Repository: cantaro86/Financial-Models-Numerical-Methods
Branch: master
Commit: 585ed1946266
Files: 66
Total size: 12.0 MB
Directory structure:
gitextract_wk7icysq/
├── .flake8
├── .github/
│ └── FUNDING.yml
├── .gitignore
├── 1.1 Black-Scholes numerical methods.ipynb
├── 1.2 SDE simulations and statistics.ipynb
├── 1.3 Fourier transform methods.ipynb
├── 1.4 SDE - Heston model.ipynb
├── 1.5 SDE - Lévy processes.ipynb
├── 2.1 Black-Scholes PDE and sparse matrices.ipynb
├── 2.2 Exotic options.ipynb
├── 2.3 American Options.ipynb
├── 3.1 Merton jump-diffusion, PIDE method.ipynb
├── 3.2 Variance Gamma model, PIDE method.ipynb
├── 3.3 Pricing with the NIG Process.ipynb
├── 4.1 Option pricing with transaction costs.ipynb
├── 4.2 Volatility smile and model calibration.ipynb
├── 5.1 Linear regression - Kalman filter.ipynb
├── 5.2 Kalman auto-correlation tracking - AR(1) process.ipynb
├── 5.3 Volatility tracking.ipynb
├── 6.1 Ornstein-Uhlenbeck process and applications.ipynb
├── 7.1 Classical MVO.ipynb
├── A.1 Solution of linear equations.ipynb
├── A.2 Optimize and speed up the code. (SOR algorithm, Cython and C).ipynb
├── CITATION.cff
├── Dockerfile
├── LICENSE
├── README.md
├── data/
│ ├── historical_data.csv
│ ├── spy-options-exp-2020-07-10-weekly-show-all-stacked-07-05-2020.csv
│ ├── spy-options-exp-2021-01-15-weekly-show-all-stacked-07-05-2020.csv
│ └── stocks_data.csv
├── docker-compose.yml
├── environment.yml
├── latex/
│ ├── A.3 Introduction to Lévy processes and PIDEs.bbl
│ └── A.3 Introduction to Lévy processes and PIDEs.tex
├── list_of_packages.txt
├── pyproject.toml
├── requirements.txt
├── setup.py
└── src/
├── C/
│ ├── BS_SOR_main.c
│ ├── BS_sor
│ ├── Makefile
│ ├── PDE_solver.c
│ ├── PDE_solver.h
│ ├── SOR.c
│ ├── SOR.h
│ └── mainSOR.c
└── FMNM/
├── BS_pricer.py
├── CF.py
├── FFT.py
├── Heston_pricer.py
├── Kalman_filter.py
├── Merton_pricer.py
├── NIG_pricer.py
├── Parameters.py
├── Processes.py
├── Solvers.py
├── TC_pricer.py
├── VG_pricer.py
├── __init__.py
├── cost_utils.py
├── cython/
│ ├── __init__.py
│ ├── heston.pyx
│ └── solvers.pyx
├── portfolio_optimization.py
└── probabilities.py
================================================
FILE CONTENTS
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FILE: .flake8
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[flake8]
ignore =
E266 # too many leading '#' for block comment
W605 # invalid escape sequence '\i'
E203, # E203: whitespace before ':'
W503 # W503: line break before binary operator
max-line-length = 120
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FILE: .github/FUNDING.yml
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# These are supported funding model platforms
github: cantaro86 # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
patreon: # Replace with a single Patreon username
open_collective: # Replace with a single Open Collective username
ko_fi: # Replace with a single Ko-fi username
tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
liberapay: # Replace with a single Liberapay username
issuehunt: # Replace with a single IssueHunt username
otechie: # Replace with a single Otechie username
custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
================================================
FILE: .gitignore
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*.aux
#*.bbl
*.blg
*.log
*.maf
*.toc
*.mtc*
*.out
*backup
*.sty
*.png
#*.pdf
*~
.DS_Store
.ipynb_checkpoints
temp
build
python-venv
src/FMNM/__pycache__/
src/FMNM/cython/build/
src/FMNM/cython/__pycache__/
src/C/*.o
src/FMNM/FMNM.egg-info
src/FMNM.egg-info
src/FMNM/cython/*.so
src/FMNM/cython/*.c
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FILE: 1.1 Black-Scholes numerical methods.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Black Scholes valuation methods\n",
"\n",
"[*Black Scholes model WIKI*](https://en.wikipedia.org/wiki/Black%E2%80%93Scholes_model). \n",
"\n",
"\n",
"The purpose of this notebook is to review the most common algorithms and implement them numerically. \n",
"\n",
"## Contents\n",
" - [European option](#sec1)\n",
" - [Put-Call parity](#sec1.1)\n",
" - [Numerical integration](#sec2)\n",
" - [Monte Carlo method](#sec3)\n",
" - [Binomial tree](#sec4)\n",
" - [Limits of the model](#sec5)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from FMNM.BS_pricer import BS_pricer\n",
"from FMNM.Parameters import Option_param\n",
"from FMNM.Processes import Diffusion_process\n",
"\n",
"import numpy as np\n",
"import scipy.stats as ss\n",
"from scipy.integrate import quad\n",
"from functools import partial\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## European option\n",
"\n",
"Under the Black-Scholes (BS) model, the best method to price a vanilla European option is to use the [BS closed formula](https://en.wikipedia.org/wiki/Black%E2%80%93Scholes_model).\n",
"\n",
"The **BS formula** for a call is:\n",
"\n",
"$$ C(t,T,S,K,r,\\sigma) = S \\, N(d_1) - K e^{-r(T-t)} N(d_2) $$\n",
"\n",
"with \n",
"\n",
"$$ d_1 = \\frac{1}{\\sigma \\sqrt{T-t}} \\biggl[ \\log \\biggl( \\frac{S}{K} \\biggr) + \\biggl(r + \\frac{\\sigma^2}{2} \\biggr) (T-t) \\biggr] \\quad \\text{and} \\quad d_2 = d_1 - \\sigma \\sqrt{T-t} $$\n",
"\n",
"where $N$ is the cumulative distribution function of a standard normal random variable. \n",
"The formula for a put is similar and can be found in the wiki page.\n",
"\n",
"The value of an option can be also computed as the discounted expectation of a future payoff in this way:\n",
"\n",
"$$\\begin{aligned}\n",
" C(s,K,t, T) &= e^{-r(T-t)} \\mathbb{E}^{\\mathbb{Q}}\\biggl[ (S_T - K)^+ \\bigg| S_t=s \\biggr] \\\\\n",
" &= e^{-r(T-t)} \\int_0^{\\infty}\\, (s' - K)^+ \\, f(s'|s) \\, ds' \\\\\n",
" &= e^{-r(T-t)} \\int_K^{\\infty}\\, (s' - K) \\, f(s'|s) \\, ds' \n",
"\\end{aligned}$$\n",
"\n",
"where $f(s'|s)$ is the risk neutral transition probability of the process $\\{S_u\\}_{u\\in [t,T]}$ starting at $S_t=s$. This is a log-normal density function \n",
"\n",
"$$ f(s'|s) = \\frac{1}{s' \\sigma \\sqrt{2\\pi (T-t)}} \\; e^{- \\frac{ \\biggl[\\log(s') - \\bigl(\\log(s) + (r-\\frac{1}{2} \\sigma^2)(T-t) \\bigr) \\biggr]^2}{2\\sigma^2 (T-t)}} $$\n",
"\n",
"\n",
"#### Comment:\n",
"Usually in statistics random variables are indicated by capital letters, and non-random variables are indicated with small letters. However, it is very common to indicate the stock price in the Black-Scholes formula with a capital letter e.g [wiki notation](https://en.wikipedia.org/wiki/Black%E2%80%93Scholes_model#Notation). I'm going to use a capital letters to indicate random variables, and small letters for non random. However there will be exceptions where it will be clear from the context e.g. strike K and maturity T are non random, or $S_0$ most of the time is a fixed variable."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Let us analyze better the previous formulas\n",
"\n",
"\n",
"\n",
"\\begin{align*}\n",
" C(s,t,K,T) &= e^{-r(T-t)} \\mathbb{E}^{\\mathbb{Q}}\\biggl[ (S_T - K)^+ \\bigg| S_t=s \\biggr] \\\\\n",
" &= e^{-r(T-t)} \\mathbb{E}^{\\mathbb{Q}}\\biggl[ S_T \\mathbb{1}_{S_T >K} \\bigg| S_t=s \\biggr] \n",
" - e^{-r(T-t)} \\mathbb{E}^{\\mathbb{Q}}\\biggl[ K \\mathbb{1}_{S_T >K} \\bigg| S_t=s \\biggr] \\\\\n",
" &= e^{-r(T-t)} \\mathbb{E}^{\\mathbb{Q}}\\biggl[ S_T \\mathbb{1}_{S_T >K} \\bigg| S_t=s \\biggr] \n",
" - e^{-r(T-t)} K \\, \\underbrace{\\mathbb{Q}\\biggl[ S_T >K \\bigg| S_t=s \\biggr]}_{N(d_2)} \\\\ \n",
"\\end{align*}\n",
"\n",
"Let us introduce the following change of measure (under the stock numeraire):\n",
"\n",
"$$ \\begin{aligned}\n",
"\\frac{d \\tilde{\\mathbb{Q}} }{ d \\mathbb{Q} } &= \\frac{S_T}{\\mathbb{E}^\\mathbb{Q}[S_T | S_t]} = \\frac{S_T}{S_t e^{r(T-t)}} \\\\ \n",
" &= \\frac{S_t e^{(r -\\frac{1}{2}\\sigma^2)(T-t) + \\sigma W_{T-t}} }{S_t e^{r(T-t)}} \\\\\n",
" &= e^{ -\\frac{1}{2}\\sigma^2(T-t) + \\sigma W_{T-t} } \\quad \\text{(exponential martingale)} \n",
"\\end{aligned} $$\n",
"\n",
"By [Girsanov theorem](https://en.wikipedia.org/wiki/Girsanov_theorem), under $\\tilde{\\mathbb{Q}}$ the driving Brownian motion has the new dynamics \n",
"\n",
"$$ \\tilde{W_t} = W_t - \\sigma t $$\n",
"\n",
"and the corresponding stock dynamics becomes\n",
"\n",
"$$\\begin{aligned}\n",
" \\frac{dS_t}{S_t} &= r dt + \\sigma dW_t \\\\\n",
" &= (r+\\sigma^2) dt + \\sigma d\\tilde{W}_t \n",
"\\end{aligned}$$\n",
"\n",
"The first term is\n",
"\n",
"$$ \\begin{aligned}\n",
" e^{-r(T-t)} \\mathbb{E}^{\\mathbb{Q}}\\biggl[ S_T \\mathbb{1}_{S_T >K} \\bigg| S_t=s \\biggr] =& e^{-r(T-t)} \\mathbb{E}^{\\tilde{\\mathbb{Q}}} \n",
" \\biggl[ \\frac{d \\mathbb{Q} }{ d \\tilde{\\mathbb{Q}}} S_T \\mathbb{1}_{S_T >K} \\bigg| S_t=s \\biggr] \\\\\n",
" =& e^{-r(T-t)} \\mathbb{E}^{\\tilde{\\mathbb{Q}}} \n",
" \\biggl[ \\frac{e^{r(T-t)}S_t}{S_T} S_T \\mathbb{1}_{S_T > K} \\bigg| S_t=s \\biggr] \\\\ \n",
" =& \\; s \\, \\underbrace{\\tilde{\\mathbb{Q}} ( S_T > K | S_t=s)}_{N(d_1)}\n",
"\\end{aligned}$$\n",
"\n",
"We have just seen how to interpret the terms $N(d_1)$ and $N(d_2)$. These are the risk neutral probabilities of $S_T > K$ in the stock and money market numeraires respectively."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I implemented the BS closed formula in the class `BS_pricer`. \n",
"Let us consider the following set of parameters, that will be recurrent in all the next notebooks. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"S0 = 100.0 # spot stock price\n",
"K = 100.0 # strike\n",
"T = 1.0 # maturity\n",
"r = 0.1 # risk free rate\n",
"sig = 0.2 # diffusion coefficient or volatility"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Call price: 13.269676584660893\n",
"Put price: 3.753418388256833\n"
]
}
],
"source": [
"call = BS_pricer.BlackScholes(\"call\", S0, K, T, r, sig)\n",
"put = BS_pricer.BlackScholes(\"put\", S0, K, T, r, sig)\n",
"print(\"Call price: \", call)\n",
"print(\"Put price: \", put)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Put-Call parity\n",
"\n",
"This is an important formula [wiki page](https://en.wikipedia.org/wiki/Put%E2%80%93call_parity)\n",
"\n",
"$$ Call - Put = S_0 - K e^{-rT} $$\n",
"\n",
"Let us check if it works:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13.269676584660893\n",
"13.269676584660886\n"
]
}
],
"source": [
"print(call)\n",
"print(put + S0 - K * np.exp(-r * T))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Numerical integration\n",
"\n",
"I want to play a bit with the different formulas written above. \n",
"\n",
"Let us compute the option prices by integrating the log-normal density:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"e_ret = np.log(S0) + (r - 0.5 * sig**2) * T # expected return of the log-price\n",
"vol = sig * np.sqrt(T) # standard deviation of the log-price\n",
"\n",
"\n",
"# log-normal density (defined above)\n",
"def log_normal(x, e_ret, vol):\n",
" return 1 / (x * vol * np.sqrt(2 * np.pi)) * np.exp(-((np.log(x) - e_ret) ** 2) / (2 * vol**2))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 5))\n",
"x = np.linspace(30, 190, 100)\n",
"plt.plot(x, log_normal(x, e_ret, vol))\n",
"plt.title(\"Lognormal distribution, conditioned on $S_0=100$\")\n",
"plt.xlabel(\"$S_T$\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function `log_normal(x, e_ret, vol)` defined above, corresponds to the scipy.stats function: \n",
"```\n",
"ss.lognorm.pdf(x, vol, scale=np.exp(e_ret) ).\n",
"```\n",
"\n",
"In the next calculation, I'm going to use the scipy function. \n",
"Let us perform the integration with the `scipy.integrate` function [quad](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.quad.html):"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Call price: 13.269676584660926 \n",
"Put price: 3.753418388256828 \n"
]
}
],
"source": [
"def integrand_LN(S, strike, e_ret, vol, payoff):\n",
" if payoff == \"call\":\n",
" return (S - strike) * ss.lognorm.pdf(S, vol, scale=np.exp(e_ret))\n",
" elif payoff == \"put\":\n",
" return (strike - S) * ss.lognorm.pdf(S, vol, scale=np.exp(e_ret))\n",
"\n",
"\n",
"call = quad(integrand_LN, K, np.inf, args=(K, e_ret, vol, \"call\"))[0] * np.exp(-r * T)\n",
"put = quad(integrand_LN, 0, K, args=(K, e_ret, vol, \"put\"))[0] * np.exp(-r * T)\n",
"\n",
"print(\"Call price: {} \\nPut price: {} \".format(call, put))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The put option payoff $(K-S_T)^+$ is positive for $S_T < K$. \n",
"- In the call case, the integration is from $K$ to $\\infty$.\n",
"- In the put case, the integration is from $0$ to $K$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What if we use the change of measure proposed above? In this way the integrations are simpler. \n",
"Let us compute $\\tilde{\\mathbb{Q}}( S_T > K )$ and $\\mathbb{Q}( S_T > K )$."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Risk neutral probability under stock numeraire,\n",
" Q1 = 0.7257468822499276\n",
"Risk neutral probability under money market numeraire,\n",
" Q2 = 0.6554217416103069\n",
"BS call price: 13.26967658466257\n"
]
}
],
"source": [
"# expected return of the log-price under the new measure\n",
"e_ret_1 = np.log(S0) + (r + 0.5 * sig**2) * T\n",
"\n",
"Q1 = quad(lambda S: ss.lognorm.pdf(S, vol, scale=np.exp(e_ret_1)), K, np.inf)[0]\n",
"print(\"Risk neutral probability under stock numeraire,\\n Q1 =\", Q1)\n",
"Q2 = quad(lambda S: ss.lognorm.pdf(S, vol, scale=np.exp(e_ret)), K, np.inf)[0]\n",
"print(\"Risk neutral probability under money market numeraire,\\n Q2 =\", Q2)\n",
"\n",
"print(\"BS call price: \", S0 * Q1 - K * np.exp(-r * T) * Q2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is quite common to compute the Black-Scholes formula using $N(d_1)$ and $N(d_2)$. \n",
"The reason is that the cumulative function of the standard Normal distribution is more accessible (I guess). In the `BS_pricer` class I used the function `scipy.stats.norm.cdf`. \n",
"\n",
"For completeness, let me recall that if $X_T$ is a Normal random variable, then $S_T = S_0 e^{X_T}$ is Log-Normal. Therefore we have:\n",
"\n",
"$$ \\mathbb{Q}( S_T > K ) = \\mathbb{Q}\\biggl( S_0 e^{X_T} > K \\biggr) = \\mathbb{Q}\\biggl( X_T > \\log \\frac{K}{S_0} \\biggr). $$\n",
"\n",
"This permits to use the Normal cumulative function."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Monte Carlo method\n",
"\n",
"I'm going to simulate the random variables: \n",
"\n",
"$$ S_T^i = S_0 e^{(r -\\frac{1}{2}\\sigma^2)T + \\sigma W_{T}^i} $$\n",
"\n",
"for $1 \\leq i \\leq N$. \n",
"Then use the approximation for a call option:\n",
"\n",
"$$ \\mathbb{E}^{\\mathbb{Q}}\\biggl[ (S_T - K)^+ \\bigg| S_0 \\biggr] \\; \n",
"\\approx \\; \\frac{1}{N} \\sum_{i=1}^N (S_T^i - K)^+\n",
"$$\n",
"\n",
"For a put option I use this payoff $(K - S_T )^+$ inside the expectation."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(seed=44) # seed for random number generation\n",
"N = 10000000 # Number of random variables\n",
"\n",
"W = ss.norm.rvs((r - 0.5 * sig**2) * T, np.sqrt(T) * sig, N)\n",
"S_T = S0 * np.exp(W)\n",
"\n",
"call = np.mean(np.exp(-r * T) * np.maximum(S_T - K, 0))\n",
"put = np.mean(np.exp(-r * T) * np.maximum(K - S_T, 0))\n",
"call_err = ss.sem(np.exp(-r * T) * np.maximum(S_T - K, 0)) # standard error\n",
"put_err = ss.sem(np.exp(-r * T) * np.maximum(K - S_T, 0)) # standard error"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Call price: 13.263338006636662, with error: 0.005093638687208466\n",
"Put price: 3.7553894006350093, with error: 0.002214066662789331\n"
]
}
],
"source": [
"print(\"Call price: {}, with error: {}\".format(call, call_err))\n",
"print(\"Put price: {}, with error: {}\".format(put, put_err))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### BS_pricer\n",
"\n",
"In the next notebooks I will present better the class `BS_pricer`. But now let's have a look at the prices obtained by different pricing methods:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Creates the object with the parameters of the option\n",
"opt_param = Option_param(S0=100, K=100, T=1, exercise=\"European\", payoff=\"call\")\n",
"# Creates the object with the parameters of the process\n",
"diff_param = Diffusion_process(r=0.1, sig=0.2)\n",
"# Creates the pricer object\n",
"BS = BS_pricer(opt_param, diff_param)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"13.269676584660893"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BS.closed_formula()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"13.269676584660623"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BS.Fourier_inversion()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([13.26753511]), array([0.00294085]), 0.679786205291748)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BS.MC(N=30000000, Err=True, Time=True)\n",
"# output is: price, standard error and execution time"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"BS.mesh_plt() # PDE method"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The PDE approach is the topic of the notebook **2.1**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Binomial tree\n",
"\n",
"\n",
"Of course I cannot forget about the Binomial model!\n",
"This is a simple but very powerful numerical method!\n",
"\n",
"I expect you to be familiar with this model. If not, have a look at the [wiki page](https://en.wikipedia.org/wiki/Binomial_options_pricing_model). \n",
"Although I said I expect you to know the model, I'm not expecting that you have already implemented it! \n",
"Therefore, here I present an efficient implementation:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BS Tree Price: 13.269537371978052\n"
]
}
],
"source": [
"N = 15000 # number of periods or number of time steps\n",
"payoff = \"call\" # payoff\n",
"\n",
"dT = float(T) / N # Delta t\n",
"u = np.exp(sig * np.sqrt(dT)) # up factor\n",
"d = 1.0 / u # down factor\n",
"\n",
"V = np.zeros(N + 1) # initialize the price vector\n",
"\n",
"# price S_T at time T\n",
"S_T = np.array([(S0 * u**k * d ** (N - k)) for k in range(N + 1)])\n",
"\n",
"a = np.exp(r * dT) # risk free compounded return\n",
"p = (a - d) / (u - d) # risk neutral up probability\n",
"q = 1.0 - p # risk neutral down probability\n",
"\n",
"if payoff == \"call\":\n",
" V[:] = np.maximum(S_T - K, 0.0)\n",
"else:\n",
" V[:] = np.maximum(K - S_T, 0.0)\n",
"\n",
"for i in range(N - 1, -1, -1):\n",
" # the price vector is overwritten at each step\n",
" V[:-1] = np.exp(-r * dT) * (p * V[1:] + q * V[:-1])\n",
"\n",
"print(\"BS Tree Price: \", V[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The stock price on each node of a binomial tree is\n",
"$$ S_n^{(k)} = u^{k} d^{n-k} S_0 \\quad \\text{for} \\quad k \\in \\{ 0, ..., n \\} \\quad \\text{and} \\quad n \\in \\{ 0, ..., N \\}$$\n",
"where the price at $S_n^{(k)}$ is obtained starting from $S_0^{(0)}$ := $S_0$ and applying $k$ up factors and $(n-k)$ down factors. \n",
"\n",
"In a (recombining) binomial tree at each time point $n$ there are $i+1$ nodes.\n",
"The total number of nodes of a binomial tree is\n",
"\n",
"\\begin{align}\n",
" \\sum_{i=0}^N (1+i) &= \\sum_{i=0}^N 1 + \\sum_{i=0}^N i \\\\\n",
" &= (N+1) + N(N+1)/2 \\\\\n",
" &= (N+1)(N+2)/2.\n",
"\\end{align}\n",
"\n",
"Since $u = 1/d$, we can rewrite the formula above as\n",
"$$ S_n^{(k)} = u^{2k - n}\\, S_0 \\quad \\text{for} \\quad k \\in \\{ 0, ..., n \\} \\quad \\text{and} \\quad n \\in \\{ 0, ..., N \\}$$\n",
"\n",
"In this way, we can decrease the number of operations and increase the speed of the program:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.64 ms ± 37.9 µs per loop (mean ± std. dev. of 10 runs, 20 loops each)\n"
]
}
],
"source": [
"%%timeit -n 20 -r 10\n",
"S_T = np.array([(S0 * u**j * d ** (N - j)) for j in range(N + 1)]) # price S_T at time T"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.59 ms ± 24.1 µs per loop (mean ± std. dev. of 10 runs, 20 loops each)\n"
]
}
],
"source": [
"%%timeit -n 20 -r 10\n",
"S_0N = S0 / u**N\n",
"S_T = np.array([S_0N * u ** (2 * j) for j in range(N + 1)]) # price S_T at time T"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.49 ms ± 25 µs per loop (mean ± std. dev. of 10 runs, 20 loops each)\n"
]
}
],
"source": [
"%%timeit -n 20 -r 10\n",
"S_0N = S0 / u**N\n",
"S_T = np.array([S_0N * u ** (j) for j in range(0, 2 * N + 1, 2)]) # price S_T at time T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The last approach for the computation of $S_T$ is the fastest."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Limits of the model"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"BS_sigma = partial(BS_pricer.BlackScholes, \"call\", S0, K, T, r) # binding the function\n",
"sigmas = np.linspace(0.01, 10, 1000)\n",
"\n",
"plt.plot(sigmas, BS_sigma(sigmas))\n",
"plt.xlabel(\"sig\")\n",
"plt.ylabel(\"price\")\n",
"plt.title(\"Black-Scholes price as function of volatility\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The BS formula is an increasing function of the volatility. \n",
"However, for higher volatilities, the graph becomes almost flat!!\n",
"\n",
"We can conclude that the model is reliable for volatilities in the range $0 - 400\\%$.\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: 1.2 SDE simulations and statistics.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SDE simulations and statistics\n",
"\n",
"## Contents\n",
" - [Brownian motion](#sec1)\n",
" - [Confidence interval](#sec1.2)\n",
" - [Hypothesis testing](#sec1.3)\n",
" - [Geometric Brownian motion](#sec2)\n",
" - [CIR process](#sec3)\n",
" - [Euler-Maruyama method](#sec3.1)\n",
" - [Parameter estimation](#sec3.2)\n",
" - [Change of variables](#sec3.3)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy as scp\n",
"import scipy.stats as ss\n",
"import matplotlib.pyplot as plt\n",
"from statsmodels.graphics.gofplots import qqplot\n",
"from scipy.special import iv\n",
"from scipy.optimize import minimize"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Brownian motion\n",
"\n",
"Let us simulate some Brownian paths. \n",
"Remember that the (standard) Brownian motion $\\{X_t\\}_{t\\geq 0}$ is a continuous time stochastic process, that satisfies the following properties:\n",
"- $X_{0} = 0$.\n",
"- The increments are stationary and independent. (see **A3** for the definition)\n",
"- It is a martingale.\n",
"- It has continuous paths, but nowhere differentiable.\n",
"- $X_t - X_s \\sim \\mathcal{N}(0,t-s)$ for $t\\geq s \\geq 0$.\n",
"\n",
"For more info see here [wiki](https://en.wikipedia.org/wiki/Brownian_motion).\n",
"\n",
"In our simulation, each increments is such that:\n",
"\n",
"$$ X_{t_i+\\Delta t} - X_{t_i} = \\Delta X_i \\sim \\mathcal{N}(\\mu \\Delta t,\\, \\sigma^2 \\Delta t). $$\n",
"\n",
"The process at time T is given by $X_T = \\sum_i \\Delta X_i$ and follows the distribution:\n",
"\n",
"$$ X_T \\sim \\mathcal{N}(\\mu T,\\, \\sigma^2 T). $$"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# scipy.stats uses numpy.random to generate its random numbers\n",
"np.random.seed(seed=42)\n",
"\n",
"paths = 4000 # number of paths\n",
"steps = 1000 # number of time steps\n",
"\n",
"mu = 0.1 # drift\n",
"sig = 0.2 # diffusion coefficient or volatility\n",
"T = 100\n",
"T_vec, dt = np.linspace(0, T, steps, retstep=True)\n",
"\n",
"X0 = np.zeros((paths, 1)) # each path starts at zero\n",
"increments = ss.norm.rvs(loc=mu * dt, scale=np.sqrt(dt) * sig, size=(paths, steps - 1))\n",
"\n",
"X = np.concatenate((X0, increments), axis=1).cumsum(1)\n",
"\n",
"plt.plot(T_vec, X.T)\n",
"plt.title(\"Brownian paths\")\n",
"plt.xlabel(\"T\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we consider the terminal values of each path. These values should give us some indications about the distribution of $X_T$.\n",
"\n",
"We expect to have $\\mathbb{E}[X_T] = \\mu T$ and $Std[X_T] = \\sigma \\sqrt{T}$."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Expectation of X at time T: 9.9827\n",
"Standard Deviation of X at time T: 1.9964\n"
]
}
],
"source": [
"X_end = X[:, -1]\n",
"print(\"Expectation of X at time T: {:.4f}\".format(X_end.mean()))\n",
"print(\"Standard Deviation of X at time T: {:.4f}\".format(X_end.std(ddof=1)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here I'm using the unbiased standard deviation estimator, with one degree of freedom i.e. `ddof=1`. Since the number of steps is quite big, there is no big difference between this and other possible estimators. \n",
"Let us recall that a common alternative is the MLE (maximum likelihood estimator), with `ddof=0`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Confidence intervals\n",
"\n",
"Ok... what we have seen so far was quite easy. Right? \n",
"\n",
"But... we want to be precise! We want to associate a confidence interval to each estimated parameter! \n",
"In other words, we want to indicate how close the sample statistic (the estimated parameter) is to the population parameter (the real value of the parameter).\n",
"\n",
"We can find an interval of values that contains the real parameter, with a confidence level of $(1-\\alpha)100\\%$, for $0<\\alpha<1$. This is called **confidence interval**.\n",
"\n",
"Let us introduce some basic concepts:\n",
"\n",
"Let $X_i$ i.i.d (independent and identical distributed), for $1\\leq i \\leq n$, with $\\mathbb{E}[X_i]=\\mu$ and $Var[X_i] = \\sigma^2$. \n",
"We call $\\bar{X} = \\frac{1}{n}\\sum_{i=1}^n X_i$.\n",
"- $\\bar{X}$ has expectation: $\\mathbb{E}[\\bar{X}] = \\frac{1}{n}\\sum_{i=1}^n \\mathbb{E}[X_i] = \\mu$.\n",
"- $\\bar{X}$ has variance: $Var[\\bar{X}] = \\frac{1}{n^2} Var[\\sum_{i=1}^n X_i] = \\frac{1}{n^2} \\sum_{i=1}^n Var[X_i] = \\frac{\\sigma^2}{n}$.\n",
"- Central limit theorem: $$ Z_n = \\frac{\\bar{X} - \\mu}{\\frac{\\sigma}{\\sqrt{n}}} \\underset{n\\to \\infty}{\\to} Z \\sim \\mathcal{N}(0,1) $$\n",
"\n",
"##### Normal distributed variables \n",
"For $Z \\sim \\mathcal{N}(0,1)$, we call $z_{\\alpha/2}$ the value such that:\n",
"\n",
"$$ \\mathbb{P}(Z< -z_{\\alpha/2}) = \\mathbb{P}(Z > z_{\\alpha/2}) = \\alpha/2 $$\n",
"\n",
"Let $X_i \\sim \\mathcal{N}(\\mu,\\sigma^2)$. **Let us assume $\\sigma$ is known**.\n",
"\n",
"The sum of normal random variables is again normal, i.e. \n",
"$\\sum_{i=1}^n X_i \\sim \\mathcal{N}(n\\mu,n\\sigma^2)$, therefore we have that $\\bar{X} \\sim \\mathcal{N}(\\mu,\\frac{\\sigma^2}{n})$. We can standardize and obtain $Z = \\frac{\\bar{X} - \\mu}{\\frac{\\sigma}{\\sqrt{n}}} \\sim \\mathcal{N}(0,1) $. \n",
"\n",
"We can write:\n",
"\n",
"$$ \\begin{aligned}\n",
"(1 - \\alpha) &= \\mathbb{P} \\biggl( -z_{\\alpha/2} < \\frac{\\bar{X} - \\mu}{\\frac{\\sigma}{\\sqrt{n}}} < z_{\\alpha/2} \\biggr) \\\\\n",
" &= \\mathbb{P} \\biggl(\\bar{X} - z_{\\alpha/2} \\frac{\\sigma}{\\sqrt{n}} < \\mu < \\bar{X} + z_{\\alpha/2} \\frac{\\sigma}{\\sqrt{n}} \\biggr)\n",
"\\end{aligned} $$\n",
"\n",
"Then, the $(1 - \\alpha)100\\%$ confidence interval for the mean $\\mu$ is:\n",
"\n",
"$$ \\biggr[ \\bar{x} - z_{\\alpha/2} \\frac{\\sigma}{\\sqrt{n}} \\, , \\; \\bar{x} + z_{\\alpha/2} \\frac{\\sigma}{\\sqrt{n}} \\biggr] $$\n",
"\n",
"where $\\bar{x}$ is one realization of $\\bar{X}$. \n",
"Recall that confidence intervals are random variables, and can have different values for different samples!\n",
"\n",
"Usually it is common to calculate the $95\\%$ confidence interval. \n",
"For a $95\\%$ confidence interval, $1-\\alpha = 0.95$, so that $\\alpha/2 = 0.025$, and $z_{0.025} = 1.96$.\n",
"\n",
"Let $X_i \\sim \\mathcal{N}(\\mu,\\sigma^2)$. **Let us assume $\\sigma$ is unknown**.\n",
"\n",
"We can consider instead the [sample variance](https://en.wikipedia.org/wiki/Variance#Sample_variance) (unbiased version, $\\mathbb{E}[S^2] = \\sigma^2$):\n",
"\n",
"$$ S^2 = \\frac{1}{n-1} \\sum_{i=1}^{n} (X_i - \\bar{X})^2 $$\n",
"and the statistics\n",
"$$ T = \\frac{\\bar{X} - \\mu}{\\frac{S}{\\sqrt{n}}} $$\n",
"\n",
"Let us recall that:\n",
"- $\\bar{X}$ and $S^2$ are independent.\n",
"- $S^2 \\sim \\frac{\\sigma^2}{n-1} \\chi^2_{n-1}.$\n",
"- $T \\sim t_{n-1}$ (**student t distribution**) [wiki]().\n",
"\n",
"Following the same steps as above, we can compute the **t-confidence interval for the mean**:\n",
"\n",
"$$ \\biggr[ \\bar{x} - t_{\\alpha/2,n-1} \\frac{s}{\\sqrt{n}} \\, , \\; \\bar{x} + t_{\\alpha/2,n-1} \\frac{s}{\\sqrt{n}} \\biggr] $$\n",
"\n",
"where $\\bar{x}$ and $s$ are realizations of $\\bar{X}$ and $S$. \n",
"For $T \\sim t_{n-1}$, we call $t_{\\alpha/2,n-1}$ the value such that: \n",
"\n",
"$$ \\mathbb{P}(T< -t_{\\alpha/2,n-1}) = \\mathbb{P}(T > t_{\\alpha/2,n-1}) = \\alpha/2. $$\n",
"\n",
"Recall that the student t density is symmetric. \n",
"The term $\\frac{s}{\\sqrt{n}}$ is called **standard error** for the mean.\n",
"\n",
"For big $n$ the values of $t_{\\alpha/2,n-1}$ and $z_{\\alpha/2}$ are very close. Therefore they are interchangeable.\n",
"\n",
"\n",
"##### Non-normal data\n",
"\n",
"Thanks to the central limit theorem, the ratio $\\frac{\\bar{X} - \\mu}{\\frac{s}{\\sqrt{n}}}$ approaches a standard normal distribution for $n$ big enough. In general $n>30$ is enough. \n",
"\n",
"\n",
"#### Confidence intervals for the variance\n",
"\n",
"The Chi-square distribution is asymmetric, therefore we define $a = \\chi^2_{1-\\alpha/2,n-1}$ and $b = \\chi^2_{\\alpha/2,n-1}$ and write:\n",
"\n",
"\\begin{align*}\n",
"(1 - \\alpha) &= \\mathbb{P} \\biggl( a < \\frac{(n-1) S^2}{\\sigma^2} < b \\biggr) \\\\\n",
" &= \\mathbb{P} \\biggl( \\frac{(n-1) S^2}{b} < \\sigma^2 < \\frac{(n-1) S^2}{a} \\biggr)\n",
"\\end{align*}\n",
"\n",
"The confidence interval for the variance is therefore:\n",
"\n",
"$$ \\biggl[ \\frac{(n-1) S^2}{b} \\, , \\, \\frac{(n-1) S^2}{a} \\biggr] .$$\n",
"\n",
"For the standard deviation $\\sigma$, the confidence interval is:\n",
"\n",
"$$ \\biggl[ \\sqrt{\\frac{(n-1) S^2}{b}} \\, , \\, \\sqrt{\\frac{(n-1) S^2}{a}} \\biggr] .$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### A step back\n",
"Ok... cool... a lot of theory. \n",
"Let's see how it works in practice for the terminal value of the Brownian motion $X_T$. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The expectation of the mean is: 9.982654\n",
"The confidence interval is: (9.920766648111107, 10.044541975623867)\n"
]
}
],
"source": [
"print(\"The expectation of the mean is: {:.6f}\".format(X_end.mean()))\n",
"print(\"The confidence interval is: \", ss.t.interval(0.95, paths - 1, loc=X_end.mean(), scale=ss.sem(X_end)))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The estimated Standard Deviation is: 1.996432\n",
"The confidence interval is: 1.9536249339404514 2.041170826805831\n"
]
}
],
"source": [
"s2 = X_end.var(ddof=1) # unbiased sample variance\n",
"AA = s2 * (paths - 1)\n",
"print(\"The estimated Standard Deviation is: {:.6f}\".format(X_end.std(ddof=1)))\n",
"print(\n",
" \"The confidence interval is: \",\n",
" np.sqrt(AA / ss.chi2.ppf(0.975, df=paths - 1)),\n",
" np.sqrt(AA / ss.chi2.ppf(0.025, df=paths - 1)),\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Sometimes the confidence interval fails!\n",
"\n",
"We chose a $95\\%$ confidence interval. This means that $5\\%$ of the times it fails!\n",
"\n",
"In the following cell, I want to check how many times the confidence interval fails to contain the population mean (in this case it is $\\mu T = 10$), if we repeat the experiment 100 times. For this purpose we just need to change the seed of the random number generator."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"seed: 23 \n",
" 10.0 is not contained in (9.8494, 9.9735)\n",
"seed: 68 \n",
" 10.0 is not contained in (9.875, 9.9985)\n",
"seed: 99 \n",
" 10.0 is not contained in (10.0291, 10.1555)\n"
]
}
],
"source": [
"for n in range(100):\n",
" np.random.seed(seed=n)\n",
" XT = ss.norm.rvs(loc=mu * T, scale=np.sqrt(T) * sig, size=paths)\n",
" low, high = ss.t.interval(0.95, paths - 1, loc=XT.mean(), scale=ss.sem(XT))\n",
" if (mu * T < low) or (mu * T > high):\n",
" print(\"seed:\", n, \"\\n\", mu * T, \"is not contained in \", (low.round(4), high.round(4)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Well... it failed 3 times!!**\n",
"\n",
"The parameters can be estimated by the following `scipy.stats` function:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameters from the fit: (9.982654311867487, 1.996182453394599)\n",
"The MLE estimator for the standard deviation is: 1.996182453394599\n"
]
}
],
"source": [
"param = ss.norm.fit(X_end)\n",
"print(\"Parameters from the fit: \", param) # these are MLE parameters\n",
"print(\"The MLE estimator for the standard deviation is: \", np.std(X_end, ddof=0))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(X_end.min(), X_end.max(), 100)\n",
"pdf_fitted = ss.norm.pdf(x, *param)\n",
"\n",
"plt.plot(x, pdf_fitted, color=\"r\", label=\"Normal\")\n",
"plt.hist(X_end, density=True, bins=50, facecolor=\"LightBlue\", label=\"frequency of X_end\")\n",
"plt.legend()\n",
"plt.title(\"Histogram vs Normal distribution\")\n",
"plt.xlabel(\"X_end\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Hypothesis testing\n",
"\n",
"Hypothesis testing is a broad topic. \n",
"A summary of the topic can be found here [statistical hypothesis testing](https://en.wikipedia.org/wiki/Statistical_hypothesis_testing). The two fundamental concepts to understand are: the [Test Statistic](https://en.wikipedia.org/wiki/Test_statistic)\n",
"and the [p-value](https://en.wikipedia.org/wiki/P-value). \n",
"\n",
"For what concerns practical applications, only the **p-value** is necessary. \n",
"It is the probability of obtaining values of the test statistic more extreme than those observed in the data, assuming the null hypothesis $H_0$ being true. \n",
"The p-value is the smallest significance level $\\alpha$ that leads us to rejecting the null hypothesis.\n",
"\n",
"Here I want to use some \"classical tests\" for testing the normality of $X_T$. \n",
"- **Shapiro-Wilk**, [doc](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.shapiro.html) \n",
" The null hypothesis $H_0$ is that the data are normally distributed. \n",
" It returns the test statistics and the p-value.\n",
"- **Jarque-Bera** [doc](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.jarque_bera.html) \n",
" The null hypothesis $H_0$ is that the data are normally distributed. \n",
" It returns the test statistics and the p-value. \n",
"- **Kolmogorov-Smirnov**, [doc](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.kstest.html) \n",
" It compares two distributions. \n",
" The null hypothesis $H_0$ assumes the two distributions identical.\n",
"\n",
"Other common tests are: The [Anderson-Darling](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.anderson.html#scipy.stats.anderson) test and the [D'Agostino](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html) test.\n",
"\n",
"It is also quite useful to visualize the **Q-Q plot**.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Shapiro - Wilk:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ShapiroResult(statistic=0.9995088577270508, pvalue=0.4110822081565857)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ss.shapiro(X_end)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assuming a confidence level of $95\\%$. Since the p-value is not smaller that 0.05, we cannot reject $H_0$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Jarque - Bera:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SignificanceResult(statistic=1.8821352125475082, pvalue=0.39021102116710205)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ss.jarque_bera(X_end)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assuming a confidence level of $95\\%$. Since the p-value is not smaller that 0.05, we cannot reject $H_0$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Kolmogorov - Smirnov:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KstestResult(statistic=0.010299978780936525, pvalue=0.7857293135081154, statistic_location=8.92058869860379, statistic_sign=1)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ss.kstest(X_end, lambda x: ss.norm.cdf(x, loc=10, scale=2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assuming a confidence level of $95\\%$. Since the p-value is not smaller that 0.05, we cannot reject $H_0$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Q-Q plot:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"qqplot(X_end, line=\"s\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Geometric Brownian Motion\n",
"\n",
"The GBM has the following stochastic differential equation (SDE):\n",
"\n",
"$$ dS_t = \\mu S_t dt + \\sigma S_t dW_t $$\n",
"\n",
"By applying the Itô lemma on $\\log S_t$ it is possible to solve this equation (see the computations [here](https://en.wikipedia.org/wiki/Geometric_Brownian_motion)). \n",
"The solution is:\n",
"\n",
"$$ S_t = S_0 e^{(\\mu-\\frac{1}{2}\\sigma^2)t + \\sigma W_t} $$"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(seed=42)\n",
"mu = 0.1\n",
"sig = 0.2\n",
"T = 10\n",
"N = 10000\n",
"S0 = 1\n",
"\n",
"W = ss.norm.rvs(loc=(mu - 0.5 * sig**2) * T, scale=np.sqrt(T) * sig, size=N)\n",
"S_T = S0 * np.exp(W)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we consider a lognormal random variable $S \\sim LN(\\mu, \\sigma^2)$,\n",
"the two parameters $\\mu$ and $\\sigma$ are not the location and scale parameters of the lognormal distribution. \n",
"They are respectively the location and scale parameters of the normally distributed $\\ln S \\sim \\mathcal{N}(\\mu,\\sigma^2)$. \n",
"\n",
"Using our specific notation $S \\sim LN(\\mu -\\frac{1}{2}\\sigma^2, \\sigma^2)$, the scale parameter is $e^{(\\mu-\\frac{1}{2}\\sigma^2)T }$ and the shape parameter is $\\sigma \\sqrt{T}$. \n",
"The location is zero, because the distribution starts at zero. (changing the $loc$ parameter would shift the support of the distribution on the domain $[loc,\\infty]$ )"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitted parameters: (0.6334937910478082, -0.003211120875913497, 2.226462581309446)\n",
"Shape: 0.632455532033676\n",
"Scale: 2.225540928492468\n"
]
}
],
"source": [
"param_LN = ss.lognorm.fit(S_T)\n",
"print(\"Fitted parameters: \", param_LN)\n",
"print(\"Shape: \", sig * np.sqrt(T))\n",
"print(\"Scale: \", np.exp((mu - 0.5 * sig**2) * T))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(S_T.min(), S_T.max(), 100)\n",
"pdf_LN_fitted = ss.lognorm.pdf(x, *param_LN)\n",
"\n",
"plt.plot(x, pdf_LN_fitted, color=\"r\", label=\"Log-Normal\")\n",
"plt.hist(S_T, density=True, bins=50, facecolor=\"LightBlue\", label=\"frequency of X_end\")\n",
"plt.legend()\n",
"plt.title(\"Histogram vs Log-Normal distribution\")\n",
"plt.xlabel(\"S_T\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Parameter estimation:\n",
"\n",
"In order to estimate the parameters, we need to take first the logarithm of the data!\n",
"\n",
"The variable of interest is\n",
"$$\\log S_T \\sim \\mathcal{N} \\biggl( (\\mu-\\frac{1}{2}\\sigma^2)T, \\sigma^2 T \\biggr)$$\n",
"\n",
"Now we can estimate the mean and the variance of a Normal distributed sample. This is quite easy!"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Volatility or coefficient sig: 0.200682\n",
"Drift or coefficient mu: 0.100002\n"
]
}
],
"source": [
"std_S = np.std(np.log(S_T), ddof=0) / np.sqrt(T)\n",
"mu_S = np.mean(np.log(S_T)) / T + 0.5 * std_S**2\n",
"\n",
"print(\"Volatility or coefficient sig: \", std_S.round(6))\n",
"print(\"Drift or coefficient mu: \", mu_S.round(6))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Path simulation\n",
"\n",
"There is no need to use advanced techniques. \n",
"\n",
"If we want to simulate GBM paths, it is enough to simulate Brownian paths (with the adjusted drift) and then take their exponentials!\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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DA4mOjmbjxo18+umn+Pr62t3LnMF04cKFeHl54erqSkxMjMPmlobMmjWLQYMGMWrUKIKCgkhKSuLNN98kKiqK3r17t9jrFkJGu4hOb9++fcrcuXOVmJgYRa/XK66urkqvXr2UW265pd5IC0VRlP379yvXXnutEhwcrOh0OiU0NFSZNm2a8uGHH9qdd/DgQWXWrFmKj4+P4uLiogwdOtRu1IiiWEctLFu2zG7/559/rgDKzp077fabR2KYh0iaffbZZ8rYsWMVDw8Pxc3NTenZs6dyyy23KLt27bKck5+fr1x99dWKr6+votFoFPN/X/OIlldffbXea3U02kVRFOXIkSPKNddcowQEBCguLi5K9+7dlTlz5iiVlZWOf8m1qDPKRafTKT169FDmzp2rnDhxwu7cyZMnKwMHDnR4n6SkJOWGG25QAgICFJ1Op/Tt21d59dVXLaONbF1xxRUKoHz99deWfdXV1YqHh4fi5OSkFBQU2J3f0O/Y/G+SkJBg2edotMtLL72kREdHK3q9Xunfv7/y8ccfW+5Z93dx33331SvvqUbQKIr17+arr75SHnzwQSUoKEjR6/XKxIkT7f7NFUVRUlNTlauuukrx8/NTvLy8lIsuukg5dOiQw+e8+eabSkxMjKLVau3+3Rv6t7j11lvtRlC9/vrryrhx45TAwEDL38Xtt9+uJCYmNvp6hGgujaLY1PkJIYRodX/++SdTp05l2bJlXH311e1dHCHanPT5EEIIIUSbkuBDCCGEEG1Kml2EEEII0aak5kMIIYQQbUqCDyGEEEK0KQk+hBBCCNGmmp1k7K+//uLVV19l9+7dZGRksGLFCi6//HLLcUVRePbZZ1m4cCEFBQWMHTuW9957j4EDBzbp/iaTifT0dLy8vJqULloIIYQQ7U9RFEpKSggPD8fJqfG6jWYHH2VlZQwdOpS5c+dy1VVX1Tv+yiuv8MYbb/DFF1/Qp08fXnjhBc4//3xiY2NPmTkR1PkdWmqeBiGEEEK0rZSUlFNOXnlGo100Go1dzYeiKISHh/PQQw/xxBNPAOoESCEhIbz88svcfffdp7xnUVERvr6+pKSk2KXFFkIIIUTHVVxcTGRkJIWFhaecgqJF53ZJSEggMzOTCy64wLJPr9czefJktm7d6jD4qKqqspsxsqSkBFDnNJDgQwghhOhcmtJlokU7nJqnhw4JCbHbHxIS4nDqaIAFCxbg4+Nj+ZEmFyGEEKJra5XRLnWjHkVRGoyE5s+fT1FRkeUnJSWlNYokhBBCiA6iRZtdQkNDAbUGJCwszLI/Ozu7Xm2ImV6vR6/Xt2QxhBBCCNGBtWjNR0xMDKGhoaxZs8ayr7q6mo0bNzJu3LiWfJQQQgghOqlm13yUlpZy4sQJy3ZCQgL79u3D39+f7t2789BDD/Hiiy/Su3dvevfuzYsvvoi7uzs33HBDixZcCCGEEJ1Ts4OPXbt2MXXqVMv2I488AsCtt97KF198weOPP05FRQXz5s2zJBlbvXp1k3J8CCGEEKLr63Cz2hYXF+Pj40NRUZEMtRVCCCE6ieZ8fsvcLkIIIYRoUxJ8CCGEEKJNSfAhhBBCiDYlwYcQQggh2pQEH0IIIYRoUxJ8CCGEEGcLkwmqy9q7FBJ8CCGEEGcFRYEvLoEXI+Doz+1aFAk+hBBCiLNBdSkkbwUUOLG2XYsiwYcQQghxNqgstq5Xl7dfOZDgQwghhDg7VNkEHxX57VcOJPgQQgghzg62NR/lee1XDiT4EEIIIc4OtjUf5VLzIYQQQojWVllkXZfgQwghhBCtrqrEuu4dDkZDuxVFgg8hhBDibGCu+RhyPdy/A7TO7VYUCT6EEEKIs0H+SXXpG9m+5UCCDyGEEOLskH1MXQb1a99yIMGHEEIIcXbIO64ug/q2bzmQ4EMIIYTo+gzVUFGgrnuFt29ZkOBDCCGE6PrKc9WlRgtufu1bFiT4EEIIIbq+0mx16REITu3/0d/+JRBCCCFE6yrLUZcewe1bjloSfAghhBBdXVlts4tHQPuWo5YEH0IIIURXZ57XxdW3XYthJsGHEEII0dWZgw+9V/uWo5YEH0IIIURXZ57XRe/dvuWoJcGHEEII0dVZgg+p+RBCCCFEWzAHH65S8yGEEEKItiA1H0IIIYRoUxJ8CCGEEKJNVdaOdnGR4EMIIYQQbaEiX126+7dvOWpJ8CGEEEJ0ZYpik149sH3LUkuCDyGEEKIrqyoGY7W67i7BhxBCCCFam3leFxdPcHFv37LUkuBDCCGE6Mosk8p1jFoPkOBDCCGE6NrM/T06SJMLSPAhhBBCdG2WzqZB7VsOGxJ8CCGEEF1ZuTS7CCGEEKKtlOfDjk/UdQk+hBBCCNHqlt0KpZnqujS7CCGEEKLVJfxlXfcIbr9y1CHBhxBCiNZxcj2k72vvUnR9iVugLO/U5/n3aP2yNJEEH0IIIVpecTp8dQUsnKym9xat49iv8MXF8Ml0dTv+T8g6rK4ba+zPDejZpkVrjAQfQgghWp55eCdAYXL7laOr2/u1uixIgPwE+PIy+GCcuq8k0/5cN982LVpjJPgQQgjR8kwG6/rCKe1WjC6vLNu6XpBgXY/9HUptjt31Z5sVqSkk+BBCCNHyaiqs6+bp3EXLs61hUkzW9aXXQ2mWuh4xEsKHt225TkGCDyGEEC2vutx+u6q0fcrR1dl2NK0otD9mDj48Q9qsOE0lwYcQQoiWV1Nmv23+IBQtq9omqKsosD9WnK4uPTvOEFszCT6EEEK0vLo1H+YPQtHCbEYSVRbaH8o5pi47UH4PMwk+hBBCtLyaOsGHbd8E0TrqNrtkH1WXHbDmw7m9CyCEEKILqht8VJW0Tzm6qtzjsPlN+311g4+84+qyA6VVN5PgQwghRMur1+G0uH3K0VUtvgoKk+z31e3zYSYdToUQQpwV6gYblRJ8tJiKwvqBBzTcqbcDNrtI8CGEEKJlmUxwZKW67lRbwS7NLi2noYyxabvUpd7Hfn8HbHaR4EMIIcSZUxQ4sAxSd6tTuBenqvvPmacupdml5RSlqEuf7hAyuP5x3+7WdWdX0Hu1TbmaQYIPIYQQZ277R7D8Dlh2q/WbuW8U+EWp65VF7Ve2rsb8+40YDvdurn/cN9K67hEMGk3blKsZJPgQQghx5vYuVpdFKXDgO3Xdt7u1CUBqPlqOJbjr7vi4j03w0QH7e4AEH0IIIVqCbabNXZ+qS79o8AhQ120nORNnxrbZpa5e59kHJRJ8CCGE6LKqy+rvG3SlGoAAFCSp/ULEmSusDT5sm1cABl8LN/0Art7WfR2wsylI8CGEEOJMKYrjZpUeU9UmAI0TGCog+e+2L1tXZKn56Ga/3yNQXbrajHaRmg8hhBBdUlUJGCrt9w2/We3oqNVZv33/9Urbl62rqS6D8tqZbH3q1HxEjKy/vwPO6wKtEHwYDAb+/e9/ExMTg5ubGz169OC5557DZDK19KOEEEJ0BHUnNAPQuVnXxz2gLnNi26Q4XVpRmrrUe4Obr7p+y0qY/hQMukrdNjd1gX0TTAfS4unVX375ZT788EMWLVrEwIED2bVrF3PnzsXHx4d//OMfLf04IYQQ7a2qtP4+Z1fr+sg5sPrfUJwGZXnWTqii+YpqR7rY1m70mKz+mLn5WdedOuYsKi1eqm3btnHZZZdxySWXABAdHc3SpUvZtWtXSz9KCCFER1DtIPiwrfnQe4FXOJSkQ2GiBB9nIvuYuvSPafgcjQbGPwRJW6DvxW1SrOZq8WaXCRMmsG7dOuLi4gDYv38/mzdv5uKLO+YvQAghxBlylDrdtuYDrJ0ji1JbvzxdVVkerP6Xut5tdOPnnv8s3LEWXNxbv1ynocVrPp544gmKioro168fWq0Wo9HIf//7X2bPnu3w/KqqKqqqqizbxcWSiEYIIToVRzUfjoKP1B0SfJyJQz9Y13uf337laAEtXvPx7bffsnjxYpYsWcKePXtYtGgRr732GosWLXJ4/oIFC/Dx8bH8REZGOjxPCCFEB2Xu82Hbv0BXN/iIUJcSfJy+hI3qcuRcCBnYvmU5Qy0efPzzn//kySef5Prrr2fw4MHcfPPNPPzwwyxYsMDh+fPnz6eoqMjyk5KS0tJFEkII0ZrMNR9B/az7nN3sz3HzV5cyx8vpK8lUl5281gNaodmlvLwcJyf7mEar1TY41Fav16PX61u6GEIIIdqKuc9HQE/IOuT4HPOQTwk+Tl9Fgbq0Hc3SSbV48DFr1iz++9//0r17dwYOHMjevXt54403uO2221r6UUIIIToCc82HV7h1X90gQ18bfMgEc6dPgo+GvfPOO/znP/9h3rx5ZGdnEx4ezt13381TTz3V0o8SQgjREZj7fOg9rfu0OvtzLMGHg5ExXZGiwI/3grEGrvrkzKe1N5msydwk+KjPy8uLN998kzfffLOlby2EEKIjMtd8uHjCrLfh+GoYdoP9OZZml7Ok5qM0G/YvVddnvGydd+V0VRWBUtt9QYIPIYQQZ71qm5qPkbeqP3XpvdTl2VLzUWQzeKKmovnXV5fD0uvUGpTrvrI2ueg8wLnz95OU4EMIIcSZMTe7uHg1fI652aUsW/1APdNmiI6uING6XlPe/OtTd0DCX+p64hbwi1LXbZu2OjGZ1VYIIcSZsTS7eDR8jm1TwYFvW7c8HYFt8OEoCdupmGs6AOI3gKFaXdd2/loPkOBDCCHEmXLU4bQuV28IG6qu551o/TK1t8Ik63r1adR8VBRa13d+Amm71fW6HXk7KQk+hBBCnJnqJjS7APQ6T10WZ6hNL11ZgW3wUdb8680jW8xOrlOXXaC/B0jwIYQQ4kyZO5Geqj+CudPpvsWw/E7H56TuhkPLwWhoufK1B9uaj5rTCD5sm13AOkpIaj6EEEKclQ79AId/VNcVxX6obWP0NjUjB5fVP26ohkWz4Pu5sP2DFilquynLta6fTs1HTqz9dvJWddlF+nzIaBchhBBNV54P39dmrO6TpS5NtbUUp6z58G78eHWptZYg++jpl7G9VBZB8nboOdV+eG1z+3xsWABxq1q2bB2MBB9CCCGarjTbum6otAYe0LyaD4DUXeow1JhJ6rZtDUFx+pmVsz0snQ1JW+C8Z0AxWvc3Z7RLRQFsfKnh47b37cSk2UUIIUTT2SYJM1RZt3Xu4KRt/FpnV/vtT6arzSxleeq2bT6Mok44w3nSFnW5e5H9fvNstKey7nl4Obrxc0ydvC9MLQk+hBBCNF1FvnXdUNn0/h4AxmrH+8trgw+7mo+M0ytfezm8wrruHmB/LLdO/w1HjDWw6TX7fXWDNVDneOkCJPgQQgjRdOZAAWprPpqQ48Msarz9zLdm5m/ztjUfNWVg6kRNDPttEqe5uNsfy4k79fWpO+vvm/c36H3s90mzixBCiLOOXfBRaa2taErNh94THjoA3t3s95vvUXcOlM40D0z2Eet6eW3tkKb2I7Y089Tzu2z/yH77mkXgHwNPJtnvl2YXIYQQZx1zpk1Qm1GqzTk+TpFgzEyrA9c63+b3LYavr6nfybSzBB/GGvu8HmU56tIrzDrCp/AUfVjy49Xl7G/gmSIYeLm6XXcOnM5UG9QIGe0ihBCiaUwmiFtt3TZU2kwq14wJz0IHQ/Zh6/buL9Rl7nH7805nTpT2YJsKHawjgnRu6pw2WYegMBmC+tS/NicWNr0OmQfUbY+gxp8lNR9CCCHOKsWp9tk6bTucNme2VXOa9bpsm3SgfWs+8hPqZxltSN1U6NSmjnd2A9/u6nphneYTs6+utJ9oz3YCPrPrl9jcWjqcCiGEOJvUrZkwVFkDBjf/pt/HP8bx/qrixrfbSnE6vD0M3hrWtPMbClJ0rjbBR3IDz0q133Z38Hvsd4l1XWo+hBBCnFXM/RLMVv/b2r/hVM0Ftny6nfocaL+aj7Q96rKysGnZSbe+oy6d6sy74tyE4MOWxqn+6Ja6ukifDwk+hBBCnFpJFuxfar8vP17dD+ARUP+ahngEN348qL+6rGqnPh+2k7cdXgEfTYYTaxs+/+hKdWmqsd+vs2l2yTtx6ue6+YHTKT6WA3uf+j6dgAQfQggh6lMUqKm0bn881X6ki1nc7+qyOTUfTk7Q+8KGjwf1VZdtWfORtA3Wv6COXKkssu7/aR5k7IPFVzm+rrF+IS6eEDII0KgdSh39/mw1ViN053oYdBVc8WHj9+gkZLSLEEIIq9hV4KyHg9/DkZ/g/h3gHQ7FaY1f5x7YvOfMXqp26vzrVQjoCdves3bcNHe6bIvgozgd0MDnF6nbniHNu/6rK6zrNy1XZ+Q1By8uHmr/lj4XqUFa4mZY87Saiv6Gb9VhtE46a42JucbHkYiRcPVnzStbBybBhxBCCFVlESy9zn7f3sUw+fFTXxsysHnPctJCYC+4sja51r6vrcGHeeRMtU3wsfUd8IuB/jOb95zG1FTAG3U+8LOPgHdE0++Rvte63mu62vHWEnzUvo7I0WrwEfeHdf6XigK1c6mzHqrNwUff03sdnZA0uwghhFA5akLQualNMLaGXG+/PfgacPM9s2frbFKSmxNzmWs+sg6rnVu/vbHpw1+boshBbY5ism92aYxtn5RBtc0ytvO6mIOowNr8Hrb9PlK2w+Ef7Sfji57YtOd2AVLzIYQQQmU7sZuZs2v91ODnPwdOzmpmUmh+k4sjUeOsKcrN2VLNwUeJzSRzJ9bB4KvP/HlgHaljS1GaHnzYjv657H11aTtU1sWjdulZ/3lL6wRwABEjmvbcLkCCDyGEECpHwUdJJvz9nnXbPRC8QuxTpNdNl346pj+t1rIMulrNCArWmoVSmw/tolOkKW+OEgcz5xqr1eRpjphM9qNRymozmYYMVnN6gH3NhznoMC8bSxA2+1v7WpAuToIPIYQQKkfBR91p3udtU5fmb/UArt5n/mxXb7jgBXW9IFFdmms+zB/yoAZDLcXRvcpyG5459uAyGHKtdb4Vc/lsX79tsjVL8FFnlltHmjNaqAuQ4EMIIYTKHHx0G6N+ANcdGtr3YvCszdFhF3y0QM2HrbrNLqW2wYeD2orT5eheWYfU0T2OrLgLKvLh6M9qLcWAy9T9tvPaOGp20TUh+HDWN63MXYQEH0IIIVTm4MPFA2Im2QcfWheY9bZ1u1WDj9qaBPNoF9s5X4pbOfgoyWi8U+uqJ63r5nLazujbWJ+PxujcTn1OFyKjXYQQQqjMk8S5eNh/oALMegs8bZoGbI83NzfGqZhHiZhrPmzzfdSbxO0MNNSE01Cfj7qO/aIubSfVc7YJIsyjXJrS7HKW1XxI8CGEEEJlW/Ph6mt/rO63d9vOk4EOpoo/E3WbXWwnmGvKXCtNkX0MEjc1fo7GpgNo5DkNn2cbiPU+H/x7wIRHwDdS3efchFqNptSOdCESfAghhFDV1H6wu3iAR53hs7bNLKCmITc70xwfdZk/zI3VsO55+5qPGgedYk/HriZkC/W0mYNmwkP2x2xzcuhtOpx6BMKDe+G8p637Gpuvpec0tTmrpX+HHZz0+RBCCKEqTleXbn71R1/UbYYZdBUc+FZNHd7SbGsB6o62aYmaj9jfYUdtZlW9D1Q5yOuhcVKHzZr7hfS+EMb/Q60xGX27OmHc+7W1Icaa+tc3hasP3Lzi9K7t5CT4EEIIoTJPJR82zP5bP9RvFtB7wtzfWqccjeW7MFap08qfSU4M2wRfDx2ADS+qwUjESGsnW88Q+1EqTk5qcjUz26yv3mGnVw6dx6nP6aKk2UUIIQQYDdYMo+HDa/NVaKzH9R2oT0JNA7Uf+7+Bkxuafh8nndrcMeNl+Gc8DLvReswrFGa8pGZynfhY/Ws1Grh/N0z7j/11DXnsRP19TemI2kVJ8CGEEAJW/8uaXMs7HLTO9k0tHalDpKOml5QdsOJu+OpydUbexphH59z4nbrUaMAjAAbazFBbnKHWhDyZDNP/4/g+gb1g0mNNG6niGQQ3/wiDr7Xu07dAcrZOSoIPIYQ4251cD9s/tG6bmzQ0NjUfdUe/tAfz6BNHNR8Jf1nXf7i94Xuk7ITSLHXdv6f9MXd/8KptQgkdrC7rdrQ9Ez2nwrDZ1u26nXrPIhJ8CCHE2e6rKxzvt53MtrERG63hagejUcwJvBwFH7nHm3bfT8+zrjtKjnbHOhh9J8x8o2n3ay7bYbctMSFfJyXBhxBCnM1sO04Cdv08GpsIrbUNukrte2J2z2ZrFlBHzS7m2gyzmiYkCnPU7OETAZe8po5maQ3mCejAPhvqWUaCDyGEOJvVTSV++xqbjbqBSRurqbCuhw62jg4xZ2K1ZTtdPUB57qnv39a1OWAf8JxlWU1tSfAhhBBni/J8OPyjfa1AYZL9OZGjres9pqhLr9McSnqmptTOozL8ptpy1HYUNecjsWU7+RzA/wbW32fbL6S9+PewrtfNnXIWkTwfQghxNljzFGx5S13vMRVu+VFdL0xu+JpZb0Fgbxh+c6sXz6GBV6hNLz61acr9otVlQaL9eUaD45qOXx+F676ynrNolvXYnF9burRNo9HAbX/A/qVq35KzlNR8CCFEV2eosgYeAPE2uTBW/9u6fsVH9td5BMJ5z0BAnVEhbckv2jr6xi9GXeaftD8neZvaP8XVF2a+Cc61/SqOrrROHlddYn9N9IRWKnATdD9HDew6Uu6UNibBhxBCdHXVDuZDURRYdKm15mPYTTD0+vrndSRhQ9Rl7CqoKLTuNzen9LkIRs2Ff2epKeLBel6Vg34iot1I8CGEEF2dow6apdmQsNG63X1s25XndPWYqvY/qSmDnGPW/eZ+K8H9rPtcavtTmF+7o9+BaDcSfAghRFfn6Ft/bpz99tAb2qYsZ0KjsQ6BNTengLX2xnZ4rLlJwzwjblWdZhfRriT4EEKIrs5Rs4ttzcHsb9V06p2BOTW6Oa/HH/9S+3wA+EZZzzOngzfXeMStapvyiSaR4EMIIbo6R00Ov9VOltb/Uuh7UduW50x4harL3x+HsjzY86X1WHB/67ql5qMUTCbY9Lr12AX/bf1yikZJ8CGEEF2dOfjwjqh/rNd59fd1ZLav4dUeUFWsrs9dZT8Pi23NR368/T3Ova91yyhOSYIPIYTo6szNLoF96h8bdGXbluVMDZ3teH/d12ZO4FVVAuuetT9mO2GeaBcSfAghRFdnrvmom1cifETny7LpFQJPFdTf71pnnhZzzcfh5Wq+D7N+M1uvbKLJJPgQQoiurrK2acKlTvBx0UttX5aW4GhOFq3Ofts9QF1mHrTu84uGy99vtWKJppPgQwghujrzUFSfbtbmias+7Ry5PRpyzin6bYQMqL9v6Gxw9Wmd8ohm6SRjq4QQQpyW3OOw+3N13S9GnVck8wDETG7fcp2paf+GpC2Qsc/x8dAh9fcZa1q1SKLppOZDCCG6sp2fWNf9osDdX52ttrN3unRxh+uXqHk/Jv2z/nG/KDivTkfTsKFtUzZxShJ8CCFEV2Y7zDR8RPuVozX4RMCjsWotiCMTHrLf7j/L4Wmi7UnwIYQQXVneCXU551e1tqCraWoNzug7On9tTxciwYcQQnRlZXnq0pyW/Gxz4w8w8AqY+q/2LomwIR1OhRCiqzJUQVWRum4eenq26X2e+iM6FKn5EEKIrqosV106OYOrb7sWRQhbEnwIIURXVV4bfLgHOk7MJUQ7kb9GIYToqkoy1aVnUPuWQ4g6JPgQQoiuKu+kuvTv0b7lEKIOCT6EEKKrMc/lYh5mG9Cr/coihAOtEnykpaVx0003ERAQgLu7O8OGDWP37t2t8SghhBC24jfCS5GwYQHkxqn7JPgQHUyLD7UtKChg/PjxTJ06ld9//53g4GBOnjyJr69vSz9KCCFEXb/Vphrf+JJ1ErWQQe1XHiEcaPHg4+WXXyYyMpLPP//csi86OrqlHyOEEMIRvZd1vbIInHQQ1K/9yiOEAy3e7LJy5UpGjRrFNddcQ3BwMMOHD+fjjz9u8PyqqiqKi4vtfoQQQpyG4nRI22W/L3wYOLu0S3GEaEiLBx/x8fF88MEH9O7dmz/++IN77rmHBx98kC+//NLh+QsWLMDHx8fyExkZ2dJFEkKIs8P759TfFzO57cshxCloFEVRWvKGLi4ujBo1iq1bt1r2Pfjgg+zcuZNt27bVO7+qqoqqqirLdnFxMZGRkRQVFeHt7d2SRRNCiK6rugxeDK+//4E9ENCz7csjzjrFxcX4+Pg06fO7xWs+wsLCGDBggN2+/v37k5yc7PB8vV6Pt7e33Y8QQohGpO6GpTfAT/erQQdYO5rWJYGH6IBavMPp+PHjiY2NtdsXFxdHVFRUSz9KCCHOPuX58Mk063b0ROg5DfZ93X5lEqKZWjz4ePjhhxk3bhwvvvgi1157LTt27GDhwoUsXLiwpR8lhBBnn8Ik++0VdzV87k0/tG5ZhDhNLd7sMnr0aFasWMHSpUsZNGgQzz//PG+++SY33nhjSz9KCCHOPiVZTTvvro3QS6aSFx1Ti9d8AMycOZOZM2e2xq2FEOLsVpLR8LEL/gur/wUeQZJYTHRorRJ8CCGEaCXmmWp7nQcn1lr3X/sV9J8FESPUwEMrb++i45KJ5YQQojMpz1WX4cMhdLB1/4BLQaOBqHHgKqMGRccmwYcQQnQmFQXq0s0fNPIWLjon+csVQojOxBJ8+MHwm9X1iFHtVx4hToM0CgohREeTeQg8Q8AzqP4xS/DhC0OuBf8YiBjZpsUT4kxJ8CGEEB1J3kn4cDxo9fDwIfAMtj9eUagu3fzASSvDaUWnJM0uQgjRkeQeV5fGKkjcXP+4bbOLEJ2UBB9CCNGRlOVY16tL7Y+ZjFBZpK67+rZZkYRoaRJ8CCFER1KWbV2vKrE/Vp4PKIAG3APaslRCtCgJPoQQoiMpy7Wu1w0+zLUi7v6SREx0avLXK4QQHUmpTc3HnwvAWQ9Zh8G/B3iFqvs9HIyCEaITkeBDCCE6isTNcOh7+31rn6l/Xk5smxRHiNYizS5CCNFRLJrVtPO0utYthxCtTIIPIYToKBRT086b/U3rlkOIVibBhxBCdDY9p7V3CYQ4IxJ8CCFEZ6PRtHcJhDgjEnwIIUR7K0qD35+0bvtE1j9HkoqJLkRGuwghRHsy1sDH06A007rvns2wbwnEb4D8eHD1AY0TpO5sv3IK0YIk+BBCiPYUt8o+8Oh7sTpj7bnz1B9FUZtZ4lbDkmug/6XtVlQhWooEH0II0Z6O/Wpd7zkdrvjI/ri5f0efC+D+3eDbve3KJkQrkeBDCCHaU8p2dXnTcug1vfFzA3u1fnmEaAPS4VQIIdpLVYnapwMgbGj7lkWINiTBhxBCtBdz4OERBB6B7VsWIdqQBB9CCNFeqkrVpQyjFWcZCT6EEB2bokDsKshPaO+StLzqMnXp4tG+5RCijUmHUyFEx5a6C5Zep64/U9S+ZWlp1bU1Hy6e7VsOIdqY1HwIITq27CPW9eL09itHa7DUfLi3bzmEaGMSfAghOo/0ve1dgpZVU64updlFnGUk+BBCdGwVBdb1orT2K0drsDS7SPAhzi4SfAghOjbb4OPgd+1XjtZgaXaRPh/i7CLBhxCiY7MNPlJ3QmFy+5WlpZmDD530+RBnFwk+hBAdW0W+/XZBUvuUozXIUFtxlpLgQwjRsVUU1tkucHhap2R+La4+7VsOIdqYBB9CiI7N/AGtdandzm/43M6mKFVdeke0bzmEaGMSfAghOjZz8OHfU13u/7b9ytLSzHlLfCT4EGcXCT6EEB2bOfgwT7yWvBX2LW2/8rQUQxWUZavr3t3atyxCtDFJry6E6Ji2fwS5x62JuGxHhOz7GobNbp9ytYSKAsg6rK47u4K7f/uWR4g2JsGHEKLjqS6D3x+3bmu0cOGLkPI3VBZB8jYwmcCpk1beLpwKBbUT5XmHg0bTvuURoo110v+5QoguLeOA/babHwT2gn+eVLdNhs476mXPl9bAA6SzqTgrSfAhhOh4sg7Zb3uHq0utzjostTzXwXVHrEGJyQSb34Tkv1utmKdl6zv223rv9imHEO1Igg8hRPsqzYZvboTja637zENQzXxsOmRqtOryi5n25yT/DR+cC28OUYOQpM2w9mn47MKO0UG1phI+nAC5cfb7e0xpl+II0Z4k+BBCtK91z8GxX+Drq6z7SjLtzynNsq6b83yUZUNZnnW/ubakqhg2vWYfwGz/oGXLfDqSt0HmQft9Ab1g9B3tUx4h2pEEH0KI9uVorpaSdPvtUbc7vjZ5q3W9OMO6nroLfrzXup19FIw1p1/G03ViLax8EGoqQFPn7fam5XDP5s7baVaIMyB/9UKI9uXsal03mdSluebjph9gzm8w9HrrOdd+ZV2P+wM+ngbxG+1rSwrrzP9irIb8BNj1GSRtpc0svgr2LIJXe9ev9eg1HXRubVcWIToQCT6EEO3LyWbEv7nppLy2OcUrHKLHg5PWes6AS2H8P9T1vV9B2m748lLI3N/4c3Z/Dr88DJ/PaLmyN6bYpvamugRW/8u67RXWNmUQooOS4EMI0bZMJohbDVUl6naJTXPJzk/AZLQGH25+ju/hE1l/X92ahbr+ft+6rihNL+/paqw8c39r/ecL0YFJ8CGEaFu7PoUl18CyuZATC+l7rMfSdsP3t1m3Gwo+GsuN0et8++2QwfXPqSxscnFPW+5xx/tH3Q7+PVr/+UJ0YBJ8CCHa1t+1I09OrIGU7eq6e4C6zD4KR360nqtzxaHGpqAfZDNq5h/7wddBLcmq/7P2L2ktx1c73t9Y2YU4S0jwIYRoW3ov6/rJDepywOXgpAPF2LR7uHg43u8eCEOug8lPqunY/aLtz3XSqcv9SyDu9+aWvOkKUyBho7ruF21/zJwwTYizmAQfQoi2YzJC3knr9uHl6tI/pnmTq9kGMADnzAP/nnDLT+rQ1anz4dz71GPlNrlAgvtZ1wvqjIhpSUUp6tInUq19eTTWeqzXea33XCE6CZlYTgjRdnLj1JEfdQX0Ajd/+2Rig69t+D51az7GPQAXLXB8ru0QXDebAGftM3DuvFMW+bRs/1Bd+nZXl16hcN6zaq4P/5jWeaYQnYjUfAgh2k7dCePMQgdb+30ARE+EKz5s+D4unvbbbo3UmnQ/V116BMMFL1j3G6sgJ87xNWeiugyO/KSuVxVb9094CMY/2PLPE6ITkuBDCNF2zNlMh98Es7+x7veOAHebkS0hA+1ze9Slc6+z3UDHVIDpT8G0/8AdayFsCMx4xXqs7gR2hcnw12vWYcCnozDFui75PIRwSJpdhBBtx5x51Kc79LkILn1HHXaq0djXfHgENX6f5qQkd/OFSY9Zt0ffCb8/rq5/P1fNsNrvYnX0y6JL1enuy/MabsY5Fdvsqhe9dHr3EKKLk5oPIUTbMXfE9I1UA44Rt0D0BHXfsBvBu3b22rChTb+neQRLk893Ujunmn0zW818+sE4NfAA2P7R6SciM9fu9JsJAT0bP1eIs5TUfAgh2o75g9ncEdNW5Bj4xz61g6ij3Bx1OTmDyaB2Vm2umImQbzPqZtdn9scVI6TuVMvUXOYOrtLkIkSDpOZDCNE2TCbrNPeO0qMDaHVNCzwArlwIvS+AmW80vyznPwcBvRs/Z9Gs5t1TUdTZdHNrO7HaNiMJIexIzYcQom2UZqmzy2q0jadHb6pBV9lnM20OVx+Y8ZI662xdLp5QXQqGSjWg0GhOfb+D39fOmLvFuq85eUuEOMtIzYcQom2Y+3t4h4O2A3zvcXYwnb1fNDxyxLpdlnvq+6Tthh9utw88oPHhv0Kc5ST4EEK0DfMHuWdw+5bDLHKMmk/EzCMY7tms1oqYO77mx5/6Psd+dbxfaj6EaFAH+PohhDgrVOSry4Zmqm1rWh3M+QViV6nzvJz3rDVtu2cwFKday9yYikLH+3UOalaEEIAEH0KItlJuDj46WI1A34vUH1v62gyqVaWnvr66zPH+sGFnVCwhurJWb3ZZsGABGo2Ghx56qLUfJYToyCoK1GVnaI5wqa0BsU2PDo5zf1TXCVA8guCRY+DiXv9cIQTQyjUfO3fuZOHChQwZMqQ1HyOE6AwqOmjNhyPm5pdfH1ETnnUbBT/dD8fXwN0b1YnizMw1H1d8BEOvV4cUNycDqxBnoVb7H1JaWsqNN97Ixx9/jJ9fB2njFUK0n/IO1uejMXqbiesWXQrJ22HvV1CaCZvfVJOl7focDFVQU66eZ55pVwIPIU6p1f6X3HfffVxyySWcd955rfUIIURn0pmaXcw1HwA1ZfDZBdbt3Fj44U745SH44/+sNR/m4EMIcUqt0uzyzTffsGfPHnbu3HnKc6uqqqiqqrJsFxcXN3K2EKLTMgcfnaHmw8Wz4WMn11vXd37StGuEEHZavOYjJSWFf/zjHyxevBhX10amua61YMECfHx8LD+RkU1MrSyE6Fw6U7NLZVHzr5GaDyGarMWDj927d5Odnc3IkSNxdnbG2dmZjRs38vbbb+Ps7IzRaLQ7f/78+RQVFVl+UlJSWrpIQoiOoDM1uwQPaP41EnwI0WQt3uwyffp0Dh48aLdv7ty59OvXjyeeeAKtVmt3TK/Xo9frW7oYQoiOpKYCDBXqemcY7TLkWnXW279ete6buwoSNsKfCxxf4xnSNmUTogto8eDDy8uLQYMG2e3z8PAgICCg3n4hRBdlMqkf0hEjoO8Ma5OLk7N9Z86OykkL4x6wDz4Ce1trb+r6Z7xkNBWiGWRMmBCi5R1dCX+9AkuvV7fLa+d1cQ9o2iyxHYHe237b1ReC+1u3u49Tl0H9wSOgzYolRFfQJunV//zzz7Z4jBCiI1AUSNxk3TYaoCRTXfcKa58ynY66QZLWWZ31tu8lYKiEqz+DjP0QIjW6QjSXzO0ihGhZe760H4JakgEn1qnrnSn4ALUfR2mWtZ+KRgOzl1iP95jcPuXqIlJSv8RgKCEm+r72LopoY9LsIoQ4fcXpkJ9gv8+2nwTA5v/Bjo/Uddu05J3B9UvV5pVbV7Z3Sboco7GSuLhniY9/g/LypPYuTqejKAqPfrefaz7cSnJeeXsXp9mk5kMIcXoUBRZOUWsGHo2Fsly1GcK3OxTZDJnf9al13TuizYt5RrqNhNt+b+9SdEkVlda/kcrKVLRaN5ydvdFqT50fypG0tKUARETMbpHydXTJ+eX8sCcVgF8OpjNvSq92LlHzSPAhhGiaNU9D1mG49B3QucKyuWrgAfDOyPqzu+q9688KO/S6timr6PAqK6zBR3bOatL3305Q0AUMHvR2s+91/MQCkpPVpr7g4IvR6XwaPd9orCIh4U0CAqbg5ze22c/rCHJKrJnBUwsqqDaYcHHuPI0ZnaekQoj2U1kEW96EE2vUIbS/PALxG6zH6wYeoM4Ga2vqv9RaEXHWS0n9iv0H7rRsp6UtRlFqyM7+FaOxqpEr6ysuPmAJPAAqKk7dhJOZuYKk5IXs2XtDs57VkfxyIMOyvmR7MqNeWEN6YUU7lqh5JPgQQpxais08TfnxENuEpoiIEfbbAT1btkyi00pIeKfBY6WlR5p1r4KCv+22y8sTT3lNdXWOZb2mprBZz+sIqgxGvtiaaLevuNJQb19HJsGHEOLUsg9b1/PjwdnFuu1oqGnEqPo1H66NV4WLri8vfzM7dl5GTU1eg+dUVWU3656Vlel224ePPExW9m8Oz1UUI4qioCgmy77S0mPNel5HkFXkuHZof0ph2xbkDEifDyFEw0xGtZllz5fWfcVp9udc9BIoJnVY6q7PIDcOZn8D6Xvtz3PtBBPKiVa1b9+tDR7T6QKoqcmjupHAxJHKSvXv0dU1wrJ+6NADhEy72HJOcfEBMjJ/JDV1Ee7uPfHxsdbKNTfY6QjSi6zNK5cNC+enfWoAtj0hn6ziSkK8T6/TbluS4EMI0bB9X9cfOmvrlp8gZqJ1++JXrOve4fbndoa06qLNODm5MGzo55SXJ6LVulNQsI30jO+oqc5v8j0URaGsPB4AP79xZGQssxyrrEwnNW0JJlMlKSmfW/aXl5+0a2qxbYLpLD7ceBKAUG9X/nftMO6e1JPrPtpGSZWBtMKKRoMPo7GcnbuuQq8PYeiQj3Byap+51ST4EEI0zJwczGzUbWrtBoBnKPSY0vC1dROKBfZu0aKJzsVkMthtBwRMw8/vHPz8zgGgtCwWgOqapgcfhUW7qKhIxMlJT2joZXbBx7HY/5CX96fD62ybfao6WfBRYzTxZ6xa5om9A3Fy0jAg3JsewZ7sTym0GwXjSFVVFmVlcVRWpqHRuDR6bmuSPh9CiIbV1EledO791nU338avte0XMuPVzjOni2gVu3dfY7ft4mI/H46LTs0iW1YWh6Iop7xfVVU2x4+/AEBo6BX4+Z5Dt4ibLccdBR6+vvWH1VZXda7g40BqoWX9nxf1tawHeao1GLmljQcflVXqVAd6fQiadvw/KcGHEKJhlTZ5OgJ62Y9YiWlCavEp86HbaBh2diR+Eo4ZjRUUlxyw2+frM9Ju2929BwAFBds4fuK/GAxlHDz0ADk5qzEYSjAaKy3nZmevYvOWcykpOQRAdNTdaDQa+vZ9hn59X3BYhm7dbmXE8K8JDb3cbn9Vdefq87HwL7WZacagUIK9rM0rQV5q8HGqmo+M9O8BNfhoT9LsIoRoWGWhdT1ilLq8cz3s/wam/fvU1095Uv0RZ7Wq2m/bZn36PE1w8MV2+wICJuHuHkN5eQIpKV+gKCays38jO/s39C4haJ09OGfsH4CaVMzM338ibm7W/DEhIZdyLLb+32ZU9zvQaDQMHPA6EeGzKSs/ybFj/9fp+nzsSS4E4NZx0Xb7Q2v7eRxOr5PYr5bRWEV8/OtkZv0ItP8QY6n5EEI0rKJAXbp4wgXPq+sRI+HiV8HVu+HrxFkt7vgL7Nl7EyaT+i38+ImXLMdGjPiGyG634OSks7tGo9EyZvQvODv7AAqFhTssx6qqsygvj6emppCysuNUVqppxQf0f5Uhgz+wu4+zswcuLkH1ymT7Td/XdxQ+3sPVe7fTaJes4kqu/mArP+5NO/XJtSqqjZaajX6h9h24ZwxW501afyybyhpjvWtTUr8gOcU61YG/37jTKXaLkeBDCNGwikJ1ee9W8Axu16KIzsForCIl5XMKCrZRUPA3imIkN3et5bif7+gGr9VqXfH2GgxAaenReserq3OIj/+feh+/cYSFXYlW61bvvKFDFqJ3CaFXryfRaj3w9R2DRqO1O0evV/+eDYaiZmdVbQn/WxPHrqQCHvp2X5OvSS1Q+2B56Z3xcbMP3noHe6LTajCaFPLLqutda5vOHiA6el7zC92CJPgQQjhWUwHmN+VTdS4VolZ5+QnLelVVjl3Cr1695p/yenf3mAaPZWb+SE7uGgC6R97W4Hne3kOYMGErUd3vZML4LQwf9mW9c5ydfdBq3QH4e/v55OZuqHdOa8pzECCcSmxWCQBRge71OotqNBp83dVO3gXl9veuqDaSW1ppt0+n823281uSBB9CCMfMScKcnMFFcnSIpiktjbOsFxXvITb2KQCCgi4kqvsdp7xerw9t8FhS8kIAwsOuJTBwapPK4+zsVa+JB9QP624RNwFqorJDhx9q0iib05FdUsklb2/i47/i+Ssuh9SCctYcybIcd9RM4siWE+oQ4THRAQ6P+7mrr7OgrAYAg9HEtR9uY/izP1JVvMJyXt3+Nu1BOpwKIRw7/KO67HcJOMn3lLONoijk5q7Fx2dEvWGxjamo7Y8BkJ7+LQB6fRj9+73U0CV2zM0hZk5OLphM9t/ke/R4pMnlaUxk5G2WgMZoLMVgKEGna/m+TO+sO8Hh9OIGO4OmFpTTK1gN8I0mhQOphQwI90bvbG0qMhhNrDmidtyd1CfQ4X38ams+3loXx7sbjvPAtN7sSMznnLCDlnNCQi6lT+//tMjrOhPyjiKEcKyo9kMkZlL7lkO0i4yM7zlw8B62/X0+SUkfYTCUWI6ZTNXs3jObo0frN6OYU5zb8vYa1OQPdduOoa6uEUydcpQeMQ9Z9rm5RaPX1+9Qejr0+iCGDrV2wqyqymjk7NNzILWQr/5ufKbdnBJrcPXF1kSueH8rz/5sP8Hetvg8ckur8XPXMb5X48HHzsQC/o7P56mfDuGkMXLn4K8s5wzo/wouLv6n+3JajAQfQgjHSmon7PIKb/w80SWlpX8DqB0yT5x8hZSULyzHSkoOU1i4g/SM7ygtO27Zn5OzmqysX+rdy8OzT5Ofa9vsYs794e0z3LIvKOj8Jt+rKQIDpuDp2R+AyhYOPkoqa7j03S2nPC+vzNrh9c01arPVku3JduesrJ2/5eLBYei0jj+6/TzsM5aezCmjh4818NldPJ+CchMf/HmS4sqapr2IViLBhxDCseLa4MM7rPHzRJdjMlVTUnLYbl9u3kbLeo2hyLK+b98cyssT2LtvDgcO3ovJVGF3XXjYtUR1v7PJz3Z3j7asu+rVvz0/39EEBkzD23sYkd1uac5LaRLzc6oqWzb4+Csut9Hjk/uoNTi2zTH+ntYAwtwHpbzawKrDapPLpUMb/jIQ4WtNOubjUoSPSxE39Pvesu9w/mCeXH6Ql1cd49Hv9jfjlbQ86fMhhFCVZkPsbzD4WkjdAaW1HeK8u7VvuUSbKys7jqLU4OzsQ3jY1SSnfIqTk/VDsbrK+qFaVZXJ9h2z7IKOoKCLGDzoXRTFiJNT8z5mNBotgwa9Q3Lyp0RH3weAk5OeoUM/PsNX1TC9q1rbYq75MJkMaDTaM0o/fjSjmPuW7AGgb4gXH948kg3HsnnuF2tzijkr6Qd/nmRav2B6BHrg5Wr9feWWVhPkpeeH3amUVBqICnBndHTDTSYRfuqw4xjvJP5v7Bs4aawdaHdmDmPbyXyqjSYA1hzJosZoarAWpbVJ8CGEAJMR3h4B1SXqENut76j7o8aDZ8u0r4vOo7JSrfVyd48mMOh8klM+tasVqK62/0Zft7ZjQP+X0Gg0aDSn9xETEnwxIW04IsNc85GY+B4e7r2IT3gLnc6XUSO/P+0A5Md91r4vn9w6ikh/d2ImxLDuWBZbTuRx+bBwqgwmyznXfLgNV50TlTXWfSkF5QR56dmdpCb7u2pEN5ycGi5PkKda83FZr1/tAg+AEmMvS+ABcF7/YIoqagj0lFlthRDtJe+kGngArLJJh37Fh+1THtGuDAa1GcDZ2RtXvVrNX1mViaKY0GicqK5puDkhPPx6nJ0719BsF5sRNoePPAxARYU6OZ2v76jTej1pBWpAdsu5UUT6u1v2v37NMFbsTePmc6NIK6jg5/3plmO2gQdAakEFA8K8+bG2v0fdrKZ1De7mg6+7Dl9X+6G7YeE3E+10GRxW7+Pl6swntzac7K0tSPAhhIDcuPr7vMLAt3v9/aLLM49scXb2qh36qkFRqolPeIuysuOWeUH0LiFUVavNc56e/fHxGUHPFhoG25Y83Hs53L//wB04Oek5Z+xq3Nya1/yYXqgGH+f0sB+mHOrjyr1T1Aka+4Z68fUdY7nxk+0O7/Hy78d4cOley3a/0MZHDPm46Vjz4CAO7LF2Ah537hbc3EKpci8A1OAjwrd+Vti2Jh1OhRCQc6z+vsDebV8O0SHUWIIPb5ycdOhd1JqBxMR3ycn5g8JC9cMyMOg8yzUR4dfTr+9z7Z4583R4ew9lQP9X0en86h0zmarqzcjbGEVR+HZnsmUCuFN90A+L9CXE277p444JapbXtEJrc5arzolwHxOK0nhCsuST1uBv0sQ9uLmp/Vn6h1kDF9vmnvYiwYcQApJqhwMG2HwDDBvWLkUR7c/c7KJzVj+w9K71RzzpdP52Kc59fEa2TeFagUajISzsSvwamGytOaNgdiYW8MQPalKvIC89fU/RVOKhd2btI5OZV1sbAnDnpB7onZ3QaoxMi9xIuEcGT13kxpatE9mz9+ZG71dYtNOyrtP5WNZddVrO6aF2Vp3at/3naZJmFyHOdjWVkLRVXb9uMez8FI79AmPuat9yiTYVH/8WxSX76dnjUVJSPgPUmg8AN9duFBfvszs/ImI27u7RjB61gorKNLy8+rd1kVtc3cnnzI6feBFvn2H4NiHASswts6z/79phuOoc39OWl6uOe6b05GBaETMGhRHi7cpvD4wg8bAaDKWUhONvjMBgKKawcDsGQ4nDfijFxdYaGn+/CfWOfzF3DGuPZjGhgSRlbUlqPoQ4Gxiq1CDDkZS/wVCp9vEI6geXvAaPHgPfyLYto2gzlVWZ7N5zAzk56iRtFRVpJCS+TV7eRnbsvNRynvnDOCBgcr17eHiozXLe3kMICZ7RBqVufTHR9+Hu3pNBA99m2LBFdsd2776WyibUgOTWJgyb0CuQCb2b/iHv7arjq9vHcsNYtZ+Vr7O1KTTSKx2dYbdlu6TEPvtpZWU6hYW7OHrs/yz7hgxZWO8ZrjotM4eEWyaga09S8yFEV1ddDu+MBL0X3Lke9J72x81zuPScBmeQ10B0HidPvEph4XYKC7czfdpJ4o4/6/A8c2bS4OAZpKUvpahoj+WYeXhqV+Lh0Ytzz1lt2T73nHVs+3u6ZTs27hmGDvkIRTERd/w5qqty0bn4oyhG+vT+D0nJC9FWVNHdy4uhEQ03bVRUpLBnzw0EBV9In97/dnhOZVVmnT3Wfhr5BVtwc4vE1TUck6mGLVsn1rteq22fIbRNJcGHEF1d5gE1VXoJsO9rGHu3/fHjtW+2g69u86KJ9lFjKLSsZ2auJC/vT7vjvXv/G1d9OAH+6rw+Wq0bo0YuQ1EUdu+5lsrKdEtK8q7M3KwUn/A2eXkbyM1dS1b2bzhpdKSmfmV3bllpLEXFe+mlh6fPhQrNSOC7evc0mQzExj1DZVU6KSmf07vXvxzmEqmqVIMPZ2dvFMWAohjx8zuXvLw/SUx8j8TE9+gWcTOpaV/Vu3b0qB9b5PW3Jgk+hOjqMq0zWpK0xRp8lOeDokBxbTKk0CFtXzbRLkwm61wi5rwWnp796df3eQyGUgIC6n+TBrVj5ojhXwNOzc5c2ll5ew8hqvud5OVtACAu7llcXesPuy0q3mu37abspqBgO35+Yy37cnLWcvDQPLsRK7l56wkKnE5dVbU1H90jb6Nbt5swmWooKT1iFyg6CjwA3Nw6fpOp9PkQoquL/9O6nrxdDTjK8+GdEfCqOnEXWj24N33adNF5VVfnU1xcf16PoKAL8PEZ3mDgYebk5HLWBB5m3t6DLetGY6Wl8+24czfg6Tmgwev27L2BzVsmcPLk6xiNFSQmvldvqOyBA3dRWlo/z455dmC9axg6nR96fTB+vuc6fI65/w2Al9fATjHcWYIPIbqyslyIW2XdLs2Et4aoo1kqCqz7tS7S36OLKireT3LyZyiK2mcg7vjzGI3leHoOwMtzoOU8X9/2zXjZkWm17kwYvw0Ao7EUACcnV4qrg/jPxtl25644fgnfxV1m2a6qyiAx6X3i4/+H1rlOf6taBw7eXW9feUUiAO5u0Tbl0NO//8t25w0Z/AED+r9i2e4WcVPTX1g7OrvCVyHONkd/BpNBzdnhpIW03VCYDCsfsD9v9O3tUjzR+nbtuhIAnc6X4OCLyclR+/j06fMUaWlLKClVZ6/18uzXbmXsDFxcAtFonFEUA6AGJD8fyCAuzz7r6C8JFzKxdyC+Pul2OTdy89aj0egc3ruiItluOy9vo6Xmw81mll+A8LCrCfCfRGzc06AoBAZOR6PR0r/fAsrK0/jttyL8/Zdz5ZVXOnxWTk4O69evp6amhptuar9ARYIPIboyc+bSmElq34603fbHJ/0Txt4DHu0/7l+0rrjj/6W45AAmUyUuLsH4+ozCRedPXt5fuLtHO8zuKaw0GidcdAGWdPIGQzF/xGYC9jWGs8d0558X9sVd+4bdKJTy8oR69+zW7WZLx1Xb3B1xx1+0nOOiq98cqtcHM2TwB3b7wsOv5dChQ6Smfk9qahr5+fn4+fkRHR3NyJHW/CTbtm3j6NGjgJqN9Uxm7j0T0uwiRFeWX/uG598DnF3rHx99pwQeXZjJZLCsGwyFlg86d/cYNBoNHh49GT9uIyNHfNteRexUDEZrArETVf9gZ6LadBlfZJ0DacGVg/H3cMHVNZxePZ9o8F4hwTPp0/spXF0jAEhO+RyAmpoiystPADBs6OfNCg4SEqwBTmpqKgcPHuTnn3+muLjYsj8rSw2eBg8ejKIo9e7RViT4EKKrqqmAjNqOhf4x9ft0DLkevELavlyizdTU5Dvc7+oabll3dvY86zqQnq7eveYDEBAwjY93qzlQNBq4Yuon+PtPZPgw+9EnkZG3MXbMb/Tq+bhlX/9+C+jd+9/07/8yGo0TUd3V/h4JCW9x6PDDZGX/CoCzsw8BAZOaVb78fMf/3snJyXzzzTfs2rWL7OxsACZNmoSTU/uFAPIXJ0RXVJoN741RO5W6eKrDaPPjYe9i9fid62XulrNAdXWuw/3mb9uieSIirsfTqz8FlUHklOxFp9Vw6NkL0TtrCRz2Rb3znZyc8fTsi6dnX4KCLsDVNRwnJ/vkX75+YyzrWVkrycpaCYCz1qPZ5TMHHxdeeCHr1q3DYFBrvr7//nsAjh1Tm2Hd3d3x9/dv9v1bkgQfQnRFKx+wjma5aAG4+8PwW9R9MVMgovNOAiaarqIyxeF+T8++bVySrsPHeyh7M9Tag5hAD/TOp567BdSmLkc83HsRGnoFmZkr7PZXVqU3q1w5OTkUFRUBMGjQIAYNGsSyZctITk6ud+7gwYPRaptW7tYiwYcQXZHt8NrhtbNgap1h4qPtUx7R5vLyN3Pw4DwAfH3HUl2dben0aM5cKk7PiWx1uG2vYMdDZ5tDo9EwcMBrDOj/Kjt2zqS09Fjt/lMHB7GxsSxdutRun6urK56enmg0GgYNGlQv+HB2dmbixMZzubQFCT6E6IqcdGCqgRG3SP6Os5DJZCAu7nnLdmS3WwgOvoj8/K04O3vi3EC+iVOxHR1RY6whqzyLbl71s30qikJcQRy9fHuhdWrfb9it4WhGCQC9g+vPLHu6NBoNQ4d8QmbWStJSF9O7j+M5X8ySk5PrBR6gBhfmf6OYGPvalvnz56PXd4w5X6TDqRBdjaFaDTwApj/dvmUR7SI5+RPLiIkB/V8hKOgCAPz9x+HtfXpp9BOKEpjy3RTe3vM2AK/vfp0Zy2fw9dGv7UZNKIrC838/z9U/X82ru149w1fSMR1KU5s3Bkf4tOh9XV3DiI66m/HjNxEcdGGD5x05coTPPvvM4bGxY63p3AMDA/Hz86u9t2uHCTxAgg8hWs+P8+C1vmqW0bZknqvFSScp089ClZXpJCapOSD69n2esLCr0GjO7K2+2ljNpT9eSn5lPh8f/BiAr49+DcBLO15iyJdD2JGxgxpTDcuPL2dZ3DLLOZ8e/PSMnn26TIqJnZk72Zy2GZNiOvUFTVRRbeR4tlrzMbhbywYfTZWammq3rdPpCA0N5Y477mDcuHGW/RqNhuuuu47BgwdzySWXtHUxGyXNLkKcyvoXYOenMO1fMPqOpl1jqFJnkAU49EP9mWRb06Ef1GXYUGly6eIUReHQoQeoqExl5Ihv0Wr1ZGX9jNFYit4lhPCwq1rkOeZgwva5zk7OGGzyiNy+2nGW3Lf2vMXMHjMJ8Wi7Yd0nC09y+U+XW7YfHvkwtw26rUXufSSjGJMCQV56Qrwd5M5pAyUlJXbb8+fPb3DYbGhoKFdd1TJ/By1Jaj6EaExpNvz1KlTkw45Pmn5d5iHruu0cKq2lPB9Sd4HJqAZKAGPubP3ninaVl/cn2Tm/U1JykJJS9W+usjIDgNCwK+sN6zwdNcYaPjtoX8WfVZ6FwWRAg4Z7h97r8LpPLviE/v79UVB4acdLZ1yO5libtNZu+3+7/0dJdUkDZzfPlhNqTWZLN7k0hzlp2IgRI7jzzjvbNV/H6ep8JRaiLSX8ZV3PjVMTdzmStA0WToHtC9XttF3217W2FXfDJ9Phh9uhJB107jDwitZ/rmg3BkMpccetnUrLy9SRLOYhmraJxM7Evpx9ZFdk46e3pl8314SEeoQyb9i8etdc2vNSxoSO4Y7Bak1hSzd9nEpaaVq9fStPrmyRe3+/W23ymDU0rEXu11wFBQUkJSUBMHToUCIimpazxaSY+PLwl7y9523Ka8pbs4hNIsGHEI3JPmJdV4yQcUBdryqxr9H49VFI3wu//xOyDkOqdUIpips3Xv+UDn4PH0yAja+CoqhNPMfVycI4XJsrwCcSnDtO5zLR8vbvv4OKiiTLdnn5SUymKgoL1b89V33LfDj+nfE3AOMixhHsFgzAwgNqkN3dS00rPjLEmjfm7alv898J/0Wj0TCt+zR0TjoqjZVklGWwN3sv96y5h4Si+vOctCRHwcfe7L1nft/CCpLzy9E6aTh/QOgZ38+s0lDJkbwjpz4R+HX7r5b14ODgJj9j+fHlvLrrVT4++DFjl4y1/Lu2Fwk+hGhM9lH77eRtapDxam/432AoyQJjjXUCN4APxsHBZfbXVBbTIgoS1dqNrIOw4QVI2mIf6Jh1P6dlnic6pOLiA5YZU80TwiWnfM6ePTdiMKh/a65u9YfAno7jBccBGBw4mGv7Xmt3zDzM9vlxzzOrxyx+veJXpnafajnu7ORMlHcUAHuy9vDQhofYkr6FB9c/2CJla4g5+Li6z9Xc1F+duXVz2uYzbnrZkZAHwKBwbzz1Lddl8t2973LdL9dZOvGWlJTU69cB8FfqX/x6VA0+hg8fjpubW6P3PZR7iJ2Z6t/Jzyd/tjt2z5p7iC+Mb4ninxYJPsTZo6JAHYGStLVp5xckQexv6nr/Wepy7dNq84qhAqpL1EDkp/vUWhEA21EFwQOt6789dsbFpywPkut8W/niEtj0ev1zx7Xum7toX+kZP1jW+/R+CgBFqaGo2Prt3t0t+oyekVCUwORvJ7MhZQMAMT4x3D30bvr597OcMzBQ/RuP9I7kxYkv0t27e737TOs+DYB/b/k3+ZVq+u/E4kRqjDVnVL6GGEwGMssyAbh7yN38c/Q/ifaOpqymjPXJ6+udbzQZqTBUcCz/GIqiUFJd4nDCNZNJYdUh9b5je7TsKLJFRxYB6sih9OJ0PvzwQ95++20yM9XnJSQk8Nprr/Hr578SUKk+28uv8RwjsfmxzP51NnetuYu8ijyO5R+zOz6732x6+PZo0dfRHBJ8iI7NZFSbFlrChhfVESifz2j4nKStkKt+02P5XerSPQAu/wD8HKRHLkyy9gtx8YKrP4fgAXDJ63D7aut5B85w1tD4P+HVnmrfjrpO1nlDvfF7COx1Zs8THVZNTRFpaeocPVHd7yYkxPEQSicn3Rk95+eTP1uCBYAYb/Xv/5VJr3Bu2Lk8MfoJrux15Snvc/ug2xkePLxen48j+U1rZmiurPIsjIoRnZOOYPdgnDROXBRzEQC/J/5OtbGaxzc+znv73uPG325k2FfDGPP1GK75+RrmrJrDuKXjGPLlELam239JeeHXo/xxWJ0R9pweLTcviqIoONl8aXnpz5coKyujpqaGnzertRU7duygtLQUN6MbgVXqLNTZpuxG7/ttrPqeYzAZ2JK+hXJDOc4aZ5ZcvISPzvuIx0c/3uj1rU2G2oqOqyQL3j8Hup8LV38GujMc1mbbPJGfoM70amasgcVXQcLG+tfN/B/oveDC/8KyOaDVq8NYkzarnUlLa98Ebl0JESNg4OWOn1/3mc0R9wdgG4Rp6mwDl76j1u70Ou/0niE6hdIyawfmbt1uQqPRMmjQOxw69EDtXg2DB713Rs+wzecBMDx4OKEeah+HGJ8YFl6wsMn3cte5s/D8hby04yX2ZO8hvTSdKmMVe7P2MjRo6BmV0+ynEz+RXJLMvUPvJb1U7WMV7hlu+VCf1WMWCw8sZEvaFkYubnheoz3Zeyzr7+17j3Hhas6M8moDn22x9lOZ2DuoRcqdVJzEnqw9doFZXEoc56A2m26P384cwxwSEur3kdlbspfrub7Be/+Vau0svyFZrb3q5tWNwUGDW6TsZ0pqPkTHlbhJHeIa+6vatHG61r8Az/ioTSRm6dY3GRL+gucDHQce3cbAgMvU9X6XwBOJ8FgsDK4dN39gmdrk4uoL4cPrX/+AzXN2NP0Nu56sw/bbM9+of87wm2H8PyS3RxdXXqZmLg3wn2QZ0WLbxDJ50l6CgxvOjulI7gcfcOKCC6nJUgPp9/Zag5fBgYN5d/q7lpTdp8PV2ZVnxj3DystXcv+w+wHYnb37tO9nq7ymnH9v+TcLDyxk0eFFpJaoo1HCPayjfbp7d+fC6Ob9TmyZ53IB+PXBCZSVFLN8+XJSU1P58ssv2bq1iU25NhRF4a7Vd/HU1qcs+7yrvTknx9pfq1t5N5auW0plZWW961flrmLwosEMXjSYfdn77I7lVuSSVZ5l2d6Yqr63hXu2zAioliDBh+i48k5Y1w99r9YcNNfxtWqejroy9qvL6nJYdGnD10eda7/t4qH+xExWt6vUNMuEDnb8oR/QE66oDTqS/wajof45TWHu+HrtlzD7GxhxK9y8Avxr22wveEGCjrOEuebD3cPatObp2Z+YmIfo3+8lnJ2bP99IzltvU5OcTP7nnwNwOM8a7M4ZOAdvF+8zLLXV2DA1/fem1E0cyj10irPrUxSF2PxYqo3VADyy8RHLsXf3vssfiX8AEOFlPwT1zsGO897cMfgOBgcOtnSMNSuotI5mS8pTh6aOjPJjYLgPS5cu5cCBA3zyySfEx8ezevVqTKbmDSWOK4gjvcw6Eu7KHldyftr59c6L36Z2Cs3T55Grt2ZLNjlZn2ebR2XlyZU8t+05u3vU1E63EOze9NExrU2CD9ExHVkJfy6w33direNzG2PuMAow/iFrR8yc2qrr727B0nwx8Aq16eKezTDsJpj0T/UaRwJ62k9LHzLQ8XkAkaPVZfoeeKl7/VqMUynLhbJsQKM2qfSdAU5a6DkN7tsJ926Fc+9v3j1Fh1JRkYbRWNXoOcdPLGDL1slkZ6szFnt7WavPNRoNPWIeIDz8mmY/21RdbVk3FhZiMplILlaH8E7vPp3zolq2Ga9/QH/Ojzofo2I8reRjHx74kKt/vpoP93+Ioijsz95vOWZQ1P4NABGe9sFHb7/evDjhRQCmRk7luXHPce/Qe3lg+AMsuWQJ/53wX7QaLWNCxwCQUpLCzsyd7Nixg8Pb1qPFSJS/OwBZWVnUZe4c2lR1h7qOcRrT6Pnp7ulsDNvI5IsmM/wy+1rWk4UnMSkm4gri+Nfmf1k6Cd8x+A48dB6W89yd3ZtVxtYkfT5Ex7RqvnW930w49gsc+an5WTuLaudAmPkmjJoLJzfA1rch7nf4419wYo16fNiNcOm7YM4UeHkT2szPfx6+uFhdb2xoq6/NN6qaMvW5t/zY9NeQskNd+kWrtS62tM6NBz6iw6mpKUar1VuyjyanfM7x4y8QFHQBQwZ/4PCapOSPSU62z7Dr4zOiZcqTZJMrJCuNHdPGMl9Tyr9vdealiS/ZdYZsKU+OeZJ1yevYn7OftNK0eoFCQzJKM3h/3/sAfHzwYyZ1m0RpTanDcx3dc1bPWczqOcvh+UODhrLumnV467258dcbOZp/lMc2PsbUo+rQ4ZtdwenEEVavdtzRMzExkfDwpjdrbMvYBgpo0DCr1yxit8faHfft60thbKFlO9c1FzQwaUztaz5gPbfSWMmWtC2W/i6gBlsPDH+AOQPnMOGbCU0uV1uRmg/RMdVWqeIZCjNeVoewJm6yBhN1Hf0Z1j2vjo6xZZ5kzac250GAzSiQbe+qy6B+cPn71sCjqSJGqIFFUH/o28ikTXWnFC/Lafozdn0G38xW16M73huIaBpFUSgtO055eRKbt5zLocNqU0F8/JscP/4CADk5qxu8PjHxfbttd/eeuLVAHo+ajAxS7rGmR6/ZthOfzFJ6ZcCwimBcnVtn7pJg92AGBQwC4JLll7Bg+wK74a01xhqO5h2tN+T13X3v2m3f/PvNAPTytR/dNaXbFCZGTGx2uQLcAtA56Xh6nDobdFF5kd1xU01lvf4d/fqpQ4+Tk5Ob/Jyqqiry4/O5KvEqrky8kt4neltSppuFRoeyJ2APB4MOsi14G/mu+fx42Y9onbT46H14Z9o7vD7ZOsz+vnX38eGBDy3bb055EyeNEz56H54b9xwDAgZYMs52BFLzIU5tx8fw9wdqM8OVC1u/b4GhGsrVZD7ctQG8wyF0CGTsg5UPqH0dAEwm2PKm+mH+d+2bc+ZBuPE7tRNp3B+QVdum7B1hv7Q1/an6+5pC5wb37wIUcHZp/NyRc2D3F+p6czKe/vKwdX3av5tZQNFRpKZ+Sdxxazt8Ts4qDh56gOzs3+zOMxor0GqtiaMUxcTuPbMticPMQkMva5Fy5X38CTVp9bOBAozIajyB1ZkaHDSYA7kHMCpGlhxbwpjQMUyPmo6iKDyw4QG2pG3hf1P+Z2n2Ka8pZ3Wi4wCtj18fhgcPZ1ncMv499t9c1++6MyrbwICBzIiZwa4juxo854knnkCn05GcnMyxY8c4duwYu3btYsiQIbi4NP5+8MOPPzAgfYBlOzEx0bI+YcIEiouLiYiKICHO2s9tRswMevr2tGxPiZyiLuOn8GfKnygo5FaofULWXbPOrn/HFb2v4IreHWu6Ban5EI07uV5NkJV/Eg5+B8fXtP4zd32mjiBx8QKv2hTR5m/9J9fDwqkQ+7s6e+u6Z62BB8DxPyD7GCyaZa3ZcPNTmyxArd14ss43lG6jT7+szi5NS2N+wQvWoKkiv+E5YhoSOgS8Wi6ds2g7iqLYBR5mdQMPgIoK+7/NyspUioqsH4B9+jxDt263ENW9Zb7BVqc1UJMIjKhq3blLpnefbrf9ys5XUBSF1JJUtqSp/Tae+OsJS8fSfTn7qDRWEu4Rzohg+yan3n69mT92Pl9c9AVX97m6yWXIy8vDYHDcCXxq5FQmZU6y7ug7zbI6a9Ys3NzccHZ2xt/fmvPjl19+YfPmzQ7vd6LgBE9uepKXd7xM3FHH8z1NnDiR8847jyuvvJIhwUPwcrF2Hr60p+OO8a9Osu9QP7PHzA7VsbQhEnyIxu1bYr/9++Mtl/SrrpJMNbhY9YS6XV1irWWZ+Kj1vPQ9sPR6WN7AG/Af8+23x94DLjYdrVzrzEbpHnhm5W4KvRf0mArOtd8mv75GbSKKre17cvTn+tdsfce6bg5cRIeTlraUxETHfTVMphp27GxkNFUtV1e1CaWiIsVuv3mGWrPIbjfTt8/TZzRbrVJjzSxqyFGbACM/+pDC4fbZLgNXbKYmvYXnJbIxOnQ0z49/nvuG3YfOSUd6WTpDvhzCX2nW/BTVpmpLvgrzfDD9A/rzwXkf8J9z/oMG9f1hbOhYdE46RoaMRFu3mbMBe/fu5Z133uGPP/6w7CuurMFkUt/fzDULZpdO9eOCCy6gT58+DBhgrbXw9rYfCeSo+SUnJ4f/ffQ/MrZn8Ou+X+2OTZtmDWp69+5tWXdzdmNChLWp1Xb+HFuuzq6EeVgDxd5+vR2e19FI8CEaVlMJsWrPeq6vDUIKEtSkWik7Tm/0SWN+eRi+sqkaDLFJhuPuD/O2O74ubBjcvgbG1GYkrZvxs1f94Wt22mo6ao3GmmQscRO8EKwGUdvehW9vgkPL1X4r1eVqk9Jqm2YW95ZN5yxaxuHDj3As9t+cjH+tXq0FQHbOKkpLG8/k2a/fi3h7DwEc1XxYm0TMadSbK2vBAo5PmkxNWhoF33zLseEjSP3HQwAYstXgwzk4mNeml7Glv4b90yKt1778ymk9s6ku73U59wy9x5KCHag3AuZ44XEqDZWW/d29u+Ouc+favtey4doNrL5qdZMTZ6WlpZGSksLvv//OTz/9BMDOnTsxGo0cSitixHNrmPnOZkoqa9BhzRAb6xNLbmUG48aN44YbbrCbU8XJyQlPT0/LdmpqKqWl1k6wOTk5vPfeewRVBhFTGsO0dOtrveaaa5gwYQIDBw5k5MiRREZaf/eg5lgxc3NuuBlsZo+ZlnXzZH8dnfT5EA079ota++AdAX1mgH9Ptfnl7/fVvhYADx+2duY8U7F1qqGvsu/dT3A/iDwHUurMb3L1Z+rQV1cf+0Re/WdB1AS1Y2hdo25Tm3cm/bNlyt5Us96GT2uHLprqVPd+P1ddluXABJu+Hpe+Izk82kB5eSJOWldc9U1r3jIYSsjM+smyXVmVhZub/Rt/QcE2y3pU97tJSv4IvT6MHjEP4enVD71LEC4uwVSUqyNOjp/4L35+56LXB5OXv4ljsWoA6unRl27dbmn2a1IUhfxFXwKQ+fwLlP75JwAlf/yBsaQEY57at6rGz4s4XR5xl2tZ0fdRatY/pL6mw4cxVVbi5No6HU/NXpn0iiU/R13v73vfMsIFoLev9Zt9gFvTg/Kamho+/vhjh8eSk5PZngYGk8KRjGK+3JZEuLtaA2JE4ZDfIVJKUhxeCzB37lxWr15NbGwsBoOB/fv3M378eBRF4Y/Vjl/XmDFjGDhQHal2zTWOh0jP7jebSkOlXQ2II3cMvsOSkbYzNLmA1HyIhigKbHlLXR9+s1o7EKr2TrcEHmCd1+RM1Z1kKqC3GmzUNeNlNZuoWe8LrHOuBPW1z3dx3rNwzj2OP7jPf06tLZn6rzMuerNEjoZxDzR+zp5F8PYwdd2/J4xo/oeOcExRTJw48TJ7980hI+MHtmydQkHBdnbuvIJtf09n+/aLqK7OP/WNgIIC+5q4KpsmEqOxipycNaSnq/NrDB3yCb16Pc7IEd8yYvhiwsOvxttrEHp9CBqNBg8P6wfqyZOvsGnzORw58igmk5r7o1ev+aeVYdS2M6k58LBsb/xL/X/urOWPQnUEh6/el17nXIj/XDUQrklNJXb4CPK++KLZz24Ok9HEP7T/YEr6FFyMamfNuv06zE43U2l2dv0hsuYp6WNjY0nKK7Psf/WPWP77o5qduEqjgAZSS+v3j6kx1nCy8CT+/v7Mnj2b885Tv1ik1f7eU1NTOXH8BAoKG0M3MuiiQZZrzaNkGuPs5MydQ+6kf0D/Rs9z17nz0IiHuLbPtQwKtD5DURQMplZqJj9DLV7zsWDBApYvX86xY8dwc3Nj3LhxvPzyy/Tt27elHyWaK/c4fH+bOvw0eABc8WHDtRaZB9QfnTuMrZ3M7MIX1Vwbtn68V53mfcxd4HEGfSeK6nyr8G2g6jB8GPxjP6x6EoZcBz2n2h8fORe2f6gGJP49HN4CUPtgRDae1KfVjPuH2ozSczpUFavlXHw1ZDtIPtZKM3+erXJy15CUrNaO5edvAmDP3hssxw2GEnLz1hMe1ninRUVRSE75zG5fcfF+NBotXl6DyMxaSULCm5Zjvr6j7JZ1hYTMJDXtK4qL95OXbx/Qx0Q/QEBA84eNAhT//nuDx5L+Mx8dUOat59ntzwPWKvvgxx61ZDtFUch+6WVMxSUEPXiKwLmZSktL2bRpE9u3q4FcAAEMzx2Ou+LOzeNvZpHLIktqcICLoi/CRXuKkWUNcJQEbPLkySxbtoxjx2JJ9PKzO+ahUTu66tzU/jWOaj7+tflf/J6o/o51TjrmRc4D4MiRI8TFxfHNN98AkOyZTJVPFVeNvYqgqiC0Wi09erTsjLK3D77dbltRFGbuOU5ejYG1o/ri6dy0vjBtpcWDj40bN3LfffcxevRoDAYD//rXv7jgggs4cuQIHh4ep76BaD3bP1QDClD7HLx3Dtyx1r6GQVHUmgLzJGzdz1X7W4AaqPS9RJ1rxdbGl+HYb3D3X/b9J6rLYNMbamrw85+rP9NqSZY6pHbTa/bT3I+4FUY30pvfzVcNnBwJ7AXz/laDi47aVOEZZN+sAupw36UOhgee93TblOksUFNTxNGjT57yvKKiPYSHXU1VVQ6JSe8TEXEDnh72nfiKSw5QWLgdjcYFf/9zycvbSErqF6SkfoFGo0VRrPlmPDz6nDLluZOTjlEjv2f9hvqdBc39QZqjYv9+Ko8eJed1dQ4gXbdu1KSq39wTergTE1+OrkINbNNcyzF/FPTyU/+ParRa9H37UhVrTXyVu3Ah3jMvwVhYhPsIB/MYOVAVn0DJ6j/wu+kmtDb9IkD9cFy8eHG9oKBbufqFaMcfO3j9ntdZenQpOq2OFcdXcOeQhpMM5ufnk5GRgU6nw2Qy8euvv1JeXs6oUaOYMGEC8fFqmvLg4GDy8vKIiooiKqYnJjQUFhaQkH2cbk4aUk0+gAYfjTqfSky3EKjBMmeMLXPgAWoK83cT3uVyLgdgyRJrZ/0UjxQWz1iMRqNh8uTJTfrdnam48ip2F6tp4X/PLeKa0JabibcltHjwsWrVKrvtzz//nODgYHbv3s2kSZMauEq0qmO/qcNli+uM568ugffHwtOF6gf1qvlw4DsYeat1HpW6/SX6XFg/+ADIOgi/PASXvq12VF12K8TZ/C3kn1TzVPSvzS6Ye0KdsdZU55v9hIfhvGfO4MUCgZ2jt7edvhfB/FRYYFMTdf0SdTI70SLi4p6rly/DVmjo5WRm/kh6+rd0i7iZ+IQ3yc1dS3b270wYv5XCwl0UF+8jPuFtfH3UkQc+PsPp2+d5tm6zvrfZBh7eXkMYNeqHJpVPo3HC328C+QWb8fDog4/PcEpLj+Hr27waOlNZGclzb8NUXm7Z1+29d3Ht25flx5ezcOVT/C/een6BpzVID3Sz1l6G/fe/VOzZg+/VV3HyohkYsrOJv1j9e4z+4XvcBjaeWbfiwAESr1UD6vK9e+n+0Uf2xysqGk1JnpOTw9GDR5kzbA4AN/a/scFzq6qqWLhwocMJ2LZv326pWQE1j0avXr3Q6/XEZpWRbvSmm7aI6S7qXFIVURP4NrYKf63a5BUREgGpkFaaxhN/PcHLk15usBxGJyMluhK8aqzBZq4+lzz3PCK91M6kSRVVaDUaurk2rQbnk9QcthWW8myviCZfAzB5xzHL+h9nQ/BRV1GRmiHOdiy0raqqKqqqrHMa1M3yJlqAOUOm2e1r4FObESDZR9RmCnO+jE3WrHkE1WmXHHIdFCZD9Hg1f0b8n7D2GfVY3Cq15uTwCvvAAyDnmDqi48pPwFABa5+1Dzy6jQG/KDjnDGav7ez0XnDJ67BvKcxeCp6do+NYZ1Benkhm1o8NHvf2HkZ4+PVkZqrn7N5zPUajOmKhujqHvzaNwmCwZrvML1BzObi5RuDmFkGvXvNJSHiHkOCLSc/4DgCt1p0RI75B04z05P36PU9KyiKiou5Crw9p5qtUlf71l13gEfrMM7j27YuiKDy99WkI1HAwSsPgJLUvQHptn81wj3Cu7XOt5Tq3QQNxG6QGGG4jR1Dyu/X/dNbzLxC1dEmD/VCMpWV2mVPLNv5FTXo6poAAlixZQmBgIKNGqU1QHh4e3H///RgMBrZv326XJ2Pt2rUMGzYMgG3btrF161ZuvfVWAgPVICm9sIIDqUVEaQsdBh6OfLq3CO/4eJ67bCAr9qaSp7jTDeu/7ZQI6NGjH2X7EynOh6iwKKit9Pgt4TeeH/+8w6afTddtYuK3E9kUuomLU9RpF7aEbCHTPZNzws5Bp9VRajBywa44igxGHokO4eGoUHRODdfQKorCCyfTqTQplBpMfDusZ4Pn2io22Gd6XpdXQo1JafRZba1Vgw9FUXjkkUeYMGECgwYNcnjOggULePbZZ1uzGGe3gkT77Ss/qd/X4dAP1lla6/KxH/qFzhWm/8e6HT5cbSJZ0A1Ks+B/g+xngr36c7U2Ja62enLDC/XLNOp2x1PEn41G39F4k5NoEqOxHEVRcHZWm3pt+2ece85a4hPext0tGj+/c8nIXE7PHo+oKfwt19vPF2IbeNgy59uI6n4HUd3voKIi2RJ8BAfNQKttXj4ON7fu9Onzn1Of2IhSmw9v39nX43vN1WSWZXLnamuTxb5ekTx39+PctOpHlo/ewGOjHuPWgbc2eE+/62fbBR8V+/ZxrP8Agh5R08T733oLGhcXin/5lbxPP6Xq2LF69yj49juSJ04gKSmJpKQkfH19ATVPhnno6vTp0xkwYAClpaUsWbKE0tJSysrK8PDwsOTj+Oabb5g9ezYmk4n5K+PZejyLm0LUzr7h4eGk1+YmmT59Ok5OTqQo/qz6YzXR2gJ8+53LF/uKgWK++lsdYdS3TiCRcCIOnS6B4nx1GHJYWBgLz1/IXWvUofxZ5Vl08+yGQbGOVruh3w34uvry4PAHeXvv26yJWIN3tTeZ7mrNzjV91NEsu4vLKaoNDN5IzGJNbjGrR/VpMIjLqjZQWdth9ER548HV8bJKig1GRvp4cKCk3O5YhclEfEUVfT1ad9RSc7Rq8HH//fdz4MCBBjO+AcyfP59HHrFOiVxcXFxvrLM4TcYaeGuodXvsvTCkdkjXRS+pnTZB7d/h2cC3rIY6ftrSe0H/S+HoSihOhYPL1P3XL4V+F1snRoP6gYdWr45gEa2msrKS2NhY+vXrh15/+smpOgujsYJtf6s1eyNHfIubWzcqyhMBiIi4AXf3GAYN/J/lfD8/azA+oP8rnIz/H1VV9sm9zHr1mk9u7noKC9VqfC8v+6YHV9dI9PpQqqoy6dbt5pZ8WU2iKApl29ThvZEff4znRHWI5qLDi0gsTrSct+fm18mtMPDm7NtZ3mc24yLGNXpfj7Fj6P7FF+jCw0i5626qa9OB57yhfmko370LQ3YOVUeP1rs2/PXXSH/0MUo3bOCQn69l/7p16wDw8bEm/dNoNJbJ2Xx9fSksLCQzM5OoKOvkjLm5ubzzjpqAr8gQznkuxShFarA4ceJEnJycyM3N5ZxzzkGr1fLBnyf5s6YX1AD76mczLVPsg4+6o2J8fX051+9coryjSCpO4tIfL8VgMuClszatPDbqMUDt9DkkaAh3rL6DYhe1Fn9I4BCmRqod4z9JtZ/X6WBpBZEb97N0SE9SKqvp6a5nrK8nKZXVfJaaw1Ava3LE9KoayoxGMqpqCHHR4VXbgdSkKOTVGLhkTxzFBhPuWici9GqOkqtC/EisUPt+zDuSSLSbnrf7dcejA3Q+bbXg44EHHmDlypX89ddfdOvWcB4IvV5/VrwhtgvzXCIA0/4Dkx6zbp9zL0SMUnNOpOxoeMhsQ0FJXTNeUYMPs76XQJ+L1PWxd8HOT+r379C5w10bQatDtJ7ff/+d/fv306tXL0addym3fLqD+6b24tZx0e1dtBZTU1NAXt5fBAWdT3bOaqqq1G+cBw7eQ2S3OeQXqOm6Q0ManxMlLOwq0Gg5cuRRh8e7R95eW8ORRk7uasLqjIrRaDSMGL6YmpoivL2blviqOU6WV+Knc8ZfZ33rTq6o4p3kbCL0Ou6lEkN6BhqdDveRan+t3Ipcfjr5E0atH9f3vIAZva5i1iFrU3f3AMcjcOryOGcsAK4DBliCD7OyjY7fPwLn3Yv7sGGYNBry0tLs5jAxG9hA35Ho6Gj27dvHnj17cHd3PBX8MGdrBlaDxpkePXqg1+vtRlcm55c7utSiXLG+//Tu3Zvjx49btnv06GGplQh1DyWpOAlDbX6ekpoSALx0Xuhq38OcNE6MDRvLs+OeZXfWbp4+92lctC5Um0zcfCCeNXnFOAGfD47h1oNqvzqDAtfsP2l55vsDongtIZP4Cuu/EYACPHMina/S8zjHx4P+nm58npaL3knDBQE+FBtM6usxmjherl57c3gAq3KL2F1czuHSSg6XVnJBQBHXhbV//48WDz4UReGBBx5gxYoV/Pnnn8TExLT0I0RjSnPUuUZcvSGhdoia3tua/dNWQG37oaGh6jxN07N/eofBlPnw5wI1KdmsN63X+veAR46qzTurnoDQwXDHejXo6KgjUrqQ/fv3A3DixAk+OrKSbJMfT688zK3jojmYWsS2+FzumNADpw7UHtwciqJw4OA8Cgt3EBR0AYWFOy3HSkuPcvTYE5Ztd/dTD28MCZ5BUeEufH3HEBAwmZPxb5CWtpjevf5l+SByc4uge+Rch9e7u7fce15WVQ2BLs7kVBvYXFDC/UeT8dA6cVtEILFllTzRI4zL9hyn1Kh+8LwEfOfjR/e+vXGq/cD+Ie4HcrV9KQ79B8V+vlx9xL4J6cOUbJ7vFdHkPCL6/v3gt/rz0gAEPnA/GE24jx2L1scbfY8eoNOxdfw40mq/hEZHR+Pk5ER8fDzh4eEMHuw4SDvnnHPYt28fhw8ftusXePHFF5NY7syODavwrB0OC/BT1SAeV5zQA0aTwsurjhGfU8b2eDWRWnSAO4l5aiCy5M6xDAz34fXVsaTllzPAQ4Ofny/jx4/nlVfUrK6RkZFceeWVlvsPDR7K9sz6WZYviL6g3r4re1/Jlb2t1y5Ky2NNnloTcnGQDxcG+nBTWACLM/LqXTvvSJLD3wfAV+nq+X8XlfF3kZqXpMqk8HNOIQDheh3pVeqXPC+tE2N9POjn4crP2YWk1e7/OiOPy0N80bdVZucGaJS6cxafoXnz5rFkyRJ++uknu+jTx8fHLiVtQ4qLi/Hx8aGoqKheznxxCrG/q+m6vcLhgd3w7mi1GeTWXyCmgTwBbw+HfJuu74/GQk6sOjpm1lsQ1Xh1rJ2aCnXIbMxk0DYQ12YehMA+TZuMTbSIZ555xm57d003ThgD2f/CLPr8W23Hf/bSgR26JkRRFIzGcksfDlsZGT9w5Ojj9fZrtR4YjdbEUYMGvUtI8Ix69wVOK4HXmTApJpxO0RH1p+wC7j7c8AdRY24ty+fZGVO4Zt9JEgoOkKttvKPi630jmezvRbCLMy6n+FAylZdTsm4dhtw8StauxVRcRNVxdaRI9y++sNSQgDrgYNeuXWzatMmy7/rrryc6Opq///6bvn37EhbW8AR2ixYtIiHBOrPr2LFjmTFjBncs2snJ2KNMdolH0WjZ4zacg/kanrioHy+vqt/fJCbQg+/uPpe5X+zAYFRYef8EXJwdv87Y2FiqqqoYMsR+iHN5TTkXL7+YvEr7gGHb7G14utgPI7ZVZjAy9u+j5NaoNSYrh/diTG3Tyg37T+LipOFwaeP9OTSoNR+n8uXgGL7NzOfXnCL+1y+S2WFqb+Kkiiq+SMvls7RcqkwKj0SH8HhMy08c2JzP7xYPPhr6T/z5558zZ86cU14vwcdpKsmE1x0kctO5qwGFawO/y+0L4ffaFOM3r4Ce0xyfJzqdmpoaVqxYwZEjjucW6TtqIvM3q296Fw0M5cOb609cVVFRwccff0x+vpr1s2fPnsyePRtn57aZmcFkquLwkcfIzv4NjUbL8GGL7fpoAOw/cDe5ufbzDLm796RbxI2W2WQHDVJHotiqMJq4cFccgS7OfD4oGh9d27ymBdsX8OOJH3lm3DPMiJnR4HmhG/ad0XNWDu/FpXtPODzm46zllvAA3kmun/Vz1cg+RLjq8NRqcdPaf0CbFIX5cank1hh4f0AUeicncj/8iJw33wSg9+ZNOAdah+t+8MEHZGVlWbbHb97MpIUfo+/RtNqhpKQkPjcnOwNuueUWevTowRXvb2FvciHBmhJevWEsR/JMDoMOy+/i/vEM6eaLyaSg0Zx+sJlbkcuRvCN4uXjxf5v+jyfHPMnkyMbzdqzMLuSuw4kAfDYomouDfOuds6e4DA+tlpXZBRwureCaUH/+GZtCfo3aOfXKED+WZxUA1kDEQ+vEFcF+rMguoKy25itp8hCqTQoHSyo419ej3uv8Mi2Xx+NSGevjwU8jWj4lQXM+v1ul2UW0A0ezooI6l0hDgQfYZwgNHdrweWehitISEvfvIXrIcNy82i8QVhSFikN56KPUxGnGwioqY/PR9/JFH+1T7/zS0lI+/fRTCgoKLPt0nv58nhvNNN0JwrVq9W/srk14aoZQquhJL6qgqKIGHzdr+7fRaOTll+07A588eZJjx441OHrtdFRWpnP48CMEBk4lKkrNpltadpz9+++gstKa2ElRjJw4+RKjRy23u76kRM0M6+09nOLivQB4efYnImI2NTWF+PiOJMC//twYh0oriCuvJK4cbj2YwA/De6Ft5RqQpOIklhxTk089/tfjTOs+DX2dETG5FbnctX4+uFuHnd/ZLZAoNz151QYi3VzIrKrhlQRrjoz7vXXkfL+cby+YZdm3s9hxX4dneoZzT/dgFEUhrrySP3Lt0xtctFud7n2Ilxu/j+xj+Z2UGozM2nOco2VqwLoyu5BrQv3xm309huws3EaMtAs8ALvAY2R+Ad1S0yjbsqXJwUdUVBRPP/00u3fvtssKmlTbfPLJfRcxpJsv/UsqeW11LEabVOLXjYqktNrAvZN7MihC/X9ypk2LgW6BTOqm5nT5/aqGs8fa2lXbPDInItBh4AEwwlut0funTW3Ey/GZluDj2V7h7CwqI6Wymv/1i6Sfhxu9PfR4aLXc1z2Y7UWlXBvqj5NGg94Jxvk5rokZ7aM+51BpBSZFwakdm71lYrmu4vCP1nUnZ/AOV4dsmke3NCSwt5p91NkVPGTmVDNFUVjx0jNkHI8loFt3bnn1HZyaOFV3S6vYl0P+t7FoXLWABqVSrb4tW3ecsPlj8Pb2Vvs9HDhAbm4u8fF/0LPXH+TldichYThefkF8nBGJAS1TZl5J3O9fWO59gS6W5dWDOZBaxNBnV/PaNUO5emQ34uPj+fLLLx2W58CBA/Tv3x+ttmV+H1nZv1JYtJPCop3EJ7zJoEHvEhv7dL0RJwqws7iS3mWp+Hp0o9JoIrvoIFVVGWg0WgYPeoeExHfROrniE3Yrv+SWMyP6QXROGo6XVWJQFPp7qk2/+0vKue2QtTr/76IyPkzJ4b7urZdbJaUkhZkrZtrt+yPxDy7teSkAP5/8mf/b/H8AlPpca3fe/d1DCNHbd8x+NSETBRjn68ncHxdTvOJbbk09ycW3PQTA8yfVzpgaYxFBhlh+nnwrvs5aSw2PRqNh0eAevJuUxU/ZhRwsrbC7/4GSCsZsO8LvI/vwwNFkNhaU2B1fmV2IBnDXOjHjP/+xfMveXFBCb3fXeuUd2Kc3VatXU7F/P9x8U5N/bxqNxpITBODn/enkl1Wj0UDPIPVDNtjLlWn9gllzRA127p/ai8cu7BhTemzIV4O7c3yal+H7zshA/hmbyiBPN4JcdKwf3ZeT5VUM9XKzq9GIcdcT4960Zuze7q74OGspMhjZmF/C1ID2+1IlwUdXUJIFSWpvfh7YY+1I2lTj/9HyZerktny7mIzjamrpvNRkdq5cTkSf/nQb0PA3fsVoApOCRteyQUrF4Vz1/pXWxEGZmkJ+ddmD7t0dPPDAA6SlpbFixQoAJk5SM2p2izyCweDCUf0jVGYUAnDJkAjCXG9h4wo1sPB2quIy/RF+qlJHHKw7msUFvb3rBR733Xcf69at49ixY8TFxfH+++9z7733Nqv5xZxAavr06ZbEUQZDCSdOWKdQN5mqOXCgfufoQQPf4suTO3i96kp2HEvmi5HduOtwIhvzqllACENDzsXVNYz+/f4LwJ2HEvk5J5E5wT481z+KibXZHt/p351zfD25cFec5d7OGnXEwU/ZBa0WfCiKwhu76uey+fro18zqMYvSmlJL4AFQ5aHmyvEo+IbrIyIJchlCdnk2W9K2MDZsLOGe4Xw5OIafsgt5JiaEnG/VCex6XHU514b68V2mtdbLvfh3Zkf4EOXm+APq/qgQ7o8Kodhg5HhZJdsKSyk2GHk7OZu0qhrmHEpgj4NalDV5xZZOlN1dXXggKphwFx03HkzA29mJZ9xMlnPPnTwFp9pa8fLdu1EUpdlNH/ll1Wg1Gh5YqtZu6Z2d8NBb//6emjmAbn5ujIzyY+aQ8Gbdu7WkVVZzvLwKZw1M9W88xX5dN4UFEK53Iab2383LWcswb8ejfprK2UnDNaF+fJKay6L0XAk+xBk6uhJQIGJk8wOPJjCYFP4qKCHGrekRdmeVcSKWX99+laIs+7TPm5cuAiBm+CiueOJph2+cuZ8eoiarnJCHR6D1dJwGuTq1BEN+Je5DgppUHsWkUJVYP+tvvDYLRaNQXV3NmjVrLM2d4RH2/Tu6Rx3AqXwBh/2nMWHgRfi46RgS48Pjph5McU4k0i8J8roxLNyDfell/H4ok8u62Xd+u+mmmwgKCuL666+3dF7Ny8vj0KFDliAC1BEGP+xOxbk4FS9TKYqiEBAQQN++famuruaPP/7AZDLx448/0r17d6qqqigt/bPB1+6ie4a9e/8kMCiEEcPH84tJ/ea4qtidHzZdzGrDi4CORzTv85CzjvKiMkb7ePBhcral9/8X2UWkZ2wDrXrtA0eTGVPnG+jjMWG8GJ9BSkU1LeH3gxnE55Zx2/gY4rJKCPVxpdiYwtrktdS49ERxcuelEdfxwt/PcSTvCLuzdhNbYJ1DpUbfB5NzICgm3ErXsvJYFSuPWecyCvcIZ8VlKxi1YTUjK6uo+cnaNKWPjuYcH09L8OFUk4Vr2UYu6WGf2twRb2ctI308GFn7+yk1mvgsLdcu8Lgw0JuPB0YzdMthCmyyaCZXVvPPWGs5ig0m5udXc6PWGQ9nLS97hBJfXsnn/oGEZmSQNPsGIj9eiNZL/UBWamrQ6BwPua8ymfghJZfnvztIeVEVaECjqLUbtiL93Xl6VuMp39tKtclEkcFoaaLq4eba7D5FGo2G6WcYHBSvWYOpuBjfq66y7LslPJBPUnNZnVtMemU14c1I2d6SWrzD6ZmSDqfNdHwNfHuzmrL8/Odh/IMt/oj3k7N57mQ6Xlondo8biHdtgprE2jkKItvpj7clKIrC3z98Q1V5KRNvmMtnD91NcY5adRsc05NLHnyczx++2+6aSTfdxuhZ6jC68iO5GIzVZGXG47ZO/a/ke2lPjKXVGIuq8RgZgr6H2t5sKKwk8yV1GGjALQNwG2DfzFV5shCNzgl9d+vffcmmNIp+jadYU8E+bQI9dGH0Gdqfz/cup0RjX02u0RgZP+FHNBrHbf1OTh74+o6gqGg3BmMVGtQPjxMnRqMwnZfiwwANI51TGOysBl8uLi48+uijllw8Bw8e5Icf1JqVgQMHcs011ma9b3Yk89TyvdzgutfuuT4+PtTUGCgvL8PJqQadroqqKk+02moGDV6Lt7c6eqAg/3wiu6dSWprNgf0jKCoKtbvPpqlTOWyq38fF1orhvbiigU6WdUW5unBzeABzIwLpuekgAC/16caciNOfnbnaYKLPv+v3BRgyaDcnWE9B+BsoGicuCPAmsPhbViWuQWu0Jp66rs/1rDRdQHK1Ds/qONwyn7e7j3eZQpkrvO5xK+FPf1b3MfTethXFx5e5Wz5mV+qv6KrjGR8+ng/Pb2AixkYklFdx7nb7pGEHxg0kWK/jviNJ/JBV0MCVVucd2UnmiHM5VNtU+K8Df3PeB28B8Oekq4k//2r+mfc3ebUdVhkyjE0EkHbhVcy/ZRKpBRW8n5LNEptpN5wyynkkIIBHL7BmBt1TXEZhjZFp7fhN3qzMaOSyPSc4ZNOMNSPQh88Ht23aCcVg4NggdRiz34034hwYgPvo0biPGsXL8RmM9fVgkp9Xi/b7aNcOp6KNrX9eDTwABp+if8dpUBSFbzLUkQ4lRhP3H0lifo8wAl2cmbD9KHonJ9aM6kuPTlojEr9nB1uXfQ3A4T/XUVmmZkqMGT6KcdfciH94BEPPn8H+NdYPlM1LvyQoMorUdfuJzlF7jOtMNeCkfnMrXGlNGFS+Owu/a/rgHORGzvv7LfvzvjyCz8weuHT3Qt/dG2NxFbmfHgQTeN8WRlzVDzgXjSHwV2cUFDZ4HyWnqoA4JYNV+/bUfvvToGjM3x1MDBsWWxt4OB6YZzKVWaaRt327iY7ex7at/bgs2I2fsv3w1Kg5FbyiBjJ69GD27b+SsrITjBnzC4MHD8bX15dPP/2Uw4cPc/jwYQYNGsSll17K31v+4iKXtHrPLSoqIjAwicFDduDion4T3LfvQrpHHrIEHomJQ0lJDmXSpOf45ptvKCrKt7uHQePE8Wr3U75jnSrwCCnKI8tHDfpe8dMxOco+id6TcanMDvOnxqTUm4I8v8bAbzlFTPP3snxb3F1UxvX7T/J0rwhuCg/gYGqhdWZoG3GFhzAFx6DUDq9dnVcMzIDw6fhmvYSuWk1sNaXP3bx7IBV3rRObplzOrlQ3S3NM/2SFZ7821zbUDzzK776W7eVHGOc7jv3x72CuRxgfMb7R30lDYtz1rB3Vh/Nqm6gOjh9IkIt61yd7hNHTXc/VIX48u+8Yf+cWkuepBoahRbkElBZzOKIHaweMhkprVtHUiy4hNMSPzGeeYcpf32NISCAvZbf1oQf2MRHYnpXOJYUaTuaUUTktDHTWUTemMHcemdIHgO2FpfySU8jnabkYFPhtRG9GNLNvRVN9mppDoIszlwX7NXre95kFdoEHQK92eH802GRqLfj6a8t63927eKJHyw+zbS4JPjozRVFnhwW49Wc10VcLOlpawXvJ2cTZzCmwOq+49o1TZTCa+CAlm1f7ds6U+Al7d1nWzYHHhOtvYewV1g5/02+fx9DzL8YvLIKP77+N8qJCdry9lHHB1myZzk4NZ2ktWBbncH/RL2p+lYgXJ1CdXAImMGDkSNKjlGkPAQvRB99HlfMwcvLrf8sc6TuQ4Bwdv7nsJSrqAJ5e6rd336TzCEi4BINLMeUBh6nwPUF5wGFcSrtR6Vv/w1mrrUGrrcGv+ATn6/3QK1VERh7E4LGNxZsGcV1ftb/EoUMPcM7YVXTr1o2YmHAyMmKprPTi0KFDpKen41uUD3VSJ9x+++18+eW79B9gnwFz2LA/7LYrytVvScnJyRQWFtYrY6p/MNXOOjSKCWeMGNAyixUMYR8nA55gab597/6pxgpq4uM40K0XY+MP09PHi4DyYpLyClg9cAwxuRmUV/pTGR2Jq6srb/Xrzj+OJQMQtfEAWg38PKK3ZRQCwJyDCewoKkPvpOG+7sHcGBbAzQfjKTGaeCw2hZvCAyh+5j98t3sb9099mBxPHVqPBEw1fjh7nKDcZRb1aFwoCn4U79x3CfaI5rnahFgXBngTpndhVs9ZDAsaxqa0TYxZ8R01xNa/B/DDOA3f+i+Htcu5e4i1ps7bxdsu2VVzDfJyZ82oPhgULIEHQKSrC49EhxIfH0/kHyuIBExAjdYZndFIpo8/hyPqJ3RLqqim9BybGYBDXVkZfR6XbF7HP296jAv2beGig1sZm3WUN1MyULx80VQZUXT2f1gvxmfweVquZYip2S0HE1g6tAeDvc6sb0RdsWWV/Ou4GljffySZYV7uDPN2485uQQS4OPNKQiYT/byY7u/Ft5n59a6/IqTxgKU11DQwY3Dpps14X3RhG5emPgk+OrO8E1BTpo5u6X7uqc9vpoePpbCvdoKi3u56S8reur5Kz+PbjHwuCPRmnK8ni9PzGOHtwWv9Wj4gid+7k/LCQgZMmobTGYy2KCssYONXn3J08592+6OHjWTULPs3a41GQ1CUWmU6cPJ0jvy21i7wMCutKWR1+hdEeQzAIyyQifffTtH3J6jJLEOpMdU7v9IrkaLwLbgmOaFLjaCcKla4/8Uo7SHLOenD3uPwAXV20OjoGIxGAykpKRzFn01Zrrzvk82AwJMERKmBh0fOEIKOX4OTyQX38YNw3dAdbPJUVXmkU+WVDIracJ7TexkGtzwCAlLIzu5BjD6NgYPW4+mpBju9Aq3BSlnZceLj3yI8/Bq6RX1CRLd8Dh2aRmFBOBrNPjw9PXByMlJa6ofJpKPcM5IckxvdBzU8t5NZcPAUcnMT+eWXXyz7Bg0ahKurK4cOHaIwQO0I2i87laFJsfh5ZDFhwAqqqz25sXtvXhsSzLeZ+fyWXUh/YyUVv6zE2WRiUHoCer2eO667gj179pCXkcGVe9VAaHsK7Nq1i1tuuYWBJSVE6nWk1GaBNCpw1d4TvD8gihlBviRWVLHDJqPkG4lZvJGYZfca9kyZRqlWR2hNBYtWvwjAq1c6sbOvEwpaKjzVN/w5vj58UWjNMqo4eVAU/IQ6r2ppBU5GBc/0CpQBau1VSBFcctyT9GPWwOO52U6UDIz6//bOOrqKc+vDzxyX5MTdQwQSNLhDobSFektbKtRb6nZv21t3u7eu1L2lCtRLcXcIGkLcXY7LzHx/HEg4JKG0l0Iv3zxrsRYZfTM5Z979bvltMn0R1O3exPbUTk/L7Hx/fkd2WDYfnvIhJu1/NxEfaiLfva+BnE6n47rrruPHH3+ksLCQRFsrl0cG8ePeVlq3NiIFa/HmRVBmd/FNowRJQ9g+ZgDfjZsMwPMXXgnA5lF5rCicTL3egDU0Crel06g8zadlgc7faO2VbvRJBKDR6+Oj6iaeye4c80tldbxZ0cCXA3t1VDsdiv3JsLIs45RkTGoVvzZ2/r28ssz6djvr2+2sbrUzINjIJzXNzK5oINGgpdLlRSPA5wN6dfRhyTmM+x5pvNXd9ydyrF//tzA+lJyP/1Vs9fDWJGgrh7gBcG0PvVkOQJT94jM5QYbfVTFs8Hjpt3JHx88LhmRR5/HxVW0zc+tbAf/qJ89iYt6+nw9m4dBsVPi//Pf3iv+vE5sqd21nzkP+ZnhxGdn0m3QSfSeeeMis+d2rlmFrbsJttxGVmk7W8NGIPi/v3X4dbXW1SGoN7uhEtLJElCBy2bOvHdKo8TY5qPt3p5vY3t9DSE0IvgYnm5oWUNi+KeD4kOgYZjz2LDqPDseGOuwb61CZtXjr7JSOuhdPUDUaQojfey0V7Y0UhWwlvdfGgGts2jgNn5zMx21ZXD46FGvjbEbFr6O0PZm04ErUar9b29zQn4TNtyEgsKd/GO857WQUtnMlPXeybB4wn4aYb3rc/3u4XGZ27RrHoEGBeQ5z90xjdOIaokydapDN0iRKremsLfVx3YBO4ajNe8Ziib2U6i1LkYHC6ESa41P45rRJmNQqmjw+rtxewpo2O49mxFP1zmv+Zxtag8MeSq9eg7jgggsAfwv2AxtZ3nbbbZjNZjQaDXV1dcyePRuLxdKtd8U5Mo8PdIfRSPF3uPGL9zlncadn5+2Ts/h6zBhcEf5md9M//waDGqyXns96kQ7Za/8gfOg2NqGy+zh/UBwzmraif+mZjt261FSeuNjAOvHQ4SWVoOL5Cc+TbhpOsEFDRNCRd/u7XC5effVVrFYr5513Hjk5OQB4PB5sDienvbGRqlZ/+EEK0uAZHQMeEcPiWsQ4I97+h99fZIBez7yhWVR6vUzduKejj8l7fVN5sriWs2JCSTPqmbWzjEyTnuXD+/jHIkkkL80HYHxY8O+2pF/Y1M51O0u5IiGK7TYnvzV1TfY+HEaEmJn7F4h4/REaXnqJxtdeRzAYkF2d3mtBpyNj8SI0EUdeWkHJ+ThesTX4W9oDfHut3/AIioXTX/ndU52ixKXbilnWYuPm5GguSYhkXl0LFS4PF8VH0P+A1Y1XklnU5K/pTzHo+C4vk2i9ln7A5AgLr+fIbGx3kBNkxKRWMT22nXcrG1jUHKgDMGl950ptXn0ra0b0IbmHcr9D4XW72LrgJ5Z90jlh1ewtoGZvASHRMST37V4czetx88OLzwRsu+Xjb6kp3N1RzeKJiMUXGokPOGH4OSACh3CoeMs7W62bR8aReEYGot2Lp7yd7BZIbBuKRqtlwVuvADJt9XV8//JdxA2xkzvwPuJOHg4yVH78E54gvw6DjzbKM/zj3O+oLi4aTGRkBZaQemJi97KgaTKS4CFduJmQRP9zzggtDRhbZNFZCAhsxcdN+WVIwHJg4JnZDF1Ug8qoQWXU4Km0EXPzIGSfRISrNy27luIzdhoJoqhGrRYDrv3lntOZnjWfgzEY7F0MD4Azs37osu2EvJmU2XJ5Y/1qSpwtjErX8M/vx7KnScJb6qCvPoXiIek078sdeHBTKXf0T2Lo6p14962RBlvM7M8qaWv1hxkLCgqoqakhMjKStWs7e2/o9fqAjqkxMTHMmjULk8nEtm3bOlq0dxy/fgMRKU9iDb8cjylQRXU/V837jCE7tzPrX493ux/glfMu49VzZyKrVBjcLlz6QOPvog0/E2K34tmwgCu++JLHKq1sWVzOSWVrGVG8DSEugaSCTUR+14peDGzGGP/GG7ycEMW0b06j0VlPbLMeIoJ46dTXWV62nfXbE1hWvBePCFfsdAJLSAg18utt4wLKUsFfneSTJGQZDD2Uh4uSzPmzV+MVJb66bhTafYqnDoeDZ599FlEUsVgsZGVldZyj0+lYvK2+w/AAENz7PH86Nb5kM2L2oROHD+aJ3GQMWjUZWjWv56Ry2+5yrkyI4pSoUE7ZJ9zVsk++vNDhpszpJsWoZ8kB76SlLVbO31LEm7kp3VaeNHl8XJTvD4W+UFbXZf9+0o16BgQbqXB52HBAJdA96f6qKegU8zpWSHY7LZ99DkDMvfcQNn06sihSdMpUvOXlFJ92OpkrVxz1tgIHohgff1c8dihfDRoj7PkJ6nZA0aKux01/D+L6d91+EF/WNrOsxT9xvlRez0sHuC2/qG3h60G9yLOYmV/fyrU7SjvSFc+OCSP6ILEgQRAYcsCXa3KEhckRFh7aW8UbFYEto/cjAePXFfDj4MzDcn3up3L3DtbP/5rijeu63b/w3TeY8ei/MZi7Kvq11nZ1O1bu3EbVbr9HR9Lp8UV0VlTULC9Cs7SV6JsHoYvvXiHQW+9/2QhaFaGn+1dRarMWY58IsvH3zxFFH/XW1xF1uyicn4I5azlenZs1K89nyhnLKSl9CdUoA5R3/zs3N8dTVdUHg8GGJaSehIQCbA0FnJe1hRC9tdtzVlYP45r2CAZhZzsiBwZ5Th+ehDwoHgQBQQDJJaIO9nuhZNFM8hf/oiL333jNdYSF3c2egiBOOWUqFouF8op3cPrM/PyrhQprArcPft3/O0oq1KquoaRDEWIZRF64mXX3TMJiPBmtWsWLETYmP+dvgLg5LQMxqFML4dPt1WyubsVr8U94qUYdgywmHJMn89tvv3HxxRezcuVKSkpKWLVqFbm5uXi9/sk6KyuLCRMmdBlDdLQ/fDN8+HBiY2NZtGgRFRUVAKh8KsweEVXDy4jhT9Mc3FUrQh2sp1dVGePL97I0OYNbPnuXzIpSbrzzkYDj5H2exYMNj2E7thBi9/8Nde2taKedDCffyRPrv2Zw/b7coOrtdMcFpzzE16ZwEj0yKZtPJba9giFNezCFhSOPC+OJL0xACxC4oq1qdTJvSzUXDu/06vyQX8OD87fTaPOXF79x8WBO7htYXQSwvLCBDWX+ENyumnZCxDY++ugjfL7OJNKTTz45QO/F6vJyx5edydWL/zGBz9eX8yL+sK2vTyjgV1D9amAGc2qaeay4msEWMw0eL+fHhvP9kpXEV5Tyz+EDsA8b0VH+CzApwsLWUbldJs4wrYbeZgO77S6Gr9nFvEEZfHtQRc7SFiuPFdd0m6O2X5RtP9lmAwaVwFar34j6pH86aUY9oVp1R1fhrVYHZ24qZFpUKDenxBCp1fBLUxsXxx87wUbZ56P0ggsQ9ykcm4f7e+0IajXamBi85eWIzc14SkvRH8PGr0rY5a+itRzmXAzxg+DUF/5Y91bRB++eBFUbDn3coIv9Xo/DuPbtu8v5tKZrItR+zowO5eU+KeSt3kGDZ58bX61i2bDeJBxmuKTN6+PHxjbSjHrO3ryX7qYmi0bFkqG9A0IwRQ4XPzb4v7CLmtrJs5hJNeqoKyrkk3tvDzg/Y+hIgsLD2brgJ2TJf4cRZ5/P6PMvCTiurqSIuqLCfR6ITnLHT6Zw3Spcooy9V6AmwChvFjliEobe4URe1lUvQJZl6l/dgrfSRuhp6QSNTujYfuCLsKlpGVu2+jueNheEEJ7dGS+OCDuJppbOFXdoxQm4nME4M+cjCDKipOKF5f/itDCZJlsh/fotJCi4699NlHXodNGEBKeR0+dpfETw9vIS2l1e3lnRqdz5zLn9OW/IoXNvmj7ZhWNbPSAQc8MgBJ0KbUzgyu2y99axtKCOmwe9ybisCNKzXiN/8zR8Hr8FJckCi6rP59qxSSQmXsTSZQM7zrXLOUwd/ykaTfciS20OL1tq2zi/rLLb/QB9TFoiWt9hfHSaP6FShLX1a9E2afn525+JjIwkISGBrVu3Umgp5I6L7iA7PBuz9vdXoL9s/oXF3y3GIBmQLOVkDpnA9mWVtJnMtBmDWNzb3/PmspU/YPB56bttGyHX3URTVBQDFy3h043VrI5LpSA0GXGgDtHUc3Lh82tW8p0xG8/WLTy6+p1DjkvGn8fwav+zWBnfjxaDhQnZUZzStpLK1QsDjt1q6cuyiB6aRwJjMiLpnxiCIIDdLfL+qtKA/ZP7xPD2pUOobnUSbhAoLy8nIyODh7/byQer/UlDD0zNwpH/C42NjQHn3n///R1qt402N7fN2cLyQv8x7102lIm9/Qbf3Xsqeb+q89wlw7LpbfYvRERZDpC2r338CVo++giAqFtuJuSss9DExPzuSv2lsroO70OGSU+Vy4tTkkgz6ig5QMclXKvukC6fFG7hqsRIbtxVTpPXx+zcFAYEm0gx6HBLMnftqaTJ6+OdvqnddoIVZRkVR785YU/Yli6l4tpZHT/33rWzY2y2ZcuouMafkCxotfTeln9E731MG8v9t/xPGx8+D6ybDcZwmHd95/bbd/nlzg95rhvm3eDv+tpWCR7boY8HeKit281lTjc7bM6APgIT1u1mt93FYIuJjftchR/0SyNIreKcLf7S0AlhwSzZJ6F8SngQs1JiGR7ac7fGg5FFieY5BahMWryTk7hwV2mXkjPwCxW9k5uGRiXgEiXyVu/oeBEAZBp0XP3t6zSUdU6iwZFRDDhxKv0nndTRZ2XH0oX8/NrzRKWmM/PplwB/iOa1qy/Cd0AL7sEjTyNpxEDmPu/XTNCqDLRm9UMSAj/6SWIEU7wDEFQqom8ahNjmRqVXo08LwV3e3lEqK2hVxP5zKG6DivNnr2FbVRtnDown2mLg0pGJ7N58HtB9hcvB1BVOYU9NDCATHV3CFmcKZaoTeXhGDu98/jm9xV0MHPRzx/FxiTezq2UgJ/fPwWzsXqhs8e56nvmlgGenDyAnvufv0Ld1LRQ53FzhVOP4MLApV+RVfTFkdE6iDVY3j3y/k4uGJzMi3b+q8/h8/PO9m9GrvXy553R2PTq1w31vt+9lyZYP+HrPiTx73gjCzD0bsHsdLsas7bz/HQYLz7oCY+1nadayorjTkDwv6zy+2PMF4UI4E4snBhy7JG4JTQZ/GOnC3hfyr+H/6vHeAK+sexnbL024RFD7fEhqNfIBE0lNSARan49Ie/fft89dA3Gh5a1Lc5mz+iOKwqcS6fNxy6QcRgYZyP96Lo8JJibKXmadM43fFi6kXRXMuk++Z+bG79Hu8yLU5QxhxLOPUvWPO2guLGR1ZgJxAwbxunYcxW0iapWAKMncVPJ6t+PYGDKQVeEj+eCKYcRY9Njd/nPOfHVlt8cH6TVcNCKZ2Uv9oYYYi566djfnRdVgslYyefJkHtsgsbvWCsicqN1Dwr6+QBEREaSlpTFgwACSkvzGrSzLTHtpBTtrOv922x8+iaB94R5JlrlsWwm/NrWTatSxenifHids28qVVN/5FJKjGdnZaXwnv/cu5pE9J9f7JJmnS2oCGuaFa9UsG9aHu/ZU8END93/D/agF2Du2f5eGen93bMtX4Ny8mYirrqT28cdp+9qfxxVz772EHyRlX/PgQ7TuU8Ttnb8VQXfkdJoU4+NYMP9m2PRBDzsFeKj10Oeve8vfxv5ATJHgCFxlcOn38MVMv5jYmNu6vVTa0q04JZmP+6VxQnAQBV4PE9cXoBUEto7OxbMvg1tfbaetqJWp6jYqxc7J/+TF3zBg71Yue+51QmPikEQfak3PpaT7ce5upun9ziRV1w39eKK1hSmRFspdHiK0mo5yNYtGxXPZyTgkiZt3dY0/XPfBUwQ5Ow2wS55+iejUwNI9R1srs6+7DEn0cd6DTyJ6PGz4YS5l+X6RK7MmlFOT9ln5BjUNzkqa2itpVNnYFdXplznJM4AF2nwkQWaIuTcDmxIC7qMONyA2dyZsOTJDmJ+ox+UVeXtFCSAzLmEVMgIjYjbSO7KwO6mHrs/LGcTGDacjy/4JuyE4k+/sEZw7No1PcaICntd5cDY9SKK5kNSst8hImnjoi+6jyeMj3+pgQnhwty/4b+tauG6nf0U7PMTM1jYHye0+mnUCl5V4cMaaONtkJntIPJrInsNkqXf7cztkAUqemPqHBYvckkTK0s7Vl1mtomhcf2bmF3eWdEsy46zvsattcbfXOLnxZMxWv4ej3lDP8tjlAUImNw68kUnJk8gI61TElGWZtrnzaPltNdrgUylU1bBUF6gOa1KrMdXWMnL1ahpuuYi5zkJia7uGJvaKEQwaP4bYhJ0Yvm5juM0v7BR13QD0KYHvsN27d/P55593/BxTVkX/TVspu/J2Lrz5AvJ/+7mLt05nNHH24y/w9mdf01hdT3LtNhAEVD4v2zOmoq8pINPuX0AIKhWX/vtVIhL9BoEkyaTf8yMAaiREBEAgPkjNM+cPZmSvSHrd8yNGPISrnFhlPWfrt3Xc+3PXQNLVTRgFL/00tQiywAZvJo9fP5X+iaEB43xpYSHPLeg0ur+5fhR5yYFeIFmW2dzuIEqvPaQ4oSO/geZPdyPZG7H/dj/Ine+n7ibUg7m/sJK3Kv3vzgOTP4scLh4tqubnxu4TSfsFGVkw9O/RE+ZwkX0+CseO6wiz7CfprbcIGtu1oaKvpYW2ufPQREYQPGUKKv2RS0ZWjI+jiSzDhnfghzsOfdyt2yC0aya97JUQWvfCeyfjssZjVV2OJkyHZbgGdf8T4cvLoH6X3wgZcgWc+nyHiJEsy0h2L4JOjUrnn8AeLarm1X1W/+U6M4Ydzbye6f9wTXKr+eiEHFw7mxBtXtp/KUX2StQaBK4bYqLCrOKsahszVm1lR8sKHKKVjKEjqdiRz6QrZtFnbM8T34Ehif0ET0wi5KTUjp/LnW6GrdnVzdn+SSdBr+vQFEmqKub8795FAKbf/wTJfbvPa1n66lu0banEGeWmssgfK5dVanymYOKih9GsdZHnS8MheNitrmKStx81qhZWa/cQLBk5zzMSAYHdoxys2LQagMHqDAbZU3r8XS/DRnD0ZpKCqvm+ZAp9I3ZzS16gfHVp6QBaWuKJiKjA6zFgNluJjStA9KmoWpaAMS2YkoYs3C5/KKJd0vOVbhCekTFwUOfNV7OjOTnEidncc6Z+i9fHdquTObXN1Li92EWJLVYH4Vo1TlEmzajjnb5pbLM5+aCqkZWtv+9ZG9no48VyiL0lD52m+5Xgt1uruGFPBVKkgRPCg7k0IZJSp5trEqMQBAFJlnFLco8ryS9rm7npAOPz/b5pnBwVgiTLiDL865edfLV1M0ExzyGovN1ew+Az0L+5P261m53BW/BpCfBc7Oep4e8xrbe/QVnr3Hk0fbAaffY0ACRkVmh2sUfjd9lnVdQzJvwcBElEk9BK0LDR7Ijy8OkXszHJsCZyM30ahxMj+X+vUksxVm8tl7rPJc/hr7Qw9o8k4sI+AWNYsGABK1cGeiLm+Iby5QW9WP3GvzvUdWUEPFFxaGxtqFwODHmjaTxAb0fwebnm2lnEJSVha2nmwztvwtnuX9Ubgi1c/9YnHUbnnLUlvDpvJRO0eymXwhiWHER9VQX9+/fn7LPP5t53fkBbsb7bZ2uTdQQJneGKODGMdO8A/hOj4oebx6A54O963uzVrCvxeyneuXQIk/rEdLne4SBaPdQ83pk47Nz4Hr6K1R0/q4KCyFy54pCTZovXx1mb97Lb7uKO1JiAbrEHs6LFyp0FlbT5RGbnpjAm7I/1YDlWOLdto+K66xEPCoMBRN5wA1E33XjUx6QYH0eTtbPhpzs7f57+PuxdCDozBEXDwn2JaP3Og3PeguYSqNmKHD8MV52Bpvd3YIldR3DL49R4P0Xa179CZdFhyAjF1+gEQUYX1ErI9GEIBjOyJFP/+la8FZ3JhzH/GEKrRUPeqs7KgIN5dpOD8Q1it/vatLDLomZok9hR7LG24QfqnGU4Rf99zvnHQ6QO7ewuKUsyviYnsk/GsaEW28rAhC1DdhiRl/sbsbUvLqf9lzKuGGYiP6xrZv0Pg3qhnv0rn8ck8H6KP0nvKkcbtw1IZ5HGzE6bk3vT49EcMDFLbh/VT64Fl4Qki8wvfw2P5EKVlUfrYUiADAvqQ/+meCIuyYE0E7Nnz+4owezvS6GfLxkj/tWZmGBmByLf2uzoI77mrAz/anJt4wWMT1yJy+VPWvT4dHicQby//grUgFdW89C10ylat4Tt27cAAtkpyRTXNeF12RG8bhzoWWrsRenofj2Ode2IPiTrtSz79H0M5qAAETS3JDF5fUGPOiw9cWpUCJvaHVS7u5/UAbSSTB+1ll/GByb4/drYxq27ywPCZQfyzcAMhoaYuWBrEZutDt7tm0qwWs19hVW4JIlddheTIyysbrVhFyV0gkzZ+IFdvDTzt+3hX2svR6Vtx2fLYLjpLp6YnsyrW16lX2Q/dpRuZX7tj0iCRHK9zBPviyzvK7C5l8BliwX+cwYUx3Ve87KM+xhitxH/n5UY8y4PuJcDN5/p/WW6F7X0xWDqOnlerKqlOeU1ZIP/hT+4YTCptlQABEnics8kVAcoralD9ainxrGmcCMun4edu3Z2uea0GVdS+f3HFK/rNEqcCen4LIcuRc3NzeWcc85BpVJRvWcXn93/z459wRFRDDxpGhsXLaAxPB7xCL3moyQLZ3iGMoZ2PrlqOH0TQrh/7nbmb+387n9/05iOFvZ/BvvGui7CfNroSsIvnMjeCf4FUNLsNwgaP77LufunM0EQcIgSq1ptjAoNwnQYYZQ/0+zuSOJracG9pxDz8O4rrQ7EtWcPJad31RrSZ2UR86+7Dxma+itRjI8jTXMJhKbAgclGzhZ/T5XS5Z3bJj0IYw9IkBR9sPZ1+PW+jk2SbKTG/T4ygYlwZvWPtIsn40XEQM8hjvALe6OND6LuP12TUZeek8wdtu77LZxd4eFfO90BstqqIC3h52dT/3Y+KqH7L6fTZ6PRXUWCKQOVoEaMgs3eJWSPHUdcaxK25YFy2ppoI5YTkmn+3F9mG3P7YCS7l4bZna71t9N1vJHZuWp5oXcSI3YXo/nFRatW4KKRJuqMKtSSzI2igRe1nZNq2fj+qOqdNH+5B9kn4avrLHWzeVup1ztYpOveu3IwV19+FdGmcLRRnWXGn3zyCYWFfqlrvaAlJigSISScV4pkNEY7/xr2PME6e7fX69/vDT77bCfNzS24Ukfz+W7/ivHKMWlsKq6jf9PSgFCMLMsEF2xGkCW+mHYZZUmdYYFEFfRfNp8fx/jbrZ8XG8Z1TSX8+PJ/ALj29Q8wh4Vze0EFS5qt1BzCgDiYVKOO21JiOT8unMu3lfDTPgGlS+vKWZ2WyZTVG3llQGD33l/DY8hJDccTpOWT6ibu39tVRv1Abk+NIVit5uGDKgh6IrT2YV4edT219lpq7bVIssSZGWdy7nfnAiB5LThKbkEWzXxz/SgGhJioebKzAuqt6K85obCK6MVdK0WcWvhsgoqfh6gwuWRent+X+H43BBxjnX89+pyzsWYOR4VAmNxzrlNBmpb57reY2DaURcHr0LQasOBPqrzANRqLOQjJ3lkN8oVuFe2qwNwnc2E+9ox+XWJzQzLTiY1P4PulyzkYlcOG2uXAG97ZefeEE05g3LhxyLLMph/ns+brzzqUegGc8en4Qg5fTwMgVPbRGJWDptFvAKSnp+O0Oaipr2WkN4tcMYnHcGLKi0aW4dvNgZ+FXY+cjFF3+AKAos2DoBJQmfzvvabPduPc2oAhJwLXzs4ScNQCKtbR9vVbBJ94IgnP/qcjX0GWZVo++pi6p54CSSJ1zucYcnMR9lXhuAr2UH7VlQSNGk3ENVfjLigg+KSTEP4LocIjTfmVV2FfuZLou+/CtWMn4ZdcjLF/p8e3/ddfUen16Hv3YW83hlfI2WcT/0TPJeBHA8X4OEI4Gjayc/M1JBWWE5N9A4y/299LpddE+PicwINVWrhpI4R1465/eQhSYxXN3n/gknq2an/SbqZO28aUkEHE1/yxlcPHKVpe6O33GFxa7GZptIbSIDV37HIxozxwYjL0CSfs7Ezsrlb2rF3Jnq8WIyCgNRoJJQp1sI5M1cAe79XsrmVV/VxOTZoVsF0waIj9x2AEjYqax9cieyXMI+NQm7W0/9bpWndrXLgx8FiugYyaEvo2b2GgazQmjf/vLQPnjDFTbu5qEL3nNNFvWfc1+G68fKJfjiTIaGQV4zT9aMgU2bbbPyGFh4f7BZBsNvr27cs555zTZaVTW1vLG28ENuFSqbwkJ29DHdpMtKWBKhKIogGdrEUj+CfuoKAckhJn8+abbwLwj3/8gxWlNmZ93CkYNkG7l1R1Cy3GIDYnZ+FzSVy5czF1tZW8cum/kNQazvnhA+qi4hnkasewczPl8WnMOf3KjmskVZdw7vfvM+2aG9mbM4RZ+/I2BOC82HBSjDo+qGqkzuPj4rgIDGqBaVGhfFXbzCf7qp2KxvXDvO+lu6O+kZO3FHPa8oXc9OWHxD74ALUPP8L2gRO46dpOme7+LSI+vYqdpsDn9Zbey6C8AZy3pYhi5+F5XkaFBrHqgLBPUPMHGG2/dTku3BBOs8s/5jsGPshDn/lzT4amhvFOZiLtC8oCjvc17MC58sUe7/vWiTqyarxMMV2DLt5fwbIqaAsWWy0pc78BrZn6CXfTy+z3eIiyxKmCjXcIIv5g3fh9tKvsrNL+SIsUg13tZbKnP2XxdYhuDScGDaGutIa5+sAy8ZD6RqSmUlyxKXjDDq+7cZIYib1kJV6vlbghoyh0eDtW6jNmzOjQ2ZBlme+ee5LCdauQAXvmAOR9+Vqa1kZkrQ5BFNFYm3EldIbxdPWVGFoaSY0eTajPgHdkCGuLSwnXhXDuiafj3dRMSUkJqVI0KgQKEbmcrkb4g6flcPnozvJNWZRp+6kElVmDeUhsR4k3gOwVsa2uoe3HEtQhOmL/MQRPjZ3Gt7YheyWirulPw5tdqzGca1/DV7MFY14eKR9+gLuoiPpnn8W+LNBgE4xGom68gYgrr6Tqzjtpn/9dwP7IG28k6sZAI/RY0frNt9Tcc0+X7ek//Yhrx04EjYaqW2/tsj/2kYcRm1tomTOHlI8+RJeYeBRG2zOK8XEEENsrWLtiAs59ZfqTlnWNqwHQ6wQ46Ql/mKWbnA4ANrxH+9yVtPsu7f5eSKzSFFCg8a8SBZ+PPvVgSjAx9e67EFtdLFq4iNo9lQxz9SIY/0vYOCSaVRVzaXRZ+NcIv5tNkGW+WmHH7IM2rcDwGTlYW5vYOHcuhpRQPFY7fU89iW2LfmX74l87xpCeN5Qz/nkf9pYWzCFhtH1XjH1tYG8At+hAr+5ealkWZGJvG4I22r9/f8LYwXxV+iyi7CPe2Iuxsf5VbZltJylBfnVEh8+KNt7Me1NSebPB78XJbhcpsPgnywEtPq4r9NCnXcR8kMe/UtXEz7otANx47lVEZMfRZreyfPly+vXrR2pqardjP5hVq1aRn78Zl3sXXq9AXp4/xLKa0bwi+D1begFmR/6Ktt6f6xEZcSvz57ciiiLZ2dnMmDEDp0ck79EFOPeFJib0Csaqa2RlcmfibJBahW1ff4rwlnqunPNSwFhElZrnrnk4YNvUhV9xUnQYt/Sb1LFt4dBscvfpp1h9IoUOF4OCTR3GVbPXx8z8YoaEmHkoozOhtm3ePErvvR+N6EN10Ktg2cChfDn9OraHd59w+sozD5BbUkjMPfegG9CfeUHhOPUGniyqxC757yuIVrTuQjymPACuTYriwV5xnPfT9azQX44gu4movg1B7swrCBJNuAUPyZ5YRrcPInJMGpePvJrVRU3MeGsNAG9jpnc3anDuXS9Q0e4ipsZfweHQGjB5O3MldL1PQ9/b32NlQdUHlKsqaR51E7Oe9yd773jwJWLXVpBqyOQ3+yJ6TR5AZXAa8oIdjJO7L1l2iXYWGQqoVnf1OiaIoVSpWwEIl4IY5ssg3KMj7JxMPpx9H/boJCSjuYsHRC2rGO7LpELVSJoYQ5YUx+Kaz2j0VnHRE88TlZLGGy+9Rl1LA7Gxscya1bkQaK2t4e1brsGenou8T2fkgvOm4ygrYvH7b3YcJwsqok+YSkxKGjnJCRS9t5QMlX+lvdb+E2OmXop7aR2qff5Sn+RlWd2XTIybgYDAuVip3acItPORkzDpuspGOXc20fThvlCTWiD2H0PQhBlo+6kE69KeS6tVJg1x942g/dcyXHuaEW1epPbOz0jr9zeg9nn9z+13pjBVUBCSrfscJ8vUqcQ/9WRAxYd9fSGS00vwuJxDXvdIYV+7jvJLu58bDolGQ+aSxWgi/3wX5iONYnz8F/h8dlpb15Kffy3yAUoV2uWpFMq9OINfiKTVvzEoFm7fCapDu+4kp5fqh9d0/KwONxDZZw3edb9i6J/Cd1uHsllTEnCOtrUBQ00Zlzw/m8ULF7KryP8yTZTCONnjf5GvcfxIWd22AJf9Dcs3MEOXgtkcRtjpmUihMm/MmtmhidEd4fGJzHj0PxiCAl3Nzh1NNH3kf3HE3T+CTQvnI//WTrypMzxQYS8AZDY0/kLOiZPYvWIpEUnJTJh5NaYyPe0Ly/2NMoCF1R9j07bjslkRUHFWys1oVZ3hlwZXJUHnJpM5ZjR2UeT7z7ahq7ZjaCsjPwSeH9r5Mshzwju7RVTpFpwDIolTqVnwzjw2qIrI7Z3D9As6cyL280diusXFL1JSGmgIXM0HOITOZ3RqiJsZrRcCsH7dGbhc/s/rSSedREhWCA+uehCT2Iela4aBWUP0iDbKNT1n0o9b8wtXhOgp3boRe2vnRPbD6ZezM74XOo8Lj85ATEMVfSr3smSQ3/X6RXIIA0OCsEQe3ip6P7Iss3fSJHzd9ICIuvUWmt5+B4xJvH7pLXzey++JC/VKTF76E6cv/42k+kDjVJeejuOdx7lxbx0FPr/3IKj5Awy2hdjCryTSGI6l5X1MGiNl7WX4NLEIsg+16DfsNYKGt4e+QdQnblrldsJF/z3t/dRknjWUxg93Yq20IvkkjPiTWS93VfHKov8QPO0FAO63leJpq+T2zZ8xt9c4vu81HLPby53b38YQnEROX38SXo2jmGV1XwLgVBlYGzyKcJ+XK6als/nLTwnShNDmDVxwCAicnX47GllDq6cBt2gnxpgKwCpNATs1PU+m0VX19FKnkBs6qmObvb+XoiWrKLPtwJPalxad30MZLgUx2dsPi+w35DXRJnz1DswT4tEOCyUoPAJnQTMF769mrn49OrWWOy6/GWtRIx+u+pp2d+BEO2XKFEaNGkVrbQ3v3HJ1x/arX3kXS5Q/hCNaPdS9sAnJ3nP4bl3DT5TY8pkYO4NoYzIv4uJL/AZB6VPTuhzva3bRPKcAT1lnZUnYOZnIHpHW74p7vA+AaXAM4dOzAraVf7se1Vq/IVnpXUnID12rCzMWL6L86qvx7C3qsk/fx5/8694VGJY19O1L0puz0YSH4y6ppv613QhqLZaTQgka3rsjHHS4yJJEy8efYBychzE3UC/I19KCfdkyzGPHogn3h8NKz78A59atmEaMIOLKK6i++1+ITU3dXRpjXh5IEkHjxxE2Ywbq0NBDjkVsd6MK0iGojk4ui2J8/Al8PhubNl2I1baj2/0lxXlUVuaSSTEzmE8FcSyPm0VOZi5pOTmExXbqeDRXV9JUWU563jBwSTTMzsfX4I/5RlzcByHJgNqgQdteDqHJPPfE87TLbgxON3JLDe74VAAMVcW4uukMaXB68Oo0aOorkBxWXrnsHmSVisvnvEhkS6fCaHxWH1QaNZU7u1dMBLj2jQ8JCus5Juza04ImxoQmRI/o87Fmzuc4K5tJa+yNWlDzQ+WbuMSu7lejJYQZjzyDsNeH7Qd/yGVb/DpOufkOti5ewI6NG8gJm0hkoRcfIoJeQD8znpj0jA4DobWgjhc+69Q0+HbQOOq6ScJT1Tg4O8hE5Ka5QOfLdj82n8i4dbupdvvVE1/onXRII6Swro0tGycSbujUBHCh50rh0y7H3qH5gMTmbRRsHs3++s6NCRsp1ZXi0ybi1WeTYhnALs1ApH37Ne49hNT/m6aktzqu81RWIhdHW9BodZTmb+abJx7EEBzMOfc8giEmjjcfvBtvYwOzL70L9wHf2LPW/ETGlpUER0Zx1ctvo/odQ/hAmj/+hLrHHgMg7qkn0UREIlnbUUdGYh42DHdREaUXzECyWqmbdjaJ+ikYJBXuXfPQZwhEXnM19a++Qkn+cqJr/ZNCfqpARUwG3066CGO0l9eGj+TCH87HLXYNyfxjyD/4zwZ/DsvMnJnM6D0D00/NODZ11WIwDY7BsTEw3Oat2oBrvX8lbxx9O5qo3jwtO2hqWcHgts0BxxrUZs5I7sz+f7H6A1aEj2BS4xLCvK0UmdKYfvf9VLzzaIC+TJdxaCxoBB3t3kb0KiPTkq5Fq9JjN3j5VrMWl8//ewpuV4fXAeDm669DoxLQ1Kho/qRrTpIHH268aNGgR4Ow77MSfeNAvPUOWr7Yg6BTEXpaL9qXVCA2ufAh8r5hCQAXu8axS13JRm3gpJ6SksLll3cm1u5asQS9yUx63tCA41rm7sW+pvtGZCXWbTS7a9hr9T/TjOA8BkeeSJtW4ALZypsX5TEiOzpggnNsrqd5Tmd7BX2vENxFbWiijPha3OCT0EQaiby6HyqtioZ3tyM2u9D3CkWXFIxpcAxqc+Ck//Nrz6PK95ITOpJ6ZznxGU3Q0oZ95Up8ra0kvfIypqFD8dbX0/T22wSfcAKNr76GY72/kifiulmEz5yJbdFigk+cTNEpUzsmecFgIOGVl2n9qgzBEOjB1kbvJfSMceh7HbovzH7KZl6KY50/1Bb76CMEn3ACmogIZFmm5MyzcBcUoEtJIfahB2mbN5+2uXNBqyVj4W9oo6ORZZmmt9+m4dnnQKv1q5JW+g3brDWrf9fg2M9+r1PwCUmETEkN2OfY1oA+JQS15chpfIBifPwpNqw4kTZPz9a41RrBls1TAcg11LHD5V/Zqe1WkmUX0Wm98Hk8pPQfxOL3ZuPzehhw4ikMDp+CfV0tMjKqKAOb1UvYvdrfBG7izKuIzenPW+9/AIKAuXArgiRhyx502OPeFBLBuoFjifW6+Mzk6khIPBC1RsOgU86grqkJc1wilSsWYrCEcME9j6A3/fGOl7IsU7l+K201dTRZq9i1Ygn2lu7VUwUEBoRPxOZtJeHMQfQ/+Qze+/wbavZup0q0kC3GYtPtYWzGUEadcwLFxcVExMbzy44GbDsW0VDXubpuNZrZE5OMU9CxK7mrLPCpW1di0xtJHj4Sj9HM2LAgzokN5+PqJv5RUNFx3NcDexGr17K42YpTlJgcYSFNr2NDaTMNWjff73mHczWfocNLeXsCLu0prIk/hV9aweK0MWPdbyzsM4S90YkYPG4uWP8bBp9/xegVvPyc9DMurZamxMDcEUGyY2r7jr6acopbtmEPOQuPZSpPZoTRW9fO0Fj/hLC+dj03/nAt/eMH8dpJb6BVabG1NOOytvOMU82H1Z2rolvffhjtvnunDsjjhMuvJSQ6FkEQcDsd3crOg18bYO+UKfiqazAOHEjyB+93W7pY//wLNM32h5YMQ69Bm9BZ7RQxM4fi6Fou+OECTlkvcflvgd411Xmnkf3IM/xQ/AN3L7+7Y3t6jczV9sFkLymhNTGU74bBzbPeRfg6H+f2Q68yDb3D8VRY8TaU4lj0NPj8Rk/RmOsZGDkQgDX131Fm30mSuQ/xpnTym5cyIHxiR2hvRd03bB99EuNGDuTHpRtJWvo6Ko2Wix5/lo/uuhkEgfRBQyjetG/SSkymqdJvQIfExJKQ1QdTaBiDTj6N6oKdxMVkYUmJweq0UVNTQ1ZWFp8/cCclLe14IuMwmUzceWdnRZy33kHdC5tA6vrqDTsnk/bfykAlEHFRH3SJwciSTMPs/AAPAoBg1PCpail20UW2L54idR0+wR/iG5w9EILUjB02Gu/3VegSgjD2jUSbENRheMuSjH11NQgCrfP9noLQszJo+qEAtafTiJ1T8jQ6o5ETLp9F9qhxvHHlxUyJvhTTgYq1aoHQaekEjYrHW2v3/3770GeGEjwhica3OvVDVCYNMXcMQW3WUrhuFTqDiZT+A7s8j/aGeuY9+zj1Jf7xBWvDmZp4NT7Jy7dlL3DR0y8QnZqOLMogywgHlYRLDgeNb72F7HAQdeutqIxGvPUOBK0KsbWGtm++oW3efHz19egHXIgubUKXMYhNRTiWP0PkTTcQfsklqHuYl2RRpGjqVLxlXXWLYu67D01EOFW33d7NmWAeNYrkdzsVb2Wfj9Zvv0WXkIBp5Eh8NTXIoogu6fA7hVc9uArZ7f88hF/UB2NuBIJKwNfqpvbf60EQiL0tD03Ekeu4qxgff5DanS+yo9bvYpetelZtPQutzrVv7SEzdNg8ZBkqK66gtLTTLanVOpEkNcadOxAkEWOEi/DsVuq2ROBz+F+ipyRehVkbxhf8ht2gRvB5ETxuJFMQ+tpyJK0Ob0QsKqcdc+kuBEFF+tkXsWVn4OrIq1LhsbczqE8uLklmbasNn1rNwj5DcWl1PJOZgGbZAkpLSogMMhHqbMOFmqjwcNrNIewq7NoF85JLLqHXYVrzh0KWZbb88j2bfprPxEuvYc23c6jZsxsZkNJiiU0vw9loIW7ILdyxqJVz9PkYBN/vXnc/ap0BKTwVubYzh2RDSjYbUvsc4qzDp5dRT5XVhUvTuXKbIv9AVGkVnvSbqQvRsKzVhgqZSTvW06uxGrdayzd542gzBRPisJFe/RPV2u+RVXGoE66iRpXR5T6h7V8zb+zFJFuS+XTXp7yw6QVkBIR9cfNHRz/KmRlncvuS21lQtgCAvhF9eXXyq4Qb/B6fVq+P0Wt20eQTyd67jdN/m9PlPsERUWgNBpqrKug3fhLmyGgSsnqTMiCvY+JpeO01Gl962a+ZsHwZKmP3LyBZlqmYNQv70mWoQlMwT7i3yzFzwxbxXuTXfPzvQJe9PjubmH8/TdMz/8F1+gQ+jypi0AobZavzSW5qJ6tdwDD4CmRAbi9Al35ax7nW+dejCo7HMOAi1OF+Q1ObFEzk5dm0f/8dtQ88CEDIWWdh6J3Noq0FjNCd0nF+fvNS+of7w1KSLHVUc9VpKgi+ZBiZWb062qbPvu7SAOO515ARDDntLOY8eBd9J57ISbNu8V9HFA/Z8fhAWmqq2PjjPFRJvUjPyu6SbyS2u6l5en1HSBL8Xg5dYvcaEwG5E4AxN4KQU9L4de1i1q3vTGi1aM2ca/VX7FhOTOmSlBs8KZmQE/1J8dZllbT92OnlEfRq4h8Y4V95l5bhW9WKNj2YiFG9EA6o9Jv/7BNIu+wMiTy5yzjDL+pDy1d7OiY9gJBTUgkal0j1I2uQnf7vffxDI1EZNLTW1fLOzVcBcP3bn3aoF+/n59deYMfSwGTkM1NuQq8yUdS+hY3NvzEgbBzZIcPAoMI7ToNL6yA+OwdJ9BEWFygYKNq91D61zytx9zDUZi3O/HwaXn4NIeQcBI0BybEXfaoFb31nVZFz0/v4ylcRNvMSYg9IDHWXtGHfWIc2vIHKG67+3fyTnkh+9x3MB3hr/1scW+tp/qwgYFvw+ERCTknr8HLp00OIuub3+4L9ERTj4w/gclWzesVYJBXQYmL5trM5UB7xzDPPROZVmpsXkZR0Nb/8bKapqQmtzsGQIfPxeIy0VwZBu4fw3q3ognx42oOoXT6MtqoGzk65jUJNLcu1hy7/PPXEyeT274+jrY3g6Bjef/99qqurSQgNZtrMyzl7dzWlbh+3mBYzJS6T04sS9qkVQqgKzls8DxV/7E+ZmZnJRRdd9EcfGQArVqzAZrMxZcoUVAf1O2itrWHlFx9TVllFWN42oqNLkSQVK1dcSID05GFQIYaw0JsFyKQEyWS495Kk9rvlnRod28aczCb58KSQNbtb8fUO/UP3PxBLw0uMLBdJs6XRrm1nT2QQGzPPQhYEtPgIqbgWQ9JDVNK5OplmrkDvraBN0vDMoDNICOrUjrjylytZVxtYCTE5eTK/lQe+bMMN4dwx5A5Gxo1kbe1agrXB9I0ZjVH0snvJbyT2yfWv2H+HE6+8nv5TpuIpLaXknHOR7HbiHnuU0HP9ib+iJFJtryYpOHB1VXP//bR++ZV/LNc8hs0toW2L6UhE7DjOUEXoql/xla/GrtOwKTUWq1FPcmMbSc3tDFm2gjeunIFbgPSg/gyNOoXu8FasxbUxsO9JzENPY1+xEG9FGe4C/0tVm5RExoJfaaos5/07rifGmMrwyGkYNd17e3SpFqKu7d8l5Lbqy09Z/VVnSG3azf+k9+jxtDc2YA4NQ63pmkh5JJAcXmSfhHVZFdp4M+a8Q4ty2TfW4Wt0Ejw+EZXBPya3282LL76Iw+EvOR/WO4/+W3ruLaONMxNzSx6yLFP3/CZ89Z2l6pYpKVhO6CFp/gCqdu/k6yceJM9yAqlBfXs8ThWqw9gngtCp6TjsbVi31SAtsxJ6Wjqm/lG01Fbz7i3XdBxvDgtn1hsfUrRxHcs+eY/W2hokMXCRcuHjzyKsdeLb5tce2taynH5hnT1tRNnHqvp5NLqq8EhOQmPiuOCRZzCH+p/JgSrM5pFxhJ6ajq/JhauwhbbvikF2EzWrFyqTmb3jx6PLORt9lt/I8hQvxp3/OSFnnkHM3XdR95//IPlOgH3SCPalTyG1+L3nKZ9+gtjaRuX1B7TZ2Efyhx9gXfAbrh07MA4YQNCECYel7XG4uPa20vj2tm73CQYNssv/TCOv7oehV+gRuy8oxsdhYbPtIX/bLJxO/8pAbdWwbOu5SJKWvr0SmHrORdTU1JCenk5V1acU7HmAiIiJZGe9zMfvvk1C+mz0+q59S/aj06Rg/WkGMcSyWltAo6pnRcnQ4GBuvu22jknc5a7FSjhTN+2lwuXpcvxI1SZWS3kdP0/euZ6MhkPrLpjNZmbOnIksywGlpHl5ebhcLsaNG0d7ezs+n4/09HQMBkOP1yovL+fdd98F/F1C9Xo9Op2OkpIS+vbtyymnnEJxcTFffvklw4Z/jV7vf8Fty59Ma2un0uAOTRZup51kdQuRqs6XYI0YjFHwIiGw2JuBVe4ci4CEER/nxDSibqtCBpxaPaIIn4ztnMwEUUYHuNX+iSZJUlG/oAIx3oTYK5hguY0xwjJ+NJ11yOe2H7XYSljVTahkAaPPiEPr4LHRj5EWcyJTNvj1EEI0Au0+ucME/LR/OidEWJAlieYPP0R2uTEOHIig02HKG8S8vfO4b+V93d4vJyKHMEMYK6u678ux4oIVhOj9CZmyKFL3xhvkz/2KWoOWBlP3cdwYs4UJQVFYf/0Vl0ZNS1oMw+Z8Q5AphHZPOx/s+IA38/05FNGmaIbEDOGR0Y8g7dxD6fTzMI0cwZq7TuaxtY/R25nKQxXXESJ2XambBrr5beGHVLR3DcVpBR25YWPIDhnaZR+AoK1GNzqC5RuX4ampIWRzPilN7d2arIWjBlNn0pOU048dS38jODKKYFcIY6P8ZfCqIC3mobFYF/tDbt3JnYM/QfCrx++nfPtWNFodV7/2HibLnxfJOtp4vV4qKyupqakhLy8P5y+V2Fd35m8IWhWCUeOvFhEg7r4RiO0e6l/sDI0EjUsgdGrX/LJDIcsyXz/xAGX5m+kbNjYgmXZh9cckjB9Ict/+bF3wE+XbtvjHolIx6YrrqCvZS+GalQGaJAAXP/kCnz3wT0Rv18RXrcHoV251E6CA2h0On5XV9fNo8zbildxc/vxsQkKiaf5kF+7invu7GPqEE3mpP0nUumgRbd//Bgd41PYbIILRgOx0EnxmZ/WQ5GgE5wKi/3krhv2lzz4frV99Td2TTyKo1STNfgPT0O4/+0eKpo934tzehDpUT/h52ViXV+IubgvwRnWX0HskUIyPw6Cw8AnKKzpXVw27h7C7vg8DBw7k9NNPD1jNt7SsY9PmGQCMGP4La75/CaJ+6Pa6DkcYJpO/UmFPwUjq6vzud41Gw4Vnn0ViRiY7duxg2bJl6PTrMBnbGTbsCXr37sveoqcpL/cnIW4I+RfPtw/p9h77Sfa5SS7ZTd/qTtfprFmzcLlcfP/99x3dJ7Ozs5k+fXpH2+uioiI+2tcxsieuvvpqEhI6XZaSJFFZWUlYWBjvvvsuLQf1ETiQxMREKisr0ensDB/xTcC+Hdsn4nCEcO6515GRkUGb08sv+ZXs/Mmfud4u6fnG05/TB8TT5vRS1epkXGYUdrePORv8k8iMYUlc0tfE93NfJj6hgNraDNbah7FWF4cvJ4RxvkX0FzexqmwIm5LGImtU6DY0sr+PXLSxgSfH+pvMvV5/LatdozgnKIgTsn7hp0YbZ4RY+Yfzyg71TkFyE1L/JGMjotCqtCyvWs45mefw0KiHAHiyuIYXyzqTIafHhvFcdjLafQl4LZ9/Tu1DgeWyupQUYh5/lHnmPfSN7MvrW1/vMDTyovN4cuyTxAfF8/Lml3l/+/t4pEAj9PExj3N6r9OxrVxJxZVXdWyXgW1JUQiSTG5VI7WhZmRBYGuyf1U9sKyWCKuTZb2T8WrUbEtvY3uus9uEUICL+1zMnUPv5MfFb/KbYzO/NQUaQ6fEnsTNkVdj3C3h3Ob/vMkxapZu+RSNSofN24xJYyFcH0eILpIkc++Ocz2ii6W1c0gPHkC4Po6V9XM558nHWPTuG1Tu6kySHlpUTdRBDQp1OX2Yqw18JidffxvZI8fiXFuPaPVgmZSCoFXh3NGIoFNjzO45sVqSREo2byAkOpbIpJ6l9f9X2K8+rDZrQaNCpVNT99ImvNX2gF5FhtwIwk7vhTrkz/X3kCSR7Yt/o/iHVQzRTQbA6m3hx8o3f+fMTkacM4PKnduo3LWdzOGjKFy7CgCVWoNGp2PwtDNJyu2HVqcnNsM/YbYvLA8IKRWatuGsbCY9dABBqtCA669v/JlqSjh38J14y6wcirDzsrp4oDxVNprnFHR4iSRHE97yVUit5RhHBOqEaCKNmIbEIDa50KVaMPaLRKVT+5VXRREENa49zbgKWtDGmDAPi0U4Ak3sJIe/zYZ9Ux2t3/hD7DG35qGN9YtZHpiHY8qLJmx61l+i5qoYH4dAliS2LPqMZtUDAFg9oexsG469IA6tJHLNzbdgDA4mTOufqJ27m7HvqCTfch6y2m+Nix4tal2gZW61huN0WijcM5K8wd9hNNpwOoPYln8iKpVEdu/BnHmGP8Rhtxezu+A+Wlv91rsq+gpWMpKM+nuIwl+t8iJ3sE7oXElYaOMU+Tu+YAayoCZHVcS8vIl8//0v2Gw2cnNzGTp0KFptZ8JeS0sLLS0tpKWlBXzQJEliyZIlLFu27JDP6qqrrqKhoYH4+HjmzZtHdXWnWqVOp2PIkCGsWrWqx/P75LQQGfl9l+0qQUe/fq9hte4gKelSbLYCtNoMduzYQ1JSEmGR0eg1KlrbNmAypqLX+0tI522pIsSoZVSaBpXKxMJFo9FoWhFFHSEpX/HF8peYmhYYsnhx0zXkN/pdwxnRJp6YMAdr64KAY8yR1zM45ybWrpmA21PHoIEfEhY2iq1tTVw87wRARaolkTmnzkGr0rKnZQ85ETkdeQRlTjfD9/WsGWoxMzcvA5Uo4mtqwrl5S7fiQPvJ2rABdZCZVlcr3xd/z1mZZ3VpAy9KItW2amb+PJNGp3+CTwpO4vuzvqfqhpuwLVrU7bWDL7sE/TUz+Xf+i0TPXocgdfVSSILM55Mr8GhlLDYNaTVmaiJcZPcbxvIqv2jTmIQxrKhawej8CCx2DQXDVQxwpZDbGsO0a28jKDyCPWtX0rKkmISW1IDre1UeVKIKtdA1dLG24Qc8CRInzbqZ926b1WX/fsKdHobvqejwfnhzerMy0oyjrTXguBve+bxLubhCJwc3fgSIun4A+uT//j3rsTmo/fcGZFlGfVI4i+bOpqG8NOAYc2hYQAk5QFJOP8578ElWffkJq7/6rGN7Sv9BnHvvoz3ez9fsovaZzn40CY+Nxid50Wh1ODbX0/JFpzy7LMtsbl5IXoTfOKpOqGTo9edTdd9KkEETY8KQFYY22oRpcEy3ZamSy0fd85sQ27oa6ZKrDbVhBzJdczb0maFEXtG34/1rXVpJ20+di8Wg0fGEnvbn8+6cOxppX1wR0FMLOsNrB9Iydy+u3c1EXz8AteXINZM7EMX4OASbfvmUFu39gL988p++12jWhjK4dDe9m2v4qt9o9Eg8uHM5bdu3Mi3yStRocAVVUDbq/oBrDcr9EZXeRltFKx+VBPODLDGsaAsRtgqGDJ2HoPeho9NIMZkyiIs9k5rar3E4Oj+AT3Mv+UIewXIbL3Etu8jlGcF/rxcs35BlVFFZ+w3h+mD6DfyM9zY+QrpvLSP6/ofo6JP+9LMoLy+nqKiIpUuXApCUlER7ezttbYduOw0QnjGI4YNFqipeJzhsOsmW8Xz22WeATExMEUaTkZSU9UiSFaeQR3419Imsx6LtXgshOelKMjP9iVx2exFr1k4BwGhIZuTIhdhsu6io/AijIYHikhfQ6aLxeDrbZqtUBiTJ1e21AUzhV5IQmUXhnru63R8RMZGmpsUIgpbx47awtXEns36bhdPnX3E/N+E5Tkw5scfr+ySZZTt2E/7vp+h1xeW0fPQx9oMaiGUsXgSyjG3Fis6EycOURG757DPat21FSo5jwZJ3+XCsyGXySEa/4u9FEv2POzAOHkz5pZchDxvAE712siXe3aESqnermLGwM5dDVoGwrzjFF6KjFSuRbf4XkqBSccoNtzPXtoiCpYvxaCSyKoIJdnY1INIGDiZ3wmS+f+FpTBoLpyVd97u/S/D4RFS5ZvJX/8Kgk0/DFBLKis8/ZO23X3Qc02/SSYTFJbDisw874v6TL76S5kUL2VRd2uWaI8+9kFHTL/zde/9/p21BGdaF+yp3pqYRPO7IKWJKHhFBEBC0KjwuJ3XFe9EZTSz75D0S++Qy4uwLaK2rITgiitVffUrp1k2cNOsWolPTqSrYxecPdPamGXvhZQw749xD3s9V1IptRRWGrDCCRsYH7PPU2LEtq8Sxub7LeXNKnkarN9CnzxgG552KZUQSmsPw/MiihLfeSdP7+YhtB+SiSDuJ/efZOHe6aPuptMt5QWMTCJmSinVZZZcEYEGvJv7+EQFVOp4qG5pIA5JTpGH2VsQ2NzG3DUZl0KAyajqOta6oou377is0D27qebRQjI8eEEUXS5b643m1xPKy53ZK9d1bnePW/MJZBdUdKpwAtsgtVOW9gAyoPVlMPPkn1jRbuWz9Xlp1fss2q81D75L1LOszCK8e/ik/SiZ7ur1HZNRUGuzVXOK4B1nwZ9EPt2jZaPXi2/dX+VCejnqf2FlU1En07/caewofp6LiXSIiJqLXx9DWtpF+fV/BbO5aYdEdrXUONv5cSkJWGL1HxlFcXIxarSYlJQWr1cq8efPYu7drdUxYWBhbVBkU1TTToNLz0sR7UAkyoqSiyvAzmTEWYoVPKCt/ueOcJmcEP9f/h0UFVm6cmMa05I+orvmiy7UBxo5ZT3PLSnbsuDVge1LS5VRUvHdYv5tabSY0dChudz02W9cmXvsZOOBdCvc+id1eGLA9Lu5csrIfZ9KXkzqkvS/NuZQ7htzRsXrxNTXha2zCkN0ZM/W1tFB24UV4SrrXiIh95GHCzvMLn8miyN6JJ+Cr978Y0+bP64gRA4g2O5LNijbW3769uyZSdj2Y9y3CVCEhZK1YzudFX/HU6seRehAUujL9EpzfbiQyMZnzZ95JxY58fn7t+R6f0Z9hRNTpxIdnEDQsDs/6loA4c/DEJCwnJCNou7qZbS3NLP/kPRrKS+kzZgJDT/fnbSx89w22/NLVewYweNqZjLvoclx2G8ZgyzFtCva/gizLuIvaABl9r9C/1TOrKSxAazCg1moJjYk7ImNzbGug+ZPOKrmtzYvZ3daZ5N1/0smceM0f6/4qixJ1z23E1+Qi4pI+GPr4S1hlWca2shqVXo15aCy21dW0zvOXCAtaFbK3sxQ97t7hHaJulsnJWCb7Q32OLfU0f16A2qJDMGoCelftJ+LSHASVQON7fi+WYNBgmZiEKkiLt8aOyqghaFQ8KuNfkyh9KBTjowcWrHiErzxecsnnVel22tSHTir7aYmNKLdMhb2AovYtxGTP4P4BKur0KpKDzAwLC+HrmmYcv/MIP8iJJKHxWerq5ndsS824n380jWZ1a/dNygDuEZ4hV/KHZgRBQ27uC8REn7LPM3AyHKDAGhE+joEDf3+Cdto8fHjvanxuEbVWxWVPjcawT8xHlmXqStoJjzMjCSK1tTXU1dXx888/o9FomHnVdYx93t/aenziSmbmdJZ5FrWmIAgyqZYKVPuSK7Y19uGrPadTafPnjsy+ZDAn9olkd8F9IIsEBedQWPhYxzV0umi83mZk+fDLcMPDRtPath5J8qDTRTF61HJUKi2i6Ka5ZQU7d96Jz9cacE7fvi8TEz11n4FSQFHxs1it/uzwIYO/pkm2cMY8/2T//snvMzhmcMe53qoqik47HdnhwDR0KK5du3qUbgYQtFqi77qL8Iu7VhVV3nQz1gULCJ1xAXEPPtixvfTii3HlbyPtm69RR0RQeu50vFU9JxRbzxhH2oOPccKXJ3TZF2OK4fNTP6fN3UZ6SHqXF/qmn+az6af5tNXVYg4LZ9pN/2DNN59Tvr1rTw2Ac/71MDuWLUIQBArXr8bn9ltAOqOJs+9+iNiMTARBhUqtxtfsouHtbUh2L1FX9+uxjPRQeD1uln38XoABEhoTR9bIMYw5/5KAElAFhe7wNjjY/Z+fKGnOp7B9I6kDB1O6xd9zSaXWcO3r72MKCf1D15QcXmRRDuhV0x3NX+7pIo4HkPjUWFrnF2Fb5Q9lW05KxTw4mvpXtiC2dy0yOBBBq8KQG4FzSwOqYC1xdw9HUP89jEjF+OiGPYvXcalYSYk6MKM7u7aMARV7+aHvcJw6A8NsMmtC/JNxtEvivbX1vKjbwcKh4xA13QsgGX0yi5oMvDowiI9rusri9gsy8umAdCTrWjbUbWVA1EA+sqYHJCnOSorijYpOddIlw7LJNhlobl5OcHAOarUZtbpTi2HjxgtobVt/wF1UjB2zGp2uZ51/p7OO1d9uY9eyzhf2CTN7E51i4cfX82lv9IcttHo1glqgzeNjfaqGu09JIUzXyhurPmZPUxgTEleQG1nQ020orBnIU9su58Cy2stGpfLAqTmoDlqVezyNNLes7uLtCDJnk5h0Kbt3d9bU7yYTc9R0pqZP5ZO11zIgOJShfe7B4SimrnY+qanXozb2QkDApO0UT2tuXonDUUpR8b8xm7MYNPAj1OpON2t19Zfs2n03Km0UsTnvsLe1mHtW3MPAqIF8NPUjvHV11NxzL5apU3Ht2E7Lp5/xewgGA6mffYqhT89aJPZVqyi/4koEg4G4hx/C19iI9beFODf7lSQtU6eiTUyk6c03EYxGEl96CZXZTNO772D7bSEAm9IFnjtbhUcb+Fwv73s5w2OHkxqSSkJQQpd7Hwqvy8WOpQtZO9fvoUrpPwiNTs+YCy4JEC1rb6ynpnAPyX37ozeZu9XAkCUZRAlB+991D3W0t7F+/tdkDBlBQu+j03ND4fihtbaGmsLdJPTp29GG4ON/3UZdcSGTr7qeASdO/Uvu6ypqDRBXA38YJnRaOqLV4+/MfLDYnAD7S+Y0UUZkSUZs6j6kHHFJDsbciL9g5H8Oxfjohne/fZuHQ3JxC50TeO+aMibs2UyGKhprVQvzhRDODu1DcUYQH6bpEFUCY4PNLLcGeicm1XpZGOs3RGKcEjcVupl5Tl/csSau2VHKgGATd6XFUuHyMHLtrgN1hHpk9fA+bLc5+aauhasToxgVdujEufr6n9m5604Mmn44HPXI6mKyMh9mVe04oi0GJmZ3CuQUbapn+7JyjFk3ozbUUbrgXiRnLzwu8RB3OABBJO2kh9BbarvsSoz5J2XlPyDJ1WgNrTgaMqhYdisvmiUmD4hjSq4/c/z0AfFdVt1LKpZg0VkIN4Szbt0pRKg9lLhVvFKv594RD3BWr9PZsnUmze3beKlWoMzTdQIL1gYzOmE09424j892f8arW17FrDXzyzm/oFPrMGr8f2/Hps00f/Yx+uQ0Iq69BtW+RlI1thp8ko9tNYu5f+0zeOTOMV6cOYOZv3po/byrkNd+1GFhiDYbeL2ozGbS5s3FW12NLiUVbUx0j+eBP/m56MQph/Rq7CfhuWexTPW/IGVZxlNUxIb2Hdy26/GOvBSAC3tfyD+H/hON6ui7XBUU/pdYN+8rln/6PqkD8sgcNooN33/LyOkX0md013b1fxZZkmmdtxfZKxF6Wi8khxd1iL4jb0Ny+qh/fQu+ev93WNCribqmP7JPwrm9keCxCQHJoc5dTTR90BlOjrt/RBcZ+mOJYnx0gySKvPrwq/yam0KRIZF4h8RzO12EeCXU3bTL/iJRwzO5gaqPkfVuno7T0e+ndlSyimadQKhHxnKIGvkvSxu4qaT7ySXNoOHaGB8u0csJkSFkhR1+3bUoSnz+yDpa6xyEZ/1K9MAvcTjjuG3lPzBpnFwyVGDm2EkU7biC1vo2ZEmFIaxTYlyjimHHlw8gizpUOhvR/b8iOGEnkeHX89OqHKqK28n1+icwc8x2ksZ3bVXeuONUWgvPwueRAAljZBGu5lRkSUufc9IZc0I862rX0TeiL/OL5mPQGHD6nDh9TkRZ5I2tnXojcVqJoSYfC63ajo6oAFlhmZS07sEr/3G3ol6tZ0bvGZy3XMY2u7OsWtDrib3/Popzwvl49k3sTBaoiux6/fe0V2N+5PUu29FqiX/iccSWFkLOPgexuQlVcDCasJ7FnXqi4dVXaXz5la5jz8zAfYAq7f6qmG6v4Wjgx5If8YgeLut7GVrV3+dlpKDwd6W5urLbKqubP/wKrb5nnaMjjeQWafl6DyqTltDTex2yCZwsy9T+ZwNikwt9ZihRV/b73et7nA6+fvIhjMHBxGVkU5q/icqd2xl/8RUMOe3sI/mrKMZHj9dudPLbm5vQrviBjOJvcQ65gaiEroqHVfY6/mMKYtOwcOzhOoQ2D9qtzaicIqa0F4lVu5hVO51RtoHokoOJuro/glaFV/KiETQd19u1qppFH+6mKUjFZ+OCyWoUCS62Yo/RUhotI/leQvL5k1FNGjNfTfueTaVuXlm0l+lDEpk5IpWSDXWYLHoS+4Th2riBhueex9r/RFZXp+D1+e+j0jroNfVfqPVdk5MOjY7ksC9otL2CwxtYorqkcjjupt4Ms7gIS/Ov/FuLx2KtzMPdHofosiBLgZOcLhQ8rZ0/L+71KQXRhxYD+iPM6D2DguYC8hvzOTX9VH4p/SVg1Q8Q1yRz4maJ7SkCSY0waYtEbGv31zuY1b0Flgw1EKYPo1/sQMa+uBzJ6tcFMPTrR9xjj+Halo8mJoagsWN/52qHh9jWRtlll+OrqyP9h+8RtDq8VZUYsrNp/vRTGp59jui77uxIVlVQUDhyfPX4/ZTlBzYhTMrtz+m33/O3Ldv21tlx723F2D/qd3NOAJZ/9gHr5n7Z7b7Lnn2diMTD7xfzeyjGx+/Q8sWX7H36YZbmyITqbiXalEGOUU2TT6apfg/6ytXs7n0xkgA79dX8aAjFIqk4w6GmPmoFq1PnAqDHxEVZV+HVtODwOlhVtJYEMY2bc2+nYZubvevrDin1XxlSwMrUbwi21ZBWK/Nb1DRcreNJ9qqIESFFFEjz+j9cOr0KQ3MZbnUQbkPgCrtYI9IrajvZY7uuoAEkn45Gl5qSKi+Ve9M4ZVIxJsNhhlwOvM+26Xh2+UtgK0J2saTXZ4wtmU5Sax+Wp33J7pg1JLfkMHX3tR3nvDv0Ljya7uOVGkHDI6MfwaKz4JE8jEschwoVq2tW88DKB9CpdbR72ukT3odHRj/SRfbbLbqpslUxff50JCQuzbmUU1/bgndlV4NndwI8eImaOU/9sd/bOGAACS++gCYm5i+rDJBFEUQRQXdkO0wqKCgcGp/Xy8bvv6VgzQqyho9m3byv8LqcRCalMPmqG6gq2IksSQw57SzUPeT8/Z1pqani3dtmdek5YzAHMf6SK+k7sWf5gD+DYnwcBo3ORtbUrGFn3S5q13joVTCyx2NDbJtpDUpBwK+Q+E3mR9SYa1EbOzUrMhsGM2nvzC7nNpmrmd/nZWJsaUwovBiT2LWLbNaezwlr3cPPQwfRJIwkg56TRg8kb/NzPD0qmq1BE5lSUc/IMBPBfVcj+fQgq3A0ZOK1RWMrf47hBW5C9zlGZGTemClwxojOrOq1Ng3vbruMG/p8T//QrrkdPhmeqAwmrLE/TaZq7EHNeCW/holKUiGp/JU3gqxiUuElZDT5BW4awkr5odebnNH3VM7JPIeSthJOSj0Jr+RFlMUugloHI8kSAsIhJ/7qmkLcz76Gd+VaxG6UV5uD4JEL1VRHCITYZc5eKdEQIlAeBffNkbq5oh9VUBAZS5b0GO5QUFA4vmgoK+GbJx/EdlCX7tQBeZx114OH3VjwWOKy2zCYg6jcvYM5D/p1jRJ653DeA08iqFR/aXm1Ynz8QTyih1t/uQNxfRj9yxNBe2i9jDJDKwsjtLiCfyDEuAuXxs6UXTeRZAtcmbfpG/mm37O4tZ3hELM7lDN6ncX45pPJ/7UUUX3o2KKh9TsG7NlMQeYFtIZlISOyKfJpLlxYQ0JL4MTpNIRTHTeKkPZSHMZo7OZYsvfMQSV3v9p3DBFpPU2kuUjNh2otpbH7c19kdALEaiWCVTLVXhUGFdR4/fsHRA3g5RNexqQ1kd+QT4W1ggdX+UtFs8KyyA7LJnhdJpZCf+16RLKJ6XcOQ63578siXbt3ow4PRxsdTdP779P09juI+2Tk92OZOpXI225DGxmBaLUihlvIb8jnne3vsKrar8iqV+sZlziOO/vfSqQQjHPzFhwbNyA2t2DIzcX6889E3XLzX96HQUFB4e9Fe0M9P7/+AhU7upaba7Q6hp99PoNOPhW96e+zKPF63OQv+Jlti36hqbKcsLgEWmo6cw1Pu+1uskaM+cvHoRgffwJZlnGJLq54ZyoXzrHi1DpZl5tDZsvpSJoIJLWB2JrV1Mb17CEBCCr7kvembCfcpaYk2orTmYG7bhrBGc8hIxJjimHemPconzgFUaVlzYiHcesC9Ubiq5cT3lKA3t1CSHspAJKg4sfRE1mSW095+C7C2/Tc+KOLvqWBhsWqPgKJjTLJDeAI1mKy+r0T6shI4p9+CrGxkbonn0JsbQ04z24QmHWDCvc+sbTJyZO5Z/g9fLHnC+xeO1f0vYJ2TztmjZkYc9fum06fE5/kI1jn13KoLGhh3vOBsdTk3HBOurovOkPPlRiyKIIgBOg3uAr2UHr++ciu7sM3Xo2Z1tAM2izpOOJ6I6dk0dbg4ozbBxGbFvhsJVnC6rFi0SmiVAoK+6kpamP5nD2IPonUfhGMOLNXj9+PrYsqqCpoITrVQmJ2GFqDmoj4wPwIt8OLzujPf3NaPehMGlSqQ3sw/07Isoy1qRFHWyu/zn6JhrKu4oFn3nk/vQYPPwajC0SSRL54+F9U7e5eVDGhdw7n3vc4Gu1fHzZSjI//AlmW2dm8kyZnE7csugWf7CNWHc/zu05G9fVbrBz5GB59aLfnRjbm02/77A51CzEvlw3TrsOcnUVQxHZ+LP6Byxv6YHyoMzcj9NF/U6rpw64VlcguKyHir+QuOaj3yM2zKD3pDL4tnM/YtF6clTUVQRCQvF4q353Nor0/01ZZzJJ+KmKGjsEreYm367hjymOYGq20zptHxOWXo973PCWPB09pKbLbQ+k9d0Kh/4tlu/psKk8fwhkZZ3T0LflvcNm81BS38eNrnSuICRdlkzMqloaXX0aflkbIGWfgKthDw/PP462pwVdfjyxJxN53L+YxY5Da26m89Tbcu3YFXLvVkk5lwnjqo/PgEGNNzgln7AVZhEZ3DXcBeFw+XDYvlkhjt/sVFI53diyvYskngbo9MWkWDGYtY6Zn0lBhxdbiJmtYDLtW1rB2fldJb41ejW+fmq1KJSDt066ISDDTVOWXKtCbNJhD9YTFmjnxipwj4gk9GsiyTFn+Zr5+4oGA7YJKxVUvvY05LBy15q8vbbc2u1j6WQHtDU60Bg225ipE91Laag9U0FaxX3wytlcmFz7+XIfB11RlY94Lm4lKCiYuI4Ss4bFYIo7se08xPo4QX+75kl9Lf+XWvFvpE9SLuieepOSXTRTlnE9c6VLUkpvGiL7Igpqohs3E1G9CoOvj1CYkoE1Kwl20F7GhM0QQfOJkEl58sYtKo3PbNprefgddcjK69HRCTj8N4RCxxkXli7hl8S0YNUZ+OOsHokxRf+j3rH30MVo++QRBqyXprTdx7ykEtYqQM85AfQQyvqsKWpi7zwsS1yuEMcnl1D/8IDVxo/Cl5RKzYQ5GVxMy0BLWm9aQXkgqHUZnA3G1a2iIGoDWayespYCohx5mzW9NlGu6L0vWmzQk50ZQuL5TwC06JZhz7xrSUcImiRKCIFBb0s7Ps7fh2KcoOPDEZEad3fOKT0FhP5IkdxHM+1+kcncz817YAkBsuoX6ciuS76+fEsZdkEW/CYm0NzmRfDKhMd0vDv5OFG9aj8tuY93cL2mqLO/YHpuRxZBTz8ba1MCOJb+R1Lc/Ey+95k+/R2RZpq6oEHN4HKu+KSMo1Et0sppV3/qNQADJV4PHGih2qDWdjErXB2QXA04IIzgymrQBCVgiDexcUd3FwNSbNZx755Aj+uwV4+MoILndSO3tqCMjsa9chae0lLALZ+DauYvGl19GcjhwrF/f4/nR//wnEVdeccTGU9hSiEFj6FIRcjhITieVN9yAfdXqgO2a2FiSZs8O6GFyKFw7d1L/wguETp+O5cTALOrWOgdzHluHz9s1wTPIVkmvom8pSZ1Ge0j3eik9YQ7R4XGLnHhFLhEJ5g5Lfu/GenavrqFsu19xdtQ5GfQZFceST3ZTtKmhx+ulD4pi8mU5aPV//8QyhWPD1oUVrPp6L8l9I7C1uLBEGMkeEYsxWEdtURv9JyWiVqtorLSyc2UNjjY3aq2K7OGx6AwaYtMP3dbhaNFUbeOrpzfic4tkDYth0qV92LO+joXv7+rxHEOQlv4TExkwKYmKnc3IMiRmhzH/pS00lHe2q+83IZEdy6uQJJn+ExOpL22ntri922sKApx8bT/SB/6xRdOxZO+Gtcx/9nFkqeeE9XEXX8GAySej1uoo3rQOr8tFRGIyMekZ1JcWY2tuIm3QEGoKCwCZ+Kw+lGytYd6zbyK6u84d2qDpqNQR+JzLET2B3Ym1ppNR63PolRdN0abOZnpBYXrSB0WRv6hrQ8+knHBOu3HAIXVF/iiK8fE3QXK5cO3chX3FChpfew1Bryfl008QNFr0WZl/qxW2t66e4lNOQXIEaoVooqOJf+YZjAP6ozL27KJzF5dQfPrp4PP3ZUn75msMOTmI7e1YFy0i+IQT2PH2T6zaZUFU//ftnPuMjmP8jOzfdd1u+qWM1d8W9bjfEmkgPjOU3as7K3xi0ixMuSr3iLskFf53EH0SbocPkyWw/LmtwcnH96/u4Sw/vUfGUl3Y2tGu4GBOmXVkJlqvR6Q0v5Hdq2vR6tVkDokmbUAkKnXnd6K9yYm1yUVcrxAElcCW3yrY+HMpbntg/6SLHh5BaIwJWZZxtHlwO3z+FfPKaqKSLRjMGvasryN3TAJBYd1/f30eEY0u0GgXvRJqrQpZkqnY3YzOoOHrZzZ2e36f0XEIQEq/SNIGRP6t3o8HI0kyZdtK2PLLxxRvXPf7J/SAoFIjS/5wlT4oFbe9AeSe+30dzMhzZ5DQ50QqC9oZcEISOqOaD+9ZhdvRtT+WoBIYd0EWaf0jMZi1+LwietORzQNRjI+/Ic4tW9DExaGN6Zqs+XfBtnQp1oWLCJ95CeqICIpPPgWxrS3gmJh7/kX4zJl46+qxLV5E8EkngShSfPoZiM2B5WmhF5yPfdlyvNXVHdt8agMFmefTnjac1LxYRK/EnnWdIZJeeVEMOy0dj9NH0aZ6tvxWQa+8KFRqFVq9mvZGJ3qTlgkXZXc0xDsUolfih9fzqdgZOLZ+ExMJjTbRKy8Kc4geSZRYPbeYLQs63al5J6cw4oyuzdgOxtHuYeuiCtobnQiAoBYYOz0LrUHNb+/tpLnGTlRyMMNOS1MMmv8BPC4f857fTH2ZlV6DoohMCqLv+EQqd7fwy1vbO45LyAqlscrWZSI/HM67ZyhRycGIokRrnaMjYVOSZFZ9tRdbi99w0Rk1GIN1lO1owtrkAlkmLM5MvwmJbP61rCOfYj/JOeGcPKsfdcVtVO5uYctvFYg+ieAIA3qThsaKwCaI8ZmhpA2IZODk5D/8O/xZSvIb+fH1fJBBo1Wh1qq6TJZ5J6Uw8qzuO44fKWRJZs38Ykq2NBAeb2bkWRmERPm/n83VdnasqKLf+MSOsERDhZX60nbqStop3daI0+olITuUzMHtuO1WzKGhRKWkseG7b9i7fg1Oa/eensPFEj0YlcpKcGQUFdtXdmw3h4WTd8rpZA0fTWhsXJfz6krbKVxfh9ctsnNFNeYQHdkjYhl6ahqa/7LH0u+hGB8KRwTr4sXU//s/+JqakA4wQoImT8K5eQtiU9cmeqEzLqD1s897vKYhJ4fwyy8j5LTTAP/KbM23RST3jSAkykRMmqUjli7LMrIkB6zk/ixl25vYuaKamDQLueMS0PfQbrqp2sa8F7bg3JcHkj4oiswhMcSmhxAUpsdl99JS68Dt8FK0uQFLhIF133XNhAeISAyiqbLzZR/XK4Sp1/fvYjR5nD6+f3UrboeP0BgT/cYnkNg7/E/9nrIk47R52b26BrVWRe8RsUd8dfN7/B3yIaoLW9i9upaMIdEk9g5HEKCxwkZrvYMdy6tpq3cQHGFAq1eTMTgGn0ekdFsjgkqgvrQd574qsZ6Y8eBwwuP8pZYlWxtwO3xEJgXz/Stbsbf64/IZg6M5YWYfEMDe4qa2uI2FH/hDGmGxJi54YDiLPthFwdpaErJDmXpdf4o3N3QcczgcmNy5n6AwfUduQE8MmpJM+qCoLtVgRwufV2T36loSe4ehN2mo3N1CTVEb2xYHhgf6jIpj4iW9EQR/y3qgx8WAx+XD55HQ6tV4XD7MIYf2sK74qpCtv3W2nNAZ1Iy/KJteedF89tBa2hr86smDpiSTnBvRpXpvP1qDmr7jEhhxRnrHu0qWZd6/43qaqyqITE5l7IxLsURF8/lDD+O2NaE1n4RaXYrLWobGMAxJrEPy+KtVYtIzOeHya4nP6t1xj+bqKip2bMXR1ka/SScRFPb774f9XcojE4O6eKT+KhTjQ+GIIlqt1D3xJG3fftvzQRoNKe+9i2noUJw7dlD74EOI7e1Ypk0leMIEWuZ8gbF/f8IuOP/oDfxP0lRl4/NH/5wrNSzOTEtN4Go0YDIQ4IL7hqE1qKnc3cLuVTXUFAV6l9RaFekDozqSZo3BWoIjjMRnhNBvYiKWCCM1e1vZ9EsZMWkWBk5OZs28YnYsr9rXZyfw3idd3RdzqJ7g8L+2X0XJ1gYWf7y7Y+IOiTKSNiCSPqPjUakFWmrsRKda+OXN7dQUtf3h8IMsywiCgMfpo7a4DY1eTWi0idL8Rsp2NDHyzF7ojBq2Lalkw4+l/9Xvotao6D0qjh3LAvsypfSNYMpVuYcsF/e4fEii3K1nriS/MaD663AwWnTIoozL7g0wNnRGDaffMpDolGAkUaZiVzM/vpbfIWYZnRJMTHoIcb1CWPj+LqJTghl6ahqJvcP+tiENSZT48qkNAR6aiMQg9EYN1YWtAGQOjaFmr///CdlhHTk0K78sDMgp6z0ilrB4Mym5EWxfWkXBuloGnZjM0GlpNNfYmfPoOiRJZvjpaZTkN1FfenieCpVKwBCsZeCkZDb8WBLQoDMmzcLoczKwtrgwWSAsRo/DqqGlxk7ptqaO7/SY8zLpPzER0SdRtLGe4q2NmIN3ktSnF2mDhvw3j/CYohgfCn8ZDa+8SuMbbxA0YTyx995L+y+/4N61i4irrkKfmXmsh3fEkCSZxR/vZu/G+o4SwgPRGdQBL51TZvXriFOX72ziu5e2IggwaEoKw89Ip760nbnPbUb09ZygFhJl7Fht9Yjgr+j5o65+jVbFWf/IQxAEGittZA+POSIepf2s/KqQLQesIg8HnUHNzCdG/a5nRpJklnyym10ra/yxap/U7d/kj5LYOwx7qxtrs6vDaItKDiYoTE/eSSkdk1rhhjoaK20YzP5ky/+2RHT5F3sCEgDVWhXiAZNmzph4xp6fCbJ/38GGgs8r4nb40Bk1aA9a0daW+MMtMWkWkg7wnnk9Ypdj/640VdnY9GsZsihTuKH+90/4L4jrFcLZ/xyMy+5l7nObAsJYOaPjCI0xs+obf4PHuIwQpl7n91zu9+75vCK/vbcrIMnz9+g3MZFx5x9+E9H/JRTjQ+EvRfZ4QKv9266ejiSyLON2+NjwYykGswatQUNKbkRHHNhp9WDsprmTzyPidga6fgs31LHiy0Icbf6QjjFYS0iUkbiMUHJGxxMaY8Lt8LLow92U5jd2rHDHTM/E4/JRuq3n1ZlKLRCbHkLOmHgSs8MwBGkp2doYkKNwML1HxjJwcjIRCX+8nLqyoIXK3c2YQ/TUFLV1rOjSB0Wh1aup3NWMSqPy5ykcgoSsUE6+th+7V9ewc0U1LbUOMofGMHRaKmGx/rDG2vnFf8iTYTBrScgOZfLlOTitXkq2NtBYYSM61ULG4Gj0Jr/XokP/oNpGXXE7fUbFHdHM/55orXPw3StbiUwMIqVvBDmj42lvdNJYaSM6JZigsKPXUfXvTm1JGz+8ko/L7u0w0ozBWpxWL/0mJCJLMpUFLehNGqKSgolKCcZt93vGqva0dOSSHKhDsp+QKCOn3jig47vs84psXViBuK/UuN+EBIxBOpqqbNha3CRmh6HWdjU8ZVlm409l1Je101rnoKW25wafSTnhnHJtv+O2mk4xPhQU/qb4PCK7VtUQk2YhOuXQn2+Py//i3O/ilySZNXOLcLZ7GDA5icjE4I5clkEnJXcbv6/Z24ooyvz4ej5eVzceHKOGCx8ajuiTaKt3otaoMJi12FvdJOV0rpwL1taya1U1OWPiUWtU/PLWDuSDcg2GnZbG0GlpAdtcdi/Vha2ERBkxhejYs7aO6JRgNHo13/5nE95DeDDMITrsbZ39h4LC9PQeFYfBpKVXXhQmiw6fV8LrFnFavdiaXUSnWrpUqCj8b+N1i4g+6bASzA9E9EmIPgmP01+1JKgECtbWsvD9XZhCdFzy6MgjngshSTKVu5uxRBppq3dStq0RvVlLzph4NDoVxqDj+7OpGB8KCgoB1Je1s/KrvR3VDUWb6ilYUxswuR/MgMlJNJZbqdrT2uMxaq2KnNHxpA+KIjE7rMfjuqN8ZxM/vJaP5JMJCteT2jeS9kYndaXtXaofMgZHM+Wq3P8X3jaFv5a2BgdqjbrHkmGFP49ifCgoKPwutSVtfPufTUjiH38FRKcEc/Y/BiOK0iGTL38Pe6sbUZQICjMEVMjUFrfRUuugtd5BcLiB3LHxiuGhoPA3RzE+FBQUDgtrs4uaola8LpHeI+KwNvtzNIo215O/uBKdQUNSTjhZQ2OITrWw+pu9eN0iI87ohSHo6JbwKigo/L1RjA8FBQUFBQWFo8ofmb//N9oKKigoKCgoKBw3KMaHgoKCgoKCwlFFMT4UFBQUFBQUjiqK8aGgoKCgoKBwVFGMDwUFBQUFBYWjimJ8KCgoKCgoKBxVFONDQUFBQUFB4aiiGB8KCgoKCgoKRxXF+FBQUFBQUFA4qijGh4KCgoKCgsJRRTE+FBQUFBQUFI4qivGhoKCgoKCgcFRRjA8FBQUFBQWFo4pifCgoKCgoKCgcVTTHegAHI8sy4G/Nq6CgoKCgoPC/wf55e/88fij+dsaH1WoFICkp6RiPREFBQUFBQeGPYrVaCQkJOeQxgnw4JspRRJIkqqurCQ4ORhCEI3rt9vZ2kpKSqKiowGKxHNFrK3SiPOejg/Kcjx7Ksz46KM/56PBXPWdZlrFarcTHx6NSHTqr42/n+VCpVCQmJv6l97BYLMoH+yigPOejg/Kcjx7Ksz46KM/56PBXPOff83jsR0k4VVBQUFBQUDiqKMaHgoKCgoKCwlHl/5XxodfrefDBB9Hr9cd6KMc1ynM+OijP+eihPOujg/Kcjw5/h+f8t0s4VVBQUFBQUDi++X/l+VBQUFBQUFA49ijGh4KCgoKCgsJRRTE+FBQUFBQUFI4qivGhoKCgoKCgcFT5f2N8vPbaa6SlpWEwGBg8eDDLly8/1kM67njyyScZOnQowcHBREdHc+aZZ1JQUHCsh3Xc8+STTyIIArfeeuuxHspxR1VVFRdffDERERGYTCYGDhzIxo0bj/Wwjit8Ph/33XcfaWlpGI1G0tPTeeSRR5Ak6VgP7X+eZcuWcdpppxEfH48gCMydOzdgvyzLPPTQQ8THx2M0GpkwYQI7duw4KmP7f2F8zJkzh1tvvZV7772XzZs3M3bsWE455RTKy8uP9dCOK5YuXcoNN9zAmjVrWLBgAT6fjylTpmC324/10I5b1q9fz5tvvkn//v2P9VCOO1paWhg9ejRarZaffvqJnTt38uyzzxIaGnqsh3Zc8fTTT/PGG2/wyiuvsGvXLp555hn+/e9/8/LLLx/rof3PY7fbGTBgAK+88kq3+5955hmee+45XnnlFdavX09sbCwnnnhiR4+1vxT5/wHDhg2TZ82aFbCtd+/e8t13332MRvT/g/r6ehmQly5deqyHclxitVrlzMxMecGCBfL48ePlW2655VgP6bjirrvukseMGXOsh3HcM23aNPmKK64I2Hb22WfLF1988TEa0fEJIH/77bcdP0uSJMfGxspPPfVUxzaXyyWHhITIb7zxxl8+nuPe8+HxeNi4cSNTpkwJ2D5lyhRWrVp1jEb1/4O2tjYAwsPDj/FIjk9uuOEGpk2bxuTJk4/1UI5L5s+fz5AhQ5g+fTrR0dEMGjSIt95661gP67hjzJgxLFy4kD179gCwdetWVqxYwdSpU4/xyI5vSkpKqK2tDZgb9Xo948ePPypz49+usdyRprGxEVEUiYmJCdgeExNDbW3tMRrV8Y8sy9x+++2MGTOGvn37HuvhHHd8/vnnbNq0ifXr1x/roRy3FBcX8/rrr3P77bdzzz33sG7dOm6++Wb0ej0zZ8481sM7brjrrrtoa2ujd+/eqNVqRFHk8ccfZ8aMGcd6aMc1++e/7ubGsrKyv/z+x73xsR9BEAJ+lmW5yzaFI8eNN95Ifn4+K1asONZDOe6oqKjglltu4ddff8VgMBzr4Ry3SJLEkCFDeOKJJwAYNGgQO3bs4PXXX1eMjyPInDlz+Pjjj/n000/Jzc1ly5Yt3HrrrcTHx3PppZce6+Ed9xyrufG4Nz4iIyNRq9VdvBz19fVdLD6FI8NNN93E/PnzWbZsGYmJicd6OMcdGzdupL6+nsGDB3dsE0WRZcuW8corr+B2u1Gr1cdwhMcHcXFx5OTkBGzr06cPX3/99TEa0fHJP//5T+6++24uuOACAPr160dZWRlPPvmkYnz8hcTGxgJ+D0hcXFzH9qM1Nx73OR86nY7BgwezYMGCgO0LFixg1KhRx2hUxyeyLHPjjTfyzTffsGjRItLS0o71kI5LJk2axLZt29iyZUvHvyFDhnDRRRexZcsWxfA4QowePbpLqfiePXtISUk5RiM6PnE4HKhUgVORWq1WSm3/YtLS0oiNjQ2YGz0eD0uXLj0qc+Nx7/kAuP3227nkkksYMmQII0eO5M0336S8vJxZs2Yd66EdV9xwww18+umnzJs3j+Dg4A5vU0hICEaj8RiP7vghODi4Sx6N2WwmIiJCya85gtx2222MGjWKJ554gvPOO49169bx5ptv8uabbx7roR1XnHbaaTz++OMkJyeTm5vL5s2bee6557jiiiuO9dD+57HZbOzdu7fj55KSErZs2UJ4eDjJycnceuutPPHEE2RmZpKZmckTTzyByWTiwgsv/OsH95fX0/xNePXVV+WUlBRZp9PJeXl5SvnnXwDQ7b/33nvvWA/tuEcptf1r+O677+S+ffvKer1e7t27t/zmm28e6yEdd7S3t8u33HKLnJycLBsMBjk9PV2+9957ZbfbfayH9j/P4sWLu30nX3rppbIs+8ttH3zwQTk2NlbW6/XyuHHj5G3bth2VsQmyLMt/vYmjoKCgoKCgoODnuM/5UFBQUFBQUPh7oRgfCgoKCgoKCkcVxfhQUFBQUFBQOKooxoeCgoKCgoLCUUUxPhQUFBQUFBSOKorxoaCgoKCgoHBUUYwPBQUFBQUFhaOKYnwoKCgogkCMQgAAAURJREFUKCgoHFUU40NBQeEvRRCEQ/677LLLjvUQFRQUjjL/L3q7KCgoHDtqamo6/j9nzhweeOCBgIZtSt8fBYX/fyjGh4KCwl/K/tbd4G8yKAhCwDYFBYX/fyhhFwUFBQUFBYWjimJ8KCgoKCgoKBxVFONDQUFBQUFB4aiiGB8KCgoKCgoKRxXF+FBQUFBQUFA4qijGh4KCgoKCgsJRRTE+FBQUFBQUFI4qgizL8rEehIKCgoKCgsL/HxTPh4KCgoKCgsJRRTE+FBQUFBQUFI4qivGhoKCgoKCgcFRRjA8FBQUFBQWFo4pifCgoKCgoKCgcVRTjQ0FBQUFBQeGoohgfCgoKCgoKCkcVxfhQUFBQUFBQOKooxoeCgoKCgoLCUUUxPhQUFBQUFBSOKorxoaCgoKCgoHBUUYwPBQUFBQUFhaPK/wEj+qV1GN0IFwAAAABJRU5ErkJggg==",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"np.random.seed(seed=42)\n",
"paths = 10 # number of paths\n",
"steps = 1000 # number of time steps\n",
"T = 10\n",
"T_vec, dt = np.linspace(0, T, steps, retstep=True)\n",
"\n",
"X0 = np.zeros((paths, 1)) # each path starts at zero\n",
"W = ss.norm.rvs((mu - 0.5 * sig**2) * dt, np.sqrt(dt) * sig, (paths, steps - 1))\n",
"X = np.concatenate((X0, W), axis=1).cumsum(1)\n",
"\n",
"S_T = np.exp(X)\n",
"plt.plot(T_vec, S_T.T)\n",
"plt.title(\"Geometric Brownian paths\")\n",
"plt.xlabel(\"T\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# CIR process"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Cox-Ingersoll-Ross process [CIR](https://en.wikipedia.org/wiki/Cox%E2%80%93Ingersoll%E2%80%93Ross_model) is described by the SDE: \n",
"\n",
"$$ dX_t = \\kappa (\\theta - X_t) dt + \\sigma \\sqrt{X_t} dW_t $$\n",
"\n",
"The parameters are:\n",
"- $\\kappa$ mean reversion coefficient\n",
"- $\\theta$ long term mean \n",
"- $\\sigma$ volatility coefficient\n",
"\n",
"If $2\\kappa \\theta > \\sigma^2$ (Feller condition), the process is always strictly positive!\n",
"\n",
"**Curiosity for mathematicians** \n",
"The diffusion coefficient of the CIR equation does not satisfy the Lipschitz conditions (square root is not Lipschitz)!! However, it can be proved that the CIR equation admits a unique solution. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here I recall a common technique used to solve SDEs numerically. [wiki](https://en.wikipedia.org/wiki/Euler%E2%80%93Maruyama_method)\n",
"\n",
"\n",
"## Euler Maruyama method\n",
"\n",
"Let us divide the time interval $[0,T]$ in \n",
"$$0=t_0, t_1, ..., t_{n-1}, t_n=T.$$ \n",
"We can choose equally spaced points $t_i$ such that $\\Delta t = t_{i+1} - t_i = \\frac{T}{N}$ for each $1 \\leq i \\leq N$.\n",
"\n",
"A generic Itô-Diffusion SDE\n",
"\n",
"\\begin{align*}\n",
"&dX_t = \\mu(t,X_t) dt + \\sigma(t, X_t) dW_t \\\\\n",
"&X_0 = 0\n",
"\\end{align*}\n",
"\n",
"can be approximated by:\n",
"\n",
"\\begin{align*}\n",
"& X_{i+1} = X_i + \\mu(t_i,X_i) \\Delta t + \\sigma(t_i, X_i) \\Delta W_i \\\\\n",
"&X_0 = 0\n",
"\\end{align*}\n",
"\n",
"with $X(t_i) = X_i$ and $W(t_i) = W_i$. \n",
"The quantity to simulate is $W_{i+1} - W_i = \\Delta W_i \\sim \\mathcal{N}(0, \\sqrt{\\Delta t})$.\n",
"\n",
"Applying this method to the CIR SDE we obtain:\n",
"\n",
"$$ X_{i+1} = X_i + \\kappa (\\theta - X_i) \\Delta t + \\sigma \\sqrt{X_i} \\Delta W_i $$\n",
"\n",
"Despite the presence of the Feller condition, when we discretize the process, it is possible that $X_i$ becomes negative, creating problems in the evaluation of the square root. \n",
"Here I summarize the most common methods to overcome this problem:\n",
"\n",
"1) $ X_{i+1} = X_i + \\kappa (\\theta - X_i) \\Delta t + \\sigma \\sqrt{X_i^+} \\Delta W_i $\n",
"\n",
"2) $ X_{i+1} = X_i + \\kappa (\\theta - X_i^+) \\Delta t + \\sigma \\sqrt{X_i^+} \\Delta W_i $\n",
"\n",
"3) $ X_{i+1} = X_i + \\kappa (\\theta - X_i) \\Delta t + \\sigma \\sqrt{|X_i|} \\Delta W_i $\n",
"\n",
"4) $ X_{i+1} = |X_i + \\kappa (\\theta - X_i) \\Delta t + \\sigma \\sqrt{X_i} \\Delta W_i |$\n",
"\n",
"where ^+ indicates the positive part. \n",
"The methods 1) 2) and 3) just resolve the square root problem, but the process can still become negative. \n",
"The method 4) prevents this possibility!\n",
"\n",
"Let us have a look at the implementation of the **method 4)**"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(seed=42)\n",
"\n",
"N = 200001 # time steps\n",
"paths = 2000 # number of paths\n",
"T = 3\n",
"T_vec, dt = np.linspace(0, T, N, retstep=True)\n",
"\n",
"kappa = 4\n",
"theta = 1\n",
"sigma = 0.5\n",
"std_asy = np.sqrt(theta * sigma**2 / (2 * kappa)) # asymptotic standard deviation\n",
"\n",
"X0 = 2\n",
"X = np.zeros((N, paths))\n",
"X[0, :] = X0\n",
"W = ss.norm.rvs(loc=0, scale=np.sqrt(dt), size=(N - 1, paths))\n",
"\n",
"for t in range(0, N - 1):\n",
" X[t + 1, :] = np.abs(X[t, :] + kappa * (theta - X[t, :]) * dt + sigma * np.sqrt(X[t, :]) * W[t, :])\n",
"\n",
"X_T = X[-1, :] # values of X at time T\n",
"X_1 = X[:, 0] # a single path"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feller condition is: True\n"
]
}
],
"source": [
"print(\"Feller condition is: \", 2 * kappa * theta > sigma**2)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 5))\n",
"plt.plot(T_vec, X_1, label=\"CIR process\")\n",
"plt.plot(T_vec, (theta + std_asy) * np.ones_like(T_vec), label=\"1 asymptotic std dev\", color=\"black\")\n",
"plt.plot(T_vec, (theta - std_asy) * np.ones_like(T_vec), color=\"black\")\n",
"plt.plot(T_vec, theta * np.ones_like(T_vec), label=\"Long term mean\")\n",
"plt.legend(loc=\"upper right\")\n",
"plt.title(\"CIR process\")\n",
"plt.xlabel(\"T\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the plot above I also included the asymptotic mean and standard deviation:\n",
"\n",
"$$ \\mathbb{E}[X_T|X_0] \\underset{T\\to \\infty}{\\to} \\theta $$ \n",
"$$ Var[X_T|X_0] \\underset{T\\to \\infty}{\\to} \\frac{\\theta \\sigma^2}{2\\kappa} $$ \n",
"\n",
"You can find the complete formulas [here](https://en.wikipedia.org/wiki/Cox%E2%80%93Ingersoll%E2%80%93Ross_model#Properties).\n",
"\n",
"Let us also recall that the conditional distribution of $X_T$ is a (scaled) non-central Chi-Squared distribution:\n",
"\n",
"$$ X_T \\sim \\frac{Y}{2c} $$\n",
"\n",
"with $c=\\frac{2\\kappa}{(1-e^{-kT})\\sigma^2}$, and the random variable $Y$ follows the non-central $\\chi^2$ distribution:\n",
"\n",
"$$ f\\bigl( y|x_0;\\, K,\\lambda \\bigr) = \\frac{1}{2} e^{-\\frac{(\\lambda+y)}{2} } \\biggl( \\frac{y}{\\lambda} \\biggr)^{ \\frac{K-2}{4} } I_{ \\frac{K-2}{2} } (\\sqrt{\\lambda y}) $$\n",
"\n",
"with $K=\\frac{4\\theta \\kappa}{\\sigma^2}$ degrees of freedom and non-centrality parameter $\\lambda = 2 c X_0 e^{-\\kappa T}$. (`scipy.stats.ncx2` uses this parameterization, see [link](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ncx2.html))\n",
"\n",
"After the variable transformation, the density of $X_T$ is:\n",
"\n",
"$$ f(x|x_0; \\kappa,\\theta,\\sigma) = c e^{-(u+v)} \\biggl( \\frac{v}{u} \\biggr)^{q/2} I_q(2\\sqrt{uv}) $$\n",
"\n",
"with $q = \\frac{2\\theta \\kappa}{\\sigma^2} - 1$, $u = c x_0 e^{-\\kappa T}$, $v = c x$. \n",
"\n",
"The function $I_q$ is a modified Bessel function of first kind. See [1] for more details.\n",
"\n",
"The two parameterizations are related by $K=2q+2$ and $\\lambda=2u$."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"def CIR_pdf(x, x0, T, k, t, s):\n",
" \"\"\"\n",
" Density of the CIR process\n",
" x: array value\n",
" x0: starting point,\n",
" T: terminal time,\n",
" k,t,s: kappa, theta, sigma\n",
" \"\"\"\n",
" c = 2 * k / ((1 - np.exp(-k * T)) * s**2)\n",
" q = 2 * k * t / s**2 - 1\n",
" u = c * x0 * np.exp(-k * T)\n",
" v = c * x\n",
"\n",
" return c * np.exp(-u - v) * (v / u) ** (q / 2) * iv(q, 2 * np.sqrt(u * v))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Parameters estimation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### OLS\n",
"\n",
"We can rewrite the equation as:\n",
"\n",
"$$ \\frac{X_{i+1} - X_i}{\\sqrt{X_i}} = \\frac{\\kappa \\theta \\Delta t}{\\sqrt{X_i}} - \\kappa \\sqrt{X_i} \\Delta t + \\sigma \\Delta W_i. $$\n",
"\n",
"Now we can find the parameters by OLS. \n",
"\n",
"$$ (\\hat{\\kappa}, \\hat{\\theta}) = \\arg \\min_{\\kappa,\\theta} \\sum_{i=0}^{N-2} \\biggl( \\frac{X_{i+1} - X_i}{\\sqrt{X_i}} - \\frac{\\kappa \\theta \\Delta t}{\\sqrt{X_i}} + \\kappa \\sqrt{X_i} \\Delta t \\biggr)^2 $$\n",
"\n",
"After a lot of calculations, it is possible to find an expression for $(\\hat{\\kappa}, \\hat{\\theta})$. I will not write it in latex (it is long), but just in python in the following cell. \n",
"\n",
"We call:\n",
"- $YY = \\frac{X_{i+1} - X_i}{\\sqrt{X_i}} $. \n",
"- $XX_1 = \\frac{1}{\\sqrt{X_i}} $. \n",
"- $XX_2 = \\sqrt{X_i} $. \n",
"\n",
"The parameter $\\sigma$ can be estimated from the residuals. The choice of `ddof=2` is common when estimating the standard deviation of residuals in linear regressions (it corresponds to the number of parameters already estimated).\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"kappa OLS: 4.436449146267847\n",
"theta OLS: 0.9740868705073866\n",
"sigma OLS: 0.5001608939850056\n"
]
}
],
"source": [
"# formulas for the OLS estimators kappa and theta\n",
"num = (\n",
" N**2\n",
" - 2 * N\n",
" + 1\n",
" + sum(X_1[1:]) * sum(1 / X_1[:-1])\n",
" - sum(X_1[:-1]) * sum(1 / X_1[:-1])\n",
" - (N - 1) * sum(X_1[1:] / X_1[:-1])\n",
")\n",
"den = (N**2 - 2 * N + 1 - sum(X_1[:-1]) * sum(1 / X_1[:-1])) * dt\n",
"kappa_OLS = num / den\n",
"theta_OLS = ((N - 1) * sum(X_1[1:]) - sum(X_1[1:] / X_1[:-1]) * sum(X_1[:-1])) / num\n",
"\n",
"# residuals of the regression\n",
"YY = (X_1[1:] - X_1[:-1]) / np.sqrt(X_1[:-1]) # response variable\n",
"XX1 = 1 / np.sqrt(X_1[:-1]) # regressor 1\n",
"XX2 = np.sqrt(X_1[:-1]) # regressor 2\n",
"sigma_OLS = np.std(YY - theta_OLS * kappa_OLS * dt * XX1 + dt * kappa_OLS * XX2, ddof=2) / np.sqrt(dt)\n",
"print(\"kappa OLS: \", kappa_OLS)\n",
"print(\"theta OLS: \", theta_OLS)\n",
"print(\"sigma OLS: \", sigma_OLS)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are lazy... and you don't want to read, write or understand this extremely long OLS formula, you can use a solver for minimizing the sum of the squares. \n",
"\n",
"Here I'm using the `scipy.optimize` function [minimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html).\n",
"\n",
"There are several algorithms that work quite well. An alternative to Nelder-Mead can be the L-BFGS-B algorithm. \n",
"Since the variables are positive, I'm also specifying the bounds (but it helps only for L-BFGS-B).\n",
"\n",
"The coefficients are given in the last row. We can see that they coincide with those obtained before."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" message: Optimization terminated successfully.\n",
" success: True\n",
" status: 0\n",
" fun: 0.7504752547880302\n",
" x: [ 4.436e+00 9.741e-01]\n",
" nit: 75\n",
" nfev: 163\n",
" final_simplex: (array([[ 4.436e+00, 9.741e-01],\n",
" [ 4.436e+00, 9.741e-01],\n",
" [ 4.436e+00, 9.741e-01]]), array([ 7.505e-01, 7.505e-01, 7.505e-01]))"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def least_sq(c):\n",
" return sum((YY - c[1] * c[0] * dt * XX1 + dt * c[0] * XX2) ** 2)\n",
"\n",
"\n",
"minimize(least_sq, x0=[1, 1], method=\"Nelder-Mead\", bounds=[[1e-15, None], [1e-15, None]], tol=1e-8) # L-BFGS-B"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of course, if you change the initial parameters of the simulation, the estimation procedure can give different results.\n",
"\n",
"I tried to change the seed, several times. \n",
"After some trials, I found that $\\kappa$ is the most \"volatile\" parameter, while $\\theta$ and $\\sigma$ are quite stable.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, let us compare the density with the histogram of all realizations of $X_T$.\n",
"\n",
"We can also use the `scipy.stats.ncx2` ([doc](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ncx2.html)) class to fit the scaled Non-Central Chi-Squared density. However, the results can be very different. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `CIR_pdf` function defined above, corresponds to the function `ss.ncx2.pdf` \n",
"after the change of parameterization."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
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Yfa57mVo7gCEN29my9B9dlFTEe2RkZLBx40amTZvGj370I4YNG8all17KkiVLeO659p8WHxsby3XXXcfDDz+Ms51F/9e//jV33303Y8eOpaSkhHfeeYfAwEAAJk6cyIsvvsgf//hHRo0axaJFi/h//+//dWgfz5dle9KHRm2orKwkMjKSiooKIiIiTMcR8Vmr//YIE/f+ngrCaPzhWvrEJ3focVb96XayS14n15FG6oMb8fP3/M/2xbPV1taSm5tLRkZGhy9T35MsW7aMadOmcezYsXadPnw+zvbatPf3t46MiAgApYX7GLHnGQB2D5vb4SICMOTah6kkjAxnPhvf1em+InJ2KiMiAkDRP+YSatWREzCEcdfcfV6PFRkdy85+NwGQsvkJak+eOXBPRDpu9uzZ9OrVq9Xb7NmzTcdzm46dighlRbmMrFwOFgRe9btznj3THqO+fj9lj71OAodZvfBxJn7noXNvJCLt8uijjzJ37txWvxYREUFcXJxHnbp7LiojIsL+Rc8TZznJCRjK4BHuTarUluDQXmwdfjdx2x5i0N4XqDx+BxFRfTrlsUV6uri4OOLi4kzH6DT6mEakh2tqbCQj33WqYPXw6zr1scd8+TbyHSlEcYId/3y0Ux9bRHyHyohID7f9k7dI4DAVhDFseueWEf+AQI5OfACAUQffoOLo4U59fBHxDSojIj2cc93LAOTEXUFwaK9Of/xR079NriOdEKueXYte7PTHFxHvpzIi0oOVFeUyvNo1/XPitK4ZgW85HBzK/CYAMXvf1KysInIGlRGRHmz/oufx/3zgatrgsV32PINn3ESdHUD/plz2bf2sy55HRLyTyohID/XFgasnhn23S58rsk882yKnAHD0E31UIyItqYyI9FBfHLg6/NLru/z5gi+4AYAh5f+l5kRFlz+fiK/Ly8vjpptuIiMjg5CQEPr3789DDz1EfX296WhuUxkR6aGc614Bum7g6umGZF9BkRVPuHWSHR/9tcufT8TX7dq1C6fTyZ/+9Cd27NjBH/7wB55//nkefPBB09HcpjIi0gMdLs5jePUqoOsGrp7O4edHYdrXAei1441ueU7xYbYN9dVmbm7MbDp16lTuuusu7rvvPqKjo0lISODhhx9u/vrx48e55ZZbiI+PJzg4mGHDhvHuu+8C8P3vf58RI0ZQV1cHQENDA2PHjuU73/kOAJdddhmvvPIKM2bMoF+/fnz5y19m7ty5LFy4sPO+z91EM7CK9ED7l/2ViZaTXQFDGNSFA1dP13/GLTT96VkGN+wgf/dm0rJGddtzi49pqIFfJZl57geLITCs3au/9tprzJkzhzVr1rBq1SpuvPFGJk+ezCWXXMKsWbOoqqri9ddfp3///uzcuRO/zy/H8NRTTzFy5EgeeOAB/vCHP/DTn/6U8vJynn322Tafq6Kigujo6PPexe6mMiLSA/XK/wiA4+mzuvV5Y5PS2Rw2kVE1qyhZ+gJpWW3/UBXxFSNGjOChh1zXZsrMzOTpp59myZIl2LbN2rVrycnJYeDAgQD069evebtevXrx+uuvM2XKFMLDw/nd737HkiVLiIyMbPV59u/fzx//+Ed+97vfdf1OdTKVEZEepvL4EbJqt4EFyRO+2v0BRl8Pn60is/Rd6ut+T2BQcPdnEO8XEOo6QmHqud0wYsSIFvcTExMpKytj8+bNJCcnNxeR1mRnZzN37lx+/vOfc//993PxxRe3ul5xcTGXXXYZ1157LTfffLNb+TyByohID7N35duMtZrIdySTNmBYtz//sKlf5/BnDxLLMTYte5PRM2/o9gziAyzLrY9KTAoICGhx37IsnE4nISEh59zW6XTy2Wef4efnx969e1tdp7i4mGnTppGdnc0LL7zQKZm7mwawivQw9q4PACiJm2Lk+f0DAtmXdJXrzpY3jWQQ8QQjRozg4MGD7Nmzp811fvvb35KTk8Py5cv573//yyuvvNLi60VFRUydOpUxY8bwyiuv4HB4569170wtIh3S2FDPgErXWTQRI68yliNu4v8AMKh6HSerq4zlEDFpypQpXHzxxXzta19j8eLF5Obm8sEHH/Dhhx8CsHnzZn72s5/x0ksvMXnyZJ588knuvvtuDhw4ALiOiEydOpWUlBQef/xxDh8+TGlpKaWlpSZ3q0NURkR6kL0blxLFCSoIY+C4S4zl6DdsIsVWHCFWPTmfvm0sh4hpCxYsYPz48fzP//wPQ4YM4b777qOpqYna2lq+853vcOONN3LVVa7/ONx0001Mnz6d6667jqamJhYtWsS+ffv4+OOPSU5OJjExsfnmbSzbduOEaUMqKyuJjIykoqKCiIgI03FEvNaqP91OdsnrrI+Yzrg5C4xmWf3crUw89L+si5zB+Hv/aTSLeL7a2lpyc3PJyMggOFiDnj3J2V6b9v7+1pERkR4k6dByAKys7j2ltzURo68BIKviMxrq6wynERGTVEZEeoiiAztIcxbSYPuROfka03HIGjedo0QQQTW7Vn9oOo6IGKQyItJDFK52TRG9O3g4EVF9DKcBP39/9vV2zZlQs/Vts2FExCiVEZEe4tSsqydSzQ1cPV3Q8K8AkFG+DGdTk+E0ImKKyohID9A86yqGZl1tw6BJV3LCDiGOo+zZtMx0HPECXnDORY/TGa+JyohID7B35dsEfD7rarKBWVfbEhQcyu6IiQAc2+B9VxqV7nPq4nH19fWGk8jpampqgDNnmnWHpoMX6QG+OOtqmuEsZxh0JaxbSnLpx9hOJ5aXziApXcvf35/Q0FAOHz5MQECA18406kts26ampoaysjKioqKaC2NHqIyI+DhnUxP9K9cAZmddbcvAC79K/dr7SaGYvN0bSR88znQk8UCWZZGYmEhubi75+fmm48gXREVFkZCQcF6PoTIi4uPyd28kg0pq7CAyx37JdJwzhEdGsyV0DCNPrqVk9T9VRqRNgYGBZGZm6qMaDxIQEHBeR0ROcauMzJ8/n4ULF7Jr1y5CQkKYNGkSv/nNb8jKyjrrdsuXL2fOnDns2LGDpKQk7rvvPmbPnn1ewUWkfcq2fUwGsD94CMMDg0zHaVXdgCtg21piDy4GfmM6jngwh8OhGVh9kFsfui1fvpzbb7+d1atXs3jxYhobG5kxYwbV1dVtbpObm8vll1/ORRddxKZNm3jwwQe56667WLDA7FTUIj2Ff+FKAE4kTDCcpG0DLrqWJttiQNN+Sgtav0y6iPgut46MnLqS4CmvvPIKcXFxbNiwgYsvvrjVbZ5//nlSU1N54oknABg8eDDr16/n8ccf52tf+1qr29TV1VFX93/TQ1dWVroTU0Q+ZzudpJ3YDEDk4Glmw5xFdFxfdgUOZlDDTgrW/oeE1DmmI4lINzqv4cgVFRUAREdHt7nOqlWrmDFjRotlM2fOZP369TQ0NLS6zfz584mMjGy+paSknE9MkR6rcN9WYjhOnR1A/1Gt/4fBUxxLvBAA/7ylhpOISHfrcBmxbZs5c+Zw4YUXMmxY2/MWlJaWEh8f32JZfHw8jY2NlJeXt7rNvHnzqKioaL4VFhZ2NKZIj1ayZQkA+4IGERQcajjN2fUecRkAA05soKmx0XAaEelOHS4jd9xxB1u3buXvf//7Ode1LKvF/VOztZ2+/JSgoCAiIiJa3ETEfX6fjxepjPfc8SKnDBh5EZWEEUE1ezcvNx1HRLpRh8rInXfeyTvvvMPSpUtJTk4+67oJCQmUlpa2WFZWVoa/vz99+pi/WJeIr7KdTlIrNwIQnjXFcJpz8w8IZF/YWACObf2v4TQi0p3cKiO2bXPHHXewcOFCPv74YzIyMs65TXZ2NosXL26xbNGiRYwbN+68po4VkbMrzsshjqPU2370H+25g1e/qDHDlTOq5BPDSUSkO7lVRm6//XZef/113njjDcLDwyktLaW0tJSTJ082rzNv3jyuv/765vuzZ88mPz+fOXPmkJOTw8svv8xLL73E3LlzO28vROQMxZtdV+ndH5hFSFi44TTtkzzuCgAy63dRefyI4TQi0l3cKiPPPfccFRUVTJ06lcTExObbm2++2bxOSUkJBQUFzfczMjJ4//33WbZsGaNGjeLnP/85Tz31VJun9YpIJ8l3jRc5HneB4SDtl5SeRaGVhL/lZN+a903HEZFu4tY8I+25TPCrr756xrIpU6awceNGd55KRM5T38/Hi4RlevYpvacrjplEyuF/0bDnI5h5nek4ItINdNlDER9UWrCXJLuMRttBvzGedz2aswkadCkAKUdXYTudhtOISHdQGRHxQYWbXIPGDwQMoFdEb8Np3JN5wWXU234k2YcoOrDTdBwR6QYqIyI+yM77FICjMeMNJ3FfWHgUe4OGAlC04V3DaUSkO6iMiPigpOOu8SIhA71rvMgplX0vAiAwX5OfifQEKiMiPuZwcR7JdglO2yJjzKWm43RI7KjLARhYvZGG+rpzrC0i3k5lRMTH5G9cBMAB/35ERHnnLMf9hmVzjAjCrFr2bvjYdBwR6WIqIyI+pil/DQDl0WMMJ+k4h58f+8Nd410qtmtqeBFfpzIi4mN6H9sKgH+a90x21hpnP9fU8H0OfWY4iYh0NZURER9SV1tDesMBABKHTDac5vykjJkBQL+GfZyoPGY4jYh0JZURER+Sv3MtgVYjx4ggKX2w6TjnJTEti2IrHn/LyYENS0zHEZEupDIi4kOO7nZdj6YgZBCWw/vf3gcjxwJQvUen+Ir4Mu//aSUizfxLXPOL1MSOMhukk1jpFwIQfXiN4SQi0pVURkR8SHzVDgDCMiYYTtI5kkdPB6B/w16NGxHxYSojIj6i4sghUuxiAFKHX2g4TefQuBGRnkFlRMRH5G9zXY/moJVIVEyC4TSdR+NGRHyfyoiIj6jOXQtAafhQw0k6l8aNiPg+lRERHxFatgmAxkTvnXm1NRo3IuL7VEZEfIDtdJJyMgeA3pnZhtN0Lo0bEfF9KiMiPqAkfw/RVFJv+5E21DfOpPkijRsR8W0qIyI+oHjnJwDkBfQnOCTMcJrOp3EjIr5NZUTEBzQWrAfgWNRww0m6hsaNiPg2lRERHxB11HWlXkfKOMNJuobGjYj4NpURES/XUF9Hev1eABK8/Eq9Z6NxIyK+S2VExMvl56wj2GqgkjCS+/vmxzSgcSMivkxlRMTLHdm9CoC8YN+4Um9bNG5ExHf57k8ukR7CUbwBgJqYUWaDdDGNGxHxXSojIl4urnI7AMEZFxhO0vWKIkYDUL3vU8NJRKQzqYyIeLETlcdIaToIQMow37hS79nYqRMBiDi83nASEelMKiMiXqxg5xoclk0pMfSJTzYdp8slDJ8GQP+6XdTV1hhOIyKdRWVExItV5rrGi5SEDjScpHukDBjBMSIIthrI3bbSdBwR6SQqIyJezK/UNdlZbcwww0m6h+VwkBfqOn35+K4VhtOISGdRGRHxYn2qdgEQkjLacJLuU5fkGqgbXLLWcBIR6SwqIyJeqvZkNalNBQAkDva9K/W2JXrIVADSa7bhbGoyG0ZEOoXKiIiXKshZj7/l5CgRxCVlmI7TbTKGZXPSDiSKExTu2Ww6joh0ApURES91bL/r9NaDwZk+PfPq6QICgzgQNBiA0u1LDacRkc7Qc36Cifia0i0AVEcPNRyk+1XGjwfAr3C14SQi0hlURkS8VO+KHAACk8cYTtL9emW6JnjrW7nZbBAR6RQqIyJeqKG+jrSGXADiB443nKb7ZYyaSpNtkchhSgv3mY4jIudJZUTECx3cu5kgq4EqO4SkjMGm43S7XhG9OeDfH4CDWzVuRMTbqYyIeKHyvesAKAwagMPPz3AaM470cX081ZSrmVhFvJ3KiIgXaip2DV6tjBpiOIk5gRmTAIg9utFwEhE5XyojIl4o4thOAPz6jjIbxKDU0ZcAkN6UT8WxcsNpROR8qIyIeBlnUxOp9fsBiMnseYNXT4lJSOWglYjDssnb9LHpOCJyHlRGRLxMUe5OelknqbUDSMkcaTqOUSWRowCo2fep2SAicl5URkS8TNnuNQAUBGTgHxBoOI1hqdkARB1ebziIiJwPlRERL1N/0DV49Vhkzx28ekr8sCkA9KvfQ31dreE0ItJRKiMiXqbX0e0AWIk9+yMagJQBIzhGOEFWAwe2fWY6joh0kMqIiBexnU6S6/YC0Lv/OMNpzLMcDvJDhwFwfLfGjYh4K5URES9yqOgAvamiwfYjZdBY03E8wskE1xlFgcXrDCcRkY5SGRHxIiU5rqvUFvinEhwSZjiNZ4gcOBmA1Opt2E6n4TQi0hEqIyJepLZwEwBHwwcZTuI5+o24kHrbjxiOU5y323QcEekAlRERLxJ8xDXzalPcMMNJPEdwaC9yAwYAULJ9mdkwItIhKiMiXiT+5D4AwtNGG07iWY6dumhe/mrDSUSkI1RGRLxE5fEjJNllACQPvsBwGs8SmDERgNhjm80GEZEOURkR8RIHd7nOFiklhsjoWMNpPEvqiGmA66J5lcePGE4jIu5SGRHxElV5rsGrpSEDDCfxPDFJaRRb8Tgsm/wty03HERE3qYyIeAnrkGvm1ZPRgw0n8UzF4SMAOLFPM7GKeBuVEREvEVW1B4DAvpoGvjVNya5xNL3KNhhOIiLuUhkR8QJNjY2kNOQBEJc5xmwYDxU75POL5tXm0NhQbziNiLhDZUTECxQd2E6IVc9JO5CkjKGm43iktEFjqbJDCLNqyc9ZbzqOiLhBZUTECxze5/rooTAgHT9/f8NpPJOfvz+5IUMAKM9ZYTiNiLhDZUTEC9QXbQXgeESW4SSerTrOdfFAvyJdNE/Em7hdRlasWMFVV11FUlISlmXx9ttvn3X9ZcuWYVnWGbddu3Z1NLNIjxN6NAcAO17TwJ9NrwEXAtC3covhJCLiDrfLSHV1NSNHjuTpp592a7vdu3dTUlLSfMvMzHT3qUV6rPiT+wGISNc08GeTMepimmyLRA5TVpRrOo6ItJPbHz7PmjWLWbNmuf1EcXFxREVFub2dSE9XceQQCZQDkDxovOE0nq1XRG/2+2fQv+kAhVuWEdc3w3QkEWmHbhszMnr0aBITE7nkkktYunTpWdetq6ujsrKyxU2kpzq4y3VmSLEVT3hktOE0nq+89ygAGvJWmQ0iIu3W5WUkMTGRF154gQULFrBw4UKysrK45JJLWLGi7dHu8+fPJzIysvmWkpLS1TFFPFZVvmsa+EMh/Q0n8Q5+aa6L5kUf3WQ4iYi0V5efI5iVlUVW1v+dAZCdnU1hYSGPP/44F198cavbzJs3jzlz5jTfr6ysVCGRHstRtgOA2j5DDCfxDknDp8IGyGjYT23NCYJDe5mOJCLnYOTU3okTJ7J37942vx4UFERERESLm0hPFV21G4Dg5BGGk3iHxNRMDtObAKuJA1s/NR1HRNrBSBnZtGkTiYmJJp5axKs0NtST0lgAQFzmOMNpvIPlcFAYNhyAij26aJ6IN3D7Y5oTJ06wb9++5vu5ubls3ryZ6OhoUlNTmTdvHkVFRfzlL38B4IknniA9PZ2hQ4dSX1/P66+/zoIFC1iwYEHn7YWIjyrat400q4FqO5jEtEGm43iN+qRxsHcFwaWaFl7EG7hdRtavX8+0adOa758a23HDDTfw6quvUlJSQkFBQfPX6+vrmTt3LkVFRYSEhDB06FDee+89Lr/88k6IL+LbDu/fQBpQGNiPQX5+puN4jaisC2Hv70mv2Y7tdGI5NNm0iCezbNu2TYc4l8rKSiIjI6moqND4EelRVv3pTrJL/sKaPlcz4c7XTMfxGnW1NTA/lSCrgcLvfkrKgOGmI4n0SO39/a3/Loh4sLBjrmng0TTwbgkKDuVAoGuW59Ltyw2nEZFzURkR8WAJta5p4CMzNA28uyr6uL5nzoI1hpOIyLmojIh4qGOHS4jjKE7bImWQzqRxV1C/SQDEHddF80Q8ncqIiIcq2r0OgGJHPGHhUWbDeKGUEVMASGsqoOJYueE0InI2KiMiHupEget/9IdDBxhO4p1iElI4aCXisGzyNy8zHUdEzkJlRMRDOcp2AlAbPdhwEu9VEuGatbZm/0rDSUTkbFRGRDxU7xOuSyYEJ+u01I5yJl8AQK/DGwwnEZGzURkR8UBNjY2kNOQBENN/jNkwXixuyEUAZNTuorGh3nAaEWmLyoiIByrO3UGw1UCNHURSuj6m6ajUrLFU2SGEWbXk52hqeBFPpTIi4oEO79sIwMGANPz83b5qg3zOz9+f3JAhAJTnfGI4jYi0RWVExAPVF20D4Hh4puEk3q86biwAfkVrDScRkbaojIh4oKCjrmngnXFDDSfxfr0GuCY/S6raajiJiLRFZUTEA8WddE0DH546ymwQH5A+cgpNtkWSXUZ5cb7pOCLSCpUREQ9zovIYfe1DAPTNGms4jfcLj4wmzz8dgIItH5sNIyKtUhkR8TAHd7vmxCgjmqiYBMNpfEN5b9dF8+pzVxlOIiKtURkR8TAVea5p4EuD+xtO4jv80iYCEH10k+EkItIalRERT3NoOwA1UQMNB/EdScOnApDRsJ+T1VVmw4jIGVRGRDxMeMUeAPyTNA18Z0lMzaSMaAKsJg5s0XwjIp5GZUTEg9hOJ8kNBwCI7qdp4DuL5XBwsJer3FXt+dRwGhE5ncqIiAc5VHSACGposP1IzhxpOo5PqU9yXTQv5NA6w0lE5HQqIyIe5NBe1/VTDvolExgUbDiNb+kz+GIAMk7uwNnUZDiNiHyRyoiIB6kpdM0SeqSXpoHvbOlDJ1BjBxFBNYV7NpuOIyJfoDIi4kECyncB0NBnkOEkvicgMIjcoCwADu1YbjiNiHyRyoiIB4mp3gtAaIrGi3SFyljXjLZW4RrDSUTki1RGRDxEXW0NyU0HAUgYqGngu0LogMkAJFZuMZxERL5IZUTEQxzcuxV/y0klYcQlZZiO45PSR03DaVsk2yUcOXTQdBwR+ZzKiIiHOHpgIwAHA/thOfTW7AqRvWPI90sFoGDLMrNhRKSZfuKJeIimEtc08FURmga+K5VFucbj1B1YaTiJiJyiMiLiIUKPu86kIX6o2SA+zpHqumheVPlGw0lE5BSVEREPkVjrmgY+KmO04SS+LWnEVAD6Neyl9mS12TAiAqiMiHiEo2VFxHLMNbgyS9ek6UpJ6YMpJ4pAq5G8rZ+ZjiMiqIyIeITiPRtcfzoSCAuPMhvGx1kOBwVhrovmHdutK/iKeAKVEREPcKLANe9FWegAw0l6hvqk8QCElKw1nEREQGVExCM4ynYCUNdnsOEkPUP04KkAZJzcpovmiXgAlRERD9C7ag8AQX1HGE7SM/Qbnk2NHUQk1eTv1lk1IqapjIgY1thQT0pjPgDxmRq82h38AwLZHzwEgLLtSw2nERGVERHDig7sJNhqoMYOIjFNV+vtLifiXeNG/ApXG04iIiojIoaV73edSVMYkIHDz89wmp4jfOBFACRX6aJ5IqapjIgYVl+0DYDjmga+W/UbNYUG248EyinJ3206jkiPpjIiYljIMU0Db0Jor0hyA/oDULTlY8NpRHo2lRERw+Jr9gEQnjbKbJAe6GifsQA05a8ynESkZ1MZETGo8vgREjkMQN+scYbT9DxB/S8EIP6YTu8VMUllRMSgot3rASgllsjeMYbT9Dzpoy9x/eks5Hh5qeE0Ij2XyoiIQZV5mwEoDelvNkgP1Ts2kXxHCgB5mzRuRMQUlRERk8p2AHAyWvOLmFIaNRqA2v2fGk4i0nOpjIgYFFnhmgY+MGm44SQ9l1/6JAB6l683nESk51IZETHE2dRESkMuADH9NQ28KUkjvgRAv4Z91JyoMJxGpGdSGRExpCR/D2FWLXV2AH37DzMdp8dKTM3kEH0IsJo4sGWF6TgiPZLKiIghZfs+nwbePxX/gEDDaXouy+GgMHwUAFW7PzEbRqSHUhkRMaT24FYAjoVrGnjTmpInANDr0FrDSUR6JpUREUOCjuQA0BQ7xHASiRs2FYD+tTtpbKg3G0akB1IZETEk9vNp4HuljjScRNIGjaOCMEKtOg5sW2k6jkiPozIiYkDNiQr6OksASNI08MY5/Pw4EOoqhUe3LzGcRqTnURkRMeDgnk04LJtyooiO62s6jgB1ya75RkJKVhtOItLzqIyIGHBsv+tMmuJgTQPvKWKGuuYb6V+zTeNGRLqZyoiICYe2A1Dde7DhIHJKxtAJVBJGL+skudt1dESkO6mMiBgQWbELgIC+GrzqKfz8/TkQOgKAIzs0bkSkO6mMiHQzZ1MTKfUHAIjtP9ZwGvmi2r7ZAAQX6ciISHdSGRHpZsW5OYRZtdTaAfQdoAvkeZI+zeNGttDU2Gg4jUjPoTIi0s3K9q4DoDAgXdPAe5h+w7KpskMIt05yYPsq03FEegyVEZFuVld0ahr4LMNJ5HQtx418bDiNSM+hMiLSzUKO7ATAjteVej3RyeZxIzoyItJdVEZEulnCSdc08BEZYwwnkdZED3GNG+lXs1XjRkS6idtlZMWKFVx11VUkJSVhWRZvv/32ObdZvnw5Y8eOJTg4mH79+vH88893JKuI1zteXkoC5QAkDxpvOI20pt/wbE7YIURQTe6ONabjiPQIbpeR6upqRo4cydNPP92u9XNzc7n88su56KKL2LRpEw8++CB33XUXCxYscDusiLc7uMt1ifoiK57wyGjDaaQ1/gGB7A9xfYRWrnEjIt3C390NZs2axaxZs9q9/vPPP09qaipPPPEEAIMHD2b9+vU8/vjjfO1rX2t1m7q6Ourq6prvV1ZWuhtTxCOdyN8MwKHQgeiKNJ7rZFI2HFhH0EFdwVekO3T5mJFVq1YxY8aMFstmzpzJ+vXraWhoaHWb+fPnExkZ2XxLSUnp6pgi3cKvzDUNfF2fIYaTyNn0HjINgH41W3A2NRlOI+L7uryMlJaWEh8f32JZfHw8jY2NlJeXt7rNvHnzqKioaL4VFhZ2dUyRbhF9Yg8AISmjzAaRs+o3YjLVdjCRVJO7c53pOCI+r1vOprEsq8V927ZbXX5KUFAQERERLW4i3q6+rpaUxgIA4gdqGnhPFhAY1Dxu5PB2XadGpKt1eRlJSEigtLS0xbKysjL8/f3p06dPVz+9iMco3L2RQKuJSsJISMk0HUfOoTpxIgBBhZ8aTiLi+7q8jGRnZ7N48eIWyxYtWsS4ceMICAjo6qcX8RhHD2wEoDCwH5ZDU/x4uj7DXWPd+tdsprGh3nAaEd/m9k/EEydOsHnzZjZv3gy4Tt3dvHkzBQWuw8/z5s3j+uuvb15/9uzZ5OfnM2fOHHJycnj55Zd56aWXmDt3bufsgYiXaCp2TQNfFTXYcBJpj/4jJlNJGBHUsH+Ljo6IdCW3y8j69esZPXo0o0ePBmDOnDmMHj2an/3sZwCUlJQ0FxOAjIwM3n//fZYtW8aoUaP4+c9/zlNPPdXmab0ivir8+C4AHIkjDCeR9vDz92d/mOvn3LHtHxlOI+Lb3J5nZOrUqc0DUFvz6quvnrFsypQpbNy40d2nEvEZttNJcr1rGvjofpoG3lvUp14MOZ8SXqwjIyJdSR9ci3SDQwf3E0k1DbYfKVmjTceRdkocPROAzLodnKyuMpxGxHepjIh0g9I96wE46JdCUHCo4TTSXikDRnCIPgRajezboI9qRLqKyohINzhZuBmA8vCBZoOIWyyHg4KoCwA4kaMyItJVVEZEukFQ+U4AmmKHGk4i7rL6TQEgpmy14SQivktlRKQbxNfsBqBXmsaLeJv0cZcD0L9xPxVHDhlOI+KbVEZEuljF0cP0tV2/xFKGTjKcRtwVk5RGniMFh2Wzf90HpuOI+CSVEZEuVrjDdRn6YiueyOhYw2mkI0r7uKaGb9i71HASEd+kMiLSxU7kbQCgNCzLcBLpqOCsSwBIOrrWcBIR36QyItLFAspc08DXxQ43nEQ6qv/4mTTaDlLsYkryd5uOI+JzVEZEuljcic8Hr6aPM5xEOio8Mpp9Aa4jWwc3fGg4jYjvURkR6UKVx4+QYhcDkDwk23AaOR/HElyvn5W3wnASEd+jMiLShQp3uOamKCWW3rGJhtPI+YgYcikA6ZXrsZ1Ow2lEfIvKiEgXqspzTQNfosGrXm/AmKnU2EHEcJzcnetMxxHxKSojIl3I/5Br8GqtBq96vaDgUPaGjgSgbOO7htOI+BaVEZEuFHciB4DQtLGGk0hnqE37EgDhB5eZDSLiY1RGRLrIicpjJDdp8KovSb7gywAMrNvBicpjhtOI+A6VEZEuUrBzDQ7L5hB96BOfbDqOdIK+/YZy0EokwGpi7+r3TMcR8RkqIyJdpPKAa/BqcagGr/qSopjJANTv+q/hJCK+Q2VEpIv4HdoCQG2MBq/6kuAhlwGQdnSVTvEV6SQqIyJdJLbKNXg1JG2M4STSmQZecBl1dgAJHKZg9ybTcUR8gsqISBeoOVFBStNBAJIHa/CqLwkJC2d3iOsU35IN/zGcRsQ3qIyIdIGCnWvxs2wO05uYpDTTcaST1aROAyCscJnRHCK+QmVEpAtUnBq8GjLQcBLpCknjrgQgq3Yb1VXHzYYR8QEqIyJdwFHqGrxao8GrPillwAiKrXgCrUb2rvnAdBwRr6cyItIFYio/H7yaqplXfZHlcFDYx3WKb92uRYbTiHg/lRGRTlZbc4KUpgIAEgdPMJxGukrQ4JkApBz5TKf4ipwnlRGRTpa/cy3+lpMjRBKXlGE6jnSRgRNmUW/7k2QfonDfVtNxRLyayohIJzu+bw0AB4MHYjn0FvNVob0i2R3sGhNUvF6n+IqcD/2kFOlkfsUbAKiJG204iXS16lOn+OZ/bDiJiHdTGRHpZPFVOwAIy9B4EV/X94KrAciq3ULl8SNmw4h4MZURkU50vLyUFLsYgLQRFxlOI10tJXMk+Y5kAq0m9ny20HQcEa+lMiLSifK3fQJAoZVEZJ94w2mkOxQnfAkAa9f7hpOIeC+VEZFOVHPANXi1NGKY4STSXXqPuRqAgZWrqK+rNRtGxEupjIh0orDDrqu4OpPGGU4i3WXg6KmUE0W4dZLdq3V0RKQjVEZEOontdJJW65p5NTprkuE00l0cfn4ciHaND6rZplN8RTpCZUSkkxzcv41Iqqm1A0gfcoHpONKNgoZ9GYCM8mWajVWkA1RGRDpJ6Y5PAcgNzCQgMMhwGulOWdlXUGMHEcdR9m351HQcEa+jMiLSSZwH1wNQET3ScBLpbsEhYezq5ZpXpnzDW4bTiHgflRGRThJ9zHV9koC08YaTiAlNA2cBkFCs2VhF3KUyItIJamtOkN6YC0DfYRcbTiMmDLzwazTaDjKceRQdyDEdR8SrqIyIdIK8bSsJsJooJ4r45P6m44gBkX3i2R3kunBe4ap/Gk4j4l1URkQ6wfG9KwEoDB2iK/X2YFXpMwAIz19kOImId9FPTZFOEFi6EYDa+DGGk4hJqZOuBWBQ3XaOl5caTiPiPVRGRDpB0omdAIQPmGg4iZiUlJ7Ffr8M/CybPcv/13QcEa+hMiJynsqL80ngME7bIn34habjiGGHUy4HIGTPvw0nEfEeKiMi56lwu+tKvfl+afSK6G04jZiWcvF3ABhcu5kjhw4aTiPiHVRGRM5TbZ7rSr2HI3WlXoG+/Yay1z8Tf8vJvuVvmI4j4hVURkTOU0T5ZtdfknWlXnE5kn4FAL32vWM4iYh3UBkROQ8N9XVk1O0GIHawxouIS/rF3wVgcN12DhfnmQ0j4gVURkTOw4GtnxFq1XGcXqRl6bRecUlIzWRXwBAcls3+Za+bjiPi8VRGRM7DsZxlAOSGjsDh52c2jHiU4/2uBCDqwLuGk4h4PpURkfMQXOwavFrXV/OLSEsDpnwXp20xqDGHkvzdpuOIeDSVEZEOampspN9J15V6+wyZZjiNeJqYpDRyPr9WTf6KvxlOI+LZVEZEOigvZz0R1FBtB5MxTEdG5EwnBnwZgJg8fVQjcjYqIyIddHj7xwDsDxmGf0Cg4TTiiTKnfptG28GApv0c3LfddBwRj6UyItJBgQdXAVCdOMFwEvFU0XF92RkyGoDCT3VWjUhbVEZEOsB2Okmv3gxA70FTjWYRz1Y38CsAJBW8i+10Gk4j4plURkQ6oGDvVqKppNYOIGOkJjuTtmV96bvU2gGkOQvZu3mF6TgiHkllRKQDSrcuAWB/0GCCgkMNpxFPFhHVh+2RUwA49tkrhtOIeCaVEZEO8CtcCUBl/AWGk4g3CB5/PQCDjyyituaE4TQinkdlRMRNttNJSuUmAMKzphhOI95gyKQrKSGWCGrYvkRzjoicTmVExE0l+XuI5wgNth/9R2uyMzk3h58feSlXAxC0/e9mw4h4oA6VkWeffZaMjAyCg4MZO3Ysn3zySZvrLlu2DMuyzrjt2rWrw6FFTCra8hEA+wMGEhIWbjiNeIu0L90MwNDazZoeXuQ0bpeRN998k3vuuYef/OQnbNq0iYsuuohZs2ZRUFBw1u12795NSUlJ8y0zM7PDoUVMsvM+A+BY7HjDScSbJGUMYkfgSByWTd6Sl0zHEfEobpeR3//+99x0003cfPPNDB48mCeeeIKUlBSee+65s24XFxdHQkJC881PVzgVL5VU4RovEpp5keEk4m1qhn4LgLTCt3E2NRlOI+I53Coj9fX1bNiwgRkzZrRYPmPGDFauXHnWbUePHk1iYiKXXHIJS5cuPeu6dXV1VFZWtriJeILDxXkk2yU02RYZYy4xHUe8zLDp13HCDiHJPkTO6g9NxxHxGG6VkfLycpqamoiPj2+xPD4+ntLS0la3SUxM5IUXXmDBggUsXLiQrKwsLrnkElasaHvyn/nz5xMZGdl8S0lJcSemSJfJ37QYgFz/fkRE9TGcRrxNSFg4O/tMB6Bm7WuG04h4Dv+ObGRZVov7tm2fseyUrKwssrKymu9nZ2dTWFjI448/zsUXX9zqNvPmzWPOnDnN9ysrK1VIxCM497mO6pXHjGeA4SzinSKyb4T3/sOw40upqjhKeGS06Ugixrl1ZCQmJgY/P78zjoKUlZWdcbTkbCZOnMjevXvb/HpQUBAREREtbiKm2U4n6cdcF8cLGzLTcBrxVlljv0SBoy8hVj07/6uBrCLgZhkJDAxk7NixLF68uMXyxYsXM2nSpHY/zqZNm0hMTHTnqUWMy9u1gTiOctIOJPMClRHpGMvhoLi/ayBr/K6/6OJ5InTgbJo5c+bw4osv8vLLL5OTk8O9995LQUEBs2fPBlwfsVx//fXN6z/xxBO8/fbb7N27lx07djBv3jwWLFjAHXfc0Xl7IdINDm18F4A9ISMJDgkznEa82eDLb6PGDiLdWcCOle+ajiNinNtjRr75zW9y5MgRHn30UUpKShg2bBjvv/8+aWlpAJSUlLSYc6S+vp65c+dSVFRESEgIQ4cO5b333uPyyy/vvL0Q6Qa9CpcBcDJ1qskY4gMie8ewJmYWE468TcPK5+HCL5uOJGKUZdu2bTrEuVRWVhIZGUlFRYXGj4gRNScq8P9tPwKtRgq+vZzUgaNMRxIvl5+zgbQ3v0STbXHoe2tISs8690YiXqa9v791bRqRdti79gMCrUaKrThSBowwHUd8QNrgsWwLGo2fZVPw4ZOm44gYpTIi0g61OxcBUBg9Ccuht410jqbxtwIwuPRtTlZXGU4jYo5+qoq0Q98jrhmGA7MuNZxEfMnwqddSbMUTSTXbPviz6TgixqiMiJzDwX3bSbZLaLD9yJx4hek44kP8/P0pGPAdAGJ3vqbTfKXHUhkROYeiDZ+f0hs0hF4RvQ2nEV8z+PLbqbGDyHDmsXPVB6bjiBihMiJyDkH5ywCo7DvFbBDxSZG9Y9gWMwuA+pXPGk4jYobKiMhZ1NXWMLB6IwBxo/URjXSNhOl3ATDyxGcU7NlsNoyIASojImexd/1HhFp1lBNFv2ETTccRH5U2eCybQifhsGwOvTffdByRbqcyInIWJ7Z/CEBu5ESd0itdKmz6AwCMPr6IogM5htOIdC/9dBU5i/iyzwCwMi8xnER83cAxU9gaPA5/y8nBd39lOo5It1IZEWlDce4uMpx5NNkW/SdcZTqO9ACB0+4HYPSR9zh0cL/hNCLdR2VEpA0Fn74BwK6gEfSOTTScRnqCQRNmsCNwBIFWE3n/1tER6TlURkTa0Cf/fQBODNBREek+9kVzARhZ9m/KSwvOsbaIb1AZEWlFce4uMhv30mRbDJjyP6bjSA8ydPJV7PIfTLDVwL63f206jki3UBkRacUXP6LpE59sOI30JJbDQd2kOQCMKPkXxw6XGE4k0vVURkRaEZ3vmpZbH9GICSOmfp19fv0JterYveDnpuOIdDmVEZHTFOfuYmDjHn1EI8ZYDgfVk+8DYHTJPyjO2204kUjXUhkROY0+ohFPMGLqN9geNIogq4HiBfNMxxHpUiojIqfRRzTiCSyHg5Ar5uO0LcZVLWHPxmWmI4l0GZURkS/QRzTiSfqPmMSGqJkANH3wILbTaTiRSNdQGRH5An1EI54m9dpfcdIOZHDDDjYt/pvpOCJdQmVE5Av0EY14mvjk/mxO+S4Acat/SX1dreFEIp1PZUTkc8V5u/URjXik4d/4GeVEkWyXsHHh70zHEel0KiMinytY8Tqgj2jE8/SK6M3+oXcBMGj3sxwtKzKcSKRzqYyIALbTSd/cfwFQPfBqs2FEWjH26js54EgnihMceP0u03FEOpXKiAiwY+W7pNjFnLBDGDrz+6bjiJzBPyCQxiufosm2GFf5EVs+/l/TkUQ6jcqICFC/+kUAdsTOIiw8ymwYkTYMHDOFdQnfAiB+xU84UXnMcCKRzqEyIj1eeXE+w6s+BSB26mzDaUTObsR1v6HYiieBcnb85Uem44h0CpUR6fH2LnqOAKuJXQFD6Ddsguk4ImcV2iuSI9N+C8D4wwvZtXax4UQi509lRHq0psZGMvJcA1erhl1nOI1I+wy/+Cusi5qFw7IJ+fBe6mprTEcSOS8qI9KjbV/xLxI4zHF6MXzGDabjiLTbwOue5AiRpDkL2fiX+03HETkvKiPSs617GYBd8VcRHBJmOIxI+0X2iSd/4qMATCj6K1uXLTCcSKTjVEakxyrJ383wmrUA9J3+Q8NpRNw35rIbWdPnahyWTfKyezhcnGc6kkiHqIxIj5W36Dkcls32oFGkZI40HUekQ0be/Cz7/TKIppKyV79LY0O96UgiblMZkR6pvq6WzKK3XH8f/T3DaUQ6LjgkjMBvvUa1HczQ+m2se03jR8T7qIxIj7Tp308Rw3HKiGb4l3RRPPFuKZkjyRn/cwAmFL7CtuULDScScY/KiPQ4tTUnyNj5HAC5g28lIDDIcCKR8zfuyltYE/1lHJZN36V3U5y323QkkXZTGZEeZ/PbfyCOo5QSw6iv6IJj4jtG3vwc+/36EU0lDX/5KhVHDpmOJNIuKiPSo9ScqCBzz58BKBh+B0HBoYYTiXSe4NBe9PrevygjmjTnQYqe/yq1J6tNxxI5J5UR6VG2LHycPlRQZMUz+qrbTMcR6XTxyf2pvvZ/qbJDGNKwnR3PfBtnU5PpWCJnpTIiPUZVxVEGH3BNclY88m6NFRGflTF0AvnT/0S97cfYE8tY++c7TUcSOSuVEekxti/8DVGcIN+RzJgrbzUdR6RLDbvoK2wZ8wsAJpb+jdV/e8RwIpG2qYxIj1Bx9DBD8/8CQPnYe/Hz9zecSKTrjf/KbaxOvx2AiXt/z6q//NRwIpHWqYxIj7Dzn48SQQ25jnRGX6ZJzqTnmHD9L1idfBMA2QeeYtVLc7GdTsOpRFpSGRGft3fzJ4wvfh2AikkP4PDzM5xIpPtYDgcTb/49qzJcR0iyC//M6j/fqUIiHkVlRHxafV0t/u/cjr/lZEOvqYyartlWpWfKvuFXrB74Y9ffS15n7bM36ywb8RgqI+LTNvx1HhnOfI4SQcb1z5qOI2LUxG//P9YM/RlO22JC+QK2Pn45lcePmI4lojIivmvv5k8YX/gqAHkTHiU6rq/ZQCIeYMK1P2Lj+MeoswMYdXI1x5+6iII9m03Hkh5OZUR80ukfz4yZpUGrIqeMu/IWCq5eyCH6kOosovffLmPLx/9rOpb0YCoj4pP08YzI2WWOvhi/2cvJCRhKuHWS4ctns+qluTTU15mOJj2Qyoj4nO2f/Fsfz4i0Q0xCCv3nfsyaPlfjsGyyC/9M3m8mkbtznelo0sOojIhP2b91Jekf3Yq/5WR9xKX6eEbkHAKDgplw52usH/sYFYSR2bSPvm9exqrXfkJjQ73peNJDqIyIzyjO203kwv+hl3WSHYEjGPbD10xHEvEa4666lYZbVrE5ZCKBViPZuU+z/zcXsnfzJ6ajSQ+gMiI+4djhEhr/cg0xHCfXkU7KbW8THBJmOpaIV4lJSmPkjz9g3ahfUkkoWY27yXz7Stb94VpKC/eZjic+TGVEvN7J6irK/nQ1qc4iSokl7Ka3iYjqYzqWiFeyHA7GX30HJ2/+lPURlwIwvmIRUS9OZNWf7+ZE5THDCcUXqYyIVzteXsqBJ68gq3EXFYRR961/ENc3w3QsEa8Xn9yfcXP+xd6v/IedgcMJthrILnqVpt8PZdWf76G8tNB0RPEhlm3btukQ51JZWUlkZCQVFRVERESYjiMeYv+21YQsvI4ku4waO4iCy19n0IQZpmOJ+Bzb6WTzR2/QZ/WvSHUWAVBnB7C5zyySZs0lJXOk4YTiqdr7+1tlRLzShvdfYvCaeYRadRRZ8dRf+zcyhow3HUvEpzU1NrJlyd8JW/dHshp3A+C0LXYGj6R26P8w9JLvEBIWbjileBKVEfFJ9XW1bHjtx2QX/wWArcFjSfvB34nsE284mUjPYTud5KxdRP3yPzDq5Orm5VV2CDl9ptNr/HfIGn8pfv7+BlOKJ1AZEZ9iO51sXvxXYlfPJ9kuAWBV4ne54KYn9QNPxKDi3F3kL32JtMK3SbLLmpcfI5x9kZPxH3I5Ayd9hbDwKHMhxRiVEfEZezYup/GDBxnSsB2AcqLIv+Bhxl6uCc1EPIWzqYmcNf+lZu1fGHh8BZFUN3+t3vZnb9BgKuMnED5oKgNGTyM4tJfBtNJdVEbEq9XX1bJj+b9g898YXbMSgJN2IJtTvsvwb/yMXhG9DScUkbY0NtSze91HVG35NyllS+lrH2rx9XrbnwMBmVT0Hoqj72hiBk4gJXMk/gGBhhJLV1EZEa9jO53s37aS8s9eI6vsQ3pT2fy1dZEzSb12PvHJ/Q0mFBF32U4nhfu2UrJlCX4Fn5FatYk4jp6x3kk7kCL/FI6HZdAQnUlQwiB6pw4lPnUgob0iDSSXztClZeTZZ5/lt7/9LSUlJQwdOpQnnniCiy66qM31ly9fzpw5c9ixYwdJSUncd999zJ49u93PpzLimxrq68jbsYYjO5cSVLSG9JptLQpIOVHsS7iCxCk3kTZ4rMGkItJZbKeTogM7Kd35CY0HNxFxbAdp9fsIs2rb3OYoEZT7J1AVnERDWCKEJ+AflURIdDLhMUlE9kkkPCoGh59fN+6JtEd7f3+7PfLvzTff5J577uHZZ59l8uTJ/OlPf2LWrFns3LmT1NTUM9bPzc3l8ssv5wc/+AGvv/46n332GbfddhuxsbF87Wtfc/fpxUvYTidVlceoKC+m+kgJNccP0XC8GPvoAUIqc4muLSTBeYhMq4nML2x30g4kJzwbvzHfZehFVzNRh21FfIrlcJA8YBjJA4Y1L3M2NVF4YDvluduoLdmJ/9F9RFbnktBYRATVRFNJdGMlnNgDJ4BDZz5uo+3gmBVOlSOCk34R1PmH0xgQTlNgBM6gCAgKxxEUhiMoHP+QXvgF9cI/OIyAoFACg8MICA4jMCiEgOBQAoOCCQwK0eD4buT2kZEJEyYwZswYnnvuueZlgwcP5uqrr2b+/PlnrH///ffzzjvvkJOT07xs9uzZbNmyhVWrVrX6HHV1ddTV1TXfr6ysJCUlpdOPjKx7+xmaijd32uMZ1Y6X0aKVdWwbvrjcdp76C5btdN23bSy7qeXN2YifswGH3YCfswF/u55A50mCnScJ4SShdi3+lvPM5ztNJWEcCBlObeIFRA2eQr8RFxIYFNy+fRYRn1dxrJzywj1UlOynvjwXKovxP3mYkNoyIhrK6e08Ri/rZJc8d4PtRyN+NFj+NOK6NeFHk+VHk+WPEz+c1uc3HDgtP2zLDxsHtmXhtPwAB7b1+Q0LLAc2DrD4/E+r+U8XC9uy4AvLbD7/mmXBF/7ecjn/97XWWGebcN21XfSF3yNzVNufcnRElxwZqa+vZ8OGDTzwwAMtls+YMYOVK1e2us2qVauYMaPlrJgzZ87kpZdeoqGhgYCAgDO2mT9/Po888og70TrE2r+EiVVLuvx5eqTP3xPVdjDHHZGc8OtNTWA0deFpWH36E5aURUzaEOKSMhilQ6si0obI3jFE9o6BEZPaXKeutobKo2VUHT1EzbFS6k8cpbGmAufJ49i1FThqK3A01uDXWI1/Yw2BTTUEOGsJdNYSYNcTRB3Bdh1BNOCw/u8/ZwFWEwE0EUJ9yyc87f9wvmJ9UTZ0chlpL7fKSHl5OU1NTcTHt5xgKj4+ntLS0la3KS0tbXX9xsZGysvLSUxMPGObefPmMWfOnOb7p46MdDZr0OWsKu78x/UspzVl68zmbDU38c/Xt1ztHcvC+rzhWw4/cPiB5ef6u18gln8gjoAgHP6BOAKCCQiJIDA0nKDQcILDIgmPiiEsLBxdO1dEulJQcCixSenEJqWf1+PYTicNjQ3U152kvvYkDfW1NDbU09Rw6s86mhrqsZsaaWpqxNnYgN3UgG03Yjc5sZ0NOJuc2HYjOJ3YziZsZxM4mwAb23nqaHMT2Db2qSPTnx+Bdv398z+driPLtisYrqPVn+fEeebR8Ob79mn3v7Dsc1YbR9Lj00d09Ft33jr0gZh12i8027bPWHau9VtbfkpQUBBBQUEdieaWsVfc3OXPISIi3sFyOAgIDCIgMEiTtHUzt67aGxMTg5+f3xlHQcrKys44+nFKQkJCq+v7+/vTp48u8y4iItLTuVVGAgMDGTt2LIsXL26xfPHixUya1PrnednZ2Wesv2jRIsaNG9fqeBERERHpWdwqIwBz5szhxRdf5OWXXyYnJ4d7772XgoKC5nlD5s2bx/XXX9+8/uzZs8nPz2fOnDnk5OTw8ssv89JLLzF37tzO2wsRERHxWm6PGfnmN7/JkSNHePTRRykpKWHYsGG8//77pKWlAVBSUkJBQUHz+hkZGbz//vvce++9PPPMMyQlJfHUU09pjhEREREBNB28iIiIdJH2/v52+2MaERERkc6kMiIiIiJGqYyIiIiIUSojIiIiYpTKiIiIiBilMiIiIiJGqYyIiIiIUSojIiIiYlSHrtrb3U7Ny1ZZWWk4iYiIiLTXqd/b55pf1SvKSFVVFQApKSmGk4iIiIi7qqqqiIyMbPPrXjEdvNPppLi4mPDwcCzLMh2ny1RWVpKSkkJhYaHPT3vfk/YVetb+al99V0/aX+1r57Btm6qqKpKSknA42h4Z4hVHRhwOB8nJyaZjdJuIiAif/8d/Sk/aV+hZ+6t99V09aX+1r+fvbEdETtEAVhERETFKZURERESMUhnxIEFBQTz00EMEBQWZjtLletK+Qs/aX+2r7+pJ+6t97V5eMYBVREREfJeOjIiIiIhRKiMiIiJilMqIiIiIGKUyIiIiIkapjIiIiIhRKiNd6NlnnyUjI4Pg4GDGjh3LJ5980ua6Cxcu5NJLLyU2NpaIiAiys7P573//22KdV199FcuyzrjV1tZ29a60izv7u2zZslb3ZdeuXS3WW7BgAUOGDCEoKIghQ4bw1ltvdfVutIs7+3rjjTe2uq9Dhw5tXsdTX9sVK1Zw1VVXkZSUhGVZvP322+fcZvny5YwdO5bg4GD69evH888/f8Y6nvi6uruv3v6edXd/vfk96+6+evN7dv78+YwfP57w8HDi4uK4+uqr2b179zm3M/2+VRnpIm+++Sb33HMPP/nJT9i0aRMXXXQRs2bNoqCgoNX1V6xYwaWXXsr777/Phg0bmDZtGldddRWbNm1qsV5ERAQlJSUtbsHBwd2xS2fl7v6esnv37hb7kpmZ2fy1VatW8c1vfpPrrruOLVu2cN111/GNb3yDNWvWdPXunJW7+/rkk0+22MfCwkKio6O59tprW6znia9tdXU1I0eO5Omnn27X+rm5uVx++eVcdNFFbNq0iQcffJC77rqLBQsWNK/jqa+ru/vq7e9Zd/f3FG98z7q7r978nl2+fDm33347q1evZvHixTQ2NjJjxgyqq6vb3MYj3re2dIkLLrjAnj17dotlgwYNsh944IF2P8aQIUPsRx55pPn+K6+8YkdGRnZWxE7l7v4uXbrUBuxjx461+Zjf+MY37Msuu6zFspkzZ9rf+ta3zjvv+Tjf1/att96yLcuy8/Lympd58mt7CmC/9dZbZ13nvvvuswcNGtRi2a233mpPnDix+b6nvq5f1J59bY03vWe/qD37683v2S/qyGvrre9Z27btsrIyG7CXL1/e5jqe8L7VkZEuUF9fz4YNG5gxY0aL5TNmzGDlypXtegyn00lVVRXR0dEtlp84cYK0tDSSk5O58sorz/hfmAnns7+jR48mMTGRSy65hKVLl7b42qpVq854zJkzZ7b7e9gVOuO1femll5g+fTppaWktlnvia+uutl6z9evX09DQcNZ1TL6uncGb3rPnw9ves53Bm9+zFRUVAGf8u/wiT3jfqox0gfLycpqamoiPj2+xPD4+ntLS0nY9xu9+9zuqq6v5xje+0bxs0KBBvPrqq7zzzjv8/e9/Jzg4mMmTJ7N3795Oze+ujuxvYmIiL7zwAgsWLGDhwoVkZWVxySWXsGLFiuZ1SktLz+t72BXO97UtKSnhgw8+4Oabb26x3FNfW3e19Zo1NjZSXl5+1nVMvq6dwZvesx3hre/Z8+XN71nbtpkzZw4XXnghw4YNa3M9T3jf+nfKo0irLMtqcd+27TOWtebvf/87Dz/8MP/+97+Ji4trXj5x4kQmTpzYfH/y5MmMGTOGP/7xjzz11FOdF7yD3NnfrKwssrKymu9nZ2dTWFjI448/zsUXX9yhx+xOHc316quvEhUVxdVXX91iuae/tu5o7Xtz+nJPfV07ylvfs+7w9vdsR3nze/aOO+5g69atfPrpp+dc1/T7VkdGukBMTAx+fn5nNMaysrIzmuXp3nzzTW666Sb+8Y9/MH369LOu63A4GD9+vPEmfj77+0UTJ05ssS8JCQnn/Zid7Xz21bZtXn75Za677joCAwPPuq6nvLbuaus18/f3p0+fPmddx+Trej688T3bWbzhPXs+vPk9e+edd/LOO++wdOlSkpOTz7quJ7xvVUa6QGBgIGPHjmXx4sUtli9evJhJkya1ud3f//53brzxRt544w2uuOKKcz6Pbdts3ryZxMTE8858Pjq6v6fbtGlTi33Jzs4+4zEXLVrk1mN2tvPZ1+XLl7Nv3z5uuummcz6Pp7y27mrrNRs3bhwBAQFnXcfk69pR3vqe7Sze8J49H974nrVtmzvuuIOFCxfy8ccfk5GRcc5tPOJ92ynDYOUM//u//2sHBATYL730kr1z5077nnvuscPCwppHYz/wwAP2dddd17z+G2+8Yfv7+9vPPPOMXVJS0nw7fvx48zoPP/yw/eGHH9r79++3N23aZH/ve9+z/f397TVr1nT7/p3O3f39wx/+YL/11lv2nj177O3bt9sPPPCADdgLFixoXuezzz6z/fz87F//+td2Tk6O/etf/9r29/e3V69e3e3790Xu7usp3/3ud+0JEya0+pie+tpWVVXZmzZtsjdt2mQD9u9//3t706ZNdn5+vm3bZ+7rgQMH7NDQUPvee++1d+7cab/00kt2QECA/a9//at5HU99Xd3dV29/z7q7v978nnV3X0/xxvfsD3/4QzsyMtJetmxZi3+XNTU1zet44vtWZaQLPfPMM3ZaWpodGBhojxkzpsWpVTfccIM9ZcqU5vtTpkyxgTNuN9xwQ/M699xzj52ammoHBgbasbGx9owZM+yVK1d24x6dnTv7+5vf/Mbu37+/HRwcbPfu3du+8MIL7ffee++Mx/znP/9pZ2Vl2QEBAfagQYNa/OAzyZ19tW3bPn78uB0SEmK/8MILrT6ep762p07nbOvfZWv7umzZMnv06NF2YGCgnZ6ebj/33HNnPK4nvq7u7qu3v2fd3V9vfs925N+xt75nW9tPwH7llVea1/HE9631eXgRERERIzRmRERERIxSGRERERGjVEZERETEKJURERERMUplRERERIxSGRERERGjVEZERETEKJURERERMUplRERERIxSGRERERGjVEZERETEqP8Prx24ZAF21/YAAAAASUVORK5CYII=",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(X_T.min(), X_T.max(), 100)\n",
"\n",
"plt.figure(figsize=(14, 7))\n",
"plt.plot(x, CIR_pdf(x, X0, T, kappa, theta, sigma), label=\"original parameters\")\n",
"plt.plot(x, CIR_pdf(x, X0, T, kappa_OLS, theta_OLS, sigma_OLS), label=\"OLS parameters\")\n",
"plt.hist(X_T, density=True, bins=70, facecolor=\"LightBlue\", label=\"frequencies of X_T\")\n",
"plt.legend()\n",
"plt.title(\"Histogram vs Non-Central Chi-Squared distribution\")\n",
"plt.xlabel(\"X_T\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" \n",
"In practice, when working with financial market data, we just have 1 realized path! (we just have one realization of $X_T$). \n",
"\n",
"The OLS method described above is the right method to use! An alternative can be to use the MLE method, considering the conditional probability density.\n",
"\n",
"\n",
"### Comment\n",
"\n",
"If we know the conditional density, why do we need to solve numerically the SDE? Why can't we just generate random variables from this density?\n",
"\n",
"**Well, we can! And in some circumstances it is the best thing to do!**\n",
"But the CIR process is mainly used as a model for the volatility dynamics in the Heston model, where the SDE approach is easier to implement!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Change of variable"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A nice trick to solve the problem of negative values is to change variables. (this is a recurrent trick in financial math)\n",
"\n",
"Let us consider the log-variable:\n",
"\n",
"$$ Y_t = \\log X_t $$\n",
"\n",
"The new variable $Y_t$ can be negative for $0"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 5))\n",
"plt.plot(T_vec, np.exp(Y_1), label=\"CIR process\")\n",
"plt.plot(T_vec, (theta + std_asy) * np.ones_like(T_vec), label=\"1 asymptotic std dev\", color=\"black\")\n",
"plt.plot(T_vec, (theta - std_asy) * np.ones_like(T_vec), color=\"black\")\n",
"plt.plot(T_vec, theta * np.ones_like(T_vec), label=\"Long term mean\")\n",
"plt.legend(loc=\"upper right\")\n",
"plt.title(\"CIR process - by log-transformation\")\n",
"plt.xlabel(\"T\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As expected, the path did not change. (under same parameters and same seed as before). \n",
" \n",
"**This is (in my opinion) the best theoretical approach!** \n",
"\n",
"However, in practice, this is not the perfect method. It can have several problems for small values of $N$. \n",
"For instance, as the algorithm involves an exponential function, it can generate huge numbers and even NaNs.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"[1] Cox, J.C., Ingersoll, J.E and Ross, S.A. A Theory of the Term Structure of Interest Rates.\n",
"Econometrica, Vol. 53, No. 2 (March, 1985)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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================================================
FILE: 1.3 Fourier transform methods.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fourier transform methods\n",
"\n",
"\n",
"In this notebook I want to show how the characteristic function can be used to price derivatives. \n",
"Sometimes it will be convenient to use this method, because for some stochastic processes the probability density function is not always known, or can have a quite complicated formula, while the characteristic function is frequently available. \n",
"For Lévy processes, in particular, the characteristic function is always known, and is given by the Lévy-Kintchkine representation (see **A3**). \n",
"\n",
"I will present some examples in which I'm going to price European call options using the Normal, the Merton and the Variance Gamma characteristic functions. \n",
"For a complete presentation of Fourier methods, I suggest to have a look at [1].\n",
"\n",
"## Contents\n",
" - [Inversion theorem](#sec1)\n",
" - [Numerical inversion](#sec2)\n",
" - [Option pricing](#sec3)\n",
" - [Lewis method](#sec4)\n",
" - [Fast Fourier Transform](#sec5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us recall the definition of the **characteristic function** $\\phi_{X}(\\cdot)$ of the random variable $X$:\n",
"\n",
"\\begin{align}\n",
"\\phi_{X}(u) &= \\mathbb{E} \\bigl[ e^{iuX} \\bigr] \\nonumber \\\\\n",
" &= \\int_{\\mathbb{R}} e^{iux} f_X(x) dx.\n",
"\\end{align}\n",
"\n",
"where $f_X$ is the density of $X$. \n",
"The characteristic function is nothing but the **Fourier transform** of $f_X$. \n",
"Let us review some properties:\n",
"- $\\phi_{X}(u)$ always exists. This is because $\\bigl| \\int_{\\mathbb{R}} e^{iux} f_X(x) dx \\bigr| \\leq \\int_{\\mathbb{R}} | e^{iux} f_X(x)| dx < \\infty$. Recall that $|e^{iux}|=1$ and $\\int_{\\mathbb{R}} |f_X(x)| dx = 1$.\n",
"- $\\phi_{X}(0) = 1$.\n",
"- $\\phi_{X}(u)^{*} = \\phi_{X}(-u)$, where $*$ means complex conjugate.\n",
"\n",
"The density function can be obtained by taking the **inverse Fourier transform**:\n",
"\n",
"$$ f_X(x) = \\frac{1}{2\\pi} \\int_{\\mathbb{R}} e^{-iux} \\phi_X(u) du $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Inversion theorem\n",
"\n",
"Let us consider the following representation of the cumulative density function:\n",
"\n",
"\\begin{align*}\n",
"F_X(x) &= \\mathbb{P}(X0.\n",
"\\end{cases} $$\n",
"\n",
"The step function has Fourier transform equal to \n",
"$$\\mathcal{F}[sgn(x)] = \\frac{2}{iu}$$ \n",
"(intended as the [Cauchy principal value](https://en.wikipedia.org/wiki/Cauchy_principal_value) ).\n",
"\n",
"In order to prove it, let us consider the function\n",
"\n",
"$$ g_a(x) = \\begin{cases}\n",
" -e^{ax} &= x<0 \\\\\n",
" 0 &= x=0 \\\\\n",
" e^{-ax} &= x>0.\n",
"\\end{cases} $$\n",
"\n",
"with $a>0$. When $a \\to 0$, the function $g_a(x)$ converges to $sgn(x)$.\n",
"\n",
"The Fourier transform of $g_a$ can be computed easily by splitting the integral in two (if you are lazy, have a look at the table [here](https://en.wikipedia.org/wiki/Fourier_transform#Square-integrable_functions,_one-dimensional)):\n",
"\n",
"\\begin{align*}\n",
"\\mathcal{F}[g_a(x)] &= \\frac{1}{a+iu} - \\frac{1}{a-iu} \\\\\n",
" &= - \\frac{2iu}{a^2 + u^2} \n",
"\\end{align*}\n",
"\n",
"We can send $a\\to 0$ and obtain the relation we were looking for.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Proof - step 2\n",
"\n",
"The convolution of the density function $f_X$ with the sign function is given by:\n",
"\n",
"\\begin{align*}\n",
"(f_X * sgn)(x) &= \\int_{-\\infty}^{\\infty} f_X(x+t) \\, sgn(t) \\, dt \\\\\n",
" &= \\int_{-\\infty}^{0} f_X(x+t) \\, (-1) dt + \\int_{0}^{\\infty} f_X(x+t)\\, (1) dt \\\\\n",
" &= - F_X(x) + 1 - F_X(x) \\\\\n",
" &= 1 - 2 F_X(x)\n",
"\\end{align*}\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Proof - step 3\n",
"\n",
"By the convolution theorem [wiki](https://en.wikipedia.org/wiki/Convolution_theorem) we know that:\n",
"\n",
"$$ (f_X * sgn) = \\mathcal{F}^{-1} \\, \\biggl[ \\mathcal{F}[f_X] \\cdot \\mathcal{F}[sgn] \\biggr] $$\n",
"\n",
"Therefore we have that \n",
"\n",
"\\begin{align*}\n",
" (f_X * sgn)(x) &= \\frac{1}{2\\pi} \\int_{\\mathbb{R}} e^{-iux} \\phi_X(u) \\cdot \\frac{2}{iu} \\, du\n",
"\\end{align*}\n",
"\n",
"#### Proof - conclusion\n",
"\n",
"We can put together the steps 2 and 3:\n",
"\n",
"$$ 1 - 2 F_X(x) = \\frac{1}{2\\pi} \\int_{\\mathbb{R}} e^{-iux} \\phi_X(u) \\cdot \\frac{2}{iu} \\, du $$\n",
"\n",
"Rearranging the terms, we can conclude the proof:\n",
"\n",
"$$ F_X(x) = \\frac{1}{2} - \\frac{1}{2\\pi} \\int_{\\mathbb{R}} e^{-iux} \\phi_X(u) \\cdot \\frac{1}{iu} \\, du $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gil Pelaez formulas\n",
"\n",
"Recall that for a complex number z,\n",
"\n",
"$$ Re[z] = \\frac{z+z^*}{2} \\quad \\quad Im[z] = \\frac{z-z^*}{i2} $$\n",
"\n",
"Starting from the inversion formula, **Gil Pelaez** [5] obtained the following expression:\n",
"\n",
"\\begin{align*}\n",
" F_X(x) &= \\frac{1}{2} - \\frac{1}{2\\pi} \\int_{\\mathbb{R}} e^{-iux} \\phi_X(u) \\cdot \\frac{1}{iu} \\, du \\\\\n",
" &= \\frac{1}{2} - \\biggl( \\frac{1}{2\\pi} \\int_{-\\infty}^0 e^{-iux} \\phi_X(u) \\frac{1}{iu} \\, du\n",
" + \\frac{1}{2\\pi} \\int_0^{\\infty} e^{-iux} \\phi_X(u) \\frac{1}{iu} \\, du \\biggr) \\\\\n",
" &= \\frac{1}{2} - \\frac{1}{2\\pi} \\int_0^{\\infty} \\biggl(-e^{iux} \\phi_X(-u) + e^{-iux} \\phi_X(u) \\biggr) \\frac{1}{iu} \\, du \\\\\n",
" &= \\frac{1}{2} - \\frac{1}{\\pi} \\int_0^{\\infty} \\frac{Im[ e^{-iux} \\phi_X(u) ]}{u} du\n",
"\\end{align*}\n",
"\n",
"This formula can also be written as:\n",
"\n",
"$$ F_X(x) = \\frac{1}{2} - \\frac{1}{\\pi} \\int_0^{\\infty} Re\\biggl[ \\frac{ e^{-iux} \\phi_X(u)}{iu} \\biggr] du $$\n",
"\n",
"We obtain the expression for the density by taking the derivative:\n",
"\n",
"$$ f_X(x) = \\frac{1}{\\pi} \\int_0^{\\infty} Re\\biggl[ e^{-iux} \\phi_X(u) \\biggr] du $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pricing formula by Fourier inversion\n",
"\n",
"In the notebook **1.1** we found that the pricing formula for a call option with stike K and maturity T is: \n",
"\n",
"$$ C(t, S_t,K,T) = S_t \\tilde{\\mathbb{Q}} ( S_T > K ) - e^{-r(T-t)} K \\, \\mathbb{Q}( S_T >K ) $$\n",
"\n",
"where $\\tilde{\\mathbb{Q}}$ is the probability under the stock numeraire and $\\mathbb{Q}$ is the probability under the money market numeraire.\n",
"\n",
"#### This formula is model independent! \n",
"\n",
"When the stock follows a geometric Brownian motion, this formula corresponds to the Black-Scholes formula.\n",
"\n",
"Now let us express $\\tilde{\\mathbb{Q}}$ and $\\mathbb{Q}$ in terms of the characteristic function using the Gil Pelaez formula. \n",
"Let us call $k = \\log \\frac{K}{S_t}$ and $S_T = S_t e^{X_T}$\n",
"- For $\\mathbb{Q}$ we can write: \n",
"\n",
"\\begin{align*}\n",
"\\mathbb{Q}(S_T >K) &= 1 - \\mathbb{Q}(S_T K) &= \\frac{1}{2} + \\frac{1}{\\pi} \\int_0^{\\infty} Re\\biggl[ \\frac{ e^{-iuk} \\tilde{\\phi}_X(u)}{iu} \\biggr] du \\\\\n",
" &= \\frac{1}{2} + \\frac{1}{\\pi} \\int_0^{\\infty} Re\\biggl[ \\frac{ e^{-iuk} \\phi_X(u-i)}{iu \\, \\phi_X(-i)} \\biggr] du \n",
"\\end{align*}\n",
"\n",
"where\n",
"\n",
"\\begin{align*}\n",
" \\tilde{\\phi}_X(u) &= \\mathbb{E}^{\\tilde{\\mathbb{Q}}} \\bigl[ e^{iuX_T} \\bigr] = \\mathbb{E}^{\\mathbb{Q}} \\biggl[ \\frac{d\\tilde{\\mathbb{Q}}}{d \\mathbb{Q}} e^{iuX_T} \\biggr] \\\\ \n",
" &= \\mathbb{E}^{\\mathbb{Q}} \\biggl[ \\frac{S_t e^{X_T}}{S_t \\mathbb{E}^\\mathbb{Q}[e^{X_T}]} e^{iuX_T} \\biggr] \\\\\n",
" &= \\mathbb{E}^{\\mathbb{Q}} \\biggl[ \\frac{e^{(iu+1)X_T}}{\\phi_X(-i)} \\biggr] \\\\\n",
" &= \\frac{\\phi_X(u-i)}{\\phi_X(-i)}\n",
"\\end{align*}\n",
"\n",
"\n",
"(see the notebook **1.1** for a discussion about this change of measure)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Numerical inversion\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import scipy.stats as ss\n",
"from scipy.integrate import quad\n",
"from functools import partial\n",
"from scipy.fftpack import fft, ifft\n",
"from scipy.interpolate import interp1d\n",
"\n",
"from FMNM.BS_pricer import BS_pricer\n",
"from FMNM.Parameters import Option_param\n",
"from FMNM.Processes import Merton_process\n",
"from FMNM.Merton_pricer import Merton_pricer\n",
"from FMNM.Processes import VG_process\n",
"from FMNM.VG_pricer import VG_pricer"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def cf_normal(u, mu=1, sig=2):\n",
" return np.exp(1j * u * mu - 0.5 * u**2 * sig**2)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def cf_gamma(u, a=1, b=2):\n",
" return (1 - b * u * 1j) ** (-a)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def cf_poisson(u, lam=1):\n",
" return np.exp(lam * (np.exp(1j * u) - 1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us consider the following characteristic functions:\n",
"\n",
"##### Normal:\n",
"$\\mathcal{N}(\\mu,\\sigma^2)$, $\\sigma>0$.\n",
"$$ \\phi_N(u) = e^{i\\mu u - \\frac{1}{2}\\sigma^2 u^2} $$\n",
"##### Gamma (shape-scale parameterization) \n",
"$\\Gamma(a,b)$, $a,b >0$.\n",
"$$ \\phi_G(u) = (1-ibu)^{-a} $$\n",
"##### Poisson\n",
"$Po(\\lambda)$, $\\lambda>0$.\n",
"$$ \\phi_P(u) = e^{\\lambda (e^{iu}-1)} $$\n",
"\n",
"I want to check if the Gil Pelaez formula works:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Gil Pelaez formula for the density\n",
"# right_lim is the right extreme of integration\n",
"# cf is the characteristic function\n",
"def Gil_Pelaez_pdf(x, cf, right_lim):\n",
" integrand = lambda u: np.real(np.exp(-u * x * 1j) * cf(u))\n",
" return 1 / np.pi * quad(integrand, 1e-15, right_lim)[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Normal"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"xx = np.linspace(0.1, 20, 100)\n",
"a = 1 # shape parameter\n",
"b = 2 # scale parameter\n",
"c = 9 # shape parameter\n",
"d = 0.5 # scale parameter\n",
"lim_ab = 24\n",
"lim_cd = np.inf\n",
"cf_gamma_ab = partial(cf_gamma, a=a, b=b) # function binding\n",
"cf_gamma_cd = partial(cf_gamma, a=c, b=d) # function binding\n",
"\n",
"fig = plt.figure(figsize=(15, 4))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"ax1.plot(xx, ss.gamma.pdf(xx, a, scale=b), label=\"pdf Gamma\")\n",
"ax1.plot(xx, [Gil_Pelaez_pdf(i, cf_gamma_ab, lim_ab) for i in xx], label=\"Fourier inversion\")\n",
"ax1.set_title(\"Comparison Gamma. shape=1, scale=2\")\n",
"ax1.legend()\n",
"ax2.plot(xx, ss.gamma.pdf(xx, c, scale=d), label=\"pdf Gamma\")\n",
"ax2.plot(xx, [Gil_Pelaez_pdf(i, cf_gamma_cd, lim_cd) for i in xx], label=\"Fourier inversion\")\n",
"ax2.set_title(\"Comparison Gamma. shape=9, scale=0.5\")\n",
"ax2.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Comments:\n",
"\n",
"If you try to increase the value of `lim_ab = 24`, you will see that python will raise: \n",
"`IntegrationWarning: The maximum number of subdivisions (50) has been achieved.`\n",
"\n",
"When we solve this kind of problems we have to be careful, and analyze them case by case.\n",
"\n",
"For $c=9$ and $d=0.5$, we could set `lim_cd = np.inf` because the integrand has a good behavior. \n",
"But for $a=1$ and $b=2$ the integrand is not so nice. There are many oscillations (originated by the term $e^{-iux}$) that create the problem.\n",
"\n",
"Let's have a look at the plot of the integrands: (at the point x=3)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"k = np.array(range(20))\n",
"lam = 4\n",
"cf_poisson4 = partial(cf_poisson, lam=lam) # function binding to lam=4\n",
"\n",
"fig = plt.figure(figsize=(15, 4))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"ax1.plot(k, ss.poisson.pmf(k, 1), linestyle=\"None\", marker=\"p\", label=\"pmf Poisson\") # with lam=1\n",
"ax1.plot(\n",
" k, [Gil_Pelaez_pdf(i, cf_poisson, np.pi) for i in k], linestyle=\"None\", marker=\"*\", label=\"Fourier inversion\"\n",
") # lam=1 by default\n",
"ax1.set_xlabel(\"k\")\n",
"ax1.set_ylabel(\"P(X=k)\")\n",
"ax1.set_title(\"Comparison: Poisson pmf - Gil Pelaez, lambda = 1\")\n",
"ax1.legend()\n",
"ax2.plot(k, ss.poisson.pmf(k, lam), linestyle=\"None\", marker=\"p\", label=\"pmf Poisson\")\n",
"ax2.plot(k, [Gil_Pelaez_pdf(i, cf_poisson4, np.pi) for i in k], linestyle=\"None\", marker=\"*\", label=\"Fourier inversion\")\n",
"ax2.set_title(\"Comparison: Poisson pmf - Gil Pelaez, lambda = 4\")\n",
"ax2.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Comments:\n",
"\n",
"In this case, we considered only the integration in the region $[0,\\pi]$.\n",
"\n",
"As you can see in the plots below, the integrand is an even periodic function. Therefore, if we had integrated on $[0,\\infty]$ the integral would have been infinite.\n",
"\n",
"The two plots below consider the characteristic function with $\\lambda=1$. \n",
"The integrands inside the `Gil_Pelaez_pdf` function are functions of $u$ with fixed values of $x$. For this exaple I chose x=1 and x=10 ($x$ corresponds to $k$ in the plots above). \n",
"The characteristic function of a Poisson random variable does not have a damping factor. It is a pure periodic function. For this reason it is helpful to define it only on $[-\\pi,\\pi]$. \n",
"The integrand of the Gil Pelaez function simply inherits these features."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"X1 = 1\n",
"X2 = 10\n",
"y = np.linspace(0.0 - 15, 15, 2000)\n",
"ff = lambda u: np.real(np.exp(-u * X1 * 1j) * cf_poisson(u)) # integrand\n",
"gg = lambda u: np.real(np.exp(-u * X2 * 1j) * cf_poisson(u)) # integrand\n",
"\n",
"fig = plt.figure(figsize=(15, 5))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"ax1.plot(y, ff(y))\n",
"ax1.set_title(\"Integrand at x=1, for lambda = 1\")\n",
"ax2.plot(y, gg(y))\n",
"ax2.set_title(\"Integrand at x=10, for lambda = 1\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Option pricing\n",
"\n",
"Let us apply the pricing formula introduced above using the following processes:\n",
"- Geometric Brownian motion\n",
"- Merton process\n",
"- Variance Gamma process\n",
"\n",
"These processes will be described better in the next notebooks. If you are not familiar with these concepts, have a look at the Appendix **A3**. Otherwise you can skip this part, for now. And come back again after having read the relevant notebooks.\n",
"\n",
"First, I have to implement $\\mathbb{Q}$ and $\\tilde{\\mathbb{Q}}$:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def Q1(k, cf, right_lim):\n",
" integrand = lambda u: np.real((np.exp(-u * k * 1j) / (u * 1j)) * cf(u - 1j) / cf(-1j))\n",
" return 1 / 2 + 1 / np.pi * quad(integrand, 1e-15, right_lim, limit=1000)[0]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def Q2(k, cf, right_lim):\n",
" integrand = lambda u: np.real(np.exp(-u * k * 1j) / (u * 1j) * cf(u))\n",
" return 1 / 2 + 1 / np.pi * quad(integrand, 1e-15, right_lim, limit=1000)[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the following, I present European call option values, with the following parameters:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"S0 = 100.0 # spot stock price\n",
"K = 100.0 # strike\n",
"T = 1 # maturity\n",
"k = np.log(K / S0) # log moneyness"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Geometric Brownian motion"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"r = 0.1 # risk free rate\n",
"sig = 0.2 # volatility"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fourier inversion call: 13.2696765846609\n",
"Closed formula call: 13.269676584660893\n"
]
}
],
"source": [
"cf_GBM = partial(cf_normal, mu=(r - 0.5 * sig**2) * T, sig=sig * np.sqrt(T)) # function binding\n",
"call_BS = S0 * Q1(k, cf_GBM, np.inf) - K * np.exp(-r * T) * Q2(k, cf_GBM, np.inf) # pricing function\n",
"\n",
"print(\"Fourier inversion call: \", call_BS)\n",
"print(\"Closed formula call: \", BS_pricer.BlackScholes(\"call\", S0, K, T, r, sig))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Merton process"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# characteristic function of the Merton process at time t\n",
"def cf_mert(u, t=1, mu=1, sig=2, lam=0.8, muJ=0, sigJ=0.5):\n",
" return np.exp(\n",
" t * (1j * u * mu - 0.5 * u**2 * sig**2 + lam * (np.exp(1j * u * muJ - 0.5 * u**2 * sigJ**2) - 1))\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"r = 0.1 # risk free rate\n",
"sig = 0.2 # diffusion coefficient\n",
"lam = 0.8 # jump activity\n",
"muJ = 0 # jump mean size\n",
"sigJ = 0.5 # jump std deviation\n",
"m = lam * (np.exp(muJ + (sigJ**2) / 2) - 1) # martingale correction\n",
"\n",
"# Creates the pricer objects\n",
"opt_param = Option_param(S0=S0, K=K, T=T, exercise=\"European\", payoff=\"call\")\n",
"Merton_param = Merton_process(r=r, sig=sig, lam=lam, muJ=muJ, sigJ=sigJ)\n",
"Merton = Merton_pricer(opt_param, Merton_param)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fourier inversion call: 22.0163676219057\n",
"Closed formula call: 22.016367621905697\n"
]
}
],
"source": [
"# function binding\n",
"cf_Mert = partial(cf_mert, t=T, mu=(r - 0.5 * sig**2 - m), sig=sig, lam=lam, muJ=muJ, sigJ=sigJ)\n",
"# call price\n",
"call_Mert = S0 * Q1(k, cf_Mert, np.inf) - K * np.exp(-r * T) * Q2(k, cf_Mert, np.inf) # pricing function\n",
"\n",
"print(\"Fourier inversion call: \", call_Mert)\n",
"print(\"Closed formula call: \", Merton.closed_formula())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Variance Gamma"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# characteristic function of the VG process at time t, with an additional drift mu\n",
"def cf_VG(u, t=1, mu=0, theta=-0.1, sigma=0.2, kappa=0.1):\n",
" return np.exp(t * (1j * mu * u - np.log(1 - 1j * theta * kappa * u + 0.5 * kappa * sigma**2 * u**2) / kappa))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# Creates the object with the parameters of the process\n",
"VG_param = VG_process(r=0.1, sigma=0.2, theta=-0.1, kappa=0.1)\n",
"# Creates the VG process\n",
"VG = VG_pricer(opt_param, VG_param)\n",
"\n",
"theta = -0.1\n",
"sigma = 0.2\n",
"kappa = 0.1 # VG parameters\n",
"w = -np.log(1 - theta * kappa - kappa / 2 * sigma**2) / kappa # martingale correction w"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fourier inversion call: 13.314022\n",
"Monte Carlo call: 13.316149 with standard error: 0.002253\n"
]
}
],
"source": [
"# function binding\n",
"cf_VG_b = partial(cf_VG, t=T, mu=r - w, theta=theta, sigma=sigma, kappa=kappa)\n",
"# call price\n",
"call_VG = S0 * Q1(k, cf_VG_b, np.inf) - K * np.exp(-r * T) * Q2(k, cf_VG_b, np.inf) # pricing function\n",
"\n",
"print(\"Fourier inversion call: \", call_VG.round(6))\n",
"print(\"Monte Carlo call: {0[0]:.6f} with standard error: {1[0]:.6f}\".format(*VG.MC(N=50000000, Err=True)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The closed formula for the VG price is also available, but it is not very reliable (it has a negative bias). See also the comments in the notebook **3.2**. \n",
"For this reason I prefer to compare the Fourier inversion price with the Monte Carlo price!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Warning: infinite oscillatory integrals!\n",
"\n",
"The naive implementation of $\\mathbb{Q}$ and $\\tilde{\\mathbb{Q}}$ in the functions `Q1` and `Q2` uses the scipy method `quad` which is not very appropriate for infinite oscillatory integrals.\n",
"\n",
"A possible solution could be to set the parameters `weight` and `wvar` in [quad](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.quad.html) appropriately, or use a more advanced algorithm. However, these integrals must be treated on a case-by-case basis. \n",
"For more information about oscillatory integrals have a look at this\n",
"[link](https://scicomp.stackexchange.com/questions/58/whats-the-state-of-the-art-in-highly-oscillatory-integral-computation).\n",
"\n",
"#### Example:\n",
"As for the pricing problem we are considering, the naive approach above works quite well for a large time to maturity. \n",
"For a short time to maturity, OTM and ITM options have problems. \n",
"In the following plot we can see that the integrand function has different shapes for different moneyness. \n",
"In this example, the `quad` method produces a negative price for the call with K=120."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Strike= 105.0\n",
"Fourier inversion Black-Scholes call: 0.005750947197630807\n",
"BS Closed formula call: 0.005750947197631251\n",
"Strike= 120.0\n",
"Fourier inversion Black-Scholes call: -1.103565016036534e-15\n",
"BS Closed formula call: 1.4557223869346492e-20\n"
]
},
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"S0 = 100.0\n",
"K1 = 105.0\n",
"K2 = 120.0\n",
"T_1 = 0.01\n",
"k1 = np.log(K1 / S0)\n",
"k2 = np.log(K2 / S0) # two different moneyness\n",
"cf_GBM_1 = partial(cf_normal, mu=(r - 0.5 * sig**2) * T_1, sig=sig * np.sqrt(T_1))\n",
"call1 = S0 * Q1(k1, cf_GBM_1, np.inf) - K1 * np.exp(-r * T_1) * Q2(k1, cf_GBM_1, np.inf) # pricing function\n",
"print(\"Strike=\", K1)\n",
"print(\"Fourier inversion Black-Scholes call: \", call1)\n",
"print(\"BS Closed formula call: \", BS_pricer.BlackScholes(\"call\", S0, K1, T_1, r, sig))\n",
"call2 = S0 * Q1(k2, cf_GBM_1, np.inf) - K2 * np.exp(-r * T_1) * Q2(k2, cf_GBM_1, np.inf) # pricing function\n",
"print(\"Strike=\", K2)\n",
"print(\"Fourier inversion Black-Scholes call: \", call2)\n",
"print(\"BS Closed formula call: \", BS_pricer.BlackScholes(\"call\", S0, K2, T_1, r, sig))\n",
"\n",
"u = np.linspace(1e-15, 100, 200)\n",
"fig = plt.figure(figsize=(15, 4))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"ax1.plot(u, np.real((np.exp(-u * k1 * 1j) / (u * 1j)) * cf_GBM_1(u - 1j) / cf_GBM_1(-1j)))\n",
"ax2.plot(u, np.real((np.exp(-u * k2 * 1j) / (u * 1j)) * cf_GBM_1(u - 1j) / cf_GBM_1(-1j)))\n",
"ax1.set_title(\"Integrand of Q1, k={0:0.2f}\".format(k1))\n",
"ax2.set_title(\"Integrand of Q1, k={0:0.2f}\".format(k2))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Lewis method\n",
"\n",
"Fourier inversion methods in option pricing allow the use of semi-closed valuation formulas for European options whenever the characteristic function of the log-price is known.\n",
"There is a large body of literature dealing with Fourier-based pricing methods. Among the most important there are the methods of Carr and Madan (1999) [3] and Lewis (2001) [4].\n",
"\n",
"The Gil-Pelaez approach described above, consists in expressing the \"in the money\" risk neutral probabilities as the integral of an expression containing the characteristic function of the log-process. \n",
"Here I present the method of Lewis which instead provides an integral expression for the option price itself.\n",
"The paper of Lewis can be found here http://optioncity.net/pubs/ExpLevy.pdf. \n",
"\n",
"**Tecnical comment** (irrelevant to the understanding of the derivation): \n",
"The functional space $L^2$ is not a subset of $L^1$ (see [Lp space](https://en.wikipedia.org/wiki/Lp_space)), and the definition of the Fourier transform is therefore not directly applicable to every $L^2$ function. The definition does apply, however, if a function $f$ is in $L^1 \\cap L^2$, and\n",
"it turns out that the $\\mathcal{F}[f] \\in L^2$, and $\\lVert f \\rVert^2 = \\big \\lVert \\mathcal{F}[f] \\big \\rVert^2$ (see [Placherel and Parseval theorem](https://en.wikipedia.org/wiki/Fourier_transform#Plancherel_theorem_and_Parseval's_theorem)). \n",
"This isometry of $L^1 \\cap L^2$ into\n",
"$L^2$ can be extended to an isometry of $L^2$ onto $L^2$, and this extension defines the Fourier\n",
"transform of every function in $L^2$.\n",
"\n",
"\n",
"#### Derivation of Lewis formula:\n",
"\n",
"The Lewis approach makes use of the [Parseval formula](https://en.wikipedia.org/wiki/Fourier_transform#Plancherel_theorem_and_Parseval's_theorem), i.e. the Fourier transform preserves the inner product. \n",
"If we consider the log-price $X_T = \\log S_T$ and $s=\\log S_0$, the CALL option price is defined as \n",
"\n",
"$$\n",
"\\begin{aligned}\n",
"C(S_0, K,T) &= e^{-rT} \\mathbb{E}^{\\mathbb{Q}}\\biggl[ \\big(e^{X_T} - K\\big)^+ \\bigg| \\log S_0 = s \\biggr] \\\\\n",
" &= e^{-rT} \\int_{\\mathbb{R}} \\big(e^x-K\\big)^+ f_T(s+x) \\,dx \\\\\n",
" &= e^{-rT} \\int_{\\mathbb{R}} \\mathcal{F} \\bigg[\\big(e^x-K\\big)^+ \\bigg] \\; \\mathcal{F}\\big[f_T(s+x)\\big]^* \\; \\frac{du}{2\\pi}.\n",
"\\end{aligned} $$\n",
"\n",
"Since the function $(e^x-K)^+$ is not integrable, the idea is to consider the *Generalized Fourier Transform* i.e. replace the real variable $u$ with a complex variable $z = u + iv$:\n",
"\n",
"$$ \\mathcal{F}\\bigg[\\big(e^x-K\\big)^+ \\bigg] = \\int_{\\log K}^{\\infty} e^{ixz} \\big(e^x-K\\big) \\, dx \n",
"= - \\frac{K^{iz+1}}{z^2-iz} $$\n",
"\n",
"The integral above exists only for some $v_1 = Im[z] > 1$. \n",
"Using the translation property and the property of the complex conjugation of the characteristic function, we can write \n",
"\n",
"$$ \\mathcal{F}\\big[f_T(s+x)\\big]^*(z) = e^{-izs} \\phi_T(-z).$$ \n",
"\n",
"The pricing function becomes:\n",
"\n",
"$$ \\begin{aligned}\n",
"C(S_0, K,T) &= -e^{-rT} \\int_{iv_1-\\infty}^{iv_1+\\infty} e^{-izs} \\frac{K^{iz+1}}{z^2-iz} \\; \\phi_T(-z) \\; \\frac{dz}{2\\pi} \\\\\n",
"&= -K e^{-rT} \\int_{iv_1-\\infty}^{iv_1+\\infty} e^{-izk} \\frac{1}{z^2-iz} \\; \\phi_T(-z) \\; \\frac{dz}{2\\pi}. \\end{aligned}$$\n",
"\n",
"where we introduced the log-moneyness $k = \\log S_0 - \\log K$. \n",
"At this point, Lewis uses the [Residue theorem](https://en.wikipedia.org/wiki/Residue_theorem) in order to write this expression in a different form, where the integral is on the orizontal line with imaginary level $Im[z] = v_2 \\in (0,1)$. \n",
"The integration is made on a rectangular region containing the pole at $i$. \n",
"The integral is split on each side of the rectangle: \n",
"$$\\int_{-\\infty + iv_2}^{+\\infty + iv_2} + \\int_{+\\infty + iv_2}^{+\\infty + iv_1} + \\int_{+\\infty + iv_1}^{-\\infty + iv_1} + \\int_{-\\infty + iv_1}^{-\\infty + iv_2} = \n",
"\\int_{-\\infty + iv_2}^{+\\infty + iv_2} - \\int_{-\\infty + iv_1}^{+\\infty + iv_1} = \\; 2 \\pi i \\, Res(i) $$\n",
"\n",
"where the terms $\\int_{-\\infty + iv_1}^{-\\infty + iv_2}$ and $\\int_{+\\infty + iv_2}^{+\\infty + iv_1}$ are zero because the integrand is zero at $Re[z]= \\pm \\infty$. \n",
"The residual is \n",
"$$ \\begin{aligned}\n",
"Res(i) &= \\lim_{z\\to i}\\; -(z-i)\\, e^{-rT} e^{-izk} \\frac{1}{z(z-i)} \\; \\phi_T(-z) \\; \\frac{K}{2\\pi} \\\\\n",
" &= -\\frac{S_0}{2\\pi i } \n",
"\\end{aligned}$$\n",
"\n",
"where we recall that $\\phi_T(-i) = e^{rT}$. Rearranging the terms we get the alternative expression:\n",
"\n",
"$$ C(S_0, K,T) = S_0 - \\frac{K e^{-rT}}{2\\pi} \\int_{iv_2-\\infty}^{iv_2+\\infty} e^{-izk} \\frac{1}{z^2-iz} \\; \\phi_T(-z) \\; dz.$$\n",
"\n",
"For $v_2=\\frac{1}{2}$, we can replance $z=u+\\frac{i}{2}$ and obtain the final form: \n",
"\n",
"$$ C_0 = S_0 - \\frac{\\sqrt{S_0 K} e^{-rT}}{\\pi} \\int_{0}^{\\infty} Re \\biggl[ e^{iuk} \\phi_T \\bigg(u-\\frac{i}{2} \\bigg) \\biggr] \\frac{1}{u^2 + \\frac{1}{4}} du. $$"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"def Lewis(K, cf):\n",
" \"\"\"Numerical integration of the Lewis expression\n",
" K = strike\n",
" cf = characteristic function\"\"\"\n",
" k = np.log(S0 / K)\n",
" integrand = lambda u: np.real(np.exp(u * k * 1j) * cf(u - 0.5j)) * 1 / (u**2 + 0.25)\n",
" int_value = quad(integrand, 0, 2000, limit=1000)[0]\n",
" return S0 - np.sqrt(S0 * K) * np.exp(-r * T) / np.pi * int_value"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Black-Scholes: Lewis call: 13.269676584660886\n",
"Black-Scholes: Fourier inversion call: 13.2696765846609\n",
"\n",
"Merton: Lewis call: 22.0163676219057\n",
"Merton: Fourier inversion call: 22.0163676219057\n",
"\n",
"Variance Gamma: Lewis call: 13.314021941453163\n",
"Variance Gamma: Fourier inversion call: 13.314021941453447\n"
]
}
],
"source": [
"print(\"Black-Scholes: Lewis call: \", Lewis(100, cf_GBM))\n",
"print(\"Black-Scholes: Fourier inversion call: \", call_BS)\n",
"print()\n",
"print(\"Merton: Lewis call: \", Lewis(100, cf_Mert))\n",
"print(\"Merton: Fourier inversion call: \", call_Mert)\n",
"print()\n",
"print(\"Variance Gamma: Lewis call: \", Lewis(100, cf_VG_b))\n",
"print(\"Variance Gamma: Fourier inversion call: \", call_VG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# FFT method\n",
"\n",
"Above we have numerically calculated the integral of the Lewis expression. \n",
"However, when we want to compute the price of a large number of options with the same maturity, the previous method is no more efficient. \n",
"A solution is to take advantage of the [FFT](https://en.wikipedia.org/wiki/Fast_Fourier_transform) (Fast Fourier Transform) algorithm to reduce the computational cost.\n",
"\n",
"Let us recall that the *real part* of a complex number is linear, i.e. for $a,b\\in \\mathbb{R}$ we have $Re[a z_1 + b z_2] = a Re[z_1] + b Re[z_2]$, and the real part of an integral is the integral of the real part. \n",
"Thanks to this property, the integral pricing formula can be written in the following form:\n",
"\n",
"$$ C_0 = S_0 - \\frac{\\sqrt{S_0 K} e^{-rT}}{\\pi} Re \\biggl[ \\int_{0}^{\\infty} e^{iuk} \\phi_T \\bigg(u-\\frac{i}{2} \\bigg) \\frac{1}{u^2 + \\frac{1}{4}} du \\biggr]. $$\n",
"\n",
"At this point we can discretize the integral. Following [3], we use the [Simpson rule](https://en.wikipedia.org/wiki/Simpson%27s_rule). \n",
"The domain of integration is truncated in $[A,B] = [0,B]$, and divided in $N$ steps of size $\\Delta x = \\frac{B}{N}$.\n",
"We have that $x_0 = 0$ and $x_n = x_0 + n \\Delta x$, for $n=0,1,2,...,N$. The integral is evaluated in $N+1$ points $f(x_n) = f_n$. \n",
"The Simpson rule is a 3 points rule that approximates the integral as:\n",
"\n",
"$$ \\int_{x_0}^{x_2} f(x) \\, dx \\approx \\frac{\\Delta x}{3} \\big[ f_0 + 4 f_1 + f_2 \\big]. $$\n",
"\n",
"if we sum over the integration domain $[x_0, x_{N-1}]$ we get\n",
"\n",
"$$ \\int_{x_0}^{x_{N-1}} f(x) \\, dx \\approx \\frac{\\Delta x}{3} \\sum_{n=0}^{N-1} w_n \\, f_n. $$\n",
"\n",
"with $w_n=1$ for $n=0$ and $n=N-1$, and $w_n=4$ for $n$ odd, and $w_n=2$ for $n$ even. Notice that we are not considering the last point!\n",
"\n",
"Let us define a set of $N$ values $k_j \\in [-b,b]$, such that $k_j = -b + j \\Delta k$, for $j=0,1,2,...,N-1$. We define the step $\\Delta k$ as \n",
"\n",
"$$ \\Delta k := \\frac{2 \\pi}{B} = \\frac{1}{\\Delta x} \\frac{2 \\pi}{N} $$ \n",
"\n",
"such that we can obtain the value $b=\\frac{N \\Delta k}{2}$. \n",
"The integral in the pricing function is:\n",
"\n",
"$$ \n",
"\\begin{aligned}\n",
"I(k_k) &= \\int_{0}^{\\infty} e^{ixk_j} \\phi_T \\bigg(x-\\frac{i}{2} \\bigg) \\frac{1}{x^2 + \\frac{1}{4}} dx \n",
"\\quad \\approx \\quad \\frac{\\Delta x}{3} \\sum_{n=0}^{N-1} w_n \\, e^{ik_j x_n} \\phi_T \\bigg( x_n-\\frac{i}{2} \\bigg) \\frac{1}{{x_n}^2 + \\frac{1}{4}} \\\\\n",
"&= \\frac{\\Delta x}{3} \\sum_{n=0}^{N-1} w_n \\, e^{i (-b + j \\Delta k) n \\Delta x} \\phi_T \\bigg( x_n-\\frac{i}{2} \\bigg) \\frac{1}{{x_n}^2 + \\frac{1}{4}}. \\\\\n",
"&= \\frac{\\Delta x}{3} \\sum_{n=0}^{N-1} \\, e^{i 2\\pi j \\frac{n}{N}} \\; w_n e^{-i b n \\Delta x} \\phi_T \\bigg( x_n-\\frac{i}{2} \\bigg) \\frac{1}{{x_n}^2 + \\frac{1}{4}}.\n",
"\\end{aligned}$$\n",
"\n",
"The [scipy function fft](https://docs.scipy.org/doc/scipy/reference/generated/scipy.fftpack.fft.html) returns the following: `y(j) = (x * exp(-2*pi*sqrt(-1)*j*np.arange(n)/n)).sum()`. \n",
"The [scipy function ifft](https://docs.scipy.org/doc/scipy/reference/generated/scipy.fftpack.ifft.html#scipy.fftpack.ifft) returns: `y(j) = (x * exp(2*pi*sqrt(-1)*j*np.arange(n)/n)).mean()`. \n",
"Therefore we can use `ifft` and then multiply the output by N."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"def fft_Lewis(K, S0, cf, interp=\"cubic\"):\n",
" \"\"\"\n",
" K = vector of strike\n",
" S = spot price scalar\n",
" cf = characteristic function\n",
" interp can be cubic or linear\n",
" \"\"\"\n",
" N = 2**12 # FFT more efficient for N power of 2\n",
" B = 200 # integration limit\n",
" dx = B / N\n",
" x = np.arange(N) * dx # the final value B is excluded\n",
"\n",
" weight = np.arange(N) # Simpson weights\n",
" weight = 3 + (-1) ** (weight + 1)\n",
" weight[0] = 1\n",
" weight[N - 1] = 1\n",
"\n",
" dk = 2 * np.pi / B\n",
" b = N * dk / 2\n",
" ks = -b + dk * np.arange(N)\n",
"\n",
" integrand = np.exp(-1j * b * np.arange(N) * dx) * cf(x - 0.5j) * 1 / (x**2 + 0.25) * weight * dx / 3\n",
" integral_value = np.real(ifft(integrand) * N)\n",
"\n",
" if interp == \"linear\":\n",
" spline_lin = interp1d(ks, integral_value, kind=\"linear\")\n",
" prices = S0 - np.sqrt(S0 * K) * np.exp(-r * T) / np.pi * spline_lin(np.log(S0 / K))\n",
" elif interp == \"cubic\":\n",
" spline_cub = interp1d(ks, integral_value, kind=\"cubic\")\n",
" prices = S0 - np.sqrt(S0 * K) * np.exp(-r * T) / np.pi * spline_cub(np.log(S0 / K))\n",
" return prices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us select a set of strikes:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180\n",
" 190 200]\n"
]
}
],
"source": [
"KK = np.arange(10, 201, 10)\n",
"print(KK)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We run the `fft_Lewis` function for the three models used in this notebook. \n",
"After that, we compute the error (L1 norm) which is considered as the sum of absolute differences between option prices computed by closed formula and FFT.\n",
"This error is mainly due to the interpolation method used. \n",
"One property of the FFT method is that the resulting values are on an equally spaced log-moneyness array. \n",
"But most of the time, the desired values are not on this equidistant array, and therefore we are forced to use interpolation methods to find the intermediate values.\n",
"\n",
"We can see that linear interpolation does not work very well, while cubic spline provides the desired results.\n",
"\n",
"#### Black Scholes"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BS linear interp Error: 0.0607495988699249\n",
"BS cubic spline Error: 1.998515423570768e-05\n"
]
}
],
"source": [
"prices_BS_cub = fft_Lewis(KK, S0, cf_GBM, interp=\"cubic\")\n",
"prices_BS_lin = fft_Lewis(KK, S0, cf_GBM, interp=\"linear\")\n",
"\n",
"print(\"BS linear interp Error:\", np.linalg.norm(prices_BS_lin - BS_pricer.BlackScholes(\"call\", S0, KK, T, r, sig), 1))\n",
"print(\"BS cubic spline Error:\", np.linalg.norm(prices_BS_cub - BS_pricer.BlackScholes(\"call\", S0, KK, T, r, sig), 1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Merton"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"Merton_prices = np.zeros_like(KK, dtype=float)\n",
"for i in range(len(KK)):\n",
" Merton.K = KK[i]\n",
" Merton_prices[i] = Merton.closed_formula()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Merton linear interpolation error: 0.037010068189591294\n",
"Merton cubic spline error: 1.0136974953667277e-05\n"
]
}
],
"source": [
"prices_M_cub = fft_Lewis(KK, S0, cf_Mert, interp=\"cubic\")\n",
"prices_M_lin = fft_Lewis(KK, S0, cf_Mert, interp=\"linear\")\n",
"\n",
"print(\"Merton linear interpolation error:\", np.linalg.norm(prices_M_lin - Merton_prices, 1))\n",
"print(\"Merton cubic spline error:\", np.linalg.norm(prices_M_cub - Merton_prices, 1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Variance Gamma"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"VG_prices = np.zeros_like(KK, dtype=float)\n",
"for i in range(len(KK)):\n",
" VG.K = KK[i]\n",
" VG_prices[i] = VG.Fourier_inversion()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"prices_VG_cub = fft_Lewis(KK, S0, cf_VG_b, interp=\"cubic\")"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"VG cubic spline error: 2.3181008812489523e-05\n"
]
}
],
"source": [
"print(\"VG cubic spline error:\", np.linalg.norm(prices_VG_cub - VG_prices, 1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Performances:\n",
"\n",
"When we want to price several options with same maturity (let's say, more than 10) the FFT method is the best."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"200 ms ± 10.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
}
],
"source": [
"%%timeit\n",
"VG_prices = np.zeros_like(KK, dtype=float)\n",
"for i in range(len(KK)):\n",
" VG.K = KK[i]\n",
" VG_prices[i] = VG.Fourier_inversion()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.51 ms ± 30 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"prices_VG_cub = fft_Lewis(KK, S0, cf_VG_b, interp=\"cubic\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"[1] Martin Schmelze (2010), Option Pricing Formulae using Fourier Transform: Theory and Application. \n",
"\n",
"[2] N.G. Shephard (1991), \"From characteristic function to distribution function, a simple framework for the theory\", Econometric Theory, 7, 519-529.\n",
"\n",
"[3] Carr, P. and D. Madan (1999). \"Option valuation using the fast Fourier transform\",\n",
"Journal of Computational Finance 2(4), 61–73.\n",
"\n",
"[4] Lewis, A. (2001) \"A Simple Option Formula for General Jump-Diffusion and other Exponential Lévy Processes\", Envision Financial Systems and OptionCity.net, California.\n",
"\n",
"[5] Gil-Pelaez, J. (1951) \"Note on the inversion theorem\", Biometrika 38(3–4), 481–482."
]
}
],
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"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
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================================================
FILE: 1.4 SDE - Heston model.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SDE - Heston model\n",
"\n",
"## Contents\n",
" - [Correlated Normal random variables](#sec1)\n",
" - [Heston model](#sec2)\n",
" - [Distribution of log-returns](#sec2.1)\n",
" - [Characteristic function](#sec3)\n",
" - [Option pricing](#sec3.1)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy as scp\n",
"import scipy.stats as ss\n",
"import matplotlib.pyplot as plt\n",
"import scipy.special as scps\n",
"from statsmodels.graphics.gofplots import qqplot\n",
"from scipy.linalg import cholesky\n",
"from functools import partial\n",
"from FMNM.probabilities import Heston_pdf, Q1, Q2\n",
"from FMNM.cython.heston import Heston_paths_log, Heston_paths\n",
"from scipy.optimize import minimize\n",
"\n",
"from IPython.display import display\n",
"import sympy\n",
"\n",
"sympy.init_printing()\n",
"\n",
"\n",
"def display_matrix(m):\n",
" display(sympy.Matrix(m))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Correlated Normal random variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to generate $n$ correlated Gaussian distributed random variables\n",
"\n",
"$$ Y \\sim \\mathcal{N}(\\mu, \\Sigma) $$\n",
"\n",
"where $Y=(𝑌_1,...,𝑌_n)$ is the vector to simulate, $\\mu = (\\mu_1,...,\\mu_n)$ the vector of means and $\\Sigma$\n",
"the [covariance matrix](https://en.wikipedia.org/wiki/Covariance_matrix),\n",
"\n",
"1) we first need to simulate a vector of uncorrelated standard Normal random variables, $Z$\n",
"\n",
"2) then find a square root of $\\Sigma$, i.e. a matrix $C$ such that $C C^T = \\Sigma$.\n",
"\n",
"The vector is given by $$Y = \\mu + C Z .$$ \n",
"\n",
"A popular choice to calculate $C$ is the [Cholesky decomposition](https://en.wikipedia.org/wiki/Cholesky_decomposition).\n",
"\n",
"#### Example:\n",
"\n",
"Let us consider the following 2-dimensional covariance matrix:\n",
"\n",
"$$ \\Sigma = \\left(\n",
"\\begin{array}{cc}\n",
"1 & \\rho \\\\\n",
"\\rho & 1 \\\\\n",
"\\end{array}\n",
"\\right) $$\n",
"\n",
"where for simplicity I chose $Var[Y_1]=Var[Y_2]=1$, such that $\\Sigma$ corresponds to the [correlation matrix](https://en.wikipedia.org/wiki/Correlation_and_dependence#Correlation_matrices). I also set $\\mu = 0$.\n",
"\n",
"In python we can use the built in function `scipy.stats.multivariate_normal` as follows:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"COV: \n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0 & 0.6\\\\0.6 & 1.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0 0.6⎤\n",
"⎢ ⎥\n",
"⎣0.6 1.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"correlation matrix: \n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0 & 0.6\\\\0.6 & 1.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0 0.6⎤\n",
"⎢ ⎥\n",
"⎣0.6 1.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"SIZE = 1000000\n",
"rho = 0.6\n",
"\n",
"MU = np.array([0, 0])\n",
"COV = np.array([[1, rho], [rho, 1]])\n",
"print(\"COV: \")\n",
"display_matrix(COV)\n",
"\n",
"Y = ss.multivariate_normal.rvs(mean=MU, cov=COV, size=SIZE)\n",
"\n",
"print(\"correlation matrix: \")\n",
"display_matrix(np.corrcoef(Y.T).round(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we want to use the algorithm proposed above, we have to find the matrix C.\n",
"\n",
"In two dimensions it has the simple form:\n",
"\n",
"$$ C = \\left(\n",
"\\begin{array}{cc}\n",
"1 & 0 \\\\\n",
"\\rho & \\sqrt{1-\\rho^2} \\\\\n",
"\\end{array}\n",
"\\right). $$\n",
"\n",
"By a simple matrix multiplication we can verify that:\n",
"\n",
"$$ \\left(\n",
"\\begin{array}{cc}\n",
"1 & 0 \\\\\n",
"\\rho & \\sqrt{1-\\rho^2} \\\\\n",
"\\end{array}\n",
"\\right) \\cdot \n",
"\\left(\n",
"\\begin{array}{cc}\n",
"1 & \\rho \\\\\n",
"0 & \\sqrt{1-\\rho^2} \\\\\n",
"\\end{array}\n",
"\\right)\n",
"= \\left(\n",
"\\begin{array}{cc}\n",
"1 & \\rho \\\\\n",
"\\rho & 1 \\\\\n",
"\\end{array}\n",
"\\right)\n",
"$$\n",
"\n",
"Therefore, in 2 dimensions there is no need to use the Cholesky decomposition. We simply have:\n",
"\n",
"$$ \\begin{cases} Y_1 = Z_1 \\\\ Y_2 = \\rho Z_1 + \\sqrt{1-\\rho^2} Z_2 \\end{cases} $$\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"correlation matrix: \n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0 & 0.6\\\\0.6 & 1.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0 0.6⎤\n",
"⎢ ⎥\n",
"⎣0.6 1.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Y_1 = np.random.normal(loc=0, scale=1, size=SIZE)\n",
"Y_2 = rho * Y_1 + np.sqrt(1 - rho**2) * np.random.normal(loc=0, scale=1, size=SIZE)\n",
"print(\"correlation matrix: \")\n",
"display_matrix(np.corrcoef(Y_1, Y_2).round(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For higher dimensional problems, the function `scipy.linalg.cholesky` [doc](https://docs.scipy.org/doc/scipy-0.16.1/reference/generated/scipy.linalg.cholesky.html) can help:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cholesky: \n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0 & 0.6\\\\0 & 0.8\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0 0.6⎤\n",
"⎢ ⎥\n",
"⎣ 0 0.8⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Correlation matrix: \n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0 & 0.6\\\\0.6 & 1.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0 0.6⎤\n",
"⎢ ⎥\n",
"⎣0.6 1.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(\"Cholesky: \")\n",
"display_matrix(cholesky(COV))\n",
"print(\"Correlation matrix: \")\n",
"display_matrix(cholesky(COV, lower=True) @ cholesky(COV))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Heston model\n",
"\n",
"The [Heston process](https://en.wikipedia.org/wiki/Heston_model) is described by the SDE: \n",
"\n",
"$$ \\begin{cases}\n",
"dS_t = \\mu S_t dt + \\sqrt{v_t} S_t dW^1_t \\\\\n",
"dv_t = \\kappa (\\theta - v_t) dt + \\sigma \\sqrt{v_t} dW^2_t \n",
"\\end{cases}$$\n",
"\n",
"The stock price follows a \"geometric Brownian motion\" with a stochastic volatility. The square of the volatility (the variance) follows a CIR process. \n",
"Have a look at the notebook **1.2** for more information on the CIR process.\n",
"\n",
"The parameters are:\n",
"- $\\mu$ drift of the stock process\n",
"- $\\kappa$ mean reversion coefficient of the variance process\n",
"- $\\theta$ long term mean of the variance process \n",
"- $\\sigma$ volatility coefficient of the variance process\n",
"- $\\rho$ correlation between $W^1$ and $W^2$ i.e.\n",
"$$ dW^1_t dW^2_t = \\rho dt $$\n",
"\n",
"We will also require that $2\\kappa \\theta > \\sigma^2$ (Feller condition)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Path generation\n",
"\n",
"The Heston process can be discretized using the Euler-Maruyama method proposed in the notebook **2.1**.\n",
"\n",
"In the next cell I present the algorithm that produces sample paths of the Heston process. In this example, I prefer to use the log-variables $X_t = \\log(S_t)$ and $Y_t = \\log(v_t)$ in order to avoid negative values for the process $\\{v_t\\}_{t\\geq 0}$. \n",
"\n",
"$$ \\begin{cases}\n",
"dX_t = \\biggl( \\mu - \\frac{1}{2} e^{Y_t} \\biggr) dt + e^{Y_t/2} dW^1_t \\\\\n",
"dY_t = e^{-Y_t} \\biggl[ \\kappa (\\theta - e^{Y_t}) - \\frac{1}{2}\\sigma^2 \\biggr] dt + \\sigma e^{-Y_t/2} dW^2_t \n",
"\\end{cases}$$\n",
"\n",
"This is an arbitrary choice! \n",
"\n",
"**Comment:** \n",
"From a theoretical point of view, using log-variables is the best choice, because it avoids negative values without any tweak of the original process. \n",
"From the practical point of view, it is better not to use log-variables! \n",
"The reason is that the algorithm can produce NaNs or overflows when the time steps are not small enough (e.g. for $N<20000$). This happens quite frequently. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 9.93 s, sys: 32.8 ms, total: 9.96 s\n",
"Wall time: 10.1 s\n"
]
}
],
"source": [
"%%time\n",
"np.random.seed(seed=42)\n",
"\n",
"N = 1000000 # time steps\n",
"paths = 3 # number of paths\n",
"T = 1\n",
"T_vec, dt = np.linspace(0, T, N, retstep=True)\n",
"dt_sq = np.sqrt(dt)\n",
"\n",
"S0 = 100 # spot price\n",
"X0 = np.log(S0) # log price\n",
"v0 = 0.04 # spot variance\n",
"Y0 = np.log(v0) # log-variance\n",
"\n",
"mu = 0.1 # drift\n",
"rho = -0.2 # correlation coefficient\n",
"kappa = 2 # mean reversion coefficient\n",
"theta = 0.04 # long-term variance\n",
"sigma = 0.3 # Vol of Vol - Volatility of instantaneous variance\n",
"std_asy = np.sqrt(theta * sigma**2 / (2 * kappa)) # asymptotic standard deviation for the CIR process\n",
"assert 2 * kappa * theta > sigma**2 # Feller condition\n",
"\n",
"# Generate random Brownian Motion\n",
"MU = np.array([0, 0])\n",
"COV = np.matrix([[1, rho], [rho, 1]])\n",
"W = ss.multivariate_normal.rvs(mean=MU, cov=COV, size=(N - 1, paths))\n",
"W_S = W[:, :, 0] # Stock Brownian motion: W_1\n",
"W_v = W[:, :, 1] # Variance Brownian motion: W_2\n",
"\n",
"# Initialize vectors\n",
"Y = np.zeros((N, paths))\n",
"Y[0, :] = Y0\n",
"X = np.zeros((N, paths))\n",
"X[0, :] = X0\n",
"v = np.zeros(N)\n",
"\n",
"# Generate paths\n",
"for t in range(0, N - 1):\n",
" v = np.exp(Y[t, :]) # variance\n",
" v_sq = np.sqrt(v) # square root of variance\n",
"\n",
" Y[t + 1, :] = (\n",
" Y[t, :] + (1 / v) * (kappa * (theta - v) - 0.5 * sigma**2) * dt + sigma * (1 / v_sq) * dt_sq * W_v[t, :]\n",
" )\n",
" X[t + 1, :] = X[t, :] + (mu - 0.5 * v) * dt + v_sq * dt_sq * W_S[t, :]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 5))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"\n",
"ax1.plot(T_vec, np.exp(X))\n",
"ax1.set_title(\"Heston model, Stock process. 3 paths\")\n",
"ax1.set_xlabel(\"Time\")\n",
"ax1.set_ylabel(\"Stock\")\n",
"ax2.plot(T_vec, np.exp(Y))\n",
"ax2.set_title(\"Heston model, Variance process. 3 paths\")\n",
"ax2.set_xlabel(\"Time\")\n",
"ax2.set_ylabel(\"Variance\")\n",
"ax2.plot(T_vec, (theta + std_asy) * np.ones_like(T_vec), label=\"1 asymptotic std dev\", color=\"black\")\n",
"ax2.plot(T_vec, (theta - std_asy) * np.ones_like(T_vec), color=\"black\")\n",
"ax2.plot(T_vec, theta * np.ones_like(T_vec), label=\"Long term mean\")\n",
"ax2.legend(loc=\"upper right\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Distribution of log-returns\n",
"\n",
"The python code presented above is very slow when we want to generate a big number of paths. \n",
"For this reason I implemented it in Cython. \n",
"\n",
"For a short introduction to Cython, have a look at the notebook **A2**. \n",
"This is the [Cython code](./functions/cython/cython_Heston.pyx), that makes also use of some C functions.\n",
"\n",
"\n",
"##### CYTHON implementation\n",
"\n",
"1) The function `Heston_paths_log` contains the code presented above, but translated in Cython. \n",
" The code returns only the \"good\" paths. It means that if there are overflows, they are ignored. \n",
" Since this method returns a \"random\" number of paths, I will prefer to use the next method.\n",
"\n",
"2) The function `Heston_paths` implements the discretization of the original process $(S_t,v_t)_{t\\geq 0}$.\n",
" The CIR process is discretized using the method (4) presented in the notebook **1.2** i.e. by taking the absolute value of the variance at each time step.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I import the following cython functions:\n",
"```python\n",
"from FMNM.cython.cython_Heston import Heston_paths_log, Heston_paths\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us re-define the parameters of the process, together with the parameters of the CIR density."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"mu = 0.1 # drift\n",
"rho = -0.9 # correlation coefficient\n",
"kappa = 2 # mean reversion coefficient\n",
"theta = 0.04 # long-term mean of the variance\n",
"sigma = 0.3 # (Vol of Vol) - Volatility of instantaneous variance\n",
"T = 1 # Terminal time\n",
"v0 = 0.04 # spot variance\n",
"S0 = 100 # spot stock price\n",
"\n",
"c = 2 * kappa / ((1 - np.exp(-kappa * T)) * sigma**2)\n",
"df = 4 * kappa * theta / sigma**2 # degrees of freedom\n",
"nc = 2 * c * v0 * np.exp(-kappa * T) # non-centrality parameter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next cell just wants to check if there are overflows for small value of $N$ (big time steps)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING. 8 paths have been removed because of the overflow.\n",
"SOLUTION: Use a bigger value N.\n"
]
}
],
"source": [
"_, _ = Heston_paths_log(N=500, paths=20000, T=T, S0=S0, v0=v0, mu=mu, rho=rho, kappa=kappa, theta=theta, sigma=sigma)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK.... now we are going to use the other function:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 16.4 s, sys: 53.4 ms, total: 16.4 s\n",
"Wall time: 16.6 s\n"
]
}
],
"source": [
"%%time\n",
"S, V = Heston_paths(N=20000, paths=20000, T=T, S0=S0, v0=v0, mu=mu, rho=rho, kappa=kappa, theta=theta, sigma=sigma)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"qqplot(log_R, line=\"s\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**The distribution of the log-returns is far from being normal!!** \n",
"(as you can see also from the q-q plot)\n",
"\n",
"This is a good property, because we can describe better the features of the empirical distributions obtained from market data!\n",
"\n",
"You can play with the correlation parameter $\\rho$ in order to obtain different skewness values.\n",
"\n",
"- For negative $\\rho$, we generate a log-return distribution with negative skewness.\n",
"- For positive $\\rho$, we generate a log-return distribution with positive skewness."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 16.4 s, sys: 72.9 ms, total: 16.5 s\n",
"Wall time: 16.7 s\n"
]
}
],
"source": [
"%%time\n",
"S2, _ = Heston_paths(N=20000, paths=20000, T=T, S0=S0, v0=v0, mu=mu, rho=0.9, kappa=kappa, theta=theta, sigma=sigma)\n",
"log_R2 = np.log(S2 / S0)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(-1, 1, 70)\n",
"\n",
"fig = plt.figure(figsize=(16, 5))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"\n",
"ax1.plot(\n",
" x,\n",
" [Heston_pdf(i, t=T, v0=v0, mu=mu, theta=theta, sigma=sigma, kappa=kappa, rho=0.9) for i in x],\n",
" color=\"blue\",\n",
" label=\"Heston density\",\n",
")\n",
"ax1.plot(x, ss.norm.pdf(x, log_R2.mean(), log_R2.std(ddof=0)), color=\"r\", label=\"Normal density\")\n",
"ax1.hist(log_R2, density=True, bins=100, facecolor=\"LightBlue\", label=\"frequencies of R_T\")\n",
"ax1.legend()\n",
"ax1.set_title(\"Histogram vs log-returns, Positive skew\")\n",
"ax1.set_xlabel(\"log(S_T/S0)\")\n",
"\n",
"ax2.plot(\n",
" x,\n",
" [Heston_pdf(i, t=T, v0=v0, mu=mu, theta=theta, sigma=sigma, kappa=kappa, rho=-0.9) for i in x],\n",
" color=\"blue\",\n",
" label=\"Heston density\",\n",
")\n",
"ax2.plot(x, ss.norm.pdf(x, log_R.mean(), log_R.std(ddof=0)), color=\"r\", label=\"Normal density\")\n",
"ax2.hist(log_R, density=True, bins=100, facecolor=\"LightBlue\", label=\"frequencies of R_T\")\n",
"ax2.legend(loc=\"upper left\")\n",
"ax2.set_title(\"Histogram vs log-returns, Negative skew\")\n",
"ax2.set_xlabel(\"log(S_T/S0)\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The skewness for rho= -0.9 is: -1.1550880187314136\n",
"The skewness for rho= +0.9 is: 1.1820448066432543\n"
]
}
],
"source": [
"print(\"The skewness for rho= -0.9 is: \", ss.skew(log_R))\n",
"print(\"The skewness for rho= +0.9 is: \", ss.skew(log_R2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the pictures above we can see the difference between the Normal distribution and the Heston distribution!\n",
"\n",
"I used the function `Heston_pdf`, which implements the Fourier inversion of the Heston characteristic function using the Gil-Pelaez formula. \n",
"\n",
"For more information on Fourier inversion see the notebook **1.3**. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Characteristic function\n",
"\n",
"The characteristic function (CF) for the Heston model was presented for the first time in the original paper of Heston (1993) [1]. \n",
"\n",
"The Heston CF is very useful for the computation of option prices. All the other methods, such as Monte Carlo or PDE, are quite slow, while Fourier inversion (as we have seen in the notebook **1.3**) is super fast!\n",
"\n",
"##### Issue with the Heston CF\n",
"\n",
"The Heston CF works well for short time $T$. \n",
"However, when the time increases this CF exhibits discontinuities due to the multivalued complex functions (the complex square root and the complex logarithm need to have a specified [Principal brach](https://en.wikipedia.org/wiki/Principal_branch)).\n",
"A consequence is that the numerical integration becomes unstable.\n",
"\n",
"In the important articles [2] and [3], different (but equivalent) forms of the Heston CF are presented. These CFs consider different [Riemann surfaces](https://en.wikipedia.org/wiki/Riemann_surface) and resolve the problem.\n",
"\n",
"In the following I will show how the CF proposed by Schoutens (in [2]) performs better than the Heston CF. \n",
"In these notebooks, all the functions that consider the Heston CF are implemented using the CF `cf_Heston_good` proposed by Schoutens."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"r = 0.05 # drift\n",
"rho = -0.8 # correlation coefficient\n",
"kappa = 3 # mean reversion coefficient\n",
"theta = 0.1 # long-term mean of the variance\n",
"sigma = 0.25 # (Vol of Vol) - Volatility of instantaneous variance\n",
"T = 15 # Terminal time\n",
"K = 100 # Stike\n",
"v0 = 0.08 # spot variance\n",
"S0 = 100 # spot stock price\n",
"k = np.log(K / S0) # log moneyness"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def cf_Heston(u, t, v0, mu, kappa, theta, sigma, rho):\n",
" \"\"\"\n",
" Heston characteristic function as proposed in the original paper of Heston (1993)\n",
" \"\"\"\n",
" xi = kappa - sigma * rho * u * 1j\n",
" d = np.sqrt(xi**2 + sigma**2 * (u**2 + 1j * u))\n",
" g1 = (xi + d) / (xi - d)\n",
" cf = np.exp(\n",
" 1j * u * mu * t\n",
" + (kappa * theta) / (sigma**2) * ((xi + d) * t - 2 * np.log((1 - g1 * np.exp(d * t)) / (1 - g1)))\n",
" + (v0 / sigma**2) * (xi + d) * (1 - np.exp(d * t)) / (1 - g1 * np.exp(d * t))\n",
" )\n",
" return cf"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def cf_Heston_good(u, t, v0, mu, kappa, theta, sigma, rho):\n",
" \"\"\"\n",
" Heston characteristic function as proposed by Schoutens (2004)\n",
" \"\"\"\n",
" xi = kappa - sigma * rho * u * 1j\n",
" d = np.sqrt(xi**2 + sigma**2 * (u**2 + 1j * u))\n",
" g1 = (xi + d) / (xi - d)\n",
" g2 = 1 / g1\n",
" cf = np.exp(\n",
" 1j * u * mu * t\n",
" + (kappa * theta) / (sigma**2) * ((xi - d) * t - 2 * np.log((1 - g2 * np.exp(-d * t)) / (1 - g2)))\n",
" + (v0 / sigma**2) * (xi - d) * (1 - np.exp(-d * t)) / (1 - g2 * np.exp(-d * t))\n",
" )\n",
" return cf"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"cf_H_b = partial(cf_Heston, t=T, v0=v0, mu=r, theta=theta, sigma=sigma, kappa=kappa, rho=rho)\n",
"cf_H_b_good = partial(cf_Heston_good, t=T, v0=v0, mu=r, theta=theta, sigma=sigma, kappa=kappa, rho=rho)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"u = np.linspace(-4, 4, 100)\n",
"\n",
"plt.figure(figsize=(12, 5))\n",
"plt.plot(u, np.real(cf_H_b(u)), label=\"Heston CF\")\n",
"plt.plot(u, np.real(cf_H_b_good(u)), label=\"Schoutens CF\")\n",
"plt.title(\"Comparison of stable and instable Heston characteristic functions\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Option pricing\n",
"\n",
"We can compare the values of an European call option computed with the following two pricing methods: \n",
"\n",
"(as usual we consider the parameters defined above as the *risk neutral* parameters)\n",
"\n",
"##### Monte Carlo"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Heston Monte Carlo call price: 64.8311510602247\n",
"With standard error: 1.1100688417854097\n",
"-----------------------------------\n",
"CPU times: user 16.4 s, sys: 668 ms, total: 17 s\n",
"Wall time: 17.3 s\n"
]
}
],
"source": [
"%%time\n",
"S, v = Heston_paths(N=20000, paths=20000, T=T, S0=S0, v0=v0, mu=r, rho=rho, kappa=kappa, theta=theta, sigma=sigma)\n",
"DiscountedPayoff = np.exp(-r * T) * np.maximum(S - K, 0)\n",
"V = np.mean(DiscountedPayoff)\n",
"std_err = ss.sem(DiscountedPayoff)\n",
"print(\"Heston Monte Carlo call price: \", V)\n",
"print(\"With standard error: \", std_err)\n",
"print(\"-----------------------------------\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Fourier inversion"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Heston Fourier inversion call price: 65.27786862734644\n",
"-----------------------------------\n",
"CPU times: user 5.64 ms, sys: 174 µs, total: 5.81 ms\n",
"Wall time: 6.57 ms\n"
]
}
],
"source": [
"%%time\n",
"limit_max = 1000 # right limit in the integration\n",
"call = S0 * Q1(k, cf_H_b_good, limit_max) - K * np.exp(-r * T) * Q2(k, cf_H_b_good, limit_max)\n",
"print(\"Heston Fourier inversion call price: \", call)\n",
"print(\"-----------------------------------\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"[1] Heston, S. L. (1993). \"A closed-form solution for options with stochastic volatility and applications to bond and currency options\". Review of Financial Studies\n",
"\n",
"[2] Schoutens, W., Simons, E., & Tistaert, J. (2004). \"A perfect calibration! Now what?\". Wilmott Magazine\n",
"[link](https://perswww.kuleuven.be/~u0009713/ScSiTi03.pdf)\n",
"\n",
"[3] Yiran Cui, Sebastian del Baño Rollin, Guido Germano, (2017), \"Full and fast calibration of the Heston stochastic volatility model\" European Journal of operational research. [link](http://www0.cs.ucl.ac.uk/staff/g.germano/papers/EurJOperRes_2017.pdf)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: 1.5 SDE - Lévy processes.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SDE - Lévy processes\n",
"\n",
"\n",
"## Contents\n",
" - [Merton process](#sec1)\n",
" - [Merton Density](#sec1.1)\n",
" - [Parameter estimation](#sec1.2)\n",
" - [Variance Gamma process](#sec2)\n",
" - [VG Density](#sec2.1)\n",
" - [Parameter estimation](#sec2.2)\n",
" - [Normal Inverse Gaussian process](#sec3)\n",
" - [NIG Density](#sec3.1)\n",
" - [Parameter estimation](#sec3.2)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy as scp\n",
"import scipy.stats as ss\n",
"import matplotlib.pyplot as plt\n",
"from statsmodels.graphics.gofplots import qqplot\n",
"import scipy.special as scps\n",
"from functools import partial\n",
"from scipy.optimize import minimize\n",
"from math import factorial\n",
"\n",
"from FMNM.probabilities import Gil_Pelaez_pdf\n",
"from FMNM.CF import cf_VG, cf_mert, cf_NIG"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Merton Jump-Diffusion process\n",
"\n",
"The Merton process is described by the following equation:\n",
"\n",
"\\begin{equation}\n",
"X_t = \\mu t + \\sigma W_t + \\sum_{i=1}^{N_t} Y_i, \n",
"\\end{equation}\n",
"\n",
"where $N_t \\sim Po(\\lambda t)$ is a Poisson random variable counting the jumps of $X_t$ in $[0,t]$, and \n",
"\n",
"$$Y_i \\sim \\mathcal{N}(\\alpha, \\xi^2)$$ \n",
"\n",
"are the sizes of the jumps. \n",
"In the following I indicate $\\mu \\to$ `mu`, $\\sigma \\to $ `sig`, $\\lambda \\to $ `lam`, $\\alpha \\to $ `muJ` and $\\xi \\to $ `sigJ`.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"mu = 0.05 # drift\n",
"sig = 0.2 # diffusion coefficient\n",
"lam = 1.2 # jump activity\n",
"muJ = 0.15 # jump mean size\n",
"sigJ = 0.5 # jump std deviation\n",
"T = 2 # terminal time\n",
"N = 1000000 # number of random variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Merton Density"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Simulation"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(seed=42)\n",
"W = ss.norm.rvs(0, 1, N) # The normal RV vector\n",
"P = ss.poisson.rvs(lam * T, size=N) # Poisson random vector\n",
"Jumps = np.asarray([ss.norm.rvs(muJ, sigJ, i).sum() for i in P]) # Jumps vector\n",
"X_T = mu * T + np.sqrt(T) * sig * W + Jumps # Merton process"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Merton distribution\n",
"(I took this formula from section 4.3 of [1] )"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def Merton_density(x, T, mu, sig, lam, muJ, sigJ):\n",
" tot = 0\n",
" for k in range(20):\n",
" tot += (\n",
" (lam * T) ** k\n",
" * np.exp(-((x - mu * T - k * muJ) ** 2) / (2 * (T * sig**2 + k * sigJ**2)))\n",
" / (factorial(k) * np.sqrt(2 * np.pi * (sig**2 * T + k * sigJ**2)))\n",
" )\n",
" return np.exp(-lam * T) * tot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Merton characteristic function"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"cf_M_b = partial(cf_mert, t=T, mu=mu, sig=sig, lam=lam, muJ=muJ, sigJ=sigJ)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Plot"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"qqplot(X_T, line=\"s\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Parameter estimation\n",
"\n",
"In this section I will use the function `scipy.optimize.minimize` to minimize the negative of the log-likelihood function i.e. I'm going to **maximize** the log-likelihood.\n",
"\n",
"This is not an easy task... The model has 5 parameters, which means that the routine will be very slow, and we are not sure it will work. \n",
"It is possible that the log-likelihood function has several local maximum points, or that some overflows appear.\n",
"\n",
"We have to try several methods until we find the one that better performs.\n",
"\n",
"Let us firts analyze our simulated data:\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Median: 0.3785193938994046\n",
"Mean: 0.4597291804315835\n",
"Standard Deviation: 0.857134840167505\n",
"Skewness: 0.44466395968028455\n",
"Kurtosis: 1.0056718465894816\n"
]
}
],
"source": [
"print(f\"Median: {np.median(X_T)}\")\n",
"print(f\"Mean: {np.mean(X_T)}\")\n",
"print(f\"Standard Deviation: {np.std(X_T)}\")\n",
"print(f\"Skewness: {ss.skew(X_T)}\")\n",
"print(f\"Kurtosis: {ss.kurtosis(X_T)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Maximum Likelihood Estimation\n",
"\n",
"Initial guesses are important! We can gain some information of the unknown parameters from our data.\n",
"\n",
"I use:\n",
"- The mean of a Merton process is $\\mathbb{E}[X_T] = (\\mu + \\lambda \\alpha) T$. See **notebook A.3**."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.22986459021579175\n",
"0.22999999999999998\n"
]
}
],
"source": [
"print(np.mean(X_T) / T)\n",
"print(mu + lam * muJ)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- therefore we can assume equal contribution from `mu` and `lam * muJ`. So, `mu`=0.1. Of course this is just an initial guess.\n",
"\n",
"- Diffusion coefficient of 0.5 is quite reasonable. A standard deviation of about 0.85 computed above contains also the effects of jumps.\n",
"\n",
"- We don't have information on lambda, so we choose $\\lambda=1$ in order to keep the formula for the mean consistent. \n",
"\n",
"- The histogram has positive skew. The parameter $\\alpha$ (or `muJ`) affects the skewness. The value 0.1 makes sense.\n",
"\n",
"- About `sigJ` I set it to $1$, because I expect that a jump has a standard deviation higher than the usual diffusion standard deviation."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Desired error not necessarily achieved due to precision loss.\n",
"Number of iterations performed by the optimizer: 38\n"
]
}
],
"source": [
"def log_likely_Merton(x, data, T):\n",
" return (-1) * np.sum(np.log(Merton_density(data, T, x[0], x[1], x[2], x[3], x[4])))\n",
"\n",
"\n",
"result_Mert = minimize(\n",
" log_likely_Merton, x0=[0.1, 0.5, 1, 0.1, 1], method=\"BFGS\", args=(X_T, T)\n",
") # Try also Nelder-Mead\n",
"\n",
"print(result_Mert.message)\n",
"print(\"Number of iterations performed by the optimizer: \", result_Mert.nit)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original parameters: mu=0.05, sigma=0.2, lam=1.2, alpha=0.15, xi=0.5 \n",
"MLE parameters: [0.04779053 0.1997717 1.20723181 0.15081947 0.49841832]\n"
]
}
],
"source": [
"print(f\"Original parameters: mu={mu}, sigma={sig}, lam={lam}, alpha={muJ}, xi={sigJ} \")\n",
"print(\"MLE parameters: \", result_Mert.x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Very good!!\n",
"The BFGS and Nelder-Mead methods work! The results are printed in the last line, and we can see that the values correspond to the original values.\n",
"\n",
"I tried also L-BFGS-B and SLSQP which instead do not work.\n",
"\n",
"See here for a list of possible methods: [link](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Variance Gamma process\n",
"\n",
"\n",
"Usually the [Gamma distribution](https://en.wikipedia.org/wiki/Gamma_distribution) is \n",
"parametrized by a shape and scale positive parameters \n",
"\n",
"$$T \\sim \\Gamma(a,b).$$ \n",
"\n",
"The random variable $T_t \\sim \\Gamma(a t, b)$ has the shape parameter dependent on $t$ (the shape is a linear function of $t$). \n",
"The density function is \n",
"\n",
"$$ f_{T_t}(x) = \\frac{b^{-a t}}{\\Gamma(a t)}x^{a t -1}e^{-\\frac{x}{b}}$$ \n",
"\n",
"with \n",
"\n",
"$$\\mathbb{E}[T_t]= a b t, \\quad \\text{and} \\quad \\text{Var}[T_t] = a b^2 t. $$ \n",
"\n",
"When working with the VG process, it is common to use a parametrization such that \n",
"\n",
"$$\\mathbb{E}[T_t]=\\mu t \\quad \\text{and} \\quad \\text{Var}[T_t] = \\kappa t $$ \n",
"\n",
"therefore $\\mu = ab$ and $\\kappa = a b^2$.\n",
"Inverting we obtain $b=\\frac{\\kappa}{\\mu}$, $a=\\frac{\\mu^2}{\\kappa}$.\n",
"\n",
"The new parametrization is with respect to the new parameters $\\mu$ and $\\kappa$, such that \n",
"\n",
"$$T_t \\sim \\Gamma(\\mu t, \\kappa t)$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Variance Gamma\n",
"\n",
"If we consider a Brownian motion with drift \n",
"\n",
"$$X_t = \\theta t + \\sigma W_t$$ \n",
"\n",
"and substitute the time variable with a Gamma random variable \n",
"$T_t \\sim \\Gamma(t,\\kappa t)$,\n",
"we obtain the **Variance Gamma** process:\n",
"\n",
"$$ X_{t} := \\theta T_t + \\sigma W_{T_t} .$$\n",
"\n",
"where we chose $T_t \\sim \\Gamma(\\mu t, \\kappa t)$ with $\\mu=1$ in order to have $\\mathbb{E}[T_t]= t$.\n",
"\n",
"It depends on three parameters:\n",
"\n",
"- $\\sigma$, the volatility of the Brownian motion\n",
"- $\\kappa$, the variance of the Gamma process\n",
"- $\\theta$, the drift of the Brownian motion\n",
"\n",
"The characteristic function is: \n",
"\n",
"$$ \\phi_{X_t}(u) = \\biggl( 1-i\\theta \\kappa u + \\frac{1}{2} \\sigma^2 \\kappa u^2 \\biggr)^{-\\frac{t}{\\kappa}}.$$\n",
"\n",
"The first four moments are: \n",
"\n",
"$$\n",
"\\begin{aligned}\n",
" \\mathbb{E}[X_t] &= t\\theta. \\\\ \\nonumber\n",
" \\text{Var}[X_t] &= t(\\sigma^2 + \\theta^2 \\kappa). \\\\ \\nonumber\n",
" \\text{Skew}[X_t] &= \\frac{t (2\\theta^3\\kappa^2 + 3 \\sigma^2 \\theta \\kappa)}{\\bigl(\\text{Var}[X_t])^{3/2}}. \\\\ \\nonumber\n",
" \\text{Kurt}[X_t] &= \\frac{t (3\\sigma^4 \\kappa + 12\\sigma^2 \\theta^2 \\kappa^2 +6\\theta^4\\kappa^3)}{\\bigl(\\text{Var}[X_t]\\bigr)^2}.\\nonumber \n",
"\\end{aligned} \n",
"$$\n",
"\n",
"Additional information can be found in **A.3**.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## VG Density\n",
"\n",
"For the formula of the VG density see **A.3**. For information on Fourier inversion formula see **1.3**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Simulation"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"T = 2 # terminal time\n",
"N = 1000000 # number of generated random variables\n",
"\n",
"theta = -0.1 # drift of the Brownian motion\n",
"sigma = 0.2 # volatility of the Brownian motion\n",
"kappa = 0.5 # variance of the Gamma process"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(seed=42)\n",
"G = ss.gamma(T / kappa, scale=kappa).rvs(N) # The gamma RV\n",
"Norm = ss.norm.rvs(0, 1, N) # The normal RV\n",
"X = theta * G + sigma * np.sqrt(G) * Norm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### VG Density"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def VG_density(x, T, c, theta, sigma, kappa):\n",
" return (\n",
" 2\n",
" * np.exp(theta * (x - c) / sigma**2)\n",
" / (kappa ** (T / kappa) * np.sqrt(2 * np.pi) * sigma * scps.gamma(T / kappa))\n",
" * ((x - c) ** 2 / (2 * sigma**2 / kappa + theta**2)) ** (T / (2 * kappa) - 1 / 4)\n",
" * scps.kv(T / kappa - 1 / 2, sigma ** (-2) * np.sqrt((x - c) ** 2 * (2 * sigma**2 / kappa + theta**2)))\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Fourier inversion of the VG characteristic function"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"cf_VG_b = partial(cf_VG, t=T, mu=0, theta=theta, sigma=sigma, kappa=kappa)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Histogram vs density vs Fourier inversion"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"qqplot(X, line=\"s\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Parameter estimation\n",
"\n",
"\n",
"Let us consider a VG process with an additional (deterministic) drift $c$:\n",
"\n",
"$$ Y_t = c t + X^{VG}_t $$\n",
"\n",
"such that $\\mathbb{E}[Y_t] = t (\\theta + c)$.\n",
"\n",
"This is a more general setting that keeps into account a possible \"location\" parameter for the VG distribution (the function `VG_density` already considers the location parameter $c$).\n",
"\n",
"The formulas for higher order moments are unchanged. \n",
"The characteristic function if $Y_t$ is:\n",
"\n",
"$$ \\phi_{Y_t}(u) = e^{icu} \\biggl( 1-i\\theta \\kappa u + \\frac{1}{2} \\sigma^2 \\kappa u^2 \\biggr)^{-\\frac{t}{\\kappa}}.$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Approximated Method of Moments\n",
"\n",
"This method suggests to consider only the first order terms in $\\theta$ and ignore $\\theta^2$, $\\theta^3$ and $\\theta^4$. \n",
"This method is motivated by the fact that usually $\\theta$ is quite small.\n",
"\n",
"Since there are 4 parameters to estimate, we need 4 equations i.e. the formulas for the first 4 moments. \n",
"We get:\n",
"\n",
"$$ \\begin{aligned}\n",
" \\mathbb{E}[X_t] &= t \\, (c+\\theta). \\\\\n",
" \\text{Var}[X_t] &= t \\, \\sigma^2. \\\\ \n",
" \\text{Skew}[X_t] &= \\frac{1}{\\sqrt{t}} \\frac{3 \\theta \\kappa}{\\sigma}. \\\\\n",
" \\text{Kurt}[X_t] &= \\frac{1}{t} 3\\kappa \n",
"\\end{aligned} $$\n",
"\n",
"Inverting we have:\n",
"\n",
"$$ \\begin{aligned}\n",
" \\kappa &= \\frac{t \\text{Kurt}[X_t]}{3} \\\\ \n",
" \\sigma &= \\frac{\\text{Std}[X_t]}{\\sqrt{t}}. \\\\ \n",
" \\theta &= \\frac{\\sqrt{t} \\text{Skew}[X_t] \\sigma}{3 \\kappa}. \\\\\n",
" c &= \\frac{\\mathbb{E}[X_t]}{t} - \\theta. \\\\\n",
"\\end{aligned} $$\n",
"\n",
"From the formulas above we obtained interesting information:\n",
"- The parameter $\\theta$ is connected with the **skewness**. (The sign of $\\theta$ indicates if the distribution has positive or negative skewness)\n",
"- The parameter $\\kappa$ represents the amount of **kurtosis** of the distribution. "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Estimated parameters: \n",
"\n",
" c=-0.01854377725232363 \n",
" theta=-0.08144922792709257 \n",
" sigma=0.21200595513004386 \n",
" kappa=0.5904713302435356\n",
"\n",
"Estimated c + theta = -0.0999930051794162\n"
]
}
],
"source": [
"sigma_mm1 = np.std(X) / np.sqrt(T)\n",
"kappa_mm1 = T * ss.kurtosis(X) / 3\n",
"theta_mm1 = np.sqrt(T) * ss.skew(X) * sigma_mm1 / (3 * kappa_mm1)\n",
"c_mm1 = np.mean(X) / T - theta_mm1\n",
"\n",
"print(\n",
" \"Estimated parameters: \\n\\n c={} \\n theta={} \\n sigma={} \\n \\\n",
"kappa={}\\n\".format(\n",
" c_mm1, theta_mm1, sigma_mm1, kappa_mm1\n",
" )\n",
")\n",
"print(\"Estimated c + theta = \", c_mm1 + theta_mm1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The approximated method gives very good results!! \n",
"\n",
"Only two remarks: \n",
"- We simulated the process with $c=0$ and $\\theta=-0.1$. Therefore, only the parameter $\\theta$ contributes to the mean of the distribution. The results of this estimation process, assign a little part of the true value of $\\theta$ to $c$, which instead should be zero.\n",
"- The estimated value of the parameter $\\kappa$ is not very precise.\n",
"\n",
"Let us see if we can do better!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### MLE\n",
"\n",
"An alternative method to estimate the parameters is to use the Maximum Likelihood Estimator.\n",
"\n",
"In the following cell I use the function `scipy.optimize.minimize` to minimize the negative of the log-likelihood, i.e. I maximize the log-likelihood.\n",
"\n",
"Since there are 4 parameters to be estimated, the routine can be very slow. \n",
"Fortunately there are some tricks we can use in order to speed up the task.\n",
"\n",
"1) We can use the values estimated with the *approximated method of moments* as **initial guesses**. It is reasonable to expect that they are quite good initial guesses.\n",
"\n",
"2) Look at the **Histogram** above. From the histogram we can extract some information:\n",
" - The range for the mean $c$ i.e. $[-1,1]$ \n",
" - The skewness is negative, therefore $\\theta$ must be negative. I chose the interval $[-1,-10^{-15}]$.\n",
" - For $\\sigma$ and $\\kappa$ I chose the reasonable intervals $[10^{-15},2]$ and $[10^{-15},\\infty)$. \n",
"\n",
"Using a method with constraints such as 'L-BFGS-B' is helpful to reduce the computational time. \n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
"Number of iterations performed by the optimizer: 18\n",
"MLE parameters: [ 0.00442153 -0.10220385 0.19960387 0.48852015]\n",
"CPU times: user 41.8 s, sys: 822 ms, total: 42.6 s\n",
"Wall time: 42.6 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"\n",
"def log_likely(x, data, T):\n",
" return (-1) * np.sum(np.log(VG_density(data, T, x[0], x[1], x[2], x[3])))\n",
"\n",
"\n",
"result_VG = minimize(\n",
" log_likely,\n",
" x0=[c_mm1, theta_mm1, sigma_mm1, kappa_mm1],\n",
" method=\"L-BFGS-B\",\n",
" args=(X, T),\n",
" tol=1e-8,\n",
" bounds=[[-1, 1], [-1, -1e-15], [1e-15, 2], [1e-15, None]],\n",
")\n",
"\n",
"print(result_VG.message)\n",
"print(\"Number of iterations performed by the optimizer: \", result_VG.nit)\n",
"print(\"MLE parameters: \", result_VG.x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Very good! \n",
"\n",
"These values (the last line of the output) are very satisfactory!\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## NIG process\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Inverse Gaussian (IG) distribution\n",
"\n",
"Usually, the [IG distribution](https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution) is parametrized by two parameters: the mean ($\\mu$) and the shape ($\\lambda$):\n",
"\n",
"$$ T \\sim IG(\\mu, \\lambda) $$\n",
"\n",
"where\n",
"\n",
"$$ f_{T}(x) = \\sqrt{\\frac{\\lambda}{2\\pi x^3}} e^{- \\frac{\\lambda(x-\\mu)^2}{2\\mu^2 x} } $$ \n",
"\n",
"with \n",
"\n",
"$$\\mathbb{E}[T]= \\mu, \\quad \\text{and} \\quad \\text{Var}[T] = \\frac{\\mu^3}{\\lambda}. $$ \n",
"\n",
"The python function [scipy.stats.invgauss](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.invgauss.html#scipy.stats.invgauss) is implemented for $\\lambda=1$. \n",
"To generate a distribution with a different $\\lambda$, we have to scale both the variable $x$ and the mean $\\mu$:\n",
"\n",
"$$ x \\to \\frac{x}{\\lambda} \\quad \\mu \\to \\frac{\\mu}{\\lambda} $$\n",
"\n",
"The **IG distribution** is the distribution of the first passage time of a Brownian motion with drift $\\gamma \\geq 0$, for a barrier $\\delta>0$. \n",
"\n",
"If we let the barrier to be a linear function of the time $t$, we obtain the IG process. \n",
"\n",
"##### IG process\n",
"\n",
"The IG process $T_t$ can be defined as:\n",
"\n",
"$$ dZ_t = \\gamma dt + dW_t $$\n",
"\n",
"$$ T_t = \\inf \\bigl\\{ s>0 : Z_s = \\delta t \\bigr\\} $$\n",
"\n",
"where $\\{Z_s : s\\geq 0\\}$ is a Brownian motion with drift $\\gamma \\geq 0$ and unitary diffusion coefficient. \n",
"The IG process at time $t$ is distributed as \n",
" \n",
"$$ T_t \\sim IG \\biggl( \\frac{\\delta t }{\\gamma}, \\delta^2 t^2 \\biggr).$$\n",
"\n",
"It is useful to consider the parameterization in terms of mean ($\\mu$) and variance ($\\kappa$). \n",
"The variance is $\\text{Var}[T] = \\frac{\\mu^3}{\\lambda}$. By changing variables we get $\\text{Var}[T_t] = \\frac{\\delta t}{\\gamma^3}$. Let us call $$\\kappa = \\frac{\\delta}{\\gamma^3}.$$\n",
"\n",
"Ok... let us see if it works..."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(seed=42)\n",
"paths = 40000 # number of paths\n",
"steps = 10000 # number of time steps\n",
"\n",
"t = 2\n",
"delta = 3 * t # time dependent barrier\n",
"gamma = 2 # drift\n",
"T_max = 20\n",
"T_vec, dt = np.linspace(0, T_max, steps, retstep=True)\n",
"X0 = np.zeros((paths, 1)) # each path starts at zero\n",
"increments = ss.norm.rvs(loc=gamma * dt, scale=np.sqrt(dt), size=(paths, steps - 1))\n",
"\n",
"Z = np.concatenate((X0, increments), axis=1).cumsum(1)\n",
"T = np.argmax(Z > delta, axis=1) * dt # first passage time"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Theoretical mean: 3.0\n",
"Theoretical variance: 0.75\n",
"Estimated mean: 3.017242724272428\n",
"Estimated variance: 0.7578786228647868\n"
]
}
],
"source": [
"x = np.linspace(0, 10, 10000)\n",
"mm = delta / gamma\n",
"lam = delta**2\n",
"mm1 = mm / lam # scaled mean\n",
"\n",
"plt.plot(x, ss.invgauss.pdf(x, mu=mm1, scale=lam), color=\"red\", label=\"IG density\")\n",
"plt.hist(T, density=True, bins=100, facecolor=\"LightBlue\", label=\"frequencies of T\")\n",
"plt.title(\"First passage time distribution\")\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"print(\"Theoretical mean: \", mm)\n",
"print(\"Theoretical variance: \", delta / gamma**3)\n",
"print(\"Estimated mean: \", T.mean())\n",
"print(\"Estimated variance: \", T.var())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Normal Inverse Gaussian (NIG)\n",
"\n",
"Following the same argument used to build the Variance Gamma process, we consider a Brownian motion with drift \n",
"\n",
"$$ X_t = \\theta t + \\sigma W_t$$ \n",
"\n",
"and substitute the time variable with an Inverse Gaussian random variable \n",
"$T_t \\sim \\Gamma(t,\\kappa t)$,\n",
"we obtain the **Normal Inverse Gaussian** process:\n",
"\n",
"$$\n",
" X_{t} := \\theta T_t + \\sigma W_{T_t} .\n",
"$$\n",
"\n",
"where we chose \n",
"\n",
"$$T_t \\sim IG\\biggl( \\mu=t, \\lambda= \\frac{t^2}{\\kappa} \\biggr),$$ \n",
"\n",
"with $\\delta=\\gamma$ and $\\kappa = 1/\\gamma^2$ in order to have $\\mathbb{E}[T_t]= t$.\n",
"\n",
"The NIG process depends on three parameters:\n",
"\n",
"- $\\sigma$, the volatility of the Brownian motion\n",
"- $\\kappa$, the variance of the IG process\n",
"- $\\theta$, the drift of the Brownian motion\n",
"\n",
"The characteristic function is: \n",
"\n",
"$$ \\phi_{X_t}(u) = \\frac{1}{\\kappa} \\biggl( 1 - \\sqrt{1 - i2 \\theta \\kappa u + \\sigma^2 \\kappa u^2} \\biggr) .$$\n",
"\n",
"The first four moments are: \n",
"\n",
"$$ \\begin{aligned}\n",
" \\mathbb{E}[X_t] &= t\\theta. \\\\\n",
" \\text{Var}[X_t] &= t(\\sigma^2 + \\theta^2 \\kappa). \\\\\n",
" \\text{Skew}[X_t] &= \\frac{t (3\\theta^3\\kappa^2 + 3 \\sigma^2 \\theta \\kappa)}{\\bigl(\\text{Var}[X_t])^{3/2}}. \\\\ \n",
" \\text{Kurt}[X_t] &= \\frac{t (3\\sigma^4 \\kappa + 18\\sigma^2 \\theta^2 \\kappa^2 +15\\theta^4\\kappa^3)}{\\bigl(\\text{Var}[X_t]\\bigr)^2}. \n",
"\\end{aligned} $$\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## NIG Density\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Simulation"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"T = 2 # terminal time\n",
"N = 1000000 # number of generated random variables\n",
"\n",
"theta = -0.1 # drift of the Brownian motion\n",
"sigma = 0.2 # volatility of the Brownian motion\n",
"kappa = 0.5 # variance of the Gamma process\n",
"\n",
"lam = T**2 / kappa # scale\n",
"mus = T / lam # scaled mu"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(seed=42)\n",
"IG = ss.invgauss.rvs(mu=mus, scale=lam, size=N) # The IG RV\n",
"Norm = ss.norm.rvs(0, 1, N) # The normal RV\n",
"X = theta * IG + sigma * np.sqrt(IG) * Norm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### NIG Density"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"def NIG_density(x, T, c, theta, sigma, kappa):\n",
" A = theta / (sigma**2)\n",
" B = np.sqrt(theta**2 + sigma**2 / kappa) / sigma**2\n",
" C = T / np.pi * np.exp(T / kappa) * np.sqrt(theta**2 / (kappa * sigma**2) + 1 / kappa**2)\n",
" return (\n",
" C\n",
" * np.exp(A * (x - c * T))\n",
" * scps.kv(1, B * np.sqrt((x - c * T) ** 2 + T**2 * sigma**2 / kappa))\n",
" / np.sqrt((x - c * T) ** 2 + T**2 * sigma**2 / kappa)\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The NIG density usually is indicated with the parameters:\n",
"$$ \\beta = \\frac{\\theta}{\\sigma^2}, \\quad \\quad \\alpha = \\sqrt{ \\beta^2 + \\frac{1}{\\kappa \\sigma^2} } $$\n",
"$$ \\delta = \\frac{T \\sigma}{\\sqrt{\\kappa}}, \\quad \\quad \\mu = c T $$\n",
"\n",
"See [3] for instance. However, I prefer to use the ($\\theta,\\sigma,\\kappa$) parameters, as in [1].\n",
"\n",
"The following function corresponds to the NIG($\\alpha, \\beta, \\delta, \\mu$) density:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"def NIG_abdmu(x, T, c, theta, sigma, kappa):\n",
" beta = theta / (sigma**2)\n",
" alpha = np.sqrt(beta**2 + 1 / (kappa * sigma**2))\n",
" delta = T * sigma / np.sqrt(kappa)\n",
" mu = c * T\n",
" g = lambda y: np.sqrt(delta**2 + (x - mu) ** 2)\n",
" cost = delta * alpha / np.pi\n",
" return (\n",
" cost\n",
" * np.exp(delta * np.sqrt(alpha**2 - beta**2) + beta * (x - mu))\n",
" * scps.kv(1, alpha * g(x - mu))\n",
" / g(x - mu)\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Fourier inversion of the NIG characteristic function"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"cf_NIG_b = partial(cf_NIG, t=T, mu=0, theta=theta, sigma=sigma, kappa=kappa)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Histogram vs density vs Fourier inversion"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"qqplot(X, line=\"s\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Parameter estimation\n",
"\n",
"Since the moments at first order in $\\theta$ are the same as in the VG process, we can use the same technique to get an approximated estimate of the parameters.\n",
"\n",
"After that we can use those approximated values as initial guess for the MLE estimation."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Estimated parameters: \n",
"\n",
" c=-0.029490461790484923 \n",
" theta=-0.0703900720616428 \n",
" sigma=0.21211113318032002 \n",
" kappa=0.708309868095375\n",
"\n",
"Estimated c + theta = -0.09988053385212772\n"
]
}
],
"source": [
"sigma_mm1 = np.std(X) / np.sqrt(T)\n",
"kappa_mm1 = T * ss.kurtosis(X) / 3\n",
"theta_mm1 = np.sqrt(T) * ss.skew(X) * sigma_mm1 / (3 * kappa_mm1)\n",
"c_mm1 = np.mean(X) / T - theta_mm1\n",
"\n",
"print(\n",
" \"Estimated parameters: \\n\\n c={} \\n theta={} \\n sigma={} \\n \\\n",
"kappa={}\\n\".format(\n",
" c_mm1, theta_mm1, sigma_mm1, kappa_mm1\n",
" )\n",
")\n",
"print(\"Estimated c + theta = \", c_mm1 + theta_mm1)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
"Number of iterations performed by the optimizer: 19\n",
"MLE parameters: [ 0.0010417 -0.10092221 0.19980898 0.49902213]\n",
"CPU times: user 43.9 s, sys: 939 ms, total: 44.8 s\n",
"Wall time: 44.8 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"\n",
"def log_likely_NIG(x, data, T):\n",
" return (-1) * np.sum(np.log(NIG_density(data, T, x[0], x[1], x[2], x[3])))\n",
"\n",
"\n",
"result_NIG = minimize(\n",
" log_likely_NIG,\n",
" x0=[c_mm1, theta_mm1, sigma_mm1, kappa_mm1],\n",
" method=\"L-BFGS-B\",\n",
" args=(X, T),\n",
" tol=1e-8,\n",
" bounds=[[-1, 1], [-1, -1e-15], [1e-15, 2], [1e-15, None]],\n",
")\n",
"\n",
"print(result_NIG.message)\n",
"print(\"Number of iterations performed by the optimizer: \", result_NIG.nit)\n",
"print(\"MLE parameters: \", result_NIG.x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"[1] Rama Cont and Peter Tankov (2003) \"Financial Modelling with Jump Processes\", Chapman and Hall/CRC; 1 edition. \n",
"\n",
"[2] Madan D. and Seneta E. (1990) \"The Variance Gamma model for share market returns\". The journal of Business. 63(4), 511-524 \n",
"\n",
"[3] Barndorff-Nielsen, Ole E. (1998) \"Processes of Normal Inverse Gaussian type\" Finance and Stochastics 2, 41-68."
]
}
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================================================
FILE: 2.1 Black-Scholes PDE and sparse matrices.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Black-Scholes PDE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[*Black Scholes PDE, WIKI*](https://en.wikipedia.org/wiki/Black%E2%80%93Scholes_equation). \n",
"\n",
"In this notebook I want to show how to solve the famous Black-Scholes (BS) Partial Differential Equation (PDE).\n",
"\n",
"\\begin{equation}\n",
"\\frac{\\partial V(t,s)}{\\partial t} \n",
" + r\\,s \\frac{\\partial V(t,s)}{\\partial s}\n",
" + \\frac{1}{2} \\sigma^2 s^2 \\frac{\\partial^2 V(t,s)}{\\partial s^2} - r V(t,s) = 0.\n",
"\\end{equation}\n",
"\n",
"I decided to use a fully implicit method, because it is an efficient and stable method. The illustrated approach can be used to solve more complex problems, such as the pricing of different types of derivatives (barrier options, digital options, American options, etc) with small modifications.\n",
"\n",
"## Contents\n",
" - [The BS PDE](#sec1)\n",
" - [PDE in log-variables](#sec1.1)\n",
" - [Implicit discretization](#sec1.2)\n",
" - [Introduction to sparse matrices](#sec2)\n",
" - [Numerical solution of the PDE](#sec3)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import cm\n",
"\n",
"%matplotlib inline\n",
"\n",
"from scipy import sparse\n",
"from scipy.sparse.linalg import splu\n",
"from scipy.sparse.linalg import spsolve\n",
"\n",
"from IPython.display import display\n",
"import sympy\n",
"\n",
"sympy.init_printing()\n",
"\n",
"\n",
"def display_matrix(m):\n",
" display(sympy.Matrix(m))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## The BS PDE\n",
"\n",
"Ok ok... *Nihil novi sub sole*. The PDE derivation can be found in every textbooks... I know. \n",
"\n",
"I suggest the interested reader to have a look at [1] for a clear exposition. If you are already familiar with these concepts, maybe it can be interesting for you to have a look at the notebook **A3**, where I present the derivation of the P(I)DE for general exponential Lévy processes. The BS PDE appears as a special case."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## PDE in log-variables\n",
"\n",
"Let us consider the geometric Brownian motion (GBM), described by the SDE: \n",
"\n",
"\\begin{equation}\n",
"\t \\frac{dS_t}{S_t} = \\mu dt + \\sigma dW_t.\n",
"\\end{equation}\n",
"\n",
"Let $V(t,s)$ be the value of a European call option. \n",
"\n",
"By the **martingale pricing theory**, in an arbitrage-free market there exists an equivalent martingale measure $\\mathbb{Q}$ such that the discounted option price is a $\\mathbb{Q}$-martingale. (Remember that under $\\mathbb{Q}$ the drift $\\mu$ is replaced by the risk free drift $r$.)\n",
"\n",
"The PDE for the option price is:\n",
"\n",
"\\begin{equation}\n",
" \\frac{\\partial V(t,s)}{\\partial t} + \\mathcal{L}^{S} V(t,s) -r V(t,s) = 0 \n",
"\\end{equation}\n",
"\n",
"where $\\mathcal{L}^{S}$ is the infinitesimal generator of $\\{S_t\\}_{t\\in[t_0,T]}$. \n",
"In the Black-Scholes model, the measure $\\mathbb{Q}$ is unique and the infinitesimal generator under this measure is:\n",
"\n",
"\\begin{equation}\n",
" \\mathcal{L}^{BS} V(t,s) = \\; r s \\frac{\\partial V(t,s)}{\\partial s}\n",
"+ \\frac{1}{2} \\sigma^2 s^2 \\frac{\\partial^2 V(t,s)}{\\partial s^2},\n",
"\\end{equation}\n",
"\n",
"In order to simplify the computations, it is better to pass to the log-variable $x = \\log s$. This change corresponds to the following change in the operators:\n",
"\n",
"\\begin{equation}\n",
"s \\frac{\\partial}{\\partial s} = \\frac{\\partial}{\\partial x}, \\hspace{2em} \n",
"s^2 \\frac{\\partial^2}{\\partial s^2} = \\frac{\\partial^2}{\\partial x^2} - \\frac{\\partial}{\\partial x} . \n",
"\\end{equation}\n",
"\n",
"In log-variables the BS PDE is:\n",
"\n",
"$$ \\frac{\\partial V(t,x)}{\\partial t} \n",
" + \\biggl( r -\\frac{1}{2}\\sigma^2 \\biggr) \\frac{\\partial V(t,x)}{\\partial x}\n",
" + \\frac{1}{2} \\sigma^2 \\frac{\\partial^2 V(t,x)}{\\partial x^2} - r V(t,x) = 0. $$\n",
"\n",
"For an option with strike $K$ and maturity $T$, the boundary conditions are:\n",
"\n",
"CALL:\n",
" - Terminal:\n",
" $$ V(T,x) = \\max(e^x-K,0), $$\n",
" - Lateral:\n",
" $$ V(t, x) \\underset{x \\to -\\infty}{=} 0 \\quad \\text{and} \\quad V(t, x) \\underset{x \\to \\infty}{\\sim} e^x - Ke^{-r(T-t)}. $$\n",
"\n",
"PUT:\n",
" - Terminal:\n",
" $$ V(T,x) = \\max(K-e^x,0), $$\n",
" - Lateral:\n",
" $$ V(t, x) \\underset{x \\to -\\infty}{\\sim} Ke^{-r(T-t)} \\quad \\text{and} \\quad V(t, x) \\underset{x \\to \\infty}{=} 0. $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Derivative approximation\n",
"\n",
"Finite difference methods are a technique for obtaining numerical solutions of PDEs. \n",
"The idea underlying finite-difference methods is to replace the partial derivatives occurring in the PDE by finite difference approximations. If we assume that $V$ is a smooth function, we can use the Taylor series expansion near the point of interest.\n",
"For a $\\Delta t > 0$ we can write\n",
"\n",
"\\begin{equation}\n",
" V(t+\\Delta t,x) \\approx V(t,x) + \\frac{\\partial V(t,x)}{\\partial t} \\Delta t + \\frac{1}{2} \\frac{\\partial^2 V(t,x)}{\\partial t^2} \\Delta t^2 + \\mathcal{O}(\\Delta t^3).\n",
"\\end{equation}\n",
"\n",
"\\begin{equation}\n",
" V(t-\\Delta t,x) \\approx V(t,x) - \\frac{\\partial V(t,x)}{\\partial t} \\Delta t + \\frac{1}{2} \\frac{\\partial^2 V(t,x)}{\\partial t^2} \\Delta t^2 + \\mathcal{O}(\\Delta t^3).\n",
"\\end{equation}\n",
"\n",
"An analogous approximation can be done for $V(t,x+\\Delta x)$ with $\\Delta x > 0$.\n",
"\n",
"If we want to approximate the partial derivative with respect to time, we obtain the following finite difference approximation\n",
"\n",
"\\begin{equation}\n",
" \\frac{\\partial V(t,x)}{\\partial t} \\approx \\frac{V(t+\\Delta t,x) - V(t,x)}{\\Delta t} + \\mathcal{O}(\\Delta t)\n",
"\\end{equation}\n",
"\n",
"also called **forward difference**, since the differencing is in the forward $t$ direction.\n",
"We can also consider the **backward difference**\n",
"\n",
"\\begin{equation}\n",
" \\frac{\\partial V(t,x)}{\\partial t} \\approx \\frac{V(t,x) - V(t-\\Delta t,x)}{\\Delta t} + \\mathcal{O}(\\Delta t)\n",
"\\end{equation}\n",
"\n",
"and the **central difference**\n",
"\n",
"\\begin{equation}\n",
" \\frac{\\partial V(t,x)}{\\partial t} \\approx \\frac{V(t+\\Delta t,x) - V(t-\\Delta t,x)}{2 \\Delta t} + \\mathcal{O}(\\Delta t^2).\n",
"\\end{equation}\n",
"\n",
"The use of the forward and backward difference approximation leads to the **explicit** and **implicit** finite difference \n",
"schemes respectively. The central difference is not used for the time variable because it leads to bad numerical schemes.\n",
"But it is common to use it for the space variable.\n",
"\n",
"For second order derivatives, such as $\\partial^2 V(t,x)/\\partial x^2$, we can use the symmetric central difference approximation for a $\\Delta x > 0$:\n",
"\n",
"\\begin{equation}\n",
" \\frac{\\partial^2 V(t,x)}{\\partial x^2} \\approx \\frac{V(t,x+\\Delta x) + V(t,x-\\Delta x) - 2V(t,x)}{ \\Delta x^2} + \\mathcal{O}(\\Delta x^2).\n",
"\\end{equation}\n",
"\n",
"If you need more details, have a look at [2]."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Implicit discretization\n",
"\n",
"\n",
"First we have to restrict the theoretical infinite domain to the finite region $[t_0,T]\\, \\times \\, [A_1,A_2]$, with $A_1 < A_2$. \n",
"\n",
"The next step is to replace $[t_0,T]\\times [A_1,A_2]$ by a discrete grid:\n",
"\n",
"For $n = 0,1, ... N \\in \\mathbb{N}$, define the discrete time step $ \\Delta t = \\frac{T - t_0}{N} $ such that\n",
"$t_n = t_0 + n \\Delta t$. For $i = 0,1, ... M \\in \\mathbb{N}$, define the discrete space step $ \\Delta x = \\frac{A_2 - A_1}{M} $ such that\n",
"$x_i = A_1 + i \\Delta x$.\n",
"\n",
"The grid is divided into equally spaced nodes of distance $\\Delta x$ in the x-axis, and of distance $\\Delta t$ in the t-axis.\n",
"\n",
"The mesh points have the form $(t_0 + n \\Delta t, A_1 + i \\Delta x)$.\n",
"At this point we concern ourselves only with the values of $V(t,x)$ on the mesh nodes. We call \n",
"\n",
"$$ V(t_0 + n \\Delta t, A_1 + i \\Delta x) = V^n_i .$$\n",
"\n",
"We apply the backward discretization (implicit scheme) for the time derivative, and a central discretization for the first order space derivative. \n",
"\n",
"We are interested in the value of V at time $t_0$. We know the values $V^N$ corresponding to the terminal conditions. The algorithm consists in finding the values $V^n$ given the knowledge of the values $V^{n+1}$. \n",
"\n",
"**Comment:**\n",
" A common practice is to invert the time flow i.e. introducing a new time variable $\\tau = -t$ and change the variables in the PDE. It follows that the terminal conditions become initial conditions, and the problem becomes a forward problem. However, I am not using this change of variable in this notebook.\n",
"\n",
"The discretized equation becomes\n",
"\n",
"$$ \\begin{aligned}\n",
"\\frac{V^{n+1}_{i} -V^{n}_{i}}{\\Delta t} + \n",
"(r-\\frac{1}{2}\\sigma^2) \\frac{V^{n}_{i+1} -V^{n}_{i-1}}{ 2 \\Delta x}\n",
"+ \\frac{1}{2} \\sigma^2 \\frac{V^{n}_{i+1} + V^{n}_{i-1} - 2 V^{n}_{i}}{\\Delta x^2} - r V^{n}_i = 0.\n",
"\\end{aligned}$$\n",
"\n",
"Rearranging the terms: \n",
"\n",
"$$ \\begin{aligned}\n",
" V^{n+1}_{i} &= V^{n}_{i} \\biggl( 1 + r\\Delta t + \\sigma^2 \\frac{\\Delta t}{\\Delta x^2} \\biggr) \\\\\n",
"& + V^{n}_{i+1} \\biggl( -(r -\\frac{1}{2}\\sigma^2)\\frac{\\Delta t}{2 \\Delta x} -\n",
"\\frac{1}{2}\\sigma^2 \\frac{\\Delta t}{\\Delta x^2} \\biggr) \\\\\n",
"& + V^{n}_{i-1} \\biggl( (r -\\frac{1}{2}\\sigma^2)\\frac{\\Delta t}{2 \\Delta x} - \n",
"\\frac{1}{2}\\sigma^2 \\frac{\\Delta t}{\\Delta x^2} \\biggr).\n",
"\\end{aligned} $$\n",
"\n",
"We can rename the coefficients such that: \n",
"\n",
"$$ V^{n+1}_{i} = a V^{n}_{i-1} + b V^{n}_{i} + c V^{n}_{i+1}, $$\n",
"\n",
"and write it in matrix form:\n",
"\n",
"$$\n",
"\\left(\n",
"\\begin{array}{c}\n",
"V^{n+1}_{1} \\\\\n",
"V^{n+1}_{2} \\\\\n",
"\\vdots \\\\\n",
"V^{n+1}_{M-2} \\\\\n",
"V^{n+1}_{M-1} \\\\\n",
"\\end{array}\n",
"\\right) = \n",
"\\underbrace{\n",
"\\left(\n",
"\\begin{array}{ccccc}\n",
"b & c & 0 & \\cdots & 0 \\\\\n",
"a & b & c & 0 & 0 \\\\\n",
"0 & \\ddots & \\ddots & \\ddots & 0 \\\\\n",
"\\vdots & 0 & a & b & c \\\\\n",
"0 & 0 & 0 & a & b \\\\\n",
"\\end{array}\n",
"\\right) }_{\\mathcal{D}} \\cdot\n",
"\\left(\n",
"\\begin{array}{c}\n",
"V^{n}_{1} \\\\\n",
"V^{n}_{2} \\\\\n",
"\\vdots \\\\\n",
"V^{n}_{M-2} \\\\\n",
"V^{n}_{M-1} \n",
"\\end{array}\n",
"\\right)\n",
"+ \\underbrace{\n",
"\\left(\n",
"\\begin{array}{c}\n",
" a V^{n}_{0} \\\\\n",
" 0 \\\\\n",
" \\vdots \\\\\n",
" 0 \\\\\n",
"c V^{n}_{M} \\\\\n",
"\\end{array}\n",
"\\right) }_{\\text{B (boundary terms)}}\n",
"$$\n",
"\n",
"The system \n",
"\n",
"$$ V^{n+1} = \\mathcal{D} V^{n} + B $$\n",
"\n",
"can be solved easily for $V^{n}$ by inverting the matrix $\\mathcal{D}$.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Introduction to sparse matrices \n",
"\n",
"A [*sparse matrix*](https://en.wikipedia.org/wiki/Sparse_matrix) is a matrix in which most of the entries are zeros.\n",
"\n",
"If most of the elements are nonzero, then the matrix is considered *dense*.\n",
"\n",
"A dense matrix is typically stored as a two-dimensional array. Each entry in the array represents an element of the matrix and is accessed by the two indices i and j.\n",
"\n",
"Since a sparse matrix is almost empthy, storing all the zeros is a waste of memory. For this reason it is more efficient to use different data structures from the usual 2D arrays. More information on these can be found in the wiki page. \n",
"\n",
"Have a look at the manual page of the **scipy.sparse** library. [link](https://docs.scipy.org/doc/scipy/reference/sparse.html).\n",
"\n",
"There are many ways to represent a sparse matrix, Scipy provides seven of them:\n",
"- Block Sparse Row (BSR)\n",
"- Coordinate list (COO)\n",
"- Compressed Sparse Column (CSC)\n",
"- Compressed Sparse Row (CSR)\n",
"- DIAgonal storage (DIA)\n",
"- Dictionary Of Keys (DOK)\n",
"- List of lists (LIL)\n",
"\n",
"Each format has its pros and cons. But we can roughly divide them in two groups: \n",
"- CSR and CSC are better for efficient matrix operations.\n",
"- All the others are better for the initial construction the sparse matrix.\n",
"\n",
"The good thing is that it is very easy to convert one representation into one other. Here I pasted some recomandations from the scipy manual page:\n",
"\n",
"*To construct a matrix efficiently, use either dok_matrix or lil_matrix. The lil_matrix class supports basic slicing and fancy indexing with a similar syntax to NumPy arrays. The COO format may also be used to efficiently construct matrices. Despite their similarity to NumPy arrays, it is strongly discouraged to use NumPy functions directly on these matrices because NumPy may not properly convert them for computations, leading to unexpected (and incorrect) results. If you do want to apply a NumPy function to these matrices, first check if SciPy has its own implementation for the given sparse matrix class, or convert the sparse matrix to a NumPy array (e.g. using the toarray() method of the class) first before applying the method.\n",
"To perform manipulations such as multiplication or inversion, first convert the matrix to either CSC or CSR format. The lil_matrix format is row-based, so conversion to CSR is efficient, whereas conversion to CSC is less so.\n",
"All conversions among the CSR, CSC, and COO formats are efficient, linear-time operations.*\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Example: Matrix multiplication and operations"
]
},
{
"cell_type": "code",
"execution_count": 3,
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"outputs": [
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0\\\\1 & 3 & 0 & 0 & 0\\\\1 & 1 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡0 0 0 0 0⎤\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢1 3 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢1 1 0 0 0⎥\n",
"⎢ ⎥\n",
"⎣0 0 0 0 0⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CSR representation: (ordered by rows)\n",
" (2, 0)\t1\n",
" (2, 1)\t3\n",
" (3, 0)\t1\n",
" (3, 1)\t1\n",
"CSC representation: (ordered by columns)\n",
" (2, 0)\t1\n",
" (3, 0)\t1\n",
" (2, 1)\t3\n",
" (3, 1)\t1\n"
]
}
],
"source": [
"np.random.seed(seed=44)\n",
"dense_mat = np.random.binomial(n=1, p=0.1, size=(5, 5)) # Sparse matrix stored as a dense matrix\n",
"dense_mat[2, 1] = 3\n",
"sparse_mat = sparse.csr_matrix(dense_mat) # Sparse matrix stored as a CSR sparse matrix\n",
"\n",
"display_matrix(sparse_mat.toarray())\n",
"print(\"CSR representation: (ordered by rows)\")\n",
"sympy.pprint(sparse_mat)\n",
"print(\"CSC representation: (ordered by columns)\")\n",
"sympy.pprint(sparse_mat.tocsc())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Feel free to convert the sparse matrix into a different representations and pprint it.\n",
"\n",
"Now let's see some math operations. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}0 & 0\\\\0 & 0\\\\8 & 8\\\\4 & 4\\\\0 & 0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡0 0⎤\n",
"⎢ ⎥\n",
"⎢0 0⎥\n",
"⎢ ⎥\n",
"⎢8 8⎥\n",
"⎢ ⎥\n",
"⎢4 4⎥\n",
"⎢ ⎥\n",
"⎣0 0⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}7 & 13 & 0 & 0 & 0\\\\17 & 33 & 0 & 0 & 0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡7 13 0 0 0⎤\n",
"⎢ ⎥\n",
"⎣17 33 0 0 0⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"two_dense = 2 * np.ones(shape=(5, 2), dtype=np.int32) # Example of dense matrix\n",
"range_dense = np.reshape(np.array([i for i in range(1, 11)]), (2, 5)) # Second example of dense matrix\n",
"\n",
"# The product is a dense matrix\n",
"display_matrix(sparse_mat @ two_dense)\n",
"display_matrix(range_dense @ sparse_mat)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Depending on what you prefer to use, you can call *toarray* or *todense* to convert the sparse matrix in a np.array or in a np.matrix. \n",
"\n",
"Be careful because the the **'*'** operator represents the element-wise product for numpy arrays and the scalar product for the numpy matrices.\n",
"\n",
"I prefer to use the numpy arrays, and therefore I will use the **@** operator for the scalar product operation. This operator handles very well hybrid multiplications between sparse and dense matrices. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n"
]
}
],
"source": [
"print(type(sparse_mat.toarray()))\n",
"print(type(sparse_mat.todense()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Example: Diagonal matrices\n",
"A diagonal matrix can be initialized as follows, by giving as the second argument a list of diagonals to be filled, and as first argument the list of values in the respective diagonal. (enter ```sparse.diags??``` in a cell for more information)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Ix1Lf0y57D/+wEu40Rnytm7b92EyRaOnTyuRFbJ/cQcHIfFVJiEvNOIuzzvKnUVHpWT1jp0lax8/8A8bNJYoXrtWVaOmlbJ+riVe7QOElymDoGj1jJye949/gYe1VtPRs2+dyUGx3S7jz0IXxn/me/zSH0e72erY9nRM7xXsVLT3b9lkcFIwk4c7dv+jgfcAHm7bfYvluiHD807nttzAtH1IOGWldtNQ6tO3t3L7UsjxjSSJMmrwPao9Cwp29ipayCLC22CP+5kVL4XjoYBlud6u8wqQS7hxZ10oMiIE6GIBTlHBnHaZQLsSAGIgxkKUPKpYBpYkBMSAGQgzIQYWYUbwYEAPFGZCDKm4CZUAMiIEQA3JQIWYULwbEQHEGpp8ZcGKteYY4qJGTTimIATEgBi7OAPzLR1x0G7owHRTHwb0KHOByjFwAq6LFgBjIz4AJqSze+f+FtLFmljGmEgAAAABJRU5ErkJggg==",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0 & 5.0 & 0 & 0 & 0\\\\-2.0 & 1.0 & 5.0 & 0 & 0\\\\0 & -2.0 & 1.0 & 5.0 & 0\\\\0 & 0 & -2.0 & 1.0 & 5.0\\\\0 & 0 & 0 & -2.0 & 1.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0 5.0 0 0 0 ⎤\n",
"⎢ ⎥\n",
"⎢-2.0 1.0 5.0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢ 0 -2.0 1.0 5.0 0 ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 -2.0 1.0 5.0⎥\n",
"⎢ ⎥\n",
"⎣ 0 0 0 -2.0 1.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The representation type of the diagonal matrix is: \n",
" (1, 0)\t-2.0\n",
" (2, 1)\t-2.0\n",
" (3, 2)\t-2.0\n",
" (4, 3)\t-2.0\n",
" (0, 0)\t1.0\n",
" (1, 1)\t1.0\n",
" (2, 2)\t1.0\n",
" (3, 3)\t1.0\n",
" (4, 4)\t1.0\n",
" (0, 1)\t5.0\n",
" (1, 2)\t5.0\n",
" (2, 3)\t5.0\n",
" (3, 4)\t5.0\n"
]
}
],
"source": [
"diagonal = sparse.diags([-2, 1, 5], [-1, 0, 1], shape=(5, 5))\n",
"display_matrix(diagonal.toarray())\n",
"print(\"The representation type of the diagonal matrix is: \", type(diagonal))\n",
"sympy.pprint(diagonal)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Example: Solving the linear system: \n",
"$$A x = b$$\n",
"\n",
"- **Using spsolve**\n",
"\n",
"- **Using splu** (based on LU decomposition [link](https://en.wikipedia.org/wiki/LU_decomposition) )\n",
"\n",
"A *SparseEfficiencyWarning* is raised if the sparse matrix is not converted in the most efficient format.\n",
"So it is better to convert from \"dia\" to \"csr\"."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The unknown 'x' is exactly what we expected:\n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0\\\\2.0\\\\3.0\\\\4.0\\\\5.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0⎤\n",
"⎢ ⎥\n",
"⎢2.0⎥\n",
"⎢ ⎥\n",
"⎢3.0⎥\n",
"⎢ ⎥\n",
"⎢4.0⎥\n",
"⎢ ⎥\n",
"⎣5.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0\\\\2.0\\\\3.0\\\\4.0\\\\5.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0⎤\n",
"⎢ ⎥\n",
"⎢2.0⎥\n",
"⎢ ⎥\n",
"⎢3.0⎥\n",
"⎢ ⎥\n",
"⎢4.0⎥\n",
"⎢ ⎥\n",
"⎣5.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"A = 3 * sparse.eye(5)\n",
"x = np.array([[i] for i in range(1, 6)])\n",
"b = A @ x\n",
"\n",
"print(\"The unknown 'x' is exactly what we expected:\")\n",
"# Solution by spsolve\n",
"display_matrix(spsolve(A.tocsr(), b))\n",
"\n",
"# Solution by LU decomposition\n",
"A_LU = splu(A.tocsc())\n",
"display_matrix(A_LU.solve(b))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Numerical solution of the PDE\n",
"\n",
"Ok, let us solve the vector equation derived in the section [\"Implicit discretization\"](#sec1.2).\n",
"\n",
"In this case we consider a call option with strike $K$, maturity $T$. The stock price $S_0$ is not relevant for the algorithm. We will use it in the end to compute the value of the option for $S_0$.\n",
"\n",
"A common practice is to choose the computational region between $3K$ and $K/3$. Then we have $A_1 = \\log K/3$ and $A_2 = \\log 3K$.\n",
"\n",
"The values of the parameter are:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"r = 0.1\n",
"sig = 0.2\n",
"S0 = 100\n",
"X0 = np.log(S0)\n",
"K = 100\n",
"Texpir = 1"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"Nspace = 3000 # M space steps\n",
"Ntime = 2000 # N time steps\n",
"\n",
"S_max = 3 * float(K)\n",
"S_min = float(K) / 3\n",
"\n",
"x_max = np.log(S_max) # A2\n",
"x_min = np.log(S_min) # A1"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"x, dx = np.linspace(x_min, x_max, Nspace, retstep=True) # space discretization\n",
"T, dt = np.linspace(0, Texpir, Ntime, retstep=True) # time discretization\n",
"Payoff = np.maximum(np.exp(x) - K, 0) # Call payoff"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"V = np.zeros((Nspace, Ntime)) # grid initialization\n",
"offset = np.zeros(Nspace - 2) # vector to be used for the boundary terms\n",
"\n",
"V[:, -1] = Payoff # terminal conditions\n",
"V[-1, :] = np.exp(x_max) - K * np.exp(-r * T[::-1]) # boundary condition\n",
"V[0, :] = 0 # boundary condition"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# construction of the tri-diagonal matrix D\n",
"sig2 = sig * sig\n",
"dxx = dx * dx\n",
"\n",
"a = (dt / 2) * ((r - 0.5 * sig2) / dx - sig2 / dxx)\n",
"b = 1 + dt * (sig2 / dxx + r)\n",
"c = -(dt / 2) * ((r - 0.5 * sig2) / dx + sig2 / dxx)\n",
"\n",
"D = sparse.diags([a, b, c], [-1, 0, 1], shape=(Nspace - 2, Nspace - 2)).tocsc()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Backward iteration\n",
"for i in range(Ntime - 2, -1, -1):\n",
" offset[0] = a * V[0, i]\n",
" offset[-1] = c * V[-1, i]\n",
" V[1:-1, i] = spsolve(D, (V[1:-1, i + 1] - offset))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comment:\n",
"\n",
"You may be tempted to think: \n",
"\n",
"*Why not calculating the inverse of the matrix **D**? Something like this:*\n",
"\n",
"```python\n",
"from scipy.sparse.linalg import inv\n",
"Dinv = inv(D)\n",
"# and inside the block of the backward iteration substitute\n",
"V[1:-1,i] = Dinv * (V[1:-1,i+1] - offset)\n",
"```\n",
"\n",
"Well... you can try... It works, but it is very slow! \n",
"\n",
"The reason is that **inv** computes the sparse inverse of A (using the same spsolve method). However the inverse of a tridiagonal matrix, in general, is not sparse!! \n",
"For this reason, all the efficiency gained by using scipy.sparse is lost.\n",
"\n",
"We will see very soon that the right approach is instead to use **splu**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Here we are!\n",
"\n",
"we can now find the value for $S_0 = 100$ and plot the curve at $t_0$."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13.269144076064563\n"
]
}
],
"source": [
"# finds the option at S0\n",
"oPrice = np.interp(X0, x, V[:, 0])\n",
"print(oPrice)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"S = np.exp(x)\n",
"fig = plt.figure(figsize=(15, 6))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122, projection=\"3d\")\n",
"\n",
"ax1.plot(S, Payoff, color=\"blue\", label=\"Payoff\")\n",
"ax1.plot(S, V[:, 0], color=\"red\", label=\"BS curve\")\n",
"ax1.set_xlim(60, 170)\n",
"ax1.set_ylim(0, 50)\n",
"ax1.set_xlabel(\"S\")\n",
"ax1.set_ylabel(\"price\")\n",
"ax1.legend(loc=\"upper left\")\n",
"ax1.set_title(\"BS price at t=0\")\n",
"\n",
"X, Y = np.meshgrid(T, S)\n",
"ax2.plot_surface(Y, X, V, cmap=cm.ocean)\n",
"ax2.set_title(\"BS price surface\")\n",
"ax2.set_xlabel(\"S\")\n",
"ax2.set_ylabel(\"t\")\n",
"ax2.set_zlabel(\"V\")\n",
"ax2.view_init(30, -100) # this function rotates the 3d plot\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the BS_pricer class there are more methods implemented. Let us have a look:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from FMNM.Parameters import Option_param\n",
"from FMNM.Processes import Diffusion_process\n",
"from FMNM.BS_pricer import BS_pricer\n",
"\n",
"# Creates the object with the parameters of the option\n",
"opt_param = Option_param(S0=100, K=100, T=1, exercise=\"European\", payoff=\"call\")\n",
"\n",
"# Creates the object with the parameters of the process\n",
"diff_param = Diffusion_process(r=0.1, sig=0.2)\n",
"\n",
"# Creates the object of the pricer\n",
"BS = BS_pricer(opt_param, diff_param)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default the method PDE_price uses the LU solver. Let us compare the execution times of three implementations:\n",
"- splu\n",
"- Thomas: I wrote a wrapper of a LAPACK function. [code](./src/FMNM/Solvers.py)\n",
"- spsolve\n",
"- SOR: I wrote the SOR solver in cython, such that it would be easily integrated with the python code. However it is still very slow. [Cython code](./src/FMNM/cython/solvers.pyx)\n",
" From this cell it is also not possible to modify the SOR parameters (if you want to modify it, you need to enter the *BS.PDE_price* method in [BS_pricer class](./src/FMNM/BS_pricer.py) ). \n",
" \n",
"The SOR and Thomas algorithms are explained in the notebook **A1** and the code optimization for the SOR algorithm is the topic of the notebook **A2**. Further comparisons (with cython and C code) are in the notebook A2."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Price: 13.269418 Time: 0.404955\n",
"Price: 13.269418 Time: 0.651605\n",
"Price: 13.269418 Time: 3.438750\n",
"Price: 13.269418 Time: 24.499912\n"
]
}
],
"source": [
"print(\"Price: {0:.6f} Time: {1:.6f}\".format(*BS.PDE_price((5000, 4000), Time=True, solver=\"splu\")))\n",
"print(\"Price: {0:.6f} Time: {1:.6f}\".format(*BS.PDE_price((5000, 4000), Time=True, solver=\"Thomas\")))\n",
"print(\"Price: {0:.6f} Time: {1:.6f}\".format(*BS.PDE_price((5000, 4000), Time=True, solver=\"spsolve\")))\n",
"print(\"Price: {0:.6f} Time: {1:.6f}\".format(*BS.PDE_price((5000, 4000), Time=True, solver=\"SOR\")))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Thomas and SPLU are super fast!!!**\n",
"\n",
"\n",
"A put option can be priced by changing the terminal and boudary conditions. You can read the implementation code inside \"BS_pricer\"."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"price: 3.7532654035665085\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"put_param = Option_param(S0=100, K=100, T=1, exercise=\"European\", payoff=\"put\")\n",
"# Creates the object of the put pricer\n",
"put = BS_pricer(put_param, diff_param)\n",
"print(\"price: \", put.PDE_price((5000, 4000)))\n",
"put.plot(axis=[50, 140, 0, 50])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### References\n",
"\n",
"[1] Björk Tomas (2009). Arbitrage theory in continuous time, Oxford Financial Press. \n",
"\n",
"[2] Wilmott Paul (1994). Option pricing: Mathematical models and computation. Oxford Financial Press."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: 2.2 Exotic options.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Binary, barrier and Asian options\n",
"\n",
"In this notebook we want to study the problem of pricing some exotic derivatives by solving their associated PDE.\n",
"\n",
"I will use the same framework, based on the implicit discretization, presented in the Black-Scholes PDE notebook **2.1**.\n",
"The results are then compared with the values obtained from other numerical methods.\n",
"\n",
"## Contents\n",
" - [Digital/Binary options](#sec1)\n",
" - [Numerical solution](#sec1.1)\n",
" - [Barrier options](#sec2)\n",
" - [Up and Out CALL -- Down and In CALL](#sec2.1)\n",
" - [Asian options](#sec3)\n",
" - [Numerical solution: Monte Carlo](#sec3.1)\n",
" - [Fixed strike](#sec3.2)\n",
" - [Floating strike](#sec3.3)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy.stats as ss\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.gridspec as gridspec\n",
"\n",
"%matplotlib inline\n",
"\n",
"from scipy import sparse\n",
"from scipy.sparse.linalg import splu\n",
"from scipy.sparse.linalg import spsolve"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Option variables\n",
"S0 = 100.0 # spot stock price\n",
"K = 100.0 # strike\n",
"T = 1.0 # maturity\n",
"r = 0.1 # risk free rate\n",
"sig = 0.2 # diffusion coefficient or volatility\n",
"X0 = np.log(S0) # logprice\n",
"B = 120 # Barrier 1\n",
"BB = 90 # Barrier 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Binary/digital options\n",
"\n",
"For more info have a look at the [wiki page](https://en.wikipedia.org/wiki/Binary_option)\n",
"\n",
"Let us consider the case of a **cash or nothing CALL** binary option:\n",
"\n",
"\\begin{equation}\n",
"V(t,s) = e^{- r (T-t)} \\mathbb{E}^{\\mathbb{Q}} \\bigl[ \\mathbb{1}_{\\{S_T > K\\}} \\, \\big| \\, S_t=s \\bigr].\n",
"\\end{equation}\n",
"\n",
"where, as usual, $\\mathbb{Q}$ is the risk neutral measure."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Numerical solution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Closed formula"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The price of the ATM digital call option by closed formula is: 0.5930501164033175\n"
]
}
],
"source": [
"d2 = (np.log(S0 / K) + (r - sig**2 / 2) * T) / (sig * np.sqrt(T))\n",
"N2 = ss.norm.cdf(d2)\n",
"closed_digital = np.exp(-r * T) * N2\n",
"print(\"The price of the ATM digital call option by closed formula is: \", closed_digital)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Monte Carlo"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The price of the ATM digital call option by Monte Carlo is: 0.593071342432063\n",
"with standard error: 9.615080192805355e-05\n"
]
}
],
"source": [
"np.random.seed(seed=42)\n",
"N_simulation = 20000000\n",
"W = (r - sig**2 / 2) * T + ss.norm.rvs(loc=0, scale=sig, size=N_simulation)\n",
"S_T = S0 * np.exp(W)\n",
"\n",
"MC_digital = np.exp(-r * T) * np.mean(S_T > K)\n",
"digital_std_err = np.exp(-r * T) * ss.sem(S_T > K)\n",
"print(\"The price of the ATM digital call option by Monte Carlo is: \", MC_digital)\n",
"print(\"with standard error: \", digital_std_err)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### PDE method:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The price of the ATM digital call option by PDE is: 0.5930462329429435\n"
]
}
],
"source": [
"Nspace = 6000 # M space steps\n",
"Ntime = 6000 # N time steps\n",
"S_max = 3 * float(K)\n",
"S_min = float(K) / 3\n",
"x_max = np.log(S_max) # A2\n",
"x_min = np.log(S_min) # A1\n",
"\n",
"x, dx = np.linspace(x_min, x_max, Nspace, retstep=True) # space discretization\n",
"T_array, dt = np.linspace(0, T, Ntime, retstep=True) # time discretization\n",
"Payoff = np.where(np.exp(x) > K, 1, 0) # Binary payoff\n",
"\n",
"V = np.zeros((Nspace, Ntime)) # grid initialization\n",
"offset = np.zeros(Nspace - 2) # vector to be used for the boundary terms\n",
"\n",
"V[:, -1] = Payoff # terminal conditions\n",
"V[-1, :] = 1 # boundary condition\n",
"V[0, :] = 0 # boundary condition\n",
"\n",
"# construction of the tri-diagonal matrix D\n",
"sig2 = sig * sig\n",
"dxx = dx * dx\n",
"a = (dt / 2) * ((r - 0.5 * sig2) / dx - sig2 / dxx)\n",
"b = 1 + dt * (sig2 / dxx + r)\n",
"c = -(dt / 2) * ((r - 0.5 * sig2) / dx + sig2 / dxx)\n",
"D = sparse.diags([a, b, c], [-1, 0, 1], shape=(Nspace - 2, Nspace - 2)).tocsc()\n",
"DD = splu(D)\n",
"\n",
"# Backward iteration\n",
"for i in range(Ntime - 2, -1, -1):\n",
" offset[0] = a * V[0, i]\n",
" offset[-1] = c * V[-1, i]\n",
" V[1:-1, i] = DD.solve(V[1:-1, i + 1] - offset)\n",
"\n",
"# finds the option at S0\n",
"oPrice = np.interp(X0, x, V[:, 0])\n",
"print(\"The price of the ATM digital call option by PDE is: \", oPrice)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plots"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"S = np.exp(x)\n",
"fig = plt.figure(figsize=(18, 6))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122, projection=\"3d\")\n",
"ax1.plot(S, Payoff, color=\"blue\", label=\"Payoff\")\n",
"ax1.plot(S, V[:, 0], color=\"red\", label=\"Binary curve\")\n",
"ax1.set_xlim(60, 200)\n",
"ax1.set_ylim(0, 1.1)\n",
"ax1.set_xlabel(\"S\")\n",
"ax1.set_ylabel(\"V\")\n",
"ax1.legend(loc=\"upper left\")\n",
"ax1.set_title(\"Curve at t=0\")\n",
"\n",
"X, Y = np.meshgrid(T_array, S)\n",
"ax2.plot_surface(Y, X, V)\n",
"ax2.set_title(\"Digital option surface\")\n",
"ax2.set_xlabel(\"S\")\n",
"ax2.set_ylabel(\"t\")\n",
"ax2.set_zlabel(\"V\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Barrier options\n",
"\n",
"For more info have a look at the [wiki page](https://en.wikipedia.org/wiki/Barrier_option)\n",
"\n",
"Let us consider the case of an **Up and Out CALL** European option:\n",
"\n",
"\\begin{equation}\n",
"V(t,s) = e^{- r (T-t)} \\mathbb{E}^{\\mathbb{Q}} \\bigl[ (S_T - K)^+ \\mathbb{1}_{\\{M_T < B\\}} \\, \\big| \\, S_t=s \\bigr].\n",
"\\end{equation}\n",
"\n",
"where I introduced the running max:\n",
"\n",
"$$ M_T = \\max_{0\\leq t \\leq T} S_t. $$\n",
"\n",
"The parameter $B$ is the barrier. \n",
"We have to assume that $B > K$, otherwise the payoff would be always zero. \n",
"\n",
"We can also consider the **Down and In CALL** European option:\n",
"\n",
"\\begin{equation}\n",
"V(t,s) = e^{- r (T-t)} \\mathbb{E}^{\\mathbb{Q}} \\bigl[ (S_T - K)^+ \\mathbb{1}_{\\{\\tilde M_T \\leq B\\}} \\, \\big| \\, S_t=s \\bigr].\n",
"\\end{equation}\n",
"\n",
"where I introduced the running min:\n",
"\n",
"$$ \\tilde M_T = \\min_{0\\leq t \\leq T} S_t. $$\n",
"\n",
"In this case the option expires worthless if the barrier is not triggered. If instead the barrier is hit, the option becomes a vanilla option.\n",
"\n",
"The numerical methods for the **Down and Out CALL** and **Up and In CALL** are straightforward modifications of the following numerical methods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Up and Out CALL -- Down and In CALL"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Closed formula:\n",
"\n",
"I took the following formula from Chapter 7.3.3 of the reference book [3] and from [4]. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"d1 = lambda t, s: 1 / (sig * np.sqrt(t)) * (np.log(s) + (r + sig**2 / 2) * t)\n",
"d2 = lambda t, s: 1 / (sig * np.sqrt(t)) * (np.log(s) + (r - sig**2 / 2) * t)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"closed_barrier_u = (\n",
" S0 * (ss.norm.cdf(d1(T, S0 / K)) - ss.norm.cdf(d1(T, S0 / B)))\n",
" - np.exp(-r * T) * K * (ss.norm.cdf(d2(T, S0 / K)) - ss.norm.cdf(d2(T, S0 / B)))\n",
" - B * (S0 / B) ** (-2 * r / sig**2) * (ss.norm.cdf(d1(T, B**2 / (S0 * K))) - ss.norm.cdf(d1(T, B / S0)))\n",
" + np.exp(-r * T)\n",
" * K\n",
" * (S0 / B) ** (-2 * r / sig**2 + 1)\n",
" * (ss.norm.cdf(d2(T, B**2 / (S0 * K))) - ss.norm.cdf(d2(T, B / S0)))\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The price of the Up and Out call option by closed formula is: 1.1789018151004917\n"
]
}
],
"source": [
"print(\"The price of the Up and Out call option by closed formula is: \", closed_barrier_u)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"closed_barrier_d = S0 * (BB / S0) ** (1 + 2 * r / sig**2) * (ss.norm.cdf(d1(T, BB**2 / (S0 * K)))) - np.exp(\n",
" -r * T\n",
") * K * (BB / S0) ** (-1 + 2 * r / sig**2) * (ss.norm.cdf(d2(T, BB**2 / (S0 * K))))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The price of the Down and In call option by closed formula is: 2.0364883889158847\n"
]
}
],
"source": [
"print(\"The price of the Down and In call option by closed formula is: \", closed_barrier_d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Monte Carlo:\n",
"\n",
"Here we use the correction proposed in [5] to adjust the Monte Carlo price of the Barrier option problems.\n",
"\n",
"We call $V_d$ the discete option price and $V$ the continuous option price. The two satisfy the relation:\n",
"\n",
"$$ V_d(B) \\, = \\, V(B e^{ \\pm \\beta_1 \\sigma \\sqrt{\\Delta t}} ) $$\n",
"\n",
"where $+$ for an up option and $-$ for a down option.\n",
"\n",
"The parameter $\\beta_1 = -\\frac{\\zeta(1/2)}{\\sqrt{2\\pi}} \\approx 0.5826$ with $\\zeta$ the Riemann Zeta function. \n",
"\n",
"Here below we want to replicate the continuous option price $V$:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# correction\n",
"beta1 = 0.5826\n",
"B = B * np.exp(-beta1 * np.sqrt(dt) * sig)\n",
"BB = BB * np.exp(beta1 * np.sqrt(dt) * sig)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The price of the Up and Out call option by Monte Carlo is: 1.1722841699125972\n",
"with standard error: 0.013831717711381595\n",
"\n",
"The price of the Down and In call option by Monte Carlo is: 2.0397088652568667\n",
"with standard error: 0.028168560614225728\n",
"CPU times: user 9.92 s, sys: 1.46 s, total: 11.4 s\n",
"Wall time: 11.6 s\n"
]
}
],
"source": [
"%%time\n",
"np.random.seed(seed=42)\n",
"N = 10000\n",
"paths = 50000\n",
"dt = T / (N - 1) # time interval\n",
"\n",
"# path generation\n",
"X_0 = np.zeros((paths, 1))\n",
"increments = ss.norm.rvs(loc=(r - sig**2 / 2) * dt, scale=np.sqrt(dt) * sig, size=(paths, N - 1))\n",
"X = np.concatenate((X_0, increments), axis=1).cumsum(1)\n",
"S = S0 * np.exp(X)\n",
"\n",
"M = np.amax(S, axis=1) # maximum of each path\n",
"MM = np.amin(S, axis=1) # minimum of each path\n",
"\n",
"# Up and Out -- Discounted expected payoff\n",
"MC_barrier_u = np.exp(-r * T) * (np.maximum(S[:, -1] - K, 0) @ (M < B)) / paths\n",
"barrier_std_err_u = np.exp(-r * T) * ss.sem((np.maximum(S[:, -1] - K, 0) * (M < B)))\n",
"\n",
"print(\"The price of the Up and Out call option by Monte Carlo is: \", MC_barrier_u)\n",
"print(\"with standard error: \", barrier_std_err_u)\n",
"print()\n",
"\n",
"# Down and In\n",
"MC_barrier_d = np.exp(-r * T) * (np.maximum(S[:, -1] - K, 0) @ (MM <= BB)) / paths\n",
"barrier_std_err_d = np.exp(-r * T) * ss.sem((np.maximum(S[:, -1] - K, 0) * (MM <= BB)))\n",
"\n",
"print(\"The price of the Down and In call option by Monte Carlo is: \", MC_barrier_d)\n",
"print(\"with standard error: \", barrier_std_err_d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is well known that Monte Carlo methods are slow.\n",
"\n",
"When working with path dependent options, such as Barrier options, it is necessary to generate the entire paths. This is SUPER slow!! \n",
"\n",
"With C and Cython it is possible to improve the performance.\n",
"\n",
"There are several methods to improve the algorithm, for example by using antithetic variables [wiki](https://en.wikipedia.org/wiki/Antithetic_variates) to reduce the total variance."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### PDE method - Up and Out:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The price of the Up and Out option by PDE is: 1.1470312860184584\n"
]
}
],
"source": [
"# PDE\n",
"Nspace = 14000 # M space steps\n",
"Ntime = 10000 # N time steps\n",
"S_max = B # The max of S corresponds to the Barrier\n",
"S_min = float(K) / 3\n",
"x_max = np.log(S_max) # A2\n",
"x_min = np.log(S_min) # A1\n",
"\n",
"x, dx = np.linspace(x_min, x_max, Nspace, retstep=True) # space discretization\n",
"T_array, dt = np.linspace(0, T, Ntime, retstep=True) # time discretization\n",
"Payoff = np.maximum(np.exp(x) - K, 0) # Call payoff\n",
"\n",
"V = np.zeros((Nspace, Ntime)) # grid initialization\n",
"offset = np.zeros(Nspace - 2) # vector to be used for the boundary terms\n",
"\n",
"V[:, -1] = Payoff # terminal conditions\n",
"V[-1, :] = 0 # boundary condition\n",
"V[0, :] = 0 # boundary condition\n",
"\n",
"# construction of the tri-diagonal matrix D\n",
"sig2 = sig * sig\n",
"dxx = dx * dx\n",
"a = (dt / 2) * ((r - 0.5 * sig2) / dx - sig2 / dxx)\n",
"b = 1 + dt * (sig2 / dxx + r)\n",
"c = -(dt / 2) * ((r - 0.5 * sig2) / dx + sig2 / dxx)\n",
"D = sparse.diags([a, b, c], [-1, 0, 1], shape=(Nspace - 2, Nspace - 2)).tocsc()\n",
"DD = splu(D)\n",
"\n",
"# Backward iteration\n",
"for i in range(Ntime - 2, -1, -1):\n",
" offset[0] = a * V[0, i]\n",
" offset[-1] = c * V[-1, i]\n",
" V[1:-1, i] = DD.solve(V[1:-1, i + 1] - offset)\n",
"\n",
"# finds the option at S0\n",
"oPrice = np.interp(X0, x, V[:, 0])\n",
"print(\"The price of the Up and Out option by PDE is: \", oPrice)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plots"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"S = np.exp(x)\n",
"fig = plt.figure(figsize=(18, 6))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122, projection=\"3d\")\n",
"\n",
"ax1.plot(S, Payoff, color=\"blue\", label=\"Payoff\")\n",
"ax1.plot(S, V[:, 0], color=\"red\", label=\"Barrier curve\")\n",
"ax1.set_xlim(40, 130)\n",
"ax1.set_ylim(0, 1.5)\n",
"ax1.set_xlabel(\"S\")\n",
"ax1.set_ylabel(\"V\")\n",
"ax1.legend(loc=\"upper right\")\n",
"ax1.set_title(\"Curve at t=0\")\n",
"\n",
"X, Y = np.meshgrid(T_array, S)\n",
"ax2.plot_surface(Y, X, V)\n",
"ax2.set_title(\"Barrier option Up and Out surface\")\n",
"ax2.set_xlabel(\"S\")\n",
"ax2.set_ylabel(\"t\")\n",
"ax2.set_zlabel(\"V\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### PDE method - Down and In:\n",
"\n",
"This is a second order contract. We have to first solve for the vanilla call and then for the barrier problem. When solving the barrier problem we have to impose the terminal condition $V(T,s) = 0$ and the lower lateral condition $V(t, B) = \\text{vanilla price}$ and $V(t, s_{max}) = 0$.\n",
"\n",
"This is more an exercise, because the computation can be reduced using the property **In + Out = Vanilla**. \n",
"But let us do this exercise."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The price of the vanilla option by PDE is: 13.269458211800428\n"
]
}
],
"source": [
"# PDE for vanilla\n",
"Nspace = 5000 # M space steps\n",
"Ntime = 5000 # N time steps\n",
"S_max = K * 3 # The max of S corresponds to the Barrier\n",
"S_min = K / 3\n",
"x_max = np.log(S_max) # A2\n",
"x_min = np.log(S_min) # A1\n",
"B_log = np.log(BB)\n",
"\n",
"x_b, dx = np.linspace(B_log, x_max, Nspace, retstep=True) # space discretization starting from the barrier\n",
"x = np.concatenate((np.arange(B_log, x_min, -dx)[::-1][:-1], x_b)) # total x vector\n",
"N_tot = len(x)\n",
"\n",
"T_array, dt = np.linspace(0, T, Ntime, retstep=True) # time discretization\n",
"Payoff = np.maximum(np.exp(x) - K, 0) # Call payoff\n",
"\n",
"V = np.zeros((N_tot, Ntime)) # grid initialization\n",
"offset = np.zeros(N_tot - 2) # vector to be used for the boundary terms\n",
"\n",
"V[:, -1] = Payoff # terminal conditions\n",
"V[-1, :] = np.exp(x_max) - K * np.exp(-r * T_array[::-1]) # boundary condition\n",
"V[0, :] = 0 # boundary condition\n",
"\n",
"# construction of the tri-diagonal matrix D\n",
"sig2 = sig * sig\n",
"dxx = dx * dx\n",
"a = (dt / 2) * ((r - 0.5 * sig2) / dx - sig2 / dxx)\n",
"b = 1 + dt * (sig2 / dxx + r)\n",
"c = -(dt / 2) * ((r - 0.5 * sig2) / dx + sig2 / dxx)\n",
"D = sparse.diags([a, b, c], [-1, 0, 1], shape=(N_tot - 2, N_tot - 2)).tocsc()\n",
"DD = splu(D)\n",
"\n",
"# Backward iteration\n",
"for i in range(Ntime - 2, -1, -1):\n",
" offset[0] = a * V[0, i]\n",
" offset[-1] = c * V[-1, i]\n",
" V[1:-1, i] = DD.solve(V[1:-1, i + 1] - offset)\n",
"\n",
"# finds the option at S0\n",
"oPrice = np.interp(X0, x, V[:, 0])\n",
"print(\"The price of the vanilla option by PDE is: \", oPrice)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The price of the Down and In option by PDE is: 2.1016847221584603\n"
]
}
],
"source": [
"# PDE for barrier\n",
"VB = V[-Nspace:, :]\n",
"offset = np.zeros(Nspace - 2) # vector to be used for the boundary terms\n",
"\n",
"VB[:, -1] = 0 # terminal condition\n",
"V[-1, :] = 0 # lateral condition\n",
"\n",
"# construction of the tri-diagonal matrix D\n",
"sig2 = sig * sig\n",
"dxx = dx * dx\n",
"a = (dt / 2) * ((r - 0.5 * sig2) / dx - sig2 / dxx)\n",
"b = 1 + dt * (sig2 / dxx + r)\n",
"c = -(dt / 2) * ((r - 0.5 * sig2) / dx + sig2 / dxx)\n",
"D = sparse.diags([a, b, c], [-1, 0, 1], shape=(Nspace - 2, Nspace - 2)).tocsc()\n",
"DD = splu(D)\n",
"\n",
"# Backward iteration\n",
"for i in range(Ntime - 2, -1, -1):\n",
" offset[0] = a * VB[0, i]\n",
" offset[-1] = c * VB[-1, i]\n",
" VB[1:-1, i] = DD.solve(VB[1:-1, i + 1] - offset)\n",
"\n",
"# finds the option at S0\n",
"oPrice = np.interp(X0, x_b, VB[:, 0])\n",
"print(\"The price of the Down and In option by PDE is: \", oPrice)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plots"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"S = np.exp(x)\n",
"S_b = np.exp(x_b)\n",
"fig = plt.figure(figsize=(18, 6))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122, projection=\"3d\")\n",
"\n",
"ax1.plot(S, Payoff, color=\"blue\", label=\"Payoff\")\n",
"ax1.plot(S, V[:, 0], color=\"darkred\", label=\"Barrier option curve\")\n",
"ax1.set_xlim(50, 150)\n",
"ax1.set_ylim(0, 10)\n",
"ax1.set_xlabel(\"S\")\n",
"ax1.set_ylabel(\"V\")\n",
"ax1.legend(loc=\"upper right\")\n",
"ax1.set_title(\"Curve at t=0\")\n",
"\n",
"X, Y = np.meshgrid(T_array, S[1500:6000])\n",
"ax2.plot_surface(Y, X, V[1500:6000, :])\n",
"ax2.set_title(\"Barrier option Down and In surface\")\n",
"ax2.set_xlabel(\"S\")\n",
"ax2.set_ylabel(\"t\")\n",
"ax2.set_zlabel(\"V\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Asian options\n",
"\n",
"For more info have a look at the [wiki page](https://en.wikipedia.org/wiki/Asian_option)\n",
"\n",
"Let us consider the two cases: \n",
"- **Fixed Strike Arithmetic Average CALL** European option:\n",
"\n",
"\\begin{equation}\n",
"V(t,s) = e^{- r (T-t)} \\mathbb{E}^{\\mathbb{Q}} \\biggl[ \\biggl( A_T - K \\biggr)^+ \\, \\bigg| \\, S_t=s \\biggr].\n",
"\\end{equation}\n",
"\n",
"- **Floating Strike Arithmetic Average CALL** European option:\n",
"\n",
"\\begin{equation}\n",
"V(t,s) = e^{- r (T-t)} \\mathbb{E}^{\\mathbb{Q}} \\biggl[ \\biggl( S_T - A_T \\biggr)^+ \\, \\bigg| \\, S_t=s \\biggr].\n",
"\\end{equation}\n",
"\n",
"where the term \n",
"\n",
"$$ A_t = \\frac{1}{t} \\int_0^t S_u du $$ \n",
"\n",
"represents the continuous time **arithmetic average** of the price process.\n",
"\n",
"The PDE of the Asian option is obtain by an augmentation of the state i.e. the new state variable $A_t$ is introduced. This state variables has a dynamics described by the SDE:\n",
"\n",
"$$ dA_t = \\frac{S_t - A_t}{t} dt. $$\n",
"\n",
"The Asian PDE is:\n",
"\n",
"$$ \\frac{\\partial V(t,a,s)}{\\partial t} \n",
" + r\\,s \\frac{\\partial V(t,a,s)}{\\partial s} + \\frac{s - a}{t} \\frac{\\partial V(t,a,s)}{\\partial a}\n",
" + \\frac{1}{2} \\sigma^2 s^2 \\frac{\\partial^2 V(t,a,s)}{\\partial s^2} - r V(t,a,s) = 0. $$\n",
"\n",
"with \"unknown\" lateral boundary conditions. It is not clear how $V(t,a,s)$ behaves when $a, s \\to \\pm \\infty$. \n",
"A derivation of a similar Asian PDE can be found in Theorem 7.5.1 of [3]. \n",
"\n",
"In order to simplify the problem, it is better to consider the two-dimensional PDE obtained by a *change of numeraire* or a *change of variable* for the case of **fixed strike** (see Theorem 7.5.3 of [3]) and **floating strike** (see Section 6.1.2 of [1]) respectively. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Numerical solution\n",
"\n",
"The price of the Asian option is not known in closed form. We will compute it by using Monte Carlo and PDE methods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Monte Carlo:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The price of the fixed strike average call option by Monte Carlo is: 7.026203431709784\n",
"with standard error: 0.03800140560040339\n",
"\n",
"The price of the float strike average call option by Monte Carlo is: 7.2851688797595715\n",
"with standard error: 0.04138857308481101\n",
"\n",
"CPU times: user 9.88 s, sys: 1.48 s, total: 11.4 s\n",
"Wall time: 11.6 s\n"
]
}
],
"source": [
"%%time\n",
"np.random.seed(seed=41)\n",
"N = 10000\n",
"paths = 50000\n",
"dt = T / (N - 1) # time interval\n",
"\n",
"# path generation\n",
"X_0 = np.zeros((paths, 1))\n",
"increments = ss.norm.rvs(loc=(r - sig**2 / 2) * dt, scale=np.sqrt(dt) * sig, size=(paths, N - 1))\n",
"X = np.concatenate((X_0, increments), axis=1).cumsum(1)\n",
"S = S0 * np.exp(X)\n",
"\n",
"A = np.mean(S, axis=1) # Average of each path\n",
"# A = S[:,-1] # Uncomment to retrieve the Black-Scholes price\n",
"\n",
"# FIXED STRIKE\n",
"MC_asian_fixed = np.exp(-r * T) * np.mean(np.maximum(A - K, 0))\n",
"asian_fixed_std_err = np.exp(-r * T) * ss.sem(np.maximum(A - K, 0))\n",
"print(\"The price of the fixed strike average call option by Monte Carlo is: \", MC_asian_fixed)\n",
"print(\"with standard error: \", asian_fixed_std_err)\n",
"print()\n",
"\n",
"# FLOAT STRIKE\n",
"MC_asian_float = np.exp(-r * T) * np.mean(np.maximum(S[:, -1] - A, 0))\n",
"asian_float_std_err = np.exp(-r * T) * ss.sem(np.maximum(S[:, -1] - A, 0))\n",
"print(\"The price of the float strike average call option by Monte Carlo is: \", MC_asian_float)\n",
"print(\"with standard error: \", asian_float_std_err)\n",
"print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Fixed strike. PDE method:\n",
"\n",
"Let us consider the PDE:\n",
"\n",
"$$ \\frac{\\partial g(t,y)}{\\partial t} \n",
" + \\frac{1}{2} \\sigma^2 \\bigl(\\gamma(t) - y\\bigr)^2 \\frac{\\partial^2 g(t,y)}{\\partial y^2} = 0. $$\n",
"\n",
"with boundary conditions:\n",
"\n",
"$$ g(T,y) = y^+ $$\n",
"\n",
"$$ g(t, -\\infty) = 0 \\quad \\text{and} \\quad g(t,y) \\underset{y\\to \\infty}{\\sim} y $$\n",
"\n",
"where the function $\\gamma(t) := \\frac{1}{rT} (1 - e^{-r(T-t)})$.\n",
"This is derived in Section 7.5.3 of [3].\n",
"\n",
"The solution of this PDE is related with the option price by the relation:\n",
"\n",
"$$ V(t,a,s) := s \\, g \\bigg(t,\\frac{x}{s} \\bigg) $$\n",
"\n",
"with $x := \\frac{1}{rT} \\bigl( 1-e^{-rT} \\bigr) s - e^{-rT} K $.\n",
"\n",
"We can discretize the computational domain and call $g(t_n, y_i) := g^n_i$, and then discretize the PDE using the usual implicit method (for a review, see the notebook **2.1**). We obtain:\n",
"\n",
"$$ g^{n+1}_{i} = a^{n}_{i}\\, g^{n}_{i-1} + b^{n}_{i}\\, g^{n}_{i} + a^{n}_{i}\\, g^{n}_{i+1} $$\n",
"\n",
"with\n",
"\n",
"$$\n",
" a^{n}_{i} = - \\frac{1}{2}\\sigma^2 \\frac{\\Delta t}{\\Delta y^2} \\biggl(\\gamma(t_n) - y_i \\biggr)^2 \\quad\n",
" \\text{ and } \\quad b^{n}_{i} = 1 + \\sigma^2 \\frac{\\Delta t}{\\Delta y^2} \\biggl(\\gamma(t_n) - y_i \\biggr)^2 $$\n",
" \n",
"The coefficients depend on time and space. We have to construct a new matrix $\\mathcal{D}$ at each time step."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"def gamma(t):\n",
" return 1 / (r * T) * (1 - np.exp(-r * (T - t)))\n",
"\n",
"\n",
"def get_X0(S0):\n",
" \"\"\"function that computes the variable x defined above\"\"\"\n",
" return gamma(0) * S0 - np.exp(-r * T) * K"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The price of the Fixed Strike Asian option by PDE is: 7.0508906916853284\n"
]
}
],
"source": [
"# PDE algorithm\n",
"Nspace = 6000 # M space steps\n",
"Ntime = 6000 # N time steps\n",
"y_max = 60 # A2\n",
"y_min = -60 # A1\n",
"\n",
"y, dy = np.linspace(y_min, y_max, Nspace, retstep=True) # space discretization\n",
"T_array, dt = np.linspace(0, T, Ntime, retstep=True) # time discretization\n",
"Payoff = np.maximum(y, 0) # payoff\n",
"\n",
"G = np.zeros((Nspace, Ntime)) # grid initialization\n",
"offset = np.zeros(Nspace - 2) # vector to be used for the boundary terms\n",
"\n",
"G[:, -1] = Payoff # terminal conditions\n",
"G[-1, :] = y_max # boundary condition\n",
"G[0, :] = 0 # boundary condition\n",
"\n",
"for n in range(Ntime - 2, -1, -1):\n",
" # construction of the tri-diagonal matrix D\n",
" sig2 = sig * sig\n",
" dyy = dy * dy\n",
" a = -0.5 * (dt / dyy) * sig2 * (gamma(T_array[n]) - y[1:-1]) ** 2\n",
" b = 1 + (dt / dyy) * sig2 * (gamma(T_array[n]) - y[1:-1]) ** 2 # main diagonal\n",
" a0 = a[0]\n",
" cM = a[-1] # boundary terms\n",
" aa = a[1:]\n",
" cc = a[:-1] # upper and lower diagonals\n",
" D = sparse.diags([aa, b, cc], [-1, 0, 1], shape=(Nspace - 2, Nspace - 2)).tocsc()\n",
"\n",
" # backward computation\n",
" offset[0] = a0 * G[0, n]\n",
" offset[-1] = cM * G[-1, n]\n",
" G[1:-1, n] = spsolve(D, (G[1:-1, n + 1] - offset))\n",
"\n",
"X0 = get_X0(S0)\n",
"oPrice = S0 * np.interp(X0 / S0, y, G[:, 0])\n",
"print(\"The price of the Fixed Strike Asian option by PDE is: \", oPrice)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"S = np.linspace(70, 130, 100)\n",
"asian_PDE = S * np.interp(get_X0(S) / S, y, G[:, 0])\n",
"\n",
"fig = plt.figure(figsize=(10, 5))\n",
"plt.plot(S, np.maximum(S - K, 0), color=\"blue\", label=\"Payoff\")\n",
"plt.plot(S, asian_PDE, color=\"red\", label=\"BS curve\")\n",
"plt.xlabel(\"S\")\n",
"plt.ylabel(\"Price\")\n",
"plt.text(98, 8, \"ATM\")\n",
"plt.plot([S0, S0], [0, oPrice], \"k--\")\n",
"plt.plot([70, S0], [oPrice, oPrice], \"k--\")\n",
"plt.legend(loc=\"upper left\")\n",
"plt.title(\"Fixed Strike Asian CALL price at t=0\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Floating strike. PDE method:\n",
"\n",
"In this case we can simply make a change of variable in the three-dimensional Asian PDE introduced above:\n",
"\n",
"$$ V(t,a,s) := a\\, W(t,x) \\quad \\text{with} \\quad x=\\frac{s}{a}$$\n",
"\n",
"Let us recall how the partial derivatives change: \n",
"\n",
"$$ \\frac{\\partial}{\\partial s} = \\frac{\\partial x}{\\partial s} \\frac{\\partial}{\\partial x} = \\frac{1}{a} \\frac{\\partial}{\\partial x}, \\quad \\frac{\\partial^2}{\\partial s^2} = \\frac{1}{a^2} \\frac{\\partial^2}{\\partial x^2}, \\quad \\frac{\\partial}{\\partial a} = \\frac{\\partial x}{\\partial a} \\frac{\\partial}{\\partial x} = \\frac{-s}{a^2} \\frac{\\partial}{\\partial x}$$\n",
"\n",
"The new PDE in two dimensions is:\n",
"\n",
"$$ \\frac{\\partial W(t,x)}{\\partial t} \n",
" + r\\,x \\frac{\\partial W(t,x)}{\\partial x} + \\frac{x - 1}{t} \\biggl( W(t,x) - x \\frac{\\partial W(t,x)}{\\partial x} \\biggr)\n",
" + \\frac{1}{2} \\sigma^2 x^2 \\frac{\\partial^2 W(t,x)}{\\partial x^2} - r W(t,x) = 0. $$\n",
" \n",
"with terminal conditions:\n",
"\n",
"$$ W(T,x) = (x-1)^+ $$\n",
"\n",
"and lateral conditions:\n",
"\n",
"$$ W(t,0) = 0 \\quad \\text{ and } \\quad W(t,x) \\underset{x\\to \\infty}{\\sim} (x-1)^+ $$\n",
"\n",
"We can use the usual implicit discretization in time. \n",
"However, it turns out that the central finite-difference approximation in space creates stability problems.\n",
"Instead, the [Upwind scheme](https://en.wikipedia.org/wiki/Upwind_scheme) makes a good job here. \n",
"The discretized PDE is the following:\n",
"\n",
"$$ \\begin{aligned}\n",
"& \\frac{W^{n+1}_{i} -W^{n}_{i}}{\\Delta t} \n",
" + \\max \\biggl( r\\,x_i - x_i \\frac{x_i-1}{t_n},\\, 0 \\biggr) \\frac{W^{n}_{i+1} -W^{n}_{i}}{ \\Delta x} \n",
" + \\min \\biggl( r\\,x_i - x_i \\frac{x_i-1}{t_n},\\, 0 \\biggr) \\frac{W^{n}_{i} -W^{n}_{i-1}}{ \\Delta x} \\\\ \n",
"& + \\frac{1}{2} \\sigma^2 x_i^2 \\frac{W^{n}_{i+1} + W^{n}_{i-1} - 2 W^{n}_{i}}{\\Delta x^2} \n",
" + \\biggl( \\frac{x_i - 1}{t_n} - r \\biggr) W^{n}_i = 0. \n",
"\\end{aligned} $$"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The price of the Floating Strike Asian option by PDE is: 7.3005262375145765\n"
]
}
],
"source": [
"Nspace = 4000 # M space steps\n",
"Ntime = 7000 # N time steps\n",
"x_max = 10 # A2\n",
"x_min = 0 # A1\n",
"x, dx = np.linspace(x_min, x_max, Nspace, retstep=True) # space discretization\n",
"T_array, dt = np.linspace(0.0001, T, Ntime, retstep=True) # time discretization\n",
"Payoff = np.maximum(x - 1, 0) # Call payoff\n",
"\n",
"V = np.zeros((Nspace, Ntime)) # grid initialization\n",
"offset = np.zeros(Nspace - 2) # vector to be used for the boundary terms\n",
"V[:, -1] = Payoff # terminal conditions\n",
"V[-1, :] = x_max - 1 # boundary condition\n",
"V[0, :] = 0 # boundary condition\n",
"\n",
"for n in range(Ntime - 2, -1, -1):\n",
" # construction of the tri-diagonal matrix D\n",
" sig2 = sig * sig\n",
" dxx = dx * dx\n",
" max_part = np.maximum(x[1:-1] * (r - (x[1:-1] - 1) / T_array[n]), 0) # upwind positive part\n",
" min_part = np.minimum(x[1:-1] * (r - (x[1:-1] - 1) / T_array[n]), 0) # upwind negative part\n",
"\n",
" a = min_part * (dt / dx) - 0.5 * (dt / dxx) * sig2 * (x[1:-1]) ** 2\n",
" b = (\n",
" 1 + dt * (r - (x[1:-1] - 1) / T_array[n]) + (dt / dxx) * sig2 * (x[1:-1]) ** 2 + dt / dx * (max_part - min_part)\n",
" ) # main diagonal\n",
" c = -max_part * (dt / dx) - 0.5 * (dt / dxx) * sig2 * (x[1:-1]) ** 2\n",
"\n",
" a0 = a[0]\n",
" cM = c[-1] # boundary terms\n",
" aa = a[1:]\n",
" cc = c[:-1] # upper and lower diagonals\n",
" D = sparse.diags([aa, b, cc], [-1, 0, 1], shape=(Nspace - 2, Nspace - 2)).tocsc() # matrix D\n",
"\n",
" # backward computation\n",
" offset[0] = a0 * V[0, n]\n",
" offset[-1] = cM * V[-1, n]\n",
" V[1:-1, n] = spsolve(D, (V[1:-1, n + 1] - offset))\n",
"\n",
"# finds the option at S0, assuming it is ATM i.e. x=s/a=1\n",
"oPrice = S0 * np.interp(1, x, V[:, 0])\n",
"print(\"The price of the Floating Strike Asian option by PDE is: \", oPrice)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"S = np.linspace(10, 200, 100)\n",
"A = np.linspace(80, 120, 100)\n",
"VV = np.zeros((100, 100))\n",
"for i in range(len(S)):\n",
" for j in range(len(A)):\n",
" VV[i, j] = A[j] * np.interp(S[i] / A[j], x, V[:, 0])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ujykoKFAAZcWKFe5ljzzyiAIoTz31lMe6N998s2I0Gt3b/eqrrxRAefnllz3We/zxxxVAeeSRRw719Cjnn3++kpaWdsh1nn76aQVQCgoKut0XFxenaDSaHp+Pg5/T1atXK4Dy0UcfKdu3b1ciIyOV0047Tamrq3Ov8/777yuA8sknn3hsKz09XQGUl1566ZCxKoqiLFmyRAGUb775RlEURcnPz1dUKpVy9dVXe6zXl2N3nT89HbuiKIrD4VCsVqtSVFSkAMp//vMf9319fQ170tDQoJhMJuW8887zWF5cXKwYDAblyiuvdC+79tprFUD55z//6bHu//3f/ymA8uOPP7qXeXl5ebwmLq7XZvXq1YqiOM/jyMhIZcyYMYrdbnev19LSooSGhirTp08fkOPcsmWLAiifffZZr+v09P5wOfgcd8Xy8MMPd1vX9Vqmp6f3uq9DvZ6/+c1vFH9/f6W6urrXx99www2Kt7e3UlRU5LH8mWeeUQAlIyOj18f2FsvatWsVQNm5c6f7vltuuUXpz5/CuXPnKnFxcT3ed/Bz6HqebrvtNo/1LrroIgVQnn32WY/laWlpyoQJE9y/D8R72KWtrU2ZMmWKEhERoRQWFrqXBQYGKhdccIHHuna7XRk3bpwyZcoU97Lezodvvvmmx3P2ww8/VADltdde63OMQgghxFB4++23FUB55ZVXFEVxXqN5e3srp512msd6B1/jufR2ffXkk08qgLJy5Url2muvVUwmk7Jr1y6Pda699tpu1xUHX/cf6vrNxWazKRaLRRk2bJjy5z//uVvMp59+erfHuO5buXKle1lZWZmi1WqVxYsX97ovF0C55ZZbPJa5rhcO1tPngLi4OMVoNHpc63V0dCiBgYHKDTfc4F523XXXKTqdTsnMzOw1Fte1UU/P0cExZWVlKYBy8803e6y3adMmBVAeeOAB97KZM2f2+Dl21KhRyjnnnNNrPC533HGHAijl5eWKoijKbbfdpkydOlVRFEX5+uuvFY1GozQ1NSmKoigLFixQNBqN0tzcfNjtHshmsylWq1VZuHChMn78eI/7evu80pOamppeP/P29Pq5npstW7a4l9XV1SkajUYxmUxKWVmZe/mOHTsUQHn++efdy8455xwlOjraffwut956q2I0GpX6+vo+xa0oirJ+/XrFaDQqV111lfvzUn+uo3v7HH755ZcrBoNBKS4u9lg+Z84cxWw2K42NjX2OUYgDHdHQ1Lfffpv09HSPn0NVxK1atYpRo0YxZcoUj+Xz589HURSPbwJWrVrFmWeeiZ+fHxqNBp1Ox8MPP0xdXR3V1dVHEq7bb3/7W4/fx44dS2dnp3u7a9euBeDSSy/1WO+KK67o0/anTJnCzp07ufnmm/n2229pbm7ud4xjx45l+PDhfV7/22+/5bTTTuP000/nu+++IzAw0H3fl19+ib+/PxdccAE2m839k5aWRnh4+GG7eiqK4h6OetZZZwHOIbWzZs3ik08+8Ti+Iz326upqbrzxRmJiYtBqteh0OuLi4gC6DRuFw7+GPdmwYQMdHR3dhvbGxMTwm9/8ptsQPICrrrrK4/crr7wSwOMbrb7Kzs6mvLycq6++GrX6l7ect7c3F198MRs3buw29OFIjjM5OZmAgADuvfdeXnnlFTIzM/sda08uvvjiPq/bl9ezvb2dtWvXcumllx7yW7Yvv/yS2bNnExkZ6XH+zpkzB/jl/dqb/Px8rrzySsLDw91/S1xVnD2dW8fSwd2GXR20Dp50eeTIkRQVFbl/P9r3sIvdbueyyy4jKyuLr7/+2v2a/Pzzz9TX13Pttdd6bN/hcHDuueeSnp7uMZQXup8Prr/fB7+/LrnkEry8vHp8fwkhhBDHk2XLlmEymdzNBry9vbnkkktYv349OTk5R7zde+65h7lz53LFFVfw1ltv8cILLzBmzJgBidlms/G3v/2NUaNGodfr0Wq16PV6cnJyerzO6el6btasWYwbN85jKOArr7yCSqXi+uuvH5A4DyctLc1jrnGj0cjw4cM9rof++9//Mnv27B47kB4J1/X8wdcuU6ZMYeTIkd2uXcLDw7t9jh07dqxHjL05eJ64NWvWuIeHukbErFu3zn3fpEmT+tQA7aOPPmLGjBl4e3u7r7mXLVs26Ne4ERERTJw40f17YGAgoaGhpKWluSvf4JdrX9dz1tnZyQ8//MDvfvc7zGazx3XoeeedR2dnJxs3buxTDFlZWfz2t79l+vTpLF++3D0Mub/X0T19Dl+1ahVnnHEGMTExHsvnz59Pe3v7kDQBESeHI0rEjRw5kkmTJnn8HEpdXV2PQ71cb866ujoANm/ezNlnnw3A66+/zk8//UR6ejoPPvggcPQTJgYFBXn87hou5dpuXV0dWq3WI5kFEBYW1qft33///TzzzDNs3LiROXPmEBQUxBlnnMGWLVv6HGN/O9F89tlndHR0cNNNN3Ub/lVVVUVjYyN6vR6dTufxU1lZ2eO8XQdatWoVBQUFXHLJJTQ3N9PY2EhjYyOXXnop7e3tvP/++0d17A6Hg7PPPptPP/2Uv/zlL/zwww9s3rzZ/Ue3p9f7cK9hT1znV2/noOt+F61W220/rslnD163Lw63f4fDQUNDg8fyIzlOPz8/1q5dS1paGg888ACpqalERkbyyCOPdJt3rD/6ek729fVsaGjAbrcftqlBVVUVX3zxRbdz11Vufqjzt7W1ldNOO41NmzaxdOlS1qxZQ3p6Op9++qlHLIPl4L8per2+1+WdnZ3u34/2Pexy44038s033/Dxxx+TlpbmsX2AefPmddv+k08+iaIo1NfXe2zr4PPB9Xfz4KSqSqUiPDz8iN4zQgghxGDJzc1l3bp1zJ07F0VR3Ne78+bNAw4//cmhuOar6uzsJDw83GNuuKN155138tBDD3HRRRfxxRdfsGnTJtLT0xk3blyP1zm9Xc/dfvvt/PDDD2RnZ2O1Wnn99deZN2/eoDVeOPiaF5zXvQceQ01NzYA2w+rvZ4O+xNibmTNnolarWb16NXV1dezZs8f9xbCPjw/jx49nzZo1FBcXU1BQcNhhqeAcgnnppZcSFRXFO++8w4YNG0hPT+e6667zuI4cDAdfy4Lzera3a19XfHV1ddhsNl544YVu16DnnXcecOhrfZfy8nLOPfdcoqOj+fTTT937gf5fR/d0PvQ1jyFEfx3RHHH9FRQUREVFRbfl5eXlAO4x+B988AE6nY4vv/wSo9HoXq+vLZcHIk6bzUZ9fb3HH4+DW073RqvVcuedd3LnnXfS2NjI999/zwMPPMA555xDSUkJZrP5sNvoaXLTQ3nuuef48MMPmTNnDitXrnQnMsH5vAYFBfU4MSZw2G9bXJPWPvvsszz77LM93n/DDTcAR3bse/bsYefOnbz55pse82/k5uYe/sD7wfWfZ2/n4IFzQIDzG8a6ujqP/3Rd50BP/xEf7f7VajUBAQH93m5PxowZwwcffICiKOzatYs333yTJUuWYDKZuO+++9zvqwMbIsCh/xPp6znZ19czMDAQjUbTbTLcgwUHBzN27Fj+7//+r8f7D/yW7WCrVq2ivLycNWvWeMxleKI1Dzja9zA4J7N94403WLFihcffB9f2AV544YVeu4kd/EXEweeD6+9mTU2NRzJOURQqKyvdkycLIYQQx6Ply5ejKAoff/wxH3/8cbf733rrLZYuXYpGo+n1Oqq3hEFFRQW33HILaWlpZGRkcPfdd/P8888PSNyuOV7/9re/dYulp46bvV3PXXnlldx7773861//YurUqVRWVnLLLbcccVwHPkcHFgn09cvDnoSEhBz2urE/Drw2PzjB19Nng6Ph5+fnTratWbMGtVrNjBkz3PfPnDmT1atXuysl+5KIe+edd0hISODDDz/0eF0PPi+PZwEBAWg0Gq6++upez7eEhIRDbqO5uZnzzjsPh8PB119/jZ+fn8f9/b2O7uk90tc8hhD9NShdU8844wwyMzPZtm2bx/K3334blUrl/oOjUqnQarUek4V2dHTw73//u9s2+/otRH+4PrB/+OGHHss/+OCDfm/L39+fefPmccstt1BfX+/uMNOXyqb+MBqNfPrpp5x//vn89re/9eiWc/7551NXV4fdbu9WwThp0qRuk7geqKGhgZUrVzJjxgxWr17d7eeqq64iPT2dPXv29PnYD+b6Y3dwJd+rr756BM9E76ZNm4bJZOKdd97xWF5aWuouNz7Yu+++6/H7e++9B+DRaaiv52BKSgpRUVG89957Ht1m29ra+OSTT9ydVAeSSqVi3LhxPPfcc/j7+7vfe2FhYRiNRnbt2uWx/sFdlo50n3D419NkMjFz5kw++uijQ16UnX/++ezZs4ekpKQez99DJeL6c2719z15LP729OZo3sPgTJYvXryYJUuW9Nh1ecaMGfj7+5OZmdnj9idNmuTxzWJPXO+fg99fn3zyCW1tbT2+v4QQQojjgd1u56233iIpKanH69277rqLiooK/vvf/wK4u5sefB31+eef97jtK664ApVKxX//+18ef/xxd8O2gaBSqbpd53z11VeUlZX1aztGo5Hrr7+et956i2effZa0tDSPRFF/9fYcuRqeHYk5c+awevVqsrOze12nP9dzv/nNb4Du1y7p6elkZWUN+LXL7NmzycnJ4b333mPixIkeCaCZM2eyY8cOPvvsM3Q6XZ+ee5VKhV6v90gcVVZW9ng935/r1oH+nHooZrOZ2bNns337dsaOHdvjNeihCiAsFgu/+93vKCws5L///W+PFZNHex0Nzutc1xf8B3r77bcxm829fpEtxOEMSkXcn//8Z95++23mzp3LkiVLiIuL46uvvuKll17ipptuco/Fnjt3Ls8++yxXXnkl119/PXV1dTzzzDM9dtxzVf58+OGHJCYmYjQaj3rOhXPPPZcZM2Zw11130dzczMSJE9mwYQNvv/02gMf8Xj254IILGD16NJMmTSIkJISioiL+8Y9/EBcX5+7K6Yrxn//8J9deey06nY6UlJQ+Vbb0RqfT8f7777No0SLmzZvH22+/zRVXXMHll1/Ou+++y3nnncef/vQnpkyZgk6no7S0lNWrV3PhhRf22g3p3XffpbOzk9tvv73HNtdBQUG8++67LFu2jOeee65Px36wESNGkJSUxH333YeiKAQGBvLFF1/w3XffHfFz0RN/f38eeughHnjgAa655hquuOIK6urqWLx4MUajsVtHHr1ez9///ndaW1uZPHmyu2vqnDlzPLqbjhkzhjVr1vDFF18QERGBj49Pj3/Q1Wo1Tz31FFdddRXnn38+N9xwA11dXTz99NM0NjbyxBNPDMhxfvnll7z00ktcdNFFJCYmoigKn376KY2Nje45/lQqFX/4wx9Yvnw5SUlJjBs3js2bN7sTjUejP6+nq5PqKaecwn333UdycjJVVVV8/vnnvPrqq/j4+LBkyRK+++47pk+fzu23305KSgqdnZ0UFhby9ddf88orr/Q6TGH69OkEBARw44038sgjj6DT6Xj33XfZuXNnt3Vd78knn3ySOXPmoNFoGDt2bK8JqDFjxvDpp5/y8ssvM3HiRNRq9WGH5x+po3kPb9iwgRtvvJEZM2Zw1llndZtnY+rUqXh7e/PCCy9w7bXXUl9fz7x58wgNDaWmpoadO3dSU1PDyy+/fMgYzzrrLM455xzuvfdempubmTFjhrtr6vjx4wd0GI4QQggxkP773/9SXl7Ok08+2eP17ujRo3nxxRdZtmwZ559/PuHh4Zx55pk8/vjjBAQEEBcXxw8//NBjcu2RRx5h/fr1/O9//yM8PJy77rqLtWvXsnDhQsaPH3/Yap/DOf/883nzzTcZMWIEY8eOZevWrTz99NNHNITz5ptv5qmnnmLr1q288cYbRxXXeeedR2BgIAsXLmTJkiVotVrefPNNSkpKjnibS5Ys4b///S+nn346DzzwAGPGjKGxsZFvvvmGO++8030NajKZePfddxk5ciTe3t5ERkb2+MVtSkoK119/PS+88AJqtZo5c+a4u6bGxMTw5z//+Wiegm5mz57NM888w8qVK7n77rs97jvttNMA55fi06dPx8vL67DbO//88/n000+5+eabmTdvHiUlJTz22GNERER0m9Owr59XwFkhFhcXx3/+8x/OOOMMAgMDCQ4OdidXB9o///lPTj31VE477TRuuukm4uPjaWlpITc3ly+++MJjHvmD/fnPf2bVqlX87W9/o7W11eM6NyQkhKSkpKO6jnZ55JFH3PNWP/zwwwQGBvLuu+/y1Vdf8dRTT3WrwhOiz/rT2aEvnRIVpXunH0VRlKKiIuXKK69UgoKCFJ1Op6SkpChPP/20RxdJRVGU5cuXKykpKYrBYFASExOVxx9/XFm2bFm3Li2FhYXK2Wefrfj4+CiAu9vQobqm1tTU9Hg8B263vr5eWbBggeLv76+YzWblrLPOUjZu3NhjJ82D/f3vf1emT5+uBAcHK3q9XomNjVUWLlzo7lDocv/99yuRkZGKWq326LwUFxenzJ07t0/P6YFdU10cDody++23K2q1Wnn99dcVRVEUq9WqPPPMM8q4ceMUo9GoeHt7KyNGjFBuuOEGJScnp9djSUtLU0JDQ5Wurq5e15k6daoSHBysdHV19enYe3q+MzMzlbPOOkvx8fFRAgIClEsuuUQpLi7utYNnX17D3rzxxhvK2LFjFb1er/j5+SkXXnhht86b1157reLl5aXs2rVLmTVrlmIymZTAwEDlpptuUlpbWz3W3bFjhzJjxgzFbDYrgLuLb28dtT777DPllFNOUYxGo+Ll5aWcccYZyk8//eSxztEc5969e5UrrrhCSUpKUkwmk+Ln56dMmTJFefPNNz3Wa2pqUhYtWqSEhYUpXl5eygUXXKAUFhb2+Tk/MJ6D/xb09fV0rXvJJZcoQUFB7nNm/vz5Smdnp3udmpoa5fbbb1cSEhIUnU6nBAYGKhMnTlQefPDBbq/HwX7++Wdl2rRpitlsVkJCQpRFixYp27Zt6/b3oaurS1m0aJESEhKiqFSqwz7P9fX1yrx58xR/f3/3+i4HH2dvz1Nvz63r/DvQkb6HD9Xl+uA//WvXrlXmzp2rBAYGKjqdTomKilLmzp3r8fflUOdDR0eHcu+99ypxcXGKTqdTIiIilJtuuklpaGjoNT4hhBBiqF100UWKXq8/ZBf3yy+/XNFqtUplZaWiKIpSUVGhzJs3TwkMDFT8/PyUP/zhD+7O9a7ri//973+KWq3udu1TV1enxMbGKpMnT3ZfYx9p19SGhgZl4cKFSmhoqGI2m5VTTz1VWb9+vTJz5kz3Nami9PyZoSezZs1SAgMDlfb29kOudyB66JqqKIqyefNmZfr06YqXl5cSFRWlPPLII8obb7zRY9fUnj77HHwMiqIoJSUlynXXXaeEh4crOp1OiYyMVC699FKlqqrKvc7777+vjBgxQtHpdB7XZD11crXb7cqTTz6pDB8+XNHpdEpwcLDyhz/8QSkpKekWS2pqarcYe3rdetPc3KxotVoFUL788stu96elpSmA8uCDD/Zpe4qiKE888YQSHx+vGAwGZeTIkcrrr7/e43H29nmlN99//70yfvx4xWAwKID7POyta2pPz01vr2tP50tBQYFy3XXXKVFRUYpOp1NCQkKU6dOnK0uXLj1knK6OrT39HPje6et19KE+h+/evVu54IILFD8/P0Wv1yvjxo07ZAdjIfpCpSgHjJUTPXrvvfe46qqr+Omnn5g+ffpQhyOOofnz5/Pxxx/T2to61KEIIYQQQghxzFVXVxMXF8dtt93GU089NdThCCHESW9QhqaeSN5//33KysoYM2YMarWajRs38vTTT3P66adLEk4IIYQQQghxUigtLSU/P5+nn34atVrNn/70p6EOSQghfhUkEXcQHx8fPvjgA5YuXUpbWxsRERHMnz+fpUuXDnVoQgghhBBCCDEg3njjDZYsWUJ8fDzvvvsuUVFRQx2SEEL8KsjQVCGEEEIIIYQQQgghBsGh24AKIYQQQghxHFm3bh0XXHABkZGRqFQqPvvss8M+Zu3atUycOBGj0UhiYiKvvPLKsQ9UCCGEEKIHkogTQgghhBAnjLa2NsaNG8eLL77Yp/ULCgo477zzOO2009i+fTsPPPAAt99+O5988skxjlQIIYQQojsZmiqEEEIIIU5IKpWKlStXctFFF/W6zr333svnn39OVlaWe9mNN97Izp072bBhwyBEKYQQQgjxC2nWIIQQQgghTlobNmzg7LPP9lh2zjnnsGzZMqxWKzqdrsfHdXV10dXV5f7d4XBQX19PUFAQKpXqmMYshBBCiMGnKAotLS1ERkaiVh+7AaSSiBNCCCGEECetyspKwsLCPJaFhYVhs9mora0lIiKix8c9/vjjLF68eDBCFEIIIcRxpKSkhOjo6GO2fUnECSGEEEKIk9rBFWyumVkOVdl2//33c+edd7p/b2pqIjY2lpKSEnx9fY9NoEIIIYQYdFu2wJlngqI0AzH4+Pgc0/1JIk4IIYQQQpy0wsPDqays9FhWXV2NVqslKCio18cZDAYMBkO35b6+vpKIE0IIIU4SXV1w222gKHDppfD//t+hv6gbCNI1VQghhBBCnLSmTZvGd99957Hsf//7H5MmTep1fjghhBBC/Do89hhkZkJoKDz55ODsUxJxQgghhBDihNHa2sqOHTvYsWMHAAUFBezYsYPi4mLAOaT0mmuuca9/4403UlRUxJ133klWVhbLly9n2bJl3H333UMRvhBCCCGOE9u2wRNPOG+/9BIEBg7OfiURJ4QQQgghThhbtmxh/PjxjB8/HoA777yT8ePH8/DDDwNQUVHhTsoBJCQk8PXXX7NmzRrS0tJ47LHHeP7557n44ouHJH4hhBBCDD2LBRYsALsdLrkEBvOyQKW4ZqsVQgghhBBC9Ki5uRk/Pz+ampp6nSPO4XBgsVgGOTLRVzqdDo1GM9RhCCGEOA4sWQKPPAJBQb8MTe3L//UDQZo1CCGEEEIIcZQsFgsFBQU4HI6hDkUcgr+/P+Hh4cd8Im4hhBDHr927YelS5+0XXnAm4QaTJOKEEEIIIYQ4CoqiUFFRgUajISYmBrVaZn853iiKQnt7O9XV1QBEREQMcURCCCGGgs3mHJJqtcKFF8Lllw9+DJKIE0IIIYQQ4ijYbDba29uJjIzEbDYPdTiiFyaTCYDq6mpCQ0NlmKoQQvwKPfMMbN0K/v7w8sswFAXS8nWdEEIIIYQQR8FutwOg1+uHOBJxOK5EqdVqHeJIhBBCDLasLOe8cAD/+AcMVXG0JOKEEEIIIYQYADLv2PFPXiMhhPh1stvhuuuc3VLnzIFrrhm6WCQRJ4QQQgghhBBCCCFOWv/8J2zcCD4+8OqrQzMk1UUScUIIIYQQQogh8dprr7kbXPzjH//odZkQQghxpHJy4MEHnbf//neIiRnaeCQRJ4QQQgghxK/Q/PnzUalUqFQqdDodiYmJ3H333bS1tQ3K/pubm7n11lu59957KSsr4/rrr+9xmRBCCHGkHA5YuBA6O+GMM2DRoqGOSLqmCiGEEEII8at17rnnsmLFCqxWK+vXr2fRokW0tbXx8ssvH/N9FxcXY7VamTt3LhH7Z8zes2dPt2VCCCHEkXrpJVi/Hry84I03hnZIqotUxAkhhBBCCPErZTAYCA8PJyYmhiuvvJKrrrqKzz77jHfeeYdJkybh4+NDeHg4V155JdXV1QAoikJycjLPPPOMx7b27NmDWq0mLy8PcCbaLrzwQry9vfH19eXSSy+lqqoKgDfffJMxY8YAkJiYiEql6nFZYWHhID0TQgghTjYFBXDffc7bTz4J8fFDGo6bJOKEEEIIIYQYQIoCbW1D86MoRxe7yWTCarVisVh47LHH2LlzJ5999hkFBQXMnz8fcHYeve6661ixYoXHY5cvX85pp51GUlISiqJw0UUXUV9fz9q1a/nuu+/Iy8vjsssuA+Cyyy7j+++/B2Dz5s1UVFRwySWXdFsWM9QT+QghhDghKYpzGGpbG5x+Otx001BH9AsZmiqEEEIIIcQAam8Hb++h2Xdrq3P4zZHYvHkz7733HmeccQbXXXede3liYiLPP/88U6ZMobW1FW9vbxYsWMDDDz/M5s2bmTJlClarlXfeeYenn34agO+//55du3ZRUFDgTqb9+9//JjU1lfT0dCZPnkxQUBAAISEhhIeHA/S4TAghhOiv11+HVavAZIJly0B9HJWhHUehCCGEEEIIIQbTl19+ibe3N0ajkWnTpnH66afzwgsvsH37di688ELi4uLw8fFh1qxZgHO4KUBERARz585l+fLl7u10dnZyySWXAJCVlUVMTIxHRduoUaPw9/cnKytrcA9SCCHEr0pxMdx9t/P2//0fJCcPbTwHk4o4IYQQQgghBpDZ7KxMG6p998fs2bN5+eWX0el0REZGotPpaGtr4+yzz+bss8/mnXfeISQkhOLiYs455xwsFov7sYsWLeLqq6/mueeeY8WKFVx22WWY9wegKAqqHmbE7m25EEIIMRAUBW64AVpaYNo0uP32oY6oO0nECSGEEEIIMYBUqiMfHjrYvLy8SD6oVGDv3r3U1tbyxBNPuCvatmzZ0u2x5513Hl5eXrz88sv897//Zd26de77Ro0aRXFxMSUlJe5tZGZm0tTUxMiRI4/hEQkhhPg1e+st+OYbMBhg+XLQaIY6ou5kaKoQQgghhBDCLTY2Fr1ezwsvvEB+fj6ff/45jz32WLf1NBoN8+fP5/777yc5OZlp06a57zvzzDMZO3YsV111Fdu2bWPz5s1cc801zJw5k0mTJg3m4QghhPiVKC+HP//ZeXvxYhgxYmjj6Y0k4oQQQgghhBBuISEhvPnmm3z00UeMGjWKJ554gmeeeabHdRcuXIjFYvFo7gDOzqqfffYZAQEBnH766Zx55pkkJiby4YcfDsYhCCGE+JVRFGdn1MZGmDQJ7rprqCPqnUpRjrbJuRBCCCGEECe35uZm/Pz8aGpqwtfX1+O+zs5OCgoKSEhIwGg0DlGEQ+Onn35i1qxZlJaWEhYWNtThHNav+bUSQoiT2XvvwVVXgU4H27bB6NH938ah/q8fSDJHnBBCCCGEEKJfurq6KCkp4aGHHuLSSy89IZJwQgghTk5VVXDbbc7bDz10ZEm4wSRDU4UQQgghhBD98v7775OSkkJTUxNPPfXUUIcjhBDiV+zWW6G+HsaNg/vuG+poDk8ScUIIIYQQQoh+mT9/Pna7na1btxIVFTXU4QghhPiV+vhj549WCytWOIemHu8kESeEEEIIIYQQQgghTii1tXDLLc7b990H48cPbTx9JYk4IYQQQgghhBBCCHFC+dOfoLoaUlPhr38d6mj6ThJxQgghhBBCCCGEEOKE8fnnzk6parVzSKrBMNQR9Z0k4oQQQgghhBBCCCHECaGhAW680Xn77rth8uShjae/JBEnhBBCCCGEEEIIIU4Id94JFRUwfDg8+uhQR9N/kogTQgghhBBCCCGEEMe9//4X3nwTVCrnkFSTaagj6j9JxAkhhBBCCCGEEEKI41pzM1x/vfP2n/4E06cPbTxHShJxQgghhBBC/Mq88sor+Pj4YLPZ3MtaW1vR6XScdtppHuuuX78elUrFvn37BjtMIYQQwu2ee6C0FBITYenSoY7myEkiTgghhBBCiF+Z2bNn09raypYtW9zL1q9fT3h4OOnp6bS3t7uXr1mzhsjISIYPHz4osSmK4pEgFEIIIX74AV57zXl72TLw8hraeI6GJOKEEEIIIYT4lUlJSSEyMpI1a9a4l61Zs4YLL7yQpKQkfv75Z4/ls2fPPuT2li9fTmpqKgaDgYiICG699VYACgsLUalU7Nixw71uY2MjKpXKve81a9agUqn49ttvmTRpEgaDgWXLlqFSqdi7d6/Hfp599lni4+NRFAWAzMxMzjvvPLy9vQkLC+Pqq6+mtrb2KJ4ZIYQQx5vWVli0yHn75pth1qwhDeeoSSJOCCGEEEKIgaQo0NY2ND/7E1R9MWvWLFavXu3+ffXq1cyaNYuZM2e6l1ssFjZs2HDIRNzLL7/MLbfcwvXXX8/u3bv5/PPPSU5O7vfT9pe//IXHH3+crKws5s2bx8SJE3n33Xc91nnvvfe48sorUalUVFRUMHPmTNLS0tiyZQvffPMNVVVVXHrppf3etxBCiOPX/fdDYSHExcETTwx1NEdPO9QBCCGEEEIIcVJpbwdv76HZd2trn8frzJo1iz//+c/YbDY6OjrYvn07p59+Ona7neeffx6AjRs30tHRcchE3NKlS7nrrrv405/+5F42efLkfoe+ZMkSzjrrLPfvV111FS+++CKPPfYYAPv27WPr1q28/fbbgDMBOGHCBP72t7+5H7N8+XJiYmLYt2/foA2lFUIIceysWwcvvui8/frr4OMztPEMBKmIE0KcUBRFQVEUHA4Hdrsdm82G1WrFYrHQ1dVFZ2cn7e3ttLa20traisPhGOqQhRBCiOPS7NmzaWtrIz09nfXr1zN8+HBCQ0OZOXMm6enptLW1sWbNGmJjY0lMTOxxG9XV1ZSXl3PGGWccdTyTJk3y+P3yyy+nqKiIjRs3AvDuu++SlpbGqFGjANi6dSurV6/G29vb/TNixAgA8vLyjjoeIYQQQ6u9HRYudN5etAgO+K7mhCYVcUKIIeOa38WVXOvpx+Fw9Hj74MerVKpuywAMBgNqtXznIIQQYhCZzc7KtKHadx8lJycTHR3N6tWraWhoYObMmQCEh4eTkJDATz/9xOrVq/nNb37T6zZMJtMh9+H6P1g5YMis1WrtcV2vgyr5IiIimD17Nu+99x5Tp07l/fff54YbbnDf73A4uOCCC3jyySe7bSsiIuKQcQkhhDj+PfQQ5OZCVBQ888xQRzNwJBEnhBgQfUmq9ZZYO/jxLlarlY0bNzJjxgw0Go072eb698AEm2uZi81mc1fMabXabvcLIYQQx4xKdcK0c5s9ezZr1qyhoaGBe+65x7185syZfPvtt2zcuJEFCxb0+ngfHx/i4+P54Ycfehy+GhISAkBFRQXjx48H8GjccDhXXXUV9957L1dccQV5eXlcfvnl7vsmTJjAJ598Qnx8PFqtfKwRQoiTyYYN8Nxzztuvvgp+fkMbz0CS/7GEED06XELt4GTa4ZJqLgcn01y3XT8H3ucaVqrT6Y64qs0Vm0ajOaLHCyGEECez2bNnc8stt2C1Wt0VceBMxN100010dnYetmPqo48+yo033khoaChz5syhpaWFn376idtuuw2TycTUqVN54okniI+Pp7a2lr/+9a99ju/3v/89N910EzfddBOzZ88mKirKfd8tt9zC66+/zhVXXME999xDcHAwubm5fPDBB7z++uvyf78QQpygOjvhuuuc/Yeuvhrmzh3qiAaWJOKE+BUY6KTapk2bSEtLw2g0upcdmETrKal2pHEf6TZcw1VVKhVWqxW1Wi1VcUIIIcRBZs+eTUdHByNGjCAsLMy9fObMmbS0tJCUlERMTMwht3HttdfS2dnJc889x913301wcDDz5s1z3798+XKuu+46Jk2aREpKCk899RRnn312n+Lz9fXlggsu4KOPPmL58uUe90VGRvLTTz9x7733cs4559DV1UVcXBznnnuuTEshhBAnsMWLYe9eCA+Hf/xjqKMZeCqlp5IVIcRxq6cE2sHLHQ5HtyGgrnUO/PfAxNSB86z19q/L6tWrmTFjBgaD4RgeKbS3t7Np06bDfhPfE6vViqIoeHl5oSgKer1evhkXQghxxJqbm/Hz86OpqQlfX1+P+zo7OykoKCAhIcHjSypx/JHXSgghjm9btsDUqWC3w8qVcNFFg7fvQ/1fP5CkIk6IITRQzQocDgc7d+5k9OjR6HS6XpNqB1aqHXhff7iSeoNRXXbgcRwplUqFoijYbDapihNCCCGEEEKI45TF4hySarfDZZcNbhJuMEkiTogBciw7gPZWoXbgsIuWlhY0Gs0xn6z4aIaLHsm+jmZoyYHP1759+0hMTOzWkU0IIYQQQgghxND7v/+D3bshJAReeGGoozl2JBEnRA8GogNoZ2cnhYWFpKSkeGy7L0m1/ia5BmI+tr4azEScw+EYkP2oVCqKioqIjIzEbDZLVZwQQgghhBBCHEd27oS//c15+8UXncm4k5Uk4sSvwlB0AAWoq6tDp9Md68PrMbZjva8TqSIOfhlSKx1UhRBCCCGEEOL4YbXCggVgs8HvfgeXXDLUER1bkogTJ5yjSao1NjbS0dHh0RXMpae50wayA+hgGMyKuMHQW0Wc63W1WCxYrVaPH9eyrq4uAgMDSUpKcp8TarVa5ooTQgghhBBCiOPIU0/B9u0QGAgvvQQn+0c1ScSJITXYHUDb29upr68nKiqq22NOZCd6RZzD4egxmdbc3IzNZiMjIwOLxUKXxYLdZsNqtbrPA51O5/7R6/Xu21qtlry8PKKjo93z5mk0Gvf5JFVxQgghBtpg/n8sjozr+kEIIcTxISMDlixx3v7nPyE8fGjjGQySiBMD5kibFVRXV+Pt7d1rC/mB7ADqGuZ4siTgDnY8zBHn6lB6cLXaoarX7HY7AFqt1iOx5qqI8/b2Zm9xFS9+sprlf12E2WRCr9ej1Wp7jcNisdDW1kZBQQFJSUnAL6+/VMUJIYQYSDqdDpVKRU1NDSEhIfL/y3FIURQsFgs1NTWo1Wr0ev1QhySEEL96NpuzS6rFAnPnwlVXDXVEg0MScaJH/W1WcDQdQEtLS4mKivLoZnksL2BPxm+rj9UxKYqC3W73SJy1t7cDkJ+f32uCDZxJr54q1UwmE76+vh7LXD8HzwdXU1NDYWEhOwprePiVj4kODWT9rnx+/5spfYo/NjaWXbt2ER0d7Y5JpVJht9ulKk4IIcSA0Wg0REdHU1paSmFh4VCHIw7BbDYTGxt7VHPQCiGEGBjPPQebN4OfH7z66sk/JNVFEnG/Av1NqlksFlpaWvDz8+v2+IMNRAfQwaxSO9m/oT7c8R2YVOtrtZormepKlrmSVzabDb1ej5eXV48Jt4FIclmsNt78Np28qkZ8vUzszi3h7a/WcdGsSYe9gFYUBR8fHwICAiguLgbwqIKTqjghhBADydvbm2HDhrm/kBLHH41Gc8hqeiGEEIMnOxseesh5+9lnYf/sUb8Kkog7AR3rDqAtLS1kZWUxderUQWlWMNgXQydLRZzD4cC2f740V5VaeXm5e1lPybUDh4D2VJFmNBp7XH7gRWtbWxvp6emkpKQc0+Nram3nz/98n6q6Bmqb2hkeF4lapaKsup7/bdzFudPTDrsNlUpFUlISW7Zscf8OzoSczBUnhBBioGk0Gvl/RQghhDgMux0WLoSuLjj7bGfH1F8TScQNsb4m1To6OtBoNKhUqn4l1Q683dekmlarRVGUQbuQPPCYBmNfxyPXvGp9qVRzLbfZbMAv3+4C1NbWuivTDqxUO7BaTavVHtVwjAOHGx8reaVVXLf4Zfy8jBRVNuBQFPQ6DVX1zbR3dvHxD5s4Z9q4w8ahUqkICAjA29ub5uZmj+WuRKZUxQkhhBBCCCHE4HnxRfjpJ/D2htdf//UMSXWRRNwAOpoOoK71etvu9u3biY+PJzg4GDjyZgV9MZiJsaHY32Dsy26309XVhaIo1NXV9SnBduAQ0IOr0gwGAz4+Pt0q1VxDQLu6uvjpp58YN+7wyamjdawTcWu3ZvLGZ6sI8vNhS1Y+AKeMTmbDrhzShseh0WhYvSWD9Tv2cvr4kb1u58AYo6OjycjIwGq1otPpAKmKE0IIIYQQQojBlpcH99/vvP300xAbO7TxDAVJxPXR3r17CQ4OxtfX95AdQA9VpeZafiTzqrmq4QYjYeCqFhosx3tFnMPh6Necalar1eP5y8nJ6bFhQW/zqh1JjIfrZDqQjlUiTlEU3vhsFS99/B2xYUHsySvBoNMSFx7Ipj25qFTg5+3F2m2ZAHy+dushE3Hwy/Ph7e2NSqWiuLjY3UHV1bTBarVKVZwQQgghhBBCHGMOByxaBB0dMGsWXH/9UEc0NCQR10dXXXUVd9xxB3PnzvVYPhDNCvrCVb0zGNRq9UlbEefqAtrW1tbnpgUHDgHtqVrNy8ur2zK9Xk9nZyfbtm1j6tSpg3Jcg+VYJOK6rFYeePEDtmblo1Gp2J5dyKnjhlNUUcO+kmq8TQYSokJZuy2TscmxGA06/t93G7j87OlMGpXYa5wH3tbpdBQWFhIXF+ceytvU1MSePXs444wzpCpOCCGEEEIIIY6hV1+FNWvAbIZly+DX2sBaEnF9ZDKZsFgs7mFtg20wq9QGuyIOjiyR5Eqq9adSzdXJLD09vcf500wmE76+vj02LOjvvGpdXV39PqajMZgVXQO5r+r6Ju589m1a2jspr2mg02IlJS6CmsYWSmsaCPX3Rm/Qszu3BF8vM34+ZtZv34tWo+azNem9JuIO5HA40Gq16HQ6SkpKSEhIcC9Xq9UyV5wQQgghhBBCHENFRfCXvzhvP/44JB7+Y9xJSxJxfWQ2m+no6Biy/Ws0mkGtiIPBmZQffqmIcw0B7Usy7cAhoK551Xr6ObhhQVNTE5WVlUyaNOmkSroMZkWc6zkfCLtzi1n02KukJsawPXsvAJNHJbFjXyFWm51TRsWzO7+c9sZW4iKCsVhtrN++l7ThcXR0WXj7q3Vcee4MRiVGHzZmjUZDUlISe/bsITY2Fo1Gg6IoMlecEEIIIYQQQhxDigJ//CO0tsKMGXDrrUMd0dCSRFwfDXUibrAr4oCjSkwoitLnedXa29upqakhLy8PwF25dPAwUKPR2OPQUK1W2+fEUGdnZ7cmFyeLwTqmgUrQfrF+Kw+99CGx4cGkZ+ZhNuoZnRTD5gzneXDK6GQqaupp77Qwblgs+4or6eiykBQdhtGgZ8e+IgL9vPngfz+z5MZLD7kvV+VbSEgIRqORkpIS4uPj3ctd1ZVSFSeEEEIIIYQQA2v5cvjuOzAanbd/rUNSXSQR10cmk2lIE3GDPUcc4NF4wm6397lSzWKxdJtX7eBhoGaz2f17eXk5JpOJhIQEtFptv4eAihNrjjiHw8Gz737FR99vxNfbzI59RfiYjUxJTWbN1kx0Gg3jhsexaU8uAFNGxrM5qxCA8SnxZBWUkVdaxeRRSRSUV/P2V+tYcMEsEqJCu+3rwKSyK8mWlJREZmYmMTEx7uSbWq3Gbrej0WikKk4IIYQQQgghBkhZGdx5p/P2Y4/B8OFDG8/x4IROxK1bt46nn36arVu3UlFRwcqVK7nooot6XPeGG27gtdde47nnnuOOO+7o977MZjPt7e1HF/BRGMhE3IHzqvWWSAPYsmULNpsNq9XqTr70VKlmMBjw9vbusVqtL0mNuro692OPtcFsDDHYToQ54to6OrnzuX9TXddER6eFitpGYsOCsDsc7C0sI9jfh0Bfb7Zk5aPXaUmKDKa2qRWAqaOT2bg/OTdxhHOOt9rGFobFhPPWl2t59IZLet2vKxEHEBoaSk5ODmVlZahUKneCzuFwSFWcEEIIIYQQQgwQRYEbboDmZpgyBf7856GO6PhwQifi2traGDduHAsWLODiiy/udb3PPvuMTZs2ERkZecT7MpvNdHZ2HvHjj1ZviTiHw4HNZuuWUDtU9ZprOz3NqabX6zEajdTU1BAXF+cxx5pGozkmCYqTOTk2WE6EirjiyloWPfYqPmYjOp2WxtZ2xibHkldWRVtHF0nRYYT4+7BxTy4h/r74epvIKqpEr9Vw6rgUftyZDTiHrG7ak4tKBTPGpbA5I5fCihr++LsziAoN7HHfB85r56qKy87OJj4+3p2gk6o4IYQQQgghhBg477wDX30Fer1zSKp8zHI6oRNxc+bMYc6cOYdcp6ysjFtvvZVvv/2WuXPnHvG+zGYzDQ0NR/z4vlAUxV2BdnAyrbm5GbvdTltbm0dy7eAhoAdXpLkSaT11Ae0tmaIoCnl5eQQEBGA0Go/pMcPgJ+JO1qTf8TxH3MbdOdz8xDISo0LZnJGHt8nAtLHD2Lg7F0VRGJ8ST2ZBGQ6Hg+GxEdQ2NpNXWkWQnzcGnYbWjk68jAYSo8PcSbgpqclYrFasNjsTRyaw/PPVPLSo54T8gRVxAOHh4eTm5tLQ0OBeLlVxQgghhBBCCDEwKivhT39y3n74YUhNHdp4jicndCLucBwOB1dffTX33HMPqUf5qpvNZsrLy/u8vmtetZ46fR5qXjVXF8eDk2bgTLYFBQX1ODx0IOdVczUzGMzmEIOVHJPkytHrbyLukx82svSNlYQF+7M5Iw+9VkNKXCQOu4KiKB5DToP8fOiyWqlvbmNkQhSllbXUNbWiUqkZFhvOjn1FeJsMJESFsmlPLnrdL9VyGfml3DTvLIL9fXE4HB7nlOt95aJSqUhMTGTv3r0EBQW5l7uq4vrTAEQIIYQQQgghxC8UBW6+GRoaYPx4+Mtfhjqi48tJnYh78skn0Wq13H777Ue9Lb1eT21tLZs3b0aj0RAdHX3Y5JprOJxWq+2xIu3AYZ8Hz6t2cBKgoKCAjo4OoqOjj/pY+sLVSXIwSEXc0RuoTqYDuS+rzc7i1z5m7dZMIoL92ZNfSpC/D4E+XmzdW0BMaCDjU+LZeECF26Y9uYyMj2RKahJbswqwOxwMiwmlsr6FiOAAokIDUalgd24JPmYj8ZEhWO12AMYNi2PZf1Zz77UXumM4uFnDgSIiIsjKyqKrq6vb8dlstkNWjQohhBBCCCGE6NlHH8HKlaDVwooVsL+2SOx30ibitm7dyj//+U+2bdt2RB+mN27cyJ/+9Cdqa2upra2lubkZgPXr13POOedw6623elSl+fj49FipNlCVNYPZNRUY9Iq4wdyXODp9ScQ1NLdy0xPLaG3vpKaxhbAgP5JjwmhsaSenpJKIIH/UGme3Uh+zkbiIEDbtyUWjVuFlMlDf3Ibd4SA1IYKswkocikKX1YZOo6Gwoobo0EDYn5AL9vNl4sgENu3JJTO/lBsvPhMfswk4dCJOrVbj7+9PY2Oj+5hczRukKk4IIYQQQggh+q+mBm65xXn7gQdg3Lihjed4dNIm4tavX091dTWxsbHuZXa7nbvuuot//OMfFBYWHvLx8fHx3HPPPQQHBxMcHMx3333Hf/7zH7744otjHHnPBrNCbbD3JxVxR+94qojLLixj4WOvEhEcwO7cEhTFOQS1saWd2sYWRsZHUlHbSEVdI/7eXvh5m9mTV4Kft5nIkAC2ZBWQNjyOSaMS2ZKZDzgbNKRn5jFpVBLeZiOF5TW0dnS6K+S0Gg1mo56EqFD+/dV6br7kbI8Ye0rEwS9zP1ZWVhIREeFe3tHRQUdHBwEBAZKME0IIIYQQQog+uu02qK2FMWPgwQeHOprj00mbiLv66qs588wzPZadc845XH311SxYsOCwjw8PD2fevHnu3/fs2YPFYhnwOPtqKCriTsZEnCRVjt6hEnHfbdrNq598R4i/L5v2z/vm6nJ6yuhkEqJC2bZ/yOmY5BjySqtIiApBo9HQZbGSVVBGoK8XnRYrLY0taNQqRiVEurelUakora6ntaOT0UnRFFXU0tLeiVGvIyokkF05xdQ1trLggploNb8k3npLxCmKgr+/P3l5eYSHh7ur4qqrq2loaOCUU06Rc0YIIYQQQggh+mDlSvjwQ2d31BUrnN1SRXcndCKutbWV3Nxc9+8FBQXs2LGDwMBAYmNjPSZhB9DpdISHh5OSktLvfZlMJtrb24865iN1sg9NPRmr1AbTUFfEKYrCvz76H29+vgZfLxNWmx0vo4GkmDD3kFONWs22zDzsDgenpCazOTPPmQjz9iK3pIqOLguJUaG0tneyt7CcqaOTUeNgd14ZOo2GscPj2LA7hympSahUKtIz83A4FEbGR1JaXU9ydCjBft6oUHjh3c+58NSxjBgxAq1Wi8PhcDc9OZDD4cDPz4+ysjKqqqoIDw93L1er1TJXnBBCCCGEEEL0QX093HST8/Y998DEiUMbz/Fs4FptDoEtW7Ywfvx4xo8fD8Cdd97J+PHjefjhhwd8X2azmY6OjgHfbl8NZmIMTt6hqZL0O3oHJ+I6uyzc/vSbrFy1GbvDQW5pFVGhgQT5+7ArpxhfLzPD4yL5edc+JqTEM3FkIpsyct0dU9fv2EtkSABpw+Mor2mguqGZ+IhgCsqqaWrtwM/LSGSwH1uz8tGoVbS2tLJ9bwEOh8LwqCCyiypoae+kta0du91OaU0D327OpLaujrKyMqD3ijiHw4FGoyEhIYH8/Hz3ueFabrfb5XwRQojj1EsvvURCQgJGo5GJEyeyfv36Q67/7rvvMm7cOMxmMxERESxYsIC6urpBilYIIYQ4ud1xB1RVwciR8MgjQx3N8e2ETsTNmjXLPf/UgT9vvvlmj+sXFhZyxx13HNG+hjoRd7JXxA2mkzGxMlQVcZW1jfz52bcpra6jsKKGuqZWUhOjyS4qp6W9g9iwIEwGLVkFZQR4m+jo7GJrVj56rYaUmBA27h9y6q1Xs2NfEZ0WK8mRQZTVNFDV0Ex8mD+goqiqHn9vE3HhwWQWVzF2WCwTRsSzr6wOh6IweVQSBZX1JMVGkhwTTlN7FxllzRQVFeFwOA6biIuJiaGzs5Oamhr3clcy2mazDcrzKoQQou8+/PBD7rjjDh588EG2b9/Oaaedxpw5cyguLu5x/R9//JFrrrmGhQsXkpGRwUcffUR6ejqLFi0a5MiFEEKIk89XX8G//w0qFSxfDkbjUEd0fDuhE3GDyWQy/aoScSdzRdzJ6Fg/fw6Hg87OTlpaWmhra6Orq4tv1m1izu1/o7q2nq1ZBVhtdkbEhpJVUEZdUysxQd5U1TdSVd9MVJAfCrArr5QxiVGEBfqSXVKDUa9ldGI0u/IriAj255TRyeSW12G12Zk4IoHCqkYsNjtJ0WHotFryy2sI8DGjKLArpxi1SsWU1CTSM/Ow2R0YdBpKq+qobWzhh217UanVlJWVHTIRp1ar3VVxeXnO4bKuBJ2rg+pgvveEEEIc3rPPPsvChQtZtGgRI0eO5B//+AcxMTG8/PLLPa6/ceNG4uPjuf3220lISODUU0/lhhtuYMuWLYMcuRBCCHFyaWqCG25w3v7zn2Hq1KGN50RwQs8RN5jMZrPMEXcM9yVdUwePq8rLarVitVqxWCyHvW232wHQarWoVCp+3FPIe6t3EBrgQ/reIhIjg/ExG9mZWwrA5FGJ5JRUgUrNhBGx7MkrwWK1kRIXiV6vp6S6jIggf/R6LXvyS/EyGhiZEMWq9AzglwYPAKeNHc7mvQV0WazuOeS27i1g6phkmls72JyRh0oFU0Yl89NO5/BXu0NhZ04xebVpqMjHy8vrkIk4gJiYGPLz86mtrfWYU871fOllplEhhDguWCwWtm7dyn333eex/Oyzz+bnn3/u8THTp0/nwQcf5Ouvv2bOnDlUV1fz8ccfM3fu3F7309XVRVdXl/v35ubmgTkAIYQQ4iRy111QVgbJyfDYY0MdzYlBEnF95BqaOphDAA8kFXEDt6+TjcPhwGKxoCgK9fX1h02qWa1W93ms1+vR6XTodDr3bZPJhJ+fn3u56z6tVouiwIMv/Jvvt2Ti5+NFTmkN/t5mYiNCWbM1E4NeS2pCNOmZ+QCcOWU032/eA8CkkYnszCnCarMzfexwMvJLqahrJDIkAK1GzU87s4kI8ick8JeOqyNjQ8kodFa0jU+JJ6ugjE6LlbiIENo7LWQWlOFjNhIfGcKmDOdjfLxMrNu+F0VRWLsjh6QzxtDZ2XnYRJxWqyU+Pp68vDzMZjMGgwGVSuVRFdfTNoQQQgyu2tpa7HY7YWFhHsvDwsKorKzs8THTp0/n3Xff5bLLLqOzsxObzcZvf/tbXnjhhV738/jjj7N48eIBjV0IIYQ4mXz3HSxb5ry9fDmYzUMbz4lCEnF9ZDKZUBSFrq4ujEMw4Fkq4n4dFEXBbrcfMol28H2uajWA7Oxsj6SaK7F2cLJNp9Oh0Wj6lZhsbuvg9qdW0N7eRluHheqGVhIinQmxdduyGB4bgd3hYFt2IWajnuSYcPYWlqNRq5g4KonN+5NrU1KTsFitNLW2MyoxitKqeprbOogKDSApKox12/e6O67uynHO9XNgQm/csFj2FVdSVFHDjLHDKaqsZXduCUa9jhHxkazdlkVqYjRmo57vN+/m3CmjCDZYejymg5NrcXFxFBQUoFar8fb29lhXquKEEOL40lMH797+X8vMzOT222/n4Ycf5pxzzqGiooJ77rmHG2+8kWWuTxAHuf/++7nzzjvdvzc3NxMTEzNwByCEEEKcwFpawDXV6q23wmmnDW08JxJJxPWReX9qt729/VeRiBvMijgYvOGiQzEMtqurq89JtQOr1Q5OnOl0OoxGIz4+Pt3ua21tJScnh6nHaEB+YXk1Cxa/QpCfNxW1TbR1WkgbHkd2UQUdXRaGx0YQEuDrrGoLDkCn1bArpxgfs5Hp41JYv30vGrWKCSMSnUNJgVPTRrBh1z7sDgepiTEUV9WyK6eY+IgQHIqDXTnFGHQahsVEkF1UAXgOWR2dFENzWwel1fWEB/lhNOjZsa8InUZDsL83a7ftBeDHPfn8dnwMra2t3Y7r4EScqyquqKiI0NBQ4JcPelIVJ4QQx4fg4GA0Gk236rfq6upuVXIujz/+ODNmzOCee+4BYOzYsXh5eXHaaaexdOlSIiIiuj3GYDBgMBgG/gCEEEKIk8C990JxMSQkwOOPD3U0JxZJxPWRyWQCoLOzc0j2PxQVcSdrcuxIuarV+jq3msViweFw8NNPP6HRaLolz/R6fY9JNb1e3+9qtdbW1mM27Hb99r386ekVxEeFsTkjD4ApI+PYnFUE/DLktKK2gXHDYykoq6G5zdkx1e5wUNvYgp+3mcjgANIz89Bq1KSlJGCx2bA7HJwyOpn0zDwcDoXUxGh8vIxs3J1LRJA/DruNPfmlGPRaZk0cxZqtmYAzIbc5w9lY4dS0FPbkllBZ10SgrxfB/r6s3baXEXER+Pl48emqzYwK98FsriYlJcXjeeopsRYXF0deXl6P73WpihNCiKGn1+uZOHEi3333Hb/73e/cy7/77jsuvPDCHh/T3t6OVut52avRaACZO1YIIYTorzVrwNUf6fXX4aDBROIwJBHXR2q1GoPBMGQNG1wVaoM1R93JPDTVtS9FUfrVsMBqteJwONzVagcP9zQYDO7Emk6nw2azkZWVxWmnnXZCVlEpisJbX6zljc9WERTgy5bMPIx6HQkRQTS1djgbJKT+UqE2PiUecA5hHZscS15ZFW0dXWi1GuIjQ9i5r4gAHzOhQf5syczD18vE1NHJbDxgyOqWrHzCg/xJTYymtLqeptZ2Qvx98PEyUVHTgF6rYcywOPc+p6Qm0dzWQWNrO0nRYTS3trOvuAIfs5HQAF/W7cgGYGNWMSnRQdTV1REcHOw+xp4Sca7Xsra21r3MdY5KVZwQQhwf7rzzTq6++momTZrEtGnTeO211yguLubGG28EnMNKy8rKePvttwG44IIL+OMf/8jLL7/sHpp6xx13MGXKFCIjI4fyUIQQQogTSlsbLFzovH399XDGGUMbz4lIEnH94GrYMBRcH/wdDof7G9xjvb8TrSKuL3OrdXZ2YrVaWbduHTabDcBdrXZwZZqXlxcBAQHdlrs6hx6Oq0ptMJI2A52gtVht/PXlD9m0O4fo8CB+3rmPsEBfzEYDWUWVeBl1TBnlTMIdOOQ0Itjfo1rNNXw12M/H3fE0u7CcQF8vQgJ8UWB/hVy8u9ouOjQQq81OU2s70cF+tHZZyS+rJsjfh/EjErrtE+C08SPYtCcXi9VGTFgQDoeDdTuyGREfidlk5PvtOVx4+njy8/MPm4gDZ7VFa2srjY2N+Pv7A87ztLOzE4vFgp+f34A910IIIfrvsssuo66ujiVLllBRUcHo0aP5+uuviYuLA6CiooLi4mL3+vPnz6elpYUXX3yRu+66C39/f37zm9/w5JNPDtUhCCGEECekBx+E/HyIiYGnnx7qaE5MkojrB5PJNOSJuMFMjg1lRdyB1Wp9rVpzxdvT3Gp6vR4vLy/sdjvt7e2MHz/eo2mB+EVtYws3/O11rFY7lXVNdFpsjIiLpLK+iar6GsICfEClQkHB39tMWLA/6Zl56LUaIoMD6LQ457k7cD43RVGwWG1UNzQ7K9faOsguqiAhMoRhsRFsyczfn1xLYHNGHskxYUwelcS2vfnYHQrJMeE0trTR0WXxGOYKzmGqbR2dWKw2j0q8EH9fwoL8Wbs1ExWwIasEfwM0NDQQEBAA9J6IA+ccRHl5eUycOBFwnqelpaV0dHQwceJEqYoTQoghdvPNN3PzzTf3eN+bb77Zbdltt93GbbfddoyjEkIIIU5eP/0Ezz/vvP3aa+DrO7TxnKgkEdcPx0tF3GDtbyCTfoeaW625uZn29na2bdvmcZ8rjp7mUPPy8upx+eGq1VpbWykuLsbHx2fAju14MFAVcVkFZVy3+GWiw4LYsa8QcDZFsNhsNLa0MTophvzSKtq7LPj7eOHv60V2YTlB/j4E+HixdW8BqYnRjE+JdyfhXMNPTxmdTEiAL5kFZXRZrCRFh9HS1kGQnw/+3mbCg/1Jz8xHrVIR6OtNS1sHdofC+JQ4MvLLsFhteJuNxIYHsTu3BC+jgcToMPd+Ths/gh93ZKMoCsNjI6hpaGbt1kxS4iPpbG/nvf9t4OzJKeTn57uTa70l4hwOB+Hh4ezZs4fm5mZ89/8P41pf5ooTQgghhBBC/Jp0dMB114GiwPz5cO65Qx3RiUsScf1gMpmGbI44V5JlMKvUetuXoijYbLbDzqd24O92ux2gx7nVtFotarWayMjIbvcdi2o1mZS5Z1//tJ2HXv5/JEaFsmlPLmqVikmjnMM/RyZEegw5HREXTlFlHWOHxaLVaGhobiW3pJLQQF/aO7vo6LJgMugYHhfpngNOrVaRXVRBl8XK+JR4d0IuOiwQg17H3sJyfL3MRIcGsDkjj9FJ0aREB7M929kUIm14PNlF5QT5eRMVGoBapWZ3bjF6nZbRSTF0djkr8SaPSmJ7diE2u534yBDCAv1YV1iOXqfh56wSTk0KdCfXDpWIMxgMxMTEkJeXx/jx493LNRqNzBUnhBBCCCGE+FV55BHYtw8iIuDZZ4c6mhPbCZ2IW7duHU8//TRbt26loqKClStXctFFFwFgtVr561//ytdff01+fj5+fn6ceeaZPPHEE0c8Ka/JZBqyrqmuucaORSLO4XB0S5y1tbXhcDjIzs7ulmCzWp0JD1e12sHJM5PJhJ+fX4/zrvVUtVVbW0t7ezvh4eEDfmwHG4xGF0PhaCriHA4H//zgv7z/zU94mwwA+HiZiA0LYnNGHjqNBrPBQGVtI4qiMCYxkt355QBo1CpKq+rotFhJiYuguqGZgvIapo1Jxu5wsHNfkTMhFxvBhl05TElNQq1SuZNzaSnxZOaXkRwTjtGgw2K1kVlQho/ZCEBDi7MC9cBhrlarHb1WS0F5DSH+vvh6m9i2twCjXsv0scP5edc+AMYmx5JbWklpVR3Rwb7oDUaWfbaa0x+8lvz8fMaOHes+jw9mt9tRq9UkJCSwbt06Wlpa8PHxweFwoNVq3cloqYoTQgghhBBCnOw2b4a//915+5VXYP9MP+IIndCJuLa2NsaNG8eCBQu4+OKLPe5zDXV86KGHGDduHA0NDdxxxx389re/ZcuWLUe0P7PZPGQVcdC3edtcnR370gXUddtVrabVat1JM4vFgkqlQqvVYjKZekyqaTSaAUlsDXbXVPGL9s4u/vzs2+SXVtFltVFZ14RJryPA20xGfimBvl4E+/uydW8Bk1MTCfDzZkd2IeBMjv28K4e04XHotFp27CvEarMzMiGKfcWVAEQE+aPXadmZU4zZqEejVrMpI9f9eFdyLcDXi7zSKjq6LESHBgKwJ6+U5IhAIsKC3OudMjqZ9Mw8Jo1MRK/VUtPYTF5plbuSzr7//TElNcndyGFEfCQqu5Wskmp8zEY27C3jlHg/WlpaAHqtiNNoNBiNRqKiosjLyyMtLQ273Y7BYEClUklVnBBCCCGEEOKk19UFCxaAwwFXXgm//e1QR3TiO6ETcXPmzGHOnDk93ufn58d3333nseyFF15gypQpFBcXExsb2+/9DcUcca5qNdecabW1tTQ2Nh5yGKirOqqnOdRMJhO+vr49zq12YEIhLy8Pq9VKUlLSMT/GwUzEnaxJvyM5ptLqOv5v2UpqG1rIL6vGarO7mx2kxEWi02ppbmtnX3EFYYG+NLW0OxN1Bh2xYYHu4asBPl6s3poJwOTUJLZlFWB3OJg9cRRb9xZQUdfoTsht2J3D5JFJWOw2jznk1m/fS0JkCF4mI4XlNbR2dBIW6Etbp4XGjgZ0Gg1jh8e5H6MCappaqG9uIy4i2F1JF+jnzeRRvyThpqQmsz27ABSFEfGRtLZ38vpnqzj1gWsoLCwEek/EuZYnJiayfv16Wltb3ctdSXFX5ZwQQgghhBBCnIweewwyMyE09JdGDeLonNCJuP5qampCpVLh7+9/RI8/2qGprmq1w82nduBtm80GOKvVHA4HVVVVHhVqRqOxW1JtIKrVBrpZw6GcrMmxwdTfoambM3K56fFlxEcGU1pdj9Vm95gDzsuoJyO/lC6LlRHxkVTWNbGvuJLT00aQXVRGdnGVe/jq6q2ZDIsJx9/Hi/T9CbBTUpPZsa+ILquNUQnRlFbXuRNyaq2KnVlFGPRaRiVEu4epJkSHsjo9E0VRGBkfSXltI02t7UwbM4zapha2ZuWjUsHkUclsynA2f+gMs7KvqIKOLgthgb6YjQbUahVqlYqJIxPYvL/6Lj7Mn0Bfb/YWlhPo68WmfeWMj/IGDp+IM5lMREREkJ+f766UU6lU7qo4jUYjyTghhBBCCCHESWf7dnjiCeftf/0LgoKGNp6Txa8mEdfZ2cl9993HlVde6e6A2F9eXl4eQ1MdDgc2m61Pw0Bdvx9YrXZw8sxgMODj49PjMFC1Ws2GDRsYNmwYgYGBA/W09Kovw2AHcl+S9Bs8H3z7M0+++R9nl9KMfKakJhEZEuBZobYjm6ToMAJ8vdiRXYjN7mB0UjQl1XXUNLYSGeQHajUZ+aX4eZuJDg1k9dZMNGoVE0YkuoefnjllNKu3ZGJ3OBiZEEV5TQObducycWQiDc2tbM8uRK/VMGZYHGu3ZBLk501iVChb91fVRQT6UFZdT11z6wHz1jm3rVVryC4sovOAZGFVfQ02u4NRidGkZ+YDzuGsm/fkUtfSSWpSNDX1zby2cjXL7rmCrvaWbglMRVG6zR2XlJTEjz/+iJ+fn3u5VMUJIYQQQgghTlYWi3NIqt0O8+Y5f8TA+FUk4qxWK5dffjkOh4OXXnqpz4/bs2cP//vf/6ipqaG2tpa1a9fS1tbGm2++yYUXXshZZ50FgEaj6XEYaG9JNa1We0TVaseqWUNv+5Lk2InlcOeUzW7n+fe/4dNVmzAadGTklxLk70NHl4Wc4krMRj3DYsLZuH/IaXxkCD9s3gPAlNHJbMnMw+FQmDQ8hoyiSjq6rMRHhtDZZWX9jr3ERYRgMuhIz8xzJ+SyiyqwOxxMSU1ma1Y+doeDlLgItBo1+WXVBPn7EOjjxdasfDRqFWnD4/h+/z7ThsexJ68Em72F0yeMJLekkoz8Uox6HSMTovhpVzZTRiWhANuzC7DZHe5hqka9Dr1Oy5jkWHeCcXhcJHqdloy8UsKC/Ni4r5zx0T5YLBYMBoP7eXK9xw5MrpnNZsLDw6mtrfVIxElVnBBCCCGEEOJk9OSTsHOnswruxReHOpqTy0mfiLNarVx66aUUFBSwatWqflXDFRcXs2HDBkJCQoiIiCAhIQGHw8H111/PyJEjiY2NRavVotFojuER/GIwE3GDXRE3mE7GpN/hjqmxpZ1bnlyG3W6nrrmVto4ukmPCaWxpY3duCaemjaCkspadOcX4eJmICQti3dYsQvx9iIsIYfMBzRKqauvp6LKSNjyO7P3DQhMiQ4gKDeTHHdkE+JgJDfJ3J+R+MymVVVsyAJg8Kont2c5qtwkjEiiurCWnpBJ/bzMRwQFs2JWDj9nIqMRodwJt7LA4Csurqa5vcg8/3Z5diEatQq/X8vOufTgcikc8PiYDsWFBbM1yVsWlRAezNSufQD9vxgyLpaSyjhVfrmfkwrMpLCwkJSXF/Vy5mpccnFhLTEykvLzcfT9IVZwQQgghhBDi5LN7t3NuOHDOCxcWNrTxnGxO6kScKwmXk5PD6tWrCerngObzzjuP8847z/37X//6V8rLy/ntELUJkYq4gdnXyaq3Y8sprmThklcIC/Jj055cEqNCCfDxYndeCZb9c7g1NLdSVFlLbHgQNruDzP0dU1OTYlizNRO9TsvopJhfupemJrIpw5nkSkuJJ7uwnMaWNobFRtDc2k52Ybk7uVZV34RapWLSqER3E4WJIxPQaTXUNraQEBlCe5eFrMIyvE0GJqcmsSrdmbgbERPCrpwiAGZNHMWOfUVU1dfg520mMjiAH3dkMz4lHr1O644tJTaC6sZmEiJC8DYZiY8MZk9eKQDDYsJBpaKxpY3IEH9+zizBy2ggMTERnU4H9FwRB+Dt7Y1Wq6W6utrd7OXAqrgjrXQVQgghhBBCiOOFzeYckmq1OjukXnHFUEd08jmhE3Gtra3k5ua6fy8oKGDHjh0EBgYSGRnJvHnz2LZtG19++SV2u53KykoAAgMD0ev1/d6f2Ww+qmYNR+tkrogb7ERcf5sbHO96e/5Wpe/hruf+TXxkqDtRlRgV6h7+OWVUElv3OivUTh8/ki1Z+bR3dpEUHUZLWwd78koID/LH22Rg294CjHodsaH+tHdaAGeFnDsBFh+JXqMhp7jCPWQ1q7AMHy8Tk1OTfkni7X+MyaBjcmoSe3JL6OiyEB0aCCrYk1uCl8lAUlQou3JLAGclXVFlLY0tbcRHhNDe2UVWYRkGnRadWuXe9qj4cPaVVGGzO4gI9CXQz5s9eaWoVTA51bnfiGB/xg2PJ6+kgs83ZPLbU8dRXFzs7hDscDhQqVQ9VrhptVrq6uro7OzEaDQCv7xXbDabO5knhBBCCCGEECeiv/8dtm4Ff394+WU4iT42HzdO6ETcli1bmD17tvv3O++8E4Brr72WRx99lM8//xyAtLQ0j8etXr2aWbNm9Xt/JpPJo1nDYBvsRNzJWBF3MjswsagoCq99+gNrt2YS6OvN1qx8vIwGkmLCWL9jL/7eZobFRrA5c3+X09HJtHZ00N7ZxfiUeDILyuiyWBke6+yGujkjj9BAX7xNRvaVVuNtMjClh+RaQmSIxxBR15xtdrsDb5OBhKhQjyGnAB1dFlIToygsr6Wts4tgX29GxIazNbsItQpGxIaRvj/OMQlhZJfUYrHZCfAxYdRp2ZxVQHJUMCaDnt355QAMjwklv7yG4XGRNLWa8TPp3fuNiwzBZnPQ2tFFmL83GWVNdHV2EBcX5+5OfKhhpv7+/hQUFDBy5Ej3815RUUFDQwMTJ048qRK8QgghhBBCiF+PvXvhkUect597DiIjhzaek9UJnYibNWvWIRM4A53cMZvNdHR0DOg2+2Mwh4uerENTXU7GijjX8XRarNz3wnvsyinCqNeTU1JJZEgAWo2aXTnF+HmbSUuJ7zbkVKNWcWracH7csQ9wDh/dlVNMYpSKlLgIquqbya+vJsjHjMlkQKVSYTLoGB4X6U5yhQT60dza7k6u5ZVW02mxolZBiL83u3NLMOg0xIT4s2lPLr4mAynRwWTml6EAsaH+NLR2UFnXhLfZQICXkcyiKmc8I+Jp6+jCYrMzMj6SitpGKupb8PUyExkazLrte4H9XVIz8pydT1Ew6LQU1zSi12oYOzyejbtyiIsIZnRiFDklVbz++VqeveF8SktLiY+PP2QizuFwEB0dTWZmJomJie4mD67OyFIVJ4QQQgghhDgR2e3OIaldXTBnDlx77VBHdPI6oRNxg+14SMQdOFH8sXSyD009WVXVN/HHx15FrVZTXFmHxWrjtLQUduYU09zW4R4ymlNSQWiAL75eJrbtLXAm1GIjsNocqFQwJfWXIafeJgONLe00trSRHB1KRW0jdS3taIAgXy927itCr1UTE+LP5j25xIf5MyImhIz8MgCSIoOoqGsmOTqUEH8fDDotueV16LQakmLDna8/tUwelcSOfYVYbXaC/X1JCvBhZ06JO9m3dW8hADMnjuLH7XuxOxzERYRgsVpZt30vKXGReJsM7rhHJ0aRkV9GXEQIXRYLoYH+bNlfWRcRHEhLWxtdVht6rYb82nbstgJiY2MPm4jz9fUlMDDQo8mDw+FAo9HIXHFCCCGEEEKIE9Lzz8PGjeDjA6++KkNSjyVp89cPJpNpSBNxg5mwGsyKOBj8TqYn21BYRVHYV1rN/EdewqDXsW1vARarjSmpSbR1Wmhu6yBteBxVdU1U1jVi1OkID/Qlt7SKEH8fAnzM7MwpprC0iqSIYDbtyUUFDI8KYuveQtQ4GBETSn5ZDW2dFuLDAqhvacff20yIvw/hQf7kVdRj0GkJ8PejudMGwCmpyeRX1NPeZUWjdVaKldY0ELS/G+v27CIaW9qZPCqJ9Mw8rDY7aSnxVNY10WW1EehjJjTQj537itCoVUwelURzazt2h4O04XFU1zdRUdtIsL8PYYG+bN1bAMDkUYnsyS/Danfga9ajKAo5JZUYDTomjEhg4+59tHdYGB4dQnlNA298sR6NVktZWdkhO6C67ktKSqK4uBiLxTlXnisR55orTgghhBBCCCFOFLm58OCDztvPPAMxMUMbz8lOKuL6wWw2D+kcca4P+oNhMCviXEmPwRguejJVKimKgt1ux2Kx8OWP23nxk9WMTorh5925qFUqRsaFszkjDzUwOj6MHfuc3UeTIwMprKwlLiyA+PBAapvaqGnsIjzQF1QqTEYj3mYj0SEB7C2qQKNWEeDvR3ZRBQ5FYURsKDmlNdgdCt5eZkprGqlpbCE0wBdvs5Ht2UVMSU0kIjiATRnO6rQpqclszcpn/Ih4/H28qG9uJbekEl8vM0a9jrYOZxOUA5s/OOwOfMwGiipqCfAxExbkT3pmHirg9PEj3ENRh8dGUNvUwvode0mMDMHby0R6prOj67jkGHbmlhLi542X2YRRr2fb/mRdgK+ZhqYWOi1WQKG40YKSn8/IkSPRaDQ9Pt+KoqDRaPDy8sLf35/CwkKGDx+O3W5Ho9FIB1UhhBBCCCHECcXhgIULoaMDzjgD/vjHoY7o5CeJuH4Y6q6pg50cO5k7mR6PFXGKomCz2bBYLFit1m7/9rTM7nDw6U8Z/JxZjFGvJTO/FD8vE4G+ZjIKK9BrNQyPDceg16ECJqcmsTnDOTwz0N+PspoGWju6GJ0UQ2F5Da0dnUSFBOFjNrK3qAI/bzORIQFszsjjlNHJOByKu3HC5FFJbMnKJyEqlLAgP6obmskvqybI34e2DguFFTVo1ComjEhg8/6EnFGvY1dlLRarzd3IIbOgjHHD4xk3PM6dhJuSmsTWrHyGRYWQGBVKW0cXewvLMep1jIiP3J88g0kjE9mZU4TVZicyJIDosCDWbd+LWqVi0qhE97GGBXhTWO2snvMyGUiOCWdbdhGJkUFMGpnI7rwS3vr6Zx687DTq6up6rIhzvfdc9yUnJ7N161YSEhLcFXHSQVUIIYQQQghxInn5ZVi3Dry84PXXZUjqYJBEXD8cD11TT8Y54lwGIzk2mFVKrgqq1tbWPiXWbDabs8GAWo1Op0Ov13v86+XlRUBAgHtZp8XOff/6kIraNiw2B/UtHSREhhAS4Ovscrq/Qm1Pfhmx4UGMSY5lc0be/uRYIumZeUxJTSImLMjd3GDssDgyC0qJCPZ3z7+WVVCGn7cZu93BtuwCVEDa8Dh3Qi46LIgft+/FarOTHBNOY0sbGfmlTB87nPrmVtIz853zzo1K4scd2YxOikGjUbNvf2fViCB/GltacTgUtBo1aSkJ7gSaoih0WmxU1TcREeSPwaBjx74izAY908cO5+ddzsYSqYkxFFfVsnF3DjHhQXgbDe5tjE6IYE9hJSEBfviYjajVanburw40G/Q0t3XQZbHS0dVFdbsCVVXuJgwHOjgRFxAQgK+vL0VFRdjtdnQ6HSqVSqrihBBCCCGEECeEggK4917n7SeegISEoY3n10IScf1wPDRrsFqtg7avoaiIGyxHsi+Hw9GnKrUDE2sAW7du7ZZU0+l0mEwm9+0D7+9pWOTBCitquG7xy0SGBJBdVI7d4WB4TCgl1Y14mYykxEVS3dBEflk1UaGBOBQFlVrlrnBLz8xDp9GgVqvZmpWPoigew0Jjw53zxHV0WdwNHrZk5XPK6CQqa+rZvq9of9VZEuu2ZuLv40V8ZAi7c0uwWG0kRIbQ2NpGdlEFPmYjcREhbNqfGAsN8GXVlgwARiVEU1ZTT0VdI1NHJ2M26t0NFcYPi2V7TjGnjE7G39tMWU0DFXWN+HiZiAkLwr4/MTYl1VmZ53AoxEUEExsezPrte9Fq1EwYmcjm/ceUHB3KzpxiWto78TYZiQrxZ09BBeNT4pk0KpFdOcW8+91mbps7Ea22+59GVxL8wGq5pKQkduzYQWBgoPt1k6o4IYQQQgghxPFOUZzDUNva4PTT4eabhzqiXw9JxPXD8ZCIO5k7mQ52RZxrfrXDJdbcw0D3J2K0Wm2PiTUvLy+PZVarld27dzNz5swBPYafd+7j1qeWkxAZSmeXc3hq2rAYduSUAGAy6GhubaehuY3RSTEUVdTQ0t5JVEggJoOerIIyAv28CfLzZuPuHCanJmG3O9xJuFNGJ7Nxdw46nZa0uDiy91euxUWE4HBAUVUD3mYjceHB7iGn40ck8P2m3QCkpcSzt6CMTouVU8cNp6Cilj15JRj1OkYmRLFqSwYxYYFEBAewNasAu8NBUnQYJVV1tHVaMBv1JMeEsz2n2P2alVTV0dLeSWx4EFabncz8UkL8fZhywFDbccPj2FdUQX1TKxFB/vh6m9xJuJGxoWzJzMdkNBATFoQCZBdXAmDQa6msbcJitVHX1EJls4Vwug+VdjgcqFQqj0RcYGAgXl5etLW1ERAQ4I5XquKEEEIIIYQQx7M33oAffgCTCZYtg1761YljQBJx/WAymdyVTj1VzBxrg52IG8x9wdEl4g5sXHCoKjVXl8uffvrJfXw9DQM1GAz4+Ph0u0+r1fbaUfNgra2tA5qEURSFf3+1nufe+4rQAD/SM/MI9vdh0shEtmTlO+eAG53M5j25TElNImJ/5ZvDoZA2PI5dOcUE+HqRFB1Gc2s7OcWVhPj7olVrSM/IcyfKXAm5U9NG8P3m/cm14c6EXFFFDcOjgmjqsJGRX4qP2Uh8ZAhbMvLQ67SMT4l3P35sciwtHV2UVdcTFuiL2Whge3YhWo2alLhIvt+8B4AJIxLIyC+ly2Ll1LQUiivr2JVTjFqlYkRsGBt35zAlNZkuq9U9nDXI3wd/Xy/3c3NgNV9SVBihgT5s2J2LXqshJS6c3XllAEwdm8yWrEJa2zvxMuqJDPZn4+5cJo9KIjjAh925JXyyfic3njuBuro6goOD3ftwOBzdXnuVSkVSUhLbtm3rtlyq4oQQQgghhBDHo5ISuOsu5+2lSyE5eWjj+bWRRFw/mM1mADo6OvDx8Rn0/Q9mIm6oh6YqitKtQu1w86y5KpgOrFJz3TaZTPj5+aHVasnIyCAtLQ0vL68TpmLJYrXxzNuf883GXei0WrIKy9zJLQAvo57wQF8278lFp9Gg0ajZkukcrnlgkmpa/DB+3JGNxWpzdxvdsHsfaSnxNLW0sT270NkQISGKjLwS59xuqb88fuywOKxdHVTV1xEdGggq2J1bgsmgY8a44azekgk4E2OueeemjR1OVn4pVfXNBPp6ERLgy7rte/H3MZMSF+nedmqis2FEdUMzAT5mAny8yCxyVq3pdRq27c3HZne456HLKa4kLjzII/k3YUQCGXklNLd1EB7oj4+3yZ2EG5sUxZbMfBRFRWx4MF2WLnJKqwHQatQUVtTQZbFSWtNIVXMn+fn5Hok4u93eYxI2ODgYtVpNY2Oje5lUxQkhhBBCCCGOR4oC118PLS0wbRr86U9DHdGvjyTi+sGViOvs7DzpE3EDXRHncDh6Tay5qtR2797tUdUGzmPuaRjowdVqB86vdqikh+uYzGbzCVOpVNfUwk2PL6Ozy4K3yUhheQ0pcRFU1TdTVV9DgI8X3mYjeeW1BPp6Eezvy4ZdziGnNpvdY8jplsx8wLPb6Ij4SMwGPTuyC93JvR37E3Knjx/J2m1Z7sdv2pOLTqMmbVgMuaXOLqsRQf7o9VoKy2vQ67SMTorx6H5qs9lobG0nKTqMlrYOsosq8DIamDAigVXpzrnipoxOZsv+6r2ZE0aSVVBGfnkNeq2GUUkx/Lgjmwkp8ajUanbtjzsiyB+V6pfE2IEJR38fE4F+Ps54tRqSo0LYtT8hN3vSKDZn5NHW0YXZoCM5NpINu3M4ZXQykSGB7Mkt5ouNWUT4m2loaHAPOe2pIg6c7xWDwUBtbS12u13mihNCCCGEEEIct95+G775BgwGWL4c+jBFuRhgQ5qIc3WVVBQFh8Ph/tBqt9vd/4aEhAxliB50Oh1qtXrIOqcOdkUcdJ8ny8Vut/erYs3VuECj0fSYWAMIDQ11z7PWn8YFR2IwG0McjayCMhYueYWo0EB27CtiwogEjyTamOQYsosq8PcxER3iT5fNwb7iCkIDfdFpnENOXXOubdqTi0oFv5k8mh/2DwudPCqJ7dkFaDRqxg2Po6iilqr6GndCrq6pFYNeS2pC9C9DPyOD0Gq1tHZ0Miohyt1EIdjfx12dplGrmDgikc0ZzqYQU8cksz27iC6LlajQANQqNTuyCzEb9IxKjHbP5TZhRAKl1fVUNzQT7OeNXqdhR3YhKhX4eptYs9WZFByZEEX5/v2ajDrGJP+S/Js4MoFdOcVYrHbCgvzQa9Rk7a+sG5MYyY7sQixWG+GBvgDsynF2UFVr1BSWVNJltZFfXoei9yI/P5+JEycCzkRcb+ejq9NtaWkpcXFxgDMRZ7PZaGhoIDIyUqrihBBCCCGEEEOqvBzuuMN5+9FHYcSIoYzm12vIEnF/+MMf6OzsdCd0Ojo60Gg02O127HY7DoeDmpoa9uzZc8hkzLp163j66afZunUrFRUVrFy5kosuush9v6IoLF68mNdee42GhgZOOeUU/vWvf5GamtrvmFUq1ZA2bDhWiTjX/GoHJs5cx5iTk4PNZuuWaHM1LnAlzQ7XuMD1b2/zq5WUlBAcHOyuOjxWTqRkyKr0Pdz13L/dHUxVKtBpNGQVlmG12T2Gf45NimRrdjEWm52UuAiqG5r5edc+xg2Po6G5jV05xe7upSWVtfs7nia6Gx2MT4l3Dq9saWNEfCSVdU1U1dcQGuDLqIRotmUXotdqGDMsjq1Z+YQEdDIlNcndbGF4bDi1Ta10WW3OzqzBAWze3/10wsgEFAW6LFZGJ0VTVFlHS1sHgb5eTByVyPrtewHPirbTJ4xg+95Capta3XGv2ZrFiLhI/LzNbMnKx+5wEB0aSEt7Jz5mU7djMhn0hAT6sSUzD71Ww+hhcWzLclYEzp6YysY9++josuJlMpAcE86GnfuYOmYYIc0tFFbU8cXGTC4YH0tzczO+vr69VsSB8z0UERFBfn4+MTEx7vXa29vZtWsXoaGhUhUnhBBCCCGEGDKKAjfdBI2NMGkS3H33UEf06zVkiTidTodKpcJoNGK1Wnn77bf505/+hL+/P3q9HpVKxQMPPEBHRwfe3t69bqetrY1x48axYMECLr744m73P/XUUzz77LO8+eabDB8+nKVLl3LWWWeRnZ19RMNLhzoR15dKLkVRekyeHWp+NVdHSFcizdWMwuFwYDKZ8PX17TYMtD+NCw5nMLu0wvFdEacoCi98+A0rV28m0Neb7dmF+JiNzoRcRi5TRydjPaDL6dTRyezMKQKVyqNablRiFF5GAzv3FTk7hSoKe/JK8PEyMSU1iY17cj2SV1GhAUwalciO7EJsdgcpcZFUNzSh02oI8vchwMeLrVn5qFUQExZEl9WG3eHw2Ke/t5nIkACyCso8KvECfMycMnoY6Zm5OBwKyTFhNLW0U1pVh5fRQFJMmMdw1vqmNlraOwkP9EWj1bInrwStRk1kaIB7OOuY5BgKypzDY8enxDEyIcqdhJs4MoGd+4oYFhtBkK8XJqPenYQbnRBBVkEJnRYr4YF+6PU6du5zVsVp1CpKqurptFhJz8znoump5Ofnk5aW1uscceCsEA0ODqa6uprS0lJiY2Pdr6XrCwaZK04IIYQQQggxVD74AD7/HHQ655DUIeg/KfYbsqd+xYoV7tsWi4XPP/+cp59+2qMb6ZIlS2hraztkIm7OnDnMmTOnx/sUReEf//gHDz74IL///e8BeOuttwgLC+O9997jhhtu6HfcJpNp0BNxrsYFrqRZdXX1UTUuOLha7cDEmitRYLfbWbt2LUlJSYNSyTNYibjjPRHS0Wnh7n++w77Ccuqb24iLCCE2LAi7w0FGfimBft44FIWtWfmYjXqGxYSzcU8uKmDKqDg27Z8DzlWtZjbqSRseR25JFa0dncSGB2G12bE5HPh6mYkKDWBzRh5qlYqokEDaO7uw2T2Ta60dXRj1OnJLKgnwMeNl0LJtbyEjEyI5JTWZTRnOBFpaSjzZheWMHRZLZEgAWo2GXTnF6DQaEqPD91e6KkwckcDuvBIsVhs6nZZRSdGkZ+ShUauYMCLBnUybMDyGrMJKOixWd5OHVekZxIQFEhkS6K4GjIsIprKuCb1Wu38biaTvr8YDCPLzYl9JNUa9jtSkGLbuT8hNGB5NdkkNbfVNmAx6RsRH8tPOfYxNiqLLYqOsrolvtuRwVmo4bW1thxya6rovMTGRnJwcoqOjUavV7uSdq+p0KLotCyGEEEIIIX7dqqrgttuct//6VxgzZmjj+bU7buaIs1qtFBYWkry/b25jYyNGo5Gurq4j3n5BQQGVlZWcffbZ7mUGg4GZM2fy888/H1Eizmw2H/UccT01LjjcPGvwS0VcUVGRx5DPI21ccCiuxw1mc4jBMtjVd31VXtPAosdexajXkV9ejdVmJzUhmpzmCto6ukiOCaOxpZ3NGXlMTk2iur6JnfuHnIYH+lBR24xWoyYt5Zdk1qjEaOx2B60dnYwdFkduSSXtnV14mwwE+3uTVVCGr5eZ6P0JuXHDYpmSmszmjF/mW9udW8K4YXHotRraOy2U1jbhYzaiVqmprGsEPIeVWqxWrFYb5TUNBPp5E+TnzdasfBIiQzzWcw1T7bJYCfAxExrkT/r+ROIpo5NpbW2jw2IlKSqMlnZnkwe9Tsvw2Eh+SHfOcTdueBz7iiro6LIwJTUJs8ngTsJNSU1m2958kiKDCfb1xs/Xy52ES40Lp6qumbaOLiKCAzDotWzPLgRArYKi/VVx67Zncf7k4eTn5xMQEHDIiji1Wk1ERAS5ubmUl5cTHR3tkbyz2WxH9Z4UQgghhBBCiCNx221QVwfjxsH99w91NGJIE3GuroIGg4GQkBCWL1/OwoULMRqNvPrqq0RFRR3VZP2Vlc4J2sPCwjyWh4WFUVRUdETbNJlMdHZ2eizrqXHBoRJsrsYFWq22W7WaXq/HZDL1OL9aW1sbu3btYvLkyUcUe3+4kgWDlbA6XpNjg2VrVj7//OAbvEwGjy6nG3bvI9jflxFxke4qspEJUWjVaooqaokJD8Jhd5BTWoO32cCEkQls3pPnns9t055ckqJCmTo6mY37t+tKXo1JjsHucGCx2sgsKMPHy4TN7qCyogaVCqaM+qXaTafVUFnXREeXhWA/M3qdnoz8UqakJhLg6+URc3pmHhNHJODn40VjSxs5xZX4mI14m400tzmrSaekJrMly9kltbW9k8iQQDLySzHotYza3xhCrVKRNiyGrKJKuixWQvx98fU2sW5bFoG+XgyLjXDvNyEyhLLqeixWGxq1irSUeHcy0WK14e9rJre0CoNOy5hhse7usaekJpJVVElFbQMGnZbRybFszconbVgMXTaFitoGfthVwKnJQZhMpl4Tca6Em0qlIikpiby8PCIjI91dVF3nt1TFCSGEEEIIIQbTJ5/ARx85u6MuX+4cmiqG1pB/InQl2h544AEeeughvvvuO3x8fNi0aRMPPfTQgHRNPbgCpbdOoD3ZuXMn27dvp6amhpqaGkpKSnj44YdZvHgxN954I8nJyYdsXGAwGPD29u5X44JDHcdgVqgN9v5+rXPEffT9Rv5v2Upiw4PIKancn0SLdSeZxibH8t3m3QBMTk1iW5azy+nEkQnsLSynraOL6BB/OrqsKAoe87npdVp8vUxUNzQDnpVrGrWamoYW2ju7iAkPwm53Dn+dNiaZ0EBfdxLulNHJ/LxrHyMTotBpNWQXltFlbSciyJ/GlnYqahvRaTSMGx7n3rZep6W4shaL1UZ0aCCAs7JueBwTRya6k2TjU+LJKihjbHIsoYG+eJuM7sq01IQIFEWhy2IlJTaCmsZm8kqrMBl0jE9JcFfFubbRabEyfeww6ppa2ZpVsD/2YWzJzCU1MYrQQD98vUzuJFxKdDB1TS00t7YTFuiHj5fJXTGn02jYW+Tc5jcbdnJW2kXU1tZiMBi6vX4OhwNFUdzvZ1dVXGVlpbvBg+v8lqo4IYQQQgghxGCprYWbb3bevu8+mDBhaOMRTkOWiHN9KHVVk1155ZV0dHTw//7f/6Ouro5HHnmEO+64A71ef8T7CA8PB5yVcREREe7l1dXV3arkerN69Wo+//xzQkJCCAkJwcfHhxEjRnD22WczY8YMIiIi3Im1Y/3h+lh1TT3U/k7GirjjJQlis9v52/LP+ObnHZiNenbsK2L62GHUNLSwNavA3cRgZ04ROq2GtOHxpO8fcjppVCKKotDW0cX4lHgy80vpstoIbe/AtH8+N1cF2fZ9RUwalYift/mgaruc/ck1rXvIanRoIA3N7eSVVbnnoHM9JjI4gFVbMlAUhRHxkVTUNlJR18iMccOpaWhhS1a+s5IuNZmfdu4jNTEatVrlbqgQ6OdNR2cXLe2d7hhc227v7EKv05JfVo23yUBCVBi7c4vxNhmYMjqZ7XsLsNrsRAT5Y9DrSM/Mx2TQMXbYL8m/pOgwymsaqGlsQaNWMXFkEpv25ADQ2WnFx2wkt6Tyl+dy/xDWGeNGkFVQSm5JJVqNmmHRIaTvLWRyahKNzW00trbzY1YZE6K9CQ0N7fY6ut6Tri8V1Go1iYmJ5OXlERMT414uVXFCCCGEEEKIwXTHHVBdDaNGwUMPDXU0wmXIPg0uW7aMrVu3otPp6OzspKOjA4fDQVpaGo2NjVx11VXo9Xp3RcmRSEhIIDw8nO+++47x48cDzsYQa9eu5cknn+zTNu644w7uuOMO9+8XXXQRM2bMYP78+UcU09FwJeL6U9F3NKQi7thpam3nsTc+JTO/lMaWdlo7OkmOCaO9y0pOSSURwQHotM5mB37e5v3JrWxntVyys1ouLjzYI5k1Kj6c3NJqxg6Lw2w0UNvUQl5pFaGBvrS0d1JaVecx9BMgMtifHw7qQlpaXc+MccMprapnZ04xep2W0UkxrN6SQZCfN4FeenKKK7E7HCRFh1HX1EpOiXP4aXxkiHvboYF+rNmaiaIoJMeE09jSxr7iSk4ZnUxIgK97vcmjEtmeXcjEkYmgACrYnVuMSgVJUSHOOSRtdkYlRFNWU09FXSNeRgNTxwxn9RZn7BNGJJCRV0KX1caU1AQamtrdVXcj48LYV1rD5FFJNLW2E+jr7U7CDY8OpqWtjfrmVgL9vAkN8CWrsBwAo15PSVUxnRYrK9dsYcI1Z/Y4P6TrPXLg36moqCjy8vJobm52L5eqOCGEEEIIIcRg+eILePddUKthxQroYXCPGCJDlojLyspiw4YNhISEuIdp1tXV8cUXXxAaGkptbS1RUVGH/bDa2tpKbm6u+/eCggJ27NhBYGAgsbGx/5+9/46TLa/r/PFn5ZxDV66u0Ln75gRDGOS7urugrrssumBEMbArAgoi67I4Iiy44CgqSBQRw89d2VVX0FEYJt7QN3cOVdWxOofqqq58zu+PU31u99w7A3Nn5vY4c56Pxzy6bp2qz/mc6qo7U695vd4v3vWud/GRj3yEjo4OOjo6+MhHPoLZbOYtb3nLXe37MFpT99j7Qn+vhDjFEffCkFlY5qc+9GnaQ36GM/OIosiJ7gRDrRlwrzzSydD0HIVSmXjQR7VWp1Su3IqcjmUxGXQ47RY2CkUAjqTC3JheAMBs0HF9Qip76IoHWdksMJ5b5FVHO1lc2+LqeE4W1x6+PILbbqUjFpBbSHuTYYq7FWaW1mRX3ZWxLBq1iqMdMVm4299++sojncwurXFzau6AcBfyuQh6ndyYnKXeaBLyudgolFhY2ZBca91JLrZEsaYgUCxX2dopYbeY8diNXJ+ap81l53hnjBtTczQFEb/Lilql5ubkDGoVdEX9XBmToqgBl5WFpXWWNiVX3KnelCz4FXfLmI0GJmbzaNRqOsIexuZWATjdm2Imv8pYbhEVcKIrzqNXRznRnWBtawdBEHh8dI77uoLU6/UDTcJ70fT9QpxarSaRSDA9PY3X65XvV1xxCgoKCgoKCgoKCgovNJubsNdN+cu/DGfOHO5+FA5yaN8EP/GJT9zx/j/7sz/j4sWLWCyW72qdwcFBXve618l/fs973gPAT/zET/DHf/zHvO9976NcLvOOd7yDzc1Nzp49yz/+4z9is9nuat/PR2vq3bL3Rf+5uASfDfdaHHupOeLudI5vXx7h3Z/4Mu0hP98aHMbvspMI+2Wx6ExfiqYgUCiVOdYZZ7zVCOqwmXBYTEzNLRHwODDq9VyfmOFMb4qBdJQbU3OokGbIPXJ1jFSkDbfdyrWJnCzIbRV3ySys4HfZsVkkcU2v1XCiu51/ujgkn//yaJamIPCKgQ7GZxaZnl+WW00vj2XRqlWc6EnKzaxH0jEq1RrzKxt4nTacVjNXxrKoVNCXjPDQBWm+XX8ywszSGourm5zuSbC5U5JFuIFkiMujGToiPoxaNdV6g2x+A7UKHGY9O8USTUEkFfKytFGgVKlhNug505vk/LA01+1YV5zRzALVeoOBRJBSpS6/rsc6YoxkFznZk2Bnt0zA42Q0KwmXHWEPgiCwslnAZjHhtZm4PC6VuZiNBta3pTl8f/vEEK/qiTA7O0sqlZJ/p/vnwO0nEokwMTFBrVaT71NccQoKCgoKCgoKCgoKLzS//MuQz0NnJ/zGbxz2bhSeyqFaMkRRlP8RBAGVSsVb3vIWPv7xj+P1evn1X/916vX6M86Ju//++59RVFGpVHzoQx/iQx/60POy5zu1pt4r9gtx94KXajT1MMQPURT54t88zB/81T/gdtgYHM1gMRroS0f41qURNGoVJ7olcctuMR1oOT3Zk+Dm1BzHu9ox6LTk17ZYWt8m7HexUSiyuLaFxagn6HFwcXgatUpFIuSTxbXTvSlZkLvvaCej2QWm55fxOG24bRZutOKnRzpisrh2qidJUxDYKJRIhHzsVmqM5xaxmAz0xPzy4073JhkczSKKIsc6ouTya0zNL2M26Ah6HDx6dRSDVkN7wMVwdh5RlFxrKxtbzC5votOoSUf83MxIcVCHxcLs8haVWh2byYDPZWdiYZWOaJvcyCoIolQAoYKNHUkU3x/RTYR8CKjJ5NdQq1R0RX1cm5wFoFSuYjEZZRHuVE+CwdEsGvUGRzpirG8VyS5tAPCKI508dm2MgY4YK+vbaDVqLmdW0KhVxONx2dH2dMK4RqPBbrdTKBQOuFgVV5yCgoKCgoKCgoKCwgvFN74hRVFVKqkl1WQ67B0pPJUX3lb1DKhUKtRqNRqNBp1OJ38pfdOb3kRfXx/AcypreCGwWCwvCkfcvTrfS9URdy+p1uv8yoN/ysODw6hQMTGbJ+h14XHaGM/lcdnMpGNBLo1MY9BrSUXaqNYbgCQwXR7NUqs30Gk1TM0vs1XcpS8ZpVAsMzW/zMnuJFaTgamFVWwWEz2JMI9fH8dmNnKmL8WlkWnqjSanepPU6k02CiXS0QAqYHJuiXqjwZm+FIMjGdQqFSe72xkczXBtIsfRdITF1U2WN7bxO61YDDrWC7toNWpSQTeXRjKIokhXxEetVmOrWCbgtmOzGJleXKPRFDjTn2J8fg1RhONdcbZKVWaWNznbnyYe9DE6syRf66WxHO0hH6lIGxqNmsziKnqtBpvFzMZ2EUEQGUhH2drZZX55g8XVTV51rEsW4Y53tZNf22I4u0BvvI1k2Mfo7AoAJ3uSjM/mCflc2Cwm4n4ng6121bjfiUmvY2F1A71WzZF0lCdvTABgt5ioN5rMrWzyv799DZ1Oz/z8vPz7bTabciHDUzGZTDQaDdbW1uT79gS5RqPxkn3PKygoKCgoKCgoKCjcewoFePvbpdvvfCfcd9/h7kfhzhyaHUMQBEqlEo1Gg2q1SqVSoVarUalU+Pf//t8TiUQAuHDhAnq9Xi5bOGzMZjOLi4uHcm6VSnXPXWr3sqX1pSj6bRXL/Miv/R6CIFCu1ljdLNCbDDO/vEGhVCYR8tHmdnB1PIffbcdqMnJ1PMeRdJSBdJQLQ1OoVSpO9SZ57No43e0hHFaz7AwbSEdZL+ywvLlD0GNHpdYwnJnHZjFxoqudb18ZRYUkgA2OZNBp1BxNRxjJ5ak3moS9DkqVGvOLK5gNWrx2C5fHcgB0hLw0m02q9QYdET+La1uUKjXsJgNH0jGujOdae0txcXgKk0HHye4EYzNSlNPtsOJxWMksrqJSwdm+Wy6/rliQeqPJ1PwyJoOOznhIFtMCHidP3JigVm/gtltwO2xcGctyvCvO2f60PMsu4ncDyC2s+12E8YAHo8nESOtaJOebFGGtVRs4rGZmltel5w10cP7mJAvrBdLRADvFIjem5gA429/B+RsTdMSCVOs1TEYd0+tlms0ssVhMLlB5pqi4y+ViamoKr9d7wBWXz+fxer13HZNXUFBQUFBQUFBQUFDYz/veB/PzkEzCb/3WYe9G4ek4NCHuq1/9Kg888AB+v59qtSoLPlqtlvn5ed73vvfxrne9i7/927/FZDK9aIS4w4ymwq3m1Ht1rpeiOHavGMku8ME/eYiOeJgLLRHoNcd7ePz6OE1B4FhXO+O5RdwOG13xECub22QWVgj7XWzu7FJrNLBbzET8Li4OT6PVqAl6HHzr8igAxztjXJucRRRhoD3A5MIalXqDNqeVerPJeHYOs16Lz2nlyvgMKqAz2oYgCNQbTfraQ0wtrFCtN7CZTbSH2hjJLmA26umIBbk+MUPY52q56iTnW0e0jaW1TVRqNTaLiVibR24nPdIRRxRFSuWq3JI6ObuE2ajnVce6efTqGACnepNcn5ih3mhyoqud9UKR6xPSTLaz/WkevjxCyOdCg0ixUmNqbgm9TotarSaXX0UURY6kY2QWViiWK2ztlDg3kOb8zb19xJicXWJmaZ1E0I0oiLLz7VhXO5MzebraQ6ysb9KTjHL+5iQAbS4rPqeVqbklqUiiN8WFIemY225hbXObybkVvvz3j/PAj72ehYUFotHoMzrims0mbrebXC7H+vq6XNygUqmYnJxEr9djtVqVWXEKCgoKCgoKCgoKCs+Jb34T/uiPpNtf+AJ8l2P3FQ6BQxPikskkb3zjG+XWVKPRiE6nQ6/X02g0OH36NAA/8iM/clhbvCNms/nQWlPh3otjL8UZcfDCu+/+7tEr/O6f/z1Oi4kLQ1NoNWqOdSWo1Go0BeHATDNEgfzaBoVShY5oG4urm5QqNY6nw8yubDKSXcBmMmA3G3j02hg2k56gx87VCWnu2UAyRL3eoFJv0NseJJtfp1ytEW3z0Onzcm0ih81spD3k4+bUHGGfi7N9aS7sE9Cm5pYI+dwEvS70Wg3XJ2bQazUEvE52KzVEUeRUb5Jr4zM0mk02C0WCHifDmXm5JfXC0BQhn4uT3QluTN1qSdVqNGwWSrKzb2++XG8yjEGvYya/Ju9v7zXpaQ/z8OURmoIgN7deHs1ypi9Fe9Anu+LCfjdqlYqmIP0+97+usYAXn8vOxRFJBD030Mn5mxOtl1zAaTHJAuC5/g4ujkyxUazgsprwuZ1cbK1zojvB1fEcLqsZvVaDxWRkvtBAzGQIh8PP6IgTBAGdTkd7e/ttDap7zxME4WmFPAUFBQUFBQUFBQUFhe9EsQg//dPS7V/4Bbj//kPdjsJ34NCEuPvuu4/7vovAcn9//z3YzXfPYbamguKIe77O9UIgCALVapUH//zr/PW3LhFvc3NxJo/NbMTrsDA4Mo3bZqIj7JXFos6wh8tjOTrDXiJeB2OzKwiiSHesjZWtIhs7u7QHvRTLVRbWC3idNnoSYR69OiYLYFfGshh1Go51RLk2KUUqj3TEmJpbJuBxyhHOm1NzmI16vE4bO2XJ1blfuKo16jQaDfJrm3icNlw2C5dHsxxJRznTl5adb4mAi6X1bQbS0QMNrCoVRP1udqt1yXGXjDK7vMZOqYzdauJ0X+pAO+zgaAaNSk1PIkyhVObm1Jwk1vWleOLGOHqthpDfy2ahxPT8MnqdFlGE4cwcoijSn4oyk19lZ7dCcbd8YP3+VJTs4gpzy2u0uSx4HHZZhOtPRZleWMFh1qPTaDjW3c75lvPNZtThtJgYnc0D8IqBTp5sPS8RMKHTabk8mkEQBH71TfextCTNt3s6Ia7ZbKJWq4nFYmSzWTY2NnC73fIxjUZDo9G4Y+uqgoKCgoKCgoKCgoLCd8MHPgC5HMRi8LGPHfZuFL4Th17Zt7CwwKOPPsrQ0BAajYbjx4/zqle96oBzZH/j4GFz2NHUl+qMuBejI04QBOr1OvV6nVqt9h1/FncrfO7rl1jZLlHcrTAxt0TAZaXeFMnm1/E6rJiNevQGAzazgWibl5HsAlqNmlDAz8OXR4CD4tj9p3p48sYU1VqdjmiAjUKR+eV12SV2ZSyLXqclEfTQaDbl5+85xuqNBrV6g5XNAkGvC51Ww/XJWY53xTnR1X5LGOtPMzgyzfGuBE6bhY1Ckam5JWwWE01BZGZpFbjVMgpQqzcRRJHp+WVsZiPxoI8Lw9P0JSPyeoIgEg96qdUbCKKITqPhaGdcdsX1p8NYzAZGswtSDLfNfcuJ1hHiRmaJRlOQBb9LI9Oc6UuhUqnka9xz8albf0fsj9IGvS48Fh1DOUlYOzfQwcXhKQRBxGczkQj7ubTXFNuKzO5W6zisJhKhNlmE602EmVtaw2I2olKB0aBnrQLaTIZ4PP6M0dS9Mpp4PM709PRtQpwgCIorTkFBQUFBQUFBQUHhrnj0UfjUp6Tbn/scKCOoX/wcqhD3xBNP8La3vY3Z2VkikQj1ep3f+73f44d+6If41Kc+haUVan6xiHBw+I64vS/u94KXmiNOEAS5qbJQKMgFIfvFtv3CWqMhNZdqNBr0er0cnd77aTab5T+vbBb5zU9+BbvFzOL6Ek1BYCAVZadYZGl5k654kOWNArPLG5ztT2G3mBnJLuC2W/C57Dx8eYRk2I/DapbFsXP9aQo7Zaq1Oid7EtyYlOKeHoeNNo+Roek5WaAanV0mEfRwojshP39PkDvdk8TrsskFEX6XnWK5yvp2sRWZbZfFL4tJz41JaX5bLOCh0RQYzsxztj9NyOuSRbg90ao3GcFs0NMURYam59BpNBj1OhZXNhAEkaOdcSZm8pSrNVSo6E6E5NKEvf25bGbSkTZKlSojmXlpPltPkomZBUQRuttDLG9sy644jUbNxSFJhNvvuqvU65zquRV97U9Jx1Y3t3CYDSQiAXkeXEc0wNLaBj63ViqS6L8VW9Wa9HQlojzRak0929/B5dFpGk2BzlgQn8vBkzcm0KhV/Od/e5JCofCM0dQ9gS0ej5PL5dja2sLhcCCKotwUrbjiFBQUFBQUFBQUFBSeLbu78La3Sbd/+qfhe7/3cPej8N1xaELczMwMv/qrv0p3dzfXrl3DaDQC8Hd/93e8//3v5xOf+AQf/OAHv2Mj4b3GbDa/rBxxL2Yhbk9YeyaX2n6hrV6vy8+dmZnBaDQeENZMJtNtgptOp/uOTqXzNyf5zx/7In3JyG2utnTIw+neFFfHszSaAkfSMUazi2g0alKRNnZKZcZn8vhddqIBD9++PCqVJUQDnB+aQq/T8MojnbIodKonyfXJGY51xumKh1jdlAQql81EoymwUSii12oY6IjJgpzRoOfyWJamIMilEJOzS9x3tJO1rR0GRzKoVHCmL80jV8ZIhv3YzEYm55bZrVTxu+2UylXGZxZRq1R0hD0Mtuau2cxGJmeX2K1ILaleh43LY1nO9KYI+93yHtLRNrZ3djEb9Bj0WvoSkQOz3HRaLVPzyzitZoI+lyymvfZ4F4/fmDzginvyxiTHu9rR67Rye2yb247ZoGdPxzrdm+TKWI6mIGA3G+iKB7k4mpOO9aW4Np6j3mjS47BhMRlvxVbTUaZn8+QWltFpNRzvTsiFDW1OKyubBco16X1UbwgUBS2q1VUcDscd3xt70VQAvV5PLBZjenqao0ePAsjim+KKU1BQUFBQUFBQUFB4tnzwgzA1BeEwfOITh70bhe+WQxPi5ufnWVhY4C/+4i8wGo3U63V0Oh1vfOMbyWazfPnLX+aDH/zgi65J02QyvWxmxB1GDPaprrRn+rknrO1F//R6/QEBzeFw3PH+Cxcu0Nvbi9PpfM77/tOvP8onvvJ3+N0OirsVDHotvS2RSa1SodWoGc7M02gKByKjrz/dx2PXJ6jW6rI4dnNylrDfjVat5vrkLFaTgUTYT6MpSEJZ762CBbVazeLaJjulMl2xIPn1TeZWNjnXn0ar0XB5NCu73R65OkpHNIDTZuHahCRApcJtrG0VmZhduhUrbQljybCff7o4BEhutKX1bYam53jFQAcbhSLjM1LM82x/mseujRMLeNDrXGztlJiYzWMy6BBFGJqS5tWd7E5wc3qOWr2B22El6vdwZTwnr3FhaIpEyEci5GO3WmM0u4BGrSId8jC/skmjKRwQHfVaDTaLiUeuSO2xPe0h8mtbLG8UaIoiJ3sSXGoJhZ2xIPnVdcZnVzDoNAykY3IU1e+wkF/fkiO95wY6uDA0JTnVdiuc7e/gsWtS0+vxrnZGMnNUt4qc60+RDPu5MDSJ3WLkx17TLbsnn8pTxbX29nYeeeQRtra25PfungituOIUFBQUFBQUFBQUFL5bzp+H3/kd6fYf/RE8jTdA4UXIoQlxWq2WcrlMMBgEQKfTycc8Hg+FQuGwtvaMvBhaU/+lRFNFUfyu56uVy2XW19eZnJw8IKzt/7lfWNv/825cRM9V4K03mjzwuf/Fw5dH0Ou0jGYXiAd9xIM+ro5LLaVhv4uxXJ4zfSmagnAgcjq9sEK1VpcdbvVGk95EBKNBx5WxrFSwoJIKFrriQUncG55CpYLTfZJ4daonCSA/vz3gYWF1k9XNghx5HRzJoFapiAe9srh2ojvBcGaeaq3OK490MrO0xtD0nCwiPnZtHKvJSE8ixNXxHI2mQDzoo9FsMj6TR6dRM5CO7XO7BXjs2ji1eoOgx4lBr+PS6DRn+9KgQn7cXsQ0FvBiMujoiofkY06bhVqjQXZxVXLFeV2M5hYAuP9kD49fn6DeaOJ12nDaLDxyZZTueEhuU20KAh6nDYNOS70uCWtn+lJcbTnfHBYtR9NRLraitQOpKBOzearbJSl263PLsdWQ28Zutc7iypr0+zrSyfmWI9Ftt1Cs1JhdWgdgdWuHjd0GOl3xju+T/Y44AIPBQCQSIZuV9rF3THHFKSgoKCgoKCgoKCh8t1Qq8FM/BYIAP/Zj8IY3HPaOFJ4NhybEeTwe2traGBkZob+/X46g1mo11tbWOHfuHPD0bYTPRKPR4EMf+hBf/epXWVpaIhgM8pM/+ZP8+q//+nOOub6chLinOuL2hLWni38+dd7anRxrTyeszczM4HQ6n3Hw/fN5Xc+FzUKR93zyK2wWiqxsFChXa7KrLeHwEw14EJoCY7k8drMBnVbLxevjUuQ0FuT8kCSovfZED99uubrO9KW4PJqlPx090AYaa/NQ3K1gMuixWUzE2jxcbD3fajbw8OWWKyweYGp+lXqzyWtPdDOaXWR8Ji8XIDx6bQyr2Uhf8lYk9EhHjGq9wcLKBm1uOxaTkavjOTRqFfcd6+KfW8Ldsc444zN5ZvKrDKQirG1uc2U8J7v0zt+cRKvRkI60sbC6SX59C5vZiFqj4skbkrh1ujcpi3qJkB+/28G1iRnglivuaGecZNhPqVxlNLeAWiWJhtvFMvVGk85YkLXtHabmlqSCC7+Lb14aBqSZb5s7JSZnl+iMBTjde2tWnN9pQaPVMbeyhQopmrp3rM3toFDcJb++DcCJrnZuTM3SaArsVuu85kSP7LwLeexUGwJDU3OcG+hApVJx/uYk/9es5ydff4TNzU1cLteB98qdhLVEIsEjjzxywP2muOIUFBQUFBQUFBQUFL5bHngAxsagrQ0efPCwd6PwbDk0IS4cDvPLv/zLLC8v09/ff2COUnd3N6997WuBuxNNPvaxj/GZz3yGL3/5y/T19TE4OMhP/dRP4XA4+KVf+qXntO+9aOphNbk+n0Lc0wlrez+3trbY2tpiZWXljsLaU8W15+JYy+fzaLXae+YGultH3PjMIj/9wGcIel2IQLlaO+Bq60vp2ZgpUipXSYZ9rG0WePz6OH3JCMVyhesTM9jMRmIBL+VqrRUfTcjCkFGnZWp5mZ3dCkfSMaYXlimVq7zyaBflao3hzLwsyD18eZR40EvA7ZQjqye6E6xtFVnZLMhtpSOZeSxGA6d7k3xr8NYMu72Y7KmeJFPzyyxvrOCwmgn5XEzM3oqf7gl3XfEgJoOe/MbOvpZU6dj/d6afbw2O0BQEYm0emqLIkzcmOdIRw6DTcWlEur7eRISpuSXcdisWo4FUtE1eX6tW0xQElje2cVrN2Ew6BkezqFRw39EuLg5PUW80ZbffNy8NE/a7CXqc8uvvsJpRq1Rs7pQASUQcycxTa5QwGfSc7IrJr3XM72SnXGc0t8grjnQiiuItV5zPRbNRZ2VjC+DAnD+jQQeiyPXJGURRZGphlWJNJJPJcPLkyQPvsac64gCMRiM+n4+VlZUD9yuuOAUFBQUFBQUFBQWF78Tly/Dxj0u3P/1pcLsPdz8Kz55DE+JMJhM/9mM/BsDOzg6FQoFms4nZbOZ7n2PVx5NPPskP/uAP8oaWP7O9vZ0///M/Z3Bw8Dnv22w2I4oi1WpVLpi4lzyTEPedhLU7/dxb806toHq9Hq1WSyQSOXD/CyES3Ot5dHfDQxdu8r7f/VN5ntpAKsqZvjQXh2+1lD55fQKHzUJ3PCTPRetNhHFYTQxn5om2eRBEUbrt99AVDzE4Mo1Oo+FIZ5wLw9Oc6U2RjgZkoexIR4zJmTw7u2XZbTecmcdmNpKKtMmusGPpKFfGpMjjq452cXksS7laI+x3oVGryS6uotdp6U9FZfHrdG8SjUbD1k6JRMhHuVpnNLuAyaDj1ce7efTqWOtxkhClUauJeh0Iag1D03PyHLrxmTxNQeBIR4zpeUk8dFrNuGyW21x/TUGgPxVFEAVuTM7Kr92FoSnO9KVQq1TsVmrMrW6jVqk41Zui3mjKs+0Ku1LBhVqloicR5p8u3ASQ9z82k+dIOsYrjnTyZCtSGvK50es0bBSlopXjnXFuTM3SFETsVpMsWAK0t7nYKFYolMosbxa5/1QfDw9Kr3HA48BkMHB+aIpzAx3UG01uTs3y95fG+eFXqSkUCtjtduCW2Hunz0swGGR5eZlisYjVapXfl4orTkFBQUFBQUFBQUHh6ajVpEhqswk//MPwQz902DtSuBsOTYjb49KlS/ze7/0e4+Pj1Go1jh07xhve8Abe9KY33fUX0Ve96lV85jOfYWJigs7OTq5fv85jjz3Gg8+DZ9NsNgNQLpdfcCFu70v5/vhnpVKhVqtRrVbvGAuFpxfWntoS+p2EtclJyR3k9Xpf0OuE5x4XfbY8G0ecKIp8+e++zR/8//4Rp83ClbEsNrMRVDAxm0ev1dC/b2baye4ED7XEoc6IV25F3e9w2ysm0Ou0uB1WPA4rl0cz6HVa9HqdXBKw37n2upO9XByZplSuSjPkgMGRLE6rGZ/DwrVWOcLZ/jS1RoNytUZ/KsLM0jo7pTI+p51jnXEuDk+jUas42Z3k4sg0bruFE93tjGYXKVdr8py34m5FelzPrZhnZzQAYpORmeUDc+gAvudUH9+6PIIoivL1ffvKKLGAF7/LLq/Rn4owMbtIvSHc5ooTRZHt4i4bhRJmg454yM/F4SmsJgNn+lJcm5ihVm/citxeHcVhNZMM+xnNLlCp1TEb9aCC3OIqAHG/k83dCoXVXbQaNSc7IlxuxWLbQz5q9QaXRzOc7U/TaDS5Mp5DFEXsFjMui56NLWleZczvYKtUY6kVY9VqNVwZzVJrNLgwmuPHv+8cmUyGY8eOAdJ8OLhzvF6r1aLVag80qILiilNQUFBQUFBQUFBQeHo+8hG4eRO8XvjUpw57Nwp3y6EKcVeuXOH+++/n3LlznDlzhs9+9rO87nWv4xd/8RfR6XT8u3/37+5q3V/91V9le3ub7u5uNBoNzWaT3/qt3+I//af/9Jz3bDKZAEmIe+o8qO/EfmHt6Vxq++es1et1RFFErVbLolmtVkOr1aJWq7HZbM9KWHu23GuX2r1qyH02ol+lWuO9v/tVxnILmI0GpueXpeilIHBzao77jnayvL7NlbGsLCpNzi2hUas42hGX20FjQS+FUplSucqxznbGZyTRKxHyoVGpmJxdkooIrGYeuzZGTyKEyWCQBaqz/WmWNrYplasMpKNkF1YpliuEfC7iQR9P3phAr9XQ13K7Wc1GzvWnuTgyjSCIdMWCrG4VEARRip96XVxsxUU7YyGq9Trlao3eRFie87azW+ZoZ7ssoEmi4BQem4VUpI1CSXKmGfRa+hIR1rd3EEWR413tsijmtltIhv08fPlWJPZSa0+vPt5NbnH1Dq64NMsbWxR2SoxmpcKGvlQUURSp1RvEgz5q9brsYHvN8R5Z+Az7XahVKm5MznK2P03Y5+TymCSsue0W/G4n5Yb0nu4Ie1lY32a3UkOv1aDTaRkcySCKIsmwn3K1xszKFqxs8epj3Tx2fQxRBJvZSCLcxmNXxzjTn2ZpZZ1iucY/XJni/i4fpVIJi8Uif3bu9HkUBAGDwcDy8rL8+L33ZqVSoVQq4fF4FFecgoKCgoKCgoKCggIA16/Db/2WdPv3fx98vsPdj8Ldc6hC3H/7b/+Nt771rXz2s59lZWWFr3zlK/zO7/wOp06d4gMf+MBdC3F/+Zd/yZ/+6Z/yZ3/2Z/T19XHt2jXe9a53EQqF+Imf+InntGe1Wo3BYGB3d5dms0mz2fyu46B3Etb2BDSdTofNZrvt/qcKaxMTE6jVatLp9HO6ju+Gl6oQ992ytLbFz3z4j9BptWQWVjje1Y7NbJRdbR3RAKVylan5ZUI+F1qNmhuTszisZk71prgwNIVWreJoZzsXhqY42ZM4MHPtRHeC6xMzmI0GOmMB1raLTM0v43Pa8bscfPvK6G1R0lcf7+axa+OIokhfMsLs8jqF4i5umxmLycDV8Rw6jYbueIh6s4kgiJzqTXJ9YqbVOrqLx2FlNCfFTzvjIc4PTdKTCHG2L83gaIamINAe8lGp1tFpNbftwW03YzRJoqTfbcdqMnJlPIdaBa861i27+dLRANs7JQZHpnFYTaSjQXmNgXSU2fwai6ubt7niNGoVq5sFdis1rGYj7a0ocNDj5HhXO2M5ScTce97l0Qw6rYaueEguudBppc/M+EweURRJRwMUiruMtZpYjyQC3MguAeB32bFbzTx2dYyj6SjlSoXZlU0qtTp6rYZ4m5Pi7i6iCLGAF0EQuDEpOer0Og1LGwVqjSZ//c2L/JuTbyGTyTAwMECz2USlUt3REddsNtHpdDidTvnxe2xubjI7O8urXvUqxRWnoKCgoKCgoKCgoEC9LkVSGw0pjvrmNx/2jhSeC4cqxJ0/f55vfOMb8sw1QRAoFot8//d/P29/+9upVCp3Ff9873vfy/vf/35+5Ed+BICBgQFmZmb46Ec/eldC3J/8yZ8wODjI6uoqq6ur1Ot17r//fkqlEl/84hdxuVx3LCnYL6w9tbzgbp0u97I1Va1W31OX2ovpXNcmcvzshz9LPODlQssRZjLoudRyTJ3sSXBjcpZ6o8m5gQ6GM/PslMqygCWK4HFYMWjVXB7LYtTrUKHi+lOaQgFeebSLR66MUm80Zefa5dEMYZ8Lo0HPlbEseq2GgY4YtXoDURQ505dicDSDIIg0BQGLUc/cyiZuhxWvw8bgaIbu9hBn9jWE7jnxBtJRyhUpFnt9YgatRo3FaGR+dYOmIBxw7NksUrHE3uy5450xrk7McqQjTnd7iKX1bTIbK5iNetLRAPVGA4CTPQluTkkz8rxOG73JiNw+uj9u+6pjXcwurcmuuHP9HTx5c5KBdJT86joGg5GhaSlyGwt6qdTqrbl3btn5BlJZxD9fGkYURclZaDNL4md3OzvFErmlNWr1Bmajns54iFpVmhXX3R5iZbPA1JwkyllMeq63Ir4+px2n3cLkbB6NeoOuiJf5tR1KlSpqlYoz/WkeuzpOV9TPemEXp93Kt27OcC7hJJ1Oy03Qd6LZbKLRaEgmkzz++OOkUik59r73PGVWnIKCgoKCgoKCgoICwG//Nly9Ci4X/OEfgvIV4V82hyrEiaIoi1IGgwGNRoPBYOCRRx4hEAhQq9XuSojb3d297QuwRqO5awFra2sLnU7H0aNH8fv9XLlyhV/7tV/jta99LclkEr1ef8++LO99Qb8XvFwdcX/9zYt87mv/hM9l58LwtCyCffvKKLE2D0GfSxbRzvRLzsSdUpljnXHGZ/KUqzX8LjtqlYrF9QIBjwOjQc9gaw5ZuVrjwtAUKhWc6UszMZOn3mgeaF/tjAVx2S1cGJrC47Thtlm4PJol4HZwuvfWzLa9EoWuaBtxjZpSpc7EbB6bxSSdf20TOCj8qVCxW62RX9/CZTPj9zgZHM1wpi9FxO8+0JK6slkg1ubBajKQCPu5OiEJXwadhrWtAls7pZYbUMONyVlcNjPn+tOcb63RGQuytr1DbmFFirAmbznr+lNRCqUys0vrmI16OmJBzg9JcwltZgMTu1XWtnelY9EAF4am6IoHD0Rz99x6k3NLiOKtCO7U3DIqFWg1ajL5dRpNgYjfjVqt5tp4Tip6iPmYmFui2RSwmowko208cXOadNiLRqdnbbPAZKs9Nh3ygEpFqVLFabUQ8rvkhlW1CpqCyNTcEisb23zPr76VbDZLOBx+WkfbXpuqxWKhra2NbDZLX1+ffEyr1Sqz4hQUFBQUFBQUFBQUGB6G3/gN6fbv/i4EAoe7H4XnzqEKcd3d3Vy6dImjR4/K888+//nP86UvfYkf/dEfleexPVu+//u/n9/6rd8iFovR19fH1atX+eQnP8nb3va2u1rvne9854E/f/SjH6Wnp4eenp67Wu+5cC/FsZeqIw7uXNbQbAp87Mt/w98+elkScdSaAyLYXhTyW4MjclvoxaEp2tx2zvaludBqT91zy3XGAhh1anbKdZbWtwn5XKjVUnzVZjbK7asqFbz2RI/cLronyGk1ATqiATYKRSbnlnDbLThsZmr1RqtN9JYgp1aJLG8UqNQaxINeqQU0u8C5vjReh+2WcNhy0qWjbXidNnZKZcZzi5gMOkRRcgICnOpNcm08R6Mp0BEJ4LRbuDk1h1qtojvq59KIJCq67Ra5DMJmNhL2e2g0BXmNvUisVq3meFdCFq/2u+JOdLeztlU84BZ84sYUbS4rGq0OrUbD9ZbzzWoyslkoUixX8DgsmFuOQYATHWGuT0vtrVaTgfawnwvDGbpjfvQGI9mFFXZ2K2jUavqSIRq1Os2mQLTNA8CN1vkdFiM3MpI4ajLo6U6EuTqWxajT0JeMsL5dlOfTne1PMzgyTXd7iN1Klc5YkCfGFzkWsuD1ep/WEbdfYEulUjzxxBOkUimMRqPsllMaVBUUFBQUFBQUFBRe3jSb8La3SW2pb3gD/OiPHvaOFJ4P7vwt8R7xwz/8wzz55JMUCgWMRiNHjx7lgx/8IMeOHeN973sfOp3urtb91Kc+xZve9Cbe8Y530NPTw6/8yq/wcz/3c/zmb/7m87Jvk8lEuVx+XtZ6tjwXZ9+z5aXqiLuTqFEolfnpBz7DY9dGKRR3mV/eIB70oAIm55YI+124HVbGsou47VJZweBIBoNeS8jnplCS3g9n+9NcHs1SbzTx2C0srhfYKu7Sl4yws1vhyRsT9KeiOKxmhqbnsJmN9CWj7FaqqFTIc9rqjSYmo55qrc76dpFUpA2NRsP4TB6NRk1Xe+hAicLN7BLtQR9HO2KsbBTIr20R9DjZ2S0znJlHq1FzuleKqTYFgZDPzfzKBiubBYIeJ363g0sj0xzpiEli3UiGRlNgIB1lJLeAIIg4rWaSIR8jM8sAaNRqpuaW2SmViQY82C0mhqbn2Nopcrq1Rr3RpCseotFsSqUIOi0nuhNcGJpCFEV6EiE0KhWzS2sYdFp622/NkfM5zGwViswuraHVqEgF3Vwey6JRCcT9Lmr1BnMrm6hUcDQdoVRt0hQEwl4HBp2GoVbE1KjXMjw9z85uBZfdQmc8yI2pebJL63SEPGwUiswtr6NRqzmajnJ5Yp5EyE/I56bN4+BqS+gLex3YzEaW1rfQatScabkMm4KIRqUi0uZhcDTDl//fo5itNhYXF7+jIw7AarXi8/nIZDLyMY1GI8fQ79VnUEFBQUFBQUFBQUHhxcXv/A5cvAh2O3zmM0ok9aXCoTrifumXfkm+LYoiX/7ylwmHw3KD4N1is9l48MEHefDBB5/jDu/MYQpxL1VHHNzZpXYvzpVdWOFjX/6/lCpVRrKS6HSyO8H0/AprWzv0JaPMLq+xUyqTDPtp8zi4NjEjFxVcHc9xui/JQFqKXe651Z4cmkarUXOqNyE3hQ6ko1jN0tyzaJsHQRQZmp4j4LZzJB3nwvCtyOqFoSnO9qfxuuwMZ+ap1urEgz6W17cp1+qtuGjbgRKFJ25K4pzcfprd4lx/ms2dEpdGbgl3j18fx6DXkQq3Mb+yTn59C4fVjFGv47Fr4/LjZNdaVzvjM3mm5lfQatQc7WzniRsTnOxJUG8ITM0tsVupYreY0Go0rG8VABhIhRnN5Wk0BURRIO53yg62roiHsdwiKlREfA6agshIToqCHmk9z2IyYjEZsZqNTC+uAuByOJhb2WBntyo1wPpcXJ+SHGr3He3iyliWclVqQu2KB7k2Nc+Rzji75QqFYlluYu2ItqFSqSgtruOyWwh4nPJ8uJDXyZXxnCywnuxu5/JYjrVCmYDHidNm4WLrdY/5ncwsrxP2S866zliQazPrdHr08ty3p7Intu2RSqU4f/48qVRKFun2Puv7/6ygoKCgoKCgoKCg8PJgYgL+23+Tbn/ykxCJHO5+FJ4/DtURtx+VSkVnZycWi+VFMyvs6TCbzezu7h7Kue9lWcPLwRH36NUx/sN7PyELVYIgSq62sSzzKxvcf6qP0ZxUxHC8q53F1U10Oi1d8SD1RpPMwgphv5utwi7jM3nsFrPsVhMEkZ6onwtDU/K6Q9PzzC6tMZCOsVEosrCyQazNg0ajQatVyw65PXFNp9VwdTxHtVbnWGeclY1tFlY36UtEWnHRWYx6HZ0RH+eHpnHbLZzpSzE+k2e7uEsq3IYgiozP5LEYDRzpiHFhaIpavcHZvhTjM4sUWiUTJoOex66N0xENHHCtHUlHGc3Os1ko4rQYaXPZuDwqubfEeo0bkzPsVqr4nRY0KhibyaNBoDfexs3pBRpNgWTQiyiC1WJBp9VwvCvO+Pw6oigVJsSCbeTXC+i1GinaO71AvSlwvKudelMgu7iKRq2SXH0jGWIBL4mQD6NBJwtrZwfSVOtSmYPHbsHntHJzWhLo3HYLs0vrrGwWMBp09LYHuTG9wPzaFmGPHa1GI69zJBnm0Wtj2K1mbGYjRzriXB7LAWAxaEmGfXL76tn+DuZXt9kultFqNZzsSfLkjQm+9LePotXrn3ae41Nnv9ntdrxeL9ls9sAxtVpNs9lUXHEKCgoKCgoKCgoKLyMEQYqkVirwr/6VdFvhpcOhOuKejhe788NsNlOpVA7l3PdaiHupzogTBIEv/s23+P2//Ac8ThtP3pjkRFeChtDkwtCUPAOutFuRRbQ9cUyrlqKU5Wqd/lSEmfwaCysbvOpYF9Pzy4xmF3DbLbgdVtZ2duWyh73nR/weNgslSuUqR9IxpheWKZWrhHwuObK6N4/usWvj9CUjWM3GAyUHS+tbzC9v0Oa2YzYamJiXhKoT3Qn+6eIQACe6Ewxn5pleWGYgHWNrp8SNyVm0ajX96QgjmXmagkBPe4DMwhrVegOn1YhZp5Jda50RLzdaLrHj6RDT+Q0W1rbRazV0RNu4MrVAe8CLzWJicn6JSrWO02pGbzSxuCoVRewVSjSaAhuFEulIG1fHpXlsZ/pSXB7N4rCaCHidWI0GLo9K5+6O+BjKzFMqV+RSiT1XnyiKbO7ssrVTkoseLtycQqfRcKwjTm5plYW1bVRAXyLIw4MjpKMBSuUKRoNedt4lgn7KlTLD2bx8TTcyksiWCHnJLojcmJT22hX1k8mvU5/NY9BrOdIR50KrXKLNbWenuCsLaG6HlZsz63QHrDQaDbTag3/VNptNDAbDgftSqRQXL17E5/NhtVoBFFecgoKCgoKCgoKCwsuQ3/99ePxxsFrhc59TIqkvNV40jrh/SRxmNPVeCnEv1bKGRlPg9//XP/Mnf/cIapWK8Zk8Qa8Li9kgN3/uzYCbWVrlRHe7HDk905fmyZtTdEQDnO1PM5JdYGe3wtHOOJVKnfzaFqlIG1qNhqm5ZQrFCn2pKJdHs+i1GtlpZreYpNlu03OUylUG0lFGc4vU6g1CPhcep5Ubk5Lbrc3jkEW4s31phjPzTM4tcd/RLqr1BtnFVawmA9E2NzcnZ1ABA8kQV8ayVGt1EgE3zVqFueV1rCY9AbeVaxOzLK5tcbIzymhuiWq9QSLkAdRMLkhRzYFUlIn5NUAqkKg0VBRKFdw2M2G/m+HsIgCpWICb03NUqnUSIR8Gg47h6Xk6YyHO9KW4NDJNoynQFQuys1vGaja2IrtJeWadz+UgHvAyNb+MRq3ieGeMsflVVjcLvOJIJzqtlvGcdL69yGxnLEDI58LrtMtFD8e729HrtWzt7GIx6mkPuBnKSqJb2O9it1Iju7CCCmmu3OBohsJuDYfFQDTgla/pWGc7N6fmqNTqqFXSfsbnVqg3mlRrdc70pbnUmtEX9Tmo1KTfg81s5BUDHQxNz/HVhy6iUqmYn5+/7T24f0bcHg6HA5fLxc7OzgG3nOKKU1BQUFBQUFBQUHj5MD0Nv/Zr0u2Pfxzi8cPdj8Lzz4vSEfdix2KxKNHUF+Bc90KIW9va4b//8Tdw2Cwsrm5SqdXpTUSYX1nn8miGzliArR0pZhrwODAa9DSaAjaLiVibh4vDkuvK53Twz4OS82zPLadRqzjXn+bqxAzVWp1k2MfaZgG9XovXacNpNXNlLItep0WtUXF1PIcoigdmsZ3qSXB5NMvObgWfy4ZRp+ORK6M4LSaCXofczNoV8bG6tsbWzi4Bl5VKrUEuv45Rr+VYR4Srk5L4c6wjyo2peewWE+loG9vFMvNrBUwGHZ3xELTEoNO9Ka5N5Kg3moR8LpIhP49dH7+tnfV4Z4zJuWWyi6vyGo9dG8NmNpKOBhjNLlCpSa64ZrMhO9/2mmDrjSZtu1WSYUno3H/uWMCL227B53ZwdUJqST3ZnWB5fYuVzcJtM/FQQalcZbu4i1aj5nhXOxeHp6VrjbRJotvSBgDnBjp48voELocVQTTR5rTKc+WsZiNWg5bRWamE4mg6IrfH3ne0i63irryfrniI/OoGuYXl1u++g0vDUwiiiFGvQ61WsbC6iSiKlKt1hufW0emyxGKxA8LbU6Ope+y54vajuOIUFBQUFBQUFBQUXh4IArz97bC7C/ffDz/3c4e9I4UXAsURdxeYTKaXRTT1peaIG8nM8wPv/jhqtYrBsZzUfLlvTlpXPITVbGJls0B3e4hKtU5ucRWzSY/dbGQ4M4/bbiER9vPotVHcdivHu9plYeh0bwpBFKnW6pzsTjC/sklht0q1UkMQBKbml3HbLQQ9Di4OTdMR9tEda5NnsXVFvAxNzrCzWyHqc1AuV5lb2cBq1JOK+BidWUKtksSw8flVZla2Od2TZLNUYatUIeB24HM5UGl0rVlrSa5NziGIIqlIGzazidV9LanXJ2YYzszxiiOdXBqZpt5o0p+S2l3z61vYLCZ6EmFZhDvTm0IQBIrl6oE1qrUG5wY6uDqeo1K75YobHM1ytLPVwtpqgk1HA6xtFdBq1GjUKtkxV280QYR40Cc733ribVweyzI+k+dsXxqnTZqJB5L4OTiSIR70yi22l1rCXnvQh8dpY3FtE41aRVfEx/mbk9RaDa52s4mphdXWOh1MzuZpopLKHaJ+WaBLhv0srm2SW1wBoDceILOwTGG3wuLaFt9zup8LQ5MIokib20HI7+byaJY2t4P+VJRytcb/euQGWq2WhYWFA+/FOzniAFwuFxqNho2NjQP377niXuzzMxUUFBQUFBQUFBQU7p7Pfha+9S0wm+Hzn5d9EwovMZRf612glDW8MOd6IUWGrz9+lbf++qfwOm0M55YQRJEzfWk5Gnm2P83V8RyCIHK6N8nU3BJbxV2OdMSYyC2xUSiRirSh0WiYmM3jsJrpiPq5Op7DpNfRHQtwfmiKxeU1elsCUq3eIB3yMJSZx6zXEvE6aDSazCyt47SaMBr0jM1KUcxjnTHG59dYK+xy/8ke8hs7FCs1OqIBNFotW0WpIbSrPSy7s451tYMKqrUGyaCHnbIUP82vbtLVHpILFfbKJ+qNJr3JMKVKlZn8GnaLmWS4Tf4dy1HbUplGs0kq0sZwZr5VkpDk4sg0YzNLdEX98hp7BRAjWUm8Ot7VTn5ti+X1bRxWMxaTQRbyTvYkmF1aY3VrB5NBT2c8JB870Z1gflUSn2xmI93xIKMzkuvsSEcMQRSZX9mQyxz2CjBMBgMajYbxmbx8DTemZllY2cDvshP2ORmfl0S3493tjGTm2S7tolGrOJqOcmFokkZTYGe3QjLkYXxuRd7P4uom2YUVjqTjHE1HGJlZot5o4nPaCbjtzOalx0Z9Dqr1Opl5ab8ajZqNQpFiucrsygaruyKZTObAZ+mpran7MRgMrK+vU6/X5fv2Ph+NRkMR4xQUFBQUFBQUFBRegszMwHvfK93+yEcglTrc/Si8cChC3F1gNpuVGXHPMy+UECcIAr/zZ/+PP/36Y9gsJq5NzGA1GUiGfKxsbmPU6zjaKlJQq1SoEBmenqfRFDiSDHNzapaNQpGeqI/ZJWlmWdhjp1ytsrS2gcduxmkzMTa7hNmgw2wysFmqoEJyrk0trtNoCnQmIixvFSnsVkhHA2g0Gq5MzJAK+0lFAlybmJVbQYvlKo2mwMmeBLn8KuvbRRpCk1jQy2h24UD76fp2kbP9abJLG5TKVTqiAWqNBiaDHqvJwED6VgOryahna2eXQqlMPOjDYtIzkl1gaX2Lkz1JWdyS2lkl15rLZiYdC8pus+54AL1WS6FUJuy/NctuYWWT153ulV1xey2sj14dJxHySWLgqCROJkI+FlY32CgUAUk825tnt1koEmnzMLZPWLs5NcelkWl62sNEA165zOFkT4IrYxlsZiNmo56jnXH5Ws1GA/GQj9llqTDiWDrC1bEca1s7HO9qJ+Jzcr1VQjGQjlEqV9Hp9AAc74hyZSxLpVbHZbdQbzbl2XFd7SEEUWBhbZup+RVec6KbhbUCWzu7GHRaTvWmOH9zkmibF4/dQsDt4EtffwJRFFlaWjrwvnw6IU6tVmM2m5mdnb3tfsUVp6CgoKCgoKCgoPDSQxThZ38WikW47z74xV887B0pvJAoQtxdcNitqfdSHPuX6IhrNpuUy2WWVtb4mQc+zd8+fImVtU1yi6uEvA50GjVT8yvkV9bxO8xcn5zFbNAR9Tm4OjFDIuCmJy61Z4qiJKiVGwL1hsCJ7nZWtksUyzWsFis2i4X8eoGw34XbaWNiboX2gJ++VFR2rp3tlwb7q1QqTvYkmGmJa8lwGwGvU3bYdcZDXBqZZiy7wLmBDi6PSi62PWHMpNcdEL/0Wg1Om4Wl9S1EUeRIOiILd5VqvRXlnJOKEXqSXBqeJtrmaa23TX5tC4/Thl6rZbdSlfd6bWKGcrXGTqmMw2ZhPLeITqOR5rxNzbO6XWQgFaVQLDO7tC4Lg+tbJUBy6i2vb7PUircmQn5ZINtzzC2tb5MI+Q4IhUc64qxs7mC3mDDotHRGfLdiu/EgboeV6fllVCo422pbrTeaBFyOA4UNJ7vbyS6uUNytYtRr6Yz6udaKm3ZEAyyvbzG/ugVIs+OGM3MUSmVmltboini5OikJdOloG3qtlsGRaXriQfqTITLzy6xvF9FpNXRGvKxtbCOIIn63g2jAy2Cr1XW3XMGg0zK3ssml4WkKDQ2ZTEZ+jz+TI67ZbBIKhcjlcjQajQPHpqamWFhYUMQ4BQUFBQUFBQUFhZcQX/oS/OM/gtEIX/iCEkl9qaOUNdwFJpPpZRFNvdei39MhCAL1ep1arSb/3H/7qfc1m03WCiV+7/+ex2TQk82v0RREjnVEGZ3JU601SAQ9bBR28XncoNZSbzaZWdnC67Thcjl57NoYZqOejmiAqxOz6DRqXnm0kyeuTwBwpiUEnepNYjUbmcmvSrPd2jwUyxWGpuewmQ14bGYuDE2hUsH3nOrjny8NA1L0cWh6jlqjQXvIR6VaZzS7gM1sJB70STPTgLMDaS7clISqRrNJuVJjo1DC47Thslm4MpblTF8ai17DjZbYNJCOkllYIehz4rZb8DrtDLZiqlqNhsvTGcmlFwuwvl1kcm6Jk90Jju1zlJ3oTjCcmedoR4yd3Qpuu0Vew+e00hSkOGfY70KjVnNjchYV8Opj3Tx6bQyAWMBDoynwxI0JbGYjvcmIvH6szcP6VpHZ5XXpOluFF4DkOvQ5mWhFSk/3prg6nmV+eYM2tx2v086FVqS1PxVhdHYRtUqFRq3iRHeSSy0xrFAskQh6GZ2RnGhHUhHGZpeo1Rv0xtrQ6vWcvznZ2qsXQRRRqaR/453sSTI0PUe1VkejVqPXabg8Ptu6fjsuu4WJWcm11x31sbJdZmpuqfU762BweJre9gDLmwVO9qb46kOD/Py/PsbKygptbW1POyMOJCHO5XKxvLzM3NwciURC/owUCgWMRiOiKCqlDQoKCgoKCgoKCgovARYW4D3vkW4/8AB0dR3ufhReeBQh7i54uURTX6i4qCiK1Ov1AwLa1tYWpVKJ8fHx24S2PVeQVqtFr9ej0+nQ6/Xo9XoMBgM2m+3A/dcn5/iLr/8/Aj6PPIPsbH8aFdI8tZ54GxNzqzQFAaNBx9p2kd2KFOtcLxR5/PoYnfEg1Wqd65Oz2MxGYgEvjUZTFnv21jXotFwamUYQRFkAm1te52x/mrmlNXLLm7K4trq1I+9lT3Ty2G1UajVyi6tE2zwIosjQ9By9yYgkjLVEuL3Cg6MdcTwOGxuFIlNzS9jMRpqCwPjcqrz2XgNrwO1kq1BiYjaPyaCjKx7i8evjHO2Mo9No5BbToMfJdqnM2mbhtv01BREVMDm7hF6roSPi52ZmkeNd7fSnorIAuSda1lq/q2OdccZn8pSrNSxGA6d6U3xrUBIhj3TEmJpbZrdS5Vx/mkq9IZ+vNxFmcXWTWJsbtQpO9qa41Hqtw34bEb+Hx6+PS/vsS3NpVHrtX3mkg82dXVmEG0hFyeVXsZv0qFVwtCMmOxT9bgcAN1rR1KOdcabmliiVq+i1anpjfnm+nttuoc3j5PL4LN3xAKJKzdpmQRbhuiJeBEFkoyC55I53tXOhJe6tbBboiLZxsXVtP/Gvz5LJZPD7/c8YTW02m2i1WlKpFENDQ8RiMfmxewJeo9FAp9MpYpyCgoKCgoKCgoLCv2BEEX7+52F7G86cuSXIKby0UYS4u+CwhThRFO+JI+a7jaaKokiz2bxNQHsm1xqARqORxTNBEBAEAa1Wi8lkkoW2veM6ne5pHUT7+Yt/eIKPffn/cqQjxrcGRzDotfQmJCdWe9DLmb6ULKKd60/zxI0JvE47PYkQNyZnpUKDRASH1cSTNyeJBjwITYHhzDz9yUhrXto0Oo2Go51xHrk6RizgIeh1yQLYQDqKWqVicW0Ln8OMwWBsOeSMnOpNyqLTnuB1pi/FkXSM6YVlSq1G0t1yhc16Qz7P3p4dVhND03PUG01ibR6aosjl0QxdET8Go15e+2R3ghtTs7LQZtDruDYxg0oFHoeVb7aceb2JMAurm+TXt3jFQJpiuSqvcaonyfXJGdKRACpUOKwmhnOSAKXTaphbXt/nitNwfXIWp9XEuYE051sCYtjvRq1SMTG7KL/m51vrBz1OmoLAtfEccMtl2BQEvA4LMb9bFuH2hL16o4lRr5PcdcPSOsmwn+WNbeZXpHlwp3uTDI5mEUWRhTWR/lREFuE6om2sb5cYmV2mpz2I027jyRuSy9FuNeGxmtBopb8W09EAheIuo1mp8dRmNnGt9R6RRLcEF4enUAHd7SFEEfn31BkPsrK+hcmgb+0pxdceu8GPvLKD9fX17xhNVavV+Hw+jEYjc3NztLe3y8e0Wq38UxHiFBQUXs784R/+Ib/9279NPp+nr6+PBx98kFe/+tVP+/hqtcoDDzzAn/7pn7K0tEQkEuG//tf/ytve9rZ7uGsFBQUFBYVbfPWr8Hd/B3o9fPGL8DRfERReYrykhbiFhQV+9Vd/la9//euUy2U6Ozv5whe+wMmTJ5/TuiaT6VCFOHjmYe/PB81mk3q9jiAIrK2tPaOoVqvVZGHwTgLaUx1rez/3739paYnFxUVSd1kN02g2+fAXvsZDF26g12m5PjlLwOPAbDRwdTyH3WLGZNCztL6FTqOmKx6UBaGjnTH+6cIQcEsMSkfaONIRY3peEscSIR8rWwU0ajVuhxWvw8bgaEZyicWC/PNF6fl7jjSrycCRdJSJ2Tyr27tyhFOjUmMy6OiMh2TBS6vVMDYxI0Um9wljrzrahc1sYnA0g0oFZ/rSfPvKKB6HjZDPJbvKvE4bet2taOp+R9vrTvZxZTxLvjWvLdbm4ZuXhvE5bSTDbQyOZmgKAhG/m91qnZtTc9K5etOy0OVx2lje3GZqfhmdVk1HpI2Lw9Oc7U8T8DhlV5zJoKM95JdjtQPpKNmFVYrlCka9jtee6OHbV0YB6E1EWFjd4NJIhr5UFJNBJ4tYqXAbmztF7Bbjbddjaxg53p2QxbO9CG21Vudsf5p6vSEXSyRCfnZKJTStz8zRdJShzDxNQUSv1eB12Hi0FTVuD/mo15tklzawW0x0hT1kl9ao1Rto1Gq6Y21cGs3Sm4iwsrmNx2HjYuv1CXlsuG0Wnmg54U73pbk+nqPWaJDLrx6INL/5/mNkMpmnjaYKgoAoimg0GlQqFalUipGREaLRKBqNRhbw9hpUFVecgoLCy5W//Mu/5F3vehd/+Id/yH333ccf/dEf8W/+zb9hZGSEWCx2x+e8+c1vZnl5mS984Quk02lWVlZum8WpoKCgoKBwr1hagne+U7r9wQ9CX9/h7kfh3vGSFeI2Nze57777eN3rXsfXv/51/H4/09PTOJ3O57y22Ww+tBlxe1+6n60Qt3/O2p1EtTvNWdtjenr6gIBmNptxOBy3iW574sHdXtfdxmC3dkq855NfYWVzm/UtKWbaFQ9h1EuCXCzgod5oMppb5FVHOxGaTYYyC1hNBhJhP9mF1dsipw6bmbnldUrlKse62hnPLVKu1njtiW5GMgtMzOblOW03p2Yx6LT0p24VD/QmI5QrVSq1Bv2pCDNL6+yUyjitZtrcTq5PzKDXauhPx3ji+gRHO+MYdFrZEZaOBljb2mFiNi9HW28VHsT5p4vDiKJIZyzI2vYON7N5om1unDaL/LgzfSkW1zbYLu7K89qGM/NoNWqOdsVl8XFPMJtf2eB4Vzu1ekMW4U71JLkymkGv1+F32THoNIzsc8WNzyzKrju9Xsu1iRnS0bYDEdmgx4nBoGOzUJL3tXedkqhplQW6/cKa12E50ITa3R5ieWOb7R1pnf0Cnd9tp9kUuNJy1x3vbmcsK/3Oao0mrzjSKYt3AY8TUWjw6PUJkmE/DquF8ZkFdis1ADpjAarVKrV6Q46mDrdccW6HlZXNbcZnJIffsc4YN6fmWN3O4HFYSceCcjTVYtTjdzloNqX3dUc0QHs8Rm58WBbbnsqeA3XvmN/vZ3JykoWFBWKxmOyE22tQVVxxCgoKL1c++clP8tM//dP8zM/8DAAPPvgg//AP/8CnP/1pPvrRj972+G984xt8+9vfJpPJ4Ha7AWS3sYKCgoKCwr1GFOEd74DNTTh+HN73vsPekcK95CUrxH3sYx8jGo3ypS99Sb7v+foPLpPJdKitqSA51vZmrX2nGOjdzFnT6XQIgsATTzzBmTNn7kkM9m6EuMnZJd72wKdJRwLcnJpDFEU5Unm8s50jHdL8r91KVSonKJSYX92izW1Hr9Nxc2oOh8XMsa4El0amZXFsz/EVC3gPFBjMLW+wurVDRzQgz2nzOm0c62rnwtAUeq2GgQ5JPOpLhumO+hjJLiAIIr2JMLn8Gm67Fa/ThtNq5spYFo1adSAuerI7wc3pOWr1BucG0swtrTM0PYe+JfaNZKXWzFO9Sa5PSHPefA4LYa+L88PTaDVqjnW1y6Liq452cXksS7law2234HPZuTExK7vs9gSzWMCD2ajn6ngOtUrFqd7kvhl7Ka5PzrKyWZCEvI44j10b52hHjHqzyfzyBvn1LQx6LTazibWtHURRlJ1v+fUtdFoN9x3tkme8paMBtnZKPHp1jDa3nfZ97ap+l43CboVCWRLHTveluDqWpdEUqNcbvGKggydbgld3e4il9W0GRzMMpKIYDTrZFWc3G/E5bfJ7qyPsY3F9m1JLdEtFAzx0/gYADosZp9XA4GiWoMdJyGOjLiBHU4+mo1y4OUm4zc1mocSpvpQsuhn1avrTUb59WRIUk2E/W4Ui47NLtHnK/Jv7jvF7730bNouJRmmbmZmZOwpxewL43rE9V9z4+DiRSOSAk27PFafX65/150ZBQUHhXzK1Wo3Lly/z/ve//8D93/u938sTTzxxx+f8zd/8DadOneLjH/84X/nKV7BYLPzAD/wAv/mbv4nJZLrjc6rVKtVqVf5zoVB4/i5CQUFBQeFlzV/9FXzta6DVSo2pOt1h70jhXvKSFeL+5m/+hu/7vu/jP/7H/8i3v/1twuEw73jHO3j729/+nNd+IRxxz2bOGiD/h+b+OWtPda09NQr63c5Z22PvXC90DBbuToj75qUhfuV3vkIs6GNtqwCInOlPy8PxdToNl8ck59XJniQ3WuUER1IhphfWKFUKJEI+dqs1tBr1AXHMqNeh02p47FqrGGCf++o1J3p48sYE9UaTzliAte0igiDKDrnLoxl0Gg0GnZbxxRUEQTzgAjve1c7N6Tmm5pdxWs0EvE4eHhzBbbfSEQvI5znSEUMUYWF1E7/Ljs1i4spYFrVKxf0ne3n48gggOdomZ/NMLazgcVrx2G0MtoSoc/1pas0m5WqNVKSNQqnM+EwevU7La0/cWuNoR4yJ2aVWUYIXq9kgi3CnepNMLSyzUSjhspmwmU1cHssC4HJYeeTKKIIg4nfbsZqMXB3PcbI7ceCaPU4bbruFRkto2hNL640mNrORvlSMb16SHHr9qSjZhWVKlRpnelNE9s2KiwY8CIIox1/3n8NiNODe567riAVZ3dhiemGVUqXGuYEOLg5PIQgiBp2GzliIb14cwueS9l2p1ZlZlubMhds8FItFxmaX0ajVdEX9XG+VO7R5nJgMelmEC3sdlKt1BoenMRv19CQiDE/PUalJsxB/9N++hl/+0TfKn71IJMLMzAylUuk2h26z2USlUh0QvgOBAFNTUywsLMjRVJVKJbviBEF4Vp9rBQUFhX/prK2t0Ww2aWtrO3B/W1sbS0tLd3xOJpPhsccew2g08rWvfY21tTXe8Y53sLGxwRe/+MU7PuejH/0ov/Ebv/G8719BQUFB4eXN6ir85/8s3f7AB+Do0cPdj8K95yUrxGUyGT796U/znve8hw984ANcvHiRd77znRgMBn78x3/8Oa393ZY17Alr361r7enmrOn1+gOOtZs3b3L06FEcDscLKpDtd9680DwbIU4QBD73tW/y+f/zTZx2K1fGsngcNo52xLk4NCU7xx6/PkFvIozNYjpQkFAqlShVahzrbGd8RoovRnxuRFFkan6ZgMeB0aDnsWvjdMaDGPU6LgxNyQ6ySrVOvdE8IO4FvU70Wg1Tc0u47Ra8TjtXxmfojvqw2ey3RK2eJOO5RbZ2SpIIWKkxllvEZjZytDMuN4vuRTv1Og1HOmLMLa8zPb+MzWIi2uahuFs58DhRFAl6DbjsNllI7EmEOT80hc1i4lRPkhtTs9TqDVnU22hFRfcXKLSHfET8bh67No5GreJkzy1X3KuPdXN9Msfs8oZcIvHw4AhBrxOH1czS+jaZjRV0Gg0ajZrR3CJNQZDdg5OzSzisZs4NdHC+JWLtlWFcHs2g02o40Z2QW2jtZiOVep3JWelL1X534+Z28cA6Eb8bVPDtK6PEAh45/ltrNNBrNUTavNQaDUkwdFoRBIGb063W1I44j10bo1Kro0ISHi+NTJMM+bGY9MTafIzkJFdcOhpgNr+K0HqvDiTDjM0uycLg/3d2gH+6cBMAo17Lx3/xP/Hmf/2aA+9fbasMIpfLcezYsQPH9hxv+4W4PVfc5OTkbaL4U0U7BQUFhZcTT/3775mKrARBQKVS8dWvfhWHQ2rP/uQnP8mb3vQm/uAP/uCOrrhf+7Vf4z376usKhQLRaPR5vAIFBQUFhZcj73wnrK1Bfz/81/962LtROAxeskKcIAicOnWKj3zkIwAcP36c4eFhPv3pT9+1EFer1VhbWyObzVIqlfjsZz9LpVLhDW94wx2Ftr2Y2Z2KCp7LnLU9F9y9cKnBi0uIq9TqvP9Tf8bc0hqNZpOpuSWpPbTlCvI57ditknPMZjYS8Dj55uCwFNfslOKjXoeFo+kI1yZyAJzsSXBjcpaQz4XfZSe/tsXS+jYhn4uA28EjV8fkeXIXhqbwu+ytSOfUgef3JSOYjQYKxV0mZvM4LGY0KhWXRqZRqaTmzD1R6/6TvZy/OUmlVpdEJCC3uIJBr6Wv1fIKMJCOoQI2CyV5zttIZh6n1cyZ3pT8uM6Il9zSJk6blYDHgckgRUxBakatN5rU6g264iFWN7eZnl/GYtQfEOH2hMlqrYHLZibQaoIFae+lSoVCqYLTZsLncjA4KrnuepMRvn15hEZTmvnmcVjlaG+zKcjON7vFTNjvptGU5qDtF9b0Oi2vPt4tx3NjfhfFcpUbk7Oc7k2iVqvla92L1+69X/aXQqhVKtKRNr45KDn9nFYjTpuVwZFpOuNBjnbEmGoVcAC84kgn529OoNGocVjMOMy3Yq1qjYqI18loS4Q71hFjJLfYigx3kAj5ZSHQZjHR5rRweWQanVZDxO/hF95wiu89d+S29/Ce2LayskKpVMJiscjHns59GggEmJyUzrU/tqoIcQoKCi9HvF4vGo3mNvfbysrKbS65PYLBIOFwWBbhAHp6ehBFkfn5eTo6Om57jsFgwGAwPL+bV1BQUFB4WfN//g/8xV9I7ahf+pLUlqrw8uMlK8QFg0F6e3sP3NfT08P//t//+67W6+/vZ3hYEgmcTieNRoMvf/nLxGIxvu/7vu+Oc9b0ev0LMkxdrVbLQ91fSPYXQ9wLvpMQt7yxzc9++LOo1Sp0Wi3r20UG0lEyCyuUylXiQR9NUWB6flkWt66OZ/E6bbjtVgZHMxj1Ojx2C5s7u7e1g3ZEAzzcEpT6khFml9eZnF2S3VY3p+awmY343Q6Ku+Xbnu+0WRjJLlCrN0iEfBTLFYZnV+iIBtDrtLKoda4/TaFUplKrHxCR/C47/akol0ezB2a0dcVDHO2MMzGTp1ytyY42Aen12h+brdUbaLUasour2MxG2kNSyUNvIszp3hTXJnJyuYJBr2PvFT/X38H5IUnoCXgchHzO2/ahVqnoivpZ2ZLcbVqNmuNdCS4NZ1Cr1XSE/GzulJicXUKtUqHXaXl0aAyAeNBHrV5nJDNPe9B7sGihdT25xVXglrBZbzQxGXTo9Toev3Zrrtz2TonxmTwuu4Vz/WkutByBDquZkM/Fo9fGcdksBLxO5pfWyOXXAPDYrWwVdymVqxj1WuIBj1zgcP/JXsZmFpld3QIkV9yNyVlSET8qoDvm59rkLCDNkqvU6mQWVgAIeuyoNVqmFqTz/Ni/fTX/9Wf+A4MXnnzaOXAajQa/308mk2FgYOC2Y09FrVYTj8cZGxs7EENVRDgFBYWXI3q9npMnT/LQQw/xQz/0Q/L9Dz30ED/4gz94x+fcd999/NVf/RXFYhGr1QrAxMQEarWaSCRyT/atoKCgoPDyZmMDfuEXpNvvfS+cOnW4+1E4PF6yg4Xuu+8+xsfHD9w3MTFBPB6/q/X++q//msXFRWq1GtmsNB/rG9/4Bn/+539OV1cXyWSSSCSC3+/H5XJhsVjQ6XQvyBfley3EvRgccdcnZ/iBd38cfatZdHl9i7P9aYam5ymVqy3xZoZypUZ/KsrWzi7zKxu4HTaiAQ8Ts3na3HaCXifjcyu4bGap5XR4Co1axZm+FMOZBRpNgbP9aUZzC+yUynicNuxWE/PLG0TbPNitZoam57CYDPQlo7IId7Y/zePXxjHodRzvaie/tsXq5g4+hyQIDWfmMRv1HO2IcX5oiqHpWV5xpIOh6XmK5Qpd8SD1lgjjsJrpag/danC1mljf3qFcrdEVD9FoNpmeX6ZcqTGQvtXUeqIrzlhukZDXTbTNg8Nq5ubUHGqVCovJwMLqBvVGk95EmN1qjVx+lfzqJkc747IIt7f3piDeto8T3Qn0Wi2bO7u47RZSkTYujUxTKO3y6mNd5PKrrG3tYLeY6W4P8ejVMfpTUY51trOysU1+bQuTQYfTZmG7KM1Y7G4PUW9dT3ZxhftP9nJ5NEu90cTvsuJ3O3j82jj9qSgnuxPM5FdZ3drBoNMS8jgo7u4iiiIhrwOtWipWqDeanOpNMjmbZ6clup3oSfDkzUmMej2xgBeLQc/47LJ8XZn5FVY3CvLvcnAkQ63eYGFlk554G6OzkuiWCPkxmwxcG8/Rl4jQEw+wubPLwsoGAP/hNUf52e9/JU6b5UCxwn72XG+JRIJ8Pn8g5v50QhxIDhCQHB8KCgoKL3fe85738PnPf54vfvGLjI6O8u53v5vZ2Vl+/ud/HpBipfsTEG95y1vweDz81E/9FCMjIzzyyCO8973v5W1ve9vTljUoKCgoKCg8n7z73bC0BN3d8N//+2HvRuEweck64t797nfzyle+ko985CO8+c1v5uLFi3z2s5/ls5/97F2t19nZKd/e+w+2SqWCzWZ7Xvb7bLiXQpxKpbpn53o6Ie5vHhnkgc/+NW0eu9xM6rJbmV1aA0TO9t1ypb3+dD/fHBxGFEWOd7Uzkl3giD0mN2sub6zid9ooV2tMzK3ILqqLrbbR1xzv5pGrkovrbF+aiyPTnO5NHnDeRds8bO3sksuvtiKrbbIY9tq+FP90USod6EuGmZpbZmp2iZDPhU6j4frkrNSsmopRbzRvaz8tlspYzUZGswvynLe9mGfQ45IdbRG/WxKzVKDVqEkFPVwZnwFArZairMVyRb6+SyMZzvRJxQd75QYhnwtUKpqtqOh+l1qhVMbvsjOaXTjginNaTbQHPJSqdcZn8tKxvhSzy+vUG00SIR/lap2RVtOo322X46Z7LrxrEzMc72rnTG+KK+NSE6rVbCTkdbK6vgVAe5uLla0iK5tFVIBGrHN5TJrp5raZMOi0DGcXiXiddMcCZPNrVOsNNGoVqZCHi0PTqFVq3DYDNquFK6PZ1htNRKtRs74jCYEnuuJcaRVPnO1Lk19ZlV+DZNhPuVpDp5P84kc7YozP5OUSBo1GTWZROq/JqOfBX/lJXtETZ3h4mER7O6IoPqMjzmq14vf7yWazsnv3mYS4vc9JJpMhGAwqbjgFBYWXNT/8wz/M+vo6DzzwAPl8nv7+fv7+7/9e/h+u+Xye2dlZ+fFWq5WHHnqIX/zFX+TUqVN4PB7e/OY38+EPf/iwLkFBQUFB4WXE//t/8Cd/AioVfPGLYDQe9o4UDpOXrBB3+vRpvva1r/Frv/ZrPPDAAyQSCR588EHe+ta3Pue19Xo9arX6eW9O/W5Rq9X3xKV2L891p/MIgsD//Mrf8bWHLxIPerk0Mi3NIGsVNLxioAOv08aF4Sl0Gg1HOuOsbxcRRfHA7DONRk12cYVqrUF/Ksr0/BIrWzuc7U8zu7TGaHZBnjtWqUnD/Qc64rK4p9WqGR2T3HL7o6SvPNLJ3PI6N6dmMRv1dMSCzLecUfvP73ZYUatV3Jyak5pZbRYuj2XpaQ9xpu/W3LhjnXHGZ/J0xAI0m015zptapUKr0fDE8ASiKB7cw9EOLEYD4zN5YE9MmyYV9tMUbOxWa4xmF9Bq1Gi1ai7cnKYpCPQlo8wur7FTKnO8u50jHTFZgDre1c5odoGjnXHsG2YibW55jwG3HbVaTW5pHZvFRKzNI7fUvvpYF4OjGcrVOiaDjkTIxzcvDeN1WHFYTCxtbFNar6FWwW6pyOLGDo2mgN9poSmITLRKGY53RLg2tYAoithaAt31zBJhnwuH1czC6ib5jR0AwgEv28VdqvVGqyTDJq/z+tP9PHljnI1WNPXcgDQP7kxfisXVDeIBryxetof81JoNZle3AcklN5KZp1Krs7pZoDfm53ormmoxGeiIBnns2hgDyRBr2yW++pF30ZuMIIoiOp2O+fl5+X39VPY75ZLJJOfPnyeVSmEwGJ7WRbf3PK1WS71eZ3l5mUAg8B0+VQoKCgovbd7xjnfwjne8447H/viP//i2+7q7u3nooYde4F0pKCgoKCgcZHsbfu7npNvvfje84hWHux+Fw+clK8QBvPGNb+SNb3zj876uSqXCbDZTqVSe97W/G+6VIw64Z444OBiBLe5W+KX/+ccsrm5SKFUIeSEdbWNrZ5fJuSV56P/NqTm5pfTyaAaXzcyJ7gTnh6ZkJ9f5m5Nye+pew2hPPIBarSa/tkUq0sZOqcz4TJ5U2E97yM/l0YwkyKVjPHH99uf3p6I0mgJzy+sEPU70ei3XJ2Yw6LS8YqCDJ1tD/LsiXkZzC5zsTtIZC7K2vcPU3BJ2ixlUKmaX1oGDbjS7xcRsfo2l9W3sFjMRv4vHr4/Tl4xgNRvlPaTCbaxs7JBZWEanUdOTDMtrRNs8PNkqg9h7fZ64PsmRjhhGvZ7BUamZNOx3sVkoUWhFRffvo1ypYTUZGMlIotLJ7nYuj+VIhb2EvQ5q9SbDrWM9MT9Lq2uUq3U8NjMajZqR7CIA6bCHi6OzCKKIw2LC57IxPr/C6d4ktXqTiVlp9t2eAKrVSKJsxO+iKYiMt4S1nmSEb14aQhDEA07EY51x0tEAWzslWYTriniZnl9ityUKdrdHOH9Tmge3tr2D22qS1z2SjjI5t0xucYWYz0E40MaTrcfazEYSYT/V8q78uqpUKrnow2k18T9+4U30JqX5QiqVikQiwdTUngh8u7ttfyGD3W7H4/GQy+Xo6up6RkfcnhAXj8fJZDK0tbUprjgFBQUFBQUFBQWFFzm/8iuwsADpNPzmbx72bhReDLykhbgXEpPJdKiOuHsljt0rR9z+aOpsfo2feuDTuGwWxnKLrUZUFfPLG1RqdfpTEWbyayysbHC8q5355XUmZvOSIKbTIoqiLGBdHJ5Gp9EQ9Ln451ZkdCAR4mZ2EY+zxImudoazC1RrdVnM8TptknPNaubyWBa9VkPQ6+KfL0nPP9OXZnB0Gq1azbGudjLzy+TXt/C77FjNRgRRij8OpKJcndiLi6pYWN2QSyVq9Tqj2QXO9ncQ8Dhk8etMnzSfzKDX3So4yC6g12lp8zjkmOeJ7gTDmXmqtTpnB9LMLixzY1KKbp7rTzM4mkFEEi+3d6QWV71Wg9dh45uD0ho97UFml6U9HUtH8Dos8j7SIS9juQWCbhtGvZaAy8blsZx0LSKIwOp2UXK+BX2M5BYx6rUc64wzPb/Mzs6uLKyNzS6h0ahJBLwUSmWm5qUZZ3qdFFOtN5r4nHYcVpMspnZH/WSXNqjWJYfikY44T1wfx2TQ47FbaYoiN6ckh5pWraZaq7O2tYNOq2EgFeVKqzG2K+yh3FRxdVyKnx7ramdiZpGo1w4bOxxNR7g+Jb1uVpMBp9Uoi3CxgBdBELgxOYtBp6Ej5GZxsyg3rv78m76X7z+VwmW/1XoKUlHMxMRE6/d+Z0fcfrEtmUwyODhIMpl82tbU/c+LRqNkMhlWV1cJBoN3fKyCgoKCgoKCgoKCwuHz0EPw+c9Lt7/wBTCbD3c/Ci8OFCHuLjGbzQeGrN9L7qVL7TuVKDzf53ni+gTv/O0vkYoG5EbLPZdWZzyIy2bh0ojk5jrRnUCn1bC6tUNvIixFFte3CPicWEx6RrILUpTVYeXScAaLyUBnLMjVlkiTjrRJscZanZPdCW5Oz1GrN+hJhFndLDA1vyxHSS8OT2E26ulJRLjYiqwe62lHEEUKpTJd8SArmwUyCyvodRqSkTZZhDvTl5KcW13tIIqMt9pPgx4ntXqdaxMzaNQqTnQn5LXvO9rJEzcmD7SkPnZ1DLvFRE/ilvOtuz0Egkh+YweTXkcy7JcjsWd727kyMUe90cRpMWIx6vnm4DA2k56Qx85YLo8I+JwWdlsz8wCOd8a4OiGJXH6vm4aoJrcsRW772gMM55Y4259GpVajVqkZyUnOt6Od7Ygi7OxWbkVwRzMAfM/pPh67Nk6t3pBjvI9fH+dEVzulSlV+vQG64iG2CjtU6w28Thsum4XB1jqvP93Hkzen2K1UW47HFBeHpzjTl6ZSrWO3mmQRLu530hRpzRKEcwMdnG85FTcKZY53JWSBLtrmodFsciO7THvIh9NmYXImT6kiiW7tbS4MBgOlxQ2Meh2feM+P8x9ef44rV67cJrap1WrC4TCZTOaO7/Wnxk9dLhd2u12eZfSdhLi9oofp6WklnqqgoKCgoKCgoKDwImVnB97+dun2f/kv8JrXHO5+FF48vGRbU19oDlOI02g0L7loqkql4qErk/yX3/4iLoeVeqOJ2aiXZ5fpNBqibR4uDE0hCCJn+9NcGcuyWShypi/F+Eye7eIuRzriTM7k2SyUSEXa0KhUTM4uYTRo6U9FuTqew6jX0h0LcGFoCrvZxNn+NJfHstTqDU72JBjLLbK5U6IjGkAURabmltDptBzvaufyaAatRt0SgKYpV2qc7k2RWViRzhluY327RKPRxGo2Em9zyvPV3HYL1yZmKFdrcnPp1fGcFK2MBbk0Igk3Z/vT5BZXKVdrpCN+avUG0/PLaDVquqL+WyJc1MfkbJ5LI9OEPTZsZj3D2QVUQH8iyMrmDvVGk2TIi0qtZmG9gE6r4XRfmvH5NUSgPxWlUmsyMbfCsc44Rzvjsgh3pCPGWG6Rcq0mNb52xhnOSXHORlNgt1xjbnkdvU7Lie4EF4amyK9v0hkLIrReN5UKzvalmF9ep1ZvEPa78DrtXG+JlDaLien5ZTYKJawmA0darbI7uxWSIS+CKDI5J53zXH+asZwkYjqsUjPrnnBZrlTQ6zRMt8S8cwMdzK1uM7W4TjraxrGudlmEi/jdiCppdiBAZ9TP2maB/NqW9OdYkGvjOUqVKiaDnmOd7YzPrzG/to3faeEvPvKL/IfXnwN4Wgebz+cDYH19/bZjd3pOKpUil8tRr9e/oxAHEI1G2d3dZW1t7c4fKAUFBQUFBQUFBQWFQ+X974eZGWhvh49+9LB3o/BiQnHE3SVGo/Fl4Yi7F9HUeqPJb3z+azw8OIFGrWE8t8jJniRep50bk7PyjLObU7NYjAZSUWk2mEatwmE1Mz2/TFMQDsw3e/3pfh69Nkat3pDdas2mQNDrAqHJ2OwSVpMBEZHBkQwqFZzpvdW++rqTfTx2fYx6oynPptut1HDZzPjdDgZHplGrVJiNBjILy9QbzQNx0a54kHK1xszyFka9ju72EI9cGcVqMtARbePG1BxNQSTic6ISm4znFjFoNUT8Tvka+uNtjM6t0hQE/E4rKpWKla0d1CoVA+kI11tR1N5EGLFZY3R2FZvZSDzoY2haOvaqY11cGJqS4592q0luND3bn5bnzQW9LmxmE49eG5OP7e3j1ce7mMmvy+LZ8c4YV8ayHO+Mo1vRYLOY5ObRgNtJqVJlY7so7+VCS4h81bEurk/MsLNbaTkAk3z7yiiJkI9GU0BE5EarEMFmNtAQYKMVf+2Kh2Sn333HupiaW5Kv43RfiqtjWU50J1jd2qEvGZFFN4/NRMjr5pGrowAc65SiqbvVGnbrDueOdHD+hvRYk0FHyGPnm5eG8Dpt6HU6dFqNPA+uKx7iPW96LRZ1Q37vPl25gkqlQqPRkMlk8Hq9B47daQ6cx+PBZDKxvb2N3W6/4+dk//O0Wi3t7e3kcjklnqqgoKCgoKCgoKDwIuPhh+EP/1C6/fnPg9V6qNtReJGhOOLuErPZ/LKYEfdCi34b20Xe8T8+z2h2kc2dMsvr2/Qmw2QXllnf3pFcbRoNE7N5NGoNXe1BbkzO4rSa6YyFuDSSoSMW4ERXuywcne1Ps1EoUqs3DrjVms0m1VqN/EYBv8uGy27l0kiGox1x+pJRLgxPSQ6u/jQ7u7vUG01OdieYXVpnbWuHUrmCzWJmfCaP3WKiqz3EpZFp4gEPJzrjXBnLUq3V6Yz6mcgtsLS2hdNqxGExcG1iBkEQSIfcXJ2YpSmIdMX8rGzuMDG3QsjjwO92ML24jlql4nRvCrPVRlOQmlp3qw2WN3fY2qlwqjcpi3Bn+tKMzSyyUSgT9rmwW80MTc+hVqk405ei3mhSbzTpiodoCk3ZffbaEz1cGJpCFEX6klGK5QpP3pzA73FwfN9r2Z+KsLCyKTvfOiM+rk7MIooiBoOOelNac+98l0amMei0xINebBaTLAie7U9TrTXY2a3gsplbDkBJoIuHvGwUiswvb6BRS9c+NruCyaAn6HXhdzu41hIBT3QnKO5WWF7fRqtRc7o3yaXhaRpNgWKpTMTvlqPH/ckIu9U6529O4LCaecWRTq5N5Nit1jDqtNitZoolqXDF75SaXacX12k0BY52xinulpnJrwLwr46n+Mv/8S6OD/SSz+flopanc8Q1m030ej3b29tsbW3dduyp4p1KpSKVSrG9vf2Mran7zxWPxzlx4sR38SlTUFBQUFBQUFBQULhXlErw0z8t3f7Zn4XXv/5w96Pw4kNxxN0lL5fW1BfSETeWW+Btv/EZwn43xXKZWqPJmb4Ul0ezNAWB15/u57Hr41RrdXoTEeZX1tFpPSRCPnarNUZzC/icdjRqNVfGcwfcchaj/sBMsL11exJh3HYz+bVtVjZ3iPjdWM1GHr06JkVJA1L81WYycLwzxuWW06s75md6cZVkwE2b00qt0WQ0u4BBp6FerXAzJ0UiB5IhbmakmWmnu9sZyS2yVSzitJpp8zjYrUvXvt9xlo4GMBsNDI5mcFjNhH0uLo1M43ZYD7jW9q5bpVLJRQh70UyLUY/OYGiJhGYibW4uDktrnO5NcW0iR73RlOfNFUpleR97M/cCHgedsSCPXG254vrSXGq1q57pT7O+tcNEK/p5ujfFpeFpgl4XoiAS9LnkCK4oCjQaTRZXN9HrpEjwhaEpDHotfakIqxsFxltz5c72p7lwcwqtVovLZqbN45QFukZDQK/VMJNfu+0160uGaTQFOc7bkwizuLpJNOAB4BVHOjl/cxJRFNFrNZzuS/FPF24CEPQ6aTYaXBvPcaQjzpGOONPzS3IJwysGOrg0kqHRbGLQaXng5/8jIVMTvU6HXqfD6/UyMzMjt5zeSTjbE+gikQjZbJbjx4/fduyp+P1+1Go1pVLpjp+XpwpxWq0WrVb5K1xBQUFBQUFBQUHhxcSv/zpkMhCJwMc/fti7UXgxojji7pKXS2vqC+WI+4cnr/OfPvB7eF12LgxN4XXY6Qh7uDg8TVMQONdytVVr9dYMuEUKpTJqFSytb7G8vk1XLEhTaPLEjQk6Y0E8Tis3JmexWUzEQ34q1XorAtkur2s365leWKNUqZEKeVjf3uHyyDR+pwWDVs1wZgGjXovPaaFYkn6/R1JhxmZXpNIDh5VCucpmsUzY78LvdnIzt0wi5Od4V7sswp3pS9EURek8kTb0Oi3jM3nml9cPCEp78+1qjSbtIR8mg1QyoddqSAR97FaqiKLI8a528mvSda9sFEiE/XIRwtn+NJmlDSwmA/GgTyqqyMyjUatIR9qo1ustV1yQRsvBNp5b5NxAhzxzrzcRplytt8omtJzsTnBhWDrW3R5CBS3nGxzrjHFpRHKhtYd8GAw6RvfFXa9NzBJp8+B324m2eeTYan8yitVkZGWzgFGvk5135WqdE13t6LRaxloCXW/Mz/hsnoDXic1sZCAd3SdctuGyWRmfycuv4cRMnq3iLk1B5HhnO0/emEAURdw2E85WG6tOq+FoR5xCsczKVhEAs1HHdrFEqVxFp1HTEfby5M1JCsVd7jvWzdc++T5+6P5TB8S2RCLB3NwcjUbjGR1xe6UKq6urFIvF2449FZVKhdlsZnNz846fuTs9T6VSPePnTEFBQUFBQUFBQUHh3vH44/C7vyvd/tznwOE43P0ovDhR7BR3yWGWNfxLdsSJosiX/vZhPvO/HsJuMXFtPIfTagaVyOTCOiaDjs7WPDCrycCZvpTstDrbn+bJm1NE2zx4HVZuTs/RaAp0RHwYdWomZtdpc9toNgRGMvOkQ26CbhtXxnKSKBX2cm1iFhXQE29jdHYZUYSe9hBarYabU3OE/S5UKhWZ/AYne9o5YjbLc8vO9acZHM1gNRlJRQLM5FfZ2a3gd9uJBTx8+8qoPPvs4vC0dC1hLzMrG1RrdUI+F9pWQYBWo+ZYV7t8bRq1ikq1ztL6ljzL7fJYluNd7ZzrT8vz0TpjAda3i4T9bgx6Lb2JiCxQiYLI9k6JreIuDquZUMuldrQjxuneFFfHszSagtxoKrbeQ/tdiHqt9kA0de95giCSDPtp1Gtca5U5HO9qZzS7wM5uRW5C3XtevdGk2RSYnl8+MH8v5HMR9DgxGHRyhPRkd4Lc4gqrWzvodVoGUlHZibhd3MVtt3JzSoq4nuxJcmNyhrnldTwOK4mQX34NAx4Hu+UKe36yvmSY7MIKu1Xpc/q9547w0IWbskuuPx3j/E2pcbVab6ABJhfWWq9Jmk+8+8fxux1sbm7e1nJqtVqZm5tDEISndcSp1WqMRiPBYJBsNsvAwADw9HPlAHQ6HeVymaWlJUKh0IFjTyfgKSgoKCgoKCgoKCgcPuUyvO1tIIrwkz8J//pfH/aOFF6sKI64u+TlIsQ9n464cqXGf/n4l/jLf3gCtVpNdnFVclTpdVwcztDud+KxW7k+MYPNbMTnsLK5VUCnVZMOeWSRx2PRcXVihkZToDfexvTCGrvVGt2xAIVShbVCiXjQS7UBq9u7uB1WUpEA43OrNJoCJzqjjMxIItzZ/jRjM3n0Oh39qSiFYpn55Q2CXheFYpmJmbzs3jo/NEWjKXCqN8VIdp6d3Qrd7SHqjSZT80u3zT7rTUYQRJFqrU5/KsLOboXZpXU2WrPvBve1pF4dzxELeGSX3/T8MhajARAZzswDcKonSXZxlfXtIga9jrDv1jy07oiPqxMzJCNtJEI+jC2XmkatwqDXMT2/RKMpHGg0XdnY5lRPUnYL7s2RazSarVltSdn5lgj5CflczK5sHdjz2tYOp3qS+PY1oZ7pTXFjcoaI343NbJTn7wE4bWbavE5yi6vyPL7LY1lmltY505ci2uaRRbi+ZJjZpXW8Tps0g64/zeXRDPVGE6vZxEA6ymDLFdibCFOp1snl1/A6rLziSCcj2UV2q3WMeh2poJuJ2TyiKOJ32fA4LLJTr1ytoddqWWi1pr759Wf4X7/9y/jd0v++upPrLZFIkMvlaDQadxTV9otmiUTiu5ort3fM7/eTyWRuE8AVIU5BQUFBQUFBQUHhxcuHPgQTExAMwic/edi7UXgxowhxd4nJZHpZCHHP1hEniiKNRoNyucz29jarq6ssLi5y4eoN3vhLH2V6ZoHJ2SUseg2poJvFlQ2WN7aJ+hwYDVrmVzcJeewYdBqyS+tYzUaCHidTi+tYTQb6khG2Kk20GjWnepOMzCwjiCIep538RoFytc7xrnaW17eZW9ngTH8HGpWKidk8HqeNWNBHuVpHq1FL8ctWYYFeq2ZyLs/OboXeZJhSucLk3DInutsJep1cHc+h02g42ZNkfmUdQRA53Ztiam6JzUIJg05HNOBlPLeIxWjgSEdMcpk1Bc70pRnJLrBTKpMKt7FVlN43JoOOo51xWWA0GHRkFlfYKJQI+Vx4nFaujs/Qkwhzpi/FYEuE6mkPkV1YYb3VKHq0M87YvFQqYDUZ5AjrXqHFxeFpOmLBlpC3wsZ2EbfDik6nY2dX2otUaiEVOcyvrNOfisnz1052J5hf3WBiNo/ZoKM3ETpQ5rBTKjOztNYqT0hxsSXeGfS6A4UNZ/rTjOUWEQThlkDXWqe7PYQgSA46gO6oj+HMAruVKpVag+5EmIuyKzAIoshIdkEW88Zb0VSDTjL5jmUXWtFUM0Gfi+n8BrnFVV57spdytUZ+vQDAuYEORjLzhPxutBo1P/fGc/zo9xxFr7tlFr6T683v96PRaJ5WHNvverNarfh8PnK5nHzs6QS1ZrOJx+OhXq+zsrJy2zFFiFNQUFBQUFBQUFB48XHxIvzP/ynd/sxnwOU63P0ovLh5WUVTP/rRj/KBD3yAX/qlX+LBBx98TmtZLBY2Nzefn409Sw7DEVetVqnX69RqNfmf/X/ef1sQBFQqFTqdDr1eT3Z5i4/+2UOEvS5uZqRZYgGvm0evTwBwujfJlbEcAZeFox1xJmbzlKs1uuJB1gq7LKxuEm3zIIqSO8xpNXO0M87gSGZfGcA0p3qSaDUazg9JBQ0nuxNUqlVWt3boiAbYKBSZmlsi4LYR9bu4PJaVSw+evDnFkXQUo9HAYKu8oLs9RLMpkl1cxe2w4nFYuTyaQaNWcd+xTh6/Ju1/L6J5pCNG2O9CrVJzY3IWrUaNTqtmYmYRQRA50Z1gODNPtVanIxqgUqtzfWIGtUrFqd4kj14ZIxbwYrcYmVlaZ6dUxmYxodfpeOyaVKBwpjfF5TEpRvrqY93MLq3JTrTTvUkeuTpGwOPEZNCxW5EKLdQqFXqtlkeHpDXS0QBbOyUmZvOc6E5wpiWeAaTCbezsljHotVKktO/WPDuDzo7bb2Ak2ypa2FfmcCQdpVpvyG7AY51xhqbncFjN6LUajnTEZSGtUNwlFvDKAt3pvhRXx6TYbDLslyLLrWtKhHysbRWwW0yta7xVPKHXanjdyT6+OTgsvac8Dox6PZfHcrxioJPNnR3JgbggCVpdES/bOyV2dqtoNWqOdyXkMo+N7R3+4JffSsxrY3V1lUKhgN1uB+4sxKlUKhKJBMPDw3ec0/ZU11sikeDSpUskk8lnjKYKgoBOp6O9vZ1MJoPf75fXV2bEKSgoKCgoKCgoKLz4qFalSKogwFveAj/wA4e9I4UXOy8bIe7SpUt89rOf5ciRI8/LemazmXw+/7ys9Wx5rnPbRFGk2WzeJqDdSVzb3d1lbW2NyclJtFoter1eFtj0ej0GgwGbzXbb/VqtFpVKxV/903m+8A+XCHrd3MzMy3PNrk3OotdpOdYZl2d8WYx65lfWKVdrB2aaveZEN1dGcxTLFZJhP8XdClqNRp6ldmUsi8mgw2o28vDlEeBWw6bZqOd0b5JrEzPUG006ogFWN7exW0x4nDbcNguXRzNoNWp8Lgf/fGkIkEStq+M5VKjoT0dZWtticnZJnr3WaAgHzgPQFARKu1W2irt4nDacFhMTCyuc6UuhUqnkx/Umw+Tyq6xuFqSGU/+txtHOeJBvXhpCEETiQS+1eoPHro0RD3rxuxyyYNaXjLK5U2RmaQ2L0YDfYZYdbL3JME9cn6BSq8v7ffTaGF3tIawmIzcmpdfCaTVTbzQYbwlVJ7sTDLWEQofNfMCxdqQjxvT8MjqnRZ6vthc3TUXasFvNPHZt/LbX5FhnHKvJKEdIj3XGGZ/Jc6TDKYthl1rXHvS6iPjdcmvrkXSUidklKrU6YZ/rgGDoc9px2MwsrklieG+rNXVpfRsAjVZNdmGVar2BQadloCPO4Mg0sEZHxE+lVpdFwxPdCb74399BcWuNcrlMOBwml8vJf1c8nXAWCAQYHh5me3sbi8Vy4NhTn+N0OnE4HPJcuWdyxGk0GqLRKJlMhvX1dbxe7zPuQ0FBQUFBQUFBQUHh8Pjwh2F4GPz+W0UNCgrPxMtCiCsWi7z1rW/lc5/7HB/+8IeflzVfbNFUQRCe1qF2J4Ftz7W2J5ztF9Hsdrv857m5OWw2G4lE4lmJAM2mwEe+9Nf8wxPXabQEBL/bjtVk5Op4DofVzMmeJE/emMCg19LTHuHaRI5TPQlSkQAXWyLP2f40lWqdYrkiO88qtTrxoJemKEUZgx4nBr3kGvO57AS9Ti4MTaFSwUAqRqXVGro36L/eaJIMetjYKTM5t4TbbsHrtPPEjXHMRj19yags0pzsSaBSqVjb2iEe9FGr1xnNLtAZC3CkIyYLTmf701wamaY3EcHrsrO1U2J6YQWjXotBr+PRlri0vxjh1ce6mJpflltSBzriXBvPIYq3BKtytYbLZiYZ8vOtlsh4pj8tu/ZOdCdY2yqQXVqX9zGSmadSq5MISTHc0VaEM+J3888XJaExEfKxW6lxc2qO030p1PuEwmjAw265irrltjrbn+bi8DSiKLKhUdEZC8rz1U72JLg5Ncfi6gZep42w3y2v0xULsrCySX59S15n71h+bZP/P3v/HebYnt914i/lnHOOlatznLkzHodZAwb8YDCw/GzAAa+NjbHXgG1sMLAsTuCIIzYedh8MLCzBsMSxx3PT3M6pcpBKKqlKVaqgKuWs3x9HOl3VXX3nxmk897yep59RHUlffc+R1HPr3e/35z0e8YvXeTYRJLtzMBRV1YTdFp4MCxpsJj2tbpdMQYjfTkT87JWOWc/tAPCpy1O88WhFKJsYOiTfeLjMhWSIzd197BbzUISDkMuCWa9lLS+Ij3/uD7/CT37vN6FRq1g52EWhUBCJRHjjjTcYHx9Hq9W+UDgbudE2NzefK1Z40Vy5J0+eoFarv6gQp1QqiUQipNPpU0KcUvmR+CtbQkJCQkJCQkJC4g8EDx/CT/yEcPuXfxmG/+kuIfG2fCR+q/ue7/ke/ugf/aN8+tOf/sCEuA+7rOGka+1ZMe3o6IhqtcqDBw/E+7rdLoDoWjsprul0OiwWyymxTaVSia61t2N3dxe5XP6uRLjjap3v/enPcHBc4aBcpdZo8ZWXp3m8niW9VRQFrV6vJ4pzj1Yz6DVKdBo1rz9aEYWp2/PruG1mbswkRQfW1SnB4WYzGZgaOqEKB0cE3DbCHidvza1h0muJ+l3cXljnymTslAg0HfWxvr1Po9UhEfBQrjdY3SxgMeo5lwzxxqOVU02fs4nQKWHM77LRaHVotDqn9gngspl48/Eq7U4Xv8tKp93h9YfLhL0O3HaL6Hy7OB6h0e5Q2D865cwD+OprM3zurhC3TAQ9VGoNFtJ5NCols8mwGPGcTYTQqlVs7hygUSlOtZZ+6vI0t+fXaLY7mAw6wh4Hbz5aQa/VMBn1i2KdSa9Fo1TwxjAmfH4swnpuh3qzxdWp2KkG1YmIj63iAVqN6rnYqt9lIx5wi664kxFSQUxti49NhrwcV2q4bEL0cyRi9vsDTHotN2cToug4isoupPJcn0kwADHGOoq81hotev2++FkaiYQKhZxO9+ncueuzSe4vptgtVbEYtPzwt/1JvuWPf6X4HRiJ0waDAZfLRTabZWJi4oXNqL1eD4BKpcLx8TGWE93kZ8VInU4nWq2WZrP5yCxZOQABAABJREFUwu/TyeeFw2E2NjYolUrYbDbJESchISEhISEhISHxPxGdDnzrt0KvB9/4jcIfCYl3wpe9EPev/tW/4sGDB9y9e/cDXfe9CHH9fv9tZ6s9+/NgMEAulz8noMnlchQKBX6//8z7PkjebQw2vbXLj//Wf6DRajOfyjMYDLg6FWfvqEypXOPCeITVoaDlc1jpdHqkD4uEPA7q9Tpvza0RdNvRadTcX0qj06jwuWwclKunxDGAc2NhPn9vkV6/z0xccD/VW22CbjsAc+s5THot/cGAB8sbz4lHX3Vlhi/MrdJqd4j6XTRbHWqNFia9lojPJb6OQasR59aNXqdSa/DKxXG0ahX3l9Li2nfmBaHtXDJEeqtIrdHCbjYQD3iei80qFXLOj4XZKh6ylttBp1ExEfFzXKkDnJop57CauDwZ463hTLORS81i0BH2OqnX68ynhHbVm7NJKvWG6Bxsd7pi8+rXXJ8VXXEhr4N+r88bj1eJ+V247Rbx2ngdFsq1BoVhk+goqtvt9Skelk/FVmcTIbKFPfLFQxRyGVcm42KENOC2AQMer20CnHIljs5ptE7Y46A3GJAeRmUvTURYSG/R7nRRKuQolQruDJtrR9HUe0tpVAoFlyaibGzvkT4Unnvz3Bh3F9YJuqw02x0uT8ZFAdOgVfL3/9LX82f+2Fed+uyO5rOB4F67f/8+iUTihQLYyJUaDAbZ2Njg4sWLZ641QiaTEY/Hefz48ZkC+Eh8H72WWq0mHA6TTqe5cuWKNCNOQkJCQkJCQkJC4n8ifvIn4fFjcDjgl37pZe9G4g8SX9ZCXC6X4/u+7/v4H//jf6DVaj/QtXU6HfV6nWKxSL1ex2KxvFBgG90eudZG4tlJEW3kWnvWzaZQKJ77ZXtvb49MJoPX6/1Az+ksRmUN74TXHizx/T/zfxEPeHi4sgEMBHFqYR0Z8MqFcd4UCxoEx1TQbSfkdbCe26XebBEPuHFaTdxZSOFzWFGrlTxezXLz3BhatYrbC+so5DIuT8bZLh7S6/fFRtF+fwADaLU77B1VCLrtgoNqJcO16TjNdkcUfc7F/ZTrdVrtDhfHo6xkt2m02hi0atx2C/OpnCiM3V5Y59pUHJlcJr7OWMjL4XGNtdzOU+FuuPbXXJvlc/cWGAwG+B0mOj1EoW084j8xPy2KXC7n4LgqxmsfrWbRaVR87PwYbz0RRLdR0US70xUceMmnkdigx4FapeDBzj56rZpkyMut+XXUSgVXp+MspPI0Wm2MOg2xgJtsYR847XzTaVTEAx5xPt50LMjW3iE7B8fcmEnSHwzECKnbaqTd7SKXPx9bVSrkXJ9JimLhuWSIzPYet4spEkEPDqtJFMPcdjOtthAZfnY/Mhmci3p4uCIUNtjNBlw2M194vMrlyRi1RpO9UlmMpl6ajCKXyTmq1FAq5Fwcj4olDHqt+lTU+fxYmG/96nNMhNzPfX5POt+sVisGg4F8Pv/CaOpINItGo7z++uvU63X0er1431nP8Xg8AJRKJex2+6n7RoL3yedFo1FeffVVyuXyc/uQRDgJCQkJCQkJCQmJl8PcHPz9vy/c/sVfhOF/5ktIvCO+rIW4+/fvUywWuXLlinis1+vx2muv8Uu/9Eu0Wq0Xzmp6llqtxnd913dRLBYpFotsbm5yeHhIMpnk4x//OD/2Yz92prg2uj2674Nwrb0bcez98k4ccYPBgH/6H36fX/u3n8VmMnBnGAdttjvcWVgXCxqa7a7YEDqKaCaCHn7vriBaJf0ONouHWJ+JnIY8DhrNttjC6XfauLuYQq1S8LFzY8+5xC6OR/A6rWxs7VFtNAm67Wg1au4upjFoNQRdVubS2xi0am6eGxNFm5GLbSLiPyWMqRQKDCeKIE66uq5OxynslZhP5cT5ZPvHFQaDARfGwiyk83R7fXwOK9OxIPeXN05dg4mwTxS+CgdHomDW6QgC1dWpOI+Hr6VVq0mGvdwfRi9H53suGcJh0qHX63gydJ5dmoox6A9otNpC4ywD5oYz1z51eZJXHwgz60Zi5+35NbE8YzTDzmbSI1PIuPvkqXC4mM7R7vbw2K2n3GyjCG17KDafFOjMBj1hr5PfH7abTkX9FA6OKR7ucXUqfio2bDLoCHns1OqC23QUY13JCsUoJr1QNtHt9VEpFFyYEMo+HFYTXrsVg14tFkPE/E52DspoNBoAvvHTN/mH3//nuX/3DuVy+bnP8UkhTiaTEY1GWVlZwe/3v9ARp1Ao0Ol0eDweMpkM09PTz611ktGxra0t4vH4KTFtFHU9+XeSRqMhGAySTqfpdrvv+O8rCQkJCQkJCQkJCYkPh25XaEntdISG1D/35172jiT+oPFlLcR9zdd8DXNzc6eOfeu3fiuTk5P80A/90Lv6pVar1TI5OcmnPvUp3G43mUyGn/u5n+O1117DbDZ/SWc3nVXW8GHxxUS/VqfDz/3z/8L/uP2Y/mDAWm6HkMeBXqfh/vIGbpsZk0HHw5UMlydjTMUC3FlIiaLVxvYeg8HglBijVirFGWYjcSy3e8DVqTj54iFLmS2xYGE0K+zknDa72cDn7i0yGAyYTQiurAdLG0S8TgYMWMntolYKM9UarTZwWjjSadTUW20KB0fYLUacFhOfv7+Ix24m6nefahJVKxVs7ZWEqOSwwVWlVPDx8+N84Yng/puOBckXD1Ao5OK8tpEQaTHp2d4rcVytC5FamRCpnYr5uT6TEB83KijwuazitRvtY9AX3qfc7uFTF9/cOomgh/PJMKmtXWqNlnhfoy2IZdPxAPldQQBUKRR88tIknxuKZaPZbLeerDEW8mK3GE/MwTNTbTSp1ASx7GSEttFqn3ovR4URbz1ZRa9VM5sIiUKfTqOiPxiwsrEFIM4OXExvIQM+dn6Me4tpOt0eSrmcc8kQrz5YIuyxU641sBh03Bs2xeqUcgxaJSvDEoZrM0kerWzQ6fb4eCLMd/+ZP8R3fMPXIJPJ0Ov17O3tUavVTrWdPiueeTweVlZWqFQq6HS65z77Jx8fi8W4desWyWQStVr9Qkfc6LvU6XTY29vD7X7qzDtLiBut/frrr79t26qEhISEhISEhISExJeGn/kZuHcPrFb41V8FKagi8W75shbiTCYTs7Ozp44ZDAYcDsdzx78YCoWCH/3RHxV/fuONN+j1elit1g9iq++K/1mEuL1Smf/tH/wGWrWSzZ0D2p2uKJy1Oh3Gwz72j8qk8ruEvA6OylVyRaFd02rU82B5A4NWzbXpBLfn15HL4OJElFvza8zEgxj1WlEcuzgeQaVSsHNwdKpg4dJ4lIjPxf2ltBjbfPPxKiqlMDds9PyZeAi1SsHDlQxOixG1SsGj1SyXJ6NcOeHsuj6T4MFyGrlcTjLk4bhSZ3WzgEmvZToeEl1dI+HOYTEKItlRmfX8rhhTbQ8jlxMhFyvZbXr9PpW6IB4tpPNChHK4vxuzSexmI+mtItVGE5NBh1wmJ7MttISeLD4IdXuEPA6xkGC0jzG/HaVKjUal5NGqEOl0Wk0UD4+pNVqnHH5mg+5U86rDasJuNrC9XwLgymSM+aGwpteqCXkdYnlE0Gmh2uyQyu9yczZJLPBUmPTYzRh0TyPgJ1tulQo5n7o4Lc6n8zqsqJRyHixvcHEsTLvTIbW1R6vTRSaDsYCDUumITreHUafGatDycHheDpOOcq1BdvcQgAvJEAsbW0R8TuQyGeNBF3eHUVSzXsv3/7mv4yuuTJ/6TJtMJrLZrOhgg+eFuFHsNJ1Oi5HTk5wU28xmMzabjWw2y9jY2BedKzcqYnhWiJPL5c9FTnU6HV6vl+3tbUmIk5CQkJCQkJCQkHiJLC/D3/k7wu2f+znw+1/ufiT+YCJV8L1HPuzW1LfjSynEvSiaOp/K8fX/+0+jVMh58/EqVqOeG7NJ5lN5ao0WYY+TXr/PYbnG+bEwh8dV0tt7fGx2jP5gwHp+l4Dbht1iQiYDi1FPwGnhwXIGtVKB1yEUB4zcco9WsxQPylyejJHfO2SvVCYZ8rK9X2Ln4Ain1UTY5+L+8gb9fp+Pnx8Xn399JsFSJo9cLmMqFqDV6bK9f4zTaqLZ7jCXyqGQy7g2LTjQGq0ON2bH2Nw5YO+oQsjrwGzQkdvdR6NWcnnYJDoYDIj6XSgVcg6Pq4Q8DixGPfOpHPVGk0vjEVZye/T6fS6MR8hs72MzG8TyhpGbS6VSMp/OUW00ificGHUaFtJ5YkE312cS3F1M0en2mIj42CzsU2+2UKuUYjR09P7Umy2yO8IeR22nLruF6XiAWrNFprAnOgE7nS79/oDxsBcGA9Y2d1jObPPJS5PcX96g1e4QcNtxWk184fEqOo0gmBYOyxxV60IJRv/p7LjJqJ9mu0N6q8jmzh4Xx0I8XMnQbHewGLT4HGbmVjMARNwWjqs1ckMhjX6XpWyBVqeLUachEXCzmj9gp1RlPOxFq9GQ3xeipNemE6zk90AmF9+zx+s5ur0+B0dVrk7FWM4Jrrhk0M0v/8D/ekqEA0EMczgcbG1t0el0xONniWfBYJBOp3PqcSfXOfn4eDzO5uYmvV7vbefKgSDEVSoVSqXSqfVeJLSFw2GAU/uQZsRJSEhISEhISEhIfOno9YRIaqsFf+gPwV/8iy97RxJ/UPmydsSdxec///kPZJ2PihB3liPuv775iB/55X9JwG3n9rAYYDYZ5nN355HJnraC3phJiMULg8GAK1MxWt0uh8fVU+2jXocFnUZNbu8Ih8WIzWzk4WoWvVbNeNj3NHJqMbK5s0+r3eHKZIy5VI52p8tXXp5iLpVjPbeDw2rCZjJQrjWEGWLjETHeqVIoyGzv0Wi1CXtsVJtCDPLGTJKjWl0UlW7MJtkrlWl3upwfC5PKC9FOt83MVDTAg5XMqTlv16YTpyKgdrNBaBctHYrrjc7BbNCxI5ezulkQI6ZvPFxmPOxDr1WzMmyUtRj1MJCJJQPXphM8XNmg2+vzSnwCvVYjuuJGYl3M70anUaHXani4kgFAIZeRyu/SandxDq/N/aU0s4nQqflzopNvGFudHRYtVOtNFHI5HzuX4I3Hwiw9r8NMt9vjzmKKuNeGUqlgNVugPxigVSvRqZQcV2oARLx2yrUmueIRAF9xcYI3Hq/SHwww6jTEgx4erW1yLhmiWm/SaHVYH8ZLkyEfA2B1c+e5NtZLE1H2j6vie5YIeijXGrSHQtdXXEjyo3/xj6JSnO1KMxgMmM1m8vk8sVhMPP6sEKZUKjEYDBwfHz+3zrPCnd1uR6fTkc/nX+iIGwlxarWaUCgkNqKetd5JRmUzm5ubp1x8EhISEhISEhISEhJfGn7xF+Gtt8Bkgt/4DSmSKvHe+cgJcR8Uo9bUwWDwJXemvCxHXL/f5+d++79wa34No07Lk7VNIdZoMrBfKp9qD1UrFWg1al59sCSIczNJbi+s47AYT7WcXhyPsJzZZjCAgMNMo9NjPbeDx24mFgtyfykttoHeXUxxfSZB1P+0ofTKVJxyvcnBcZVkyMtRpcZ6boeg20Yy5OHeUnrYshrj1vw658fC9Hs9lrMFur0+8YAbpULOSmYbk14rrq2Qy/j4+XHemltjMBgwEfFRLJXRqFXDQoGnc96USjmPVrfodHuCIFQV4qzXp+NYdOqne52MMb8ulB2MZueNxLSw18HvDmObMb+LeqvN7fk1JiI+LEb9iVlxPvZKQtx3NBtvdF/AZWdufZPdw7IoQr71ZI2L4xHqzTb7xxXWcjvIZTJUChmZ7SKdbg+fQ5i1N5/KYdCqmQq5WFjPMQBMOg1Wo5aNoTgW89ooHFZotrsoFXICHievDwW6kMfBYDAgXTjg8mQUh93K41VB6BuJjpVGi/5gIBZIjMol7GYTa5s7NNsd5DIZYwEH95bSjIW82Ex6PA6rKMIlQx6KpTLp4Z6uTMWYWxdE2aNKjb/5LV/PhEOFbNBHJjt7Ttsodrq0tEQkEhG/U2cJYVqtloODgzNnyj3bYhqLxVhdFWYDvmhG3KgJORKJ8Nprr1GpVDCZTC+cKweCSCeTycjn8yQSCbGAQkJCQkJCQkJCQkLiw2d9HUaTqv7RP4JQ6OXuR+IPNlI09T2i1+sZDAa0Wq0v+WuPxLEv1mb6QSCTyRgMBtQaTb7rx3+Tz96eY3Nnn3zxkGTIgwxYy+1QqTdw2czMp3I4rCbCXievPlgi6LYLxQIL6ygVcmIBNwfHVfr9p5HTSr3JjdkEu0dVDss1JqN+Wp0eSoUcv8uG02rmydomRp0GrVrF7fn1obiX4P5SmvlUjuszcTZ39tk/qpAIemi1u6hUSqxGPWNhH3cX08hk4LKamU9v0e31uTQRZXuvxIOVDeIBNxajnrn1HBq1kvNjEdrdLoPBgKvTcVL5XUrlGs12G6NOw2I6L8ZD33qyxlQswJXJGLldIc7qc1jp9vqkCgeiS/D+8gbHtQbXZxN0ej1S+V10GhUXxyPcXkiJc+0K+0fsHhxjNerxuWyi0HZ1Ok56q8jqZoEr0zFxNh7AeMDBk/UstWYLq1GH32kR20Ppdkjldzg8rqLXqAi6LDxc3cRjNTIeclOq1Nk7qqJWKgRhUq1hACSDHrQaNbm9Y7YOjvnUpUk2dko0210cVhPxgIfXH6/hcViE+HG5Sr54KAh9SiXp/C6dbg+33UzQbefB8gZP1rJcn04Ij9196hh87eEiFpMei1HPWNjLSn4fgF6/T8jrZDmzDQjC6+bOAWubO1wYD3N9Nsn9pQ3anS5mg45f++Fv47v/zB/G5/NRLpdfOKdNLpfjdruRyWTs7u6eOn7Wd8BkMpHJZM5c5ySeYW95p9N5oSNudFyr1RIIBNjY2BDvezshTqlUYrfbyWazZz5GQkJCQkJCQkJCQuKDp9+Hb/92aDTgq78avuM7XvaOJP6gIwlx75HR8PaXEU8d/SL/pXDFyeVyCgfH/Im/9o84LFdZyW5T2D/ik5cm2dw5YP+owmwixF6pglGvZSwkzBwTZsDZCfuczK3nsJn0JILCXDS3zSzOMANBiDks1wRxbDzMem6Ho0qNXr9Ptd5kc2efgNuO1Wzg9UfLBN12ZuIhbi+kkMmEmGKn26fd6Z4Sw4w6LRq1iuXMNiaDjulYkDsL6yjlcmZjPnGGWTLkxWoykC8e4raZCbjsPFzJUG+2BPfeYppur8/F8QgrmQJqpRK37am4BEKT6P1lQRCajgWot9o8WMkQdJqFvc6PIqZxSuUapXINn8OK224RxMhag6+4NCnuKeJzolIquL+YRqNScC4eEBtEQy4rrVp96G4Tig1Wtw44rjaYjXoByO4eCuUXYyEep7exmgxEfU7MRj2bw5io3WZhfWuPZrsriGUeB3OpPANkguA1vI56rZoL4xFqTUF0Hr3Hq5sFAM4lwjxZ26TWaGE16pmI+rk9v85Y2MdULEC70yO9JbjXrk4nkMtl1BotTHot55Kh4Zw7oRlWq1GxkhXWvTIVI188RK1UIpfJhqJbmnani8Wox6DVcGd4XccjPv7jz/4NPnlpEoBIJEKtVjtTrD5ZihCJRESB7UVCXL/fx+VysbW1RbvdPrXOs8KZXC4nEom8bVnDyedEo1EKhQL1ev2LCnEKhYJEIsHm5iadTkeaESchISEhISEhISHxJeBXfxVeew0MBvjN35QiqRLvH0mIe4/odDrg5QpxXwpH3KO1HL/yO69jNui4s5Ci1+9zYzZJo9Wm3elyYzbJQjpPtdHEZTGRKexxcFxlNhGkXK1Ta7RIBD2olEpWsgXcdjO9fp+HKxmMOo0oxKTyu0yFXTxc3aTb63N9NsmD5Q2sJj2ziRDHlTr53UOCbgdhr5P5VA6TXiuKXDKeus7anS6XJqKsbhY4OBbKFkx6LQvpPL2+IPbNbwhiz6hgYhQ/7fQE0chi1CNDmE82etyj1SzNdoeo3y0+zqTXMpsI8eajFTQqJTdmkqxkCxxX6wRcNmxGHfPDMojLExHuLqaZW9tkNuanUm+QLeyjVStJ+B1k8sKeEl47hb0Se0cVBoM+kxEvc+ktAM4lghQOy6wXDvE6LCRDXta2DgC4OhVjIFdyVG1gMeqZiAZ4tCZETC+OR9g9LLNzcIxOo+LCeITXH60wFQ0wGfWLYplcJkOvVZPfFVpw/S4bLquZx6tZFtNbTAQc4nus06i4OBHl4WoGhVxOPOBGo1axtCHsVa1UiKKqVi089vb8OuntIlGfC7NBx9x6DoDLkzGWM1scHleRy2RMhlyi0y1b2BNm/Q1Ft5jfhU6j5vVHK8QDbv7Ixy/yH/7RXyfqdyGTyZDJZFitVpRKJdVq9bnP9EnBLRgMUq1WKZVKbyvEGQwGrFYruVzuzHVOEgwGGQwGVCqV5+57VmwzGAx4PB4ymcw7EuJsNhsmk4nNzc0zHychISEhISEhISEh8cGRycAP/ZBw+yd/EobjpSUk3heSEPcekcvlaDQa6vX6S3lt+PAdcb/9X9/gR3/t39HpdLm7KMxqOz8W5vb8OsWDI65MxU81mz5Zz9Hp9rgxm2RxY4tKvYlKoWCvVKZYKjMR8dHu9LizkGI2EcRqNjC3nsNs0BNw22m2eyjkcqEEYX592OrpF4W+c8kQh+Uq1XqTkMeBedhQatRpAJnoTrsxm+ThSob9owqfuDjJwVGV7b2SGHNtdjqoFPJTraNqpZLtvRKlco3oUOhZSG8xGfEL0dGhCCTEQ4WYasBlw6jXMp/K0el2uTQuRHB7/T4Jv5OD4wr5/WMMWjU+u4kHK0KkcCriAQZUGy3cNhN2s5HU9gGbxSNuzCZJ7RzS7vaI+V0YdDqQDx1hM0nmUnm6vT5Bj52oz8XqpjDzbTzg4N7SBo/XspxPhtFqngpiN2eTYgmEz2nDbbPweFXYi9dhEcUys0HPVCzAnYUUUb9LmOlWb5Ld2UcukzGTCNIfMJwrZ8XjsPJoJcPBUYVPXZ5ia6/E7uExKoWCq1NxXn+0wmTUP3yshUfDAgmf04bNbGBrryTGdh8sb1A8FFpxx0IelnN7gCC6yRVy1CphnOXlyRiF/SN2Do6QyWR88x/5BL/6N78do1773OdXr9dzfHz8nGB9UkBTKpUEg0Gy2ewLhbCRuy0Wi4mPO3n8WUZrbG9vn7nWs68Rj8fJ5/O02+0XljWcfF48HieTydDtds98rISEhISEhISEhITE+2cwEGKotRp88pPw3d/9snck8eWCVNbwPtDpdDSbzS/5644iaR+WENfp9vj7v/Fv+f37iyiUcuY2dpiM+Km32jxZ28RuMaJQKsgXDzDqNMQCHm7Pr6NSKPjExQneeLQCPG0LPZcMoVWrxdbP2UQIg07DfCpPxOek3emytLHFdNhN1O/k3mIalULB+fEImcKeKPSN2ldlMmi2O+yVygTcdmQyeLCywZXJON1e91TktdZoUW+2TrW0BpwW/E4LD5Y3xPbT2wvrXBiLMBj0WR2WBrisJpqtJo/WcshkMBkSorUA02E36cIhzU4Xk06Dzazn8FhoCr04FuLxeo7BAKwGLSGfnSdrm2KBwr2lNImgh9lEiGxhj0q9Il7H0Xt6aSLK0sYWzXaHbq/H+fGI2KA6aoxVK1VYjHr8TitLwxlqF8ejaNUqdtePMWg1JEIebg2vxycvTvJoTYjBKhVyLk5Eee3BMnqtBrvFSKvdYSGdB0CpULCcEWbpWYx6/C4bdxfT+O0mZuJBcrsHFA6OxOtcLB3TandwWE04zEZxPp3FpCdT2Kdy4rG359eZTYROlXuAILr1en1Whi7Ey5MxFtN5mu2O6HgcPdak1/ILf+Nb+PT1cy/8HKtUKgaDAXt7e7jdbvH4YDA4JXhFIhFef/11ZDLZ28ZJnU4narWaQqFAMBg8s2V19HiAo6MjsYhhxFnindlsxm63c3BwgEqlOvNcTgpxTqcTnU5HLpdjbGzshecvISEhISEhISEhIfHe+c3fhN/9XdBq4Z/+U3jBv5lLSLxrpI/S+0Cv178UR9xIMPgwhLhSucoP/eJv83A1Q/HwmL1ShajHhtmkZ3Nnn0TAg0ImYz23y3jIi81sZG59E7vZQCzgptPtidHH2/PryGUyPHYLdxdTQuR0JsniRp5ao8mFsTDFwzKF/SMSQQ+VRptUvojdYiQaEIoINnf2uHlu7DnnXWhYAlGuCpFVn8OKQafm8domGrVSnEG3kt3mymSUpY08lVqDmM9JpdFCJgODTk3IbRXLEOi2eLKeE+KnHhuNVotHazkCLisJv5ulTWGo/7WpGEajkWanSyLgQavVsLlbYvvgmOszCSEOOoDzyRCHlQZatQq7xUgs4BYFKqfFSLnWoFJvivPv5tY3OarWRUdfs90h5nehUipRKxVPCyqG8dtGq4XPaRNFuBszCR6uZMgXDwm67dgtRrGV9MZMkma7TaXWwG42EA8IomKn1+PadILdg2MK+0diAcWbj1eYigWIB9yn3HVGnZpGq0251hBjubfn15lP5bk6FT81O+7GbJJbT9bQqVXinLmRkFapNcSIMSCWVNxbShPzO5kKu3mwvEGz3cGk1wpOxnYHgETQw3/8uR98oQg3EqoHgwF2u/25koVnxTC9Xo/b7X7baOpoplw0GiWTyTAYDN52DhyAz+cTixhO3neWeBeLxSiVSi+c+3ZSiJPJZCQSiZfyd4+EhISEhISEhITER4FcDv7aXxNu/4N/ANK/f0t8kEhC3PtAr9e/lBlxwIcixK1uFvjj//tPUyrXeLyapdFqc2k8zGbxiE6ny+XJGPm9Q/aOKswmgjTaHXK7B4I4p1Cwulmg2ergtgvRR7NBz2QswJO1TdRKBVem4txZECKnBp2WVL4ovMZElHzxkNzeEVcnI8hlMtY2d3BaTYQ9Tvr9Pmql4lSU1KhTs5jOU6k3SQbcVOoN7i+lcZj12E16Hq5kUMhleCx69g+P6A8GTIbd5IqHlGtNjFo1eo2G7G4JnUbFuWSI5fw+WrWK6zMJcnvHVBttYn4XUb+b9a0iGrUgUt1d2iBT2BeKIYoH7JXKeOxmXDYz/WEK8uZQMGx3ezSaLVTD66NWKrgyGeP2Qgqf08q55NP5d6NW2M0doTF0JE7tHBxRaTTFggqAixNRdg6OsRh1GHQaoh6reJ912D6a2z048XrrzKdyzMSDKORyUSy7OZtkY2uXRquN224m5HGIEV+Hxcj2Xondg2PUSgWXxiOsbh3gtJoIeR2YDTpRSLs6FUepVHBwXBXFvNvz63R7fSaifpxWkxiHvTgeoVgqi3HSk8KjSa/F77SxtCmUO4RPvE632+PT18/xOz/7N0gEPc99fs+KoDocDtGZdvL4swJaJBIR73uWk4/3+Xy022329/dfKKqNoquxWIydnZ1Tf0e8SLyz2+2oVKoX/n3ybKTV4/EwOzt75mMlJCQkJCQkJCQkJN47gwF853dCpQI3b8L3fd/L3pHElxtSNPV9oNVqX5oQJ5PJPlAh7nfvzPGDP//PCXmd3FlYR61ScmEszN1hFFOnVovzz67PJrm3mEKpUHBtKs6TVI5Wu8N0LEC2sIdMLiPsddDp9lhM57FbjJwfEyKZGrWS6ViQ2/PrXJqIolYpRZfUeNBJp9Nj/6hCxGvnuNpgPb9LctDFaTHwYHkDhVxGzGvn1rzQmDoT8bK0uUu/PyDudwqz3Ta2sZn0OK0m1nK7XJtJ4HE5xWjndNTP1v4R5XoTn8OKWq1kbl0oVPiKy9P8/r0FQBC7VjLbqFUq3DYzRr1WFKliATfH1TrtTpfJqJ+dg2N2D/cwaLWcHwuLcdCxgJPlbIHJqJ9+f4DFqOP+cA2NSsHdxTz9/oCg2w4ymFvPcWM2QdjrFK9L2OvgqFxDoRAEnJMRzWqzhc1kILN7KN53ZyHF1akYbpsZk+Hp603HQ8hksHdUeS62+vHz4yxubJHK74rrvP5wGbNRj9mgw2TQ8XAopI3abEvlGgq5bCiwprCbDfhdVnQajXidziWF+O1WsfTc3nO7+1wYe+qSi/hctDsdYR6hRsV4xC/OtgP4muuz/MA3/dEXzlF7lsFggFqtxu/3k81mReHqLCHObDYDsLu7i9FoPHXfSfFMoVAQDofJZDJotdoXOuJkMhlGoxGXy0U2m2VyclK87yzxTiaTYTKZXlga8XZFDhISEhISEhISEhISHxz/9/8N//W/gkYDv/VbIP1nuMQHjeSIex98OTjiBoMBv/nvf48f+aV/idk4cpLJuTwR5e5iGp1GRcJn5wtzq0zGAlydelqkcHkiSqvbpdXucH06wUq2wFG1zsXxCPtHFQr7R0R9ThgM6HTbOMwGnGYDD1cyaFQKBt2WKMJMBJ2s5vfZOTxmKuJha++Io6oQJT2qtijXW9iG8df17QP6gwGfvDzFQmaHfn/A1ek4m7uH6PU6EkEPSoWCtdwuJoOOQX/A3UXBLXZjNsliZptStc4rF8apNVtkC/vYTHqSYR+lsjDn7ebsGI9WMkMRaIDJoBNbUkdNryadluszCbHsIOC2Ua7X2R6WEFyZjLK2tU+n28NiNNDr91jP74qOsdcerjAW8nIuGeJo6IozaDXI5XLxulwYC7N/VKWwf0TQbRfLMgDOJ8Nkt/bQadVC+cQwjjsYDGi22sjlMlL5XbEQ4f5SWpiR90xs9fp0gl6/z1Gldur8ev0BF8cjdHo9UaCbDDq5v7RBIuDBZtIzHvaL0V6P3ULA5Tgl5s2n8mzuHHB5Mnpq7yGvAxkylErhr6CL41GKh0I8tt3tMRP18mjoyjToNPyTH/0O/vqf/+PvWISDp4JbJBJhe3ubdrstfmeeXWfkpsvn82c6606KYOFwmFKpRLPZfKEjbnQ8FouRy+XodDrP3fcsGo3w3r+o5OHZPb8oxiohISEhISEhISEh8d4oFOD7v1+4/Xf/LkxNvczdSHy58mUvxP3ET/wE165dw2Qy4Xa7+RN/4k+wsrLygaz9smbEwQcjxDVbbb7vH/0zXn24RKvTJb1VJOZ3odGoGCC0WzosRlKFQ2wmIfJ5b0kQ56YiPm7Nr9PvdpmOermzmKLX7zMZcpHN71Bvtkn6HWztlTgs1+h2OnR6Pbb2j3HbTLhsZtYLJcwGHbOJECv5fWQILZ5HtRbdXp8rU3HyeyX2j6tcmYyjkMtZz+1iNxtIBD1U681hm2iCe4tpur0+CpmMraIQnw17HRh1Gu4tpZlNBE+1n85EvDTbHcq1BomgB5VSyUpmm2yhyOXJGLfm1wC4Np0gld/FaTES8jiwGPWie06lUnJvKS0WUJSrDTZ3DhgLe5mOBbm/nBHXeLKWpd58Pv4Z8jiYTwmtsAG3DbvFyFtP1hgLe7kxm+Tx2ib1ZouA206t0X468202yVwqR7XRxGYy4DDrebAyer04S5lt/C77sAk1KJ63WqVAp1aR2z1AqZBzbTrBncUU8+s5EkEP5uH5jfad3ipSKtfQa9WcSwRZzguxWZkM1CoVSxlhdtz1mQSrm8L7fnJG32AwIOC2o1IqxL1fGI+wX6qwtVeiPJyJ92hVED0NWg1TER8bO4LDL+p38R9+5q/zhz9+8V1/vkdCnMlkwmq1ks/nXyjEjeKk/X6fYrF45joj1Go1gUCAarX6tjPlAKxWK2azmVwuJ77Oi4S4fr+P3W5nY2PjOTFQcsRJSEhISEhISEhIfLgMBvCX/zIcHcGVK/DX//rL3pHElytf9kLcq6++yvd8z/dw69YtPvvZz9Ltdvnar/1aarXa+177ZbWmwvsX4gp7Jb7xB3+ObGGP/M4+pXKNqahPnAlWrVYpV2vkiyW8ViP9fp+VTB67SYdZr2UpW8Co04AMFjM7qJUKzidDLOf22D2qcnM2yfr2AZ1uj6vTcVZyeyiVQiy12e6SL5awGPWMhb3Mp3KY9FpiPgeP1/OEvU7RwdXp9rgyFaPWbLF/VBHdbivZAjv7R0zFAqIj68Zsklvz60R8zlNOMp/Dit1s5NFqFqVCzvlEkIXsDivZgjD3rnhIsVTG57BiNugBUMhlXJ9OiCUTcoWcw3KVfPFQdM994ckqU9EAN2aTLAzn1YW9DjqdHgvpPHKZjImgk7uLKcq1Bjdnx2h3BHfZaN7cnYU0CrlMFPJyuwfoNCpCHocono1KKebWNzk/Fj4lck1F/eR299kv11HIZVybTnB3KEoqFXIMw1l6o+szn9rCYTWJYubIKZgIebGbDWwVD4cCXZy7iymyhX2uzyRwWs3MpYR1Lk/GeLy2iVatQqmQc3VaiKb2+n0OjstMRgM8HIqCoxl4X3iyRsBlE8TFodPNqNOg1agpHh4DEHTbsVkMLGUL7B/X+HN/6OP8x5/9QSYi/nf0mR6JVydbhUeCWCQSYXNzk263K35/TjKKk0YikVPlDv1+/7mWVYBoNEqr1RIFvJM8K5rFYjGy2Sz9fv+FM+JGz7PZbHS7XXZ3d992TQkJCQkJCQkJCQmJD5Z/9a/gd34HVCr4zGdAKQ3ykviQ+LL/aP23//bfTv38mc98Brfbzf379/mKr/iK97X2/2zR1F6vR6fTod1un/rz7LGlTIGf+3ev47IYWd0SHE7nYj7mNoQB/ucTARY2tjHqtczE/axmd+j0+kzFAjRbbTa294j6XTRbHebT25xLhqk3hcbR0eyxytCtNhJpAC6NR/n8/UV6fcFBli3sEXDZCHkc9AcD0oUDDFo1GpWS1x4uA09niuk0Kq5OxZhL5Wm1O+I8sf5ggFGnIRbwPI09epz87p05AKZjQbb2DllMb+GwGHFaTTwZCkqTUT/VelOYbxcPkN89pHBwhNNqEiKXJ+Kst+bW8DqseOwWao2WMDtOqcDrtPJ7d+YBIUa6lttlc+eA8YgPBgNWNncAwV12VK1xVKnhtpsx6p7Om/vqazP8/r1FBoOBOLPu3uIGSoVcnL82GAzw2M3YTAZefbAECC60+0sbQyeiExRqUVi7OBFlbj2H2aA7NZcPoNnqCA7A7NN209vz60xF/djNBlw2szgbcDYRQi6Xs7kjOBYnQi5x336XDY1axb3hY0fXWqtWAUIRxO3h3vVaNZNRP793d2H4HjkYMGBuPce16Tgmg45UfpdaowXAN37qIj/+PX8OmeypGAa88H9HyGQy8dhJIc7tdrO8vCy63c4S4uRyOaFQiFQqxfHxMRaLRfyOPSuCGQwGVCoVpVKJZ3lWbHO5XCiVSra3t+n3+6hUqueeM3qeUqkkFouxsbGBx+MRRUVJiJOQkJCQkJCQkJD48CgW4Xu/V7j9t/4WnDv3cvcj8eXNl70Q9yzHx4L7xm63v++1DAbDhyrEDQYDer3ec8Jau92m1WqRzWbJ5XLisZE7R6VSoVarT/0xGAzYbDY+e3eJ//JgA5/LweLGlijSyOVyFHKZ0Ao6FFauTiX4vbuCyHR9OsH95Q0uT8awGPXiEP3xsA+TQcvc+iZ+lw2lQogg3pwdY3LoVlMq5FyciLF3VKHX73NjNsndxRT9/gAGMkrlGtVGE7fViFwu5/VHy/idVhxW09N5aGMRGq02rXaHi+MR8fUTfg+VeoO59U3xXOZTw9lnJ4SqRMCDQa/hydomOo2KgNPC7fl1rk7FxfKJfn9AzO9i5/CYUrmGWqVkNhES9zAdC/LG4xVa7Q4uqxmLUced+XXBFTYVF4sPYn4XbpuZNx6tIJfBlSnBWaeQy7g4ESGzvU/6sCiuXyrXGAwGp8RAtUrJKxcnefX+IgBTsQCFvRJ3FtYx6bVMRJ/OZov5XQwGA1Yywmyxk4UIlydjbO8dig61K1NxnqxlmYwGKNfqTET84mM7vR4eu1WMm46KH1RKBT6nFatRz9LwNcbDXrb3SmzvC0LUyWuYLexzZSomXo+A245CLuPe0gYqpYKpaIDU1lPRTS6Xk94qUmu00GnU/OA3fS1XEj56ve6Z34uROCWTycQ/o59HrjOFQnFKiJPJZITDYfL5PDKZ7IVCnEqlIhAIkM1mOX/+/AujrCBEVA8ODuh2uyhP/HPZszPlZDKZKK6ZTCZ0Ot2Z5zXat9frJZVKcXh4iMPhOHXfWddBQkJCQkJCQkJCQuL98Vf+ChwcwPnz8MM//LJ3I/HlzkdKiBsMBvzAD/wAn/jEJ8QGxffDe2lNHQwG78i1Njo2isw9K6zJ5XJ0Oh1Op/PUcZVKdeYv6L1en5/8v36H//LGQyJeJ4sbW6Iz6+FKhqvTccbCPrGgYTzip1QRWjETPrvoDjMZtHxu6Gq6OhXn8VoWhVzGTDzE5u4+lVqDsNdBrdlkMZ3HZtLjdli5t5jCpNdybTohCj8joSfkcRAPuFnbLNBodwi67YS8Dt56siY67ATRLHZKZDqXDJHbOyS/e4jHbsYwPBelQs4nL03y+tBVd2Uyxnw6z8WxCAG3nUG/z/rWPiqFApVKye35Nfr9AZcmoixtbNFsd/j4+TF2D8ui++vGbJL0dpFWu8NE2MfecYX1/C5atYpXLkyILrXRGt1eH6tRj0mnEl1qV6bigExwxQ0bTR8sb6BSyPnY+TFuD0sw3HYzJr2O44owf/DGTJJ7S2l6/T52s4GZROj0uaVytDpdQh47NrNRvD5TsQB7pWM2tveecyfqNCrcNguPhk2oI4Hu8mQMjVrJzAkHnc9pJeh28OZjYbbi1ek4j1ezdLo9rk3H6fcH3Bk9dujo6/UEAWs2ESSzvUd1KLp99dVpPndvUdzDeNgnvLfTcXYPjvm1H/l2fFYDi4uLyGQyFArFc4Lb2xU2jL4zoz8nHxsMBllbWzvz+/FsjPWNN95gfHxcvP9Fr6nVasnn80SjUfHYWfFTv9/P2toazWYTq9V65lojAU+pVBIOh0mn028rxElISEhISEhISEhIvH/+7b+Ff/NvhHbUz3wG1OqXvSOJL3c+UkLcX/krf4UnT57wxhtvfCDrjcoaarUarVYLpVL5jsQ1EKJuz4prGo0Gk8n03PGRGHGSx48fY7Va8Xq9X3Sf5VqDv/rTn6FYKlMq1yiVq0xF/ewcHosFDaVyjfRWURRSHq9mCbrsjIW8LGcLotvsrSerqFVPh/EDmAw6VrIFKrUG58cirOd22Nw54OpUnM2dfVYy29gtRhwWI812B7VSwbnk0wbNibCX3727wGAwYCLsZWtPiIcG3DYUcjlPhvPIFArFqXlwo8jmK+fHWdjYYvewKEYr252u+LjR6/QZUK7WqdSbmPVavE4bbz1ZZSYexKjXio+bjPrp9Qek8runhECAT16c5Nb8Gp1uD5/DikatolIX5gTeHM6oA1ApFfhdVhY3todOQ0EEm4j4mIj4KJbKpPK7qBQKLoxH6HR79PsDJqN+dg+PSeV30WvU3DyX5NacsGYy5OG4UufwuIpMBtdnnp6b06wn6nPz+iNBoDvpBrw4HqXd7YrXbjYRZC23w6A/QC4T5srdXhDWOTyuEHQ7xOKHC+MRVrMFZMiQy2A86BKjqE6rCaVSwVtPhGKL6ZiffLFE4eAInVrF9RlhXt1gMBiKbn629gQHnc9pQ61U8HhY4OBzWPnM3/1u7GYjg8GAVCrFwcEBgUDgi36+T6JQKE6JcSe/NyqVCrfb/dz8NTgtxBkMBpxOJ5ubmwQCgVMi4EkGgwE+n49sNks4HBaf/6wjDhDbW9Pp9AsFtZMC3mhW3SgiKwlxEhISEhISEhISEh88Bwfw3d8t3P7hH4bLl1/ufiQ+GnxkhLjv/d7v5T/+x//Ia6+9RjAYfE9rpFIpfuZnfobd3V12d3dZWlqiXC7zK7/yK/yFv/AX+LN/9s++MBL6rGvt/f5SLZfLn5uPdRaZ7SJ/+1f/Nc12h8WNPP3+gKtTcSr1JqVy7VTM85WLE8yv5ygcHBH1u2i02nR7fSwGDXarhYcrGUx6LZcnI7z5eGUYOY1yZyHFpYkoGpVSFKIuTURRKhUUS2USQQ/lap21zR1uzo4R9jq5v7whRjOzOwcMBgNmYz4WMgUGAxj0BzRbHQ6Oq3gdFrQaNbfn15mJB9FqVKIAdX0mQW/Q56hSIxEQYqor2QKJgJtzydBz7rt4wI3DYqBUrrG6WUCvVeNxWESX37XpBA9XNpDLZCSDHlqdLk/WNkVHWbvbpdPtifPQCgdHlOsNLk1GxXO/PBljIZ1nNh7EqFMT8DhFV5zZoGN7/4hSuSaUJpgM3FtKMxUNcH0mwYPlDbq9PnaLEafFRK8nvMdXpmLMredod7o0O12uTMWfRmbjftL5ItnCnijsjUS3qN+FUa/hjUcZ8XrdW0rT7w+4eS7BcbUpinCTUT+F/SPCXof42NE6pUqNy5NR7i0J64xHfOwdlnlrWMIQdDu4uyREU7VqFTqthnzxUGhNddlQKhU8XhPcd69cmGBufZNCTXCTfuef+jQ/8m3fgHL4nZDJZIRCIfL5/LsW4uRyOTKZjG63+5wjDsDj8bCzs0Oz2USr1YrHnxW6otEojx49wu12v61w5nA42NraYnd3F5/PJx4/y0EXCoVYXV19YcHLyT2Mmlk3Nja4ePHimeKehISEhISEhISEhMT74/u+T5gPNz0Nf/tvv+zdSHxU+LIX4gaDAd/7vd/Lv//3/57Pf/7zxGKx97yWUqnEYrEwNjaGx+Phs5/9LFtbW/zCL/wCXq/31JyoD5t30pr6xqNl/uo//GdcHI/w6oMl0UV1Z2GdGzOJU26xazMJet0+x9X6KXFu4pKXnf0SG1tFsVSh0+2JzrN7i2nUKiU2s5HPDefJicP/YwGuTMaYSwkC0njYy/5xmfX8Lk6rCatRz4PlDdQqxSnn10gsmk2EcNstFPZK7Bwc47CacNnMfP7+4imXWdjjEMWv1jDa2up26VT7z7nvIj4nbzxcpt3tiTPtHi4Ls8suDUVFEGKvWrWKu4tpLEY9fpeNOwspgm7bKbfZyLXHUBM9eU0b7TZKhZyVzNAVNxXn7kKKG7NJDFoNB8cV1nI7KOQyDHoNG1tFur0+yZCXo4ogFE5EvKfWDHsddHt94TWH12rkOqu1WlyciIgz/i6OR1jObrN7cITFoCMe9IjnF/E5aTQ7LG0I8+CuTcd5sJyh1++jkLs4PxYWH5sIeqjWm8jkghB0cSzEfFqI32pUglPys8NyDJ/Thkat5OFKhpuzSRwWI5ntvaeuwXNjdHs9yrUGWrWKn/6+b+ZPfc2N5z67Pp+PtbU10RH2blAoFC9sR9VoNMjlcnK5HGNjY+LxZ0U7u92OVqulWCy+MJba7/dRKpVEo1EymQxer1ecU3eWaDaa3bi/v3/qtUec1bb62muvUavV6Ha70ow4CQkJCQkJCQkJiQ+Q//Sf4Ld/G+RyIZKq0bzsHUl8VPiyF+K+53u+h3/xL/4Fv/M7v4PJZGJnR2ixtFgsLxya/iIikQg/8RM/If68u7tLqVR6zw6798PbCXGDwYB/9p9e5Zf+9X/HbjayltvBpNcS8bm4s7CORq1Eq1Hz6oMlFHIZV4aC1ljotOhzbTrB7mGZarPNbCI0nPXVFCKJCgUr2cLTWWdLaQxaDcmwV3y+2aBjMZ2nPXRwPVkT5ordPDfG6mbhqSBnMgzFJRlTUZ8oADmtZl4dtqyOh70cHFdZyWxjNerxOW2iy8zrtFHYL9Fqd07t8xMXJlArFdxf3hBFyNXsNu1uj5jPzn65QaXWwGrUc3U6wVtPVoGn7rlzybDYDru0sYVSIcfrtLF7cHyq+bVSb2K3mE458C5PxlhI5XBbDchkcjwOK3eH56VUKNjYLj4VNO0W7i2muTGbJOxz8mRtk063h8mgQyFXkC3sAXA+GWYtt0Oj1UatUnLxhPMt4DDR6A4YmSRPutncNjNjYS9vPhbO79JElOXMNtnCPhNhP2ajTryWAbedo0qd8tCtdmUqLsyga3dotttcHA/zaFWIk45m2d1ZFAodxiN+cjv7FPaF5yoUClY3d2i1O2jUSmbjIW7NrWHQaYgH3PzyD38758ciZ36GlUolPp+PfD7/roW4kRgGZ7ejqlQqcrkciUTiVJz05GNlMhmRSIT19fUXCl6j5wSDQdbX1zk6OsJms73QETc6r3K5TK1Ww2AwnLrvWSFOp9Ph8/nY2Ng4swBCQkJCQkJCQkJCQuK9cXQE3/mdwu2/9tfg+vWXuh2Jjxhf9kLcr/7qrwLwlV/5laeOf+Yzn+FbvuVb3tfaer3+hTGzD5sXCXHtTpcf/61/z6v3hfKA1c0CIbcdv9vOfConFjS89nCJgNuG2aDnzmIKrVqFyaBlIZV/brD/VMjFQjrPYCAUGiym89jMRiYifvZKwjwzn8NKImjj/vIGBq2GeFCYq3Z5MoZKqRAFqqtTcQAOj6uMhbwclqus53a4PBUj4LIxn346U+3Rygb9wUAsheh0e8T8LhxWI/OpvDi/7c7COtdnEqJrbTAYMB72cVyrs57fFUXI0R5uzsS5syisHfO7qLfadHu951pSFXIZ1XqT/aMKdrMBp1VwAN6YSeB1WsXm16DbzlGlxn6pApx2xVkMWto9WM5sI5fJuDId583HK0R9LuRyGZVag5XMNjIZqE9cp4jXSbvTZXFji+vTccJe51NhzW5GBqLL7MpUjMerWbq9Pmql8pSzbzIaYPdQiMKC4Ei7NSfMdHNaTfjdNn7/nhDLPT8WJr1VpFpvcvNcEgaIcVu7xYjLakapEP7KmAh7KR5VSOWFeWufvnGOz92dp98fCGUP8RBvPl4RW1t1Wg33h8UX55Jhfu1HvgOXzfy2n/FQKMTt27cZGxtD/YKJqd1u97k24Xa7/cISlZEQNxgMKBQKYvT1rBir3+9neXn5THfbqNFYLpejVCoJhUJsbGxgs9neNkY6GAyw2+1kMhlmZmZOHT9rD7FYjLfeektoNZaiqRISEhISEhISEhIfCD/wA1AowPg4/L2/97J3I/FR48teiHsnc9TeKzqdjnq9/qGt/3acJcTtH1X4zh//DUx6Hfm9Q1rtDueSIdJbRdwOKxMRP8WSUNAQ8bnwOCzcmV/HbTdj0Gp4sJzh+kyCWqPFnYUUaqWC2WSY4+NjBoPBKYFpJhHktQdLw3lpAbb2SiiVCgJuG3KZnLn1TUx6LRajjt+/t3iqXCAR9JxyyCWCHnYPjikeHmPSawm47dxdTCGTwVdcmhIbSS9ORFne2OLCeER8nSdrm6iVCrQaNa8NH3dSuDs/FubguMp8KodKoeD8eIRWp0t/cLol1aDREPE6xZbUm7Nj3Jpf48pUHKtJz3GlzupmAbVSaFp945HQIHouGWJjq0i10eLGTIJasyVeo/OJIJs7B1QaLcwGHQGXTXTFxQIu3ny8SrvTxWTQEXLbef3RChGfE5vJIEaDdRo1KrWKN4evNx0LsL1fonhY5sZsEqfVJL6e22pEpXwq1lyfSfJgOU2316fT6Z4S4cbDPg6OK9xbTKFWKbk8GeP2/DqDwQCTQYdMJuetOcFBNxbyclSts5LdZjzs41zcx2Jml15fiKbOJsMU9kv0+wO8Dgs6rUa8jlqNikarw87BMQDf9vVfxY/9b994ap8nGQwGorjW6XTQarUsLi5iNBqfKz5ptVovbBVWq9WMj48/5xwbiWSBQIBsNovf7xcddM+KYHK5HJfLRbFYPHOfgCiORSIRXn31VWq12ts64vr9PoFAgPn5eZLJJJqh/330XX5WbDOZTDgcDvb29iQhTkJCQkJCQkJCQuID4L//dyGKKpPBb/0WvMugnITE++bLXoj7MNHr9S903nzYyGQyer2e+PNCKsd3/J//hIDbzv5RmVa7c6pZ1KzXisLXhbEwq5s7uGwmscFzY3uPkNeBRq3izkJKLBJ4sLxB0mcXY5cyGVybSbJXKtPp9k7NSwMoVxtU6k1xntyD5Qxmo46wxyk+32E2kN7eo9PtnZohd2ksyOZuieXMtuhia7aFltmRMCac+0B8HZfVjNmo47UHS3gdllPOsfPJMAathidrm8L5mA3cX0rjd1qYjnh4OGwGHTn73HYLeq2asZBXfC2DVs3c+ibtThen1YTZoOONRytEfU7cdos4ny3otiOXyZhP5YHT0dAbMwmyO/ssDZ1v12eSLKS3aHe6RH0u0fkGgkD22dvCvDW/y4ZaqeTNRyuMhbzYLUbuLabp9ftYjHo6vR5PVoUChNlEkPXcDs2jKnazgWszCe4MSxhCHscpQfradIJHqxk63R5atYpPXJjgc0NXXNTnot3t8taTVSajfox6LU9WN2l3u6iUCswGHTsHR/T6fTGaen9JmEl3Y3aM5cyWKLrdmB3j1pM1/C4bjWabv/edf4o/9soFDvb3nnOwnfwzEtc0Gg0ymYyDgwNxvprRaHxOcFOpVGdGNTudjhj3fDaC6vf7WV1dFeOkZwlxIMyKKxQKVCoVTCaTePzZ6KtWq8Xn85HJCJ8plUp11teWXq+HyWTCbrezubkpzoobfZfPEttisRh7e3sf6j8qSEhISEhISEhISHwUKJfhO75DuP293wuvvPJy9yPx0UQS4t4HL1OIk8vldDqCSPVf3nzIj/7yvyLocXB7fh2LUcfVYaumTAbXZ5NP57PNJsW4oVqhIL1VFJ1j67ldWq2OWBawltvB57DS7vZYX889nTM3v45RpzktNs0mubuQwmo2iC68WqNFyOPAaTPzcHnj6fMX01ybSZAYxldBmFvW63Q4KNcIeR30e33mUznifhcXxiOiMHZj6Kpz2y34nTb2joV4pMmgYzIa4PP3F8X93FlIkQx5np7P5g56jRqzQcf+cRUYxTo36fZ6TER81JsmHq9tIpPB1ek4X3iyilatIup1sndcIb1VRKNSEg96xKbV82NhUvldtvdKBFw2HBaTeF0mQh4azRY7B8eYDDqiJyKyX3llhjsLa9SbbfRaNeNhH4+H89dmEyE2d/Yp1xooFXIifhe/OxToYgE3jWaLB0sbXBiPoBs2yg4GA6xGHZ1uj83dg+HeIqTyO9QaLfr9Addnk9wZvr7PYUWrUbNbKovvwUp2m3qzjVwmI+R2iCUMTqsJu9nIvSVhlp2cPke1FsXD4XPHw7RaLY6rdZQKOcmAi9vD98xhVPNtn/4kAX2fhYUFNBqNKKJpNBpMJtOpY2q1GqVSKTrVXn/9dTweD06n8119R0alDSdFttFtpVJJMBgkk8m8rRAnl8tRq9Vks1lmZ2fF42cJZ9FolFu3buF2u081sp5kJAzGYjEePXpELBZDqVSK6521B7NZiPAWCgVRuJNmxElISEhISEhISEi8e37wByGXg3gcfvzHX/ZuJD6qSELc+0Cn071UIa7b7fGzv/2fubOwjlGv5fFqFrvFiMNspNponhLONGolr1yY4M3HK8MZcAm+MLdGyGPH77KLzrmpqJ/DcpX9o4oYOT2u1rkyGWXn4Jj5VA6rUY/XaaXebItxT7GkYCLK791dYDAYcD4ZJrW1i99lEx1y86kcRp0GvUbDqw+eima359exGHRMhDzk9o6oN1v4HFa6vT4HxxWh/XQswu2hy2s2HuS1h8t0uj3CHgedXo+dgyOhxTMeeFoYodextVdi/6iCz2lDqZCznN1hKuQi4neLDaPT8SBbeyVyu4fidbu7INx389w4rz5YpNPtiS6wpfTW8L4xUQQLeRxCK+swRjqKgmpUChJBD812h7n1TfG+erNJvdkm4LahlCt4NHS3fdW1GV69v0i/P8BhMeKwmLi7kEKpkHN+LMJiOk+z3UGrVuG0mPi9YVtt1OfgqFxjcWOLG7NJZMhEAdNq0mMx6akN58pNx4NsFQ8pHBwB8MlLk7z+cFm4ZgYdXoeF1x4uodOo8NjMHFWFaC5ApXzMzmGFdreHUi4j7nfwcHUTs16Ly2JEp1WzvCnMjrs0EeGXf/BbCXicKBSKdy0gyeVyAoEAuVzuXQtxI1Gr2+2KjcYnBbdwOMzrr79Oo9F4YZy03++j1+vZ3t5mbGzsuSjpyfMxm81YrVaq1eqZBRP9fp/BYIBcLsdut6PT6cjn80SjUVGgO+v6jES6XC5HPB6XIqoSEhISEhISEhIS74HPfQ5+/deF27/5m/BMd5qExJeMswcZSbwj9Hr9S5sR1+p0+fHf/u/8j7cewwCyhX0SQQ8KmYy13I4Yo5xP5XDbzARcdro9oYlzKhbgzsI6apWSsbBPFJJuzCa5v7yBy2bm+kyClWyB42qdmMeGQiFna69EzO9Co1GxnNnGqNcSDbi4v5RGrVRwZTLG/lFFXGsulaPWaIEMDstVtoqHBNx2rGYDb82tYjMZuHBCxIt4bTTbHerNFtOxALVmi83dA8JuOyGvk/tLaeQyGdem42ztlQQnXzLM/nGFwr5QSJAMeXm4kh1GQBPcX94g5HUwmwhSbTTJ7R5gMeqRy2WiCHdjNslyZovNnQNuziYxG/XMp3LIZTJuzCbZPTym0+0xFQvQ6fRI5Xc5rFT5xMVJbs2tCaLjWJjDcpWD4yoGnUZw8Q3nsQUcZkw6DVvFQ/RaNReH9y1nClwYj1CuNsju7AvXcCpOtdak3x8wHvYik8lY3RTeh09dmebB8gbNdgevw4LPaeO1h0uYDTquTsfZ2jviqNZEq1aiUSlFES4ecKNSKlna2EIuG3AhGWR5Y4vjah2tWknC72BvX3DQeaxGFDKEttNOl3MxP/m9EkdVwZl3cSzM4uYeU7EAFr2GsM/Fan4fAL/bQTToYXP3EIBv/rpP8u/+0d8gEvCKDrf3QjAY5ODg4D2J3nK5XCxWgNNCnF6vx+12s7m5+cKChX6/j1qtxmazkcvlxOMvEs5isRi1Wu3Mcz05B04mkxGPx8lkMvT7/bedKzfau1qtZmtr611fAwkJCQkJCQkJCYmPOtUq/KW/JNz+ru+Cr/qql7sfiY82khD3PnhZralbxUO++2f+BaVyjZVsgUxhj8uTMXK7B+wdVTifDHNUrbO1V2Ii7KPb65HeKtLr9TDrtSyk87isZkIeB/ulCkadRpwBp5DLMOi03FlI0ev3uTGbJLNb4vC4xsWJKIX9I3YPjkkEPZRrDdY2BdEv7HVyf3mD7M6+GIsdDIQo5O25dQw6DbPxIMfVOvndQxxmA2MhL49Xs6hVSi6OR3iS2saoU3NtOs5KtkC51iAZ9NBod5/GT2N+7i6mWc1u8/EL4zxZ36TebDMZDdDudtFrNZgNOmbiITEeatRpWdzYolJrEPO70WnULGSL+J0Wrk4Le+33B1wYiyCTydgqHmIx6pmMCc66hdQmHz8/ztpmgVKlhtdhwe+y0+kKAsnNc2PMrQuiY6XWIOpz8Xjobrt5boz0Tolmq0XAZcNlNYvOt6mY0NhZqTdx2cyEfYKoubSR5/pMgsz2PvtHwvtzfizM4bHQfDodC9JottnYLtLr9bmQDHJvMU2n28Nu0mMxaHnt4TJem4mkz05uZ5+9UhmlXEav02U1t0t/MMBjN2M3G0ltH1Cqtbg0EeG43qJUbaCQy7g+m6RUb9Ht9XFaTcSDHh6tCY4+o17LABnpLaHI4PpMkvVcgWazg0qp4Kf+6jfxU3/1m1Cr3r/pVqvV4nK5yOfz7/q5I3HrLCEOhJKFfD5Pt9t9oSNOLpcTjUZFwe6sdUY4nU7kcjmVSuW5+56Ns3o8HmQyGTs7O2/btNrr9VAqlcTjcTY2NsQZehISEhISEhISEhIS74wf+RHY2IBwGH76p1/2biQ+6khC3PvgZbSm3l1M8Vd++jPotWoWs0JzZcjtYKt4SLvTFZ1oC6kcN2eTpLeLHJZrnB8Lk94qsndUYTzspTfok8rvUqnVcdkszK0LkdOxsI83Hy1j0mufNmkCFqOOdH6XZrsjin6L6TxXpmL0+wPW87s4rSZsJgO1ZhO1Ssmliag4j2w2HmQxs0W13mQqFqDWbNMfDHDbzATddh6tZlHI5ShkA+4OCwmuTsfZ3Dng4UqG2UQQk07LYnoLrVrFhfEo7U4XEJxv67kCpXINuVyGUa9lPpVDrVRwdTrO5+8vYDbouDQZZefgiJ2DI+xmA06TjntDV9zN2TEer2VZ3SwQD7jRaVQspvMo5EKMt9Pt0e31mY4FaLY7bGwVye8ecHkyJrriziVDHFfrGPVadBo1lyaioiuu1WrT6w/I7uyjUSm5MhXn9vwaCrmciaifXq/Hem4HuUzGWNhLrd6g3e3itZvRa9U8WdvkyeoGUyEXy5ktjmsNDBoVYbeVTEFwpCUDLtrdHrulKjJgMh5kvXBIp9fHZTMTDXhY3NxlNhnm/FiYWrPN9v4RALGAh3anR7PdwWYyMB7xc2d+nbXNHW6cSzIAVrNCNFWYM5dCp1WjkMu4Ni0UQ3R7fYqlI/7NT/0A3/x1n/xAP/fBYJCtra3nmoK/GHK5XGwYPst5ZrPZ0Gq1VCqVM4W10eOdTicqlYrt7e1Tx59l1OC6v7//XLnCSIgbiWgymYxYLMbGxgbdbvdthTiFQoHX6wVgd3f3XV0DCQkJCQkJCQkJiY8yr78O//gfC7d/4zfgRAebhMRLQRLi3gd6vZ52u32qvfTD5P/5H1/gO//Bb2DSa3m8lkOjUnB+LMzthXUiPidXpmLDmOeA6zNJGq0OnW5PEOfWcxyWa7xyYYKN7T0Oj6vMJoLsH1fRaVREfS7UaiXLmW3MRj3T8SAPljfQaVQkfA7uL2dIBNxcn4nzYHmDdqfL5YkoCrmcw3KVsbCHfn9AequI1WQk5HHwcCUjxi33jir0+0JkdXXoduv2+3R7fdJbRexmA8mQhycbu/icVm7MJrm3mKbd7XJxPIpBp2V7vyRGMh+uZIatrUIpQ7fX58JYhJXsNkeVOk6riYjfzb3FNIOB0BT6cDlDo9VmPOJDJpOzX66hVSu5NBETY5whjxOdRs3OwTE2k4GxsI87Cyma7TY3ZpOsZAscVep4HRbUaiX1ZguAm7NJ5lN5KvUmzXYHj8MitrJenYqRKZZwmPXYzQbcNpPYNFqvV8kV9jgs19BrVIRcFh6uZGm3O4wH3RzXGhRLFZQKOeeSYXQGA/3BgKjfhdlkILNbIlcs8RWXp0gX9qk2Wug1KiYiPh6tZlEq5ExG/PR6fdZzOwAYdRrm1nNU601MBh3nkmFuza2h1ahIBD2oVEKMFYTrJkfGwZGwh2szCe7Mr9Pp9UiGffjsZu4uCs7DSxNR/vMv/k2uzSQ+8M++3W5HpVK9JxHqZDz1WSebTCYjEolQqVReGCcdRUkjkQjZbJbBYPC2DrZR+cL+/v4L1xoRCARoNpscHR19USFu5MxLp9NSg6qEhISEhISEhITEO6Beh2//duH2t30bfO3Xvtz9SEiAJMS9L/R6PcCHXtjQ7fX4u//k/+WX/vV/R6VUsHt4jMduxmrU82RtE7vFiE6j5v6S0Ew6HQ9ye34dlUrOpYnoCXEuQa3RoNPtcW06LsQ1603cNjM7h0cUD8skQ176fUFo8DosuO0WUoUDdBoVVrOBOwtpZDJhrtqDlQz1Vpur00KM8rBcZTzs47hSIzV0yEX8bu4vpUnld7kxLDbo9ftcm06wkMphtxhJBD0oFQpWsgUMWjVht02cG3fz3BiPVjM0Wp2nbrTtIhajnkF/wJO1pxHQx2tZjip1rk3HGQBrmwVxJtvuwTEgNKFmtvY4OK7QagtFDw9XNsQ1Hq5sYNRrRUFqObONUiFHq1GztLFFr99nMuoXoqFbRTRKOTMxP7eGUdyY185adptypYpSISPps3NvaYPBAAa9Dt1uj1yxhFwm49J4mOXNPSaifqI+F2ajgWzxCACH3cruUZVGq4PTaiIWEGKhjVaHSxMxdg+OKewfia6/drtLvz8g6nNh1GtZHgqGn7o6zXp+h8NyFa1axaWJGL9/b5GAy0bE68Sk14oFEgqZnFK5RvHwWIimziS5u5hicWMLj91CIujh7jDuOx72UTw8ZvtAaE39s1/7cf7ff/jX8DltH8p3QCaTEQqFTs1pe6fI5XJkMtmZQhyAz+ej3+/TarWee+5J59tINDs8PHxhNBUEwc3tdpPJZJ5b61mxTaFQEIlE2N3d/aJCHAjOwFarxd7e3js6dwkJCQkJCQkJCYmPMj/2Y7C2Bn4//MzPvOzdSEgISELc+0Cn0wEfrhB3VKnzPT/1W9xbTFE8LFPYP0Iug26vz26pQiLoRgY8WcsSdNsxGXQspPI4rSYYwMOVDEadhulYkDsLKfZLFa5Mxbi7mBYdatmdfZqtDlen4mQLexyWqwwY0Gx3yBb28djNmPVaHq9tYjZomYkL8+RkMjBqNcytb9Lp9gSRa3uPxY0trk7HAUEMs5sNhDxOmq22WIBwdzFFp9sj6nOSLx5SLJUJex2Y9Dp2D46GotHTaKdOoyK3e8BRpU7M70KnUfFkfZPZREiMh4IgtNWabQ6OKvhdNlw2YSbbcnaLj58fF11207EAnf4AOX0MWjXnEiFxjXarRX73gOLhMWa9Fr/DzN2FFG6LjvGAg9VsgeNaA6tRS73RZDEjxBUvjIXI7paotzpMRgOEvS7WC0JxweWJKCv5fZRKJTaTgYmon4erggBmNujYPTxm55nznoj4mIj4GQwGrA0bS80GLTsHJRqttjhX7t5imkyhyKWJGMXSMcVSBbkMrk3HOTiq0O318TlteJ1WUXSciPrZOyqzvVcShdU7iymSIS9288gJKIihHruZiN/JyjCaem06wcZ2kbXNHc6PhfnzX3OR//O7vhGtWvWhfQ9AEMwqlcqZ89e+GAqFQnTFPSugKRQKdDodR0dHzz3vpOCmUCgIhUJkMpkzRbURvV4Pj8dDqVSiXC6fOn6WeBcOh6nVai+M3Z583ki4e1bkk5CQkJCQkJCQkJA4za1b8HM/J9z+9V8Hq/WlbkdCQkQS4t4HarUauVz+nubEjWZWjeZW9Xo9ut0unU6HTqdDu91mKZ3jj3//T9Fqt3mytkmj1ebadJzlTAGn1cSY30Fu95CD4yoBtwOtRsX2XomxsJf+YMC9pQ1mE0FsZiML6Tx2ixGlSkl6qygKPrfn18ls7/HKhQnuLQkD/6/PJnm4nEGrVjGbDFFrttktVTBoNcQDHuZTOUzDQoRb8+tMRv2noqRXp+MoFQr2jyqC202pZCW7jU6tFgsQQHCgbWzv0Wp3uDAeYf+oys7hMZV6k4jPycOVzNCZleD2/DqJkJdLEzEK+0fsHBzjcVgw6LU8WN4QxaR7i2kW0znOJUNUag2yhX2Meg0Rr4PjoYAzE/WynNmmXGvQ7nTRa1TMpQSn1XTEw8O1HD6HmbDHjlKpYLN4hEIuJ+Bxs7p1QH8wYDIaQCZXsLa1z2wixNXpOI/XcsJ9ET+VRov0VlHc/4OVDL3+gInI6ejnzXNjvP5wBa1Ghc9hHYplGQD0WjXprV0OjqtiYcPt+RQRn+u5uGnU76bZblNvtrFbjARdNu4upplb2+TKVIxao0lme098zbmhu25UbDF6TxgMUCoULA/FxWtDt2Oj2RYLHEYiqsNi4m/9pT/Fn/30DQqFwnv7Er0LVCoVPp/vPbvigBeWMmg0Gur1OrVa7dTxZyOo4XCY/f19Go3G2zritFotgUDglGD2ojirWq3GYrG8sPhlVNYwIhKJcPHixReeq4SEhISEhISEhMRHnWZTiKL2+/DN3wx/7I+97B1JSDxFEuLeBzKZ7FRz6rPiWrfbfU5cG/0Z3dftdkUh7uTcp9ceLvNNf/uXsVuMHJXr4lD8UZFByO1gbftgWNAwxkI6j8Ni4up0nOz2PofHVc4lQ+i1WnK7B4IgJpezmi0wEwuKc9ZMei2zyTDNdkeMI96ZX6fXHzCbjLCYzlOtN4l77RweV9Fq1IS9DswGnSjIOSwm0SE3EsOqjSaXp2KC2+3wmKjPxXG1zmI6L4pKt+bWSOV3+eSlSR6vZqk3W0zHArS6fVQK+akZbQB2s4GHKxs02x3GQh4ajRb3F9cx6bXEvA5RTAo5zYJzqt7EYzOiUSpZyxXpdnucSwRYyOzQHwyYivnZKVXp9geY9FrOJcMsZoUZZMlwgJ3DMoflGnaLkWTIy+uPVjAbdFyfTYrlEC6rCafVLJY+XJtJsp7fYW49x0w8eGr/M1Efu/tHFA+PMZy4Bv3BgMsTUeqtNpntPVHw+vz9JRwWYd6ezWzkybCxVKlQiHHTUfT21twaRp2WsZAXhVzOZrEEwNXpJEqFgnKtgUGnER9bLJV55fw4Bp2G+aEIeXU6zsPVjFjCMHIutrtdtouHXByPieUb55Jh/us//pt87Py4WKTwpZhbFgwGKRQKdDqdd/3cUWnDWbPgZDIZZrOZzc3NU8efjaBqtVp8Ph/FYvGFQtzILReNRtnZ2RH/fng7F53VaqXVap3prn3WSadUKtFoNF/8hCUkJCQkJCQkJCQ+ovwf/wcsLYHHA7/wCy97NxISp1F+8YdIvIhR1C2VShGLxc58zMlf+kezqk4eH/2CffL4r/+73+Wf/LvfxWo08HAlQ8TrZDzi5+5iCoNOQyLoIbuzPxzE7+P2/JoQE9Vr+b078wDcmB3j7uI6lyZiXJ6KsZDK02p3mIoF6A8GbGwXCbodwIC59U2uzySEpsyFdVRKBRfHo+wfHdPvD7g5m+T2wjqDASjkcvaPqtSbLcJeB73+gMV0DpNeS9TvFsUwo07L49UsrXaHixMRVrMF6s02V6fi7B4e82RtE41ayVQ0QL0hCBUXx0LMpYQ5bP1uBxiwnNlGo1Lgd5i59WQVgMmQm9V8kf5ggM9hxm42srAhzHKbTYR4tJrl2nQCq9Uivq7dYkSl1rA2dJCNRKZ+f0DS7+Cw1mZufXMoJo7xZG2TdqdLMuShXG2wkt0WBbLfvT0HCHPSDo4rPFzZQK1ScnE8KsY5E0EPdouR1x8uA3DzXJJbcyMRK8RRpS4Kazdmk+T3ShxX61hNenxOmyh4zcSD3Jpfp9ZooZDLuDIV541Hy4yFvNRbbRRyOY9Ws8PPkkxwFNYaqJQKIm4Lt+fXSAQ9BNx25DKZ+NhLEzHavd4w6izj2tB1COB32dFrNeLPo2ugUgki0p/86hv89Pd9EzqNGgCHwwHA/v4+LpfrnXx13jNmsxmTyUShUCAcDgPC97Db7dJqtU6J3Sd/Ht1utVrYbM/Psev3+7hcLjY2NhgbGxMdaGfNgotEIty6dUs877PWksvl6PV6nE4n2WyWiYmJF0ZT4Wk8NpPJMDU1deq+txPwJCQkJCQkJCQkJCROc/8+/PRPC7d/9VfBbn+5+5GQeBZJiHsfyGQy7HY7GxsbYhviyT8jXvTL97M02x1+6Bd/m+LhMd1uj/X8DjG/m0arRX8wIOCyoVAoeLK2ictmJu53MZ/ewqjTEAt4eOvJKjqNiplEiNvDFlCtWsWbj1cAwa31cDmN1WTgfDJMertItd4k5ndxVKmzulnAbjHispq4u5jCZjZwbTrBraEgMxV2c3t+Da1GzfmxCOu5HerNFiGPg1jAwpO1TSG+GnRza26NmZgftUrJwxVB/EkGXLRaDXK7B1iNWrQqJY9Ws0TdViZDLh6tCc6sqYiXXPGQequL125BqVSwsXOIQi7jK69M8/n7iwBcHI+yurlN2OfGaTVhNxtFoUmtVvLgyQa9fl8UkuZTOa5OCUUOI5Hp/FiYSqVCvljCZNAR8TrFWXFfdWWaN5+s0u50sZkMeBwWdvaPhGs5neDRqtDcajMZuDqd4AvD63x1Ks6T9U2a7TYmg5aozy2KcHGfA41SQW73ALVSwfnxiLiXy1MxtnYPT8VW1/O71BotoW3VbhHddUGPg7uLKar15ikh7cpUnNzOAWaDjvW8IDrqtephGca2uO6tuTXUSgV+lw2bySDuIRH0sFc6FmOsV6bizK0LoqRKqeTHvuNP8b/9yU8/9/n2+/1sb29/YELc24lrMpmMtbU1tre3xeODwQC5XI5Go0GtVot/tFotZrMZtVqNRqNBLpej1WqfE9j6/T5GoxGj0Ug+nycajQJnz3WzWCxotdozo6T9fp/BYCAKZ9FolAcPHpBIJN62abXX62GxWMjn8ySTSVQq1an7nn3eWa4+CQkJCQkJCQkJiY867TZ867dCrwd/5s/AN3zDy96RhMTzSELc++RP/+k/TSqVQq1Wv691dg+O+Ut//9eQy2WoFEoOjqtcnIiwkinQaLX56qsz3FtKU641GAt5KVVqaNQqnGY9Op2OuXWhPXUs5OX2/Dp6rZrxsI83H6+QDHmwW0yiyyoR9LJ7cES13uTSRJTlzDaNVpubs0lSW0VWsgVcVhNWk4Fqo4lapWQ6FhBFrguJIF+YFxxyY0EX+eIRRo0Ch1mPXCZjbj2HWilHo5DxYFhIcD4RYC69RdTrZCzoYe+4wk6pis2kx2gysp4TIqEjkQjgYzMJFjLblA8bohDWaLWfe1yrLcQUVzcL6DRqJqJ+3ny0wlQsgEGr4clQSHLZzBj1WlHIuzlscR0MBiSDLuqtLvOpnOiKq7c6tDtdEkEPlXqT5cw2KoWcj18Y5wuPV4fXUriv0+mKzxvtSyGXkwh6eTSc+XZzdozbC2vUWx3x+o4irVcmY2hUKnYPj9GqVUzHg+I6N8+Nkc7vinPbbp4b4/FqhmarjdmgJ+Sxi0KaVq2k0+uKItxU2M18Ks/1mYQQ4fW7xXUDbjt+l10UakcCYrvT5fJkDJVSIa5rNRn4mR/4C3zy0uSZn99AIMCbb75Jq9V6YWzy3TjX3k5cs1qtlMtlnE4nTqdTPD4Sw9+Ofr8vxsGfFeLkcjmRSIT19XUikQgymeyF4pnFYqFYLJ4p6MFT8d1ms2EwGMjn88hksrcV4nQ6HRaLhc3NTRKJxKn7JEechISEhISEhISExBfnx38c5ubA6YRf+qWXvRsJibORhLj3ySc/+Ul+7Md+7H2t8Xg1y1/6+79G2OPg1vw6Ub/rlNB0Y3aMo2qDcq1xyqE0mwixnttlv1wnGfJwXG0gk8nwu2yoFAoerWYxG/QEPQ4+f2/xhANrjeszCVw2E3eHQtC5RIBmq8leqUzYbeOo1mAtt8N02I3dqOXRahalQkbMY+ewXGEweCquDQZgMhrJ75ep1Jt4HRZ0Gg25g4r4miPByWkzky3sC+2nATf1Rov5VJ5rM3Ha7Z54zucTAap14ZzjQQ+1oRAW8TpPtalenY7zZHWTkNeBSqlEo1KKwlfQ7eCzt58AMBHxs39U5u5iCpNey1jYJ64xE/PT73XZ3jvtinPbzVyZijOfytFqd7AY9QTcdrpdQWy5MhVjfhj5VSpknE+GxTUvjkdY3dzB57ShUSuHxRZDgU42wGY1sSQKa0JsNeRx4HNY0WrUPFjeEM9PJoNiqfx0neFrfOLiBKn8LgvpPADXZ5PcWUhhNurRqlVMxQJi8UOlWhfmzK0LwujlyRiL6TxKhQKZDK7PjIkuSotRj9mg5fP3lwCYigX4rb/zlwl7nc99dkfiWq/Xw2g0srKyIs47ezfi2rPOtXcirtVqNZLJ5Dv6jo0YxcNH4tZIMBv97HQ6WVlZYW9vD7fb/cI4qU6nQyaTsbOzg9/vF4/3ej0AUTiTyWREo1FWV1cJBoNvO1dOo9EQi8WYm5sjGo2Ka7ydk05CQkJCQkJCQkJCQuDJE/gH/0C4/Uu/BB/y1BwJifeMJMS9T1555RUWFxcplUpnzp56J/zkP/sdHBYTt+bX0aiV2M3GoUgi59JkjNvza1iNem7MJk81jmZ39mm0O8zE/Kzli7Q7XeI+J5Vag0q9SdBlo9XpkN/Zw2rQYtRruLeYRqWU06xXebIhuNCmIx7mUlvEfQ5mYn5Wc7t0uj2SQQ9ylYqdUhG3zYJWrWBt+wCf08KVqTj3lwRx7cbsGA9XNlAq5EzHAmzvl9g5OMZpNXFuTBDh5DIZV6cT3FlY59p0gqDHwWI6T7PdIeC2odNouLuwJM6nu7uYwmbUcnkyxkJaELt8DisyuYxSuTYUj55ej7DXwYPlDIX9kjiv7sm64OA7GSP1OCynXGEjwTPsthLy2On1B2J5QTzg5uC4RqvdIeZ30Wi1WUznmYoFTgmlEa+TTq/HqKrg5H2lco2A2yEKa9dnEtxf2sDtsIn7HMVWzUYdgwEspvOnzs9hMeJ32tCoVeI6lyejtDpdCvtH4udk5HicigXY2j0URbjxkJvMzgHJkIfNndP72zk85sbMmCgSxgJuGs0Wrz5Ywm42cH06zt/61j8O7Tpra2tvK64pFAqq1Sr9fv89i2vvlGAwyJtvvkmz2USr1b6r5yoUCrrd7ik32+i2XC4nHA6TzWZxu91nzogbPd5sNpPNZvH5fOL5jIS4k+fn8XhYWVmhUqm8UFAbiW0jh9/29jahUAgQml6fddxK0VQJCQkJCQkJCQmJp3Q6QiS124U/8SeEWKqExP+sSELc+8Tj8TA2Nsbt27f5w3/4D7+nNS6MhfnH/89/w203Y9RpebC8wY3ZJMfVOncXUui1agJuG4W9w2FBg/epmyvqYWFDcFaNB5w8WMkgk8lI+h3k9o5pdbqYdBpUKhX5vWPcNhM6rYaV/AFWk56w18mTNaGkwOW082Q1S6fbOyVeXZqMkt3eZ3P3GIteg0Gr5eC4gkqp4MLQYQfwyUuzfP7eIr1+n/GIj4PjKiqlAotRj99lE4sMLEYdvzsslTiXCJMpFJlP5XBYTTjMRu4uCnPQvDYTe6VjWu0O07EAW3slCgdHfPz8OEa9VhThbsyOkS3sc1ytE/Y66fX7zK1vIpfJ+NTlaV59IERRp2IBCvsler2+6BgbXUeNWslRtcneUfWUQHZjdoxLEzGWNgTR0KTXolIqWBm62UZz6urNNn6XjYsn3HojUdLrsA6LFhLiNTiuVDEb9cytPy1suLuY4tp0ArNBR8jztAXW47CiUSlFYe3kjLegx45Bq+XuqJk1EeTwuMrGdhGAa1Nx7g4FU6VCwUz86TmH3DYarQ61ek34/ARdZHYOaHd7yIA/dn2cr7sxzd7uzjtyrg0GA1577TXC4TD2D3kiqk6nw+FwsLW1dSrG+U4YCWvdbvfMUoZQKEQqlaJSqbytEDea6XZ0dCSK8CNB7dkZetFolI2NDdxu95l7GjnyZDIZsViMVCpFMBh823ishISEhISEhISEhITAP/yH8OAB2GxCQYP079YS/zMjCXEfAK+88gpf+MIX3pEQNxgM6HQ6p2ZkxdwWYj4HB8c1iodlfHYTtVqV5cwOdpMOhVzOQnqLczEvOC3Mp7fRaVREvE6QyZDL4OJElAfLGQC++toMn7u7ADwdtp8MeXHazBT2SxQL+wTcNnxOG/cW05j0WiI+F7fn1rg4HkGjVoki0LXpBIPBgMNylfGwj539Q9bzu3ziwjhqpZJ7i2mxzbNSa9Dr98W4aLvbJeZzodOoWdrYwqDTkAx6+cKTVZQKOVem4mJz6VjIi06j5sn6ptgM+2Rtk3NxP/7ZJPcW0/T6fcF91hVmuSkVci5NxEQh8OMXxnm8mqXWaIkx0lZHmB9348Qah+UKAbf9OWFrMuRCp9WKQh6AWqUQ1w/7nPR6fZ6sCS2zcrlcFLX8LhuVepONfPG515uKBcRGWhAKIlYyW4S9rucKG6r1Jia9Toyb3pgd495iikuTUYw6DfGgR3zNsM+F02oSf748EebRao7+YEDM50ClkIsinMusJ1/YpdIQZuyNB11kdg9pd3r0+j0ujYV4OCzLMBt0/OMf/FY+feP8u/gWCC4tn8/H1tbWhy7EgSCYLSwsEIvF3nEhygi5XE6/3xcFsJOCm1qtxufzkc1mXyiCjaKkwWCQTCYjCnEvirIGg0FWVlboDD+PZ603eh2fz8fq6irFYhGPxyPNiJOQkJCQkJCQkJB4GxYX4e/9PeH2L/wCeL0vdz8SEl8MSYj7AHjllVf4tV/7Ne7cuUO1WmVqaurMCN+L5mQFHQa29wX32nTMT3prD7VGw1TUT754yGGlRiLowWgyMbexQ8BlQ6lQsJwtcHM2ScBp5cFyRpwhVhuKLTdnn0YO/S4rn7+3SLfXZzYRYnNnn5BbQcjjoD8Q4phmgw67xcTn7s6fauK8OhXn6nSCJ6tZ2t0uYbeddrfH6mYBq0mPz2njzkIKt810Kj57aSLK4oYwh2zU+Pp4LYtaqeCVixO8OpxBNhLuLk/FCLjtyGUynqxtopDLkTEQ1xPmrhXI7uyTDHmRy2Sie+7muTH6vQG1RmvYNCvESKNep3geANPxIFvFQwJuOzqNmsmo/0S5goz9o2PqzbYoGr7+cJmxkBeDTsPqptASq9Oo0Ws1YunD6Hpu75W4PBlFIVc800JaIb31fBmFSacm4nOJ8/MuTUZZ3tgm5ndRLAnutZEIWNwvYTXqeLImCITjASfp7SKtRh2lXEbc5+DBinCf22rEZTFxZyjMToQ9ZAoHtDpdrs8mkctkT/dg0BFwOxE8cDAe9vFP/853EQ943tN3IRAIcPv2bTqdzqnmzw8Dh8OBQqGgWCzifZf/b/t2QhxAJBLh1q1bKJXKFzriFAoFgUCA1157jXq9jl6vf6Fwp1QqMRgMVCqVM/dzUmw76aCThDgJCQkJCQkJCQmJF9Prwbd9m9CW+nVfB9/8zS97RxISXxxJiHuP/Pk//+eZm5tjd3eXvb09er0eX/d1X8fExAQ///M/L0b2Rk2Izw6nH0XiRkzFfg+tRi0KJB67hbXcDpV6k8tTMRZSeYqHx5xLhsgW9inXGsQCbsr1Jrm9IxxmA1azkQfLG4yFvUJxwPya4FabTpDd3qfb64sRyH5/gFwu47BcpdZoEfY66Pb6rG0WTkUjZTLQqFVis+aFZIjFjS229ktMxfyUyjWWNrYwGXS47VZ2D4+B04LTV12d4f6w8dVpNWEzGWi2Os+1jAKUq3Uq9SZ2swGXzcyTdAG72cB45KlgNhby4rKb+cLj1VMFBpMRP5cmYixntmi02tjNBtRqFTv7R8Bpl9psMki9aTnliru7sI5WrRLFwMdrwoy5aMDFZ2/NAYLzTaVU8OqDRWwmA+MRn3g9nVYTZoNeFOiuTMaYH863uzgeod/vi+cQ9djZ3jukeCTEQqcjHh4OhbNeu4XLrOfRsHF2LOgiv3dE3O9ie/+Ii+MRHqwIe2v1YHYsIhZUnEuGyGzvsba1h1Ih5+p0QnxNo06DVq3ktQfLwNPZdvOpHJcmovyRj1/k5//6t2DUv7uZaycxGo2YzWYKhQLhcPg9r/NOkMlkBINB8vn8exLiTopxzwpxZrMZi8XC8fHxmSLY6PE6nQ6Px0M2m2VqauqFjjgAvV7P3t4etVoNg8Fw6r5nnzeKx5ZKpTOFOGlGnISEhISEhISEhAT8/M/D7dtgNsOv/7oUSZX4g4EkxL1HvuEbvoE//af/NB6PB7fbzY0bN/iX//Jf8rGPfew9rffV12f52X/+n1HIZVyeinNnIcWliSgTJwSoi+NRDo8rlGsNLk1EWc5s02i1uZAMkd4uksrvEnDbabY67JXKT2ezza+jVilOiWOj22ajjgvjEdaGbq+wx0HAbWchnRcbRN98vMJYyIvjRAzywlgEuVzGzsE2Ya9DLDn4+IVxrEbDqcbXyrD9dDQ3bi23Q6zvPtUAevOc0Nrpsllw2y1Uag1WsgXUSgXjwaflCqOorVatxuOwYNBqxAIDq0nP6uYOjVabRNBDpdZgdbPAx86N4XVYRZfaTCLIVvGIzPbecFZcUFx/POhibfuAar0pzpGbG7rQZpMhNociqFqp4NJElM/dEyLAcb+TUqXOW09W0KiUJINO7g/3ZTfq6LYazGeF2Opk2M36liCMTkW8DJCxmC0AgshYb7fZLzdOvU8AGrWayVhQFOEmowF2D4/QqlUnruE6g8EAg07DKxcmePWB4Dr02k0MBjJee7BMyOPAaTWxkhVm28lkMj594xx/9X/9I+864nkWgUCAbDZLKBT60AUjv99PKpWiWq1iNBrf1XPlcjndbpdutwvwnNgVjUZ5+PDhmedwUhyLRCLcu3ePsbGxLzrPzWQykc1mmZ6ePnX82ecplUpCoRAbGxuSI05CQkJCQkJCQkLiDFZX4W/9LeH2z/4sBIMvdz8SEu+U9/9b90eUP/kn/yRf//Vfz40bN4jFYnziE5/gzTfffM/rXRiLYjXpGQv7xIIGu8XIrbk1lAo516YT3J5fw24xcvPcGA9XMjRaba5NJ1CqlFTqLWYSQcrVOrndA84nI+JsNrvFSMzvptXuoFYquDIV59bcGv3BgKtTCZ6sbVJvtrgwFmH/uIpRryXsdWA26JhP5TDptYS8zhMFEV4er2XFsob9oypbxUN8TmFO1qPVDCqlgqvTcW7Pr7Ga3eHadJzM1h4HRxXCXiftTpfjav3UfgYDOD8WIl88pFgq43NYcdvN7B1VkMkEoen+Upp2p0t/0EMhl5PeKmLQabgwHuHW/DqJkIerU3FyuwcUS2UcFiNy+dMI643ZJEsbW2S2i1ybjuNz2ni4siHed1StU6038djNuKwmHq5k2Dk45upEmMVUnnKtgcWgwWHWs5YRZqpNBF1s7h5SqtRRKhScTwZZ2NgBYCrqp9MfkN0ro1WruD6TZHmzSLfXx2MzYdRpWR6KcNdnEmQKe+R2D7k4HuHSieKHeNDDzsERhb3S8LFJ1nMFSuUauwfHXJ6MDa/hgIDbjt1spFIXxLyL41GOay12S2UAJmMBHq5kqDfbmPRafuvv/GW+///3Rz8QEQ6EApNms0m5XP5A1ns71Go1Ho+HfD7/rp8rl8uRyWTi3LZnz9817Ds/PDx87rknHXQ2mw2j0Ug+n39bR1y/38fpdLK1tUW73T5131liWyQSYX9/n06nc+o+yQ0nISEhISEhISHxUaffh2//dmg24X/5X4R4qoTEHxQkIe4D4pOf/CRvvfXWe37+tek4eq2G5cw2fpcNl9VMOl/EZjKQDHm5u5hCq1ah12q4NbeGXCYTY6aDwYCpsIel9BaVepOLExE63S47B0ckgh6Ucjkr2QJatZqwz8X9pTQatZLLkzGOq3UGgwE3z43xeC0rzkAbiWtBtwOLUc/2Xgm9Vs3F8QgLGUFk0qiVoog3HQ9SazS59WSNkMdBzO/m3mIamQymYn7KtSbtbpfzYxEOjitsFQ+J+JzifkAQ2nYPhi2p8SCNdpt8sUSxVOb8WFgUpa7PJlnJFvA6rATcNhwWE49Xs8hkQtHAvaFYNxbyIpPJePPxKj6nlYvDUoR+f0Ai4KLTbrOxXUSlkDMWEKK4m8Uj4l4b1XqTXFFoqZ2Oeun0+vQHAxIBN3K5ksJhhe3DCp+4OMFKfo9uTyiSsJqNdPvCe3pjNsnKZoFKvYlSIQiTTwsbIlQaLebSeXQaQaC7s5Ci0+3hd9nQaFRibPbyVIzt4iHb+0dMRH1cmxHaV7u9Pl6HBWRQrjWG64Y5Hoqx86kcr1wY59GqINpqVUpm4gEeDZt1E0EP/98v/DBfe/PdlTJ8MRQKBV6v9z2JY++FUCjE9va26Gx7NygUCnq9HvC8EDcYDADY2tp67nnPCm7RaJRsNvu27rVer4fRaMRisZDL5Z6779nnabVafD7fc0KchISEhISEhISExEedX/5leOMNMBrhN35DiqRK/MFCEuI+ID7xiU9w69Yt+v3+e3q+xWTAaTUxHQ9SrTfJ7uwjk4HNbGA5s43XYcHntHFrbg272chULMDt+XVUSgU6jYalzV36Q0Ht0UqWTGGfK5Mx0V02HvFxVK2xntvBbbMQdDt4sLzB9l6JS0M3FQhi2KPVLM12m/PJMEfVGvniIdV6g5DHyaNVoWwh4bfz+sMVQm67IDhltinXGiSCHnxOK6ubBUwGHTPxELfn17Ga9Nw8N8bc+ia1RouJiJ9mq8N6bgeTXsu5pCC0LW1s8bHz46xktjmq1Al7HWjUKjqdLgq5jOszCe7MCyKUXq3iuFJnc2cfg1ZNMujm1XsLaFRKJkIuNraL7B9VMOs0OIwaHq0Kkc6ZqI+Nwj75/WOcZiN+l421rQMAziWCKJVKas02LpuZqN/NYmaH4lGNa9MJNncPKFVqWIx6JqIBOl1BxLk4HmHvSBAYU7kdrg9LK/r9AYmgB5VSQaPVEa/x3Pom9WYbhULOxfGIKNDNJkNU601uz61h0mu5eW6MB0sbNNsdHBYjCrmCuwuCu286HqTR6rCxVcRuNgzXzVGtN9Fr1UzHguL+Qh4HFpOOhfQWe6Uy3/x1n+T/+4UfJhn6cCqFAoEAu7u770kce7eYzWb0ej2FQuFdP3c0Jw6ed5qNjlerVY6Pj5+776Q45vF4GAwGHB0dva0QJ5fLicViomh38r6znheLxcQ5dhISEhISp/mVX/kVYrEYWq2WK1eu8Prrr7+j57355psolUouXrz44W5QQkJCQuJDIZ2GH/5h4fZP/RREIi93PxIS7xZJiPuAuHTpEp1Oh6Wlpfe8xh/5+CVR0Lo0EWN7r4TFqGcqFqDZ7rCxXcRlMxHxOVlI53EMI6dvPl4h6nMyFngaHw17HWzvl2i1O1wdxkKXNra4Oh2n0+uSyu/itpvRalTsl8qiQ+7W3Brlap1PXZ5mLiWIOjOJIOVaA71WLYh4Xiep7UMUcjnjUT+359fp9ftcnY6TLewzGHAq2qpRK9GqVWJ08tp0gvTWLg+WNxiP+LAY9cytb6JU/P/Ze/PwyM7yTP8+te/7vqiqJJV2qffF7jbYJjFjIISBwUmcAcLiJDiBEGMWQxLbDEMIJJAEwgQyBM8EEjIzASbDj5nEYGhjd7v3Tfu+V2nfVXvV74+jOi211Ha33K22nXNfl686Veec7/vOqZLa9eh530dBS7yCpZVVCsUi9RE/U7OLTC+sUMjlCLpsnFoToWpDLs51DZDKZPHYTBh1GnpGJikUS+yvi9A1IrrUqkJeNFoNRYUatUrJgYYq2gYTa+EKFjwuG0PJGamU9kLPCPl8gZoKH4VCkd6RJEqFQNjnZHZxmVy+QCzgRq/V0N4/yvzSiiRerqYzeOxW3A4rhYIo4qwvkx0Ym2B/Q6V0H6pCXjQqFfOLy4CYctveL5a/arWaDUELV9x9XQTcdg41xekaHGdheRWdRo0gCJxu65NKU102Cxe6h1hcSbGrJsLs4jITs2Ji5+/92v187nd+FYtRv+3P6ktRFseSyeQtm6OMIAiEw2FGR0clF1upVCKbzbK8vMzMzAyJRILBwUG6u7tpbW3l7NmznDhxgmPHjnHy5Ek0Go10bpmyEBcIBBgaGtq0b70jTqFQEIlEmJmZedHSVKVSicvlQq1WS8JhsViUkpSvphzqsF5klEtTZWRkZOAf//Ef+chHPsKnP/1pzp8/z1133cX999/P8PDwi563sLDAu9/9bt7whjfs0EplZGRkZG4mxSJ84AOwugp33w2//du3e0UyMjeOHNZwk1CpVBw+fJgTJ07Q2Ni4rTHqogEKxeKGBv0Oq4ljZ9vJF4pSqELA7aA67GVhWQwj8DgshLxOnrvQhVGnoSrs42RrLwcbq6jwuTnZKo51oKEKQYC5xRVqowEmZxfoG53grt21aNQqznUOoFEpaY5HSGWylEqlDSmrZoOOXCFP/+gEFoMOp9VIYnoeQRB7lpXDEAQBZhbENFaP3YJRr+XYuQ5cNhMht13q11YddCMU84xOzmLWa7AYdJzrHKQm6KIx6qNtUBQfwh4bi6spknPLqFVKmqrCUtnm3fsaONXWx2o6I6W9pvOioLK/oZJL3cNk83kEQaAhFpLmPthUzbmOfvbXV4lJrhYTZ9rFElmdVs3QxCwr6Sx2sxGfy8ap1l4ON8exmvS094+RzubQazUYdFp6R5Jr758YntA3OkFLdQWHmq7cE7/Thk6roVhcW1t9JZd6h8nm8mSyWQ42VvHC2vsU8bnIF4tMzi2sXUcVl7qHyObzaFRKGipD/PikmOLqd9nRalScbO2lJS6mlPaPTbK8mgbAajIwOjHDSiqDQaflg285zINvPHTT+sG9GMFgkLGxMUI3qWtqqVQin8+TyWTIZrPSYzabJZ1Os7KywvHjx8nn8+RyOUqlEkqlckNisVarxWAwYLPZpNfVarUU3KDRaKT5yi60SCTC8ePHqa2tRavVSvuuvoehUIju7u5Niajrx1MqlQiCQDQaZXBwkGAwKAl+10pnBVGIq62t3ZS2LCMjI/NvlS996Uu8//3v5wMf+AAAf/7nf86//Mu/8F/+y3/hj//4j6953m/91m/x4IMPolQq+cEPfrBDq5WRkZGRuVn8zd/AT38Kej381/8KO/C1RkbmpiN/q7uJHDlyhBMnTkj/U3ijHGyqllxpSoXAvoYq+kYmyBeKGxIxNSoVw8kZsrm8KP7MzLOSyuCyGNFq1VzqGcZs0GHS63jmTBsKQeBAYxUnW3vZWxflYGMV57sGyeUL1EUCZPNF+kYnREHKbORsRz+1Ef9aQIQoJB1srKJvdIK5xRWqgl7mlpYZSMxgM61SHwlIxzXF/Jxq68PvMGM3aplfTjM5t4hRpybssnC+R+yNtas6xKW+UZqrwlQG3CytphmbEQMNHA77lZTUuhit/aNksjlaqsOsZnKSCHe4OU4qk2U1nSHqd5PN5WnrH8Vi0nO4KS4JWzUVPmYXV9CoVaiUCvbUxTi1tt58oUAJ6BlOSPf8VGsvNSE3BRQsrabpGBB7hGlUSmldAbcdtUrJ+a5BDjZWURnycq6jn3yhiMWoRxAE2vvFHmmNVSFGkjMkZuaxmDYKdB6HBSUlsmuBAbtrI3QPJVhNZ5mYmefo7lqeu9AFgNtuwWoySOtprAozkpwmMZ2SxvrJqTZKpRJ6rYbaNbfi4eY4arWKv/2jD6IuphkfH8fnuzUlqevx+Xx0d3eztLSE2Wze8piyuFYW1NaLa1sJbmXnWFlEKz+WxbVisUhjY6P0+vX2Vsvn8xSLxQ1Ot7KDzWQy4XA4GBkZobq6esO+9ajVasxmMysrK1vOsV68CwQC9PT0MD09jcViAbYW4spioNFoZGRkhFgsdl3XIyMjI/NaJpvNcvbsWT5Zrkta47777uP48ePXPO9b3/oWfX19fPvb3+azn/3sS86TyWTIZDLS850IIZKRkZGRuTbDw/Cxj4nbn/scVFXd3vXIyGwXWYi7idx111089dRT2z7fZjaSyeWxmgwEPQ5OtfaiUSs51FS9oYfbC61iaePBxirOdQ6QLxSJ4iadyzO9uELY66RYKnG+axCLyUDY4+Bkay8KQUCv0fL8JVHYOdBQxYXuQVRKJbURP1NzS/SMJLGb9OTzedr7R8SwhYhPKgltjLjpHp0iVyjithgoCQK5vJjGWh3y0Doguthqo0Geu9gtrs3vJpPLUVKo0apVNFWFOdspJpXqdVp6RiZIZbL4nFb0a2EUtRE/dotJuu6oz4lSKNE7kkSrUdFYGeaFyz14HBZ210bpHhpnNZ3FYhSvdzklOsLWu+IqfE4x+GLtWg40VHG+awC/y07BbMDvsksCnValoHtshkwuj0mvpTLk5dnznVSFvOh1GoYT0yyupFApFRh0On52tk1c55ogeLFniP31lSiVCslRaDHqoQStfaIYWR8LkpyeZ3JpBaNOu8EJaVtbT7nHW10kwOTcouS+u2d/I8fOtVMsltBp1NTHQvz8XCcGnQab2YBKoeTCmmDptpn55h/9NjazkVQqRX9/P5lMRnJ33WxKpRKFQoFcLofdbqenpwe3272lyJbNZiXxa72wVt62WCybXr+WK2xlZYUTJ06g1+tv+NrKjrj1Ytl6US4SiXD58mUqKytRKBTXTEc1m82MjY2RTqfR6XQb9q0X75RKJRUVFQwODtLY2IggCFuOVxbiYrEYnZ2dRCKRHXEzysjIyLySmZ6eplAo4PV6N7zu9Xqv2RKhp6eHT37yk/z85z+/bnfxH//xH/Pkk0++7PXKyMjIyLx8SiV46CFYWoI774QPfeh2r0hGZvvIQtxN5NChQ4yNjTE6Orrtcrw3Hm7hH/7ledr7R0UHlNFANpfHqNdSHfJJiamv21vPz862i/M2xTnd3kuF14nHamRirSy0wuvEbNTR1j+K2aDD57Dw/KUuKjx2zAatVKYZ9dhYXV1ldnGZgNPCaiZH39gUzZUBVjM52tdSUvfVxxAQyBWmaIlH6B4aI53NUxMJUCgJtA9ecZUlZhfJF4rsqY3StSaSqZVKogGPJMKVhae6aACFIDA+PUdyZgGzQUfQ4+SZ061AWTAbZCWVlsS6c2tjVAa9TM4tsprOEg24yWRztPWPcqipikNNcakstzrsZWJmgbmllQ0OQYDKkIfOgXHJaXa4uZrznQMUS2LIQalU4lLP8Np8Hn5yupVisYTDasJts/Czs2247RZCHod0rRqVEotJzzOnrxbohtlXX4lKqeBMez+FYhG9Vk2pVKRzbf7KkJeVNSdeVcgjldHmC0VRyKyuYDWdoVgsSaWp57vE+3Fkdy2n2/pYWitN/d0H3sjH3/PLKJWieKPX67Hb7SQSCaLR6A19NguFwjWdaldvF4tFBEFApVKRW3P7abVatFotJpNpg7BWFtdebu8zo9GIw+FgbGyMysrKGzpXoVAgCIJUPloOcSiLXuv7ugUCgS0dcSAKbAaDgaGhIWprazfsuzqQoaKigv7+fhYXF1804EGpVOL1eunu7iaRSFBRUXFD1yYjIyPzWuXqfzdKpdKW/5YUCgUefPBBnnzySWpqaq57/Mcee4xHHnlEer64uEg4HN7+gmVkZGRkts1TT8G//itotfC3fwvXWfgiI/OKRBbibiJms5ndu3dz/PhxHnjggW2N0VgVJjE9T02Fn5mFJXpHkxzZVYPTauZizxAWo56A287SyipKhUBjLCiJTW6LgTNdw5SAmM/O+Mw8erUdr91EvlCkZ3QSk06D32XjZPsAArC3LsrZzkEONVXjcTlp7Rshk80RC3iwWS1cPt8pOcLOdgzQVBXeUCYb8zmYml+mf2xScnGdau1FQBSFnl8rq6yLBJiYW8BiMmA26IgGPJL7K+R18tPTbRSKRUn4Ghib3CSY6dRK1Fot/WOTUunlC5d7xN5tRj2dg+OkMlksRj3FIpxuF8df34/tUFM1S6tpacx9dTEmZhaZmF1Yty5x396aIF0j06ykMpIA1j4wSrFYIh72Mb+8StfQOAC7a6M8/cIl8X1YKyE9drYdq8lAZdAjrU2lVGAzGfjJmsgY9NhRKpT0JabZUxNBoVRK74FKqcBhMZGYmiNfKOJxWDAb9Jzt6MduNtIcr2BofEoqTT3cHGdidpGl1TR6rYY/e+Td/PLr92/6jPn9foaGhohEIhSLxS3FtK0Et0KhgCAIGwS09aWhV7vZyo6DEydO4Pf78fv92/qZuBFCoRCdnZ1Eo9Ebdo4plUry+bwU2rDe9SYIAhUVFQwNDUllvddysNlsNkZHR6mqqpLuwVaBDBqNhmAwyOjo6EsmrZb7yg0MDMhfAmVkZP7N43K5UCqVm9xvk5OTm1xyAEtLS5w5c4bz58/zu7/7u8CV38sqlYp//dd/5d577910XvkPSDIyMjIyt5exMfj93xe3P/MZuOrv3TIyrzpkIe4mU+4Td71C3NUN6GMeCy1VIToGE+QKBSoDTkYSkwxPzuOxGskXi3QOjlPpcxB0WbnUN4pKqaC2wsdiKksJsf/axV6xP5nH7WJkup/VdJaIz0UuX2A5k8ds1BPxuTjbOQiAQafhp2dEh92e2uiacJQh6HGgEAQu9Qyj1ajQa9Wc6xygVCqxty7Kxe4hCsUS++orGZucoWNgDL1WQ100QCqdBdhQQttUGWZuaUVKSd1TF6N3OEmhWKQlXiEFDViMevbWxyTBrBwa0Rwziy4wtUoqvdRqVFdKWNecZ6fb+6ip8OOwXilvjQXcqFVKqXfb+lLQlngFMwvLXO4dltY8Oze3FjhhxWIUBTCA1+2t54VLPWTzeQw6DTUVfoYT0wDURgNMzV4pIT3QUMWPT4nBCi6bGYfFxE/PtGE1GYj4XPSPi9crACqhxOm1Ocpuu9Pt/RxujmMy6EhOi0EQ5XlWUxkWV1LoNGoaKkPSteyujfLZ334nVUE3U1NTm4S1dDrN8vIyP/3pT6XSx6uFNY1Gg81m2/S6Wq2+YedaObRhJ4Q4t9tNV1cX09PTeDyeGzq3LJLl83mUSuUm11swGKSnp4fZ2Vng2j3dzGYzS0tLjI+PS+61awUyRKNRnnvuuWt+0VvvogsGg/T29jI1NUUgELiha5ORkZF5LaHRaNi3bx9PP/00//7f/3vp9aeffppf/uVf3nS8xWLh8uXLG1772te+xjPPPMP/+l//S+6/KSMjI/MKplQSk1EXFuDAAVhnVJaRedUiC3E3mbvuuosnn3ySiYkJJicnCQaDL9qIfqsG9EsrKXKFAi3VIVr7xiiWSuyri9I+ILqqqkJeVtMZ5hZXcNvM2MxG2gbGsZkNNFUGJBHucHOcsx39ZHN5dtdE6B5OsprOoNOocFpNtPaNSG63n55px2zQ0VgVlgSd6rCXVDpH72hScmOdbu+npSaCTqOW+qm1VIdRq5QkZxbwO21otWrOdw2yv6GKg43VnGpbOy4eYXx6jrHJWRwWIx6HVerX9ro9dfz8Qpfosgu4SWVyKBUKSawrC3KFfJ6VVJrEdEoSwY6d7cDntOF32aTSUPNagmrZeba3Lkp7/xiTc4vYLSZCHod0nbviEQw6DZd6hiVR61RbH3qtmnjYy/TCMr2jScmhl83myObzkpvtQvcQAEd313GytYdcviCVEidm5gCoifiZWVime1jsoXewsYofn2qlVCqJa/XYOd01jGfNTTe3tCK57dQqJT3DCfKFIhqVkro1J2BjLIDLakKjVkqluo0RL++7t4np0X4WJkY2lYBaLBbJSaDRaKipqUGj0bzsstAXw+/309PTw8rKyjUTRW8WgiAQCoUYGRm5YSEOkEpSrw5uADEZORQKMTw8LB17NcViEZVKRTQapa+vj3A4LJW8wmYhzmg0YrFYWF1d3XI964U4pVJJJBKhv79fFuJkZGT+zfPII4/wrne9i/3793PHHXfwjW98g+HhYX77t38bEMtKx8bG+O///b+jUChoamracL7H40Gn0216XUZGRkbmlcXf/z388IegVsO3vgXX2eZTRuYVjfwxvgk89thjXLx4kYmJCRKJBIlEgsrKSqxWK//jf/yPDa4inU73kg3of+HcEBe6B6Ueboeb42RzeVKZLHvrY7T1iSmid+2po71/lJ6RK0JZJldAo1LQVB2RhKZ7DjTy07VeZbtqIvQMJ6kMeja43XQaNXvrYxw72wGIDrRTbX0cbKxCpQoyMSu6sWxmAy6LiWfOiOM1Vwa41DtCc1UFDZUhxiZnSczMYzUZKBaLnCn3g2uKc7JNLGc90FDFUGKKzsFxKXghkxNLAvfURelcExz9LhtVoSvhCgcbqzjb0Y9RryPotqNSXhHBmqvDPH1S/Gt3xOciVyhIotd651uFz4XPaZPEwfK+2mgAv9OGTquRRK2asJeVVJq5xRUxBMIrhl6EPE6aq8MMjk+xtJoW++LVV5HL58nlC9J9vdgzhCDAkV01nG7rJ5vPS0Lf2OQspVKJiM9JNlegfUAU3WI+O+e6R8gViigVAvGgm5+f7yTgMLOSyWLQarjUK4Y9ZLM5oMT49AIA//Hf3ckn3vNLGPR6NBrNi5Zm6nQ6Ll26tC2H242i0WjweDyMj48Tj8dv6VwgJpL29fVtS/grC3GFQmHLQIaKigp+/vOfA5t7E8GVUlK/3093dzdTU1N4PB5JiNvqHLfbTW9vL/l8flMD8a36yskinIyMjAz8yq/8CjMzM3zmM58hkUjQ1NTEj370IyKRCACJREL6w4mMjIyMzKuTZBI+/GFx+4/+CBobb+96ZGRuFrIQdxOoqqqioqICr9eL1+vlwQcf5D//5//M29/+9m2Nd7Cpir/5wU82pIMebKzicJOYmApiGmg+X2RmYZnaaIDJ2QX6Ric4uquWxeUVznUOoNWoaIiFpRLR9b3dPHYzQwlRSCq72FJrIQPN8YjkQDPotJxdCwqIBT2spjL0jk5IbrSyEGbQa2jtG2EllVlztGU51zlAQyyITquR1r2vPoZSqWByblESD891DtBcHd4gmFWHvUzMLjC/tIJapWRXTeRKcmssQNvAOIsrZVdcgLEp0Xm23vln0Gm4oznOiXXOt56RpBj4sFY+W55PpVCg1aoZGJ9cE9YqOdXWRzzg3BACARDyOpicXWBpNS05+0619VIT8YvltaMTLKcyKBUCLdVhlldWyebzuCxGNGqVJPTVBZ0MTM6TyRUQgNqwm+HkDLlCEZtZj91spHNkUrwfkSCXekdIzC4BV0p199bFWE6N8IXf+4+84w2HrvszZrfbUSqV2yrh3A7BYJDW1laqqqpueeqnVqvF6/UyOjq6KTDhpVAoFFIq6lZCnMFgwG63Mzc3t6WoVi5nVSgUUk85j8cjvb7VOTqdDqVSyejo6KYAjauFOLVajV6vv6FrkpGRkXmt8vDDD/Pwww9vue+lUuyfeOIJnnjiiZu/KBkZGRmZm0KpBL/zOzA7C3v2wCc+cbtXJCNz87i134hvE88++yy/9Eu/RCAQQBAEfvCDH0j7crkcn/jEJ2hubsZoNBIIBHj3u9/N+Pj4hjEymQwf+tCHcLlcGI1G3vrWtzI6OrrlfB/4wAf44Ac/yNvf/naOHDnCvffey6VLl7a9/sPNNXidVoJuB+c6BzDqtajVKl5o7UEQRBHmTHs/M4tLHGioon90grnFFepjQVYzWSbmlnFZTQTdDs53DbCwvMq+uhgvXO6hVCpxqKmakclZllbTNFSGWM1kGRyfYn5xhQq/m7Md/SgEgYON1Zy41I0gCOyti5KYmmNidoFcPk9l0MuF7iFUSgXxgJOTrb1UBb3sqY2RmJ4nObOAx2HBY7dIwtPh5mrOdgywtJqiLhoklyvQtybqqVVKqefb/vpKhpMzjE/NsbsmSjTg4Uy72Dttd7yCuYUlFldSBN123HYLF7oH6RwY5ciuGi50D7GazhDyOHFazaz13edwU5yLPUNSSa/PZeP82nyHmqrpHBzHY7fisBiJV/gl0U+lUjI5u0Bieh6tWkVLtSiMWoxaKjwOSqUSnYPiZ0co5BhJTLKcymDUaQi6rJzvHmZheZWqgItMPs/4jOheO1Afw2y1kskVsJoM1FeG6ByZIjG3zMHGSpQKJQPj09L62gfGWFxZRaNSsq++kpOtvRSLJbRqFd//s0dvSIQD0Znl9/tJJBLb/pzeCA6HA6VSydTU1I7MFwqFGB8fl5xoN4JCoZB6N27VB87v91MqlbYce714Fw6HmZubY2lpaZOgtp5isSglrZZ7ya0f71rnycjIyMjIyMjIyLxW+V//C773PbEU9W//VixNlZF5rfCaFOJWVlbYtWsXX/3qVzftW11d5dy5c/zhH/4h586d43vf+x7d3d289a1v3XDcRz7yEb7//e/z3e9+l+eee47l5WXe8pa3XNcX+6NHj3LixIltr99hNRFw2ekfmyTotuO0mnn+Qhdhr5PGyjAnW3tRCAJ2s5G2/lFy+QIHGqvpGU5wqWeI2govmVyO/rFJPHYrhUKBjsExNCol+xtEEadnOMmdLTV0DY6zsLwq9jBbWiGVyWI1GaiLBTnV1ks6m+PonjrOdQ6SzuZoqgqzksqg12lwWE1Uhbz0jM8A4HVYOd81QDqboz4mCm3j03OYDTqaqyukNFKTTsv41CxzSyuimGazcK5zkLpYgMPNcc6s9bWrifgRBIGe4YQkQF3oGWYgMUNLdQVLq2mGEtPotRpaaqKks3lADF6YX15hZGKGhZUV9tRGJUdeY1WIidkFtGo1aqWSvXVRTrb2UigWyWYzAJKw1hjx0jE8icusx2bUYTfppLJQCgUSswvMLa2iVatorgrRNTpNdUWAqpAXk0HP8OQ8AB6nnZVMjqXVDEa9ll01EU53iAJpZciLXquWAiTqKjwU8gVmFpZRq5QcaBBTY6fnlzjYWE2Fzy2FRtzRHOdrj32AlnhkW58zv98vhTncagRBIBAIMDY2dsvnArDZbOh0uk2JetdDOaU0n89v6WAzGo0IgrBJvAc2BDxoNBoCgQCDg4NbuuvKFAoF9Ho9pVKJycnJTfuuFuJudSmxjIyMjIyMjIyMzO1kakp0wwE89hjs3n1blyMjc9N5TZam3n///dx///1b7rNarTz99NMbXvvKV77CwYMHGR4epqKigoWFBb75zW/yd3/3d/zCL/wCAN/+9rcJh8P8+Mc/5o1vfOOLzn/06FF+93d/l0wms+3Y+101EbL5AiPJaRZXUoS9ToJrAQOWtSCCk6297K6JoFkXnHCgsZqV1RRLqxlqIwEm5xboHk5yuKmamYVlzrT3o1QI7K2vJJ3NUSgW2d9QxaXuIbL5PHfvradzKEF7/ygGnYZ4hV8qbS2XQxaLJdRKJSqFgq6hBDqNigqPg/YBUUxan5JqNuoIuB1c7h0WSz7XhKU9tVFyhQLDCfH6zEY9DouJZ9Z62ZXXNDm7gNdhxbwutbQq4CSby7K4ktqQoLqnNsqhxmpOtfdRKpXwOiwsraRITs8D0BT1094vhl8EnBncVgPn1lJja8MeOgbHUavEZNioz0XbkOgWsxi0LKRyJOeWEQQ42FjNydZeIn4X2WwenVbD5T7x2nVaDaOTI2SyOTQqJS1rJbWHmqrFkkVB4OJaOa/JoGdmYYnkzILUZ+5UWy9uqxGXzYzdYpL6BNbHgigVAr2jorD03rfezeO/+U7Uqu27pcpBARMTE4TD4W2Pc70EAgH6+/tJpVK3vLxSEATC4TAjIyOSM/ZGKKembiWelUol1Go1Q0NDhEKhDWNfLbhFo1GOHz+O0+l8UUdcOeBhYGAAr9crjXl1cquMjIyMjIyMjIzMa50Pf1gU45qa4A/+4HavRkbm5vOadMTdKAsLCwiCgM1mA+Ds2bPkcjnuu+8+6ZhAIEBTUxPHjx9/yfFqamowmUycP39+22t63d4GOgZGWVxJ0RKvYHZxmWKxSNTvxqTX0dY/itVkwG41cWrNIXewsZrTbb2YjXpqgk761kpWayp8qFQqekaS2M1i6eXptj5USsVamWsf2XyePXVRltNZkjPzklPtYvcQ03OLkpOuWCyxrz5Gx+A4MwvLBN12XDYL3aOTJKbnOLqnjlNtfeQLRVriFcwsLGMy6LCbjdREApJg6LSZae8Xr6/C58Ri1HOpRxTr1q/J67DhcVjoHUmiVilpqQ7TPjyJUCpSFXSxuLzCYGIKtVLByvIyZzv7xRAEj43l1TSjk3OE3XYaYwFaBxMUSyWqgh4KJSXjs0tSqW/XyCSZXIG9dTFcNgsdayLcoaY4I1OLLK2ksBj1kiMRoCroYTWTZWB8EkEQy19/fr4Tu9mI224h4ndLJbVKpYKF5VVGJmbEY5vjnOscwO+yX7k3awESGpUCn9NKj5SwKrodz3cNYjbo+dIj7+azD//qyxLhygQCgS2dXbcCnU6H0+ncsfl8Ph+rq6ssLi5u2lcqlchmsywtLTEzM8P4+DiDg4N0dXVx+fJlzp8/z9jY2JZCXKFQQK1Wk8lkmJ2d3bDvauHMZDLhcDiYnJy8pqBWdr2FQiFWVlaYn5+X9l2rPFZGRkZGRkZGRkbmtcgPfgDf/S4oFGJKqkZzu1ckI3PzeU064m6EdDrNJz/5SR588EEsFgsAyWQSjUaD3W7fcKzX672uUjdBEDhy5AjHjx/n8OHD21rXvvoYxWJpQ8CCUqFgcm6B1XSWaMBNOpNjODGN1WQg4LZzqk0U5PQ6DSfHxHLR/Q2VXOoeJpcvUBXysrSapnNwHItRj0IhSOWi5aAEo15LS7yCwfEpFldSOK0mFAoFfaMTa8dVS+fctaeei92DLK6kxJ5oHge5nFgeuj4lVadRo1Gr6BgYk8IgzncOUCyWaKoK0Tc6SSqTxWTQ0hgLSEJXbdhN70iC6oATq0GLUa+VSkOFUpGBxAzFYgmXzYxRr6V7bJqmqhBGvU5y7rltZqxWCz8/L6bBHmio4kL3ILl8gaYq0QVWnq8lXsFqJsvIxAxqlZLdNVFOrpW0tsT8TC+lae0T5z/UXM3A+BQLy6uYjXqifrdU/lofC3Kxe4iekaR0b09c6sbrsCIIAhGfSwqJyOUKqFVKOgbEkk0xGXYAt8MmraEs0Llser75+AfZXRPd1mdqK7xeL11dXdtKGN0OwWCQrq4uKisrb3qJZbmvWzabJZPJkM1mMZvNtLW1YbVapdcymQy5XI5SqYRKpdqQYKzVajGZTFKSsclk2uSMKzvYvF4vg4ODOJ1Oad9WpaSRSIQLFy5gMpm2XHf5HJVKRSgUYnBwUPrdIzviZGRkZGRkZGRk/q0wOwsf/KC4/bGPwf79t3c9MjK3in/TQlwul+NXf/VXKRaLfO1rX3vJ40ul0nWLB3fddRfPPPPMttfmsll44x27+JcTF4E1MedyD06bmdpIgM7BcVKZLLGAB6fVRMfAGBajgbDXwc/OtGM2aKnw2CVHlt1iYnxqlsnZBSp8TvKFIi9c7qUy6MFiNEjCUFNVmIXlVRZXUsTDPuaWVugeTnC4Oc5KKiOJcIeaqsnn8yyupIgFPSwsLdM9nKSpKsSueMWVnmyxAB39o8wtr2Az6jDqtVKJaVPUS+taSaffYSFXENNDFYJASzzMhe5hQHQVKRfTjM8siuWe8QhnOvqJ+l3odVoS03MMJabRqJT4XHZ+fPIyALWRANPzixy/2Cn2vYteEfmiATcOi4ln1wS6snCoVAiEvU70Wo1UFtpSXUEuk2J8ek4qNz25dh/21VeSmJ7jcq+41gON1YxPzTG7uIxOo6ahMiTd25pIgN6RpCTm7auv5FLPEH6XfUNpKsDE7AKxoEdaw/6GSv7mD34Lj8O67c/UVqjVatxuN+Pj48Tj8Zs69la4XC46OjqYmZnB5XJd1zmFQmGDiLbVY3m7VCqhUCgkYU2pVLKysoLL5cJqtW4Q3cr7r0WxWCSfz28qNy0LcxUVFRw7dozV1VUMBsOGfVdfs0qlIp/PX/P6yiXskUiEZ599VhJG1+8rI/eIk5GRkZGRkZGReS3y+78PySTU1YEcbC3zWubfrBCXy+V44IEHGBgY4JlnnpHccCCWtGWzWebm5ja44iYnJ7nzzjuva/y77rqLz33uc9fsM3U9eBxWtBoVjZVhSczZUxPlx6dEoWlPbZTOwXFqIwGifjeZXI62/lHMRj3NVSGOXxLPKbvd9tXF8Dps9IwkWU1n8LvsBNwOnrvQiVqlZFdNhJOtvRxujktOumw+T8jjQCHA5d5h1EoFdRE/J1t7CXts1IY9DCRmyOYLWI1a5hYWmZhbBqAx6qNtQCxD3FcboX98irHpBXQaFbXRIHqNBphgT12UzgFRWPQ4bdRGA5IIV+4357ZbpL5pZ9aEPL/DzKmOIQrFIm6bGZvZyPm1hNb1zjeb2UBLPMKz5zo23LdSsXRFLFsTDitDPuxmoyTYle+d1aDDZTVhs5iuJLjWRtBqVIxPzW0S0pqqwiyn0lJi7O7aCNPzi4xPzaEQBA40VklzVHidGHQa6dyaCh+JqTlsZtFB9ev338V/+uADaDW3JirI7/fT0dFBdXX1LRd5FAoFwWCQ0dFRTCbTNQW19Y+FQgFBEDaJaGX32tWvKZXKDddx5swZNBoNoVDohteqUCgkx1r557j8XKfT4fV6GRoaor6+nlKptKWDTRAE7HY7U1NTW4r564U+vV6Pz+djaGiIhoYGOTVVRkZGRkZGRkbm3wQ/+hH89/8OgiCmpOp0t3tFMjK3jn+TQlxZhOvp6eGnP/3phtIygH379qFWq3n66ad54IEHAEgkErS2tvKFL3zhuubYs2cPqVSKrq4u6uvrt7XOu/bU8fyFTs51DqDTqKmPBVlcWQWuCEQADquB7uFxVtNZKnwuCsUi+UIJrVpJddi37jgjT59sBaAhFmRkcob5pWXsZgNWk54z7f0IQDad4lyP6FSLem0kZxdZWFrBatRhMei43C+WUTrMJgYnRBEuHvYyPjXH2PQie+tj5PMFLvWsucQaqlAplcwtreJzWtFrtVzsHqI65N1wHbGgh9mFZbK5wiZhqy4S4FLfCD3DCVRKBU2VIQaT0xSKRWoq/MwsLNEzkkSpEHj9vnqOne2QxkylMyyvpsT71hSXRLd0NktdNCCJZQcaqrjYPYSxSiyDrQ5duXdOqwmlUthQbvrC5R4iPhcOqwmP3SKttbEqjMWol5xv668xXuFDo1JJIlzZdVhOaj3YWMX5LlFA1KgUfP5DD/KuN79uW5+f68XpdFIqlZidnd30s3AjlPuuvZhjrfyYz+eZmpraIKCVH20226bXVCrVtkXCcDhMT08PkUjkhsdQKBSbXHHrxfVoNMrp06eJx+PS2FsJ7yaTiYmJiS2dgFeLd7FYjJMnT1JdXS0LcTIyMjIyMjIyMq95FhbgN39T3P7IR+COO27rcmRkbjmvSSFueXmZ3t5e6fnAwAAXLlzA4XAQCAT4D//hP3Du3Dl++MMfUigUpL5vDocDjUaD1Wrl/e9/Px/96EdxOp04HA4effRRmpubpRTVl0KtVnP48GFOnDixbSHuQEMV/WOTknh1vmuQqN/NntqoJOwcbo7TPzbFajpLc3WY/tFJVtIZzDo1NpOetn6xL1vM7+TcmpOrNuiic3CcYqnE8vIylEoMJmbEPm9uO+d7RjHqNNTHgpztHKRUKlHhF8vr2vtHJZHqYs8Q++orUSoEKSU14LJh1us4dq5jg/OrKuSloTLE2OQsyZkFDDoNJqOOrjUBam9dlPb+MdLZHEd21TK7uCwJWwcaqphdXGFmfgmH1YTbZuHCmsh3R3M1ZzoGJOeb32Unk8mvjRmjvX+UdDZHrlBkb11MEuHKa9FpNJscavNLK/icNi72iOmmBxqrOd/ZT5XfgVGnpbriikCnVinxOW2094vCZTlZNhbwiPdpnRAaC7jxOm08d74TYIPrsKbCh81iksIsHBYj77yr+ZaLcCAKRz6fj/Hx8U1CXLnv2vU417JZMV1XpVJt6VxzOp3S887OTpxOJ5WVlbf8+txuN52dnUxPT+N2u2/oXIVCgSAIG1xx60U5m82GyWRidHSUQCAAsKVwViqVMJlMDA0NbRLirhbbLBYLVquVkZERWYiTkZGRkZGRkZF5zfOxj8HYGFRXw2c/e7tXIyNz63lNCnFnzpzhnnvukZ4/8sgjALznPe/hiSee4J//+Z8B2L1794bzfvrTn3L33XcD8OUvfxmVSsUDDzxAKpXiDW94A0899dQNfSk+cuQIJ06c4H3ve9+2rsNtt3DvgSbOdvSTnFnAbTOTz+cYHJ9Eo1YS8zklkac+7Ka1d4QSEPXaGZ6YRaVUYjPpMBv0dI1MolQoONRYxcm2K33HLnUPE/G7MJsMFIolukcm0KpVHGis5mdn2zcct7c+StBjRyEouNgzhFIhYLcYpZ5sNWEvo5Oz9I1OYjUZCHockrjltJoYXAs3CLrtqJRKLnQNsb+hEpVSucEVByU6BsY2CGQqpYLGqjCTswt0DY2jEATqIl5WVtPk8gUqQ+J2x8AYFV7nBudb0ONAqVCQLxQA1lJZ+ykUiyylUtSu6x3XEq+gf2yS6pBvkysvlc1jMxu42D105b70DLO3LoZWraKpukIaJ53NURn0SseWRUEQNgl/DouRoNfJT0+3AbC7JsLXP/UQXW0XWV5evmaT/+1SKpUoFAobRDSFQkEymUQQBHK53IZ96/uurRfYDAYDNpttk+h2PaXY4XCY3t5eYrHYjpTDhkIhRkdHb1iIA1FYy+fzlEolYLODLRKJ0Nvbi9frlea7mmKxiMViIZFIbHpPtxLbotEobW1t6HS6LUtdZWRkZGRkZGRkZF4L/PjH8Dd/I25/85uw1npZRuY1zWtSiLv77rulL81b8WL7yuh0Or7yla/wla98ZdvruOuuu/j2t7+9ae71zedfzF2UzWYpZlZYWF6lwmNjdmmVqfksu6uDTC+s0jUyiUalpD4WRBAESlxxZBWLJV63p4pzHf2MTM6tOcnMFBHr7g82VktCUHgtxXM1ncFjt2I26lhNZ9aOi0vJoSqFgsXlFEuracl9dm6tX9v6dFePUkHAbae9fxSVUsGe2hin2vo40FCFx2llODHN4koKk16LxajnmTUBqixWJafncdvMuB1WaY27a6MUiyWm5hYlka+9f5RKv5O99THa+kbJZHOYDTqsJgMjk2JqbHN1mMHxKZZW03gdlRxouCKAVYa8zMwvUSiWNl2DRq0kXuGXRLiW6gp6R5K4rMZNQtriSpqQ1ymFUJTFvLDHKY1bFhpnFpbYv24N8bCPheVV2tbKWH/lvjv53O/+GjqNmpkJF8lkkurq6uv6vJXFtespDS0WiwiCsEFEU6vV5HI5XC7Xpn5sKtXN/VXh8Xjo7Oxkbm4Oh8NxU8feimAwyMDAwIZghRsln8+jVCo3hTf4fD66urqYmpqSHHRXUygUUKvVUv+3xsbGDfuuFtvcbjcqlYpMJiM74mRkZGRkZGRkZF6TLC3BBz4gbv/O78Drbn0xkIzMK4LXpBB3O7l06RJ9fX1MTEwwNDTE4OAg9957L0tLSzz55JNotdotRZDy9tXN52eLelJFpeTgqo8FMRpNXOgdk0Sziz3DHGqsYn9DpSTwHGisJp3NsZzOEgu4WEll6RpKsK++ksbK8IYwgomZeVbTGeoiASbmFugbnaBUKtEQC0ki3OHmOKc7+hEQNrjPtGoVR3fX8tyFLgCq/A7GZuZpiUck8a+c/Gkx6jnb2U+xWCLkcSIIcLqtD5VSwf76KsnBFgu4cVjNnO3o3yAa7m+olHq+lUtB1UroHBgjk80R9oq9zi73jXCoqZqw1ykJay6bmVy+KJWb7q2P0d4nlq3e2VxD2HvFXVgd9jIxu8DY5Kx07eVxjFordbGgdP/qY0HGp2YwG/Sbjp1fXqG5ukIatzLkZTWVRqEQhZqyoy6by6NUKPjiR/4jv/bGI5KQ4/P56O7uJhQKbRLTttoup3JuFWpgMBhesu/a0NAQk5OThMPhm/gTsTUKhQK/38/Y2NiOCHE6nQ63283Y2NgNp8OWQxuKxaL033pxrJygOjY2dk03YLFYRK1WE41GeeGFF4jH42g0GoBNwh6IrrdoNEp7e/uGfbIbTkZGRkZGRkZG5rXCY4/B0BBEIvD5z9/u1cjI7ByyEHeT+dKXvsTly5fxer34fD7cbjc+n49f+7Vfo6WlBbvdLjmMrudL9dE9DXzkz/47cKVXmddhpTYaYGp2kb7RCZxWEyUEzrT3bSin9DttNER89IxNkcsXqPA5WV5N0TWU2JDGqlYpOdxUzZmOfvKFIlG/m1Q6SyqTRaNS0hyPSGLSvfsbOX6pm3Q2JwmB+UIR2BiEoFAIKBUKuoYS0hin2noREGiJh+kfm2R5NY1Jr+VQc5zn14S8cq84j8OKxainwueSRC+lQmBiZoHVdAazQUc04OFy7zCNlUFUKhW9I0lWUhm0GhV6rUYqrS2HOVzoHqQuGsBmNkrX43faENZ63AHsq6/kcq8oju2tiwFs6PGWyuYZHJkCRIHwXKd4z+pjIVw2s3Rshc/J0kpKtB8C++pitPaLrj2NWsmBhkpOr/Xss5kMfPY330Zj1ENbW9sG51oul+PnP/85arV6k8BmsVg2vabRaLYt1vj9fnp6ekilUuj1+m2NcSMEg0FOnjxJLpdDrb41ibDrCYfDXLp0icrKyht2mZWFuEKhIIlqV4/d29t7Tedg2fVmsViw2WyMjo5K/fGu1QcuEAjQ1tbG8vLypr5yMjIyMjIyMjIyMq9mjh2Dv/orcfu//le4yd14ZGRe0bymhbhnn32WL37xi5w9e5ZEIsH3v/993va2t0n7v/e97/H1r3+ds2fPMjMzw/nz5zf1jbv77rs5duzYhtd+5Vd+he9+97tbzvnUU09teP6hD32IfD7PQw89tK1r8LtsVIe9OCxmqUzS77IzPjXH7OKylLp5qq2XWNCDTqOWjosEXPSPTpLLF9hVE6FnOMlqOsPu2igLy6uc6xxAqRDYUxtjJZ0hXyiyuyYqJbAe2V2LUa+TSi4PN8dZSWdIZ3Mb3HM6jYbdNRFJhKsLe+gcGGMlncFtt2A1GaQx7jnQyM/OtFMqia44KFEoC3nrSjhXU1mMet2m5NHaSIBUJkuhWORyrxjYYFArOd0lOt28TitGnZafnW3HbjZSHfZxoVtMITUbdAQ9Dn5yai05di2w4cSlbuxmAzWRK73i/E4bFqNeEvPWh0lUeO14nY4N70e+kOd81yAAzVUhekcnSGVy7KoO4TDpOLsm9Bl1GkqFAkPjEwBEfXY+/ev3EfRYKRaLm/quDQ0NodFoqKur29bn50bQaDQ4nU7Gx8epqqq65fOZTCbMZjOJRIKKiopbPl9ZBJ+YmJCCFa6XsiuuUCiQz+fRXZWnXr53c3NzW56/3vVW7v8WjUYlgW8rIa4cDpFIJIhGoze0XhkZGRkZGRkZGZlXKqur8P73i9sPPQTXmYcoI/Oa4aW7qr+KWVlZYdeuXXz1q1+95v4jR47w+ZfwwT700EMkEgnpv69//evXvYbXve51nDhx4obWfTWv39fIqbZeFIIgBg109ON329lXX8lQcprp+SUxkdNhpWNgDJNeS0t1BS9c7iXsc1IbcnGxe4jVdEYsbdVpGRgTAxVqIgFOtfVi0Gk53BznQvcgq+kstZEAxUKJ3pEkRr2WlrhYYpmYnudgYxW9o0nmFlcIuu2spjN0DycRBDjQUEnnyCTzy6vc0RynWCzSO5JEq1axry5GKi02/2+JVzC/vMLo5Cwz84vS+AB7aqP0jSVRq5TitazbF/TYmZ5fWks8VbMrXkHbYAKFQqA+FiSbzdM/NolSIbC3Psbp9j7JDWgxGegcGAPgUFOcrsFxFpZXMRn0tMQjkgjXUBliNZPlYrcYSHGgoZJznYOkszlsJj02g1Yqt630OVhYWuZ0Wz9Os566kJvLfaOkMjmMOg0CcLlfTIaN+JwY9TpGphaIhfy8/Z6D/Pivn+RNv3gPu3btoq6ujsrKSkKhEG63G6vVSjAYZGJi4rr6Gt4MAoEAiURix+YLhUKMjY3tyHyCIEihDdtBoVBIPR63KkF1u91Sn76rWe96c7vdKJVKKa35xZJRi8UiS0tLLC4uStcgIyMjIyMjIyMj82rmD/4A+vogFIIvfvF2r0ZGZud5TTvi7r//fu6///5r7n/Xu94FwODg4IuOYzAY8Pl821rDkSNHuHz5MgsLC1it1m2NsSsewWI0EPZeSSG1m438+JSYVrqnLkrnwDgum0XqvXapdxitRoXNZORsh+jGOthYxbnOARpiIbHMMpOlY2AMnUaNVqPm2XMdABxoqOJC9yDjU7OEvWLgwKWeYZQKAa/DwmBimnyhSFN1eC14YY6DjdWspjNSueXumgpy+QIzC8t4HBbMBj1nOweI+Fwb+qiFPE7S2RxLq2lgoysu6ndTLBa51DMs7esbnWA1ncHvtKHTari4tu9oS5wTrX0UikUpSGJmfmltLRG619yAGpWSo7vreO5CJwAVXgfpbI7JGdHJ1BTz0z4wRrFUQq9RURtySdcUcltZTmUZnxFFkd3xMJd6RymWShh0GlpqY/z0TLu09mwuz4XeUUIeB16njda+ETLZHAqFwJuO7uX9v3zPSwordrsdYMdCDdxuN+3t7czPz0tz30q8Xi9dXV0sLi5u++fjRvD7/fT29rK4uIjFYrmhc8tBDIVCYcv3TavVolQqN5SdllnvehMEgUgkwuDgID6f75pCXLEoOkW9Xi+Dg4O0tLTc0HplZGRkZGRkZGRkXmkcPw5//ufi9je+ATvwFUBG5hXHa9oRd7P4zne+g8vlorGxkUcffZSlpaXrPjcQCBCNRjl16tS25z/cHMdk0NLWP4rZoKO5uoK2tbCCw81xzncOkspkERCYX15hZGIGj0MU5c53ieWnTZUBTrX1kS8U0ahVzCwsk5xZwOe04nfZefZcBy6rSUpdzeULRANuzEY9IxMz2NZKN0+39xPxOTncHKe9f5TFlRQVPic6jZrWvhEUgkBzZZAL3cOMT81RHwuSyxXoG51Aq1HhsJqkMIiyK25kYpZY0MPumqgkwrXEIyyurDK65nzbUyvuG0pMc6ipmpV0hoHxSVRKBU2xAAvLKxSKRaJ+F2qlko6BMdr7R9ldHeLCmhvQatThshqZmp4GoCrgIDm7wOTcEkPJGZorg7QOJCiWSoTcduwWE0aTKNYcaKhicm6F+eUUC6tp9sRDXOgZoVgqEfQ4cFrNDCXFpNY9tVEmZhcYn55DEKC+MsTZjn4y2Rw2s5HvfPbDfOBt916Xu0mhUOD1eiX31K1GoVDg8/lIJBI7Mp9SqcTn8zE2NrYj85WTS0dGRq77nFKpRDabZWlpifn5edLp9JaOuGKxiE6nY3h4WBLRylztogsGg6yurjI7KwaCbCXEFQoFQCxlTSaTpNPp616zjIyMjIyMjIyMzCuNVAre9z4oleA974EX8czIyLymeU074m4Gv/7rv04sFsPn89Ha2spjjz3GxYsXefrpp697jKNHj3LixAl+8Rd/cVtrCHocqFXKK6mgvcPoNGoONVVLwlXZSeZzWgl5nEzMzNM3OoHNbKC5OsyF7uENx9XHgoQEgfGpWZIzC1iMBuorw/z8vOiKO9QU51RbL4eaqqWk0o6BMTQqJRaTQeqzVnabZXN5rCYDAbedy/2iqOJ1WBmdmmVuaUVyxZ3vGmR/QyVVIa/kigu67SgVSi50X7mWk629CEBV0Eu+UJD6r+2Oh0mlUiyupLAadVgMOloHxnGa9VT5HQxPzpIrFNGqlUR9TlLZHABVQTcziyuMzywSC7g51FQtuQvddgs2kwHVWgP+9Q66YqnEwaZqTq0d63FYMGi1pNIZAFriFVLwBMDr9tZLzsJy2ETPsChq1ceC/O3jH6TCd2ON930+H+fPn6euru6aqZw3k0AgwLlz56itrb3hUIPtEAwGOXPmDDU1NdcMO7iZhMNhTp48SWVlJcVicUMC7VaPuVyOUqmESqVCo9GgVqsxGo2bxi0UCuh0OlZWVpicnNzgor26D5xKpSIcDjM0JPY23Op9LQtxZrMZp9PJ0NAQ9fX1N/t2yMjIyMjIyMjIyOwITz4JXV3g88GXv3y7VyMjc/uQhbiXYH3IQlNTE/F4nP3793Pu3Dn27t17XWMcPXqUb3/72y9rHW+5ax9P/Z+fsZLK4HfZ0aiVFIpFtGoVTdVXeqg1VYX52dl28oUisaCH1VQGjVqNVqWkNhqUjgu5HTxzpo1CsUgs4CGdzZLOZKXxTq4FL6hVShJTc6SzOdw2M1azkVNtfSgEgYPrhECvw4LTaqatfxSlQqDK7+RUex8HG6uwmgwkp+clV5zFoOeZM20A1EX8jE7OcrK1B5NeQ9Blk8YMe2woSnmGkjMoFQLxoJsLPSNUBdxEfU4WV9KMTM0D4LaaWMrkyRWKBNx2NCoVXSOTHGio4mCjifNdYmCDSa/FbNAzMC4mn5bTZ3tGkuyri20ojbWZDdgtJuYXVwBRSEtOzzM4O4XbamBfXZRzXUOUSiVMei2VIS/ZbB6AaMBNNpuXwibe9ea7+KOH/gMGnfaG33ur1YpKpWJmZga3233D598o5TTWyclJ/H7/jsxnMBhIJpOEQqGXNdb1CGvZrNin8LnnnkMQBCkYo/yo0+mwWCybXi8Lafl8nlKpRLFY3CCglcW2SCTC0NDQBiFuq/LTiooKnn32WeDajjhBEFAoFMRiMc6dO0dNTc2OJMzKyMjIyMjIyMjI3ExOn77SD+6v/xp2oAuOjMwrFlmIu0H27t2LWq2mp6fnhoS4D3/4w2SzWTQazbbmjVf4WUllaKwKMZKcITGdwue0E/a5ONvRj1IhsK++irmlVfKF4saUT78Lm9nApd5hVEoFe+piDE/MUCgW2VMbpXNwnFQmi1GnJeR1bkhJPX6xC61GQ20kwPT8Ir0jSXQaNXfuqpX6rJXnaq4OYzMZcNnMdI+KqaBCKU/X0DjFYgmbSYdOreb5C50oBKgJuekaTlIqlXBajATdNi71iW66fXVRzncNsbsmgsuWxW420jkilmc6bGZ6RyeYW1rBbNARDXi43DvMruoQdqtprW9dCqVCQK1ScuJyj9SPrtw/71BTNRG/m/Od/eQLRQw6DQqFwJlyCEPIy8pqmo6BMQ41VYshGe39FIpFdBo1DquZsclZSqWS5FS81DNMVcgr9exLZbIIgsBj730bD7/zvm032hcEAZ/PRzKZ3BEhThAEKbRhJ4Q4EF1xY2NjWwpxpVKJXC4nCWlXi2rrt3M50QGp0Wg2iGharRaDwSBtLywsMDY2xp133nnDLkOFQkE+n99UbloW20KhEL29vRv6Qm4lxOn1epxOJ9PT01t+NtafY7fbMRgMjIyMEI/Hb2i9MjIyMjIyMjIyMreTTAbe+14oFuHXfg1++Zdv94pkZG4vshB3g7S1tZHL5W5IoKirq8NoNHLhwgUOHjy4rXnvaI5zqCnOmXYxkKA2EmB+aYXekaQUTnCqrRe3zbyh7LKpKszYxAyT80tYDDoCHgen2/pQKgTubKnh+KVu4IozzKDTYNRrqQ75JGfYgYYYz1/sIV8o4LaZ0aiVLCyIgQX1FR7OdQ4CsLCwAKUivaMTKASB6qCTi71jCAhUh9xMzS+RnFtCrVJy1556jq2VcDZUhhifmkWn06NRKWmpiXBmLSAhVyiiEAR6RpIoBIEDjVWcbO1lb10Ms2GRQlEs1QUQSkXaBxIUiyXsZiM+l43jl7qpiwbQqNX0jSZZSWVQKgSMeh3PnBbLa4NuOyqlktPt/TRXV6BRq6RgBbVKiVqlkkRHv8uOVqOia3iCurAbrzNC79q4AE6ridHJWVKZLFaTga9+4n3ce6BpW+/5enw+H6dOnXrRhM2bid/vp6+vj3Q6jU6nuyVz5PN5SURTKBQsLS3R3t4u9WRbL7SVSiWUSuUml5rJZNokuKnV6pcU12w2G8PDw8zNzeF0Om9o3QqFQhLjlEqlNFfZIadWqwkEAgwNDUkBC9dKWvX7/UxPT5PP5zeV5a5/rwVBIBaLMTg4KAtxMjIyMjIyMjIyryr+83+GtjZwu+Ev//J2r0ZG5vbzmhbilpeX6e3tlZ4PDAxw4cIFHA4HFRUVzM7OMjw8zPj4OABdXV2AKHr4fD76+vr4zne+w5ve9CZcLhft7e189KMfZc+ePRw5cuS61yEIAnfeeSfHjx/fthAX8joZn5qlUCxKqaa5fIE9tVES03N0DIxh0GkIuB2MTYoN4MvBC8Viif11UXpHknQOjqPXqomHfSwsiSWXLVUh2gfHyReKVHgCZLNZLvYMIQDxoJPE5DT5QoGYz8HUwgpT8xl0WjX1UT8dg2L/sz21UfpHJ0hl8zisJpxWE93DooPt3gONPHuug3yhiNtuwWo0kF7r3bbeaTa3tEIk4JZEuPJ1VoW8pLP5DamxFqOezsFxVtMZtGoVjVUhznUOYjMZcNrMLK2k6BgQ3XVhr5OnT4oJsw6rCY/dws/OtGEx6on4XQyOT7G0mkYQwOu08uO1Y102Mw6LiecudBILeDDotYwkp0lMpwDQqJRc7BF7fJVLU0+19XG4KY7ZoOebj/82sYBnW+/31ZhMJvR6PVNTU9tO8L0RdDoddrudZDJJNBq97vPKpaFXu9e2Kg8tl16WRTSNRsP8/Dwej0cqj10vsN1MAVKhUBAMBhkZGblhIa58frFY3CCwrRfOIpEIx48fp7a2Fq1Wu6lHXBmDwYAgCIyOjm66z1eLrl6vd8ccijIyMjIyMjIyMjI3gwsX4I//WNz+q78C1421y5aReU3ymhbizpw5wz333CM9f+SRRwB4z3vew1NPPcU///M/8973vlfa/6u/+qsAPP744zzxxBNoNBp+8pOf8Bd/8RcsLy8TDod585vfzOOPP37DosDrXvc6jh07xkc+8pFtX8+dLTUMJqYkMWpPbQyVUkFyZoGA245SoeBizxB7ayuwmw3ScXUVXlLpFPMraRwmPSqVgku9I9SH3TREvFzqExNYa8IeSoKSyfllLEY9QbedjsFx/C7bBjde0OOgUICR2TkADjdX88Jlca679tTSNZigZ1h0sLXEw6ymMuQLRcl11zuaJF9wcaChSlpjTYWfmYUlTHodSoXA/nX7PA4r80urG5NiuwZJZ7N47FbMRp3kymuM+TndOUQ2l0ev1VAXDdA+IJ5XHfayuJyic1AUXg82VfOTU62USiUpWOFcRz+CIFBT4Wd6fpHutaCF6rCXn5xupVgsYdBpqIkEuNA1iNtmQq/TUSwWudQjOvMifhf/7TO/g8lw85xk5fLURCKxI0IciKENAwMDVFRUbHCvXasstBxsAGI66dXuNZvNtklcU6lUUlnm3NwcFy5cIBaL7VhIxPPPP78t1996F1zZCbe+Z5zJZMLhcDAyMkJlZSWlUumagQxqtZqhoSEikciGEtWrxTuFQrEjYRYyMjIyMjIyMjIyN4NcTixJzefhHe+Ad77zdq9IRuaVwWv6W93dd99NqVS65v7f+I3f4Dd+4zeuuT8cDnPs2LGbspajR4/y+c9/nlKpdN29worF4gbBozbs5h+fPgFAU8zP+a4BakNuIl4bk/PLpDI5LHotxXyey32i2NRSHeJS7yh1ET9Rn4PphRVml1NYTQbsdrtUmlpOSTXqNFSGvKymMnQMjqNSKgi6HQwlJikUizRXhyUH2aHGalYzWUmE21dfSb5QYnJuEYfFiNWo40L3MLGAh4ONVVJggtNqQq1SMTIxDbDB4VcXDWDQayURbm9dlOT0HMmZefRaDbXRgFQye9fuOlr7R+gbnUAQYFd1mMnZRbK5vFRCWk5bfd2eel5o7SGby6PVqGiqDDM9v0ipVCLqd5PNXwlWuGdfA89d7CKXL0jhFd1DYslr0G1HpVJyYW3ckNNC19gMq2spqh9791v58K/+u1uSburz+ejv7yeXy92Uhv2FQuFFQw0ymQwrKys888wzUmnoVn3X7Hb7htc1Gs22rt9ms6HVancsJEKv1+NyuRgdHaW6uvqGz1cqlRQKBel3TKFQQKu9EsYRiUS4fPkyFRUV0vFXUywW0Wg05PN5JiYmNois5dJXGRkZGRkZGRkZmVcjf/InoiPO4RDdcDIyMiKvaSHulcTevXtZWVmhvb0di8WCWq1GpVK9qMsonxcTOMviRsiuR6dREfE5aR0QnVo2q4XT7f0USyXiFT7mFlcYnlrAbNARC3i4tNY/zWYxcb5zikwuTyzgIZXJcvxSNz6nlaDHKaWkxiN+0pkc/aMTOKwm3DYzZzr6OdBQSdTv5mRbH6VSiQqfC51Wzcm2XgQBDjaKfekiPpfkPBtIzKAQxBLP/rFJcvkCNRU+ZhdX6BlJcnAtMKEsulWGvOTyBamkdH2CaVN1mKWVlCSAHWyqJl8oMLe4IrnZLvSMoFYqaI5XMDQ+RWI6Ja0tm8+TzeXxOq0YdVrOdg6g1ajYV19Je/8oqUxWCrxYzWTJ5Qt4HBbMBr0UXnFnSw2Xe4dZWk2L62uqZiQxyWo6g8mg4ysffx/3HW65ZZ8hg8GA2WxmYmLimumiV5eGXqssNJPJSKWhV4trGo0Gs9mMRqNhbGwMjUZDXV3dLXdjlUMixsbGdqwEMxQK0dbWRmVl5Q2Lh4IgIAiCJJhdnaLqcrlQqVQkEuLP6rWSUVUqFcFgkMHBwQ1C3LXKWWVkZGRkZGRkZGRe6bS2wmc+I27/5V+C13t71yMj80pCFuJuId/73vf43//7fzMxMUEymaRQKHDo0CFKpRKPPfYY995774Z0R7PZvMFVdHXj+VKpxF//f6c41zmAWqVkV02Ek219+Jw2Ql4Hl7qHyebzhL1O7BYjl3qGUauU7K6J8sLlHmoqfBRyWcZn5kllsrjtFqrDPp67IPbGO9wU54XWHg43x4mHfcwvr9I1lECrVmExGvjJWrjBrpoIPcNJsrkcZqOOiO+KmOayW0jOzDM5t4jdbMCoVXG6vY8DDVVEA25pjTazAb1WzbGzYmDD3voYbX2jDI5N4nFY8DltG0Q4k15Ha++IlPp6qrWXCp+LaMBNNnvFzRYPusnnciyupLAYDVJfuZoKnxQKMTGzAIilvalMllQmK4U7nGrrxee0Uh8NkpyZp28t/fVwc5xiqcTSalosTa0I8MLaNTdXBvjqY79JdfjWlIyWSiXy+TyZTAaLxcLw8PA13WzrS0OvFtgsFssmwU2tVr+oQ1OtVnPp0qVb4vDbikAgQG9vLysrKxiNxls+n9PpRKlUMjk5ecMlv+Vy1FKpJPWLWy+cCYJAJBJhZGREen41L5a0upUjbrvJuzIyMjIyMjIyMjI7RT4vlqTmcvBLvwQPPni7VyQj88riVSvEpVIpvvKVr/BP//RP+Hw+/uzP/mxb5WW3Eo1GQ3V1NUePHsXr9fK9732P2dlZvvnNb25LZBAEgWjAw3ByGrvFxJn2fgRBTEb98SkxYKAlXkHf6AQBtx2n1YTTauZ0ex8AFX4XPz4pimnlfm2ZbH5NWPLzwporTqNWMpScJpvLSz3YTq/NdajpikvNbTPjtJpp7RvZsO9AYxU6jZql1TSj0wsoBLCaDNIaK0NeVlbTnLzci1atYk9dTBoz5HES9jo4sfa8HDjRGAuvOfQsnG4Tr8dhMTG/vML49BwalZLmeISzHf00Rv3EAh7S2azUV85mNjKYmGZ+aRWTXkvVWirs/oZKycFXduJF/G6m55eYW1pBq1HRWBnmhcs9VIe9BD12lAolF7oHAXjd7hp+4xd2URW68T/xFAqFa7rVrn4s9xjTaDSk02lmZmYwGAwYDAappHO9wHazhDO73Y5SqWR6ehqP5+YET7wYGo0Gj8fD+Pj4jqSDCoJAOBxmZGRkW733yumphUJhkyMOxD503d3dKBSKFxXi1Go1oVCIwcFBdu3aBciOOBkZGRkZGRkZmVcnX/oSnDkDViv89V+D/LdkGZmNvCqEuFQqxfHjx3n9618vlcf94Ac/4E//9E954IEH6Ojo4P3vfz9f+9rXaGxs3HKMZ599li9+8YucPXuWRCLB97//fd72trdJ+7/3ve/x9a9/nbNnzzIzM8P58+fZvXv3hjEymQyPPvoo//AP/0AqleINb3gDX/va165ZJviWt7yFt7zlLdJzjUbDww8//LKcPvfsa+DYuXZ6hhOYjXoiPhfzy2L66eHmOCdbeyXRRhAEuocTktA2OiGGKzRXBekcSpLLF/CmrLhsFi50D0llnM9f6EKrUVMZCDAxt0Df6AQ6jZoju+p47kInALtronQPj+Nz2aR1lMU0k17HpZ5hMtkcJoMWt9XEC5e7USoU7KqN0NY3Siabw2zQsbsmys/XxmyJV9A/NonPZZV6s5Wddtl8DoNWQ9eQ2Puu7N471FTNaiqLxXilhDSfy5GcWSSVyUqOwFNtfRxsrEKrFj8/5bRTlULB2OTchmNPtvZyuDnOSiqNXqvlXOcAAAathnyhyOD4FAC/92tv4vcfvJ+f//znLC0tYbFYKJVKW5aDblV+nM/npdLQq91rJpNpkztSqVQiCAJnzpzB6XQSiUS2/Tm6XgRBwO/3Mz4+viNCHIjiVWtrK1VVVTvixCu78JaWljCbzTd0rkKhQKFQUCgUtnSwqVQqPB4PExMTW56/PnU1Eonw85//nNraWnQ63SaHnYyMjIyMjIyMjMwrnc5O+KM/Ere//GUIBG7vemRkXom8KoS4v//7v+erX/0q//iP/0hNTQ0A3/72t3nd617HV7/6VUZGRvjwhz/M3/3d3/H5z39+yzFWVlbYtWsX733ve3nHO96x5f4jR47wzne+k4ceemjLMT7ykY/wf/7P/+G73/0uTqeTj370o7zlLW/h7Nmz1/WF+Y477mBwcJBEIrHtHlh76mPMzC9R4XNSKJZo7Rsh4Lazd52rbH9DJd1D48wurojhAkolF7qH0KiV7Kmp4Hy32DeuqTrMcGIak0En9VkrC193NNdw7Fw7+UIRn9OKXqslXygAG3u3UQKzQSeVhh5uruZsRx+5fLkXXYaBxAwA9+xv5Kdn2gCo8LkoFItk1/rgrRcRZ+eXqfC5JGFtf0Mll7qHaagMMbe0QnXYJ7n3FAqBXCFP72hSHKepmhdae6kMuFlaTWOzmCRHoFajZnZxmZVURhIdX2jtpT4WZGpuEce6Y0ulEqlMjuRaGevBxipOt/eztzbC5OwCf/Cet3BnU4yuri6USiXnz58HIJvNAptLQzUaDRaLZZPg9lKloVvh8/kYGxvbESEORKHq+PHjZLNZNBrNLZ/P4XCgUCiYmprCuwPNJNRqNT6fj9HRUerr62/4/PWuuK1+D7jdbhKJBKlUCr1ev2Hf+nMMBgNut5uhoSFqa2tlIU5GRkZGRkZGRuZVRaEA73sfZDLwxjfCi+Qiysj8m+YVLcSVS71mZmaoqamRnGe5XA6LxYJOpwPEdNP77ruPr3/969cU4u6//37uv//+a871rne9C4DBwcEt9y8sLPDNb36Tv/u7v+MXfuEXAFEMDIfD/PjHP+aNb3zjS16P1WqlpaWFEydO8Pa3v/0lj9+KqN/N3fsbOdXay2o6g8duRa/VkJye3xCaAHB0dx2XeoZYXBFTUgNuO9m8KKYdbKzmTEcfxWKJ3bVRekeStPaNiIEFDVUsp9LkC0Wpr1pyZgGDTsvu2qgkwu2uidA7mmRheRWzQUc04JESVO/e18DJ1l5SmSx6jYpYUAyIANgVF89bSWVQq5QbRMS6aJCJ2XlcNgsKQeBAY5V0PQa9BofFxMXuIekaznUMoFarMOm1VIa8Ut82h8XAwkqKnmGxUf7hJlHoUykVG3rHATjMBpLTc3SvHdtcGeBUWy9OixGNSkHYbePUWjlsOpXijz/wZuJhL/l8Hr1ej9vtZmpqil27dqHVatFqtbfUyeX1euns7GR1dRWDwXDL5iljMBiwWq0kk0kpAfRWIggCwWCQsbGxHRHiQAxtOHv2LPF4/IZDKcrvdTn84mrUajVKpZLh4WFqa2s37Lu6/DQajXL+/HmqqqooFAqb0nHlHnEyMjIyMjIyMjKvVL7yFThxAsxm+MY35JJUGZlrsTMd2F8mDQ0NnDt3Dq1WC8Dq6ipGo3GD2FFTU8PS0tItW8PZs2fJ5XLcd9990muBQICmpiaOHz9+3eMcPXr0ho6/GkEQsJsNrKYz1EYD5Ap5+kYniAbdNFaGJXHpcHOcXD7P4kqKWMCDXquhY2AMs9FAPOjiVFuvKMLVRElnsoxPzWE3G6mJBDjV2ktyep5DTXG6BseZX1rF77KTzmbpGhyXxr/QPcTMwjL7G6qwmAxcXktoPdwcJ53NkcpkqfC5sBj1tA+Mk5yZ53BznIs9Q6ykMvidNihBcmYeEIW13pEEc4sruO0m6mNB6Xpa4hWMJGcYmZhBpVRwsLGKU229ZHJ59tREsZnFcAqAXdVhxidnmVlYRqdR0RAV+99lc3niQRdqJVLvuHjQSdfgGHNLq6iUChqiPi73j1MqQVXIi89lpy8xC8Dd+xv4H3/6cf7DW97Irl27qK+vp6qqitraWvL5PCqVCr1ef8vLKdVqNU6nk2QyeUvnWU+5PHWnCAQCzM7OkkqldmQ+q9WK0WiUEk5fjEKhQCqVYn5+nsnJSUZGRhgaGiKTyWwplBWLRbRaLaOjoxTWXKXrx1ovxNntdvR6PePj47IjTkZGRkZGRkZG5lVDby986lPi9he/CDvw93sZmVctr2hHXFnQeN3rXsfExATDw8PEYjGsVivt7e289a1vlb6sjoyMsG/fPqampnC73Td9LclkEo1Gg91u3/C61+u9IUHkrrvu4k/+5E9e1loON9cwNjnL+a5BcvkCFT4nSoWC1r4RNColLTURXrjcQ0u8gj11UToHxsWUVJsZQYCesem1ccQSU4vRQHXIx+KqGFigUipw2y30jiYpFIs0VoUZSU6TmJ5jb12MYqkkOdh21URQKgTGJmcx6rVi2ejlHuJhH7tqIvSOiM43jUqJzWyQHGoNlSHGJmdJzMxzuLmaoMfBqTZRdIsF3GSyBUksO9RUzam2PkqlEk2VQVKZnORQqwl7mJ2fZ3RyFoUA1QEnF3vFMtmI104mV6B9cG3OqB9BqWJmcRWlQmBPXYwz7WL56+6aCMurGdoHxfeysSpMOldgOCmW1T78zvv45G+8DaVys8imVCpxuVxMTExgMple1nt7vfh8PgYGBojFYjvikvJ6vXR1dbG8vLwj16jT6XA6nYyPj1NVVXXL5yuVSvj9foaGhtDr9Vv2+Ss/5tfKqcvlxeUS42AwuGX/x0KhgEajQalUMj4+Tjgc3rSvjCAIRKNRent7MZlMshAnIyMjIyMjIyPziqdYhA98AFIpuPde+M3fvN0rkpF5ZfOKFuJA/IJssVg4cuQIH//4x3nPe97DwsICZ86c4ZFHHpG+qO7evZvGxkZsNtuOr+9GhJAjR45w8eLFbTWGL3NnSw2f+MvvAGKZZ89IklQ6i8tmxm42SuKSSa/jZGsvhWKRmgo/MwtLnLjUg9NqwmHWS2JaPOxlcTXN5OzCWjKpmTMd/eyrj1Ed9nK6TSxh9bvsWAx6fnauHbgi5FWFvAQ9DhSCIJWNOqwmuocSkvOtUMhxoWtobUwfZ9r7KRSLOCxGlILAC2siXEPUT+/YFAPjU7gsBuxmg+SKCzjMUMzTNzYJiKETl/vGMOm1OCwGnFYL3SOikFYVEJ1453vWetethTuYjXpcNjNuu0W6T3XRICaDjgtraz/UFOd0u+gYDHudPPa+f88vv37/i74nXq+XgYGBHRGNADweD+3t7SwvL2/7c3QjqNVq3G434+PjUp/GW00wGKSrq4vKyspti41bJdNeK522VCoB0N7ejl6vl0Q2k8mE0+nc1PPv6jXl83lKpdKm9NTyHwvKqaihUEg6dyvXm8/no6uri3Q6vSNhFTIyMjIyMjIyMjIvh7/+azh2DAwG+K//VS5JlZF5KV4VQpwgCDz55JM8/vjj/NZv/RbT09M88MAD3H333YD4ZXbXrl231Bnk8/nIZrPMzc1tcMVNTk5y5513Xvc4oVCIiooKTp06xRve8IZtrSUW9OB1WIgFvZKYFvI6oMSGlNTjl7qJV/ixmQxc6Bbdc36njQqfk5NrjrKyQHW4uRrCPuaXV+kaSqBRKbGbTfz41GUAyRXXPz6JTqOmPhaU5jbqtGSyOUYmZtBp1NRFA5xs7aWlOozbZmZ8eo7lVAalQoBCnpMdYgppwGFmOZ3lZFsfGpWCeNhD24DoXnPZzFT6XZxaO3ZffYxLPcMYjAb0Wg110QDnuwbF98Zlw2zQS88PNVVzuq2PiNd+pXfdWriD12HBbDBwvksc92BTNec6+slkXeuSWsVjw14nf/v4B2mo3DoVdz0ul4u2tjZWVlZeViru9aJUKvF4PCSTyR0R4kAsF21ra6O6unpHBCKXy0VHRwczMzO4XC7p9VKpRC6Xuy5x7cWSac1m86bX+vr6SKfTtLS03PB614c2rL8/5T5wfr+frq4uZmdncTqdAJuOLY9TUVHBwMDApn51co84GRkZGRkZGRmZVxKDg/Dxj4vbn/88xGK3dTkyMq8KXvFCXPlL6sGDB/nWt77Fs88+S0VFBYcPH5aOWe8ouVGH2vWyb98+1Go1Tz/9NA888AAAiUSC1tZWvvCFL9zQWEePHuXEiRPbFuIEQeD+I3t46v8cA2B/QxWXuofYVRvZkJKqEAQiPtc6MS3E6MQsJQR0GhWVwSsClU6jYSgxTTafF511FhMXuweBjQ4xnUZF1O+URK/mygCX+4aJB904zHrUa3MDqIQCvaMTFEslrEYdZr2WrpFJFILArngFbQNjZHN5THote+pi/Px8J3ClbDVXLG0KbFhNZakMeqT599VXcrl3mD210XVC2tqx6Qweh1XqXbe/oZJLPcPsqTWjUirYUxfj1Nqxy6k0dbGglNR6ZHctf/3YQzis11eGqVKpcDqdTE5OEtuhf318Ph+dnZ1UV1fviEBTFo9mZ2c3CGM3i0KhsElIMxgMdHR0YDKZpNfK7jWVSrVJSHu5ybShUIjjx4+TyWSknpTXi0KhQBAEyeW2PsRBoVCgVCoJh8MMDQ1J9/LqsIYy4XCYnp4eMpmM9JoswsnIyMjIyMjIyLySKJXgoYdgZQWOHoXf+Z3bvSIZmVcHr3ghruxoUSqV+Hw+SQQDOH/+PD/72c84fvw43//+95mcnMThcGw5zvLyMr29vdLzgYEBLly4gMPhoKKigtnZWYaHh6WG9F1dXYAodvh8PqxWK+9///v56Ec/itPpxOFw8Oijj9Lc3CylqF4vR48e5R/+4R9u9FZsoDYa3CRS6TUallbTUkpq0OOQghAONVVL5aCpdAbrWoCCIMC+uhgnLnVTLBWJ+pzML6/SM5xApVTQFPVKDrEKj5WZxRQOkx61UkF1yMPlfvF+OawWknPLzC4tS33q+scmKZZK1FT4mZ5fZHR6AUGA1+9r4GdnxPLWsFcUJLK5/No645xp76NQLJKcmaexMiRdX1NVmOHkNEGPY1NC7MJKipDXKQlpu2sidA6O4XbYNt2nuYVlqkJiyS1AfSxIcnoenUZMqPzNt/8Cn37/v0d1g/25PB6P1MdwJ3A6neTzeRYWFnakJFsQBPx+P4lE4rqFuFKp9KL91taLa+vda+uFtPn5eUKhEEajccO+W9E/zWAw4HA4GB0d3VaZsVKpJJ/PbyhPXV9+WlFRwbFjx6TE22sFMmg0GlQqFdPT0zv2eZKRkZGRkZGRkZG5Eb75Tfjxj0Gng7/9W5C7qsjIXB+veCGuXJqVzWZ59tlnef755/l//+//ce7cOfL5PEajkeXlZd72trdJPZ624syZM9xzzz3S80ceeQSA97znPTz11FP88z//M+9973ul/b/6q78KwOOPP84TTzwBwJe//GVUKhUPPPAAqVSKN7zhDTz11FM3LAgcPXqU3//93yeXy6FWq2/o3DJ3NMc3pIoebo7TNTTO4kqKiN9NOpOlvX8Uo05DS/UVMas64KJnOIFGrcSgVeGyGDlTLv+sCXGxb5x8oYjdYsBmNKDV6gE40FjF2Y5+isUSFquFipJAx5DYj+1QUzX9Y5MsrqRw2y3YTAap/9rr99Vz/GI3uXwBo1ZN2OcinckB0BKP0DcqhjlAif31lZLoFwu4SWVyFNbe0/WuvEKhSEPsyjXVx4IkpuYwGnTSvSiXzZYK+Q33qSbiZ2ZhGYtBL629LFCuprP85cfeyzvecGhb74nb7aa9vV0SWW41CoVCCgvZqd6IgUCAkydPkk6nKRaL1ywJLYtsuVxOcq+tF9c0Gs0G91r5ta3ca2fPnkUQBDwez45cYzgcpqOjg1gsdsMluFe74BQKxQbXm06nw+v1MjQ0RH19/YsmoyoUCmZnZ7flzpORkZGRkZGRkZG5lYyOwkc/Km5/9rMQj9/e9cjIvJoQSi+mXr0C+PznP8+f/dmfsbCwgMFgYHFxEZ1Ox6c+9SnuueceTCYTZ8+e5ZlnnmH37t08+uijL/rl9pVAsVjE5XLxgx/8gP37XzwEoMzVZXuZTIY3f/TPWVxNUx100zk8AUBD2E1vYpZsvoDFoMNq0qFUKBhMzrKrOsSlvlFKJbijqYquoXFml1KSY0wAXmjtlYIdZhaWaamuQKNRScJafSzIairDUHIatUrJ7poop9vX+s01x+keSjC7uLxpzGjAzfLKKtMLK1QG3XgcNk629lIqlXDbzNjMRtJrfeb21kVp7x8jnc1xuCVOLleQnG6xgAcoMTo5Sy5f4GBjNWc7RCHtjl01pFJZLqyV1Eb8LjLpDFMLKxSKRQ40VEm98u5siZPLF6W1B9x2vvlHv01LPPKy3ttz587hcDiIRqMva5zrZXZ2lsuXL3PXXXe97L5tV7vXriWyra6uAqJD7mpx7VqPL+fnMZlM0tfXx5133rkj5ZmlUonnnnuOmpoavF7vDZ9fKBQ2JKW2t7ejVCqpra0FYG5ujrNnz3L33Xdz/PhxGhoatnQY/uQnP8FoNOJ0OonH45JbUEZGRkbm9rC4uIjVamVhYQGLxXK7lyMjIyNz2yiV4C1vgR/9CA4dguefh1fw128Zmetmp/6tf8U74rxeL+985zu577778Pv9fPOb32Tfvn381m/9lnTMrl27UKvVPPHEEzz66KOv+F5KCoWCI0eO8Oyzz+JwOMjlcjgcjms6i7LZLIVCYYPwodFoONQYpW0gKYlw++tjUIJsfop42Mfc0gojk/Pc2VyD02blbKfofNtdGwFBwexSCrNBR9jn4uSaALderHLZzBRLJc53DgJXgg3yhSK7a6KspDOSkHWwUSzjm11cxmY24HfZOdnaS0MsyJ66KJ0D46QyWTQqJVaTka7BcUqlErWRANPzi/SMJDnUVE3Q7ZD61nkcFgr5oiTC7amN0Tk4RiqTZXdNBLVKxam1tNWgx45QQhLh1ot59VE/FpNRcsW5bWZKCBsExK9/+iFctpf/g+b1ehkdHd0xIa4cHDI3Nyf1HbuafD6/oQT0xUQ2QHKvXd17rfx8dnaW2dlZDh48uCM/ax6Ph87OTubm5q5Zen4zEQSBUCjE6OjotoS48j3J5/MolUoKhcIG56vNZsNoNDI2NvaifzQoFAoEg0F6enqorKzctntWRkZGRkZGRkZG5mbyd38ninAajViSKotwMjI3xiteiHv3u9/Ngw8+iFarZXBwkJ/97Gd8/vOf33RcuQTu5ZR73krOnz/Pl7/8ZZLJJMlkkv7+fn74wx/yh3/4h/y7f/fvePTRRzc4iKxW6yYxRKVSbRA+jo4u8H9faJdSUs90DLCvLiaGEnQPk83n8TqtKBSCJMKVyza9TitBt41MNkd7/ygKQcBuMdLaN0IuX5BKOFv7RmioDGHUa6Vgg5oKP0aDlgvdgygVAvvqqzjV1ktTVZjKkJeV1TQdA2MAWEwGLvcOk8pk8TmtFAt5zncNsq++EqVC4HyXKPqZ9FoMWg0/PSv2jiv3bTvd3kfAbafC55LKTV02M3azUTq2uTrM4PgUL1zuwWLU01AZko61GHSY9BpJhKuNBpiaXeTEpW48dgtvvmsvj//mO1Grbs6/Hm63m46ODlKpFHq9/qaMuRXFYlFKDrVarfT397OwsPCSIu56IVen02G1Wjc52F7KWWc2mxkaGiKVSu1YCa7f72dsbGxHhDiAYDBIf3//tlJwyyWppVKJYrG4KZBBEAQikQi9vb3XFOLK5zqdToaGhkgkElRUVLzs65KRkZGRkZGRkZF5OSQS8Hu/J24/8QQ0NNzW5cjIvCp5xQtxSqVS+qIajUYZHh7mhz/8Ie9+97spFosMDw/z/PPP86EPfYhHHnnkZZfn3Sr0ej21tbW8/vWvx+fzMTExwaOPPkpvby86nW5bY97RXLMhJVUQQK/T8NwFMWiiLGadbOvFZjYQDXgkgSrgcjA1N8/0wooU7HCytZddNRE0KhUXu4fI5vM4rCa8Dgs/XQtXKDvm1GoldrMRn8smudJ0WjUjEzMsLK9i1GupDvl44XIPu2si5AoFRpIzLK6kUCgE7BYDPz7ZCkCFz0WxWOS5i13oNGp21USkvm0mvZbaSICfnmkDrvR46xwUQyION8elElezUc+e2ijHznUAUB32Mr+0St/oJCC69srCn0at4o9+8z/w7+85uK17fy00Gg12u53JyUkikRsrcy2VSlsmh27lYCu719RqNSqVinQ6jVarlcS1lxJxX+41ulwuEonEtgINtkMwGOTkyZM7JrRrNBq8Xi8jIyPU1dXd8PkKhYJ8Pi+VqV79e8nn89HV1XVNIa5QKACiOzESiTA4OEg4HN7excjIyMjIyMjIyMjcBEol+OAHYX4e9u2Dj33sdq9IRubVySteiCtT/sL6yU9+kk996lN88YtfZPfu3ayurtLb28v73/9+PvGJT1xXL6pnn32WL37xi5w9e5ZEIsH3v/993va2t0n7S6USTz75JN/4xjeYm5vj0KFD/NVf/RWNjY3SMXfffTfHjh3bMO6v/Mqv8N3vfnfLOevq6vj0pz8tPc9kMjz88MOMjIwQ32Zny5qIH5VKyVBiGotRT9jr5LkLXYQ8DgIeB2fXxCy/y07I49hQivnC5R72N1SSy+VAUEquOLfNwo9PXQYgHvYxv7zK+NT8puTRXC6PyaCTnG/lMZurK6QecRd7hgBw2Sw8c6ZVDHow6HFYDJxu60cQBFqqK+gZSbKazqBSKrijpUYS3cJeJ6VSien5RYANZbNajYoju2p5/qIoOkZ8LvLFIsuraQD21VdyuXeYbC6PQoA7W+IcvySKkD6nlb/5w99mb92tSaP0eDwkk0lJiCsWi9eVHJrJZKS0zatdanq9HpvNtqn3Wtl59fzzz+Pz+XYs0CAQCNDV1UVlZeWOlKeaTCbMZvOOOsNCoRDnz58nHo/fcI87hUKBIAiSEHf1+QqFgnA4TG9v74sKcUqlkkAgQHd3N9PT0wQCge1fkIyMjIyMjIyMjMzL4B//Ef73/wa1WixJVb1q1AQZmVcWr5ofnbKj5FOf+hQHDhzg5MmTdHR0EAwGefDBB3nDG96AyWS6rrFWVlbYtWsX733ve3nHO96xaf8XvvAFvvSlL/HUU09RU1PDZz/7WX7xF3+Rrq4uzGazdNxDDz3EZz7zGen5jZQiarVaDh48yPHjx7ctxAmCQGNVGAGBbC5PW/8oSoVAbTTAT06JbrPGqjAjyWmUCoXoUgv7JFecWa9jbilFNl+QXHE9wwkAsby1RxSyioUCzdUVkgi3r76S1r4Rair8zC+tbHDauexmBsYnWV5No1EpaamJ0NY/SrFYoirkZXF5lcHkDAB372/kZ2uim9Nqwmk1s5zKALCrJkLviJioOrOwxB0tcU6sE9L0Wi2FYhEQe951D4li3tJqakNqqsNqwqLXsLKakq7rG5/+LbxO67bueZlSqUQ+n79mmMH8/DzPP/88uVxOFDsR3WtXC2k2m21TcuiNutcEQcDn85FIJHZMiHO5XLS3t+9Y3zYQXXHDw8OEw+EdEf+sVit6vZ5EIkEoFLrh85VKpeSK20psCwQC9Pb2kkqlNrliy0JcWdCrqKhgcHBQFuJkZGRkZGRkZGRuC5OT8Lu/K25/+tPQ0nJ71yMj82rmVSPElb94azQa3vzmN/OmN70JQRDI5/OMjo4yMzODzWa7rrHuv/9+7r///i33lUol/vzP/5xPf/rTvP3tbwfgv/23/4bX6+Xv//7vN4REGAwGfD7ftq/pyJEjHD9+nPe85z3bHuPeA0387Ewbq+ksDosRj8PK3OIKAIeaqjnd3kexWCKTy+GwmLjYLbrUDjfHudQ7TDZfIOCyUiwJtPePIgBHd9fx3IVOAKnnW1n3WC9yWU16qQccwOGmOAOjogjntluwmQxS2urR3bWcbu8nk82hUSmpjQRIZ8TyypoKH7OLK3QPJ/A4LBxuquaFNdGvXP66mhaPbagMMTY5S3JmAY1KuWE9FqOBsMfB4oooupUdfYPJWULuAr9+/1189uFfQaO+9se+7F67VpjBVu61q8U1s9nM8vIyDoeDQCCwwb12q/D5fJw8eZJ8Po9qB/40pVAoJPFvp4S4cjlnOcnmVrM+tCEYDN6w+Fd+v/P5/JbnlsW5sbExKXSjTFm8K59XUVHB+LgYcPJKD6ORkZGRkZGRkZF57fGhD8HMjCjAPfbY7V6NjMyrm1eNEHc1giDwD//wD/zTP/0TyWSSoaEhSqUSX/jCF3jHO96BVqvd1rgDAwMkk0nuu+8+6TWtVsvrX/96jh8/vkGI+853vsO3v/1tvF4v999/P48//vgGx9xLcdddd/HhD394W+sss7smymo6S1XIy9Jqms7BcTx2C/sbKiUHW3N1mIGxSTK5/IbebQCHm6o41zlINl/AYtRT4XORWhPI9tbFaO8fJZ3NEQt4aK6ukM7bFY8wPbfE2OQsWo2KxsqwlHa6vz5G/9gUPSNJBAEONcXJ5QtksjkCbjulYpHLfSPURMSU1nI/Or1WQ9DtIDEzDyBdU8fAGAcaqjjUXM3pNlFYNOq1GPRa2vtHAYgF3KQyOdr6RznUVL0hsEKtUnJvS4xH/+O/Y3lp8UXFtavda+tFNoPBsEl0u5Z7TaVSMTk5uSOCEYilmwaDgcnJyR1zTfn9fs6ePUtdXd0Nl25uB6VSic/nY2xsbMfuq9/vp6enh4WFhesS+q/u8ZdOp8lkMluKsGXXWyKRoLa2dkPvu6sDHrRaLdXV1bIIJyMjIyMjIyMjs+N873vwP/6HmI76rW+JaakyMjLb51UrxH3yk5/kS1/6Em9605t4+9vfTl1dHc8++yxPPvkkq6urfOADH7hmSdiLkUwmAfB6vRte93q9DA0NSc9//dd/nVgshs/no7W1lccee4yLFy/y9NNPX/dcd955J/39/SSTyW0762ojfl63t56Trb1ksjncNjNWk4HJWbGv2uGmak629VEqlbizpYaRiRmpd9vh5rjoAMsXqPA6yReKtPaNcKChksNNcUlYq/A5yeRyV5xv61xotZEA2Xyec2uprAcaq4ESs4vLkrD3wuUeWuIVNFWFGU5Os7iSQhDAZjLQNZQgm88TcNvRqFSc7xrkcHM1bpuFy30jkoNOo1ZJ/eCCHgcKQUxc3VMbpVgs0jk0TiYr9oPLZ7Oc7RbXajXq+N233kE84OD8+fPodLpN4prdbt8grt0M95rH46G7u5tsNotmh/6l8vl8JJPJHRPiLBYLOp2OyclJ/H7/jswZCoU4c+YMNTU1O+L8UyqV+P1+hoaGUKlUm3r6XS3mlt1v6z9PLpdry+TVsthmsVgYHR0lFrvSszCfz2/63SWLcDIyMjIyMjIyMjvNzIwY0ADwiU/A3r23dz0yMq8FXpVCXFtbG//v//0//vzP/5yHH35Yev1Nb3oTGo2Gp556ig984AMva46rv/ReXRL20EMPSdtNTU3E43H279/PuXPn2Hudv51sNhvNzc288MILG8IibgSFQoFBpyGTzVFT4WdmYYmekSSHm+O47WapxLMlXoEgCIxMzGxwxe2KR4iHXIxMLpDO5jDptSiUyg3Ot56RJMPJGcJeB06bRdrXUBnCpNdyqq0PpUJgX30Vp9p62VsXI+p3k83lae0bAcCg03Kha5B0NofNZMCsV3OqrY999TEyuTzDiSsCnV6j4aeXxZRWp9WISa/j+YtdBJxWDFo1Y9MLpLKic62YTXNxQBRPTXotHpuJs93D+J1WHFYTf/nRdxH2uZmammJ6epr9+/dv6z7fKDqdDovFwuTk5Lb6i20Hn89HX1/fjol/giAQCAQYHx/fMSHOYrFgMBhIJpMv+76uL0N+MXEtk8lQKpWYmZnZIOJqtVrMZvMm56Rard7wuyKfz1MqlaRS5jLlPxREIhE6OzuJRCLS/qsdcTIyMjIyMjIyMjK3g498ROwPV18Pf/RHt3s1MjKvDV5VQlxZDEskEszOzvLggw9SKpXI5XIIgoBaraaqqorvf//7ANv6Ilt2piWTyQ3iwuTk5CaX3Hr27t2LWq2mp6fnuoU4uNInbrtCHMAdLTXMzC9LiaIeuxWtWnWl/LQ5zsnWXqpDXslNVnbFGfU6LvcNUyyWpJTSk5d7CHochL1OaQy/y07E5+a5NVfaoaZqzrT3s6c2KvWmO9Umin5KhYKp+UVWUhm0ahVNayWtDbEgq+kMK6kMI1MLAKgocb5vlGKphEGrxmU1cuxcO1q1Co/NyPxKmqEFMdwhFnBxvLWPUgnMBh0Rv4v+5AwKhUAs4GF5NU1/Qjz2HW84zO//x7eg04jlfgqFgt7eXnK53IYSwFuJ1+vdUSFOr9djsViYmJggHA7vyJw+n4/e3l7S6fSmwIFbRTAYZHx8fMv7eq0QjRcrQy67INcLaiaTaYO41t7ejtPp3OBau14UCoUU2nC1EKdQKPB4PHR2djI5OSn9/tmOm1dGRkZGRkZGRkbmZvLDH8K3vw0KhViSus3uTzIyMlfxqhLiyi6Tffv2MTU1RSKRkFInAUZGRvjCF77Am970pm03rS+Xmz799NPs2bMHgGw2y7Fjx/iTP/mTa57X1tZGLpe7YWfQXXfdxZ/+6Z/e8DrXc2dzLY//9f8EoC4aZGJ2nks9w+g0ahoqQ5KYptdrWVpNMzYzj0GnoaYiwPFLXVQFPRQLeSYXlllJZdBrNdRW+HlmLdG0nLyaymZRq5TsrolK/efS2SxKhYLOwXEAdsfDnG7vI+yxo1Ep0GvUnO0QAxtUpTxjU7Pk8kVUSgUxn4PO4QmKpRJhj4NcocDw5Lx4X/bU87OzbeQLRek6ZhZTlEpiqWyhWKK1T+wPd8/+Bo5f6iGTzaFUKHjyt9/Jb/zS3RtcSXq9HqPRyMzMzMsK2LgRPB4PPT09Oyr++f1+ksnkjglxOp0Oh8NBIpHYlkh1vZRKJUlAU6vVLC4u0tkpBopcLbRdK0Tj5ZQhh8Nhuru7iUaj2wptEARBEtfK861/HolEGBoakoU4GRkZGRkZGRmZVwTz81Buj/7II3Do0G1djozMa4pXlRAH4hdyu93OW9/6Vt72trfx3ve+F7/fz/PPP8/3v/99Kioq+L3f+z1UKtU1BZDl5WV6e3ul5wMDA1y4cAGHw0FFRQUf+chH+NznPkc8Hicej/O5z30Og8HAgw8+CEBfXx/f+c53eNOb3oTL5aK9vZ2PfvSj7NmzhyNHjtzQ9Rw9epR3vetdLC8vYzKZtnVP6mIBrCYDtRE/5zoHyBeKeBwWooGg1Lut7IpriVegVAiolEoudA8CEAt5+cnJy5SAgNuOWqVkZmEJgL21ES50D1MslUhMzuJ3WDjd3gdAdcBB5+A4TosBrVpJ2GPnQo9Yihpw2egcSjI+s4gAHGisYnJ2kVxeXJtBq6FnbBqA1++t51RbH6lMFpVSwZ66GAvLq+QLRfwuOzqNmnOdAwiCOE5b3wir6awUBLGayZHJ5nBYTXz9Uw9x567aLe+T2y2WqO6UEKfX6zGbzUxNTe1Y3zav10tXV9eOOtT8fj8DAwPbEqmuDjd4MRcbiCEaGo0GtVrNzMwMbrdbEuPXO9quFaKxXdxuN11dXUxPT+N2u2/4fKVSSS6X21Ceur78NBQK0dvby+LiIhaLZUshTu4RJyMjIyMjIyMjs1N89KMwPg7xOHzmM7d7NTIyry1edUJcma985St861vf4m/+5m9YWFigvr6ej33sYzz00EPY7XbGx8evKX6cOXOGe+65R3r+yCOPAPCe97yHp556io9//OOkUikefvhh5ubmOHToEP/6r/8qJaJqNBp+8pOf8Bd/8RcsLy8TDod585vfzOOPP37DLpZwOEwwGOTUqVPce++927oXCoWCt75+P3/3/z0LQH0sSHJ6Ho1ahVajoqkyLLniLAYdfaMTLK+m0apVVAZcDIwkKAERj42phWVWMzlm9RriQSfnusQS1ojXwcJKGrNBiwDsrYtytnMQgNpYmMHxKXrXhLVDTdUsr6ZZWElhNuqJ+t2cahPFu/31lfSNTjA4Kx57oKGSTC5PKpPFYTXhtlk43daHy2aWwh0S03Nr48bJ5fOsprMbgiAqfE6aqsJ8849+m5DXec375Ha7OXfu3KZeXbcSj8fDxMTEjglxGo0Gh8NBMpkkGo3uyJwej4eOjg4WFxexWq1SufhLlYZmMhkKhcKmcAOtViv12Lu6/1r5fZubm+PChQtUVVXtiHNMoVAQCoUYGRnZlhBXdsXl83kUCgUKhWKD2KZWqwkEAgwODtLS0iI74mRkZGRkZGRkZG4b//Iv8Ld/C4IgPur1t3tFMjKvLV51QpwgCJRKJTweDx/72Mf4+Mc/vskp8sILL/C5z32OBx98kJ6eHv7wD/9ww/67776bUqn0onM88cQTPPHEE1vuD4fDHDt27GVfS5mjR49y4sSJbQlxhUKBbDaL3yGKhLviYVr7xigUiywuLeI0Gzi75oqrDbnoHRpjeTWNw2xAp1XTMZREpVTQXOmndSBJqVQSk1fNRsq36EBDldR/rrEqhNVskkS4luoKVlJphpPToputNsbJ1l4UgkBNxM/yalpKWz3UVE2xVGJuaQWTXovfaeF0ez+10QA1FT5mF1foGhJLXKtCPqbmFlhcSWHUa4mHxXCJ/fWVxAIeMtmcFASxr66SL3zkP2LQvXjTAovFgkKhYH5+HofDccP3ejt4vV76+vp2tDzV5/MxPDx804W4YrF4TXFNpVJx/vx5FAoF2WyWUqmEUqncJKRdT7jB9WCz2dBqtTua2BoMBunv72d1dRWDwXDD5yuVSgqFgvS752qxLRKJcPz4cWpra2UhTkZGRkZGRkZG5rawuAjlXMIPfQiOHr2965GReS3yqhPi4EqJVtkdk0gk6Ovro62tjY6ODnp7e/nRj37E+Pg4giDw8MMP43Re2yl1uzl69Cj/83/+T+n59TqKstks+XweQRCwqvPUhT1cXCsNrQq4WFjNMj6ziEalpLEqxPk1d9veuhh9oxOMTy+gUSlpqYmwtJKiVCpRGwkwNbdI70iSw01xnDaT1A8u6ndTKiEJYOVyV7VSgd9pw2LSS2WrLTUVCAh0DyU29JXbUxuTQiF6RqcAMOt1DIxPMrOwLIU7nGzt4XBznEwuj0IQuNAtrl2hEJiYXWA1nUGhEPiD97+D33z7G65LyBEEQSpP3SkhzmAwYDQamZ6e3jHByO1209HRwcrKCkaj8UWPvTrc4FrOtWw2uyHc4Or0UI/Hw9jYGC0tLeh0OrRa7S0VksqJrWNjYzt2X8vXOTo6Sk1NzQ2fX/6MlkW2q8U2k8mE3W5nZGREFuJkZGRkZGRkZGRuC5/4BIyMQCwGn/vc7V6NjMxrk1elELeeb3zjG/zoRz9iamqKfD6PyWQiGo3i9Xq57777eOyxx15SjNhpCoUCP/jBD0gmkyQSCdrb2zl27Bi7d+9mYWGBr3/96wiCgEql2iR6WCyWTa+p1WqKxRJ/8o9iaerBpmrOtvdTKBZpiVewkspIItyhpmqyuTwLy6u47RasJgNn2vs53FxNTchF3+gE+UIBm9mAVqPi2LkOAPbURukaGic5M4/ZoCNe4ZfKXaMBN0aDnvPr+tG9cLmHg43VuGxmHBaTJNApFALzy6ssraRQCALxoJMzHf3srYuhVCowG/RSuINSIbCwvMryanrDuLURPxOzi/yXxz7A6/bW39C9L/f6qqmp2bGeW16vl4mJiR0TjNRqNU6nk9HRUfx+/0sKbFuFG2i12k3hBuXP2lZlvaVSienpaXK53I6JnIFAgN7e3usSHG8WoVCIS5cubasktlySWiwWKRaLm1JUAaLRKK2trXg8nk1hM3KPOBkZGRkZGRkZmVvJM8/AX/+1uP3Nb8Ir7Gu0jMxrhle1EPdP//RP/Kf/9J84fPgwv/7rv05TUxP19fW43W7e97730d3djdlslvoyvVJQKBR88YtfxO124/f7aWxs5P/+3//LO97xDu644w4OHz6MXq+/oS/6SqXAHS01zC4uc2rNwfb/t3ff4VGV6f/H35NJZtLLJJPJTBotCSGh9yaiFLHsKnxF1FWaLq5YWOwiGuygsrh8EeS7tBVF3J9iWd1VXAUWKUrohpJASAKkExLSM5nz+2OYMb1nAuF+XZeXM2fOnPOcJAeYT+7nuXuGm/Bwc+VwYmq1qrQhMT2I6mIi+4K18k3tpMJNq+XkWeu6bd1CDBQVl7LnSCIaF2cGRHVlz1Fr6Bbk70NYUMBva7716s7hkykM6NnlcifW3wK68kozCnAyNR2oEaQ5qTDpdSSctnY+9XBzpaKiklNnM+377j2ahLurFi93V7oGB9qPGxlmYl3cw4Qbm79Wl06no6ysjKKiohY3x2iuwMBAkpOTW9zJtypbc4PGGhzYmhukp6fXamTQHs0NVCoVRqOR8+fPYzAYWnWNTaXRaAgMDOT8+fNEREQ45Jy2YLKl6/45OTlhNpuprKys1qzBJiAgALVaTWFhocMCTSGEEEIIIYqK4IEHrI8fegiqLKkuhGhjV2UQZ5u2lZmZSZcuXapN67T5n//5H/71r38BNBh+7Nixg7feeov4+HjS09PZsmULt99+u/11RVFYtGgRq1evtjduWLFiBTExMfZ9ysrKePLJJ9m0aRMlJSXceOONvPfee4SEhNR5TpVKxZ49e6pt279/P35+fowbN645X4pqhvWOYNHq/wfA4JgeHDh+mt6uYbWq0rQuak6fzaTCXImflwdBAb5sj0/AVeNCuMGPM5kXKCuvwMNNy4Corvz34HEAenUL4Xz2BWtlj0rF4Jju9mmrJWXlBPh626eQ2taV0/t546bV0LOLyR6kGfx9uXip2B7C9e5m5GTKefIuFeGqcSG6a4h9376R4Zw+l8XhROs6c7eOHsDSJ+5vdD24+qjVavz9/cnOznZYEOfp6Ymbmxs5OTl1dmxtbCpy1ce25gZVq9c0Gg2urq74+PjYtzk7O7Nnzx4GDBiAt7e3Q67TZDKRnJxMeXk5Go3GIecMDg7m6NGjdO/e3SFhu0qlIjQ0lLS0tBYHcSqVisrKSsxmc52dUcPDw0lKSmpRUwghhBBCCCFa4vnnITkZwsJgyZKOHo0QndtVGcTZPnAPGjSIefPmAVTrRghw0003MXr06EaPVVRURN++fZk5cyZTpkyp9fqSJUtYunQp69evJzIykldffZXx48dz4sQJexfVefPm8dVXX/Hxxx/j7+/PE088wa233kp8fHyTq9pGjx7N7t27eeSRR5q0f12GxkagdlIxMLo7P/9qDcjMlZWAtSrN9tp/D55A5+2Jn7cHl4pLOZZ87vL7u7N9vzV0Cw7U4aRSUXH5/UNje7Dv8nTXgqJienYNtodwfXqEcfpcFs7O6lrn7x5iIC0jlwMnzgAwMLobOXmXyLyQj4uzmphuwRw8aQ3ZBvTsyoX8Qg6csE5x7RfZhQpzJeeyLqBSqXh2xu+ZO3Viq6fo6fV6zp07R9euXVt1nMbYGmmUlZXh4eHBmTNnKCwsrHOtP0VRGp2KbNve1OYGBoOBjIwMhwVxbm5u+Pj4kJGRQVhYmEPOqdPpcHJyIicnh8DAQIec02g0kpiYSH5+Pj4+Ps1+v1qtpqKiArPZjFZbO1AODg7m+PHjlJWVVdsuU1OFEEIIIUR72LkTli+3Pl69Gi5/zBVCtJOrMoizfSAdMmQIf//73+uswHFycrIHZQ2ZNGkSkyZNqvM1RVFYtmwZCxYsYPLkyQBs2LABg8HARx99xJw5c8jPz2fNmjV88MEH9mq2jRs3Ehoayvfff8/EiRObdE2jRo1i2bJlKIrS4g/cMd1D6BsZbg/BbFVpIYH+VFZaCArwtb/Wv2cXdh48QVl5BV7urnQxBVJabgagV1cTqZkXKCwuxU2rYXCv3yrfugYHkldQVG3dtr1Hk1AUhRF9Ism7VGQ/R2yPUMrKzZxJz0algqExEfYprl2MejQuzvYQLrqrCVetC2fSs+3H3XMkEZ23Bz6e7vzvM7O4YXBsi74uNQUEBHDs2DHKysrqDEIaYmtu0FgjjbKyMsxm69fTNvWzuLgYT09PXF1d8fT0rNU9tK0X5w8KCiIhIYGIiAiHhTgmk4m0tDSHBXEqlYrg4GDOnj3rsCDO2dkZo9HI2bNnWxTE2arizGZznd1XbYHsxYsX22C0QgghhBBC1K+kBGbNAkWx/r+JH1+FEK1wVQZxVU2bNq3djp2cnExGRgYTJkywb9NqtYwZM4Zdu3YxZ84c4uPjqaioqLaPyWQiNjaWXbt2NTmIGzRoEHl5eZw6dYoePXq0aLzOajW+Xp61po12MQbwa2kZx5LPXQ7EelBQVEJZeQVhQf5UWhSOJKXSxaSndzcTR5PPoyig9/VCpYJjZ6wVcwN6diXh9FlKyysY1KsbFotin0LaPcSAk5OqSnVdBL8kJOGqcUHn7YlJ72cP4SLDjPh6u/PzUetU2ZiuQRw7k05UF1OtdeYCfL1Z8+JDdAtpu3XHbJVmOTk5BAcHA2CxWOxrqzWnuUHVIM3T07NWRZtGo0GlUqEoCrt27SIwMNBhgZFOp8NisZCXl+ew9cYMBgPHjx/n0qVLTQrC24LJZOL06dOUlpbi6urqkHOGhoayd+9eIiMjcXFxafb7bUFcfQGpRqOhoKDAfk1SDSeEEEIIIdrDiy9CYiKYTPDOOx09GiGuDVd9ENeeMjIyAGotPm8wGEhJSbHvo9Fo8PPzq7WP7f1N4erqyuDBg9m1a1eLgziAMQOjyci9aA/hBkZ341JxKVkX8vHycCM8KIA9R5MI1vvRLzKck6kZFJeWodU44+/tycnUdBTFGpbl5l/iREo6/SLDcdVq7OFYkL8Pnm6ubItPuHyOrhw9dZaCwmK0Gmdiu4ey93LoFujng87Hk/2XO6raqvT6eoajdXEmtkco8cesrxWVlBIS6G9fZ+6mEX1598mZeLq3PFypWb1m+7/FYiExMZHU1FTKysqoqKgArB1H6+oeWnObWq1uVjiiUqkIDAwkMzPTYUGck5OT/efQUUGcs7MzgYGBpKenOyyIc3V1xd/fn3PnztG9e3eHnNPT0xNvb2/Onz9PeHi4fXt9zTRqPrf9vNXXSVdRFLy9vUlNTSUyMtIh1ySEEEIIIa4te/fC0qXWx++/D76+HTocIa4ZnSaIUxQFoM5OhK1VM3BpyvTRlkwxHTlyJLt37+b+++9v9hhtBvTsxkur/mGvfNtzNAlvDze6GPWUm80cPZUGQIjBn5T0bIpLyzD4++DhqiX+eDJ9I8MpLiwk+XwWFeZKvNxd8ffx4j+/HAWsDRvOZV0gMS3deo7YCHtAp/J0I6bbb8Fa/6gunEg5T1CAr3XtuF7d7R1d8y8VEWLwt+/bxeBLTn4REWHW8ObJ+27j8bsn1bkAf9XmBo01OKivuYGPjw+FhYX07NkTNzc3+/b2XPDfYDCwb98+e0WdIwQFBXHo0CF69uzpsHOaTCaOHj1Kjx49HHbO4OBgTpw4Qbdu3dq8eqzqdOSqP1tqtZpTp06RlZXVYDMNrVaLm5sbvr6+1bbbpqjW9fNQWVlJcHAwp0+fpnv37q3utiuEEEIIIURVZWUwcyZYLPCHP8Ctt3b0iIS4dnSaT3e2D99VQ7isrCwKCgoICwtrURdHW4fLjIyMapUrWVlZ9iq5oKAgysvLycvLq1YVl5WVxYgRI5p1vtGjRzN//vxmj7Oq3j3CCPL3IcDXmz2XQ69uIYEUlZRxJj0bN62GqC4m9h5NYlCvbuh8vDiffYHM3HzUTioCfb3ZerkizTZtNTHNWtk3NDaCfQmnqLRYsFgsDIrubg/henUNJj3nIi7O1q//sNjf1oMrKCwhIsxoD+Fs+/p6eVj37R3B3iOJKIDWxZnlT97PyN7dOXfuXJ3hWtXmBjUDNm9v71rb6mpuoCgKubm5qNXqFq3z1RJeXl64uLiQm5vrsI6Yvr6+qNVqh55Tp9OhUqkcek7bun+5ubkEBAQ06T2KotRbtbfyNB4AAGIXSURBVFbXdGS1Wl2rUvLixYt4e3sTEBBgf62pzTQsFgtms5nKyspaQZzFYrEHd+fPn3fYmntCCCGEEOLa8PLLcOwYGAywbFlHj0aIa0unCeLOnz/P//3f/3Hs2DGmT5/OpEmTSEtLY926dUyYMIHf/e53VFZWNqtarmvXrgQFBbF161b69+8PQHl5Odu3b2fx4sUADBw4EBcXF7Zu3crUqVMBSE9P5+jRoyxpZt/nkSNHkpSURFZWVounL7o4q+kf1ZV/7ToI/Nb0YGhsD0x6PzTOzhy83MHUx9OdA8fPUGmx4OflQVCAL8fOnAcgKsxAWtZFikvLcFE7MbxPJLsPnwSgi0lP+eXGDlC9o2ppeQV9I8LtIVyPUAM5FwsouzwVr39kGIeT0qi0KIQG+hAZov9tyqufJ78b1BV/lwqSk5OrBWpeXl61uoe2pvJRpVKh1+vJzs52WFikUqkwGAxkZWU5/JwZGRkOPafRaCQ9Pd1h53RycsJkMnHu3Dn8/PzqrZKsGbaBdTpy1Z+tqtORq/4M1lWV5uzsTGFhIf7+/i0as0qlsv+5VDWMM5vNODs7Ex4eTkpKigRxQgghhBCizcTHw+WPs7z3HrTgn7JCiFboFEFcQUEBc+bM4eeffyY2NpYFCxaQlZXF9OnT2bhxI6tXr+Z3v/tdne8tLCwkKSnJ/jw5OZmDBw+i0+kICwtj3rx5vP7660RERBAREcHrr7+Ou7s799xzDwA+Pj7Mnj2bJ554An9/f3Q6HU8++SS9e/e2d1FtKj8/P2JiYtizZ0+9422KflFd2L4/gcgwkz3kcnF2prC4lIKiElyc1fSL7MKRpFQqLRa6hxgoLC61N1oY0qsbPyecBkDv542vpzsVlzuA2qabFpeWE+jnRf/IcPt6dEE6b3IvFpCRmw9ARHAAyeezMVdaiAj2x+DryYHLXVL1vp44q505edYa/I3o3YPZ4/sxYdwNDpvOqNfrOXLkSKs61TZXYGAgBw4cIDo62qHTU/ft29fsILo1jEYje/fupaKiokXNDGqyTQ9taBpySUkJJSUl/PDDD6hUKjQaTa2AzVYxWXOKaEuFhISwc+dOSkpKcHNza/b71Wo1ZrO52vRURVHsFXgmk4mTJ09y4cIFe4WuEEIIIYQQLVVebu2OWlkJU6fC5MkdPSIhrj1XdRBnC1B+/fVXfv75Z/7xj3/Qt29f/vrXv7Jy5UqmT5/O1KlTueOOOwDqDCH27dvH2LFj7c9tU0OnT5/O+vXrefrppykpKeHhhx8mLy+PoUOH8t1331VbiP4vf/kLzs7OTJ06lZKSEm688UbWr1/fotBj5MiR7Nq1q1VB3Ig+kXz0r50cPHkGsFbFHTmVyqXiUgJ8vdB5e/JLgrVj6ah+UfyScJqy8grcXTVEhBkpN1cC0M2k50JBIYlpGUSHG4jpEsSBy9V03m5aLhYUcCbzIgA9ggM4n5NPcVkFkaEGPN207L8cugX4ehEcZLA3d4jtHkpqRg6HT53F3VXLg3fcyJ/vmcSOHTsoKSnBw8OjxdfeHL6+vlgsFgoKChw2PdXHxwe1Wk1eXl6LqqhawsvLC1dXV7Kzsx0W5nh6euLp6UlmZiYhISH17tfQen81n1ftVls1YPPw8ECn06HVajl16hT+/v50797dIeGqq6srer2ec+fOtajJii18s1XAgfVroigKarUatVpNaGgoZ86ckSBOCCGEEEK02htvwOHDEBAAy5d39GiEuDZd1UGc7YN2VFQUBQUF9OnTBx8fH+68807efvttFEUhMzMTb29vcnJy6lw76vrrr7c3eqjvHHFxccTFxdW7j6urK8uXL2d5G/xJNnr0aJa1cpJ+74hwsvIKLnclDbNXxQ3p1Y2ks5mcTE1HBfTuFsyFvHzKyivw93ZH7aTi0MkUIkz+RAT7czo9l0qLBTeNC1oXFw4mnQWgR4iBCwVFnMm8SLfgQAL9fOxTUb093Ak2BPDjvl8BiAo3kXOxwN4kYljvCH7+NQmLRcHdVcuKZ2czYVgfwFoRmJOT47AgzsnJiYCAALKzsx0WxFXtnuqoIK7q9FRHhTkWiwW9Xk9qaioajabephr1rfen1Wrx8fGpc3poQwGb2Wzm1KlTDuueCtaquCNHjtCtW7cWVdc5OTlhsVjsFYuVldYg3Bbkh4WFkZCQ4NAmH0IIIYQQovM5fBhefdX6ePlyaOFqSEKIVrqqgziwVo/odDomTJjAihUrmDZtGj179iQwMJC3336bzz//nNtvvx2dTtfRQ22SUaNGMWPGDIqKihoNpCorK+tdYH5gZAinz+cQf8w6xTTC5E/BpUtcKCjCXeuCMcCHw6fPEdstmJ5hRs7m5FFYXIraSYUhMICdB08AEBLoj0oFB5POEmrwxxjgy4ETZ6gwV+Km1dDVFGjvqNrVpKekrILUjBwABsf04MDx05grLWhdnBnVL8p+3HBjAGtf+hM9uwTbr8ff35/c3FzCw8Pb/OtaH71eT3JycouqmVrKYDA4vJNpUFAQycnJrZ4qavuZa6xjbcXlNQEBEhMT7Z1ptVotnp6etQK2tpoyGxgYyPHjx8nLy3PYPa/T6XBxcSEzM7NaU5emaiyIc3V1ZfDgwRLCCSFEDe+99x5vvfUW6enpxMTEsGzZMkaPHl3nvp999hkrV67k4MGDlJWVERMTQ1xcHBMnTnTwqIUQomOYzdYuqWYz3H473HVXR49IiGvXVR/E2apj/u///o9bbrmFtWvX4ubmxqlTp3jzzTcZMWIEs2fPvmo+xIaGhhIYGMiHH35IYGAgBoMBo9FYZyWR2Wyudy2smG6h7DxyGieVigHRXdmXcJq+EeF0MeopN5s5dc4alvl5e/LT4ZNYLAp+Xh4Y/H3YefAERn8f3DRqMi8WUlRShtpJRVQXE9/vPQJgb/yQecG6HtyAnl1JOH2W0vIKXNROjOgTya7LzR0M/j54urlScXnK65gBvVjx3Gz8vKoHjQEBASQlJTl0LTN/f3+OHj1KcXEx7u7uDjmnr68vKpWKixcvOiws8vDwwNPTk6ysLIKDg6u9Zlt/rb6ArerzyspKVCpVreo1Nzc3e5dP22sajYYjR47g4eHhsKDTyckJo9HI+fPnHfa1ValUhISEcPbs2RYHcbYwzhbI2Ro5CCGEqNvmzZuZN28e7733HiNHjuT9999n0qRJJCQk1NngZseOHYwfP57XX38dX19f1q1bx2233cbevXvtDbmEEKIze+st2L8f/PysDRrkn5pCdByV0tC8zKuAbbrWpk2buPfee7n55pvp2rUrQ4cOpU+fPvTp06ejh9iod955h08++YSMjAwyMjIoLy/H2dmZwMBApk+fzu9+97taHUNtj11cXOr8wP7Lr0nMiHsPY4CfvQnDoF7dOZ58jsKS0svTVkOJP5ZMqMEfrcaZgsISsvIKALhxSCw//HwUBewdVbUaZw6eSCG2Ryip6TkUFJWgdXFmQHRXdh+2Tk3V+Xii9/XGw03L/uPJ9OoazPmcPC5eKia2RyhjBvTimem/R62uHYwqisLOnTuJjo6ucxpxe4mPj0ev1zu0M+WxY8cAiI6ObrdzWCwWysvL7SFaeno6BQUF+Pv71wrbbGuS1fz5qhnwajSaen/m6pKVlcWJEycYNWqUw4KlwsJC9u7dy3XXXdcmjSKaoqKigh07djBkyJBq60c2lcVisa8TV1JSwi+//MKNN95of12tVtfZtVUIIa5VQ4cOZcCAAaxcudK+LTo6mttvv5033nijSceIiYnhrrvu4sUXX2zS/rY1ZfPz8/H29m7RuIUQoiMkJED//tZGDRs2wP33d/SIhLgyOerv+qv+k52t0u2OO+5g//79BAUF2StzbB/8Dx06RFxcHH//+9+b/SH50qVLLFy4kC1btpCVlUX//v159913GTx4MAAzZsxgw4YN1d4zdOhQ9uzZ0+RzDB48mNDQUIxGI0ajkS+//JJvvvmGL7/8slljrapvZBd8PD3sIdyw3hHsOZJI9xAD7sUavNzdiD+WDFjXcdtx4BjlFWZcNS5Edw0mr6AQBQg3+FNSYeZY8jm83F0ZFtuDnxNOYbEoeLm70sUUSEFRKQARoUFcLCzmRMp5BkV3Y2hsD/YlnKbSYsFV68Kfpozn9rFD6h2zSqUiICCg3vX82oteryc7O9uhQVxgYCBHjx6lZ8+ezQ6oKisr650SWrNqEsDFxcW+vlpJSQkqlQo/P786119rawEBASQkJDh0qqinpydeXl6kp6c77Hvq4uKC0WgkLS2NXr16Nfv9tgq4yspKKioqalWESnWcEEL8pry8nPj4eJ599tlq2ydMmMCuXbuadAyLxcKlS5ca/LvJ9veqTUFBQcsGLIQQHaiy0toltbwcbr4Z7ruvo0ckhLjqgzgbV1dX+vXrV21bUVERn332Ge+99x4qlYrc3NxmB3EPPPAAR48e5YMPPsBkMrFx40bGjRtHQkKCfYrfTTfdxLp16+zv0Wg0zTrHddddV+35uHHjeOmll6p1UmwujYszIQYd2Xn5RIYZ7Q0bQoP8OXQihVNnM1GpYEhMD3LzCymvMGMM8EOrcebAiTP4erkT29XEibRMKsyVaF2ciQw3kZtfiMWiEBbkT6VF4UhSKsN6RzCoVzcOn0yl3GzGxVmNq8aFnYds68zpWBv3MDHd6u+eaePv709iYmKLrrml9Ho9J0+ebPX6ac3h5+eHoihcvHjR/thsNjcYqtm21Tcl2dXVFW9v71oBW9Vp2fv27cPd3d1hAZWTkxNBQUEOnSoKEBwcTGpqKqGhoQ4LsUJCQti3bx8REREt+jlSq9WYzWbMZrPDpmYLIcTVKCcnh8rKSgwGQ7XttsZETfHOO+9QVFTE1KlT693njTfeYNGiRa0aqxBCdLRly2DvXvD2hvfflympQlwJOk0QV9PZs2d55ZVX+M9//sOtt97KvHnz6NKlS7OOUVJSwqeffsoXX3xhD8vi4uL4/PPPWblyJa9ebjmj1WrbtBtlTEwMarWaI0eOtGrdkhsHxZKWkcvBkykADI3twcVLxeRdKsLbw51Qg469R5OsoVv3UFIzckjPKUGlslbJpefkUWGuJFDnfbmC7jTDekfg4a7lZEo6xaXlOKlUuGpd2LbPGp7pfb3w9fJg56EThBr8CTMGsOq5B9H5eDZpzDqdjpKSEoeu2ebm5oaHhwc5OTktWuOrIYqiUFFRUWe4ZvseOzk5UVZWZp9mXXNKqKenZ63pohqNpkUBky0Uc2T1n8lkYt++fa0KlpsrKCiIEydO2EuLHcHb2xtPT88WV+LZAtPy8nIJ4oQQoglq/j2oKEqT/m7ctGkTcXFxfPHFFwQ20DLwueeeY/78+fbnBQUFhIaGtnzAQgjhYCdPwgsvWB+/8w6ENF4XIYRwgE4XxGVkZLBq1SqWLVtGt27deOedd/j973/fomOZzWYqKytxdXWttt3NzY2dO3fan2/bto3AwEB8fX0ZM2YMr732WoP/sGuMWq1m+PDh7N69u1VBXN+oLqT+7VNcnNX0i+zC3qNJeLpp6RYcSElZOb+ePgtAVHgwWXn5FBSV4O3hRlhQAHuPJjE0tgeK2cyl0gpOXcgEwN1VY6+u8/Vyxxjgx7Z9CXh7uGHU+5F9oYDENOtvo++eOJK5d03EuRmhgrOzM76+vuTm5josiIPfpqc2NYizrb/WWIOD8vJyFEXB2dm5VsCm0+nIysoiJiYGV1dXNBoNzs7O7VrBZesqWlJSgpubW7udpyovLy/c3NzIysrCZDI55JxqtZqgoCDOnTvnsCAOrFVxZ86caXElnpOTk71ZgxBCiLoFBASgVqtrVb9lZWXVqpKrafPmzcyePZt//OMfjBs3rsF9bX9fCyHE1chigdmzobQUxo2zPhZCXBk6VRBXWFjIggULSE1NZdWqVUybNq1Vx/Py8mL48OG88sorREdHYzAY2LRpE3v37iUiIgKASZMmceeddxIeHk5ycjILFy7khhtuID4+vlX/eBs9ejS7d+/m4YcfbvEx+kd1waT3w9PNlV8STgEQEWaiuLSU0+cuXm7YEMbeo4kMje0BCpSWl3P0VBoAHq6unLtQgMWi4O6qITLMyM4Dx9G4OBNq8KewuPS3RhAx3dkRn4C50oKrxoUlj/+BKTcObdG4bevEOfK3znq9nv3791NeXk5FRUWdU0KrPq+oqACwB2s1K9hqNjioq8LJYrGQlZWFs7MzHh4etV5vDxqNBn9/fzIyMujatatDzqlSqTAajaSnpzssiAPr9NT4+HgiIyMdVolnMBg4efJks9bEq9q1trS0lPz8/FpBnKwRJ4QQv9FoNAwcOJCtW7dyxx132Ldv3bq1wV++btq0iVmzZrFp0yZuueUWRwxVCCE6zIoVsHMneHjA//2fTEkV4krSqYI4T09P+1ptFRUVHD58mK5du2I2mwkJCWHYsGH2NbSaOn3hgw8+YNasWQQHB6NWqxkwYAD33HMP+/fvB+Cuu+6y7xsbG8ugQYMIDw/n66+/ZvLkyS2+llGjRrF8+fImj7MuWo0LUeEmftz3K/Bbw4ZhsREE+vng5eFK/LHTALhqXEjPyaO0vOK3CrpfE1EBgb6euLq62qe4jh0Uw67DJykrr8BZ7cSAnl0pLCrBXGnBGODHmhcfom9keIuv3d/fn9OnT9unaraWbXpoYw0OzGYz27dvR6VS1Vpnzc3Nzd4EpOr00NaMz8nJCb1eT2ZmpkOrtoKCgjhz5ozDgjgAo9FIUlKSQyvxvL29cXd3JzMz076eY3tTq9UEBweTlpaGn59frWnJ9f3sWSwWe9daFxcXDAZDm/38CyFEZzR//nzuu+8+Bg0axPDhw1m9ejWpqak89NBDgHVa6blz5/j73/8OWEO4+++/n3fffZdhw4bZq+nc3Nwc+newEEI4wunTYOtns2QJNHOFJiFEO+s0QZztQ+uGDRtITk6mrKyM+Ph4Nm7ciF6vx2KxcOjQIV577TWee+65Jgdc3bt3Z/v27RQVFVFQUIDRaOSuu+6qN8QwGo2Eh4e3uuHA4MGDyc3NJTk5mW7durX4OP2juvDLr0l0CzHYp5Q6qVVUVJo5ddY63XRY7wj2H0+mrMKM3tcLP29PewXdiN4R7D+RTNbFQlQqGBobQUlZOWXlFeh8PNH7evPzr6cwBvgxrHcE7y94kADf1rX59fT0xNnZmby8PPz9/evdzzY9tCkNDhRFsQcdVavXvL297c/Pnj2Li4sL0dHRDqtAMhgMnDhxgoiICIedU6/Xk5CQQGFhIZ6eTVu7r7VsU3EdXYkXHBzMuXPn2jSIqzotua6fvZKSEgoLC/nPf/5TbVpy1Z89W6hb9WfRVrVnsVjs0+IliBNCiLrddddd5Obm8vLLL5Oenk5sbCzffPMN4eHWXwSmp6eTmppq3//999/HbDYzd+5c5s6da98+ffp01q9f7+jhCyFEu1EUeOABKC6GMWPg8u8nhBBXkE4TxNk+sN53uR+z2WzGyckJJycncnJySE5OxmKx2LumNvcDroeHBx4eHuTl5fHtt9+yZMmSOvfLzc0lLS2t1Yv+u7m5MWjQIHbt2tWqIG5k3yg+2bqbw4nWf4wOie3Br6fSuFRcal0vrkpAN6JPJCdSznMyNR2wBnQWBUrLzXi6u9LFqGfPkUQiQoOIDAviQkERJ1LOAzBxeB9e+uOdaFxa/yNlsVjw8fHh3Llz9il7dVWzlZeXA+Di4lIr1PDw8KjV4KCx6YkWi4UTJ060evzNodPpKC8v59KlS3h7ty7AbCpnZ2f0ej0ZGRn06NHDIecEa9OGU6dO0aVLF4eFjkFBQZw8eZJLly412jHZYrHUW7XW0M9d1Z8927TklJQUvL29iYiIaHbjBScnJ1QqFZWVlajVavtzIYQQ1T388MP1LuFRM1zbtm1b+w9ICCGuAKtXw48/gpsb/O1vIL/XFeLK02mCuKqqhnDl5eW4ubkxePDgFh3r22+/RVEUoqKiSEpK4qmnniIqKoqZM2dSWFhIXFwcU6ZMwWg0cubMGZ5//nkCAgKqrVnSUqNGjWL37t384Q9/aPEx+vXsSlZeAc5qJ/r37MrPR5MAayB3PuvCbwFdTHcURSE3vxBXjQu9uoWw50givbqFYPT3ptKi2NeO8/FyJ/lcFrn5hWhcnHnjkbuZNnFkg+OwrYPVUIMD22Oz2QxYK5qKi4vtYYerq6t9anHVAKStqoZ0Oh1lZWUUFRU5rFJMrVaj1+vJyspyWBAHvwVU3bt3d2gl3rFjx8jPz8fX19ch53RxcSEwMJCUlBSCg4MbDNhqrvtX9b+qP3e21xv6uXNyciIhIYHIyMgWjVutVmM2m1EUpUXvF0IIIYQQ157UVHjqKevj118HB/7OXQjRDJ0yiLNVPl24cIGNGzeSkJDAkSNH6NmzJ4sWLSKkGX2b8/Pzee655zh79iw6nY4pU6bw2muv4eLigtls5siRI/z973/n4sWLGI1Gxo4dy+bNmxutvmmK0aNH85TtT9IWctW4cP3AXqSkZ/PLr9bppn0jwlE7OXE26wJqJxUDo7vz869J9OkRhjHAD63Gmf3HkwFw07iQVlLOpeJSnFQqBsd0Z+/RJPpFdcHFWc3qF+YQ2y2YS5cuNbgGVllZmX36cF1VRDqdrlrwAbBjxw769etXq2tte1Gr1fj7+5Odne2wIA6snUyTkpIcGooFBATw66+/OjQUU6vVGAwG0tPTW31ORVGorKxs0vprtmD3woUL1X7Gaq77Z1ufrS2CXVtHv+zs7EY7+NXFNoaKiopmV9QJIYQQQohrj6LAH/8Ily7BiBHw6KMdPSIhRH06ZRAH8MMPPzB9+nRyc3MpLy9n/PjxODk5ce+99xIXF8fYsWPtU78aMnXqVKZOnVrna25ubnz77bftMXwARowYwcmTJ8nOzkav17f4ODHdQvhuz2Hgt4YNfXqEofP2IFDnw8+/WqvkVE4qysorSM/JA6xVcj//eoqIkEAslkqCdL7svVxR56VR8cd7byDv3Gl2nD1V5zpYPj4+tarXnJ2dmxw2+fj4kJOT06zgtLX0ej3nzp1zaCODgIAAjh496tBKPCcnJwIDA8nIyHBYEAfW6akHDhwgMjKyznuvagfRhgK2msFuza61/v7+1Tra/vLLL3Tv3r3VU8abSqVSERISQlpaWouCOLB+jywWC5WVlW08OiGEEEII0dmsXw/ffgtaLaxZA/K7XCGuXJ0yiCsvL+f999+nf//+fPnllzz55JOUlZWxfPly5s6dy9tvv83YsWM7epiN8vf3Jzo6mr1793Lrrbe2+DhDe0fipv2eqC4m+3pwpeVlOKudOH7GusZb724mjiSmEhzgQ7HGmSA/T36+XEHnrlXjolZz6nw2AL8f3Y/npt+Gl6eHPehoj6odf39/cnNzHR7EHTt2jLKyMntlXntTq9UEBASQmZnp0Eq8oKAgjh49SlRUVLtX4imKQnl5uX3K+MmTJ9FqtXVOT67aQbRmY426Ghw0ZewhISGcO3fOYUEc/LYmXkubYlQN4mSNOCGEEEIIUZ/z5+HPf7Y+fvll6NmzY8cjhGhYpwzijh8/zokTJ3jnnXcA6NevHwsXLmT58uVMnjyZe++9F+CqmPI1cuRIdu3a1WAQpygKFRUV9U4JpagQP09XDp44A0APk47kc9kE6bxxUTsREWrgyGlrIBcc6E8lKs5kWqviBkV3o6SsnItFpaidnHh17jTuv+W6dr9usFaKpaSk2CufHEGj0eDt7U12drZDA0CDwUBycjLdu3d32Dl1Oh1gnbLZUHfahjTUQbRmgwNbB1GVSkVmZib+/v6NdhBtKyaTiaSkJIqKivDw8GjTY9dHo9FgMBg4e/YsPVvwryFbaGmxWBx6DwghhBBCiKuHolg7o+bnw+DBMH9+R49ICNGYThnEBQcHk5SURK9evQBrmHXhwgXAGjpER0c3qYtiRzObzcTGxrJy5Ur69OlDTk4OkyZNqrPBgaIo9VYRBQQEYPD3I+PCJQb16mavdOsSbECj1ZJwxtoltW9EOGVmC+ez81CpYEhMD/YeTcJJpSJY78fDvxvusBAOwMvLC5VKRX5+Pn5+fg47r16vd3gQZ1uzzZFBkUqlwmAwkJGRUSuIq9pBtKF1/+rqIGr7GbR1EK26Ta1WU1JSwk8//URkZKTDqg41Gg16vZ7z588TERHhkHMChIaG8sknnxAUFFTvFGBFUVAUhYKCAtLT08nMzOT8+fNkZmaSnp5Oeno6999/P7///e8dNm4hhBBCCHF1+Ogj+OorcHGBtWuhjX+fLYRoB53uNlUUBX9/f3Q6Hbt372by5MmEh4fj7+/PPffcw3fffcdzzz13xYZwp0+fZvLkyWRkZJCVlWXvmvjyyy9jMBi47rrrcHNzw8PDo1Z3x4Yq/MYN60O5udIewkV3Daa0wsyps5nAb2vHaV2cCfD1xFBlPbg+EWGseHomScePUlFRgYuLSzt/FaxUKhUBAQHk5uY6PIg7ffp0k9YQbCvOzs74+/uTmZlJt27d2u08NRscAKSnp9eqbGtqB9GWdK61NUnIyMggPDy8Xa6zLiEhIRw9epTu3bs7pLpMURQ8PT1Zvnw5J0+e5KabbiI9PZ2MjIxa/2VmZlJcXIybmxtBQUEEBQVhNBoJCgqif//+Dv06CSGEEEKIq0NmJjz2mPXxiy9CbGzHjkcI0TSdLoizrS81ZcoUNm3aRHR0NNHR0fzud7/jn//8Jy+99BKPtqKFzKVLl1i4cCFbtmwhKyuL/v378+677zJ48GDA+uF70aJFrF69mry8PIYOHcqKFSuIiYlp0vENBgOLFi3CaDRiNBoJDAykW7durFq1iuuua3k12oCeXXnr718B1kq3+GOncXFW4+3hRrdgg33tOJNeh5+3h71r6p3jhvHmY/fiqnHhXMopLl682KrGEc3l7+9PSkoKPRzYe9vDw7r2XW5uLoGBgQ47b2BgIKmpqc0O4mwNDhqaGlq1g6hKpapWOenk5ISiKBgMhloBW3utTWYymUhJSXFowKTT6XByciInJ6dV31dbBVtubi4ZGRnVwjXb48zMTPu2srIyVq5cybfffmsP14xGI4MHD8ZoNBIcHIzRaMRkMuHr64tKpZI14YQQQgghRKPmzoULF6BfP3jmmY4ejRCiqTpdEGf7APunP/2Jbdu2kZ+fD8D8+fOZPn06ffr0obKykpKSkhYtoP7AAw9w9OhRPvjgA0wmExs3bmTcuHEkJCQQHBzMkiVLWLp0KevXrycyMpJXX32V8ePHc+LEiSZV4Xl4eNSagjZ69Gh2797dqiBuUK/uuGk1xHYPtXdJDfLxISjAl/hj1tBtQM8uJJw+h8HfB7WTE3Fz7mTm7663f011Oh0XLlxweBB39OhRhzZPUKlU9umpjgzi9Ho9CQkJFBcX4+7uXu/af3VNFW1qB1GtVouLi0u1oCcpKYni4mKHTsUNDAzk2LFjDp0ibgsg33//fRYuXFjrdUVRsFgsZGdn1wrWalavZWRkUFFRgbe3NwaDwR6cBwUFMXLkSHuwZgvXevXqxbp16xg9erRDrlUIIYQQQnRu//gHfPqpdSrqunXWqalCiKtDpwvibFPOIiMjiYyMBKxrrdkWhN+1axcJCQn8/PPPLF++HHd39yYfu6SkhE8//ZQvvvjCHorFxcXx+eefs3LlSl555RWWLVvGggULmDx5MgAbNmzAYDDw0UcfMWfOnBZd06hRo/jiiy9a9F4bd1ct1w2I5tvdhwDo0yOM5PNZhAYFAL9NTQUorzDz8RuPM6JvVLVj6HQ6Tp8+3apxNJeteUJubi4mk8lh59Xr9Rw5cgRFUdq8OsnWQbS8vJzS0tJqoZqLiwvx8fEAja7919IOojUFBQWxd+9ezGZzmzdJqI+zszMGg4Hz588TFRXV+BtaQVEUKisrycrKIiMjgyVLluDs7IzFYrGvyWYL2bKysux/XlSdImoymYiKiqpVwWYL8xv7us+cOZOVK1dKECeEEEIIIVotJ8daDQfw3HPWijghxNWj0wVxVZ04cYLExESOHTtGfHw88fHxJCcn4+zsjEaj4dlnn23Wwu1ms5nKykpcXV2rbXdzc2Pnzp0kJyeTkZHBhAkT7K9ptVrGjBnDrl27WhXEPf/8861esywiNIhvdx9iWO8I9h5NQlEUikrL6NMjzB7CxXYPZeVzDxBiqN1F08/Pj8LCQsrLy9FoNC0eR3P5+/s7PIjz9fXFYrGQn59f7yL7NVVdZ62hLqJVO4jWnArq5+dHQUEBMTEx9pCtvcMxT09P3N3dyc7Oxmg0tuu5qjIajRw9epSIiIgWrdlmqxisGqTVV8GWlZWFxWJBp9Ph5ubGhx9+yMiRIwkKCqJ37972ijZbyGYL6NsqhH366aeprKxsk2MJIYQQQohr22OPQXY2xMTAggUdPRohRHN1yiDOVsX09ttvs2bNGkJCQoiNjWX27NmMHDmSr776ivLycvLy8gCaHHB5eXkxfPhwXnnlFaKjozEYDGzatIm9e/cSERFBRkYGYF3nrSqDwUBKSkqLr6d3794AHD16lL59+7b4OMP7RrLr8El76BYW5E9RcSmpGTkA3DF2MG89fh9urnWHbBqNBi8vLy5cuEBQUFCLx9FcAQEBHDx4sF2q0+rj5ORkn57q7e3d4LTQqgEbNK+DaE3l5eXs2LEDV1dX3NzcHHKtYK2Ky8jIcGgQp9Pp+PLLL7l48aJ9OratOUl5eXmtcK1m9VpGRgY5OTkoikJAQIB9aqjJZLI3OTCZTPbqNaPRiKurK1988QWPP/4469evd1gzDkfeL0IIIYQQovP64gvYtAmcnKxTUh20eo8Qog11yiDO1rDhz3/+M08++WStqW8jR45k1apV3HrrrWRlZTUr3Pnggw+YNWsWwcHBqNVqBgwYwD333MP+/fvt+9Q8XmsDJLVazfDhw9m9e3ergrhB0d05dNIaCPaL7MLJ1HSKS8vo1S2EKTcOZc7kcY2O07ZOnCODBW9vbxRFoaCgAB8fnzY7bs0OojVDtqKiIsrKyjhz5gzQPh1Ea7JVxWVlZTm0kUFQUBCnTp1qt2pHW8BWWlparWrt4MGD/Otf/+Lrr7+uFrTl5ubaw1Db9FCj0UhISAhDhgypVsEWFBTUrMYSt9xyC3PnzmXfvn0MHTq0za9VCCGEEEKI9pCXBw89ZH381FNwuV+gEKKtXP7c2t46ZRBnq3Lp1auXfZttOqCLiwtOTk6MGzeO//73v82e7tm9e3e2b99OUVERBQUFGI1G7rrrLrp27WoPp2pWFmVlZdWqkmuu0aNHs2vXLh6y/cnbAp7urvSJCEOrcbFXxfl6efDSH6cwql90k47h5+fHiRMnWjyGlnBycsLf35+cnJxGg7imdhAtKyujsrKyVgdRrVaLu7s7vr6+ODs7c+TIEQYPHoyPj4/DqvEMBgPp6ekODeLc3Nzw9vYmKyurWU0bbAFbcXEx6enptTqIZmZmkpmZad9+8eJF1Go1BoOBoKAg3N3dOXv2LEFBQYwYMaJaBZvBYKjVWKItuLi4cOzYMby9vdv0uEIIIYQQQrSnP/8ZMjIgKgri4jp6NEJcZRTFusBiWhqcPfvb/6s+TktzyFA6ZRBXla0arWaVT3BwMGvWrGnx1DQPDw88PDzIy8vj22+/ZcmSJfYwbuvWrfTv3x+wBoDbt29n8eLFrbqOUaNGsXLlylZX140dFMPSD78GILpLMGteeohwY9O7oPr5+VFaWkpJSYlDp076+/uTlpZGYGBgoyFbazqI1nTu3LlmrRPXFvR6PcePH6e0tLTWeoTt6ezZs7zyyit89dVX9oCtsLCQ8+fPVwvTak4RzczMJD8/HxcXF3sHUVsVW8+ePbn++uurdRHV6/XVGksMGzaMHj16tCpkbi4J4YQQQgghxNXkX/+CDRtApYK1a8GBHxOEuPIpinXhxIYCtnPnoKyso0cKXANBnO3D/qFDhzh06BBDhw4lKiqK1NRUvvvuO/r27cv1119vD28a8+2336IoClFRUSQlJfHUU08RFRXFzJkzUalUzJs3j9dff52IiAgiIiJ4/fXXcXd355577mnVdQwZMoTs7GxSUlLo0qVLi4/TL8r63ltGDeAvT9yPh1vz/gR3dnbGx8eHCxcuEBwc3OJx2Ng6iNZXtVb1uaIo/PLLL9WmgWq1Wnx8fGpta2kH0Zr0er3Dp4lqtVp8fX3Jzs4mNDS0TY+tKIp9mm/NYO3EiRP8+OOPjBkzhry8PDIzMyksLESj0djDNVvAFhsby/jx4+1TRE0mEwEBAajV6mZ/3WfOnMmvv/7aptcphBBCCCFEZ5GfD3/8o/XxvHkwYkSHDkcIx7JYGq9kO3sWLq/Z3iiDAUJDISTkt//bHvv4QCuWA2uqTh/EAbz99tssXLgQg8FAeHg4zz//PBMnTmT16tVs3bqV66+/3l4B1Jj8/Hyee+45zp49i06nY8qUKbz22mu4uLgA1u6IJSUlPPzww+Tl5TF06FC+++47vLy8WnUN7u7uDBgwgF27drUqiBsS04PnZ93Bw3dOaHFQ5efn12gQV7ODaH0hW2MdRKs+P3ToEN26dXPo+nR6vZ6TJ09SUVFh/x47QmBgIOfOnWtyEGcL2PLy8uyVarZKtrqq2GwVjbZgzRaydevWjW7dujFz5kz7NFGdToeTk1O7Tc394x//6LBpv0IIIYQQQlxtnnrKmjN07w6vvtrRoxGiDVksTatka0rIplJZQ7aqAVvNoM1kgobWRC8oaLtra2ioSlMTqKtUcnIyN910E3fddRdTpkxh9erVHDlyhB07dnDgwAFuvPFGLly40NHDbJInn3ySvLw8li9f3mFjqKysJCsri+PHjxMdHW0P02pOFa2oqABqdxCtOV20oQ6iNSUmJlJWVkZsbGx7X2Y1u3fvpkuXLg7tKJqVlUWfPn3Yt28fJpOJ3Nzcah1Eaz62hWxlZWV4eHjUqmCrOjXU1uTAtu5d1RBs7dq1vPfee+zbt89h1yqEEEJcDWxNo/Lz82WJAyGEw3z/PYwfb328bRuMGdOhwxGi6WwhW0OVbM0N2eoL2EJCGg/ZmsBRf9d3+oo4k8lEamoqDzzwAGFhYcybN49hw4aRnZ1NQEAAGo2GpKQkevTo0dFDbdTo0aN57rnn2uXYZrO5VtVaXQGb2Wy2v+f06dO4ubmh1WpxdXVt8w6iNfn7+3PkyJFWr5PXXHq9nuzs7DYN4hRFwWKxkJ2dXWewlpGRgdlsZujQoRQVFVFRUYG3t7d9DTZb0DZy5MhqIZvJZLL/gdGSr9HkyZP5/PPPHb4GoBBCCCGEEKK6wkJ44AHr47lzJYQTVxCLBbKyGq9ku1yg0yCVCoKC6g/YQkPBaGx1yHYl6dRBnMViQavV0qtXL7744gtmzZpFREQEoaGh/PTTT+zfv59+/fo12onzSjFixAiOHz9Obm4u/v7+je5v6yBaX6hWdVvVDqJVq9ZsHURrBmz79+9Hr9cTFhbmgCu38vX1pbKykkuXLjn0N9F6vZ5t27YRFRWFVqttcF9FUexVg3VVr9maHmRmZpKVlYXZbMbPz8/eRdRWsRYVFYWvry/Hjx9n48aNmEwmPD09gZYFbE3l6+vLl19+2W7HF0IIIYQQQjTNs89CSgqEh8Obb3b0aMQ1wxayNVbJ1pyQrbFKNgcuA3Ul6NRBnM3mzZt5+umn+c9//kPPnj05f/4806ZNw9vbm2XLlqHXN71raEfS6/VERUWxe/duBg4cSGlpKd7e3k3uIFo1YPPy8qq1rbEOolXpdDouXLjg0CDOyckJnU5Hbm6uQ4M4Ly8vHn/8cd555x2GDBlSbwWbLWjLysrCYrGg0+mqTQ0NCgqid+/e9ufBwcEYjUbc3d2B2gFbSkoKPXr0ICAgoNVrDAohhBBCCCGuHjt2wIoV1sd/+xtc/p28EK1jsUBmZsOVbOfPNz1kMxobr2S7xkK2pujUQZwt2MjKyuKrr74iODiY48ePc/PNNzN48GBuvvlmunbt2sGjbNinn37KDz/8wPnz50lPTyctLY1p06ahKAq33norjz32mMM6iFal0+k4c+aMw6eJBgQEkJ6e3ibfN9vyiOXl5XWuv1a1uUFBQQH3338/YA1EbeuvmUwmgoKC6N+/v72azfZ/18s9xVv69QkPD2fBggUUFRU1qQJSCCGEEEIIcfUrLoZZs6yPH3wQxo3r2PGIq0RlZdMq2aosN1UvW8jWUCWbhGwtdk0Ecf369ePtt98mMjKSqKgoTCaTPSRpKbPZTFxcHB9++CEZGRkYjUZmzJjBCy+8YF8XbcaMGWzYsKHa+4YOHcqePXuafJ7i4mI8PT0ZM2YMJpOJgwcP8s9//pNvv/22Q6fU2irSbIsZOoq/vz/ffPMNPXr0wM/Pr859bAFbaWlpneFazWmiFy5cwMnJCb1eb69cMxqNhIaGMmTIEIxGI2fOnGHZsmUkJiai1WodFj7GxcU55DxCCCGEEEKIK8MLL8CpU9as4623Ono04opQWdm0SramhGxOTo1XsgUFScjWjjp919S6Krb27dvHoUOHyMnJwcvLi+uvv55evXo167ivvfYaf/nLX9iwYQMxMTHs27ePmTNn8uqrr/L4448D1iAuMzOTdevW2d+n0WjQ6XQtvp5Tp04RHR1Nenp6hy+mf/DgQXx8fBxWVWj7UY2Ojmb27NkMGzasVshWNWi7ePEiarXavv5a1Qq2qpVrwcHBBAYGNjg1t7S0FH9/f/bs2UPv3r0dcr1CCCGEuHJI11QhhCPs3g0jR4KiwDffwKRJHT0i0e5sIVt9AdvZs80P2RqrZHPu1DVZLSZdU9tI1WAlLy+PF198kS+//BIXFxcURaG4uJjVq1fzxBNPcN9991FZWYlarW70uLt37+b3v/89t9xyCwBdunRh06ZN7Nu3r9p+Wq2WoKCgNruebt26ERAQQHx8PKNGjWqz47aETqcjOzu71UGcLWC7dOmSPVA7f/58req1qtNEFy1aZF9jzRas9ezZk+uvv75aF1G9Xt8mU3NdXV1ZvXo1vr6+rTqOEEIIIYQQQtSltNQ6JVVRYPp0CeE6hcpKyMhovJKtsrLxYzk5WRsbNFbJJiHbFa9Tf4cqKirsUwm7d+/Ou+++y+bNm3nnnXcYN24cRqORgoICXnjhBeLi4rjvvvuaHNiMGjWKVatWcfLkSSIjIzl06BA7d+5k2bJl1fbbtm0bgYGB+Pr6MmbMGF577TUCAwNbfE0qlYpRo0axe/fuDg/ifHx8WLt2Lb169aqzOk9RFBRFoaCgwB6o1ZweWrXJQWFhIRqNplq4ZmtwMHHiRPu2pKQknnjiCVJSUuzTgB3h3nvvddi5hBBCCCGEENeWuDg4ftyapSxd2tGjEY2yhWyNVbI1J2SrL2ALCZGQrRPp1FNTz58/zzPPPMO8efPo2rUr/fv35+mnn2bu3LnV9svKyiIqKoq0tDQ8m9iORlEUnn/+eRYvXoxaraayspLXXnuN5557zr7P5s2b8fT0JDw8nOTkZBYuXIjZbCY+Ph6tVtvi6/rrX//K119/zZYtW1p8jNawBWy5ubn06dOHxx57jJCQEHvVWs1GByUlJbi5uVUL14KCgggODrZPF7VNE9XpdDg5OTUYiJaWlqLT6fjll1+IiYlx4JULIYQQ4lolU1OFEO3pl19g2DBrU8vPP4ff/76jR3SNq6yE9PT6K9maE7Kp1Q1XsknIdsVw1N/1nTqIUxQFX19ftm/fTr9+/fDy8uKHH35g8ODBWCwWezXVmjVruHTpEn/605/QarVN6gT68ccf89RTT/HWW28RExPDwYMHmTdvHkuXLmX69Ol1vic9PZ3w8HA+/vhjJk+e3OLr2r9/PzfccANpaWlNmkbbVFUDtppNDmpWr2VkZFBWVoZarcbLy4vY2NhqlWxVw7Xg4GB8fHxQqVRt1uRg69atDB48WKaKCiGEEMIhJIgTQrSXsjIYNAiOHoVp02DTpo4eUSdnNjdeyZae3ryQrbFKtjb83C7aj6wR1wZUKhVhYWHs2rWLfv36MX36dP7xj39QXl7OyJEjKSkp4euvv2bRokWcPXuW3NxcXnnlFSwWS6MB11NPPcWzzz7LtGnTAOjduzcpKSm88cYb9QZxRqOR8PBwEhMTW3Vdffr0obKykoSEhCY1DlAUBYvFQnZ2dq1wrWb1WmZmJhUVFXh7e2MwGDAajfaAbeTIkfZgzRa2/b//9/9Yu3Yt//3vf1t1Tc01fvx4h55PCCGEEEIIIdrDa69ZQzi9HpYv7+jRXOXM5t8q2eoK2GyVbBZL48dSqyE4uP6ATUI20UKdOogD+MMf/sCKFSsoKytjzpw5LFiwgFWrVuHv709qaioajYaBAwdy8803M3DgQIAmVZkVFxfXWp9MrVZjaeCGzs3NJS0tDaPR2KprcnZ2ZtiwYfz0009ER0eTlZVVZwWbbS22zMxMsrKyMJvN+Pn52buI2jqI9uzZs1oFm8lksk/RbayCbdy4cTz00ENcunQJLy+vVl2XEEIIIYQQQlxLDh6EN96wPl6xAgICOnQ4VzZbyFbfVNG0NOvrTQnZnJ0br2QzGCRkE+2i0wdxDz74ICqViri4OJ544gkMBgNjx46le/fuDBkyhN69exMUFISzs3OzSg9vu+02XnvtNcLCwoiJieHAgQMsXbqUWbNmAVBYWEhcXBxTpkzBaDRy5swZnn/+eQICArjjjjtafV3e3t48//zzPPXUU1gsFnQ6XbWpoUajkd69e9eaJuru7g40HrA1VXh4OHv27LEfVwghhBBCCCFE4yoqYOZMa740eTL8z/909Ig6kNlsrVRrqJKtOSFbY5VsErKJDtSp14irKjk52d5UwdXV1V6VVlRURGFhIQaDoVnHu3TpEgsXLmTLli1kZWVhMpm4++67efHFF9FoNJSUlHD77bdz4MABLl68iNFoZOzYsbzyyiuEhoa2+nr27t1LYmIiI0eOJCgoCFdXV6DtAjYhhBBCCPEbWSNOCNHWXn0VFi4EnQ4SEqzZUKdUUdF4JVtGRvNCtoYq2QIDJWQTLSLNGtpQXc0XMjMz+dvf/kZ8fDxFRUW4uroybdo07r777g4apRDiavXGG2/w2Wefcfz4cdzc3BgxYgSLFy8mKirKvo+iKCxatIjVq1eTl5fH0KFDWbFiRbXOv2VlZTz55JNs2rSJkpISbrzxRt577z1CQkI64rKEEEJUIUGcEKIt/for9O9vzag2boR77+3oEbVQRUXTKtmaEju4uDStkq3GElFCtBVp1tCGaoZwiYmJ3HfffWRkZODu7k5RUREPPvgg7777LqdPn2bBggVUVla2aUdSIUTntX37dubOncvgwYMxm80sWLCACRMmkJCQgIeHBwBLlixh6dKlrF+/nsjISF599VXGjx/PiRMn7Osrzps3j6+++oqPP/4Yf39/nnjiCW699Vbi4+PlzyMhhBBCiE7CbLZOSa2ogFtvhXvu6egR1aNqyFZXwGarZGtOyNZYJZuEbOIacE1UxNW0ZMkS1q5dy86dO9m3bx+PPPIISUlJfPLJJzz22GNkZGR09BCFEFex7OxsAgMD2b59O9dddx2KomAymZg3bx7PPPMMYK1+MxgMLF68mDlz5pCfn49er+eDDz7grrvuAuD8+fOEhobyzTffMHHixI68JCGEuOZJRZwQoq0sWQLPPAM+PtbKuODgDhhEeXnjlWzNCdls4Vp9lWwSsomrgFTEtROz2cynn37Ko48+SkBAAH369CErK4uUlBSmTp3K448/zvHjx+nZs2edU1qFEFe+HTt28NZbbxEfH096ejpbtmzh9ttvt79e3329ZMkSnnrqKQCuv/56tm/fXu31u+66i48//rjR8+fn5wOg0+kA6xqVGRkZTJgwwb6PVqtlzJgx7Nq1izlz5hAfH09FRUW1fUwmE7GxsezatUuCOCGEEEKITuDECXjxRevjv/ylnUK4qiFbfZVsmZnNC9kaqmTT6yVkE6IZrqkgzmKx4OzszIULFwi43BfaZDIREhJCamoqZWVlBAYGUlBQAEjjAyGuVkVFRfTt25eZM2cyZcqUWq+np6dXe/6vf/2L2bNn19r3wQcf5OWXX7Y/d3Nza/TciqIwf/58Ro0aRWxsLIC9yrZmUxiDwUBKSop9H41Gg5+fX619pEpXCCGEEOLqV1kJs2ZBWRlMnAgzZrTgIOXlcO5cw5VsTQ3ZNJrGK9kkZBOizV1zQZyTkxMjR47khx9+sE//Gjp0KA8++CBubm4MGDCAIUOGNKsazmw2ExcXx4cffkhGRgZGo5EZM2bwwgsv4HT5D62mLNQuhGgbkyZNYtKkSfW+HhQUVO35F198wdixY+nWrVu17e7u7rX2bcwjjzzC4cOH2blzZ63Xav6Z0pQ/Z6QyVwghhBCic1i+HHbtAi8vWL0aav0Tr6ysaZVsTWEL2eqqZLM9DgiQkE2IDnBNBXG2UOzuu+9m4cKFbNiwgenTpzNlyhT71K/nnnsOaF413OLFi1m1ahUbNmwgJiaGffv2MXPmTHx8fHj88ceBpi3ULkRn1Ng00RkzZrBhw4Zq7xk6dCh79uyxP2/PbqKZmZl8/fXXtcYA8OGHH7Jx40YMBgOTJk3ipZdeavB+ffTRR/nyyy/ZsWNHtbHZwjxbUG+TlZVlr5ILCgqivLycvLy8alVxWVlZjBgxotXXKYQQQgghOs6phDJWP3uO0ZzlpTvPErapnkq2ptBqm1bJJr/MFeKKdE0Gcddffz1PPvmk/QPxDTfcwCeffELPnj3RarXNPu7u3bv5/e9/zy233AJAly5d2LRpE/v27QOsFS3Lli1jwYIFTJ48GYANGzZgMBj46KOPmDNnTltcnhBXpMamiQLcdNNNrFu3zv5co9FUe709u4lu2LABLy8v+71pc++999K1a1eCgoI4evQozz33HIcOHWLr1q21jqEoCo8++ihbtmxh27ZtdO3atdrrtuNs3bqV/v37A1BeXs727dtZvHgxAAMHDsTFxYWtW7cydepUwDqF9ujRoyxZsqRV1yiEEEIIIdpRWdlv00XrqGRT0tLonpVFgm3/tQ0cq2rIVlfAZqtkk5BNiKvWNRXE2Wi1WvsHXbBOP+vbt2+Ljzdq1ChWrVrFyZMniYyM5NChQ+zcuZNly5YBTVuoXYjOqrFpomC9H+qbApqfn8+aNWv44IMPGDduHAAbN24kNDSU77//vtVNDNauXcu9996Lq6trte0PPvig/XFsbCwREREMGjSI/fv3M2DAgGr7zp07l48++ogvvvgCLy8v+5puPj4+uLm5oVKpmDdvHq+//joRERFERETw+uuv4+7uzj2X+9X7+Pgwe/ZsnnjiCfz9/dHpdDz55JP07t3bft1CCCGEEMLBysp+q1ira6ro2bOQldXgIWyRWSla1OGhuHStZ6poSIiEbEJcA67JIK6tPfPMM+Tn59OzZ0/UajWVlZW89tpr3H333UDTFmoXwlEamipaUVHBCy+8wDfffMPp06fx8fFh3LhxvPnmm5hMJvsxWtNRtC7btm0jMDAQX19fxowZw2uvvUZgYCBAu3YT/e9//8uJEyfYvHlzo/sOGDAAFxcXEhMTawVxK1euBKxfl6rWrVvHjMur8D799NOUlJTw8MMP29eJ/O6776pNdf3LX/6Cs7MzU6dOtU/BXb9+faur/oQQQgghRB1KSxusZCMtDbKzm3YsV9c6K9kyXUK449EQTpaG8tK7/jz6mIRsQlzrJIhrA5s3b2bjxo189NFHxMTEcPDgQebNm4fJZGL69On2/VqyULsQba2hqaLFxcXs37+fhQsX0rdvX/Ly8pg3bx6/+93v7FOtbVrSUbQukyZN4s477yQ8PJzk5GQWLlzIDTfcQHx8PFqttl27ia5Zs4aBAwc2qSL2119/paKiotoabzZKE7pSqVQq4uLiiIuLq3cfV1dXli9fzvLlyxs9nhBCCCGEaEBpaeOVbM0J2RpqehASAv7+tSrZFAXumwi7S2HUKJj7SDtcpxDiqiNBXBt46qmnePbZZ5k2bRoAvXv3JiUlhTfeeIPp06c3aaF2IRyloamiPj4+tdZAW758OUOGDCE1NZWwsDD79pZ0FK2LrXsxWKeADho0iPDwcL7++uta67ZV1VCQXVhYSFJSkv15cnIyBw8eRKfT2a+hoKCAf/zjH7zzzju13n/q1Ck+/PBDbr75ZgICAkhISOCJJ56gf//+jBw5sqWXKoQQQggh2kJJSeOVbDk5TTtW1ZCtvjXZdLoWTRdduxa2brWeYu1aaVAqhLCSIK4NFBcX2xtB2KjVaiwWC9C0hdrFtamxjqKKorBo0SJWr15tn864YsUKYmJi7Pu0Z0dRsK7RplKp8PX1rba9uR1Fm8poNBIeHk5iYiLQsm6i+/btY+zYsfbn8+fPB2D69OmsX78egI8//hhFUexTyKvSaDT85z//4d1336WwsJDQ0FBuueUWXnrpJZkmKoQQQgjRnmwhW30B29mzTQ/Z3NwaDthCQlocsjXm7Fm4/E9QXn0VIiLa/BRCiKuUBHFt4LbbbuO1114jLCyMmJgYDhw4wNKlS5k1axZAkxZqF9emxjqKLlmyhKVLl7J+/XoiIyN59dVXGT9+PCdOnLCHXu3ZUbS0tJRnn32We+65B29vb/v25nQUba7c3FzS0tLs1aMt6SZ6/fXXNzpd9I9//CN//OMf63wtNDS01hp4QgghhBCilUpKGp4qmpYGublNO5abW+OVbH5+HdL4QFFgzhwoKIChQ2HePIcPQQhxBVMpTVncSDTo0qVLLFy4kC1btpCVlYXJZOLuu+/mxRdfRKPRAL9VNr3//vvVKptiY2M7ePTiSqFSqapVxCmKgslkYt68eTzzzDOAtfrNYDCwePFi5syZQ35+Pnq9ng8++MA+xfP8+fOEhobyzTffNNrIoOY5q6qoqODOO+8kNTWVbdu2VQviaoqPj2fQoEHEx8fXamRQdZpo//79Wbp0KWPHjkWn06HT6YiLi2PKlCkYjUbOnDnD888/T2pqKseOHbOHjX/605/45z//yfr16+3dRHNzc9skbBRCCCGaoqCgAB8fH/Lz8xv8O1GIa1ZxceOVbM0N2RqqZOugkK0pPvgA7r8fNBo4cAB69eroEQkhmsJRf9dLRVwb8PLyYtmyZSxbtqzefZqyULu4snTp0qXOrrYPP/wwK1asYMaMGWzYsKHaa0OHDmXPnj1tcv7k5GQyMjKqdQvVarWMGTOGXbt2MWfOnHbrKFpRUcHUqVNJTk7mhx9+aPQPoYY6ijY0TXTlypUcOXKEv//971y8eBGj0cjYsWPZvHmzdBMVQgghhLhSFBc3Xsl24ULTjuXu3nDAFhoKvr5XbMjWmPR0ePxx6+O4OAnhhBC1SRAnRD1++eUXKisr7c+PHj3K+PHjufPOO+3bbrrpJtatW2d/bquAbAu2jqA1G3oYDAZ7QNgeHUVtIVxiYiI//vgj/v7+jb6noY6ijU0T/fbbbxs9vnQTFUIIIYRoJ7aQraFKtuaGbA0FbVdxyNYYRYGHH4a8PBgwAJ58sqNHJIS4EkkQJ0Q99Hp9tedvvvkm3bt3Z8yYMfZtWq22TTqHNqRmZ9CGuoU2ZZ+GOoqaTCb+53/+h/379/PPf/6TyspKe6Cn0+nQaDTSUVQIIYQQ4mpRVNR4JVteXtOO5eHReCWbj0+nDdma4pNP4PPPwdkZ1q0DF5eOHpEQ4kokQZy4KsTFxbFo0aJq26pWfTWlu2hrlJeXs3HjRubPn18t4Nq2bRuBgYH4+voyZswYXnvtNQIDA9vknLaALyMjo1qlWVZWlr1Krq07isbFxfHll18C0K9fv2rv+/HHH7n++uulo6gQQgghxJXAFrI1VMnW3JCtoaDtGg/ZGpOdDY88Yn28YAH06dOx4xFCXLkkiBNXjZiYGL7//nv786qhT1O6i7bG559/zsWLF5kxY4Z926RJk7jzzjsJDw8nOTmZhQsXcsMNNxAfH49Wq231OW1dSbdu3Ur//v0BayC4fft2Fi9eDLRPR9HG+rdIR1EhhBBCiHZWVNRwwJaWBhcvNu1Ynp6NV7J5e0vI1kqPPAI5OdYA7vnnO3o0QogrmQRx4qrh7Oxc5zRQRVFYtmwZCxYsYPLkyQBs2LABg8HARx99xJw5c1p97jVr1jBp0iRMJpN9m61LKUBsbCyDBg0iPDycr7/+2j6OxjQ0TTQsLIx58+bx+uuvExERQUREBK+//jru7u7cc889APj4+DB79myeeOIJ/P397R1Fe/fuzbhx41p93UIIIYQQoo0VFjYcsJ092/yQrbFKNtGuPvvMOi1VrYa1a63dUoUQoj4SxIlmeeONN/jss884fvw4bm5ujBgxgsWLFxMVFWXfp726iSYmJmIymdBqtQwdOpTXX3+dbt26Nam7aGukpKTw/fff89lnnzW4n9FoJDw8nMTExCYfu6FpouvXr+fpp5+mpKSEhx9+2D7l9rvvvpOOokIIIYQQV6LCwsYr2fLzm3YsL6+mVbKJDpWba23QAPD00zBwYMeORwhx5VMpjc1DE6KKm266iWnTpjF48GDMZjMLFizgyJEjJCQk4OHhAViDuMzMzFrdRHU6XYvP+69//Yvi4mIiIyPJzMzk1Vdf5fjx4/z666+cOHGCkSNHcu7cuWoVa3/84x9JSUlpUlfOhsTFxfH++++TlpaGs3P92XVubi7BwcGsXr2a+++/v1XnFEIIIcSVpaCgAB8fH/Lz8/GW8OPadOlS45VsTQ3ZvL1rh2o1gzb5Obsq3HcfbNwI0dGwfz+4unb0iIQQLeWov+ulIk40y7///e9qz9etW0dgYCDx8fFcd9119u1t3U100qRJ9se9e/dm+PDhdO/enQ0bNjBs2DCgZd1FG2OxWFi3bh3Tp0+vFsIVFhYSFxfHlClTMBqNnDlzhueff56AgADuuOOOVp1TCCGEEEI42KVLjVeyFRQ07Vje3g0HbBKydRr//Kc1hHNysnZJlRBOCNEUEsSJVsm//Fu/mtVu7dlNFMDDw4PevXuTmJjI7bffDjTcXbSlvv/+e1JTU5k1a1a17Wq1miNHjvD3v/+dixcvYjQaGTt2LJs3b26T5hBCCCGEEKKNFBQ0XsnW1JDNx6fhqaLBwRKyXSMuXgTbCjh//jMMHdqhwxFCXEUkiBMtpigK8+fPZ9SoUcTGxtq3t3c3UYCysjKOHTvG6NGjm9RdtKUmTJhQZxdRNze3Vk95FUIIIYQQrVRQ0Hgl26VLTTuWj0/jlWzyC1dx2RNPwPnzEBEBr7zS0aMRQlxNJIgTLfbII49w+PBhdu7cWW17W3QTrenJJ5/ktttuIywsjKysLF599VUKCgqYPn06KpWq0e6iQgghhBDiKpOf33glW1NDNl/fxivZJGQTTfTdd9buqCqV9f9ubh09IiHE1USCONEijz76KF9++SU7duwgJCSkwX1b0k20prNnz3L33XeTk5ODXq9n2LBh7Nmzh/DwcIAmdRcVQgghhBBXAEWpXslWV8DW3JCtsUo2T892vSRx7bh0CR580Pr4kUdg1KiOHY8Q4uojXVNFsyiKwqOPPsqWLVvYtm0bERERjb5HuokKIYQQ4monXVObSFGaVslWWNi04/n5NV7JJiGbcKA//QlWrYKuXeHIEfDw6OgRCSHainRNFVekuXPn8tFHH/HFF1/g5eVFRkYGAD4+Pri5uUk30U4gPz8fRVHw9fVt8nsURcFisQDW7rVOTk7tNDohhBBCdBhbyNZYJVtzQraGKtkkZBNXmB9/tIZwAH/7m4RwQoiWkYo40SwqlarO7evWrWPGjBmUlJRw++23c+DAgWrdRF955RVCQ0MdPFrREn369OGuu+5iwYIF9m0StAkhhLjWdfqKOEWxtoFsrJKtqKhpx9PpGq9kkxRDXEWKiqBPHzh92tot1RbICSE6D0f9XS9BnBBXEUVRUBSlTYIw27FUKhUWiwUnJydUKhXl5eVoNJpmHevAgQN88803pKam0rdvX6ZNm4ZOp7MfXwghhLjaXdVBnC1ka6ySrTkhW30Bm62STUI20ck8/jj89a/WH/OjR+Fq+2NACNE4CeKEEC1WNWSD+isZ63qf2WwmNzeXoKAg+/aEhATefvtt9uzZQ2VlJbNmzWL+/Pm4uLiwfft24uLicHFxITg4mKSkJPz9/XnvvfcwmUztcn1CCCGEo12xQZyiQF5e/ZVstsfFxU07nr9/45Vs7u7te01CXGF27oTrrrPebv/+N0yc2NEjEkK0B1kjToirlC3btgVhbVkRdvDgQdauXctNN93EzTffbK9kq6mu85aUlKDRaFCr1QAcOXKE//znP/Tu3Zv333+f/fv3884775CQkMD7779PYmIiLi4uJCQk8PDDD+Pu7s7zzz9vD/nKysq4cOECb7/9NkOGDGHx4sUApKam8vjjj/O///u/vP7662127UIIIcQ1xxayNVbJ1pyQrbFKNgnZhKimpARmz7bejjNnSggnhGg9CeKEaGNNrUKzWCxNDuoqKipwcXFBrVazd+9eBgwYAFDvFNUTJ06wfv16oqKi+PHHH/nwww/p3r07jz32GHPnzgXg3LlzzJ8/n1GjRnHTTTfx8MMPEx0dTW5uLgBmsxkXFxc2b95MYmIi+/fvx2Aw2F9zdnbm2LFj7Ny5k5tvvpmlS5dy6tQpzp49y/fff09qaqoEcUIIIdrNe++9x1tvvUV6ejoxMTEsW7aM0aNH17v/9u3bmT9/Pr/++ismk4mnn36ahx56yIEjrkFR4MKFhgO25oRsAQH1V7LZ/nNza99rEqITeuklOHkSTCZYurSjRyOE6AwkiBOijSiKwunTpzl8+DCnT5+muLiY6OhoJk6ciJeXV6310hpb5y0nJ4eLFy/i7++Pn58fly5d4uOPP+bw4cM89dRTzJo1iz59+vD1118THBxsH4Ntnbf169fj4eHBfffdR0JCAp988gkvvfQS7u7uzJw5k65du+Lu7o5er+f555+3n9dgMJCbm0t5eTmlpaX89NNPTJo0CYPBYD++s7P1jw6NRkN+fj7vvfceBoOBkJAQYmNjufXWW4mOjm6Hr7IQQggBmzdvZt68ebz33nuMHDmS999/n0mTJpGQkEBYWFit/ZOTk7n55pt58MEH2bhxIz/99BMPP/wwer2eKVOmtP0AbSFbY5VsJSVNO15AQOOVbBKyCdHm9u6Fd96xPn7/ffD17dDhCCE6CQnihGgjX3/9NYsWLaKiooKAgAA8PDxIS0ujR48e9OvXzx7Cmc1mTp8+zbFjx3Bzc+O6667D1dXVfpyffvqJhx56iKSkJAICAhg9ejRvvvkmwcHBhIeHExYWRlRUFG+++SY+Pj72EA5+q8IzGo3odDrc3Nx46aWXAHjqqafIzc3lrbfeYubMmfj5+eHl5cXIkSOrXYfRaKSkpIT8/HwCAwNRFAU/P786r1mv1wOwfPlyrr/++jb7WgohhBANWbp0KbNnz+aBBx4AYNmyZXz77besXLmSN954o9b+q1atIiwsjGXLlgEQHR3Nvn37ePvtt5sfxFkskJUF585Vr1yrWcnW1JBNr68/YLP9V+XfCUIIxygrg1mzrLf8vffCrbd29IiEEJ2FBHFCtIHExETmzJnD3Xffzcsvv4y7uzv5+flkZGTYp3P++uuvDBgwgCVLlrBp0yYyMzNRq9XceOONvP/++wBkZmYybdo0Jk2axLZt2zh58iRz585l5syZ/Oc//2HmzJn88MMPeHh40KtXr3rH4+XlhY+PDz179rRv02q1XHfddaxYsQKAgIAAtFqtvbrNxha+ZWdnExYWRrdu3fj555+B6tNtFUXBZDIxcuRI3njjDQIDA+1j+uWXXzh48CD3338/Wq22Db7CQgghhFV5eTnx8fE8++yz1bZPmDCBXbt21fme3bt3M2HChGrbJk6cyJo1a+zLP9RUVlZGWVmZ/Xl+fj4AuX56wNykseap/cnWhpCjMZHtEky2NphsTTDZLsHkaE3kuJgoU10O2dIv//dzrSsGymmP9mrNOWZT95VjXvnH7Ojzt9c1tfUxS0ogI8NakPrqq1BQ0PZjEUJcWQou3+jt3dNUgjgh2kBxcTEZGRnMnTsX98uLHPv4+ODj42Pfx2QyUVFRwV/+8hc++OADRo0axZYtW7j33nsZNWoU9913H1u3bqWwsJAVK1bg4uLC8OHDWbt2LQMGDCAxMZEePXrg6upKXl5eg+PRarV4enqiVqspLy9Ho9EA4OHhgZOTE3l5efj5+eHu7k5eXh6VlZX2Jg4+Pj64urqSlZUFwOzZs7nxxht57rnnmDdvHjqdjm+//Raj0cjAgQN57733eOCBB3j00Ufx9PTkwoULlJeXM3nyZCwWS3t8uYUQQlzDcnJyqKystP+iy8ZgMJCRkVHne6r+Yqzq/mazmZycHIxGY633vPHGGyxatKjW9m5NDOEAqMyF4lwoPtT09wghrig5OdC1a0ePQgjhSLm5udU+y7c1CeKEaAORkZEEBATw1FNPce+99+Ls7ExgYCADBgyw/5bdz88PlUrFHXfcYV9MevLkyUybNo0PPviAP/zhD5w6dYqePXvaE3iLxUK/fv1wc3PjwIEDRERE4OPjw/nz5yksLMTT07PWWGzruOl0Ov773/9WC+L27NlDz549KSkpwc/PD71eT35+PmVlZbi7u6MoCh4eHmg0GtLS0gAYNmwYb7/9Nhs2bOCjjz6itLQULy8vVq1aBUDv3r356quv+OSTT0hLSyMsLIzY2Fj69u2Lm6xXI4QQop3UbHZUcy3Wpuxf13ab5557jvnz59ufX7x4kfDwcFJTU9v1H+dXooKCAkJDQ0lLS8Pb27ujh+NQ1/K1w7V9/dfytcO1ff1y7dfmtYO1+j0sLAydTteu55EgTog24ObmxmeffcaTTz7Jo48+SnBwMM7Ozmi1WhYsWMD111+PWq3Gw8OD0NBQe9dRsAZZR44cobi4GHd3d1QqFRcvXiQwMND+4UCtVlNZWQlYAz2z2Ux+fn6DQVxQUBAnT57kscce44knniAnJ4dVq1bxyCOP4O/vb9/3xIkT9nODtYlE7969q30w+dOf/sTYsWM5f/48er2esLCwah9CAgMDeeSRR9rniyuEEEJUERAQgFqtrlX9lpWVVavqzSYoKKjO/Z2dne1/J9ak1WrrXF7Bx8fnmvxwAuDt7S3Xfo26lq//Wr52uLavX6792rx2aLyxYquP365HF+IaMnLkSHbv3s3hw4dZunQpjz76KKWlpTz22GOkpKQA1hDt3Llz1d5XWlqKs7MzRUVF9O/fn6SkJI4fPw5Yf0v/73//G41GQ2BgIABdu3YlOTmZgwcPYrFY7GvW1OTh4cF1112HVqtl2rRpjB8/nokTJ/LQQw/ZP1i88MILPP300/ZQzRa+7dixgwcffLDa8Xr27MkNN9xA796966wEsFgsVFZWUllZ2e5z6oUQQly7NBoNAwcOZOvWrdW2b926lREjRtT5nuHDh9fa/7vvvmPQoEF1rg8nhBBCCNFeJIgToo3pdDpGjhzJtGnTWL58OefOnePYsWMAhIaG8vXXX5OQkGDf/4svviA6Ohpvb2/GjRvHkCFDeOCBB/joo4/4/vvvefHFF7n11lvp378/ALfffjsTJ05k1qxZODs7s2jRomqLSdtCMFvFwD333MORI0coLy9n3bp19jJbRVGYMGECY8aMqfNDSF3ruymKUm/I5uTkhFqtRq1WNzg1SAghhGit+fPn87e//Y21a9dy7Ngx/vznP5OamspDDz0EWKeV3n///fb9H3roIVJSUpg/fz7Hjh1j7dq1rFmzhieffLKjLkEIIYQQ1yiZmipEGyktLbWvvWbzxRdf4OHhgaurtSNaVFQUu3bt4p133mH48OHs3r2bvLw8pk2bZt9nzZo1rFixgmeeeYaysjImTZrEokWL0Ol0WCwWfHx8WLx4MQsWLMDPz6/W9FRbCGYymSguLqaiogKwBmsqlcr+uu3/FoulztLburZJwCaEEOJKcNddd5Gbm8vLL79Meno6sbGxfPPNN4SHhwOQnp5Oamqqff+uXbvyzTff8Oc//5kVK1ZgMpn461//ypQpU5p8Tq1Wy0svvXRNdgOXa782rx2u7eu/lq8dru3rl2u/Nq8dHHf9KkXmkAnRJl5++WXi4+O57rrrKCgoICUlhcOHD3PPPfcwd+5c3NzcmD9/Pr/88gszZsxg06ZNqNVqHnroIW6//fZqIVddC043tgi1jS1Y++abb3jooYd4/fXX+cMf/tDk9wshhBBCCCGEEKJ9SBAnRBvZv38/69atIzExEbVaTffu3bnxxhsZP368vRHCX//6V95991327t1LQEBAo8e0WCwoioKTk1OzQ7T8/HxOnTpFjx49rumFNoUQQgghhBBCiCuFTE0Voo0MGDCAAQMG1PlaZWUlarWaLl26kJubS15eHgEBAZSXl+Ps7FxvV5bWdGvx8fGpdzxCCCGEEEIIIYRwPAnihGhDiqLYq9hs67E5OTnZA7XQ0FAURcFsNgPWzm9CCCGEEEIIIYS4NsjUVCGEEEIIIYQQQgghHKDl896EEEIIIYS4Sr333nt07doVV1dXBg4cyH//+98G99++fTsDBw7E1dWVbt26sWrVqlr7fPrpp/Tq1QutVkuvXr3YsmVLew2/VZpz7Z999hnjx49Hr9fj7e3N8OHD+fbbb6vts379evtMgKr/lZaWtveltEhzrn/btm11Xtvx48er7dcZv/czZsyo89pjYmLs+1wt3/sdO3Zw2223YTKZUKlUfP75542+pzPd8829/s503zf32jvbPd/c6+8s9/0bb7zB4MGD8fLyIjAwkNtvv50TJ040+j5H3fcSxAkhhBBCiGvK5s2bmTdvHgsWLODAgQOMHj2aSZMmkZqaWuf+ycnJ3HzzzYwePZoDBw7w/PPP89hjj/Hpp5/a99m9ezd33XUX9913H4cOHeK+++5j6tSp7N2711GX1STNvfYdO3Ywfvx4vvnmG+Lj4xk7diy33XYbBw4cqLaft7c36enp1f5zdXV1xCU1S3Ov3+bEiRPVri0iIsL+Wmf93r/77rvVrjktLQ2dTsedd95Zbb+r4XtfVFRE3759+d///d8m7d+Z7nlo/vV3pvu+uddu0xnueWj+9XeW+3779u3MnTuXPXv2sHXrVsxmMxMmTKCoqKje9zj0vleEEEIIIYS4hgwZMkR56KGHqm3r2bOn8uyzz9a5/9NPP6307Nmz2rY5c+Yow4YNsz+fOnWqctNNN1XbZ+LEicq0adPaaNRto7nXXpdevXopixYtsj9ft26d4uPj01ZDbFfNvf4ff/xRAZS8vLx6j3mtfO+3bNmiqFQq5cyZM/ZtV9P33gZQtmzZ0uA+nemer6kp11+Xq/m+t2nKtXeme76mlnzvO8t9n5WVpQDK9u3b693Hkfe9VMQJIYQQQohrRnl5OfHx8UyYMKHa9gkTJrBr164637N79+5a+0+cOJF9+/ZRUVHR4D71HbMjtOTaa7JYLFy6dAmdTldte2FhIeHh4YSEhHDrrbfWqpy5ErTm+vv374/RaOTGG2/kxx9/rPbatfK9X7NmDePGjSM8PLza9qvhe99cneWebytX833fUlf7Pd9WOst9n5+fD1DrZ7gqR973EsQJIYQQQohrRk5ODpWVlRgMhmrbDQYDGRkZdb4nIyOjzv3NZjM5OTkN7lPfMTtCS669pnfeeYeioiKmTp1q39azZ0/Wr1/Pl19+yaZNm3B1dWXkyJEkJia26fhbqyXXbzQaWb16NZ9++imfffYZUVFR3HjjjezYscO+z7XwvU9PT+df//oXDzzwQLXtV8v3vrk6yz3fVq7m+765Oss93xY6y32vKArz589n1KhRxMbG1rufI+9752btLYQQQgghRCegUqmqPVcUpda2xvavub25x+woLR3npk2biIuL44svviAwMNC+fdiwYQwbNsz+fOTIkQwYMIDly5fz17/+te0G3kaac/1RUVFERUXZnw8fPpy0tDTefvttrrvuuhYdsyO1dJzr16/H19eX22+/vdr2q+173xyd6Z5vjc5y3zdVZ7vnW6Oz3PePPPIIhw8fZufOnY3u66j7XirihBBCCCHENSMgIAC1Wl3rt9dZWVm1fsttExQUVOf+zs7O+Pv7N7hPfcfsCC25dpvNmzcze/ZsPvnkE8aNG9fgvk5OTgwePPiKq45ozfVXNWzYsGrX1tm/94qisHbtWu677z40Gk2D+16p3/vm6iz3fGt1hvu+LVyN93xrdZb7/tFHH+XLL7/kxx9/JCQkpMF9HXnfSxAnhBBCCCGuGRqNhoEDB7J169Zq27du3cqIESPqfM/w4cNr7f/dd98xaNAgXFxcGtynvmN2hJZcO1grYmbMmMFHH33ELbfc0uh5FEXh4MGDGI3GVo+5LbX0+ms6cOBAtWvrzN97sHYfTEpKYvbs2Y2e50r93jdXZ7nnW6Oz3Pdt4Wq851vrar/vFUXhkUce4bPPPuOHH36ga9eujb7Hofd9s1o7CCGEEEIIcZX7+OOPFRcXF2XNmjVKQkKCMm/ePMXDw8PeFe7ZZ59V7rvvPvv+p0+fVtzd3ZU///nPSkJCgrJmzRrFxcVF+X//7//Z9/npp58UtVqtvPnmm8qxY8eUN998U3F2dlb27Nnj8OtrSHOv/aOPPlKcnZ2VFStWKOnp6fb/Ll68aN8nLi5O+fe//62cOnVKOXDggDJz5kzF2dlZ2bt3r8OvrzHNvf6//OUvypYtW5STJ08qR48eVZ599lkFUD799FP7Pp31e2/zhz/8QRk6dGidx7xavveXLl1SDhw4oBw4cEABlKVLlyoHDhxQUlJSFEXp3Pe8ojT/+jvTfd/ca+9M97yiNP/6ba72+/5Pf/qT4uPjo2zbtq3az3BxcbF9n4687yWIE0IIIYQQ15wVK1Yo4eHhikajUQYMGKBs377d/tr06dOVMWPGVNt/27ZtSv/+/RWNRqN06dJFWblyZa1j/uMf/1CioqIUFxcXpWfPntU+uF1JmnPtY8aMUYBa/02fPt2+z7x585SwsDBFo9Eoer1emTBhgrJr1y4HXlHzNOf6Fy9erHTv3l1xdXVV/Pz8lFGjRilff/11rWN2xu+9oijKxYsXFTc3N2X16tV1Hu9q+d7/+OOPDf4cd/Z7vrnX35nu++Zee2e751vys98Z7vu6rhlQ1q1bZ9+nI+971eVBCiGEEEIIIYQQQggh2pGsESeEEEIIIYQQQgghhANIECeEEEIIIYQQQgghhANIECeEEEIIIYQQQgghhANIECeEEEIIIYQQQgghhANIECeEEEIIIYQQQgghhANIECeEEEIIIYQQQgghhANIECeEEEIIIYQQQgghhANIECeEEEIIIYQQQgghhANIECeEEEIIIYQQQgghhANIECeEEEIIIYQQQgghhANIECeEEEIIIYQQQgghhANIECeEEEIIIYQQQgghhAP8fy1Q4X/v8IFgAAAAAElFTkSuQmCC",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(18, 5))\n",
"gs = gridspec.GridSpec(1, 2, width_ratios=[3, 2])\n",
"ax1 = fig.add_subplot(gs[0], projection=\"3d\")\n",
"ax2 = fig.add_subplot(gs[1])\n",
"\n",
"X, Y = np.meshgrid(A, S)\n",
"ax1.plot_surface(Y, X, VV)\n",
"ax1.set_title(\"Floating strike Asian option surface at time zero\")\n",
"ax1.set_xlabel(\"Spot price\")\n",
"ax1.set_ylabel(\"Spot average\")\n",
"ax1.set_zlabel(\"Price\")\n",
"ax1.view_init(30, -100)\n",
"\n",
"ax2.plot(x, Payoff, color=\"blue\", label=\"Payoff\")\n",
"ax2.plot(x, V[:, 0], color=\"red\", label=\"W curve\")\n",
"ax2.set_xlim(0, 2)\n",
"ax2.set_ylim(0, 1)\n",
"ax2.set_title(\"Auxiliary function W at time zero\")\n",
"ax2.legend(loc=\"upper left\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### References\n",
"\n",
"[1] Daniel Sevcovic, Beata Stehlikova, Karol Mikula (2011). \"Analytical and numerical methods for pricing financial derivatives\". Nova Science Pub Inc; UK. \n",
"\n",
"[2] Wilmott Paul (1994), \"Option pricing - Mathematical models and computation\". Oxford Financial Press.\n",
"\n",
"[3] Steven Shreve (2005), \"Stochastic calculus for finance\". Springer Finance.\n",
"\n",
"[4] Paul Wilmott (2006), *Paul Wilmott on quantitative finance*\n",
"\n",
"[5] Broadie, Glasserman, Kou. *Connecting Discrete and Continuous Path-Dependent Options* "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
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}
================================================
FILE: 2.3 American Options.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# American options\n",
"\n",
"\n",
"## Contents\n",
" - [Optimal early exercise boundary](#sec1)\n",
" - [Binomial tree](#sec2)\n",
" - [Longstaff - Schwartz](#sec3)\n",
" - [Perpetual put](#sec4)\n",
" \n",
" \n",
"The American option problem is an optimal stopping problem formulated as follows:\n",
"\n",
"$$ V(t,s) = \\sup_{\\tau \\in [t,T]} \\mathbb{E}^{\\mathbb{Q}}\\biggl[ e^{-r(\\tau-t)} \\Phi(S_{\\tau}) \\bigg| S_t=s \\biggr]$$\n",
"\n",
"where $\\Phi(\\cdot)$ is the payoff, also called *intrinsic value*. \n",
"This formula resembles the formula of a European option, except that in this case the option can be exercised at any time. But... at which time? The problem is to find the best exercise time $\\tau$ that maximizes the expectation.\n",
"\n",
"Notice that the time $\\tau$ is a random variable! It can be different for different paths of the stock process. \n",
"The solution of the problem provides a strategy, that at each time assesses if it is optimal to exercise the option or not, depending on the current value of the underlying stock.\n",
"\n",
"It can be proved that the function $V(t,s)$ is the solution of the following PDE:\n",
"\n",
"\\begin{equation}\n",
"\\max \\biggl\\{ \\frac{\\partial V(t,s)}{\\partial t} \n",
" + r\\,s \\frac{\\partial V(t,s)}{\\partial s}\n",
" + \\frac{1}{2} \\sigma^2 s^2 \\frac{\\partial^2 V(t,s)}{\\partial s^2} - r V(t,s), \\; \n",
" \\Phi(s) - V(t,s) \\biggr\\} = 0.\n",
"\\end{equation}\n",
"\n",
"$$ V(T,s) = \\Phi(s) $$\n",
"\n",
"The numerical algorithm for solving this PDE is almost identical to the algorithm proposed in the notebook **2.1** for the Black-Scholes PDE. \n",
"\n",
"Let us have a look at the differences between the implementation of the European and the American algorithm, in the class `BS_pricer`:\n",
"\n",
"```python\n",
"if self.exercise==\"European\": \n",
" for i in range(Ntime-2,-1,-1):\n",
" offset[0] = a * V[0,i] \n",
" offset[-1] = c * V[-1,i]\n",
" V[1:-1,i] = spsolve( D, (V[1:-1,i+1] - offset) )\n",
"elif self.exercise==\"American\":\n",
" for i in range(Ntime-2,-1,-1):\n",
" offset[0] = a * V[0,i]\n",
" offset[-1] = c * V[-1,i]\n",
" V[1:-1,i] = np.maximum( spsolve( D, (V[1:-1,i+1] - offset) ), Payoff[1:-1])\n",
"```\n",
"\n",
"The European and the American algorithms just differ in one line!! \n",
"\n",
"I don't consider dividends in the notebook. In absence of dividends the American call has the same value of the European call. For this reason, I'm going to consider only the pricing problem of a put option\n",
"\n",
"$$ \\Phi(S_{\\tau}) = ( K - S_{\\tau} )^+ $$\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from FMNM.BS_pricer import BS_pricer\n",
"from FMNM.Parameters import Option_param\n",
"from FMNM.Processes import Diffusion_process\n",
"from FMNM.cython.solvers import PSOR\n",
"\n",
"import numpy as np\n",
"import scipy.stats as ss\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"from IPython.display import display\n",
"import sympy\n",
"\n",
"sympy.init_printing()\n",
"\n",
"\n",
"def display_matrix(m):\n",
" display(sympy.Matrix(m))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Creates the object with the parameters of the option\n",
"opt_param = Option_param(S0=100, K=100, T=1, exercise=\"American\", payoff=\"put\")\n",
"# Creates the object with the parameters of the process\n",
"diff_param = Diffusion_process(r=0.1, sig=0.2)\n",
"# Creates the pricer object\n",
"BS = BS_pricer(opt_param, diff_param)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The class `BS_pricer` implements two algorithms to price American options:\n",
"- the Longstaff-Schwartz Method (see section [below](#sec2))\n",
"- the PDE method"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle 4.8181988706184$"
],
"text/plain": [
"4.818198870618403"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BS.LSM(N=10000, paths=10000, order=2) # Longstaff Schwartz Method"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle 4.81563894941683$"
],
"text/plain": [
"4.815638949416825"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"N_space = 8000\n",
"N_time = 5000\n",
"BS.PDE_price((N_space, N_time))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot American put curve"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(9, 5))\n",
"BS.plot([70, 140, 0, 30])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Optimal early exercise boundary\n",
"\n",
"Let us define a free boundary by the value $0 S_f \\quad $ called **continuation region**\n",
"\n",
"In the stopping region, the value of the option corresponds to its [intrinsic value](https://en.wikipedia.org/wiki/Intrinsic_value_(finance)) i.e. $V(t,S_t) = K-S_t$. \n",
"\n",
"In order to find $S_f$ we have to find the maximum value $s$ such that $V(t,s) - (K-s) = 0$:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"payoff = BS.payoff_f(BS.S_vec).reshape(len(BS.S_vec), 1) # Transform the payoff in a column vector\n",
"mesh = (BS.mesh - payoff)[1:-1, :-1] # I remove the boundary terms\n",
"optimal_indeces = np.argmax(np.abs(mesh) > 1e-10, axis=0) # I introduce the error 1e-10\n",
"T_vec = np.linspace(0, BS.T, N_time) # Time vector"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(9, 5))\n",
"plt.plot(T_vec[:-1], BS.S_vec[optimal_indeces])\n",
"plt.text(0.2, 94, \"Continuation region\", fontsize=15)\n",
"plt.text(0.6, 87, \"Stopping region\", fontsize=16)\n",
"plt.xlabel(\"Time\")\n",
"plt.ylabel(\"$S_f$\")\n",
"plt.title(\"Early exercise curve\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Binomial tree\n",
"\n",
"One of the most used methods for pricing American options is the Binomial tree. \n",
"\n",
"We have already encountered the binomial tree in the notebook **1.1**. The following algorithm is almost a copy/paste from that notebook. \n",
"There are just two additional lines:\n",
"- `S_T = S_T * u`. This an efficient method to retrive the price vector at each time steps. \n",
"- `V = np.maximum( V, K-S_T )`. This line computes the maximum between the conditional expectation V and the intrisic value. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"S0 = 100.0 # spot stock price\n",
"K = 100.0 # strike\n",
"T = 1.0 # maturity\n",
"r = 0.1 # risk free rate\n",
"sig = 0.2 # diffusion coefficient or volatility"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"American BS Tree Price: 4.81624866310944\n"
]
}
],
"source": [
"N = 25000 # number of periods or number of time steps\n",
"payoff = \"put\" # payoff\n",
"\n",
"dT = float(T) / N # Delta t\n",
"u = np.exp(sig * np.sqrt(dT)) # up factor\n",
"d = 1.0 / u # down factor\n",
"\n",
"V = np.zeros(N + 1) # initialize the price vector\n",
"S_T = np.array([(S0 * u**j * d ** (N - j)) for j in range(N + 1)]) # price S_T at time T\n",
"\n",
"a = np.exp(r * dT) # risk free compound return\n",
"p = (a - d) / (u - d) # risk neutral up probability\n",
"q = 1.0 - p # risk neutral down probability\n",
"\n",
"if payoff == \"call\":\n",
" V[:] = np.maximum(S_T - K, 0.0)\n",
"elif payoff == \"put\":\n",
" V[:] = np.maximum(K - S_T, 0.0)\n",
"\n",
"for i in range(N - 1, -1, -1):\n",
" V[:-1] = np.exp(-r * dT) * (p * V[1:] + q * V[:-1]) # the price vector is overwritten at each step\n",
" S_T = S_T * u # it is a tricky way to obtain the price at the previous time step\n",
" if payoff == \"call\":\n",
" V = np.maximum(V, S_T - K)\n",
" elif payoff == \"put\":\n",
" V = np.maximum(V, K - S_T)\n",
"\n",
"print(\"American BS Tree Price: \", V[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Longstaff - Schwartz Method\n",
"\n",
"This is a Monte Carlo algorithm proposed by Longstaff and Schwartz in the paper [1]:\n",
"[LS Method](https://people.math.ethz.ch/~hjfurrer/teaching/LongstaffSchwartzAmericanOptionsLeastSquareMonteCarlo.pdf)\n",
"\n",
"The algorithm is not difficult to implement, but it can be difficult to understand. I think this is the reason the authors started the paper with an example. \n",
"Well. I think they had a good idea, and for this reason I want to reproduce their example here. \n",
"\n",
"The same code is copied in the class `BS_pricer` where the function `LSM` is implemented.\n",
"\n",
"**If in the following code you feel that something is unclear, I suggest you to follow the steps (not in python) proposed in the original paper**."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"N = 4 # number of time points\n",
"r = 0.06 # interest rate\n",
"K = 1.1 # strike\n",
"T = 3 # Maturity\n",
"\n",
"dt = T / (N - 1) # time step\n",
"df = np.exp(-r * dt) # discount factor per time step"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"S = np.array(\n",
" [\n",
" [1.00, 1.09, 1.08, 1.34],\n",
" [1.00, 1.16, 1.26, 1.54],\n",
" [1.00, 1.22, 1.07, 1.03],\n",
" [1.00, 0.93, 0.97, 0.92],\n",
" [1.00, 1.11, 1.56, 1.52],\n",
" [1.00, 0.76, 0.77, 0.90],\n",
" [1.00, 0.92, 0.84, 1.01],\n",
" [1.00, 0.88, 1.22, 1.34],\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0 & 1.09 & 1.08 & 1.34\\\\1.0 & 1.16 & 1.26 & 1.54\\\\1.0 & 1.22 & 1.07 & 1.03\\\\1.0 & 0.93 & 0.97 & 0.92\\\\1.0 & 1.11 & 1.56 & 1.52\\\\1.0 & 0.76 & 0.77 & 0.9\\\\1.0 & 0.92 & 0.84 & 1.01\\\\1.0 & 0.88 & 1.22 & 1.34\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0 1.09 1.08 1.34⎤\n",
"⎢ ⎥\n",
"⎢1.0 1.16 1.26 1.54⎥\n",
"⎢ ⎥\n",
"⎢1.0 1.22 1.07 1.03⎥\n",
"⎢ ⎥\n",
"⎢1.0 0.93 0.97 0.92⎥\n",
"⎢ ⎥\n",
"⎢1.0 1.11 1.56 1.52⎥\n",
"⎢ ⎥\n",
"⎢1.0 0.76 0.77 0.9 ⎥\n",
"⎢ ⎥\n",
"⎢1.0 0.92 0.84 1.01⎥\n",
"⎢ ⎥\n",
"⎣1.0 0.88 1.22 1.34⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_matrix(S)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the previous cell we defined the stock matrix S. \n",
"It has 8 rows that correspond to the number of paths. \n",
"The 4 columns correspond to the 4 time points, i.e. each row is a path with 3 time steps."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Example price= 0.11443433004505696\n"
]
}
],
"source": [
"H = np.maximum(K - S, 0) # intrinsic values for put option\n",
"V = np.zeros_like(H) # value matrix\n",
"V[:, -1] = H[:, -1]\n",
"\n",
"# Valuation by LS Method\n",
"for t in range(N - 2, 0, -1):\n",
" good_paths = H[:, t] > 0 # paths where the intrinsic value is positive\n",
" # the regression is performed only on these paths\n",
"\n",
" rg = np.polyfit(S[good_paths, t], V[good_paths, t + 1] * df, 2) # polynomial regression\n",
" C = np.polyval(rg, S[good_paths, t]) # evaluation of regression\n",
"\n",
" exercise = np.zeros(len(good_paths), dtype=bool) # initialize\n",
" exercise[good_paths] = H[good_paths, t] > C # paths where it is optimal to exercise\n",
"\n",
" V[exercise, t] = H[exercise, t] # set V equal to H where it is optimal to exercise\n",
" V[exercise, t + 1 :] = 0 # set future cash flows, for that path, equal to zero\n",
" discount_path = V[:, t] == 0 # paths where we didn't exercise\n",
" V[discount_path, t] = V[discount_path, t + 1] * df # set V[t] in continuation region\n",
"\n",
"V0 = np.mean(V[:, 1]) * df # discounted expectation of V[t=1]\n",
"print(\"Example price= \", V0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The matrix `H = np.maximum(K - S, 0)`, is the matrix of intrinsic values:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}0.1 & 0.01 & 0.02 & 0\\\\0.1 & 0 & 0 & 0\\\\0.1 & 0 & 0.03 & 0.07\\\\0.1 & 0.17 & 0.13 & 0.18\\\\0.1 & 0 & 0 & 0\\\\0.1 & 0.34 & 0.33 & 0.2\\\\0.1 & 0.18 & 0.26 & 0.09\\\\0.1 & 0.22 & 0 & 0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡0.1 0.01 0.02 0 ⎤\n",
"⎢ ⎥\n",
"⎢0.1 0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢0.1 0 0.03 0.07⎥\n",
"⎢ ⎥\n",
"⎢0.1 0.17 0.13 0.18⎥\n",
"⎢ ⎥\n",
"⎢0.1 0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢0.1 0.34 0.33 0.2 ⎥\n",
"⎢ ⎥\n",
"⎢0.1 0.18 0.26 0.09⎥\n",
"⎢ ⎥\n",
"⎣0.1 0.22 0 0 ⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_matrix(H.round(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The matrix V contains the cash flows.\n",
"\n",
"**Important** \n",
"To simplify the computations, the discounted cashflows are reported at every time points. \n",
"\n",
"For instance: \n",
"In the third row the final cashflow (0.07) is discounted at every time point, till t=1. \n",
"In the paper, the authors just consider the cashflow (0.07) at time t=3 and the discount is performed at the end of the algorithm."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0\\\\0 & 0.0621 & 0.0659 & 0.07\\\\0 & 0.17 & 0 & 0\\\\0 & 0 & 0 & 0\\\\0 & 0.34 & 0 & 0\\\\0 & 0.18 & 0 & 0\\\\0 & 0.22 & 0 & 0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡0 0 0 0 ⎤\n",
"⎢ ⎥\n",
"⎢0 0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢0 0.0621 0.0659 0.07⎥\n",
"⎢ ⎥\n",
"⎢0 0.17 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢0 0.34 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢0 0.18 0 0 ⎥\n",
"⎢ ⎥\n",
"⎣0 0.22 0 0 ⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_matrix(V.round(4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Perpetual put\n",
"\n",
"\n",
"An American option is called *perpetual* if it has no expiration date i.e. $T=\\infty$:\n",
"\n",
"$$ V(t,s) = \\sup_{\\tau \\in [t,\\infty]} \\mathbb{E}^{\\mathbb{Q}}\\biggl[ e^{-r(\\tau-t)}\\, ( K - S_{\\tau} )^+ \\, \\bigg|\\, S_t=s \\biggr]. $$\n",
"\n",
"By the time homogeneity of the problem we can set $t=0$ and obtain:\n",
"\n",
"$$ V(s) = \\sup_{\\tau \\in [0,\\infty]} \\mathbb{E}^{\\mathbb{Q}}\\biggl[ e^{-r \\tau}\\, ( K - S_{\\tau} )^+ \\,\\bigg|\\, S_0=s \\biggr]. $$\n",
"\n",
"The PDE for the perpetual put option is:\n",
"\n",
"$$\n",
"\\max \\biggl\\{ \n",
" r\\,s \\frac{ d V(s)}{d s}\n",
" + \\frac{1}{2} \\sigma^2 s^2 \\frac{d^2 V(s)}{d s^2} - r V(s), \\; \n",
" ( K - s )^+ - V(s) \\biggr\\} = 0.\n",
"$$\n",
"\n",
"In order to solve this obstacle problem we need to find a value $s_0 \\in (0,K)$ such that:\n",
"\n",
"$$ V(s) = ( K - s ) \\quad \\text{for} \\quad s \\in [0,s_0] \\quad \\text{ (stopping region)}$$\n",
"\n",
"and \n",
"\n",
"$$ r\\,s \\frac{ d V(s)}{d s}\n",
" + \\frac{1}{2} \\sigma^2 s^2 \\frac{d^2 V(s)}{d s^2} - r V(s) = 0 \\quad \\text{for} \\quad s \\in [s_0, \\infty) \\quad \\text{ (continuation region)}$$\n",
" \n",
"We can search for the solution of the linear second order ODE in the continuation region, by guessing a solution in the form $V(s) = s^n$. \n",
"We then obtain a quadratic equation in $n$ that has two solutions $n_1=1$ and $n_2 = -\\frac{2r}{\\sigma^2}$. \n",
"We can express \n",
"\n",
"$$V(s) = A s + B s^{-\\frac{2r}{\\sigma^2}} \\quad \\text{for} \\quad s \\in [s_0, \\infty).$$ \n",
"\n",
"Since $V(s) \\to 0$ as $s\\to \\infty$, we must have $A=0$. \n",
"We then require that *V(s)* is **continuous** and **differentiable** at $s_0$:\n",
"\n",
"$$ B s_0^{-\\frac{2r}{\\sigma^2}} = K -s_0 \\quad \\text{and} \\quad -\\frac{2r}{\\sigma^2} B s_0^{-\\frac{2r}{\\sigma^2}-1} = -1$$\n",
"\n",
"These equations are satisfied for:\n",
"\n",
"$$ B = \\frac{\\sigma^2}{2r}\\, s_0^{\\frac{2r}{\\sigma^2}+1} \\quad \\text{and} \\quad s_0 = \\frac{K}{\\frac{\\sigma^2}{2r}+1}. $$\n",
"\n",
"It is also interesting to have a look at [2], where the author derives the formulas above using a probabilistic approach."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def perpetual_put(S, K, sigma, r):\n",
" \"\"\"analytical formula for perpetual put option\"\"\"\n",
" s0 = K / (sigma**2 / (2 * r) + 1)\n",
" B = sigma**2 / (2 * r) * s0 ** ((2 * r) / sigma**2 + 1)\n",
" return np.where(S < s0, K - S, B * S ** (-2 * r / sigma**2))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"S = np.linspace(60, 160, 1000) # stock price\n",
"K = 100.0 # strike\n",
"r = 0.1 # risk free rate\n",
"sig = 0.2 # diffusion coefficient or volatility\n",
"s0 = K / (sig**2 / (2 * r) + 1) # optimal excercise value"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12, 5))\n",
"plt.plot(S, perpetual_put(S, K, sig, r), label=\"perpetual option price\")\n",
"plt.plot(S, np.maximum(K - S, 0), label=\"option intrinsic value\")\n",
"plt.axvline(x=s0, color=\"k\", linestyle=\"--\", label=\"optimal excercise value\")\n",
"plt.xlabel(\"s\")\n",
"plt.ylabel(\"V(s)\")\n",
"plt.title(\"Perpetual put option\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Numerical computation of perpetual put"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can pass to the log-variable $x = \\log s$ and obtain the simpler equation with constant coefficients:\n",
"\n",
"$$ \\max \\biggl\\{ \n",
" (r - \\frac{1}{2} \\sigma^2) \\, \\frac{ d V(x)}{d x}\n",
" + \\frac{1}{2} \\sigma^2 \\frac{d^2 V(x)}{d x^2} - r V(x), \\; \n",
" ( K - e^x )^+ - V(x) \\biggr\\} = 0. $$\n",
"\n",
"equivalent to:\n",
"\n",
"$$ \\min \\biggl\\{ \n",
" -(r - \\frac{1}{2} \\sigma^2) \\, \\frac{ d V(x)}{d x}\n",
" - \\frac{1}{2} \\sigma^2 \\frac{d^2 V(x)}{d x^2} + r V(x), \\; \n",
" V(x) - ( K - e^x )^+ \\biggr\\} = 0. $$\n",
"\n",
"This **variational inequality** can be reformulated as a **linear complementarity problem**:\n",
"\n",
"$$ \\biggl( -(r - \\frac{1}{2} \\sigma^2) \\, \\frac{ d V(x)}{d x}\n",
" - \\frac{1}{2} \\sigma^2 \\frac{d^2 V(x)}{d x^2} + r V(x), \n",
" \\biggl)\\, \\cdot \\biggl( V(x) - ( K - e^x )^+ \\biggr) = 0, $$\n",
"$$ -(r - \\frac{1}{2} \\sigma^2) \\, \\frac{ d V(x)}{d x}\n",
" - \\frac{1}{2} \\sigma^2 \\frac{d^2 V(x)}{d x^2} + r V(x) \\geq 0, \n",
" \\quad \\quad V(x) - ( K - e^x )^+ \\geq 0.\n",
" $$ \n",
" \n",
"At this poins, we can discretize the equations by finite difference using a central approximation for the first order derivative;\n",
"\n",
"$$ -(r - \\frac{1}{2} \\sigma^2) \\frac{V_{i+1} -V_{i-1}}{ 2 \\Delta x} \n",
" - \\frac{1}{2} \\sigma^2 \\frac{V_{i+1} + V_{i-1} - 2 V_{i}}{\\Delta x^2} + r V_i \\geq 0 \n",
" \\quad \\quad V_i - ( K - e^{x_i} )^+ \\geq 0. $$\n",
"\n",
"That in matrix form becomes:\n",
"\n",
"$$ A \\, V - B \\geq 0 \\quad \\quad V - C \\geq 0 \\quad \\quad (A \\, V - B)\\cdot(V-C) = 0. $$\n",
"\n",
"where as usual, $A$ is a tridiagonal matrix and $B$ is the vector containing the boundary values. $C$ is the payoff.\n",
"\n",
"A description of numerical methods to solve the linear complementarity problem can be found in [3] or [4]. \n",
"Here it is important to choose a big value of `S_max` because the perpetual put price goes to zero asimptotically. \n",
"The PSOR method is a slight modification of the SOR method introduced in the notebooks **A1** and **A2**. "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"Nspace = 10000 # space steps\n",
"S_max = 10 * K # very large in order to minimize the error due to asymptotic boundary condition\n",
"S_min = K / 3\n",
"x_max = np.log(S_max)\n",
"x_min = np.log(S_min)\n",
"x, dx = np.linspace(x_min, x_max, Nspace, retstep=True) # space discretization"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"sig2 = sig * sig\n",
"dxx = dx * dx\n",
"\n",
"a = (r - 0.5 * sig2) / (2 * dx) - sig2 / (2 * dxx) # -1 diagonal\n",
"b = sig2 / dxx + r # main diagonal\n",
"c = -(r - 0.5 * sig2) / (2 * dx) - sig2 / (2 * dxx) # +1 diagonal"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"C = np.maximum(K - np.exp(x), 0) # payoff\n",
"\n",
"B = np.zeros(Nspace) # vector to be used for the boundary terms\n",
"B[0] = -a * (K - S_min) # boundary condition\n",
"B[-1] = 0 # boundary condition"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Convergence after 30017 iterations\n"
]
}
],
"source": [
"V = np.asarray(PSOR(a, b, c, B, C, w=1.999, eps=1e-12, N_max=1000000))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 5))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"ax1.plot(S[::40], perpetual_put(S[::40], K, sig, r), \"*\", label=\"Theoretical price\")\n",
"ax1.plot(np.exp(x), V, label=\"Numerical value\")\n",
"ax1.plot(np.exp(x), C, label=\"Payoff\")\n",
"ax1.axvline(x=s0, color=\"k\", linestyle=\"--\", label=\"optimal excercise value\")\n",
"ax1.axis([30, 200, -1, 70])\n",
"ax1.set_title(\"Numerical computation - Perpetual put option\")\n",
"ax1.set_xlabel(\"s\")\n",
"ax1.set_ylabel(\"V(s)\")\n",
"ax1.legend()\n",
"ax2.plot(np.exp(x), perpetual_put(np.exp(x), K, sig, r) - V, label=\"error\")\n",
"ax2.set_title(\"Difference: theoretical - numerical\")\n",
"ax2.set_xlabel(\"s\")\n",
"ax1.set_ylabel(\"Error\")\n",
"ax2.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"[1] F. Longstaff, E. Schwartz (2001) *Valuing American Options by Simulation: A Simple Least-Squares Approach*, The Review of Financial Studies, vol 14-1, pag 113-147. \n",
"\n",
"[2] Steven Shreve, *Stochastic calculus for finance, volume 2*, (2004).\n",
"\n",
"[3] Daniel Sevcovic, Beata Stehlikova, Karol Mikula (2011). \"Analytical and numerical methods for pricing financial derivatives\". Nova Science Pub Inc; UK. \n",
"\n",
"[4] Wilmott Paul (1994). \"Option pricing - Mathematical models and computation\". Oxford Financial Press."
]
}
],
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================================================
FILE: 3.1 Merton jump-diffusion, PIDE method.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Merton Partial Integro-Differential Equation\n",
"\n",
"## Contents\n",
" - [The Merton PIDE](#sec1)\n",
" - [Discretization](#sec1.1)\n",
" - [Numerical solution of the PIDE](#sec2)\n",
" - [Comparison with Monte Carlo and closed formula](#sec3)\n",
" - [Model limitations](#sec4)\n",
" - [Comparison with the Black Scholes PDE](#sec5) \n",
" \n",
" \n",
"The notebook **2.1** is a prerequisites for understanding this notebook. If you don't know the Merton model, have a look at the notebook **A.3**.\n",
"\n",
"In this notebook we only consider the problem of solving the Merton PIDE. \n",
"Let us recall that, this is the pricing PIDE associated to the process: \n",
"\n",
"\\begin{equation}\n",
"X_t = \\mu t + \\sigma W_t + \\sum_{i=1}^{N_t} Y_i, \n",
"\\end{equation}\n",
"\n",
"where $N_t$ is a Poisson random variable representing the number of jumps of $X_t$ up to time $t$, and $Y_i \\sim \\mathcal{N}(\\alpha, \\xi^2)$ is the size of each jump. (more details in **A.3**)\n",
"\n",
"This is an **incomplete model**. It means that the risk neutral measure is not unique (for more information see Section 3 of **A.3** or the [wiki page](https://en.wikipedia.org/wiki/Fundamental_theorem_of_asset_pricing)). We are not considering any change of measure here. \n",
"Rather, we are going to consider all parameters as *risk neutral* parameters i.e. obtained from the options market through a calibration process (see Notebook **4.2**). \n",
"The risk neutral process has drift:\n",
"\n",
"$$ \\mu = r - \\frac{1}{2} \\sigma^2 - m $$\n",
" \n",
"with $r$ risk free interest rate, and $m$ defined below.\n",
"\n",
"##### Closed formula\n",
"Just for information, there exists a closed formula for pricing European call/put options under the Merton model.\n",
"This formula was presented by Merton in [2]. We just report it without derivation.\n",
"\n",
"$$ \\sum_k \\frac{e^{-\\psi T} \\psi T}{k!} \\; V^{BS}\\biggl( S_0,\\, T,\\, r - m + \\frac{k (\\alpha + \\xi^2/2)}{T} ,\\, \\sqrt{\\sigma^2 + \\frac{k \\xi^2}{T}} \\biggr) $$ \n",
"\n",
"with $\\psi = \\lambda \\bigl( e^{\\alpha + \\frac{1}{2} \\xi^2} \\bigr)$, and $V^{BS}(S,T,r,\\sigma)$ is the Black-Scholes closed formula."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## The Merton PIDE\n",
"\n",
"In this notebook I want to show how to solve the Merton PIDE: \n",
"\n",
"\\begin{align}\n",
" \\frac{\\partial V(t,x)}{\\partial t}\n",
" + \\biggl( r -\\frac{1}{2}\\sigma^2 -m \\biggr) \\frac{\\partial V(t,x)}{\\partial x}\n",
" + \\frac{1}{2} \\sigma^2 \\frac{\\partial^2 V(t,x)}{\\partial x^2} \n",
" + \\int_{\\mathbb{R}} V(t,x+z) \\nu(dz) - (\\lambda+r) V(t,x) = 0.\n",
"\\end{align}\n",
"\n",
"with\n",
"\\begin{align} \n",
" m :=& \\; \\int_{\\mathbb{R}} ( e^{z} - 1 ) \\nu(dz) \\\\\n",
" =& \\; \\lambda \\biggl( e^{\\alpha + \\frac{1}{2} \\xi^2} -1 \\biggr).\n",
"\\end{align}\n",
"\n",
"The Lévy measure is:\n",
"$$ \\nu(dz) = \\frac{\\lambda}{\\xi \\sqrt{2\\pi}} e^{- \\frac{(z-\\alpha)^2}{2\\xi^2}} dz. $$\n",
"\n",
"The Lévy measure is a scaled Normal distribution, such that $\\lambda = \\int_{\\mathbb{R}} \\nu(dz)$.\n",
"\n",
"For more information on the derivation of this PIDE, have a look at the document **A.3**. It includes a short introduction to general pricing PIDEs and the description of the Merton model. \n",
"\n",
"This equation is an \"extension\" of the Black-Scholes equation. It coincides with the BS equation for $\\lambda = 0$.\n",
"\n",
"Let us first introduce the discretization method, and then write everything in python!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Discretization\n",
"\n",
"The discretization of this equation is quite similar to the discretization of the BS equation. \n",
"However, here there is an additional integral term!!\n",
"\n",
"We will adopt the Implicit-Explicit **(IMEX)** scheme proposed by Cont-Voltchkova [1]. The differential part is discretized by an implicit scheme (the same approach used for the BS equation). The integral part in instead discretized by an explicit scheme. The reason of the explicit choice, is that it avoids the inversion of the dense \"jump\" matrix (below we will construct the jump matrix).\n",
"\n",
"The integral part is computed using **fourier methods** (it is a convolution integral) in order to increasing the efficiency."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we have to restrict the problem to a bounded region, we consider the equation: \n",
"\n",
"\\begin{align*}\n",
" \\frac{\\partial V(t,x)}{\\partial t} \n",
" + \\biggl( r -\\frac{1}{2}\\sigma^2 - \\hat m \\biggr) \\frac{\\partial V(t,x)}{\\partial x} \n",
" + \\frac{1}{2} \\sigma^2 \\frac{\\partial^2 V(t,x)}{\\partial x^2} \n",
" + \\int_{-B_1}^{B_2} V(t,x+z) \\nu(dz) - (\\hat \\lambda + r) V(t,x) = 0.\n",
"\\end{align*}\n",
"\n",
"with \n",
"\n",
"$$ \\hat m = \\int_{-B_1}^{B_2} \\bigl( e^z-1 \\bigr) \\nu(dz)$$ \n",
"and \n",
"$$\\hat \\lambda = \\int_{-B_1}^{B_2} \\nu(dz).$$\n",
"\n",
"For $0 < K_1 < K_2$ we choose $B_1,B_2$ such that $ \\bigl[-B_1,B_2\\bigr] = \\bigl[ ( -K_1-1/2 )\\Delta x , ( K_2+1/2 )\\Delta x \\bigr] $, and $\\Delta x$ is the space step. \n",
"\n",
"The computational domain of interest becomes $[0,T]\\, \\times \\, [A_1-B_1,A_2+B_2]$, where in the regions $[0,T]\\, \\times \\, [A_1-B_1,A_1]$ and \n",
"$[0,T]\\, \\times \\, [A_2,A_2+B_2]$ we need to define the boundary conditions.\n",
"\n",
"Let us discretize the integral as follows:\n",
"\n",
"$$ \\int_{-B_1}^{B_2} V(t_n,x_i+z) \\nu(dz) \\approx \\sum_{k = -K_1}^{K_2} \\nu_k V^{n}_{i+k} $$\n",
"\n",
"where\n",
"\n",
"$$\n",
" \\nu_k = \\int_{(k-\\frac{1}{2}) \\Delta x}^{(k+\\frac{1}{2}) \\Delta x} \\nu(z) dz, \\hspace{1em} \\mbox{ for } \\hspace{1em} -K_1 \\leq k \\leq K_2. \n",
"$$\n",
"\n",
"We have that $ \\hat \\lambda = \\sum_{k = -K_1}^{K_2} \\nu_k $. For large values of $B_1$ and $B_2$, the parameter $\\hat \\lambda$ is a good approximation for $\\lambda$, since\n",
"\n",
"$$\\lambda = \\lim_{B_1,B_2 \\to \\infty} \\hat \\lambda = \\lim_{B_1,B_2 \\to \\infty} \\int_{-B_1}^{B_2} \\nu(dz). $$\n",
"\n",
"The discretized equation using the IMEX scheme becomes: \n",
"\n",
"$$ \\begin{aligned}\n",
"&\\frac{V^{n+1}_{i} -V^{n}_{i}}{\\Delta t} + \n",
"(r-\\frac{1}{2}\\sigma^2 - \\hat m) \\frac{V^{n}_{i+1} -V^{n}_{i-1}}{ 2 \\Delta x} \\\\ \\nonumber\n",
"&+ \\frac{1}{2} \\sigma^2 \\frac{V^{n}_{i+1} + V^{n}_{i-1} - 2 V^{n}_{i}}{\\Delta x^2} - (r+\\hat \\lambda) V^{n}_i +\\sum_{k = -K_1}^{K_2} \\nu_k V^{n+1}_{i+k} = 0.\n",
"\\end{aligned} $$\n",
"\n",
"Rearranging the terms: \n",
"\n",
"$$ \\begin{aligned}\n",
"\\underbrace{ V^{n+1}_{i} + \\Delta t \\sum_{k = -K_1}^{K_2} \\nu_k V^{n+1}_{i+k} }_{\\tilde V^{n+1}_i} &= \n",
"\tV^{n}_{i} \\biggl( 1 + (r+\\hat \\lambda)\\Delta t + \\sigma^2 \\frac{\\Delta t}{\\Delta x^2} \\biggr) \\\\\n",
"& + V^{n}_{i+1} \\biggl( -(r -\\frac{1}{2}\\sigma^2 -\\hat m )\\frac{\\Delta t}{2 \\Delta x} +\n",
"\\frac{1}{2}\\sigma^2 \\frac{\\Delta t}{\\Delta x^2} \\biggr) \\\\\n",
"& + V^{n}_{i-1} \\biggl( (r -\\frac{1}{2}\\sigma^2 - \\hat m)\\frac{\\Delta t}{2 \\Delta x} + \n",
"\\frac{1}{2}\\sigma^2 \\frac{\\Delta t}{\\Delta x^2} \\biggr).\n",
"\\end{aligned} $$\n",
"\n",
"We can rename the coefficients:\n",
"\n",
"$$ \\tilde V^{n+1}_{i} = a V^{n}_{i-1} + b V^{n}_{i} + c V^{n}_{i+1}, $$\n",
"\n",
"and solve the system for $V^{n}_{i}$, for every $1 \\leq i \\leq M-1$:\n",
"\n",
"$$\n",
" \\begin{cases}\n",
" \\tilde V^{n+1}_i = V^{n+1}_{i} + \\Delta t \\sum_{k = -K_1}^{K_2} V^{n+1}_{i+k} \\nu_k \\quad \\text{ for } \\quad 1 \\leq i \\leq M-1 \\\\\n",
" V^{n} = \\mathcal{D}^{-1} \\biggl( \\tilde V^{n+1} - B \\biggr) \n",
" \\end{cases}\n",
"$$\n",
"\n",
"where $\\mathcal{D}$ is the tridiagonal matrix formed by the coefficients $a,b,c$, and with boundary terms $B = (a V^{n}_{0}, 0, ... , 0, c V^{n}_{M})$. \n",
"\n",
"Introducing the *jump* matrix $J$, the system becomes:\n",
"\n",
"$$\n",
" \\begin{cases}\n",
" \\tilde V^{n+1} = V^{n+1} + \\Delta t \\, J \\, V^{n+1} \\\\\n",
" V^{n} = \\mathcal{D}^{-1} \\biggl( \\tilde V^{n+1} - B \\biggr) %\\quad \\mbox{ for } \\quad 1 \\leq i \\leq M-1. \n",
" \\end{cases}\n",
"$$\n",
"\n",
"However, we will see that it is better to avoid to use this matrix, and solve the integral by **fft** methods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Numerical solution of the PIDE\n",
"\n",
"Ok... we are ready to implement the previous algorithm. \n",
"\n",
"In order to have a clear presentation, I prefer to choose a small number of time and space steps. It will be easier for you to follow what I'm doing.\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from scipy import sparse\n",
"from scipy.sparse.linalg import splu\n",
"\n",
"import numpy as np\n",
"import scipy as scp\n",
"import scipy.stats as ss\n",
"from IPython.display import display\n",
"import sympy\n",
"\n",
"sympy.init_printing()\n",
"from scipy import signal\n",
"from scipy.integrate import quad\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"def display_matrix(m):\n",
" display(sympy.Matrix(m))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"r = 0.1\n",
"sig = 0.2 # risk free rate and diffusion coefficient\n",
"S0 = 100\n",
"X0 = np.log(S0) # spot price and log-price\n",
"K = 100\n",
"Texpir = 1 # strike and maturity\n",
"\n",
"lam = 0.8 # lambda\n",
"muJ = 0 # (or alpha) is the mean of the jump size\n",
"sigJ = 0.5 # (or xi) is the standard deviation of the jump size\n",
"\n",
"Nspace = 5 # M space steps\n",
"Ntime = 3 # N time steps\n",
"S_max = 3 * float(K)\n",
"S_min = float(K) / 3\n",
"x_max = np.log(S_max) # A2\n",
"x_min = np.log(S_min) # A1\n",
"\n",
"dev_X = np.sqrt(lam * sigJ**2 + lam * muJ**2) # std dev of the jump component\n",
"\n",
"dx = (x_max - x_min) / (Nspace - 1)\n",
"extraP = int(np.floor(3 * dev_X / dx)) # extra points\n",
"x = np.linspace(x_min - extraP * dx, x_max + extraP * dx, Nspace + 2 * extraP) # space discretization\n",
"T, dt = np.linspace(0, Texpir, Ntime, retstep=True) # time discretization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What I did was: \n",
"- define tha parameters. \n",
" I called muJ and sigJ the mean and standard deviation of the (normal) jump random variables. They correspond to $\\alpha$ and $\\xi$. \n",
"- obtain the boundary values: A1 and A2. And the space step *dx* and time step *dt*.\n",
" The variable `x` takes values in the extended computational domain $[A_1-B_1, A_2+B_2]$. \n",
"- compute the standard deviation of the jump component (at $t=1$). Let us recall the variance formula for the compound Poisson process (obtained from the formula of conditional variance):\n",
"$$ Var \\biggl[ \\sum_{i=1}^{N_t} Y_i \\biggr] = \\lambda (\\alpha^2 + \\xi^2) t $$\n",
"- compute the extra points *extraP* indicating the number of points to use as extra bounday conditions. The extra points are the points in the regions $[A_1 - B_1, A_1]$ and $[A_2, A_2 + B_2]$.\n",
"\n",
"Why did I choose the extra points in that way?\n",
"\n",
"The idea is that we want to cover at least 3 standard deviations of the domain of the Lévy measure. The choice of 3 is arbitrary. The higher is the better. \n",
"The important fact to remember, is that the smaller is $\\Delta X$, the higher is *extraP*. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Under this discretization there are 2 extra points\n"
]
}
],
"source": [
"print(\"Under this discretization there are {} extra points\".format(extraP))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 73.21\\\\209.52 & 204.88 & 200.0\\\\429.13 & 424.49 & 419.62\\\\809.52 & 804.88 & 800.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡ 0.0 0.0 0.0 ⎤\n",
"⎢ ⎥\n",
"⎢ 0.0 0.0 0.0 ⎥\n",
"⎢ ⎥\n",
"⎢ 0.0 0.0 0.0 ⎥\n",
"⎢ ⎥\n",
"⎢ 0.0 0.0 0.0 ⎥\n",
"⎢ ⎥\n",
"⎢ 0.0 0.0 0.0 ⎥\n",
"⎢ ⎥\n",
"⎢ 0.0 0.0 73.21 ⎥\n",
"⎢ ⎥\n",
"⎢209.52 204.88 200.0 ⎥\n",
"⎢ ⎥\n",
"⎢429.13 424.49 419.62⎥\n",
"⎢ ⎥\n",
"⎣809.52 804.88 800.0 ⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Payoff = np.maximum(np.exp(x) - K, 0) # Call payoff\n",
"V = np.zeros((Nspace + 2 * extraP, Ntime)) # grid initialization\n",
"offset = np.zeros(Nspace - 2) # vector to be used for the boundary terms\n",
"\n",
"V[:, -1] = Payoff # terminal conditions\n",
"V[-extraP - 1 :, :] = np.exp(x[-extraP - 1 :]).reshape(extraP + 1, 1) * np.ones((extraP + 1, Ntime)) - K * np.exp(\n",
" -r * T[::-1]\n",
") * np.ones(\n",
" (extraP + 1, Ntime)\n",
") # boundary conditions for the call\n",
"V[: extraP + 1, :] = 0\n",
"\n",
"display_matrix(V.round(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the previous cell we created the grid for $V$. \n",
"The time grows from left to right. The stock price grows from up to down.\n",
"\n",
"- The first 2 rows, and the last 2, are the extra boundary conditions. (In agreement with extraP)\n",
"- The third row and the seventh row are the usual boudary conditions.\n",
"- The last column corresponds to the terminal boundary condition."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us create the vector $\\nu_k$:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.00236098 0.03733859 0.19337038 0.3337637 0.19337038 0.03733859\n",
" 0.00236098]\n"
]
}
],
"source": [
"cdf = ss.norm.cdf(\n",
" [np.linspace(-(extraP + 1 + 0.5) * dx, (extraP + 1 + 0.5) * dx, 2 * (extraP + 2))], loc=muJ, scale=sigJ\n",
")[0]\n",
"nu = lam * (cdf[1:] - cdf[:-1])\n",
"print(nu)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The integral:\n",
"$$ \\hat m = \\int_{-B_1}^{B_2} \\bigl( e^z-1 \\bigr) \\nu(dz)$$ \n",
"can be computed using two methods:\n",
"- or using the scipy function `quad`\n",
"- or using the discretization:\n",
"$$ \\hat m \\approx \\sum_{k = -K_1}^{K_2} (e^{k \\Delta x}-1) \\nu_k $$\n",
"\n",
"These two methods are equivalent. We show both in the next cell. Of course the values are different because $\\Delta x$ is quite big now."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Truncated jump activity: 0.7999036142791525\n",
"True value: 0.10651876245346106\n",
"Truncated value, using quad: 0.10623607827426487\n",
"Approximation value: 0.11761420996944338\n"
]
}
],
"source": [
"lam_appr = sum(nu) # sum of the components of nu\n",
"print(\"Truncated jump activity: \", lam_appr)\n",
"\n",
"m = lam * (np.exp(muJ + (sigJ**2) / 2) - 1) # coefficient m\n",
"print(\"True value: \", m)\n",
"m_int = quad(lambda z: lam * (np.exp(z) - 1) * ss.norm.pdf(z, muJ, sigJ), -(extraP + 1.5) * dx, (extraP + 1.5) * dx)[0]\n",
"print(\"Truncated value, using quad: \", m_int)\n",
"m_appr = np.array([np.exp(i * dx) - 1 for i in range(-(extraP + 1), extraP + 2)]) @ nu\n",
"print(\"Approximation value: \", m_appr)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# construction of the tri-diagonal matrix D\n",
"sig2 = sig * sig\n",
"dxx = dx * dx\n",
"a = (dt / 2) * ((r - m_appr - 0.5 * sig2) / dx - sig2 / dxx)\n",
"b = 1 + dt * (sig2 / dxx + r + lam_appr)\n",
"c = -(dt / 2) * ((r - m_appr - 0.5 * sig2) / dx + sig2 / dxx)\n",
"D = sparse.diags([a, b, c], [-1, 0, 1], shape=(Nspace - 2, Nspace - 2)).tocsc()\n",
"DD = splu(D)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the previous cell we created the \"diffusion\" matrix, in the same way we did for the Black-Scholes equation in the notebook **2.1**.\n",
"\n",
"In the next cell we create the \"jump\" matrix $J$. As you can see, it is a dense matrix!"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}0.0024 & 0.0373 & 0.1934 & 0.3338 & 0.1934 & 0.0373 & 0.0024 & 0.0 & 0.0\\\\0.0 & 0.0024 & 0.0373 & 0.1934 & 0.3338 & 0.1934 & 0.0373 & 0.0024 & 0.0\\\\0.0 & 0.0 & 0.0024 & 0.0373 & 0.1934 & 0.3338 & 0.1934 & 0.0373 & 0.0024\\end{matrix}\\right]$"
],
"text/plain": [
"⎡0.0024 0.0373 0.1934 0.3338 0.1934 0.0373 0.0024 0.0 0.0 ⎤\n",
"⎢ ⎥\n",
"⎢ 0.0 0.0024 0.0373 0.1934 0.3338 0.1934 0.0373 0.0024 0.0 ⎥\n",
"⎢ ⎥\n",
"⎣ 0.0 0.0 0.0024 0.0373 0.1934 0.3338 0.1934 0.0373 0.0024⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"J = np.zeros((Nspace - 2, Nspace + 2 * extraP))\n",
"for i in range(Nspace - 2):\n",
" J[i, i : (len(nu) + i)] = nu\n",
"display_matrix(J.round(4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have all the ingredients to solve the backward in time algorithm:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"15.205937181777447\n"
]
}
],
"source": [
"# Backward iteration\n",
"for i in range(Ntime - 2, -1, -1):\n",
" offset[0] = a * V[extraP, i]\n",
" offset[-1] = c * V[-1 - extraP, i]\n",
" V_jump = V[extraP + 1 : -extraP - 1, i + 1] + dt * (J @ V[:, i + 1])\n",
" V[extraP + 1 : -extraP - 1, i] = DD.solve(V_jump - offset)\n",
"\n",
"# finds the option at S0\n",
"oPrice = np.interp(X0, x, V[:, 0])\n",
"print(oPrice)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Is the previous method efficient?\n",
"\n",
"Well NO!\n",
"\n",
"Since the integral we are considering is a convolution integral, we should take advantage of the [convolution theorem](https://en.wikipedia.org/wiki/Convolution_theorem).\n",
"\n",
"We can use two python functions:\n",
"- [fftconvolve](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.fftconvolve.html#scipy.signal.fftconvolve) Convolves two arrays using FFT.\n",
"- [convolve](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.convolve.html) Convolves two arrays. The user can specify the method. If the length of the array is smaller than 500 it is better to use the definition of convolution, otherwise it is better to use the FFT (and it calls fftconvolve).\n",
"\n",
"Let us check that the output is the same:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 3.2055702 22.61411538 80.66390056]\n",
"[ 3.2055702 22.61411538 80.66390056]\n"
]
}
],
"source": [
"# example\n",
"from scipy import signal\n",
"\n",
"print(signal.fftconvolve(V[:, -1], nu, mode=\"valid\"))\n",
"print(J @ V[:, -1])"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"15.20593718177744\n"
]
}
],
"source": [
"# Backward iteration using fftconvolve:\n",
"for i in range(Ntime - 2, -1, -1):\n",
" offset[0] = a * V[extraP, i]\n",
" offset[-1] = c * V[-1 - extraP, i]\n",
" V_jump = V[extraP + 1 : -extraP - 1, i + 1] + dt * signal.fftconvolve(V[:, i + 1], nu, mode=\"valid\")\n",
" V[extraP + 1 : -extraP - 1, i] = DD.solve(V_jump - offset)\n",
"\n",
"# finds the option at S0\n",
"oPrice = np.interp(X0, x, V[:, 0])\n",
"print(oPrice)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Comparison with other numerical methods\n",
"\n",
"\n",
"In the class `Merton_pricer` I made the following choice for the parameters: \n",
"```python\n",
" S_max = 6*float(self.K) \n",
" S_min = float(self.K)/6\n",
"```\n",
"and\n",
"```python\n",
"extraP = int(np.floor(5*dev_X/dx))\n",
"```\n",
"The reason is that I want the parameters $A_1$, $A_2$, $B_1$, $B_2$ to be as large as possible.\n",
"\n",
"In the backward iteration I used the FFT approach:\n",
"\n",
"```python\n",
"V_jump = V[extraP+1 : -extraP-1, i+1] + dt * signal.convolve(V[:,i+1],nu[::-1],mode=\"valid\",method=\"fft\")\n",
"```\n",
"\n",
"The correct code to use is `nu[::-1]`. \n",
"This is because our convolution has the form\n",
"$\\sum_{k = -K_1}^{K_2} \\nu_k V^{n}_{i+k}$, but the convolution in the [numpy.convolve](https://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html) function is defined as\n",
"$\\sum_{k = -K_1}^{K_2} \\nu_k V^{n}_{i-k}$. Therefore it is necessary to invert the vector! \n",
"(In the code above, I did not invert `nu` for clarity. The code worked because the considered `nu` was symmetric).\n",
"\n",
"Let us compare the solution of the Merton PIDE with other numerical methods: "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from FMNM.Parameters import Option_param\n",
"from FMNM.Processes import Diffusion_process, Merton_process\n",
"from FMNM.BS_pricer import BS_pricer\n",
"from FMNM.Merton_pricer import Merton_pricer"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Creates the object with the parameters of the option\n",
"opt_param = Option_param(S0=100, K=100, T=1, exercise=\"European\", payoff=\"call\")\n",
"opt_param_p = Option_param(S0=100, K=100, T=1, exercise=\"European\", payoff=\"put\")\n",
"\n",
"Merton_param = Merton_process(r=0.1, sig=0.2, lam=0.8, muJ=0, sigJ=0.5)\n",
"Merton = Merton_pricer(opt_param, Merton_param)\n",
"Merton_p = Merton_pricer(opt_param_p, Merton_param)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**PIDE price:**"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(22.01403904360017, 40.36621618270874)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Merton.PIDE_price((12000, 10000), Time=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Closed formula:**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"22.016367621905697"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Merton.closed_formula()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Fourier inversion:**"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"22.0163676219057"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Merton.Fourier_inversion()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Monte Carlo** method gives the value: (the output includes the price, the standard error and the execution time)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(22.01260319665876, 0.05643812936401397, 64.60239958763123)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Merton.MC(1000000, Err=True, Time=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot of the call:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ixahVqxYAZ6fjbMls2bJxyy23AJA9e3aaNWvGl19+ya233srUqVPp06cPc+fO5eeff+aqq64CNEFdccUV6ftHMiZIfvlFz9LYvVuHqBo08Kypffs0P61aBePH67aQSBLdicOvuuohN910E927dycmJoa9e/cev+6c4/PPP6d8+fL/+P7FixeTL1++NF/PnaTUeq4UBfmzZctGYmLiv54rqUzsZc+enaSkpOP3k0u9A+TOnZts2bIdv9+uXTsGDx5M4cKFqVWrFgUKFMA5R5MmTRgzZkyasRkTeD/9BE2a6HrY2bMh9AHLC7t3a35at06X3Z7wuTIi2ByHh+6991569uxJpUqV/nH9uuuuY9CgQccTwYoVK1J9foECBTh48ODx+/Xq1eOTTz4BYP369WzZsuVfySctFSpU4Pfff2fp0qUAHDx4kMTERMqUKcPKlStJSkpi69atLFmyJM3XaNCgAd9//z3vv//+8fPN69aty4IFC44fj3v48GEroWIiy6JFkHxkwdy5niaNnTu1I7N+vR71GolJAyxxeKpkyZI89thj/7r+wgsvkJCQQOXKlalYsSIvvPBCqs+vXLky2bNnp0qVKgwYMICHHnqIY8eOUalSJdq1a8fIkSP/0dM4mZw5czJu3Di6du1KlSpVaNKkCXFxcVx11VWULVuWSpUq0b17d6qf5CyBbNmy0bx5c6ZNm3Z8aXHRokUZOXIkHTp0oHLlytStW5e1a9emKyZjfDdzpn78L1wYFiyAihU9a2r7dk0amzfrstumTT1rynNysuGPoKtZs6ZbtmzZP66tWbOGSy+91KeIzMnY/40JlM8/h9tug/Ll4ZtvoFgxz5raskVrIu7apUnj6qs9aypdRGS5c67m6T7fehzGmKxn+HBo2xZq1NDhKQ+Txq+/Qv36usp3+nT/k0ZmsMRhjMla+vaFTp10MnzGDAiduumFDRugXj346y+YNQtOOPgzYkVl4ojk4bdoZf8nxnfOab2p//5XexuTJ8NJVjGeqbVrtacRFwdz5mjnJlpEXeLInTs3e/futTeqAHHOsXfvXnLnzu13KCarSq5w+9prWtNj9GjImdOz5lav1qSRlKQHBFap4llTvoi6fRwlS5Zk27Zt7N692+9QTAq5c+emZMmSfodhsqKUFW579NBDLjw8FWnlSl2olSuXbglJ54r5iBJ1iSNHjhyULVvW7zCMMUFw+LDWKv/6a+jTR4epPLRsmS6zzZ9fk8bFF3vanG+iLnEYYwwAf/6ptaYWLdJDmDp39rS5hQu19lThwjqnUaaMp835ypc5DhF5XER+EpHVIjJGRHKLSGERmSEiG0K33i11MMZEt+Qt2kuX6hCVx0lj3jztaZx3nla6jeakAT4kDhEpATwK1HTOVQSyAe2BHsAs51w5YFbovjHGZMyvv+pmiU2bYOpUuPVWT5ubPVt7GiVL6paQFLVFo5Zfq6qyA3lEJDuQF/gdaAmMCj0+CmjlU2zGmEj100+aNPbt03IiTZp42tw33+ghTBddpKunihf3tLnACHvicM5tB/oCW4AdwF/OuenA+c65HaHv2QGcl9rzRaSLiCwTkWW2csoYc9zixbrbzjkdL/J4t92UKXrca4UKOqdx/vmeNhcofgxVFUJ7F2WB4kA+Ebkjvc93zg11ztV0ztUsWrSoV2EaYyLJzJl6al/BgjB/vqfFCkHPeGrdGipX1h3hRYp42lzg+DFU1Rj41Tm32zmXAEwArgT+EJFiAKHbXT7EZoyJNBMm6HhR2bKaNC66yNPmPv1UT+urUUPzVRrnrkU1PxLHFqCuiOQVPVmoEbAGmAx0DH1PR2CSD7EZYyLJ8OF/v4t/+62nxQoBPv4YOnSAK6/UgoWhQzeznLDv43DOLRaR8cD3QCKwAhgK5Ac+FZFOaHKJoBN4jTFh168fdO+u62AnTPC07hRojurcWVf5fvml580Fmi8bAJ1zLwIvnnA5Hu19GGNM2pyD55/X0iFt2sBHH2l9Dw+99x488IDmqC++gLx5PW0u8KKuyKExJoodOwYPPaRJ4777YMwYz5PGoEGaNG68ESZNsqQBljiMMZHi6FG44w4YMgSeflq7Admyedpk377w6KNw8806GmYFnpXVqjLGBN/hw7oDfNo06N1bE4fHXnlFR8TatdPRsBw5PG8yYljiMMYE2/79Wqzwu+9g6FAdovKQc/DSS/Dyy9rBGTECsts75T/YP4cxJrj++AOuuw5+/lmLFbbxdrFl8iGBvXvDPfdoUV2PR8MikiUOY0ww/fKLLmPasUPrezRt6mlzzsGTT8KAAToZPngwnGWzwKmyxGGMCZ5Vq7SncfSo1vTwuO5UUpJOgg8erLcDB3p6SGDEs3xqjAmWefO0WGH27Pp1GJLG/fdr0uje3ZJGeljiMMYEx5df6pDUBRfAggVw2WWeNnfsGNx7L3zwATz3nJ4ua0nj1CxxGGOCYdQo3TBRsaL2NEqV8rS5xES46y5t9uWXoVcvSxrpZYnDGOO/fv3g7ru1ENTs2eDxkQkJCVqscPRoXUH1wgueNhd1LHEYY/zjnG7m695dl9pOnQoFCnjaZHy87iUcPx769w/LXsKoY6uqjDH+SEzUWenhw3X969tve75p4sgRuOUW3YD+9tvw8MOeNhe1LHEYY8IvLk7HiiZOhJ49dau2xxMMhw9Dy5a6ujcMG9CjmiUOY0x4/fWXvoPPnQtvvQVdu3re5KFD0KKFnvU0YgR07Hjq55i0WeIwxoTPH39As2awerXOTHfo4HmTBw7ADTfAokV/n+BnzowlDmNMeKQsIfLll5pAPPbnn9rM99/D2LE6KW7OnCUOY4z3wlxCBGDvXmjSRDs348fr6JjJHLYc1xjjrfnztYRItmxhKSECsGsXXHutFtWdNMmSRmazxGGM8c6UKfqx//zzw1JCBHQk7NprYeNGbf766z1vMsuxxGGM8caHH0KrVlpCZP58KF3a8ya3b9fN57/9pns1Gjf2vMksyRKHMSbz9euna17DVEIENFnUq6c9jm++gfr1PW8yy7LEYYzJPM5Bjx5aQuTWW8NSQgR0wVb9+johPmMGXHWV501mabaqyhiTORITtXTIsGFhKyECsGEDNGyoO8Nnz4bq1T1vMsuzHocx5szFxUHbtpo0evaEd94JS9JYs0aHp+LjYc4cSxrhYj0OY8yZ2b8fbrpJJ8DDVEIE4McfoVEjzU8xMWFZsGVCLHEYY07f9u263nXtWhgzBtq1C0uzK1boKt/cuXV46pJLwtKsCbHEYYw5PWvX6m7wP//Uta+NGoWl2aVLtXLJ2Wdr0vjPf8LSrEnB5jiMMRm3aJEuXYqL03GiMCWN777TvRmFCmmlW0sa/rDEYYzJmKlTdRlToUL6Th6mGelvv9Wexvnn69dh2E9o0mCJwxiTfiNHauGnSy/VEiJh+sg/a5ZWub3wQj3Go2TJsDRr0mCJwxhzas5B795wzz1aCComRj/6h8HXX0Pz5pqjYmKgWLGwNGtOwhKHMebkkpKgWzd45hk9BSlMu8FBj+1o2RIqVNB9GmHKVeYULHEYY9IWHw+33ab7M7p10yP0cuYMS9MTJkDr1lCliq6eKlIkLM2adPAlcYhIQREZLyJrRWSNiFwhIoVFZIaIbAjdFvIjNmNMyIEDcOONMG4c9OkD/fvDWeF5yxg7Vjei166ttacK2btBoPjV43gT+No5VwGoAqwBegCznHPlgFmh+8YYP+zcqZVtY2Jg1Cj4739BJCxNf/gh3H67rvb9+ms455ywNGsyIOyJQ0TOBuoBwwCcc0edc/uBlsCo0LeNAlqFOzZjDHoC0pVXwrp1Oslw111ha3rYMLj7bs1ZX30VtqkUk0F+9DguAnYDI0RkhYh8ICL5gPOdczsAQrfnpfZkEekiIstEZNnu3bvDF7UxWcGyZZo0DhzQiYUwHp/37rvQubNuRp8yBfLlC1vTJoP8SBzZgerAu865akAsGRiWcs4Ndc7VdM7VLBqGw2GMyTJmzNCP+nnz6h6NOnXC1vSbb8JDD0GLFjBxIuTJE7amzWnwI3FsA7Y55xaH7o9HE8kfIlIMIHS7y4fYjMmaRo/WifD//Ed3g5cvH7am+/TRBVutW8P48ZArV9iaNqcp7InDObcT2CoiyT+ZjYCfgclAx9C1jsCkcMdmTJbUv7/ORl95pW7LLl48bE336gVPPw3t2+tKqjCt9DVnyK/quF2BT0QkJ/ALcA+axD4VkU7AFqCNT7EZkzUkJcFTT+n54Lfcons0cucOS9POwYsvwv/+B3feCSNGhOXcJ5NJfEkczrmVQM1UHgpPiU1jsrr4eF2+NHasTi689VbY3rmTjyXv0wc6dYL33rOkEWnsPA5jspr9+6FVKx2W6t1bex1h2qPhHDz+uE6GP/igHksepj2FJhNZ4jAmK9m6VZfYrl+vQ1O33x62ppOS4JFHdNntY4/BgAFhy1cmk1niMCar+PFHTRoHD4b1xD7QpNGli27we+op7ehY0ohc1kk0JiuYPRuuvlrHiubNC2vSOHZMq7EPGwYvvGBJIxpY4jAm2o0eracglSwJCxdC5cphazohAe64Q+tP/e9/8PLLljSigSUOY6KVc/DGG3/v0Zg/H0qVClvzR4/+vT+jTx94/vmwNW08lu7EISKlRaRx6Os8ImLlx4wJqmPHdAb6qae0Pvk334S1Nnl8PNx6q56pMXCgFtc10SNdiUNE7kNLg7wXulQSmOhVUMaYM3DkiCaLQYPgiSdgzJiw1vE4ckRX+375JbzzjuYvE13Su6rqYaA2sBjAObdBRFKtXmuM8dHevXrW6nff6XrXbt3C2nxsrDY/ezZ88IFu8DPRJ72JI945d1RCs1oikh1wnkVljMm4zZt1EnzzZj21r014q/YcPAjNm+tUyqhRWkrERKf0Jo65IvIskEdEmgAPAV96F5YxJkNWrIAbboC4OC2Pfs01YW3+r790i8iSJfDJJzopbqJXeifHe6CHL/0I3A98BdgaCWOCYPp0qFdPS8suWBD2pPHnn9CkCSxdqh0dSxrRL709jjzAcOfc+wAiki107bBXgRlj0mHYMLj/fqhYUc9aDWNJdIA9e6BpU/jpJ11B1aJFWJs3Pklvj2MWmiiS5QFmZn44xph0cU43RnTuDI0bw7ffhj1p7NoFDRvCmjUwaZIljawkvT2O3M65Q8l3nHOHRCSvRzEZY04mPl5reIwZo4njnXcgR46whrBjh1Yt2bxZzwcPYwUTEwDp7XHEikj15DsiUgM44k1Ixpg07d2rEwpjxsBrr8HQoWFPGtu2Qf36sGULfP21JY2sKL09jm7AZyLye+h+MaCdNyEZY1K1aZOunNq8Wet4tAv/r+Bvv+nw1J49Oid/5ZVhD8EEQLoSh3NuqYhUAMoDAqx1ziV4Gpkx5m8LF8JNN2l98lmztNJtmG3apEnjwAGYORNq1Qp7CCYgTpo4RKShc262iLQ+4aFyIoJzboKHsRljAD7/XEvMliih52iUKxf2ENat0yGpuDjdFV6tWthDMAFyqh5HfWA2kNp6CQdY4jDGK85Bv35aqPCKK2DiRChaNOxh/Pyz9jSSkmDOHKhUKewhmIA5aeJwzr0oImcB05xzn4YpJmNMYiI8+qies9qmjR5okTt32MNYtUpX+2bLBjExcNllYQ/BBNApV1U555KAR8IQizEG4NAhrRT47rva2xg71pek8f33cO21uiF97lxLGuZv6V1VNUNEugPjgNjki865fZ5EZUxW9fvvWilw1SoYMkR3hftgyRK47jo45xyd07joIl/CMAGV3sRxLzqn8dAJ1+3HyZjM8uOPutx2/349zOL6630JY8ECbbpoUU0apUv7EoYJsPRuALwMGAz8AKwEBgGXexWUMVnO1Km6KSIpCebN8y1pzJ2rPY1ixbSKiSUNk5r0Jo5RwKXAW2jSuDR0zRhzJpyDN9/UPRqXXKJjRFWr+hLKzJmar0qX1onwEiV8CcNEgPQOVZV3zlVJcX+OiPzgRUDGZBkJCbpyasgQaN1aV07ly+dLKNOmwc03a+6aORPOs/M9zUmkt8exQkTqJt8RkTrAAm9CMiYL2L8fbrxRk0aPHvDZZ74ljcmT9Yzwyy/XfRqWNNm/0BYAABcwSURBVMyppLfHUQe4S0S2hO6XAtaIyI+Ac85V9iQ6Y6LRpk26cmrTJhgxAu6+27dQxo+HDh2genX45hsoWNC3UEwESW/iaOZpFMZkFfPm6ZiQczomVK+eb6GMGaPngtepo0NVZ5/tWygmwqS3yOFvXgdiTNT78EM9P+Oii/QQi4sv9i2UUaPg3nv1lNkpUyB/ft9CMREovXMcxpjTlZQEzz0HHTtqD2PhQl+Txgcf6DlQDRvqabOWNExGWeIwxkuHD0PbtvDqq9Cli44JFSrkWziDB8N990GzZrrHMK+d42lOQ3rnOIwxGfX771pzavly6N8funUDEd/CGTAAnnhCt4x8+inkyuVbKCbCWeIwxgtLluga1wMHYNIkaJHayQTh8/rruur3lltg9GgtXGjM6fJtqEpEsonIChGZErpfWERmiMiG0K1//XljzsTHH+tcRu7cOp/hc9L43/80aXTooIV2LWmYM+XnHMdjwJoU93sAs5xz5YBZofvGRI5jx7QM+p136sFLS5b4euqRc/D889Czp87Lf/QRZLcxBpMJfEkcIlISuBH4IMXllvxd/2oU0CrccRlz2v76SycP3ngDHnoIpk+HIkV8C8c5zWGvvKKT4cOH62FMxmQGvz5/DASeAgqkuHa+c24HgHNuh4ikWvhARLoAXQBKlSrldZzGnNqGDZo0Nm7Uw5ceeMDXcJzTefi33oKHH9bbs2z9pMlEYf9xEpHmwC7n3PLTeb5zbqhzrqZzrmZRH85fNuYfpk+H2rVh927dCe5z0khK0g7PW2/B44/DoEGWNEzm8+NH6irgJhHZDIwFGorIx8AfIlIMIHS7y4fYjEkf52DgQK1DfuGFsHQp1K/va0jHjumwVHLdxH79fF39a6JY2BOHc+4Z51xJ51wZoD0w2zl3BzAZ6Bj6to7ApHDHZky6xMdDp076kb5lS/juOyhb1teQEhO1VuLw4fDii7rf0JKG8UqQOrG9gSYisgFoErpvTLD8/jtce61WtX3xRS0v63PNjoQEuP12XQXcqxe89JIlDeMtXxfnOedigJjQ13uBRn7GY8xJLVgAt96qm/o+/RTatPE7Io4ehfbt4YsvdEFX9+5+R2SygiD1OIwJJufgnXegQQM9bGnx4kAkjbg43Qn+xRd6+qwlDRMuljiMOZkjR7T++MMPQ9OmsGwZVKzod1QcOaLTK1Om6GT4o4/6HZHJSixxGJOW337TAytGjtTt119+GYgj8mJj9dTZGTN0Mvz++/2OyGQ1VoDAmNTMng3t2ukkwqRJusEvAA4e1KSxYIGeC3XHHX5HZLIi63EYk5Jz0LcvNGkCRYtqvamAJI2//oLrrtPVv6NHW9Iw/rEehzHJYmN1f8a4cTrrPGIEFChw6ueFwb59mjR++AE++0yPLTfGL9bjMAZg7VqoU0fflV97TW8DkjT27IFGjWDVKpgwwZKG8Z/1OIwZO1ZrdeTODV9/rcNUAfHHH5o0Nm2CyZO112GM36zHYbKu+Hh45BE94ahyZVixIlBJ4/ffdevIr7/C1KmWNExwWI/DZE2bN0Pbtlqc8MkndXgqRw6/ozpu61Zo2BB27tRO0DXX+B2RMX+zxGGynqlT9ZS+Y8fg88+hdWu/I/qHzZs1aezdq1Xbr7jC74iM+ScbqjJZR2IiPPssNG8OpUvD8uWBSxobN+px5fv3w6xZljRMMFmPw2QNO3dqNcC5c3Ui/M03IU8ev6P6h3XrtKcRH6/7D6tW9TsiY1JnicNEv+nTdWjq4EEYNQruusvviP7lp5909ZRzEBMTiHJYxqTJhqpM9Dp6FJ56SpcjJe8CD2DS+OEHXT111lnaIbKkYYLOehwmOv3yiy6zXbJEqwD27w958/od1b8sX64rgPPl0+GpcuX8jsiYU7PEYaLPuHHQpYseg/fZZ3r4UgAtWgTNmmnB3TlzfD991ph0s6EqEz1iY6FzZ50Ev/xyWLkysElj/nw93qNIEfj2W0saJrJY4jDR4ccfoVYtPaDimWd0sqBMGb+jSlVMjPY0ihfXMEuV8jsiYzLGEoeJbElJMHCgJo0//9QVVK++Gqhd4CnNmAE33KDbSGJioEQJvyMyJuMscZjItW2bjvc8/rjOMP/wAzRu7HdUafrqK2jRAi65RJPGBRf4HZExp8cSh4lM48ZBpUqwcCEMHaqlY887z++o0jRpErRqpUttZ8/W1cHGRCpLHCay7N+vR9+1bw/ly+sE+H336QqqgEpe2FW9OsycCYUL+x2RMWfGEoeJHHPmaPnzsWPhpZd0aVLANz6MHq05rm5dnX4pWNDviIw5c5Y4TPAdOQLdu2tNjty5YcECePFFyB7sbUgjR2rnqH59mDYNzj7b74iMyRyWOEywLVwI1apBv366qW/FCj3iNeCGDoV77tG5+ilTIH9+vyMyJvNY4jDBlNzLuOoq/Xr6dBgyRGtzBNzbb2uVkxtu0Dn7AFY6MeaMWOIwwfPdd1pTPLmX8eOPgTrS9WT694euXaFlS5gwQUfWjIk2ljhMcBw+rMe4Xn01xMXpbrkhQyJmcuC11zT8Nm10JVWuXH5HZIw3LHGYYPj2W+1l9O+v4zyrVwd6M19KzsH//Z8eLnj77bqSKqAb143JFJY4jL/27dPChPXrQ0KCbnR4910oUMDvyNLFOXj+eV0dfPfdek5UwBd7GXPGLHEYfzgHY8bApZfqutX//ld7GY0a+R1ZujmnYb/6qk7FDBsG2bL5HZUx3rPPRib8fv0VHnwQvvkGatbU2wg7YNs5eOwxGDQIHnkE3nor0JvXjclU1uMw4ZOQAG+8oWdlLFgAb76ppxlFWNJISoIHHtCk8eSTljRM1hP2xCEiF4rIHBFZIyI/ichjoeuFRWSGiGwI3RYKd2zGQ3PnQo0aegZ406bw88/w6KMRN7Zz7Bh06qQb/J59VvOgJQ2T1fjR40gEnnTOXQrUBR4WkcuAHsAs51w5YFbovol027fDbbdBgwZw4IBubpg4ES680O/IMiwxEe66S6dkXnoJevWypGGyprAnDufcDufc96GvDwJrgBJAS2BU6NtGAa3CHZvJREePQp8+WsF2wgTo2VN7GTff7HdkpyUhQfPf6NE6Gf7ii5Y0TNbl6+S4iJQBqgGLgfOdcztAk4uIpHq4goh0AboAlLIzN4Np+nTdPr1+Pdx0EwwYABdd5HdUpy0+XivcTpyom9mfeMLviIzxl2+T4yKSH/gc6OacO5De5znnhjrnajrnaha103CCZd06rbVx3XU6gzx1qp5gFMFJIy4OWrfWpDFokCUNY8CnxCEiOdCk8YlzbkLo8h8iUiz0eDFglx+xmdOwZ4/2MJKPt3v1Vd2TccMNfkd2Rg4f1g7TV1/Be+/psltjjD+rqgQYBqxxzvVP8dBkoGPo647ApHDHZjIoPl6XFV18Mbzzju4A37gRnnkm4gs1xcZC8+a6kX34cN3gZ4xRfsxxXAXcCfwoIitD154FegOfikgnYAvQxofYTHo4B59+Cj16wObN2rN44w247DK/I8sUBw7AjTdqkd6PPtL6U8aYv4U9cTjn5gNprUeJnHoTWZFzWrH2+edh6VKoUkXvR0gxwvTYvx+aNYPly/WE2jb28cWYf7Gd4yZ9FiyAa6/Vie9du2DECH13jaKksW+f/nW+/x7Gj7ekYUxaLHGYk1uxQsdtrr4a1q7V4+3WrdNSsBG26/tkdu+Ghg11Tn/iRF0cZoxJnSUOk7qffoK2baF6dT33u3dv2LQJHn444ie+T7Rzp3am1q3To14jfDGYMZ6z6rjmn77/Hl55RXd7588PL7ygmxcKFvQ7Mk9s366V3Ldu1WW3117rd0TGBJ8lDqMWLtSEMXUqnHOOJozHHoNzz/U7Ms9s2aLDU7t2aWX3q6/2OyJjIoMljqzMOa1a26sXzJqlSaJXL93pds45fkfnqV9/1aTx559aIaVuXb8jMiZyWOLIihITdSiqXz9YsgTOPx/69tWzvvPn9zs6z23cqENSsbGaL2vU8DsiYyKLJY6s5OBB3QY9cKBu3Lv4Yhg8GO65B/Lk8Tu6sFi7VnsaCQkwZ45uRTHGZIwljqxg+3at0DdkCPz1lw7mDxgALVpE1ZLaU0k+0lwEYmL0IEJjTMZZ4ohWzsG8eVpD6vPPtVrtLbfoWad16vgdXditXKmb+3Ll0jqM5cv7HZExkcsSR7Q5eBA+/lgTxurVuoy2a1f9U7as39H5YtkyPa02f35NGhdf7HdExkQ2SxzR4uefNVl8+KEmj2rV4IMPoEMHyJvX7+h8s2iRVkkpXFjnNMqU8TsiYyKfJY5IdvAgfPaZ1o2aPx9y5oR27eChh3Q4KoufbTpvnu4Cv+AC7WlE4DHnxgSSJY5I45wWHBw+XEubx8bqgP3rr+vqKDsVEdBE0aIFlCqlS26LF/c7ImOihyWOSLF9ux4OMXw4bNigA/bt20OnTrp7LYv3LlL65hto1UrnMmbO1G0qxpjMY4kjyPbt0xVRY8bo+lHn4Jpr4Nln4dZbs8RmvYyaMkUXj112mR4VUqSI3xEZE30scQTN4cPw5ZcwejRMm6Y71S65BHr21KPoypXzO8LA+uILneKpUkV7HYUL+x2RMdHJEkcQHDmiH48/+0zf/WJjdVC+a1e47TYtbW5DUSf16af6T1WrFnz9ddSX2jLGV5Y4/HLggNbxnjBBb2NjoVAhffe77TYdkspCu7rPxMcfQ8eOcNVVWty3QAG/IzImulniCKc9e/SkoAkTtIdx9KjO3N55J7RuDQ0aQI4cfkcZUUaM0PUBDRroCF++fH5HZEz0s8ThJed09/bUqdqrWLBAS3+ULq0n6bVuDVdcYT2L0/Tee/DAA7or/IsvsvQ+R2PCyhJHZouN1U0EX32lCWPrVr1etSo884wmi2rVbM7iDA0aBI8+qsehjx8PuXP7HZExWYcljjPlHKxZoxsGpk3Tuhbx8Tpm0qSJroa6/nooUcLvSKNG377w3//CzTfD2LG6Yd4YEz6WOE7Htm26HXnWLE0YO3bo9XLl4MEH9WPwNddoKVaTqV55BZ5/XpfdfvSRTQkZ4wdLHOmxb58WPpo5U/+sXavXixbVAx4aN9Zbq6DnGefgpZfg5Zfhjjt0Ujy7/fQa4wv71UvNtm2aKJL/rF6t1/Pmhfr14b77NFlUrAhnneVvrFmAc7pZvndvLcf1/vu2nsAYP1nicA7Wrftnoti8WR/Lnx+uvFLHRerV05pQNqAeVs7p2VMDBugKqsGDLVcb47eslzh274YlS2DxYr1dsgT+/FMfK1pU5yYee0xvq1Sx8RAfJSXpyqnBg/V24EBbjGZMEET3u2JsLPzww99JYvFi+PVXfeyss/TQ6Vtu0bMrrrlGa0LZO1MgJCVpD+P996F7d+jTx/5rjAmK6Ekcf/yhB0uvXAkrVujt+vU61gF6ik/t2rrqqXZtqFHDqssG1LFjuht81Ch47jn43/8saRgTJJGdOLZv1z0SK1fCzp1/Xy9dWjfZdeigG+9q14ZixfyL06RbYqLWnRo9WldQvfCC3xEZY04U2Ylj507dQ3HddZogqlbVeYlChfyOzJyGhASt7zh+vK6gevppvyMyxqQmshNH9eqwfLnfUZhMEB8PbdtqDcj+/eHxx/2OyBiTlshOHDbwHRXi4rSE17Rp8PbbWv/RGBNckZ04TMQ7fBhattTqLUOH6t5KY0ywBW4rlYg0E5F1IrJRRHr4HY/xzqFDWtZr9mwtIWJJw5jIEKgeh4hkAwYDTYBtwFIRmeyc+9nfyCJT8krk9Fz36nvTun7okPY0Fi3SE/w6dEj9ucaY4AlU4gBqAxudc78AiMhYoCWQauJYseLfWzGC/GbpZXuRKHt2LYt+661+R2KMyYigJY4SwNYU97cBdVJ+g4h0AbqE7h6KjZV1HsVSBNjj0Wt7IdLiJTGRIm3aRFbMRN6/c6TFC5EXc6TFC1D+TJ4ctMSR2jKpf3zGds4NBYZ6HojIMudcTa/bySyRFi9YzOEQafFC5MUcafGCxnwmzw/a5Pg24MIU90sCv/sUizHGmFQELXEsBcqJSFkRyQm0Byb7HJMxxpgUAjVU5ZxLFJFHgG+AbMBw59xPPoXj+XBYJou0eMFiDodIixciL+ZIixfOMGZx0bRMxxhjjOeCNlRljDEm4CxxGGOMyRBLHICIFBSR8SKyVkTWiMgVIlJYRGaIyIbQbaBqtYvI4yLyk4isFpExIpI7SDGLyHAR2SUiq1NcSzM+EXkmVGZmnYhcF6CY3wj9XKwSkS9EpGDQY07xWHcRcSJSJMU1X2NOK14R6RqK6ScR6ROUeEMxpPZzUVVEFonIShFZJiK1gxKziFwoInNC72U/ichjoeuZ9/vnnMvyf4BRQOfQ1zmBgkAfoEfoWg/gdb/jTBFvCeBXIE/o/qfA3UGKGagHVAdWp7iWanzAZcAPQC6gLLAJyBaQmJsC2UNfvx4JMYeuX4guMvkNKBKUmNP4N74WmAnkCt0/LyjxniTm6cD1oa9vAGKCEjNQDKge+roAsD4UV6b9/mX5HoeInI3+YAwDcM4ddc7tR0udjAp92yiglT8Rpik7kEdEsgN50f0ugYnZOfctsO+Ey2nF1xIY65yLd879CmxEy8+EVWoxO+emO+cSQ3cXoXuLIMAxhwwAnuKfG2h9jzmNeB8Eejvn4kPfsyt03fd4Q/GkFrMDzg59fQ5/7zfzPWbn3A7n3Pehrw8Ca9APm5n2+5flEwdwEbAbGCEiK0TkAxHJB5zvnNsB+h8BnOdnkCk557YDfYEtwA7gL+fcdAIcc0ha8aVWaqZEmGNLj3uBaaGvAxuziNwEbHfO/XDCQ0GN+RLgGhFZLCJzRaRW6HpQ4wXoBrwhIlvR38VnQtcDFbOIlAGqAYvJxN8/Sxz6yb068K5zrhoQi3bjAis0NtkS7VYWB/KJyB3+RnVGTllqxm8i8hyQCHySfCmVb/M9ZhHJCzwH9Ezt4VSu+R4z+jtYCKgL/Bf4VESE4MYL2kt63Dl3IfA4oRELAhSziOQHPge6OecOnOxbU7l20pgtcWh23eacWxy6Px5NJH+ISDGA0O2uNJ7vh8bAr8653c65BGACcCXBjhnSji/QpWZEpCPQHLjdhQaFCW7M/0E/UPwgIpvRuL4XkQsIbszbgAlOLQGS0MKBQY0XoCP6ewfwGX8P7QQiZhHJgSaNT5xzyXFm2u9flk8czrmdwFYRSa4W2Qgt4z4Z/eEgdDvJh/DSsgWoKyJ5Q5/MGqHjmEGOGdKObzLQXkRyiUhZoBywxIf4/kVEmgFPAzc55w6neCiQMTvnfnTOneecK+OcK4O+KVQP/ZwHMmZgItAQQEQuQReo7CG48YK+sdYPfd0Q2BD62veYQ+8Jw4A1zrn+KR7KvN+/cM72B/UPUBVYBqxCf4gLAecCs9AfiFlAYb/jPCHm/wPWAquBj9AVEYGJGRiDzr8koG9enU4WHzq8sglYR2i1SkBi3oiO/64M/RkS9JhPeHwzoVVVQYg5jX/jnMDHoZ/l74GGQYn3JDFfDSxHVyMtBmoEJeZQbC70fpb8c3tDZv7+WckRY4wxGZLlh6qMMcZkjCUOY4wxGWKJwxhjTIZY4jDGGJMhljiMMcZkiCUOYzKZiDwXqkq6KlQ9tY7fMRmTmQJ1dKwxkU5ErkB3mVd3zsWHSprn9DksYzKVJQ5jMlcxYI/7u9LrHp/jMSbT2QZAYzJRqLDcfLTU/UxgnHNurr9RGZO5bI7DmEzknDsE1AC6oOX6x4nI3b4GZUwmsx6HMR4SkVuBjs65Fn7HYkxmsR6HMZlIRMqLSLkUl6qix7caEzVsctyYzJUfGCQiBdGDnzaiw1bGRA0bqjLGGJMhNlRljDEmQyxxGGOMyRBLHMYYYzLEEocxxpgMscRhjDEmQyxxGGOMyRBLHMYYYzLk/wF6neOk5+WJtAAAAABJRU5ErkJggg==",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"Merton_p.plot([40, 180, 0, 80])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### OK cool! It works!\n",
"\n",
"But what happens if we select some \"strange\" set of parameters? \n",
"Let's try."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PIDEprice: 50.19209080990695\n",
"Closed formula: 92.84579753439006\n",
"Monte Carlo price and std error: (0.021836642589105403, 0.010662297105708677)\n"
]
}
],
"source": [
"Merton_param = Merton_process(r=0.1, sig=0.1, lam=0.1, muJ=0, sigJ=3.1)\n",
"Merton = Merton_pricer(opt_param, Merton_param)\n",
"\n",
"print(\"PIDEprice: \", Merton.PIDE_price((12000, 10000)))\n",
"print(\"Closed formula: \", Merton.closed_formula())\n",
"print(\"Monte Carlo price and std error: \", Merton.MC(500000, Err=True))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What happened? \n",
"Three methods... three different outputs. This is not good. \n",
"\n",
"Do not worry. The model is fine. And the algorithms are fine too. \n",
"The last set of parameters is the problem. \n",
"To be precise, the model is fine under a certain set of parameters. Let us investigate this feature."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Model limitations\n",
"\n",
"Let us inspect the behavior of the pricing function (Merton closed formula) under different values of `lam` and `sigJ`. \n",
"\n",
"In the plot we can see the curves obtained for different values of `lam`. \n",
"\n",
"The model works properly for `sigJ` smaller than a certain level dependent on `lam`. \n",
"For `sigJ` too big, the price drops to zero. However, even at the peak of the curve, the three different numerical algorithms can give different outputs. \n",
"Under several numerical tests, I found that the model works properly for all values `sigJ` on the left side of the peak of the curve (but not too close to the peak).\n",
"\n",
"As you can see, for high values of `sigJ`, the calculations produce overflows and NaNs. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jovyan/work/functions/BS_pricer.py:72: RuntimeWarning: overflow encountered in exp\n",
" return S0 * ss.norm.cdf( d1 ) - K * np.exp(-r * T) * ss.norm.cdf( d2 )\n",
"/home/jovyan/work/functions/BS_pricer.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
" return S0 * ss.norm.cdf( d1 ) - K * np.exp(-r * T) * ss.norm.cdf( d2 )\n",
"/home/jovyan/work/functions/BS_pricer.py:72: RuntimeWarning: overflow encountered in double_scalars\n",
" return S0 * ss.norm.cdf( d1 ) - K * np.exp(-r * T) * ss.norm.cdf( d2 )\n",
"/home/jovyan/work/functions/Merton_pricer.py:78: RuntimeWarning: overflow encountered in double_scalars\n",
" tot += ( np.exp(-lam2*self.T) * (lam2*self.T)**i / factorial(i) ) \\\n",
"/home/jovyan/work/functions/Merton_pricer.py:78: RuntimeWarning: invalid value encountered in double_scalars\n",
" tot += ( np.exp(-lam2*self.T) * (lam2*self.T)**i / factorial(i) ) \\\n"
]
}
],
"source": [
"sigJs = np.linspace(0.01, 10, 1000)\n",
"lambdas = [0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10]\n",
"Mert_prices = []\n",
"\n",
"for j in lambdas:\n",
" curve = []\n",
" for i in sigJs:\n",
" Merton_param = Merton_process(r=0.1, sig=0.2, lam=j, muJ=0, sigJ=i)\n",
" Merton = Merton_pricer(opt_param, Merton_param)\n",
" curve.append(Merton.closed_formula())\n",
" Mert_prices.append(curve)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15, 8))\n",
"for i, j in enumerate(lambdas):\n",
" plt.plot(sigJs, Mert_prices[i], label=\"lam=\" + str(j))\n",
"plt.plot(\n",
" sigJs,\n",
" [BS_pricer.BlackScholes(payoff=\"call\", S0=100.0, K=100.0, T=1.0, r=0.1, sigma=0.2)] * len(sigJs),\n",
" label=\"BS price\",\n",
" color=\"black\",\n",
")\n",
"plt.xlabel(\"sigJ\")\n",
"plt.ylabel(\"Mert price\")\n",
"plt.title(\"Merton price as function of sigJ\")\n",
"plt.axis([0, 7, 0, 110])\n",
"plt.legend()\n",
"plt.figure(figsize=(28, 60))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Comparison with the Black Scholes PDE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let us compare the Merton curve with the Black Scholes curve, for a European call option. \n",
"The volatility of the BS model is chosen equal to the standard deviation of the Merton process. \n",
"\n",
"Looking at the plot we can see the different shape of the two curves."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"standard deviation: 0.8988882021697694\n",
"kurtosis: 2.823252622291932\n"
]
}
],
"source": [
"# Creates the object with the parameters of the option\n",
"opt_param_c = Option_param(S0=100, K=100, T=1, exercise=\"European\", payoff=\"call\")\n",
"\n",
"# Creates the object with the parameters of the process\n",
"Merton_param = Merton_process(r=0.1, sig=0.2, lam=1.2, muJ=0, sigJ=0.8)\n",
"diff_param = Diffusion_process(r=0.1, sig=np.sqrt(Merton_param.var))\n",
"\n",
"print(\"standard deviation: \", np.sqrt(Merton_param.var))\n",
"print(\"kurtosis: \", Merton_param.kurt)\n",
"\n",
"# Creates the object of the pricer: call\n",
"BS_c = BS_pricer(opt_param_c, diff_param)\n",
"Merton_c = Merton_pricer(opt_param_c, Merton_param)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Merton closed formula, call: 39.525220975930694\n",
"BS closed formula, call: 37.987106518471414\n"
]
}
],
"source": [
"print(\"Merton closed formula, call: \", Merton_c.closed_formula())\n",
"print(\"BS closed formula, call: \", BS_c.closed_formula())"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Merton PIDE call: 39.4908331514349\n",
"BS PDE call: 37.9848259795486\n"
]
}
],
"source": [
"print(\"Merton PIDE call:\", Merton_c.PIDE_price((13000, 10000)))\n",
"print(\"BS PDE call:\", BS_c.PDE_price((8000, 5000)))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
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"text/latex": [
"$\\displaystyle \\left[ \\right]$"
],
"text/plain": [
"[]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 7))\n",
"plt.plot(BS_c.S_vec, BS_c.price_vec, color=\"red\", label=\"BS curve\")\n",
"plt.plot(Merton_c.S_vec, Merton_c.price_vec, color=\"blue\", label=\"Merton curve\")\n",
"plt.plot(Merton_c.S_vec, Merton_c.payoff_f(Merton_c.S_vec), color=\"black\", label=\"Payoff\")\n",
"plt.axis([50, 190, 0, 120])\n",
"plt.xlabel(\"S\")\n",
"plt.ylabel(\"price\")\n",
"plt.title(\"Merton vs Black-Scholes\")\n",
"plt.legend()\n",
"plt.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# References\n",
"\n",
"[1] Cont-Voltchkova (2005), \"Integro-differential equations for option prices in exponential Lévy models\", Finance and Stochastics, 9, 299--325.\n",
"\n",
"[2] Merton, R. (1976). \"Option pricing when underlying stock returns are discontinuous\", Journal of Financial Economics, 3, 125--144."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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================================================
FILE: 3.2 Variance Gamma model, PIDE method.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Variance Gamma Partial Integro-Differential Equation\n",
"\n",
"## Contents\n",
" - [The VG PIDE](#sec1)\n",
" - [Numerical solution of the PIDE](#sec2)\n",
" - [Comparison with Monte Carlo, closed formula and Fourier inversion](#sec3)\n",
" - [Comparison with the Black Scholes PDE](#sec4) \n",
"\n",
"I suggest the reader to read the notebook **3.1** on the Merton PIDE, and the Appendix **A3** for an introduction to the Variance Gamma (VG) process.\n",
"\n",
"The knowledge of the VG process, is not necessary to understand this notebook, which is focused on the numerical solution of a PIDE. \n",
"In my opinion the [wiki](https://en.wikipedia.org/wiki/Variance_gamma_process) page is not that clear, and if the reader is interested to understand something more, I suggest to have a look at the notebooks **1.5** and **A3**.\n",
"\n",
"Now I'm going to present an approximated equation, whose solution converges to the solution of the original VG equation for $\\epsilon \\to 0$. The reason for using an approximation, is that the Lévy measure is singular in the origin.\n",
"Therefore, we have to remove the singularity from the integration region. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## The VG PIDE \n",
"\n",
"The approximated VG PIDE has the following form: \n",
"\n",
"$$\n",
" \\frac{\\partial V(t,x)}{\\partial t} +\n",
" \\bigl( r-\\frac{1}{2}\\sigma_{\\epsilon}^2 - w_{\\epsilon} \\bigr) \\frac{\\partial V(t,x)}{\\partial x} \n",
" + \\frac{1}{2}\\sigma_{\\epsilon}^2 \\frac{\\partial^2 V(t,x)}{\\partial x^2}\n",
" + \\int_{|z| \\geq \\epsilon} V(t,x+z) \\nu(dz) = (\\lambda_{\\epsilon} + r) V(t,x).\n",
"$$\n",
"\n",
"with parameters \n",
"\n",
"$$\n",
" \\sigma_{\\epsilon}^2 := \\int_{|z| < \\epsilon} z^2 \\nu(dz), \\quad \\quad w_{\\epsilon} := \\int_{|z| \\geq \\epsilon} (e^z-1) \\nu(dz), \\quad \\quad\n",
" \\lambda_{\\epsilon} := \\int_{|z| \\geq \\epsilon} \\nu(dz) .\n",
"$$\n",
"\n",
"and Lévy measure: \n",
"\n",
"$$\n",
" \\nu(dz) = \\frac{e^{\\frac{\\theta z}{\\sigma^2}}}{\\kappa|z|} \\exp \n",
" \\left( - \\frac{\\sqrt{\\frac{2}{\\kappa} + \\frac{\\theta^2}{\\sigma^2}}}{\\sigma} |z|\\right) dz,\n",
"$$\n",
"\n",
"where I used the parametrization coming from the Brownian subordination. See equation [35] in **A3** or [wiki subordinator](https://en.wikipedia.org/wiki/Subordinator_(mathematics)). \n",
"If you are reading quickly, and you have no time to study the Brownian subordination, just think of $\\theta$, $\\sigma$ and $\\kappa$ as the three parameters of the model.\n",
"\n",
"As I said before, the Lévy measure has a singularity at $z=0$. \n",
"\n",
"The activity of the VG process is infinite, i.e. $\\lambda = \\int_{-\\infty}^{\\infty} \\nu(z) = \\infty$. Since the interval $-\\epsilon < z < \\epsilon$ is removed from the region of integration, all the parameters defined above are finite! "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This equation is almost identical to the Merton PIDE, except for the truncation in the integral.\n",
"\n",
"At this point, we can restrict the computational domain on $[A_1,A_2]$ and the integral region on $[-B_1,B_2]$, following the idea presented in **3.1**. \n",
"\n",
"Using the same discretization used for the Merton PIDE, we can solve the problem with the IMEX scheme.\n",
"\n",
"I will not re-write the discrete equation. Everything will be clear (hopefully) from the following python code:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Numerical solution of the PIDE"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from scipy import sparse\n",
"from scipy.sparse.linalg import splu\n",
"\n",
"import numpy as np\n",
"import scipy as scp\n",
"import scipy.stats as ss\n",
"from IPython.display import display\n",
"import sympy\n",
"\n",
"sympy.init_printing()\n",
"from scipy import signal\n",
"from scipy.integrate import quad\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"def display_matrix(m):\n",
" display(sympy.Matrix(m))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"r = 0.1 # risk free rate\n",
"theta = -0.1\n",
"sigma = 0.2\n",
"kappa = 0.3 # VG parameters\n",
"\n",
"W = -np.log(1 - theta * kappa - kappa / 2 * sigma**2) / kappa # martingale correction w\n",
"\n",
"dev_X = np.sqrt(sigma**2 + theta**2 * kappa) # std dev VG process\n",
"\n",
"S0 = 100\n",
"X0 = np.log(S0) # stock, log-price\n",
"K = 100\n",
"Texpir = 1 # strike and maturity\n",
"\n",
"Nspace = 7 # space steps\n",
"Ntime = 3 # time steps\n",
"S_max = 3 * float(K)\n",
"S_min = float(K) / 3\n",
"x_max = np.log(S_max) # A2\n",
"x_min = np.log(S_min) # A1\n",
"\n",
"dx = (x_max - x_min) / (Nspace - 1)\n",
"extraP = int(np.floor(3 * dev_X / dx)) # extra points\n",
"x = np.linspace(x_min - extraP * dx, x_max + extraP * dx, Nspace + 2 * extraP) # space discretization\n",
"T, dt = np.linspace(0, Texpir, Ntime, retstep=True) # time discretization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In **A3** we defined the \"martingale correction\" term:\n",
"\n",
"$$\n",
" w := \\int_{\\mathbb{R}} (e^z-1) \\nu(dz) = - \\frac{1}{\\kappa} \\log \\left( 1-\\theta \\kappa -\\frac{1}{2}\\bar\\sigma^2 \\kappa \\right).\n",
"$$\n",
"\n",
"In the previous cell I defined $w$ (called `W`) just to compare it with $w_{\\epsilon}$. We expect that $w_{\\epsilon} \\to w$ as \n",
"$\\epsilon \\to 0$.\n",
"\n",
"Following Section 2.3 of **A.3**, I introduce the auxiliary parameters A and B. \n",
"They will make the Lévy measure definition more readable."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"A = theta / (sigma**2)\n",
"B = np.sqrt(theta**2 + 2 * sigma**2 / kappa) / sigma**2\n",
"\n",
"levy_m = lambda y: np.exp(A * y - B * np.abs(y)) / (kappa * np.abs(y)) # Levy measure VG"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The extra points are obtained by `extraP = int(np.floor(3*dev_X/dx))`, where `dev_X` is the standard deviation of the VG process. The choice of 3 stardard deviation is arbitrary, and in the pricer class this number is different (it is 5).\n",
"\n",
"The extra points are the points in the intervals $[A_1 - B_1, A_1]$ and $[A_2, A_2 + B_2]$. \n",
"In this case we have:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Under this discretization there are 1 extra points\n"
]
}
],
"source": [
"print(\"Under this discretization there are {} extra points\".format(extraP))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 44.22\\\\0 & 0 & 108.01\\\\209.52 & 204.88 & 200.0\\\\342.19 & 337.55 & 332.67\\end{matrix}\\right]$"
],
"text/plain": [
"⎡ 0 0 0 ⎤\n",
"⎢ ⎥\n",
"⎢ 0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 44.22 ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 108.01⎥\n",
"⎢ ⎥\n",
"⎢209.52 204.88 200.0 ⎥\n",
"⎢ ⎥\n",
"⎣342.19 337.55 332.67⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Payoff = np.maximum(np.exp(x) - K, 0) # Call payoff\n",
"V = np.zeros((Nspace + 2 * extraP, Ntime)) # grid initialization\n",
"offset = np.zeros(Nspace - 2) # vector to be used for the boundary terms\n",
"\n",
"V[:, -1] = Payoff # terminal conditions\n",
"V[-extraP - 1 :, :] = np.exp(x[-extraP - 1 :]).reshape(extraP + 1, 1) * np.ones((extraP + 1, Ntime)) - K * np.exp(\n",
" -r * T[::-1]\n",
") * np.ones(\n",
" (extraP + 1, Ntime)\n",
") # boundary condition\n",
"V[: extraP + 1, :] = 0\n",
"\n",
"display_matrix(V.round(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us define the cutoff term $\\epsilon$ and the relevant parameters.\n",
"\n",
"Note: The integrals are performed using the function `quad`. The integrals are split in the two regions: \n",
"$[-B_1, -\\epsilon]$ and $[\\epsilon, B_2]$"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"eps = 1.5 * dx # the cutoff near 0\n",
"\n",
"lam = (\n",
" quad(levy_m, -(extraP + 1.5) * dx, -eps)[0] + quad(levy_m, eps, (extraP + 1.5) * dx)[0]\n",
") # approximated intensity lambda\n",
"\n",
"int_w = lambda y: (np.exp(y) - 1) * levy_m(y) # integrand of w_eps\n",
"int_s = lambda y: np.abs(y) * np.exp(A * y - B * np.abs(y)) / kappa # integrand of sigma_eps\n",
"# avoid division by zero\n",
"\n",
"w = quad(int_w, -(extraP + 1.5) * dx, -eps)[0] + quad(int_w, eps, (extraP + 1.5) * dx)[0] # w_eps\n",
"\n",
"sig2 = quad(int_s, -eps, eps)[0] # the small jumps variance\n",
"sigJ = quad(int_s, -(extraP + 1.5) * dx, -eps)[0] + quad(int_s, eps, (extraP + 1.5) * dx)[0] # big jumps variance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we computed $\\sigma_{\\epsilon}^2 := \\int_{|z| < \\epsilon} z^2 \\nu(dz)$ we can compute also $\\sigma_J^2 = \\int_{|z| \\geq \\epsilon} z^2 \\nu(dz) $. \n",
"We expect that $\\sigma_{\\epsilon}^2 + \\sigma_J^2 = \\text{dev\\_X}^2$. \n",
"Due to the limitation of the integral in $[-B_1,B_2]$, the two are not perfectly equal. But for a big enough choice of $B_1$, $B_2$, the equality is satisfied, as we can see in the output of the cell below.\n",
"\n",
"Let us inspect better the parameters we obtained: "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"eps = 0.5493061443340548\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The intensity lam=0.001472 is very small because the mass is concentrated inside [-eps,eps].\n",
"\n",
"Theoretical w: -0.07905508872438688\n",
"Approximated w, w_eps = -0.0005999810394699548\n",
"Integral of int_w inside the truncation [-eps,eps]: -0.07844382431861505\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The VG variance 0.042981 is equal to the sum of the Var of the decomposed processes 0.043000.\n",
"Under this discretization the diffusion variance 0.042396 is the major component. \n"
]
}
],
"source": [
"print(\"eps = \", eps)\n",
"plt.plot(np.linspace(-1, 1, 100), levy_m(np.linspace(-1, 1, 100)))\n",
"plt.title(\"Lévy measure\")\n",
"plt.show()\n",
"print(\n",
" \"The intensity lam={:.6f} \\\n",
"is very small because the mass is concentrated inside [-eps,eps].\".format(\n",
" lam\n",
" )\n",
")\n",
"print(\"\")\n",
"\n",
"print(\"Theoretical w: \", W)\n",
"print(\"Approximated w, w_eps = \", w)\n",
"print(\"Integral of int_w inside the truncation [-eps,eps]: \", quad(int_w, -eps, 1e-10)[0] + quad(int_w, 1e-10, eps)[0])\n",
"plt.plot(np.linspace(-1, 1, 100), int_w(np.linspace(-1, 1, 100)))\n",
"plt.title(\"(e^z-1) * nu(z)\")\n",
"plt.show()\n",
"\n",
"print(\n",
" \"The VG variance {0:.6f} is equal to the sum of the Var of the decomposed \\\n",
"processes {1:.6f}.\".format(\n",
" sigJ + sig2, dev_X**2\n",
" )\n",
")\n",
"print(\"Under this discretization the diffusion variance {:.6f} is the major component. \".format(sig2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The parameter $w_{\\epsilon}$ (i.e. w) is very different from the theoretical $w$ (i.e. W). This is a consequence of the discretization. \n",
"I showed that the integral of $(e^z-1)\\nu(dz)$ on $|z|>\\epsilon$ is almost zero. \n",
"It follows that the integral in the region $|z|<\\epsilon$ is almost equal to $w$.\n",
"\n",
"Ok... now that we have some confidence on this topic, we can continue with the solution of the PIDE. \n",
"As usual we create the diffusion matrix:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"dxx = dx * dx\n",
"a = (dt / 2) * ((r - w - 0.5 * sig2) / dx - sig2 / dxx)\n",
"b = 1 + dt * (sig2 / dxx + r + lam)\n",
"c = -(dt / 2) * ((r - w - 0.5 * sig2) / dx + sig2 / dxx)\n",
"D = sparse.diags([a, b, c], [-1, 0, 1], shape=(Nspace - 2, Nspace - 2)).tocsc()\n",
"DD = splu(D)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the following cell, I create the Lévy measure vector. \n",
"\n",
"The three points in the middle are zero! \n",
"This is because the Lévy measure is truncated near the origin, and the region $[-\\epsilon, \\epsilon] = [-1.5\\Delta x, 1.5\\Delta x]$ is excluded."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}0.00140784532775271\\\\0\\\\0\\\\0\\\\6.46388528653557 \\cdot 10^{-5}\\end{matrix}\\right]$"
],
"text/plain": [
"⎡0.00140784532775271⎤\n",
"⎢ ⎥\n",
"⎢ 0 ⎥\n",
"⎢ ⎥\n",
"⎢ 0 ⎥\n",
"⎢ ⎥\n",
"⎢ 0 ⎥\n",
"⎢ ⎥\n",
"⎣6.46388528653557e-5⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nu = np.zeros(2 * extraP + 3) # Lévy measure vector\n",
"x_med = extraP + 1 # middle point in nu vector\n",
"x_nu = np.linspace(-(extraP + 1 + 0.5) * dx, (extraP + 1 + 0.5) * dx, 2 * (extraP + 2)) # integration domain\n",
"\n",
"for i in range(len(nu)):\n",
" if (i == x_med) or (i == x_med - 1) or (i == x_med + 1):\n",
" continue\n",
" nu[i] = quad(levy_m, x_nu[i], x_nu[i + 1])[0]\n",
"\n",
"display_matrix(nu)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following code is the same we used for the Merton PIDE. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Backward iteration\n",
"for i in range(Ntime - 2, -1, -1):\n",
" offset[0] = a * V[extraP, i]\n",
" offset[-1] = c * V[-1 - extraP, i]\n",
" V_jump = V[extraP + 1 : -extraP - 1, i + 1] + dt * signal.convolve(\n",
" V[:, i + 1], nu[::-1], mode=\"valid\", method=\"auto\"\n",
" )\n",
" V[extraP + 1 : -extraP - 1, i] = DD.solve(V_jump - offset)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10.467523461720827\n"
]
}
],
"source": [
"# finds the option at S0\n",
"oPrice = np.interp(X0, x, V[:, 0])\n",
"print(oPrice)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### All good!!\n",
"\n",
"**Alternatively** we could have used the Jump matrix. \n",
"Let us do it for completeness, but remember that the `signal.convolve` method is more efficient."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}0.00140784532775271 & 0 & 0 & 0 & 6.46388528653557 \\cdot 10^{-5} & 0 & 0 & 0 & 0\\\\0 & 0.00140784532775271 & 0 & 0 & 0 & 6.46388528653557 \\cdot 10^{-5} & 0 & 0 & 0\\\\0 & 0 & 0.00140784532775271 & 0 & 0 & 0 & 6.46388528653557 \\cdot 10^{-5} & 0 & 0\\\\0 & 0 & 0 & 0.00140784532775271 & 0 & 0 & 0 & 6.46388528653557 \\cdot 10^{-5} & 0\\\\0 & 0 & 0 & 0 & 0.00140784532775271 & 0 & 0 & 0 & 6.46388528653557 \\cdot 10^{-5}\\end{matrix}\\right]$"
],
"text/plain": [
"⎡0.00140784532775271 0 0 0 \n",
"⎢ \n",
"⎢ 0 0.00140784532775271 0 0 \n",
"⎢ \n",
"⎢ 0 0 0.00140784532775271 0 \n",
"⎢ \n",
"⎢ 0 0 0 0.001407845327\n",
"⎢ \n",
"⎣ 0 0 0 0 \n",
"\n",
" 6.46388528653557e-5 0 0 \n",
" \n",
" 0 6.46388528653557e-5 0 \n",
" \n",
" 0 0 6.46388528653557e-5 \n",
" \n",
"75271 0 0 0 6.463885\n",
" \n",
" 0.00140784532775271 0 0 \n",
"\n",
" 0 0 ⎤\n",
" ⎥\n",
" 0 0 ⎥\n",
" ⎥\n",
" 0 0 ⎥\n",
" ⎥\n",
"28653557e-5 0 ⎥\n",
" ⎥\n",
" 0 6.46388528653557e-5⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"J = np.zeros((Nspace - 2, Nspace + 2 * extraP))\n",
"\n",
"for i in range(Nspace - 2):\n",
" J[i, i : (len(nu) + i)] = nu\n",
"\n",
"display_matrix(J)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Backward iteration\n",
"for i in range(Ntime - 2, -1, -1):\n",
" offset[0] = a * V[extraP, i]\n",
" offset[-1] = c * V[-1 - extraP, i]\n",
" V_jump = V[extraP + 1 : -extraP - 1, i + 1] + dt * (J @ V[:, i + 1])\n",
" V[extraP + 1 : -extraP - 1, i] = DD.solve(V_jump - offset)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10.467523461720827\n"
]
}
],
"source": [
"# finds the ATM option at S0\n",
"oPrice = np.interp(X0, x, V[:, 0])\n",
"print(oPrice)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Comparison with Monte Carlo, closed formula and Fourier inversion\n",
"\n",
"\n",
"Let us compare the ATM prices obtained from different numerical methods. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from FMNM.Parameters import Option_param\n",
"from FMNM.Processes import Diffusion_process, VG_process\n",
"from FMNM.BS_pricer import BS_pricer\n",
"from FMNM.VG_pricer import VG_pricer"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# Creates the object with the parameters of the option\n",
"opt_param = Option_param(S0=100, K=100, T=1, exercise=\"European\", payoff=\"call\")\n",
"# Creates the object with the parameters of the process\n",
"VG_param = VG_process(r=0.1, sigma=0.2, theta=-0.1, kappa=0.3)\n",
"# Creates the VG pricer\n",
"VG = VG_pricer(opt_param, VG_param)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PIDE price: (it takes about 5 minutes to run)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV4AAAAVCAYAAAAOw4YBAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAL1UlEQVR4Ae2c4bUVNRDHL+9QwBMqUDoQ6AA6QKxA6ECPn+AbRzpQKxDsQK1ApAOxAvB1gP9fbiZks0l2sntBnm7O2ZtkMvnPZDKZZHPve1fevn17yNPjx48/0/Mqp+3l3QK7BXYL7BZYZ4FaTD3LocTwteqf57S9fLRANN4D5eentInwZvZGBvJOKedjwmJsek5uy94YJe9S2vmy6t2bi/9hG/5ObE3pqpXUcE/l68qfGi3PowM8F+2myhd5G2XRCBQPI53gRP070X+NtG420l+832dg11T+SrSJThHvm8h3S/kbPd+I/jLSJpkDk4WL3O/FO+kbKxeif0JZOWP3yv5N/NjL9KJMunnMpp/iRY/mPBi3+L6L5dfKb+hhLqpvMhmvdf9JNNPHaClXGzo8VG7zndooiL40P25bToC3Vdx2dujf1ER9h9aB+Jfm0603Sjnw4BnS0QYbsavzrja3vxlepu8MU3isg28zXurPRV+MJz09o0zT1eCr/h5xPGut6+/oDJYeDhs/IDQEXlXCIJVPFnuk/yg+ghbBiwmbpchHUEsLUWUC+S/Kv9Dz86xTRlA78hf7R74/xEvwCxuEchz3L+VsCCGwKEdPeO4qD0lljP0HND1p8lRG9iKmeG7roV8teN0RPQRa4bllqw8J25IYB9jY6olwLpSHFHVcnIeMn/GAEewe+9vYk/6ioyuOhe2DTZRT5yFYtxLts8BsctTWnR+1u2zZEr6S7rWzxxeqKsTxe/3YO59evV14Xh2rAzz6RW3eXf42gileDgopntBXdQIvb4PdeCLWln8u+jv46j9iS5e/CPepHtbgMz0XduIlKOVRW9UwUBb/F7Hcu4Z4IB6iOYHWjGLBjV3LaEDVkrc/BrkmGelUrvJLPS9ER38LtIynnDQWBHKYlHAyVU7yYh7U3/CPPfUpGpNJbmMckU1X9A82plJLavfOA3owxolz0l8P+uU2QhS2YLe3uYKG46XgDCFP4p28MuVtKp/algX8puqinUf0b2ji8mPmQ/096woxi3oP4rl0LMfXmnfRR/xtAruASUAr01ci/KbH1lrZfmhhRsZFf1f/kblx+3uUz/oL8eEsEu5LYDgCx/poxi6IwjwhxQFYdSn39ucUXQsK9L8jmedRECfQP7N6JIcTK0EpBMtI9GL+biBFXu7MI7ILqJNUWdCzU4lo6J9sJBswbk7Zk3kXnTeC2QYjvoPo8E/mGXqWTm3LDPqDFL36t5Tx+nGr/4egD+u4MO8ufysHtoDJ21bVB0ucvN7DVNuwv+fYjfKQv0gH1hqx9vxqVKgWzBqy5mRhcGLKT5GHiAszUb6bPP3FY0H1TQXsdaTdUo4uPNypECRqKWCNYIp3tsuKxu71pBDgkl30OWWVwD8JphHc5ph2xsIbASfhlo1it0n2pfgnr9LWKrp7fsTrtaXBv/d8RP+WMsLYtA5auKekr9SxOe/Szetv5TB6mBwSvpauvyjnqtJ8lPXWiyc9zDX+Xuqc6tLJ7e+p07HAOrx/VR/sLDjMyZKUYjLCkVrlWhDoyqr1F40gQb9rlc7XIy2cZMXXenXnxHZQezgRKndjljLVF1kEd/sSLbCo7pKd46lPeF0TjXGAy/1s0DHnWyqrjzlDj9Xsxyb1Sn2wyZd62Lw4aVS/wBAfVwxNp1f7yW0peSdN0rFp5y36t5QU5qZ1YLg9vY1nbb6ko9qb8662EX9LKvYwYVL7zzwqcqL8W2XWGL6ZX2Wq+i6Jp6ln5Bry93fI9ZLkrfV3Yu1dAi8KNRdUXWydKmVYxDjbbT0Ejhd63MnRn8kAv0whoIrYdISITVCbBErV12KysfAspo5s+qJzuHCnIl505Jpk8iUgbY5kQfWiw2s2svyWZCWbqIyj8ysR7BKSyuiEo9mpObbMsvduy5lEP8Fj57X6T7SQnTatgwnYaf0jQXt0dMz7iL8F2Q5M4+OkS1xis2Sd4XvV9ebEdPu75HjTGn/5U+B3zvSBQrXXd6/wxCcD8EUA395x6vtJD9/isWu5kqM/l+sH8aXgqzJOfhEF9AIDF+vspOmLudhnGFMYBKI7yr1vCi3ZB2EQYE1/6owB3JNshnGMecZPBs0JObGXbyTPxPxjxkNffu5T8uWYVv4QtjRZQ7n099h5WP+aEpK1aR3kmE698y6uslNH77z3ZNrbqPG4MKUfcYN1wUmX9cCa40BSiyddTPUZ9XeJcqU1/kKs/YzAy66VFr5LnINJg2U3ADf8BMTRZcJS6y8aeJ/qYTfkDogdkAnhTohUDbziI4jxWj27BhBtDSb3RVVZKJGnnuycryiDzY+uGdtI6m2gdjqx+3Bwa2Pg2+RzPbwJHaQDJw7XJiDe92pL9Dlxmth5pf5dlYS5aR00wCd6N3jc5JqOonnmfcjfnJiHyMdGyfcJrNu7Goyt3cmhIPK6/FMYi/7uNpoYJXuNv6PDOVcNm5MU4NSJIi8LsBeqczrlwQGraaS/eBksgS8l0ewVZGZYteFA15QzedU0iikQdt2ZrBJ8Sbba+fIA3Sa/n85wCIDuxDj0wF/rZzQc2fiwZStZ4D8X/+JYDQRsld3zI16XLQ1/TS6d3HZeoX9SSX03rYMEFAsjepd9W3WPjuJhHS/Ou/jMj8y3crFGC74jXg4Ri5gRgPXMASsl9edtldNveFVXTt2FKT7Ts+vvSdhAAWyxj/h7OOgSeNm1zEgDIiesnJIOUuKTqMik0VHZ2h+H569DJoZVnUV9Q7ntlgeVwylS+VIwaWFiKzDKTUakd0n4Htm31AP7l4nJQdeujLJTrNtrWdkcMEW065EWn/XDPozztvTgqiRP2IbADJ1Anu6Ic6asvMmWGc7a4lY7V/WvKLPVj0vIrXqXeNQ9Oo7Me8uPSn9zYUq/J3oI0JO1jOKi4Wsc4Azbhak++GdLT6BJS/HgyOX77PkL8eMNgdcWmA+yzoWRZoFPNByHZIv9WJt/uvrLgPfUlR8tf6pymBjlDIQT9eTUKDqDJ2iUQQGMdF+pdjem+pFsTLWAGRi8ssX8Q0U/MBjPks3gqyWCob0B5O3Yh7vHYDeVeT0rAyr88HFCMPmW0xaS2v5WgflOGxoNqp/clhG3uhBpcyaXnVfoX4p3+XHZqVN36d3pX2ta1FF2CDxlZ9Fr8+7yN/XFj1y+hHw9bOy1YHhuOCOY6uP193LYzbrkj/o7WGwar870wanqtp6ldD0y2G6T8xPceJ1LKSqFkbj4ZiIPyllAb/WEXTcxH39psNhf/OxwZcBj4pGRTocqwwcdefz5anoib9BHZZIL88gaPhkTKcc4UvQ5KDvolTof+9tfhk2CWsbTmwfks6m8UY5ThKQyOt/XE74MgCgaJweCZwrSNT54Kwk8s0PefFJbAhx14pcWpc/kcpfKXju79EcnPVv8ONe3N59evb148LnWag6YlWfzLju4/C3DKIszTDHg+7PvhiSLa0N+UlYLyDnuDFN91vh7b26Q5/KXXDGVb+p5eeXRo0ecDBkk9yezJLqdijiFMSACHAPHAPnJkfY8WKAUf9U12eVU546GBV/ei3j7W6BAF9Lsd6dRBvJriVMfg09J9UVMYxYvuASByU+usnbGNyIbXjuVs6mxsfClwoXylFR3zQMdxIttGBMYr/WwsT4RPW1OqockGnxmS+RX+WAWL6cG9GWuSDjz76KnX4pEPNoMczY/NJLE27XlkSvwYVNS9R80HZv6n1GWx84uXxDeVj92zeeA3i48rCRM11ozi4q/O+9qd/vbACZx6Vs9rAdLTV+CYUnPyLPo78IZsaXLX2wAwsZvHl7h//HGCr8UmC1M67DnuwX+TQvINznB166z/k21dtm7BdwWkA9z0ODAeuMs9iJqT06gbrSdcbfAh7EA9/Wc4Pe0W+CyWoA3rnBCDoFXDs2Vgf186LIOatf7P2oB+SevslyZ7Gm3wKW0gHyY0y4xNlzP2omXwXA/y13OnnYLfGwWeCCHTffIH5tyuz67BRwWILamW4Vwx2udYlS+tzu5WWTPdwvsFtgtsM0Ciqf8Uok/+Ei/xvgHn7nfxjdu4hcAAAAASUVORK5CYII=",
"text/latex": [
"$\\displaystyle \\left( 13.3929725106497, \\ 59.3151340484619\\right)$"
],
"text/plain": [
"(13.3929725106497, 59.315134048461914)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"VG.PIDE_price((30000, 30000), Time=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"VG_p.plot([50, 150, 0, 60])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Remark:\n",
"\n",
"There are some discrepancies between the outputs of the four numerical methods. \n",
"\n",
"- The Monte Carlo and Fourier prices are the most reliable\n",
"\n",
"- The convergence of the PIDE, when considering the Brownian approximation, is quite slow.\n",
" - For the put option we didn't need a huge grid in order to obtain the right price.\n",
" - But for the call option I had to use a huge grid (30000x30000). I expect to achieve a better convergence level for higher grid resolution, in particular when increasing the number of time steps.\n",
" - Increasing the computational domain $[A_1,A_2]$ may also help.\n",
"\n",
"- The semi-closed formula used in VG_pricer is taken from [1]. In my opinion, this formula is not very accurate. \n",
" Unfortunately, I was not able to implement the closed formula proposed in the same paper [1], which relies on the Bessel function of second kind (well...I tried, but it doesn't work. In VG_pricer it is called `closed_formula_wrong`). \n",
" Therefore I decided to use the semi-closed formula, which is based on numerical integration. \n",
" However, this formula still does not produce very accurate values. \n",
"\n",
"If you have any comment on these features, please let me know."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Comparison with the Black Scholes PDE\n",
"\n",
"Now let us compare the VG curve with the Black Scholes curve, for a European call option. \n",
"The volatility of the BS model is chosen equal to the standard deviation of the VG process. \n",
"\n",
"In this case I'm going to select some parameters with the purpose to have high values of skewness and kurtosis for the VG distribution. \n",
"In the VG process, the parameter $\\theta$ is associated to the skewness, and the parameter $\\kappa$ is associated to the kurtosis.\n",
"\n",
"Looking at the plot we can notice the different shape of the two curves."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"standard deviation: 0.37416573867739417\n",
"skewness: -3.05441419328485\n",
"kurtosis: 14.387755102040819\n",
"Changing the sign of theta, the skewness becomes: 3.05441419328485\n"
]
}
],
"source": [
"# Creates the object with the parameters of the option\n",
"opt_param = Option_param(S0=100, K=100, T=1, exercise=\"European\", payoff=\"call\")\n",
"\n",
"# Creates the object with the parameters of the process\n",
"VG_param2 = VG_process(r=0.1, sigma=0.2, theta=-0.2, kappa=2.5)\n",
"VG_param3 = VG_process(r=0.1, sigma=0.2, theta=+0.2, kappa=2.5)\n",
"diff_param = Diffusion_process(r=0.1, sig=np.sqrt(VG_param2.var))\n",
"\n",
"print(\"standard deviation: \", np.sqrt(VG_param2.var))\n",
"print(\"skewness: \", VG_param2.skew)\n",
"print(\"kurtosis: \", VG_param2.kurt)\n",
"print(\"Changing the sign of theta, the skewness becomes: \", VG_param3.skew)\n",
"\n",
"# Creates the object of the pricer\n",
"BS = BS_pricer(opt_param, diff_param)\n",
"VG2 = VG_pricer(opt_param, VG_param2)\n",
"VG3 = VG_pricer(opt_param, VG_param3)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left( 19.3873623868759, \\ 0.536520004272461\\right)$"
],
"text/plain": [
"(19.38736238687595, 0.5365200042724609)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BS.PDE_price((7000, 4000), Time=True)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left( 16.1178393915308, \\ 12.3674609661102\\right)$"
],
"text/plain": [
"(16.117839391530826, 12.36746096611023)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"VG2.PIDE_price((14000, 10000), Time=True) # negative skewness"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left( 18.1266742827993, \\ 12.1112761497498\\right)$"
],
"text/plain": [
"(18.12667428279933, 12.111276149749756)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"VG3.PIDE_price((14000, 10000), Time=True) # positive skewness"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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nTk2SATDffPNNkscvUqSICQkJcXw/fPhw4+LiYnbv3p3kuE8//dQAZs2aNcaYlNXsKfnMI3IramxLqjNnzhwDmE8//dQY8/9/qCpWrJjkuFsVuR988EGSQvLffzG9lYYNG5ps2bI5/hG53nR78803k+Vq1qyZWb58ueOv0CkBmCZNmpjdu3eb3bt3m23btpkPP/zQZM6c2VSoUCHZa7hw4YKZP3++6dq1q3nuueeMi4uLsdlsyZpON7peXF3/+ndj7EYXLlww6dOnN4UKFTKTJk26ZUH0/fffmzp16pgMGTIkK9K//vprx3GVKlUyQUFBye4fFBRkypYtm2RbbGysAUzPnj2NMcZcvXrVuLm5mddffz1J/ri4OEej8fo/rLeSkvfQGGM2btxoqlSpYtKmTZvs9fz7r/S5cuVKVqD+e9+NRe7ixYtNuXLljK+vb5LH9PLySnLc9Z+hGwUGBppXXnnFGGNvprq6upqOHTve9jVny5bN1KlTJ9k5uz7aaeLEibe87/UCP6UfBq8XkD///HOS7ZMnT05y7vr3729sNps5depUslxlypRxNIKvv69Lly5N9ly5c+dO1thO6fm9k8b2qlWrDGDWrl3r2Na4cWMTGBjoOB/r1693/D668fX06dPH2Gy2ZB9U/23w4MHGZrOZV1991URERCT7EPfvc9utWzfj6upqxo4dm+yYlL7XV69eNV5eXmbAgAHGGGPatGlj6tSpY1auXGk8PT1NdHS0OXbsmAHMjBkzkmW4X+9vSn6/bN261QAmODjYLFq0yPz555+3PI8iIpL6qOZXza+a/9Gt+f/9hwtj7P9/urm5mfbt2xtjjJk4caIBHKPb/61w4cKmdOnSxpiU//zdS2PbGPsfxby8vBzN1/j4eBMUFGQaN27sOCaldeytXP8jxJAhQ8zOnTuT/FHuukqVKpl8+fKZOnXqmPTp05stW7Yk2X/o0CEDmDFjxiTLcP2cXh/Jfv3zy6ZNm4wx9s9I77//vunRo4epUKGCMcaY2bNnG8D88ccfSTIEBgYmy9asWTPzxBNPOL4vX768KV68eLIcly5dMjabzdHoT0nNnpLPPCK3ojm2JdVp1KgR/v7+jnna1qxZw6lTpxwLyFyXM2dOAI4ePZpke/Pmzdm9eze7d+9OMp/W7bRr144TJ06wfv16wL5oTWxsbJI51Vq1asWMGTM4evQoDRs2JEuWLJQuXdpxn/+SOXNmSpUqRalSpahYsSKvv/46H374IV9++SWzZs1Kcqy/vz+hoaGMHz+eb775hn379hEQEED//v25cOHCLZ+jSpUquLu7O75uN1etv78/W7dupUSJEvTr148nn3ySrFmz8u677zrm0jt27BgVK1bkxIkTjB8/nu3bt7N7927HHHhXr15N8pgZMmRI9jweHh7Jtnt4eAD2eerAPs9gfHw8EyZMSJLf3d2dmjVrAiSZ7/hmUvIe7tq1i+DgYACmTZvGjh072L17N/3797/p67nVvIc3WrZsGU2aNCFbtmyEh4ezc+dOdu/eTbt27Ryv8d8yZsyYbJunp6fj+c+fP09CQsJ/Lopy6tQpVq1aleycPfnkk8Dtz9ngwYOT3Cdfvnwpeq03Zvf09AT+f+5OnTqFMYaAgIBkub7++mtHputzSwYEBCR7jhu33en5TakaNWoQFBTk+F1z/vx5Vq5cSevWrR1zYJ46dQqw/1668fWMHDkSYwznzp275XP07duXMWPG8PXXX1OjRg0yZsxIlSpV+Pbbb5MdGx4eTrZs2WjWrFmyfSl9r728vJLM9bdx40aqVatG5cqVSUhIYPv27Y7/R6pWrZrsee7X+5uS3y/PP/88y5cvJz4+ntatW5M9e3aKFi3KggULbnk+RUQk9VDNr5pfNf+jW/MHBgYm+d7NzY2MGTM6avjbzROfNWtWx/6U/PzdD9ffg4ULFwKwdu1aTp48mWRx1pTWsbeyaNEi2rRpwyeffELZsmXJkCEDrVu3JjIyMslxp0+fZu3atZQtW5Zy5col2Xf9s0WvXr2SZbi+mO71HOXKlcPHx4cNGzbw22+/ceTIEapVq0bVqlX55ptvuHz5Mhs2bCBv3rzkyZMnyfP818/e9Sz79u1LlsPPzw9jjCNHSmr2O/nMI3IjN6sDiNxv3t7ehIaGMm3aNE6ePMmMGTPw8/OjcePGSY6rVq0aU6dOZeXKlfTq1cuxPUuWLGTJkgUAPz+/JItu3EpISAhZs2Zl5syZhISEMHPmTEqXLk2RIkWSHNe2bVvatm1LdHQ027Zt491336V27dr8+uuv5MqV645fa/HixQHYu3fvbY978sknadasGePGjePXX3/lueeeu+lxU6ZM4dKlS47vM2XKdNvHLVasGAsXLsQYw759+5g1axaDBw/G29ubt956i+XLlxMdHc2yZcuSvL49e/ak8BWmTPr06XF1daVVq1a89tprNz3mxn+sb5SS93DhwoW4u7uzevVqvLy8HNuXL19+08f896IktxMeHk6ePHlYtGhRkvuk5GfvZjJkyICrq2uyhVdulClTJooXL85777130/1Zs2a95X07duxI7dq1Hd9fb2Deq0yZMmGz2di+fftNH/P6tuvF1vXi7t8iIyPJnTu34/v7fX6vu/4z9+GHH3LhwgXmz59PbGxskgL4+v9DEyZMoEyZMjd9nJs1569zc3OjR48e9OjRgwsXLrBhwwb69etHSEgIx48fx8fHx3FsREQETZs2pWLFimzcuDHJ/3N38l5XqVKFAQMGsGvXLv7880+qVauGn58fzz77LOvXr+evv/6iYMGC5MiRI2Un6l9S+v7Cf/9+AahXrx716tUjNjaWr7/+muHDh9O8eXNy585N2bJl7zifiIg4D9X8yanmV81/M1bU/JGRkWTLls3xfXx8PGfPnnXU8Nf/e/LkyWSN+b/++ivJz2RKasJ7VaRIEZ577jlmzpxJp06dmDlzJlmzZnX8gQPurI69mUyZMjFu3DjGjRvHsWPHWLlyJW+99RanT59Oskhqzpw5GTt2LC+99BINGjRgyZIljp/D6+elb9++SRY7/bdChQoB9j8MVahQgQ0bNpA9e3YCAwMpVqwYefPmBeyLbG7cuDHJ+3snMmXKhLe3NzNmzLjl/uv+q2a/k888IjdSY1tSpfbt2zN58mRGjx7NmjVrCAsLS/bL8KWXXqJIkSIMGzaM2rVr88QTT9z1810vsMaNG8f27dv59ttvmTJlyi2P9/X1pUaNGly7do369euzf//+uypyrxeL14vys2fP4ufn5xjd8G8///wzcPvC5fo/gnfKZrPx1FNP8cEHHzBr1iy+//57x3ZI+o+8MYZp06bd1fPcio+PDy+88AI//PADxYsXv+nr/y8peQ9tNhtubm6O0bhgH7Exd+7ce8pvs9nw8PBIUuBGRkbedIX0lPD29qZSpUosWbKE995775YfVmrXrs2aNWvIly8f6dOnv6PnyJo1621/lu5W7dq1GTFiBCdOnKBJkya3PK506dJ4enqyaNGiJEXd119/zdGjR5M0tu/k/N44EuG/tG3bllGjRrFgwQJmzZpF2bJlk/wuKV++POnSpePAgQN06dIlxY97M+nSpaNRo0acOHGCbt26ceTIkSQfwnLlysX27dupWrWqo7ldoEAB4M7e66pVq9KvXz/eeecdsmfP7ng9VatWZeXKlURGRtKwYcO7eg0pfX//7Va/X/7N09OTSpUqkS5dOtauXcsPP/ygxraIyGNANb9q/julmv/h1Pzz5s3jmWeecXy/ePFi4uPjqVy5MgAvvvgiYG/2P/vss47jdu/ezcGDBx2j4/8tJTXhdTdeNZgSbdu25dVXX+XLL79k1apV9OjRI8nPwN3UsbeSM2dOunTpwsaNG9mxY0ey/cHBwaxdu5ZatWpRu3ZtVqxYga+vL4UKFaJAgQLs3buXYcOG/efzVK1alb59++Ln5+e42tLX15cyZcowYcIE/vrrr5tehZkStWvXZtiwYWTMmPE//6B0XUpq9v/6zCNyIzW2JVUqVaoUxYsXZ9y4cRhjkl2SCPaiZvny5YSEhPDcc8/RoUMHKleuTPr06blw4QLffPMNe/fupXDhwil6znbt2jFy5EiaN2+Ot7c3TZs2TbK/Q4cOeHt7U758eYKCgoiMjGT48OH4+/sn+cf8Vk6dOsXXX38N2C/H27NnD0OHDiVdunSOEaKbN2/mjTfeoEWLFpQrV46MGTNy+vRpFixYQEREhOPSn/th9erVTJw4kfr165M3b16MMSxbtowLFy5QrVo1wD5CxsPDg9DQUHr37k1MTAyTJk3i/Pnz9yXDv40fP54KFSpQsWJFXn31VXLnzs2lS5f47bffWLVqFZs2bfrPx/iv97BWrVqMHTuW5s2b07FjR86ePcuYMWPuebRy7dq1WbZsGZ07d6ZRo0YcP36cIUOGEBQUxKFDh+7qMceOHUuFChUoXbo0b731Fvnz5+fUqVOsXLmSKVOm4Ofnx+DBg1m/fj3lypWja9euFCpUiJiYGI4cOcKaNWuYPHnyfft5Sany5cvTsWNH2rZty7fffsvzzz+Pr68vJ0+e5Msvv6RYsWK8+uqrZMiQgR49ejB8+HDSp0/PSy+9xJ9//smgQYMICgrCxeX/M23dyfktVqwYW7ZsYdWqVQQFBeHn53fbD39PPPEEZcuWZfjw4Rw/fpypU6cm2Z8mTRomTJhAmzZtOHfuHI0aNSJLliycOXOGvXv3cubMGSZNmnTLx69Tpw5FixalVKlSZM6cmaNHjzJu3Dhy5crlaFr/W1BQEFu3biUkJITnn3+e9evXU7Ro0Tt6r5955hnSp0/PunXrkow+r1q1KkOGDHHcvhspfX9T8vtlwIAB/Pnnn1SpUoXs2bNz4cIFxo8fj7u7O5UqVbqrfCIi4lxU86vmV83/aNb8y5Ytw83NjWrVqrF//37eeecdnnrqKUdDuFChQnTs2JEJEybg4uJCjRo1OHLkCO+88w45cuSge/fuQMp+/m4mX758eHt7M2/ePAoXLkyaNGn+s0kfGhpKjx49CA0NTTY9DaS8jr2Zixcv8sILL9C8eXOeeOIJ/Pz82L17NxEREbcceV2hQgU2btxI9erVCQ4OZs2aNfj7+zNlyhRq1KhBSEgIYWFhZMuWjXPnznHw4EG+//57lixZ4niMKlWqkJCQwMaNG5k9e7Zje9WqVXn33Xex2WyOPzLcqW7durF06VKef/55unfvTvHixUlMTOTYsWOsW7eOnj17Urp06RTV7Hf6mUckiYc+q7fIQzJ+/HgDmCJFitz2uIsXL5phw4aZZ5991qRNm9a4ubmZLFmymGrVqpmPP/7YREdHp/g5y5UrZwDTokWLZPtmz55tXnjhBRMQEGA8PDxM1qxZTZMmTcy+ffv+83G5YcESd3d3kzdvXtO2bVvz22+/OY47fvy4efvtt0358uVNYGCgcXNzM35+fqZ06dJmwoQJ/7nYx534+eefTWhoqMmXL5/x9vY2/v7+5rnnnjOzZs1KctyqVavMU089Zby8vEy2bNnMm2++ab744otkC/FdXwH6Rrly5TK1atW66Tl57bXXkmw7fPiwadeuncmWLZtxd3c3mTNnNuXKlTNDhw5N8eu63XtojDEzZswwhQoVMp6eniZv3rxm+PDhZvr06QYwhw8f/s/c1/fduJDMiBEjTO7cuY2np6cpXLiwmTZt2k0XNrnZ677VYx44cMA0btzYZMyY0Xh4eJicOXOasLCwJAsPnTlzxnTt2tXkyZPHuLu7mwwZMphnnnnG9O/f/7aLGt6p66/lxgWUZs6cmezcGWM/z6VLlza+vr7G29vb5MuXz7Ru3TrJQjSJiYlm6NChJnv27MbDw8MUL17crF692jz11FPmpZdeSvJ4KT2/e/bsMeXLlzc+Pj4GcCxCebPFI6+bOnWqAYy3t/ctFzrZunWrqVWrlsmQIYNxd3c32bJlM7Vq1TJLliy57Xl7//33Tbly5UymTJkc72H79u3NkSNHbntuL1y4YMqXL28yZMjgWKn8Tt7rl156yQBm3rx5jm3Xrl0zvr6+xsXFxZw/fz7J8ff7/U3J75fVq1ebGjVqmGzZshkPDw+TJUsWU7NmTbN9+/bbnlMREUldVPOr5lfN/+jV/N99952pU6eOSZMmjfHz8zOhoaHm1KlTSY5NSEgwI0eONAULFjTu7u4mU6ZMpmXLlub48eOOY1L683ez87JgwQLzxBNPGHd3dwOYd999N0nGm2nevLkBTPny5W/5GlPyOeVGMTEx5pVXXjHFixc3adOmNd7e3qZQoULm3XffTfK752b/f/z0008mMDDQPP30045ae+/evaZJkyYmS5Ysxt3d3QQGBpoXX3zRTJ48Ocl9ExMTTaZMmQxgTpw44di+Y8cOA5inn346WdZb/T/apk0bkytXriTbLl++bN5++21TqFAh4+HhYfz9/U2xYsVM9+7dHYuspqRmT8lnHpFbsRljzINrm4uIiDw8hw8f5oknnuDdd9+lX79+VscREREREXlsDBw4kEGDBnHmzJn/nLtdROR+0FQkIiLilPbu3cuCBQsoV64cadOm5ZdffmHUqFGkTZv2ppcii4iIiIiIiEjqoca2iIg4JV9fX7799lumT5/OhQsX8Pf3p3Llyrz33nsEBARYHU9EREREREREHiBNRSIiIiIiIiIiIiIiTsXFyicfOHAgNpstyVdgYKBjvzGGgQMHkjVrVry9valcuTL79++3MLGIiIiIiNxKfHw8b7/9Nnny5MHb25u8efMyePBgEhMTHceoxhcRERGR+8HSxjbAk08+ycmTJx1fP/74o2PfqFGjGDt2LB999BG7d+8mMDCQatWqcenSJQsTi4iIiIjIzYwcOZLJkyfz0UcfcfDgQUaNGsXo0aOZMGGC4xjV+CIiIiJyP1je2HZzcyMwMNDxlTlzZsA+kmPcuHH079+fBg0aULRoUWbPns2VK1eYP3++xalFRERERORGO3fupF69etSqVYvcuXPTqFEjgoOD+fbbbwHV+CIiIiJy/1i+eOShQ4fImjUrnp6elC5dmmHDhpE3b14OHz5MZGQkwcHBjmM9PT2pVKkSX331FZ06dbrp48XGxhIbG+v4PjExkXPnzpExY0ZsNtsDfz0iIiIi8nAYY7h06RJZs2bFxcXy8RoCVKhQgcmTJ/Prr79SsGBB9u7dy5dffsm4ceMAVOOLiIiIyG3dSY1vaWO7dOnSzJkzh4IFC3Lq1CmGDh1KuXLl2L9/P5GRkQAEBAQkuU9AQABHjx695WMOHz6cQYMGPdDcIiIiIvLoOH78ONmzZ7c6hgB9+vTh4sWLPPHEE7i6upKQkMB7771HaGgogGp8EREREUmRlNT4lja2a9So4bhdrFgxypYtS758+Zg9ezZlypQBSDYCwxhz21EZffv2pUePHo7vL168SM6cOTl+/Dhp06a9z69ARERE5M7FxsdSZU4Vfjz1I5VOuLF8bjwuYWEwfrzV0ZzCggULeOWVVxyNUz8/P6sjyT8WLVpEeHg48+fP58knn2TPnj1069aNrFmz0qZNG8dxqvFFREREhJ07oX59iImBevVgxgyGbR7FyEYjU1TjWz4Vyb/5+vpSrFgxDh06RP369QH7qI6goCDHMadPn042wuPfPD098fT0TLY9bdq0KnpFRETkkdBjbQ9+vPgjGV3cmL88nnQlS8KkSeDlZXW0R96xY8fo3bs3AP3792fw4MGaiuIR8uabb/LWW2/RrFkzwD545ejRowwfPpw2bdoQGBgIqMYXEREReex99x00aWJvalevDosX88lPcxj57Ugg+UCIm3mkJiOMjY3l4MGDBAUFkSdPHgIDA1m/fr1j/7Vr19i6dSvlypWzMKWIiIjI3fvi0Bd88PUHAMz6NJ6sLv6wZIma2imQmJhI27ZtiYqKomzZsrzxxhtWR5IbXLlyJdlciK6uriQmJgKoxhcREREROHAAQkIgKgqefx6WLmXZ76vptPrm663ciqUjtnv16kWdOnXImTMnp0+fZujQoURFRdGmTRtsNhvdunVj2LBhFChQgAIFCjBs2DB8fHxo3ry5lbFFRERE7krk5UjaLLdPx/D6N1D7V+CzWZAvn6W5nMXHH3/Mpk2b8PHxYfbs2bi5PVIXHwpQp04d3nvvPXLmzMmTTz7JDz/8wNixY2nXrh2AanwRERGRx93vv0PVqnD2LDz7LKxaxeZT3xC6NJREk0irp1oxl7kpeihLPw38+eefhIaG8vfff5M5c2bKlCnD119/Ta5cuQDo3bs3V69epXPnzpw/f57SpUuzbt06zaMoIiIiTifRJNJmeRvOXDlD8TMujFqfCD172ueUk//0888/O6YgGT16NAUKFCAqKsriVHKjCRMm8M4779C5c2dOnz5N1qxZ6dSpEwMGDHAcoxpfRERE5DH155/2pvbJk1C0KHzxBT9E/069hfW4lnCNl554iXEh41Lc2LYZY8wDjmypqKgo/P39uXjx4i3n3zPGEB8fT0JCwkNOJ/fK3d0dV1dXq2OIiIj8p/e/ep9e63vhHW/j2ymGIgXLw+bN4O5udbRHXnx8POXLl2fXrl1Uq1aNtWvXYrPZUlTnSeqkGj91U40vIiKSCp0+DZUqwc8/Q/78sH07h9wvUWFmBU5Hn6ZSrkpEtIzg2pVrKa7xH/vrN69du8bJkye5cuWK1VHkLthsNrJnz06aNGmsjiIiInJL3/31HX039gVg3BeGImSGRYvU1E6hESNGsGvXLvz9/ZkxY4YWi5T/pBrfuanGFxERSWUuXLDPqf3zz5AjB2zYwF++iQTPCOZ09GlKBJZgRbMVeLl5cY1rKX7Yx7qxnZiYyOHDh3F1dSVr1qx4eHjog5ITMcZw5swZ/vzzTwoUKKBRHSIi8ki6fO0yoUtDiUuMo8EB6PA9sG4+ZMtmdTSn8P333zNo0CDAPsd29uzZLU4kjzrV+M5NNb6IiEgqc/ky1KwJe/ZAliywYQPns6Sl+qxKHLlwhHzp8xHRIgJ/L/87fujHurF97do1EhMTyZEjBz4+PlbHkbuQOXNmjhw5QlxcnIpeERF5JL3+xescOneI7FE2pq0y2AYOss8rJ/8pJiaGVq1aER8fT6NGjbS4oKSIanznpxpfREQklYiJsa8ptHMnpEsH69dzJU926swN5sfTPxKYJpB1rdYRkCbgrh7+sW5sX+fi4mJ1BLlLGn0jIiKPsoU/LWTWnlm4JMK8pYYMz4fA229bHctpvPPOOxw4cICAgAAmTZqkf/fljqjGd176f11ERCQViIuDpk1h40ZIkwYiIoh7sjBNFzdgx/Ed+Hv6s7blWvKmz3vXT6HGtoiIiMgDcPj8YTqt7gTA29vg+YTsEB4OaralyLZt23j//fcBmDZtGpkyZbI4kYiIiIiIpEhCAoSFwcqV4OUFq1aR+NyzdFjRjtW/rsbLzYtVoasoHlD8np5GjW0RERGR+ywuIY7my5oTFRtF+WPwzg5X2LoY1JxNkUuXLtGmTRuMMbRv3546depYHUlERERERFLCGOjcGebPBzc3+PRTqFyZPuveZPbe2bjaXFncaDEVc1W856fSkCERERGR+2zQ1kF8/efX+MfAvKXgNmoMlC1rdSyn0bNnT44cOUKuXLkYO3as1XFERERERCQljIE334SpU+1Xqs6bB7VqMWrHKMbsHAPA9LrTqVPo/gxcUWPbCYWFhWGz2RxfGTNmpHr16uzbty/JcVOmTOGpp57C19eXdOnSUbJkSUaOHGlRahERkcfDliNbGLZ9GADTVkKuqg3hjTcsTuU8Pv/8c6ZNm4bNZmP27NmkTZvW6kgiD4VqfBEREXF6Q4bAP9MJMm0aNGnCjB9m0GdDHwBGVxtNmxJt7tvTqbHtpKpXr87Jkyc5efIkGzduxM3Njdq1azv2T58+nR49etC1a1f27t3Ljh076N27N5cvX36oOa9du/ZQn09ERMRKZ6+cpeWylhgM7b+Hxtfyw/TpoIXQUuTs2bO8/PLLAHTv3p1KlSpZnEjk4VKNLyIiIk7rgw/g3Xftt8eNg3btWPHzCjqs6gBA73K96VWu1319SjW2b2QMREc//C9j7iimp6cngYGBBAYGUqJECfr06cPx48c5c+YMAKtWraJJkya0b9+e/Pnz8+STTxIaGsqQIUNu+7j79++nVq1apE2bFj8/PypWrMjvv/8OQOXKlenWrVuS4+vXr09YWJjj+9y5czN06FDCwsLw9/enQ4cOlC1blrfeeivJ/c6cOYO7uzubN28G7MVx7969yZYtG76+vpQuXZotW7bc0TkRERGxkjGGdivbceLSCQr9DeM3e9rnk/P3tzqaUzDG8OqrrxIZGUnhwoV57733rI4kqYVV9b1qfNX4IiIij4tPPoEePey3hwyBN95g29FtNP20KYkmkbYl2jKi6oj7/rRaPPJGV65AmjQP/3kvXwZf37u862XmzZtH/vz5yZgxIwCBgYFs3bqVo0ePkitXrhQ9zokTJ3j++eepXLkymzZtIm3atOzYsYP4+Pg7yjN69Gjeeecd3n77bQAiIiIYPXo0w4cPx/bPiLVFixYREBDgGInVtm1bjhw5wsKFC8maNSufffYZ1atX58cff6RAgQJ39PwiIiJW+Hj3x6z8ZSUe8bDwU/AdNxGeesrqWE5j4cKFLFmyBDc3N+bOnYuXl5fVkSS1sKq+B9X4qvFFRERSv4ULoWNH++3evaF/f/ZG7qXOgjrEJsRSt1BdptaZ6qgX7ic1tp3U6tWrSfNPgR4dHU1QUBCrV6/GxcU+CP/dd9+lQYMG5M6dm4IFC1K2bFlq1qxJo0aNHMfc6OOPP8bf35+FCxfi7u4OQMGCBe8424svvkivXv+/tKBp06Z0796dL7/8kooV7Suezp8/n+bNm+Pi4sLvv//OggUL+PPPP8maNSsAvXr1IiIigpkzZzJs2LA7ziAiIvIw7Y3cS6919n/7xqyDEtXDoF07a0M5kRMnTtC5c2cA3nnnHZ555hmLE4lYQzW+iIiIOJVVq6BVK/tVaq+8AiNG8Pv5PwgJDyEqNorncz3PwoYLcXN5MC1oNbZv5ONjH1lhxfPegRdeeIFJkyYBcO7cOSZOnEiNGjXYtWsXuXLlIigoiJ07d/LTTz+xdetWvvrqK9q0acMnn3xCRETETQvfPXv2ULFiRUfBe7dKlSqV5PvMmTNTrVo15s2bR8WKFTl8+DA7d+505P/+++8xxiQrsGNjYx2jU0RERB5V0deiafppU2ITYqnzC3S5WhQ+/tjqWE7DGEP79u25cOECpUqVom/fvlZHktTGqvr++nPfAdX4IiIi4jQ2bYLGjSE+Hlq2hI8/JjL6FMHhwZyKPsVTAU+xstlKvN29H1gENbZvZLPd9eWCD5Ovry/58+d3fP/MM8/g7+/PtGnTGDp0qGN70aJFKVq0KK+99ppjNMXWrVt54YUXkj2mt/ftf9BcXFwwN8wTGBcXd9NsN2rRogVvvPEGEyZMYP78+Tz55JM89c/l2YmJibi6uvLdd9/h6uqa5H5prLpsVEREJIXeiHiDX87+QtYomLHRF9v2pXfczHqcTZkyhbVr1+Ll5cWcOXPuufkmkoyT1PegGl9EREScxNdfQ926EBsL9evDzJlcuBZF9fDq/HH+D/Kmz0tEywj8vR7sekNaPDKVsNlsuLi4cPXq1VseU6RIEcB+WePNFC9enO3bt9+0kAX7qIyTJ086vk9ISOCnn35KUb769esTExNDREQE8+fPp2XLlo59JUuWJCEhgdOnT5M/f/4kX4GBgSl6fBERESss+mkR03+Yjs3AvGWQ6aOZcBeX+D+ufvvtN3r27AnAiBEjKFy4sMWJRB4tqvFFRETkkbN3L9SoYV8ou1o1WLiQqyaOegvrsffUXgJ8A1jXch2BaR78v/dqbDup2NhYIiMjiYyM5ODBg7z++utcvnyZOnXqAPDqq68yZMgQduzYwdGjR/n6669p3bo1mTNnpmzZsjd9zC5duhAVFUWzZs349ttvOXToEHPnzuWXX34B7PPqff7553z++ef8/PPPdO7cmQsXLqQor6+vL/Xq1eOdd97h4MGDNG/e3LGvYMGCtGjRgtatW7Ns2TIOHz7M7t27GTlyJGvWrLm3EyUiIvKAHD5/mI4rXwbg7W1QuW5X+6V4kiIJCQm0adOGK1eu8MILL/D6669bHUnEcqrxRURE5JH2888QHAwXLkD58vDZZ8S7uxK6NJRtR7eR1jMtES0jyJch30OJo8a2k4qIiCAoKIigoCBKly7N7t27WbJkCZUrVwagatWqfP311zRu3JiCBQvSsGFDvLy82Lhx4y3ntMuYMSObNm3i8uXLVKpUiWeeeYZp06Y5Lglu164dbdq0oXXr1lSqVIk8efLc9HLHW2nRogV79+6lYsWK5MyZM8m+mTNn0rp1a3r27EmhQoWoW7cu33zzDTly5Li7EyQiIvIAxSXEEfppM6LiLlP+GAy4+hyMHm11LKcyZswYvvrqK/z8/Jg5c+YtF74TeZyoxhcREZFH1uHDULUqnD4NTz8Nn3+O8fGh06pOrPhlBZ6unqxstpISgSUeWiSbuXFCtVQmKioKf39/Ll68SNq0aZPsi4mJ4fDhw+TJkwcvLy+LEsq90HsoIiJW6LuhLyN2jCDdVdiz0J9c2/bBDQ0dubV9+/ZRqlQp4uLimDlzJmFhYXf1OLer8yR1U42fuuk9FBERecScOAEVK9qb20WKwNatkCkTb214i5E7RuJic2FZk2XUe6LePT/VndT4GhojIiIicgc2/LGBkTtGAvDJSsg1aYGa2ncgNjaWVq1aERcXR926dWnTpo3VkURERERE5FZOn7aP1D58GPLlgw0bIFMm3v/qfcfnoml1pt2XpvadUmNbREREJIVOR5+m1ZJQDIZO30LDhm/bF06RFBs0aBD79u0jU6ZMTJ06FZvNZnUkERERERG5mfPn7XNq//wz5MgBGzdCUBCz98ym1/peAIysOpJ2JdtZEk+NbREREZEUSDSJtPm0JZExf/PkafggphIMHGh1LKfy1VdfMXKkfVTH1KlTCQgIsDiRiIiIiIjc1KVL9kE8e/dCYKC9qZ0rF6t+WUX7le0B6Fm2J2+We9OyiG6WPbOIiIiIExm38wMijqzHKw4WbcmE96ZF4OpqdSynER0dTZs2bUhMTKRVq1a89NJLVkcSEREREZGbuXIFateGb76BjBlh/XooUIAvj31Jk0+bkGASaPNUG0ZVG2XpFZgasS0iIiLyH7776zveWt8HgHHrbDw5eRlotPEd6d27N7/99hvZs2fnww8/tDqOiIiIiIjcTGwsNGwI27ZB2rSwdi0ULcq+U/uoPb82MfEx1C5Ym2l1puFis7a1rMa2iIiIyG1cir1Es3n1iSOBhgegY5NR9hXBJcXWrl3LxIkTAZg5cybp0qWzNpCIiIiIiCQXHw+hoRARAT4+sGYNPPMMh88fpnp4dS7GXqRCzgosarQId1d3q9OqsS0iIiJyO52XteO3K3+S8wJMM3Ww9expdSSncv78edq1sy8m8/rrr1O1alWLE4mIiIiISDKJiRAWBp99Bp6esHIllC/PqcunCA4P5uTlkxTLUoyVzVbi4+5jdVpAc2yLiIiI3NLcH2YT/uunuCbC/F05SP9FOFg4h5wzev311/nrr78oWLAgI0aMsDqOiIiIiIjcyBh49VWYNw/c3GDJEqhShYsxF6kxrwa/nfuN3OlyE9EygvTe6a1O66AR2yIiIiI3cejsIV5d0QGAgTvcKT9ljX2OOUmxJUuWMG/ePFxcXJgzZw4+Po/GyA4REREREfmHMdCzJ0ydCi4uEB4OdeoQEx9D/UX1+SHyB7L4ZmFdy3Vk9ctqddok1NgWERERuUFsfCzNZtQg2hZH5cPQN2w6FC1qdSynEhkZyauvvgpAv379KF26tMWJREREREQkmYED4YMP7Lc/+QSaNiUhMYHmS5uz5cgW/Dz8iGgRQYGMBSyNeTNqbDuZOnXq3HJuyp07d2Kz2fj+++8d25YuXcqLL75I+vTp8fHxoVChQrRr144ffvjhYUUWERFxOn2Xdeb7K7+T8QqEp22Da8tWVkdyKsYYOnTowNmzZylZsiTvvPOO1ZFEHmmq8UVERMQSo0bB4MH22xMmQNu2GGN4ZfUrfPbzZ3i4erAydCUlg0pam/MW1Nh2Mu3bt2fTpk0cPXo02b4ZM2ZQokQJnn76aQD69OlD06ZNKVGiBCtXrmT//v1MnTqVfPny0a9fv4ea+9q1aw/1+URERO7Wmv3L+eDgDABmHixIttFTLE7kfGbMmMHq1avx8PBgzpw5eHh4WB1J5JGmGl9EREQeuokToU8f++0RI6BLFwDe3vQ2n/zwCS42FxY0XEDl3JWty/gf1Ni+gTGG6GvRD/3LGJOifLVr1yZLlizMmjUryfYrV66waNEi2rdvD8DXX3/NqFGjGDt2LGPHjqVixYrkyZOHSpUq0b9/f9asWXPb5/nzzz9p1qwZGTJkwNfXl1KlSvHNN98AEBYWRv369ZMc361bNypXruz4vnLlynTp0oUePXqQKVMmqlWrRmhoKM2aNUtyv7i4ODJlysTMmTMd53/UqFHkzZsXb29vnnrqKT799NMUnRsREZF7dfLSScIWhQLw+l4v6ny8wb4iuKTY4cOH6datGwDvvfceRTWFi1jMqvpeNb5qfBERkUfW7Nnw2mv222+/7Whwj/t6HMO+HAbA5FqTaVC4gVUJU8TN6gCPmitxV0gzPM1Df97LfS/j6+H7n8e5ubnRunVrZs2axYABA7DZbIB9caZr167RokULABYsWECaNGno3LnzTR/n+v1umuXyZSpVqkS2bNlYuXIlgYGBfP/99yQmJt7Ra5o9ezavvvoqO3bswBjDb7/9RpMmTbh8+TJp0tjP8dq1a4mOjqZhw4YAvP322yxbtoxJkyZRoEABtm3bRsuWLcmcOTOVKlW6o+cXERG5E4kmkVYfV+GMawxPRcKoTp9CjhxWx3IqiYmJhIWFcfnyZSpWrEj37t2tjiRiWX0PqvFV44uIiDyCliyBdu3st7t1c0xFEr4vnO5r7fX7sBeH0eGZDhYFTDk1tp1Qu3btGD16NFu2bOGFF14A7JcoNmjQgPTp0wPw66+/kjdvXtzc/v8Wjx07lgEDBji+P3HiBP7+/skef/78+Zw5c4bdu3eTIUMGAPLnz3/HOfPnz8+oUaMc3+fLlw9fX18+++wzWrVq5XiuOnXqkDZtWqKjoxk7diybNm2ibNmyAOTNm5cvv/ySKVOmqOgVEZEHauSSN9gYexCfa7Aoa1e8QmpZHcnpjBs3jm3btuHr68usWbNwdXW1OpKI01CNLyIiIg/c559D8+aQmAgvvwxjx4LNxppDa2i7oi0A3Up3460Kb1kcNGXU2L6Bj7sPl/tetuR5U+qJJ56gXLlyzJgxgxdeeIHff/+d7du3s27duiTH3Thio127dtStW5dvvvmGli1b3vLSyD179lCyZElHwXu3SpUqleR7d3d3GjduzLx582jVqhXR0dGsWLGC+fPnA3DgwAFiYmKoVq1akvtdu3aNkiUfzUnqRUQkdfj6wDre2f8RuMBHfxaj0MwPrI7kdPbv3++Y3/eDDz4gb968FicSsbOqvr/+3CmlGl9EREQeqE2boGFDiI+3N7cnTwabja+Of0WjxY2IT4ynZfGWvB/y/m2vAnuUqLF9A5vNlqLLBa3Wvn17unTpwscff8zMmTPJlSsXVapUcewvUKAAX375JXFxcbi7uwOQLl060qVLx59//nnbx/b29r7tfhcXl2QFc1xcXLLjfH2Tn8cWLVpQqVIlTp8+zfr16/Hy8qJGjRoAjssgP//8c7Jly5bkfp6a31RERB6QC1fOERpenwRPaPaHL2FjN4OLliG5E3FxcbRu3ZrY2Fhq1KjByy+/bHUkEQdnqe9BNb6IiIg8IF99BXXrQmws1K8Ps2aBqys/nf6JWvNrcTX+KjUL1GRG3Rm42Jzns5DzJJUkmjRpgqurK/Pnz2f27Nm0bds2yV9TQkNDuXz5MhMnTrzjxy5evDh79uzh3LlzN92fOXNmTp48mWTbnj17UvTY5cqVI0eOHCxatIh58+bRuHFjPDw8AChSpAienp4cO3aM/PnzJ/nKoTlORUTkATDG0On9yhzxvEqe8zC58+fYMma0OpbTGTp0KN9//z0ZMmRg+vTpTjPCQ+RRoxpfRERE7rvvv4eaNSE6GoKDYeFCcHfnyIUjhISHcCHmAuVylGNJ4yW4u7pbnfaOaMS2k0qTJg1NmzalX79+XLx4kbCwsCT7y5YtS8+ePenZsydHjx6lQYMG5MiRg5MnTzo+cLrcYjRaaGgow4YNo379+gwfPpygoCB++OEHsmbNStmyZXnxxRcZPXo0c+bMoWzZsoSHh/PTTz+l6FJCm81G8+bNmTx5Mr/++iubN2927PPz86NXr150796dxMREKlSoQFRUFF999RVp0qShTZs293TOREREbjR9fi8WJ/6IWwIsKNgP/9Ka6/VO7d69m/feew+ASZMmERQUZHEiEeelGl9ERETuq/377c3sixehYkX47DPw9OR09GmC5wbz16W/eDLzk6wKXXVHU6g9KjRi24m1b9+e8+fPU7VqVXLmzJls/5gxY5g/fz4//PADtWvXpkCBAjRu3JjExER27txJ2rRpb/q4Hh4erFu3jixZslCzZk2KFSvGiBEjHAtAhYSE8M4779C7d2+effZZLl26ROvWrVOcu0WLFhw4cIBs2bJRvnz5JPuGDBnCgAEDGD58OIULFyYkJIRVq1aRJ0+eOzgzIiIi/+3gT5vpenAsAEOjSlG683sWJ3I+V69epVWrViQkJNCsWTOaNGlidSQRp6caX0RERO6L336DqlXh7Fl49llYvRp8fLgUe4ma82py6NwhcvnnYm3LtWTwvrc1OKxiM7daXSSViIqKwt/fn4sXLyYr8mJiYjh8+DB58uTBy8vLooRyL/QeiojI3Yi5EkXpAUHs87tCtdN+RIw+iYuPc8zB+yjp1q0b48ePJygoiJ9++umeF6W7U7er8yR1U42fuuk9FBERuUfHjtlHaB87BsWKwZYtkCEDsfGx1Jxfk02HN5HJJxM72u2gYMaCVqdN4k5qfI3YFhERkcdOryEV2Od3hSzRNuZ0Xq+m9l3YtGkT48ePB2DGjBkPvaktIiIiIiI3cfIkVKlib2oXLAjr10OGDCQkJtBiWQs2Hd5EGo80RLSIeOSa2ndKjW0RERF5rCyf1oOPvX4EYHbxAQQ+WdriRM7n4sWLtG3bFoBOnTpRvXp1ixOJiIiIiAhnz0K1avZpSHLnho0bISAAYwyvrXmNpQeX4uHqwYpmK3gm6zNWp71namyLiIjIY+P47g20+/0DAHomlKZ6y4HWBnJS3bp149ixY+TNm5cxY8ZYHUdERERERC5ehJAQ+4KRWbPChg2QPTsA7255lynfTcGGjXkN5vFinhctDnt/qLEtIiIij4WEixdoOasu572h1CU/hr292epITmnFihXMmjULm83GnDlzSJMmjdWRREREREQeb9HRUKsWfPcdZMpkb2rnywfAh998yJBtQwCYVGsSjYo0sjLpfaXGtoiIiKR+xjC0X3m2ZblKmms2FnRaj4eHt9WpnM7p06fp0KEDAG+++Sbly5e3OJGIiIiIyGMuJgbq14cdOyBdOvuc2oULAzD/x/m8EfEGAENeGEKnUp2sy/kAqLEtIiIiqd6297syONMBACY9/Q75C2he7TtljOGVV17hzJkzFC1alMGDB1sdSURERETk8RYXB02a2Edo+/rCF19AiRIARPwWQZvlbQB4/bnX6V+xv4VBHww1tkVERCRVO7d5DS1OfkSiC7T2eI6WDQdZHckpzZ07l88++wx3d3fmzp2Lp6en1ZFERERERB5fCQnQqhWsWgVeXrB6NZQpA8DXf35Nw8UNiU+MJ7RoKOOqj8Nms1kc+P5TY1tERERSLRMZSfvZDfgzLRS45sfHPTZYHckpHT9+nNdffx2AgQMHUuKfUSAiIiIiImKBxETo0AEWLQJ3d1i6FCpXBuDAmQPUml+LK3FXCMkXwqz6s3Cxpc4WcOp8VSIiIiLx8UzqUZHleWJxT4CF7daQxtPP6lROJzExkbZt2xIVFUWZMmXo3bu31ZHkEZc7d25sNluyr9deew2wT2szcOBAsmbNire3N5UrV2b//v0WpxYRERFxEsZAt24wcya4uMCCBVCzJgDHLh4jJDyEc1fPUTpbaZY2WYqHq4e1eR8gNbblrk2dOpUcOXLg4uLCuHHjbrlNRETECvv6tqNHvt8AGPlMH57OV8HiRM7p448/ZuPGjfj4+DBnzhzc3NysjiSPuN27d3Py5EnH1/r16wFo3LgxAKNGjWLs2LF89NFH7N69m8DAQKpVq8alS5esjC3/UI0vIiLyiOvfHyZMAJsNZs+Ghg0BOBN9huC5wfwZ9SeFMxXm8+af4+vha3HYO2eMSfGxamw7qbCwMMfoF3d3d/LmzUuvXr2Ijo5+KM8fFRVFly5d6NOnDydOnKBjx4433SYiImKFKwvm0OzqXGLdoKbf03SrO9zqSE7pl19+oU+fPoC9GVmgQAGLE4kzyJw5M4GBgY6v1atXky9fPipVqoQxhnHjxtG/f38aNGhA0aJFmT17NleuXGH+/Pm3fMzY2FiioqKSfKVGqvFFRETktt57D4b/89lm0iRo2RKAS7GXqDm/Jr+c/YWc/jlZ12odGX0yWhj07o0YMSLFx6qx7cSqV6/OyZMn+eOPPxg6dCgTJ06kV69eD+W5jx07RlxcHLVq1SIoKAgfH5+bbhMREXno9u+n25L2HMwMgcaXWZ0iUuVCKQ9afHw8rVu35urVq1SrVo1XX33V6kjihK5du0Z4eDjt2rXDZrNx+PBhIiMjCQ4Odhzj6elJpUqV+Oqrr275OMOHD8ff39/xlSNHjocR3xKq8UVEROSmxo6Ft9+23x4zBjp1AiA2PpaXFr3Et399SyafTKxruY7sabNbGPTuTZw4UY3te2GMITo6+qF/3ckw++s8PT0JDAwkR44cNG/enBYtWrB8+XLCw8MpVaoUfn5+BAYG0rx5c06fPu14ffnz52fMmDFJHuunn37CxcWF33//HbAXtfXq1SNNmjSkTZuWJk2acOrUKQBmzZpFsWLFAMibNy82m+2m244cOXK3b4OIiMjduXiRJW9UZdpT8dgMhLdYRmbfzFanckojRoxg165d+Pv7M2PGDFxcVDbKnVu+fDkXLlwgLCwMgMjISAACAgKSHBcQEODYdzN9+/bl4sWLjq/jx4+nOINV9b1qfBEREblvJk2Cnj3ttwcPdtxOSEyg5Wct2Xh4I77uvqxpvoZCmQpZGPTuLV68mC5dutzRfTRJ4g2uXLlCmjRpHvrzXr58GV/fe5v3xtvbm7i4OK5du8aQIUMoVKgQp0+fpnv37oSFhbFmzRpsNhvt2rVj5syZSUZ+zJgxg4oVK5IvXz6MMdSvXx9fX1+2bt1KfHw8nTt3pmnTpmzZsoWmTZuSI0cOqlatyq5du8iRIwd+fn7JtmXOrEaCiIg8RImJHO7QiA7P2ptjbz3zBlUKBP/HneRmfvjhBwYNGgTARx99RPbszjniQ6w3ffp0atSoQdasWZNsv/EqCmPMba+s8PT0xNPT864yWFXfg2p8ERERuQ9mzYLOne23+/Z1jNo2xtBlTRc+PfApHq4eLG+2nGezPWtdznuwfv16WrZsiTGG9u3bM3369BTdT43tVGLXrl3Mnz+fKlWq0K5dO8f2vHnz8uGHH/Lcc89x+fJl0qRJQ9u2bRkwYAC7du3iueeeIy4ujvDwcEaPHg3Ahg0b2LdvH4cPH3Zc5jl37lyefPJJdu/ezbPPPkvGjPZ5eq7PoQjcdJuIiMjDEjf8PUL9N3DRC8qkK8qgmqOtjuSUYmJiaNWqFfHx8TRs2JAWLVpYHUmc1NGjR9mwYQPLli1zbLteI0ZGRhIUFOTYfvr06WSjuEU1voiIyGNv4UJo395++4037HNs/zMY4N0t7zL5u8nYsDGvwTyq5q1qYdC7t3v3bl566SXi4uJo0qQJo0ePVmP7bvn4+HD58mVLnvdOrV69mjRp0hAfH09cXBz16tVjwoQJ/PDDDwwcOJA9e/Zw7tw5EhMTAfulh0WKFCEoKIhatWoxY8YMnnvuOVavXk1MTIxjpfqDBw+SI0eOJHMXFilShHTp0nHw4EGefdY5//ojIiKp2Lp1vL11AN+Uh3Q2Hxa0WYW7q7vVqZzSO++8w/79+wkICGDSpEman1zu2syZM8mSJQu1atVybMuTJw+BgYGsX7+ekiVLAvZ5uLdu3crIkSMfSA6r6vvrz32nVOOLiIgIAJ99Zl8cMjHRPp/2Bx84mtoffvMhQ7YNAWBSrUk0KtLIyqR37eeff6ZGjRpER0dTtWpV5syZQ2xsbIrvr8b2DWw22z1fLviwvPDCC0yaNAl3d3eyZs2Ku7s70dHRBAcHExwcTHh4OJkzZ+bYsWOEhIRw7do1x31ffvllWrVqxQcffMDMmTNp2rSpo/C+1aWg/3WJqIiIiCWOHGFtn4aMqm//9pNGc8idLreViZzWtm3beP/99wGYNm2aphyQu5aYmMjMmTNp06YNbm7//8hhs9no1q0bw4YNo0CBAhQoUIBhw4bh4+ND8+bNH0gWZ6rvQTW+iIiIABER0LQpJCRAq1YwcaKjqT3/x/m8EfEGAENeGEKnUp2sTHrX/vzzT0JCQjh79iylSpVi2bJleHp6qrH9uPD19SV//vxJtv3888/8/fffjBgxwjEa49tvv01235o1a+Lr68ukSZP44osv2LZtm2NfkSJFOHbsGMePH3c8xoEDB7h48SKFCxd+gK9IRETkDsXEcLJFPVpVs4/GfLVkRxoWaWhxKOd06dIlwsLCMMbQrl076tSpY3UkcWIbNmzg2LFjSabPuK53795cvXqVzp07c/78eUqXLs26devw8/OzIOmjRzW+iIjIY27TJnjpJYiLg8aNYcYM+Gch94jfImizvA0Arz/3Ov0r9rcy6V07e/YsISEhHDt2jEKFCrFmzZq7qgW1vH0qkzNnTjw8PJgwYQJ//PEHK1euZMiQIcmOc3V1JSwsjL59+5I/f37Kli3r2Fe1alWKFy9OixYt+P7779m1axetW7emUqVKlCpV6mG+HBERkVszhoTXXqVV/n2c8YXi6Z9gbM3xVqdyWj179uTw4cPkypWLDz74wOo44uSCg4MxxlCwYMFk+2w2GwMHDuTkyZPExMSwdetWihYtakFK56EaX0RE5DGxYwfUrQsxMfb/zpsH/1z9tvP4Thoubkh8YjzNizVnXPVxTnnVVXR0NLVr1+bAgQNky5aNtWvX3vWVompspzKZM2dm1qxZLFmyhCJFijBixAjGjBlz02Pbt2/PtWvXko2ksdlsLF++nPTp0/P8889TtWpV8ubNy6JFix7GSxAREUmZadMYeWgWG/OCj4sXC0OX4eXmZXUqp/T5558zbdo0bDYbs2fPJm3atFZHEpF/UY0vIiLyGPj2W6hZE6KjITgYFi0Cd/u6QftP76fW/FpcibtC9fzVmVlvJi4252vrxsXF0ahRI77++mvSp0/P2rVryZUr110/ns0YY+5jvkdOVFQU/v7+XLx4MdmHtJiYGA4fPkyePHnw8nr8Pgjv2LGDypUr8+effzrtKvSP+3soIvLY2rWLHaHlqdQyngQXmFF3Bm1LtrU6lVM6e/YsRYsWJTIyku7duzN27FirI6XY7eo8Sd1U49+aanwREREntG8fVK4M589DpUqwZg38s07G0QtHKT+jPCcunaBs9rKsb7UeXw/nWT/kusTERFq3bs28efPw9vZm48aNSa4uu+5OanzNsf0Yio2N5fjx47zzzjs0adLEaQteERF5TJ0+zbnmLxFaz97UblGsBWElwqxO5ZSMMbz66qtERkZSuHBh3nvvPasjichdUo0vIiLipH7+GapWtTe1y5SBVascTe0z0WcIDg/mxKUTFMlchNXNVztlU9sYQ48ePZg3bx5ubm4sXbr0pk3tO+V8Y9blni1YsIBChQpx8eJFRo0aZXUcERGRlIuPxzRrSvtSf3HcH/Kny8ukWpOccm65R8HChQtZsmQJbm5uzJ07F29vb6sjichdUo0vIiLihH7/HapUgTNn4Omn4Ysv4J9FFC/FXqLGvBr8evZXcvrnZG3LtWTwzmBx4LszfPhwxo+3r4c0a9YsatSocV8eV43tx1BYWBgJCQl89913ZMuWzeo4IiIiKdevHxMvb2F5YXC3ubGw8WL8PO989WyBEydO8NprrwHw9ttv88wzz1icSETuhWp8ERERJ3PsGLz4Ivz1FxQtCuvWQbp0AMTGx1J/UX2+O/kdmXwysb7VerKnzW5t3rs0bdo0+vfvD8C4ceNo0aLFfXtsNbZFRETEOSxdyp65o+kRYv92dPAYnsmqZuzdMMbQvn17zp8/T6lSpejXr5/VkUREREREHh9//WVvah87BgULwvr1kDEjAAmJCbRY1oJNhzeRxiMNES0iKJixoMWB786yZct45ZVXAOjXrx9vvPHGfX18Nbaxf7gT56T3TkTkMXHwIJc7tKFpI7jmBnUK1qFr6a5Wp3JaU6ZMYe3atXh5eTFnzhzc/1ltXSQ1UZ3ovPTeiYhIqnbmjH1O7d9/hzx5YONGCAwE7P8Gdv68M0sPLsXD1YMVzVY47WCezZs3ExoaSmJiIh06dGDo0KH3/Tke68b29Q9xV65csTiJ3K1r164B4OrqanESERF5YC5dggYN6FIpml8zQTa/bMysN1Pzat+l3377jZ49ewL2ue4KFy5scSKR+0s1vvNTjS8iIqnWuXNQrRocPAjZs9ub2tn/P8XIO5vfYer3U7FhY36D+byY50ULw96977//nnr16nHt2jUaNGjApEkPZl0kt/v+iE7E1dWVdOnScfr0aQB8fHz0IdmJJCYmcubMGXx8fHBze6x/lEVEUi9joG1b5nr8zOwS4GJzYX7D+WT0yWh1MqeUkJBAmzZtuHLlCpUrV6ZrV416l9RHNb5zU40vIiKpVlQUVK8Oe/faR2hv3Ggfsf2P8V+P573t7wEwufZkGhZpaFXSe3Lo0CGqV6/OpUuXqFy5MvPmzXtgf6x+7CuFwH+G+l8vfMW5uLi4kDNnTn1YERFJrUaP5tctS3m1k/3bdyu9y/O5nrc2kxMbM2YMX331FX5+fsyaNQsXl8f64j1JxVTjOzfV+CIikupER0OtWrB7t30u7Q0b7HNr/2Pevnl0W9sNgKEvDKXjMx0tCnpv/vrrL4KDgzlz5gwlS5ZkxYoVeHl5PbDne+wb2zabjaCgILJkyUJcXJzVceQOeXh46EO5iEhqtXEjMW+/RdN2EO0BlXNXpn/F/lanclr79u1jwIABAIwfP55cuXJZnEjkwVGN79xU44uISKpy9SrUqwdffgn+/vaFIp980rH7i0NfELYiDIA3Sr9Bv4rOubD7hQsXqF69OkeOHCF//vx88cUXpE2b9oE+52Pf2L7O1dVVc7iJiIg8Ko4dg2bN6F3VsCcIMvlkYl6Debi66N/quxEbG0urVq24du0adevWJSwszOpIIg+FanwRERGx1LVr0KiRfdqRNGkgIgJKlnTs/ur4VzRc3JD4xHhaFm/J2JCxTnnF0pUrV6hTpw4//vgjgYGBrFu3joCAgAf+vPozuIiIiDxaYmKgUSNWZPybCaXtm2bXn01Wv6zW5nJigwYNYt++fWTKlImpU6c6ZbEsIiIiIuJU4uMhNBTWrAFvb/j8cyhTxrH7p9M/UWt+La7GX6VG/hrMqDsDF5vztWrj4uJo2rQpX375Jf7+/qxdu5Y8/5o7/EFyvrMlIiIiqdvrr3Ps1920fcnefO1Ztic1C9S0OJTz2rlzJyNHjgRgypQpD2XkhIiIiIjIYy0hAdq0gWXLwMMDVqyA5/+/VtCRC0cICQ/hQswFymYvy5LGS3B3dbcw8N0xxtChQwdWr16Nl5cXq1atonjx4g/t+dXYFhERkUfH1KnEz/iE5g3hvJehVNZSDKsyzOpUTis6OprWrVuTmJhIq1ataNCggdWRRERERERSt8RE6NQJ5s8HNzf49FOoVs2x+3T0aYLnBvPXpb94MvOTrG6+Gl8PXwsD370+ffowe/ZsXF1dWbx4MRUrVnyoz6/GtoiIiDwadu6ELl0YVAl25AQ/Dz8WNlyIh6uH1cmcVu/evfntt9/Inj07H374odVxRERERERSN2PgjTdg+nRwcYEFC6BOHcfuqNgoasyrwaFzh8jln4u1LdeSwTuDhYHv3ujRoxk9ejQA06dPp86/XufDosa2iIiIWO/kSWjYkI3Z43jvnyv0ptWZRr4M+azN5cTWrVvHxIkTAZg5cybp0qWzNpCIiIiISGpmDPTpAx99BDYbzJ5tXzjyHzHxMdRfWJ/vT35PZp/MrG+1nmxps1kY+O7NnDmT3r17A/YGd5s2bSzJoca2iIiIWOuflcJPR52kZRM3jA1eLvkyTYs2tTqZ0zp//jzt2rUDoEuXLlStWtXiRCIiIiIiqdygQfDPCGamTIGWLR27EhITaLGsBZuPbMbPw4+IlhEUyFjAoqD3ZuXKlXTo0AGAN998k169elmWRY1tERERsVa3biTu/Io2jdyI9I6nSOYijK8x3upUTu3111/nxIkTFCxY0LFwpIiIiIiIPCAjR9ob2wDjx8M/jV+wL7D4yupXWHZwGR6uHqxotoKng562KOi92b59O02bNiUhIYGwsDDLP2uosS0iIiLWmT4dJk3i/XIQkSceLzcvFjVahI+7j9XJnNann37KvHnzcHFxYfbs2fj46FyKiIiIiDwwH34Ib71lvz1iBHTtmmR3/039+eSHT3CxubCg4QJeyPOCBSHv3d69e6lTpw4xMTHUqVOHadOmYbPZLM2kxraIiIhYY9cu6NyZb7JBv2r2kmR89fEUzVLU4mDOKzIykldeeQWAvn37UqZMGYsTiYiIiIikYtOm2ReLBHj3Xfsc2//ywc4PGP7lcAAm15pMg8INHnbC++KPP/6gevXqXLx4kQoVKrBo0SLc3NysjqXGtoiIiFjg1Clo0IALLtdo1tqHeBJp8mQTOjzd4b/vKzdljKFDhw6cPXuWEiVKMGDAAKsjiYiIiIikXuHh0KmT/fabb9ob2/8yd+9ceqzrAcCwF4fR4Rnn/Kxz6tQpgoODiYyMpFixYqxatQpvb2+rYwFqbIuIiMjDFhcHjRtjTpygY3M/jnheIXe63EytPdXyS9mc2cyZM1m9ejUeHh7MnTsXDw8PqyOJiIiIiKROixZBmzZgDHTpYp9j+1+fZT7/9XParmgLQPcy3XmrwltWJb0nFy9epHr16vz+++/kzp2btWvXki5dOqtjOTwyje3hw4djs9no1q2bY5sxhoEDB5I1a1a8vb2pXLky+/fvty6kiIiI3LtevWD7dqaV82RJzku4ubixqNEi/L38rU7mtA4fPswb/1wCOXToUIoW1XQuIiIiIiIPxGefQYsWkJgIL79sXyzyX03tHcd20HhJYxJMAi2Lt2RM8BinHMATExNDvXr12LNnD1myZGH9+vUEBQVZHSuJR6KxvXv3bqZOnUrx4sWTbB81ahRjx47lo48+Yvfu3QQGBlKtWjUuXbpkUVIRERG5J3PmwIcf8lMWeCPEADC8ynCey/acxcGcV2JiIm3btuXy5ctUqFCBHj16WB1JRERERCR1Wr0amjaFhARo3RqmTAGX/7dXfzz1I7UX1OZq/FVqFajFjLozcLE9Eu3XOxIfH09oaChbt27Fz8+PiIgI8ufPb3WsZCw/s5cvX6ZFixZMmzaN9OnTO7YbYxg3bhz9+/enQYMGFC1alNmzZ3PlyhXmz59vYWIRERG5K999B506ccUdmr6amRhzjer5q9OjrBqx92L8+PFs3boVX19fZs+ejaurq9WRRERERERSn3XroGFD+9SKzZrBjBlJmtqHzx8mJDyECzEXKJ+jPIsbL8bd1d3CwHfHGMOrr77K8uXL8fDwYOXKlZQsWdLqWDdleWP7tddeo1atWlStWjXJ9sOHDxMZGUlwcLBjm6enJ5UqVeKrr7665ePFxsYSFRWV5EtEREQsduYMNGgAMTG80TEHB2xnCEoTxOz6s51yBMOj4sCBA/Tt2xeAsWPHkjdvXosTiYiIiIikQps3Q716cO2a/XPNnDnwrwElpy6fIjg8mJOXT1IsSzFWha7Cx93HwsB37+233+aTTz7BxcWFBQsWULlyZasj3ZKblU++cOFCvv/+e3bv3p1sX2RkJAABAQFJtgcEBHD06NFbPubw4cMZNGjQ/Q0qIiIidy8+3n653rFjLKwayCeZj2PDRniDcLL4ZrE6ndOKi4ujVatWxMbGUqNGDTp0cM5V1kVEREREHmlffgm1a0NMDNSpAwsWgPv/R2JfjLlIjXk1+O3cb+ROl5uIlhGk905/mwd8dI0bN45hw4YBMHnyZBo0aGBxotuzbIjU8ePHeeONNwgPD8fLy+uWx904ubox5rYTrvft25eLFy86vo4fP37fMouIiMhd6NMHNm/m92w+dKxsXyejf8X+vJjnRYuDObehQ4fy/fffkyFDBqZPn+6UC9KIiIiIiDzSvvkGataEK1cgJASWLAEPD8fumPgY6i2sxw+RP5DFNwvrWq4jq19WCwPfvfDwcLp37w7AsGHDnGLgjGUjtr/77jtOnz7NM88849iWkJDAtm3b+Oijj/jll18A+8jtf6+4efr06WSjuP/N09MTT0/PBxdcREREUm7+fBg7lmuu0KxrEJeu/k6FnBV4t/K7Vidzart37+a9994DYOLEiY/c6uQiIiIiIk7vu+/szexLl+DFF+Gzz+BfPcf4xHhCl4ay9ehW/Dz8iGgRQYGMBSwMfPfWrFlD27ZtAejWrRtvvfWWxYlSxrIR21WqVOHHH39kz549jq9SpUrRokUL9uzZQ968eQkMDGT9+vWO+1y7do2tW7dSrlw5q2KLiIhISu3ZAy+/DEC/fqX59urvpPdKz/wG83FzsXQ2NKd29epVWrVqRUJCAs2aNaNp06ZWRxIRERERSV327oXgYLh4ESpWhJUrwdvbsdsYwyurX2H5z8vxdPVkZehKSgY9mgss/pedO3fSqFEj4uPjadGiBe+//77TXA1q2adKPz8/ihYtmmSbr68vGTNmdGzv1q0bw4YNo0CBAhQoUIBhw4bh4+ND8+bNrYgsIiIiKXX2LLz0Ely9yufNnuZ9128AmFV/Fjn8c1gczrn17duXX375haCgID7++GOr44iIiIiIpC4HDkDVqnDuHJQpA59/Dr6+SQ7pt7Ef03+YjovNhYWNFlI5d2Vrst6j/fv3U6tWLa5evUqNGjWYOXMmLi6WjYO+Y4/0cKnevXtz9epVOnfuzPnz5yldujTr1q3Dz8/P6mgiIiJyKwkJEBoKR45w4smchJU4CjHQ9bmu1C1U1+p0Tm3z5s2MHz8egOnTp5MhQwaLE4mIiIiIpCK//gpVqsDff8Mzz8AXX8ANfcixO8cyYscIAKbWnkr9J+pbEPTeHT16lJCQEM6fP0/ZsmVZsmQJ7v9aFNMZ2IwxxuoQD1JUVBT+/v5cvHiRtGnTWh1HREQk9XvrLRg5kgRfb6qOfJItf39LycCS7Gy/E083rYNxty5evEjx4sU5duwYnTp1YvLkyVZHspzqvMeX3nsRERG5737/HSpVghMn4KmnYNMmuGEgyZy9c2izvA0Aw6sM560KzjEX9Y3OnDlDhQoV+PXXXylSpAjbt29/ZAbN3Emd5zxjy0VEROTRt3gxjBwJwNCRtdjy97f4uvuysNFCNbXvUbdu3Th27Bh58+ZlzJgxVscREREREUk9jh61LxB54gQ8+SSsX5+sqb3ql1W0W9EOgB5letCnfB8rkt6zS5cuUbNmTX799Vdy5szJ2rVrH5mm9p1SY1tERETuj59+gnb2Qm9zn6YM+nspAJNrT6ZgxoJWJnN6K1asYNasWdhsNmbPnk2aNGmsjiQiIiIikjr8+ae9qX3sGBQsCBs2QObMSQ758tiXNPm0CQkmgdZPtWZ08GinWWDx32JjY3nppZf49ttvyZQpE+vWrSN79uxWx7pramyLiIjIvTt3DurVg+hoTtd4nhaZt2EwtCvRjpbFW1qdzqmdPn2aDh06APDmm29SoUIFixOJiIiIiKQSJ0/a59T+4w/Il88+/UhgYJJD9p3aR+35tYmJj6F2wdp8UucTXGzO11JNSEigVatWbNy4kTRp0vDFF19QqFAhq2PdE+d7F0REROTREh8PTZvCH3+QmCc3rRq5cvLySYpkLsKHNT60Op1TM8bwyiuvcObMGYoWLcrgwYOtjiQiIiIikjqcOQNVq9oXjMyVy97UzpYtySF/nP+DkPAQLsZepELOCixqtAh3V+daYBHsnyu6dOniWCDys88+o1SpUlbHumdqbIuIiMi96dPHfrmejw+jRtZl3fHNeLt5s7jRYnw9fK1O59TCw8P57LPPcHd3Z+7cuXh6ap5yEREREZF7dvasval94ABkz25vaufMmeSQyMuRBM8NJvJyJMWyFGNV6Cp83H0sCnxvBg0axOTJk7HZbISHh1O1alWrI90XamyLiIjI3ZszB8aOBWDHpL68ffBjACbUmMCTWZ60MpnTO378OF26dAFg4MCBlChRwtpAIiIiIiKpwYULEBwM+/bZpx3ZtAny5k1yyMWYi1QPr87v538nT7o8rG25lnRe6SyJe68+/vhjBg0a5LjdpEkTixPdP2psi4iIyN3ZvRs6dgTgbP/uhJ6dSoJJoEWxFrQr2c7icM4tMTGRtm3bEhUVRZkyZejdu7fVkUREREREnF9UFISEwPff2xeI3LgRChRIcsjVuKvUXViXvaf2EuAbwLpW6wjyC7Io8L1ZuHAhr7/+OmAfLPPqq69anOj+UmNbRERE7lxkJLz0EsTGYurUpm2RQxyPOk6BDAWYVGuSU64Q/iiZOHEiGzduxNvbmzlz5uDm5mZ1JBERERER53b5MtSsCbt2QYYM9ukUixRJckh8YjyhS0PZdnQbaT3TEtEygvwZ8lsU+N6sW7eO1q1bY4zhtddeY8CAAVZHuu/U2BYREZE7c+0aNGoEJ07AE08wrkc5Vh1ajaerJ4sbL8bP08/qhE7tl19+cYzQHj16NAVuGEEiIiIiIiJ36MoVqFMHduyAdOlg/XooXjzJIcYYOq3qxIpfVuDp6snKZispEVjCkrj3ateuXTRo0IC4uDiaNm3Khx9+mCoHH2n4j4iIiNyZ11+3F4T+/uyaMYQ+G5oDMDZkrNMWfo+K+Ph4WrduzdWrV6lWrVqqu1RQREREROShi4mB+vVhyxbw84O1a+Hpp5Md9taGt5ixZwYuNhcWNlpIpdyVHnrU++Hnn3+mZs2aREdHU61aNebMmYOLS+oc25w6X5WIiIg8GJMnw9SpYLNxYc5Umu3qTVxiHI2KNOLVUmrC3quRI0eya9cu/P39mTFjRqotQEVEREREHorrV5uuXw++vvDFF/Dcc8kOG/PVGEZ9NQqAaXWmUf+J+g856P1x/PhxgoODOXv2LM8++yzLli3Dw8PD6lgPjD4tiYiISMps22YfrQ2Y996jQ+wSDl84TJ50eZhWZ1qqvLTtYfrhhx8YOHAgAB999BHZs2e3NpCIiIiIiDOLi4OmTeHzz8HbG1avhvLlkx02a88s3lz/JgAjq46kXcl2DzvpfXH27FlCQkI4fvw4hQoVYs2aNaRJk8bqWA+UGtsiIiLy344ds490iI+Hpk2ZXNWfTw98iruLO4saLSKdVzqrEzq1mJgYWrVqRXx8PA0bNqRFixZWRxK5aydOnKBly5ZkzJgRHx8fSpQowXfffefYb4xh4MCBZM2aFW9vbypXrsz+/fstTCwiIiKpTnw8tGwJy5eDpyesWAGVKyc7bOUvK3l55csA9Crbi97lez/cnPdJdHQ0tWrV4uDBg2TLlo1169aRKVMmq2M9cGpsi4iIyO1duQIvvQRnzkCJEuwZ/gbd1/YA7CMans32rMUBnd+AAQPYv38/WbJkYdKkSRr9Lk7r/PnzlC9fHnd3d7744gsOHDjA+++/T7p06RzHjBo1irFjx/LRRx+xe/duAgMDqVatGpcuXbIuuIiIiKQeCQnQti0sXgzu7rBsGVSrluywbUe30fTTpiSYBMJKhDGq2igLwt67a9eu0bBhQ7755hvSp0/PunXryJkzp9WxHgotHikiIiK3Zgx06ADffw+ZMnFpSThNVr9EbEIstQvWpluZblYndHrbt29nzJgxAEybNo3MmTNbnEjk7o0cOZIcOXIwc+ZMx7bcuXM7bhtjGDduHP3796dBgwYAzJ49m4CAAObPn0+nTp0edmQRERFJTRIToWNHCA8HNzd7c7tmzWSH7Y3cS50FdYiJj6FOwTpOO7ViYmIiYWFhrF27Fh8fH9asWUORIkWsjvXQaMS2iIiI3Nr778P8+eDqilm8mFf2DePQuUNkT5udWfVmOWXx9yi5dOkSbdq0wRhDu3btqFu3rtWRRO7JypUrKVWqFI0bNyZLliyULFmSadOmOfYfPnyYyMhIgoODHds8PT2pVKkSX3311S0fNzY2lqioqCRfIiIiIkkYA507w4wZ4OJi/xxTv36yw34/9zsh4SFExUZRMWdFFjVahJuL8439NcbQrVs3FixYgJubG0uXLqVMmTJWx3qo1NgWERGRm1u7Fvr0sd8eN46Z6Q4z/8f5uNpcWdhwIRl9MlqbLxXo1asXhw8fJleuXHzwwQdWxxG5Z3/88QeTJk2iQIECrF27lldeeYWuXbsyZ84cACIjIwEICAhIcr+AgADHvpsZPnw4/v7+jq8cOXI8uBchIiIizscY+0L3U6aAzQZz5kDjxskOO3npJMHhwZyKPsVTAU+xMnQl3u7eFgS+d8OGDWPChAmA/Qq46tWrW5zo4VNjW0RERJL77Tdo1sx+KV+7duxvXJkua7oAMPTFoZTPmXw1cbkza9asYerUqQDMmjWLtGnTWpxI5N4lJiby9NNPM2zYMEqWLEmnTp3o0KEDkyZNSnLcjVd7GGNuewVI3759uXjxouPr+PHjDyS/iIiIOCFjoHt3+Phje1N75ky4yWLsF2IuUGNeDf44/wd50+clomUE6bzSPfy898HUqVN5++23ARg/fjzNmze3OJE11NgWERGRpC5dgnr14MIFKFOG6HGjabK0KVfjrxKcL9hpVwp/lJw9e5b27dsD0L17dyrfZIV2EWcUFBSUbF7HwoULc+zYMQACAwMBko3OPn36dLJR3P/m6elJ2rRpk3yJiIiIYAz06gXjx9u//+QTaNMm2WFX465Sd0Fd9p7aS4BvAOtariMwTeBDDnt/LF26lFdffRWA/v3707VrV4sTWUeNbREREfm/xERo1QoOHICsWWHZMrpuepMDZw4QmCaQuS/NxcWm8uFevfbaa0RGRlK4cGHee+89q+OI3Dfly5fnl19+SbLt119/JVeuXADkyZOHwMBA1q9f79h/7do1tm7dSrly5R5qVhEREXFyxsBbb8HYsfbvp0yBdu2SHRafGE+zpc3Yfmw7aT3TEtEygnwZ8j3ksPfHpk2baN68OYmJiXTs2JEhQ4ZYHclSzjczuoiIiDw4gwfDihXg4QHLlhF+ZiMz9szAho35DeaTxTeL1Qmd3sKFC1m0aBGurq7MmTMHb2/nnNNP5Ga6d+9OuXLlGDZsGE2aNGHXrl1MnTrVMe2OzWajW7duDBs2jAIFClCgQAGGDRuGj4/PY3sJrYiIiNwFY+Dtt2HUKPv3EydCx443OczQYVUHVv6yEi83L1aFrqJEYImHm/U++e6776hXrx7Xrl2jQYMGTJw48bZTuT0O1NgWERERu2XLYNAg++0pU/g1f3pemfIKAAMqDeCFPC9YGC51+Ouvv+jcuTMA77zzDqVKlbI4kcj99eyzz/LZZ5/Rt29fBg8eTJ48eRg3bhwt/jXPZe/evbl69SqdO3fm/PnzlC5dmnXr1uHn52dhchEREXEqgwbBsGH22x9+CP9MzXGj3ut7M2vPLFxtrixqtIjncz3/EEPeP4cOHaJGjRpcvnyZF154gXnz5uHq6mp1LMvZjDHG6hAPUlRUFP7+/ly8eFFz8YmIiNzKTz9BmTIQHQ1duxLz/kjKfFKGvaf2Ujl3ZTa02oCriwqne2GMoWbNmkRERFCqVCm++uor3N3drY7l1FTnPb703ouIiDzGhgyBAQPst8eOtS8ceROjd4ym9wb7+kAz680krETYQwp4f/3111+UL1+eI0eO8PTTT7N58+ZUXf/cSZ2nEdsiIiKPu7Nn7YtFRkfDCy/AmDH0WPsGe0/tJbNPZuY1mKem9n0wdepUIiIi8PT0ZM6cOWpqi4iIiIjcqeHD/9/UHj36lk3tmT/MdDS1R1cb7bRN7fPnzxMSEsKRI0fInz8/X3zxRapuat8prf4kIiLyOIuLg8aN4Y8/IHduWLyYJb8uZ9K3kwCY+9JcsvpltTZjKvDbb7/Ro0cPAEaMGEHhwoUtTiQiIiIi4mRGj4Z+/ey3hw+HXr1uetiKn1fw8qqXAXiz3Jv0Knfz4x51V65coU6dOvz0008EBQWxbt06smTRmkf/psa2iIjI46xbN9i8GdKkgVWr+MM1ylEEvlX+LULyh1ibLxVISEggLCyMK1euULlyZbp27Wp1JBERERER5/LBB9DbPgKbIUPgrbduetjWI1tp+mlTEk0ibUu0ZWTVkQ8x5P0TFxdHkyZN2LFjB+nSpWPt2rXkyZPH6liPHE1FIiIi8riaPNm+erjNBuHhXCtckKYzyhMVG0W5HOUY/MJgqxOmCu+//z47duzAz8+PWbNm4eKicQUiIiIiIik2YQL8c/Uj774Lb79908N+OPkDdRfWJTYhlnqF6jG1zlRsNttDDHp/JCYm8vLLL/P555/j5eXFqlWrKFasmNWxHklqbIuIiDyOtmyB11+33x46FOrV4621Pfj2r2/J4J2BhQ0X4u6qOaDv1b59+3jnnXcAGD9+PLly5bI4kYiIiIiIE5k4Ea5f8di/v72xfRO/nfuN6vOqExUbxfO5nmdBwwW4uThf29MYQ+/evZkzZw6urq4sWbKEChUqWB3rkaUhQyIiIo+bP/6ARo0gPh5CQ6FvX1b+spIPvv4AgFn1ZpHDP4fFIZ3ftWvXaN26NdeuXaNu3bqEhYVZHUlERERExHlMnQqvvWa/3bu3fQqSm4zAPnnpJMFzgzkdfZqnAp5iZbOVeLt7P+Sw98fo0aN5//33AZgxYwa1a9e2ONGjTY1tERGRx8mlS1CvHpw9C888A9OncyzqOGHLwwDoXqY7dQrVsTZjKjFo0CD27t1LpkyZmDrVOS+DFBERERGxxIwZ0KmT/XaPHjBixE2b2hdiLlB9XnUOXzhMvvT5iGgZgb+X/0MOe3/MmDGDPn36ADBmzBhat25tcaJHnxrbIiIij4vERGjVCn76CQIDYcUK4jzcCF0ayvmY85TKWooRVUdYnTJV2LlzJyNG2M/l5MmTCQgIsDiRiIiIiIiTmD0bXrYvaE/XrjBmzE2b2lfirlBnQR32ndpHYJpA1rVaR2CawIcc9v5YuXIlHTp0AKB379707NnT4kTOwfkmmxEREZG7M2AArFgBnp6wfDlky8aADX356vhXpPVMy6JGi/Bw9bA6pdOLjo6mdevWJCYm0qpVKxo2bGh1JBERERER5xAeDm3bgjHQuTOMG3fTpnZcQhxNP23Kl8e+xN/Tn7Ut15I3fd6Hn/c+2LZtG02aNCExMZG2bds6BsjIf9OIbRERkcfBwoXw3nv229OmQenSRPwWwYgd9qJpet3pTlsIPmr69OnDb7/9Rvbs2fnwww+tjiMiIiIi4hwWLoQ2bexN7U6dYMKEmza1E00iL696mdW/rsbLzYtVoasoHlDcgsD3bu/evdSpU4fY2Fjq1q2rKQzvkBrbIiIiqd2339pHPQC8+Sa0asVfl/6i1WetAOhcqjONijSyMGDqsW7dOj7++GPAPkdeunTprA0kIiIiIuIMliyBli3t0ye2bw8TJ4JL8ralMYY3173JnL1zcLW5srjRYirmqmhB4Hv3xx9/EBISQlRUFBUrVmThwoW4uWlyjTuhxraIiEhqdvIk1K8PMTFQqxYMH05CYgLNlzbn7yt/81TAU7wf8r7VKVOF8+fP065dOwC6dOlCtWrVLE4kIiIiIuIEli2D0FBISICwMJg69aZNbYBRO0Yx9uuxgP2qU2dd+D4yMpLg4GBOnTpF8eLFWblyJd7e3lbHcjpqbIuIiKRWMTHw0ktw4gQULgzz54OrK0O2DWHr0a34uvuyuPFivNy8rE6aKnTt2pUTJ05QoEABRo4caXUcEREREZFH34oV0LSpvandsiV88sktm9rTv5/OWxvfAmBMtTG0KdHmYSa9by5evEj16tX5/fffyZs3LxEREbrS8y6psS0iIpIaGQMdO8I330D69LByJaRNy8Y/NjJ462AAptSeQsGMBS0Omjp8+umnhIeH4+Liwpw5c/Dx8bE6koiIiIjIo231amjcGOLj7SO2Z80CV9ebHvrZwc/ouLojAH3K96FnuZ4PMej9ExMTQ926ddm7dy8BAQGsW7eOoKAgq2M5LTW2RUREUqP334e5c+2F4ZIlkD8/kZcjabGsBQZD+5LtaVG8hdUpU4XIyEheeeUVAPr27UuZMmUsTiQiIiIi8oiLiICGDSEuzt7cnjPnlk3tzYc3E7o0lESTSPuS7RleZfhDDnt/xMfHExoayrZt20ibNi1ffPEF+fLlszqWU1NjW0REJLVZswZ697bfHjcOqlRxzKt9KvoUxbIUY0KNCZZGTC2MMXTs2JGzZ89SokQJBgwYYHUkEREREZFH2/r19nWArl2DBg1g3jy4xaKJ3/31HfUW1iM2IZb6T9Rncu3J2Gy2h5v3PjDG8Morr7B8+XI8PT1ZsWIFJUuWtDqW01NjW0REJDU5eNB+Gd/1qUheew2AIduGsPnIZse82t7uWpjkfpg5cyarVq3Cw8ODOXPm4OHhYXUkEREREZFH16ZNULcuxMZCvXqwYAG4u9/00F/P/kqNeTW4dO0SlXNXZkHDBbi53LwB/qjr168f06dPx8XFhYULF1K5cmWrI6UKamyLiIikFufO2YvEqCh4/nmYMAFstmTzaj+R6QmLg6YOR44c4Y033gBg6NChFCtWzOJEIiIiIiKPsM2boXZt+yL3tWvD4sVwi4Ehf0b9SbW51Thz5QxPBz3NimYrnHbR+7FjxzJixAgApk6dSv369a0NlIqosS0iIpIaxMfbVxP/7TfInRs+/RQ8PDh56STNlzXHYHi55MuaV/s+SUxMJCwsjMuXL1OhQgV69OhhdSQRERERkUfX5s1QqxZcvQo1ajg+r9zM2StnCQkP4djFYxTMWJAvWnxBWs+0Dznw/TF37lx69rQvdDl8+HDat29vcaLURY1tERGR1KBHD9iwAXx9YcUKyJzZPq/2suacjj5NsSzF+LDGh1anTDXGjx/P1q1b8fX1ZdasWbjeYqEbEREREZHH3pYt/29qV68Oy5aBp+dND7187TK15tfiwJkDZPPLxrqW68jim+Xh5r1PPv/8c9q2bQtA9+7d6dOnj8WJUh81tkVERJzdtGn2aUcAwsOheHEABm8dzJYjW/B192VJ4yWaV/s+OXDgAH379gXslxVqJXMRERERkVvYujVpU/uzz8Dr5lOKxMbH0mBRA7458Q0ZvDOwrtU6cqXL9ZAD3x87duygcePGJCQk0LJlS8aMGeOUi14+6tTYFhERcWabN0PnzvbbQ4bYVxcHNvyxgSHbhgD2ebULZSpkUcDUJS4ujtatWxMbG0uNGjXo0KGD1ZFERERERB5NW7dCzZpw5cp/NrUTEhNovbw16/9Yj6+7L2uar6FI5iIPOfD98dNPP1G7dm2uXr1KzZo1mTFjBi4uasE+CDqrIiIizurQIWjY0D6/dvPm0L8/ACcvnaTFshYYDB2e7qB5te+j9957j++++4706dPzySefaNSFiIiIiMjNbNv2/6Z2SMhtm9rGGF5b8xqL9y/G3cWdZU2XUTp76Ycc+P44cuQIISEhXLhwgXLlyrFkyRLc3d2tjpVqqbEtIiLijM6ft68kfv48lCkD06eDzUZ8YrxjXu3iAcUZX3281UlTjd27dzN06FAAJk2aRNasWS1OJCIiIiLyCNq2zb5A5JUrEBwMy5ffsqkNMGDzAKZ8NwUbNsIbhBOcL/jhZb2PTp8+TXBwMH/99RdPPvkkq1atwsfHx+pYqZoa2yIiIs4mLg4aN4Zff4UcOZIUitfn1U7jkYbFjRZrXu375OrVq7Ru3ZqEhASaNm1K06ZNrY4kIiIiIvLo2b79/yO1U9DUHv/1eIZutw8emVhrIk2ebPKQgt5fly5dombNmhw6dIicOXOydu1aMmTIYHWsVE+NbREREWdiDLzxBmzcCL6+sGoVBAQAsP739QzdZi8Kp9aeqnm176N+/frx888/ExQUxMcff2x1HBERERGRR8/27faR2tHRUK2avantfeuBNuH7wum2thsAQ18YyiulXnk4Oe+z2NhY6tevz3fffUemTJlYv3492bJlszrWY0GNbREREWfy8ccwaRLYbDB/Pjz1FAB/XfrLMa92x6c7Elos1OKgqcfmzZsZN24cANOnTydjxozWBhIRERERedR8+WXSpvaKFbdtan/+6+eELQ8DoFvpbvSr2O8hBb2/EhISaNmyJZs2bSJNmjR88cUXFCxY0OpYjw01tkVERJzF2rX20doAI0dC3boA9nm1lzbnzJUzPBXwFOOqj7MuYyoTFRVFWFgYAJ06daJGjRrWBhIRERERedT8u6ldtep/NrW3H91OoyWNSDAJtCreivdD3nfKRdmNMbz22mt8+umneHh4sHz5ckqVKmV1rMeKGtsiIiLO4OBBaNIEEhOhbVvo1cuxa/DWwWw9utU+r3Zjzat9P3Xr1o1jx46RN29exowZY3UcEREREZFHy44d9qb25cspamrvjdxLnQV1iImPoXbB2kyvOx0Xm3O2JwcOHMiUKVOw2WyEh4dTpUoVqyM9dpzzJ0dERORx8vffULs2REVBxYr/n4oEWPf7uiTzahfMqMve7pcVK1Ywc+ZMbDYbs2fPJk2aNFZHEhERERF5dOzYAdWr25vaVarYm9o+Prc8/PdzvxMSHsLF2ItUyFmBxY0W4+7q/hAD3z8TJkxg8ODBAEycOJHGjRtbnOjxpMa2iIjIo+zaNWjYEP74A/LkgWXLwNMTsM+r3XJZSwyGTs900rza99GZM2fo2LEjAG+++SYVKlSwOJGIiIiIyCPkq6/+39R+8UVYufK2Te2Tl05SbW41TkWfonhAcVaFrnLaK00XLFhA165dARg8eDCvvOKci16mBmpsi4iIPKqMgVdfhW3bIG1aWL0aMmUCks+r/UHIBxaHTT2MMXTq1InTp09TtGhRx0gMEREREREheVN71arbNrXPXz1PSHgIhy8cJl/6fKxtuZZ0XukeXt77aO3atbRu3RqALl268Pbbb1uc6PGmxraIiMijauxYmDEDXFxg4UIoUsSxa9CWQZpX+wEJDw/ns88+w93dnblz5+L5zwh5EREREZHH3s6d9qb2pUvwwgv/2dS+EneFOgvq8OPpHwlME8i6VusITBP4EAPfP9988w0NGzYkPj6eZs2aMX78eKdc9DI1UWNbRETkUbR6Nbz5pv322LH2BVn+se73dby3/T0AptWZpnm176Pjx4/TpUsXwL4YTIkSJawNJCIiIiLyqNi5E0JC/t/UXr36tk3tuIQ4Gi9pzI7jO0jnlY61LdeSN33ehxj4/jl48CA1a9YkOjqa4OBgZs+ejYuL2qpW0zsgIiLyqNm3D0JD7VORdOoE/8zfBknn1X7lmVdoVrSZhUFTl8TERNq2bUtUVBRlypShd+/eVkcSEREREXk0fP31/5valSv/50jtRJNI2xVtWXNoDd5u3qwOXU3xgOIPL+99dPz4cYKDgzl37hzPPfccS5cuxcPDw+pYghrbIiIij5ZTp6BOnf/PVzdhAvxzeVt8YjyhS0P/P692dc2rfT9NnDiRjRs34u3tzezZs3Fzc7M6koiIiIiI9b75JmlTe/Vq8PW95eHGGLpFdGPej/Nwc3Hj0yafUj5n+YeX9z46e/YswcHB/PnnnzzxxBN8/vnnpEmTxupY8g81tkVERB4VMTHw0ktw7BgUKABLloC7u2P3wC0D2XZ0G34efixpvAQvNy8Lw6Yuv/76q2OE9ujRoylYUNO7iIiIiIjwzTcQHAxRUVCp0n82tQGGbhvKhF0TAJhdfzY1C9R8GEnvu8uXL1OrVi3+x959x9d4v38cfyUhESv2prS0KFotVbSoEXvvPWrv2mrThlJb7ZXEJrElYrRaVItWrSpKrdojS3bu3x/376vLCM7JnfF+Ph55OHfOOZ/77etb7nPlc1/XmTNnyJMnDzt37iRLlixWx5K/UWFbREQkITAM6NLF7FuXIYN5a1+mTI+e3nl+Jx7feQBmX+1CmQtZFDTpiY6Opl27doSFhVG1alV69OhhdSQREREREev9u6i9ffszi9pzD89l9DejAZhVYxatireKj6Q2FxkZSePGjfnhhx/IlCkTAQEB5MuXz+pY8i8qbIuIiCQEEyfCihXg5GTu1H7jjUdPXQu6RpuNZl/tHqV60LxYcwuDJj1ffPEFP/zwA25ubixdulRDYEREREREfvzxr6J2hQpxKmqvObmG3jvMQeyjK4ymT5k+8ZHU5mJjY2nfvj0BAQGkTp2aHTt2UKRIEatjyWPok5uIiIjVNmyAESPMx3PmQNWqj576X1/tOw/v8HaOt5lWfZpFIZOmn3/+mbFjxwIwZ84c8ubNa20gERERERGr/b2o/eGHcSpq7zy/k3Yb22Fg0LNUT8ZWGhs/WW3MMAz69evHmjVrSJEiBb6+vpQpU8bqWPIEKmyLiIhY6ccfoW1b83GfPtC9+z+eHvP1GL67/B3pnNOxrsk69dW2ofDwcNq1a0d0dDSNGjWidevWVkcSEREREbHWoUNQrRoEBppF7R074BnDEg9dPUSjdY2Iio2iRbEWzK41GwcHh3gKbFuff/45c+bMAcDLy4vq1atbnEieRoVtERERq1y6BPXqmUMja9eG6dP/8fTO8zvx2G/21V5cb7H6atvY6NGjOXnyJNmyZWP+/PmJ9uJbRERERMQmvv/+n+1H4lDUPnXrFLVW1uJh1EOqv1YdzwaeODokznLj/PnzGTVqFACzZs2iZcuWFieSZ0mc/08TERFJ7AIDzWL2zZvw1luwerXZX/v//a+vNkDPUj1p9mYzq5ImSd999x1ffvklAIsWLSJr1qwWJxIRERERsdCBA2ZROzgYKlWKU1H7jwd/4L7Cnfvh93k/z/v4NPPB2ck5fvLa2IYNG+jZsycAo0aNok+fxNkfPLlRYVtERCS+RUdD8+Zw6hTkzAlbt0K6dH89/be+2iVzlGRq9akWhk16goODad++PYZh0LFjR+rVq2d1JBERERER63z3HVSvDiEhULlynHpq3wy5STXvavwZ/CdvZn2T7a22k8b56e9JqPbs2UPr1q0xDINu3boxbtw4qyNJHKmwLSIiEp8MA/r2hZ07IXVqs6j9r4GF/+ir3VR9tW1t0KBBXLx4kVdeeYUZM2ZYHUdERERExDr79kHNmhAaag6x37rV/JzyFIHhgdRcWZPz986TP0N+drbZSSbXTPEU2LaOHj1KgwYNiIyMpHHjxnz11VdqUZiIqLAtIiISn2bOhHnzwMEBVq6Ed9/9x9P+5/0f9dVeUm8JBTMVtCJlkrVjxw4WLlwIwPLly0mfPr3FiURERERELPL111CrllnUdneHLVueWdQOiwqj3pp6/HzjZ7KlyUZAmwByp88dT4Ft6+zZs9SsWZOQkBAqV67MypUrcfpbe0hJ+FTYFhERiS9bt8KAAebjKVOgQYN/PH0l8AptfP/qq930zabxHDBpu3v3Lp07dwagf//+VKpUydpAIiIiIiJW2bPHnPnz8CHUqAGbN4Or61PfEh0bTQufFnx76VvSu6THv7V/oh1wf+3aNdzd3bl9+zbvvPMOGzduxMXFxepY8pxU2BYREYkPP/8MLVuarUi6dv2rwP3/omKiaL6hOXfD7vJuzneZVn2aRUGTrl69enH9+nUKFy6Mh4eH1XFEkqyxY8fi4ODwj68cOXI8et4wDMaOHUuuXLlwdXWlUqVKnDp1ysLEIiIiycyuXVCnDoSFmTu2N26EVE9vf2gYBl22dmHLb1twcXJhS4stlMxZMp4C29a9e/eoXr06ly5dolChQvj5+elOzkRKhW0RERF7u3bNvHAMDYVq1WDOHLMVyd8M2z2M769+j5uLG+ubrsclhXYL2NKaNWtYu3YtTk5OeHt74/qM3Sgi8nLefPNNrl+//ujrxIkTj56bPHky06ZNY86cORw+fJgcOXJQrVo1goODLUwsIiKSTOzcCXXrQni4+auvb5yK2oN3DWb5seU4OTixruk6KuavGE+Bbevhw4fUrVuXU6dOkTNnTgICAsiWLZvVseQFqbAtIiJiTyEh5gXjn39C0aKwfj2kTPmPl2z8dSPTDpk7tD0beFIgYwErkiZZf/75Jz179gRg5MiRlCpVyuJEIklfihQpyJEjx6OvrFmzAuYH4xkzZjBixAgaNWpEsWLF8PT05OHDh6xatcri1CIiIkmcnx/Urw8REeavGzZAHNpvTD4wmanfTwXMOUD13qhn76R2ERUVRdOmTTl48CAZMmRg586d5M+f3+pY8hJU2BYREbGXmBho1cpsQ5ItG2zfDm5u/3jJ7/d+p+PmjgAMKjuI+oXrW5E0yTIMg48//pj79+/z7rvvMmLECKsjiSQL586dI1euXBQoUIAWLVpw4cIFAC5evMiNGzdwd3d/9FoXFxcqVqzIwYMHn7heREQEQUFB//gSERGR57BtmznjJyICGjaEdevA2fmZb1t0dBHD9gwDYKr7VNq/3d7OQe0jNjaWTp06sWPHDlxdXdm2bRvFixe3Opa8JBW2RURE7GXwYHNgpIuLOYzlX7sBwqPDabq+KYERgZTLWw6PKur7bGsLFy7E398fFxcXvL29Sfmv3fIiYntlypTBy8uLnTt3smjRIm7cuEG5cuW4e/cuN27cACB79uz/eE/27NkfPfc4EydOxM3N7dFX3rx57fp7EBERSVK2bIFGjSAyEho3hrVr41TU9jntQ/ft3QEY/sFwBpQd8Ix3JEyGYTBo0CBWrFiBk5MT69evp3z58lbHEhtQYVtERMQe5s2D6dPNx15e8P77/3nJJ/6f8PONn8mSOgtrm6wlpZOKrrb0+++/M3DgQMAsihUpUsTiRCLJQ82aNWncuDHFixenatWqbN++HQBPT89Hr3H415wBwzD+872/Gz58OIGBgY++rly5Yp/wIiIiSc2mTdCkCURFQdOmsHr1f1ojPs6eC3to5duKWCOWLu904fPKn9s/q51MnjyZ6f//2Wzp0qXUrl3b4kRiKypsi4iI2Jq/P/TpYz7+/HNo1uw/L1l1YhXzj87HAQdWNlpJnvR54jlk0hYTE0P79u0JDQ2lYsWK9OvXz+pIIslWmjRpKF68OOfOnSNHjhwA/9mdfevWrf/s4v47FxcX0qdP/48vEREReQYfH7OYHRUFLVrAqlVxKmof+fMIDdY2IDImksZFGjOv9ryn/gA6IVuyZAnDhv1/K5WpU2nXrp3FicSWVNgWERGxpZMnzUJ2TAy0bw/Dh//nJb/e/pWuW7sCMLLCSNxfc//Pa+TlTJ06lQMHDpAuXTqWL1+Oo6MueUSsEhERwa+//krOnDkpUKAAOXLkYNeuXY+ej4yMZN++fZQrV87ClCIiIknM+vXQvDlER5tzf7y9IUWKZ77tzJ0z1FxZk5DIEKoUqMLKRitxcnSKh8C2t2nTJrp2NT93DR06lAEDEmcrFXmyZ/8/WkREROLmxg2oXRuCg6FiRVi4EP61syE0MpSm65sSGhVK5QKVGVNxjEVhk64TJ04watQoAGbMmKFJ5yLxbNCgQdStW5d8+fJx69YtPvvsM4KCgmjfvj0ODg70798fDw8PChUqRKFChfDw8CB16tS0atXK6ugiIiJJw9q10Lq1udmmbVtYtgycnl2cvhJ4BXdvd+48vEPpXKXZ2HwjLilc4iGw7e3bt48WLVo8Gho5ceJEqyOJHaiwLSIiYgsPH0L9+nD5MhQqBL6+/xnIYhgGPXf05NTtU+RIm4NVjVYl2t0PCVVkZCRt27YlMjKSunXr0rFjR6sjiSQ7V69epWXLlty5c4esWbPy/vvvc+jQIV555RUAhgwZQlhYGD179uT+/fuUKVOGgIAA0qVLZ3FyERGRJGD1amjTBmJjzTtIlyyJU1H7zsM7uK9w50rQFQpnKcyO1jtI55I4/20+duwY9erVIyIigvr167NgwYJE20pFns7BMAzD6hD2FBQUhJubG4GBgerFJyIi9hETY/au27gRMmWCQ4fM4va/LPlpCZ23dsbRwZG97fZSMX9FC8ImbSNGjMDDw4MsWbJw8uTJp/bslcRP13nJl/7sRUREHmPFCrOYHRsLHTvCokVxKmoHRwRTxasKh/88TN70eTnQ6QB53fLGQ2Db+/333ylfvjw3b96kQoUK+Pv74+rqanUseQ7Pc52nhpMiIiIva9Ags6jt7GxOHX9MUfuXG7/Q2683AJ9X/lxFbTs4dOgQkyZNAmD+/PkqaouIiIhI8uHlBe3amUXtzp1h8eI4FbUjoiNouLYhh/88TGbXzAS0DUi0Re3r16/j7u7OzZs3eeutt9iyZYuK2kmcpYXtefPmUaJEiUeTzcuWLYufn9+j5w3DYOzYseTKlQtXV1cqVarEqVOnLEwsIiLyL7NmwYwZ5mMvL/jww/+8JCgiiCbrmxAeHU6tQrUYUn5I/GZMBkJDQ2nXrh2xsbG0adOGxo0bWx1JRERERCR+LFsGHTqAYUC3brBgAcRheHpMbAytfVuz5+Ie0jqnxa+1H4WzFLZ/Xjt48OABNWvW5MKFC7z66qv4+/vj5uZmdSyxM0sL23ny5GHSpEkcOXKEI0eOULlyZerXr/+oeD158mSmTZvGnDlzOHz4MDly5KBatWoEBwdbGVtERMS0eTP0728+njTJnDr+L4Zh0HlLZ87fO08+t3x4NfDC0UE3TNna0KFDOXfuHLlz52b27NlWxxERERERiR9LlsDHH5tF7R49YO7cOBW1DcOgx/Ye+Pzqg7OTM5uab6J07tLxENj2wsLCqF+/Pr/88gvZs2cnICCAHDlyWB1L4oGln6zr1q1LrVq1eP3113n99df5/PPPSZs2LYcOHcIwDGbMmMGIESNo1KgRxYoVw9PTk4cPH7Jq1SorY4uIiMCPP0LLluYFZNeuMOTxu7Dn/DiH9afXk9IxJeuarCNz6szxHDTp27VrF1999RUAy5YtI0OGDNYGEhERERGJD/PmmW1HDAN69YKvvopTURvg0z2fsuinRTg6OLK68WqqvFrFzmHtIzo6mpYtW/Ltt9+SPn16/P39ee2116yOJfEkwWwZi4mJYc2aNYSGhlK2bFkuXrzIjRs3cHd3f/QaFxcXKlasyMGDB5+4TkREBEFBQf/4EhERsamLF6FuXQgLg5o1zQvIx0zZ/vHajwwMGAjAlGpTKJOnTHwnTfLu379Px44dAejVqxfVqlWzOJGIiIiISDyYORN69jQf9+sHs2c/9jPJ40w5MIVJB8zZNAvqLKBRkUb2SmlXhmHQrVs3Nm/ejIuLC1u2bOHtt9+2OpbEI8sL2ydOnCBt2rS4uLjQvXt3Nm7cSNGiRblx4wbAfwY/Zc+e/dFzjzNx4kTc3NwefeXNmzgb3ouISAJ1/z7UqgW3bkHJkrB2LaRI8Z+X3Qu7R7P1zYiKjaJxkcb0LdPXgrBJX9++fbl27RqFChXiiy++sDqOiIiIiIj9TZnyV0vEIUNg+vQ4F7UX/7SYIbvNu00nV51M53c62ymk/Q0fPpylS5fi6OjI2rVrqVixotWRJJ5ZXth+4403OHbsGIcOHaJHjx60b9+e06dPP3re4V//YRqG8Z/v/d3w4cMJDAx89HXlyhW7ZRcRkWQmIgIaNoQzZyBPHti2DdKl+8/LYo1Y2m9qz6XASxTMVJAl9ZY89d8ueTE+Pj6sWLECR0dHvLy8SJMmjdWRRERERETs67PP/mqDOGqUOesnjp81fE770G1bNwCGlh/K4PKD7ZXS7qZOnfpoY8uiRYuoX7++xYnECv/dYhbPnJ2dKViwIAClSpXi8OHDzJw5k6FDhwJw48YNcubM+ej1t27d+s8u7r9zcXHBxcXFvqFFRCT5MQzo1An27YP06WHHDsiV67EvnXJgCtvObsPFyYX1TdfjlkrTuG3txo0bdOtmXpQPGzaM999/3+JEIiIiIiJ2ZBgwZgxMmGAeT5gAI0fG+e27ft9FK99WxBqxdHmnCxOrTLRTUPvz8vJi0KBBAEyaNIlOnTpZnEisYvmO7X8zDIOIiAgKFChAjhw52LVr16PnIiMj2bdvH+XKlbMwoYiIJEujRsGqVWbbER8fKF78sS/79tK3jNg7AoDZNWfzdo634zFk8mAYBl27duXu3bu89dZbjBkzxupIIiIiIiL2YxgwbNhfRe3Jk5+rqH3o6iEarm1IZEwkTYs2ZV7teYn2jtJt27Y9KmQPGDCAIf/bvS7JkqU7tj/99FNq1qxJ3rx5CQ4OZs2aNXzzzTf4+/vj4OBA//798fDwoFChQhQqVAgPDw9Sp05Nq1atrIwtIiLJzeLF8Pnn5uOFC6Fq1ce+7FboLVpsaEGMEUObEm0Sdb+6hGzZsmVs3boVZ2dnvL29cXZ2tjqSiIiIiIh9GAYMGAAzZpjHM2aYwyLj6OStk9RaWYvQqFDcX3NnRaMVODk62SWqve3fv5+mTZsSExND27ZtmTJlSqIt0IttWFrYvnnzJm3btuX69eu4ublRokQJ/P39qVatGgBDhgwhLCyMnj17cv/+fcqUKUNAQADpHtPPVERExC527oTu3c3Ho0ZBx46PfVlMbAytfFpxPeQ6RbMWZX7t+brIsoM//viD/v8/KGfChAkUf8LOeRERERGRRC82Fvr0gblzzeO5c6FHjzi//eL9i7h7u3M//D5l85TFt5kvzk6Jc1PIiRMnqFu3LuHh4dSuXZslS5bg6JjgGlFIPHMwDMOwOoQ9BQUF4ebmRmBgIOnTp7c6joiIJCa//AIffgjBwdC2LXh6PnEwy9hvxjJu3zhSp0zN4S6HKZq1aDyHTfpiY2OpXLky+/bto3z58uzbtw8np8S520RsQ9d5yZf+7EVEJMmLjYVu3cy7Rx0cYNEi+PjjOL/9evB1Plj2ARfuX6BYtmLs67CPTK6Z7BjYfv744w/KlSvH9evXKVeuHLt27SJ16tRWxxI7eZ7rPMuHR4qIiCRIV69C7dpmUfujj/66oHyMgN8DGL9vPAAL6ixQUdtOZs6cyb59+0iTJg2enp4qaouIiIhI0hQTYw6u9/ICR0dYvtzcaBNH98PuU31FdS7cv8CrGV8loE1Aoi1q37p1i2rVqnH9+nWKFSvGtm3bVNSWR1TYFhER+begILOofe0aFC0Kvr7whD7O14Ku0dq3NQYGXd/pSpsSbeI5bPJw+vRphg8fDsDUqVN57bXXLE4kIiIiImIH0dFmEXvNGnBygpUroXnzOL89NDKUOqvrcOLWCXKmzcmutrvImS6nHQPbT1BQEDVr1uT8+fO88sor+Pv7kzFjRqtjSQKiwraIiMjfRUVBs2Zw/Dhkzw7bt0OGDI9/aUwUzTc0587DO7yd421m1pwZv1mTiaioKNq1a0dERAQ1atSga9euVkcSEREREbG9yEho1Qp8fCBlSrO43ahR3N8eE0njdY05eOUgGVJlYGebnbya8VU7Braf8PBwGjRowE8//UTWrFkJCAggd+7cVseSBEaFbRERkf8xDOjZ0xwYmTo1bNsG+fM/8eUj9o7gwJUDpHdJz/qm60mVIlX8ZU1GPv/8c44ePUrGjBlZsmSJhnKKiIiISNITEQFNm8LWrebdohs2QN26cX57TGwMbTe2ZefvO0mdMjU7Wu2gePbEOWg9JiaGNm3a8PXXX5M2bVr8/Px4/fXXrY4lCZAK2yIiIv/z2WdmL21HR1i7FkqVeuJLt/y2hSkHpwCwrP4yCmYqGF8pk5UjR47w2WefATB37lxy5cplcSIRERERERsLCzN3Zvv7Q6pUsHEj1KgR57cbhkGvHb1Yd2odKR1TsrH5RsrmLWvHwPZjGAY9e/bEx8cHZ2dnNm/ezLvvvmt1LEmgVNgWEREBWLYMRo82H8+ZA3XqPPGlF+9fpP2m9gD0L9OfRkXifnugxF1YWBht27YlJiaG5s2b06JFC6sjiYiIiIjY1sOHUK8e7NkDrq7mju0qVZ5riRF7R7Dg6AIccGBlo5W4v+Zup7D2N3r0aBYuXIiDgwMrV66kcuXKVkeSBEyFbRERET8/6NLFfDx8OPTo8cSXhkeH02R9Ex6EP6BM7jJ8Ue2LeAqZ/Hz66aecOXOGnDlz8tVXX1kdR0RERETEtkJCzA01+/ZB2rTmfJ8KFZ5riS8PfsnE/RMBWFBnAU3fbGqPpPFi1qxZj+7WnDdvHk2aNLE4kSR0KmyLiEjyduSI2csuJgbatYPPP3/qy/v69eWn6z+RJXUW1jddj7OTczwFTV6+/vprZsyYAcDixYvJnDmztYFERERERGwpMBBq1YKDByF9erMNSdnnax+y5KclDN41GIAvqn5Bl3e72CNpvFi1ahX9+vUDYMKECXTr1s3iRJIYqLAtIiLJ14ULULs2hIZCtWqwaBE8ZTCh5zFPFv206NEtfnnd8sZj2OQjKCiIDh06ANC1a1dq1aplbSAREREREVu6fx+qV4fDhyFDBggIgNKln2sJn9M+dN3WFYAh5YYwpPwQOwSNH/7+/rRvb7Z67NOnDyNGjLA4kSQWKmyLiEjydPu2OZDl1i14+23w8TGnjz/B8ZvH6b69OwBjKo5J1H3rErr+/ftz+fJlXn31VaZOnWp1HBERERER27l719xU8/PPkDkz7NoFJUs+1xK7L+ymlW8rYo1YOpfszKSqk+wU1v4OHTpE48aNiY6OpmXLlsyYMQOHp2w2Evk7FbZFRCT5efgQ6taFc+fglVdgxw5Il+6JLw8MD6TxusaER4dT/bXqjKo4Kh7DJi9btmxh2bJlODg4sHz5ctKmTWt1JBERERER27h50yxqnzgB2bLB7t1QvPhzLfHD1R9osKYBkTGRNCnahPl15ifaQvDp06epXbs2Dx8+pHr16ixfvhxHR0erY0kiosK2iIgkL9HR0LIl/PADZMpk9rLLmfOJLzcMg05bOnH+3nnyps/LikYrcHTQxZY93L59my7/P8Rz0KBBfPjhhxYnEhERERGxkatXoUoVOHvW/PyxZw8UKfJcS5y8dZKaK2sSGhVKtVersaLhCpwcnewU2L4uX75M9erVuXfvHmXKlMHHxwfnp9xBK/I4+mQuIiLJh2FA796wZQukSmX+WrjwU98y/dB0fH/1JaVjSjY020CW1FniKWzyYhgG3bt359atWxQrVozx48dbHUlERERExDYuXIAPPzSL2vnywbffPndR++L9i7h7u3M//D7v53kf3+a+uKRwsVNg+7pz5w7Vq1fn6tWrFClShO3bt5MmTRqrY0kipMK2iIgkHx4esGCBOSBy1SooX/6pL99/eT9DdplDWKZXn857ud+Lj5TJ0sqVK/H19SVFihR4eXmRKlUqqyOJiIiIiLy8M2egQgX44w8oWBC++8789TncCLlBNe9qXA+5TrFsxdjeajtpnRNny76QkBBq167NmTNnyJs3Lzt37iRz5sxWx5JESoVtERFJHjw9YeRI8/GsWdCw4VNffjPkJs3WNyPGiKFlsZb0LN0zHkImT1euXKF3794AjB07lpLPOTxHRERERCRBOn4cKlaEa9egaFFzp3a+fM+1xP2w+1RfUZ3f7/9OgQwF2NlmJ5lcM9kpsH1FRkbSqFEjfvzxRzJnzkxAQAB58+a1OpYkYipsi4hI0rdzJ3TubD4eOtRsR/IU0bHRtPRpyfWQ6xTJUoSFdRcm2oEsCV1sbCydOnUiMDCQMmXKMHToUKsjiYiIiIi8vCNHoFIluHUL3n4bvvnmqbN9Hic0MpQ6q+tw/OZxcqTNwa62u8iVLpc90tpdbGws7dq1Y9euXaRJk4YdO3ZQ+BltIUWeRYVtERFJ2n76CRo3NodGtm5ttiN5htFfj+brP74mTco0+DTzSbS3+SUG8+bNY/fu3bi6uuLl5UWKFJprLSIiIiKJ3IED5qDI+/ehTBnYuxeyZn2uJSJjImmyvgkHrxwkQ6oMBLQJ4LVMr9kpsH0ZhkG/fv1Yu3YtKVOmxNfXl/feU5tHeXkqbIuISNJ18SLUqgWhoeaF5dKl4Pj0f/q2/raVifsnArCk3hKKZH2+oS4Sd2fPnmXw4MEATJ48mddff93iRCIiIiIiL2nPHnB3h6Agsw3Jrl2QMeNzLRETG0O7je3wP+9P6pSp2d5qO8WzF7dTYPubMGECc+bMwcHBAU9PT9zd3a2OJEmECtsiIpI03b0LNWvCzZtQogT4+ICz81PfcuH+BdptagdAn/f60LxY8/hImixFR0fTrl07wsLCqFq1Kj17qoe5iIiIiCRy27dD7drw8KFZ3N6xA9Kle64lDMOg145erD21lpSOKfFt5ku5vOXsFNj+5s2bx5gxYwCYNWsWLVu2tDiRJCUqbIuISNLz8CHUqwe//WYOZ/HzAze3p74lPDqcpuub8iD8Ae/neZ8v3b+Mp7DJ0+TJk/nhhx9wc3Nj6dKlOD5jJ72IiIiISILm42MOqI+IgPr1YcsWSJ36uZcZuXckC44uwAEHVjRaQfWC1e0QNn6sX7+eXr16ATB69OhHA+NFbEWfIkVEJGmJjobmzeHgQciQwSxq53r2gJV+fv346fpPZEmdhXVN1uHs9PTd3fLifv7550e7NmbPnq1J6CIiIiKSuK1YAc2aQVQUtGgB69eDi8tzL/PlwS/x2G/OBJpfZz7N3mxm66TxZvfu3bRu3RrDMOjevTtjx461OpIkQSpsi4hI0mEY0LUrbNsGqVLB1q1QtOgz3+b1ixcLf1qIAw6sbLSSvG4qtNpLeHg47dq1Izo6mkaNGtGmTRurI4mIiIiIvLiFC6FdO4iNhY4dzSJ3ypTPvczSn5cyeJc5f2ZilYl0fberrZPGmyNHjtCwYUOioqJo0qTJo/7aIramwraIiCQdI0bAsmXmgMi1a+GDD575lhM3T9B9W3cAxlQcg/trGmRiT6NHj+bkyZNky5aN+fPn6wJXRERERBKvmTOhWzdzg02vXrB4MTg5PfcyPqd96LK1CwCDyw1maPmhtk4ab3777Tdq1qxJSEgIVapUYcWKFTi9wP8mInGhwraIiCQNM2fCxInm44ULzR7bzxAYHkjjdY0Jiw6j+mvVGVVxlJ1DJm/79+/nyy/N3uWLFi0ia9asFicSEREREXlBHh7Qv7/5ePBgmD3b3GDznHae30lLn5bEGrF8XPJjvqj6RaLd/HHt2jXc3d25c+cO7777Lhs3bsTlBVqyiMSVCtsiIpL4rVnz10Xl55/Dxx8/8y2GYdBpSyfO3TtH3vR5WdFoBY4O+mfRXoKDg2nXrh2GYdCxY0fqxeEHDyIiIiIiCY5hwMiR5t2iAGPHwhdfwAsUo/df3k/DtQ2Jio2iadGmLKizINEWte/du0f16tW5fPkyr7/+On5+fqRLl87qWJLEpbA6gIiIyEvZtcvsaQfQpw8MHx6nt00/NB3fX31J6ZiSDc02kCV1FjuGlEGDBnHx4kVeeeUVZsyYYXUcEREREZHnZxgwYAD873p28mRzt/YL+Pn6z9ReVZuw6DBqFqzJikYrcHJMnC07QkNDqVOnDqdOnSJXrlzs3LlTd2dKvHjhrWne3t6UL1+eXLlycenSJQBmzJjB5s2bbRZORETkqY4cgUaNzOnjzZqZF5hx2OGw//J+huwaAsD06tN5L/d7dg6avPn5+bFw4UIAli1bRvr06S1OJCJPomt8ERGRJ4iNhR49/ipqz5nzwkXtM3fOUH1FdYIigvgw34dsaLYBZydn22WNR1FRUTRt2pTvv/+ejBkzsnPnTvLnz291LEkmXqiwPW/ePAYMGECtWrV48OABMTExAGTIkEG7sEREJH6cOwe1akFICFSpAl5eceppdzPkJs3WNyPGiKFlsZb0LN0zHsImX3fv3uXj/28N079/fz766COLE4nIk+gaX0RE5Amio6FDB1iwwNxIs2SJOSzyBVx6cIlq3tW4/fA27+R8h60tt5I6ZWrb5o0nsbGxdOrUCT8/P1xdXdm2bRvFihWzOpYkIy9U2J49ezaLFi1ixIgR/5hsWqpUKU6cOGGzcCIiIo91/Tq4u8Pt2/DOO7BxI8RhKEl0bDQtfVpyPeQ6RbIUYWHdhYm2h11i0atXL65fv07hwoXx8PCwOo6IPIWu8UVERB4jIgKaNwdvb3BygpUroVOnF1rqZshNqnpX5WrQVQpnKYx/a3/cUrnZOHD8MAyDgQMHsmLFCpycnNiwYQPlypWzOpYkMy/UY/vixYuULFnyP993cXEhNDT0pUOJiIg8UWAg1KgBf/wBBQuCnx/EcSjJmK/H8PUfX5MmZRp8mvmQ1jmtfbMmc2vWrGHt2rU4OTnh7e2Nq6ur1ZFE5Cl0jS8iIvIvoaFm68OAAHB2hrVroUGDF1rqfth93Fe4c/7eefJnyM/utrvJmibx9qGeNGnSozu6li1bRq1atawNJMnSC+3YLlCgAMeOHfvP9/38/ChatOjLZhIREXm88HCoXx+OH4fs2WHnTsiWLU5v3XZ2Gx77zR3DS+otoUjWIvZMmuz9+eef9OxptnkZOXIkpUqVsjiRiDyLrvFFRET+JjAQqlc3i9qpU8P27S9c1A6JDKHWqlocv3mcHGlzsKvtLnKnz23bvPFo8eLFfPrppwBMmzaNtm3bWpxIkqsX2rE9ePBgevXqRXh4OIZh8OOPP7J69WomTpzI4sWLbZ1RREQEYmKgdWvYt8/coe3vD6++Gqe3Xrx/kbYbzYutPu/1oXmx5vZMmuwZhsHHH3/M/fv3effddxkxYoTVkUQkDnSNLyIi8v9u3zaL2j//DG5usGMHvGCbjfDocBqsacChq4fImCoju9ruomCmgjYOHH82btxIt27dABg+fDiffPKJxYkkOXuhwnbHjh2Jjo5myJAhPHz4kFatWpE7d25mzpxJixYtbJ1RRESSO8Mwh7P4+pq3AG7eDG+/Hae3hkeH02R9Ex6EP+D9PO/zpfuX9s0qLFq0CH9/f1xcXPDy8iJlypRWRxKRONA1voiICHD1KlSrBmfOQNas5o7tOH72+Lf/zfjZc3EPaZ3T4t/Gn2LZEu9wxW+++YaWLVsSGxvLxx9/zOeff251JEnmHAzDMF5mgTt37hAbG0u2ON4KHt+CgoJwc3MjMDCQ9OnTWx1HRERexLhxMHasOYF83Tpo0iTOb+28pTNLfl5CltRZ+KnrT+R1y2u/nMLvv//OW2+9RWhoKNOmTdMODrErXefZj67xRUQkWTp/HqpWhUuXIE8e2L0b3njjhZaKNWLpsKkD3se9cXFywa+1Hx8V+MjGgePPzz//TMWKFQkODqZBgwasX7+eFCleaL+syFM9z3XeCw+PjI6OplChQmTJkuXR98+dO0fKlCnJnz//iywrIiLyX/PmmUVtgK++eq6i9uKfFrPk5yU4OjiyuvFqFbXtLCYmhvbt2xMaGkrFihXp16+f1ZFE5DnoGl9ERJK1kyfNndo3bphD6nfvhldeeaGlDMOgn18/vI974+TgxPqm6xN1Ufv8+fPUqFGD4OBgKlasyOrVq1XUlgThhYZHdujQgYMHD/7n+z/88AMdOnR42UwiIiKmNWvMFiQAo0ZBjx5xfuuRP4/Qe0dvAD776DOqvlrVHgnlb6ZNm8aBAwdIly4dy5cvx9HxhS4zRMQi9rjGnzhxIg4ODvTv3//R9wzDYOzYseTKlQtXV1cqVarEqVOnXjC1iIiIDRw+DBUrmkXt4sXhu+9euKgNMOrrUcw5PAcHHPBq6EXdN+raMGz8un79Ou7u7ty6dYu3336bzZs3kypVKqtjiQAvWNj++eefKV++/H++//777z92krqIiMhz8/eHtm3N/to9e5rtSOLozsM7NF7XmIiYCOq/UZ+hHwy1Y1ABOHHiBCNHjgRgxowZ2tkpkgjZ+hr/8OHDLFy4kBIlSvzj+5MnT2batGnMmTOHw4cPkyNHDqpVq0ZwcPCLRhcREXlx33wDlSvDvXtQpox5nCPHCy835cAUPv/O7D09t/ZcWhVvZZucFnjw4AE1atTg4sWLvPbaa/j7++Pm5mZ1LJFHXqiw7eDg8NgLz8DAQGJiYl46lIiIJHMHDkCjRhAdDS1awOzZZn/tOIiJjaG1b2suB16mYKaCeDbwxNFBO4ftKTIykrZt2xIZGUndunXp2LGj1ZFE5AXY8ho/JCSE1q1bs2jRIjJmzPjo+4ZhMGPGDEaMGEGjRo0oVqwYnp6ePHz4kFWrVr3070FEROS5bN8ONWtCSAh89BHs2gWZMr3wcouOLmLI7iEATKoyie6lutsqabwLCwujXr16HD9+nBw5chAQEED27NmtjiXyDy/0Sf/DDz9k4sSJ/7jAjYmJYeLEiXzwwQc2CyciIsnQ8eNQpw6EhZkXmZ6e8BwtLcbtG0fA7wG4pnDFt5kvbqm0o8Dexo8fzy+//ELmzJlZuHAhDnH8IYSIJCy2vMbv1asXtWvXpmrVf7aBunjxIjdu3MDd3f3R91xcXKhYseJj26D8T0REBEFBQf/4EhEReSlr10KDBhAeDnXrwo4dkC7diy93ci3dtnUDYFj5YYn6rtHo6GhatGjBd999R/r06fH39+fVV1+1OpbIf7xQp/fJkydToUIF3njjDT788EMAvvvuO4KCgti7d69NA4qISDJy/jy4u8ODB1C+PGzYAM7OcX77trPbmPDtBAAW1V1E8ezF7RRU/ufQoUNMnDgRgAULFpDjJW7bFBFr2eoaf82aNfz0008cPnz4P8/duHED4D87vrJnz86lS5eeuObEiRMZ9xwtqURERJ5q8WLo2tVse9iypbmZJmXKF15u+9nttNnYBgODHqV64FHFw4Zh45dhGHTp0oUtW7aQKlUqtm7dyltvvWV1LJHHeqEd20WLFuX48eM0a9aMW7duERwcTLt27Thz5gzFihWzdUYREUkO/vzTnEJ+8ya89RZs2wapU8f57b/f+502vm0A6F26N61LtLZXUvl/oaGhtGvXjtjYWNq0aUPjxo2tjiQiL8EW1/hXrlyhX79+rFix4qmDpf59Z4dhGE+922P48OEEBgY++rpy5UrcflMiIiL/Nm0adOliFrW7dQNv75cqau/7Yx9N1jchOjaaVsVbMafWnER9B+OwYcNYvnw5Tk5OrF27lgoVKlgdSeSJXmjHNkCuXLnw8Ei8P4ESEZEE5N49c6f2H3/Aa6+ZgyMzZIjz2x9GPaTxusYERgRSNk9Zplafareo8pdhw4Zx7tw5cufOzezZs62OIyI28LLX+EePHuXWrVu8++67j74XExPDt99+y5w5c/jtt98Ac+d2zpw5H73m1q1bT+3b6eLigouLywvnEhERwTBg7FgYP948HjIEJk2K8yyfxzny5xHqrq5LeHQ49d6ox/L6yxP1fJ8vv/ySyZMnA7Bo0SLq1atncSKRp4tzYfv48eMUK1YMR0dHjh8//tTX/nvyuYiIyBOFhECtWnDqFOTKZQ5seY52FoZh0H1bd365+QvZ0mRjfdP1ODvFvX2JvJhdu3YxZ84cAJYtW0aG5/hBhIgkHLa+xq9SpQonTpz4x/c6duxI4cKFGTp0KK+++io5cuRg165dlCxZEjAH0O7bt48vvvjixX8jIiIiTxMbCwMGwMyZ5vHnn8Pw4S9V1D59+zQ1VtQgODKYj/J/xNoma0np9OI7v63m6enJ4MGDAfjiiy80EF4ShTgXtt9++21u3LhBtmzZePvtt3FwcMAwjP+8zsHB4bmnpouISDIVEQGNGsEPP5jTxwMCoECB51pi/pH5eB/3xsnBibVN1pI7fW47hZX/efDgwaML3V69elGtWjWLE4nIi7L1NX66dOn+07YkTZo0ZM6c+dH3+/fvj4eHB4UKFaJQoUJ4eHiQOnVqWrVqZZvflIiIyN/FxJj9tJcuNY9nzYI+fV5qyQv3L1DVqyp3w+7yXu732NxiM6lSPLkFV0K3detWPv74YwAGDhz4qMAtktDFubB98eJFsmbN+uixiIjIS4mJgTZtzB3aadKYU8jffPO5lvjh6g/08+8HwMQqE6mUv5Idgsq/9e3bl2vXrlGwYEHtsBRJ5Ky4xh8yZAhhYWH07NmT+/fvU6ZMGQICAkiXLl28nF9ERJKRiAho2xbWrwdHR7O43b79Sy35Z/CfVPOuxvWQ6xTLVgy/1n6kc0m8/4bt37+fZs2aERMTQ/v27Zk8eXKi7hEuyYuD8bgtGU8RFRVF165dGTVqFK+++qq9ctlMUFAQbm5uBAYGkj59eqvjiIgImP3tunY1p5E7O8P27VC16nMtcSv0Fu8ufJerQVdpVKQRG5pu0AVYPPDx8aFJkyY4Ojqyf/9+ypYta3UkScZ0nWc7usYXEZEkJyTEvDt01y5zOOTq1fCSw85vh96mkmclTt8+zWsZX+O7jt+RM13OZ74voTp+/DgVKlQgMDCQOnXq4OvrS8qXGKQpYgvPc5333B3tU6ZMycaNG184nIiICMOHm0VtR0dYteq5i9rRsdG09GnJ1aCrvJH5DZbVX6aidjy4efMm3bp1A8zBkSpqiyQdusYXEZEk5e5d8zPG/+4O3b79pYvaD8IfUH1FdU7fPk2e9HnY3W53oi5qX7x4kRo1ahAYGEj58uVZu3atitqS6LzQqNaGDRuyadMmG0cREZFkYfJk+F/7igULXugCc9TeUey9uJc0KdPg29yX9C7arWdvhmHQpUsX7t69y1tvvcWYMWOsjiQiNqZrfBERSRKuXYMKFf6a47NnD7zkTJiQyBBqrazFzzd+JluabOxuu5v8GfLbJq8Fbt68ibu7O9evX6d48eJs3bqV1KlTWx1L5LnFucf23xUsWJAJEyZw8OBB3n33XdKkSfOP5/v27WuTcCIiksQsXgxDh5qPJ0+Gzp2fe4mNv25k0oFJACypt4SiWYvaMqE8wfLly9m6dSvOzs54e3vj7OxsdSQRsTFd44uISKJ37pxZxL50CXLlMofTP+ccn38Liwqj3up6fH/1ezKmysiutrt4I8sbNgoc/4KCgqhZsybnz58nf/78+Pv7kzFjRqtjibyQ5+6xDVCgQIEnL+jgwIULF14qlC2p/56ISAKxdi20bGn21x46FCZNeu4lzt49S6mFpQiODKZ/mf5MrzHdDkHl3/744w9KlChBcHAwX3zxBUOGDLE6kgig6zxb0zW+iIgkaseOQfXqcOsWFCxotiHJn/+lloyMiaTR2kZsP7eddM7p2NNuD6Vzl7ZJXCuEh4dTq1Ytvv76a7JmzcqBAwcoVKiQ1bFE/uF5rvNeaMf23yem/68urt6mIiLyRFu3Qps2fw2NnDjxuZcIjQyl0dpGBEcG80G+D5hcbbIdgsq/xcbG0rFjR4KDgylfvjwDBw60OpKI2Imu8UVEJNH67juoUweCguDtt8HfH7Jnf6klo2OjaePbhu3ntuOawpVtrbYl6qJ2TEwMrVu35uuvvyZdunT4+/urqC2J3gv12AZYsmQJxYoVI1WqVKRKlYpixYqxePFiW2YTEZGkYM8eaNoUoqOhdWuYNw+es1BiGAZdtnbh1O1T5Eibg3VN1pHSSYNN4sOsWbP45ptvSJMmDZ6enjg5OVkdSUTsSNf4IiKS6GzbBu7uZlH7ww/hm29euqgda8TSeUtn1p9ej7OTMxubb6TCKxVsk9cChmHQo0cPfH19cXZ2ZvPmzbzzzjtWxxJ5aS+0Y3vUqFFMnz6dPn36ULZsWQC+//57PvnkE/744w8+++wzm4YUEZFE6vvvoX59iIgwf122DByf/2eqs3+czeqTq3FycGJdk3WJevp4YnL69GmGDRsGwNSpU3nttdcsTiQi9qRrfBERSXRWrIAOHSAmxtyxvW4duLq+1JKGYdBnRx88f/HEycGJtU3WUr1gddvktcioUaNYtGgRjo6OrF69mo8++sjqSCI28UI9trNkycLs2bNp2bLlP76/evVq+vTpw507d2wW8GWp/56IiEWOHYNKlSAw0BzgsnUruLg89zIHLh+gkmclomOjmV59Ov3f72/joPI4UVFRlC1blqNHj1KjRg127NihlgSS4Og6z7Z0jS8iIonKrFnQr5/5uE0bWLoUUr7cXZ2GYTB091CmHJyCAw6saLSCVsVb2SCsdWbOnEn//v0BWLBgAV27drU2kMgzPM913gu1IomJiaFUqVL/+f67775LdHT0iywpIiJJyZkz5u2AgYHwwQewceMLFbVvhNyg6fqmRMdG0/zN5vQr088OYeVxPDw8OHr0KBkzZmTJkiUqaoskA7rGFxGRRMEwYMyYv4raffuCp+dLF7UBPvv2M6YcnALAwroLE31Re+XKlY+K2p999pmK2pLkvFBhu02bNsybN+8/31+4cCGtW7d+6VAiIpKIXbwIVavC7dvw7rtmz7s0aZ57maiYKJpvaM71kOsUyVKExfUWq7gaT44cOcKECRMAmDt3Lrly5bI4kYjEB13ji4hIghcbC336wPjx5vH48TBjxgu1O/y3ad9PY/Q3owGYXn06nd/p/NJrWsnPz48OHToA0LdvXz799FNrA4nYwQv12AZzsExAQADvv/8+AIcOHeLKlSu0a9eOAQMGPHrdtGnTXj6liIgkDteuQZUq5q9Fi5rTyN3cXmip4XuG8+2lb0nnnA7f5r6kdU5r47DyOGFhYbRt25aYmBiaN29OixYtrI4kIvFI1/giIpJgRUVB+/awerU5jH72bOjVyyZLLzy6kIEBAwH47KPPEn37w++//57GjRsTHR1Nq1atmD59ujYJSZL0QoXtkydPPpqe+vvvvwOQNWtWsmbNysmTJx+9Tv/RiIgkI7dvmzu1L16E116D3bshS5YXWmrtybVM/X4qAMvqL6NwlsK2TCpPMWLECM6cOUOOHDn46quvrI4jIvFI1/giIpJgPXwITZqAnx+kSAFeXvCvmRAvasXxFXTf1h2AYeWH8emHiXtn86lTp6hduzZhYWHUqFGDZcuW4WiDHe0iCdELFba//vprW+cQEZHE7MEDs6f2mTOQJw/s2QM5c77QUidvnaTTlk4ADC43mMZFG9swqDzN119/zfTp0wFz12bmzJktTiQi8UnX+CIikiA9eAB16sCBA+DqChs2QK1aNlna91dfOmzqgIFB79K98ajikah/gHv58mWqV6/O/fv3KVOmDBs2bMDZ2dnqWCJ288KtSERERAAICTEvLI8dg2zZzKL2K6+80FIPwh/QcG1DHkY9pEqBKnhU8bBtVnmioKAgOnbsCEDXrl2pZaMPCyIiIiIiL+zGDaheHY4fN1scbttmDqe3Af/z/rTY0IIYI4aOb3dkZs2Zibqoffv2bdzd3bl27RpFihRh+/btpHmBWUciiYkK2yIi8uLCw6F+ffj+e8iQAXbtgtdff6GlYo1Y2vi24fy98+Rzy8eaJmtI4ah/puLLJ598wqVLlyhQoABffvml1XFEREREJLk7f968K/TiRcieHQICoEQJmyy97499NFzbkKjYKJq/2ZxFdRfh6JB423UEBwdTq1YtfvvtN/LmzcvOnTt196UkC4n3v1oREbFWVBQ0awZ790LatOagyJe40By/bzzbz23HxckF32a+ZEn9Yv255flt2bKFpUuX4uDggKenJ+nSpbM6koiIiIgkZ0ePQrlyZlH71Vdh/36bFbV/uPoDdVbXITw6nLqv18W7oTdOjk42WdsKERERNGrUiCNHjpA5c2YCAgLImzev1bFE4oUK2yIi8vyio6FtW9i6FVKlMn8tU+aFl9v621bG7RsHwII6C3g317u2SirPcPv2bbp06QLAoEGD+PDDDy1OJCIiIiLJ2u7dUKmSOZz+7bfN3toFC9pk6Z+u/0T1FdUJiQyhSoEqrGu6jpROKW2ythViYmJo3749u3fvJk2aNOzYsYPChQtbHUsk3qiwLSIizycmBjp1grVrIWVK8PExLzxf0Lm752izsQ0AvUr3ov3b7W0UVJ7FMAy6d+/OrVu3ePPNNxk/frzVkUREREQkOVu71pzfExICH30E+/ZBjhw2Wfr4zeNU865GYEQg5fOWZ1OLTaRKkcoma1vBMAz69u3L2rVrSZkyJRs3buS9996zOpZIvFJhW0RE4i42Frp1A29vcHKCdeteaiJ5SGQIDdY2ICgiiPJ5yzOt+jQbhpVnWblyJb6+vqRIkQJvb29SpUq8F/YiIiIiksjNng0tW5otD5s0AT8/SJ/eJkufvn2aql5VuRd2jzK5y7Cj9Q7SOqe1ydpWGT9+PHPnzsXBwQFvb2+qVatmdSSReKfCtoiIxI1hQO/esGQJODrCqlXQoMFLLGfQaXMnTt8+TY60OVjfdD3OTs62yytPdeXKFXr37g3A2LFjKVmypMWJRERERCRZMgwYMQL69jUf9+wJa9aAi4tNlj979yxVvKpw++Ft3sn5Dv5t/EnvYpuCuVXmzp3L2LFjAZgzZw7Nmze3NpCIRVJYHUBERBIBw4ABA2DePHBwAE9Pc3DkS/jy4JesP72eFI4p2NB0AznT5bRRWHmW2NhYOnXqRGBgIGXKlGHo0KFWRxIRERGR5Cg6Grp3NzfPAIwfDyNHmp85bODC/QtU9qzMjZAblMhegoA2AWRIlcEma1tl3bp1jzaojBkzhp49e1qcSMQ6KmyLiMjTGQZ8+inMmGEeL14Mbdq81JJ7Luxh2J5hAMysMZPy+cq/ZEh5HvPmzWP37t24urri6elJihS6HBARERGReBYWBi1awJYt5h2h8+fD/w81t4VLDy5R2bMy14KvUTRrUXa13UXm1Jlttr4Vdu3aRZs2bTAMgx49ejBmzBirI4lYSp9kRUTk6caNg0mTzMdz55qDI1/CpQeXaL6hObFGLB3e7kCPUj1sEFLi6uzZswwePBiAyZMn88Ybb1icSERERESSnfv3oV492L/fbDmyejU0bGiz5a8FXaOyV2UuBV6iUKZC7G67m2xpstlsfSscPnyYhg0bEhUVRbNmzZg9ezYONtrZLpJYqbAtIiJPNnGiWdgGmD4derxcETosKoxG6xpxN+wu7+R8h7m15upiLB5FR0fTvn17wsLCqFKlim5bFBEREZH4d+0aVK8Op06Bm5u5Y7tCBZstfyPkBpW9KnPh/gUKZCjA3vZ7E33bwzNnzlCzZk1CQ0OpWrUqXl5eODk5WR1LxHIqbIuIyONNm2a2IAFzx3b//i+1nGEY9Njeg5+u/0Rm18z4NvPFNaXry+eUOJs8eTKHDh3Czc2NZcuW4eioGdIiIiIiEo/OnDGL2pcvQ86c4O8PJUrYbPnbobep4lWFs3fPks8tH3vb7yVP+jw2W98KV69epXr16ty9e5dSpUrh6+uLi40Ga4okdvpEKyIi//XVVzBwoPl43DiwwXDBeUfm4fmLJ44OjqxpsoZXMrzy0mtK3B07duzR5PTZs2eTN29eawOJiIiISPLyww/wwQdmUfv11+HgQZsWte+F3aOadzVO3z5NrnS52NtuL/kz5LfZ+la4e/cu1atX5/Lly7z++uvs2LGDdOnSWR1LJMFQYVtERP5p0SL4/ynbfPopjBr10ksevHKQfv79AJhUZRJVX6360mtK3EVERNC2bVuioqJo2LAhbV5y+KeIiIiIyHPx84PKleHuXShd2uytnT+/zZZ/EP4Ad293frn5C9nTZGdvu728luk1m61vhdDQUOrUqcPp06fJnTs3AQEBZM2a1epYIgmKCtsiIvIXT0/o1s18PHAgfPYZvGQP7OvB12m8rjHRsdE0LdqUQeUG2SCoPI/Ro0dz8uRJsmXLxoIFC9TXXERERETij7e3OSjy4UNwd4e9e8GGBdrgiGBqrqzJ0etHyZI6C3va7eGNLIl7QHpUVBRNmjTh0KFDZMyYkZ07d/LKK7rjVeTfVNgWERHTmjXQqRMYhrlje8qUly5qR8ZE0nR9U26E3ODNrG+ytP5SFVXj2f79+5kyZQoACxcu1C4PEREREYkfhmF+pmjXDqKjoXVr2LoV0qa12SlCI0Opvao2h64eImOqjOxuu5s3s71ps/WtEBsbS8eOHfH398fV1ZXt27fz5puJ+/ckYi8qbIuICKxbB23aQGwsdOkCM2e+dFEbYMDOARy4coD0LunZ2HwjaZ1tdxErzxYSEkL79u0xDIOOHTtSv359qyOJiIiISHIQEwP9+sGQIebxJ5+Alxc4O9vsFKGRodRdXZfvLn9Hepf0BLQN4K0cb9lsfSsYhsGAAQNYuXIlKVKkwMfHh7Jly1odSyTBSmF1ABERsdj69dCqlXnx2aEDzJ8Pji//c0/PY558dfgrAFY2WkmhzIVeek15PoMGDeLChQvky5ePGTNmWB1HRERERJKDsDBo2xZ8fMzjqVNhwACbnuJh1EPqrq7L1398TTrndPi39qdUrlI2PYcVJk6cyMyZMwFYvnw5NWvWtDiRSMKmwraISHK2fj20bGkWtdu3h8WLbVLU/un6T3TbZvbqHlNxDHVer/PSa8rz8fPzY8GCBYB5UZw+fXqLE4mIiIhIknfvHtSvbw6HdHY2Z/i0aGHTUzyMekidVXX4+o+vSeucFv82/pTNm/h3NS9atIgRI0YAMGPGDFq3bm1xIpGET4VtEZHkasOGfxa1lywBJ6eXXvbOwzs0XNuQiJgI6rxeh9EVR9sgrDyPe/fu8fHHHwPQv39/PvroI4sTiYiIiEiSd+kS1KwJv/4Kbm6waRNUqmTTU/x9p3Za57TsbLOTcnnL2fQcVvD19aV79+4AfPrpp/Tr18/iRCKJgwrbIiLJkY+PuXMiJsYc5mKjonZ0bDQtfVpyOfAyBTMVxLuhN44OGucQ33r16sX169cpXLgwHh4eVscRERERkaTu2DGoVQuuX4fcucHPD4oXt+kpHkY9pN7qeuy9uDdJFbW//vprWrZsSWxsLJ07d+azzz6zOpJIoqFqg4hIcuPjA82bm0Xttm1h6VKbFLUBhu0exu4Lu0mdMjUbm28kQ6oMNllX4m7NmjWsWbMGJycnvLy8cHV1tTqSiIiIiCRlu3dDhQpmUbtYMTh0yG5F7T0X95jtR1r7J4mi9k8//UT9+vWJjIykYcOGzJs3DwcHB6tjiSQalha2J06cSOnSpUmXLh3ZsmWjQYMG/Pbbb/94jWEYjB07lly5cuHq6kqlSpU4deqURYlFRBI5X9+/dmq3bQvLltmsqL3y+Eqmfj8VAM8GnhTLVswm60rc/fnnn/Ts2ROAkSNHUrp0aYsTiYiIiEiStmKF2X4kONhsO/Ldd5Anj01PERYVRv019R8Vtf1a+1E+X3mbnsMK586do0aNGgQHB1OpUiVWrVpFihRqrCDyPCwtbO/bt49evXpx6NAhdu3aRXR0NO7u7oSGhj56zeTJk5k2bRpz5szh8OHD5MiRg2rVqhEcHGxhchGRRMjX19ypHR0NbdrYtKj90/Wf6Ly1MwCffvApTYo2scm6EneGYdC5c2fu37/Pu++++2jwjIiIiIiIzRkGTJpkbpaJjjY/Z/j7Q4YMNj1NWFQY9dbUY/eF3aRJmQa/1n58kO8Dm57DCn/++Sfu7u7cvn2bkiVLsnnzZlKlSmV1LJFEx8EwDMPqEP9z+/ZtsmXLxr59+6hQoQKGYZArVy769+/P0KFDAYiIiCB79ux88cUXdOvW7ZlrBgUF4ebmRmBgIOnTp7f3b0FEJGHauBGaNfurqL18uc2K2rdCb1FqYSmuBF2hVqFabGmxBSdH26wtcbdw4UK6deuGi4sLP/30E0WLFrU6kojd6Tov+dKfvYiIhWJioF8/+Oor83jgQJg8GRxtu3fyfzu1d13YRZqUafBv458kitoPHjygQoUKnDhxgtdee40DBw6QPXt2q2OJJBjPc52XoHpsBwYGApApUyYALl68yI0bN3B3d3/0GhcXFypWrMjBgwcfu0ZERARBQUH/+BIRSdb+XtRu3dqmRe2omCiarW/GlaArFMpUiJWNVqqobYHff/+dAQMGAGabLxW1RURERMQuwsKgaVOzqO3gANOnw5df2qWo3WBtg0dF7aSyU/vhw4fUrVuXEydOkCNHDgICAlTUFnkJCaawbRgGAwYM4IMPPqBYMbMv640bNwD+8x959uzZHz33bxMnTsTNze3RV968ee0bXEQkIdu06a+idqtW4Olps6I2wMCAgey7tI90zunY3GKzhkVaICYmhg4dOhAaGkrFihXp16+f1ZFEREREJCm6exeqVjU3zjg7w9q10L+/zU/zMOoh9dfUJ+D3gEdF7Q9f+dDm54lvUVFRNG/enP379+Pm5sbOnTt59dVXrY4lkqglmMJ27969OX78OKtXr/7Pc/+eCGsYxhOnxA4fPpzAwMBHX1euXLFLXhGRBG/zZnM3hZ2K2st+XsbsH2cD4N3QmyJZi9hsbYm7adOmsX//ftKmTcvy5ctxtPFuGRERERERLl6E8uXh4EGzj/auXeZnDRsLiQyh1spaj3Zq72i9I0kUtQ3DoEuXLmzbto1UqVKxdetWSpQoYXUskUQvQYxb7dOnD1u2bOHbb78lz9+m5+bIkQMwd27nzJnz0fdv3br1xFs1XFxccHFxsW9gEZGEbuPGvwZFtmxpFrVtOGH7h6s/0H17dwDGVhxL/cL1bba2xN2JEycYOXIkADNnziR//vzWBhIRERGRpOenn6BWLbh5E/LmBT8/ePNNm58mMDyQWqtqcfDKQdK7pMevtR/l8paz+XmsMHToUDw9PXFycmLdunV8+GHiL9aLJASWbusyDIPevXvj6+vL3r17KVCgwD+eL1CgADly5GDXrl2PvhcZGcm+ffsoVy5p/OUmImJza9eauyeiosyitpeXTYvaN0Ju0GhdIyJjIqn/Rn1GVRxls7Ul7iIjI2nXrh2RkZHUqVOHjh07Wh1JRERERJKaHTugQgWzqF2iBHz/vV2K2vfD7lPNuxoHrxwkQ6oM7G67O8kUtadMmcKUKVMAWLx4MXXr1rU4kUjSYemO7V69erFq1So2b95MunTpHvXNdnNzw9XVFQcHB/r374+HhweFChWiUKFCeHh4kDp1alq1amVldBGRhGnFCmjfHmJjoW1bWLbMpu1HImMiabKuCX8G/0mRLEXwauiFo4NaX1hh/PjxHDt2jMyZM7No0aIntugSEREREXkh8+dDr17mZ4sqVcDHB9zcbH6aOw/v4O7tzs83fiaza2Z2t9vN2znetvl5rLBs2TKGDBkCwOTJk+nQoYO1gUSSGEsL2/PmzQOgUqVK//j+smXLHv3HPmTIEMLCwujZsyf379+nTJkyBAQEkC5dunhOKyKSwC1fDp06gWHAxx/DggU2LWoD9PPrx4ErB3BzcWNTi02kd0lv0/Ulbg4dOsTEiRMBWLBgwaPWXSIiIiIiLy02FoYNg//fZUyHDuZnC2dnm5/qZshNqnpX5eStk2RLk4097fZQLFsxm5/HClu2bKFLly4ADB48mMGDB1ucSCTpcTAMw7A6hD0FBQXh5uZGYGAg6dOrACMiSdTChdCtm/m4e3f46iuw8RDBhUcX0m1bNxxwYFurbdQqVMum60vcPHz4kLfffptz587RunVrVqxYYXUkEcvoOi/50p+9iIidhIWZd4CuX28ejx8PI0eCHe4O/DP4T6p4VeHMnTPkTJuTve33UjhLYZufxwrfffcd7u7uhIeH06FDB5YuXao7LEXi6Hmu8xLE8EgREXkJX30FvXubj/v2hRkzbH7hefDKQXrvMM/xeeXPVdS20NChQzl37hy5c+dm9uzZVscRERERkaTizh2oXx8OHoSUKWHpUmjTxi6nuhJ4hcpelTl/7zx50+dlb/u9FMxU0C7nim+//PILdevWJTw8nDp16qhtoIgdqTGqiEhiNn36X0XtgQPtUtS+FnSNxusaExUbRZOiTRj2wTCbri9xt3v3bubMmQOYbbsyZsxocSIRkf+aN28eJUqUIH369KRPn56yZcvi5+f36HnDMBg7diy5cuXC1dWVSpUqcerUKQsTi4gI585B2bJmUTtDBggIsFtR++zds5RfWp7z985TIEMBvu34bZIpal+4cIEaNWoQGBjIBx98wLp160iRQntKRexFhW0RkcRq8mQYMMB8PHy42QPPxkXt8OhwGq1rxI2QGxTPVpxl9Zdpt4FFHjx4QMeOHQHo2bMn1apVsziRiMjj5cmTh0mTJnHkyBGOHDlC5cqVqV+//qPi9eTJk5k2bRpz5szh8OHD5MiRg2rVqhEcHGxxchGRZOrAAbOoff485M9vFrf/NQvNVn6+/jMfLP2AK0FXeCPzG3zb8VvyZ8hvl3PFt5s3b+Lu7s6NGzcoXrw4W7duxdXV1epYIkmaCtsiIonRZ5/B0KHm4zFj4PPPbV7UNgyDXtt78eO1H8mYKiObWmwirXNam55D4q5v375cvXqVggULMnnyZKvjiIg8Ud26dalVqxavv/46r7/+Op9//jlp06bl0KFDGIbBjBkzGDFiBI0aNaJYsWJ4enry8OFDVq1a9cQ1IyIiCAoK+seXiIjYwLp1UKUK3L0LpUvDoUNQpIhdTvXdpe+o5FmJ2w9v807Od/iu43fkSZ/HLueKb4GBgdSoUYPff/+d/Pnzs3PnTjJkyGB1LJEkT4VtEZHExDBg9GgYNco8/uwzGDvWLsNc5h6ey9JjS3F0cGRtk7W8mvFVm59D4sbX1xdvb28cHR3x8vIiTZo0VkcSEYmTmJgY1qxZQ2hoKGXLluXixYvcuHEDd3f3R69xcXGhYsWKHDx48InrTJw4ETc3t0dfefPmjY/4IiJJl2GYd4A2bw4REWZv7a+/huzZ7XK6Hed24L7CnaCIICq8UoG97faSNU1Wu5wrvoWHh1O/fn2OHTtGtmzZCAgIIGfOnFbHEkkWVNgWEUksDAM+/RQmTDCPJ0+GESPscqp9f+yj/87+5mmqTqbaa2p7YZWbN2/SrVs3wBwcWbZsWYsTiYg824kTJ0ibNi0uLi50796djRs3UrRoUW7cuAFA9n8VTrJnz/7ouccZPnw4gYGBj76uXLli1/wiIkladDT07PnXHaB9+4KPD9hp88TqE6upv6Y+4dHh1C5UG//W/rilcrPLueJbdHQ0LVu2ZN++faRLlw5/f38KFSpkdSyRZEMd7EVEEgPDgEGDYNo083jGDOjXzy6nuhx4mabrmxIdG02r4q0YUHaAXc4jz2YYBl26dOHOnTu89dZbjB071upIIiJx8sYbb3Ds2DEePHiAj48P7du3Z9++fY+e//e8BsMwnjrDwcXFBRcXF7vlFRFJNoKDzV3afn7mXZ/Tp9vtcwXA7B9m08+/HwYGLYu1xLOBJymdUtrtfPHJMAx69OjBpk2bcHZ2ZsuWLZQsWdLqWCLJigrbIiIJXUyMuaNi4ULz+KuvzGM7CIsKo+Hahtx+eJu3c7zNorqLNCzSQsuXL2fr1q04Ozvj5eWFs7Oz1ZFEROLE2dmZggULAlCqVCkOHz7MzJkzGfr/uwNv3Ljxj9u0b9269Z9d3CIiYmPXrkGdOnDsGLi6wqpV0KCBXU4Va8QyOGAw0w6ZG3N6lOrBnFpzcHRIOo0DRo4cyeLFi3F0dGT16tVUstPATRF5sqTzN4qISFIUHQ3t25tFbUdHWLLEbkVtwzD4eMvH/HT9J7KkzsKm5ptInTK1Xc4lz3bp0iX6/f/umfHjx1OiRAmLE4mIvDjDMIiIiKBAgQLkyJGDXbt2PXouMjKSffv2Ua5cOQsTiogkccePw/vvm0XtbNngm2/sVtQOiwqj2fpmj4raHpU9+KrWV0mqqD1jxgw8PDwAmD9/Po0aNbI4kUjypB3bIiIJVUQEtGgBmzZBihSwYoV526CdTD4wmdUnV5PCMQXrm67nlQyv2O1c8nSxsbF06NCB4OBgypUrx6BBg6yOJCISZ59++ik1a9Ykb968BAcHs2bNGr755hv8/f1xcHCgf//+eHh4UKhQIQoVKoSHhwepU6emVatWVkcXEUmatm83P1eEhEDhwrBjBxQoYJdT3Q69Tf019fn+6vc4OzmzrP4yWhVPWn+/r1ixgk8++QSAzz//nC5dulicSCT5UmFbRCQhevgQGjaEgABwcYH166FuXbudbtvZbQzfMxyAWTVmUSl/JbudS55t1qxZfPPNN6RJkwYvLy+cnJysjiQiEmc3b96kbdu2XL9+HTc3N0qUKIG/vz/VqpmDiIcMGUJYWBg9e/bk/v37lClThoCAANKlS2dxchGRJMYwYNYsGDAAYmOhcmXYsAEyZrTL6U7fPk291fX4/f7vZEiVgU3NN1Exf0W7nMsqO3bsoGPHjgD069eP4cOHW5xIJHlzMAzDsDqEPQUFBeHm5kZgYCDp06e3Oo6IyLMFBZm97777zpxMvnkzVKlit9P9evtXyiwuQ3BkMN3f7c68OvPsdi55tl9//ZV33nmH8PBw5s2bR/fu3a2OJJJg6Tov+dKfvYjIM0RHQ9++MO//r+07d4a5cyGlfQY3bv1tK619WxMcGUz+DPnZ0WoHRbIWscu5rPL9999TpUoVwsLCaN26NV5eXjg6Jp32KiIJxfNc52nHtohIQnL3LtSsCYcPg5ubeZugHXuO3g+7T7019QiODKbCKxWYWXOm3c4lzxYVFUW7du0IDw+nevXqdOvWzepIIiIiIpLYBAZCs2bm3Z8ODjB5MgwcaD62McMwmLh/IiP3jsTAoOIrFVnfdD1Z02S1+bmsdOrUKWrXrk1YWBg1a9Zk2bJlKmqLJAAqbIuIJBQ3bkC1anDyJGTObF6IvvOO3U4XHRtN8w3NOX/vPK+4vcKGphtwdnK22/nk2Tw8PDhy5AgZM2ZkyZIlONjhw4eIiIiIJGEXL5p3f54+DalTw8qVdhsS+TDqIZ02d2LtqbUA9CzVkxk1ZpDSyT67wq1y6dIl3N3duX//Pu+//z7r168npZ12vovI81FhW0QkIbh8GapWhXPnIGdO2L0biha16ykHBwxm14VdpE6Zms0tNie5XRWJzZEjR/jss88AmDt3Lrlz57Y4kYiIiIgkKgcPmkXs27chVy7YutVuG2UuB16mwZoG/HzjZ1I4pmBOzTl0K5X07ja8ffs27u7u/PnnnxQtWpTt27eTJk0aq2OJyP9TYVtExGrnz5s9tC9fhldegT174LXX7HrKZT8vY8YPMwDwauDFWznesuv55OnCwsJo164d0dHRNGvWjBYtWlgdSUREREQSk1WroFMniIiAkiXNoradNkrs+2MfzTY041boLbKkzoJPMx8qvFLBLueyUnBwMLVq1eLs2bPky5ePnTt3kilTJqtjicjfqCGQiIiVTp2CDz80i9qvv24OjLRzUfvglYN0324OJBxbcSyNiza26/nk2UaMGMGvv/5Kjhw5mDt3rtVxRERERCSxMAwYOxZatzaL2g0amJ8p7FDUNgyDKQemUMWrCrdCb/FW9rc40uVIkixqR0RE0LBhQ44cOUKWLFkICAggT548VscSkX/Rjm0REascPmwOirx7F0qUMHtqZ89u11NeCbxCo7WNiIyJpHGRxoyqOMqu55Nn++abb5gxYwYAS5YsIXPmzNYGEhEREZHEITzc3KW9erV5PHgwTJoEdhhqGBgeSIfNHdh0ZhMAbUu0ZX6d+aROmdrm57JaTEwMbdu2Zc+ePaRJk4YdO3bwxhtvWB1LRB5DhW0RESvs2WPupggJgffeAz8/sPNtbQ+jHtJwbUNuht6kRPYSLG+wHEcH3bhjpaCgIDp06IBhGHTp0oVatWpZHUlEREREEoNbt8zPE99/DylSwLx50LmzXU51/OZxGq9rzPl753F2cmZWjVl0fbdrkhx0bhgGvXv3fjQgctOmTZQuXdrqWCLyBCpsi4jEN19faNkSIiPN3tobN0K6dHY9pWEYfLzlY45eP0qW1FnY3GIzaZ3T2vWc8myffPIJly5dokCBAkydOtXqOCIiIiKSGJw6BXXqwB9/QIYM4OMDlSvb5VTev3jTbVs3wqLDyOeWjw1NN1A6d9It9I4bN4758+fj4ODAihUrqFq1qtWRROQptFVPRCQ+LVkCTZuaRe3GjWH7drsXtQG+OPAFa06uIYVjCjY03UD+DPntfk55uq1bt7J06VIcHBzw9PQkXTz8/0BEREREEjl/fyhXzixqv/YaHDpkl6J2RHQEPbb1oN2mdoRFh1H9ter81PWnJF3U/uqrrxg3btyjx82aNbM4kYg8iwrbIiLxZcoU8/bA2Fjz17VrwcXF7qfd+ttWPt3zKQCza86mYv6Kdj+nPN3t27fp/P+3ig4cOJAPP/zQ4kQiIiIikqAZBkybBrVrQ1AQVKgAP/wAduj9fP7eecovLc/8o/NxwIGxFceyvdV2MqdOurNg1qxZQ58+fQAYO3YsPXr0sDiRiMSFWpGIiNibYcCwYTB5snk8dChMnAjx0JPu9O3TtPZtjYFBj1I96F6qu93PKU9nGAbdu3fn1q1bvPnmm0yYMMHqSCIiIiKSkEVEQI8esGyZedy5M3z1FTg72/xUq0+sptu2bgRHBpPJNRMrG62kRsEaNj9PQhIQEEC7du0wDINevXoxevRoqyOJSBypsC0iYk8xMdC9OyxebB5PnmxOK48H98LuUW91PYIjg6n4SkVm1pgZL+eVp1u5ciW+vr6kSJECb29vUqVKZXUkEREREUmobt2CRo3gwAFwdDR3bffta/NNMqGRofT168vSY0sBqPBKBVY2Wkme9Hlsep6E5scff6RRo0ZERUXRvHlzZs2alSSHYookVSpsi4jYS0QEtGplDot0dIRFi6BTp3g5dXRsNM03NOf3+7+TP0N+1jddT0qnlPFybnmyq1ev0rt3bwDGjBlDyZIlLU4kIiIiIgnWL79AvXpw+TK4uZmtDKtXt/lpTtw8QfMNzfn1zq844MDoiqMZWWEkKRyTdsnozJkz1KpVi9DQUKpVq4aXlxeOjurYK5KYJO2/pURErBIcDA0bwp495i2Ca9aYx/FkUMAgdl/YTZqUadjcYjNZ02SNt3PL48XGxtKxY0cCAwN57733GDZsmNWRRERERCSh2rgR2rSBhw+hUCHYutXm/bQNw2Dh0YX039mf8OhwcqXLxcpGK6mUv5JNz5MQXblyBXd3d+7evUvp0qXx8fHB2Q6tXUTEvlTYFhGxtTt3oFYtOHwY0qaFzZvtMqn8SZb8tISZP5htR7wbelMie4l4O7c82bx589i9ezeurq54eXmRIoX+CRYRERGRfzEM+PxzGDXKPK5aFdatg4wZbXqawPBAumztwvrT6wGoVagWy+svTxYbYu7evUv16tW5cuUKb7zxBjt27CBdunRWxxKRF6BP1SIitnTlCri7w5kzkDkz+PlB6dLxdvpvL31Lj+3mBO+xFcfSsEj87RKXJzt37hyD/7+3+hdffMEbdpheLyIiIiKJ3MOH8PHH5t2eYPbSnjoVbLwh4rtL39F2Y1suBV4ipWNKJlWdRP/3++PokPTbcISGhlK7dm1+/fVXcufOTUBAAFmyZLE6loi8IBW2RURs5eRJqFEDrl2DPHlg1y4oXDjeTn/h/gUarW1EVGwUzd5sxuiKmuadEERHR9OuXTvCwsKoUqUKvXr1sjqSiIiIiCQ0165B/fpw9KhZyP7qK+ja1aaniIyJZMzXY/jiwBcYGLya8VXWNF5D6dzxtxHHSpGRkTRu3JgffviBjBkzEhAQQL58+ayOJSIvQYVtERFb+PZb80L0wQMoUgT8/SEeL5ICwwOpu7oud8PuUipXKZbVX6Zp3gnE5MmTOXToEG5ubixbtkwDaURERETkn378ERo0gOvXzbs+fXygYkWbnuLX27/S2rc1P9/4GYBOb3diRo0ZpHNJHi04YmNj6dChAzt37iR16tRs376dokWLWh1LRF6SCtsiIi/L1xdatYKICChfHrZsgUyZ4u30MbExtPRpyenbp8mVLhebW2wmdcrU8XZ+ebJjx44xduxYAGbNmkXevHmtDSQiIiIiCcuqVdCpk/lZ4s03zSGRBQrYbHnDMJh7eC6Ddg0iPDqcTK6ZWFR3EY2KNLLZORI6wzDo378/q1evJkWKFGzYsIGyZctaHUtEbECFbRGRlzF3LvTubQ55adDAvDB1dY3XCIN3DcbvvB+uKVzZ3GIzudLlitfzy+NFRETQtm1boqKiaNiwIW3btrU6koiIiIgkFDExMHIkTJpkHtetCytWQPr0NjvFjZAbdNrcCb/zfgC4v+bOsvrLkt3nBQ8PD2bPng3A8uXLqVmzpsWJRMRWVNgWEXkRhmFeiHp4mMfdupl98Jyc4jXGoqOLmH5oOgCeDTwplatUvJ5fnmz06NGcPHmSbNmysWDBArWGERERERHT/fvmHZ/+/ubx0KHw+ec2/Syx6cwmumztwp2Hd3BxcmFKtSn0eq9XshgQ+XcLFy5k5MiRAMycOZPWrVtbnEhEbEmFbRGR5xUVZRayly0zj8ePN4vc8Vy4/OaPb+i5oycA4yqNo+mbTeP1/PJk+/fvZ8qUKYB5MZ01a1aLE4mIiIhIgnDqlHmn5/nz5p2eS5ZAy5Y2Wz4wPJBPdn7CsmPmZ5W3sr/FykYreTPbmzY7R2Lh4+NDjx49ABgxYgR9+/a1OJGI2JoK2yIizyM0FJo2BT8/cHSEBQugc+d4j/H7vd9pvK4x0bHRtCjWglEVRsV7Bnm8kJAQ2rdvj2EYdOjQgfr161sdSUREREQSgk2boG1bCAkxB81v2gQlS9ps+Z3nd9J5a2euBl3FAQcGlh3IZ5U/wyWFi83OkVjs3buXVq1aERsbS5cuXZgwYYLVkUTEDlTYFhGJq9u3oU4dc2q5qyusXWv2wotngeGB1Fldh3th9yidqzRL6y1Vm4sEZNCgQVy4cIF8+fIxY8YMq+OIiIiIiNViY2HcOPNOT4BKlWDdOrDRXX3BEcEMDBjIop8WAfBaxtdY3mA5H+T7wCbrJzZHjx6lfv36REZG0qhRI+bNm6fPSyJJlArbIiJxceEC1KgB585BpkywfTu8/368x4iOjab5huacuXOG3Olys7nFZlxTxu+wSnkyPz8/FixYAJiDadzc3CxOJCIiIiKWCgoyd2lv2WIe9+sHU6ZAypQ2WX7PhT18vOVjLgVeAqDPe32YWGUiaZzT2GT9xObcuXPUrFmTkJAQPvroI1auXIlTPM9BEpH4o8K2iMiz/PQT1KoFN2/CK6+YQ14KF7YkyqCAQez8fSeuKVzZ0nILOdPltCSH/Ne9e/f4+OOPAejXrx8fffSRxYlERERExFJnz0L9+nDmDLi4wPz50KGDTZYOiQxh6K6hzD0yF4D8GfKzrP4yKuWvZJP1E6M///wTd3d3bt++TcmSJdm0aROpUqWyOpaI2JEK2yIiT7N9OzRvbvbWfust2LEDcuWyJMrCowuZ+cNMALwbevNOzncsySGP16tXL65fv07hwoWZOHGi1XFERERExEo7dkCrVhAYCLlzg68vvPeeTZbe98c+Om7uyMUHFwHoUaoHk6tNJq1zWpusnxjdv3+f6tWr88cff1CwYEH8/PxInz691bFExM4crQ4gIpJgzZsH9eqZRe2qVWHfPsuK2l9f/JpeO3oBMOGjCTQu2tiSHPJ4a9euZc2aNTg5OeHl5YWrq9rDiIiIiCRLhgETJ5qzeQIDoXx5OHLEJkXt4Ihg+vr1pZJnJS4+uEg+t3zsaruLubXnJuui9sOHD6lbty4nT54kZ86cBAQEkD17dqtjiUg80I5tEZF/i42FYcPM3ncAHTvCggU264P3vM7dPUfjdY2Jjo2mZbGWjPhwhCU55PH+/PNPevbsCcCIESMoXbq0xYlERERExBIhIeZnhw0bzOPu3WHmTHB2fuml/c750X17dy4HXgagc8nOTK0+lfQuyXtXclRUFM2aNePAgQNkyJCBnTt3UqBAAatjiUg8UWFbROTvwsOhXTtYv948njABRowAi6ZoPwh/QN3Vdbkffp/3cr/HknpLNNE7ATEMg86dO3Pv3j3eeecdRo4caXUkEREREbHChQvQoAGcOGFuiJkzB7p2fell7zy8Q3///qw8sRIwe2kvqLMA99fcX3rtxC42NpbOnTuzfft2UqVKxdatWylevLjVsUQkHqmwLSLyP3fumMNdDh40L0aXLoU2bSyLEx0bTfMNzfnt7m/kSZ+HTc034ZpSLS4SksWLF+Pn54eLiwve3t6ktGhXv4iIiIhYaMcOaN0aHjyAHDnAxwfKlXupJQ3DYPXJ1fTz78edh3dwdHCkX5l+TPhoAmmc09gmdyJmGAZDhgzBy8sLJycn1q1bxwcffGB1LBGJZypsi4gAnD8PNWuav2bIABs3QqVKlkb6xP8TAn4PIHXK1GxpsYWc6XJamkf+6cKFC3zyyScAeHh4ULRoUYsTiYiIiEi8io017/AcN87srV2mjFnUzp37pZa9HHiZHtt7sOPcDgCKZSvGknpLeC+3bYZPJgVTpkxh6tSpACxZsoS6detanEhErKDCtojIwYPmkMi7d+GVV8wdFxYXKWf/MJs5h+cA4N3Qm5I5S1qaR/4pJiaG9u3bExoaSsWKFenfv7/VkUREREQkPt2/b97ducMsPtOjB0yfDi4uL7xkrBHL3MNzGb5nOCGRITg7OTOqwiiGlB+Cs9PL9+lOKpYuXcrQoUMB+PLLL2nfvr3FiUTEKipsi0jytmGDeUEaEQGlSsHWrebtgxbafnY7/Xf2B2BSlUk0KtLI0jzyX9OnT2f//v2kTZuW5cuX4+joaHUkEREREYkvx45B48ZmX+1UqWD+fHjJ4uqpW6fouq0rB68cBKB83vIsqruIIlmL2CBw0rFlyxa6dOkCwJAhQxg4cKDFiUTESipsi0jyZBgwdSoMHmwe160Lq1dDGmv71f1y4xda+LQg1ojl45IfM6T8EEvzyH+dPHmSESNGADBjxgzy589vbSARERERiT/e3uZQyPBwKFDAbD1S8sXvrgyNDGXCtxOY+v1UomOjSeucli+qfkH3Ut1xdNDmib/79ttvadasGbGxsXTs2JFJkyZZHUlELKbCtogkP5GR0KsXLF5sHvfuDTNmgJOTpbGuB1+nzuo6hESGULlAZebWnouDg4OlmeSfIiMjadu2LZGRkdSpU4dOnTpZHUlERERE4kNkJHzyCcydax7XrAkrVkCmTC+85Laz2+i9ozeXAi8B0KBwA2bVmEVet7y2SJyk/PLLL9StW5eIiAjq1avHwoUL9VlJRFTYFpFk5t49aNIEvv4aHB3NXdv9+oHFF0WhkaHUXV2Xq0FXeSPzG2xoukF99BKgCRMmcOzYMTJnzsyiRYt0MS0iIiKSHFy7Zn6GOHTIPB4zBkaPNj9PvIArgVfo59+PjWc2ApDPLR+za86m3hv1bJU4Sblw4QLVq1cnKCiIDz/8kDVr1pAihcpZIqLCtogkJ2fPQp06cO4cpE0La9ZA7dpWpyLWiKXtxrYcvX6UzK6Z2d5qOxldM1odS/7l0KFDeHh4ADB//nxyWNyLXURERETiwb590KwZ3LoFGTKYu7Rf8DNEVEwUs36YxZhvxhAaFUoKxxQMLDuQURVGkcbZ2paICdWNGzdwd3fn5s2blChRgi1btuDq6mp1LBFJIFTYFpHkYe9ec5fF/fvwyivmkMjixa1OBcDw3cPZeGYjzk7ObGqxidcyvWZ1JPmXhw8f0q5dO2JjY2ndujVNmjSxOpKIiIiI2JNhwPTpMGQIxMTAW2+Z/bRfe7Fr9e+vfE/37d05fvM4YA6HnF9nPsWyFbNl6iQlMDCQGjVq8Pvvv1OgQAH8/f3JkCGD1bFEJAFRYVtEkr5Fi6BnT4iOhvffh02bIHt2q1MBsPinxUw+OBmApfWW8kG+DyxOJI8zdOhQzp07R+7cuZk9e7bVcURERETEnoKDoXNnWLfOPG7TBhYsgNSpn3upuw/v8umeT1n400IAMrlmYkq1KXR4u4OGQz5FeHg49erV45dffiFbtmwEBASQM2dOq2OJSAKjwraIJF0xMeYOi2nTzOOWLWHpUkiVytpc/2/PhT302N4DgLEVx9K6RGuLE8nj7N69mzlz5gCwdOlSMmZUmxgRERGRJOvECfNOz7NnIUUKc8h8z57PPZMnJjaGRT8tYsTeEdwLuwdAx7c7MrnaZLKkzmKH4ElHdHQ0LVu25NtvvyV9+vT4+/tTsGBBq2OJSAKkwraIJE3BwdCqFWzbZh6PHw8jR1o+JPJ/fr39K43XNSY6NppWxVsxuuJoqyPJYzx48ICOHTsC0LNnT9zd3S1OJCIiIiJ2s3y5WcQOC4M8ecwd22XLPvcyBy4foI9fH36+8TMAxbIV46taX1HhlQo2Dpz0GIZB9+7d2bRpEy4uLmzevJmSJUtaHUtEEigVtkUk6bl0CerWNXdbpEoFnp7mwJcE4nbobWqvqk1gRCDl85ZnSb0lOCSQgrv8U9++fbl69SoFCxZk8uTJVscREREREXt4+BB694Zly8zjGjXA2xuyPN/O6uvB1xm6eyjex70BcHNxY8JHE+hRugcpHFV+iYtPP/2UJUuW4OjoyOrVq6lUqZLVkUQkAdPfrCKStHz/PTRsCDdvmn20t2yB996zOtUj4dHhNFjbgIsPLvJqxlfZ2HwjqVIkjNYo8k++vr54e3vj6OiIl5cXadJoUr2IiIhIknP2rNl65MQJcHQ07/QcPtx8HEeRMZHM+mEW4/eNJzgyGAcc6FSyEx5VPMiWJpsdwyct06ZNY9KkSQAsWLCAhg0bWpxIRBI6FbZFJOlYuhR69IDISChRArZuhXz5rE71iGEYdNrciYNXDuLm4sb2VtvJmiar1bHkMW7evEm3bt0Ac3Bk2Re4BVVEREREErh16+DjjyEkxNwUs2oVVK78XEvs+n0Xff37cubOGQDey/0ec2rOoXTu0vZInGR5e3szcOBAADw8POjcubPFiUQkMVBhW0QSv+hoGDgQZs0yjxs1MtuPpE1rba5/GbdvHKtPriaFYwp8m/tSOEthqyPJYxiGQdeuXblz5w5vvfUWY8eOtTqSiIiIiNhSRAQMGgT/PyCcihVh9WrImTPOS/zx4A8G7BzAxjMbAciaOitfVP2C9m+3x9Eh7ru9BbZv3/5ors0nn3zCsGHDLE4kIomF/rYVkcTt7l2oXv2vova4cbB+fYIraq88vpJx+8YBML/2fCoXeL6dIBJ/PD092bJlCylTpsTLywtnZ2erI4mIJBoTJ06kdOnSpEuXjmzZstGgQQN+++23f7zGMAzGjh1Lrly5cHV1pVKlSpw6dcqixCKS7PzxB3z44V9F7eHDYffuOBe1gyKCGL57OIXnFGbjmY04OTjRr0w/zvY5S8eSHVXUfk4HDhygadOmxMTE0KZNG7788kvNHxKRONPfuCKSeJ08afbP3rsX0qQBX18YPfq5+uHFh31/7KPTlk4ADC0/lI/f+djiRPIkly5dom/fvgBMmDCBEiVKWJxIRCRx2bdvH7169eLQoUPs2rWL6Oho3N3dCQ0NffSayZMnM23aNObMmcPhw4fJkSMH1apVIzg42MLkIpIsbNsG77wDhw9DxozmsYcHpHj2zewxsTEsOrqIQrMLMenAJCJiIqhcoDLHuh9jRo0ZZEiVwf75k5iTJ09Sp04dwsLCqFmzJkuXLsUxgX2WE5GEzcEwDMPqEPYUFBSEm5sbgYGBpE+f3uo4ImIrmzZB27ZmP7wCBWDzZihe3OpU//Hr7V8pt7QcD8If0LhIY9Y1XaddHAlUbGwsVapU4ZtvvqFcuXJ8++23ODk5WR1LRJ5C13kJ3+3bt8mWLRv79u2jQoUKGIZBrly56N+/P0OHDgUgIiKC7Nmz88UXXzyab/As+rMXkecSHQ0jR8IXX5jHZcrA2rXwyitxevueC3sYEDCA4zePA1AoUyG+dP+Suq/X1e7iF/THH39Qvnx5/vzzT8qWLcuuXbs0rF1EgOe7zlN1RUQSl9hYc1J5w4ZmUbtyZXPHRQIsat8MuUmtVbV4EP6AsnnK4t3QW0XtBGz27Nl88803pE6dGk9PTxW1RURsIDAwEIBMmTIBcPHiRW7cuIG7u/uj17i4uFCxYkUOHjz4xHUiIiIICgr6x5eISJxcvmz20P5fUbtvX/j22zgVtc/ePUu91fWo6l2V4zePkyFVBqZXn87Jniep90Y9FbVf0K1bt3B3d+fPP//kzTffZNu2bSpqi8gL0fBIEUk8QkKgQwfw8TGP+/SBqVMhZUpLYz1OaGQodVbX4Y8Hf1AwU0G2tNyCa0pXq2PJE/z666+PhtRMnTqVggULWpxIRCTxMwyDAQMG8MEHH1CsWDEAbty4AUD27Nn/8drs2bNz6dKlJ641ceJExo0bZ7+wIpI0bdoEHTvCgweQPj0sWQJNmjzzbffC7jF+33i+OvwV0bHRODk40bN0T8ZUHEPm1JntHjspCw4OplatWpw7d458+fKxc+fORz/8FBF5Xipsi0jicPEiNGgAx4+bhex58+DjhNmrOiY2hpY+LTny5xEyu2bGr7UfWVJnsTqWPEFUVBTt2rUjPDyc6tWrx/k2eBERebrevXtz/Phx9u/f/5/n/r3L0TCMp+58HD58OAMGDHh0HBQURN68eW0XVkSSlvBwGDz4rwGR770Ha9aYLQyfIiominlH5jH2m7HcD78PQO1CtfnS/UsKZyls79RJXkREBA0aNODo0aNkyZKFgIAAcufObXUsEUnEVNgWkYTP3x9atYL79yF7dnNIZLlyVqd6LMMw6O/fn61nt+Li5MKWllsomEm7fxOyiRMncuTIETJkyMCSJUt0S6mIiA306dOHLVu28O2335InT55H38+RIwdg7tzOmTPno+/funXrP7u4/87FxQUXFxf7BRaRpOPsWWjeHI4dM48HDYLPPwdn5ye+xTAM1p9ez4i9Izh/7zwAxbIVY5r7NKq9Vi0eQid9MTExtGnThr1795I2bVr8/Px44403rI4lIomcmr2KSMIVGwuffQa1aplF7ffegyNHEmxRG2D6oenMOTwHBxxY0WgF5fIm3KwCR44cYcKECQDMnTtXO0ZERF6SYRj07t0bX19f9u7dS4F/7Y4sUKAAOXLkYNeuXY++FxkZyb59+yiXgP99F5FEYsUKeOcds6idJQvs2AFTpjy1qP3NH99QZnEZmm9ozvl758meJjvza8/n524/q6htI4Zh0KtXLzZs2ICzszObNm2iVKlSVscSkSRAO7ZFJGEKDIR27WDLFvO4WzeYORMS8G4tn9M+DAoYBMCUalNoUvTZ/fvEOmFhYbRr147o6GiaNWtGixYtrI4kIpLo9erVi1WrVrF582bSpUv3qKe2m5sbrq6uODg40L9/fzw8PChUqBCFChXCw8OD1KlT06pVK4vTi0iiFRICvXuDp6d5/NFHZpE7V64nvuXEzRMM3zOc7ee2A5AmZRoGlxvMwHIDSeucNj5SJxtjx45lwYIFODg4sGLFCqpUqWJ1JBFJIlTYFpGE5+RJaNQIzp0zC9lffZVg+2n/z/dXvqfNxjYYGPQq3YsBZQc8+01iqZEjR/Lrr7+SI0cO5s6dqxYkIiI2MG/ePAAqVar0j+8vW7aMDh06ADBkyBDCwsLo2bMn9+/fp0yZMgQEBJAuXbp4TisiScIvv5itR377DRwdYexY+PRTcHJ67MuvBF5h9Dej8TzmiYFBCscUdH2nK6MrjiZ72ie3RJIXM3v2bMaPHw/AV199RdOmTS1OJCJJiYNhGIbVIewpKCgINzc3AgMDSZ8+vdVxRORZ1q2DTp0gNBTy5QMfH0jgt6mdv3eeskvKcufhHeq+XpeNzTfi5Pj4C2lJGL755hsqV66MYRhs27aN2rVrWx1JRF6ArvOSL/3ZiwiGYQ6UHzAAIiIgd25YtQoqVHjsyx+EP2DidxOZ9eMswqPDAWhStAkelT0olLlQfCZPNlavXv3obpxx48YxevRoixOJSGLwPNd52rEtIglDdDQMGwZTp5rHVaqYk8uzZLE21zPceXiHmitrcufhHUrlKsXqxqtV1E7ggoKC6NChA4Zh0KVLFxW1RURERBKbe/ega1dzEwxAnTqwbNljPzuER4cz9/BcPvv2M+6H3wegwisVmFx1MmXylInP1MnKzp07adeuHQC9e/dm1KhRFicSkaRIhW0Rsd6tW+btg998Yx4PHWoOjUyRsP+KCosKo/6a+py/d578GfKzreU20jinsTqWPMOAAQO4dOkSBQoUYOr/fpAiIiIiIonD119D27Zw7RqkTAmTJ0O/fvCvtnJRMVEsP7ac8d+O52rQVQDezPomk6pOonah2mpDZ0c//PADjRs3Jjo6mhYtWjBz5kz97y0idpGwq0YikvQdOgRNm8LVq5A2LSxfDo0bW53qmWKNWNpvas/BKwfJkCoDO1rtUE++RGDr1q0sWbIEBwcHPD091c9VREREJLGIjITRo81CtmHA66+brUfeffcfL4uJjWHNyTWM+WYMv9//HYA86fMwrtI42r/VXndX2tmvv/5KrVq1CA0Nxd3dHU9PTxwdHa2OJSJJlArbImINw4CZM2HwYLMNyRtvwMaNUKSI1cniZOiuoaw/vR5nJ2c2Nd9EkayJI3dydufOHbp06QLAwIED+fDDDy1OJCIiIiJxcvYstGoFR4+ax126wPTpkOavuyUNw2DTmU2M+noUp26fAiBbmmx8+sGndCvVjVQpUlmRPFm5cuUK7u7u3Lt3j/feew8fHx+cnZ2tjiUiSZgK2yIS/wIDzQGRvr7mcdOmsHgxJJLhT7N+mMWX338JwLL6y6iYv6LFieRZDMOge/fu3Lx5kzfffJMJEyZYHUlEREREnsUwYMkSs9XIw4eQKZP5uaFhw7+9xGDXhV2M3DuSw38eBiBDqgwMLjeYvmX6ktY5rVXpk5W7d+/i7u7O1atXKVy4MNu3bydtWv1vLyL2pcK2iMSvn3+GJk3gwgWzJ960adCr13964iVU60+tp79/fwA8KnvQqngrawNJnKxatQofHx9SpEiBt7c3qVJpx46IiIhIgnbvnrkz+3+bYSpXBi8vyJ370Uv2X97PiL0j+PbStwCkSZmG/u/3Z1C5QWRIlcGC0MlTSEgItWvX5syZM+TJk4edO3eS5TGDPEVEbE2FbRGJH4YBCxeauy0iIuCVV2D9eihd2upkcbbvj3202dgGA4NepXsx7INhVkeSOLh69Sq9evUCYMyYMZQsWdLiRCIiIiLyVHv3Qrt2fw2I/PxzGDgQ/r9X89E/jzLq61H4nfcDwMXJhZ6lezLsg2FkS5PNyuTJTmRkJI0bN+aHH34gU6ZM7Ny5k3z58lkdS0SSCRW2RcT+QkKge3dYudI8rlMHPD3NWwkTiRM3T1B/TX0iYyJpVKQRM2tosndiYBgGnTp1IjAwkPfee49hw/TDCBEREZEEKzISRo2CKVMeOyDyyJ9HGLdvHNvObgPAycGJj0t+zKiKo8iTPo+VyZOl2NhY2rdvT0BAAKlTp2b79u0ULVrU6lgikoyosC0i9nX6tNl65NdfwckJJk78x26LxOBK4BVqrqxJYEQgH+T7gBUNV2iaeiIxb948du3ahaurK15eXqRIoX/2RERERBKkM2egTZvHDog8fO0w4/aNY/u57QA4OjjSqngrxlQcQ8FMBS0MnXwZhkG/fv1Ys2YNKVKkwMfHh/fff9/qWCKSzOgTvojYj7e3uVP74UPIlQvWrIEPP7Q61XO5H3afGitrcC34GkWzFmVLiy24pnS1OpbEwblz5xg0aBAAX3zxBW+88YbFiURERETkP2JjYfZsGDYMwsP/MSDy8LXDjNv8z4J26+KtGVlhJK9nft3i4Mnb559/zpw5cwDw8vKiRo0aFicSkeRIhW0Rsb2QEOjd22w3AlC1qtmGJFvi6ncXHh1O/TX1OX37NLnT5ca/tT8ZXTNaHUviIDo6mvbt2xMWFkaVKlUe9dgWERERkQTkyhXo2BH27DGP3d1h6VJ+5BrjVtVmx7kdgFnQblOiDSM/HEmhzIUsDCwA8+fPZ9SoUQDMnDmTli1bWpxIRJIrFbZFxLaOHYPmzeHsWbPdyOjRMHKk2YYkEYmJjaGNbxu+u/wdbi5u+LX2I69bXqtjSRxNmTKF77//nvTp07Ns2TIcE1HrGxEREZEkzzBgxQro0wcCAyF1avjyS36oU5Jx33R5NBTS0cGRtiXaMuLDESpoJxAbNmygZ8+eAIwcOZK+fftanEhEkjNLP+l/++231K1bl1y5cuHg4MCmTZv+8bxhGIwdO5ZcuXLh6upKpUqVOHXqlDVhReTpDANmzYIyZcyidu7c5jTzMWMSXVHbMAz6+/fH51cfnJ2c2dRiE8WzF7c6lsTRsWPHGDNmDACzZ88mb179QEJEREQkwbhzB5o2hXbtzKJ2mTIc2LWEmum38P7Ssvid98PJwYn2b7XnTK8zLG+wXEXtBGLPnj20bt0awzDo1q0b48ePtzqSiCRzlha2Q0NDeeuttx71Zfq3yZMnM23aNObMmcPhw4fJkSMH1apVIzg4OJ6TishT3bkD9etDv37mJPO6deGXX6BiRauTvZAvDnzBnMNzcMAB74beVMpfyepIEkcRERG0bduWqKgoGjZsSNu2ba2OJCIiIiL/s307FCsGPj4YKZwIGN+eit2c+WBXS/zP++Pk4ESHtztwprcK2gnN0aNHadCgAZGRkTRu3JivvvoKBwcHq2OJSDJnaSuSmjVrUrNmzcc+ZxgGM2bMYMSIETRq1AgAT09PsmfPzqpVq+jWrVt8RhWRJ9m3D1q3hmvXwNkZvvzS7K+dSC9yvH7xYvie4QBMrz6dZm82sziRPI8xY8Zw8uRJsmbNyoIFC3SxLSIiIpIQhITAgAGwaBGxDrCpWh48aqXjaKAnXIaUjilp/1Z7hn4wlIKZClqdVv7l7Nmz1KxZk5CQECpXrszKlStxSmR35YpI0pRge2xfvHiRGzdu4O7u/uh7Li4uVKxYkYMHDz6xsB0REUFERMSj46CgILtnFUmWoqNhwgT47DNzkvkbb8CaNfD221Yne2E7z+/k4y0fAzC43GD6vd/P4kTyPPbv/7/27js6iqoP4/g3PSShtxB6rwKCEA0IwisJECEoIL2ICIhIUxQRRUQBUYqCgIh0aUrvICJFkN5BepFeQ0gvO+8fI8EIKi3M7ub5eOYkc2eWPDmXyN1f7ty7gSFDhgDw7bffkj17dosTiYiIiAgbNkCbNiScPM7McjCofhYOup2BG5DOPR0dK3bkraC3yJMhj9VJ5S7Onj1LcHAwly9fpkKFCsybNw8vLy+rY4mIAHZc2L5w4QIAOXPmTNGeM2dOTp069Y+vGzRoEP3790/VbCJp3h9/mLO01683z9u2hZEjwc/P0lgPY/u57TSc3ZBEWyItnmjB4OcHWx1J7kNkZCRt2rTBMAzatm1LWFiY1ZFERERE0raYGPjwQ2K/HMrEcgZDXnTjZPok4BoZvTLSpXIXugV2I7uvJiPYq2vXrhESEsKpU6coWrQoy5YtI0OGDFbHEhFJZreF7Vv+/hi5YRj/+mj5e++9R8+ePZPPIyIitHGYyKM0ezZ07Ajh4ZA+PYwdC82bW53qoRy+epg639chKiGK/xX8HxPCJuDqYukWBHKfevXqxfHjx8mXLx8jRoywOo6IiIhI2rZpEzdfa8PYjEcY1hUupAdIIrtPdno+05PXn3qdjN4ZrU4p/yI6Opp69eqxf/9+cuXKxcqVK8mRI4fVsUREUrDbwra/vz9gztzOlStXcvulS5fumMX9V15eXnosRiQ13LgBb74JU6ea55UqwYwZULiwtbke0tmIs9SaWovL0ZepmKsi85rMw9PN0+pYch+WLVvG2LFjAZg4cSIZM+pNkoiIiIglYmK48GFPRu4cy5gX4Ho6szlvhrz0CurFqxVexcfDx9qM8p8SEhJo3LgxGzduJFOmTKxYsYICBQpYHUtE5A52OyWxYMGC+Pv7s2rVquS2+Ph41q5dS1BQkIXJRNKg9euhXDmzqO3qCh98AL/+6vBF7Wsx1wiZFsLpG6cplrUYS1ssJb1XeqtjyX24du0ar75qroverVs3atasaXEiERERkbTp91UzeK2DP/m9xzLwb0O76AAARyhJREFUWbOoXSxTESbUn8DRrkd5M/BNFbUdgM1mo127dixduhRvb28WL17ME088YXUsEZG7snTGdmRkJEePHk0+P3HiBLt27SJLlizky5eP7t27M3DgQIoWLUrRokUZOHAgPj4+NHfwZQ9EHEZ8PPTvD4MHmxtEFiwI06aBE/xyKSo+ihemv8D+y/sJSB/AypYryeGrR+scTZcuXTh//jzFixdn0KBBVscRERERSVMMw+DXI6v5fEonFnocgyJm+9O+xekVOpCw4mG4ubpZG1LumWEYvP3220ybNg03Nzd+/PFHqlSpYnUsEZF/ZGlhe9u2bdSoUSP5/Nba2G3atGHSpEm88847xMTE0LlzZ65fv05gYCArV64kfXrNqBRJdb//Di1bwvbt5vkrr8CIEeAEm4UkJCXQ+IfGbDqziczemVnZciX5M+W3Opbcp1mzZjFjxgzc3NyYMmUK6dKlszqSiIiISJqQZEtiwaEFfL7iQ367sR88zPb6UXnp1eYbqpSq/a97Y4l9GjJkCMOHDwdgwoQJhIaGWpxIROTfuRiGYVgdIjVFRESQMWNGbty4od17Re6FYcA330DPnuZO5pkzw7hx0KiR1ckeCZtho9W8VkzfO5107ulY3Xo1z+R9xupYcp/Onz9PmTJluHbtGh9++CH9+/e3OpKIWEDjvLRLfS9ijZiEGCbvnszQX7/gaPgxADwTofURH95qNJQSjTpZnFAe1HfffUf79u0BGDp0aPLEQxGRx+1+xnl2u3mkiFjgwgV47TVYvNg8f/55mDQJcue2NNajYhgGPZb3YPre6bi7ujPn5TkqajsgwzBo3749165do0KFCvTt29fqSCIiIiJO7VLUJcZsHcOoraO4En0FgMwx0HkrdCn4Mv5jxpoTYsQhzZ8/nw4dOgDw7rvvqqgtIg5DhW0RMWdpz5wJXbrAtWvg5WWuq921q7lZpJMYuH4gX235CoBJYZOoU7SOxYnkQYwfP56lS5fi5eXFlClT8PDwsDqSiIiIiFPac3EPX/72Jd/v/Z64pDgA8odDz03Q7rw/fmO+g7p1rQ0pD2Xt2rU0bdo0edNI7VsjIo5EhW2RtO7yZXj9dZgzxzx/8kmYMgXKlLE21yP2zbZv6LvGnNn7Ze0vaVG2hcWJ5EEcP36cHj16ADBw4EBKly5tcSIRERER55JkS2LJkSWM+G0Ea06uSW6vdMmDnusSaHQA3Nt3gOWfQaZM1gWVh7Zr1y7q169PXFwcYWFhfPPNN1obXUQcigrbImnZ3LnQqZNZ3HZ3h759oU8fcLIZsD8e+JHXl7wOQN9n+9I1sKvFieRBJCUl0bZtW6KioqhWrRrdu3e3OpKIiIiI07gZd5OJuyby1eavOHbdXD/bzcWNhtf96f7jWZ4+k4BL0aLw87dQvbrFaeVhHTt2jNq1axMREUG1atWYMWMG7u4qEYmIY9H/tUTSomvXzGVGvv/ePC9TBiZPhgoVrM2VCn4+8TMt5rbAwKBjxY58XONjqyPJAxo+fDjr16/Hz8+PSZMm4epEy+SIiIiIWOX49eOM3DySCbsmEBEXAUAm70x0cA/kjREbyXfmrDkJps878MEH4O1tcWJ5WOfPnyc4OJiLFy9Srlw5Fi5cSLp06ayOJSJy31TYFklrliwxN4g8f95cP/vdd6FfP3NdbSez+cxmwmaGEZ8UT6NSjfi67td6tM5B7du3j/fffx+AESNGULBgQYsTiYiIiDguwzBYd2odIzaPYMHvCzAwACietTjdCjal9dDV+K5ZYd5cqRKMHw9ly1qYWB6V8PBw6tSpw/HjxylUqBDLly8nY8aMVscSEXkgKmyLpBU3bkCPHjBxonlevLg5Szsw0NpcqWTPxT3U+b4OkfGRPF/oeaa9OA03VzerY8kDiI+Pp3Xr1sTHxxMaGkq7du2sjiQiIiLikKLio/h+7/d8vfVr9lzck9weUjiEbk+9QcjcPbg2HABxceDjA598Yj7p6aZxtDOIiYkhLCyM3bt3kzNnTlauXIm/v7/VsUREHpgK2yJpwaJF5gaRZ8+Ciwv07AkDBoCTPm525OoRgqcGcz32Os/keYb5Tebj5e58M9LTigEDBrBz506yZs3K+PHjNeteRERE5D4dunKI0VtHM2n3pOTlRtK5p6N1udZ0DexKqaM3oEkn2PNnsTs4GMaOBT0l5zQSExNp1qwZ69atI0OGDCxfvpzChQtbHUtE5KGosC3izC5fhm7dYMYM87xIEXPGdtWq1uZKRadvnOb5qc9zMeoi5f3Ls7TFUnw9fa2OJQ9o8+bNDBw4EICxY8dqRomIiIjIPUq0JbL48GK+3vo1Px3/Kbm9cObCdK7Umbbl25IlBujdG7791ryYNSuMGAEtWpgTYsQpGIZBx44dWbBgAV5eXixcuJDy5ctbHUtE5KGpsC3ijAwDpk83i9pXr5prab/9Nnz0kdPO0ga4GHmRWlNrcfrGaYpnLc6KlivI5J3J6ljygKKjo2ndujU2m40WLVrQqFEjqyOJiIiI2L1LUZcYv2M8Y7eN5Y+IPwBwwYXQYqG8UekNggsH44oLTJlivke4csV8Ybt28NlnkC2bheklNbz33ntMmDABV1dXZs6cSfXq1a2OJCLySKiwLeJs/vgDOnWCpUvN87Jl4bvv4KmnrM2Vyq7HXCd4WjCHrx4mf8b8rGq1ihy+OayOJQ+hd+/eHD58mNy5czNy5Eir44iIiIjYLcMw2HRmE19v/Zof9v9Agi0BgKzpstK+Qns6PdWJApkKmDcfOACdO8PateZ56dLmsiNO/FRnWjZ06FA+++wzAMaNG0eDBg2sDSQi8gipsC3iLGw2c0D67rsQGQmenvDhh/DOO+DhYXW6VBUZH0nd6XXZc3EP/n7+/NT6J/JmzGt1LHkIq1evTi5mT5gwgcyZM1ucSERERMT+RMRFMGPvDL7Z/g07L+xMbq+cuzJvVHqDl0u/jLe7t9kYHW1uBvn555CYaG4O2a+fucG8k79fSKumTJnC22+/DcDgwYN59dVXLU4kIvJoqbAt4gwOHYL27WHDBvM8KMicpV2ihLW5HoPYxFjCZobx25nfyJIuC6taraJIliJWx5KHEB4eTtu2bQHo3LkzwcHB1gYSERERsSOGYbDt3DbGbR/HjH0ziEqIAsDb3ZtmZZrRuVJnngr429OaS5ZAly5w8qR5Xr8+fPUV5M//eMPLY7N48WLatWsHQM+ePXnnnXcsTiQi8uipsC3iyGJjzXXwBg6E+Hjw9YXBg81HC11drU6X6hKSEnj5h5f5+cTP+Hn6sbzFcsrkKGN1LHlI3bp148yZMxQpUoQhQ4ZYHUdERETELtyIvcH0vdMZt2Mcuy7sSm4vka0EHSp0oHW51mT1yZryRadPQ/fuMG+eeZ43L4wcCWFhjy23PH4bNmygcePGJCUl0apVKz7//HNctBmoiDghFbZFHNXq1fD663DkiHleu7a5FEkamXWRZEui7YK2LDq8CG93bxY1W0Sl3JWsjiUPad68eUyZMgVXV1cmT56Mr6+v1ZFERERELGMYBlvObmHc9nHM3D+T6IRoALzcvGhcujEdKnSgar6qdxYtY2PNJUcGDYKYGHB3N5cc+fBD8POz4DuRx2Xv3r3Uq1eP2NhYQkND+e6773BNA5OeRCRtUmFbxNFcvAhvvQXff2+e+/vDl19C48aQRn4LbxgGnZd0Zvre6bi7ujPn5Tk8V+A5q2PJQ7p06RIdO3YE4N133yUoKMjiRCIiIiLWCI8NZ9qeaYzbPo69l/Ymt5fKXooOFTrQqlwrsqTLcucLDQMWLjSL2CdOmG3VqsGoUfDEE48pvVjl5MmThISEEB4eTlBQELNnz8ZD66eLiBNTYVvEUdhs8O230Ls3hIebRew33jA3gMmY0ep0j41hGHRd1pVxO8bh6uLKtBenUbdoXatjyUMyDIMOHTpw+fJlypYtS79+/ayOJCIiIvJYGYbB+tPrmbBzArP3zyYmMQYw185+ufTLdKjQgaC8Qf+8pMShQ9CtG6xYYZ7nzg1ffAFNmqSZCTBp2aVLl6hVqxbnz5+nTJkyLF68GB8fH6tjiYikKhW2RRzB7t3QqRP89pt5XqECfPMNPPXUv7/OyRiGwdsr32bU1lG44MKE+hNoUqaJ1bHkEZg8eTILFizAw8ODqVOn4uXlZXUkERERkcfijxt/MHn3ZCbtmsSx68eS28vkKEOHCh1oWbYlmdNl/uc/4OZNGDAARoyAhATw9DSf8OzTR8uOpBERERHUqVOHo0ePkj9/fpYvX07mzP/yd0ZExEmosC1iz27ehP79zUFqUpI5MP3kE3Omtnva+vE1DIM+q/sw7LdhAIyrN4425dtYnEoehVOnTtG1a1cABgwYQNmyZS1OJCIiIpK6YhNjmf/7fCbumsiqY6swMADw8/SjSekmtHuyHc/keebfN/wzDHN5wnfegfPnzbbQUPO9Q5Eiqf9NiF2IjY2lQYMG7Nixg+zZs7Ny5Upy585tdSwRkccibVXGRByFYcD06dCr1+1BasOG5lraaXSQ0n9tfwb/OhiAUXVG0b5Ce4sTyaNgs9l45ZVXuHnzJkFBQbz99ttWRxIRERFJFYZhsP38dibunMj0fdMJjw1PvlY9f3XaPdmOhiUb4ut5D5tnb99uLjvy66/meZEiZkE7NDRVsot9SkpKomXLlqxZswY/Pz+WLVtGsWLFrI4lIvLYqLAtYm927YI334QNG8zzwoXNgnYaHqQOWj+I/mv7AzAseBhvVH7D4kTyqIwcOZI1a9bg4+PD5MmTcXNzszqSiIiIyCN1Oeoy0/ZMY+KuiSk2gsybIS9ty7elTbk2FM5S+N7+sLNn4f33YcoUczKMjw/07Qs9e4KWcktTDMOgc+fOzJkzB09PTxYsWEDFihWtjiUi8lipsC1iL65dgw8+gLFjzY0ifXzMQWvPnuDtbXU6ywzdOJQ+P/cBYPD/BtPjmR4WJ5JH5ffff6d3794ADB06lCJ6ZFZEREScRFxiHEuOLGHqnqksPryYRFsiAF5uXrxU8iVeKf8KNQvWxM31Hn+pHxVlbgQ5ZAhER5ttzZvDZ59Bnjyp9F2IPfvwww8ZN24cLi4ufP/999SsWdPqSCIij50K2yJWS0qC8ePNIvbVq2bbyy+bA9e8ea3NZrGRm0fy9ipzaYqPn/uYd6u+a3EieVQSEhJo1aoVsbGxhISE0LFjR6sjiYiIiDwUwzD49Y9fmbp7KrMPzE6x1EilgEq8Uv4VmpZp+u8bQf6dzQbTppkbQZ49a7YFBcHw4VC58qP9BsRhfPXVV3zyyScAjB49mkaNGlmcSETEGipsi1hp0ybo0gV27DDPy5SBr76CGjWszWUHvtn2DV2XmxsKvv/s+3xQ/QOLE8mjNGjQILZt20amTJn47rvv/n1jJBERERE7dvjqYabunsr3e7/nRPiJ5Pbc6XPT4okWtCrXijI5ytz/H7xunfn05vbt5nmBAuaM7UaNQGOnNGv69Ol069YNgI8//phOnTpZnEhExDquVgcQSZNOn4YWLczZFjt2QMaM5jraO3eqqA1M3DmRTkvMAVqvoF4MqDHA4kTyKG3fvp0BA8w+HT16tHZtFxFxMuvWraNevXoEBATg4uLC/PnzU1w3DIOPPvqIgIAA0qVLx3PPPcf+/futCSvygC5HXWbk5pEEjg+k+KjifLL+E06En8DP04+25duyuvVqTnU/xWe1Prv/ovaxY+bG8dWrm0Xt9Olh8GA4eBAaN1ZROw1bvnw5bdq0AeDNN9+kb9++FicSEbGWZmyLPE43b5qD0mHDIDbWHJS+8goMGgQ5clidzi5M3DmRVxe+CkC3wG589vxnms3rRGJiYmjVqhWJiYk0btyYpk2bWh1JREQesaioKMqVK8crr7xCw4YN77g+ZMgQhg0bxqRJkyhWrBiffPIJtWrV4tChQ6RPn96CxCL3JiYhhoWHFjJ1z1SWH11OkpEEgJuLGyFFQmj5REvCSoTh4+HzYF/gyhX49FMYPRri48HVFTp0gP799V5B+O2332jYsCGJiYk0a9aMESNG6H2SiKR5KmyLPA5JSTBxorlj+cWLZttzz5kF7ieftDSaPflux3e8tug1DAw6P9WZ4SHDNVhzMn379uXgwYP4+/szevRo9a+IiBOqU6cOderUues1wzAYMWIE77//Pi+99BIAkydPJmfOnEyfPl17LojdSUhK4KfjPzFz/0zmHZzHzfibydeeCniKVmVb0aR0E3L65XzwLxIVBSNGmMuMRESYbcHBMHSouVShpHkHDhwgNDSU6OhogoODmTRpEq6uegBfRESFbZHU9tNP5tp4e/ea50WLwuefQ/36eozwL77d/i0dFncA4M3Kb/Jl7S9V9HQya9euZfjw4QCMHz+ebNmyWZxIREQetxMnTnDhwgWCg4OT27y8vKhevTobN278x8J2XFwccXFxyecRt4p/IqkgyZbE+tPrmbF3BnMOzuFqzNXka/kz5qdl2Za0LNuSEtlKPNwXSkiACRPgo4/gwgWzrXx58wnP4GC9VxAATp8+TUhICNeuXSMwMJA5c+bg6elpdSwREbugwrZIajl4EHr1giVLzPPMmaFfP3j9ddBAJIVvtn2TvKZ2t8BumqnthCIiImjbti2GYdC+fXtCQ0OtjiQiIha48GfxLmfOlLNbc+bMyalTp/7xdYMGDaJ///6pmk3SNsMw2Hx2MzP3zWT2/tmcjzyffC2Hbw4al2pM0zJNCcobhKvLQ86UNQyYOxf69IHDh822ggXNZUiaNDGXIBEBrly5QkhICGfOnKFkyZIsWbIEPz8/q2OJiNgNFbZFHrVz5+Djj2H8eHMJEnd3eOMN+PBDyJLF6nR2Z8zWMXRe2hmA7oHdGRYyTEVtJ9SzZ09OnjxJgQIFGDZsmNVxRETEYn//t94wjH/99/+9996jZ8+eyecRERHkzZs31fJJ2mAYBnsu7mHGvhnM2j+Lk+Enk69l8s5Ew5INaVqmKc8VeA5310f01nntWnjnHdiyxTzPls18n9Cxoya/SAqRkZGEhoby+++/kydPHlasWEHWrFmtjiUiYldU2BZ5VMLD4bPP4MsvISbGbKtf31x2pFgxS6PZq6+3fE2XZV0A6Pl0T74I/kJFbSe0aNEivvvuO1xcXJg8ebI2BhMRScP8/f0Bc+Z2rly5ktsvXbp0xyzuv/Ly8sLLyyvV80nacPDyQWbvn83M/TP5/crvye2+Hr40KNGApmWaElw4GE+3R1ho3rEDPvgAli7984v5wltvmUeGDI/u64hTiI+P56WXXmLLli1kyZKFlStX6pd5IiJ3ocK2yMOKiYFRo2DQILh+3WwLCjKL3FWrWpvNjo3cPJKuy7sC8PYzbzOk1hAVtZ3QlStXeO211wBz1na1atUsTiQiIlYqWLAg/v7+rFq1iif/3EA7Pj6etWvX8tlnn1mcTpyVYRjsvbSXOQfm8OPBHzlw+UDyNS83L14o9gJNyzSlbtG6+Hj4PNovvm+fuRzh3Lnmubs7dOhgFrn//EWPyF/ZbDZat27NqlWr8PX1ZenSpZQsWdLqWCIidkmFbZEHlZgIU6aYA9UzZ8y20qVh4ECoV0+bvfyLL3/7ku4rugPwTtA7DH5+sIraTsgwDDp16sTFixcpVaoUn3zyidWRRETkMYiMjOTo0aPJ5ydOnGDXrl1kyZKFfPny0b17dwYOHEjRokUpWrQoAwcOxMfHh+bNm1uYWpyNYRjsOL+DOQfn8OOBHzly7UjyNQ9XD2oVrkXT0k0JKxFGBq9UmDF96JC5KeSsWeaa2i4u0Ly5+d6haNFH//XEKRiGQbdu3Zg1axYeHh7MnTuXwMBAq2OJiNgtFbZF7pdhwIIF5mYvBw+abXnzmutqt2oFbm7W5rNzX2z8gl6regHQu0pvBv5voIraTmr69OnMmTMHd3d3pk6dire3t9WRRETkMdi2bRs1atRIPr+1NnabNm2YNGkS77zzDjExMXTu3Jnr168TGBjIypUrtVSVPDSbYWPL2S3JM7P/uma2l5sXtYvUplGpRrxQ7AUyeWdKnRDHj5vvC6ZOBZvNbGvUyCxyly6dOl9TnMaAAQMYNWpU8hJ+wcHBVkcSEbFrLoZhGFaHSE0RERFkzJiRGzdukEFrl8nDMAxzTbx+/WD7drMtSxZ4/33o3BlUtPtXhmHQf21/+q/tD0Cfqn34pOYnKmo7qTNnzvDEE08QHh7Oxx9/zAcffGB1JBFxQhrnpV3qe7nFZtjY+MdGfjzwI3MOzuFMxJnkaz4ePtQtWpdGJRtRt2hd0nul4i9PTp+GTz6BiRPNJzvB3G+nf38oXz71vq44jTFjxtC5c2cAvvrqK958802LE4mIWON+xnmasS3yXwwDVq40dyu/tXu5ry907w69ekHGjJbGcwSGYfDOqnf4YtMXAHxa81P6PNvH4lSSWgzDoF27doSHh1O5cmXee+89qyOJiIiIE4lLjOPnEz+z4NACFhxawIXIC8nX/Dz9qFesHo1KNaJ2kdqPfs3svzt9GoYMgW+/hfh4sy0kxJy1Xbly6n5tcRo//PADb7zxBgAffPCBitoiIvdIhW2Rf2IYsHq1OUN740azzccHunSBt9+G7NmtzecgbIaNLku7MGbbGACGhwyn+9PdrQ0lqWrMmDGsWrUKb29vpkyZgru7/qkRERGRh3M95jpLjyxl/qH5LD+6nMj4yORrGb0yElYijEYlG1GrcC283R/Dk5THj5ubx0+eDAkJZluNGjBgAFSpkvpfX5zGTz/9RIsWLZL3p+nfv7/VkUREHIaqDSJ3s3atOUN73Trz3NvbXG7knXcgZ05rszmQRFsiry58lSm7p+CCC+PqjaN9hfZWx5JUdOTIEXr1MtdQHzJkCMWLF7c4kYiIiDiqU+Gnkmdlrzu1jkRbYvK1gPQB1C9Wn7ASYdQsWBNPN8/HE+rQIXOz+O+/h6Qks61GDfjgA/OjyH3Ytm0bL774IgkJCTRq1Ch5fW0REbk3KmyL3GIY8Msv5iyLNWvMNi8v6NgReveGXLksjedo4pPiaTG3BT8e+BE3FzemvDiF5k80tzqWpKLExETatGlDdHQ0NWvWTH6cUkREROReGIbB7ou7mf/7fBYcWsCuC7tSXC+dvTQNSjQgrHgYFQMq4uri+vjC7dtnrqE9e7b5vgGgdm3o21cztOWBHDp0iDp16hAZGUnNmjWZNm0abm5uVscSEXEoKmyLGAYsWQKffgq//Wa2eXhAhw7w3nuQO7e1+RxQTEIMjX5oxNIjS/F082RWo1k0KNHA6liSyj7//HM2bdpEhgwZmDhxIq6uj/HNpoiIiDikuMQ41p5ay+LDi1l4aCGnbpxKvubq4kqVvFUIKx5GWIkwimQp8vgD7thhFrTnzbvdFhZmbiBfqdLjzyNO4ezZswQHB3PlyhUqVqzI/Pnz8fLysjqWiIjDUWFb0q6kJPjxR/NRwj17zDYvL2jf3lxyJF8+a/M5qJtxN2kwqwE/n/gZb3dv5jeZT0iREKtjSSrbvXs3/fr1A2DkyJHk08+PiIiI/IOzEWdZemQpS44s4afjPxGVEJV8LZ17OoILB9OgRANCi4aS3deCfW0MA37+2dwUcuVKs83FBRo1Mgva5co9/kziNK5du0ZISAinT5+mWLFiLFu2jPTp01sdS0TEIamwLWlPQgJMmwaDB8Phw2abn5+5hnaPHuDvb20+B3Y56jJ1p9dl27lt+Hn6sbjZYqoXqG51LEllcXFxtGrVioSEBBo0aECrVq2sjiQiIiJ2JMmWxJazW1hyZAlLjiy5Y4mRXH65qFu0LvWK1aNW4Vr4ePhYEzQxEebONQva27ebba6u0KwZ9OkDpUpZk0ucRlRUFC+88AL79+8nICCAFStWkD27Bb+8ERFxEipsS9oRFQUTJ8Lnn8Pp02ZblizQrRt06WJ+Lg/sVPgpQqaFcOjqIbKmy8qyFsuolFuPZ6YF/fr1Y+/evWTPnp1vvvlGG96IiIgI12Ous+LYCpYcWcLyo8u5En0l+ZoLLgTmCSS0aCihRUMp71/e2vFDdDRMmgRDh8Lx42ZbunTmk5w9ekDBgtZlE6eRkJBA48aN2bRpE5kyZWLFihUUKFDA6lgiIg5NhW1xfhcuwKhRMGYMXLtmtvn7w1tvmRtD6rGvh7b/0n5CpoVw9uZZ8mXMx4qWKyiRrYTVseQx+PXXX/n8888B+Pbbb8mRI4fFiURERMQKtzZ+XHHULGZv/GMjSUZS8vWMXhkJKRJCaNFQ6hSpY80SI3939Sp8/TWMHAlX/iy8Z8sGb75pPs2ZLZu1+cRp2Gw22rVrx7Jly0iXLh2LFy+mTJkyVscSEXF4KmyL8zpwwJx1MW0axMebbYULQ8+e0K4deHtbm89JbPpjE6HTQ7kee51S2UuxouUK8mTIY3UseQwiIyNp06YNNpuNNm3aEBYWZnUkEREReYwuRV1i1bFVrDi2gpXHVnIx6mKK66Wyl0qelR2UNwgPNw+Lkv7N4cPw1Vfm05zR0WZbwYLmxJdXXgEfi5ZCEadkGAZvvfUW06ZNw83NjR9++IEqVapYHUtExCmosC3OxTBgzRqzoL106e32Z56Bt982dzB3c7Mun5NZfnQ5DWc3JDohmqfzPM3iZovJ6pPV6ljymPTq1Ytjx46RN29evvzyS6vjiIiISCqLT4pn4x8bWXF0BSuOrWDnhZ0prvt6+FKjYA1qF65NaLFQCmQqYE3QuzEMWLUKvvwy5fuEJ5+Ed9+Fhg3BXW+P5dEbPHgwI0aMAGDixImEhoZaG0hExInoX25xDvHx8MMPZkF7558DbBcXePFFc+ZFUJC1+ZzQ9L3TaTO/DYm2RGoXqc2PjX/E19PX6ljymKxYsYKxY8cCMGnSJDJmzGhxIhEREXnUDMPg6LWjrDy2khXHVrDm5Boi4yNT3FPevzwhhUMIKRxCUN4gvNy9LEr7D6KjYepUs6B98KDZ5uICL7xg7rVTs6Z5LpIKxo8fT58+fQAYNmyYNlkXEXnEVNgWx3buHIwdC+PGwcU/H31Ml85caqR7dyhSxNJ4zsgwDIb/Npy3Vr4FQPMnmjMxbCKebp4WJ5PH5dq1a7Rr1w6Arl27UrNmTYsTiYiIyKNyNfoqv5z8hZ+O/8SKYys4EX4ixfUcvjkILhxMSOEQahWqRU6/nBYl/Q9//GGunz1uHFy/brb5+cGrr5obx+t9gqSyefPm0bFjRwB69+5Njx49LE4kIuJ8VNgWx2MYsHGjucnLnDmQmGi2BwTA66+bR1Yth5EakmxJdF/enVFbRwHQtXJXhtcejquLq8XJ5HHq0qUL586do3jx4gwaNMjqOCIiIvIQIuMj2XB6A6uPr2b1idXsurALAyP5uoerB1XzVU0uZpfzL2e/Yz/DgF9+MTeNnzsXkv7cvLJQIXNDyFdeAT1lJo/BL7/8QrNmzbDZbLz66qsMHDjQ6kgiIk5JhW1xHDExMGMGjBp1e7kRgKpVzYHqiy+Ch51sSOOEouKjaD63OQsPLQRgaPBQejzdAxc9upmmzJ49mxkzZuDm5saUKVPw0eZKIiIiDiU+KZ7NZzaz+oRZyN58ZjMJtoQU95TOXpqaBWsSXDiY5wo8h5+nn0Vp79H16zB5svkk56FDt9tr1DCXG3nhBe2zI4/Nzp07qV+/PnFxcTRo0ICxY8fqPZOISCpRYVvs35Ej8O23MGECXL1qtnl7Q4sW5mOE5ctbGi8tuBh5kXoz6rH13Fa83LyY9tI0GpVqZHUseczOnz/P66+/DkCfPn2oXLmyxYlERETkv9gMG7su7Eqekb3+9HqiE6JT3JM/Y37+V/B//K/Q/6hZsCb+fv4Wpb1PW7eas7NnzjQnwQD4+kLLluZTnOXKWZtP0pyjR49Su3Ztbt68SfXq1ZkxYwbu2pRURCTV6P+wYp/i4mDePHNNvDVrbrfnzw+dO5tr42m5kcfi0JVD1Pm+DifCT5A1XVYWNF1AlXxVrI4lj5lhGLRv355r165RoUIF+vbta3UkERERuYskWxJ7Lu5h7am1rDu1jrWn1nIt5lqKe7L7ZKdmwZrJxexCmQtZlPYBREWZhewxY2D79tvtTzxhFrNbtIAMGazLJ2nW+fPnCQ4O5tKlS5QrV44FCxbg7e1tdSwREaemwrbYl99/N2dnT558e3a2iwvUqQMdOugxwsds/an1hM0M43rsdQplLsSyFssolrWY1bHEAuPHj2fp0qV4eXkxZcoUPD21WaiIiIg9SEhKYMf5HclF7A2nN3Aj7kaKe9J7pqd6gepmIbvg/yiTo4xjLY1gGGYRe8IEmD4dbvz5/Xl6QuPGZkE7KMh83yBigfDwcGrXrs2JEycoXLgwy5cvJ6PWcxcRSXUqbIv1YmLMTSDHjYP162+358ljzsxu1w7y5bMuXxo1ZfcUXlv0GvFJ8QTmDmRRs0Vk981udSyxwPHjx+nZsycAn376KaVLl7Y4kYiISNoVlxjH1nNbkwvZv57+laiEqBT3pPdMT9V8VamevzrVC1TnqYCncHd1wLd+V67AtGlmQXvv3tvthQpBp07Qti1k1/hUrBUTE0P9+vXZs2cP/v7+rFy5En9/B1nOR0TEwTng6EacgmHAxo3mzOzZs2/PunB1NWdld+gAtWtrdrYFkmxJ9FndhyEbhwDwUsmXmPriVHw8tElgWpSUlETbtm2JjIykWrVqdO/e3epIIiIiaUpUfBRbzm5JXlpk05lNxCbGprgns3dmquWvRrX81aievzrl/Ms5ZiEbICkJVq40i9kLFkDCnxtbenlBw4bmpJcaNcz3DSIWS0xMpGnTpqxfv54MGTKwfPlyChVyoKV9REQcnIOOdsRhnTwJU6fClClw9Ojt9vz5oX17eOUVyJ3bsnhp3c24mzSf25zFhxcD0PfZvvSv0R9XF71xSKuGDx/O+vXr8fPzY9KkSbjpl00iIiKp6kzEGTb+sZFfT//Kr3/8yq4Lu0gyklLck90nO9ULVKd6/upUy1+NMjnKOP547dAh833CpElw9uzt9ooVzWJ2s2aQObNl8UT+zjAMXnvtNRYuXIiXlxeLFi2inDYsFRF5rFTYltR38yb8+KNZzP7ll9vtvr7QqBG0aQPVq2vWhcVOXD9B/Zn12XdpH97u3kyoP4FmTzSzOpZYaN++fbz//vuAWeAuWLCgxYlEREScS6Itkb0X9/LrH7+axew/fuX0jdN33Jc7fe7k2djV8lejRLYSjrVG9j+5cMHcCPL772HbttvtWbJAq1bmpBcVCsVO9e7dm0mTJuHq6sqsWbOoVq2a1ZFERNIcFbYldcTFmY8QzpwJ8+dDdLTZ7uICNWuaxeyXXjKL22K59afW89Lsl7gSfYVcfrmY33Q+lXNXtjqWWCg+Pp7WrVsTHx9PaGgor776qtWRREREHN6N2Bv8dua35CL25rObiYyPTHGPq4sr5f3LE5QniCr5qhCUN4h8GZ1ov5nISJg3zyxmr1oFNpvZ7uYGISHmutn165tLj4jYqS+++IIhQ8ylG8ePH09YWJjFiURE0iYVtuXRSUyENWvMYvbcuRAefvtasWJmMbtlS20EaUcMw2DMtjF0X96dBFsCFXNVZEHTBeTOoOVg0roBAwawc+dOsmbNyvjx451jVpiIiMhjlGhL5MDlA2w+s5nNZ81j/6X9GBgp7svglYFn8jxDlbxmETswTyB+nn4WpU4lCQlmEfv771NOegF4+mlo0QJefhly5LAsosi9mjx5Mr169QLgs88+45VXXrE4kYhI2qXCtjwcmw1+/dUsZv/wA1y+fPtarlzQpAk0bQqVK5uztcVuRCdE02lxJ6bumQpAk9JNmBA2QZtECps3b2bQoEEAjBkzRru6i4iI/AfDMDgTcYbNZzez5ewWNp/dzPZz24lKiLrj3kKZCxGUN4gqeatQJW8VSmUvhZurE+5hkZAAP/9sbhQ/fz5cu3b7WtGiZjG7RQsoUsSyiCL3a9GiRclPMr711lvJBW4REbGGCtty/5KSzGL23Lnm2tl/3dwla1Zo3NgsZletaj5SKHbn2LVjvDT7JfZc3IObixtDag2hx9M9NCtXiI6OpnXr1iQlJdG8eXMaN25sdSQRERG7ExEXwbZz29h8ZjNbzm1h85nNnI88f8d96T3TUyl3JQJzB1I5d2UCcweSK30uCxI/JgkJsHq1OeHl78Xs7NnN9wgtW0KlSpr0Ig5nw4YNvPzyyyQlJdG6dWuGDBmi908iIhZTYVvuTVycOUidOxcWLkw5MztDBnO97KZNzfWzPTysyyn/afHhxbSc25IbcTfI4ZuD2Y1mU71AdatjiZ3o3bs3hw8fJiAggFGjRlkdR0RExHLRCdHsurCLHed3sP38drac3cLBywfvWFLEzcWNsjnLJhewA/MEUjxrceecjf1Xt4rZt2ZmX79++1qOHOb7hMaNoVo1cNfbT3FMe/bs4YUXXiA2NpYXXniB8ePH4+rqanUsEZE0TyML+Wc3b8KyZWYxe+lS8/yWzJmhXj1zoBoSAt7e1uWUe5JoS+SjXz7i0/WfAvBMnmf4ofEPWk9bkq1evZqRI0cCMGHCBDJnzmxxIhERkccrMj4yRRF7+7ntHLxyEJthu+Pe/BnzE5gnMHk2doVcFdLOkm7h4bB8OSxYYL5fuHHj9rUcOaBhw9vFbD3BKQ7uxIkT1K5dmxs3blClShVmzZqFhyZziYjYBRW2JaUTJ8wi9pIl5pp4cXG3rwUEQIMGZjG7WjXNzHYgp2+cpvmc5vz6x68AvFHpDYaFDMPTzdPiZGIvwsPDadu2LQCvv/46ISEh1gYSERFJZTfjbrLzws4URezfr/x+x0xsAH8/fyrmqmgeARUJzB1ITr+cFqS20KlT5pObCxfCL7+YG8ffkjOn+R7h5Zfh2WdVzBancfHiRYKDgzl//jxlypRh0aJF+PikkV9giYg4ABW207qEBNiw4XYx++DBlNeLFoUXXzQHqpUqgR63cjhzD87l1YWvEh4bTgavDIx7YRxNyjSxOpbYmW7dunHmzBkKFy7M559/bnUcERGRR8YwDM5Hnmf3hd3svribPRf3sOP8Dg5fPXzXInZA+oAURewKuSoQkD7AguQWs9lgxw5YtMgsZu/alfJ6iRIQFmYelSurmC1OJyIigjp16nD06FHy58/PihUr9ESjiIidUWE7LbpwwXxkcOlSWLkSIiJuX3NzgypVIDTUPEqV0sYuDiomIYa3Vr7FmG1jAKicuzIzGs6gUOZCFicTezNv3jymTJmCq6srU6ZMwdfX1+pIIiIiDyQ+KZ6Dlw+y++Lu5EL27ou7uRJ95a7358mQJ7mIXSFXBSoGVMTfz/8xp7Yjly+b7w+WL4cVK1Luq+Pqar5PqF/fPIoVsy6nSCqLjY2lQYMG7Ny5k+zZs7Nq1SoCAtLgL7hEROycCttpQWQkrFsHP/0Eq1bBvn0pr2fPDnXqQN26EBxsrp8tDm3r2a20md+Gg1fMGfjvBL3DJzU/wcNNy8dISpcuXaJjx44AvPPOOwQFBVmcSERE5N6tObGGo1FHkwvYBy8fJMGWcMd9ri6uFMtajHI5y1EuZznK+5enYkBFcvjmsCC1HUlMhC1bzEL28uWwbRsYf5nF7ucHtWqZs7Lr1jXfN4g4uaSkJFq0aMGaNWtInz49y5cvp2jRolbHEhGRu1Bh2xklJsLWrWYh+6efYNMmc8mRv6pY0ZyRXbeulhhxIvFJ8QxYO4BBGwaRZCSR0zcnU16cQnDhYKujiR0yDIMOHTpw+fJlypYty0cffWR1JBERkfvSYGYD+Nse5hm9MlLOv1xyEbucfzlKZy9NOo90lmS0K4YBR47AmjW33yuEh6e8p1w5qF3bPIKCwFN7skjaYRgGr7/+OnPnzsXT05P58+dToUIFq2OJiMg/UGHbGSQmws6dsH49rF1rbuby1+VFAAoUMGdbPP881KwJ2bJZkVRS0Z6Le2g9rzW7L+4GoGmZpoyqM4qsPlktTib2asqUKSxYsAAPDw+mTp2Kl5eX1ZFERETuS6HMhXiywJPJBexyOcuRL2M+XLSU3m2nTpmF7J9/No+zZ1Nez5zZfGqzdm3zo5ZbkDTsgw8+4Ntvv8XFxYXp06dTs2ZNqyOJiMi/UGHbEcXGmjOy160zj40bzeVG/ipzZvjf/8xC9vPPQ+HC1mSVVBcVH8WAdQMYumkoibZEsqbLypjQMTQu3djqaGLHTp06RdeuXQH4+OOPKVu2rMWJRERE7t/OTjvJkCGD1THsy7lz5mSXW4Xs48dTXvf0hGeegRo1ICTEfHpTGz+K8OWXX/Lpp58CMHbsWBo2bGhxIhER+S8qbDuCq1fNte82bjQL2Zs3Q1xcynsyZYJnn4Vq1eC55+DJJzVATQMWH15Ml6VdOHXjFAANSjRgbOhYcvrltDiZ2DObzcYrr7xCREQEQUFB9OrVy+pIIiIi8iBsNjhwADZsgF9/NT+ePJnyHjc3s3hds6Z5BAVBOi3LIvJX33//Pd27dwfgk08+oUOHDtYGEhGRe6LCtr1JSIA9e8zi9W+/mceRI3felzOnWcS+dZQpo3Wy05BDVw7x7k/vsuDQAgDyZsjLyDojCSsRZnEycQSjRo1izZo1+Pj4MHnyZNz0SzARERHHEBNjTni5VcTetOnONbJdXc11sm8Vsp99FtKntySuiCNYtmwZbdu2BaBr16706dPH2kAiInLPVNi2kmGYjwbu2GEOUH/7zdyJPDb2znuLFYOnn749K7toUdDagWnO+ZvnGbBuAOO2jyPJSMLNxY2ez/Tkw+of4ufpZ3U8cQC///477777LgBffPEFRYoUsTiRiIiI3FViojkbe+tW873C1q2wd6/Z/le+vub7hCpVoGpVCAwELc8ick82bdpEw4YNSUxMpFmzZgwfPlxr9IuIOBAVth+XpCQ4dMgsYt86du2CGzfuvDdTJnNA+vTT5lG5MmTJ8rgTix05ePkgQzcNZeqeqcQnxQNQr1g9Bj8/mFLZS1mcThxFYmIirVu3JjY2lpCQEDp16mR1JBEREQFzwsuxY2bx+taxYwdER995b65cZgG7alWzmF2uHLjrbZ3I/dq/fz+hoaHExMQQEhLCpEmTcNVT0CIiDkUjoNRw4wbs3w/79sHu3eagdPdu89HBv/PygrJloWLF24XsokW1rIgQERfBD/t/YPLuyaw/vT65/Zk8zzDwfwN5rsBz1oUThzRo0CC2bt1KpkyZ+O677zQbRURExAoxMeZ7hd27zYkuu3ebSxHebcJL+vTw1FPmGtm3jnz59OSmyEM6deoUISEhXL9+ncDAQObMmYOnp6fVsURE5D6psP0woqPh4EGzgH2rkL1vH/zxx93v9/WF8uWhQoXbR8mS4OHxWGOL/boec51lR5ex4NACFh1aREyi+csQF1yoX7w+vYJ6USVfFYtTiiPavn07H3/8MQBff/01uXPntjiRiIiIkzMMOHv29mSXW8fvv5ubPv6dp6e5AfytAnblyuZyhJrwIvJIXb58meDgYM6ePUvJkiVZsmQJvr6+VscSEZEHoML2fzEMuHABDh82j0OHzI8HD5qPCxrG3V+XJw+ULg1PPHG7iF2kiLkrucifbIaNvRf38vOJn1l0eBHrTq0jyUhKvl4iWwnalGtDy7ItyZMhj4VJxZHFxsbSunVrEhMTady4Mc2aNbM6koiIiPNISoKTJ831sA8evP3x4EG4efPur8mWzVxCpHx582O5clCihFncFpFUc/PmTerWrcvhw4fJmzcvK1asIGvWrFbHEhGRB6TCNpjF6WvX4MQJOHIkZQH78OF/HpACZM1qFq/LlLl9lC5trpMt8jeJtkQOXD7A2pNrWXNyDWtPreVazLUU95TOXpqw4mG8WPJFKuaqqOUi5KH17duXAwcO4O/vz+jRo/V3SkRE5H4ZBly+bE5sOXrUPG5Ndjl06O6bv4M5qaVoUXPpwVtF7PLlzXWy9e+xyGMVFxfHSy+9xLZt28iaNSsrV64kb968VscSEZGHkHYK25GRcOqUWbw+ccKcVXHr8xMn/r147eoKBQqYjwIWL377Y5kykCOHBqVyV4ZhcOz6Mbae3crWc+ax4/wOohNSbgLk6+HLs/mfJbhQMPWL16dwlsIWJRZntHbtWoYNGwbA+PHjyZYtm8WJRERE7FRSEpw/D8eP3y5e//X4t/cLXl7mjOuSJaFUqdsfixTRLGwRO5CUlESbNm346aef8PX1ZenSpZQoUcLqWCIi8pDSTmH7XtaT9fc3B5+3ite3CtiFCpmDVZG7MAyDPyL+YP+l/ey/bB4HLh/gwOUDRMZH3nF/es/0BOYJpEaBGtQoUIOnAp7Cw03rrMujFxERQdu2bTEMg/bt2xMaGmp1JBEREetERsLp07ePU6dSfn72LCQm/vPrXVzM5QaLFLl9lCxpHgULaslBETtlGAZdu3Zl1qxZeHh4MG/ePCpXrmx1LBEReQTSTmEbIHNmc9B5tyN/fkiXzuqEYqdiE2M5GX6S49eP3/WISoi66+u83Lwo71+eSgGVqJS7EpUCKlE8W3FcXbQJkKS+nj17cvLkSQoUKJA8a1tERMTpxMSYM63/epw7l/L87Flz6cH/4u4O+fKlLF7fOgoWBG/v1P9+ROSR+vjjj5OX45s6dSq1atWyOpKIiDwiaaewffo0aP0s+ZvYxFiuRl/lQuQFzt08x9mbZzl389wdn1+JvvKvf46HqwfFshajdI7SlM5emlLZS1E6e2mKZCmi2dhiicWLF/Pdd9/h4uLC5MmTSZ8+vdWRREREHr28eSEi4t7vz5TJLFzfOvLnT/m5v79mXos4kdGjR/PRRx8BMHLkSJo0aWJtIBEReaQcorA9evRoPv/8c86fP0/p0qUZMWIEzz777P39IRkzpk44sVx8UjwRcRF3PcJjw7kSfYUr0Ve4GnP19ufR5uf/NNP6btJ7pqdQ5kIpjsKZC1Mwc0EKZiqoArbYjStXrtC+fXvAnLVdrVo1ixOJiIjc6ZGM8W8Vtb29zQ0ZAwLMj389AgLMI18+yJDh0X8jImKXZs+eTZcuXQDo168fb7zxhsWJRETkUbP7wvasWbPo3r07o0ePpkqVKnzzzTfUqVOHAwcOkC9fPqvjyd/YDBsJSQnEJ8WTYDM/xifF39EWmxhLTEIMMYkxRCdEE5Pw58fEmBSf/70tOiGam/E3iYiL4EbsDSLiIohLinuozO6u7mT3yU7uDLkJSB9A7vTmx1vHrfMs6bLgoo1Cxc4ZhsHrr7/OxYsXKVWqFJ988onVkURERO7wyMb4W7ZA0aLmJBaN00TkT6tWraJly5bJY+N+/fpZHUlERFKBi2EYhtUh/k1gYCAVKlRgzJgxyW0lS5akQYMGDBo06D9fHxERQcaMGXl94Ot4enliYH67yR+Nv5wb3PX6rc9vXU8+v5/rybfc/fpfuyH5nr++xrj717QZNmyGDQMDm82GDdvtj7euGYb5+Z/XDMzzJCMp5fW/33/r+PN1SUYSSUYSibZEkmxJJNgSSLKZ57fabIbtP/sktXi6e+Lj4UM693T4ePiYn3ukI4NnBjJ4ZSC9V3rzo2d683NPs83Hw0cFa3Ea69at48svv8Td3Z3NmzdToUIFqyOJiKSaW+O8GzdukEEzcR3KoxrjT5kyBR8fn3v+ug/61kevs/51jpBRr7P+awHExMTQp08foqKiaNy4MTNmzMBNSwyJiDiM+xnj2/WM7fj4eLZv307v3r1TtAcHB7Nx48a7viYuLo64uNszeG/cuAHAmD5j7nq/OI/4P/8LJ9zqKCKW69WrF0WKFCHiftYdFRFxMLf+H2fn8zTkbx7lGL9169apF1REHFr16tX5+uuviYq69+UnRUTEevczxrfrwvaVK1dISkoiZ86cKdpz5szJhQsX7vqaQYMG0b9//8cRT0TEbg0aNOieZryJiDiDmzdvklH7qTgMjfFF5HFYu3YtOXLksDqGiIg8oHsZ49t1YfuWvy8TYRjGPy4d8d5779GzZ8/kc5vNxrVr18iaNWuaW24iIiKCvHnz8scff+jx3DRGfZ92qe/TLvV92pWW+94wDG7evElAQIDVUeQBaIyfUlr+WXYU6iPHoH5yDOonx6B+sn/O2Ef3M8a368J2tmzZcHNzu2PmxqVLl+6Y4XGLl5cXXl5eKdoyZcqUWhEdQoYMGZzmL7fcH/V92qW+T7vU92lXWu17zdR2PBrj/7u0+rPsSNRHjkH95BjUT45B/WT/nK2P7nWM75rKOR6Kp6cnFStWZNWqVSnaV61aRVBQkEWpRERERETkQWmMLyIiIiKPgl3P2Abo2bMnrVq14qmnnuKZZ55h3LhxnD59mk6dOlkdTUREREREHoDG+CIiIiLysOy+sN2kSROuXr3Kxx9/zPnz5ylTpgxLly4lf/78Vkeze15eXvTr1++OxzbF+anv0y71fdqlvk+71PfiiDTGv5N+lu2f+sgxqJ8cg/rJMaif7F9a7yMXwzAMq0OIiIiIiIiIiIiIiNwru15jW0RERERERERERETk71TYFhERERERERERERGHosK2iIiIiIiIiIiIiDgUFbZFRERERERERERExKGosO0kzp49S8uWLcmaNSs+Pj6UL1+e7du3J183DIOPPvqIgIAA0qVLx3PPPcf+/fstTCwPKzExkb59+1KwYEHSpUtHoUKF+Pjjj7HZbMn3qN+dw7p166hXrx4BAQG4uLgwf/78FNfvpZ/j4uJ48803yZYtG76+vtSvX58zZ848xu9CHsS/9X1CQgLvvvsuTzzxBL6+vgQEBNC6dWvOnTuX4s9Q3zum//q5/6uOHTvi4uLCiBEjUrSr70Xsk8bt9k/jbPuj8bBj0NjVMWicaf/upY8OHjxI/fr1yZgxI+nTp+fpp5/m9OnTydfTSh+psO0Erl+/TpUqVfDw8GDZsmUcOHCAoUOHkilTpuR7hgwZwrBhwxg1ahRbt27F39+fWrVqcfPmTeuCy0P57LPPGDt2LKNGjeLgwYMMGTKEzz//nJEjRybfo353DlFRUZQrV45Ro0bd9fq99HP37t2ZN28eM2fOZMOGDURGRvLCCy+QlJT0uL4NeQD/1vfR0dHs2LGDDz74gB07djB37lwOHz5M/fr1U9ynvndM//Vzf8v8+fPZvHkzAQEBd1xT34vYH43bHYPG2fZH42HHoLGrY9A40/79Vx8dO3aMqlWrUqJECX755Rd2797NBx98gLe3d/I9aaaPDHF47777rlG1atV/vG6z2Qx/f39j8ODByW2xsbFGxowZjbFjxz6OiJIKQkNDjXbt2qVoe+mll4yWLVsahqF+d1aAMW/evOTze+nn8PBww8PDw5g5c2byPWfPnjVcXV2N5cuXP7bs8nD+3vd3s2XLFgMwTp06ZRiG+t5Z/FPfnzlzxsidO7exb98+I3/+/Mbw4cOTr6nvReyTxu2OQeNs+6bxsGPQ2NUxaJxp/+7WR02aNEn+N+lu0lIfaca2E1i4cCFPPfUUjRs3JkeOHDz55JN8++23yddPnDjBhQsXCA4OTm7z8vKievXqbNy40YrI8ghUrVqV1atXc/jwYQB2797Nhg0bqFu3LqB+TyvupZ+3b99OQkJCinsCAgIoU6aM/i44mRs3buDi4pI8809977xsNhutWrWiV69elC5d+o7r6nsR+6Rxu2PQONuxaDzsuDR2tU8aZ9o3m83GkiVLKFasGCEhIeTIkYPAwMAUy5WkpT5SYdsJHD9+nDFjxlC0aFFWrFhBp06d6Nq1K1OmTAHgwoULAOTMmTPF63LmzJl8TRzPu+++S7NmzShRogQeHh48+eSTdO/enWbNmgHq97TiXvr5woULeHp6kjlz5n+8RxxfbGwsvXv3pnnz5mTIkAFQ3zuzzz77DHd3d7p27XrX6+p7Efukcbtj0DjbsWg87Jg0drVfGmfat0uXLhEZGcngwYOpXbs2K1eu5MUXX+Sll15i7dq1QNrqI3erA8jDs9lsPPXUUwwcOBCAJ598kv379zNmzBhat26dfJ+Li0uK1xmGcUebOI5Zs2Yxbdo0pk+fTunSpdm1axfdu3cnICCANm3aJN+nfk8bHqSf9XfBeSQkJNC0aVNsNhujR4/+z/vV945t+/btfPnll+zYseO++1F9L2Itjdsdg8bZjknjYcehsav90jjT/t3ayDgsLIwePXoAUL58eTZu3MjYsWOpXr36P77WGftIM7adQK5cuShVqlSKtpIlSybvhurv7w9wx29lLl26dMdvtcVx9OrVi969e9O0aVOeeOIJWrVqRY8ePRg0aBCgfk8r7qWf/f39iY+P5/r16/94jziuhIQEXn75ZU6cOMGqVauSZ7yA+t5ZrV+/nkuXLpEvXz7c3d1xd3fn1KlTvPXWWxQoUABQ34vYK43bHYPG2Y5F42HHorGrfdM40/5ly5YNd3f3/xxPpJU+UmHbCVSpUoVDhw6laDt8+DD58+cHoGDBgvj7+7Nq1ark6/Hx8axdu5agoKDHmlUenejoaFxdU/4Iu7m5Jf/2Tv2eNtxLP1esWBEPD48U95w/f559+/bp74KDu/XG4MiRI/z0009kzZo1xXX1vXNq1aoVe/bsYdeuXclHQEAAvXr1YsWKFYD6XsReadzuGDTOdiwaDzsOjV3tn8aZ9s/T05NKlSr963giLfWRliJxAj169CAoKIiBAwfy8ssvs2XLFsaNG8e4ceMA85Gs7t27M3DgQIoWLUrRokUZOHAgPj4+NG/e3OL08qDq1avHp59+Sr58+ShdujQ7d+5k2LBhtGvXDlC/O5PIyEiOHj2afH7ixAl27dpFlixZyJcv33/2c8aMGXn11Vd56623yJo1K1myZOHtt9/miSee4Pnnn7fq25J78G99HxAQQKNGjdixYweLFy8mKSkpeaZSlixZ8PT0VN87sP/6uf/7G0EPDw/8/f0pXrw4oJ97EXulcbtj0Djb/mg87Bg0dnUMGmfav//qo169etGkSROqVatGjRo1WL58OYsWLeKXX34B0lgfGeIUFi1aZJQpU8bw8vIySpQoYYwbNy7FdZvNZvTr18/w9/c3vLy8jGrVqhl79+61KK08ChEREUa3bt2MfPnyGd7e3kahQoWM999/34iLi0u+R/3uHNasWWMAdxxt2rQxDOPe+jkmJsbo0qWLkSVLFiNdunTGCy+8YJw+fdqC70bux7/1/YkTJ+56DTDWrFmT/Geo7x3Tf/3c/13+/PmN4cOHp2hT34vYJ43b7Z/G2fZH42HHoLGrY9A40/7dSx999913RpEiRQxvb2+jXLlyxvz581P8GWmlj1wMwzBSoV4uIiIiIiIiIiIiIpIqtMa2iIiIiIiIiIiIiDgUFbZFRERERERERERExKGosC0iIiIiIiIiIiIiDkWFbRERERERERERERFxKCpsi4iIiIiIiIiIiIhDUWFbRERERERERERERByKCtsiIiIiIiIiIiIi4lBU2BYRERERERERERERh6LCtoiIiIiIiIiIiIg4FBW2RUScxKVLl+jYsSP58uXDy8sLf39/QkJC2LRpk9XRRERERETkPml8LyLy79ytDiAiIo9Gw4YNSUhIYPLkyRQqVIiLFy+yevVqrl27ZnU0ERERERG5Txrfi4j8OxfDMAyrQ4iIyMMJDw8nc+bM/PLLL1SvXt3qOCIiIiIi8hA0vhcR+W9aikRExAn4+fnh5+fH/PnziYuLszqOiIiIiIg8BI3vRUT+mwrbIiJOwN3dnUmTJjF58mQyZcpElSpV6NOnD3v27LE6moiIiIiI3CeN70VE/puWIhERcSKxsbGsX7+eTZs2sXz5crZs2cL48eNp27at1dFEREREROQ+aXwvIvLPVNgWEXFi7du3Z9WqVZw6dcrqKCIiIiIi8pA0vhcRuU1LkYiIOLFSpUoRFRVldQwREREREXkENL4XEbnN3eoAIiLy8K5evUrjxo1p164dZcuWJX369Gzbto0hQ4YQFhZmdTwREREREbkPGt+LiPw3FbZFRJyAn58fgYGBDB8+nGPHjpGQkEDevHl57bXX6NOnj9XxRERERETkPmh8LyLy37TGtoiIiIiIiIiIiIg4FK2xLSIiIiIiIiIiIiIORYVtEREREREREREREXEoKmyLiIiIiIiIiIiIiENRYVtEREREREREREREHIoK2yIiIiIiIiIiIiLiUFTYFhERERERERERERGHosK2iIiIiIiIiIiIiDgUFbZFRERERERERERExKGosC0iIiIiIiIiIiIiDkWFbRERERERERERERFxKCpsi4iIiIiIiIiIiIhD+T8SRc+ZMchFcwAAAABJRU5ErkJggg==",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(18, 6))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"\n",
"ax1.plot(BS.S_vec, BS.price_vec, color=\"red\", label=\"BS curve\")\n",
"ax1.plot(VG2.S_vec, VG2.price_vec, color=\"green\", label=\"VG curve\")\n",
"ax1.plot(VG2.S_vec, VG2.payoff_f(VG2.S_vec), color=\"black\", label=\"Payoff\")\n",
"ax1.set_xlim(50, 150)\n",
"ax1.set_ylim(0, 50)\n",
"ax1.set_xlabel(\"S\")\n",
"ax1.set_ylabel(\"price\")\n",
"ax1.set_title(\"VG vs BS - same variance - negative skewness\")\n",
"ax1.legend()\n",
"\n",
"ax2.plot(BS.S_vec, BS.price_vec, color=\"red\", label=\"BS curve\")\n",
"ax2.plot(VG3.S_vec, VG3.price_vec, color=\"green\", label=\"VG curve\")\n",
"ax2.plot(VG3.S_vec, VG3.payoff_f(VG3.S_vec), color=\"black\", label=\"Payoff\")\n",
"ax2.set_xlim(50, 170)\n",
"ax2.set_ylim(0, 80)\n",
"ax2.set_xlabel(\"S\")\n",
"ax2.set_ylabel(\"price\")\n",
"ax2.set_title(\"VG vs BS - same variance - positive skewness\")\n",
"ax2.legend()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"[1] Madan, D., Carr, P., and Chang, E. (1998). \"The Variance Gamma process and option pricing\". European Finance Review, 2, 79--105."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: 3.3 Pricing with the NIG Process.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Normal Inverse Gaussian Process\n",
"\n",
"\n",
"## Contents\n",
" - [Numerical methods for option pricing](#sec1)\n",
" - [The NIG PIDE method](#sec2)\n",
" - [The Lévy measure](#sec2.1)\n",
" - [The PIDE](#sec2.2) \n",
"\n",
"The methods used in this notebook are basically the same presented in the notebook **3.2**. \n",
"As you probably have noticed when reading the notebook **1.5**, the NIG process is very similar to the VG process. They are both obtained by Brownian subordination. They both depend on three variables. Their distribution is quite similar. \n",
"\n",
"And of course, the numerical methods for option pricing are quite similar as well."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy as scp\n",
"import scipy.stats as ss\n",
"import scipy.special as scps\n",
"import matplotlib.pyplot as plt\n",
"from functools import partial\n",
"from scipy.integrate import quad\n",
"\n",
"from FMNM.probabilities import Q1, Q2\n",
"from FMNM.CF import cf_NIG"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"T = 1 # terminal time\n",
"N = 10000000 # number of generated random variables\n",
"S0 = 100.0 # initial price\n",
"K = 100.0 # strike\n",
"k = np.log(K / S0) # log moneyness\n",
"\n",
"r = 0.1\n",
"theta = -0.11 # drift of the Brownian motion\n",
"sigma = 0.2 # volatility of the Brownian motion\n",
"kappa = 0.3 # variance of the IG process\n",
"lam = T**2 / kappa # scale\n",
"mu_s = T / lam # scaled mu\n",
"\n",
"w = (1 - np.sqrt(1 - 2 * theta * kappa - kappa * sigma**2)) / kappa # Martingale correction\n",
"dev_X = np.sqrt(sigma**2 + theta**2 * kappa) # std dev NIG process"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let me recall the derivation of some formulas:\n",
"\n",
"##### Mean and Variance: \n",
"Using the Brownian subordination expression for the NIG process (where $T_t \\sim IG(t,\\kappa t)$), we get\n",
"\n",
"$$ \\mathbb{E}[X_t] = \\mathbb{E}[\\theta T_t + \\sigma W_{T_t}] = \\theta \\mathbb{E}[T_t] + 0 = \\theta t.$$\n",
"\n",
"where I used the [Tower property](https://en.wikipedia.org/wiki/Law_of_total_expectation), and\n",
"\n",
"$$ \\text{Var}[X_t] = \\text{Var}[\\theta T_t + \\sigma W_{T_t}] = \\theta^2 \\text{Var}[T_t] + \n",
"\\sigma^2 \\text{Var}[W_{T_t}] = (\\theta^2 \\kappa + \\sigma^2) t. $$\n",
"\n",
"where for the term $\\text{Var}[W_{T_t}]$ I used the [conditional variance](https://en.wikipedia.org/wiki/Law_of_total_variance) formula.\n",
"\n",
"##### Martingale correction:\n",
"\n",
"The idea is to find the correction term $w$ such that the process $e^{-rt}S_t$ is a martingale. The stock process has dynamics $S_t = S_0 e^{rt - wt + X_t}$. \n",
"We have that\n",
"\n",
"$$ \\mathbb{E}[ e^{-rt} S_0 e^{rt - wt + X_t} ] = S_0$$\n",
"\n",
"$$ \\mathbb{E}[ e^{-wt + X_t}] = 1$$\n",
"\n",
"$$ e^{wt} = \\mathbb{E}[ e^{X_t}] $$\n",
"\n",
"$$ w = \\log( \\phi(-i) ) \\quad \\quad \\text{for} \\quad t=1 $$"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The martingale correction w=-0.088817 can be computed from the CF as: \n",
"(-0.088817+0j)\n"
]
}
],
"source": [
"print(\"The martingale correction w={0:.6f} can be computed from the CF as: \".format(w))\n",
"print(np.log(cf_NIG(-1j, t=1, mu=0, theta=theta, sigma=sigma, kappa=kappa)).round(6))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"# Numerical methods\n",
"\n",
"##### Monte Carlo"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Monte Carlo, call: 13.429721691441594, std err: 0.0049326098983063895\n",
"Monte Carlo, put: 3.915092184682041, std_err: 0.0025466749195472587\n"
]
}
],
"source": [
"np.random.seed(seed=42)\n",
"IG = ss.invgauss.rvs(mu=mu_s, scale=lam, size=N) # The IG RV\n",
"Norm = ss.norm.rvs(0, 1, N) # The normal RV\n",
"X = theta * IG + sigma * np.sqrt(IG) * Norm # NIG random vector\n",
"S_T = S0 * np.exp((r - w) * T + X) # exponential dynamics\n",
"\n",
"call_payoff = np.maximum(S_T - K, 0)\n",
"put_payoff = np.maximum(K - S_T, 0)\n",
"call = np.exp(-r * T) * np.mean(call_payoff)\n",
"put = np.exp(-r * T) * np.mean(put_payoff)\n",
"call_err = np.exp(-r * T) * ss.sem(call_payoff)\n",
"put_err = np.exp(-r * T) * ss.sem(put_payoff)\n",
"print(\"Monte Carlo, call: {}, std err: {}\".format(call, call_err))\n",
"print(\"Monte Carlo, put: {}, std_err: {}\".format(put, put_err))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Fourier inversion"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fourier inversion call: 13.427965\n",
"Fourier inversion put: 3.911707\n"
]
}
],
"source": [
"# function binding\n",
"cf_NIG_b = partial(cf_NIG, t=T, mu=r - w, theta=theta, sigma=sigma, kappa=kappa)\n",
"\n",
"# price\n",
"call_F = S0 * Q1(k, cf_NIG_b, np.inf) - K * np.exp(-r * T) * Q2(k, cf_NIG_b, np.inf)\n",
"put_F = -S0 * (1 - Q1(k, cf_NIG_b, np.inf)) + K * np.exp(-r * T) * (1 - Q2(k, cf_NIG_b, np.inf))\n",
"\n",
"print(\"Fourier inversion call: \", call_F.round(6))\n",
"print(\"Fourier inversion put: \", put_F.round(6))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"# The NIG PIDE method \n",
"\n",
"\n",
"## Curiosities about the NIG Lévy measure\n",
"\n",
"The NIG process is a pure jump process with triplet $(b,0,\\nu)$. (For more information on the Lévy triplet see Appendix **A.3**)\n",
"\n",
"The expression of the drift $b$ in [2] and [3] is given for the parameters:\n",
"\n",
"$$ \\beta = \\frac{\\theta}{\\sigma^2}, \\quad \\quad \\alpha = \\sqrt{ \\beta^2 + \\frac{1}{\\kappa \\sigma^2} } \\quad \\quad\n",
" \\delta = \\frac{T \\sigma}{\\sqrt{\\kappa}} $$\n",
"\n",
"and is:\n",
"\n",
"$$ b = \\frac{2 \\delta \\alpha}{\\pi} \\int_0^1 \\sinh(\\beta x) K_1(\\alpha x) dx $$\n",
"\n",
"The Lévy measure is:\n",
"\n",
"$$ \\nu(x) = \\frac{C}{|x|} e^{A x} K_1(B|x|) $$\n",
"\n",
"with\n",
"\n",
"$$ A = \\frac{\\theta}{\\sigma^2} \\quad B = \\frac{\\sqrt{\\theta^2 + \\sigma^2/\\kappa}}{\\sigma^2} \n",
"\\quad C = \\frac{\\sqrt{\\theta^2 + \\sigma^2/\\kappa}}{\\sigma \\pi \\sqrt{\\kappa}} $$\n",
"\n",
"The function $K_1$ is a Bessel function of second kind with 1 degree of freedom.\n",
"\n",
"Let us recall from the Levy processes theory that:\n",
"\n",
"$$ \\mathbb{E}[X_t] = t \\biggl( b + \\int_{|x|>1} x \\nu(dx) \\biggr)$$\n",
"\n",
"$$ \\text{Var}[X_t] = t \\int_{\\mathbb{R}} x^2 \\nu(dx) $$\n",
"\n",
"Let's verify it is correct:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def NIG_measure(x):\n",
" A = theta / (sigma**2)\n",
" B = np.sqrt(theta**2 + sigma**2 / kappa) / sigma**2\n",
" C = np.sqrt(theta**2 + sigma**2 / kappa) / (np.pi * sigma * np.sqrt(kappa))\n",
" return C / np.abs(x) * np.exp(A * (x)) * scps.kv(1, B * np.abs(x))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.concatenate((np.linspace(-0.1, -0.001, 200), np.linspace(0.001, 0.1, 200)))\n",
"plt.plot(x, NIG_measure(x))\n",
"plt.title(\"NIG Lévy measure\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### mean:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The theoretical mean is theta: -0.11\n",
"The drift component is: -0.10992734491736125\n",
"The mean is the sum of the drift and the integration over |x>1| i.e. : -0.11000000000015915\n"
]
}
],
"source": [
"beta = theta / (sigma**2)\n",
"alpha = np.sqrt(beta**2 + 1 / (kappa * sigma**2))\n",
"delta = sigma / np.sqrt(kappa)\n",
"int_b = lambda y: 2 / np.pi * delta * alpha * np.sinh(beta * y) * scps.kv(1, alpha * np.abs(y))\n",
"drift = quad(int_b, 0, 1, points=0, limit=2000)[0]\n",
"\n",
"print(\"The theoretical mean is theta: \", theta)\n",
"print(\"The drift component is: \", drift)\n",
"\n",
"int_m = lambda y: y * NIG_measure(y)\n",
"mean_greater1 = quad(int_m, -10, -1, limit=2000)[0] + quad(int_m, 1, 10, limit=2000)[0]\n",
"\n",
"print(\"The mean is the sum of the drift and the integration over |x>1| i.e. : \", drift + mean_greater1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### variance:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The variance obtained from integration of Lévy mesure is: 0.04363\n",
"The theoretical variance is: 0.04363\n"
]
}
],
"source": [
"int_s = lambda y: y**2 * NIG_measure(y)\n",
"var_int = quad(int_s, -2, 2, points=0)[0] # I inform that the point 0 has a singularity\n",
"var_th = dev_X**2\n",
"print(\"The variance obtained from integration of Lévy mesure is: \", round(var_int, 6))\n",
"print(\"The theoretical variance is: \", var_th.round(7))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Martingale correction:\n",
"\n",
"The martingale correction can be computed from the Lévy measure as well:\n",
"\n",
"$$ w = \\int_{\\mathbb{R} \\backslash [-\\epsilon,\\epsilon]} (e^z-1) \\nu(dz). $$\n",
"\n",
"we have to choose an $\\epsilon > 0$ very small, but it cannot be zero. The reason is that the NIG process has infinite variation (see **A.3** for the definition) and this means that the integral above does not converge for $\\epsilon = 0$. \n",
"\n",
"**Comment:** \n",
"For infinite variation processes we have that:\n",
"\n",
"$ \\int_{[-\\epsilon,\\epsilon]} (e^z-1) \\nu(dz) \\approx \\int_{[-\\epsilon,\\epsilon]} z \\nu(dz) = \\infty. $\n",
"\n",
"This is the main difference with the VG process, which instead has finite variation."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Martingale correction: -0.088817\n",
"Martingale correction from integration: -0.088817\n"
]
}
],
"source": [
"epsilon = 0.0000001\n",
"int_w = lambda y: (np.exp(y) - 1) * NIG_measure(y)\n",
"w2 = quad(int_w, -10, -epsilon)[0] + quad(int_w, epsilon, 10)[0]\n",
"print(\"Martingale correction: \", w.round(6))\n",
"print(\"Martingale correction from integration: \", round(w2, 6))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## The NIG PIDE\n",
"\n",
"The NIG PIDE is \n",
"\n",
"$$\n",
"\\frac{\\partial V(t,x)}{\\partial t} \n",
" + \\biggl( r - \\int_{\\mathbb{R}} \\bigl( e^z-1-z \\mathbb{1}_{|z|<\\epsilon} \\bigr) \\nu(dz) \\biggr) \\frac{\\partial V(t,x)}{\\partial x} $$\n",
"$$ + \\int_{\\mathbb{R}} \\bigl( V(t,x+z)- V(t,x) - z \\frac{\\partial V(t,x) }{\\partial x} \\mathbb{1}_{|z|<\\epsilon} \\bigr) \\nu(dz) = r V(t,x).\n",
"$$\n",
"\n",
"for a small value of $\\epsilon$ we can use the Brownian approximation described in **3.2** and **A.3**. \n",
"The jump-diffusion type PIDE is:\n",
"\n",
"$$\n",
" \\frac{\\partial V(t,x)}{\\partial t} +\n",
" \\bigl( r-\\frac{1}{2}\\sigma_{\\epsilon}^2 - w_{\\epsilon} \\bigr) \\frac{\\partial V(t,x)}{\\partial x} \n",
" + \\frac{1}{2}\\sigma_{\\epsilon}^2 \\frac{\\partial^2 V(t,x)}{\\partial x^2}\n",
" + \\int_{|z| \\geq \\epsilon} V(t,x+z) \\nu(dz) = (\\lambda_{\\epsilon} + r) V(t,x).\n",
"$$\n",
"\n",
"with parameters \n",
"\n",
"$$\n",
" \\sigma_{\\epsilon}^2 := \\int_{|z| < \\epsilon} z^2 \\nu(dz), \\quad \\quad w_{\\epsilon} := \\int_{|z| \\geq \\epsilon} (e^z-1) \\nu(dz), \\quad \\quad\n",
" \\lambda_{\\epsilon} := \\int_{|z| \\geq \\epsilon} \\nu(dz) .\n",
"$$\n",
"\n",
"The previous approximated equation is identical to the VG equation. \n",
"I'm not repeating the calculation. I'm just going to present some numerical values and some plots.\n",
"\n",
"**Comment:** \n",
"I would like to point out that the convergence to the right price is quite slow, in particular for small values of $\\kappa$. \n",
"We have seen a similar behavior for the VG PIDE, but in the NIG PIDE is a bit worse. \n",
"The algorithm works very well for PUT options, while for CALL options it is hard to obtain good results under our grid resolution. \n",
"In order to improve the performances I also used a big computational domain $[A_1,A_2]$, but the improvements are still not satisfactory. \n",
"I expect that for a higher grid resolution (in particular for more time steps), we can achieve better performances."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from FMNM.Parameters import Option_param\n",
"from FMNM.Processes import NIG_process\n",
"import FMNM.NIG_pricer as NIG"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"opt_param = Option_param(S0=S0, K=K, T=T, exercise=\"European\", payoff=\"call\")\n",
"opt_param2 = Option_param(S0=S0, K=K, T=T, exercise=\"European\", payoff=\"put\")\n",
"NIG_param = NIG_process(r=r, sigma=sigma, theta=theta, kappa=kappa)\n",
"\n",
"NIG_c = NIG.NIG_pricer(opt_param, NIG_param)\n",
"NIG_p = NIG.NIG_pricer(opt_param2, NIG_param)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### European Call"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(13.387236700090668, 46.138726234436035)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"NIG_c.PIDE_price((22000, 50000), Time=True)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"NIG_am.plot([50, 150, 0, 60])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"[1] Rama Cont and Peter Tankov (2003) \"Financial Modelling with Jump Processes\", Chapman and Hall/CRC; 1 edition. \n",
"\n",
"[2] Rydberg Tina (1997) \"The Normal Inverse Gaussian Lévy Process:\n",
"Simulation and Approximation\", Communications in Statistics. Stochastic Models. 13:4, 887-910\n",
"\n",
"[3] Barndorff-Nielsen, Ole E. (1998) \"Processes of Normal Inverse Gaussian type\" Finance and Stochastics 2, 41-68."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: 4.1 Option pricing with transaction costs.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Davis-Panas-Zariphopoulou model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook presents a model for pricing options in a market with proportional transaction costs. The model is taken from the celebrated work of Davis-Panas-Zariphopoulou 1993 [*link-to-the-paper*](https://web.ma.utexas.edu/users/zariphop/pdfs/TZ-7.pdf). \n",
"\n",
"#### This is a very powerful model!\n",
"\n",
"However, due to its complexity (and time complexity), it is not very well known to the practitioners.\n",
"\n",
"The purpose of this notebook is to explain **in simple terms** the main ideas of the model, and show how to implement it numerically. **The results will surprise you!**\n",
"\n",
"## Contents\n",
" - [Model description](#sec1)\n",
" - [Portfolio dynamics (original)](#sec1.1)\n",
" - [Some definitions](#sec1.2)\n",
" - [Singular control problem](#sec2)\n",
" - [Maximization problem](#sec2.1)\n",
" - [Indifference pricing](#sec2.2)\n",
" - [Variable reduction](#sec3)\n",
" - [Minimization problem](#sec3.1)\n",
" - [Portfolio dynamics (2 state variables)](#sec3.2)\n",
" - [HJB variational inequality](#sec3.3)\n",
" - [Indifference price (explicit form)](#sec3.4)\n",
" - [Numerical Solution](#sec4)\n",
" - [Discrete SDE](#sec4.1)\n",
" - [Algorithm](#sec4.2)\n",
" - [Numerical Computations](#sec5)\n",
" - [Time complexity](#sec5.1)\n",
" - [Is the risk aversion important?](#sec5.2)\n",
" - [Is the drift important?](#sec5.3)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from IPython.display import display\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Model Description"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Portfolio dynamics (original)\n",
"Let us consider a portfolio composed by: \n",
"- A bank account $\\mathbf{B}$ paying an interest rate $r > 0$. \n",
"- A stock $\\mathbf{S}$. \n",
"- A number of shares $\\mathbf{Y}$ of the stock $\\mathbf{S}$. \n",
"\n",
"The 3-D state of the portfolio at time $t\\in [t_0,T]$ is $(B_t,Y_t,S_t)$ and evolves following the SDE:\n",
"\n",
"\\begin{equation}\n",
" \\begin{cases}\n",
" dB_t &= rB_t dt - (1+\\theta_b)S_{t} dL_t + (1-\\theta_s) S_{t} dM_t \\\\\n",
" dY_t &= dL_t - dM_t \\\\\n",
" dS_t &= S_{t} \\left( \\mu dt + \\sigma dW_t \\right).\n",
"\\end{cases}\n",
"\\end{equation} \n",
"\n",
"The parameters $\\theta_b$, $\\theta_s \\geq 0$ are the proportional transaction costs when buying and selling respectively. \n",
"\n",
"The processes $\\{(L_t, M_t)\\}_{t \\in [t_0,T]}$ are the **trading strategies**, i.e. the **controls** of the problem. \n",
"\n",
"The process $\\{L_t\\}_{t}$ represents the cumulative number of shares bought up to time $t$, and $\\{M_t\\}_{t}$ represents the number of shares sold up to time $t$. \n",
"They are right-continuous, finite variation, non-negative and increasing processes. If the time $t$ is a discontinuous point (there is a transaction), the variation of the processes are indicated as\n",
"$$ \\Delta L_t= L(t)-L(t^-) \\quad \\quad \\Delta M_t= M(t)-M(t^-) $$\n",
"\n",
"Let us consider an example. If at time $t$, the investor wants to buy $\\Delta L_t$ shares. Then the portfolio changes as\n",
"\n",
"$$ \\Delta Y_t = \\underbrace{\\Delta L_t}_{\\text{shares bought}} \\quad \\quad\n",
" \\Delta B_t = - \\underbrace{(1+\\theta_b)S_t}_{\\text{adjusted price}} \\Delta L_t $$\n",
"\n",
"where the **adjusted price** is the real cost of the stock (incorporating the transaction cost).\n",
"\n",
"If there are no transactions, the portfolio has the simple well known evolution:\n",
"\n",
"$$\n",
" \\begin{cases}\n",
" dB_t &= rB_t dt \\\\\n",
" dY_t &= 0 \\\\\n",
" dS_t &= S_{t} \\left( \\mu dt + \\sigma dW_t \\right).\n",
"\\end{cases}\n",
"$$\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Some definitions\n",
"\n",
"The **cash value** function $c(y,s) : \\mathbb{R} \\times \\mathbb{R}^+ \\to \\mathbb{R}$, is defined as the value in cash when the shares in the portfolio are liquidated i.e. \n",
"long positions are sold and short positions are covered.\n",
"\n",
"$$\n",
"c(y,s) := \\begin{cases} \n",
"(1+\\theta_b)ys, & \\text{if } y\\leq 0 \\\\ \n",
"(1-\\theta_s)ys, & \\text{if } y>0 . \n",
"\\end{cases} \n",
"$$\n",
"\n",
"For $t\\in [t_0,T]$, the **total wealth** process in a portfolio with zero options is defined as:\n",
"\n",
"$$ \\mathcal{W}^0_t := B_t + c(Y_t,S_t). $$\n",
"\n",
"If the portfolio contains an option with maturity $T$ and strike $K$, then the wealth process becomes: \n",
"**Writer**:\n",
" \n",
"$$ \\mathcal{W}^{w}_t = \\; B_t + c(Y_t,S_t) \\mathbb{1}_{\\{t < T\\}} +\n",
" c(Y_t,S_t) \\mathbb{1}_{\\{t = T,\\, S_t(1+ \\theta_b ) \\leq K\\}} +\n",
" \\biggl( c\\bigl( Y_t-1,S_t \\bigr) + K \\biggr) \\mathbb{1}_{\\{t=T,\\, S_t(1+ \\theta_b ) > K \\}} $$\n",
" \n",
"**Buyer**: \n",
"\n",
" $$ \\mathcal{W}^{b}_t = \\; B_t + c(Y_t,S_t) \\mathbb{1}_{\\{t < T\\}} +\n",
" c(Y_t,S_t) \\mathbb{1}_{\\{t = T,\\, S_t(1+ \\theta_b ) \\leq K\\}} +\n",
" \\biggl( c\\bigl( Y_t+1,S_t \\bigr) - K \\biggr) \\mathbb{1}_{\\{t=T,\\, S_t(1+ \\theta_b ) > K \\}} $$\n",
" \n",
"For $t_0 \\leq t K$, because the true price of the share incorporates the value of the transaction costs. Let's see the plot:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"S = np.linspace(14, 16, 100)\n",
"K = 15 # strike\n",
"cost_b = 0.01 # transaction cost\n",
"\n",
"plt.plot(S, np.maximum(S - K, 0), color=\"blue\", label=\"Zero cost Payoff\")\n",
"plt.plot(S, np.maximum(S * (1 + cost_b) - K, 0), color=\"red\", label=\"Transaction costs Payoff\")\n",
"plt.xlabel(\"S\")\n",
"plt.ylabel(\"Payoff\")\n",
"plt.title(\"Payoff comparison\")\n",
"plt.legend(loc=\"upper left\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Singular control problem"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Maximization problem\n",
"\n",
"The **value function** of the maximization problem for $j=0,w,b$ (corresponding to the three portfolios: no option, writer, buyer) is defined as:\n",
"\n",
"\\begin{equation}\n",
"V^j(t,b,y,s) = \\sup_{L,M} \\; \\mathbb{E}\\biggl[ \\; \\mathcal{U}( \\mathcal{W}^{j}_T ) \\; \\bigg| \\; B_{t} = b, Y_{t} = y, S_{t} = s \\biggr], \n",
"\\end{equation}\n",
"\n",
"where $\\mathcal{U}: \\mathbb{R} \\to \\mathbb{R}$ is a concave increasing **utility function**. The **exponential utility** is what we are looking for:\n",
"\n",
"\\begin{equation}\n",
" \\mathcal{U}(x) := 1- e^{-\\gamma x} \\quad \\quad \\gamma >0 .\n",
"\\end{equation}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Indifference pricing\n",
"\n",
"The writer (buyer) option price is defined as the amount of cash to add (subtract) to the bank account, \n",
"such that the maximal expected utility of wealth of the writer (buyer) is the same he could get with \n",
"the zero-option portfolio.\n",
"\n",
"* The **writer price** is the value $p^w>0$ such that \n",
" \\begin{equation}\n",
" V^0(t,b,y,s) = V^w(t,b+p^w,y,s),\n",
" \\end{equation}\n",
" \n",
"* The **buyer price** is the value $p^b>0$ such that\n",
" \\begin{equation}\n",
" V^0(t,b,y,s) = V^b(t,b-p^b,y,s).\n",
" \\end{equation}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Variable reduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the properties of the exponential utility, it is possible to remove $\\mathbf{B}$ from the state variables.\n",
"\n",
"$$ V^j(t,b,y,s) = \\sup_{L,M} \\; \\mathbb{E}_{t,b,y,s}\\biggl[ 1- e^{-\\gamma \\mathcal{W}^j(T) } \\biggr] \n",
"\t = 1- e^{-\\gamma \\frac{b}{\\delta(t,T)}} Q^j(t,y,s), $$\n",
" \n",
"where $\\delta(t,T) = e^{-r(T-t)}$. (for the full calculations, check the paper. Equations 4.21 -4.25).\n",
"\n",
"\n",
"### Minimization problem\n",
"\n",
"\\begin{equation}\n",
"Q^j(t,y,s) = \\inf_{L,M} \\; \\mathbb{E}_{t,y,s}\\biggl[ \\;\n",
"\t e^{-\\gamma \\bigl[ -\\int_{t}^T (1+\\theta_b) \\frac{S_u}{\\delta(u,T)} dL_u +\n",
"\t \\int_{t}^T (1-\\theta_s) \\frac{S_u}{\\delta(u,T)} dM_u \\bigr] } \\; H^j(Y_T,S_T) \\bigg] \n",
"\\end{equation}\n",
"\n",
"The exponential term inside the expectation can be considered as a discount factor, and the second term is the terminal payoff:\n",
" - No option:\n",
" \n",
" $$ H^0(y,s) = e^{-\\gamma \\, c(y,s)}. $$\n",
" \n",
" - Writer:\n",
" \n",
" $$ H^w(y,s) = e^{-\\gamma \\bigl[ c(y,s)\\mathbb{1}_{\\{s(1+\\theta_b) \\leq K\\}} + \n",
" \\bigl( c( y-1,s) + K \\bigr) \\mathbb{1}_{\\{s(1+\\theta_b)>K\\}} \\bigr] }.$$\n",
" \n",
" - Buyer:\n",
"\n",
"$$ H^b(y,s) = e^{-\\gamma \\bigl[ c(y,s)\\mathbb{1}_{\\{s(1+\\theta_b) \\leq K\\}} + \n",
" \\bigl( c( y+1,s) - K \\bigr) \\mathbb{1}_{\\{s(1+\\theta_b)>K\\}} \\bigr] }.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Portfolio dynamics (2 state variables)\n",
"\n",
"In order to simplify the numerical computations,let us pass to the log-variable $X_t = \\log(S_t)$.\n",
"\n",
"The resulting portfolio dynamics is:\n",
"\n",
"\\begin{equation}\n",
" \\begin{cases}\n",
" dY_t &= dL_t - dM_t \\\\\n",
" dX_t &= \\biggl( \\mu - \\frac{1}{2} \\sigma^2 \\biggr) dt + \\sigma dW_t.\n",
"\\end{cases}\n",
"\\end{equation} \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### HJB variational inequality\n",
"\n",
"The Hamilton Jacobi Bellman equation associated to the minimization problem is:\n",
"\n",
"$$\n",
" \\min \\; \\biggl\\{ \\; \\frac{\\partial Q^j}{\\partial t} + (\\mu-\\frac{1}{2}\\sigma^2) \\frac{\\partial Q^j}{\\partial x}\n",
"+ \\frac{1}{2}\\sigma^2 \\frac{\\partial^2 Q^j}{\\partial x^2} ,\n",
" \\; \\frac{\\partial Q^j}{\\partial y} +(1+\\theta_b) e^x \\frac{\\gamma}{\\delta(t,T)}Q^j \\; , \n",
"\\; -\\biggl( \\frac{\\partial Q^j}{\\partial y}+(1-\\theta_s)e^x \\frac{\\gamma}{\\delta(t,T)} Q^j \n",
"\\biggr) \\biggr\\} = 0. \n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Indifference price (explicit form)\n",
"\n",
"Using again the explicit form of the utility function, we obtain formulas for the option prices:\n",
"\n",
"$$\n",
" p^w(t_0,y,x) = \\frac{\\delta(t_0,T)}{\\gamma} \\log \\biggl( \\frac{Q^w(t_0,y,e^x)}{Q^0(t_0,y,e^x)} \\biggr), $$\n",
" \n",
"$$ p^b(t_0,y,x) = \\frac{\\delta(t_0,T)}{\\gamma} \\log \\biggl( \\frac{Q^0(t_0,y,e^x)}{Q^b(t_0,y,e^x)} \\biggr).\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Numerical Solution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Discrete SDE\n",
"\n",
"As usual, we introduced the time step $\\Delta t = \\frac{T}{N}$, where we assumed $t_0 = 0$ and $N \\in \\mathbb{N}$. \n",
"The time $t_n = n \\Delta t$, for $n \\in \\{0,1,2, ..., N\\}$. \n",
"\n",
"The space discretization has 2 dimensions:\n",
"- The space step $h_x$ is defined as $h_x = \\sigma \\sqrt{\\Delta t}$.\n",
"- The space step is $h_y$. In this computations we choose $h_x = h_y$.\n",
"\n",
"The discretized version of the Stochastic Differential equation is: \n",
"\n",
"$$\n",
" \\begin{cases}\n",
" \\Delta Y_n &= \\; \\Delta L_n - \\Delta M_n \\\\\n",
" \\Delta X_n &= \\; (\\mu - \\frac{1}{2} \\sigma^2 ) \\Delta t + \\sigma \\Delta W_n\n",
"\\end{cases}\n",
"$$\n",
"\n",
"Both $\\Delta L_n$ and $\\Delta M_n$ at each time $t_n$ can assume values in $\\{0,h_y\\}$. They cannot be different from zero at the same time (It is quite strange to buy and sell at the same time, right?)\n",
"\n",
"The variable $\\Delta W_n$ has $\\frac{1}{2}$ probability of being equal to $h_x$ and $\\frac{1}{2}$ probability of being equal to $-h_x$.\n",
"\n",
"The variation $\\Delta X_n$ is $\\pm h_x$ plus the drift component. We obtain a recombining **binomial tree**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Binomial tree with drift"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2.7080502]\n",
"[2.61744646 2.82157061]\n",
"[2.52684272 2.73096687 2.93509101]\n",
"[2.43623898 2.64036313 2.84448727 3.04861142]\n",
"[2.34563524 2.54975939 2.75388353 2.95800768 3.16213182]\n",
"[2.2550315 2.45915565 2.6632798 2.86740394 3.07152809 3.27565223]\n"
]
}
],
"source": [
"N = 6\n",
"dt = 1 / N\n",
"S0 = 15\n",
"x0 = np.log(S0)\n",
"mu = 0.1\n",
"sig = 0.25\n",
"h_x = sig * np.sqrt(dt)\n",
"\n",
"for n in range(N):\n",
" x = np.array([x0 + (mu - 0.5 * sig**2) * dt * n + (2 * i - n) * h_x for i in range(n + 1)])\n",
" print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Algorithm\n",
"\n",
"Using the Dynamic Programming Principle (DPP) on the minimization problem we obtain a recursive algorithm on the nodes of the grid.\n",
"\n",
"$$ \\begin{aligned}\n",
" Q^{j}(t_n,Y_n,X_n) = \\min \n",
" & \\; \\biggl\\{ \\mathbb{E}_n \\biggl[ Q \\bigl( t_{n+1}, Y_n, X_n + \\Delta X_n \\bigr) \\biggr], \\\\ \\nonumber\n",
" & \\; \\exp \\biggl(\\frac{\\gamma}{\\delta(t_n,T)} (1+\\theta_b) e^{X_n} \\Delta L_n \\biggr) \n",
" \\mathbb{E}_n \\biggl[ Q^{j} \\bigl( t_{n+1}, Y_n+\\Delta L_n, X_n + \\Delta X_n \\bigr) \\biggr], \\\\ \\nonumber\n",
" & \\; \\exp \\biggl(\\frac{-\\gamma}{\\delta(t_n,T)} (1-\\theta_s) e^{X_n} \\Delta M_n \\biggr)\n",
" \\mathbb{E}_n \\biggl[ Q^{j} \\bigl( t_{n+1}, Y_n-\\Delta M_n, X_n + \\Delta X_n \\bigr) \\biggr]\n",
" \\biggr\\}.\n",
"\\end{aligned} $$\n",
"\n",
"#### How to solve this problem?\n",
"- Create a **binomial tree** with N time steps. \n",
"- Create a \"shares vector\" **y** with dimension M. \n",
"- Initialize a two dimensional grid of size $(N+1)\\times M$, to be filled with the values of the terminal conditions $H^j(y,e^x)$ for $j=0,w,b$ (see [Minimization problem](#sec3.1))\n",
"- Create a backward loop over time and find $Q^j(0,0,X_0)$\n",
"\n",
"#### Computational complexity? Well... Quite high. \n",
"- A binomial tree with N time steps has $\\sum_{n=0}^N (n+1) = \\frac{N(N+1)}{2} + (N+1) = (N+1)(\\frac{N}{2}+1)$ nodes. \n",
"The loop over all the nodes is $\\mathcal{O}(N^2)$.\n",
"- If we assume $M \\sim N$, the loop over all the values of **y** has another $\\mathcal{O}(N)$.\n",
"- The minimum search for every point in **y**, produces another $\\mathcal{O}(N)$ operations.\n",
"\n",
"Therefore the total computational complexity is of $\\mathcal{O}(N^4)$.\n",
"\n",
"\n",
"#### For space reasons, I will not expose the code here in the notebook. The interested reader can peek the (clear and commented) code inside the class TC_pricer. [code](./functions/TC_pricer.py)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Numerical computations\n",
"\n",
"Let us import the classes we need:\n",
" - **Option_param**: creates the option object\n",
" - **Diffusion_process**: creates the process object\n",
" - **TC_pricer**: creates the pricer for this model\n",
" - **BS_pricer**: creates the Black-Scholes pricer, useful for making comparisons. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from FMNM.Parameters import Option_param\n",
"from FMNM.Processes import Diffusion_process\n",
"from FMNM.TC_pricer import TC_pricer\n",
"from FMNM.BS_pricer import BS_pricer\n",
"\n",
"# Creates the object with the parameters of the option\n",
"opt_param = Option_param(S0=15, K=15, T=1, exercise=\"European\", payoff=\"call\")\n",
"\n",
"# Creates the object with the parameters of the process\n",
"diff_param = Diffusion_process(r=0.1, sig=0.25, mu=0.1)\n",
"\n",
"# Creates the object of the Transaction Costs pricer\n",
"TC = TC_pricer(opt_param, diff_param, cost_b=0, cost_s=0, gamma=0.0001)\n",
"# Creates the object of the Black-Scholes pricer\n",
"BS = BS_pricer(opt_param, diff_param)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We expect that if the transaction costs are **zero**, and the risk aversion coefficient $\\gamma \\to 0$ (i.e. the investor becomes risk neutral), the price should **converge** to the **Black-Scholes price**"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Zero TC price: 2.246375063664713\n",
"Black Scholes price: 2.246368616746695\n",
"Difference: 6.446918018099268e-06\n"
]
}
],
"source": [
"tc = TC.price(N=2000)\n",
"bs = BS.closed_formula()\n",
"\n",
"print(\"Zero TC price: \", tc)\n",
"print(\"Black Scholes price:\", bs)\n",
"print(\"Difference:\", np.abs(tc - bs))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Wait a second!!! WE CAN DO BETTER!\n",
"\n",
"##### Let us analyze the the writer and buyer prices, for different initial stock values."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"S = list(range(5, 21))\n",
"price_0 = []\n",
"price_w = []\n",
"price_b = []\n",
"\n",
"for s in S:\n",
" TC.S0 = s\n",
" TC.cost_b = 0\n",
" TC.cost_s = 0\n",
" price_0.append(TC.price(N=400)) # zero costs\n",
" TC.cost_b = 0.05\n",
" TC.cost_s = 0.05\n",
" price_w.append(TC.price(N=400, TYPE=\"writer\"))\n",
" price_b.append(TC.price(N=400, TYPE=\"buyer\"))\n",
"TC.cost_b = 0\n",
"TC.cost_s = 0 # set to 0 for future computations"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(S, price_0, color=\"blue\", marker=\"s\", label=\"Zero costs\")\n",
"plt.plot(S, price_w, color=\"green\", marker=\"o\", label=\"Writer\")\n",
"plt.plot(S, price_b, color=\"magenta\", marker=\"*\", label=\"Buyer\")\n",
"BS.plot(axis=[10, 20, 0, 8]) # plot of the Black Scholes line"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Time complexity\n",
"\n",
"If we set the \"Time\" argument to True, the method also returns the execution time.\n",
"Let us verify that the algorithm has time complexity of order $\\mathcal{O}(N^4)$\n",
"\n",
"The following operation will be very time consuming. In case you are in a hurry, reduce the NUM."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
N
\n",
"
Price
\n",
"
Time
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
50.0
\n",
"
6.531228
\n",
"
0.008018
\n",
"
\n",
"
\n",
"
1
\n",
"
100.0
\n",
"
6.533475
\n",
"
0.029627
\n",
"
\n",
"
\n",
"
2
\n",
"
200.0
\n",
"
6.533735
\n",
"
0.136240
\n",
"
\n",
"
\n",
"
3
\n",
"
400.0
\n",
"
6.534301
\n",
"
0.613247
\n",
"
\n",
"
\n",
"
4
\n",
"
800.0
\n",
"
6.534227
\n",
"
6.132332
\n",
"
\n",
"
\n",
"
5
\n",
"
1600.0
\n",
"
6.534322
\n",
"
44.816548
\n",
"
\n",
"
\n",
"
6
\n",
"
3200.0
\n",
"
6.534285
\n",
"
407.323948
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" N Price Time\n",
"0 50.0 6.531228 0.008018\n",
"1 100.0 6.533475 0.029627\n",
"2 200.0 6.533735 0.136240\n",
"3 400.0 6.534301 0.613247\n",
"4 800.0 6.534227 6.132332\n",
"5 1600.0 6.534322 44.816548\n",
"6 3200.0 6.534285 407.323948"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"NUM = 7\n",
"price_table = pd.DataFrame(columns=[\"N\", \"Price\", \"Time\"])\n",
"for j, n in enumerate([50 * 2**i for i in range(NUM)]):\n",
" price_table.loc[j] = [n] + list(TC.price(n, Time=True))\n",
"display(price_table)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the computational times we can estimate the exponent $\\alpha$ of the polinomial growth $\\mathcal{O}(N^\\alpha)$. \n",
"\n",
"For higher values of N, the exponent converges to the expected value of $\\alpha = 4$.\n",
"\n",
"Here we are quite close."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The exponent is: 3.1840732004452104\n"
]
}
],
"source": [
"print(\"The exponent is: \", np.log2(price_table[\"Time\"][6] / price_table[\"Time\"][5]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Is the risk aversion important?\n",
"\n",
"The coefficient $\\gamma$ measure the risk aversion of the investor. We can see how the option price is affected by this coefficient:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"price_ww = []\n",
"price_bb = []\n",
"gammas = [0.0001, 0.001, 0.01, 0.05, 0.1, 0.3, 0.5]\n",
"TC.cost_b = 0.01\n",
"TC.cost_s = 0.01\n",
"\n",
"for gamma in gammas:\n",
" TC.gamma = gamma\n",
" price_ww.append(TC.price(N=400, TYPE=\"writer\"))\n",
" price_bb.append(TC.price(N=400, TYPE=\"buyer\"))\n",
"\n",
"plt.plot(gammas, price_ww, color=\"green\", marker=\"o\", label=\"Writer\")\n",
"plt.plot(gammas, price_bb, color=\"magenta\", marker=\"*\", label=\"Buyer\")\n",
"plt.xlabel(\"gamma\")\n",
"plt.ylabel(\"price\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So far we have found that:\n",
"\n",
"- The option pricing is an increasing function of the risk aversion coefficient for the writer, and a decreasing function for the buyer.\n",
"\n",
"- The option pricing is an increasing function of the transaction costs for the writer, and a decreasing function for the buyer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Is the drift important? \n",
"\n",
"As we know from the \"classical\" no-arbitrage martingale pricing theory, the option price does not depend on the stock expected value. \n",
"\n",
"However, this model is a utility based model i.e. a model that does not consider a risk neutral investor. \n",
"\n",
"We can see that in this model the option price depends on the drift. \n",
"\n",
"If we consider a high risk aversion coefficient, the option price is not very sensitive to the drift. If instead we choose a small value of $\\gamma$, i.e. the investor is risk neutral, the drift plays the role of the risk neutral expected return $r$ and therefore changing $\\mu$, is like changing $r$.\n",
"\n",
"Following Hodges-Neuberger [2], in the practical computations **it is better to set $\\mu=r$.**"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"price_mu1 = []\n",
"price_mu2 = []\n",
"mus = [0.01, 0.05, 0.1, 0.2, 0.3]\n",
"TC.gamma = 1 # high value of risk aversion\n",
"TC.cost_b = 0.01\n",
"TC.cost_s = 0.01\n",
"\n",
"for mu in mus:\n",
" TC.mu = mu\n",
" price_mu1.append(TC.price(N=400, TYPE=\"writer\"))\n",
" price_mu2.append(TC.price(N=400, TYPE=\"buyer\"))\n",
"\n",
"plt.plot(mus, price_mu1, color=\"green\", marker=\"o\", label=\"Writer\")\n",
"plt.plot(mus, price_mu2, color=\"magenta\", marker=\"*\", label=\"Buyer\")\n",
"plt.xlabel(\"mu\")\n",
"plt.ylabel(\"price\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Other references\n",
"\n",
"[1] Cantarutti, N., Guerra, J., Guerra, M., and Grossinho, M. (2019). Option pricing in exponential Lévy models with transaction costs. [*ArXiv*](https://arxiv.org/abs/1611.00389). \n",
"\n",
"[2] Hodges, S. D. and Neuberger, A. (1989). Optimal replication of contingent claims under transaction costs. The Review of Future Markets, 8(2):222–239."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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================================================
FILE: 4.2 Volatility smile and model calibration.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Volatility smile and model calibration\n",
"\n",
"\n",
"## Contents\n",
" - [How to produce the smile?](#sec1)\n",
" - [Implied volatility](#sec1.1)\n",
" - [Lewis representation](#sec1.2)\n",
" - [Market data](#sec2)\n",
" - [1 week maturity](#sec2.1)\n",
" - [6 months maturity](#sec2.2)\n",
" \n",
" \n",
" \n",
"In this notebook, we discuss in a very **basic and naive** way the implied volatility of options.\n",
"\n",
"The **Implied Volatility** is that value $\\sigma$ that must be inserted into the Black-Scholes (BS) formula in order to retrieve the option price quoted in the market:\n",
"\n",
"$$ BS\\;(S, K, T, r, \\sigma) = P $$\n",
"\n",
"where $S$ is the underlying spot price, $K$ is the strike, $T$ time to maturity, $r$ risk free interest rate and $P$ the option price quoted in the market. All these quantities are **observable**. \n",
"The only **non observable** quantity is $\\sigma$.\n",
"However, since BS is an increasing continuous function of $\\sigma$, there is a one-one correspondence \n",
"\n",
"$$\\sigma \\longleftrightarrow BS\\;(S, K, T, r, \\sigma)$$\n",
"\n",
"where all the other variables are kept fixed. \n",
"For this reasons, it is common to quote option market prices in terms of implied volatilities.\n",
"\n",
"According to the assumptions of the Black-Scholes model, the stock price follows a Geometric Brownian Motion and the implied volatility (IV) should be the same for all $K$, $T$. \n",
"In practice, however, it depends on both. This relation $\\sigma(K, T)$ is called a **volatility surface**. \n",
"The dependence of IV on $T$ is called **term structure**, while the dependece on $K$ is called **volatility smile**.\n",
"In this notebook I will consider only the volatility smile.\n",
"\n",
"But... why does the volatility change for different $K$ and $T$? \n",
"The sad truth is that the Geometric Brownian Motion is not a good model to describe the underlying dynamics!\n",
"\n",
"And here there are plenty of nice quotes to recall, such as: \n",
"\n",
"> *\"Essentially, all models are wrong, but some are useful\".* [George Box](https://en.wikipedia.org/wiki/All_models_are_wrong)\n",
"\n",
"> *\"The implied volatility is the wrong number to put in the wrong formula to obtain the right price\".* Riccardo Rebonato [1]\n",
"\n",
"Some models (still wrong, but more useful) that better replicate the price dynamics, and under an appropriate risk neutral measure, are able to reproduce the volatility surface are:\n",
"1. Local volatility models: $ dS_t = \\mu S_t dt + S_t \\sigma(t, S_t) dW_t $. \n",
"\n",
"2. Stochastic volatility models, e.g. Heston model.\n",
"\n",
"3. Exponential or Geometric Lévy processes, e.g. Merton Jump-Diffusion model, NIG, Variance Gamma.\n",
"\n",
"4. Combination of stochastic volatility and jumps, e.g. [Bates model](https://en.wikipedia.org/wiki/Stochastic_volatility_jump). These type of models are the best for this purpose (see [2]). The reason is that they describe better the empirical features of the prices, such as volatility clustering and jumps. \n",
"The presence of many parameters makes these models much more flexible.\n",
"\n",
"Here I consider only the case of stochastic volatility and Lévy processes, since I have already introduced them in the notebooks **1.4** and **1.5**.\n",
"\n",
"When considering complex models, it is very difficult to pass from the physical measure to the correct risk neutral measure (which is not unique).\n",
"For this reason, the best approach is to derive the model parameters directly from the prices (usually European call and put options with different strikes and maturities) \n",
"already quoted in the market.\n",
"The process of choosing the risk neutral parameters for a model, such that it reproduces the prices in the market is called **model calibration**."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from FMNM.Parameters import Option_param\n",
"from FMNM.Processes import Diffusion_process, Merton_process, VG_process, Heston_process\n",
"from FMNM.BS_pricer import BS_pricer\n",
"from FMNM.Merton_pricer import Merton_pricer\n",
"from FMNM.VG_pricer import VG_pricer\n",
"from FMNM.Heston_pricer import Heston_pricer\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import scipy as scp\n",
"import scipy.stats as ss\n",
"import matplotlib.pyplot as plt\n",
"import scipy.optimize as scpo\n",
"from functools import partial\n",
"from itertools import compress\n",
"import os\n",
"import warnings\n",
"\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# How to produce the smile?\n",
"\n",
"\n",
"Alright... Let us check if the \"complex models\" mentioned above, are able to reproduce the volatility smile!\n",
"\n",
"In the following cell, I'm going to create the objects for the **Black-Scholes, Merton, Variance Gamma and Heston** pricers. I chose the values of the process parameters such that the processes have similar variance. \n",
"The option is a European call with strike $K=100$, and maturity $T=1$. We will see below that changing the time to maturity can create problems.\n",
"\n",
"The notation is the same I used in the other notebooks. If you have forgotten it, check the *doc*: \n",
"You can type `Heston_param.__doc__` or `Heston_param??` in a cell."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"T = 1\n",
"S0 = 100\n",
"K = 100\n",
"opt_param = Option_param(S0=S0, K=K, T=T, v0=0.04, exercise=\"European\", payoff=\"call\")\n",
"\n",
"diff_param = Diffusion_process(r=0.1, sig=0.2)\n",
"Merton_param = Merton_process(r=0.1, sig=0.1, lam=0.8, muJ=-0.04, sigJ=0.2)\n",
"VG_param = VG_process(r=0.1, theta=-0.09, sigma=0.19, kappa=0.6)\n",
"Heston_param = Heston_process(mu=0.1, rho=-0.3, sigma=0.6, theta=0.04, kappa=5)\n",
"\n",
"BS = BS_pricer(opt_param, diff_param)\n",
"VG = VG_pricer(opt_param, VG_param)\n",
"Mert = Merton_pricer(opt_param, Merton_param)\n",
"Hest = Heston_pricer(opt_param, Heston_param)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us create a grid of strikes $[50, 55, ..., 145, 150]$, and compute the option prices. \n",
"In this case, the best approach is to use the Fourier Transform method described in the notebook **1.4**. \n",
"**This method is super fast!!**"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"strikes = np.arange(50, 151, 5) # strike grid\n",
"BS_prices = BS.FFT(strikes)\n",
"Mert_prices = Mert.FFT(strikes)\n",
"Hest_prices = Hest.FFT(strikes)\n",
"VG_prices = VG.FFT(strikes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Well... if there is a closed formula, why not use it? \n",
"\n",
"The closed formula is always the best solution, but the FFT method is very fast and has a small error. You won't notice the difference.\n",
"\n",
"For VG and Heston there is no closed formula. A possible alternative is to use the Fourier inversion method. \n",
"\n",
"Let's see how small is the error:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Closed formula prices\n",
"BS_prices_cl = np.zeros_like(strikes, dtype=float)\n",
"VG_prices_cl = np.zeros_like(strikes, dtype=float)\n",
"Mert_prices_cl = np.zeros_like(strikes, dtype=float)\n",
"Hest_prices_cl = np.zeros_like(strikes, dtype=float)\n",
"\n",
"for i, K in enumerate(strikes):\n",
" BS.K = K\n",
" VG.K = K\n",
" Mert.K = K\n",
" Hest.K = K\n",
" BS_prices_cl[i] = BS.closed_formula()\n",
" VG_prices_cl[i] = VG.Fourier_inversion()\n",
" Mert_prices_cl[i] = Mert.closed_formula()\n",
" Hest_prices_cl[i] = Hest.Fourier_inversion()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Closed vs FFT. Total absolute error BS: 1.2537767304365843e-06\n",
"Closed vs FFT. Total absolute error VG: 3.149305698890714e-06\n",
"Closed vs FFT. Total absolute error Merton: 3.2961514604412656e-06\n",
"Closed vs FFT. Total absolute error Heston: 2.10374569764582e-06\n"
]
}
],
"source": [
"print(\"Closed vs FFT. Total absolute error BS: \", np.linalg.norm(BS_prices - BS_prices_cl, 1))\n",
"print(\"Closed vs FFT. Total absolute error VG: \", np.linalg.norm(VG_prices - VG_prices_cl, 1))\n",
"print(\"Closed vs FFT. Total absolute error Merton: \", np.linalg.norm(Mert_prices - Mert_prices_cl, 1))\n",
"print(\"Closed vs FFT. Total absolute error Heston: \", np.linalg.norm(Hest_prices - Hest_prices_cl, 1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Indeed... very small!\n",
"\n",
"#### Comments:\n",
"\n",
"At short maturities e.g. $T=0.01$, the Fourier inversion method may have problems. In our case it may have problems for the VG and Heston model. The reason is that the numerical integration used in the Fourier inversion method has ad-hoc bounds for the truncation of the integral. \n",
"Usually, for small values of $T$, the tails of the integrand decay very slowly and the ad-hoc bounds produce a truncation error. \n",
"\n",
"Similar problems happen also for other numerical methods. \n",
"In general, there are always problems in pricing deep OTM (out of the money) options for very short maturities, when the closed formula is not available. \n",
"The reason is that the error of the numerical method is usually bigger than the option price itself!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Implied volatility\n",
"\n",
"How to find the value $\\sigma$ such that \n",
"\n",
"$$ BS\\;(S, K, T, r, \\sigma) - P = 0 $$\n",
"\n",
"for fixed $S, K, T, r$ ?\n",
"\n",
"We have to use numerical methods!\n",
"\n",
"The most common methods are the [Newton](https://en.wikipedia.org/wiki/Newton%27s_method) method and the [Bisection](https://en.wikipedia.org/wiki/Bisection_method) method. A more advanced method is the [Brent](https://en.wikipedia.org/wiki/Brent%27s_method) method. \n",
"Of course all of them are implemented in `scipy.optimize`: \n",
"[Newton](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.newton.html#scipy.optimize.newton), \n",
"[Bisection](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.bisect.html#scipy.optimize.bisect), [Brent](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.brentq.html#scipy.optimize.brentq). \n",
"\n",
"At first sight, the Newton method seems quite good since the derivative of BS with respect to $\\sigma$ is known analytically:\n",
"\n",
"$$ \\text{VEGA:} \\quad \\frac{\\partial BS}{\\partial \\sigma} = S \\sqrt{T} N'(d_1) $$\n",
"\n",
"where N'(x) is the standard normal density and $d_1$ is a variable defined...everywhere :) (see notebook **1.1**). \n",
"The Newton method has quadratic convergence if some conditions are satisfied.\n",
"However, for high values of $\\sigma$ the VEGA can be very close to zero, and this makes the convergence slow. \n",
"Instead, the bisection method is a very robust method. \n",
"\n",
"In the function below I use \n",
"[fsolve](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fsolve.html),\n",
"which is a very efficient method, as the default method, but with the possibility to switch to the brent method."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def implied_volatility(price, S0, K, T, r, payoff=\"call\", method=\"fsolve\", disp=True):\n",
" \"\"\"Returns Implied volatility\n",
" methods: fsolve (default) or brent\n",
" \"\"\"\n",
"\n",
" def obj_fun(vol):\n",
" return price - BS.BlackScholes(payoff=payoff, S0=S0, K=K, T=T, r=r, sigma=vol)\n",
"\n",
" if method == \"brent\":\n",
" x, r = scpo.brentq(obj_fun, a=1e-15, b=500, full_output=True)\n",
" if r.converged == True:\n",
" return x\n",
" if method == \"fsolve\":\n",
" X0 = [0.1, 0.5, 1, 3] # set of initial guess points\n",
" for x0 in X0:\n",
" x, _, solved, _ = scpo.fsolve(obj_fun, x0, full_output=True, xtol=1e-8)\n",
" if solved == 1:\n",
" return x[0]\n",
"\n",
" if disp == True:\n",
" print(\"Strike\", K)\n",
" return -1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another approach is to find\n",
"\n",
"$$ \\text{argmin}_{\\theta} \\bigg( BS\\;(S, K, T, r, \\sigma) - P \\bigg)^{2n} \\quad \\quad \\text{for } n>0$$\n",
"\n",
"This is an uncommon method. But it is useful when the root finder is not able to find a solution."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def implied_vol_minimize(price, S0, K, T, r, payoff=\"call\", disp=True):\n",
" \"\"\"Returns Implied volatility by minimization\"\"\"\n",
"\n",
" n = 2 # must be even\n",
"\n",
" def obj_fun(vol):\n",
" return (BS.BlackScholes(payoff=payoff, S0=S0, K=K, T=T, r=r, sigma=vol) - price) ** n\n",
"\n",
" res = scpo.minimize_scalar(obj_fun, bounds=(1e-15, 8), method=\"bounded\")\n",
" if res.success == True:\n",
" return res.x\n",
" if disp == True:\n",
" print(\"Strike\", K)\n",
" return -1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us compute the implied volatilities from the prices obtained before. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"IV_BS = []\n",
"IV_VG = []\n",
"IV_Mert = []\n",
"IV_Hest = []\n",
"for i in range(len(strikes)):\n",
" IV_BS.append(implied_volatility(BS_prices[i], S0=100, K=strikes[i], T=T, r=0.1))\n",
" IV_VG.append(implied_volatility(VG_prices[i], S0=100, K=strikes[i], T=T, r=0.1))\n",
" IV_Mert.append(implied_volatility(Mert_prices[i], S0=100, K=strikes[i], T=T, r=0.1))\n",
" IV_Hest.append(implied_volatility(Hest_prices[i], S0=100, K=strikes[i], T=T, r=0.1))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"IV_BS_m = []\n",
"IV_VG_m = []\n",
"IV_Mert_m = []\n",
"IV_Hest_m = []\n",
"for i in range(len(strikes)):\n",
" IV_BS_m.append(implied_vol_minimize(BS_prices[i], S0=100, K=strikes[i], T=T, r=0.1))\n",
" IV_VG_m.append(implied_vol_minimize(VG_prices[i], S0=100, K=strikes[i], T=T, r=0.1))\n",
" IV_Mert_m.append(implied_vol_minimize(Mert_prices[i], S0=100, K=strikes[i], T=T, r=0.1))\n",
" IV_Hest_m.append(implied_vol_minimize(Hest_prices[i], S0=100, K=strikes[i], T=T, r=0.1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see if `implied_volatility` and `implied_vol_minimize` produce the same output."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Are the IV values obtained by the methods above equal? True\n"
]
}
],
"source": [
"print(\n",
" \" Are the IV values obtained by the methods above equal? \",\n",
" np.allclose(np.array(IV_BS), np.array(IV_BS_m))\n",
" & np.allclose(np.array(IV_VG), np.array(IV_VG_m))\n",
" & np.allclose(np.array(IV_Mert), np.array(IV_Mert_m))\n",
" & np.allclose(np.array(IV_Hest), np.array(IV_Hest_m)),\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 4))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"ax1.plot(strikes, BS_prices, label=\"BS\")\n",
"ax1.plot(strikes, VG_prices, label=\"VG\")\n",
"ax1.set_xlim([80, 150])\n",
"ax1.set_ylim([0, 30])\n",
"ax1.plot(strikes, Mert_prices, label=\"Mert\")\n",
"ax1.plot(strikes, Hest_prices, label=\"Hest\")\n",
"ax1.set_title(\"Comparison of prices\")\n",
"ax1.set_xlabel(\"Strike\")\n",
"ax1.set_ylabel(\"Price\")\n",
"ax2.plot(strikes, IV_BS, label=\"BS\")\n",
"ax2.plot(strikes, IV_VG, label=\"VG\")\n",
"ax2.plot(strikes, IV_Mert, label=\"Mert\")\n",
"ax2.plot(strikes, IV_Hest, label=\"Hest\")\n",
"ax2.set_title(\"Comparison of Implied volatilities\")\n",
"ax2.set_xlabel(\"Strike\")\n",
"ax2.set_ylabel(\"Imp Vol\")\n",
"ax1.legend()\n",
"ax2.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Nice plot! We verified that the Heston, Variance Gamma and Merton models produce a smile!!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Lewis representation\n",
"\n",
"\n",
"In [2], Gatheral suggests an elegant implicit expression for computing the implied volatility (Eq 5.7 page 60) generated by a stocastic process with characteristic function $\\phi_T$. \n",
"The expression makes use of the Lewis representation for the option price, that I presented in the notebook **1.4**. This version looks slightly different from Eq. 5.7 in [2], but they are equivalent. \n",
"\n",
"The value of $\\sigma$ can be obtained implicitly from \n",
"\n",
"$$ 0 = \\int_{0}^{\\infty} Re \\biggl[ e^{iuk} \\bigg( \\phi_T \\big(u-\\frac{i}{2} \\big) - \\phi^{BS}_T \\big(u-\\frac{i}{2} \\big) \\bigg) \\biggr] \\; \\frac{1}{u^2 + \\frac{1}{4}} du. $$\n",
"\n",
"Replacing $\\phi^{BS}_T(u) = e^{i(r-\\frac{1}{2}\\sigma^2) u T - \\frac{1}{2} \\sigma^2 u^2 T}$: \n",
"\n",
"$$ 0 = \\int_{0}^{\\infty} Re \\biggl[ e^{iuk} \\bigg( \\phi_T \\big(u-\\frac{i}{2} \\big) - \n",
"e^{iurT} \\, e^{\\frac{1}{2}rT} \\, e^{-\\frac{1}{2}(u^2+\\frac{1}{4})\\sigma^2 T}\n",
"\\bigg) \\biggr] \\; \\frac{1}{u^2 + \\frac{1}{4}} du. $$\n",
"\n",
"The implementation of `IV_Lewis` is [here](./functions/FFT.py)."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"IV_L_BS = []\n",
"IV_L_VG = []\n",
"IV_L_Mert = []\n",
"IV_L_Hest = []\n",
"for K in strikes:\n",
" BS.K = K\n",
" VG.K = K\n",
" Mert.K = K\n",
" Hest.K = K\n",
" IV_L_BS.append(BS.IV_Lewis())\n",
" IV_L_VG.append(VG.IV_Lewis())\n",
" IV_L_Mert.append(Mert.IV_Lewis())\n",
" IV_L_Hest.append(Hest.IV_Lewis())"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(15, 4))\n",
"plt.plot(strikes, IV_BS, label=\"BS\")\n",
"plt.plot(strikes, IV_VG, label=\"VG\")\n",
"plt.plot(strikes, IV_Mert, label=\"Mert\")\n",
"plt.plot(strikes, IV_Hest, label=\"Hest\")\n",
"plt.plot(strikes, IV_L_BS, \"d\", label=\"BS Lewis\")\n",
"plt.plot(strikes, IV_L_VG, \"d\", label=\"VG Lewis\")\n",
"plt.plot(strikes, IV_L_Mert, \"d\", label=\"Mert Lewis\")\n",
"plt.plot(strikes, IV_L_Hest, \"d\", label=\"Hest Lewis\")\n",
"plt.ylim([0.08, 0.45])\n",
"plt.legend(loc=\"upper center\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ok, this method seems a bit slower than the previous, but it can be a good alternative."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Market data\n",
"\n",
"\n",
"Let's analyze some options market data (the expiration dates were chosen at random). I took the following values from [barchart](https://www.barchart.com/etfs-funds/quotes/SPY/options?moneyness=allRows&expiration=2020-05-15-m).\n",
"\n",
"The current date is 02 July 2020.\n",
"\n",
"- Maturity date: 10 Aug 2020 $ \\quad \\quad \\sim$ 1 week time to maturity.\n",
"- Maturity date: 15 Jan 2021 $\\quad \\quad \\sim$ 1/2 year time to maturity\n",
"\n",
"This is a set of option prices written on the [SPY](https://en.wikipedia.org/wiki/SPDR_S%26P_500_Trust_ETF) ETF. \n",
" \n",
"If we assume 252 business days, the time to maturity to the 10th of July corresponds to the year fraction \n",
"$T = 5/252$, where 3, 4, and 5 July are holidays. \n",
"For the 6 months maturity I'm using the Act-365 convention with $T=197/365$. Counting the year fraction considering only business days it is more common when dealing with options (see [4]) but in this case I have no idea how many holidays there are till the 15th of January. \n",
"\n",
"The current value of SPY is $S_0 = 312.23$. \n",
"\n",
"I didn't check the actual interest rate, and I will use a reasonable ad-hoc value. **This choice has a very important effect on the values of implied volatility.** In a serious analysis you should use the correct value of $r$ (which has a term structure dependent on T as well).\n",
"\n",
"##### Comment:\n",
"For for very short times to maturity ($\\sim$ days), it is very difficult to replicate the smile in the ITM and OTM regions. The reasons are several. There can be numerical error issues in the pricing and in the calibration process. Or more simply, the chosen stochastic model is not so good."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Files in the data folder: ['stocks_data.csv', 'spy-options-exp-2021-01-15-weekly-show-all-stacked-07-05-2020.csv', 'spy-options-exp-2020-07-10-weekly-show-all-stacked-07-05-2020.csv', 'historical_data.csv']\n"
]
}
],
"source": [
"files = os.listdir(os.getcwd() + \"/data\")\n",
"print(\"Files in the data folder: \", files)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## 1 week maturity"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"S0 = 312.23\n",
"r = 0.05\n",
"T = 5 / 252\n",
"opt_param = Option_param(S0=S0, K=S0, T=T, v0=0.04, exercise=\"European\", payoff=\"call\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
Strike
\n",
"
Bid
\n",
"
Midpoint
\n",
"
Ask
\n",
"
IV
\n",
"
Type
\n",
"
Spread
\n",
"
\n",
" \n",
" \n",
"
\n",
"
225
\n",
"
370
\n",
"
56.89
\n",
"
57.11
\n",
"
57.33
\n",
"
0.1387
\n",
"
Put
\n",
"
0.44
\n",
"
\n",
"
\n",
"
226
\n",
"
375
\n",
"
61.90
\n",
"
62.11
\n",
"
62.33
\n",
"
0.0025
\n",
"
Put
\n",
"
0.43
\n",
"
\n",
"
\n",
"
227
\n",
"
380
\n",
"
66.89
\n",
"
67.11
\n",
"
67.33
\n",
"
0.0000
\n",
"
Put
\n",
"
0.44
\n",
"
\n",
"
\n",
"
228
\n",
"
385
\n",
"
71.90
\n",
"
72.12
\n",
"
72.33
\n",
"
0.0000
\n",
"
Put
\n",
"
0.43
\n",
"
\n",
"
\n",
"
229
\n",
"
390
\n",
"
76.88
\n",
"
77.10
\n",
"
77.33
\n",
"
0.7404
\n",
"
Put
\n",
"
0.45
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Strike Bid Midpoint Ask IV Type Spread\n",
"225 370 56.89 57.11 57.33 0.1387 Put 0.44\n",
"226 375 61.90 62.11 62.33 0.0025 Put 0.43\n",
"227 380 66.89 67.11 67.33 0.0000 Put 0.44\n",
"228 385 71.90 72.12 72.33 0.0000 Put 0.43\n",
"229 390 76.88 77.10 77.33 0.7404 Put 0.45"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filename = \"data/\" + \"spy-options-exp-2020-07-10-weekly-show-all-stacked-07-05-2020.csv\"\n",
"data = pd.read_csv(filename, usecols=[0, 3, 4, 5, 8, 11])\n",
"data[\"IV\"] = data[\"IV\"].str.rstrip(\"%\").astype(\"float\") / 100.0 # transforms the percentage into decimal\n",
"data[\"Spread\"] = data.Ask - data.Bid # spread column\n",
"data.tail()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"CALL = data[data.Type == \"Call\"]\n",
"PUT = data[data.Type == \"Put\"].reset_index(drop=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Let us recover the implied volatility from option prices.\n",
"\n",
"We can check if the volatilities we computed correspond to the volatilities provided in the CSV file. \n",
"For this purpose, we consider the Mid-price defined as the average between Bid and Ask prices. The error interval is the interval between Bid IV and Ask IV.\n",
"\n",
"When working with this type of data, frequently happens that the CSV file contains unreliable values of IV:\n",
"1. because the solver was not able to converge.\n",
"2. because other operational errors.\n",
"\n",
"Sometimes the problem is just the choice of using the Mid-price. Choosing another value between the Ask and the Bid price can yield a reliable value of the IV. In this case we can use `implied_vol_minimize`, which is able to reduce the error introduced by using the wrong price. \n",
"In other cases, the error cannot be reduced and I think it is better to ignore those values."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plots. Minimization:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"IV_call = CALL.apply(lambda x: implied_vol_minimize(x[\"Midpoint\"], S0, x[\"Strike\"], T, r, payoff=\"call\"), axis=1)\n",
"CALL = CALL.assign(IV_mid=IV_call.values)\n",
"IV_put = PUT.apply(\n",
" lambda x: implied_vol_minimize(x[\"Midpoint\"], S0, x[\"Strike\"], T, r, payoff=\"put\", disp=False), axis=1\n",
")\n",
"PUT = PUT.assign(IV_mid=IV_put.values)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 4))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"ax1.plot(strikes, option_type.IV_mid, \".\", label=\"BS inversion\")\n",
"ax2.plot(strikes, option_type.IV_mid, \".\", label=\"BS inversion\")\n",
"ax1.plot(strikes, IV_VG, label=\"VG\")\n",
"ax2.plot(strikes, IV_VG, label=\"VG\")\n",
"ax1.plot(strikes, IV_Mert, label=\"Mert\")\n",
"ax2.plot(strikes, IV_Mert, label=\"Mert\")\n",
"ax1.plot(strikes, IV_Hest, label=\"Hest\")\n",
"ax2.plot(strikes, IV_Hest, label=\"Hest\")\n",
"ax2.axis([250, 400, 0.15, 0.4])\n",
"ax2.axvline(S0, color=\"black\", label=\"ATM\", linewidth=1)\n",
"ax2.set_title(\"Model Implied volatilities. ATM Zoom\")\n",
"ax1.set_xlabel(\"Strike\")\n",
"ax1.set_ylabel(\"Imp Vol\")\n",
"ax1.set_title(\"Model Implied volatilities\")\n",
"ax2.set_xlabel(\"Strike\")\n",
"ax2.set_ylabel(\"Imp Vol\")\n",
"ax1.legend()\n",
"ax2.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again the Merton model is the model that performs better."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"[1] Rebonato, R. (1999) \"Volatility and Correlation: In the Pricing of Equity, FX and Interest-Rate Options\", Wiley Series in Financial Engineering.\n",
"\n",
"[2] Gatheral, J. (2006) \"The Volatility Surface: A Practitioner’s Guide (Wiley Finance Series)\", John Wiley & Sons, New York.\n",
"\n",
"[3] Yiran Cui, Sebastian del Baño Rollin, Guido Germano, (2017), \"Full and fast calibration of the Heston stochastic volatility model\" European Journal of operational research. [link](http://www0.cs.ucl.ac.uk/staff/g.germano/papers/EurJOperRes_2017.pdf)\n",
"\n",
"[4] John Hull. \"Options, Futures, and Other Derivatives\"."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: 5.1 Linear regression - Kalman filter.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Linear Regression by Kalman filter\n",
"\n",
"\n",
"The Kalman filter is a great tool!!\n",
"\n",
"This is one of my favourite algorithms. It can be used in almost all practical applications in finance. \n",
"And this is the reason I decided to write a notebook on the **applications of the Kalman filter to linear regression**.\n",
"\n",
"I suggest the beginners in this area to have a look at [https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python). \n",
"In my opinion this is one of the best interactive tutorials on Bayesian Filters ever written (it is also the only one that I know). \n",
"\n",
"## Contents\n",
" - [Real market data](#sec1)\n",
" - [Data Cleaning](#sec1.1) \n",
" - [Linear regression](#sec2)\n",
" - [The Kalman filter](#sec3)\n",
" - [Kalman regression for the beta](#sec3.1) "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import datetime as dt\n",
"import pandas as pd\n",
"import importlib\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits import mplot3d\n",
"from matplotlib import cm\n",
"import matplotlib.gridspec as gridspec\n",
"\n",
"%matplotlib inline\n",
"import statsmodels.api as sm\n",
"import scipy.stats as ss\n",
"from scipy.optimize import minimize\n",
"from IPython import display\n",
"from scipy.linalg import inv\n",
"import FMNM.Kalman_filter as KF"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"# Real market data\n",
"\n",
"I have collected some market time series from different sources and saved them in `historical_data.csv`, such that now we can play with them.\n",
"\n",
"The tickers are: \n",
"- STOCKS: `AAPL`-> Apple, `EXO.MI`-> Exor (FIAT,Ferrari,etc), `FB`-> Facebook, `GOOGL`-> Google, `UBS`-> UBS, \n",
"- INDECES: `^GSPC`-> [S&P500](https://en.wikipedia.org/wiki/S%26P_500_Index), `^IXIC`-> [Nasdaq](https://en.wikipedia.org/wiki/NASDAQ_Composite), `^VIX`-> [VIX](https://en.wikipedia.org/wiki/VIX) (Implied Volatility of S&P500). \n",
"- BOND:`^TNX`-> Yield 10Y US. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
" AAPL EXO.MI FB GOOGL UBS \\\n",
"Date \n",
"2019-05-01 208.918625 NaN 193.029999 1173.319946 12.593584 \n",
"2019-05-27 NaN 59.554962 NaN NaN NaN \n",
"2019-07-04 NaN 64.400002 NaN NaN NaN \n",
"2019-08-15 201.740005 NaN 182.589996 1169.319946 10.160000 \n",
"2019-09-02 NaN 61.840000 NaN NaN NaN \n",
"\n",
" ^GSPC ^IXIC ^TNX ^VIX \n",
"Date \n",
"2019-05-01 2923.729980 8049.640137 2.511 14.80 \n",
"2019-05-27 NaN NaN NaN NaN \n",
"2019-07-04 NaN NaN NaN NaN \n",
"2019-08-15 2847.600098 7766.620117 1.529 21.18 \n",
"2019-09-02 NaN NaN NaN NaN "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[data.isna().any(axis=1)].tail()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AAPL False\n",
"EXO.MI False\n",
"FB False\n",
"GOOGL False\n",
"UBS False\n",
"^GSPC False\n",
"^IXIC False\n",
"^TNX False\n",
"^VIX False\n",
"dtype: bool"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(data < 1e-2).any()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fortunately there are no non-positive values, but there are several NaNs. \n",
"Why?\n",
"\n",
"There can be many reason, but in this case I think it is because of a different calendar (different countries have different holidays). \n",
"\n",
"##### How to solve this problem?\n",
"There are several possibilities: \n",
"- 1) We may remove the entire line \n",
"- 2) We may forward fill the missing values \n",
"- 3) We may backward fill the missing values \n",
"- 4) We may interpolate or estimate (e.g. regression) the missing values \n",
"\n",
"All these possibilities have advantages and disvantages. This is a delicate task, for more information see [Imputation](https://en.wikipedia.org/wiki/Imputation_(statistics)).\n",
"\n",
"First of all, let us select the two tickers for the regression analysis.\n",
"\n",
"### Feel free to change the tickers in the next cell:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df = data[[\"GOOGL\", \"^GSPC\"]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this analysis I use the following convention:\n",
"\n",
"- If in one day there is only one value missing, I use the method 2) i.e. **forward filling**. I can justify this choice by saying that if there is no price, it means that the price hasn't changed.\n",
"\n",
"- If in one day both values are missing, then I remove the entire line. (Probably the market was closed on that day) "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Are there still some NaNs? \n"
]
},
{
"data": {
"text/plain": [
"GOOGL False\n",
"^GSPC False\n",
"dtype: bool"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"history_len = 1000 # lenght of the time series\n",
"df.sort_index(inplace=True, ascending=True) # not necessary in general, but useful\n",
"df = df.dropna(axis=1, how=\"all\") # drops columns with all NaNs\n",
"df = df.dropna(axis=0, how=\"all\") # drops rows with at least one NaN\n",
"df = df[-history_len:]\n",
"df = df.ffill() # Forward fill\n",
"print(\"Are there still some NaNs? \")\n",
"df.isnull().any()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we call $S_t$ the price under consideration (stock, index, rate) at time $t$, we can work with three types of returns $X_t$:\n",
"- Total returns: \n",
"$$ X_t = \\frac{S_t}{S_{t-1}} $$\n",
"- Linear returns:\n",
"$$ X_t = \\frac{S_t}{S_{t-1}} - 1 $$\n",
"- Logarithmic returns:\n",
"$$ X_t = \\log \\frac{S_t}{S_{t-1}} $$\n",
"\n",
"For now, I prefer to work with log-returns. But feel free to try other options."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"returns = \"log-returns\"\n",
"\n",
"if returns == \"log-returns\":\n",
" ret = np.log(df / df.shift())[1:]\n",
"elif returns == \"linear-returns\":\n",
" ret = df.pct_change()[1:]\n",
"elif returns == \"total-returns\":\n",
" ret = df / df.shift()[1:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Plot"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"NOSD = 3 # Number Of Standard Deviations\n",
"mu = ret.mean()\n",
"sig = ret.std()\n",
"fig = plt.figure(figsize=(16, 5))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"\n",
"df.plot(ax=ax1)\n",
"ax1.set_title(\"Time series of prices\")\n",
"ax1.set_xlabel(\"Time\")\n",
"ax1.set_ylabel(\"Price\")\n",
"ret.plot(ax=ax2)\n",
"ax2.set_title(\"Time series of returns\")\n",
"ax2.set_xlabel(\"Time\")\n",
"ax2.set_ylabel(returns)\n",
"ax2.plot(ret.index, len(ret) * [mu], color=\"k\")\n",
"ax2.plot(ret.index, len(ret) * [mu + NOSD * sig], color=\"c\")\n",
"ax2.plot(ret.index, len(ret) * [mu - NOSD * sig], color=\"c\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What about the outliers?\n",
"\n",
"We can see that there are returns that are quite bigger than $3\\sigma$ (three standard deviations). \n",
"If we assume a Normal distribution for the returns, these outliers can create problems.\n",
"\n",
"Wait a second... \n",
"we calculated the standard deviation `ret.std()` using the data containing the ouliers!! \n",
"It means that the value of the standard deviation is aslo affected by the outliers!!\n",
"\n",
"The solution is to use [robust](https://en.wikipedia.org/wiki/Robust_statistics) estimators of the location and the dispersion of a distribution. They work much better in presence of outliers. \n",
"\n",
"Although there are several alternatives, I prefer to use the **median** and the [MAD](https://en.wikipedia.org/wiki/Median_absolute_deviation). These estimators are more robust than the mean and standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD instead, the deviations of a small number of outliers are irrelevant. \n",
"\n",
"As explained on [wiki](https://en.wikipedia.org/wiki/Median_absolute_deviation), in order to use the MAD as a consistent estimator of the standard deviation, we have to take \n",
"\n",
"$$ \\sigma = k \\, \\text{MAD} $$\n",
"\n",
"where under the assumption of Normal distribution, $k = 1.4826$."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"STD DEV: \n",
" GOOGL 0.014242\n",
"^GSPC 0.008324\n",
"dtype: float64\n",
"\n",
"Robust STD DEV: \n",
" GOOGL 0.010268\n",
"^GSPC 0.005255\n",
"dtype: float64\n"
]
}
],
"source": [
"median = ret.median()\n",
"MAD = ret.apply(ss.median_abs_deviation)\n",
"sig_robust = MAD * 1.4826\n",
"print(\"STD DEV: \\n\", sig)\n",
"print(\"\")\n",
"print(\"Robust STD DEV: \\n\", sig_robust);"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"outliers = ret[ret[np.abs(ret - median) > NOSD * sig_robust].any(axis=1)] # outliers\n",
"dret = ret.drop(outliers.index) # series of returns without outliers"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 6))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"\n",
"for ax, col in zip([ax1, ax2], dret.columns):\n",
" dret[col].plot(ax=ax)\n",
" ax.set_title(\"Clean series - No outliers\")\n",
" ax.set_xlabel(\"Time\")\n",
" ax.set_ylabel(returns)\n",
" ax.plot(dret.index, len(dret) * [median[col]], color=\"k\")\n",
" ax.plot(dret.index, len(dret) * [median[col] + NOSD * sig_robust[col]], color=\"r\", label=\"+3$\\sigma$\")\n",
" ax.plot(dret.index, len(dret) * [median[col] - NOSD * sig_robust[col]], color=\"r\", label=\"-3$\\sigma$\")\n",
" ax.legend(loc=\"upper right\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"We have reduced the initial DIRTY series of returns of size 999 to the new CLEAN series of size 911.\n"
]
}
],
"source": [
"print(\n",
" \"We have reduced the initial DIRTY series of returns of size {} to the \\\n",
"new CLEAN series of size {}.\".format(\n",
" len(ret), len(dret)\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Linear regression\n",
"\n",
"Let me recall some well known notions about [linear regression](https://en.wikipedia.org/wiki/Linear_regression). The linear model is:\n",
"\n",
"$$ Y = \\alpha + \\beta X + \\epsilon $$\n",
"\n",
"where \n",
"- X is the predictor variable\n",
"- Y is the response variable\n",
"- $\\epsilon$ is the error such that $Std[\\epsilon]= \\sigma$ and $Cov[\\epsilon,X]=0$. \n",
"\n",
"An additional useful hypothesis is to consider normal errors $\\epsilon \\sim \\mathcal{N}(0,\\sigma^2)$. This assumption permits to use MLE methods and to calculate confidence intervals.\n",
"\n",
"The parameters $\\alpha$ and $\\beta$ can be estimated by:\n",
"\n",
"$$ \\mathbb{E}[Y] = \\alpha + \\beta \\mathbb{E}[X] \\quad \\Rightarrow \\quad \\alpha = \\mathbb{E}[Y] - \\beta \\mathbb{E}[X]$$\n",
"\n",
"and, using the bilinear property of the Covariance:\n",
"\n",
"$$ Cov[X,Y] = Cov[ X, \\alpha + \\beta X + \\epsilon ] = \\beta Var[X] $$\n",
"\n",
"$$ \\beta = \\frac{Cov[X,Y]}{Var[X]} $$\n",
"\n",
"If we have some observations $x_i$, we can use the linear regression as a model for the respose variables only (which are random) $Y_i = \\alpha + \\beta x_i + \\epsilon_i$, where the $\\epsilon_i$ are uncorrelated. The expectation $\\mathbb{E}[Y_i]$ is intended as a conditional expectation $\\mathbb{E}[Y_i|x_i]$. \n",
"\n",
"If we have two sets of data $y_i$ and $x_i$ for $1 \\leq i \\leq n$, where $y_i$ are the realizations of $Y_i$, we call:\n",
"$\\bar x = \\frac{1}{n} \\sum_{i=1}^n x_i$ and $\\bar y = \\frac{1}{n} \\sum_{i=1}^n y_i$. \n",
"We also define \n",
"\n",
"$$S_{xx} = \\sum_{i=1}^n (x_i - \\bar x)^2 \\quad S_{yy} = \\sum_{i=1}^n (y_i - \\bar y)^2 \\quad S_{xy} = \\sum_{i=1}^n (x_i - \\bar x)(y_i - \\bar y) $$ \n",
"\n",
"The estimator $\\hat \\beta$ and $\\hat \\alpha$ for $\\beta$ and $\\alpha$ are: \n",
"\n",
"$$ \\hat \\beta = \\frac{S_{xy}}{S_{xx}} \\quad \\quad \\hat \\alpha = \\bar y - \\beta \\bar x $$\n",
"\n",
"These estimators are the **least squares** estimators. They are also **MLE estimators** and **BLUE** (best linear unbiased estimators). \n",
"Of course $\\mathbb{E}[\\hat \\beta]=\\beta$ and $\\mathbb{E}[\\hat \\alpha]=\\alpha$ (they are unbiased). We also have:\n",
"\n",
"$$ Var[\\hat \\beta] = \\frac{\\sigma^2}{S_{xx}} \\quad Var[\\hat \\alpha] = \\frac{\\sigma^2}{n S_{xx}}\\sum_{i=1}^n x_i^2 $$\n",
"\n",
"and under the assumption of Normal errors, the estimators are also Normal distributed. (the square root of the variances above, is called \"standard error\" of $\\hat \\alpha$ and $\\hat \\beta$) \n",
"The residual estimator is $\\hat \\epsilon_i = y_i - \\hat \\alpha - \\hat \\beta x_i$. We can define the estimator for $\\sigma$.\n",
"- MLE (biased) \n",
"$$ \\hat \\sigma^2 = \\frac{1}{n} \\sum_{i=1}^n \\epsilon_i^2 $$\n",
"it has $\\mathbb{E}[\\hat \\sigma] = \\frac{n-2}{n} \\sigma^2 $.\n",
"- Unbiased version:\n",
"$$ S^2 = \\frac{n}{n-2}\\hat \\sigma^2 = \\frac{1}{n-2} \\sum_{i=1}^n \\epsilon_i^2 $$\n",
"Under the normal assumption of errors, it is distributed as $\\frac{(n-2)S^2}{\\sigma^2} \\sim \\chi^2_{n-2}$.\n",
"\n",
"Confidence intervals can be obtained from the statistics:\n",
"\n",
"$$ \\frac{\\hat \\alpha - \\alpha}{ S \\sqrt{(\\sum_{i=1}^n x_i^2)/nS_{xx}} } \\sim t_{n-2} \\quad and \\quad \n",
"\\frac{\\hat \\beta - \\beta}{ S \\sqrt{S_{xx}} } \\sim t_{n-2} $$\n",
"\n",
"where the parameter $\\sigma$ is replaced by its unbiased estimator $S$. For a short (but complete) description of confidence intervals you can have a look at the notebook **1.2**. \n",
"For more info see [CI for linear regression](https://en.wikipedia.org/wiki/Simple_linear_regression#Confidence_intervals), or the deeper presentation in Chapter 11.3 of [1].\n",
"\n",
"Last, let us recall (with no proof) that if we call $\\hat y_i = \\hat \\alpha + \\hat \\beta x_i$, then\n",
"\n",
"$$ S_{yy} = \\sum_{i=1}^n (y_i - \\bar y)^2 =\\; \\sum_{i=1}^n (\\hat y_i - \\bar y)^2 + \\sum_{i=1}^n (y_i - \\hat y_i)^2 \n",
"=\\; \\frac{S^2_{xy}}{S_{xx}} + \\sum_{i=1}^n \\hat \\epsilon_i^2 .$$\n",
"\n",
"We can define the **coefficient of determination $r^2$** such that $0 \\leq r^2 \\leq 1$, as\n",
"\n",
"\\begin{align*}\n",
"r^2 &=\\; \\frac{\\sum_{i=1}^n (\\hat y_i - \\bar y)^2}{\\sum_{i=1}^n (y_i - \\bar y)^2} =\\; \\frac{S_{xy}^2}{S_{xx}S_{yy}} \\\\\n",
"&=\\; 1 - \\frac{\\sum_{i=1}^n (\\hat \\epsilon_i)^2}{\\sum_{i=1}^n (y_i - \\bar y)^2}.\n",
"\\end{align*}"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"In our regression Y is GOOGL and X is ^GSPC\n"
]
}
],
"source": [
"X = dret[dret.columns[1]]\n",
"Y = dret[dret.columns[0]]\n",
"print(\"In our regression Y is {} and X is {}\".format(dret.columns[0], dret.columns[1]))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"We can use one of the many python functions:\n",
"beta: 1.181743, alpha: 0.000058, R2: 0.428090, standard error: 0.045304.\n",
"\n",
"Or I can estimate by myself:\n",
"beta: 1.1817426272884215, alpha: 5.772407285412843e-05, sigma: 0.007842859851366457\n"
]
}
],
"source": [
"b_ss, a_ss, r_ss, _, std_err_ss = ss.linregress(X, Y)\n",
"print(\n",
" \"We can use one of the many python functions:\\nbeta: {0:6f}, alpha: {1:6f}, R2: {2:6f}, \\\n",
"standard error: {3:6f}.\".format(\n",
" b_ss, a_ss, r_ss**2, std_err_ss\n",
" )\n",
")\n",
"\n",
"beta_tot_OLS = np.cov(X, Y, ddof=1)[0, 1] / np.var(X, ddof=1)\n",
"alpha_tot_OLS = np.mean(Y) - beta_tot_OLS * np.mean(X)\n",
"res = Y - beta_tot_OLS * X - alpha_tot_OLS\n",
"std_eps_OLS = np.std(res, ddof=2)\n",
"print(\"\\nOr I can estimate by myself:\\nbeta: {}, alpha: {}, sigma: {}\".format(beta_tot_OLS, alpha_tot_OLS, std_eps_OLS))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Come on....Let's compute everything: (with the help of the scalar product between vectors `@`)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"beta: 1.1817426272884215 and alpha: 5.772407285412843e-05\n",
"sigma: 0.007842859851366457\n",
"Standard Error for beta: 0.04530406599209334\n",
"R squared: 0.42809009281099913\n",
"Confidence intervals for beta: (1.0928299019730954, 1.2706553526037476)\n",
"Confidence intervals for alpha: (-0.0004599474292502449, 0.0005753955749585017)\n"
]
}
],
"source": [
"n = len(X)\n",
"x_mean = np.mean(X)\n",
"y_mean = np.mean(Y)\n",
"Sxx = (X - x_mean) @ (X - x_mean)\n",
"Syy = (Y - y_mean) @ (Y - y_mean)\n",
"Sxy = (X - x_mean) @ (Y - y_mean)\n",
"beta_hat = Sxy / Sxx\n",
"alpha_hat = y_mean - beta_hat * x_mean # beta and alpha\n",
"print(\"beta: {} and alpha: {}\".format(beta_hat, alpha_hat))\n",
"epsilon = Y - beta_hat * X - alpha_hat # residuals\n",
"S2 = epsilon @ epsilon / (n - 2)\n",
"S = np.sqrt(S2) # unbiased estimator\n",
"print(\"sigma: {}\".format(S))\n",
"var_beta = S2 / Sxx # variance of beta\n",
"var_alpha = (X @ X) * S2 / (Sxx * n) # variance of alpha\n",
"s_b = np.sqrt(var_beta) # standard error for beta\n",
"s_a = np.sqrt(var_alpha) # standard error for alpha\n",
"R2 = Sxy**2 / (Sxx * Syy)\n",
"print(\"Standard Error for beta: \", s_b)\n",
"print(\"R squared: {}\".format(R2))\n",
"CI_b = ss.t.interval(0.95, n - 2, loc=beta_hat, scale=s_b)\n",
"b_up = CI_b[0]\n",
"b_down = CI_b[1]\n",
"CI_a = ss.t.interval(0.95, n - 2, loc=alpha_hat, scale=s_a)\n",
"print(\"Confidence intervals for beta: \", CI_b)\n",
"print(\"Confidence intervals for alpha: \", CI_a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The intercept $\\alpha$ is (always) almost zero. Therefore it is not relevant for the future analysis. \n",
"\n",
"##### Plot:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(res.min(), res.max(), 100) # res are the residuals\n",
"\n",
"fig = plt.figure(figsize=(16, 5))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"ax1.scatter(X, Y)\n",
"ax1.set_title(\"Scatter plot X-Y\")\n",
"ax1.plot(X, alpha_hat + b_up * X, color=\"g\", label=\"upper bound beta\")\n",
"ax1.plot(X, alpha_hat + b_down * X, color=\"y\", label=\"lower bound beta\")\n",
"ax1.plot(X, alpha_hat + beta_hat * X, color=\"red\", label=\"estimated beta\")\n",
"ax1.legend(loc=\"upper left\")\n",
"ax2.plot(x, ss.norm.pdf(x, 0, S), label=\"Normal distribution\")\n",
"params = ss.t.fit(res)\n",
"ax2.plot(x, ss.t.pdf(x, loc=params[1], scale=params[2], df=params[0]), label=\"Student t distribution\")\n",
"ax2.hist(res, bins=50, density=True, facecolor=\"LightBlue\")\n",
"ax2.legend()\n",
"ax2.set_title(\"Residual histogram\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first thing we notice is that, although we removed the outliers, the Normal fit is still not so good. \n",
"The distribution is better described by a Student t distribution with "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Degrees of freedom of fitted t distribution: 7.247881899338388\n"
]
}
],
"source": [
"print(\"Degrees of freedom of fitted t distribution: \", params[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, it is much easier to mantain the Normal assumption, because it permits to use confidence intervals and... also... the Kalman filter!!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# The Kalman filter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I do not have space and time to explain the meaning and the details of the [Kalman Filter](https://en.wikipedia.org/wiki/Kalman_filter) in this notebook. I assume the reader already has a basic knowledge. Otherwise, for a pedagogical exposition, I suggest to have a look at the tutorial mentioned at the top. The presentation on wikipedia, although very short, is also quite clear.\n",
"\n",
"Important things to remember: the KF (Kalman Filter) assumes a linear dynamics for the true state $\\mathbf x_k$ with $k \\in \\mathbb{N}$, and Normally distributed errors. \n",
"\n",
"$$\n",
"\\begin{aligned}\n",
"\\mathbf x_{k} &= \\mathbf F_k \\mathbf x_{k-1} + \\mathbf w_k & \\text{with} \\quad \\mathbf w_k \\sim \\mathcal{N}(0, \\mathbf Q_k) \\\\\n",
"\\mathbf z_k &= \\mathbf H_k \\mathbf x_{k} + \\mathbf v_k & \\text{with} \\quad \\mathbf v_k \\sim \\mathcal{N}(0, \\mathbf R_k) \\\\\n",
"\\\\\\end{aligned}\n",
"$$\n",
"\n",
"The first equation is the **process equation** for the true state,\n",
"where $\\mathbf F_k$ is the *state transition function* (or matrix), and $\\mathbf w_k$ is the process noise with covariance $\\mathbf Q_k$. \n",
"The second equation is the **measurement equation**, where $\\mathbf z_k$ are the measurement of the true state $\\mathbf x_k$. The term $\\mathbf H_k$ is the *measurement function* (or matrix), and $\\mathbf v_k$ is the measurement noise with covariance $\\mathbf R_k$. \n",
"\n",
"The Kalman filter equations, taken from [here](https://en.wikipedia.org/wiki/Kalman_filter#Underlying_dynamical_system_model), are:\n",
"\n",
"$$\n",
"\\begin{aligned}\n",
"\\text{Predict Step}\\\\\n",
"\\hat{\\mathbf x}_{k\\mid k-1} &= \\mathbf F_k\\hat{\\mathbf x}_{k-1\\mid k-1} \\hspace{10em} \\text{(a priori) state mean prediction} \\\\\n",
"\\mathbf P_{k\\mid k-1} &= \\mathbf F_k \\mathbf P_{k-1\\mid k-1} \\mathbf F_k^\\mathsf T + \\mathbf Q_k \\hspace{6.5em} \\text{(a priori) state covariance prediction} \\\\ \t\n",
"\\\\\n",
"\\text{Helper variables}\\\\\n",
"\\tilde{\\mathbf y}_k &= \\mathbf z_k - \\mathbf H_k\\hat{\\mathbf x}_{k\\mid k-1} \\hspace{9.3em} \\text{pre-fit residuals}\\\\\n",
"\\mathbf{S}_k &= \\mathbf H_k \\mathbf P_{k\\mid k-1} \\mathbf H_k^\\mathsf T + \\mathbf R_k \\hspace{8em} \\text{pre-fit measurement covariance} \\\\\n",
"\\mathbf K_k &= \\mathbf P_{k\\mid k-1}\\mathbf H_k^\\mathsf T \\mathbf{S}_k^{-1} \\hspace{10em} \\text{Kalman Gain}\\\\\n",
"\\\\\n",
"\\text{Update Step}\\\\\n",
"\\hat{\\mathbf x}_{k\\mid k} &= \\hat{\\mathbf x}_{k\\mid k-1} + \\mathbf K_k\\tilde{\\mathbf y}_k \\hspace{9.4em} \\text{(a posteriori) state estimate}\\\\\n",
"\\mathbf P_{k|k} &= (\\mathbb{1} - \\mathbf K_k \\mathbf H_k) \\mathbf P_{k|k-1} \\hspace{8em} \\text{(a posteriori) covariance estimate} \\\\\n",
"\\\\\n",
"\\tilde{\\mathbf y}_{k\\mid k} &= \\mathbf z_k - \\mathbf H_k\\hat{\\mathbf x}_{k\\mid k} \\hspace{10em} \\text{post-fit residuals}\\\\\n",
"\\\\\\end{aligned}\n",
"$$\n",
"\n",
"The notation $\\hat {\\mathbf {x} }_{k\\mid k-1}$ represents the (a priori) estimate of $\\mathbf {x}$ at time $k$ given an observation (a measurement) at time $k-1$. Instead, $\\hat {\\mathbf {x} }_{k\\mid k}$ represents the (a posteriori) estimate of $\\mathbf {x}$ at time $k$ given an observation at time $k$.\n",
"\n",
"##### Comments:\n",
"- Residuals are computed in the measurement space. \n",
"The variable $\\mathbf x$ needs to be transformed by $\\mathbf H$ before the subtraction with $\\mathbf z$.\n",
"- The (a priori) covariance is simply the conditional covariance of the process dynamics:\n",
"\n",
"$$\\begin{aligned}\n",
"\\mathbf P_{k\\mid k-1} = \\text{Cov}[\\mathbf x_k \\mid \\mathbf x_{k-1}] &= \\text{Cov}[ \\mathbf F_k \\mathbf x_{k-1} \\mid \\mathbf x_{k-1}] + \\text{Cov}[\\mathbf w_k \\mid \\mathbf x_{k-1}] \\\\\n",
" &= \\mathbf F_k \\, \\text{Cov}[\\mathbf x_{k-1} \\mid \\mathbf x_{k-1}]\\, \\mathbf F_k^T + \\mathbf Q_k \\\\\n",
" &= \\mathbf F_k \\, \\mathbf P_{k-1\\mid k-1}\\, \\mathbf F_k^T + \\mathbf Q_k \n",
"\\end{aligned}$$\n",
"- The term $\\mathbf{S}_k$ is the conditional covariance of the measurement. \n",
"\n",
"$$\\begin{aligned}\n",
"\\mathbf S_{k} = \\text{Cov}[\\mathbf z_k \\mid \\mathbf x_{k-1}] &= \\text{Cov}[ \\mathbf H_k \\mathbf x_{k} \\mid \\mathbf x_{k-1}] + \\text{Cov}[\\mathbf v_k \\mid \\mathbf x_{k-1}] \\\\\n",
" &= \\mathbf H_k \\, \\text{Cov}[\\mathbf x_{k} \\mid \\mathbf x_{k-1}]\\, \\mathbf H_k^T + \\mathbf R_k \n",
"\\end{aligned}$$\n",
"- Of course $\\mathbf w_{k}$ and $\\mathbf v_{k}$ are independent of $\\mathbf x_{k-1}$.\n",
"\n",
"##### Marginal probability distribution\n",
"\n",
"Let us recall the [marginal distribution](https://en.wikipedia.org/wiki/Kalman_filter#Marginal_likelihood), where the hidden state variables $\\mathbf x_k$ are integrated out. \n",
"If we define $\\mathbf z = (\\mathbf z_0, \\mathbf z_1, ... )$, we have:\n",
"\n",
"$$ \\mathbb{P}(\\mathbf z) = \\prod_{k} \\mathcal{N} \\bigg( \\mathbf z_k; \\; \\mathbf H_k \\mathbf x_{k \\mid k-1}, \\mathbf S_k \\bigg) $$\n",
"\n",
"where $\\mathbf H_k \\mathbf x_{k \\mid k-1}$ and $\\mathbf S_k$ are the mean and covariance of normal distribution of $\\mathbf z_k$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Kalman regression model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us introduce the model for the Kalman regression. \n",
"The state variable is $\\mathbf x_k = \\bigg( \\begin{array}{c} \\alpha_k \\\\ \\beta_k \\end{array} \\bigg)$. These variables are **hidden** i.e. they are not measurable directly. We can only measure $x_k$ and $y_k$. \n",
"#### Notation issue!\n",
"Here I use the variable $y_k$ to indicate the response variable in the regression and not the residual variable of the Kalman filter. Above I wanted to use the same notation of wikipedia. Below, I indicate the Kalman residuals with `r_k`. \n",
"\n",
"The model equations are:\n",
"##### process equation:\n",
"$$\n",
"\\biggl(\\begin{array}{c} \\alpha_k\\\\ \\beta_k \\end{array}\\biggr) = \n",
"\\biggl(\\begin{array}{cc} 1 & 0\\\\ 0 & 1 \\end{array}\\biggr)\n",
"\\biggl(\\begin{array}{c} \\alpha_{k-1}\\\\ \\beta_{k-1} \\end{array}\\biggr) \n",
"+ \\biggl(\\begin{array}{c} \\eta^{\\alpha}_k\\\\ \\eta^{\\beta}_k \\end{array}\\biggr) \\quad \\text{with} \\quad \n",
"\\biggl(\\begin{array}{c} \\eta^{\\alpha}_k\\\\ \\eta^{\\beta}_k \\end{array}\\biggr) \\sim \\mathcal{N}(0, \\mathbf Q_k) \n",
"\\quad \\text{and} \\quad \\mathbf Q_k = \\biggl(\\begin{array}{cc} \\sigma^2_{\\alpha} & 0\\\\ 0 & \\sigma^2_{\\beta} \\end{array}\\biggr) $$\n",
"##### measurement equation:\n",
"$$\n",
"y_k = \\bigl(\\begin{array}{cc} 1 & x_k \\end{array}\\bigr) \\biggl(\\begin{array}{c} \\alpha_k\\\\ \\beta_k \\end{array}\\biggr)\n",
"+ \\epsilon_k \\quad \\text{with} \\quad \\epsilon_k \\sim \\mathcal{N}(0, \\sigma^2_{\\epsilon}) \\\\\n",
"$$\n",
"\n",
"We see that the transition matrix is the identity $\\mathbf F_k = \\mathbb 1$, and the measurement matrix is $\\mathbf H_k = \\bigl(\\begin{array}{cc} 1 & x_k \\end{array}\\bigr) $. \n",
"We also assume $\\eta^{\\alpha}_k$ and $\\eta^{\\beta}_k$ uncorrelated."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training set\n",
"\n",
"I also rename the columns in order to clarify which column is the X and which is the Y. \n",
"I overwrite the variables $X$, $Y$ used before. "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"Y = dret.columns[0]\n",
"X = dret.columns[1]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"training_size = 250\n",
"ret_train = dret.iloc[:training_size]\n",
"ret_test = dret.iloc[training_size:]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"In the training set the OLS estimators of\n",
"alpha_tr = 0.0002276440411573649\n",
"beta_tr = 0.9751772551661367\n",
"var_eps = 6.286775484858256e-05\n"
]
}
],
"source": [
"beta_tr, alpha_tr, _, _, _ = ss.linregress(ret_train[X], ret_train[Y])\n",
"resid_tr = ret_train[Y] - beta_tr * ret_train[X] - alpha_tr\n",
"var_eps_ols = resid_tr.var(ddof=2) # a possible initial guess for var_eps\n",
"print(\"In the training set the OLS estimators of\")\n",
"print(\"alpha_tr = \", alpha_tr)\n",
"print(\"beta_tr = \", beta_tr)\n",
"print(\"var_eps = \", var_eps_ols)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rolling alpha and beta\n",
"\n",
"Here I calculate the OLS $\\alpha$ and $\\beta$ in the test set, using a rolling window. "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"rolling_window = 50\n",
"rolling_beta = []\n",
"rolling_std_err = []\n",
"rolling_alpha = []\n",
"\n",
"for i in range(len(ret_test)):\n",
" ret_temp = ret.iloc[1 + i + training_size - rolling_window : 1 + i + training_size]\n",
" beta_temp, alpha_temp, _, _, std_err_temp = ss.linregress(ret_temp[X], ret_temp[Y])\n",
" rolling_beta.append(beta_temp)\n",
" rolling_std_err.append(std_err_temp)\n",
" rolling_alpha.append(alpha_temp)\n",
"ret_test = ret_test.assign(ols_beta=np.array(rolling_beta))\n",
"ret_test = ret_test.assign(ols_std_err=np.array(rolling_std_err))\n",
"ret_test = ret_test.assign(ols_alpha=np.array(rolling_alpha))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Implementation of the Kalman filter for $\\alpha$ and $\\beta$\n",
"\n",
"In the following cell I decided to initialize the state $\\mathbf x_0 = \\bigg( \\begin{array}{c} \\alpha_0\\\\ \\beta_0 \\end{array} \\bigg)$ using the OLS values calculated with the data in the training set. The initial covariance matrix is set to $P_0= 0.1 \\cdot \\mathbb{1}$. \n",
"The variance parameters are set to $\\sigma^2_{\\alpha}=0.0000001$ and $\\sigma^2_{\\beta}=0.01$.\n",
"\n",
"Intuitively, it makes sense to design the $\\beta$ process dynamics with a standard deviation $\\sigma_{\\beta}=0.1$. The error describes how the beta changes over time, e.g. if $\\sigma_{\\beta}=0$ the beta remains constant, if $\\sigma_{\\beta}=100$ the beta can assume unreasonable values. If, for instance, the current value of beta is $1$, with $68.3\\%$ probability at the next time step the beta will be inside the interval $[0.9, 1.1]$ ( by the [three-sigma rule](https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule)). \n",
"The same argument works for the choice of the variance of $\\alpha$.\n",
"\n",
"The parameter $\\sigma^2_{\\epsilon}$ is set equal to the OLS standard deviation estimate, computed in the training set. \n",
"\n",
"**All these values must be calibrated using the training dataset!**. This is a difficult task because the variables are not observable! \n",
"In general, **the filter designer has an important role in the selection of the process and model errors.** \n",
"The parameters should be chosen in order to avoid overfitting/underfitting and to avoid unrealistic values. \n",
"\n",
"Possible methods (but there are more) are:\n",
"- (1) Using reasonable intuitive assumptions (such as the assumption I used above)\n",
"- (2) Compute the rolling $\\alpha$ and $\\beta$ in the training set and calculate the variances. Nevertheless, these values will depend on the selected time window.\n",
"- (3) Using MLE method. Later I will apply it in a simplified model.\n",
"\n",
"A useful thing to do, is to create a **validation set**. This is an additional set that can be created as a part of the training set. (I will not make use of it in this notebook, although it can be very useful) \n",
"We can run the Kalman filter on the validation set and check if the estimated parameters produce good results. \n",
"For instance, we could verify the intuitive assumption in (1), the choice of the rolling window in (2), and the quality of the MLE estimator. In case of bad results, we can repeat the estimation in the training set under different assumptions.\n",
"\n",
"#### Important!\n",
"If you have changed the tickers, the parameters described above need to be changed as well."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"x = np.array([[alpha_tr], [beta_tr]]) # initial mean of alpha and beta\n",
"P = 0.1 * np.eye(2) # initial covariance of alpha and beta\n",
"eta_alpha = 0.0000001 # variance of the alpha process\n",
"eta_beta = 0.01 # variance of the beta process\n",
"Q = np.diag([eta_alpha, eta_beta]) # process covariance matrix\n",
"R = np.array([[var_eps_ols]]) # variance for the measurement - 1 dim\n",
"F = np.eye(2) # transition matrix\n",
"H = np.vstack((np.ones(len(ret_test)), ret_test[X].values)).T # measurement matrix"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"alphas, betas = np.full(len(ret_test), np.NaN), np.full(len(ret_test), np.NaN)\n",
"for i, y in enumerate(ret_test[Y]):\n",
" # predict\n",
" x = F @ x\n",
" P = (F @ P @ F.T) + Q\n",
"\n",
" # update\n",
" S = (H[None, i] @ P @ H[None, i].T) + R # Here S is a scalar\n",
" K = (P @ H[None, i].T) @ inv(S)\n",
" r = y - H[None, i] @ x # residuals\n",
" x = x + K @ r # v is a scalar.\n",
" P = P - K @ H[None, i] @ P # K @ H[None,i] is an outer product\n",
"\n",
" alphas[i] = x[0, 0]\n",
" betas[i] = x[1, 0]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(16, 6))\n",
"plt.plot(ret_test.index, betas, label=\"Kalman Filter\")\n",
"plt.plot(ret_test[\"ols_beta\"], label=\"OLS\")\n",
"plt.legend()\n",
"plt.title(\"Comparison Beta\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Comments\n",
"\n",
"- Looking at the two plots above, we can see that the curves are very similar. An important thing to notice is that the OLS is shiftet on the right i.e. **OLS is delayed!** The Kalman estimates can reflect much better the true (spot) value of the state. \n",
"- The filter is very robust with respect to initial guesses. If, for instance, you try to set `P = 100 * np.eye(2)`, you will see that it will converge quickly to a \"reasonable\" value. \n",
"- In the matrix `H[None,i]` I had to use the keyword `None` in order to maintain the information of the array dimensions, `H[None,0].shape = (1,2)`. This is a bad feature of the language. Python automatically removes dimensions of a subarray i.e. `H[0].shape = (2,)`. \n",
"- `K @ H[None,i]` is an outer product of dimension $(2,2) = (2,1) \\times (1,2)$.\n",
"- I used inv(S) to compute the inverse of S. In the notebook **A1** I repeated many times that inverting matrices is bad. But for the Kalman filter, since usually S has small rank (here S is a scalar), we can make an exception :) \n",
"\n",
"#### PROS\n",
"The Kalman filter does not need an entire dataset (or time window) to estimate the current state. **It just needs ONE measurement!** \n",
"The OLS estimate, instead, depends on the length of the time window, and it never reflects the spot value of the state because it is affected by past data.\n",
"#### CONS\n",
"The Kalman filter performances are strongly dependent on the process and measurement errors!! \n",
"These values are not always measurable, and are hard to calibrate. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Kalman regression for the $\\beta$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have seen that $\\alpha \\approx 0$ and that it does not vary that much. \n",
"For this reasons, the parameter alpha is usually not considered as a relevant parameter. \n",
"##### Comment:\n",
"This is in accordance with the well known Capital Asset Pricing Model [CAPM](https://en.wikipedia.org/wiki/Capital_asset_pricing_model), that assumes $\\alpha=0$. \n",
"\n",
"Therefore we can simplify the process dynamics by assuming that it is a simple constant $\\alpha_k = \\alpha$. For simplicity we can set $\\alpha=0$, but now I want to present the general case $\\alpha \\not= 0$.\n",
"\n",
"##### Process equation:\n",
"$$\n",
"\\biggl(\\begin{array}{c} \\alpha\\\\ \\beta_k \\end{array}\\biggr) = \n",
"\\biggl(\\begin{array}{cc} 1 & 0\\\\ 0 & 1 \\end{array}\\biggr)\n",
"\\biggl(\\begin{array}{c} \\alpha\\\\ \\beta_{k-1} \\end{array}\\biggr) \n",
"+ \\biggl(\\begin{array}{c} 0\\\\ \\eta_k \\end{array}\\biggr) \\quad \\text{with} \\quad \n",
"\\eta_k \\sim \\mathcal{N}(0, \\sigma^2_{\\eta}) $$\n",
"\n",
"##### Measurement equation:\n",
"$$\n",
"y_k = \\bigl(\\begin{array}{cc} 1 & x_k \\end{array}\\bigr) \\biggl(\\begin{array}{c} \\alpha\\\\ \\beta_k \\end{array}\\biggr)\n",
"+ \\epsilon_k \\quad \\text{with} \\quad \\epsilon_k \\sim \\mathcal{N}(0, \\sigma^2_{\\epsilon}) \\\\\n",
"$$\n",
"\n",
"Now, we can obtain a simple expression for the Kalman filter variables:\n",
"##### Predict:\n",
"$$ \\beta_{k \\mid k-1} = \\beta_{k-1 \\mid k-1} \\quad \\text{and} \\quad P_{k \\mid k-1} = P_{k-1 \\mid k-1} + \\sigma_{\\eta}^2. $$\n",
"##### Variables:\n",
"$$ r_k = y_k - \\alpha - \\beta_{k \\mid k-1} x_k $$\n",
"\n",
"$$ S_k = \\bigl(\\begin{array}{cc} 1 & x_k \\end{array}\\bigr) \n",
"\\biggl(\\begin{array}{cc} 0 & 0 \\\\ 0 & P_{k \\mid k-1} \\end{array}\\biggr)\n",
"\\biggl(\\begin{array}{c} 1\\\\ x_k \\end{array}\\biggr) + \\sigma^2_{\\epsilon} \n",
"= P_{k \\mid k-1} x_k^2 + \\sigma^2_{\\epsilon}\n",
"$$\n",
"\n",
"$$ K_k = \\frac{1}{S_k} \\biggl(\\begin{array}{cc} 0 & 0 \\\\ 0 & P_{k \\mid k-1} \\end{array}\\biggr)\n",
"\\biggl(\\begin{array}{c} 1\\\\ x_k \\end{array}\\biggr) = \\frac{1}{S_k} \\biggl(\\begin{array}{c} 0\\\\ x_k P_{k \\mid k-1} \\end{array}\\biggr)\n",
"$$\n",
"##### Update:\n",
"$$ \\biggl(\\begin{array}{c} \\alpha\\\\ \\beta_{k \\mid k} \\end{array}\\biggr) = \n",
"\\biggl(\\begin{array}{c} \\alpha \\\\ \\beta_{k \\mid k-1} \\end{array}\\biggr) + \\frac{1}{S_k} \\biggl(\\begin{array}{c} 0\\\\ r_k x_k P_{k \\mid k-1} \\end{array}\\biggr)\n",
"$$\n",
"\n",
"$$ \\biggl(\\begin{array}{cc} 0 & 0 \\\\ 0 & P_{k \\mid k} \\end{array}\\biggr) = \\biggl[\n",
"\\biggl(\\begin{array}{cc} 1 & 0 \\\\ 0 & 1 \\end{array}\\biggr)\n",
"- \\frac{1}{S_k} \\biggl(\\begin{array}{c} 0\\\\ x_k P_{k \\mid k-1} \\end{array}\\biggr)\n",
"\\bigl(\\begin{array}{cc} 1 & x_k \\end{array}\\bigr) \\biggr] \\biggl(\\begin{array}{cc} 0 & 0 \\\\ 0 & P_{k \\mid k-1} \\end{array}\\biggr) \n",
"$$\n",
"\n",
"Considering the beta-component only, the two update equations are:\n",
"\n",
"$$ \\beta_{k \\mid k} = \\beta_{k \\mid k-1} + \\frac{1}{S_k} r_k x_k P_{k \\mid k-1} \\quad \\text{and} \\quad \n",
"P_{k \\mid k} = P_{k \\mid k-1} \\biggl( 1- K_k x_k \\biggr)\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calibration\n",
"\n",
"We can calibrate the two parameters $\\sigma_{\\eta}$ and $\\sigma_{\\epsilon}$ using the data of the training set.\n",
"\n",
"The idea is to use the MLE method i.e. to maximize the following\n",
"\n",
"$$ \\log L(\\sigma_{\\eta},\\sigma_{\\epsilon} \\mid y_k, y_{k-1}, ..., y_1, y_0) = -\\frac{1}{2} \n",
"\\sum_{k=1}^N \\biggl( \\log 2\\pi + \\log S_k + \\frac{r_k^2}{S_k} \\biggr)\n",
"$$\n",
"\n",
"which is the log-likelihood obtained from Normal distributions. For a derivation of this formula see [wiki](https://en.wikipedia.org/wiki/Kalman_filter#Marginal_likelihood).\n",
"\n",
"Let us recall that all the measurements are independent.\n",
"When the measurement equation is \n",
"\n",
"$$ y_k = \\alpha + \\beta_k x_k + \\epsilon_k $$ \n",
"\n",
"the variables $y_k$ are normally distributed with mean \n",
"\n",
"$$\\mathbb{E}[ y_k \\mid \\beta_{k-1,k-1}] = \\alpha + \\beta_{k,k-1} x_k $$ \n",
"\n",
"and variance \n",
"\n",
"$$\\text{Var}[ y_k \\mid \\beta_{k-1,k-1}] = x_k^2 P_{k,k-1} + \\sigma_{\\epsilon}^2 = S_k.$$\n",
"\n",
"The algorithms for the beta regression and the calibration based on the MLE method are implemented in the file [Kalman_filter.py](./functions/Kalman_filter.py)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Calibration using MLE"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"alpha0, beta0 and var_eps initialized by OLS\n"
]
}
],
"source": [
"KR = KF.Kalman_regression(ret_train[X], ret_train[Y])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Log-likelihood plot\n",
"\n",
"Log-likelihood can be ugly functions in general! \n",
"\n",
"It is always better (when possible) to visualize the plot. It will be easier to understand if it has a global maximum. In general the log-likelihood can be flat, or can have many local maximum points.\n",
"\n",
"In our case the log-likelihood has the following shape:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"dimens = 40\n",
"var_eps_x = np.linspace(1e-5, 2e-4, dimens)\n",
"var_eta_y = np.linspace(1e-5, 2e-4, dimens)\n",
"X_grid, Y_grid = np.meshgrid(var_eps_x, var_eta_y)\n",
"V_grid = np.ones((dimens, dimens))\n",
"\n",
"for i, i_eps in enumerate(var_eps_x):\n",
" for j, j_eta in enumerate(var_eta_y):\n",
" KR.var_eps = i_eps\n",
" KR.var_eta = j_eta\n",
" KR.run()\n",
" V_grid[j, i] = KR.loglikelihood\n",
"\n",
"KR.set_OLS_params()\n",
"V = np.ones(dimens)\n",
"for j, j_eta in enumerate(var_eta_y):\n",
" KR.var_eta = j_eta\n",
" KR.run()\n",
" V[j] = KR.loglikelihood"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 5))\n",
"gs = gridspec.GridSpec(1, 2, width_ratios=[3, 2])\n",
"ax = fig.add_subplot(gs[0], projection=\"3d\")\n",
"ax2 = fig.add_subplot(gs[1])\n",
"ax.plot_surface(Y_grid, X_grid, V_grid) # , cmap=cm.ocean\n",
"ax.set_title(\"Log-likelihood function of $\\sigma_{\\eta}^2$ and $\\sigma_{\\epsilon}^2$\")\n",
"ax.set_xlabel(\"$\\sigma_{\\eta}^2$ (var_eta) \", labelpad=9)\n",
"ax.set_ylabel(\"$\\sigma_{\\epsilon}^2$ (var_eps)\", labelpad=13)\n",
"ax.set_zlabel(\"Log-Likelihood\", labelpad=7)\n",
"ax.view_init(30, 10) # this function rotates the 3d plot\n",
"ax.ticklabel_format(axis=\"x\", style=\"sci\", scilimits=(-4, 2))\n",
"ax.ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(-4, 2))\n",
"ax2.plot(var_eta_y, V)\n",
"ax2.set_title(\"Log-likelihood function of $\\sigma_{\\eta}^2$ with fixed $\\sigma_{\\epsilon}^2$\")\n",
"ax2.set_xlabel(\"$\\sigma_{\\eta}^2$ (var_eta)\", labelpad=8)\n",
"ax2.ticklabel_format(axis=\"x\", style=\"sci\", scilimits=(0, 0))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Comments:\n",
"- The log-likelihood is a concave function of $\\sigma_{\\eta}^2$. Therefore there is a global maximum. \n",
"\n",
"- Sometimes, it happens that the log-likelihood is a decreasing function of $\\sigma_{\\eta}^2$. Therefore the maximum is at $\\sigma_{\\eta}^2 = 0$. In this case the value doesn't make sense. \n",
"If we use $\\sigma_{\\eta}=0$, it means that we are considering the process model as a perfect model (with no error). \n",
"The consequence is that the filter will hardly be affected by new measurements. \n",
"\n",
"- The log-likelihood is concave with respect to $\\sigma_{\\epsilon}^2$ and has a maximum point (for each fixed $\\sigma_{\\eta}^2$). This is very good! \n",
"We can see that the MLE estimator is very close to the OLS estimator!!"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization converged successfully\n",
"var_eps = 6.250650685447434e-05, var_eta = 6.250079206505446e-05\n",
"beta_last = 1.0132897275906843 P_last = 0.012048131591893373\n",
"var_eta_MLE = 6.250079206505446e-05 var_eps_MLE = 6.250650685447434e-05 var_eps_OLS = 6.286775484858256e-05\n",
"The post-fit R squared is: 0.4532263803195352\n"
]
}
],
"source": [
"KR.calibrate_MLE()\n",
"\n",
"print(\"beta_last = \", KR.betas[-1], \"P_last = \", KR.Ps[-1])\n",
"print(\"var_eta_MLE = \", KR.var_eta, \"var_eps_MLE = \", KR.var_eps, \"var_eps_OLS = \", var_eps_ols)\n",
"\n",
"KR.run(ret_test[X], ret_test[Y])\n",
"\n",
"print(\"The post-fit R squared is: \", KR.R2_post_fit)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(16, 6))\n",
"plt.plot(ret_test[\"Kalman_err\"], label=\"Kalman Filter error ($\\sqrt{P}$)\")\n",
"plt.plot(ret_test[\"ols_std_err\"], label=\"OLS error\")\n",
"plt.legend()\n",
"plt.title(\"Comparison Beta errors\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The error comparison above is only indicative. Since the OLS error $\\sqrt{ \\frac{\\sigma^2}{S_{xx}} }$ can be set arbitrarily small by simply choosing a bigger time window (the more data we have, the higher $S_{xx}$ will be)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Quite good!\n",
"Using the Kalman filter we obtain very smooth values of beta. The estimation error is smaller and smoother as well. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What about the $R^2$ ? \n",
"\n",
"A possible alternative to the MLE, could be to maximize the $R^2$. \n",
"Let us see if it works.\n",
"\n",
"The following function considers a fixed $\\sigma_{\\epsilon}$ and searches for the optimal value of $\\sigma_{\\eta}$ in the range `bounds=[[1e-15,1]]`.\n",
"\n",
"There are two reasons to calibrate only one parameter. \n",
"- The first reason is that we have already seen that $\\sigma_{\\epsilon}$ obtained with OLS is a good value. It is very close to the MLE estimate.\n",
"- The second reason is that the $R^2$ is maximized at $\\sigma_{\\epsilon} = 0$ and for a high value of $\\sigma_{\\eta}$. When the measurement error is zero, it means that the measurements are perfect and the Kalman filter simply selects the values of beta that solve the linear equation $y_k = \\alpha + \\beta_k x_k$. Of course $\\sigma_{\\eta}$ should be big enough to allow fast changes of beta. In this case we have an **overfitting** problem. Using a high $\\sigma_{\\eta}$ also means that our process model has a very low predictive power.\n",
"\n",
"Let us see what happens when calibrating only $\\sigma_{\\eta}$."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization converged successfully\n",
"var_eta = 1e-15\n"
]
}
],
"source": [
"KR.calibrate_R2(mode=\"pre-fit\")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization converged successfully\n",
"var_eta = 1.0\n"
]
}
],
"source": [
"KR.calibrate_R2(mode=\"post-fit\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Considering the *pre-fit* residuals, the optimizer returns the output $\\sigma_{\\eta}^2 = 10^{-15}$, which is the lowerbound imposed by myself. This is not an acceptable value.\n",
"\n",
"- Considering the *post-fit* residuals, the output is the upper bound in the parameter range `bounds=[[1e-15,1]]`. **Overfitting** \n",
"\n",
"By looking at the picture below, we see that the $R^2$ is an increasing function of $\\sigma_{\\eta}$, i.e. there is no optimal $\\sigma_{\\eta}$."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xx = np.linspace(0.000001, 10, 100)\n",
"RR2 = []\n",
"for i in xx:\n",
" KR.run(var_eta=i, var_eps=var_eps_ols)\n",
" RR2.append(KR.R2_post_fit)\n",
"plt.plot(xx, RR2, label=\"$R^2$\")\n",
"plt.title(\"R squared of the Kalman filter\")\n",
"plt.xlabel(\" var_eta\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Well... The $R^2$ maximization is not a good idea! It doesn't work.\n",
"\n",
"Let us see what happens if we run the Kalman filter in the test set using a big value of $\\sigma_{\\eta}$ and a small value of $\\sigma_{\\epsilon}$ (here I set $\\sigma_{\\epsilon}^2$ to var_eps_ols). "
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The post-fit R squared is: 0.9572452482415568\n"
]
}
],
"source": [
"var_eta_R2 = 200\n",
"KR.run(ret_test[X], ret_test[Y], var_eta=var_eta_R2, var_eps=var_eps_ols)\n",
"\n",
"print(\"The post-fit R squared is: \", KR.R2_post_fit)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(16, 6))\n",
"plt.plot(ret_test.index, betas, label=\"Kalman Filter alpha-beta\")\n",
"plt.plot(ret_test[\"ols_beta\"], label=\"OLS\")\n",
"plt.plot(ret_test.index, KR.betas, label=\"Kalman filter with huge $\\sigma_{\\eta}^2$\")\n",
"plt.legend()\n",
"plt.title(\"Comparison Beta using $R^2$ parameters\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The $R^2$ is very close to 1, which means almost a perfect fit. However the obtained values of $\\beta$ are unreasonable. \n",
"\n",
"#### Conclusions\n",
"\n",
"- We have seen that using the $R^2$ for the calibration is not a good idea because it produces **overfitting**.\n",
"\n",
"- The MLE sometimes works, sometimes not. Most of the time, the problematic parameter to estimate is $\\sigma_{\\eta}$.\n",
"\n",
"- The OLS estimator for $\\sigma_{\\epsilon}$ is very close to the MLE for $\\sigma_{\\epsilon}$.\n",
"\n",
"- The parameter $\\sigma_{\\eta}$ must be chosen by the filter designer, using reasonable assumptions. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"[1] Casella, George and Berger, Roger. (2001) \"Statistical inference\" Duxbury Advanced Series "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: 5.2 Kalman auto-correlation tracking - AR(1) process.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# AR(1) - Auto-correlation tracking\n",
"\n",
"In this notebook, I would like to show the power of the Kalman filter for tracking hidden variables. \n",
"\n",
"In the notebook **5.1** I introduced the Kalman algorithm and applied it to real financial data. However, it was not possible to verify the goodness of the algorithm (by computing the [MSE](https://en.wikipedia.org/wiki/Mean_squared_error) for instance). In the present notebook, I prefer to work with a simulated time series, where the hidden state process (the autocorrelation coefficient $\\rho$) is known!\n",
"\n",
"## Contents\n",
" - [Autoregressive process AR(1)](#sec1)\n",
" - [Regression analysis](#sec1.1)\n",
" - [Kalman filter](#sec2)\n",
" - [Non-constant autocorrelation](#sec3)\n",
" - [Example 1](#sec3.1)\n",
" - [Example 2](#sec3.2)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import statsmodels.tsa.arima.model as sml\n",
"import scipy as scp\n",
"import scipy.stats as ss\n",
"import statsmodels.api as sm\n",
"import statsmodels.formula.api as smf\n",
"from statsmodels.tsa.stattools import acf, pacf\n",
"from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
"\n",
"from patsy import dmatrices\n",
"import FMNM.Kalman_filter as KF"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Autoregressive process AR(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The AR(1) is the Auto-Regressive process of order 1 [wiki](https://en.wikipedia.org/wiki/Autoregressive_model#Example:_An_AR(1)_process).\n",
"\n",
"$$ (X_t - \\mu) = \\phi (X_{t-1} - \\mu) + \\epsilon_t $$\n",
"\n",
"where $\\mu$ is the mean of $X_t$, and $t \\in \\mathbb{N}$. We also assume uncorrelated errors $\\epsilon_t \\sim \\mathcal{N}(0,\\sigma^2)$.\n",
"\n",
"The process can be written in the form of a linear regression, where the value of $X_t$ is modelled as a linear function of $X_{t-1}$:\n",
"\n",
"$$ X_t = \\alpha + \\beta X_{t-1} + \\epsilon_t $$\n",
"\n",
"with $\\beta = \\phi$ and $\\alpha = \\mu (1-\\phi)$. \n",
"We also have to require that $\\epsilon_t$ and $X_{t-1}$ are uncorrelated.\n",
"And impose the condition $|\\phi|<1$, in order to have a stationary process.\n",
"\n",
"- The ACF (Auto-Correlation Function) [wiki](https://en.wikipedia.org/wiki/Autocorrelation) is \n",
"\n",
"$$ \\rho_k = \\phi \\rho_{k-1} = \\phi^k \\quad k \\geq 1. $$\n",
"\n",
"- The PACF (Partial Auto-Correlation Function) [wiki](https://en.wikipedia.org/wiki/Partial_autocorrelation_function), is\n",
"\n",
"$$\n",
"\\begin{aligned}\n",
"& \\text{PACF}_{k} = \\rho_1 = \\phi& \\quad \\text{for} \\quad k=1 \\\\\n",
"& \\text{PACF}_{k} = 0 &\\quad \\text{for} \\quad k\\geq 2. \n",
"\\end{aligned}$$\n",
"\n",
"An important thing to notice is the form of the autocorrelation coefficient with lag $k=1$:\n",
"\n",
"$$ \\rho_1 = \\beta = \\phi. $$ \n",
"\n",
"For simplicity I will assume $X_0 = 0$ and $\\alpha=0$. Let us simulate the AR(1) process:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(seed=42)\n",
"N = 1000 # time steps\n",
"rho = 0.7 # rho = beta = phi\n",
"sigma = 0.1 # std dev of epsilon\n",
"\n",
"W = ss.norm.rvs(loc=0, scale=sigma, size=N)\n",
"W = W - W.mean() # remove the mean\n",
"\n",
"X_ar = np.zeros(N)\n",
"for i in range(N - 1):\n",
" X_ar[i + 1] = rho * X_ar[i] + W[i + 1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can estimate the parameters in the same way we do it for linear regressions. The following considered estimators are asymptotically unbiased."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"beta= 0.6949272218592958 alpha= -4.961943743494367e-05\n"
]
}
],
"source": [
"Y = X_ar[1:] # response\n",
"X = X_ar[:-1] # predictor\n",
"\n",
"bb, aa, _, _, _ = ss.linregress(X, Y)\n",
"print(\"beta=\", bb, \" alpha=\", aa)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Plots"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 5))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"\n",
"ax1.plot(X_ar)\n",
"ax1.set_xlabel(\"time t\")\n",
"ax1.set_ylabel(\"X_ar\")\n",
"ax1.set_title(\"AR process with lag-1 autocorrelation rho={}\".format(rho))\n",
"\n",
"ax2.scatter(X, Y)\n",
"ax2.set_title(\"Scatter plot\")\n",
"ax2.plot(X, aa + bb * X, color=\"r\", label=\"regression line\")\n",
"ax2.legend()\n",
"ax2.set_xlabel(\"X[i]\")\n",
"ax2.set_ylabel(\"X[i+1]\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Regression analysis\n",
"\n",
"### Autocorrelation "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Auto Correlation = 0.6948575956471169\n"
]
}
],
"source": [
"print(\"Auto Correlation = \", np.corrcoef(Y, X)[0, 1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us compute and plot the ACF and PACF with lag $1 \\leq k \\leq 20$. \n",
"The PACF can be computed with several methods (type `pacf??` in a new cell for more informations).\n",
"The parameter `alpha` is used for the confidence interval computation."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"acf_theoretical = [rho**i for i in range(21)] # theoretical values\n",
"pacf_theoretical = [1] + [rho] + 19 * [0] # theoretical values\n",
"acf_20 = acf(X_ar, nlags=20, fft=False, alpha=0.05) # computes ACF not using FFT\n",
"pacf_20 = pacf(X_ar, nlags=20, alpha=0.05) # Computes pacf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Plot made by me"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(15, 4))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"\n",
"ax1.plot(acf_theoretical, \"o\", color=\"orange\", label=\"Theoretical value\")\n",
"ax1.plot(acf_20[0], \"^\", color=\"royalblue\", label=\"estimated value\")\n",
"ax1.fill_between(\n",
" x=range(21), y1=acf_20[1][:, 0], y2=acf_20[1][:, 1], alpha=0.5, linewidth=1, color=\"lightskyblue\", label=\"95% CI\"\n",
")\n",
"ax2.plot(pacf_theoretical, \"o\", color=\"orange\", label=\"Theoretical value\")\n",
"ax2.plot(pacf_20[0], \"^\", color=\"royalblue\", label=\"estimated value\")\n",
"ax2.fill_between(\n",
" x=range(21), y1=pacf_20[1][:, 0], y2=pacf_20[1][:, 1], alpha=0.5, linewidth=1, color=\"lightskyblue\", label=\"95% CI\"\n",
")\n",
"ax1.set_title(\"ACF\")\n",
"ax2.set_title(\"PACF\")\n",
"ax1.legend()\n",
"ax2.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function `acf` uses the Bartlett's approximation for the computation of the standard error (necessary for the computation of confidence intervals). The formulas for the standard errors used in `acf` and `pacf` are:\n",
"\n",
"$$ SE_{\\rho_k} = \\sqrt{\\frac{1}{N} (1+2\\rho_1^2 + ... + 2\\rho_{k-1}) } \n",
"\\quad \\text{and} \\quad SE_{\\text{pacf}} = \\sqrt{\\frac{1}{N}} $$\n",
"\n",
"Alternatively, we can use the `statsmodels` functions:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 4))\n",
"plot_acf(X_ar, lags=20, ax=axes[0])\n",
"plot_pacf(X_ar, lags=20, ax=axes[1])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The autocorrelation function exponentially decays in two forms depending on the sign of $\\phi$.\n",
"If $\\phi>0$, then all autocorrelations are positive. If $\\phi<0$, the sign shows an alternating pattern and the graph looks like a dumped cosine function. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Estimation using statsmodels methods: OLS and ols\n",
"\n",
"##### Method 1"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"alpha= -4.96194374349443e-05 beta= 0.6949272218592948\n"
]
}
],
"source": [
"X1 = sm.add_constant(X) # creates a column of only ones\n",
"results = sm.OLS(Y, X1).fit(method=\"pinv\") # OLS capital letters\n",
"print(\"alpha=\", results.params[0], \"beta=\", results.params[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Method 2\n",
"\n",
"The library `statsmodels` lets you use *R-style* formulas to specify the regression parameters. This is very useful, in particular when working with multiple regression. \n",
"The right method to call is `ols`, written with small letters."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"alpha= -4.96194374349376e-05 beta= 0.6949272218592939\n"
]
}
],
"source": [
"df = pd.DataFrame({\"X\": Y, \"X_lag\": X}) # dataframe\n",
"result = smf.ols(formula=\"X ~ X_lag\", data=df).fit(method=\"qr\") # lowercase ols for R-style\n",
"print(\"alpha=\", result.params[0], \"beta=\", result.params[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Method 3\n",
"The same effect of `sm.add_constant` can be obtained with the `dmatrices` from the [patsy library](https://patsy.readthedocs.io/en/latest/). This function lets you create a regressors matrix by selecting the columns of a dataframe and using *R-style formulas* (for more information see [here](https://www.statsmodels.org/devel/example_formulas.html) ). This is the code to use: "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Intercept -0.000050\n",
"X_lag 0.694927\n",
"dtype: float64\n"
]
}
],
"source": [
"Y_m, X_m = dmatrices(\"X ~ X_lag\", data=df, return_type=\"dataframe\")\n",
"result = sm.OLS(Y_m, X_m).fit()\n",
"print(result.params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that we used again the function `sm.OLS` with capital letters. \n",
"\n",
"**Comment:** \n",
"Regarding the [fit methods](https://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.OLS.fit.html), there are two possibilities: \n",
"The OLS method for multiple linear regression returns an estimated vector $\\beta$ that requires the computation of the inverse of a matrix [see here, wiki](https://en.wikipedia.org/wiki/Linear_least_squares).\n",
"- pinv method: uses the [Moore-Penrose inverse](https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse) \n",
"- qr method: uses the [QR decomposition](https://en.wikipedia.org/wiki/QR_decomposition)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Estimation using ARMA class\n",
"\n",
"##### Method 4"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['const', 'ar.L1', 'sigma2']\n",
"[-1.62198920e-04 6.94233969e-01 9.57630062e-03]\n",
"[-1.64760901e-04 6.94227783e-01 9.57534792e-03]\n"
]
}
],
"source": [
"AR1 = sml.ARIMA(X_ar, order=(1, 0, 0), trend=\"c\").fit(method=\"innovations_mle\")\n",
"print(AR1.param_names)\n",
"print(AR1.params)\n",
"AR1 = sml.ARIMA(X_ar, order=(1, 0, 0), trend=\"c\").fit(method=\"statespace\")\n",
"print(AR1.params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Above I presented the results obtained by two different methods.\n",
"\n",
"The first parameter above corresponds to what we previously called $\\mu$ (the mean). We can calculate the regression intercept $\\alpha$ by:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-5.037930605521569e-05"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"AR1.params[0] * (1 - AR1.params[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Kalman Filter\n",
"\n",
"We are ready to add some noise to the coefficient $\\rho$.\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(seed=42)\n",
"N = 1000 # time steps\n",
"rho = 0.7 # rho\n",
"sig_mod = 0.02 # model error\n",
"sig_meas = 0.2 # measure error\n",
"\n",
"err = ss.norm.rvs(loc=0, scale=sig_mod, size=N - 1) # process error\n",
"err = err - err.mean() # remove the mean\n",
"W = ss.norm.rvs(loc=0, scale=sig_meas, size=N - 1) # measurement error\n",
"W = W - W.mean()\n",
"beta = rho + err.cumsum()\n",
"\n",
"X_ar = np.zeros(N)\n",
"for i in range(N - 1):\n",
" X_ar[i + 1] = beta[i] * X_ar[i] + W[i]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Plot of true rho and the rho with model errors\n",
"\n",
"I use the name `beta` to call the \"rho with model error\"."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 5))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"\n",
"ax1.plot(X_ar)\n",
"ax1.set_xlabel(\"time i\")\n",
"ax1.set_ylabel(\"X_ar\")\n",
"ax1.set_title(\"AR process with autocorrelation rho={}\".format(rho))\n",
"ax2.scatter(X, Y)\n",
"ax2.set_title(\"Scatter plot X[i-1] vs X[i]\")\n",
"ax2.plot(X, aa + bb * X, color=\"r\", label=\"regression line\")\n",
"ax2.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Train and Test sets"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"In the training set the OLS estimators of\n",
"alpha_tr = 0.009396572931008583\n",
"beta_tr = 0.5966757723436933\n",
"var_eps = 0.04211570316687364\n"
]
}
],
"source": [
"training_size = 499\n",
"X_train = X[:training_size]\n",
"X_test = X[training_size:]\n",
"Y_train = Y[:training_size]\n",
"Y_test = Y[training_size:]\n",
"\n",
"beta_tr, alpha_tr, _, _, _ = ss.linregress(X_train, Y_train)\n",
"resid_tr = Y_train - beta_tr * X_train - alpha_tr\n",
"var_eps_ols = resid_tr.var(ddof=2) # a possible initial guess for var_eps\n",
"print(\"In the training set the OLS estimators of\")\n",
"print(\"alpha_tr = \", alpha_tr)\n",
"print(\"beta_tr = \", beta_tr)\n",
"print(\"var_eps = \", var_eps_ols)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"alpha0, beta0 and var_eps initialized by OLS\n",
"Optimization converged successfully\n",
"var_eps = 0.04120093850377862, var_eta = 0.0002028356713903334\n"
]
}
],
"source": [
"KR = KF.Kalman_regression(X_train, Y_train)\n",
"KR.calibrate_MLE()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original model variance: 0.0004\n",
"MLE estimated model variance: 0.0002028356713903334\n"
]
}
],
"source": [
"print(\"Original model variance: \", sig_mod**2)\n",
"print(\"MLE estimated model variance: \", KR.var_eta)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alright... **MLE method doesn't work very well.** \n",
"As discussed in the notebook **5.1**, sometimes the log-likelihood function assumes a maximum at 0. If we try to set `sig_mod` to smaller values the MLE doesn't work anymore. \n",
"\n",
"#### Window-dependent estimation:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.021466236160052832\n"
]
}
],
"source": [
"rolling_window = 60\n",
"set_train_beta = []\n",
"for i in range(rolling_window, len(X_train)):\n",
" beta_temp, _, _, _, _ = ss.linregress(X[i - rolling_window : 1 + i], Y[i - rolling_window : 1 + i])\n",
" set_train_beta.append(beta_temp)\n",
"sig_eta = np.std(np.diff(set_train_beta))\n",
"print(sig_eta)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The previous method is an informal method that helps to get an approximative value of the standard deviation of the process noise $\\sigma_{\\eta}$.\n",
"\n",
"The estimation of $\\sigma_{\\eta}$ is a difficult task, and it is considered as one of the hardest parts in the design of a filter. In this case, the designer has a strong responsibility, because his decisions will affect the performances of the filter.\n",
"\n",
"#### Kalman filter application:\n",
"\n",
"The last values of $\\beta$ and $P$ in the training set are used as initial values for the Kalman filter in the test set."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"beta_last = 0.7256458012588978 P_last = 0.009242406598071193\n"
]
}
],
"source": [
"KR.run()\n",
"print(\"beta_last = \", KR.betas[-1], \"P_last = \", KR.Ps[-1])\n",
"\n",
"KR.beta0 = KR.betas[-1]\n",
"KR.P0 = KR.Ps[-1]\n",
"KR.run(X_test, Y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Rolling regression beta"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"rolling_window = 60\n",
"rolling_beta = KF.rolling_regression_test(X, Y, rolling_window, training_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### RTS Smoother\n",
"\n",
"In the following cell I also calculate the [Rauch-Tung-Striebel](https://en.wikipedia.org/wiki/Kalman_filter#Rauch%E2%80%93Tung%E2%80%93Striebel) smoother. "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"betas_smooth, Ps_smooth = KR.RTS_smoother(X_test, Y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Plot of betas"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(16, 7))\n",
"plt.plot(beta[training_size:], color=\"springgreen\", marker=\"o\", linestyle=\"None\", label=\"beta with model error\")\n",
"plt.plot(KR.betas, label=\"Kalman filter betas\")\n",
"plt.plot(rolling_beta, label=\"Rolling beta, window={}\".format(rolling_window))\n",
"plt.plot(betas_smooth, label=\"RTS smoother\", color=\"maroon\")\n",
"plt.fill_between(\n",
" x=range(len(KR.betas)),\n",
" y1=KR.betas + np.sqrt(KR.Ps),\n",
" y2=KR.betas - np.sqrt(KR.Ps),\n",
" alpha=0.5,\n",
" linewidth=2,\n",
" color=\"seagreen\",\n",
" label=\"Kalman error: $\\pm 1 \\sigma$ \",\n",
")\n",
"plt.legend()\n",
"plt.title(\"Comparison estimated Betas\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us compare the [MSE](https://en.wikipedia.org/wiki/Mean_squared_error):"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE Kalman Filter: 0.10460636220981036\n",
"MSE Rolling regression: 0.15807556989175944\n",
"MSE RTS Smoother: 0.086624745332202\n"
]
}
],
"source": [
"print(\"MSE Kalman Filter: \", np.mean((KR.betas - rho) ** 2))\n",
"print(\"MSE Rolling regression: \", np.mean((np.array(rolling_beta) - rho) ** 2))\n",
"print(\"MSE RTS Smoother: \", np.mean((betas_smooth - rho) ** 2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- As expected the RTS smoother has smaller MSE than the Filter output.\n",
"- When we compare the rolling regression MSE with the Kalman MSE, we have to remember that the rolling betas depend on the window size! \n",
"\n",
"The Kalman estimator, represents the spot value of beta. This value is however affected by the designer choice of the unkown parameter $\\sigma_{\\eta}$. \n",
"Let us recall that a small process error $\\sigma_{\\eta}$ means that we are very confident in the model, and the filter will not be very sensitive to new measures."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Non-constant Autocorrelation\n",
"\n",
"In the previous section, we considered a constant true value of $\\rho$. In this case, regression analysis works well and the results are quite satisfactory. \n",
"But what happens if $\\rho$ is a function of time?\n",
"\n",
"In the next two examples, when we try to compute the autocorrelation coefficient of the process using the entire dataset, we will obtain just a constant \"average value\". But we will not be able to acquire knowledge of the spot autocorrelation. \n",
"\n",
"In this section, we will see that the Kalman filter is a good solution for this problem! And can be a good alternative to the rolling regression approach."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Example 1: Smooth behavior"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"beta= 0.05218486591534547 alpha= -0.0014587952488626184\n",
"Auto Correlation = 0.052179481199993\n"
]
}
],
"source": [
"np.random.seed(seed=42)\n",
"N = 1000\n",
"sig_mod2 = 0.02 # model error\n",
"sig_meas2 = 0.2 # measure error\n",
"\n",
"#################### continuous autocorrelation dynamics #####################\n",
"omega = 2 * np.pi / N # frequency\n",
"C = 0.8 # amplitude\n",
"x = np.array(range(N))\n",
"rho_nc = C * np.sin(omega * x - np.pi / 4) # rho non constant\n",
"################################################################################\n",
"\n",
"err2 = ss.norm.rvs(loc=0, scale=sig_mod2, size=N - 1)\n",
"err2 = err2 - err2.mean()\n",
"W2 = ss.norm.rvs(loc=0, scale=sig_meas2, size=N - 1)\n",
"W2 = W2 - W2.mean()\n",
"beta_nc = rho_nc[1:] + err2 # rho + noise\n",
"\n",
"X_ar2 = np.zeros(N)\n",
"for i in range(N - 1):\n",
" X_ar2[i + 1] = beta_nc[i] * X_ar2[i] + W2[i]\n",
"\n",
"Y = X_ar2[1:]\n",
"X = X_ar2[:-1]\n",
"\n",
"bb, aa, _, _, _ = ss.linregress(X, Y)\n",
"print(\"beta=\", bb, \" alpha=\", aa)\n",
"print(\"Auto Correlation = \", np.corrcoef(Y, X)[0, 1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The auto-correlation value computed above is not informative of the real auto-correlation dynamics. \n",
"The two plots below show the true dynamics of $\\rho$ (left), and the bad estimation of the regression line with slope computed above (right). "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 5))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"\n",
"ax1.plot(beta_nc, color=\"springgreen\", marker=\"o\", linestyle=\"None\", label=\"rho with model error\")\n",
"ax1.plot(x, rho_nc, color=\"black\", alpha=1, label=\"True rho\")\n",
"ax1.legend()\n",
"ax1.set_title(\"Autocorrelation coefficient\")\n",
"ax2.scatter(X, Y)\n",
"ax2.set_title(\"Scatter plot X[i-1] vs X[i]\")\n",
"ax2.plot(X, aa + bb * X, color=\"r\", label=\"regression line\")\n",
"ax2.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Train-Test split. \n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"In the training set the OLS estimators of\n",
"alpha_tr = -0.013987279981421737\n",
"beta_tr = 0.40279388929587046\n",
"var_eps = 0.05831835489065563\n"
]
}
],
"source": [
"training_size = 400\n",
"X_train = X[:training_size]\n",
"X_test = X[training_size:]\n",
"Y_train = Y[:training_size]\n",
"Y_test = Y[training_size:]\n",
"\n",
"beta_tr, alpha_tr, _, _, _ = ss.linregress(X_train, Y_train)\n",
"resid_tr = Y_train - beta_tr * X_train - alpha_tr\n",
"var_eps_ols = resid_tr.var(ddof=2) # a possible initial guess for var_eps\n",
"print(\"In the training set the OLS estimators of\")\n",
"print(\"alpha_tr = \", alpha_tr)\n",
"print(\"beta_tr = \", beta_tr)\n",
"print(\"var_eps = \", var_eps_ols)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### MLE estimator\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization converged successfully\n",
"var_eps = 0.040955571010706945, var_eta = 0.001224766491995262\n",
"Estimated process error vs true process error: 0.03 0.02\n"
]
}
],
"source": [
"KR = KF.Kalman_regression(X_train, Y_train, alpha0=alpha_tr, beta0=beta_tr, var_eps=var_eps_ols)\n",
"KR.calibrate_MLE()\n",
"\n",
"var_eta, var_eps = KR.var_eta, KR.var_eps\n",
"print(\"Estimated process error vs true process error: \", np.sqrt(var_eta).round(2), sig_mod2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Yeah! It works!** \n",
"Although the value of var_eta is a little higher than expected, we will use it. \n",
"\n",
"#### Kalman filter application\n",
"\n",
"The last values of $\\beta$ and $P$ in the training set are used as initial values for the Kalman filter in the test set."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"KR.run(X_test, Y_test)\n",
"betas_KF, Ps_KF = KR.betas, KR.Ps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Rolling betas and smoothed betas"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"rolling_window = 60\n",
"rolling_beta2 = KF.rolling_regression_test(X, Y, rolling_window, training_size)\n",
"\n",
"betas_smooth, Ps_smooth = KR.RTS_smoother(X_test, Y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot all betas"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(16, 7))\n",
"plt.plot(beta_nc[training_size:], color=\"springgreen\", marker=\"o\", linestyle=\"None\", label=\"rho with model error\")\n",
"plt.plot(x[: (N - training_size)], rho_nc[training_size:], color=\"black\", alpha=1, label=\"True rho\")\n",
"plt.plot(betas_KF, label=\"Kalman filter betas\")\n",
"plt.plot(rolling_beta2, label=\"Rolling beta, window={}\".format(rolling_window))\n",
"plt.plot(betas_smooth, label=\"RTS smoother\", color=\"maroon\")\n",
"plt.fill_between(\n",
" x=range(len(betas_KF)),\n",
" y1=betas_KF + np.sqrt(Ps_KF),\n",
" y2=betas_KF - np.sqrt(Ps_KF),\n",
" alpha=0.5,\n",
" linewidth=2,\n",
" color=\"seagreen\",\n",
" label=\"Kalman error: $\\pm 1 \\sigma$ \",\n",
")\n",
"plt.legend()\n",
"plt.title(\"Comparison Betas using MLE parameters\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### MSE:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE Rolling regression: 0.012824987450082672\n",
"MSE Kalman Filter: 0.014099470973574635\n",
"MSE RTS Smoother: 0.0023806012025244243\n"
]
}
],
"source": [
"print(\"MSE Rolling regression: \", np.mean((np.array(rolling_beta2) - rho_nc[training_size + 1 :]) ** 2))\n",
"print(\"MSE Kalman Filter: \", np.mean((betas_KF - rho_nc[training_size + 1 :]) ** 2))\n",
"print(\"MSE RTS Smoother: \", np.mean((betas_smooth - rho_nc[training_size + 1 :]) ** 2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comments:\n",
"\n",
"We see that the MSE of the filter is similar to the MSE of the rolling regression. However the Kalman filter does **not depend on the window size!**, whether the rolling regression output does! \n",
"The presented result looks nice for a rolling regression with `rolling_window=60`, but if you try to increase it to `rolling_window=100` or more, you will notice a very bad performance. \n",
"This behavior is the opposite of what we saw for a constant beta.\n",
"\n",
"In the Kalman filter instead, we used the MLE estimated parameters.\n",
"\n",
"The smoothed beta has an incredibly small MSE! In general the smoothing approach has the purpose to remove the noise. It produces a very good estimate of the true value of beta (i.e. rho)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Example 2: Discontinuous behavior \n",
"\n",
"In the following example, I will give space to my imagination to create a highly irregular (but not too much) function.\n",
"\n",
"The purpose is to test the behavior of the Kalman filter when the process model is completely wrong! \n",
"Let us recall that we assumed (see notebook **5.1**) a beta process following a random walk:\n",
"\n",
"$$ \\beta_k = \\beta_{k-1} + \\eta_k \\quad \\text{with} \\quad \\eta_k \\sim \\mathcal{N}(0, \\sigma^2_{\\eta}) $$\n",
"\n",
"A process with big discontinuities cannot be well described by a random walk. \n",
"Each discontinuity is interpreted as an extreme Gaussian event whose probability is negligible. \n",
"However, we will see that the Kalman filter works well even in these circumstances. Although it takes some time to adjust to a new measurements (this time is inversely proportional to the process error $\\sigma_{\\eta}$) "
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"beta= 0.18237876224021476 alpha= -0.002611655271123935\n",
"Auto Correlation = 0.18237764758337366\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"KF.plot_betas(X, Y, rho_nc, beta_nc, var_eta=0.001, training_size=training_size, rolling_window=60)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Comments:\n",
"\n",
"The two plots above contain the results of the Kalman estimation compared with rolling regression and smoothed values.\n",
"- Smoothed values are always the best in terms of MSE. Of course they cannot be used \"online\" as they require the knowledge of the estimations of betas at each time in the dataset.\n",
"- In the first plot I used MLE parameters. The estimated value of `var_eta` is very close to the true value. However, this small value creates problems in presence of jumps. For this reason the MSE is higher than the rolling regression one.\n",
"- To overcome this problem, in the second plot, I increased the parameter `var_eta` in order to make the filter more reactive to the jumps in the process. \n",
"This example is important because it shows that in some situations the filter designer decisions matter. \n",
"- Recall that when you choose a too small window in the rolling regression, the MSE instead of decreasing it increases. This is because the variance of beta increases.\n",
"\n",
"One last tip to remember: \n",
"Always compare the Kalman output with the rolling regression output, to get a better understanding of the functioning of the filter."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
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},
"nbformat": 4,
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}
================================================
FILE: 5.3 Volatility tracking.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Volatility tracking\n",
"\n",
"This is another notebook where I use the Kalman filter to track hidden variables. \n",
"This time it's the turn of **volatility**! \n",
"Well, actually I will track the variance, but the concept is the same.\n",
"\n",
"I will use the values simulated by a Heston process, and implement several methods to track the (hidden) volatility process.\n",
"We will compare the simple \"rolling\" method with the very famous GARCH(1,1) method, and we will see that the Kalman filter is slightly superior to both of them.\n",
"\n",
"This notebook also contains a small digression on the Variance Gamma process, which however is not related to the main theme and therefore you can skip it if you are not interested.\n",
"\n",
"In this notebook, I will make use of the [ARCH](https://arch.readthedocs.io/en/latest/index.html) library.\n",
"\n",
"## Contents\n",
" - [Heston Path generation](#sec1) \n",
" - [Log-return analysis](#sec1.1)\n",
" - [Hypothesis testing](#sec1.2)\n",
" - [A digression on the Variance Gamma process](#sec1.3)\n",
" - [Garch(1,1)](#sec2)\n",
" - [GARCH with the arch library](#sec2.1)\n",
" - [GARCH from scratch](#sec2.2)\n",
" - [Rolling variance](#sec2.3)\n",
" - [Kalman filter](#sec3)\n",
" - [Linear Gaussian State Space Model](#sec3.1)\n",
" - [Algorithm implementation](#sec3.2)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy.stats as ss\n",
"import statsmodels.api as sm\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from FMNM.Processes import Heston_process, VG_process, GARCH\n",
"from arch.unitroot import PhillipsPerron, KPSS, ADF\n",
"from arch import arch_model\n",
"from statsmodels.graphics.gofplots import qqplot\n",
"from statsmodels.tsa.stattools import pacf\n",
"import matplotlib.gridspec as gridspec\n",
"from FMNM.probabilities import VG_pdf\n",
"from scipy.integrate import quad\n",
"from scipy.optimize import minimize"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Heston path generation\n",
"\n",
"Let us consider a stock price $\\{S_t\\}_{t\\geq 0}$ following a Heston process. We introduced the Heston process in the notebook **1.4**, but it is better to recall the SDE: \n",
"\n",
"$$ \\begin{cases}\n",
"dS_t = \\mu S_t dt + \\sqrt{v_t} S_t dW^1_t \\\\\n",
"dv_t = \\kappa (\\theta - v_t) dt + \\sigma \\sqrt{v_t} dW^2_t \n",
"\\end{cases}$$\n",
"\n",
"The process $\\{v_t\\}_{t\\geq 0}$ is the variance process and follows a CIR process (see notebook **1.2**). \n",
"\n",
"The parameters are:\n",
"- $\\mu$ drift of the stock process\n",
"- $\\kappa$ mean reversion coefficient of the variance process\n",
"- $\\theta$ long term mean of the variance process \n",
"- $\\sigma$ volatility coefficient of the variance process\n",
"- $\\rho$ correlation between $W^1$ and $W^2$.\n",
"\n",
"I will simulate the process considering `N=2500` time points and `T=10` years. This is quite realistic as in ten years there are about 2500 days."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"N = 2500 # time points\n",
"T = 10 # time in years\n",
"T_vec, dt = np.linspace(0, T, N, retstep=True) # time vector and time step\n",
"S0 = 100 # initial price\n",
"v0 = 0.04 # initial variance\n",
"mu = 0.1\n",
"rho = -0.1\n",
"kappa = 5\n",
"theta = 0.04\n",
"sigma = 0.6 # alternative values: rho=-0.3 kappa=15 sigma=1\n",
"std_asy = np.sqrt(theta * sigma**2 / (2 * kappa)) # asymptotic standard deviation for the CIR process\n",
"assert 2 * kappa * theta > sigma**2 # Feller condition"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us generate the path using the function `Heston_process.path`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(seed=42)\n",
"Hest = Heston_process(mu=mu, rho=rho, sigma=sigma, theta=theta, kappa=kappa)\n",
"S, V = Hest.path(S0, v0, N, T) # S is the stock, V is the variance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Plot:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 4))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"ax1.plot(T_vec, S)\n",
"ax1.set_title(\"Heston model, Stock process.\")\n",
"ax1.set_xlabel(\"Time\")\n",
"ax1.set_ylabel(\"Stock\")\n",
"ax2.plot(T_vec, V)\n",
"ax2.set_title(\"Heston model, Variance process.\")\n",
"ax2.set_xlabel(\"Time\")\n",
"ax2.set_ylabel(\"Variance\")\n",
"ax2.plot(T_vec, (theta + std_asy) * np.ones_like(T_vec), label=\"1 asymptotic std dev\", color=\"black\")\n",
"ax2.plot(T_vec, (theta - std_asy) * np.ones_like(T_vec), color=\"black\")\n",
"ax2.plot(T_vec, theta * np.ones_like(T_vec), label=\"Long term mean\")\n",
"ax2.legend(loc=\"upper right\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Log-return analysis\n",
"\n",
"Let us compute the **log-returns** and **linear (or net) returns**. \n",
"It turns out that it is almost the same to work with log-returns or linear returns. \n",
"For small $\\Delta t$, we can use a first order Taylor expansion (around 1):\n",
"\n",
"$$ \\log \\frac{S_{t+\\Delta t}}{S_t} \\; \\approx \\; \\frac{S_{t+\\Delta t}}{S_t} -1 $$"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"ret_log = np.log(S[1:] / S[:-1]) # log returns\n",
"ret_pct = S[1:] / S[:-1] - 1 # linear net returns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Fit of Variance Gamma and Student t\n",
"\n",
"I will explain in the [VG Section](#sec1.3) why I chose the Variance Gamma distribution among many others."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
"VG parameters: 0.0005194481336264934 -0.05548824317892116 0.20503867670747206 0.003450209882217668\n"
]
}
],
"source": [
"VG = VG_process()\n",
"VG.fit_from_data(ret_log, dt, \"Nelder-Mead\") # or \"MM\" (methods of moments), or \"L-BFGS-B\" that does not work\n",
"print(\"VG parameters:\", VG.c, VG.theta, VG.sigma, VG.kappa)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Student-t parameters (3.5313949443738206, 0.0003999362308227026, 0.009120671213235513)\n"
]
}
],
"source": [
"params = ss.t.fit(ret_log)\n",
"print(\"Student-t parameters\", params) # degrees of freedom, location, scale"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us compute the mean and standard deviation for both log-returns and linear returns. I present the normalized values i.e. the values computed for $T=1$. \n",
"Adding the approximated Itô correction $\\frac{1}{2}\\theta$, the two means becomes almost equal (see below for a better explanation). "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log-returns, normalized mean=0.0673 and std=0.2065\n",
"Corrected mean=0.0873 by approximated Itô correction\n",
"Linear returns, normalized mean=0.0887 and std=0.2066\n"
]
}
],
"source": [
"mean_log = ret_log.mean()\n",
"std_log = ret_log.std()\n",
"mean_pct = ret_pct.mean()\n",
"std_pct = ret_pct.std()\n",
"print(\"Log-returns, normalized mean={:.4f} and std={:.4f}\".format(mean_log / dt, std_log / np.sqrt(dt)))\n",
"print(\"Corrected mean={:.4f} by approximated Itô correction\".format(mean_log / dt + 0.5 * theta))\n",
"print(\"Linear returns, normalized mean={:.4f} and std={:.4f}\".format(mean_pct / dt, std_pct / np.sqrt(dt)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below, you can see the returns histogram with some fitted distributions, the qq-plot and the plot of the log-return process. \n",
"As expected, the normal distribution underestimates the mass around the mean. From the histogram it is difficult to notice the presence of heavy tails. For this reason I included the qqplot as well. \n",
"The plot of the log-returns dynamics includes the mean and standard deviation levels. It clearly exhibits volatility clustering.\n",
"\n",
"\n",
"##### Plot"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(ret_log.min(), ret_log.max(), 200)\n",
"fig = plt.figure(figsize=(16, 9))\n",
"gs = gridspec.GridSpec(2, 2, height_ratios=[2, 1])\n",
"gs00 = gs[1, :].subgridspec(1, 2, width_ratios=[2, 3])\n",
"ax1 = fig.add_subplot(gs[0, :])\n",
"ax2 = fig.add_subplot(gs00[0, 0])\n",
"ax3 = fig.add_subplot(gs00[0, 1])\n",
"ax1.plot(x, ss.norm.pdf(x, mean_log, std_log), color=\"olivedrab\", label=\"Normal density\")\n",
"ax1.plot(\n",
" x,\n",
" ss.t.pdf(x, loc=params[1], scale=params[2], df=params[0]),\n",
" color=\"darkorange\",\n",
" label=\"Stud-t, df={}\".format(params[0].round(2)),\n",
")\n",
"ax1.plot(x, VG_pdf(x, dt, VG.c, VG.theta, VG.sigma, VG.kappa), color=\"indigo\", label=\"VG density\")\n",
"ax1.plot(\n",
" x,\n",
" VG_pdf(x, dt, VG.c + VG.theta * dt, 0, np.sqrt(VG.var), VG.kappa),\n",
" color=\"darksalmon\",\n",
" label=\"VG approximated density\",\n",
")\n",
"ax1.hist(ret_log, density=True, bins=200, facecolor=\"LightBlue\", label=\"Log-returns\")\n",
"ax1.legend()\n",
"ax1.set_title(\"Log-returns histogram\")\n",
"ax2.set_title(\"Log-returns qqplot\")\n",
"qqplot(ret_log, line=\"s\", ax=ax2)\n",
"ax3.plot(T_vec[1:], ret_log)\n",
"ax3.set_title(\"Log-return dynamics\")\n",
"ax3.plot(T_vec[1:], (mean_log + std_log) * np.ones(N - 1), label=\"1 std dev\", color=\"black\")\n",
"ax3.plot(T_vec[1:], (mean_log - std_log) * np.ones(N - 1), color=\"black\")\n",
"ax3.plot(T_vec[1:], mean_log * np.ones(N - 1), color=\"orange\", label=\"mean\")\n",
"ax3.set_xlabel(\"Time\")\n",
"ax3.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From now on, the quantity of interest will be the **stock return** $R_t$. \n",
"As we have seen above, there is no big difference between using log-returns or linear returns. \n",
"\n",
"Since we will work with a time series of data, it is better to consider a discrete-time model (with $\\Delta t = 1$), that can be derived from the discretization of the SDE we have seen before.\n",
"\n",
"1) Linear returns, $ R_{t+1} := \\frac{S_{t+1}}{S_t} -1 $:\n",
"\n",
"We can discretize the equation of the process $\\{S_t\\}_{t\\geq0}$ in the Heston SDE introduced on top of this notebook (the Euler-Maruyama discretization procedure is presented in the notebook **1.2**):\n",
"\n",
"$$ R_{t+1} = \\mu + \\sqrt{v_t} \\Delta W_{t} $$\n",
"\n",
"with $\\Delta W_{t} = W_{t+1} - W_{t}$.\n",
"\n",
"2) Log-returns, $ R_{t+1} := \\log \\frac{S_{t+1}}{S_t} $:\n",
"\n",
"You should make a change of variable into the Heston SDE (see what I did in the notebook **1.4**) and then discretize. \n",
"\n",
"$$ R_{t+1} = \\mu - \\frac{1}{2}v_t + \\sqrt{v_t} \\Delta W_{t} $$\n",
"\n",
"In the linear returns equation we have a constant drift $\\mu$, but in the log-return equation the drift $\\mu - \\frac{1}{2}v_t$ is stochastic.\n",
"\n",
"Since we are interested in the volatility, there is no need of a drift term. It turns out that it is almost always possible to detrend a time series. \n",
"If the trend is constant we can simply define $\\tilde R_{t} := R_{t} - \\mu$. If the trend is stochastic, detrending is more complicated but still possible. \n",
"In particular, in the process above the effect of the stochasticity in the trend is negligible for big values of $T$. Since the trend is a mean reverting process, we can approximate the mean with its long term mean $\\mathbb{E}\\big[ v_t \\big] \\approx \\theta$. Let us check it is correct. \n",
"From the [CIR conditional expectation](https://en.wikipedia.org/wiki/Cox%E2%80%93Ingersoll%E2%80%93Ross_model#Properties) formula, we can use the tower property:\n",
"\n",
"$$ \\mathbb{E}\\big[ v_t \\,\\big|\\, \\mathcal{F}_0 \\big] = \\mathbb{E}\\bigg[ \\mathbb{E}\\big[ v_t \\big| v_{t-1} \\big] \\, \\bigg|\\, \\mathcal{F}_0 \\bigg] = \\mathbb{E}\\bigg[ v_{t-1} e^{-\\kappa \\Delta t} + \\theta (1-e^{-\\kappa \\Delta t}) \\, \\bigg|\\, \\mathcal{F}_0 \\bigg] = \\mathbb{E}\\big[ v_{t-1} \\, \\big|\\, \\mathcal{F}_0 \\big] e^{-\\kappa \\Delta t} + \\theta (1-e^{-\\kappa \\Delta t}) $$\n",
"\n",
"For $t \\gg 0$ ($t$ much bigger than zero) we can write $\\mathbb{E}\\big[ v_t \\,\\big|\\, \\mathcal{F}_0 \\big] \\approx \\mathbb{E}\\big[ v_{t-1} \\,\\big|\\, \\mathcal{F}_0 \\big] \\approx \\mathbb{E}\\big[ v_t \\big]$ where the dependency on the initial information $\\mathcal{F}_0$ decays.\n",
"Solving the equation we get $\\mathbb{E}\\big[ v_t \\big] \\approx \\theta$. \n",
"\n",
"We can detrend the series by redefining $\\tilde R_{t} := R_{t} - \\mathbb{E}[R_{t}] = R_{t} - \\mu + \\frac{1}{2}\\theta$. \n",
"I called the term $\\frac{1}{2}\\theta$, *approximated Itô correction*. \n",
"\n",
"We obtain a unified model that works for both linear and log returns (we can suppress the tilde):\n",
"\n",
"$$ R_{t+1} = \\sqrt{v_t}\\, \\epsilon_{t+1} $$\n",
"\n",
"with $\\epsilon_t \\sim \\mathcal{N}(0,1)$ for each $t$. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Hypothesis testing\n",
"\n",
"Let us investigate the statistical properties of this log-return time series.\n",
"\n",
"For this purpose I want to perform some tests:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stationarity tests:\n",
"\n",
"- The Augmented Dickey-Fuller test [wiki](https://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_test) \n",
"The null hypothesys is that the process is unit root.\n",
"\n",
"\n",
"- The Phillips-Perron test [wiki](https://en.wikipedia.org/wiki/Phillips%E2%80%93Perron_test) \n",
"The test is robust with respect to unspecified autocorrelation and heteroscedasticity. The null hypothesys is that the process is unit root.\n",
"\n",
"\n",
"- Kwiatkowski–Phillips–Schmidt–Shin tests [wiki](https://en.wikipedia.org/wiki/KPSS_test) \n",
"The null hypothesys is that the process is NOT unit root.\n",
"\n",
"Let us also conside the [Jarque-Bera](https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test) test for normality and the [Ljung-Box](https://en.wikipedia.org/wiki/Ljung%E2%80%93Box_test) test for autocorrelation."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Augmented Dickey Fuller test: p-value = 0.0\n",
"Phillips Perron test: p-value = 0.0\n",
"KPPS test: p-value = 0.817\n",
"\n",
"Jarque-Bera (H0=normal), p-value = 4.223327454197681e-221\n",
"Ljung-Box test (H0=independent) for lag 1 to 3, p-values = [0.34843522 0.4224824 0.61618723]\n",
"Ljung-Box test on the squared returns, p-values = [1.14275887e-18 3.49742191e-38 1.86949489e-47]\n"
]
}
],
"source": [
"print(\"Augmented Dickey Fuller test: p-value = \", ADF(ret_log, trend=\"c\").pvalue)\n",
"print(\"Phillips Perron test: p-value = \", PhillipsPerron(ret_log, trend=\"c\").pvalue)\n",
"print(\"KPPS test: p-value = \", round(KPSS(ret_log).pvalue, 4))\n",
"print()\n",
"print(\"Jarque-Bera (H0=normal), p-value = \", ss.jarque_bera(ret_log)[1])\n",
"print(\n",
" \"Ljung-Box test (H0=independent) for lag 1 to 3, p-values = \",\n",
" sm.stats.acorr_ljungbox(ret_log, lags=3, return_df=False)[\"lb_pvalue\"].values,\n",
")\n",
"print(\n",
" \"Ljung-Box test on the squared returns, p-values = \",\n",
" sm.stats.acorr_ljungbox(ret_log**2, lags=3, return_df=False)[\"lb_pvalue\"].values,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Comments:\n",
"\n",
"1) The plot of the log-return dynamics suggests that the series is weak stationary (i.e. with constant unconditional mean and variance), but has non-constant conditional variance. This time series clearly exhibits **volatility clustering**. \n",
"The first three tests all confirm the weak stationarity! \n",
"Since the process has conditional heteroscedasticity, the most appropriate test to use in this case is the PhillipsPerron, which is very robust with respect to nonconstant variances. \n",
"In [1], it is shown that smooth and periodic variations in the variance have a negligible effect on the ADF test output, which makes it quite reliable as well. \n",
"\n",
"2) The Jarque-Bera test confirms what we saw in the histogram and in the qqplot i.e. that the log-returns distribution is not normal.\n",
"\n",
"3) The Ljung-Box test on the log-returns shows high p-values. Thus we cannot reject the independence hypothesis (we know that log-returns are independent). \n",
"However, the same test applied to the squared log-returns shows very small p-values, confirming the presence of autocorrelation. **Volatility clustering can be detected by checking\n",
"for correlations between the squared returns.**\n",
"\n",
"##### Parameters choice\n",
"\n",
"When I chose the parameters of the CIR process $v_t$, I had to select the parameters $\\rho$, $\\kappa$ and $\\sigma$ such that the stationarity and the volatility clusters can be detected from the simulated data. \n",
"The mean reversion parameter $\\kappa$ should be big enough to make the process stationary. On the other hand, if $\\kappa$ is too big, the variance process reverts too fast towards its long term mean, and the log-returns do not exhibit the volatility clusters. \n",
"If $\\sigma$ is too small, the process always stays near the long term mean, and clusters do not form."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Augmented Dickey Fuller test for the variance process: p-value = 0.00010900110759470202\n",
"Using the PACF we can calculate phi=0.9833. It is very close to the theoretical value phi=0.9802\n"
]
}
],
"source": [
"print(\"Augmented Dickey Fuller test for the variance process: p-value = \", ADF(V, trend=\"c\").pvalue)\n",
"print(\n",
" \"Using the PACF we can calculate phi={:.4f}. It is very close to the theoretical value phi={:.4f}\".format(\n",
" pacf(V, nlags=1)[1], np.exp(-kappa * dt)\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ADF test shows that the process is indeed stationary.\n",
"\n",
"We don't know the solution of the CIR equation, but we know its conditional expectation \n",
"\n",
"$$\\mathbb{E}\\big[ v_t \\big| v_{t-1}\\big] = \\phi v_{t-1} + const, $$ \n",
"\n",
"with $\\phi=e^{-\\kappa \\Delta t}$. \n",
"In order to have stationarity we must have $|\\phi|<1$. A small $\\kappa$ makes $\\phi \\approx 1$ and the process becomes similar to a unit root. \n",
"\n",
"From the last expression, we see that also the size of $\\Delta t$ matters. \n",
"When $\\Delta t \\to 0$ then $\\phi \\to 1$. \n",
"Choosing bigger value of $N$, may create problems in the detection of stationarity. \n",
"This is a consequence of the fact that **for short time scales, a mean-reverting process is indistinguishable from a random walk** (remember that in an autoregressive model when $|\\phi|=1$ the process is unit root)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## A digression on the Variance Gamma (VG) process\n",
"\n",
"Above I plotted the log-returns histogram together with some fitted distributions: the normal, the Student-t and the VG distributions. \n",
"You may ask yourself why I decided to include the VG distribution in the plot. If you look carefully, you can see that it fits the data really good!\n",
"\n",
"If you need to review the VG process, have a look at the notebook **1.5**. \n",
"Important to know, is that the process is obtained by Brownian subordination (i.e. the time $t$ in the Brownian motion is assumed to be a gamma process) and is defined as: \n",
"\n",
"$$ X_{t} = \\theta T_t + \\sigma W_{T_t} $$ \n",
"\n",
"where $W_{T_t} \\sim \\mathcal{N}(0,T_t)$ and $T_t \\sim \\Gamma(\\text{mean}=t, \\text{var}=\\kappa t)$. \n",
"I'm using the same letters $(\\theta, \\sigma, \\kappa)$ used for the Heston parameters, but remember that they have a different meaning: $\\theta$ and $\\sigma$ are the drift and std dev of the Brownian motion, and $\\kappa$ is the variance coefficient of the Gamma process.\n",
"\n",
"Alright, \n",
"let us try to answer the following three questions:\n",
"\n",
"1) How good is the VG fit? \n",
"2) How can we measure the quality of the fit? \n",
"3) Why the VG process among many others? \n",
"\n",
"In order to answer the first two questions, let us define the Empirical-Cumulative-Distribution-Function [ECDF](https://en.wikipedia.org/wiki/Empirical_distribution_function):\n",
"\n",
"$$ \\hat F_n(x) = \\frac{1}{n} \\sum_{i=1}^{n} \\mathbb{1}_{X_i \\leq x} $$\n",
"\n",
"Unlike the histogram, the ECDF had the advantage that it does not depend on the number of bins. \n",
"We can compare the ECDF with the **Normal**, **Student-t** and **VG** theoretical cumulative distributions. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def ECDF(data):\n",
" \"\"\"ECDF\"\"\"\n",
" x = np.sort(data)\n",
" n = x.size\n",
" y = np.arange(1, n + 1) / n\n",
" return x, y"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"xx, yy = ECDF(ret_log)\n",
"VG_cdf = np.array([quad(VG_pdf, -1, i, args=(dt, VG.c, VG.theta, VG.sigma, VG.kappa))[0] for i in xx])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Plot"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(15, 5))\n",
"plt.plot(xx, yy, color=\"darkslategray\", label=\"ECDF\")\n",
"plt.plot(xx, ss.norm.cdf(xx, loc=mean_log, scale=std_log), color=\"green\", label=\"Normal CDF\")\n",
"plt.plot(\n",
" xx,\n",
" ss.t.cdf(xx, loc=params[1], scale=params[2], df=params[0]),\n",
" color=\"blue\",\n",
" label=\"Stud-t, df={}\".format(params[0].round(2)),\n",
")\n",
"plt.plot(xx, VG_cdf, label=\"VG CDF\", color=\"darkorange\")\n",
"plt.title(\"Comparison of Cumulative functions\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking at the plot above, we can understand better which distribution fits better the data. \n",
"\n",
"In order to have a quantitative measure of the goodness of fit, we can calculate the Euclidean distance between the theoretical and the empirical CDFs."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Distance VG-ECDF: 0.14036726562645024\n",
"Distance Student-t-ECDF: 0.34203476140854105\n",
"Distance Normal-ECDF: 1.9583261648228136\n"
]
}
],
"source": [
"print(\"Distance VG-ECDF: \", np.linalg.norm(VG_cdf - yy, ord=2))\n",
"print(\n",
" \"Distance Student-t-ECDF: \", np.linalg.norm(ss.t.cdf(xx, loc=params[1], scale=params[2], df=params[0]) - yy, ord=2)\n",
")\n",
"print(\"Distance Normal-ECDF: \", np.linalg.norm(ss.norm.cdf(xx, loc=mean_log, scale=std_log) - yy, ord=2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### Now we have the confirmation that the VG distribution works very well!!\n",
"\n",
"Ok, but are there any theoretical motivations?\n",
"\n",
"The answer is **yes**! \n",
"\n",
"1) The variance of the Heston process follows the CIR process. I introduced the CIR process in the notebook **1.2**, where we saw that the conditional distribution of the process is a non-central chi-squared distribution. However, as time becomes large, the asymptotic distribution tends to the Gamma distribution:\n",
"\n",
"$$ f(v; \\alpha, \\beta ) = \\frac{\\beta^{\\alpha}}{\\Gamma(\\alpha)} v^{\\alpha-1} e^{-\\beta v} $$\n",
"where $\\alpha = \\frac{2 \\theta \\kappa}{\\sigma^2}$ and $\\beta= \\frac{2 \\kappa}{\\sigma^2}$.\n",
"\n",
"2) In the VG process $X_{t} = \\theta T_t + \\sigma W_{T_t}$, the variance of the Brownian motion is Gamma distributed. I will use an heuristic Brownian approximation. \n",
"Since $\\mathbb{E}[X_t] = \\theta t$ and $Var[X_{t}] = (\\sigma^2 + \\theta^2 \\kappa) t$, we can define $\\sigma^2_{VG} = \\sigma^2 + \\theta^2 \\kappa$ and write: \n",
"\n",
"$$ X_{t} \\approx \\theta t + \\sigma_{VG} W_{T_t} $$ \n",
"\n",
"where, by the law of total variance, $Var \\big[ W_{T_t} \\big] = t$. The approximated model has same mean and same variance of the original VG process. \n",
"\n",
"The process $\\sigma_{VG} W_{T_t}$ is known as the **symmetric Variance Gamma** process (the parameter $\\theta$ is the responsible for the skewness, if $\\theta=0$ the density is symmetric). \n",
"The distribution of the approximated process can be obtained from `VG_pdf`, the VG distribution $f^{VG}(x; t, c, \\theta, \\sigma, \\kappa)$, by setting the parameters \n",
"\n",
"$$ c= c+\\theta t \\quad \\theta=0 \\quad \\sigma=\\sigma_{VG} \\quad \\kappa=\\kappa $$ \n",
"\n",
"In the [plot above](#hist), we plotted both the VG and approximated VG densities. \n",
"It is very hard to distinguish them. **In this case the approximation is very good!** \n",
"\n",
"The conditional variance of the symmetric VG is: \n",
"\n",
"$$ Var \\big[ \\sigma_{VG} W_{T_t} \\,\\big|\\, T_t \\big] \\,=\\, \\sigma^2_{VG} T_t \\,\\sim \\, \\Gamma \\big( \\text{shape=}\\frac{t}{\\kappa}, \\, \\text{scale=} \\sigma^2_{VG} \\big) $$ \n",
"\n",
"Therefore... \n",
"In both cases 1) and 2), the variance is described by a Gamma distribution. \n",
"Let us plot the two Gammas, together with a Gamma distribution fitted from the data. "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"params2 = ss.gamma.fit(V, floc=0) # fit from data\n",
"beta = 2 * kappa / sigma**2\n",
"alpha = 2 * kappa * theta / sigma**2"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"vg_path = VG.path(T=T, N=N)[0] # VG path\n",
"vg_ret = vg_path[1:] - vg_path[:-1] # VG increments"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 8))\n",
"gs = gridspec.GridSpec(2, 2, height_ratios=[4, 3])\n",
"gs00 = gs[1, :].subgridspec(1, 3, width_ratios=[1, 3, 1])\n",
"ax1 = fig.add_subplot(gs[0, 0])\n",
"ax2 = fig.add_subplot(gs00[0, 1])\n",
"ax3 = fig.add_subplot(gs[0, 1])\n",
"xxx = np.linspace(V.min(), V.max(), 100)\n",
"xxxx, yyyy = ECDF(V)\n",
"ax1.plot(\n",
" xxx, ss.gamma.pdf(xxx, a=params2[0], loc=params2[1], scale=params2[2]), color=\"darkorange\", label=\"Fitted from data\"\n",
")\n",
"ax1.set_title(\"Gamma densities. Histogram of the Variance CIR process\")\n",
"ax1.plot(xxx, ss.gamma.pdf(xxx, a=dt / VG.kappa, scale=VG.var), color=\"palevioletred\", label=\"Variance of the VG\")\n",
"ax1.plot(xxx, ss.gamma.pdf(xxx, a=alpha, scale=1 / beta), color=\"forestgreen\", label=\"CIR asymptotic\")\n",
"ax1.hist(V, density=True, bins=100, facecolor=\"LightBlue\", label=\"Variance\")\n",
"ax1.legend()\n",
"ax2.plot(T_vec[1:], vg_ret)\n",
"ax2.plot(T_vec[1:], (VG.mean * dt + np.sqrt(VG.var * dt)) * np.ones(N - 1), label=\"1 std dev\", color=\"black\")\n",
"ax2.plot(T_vec[1:], (VG.mean * dt - np.sqrt(VG.var * dt)) * np.ones(N - 1), color=\"black\")\n",
"ax2.plot(T_vec[1:], VG.mean * dt * np.ones(N - 1), color=\"orange\", label=\"mean\")\n",
"ax2.set_xlabel(\"Time\")\n",
"ax2.legend()\n",
"ax2.set_title(\"VG increments with parameters from Heston logreturns\")\n",
"ax3.plot(xxxx, yyyy, color=\"darkslategray\", label=\"ECDF\")\n",
"ax3.plot(xxxx, ss.gamma.cdf(xxxx, a=dt / VG.kappa, scale=VG.var), color=\"palevioletred\", label=\"Variance of the VG\")\n",
"ax3.plot(xxxx, ss.gamma.cdf(xxxx, a=alpha, scale=1 / beta), color=\"forestgreen\", label=\"CIR asymptotic\")\n",
"ax3.plot(\n",
" xxxx,\n",
" ss.gamma.cdf(xxxx, a=params2[0], loc=params2[1], scale=params2[2]),\n",
" color=\"darkorange\",\n",
" label=\"Fitted from data\",\n",
")\n",
"ax3.set_title(\"Comparison of Cumulative functions\")\n",
"ax3.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- The first plot shows the two theoretical Gammas obtained from the VG and CIR models. It also shows a Gamma distribution with parameters fitted from the data. \n",
"- The second plot compares the cumulative density functions.\n",
"- In the third plot, you can see the simulated VG increments (with parameters fitted from the Heston log-returns). \n",
"\n",
"I decided to plot the VG dynamics because it is important to understand that,\n",
"although the VG density is very good to describe the empirical log-returns density, **the VG process is NOT able to describe the statistical feature of the Heston process i.e. volatility clusters!!**"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Distance: Gamma_fitted - ECDF: 0.8300503439368906\n",
"Distance: Gamma_VG - ECDF: 1.9777814887809424\n",
"Distance: Gamma_CIR - ECDF: 1.0003085960032356\n"
]
}
],
"source": [
"print(\n",
" \"Distance: Gamma_fitted - ECDF: \",\n",
" np.linalg.norm(ss.gamma.cdf(xxxx, a=params2[0], loc=params2[1], scale=params2[2]) - yyyy, ord=2),\n",
")\n",
"print(\"Distance: Gamma_VG - ECDF: \", np.linalg.norm(ss.gamma.cdf(xxxx, a=dt / VG.kappa, scale=VG.var) - yyyy, ord=2))\n",
"print(\"Distance: Gamma_CIR - ECDF: \", np.linalg.norm(ss.gamma.cdf(xxxx, a=alpha, scale=1 / beta) - yyyy, ord=2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Important: The plots above depend on the initial choice of the parameters $\\rho$, $\\kappa$ and $\\sigma$.\n",
"\n",
"If you try to modify the parameters, for instance, $\\rho=-0.3$, $\\kappa = 15$ and $\\sigma = 1$, you will find out that the CIR-asymptotic-Gamma distribution is a much better fit than the VG-Gamma distribution.\n",
"Other parameters can imply the opposite.\n",
"\n",
"#### Conclusive comment:\n",
"\n",
"For any choice of the CIR parameters, the VG distribution is always better than the Normal or Student-t, when fitting the log-returns!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# GARCH(1,1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the previous section we obtained the stochastic model for the returns: $ R_{t} = \\sqrt{v_{t-1}}\\, \\epsilon_{t} $ as a result of the explicit discretization of stock process SDE. \n",
"However, the most common discrete time models, prefer to consider the expression:\n",
"\n",
"$$ R_{t} = \\sqrt{v_t}\\, \\epsilon_{t} $$\n",
"\n",
"where $v_t$ usually can be deterministic or stochastic. Usually, in deterministic models such as ARCH and GARCH the variance $v_t$ is a function of variables at previous times.\n",
"\n",
"**The difficult task is to estimate the variance value $v_t$**. \n",
"Let us try two common methods:\n",
"\n",
"- 1) Let us consider the [GARCH(1,1)](https://en.wikipedia.org/wiki/Autoregressive_conditional_heteroskedasticity#GARCH) model:\n",
"\n",
"$$ v_t = \\omega + \\alpha \\, R_{t-1}^2 + \\beta \\, v_{t-1} $$\n",
"\n",
"where $\\omega = \\gamma V_L$. The parameter $V_L$ is the long term variance (unconditional variance). \n",
"The weights must satisfy: $\\alpha, \\beta >0$, $\\gamma \\geq 0$ and $\\alpha + \\beta + \\gamma = 1$. \n",
"After having fitted the parameters $\\omega$, $\\alpha$ and $\\beta$, it is possible to compute the other parameters as:\n",
"\n",
"$$ \\gamma = 1-\\alpha-\\beta \\quad \\text{and} \\quad V_L = \\frac{\\omega}{\\gamma}. $$\n",
"\n",
"If $\\alpha + \\beta = 1$ the unconditional variance $V_L$ is not well defined.\n",
"And this means that the GARCH process is not stationary anymore i.e. it is unit root.\n",
"\n",
"Usually the GARCH(1,1) is sufficient to capture the volatility clustering in the data. There is no need for an higher order GARCH(p,q) with $p,q>1$.\n",
"\n",
"\n",
"- 2) Another possibility is to compute the **rolling variance**. Using a time window of $m$ points, we can compute:\n",
"\n",
"$$ v_t = \\frac{1}{m} \\sum_{i=0}^{m-1} R_{t-i}^2 $$\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Computing the GARCH with the arch library\n",
"\n",
"The [arch library](https://arch.readthedocs.io/en/latest/univariate/introduction.html) gives the possibility to estimate the mean $\\mu$ as well:\n",
"\n",
"$$ R_{t} = \\mu + \\sqrt{v_t}\\, \\epsilon_{t} $$\n",
"\n",
"But we don't care about it, and we detrend the series: "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Comments on the output below:\n",
"\n",
"- I chose a quite big value `train_size = 1500`. If we want to compare this number with realistic data, this corresponds to 6 years of daily data. \n",
"The reason of this big number is that in order to compute the parameter $\\omega$ (proportional to the long term variance) with a good precision (small standard error), we need a lot of data.\n",
"\n",
"\n",
"- If you set `mean=\"constant\"` the parameter $\\mu$ will be estimated. You will find a $\\mu$ very close to zero, or a big *p-value*.\n",
"\n",
"\n",
"- You can try to change the GARCH values of `p` and `q`. However, in this example, $p=q=1$ produce the **smallest** [AIC](https://en.wikipedia.org/wiki/Akaike_information_criterion) i.e. provide the best model. \n",
"\n",
"\n",
"- The optimizer requires to rescale the data in some cases, see discussion on [stackoverflow](https://stackoverflow.com/questions/11155721/positive-directional-derivative-for-linesearch)\n",
"It is possible to set `rescale=True` to automatically rescale the data, but I prefer to do it manually by introducting a `scale_factor=1/dt`.\n",
"\n",
"\n",
"- If you try to set `dist=\"StudentsT\"`, you will obtain a quite big value of estimated degrees of freedom $\\nu>30$, confirming that the data was generated by a Gaussian error. "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"train_size = 1500\n",
"train = ret_log[:train_size]\n",
"train = train - train.mean() # detrend the series\n",
"test = ret_log[train_size:]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"alpha + beta = 0.9928\n",
"The parameter gamma= 0.0072\n",
"scaled omega = 2.8472e-06\n",
"The long term variance is: 0.0991\n",
"\n",
" Zero Mean - GARCH Model Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.000\n",
"Mean Model: Zero Mean Adj. R-squared: 0.001\n",
"Vol Model: GARCH Log-Likelihood: -5961.62\n",
"Distribution: Normal AIC: 11929.2\n",
"Method: Maximum Likelihood BIC: 11945.2\n",
" No. Observations: 1500\n",
"Date: Mon, Aug 07 2023 Df Residuals: 1500\n",
"Time: 18:01:03 Df Model: 0\n",
" Volatility Model \n",
"==========================================================================\n",
" coef std err t P>|t| 95.0% Conf. Int.\n",
"--------------------------------------------------------------------------\n",
"omega 2.8472 1.007 2.828 4.682e-03 [ 0.874, 4.820]\n",
"alpha[1] 0.1222 2.084e-02 5.864 4.524e-09 [8.136e-02, 0.163]\n",
"beta[1] 0.8706 1.877e-02 46.372 0.000 [ 0.834, 0.907]\n",
"==========================================================================\n",
"\n",
"Covariance estimator: robust\n"
]
}
],
"source": [
"scale_factor = 1000\n",
"am = arch_model(scale_factor * train, mean=\"zero\", vol=\"GARCH\", p=1, q=1, rescale=False)\n",
"res = am.fit(disp=\"off\")\n",
"print(\"alpha + beta = \", (res.params[1] + res.params[2]).round(4))\n",
"print(\"The parameter gamma=\", (1 - (res.params[1] + res.params[2])).round(4))\n",
"print(\"scaled omega = \", (1 / scale_factor**2) * res.params[0].round(4))\n",
"arch_long_var = (1 / scale_factor**2) * res.params[0] / (1 - (res.params[1] + res.params[2]))\n",
"print(\"The long term variance is: \", (arch_long_var / dt).round(4))\n",
"print()\n",
"print(res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If $\\alpha + \\beta \\approx 1$, as is typically the case, then the model is not very sensitive to the term $V_L$ for short horizons, however, $V_L$ becomes important for longer horizons."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Computing the GARCH from scratch\n",
"\n",
"\n",
"I implemented the GARCH(1,1) class in [Processes.py](./functions/Processes.py). \n",
"The class contains the methods:\n",
"- `path`: It uses a Monte Carlo simulation to generate a GARCH(1,1) process.\n",
"- `log_likelihood`: It computes the log-likelihood function, and optionally, it returns the last variance value. \n",
"- `fit_from_data`: It fits the GARCH(1,1) parameters from a time series.\n",
"- `generate_var`: It generates the variance process from a series of returns.\n",
"\n",
"Let us create a garch process and fit the parameters."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully\n",
" Params SE P-val 95% CI lower 95% CI upper\n",
"omega 0.000011 0.000011 1.560757e-01 -0.000010 0.000032\n",
"alpha 0.171224 0.060621 2.367780e-03 0.052409 0.290038\n",
"beta 0.785951 0.102249 7.553249e-15 0.585546 0.986356\n",
"\n",
"gamma = 0.0428\n",
"The long term variance is: 0.0629\n"
]
}
],
"source": [
"garch = GARCH()\n",
"garch.fit_from_data(train, disp=True)\n",
"print()\n",
"print(\"gamma = \", garch.gamma.round(4))\n",
"print(\"The long term variance is: \", (garch.VL / dt).round(4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### The output of my algorithm is a little different from that of the arch library: \n",
"\n",
"- I didn't check the settings in the `arch library`. I only know we are both using `scipy.optimize.minimize` with [SLSQP](https://docs.scipy.org/doc/scipy/reference/optimize.minimize-slsqp.html) to find the minimum of the negative log-likelihood. \n",
"\n",
"I used the formula for the (Gaussian) log-likelihood:\n",
"\n",
"$$ \\log L(\\omega, \\alpha, \\beta; R) = \\frac{1}{2} \\sum_{i=1}^N \\biggl[ -\\log{2\\pi} -\\log v_i - \\frac{R_i^2}{v_i} \\biggr] $$\n",
"\n",
"where the variance $v_i$ is calculated ricursively, and I assumed as initial value $v_{i=0} = R^2_{i=0}$.\n",
"\n",
"- The arch library uses a robust method for the estimation of the covariance matrix of the parameters. The provided standard errors are thus robust. \n",
"Instead, I simply implemented the formula:\n",
"\n",
"$$ \\text{COV}\\bigg[\\hat\\omega_{ML}, \\hat\\alpha_{ML}, \\hat\\beta_{ML}\\bigg] = \\bigg[\\mathbf{I}\\big(\\hat\\omega_{ML}, \\hat\\alpha_{ML}, \\hat\\beta_{ML} \\big)\\bigg]^{-1} $$\n",
"\n",
"where \n",
"\n",
"$$\\mathbf{I}(\\theta) = - \\frac{\\partial^2}{\\partial \\theta_i \\, \\partial \\theta_j} \\log L(\\theta) \n",
"\\quad \\quad \\text{and} \\quad \\quad \\theta=(\\omega, \\alpha, \\beta)$$\n",
"\n",
"is the [Fisher Information matrix](https://en.wikipedia.org/wiki/Fisher_information). The *observed Fisher information matrix* is simply $\\mathbf{I}(\\hat \\theta_{ML})$.\n",
"\n",
"The inverse of the (negative) Hessian of the log-likelihood is an estimator of the asymptotic covariance matrix. Hence, the square roots of the diagonal elements of covariance matrix are estimators of the standard errors. \n",
"I used `statsmodels.tools.numdiff.approx_hess` for the numerical computation of the Hessian.\n",
"\n",
"We can check which set of parameters is better:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log-Likelihood function of my implementation = 4382.63\n"
]
}
],
"source": [
"log_lik, _ = garch.log_likelihood(train, last_var=True)\n",
"print(\"Log-Likelihood function of my implementation = \", log_lik.round(2))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log-Likelihood function of arch parameters = 4395.54\n"
]
}
],
"source": [
"garch_from_arch = GARCH(VL=arch_long_var, alpha=res.params[1], beta=res.params[2])\n",
"log_lik_arch, last_var = garch_from_arch.log_likelihood(train, last_var=True)\n",
"print(\"Log-Likelihood function of arch parameters = \", log_lik_arch.round(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### It seems that the ARCH implementation provides better parameters in terms of maximum likelihood.\n",
"\n",
"I compared my implementation with ARCH on several simulated dataset, and the results are always very similar. Of course, as expected, the ARCH is more accurate, as we can see from the results above.\n",
"\n",
"We can use the parameters fitted from the ARCH method `fit()` to track the variance, using the GARCH recursion algorithm `generate_var`."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"var_train = garch_from_arch.generate_var(R=train[1:], R0=train[0], var0=train[0] ** 2)\n",
"var_test = garch_from_arch.generate_var(R=test, R0=train[-1], var0=last_var)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
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"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(14, 4))\n",
"plt.plot(V[train_size + 1 :], label=\"Heston variance process\", linewidth=2, color=\"steelblue\")\n",
"plt.plot(var_test / dt, label=\"GARCH variance process\", color=\"lightcoral\", linestyle=\"--\")\n",
"plt.plot(roll_var.values[train_size:], label=\"Rolling Variance\", linestyle=\"--\", alpha=1, color=\"limegreen\")\n",
"plt.title(\"Out of sample comparison: GARCH vs Rolling\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Kalman filter\n",
"\n",
"In this section I'm going to present the model initially proposed by Taylor [2], with the linearization and estimation method described in Harvey, Ruiz and Shephard, [3], [4] and [5]."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Model linearization:\n",
"\n",
"Let us consider the model for the returns defined before:\n",
"\n",
"$$ R_{t} = \\sqrt{v_t}\\, e_{t} \\quad \\text{with} \\quad e_{t} \\sim \\mathcal{N}(0,1). $$\n",
"\n",
"It is quite useful to introduce an additional scale parameter $\\bar \\sigma$, such that the model becomes: \n",
"\n",
"$$ R_{t} = \\bar \\sigma \\sqrt{v_t}\\, e_{t} \\quad \\text{with} \\quad e_{t} \\sim \\mathcal{N}(0,1). $$\n",
"\n",
"This model is multiplicative. The first thing that comes to mind is to take the log transform to make it linear!! \n",
"If you remember, already in the notebook **1.4** we found useful to define the log-variance $h_t := \\log v_t$.\n",
"\n",
"In order to remove the square root, we can square the equation on both sides, and then take the logarithm:\n",
"\n",
"$$ \\begin{aligned}\n",
"\\log R_{t}^2 &= \\log \\big( \\bar \\sigma^2 \\, v_t \\, e_{t}^2 \\big) \\\\\n",
" &= \\log \\bar \\sigma^2 \\,+ \\log v_t \\,+ \\log e_{t}^2 \\; \\pm \\; \\mathbb{E}\\big[\\log e_{t}^2 \\big] \\\\\n",
" &= \\log \\bar \\sigma^2 \\,+ \\mathbb{E}\\big[\\log e_{t}^2 \\big] + h_t + \\epsilon_t \n",
"\\end{aligned} $$\n",
"\n",
"Where we called $\\epsilon_t := \\log e_{t}^2 - \\mathbb{E}\\big[\\log e_{t}^2 \\big]$. \n",
"\n",
"We want to create a state space model with an hidden state variable (see notebook **5.1** for a review of the general theory).\n",
"The hidden variable in this model is the log-variance. We can assume it follows an autoregressive process: \n",
"\n",
"$$ h_t = \\phi h_{t-1} + \\eta_t \\quad \\text{with} \\quad \\eta_{t} \\sim \\mathcal{N}(0,\\sigma_{\\eta}^2), $$\n",
"\n",
"with $|\\phi|\\leq 1$. \n",
"However, the resulting system (state equation in $\\log v_t$ and measurement equation in $\\log R_t^2$) is NOT a linear Gaussian state space model. \n",
"The reason is that the random variable \n",
"\n",
"$$\\log e_{t}^2 \\sim Log \\chi^2_1$$ \n",
"\n",
"is not Gaussian, but follows the Log-Chi-Squared distribution with 1 degree of freedom. \n",
"It satisfies $\\mathbb{E}\\big[\\log e_{t}^2 \\big] \\approx -1.27$ and\n",
"$Var\\big[\\log e_{t}^2 \\big] = \\frac{\\pi^2}{2}$.\n",
"\n",
"In order to make the system a linear Gaussian state space model, we can approximate \n",
"\n",
"$$ \\epsilon_t \\sim \\mathcal{N}\\bigg(0,\\frac{\\pi^2}{2} \\bigg). $$\n",
"\n",
"Let us check if this approximation is good:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"def log_chi2(x):\n",
" \"\"\"Log-Chi-Squared density\"\"\"\n",
" return 1 / np.sqrt(2 * np.pi) * np.exp(x / 2 - np.exp(x) / 2)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean of the Log-Chi-Squared -1.2704\n",
"Variance of the Log-Chi-Squared 4.9348\n"
]
}
],
"source": [
"mean_chi2 = quad(lambda x: x * log_chi2(x), -30, 10)[0]\n",
"var_chi2 = quad(lambda x: ((x - mean_chi2) ** 2) * log_chi2(x), -50, 10)[0]\n",
"print(\"Mean of the Log-Chi-Squared\", round(mean_chi2, 4))\n",
"print(\"Variance of the Log-Chi-Squared\", round(var_chi2, 4))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12, 3))\n",
"x = np.linspace(-15, 5, 200)\n",
"plt.plot(x, ss.norm.pdf(x, loc=mean_chi2, scale=np.sqrt(var_chi2)), \"b-\", lw=2, alpha=0.6, label=\"Normal pdf\")\n",
"plt.plot(x, log_chi2(x), \"r-\", lw=2, alpha=0.6, label=\"Log-Chi-Squared pdf\")\n",
"plt.title(\"Approximation: Normal ~ Log-Chi-Squared\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Well... it doesn't seem so good at first sight. \n",
"But it is proved in many articles (see references in [3] and [4]) that this approximation is good enough to produce a nice output. \n",
"Indeed, this method is quite used in practice."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Gaussian linear state space model:\n",
"\n",
"**State equation:**\n",
"\n",
"$$ h_k = \\phi h_{k-1} + \\eta_k \\quad \\text{with} \\quad \\eta_{k} \\sim \\mathcal{N}(0,\\sigma_{\\eta}^2). $$\n",
"\n",
"**Measurment equation:**\n",
"\n",
"$$ \\bigg( \\log R_{k}^2 + 1.27 -\\log \\bar \\sigma^2 \\bigg)= \\; h_k + \\epsilon_k \\quad \\text{with} \\quad \\epsilon_k \\sim \\mathcal{N}\\bigg(0,\\frac{\\pi^2}{2} \\bigg).$$\n",
"\n",
"### Kalman filter:\n",
"\n",
"Let us identify the Kalman filter elements. You can find them in the notebook **5.1** or in the [wiki page](https://en.wikipedia.org/wiki/Kalman_filter#Details).\n",
"\n",
"In this model $F_k = \\phi$ and $ H_k=1$ are constant. They do not depend on $k$. \n",
"\n",
"##### Predict step:\n",
"$$ \\hat h_{k \\mid k-1} = \\phi\\, \\hat h_{k-1 \\mid k-1} \\quad \\quad \\text{and} \\quad \\quad P_{k \\mid k-1} = \\phi^2 \\, P_{k-1 \\mid k-1} + \\sigma_{\\eta}^2. $$\n",
"\n",
"##### Auxiliary variables:\n",
"$$ \n",
"\\begin{aligned}\n",
"\\tilde y_k &= \\log R_{k}^2 + 1.27 -\\log \\bar \\sigma^2 \\, - \\hat h_{k \\mid k-1} &\\quad \\quad \\text{(pre-fit residual)} \\\\\n",
"S_k &= P_{k \\mid k-1} + \\frac{\\pi^2}{2} &\\quad \\quad \\text{(conditional innovation covariance)} \\\\\n",
"K_k &= \\frac{P_{k \\mid k-1}}{S_k} &\\quad \\quad \\text{(Kalman Gain)}\n",
"\\end{aligned}\n",
"$$\n",
"\n",
"##### Update step:\n",
"$$ \\hat h_{k \\mid k} = \\hat h_{k \\mid k-1} + K_k \\, \\tilde y_k \\quad \\text{and} \\quad \n",
"P_{k \\mid k} = P_{k \\mid k-1} \\biggl( 1- K_k \\biggr)\n",
"$$\n",
"\n",
"In order to familiarize with these formulas, let us compute the conditional expectation and variance of the innovations i.e. the new measurements:\n",
"\n",
"$$ \\mathbb{E}\\bigg[ \\log R_k^2 \\, \\bigg|\\, \\hat h_{k-1 \\mid k-1} \\bigg] = \\, -1.27 \\,+ \\log \\bar \\sigma^2 \\, + \\hat h_{k \\mid k-1} $$\n",
"\n",
"$$ \\text{Var} \\bigg[ \\log R_k^2 \\, \\bigg|\\, \\hat h_{k-1 \\mid k-1} \\bigg] = \\, P_{k \\mid k-1} + \\frac{\\pi^2}{2} \\; = S_k $$\n",
"\n",
"\n",
"### Log-likelihood\n",
"\n",
"The parameters $(\\phi, \\sigma_{\\eta},\\bar \\sigma)$ can be estimated using the QML method (Quasi-Maximum-Likelihood), where the \"Quasi\" refers to the Gaussian approximation of $\\epsilon_k$. \n",
"The log-likelihood function is:\n",
"\n",
"$$ \\log L \\big( \\phi, \\sigma_{\\eta},\\bar \\sigma \\mid R_k, R_{k-1}, ..., R_1, R_0 \\big) = -\\frac{1}{2} \n",
"\\sum_{k=1}^N \\biggl( \\log 2\\pi + \\log S_k + \\frac{\\tilde y_k^2}{S_k} \\biggr)\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"h0 = np.log(train.var()) # initial value for the log-variance\n",
"P0 = 100 # initial variance of the log-variance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Above I chose an initial hidden state $h_0$ equal to the logarithm of the variance computed in the training set. \n",
"Since we have no knowledge of the true value of $h_0$, it is better to select a huge value of the initial error $P_0 = 100$.\n",
"\n",
"In the function below, I indicate $\\bar \\sigma$ with `scale`, and the residuals $\\tilde y_k$ with `r`. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Algorithm implementation:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"def Kalman(data, h0, P0, phi, var_eta, scale):\n",
" \"\"\"One dimensional Kalman filter algorithm\"\"\"\n",
"\n",
" Y = np.log(data**2) # take the log of squared data\n",
" N = len(Y)\n",
" hs = np.zeros_like(Y) # Initialization\n",
" Ps = np.zeros_like(Y)\n",
"\n",
" Y = Y + 1.27 - np.log(scale**2) # redefine Y. scale corresponds to \\bar{\\sigma}\n",
" h = h0\n",
" P = P0 # initial values of h and P\n",
" log_2pi = np.log(2 * np.pi)\n",
" loglikelihood = 0 # initialize log-likelihood\n",
"\n",
" for k in range(N):\n",
" # Prediction step\n",
" h_p = phi * h # predicted h\n",
" P_p = phi**2 * P + var_eta # predicted P\n",
"\n",
" # auxiliary variables\n",
" r = Y[k] - h_p # residual\n",
" S = P_p + 0.5 * np.pi**2\n",
" KG = P_p / S # Kalman Gain\n",
"\n",
" # Update step\n",
" h = h_p + KG * r\n",
" P = P_p * (1 - KG)\n",
"\n",
" loglikelihood += -0.5 * (log_2pi + np.log(S) + (r**2 / S))\n",
" hs[k] = h\n",
" Ps[k] = P\n",
"\n",
" return hs, Ps, loglikelihood"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The log-likelihood function may have multiple local minima. For this reason, remember to repeat the minimization several times with different initial conditions. (For instance, if you try `x0=[0.1, 0.5, 1]` you will get different locally optimal values)\n",
"\n",
"##### Parameter estimation:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
" success: True\n",
" status: 0\n",
" fun: 3374.9753216098675\n",
" x: [ 9.776e-01 3.717e-02 1.228e-02]\n",
" nit: 18\n",
" jac: [-1.000e-03 8.640e-04 -2.747e-02]\n",
" nfev: 136\n",
" njev: 34\n",
" hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n"
]
}
],
"source": [
"def minus_likelihood(c):\n",
" \"\"\"Returns the negative log-likelihood\"\"\"\n",
" _, _, loglik = Kalman(train, h0, P0, c[0], c[1], c[2])\n",
" return -loglik\n",
"\n",
"\n",
"result = minimize(\n",
" minus_likelihood, x0=[0.1, 1, 1], method=\"L-BFGS-B\", bounds=[[-1, 1], [1e-15, None], [1e-15, None]], tol=1e-8\n",
")\n",
"kalman_params = result.x\n",
"print(result)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"hs, Ps, _ = Kalman(train, h0, P0, *kalman_params) # Kalman on training set\n",
"h_test, P_test, _ = Kalman(test, hs[-1], Ps[-1], *kalman_params) # Kalman on test set"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"phi = kalman_params[0]\n",
"var_eta = kalman_params[1]\n",
"scale = kalman_params[2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Kalman Smoother:\n",
"\n",
"Let's implement the [RTS](https://en.wikipedia.org/wiki/Kalman_filter#Rauch%E2%80%93Tung%E2%80%93Striebel) smoother:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"hs_smooth = np.zeros_like(h_test)\n",
"Ps_smooth = np.zeros_like(P_test)\n",
"hs_smooth[-1] = h_test[-1]\n",
"Ps_smooth[-1] = P_test[-1]\n",
"\n",
"for k in range(len(h_test) - 2, -1, -1):\n",
" C = phi * P_test[k] / (phi**2 * P_test[k] + var_eta)\n",
" hs_smooth[k] = h_test[k] + C * (hs_smooth[k + 1] - h_test[k] * phi)\n",
" Ps_smooth[k] = P_test[k] + C**2 * (Ps_smooth[k + 1] - (phi**2 * P_test[k] + var_eta))\n",
"\n",
"smooth_h = np.exp(hs_smooth) / dt * scale**2 # Kalman re-transformed variance"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"V_kalm = np.exp(h_test) / dt * scale**2 # Kalman re-transformed variance\n",
"V_up = np.exp(h_test + np.sqrt(P_test)) / dt * scale**2 # error up bound\n",
"V_down = np.exp(h_test - np.sqrt(P_test)) / dt * scale**2 # error down bound"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Plots:"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 6))\n",
"plt.plot(V[1 + train_size :], label=\"Heston variance\", linewidth=3)\n",
"plt.plot(V_kalm, label=\"Kalman variance\", color=\"limegreen\", linestyle=\"-\")\n",
"plt.plot(var_test / dt, label=\"GARCH variance\", color=\"peru\", linestyle=\"--\")\n",
"plt.plot(roll_var.values[train_size:], label=\"Rolling Variance\", linestyle=\"--\", alpha=1, color=\"orchid\")\n",
"plt.title(\"Out of sample tracking: comparison of different methods\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is not easy to compare the methods by looking at the plot. Let's have a look at the MSE (mean square error):"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE Kalman: 0.0003109681690921269\n",
"MSE GARCH: 0.00036177556789212606\n",
"MSE Rolling: 0.00037911849190512667\n",
"MSE Smoother: 0.00021729922531328992\n"
]
}
],
"source": [
"print(\"MSE Kalman: \", np.mean((V[1 + train_size :] - V_kalm) ** 2))\n",
"print(\"MSE GARCH: \", np.mean((V[1 + train_size :] - var_test / dt) ** 2))\n",
"print(\"MSE Rolling: \", np.mean((V[1 + train_size :] - roll_var.values[train_size:]) ** 2))\n",
"print(\"MSE Smoother: \", np.mean((V[1 + train_size :] - smooth_h) ** 2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### The Kalman filter approach is the best!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"[1] Giuseppe Cavaliere (2006) - \"Unit root tests under time-varying variances\"\n",
"\n",
"[2] Taylor, S. J. (1986) - \"Modelling Financial Time Series\"\n",
"\n",
"[3] Harvey, A. C., and Shephard, N. (1993) - \"Estimation and Testing of Stochastic Variance Models\"\n",
"\n",
"[4] Harvey, A. C., Ruiz, E., and Shephard, N. (1994) - \"Multivariate Stochastic Variance Models\"\n",
"\n",
"[5] Ruiz, E. (1994) - \"Quasi-Maximum Likelihood Estimation of Stochastic Volatility Models\""
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: 6.1 Ornstein-Uhlenbeck process and applications.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ornstein-Uhlenbeck process and applications\n",
"\n",
"\n",
"## Contents\n",
" - [Ornstein-Uhlenbeck process](#sec1)\n",
" - [Numerical simulation](#sec1.1)\n",
" - [Parameters estimation from a single path](#sec1.2)\n",
" - [Expected time to reach the long term mean](#sec1.3)\n",
" - [Bond Pricing by Vasicek model](#sec2)\n",
" - [Tracking the OU process](#sec3)\n",
" - [Iterative approach for parameters estimation](#sec3.1)\n",
" - [Trading strategy](#sec4)\n",
" - [First time to exit the strip](#sec5)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy.stats as ss\n",
"from scipy.optimize import minimize\n",
"import matplotlib.pyplot as plt\n",
"from scipy import sparse\n",
"from scipy.sparse.linalg import spsolve\n",
"from mpl_toolkits import mplot3d\n",
"from matplotlib import cm\n",
"from scipy.integrate import quad\n",
"from FMNM.Processes import OU_process\n",
"from scipy.interpolate import RegularGridInterpolator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# OU process"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The [Ornstein-Uhlenbeck process](https://en.wikipedia.org/wiki/Ornstein%E2%80%93Uhlenbeck_process) \n",
"is described by the following SDE: \n",
"\n",
"$$ dX_t = \\kappa (\\theta - X_t) dt + \\sigma dW_t .$$\n",
"\n",
"The parameters are:\n",
"- $\\kappa > 0$: mean reversion coefficient\n",
"- $\\theta \\in \\mathbb{R}$: long term mean \n",
"- $\\sigma > 0$: volatility coefficient\n",
"\n",
"This process is Gaussian, Markovian and (unconditionally) stationary."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### The previous SDE can be solved! Let's do it!\n",
"\n",
"First of all we can define the new process $Y_t = X_t - \\theta$ such that the differential is equal $dY_t = dX_t$. \n",
"The SDE becomes:\n",
"\n",
"$$ dY_t = - \\kappa \\, Y_t \\, dt + \\sigma \\, dW_t .$$\n",
"\n",
"Let us introduce the function: $f(t,y) = y e^{\\kappa t}$. \n",
"This function has derivatives:\n",
"\n",
"$$ \\frac{\\partial f}{\\partial t} = y\\, \\kappa\\, e^{\\kappa t} \\quad \\frac{\\partial f}{\\partial y} = e^{\\kappa t} \n",
"\\quad \\frac{\\partial^2 f}{\\partial y^2} = 0 .$$\n",
"\n",
"We can use the Itô formula:\n",
"\n",
"$$ \\begin{aligned}\n",
"d( Y_t \\, e^{\\kappa t} ) &= \\kappa \\, Y_t\\, e^{\\kappa t}\\, dt + e^{\\kappa t}\\, dY_t \\\\ \n",
" &= \\kappa \\, Y_t\\, e^{\\kappa t}\\, dt + e^{\\kappa t}\\, \\biggl( - \\kappa \\, Y_t \\, dt + \\sigma \\, dW_t \\biggr) \\\\\n",
" &= e^{\\kappa t} \\sigma dW_t .\n",
"\\end{aligned} $$\n",
"\n",
"For convenience, let us replace the time variable $t$ with $s$, and then let us integrate both sides on $[0,t]$:\n",
"\n",
"$$ Y_t\\, e^{\\kappa t} - Y_0 = \\int_0^t e^{\\kappa s} \\sigma dW_t .$$\n",
"\n",
"At this point we can return to the original variable $X_t$ and obtain the final solution:\n",
"\n",
"$$ X_t = \\theta + (X_0 - \\theta)e^{-\\kappa t} + \\int_0^t \\sigma\\, e^{\\kappa (s-t)} dW_s .$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Moments:\n",
"\n",
"The **mean** of $X_t$ is:\n",
"\n",
"$$ \\begin{aligned}\n",
"\\mathbb{E}[X_t] &= \\mathbb{E}\\biggl[ \\theta + (X_0 - \\theta)e^{-\\kappa t} + \\int_0^t \\sigma\\, e^{\\kappa (s-t)} dW_s \\biggr] \\\\\n",
" &= \\theta + (X_0 - \\theta)e^{-\\kappa t} \n",
"\\end{aligned}$$\n",
"\n",
"where the Itô integral is a martingale, $ \\mathbb{E}\\bigl[ \\int_0^t \\sigma\\, e^{\\kappa (s-t)} dW_s \\bigr] = 0$.\n",
"\n",
"The **covariance** is:\n",
"\n",
"$$ \\begin{aligned}\n",
"\\text{Cov}[X_s, X_t] &= \\text{Cov}\\biggl[ \\int_0^s \\sigma\\, e^{\\kappa (u-s)} dW_u \\, , \\, \n",
"\\int_0^t \\sigma\\, e^{\\kappa (v-t)} dW_v \\biggr] = \\mathbb{E}\\biggl[ \\int_0^s \\sigma\\, e^{\\kappa (u-s)} dW_u \n",
"\\int_0^t \\sigma\\, e^{\\kappa (v-t)} dW_v \\biggr] \\\\\n",
" &= \\sigma^2 e^{-\\kappa (s+t)}\\, \\mathbb{E}\\biggl[ \\int_0^s e^{\\kappa u} dW_u \n",
"\\int_0^t e^{\\kappa v} dW_v \\biggr] \\\\\n",
" &= \\sigma^2 e^{-\\kappa (s+t)}\\, \\mathbb{E}\\biggl[ \\biggl( \\int_0^{\\text{min}(t,s)} e^{\\kappa u} dW_u + \\int_{\\text{min}(t,s)}^{\\text{max}(t,s)} e^{\\kappa u} dW_u \\biggr) \\cdot \n",
"\\int_0^{\\text{min}(t,s)} e^{\\kappa v} dW_v \\biggr] \\\\\n",
" &= \\sigma^2 e^{-\\kappa (s+t)}\\, \\mathbb{E}\\biggl[ \\biggl( \\int_0^{\\text{min}(t,s)} e^{\\kappa u} dW_u \\biggr)^2 \\biggr] \\\\\n",
" &= \\sigma^2 e^{-\\kappa (s+t)}\\, \\int_0^{\\text{min}(t,s)} e^{2 \\kappa u} du \\; = \\; \\frac{\\sigma^2}{2\\kappa} e^{-\\kappa (s+t)}\\, \\biggl( e^{2\\kappa\\, \\text{min}(t,s)} - 1 \\biggr) \\\\\n",
" &= \\frac{\\sigma^2}{2\\kappa} \\biggl( e^{-\\kappa |t-s|} - e^{-\\kappa (s+t)}\\, \\biggr), \n",
"\\end{aligned}$$\n",
"\n",
"where in the first line I remove the non-random variables, in the third line I use the independence property (expectiation of the product is equal to the product of expectation) and in the fourth line I use the Itô isometry.\n",
"\n",
"The **variance** is: \n",
"\n",
"$$ \\text{Var}[X_t] = \\text{Cov}[X_t, X_t] = \\frac{\\sigma^2}{2\\kappa} \\biggl( 1- e^{-2 \\kappa t} \\biggr).$$\n",
"\n",
"As $t\\to \\infty$ we obtain the **asymptotic mean**: $\\theta$ and the **asymptotic variance**: $\\frac{\\sigma^2}{2\\kappa}$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Numerical simulation\n",
"\n",
"We can discretize the SDE using the Euler-Maruyama numerical method (see Notebook **1.2**).\n",
"This discretization is commented in the code below.\n",
"\n",
"Another possibility is to generate the dynamics from the solution of the SDE. \n",
"Let us consider the solution of the OU SDE obtained above. We can compute $X_{n+1}$ and consider the initial value at time $n$.\n",
"\n",
"$$ X_{n+1} = \\theta + (X_n - \\theta)e^{-\\kappa \\Delta t} + \\sqrt{\\frac{\\sigma^2}{2\\kappa} \\bigl( 1- e^{-2 \\kappa \\Delta t} \\bigr)} \\; \\epsilon_n $$ \n",
"\n",
"with $\\epsilon_n \\sim \\mathcal{N}(0,1)$."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(seed=42)\n",
"\n",
"N = 20000 # time steps\n",
"paths = 5000 # number of paths\n",
"T = 5\n",
"T_vec, dt = np.linspace(0, T, N, retstep=True)\n",
"\n",
"kappa = 3\n",
"theta = 0.5\n",
"sigma = 0.5\n",
"std_asy = np.sqrt(sigma**2 / (2 * kappa)) # asymptotic standard deviation\n",
"\n",
"X0 = 2\n",
"X = np.zeros((N, paths))\n",
"X[0, :] = X0\n",
"W = ss.norm.rvs(loc=0, scale=1, size=(N - 1, paths))\n",
"\n",
"# Uncomment for Euler Maruyama\n",
"# for t in range(0,N-1):\n",
"# X[t + 1, :] = X[t, :] + kappa*(theta - X[t, :])*dt + sigma * np.sqrt(dt) * W[t, :]\n",
"\n",
"std_dt = np.sqrt(sigma**2 / (2 * kappa) * (1 - np.exp(-2 * kappa * dt)))\n",
"for t in range(0, N - 1):\n",
" X[t + 1, :] = theta + np.exp(-kappa * dt) * (X[t, :] - theta) + std_dt * W[t, :]\n",
"\n",
"X_T = X[-1, :] # values of X at time T\n",
"X_1 = X[:, 1] # a single path"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Mean and standard deviation\n",
"\n",
"Since we have the possibility to generate several paths, let us consider the values at time T. We compute theoretical mean and standard deviation and compare them with the values obtained from the data:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Theoretical mean=0.5 and theoretical STD=0.204124\n",
"Parameters from the fit: mean=0.506633, STD=0.205767\n"
]
}
],
"source": [
"mean_T = theta + np.exp(-kappa * T) * (X0 - theta)\n",
"std_T = np.sqrt(sigma**2 / (2 * kappa) * (1 - np.exp(-2 * kappa * T)))\n",
"\n",
"param = ss.norm.fit(X_T) # FIT from data\n",
"print(f\"Theoretical mean={mean_T.round(6)} and theoretical STD={std_T.round(6)}\")\n",
"print(\"Parameters from the fit: mean={0:.6f}, STD={1:.6f}\".format(*param)) # these are MLE parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"N_processes = 10 # number of processes\n",
"x = np.linspace(X_T.min(), X_T.max(), 100)\n",
"pdf_fitted = ss.norm.pdf(x, *param)\n",
"\n",
"fig = plt.figure(figsize=(16, 5))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"ax1.plot(T_vec, X[:, :N_processes], linewidth=0.5)\n",
"ax1.plot(T_vec, (theta + std_asy) * np.ones_like(T_vec), label=\"1 asymptotic std dev\", color=\"black\")\n",
"ax1.plot(T_vec, (theta - std_asy) * np.ones_like(T_vec), color=\"black\")\n",
"ax1.plot(T_vec, theta * np.ones_like(T_vec), label=\"Long term mean\")\n",
"ax1.legend(loc=\"upper right\")\n",
"ax1.set_title(f\"{N_processes} OU processes\")\n",
"ax1.set_xlabel(\"T\")\n",
"ax2.plot(x, pdf_fitted, color=\"r\", label=\"Normal density\")\n",
"ax2.hist(X_T, density=True, bins=50, facecolor=\"LightBlue\", label=\"Frequency of X(T)\")\n",
"ax2.legend()\n",
"ax2.set_title(\"Histogram vs Normal distribution\")\n",
"ax2.set_xlabel(\"X(T)\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Covariance\n",
"\n",
"Let us also compare the theoretical covariance with the covariance obtained from the data."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Theoretical COV[X(t1), X(t2)] = 0.0401 with t1 = 1.4876 and t2 = 1.5001\n",
"Computed covariance from data COV[X(t1), X(t2)] = 0.0403\n"
]
}
],
"source": [
"n1 = 5950\n",
"n2 = 6000\n",
"t1 = n1 * dt\n",
"t2 = n2 * dt\n",
"\n",
"cov_th = sigma**2 / (2 * kappa) * (np.exp(-kappa * np.abs(t1 - t2)) - np.exp(-kappa * (t1 + t2)))\n",
"print(f\"Theoretical COV[X(t1), X(t2)] = {cov_th.round(4)} with t1 = {t1.round(4)} and t2 = {t2.round(4)}\")\n",
"print(f\"Computed covariance from data COV[X(t1), X(t2)] = {np.cov( X[n1, :], X[n2, :] )[0,1].round(4)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Estimation of parameters from a single path\n",
"\n",
"### Least squares estimation\n",
"\n",
"We can compute $X_{t+\\Delta t}$ and consider the initial value at time $t$.\n",
"\n",
"$$ \\begin{aligned}\n",
"X_{t+\\Delta t} &= \\theta + (X_t - \\theta)e^{-\\kappa \\Delta t} + \\int_t^{t+\\Delta t} \\sigma\\, e^{\\kappa (s-t)} dW_s \\\\\n",
" &= \\theta \\bigl( 1-e^{-\\kappa \\Delta t} \\bigr) + e^{-\\kappa \\Delta t} X_t + \\int_t^{t+\\Delta t} \\sigma\\, e^{\\kappa (s-t)} dW_s \\\\\n",
" &= \\alpha + \\beta X_t + \\epsilon_t\n",
"\\end{aligned} $$\n",
"\n",
"where $\\alpha = \\theta \\bigl( 1-e^{-\\kappa \\Delta t} \\bigr)$, $\\beta = e^{-\\kappa \\Delta t}$ and with $\\epsilon_t \\sim \\mathcal{N}\\biggl( 0, \\frac{\\sigma^2}{2\\kappa} \\bigl( 1- e^{-2 \\kappa \\Delta t} \\bigr)\\biggr)$.\n",
"\n",
"So, let us use the usual OLS method to estimate $\\alpha$, $\\beta$ and $\\sigma$.\n",
"Then, we can obtain the parameters from the formulas:\n",
"\n",
"$$ \\kappa = - \\frac{\\log \\beta}{\\Delta t}, \\quad \\theta = \\frac{\\alpha}{1-\\beta}, \\quad \n",
"\\sigma = \\text{Std}[\\epsilon_t] \\sqrt{ \\frac{2\\kappa}{1-\\beta^2} }$$"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OLS theta = 0.5069818519708378\n",
"OLS kappa = 2.566173550192414\n",
"OLS sigma = 0.4972450021977947\n"
]
}
],
"source": [
"XX = X_1[:-1]\n",
"YY = X_1[1:]\n",
"beta, alpha, _, _, _ = ss.linregress(XX, YY) # OLS\n",
"kappa_ols = -np.log(beta) / dt\n",
"theta_ols = alpha / (1 - beta)\n",
"res = YY - beta * XX - alpha # residuals\n",
"std_resid = np.std(res, ddof=2)\n",
"sig_ols = std_resid * np.sqrt(2 * kappa_ols / (1 - beta**2))\n",
"\n",
"print(\"OLS theta = \", theta_ols)\n",
"print(\"OLS kappa = \", kappa_ols)\n",
"print(\"OLS sigma = \", sig_ols)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The estimate of the parameters doesn't look very accurate. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Maximum Likelihood Estimation\n",
"\n",
"We know that \n",
"$$ X_{i+1} \\sim \\mathcal{N}\\biggl( \\theta \\bigl( 1-e^{-\\kappa \\Delta t} \\bigr) + e^{-\\kappa \\Delta t} X_i \\, , \\, \\frac{\\sigma^2}{2\\kappa} \\bigl( 1- e^{-2 \\kappa \\Delta t} \\bigr)\\biggr)$$\n",
"\n",
"Let us define $\\hat \\theta^2 := \\frac{\\sigma^2}{2\\kappa} \\bigl( 1- e^{-2 \\kappa \\Delta t} \\bigr)$.\n",
"\n",
"At this point it is possible to write the log-likelihood function and impose the condition that first order derivatives are equal to zero. All these steps are done in [1]. \n",
"Here I just recall the final formulas:\n",
"\n",
"$$ S_x = \\sum_{i=1}^n X_{i-1} \\quad S_y = \\sum_{i=1}^n X_{i} \\quad S_{xx} = \\sum_{i=1}^n X_{i-1}^2 \n",
"\\quad S_{xy} = \\sum_{i=1}^n X_{i-1}X_{i} \\quad S_{yy} = \\sum_{i=1}^n X_{i}^2$$\n",
"\n",
"The parameters are:\n",
"\n",
"$$ \\theta = \\frac{S_y S_{xx} - S_x S_{xy}}{n(S_{xx}-S_{xy}) - (S_{x}^2 - S_{x} S_{y})} $$\n",
"\n",
"$$ \\kappa = - \\frac{1}{\\Delta t} \\log \\frac{S_{xy} - \\theta S_{x} - \\theta S_{y} +n\\theta^2}{S_{xx} - 2\\theta S_{x} +n \\theta^2} $$\n",
"\n",
"$$ \n",
"\\hat \\theta^2 = \\frac{1}{n} \\biggl[ S_{yy} -2e^{-\\kappa \\Delta t} S_{xy} \n",
"+ e^{-2 \\kappa \\Delta t} S{xx} -2\\theta(1-e^{- \\kappa \\Delta t})(S_y - e^{- \\kappa \\Delta t}S_x) \n",
"+n \\theta^2(1-e^{- \\kappa \\Delta t})^2 \\biggr] $$\n",
"$$ \\theta^2 = \\hat \\theta^2 \\frac{2\\kappa}{1-e^{-2 \\kappa \\Delta t}} $$"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"Sx = np.sum(XX)\n",
"Sy = np.sum(YY)\n",
"Sxx = XX @ XX\n",
"Sxy = XX @ YY\n",
"Syy = YY @ YY\n",
"\n",
"theta_mle = (Sy * Sxx - Sx * Sxy) / (N * (Sxx - Sxy) - (Sx**2 - Sx * Sy))\n",
"kappa_mle = -(1 / dt) * np.log(\n",
" (Sxy - theta_mle * Sx - theta_mle * Sy + N * theta_mle**2) / (Sxx - 2 * theta_mle * Sx + N * theta_mle**2)\n",
")\n",
"sigma2_hat = (\n",
" Syy\n",
" - 2 * np.exp(-kappa_mle * dt) * Sxy\n",
" + np.exp(-2 * kappa_mle * dt) * Sxx\n",
" - 2 * theta_mle * (1 - np.exp(-kappa_mle * dt)) * (Sy - np.exp(-kappa_mle * dt) * Sx)\n",
" + N * theta_mle**2 * (1 - np.exp(-kappa_mle * dt)) ** 2\n",
") / N\n",
"sigma_mle = np.sqrt(sigma2_hat * 2 * kappa_mle / (1 - np.exp(-2 * kappa_mle * dt)))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"theta MLE = 0.5069423415202111\n",
"kappa MLE = 2.5658456824361644\n",
"sigma MLE = 0.4972077083202338\n"
]
}
],
"source": [
"print(\"theta MLE = \", theta_mle)\n",
"print(\"kappa MLE = \", kappa_mle)\n",
"print(\"sigma MLE = \", sigma_mle)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the MLE we obtain parameters quite similar to those obtained by OLS."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Expected time to reach the long term mean\n",
"\n",
"Computation of the first hitting time density of an Ornstein-Uhlenbeck process is a long standing problem, which still remains open. \n",
"As explained in the introduction of the paper [5], the first hitting time of an OU process has many applications in applied mathematics, especially in mathematical finance and for the design of trading strategies.\n",
"\n",
"Let us define the first hitting time for the OU process:\n",
"\n",
"$$ T_{A,C} = \\inf\\biggl\\{ t\\geq 0 \\;: \\; X_t = A \\bigg| X_0 = C \\biggr\\} $$\n",
"\n",
"Here I follow the presentation of the article [4], where the author considers the standardized problem with $\\theta=0$, $\\kappa=1$ and $\\sigma^2=2$. \n",
"\n",
"$$ dX_t = - X_t dt + \\sqrt{2} dW_t .$$\n",
"\n",
"This is a common choice, and many articles cited in [4] follow this approach (see [6]). In [5] the authors use $\\sigma=1$, (which in my opinion looks better). \n",
"\n",
"The standardized form can be obtained with the following change of variables:\n",
"\n",
"$$ \\bar t = \\kappa t, \\quad \\bar X_t = \\frac{\\sqrt{2\\kappa}}{\\sigma}(X_t-\\theta), \\quad \\bar C = \\frac{\\sqrt{2\\kappa}}{\\sigma}(C-\\theta), \\quad \\bar A = \\frac{\\sqrt{2\\kappa}}{\\sigma}(A-\\theta) $$\n",
"\n",
"(the bar is then removed for brevity). \n",
"\n",
"Let $f_{A,C}(t)$ denote the density function of $T_{A,C}$. For $A=0$, the density has a nice expression:\n",
"\n",
"$$ f_{0,C}(t) = \\sqrt{\\frac{2}{\\pi}} \\frac{|C| e^{-t}}{(1-e^{-2t})^{3/2}} \\exp\\biggl[- \\frac{C^2e^{-2t}}{2(1-e^{-2t})} \\biggr] $$\n",
"\n",
"Thanks to the change of variable, the expected time it takes for a process starting at $X_0$ to reach \n",
"$\\theta$, is equal to the (scaled) expected time it takes for a process to reach $0$ starting at $\\bar C$ (set $A=\\theta$ implies $\\bar A = 0$).\n",
"\n",
"#### Let us estimate the expected time numerically"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"T_to_theta = np.argmax(X <= theta if (X0 > theta) else X >= theta, axis=0) * dt # first passage time"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The expected time from X0 to theta is: 0.8880757037851894 with std error: 0.005226665594754626\n",
"The standard deviation of the first time the process touches theta is: 0.36954410854960096\n"
]
}
],
"source": [
"print(f\"The expected time from X0 to theta is: {T_to_theta.mean()} with std error: {ss.sem(T_to_theta)}\")\n",
"print(\"The standard deviation of the first time the process touches theta is: \", T_to_theta.std())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Now we can define the density and the new starting point C.\n",
"\n",
"When comparing with the histogram, we need to take into account of the time scale: $\\kappa t$. \n",
"Therefore the density becomes: $\\kappa f_{0,C}(\\kappa t)$."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def density_T_to_theta(t, C):\n",
" return (\n",
" np.sqrt(2 / np.pi)\n",
" * np.abs(C)\n",
" * np.exp(-t)\n",
" / (1 - np.exp(-2 * t)) ** (3 / 2)\n",
" * np.exp(-((C**2) * np.exp(-2 * t)) / (2 * (1 - np.exp(-2 * t))))\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"C = (X0 - theta) * np.sqrt(2 * kappa) / sigma # new starting point"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(10, 4))\n",
"x = np.linspace(T_to_theta.min(), T_to_theta.max(), 100)\n",
"plt.plot(x, kappa * density_T_to_theta(kappa * x, C), color=\"red\", label=\"OU hitting time density\")\n",
"plt.hist(T_to_theta, density=True, bins=100, facecolor=\"LightBlue\", label=\"frequencies of T\")\n",
"plt.title(\"First passage time distribution from X0 to theta\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now compute the theoretical mean and standard deviation:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Theoretical expected hitting time: 0.8795633378038917\n",
"Theoretical standard deviation of the hitting time: 0.36757135614701975\n"
]
}
],
"source": [
"theoretical_T = quad(lambda t: t * kappa * density_T_to_theta(kappa * t, C), 0, 1000)[0]\n",
"theoretical_std = np.sqrt(\n",
" quad(lambda t: (t - theoretical_T) ** 2 * kappa * density_T_to_theta(kappa * t, C), 0, 1000)[0]\n",
")\n",
"print(\"Theoretical expected hitting time: \", theoretical_T)\n",
"print(\"Theoretical standard deviation of the hitting time: \", theoretical_std)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Bond Pricing by Vasicek model\n",
"\n",
"The [Vasicek model](https://en.wikipedia.org/wiki/Vasicek_model) describes the evolution of the interest rate process $\\{r_t\\}_{t \\in [0,T]}$ assuming it follows an OU process under the risk neutral dynamics (I indicate with $\\mathbb{Q}$ the risk neutral measure).\n",
"\n",
"We write:\n",
"\n",
"$$ dr_t = \\kappa (\\theta - r_t) dt + \\sigma dW_t^{\\mathbb{Q}} .$$\n",
"\n",
"The price of a **zero-coupon bond** has the following formula:\n",
"\n",
"$$ P(t,T) = \\mathbb{E}^{\\mathbb{Q}}\\biggl[ e^{\\int_t^T r_s \\, ds} \\; \\bigg|\\; \\mathcal{F}_t \\biggr]. $$\n",
"\n",
"Thanks to the [Feynman-Kac](https://en.wikipedia.org/wiki/Feynman%E2%80%93Kac_formula) formula, we know that the previous expression is a solution of the following PDE:\n",
"\n",
"$$ \\frac{\\partial P(t,r)}{\\partial t} \n",
" + \\kappa (\\theta - r) \\frac{\\partial P(t,r)}{\\partial r}\n",
" + \\frac{1}{2} \\sigma^2 \\frac{\\partial^2 P(t,r)}{\\partial r^2} - r P(t,r) = 0. $$\n",
" \n",
"with terminal conditions:\n",
"\n",
"$$ P(T,r) = 1. $$\n",
"\n",
"Following [2] (page 59), we can find the closed formula for the bond price under the Vasicek model:\n",
"\n",
"$$ P(t,T) = A(t,T) e^{-B(t,T) r_t} $$\n",
"\n",
"with \n",
"\n",
"$$ A(t,T) = exp\\biggl[ \\biggl(\\theta - \\frac{\\sigma^2}{2\\kappa^2}\\biggr)(B(t,T)-T+t) - \\frac{\\sigma^2}{4\\kappa} B(t,T)^2 \\biggr] \\quad \\text{and} \\quad \n",
"B(t,T) = \\frac{1}{\\kappa} \\biggl( 1 - e^{-\\kappa (T-t)} \\biggr) .$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Closed formula:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Vasicek bond price: 0.05299806665958418\n"
]
}
],
"source": [
"B = 1 / kappa * (1 - np.exp(-kappa * T))\n",
"A = np.exp((theta - sigma**2 / (2 * kappa**2)) * (B - T) - sigma**2 / (4 * kappa) * B**2)\n",
"P = A * np.exp(-B * X0)\n",
"print(\"Vasicek bond price: \", P)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Monte Carlo"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Vasicek bond price by MC: 0.053471413401116524 with std error: 0.0002741894359949401\n"
]
}
],
"source": [
"disc_factor = np.exp(-X.mean(axis=0) * T)\n",
"P_MC = np.mean(disc_factor)\n",
"st_err = ss.sem(disc_factor)\n",
"print(f\"Vasicek bond price by MC: {P_MC} with std error: {st_err}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### PDE\n",
"\n",
"As done in the notebook **2.2**, we can discretize the PDE\n",
"using the [Upwind scheme](https://en.wikipedia.org/wiki/Upwind_scheme): \n",
"\n",
"$$\n",
" \\frac{P^{n+1}_{i} -P^{n}_{i}}{\\Delta t} \n",
" + \\max \\biggl( \\kappa(\\theta-r_i),\\, 0 \\biggr) \\frac{P^{n}_{i+1} -P^{n}_{i}}{ \\Delta r} \n",
" + \\min \\biggl( \\kappa(\\theta-r_i),\\, 0 \\biggr) \\frac{P^{n}_{i} -P^{n}_{i-1}}{ \\Delta r} \n",
" + \\frac{1}{2} \\sigma^2 \\frac{P^{n}_{i+1} + P^{n}_{i-1} - 2 P^{n}_{i}}{\\Delta r^2} \n",
" - r_i P^{n}_i = 0. \n",
"$$\n",
"\n",
"For convenience, let's call $\\text{max}_i = \\max \\bigl( \\kappa(\\theta-r_i),\\, 0 \\bigr)$ and $\\text{min}_i = \\min \\bigl( \\kappa(\\theta-r_i),\\, 0 \\bigr)$. \n",
"We can rewrite the equation above as:\n",
"\n",
"$$ \\begin{aligned}\n",
"P^{n+1}_{i} =& \\; P^{n}_{i-1} \\biggl( \\text{min}_i \\frac{\\Delta t}{\\Delta r} -\\frac{1}{2} \\sigma^2 \n",
"\\frac{\\Delta t}{\\Delta r^2} \\biggr) \\\\\n",
" & + P^{n}_{i} \\biggl( 1 + r_i \\Delta t + \\frac{\\Delta t}{\\Delta r}( \\text{max}_i - \\text{min}_i ) + \\sigma^2 \\frac{\\Delta t}{\\Delta r^2} \\biggr) \\\\\n",
" & + P^{n}_{i+1} \\biggl( -\\text{max}_i \\frac{\\Delta t}{\\Delta r} -\\frac{1}{2} \\sigma^2 \n",
"\\frac{\\Delta t}{\\Delta r^2} \\biggr)\n",
"\\end{aligned}$$\n",
"\n",
"and, given $P^{n+1}$, we solve for $P^{n}$. \n",
"I also introduce the artificial lateral boundary conditions:\n",
"\n",
"$$ P(t, r_{\\text{min}}) = e^{-r_{\\text{min}} (T-t)} \\quad \\text{and} \\quad P(t, r_{\\text{max}}) = e^{-r_{\\text{max}} (T-t)}. $$\n",
"\n",
"although the resulting values are too extreme. (a high-resolution grid mitigates this problem). \n",
"\n",
"See also notebook **2.1** for the description of the application of the finite difference implicit scheme. "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Vasicek Bond price by PDE is: 0.053030189167409314\n"
]
}
],
"source": [
"Nspace = 6000 # M space steps\n",
"Ntime = 6000 # N time steps\n",
"r_max = 3 # A2\n",
"r_min = -0.8 # A1\n",
"r, dr = np.linspace(r_min, r_max, Nspace, retstep=True) # space discretization\n",
"T_array, Dt = np.linspace(0, T, Ntime, retstep=True) # time discretization\n",
"Payoff = 1 # Bond payoff\n",
"\n",
"V = np.zeros((Nspace, Ntime)) # grid initialization\n",
"offset = np.zeros(Nspace - 2) # vector to be used for the boundary terms\n",
"V[:, -1] = Payoff # terminal conditions\n",
"V[-1, :] = np.exp(-r[-1] * (T - T_array)) # lateral boundary condition\n",
"V[0, :] = np.exp(-r[0] * (T - T_array)) # lateral boundary condition\n",
"\n",
"# construction of the tri-diagonal matrix D\n",
"sig2 = sigma * sigma\n",
"drr = dr * dr\n",
"max_part = np.maximum(kappa * (theta - r[1:-1]), 0) # upwind positive part\n",
"min_part = np.minimum(kappa * (theta - r[1:-1]), 0) # upwind negative part\n",
"\n",
"a = min_part * (Dt / dr) - 0.5 * (Dt / drr) * sig2\n",
"b = 1 + Dt * r[1:-1] + (Dt / drr) * sig2 + Dt / dr * (max_part - min_part)\n",
"c = -max_part * (Dt / dr) - 0.5 * (Dt / drr) * sig2\n",
"\n",
"a0 = a[0]\n",
"cM = c[-1] # boundary terms\n",
"aa = a[1:]\n",
"cc = c[:-1] # upper and lower diagonals\n",
"D = sparse.diags([aa, b, cc], [-1, 0, 1], shape=(Nspace - 2, Nspace - 2)).tocsc() # matrix D\n",
"\n",
"for n in range(Ntime - 2, -1, -1):\n",
" # backward computation\n",
" offset[0] = a0 * V[0, n]\n",
" offset[-1] = cM * V[-1, n]\n",
" V[1:-1, n] = spsolve(D, (V[1:-1, n + 1] - offset))\n",
"\n",
"# finds the bond price with initial value X0\n",
"Price = np.interp(X0, r, V[:, 0])\n",
"print(\"The Vasicek Bond price by PDE is: \", Price)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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wcJC//OUvXHDBBRiN8m8eQggh5pZ8rtOSz3VRb8fnOrXk5GQuv/xyLr74YsrLyyPtdjk5OVRUVGjeI93d3ZotXidquu/v8847j7GxMR555JEpj3X++eejKArNzc2Tvg6LFy8+6fM9lcinZCHegVatWsWSJUu49957URRFU0oMof8Qfve73+X222/njDPOoLy8nO985zvk5uZqti19/vOfx2azsXHjRlJTU2lra+MHP/gBLpcrsuZ7Mr/+9a/5yEc+wrp167jxxhvJysqioaGBf//735G/RH/+859z2mmnsWnTJr74xS+Sk5PD4OAgVVVV/P3vf+eVV1456rgmk4knnniCq6++mosuuog//OEPXHzxxcd9PTo6Orjwwgv5/Oc/T39/P7fffjtWq5VvfvObAOj1eu6++24+85nPcP755/OFL3wBr9fLPffcQ19fHz/84Q+n9bqrnXPOOZx++ul8/etfZ3h4mFWrVvHmm2/y6KOPzug4xzv3mdq0aROXXnopd911F+3t7Zx//vlYLBb27t2L3W7ny1/+Mjk5OXznO9/hlltuoaamhg9+8IN4PB7a29vZsWNH5F+IpjLd91dsbCzZ2dn89a9/5eyzzyYuLo6EhISjVqaG5efnY7PZeOyxxyguLsbhcJCWlkZaWhrx8fFHfdCcjq9+9as8+uijfPjDH+Y73/kOFouFH/7wh4yNjZ302nohhBBiNsjnOi35XBf1dnyuW7t2Leeffz5LlizB4/FQWlrKo48+qgnVLr30Uv7f//t/fPazn+Xzn/883d3d3H333dNqdTue6b6/L774Yh5++GGuueYaysvLOeusswgGg2zfvp3i4mI+/elPs3HjRv73f/+XK664gl27dnH66acTExNDa2srb7zxBosXL+aLX/ziSZ/zKWOOBlcLIY7j5z//uQIoCxYsOOo6r9erfPWrX1XS09MVq9WqrFixQnnuueeOmvj/+9//XjnrrLOU5ORkxWw2K2lpaconP/lJ5cCBA5HbTLZ1QlFC0/rPO+88xeVyKRaLRcnPz1duvPFGzW1qa2uVK6+8UklPT1dMJpOSmJiobNiwQbnrrruOOv7TTz8duSwYDCrXX3+9otfrj7kxIHzfRx99VLn++uuVxMRExWKxKJs2bVJ27dp11O2fe+45Ze3atYrValViYmKUs88+W3nzzTc1twlvnejs7NRcHt5mVVtbG7msr69PufLKKxW3263Y7XblAx/4gFJWVjajrRPTOffLLrtMiYmJmfQ4R/5MFSW0ZeNnP/uZsmjRIsVsNisul0tZv3698ve///2o1+Oss85SnE6nYrFYlOzsbOWiiy5SXnrppWOe+3TfX4qiKC+99JKyfPlyxWKxKIBy2WWXHfPYTzzxhFJUVKSYTKYZbSI7lqqqKuVjH/uY4nQ6Fbvdrpx99tnK7t27T/q4QgghxGyRz3XyuS583dv9ue4b3/iGsmrVKsXj8SgWi0XJy8tTbrzxRqWrq0tzu9///vdKcXGxYrValQULFihPPfXUlNvE7rnnnkkfi0m2ic3kc+Xo6Khy2223KYWFhYrZbFbi4+OV973vfUdth33ooYeUtWvXKjExMYrNZlPy8/OVz33uc5O+j8TUdIqiqnMTQggxK1577TXOOussnn76aS666KK5Ph0hhBBCCHGC5HOdeC+SmUFCCCGEEEIIIYQQpxAJg4QQQgghhBBCCCFOIdImJoQQQgghhBBCCHEKmfPKoPvuu4/c3FysVisrV65k8+bNU962tbWVSy65hPnz56PX67nhhhuOus2DDz7Ipk2b8Hg8eDwe3v/+97Njx4638BkIIYQQQgghhBBCvHvMaRj01FNPccMNN3DLLbewd+9eNm3axHnnnUdDQ8Okt/d6vSQmJnLLLbewdOnSSW/z2muvcfHFF/Pqq6+ydetWsrKyOOecc2hubn4rn4oQQgghhBBCCCHEu8KctomtXbuWFStWcP/990cuKy4u5mMf+xg/+MEPjnnfM888k2XLlnHvvfce83aBQACPx8OvfvUrPve5z83GaQshhBBCCCGEEEK8axnn6oF9Ph+7d+/mG9/4hubyc845hy1btsza44yMjDA+Pk5cXNyUt/F6vXi93sj3wWCQnp4e4uPj0el0s3YuQgghhHhvUxSFwcFB0tLS0OvnvBtfCCGEEGJScxYGdXV1EQgESE5O1lyenJxMW1vbrD3ON77xDdLT03n/+98/5W1+8IMfcOedd87aYwohhBDi1NbY2EhGRsZcn4YQQgghxKTmLAwKO7LyRlGUWavGufvuu3niiSd47bXXsFqtU97um9/8JjfddFPk+/7+frKysmjU6XAqCtx3H3zmM7NyTkIIIYR47xoYGCAzM5PY2Ni5PhUhhBBCiCnNWRiUkJCAwWA4qgqoo6PjqGqhE/HjH/+Y73//+7z00kssWbLkmLe1WCxYLJajLnd+61s4v/c9uPFGWLUKVq8+6fMSQgghxHuftJkLIYQQ4p1szprZzWYzK1eu5MUXX9Rc/uKLL7Jhw4aTOvY999zDd7/7XV544QVWrVp14gf66lfhggvA64WPfxza20/qvIQQQgghhBBCCCHm2py2id10001ceumlrFq1ivXr1/Ob3/yGhoYGrrnmGiDUvtXc3Mwf/vCHyH327dsHwNDQEJ2dnezbtw+z2cyCBQuAUGvYrbfeyuOPP05OTk6k8sjhcOBwOGZ2gno9PPoorFkD5eXw0Y/Cyy9DTMzJP3khhBBCCCGEEEKIOTCnq+UB7rvvPu6++25aW1tZtGgRP/vZzzj99NMBuPzyy6mrq+O1116L3H6ysuvs7Gzq6uoAyMnJob6+/qjb3H777dxxxx3TOqeBgQFcLhf9/f04nc5QELR+PfT2woc/DM89B8Y5H7ckhBBCiHeYoz5DCCGEEEK8A815GPRONOkHuS1b4OyzYWwMrrwSfvtbkHkAQgjxnqEoCn6/n0AgMNenIt7BDAYDRqNxyplAEgYJIYQQ4t1Ayluma8MGeOopuPBCeOghsFrhV7+SQEgIId4DfD4fra2tjIyMzPWpiHcBu91OamoqZrN5rk9FCCGEEOKESBg0ExdcEAqCrrgitG4eJBASQoh3uWAwSG1tLQaDgbS0NMxms2yCEpNSFAWfz0dnZye1tbUUFhai18/ZLg4hhBBCiBMmYdBMXXZZ6H/DgVAwCL/+dWjYtBBCiHcdn89HMBgkMzMTu90+16cj3uFsNhsmk4n6+np8Ph9Wq3WuT0kIIYQQYsYkwTgRl10GDz8cqgh64AG45JLQ+nkhhBDvWlLhIaZL3itCCCGEeLeTTzMn6rLL4PHHwWQKzRL68IdhYGCuz0oIIYQQQgghhBDimCQMOhmf/jT885/gcMDLL8OZZ0Jr61yflRBCCCGEEEIIIcSUJAw6WR/4ALz2GiQmwt69sHo17N4912clhBBCzLrXXnsNnU5HX1/fXJ+KEEIIIYQ4CRIGzYaVK2HrViguhuZm2LQJ/vSnuT4rIYQQ72GXX345Op0u8ic+Pp4PfvCDHDhw4C17zA0bNtDa2orL5XrLHkMIIYQQQrz1JAyaLfn5oUDovPNgdBQ+9Sm4/fbQtjEhhBDiLfDBD36Q1tZWWltbefnllzEajZx//vkndczx8fEpLzebzaSkpKDT6U74+D6f74TvOxOBQICg/B0shBBCCDEpCYNmk8sFf/87fOUroe+/8x346Eeht3duz0sIIcT0KQoMD8/NH0WZ0alaLBZSUlJISUlh2bJl3HzzzTQ2NtLZ2Rm5zc0338y8efOw2+3k5eVx6623agKfO+64g2XLlvHQQw+Rl5eHxWJBURR0Oh0PPPAAH/3oR4mJieGuu+6atE1sy5YtnH766dhsNjIzM7n++usZHh6OXJ+Tk8Ndd93F5Zdfjsvl4vOf//ykzyUYDPKjH/2IgoICLBYLWVlZfO973wMmb0/bt28fOp2Ouro6AB555BHcbjf/+Mc/WLBgARaLhQcffBCr1XpUW9v111/PGWecMe3nIIQQQgjxXiNh0GwzGODHPw6tnrdY4B//gBUrYNeuuT4zIYQQ0zEyEloMMBd/RkZO+LSHhoZ47LHHKCgoID4+PnJ5bGwsjzzyCIcPH+bnP/85Dz74ID/72c80962qquJPf/oTzzzzDPv27Ytcfvvtt/PRj36UgwcPcuWVVx71mAcPHuTcc8/l4x//OAcOHOCpp57ijTfe4LrrrtPc7p577mHRokXs3r2bW2+9ddLz/+Y3v8mPfvQjbr31Vg4fPszjjz9OcnLyjF6DkZERfvCDH/Db3/6WkpISPvvZz+J2u3nmmWcitwkEAvzpT3/iM5/5zIyegxBCCCHEe4oijtLf368ASn9//8kdaM8eRcnLUxRQFLNZUR54QFGCwdk5SSGEELNidHRUOXz4sDI6Ohq6YGgo9N/tufgzNDTt877ssssUg8GgxMTEKDExMQqgpKamKrt37z7m/e6++25l5cqVke9vv/12xWQyKR0dHZrbAcoNN9yguezVV19VAKW3t1dRFEW59NJLlf/93//V3Gbz5s2KXq+PvJ7Z2dnKxz72sWOe08DAgGKxWJQHH3xw0uuPfFxFUZS9e/cqgFJbW6soiqI8/PDDCqDs27dPc9/rr79eed/73hf5/t///rdiNpuVnp6eaT+HIx31nlGZtc8QQgghhBBvIePcxVCngOXLQ5vFLr8c/vpXuOYaeOMNuP/+0L8ACyGEeOex22FoaO4eewbOOuss7r//fgB6enq47777OO+889ixYwfZ2dkA/PnPf+bee++lqqqKoaEh/H4/TqdTc5zs7GwSExOPOv6qVauO+fi7d++mqqqKxx57LHKZoigEg0Fqa2spLi6e1nFKS0vxer2cffbZx3/Sx2A2m1myZInmss985jOsX7+elpYW0tLSeOyxx/jQhz6Ex+OZ0XMQQgghhHgvkTDoreZ2w7PPwk9+At/4Bvzxj7B9OzzxRGgLmRBCiHcWnQ5iYub6LKYlJiaGgoKCyPcrV67E5XLx4IMPctddd7Ft2zY+/elPc+edd3Luueficrl48skn+clPfnLUcaY6/rEEg0G+8IUvcP311x91XVZW1rSPY7PZjnm9Xh/qaldUM5UmG3Rts9mOGm69Zs0a8vPzefLJJ/niF7/Is88+y8MPPzzj5yCEEEII8V4iYdDbQaeDr34V1q6FSy6BykpYvx6+973QsGm9jG4SQghx8nQ6HXq9ntHRUQDefPNNsrOzueWWWyK3qa+vn7XHW7FiBSUlJZpA6kQUFhZis9l4+eWXufrqq4+6Ply11NraGqnoUc82Op5LLrmExx57jIyMDPR6PR/+8Idn/TkIIYQQQrybSArxdtq0Cfbvh49/HMbH4etfh3POgZaWuT4zIYQQ70Jer5e2tjba2tooLS3ly1/+MkNDQ3zkIx8BoKCggIaGBp588kmqq6v5xS9+wbPPPjtrj3/zzTezdetWvvSlL7Fv3z4qKyv529/+xpe//OUZHcdqtXLzzTfz9a9/nT/84Q9UV1ezbds2fve730WeR2ZmJnfccQcVFRX885//PKq66Vg+85nPsGfPHr73ve9x0UUXYbVaZ/05CCGEmF3qalAhxOyTMOjtFhcHf/4zPPhgaDbEyy/DkiXwt7/N9ZkJIYR4l3nhhRdITU0lNTWVtWvXsnPnTp5++mnOPPNMAD760Y9y4403ct1117Fs2TK2bNky5TavE7FkyRL++9//UllZyaZNm1i+fDm33norqampMz7Wrbfeyle+8hVuu+02iouL+dSnPkVHRwcAJpOJJ554grKyMpYuXcqPfvQj7rrrrmkfu7CwkNWrV3PgwIHIFrG34jkIIYQ4ecFgEJ/PR39/P0NDQ3i9Xvx+v4RDQswynSL/rzrKwMAALpeL/v7+o4ZszqqyMrj4YgiXul9zDdxzjwyXFkKIt9HY2Bi1tbXk5uZqKkaEmMqx3jNv22cIIYR4jwkP7x8fHycQCODz+SKXh9ugjUYjRqMRg8GAwWA4ak6cEGL6pDJoLhUVwbZtoblBAA88AMuWwZtvzulpCSGEEEIIIcTbRVEU/H4/Pp8PRVHQ6/VHhT+KouDz+RgeHmZwcJCBgQGGh4elckiIEyRh0FyzWODHP4aXXoLMTKiuDs0Wuvlm8Hrn+uyEEEIIIYQQ4i0TbgsLb4nUH7FcR6fTodPpMBgMEg4JMYskDHqnOPtsOHgQLrsMFAXuvhtWrYq2kAkhhBBCCCHEe4S6GigQCESqgY5nsnBIr9ejKAper5eRkREJh4SYBgmD3klcLnjkEXj2WUhMhEOHYPXq0Ap6v3+uz04IIYQQQgghTpqiKIyPj3PgwAEaGhrQ6/UnPP8nPE/IYDBgMpkwGAyacEhdOTQyMoLX6yUQCEg4JE55Ega9E33sY6Eg6MILQyHQt78Np50G5eVzfWZCCPGeJR8KxXTJe0UIIU5cMBiMVOuEh0WrgyBFUWhububQoUM0NDQwODg4o//uHhkOqSuHxsbGGB4eZmBgIBIOhSuT5L/t4lQjYdA7VVISPPMM/OEPoYqh7dtDw6V/8hMIBOb67IQQ4j3DZDIBMDIyMsdnIt4twu+V8HtHCCHE8YXbwrxeL8FgMNIWpg5h/H4/Bw4coLKykpiYGHp7e9mzZw+bN2/m4MGDNDU1MTQ0NGvhUHt7Ozt37mRgYIDBwUEJh8QpxTjXJyCOQaeDSy+FM8+Eq66CF1+Er34Vnn4aHnoIFiyY6zMUQoh3PYPBgNvtpqOjAwC73S6rasWkFEVhZGSEjo4O3G43BoNhrk9JCCHeFcJtYYGJf9RWt4WFQ5f+/n7279+PzWZj/fr1kRAnGAwyODhIb28vnZ2dVFVVYTQacbvdeDwePB4PNptt2n93h2cOhY2OjkYeZ2xsLHKbIwdWn0wrmxDvRBIGvRtkZsK//w2/+11oDf327bB8Odx+O3z962CUH6MQQpyMlJQUgEggJMSxuN3uyHtGCCHEsQUCAcbHxyPVQOpARafTEQwGqauro7Kykvz8fHJzcyObwiAUHLlcLlwuFzk5OQSDQQYGBujt7aW9vZ2KigrMZvNR4dBMhAdXh7eUKYoSaWcbGxubdNW9hEPi3U6nSP3bUQYGBnC5XPT39+N0Ouf6dLSamuALX4B//Sv0/YoV8PDDsGTJ3J6XEEK8B4Q/sAoxlfBw0qm8oz9DCCHE2yjcFuafWIRzZEUOwP79++nv7ycYDLJ06VI8Hg8QXTc/ne1igUCA/v5++vr66O3tZWBgAIvFEgmGPB4PFotl0vt2d3dTVVXF2rVrj/k8wuFQWLhqKfx3gtFonPT5CfFOJiUl7zYZGfCPf8Af/wj/93+wZw+sXAm33ALf+haYzXN9hkII8a5lMBik9UcIIYQ4ScFgMFINBJMHQT09PXR0dGCxWNi4ceMJz2EzGAzExcURFxcHhOYO9ff309vbS2NjI4cPH8Zut+PxeCLVQ+YZ/M4UPvdwMBUOhwKBAH6/f9JV9waDQcIh8Y4nlUGTeNf8q15rK1x7LTz3XOj7JUtCs4RWrpzT0xJCCCFOVe+azxBCCPEWCFfQTNUWFr5NdXU1tbW1OJ1OnE4nxcXFmtsEg0GGhkb4+dMvUtnYzv+8bzXnrF10QuGK3++PVA319vYyNDRETEwMHo8Ho9FIR0cH69atO6nnHH7eiqJMGQ5Np8pJiLeThEGTeFd9kFMU+NOf4LrroKsLDIbQHKHbbgOrda7PTgghhDilvKs+QwghxCw6ckj0ZJUxY2Nj7N+/H5/Px9KlS2lubgY4KgwKBAJ86e6HaWjvoaali4GRMeKcMXz6A+u47IMbSI53nfB5+ny+SDjU1dWF1+vF4XBEWsrcbjfGk5jJeqxwKNxWJuGQeCeQMGgS78oPcp2d8OUvw1NPhb4vKoIHH4TTTpvb8xJCCCFOIe/KzxBCCHGSwtVAgUBgysHKHR0dHDx4kKSkJIqLizEajZSVlREMBllwxJbkZ1/dybX3PAJAnNNBTmoiPYPD1LV2kZbg4dtXnM+H1i896Tas7u5uKioqyMvLi1QOjY2NERsbGwmGTnZ75JHhEHDUMGoJh8RckJlB7xWJifDkk/CpT8EXvwhlZbBpE1xzDfzwh+A68fRcCCGEEEIIIY6knp0zVVtYMBikvLycpqYmFi5cSFpaWuQ6nU7HkbUJY75x/vLqjsj3Pf2DZCZ5SHDG0NEzQEtXL7946kV+99f/YrWYufDMVWxYXEB6oueEnoNOpyM5OZnk5OTQ44+NRYKhsrIyfD4fTqczUjnkdDpnFA5NNXNofHxcszFNwiHxdpMw6L3mwgvhzDPha18LraJ/4AH429/gV78KXSeEEEIIIYQQJ+nItrDJgqDh4WH2798PwIYNG4iJidFcP1kY9MQLb/LSjkOsXpDH7vJ6Fhdksr+yAYCUeBcLc9MoqWliZCwUpPQODvOPN/bQ1NHL6uI8NiwpZN2ifJI8x6/OnKyyyGq1kpqaSmpqKoqiaMKhlpYW/H4/LpcrMoza6XTOKLiZLBwKV1aFN5rqdDpNOBTeVibEbJI2sUm8Z0q8X30V/vd/oaoq9P3HPw6//CWo0nghhBBCzJ73zGcIIYQ4hvHxcQYHB7FarVO2hbW0tFBSUkJGRgbz58+fNDCprKxkbGyMxYsXAxAIBNl49e3Ut3YBUJyTRkBRqGhoC91BUSjKScMZY2NfZSO+cT+rinLYVVpDcU4a6PTYLCYqG9tITXCzfnEhZyybz8riXNyxMUc9fk9PDxUVFdMeIK0oCiMjI5qB1MFgEJfLFakccjgcJ1XVo24pU88cknBIzDapDHovO+ssOHAAvvtduOce+Mtf4OWX4Uc/gs9/HqT0UAghhBBCCDFNiqLg9/sZHBxk8+bNnHvuuUeFEn6/n9LSUjo6Oli6dClJSUlTHu/IyqCXdx6KBEEAFrORtu4+clMTqG3tYum8bPZX1AOQkRRHTloib+wrB6C0roWFeemMjXnJSIqjtLYZs8nIY8+/gT8QZGFeOuetX8Ki/ExWL8jHYZ/5sh2dTkdMTAwxMTGkp6ejKArDw8ORYKi+vh5FUTRr7B0Ox4yCm/Cw6TB15ZDP52N8fJz+/n7S0tI0A6klHBIzJWHQe53NBt//Pnz603D11bBzZ2iO0GOPwW9+Exo0LYQQQgghhBDHEAwG8fv9BAKBKYOHwcFB9u3bh9lsZuPGjViPs934yDDoX2/sJSXeRVt3PwCjXh9t3f1YzSaWFWbRPzQSuW1zZw8GnY61C/LYXV6HXq+nq3eQ9p7QfVcW5RJQgoyP+0GnQ6+Dex79OwBGg573r15ERqKb1FgTS5d7sVksM35NdDodDocDh8NBZmYmiqIwNDQUCYdqa2vR6/WRYMjj8WC3208qHBoeHqa6uprExER8Pl+k5ezImUMSDonjkTDoVLFkCWzdGmoT+/a3YfNmWLo09PXNN4PZPNdnKIQQQgghhHiHUVemhNuWwuFEMBjEYDCgKAqNjY2Ul5eTk5NDQUHBtMIIdRjU1t3H0y9vI87pIDctkaCiUF7fCoSGSo+Pj5PojqW2pROA5fOy2VNWR31bF9mpCeSkJvDfPWXR80Zhb1kt87NTsZiMtHX1Rq5bUpDFC1v2AWA2Gnjgn9spyExhw9L5nLZ0PkvmZWM6gfXyOp2O2NhYYmNjycrKIhgMMjg4SG9vL52dnVRVVWE0GjXhkM1mm3FwE24bC792wWAQn8+H1+uVcEhMm4RBpxKDAW64ITRI+otfhOefh9tuC62jf/BBWL9+rs9QCCGEEEII8Q5x5JDoyYYfj4+Pc+jQIfr6+lixYgXx8fHTPr46DHr2lR0EgwpdfYOM+cZZUZRNfWtn5LYGg4HtJdUsLsikrqWT3oFolVBrVx9Do2OsWZTP7sM1pCV62FNWC0B5fStrFubjjo0hyePiUHUjHd19kfvmpcZR2tBBR08/NU1t/Pj3z2G3Wli7uJCNS+ezful8FuZlYTDMfMSGXq/H5XLhcrnIyckhGAwyMDBAb28v7e3tVFRUYDabNW1lNpvtmMcMB3Lh1y/82oSvAwmHxPRIGHQqys6Gf/4ztIr+//4PSkpg40b40pfgrrtkDb0QQgghhBCnuHA1UCAQOGpIdPjr3t5eSkpKiI2NZePGjZhn2G2gPuZfXt0Z+XrM66Okupll87LZV1FPvMvBwepGAA5WNbKiKIfu3sHI7ZcUZrLzcA1dfYPkpSWRmuimoS00e8ho0FPT1E5XX+j2py2dT//wME0dPSiKQlf/cOQ42amJtHX1MjLmpb2rl7sefBqAnLRE5mens2FpERuWFTE/J/2EwpRwy5jb7SY3N5dAIEB/fz+9vb20trZSXl6OxWKJVA15PB4sM2hfO1Y45PV6j7nKXsKhU4+EQacqnQ4uvhjOOQe+8hX4/e9D6+efeQZ+/nO46KLQbYQQQgghhBCnDEVRCAQC+P1+gsHgpNvCwt/v3buXefPmkZ2dfUJhgk6nIxgMUtXYyqGJsAdgUX4meyvq6B4YYs2CPNDp6DpUHb0f0NE3wNJ52eyrqKOjdyByXe/gEK1dvaxdVMDOkmqWFmSxe6JKCKB7YJDS2mYKMlNIcMWy9WBF+InToKpEUg+YTnA7+feWvfx7y17mZ6fT1dfPhmVFnLFqEWsWzSMnLemEnr/BYCAuLo64uDggNHw7HA41NjZy+PBh7Ha7pnJIXRl0POpwKNzOF/5zZDgUHkZtNBqn3BAn3ltktfwkTsm1sC+/HGodq6wMff/BD8Kvfw15eXN7XkIIIcS7yCn5GUII8Z4xVVuYmtfr5cCBA3R3d7NixYpjbgs7noaGBjo7O3m1tIVXdx1mX1kdQUVheVFOpM0L4PTlRby+twzQ4bBZGPf78Y77AThrZTGv7i6N3Hbtwny2H6oCIC89iXhXDDtLagDITI6nsT26rWxpQQYd3X0kJXgYHfNRXtccfiGIdzvonqgmWj4/l71loWOsXVzItgOhDWYrF+Szu6SKtMQ4Pnz6KhbkZ7FxWTFpSdNvlTuW8Oaw8EDqoaEhrFYrfr+f4uJi3G43JpPphI+vDoeCwSBApK3MZDJFKockHHpvksogEXL22aE19D/8IfzgB/DCC7BwIdx6K3z1qzJgWgghhBBCiPew8JyZqaqBALq6ujhw4EBkLpDD4TipxwxXBv1j8x7K6lpYNj+HuqYOSmqaIrfJT0/i9b1lrJifw4GqRopz0thZWhO5fnBkjEV5GdS3dzM4NEJTe3fkunF/gL1ldaxdVMCuw9WkJbojYZDNYqasvhWvb5zWngFOX1FMjNXMnvI65mWlUlHfPHGOUNXQGjnmgGqjmUEXmiPU0tnDgYo6Hnzm3wCcs2E5iW4XG5cXs35pEYlx7hN6fUwmEwkJCSQkJADg8/lobGykubmZ6upqRkZGiI2NjVQNud1ujDMYfK0O+46sHBobG4vcRsKh9yYJg0SU1Qp33BFqH7v2WnjlFbjlFvjjH+GBB+D00+f6DIUQQgghhBCzKNwWFt4WNtkv+sFgkMrKShoaGiguLiY9PZ329nZOtslEp9PR3NVPWV0LAPvK6zhjRTHbDlVGbpPgjqW6uYM95XUsyE1n1Dseuc5sMlJa28zwqJfUBDeL8tLZeiB637QEN41tXWw/VEVeehIjY77IdQvy0tl9ONp6VlLdSFffIFkpCWSlJlDZ0IKiKMzLSotUDJmNBqoao8FQR09f5GuvL3pefQND/OfNPTz2z1dZt2Q+vf2DbFi+gDNXL2HlwkLcsTEn9HqZzWacTifd3d2sWbMGr9dLX18fvb29VFZWMjY2RmxsbGTekMvl0qylP57phkPhdjIJh97dJAwSR5s/H156CR57DG66CUpL4Ywz4Ior4O67YSKZFkIIIYQQQrx7BYNB/H5/pC1ssl/qR0ZG2L9/P4FAgPXr10eqgcJVPSdDp9Px5qEazWWDI6PkpiXR2NbF8JiPutZoW1dHzwAOm4Ukj5OO3gEW5qazt7wOCG0Uy0pJYGVRbmRGUFVjW+S+dquZwzVNrF1UwO7SGnyq8GZ+dhplE4FPaPC0QlpSPKkJboz66BaxgqxUSqobAIi1W1XbzhSaOqLn2T8YrR4a845TXtdMeV0zJZX17CypZHFhDh86fRUL8rNZu2Q+MbbofKLpvm4AFouF5ORkkpOTQ481NhZpKSstLcXn8+F0OiPhkNPpnJVwKDyQemxsDL1ef9RAagmH3h0kDBKT0+ngs5+FD38YvvEN+M1v4OGH4W9/g3vugcsvlwHTQgghhBBCvAuFf6EPVwNNNhsIoK2tjUOHDpGamkpRUZEmSNDr9bNSGbSzvCHyvcVk5HBNM2O+cfLTk7DbLBysig6WzktPZEdJNQnuWHLTEgmqHt9o0HO4tpnB4VHWLMhn1OvjYFX02FaziUAwyPZDVczPTo3MHAJwOeyRr1MTPDRMBFDNHT2sLM5l9aJC9pXVEBsTXfuek57MwYo6ABI9Ljp7+yPXNbR2RL7u7O2LfD0wPIKiKByoqEWnhx/89k8YDQYuPHs9aUnxbFy+gBULCrAdY4PYsV5zq9VKamoqqampkWqecDjU0tKC3+/H5XJFwqHY2Fj0qrDreI58n4TDoUAgQCAQmHKVvYRD70wSBolj83jg//0/uOwyuOYaOHgQrrwSHnkE7r8fFiyY6zMUQgghhBBCTJOiKPj9fvz+UBgyWRAUCAQoKyujtbWVRYsWkZKSctRxdDrdSYdBnX2DeMcDWM1Gxnx+FuRmsHciYKlu7uD0ZfNJcMXS1R8a5Nw3UXETXhPvVG38WpCXwYHKUPiz43A1Z64oJtHtpLNvAJPBEGlFA4i129hTVsPKolz2VdSpKnwgKzme1s6e0PFj7Owpq0VRFJI8TmxWC2aTEd+4nxhrNLBJT06IhEFZKQmRrWRWi4mWjp7I7Vo6o1+Hh1P7AwEOVtTy9L838/NHn2PVwkLMZhMbly/g9JWLWDwvF5NqDtB0t4npdDpsNhs2m420tDQURWFkZITe3l76+vpobGwkGAweFQ7NJLQJv3fCgZI6HPL7/ZHrw21l4f+dKnwUby8Jg8T0bNgAu3fDvfeG5gq9/josWwZf+1porpDdfpwDCCGEEEIIIeaSeki0+pd4taGhIfbt24fBYGDDhg3Yp/icPxttYpv3V1Lb1sOCvAxqmtoxGlUtTIpCVWMbRoOO1AQP4+N+KlSDnLNTEthXUc/qBXnsPFyDSV21pNOxp6wWvV7PgtwMjEY9ByrrI9d7feMEgwq7y+rIS43DZLbQ1t0Xev6jY5HbFWQls2diWPXA8Chv7C3F5XCQn5HM4Ei0Fcxijv5anRjnioRBmSmJVE60n8W7YyMBkF6niwROAO0Tjx3+urGtky17D/O3V7bS1N7F2iXzOXvdclYuKCTOYTmhIEWn0xETE0NMTAwZGRkoisLw8HCkcqi+PvT6qIdROxyOWQmH/H4/4+Pj1NTUkJaWRmxsrKZySMKhuSFhkJg+kykU/nzyk3DddfCPf8D3vw9PPAG/+hV86ENzfYZCCCGEEEKII4SrNQYHB3n99dc599xzj/rlW1EUmpubKS0tJSsri8LCwmO2EM1Gm9h/94ZWtB+uaWJhXgaNqk1gSW4HLV19ALgdNhbmZ9LVNxC53h8MEggG2Xm4hnWLCjhUHW0nW5CXzqGJ9rKB4RHetyrazRBjtUTmA0FoKHRZfQtrFxVS2dhKRX20gkiveo0KMlM4VNVAd/8g/UPDeJwO1i0p4mBlnWbDmFEfDaXUg6JTEuIiYVB6cjwNbaHAyGG30jswFLlde3dv5OvO3n6GR8d4Zft+KutbaGztxBljY93iAjbVd7Fx+ULm5aSfcDjkcDhwOBxkZmaiKAqDg4P09fXR09NDTU0Ner0+Eg55PB7sdvtJhUOdnZ0kJydHwiF15ZDJZMJgMETaysRbT8IgMXPZ2aHZQc89B9dfD7W1odlCH/sY/OxnkJMzxycohBBCCCGEgFDIMz4+HhkSHb5M/Uu93++npKSE7u5uli9fHlllfiwnWxk0MuZjx+Ho8GglGCTWZqHPZGRs3E92WhIdfaGQpG9olLaOLjISnDR1DWAxGSmrjQY6w2NeCrNSKKluwucPYDObo+cJbC+pZklhNrXN7RRmprKnLPq4/SOhSqAdJVWsXpDP6Jh3YtaQomkfUw95zs9Iobyumc6eflwOO26nA6fDzsDQCP1Dw5rHDnOo7h/ndkbCoNSEOCqHQ88lKd5Nx0SVkMVsomeiPQ6FyOUDw6NsP1TNf7YdBGBZUR7ZaUlsXLGQjcsWkZ2edMLhkNPpxOl0kpWVRTAYZHBwkN7eXjo7O6mqqsJoNGrCIZvNNqPHCgaDkVYxOLpyKBwOqauGJBx660gYJE6MTgcXXgjvfz/ceWeofey55+Df/w61jX31q3CMwWdCCCGEEEKIt5a6LSw81Be0Q4j7+/vZv38/NpuNjRs3YpnmZ/iTrQzavLdUM8TZajZyuLaZ7OQ4eoZH6RmIhiqxMTbq2vswGvXMy0xCCQapbI5u79LrFPaW1zEvK5Wu3gGqmqJbxIpy0impaeRAZT2pCR6sFlPkuniXg9buaLWRAhysamTVgnyGR8corW2KXNcdDmbQVvwkuJ1sO1CJzWJm7ZL5NLS0R67rG4w+h4ASDc6s5ug5OFXDqxPdrkjok5rgoW7iWHFuBz0TwZhBr6c/UomkUFHfxL6yav76ylaS4t0YDQY2Ll/ImWuWsGZxEWlJ8Ue+9NOi1+txuVy4XC5ycnIIBoP09/fT19dHe3s7FRUVmM3mSDDkdrux2WzHPGb4fRg2VVvZ+Pg4Pp8vch4SDr015FUUJyc2Fn78Y9i3L7R+fnQUvv1tWLQoFAwJIYQQQggh3lbhaguv1xv5BVz9S3cwGERRFOrq6tixYwcZGRmsWrVq2kEQnPwA6b3ltRC+v6JE5uzUt/eQk5xId1+0dWpeZgqBYBCvz09NSxfpyYnqZ0t1Uyg0qWhoJckTi90SrQyKsUW/7uzt53BtM6sXFgCQlRINShQF6lo6QadjV2ktbqeD5UV5QKiqp0YVMI2OeSNfx7tjQ5d5fbT3DNA9MMqaJUUkx7sjzwmgTxVujfujIZh61lGMPfr6u2Mdka8T3C7V17GRr2PtdkZGo+fS3TtAS0c3T//7db5178Os/uR1nPbZG7nz14/y11e20qXaeDZTer0ej8dDbm4uK1as4PTTT6e4uBiLxUJzczPbtm1jy5YtlJaW0tbWhtfrPeoYR4ZBR1JvIjOZTJFh0+Pj44yMjDA0NMTAwABDQ0OMjY0xPj5+0nOrTmVSGSRmx6JF8OqroflBX/kKVFXBBz8IH/94qHUsK2uuz1AIIYQQQoj3vCPbwtRrvcO/iI+NjVFRUcHg4CCrVq3C4/HM+HFOpk1MURSe+s8WVszPZk95PYluB52q8MdmNeN02NAbdPT0DxEIRkOnYFBhb0Udaxbms6OkmqLsdMrqoy1jRoOenv5BspNc1Hf0UakaOl2Uk8ah6iZ2Hq5m5YJ8xsaiw6Jz05KobYmuhB8aGeNgVQNL5uVit5jYdqA8cl1Nc7T6JxCIvgaJHid1zR3sOFRFVkoCi1OTaGrrpK2rh8a2aDCknhHkHR+PfK1uubKoKpjUK+1djhjae0KhToLHGRlkneB2RmYqmY1G+ieqkmob2/hDx0v85k//AmDd0mIWFmSzccVC1i0txqWqcpoJg8FAXFwccXFxQKjVsL+/n97eXhobGzl8+DB2uz1SOeR0OgFOaJW9unIoGAwyPj7O+MTrdmRbWThAEscnlUFi9uh0cMklUF4ON94IBgP85S9QXAw//CFMlPoJIYQQQgghZl8gEMDr9UbWequDIIiGDTt37kSv17Nx48YTCoLg5NrESmqaaO3qY095PfMzEsjP1K6uH/P5aGjrwmo2kZ7goVw11LkoJ43+oVF2lFSzdlEBLoeqNUmBnsFRRn1+mroGWbsgj97B6HDnoGpu0sGqBkbGfCS6QmFIUny0+sZkMERCpAOV9ej0OlYvKgQgLz2ZoZFoiNTcGR16rVNNCUqMc7HzcDVtvQNsXLGQ5PjQ66zTQVN7tMWtpy/afjbmjQZD6tdWvVpe3eYWq2oxi1NVDCXEOSNfO2KsjHmjv4ftOlTB7555gStv+QmnX/oVzvvfb3HX/Y/x350HGFY9r5kyGo3Ex8dTUFDA6tWr2bRpE/n5+ej1eurq6tiyZQsAtbW1dHZ2RsKcmVCvqQ+HP8CUlUN+v/+kh5y/l0llkJh9Tif89KdwxRXwpS/B5s3wzW/CI4+Eto69//1zfYZCCCGEEEK8Z4TbwvwT7UdHhkDh21RVVQGQmZlJQUHBSVVQnGhlUDAY5Il/vBL5vrypi7NTo2GQ1WyirC4U/rR09rKiKAdFB6OdoUDDYY8OYt5+qJLTlhWh04XavLJS42loDYUzgWAQdAbWLCxg5+FqFEWhozcavKTHx1Lb0onZaGDZ/BzNRrB52amU1ES3k7V29lHX0kFRTgZJcc5Iy1i8O5a2iY1ngGbbmdEQrmaBEe84De1dLCsuwKjXsbOkAghtK2vu6J70/iOqVjR19ZHBEK3nsGra4VTVQ7ExtHSEVtfHuZwMDYdCHo/TEalK0ut1dPcN0NXbz4HyWh77xyuMjHpZvqCA961dyqpF81ixoFDzGDNhMplITEwkMTHU0jcyMsK2bdsAqK6uZmRkhNjY2Mi8IbfbHZlpNV3hcChMXTnk8/kigeiR28qkcihkziuD7rvvPnJzc7FaraxcuZLNmzdPedvW1lYuueQS5s+fj16v54Ybbpj0ds888wwLFizAYrGwYMECnn322bfo7MUxLV4M//0vPPooJCeHKoY+8IHQavqmpuPfXwghhBBCCHFM4SHR4SAo3FqjNjY2xo4dO2hra0Ov15OSknLSvxCfSGXQ6OgoO3bs4I39FZHLrGYj/91zmLWLQnN8inLT8akGSxv1BsZ842SnhDacqdfPZyUn8Ma+MpYUZGE2GUmN11Y5dfYNsKOkmkX5mczPTqOrP9qe5XaGKml8/gClNU0ogXFMxlCwEGOLzu7xxMZQN9E+VlbfwtCoj3VL5mM06MlKiW5ds5hNkdsBqiHPE5U9Oh37KmoJKAqL5+VRlJtJRnIC/olqJaNBT6uqykg932doNFqx4/dHq5v0qh+h2RQNUmKs0cBMPaDa7YpWD8W7nJGfn9GgZ2BoBH8gwM6D5fz68b/xPzd8l4XnX8X/3vYzfv7os+w6VMG46ucyU+HQZt68eaxbt46NGzeSmZmJ3++nsrKSzZs3s2vXLqqrq+np6dFsv5uuySqHwm2Tw8PDDA4OMjAwwPDwcKSC7lSuHJrTMOipp57ihhtu4JZbbmHv3r1s2rSJ8847j4aGhklv7/V6SUxM5JZbbmHp0qWT3mbr1q186lOf4tJLL2X//v1ceumlfPKTn2T79u1v5VMRU9Hp4LOfhbKy0Bp6vR6efhqKiuDuu6V1TAghhBBCiBOgKAqBQACfz0cgEJi0LQygo6ODN998E7vdzvr16zEYDLMydHemlUGdnZ1s2bKFgM5AZUu0TSo93oU/EGT7oSrWLCzAbDRo7lff1kVP/xC9g8OsWZhPq6oSJzXBDcD+ygZy0xIZHB6NXJfkcVLTHApnDlY1khTnJDU+dHsUhRbVcYpyMyhv7CTJ4yTR7dBsBMtM1gZM1U1tbDtURXpyoiZoyU1LIqiabVSvCoaGVWGOxWzmUFUDZXUtZKensLgwB4C0pPjI/U1GA+3d0fPr6lEHQ9GKId8U4YzRoGorM0cre2JVVVXqWUFx7mhbmdlkZGgk9DqO+cZ5bcd+7v7tU3z0S7fxgatu5tKbf8QDT/2DQ5V1M/r5h28bnv9jsVhISUmhqKiI9evXs379etLT0/F6vZSWlvL666+ze/duampq6O3tnfF7NhyKThYO+Xw+CYeY4zDopz/9KVdddRVXX301xcXF3HvvvWRmZnL//fdPevucnBx+/vOf87nPfQ6XyzXpbe69914+8IEP8M1vfpOioiK++c1vcvbZZ3Pvvfe+hc9EHJfbDT//OezZAxs2wPAw3HwzLF0KL78812cnhBBCCCHEu4Z6/baiKJOGQMFgkNLSUvbv309xcTGLFy/GaDSe9Er4sOluEwsGg1RUVLBv3z6KioroHIX8jGhbmHqe8I6SKsym6EycnLTEyLDkgeFR9Ho9xTlpkev7VNU3HT39DI95SZqYl5Odpt44Bu3d/Yx6fRTlpJGVmkh7dzRgMU+0J7V09WEwGkhLjlb8eFXtWumJnkjFT31bF82dvaxZPI8YmxVXbDQYyklLYjQyp0ehWTUjKHy5TqdjPBDgUE0TBdnpFGanR26TlhgfeW0tJiOdqiqhHtWKe3Vrm9cXncETVK2xN6raqNTBUIwqGHKrgyFV9ZDFZNQEWS0d3byybS/fve+PXPXtn7D4gs9z9bd/wuP/eIWKuqZjvh+Ot0nMarWSmprKggUL2LBhA2vXriU1NZXR0VFKSkp4/fXX2bt3L3V1dfT3989qOOT1ehkZGTnlwqE5mxnk8/nYvXs33/jGNzSXn3POOZHhUidi69at3HjjjZrLzj333GOGQV6vV7P6bmBgYMrbipO0dGlohtCjj8LXvhaqGHr/+0Nbx37yE8jJmeszFEIIIYQQ4h0rPBMlEAhMGgIBDA8Ps3//fgA2bNhATEz0l329Xj8rlUHTCZXGxsbYv38/Pp+P9evX43A4+P6jL9Da1cu8rBQqGtpo7YmGGwUZybyxr4yVxXnsLaslOc4ZWvc+oXdgiNqWTpYUZlHX0kllY3RTWH5GCrtKa0hwx5KblqgZmuxy2KlsbENRFIZGxzhjxQLN2vf6NlWlUmI8u0urWb2ggANV9QyMRkMWt8NGc2cvABaTgZqmdqqUNhLcTiyqoCXBE0vdRHVRoselCXPau3ojX4fbrqqb2kn0uMlKSyHJ4yQYDFDfGrp/amI8dRPby6wWs6b9TN1Kpg6GxlTBUED1s9ar+sosqtDNrmkri6GtKzpvqHXia/MRwVD/4BCDw6M8v3knJVX1NLR2kBjn4oKz1jM/N5ONKxaSnZYceX+GQ8vp0Ol02O127HY7aWlpKIrCyMgIvb29kW1lwWAQl8sV2VYWGxs7o9bH8G3Dc4QURYn88Xq9+CY6WPR6fWTeUDhMfa/MHJqzyqCuri4CgQDJycmay5OTk2lrazvh47a1tc34mD/4wQ9wuVyRP5mZmSf8+GIa9Hq47DKoqIAvf1m7dez222Fk5PjHEEIIIYQQ4hQSHhIdbgub6pfSlpYWtmzZgsfjYd26dZogCGYvDDpem1h3dzdbtmzBZrNFgqBAIMiruw4xNDJGc2cv6xYWMDQaDW0SPaHuj92lNSwpzKJ3YDhynTPGRmVjG75xPyXVTawqztO0ZYVn73T1DTIwMqrZ7FWQmRwJrvyBIM2dPaxZVIBepyPJ7aBD1Yaln6gg2VVaTWFmSmSOEIBJFfhkpyQQnDhmV28/+8pqWJCXQaLHiV4X/TU7PSku8nWs3Uqbqv2rszdahDDuD9DY1sXu0hrsdhtrFhdhNhlxOx2R26QmRFvW7FYLfYPR10cbDEUvV4di2mAoeo6aeUOqeUmabWWqiiGT0aBpyeufeLzOnn5e3raPr//4QTZecgOX3vwjbvzB/fz536/T0tE9o7XyajqdjpiYGDIyMli8eDGnnXYaK1euJD4+nv7+fvbt28fmzZs5cOAAjY2NDA4OzriiZ7Jh0+HAc2xsjM7OTg4dOsTAwAAjIyOR/x++myuH5nyA9GRT7k82aZvpMb/5zW/S398f+dPY2DjlbcUscrvhF7+AffvgrLNgbAy+853QPKE//Sk0el8IIYQQQohTXLgtbHx8fMq2ML/fz8GDByktLWXp0qUUFxdP+sv3iW4BO9JUlUHhrWV79uyhsLAw0p4GsK+ijp6JgGd41As6yEiIzqvpHohWCdW2dGI1myMzhAozUyLhTyAYpG8oNEMIQgOQKxqi//ifnhjP4domVhbnhs5JFRrFWC1UNbaxo6Sa+TlpJLiigQco1Kpm/disFtp7B1i1IPQ4tc3R6zyu6HmnxLsZGBmjtK6FwZFRvGMjhH886ra3jORo65rZaKC5Q7ViXvXcvT4/O0uqiHXEkhjnxj4R0Lhio8FQsioYirFZGRhWVwxFQyZ1xdDomCoYUg1oVlcMmVTBkN0aDYbU85E8qmBIr9fRrwql1EFUY2sHf3rhv/zf9+/j+u/fzw0//SM3/+S3vLB5J919J96No9PpcDgcZGZmsmTJEjZt2sSyZctwu9309PSwZ88e3njjDQ4ePEhTUxPDw8MnHQ75fD46Ozsj4VB4jf369eupr68/4ecyl+asTSwhIQGDwXBUxU5HR8dRlT0zkZKSMuNjWiwWLBbLlNeLt9iiRaG5QX/5C9x0EzQ0wKc+BffdFwqLliyZ6zMUQgghhBBiTgQCAZqbmzGbzbjd7kn/kXtwcJB9+/ZhNpvZuHEjVlXbz5Heysogr9fLgQMHGB0dZe3atTidTs31L+04qPm+q2+Qjr5hFuRl0NLZQ6Uq0CnISGZ3WS0LcjOoaW7X/DKv1+moqG9laHSMtYsKGBwZ43BNdFuxxWTAHwiyu7SWtQvzKamNXleYlcq+ijoASutaKMpIJDM5nsb2brJTk6hvjQY+/kAQr8/PrtJaNi4rZm9ZTeQ6dfiSkRwfrfjR6SipaycrJZHx8XE6uqIbwmzmaDCUmZJIdVNr+C40tUVvF6746ekfpG9wBKPRzLpleRhV4Z5LFc4kxbupnVh3H2u3MTgxABoFOnv7IrcbVJ3zqLpiSLW6Xq96f5mnaCVzxdjpmHi+cU4nXX3RqqS+wejGtoEjHq+1u48//u0lSqvq2X24kuK8LD585loWFmSzdkmxZqj1TOh0OpxOJ06nk6ysLILBIIODg/T29tLZ2UlVVRVGozGyxt7j8WCz2WZUhBIMBiMtZepB1Nu3bz/hiqe5NmdnbTabWblyJS+++KLm8hdffJENGzac8HHXr19/1DH/85//nNQxxdtAp4NPfAJKS+GOO8BqDa2lX74cvvQl6O4+7iGEEEIIIYR4r1APiW5ubqavr2/SDoiGhga2bdtGSkoKa9asOWYQBLMbBqkDmp6eHrZs2YLJZGLDhg1HBUEAr+w8FPnaExtDVVM7Pn+A6qZ2Vhblao4X/vpwbRM5aYm0dEZn7czPTo2sW99+qCq6JQxAQTNnqH94lOKcjEjIYTRGfwW2mo1UtYS2lS0pzCIlPrqkSFGgtjm6VWw8EMDtjCE/I9Q6VtUYDa7UP5fctBQCwSAN7T209QySmpyIzRJqLxsajlbN2CzRuoz0pATG/eHNYAotqhXzg8OjDI6Msv1gJf4gLJ2XQ5zLoZlR5HJEQ5SEuOhziI2xMaLaPqaeXTR1MDR5xZA6GLLZJl9d74qN0bTu9ana/NTzhsIzjUprGvjvzgNc8a0fs+iCq/nyXb/iew88xms79jOiuv1M6fV6XC4XOTk5LF++nNNPP52FCxdit9tpb29n+/btbNmyhcOHD9Pa2srY2PEfKxAIYFAN49bpdAxP/DxjY2Onuts72pxVBgHcdNNNXHrppaxatYr169fzm9/8hoaGBq655hog1L7V3NzMH/7wh8h99u3bB8DQ0BCdnZ2RBHzBggUA/N///R+nn346P/rRj/joRz/KX//6V1566SXeeOONt/35iRNgt4fmBl1+eWjA9NNPhyqEnnwS7roL/vd/QzOGhBBCCCGEeI8KBoP4/f7IL+bhSgS18fFxDh06RF9fHytWrCA+Pn5ax56tbWLh4yiKQm1tLdXV1cyfP5/MzMxJKy46ewcw6vUY9DoCQYX8zBR2Ha4GQpuwBkfGWFqYxf7KBvQ6HZXqsAWw2yy4HXb6hkZwxtg0xy6tbWZFUS4HKuvJTI6ntjkaBrkcdrYfqmJxQSbVjW2a6/LTkympacIf8HKgsoGzVi2MXpeRTHVTNAzy+ny0dPZiNOjZtLyYV3dGq5zaVTOHXLHRc0uOd7PtQCWJHifzctLp64+GI6BqXVMFQ0lxbjp6+iLft6kGTg8Mj1Ba04Rep8PpsJOZkkhjWycWVcXRkcFQeLaPw25laCQaekwVDGmHT08eDKkfT7uVzBFpGYuxWTUBkLp9bEQVvoSHaAeDoa1rf3nxDe574u+sX7aAQCDAhuUL2bRqMcuK8rFaogHYTOj1+sig6dzcXAKBAP39/fT29tLc3ExZWRkWiyVyG4/Hc1Tn0JFhEMDIxKzbI+dyvVvMaRj0qU99iu7ubr7zne/Q2trKokWL+Ne//kV2djYAra2tNDQ0aO6zfPnyyNe7d+/m8ccfJzs7m7q6OiA0Lf/JJ5/k29/+Nrfeeiv5+fk89dRTrF279m17XmIWZGeH5ga9+ipcfz0cOgTXXgv/7/+FWsdOP32uz1AIIYQQQohZpShKZFtYeO5peHaJumKjt7eX/fv3Exsby8aNGzGbp/9L8mxWBo2Pj7Nnzx6GhoZYs2YNLpdrytu/vPMQu8tqWTY/h0NVDZpz0Ot0lNY2M+YbZ/n8XMZ8PkprmyPXO2PsbC+pIiMpDpPRSHNnT+S6cItXS1cvi/IyiY2xaQKfcPvWwapGls3Pprk9WnWjDjXMJiNv7i9n+fxcyuqaSXDHRsIgnY5IJZA/EGRkzMfiwmwa27pCVUCqzWRjPn/k69RED21dvXT2DjAwPEpRTjqjXi8dPf0ElGi4om7Bspujv6K7HPbIcGYgMug6qCi0dvXR1NHL8gWFmueh/toZE63aSfC4ImHQkUFNd190XpE6GPJ6VevqVT8vg6otSv14jiOCofBjmIyG0IyoCeo5RiNj0cv9/uh7XFEUdhwsZ8fBct7Yc4hDFXWsWjyfs9cuY+WieSydn4fReGJFAgaDgbi4OOLi4iYe1x8JhxobGzl8+DB2uz0SDLnd7knDoOHhYWw221GXv1vMaRgEcO2113LttddOet0jjzxy1GXTSbEvuugiLrroopM9NfFOcNZZsHcvPPAA3HYb7N8PZ5wRmil0zz0gm9+EEEIIIcR7QLgtLBz6hIMgCP3yGgwGURSFmpoaampqKCwsJDs7e8bLd2YrDPL5fHR0dBAfH8+GDRswqdqIJvPyxLygfeV1LC3Mol4VoMzPSYuEPwcq6zlr1UJNGNQ6Eeg0dfSEWsRGopus0hI8NE4EPIdqGtmweF6kgigpzkl9a3RIs8VkRAHy0pOoaWqnRRUqzctO5VBVI3vL68hJS8SvmqNTkJlCZUN0jf3gyCiHa5qIczlYNi+H13ZF298aVc/LbIz+up2TlsT+ijpsFjNrFs2jvC66tEhR/Qzj49zUTay6d9oskVXyNotJM3Q5/PW+sloWFWSzaF4eSlC73cqqCgmd6oohjzMS1NitFs3r2aMOhlSXe1UVQ0HVYxhVQYi6csehqt5yOx10qqqn+gaic4XUj+0dnyJ8MhgY843zxu5DDA2Pcud9f8Rht/GBjStYVJjDacsXsqAg+4Rn9xiNRuLj4yPVdePj4/T19dHb20tdXR1DQ0OYTCbMZjOdnZ243W5MJhNDQ0PExMS8a1fNvzsnHYlTi9EI110XWkV/zTWhaP6pp2D+fPjud2F09PjHEEIIIYQQ4h0qGAzi8/nw+/2RSiD1L5h6vZ7x8XF27dpFc3Mza9asIScn54R+CT3ZbWKKolBXV0dbWxsOh4Ply5cfNwga9/v57+7Dqu8DpCV6IivN1W1fgWCQqqY2Vi3IAyA13k1DWzTQcTnsjHp95KaGNnMNqMIEl8PO1oOVxNgspMa7yU6Nbu8C6BscoatvkNauPtYvmU9bdzSgiFFtzmrp7KWhrYtF+aF/eI5zRbd4mQzReUE9/UOMesdZu3geRoOe1MQ4elRBh3r1uzs2VKUz6vXR1NlLvMfFvOx0ANq7+iK3U/9MU5ISIl/HOaNhjtGgp121or53YIiSqgYO1zRjs1pZVpw/cbDoc7dapmgl80RnO1nMJs3Q597+aDCkriTyqUIbdfikrtTRbiKL0XytXnGvHjitfoxxVZWQTlUPEn7PDI2MUlbTwHfve4xzP/8tTvvsTXz+1p/xyLP/obKu+aTe4yaTicTERObNm8eaNWs47bTTcDqd6HQ6qqur2bx5M1/+8pf5/ve/j06nY3Bw8PgHPYbXX3+dj3zkI6SlpaHT6XjuueeOe5///ve/rFy5EqvVSl5eHg888MCMH1fCIPHukZAA998Pu3fDaaeFQqDbbguton/ySVlFL4QQQggh3lUURcHv9+Pz+QgEApOujAcYGxujpaUFs9nMhg0bjtmOdTwnUxk0Pj7Ovn37qKurIy0tDYfDMa1AatfhGgaGtaHNwapG8jOSMRv1tKnCkJQEN3Utnew6XMOahflkJmtnIY2MeukZGKazb5AlBZlU1EcrdgoyklEUhebOXrzj4xhU5xYbY6OyMXTbUa+PQDAQWT0P0Ko6h3lZqXT1DXK4tom1iwoZHIqee0FmCr7xaCtYd/8g2w9VkZ2eQkFGdIO1Qa/XDKD2+6OveUq8i9rmTioa2li/pJhhVatUrypM0qkqXVISo8FQvMsRCWF0OmjtilY41bV0sr+8nqy0FGJs1kjljl6VDFksU1QMuaPvK5PRoAmz1OelHkqtCW2IMqmqotRBWzgUg1CwMzJF+9ioN3q5OjxSzy6yqub66IB/vb6DW+59mPdd8TVWXfQlrvvur3j8H69Q19x2UnOyzGYzFouFhIQE1q1bx8aNG1m/fj0DAwP09fURFxfH+vXr+dWvfnVCxx8eHmbp0qXTvn9tbS0f+tCH2LRpE3v37uVb3/oW119/Pc8888yMHlfCIPHus3w5vP46PP54qE2soQEuvhg2boTt2+f67IQQQgghhDiucFtYeD7QZEFQMBikvLyc9vZ2nE4nS5YswWg8uUkfJxoG9ff3s2XLltBQ3w0bsNvt0/4Fe/O+Ms334bav0tpm0uNcmmqa7JRo6LGjpBqH3UJ42LLNYqK8oQWAodExjAYjC/MyIrdXVJushkbGKG9oZVF+6PrCzGTNpquRMR+7y+qYl5FIaoK2+ijGFgoZFAX2VdRht5kjG8GcqjDDbjVT2xxaRV/d2IYvEGTVwgIActOTNKFRW3d0EHR4M5dOp6N3eAS90cCqRYXodToa26JtZsOqqieTquomzhmtVEryuDTPq3ViE1ljWxe1rZ14PE7WLi1mXDVzSj3zx2ZVbSVTPTd1MKTThdbch/WrQiJ1aKMOvDSbyDRzhbTtY2ra9rFolZBXU4kUvb3JoK5EUs0rcjpo7+7j2Zfe5Gv3PMh5n7+FdZ++nht/8AB/e2ULrar2wOkKh7UAFouFSy65hC984QssW7aMmpoarrnmmhMOac877zzuuusuPv7xj0/r9g888ABZWVnce++9FBcXc/XVV3PllVfy4x//eEaPK2GQeHfS6UIBUFlZqFXMboetW2HdOrj0UmhqmuszFEIIIYQQYlLBYBCv1ztlWxiENhVt376dzs5OsrOzsVqtszKbZKZhUHh9/Y4dO8jIyGDlypWYzeYZtZu9tP0AS+eFlgQlxzk1q99NJj2JHiduR7SNKsxqNrF5XxlrFxYACvOzU7WVKHodJTVNrCzOPWoD2fycNHoHhimra2Flca7mtbOYjVRMzACqaO5iXnZ6JAAC7XaweVmp7C6tJTneTXpSHP2D0eqVvIxkTdVKS0cvuw7XsLwojyT1ineHjeaOaAChDjpcMXb6BkfYVVrDioWFpCbGRa5rVW0SU2/5CrdKASQleCJfJ7idkddHQaG1o5vO3gG2H6rAO+5n7dJiXLExqjX22mBI3drlcmorhtTBX09/dHaRuppnzBf92anfG1PNFXKpBlw7HXbNazmgDpzGosf1B9QbzqLnrm6DUw/O1ulCW9ia2rr40wv/5fu/eZJVF32JTZ+9iQPlNUzXZAOkh4aGcDgcZGVlcdlll3HppZdO+3gnY+vWrZxzzjmay84991x27drFuCo4Ox4Jg8S7m90O3/42VFbCZZeFLvvjH2HePLjjDhgePubdhRBCCCGEeLuE28K8Xi/BYHDKtrC2tja2bNmC0+lk/fr12Gy2WRn6DDNbLe/3+9m/fz/V1dWsXLmS/Pz8yPlO9zhNHd0cqGygpLqRFUU5ZKclaa73+QPUNHfgirWTluShrK4lct287BTG/QG2l1SxasHR26Oqm9oJBIPsLq3l9BVFDKpClnAljz8QZE9ZLVZVdcr8rDRN1U7/0DAJHicp8W7iXA5NWBUOiepbO/H6xiPHBYhRV6PE2mlsD1UX7S2vQ0HHgrzQzKGcFO1zVm8fUwcgQUWhqaOPtUuKSEn0aKpx1MOjA4Ho625TtUolqgIotyNGEyDVNrez/VAFo75xLCYjSfHuiWOpQhvj1JU20ecZownkelTnNdUwaPX7RN0+ZrOpX79o+GTQ6zWziwaHJx9krShTnLsq2PMcUX00OBFe1TS24lW9B45nqtXyc7FWvq2tjeTkZM1lycnJ+P1+urq6prjX0SQMEu8NaWnwyCOwc2d0ntCdd4aGTD/6KMzSX55CCCGEEEKciPDK+PC/3E8WBAUCAUpKSjh06BCLFi1i4cKFGAwGDAaDZrX8yZhuZdDg4CBbtmxhfHycDRs2RNZwh023Muil7aEtYv5AkH0V9cSowguL2UhTVyhMqG/tIiMxTjOsWb2Na9fhGowGA0ZD6FfYeVmpmpk2I2M+1i4qiHzf0BZdIV+YmcKb+ytYvaAAnU6nqSLR66CioY361i7GfOMsKczWnH9nb7RKKDXRw97yWtYtKkSn02nCmpw07S/nlQ2tHK5rYe2S+cSoVq6nJ8VrtnR1qcIUs9FIIBhk+6EqkhPiWT4xCNpkNNDcEX0+6vuHW+hA24IVDnsgFIyF5xL5/AEO1zTR2TvI/Jw0xrzRAE398zSZosGHemW8xx0dOB1js2oquaY1V0hdoaUaPB6jOvc4Vyxq6iHTUx1Xr1NVCU2xRQ2IbGcDiHdrH+dYplotPxdhEHDUfzuiM6SmXz0oYZB4b1m1KjRP6OmnIScHmpvhc58LtY9t2TLXZyeEEEIIIU4xiqIQCAQiQ6KnagsbGhpi69atDAwMsGHDBlJSUiLXzdY6+Okeq6mpiW3btpGamsqqVauwqAKcMJ1ON63KoJcmVspDaMbLtkOVE21fUJSTrlnhDjoCgSAZiR5AobIhWiWUn5HEtoOVzM9Ow2Yx4XFqfwlv6+qLVBDlpiXSptq2Fa4O2Xm4moV5GZotYukJLkYmgpK+oRGGRkZZWRzaZOaJtVMzMRMIQpVAOp2O7SVVLJuXTbdqDbu6dSs13k3XxHXbD1VhMOjJnJiFlKIKacwmIw3qGUGqQdJWs4m95XUsnpfLknm50blAikKHqo1tWBWOqNumYlWtUimqVjKjQU//8CgKUN7QTlvPAPkZyWQmx9HdG33N1POXzKrQJlYd2qjCFKNBr5klNNVcoUBQNbvIEP3/gLriSn3uVouZMVXgpK4YGptq3f0UFU6u2BjNe/bI0OlYpgqDHA7HFPd466SkpNDW1qa5rKOjA6PRSHx8/BT3OpqEQeK9R6eDiy6C0lL44Q8hNjZUMbRxI3z601BfP9dnKIQQQgghTgHqbWGKoqDT6Sb9F/2mpia2bt1KYmIia9euxW63a24zm2HQsSp6/H4/Bw4coKKiguXLl1NYWDhlpcF02sRGvT7e2BsdHl2Uk8ao18f2kirWLS7EYtaupK9t6aCzd4DewWGWFWTSPxytWjFOtASV1DSRGu+mVxU2pMa7aWgPVc7sKq0hOy1R0xbWpQptBoZGMRr0kQqkGFUIAVDZ2M7u0hrWLiogL11b7aOuEgoGFUxmEwWZKUddl5akraLaV1FHe+8AaxbNw2CI/gqek56kCnmgqT1a/RNuHztY1YCi07FuaREWswmPy6GpxulQhV5e1cwe9cBpl6oFKzneo/m59Q6MUN3SRWNnP06ng/nZaaHXrCc6r0hdlaZ+XdWhTbxHOzxZXTWlbfOKtmZphkGrt4+pKpHinEdUCWmqj6LvD79qDpJe9Z5VV4G5VFVCRoNB8/3xHGtm0Ntt/fr1vPjii5rL/vOf/7Bq1SpMJtMU9zqahEHivctqhZtvDs0T+vznQyHRU0+FWsduuQUGB49/DCGEEEIIIU5AMBjE5/Mdsy3syPBl/vz5muqOML1e/5a3iQ0NDbFt2zZGR0fZsGEDCQkJk9w7ajptYtsOVGI2R3/JVw8P3nawEqvqurz0JDp7Qy1Tw2M+7DYbRTlpkevHlejr0t0/SG/fAJ7YUJVKemK08gVCVUKZyfG4HXbi3bFUN0VXvKcmuqlp7sCo15OVHE+vam18YWZKqI1Ip2N7STXOWHtkqLLboa0SslrMtHf3U9/azfql8zRzhtQ/w5y0RIZHvfjG/ew4XIXFbCZ+oiLFrV7r7nFq2qx6+qNfm4xGth2sxONykpeWGLncbrVoQih1AKPeHqYO3dTVMIkel2ogs0J5XSvlje1kpaWQmpQYGfzcPxBtZVMPKFZX86iDFXdsjGbQc6/qvIZVc53Ug6ynah9zxEQrkWJj7JrjaquP1MOrJ68SirFHK9ziXLEzaqmaambQkcHtiRgaGmLfvn3s27cPCK2O37dvHw0NDQB885vf5HOf+1zk9tdccw319fXcdNNNlJaW8tBDD/G73/2Or371qzN6XAmDxHtfcjL85jewZw+cdRZ4vfD974eGTP/udzBLf7EKIYQQQggRrgaqrq5maGgIvV4/acATXtXu8/nYuHHjMcMXg8Hwlg6QbmlpiVQmrV69GquqtWYmxznS81v3Eed0RKpwalVhSl56Eq/vKWN+Ruh5W7S/Z9PRO0BNcwdLCrJIcMdS0xK977zsdDr7h9Hp9KR4YulUVbE4bBYqGlqpbGwjxm6lWBUoAQxOVBt19A4QVILYVIGUel4RisL+inoS3E5SEzzkZWirhDomgqvxQICRMT8ri/MiVTNtqi1giaqKGbPRwNYD5fiDCsvm52qCjfSkaHuPQa/XrJgPt7G1dffh9QcpysskLSmeNNV9dDo0K9MHVbNx1C1f6oHT8Z5oMOR0xEQep6Gtk5KaRjxuJ+uWFmMyR98PY6qWL5/qa3U1j0cVOFnMJoZVFTzq+T/qAEc9SNtgnGLDWay2kkc9M0obMqlnCamrhFTPfQbzgiAU7r5VbWK7du1i+fLlLF++HICbbrqJ5cuXc9tttwHQ2toaCYYAcnNz+de//sVrr73GsmXL+O53v8svfvELPvGJT8zocSUMEqeOZcvg5ZfhuecgPx/a2uDqq0OXv/CCtk5RCCGEEEKIGVIUJTIkuqGhgZGRkUnbwurq6iKr2qeayaP2Vs0MCgQCHDp0iNLSUpYtWzZlZdJkjlcZpCgKL207QF1LJzaLmRXzcyIBCkBynBuA8qYuCtPiGfZGK07i3Q6qmtrwjfspqWk6aqhzeIV5z8Aw6AzYbNHqjLQ4Z6QypLmjh9HRMfIzQtu8YqwWKlRziJI8Luo7+li9ID96vAm56cn09A/R0NbF0KgXh3rzlcOuCbYsZiO7SmtJTvRQnJtBo6rdS/0a5aWn4A8E6R8aYV95HXabNRJ22FTtTFmpiZpAo7VT1bIVDFJW30pn/xA56cmRqp+0xHjNWnl1IDWimkWkV70dHbZo1Y06tLKYTfQNDtPZO8C2g6ENZOuWLiDe7cRgjJ6n+r09NhoNn2JUr1WCJzpwGqB3YPL2MZ9q/o+aehaTel6R+4j5P/1D0Z+ddsW9apOZKsyJc2vP61jCc78mC4NiY2cWKk3mzDPPRFGUo/488sgjADzyyCO89tprmvucccYZ7NmzB6/XS21tLddcc82MH1fCIHFq0engox+Fw4fhJz8BjwcOHYLzzoNzzoGJ0jwhhBBCCCFmIhgM4vV68fv96HS6Sat5fD4fe/bsoa6ujlWrVpGXlzetVpW3IgwaHh5m27ZtDA4OsmHDBhITE49/5yOOc6zKoJKaJlomAonmjh5ibFayUqLVT+otWl0DI8S5XZgnWnryVbN6AsEgXX0DrJkYOm01mzTr51MS3NS2drKkMAtAU6lh1Os4WN1EY1s3+WkJZKfEaQZWjwcCKIQGS29YMo8aVTtZkirEGBwepaKhNXIOuUdUCbVPDKSub+nCZrGwvCh34holsm4ewBUbDa2cjhje2FuGy+GgODdDMwg6QVW14nLEaF6rcGXTuD/A4KgXt9PJsqI84lXn64l1aAYtq++vHrqsbqFyOqLnlqRaUa/TQUNrJ9sOljMwMkac20lmSui9ojeo1sRbo/f3jUVDHs2K+tgYTTjT0x89r2HN/B9VZY8qnFS3GarX3ev1Ok3LmHrFvaZKSJWEJcwgDAr/f++dtE1sNkgYJE5NZjPcdBNUV8NXvhL6/qWXYMUKuPxyaGqa6zMUQgghhBDvAuG2MK/XSzAYjMwGOnIdfE9PD2+++SZ6vZ6NGzfi8XiOcVSt2V4tPzY2xtatW4mLi2Pt2rXYVBUi03W8bWL/2bpf8317Tz8DwyMUZKYQ54yhsjG6DSkvPZl95XUUZKUSY7PgG4/OkrGYjZTVtbCjpIq1iwqYn52m+QXfYNDj9Y1zqLqRlcW5VDa0Rq4rys3A5w/g8weobe3GbomGFwa9jgrVbb2+cRYVZEbm4KjDlMyUeNq6+9lRUs2qBfkYVGvMPbEx1LdGW7qMRj17y2pZs6iQ9KQEzeavMVX1U/bE7J+27j5Ka1twOeyRIcrqgDAjWdsK1q6qrhoZ9dLe08e+iXa2jOSJjWWq7WFmo5F21ZBpdWuVOnRRV+C4Y6NBS6LHFWnhGvcH2HmoksaOHpYVF2juo1MFLW63O/I1qu1hDlu0As5iNmnWxA+o2trUr5P6PaZ+PHX1keeIIdP9qgovdVub+u0aP4MwKPz/vXfKNrHZImGQOLV5PPDjH0NZWWjTmKLA738PhYWhIdOqYWlCCCGEEEKoqdvCQDskOjz0ORgMUllZye7du8nPz2fZsmUz2vgTPtZsVAYFg0FaWloYHh5m8eLFFBcXT7st7EjHaxP797ZoGBTnclDR0Erf4AgtnT1kxmt/EfdODBI+XNNERlIcbT19kevmZ6dHwqHth6pIcDs0c2CqJ0KlYFBheNTLgryMyHV2a7SSJKgoVLf0RKp78tISNZutRsfG2F9RT0q8i6zkeE2olBofDVd2Ha5BUZTIEOjciRa0sNauXtDp2FFSRU56YnSVvIImNFLPwklLjOPN/eWkJ8eTl5FMr2qujkM1iyczOUEThLV3R1vBBkbGaOvpZ+3SIjyqqpm0pPhooKIcMVdI1aalDl1sqnNTt1M5HfbInJ995bVUNLRSlJ/N0qJ8TSuaWpzHHflaPcjaGaMNINXDr4dGosGQOhjUaeb/RH+26qomg16vCfKGVLOE1MeK98wsDDpyE6CiKFIZJMR7Qm4uPPEEbN8OmzbB2FhoyHRBAdx3H4xP3sMqhBBCCCFOTYFAQNMWduS2MIPBgNfrZefOnbS1tbFu3TqysrJmtMEoLNySdTKB0MjICNu3b4/8ApucnHz8O03jnCbT3t2H1Wwi/FQLMlKi5zHmw4+O5fNzADAa9FQ2RKuEbBYzBr2e1IkQxWzSVmPsKatjUX4mFrOR+dmp9KkqSlwxdraXVLF6YT46oL412qI1LyuF3sFhdpRUs2J+7lEzYxraQyFJbUsnNrOetAS36pyjQUdGUhy7y2pBB/OyUzVVQvEuh2ZekM/nZ9g7ztJ5OWSlJmiqctRBTGpi6LHqWztp7uwlwe2MtDSpw58E1VyfWLtVU3XUOzCMPxBk+8FKDEYjKxcWAmiCocQ4F15Vm5h6E9moZq5Q9D2qnpWkbq0yG430DQ5TVtvE/oo6TCYTqxfPx2Q0aFrRUL3dY1Ubx9Rr6WNsFu32MdVWtdEx9ZDp6G00W8I0VULaSh11+5h6llCC28V0BQKBSbcBztbMoLkiYZAQamvWwH//GxoyPW8edHbCl74EixfDX/8qQ6aFEEIIIU5x4Wogn8+HoiiT/pIIoRXctbW12O121q9ff1K/NIbbU040DOro6GDr1q24XC6KiopOKJA60rEqg/6z7QDbD1WxbF4OJqNBE0BYTEaqmjvZX1nPmoUFZCe58aoqNswmI80dPfj8fnJTEzVzfOZlpdI7OMyBqgaykhOIO+IX/6aOUBCz83A1axcXhNbET1CHBHvK61AUiJ2oTpmflcqQqmXJYrHQ2t1Pflo8JoOe8vrojKK0xDggtPq9pqkdk6p1KSdVO3epqaOHweFR9lfUMy8rDaNBP/HaQW1z9HkZVe1HmckJbDtYSX5mGpnJCZoWL5MqAElXtY8BNKtmE/UPjbCntJbivGxN1UyiahaQ3WrRVOP0qAIYdQWN+tycMZMfC6C0ppGdJVU4Y514XLGR13Z8XNXSp6pCU8/8Ubds6XU6TcvYgGowtKZKSPXYmoqjY1YJRQO4IwdbH8tkw6MhFLBKZZAQ7yXhIdOHDsGvfw2JiVBeDh/7GJxxBuzYMddnKIQQQggh5kAwGMTn8+GfaGs6snUkfJvS0lJ6e3uJj49n8eLFGI3GyQ43beFWrpmGQcFgkLKyMvbv38+CBQtYsGABRqNxVlrOjlUZFG4R21tex7zMFGqaopU/xXkZeH3jBIMKO0qqcDm07UJVE7ft7h/CYjFF26wAjzP6i3dlYxujXl+kgigrOZ5m1dYtBchIjiNu4j7tqiqazJR4dpRU47BZiXfacTu1v9C3dvUzHghS3dLNusWFmuqcrp5om1VKgputBypYvbAAk9GgmZuT5HGFWsYAdDp6h0bIyUghJcFNXnqyZq16T380iAm3n1U1tjHiHdfMDFIPmVYHM2lJcZpqnHDFUFldMyPecdYsLsLpsGuqfNRzhXQ6aFdtH1MHKOqV7xbVxjP1XKE4lyPyGnX3D7C3rIaAomPt0mLN/YOq94s6RHOpjqWufgLoUlUvDapCIvUgcE2VkKqtLs51xCwhVZVQvGdmlUGThUEyM0iI9yqTCa69Fqqq4FvfAqsVNm+GtWvh4ouhtnauz1AIIYQQQrwNwqulfT5fZH7IVG0j27Zto7e3l9TUVOx2+xRHnJkTCYPGxsbYsWMHXV1drF+/ntTU1MixZiMMmmqA9PCol817SiPfj46O4I61R0IZs1H7S3VlUxcr5odWx8/PTtMEI84YG5WNbZGWMvWK9fSkOPZV1OMdHyc/I5nURO1A7u7+Iaqa2rGYTSwuyKSuJTqvJ20iCGnt6mPU69dsuMpNS6KzbzD8JPH6AywvysViNmEyGGjujs4UjZloYdt1uJrMpHhNG1hWirZyp6Gti6rGNobHfOSotqWZTUbqWqJVQkEl+rNJSfCwo6Sa4vwsUhI8NKmqf9SvfdIRFT+agdEDw+wsqQKdAZcjJtJ+pg5zUuLjNG1aHar7q7d8qetxbKph0PGuaJWNxWxiYGiEkTEv2w9W0NU3yKpF80mNd+NTjd7QqY5lNau3hEWDuRibFZ8qiFNvHxtQzVXSVgkdY5aQKkxKPMkwyOfzMT4+Lm1iQrynOZ3wve9BRQVcdlkoOn/ySSgqCm0k6+o6/jGEEEIIIcS70nTbwlpaWtiyZQsej4d169ZhtVpnbR18uAJpuhvFOjs7efPNN3E4HKxfv15TvTCbYdBkx3ltd4mm7csZG0tjRy9Wi5mMJI9mPtD8nDQGR73sKa9nRVEuca4jK3T6GPcH2FdRz6ZlRTSo5vFkJE20aw0M09LZq2lBinM5qJoYLN3a3UeMzUJxTlrkevWcIZNRz76KOlYW5wGQFKdtH2ps72FveR0ZSfEsL8rVDJ22qLawDY4M097VS85ECKSuiElN8NA5sQVscGSM7v4h1iwuxGQ0kpeRrKlyUVcwhVutymqbMRlMzMtJj1ynDs3UAUjGke1jE61zg8OjtPcOkJ2eQnF+lmYzV7wnGmjExtg0lUHqVjKvauZOuOUNwGGPvg6JcW7N47d397HrcBWtPQM47HYWFeYAMK56jxhUx9Ksoj+iDXBoJFoZNawaDN0/ED1H9bHUs4SOrBI62TaxoaHQ6y+VQUKcCjIz4ZFHYM8eeP/7weeDn/0M8vNDYdHw8HEPIYQQQggh3j3UbWFTVQP5/X4OHjxIaWkpS5cujWzoms118BCaG3S8ECcYDFJRUcG+ffsoKipi0aJFR/0SO1th0FRtYtsPVkS+1umgri30D6ctnb3EOWOJd0V/efbERsOfPWU16IgOLE5PiosMY1YUBe/4OGsnNoGBdkW6zWJi5+FqVk0EOvnp2g1fQyNeqpraWVmUi8th12wKS3aHWpx2l9awdlGBJmTJTI6PtHpVN7djMhmYlx2qsDIa9FSp5hnlZqQyNOajsaOH4uxkTWtcklsbRNS1drKjpJqM1ASS1EOhY2w0tkX/oVk9aykhLpadJVXkpiWQEu+iQbWZTH07l+o1TUlwa9rROnv6qGvpoLS2CUeMLdIqpt5slqzanGbQ6+lQbXbr17RpqdbSm9XtY9FqnDhXbPR2Oh2ltY0cqm4gNzMVR4wtEuCpK7PUx3KpKntcsTGaxxxVPWd1mDbQHw3T1DOW1FVCVotZE2Adz2Rh0PDE736zVf03FyQMEmKmli2D//wHXngh9PXAAHz726HNYw88IJvHhBBCCCHe5RRFwe/3R9rCpqoGGhgYYOvWrYyMjLBx40aSkqIhxGyHQccLccbGxti1axft7e2sW7eO9PT0SW93vJXw0zXZcTq7unj8+TdYlBsKTIpz0ukbjAYIdpuFlq5eFhdkAdCkqvTJSk5gy4EKEj1OEt1OMicqf8L6h0bYXlLFiqJcEj2xVKgqjPIzUhj3B9hVWsPahQWa+TkOm4WyuuZQ4FNWy6riXE34oK5iKq9rwWG3YjGHqmbSjmg9a2jrpqa5g1UL8pmXnabZdOWbeMygAkNjAfIyU7FNrD8f9UarWFLjnZEgq7a5g6FRL6sWhEKunDRtiKV+fcJDkuvaerDZbCwvyotcp573o36fJh2x1r1tov1Lp9NR39pJ79AI65YWa7aHqQOYlASP5rXSto+pVsmr19Jb1O1j0RDMaNAzMLFBrba5ncr6FuI9LtYtLWbcH/0ZqM9FveJePQBcp9NuHPP5o+9DsyVaDTQ8HL2NuhJqJi1iEApZJwuDYmJiIi2c70bv3jMXYi7pdHDuubB7Nzz+eGg1fVsbfPGLsHAh/OlPsnlMCCGEEOJdKNwWNj4+PmVbmKIoNDQ0sH37dlJSUlizZg1WVXsLvL1hUHd3N1u2bMFisRx3c9lsVgZB6LVQFIW6ujoe/+u/GRrzcai2lVXFebhitVUTjW1djIz5KK1t4vTlxTR1RIcxp02EP7UtHeh02pXqcc6YSPizp6yWopx0HKqZNerKmANV9VjN5kgb07ysNE3lSHf/ECuL8zAa9NgsJs0MoPzMlEhLWII7lsFhdYjjprG9G38gyK7D1aQleCKVLQa9jsrGaDiVluhhX3kdiR4nGUlxdA9EA7F4TeuTQmltE7tKa1hZnE+sqq0pyeOiqy96bkOqtii3w8bOw9UsKswhJz05Oqga7cp69VyfI9vHWjp78I372X6oAvQGVi4IraJXD3ZWt1Y5HTGa+UE9qnPzqv4x3DBF+5hmULgCXX0DdPT0s+1AOT6/n7VLi0mKc2uqf4yG6LmoV9HHu5yaqrQ+1fwg9fvG6YyGPkowGjjF2q10d3dP+/+fU7WJxcTEzMpmvrkiYZAQJ0OvDw2TLiuDX/4ytHmsshI+9anQmvqXX57rMxRCCCGEENMUCATwer3HbAsbHx9n3759VFdXs2LFCgoLCyf9hVCv1896m9iRx1MUhaqqKvbs2UNhYSFLliw57uYydYhzMsLPeXx8nP3791NbW0tjfzQU2FVaAwrETAQSOWmJkW1f/kCQcb+f9YsLI7dXz6YJKAoVjW2ROT95Gcma8+0fHMETG0NKvAubxURpXXPkuqKcdHYerqYwM5VYuzagMxsNlNe3sru0hoLMFBblZWiCovCcn+qmdmxmE4Fg9PXOTElQP3kqG9uYn5OG22FnXlYaI2PRSplwONXY3o3FbCJLtXLepKqcyUpOiLRx7S6rZWB4mJzU0ONowxuFupYO1cOHfoYl1Y047DbWLJoXua5J1WbmVwUjHtXA6ESPS3O+DS0doZAtL0uzSl5dmZOsGlJt0OvpVG35mrp9LPpejFEdy+2M0ayJb+3sZfuBcrr7h3DG2MnNSOFI6vXxLlWgZjToNVvCBlXr49Xn4na5o1/H2ikrK+P1119nz5491NbW0t/fP2VIOlkY9G5fKw8SBgkxO8xmuO46qK6GO+4AhwN27QrNFjr3XNi7d67PUAghhBBCTEFRFLxeL2NjYwSDwSnbwnp7e3nzzTcJBoNs3LiR+Pj4SY4WMp0ZPzNxZEWPz+dj165dtLS0sHbtWjIzM6dVpXCia+qPFH6s7du34/P5WL9+Pa/sim4Ry0tPYuvBSpLiXCS6nZoV8QCdfYNsPVhJcXYyCW4HFao5PgUZyQwOj0bm/Kgrf+xWM6V1zTS0d+Mb97N6Qb6mGiR8XqV1zcS7HJrZQkU56ZHwpayuBbPJSMLE9iqTwUB5XUvktgkeJ7UtnawoygXQzN5J8jipb+3kcE0TFrOJRNXQ6SOrhBI8TvaW1bJ2USFGg57a5mioo17vbtDrqWjsoKG9l/mZSYyORgOWzJQETVWO+jnF2K2hKqGCbBbkZWrCkG5V9Y66YidZ9bMwG42R9rHyuhY6egdZvWg+8e5YzfspdprtYyPq9jHVni+LemC1W/166SNBYCAYpKqxjdqmdhYW5mC1RAdjawZWqyqojlwR36ua+aT+manDxLzsDDZs2MDatWtJTk5maGiI/fv3s3nzZg4cOEBjYyNDQ0OR+4RbRdWGhoaw2+1SGSSEmBAbC7ffHgqFvvzl0Hr6//wHVqwIVRBVV8/1GQohhBBCCJVgMMj4+DgHDhygtrZ2yraw6upqdu3aRU5ODitWrMCs2uA0mbeyTSwcSplMJjZs2IDTOf3NSLMVBnVNbNSNj49n1apVlNa30aJqV0qe2CpV2xxq+1JXgiTHuyLbvkrr25mXkRyZrwMwMjGLZ9wfoLy+RTPgeH52WiT86RkYZsw7ztLC0Awig15HRX00VHLYbfQMDDE/O1RhpB5OrNPBweomhsd8LMzLYH5OmiY80Ot0+Mb97CmrZd2ieZTXR4OibFWlT3tPP21dfayYGF5deESV0Jh3HJ1Ox46SKpbPz9UMNVaHXAWZKfjG/QQVhfKmLmIdDtwTAYzVEH0/GvQ6Gtuis4QCE/NySmqaMFssrF08HwiFJ+qZQ+pzUm/ZSkuK0wQlrZ097Dpczag3gMNujczaMasqzjyqIeBOh13bPqbZPqZqH9OrgiXV0OXEOG2Y09XXDzodJVUNtPf0kZOezOpF8zQTOKYaMm0xGRlShWGDqq1o6rlESXFudDoddrud9PR0Fi9ezKZNm1ixYgUul4vu7m527drFm2++SUlJCYODg0dV0g0PD7+rN4mBhEFCvDWSkuAXvwi1j11ySeiy8Dr6666D9vZj318IIYQQQrylFEUhEAhEhkQbjUYCgcBRQZDX62XXrl00NzezZs0acnJyplUN8FZsE/P7/dTU1LBr1y7y8vJYunTpcdvCjnSyYVAwGKS8vJzDhw8DkJ+fj16v559v7NG0famDIYPBQEVDK4vyMwHIPWJQclf/EGmJccS7Y4mNsVFa2xS5bl52GtsPVbGqOA+DXof6lTca9JTUNnGgqpE1CwsoyklnSBVM2Cxm+oZGqGlpZ0VRjmbDV1FOOgPDo4z6/JTVtWraoPQ6bXXPqM9LcU46dmsosFKHHPHuWCoaWtlTWsOaRYWacCJUJRQNp/R6Pb7xQOR1UJ+PW7UFzGo2sbusDkWnY+m8HE3Yl+yJ1bQ/qecF2cwmdpRUUZSXxdL5eZrbtXdHbxdUoj/7OGes6mtHZDbRqNdHeX0LCR4XK4oLNMdSr39PUq2S1+t0mu1j6jAmoApTLKr2MfX6+BibVVNZ1DcwRF1LBztLKtHpdKxbWowzxq4JljTr493aUFQdTKmDviMDKAhVlMXGxpKdnc2yZcs4/fTTWbhwITabjZGREZqamti6dStlZWW88MILtLS0zFqb2H333Udubi5Wq5WVK1eyefPmY97+scceY+nSpdjtdlJTU7niiivo7u4+5n0mI2GQEG+lvDx47LFQm9gHPwh+P/z616F19LffHtpEJoQQQggh3lbhIdE+nw9FUdDpdJEwSK2rq4s333wTs9nMhg0bcLmmv4VotsMggLq6OhoaGlizZg3Z2dkn1KISvs+JhEHhYKyzs5O1a9cC0QHS/3pjD1sPVrKyOI+ctETqVavPs1MTGBoZo6yumdUL8zUzZhw2M1VNHVQ3tWPUG1g5P1czxyfcjrSrtIbi3AzN0Oni3HSGR70oisKOkipS4t2oX5Lq5tA/wI77AwyPeinMSo1cp54nFAgG2V/ZwJqFBeh1OubnpEU2X0FoRfn+ynoS3E6yUhOoUFUJRYMtHTsOVWE06PFMtJ7Ny9JuHBsZ89E/NMKh6ibOWLmQIVXwMayq3MnPTCEQDNI/NMr+inpibLbIzJxY1VBmu8VES2f09egfCrWPldc1o9frWbdkPnq9jliHnY4e1Ywf1YY39cDo5ERt21pbZy+tXX3sLa/FarGSnxl6/dSvsVM12Dk5Xts+pp4rpK4MU0d66jAn/ogwR93m1tU3wLYDZfgCAWJj7KQlxh91/k6HKlCzmDXB0oDqPacOsKai1+vxeDzk5eXhdDopKCigsLAQg8HA9773PW6//XZ27drF1772NV544YXIqvmZeuqpp7jhhhu45ZZb2Lt3L5s2beK8886joaFh0tu/8cYbfO5zn+Oqq66ipKSEp59+mp07d3L11VfP+LElDBLi7bBsGTz/PLzyCqxeDcPD8J3vhMKiH/8YRkePewghhBBCCHHygsEgPp/vqCHR6vAmXP2yd+9e5s2bN63BzEeara1dAH19ffT19QGwcePGGYVSR9LpdCe0Xr63tzeysWzdunWRFplgMEhZXTM1E7NwdpfWkJuWhEsVEvRMrAH3B4JUN7VH2p8AMpM8BCeqRtp7+hnx+iKr580mI2Wq4dB6vQ6rxUzqxMwbyxGtenvLa1mYl0mM1cL87DR6VPNj3LF2tpdUsbwoB5PRQL1q0HJBZgpdfYPsKKmmODdDU90DUNMUem4NbV14YmMoyIwOOFYPaY5zOXhzfwVGvZHCzFScquMYDfpIaxzAmNdHcV4mcU4Hep1OUyXkUAVVsQ47r+8tJTneQ156MgFVUJaXEQ23QLuK3ufzsf1QFfkZqZHXM3q76HP3qTaBxdqiQVNaUlxkoDZASXU91U3trF40X3MO6iHRHre6yseiCWDUwZdP1bKlbptTb5/zOB2aWVDhVfJjXh/t3X20dPWwvLhAEyap2wnj3dqNer2qKqGkeA8zEQgEMJlMJCQkUFhYyObNm7nuuutYuHAhvb29XHPNNfzoRz+a0THDfvrTn3LVVVdx9dVXU1xczL333ktmZib333//pLfftm0bOTk5XH/99eTm5nLaaafxhS98gV27ds34sSUMEuLtdNZZsH07PP00zJsH3d3wta+FKoXuvx98vuMfQwghhBBCnBB1W9iRs4HCYdDIyAjbt2+ns7OT9evXk5GRcUIVOLNRGRRe2b5z505iYmJITU3FZDId/47HMZOgSlEU6uvr2bVrF7m5uZFgLBwqKYrC3zfv0dynoa2L2Bgr6Ukekj0uzRyfgswUth6sZFVxHiajQfMLv81iZl9FPYdrmlm9sIDinHTGVC1ZZqORhrYuxnzjzM9O1YQrRdlp9AwMc6i6kQR3LAkubRjQMBGU7C2vY0lhluZx41UzcEpqmhga8ZI5sc1rXlZqJMwKn8PhuubQQGi9nvKGaJVQXloyAJ19A9S2dETm7YSOo51JNDzq5XBNE+j1bFxWFJmTBDAwFP2H4rz0pInXtJuG9h6s6g1ddu0q+kHV+vmBiRatqqZ2BgaHWDY/B6PBQGpCnGZ+UFevqlNB9TaPV71+sTE2+gaH0el07DpcxcDwKOuWFmMxmzTBkHqwc1KcOnBR6FNVI6nn+qgriayqcC9OFebodTpNy9fgRNXW3rJqBkfGmJeTwfIFBZrX26lq4bJZzZrnnDRJm9ixTLZNTK/Xs2jRIn77299SW1vLrbfeOqNjQiiw2717N+ecc47m8nPOOYctW7ZMep8NGzbQ1NTEv/71LxRFob29nT//+c98+MMfnvHjSxgkxNtNp4OLLoKSEnjoIcjOhtZWuPba0Eyh3/8eZrmkWAghhBBChKpYFEWZdEi00WhkeHiYLVu24HQ6Wb9+/UkNiDUYDCiKcsLVQeEV9nV1daxatQqn03nS6+DD9Hr9tI7l9/s5cOAANTU1rFq16qh5SeHj/P31aFVCaoKH6qZ2mjp6GBrxsnBiPk7Y8ESFyK7SGhbkpNPVF22vWZCXjm/cTyAYZGdJNW5HNFww6HWUT2wc6x0cxqg3RIISAKcjWtVS39ZFR29/ZHB0bloirV19keuNBgMGvS5y/5bO6DydnLREDlU30jMwzOKCrEjLV1hdSwfBoML2kio2LJuv+fn6VavonQ47m/eWsWpBPhazCYeqvctsNERmEvX0D+HzB1m7eB46nQ6z0aAJudQBicfp4FB9O0W5GSTFuTQtaGnJcZrzbO+Jhid6vYH9lY3EOWNIdEXPw2Q00NwRrSYaVoVJFks0dExN0B67rqWT7QcrcMU6sKsCIL0uGi+oq3zi3U5NlZG6/WtM9Y/h6o1n6na4hDiX5v3a3a+6v9dHRX0ze0urMZmNrF1ShN1qwaaqEoo7YsB64jTaxNSCweBRYZB6gLROpzuhkLarq4tAIEBycrLm8uTkZNra2ia9z4YNG3jsscf41Kc+hdlsJiUlBbfbzS9/+csZP76EQULMFaMRrrgCysvhl7+E5GSorYXLL4fFi+HPf4ZZXEcqhBBCCHGq0+v1kwZBgUCA1tZWhoeHWbRoEQsXLjzql7+ZCt//RKqD+vv72bp1K4FAgA0bNuDxeGa17Ww6xxoeHmbbtm2MjY1FzuFIOp2OivoWXDH2SLtPdmpC9HkMjdDR28/qBaFNW+5Yu2Y4tNlswmg0kJoQqtRQ51MGvY5th6rIS43DYjJSOLFuPizGZmFPWS1rFxUAoYAiLD89mcrGNmqaQ6vpk45YP97a3Udn3yDNnT0syEqkUdVeFR4kPTzq5WBVIxbVL/n5Gcl09kVDljHfOOlJ8aTEuzAa9JoKqHCV0K7DNaQlxmmqggqz0zRDqAeHR9l+qIri3AyWzMvRbL5SVyVlpoSqlcrqWxj1jWOzRoMi9XlmpyZqHm9gIoDr7B/GZLWzpDAbi8lIvDNGU5nT2hWdP6R+36rb3VITPJHz6+wdoLSmicKcDIpyM/GqWs4sqo1fcaoh0WajkW7Va6huJVNXGanv71ENubaYjJr7hOckAYyO+dh+sBy9QU+8O5akiXZCZ2w0WPK4HJpjT4ff7z/qvwcjIyOzNkB6su2FU1UjHj58mOuvv57bbruN3bt388ILL1BbW8s111wz48eVMEiIuWaxhDaMVVfDD38IHg+UlsL//E9ovtDzz2v/ZhRCCCGEELNmaGiIrVu3MjY2ht1uJyUl5fh3mobw1q6ZhEGKotDQ0MCOHTtIT09n5cqVkRX2er1+1gZSHy8Mam9vZ+vWrSQmJrJ69WosFsukt9PpdDy/dT+7SmvIz0jG7bDTpfpFP9Ht5FB1IzsP17B+cSHzs9I04YNv3E9X/xD9Q6MsL8qhpEa1RSwzBe94gJrWHhLdDvRoPw+HK2u2H6pi45J59KkCggRPKDwY9wfYXVar+eU/Mzmehol5Qd5xP/5gkLULCyLXqwctZybH8/reMlYU5WIxG0k4Yg5NfWsXNc0djHjH2bi0SNOKpK4S6h8aoa6lg6XzsgFtO5XFbIxsHDtc24zZbGLRxIwfu9VMTVN0C7FRHw0k3LEOtpdUsWJBAbF2myYgSfREK2HMJiONbdGgzB9QOFDdhMvlpDg/O3J5jM2imbGkrt4x6KOxQbwqWLNaTHT29vP/2Xvz8Nbyu8zzo92yLFvWLmuzFu/23a99761UBRImaRKWAaYJBAgNpJtMwvCQAM8EaDqQNJ2eHsgU0J00kIR0htATBgINpFhqSCq13X31bku2LG+SV9myJWufP458dM7d6m6hqpLzeR4/z/U9i37nSL5V5/X7fd9YcpWpxDLm5mY6nIJgJX18keb6OO1yUVHaPrYvyRWSh0w3Pn/224Q96Trz9fu/lztgdz/PxvYuJwe6ZCNr7ofMC4J7O4MeVwyy2+1oNJo7XEBra2t3uIUO+eQnP8lTTz3FL//yL3PkyBHe+c538ulPf5rPf/7zrK6u3vWYe6GIQQoKbxRMJvjf/3fBHfTrvw4tLXDtGrzrXfDMM/Dii6/3ChUUFBQUFBQU3tRIf9teq9XEumiHw0Fvb+8TG8MCRAfSg7p5Dkey4vE4J06cIBKJ3JFp9KScQfda12Fw9ujoKIODg/T09Iii1t1Qq9U89+pNAKYSK3idVkqS1qiIr/FAe350FpPRgKGe62JubmKi7hLKHRShpqLL6xD3l+bhrGzssF+sEK2fr9NtZTvbcITs5/P4nFYxNFjaOOZ323jx+iRHugI0N+npcMjFgN39AhfHYxztDtLZ4WBe4jA63PfaVAKvwyYTK0IdTtJ14Wh3P89BqcTIUBdQr5RPSlxCXifZ3AE3ZxYYGeySCU7dgQ5ZftFmJstYLMnIUBddAY9svGpN0s7lsrXV1zaHydQka9VSSd6zkNd5V/fP2tYO+wclTvZHaW9tIdghFR9qsjDq3b2GSGSSjF956o1eIHymrk7GWd/eZeRor8z5JB3/ajc3BJTWlubbquQbQqI8ZLpxbW3me1fR70g+E6VymWqtxtWJGPlCkb5IgGO9Edy3jbw9CHfLDJKOiT0qer2ekydP8vzzz8v+/vnnn+fcuXN3PSaXy93xM3m4tof990sRgxQU3mi0tQlNY3Nz8Iu/KDiHXn4Z3vpWeOc74RGS4hUUFBQUFBQUFBocCi8zMzMcP36cnp4e9Ho9ZckD6JPgQUOks9ks58+fp1AocO7cOWw22x37fLPHxKS18WfOnHkgh9TS+g7xetMWCEHDGztZjtSdLRnJCE+72cQL1ybwu+3YLWZ6O70yEWRvb4+x+RVO94cx6LRMSlrE+sNeFlIbJNObwtjXbe6OZGqTueU1yqUSQyEPyxIxqKPuRLk1m8RhaZWNT7mtbaS2BaHj5swCfqcNj8S5silxyuQLJZbSW/SHfAA4rfIcmoXVDS6OxTjW08lQNCgTjsr18SeVSsXcchqjQScGNDdLRr1MTQYhL0il4uJYjPZWM+7DUSdTM4mVxr2WYjI2cWt2gZGhbvQ6+RhWm6Ru3WKWV8xnc3muTs1TrgntYeI9c9pk7420In4/J39PD7FZzOQOCpQrVS6NzpIrFBk50otWo5H9XBklLjPHbS4f6dqkIdNIXGHS+3V7Ff3GXVxCINz3yblFbkzP4XM7eBgOc7/uNib2uGIQwEc+8hE++9nP8vnPf57JyUk+/OEPk0wmxbGvX/mVX+F973ufuP/3fu/38pWvfIXPfOYzzM3N8corr/DzP//zDA8P09HR8VCvrYhBCgpvVBwOoXY+HocPfEDIGPrHfxRGx37wB4UAagUFBQUFBQUFhYdiZ2eHV199lWKxyFNPPYXdLmTcPIn2r9t5kHMuLS1x4cIF3G73fUeyvpljYoe18Xq9XlYb/1qcn0zKvl/b3mU/X2BsbpG3HO9lKtFo2uoKeqhWa2I4srRSXK9Vs5AWBJzLE3OMDHXJ3A9NBkEAKJbKXJueRy9xikT9brbqTVU7+weo1Gq6vA0xTVajXq6QWFmnP+QFEBvDDklv7bCXP2Ag7MdpbZUFOQdcNjJ7OaYWVhgeiMqEi84OB6nNDAA3ZhZobTEScNc/V2qVLEso1OFkMrEC1OgP+2TB1hG/W+YEWkxvsJc/4HhviLCvEZYNyAQvm6VVEJDGY/jcdrQSJ470MyMTQmqwWB+Xy+7nyezl6Y8G8TptsrYtg17H5k5DAJK2nhUOGoKNs90i/lmjVjO/lObS2Cwuu1UWgq1WN9xu0iwiR3sb5YrcISW+jsRlpJWIMtKQ6taWZtl+mdtcQoe039Yy91oc3r/b3ThPYkwM4D3veQ/PPvssH//4xzl27Bgvvvgizz33HMGgMMK3urpKMtn4OftX/+pf8alPfYr//J//M4ODg/zLf/kv6enp4Stf+cpDv7YiBikovNHxeoXa+elp+ImfENrI/vIvhZDpH/9xmJl5vVeooKCgoKCgoPCmYHt7m4sXL+Lz+Th16pRMeDkUbp7kqNj9xKBKpcLo6CjT09McP36crq6u+1bYP8kxsUMx6Pba+KNHj6KVCC33o1arMbO8jrsuHPicVjHfplqtUS5XGB6IiMLEnqSpqlguc3N2gaGwIMqEPXaKEidKLl+gzdSMz2m9Q0zpDXbw8s1pcezLdlvb12Y2z+zyJqf6QnjsbSyvZ8RtdrOR3f18XdCJsLXbEAwcllZmkqtk9/NMJpZEd9Mhh2Np1WqNZGqD9tYWDPWad9dtVeWziynWM1lO9IboDXllWUKHjput3X22drJ4HO3i+y6tjTebjMwtr7GXL3B9ah57WyvGuihmt5hZ3diWnLMhdhj0emKLaUaO9KBRq0ltNvaTjnh1OK2y92RrZ4+JuSXWM1kc1jZRdPG57LKfCek9U2ka662UG9fotltEUWtlfYvp5CpBj4PuTh8HEsFGLwm9tklyjnRajSwLaHdPUkUvWYtUZLrdJSQ9PpdvXKfb8XCZQYc/v1JnUK1WeyJjYod88IMfJJFIUCgUuHr1Ks8884y47Qtf+AIvvPCCbP//7X/73xgfHyeXy7GyssKf/Mmf4PV6H/p1FTFIQeHNQjgMX/wijI4KzqBaDb70Jejrg5/8SYjFXu8VKigoKCgoKCi8oWlvb+fMmTOEw+E7hJfHaf+6F/ca7ToMrc7lcjJ30qOc61HXValU7lsb/1qMxpKMJdLsHxQYCPvw3eay2cxkuTQep8vvIdzhZHK+MfbVE+gQ2rrmlhkMeahJwoINOi0T80ssrW2xnc3xlmO97EpaxA4rxw/HvnYk28Jep+iYuTI5T2+njxZJ+HCmLipUqzVmkys06dRo6k6VkCSvqFqtsbi2JQZH2yxmZiQZQAG3nWtT83hs7Xjs7bLQ7KBHqLHPF4pcm07gtrWLr6HVqJlONtxSAY+DS2Mx+sM+2ltNMrdP1OduiDAqFbNLKWyWVtxWM363/POSXG3kHLWajFRrNS6OztIX8stEDNmIXH387HBdhyHTxXKF9cweXreDnk4f7ZImsPZWk8xxsy8RuVol9e16iTOpuUnPzl6OZHqLmYUVWk3SkOmGsCMN1XbeVv2+IRlTk45/yaroTQ2XUHtrC0VJdpV0zZ6HzAyqVCqoVKo7nEF7e3uYzQ/nMnqjoYhBCgpvNgYG4C/+QsgO+p7vEernv/hF6O2Fn/5pIWtIQUFBQUFBQUHhDlQqleyhVcqhI+ZJikF3cwatrKzImrqamprucbScJzkmVqvViMVi962Nfy3+8uuXAcjmDpicX0YvCTD2OtvFtq/JxDIeRzthb2PMaWOr4VaZW91ErVaJjVEDEb84irSfL7CXLzAyKAQzq1QwIxndajLoWV7fZiDsB+QtWgDxpTQWswm/y0aHo53F9cbrdrodjCdWcbW30NZsIC1x2ljbWphZWBUEH3s7/Z3yLJadenNXYnUdvU4ra7ty2+QuofG5JSJ+D872Nno7veSlY1ZFQbCYmFvCYWmVjT1JR72sbS0srK6ztLbFWiYnE2i8Tqss20g6xqXWqElt7DAy1AMIY2eHSAOnAx6nLCMovbnDwuoG0wurWFpbRAFOGhgNsCwJmZaKLw6J4NIuGQVToeLS2KwYMl2UVNFrJWOD0lEuk7GJ3f1GU5o0h6osqaJvklbZ39b6trHdcAl5HA8vBt2eFwRPtlr+9UIRgxQU3qycPAl/8zdw8SJ893dDpQJ//MfQ0wP/+l9DIvF6r1BBQUFBQUFB4U3DYfvXN0sMqlQqjI+PMzk5ydGjR1+zqetu53oSzqB0Ok0mk8FkMt03o+h+VKtV/scLl8XvOzscvHhtUhwLC7jkIb3r27ssr21xNBrAbDSQSDccMGGPncmFFLY2Mz6nVeYW0Wk1TCVWuDgW40RviCNdQZnLo63FKI51ne4LsyBxyATddpKpDZbWttja3aM36JGtaa8umqxsZbG0tcjECGdrs7iOxMo6e/kCgxFBcLK2tchcQi5bG7dmk4wMCvX0UpfQYZbQTHKVYrmMXTLKpNdqmFlouITaW1uYnF9hZKgLlQqSqYZwE+poCGnVWo3J+WUGo0FsFjMdt4kb88uNKnpjk55iucLFsRjH+yLimBlAVjJ6ZWtriEstxiZW1+vvj0rF6kYGnV4vVtgf0uG0ygSgdUmGknTS0u1sCEgWs5Drcxgyvb27z6lBIfS6XG58to2ScTbnbSN40teRNolJs4ik67RZWmVZRE9CDHrSY2KvF4oYpKDwZmd4GJ57Ds6fF9rGymX47Gehu1sInk4mX/scCgoKCgoKCgrfBrzWGNSTDpE+PN/+/j4XLlxgd3eXc+fO4XQ6X/vg23jcMbHD2vhbt27R2tqKw+F4KDFKysWxGCsSJ83hA/ul8Thhr4sdiXujw9HOTHKVg2KJm7EkR7v8MrGgUHeHJFMblMsVpIlNA2Gf2Mp1bSqBpcWEq97ipVIhBjxXqzUyezk6HFbRoeS2W8Tz7OcLzK9siIKNx2YRnUsALquFRGqT4YEIAJVa43PSYtRzYybB+Nwip/pCRL0umWB16BK6OBbjVH9YJgZJs4QyezmmkoLYg0pFT2eHLD/ncITr0licU/1RWZC0SjJG197SxOpGhrH4IuVKlWaJcBLyOmU5QNKsHZVKzeZOjpGhbtQqFYmVtGRb4/zyEbQay2ubbO3ucW1qHlNzk9g6Jg2M1uu0YoA2QFbi5JFmUHkkwpBarWJpbZOrE3GajU1Uq2VxHRrJeqRV8haziQPJqJu0il7qbDJIXUISF5Vep8X6CAHSt4tBBwcHVCoVZUxMQUHhDcKZM/D3fw+vvALf9V1QKsEf/AF0dcGHPgRLS6/3ChUUFBQUFBQU3tBotdonWi+v0WjIZDKcP38eq9XKyMgIRqPxtQ+8C48jBh3Wxq+trXH27Fmam5sfS1j6ytcucWaoS/z+MDgaoFKtsra9S3fdiRNwy0eL1jL7HOkKYDYZsba1EF9uOGD8bjs3ZxZk5z5Ep9FwdXKeQrFCf8hHf8gnCzNubzVxbWoen9OKy9rKYmpTcl4b8ytrXByLcaQrQLBD7lzazu5TqVa5NBHnzFAXS+sN51JP0EutJrhdrkzOc5Dfp9kgiA3WVpMs3FqtVmPQaenyuwHBEXVIqMNJaiPDxXpGkEnyOTDotUxLXEJqlZpypcqRLqFRal4i3NjbGqNJO3t5xuKLnB7ookmvw2lpiE9ajZr4UkryvYZCsVQXm7pklfObOw1R5faGL6kTK5neZCOzx5kjvRglFe/+20KmU/cIt26ROHY8divVak28jvmVdTz2doJuG5vbjeOl44f3q6Lfl4RES8WtFlPjNd329ocWQO9VKw8oziAFBYU3GOfOwfPPw4svwnd+JxSL8OlPQzQKP//zsLLy2udQUFBQUFBQUPgW5bUau56UM6harZLNZkmlUgwODtLX1/fITpzHWZu0Nv7s2bO0tLQ8nrBULPE3L13hwugs3T4HfUE3axLRw9HeykYmy/zyGid6O5mTOHC8TsEldGs2SWtzE0eiAVk7VL5QpFarcWF0luH+CPMra+K2gYiPvfyBWO/usctzjuJ1QWpueQ17W6ssV6dDsu+t2SSlclkUqSymJplLqFqr4rZbxPPnJE4Ua6uJ0USalmYjHqsZu7lJ7hLK5ljb3iWxus5bjvUwt9xYv9PaGBGbmFtiY2eX/rAPEAQn6cjVfr7Azl6eW7NJ3nqiTxagLX3bAm47mzt7XJ6IY2tvkwUqR/xu2TmlVe2oVGT28owMCSNaC5L7LB2pko6gqVRCFX2xXOHC2CwajZaBiNC4ZpHc67YWech0ZreRZ6SW/OxJc32MBj2ZbI7VzR2Saxna29rw1wPJd3Yagk+zRIC6vYp+SyJoSa9b2ljW4ZQLkw9CuVy+Qwza29tDpVI9srD7RkERgxQUvlV5+mn42tfg61+HZ56BQgF+//chEoEPfxhSqdc+h4KCgoKCgoLCtxFPSgzK5/NcvHiRYrGI1+vF7XY/9jkfVsC5X228Wq2WiRgPw9cuj5HJCs6ImaV1Wlua8TobYsvh6FapXGF1bRNnu1kMQ/a7GiNIy+vbrG/v0t8pOIhslhYm5htO9iqCsOAVq8AbQoJGpebCqJAj1KTX0R/yykKUW5qbmEwsiWNhS5Kg44DLxtXJeda3sxzrDuKyyN0dmWyO+ZV19vJ5hgciTCUav0g9bPha286ytVfA5WhcT6vRIGYAlcoVDgpFjvd0igLG2lZDMIt4XcQW00wmhLExg8T90tykZ6beOKZSqSiUKgQ8Drz18axVyXmkjWDLa1tMJFaEMTC1CovZJDun1F1Uq9U4KJa4NB7nZH8Uh6S9K7WREf/cZJC7fwqSsbaF1XXG55Y4OdAl2+9wjKz+SqxIXFbSEa9mSVaV+7Ycn4n5ZRbXtjk50I1J4r4pFCTtZRLHj1qlkjWO7UnEMykdjocXg+7mDDrMC3qY9r03IooYpKDwrc53fAe88AL8f/8fPPUUHBzAs88KVfW/+IuKKKSgoKCgoKCgUEej0Tz2mNja2hqvvvoqra2teDyex3IDSXkYMahcLt+3Nv5xnEF//k8XxD9rNWpG40vs7OU52h1kMOKXZ+bY2hmbXyXsc+GwmGWijM9pZXxuiYnEKkOhDrr9HnFsCIQK8WRqg529PCf7QozPLYrbDl1C16aE6nbpyBNAbClFtVrj4liMp452sy2tFq+LS/lCkRszC+i1atGx4rK2iuHQ2dwBqGB4MCLqUFnJKJKpSc/LN2c51R9Br9XQE/bJ8o5W1ze5Pp3AbDQwGPLKXE72dsEVU6sJa6whBFMDdAc7ZPk3O3s55pbSbO/u8/TxXnKFhiBTkLRxRXwuMtl9Lo7H6Ap2UJE0bYV9btm9leb7VKmRye4zMtRNa0szy2uN9+hAIr44JPlHBr2OpbVNUKm4OjnHQanM8FAPKpUKs0k+CiYVgDYyDSFLKkZKhSuL2cR+/gCVSsW1yTiZbI6RI31YzCZaJRk9Oq28bU16fZsyl1DjHnW4Hl4Multm0N7eHiaTSRGDFBQU3gSoVPD2t8NLL8E//IOQL5TPw6c+BaEQ/MIvwPLy671KBQUFBQUFBYVvOvd7gNNqtY/sDDoMaL558yZ9fX0MDAyg0+me2NjZg1bLH4ZVHxwccPbs2bvWxj+qGLRdDxI+JNLhIHdQZC93wM2ZBVloswpIrgmukJmFVTwOiyyPRuoSGp1fQaPR0F4XBewWMxPzwv+b7uUPUKHiRG/ormtaWd9ibnlNzNcZCPtkLqFSuYK1tQV/fSxMKkj5XTZGF9boCrixtrXQ2SEP9s5kc1wcizEQ9hHqcMhdQn7BJXRlYg6fyy57b2wWM8sbgvCR3s5SqZTo8jaECKlbpsvv5srkHDVgMOpHKxEPzaYm0SWULxQpFMtEO2y0GJvQqFXMSlrNpE1l8aU0cyvrnB4QnFEmY5O4rd1skt2DUqlSdwnFGIwG8IqjVDXZflqJIBLwOGRizsLKOpfGY4QDHTTpJaNcVnmG0arkuvckwppBMsrllLidAJbWNrk0NkO5WqPF1Cy6kCxtjeu1SD5XGrWaTYnolJW4hLxOaTj2g1GpVO4QdL8VauVBEYMUFL69UKngHe+AV1+Fv/s7QRQ6OIDf/V3BKfShDyntYwoKCgoKCgrftjzqmNjBwQGXLl1ifX2ds2fP0tHRATx+A9jta6vVavcd70qn05w/fx673c7p06dpamq6636Puq6/fvEqhVKZvpAXgFqtcQ6DTstL1yYJe2yYTUaGogG2JKKM0WBgKrEshkMnJDXwbmsrL9+YQqvV0BPsEIWWQ/YPClwcizEY9tHhaGd8rjFONhDxk97aqde7d8nq01UqiC2lWVrbYmM7yzPH+1heb4QTe+ri1XQ9BFr6mm5bm+gSGp9bwmNvp9vfGPeT5vhk9nLMJtc41t0JQMQrF5XKaJhd3mQw7MVrb2Mx3RBFWozCerd395mYW8Zg0KGp16R33eaW2trdI766hdnUxNkjPeQOGq4bqbgS9XvY2t3j8uQcR3o6yR80HD5BmeBVI5lqvA/VGmzsZDlzpBuXrV3msJFeb7vEydPWYhIdP/HFFGuZLMNDPZhNRllFvMtqkV3L2lamcX8kP3Otpoaw47RZKNXzf/bzBaYSy7Q0NzM81ENZ4nxqlwRLS7OZANIbjXvtfYLOoObmZsUZpKCg8CZEpYJ/8S8EUej554V8IWnQ9L/5NzA//9rnUVBQUFBQUFD4FuJRxsQ2NjZ45ZVXMJlMYkCz9HxP0hkE3FXEkdbGDw4O0tvbe9/xNJVK9Uhi0J89f56tnT1mFlZ5y7EeYpImsICzjUKpzNzqJqYmg8wFpNWomUqsUK0K4dDPHO9jV1I/ftiQtb69S2xxVVYN7rS2Mll3CY3NLdHpsRP0NNrApNdxfTpBuVLFUh8bGwj7REEqXyiSLxQZHoiIGUbSxrEWo4Erk3NizlDQLW8cW8/sEl9JMzwYxd5mvi1LyEU2l+fGzALDA1GZcOJsbxUdPGNzy4R9HjolbWbLaw2xotNt45Ub04S9Ljw2C0gGz9rNJjEkO7W5Q6VaZWSoC7VahV6nlbWatZkb935mYZXljW2O151V0nYuj90qC5bezx9QLJW5OBYj6vfInFLSkGmpHOm7TWBZTG1waTyGRqulReJIsrQ2BKSW5iY2JKHjO3uSKnpJCLbU7aRRq0lvbrO5k+XyeAytWsOJfuG90qgkx0jyjww6rTDuV2d9ZZGJiQlSqRTFYkNIux93E4MOM4Pe7ChikILCtzMqlVBD/+KLQq7Q294mVNL/0R8JlfQ/9VMwO/t6r1JBQUFBQUFB4YnxpNrEarUas7OzXL9+nZ6eHoaGhu54aPxmiEG3n+/22vgHCat+FGdQbDHF1ck5YQ3VKuVKlW6/kyZ9PZRa0xBw9nJ5bs4kGKmPKQ1FA7IH/oNiiXazSRRFljca4b9Bj4NvXJ3gZG8Io0FH6LbRrc2dPRZW1xkeiNBqMspcQoNhHzdmFtBpNfQEPLLxI7VKRWwxxaXxOKEOJyd7Q6xKsnM89nYxZ2go6iezty/bNruYolypcmk8xpHuAObmhtAhvba55TUq1VpdzOGO0bOVjW1W1rc53R/B77Kxlmm4p5qbhPXOLqbY3c9RkQiTEb9Ldp7UZoZLYzG6/B5O9oZlFe6Z3cbaowE3mzt7XJ9OcLI/wo7kuqTB3wAJieBTqdZYXt/mzJEewl4XOYm7SNrc1SK5Dx57u1jxnsnmmFtZpzsUwOtoR69tiFC3BzmvrjdEuUJJWkXfOLfbYZU5i+LLKa5PzdMV9KGXiIdGSTC1yy4Ppn7LyGn0ej3JZJKXX36ZS5cuEYvF2NrauufP6f0yg97sKGKQgoKCwFvfCv/0T/Dyy/DOd0KlAl/4AvT2wo//OExOvt4rVFBQUFBQUFD4pvKgmUGFQoHLly+TSqU4c+YMPp/vrvt9s51BmUzmjtr4Bz3Xw7aJ/dnz52Xfp7d2mEqmMTXpGYz4mFlstFX1h/1kcwdcHI9xqj+MVH4z6LSMzy2ytLbF6kaGc4MRtrMNMcVZH/m5OjWPrc0sG3Hy2NuZXlilVK5waTzOqf4wOm3jQb1Uv9frmSzL6xmZC2Yg4hODpGcXUxj0OoaifnF7YqUxLpXNHbCzlydaF2AOa+gPWUht0NLcRMTnwmVtFcfMAMJeJ7PJFHv5Ase6O+Xh1TYL8aU0xVKZyxNxugOehqhUq7G527gPHXYLN2NLdHXY0Ws1ZCUijrO9TVzvTHIVVHCqPwIIDqfZxcZ6TIejgioVs4tptrM5hur5SlJhtLPDyZ7ERZPZ26NcqXJxLEaH00Z3UBh91Gk1LEjulTTs2iXJjNKo1SylN5hZWGFlM4vJ2CSGS7dKnEt2S6vsdbclVfTS9dnaGuHRBr1OdBbFFldJbWYY7AoRDXTI6uulwdTWNjM+r4doNMrw8DBvectbCAQClEolJicneemll7h+/ToLCwtks1nx5+NuYpCSGaSgoPCtyVNPwd//PVy8CN/zPVCtwpe+BAMD8CM/AmNjr/cKFRQUFBQUFBS+KTyIeLO5uckrr7yCwWDg7NmzmCUNR49yvgdFrVaL412HtfGXL1++ozb+Qc/1MM6gSqXKWHwRk1FwXYQ6HMwvCy6Szd0craZmTveHxf2lTpnYYorMXo6AWwjvHYoG2M8LAk+hWCKbL9AfdGHQaVGrVGJgMgjV5rOLaU7WR5xuF2WSqQ3sllaCbjvWVpPMJdQT9HB+NMbR7k7MzU3oJA/0Wo2a8bklRmOL9Hjt9HV2kN5quJNc7W2kNjMsrG5wqi8kG+XyOa3El9KsbmZIpjcY6grI1rRVd+VkcwekNjO0m03iawc88gDj+FIak7GJ7oCHsM/FqqTWvb0ekDy7silk50hEF1urPAtqMbXJ1ck5jveEGIgE5Nk8ksr1sM/J2vYuo7EkpwejrEtGtZySsGedVsPcUsMlVCxXmF1MM3Kkh+6gV5bxk5a4q5okTiyf2y6uuQbMLq+h0ek4PdiNRjLC6LotMFraZpaXZCJJs6DcDrmjaW1zh/F4kthiClOzEV89nFx6jP+2sT+9Xo/b7aavr49z585x+vRp7HY7Ozs7XLt2jZdffpnx8XFyudwdPyvKmJiCgsK3NsPD8Dd/A1euwP/8P0OtBl/+MgwNwQ/9EFy//nqvUEFBQUFBQUHhoXnUMbFarUY8HufatWtEo1GOHDnymgLMkwyQPjxfqVRidHSUubk5Tp48eUdt/IOe52HW9Y1rE3zt8hgWs4mgx47+tqfIhdQGl8bjnO6PEPE5mV5oCDq9QS9zy2usb+9yqi8sq0M3GQ1MJ1NMLKTpcFo5e6RbFFMA2s3N5AtFrk7N3yHKBFw2YotpFtObpLd2ONEbkokgB0XhdW7OLOBobyMnqTgfiPhFwWp6eQOntQ2bpSHqzS0LLqdSucJ2NofdYhYdPF5nY/SoVK4QW0pztCeIubkJj72d2GJK3B70OLg0HsfvtuN1WtmQ5PP4nFYSq+ukNjPEl9KEfS4hwqGOtIre2tpCMr3NyT5BcKtJHuPtrSaW6y1dN2YSaLVqhqKB+nEmUbQD0B1+XlUqFlY3OCiW6Q/7xWs5JOxzycbODkWjS2Mx2ltbRGeRublJJt5I77E078eg05De3CGTzXF5cg69Tk80IDiNTM2NKvoOh1UMjIbbquhl+UmN96qluUkcfVOpVCRW11jZ2OL0UA9aiTPM75GLQVJUKhUmkwm/38+RI0d4+umnGRoawmg0cnBwwNzcHBcuXGBmZoavfvWrbG5uPjEx6NOf/jShUIimpiZOnjzJSy+9dN/9C4UCv/Zrv0YwGMRgMBCJRPj85z//SK+tiEEKCgr35+RJ+Mu/hJs34V/+S+E/Ul/5Cpw4Ad/3fXDp0uu9QgUFBQUFBQWFJ4JWq71rgHSxWOTq1assLy8zMjJCIBB4IAHmSTqDQBBxbt68ST6f5+zZs1it1tc+6B7neRgx6E///mVACDtOb2zTIml8CnlsokhzeSKO32UnKHHAHDpu8oUiyfQGRoMBQ/0hfSDsp1h/+J9fXqNULouCh16rkYU07+cLaLUaMefGI3GHHBRLxBbTnOoLY9Brcba3itX0ANa2FuJLawzXM4ykaNQqrk7OUalUGYz4GQj7WJeINra2Fm7MLNDS3ETU5yKZaoRmB912Eivr3JxZoNlooCfgkZ17rX7tc8tr6LVaMdga5KJSpVplan6Z/pAXm8VMd8Atc+3odTrKlSpXJ+cJuWzs7DfGqoIdcpFjIrbIaCzJ8Z5OoretZ0UqprntpDYzTMwtcXqgi9RGo2VNOl5lMhpYkDS/HRTLjMYWOdnfRU9IPh65LKuib0gNznZ5w9fE3CKxpTSnh3pQqxs/R452uTspJWkC25eMkkkDxt2yXKAa6Y1tajW4Mj5LoVjizNFe2swmAh55dtP9UKvVWCwWwuEwzc3N9Pf3E4lEKBQK/PIv/zJ/9Ed/xN/+7d/yiU98ggsXLjx06PwhX/7yl/mFX/gFfu3Xfo3r16/z9NNP893f/d0k79Pu/MM//MP80z/9E5/73OeYnp7mv//3/05vb+8jvb4iBikoKDwYR47An/2ZMCb23veCWi04h0ZGhBDqr31NcA8pKCgoKCgoKLxJuZt4s729zSuvvIJGo+Hs2bO0trbe4+gHO9+jkk6nKZVKtLa23rc2/kF4mDaxzZ0s/3D+pvh9T6eXq9MLnOwTAp6bJaM4apWKsfgi6c0dTg9EiPhcModLxOvm0niMDoeVgMsmCzO2mJu5NjXP1ck5hgeiHOvulDVBNRsNJFbW2drZ53hPp8yBE/E5Sayuc2VyDpfVQm+nV5aJlN3PizlDpwciLKw2BJ1OVzt7+QKZ7D5j8UU8douYO6NWqZhNCq+zupFBo1UTcDWELrckIye9tcN8ak1sIwu4bMxLsnWc1jauTs1zsi+M0aAjJRkJ6+xwsLS2xcT8MuVKVRxzAuH3sLFkIwNIo1Gxly9wvEcYncsVGk6rUIeDzH4elUrFjZkFcntZ/HXxzGVrEx1EIGkEU6kEwU6loj8siDvSyvqQ1yW7l8vrm6hUKq5NzWHQ6zlVF9jsltZ7VtGbjI3PSHurSdzv8niMfKHEcF0UapJU0Xe4bLdV0TfG3SrVxs9Um6S1zmJuEQOsATYzWS6OzlCpVunu9PIoVCoV9Ho9DoeDoaEhJiYmeNe73sXx48cZHR3l3e9+N3/xF3/xSOf+1Kc+xc/8zM/w/ve/n76+Pp599ln8fj+f+cxn7rr/3//93/ONb3yD5557ju/6ru+is7OT4eFhzp0790ivr4hBCgoKD0d/v5AhNDkJP/mToNEIwdNvfzucPQt//ddCzpCCgoKCgoKCwhuQBx0Tq9VqzM/Pc+XKFcLhMMeOHUMnyUR5EJ6EGCStjdfr9fj9/vvWxj8ID+MM+quvX5SNEFXrwsDVyXkcbS0y4WCoK8BGJstBscTl8TgRn0uW25KoO0zmV9bQ63W0ShxGvZ1eyhVhTZfGY1SrFRz1ynmjQcd4XMgDyheKlCsVQl6nGBAtDRdOpjZIpjc4VXcYeR3tsoBnFSqa9Dq6/ELr2uFrHr7OKzdniPrduKytDEb9sgDo1uZmLo7HGIoGsLQ0M7csFbpcLKxucHE8xmDEj++2fKOV9W3xvkX9HnS6RoaRS5LXs7OXY3xuidMDUfQ6LX0hHxlJBpNKpSK7n+f6dIKRwS7Z6JxTUquuUkFyXQjSHgh5sEgEGWo1EpK1u20WVjcyTMwtMzzQxapENDJJ6uFtFrNMxNrPF7g6Oc9gNEjPbWKLrIpe8lHzOuX3RWh4ixHocGPQN8a6bBLR1WQ0sC7JPsruNYQmrSRA/Pb8odTmtrhOl02eM/Sg3C1AWq1W8/TTT/Nnf/ZnrK2t8YM/+IMPfd5Dt+E73vEO2d+/4x3v4NVXX73rMX/913/NqVOn+E//6T/h9Xrp7u7ml37pl8jn83fd/7V43cWgh52R+8Y3vsHJkydpamoiHA7zX//rf71jn2effZaenh6MRiN+v58Pf/jDHBwc3OVsCgoKj0x3t9A2FovBhz4ETU1C6PT3fz8cPQp/+qfwiJZJBQUFBQUFBYXXA41GQ7lcplgscu3aNRYWFjh9+jTBYPChc3lAeGisVCoP3dx1yOED42FtvF6vfyJOowdtE9vY2OC/fPnv6fI5aDebcNstjMYWxe3tZhOL69ucGeoCBKHlkCa9lldvzWCzmAl7nQxG/KxKRpHsFjOXJ+IcjfrRa9Wy8GRbq4mrU4l6uLQbv71NzAASXgeuTMzhsVkIe12ycbLeYAeJFcEldKK3E59LPkq3kcmS2txhfmWdp470kFxriAwDYT/5QpGZ5Cq5Qon21kYujE6jYaqehTQaSxLyOnFIcnFslsa+Y3OLrGeyDEaEPJ6w18miZISquclAYmWDkUHhvkm3dfndpLd2uDwRx223YGtrnFejVrO43rhP5UqV5qYmeutCjNSZ0x3oIJvLUwMmEimcdhv++r1wWc2yffOFemObSsVWNodWq2OgvnZpGHjgthDmw/Gx8bklqqg4c6QHrUaD322XVdFnJW4dqbgkraJPrKyxmN7iRH8XTqsFvUQs89xeRS8ZHytLhEqzSd5SdiDJMAp2uHgU7iYGSQOkNRrNQ4vEIPxsVSoVXC75ulwuF6lU6q7HzM3N8fLLLzM2NsZf/uVf8uyzz/Lnf/7nfOhDH3ro14fXWQx62Bm5+fl53vWud/H0009z/fp1fvVXf5Wf//mfl9myvvSlL/HRj36Uj33sY0xOTvK5z32OL3/5y/zKr/zKP9dlKSh8e9HZCf/5P0MiAR/9KJjNwijZj/0Y9PTAH/4hFAqvdRYFBQUFBQUFhdcdrVZLqVQSfzN/7tw5LBbLI5/v8CHyUcSgw9p4nU4n1sZrNJonEkj9Ws6gWq1GIpHgy3/zPCubu8wuraNWqxi4LSNmey9HuVLlwugsI4NRkqnGWNRQNMhe7oCl9CZL6S3ZSJVGrWK6LuDcjC0SctvQSLJjXBYTNYR8momFFB6XA6NBeOBuMRrE1rCF1AYmo45uSTZOS3NDbLg+nSC7f0DEJ+TFhDwO0c1TrlQpVSoEnBba61Xnhy1nh7x6c5rT/RH0Wg2DUT9ZyeiTWq1mMrHMyGAUnUYtjpOB0GQ2m1xlbG6RkcEodou8cW5hdUOsbT8z1CXmJoGQb3RIMrXJTDLFyFAXqFT0BN0cFBv7litVUpsZphdWeepYr6zyXZr7o9NouDo1T3pzl5HBLjq9bnGbCmRrNxv1pDYzjMeXGBmUZwnpJC4cr9NKRuKa2t3b5+JYDJ/LTsTfeD+0GjXprYbwJA2mljp51CoVS+kNrk3NsZM7wNRsFJ1fltbGtVjMJnYlApV0DdKcIofEbaXVaPC65ILSg1KpVO5w4j3JavnbReZarXZP4blaraJSqfjSl77E8PAw73rXu/jUpz7FF77whUdyB72uYtDDzsj91//6XwkEAjz77LP09fXx/ve/n5/+6Z/mt3/7t8V9zp8/z1NPPcV73/teOjs7ecc73sGP/uiPcuXKlXuuo1AosLu7K/tSUFB4SFwu+OQnIZmEf//vwW6HuTn42Z+FUAh+53dgb+/1XqWCgoKCgoLCtzn3etCq1Wqk02nK5TLBYJATJ06g1+vvuu+DcigGPYybp1arkUwmuXz5MsFgUFYb/6Taye53nkqlwujoKPPz89xcarhmtnf3GY0lOTvUhVqlEsKU0w2HhlqlQq1WM1DPncnmGg+nRoOeF69Ocqw7SJupmaPdnbLxK5VKcMf0+gXnyeZeQ5QJuu1849ok1tYWIj4nA2E/FUmWTDa7z5XJObp9DqytJsbiDefSYMTPxPwyC6sbjAxEcFjleU/L69ssrGUANSODUSYTjdDpvs4OSuUKlyfieBzt6CTNVE16HRNzS9RqNS6OxTg9EKVJEmrcamo0ZF2aiHFQKuGvixE9wQ5Skjr2WhWKpQrHujsBmFtKi9v6Q17SWztcHIvRH/LKRu6a9Dqm6uut1WoUS2V8LjvBemuW1IXV0+klXyhSqlS4NB5Dp9Xiq6+nK9Ahc11lsnvim7KRyaLRaMT2sA1JqLXH3hi7UqkQW8sWUhtCDtBgN2aTkc4OFxXJZ219s/GZMsiq3xtV9IViiamFFayWNo73RWRV9B673OklHWmTNtW1SFrK/B4H2tvcPQ9CtVqlVqvd1xn0qNjtdjQazR0uoLW1tTvcQod4PB68Xi9tbQ2hq6+vj1qtxtLS0kOv4XUTgx5lRu78+fN37P/Od76TK1euUKq/8W95y1u4evUql+oNR3Nzczz33HO8+93vvudaPvnJT9LW1iZ++f3+x7k0BYVvbywW+LVfE5xCv/u74PPB6ir80i9BMAi/+ZuwtfVaZ1FQUFBQUFBQ+GejVCpx48YN8YHqUera78bDikGHQkw8HufkyZOEQiHZOg7Hzh6Xe4lB+Xyeixcvksvl6B04wj9cuCVuO9oVZG17l/Ojs0QDbly2NtmxidV1NjJZJuaX+Y4T/cxKQo/7wl6K5TI3ZhbQ6TSYmhoigNGgY251i1K5wtTiOm890Sd7qD98neX1bRZWN2S5Mm5bG4m0IHrMLK3js7ViNzdEgFr9GsuVKtemElQqVax1l0lP0CPm7Wxn91GhYmQgKrpLNncav8TM5g4YnV1kuD8CCCJTXjKClC8U2c3lOdkbuqMFbSDs49ZskvXMLqcHwrSaGs4llQpml1Ls7ue5MbPAW0/0sydxJ0lHqmaSqyytbdPlE8SevpCXgkTEyeWLzK+ssbK+zTPH+0impGNpetmfL4zOsradZWSwi/a2hsNFr9WwvLEj2VfH5s4eo7NJBkIeWdU7ks9l2OuW3Y/NnSyXJ+LotDpRnBLOZ2Ax3Qjvlo5x2SWNYwa9jpW1LVKbGa5PJ2gyGAh5BYHE3NJ4f53WNtk42rbkPZO6eQ6PfVgOf9akYlCtVmN/fx+z2Xyvwx4IvV7PyZMnef7552V///zzz98zEPqpp55iZWWFPckv2GdmZlCr1fh8vrsecz9eNzHoUWbkUqnUXfcvl8tsbAgfqh/5kR/hE5/4BG95y1vQ6XREIhG+8zu/k49+9KP3XMuv/MqvsLOzI34tLi7ec18FBYUHxGSCn/95iMfhc5+Dri5BBPqN34BAAH75lwWRSEFBQUFBQUHhdWR3d5fz589TLpcZGRkBHs7Jcz9UdbfMg5xvf3+fCxcu3Lc2/ps5Jra5ucmrr75KW1sbw8PD/M3L1xmMBmgxCg1PFUkK8FJ6k+mFVYYiQlbNka6AmPlTq9U4KJaI+N2ie+QwPBmEkaVXbs1wdqgLjVpFuMNOUZL7srufR6NWMxjxo7tNWIn4XLx4fYrBsA9rq4ngbRk2uXKVle0sJ3qCtDU3MSk5tjvg4urUPLUaDEX9mCXOEYD48hoXx+MEPQ5O9HQSlzh0uvwe8oUilybmONIVpFhuiDBmk5Gx+KIQpjw1z7mjvbIWLEM9T+agWOL6dAJUjQas/pCfLYmAkTsoYm1tocvvQaNWMSMR1PrDPtLbu8wub3CiLywTO8wmo+gSKpUrFEplejo7xPsvrYbvDnRQrlQplspcHI+jUWvEmvvuzg7ZyNpBqf6eq1RUUVOtQdQrNJ0tLDfuj00yBmfQ68Rw6u3sPju5PEG3Fa/TRrBD/n6trEmr6BuCi99tl41WTs4vkUhtMDzUI3MJSQOzQWg6E9cuEZrCPg+Pwt3EIHgyziCAj3zkI3z2s5/l85//PJOTk3z4wx8mmUzygQ98ABB0ive9733i/u9973ux2Wz81E/9FBMTE7z44ov88i//Mj/90z+N0Wi818vck9c9QPphZuTutb/071944QV+67d+i09/+tNcu3aNr3zlK/zt3/4tn/jEJ+55ToPBQGtrq+xLQUHhCaHXw0//tNA+9uUvC+HS+/vw278t5A194AOCYKSgoKCgoKCg8M9IrVZjcXGRixcv4vV6OXXqlPhA9aTEIHgwAWdtbY3z589jtVrvWxv/pMbEpNXyh/lA165do7u7m4GBAVQqFV/8229wdXKOlmYjw/0Rbs02cl2HokJr2Gh8mR6/E53kYdmg0zIxv8TMwiq7eznedmqAZEpS5d7hoFqtcX50Fo+tjQOJs8NmaeHWbJLNnT3G4os8c7xX5vxoqTtlxuqZQbVGQTohj4PYYppqtca16QWO94ZwSNwm+fp5trP7xBZTVKsVMQNnMOxjvT4CFV9aEyrT6y4gQNbYtb6dZXktI4ZD9wU7ZG1rm5ldzCYjPUEPzU16Md8IBEfR5Yk5NFo1Q1E/TYbGaJlWo2ZqYYXl9W3iy2m+48SALKNIeo8n5pZIbe2Ia+gJdMhGsbZ395lOrJDJ5njmZJ9QHV9HI8nVaTebePXWDOuZPUYGuzBJPnfm5ibiS5IsoRYTe/kCsZUtjvd0yl8v0zh/2CsfC0tvZkiuZVjf3sXvtIs5QNbWFvGeA+zlGiHT7eaG0GI2GVnb2qFWE1rm8qUKI0d60Wo0MudUh8NKSSJkbe00zh32NTKSHobDvKDbNYj9/f0nkhn0nve8h2effZaPf/zjHDt2jBdffJHnnnuOYFAYy1tdXZXlKbe0tPD888+TyWQ4deoUP/ZjP8b3fu/38nu/93uP9Pqvmxj0KDNybrf7rvtrtVpsNmHm8dd//df5iZ/4Cd7//vczNDTED/zAD/Af/sN/4JOf/OQT+YdTQUHhEdFo4Id/GK5fh69+FZ56CopF+IM/EJrJ3vMeuE+2l4KCgoKCgoLCk0ClUlEul7l16xazs7OcOHGCSCQiunhUKtUTFYPu5wyq1WrMzMxw8+ZNBgYG6Ovru29t/JPMDKrValQqFW7dusX8/DynT58W4zJeuTFNrO6MSW1m0Gg1nB3qRl1/KJY+xKe3d1ndzNDX2QHAka4gu3URY/+gwM5+npN9IczNTUJwtMTtQq3G8maWnvroU7ffIxMSlte28bvsBFw2zM1NjErygPwuG5fG4wz3R9DrtDLhB2B2KU02d8DJ3hBuaxtzqw3XSKTDwbXpBdpbmnC2mShLnD46jYbxuSWuTMxxtLuTY92dLEnEoIDbVherlhgeiLIlyT6yW8yMzy+R2swwu5jiqSPdMqfNoZFha2eP2FIKnVYjiiODkUZAdbVaY3svR8Tnxuu0YtBrmZhv5Bn1h30sr20xNrfE8GBU5lSyW8yioyhfKFIolBmMBHC2t6FSCVXu4n3wC8/dhy4hVCoxSyjid8scTtJKebVGy0GpwnC9DW0t03A31W5zTS3Vm9JKlQobO1nsVgtDXZ343HbZ+yUNH1dJwsR9Lvl+80spLo7N4nLYaK671gAc7Y2xRY1azYrkPZMGWj8Md2sSOxwTexLOIIAPfvCDJBIJCoUCV69e5ZlnnhG3feELX+CFF16Q7d/b28vzzz9PLpdjcXGR3/md33kkVxC8jmLQo8zInT179o79//Ef/5FTp06JdW65XO6Of0A1Gg21Wu2RKx0VFBSeICoVvOtd8PLL8OKL8N3fDdUq/NmfwenT8Pa3wz/8Ayg/rwoKCgoKCgrfBEqlEufPn6dQKPDUU0+Jv1Q+RKvVUpY0Hj0uGo3mrmJQsVjkypUrpNNpzp49i8fz2g+s9zrXw3IoBh2Opd3emvbHf/OC+GedVsPk/BLnR2eI+t0MD0TERi6ADlsbK+vbTC2scnaoS9b01G42cWt2gauT8zQ3GXjriX7ZWJTHaeegWGJ6aZ0TPZ0yB0vAbWdqYYX5lTXS2zuMDMpbt6r1/1e8NBHH77TKAqv7Q16W17bE0a2BiF8cdwPI18+zltmnWK4g0R6I+uyimHVzZoFmo4HBiJDHolLJA57Xt3cplSt01sefIj6XKKBUqzWS6U3CXic+p5V2c7PMJdQf9nNhbBaXrY2Iz4X0/3yNBiGgenYxxWZmj7cc7ZVl8lQqDcFsMrFCZi9Pd1D4/IR9cmNFajPDWHyR3EGRt54YIJNtvD9lyXk89nYujsVY29plZKgLnbaRzWS3tMrcXSqVitxBkUvjcU4OdNEmaS5TS46zmSUOtxospjZYXd9mNJbE2W7BWW/88titMhdUZrchsEnDuO2WVrHqfnltk6X1bQa7QnR2OGmS5CJ5XTaZqBgJdPAo3E0MyuVy1Gq1x84MeiPwuo6JPeyM3Ac+8AEWFhb4yEc+wuTkJJ///Of53Oc+xy/90i+J+3zv934vn/nMZ/h//p//h/n5eZ5//nl+/dd/ne/7vu+7441UUFB4nXn6aXjuObh5E37iJ0Crha99Df7Fv4Bjx+BLXwJJgKCCgoKCgoKCwuOi0+no7e3l9OnTGAyGO7Y/KcHlfuc7rI3XarVibfyD8KScQZlMBkDMB5Leh5X1bVKb21jqdevHuoOigDCTXEWtUjE8EAWEivildUHAqdVqrG/vUqnVCNTdJb2djRGq9NYOi+kNenx2NGqV4PSJNUZgKrUau/t5hqIBADySKvpCscz0wgpDUT/WVhNuWxtj8Yaw0t7awuxiipFBYV3SZi+NWsX16XlMxib6OjuIeF3ElxpilstiYmIhTX/Yi7XNRKHUeK+Mei1XJ+YEF1B/hGPdnaxnGjXpDksrC6sbLK9tMzwQEV0wAD6nlemFVWJLaTYyWU72RWTiy+HY2mJ6k+3sHkaDTsxk7g/7xYavg2KJ9UyWwUgAa6uJZoOOifnGtfcEO0imNogtpRm5TYzzOa0srAoizl7+gHyhyJGuIPZ2M3qthmlJptJh21mxXOHiWJxypSqGP3felvUjzSDSqFWsrG8zMtRNk15HYrUhGjntDaHVYjayUb93KpWK1c1t9vJFzhzpocPZyMdSqWBhtfH+SEUdj6PRYAawnN5kLJ5kIb1Ja0sz7a3Cz5HN0nCJmU1GWYX9w1CtVu8qBgFPzBn0evK6ikEPOyMXCoV47rnneOGFFzh27Bif+MQn+L3f+z1+6Id+SNzn3/7bf8sv/uIv8m//7b+lv7+fn/mZn+Gd73wnf/AHf/DPfn0KCgoPyJEj8MUvCtlBH/6wED596xb8+I9DNCq0kim19AoKCgoKCgpPCKfTec+c0ictBknHxG6vjT927JhYG/+g53ocMahWqzE/P8+tW0JLWE9Pzx1TFf/3V1/k6uQ8apWKo91BdvYajo12s4mrU/NcGo9xtDvIsa4Au9Ksl7YWYosp0tu7jAxGZQ4ij62V2cU000sbBDwOTvSFZZXmtVpNyCGKJTl7pEs2ztQf8rKY3mQ0tki1WmMg7JNNfWSy+5QrVS6OxTjW3Ul6qzHGdqQryNbuPumtHaYWVgn5HLL8nfUd4eF+Ym4Za2sLFkleTXfATaHuIro0EadWKRF0CwKHXqcVq+hL5QrZ/QNaTc246vX1XonAcVAsMbu4ymDEj81ixmOzyMa+on43F8ZiRP3CWJjUBWRpaWY8vsRYfJFCqUJ/0CXLKMrV28eq1Rrzy2scFMtE6u4g6RoAEivrjMaSFApl3nKsT/Y6+5JsplCHg+vTCZbrIo9UkPG7bLIxwWKpLN77vrBf1h62IxGmbg9xnl9Oky8UuTgWQ69V0xcS3FcBj1PWkrYpEd+amxqipcduFfOkajUh66lUgZEjfTIxMOLveORmwLs5g/b29tBoNHcVkt9svO4B0g87I/fWt76Va9euUSgUmJ+fF11Eh2i1Wj72sY8Ri8XI5/Mkk0n+y3/5LzLbo4KCwhuUQAA+9SlYXITf+i1wOiGZhF/4BWHbv/t3sLb2mqdRUFBQUFBQULgf93s41Gg0T3xMrFqtirXxsVjsrrXxD8LjVMsf5gMlEglOnToFcIewVCiW+L+fexGArd19cvkCFrMJo0EYwZE6fW7OLFCqVIl0COJIa4tRDJkuFEsUSmWc1jbs9aapNkml+vzyGsnVDc4MCZkz1hYjo7FGHlClUsNkNBDxOQGhoeqQvfwB16YSnOoLY2oS9olJRrcMOi3b2X1O9YXqa2m8l80GPa/cmMHrstLZ4WAw4mNLInbZWlu4Np3gRG+IFqOBnVxDILG2mbgVX2FpbYsen52wu52sRAhrbjIwmVhhL1/kVF+YmYWG6ybqd7OwusFYfJFSqUx/SF4DfuiYmV1MoQJamhtCQ3ewkaO0ny+wvpPjWHcnlhYTdotZFKQAQh1OEqvrJFY3GBnqkok2Ub9bHMPbyx+QzR9wpCuIw9JKi9HAtOQ8hy1d5UqVy+NxDgolugKCmNPhkAtM0tY1vV7H5Pwypwa6cLS3El9s5ENJ28ICbjv5QkPwWVnbYjKxQk/Ag61VXnWflLiQpCKYS+IcU6tULKY22MsfcHFsFrVGx/G+SP3+PdqIGDQCpKUchkffL9vrzcKb/woUFBS+9Whvh1/9VVhYEAKmo1HY3oZPfAKCQfjgB5UGMgUFBQUFBYVvCt+MMbF8Ps+FCxfI5XKcO3furrXxD3quR3EG5XI5Lly4wMHBAefOnaO9XRi3uf1c/+MbV0RhAqDN3Myl8RgWs4mBsE8muoS9Tm7FFomvbHK6P8JQxC9zdJTLFUZjSXL5A/qDbhbSjXr5oaif+ZU1LozO0hP04LGaZU6fjcwuydQmiZUNnjraLcvaOdIVZDu7z5XJOVqam/A65JlPC6l1cgcFrkzOc/ZIF6nNxusORHzkC0USK+ssr22JmTUgBEdPLQjixbWpeSI+FyaJE6XL76Faq1Gp1phe2sDU3IzTIggXzQYdt+ojb/v5Qn3Eykl7PUunXZKps7ufZ2xukRM9nZhNRkIeh8xB5XVZuTQepz/sw2VtZVOSsWS3mFlIb3NjZgFUKo52BeX3bUd47yrVKisb29SAkFcQ1GxtDceTWqViNrnKaCxJvlhkZLBbNsK2udP4DHR3djCZWGF2Mc3wYJfMTRT1e2RZP3u5A1CpuDIRp8Np42h3SNy2JsmDko5tadRqUttZVCoVM0trVMpVeoNu1CoVbrvljmayQ6QCod9tpyQRcJOpda5PL9AbDnK0t9EK97Dcyxn0rTAiBooYpKCg8EamqQn+zb+BqSn48z8XAqYPDuAznxEayH74h5UGMgUFBQUFBYUnilarfaJiUKlUYm5uDqvVyvDw8D1r4x+ERxkT29jY4Pz587S3t4s5SSqVSlYvD8KY1ldfvka4Lh5Y21q4ObMAwOrGNk16HRGfC329jv3Q8QNwZTLO9m5OrDoPuO2M1Zu/coUSZnMLfSEfbaZm8ToOmV9ZY3lzV8wh6uvsEMWRSrVKuVIl4LKJOUT7+YYbJ1cocnkixshABL1Ww5GuAKnNhuhQqVSp1uBotxBDkpZss7SYeOHqBD5bK25bG0e6ArKxJp1Wy/ickBVkNOiYlwg2AbeNq9ML7OaKnO4P0xv0yFwru9k9bswsUK3VONEbuqNePr21w7XpBDqt5o48nsOg5on5JdpammmXOGX8Tov450x2n/jyGid6Q7Samgm4bDKXToe9nfmVdRbTmwwPRlmU5Bn1hXzite7nC2xl9xiI+PHYLdgsZtmI3uF7BnBzdoHtvRyD9Vwnm6Uhiui1GmYlTXFNBj1XpxL0dHrxOSwkVhr3ryoRsMI+lywYPFsoMb24jtdlp8PeyAgy6nUsrzWu4eCgIUrZLA1RT6/ViPtNJZYJeJw8KvcKkG5ubr7HEW8uFDFIQUHhjY9GAz/0Q3DxIrzwgtBGVq3C//v/CgLR294mBFE/gUBFBQUFBQUFhW9vnpQz6LA2fmdnB4fD8Zq18Q/Cw4yJHeYDXb9+nZ6eHvr7+2Wvf9godsil8Rj/cP4mydQGZ4e66A52yASOvXyBC6OzuO3tDEX9gjulzrHuTibmhVybkcEo1ha54LW8tsW1qXk0WjXnjnSL42QAQ5EAmf0DLo3HONIVpK2l8aCtUauYXUwRW0qT2trhrSf6mF5oCA79nR3kCyUujsdx2SwyJ0+TXsfE3DKZ7D43ZxZ45kQvW7sNl03Y56RarbG0uUsmm6OlubFms8koOn0uTcQ5Eg2K4cQA7rqz5aBY4vLEHHq9Hmc9K8hlbWU+JYgRO3t59rJZgq52sc1MOi61t5/n6tQ8p/rCtBgN9Ie8rEoq3NtbW7gyOcdQ1I/dYmZlvSFmhTocJFbWuTaVQKtREw3IM3mS9TWUK1V2sjn0Op2YJWSUNG8Z9Fqm5peZmFtiZy/P8W75+KJUgOkNellMbTIWW+Rkf5T9fGOMrjvYIRN1DlvjphdSGAwGRo70iA1l0nO2SxxLRoNeDI9eWtuiVFNxoj+Ko72NgOe2EOuVhvCl0zY+136PQ2x0A+gN+3lU7iYGHY6JPWoO0RsJRQxSUFB486BSwVvfCl/9qhAw/b73CQ1kX/86vPvdMDgIf/RHkM+/9rkUFBQUFBQUvm35ZmcGSWvjXS7XE3MSPOiYmDQf6PTp0/h8vjv2ud1l9Idf+SdAEA+uTM5xcFAk4LYD0BXwMF3PwEmmNmhpbuJkbwhtXVwqSESA0dkFUpsZugNuQBjrWlrbAgSBoFKtcqovjLkuvmxIRpJW17eZTq5yqi8MwNGuoCgqFEtl9g8K9HZ24Kk7RpISt0u1WuXyRJyRwShajZqhqJ89iYsou39Ak17HYMQnikyHuG1tfOPaJANhH872Vvo7vTJhI3dQYCa5yshAFHNzExNzjXydvs4OLozF2M8XONUXJuhxINHYKNXUTC6k0ajVdHVYGY01RLTBiJ/d/TxXJucwNhlwSFqwNGoV03WnzWh8EY/dgtPacGM52xtumK3dfcbjS5zoDdHW0kx3wMPqRmM8rs3cLGYJDQ92yVw6/aFGc1m+UCS1uU13wEPQbcfvsonvHQih2QCoVMwmU6ysb3OyXxjDMhnlYlp8qXF/9VoNF8diOG3tDA92kZIIXtJMp7DPJRNyUhsZrk/Ns5cvEOhwiWKStbWFjMTFlV5vfA4skpE8i9kkCnePgjImpqCgoPBGZGgI/tt/g7k5+MVfhNZWmJwUxsqCQfiN31DCphUUFBQUFBQemscdE7u9Nr6pqemJ1MHDg42J3Z4PdK8iHem5FlY3+LtXr4vbjvV0cmMmQWojw5mhLlG4gbpbJ5ni/OgsXqeVbq+dCckYVKfbSmp7j9nFNGcGu5DqbkaDnvG5JS5PxGky6PmOE/1i9TlA1O9ie1fIAzrW00lJ8j60moSA6qnECpnsPm8/1S9z0XidVrHVyuu0yrJmbJYWbs0mWc9kGYsv8dYT/RxIsm8O83TG55bIHRRkThOPvZ3R+CK1Wo2L4zGOdgVxS8KLD8O19/MFrk/PU6vVxBG6iM/J/IoQgLyzf4DF0s5AyIexnnezsdUQbA4KRS6Mz3KqP4KpycBQNEgmuy9uN+h1jM4tE/XacdvaRKEIhLa19NYO16YSqNVqfJIWMbVKxUxSEGYq1SoHhSLGJgM9YrByQ3xpa2lmMrHMTHKV1c0MvSEvWk3jXswtN9w4XQE3W7v7XJ2cZyASEJu9hPfRLRN1NuuNbcvrW1RqcKI/gq1+jxYkwlRLs1H8s8XczMq6IEQdFEusZ7I4bBaO9oTw10VK8fy7DWFoL9sQF7sCHpn77WG515iYyWS6xxFvLhQxSEFB4c2N3w+//dtCA9mnPiUIQevr8Ju/KTSQ/et/LYhECgoKCgoKCgoPwKOOid2rNv5JBlK/lhh0t3ygeyHNDPrC33yd4f6ouO2whapYLjO3tEbuoCC6hE70hsSQ6YXUBlDjdF8ItUqFSgWbWcGNU6vVWNnYZjOTpa9TEB6OdAWFkOH6a2T2c5zsC9Gk06DXamTNWPu5AsnUBid6OwHok7h18oUiqa1d+kJePDYLzU16WS5Ps0HPrdkkI4NRNGoVUa9bJg4tr29hbjYyEPZh1GsZizeODXmdvHxzhqGIH4ellYBLHva9srHN3HKakYEorvZWcZzs8PouT8xxUChxqi8kc6mAMPp0K75ES7ORswNdJCVjX552E4VimSsTcYxNelmjmKnJIGYwxVY26HC0E/W7xO1GyXhcdj/Plcl5jnV3Ym01MRjxy0QlrVbDYnqTmcUUZ450MS8RY7oDHlHEKZUrzC+v4XXa6Al20B3wyMKspbk/mztZppMpRoa60WrUslE4W1sLa5JQclQqrk0lOChVeObkALv7EiFH0s4W7JBn/SylNlhd3+bWbBK71YLfJXwePXYr2VxjKqCm1op/trcYeOmll7h16xbLy8vkH3J64F7OIEUMUlBQUHgj0doKH/4wxGLw5S/D8DAUCvDZz0J/v5Az9E//BI/x2wEFBQUFBQWFbw1ea0zsYcWb+9XGP0kx6F7neq18oLtxKCzt7OX44ldf5MLYLH2dXs4d6SKx0qjzDvucTCVWSG1kGBmMsr3bEBasbS3EV7e4PDlPh93C08f7xPpyELJ1lta2mFpY5cxQF2tbGXFbh6OdmzMLXJ2cR6/T8vTxPjLZhjDQ2mJkZy/PtakEx3s6Wc80atIDbhvjc0tMzi+T2ctx7mi3LL9Gr9eJLqGQ1yULnY74nEwvrJLe2mF8bokenx2N5s57NRpfpFqrolI1tvUEPMwtr1GtCi6h7s4OOiWixaHbaC9/wFRihWq1Jo5+DUX9opNpPZOlQk02LpeXTCZWyiVeuTFNf9CNqUlPX6hDHOWCxijfYMRP0GNnvC4UAQxGA+zu57kxs0C5UsPe3hgtMxr0TM4LglutVqNUqmAw6BmMCrk6+xJ3j8duIb6UJpnaYCa5SrDDIWYyaTVqphMrjffD46BYKnNxLEaH0y4T3ryORgg0NJxA+/kCB6UKQa+b7qAXjVrNnGS0rEnfyDVy2y2yhrO1rR1WN3cYOdJDwCN3CS2mGk6zd7z1LCdOnKC1tZV0Os2FCxc4f/48MzMzbGxsvOY46L0yg5QxMQUFBYU3Ilqt0DJ24QK8/DL8wA8IWUN/93fwXd8Fx4/DF78IxeJrn0tBQUFBQUHh246HzQw6HMu6V238w4Q+vxZ3cwaVy2Vu3rxJIpFgeHj4rvlA9zvXF//2G6KQMplYJlcocWZIGO9qbtKLjpRiuSw6N0J1AcRna6NSd5IsrW+ztrXD2aEu9FoNFnMzN2YSgCA8HBRLFEplBsLC+gIumzjCs5srEF9Kc7o/gtlkxNneyo3phLhWlUrFRibLyT6hqlxaCZ8vFJmcX6Gvs4MORzsem0UWUN3WYmQyscLIgJAlJHXrqFSwsJbB1GRgMOLH62hnNNYQVkJeJxfGZhmM+HBaWzFJ3DpqlYqp+WXRJRTxOpmUCCT9YR/XpxPkCgVO9Ydlv5DU67RMJla4MjmPXqfjmeN9srav7k4fNWBiIYVGrSK313DkWExNjM0JaxybW8RlFZrQDqlU5A1xL9+YYTASwGVtoz/slVXDF8sV0ps7jMWXOHukm9X1xthawC0PbB6PL2IyNnGsp5O+kE82FpaTCHEajZprUwlOD3RhNhllwlDQbRddZQCFYon55TVmkqu85US/rC5eGvbtddhka0msrFGpVrk0FkOr03J6sAuVSkXQ45Bd31BXJ2azmc7OTk6cOMHTTz9NNBqlVqsxOzvLSy+9xLVr11hYWCCbzd4xUqaIQQoKCgpvRlQqeOop+MpXYHYWfu7noLkZbt6En/xJ6OyET34StrZe81QKCgoKCgoK3z48TGbQ2toar7766n1r4x809PlBuF0MyuVyXLx4kUKhwLlz52hra7vP0XeeK39Q4PP/4+vi34W9Tm5MJ7gwOkvU72Z4ICob3dFo1MSW0iymNxgKeZhfbTiI+jo7mEqscH50FpfNwonesCyEuVAssbK+zficIDzEJDXoQWcbidV1Lk/E0Ws1HO0KykSE3f0cu/t5rk4KzVtS0eJINMDy2haTiRU2d7IMRHyyh/rt3X0q1arg5Al2yFxCR7uCbO8d1LOEFukNeWX5SBvbgnAxFl9Cr9XKxp+OdgdZz2RFl1CH00pPsNHotVJf436+wFJ6kxrgqYcZH4n6ye4LI0ubO3vs5Q7E8Ofbw60dVgsTi+sMhr20GPW42lvkeTy7e1yaiNPT6aE/4mV8riFm9dZH68bii+zuH9BqagYEx5q11STbt1KtUlOpOFkP717fbji8Ij4XqxsZ1jO73JxZwNneJjqeWowGpiTjfYdC3eWJOHqdTgx9BnBJquJVKhoh0yoVB6UyFVQMD3ZjajIwJ7kHaolzK+hxyD6Tm5k9rkzMEfF7CPsb97/JoL+jYU2r1eJwOOjp6eHs2bOMjIzgdDrZ2dnh2rVrvPLKK0xMTJBKpSgWi1Sr1Xu2iX0roIhBCgoK3/pEIvD7vy/kCn3yk9DRAaur8Ku/KmQO/dzPCeNlCgoKCgoKCt8WPO6Y2GFt/M2bN+nv779vbfw3a0zsYfKB7oZarearr96kpbmJzg7BBWKV1KfPLaWZSQrjXVqNmk6PQ3TclCtVqrUa7a1mOuoNVwbJWE96M8ON6QQjg10YDXq6/G5xPAmE+6dWqTgqOloa70fuoMiFsVlO9IRoNwvNWLHFhnCk0ajJ7OU43R+ur6VxbzVqNedHZ+nyuwm4bfR1djC33MjEMTc3MZNMMTIQQa/TUpCMXpmaDJy/NYtep+VYd5D+kJeEROzyOq1cGo+LDqTcQVF23isTc0wvrDI8EOF4d1DWwhX0OBiLL4nr3pKM2rWbmxmNL3JtKoEKFW890Sc2qAFipf3Y3DJqlZomQ+M+e9rNxOui2vTCKs16Haf7I2jUwv3clmQFmU1NvHB1UmgK89iJ+j0yUWlrZ49Mdp+rU/OcGeomJ3HYHAZig+CIujo1R65QZGSwi96Ql7LEjbQpcf44rW3cmF0k1OEg4HHIhLjuQIdM1Mnu58nu57k0EWewO0TE7xa3pTczknNaxD9r1Grm66HW8aU0+/kCJ/qjuGwWBqMBmXh3N5qbm/H5fBw5coSnn36agYEBDAYDi4uLvPzyy2SzWdbW1tje3hZF2CflDPr0pz9NKBSiqamJkydP8tJLLz3Qca+88gparZZjx4499hoUMUhBQeHbB6sVPvpRmJ8XRsWOHoVcDv7Lf4Hubvj+74evfU3JFVJQUFBQUPg25rXGxA5r41OpFGfOnKGjo+Oe+x6e70mOiVUqlYfOB7o7Kv7bc68wu5hiKb3JM8f7xJEwgOO9YVbWt7lQbw3r9DTGhtQq2No7IJneZHUryzPHepldXJUcG2Jrd4+LY7O0thjxORtjPlqNmthiirXtXW7OJnn6WC/rEnHkSFeAbO6Aa9PzlCtVOiSZM1qNmpnkKvv5Apcn5hgZjLIjqRgfjPjZzxeYXUyxupHB47CKbVg6jYaZhdW6kydOf8iLtOpsICKMPm3u7HFjZgGXrQ1rq0k8dnpBuL7JxAotRgNtLc3i4X2hxvjVpXGhKW0wIozD6bUapurjY/lCkUw2h1ajwe8W7kl3sINSWfh8ZPZyrG7uciQaxNneSovRwJgkoNpja+NmfIWhaACntRW/Rx6yPLec5uJ4DJe1lWNRPzGJuybU4RREzOQqS+ktjE16dHWxxO+yyZxaNWpksjlGhrpQq5BlSPWHfezs5cgdFLg0HqO5qYmITwizdlhaZa/ZahLaweZXN9ne3cfcbMSgF5xCltaGu8ag1zKbbByHCmaSKU4NdBH0OEhKRLlqrSE8hX0umftsc2eP61PzZPbyfOfpIzwMarWa9vZ2IpEIp0+f5i1veYv4b8H4+Dj/+I//yLvf/W5u3rz50EHUt/PlL3+ZX/iFX+DXfu3XuH79Ok8//TTf/d3fTTKZvO9xOzs7vO997+Ptb3/7Y73+IYoYpKCg8O2HXg8/8RNw/boQKv2udwkC0F//Nbz97YJI9NnPwmP+Q6+goKCgoKDw5uN+Y2I7Oztibfy5c+cwm8133U/KkxSDarUatVqNhYWFh8oHuhuXppIspIScmnKlSqFUxmltoy/kBRqNYgD5gxKv3Jqmx2fHaNBxojcshiHXgFyxSEtzE0eigtNH6opRq9R84/oEp/rCWFqaOd4TkuXG5AtFqCHmAUmr5ltbjHzj2iSDET8em4Vj3Z2yAOtiqcx6JsvwQAStWiU71tneytevjON1WOkOuDnaHZQ5ZTRqNZPzy3R5bZibm0hKgoc7HO28cHWSUqXKqb4wR7oCskYus8nIpfEYYa+LsNcpy/vpcLRzYSzGWHyJk70hTvaFZIJVq8nITHJVDOSWBh773TYm55e5FUuyly9yZrBbFhx9yGgsSblckQVfD4T9bGWF/3dd2dyhWCrS7bOj1wqCT0LSGtbZ4eQbVydx2Sz0hbwywQ0gmdrgoFji4liMk/1RzC2NynepM8lo0HNpPEZidZ2RwS5Zwxkguy8Rn4sLYzEsZjPHekIyB1F3oIOSRIDd2N4FlYork3PY2i0MD3WjqQueS+nG/Wpvazh0mvQ6EiuCoFUslen0ytfysOj1elQqFeFwmKeeeoqhoSFOnjzJ6uoqv/3bv000GuVDH/oQS0tLr32y2/jUpz7Fz/zMz/D+97+fvr4+nn32Wfx+P5/5zGfue9zP/uzP8t73vpezZ88+6mXJUMQgBQWFb19UKnjb2+CrXxXq5z/4QSFXaHRUqKT3+4VRskf4R15BQUFBQUHhjcvDjokd1sZfunRJVhv/IDwpMSiXy3Hjxg0ARkZGHiof6HZqtRrfuBUXvz8Me06mNpicX+a7hofYlLR3udpNlMoVppc26u6ORtBvq9HAzZkF0ps73IolefvpQVmgcMBto1qtcWVyDgBjU+NYZ3srN2YS5Aolrk7O850n+5G+M16HlVqtxlh8ke3sPiajQXTjOCytjMaSFEtlLo3HGR6IYjIa7jh2IbXB7GKKZqMBo0F4bZulhZuzCwDMLm/SHXDjruf5APicwrFCTfscer0WTz3vxtpqEsfl4ktpWpuNBFwN0eXwWICrU/NkcwVO9HY2jq07fUrlCoViCZ1WS3dAGInySEagcgcFphZW6Al24Hfb8dgszEjcO2GfSxyJC3oc6LSNR3u9VsPi5i4zyxu0thg52e0nvdV4P1uaBEFnaW2LqcQKGo0Gc93F09vZIQp9h8SX1hgejGIxNzMtyQfqC3kpFEtUqzUujccoV6uc6BVEvWCHQ8xNAsSfl/TWDsnUBs3GJjx1EcrY1Hjf2lqaxdE3EMYCL43H8bkdDA92kd5sZBlJx/zCPrds7O1EX4TH5TBAWqVS4fV6+fjHP05vby+f+cxnePbZZ1Gr1egl45EPQrFY5OrVq7zjHe+Q/f073vEOXn311Xse98d//MfE43E+9rGPPdK13A1FDFJQUFAA6O0VxsWWl+G3f1sImN7cFDKGOjvhR34Ezp9XRsgUFBQUFBS+xbldvJHWxp84cUJWG/8g3K0B7GGR5gMBDyxE3YsXr03yytgcQ2EvFnMzfZ1e2YP1YnoDg17H8Z4gzU06ZiSZPW57O6/cnOZEbwibpQW3tUUccwJYSG1QqVU5PRCh3dzMjZkFcVuww8GL16YYjPjpcLQT9rpkeTPJ9Abb2X1GBqPY2lpkx/YEO/jGtUnCXhcRn4uIzyk7Nr29S2J1ndP9YRztraLYA4Jr5sVrk5ibjRzpChD1uWXH7uwJVexHu4IE3XZZo9hA2Mf5W7Ns7WYZGYjSFfDIrvegVOLieAxHeytHuwKMxRu/ROwPeRmr5wENRfwMhLyyYwvFUl2sSnNmqIu4xL0zGPGxtLbF9ILgIhqI+NFIau4Pc4VmF1McFIoY9DpRkBqKBsSA6o2dPapqDcd7O2lvNaHTqGUB1VGvk1dvzqBRqznZF6bF2AjQ1mrUTC2sUKvVuDQeJxrokIUyS5vLrG0tXJ2c5/p0gqFogKCrMRqoAmLJxhhh2Ofm1mySjcw+I4PdpCSZQBFfIysIYGVdcJkJnys41hvGVRfuEtI8KFPDveS0tuFzyRvIHpZarXbPNjG73c73fM/38Pu///s4nc57nOHubGxsUKlUcLnkziWXy0UqlbrrMbOzs3z0ox/lS1/60mP/7EtRxCAFBQUFKRYL/OIvCoHSX/kKvPWtUKnAl78M587ByAh86UtKNb2CgoKCgsK3KIdjYrVa7Y7aeJvt4R8wH8cZVKvVmJub4/r16/T29tLX1wfw2OLS//WnXwVgdG6ZZoNenp0T9jG9sCrkr0wvMBTqwCIJli6WBNHo2tQ8eo0GteTYwYiQU7O7l+fyeJyTfWGc7a3i9kPBaSy+SKlURq1WiWHHIbeV+NKaOJ40FPUTdNsbx9ZfN76UZj2zi0qlpqnuUOoP+4gvpalWa1yemKMn6KE72BAVDu//2vYusWSKarWKtT5i5LO1ink5N2cX6HC0MxTxi8ceXl+hWOb6TIK93IE4DhXxOcU8oOX1bQx6Hf0hr5iVo9M2hISpxAqxpTVGBqKoVEJz22EVfa1Wo1qtogaOdQeF15XkQKlUKi5PzGFrayHcYac74JEFY3d2OLg4FsNuaWUg7CMrCWZuNzczGktyfTpBqVzhrSf7yUmEv8P7n8nmGJ1Nkssf4K07dgYjDVEJoFyucH06wWBUENQm5hvCV9TnFh1RY/FFVjYyYvh42OsgIxmVOxwJK5XLLK1vUSyVOdrdWb/uxufJY7OwvCZv/r0xk2B7L8dbTw3IBExpGPXpgehDCbZ34/BnTCoG1Wo19vf3H2g89LW4fX21Wu2ua65UKrz3ve/lN3/zN+nu7n7s15Xy5GQlBQUFhW8lNBr4gR8Qvm7cgN/7PfjTP4XLl+HHfxx+6ZeEsbKf/Vl4yN8IKCgoKCgoKLy+vNaYGEAqlWJ8fByv10tPT88jhjQ3xKB7Pezdi3K5zNjYGJlMhuHhYdra2sSH7ccZO7swOsuF0Vnx+6DHwflbM/SHfezt52VrNBp0TCykKZUrnBnqYi93IAuZDnY4hRp6n4tqrUZV4qA2GQ1cGItRKJY4M9TFfu6AUcmxEb+bV2/NEPTY0Wu1qGgca9BpuDQeJ18oMtwfIV8sMSoJUu7v9HF+dAaXtY0em0UWKmzQaxmLLZLZy3G8p5NqpcpNybEDER+XJ+ZoMRo43R9mfaMhNhwGVG/u7NHld9PWYuTK5Ly4/WhXgMsTc6hUKoYHIjJRTqUS8o7SWztYWpp56kgPr9yabhzbHeDK5DyrmxnCXideR7tM0FlMb7KeybKeyXJ2qIu4ZNuRaIArk3PsAGzD208PkNrcYXc/h1qlEseqVja2MRh0tJtNtJub2c7m6A52cHFMaM3dyx+wuLZFd8BDqVQmk90nttwIZ+4OuBmbW0arUdMXcMmCks0mo1hFPxZfZHgggq3dzLWJOUqVCpm9RqaS320jvpQmvpTG3taCw9JCvP46Br1WFNBAGKu7ODZLajPDUFdQJj753XZWRddQjbnlRibQQbFMq9lEv9PKrZkEcUmA+enBLh6Xw5+x251BuVzusdrE7HY7Go3mDhfQ2traHW4hgGw2y5UrV7h+/To/93M/BwhCVa1WQ6vV8o//+I+87W1ve6S1PNS/aLlcjg996EN4vV6cTifvfe972djYeO0DFRQUFN7MHDsGn/+8UE3/7/+9UE2fSsG/+3dCrtBP/ZQQRq2goKCgoKDwpudQ9BkbG3vN2vgH4fBh8mHcPIeOpEKhwNmzZ8V8IJVKhUajeSxn0J8895KYUdOk0zJRr3yfmFvC1NyEhhraujvjaHcn2dwBB8USF0ZnsZib6ekU2tOMBr3oDIktpdHrdbQ2G0W3zlA0wF7ugFK5woVRoVVssO64adLrxKr5hdUNVCoVbeYWzCZhRGkw7Cd3UKRWg0sTcarlIv2dwniSXqthpj5ylN7aYTeXR6NW46jXnx/r6hRdKNenExia9AwPRAHBBXMYMr2XL7C8vkW5UiXgsgrHdgfZlIxfqVRqRgai4jWtHIZm12okVtdZWtsSnTzHujtJbwl5Npm9HKVymf6QD69TcNlsZBp18ZnsPpcm4gwPRGhu0nOsOyjL6alUq2T38wwPCLk3GzuNsGVLSxPfuDaJWqXiVF+Yo90BWSC3rc3Mtal5ytUap/rCLEiauAJuG9MLK8wkV0mmNznZH5G5l0r1sa9ypUpqe4+d/QM6XcL6/fY22Yjbzn6ei2MxnLY2hgejzCw0xJgOu1X888bOHtOLaxzr6cRta6Mv5JM5eqTh2oViibmVNUaGujEa9BQkTWEhr4vt3cY9zBeKrG3vcm06wamBLrySsbCRocd30ByKQbf/7O/v72Myme52yAOh1+s5efIkzz//vOzvn3/+ec6dO3fH/q2trYyOjnLjxg3x6wMf+AA9PT3cuHGDkZGRR17LQ/2r9rGPfYwvfOELvPvd7+ZHf/RHef755/lf/9f/9ZFfXEFBQeFNhcMBv/ZrQjX9n/6pMDJWLMIXvgAnTsAzz8Bf/AXcp45WQUFBQUFB4Y3B3Vw6xWKRa9euAXD06NHXrI1/EA4fJh9UwFlfX+f8+fPYbDZOnz6NwWCQbX+cDKIrE3H+/J8uMLuYZijcQW/QLXsYLxUL3Iwv4Whv43hPJ1OSsOCugIeXb0wznVhheCDKid4Qu3sNF4deq+XC2CxtLc2c7AsxNd84Nup388rNGcbii5zsC3OqLyx73eYmPZcn4mg1Gk73h5mXtIIF3TbGEykmEqt0uiz0+BxsSUQBq9nEjZkF9g+KjAxGSUraprzOdi6Px7k0HqMr4OapY72sSVrSWgxaljZ3WV7PcLo/JObwANgtZq5Pz3NxPEZbSzPfeaJPNrIU8jhIbe5wY2aBI1G/LLy41WTkVizJxPwSa1u7vP3UgOzYroCHQlEIvjY1GcSxMgBTk4Gx+BL5QpFL43FGBqNoJe6UgMtGuVIls5fjyuQ8pqYmAnUhxNzcJDqosvt5iuUyraZmOjscALglAdWVapWJuSXMJiPHeoL4XFamJYJOd7CD1NYuiXSGo10BKhLXl621mWnJeJxKpeJ4bwiHRRgJXJK0iHW6rWSyOW7OLJDZy2O3mMVso3aziemFhkuovVXIn7o4Nkt7WwsGXWOQyWltBKZr1GpiEidQTaUivrzOqYEuOjuc9IUaY36PSrVaFcOjpX+3v7//WM4ggI985CN89rOf5fOf/zyTk5N8+MMfJplM8oEPfACAX/mVX+F973sfIPy8Dw4Oyr6cTidNTU0MDg4+ljD1UGLQV77yFT73uc/xh3/4h/zu7/4uX/3qV/mrv/qrJ1aVqKCgoPCmQK+HH/1RuHBB+PrRHwWtFl56Cf6X/wVCIcFBlE6/9rkUFBQUFBQU3hBIa+O1Wi1NTU2vfdADcOgMeq1npsN8oBs3boj5QHdzJKnV6kd+/vqdP/lb8bVmFtOsZbKc7g8D4LW3ElsWhJTVzQwGnY6w1yVm/jRLKsVvzSZYXtvidN25EupwcKse2Jze2kGn0RL0OPDUg37NzY17eWtmgWRqQ3S9dHY4xKDo7d19VCoVblsbgXpekN3SyBxKru2wvV9gKNyBWqXCajZyfToBCO1blWoVo0EvOp867I1mr9lkimRqg5HBKEaDjlajgblVQaCpVKsUy1X2Dgoc6QqI13QYMp3e2mFpfZuj3UGc1lbMzU3ckoRMlytVphdWGBmMolGr6At5xUr4UrlCemsHp62NgbDvjjGpNnMzL16fYijqx2OzMBjxkztotLEdFIrEl9IM90doNTWRSEnr2p28fHOGlXpNfX9Y7rqpVmvMJFdZTG1ydvDOgOqVjW3Wtne5MbNAd8Ajvl8g1Msfsrt/wOLaDsMDUdQqFUGPAylzSymuTyfI5g/4jpP9pDYbLWLSz02tVuPVWzPY29s42hUk6m/kDAGsbjSOs7WZuTQRpzfkI+xzsSMZQ+sOdpA7aOR3HgqLVybn6Av70WgePxr5XuHRwGNnBr3nPe/h2Wef5eMf/zjHjh3jxRdf5LnnniMYFFxmq6urJJPJ1zjL4/NQd2lxcZGnn35a/H54eBitVsvKysp9jlJQUFD4FmZkRHAJJRKCa8jhEKrof/3XhRGyH/sxpYVMQUFBQUHhDUytVmNxcZFLly4RCATE2vgn9QtvlUr1mgJOuVzm5s2bJJNJhoeH8Xq999z3UcfErk3O89L1SfH73qCblY0dLk/M4Xe0yUSXJr2WmeQK16bmyeYO+I5TA4zFGw+nx7pDJFbXuTwex+9owyUJidZrNcSWUtycXWAru893nOxnXJIVdLynk2R6U2in8rnwOxvjPRq1imRqg4n5ZVbWt3jmRC+TEnfSid4QS+vbjM6t4HVaGYj4ZTlFyZU15pbXmEmmeOpIj6yi/Fh3J4mVdS6OxdBp1Bzp8ssaxfIHRda2drk1m+RUf5h1iYOoJ+hhdjHFzZkFsvt5RgaiYpC2sG61GHzd6XFSlAgyIY+Dsbkllte2GJ9b4i1He2RiRWuz4Aoarecc6XUatPXtXkc7N2eTQpvXRJxOlxV/fewMwNIiuELKlSqXxuNk9w84EhXELJ/TKuY7VapVKvWA6uM9neKaDzHotVyeiLOZ3WNkMMpQNCAbW3O0t5I7KHBpPE7Q40ClaogkXX4X6/URuINiidT6Jo72VnoCHmEsL91wRPWHfeQPiqxubHMrlsTUbMBbf/99TptMgGqqi0hTiWU2d/dobW7GYhaut9XULO7X3KSXNZWdPdrLk6BSqdwhxuZyguj0OG6cQz74wQ+SSCQoFApcvXqVZ555Rtz2hS98gRdeeOGex/7Gb/wGN27ceOw1PJQYVKlU0Ov1sr/TarWUlZEIBQWFb3e8XsENtLgIf/IncOYMlEqCUHTuHJw8CZ/7HORyr30uBQUFBQUFhW86KpWKSqXC2NgYs7OznDhxgnA4LObyPMlnnPs1ih3mAxWLRc6dOyfmA92LRx0T+z/+21/R4bAyEPbRpNcxv9pwmGi1OsbnVzgz1CVk2PSE2NoVXBD5QpHM7h4+l01wtui0zEpGdMrlKpcn5zkz2IXJaOB4b1jMsCkUS+zu53FY2xiM+NBq1CxIHvgPiiVevTXD8ECEVpORE70hUptC7k65UiV/UMLcbBRzeQ4zeYRzl7kwFudkbwhbWwvHugOsSXJ5Nre3KJVKHI0KI0N7+UbbVBUV12aSDEb8eJ1W+sM+MYcIQKtWs7q5w5lBoQ1LLxlXqlRrXJ2ax+uw0hvsoNPjkAVjW9tauD6zwMm+EO3mZtrbGiNFKhVMzC9TKJYYHojgsVu4UXdUAfSFvLx0Yxq3zcJA2IfX0cjeAVjf2WN8fpW+kJf+kI9bkmDso11BJuaXuRVb5GhXUHRWHZLazLCeyXJ9OsFwf0R8fwGGIgGyuQOKpTIXx2O0thgZqt83vU4r5jsBtDQ3cXVqnpN9YaytLbSZG9en1ahZ3twltbXL9GKKgU4XOonwJXUBtZubeen6FOvbu5wZ6r6jCn5uqRGwHPW6uTQRp1ypMTzUzdp2RtzWFeiQiXpPn+jjSVCpVO6ocd/f30en090xuvlm5aHEoFqtxr/6V/+KH/zBHxS/Dg4O+MAHPiD7OwUFBYVvWwyGhhvoyhUhXLqpSQiYfv/7wecTmsji8dd7pQoKCgoKCt/W5HI5Ll68yN7e3h218Y9TB3837nW+9fV1Xn31VWw2G6dOnbrjF+9341HEoAujs3zj2iTJ1Abjc0s8faxX9iBobmmmXKlyYXSWdnOLLCcl6ndzY2aBhVXh2Lee7EetahxtMuqpVKtcGJultdkouloAgm4716bmWV7bYiy+xFtP9FOTtIa5bBYqVcHVolGraZJcv9vWxrWpedJbQi7Pd57sl113qMNBqVzh6tQ8+UKRdnOLWANvaWlmPr1NNl/gZmyRfr+Dvb2GUDQQ9pE7KDIWX2Rtawe3zYJBLzz4H45xFYolLozNcrQrIDN4H+sOsp3dJ5neZGphhc4OB9ZWwSmiUsHSmiCyXZ2cx2I2ye7Hse5OVjcy7OcFl013wEOXzy1uz2QFgWZpbYul9BaVWg17PRh7MOJjdVNwK03OL9NqauJETwijQQi3zhcaY1Nzy2tMzK8wMtiFTqNhMOIjKcnxUalVpDaF0TK9TiPLYLJbzFwYnWU0vsjRniDD/WFZVf2ho+jq5DwqtRqdRiPe98FoQLYvGgO5Ypn+oAuzUS9ziEX9HqrVWj0jKEa+UORknzA62NPZIQZ5A6L7ay9/QHJ1g1KlxlCXIBDqdTpxP6/TSkRyPx+HuzmD9vb2MJlMj11b/0bhoarlf/Inf/KOv/vxH//xJ7YYBQUFhW8pTp4UWsj+z/8T/viP4dOfFsKnf+d34FOfgu/+bvi5n4N3vhMeo6VEQUFBQUFB4eEZHR3FYrHQ29t7x0PfkxwTgzsFnFqtxvz8PPF4nP7+/vuOhd3OwwpVtVqN/+O//Q/xe1OTgVdvTVOuVBjodFNVa7k123CY+N02sWo+d1CguanhgmjSa7kyOcdBociZoS62drLMJBsOjoDHwSs3p+nr9FIolrCYm0UnkEatYnxukd29PGeGoqQ2d7g+3ahtj/hdvHRjiq6AkCPTbm4RXUIA8yvrdRdJlIXVDa7PJMRtUb+br1+doNPjwGjQ09Zi5EK9Th1gv1gmndmnP+hieSPDqMSNE/Q4+NrlMdy2Ntw2C3qdlkvjjV/aVWtClfrxnk5Sm9uyUSaPzcKL1ycxGvSc7g9TqVS5Nt1Yl91i5tJ4nLDXiVatZne/4RBvNRm5OBbjoFjiVF+YWq3K1anGsb0hoRLeaNAzMhBhP9/IEWrS65iYXxZcVxYzp/sjvHh9StzeH/ZxcSzGxbEYXqeV9tZGxo1BLzh9DkWY4z2dMsdOxOcSq+hvzizQ1+llZCjK9cl5zC3NjEqyksIdTs6PzhJw22kxGqhIHDqtpmYmE8LrTCTXOHukm93dPcbrLqOVtUbLmdfRLn4Gezu9OK1tYkC1XqtheqHhTAp47Fwai7GU3mQoGiQvyVf6jpMDT0youVdm0JMYEXuj8FBPH3/8x3/8QF8KCgoKChJsNsENNDsLf/u38C/+hZAh9Nxz8K53QXe3IBBtbb32uRQUFBQUFBSeCKdOnaK/v/+uIc3fTGdQuVzmxo0bD5QPdDce1hn0jasTVKpV2uo5KwFHK/sHRQqlCuOJFJaWZjE02WQ0iHXxE3NLGPV6jAadGAB9rEdo3ModFLkwOovT2kbYI4wy6bUa4vXRnsnEMjWVcN2HdfEn+sKkNnfqNfUx/C4bg2FhFEmlQsyomU2m2M8XhLr5FmHNx3s6SayuUyxXuDAaI+pzMxD2idd46IpJrK6ztL5FVeKoCTgsLKS3qdVqTCykGYgG6Q54xGMrRUFMSG3uiBk7XqdwTX6XTQy3vj6dwO+y0+lxim4fv9tGtVpjP1/g8sQcGrWa3k6hgc7S0iwKHHPLa+h0WtrNJtrNwjVJQ6avTM5RQ8VwfwSVShBsDsWQfKHIeibLzn6esEdwrx3pCrC7LzS5rWeyZHMH9Id8+N021CoVc8uNoGi1SsVL16c42iVUux+JNo495MbMAoMRP50dDmYl43JRv4vJxDIXx2LY2s0c7Q5QkXz2Dqvek6kNNnb20Go0YutXb2eHrIp+ayfLeGKFwWiAI9EAyxvyVrdDphdWmU6scHogirXVRF/YLwuL3pM4j/YPikwmVhke7MZsMvKdw0M8Ke4nBn2rOIOUX0UrKCgo/HOh0cC73w1/93cwMwMf/jBYLMLI2C/9kjBC9v73CyNlCgoKCgoKCt9Ubs8DkfLNEoP29/e5cOECpVLpgfKB7sbDiEG1Wo1PfuGvuDQWo1qr0udzsLCWEbeH6i6gW7NJjnV3crJPXhev0ai5OBZDq9FwZjAqChTQqJqfW91iIOzlzFCXmBUE0GYycnkijkal5vRAhEWJo8bntPLKzWluxZIc7Qry1NFeWfW632Xj0niMWq3G8EBEVkNvbWvh0kSc69MJBsI+zh3pZlbiThoI+bg0Hmc/f0Cvz4Fe33ifm/Q6JuJL3JhN0h3wMDwYZT7daLAKu9u5NB4nvZnhZK8gnkhdMyvr21wYm8Vls3C6PyI2mYGQ2XN5co6pxAon+8IMRrwUSo3cqUq1ypXJeUqVKiP9Ydm9jHhdXJuaF0KiPQ7ODnWRkVyzxdzMYnqTudVNBjo97Eq2eWwWbs4uMDG/xMraNm871U9O4iJy24TP2M3ZBbZ3czQbDGIGkrO9VRSsxuKL2NrMdPk9NDcJ43ptLQ0XTHpzh9HZJENRIWcp4nURkwR0hzocXJ9OsLOXZ3ggyuZO47PQ4bCItfVj8UWajU2MDHbRYhRcZ9mDxn0KOC2sbe9yZWKOg2IZq9mEri7KtLU0MyUJFHdY2+pjhjGMBgNvOdrDk0JxBikoKCgofHPo6hJGxZaW4A//EI4ehXxeCJk+cUIInf7iF4W/U1BQUFBQUPhn5UmX5Gg0GjKZDOfPn3+ofKC78TDV8l99+Ro3686WbO4Ao6kZl72dvpDgRipLzjO/vMbo7CJnh7owGvQMhH2iU2Y7u49KpcbW1iI6X5r0jayW2GKa6YVVzgxFadLrCHudoqMms5dDBZiMTfTUj3VZ26hWBZFlNJZkMbXBmcEoRoNezAoC2N3PUypXqdZq9NfX3OV3i/Xp43NLbO0KDVgtRgNmUxOj9dazfKFEqQoHparYsHW0OyiKLDPJVUqlMqf7I7Sbm9GoVWTypfp9qTK/nGY2uUJvwAXA8Z4gS3XBanltC7VaRX/YR4ddaPfKFRoCzMzCCtPJtBhA3RPsEOvk93IH1BCEjcN72VJ3TwEsrG4wOb/Cid4QNksLNHv1NwABAABJREFUHptFvJcgBJ8nVjcYGYyi02jwu2zivaxUq8ytrGMw6MQA65uSEcCeYAffuD6FtbWFY92ddHY4ZE6fTHafi+P10bTBKLck43RHu4OsZ7KMxhdJb2UIeZ2iaKRWqZirC0OFYont3X328gWO9YQA8NgaDWgAC6l1Lo7H0Op0fMfJAVY2M+I2l72R3VUuVzg/OkOryUhvwE3IYxevFWBNMkY4GA1gqjezPQnuJgbt7e3R0tJyjyPefChikIKCgsLrickE//pfC26gl16CH/kR0GqFAOqf/EmhpewjH4Hp6dd7pQoKCgoKCt82PElnUK1WI5/Pk0wm6e/vp6+v766jaQ+ztgdxBpUrFf7ya5dQIYy0OCxmxuaWmV9eY3J+maeORNmX5K30hb1sZ/c5PzpLc5Mep6QuvrXFyK3YArGlNFOJFb7z1ABpyQP8QKiD9NYOF0ZjmJubCHoc4jaDTkt8aY34UprpxApPH+8RRRWAk31hFlIbXBiLYTIaGAjLK9+3drIkVtaZmF/m7FAXy2sNJ8+RaICpxIpQF6/TMjLQJc/WaTKwtLbFrViSE70hmcMo1OHk+nSCyxNxSuUqbzs9yMZ2w80S9nvI7B0wlUwTcFrIZhvb2s3N3JhZ4ObMAhuZXd5+eoDFVCOgeSDsZ317V3QRuW2Ne6nTaIgvr7GQ2mAqscK5I12kNhrXdLynk/TWDtem5snliwxG/GJAM8DOfr5RY+91yAS9wYiPueU1tnb2uDo5z9HuTgJuibhS3ze1ucNMcpViqSK2jvUGO4jXBR0hvFlFqMNFxCeIYdKAamOTgZduTtHUpOdUX5gjXQHWpa6wlmbSmzvcmE7Q6WwnJ/mc9Yd84khgJrvP/kGBUIeTvpAXnUbD1ELDMTUYDVAoVdjK5pheXKNWreBuF8b/XO2tzK80xuHeefYoT5K7iUG5XI7m5uZ7HPHmQxGDFBQUFN4IqFTwlrfAf//vQj39b/0WBIOwvQ3/1/8Fvb3wtrfBn/0ZFIuvfT4FBQUFBQWF+3K/3I8nJQYd5gMdHBwQCATo6Oh47HM+6JjYF//mBb76yg08tlb6Or2EvE6K9bEllQoSq5tkcwXODnXhd9pENw6A323n61cn6A566A566A/5ZCLL6sY2u/t5zgx10WZqYjrZGBeytpn5+pVxeoIeuvxujvV0ykaGsrkCO3s5zgxFMTXpZYHMBr2Wr18dF1q2/G5O9oVkVfTVao31jBAibdBpZSJDpVLj5RtTeKxmuvwuejs7GJ9bErfrtBriS2nODEYxNRmwmBsP9flCgdFYEp/LxlDEj8du4bokzNnWbiG+us3RqJ+WZgPOtmbRnVQsV1ha26KluYlTfWGaDXoxdwkEB9U3rk1xpCuA12nleG9I1pRVLFXY2cszMhDBoNOS3mq4XYxNOl64NoGzXcj6iXhsLErEMKu5hWvTCQbDvnp+EbLXvT6dILaY5lRfmKNdASYkFfGDET83ZhZYXttmeCBKs7HhVNNpNMwmV5lJrhJfWuOZ432sSdbVXw8I39rZ48rUHKZmA91BIYfJ3NzEqKQ1TKNRM72Q4nR/BEuLiSZDw1F2WFs/v7LGZGKFp473iqNjh++3eK2tJsYSKdZ2chyJBvBYGwKbWqXiRJf3ibr5FGeQgoKCgsI/P243/OqvCllCX/0qfO/3Cm1jX/86vOc94PcL2+fnX/tcCgoKCgoKCg+NRqN57AdLaT6Q0+lEJ6nAfhweZExseTXFf/riXwOwsrlL7qBIuVLFUx9pOtUfYXl9m1KlyvnRWYIdDo7Uq7pVqkYw8MzCKpndHNVqVXQKHe0OMpVYqQdBzxL22OiwNh6QD10s0wurpDYzqFSIYc5dfjc3phNiiPTxnpAY1gzgtlqoVmvMJFeZW05jNOjFY53WVq5NzwuV76MxjvV00iYRdPyOVg6KJVa3sswupnHbLDjrgoHRoGc6sUKpXOHCWExo95I86J/sFcKtk6kNRuOL9Ie8uO2NPKft3X1qtRo3Y4u0mUxY29vRqIXrDDqFPJyNTJYrk3Oc6g/jsjaOPQzfvjWbZDOTxaDTioKHx27hev1+XByPc6I3JHNkRbwuSuUKKxvb3IolaWk20FHPAGpuaohOY/Ww75Zmg/h6R7oCohPqyuQcBr2OkcEoWo0atUrFwqogslWqVZbXtphbXud0fwSVSsXR7iBbu/viOvbzBQ6KFUYGhCr6xGqjCczvtvHKzRlmkquc6AtxpDsoCmUApXKFaq3G5Yk5jEY9ep1WDOAeigbYyzcCobd299jY2WNksAuP3cL43J1V9LVajdH4EgcVGBnsQq/VcCTiY2dznZdeeomrV6+SSCTIZrOyvKeHpVqt3jUz6FtJDHqoankFBQUFhX9GNBqhbexd74JkEj77WeFrdRU++Un4j/9RaCb7wAeEfe4ThKmgoKCgoKDw4DyuM2h9fZ2bN2/i9Xrp6elhamrqiY2dvdbakskkz37pb8nsNR6yLa3NXJuaR6/VcvZINyuSMa2gx86rt6apVmt0+d34XDa+fmVc3N7ZYefSeJwmvY6zQ11sZBotULa2FiaTaxwUSwRdVswGLWOSgN/+kJcLo0IGzZnBqKwV6rAefWt3j95gB+YWI5clle4nesO8fGNaPLZaq7G21Xjt9NYOiZV1uvxuKuUCseWGg2gg7OOFqxMY9DrODEVRoeL86KzkHqq5PBEn4nNh0Gll7qMOezsvXJ1EpYKRwSi1Wk1WNe932Tg/OovPaaW91USpVAEyABh0Gq5MxMkVSgx0eqiCLO/nSFeAl25M0d5qYjjso1qtiSNTAKmtHeaX1+jr9KLRwFi84TDq9ru4GV9Bo1Zxuj+EVq3hfL0CHsDYpOfSeJy2lmZOD4RljqsORzuXJ+ao1Wp4He30BDx87eqEZLuVyxNxLk/M0emxy4Qym6WFm7MLlCtVLo7HOTvURa5QEB1MbqtFHJG7NjVPp8fByFCUG9MJHJZWkpLPWsBl58JoDK+jHbvFzO5+TvI6Zsbii1SrNS6Oxzg71IXPZefKRJxarcbWbsNN5XfbmK6Pkzmtbbzv+9/GyMgI+Xyera0tNjc3WVhYQKPRYLPZsFqtWK3WhxJklQBpBQUFBYU3BoEAfPzjsLAAf/EX8D/9T0I9/d/9HXz/90MoJGxfXn7tcykoKCgoKCjcd0xMq9U+knhTq9WIx+PcuHFDlg/0JDOI7jUmVq1WmZiY4MqNUf7qwiRDXX6CHjv9YZ8YIl0sl6nVamRzeU70dAJgMZvEcZxkaoPx+CInekN4bBYCbjtXJucAOCiWKFeqZPbynO4PAxD2OsV69IX0FrkKHOsKYG1tpsVo4MZMAhDyZnb2cqS3djjZJ4QKH+vuFB/wpxZWKJXKnOoL47CY0Ws1LNTdJ/lCkcW1LRbTm5zsFY492RsisSJsn11MYTIaGYz4cVgEV81hpXmhWGIstsjqRoZT9TV3+d2iQBNfSmNuNtLhaMdjswCCcFKpVgXxYyzGXu6A4QGh8t3aahKvaWlti2q1hlarIVjP3TneEyJXqIdbJ1aplEv0+uyCG0etEgWa7d19FlIb7Ozl6A/76vcjyHy9En4ysUyLsZnBiB9rm+BEKdQDqivVGjdnF1la3+ZUXwi1SoXfZRPf4529HLVqDYNOx0D93F5Hu+iSWV7fZm5lnYGwj6DHXg+ZbghWZpORC2MxjnYF8DraiXrdsgynzZ0sN2eS9Id89Ie8jMYaAdVHugIkVte5OBajtaWZnvro2CFL6U1xDVvZfXQaLaEOJwBRn0s2Fpbe2uHyRBy/286ZoS5ii43GuMPQboDNTJa3nR4EwGg04vV6OXLkCE8//TQDAwPodDoSiQQvv/zyQ7mGKpXKHdleuVzuW0oMUn6NrKCgoPBmQqeDH/xB4SsWE5rIPv95oZXsYx8TBKHv+z7BLfRd3yWMlykoKCgoKCg8FI8yJlYulxkdHWVnZ4eRkRFaWyWZJmo1pVLpPkc/OHcTg4rFIjdu3KBUKvFybIPcQZHR2CIatYqnj/Wxajaxnd3HYm5mNJZkP19ga3efXr9ddq7jvSEujM6ytr2LXqvlmZN9bO1k2csXBIEmtcH69i7r27uc7A3JnD6n+sKicGTQ63jmeB8v35gEBGEms5slvZUlvbVDf6iD3EHDudTT2SEKNE16Hd9xsp8Xr0+J211WoWFsdSNDb2cHGk3j/2/srSamkmlK5QoGvY63Dw/yyo1G8cZgJMCFsVkSq+tE/W5ctjZm68KCQacltpRmcyeLXqvhmRN9XJdkJ53sC3F1Uvi+0+Mg7HPytcsN11SlUmVqYQW1WsWZoS6WJS6YkNfJzJIg7tjaTISc7VyZbTh9vPZ2rtWr6Y91B1FJ/p/NbGriVixJ7qCA0aDjSKeLycXGaNbxnk4ujsVYTG/id9kIdzhYTDcCrFc3MiyvC9lCIwNR0tuNvJ8jXQGxTl6tVvH2UwNcmZwT86TKdSHt5mySVpMRjVqFubmJbO6AnoCH6aRQET8xv8yZwSiD0QBzSym2dvepSESjXL7A+bFZOmxmzC0t6LRasZ0OwGOzcGk8jkql4lRfhG3JWFrE6yK+LORQJVMbeOwWBqMBsvt5FlbXRSEQ4JkTfdjazNyOWq2mvb2d9vZ2otEoBwcHd7iGrFar6By63TVUqVTQ3ua639vbIxwO3/Fab1aUpwQFBQWFNyvRKPyn/yS4gb70JXjmGahU4C//Et75TqG+/j/+R2GsTEFBQUFBQeGBeVgnjzQf6Ny5czIh6FHO9zBry2aznD9/Hp1Oh8Xj58vPXxC3He8J8cLVCYqlCmeGuukNdsiCoHf2C4zGFhmKBhgIy10eEb+L/+/iKBqNhpHBLo73hmQhwpVqjfG5JY51Bwl5nCytNcQIe1sLX78yRnOTgeGBCCd7Q6xuNUKkq+USt2KL9PidOCwtgtv58PrUai6Ox4VA5v4IUZ9LFm7dajJyaTxOxGPF2W4m5HPLnEDxpTQmYxOn+8NYW03cijVcL7r/n73/Dm/2oO/98Zf2lrWnJUuWvMcz/EwCYQRSUlYZPfSUA6UUTjmlpcDpr5T2wLf0yyhtodBvyyqrtKzCBRQop2FlkeRZno/33rItWbZla6/fH7d8y06ehIQkhIT7dV26EuseunVL9nPrrff7/VEo+OnQFKfaQvgc1mPl1oVSmYNMDqVCwbmuCEq5nI0jo8vT2Rw/HZqiNxok6LHTEw2Ik68qlSpUq+yk0uLI9wZDfcz57n6G1eQBLQEPzX4H9ge4cQrFIqOzy5zvjmLSa+kKB8Ry7Gy+SBE5douJ0+1hVAq5OPULoFKtcM/wFN2RAE0eB6famkQh6HD5ysY25zojGPVacvm6KKlTq7n/+iyFUpnz3VE6Qj4mFuvTvDpCfu67PoNcLudcZ+RY+bNSIWdmZYNr4/Nk8yVuPtUhimwAXZEAmVyB9eQ+U8sxnFaz2Fml06jEcu9qtUq+WGRlM8n57ha0apXohgKhg2pmZYPRuRWWN7d5zpkuikfe/6943nkeCVqtFp/PR09Pj+ga0mg0LC0t8dOf/pRr166xsLBAKpWiWq3+SjiDJDFIQkJC4qmORgO//dtw110wNgZvfSs0NMD8PLzrXULh9MtfDt//viAWSUhISEhISDxuMbF4PM7999+Pw+HgzJkzqNXqB63zRMXENjc3uXTpEn6/n5MnT/K+z36bnpYgjS4bGpWS5VpJcDqXZz2+w9LGNmc7I4CMU20hUaC5PruMyaCns7ketTp0iOwdZJhcXCO5l+Zkq1Ay3R7yiXGpoekl3PYGmjxOGoxCobPL1kCpXGF774D+iXnKlSq90SAAbnsDczHBQTO1soXFoIVSAX1NaOgM+9g7yAiFzONzuO0WuiJC3EmjVjJdE2DmYklMeh3ymnMF6vGx7b19ro7P09vSRLPfLZ67Sk10GpxaJJPLo5DLMRmEbcN+FwOTC+zsp7kyNsfFE63YzXVR4nAa28jsMqubSewNJqy1AmuTXsvY/CoH2RyXR2fpaQkil9ffX30dzWxs7zKzssH8WoJTHRGcR4qid1MHFEtlLo/OYtCqqFbLYhF3xO9iYjHGxrYwbv6mk+3YjhyX126lUqkyOrfC6tY2JoNOPC6rSc/12RXKlQpXxudo9jlpMOrEAufuSICDbI50Ns/l0VksJgOnavFBpULOfM2ds3eQYT2xw04qTW+L8DqebA2JU9Gy+QKZXAGL0UBfezMyZKxs1juL/C4bd/SPkdhLcb47wsnW0DFRUiaTkc0XuDw6i8NirpVzC8+/pyVIsvY41WqVVDpLNl/kfE8LDquZWy88+pHyh66hSCTCuXPneMYznoHP5yOdTjM0NMRPf/pTstkse3t7FI5M8X28CqQ//vGPEw6H0Wq19PX1cc899zzkut/85jd5wQtegNPpxGw2c/HiRW6//fbHfAwgxcQkJCQknl50dsLHPiYUTH/ta0Lh9H33wbe/LdwaG+ENbxBuTU1P9tFKSEhISEj8UvJIxJtqtcr8/Dzz8/N0dXU97Nj4x1sMKpfLzM3NMT8/T09PDx6Phx9fHRWLn5UKObec7ebykYJhi8nAyMySELVq8oqCAEDQ4+Dq2DzlSgWtWsULLvRy71A9ptXW5BNLlNtDPqzmujvCbNAxNr/KfiaHQafhuX1d3DtSj2md6YxweVTYtjsSwGoycM+2sG+ZDPJlWN7YxqjT0NvsOxYlam/ycu+wsK+eSACVrMrAbD1qZTLouTw6S4NRz7muKLF4PablsTdw7/AUxVKZ3mgQs1HLT4em6/sO+blvZBqzQcf57ii5fP1Dv0qhYGopxlZyj+5IAJVSfsyd1NsS5M7+cfRaDRe6W4Aql46c62KpxPXZFVqDXhRy2bFYk89h5Y5rYyjkMi50RylXKlwdnxeX280GLo/NYzfpsVuMx0RLlULB2Pwq8Z0Up9vDFAolBqaOHlcTdw9MCsfVE6Va5dh7oFiuMDI+j9dhwWNvEEuYQSgSPyzZbg16hCLtgYn6+XRYuDY+z/LmNh1h/7HOHYfFxND0IqVyha2dFM880cr23oFYju13WFnbSgqC19gcbU1eznVHGJhYwGkxc322/pr7HFYujc4R8jox6rXHomcmvZbRuRUKxRKXR2d53YtuRqd9sPj6aNFoNPh8Pnw+H5VKhVQqxdDQEPF4nKWlJarVKv/3//5fYrEYer3+Z+/wYfja177G2972Nj7+8Y9z00038alPfYrbbruN8fFxgsHgg9a/++67ecELXsAHPvABLBYLn//853nJS17C5cuXOXXq1GM6FskZJCEhIfF0RK+H3/1duPdeGB2Ft70NbDahW+iv/koonH7hC+Eb34Aj33hISEhISEhI/OzOoFKpxNDQECsrK5w/f/5hhSB46NLnn5f9/X3xsT0eD4Vikf/nk/8uLreajNw1MEGxXOZibwun20OMHIklWRqMXJ1YoMltIex3YTEZKNeOr1qtMjy9hEaj5lx3lCavQ+zNATDotNx/fZZT7WH8ThsdYT/7GaH/J53Ns57YwWI0cKYzgkmvZXKxHlfP5ovcOzLNmY4ILlsDZzojYqnyQTaPRqvDYjbRGwkAsLdfnyA1v7bB5MoWZzqacVhMtZ6hRWG9gwwyGZSrQjQOwO+yi/Gxkdll1rZ2ONsZwWERHD2H8bFUOksqnWVrZ5/TtYLq0x31SNzo3AoKuYKTbSHRNbWzL/TbZHJ5ppbWWD9SUN0e8onixvRyDLNRj99lw+8UIlKHBdWF2pj7TK7A+e4oCrmMBqNenIq2vZ+hUCyTyebw2YROnM5mH/EdYaLawOQCep2as50R0d109LgmFtZZj+/Uj6vJx8SCMGgklthFpVDgsplpCQjOKZupLvDNrGwws7opxOmcVpwWs1hQDaBWKemfXKCvPYzLZibidx8rmd5MpphYXKe7uRG/3cTIkfhhTzTA1FKMK2NzOG1mOpuPC0uHU8oWY3G2dwXn2mHJdEfYL3YbAbzyEUbEHg1yuRyLxSIca08PN910EyaTibm5OWZnZ/mDP/gDfvu3f5t//dd/JZvNPur9f+QjH+H3fu/3eOMb30hHRwcf/ehHCQQCfOITn7jh+h/96Ef50z/9U86ePUtLSwsf+MAHaGlp4bvf/e5jeZqAJAZJSEhIPP3p6oK//3tYX4evfAWe9zwhm3/77fCbvym4hf70T2Fq6mfvS0JCQkJC4mnCz4qJVSqVG04cSqfT3H///ZRKpRv2A92Ix8sZlM1mmZubo1KpHHvsz33nTvQ6jfihucnrIFcoivGfQqksTsRyWc0M1YqLl7b2MOl1aNRK3DZhX6c6wmztpNhJCXEpn9MmRoM0KiWrtZLkwalFlEoFIBNjSafaQoKjZifFtfF5+jqaafLaxeNXq5RUKlWhsLhQQq1SikJGyOvg2oRQEj0yt8rNpzuwWurnttFhIZMvcm1igdRBFrfFhEGrAcCg1TC1GGM9vsPg1CLnu6Ni5w4I7qSF9ThXx+c4yOQ40xk5dl4rlQqxxA4Dkwt0NweOCQ5hn4v+yQWujc+TymR5/vkeUaQAaG3ysbyR4Nr4PGGfUxSMAHQaNdPLMQanFtlM7vHs0x1i9AqEWNvY/CqXR2dx2y1EfTbyRx5br9exEt9jPblPb8TP7l5dHHNbTVwbn+fy6CwyZDz/XA9rm3VnVEfIz8rmNtfG52n2u3DZ6selViqYW9tienmDmZVNbjrRwtZO/TmdbG1ibSvJ4NQiW8k9TrQG0WnqDpxMLeLVP7lAqVRBIZdh1AmvRXvIJ3YHjc6vYdbr6IkGsdXcZEcnhu3up7k8NkdL0EN7yEdns5+ljXq8LOh1MDq3ysJ6nNPtzccKyzvCfk7WIm2PN9VqlUqlgkKhQKPRcO7cOb75zW/i9/t5//vfTzQa5ZOf/OSj3m+hUKC/v59bb7312P233nor99133yPaR6VSYX9/H5vN9qgf/4FIMTEJCQmJXxU0Gvit3xJuc3Pw2c/C5z8PGxvwt38r3G6+Gd74RnjVq0Cn+9n7lJCQkJCQeBqiUCiAB08U2traYmRkhMbGRlpbWx9UMPtw+3usYtDOzg6Dg4OYzWbkcrnYTRTfSfHhf/se+5kcMpmM557pYnyhHqU61xXl0qgQ/wl6HEQbPfzk2iggtLLs7qdZ3twWpoedbmfySIFwTyQgRodagh4CHvuxaVpWk4HLY7PotRou9kTFWBCA12Hh3pFpiqUync2N2M1G7jkSPWsP+7h3+DCm1UIuX6BaFUqoNSolk0vrbCVThDw2dBo1M6v1qFXIY+fuoWmMWjU9YR8qtYqBqbr7JJ0tMLGwzsnWEPuZDMux+rb2BhN3XBvHZBBEikKpxODkorjcqNcKo9Vbm9hJpTEZtKIoWClXGJ1dRqlQcL47yupW8lh8zKTXcc/QJN2RAOlsDretQYyPlcoVUuks+WKJC90tjMwssXGkkDudznJ9J0Vr0INcLkculzM+X38ddTodE0sbnOuKMLGwhs2sZ7Mm4KTSWSYWVrBbjPhdNuZWNo4VVKuVSu4enKQnGuQgk8NlMx+Lj+UKZTaTu5zrijC9FBO7gACMOi33DE6iUgrF2vl8geEjsa6WoIf7rs/UonqR43E7pYLVRIr9lTg6jZpnn+7kytiMuLwnGuTK2JwoHj2nr5P9TI6VjW00aiXjNScTQC5fZGYlxvmuKOMLq7zmhc98WEH3sXD4u3r4d+CQbDbLuXPnOH/+PH/1V3/1qPebSCQol8u43e5j97vdbjY2Nh5iq+N8+MMfJp1O89/+23971I//QCRnkISEhMSvIpEIfOADsLwsdAm96EXCGPq774bXvQ68XvjDP4Th4Sf7SCUkJCQkJJ4wHurD5OGHwMOoWLVaZXZ2luHhYTo7O2lvb3/EQtDh/h6LGLS6usq1a9eIRqOEQqFjkbP3ffabYkyrWq2yspFgZy/Nhe4WAm47Y/P1D+56rZqfXBulrclHR9hPi9/Ocm0keaFUIp0TioDPd0XRa9TspTPitrv7ae4fmaE3GiTsc9IVaWSoJjhkcnmqQCqT41x3BIVchsdhEWNaU0trLMS2ON0ewu+y4rKaGZyux7QyuTyxZC1qJYNT7SG2kkIcanFzB51Ox8m2EG57Awq5jHRtItZBrsDa9h5rW0miPsEp0Rp0i71DQ9NLOKwNNPmcOK1C1MptF8qtd1Jpro7NU6lUxXiYvcHIcG3s+vD0Mka9Fo1KhdMibHu6I8zG9h67Bxkuj84S9rnojdY7GA9fh9G5FXZSGSq1xwNoCXgYnFoknc1zaXSGk+3CRLPDd2DAbaVQKjO9vMHk4jqOBiNBj1183aYW1ymWyoJby2WjwWRCrRKEyq6Ql7X4Lhvbe/RPLNAWcBM64shS1Pqhrs8uE0vsoJDLcFmF4/K7bAxOLVAqV7gyNkc04MXntKJRC/tubfKSL5Y4yOa5MjaHUqmgr3a+tGoVUzXxcO8gQzyZIr5bj9t1NfvZzx5ORStwkMmh02g42xlBpZCzvFGfQOd3WrlrYIK1eJIzXc2c6WhmP12PYalVCrFzyKjX8RvPOcsTxeHv1wPFoMerQPqBf3eq1eojEra+8pWv8Jd/+Zd87Wtfw+VyPebjkMQgCQkJiV9lVCp42cvge9+DpSWhT6ipCfb24J/+CU6ehHPn4FOfEu6TkJCQkJD4FUAulyOTySiXy5RKJQYHB1lbW3tE/UA34ucVgyqVChMTE0xNTXH69GmCwaAYYQPon5inf3KB7lrHzvmuKLOrmxRKJS6NzhBw2+iOBNCqhUlddXFmnY3EDuVyhZDXCUBbyEf/xLwwEWtsllPtITw2izjVKuh1ks0XGJldZnkjgc1kwOuwAGBrMDIyu0IydcCVsTn6OpvF7QDOdEQEF83UIhvbe/S2BsVYkUwGqUyWrWSKaxPzdDX7OTgiQnWE/AxMLXJtYp6d1AG3nOvm4MgkquZGD5u7B8zGkgQ9NjSK+uOa9Bquzy5zdXyeVCbPc890MbNSj2md6WxmeGaZgalFogEPvS1BskecLcViWdz2Gb2trG7WxYuwz8W9w9P0Ty4QaXTz3DNdLKxvicvbQj6ujM6xs5fmXFcUY23iGQjOp5nlDa6Oz+G0GDgR9TOxVHeGnGhp4u7BSVa3kpztjHCqPczuQf2c6DRqLtWKs892NpM6cj6sJj2DMytMLMYIua2ciPjFMe4AJ1qbuG9kht2DNBe6o/hd1mPRrfjuHpeuz2LS67jY03Js2/YmL/2Ti/RPLtDksXOxO3rsuCxmA7HELgOTC0QDbo7KGx57A0PTSyRTaa6Oz3OmMyq+fwD8TlstnlXl2vg8G9t7nO+OYtJrcdvMx3qHXnC+F7PxsRU5PxzlchmZTHZMoCmXy2Sz2cckBjkcDhQKxYNcQFtbWw9yCz2Qr33ta/ze7/0e//7v/87zn//8n/sYjiKJQRISEhISAo2N8O53CyPpb79diIqpVHD1Krz5zeDxwP/4H/DjH8PjWIIpISEhISHxy4hSqeTg4ID777+fcrnMxYsXH1E/0I34eQqkD/tFtre3uXjxIna7/di+yuUKf/aPX2Z2ZYPRuRX6OprJHRkK0RLwct/IDPdfn0Gv1XDr+R6WjsSlWoJe5jZ2WIzFOdPRjP5IJ4zFqGdkZpkr43N4HRZuPt1B/0R94tWZjgj3DE0R30lxvitCW8B7rKMnvrNP/+QCzX4Xfe3hYxPCIo1ufnRllHQ2z4XuKBd7WliK1XtiquUyowvrtATctDd5KR4p8tbWRJBSpcyFniidzX6uHTkut93C9cUNOsI+Qj4HQbdN7JnJF4q1suoqF3qiWM0GZlbrH8oLpRJ3DU7SHQ0QaXRzuj0kxpfyhSKVapX9bJ4LPS1oVEqM+np8bDm2zcTiGl2RANGAG7e9gcFafKxQKpPNFZhZ3uRCdwsatZJTbSGxCHprN41SqSEa8NLeJAiNezWBpVKpMrUUY2J+jQvdUUwGLS0Bj9j5JOxDhlwm52RrCIDWoI9CTfRb3NwhlcnR6ndgNWpRKxVM1qJXhWKJtfgOU4sx4bhUSk63h8XXIrG7T7VaxWY2iuPmjzplYtt7DM+u0NbkpSXgptFlY+hIybTZoGdwepmwx0bE7ybodogl5SAURQ9OLRENeDjVGmL4ASXTc2ubXB6bBbmM3pYmlEeceL/z4mfzRFIul1EoFMfEoHRaKOc2mUw/937VajV9fX388Ic/PHb/D3/4Q57xjGc85HZf+cpXeP3rX8+Xv/xlXvSiF/3cj/9AJDFIQkJCQuI4cjnceit8/evC9LG//VthZH0uB1/6Ejz/+dDcDH/5l7C4+GQfrYSEhISExM/Nz4pmjIyM4HK56OvrEzt6fh4OnUE3KqS+EQcHB1y6dAmFQsGFCxeOjbM+FIO+8J93HRvJrZDLGJ5Z5lRbiCaPgyr1x6pUKlwancVpNXOmM0Jbk1ccFQ/Cebg+u8y52rStlqBXjDytxXdY20oKkaiWIGaDjolFQVAolSskU2mGZ5cEsUKv5WxXRHTIzK9tIZfLCXrstNWEjsNzkCsUGVtYZSmW4EJ3FI1Kic9uFh0yMyubmAw69FoNYX9tmlTITyqdrUWtZtFqVJzviqBUyNFp1MyvCY87sbhOpVJFo9EQdAsiWkfQw9zaFvuZHJdGZ4n6HUR8LtHBZDEaqFSqjM6tsLKZQKtW4XcL0TOX1czA1CKpdJZLo7P0tAbRalTitqc7hfjY2Pwqs6tbdEeCuGwN4vlN5/IcZIXHdduslEolcdtmv1BQPbUUY3Ipxi1nuzlqqekI+0imDoTuoSoEPA40tXiYWqlgKRZnaSPB0PQiZzqbOcjmxG17owEWYttMryXYzxY53xmmUq0LMkatir2DDJdGZzAb9Rh1Wg5/JawmPcMzy6xsbjM4tcgzTrRSOfL+PdUWIpk6YGopxszKJi0BD42ueqlxfEdwlC9sJNk5SFOuVMSJat2RRvG1ml3ZRK1S0RLwEGl0i++rQ8rlMvddn6bBpOdMZzPPOdNFS9DDE0m5XH5QDPRQDHqsMbF3vOMdfOYzn+Fzn/scExMTvP3tb2d5eZk3v/nNALzrXe/ida97nbj+V77yFV73utfx4Q9/mAsXLrCxscHGxgZ7j4NjXyqQlpCQkJB4aFwu+JM/gf/9v+HKFaFw+itfESJl732vcHve84Qx9q94hTDSXkJCQkJC4ilMtVplbm6OYrFIOBymra3tMe/z0FFxOKHo4TgsqW5qaiIajT5IsFIoFGyn0nzqO1c43x1lYHKBsN8lijuDU4uc74pSqVbx2i3EtndprYk/qXSWWGKXc50RTrWFGJxarI1/XxM6Y8bn6Az7UchlmA16UukMZ7siXD0iHD23r5ON7V1SR/pcMrkCl0Zn8TksqJUKtGoVuUKRSKObaxPzogB0y9lu5o9EqTpDjVwem2UtvoNJp8Znb2BzZ59ypYpeK4g7h2XGzzrVxlKsHtM61RZioFb87LE30BMN8qMr18XlFpOBgalF5HIZ5zojx0QSh9nA0OwqxVIZh9lAxO/g8kTd1XKqPcx912dQyOWc7YqgUMi5dL1efJzcSzO/tkWjy4bPYTlW9Nzsd/Hjq6PI5TLOdkVQKRTcNzItLreZtFybWsJja8DvspKr9R8BqBQKxuZX2Uym6OuIkM1mRYcRCNO1fnJ1DIfFRK/fiVwm5/Jo/biowtj8Kr3RIPuZLOkj07d0GhXDc2uUK1XOd0XZ3ksdi6Y5TDruHpyg0WXDaW1ApZQfEwx39zNMLKzREw2QLxSZPRK387us3DU4QbUKZzqaUakU3D9SP66I38XV8XkUcjlnOpqPiT06jYrxxTWxH+jmk+3Mrdf33RMJcnlslnQ2T3x3n3/7f/+QJ5pDZ9BR0uk0Go3mWKH8z8OrX/1qtre3+au/+itisRjd3d18//vfp6lJ6J6KxWIsL9ddUp/61KcolUq85S1v4S1veYt4/+/8zu/whS984TEdi6z6SOXpXyFSqRQNDQ3s7e393FZQCQkJiactmQx861uCMPTjH9fvN5uFSWW/+7tw/jw8QRMeJCR+mZGuISQknloUi8Vj8a1SqcTIyAj7+/soFAqi0Sgez2N3IZTLZX74wx/yvOc97yEdRtVqlYWFBebm5uju7sbr9d5wvXw+z6v/f3/DpSlBgPA6LLQ1+bhrYJxqVZjwVSqX2c/kUCrkPOtUB2OzK2ztCrGk891RcZJU0G3DbtQyOFefINbsdzK/Fkev1XCqtYn5tS1i27sARBvdzK1tUa1W6Y4EcDQYuXNgQtz2XFeEK2Nz2MxGogE3++mc6CIyaDVo1CqSqQNO12JHwzPLYnSos8nD+NIGHruFRpcNhVzG5SNiRE8kwPjCGqfbw6wndiiXy2xsC+4Ij72BZCqNx96AvcFEqVTm+pFo2oXuKP2TC5xuCzOzEhPHxYNgwvHaGyhXyujVKrb20iCTka718EQa3axubXOyNcTk4jotQQ/XxuvRtPPdURI7KQw6LSOzy3Q1N4o9O2qlApetAb/TxuTiOiq5jN1MVhRDupsDpHM5zHodwzPLx16bw/OJDGZXNkjuHRBp9DC3KgglRp0Gn9OGSa+lf3KekNfB0sa2KLydbg8hQ8ZGcpe1rR0udLeIU+UO9y2TyRicXKBcqWA3G9naFaaTNei12BuMaDRqJhbX6WxuPCZ4ne+OUiyV2UwK5d1nOpqPxfUifhdqBawm9pDLFBRKRbI10autycviepwTLUEmlmJ0hHzHRKeTrU1cn13hVHuI1c1typWKGKnrCPn5r//vXU/YFLFD4vE4CwsLnDt3TrxvcHCQl7/85SQSiSf88X9RSDExCQkJCYlHh14Pr3kN/OhHQkzsve+FUAhSKfj0p+HiRejqEuJlj3BMpoSEhISExJPB0Q91D+wHUqvVj3kc/CGHkZOH6g0ql8uMjIywvLzMuXPnHlIIAvjp8LQoBAE0eR3c2T+O32Wnr6OZZr9LjHhVKlUW1+PspjNc6Gkh5HUeH1eu0TA4t05r0EO00UVnk5v5NaFXKJPLk8nlSeeEbh+dRoVMJqv35GwkGJ5Z5mRrEyGvk+aa+wMgmTqgUqmwndrnbGcEuUxGdzRAMiW4fAamFskXS3Q3+7EatWhUShIpoSdnY3uX2PYO8Z19savmREsT1+dWKFcqXB2fI+i20+RxYjMbAPA6rBSKJZY3thmeXkKjVtEbFUq1bWYDI7MrtUlUs3gdVlRKBWaDDoCznRHWt/fY3DlgYXOH7rAPh+mI07laIV8ocXl0VoijqVVi+bXfaWVgcoG5tS1GZpd51qn2Y2XMp9vDrG4luTw2S75YJOhuECeAgRAfW1iPMzy7TG9rkHyx7hIK+5xcnZjnytgcmVyBF5zvFUURgO5IgOnlGP2TC4R9LkI+l/jayGUyNrdT9E8usJHY46YTbazF666qtpoAc3l0lgaTgeed7SZxZJx82O9kPpZgYnGdgMOMUlZ/32rVKqaXYwxMLrCR2OXmUx2sbtX3fbKlibm1LSaWtyiXq5zvjqI4ErlSKhTkiyWujM+jUshRyOXoNELBecBtZ2h6iXKlwrXxeQJuO80+Fw21sujff+XzfyFCzEM5g/RPMwe8JAZJSEhISPz8NDXBe94Dc3Pwk5/Aa18LOh1MTMCf/qlQSv2SlwhOoiOllhISEhISEr9MbG1tcenSpWP9QI91HPxRZDIZcrn8hvvL5XJcvnyZbDbLxYsXaWhouMEeBLL5An/xia8R8drwO624bWaGp4VIyermNvlCgWTqQBRRznVFWVjfolAscen6DG57A53NjZgMOuQyGYVaOfP08gbJVBq1Vif2sTS5rQxOL4k9OV1hP1aTAbVS+JDc3uRjZz/N0PQSSxtxAi672Alj1GmYX4uzlUxxdXyO3pbgMSHsdFuIsflVhmdXSGUL3Hy641ifkt1sYn59i8GpRVqCHgw18QWE/p7BqUUuj82SyRV43pku5tbq0bOznYJLZaRWbnyiJXis3LpQLHFpdJZKpcozeltY3UqKy5o8Dq5NLbO0tUNHyMe5jhBza/XS7UZHA/cMTSGXy7nQHcVjbxAntMllMpZicSYW1+iOBOiJBo65k+wmHQOz6ygVCi50Rzl3pFsJhAlhQ9NLdEcaaQ16MBwpqC6Vy4zOr1CpCuXXAZeNwSNlzVqNmjsHJmgL+ehsbuRMZ4S1uPC8ypWK4OLZTnG+uwWLUY/8SClR6iDDwOQiHoeFvvYwPqeV0fk1cbnZZGRkbp2I147HYiTitbOTSov7PsjmSO4dcL47itWkZ/2I6KRSKfnp8KR4vrojjcemk0UDHu6/PoNGreJsZzNOy/Fy5lhil8tjcxRLFW69cIKXPKuPXwQPJQYZjcanjSsIJDFIQkJCQuLxQC6H5z4XvvhFiMXqDqFyWRhb/4pXgN8Pb387DA092UcrISEhISEBCNGs2dlZhoeH6erqoq2tTXTxKBQKSkcmWT1WbiQu7e7ucv/992MymTh37hwajeYhthb4yJf+k8X1OHOxJLHELt3RIA01F4taqWB3P8PCepzBqUVOt4UpHXm8rkgjl0dnuTw6S6VS4fnnuo85TcJ+F0NTi8wsb3CiJYjHaReXmfRaxhfWuDI+h16r5kJnmMHpRXH5mY5m7hqcYD2xw5mOZk63h0QXEEC+WOJqzenR1x5mYa3eCeOxW7izf5y9dJbzNZHk6Bhxu9nIfSPTtAQ8dEca8Tut5AqCg6ZQLDG/tkW5XBbGpDutjC/UhYxKpcId/RN0NTfS1uTlTGezOCHsIJujXKmKzie1SoHJoBNja3Orm6zEd+ltCdLsd+FoMIodO6l0ls3tHeZWtzjXGUGtVHC2M8LyhiCEjM6toNOoiTa68doEgcNoNIjbDk4vigKKSqHA67AwUIutjc6volYrUcjlhH1OAPram4kldsUS6oDHzsnWJrFI+vCYp5ZiLKzHKVUqtDYJ7jK/y0b/xAKFUpnLo7OE/W4ajHpMBmHU/am2ENt7+6zHd+ifXCDS6KYnGhTPYaYg/A7MxbZJF0ooVUrcFqFE2WM1MTBZ37fLrMdjbxD33RHykckVREFRp9GIz1mtVIgl0rv7GaGoPL7Dmc5mFHIZJ1uDolCXyeV55olWVMqH79t6vHgoMchgMPxCHv8XhSQGSUhISEg8vjQ0wJveBPfdV3cIeTyQSMBHPwqnTsGJE/DhDwvCkcSvDKFQiI9+9KOP+35f//rX8xu/8RuP+34lJCSe/kxPT7O2tsaFCxceFM1SKpWPmzMIHiwGra2tcfXqVcLhMN3d3Q+aXvRAxuZX+cy3fyL+3BsN8uMroyR2UpzrinKhJ3rM5VKuVLg2MU9LwMOJaJCdIzEgo07LPUNTyGQyLnRHOdnaRP9EvahYq1ZzeXSWEy1NNPtdtDX5yNQ6X/YOsqxsbWPQqOgIuGgw6JhaEv49r1SqJFMH3H99lrOdETz2Bvo6mpmoCTQrm9vkchk0KiUnWgTBwWoyUCyVKRRLDE4usJVMcaE7SoNRj9vWII4rn1nZoFKBbKFIZ7MfEFxAi7G4OF0s6HHQ1dyIXiv0MslkcvHcrW4moQotAWFiVcBtp39ivjZNa5aTLSEMOo3ofDrdHiaW2GVkZpn5tS1OtDThc9anZRVLZXYPMlwZn8Nu1lMsFsTx5yGvk2vj8wzPLBNL7vO8M51k83WH9KnWELOrm1wencVqNtDV3Cg6p2QyOMjkGZ5ZYmE9zvnuKLsHaXHbsM/J/aOzXB6bxWjQ8fzz3SwccUadaAkyMLnA9PIGvbXXr3xk34eTyaoVGc840Xqs0Dvsd3HP4CSDU4tEG90870zXsX13NTcyPLtKPJWmryOM78j0MBmwvZ9hZG6NaqXKTSfbmFquX+d1hP1cHReiadYGI8/u6yR55D3pc1rZTO5xbXwel60Bs0Ev1k86rSZe8ZwzFAoFisUi5XL5ISOXjwc3EoMODg4e8ySxXzYkMUhCQkJC4omjvR0+9CFYWREcQq96FajVMDIiTClrbITbbhMmlGUyT/bRPu15/etfj0wmE292u50XvvCFjIyMPNmHBsBf/uVfisemUCgIBAK88Y1vJB6PP+x2H/vYxx7zRA0JCYlfTUKhEBcvXsRkMj1o2eMZEzu6v2q1yuTkJJOTk5w6dYpQKPQzoyelcpl3/P0XMem1nO+KYtSqWN5M1JZV2ErucW1igfPdUVxWM6fbwwzP1EUUrUaNQa+lJyL06LjtDWTzBfYzOQYmF0R3jF6rxmUzi86c4ZkldBo1xXJZdKmc64qwlkixm84xsbJF2GPD3aCvB4+qVYqlMlfH58gXS6hVClw2oVDfbTUysbzFxs4+wzPLPOtkO0pF/SPp6fYwi7E4l0ZnKZZK9EQDqFVCn4xCLuMgm2VycZ3x+TX62sPkC/WOnZZa5OjS6CwqpZIXnOtheSMhLu+OBrg2Mc/Myia90SBBt10sclYpFEK3z+gsZqOem060Mr5QjzO1N/n48bUxFmNxTreHuflUO6vxXXG522ZhYHoZo05Nq9+Bgoo4hl2tVDC+sM7KZpK+jmY6wj5R4AKwmY386MooTmsDZzubOdsZYTF2/N+96eUNQXxxWjHo6vGxvYM0Y/OrWBsMnOuM4LY2MDxTd1XlCkXuGZriZFsTIZ+TMx3NLMWEc3KQzVEuV8kXy1zoaUGjVopdSAAL61tMLsZoa/LR1dyIzWwQj7tSqXKQyTMyu8LpthAWg5ausFfsfTrI5kkmdygVS/S1h1Crjgsre/tpBiYX8NgtnOloptFlE51RILjF7h6cpNFl53R7mDe/4gWYjAZRoCmXy5RKJQqFAqVS6XEXhm4kBmUyGakzSEJCQkJC4lGjVMKLXgRf/7pQKv3JT8IzngGVCvzXf8Fv/7bgHvq934O77hLul3hCeOELX0gsFiMWi/HjH/8YpVLJi1/84if7sES6urrEsaqf+MQn+O53v8vrXve6G657+M1gQ0MDFovlF3ugEhISTwu0Wu1DTvd6ImJihUKB/v5+4vE4Fy5cwOFwPKJtP/3NHzM8vcTWTorLY7M0uW2EvQ4UckGCUauUZHIFLo/OUipX0GlUuKyCANPkddA/Oc/M8gbX51Z49ukOqg8oOZ5Z3uDS6CxKhYKeSFB0ZKgUCvYOMgxPCy6Viz0t7B0ZKd8e8jE0t8bM+jYum5mLHWEW1utCRpPbxv0jMyT30rT67djNRrFgWadRM7m0xtD0EpFGNxe6W8QJXwDRRg8/ujJKqRYBO9/TIsawQOhhGpoRpne1N3mpUn9OlUqFa5Pz6DRqzndHaWl0HZsAJpfLuHdkmpOtTYR9Tvo6hAllAIndfXL5Ilq1mnNdQgSseOR9MLMcY3Rulb6OMI0uG0GPneu1jp3ddA6FUsnOfoYWnx0Z0BZwsbG9S7VapX9iHoNOEOW8DguAGOXb2N5lfH6NvYMMZzrCyGSHEa/52rYLOC0mtGqVuG1fhxAf20qmuDI+R0uTl+5II/LaC3j4Og5NL7GZ3EWhkBP0CPE/r8PCwNSC6IzqjgTRazVi9OxMR4T1xA5TSzHG5tc42Roi7HPVz6FMRqlcYWBqkUK5QoPZjKPW+eO2NzCzlmA/m6d/cpEmpwUFFVQ14e9kW4jtvQPWEztcm5gn7HOJPVcAO/uCE2plc5vljQSve9HNKJVK1Gq1eFMqlcjlcqrVqigMPV6uoUql8pCdQU8nJDFIQkJCQuIXi9UKv//7cO+9MDMjFFCHQrC/D5/7HDznOdDcDO9+N0xPP9lH+7RDo9Hg8XjweDycPHmSd77znaysrBxz31y/fp3nPe956HQ67HY7//N//k8ODupW7sNY1t/93d/h9Xqx2+285S1voXhkCsrW1hYveclL0Ol0hMNhvvSlLz2i41MqlXg8Hvx+Py9+8Yt561vfyg9+8AOy2Sxf+MIXsFgsfO9736OzsxONRsPS0tKDYmKVSoUPfehDRKNRNBoNwWCQ97///eLytbU1Xv3qV2O1WrHb7bzsZS9jcXHx5z+pEhIST1kezpHzeDuDqtUqU1O1aNaFC4+4f2RudYPv3H1N/Lk7EmBsaZP+qSWc1gZecL6HudX69M5Io5t7h6fZ2U9zviuCzWQQHTBmg47rsytcn1uhNejlQk+Uq+Nzx7b98dVRVEolF7qjnOlqPhY9y+YLTC6uc6o1RLTRfayYuVSucH0xhsdh4XRbE81eO0MzK7VlZRQKJdOrcc51RXDZzPS2BInv7Nee4yYH2RxNHgcnWoKolQoStTHnmVye+bUtphZjXOhuwWzQ0RHyi6PMx+ZXMRv1aFUq2pp8AHSG/eyk0uzsp8WJWWc7m9GolWhUSnEk/dD0EplcnkKp7nzqCPnpn1xgayfFlbE5znVFsVlMooOpM9xIMnVA/8QCa/EkkUY3HodQ+q1UyNlM7pE8yDKzvk1L0INMVq9rDjotXBuf5+rEPFvJFM8/1y2OsAfoiQaYWopxbWIBv9NGW9ArvnZymYxkKs212rbP6G053vnkc3Lv8BRXx+fxOCzccrab6SMxrRMtTVwanWVlK0lfR5iI3yWWXwPs7qe5PDaH0aDnQk/LsfhYo8vGXQOTTCyu09Uc4OZT7Uws1ruZeqNN3Ds8RarW+xT2ucTjBighZ3Qhhl6rps3vYPKI6yrosfPT4Sn6JxZo8jh43pkuFo8Iin/wm7eiP+JYksvlKBQKVCoVGo1GFIYeT9eQFBOTkJCQkJB4oolGhdH0c3Nw993wxjeC2QxLS/C+90Fbm1BE/YlPQDL5s/cn8ag4ODjgS1/6EtFoFLtd+KYwk8nwwhe+EKvVytWrV/n617/Oj370I/7wD//w2LZ33HEHc3Nz3HHHHfzLv/wLX/jCF45FtV7/+tezuLjIT37yE77xjW/w8Y9/nK2tLR4tOp2OSqUifjufyWT44Ac/yGc+8xnGxsZwuVwP2uZd73oXH/rQh3j3u9/N+Pg4X/7yl3G73eL2z33uczEajdx999389Kc/xWg08sIXvpCCNPFOQkLiCI9nZ1A8Hufg4ACTycTp06dR1aJPP4tKpcI7PvKvtSlTwoSq7b19cblMJuOewUkcFjPnu6KcaGkSxR3hg76M0flVIUJka6At6BWLnaeXY2zvHtDsd9MbDWLUa8QI0d5Bho3tXaaXYlzojmLQajjV1iTGhAanF3FYTNjMRpr9wt/hoMfBQTZHLLHL0PQyZpOR3ogfuQxMeg3LWzuUymWujM2hUyspFoq4a/GxvvYwo3MrzK5uMjyzzE0n2nDWnE0g9Mls7+1zaXQGAJfNLI6Gt5r0TC6uMzq/ytTSOjedaCWVzonb9rWHuTaxwKXRWfRaDTef7mBn/0hXjUOIKC2sxzndFsKorwsPZoOekdllro7NYW8w8ayT7QwdKc4+2drEHdfGiSV26Y000t5oJ7lfd06ZDDpG5tfwOa2cam1CcaQAWamQcXVsjq2dPc52NtPV7D/mjLKY9Pzk2hgBt50zHc2c7Yqwsik4o8qVCqVyheWNBGc7I7htZnQajRgfiydTjC+s4nPaONMRxmkxifGxarXKfjrLfaMz9HU043cJ6xxOZDt8f1VBLHt2WExi79D4wirza1s0ua00+52YDTrG5gXRr1AssZncY3h6ifPdUWxmA6fa6m6xvXSOBosZhUJBV8iDQi5DJUM87uXNBDMrG4S8Tk61hXBZzbzu12/m4ZDL5ahUqmOuIYVCgUwmE68fDoWhR+oaeqiY2NOtQFr5ZB+AhISEhIQEcjk861nC7R/+Ab7zHWEy2e23w6VLwu2P/1gYU/+61wk9Qw9h65d4eL73ve+J32yl02m8Xi/f+973xOLSL33pS2SzWb74xS+KFz3/+I//yEte8hI+9KEPiaKK1WrlH//xH1EoFLS3t/OiF72IH//4x7zpTW9ienqa//t//y+XLl3i/PnzAHz2s5+lo6PjUR3r5OQkn/jEJzh37pzY51EsFvn4xz/OiRMnbrjN/v4+H/vYx/jHf/xHfud3fgeASCTCM5/5TAC++tWvIpfL+cxnPiM6Aj7/+c9jsVi48847ufXWWx/VMUpISDx9eTycQdVqlcXFRWZnZzGZTDidzkc1mvpz37mTy2OzgDCh6nxXFJu5iloOS1u7WM0GYokdcsk9UuksTR4H5zojDE0v4rCYGZ5Zolgqc2V8jt5oEGQyvA4LscQu57ui4r4Bnn26g939jCgWqVUqtvcSbO/N4rE3YNBqMem17GdyQvny5ILoLHluX6coVACc7YpweVTYt8squIDu7B8HhKLhSqXCwPQSCrmM3mYfu0cmjx06RYqlMs1+F0GPQ9wWBNfPXQMT6LUaLnRHqYL4WABbyRQzKxt0NTeikMtYjNV7g/RaNXf1j2PQaTnVGiJfKB6biqZSKbk8OsvJ1ib20lmcDSau1MS1zeQetgYjFpOBJq+D8flVYoldQOjQWdqIUyhV6etoZnN7F6NOK5Zyr8V38DmtFEoVznQ0MzC5QFdzIwNTgrh2dXye1kYXnSEvmzv7bCVT7NcErZXNbfYOsngdDfS1hxmYEhw01ybmqVSqXB2f41RbE0qFEq/dQmx7l76OMJdq52RtK8mzTraTzefpn1ygWgWZXEalIsTW9Fo1QY8dv8vG2lYSl80sRL+KJRK7+/S1h1HI5agUCorlMidaggwd6SW65WwXq5tJppbWATDrdSyux7k8Ooteq8ao12IzG0im0lhNekbnVsnkCiRTaU62NiEHlJs7lCoVol47M+vC+2gxFuev//C/o9M+8uu9o5MAqb3PDgWgarV6pKRbhlwuF//7QMrl8oPuP7xmejrxpDuDPv7xjxMOh9FqtfT19XHPPfc87Pp33XUXfX19aLVampub+eQnP/mgdXZ3d3nLW96C1+tFq9XS0dHB97///SfqKUhISEhIPJ7odPDqV8N//iesrsJHPgInT0KxCN/8JvzGb4DPB3/4h3D//VCt/qw9Shzhuc99LkNDQwwNDXH58mVuvfVWbrvtNpaWhAvSiYkJTpw4cezbr5tuuolKpcLU1JR4X1dX17Fvzbxer+j8mZiYQKlUcubMGXF5e3v7I+r1uX79OkajEZ1OR2dnJ4FA4FjETK1W09vb+5DbT0xMkM/nueWWW264vL+/X/xQZjQaMRqN2Gw2crkcc3NzN9xGQkLiV5PH2hlULpe5fv06i4uLnDt3Dr1e/6jEpYW1Lb5/7yCeWj9MV3Mjl8dmGZtfZWlrl5t6osiP6Eo9kQCTS+tcGZ/DZNDRHvKJPTo6jZqtnT2ujs+xlUzxrJPtbO3WI0bdzQHuGphgeGaJsM/F8892HYsYBdwOfjo8RaVa5UJ3FLvZIApBeq2asflVZleFYubTbaFjZcAum5kfXRnF1mDkXFeE890RVrZ2hXNUqVKpwkIsQYvPjrPBgFohF/e9Ht9hYmGNkE9winjtFtGdlMnlSWWyXJ9d5kJ3FKtJz7muiDg6fmx+FZ1Gg8tmpjPcCIDVaKBQKrNTKzDO5ApicbbDYuL6rOBwGZpeQimXUyiViDQKX4Kc6QgzsbDGZnKPK2NznGoPE/Q4xAliTV4X2XyB/ol5NrZ3cVrNYj+PSa9ldmWTtbjQkdMTDaBSqsRun86wn+nVLYZnV9lKpuhr8ZPN191N7SEvU0sx+icX8DmsNPvcoptGpVCwmUwJr+1Oipt6W9k6Eh+LNLq5Z2iSaxPCtrec7WZmuR4r7I0GuX9khlhih76OMC0BL4Vi/X2fSme5Mj5Hg1nP6bYgS7G6y9drt3DP4CRTyzG6o0FuOtkmlo8DdEeC3DM4STpX5Hx3Cx3hRjK5ugu3WCozML2EtcHI+a4o2WLdteMw6wkYYWpqiu3t7Z8r7nXoGjqMkx26huDGcbLDx3io0fJPt5jYk+oM+trXvsbb3vY2Pv7xj3PTTTfxqU99ittuu43x8XGCweCD1l9YWODXf/3XedOb3sS//du/ce+99/IHf/AHOJ1OXvnKVwJQKBR4wQtegMvl4hvf+AaNjY2srKzccEKAhISEhMQvOR4PvP3twm1kBP71X+Hf/k0oof6nfxJuzc1CAfVv/zY8SufJryIGg4FoNCr+3NfXR0NDA//8z//M+973PqrV6kN+a330/gdGHA7t2FC3ez+ab78PaWtr4zvf+Q4KhQKfz4dGozm2XKfTPex+dTrdw+6/UqnQ19d3ww4jp9P5qI9XQkLiqc3D/T15LDGxXC7H4OAgABcvXkSr1aJQKB7xB9pyucIff/gLXBmbQyGXc7GnhYMj3TJ2k57+qSVyhSJtQS8uWwP3DE2KyyONbn5ybQyrycCp1hAyqtw/Kgje5UqFxN4+i+tx+trD7KUzJPbq4kGuUOCnw9M0eRw4LCZyhaIYPUtn81SqVaaWN7jQHWV6OUY04OHKmLB8ZHaZkNtKo6MBjVbL/Nom27Xx4VvJFDJkqJQKzndHGZpeJOxzMbogOEpm1rfpjfjJFwo4GvQk9jK0BdwMzwn9MovEeUZvK75CkYGpBZRyBfvpHJlcgUujs/gcVhQKOQ6LicTuPpFGN1fG58R/k559ukN0PYFQnH3onjEbdJxqbRJ/BqhUq6Lw1NcRPtaB43dZuXR9hmKpTINew8nWMJfG69v2tTdzz9AkMplMiJ4ZdNw9MFF/fStVLo/N4rFbCLjtxHf2xGUNBh3jy1tk80WiPjtQPVZ+7bKZ+Un/GH6nFW/tOR86o8qVCoVSiaVYgjMdzcQSO6iORNMSu/uMzq3gsVvwu6wsb2yL8bFKpcr+QZbBtUX62sPEd1M4LWYxupbY3cdu1CCT116/qUW8Tiux7V1AcK5FGt10RQJUKhWWYnFRmMsXiqxsbLOXznC+u4W51Q08dgujc4L4Ft9J0ex3kS9VON8VZXBqgXe94RV0drSQSCQYGxujVCpht9txOBw4HI4HXR/8LH6Wa+hQ+JXJZA/pDJLEoMeRj3zkI/ze7/0eb3zjGwH46Ec/yu23384nPvEJPvjBDz5o/U9+8pMEg0E++tGPAtDR0cG1a9f4u7/7O1EM+tznPkcymeS+++4TL1Sbmpp+MU9IQkJCQuKJo7cX/vZv4YMfhB/9CL70JfjWt2B+XugXet/74NQpeM1r4Ld+C/z+J/uInxIcWqSzWaHjoLOzk3/5l38hnU6L7qB7770XuVxOa2vrI9pnR0cHpVKJa9euce7cOUD4Zm93d/dnbqtWq4+JVY+WlpYWdDodP/7xj8Xri6OcPn2ar33ta7hcLsxm8w32ICEhISHw88bE9vb2GBgYwG63H3NRPpr9ffpbPxIFlnJFGFM+Nr/CmY5mtnZSKKiwvS+M8V7eTLCXztDsd9Fg0LOZ3BMdLjv7aVLpHKtb21zojjK+sEZ7yCfuu39ygXNdEYw6LQadlrnVTawmI7HELksbCTaTe7QEPZztFKJNDouZ0bkVsvlCbQJVIzJkuKxmtnZStDU6mVqtl/8+70wX8Z2UGKdy2Rq4PrvM6lYSl9WM29rAysY26VweW4ORhdg2+5kcMpmM811h1rZ2xH11Nnm4b0QYLOF3Wuls9vOTq2Pico/Dwv0jM6iVCs51RSgWS6IQZNJrGZ1bYXvvgJaAB4fFJBZQgxBN++GVUQw6IXomk8u5f6Q+xEIpl9M/sUB3cyPFUhmlQi4eWzpfZG59C71Ww6nWEMuxbXEsfbVaZecgw/DMMidbm9jP5DAbtAzW4mEb27uEvA4y+QLnuiIMTS0SDXq4WhN/Zte3aQ26aQ+62d7bZ3s/w3Jt7PxafId8sYRRp+VcZ4SBqQXCPqe47bWJefrawyATnt/yxjan2kKi4LWe2OGZJ9rIl0oMTi5QKlfq8bHJBbRqFT6HlbDPycJ6nAa9hsWtXfKFUi1KF0KlVKBVq8gVivR1NNN/5JzecqaL2PYeO6l07fVpYD2xw+XRWdQqJV0REy5rA1s7exi0GqYWY+weZIjvpDjfHeU3n38RhUKOy+USeo7290kkEqytrTExMYHRaMThcOB0OjGbzY/6Cyi5XC4KPoeuoEqlwsHBAZlMRpz+d7je0euipwtPWkzscKziA7P5t956K/fdd98Nt7n//vsftP6v/dqvce3aNXGCyXe+8x0uXrzIW97yFtxuN93d3XzgAx942D+8+XyeVCp17CYhISEh8UuKUgkvfKHgEtrchC9/WRhbr1TC4CD8yZ9AIAC33CJMJ3sEAsSvEvl8no2NDTY2NpiYmOCP/uiPODg44CUveQkAr3nNa9BqtfzO7/wOo6Oj3HHHHfzRH/0Rr33ta8W+oJ9FW1sbL3zhC3nTm97E5cuX6e/v541vfOPPdO08Hmi1Wt75znfyp3/6p3zxi19kbm6OS5cu8dnPfhYQnp/D4eBlL3sZ99xzDwsLC9x111388R//Maurqz9j7xISEr9K/Dxi0Pr6OleuXCEUCtHT03MsavJI9zexuMY/fPW/uNjTgsWkpzca4PLoLJVKlWsT83gdFtQqJQGXFRCmi21s7zG/tsXg9CIBj53OZmG8uF6rJpk6IJXOcml0FnuDEYVcjq1BcDh0Nvu5MjbH0PQSc6ubPO9MF5Vq3QFzoqWJ67MrXB2fx2Ex0RVppFSLcGlUwgj1y2OzJFMHdAWdZIv159ca8HBH/zjX51aIBtw890wX149EiJr9Lu4anEAul3GhO0qk0cV+RohGyYD4bprV+C69LUHamzysxnfFbculEndcG8duMXGuK8Kp1iYxmlYolZHJZFyfW+FsZzN+l5WOkF90KM2sbJBKZ2l02TjdFkKjUpKsCRbpbJ6ZlQ1mloXibFuDkWijmys1gWV0fhW9VsNBJkPYYwPgTIcwcW0nlebS6CxNPgddzY2Y9FoA1EoF5UqFoekl1raSqJRKWoNC94zLamZ4ZlkYDT82R1ckgFwmR6tW1fYdZnp5k/GlDTZ30zzrRBt6bd0RYzNqWYzFuTI+h81spNFtR11zAmnUSlY2t+mfWGBlM8kzelvEke0ArUEPPx2e4uqYsO3zz3WzsFaPgJ2sOaUW1uM0e6x0NjeSL9TjY5lcnsujs2jVKi70REkccTc5LWZ+OjLN+MIqbSEvN51oY2ByUVze2yLEEncO0pzritLX0czuQUZc/r9e9QIUirpUIZPJMJvNNDc3c+7cOW6++WaCwSCZTIaBgQHuuusuRkdH2djYODbZ9JEil8tRKpUUi0WuX79OIBDAarWKo+uLxSIzMzOP6EutpxJPmjMokUhQLpcfdGHpdrvZ2Ni44TYbGxs3XL9UKpFIJPB6vczPz/OTn/yE17zmNXz/+99nZmaGt7zlLZRKJd7znvfccL8f/OAHee973/v4PDEJCQkJiV8cBgP89/8u3BIJ+PrXBcfQvffCT34i3P7gDwSx6DWvgV//ddBqn+yjflL5r//6L7EA0WQy0d7ezte//nWe85znAKDX67n99tv54z/+Y86ePYter+eVr3wlH/nIRx7V43z+85/njW98I89+9rNxu928733v493vfvfj/XRuyLvf/W6USiXvec97WF9fx+v18uY3vxkQnt/dd9/NO9/5Tl7xilewv7+P3+/nlltukZxCEhK/gvys0fKPtDOoWq0yPT3NysoKJ0+evGHs9NBp8HDkC0X+8EOfY2c/zf3XZ3BZzZiNejH6FHTbGZpeIl8QPvA+oztK8sgH/PPdUTEy5HVY6Gpu5K7+ejxJq1Fz//UZ1ColF3ta2EvXP4B77A1cGp0hkyvQEfJhazBy73DdHRP0OPnJ1TFsZiOtQQ8ymYz7rwvTvUrlChWZktV4gr6OMDupNPkjzpzt3X3iO/s0eR04GkzsHqRFB8t+Jke+WGSqNrlsZmWDloBHdLCMzCxzpqOZgEdJtSpMs9Jp1ZRqRct7+2l8DgtnO5oZnV/FbNAxMrNMqVzh6vg8HSEfpUqZkNfJYizOmY5m0RW0QJxnn+ognc+zkdilUq3S5HEwMLVIYncfrVpFTyTAfibHZnIPg1bN3OoGqZpodaazmeIRga8z7BfPiUGn4QXnusXnCXWBBYQOKKfFxJ21+JhMBqmDDHNrW1iMek61hVg9UsrttVu49/oshWKJ3pYgeo1GnK4GYNGruePaOFaTnlNBL3KFnPtHhOXVapVcocTUUoyTLU0cZHPHKhdT6SzDM0sY9Tpagx7W4kkxIgcgU6i4f3SOnmiQQrGEUacR42O7BxmoQjKVFt1nQa9DdAlNLcWgCaIBN0adlvGFVZY3hOdVLJVZWI+Tyec529nMemKHoNvB889183Co1Wp8Ph8+n49KpcLe3h6JRIKFhQVGR0exWCxinMxgMDwi11Amk6G/vx+v10s0GhW3KZfL/Nmf/Rm5XO5hOwufisiq1SeneXN9fR2/3899993HxYsXxfvf//7386//+q9MTk4+aJvW1lZ+93d/l3e9613ifffeey/PfOYzicVieDweWltbyeVyLCwsiEr8Rz7yEf72b/+WWCz2oH2C8C1pPl/P4KZSKQKBAHt7e9KFoYSEhMRTkYUF+MpXBGFovD6BhIYGeNWrBGHo5pvhAeWAEhKPlVQqRUNDg3QNISHxFOLo54Cj5HI57rzzTn7t137tYT9MlkolhoeHSafTnD59+iF7RRYWFtjb2+PkyZMPua9//Pf/4u+//H3StX6g0+1hBiYXUCuVnGoPUS6VuVb7EG7UqlHIZexl8oQ8NqxmE2PzqxRqzp3eaICR2RXsDSaiATfyI+INwLnOCP2TC5xqC7G1k8Ko0zC+sAaAVq3CZjai06oxG3Ssx5McZPPicbUGvWwm9wi5rcyubtES9B6bMHWhO0ouXySTyzO9ssGpthCDU4sAyGUyTrY1oZQrGJxaRKtVoVYqRedO2OfEaTOzEtsmtr1Ld6SR0bm6c/O5ZzpJ7h2IXTcnowGGarE4vVpJbzTA6GKMg0wOpUKO32ljaUOYKHa+K0KuUGJ4RhA6Ql4Hq1tJSuUKHnsDnc2N/HRwUjyHh+KaUiGnNxqkmMtyfWlTPJaeSIDrcyuEvE7sDUYSe/ss1aaXNRj1gCDwnWht4iCTY2ppXeweOnx92pq8aNQqtGqVGN87fOyxuVW6I42ML6wR9jnF5yyXyQh6HBh0GqpVWN6Io1QoRHeNzaSjUoGw38HMyhYBt52JxXVx32c7m8nmi5TLFSYW17jQHT3Wl3ShO0o2m2V6ZZNsoURLwCP2/2jUSnoiQbKFAmNzqzgtJvYzOXI1gbIr3IjJoGV6OUYylRbfw4c8p6+D1EGWgdr74ag4p5DL+d7f/ym9LQ/uD36kZLNZEokEiUSCZDKJWq3G6XRit9ux2WwPKoY+3ObatWu4XC5aW1vF3/dKpcJ73vMevvrVr3LnnXc+4rj8U4UnzRnkcDhQKBQPcgFtbW09pA3d4/HccH2lUondLjS1e71eVCrVsRe5o6ODjY0NCoUC6huMItZoNI+6gEpCQkJC4peYcBj+/M/hXe+C4WEhSvblL8PaGnz2s8LN7xe6hV7zGmFa2c9RdiwhISEh8dRHJpNxo+/Hj04dUipv/LEpnU4zMDCATqfj4sWLDyrXP4pcLn/YAun7RqZ5/+e+jVGn4UJPC7Iq3F9zfhRKJeQyGVenFzndFmI7dYDdbBQ/UC9vJikWS9hMOhoMOtaTKdbiQqfN9t4+Bp26NjmrhYnFNYIeuzgy/drEPOe6IhSKJVoCbmZWNultCR4TJi50t1CulBmYXECpUJDJ5dk7yDB8kMHrsKDTaXBazMR3U0Qa3Vwdn6dce67P7es8NrnsXFdEFB4cFhOnW0PcM1yfVqlRqbgyKhRnn++KkjvipnLZzFwenSWTKxDyOQm67Nx3ve5eigY8XBpfQKNS0N7oRKtVMzS7Ji4vVSoMzyzR1dwI1SrFclkUZ/YOsozNrYrumFhiV4y1lcoVNhNJYjv7nG4LsXuQwWoyiO6YxVgcj72BYqks9ve0BjxcrYkcl2v9SqfawqzHk8R3UiR2BfFraimGrcGI22oWJrFNLda6f+aoVKpcGp3ldHsYlVKB02IivrvP2a6I6ACDukC2WxOLfE4bo/NrDE6voFMrUcurmPVaUpmcMNlsdVPs8jnXGSF3JFrVEfKJr49Bq+EF5zu4Nl5/L5xqDYuOpNagF7/Lxh3X6t1NpUqZS6OzaNQqLnRHj8W/7A1GrozNkckVaHTZaPa7RPcSwEtv7ntMQhAIgyQCgQCBQIByuUwymSSRSDA5OUmhUMBms4muIZ1OJwpBTqfzmBBUrVZ5//vfz5e+9CXuuOOOp50QBE+iGKRWq+nr6+OHP/whL3/5y8X7f/jDH/Kyl73shttcvHiR7373u8fu+8EPfsCZM2fEP7w33XQTX/7yl6lUKmIh1PT0NF6v94ZCkISEhITE0xiZTBB6Tp6Ev/5ruPtuwS30jW8IwtCHPyzcOjoEYejVr4a2tif7qCUkJCQkfgk4FINKpdINxaBEIsHw8DB+v5/W1tYHTR+60f4eqjNodz/NH/3N54Si3EyOhfUtKuUKF7qjzK1uYTMbuDwm9AYNTC1ypiNMoVimPeRjcnGdc111Z8fGzgE9YQ+5fIHdfUHoUioUJHb3Sezu47SYMBt0uGxmtpIpGl02hmeWxejZzafaxQ4dOBRvhA/sLquZnmiAewbrKQ6nxcz9IzMoFXLOd0UolsqiEGQx6hmaXmJnP0004MZtM3NlrB6b8jmt/ODKdcwGHSdaoijkcu6tFTcL+6gK5cstTaRzObRqNVtJQVha20xSqVRwWRtodNlYiG2xvJkEIF8sc1Aos7S1RVujk/juATaznv4JQbwZm1/lfFeEZCrNydYmhqaX6G0JigLLpdFZznQ043VYmFhYYz+TRSaXi+ffpNdiNRvoCPmZWFzD77IyMLVIoVhiPb7DmY5mFAq5WK58trNZjIvJ5TJuOdvN7Erd5BD2OkVhqdFlI+R1srK5TYUqGrWS9XiSje09VEoFN51oZWO73s/T7Hdx18AElUqVkM9Js9fFHQN1caYnGuTK+DxqpYK2RgdKuZyx5Xo3UK5YZGRmmYjfhdmgYyu5e+y92T+xQCZf5HxXhL2DLANTdZePTAZ3XBsj5HNiNxuRyeBa7Rwfvp9mVzZqEbBdfHaLKJCtbiUxGXTYGow0eR1MLq7xrtffWAf4eVEoFDidTpxOJ9VqlXQ6TSKRYGNjg6mpKXQ6HYVCAYvFQktLyzEh6G/+5m/453/+Z37yk5/Q2dn5uB7XLwtPWkwMhNHyr33ta/nkJz/JxYsX+fSnP80///M/MzY2RlNTE+9617tYW1vji1/8IiBYK7u7u/n93/993vSmN3H//ffz5je/ma985SviNLGVlRU6Ozt5/etfzx/90R8xMzPDG97wBt761rfyF3/xF4/ouCSLt4SEhMTTnHwevv99wS303e8KPx9y8mRdGAqFnqwjlHiKIl1DSEg89SgUCjd0BoHwxfNNN910bIpQtVplaWmJmZkZOjs78T/C6ZWxWIylpSUuXLhw7P5qtcrbPvwvLG0kGJtf5SCTpb1JEBkAGow6upsDLG0kWN1K4rY1kM7mOcgKvTU3nWwjk8kxWOt4OdMRFj+QW016WvwORubWyBWPx8cUcjmn2kMoZDIu11xAJr0WnUbN1k6KaKMbh8XMyOwSmZzgzmkLeplajmHQqumOCmXHR6NnF7qjXB6b42RLE/vZLCa97lg8rNnvIlco4nVYmF3ZRKmUE9/ZB4QJYZlcgdagl4XYFnaz8Vi06UxHM+lcHqVCzvXZlQdFmy72tFCtVrk+u0ImnyfaWI82GXUaon4Xm8ldYsl9rEYd2UJJjDad7YpAFQYm5ylXqqJDB0ClkHOxO8LUyhabSUGEOdsZ4WrNLdMW9OJxNHDXwGRtfQVuewOrW0nMBh290SCLsTirW4JQFfTYiSV2KZbK9EaDmA06fnrEGXW2K8LVsTmcVhPN/gfH+063hRicXuJUW4jd/TRKhYLpZaEORaNWYjUZkMvk+N02Vje22UtnyeSE65ywz8naVpKWRhfxnRQGrYqFzV1x391hL5vJFEGvEEnr62g+5kA63RZCLpezmdxjZXObSKObuVUhNqdVqwj7XZh0WganFrGYDOxnsuI5bg16MOi07B1kmF/bOhYdBHjHa17E/37Ni/hFcXBwQH9/v9gNVq1WmZubo1gsEo/H+fSnP80Pf/hD+vr6fmHH9IvmSR0t/+pXv5rt7W3+6q/+ilgsRnd3N9///vfFUfCxWIzl5Xr2NBwO8/3vf5+3v/3t/NM//RM+n49/+Id/EIUggEAgwA9+8APe/va309vbi9/v54//+I955zvf+Qt/fhISEhISv6RoNPDylwu3vT34j/+Ar34VfvhDGBoSbn/2Z3D+vCAM/eZvSqPqJSQkJJ6mPFRMDB7s5qlUKoyPjxOPxzl79iwWi+URP85DOYO++oP7+NoP7weEWM6t53u5Nll3z7QEPNw7Mo1MJuNUWwi9RsO9I4J4oNeqWVjdYj2xQ5PHgd9lZWy+3q/jtDZwbXoFnUbN6eZGisUCI7V+nXKlQqVc5trUEidaguQLJfQ6tTj1aW5tE4VCjtVkoCsSYHpxTXSNpHMFFtcTlMplzndFWYzFaTDquTI+R7VaZXB6kdPtIXL5ktirczQetrqV5KYTrZTKFdLZPJlcAbNBx1p8h8tjsxj1WloajYS8DhZjCZwWE5OL66IAdq4rIoxCl0G1KhQzHwomRp2WW8/1cPVIT01nc6MYe+tq9mPUqrk8LiyXy2SsbSRY397DaTUTDbhZXE+I2zosZi6NL1AuVzjT0YxSIT8mQpkMOu4amCTkdeKwGFEoFKKAkkpnyRYKbO8dcL47ysJaHL1WQ7HWSTS1tI7dYqK9yYdarWR1c5vJWm9TfGcfg1ZDKp2tFTOv0+SxiyLVwOQCZzubyRVKdIR8TCyuc6r1+Oj4owJZOpdHrVJSKJUZW4yhUSsJ+d1UZQoWN7Yx6tTMrsXJFUrE99L0RAPIkInupp5IQHxsgOef7WZpIy7+fLQc295g4mRrUHR5gfB7dij+nGxtQqWsO+k89gbe/Ipb+EWRz+cZGRnBbrfT1dUFCF/mXL9+nY985CPE43F6enr4r//6LzQaDd3dD19o/VTlSXUG/bIifasnISEh8StKIgHf/CZ87Wtwxx2IozZkMqFw+rd+C175SrjBlBgJCZCuISQknooUi8WH7PK58847OXHiBFarlXw+z+DgIJVKhdOnT6N9lNMpE4kEExMTPOtZzxLvm1nZ4L+966PEav0+bU0+ZlZiKOUKTtQ+MN83ctx5c2l0lo6wH5VSgVIhF8UbmUzYPrl3QNjvYnZ1E41SyXpC2LfLZqZYKhNtdLMWT6KSy1jZEiZoAXQ3+ymUyui1Goamlx7kvOkIONFqtcyuxdnP5OgI+5moCRdGnYbeliZiiR0W1uM4LCYKxRKpdBYQipvLlSoDkwtUqlUxmgWCG+libwvXJhZI1kqkz3dFRLdSbzSAxWTg7lo0TamQ0+i2s7gex+uw0FQTjDYSuwD4XTYSu/tUKxVOtoaogujiAWEC2LXxeVoCHgw6DXIqDEyv1J9n0M3y1g4Bh5nYThqf0yo6lDQqJQ6rGUeDkXyhxGZyl3KlKj7PkNdBsVSm0W1naHqR1oCX63P1fZ/rilCpVInvpFjaSDzoHD/rZDuZfIHB2nlqa/IK07gQInc90SBTy+tsJVPYzAaKpTL7tclm57uiFEslUbDpbg4wOr9Se320XOyJ0j+5IEYAjz522GvDotcwOFcfuBR0NrAc38Ok19IV9rNzkBGPxWzQIZPJ2DvI0NXciEGrYXBqgWKtf+kwPmfSa+lqbkQmg/uvHy+ovjQ6K5Znv/Flz+WVzzvPL4JCocC1a9cwmUx0d3cfi4Z95jOf4T3veQ9f/OIXSSQS/Od//icul4tPfvKTv5Bj+0UjiUE3QLqQk5CQkJAgFhO6hb76Vbjvvvr9CgXccosgDL385fAovhWWePojXUNISDz1eDgx6J577qG9vR21Ws3g4CBWq5Xu7u4bTiT6Wezs7DA8PMxznvMcAHKFIi9+24eYW93kREsTG9u75ApFNrZ3AfDaGzjI5ok0usWY1tzqptjH09ceFjqArGYGpxY42xk5PhGqpwWqVWZWNtjeOzgm3hi0GnqiQXZSB0wtx7CZ9cL0r7wQ6emJ+NGoNQxPL1Esl2n2WJnfEEQlvVbNTSfaGJ5eYmtH6O851xURnTfdkQD2BiN31UamqxQK/C5brWTZQrPPyUIsTqwm3gTcdiF+Va1yojWETFY91it02LfT1uRFp1GjUSlFoejwPMyvbdHW5GViYR2vw8LkkiDeaNUq7BYTLquZg0yOnf00+UJRFFDCPieVahW3tYHBqUVag27GFurRtK4mD0qlks2dfTaSew8Sb559qoO9dEYUtlqDXjGy5bI10Bb0MjK7zN5BBofFRC5fFN1NzzzRxnbqQHxNDuN7AG5bA70tAe4ZnBJjVoePrZDLOdUWQqtW89PhenfTYX9U0GPHbWtgM7knjnBvMOpr4+WLnGxtIpcvMLG4LjqUmtxWljZ3aPa7sJgMyI90/wC0+R0sbO7QGfKyvr1HyOsSC8hB6CXK5PI0GHSMzCzT6BHEusP3i8VoIOC2sbyxTblSIZWux8f6OsJ8+2//98/s3Ho8KBQK9Pf3YzAY6O7uFh+zWq3yL//yL7zzne/ku9/9rvg7+nRHEoNugHQhJyEhISFxjOVl+Pd/F4Sh/v76/SoVvPCFgjD00pfCQ4wTlvjVQbqGkJB46lEqlR6y2Pn+++/HYrGwurpKJBIhHA4/7Jj5hyOVSnH16lVuuUWIw/z5P32Fz33nTnH5mY5mkEE8ucfKZpJIwM3Mcr3zpq3JR6ksTMPy2i2kMllx1HtfexiVUsn12WXSufyxcd4qpYLn9HUytbQuigNHC43bm7y47VbuGhgHQK1UYDHq2No9wKTTEHA0kEzn2KgVNx/2zggOnxAqpeL4yPqaMBRtdNNg1KNUyI+JN6daQ8ytbdIZbmR2JYbVbBS7fQxaDSaDDo+9gUyuwN5BhoNsTnyeLQE3+WIZj00Qb7qjjQxOLYn7vtjTQqVSZXkjQWx795jDCIRy7N2DDCMzy8hlMsI+J3NrQplyo8uGw6RjqjZO3WU1i6KFTAa9YQ+76QJLtZLqo+6mgNtOe5OPO/rHxOlkh2PptWoVvdEACkX9PMlkiJ1GbU1e9FoNscSOWAx9KBzJ5TI6w42k0oIr51AIPIze9UaDFEsljDqtWMwMgnA0t7ZJs9/N9dllOsON4vh2EAQ7lVLO1vYuse09/C47K5vCe8Ok1+JzWGkw6RmeXsJqNrCTSpMvloTzZDej16pJ50usJXaPnYfDc5zLFxmaXqRQKovnAYTy7Of2dTK/HmdhbQuZTMb3P/rOxzxB7JFQLBbp7+9Hp9PR09NzTAj68pe/zNvf/nb+4z/+Q/z9/FVAEoNugHQhJyEhISHxkMzOCjGyr34VRkfr9+t08KIXCcLQbbeBXv/kHaPEk4Z0DSEh8dTjocSgarXKXXfdRaFQ4OTJk7hcrsf0OOl0mnvvvZdbb72V//zpAJ/65o9Yj++wupXkXFeUK2N1x8ktZ7uIbe8yPi+4Ro4WGoe8DsI+F/cOT1EolTHqNBj1Oja2dzHoNJxuC7O8mWApJvTetAY9zK1uUa5UONESxGI0cNfghPhYh46TRpcNr9OKHBmXjxxLxGdnNb5L2GNj5yCHWqVipVaGfCj2eB1WqlTZ28+Q2N0XXR+dYT87+2mCHgfD00t0RwLHRImLPS2UKxXW4jusbSU529F8TNS4qbeVdC7P0PQSaqUCt90iihYhr5NGl42R2WVS6azoMCoUS8jlMm4+1cHq1jazK0LB8dFi7Savg9aAhzv6x0XxpiPoYmJ5C51GTU80AFVE94tSIcdja2A1vkPQ1YBKoSCRyrCXFlw+XoeFnVQag05LS8BNOptjdH5N7KI61RZieGaJk60hwSXUYDp2js93R1mP7+CxWxicXKA7GjgmsHSG/Rj1Wta2kiT29rGZjaKzymY24HPaUCkVDE4tHiuoBkG00qpVokOsr6OZ/iPn+HlnOonv7nO95ko66vJqMOg40xnh6vg8qXQGmQyafS5RQIt4bSjkMqbXtsVjyRdLpLN5rGYDvdEgIzPL7OwL0bSu5kax06onEuBZpzr4izf8Bk80xWKRgYEBNBoNvb29x4Sgb3zjG7zlLW/h61//OrfddtsTfiy/TEhi0A2QLuQkJCQkJB4Ro6N1YWi2flGHwSAIQ696Ffz6rws/S/xKIF1DSEg89biRGFQqlRgZGSGRSBAKhWhtbX3Mj5PL5bjzzjvpONHHC97yflLpLDKZjGedbCO+sy9OD+uOBBit9cxEG900eZzc0T8mdvscijdWs4HWgAcZMi7VhAW5TEY04GFudZOTbU2kMzn20llROHDZzGRzBdz2Bsx6nTjG/lAQOdnaxFIsjsdqZDm+S1c4cCwO1NvsI5vLc5DLE0se0BX2ibEqhVxGX0czlUqFwalF9FoNeq1GnMDV7HfhsTcwuRQjuXdAS8DN/FqccqWCTCY4RtbjSSZrvTRHHSWNLhsdIT93DYxTOCJyDE0vodOo6W0JUiqVxfHseq0as0HPxvYuneFGDFo1k0vrx+JhK5vbWEwGmn1Odnb3mFnfFp/nuc4I/ZMLnGxrYieVxmU1H4uHnWwJEt/Zw6BVMbuWoMltY2FDEMhUCgUhvxOb2cjC+ha5fBGlQkEyJfQh+RxWHBYTUGVkdoXWoIeZlU1ROLrQHQVgbGGV/XTumLtJJpPxvDNdrGwkmK65qY4KhX6nldYmL/cNT5Mvlo5NNlMpFZztbCaZSjNZ60DyOqwkUwfkC0VCPidBt537R2Yo1n4fTrU1MTi1hFat4kRLEJVSdSyadvgaBd1WtEoFpXKJ+Y1dcXln2M/CepzeaJDVrSRyuUwU8ywmA/f88/+DzfzEuqpLpRIDAwOoVCpOnDhxLI727W9/mze96U185Stf4aUvfekTehy/jEhi0A2QLuQkJCQkJB4V1SoMDgqi0L//OyzVv81DrxcEoVe9ShCIpCjZ0xrpGkJC4qlHuVymVCqJP2cyGdFFoFAosFgsNDc3P+bHKRQK3P6DH/LtgRX+895BQBAtrGYja1tJ2pt8mPRaZlc3RSdF2OdkLb6D1WQg5HVSKJWOjeM+2xVhZGaZEy1B1uI7BFy2Y6JFX3uYXKGIQi5ndG6Z1qBP7NPRa9WEfS6Mei3XZ5cxaDXkj5Q+twa9WEx60bVztNMG4HxHiPhuivmYIIKcagkwOCMsd1pMnGgLcWlkmoNaxOtw4pVKqagdV4mhaeG52BqMVMoVdg8ytAa9OCwmBiYXjjiMGhlfWMVqMtDW5AWZjEsPGGl/ZVwYaX+QydFgMhwrje6JBEils7hsZkbnVvDYLSzUOm10aiUNBh1NPheLNTfVfiYnjmOPNrrRalTIZTJGZlc40RJkeKY+8fpid5RsLsfU8gbZQomuJjdjS4IbSSGX85y+ThZjcXEEe2fYz3itJ6i50U3AaePekSlK5Qomgxa1Usn23gE6jZozHc2sbCbE44o2uplf36JSqdIe8uGxNXDnQN3lda4zwpXxOSxGPe0hHwqFnHuH6xO9+tpD9E8u0uJ3olAqUSgUolNHLpPR5HWQzuYJ+13MrW4hk1WJ7+wDQnQtXyzR0uhmP5OjWCqxspkUo2tdzY1s7+3jtpqZWFynydXAzHqy/n7pjlIqldk9yDC3usnfvPW3ec0Ln8kTSalUYnBwEIVCwYkTJ451fX3ve9/jd3/3d/niF794bDr5rxKSGHQDpAs5CQkJCYmfm2oVrl2Dr39duC0u1pdptXVh6MUvBpPpSTtMiScG6RpCQuKpx1ExaHt7m6GhIXw+H21tbYyPj6PRaGhpaXlcHud17/o7fjw8T2fYj1IhRyGXM1iLA8lk0N7kI5XJ4XdamVmOYdTrRCeFw2JCrVISdNuZX99Cr9GwntihUOty6YkGkMtkFIolJhbXOdsZOSaIPPt0B5lcgeFal8vRuJBJr6Un0sjY/Ap76TwatRKXtUF87AvdUYrlMv21mFXQY2czmSJfKOJ3Wml0NDA4syK6drrDPkYX1tFrNfREA6iVCu4ZmhKP5VC06IkEKFcqKORyceqWUiEn6HGQzRcIehzMrW4il8vYqnUWuW0N4lS09cQOGrWKpVhcdDf1tgQpFEto1SqGppeOxZ5AKG4ulcuMLayxn87SFfIytii4kRRyGc882c56PMnMyiYqhQKfyypG7tpDPiHiNTpLsVzGa7ewl86QyRXQa9Wc7WhmcmmNzaQgoDR7baJY1hn247Fb+Mm1MfFYDl1e9gYTLQE3VBFdXgDdzY2ML64JIlc2T7FUEkUsg1aDQadBrVLidVpZ20qyd5AR+5VCXifx3RRd4UbWEzuYDDqxrBqgr6OZvYM0ZoOeoalFznUdLyB/Rm8rlUqF+bUttnZSnGoLHRMib+pt5SCbY3hm+ZgDCcDnsBDyOZmYX2XnIIvVqCWdK4rvj5c88xQf/7Pfe0JLo8vlMgMDA8jlck6ePHlMCLr99tv5H//jf/DZz36W3/qt33rCjuGXHUkMugHShZyEhISExONCtQoDA3VhaL6e0UejEbqFXvUqeMlLQPr35mmBdA0hIfHU41AMWl5eZmpqivb2dgKBAAATE4LroqOj4zE/zjd/coU/+NBnxZ/PdUXYSOzisjUwPL1EX0f42Ifx891RZAjj55OpA9pDR0a567X0RoPEd1LMrGxgbzBSqVbZqY0NP9cVgSr0T85TrlTpam5kfEHosLGaDLX+nHl29jMAnGwJMDSzgkIu42RrCJNBy539dcfJYXQt2ujGYtSze5BhtuZ0MRt0aNUqCsUSrUEvWzu7bGzvkSsIIlWT20Zse4/uSICDTA69TnOsD+d8V5TNnT0cFhNDU4uc6Wg+PhWtFpuaW90ivps6NhVNq1Zxqi3EXjrD+PwaDUY9CrlcjGSdaAmi12oYnFokVyjSGvQyu7pBpVJFrVRwpr2Jpa1d1moixtFoWkfIh89l48dX6v2AfR1h+icWsJmNtAY95AslBmvuJpVCgc9pZWVrm5MtTeSLRWKJXXGUu8WoJVcoYTcb8TptpNLZY9PheqMBZlY26Y0GWNlM4ndZxZLvw/Owe5Cpi1w1Qe3o+0kukzG1tM7OfoaWgEcs5taqlYTdVnKlCguxbRqMemTA7oHw+nc1N2LW6xieXSKTK9AS8DC7KkTXFHI5zzndweJGQnQ3ne2KcLUmsAXcdtpDPu7qr0f4Do9NrVTQ2xJErVJy34jg5JLLZPzla55Lb0sTDocDp9OJwWD4uYvZb0S5XGZoaIhqtcqpU6eOCUF33HEHr371q/n4xz/Oa1/72sf1cZ9qSGLQDZAu5CQkJCQkHneqVRgaEsbVf/3rMFO3t6NWw6/9GvzmbwpTyRoanrTDlHhsSNcQEhJPPQ77gTY3Nzl16hRWq1VcNj09TaFQoLu7+zE9xtTSOm/8fz+NrFJgYXOXJq+TpVhcLPk93xVFJoPJxXV2DzLHPuirlAqec7qD2bUtFmrFvYeiBAiihdvewF0DE1SrgkDisplZ3tjGaTXREvSysrnNSm2SWMBtJ7G7T7lc5kRrE9lslrHFGIefCg8nkXWEfGjUqgeNcj/fFWF+fYuI383Y/AqRRo8o7shlMtpCPow6DVs7KbaSexh1GuK7gjhjMWrxOWyo1WqGZxYJuh3EtndFd9OZ9jBKpYKJxXX2DjL0tYfFHiCFXM5zz3SxsL4lihJH+3SafU7Cfjd39o9RrlSRyaAlIIx6Nxt0dDc3snOQEYUki0EHMhm7BxlOtDShUSoZml0Sj6Wtycf0cgyfw4LfZaNcqYjnHAThaGh6iRMtQdbjOzQ+MKLXESadyaPTqhiaWqKtySv2IcllEPLYaDAaWIhtUyyVMdTOGYDb2kCjx0Y6k2dyaZ2Q18FafOfY+6VKlZGZZXKF4rHzpFYpec7pDiaPTI/rCDiZWBEcRdFGNyGfg59cHRd7qA6LnY06Ld2RAJl8npFaFE6vVWPUa9lKpugM+9Fr1UwtxcT+pYDbzsb2LkadlrYmL7lCgaHpeozusHeoNxqkVK5w04lW/ux1LyaRSBCPx0kmk2g0GlEYslqtj8kxdCgEVSoVTp06hVKpFJfdc889vOpVr+KjH/0ob3jDG36lhSCQxKAbIl3ISUhISEg8oVSrMDJSF4am6tZ5VCq49VZBGHrZy8BiedIOU+LRI11DSEg89VhdXWV6eprTp0+j0+mOLZufn2d/f58TJ0783PvfT2e57a1/LTpp/E4LYZ+L0bkVdg8yeO0W0rk8qXQWtUrJM3paWNlKioJHb0tQ/GDeGW7EY2+4YdTIY7cQ9NiRHymUBuGD/tzqJr0tQbaSeyCTsViLGhl1apRyOT6XHaVCTmLv4FjUqDXgIVso4HNYuT63QmvQe8zVc6EnSqVSZS2eZG1rRzyWQ57b18n23gEjs8LxR3wO5taFyJXbaqTZ62JkfpV0roDZoEOjVhLf2UejVnG2s5mtZIrpZUFAiTS6xThYR8iHy9bA3YMTooh1trOZq+PzOCwmoo1uFHI5947U+3IOS6HDHgvFsgyL2SB2/8hlMiKNbiqVChazkemlNUwGPevxHUCYkqXTagi4bMytbqFWK0ns7pOvdRq1NfnQqpUUSxXGF1YfNG79WSfbKRSLXJ9bIZMrcK6zmSs1149SIedE2Ets54D12mj53mhQPGetQS9eRwP3DE5RqVbRqJXYzSbWEzuYDTp6ogHW47ssrAtCoa9WCp0rFOkK+6mW8kysJsTzdLhvl9VM2O9CBg9yYl0dn+dkaxOpdAab+fjks9NtIeK7+3jtFoZnlmhudIsCm0IuI+RzYTMb2UruEd9NYdBpxN4hn9PKHZ/4Pxj19d+zcrlMMpkkHo+TSCQolUrY7XacTicOhwO1Ws0jpVKpMDQ0RKlU4vTp08eEoPvvv5+Xv/zl/PVf/zX/63/9r195IQgkMeiGSBdyEhISEhK/MKpVYSrZoTA0Ubflo1LB858PL3+5IAw9xrHGEk880jWEhMRTj3K5TKFQuKEbYWlpie3tbU6fPv1z7btarfLuT/w7/3HXNeK7+0CVJpeFpa1dVEoFJ6KNyGQKcZy62aBDr9Wwsb1Ld3MjupoL47DUOdLoZmUjgdNqptFlqxVK10WHs53NjM2v0RMNsLyRoMnjOPZB/1xnhFyxSKlUYnxhnYjPzlxtipZKoaC3NYhCJmd4dgm1UolBp2VjexcQOmi8TgsLa1tsbO+J5daHTprn9HWS2E0xOicUEh8d5X4YJbp7cIJ8LT7WG/EzMreGRqUg4nWg1WoZONKf1Br0MrUUoycaRCarktxLi500VpMemUyOVq0i4LYR390nltglmy8Agoi1ltihK9xIbHsX2xHh5/A87GdzaFRKhqaXHiRi3dTbSrlSYXxhnVQ6c6w0WqVUcLGnhVhih5mVTbRqFfYGI2s14ag7EqDBoOPK2BzFchmfw8rOfppsvoBRp+VMZ4TppXXWE/X1D6fHhVwWGoxahuc3xGM5PDaXzUyzz4VcLhMjVyCIMyOzy5xobWI3lUatVonijFwmw2tvQKVS4rSamVnZQK1Uig4ke4MRhVxO2OdiZXMbhULO5vauGPdqDXqRy2XoNCoGp5Y40dLE8Ez9/faM3hYqlao4tv6B5/GWs11sJvfE98Tn3/Nmbr3Qy0NRrVY5ODggHo8Tj8fZ39/HbDaLwpDRaHxIEadSqTA8PEyhUOD06dOoVCpx2bVr13jpS1/Ke9/7Xt761rdKQlANSQy6AdKFnISEhITEk8bYmCAKfeMbwv8fIpfDM58Jr3iFIA4Fg0/eMUo8JNI1hITEU49qtUqhULjhstXVVWKxGGfPnv259v3/fe2/eP/nvi0IPy1BDDoNdx2Z/tTqtzO9tk3U70CpVKJSqsQiZZVCQZPXQa5QxOe0srC+hVKhODYmXilXEPDYWYrF0Wk1rG0lRXGmOxJAIZdTLJUYX1g7Js4AnGkNIFOqGJ5eolAqH4tcmfRazndHGZlZZmsnhVwmoyXoYWophlwuo68tjEIhFz/428wGQEYydUDI58TvsDI6v8perZOmNehlbnUTg05DZ9j/oKLkE5FGRuZXCbmsVKrQYNIzMlcvOz7bGWEzuYvHZmFoZpH2Jr/onFHIZbSH/Bj1WpZjCXYPMljNBtHVY28w4rYYyebyLGzu4HfZSO4diMLR2c5mocB6doV0Ln9M+NGoVdx8qo2ppZgYuToqeLQ3+Wh02fjxtVHReXPoCqr3ChXFknC1UoHHYWF1K8mJliZKpRLriV2294QYnctqJp3NY9ZrMOs17GdybO2lxXLsjpCfpY0EPZFGVuNJXNaGY6XO57oi7KezKBUKrs+tcLKlkaGZVXH5TSdaKZcrTC/HSKbSxxxMcpmMm062kdhJCVPfFAq8Tov4vJv9LvxOKwOTi6RzeewNRvKFEgfZXE0gi7KZTDFVi8K1Bj3MrAi9QyGvk+ee6eR9/+vVD/Pb8mDy+fyxOJlKpToWJzvsAqpUKly/fp1sNktfX98xIWhoaIgXvehF/Pmf/zl/8id/IglBR5DEoBsgXchJSEhISPxSMDEB3/wmfOtb0N9/fFlfX10YehyKTSUeH6RrCAmJpx4PJwZtbGywsLDAxYsXH/V+7xma5L/92UcfIBIs0tXciEIhR6NSHisI7gg4ie+lcdvMzK4l6IkGuTZRX36mI4xSoWAxFiexu0/Y5xILgs0GHZ3hRvYO0kwsruO0mCmVy+KI+jOdzSjlcvonFyiWykR8DhY2tqlUqliMes50NjM0vURiV4jzHBYpK+RyTrQEaTDpuePauHgsh51FkVqhdLFUFsUZlUIQqHZSadqavOLEs41aBMplNVMslwl7naRzeXL5ArHtPVHEaml0kc3l0amVzG8kaQ24mKiNaoe6oHFYKH1UnJHJZDy3r5NYYoeJxXUAoj4Hs7VoWtjnJOR1cN/1GfKFEjqNWhSO9FoNp9pCJHZSTNWiaX6nlWRKcPV0RwKYDVqujM2J4kxPNMD12RU89gaavM4bRq4GJhc40dJEYm8fl61BLKg+PI+pgyxmg46h6UU6wo2iS0guk9Ee8qJWyFmMJWpROi3bKUFgs5kNNPvdZPMFxuZX8dot7B5kRJGrK+zFbDAyMrtMOpcXe4FAEKVuPtXB3Hq9h+poeXaz30Vzo4s7r42Lz/Wwx8qg0wjvYbmc+6/XHUrtIR+Ti+t0hP1oVUp29jMsxuLi+/POT70Ht+3n70Qsl8vs7OyI4lChUMBut+NwOIjH4+RyOc6cOXNMCBodHeW2227jHe94B3/+538uCUEPQBKDboB0ISchISEh8UvH0pIgCn3rW3DPPXD0n+/29row1Ncn+OslnhSkawgJiaceDycGxeNxpqameOYzn/mo9rm4vsUL/+ivcVhNmHRakvsHxHf2yeTqXTz5YgmPvYHrcytEAx6xFwigs8lFuVQmsZ9lO5U5Nh5dLhcEj/V4XfA42dLEUC2+0xJw43fZuW94ikKpjF6rxt5gYmVzG6NWTXvYx85+Vuwk8rts7KTS5AtFTrY2oVQquDY+R7ki/Dtz2FkUDQjCj1wmPzbF6kJ3lOnlDVqDHqZXNog2uo+Ncu9rD4MMUgdZZlc3aGsSRAMAjVpJd7MQAxuaXkKv1aJRK8SOmbDPQYNOw/x6glQ2j9/RwNbuAcVSGYVczs2n21mP7zK1JOzvsPxa2NaJ3ahhcHZNfC6HwpEgnvlRKOTcO1zvFeqNBrk+t8KJaJBCuUSxWBYFN41aidMi/F33OYVx86Vyhe094Vg9dmHkfaTRzXp8B5VSwepWUix97gj7kSEUPA9NLz1oVPvhyPvDEvGjItfhc11a22K+NuY+6rMzW4v4hbwOwn4X9w4Jr7lCLiPocbCwHkev1XCiJch+JicKTVaTnmpVmCbWEfbTYNAyMrNCpiYkhf0ulmMJGox6WoMeiqUS/ZP1Yz3VFhLLs/OFIka99kGTz5Y3tgl47FyfXeYv/+ereM0LH93v0MNxNE62vLxMsVjEZDLhdDrRarV4vV6mpqa47bbbePOb38x73/teSQi6AZIYdAOkCzkJCQkJiV9qNjfhO98RhKEf/QiKxfqyYFAQhV7+ciFWdmScqsQTj3QNISHx1OPhxKBkMsn169d59rOf/Yj3d5DJ8uK3/S2TNYHCqNMQ9jnR6zSMz6+iUilRyGWi4BFpdOOymlmKJVhP7NAScLMUS1AolZHJ4FTET2LvgOW44Kw53RZioCYihH0uoo0u7hqYEHtezndFuTw2i8Wop63Ji0wu51LNwSED2sN+ZpZjnGhpIpvLk84XWKoJDGaDHp1Ghdmgw2zQsbG9x34mK3YWhX1OcoUiTR4H08sxPA4Lk4vrVGpiy5mOMFUgdZBhZmXzmNsEhELpVDrL0PQi5UpVLH0GsJuNnGwLMTi1QDKVRi6TEQ14mF6OCSXLLUHy+TyjC7HaeVWjVqpI7qeJBtw4LWYmFtbEcelem4n4XhqjXkt7k49SqUz/1CKHH39Pt4UYmhF6cLL5Ama97gEiVwsb27u4rGaGa+sdG+XeGUEml7G8kSCW2KUj5BPFObVSQV9HMwfZHNdnVzDqNBh0WjaTwmvY1uTDYTEyMrPMfiaH32Vle1cofVarlDyjt5WVzW1RsOuONIq9Oy0BDwGXjbsGJ0SRq63RwdRqAr1GRVvQg8Gg56dDU8eO9cr4HCdaghTLZVQKxbEOpc6wn539NEG3g4mlNexmk1hIrVWrcFrNuG0N7Oynie/so1DI2EkJrjOXzYzNZMSo1zI8vYTPaWU9UZ989rwzXXzxvX/wuIsx1WqVsbExUqkUvb29pFIp4vE4/+f//B8GBgbI5XK86EUv4gtf+AJGo/FxfeynC5IYdANudCGXTqcfcn25XH5s8kAmk+GhTqtMJkOv1/9c62azWSqVykMeh8Fg+LnWzeVylMvlx2VdvV4v/qLn83lKpdLjsq5OpxNL/QqFAsWjH3wew7parVbMmj6adYvF4kNeNABoNBqxvf7RrFsqlcjn8w+5rlqtFq2Pj2bdcrlMLpd7yHVVKpXY1P9o1q1UKmSz2cdlXaVSiUajAYQ/7plM5nFZV6FQoNVqxZ8f7nf50az7wN976W/EI1tX+hsh8Lj+jdjbQ3H77Si/8x0UP/gBsiO/D1WHg9KLX0z5JS9B+6IXQe33RuKJQxKDJCSemjzUNdXe3h7Xrl3jlltueUT7KZfL/OHffJ71xA6zK5ts7x3UOmgE145Jr6WvI8zc6hYrm9voNGpcNkEIkslknO1sBhCdNT6HlYNsjlQ6S6PTgsOkZ2p1i2ythLkz5GNiSRid3hHyIZPJjkV3zndFuDoxT8RjQ6ZQYjUbj42JP9PRzO5BmgajgZHpRdpDfrGzSKmQ09UcQKdRMb2yQT5fxGI2sFYrcXbbGgj5nCT39plZ2cTvsrKTyojup2f0tlAslRmYXKBcqdITCYj7tjeYONUWYmBynmRNVDgUjoR+pSZMeg139E8cO9ZrE/O0Br3oNSry+TwTy4JYIpNByGNn9yBHNOBmbiWGWqlkoya4mQ169Fo1fqeV/UyO3Vqh8+F49JDXgVqlwmzUMTK9RLPfzfRKTBS5znZGUMhlzK9vsZVMcbYzwtWaMCSTybjlbBexxK4YwzoqgjW6bHSEfPx0eFqMcPVEg1yfXUanUdPbEqBShau110WjUuKyNbCyuU1HbZT76mZSFJIcFhOFYgmVUklLwM3OfpqF9S0KReH6y2czEt/L1Bw9FVQqBWPz9f6l0+1htnf3cdkbGJlZ4mRL6Ni0sGf0tlIul1mL77C6lTzWJQXwnNMd7NUEvWr1eAG2zWzgREsT1+dWSOzuo9Wo+NE//QVh3+M7AKNarTI+Ps7u7i5nzpwRPxcATExMcOuttxIIBNjf32d9fZ23ve1tfPCDH3xcj+HpwIMr8yVuSCgUwmg03vB28803H1u3s7PzIdd9YPnc2bNnH3Ldzs7OY+vefPPND7luKBQ6tu5tt932kOu6HjCN5pWvfOVDrvtAFfW1r33tw6579EP57//+7z/suolEQlz3He94x8Ouu7xcV67/4i/+4mHXnTgyiecDH/jAw647MDAgrvuxj33sYde95557xHU//elPP+y6t99+u7jul770pYdd91vf+pa47re+9a2HXfdLX/qSuO7tt9/+sOt++tOfFte95557Hnbdj33sY+K6AwMDD7vuBz7wAXHdiYmJh133L/7iL8R1l5eXH3bdd7zjHeK6iUTiYdf9/d//fXHdTCbzsOu+9rWvPfYefrh1X/nKVx5b1+VyPeS6t91227F1pb8RAtLfCIFf6N8Ivx/dG96A6tvf5t/+/u/hP/4Dfud3KBiNyBIJVF/4AtpXvhKcTvjt34Z//3dIpZCQkJCQqPNQrgWFQvGwX3IcpVqt8uEv/SffuvMal0fnSKWzvOB8NwfZ+pdsHSE/d/ZPsLK5TXekkQs9UdGVI5fBfibHlbE5Am4757ujmAxa0ZWzny0Q202jVCo51RIk5LayFItTrVbZO8gQi+8wMrvMqbYQHSEfbU1ero7PC9Oe1rcx6HQkdvc509mMWqngXFeEaxPzzK5s0j8xz/nuFgw6LQ6LCRBEg+GZJS6NzpI6yHCuO4pBq66dL7A1GLk8OsvMyiZdYT+RRjeF2pcmJoOW+bU4V8fnsZiM3HSylVSm/qWgXqvm3uEp9jM5znQ0c647Qn+tH6lYKlMoFrmjf4LWoJe+9rAoBAFML8fQaNRs7aW50B3FZjZwMhpgIbbNzn6aq+PzuK0mPA4rrUEPIDiaNrZ36Z9cYGYlRnOjm7YmLyqFAo1KSbW232vj87hsZpxWE3azcB4cFhPTyzEujc6S2N3nmSdbxecJEPG7uLN/nLH5VZq8Dp7T18HIkYlbLquZH14ZRS6Xcb47ys0n27he61fK5gvIkHF1bI7uSIDeliAnWoKsbArxr4mFNah9J3ihO4rJoMNrt5BKZ9ne2+fy2CyVcpkmp4WQx45aqUCuVFMsVxhbWGd5I04um6M77EOpkGMzG5hd2WBpI8HVsTkCbgdyuQyPXejyCftdXBmb5fLYHKtbSW4+1UGuUH+uJ1ubuHNggsGpRZxWMy841y0WTAO0BL3c0T/O7n6avvYw737Dy58QIWhiYoKdnR36+vqOCUFLS0u84hWv4Ld+67cYGBhgdnaWgYEBfuM3fuNxPYanC8on+wAkJCQkJCQkHh/KajW89KXw0pfyo5e/nI/8xm/wcuDlgG///9/efYfHVZ75/39P1ajMaDRVvXfJ3caYahICGDASyW6S3UAIm18SkpDNLrubbJLdTdkkbNp3yRZISLIBwqYsbrRQHHChGONe1KzeNaNR10jTz++PkY4tLMA2xnK5X9fl68Jn2jNHB+uZzzzPfU/A734X/2M0wnXXxdvVr18P2dkLPXQhhDgv6fV6YrEYiqK84zYXRVEYn/Rz8IQaMIuKcti6+wgQL66b5UybU4Q5JdHEtr31ZDrSyHHb0WqPr+rp9gzhtJpnVmYU0+8bISUpkfr2+MqT+o4+st12inMzCARDdHuGmJiawj8d5EBTB3ZLMikmA+W5blr7fLhsqTR19cdX7fR6WVKah06rxZVmwTsyzuLiHF47fAxFUdBptVy/qpoe7/EP+SsqCtm2N97hsjjHTUGGk+0nrNpJSkxg5/5G7KkplOSkoyiKuppkZHySoZFJ+gZHWFFRwNR0gKlAWF0lc6yrn8QEI4VZbsxJJvp8w2rAcKyrnxy3nVgsFq9N1D2Aw5LCvoa2mXo9LSwpyUWr01GU5aS1d5CKXLe6YghgVVkuoWgMg05HOBpldVWxum0uzZLMyvJCDh47/nNzpqXyysEmdFoty8vySU4y8cqBRiC+zW7AN0ZLj4f8TCdOmwXf8IRaZHlkfAr/dA96nY7V1XmMTU6pNYf800H6fSMMjkywuDgXBYVAMKRukzva2k11UTa9gyOsri6mvq2HktwM9s3UQPIMj3F5dTHRmEJBppP2vkGWluRw4Fi3Ova1KyqZ8E/T7xshGlOoKspRa/0kmwzkOi10eEaPX9+6eBFojUbD8rICEhMMakFpk9FAc3c//b5Rspxp5LkddHmPf1GnxGDXkRZCkQirKgsJR2JqDaRINAYauPPmuV+IvleKotDY2Mjw8DArV66cs6K/t7eXW265hRtvvJH//M//VFeBV0iTjbcl28TmIdvEzvy+sgUkTraJnf59ZZtYnPwbcWb3lX8j4ub7NyIUCvHgf/0XeR4Pf5GQgO7pp6G5ee4DV6yIh0g1NbB4sRSgfg9km5gQF6ZQKDTv79twOMxLL73E9ddfr/5bfCJFUVAUhVgsRiwWQ6vVcqyrn/997jU2vrxbrV9TkZ9Jc/cAaZZkirLcRKMKexqOb71ZVVnEkZYuFhfn4h0ZJ92eOqcr1erqYqamg2i0Gg43d6ndvGZdvqiEWDTGkdYuQuEIbmsKfcPxLVJOq5my/Exaezz0+0ZJMyeh18eLNGu1GtZUlzAdCrF/JjQoyHTS5xslGApTmOUix21nT30rU4H476rZduRp5mTK8jLQabW8dvh4EebLq4vZ29DGkpI8JqeDWFMS592aZk1J4nBzFxUFWWr9Gq1Gw5LSPBIMBlp7Bhj3T5PhSFO7UqWZkynPz2R4fJKmzn4cVjPRaEztmra4MJukJBP7GtoJR6PkpzvoGRwmEo2RYjJSkeemY2CEwZlW7stK8zlwrEPtmmZJSZwTcs0WcS7IdOKwWtDpjtdfiv/cCmnq7KeyIIvO/kHcdqvaqh3itXgMeh2xmEJDey+5GQ7aZsKWxAQjxTlukhKMHG3tQafTYdDr1ILUOW47WU4bA0MjdPT7yHbZGBydIDizUmdFWS5j45O0DYwQUxQqC7LVsNCeamZZaR77Z+ovzZ73vQ1taLUaitJtJBr1HO44HprNvte8dAduWypajYY3Ttg+tqK8gIaOXqqLchjwjZJqTlZXOAGU5Wag1WowJRhp7Ojj+f/4R4pz0jlbFEXh2LFjeL1eVq5cOWduPTAwwI033siVV17Jr371K3U+dq5961vf4tvf/vacY263m4GBgQUZz7uRMGgeMpETQghxUWtsjG8ne+op2LVrbmeyvLzjwdA118AJLVrFu5M5hBAXpnA4PO+XJLFYjBdffJG1a9fO+cIImBMCQfxLmhNXDw2PTfLbF17judcO0tE/qAZD+RlORib8FOekMzE5BRoN7X1eteDu4pJcwpEoJqOBQ8c6WV5eMKfF/DXLygmGIzS09zLun57Tdcpk1FOebcczHqDfF68xU5Rho7V/GI0GKvMysFktvDJTXDjBqCfTYaO9z0teuoMMhxXv8BhtffHwxWE1E4spRKJRKguymJiepqPPh386/sVEUbabft8IZbmZBCNhUKCx83hB6WVleYxNTmNPjRdLfmsR5quWlBKJxtRaPG/toLV2RQV9g6M0dMRr3pzYNa04x02u286rMx20DHodOW47bb1erClJVBZmMeafpm6m8LI1Jf4F2rh/mny3Fb1eT69vDP9MyJWX7mBgeIxMhxWn1cLkdICmzn6iMz/fRUU5dHmGqMjPpHPAR4bdqhbyhniR5mA4TCymcKS1e857Abh2eXzL1dGWbvyB4Jzi2YkJBtYsKqG1x0vnQHz1zYnhTnVRDrbUZF450IiixAtUW5IS8I1P4UwzU5qbjmd4nJbueLhjS00hFo0xOR1gSUkeihKjoaNfXY2Vn+Ggd3CE9DQLKSYDk4Egvb5xYjPzgeqibDr7fVQWZtPjHcKVljqn89mqyiKmA0H0eh0Hj3WeVCj825/9M/6/2g9wtiiKQnNzMwMDA6xcuXLOl6Fer5d169axfPlyHn300XlD23PlW9/6Fhs2bOBPf/qTekyn0+F0OhdsTO9EwqB5yEROCCHEJcPjgWeeiQdDL74IJ64MTE2Fm2+OB0Pr1oH8TnxXMocQ4sL0dmEQwIsvvsiVV145ZyWsoihEo1F1NdHsitP5RKJRtu4+wq+f3kFdazemBCMDQ6NAvBOTOy2VhAQDh491ku6wMjQ2qYYty8ryMRkNNHX1Mzw2SXVRNvXtvcRiCglGA1cvLaOjb5CWma5TpVkOjvXGw4SqwmwyHFZe2nNUzfwrcl00dHlxpiaT6UwjwWjkzRNagi8uzqW110N1YQ6e4RESjEaaOuPdu0xGAxkOK+bkRBRFobPfR5IpQX0vTqsZZ5qFlCQTR1u7SbOkMDrhV99LdVE2KYkmugaG6PONUFmQpQZHWq2GtSsq8Q6Pq8WITwxL8jOdFGe7efVgk1rDZjZsSUowUFGQRaIpYU4HrdluYYuLcwmFI2i1GrUjl06rIcdtIxAIYUk00jc8gSUlkb6ZAM2cZMKZZsGZZqHHM8R0KIyiKGoHrUxHGm57KtFolMMt3eRnOOjzjRIKR2bGXoROq+VoaxeT08E53cCSTEauWFRKS88AHTP1oma7fUE8BMp0Wnl5T50azsy+V1eahQy7mVAwREP3oPpeV5QXcLili8UluYxPTmEyGtVi3RDfphgOR7ClplDf1oPbblVXKCUY9eS47JgTE+j2DuGfDpJg0DPqj88H7Kkp5GU4iERjHG7uIt2eyrg/oBYKX1ycQ3Kiibq2Xsb9U6ysKGTTD+9Dpzs75YkVRaGlpYX+/v6TgiCfz8ctt9xCeXk5v/3tb9XV0QvlW9/6Flu2bOHgwYMLOo5TJTWDhBBCiItYNBpVC2EvX7785KXTbjd8+tPxP1NTsHVrPBh6+mkYHDxeZ8hggLVr48HQbbdBTs65fzNCCLEA3lpE+q3bwt6tZbZep2PdFUtZd8VSmrv6eeSZnWx4aTeRaJRkk0n90J7tspGX4SQcjuKfDuK0mukaGGJobAK9TstVS8uYnAqoq26ynGm8crCJYChMnttGapKBY73D6usa9Dr+9OZRXGkWCrNcaIBdM6s3Bsf8OK0p1Hf0U57jwh8Mk+5IU8OX3XUtXFZVhH86yNLSPA4d66SyIGvOSpjV1cWggD8QYDoQIs2SQn17fAWPIzWFwkwnnQMa/NNBLMmJ+EYn1UBkdVURgPpeMuxW3jzayuR0gBy3nYJMp3pfAJPBwLa99SQmGFldlYcG1FU3U8EwoOG1Q8dYXJILCpgSDGpHtoPHOrm8ulitxVPX2k11UY76+IERWF6aSzAUIhQK4xufIsuRSmOXh7ZeLxoNXLW0nPHJKUbG/ei0GpITE9SVMvkZTvIyHAyNTxIKR0hJTKDbM8TA0CiJCUauXFLG+OTxkgYpSSZ217UwMRWgoiCLtJREDrUcD26C4TDb99XjsJopzHIRCkfUbXbekXFSEnR0+8ZZUVHAhD9ASmKCWldoX0M7l1UVMTQ2yarKQg43xwuKz77X1l4vVy4pJRqNMR0M0e8bja/WqjveGe3aZeV4hscY9fcBYDEZ1C2EGXYrFQVZ6ko1rUbDVDDE4ZZujAY9Vywu5d/u/YuzFgQBtLW10dfXd1IQNDIyQk1NDYWFhfzv//7vggdBs5qbm8nMzCQhIYHVq1fz/e9/n8LCwoUe1rxkZdA85Fs9IYQQFwu/3692fZucnJzzzfY7ikbhjTeObydrapp7+7Jl8WCopgaWLJE6QzNkDiHEhemdVgZt376dxYsXY7PZ1BVBpxoEvZ3JqQB/fO0AP9/0Eo2dfWg1Gsrzs6hv70Gj0bC0NI+URBOvHIwXLk5JTCDNkkK3Z4j8TCcZdit9g8N0zhRatpkTiSoatBoNZXkZ+ANBWnu86uqNkpx0egdHqMjPxD8dRKvT0NhxfDtXabYT/3SAZFMCbQPDLC7OYX/T8do3Vy8tJxyJcKx7gOGxyTlboOIrlMpp7/PSOrNCaXl5AftnAoqqwixcaans2N9ATFHQaTWU5GTQ2NlHhsNKXoaDCX9AbcuekpiAJSWJkXE/i4pymJwO4B0Zxzcar6XjtlnwTwdxWJKwmFMY808xODKh1jQqznYTikTIcKTR0N5Htittzta1FeUF6HRaBkfGae8bZGVFAXtPqL901ZISBofHONbtRQEWFWZypC0ejKTbrSwuzmF3XStjMwHPbJv5BKOBRUXZJJoS1ILTcLyNfHl+JkkmI4FQmPqZVu8GnY6cdDvj/mmKs910eYbQajT0eOOhnjnZhDkpkXS7leHR+DnQ6/XqlsN0eypp5mRSkhI53NKJKy0V78i4WlforauxyvMzaersVwuiX7u8guGxSQ7P1P5ZXpavBn6ZjjSqCrPZdbSZyan4KqGyLDtNvUMY9DoWF+dgSU5i277jRdG/+/mPcvf6taf4f8G7a2tro6uri5UrV87pYDs2Nsb69etxuVxs3rx5TkexhfTcc88xNTVFaWkpHo+H7373uzQ2NlJXV4fdbl/o4Z1EwqB5yEROCCHExeKMw6C3amo6Hgy9/vrcOkPZ2XDLLXDrrfCBD8AJ39xdamQOIcSFKRKJvG0DhFdffZWysjLsdvtprQg6FYqisPtoCy+8cZj/eWqb2pVqNkzJcFjJS3cQiUbVwEKv01KU7abbM0Su08rkVAC9MUEtsmxJTsSSnIg9NQVFUejzjQAaNUzJcFixpiRjToqvSnLb4h3FZsOU0mwnWhS8Y36GJ6Ypz8vgWPcAsZiCXqfl2uWV9PlG4m3PmbudqyQnnbwMBzv3NxCaqYF0eXUJbxxtxmE1U5ztRq/X8urB4wWnV1YUcri5k8UleYxO+Ek2JXBoJpzQaKAiP4tYTCE5MYH69h7slmR6BkeB+Na14px0UhITONY9QDgSIdlkUreupdtSyc90MTQ2QXP3AOn2VKYCIcb90zNjKyamKOxvbCcSjVGU7aZrwEc4Eo0X3s7LoK6tm5GJ+P2LMx209PniYUhJHskJBnaesDVtdrtXaW4GlmQTep1uTt2gy6tLONbVT2luOm29XgoyXew+oUjzZVVFRCLReGDU3suysvw5tXpWVxURA462dBMIhSjLzaSxMx5U2SzJLCrOpaXHQ693mASjHldaqtqmfkV5AUaDXu1Yl5qShF6nZWhskgyHlcIsNx39g/TOBFGZjjRGJ/1EojGWFOcSReFIc5da2yrDloJnxE+OK4201BTSLCk89q0vvOOWydPR3t5OZ2fnSUHQxMQEtbW1pKSk8PTTT59Uy+t84vf7KSoq4itf+Qr33XffQg/nJBIGzUMmckIIIS4WZy0MOpHXC88+Gw+HXnwRTuwUaDLBBz8YD4duuQVyc9/7611AZA4hxIXpncKgXbt2kZeXpxaBfWuh6LPFOzzG7158nYNNHbw405Ie4tuxDjS2x8OSST+pyUnqtiCId/cKhiMkGg0cbe2mJDdDXWVj1OtYVJyLXq+jsaMv3mnMlqoGR257KiXZ6fR4h+jo9+GwmlEUGBqbQKOBxYWZTAeCHOuNBwrZrjSGx/xMBUPkZTjIz3BwqLmL0Yn4SpXKgmwaO3sxJyVSkZ8JaHjj6PHuWysrCjjQ1Mnikngdn2RTwpyC0pdXFzM4Mo4t1cyRli6WlubNCVOq890oaPCNT+EZHmdFeYF6Lma30nmHx6lv71WDpNmta2V5GWQ6rLx+pJlgKEKCUU+63Upnv480SzKVeZmM+qfVc2dNiXddGxn3s6Qkl2g0Sluvl4mZGkiO1GT8gRBumxVXmoXRCT+dniF1VU5pTjpD45OU5KTTNTCE1ZJEQ3ufWmdqSWl8u1skGuVoa8+c9wJw7bIKpkMhjjR3MR0Kc1lloVrfKTkxgSsWl9LU2UfXzOqwE1drVRdm47an8vLeOvW7m9kOdOn2VPLSHSigbg+DeI2p5q5+FpfkMe73AxqOdQ2o5zY33YFWq8Gaksyxzj5SzUl0e+LBUVKCgX/7qxupKMrH6XRis9neU0evjo4O2tvbWblyJWazWT3u9/v5yEc+glar5dlnnz07c5r32Yc+9CGKi4t56KGHFnooJ5EwaB4ykRNCCHGxeF/CoBNNT8P27fEi1M88A11dc29fvDi+YujWW+Gyy2CB2r2eKzKHEOLC9HZhkKIovPnmm5hMJvLz8+esUHi/RKMxXtpzlMee3clUMDSnS9Pl1cX0+0ZI0EHX4BhLS/PnhCVXLimLhxYz3blWVBSyb6a+i9Gg58rFpfT5Rmjq7Eer0VBRkKWGH9WFOTisZl491EgkGiMxwagGR640Cxk2C0Nj4/T4xgFIS0lEr9fjDwSpLswhEAzR5RlStzBlu2yMTU6Rl+FEr9My4Q/QMzishiVleZkEQ2Hc9lTq23vIS3dQ19arhiWrKgvRarT0D43QNTBEabaDYz3xgssajYYPrKpicGScwzNt6U8MQ3LT7ZTlZbLr8DEmZ8Kby6qKeLOuFXOSiYqCLEwGAzsPHt/OtbQ0j0PNXSwuziGmxFAU1LpFWo2Gktx0pgIhMp1pNHX2YUky0TUThhj1WjIdVuxWC4OjE4yM+0lOPL5CyWZJpiDLRSQS43BLJ45UC6FIRN1qtqg4F3NSAkdb40WY453NRgmGIhgNupmf+xjN3fFwpjwvk2Pd/cRiChUFWbjSLLx+6BjhmWt4aWkeB4914kwzU5TlRquF1w8fv05WVRVxsKmDxcW5+ANBUpMT1bpEs+dycGQce6qZw/OEclcsLiUai9HjHaLXO8J//sOnWLukmMHBQbxeL6FQCLvdjtPpxOFwnNY2rq6uLlpbW1mxYsWc36PT09P82Z/9GeFwmOeee25OSHS+CgaDFBUV8dnPfpZ/+Zd/WejhnETCoHnIRE4IIcTF4n0Pg06kKFBXdzwY2rULTqzB4XDEu5LdeivceGO8W9lFRuYQQlyYotEokUhkzrHZQtFDQ0N0dXUxNDREcnIyLpcLt9tNcnLy+7JC6ESd/T4ef+4Vfv/iLnLT7Rxq7lRXeqyuKgYUPMNjdPT71LADZsOSSgaHJ9R6MCeGJQWZTopz0nntUJO6PWxlRSF7G9pIsyRTmpOOTqfl9cPNM88HVYU5NHX2UVmQycTkFOFwhO6Z7ltGg44spw2jQY85yURLjwdzUqK6RcmSnESm04o1JUntfKYoCkNjk0A8vMlwWBke89PcPUCO287Q2KRa86gq14lGp6exy0MkGqMsL4PWnvh/p9utVBZkcai5U32+2TDEZDSwqDgXk9Gg1l+CeBiyp66VyoJsEox6jAb9SaFbR7+P/AwHTZ19lOVlnrTdKxqLMTI+SUuPh8VF2Rw+oeD1itIcwlGF+o4+ItEY1UU5ape0dHsqFQXZHJ4Zr16nJT/TSUu3B6NBz9KyPDQKajiTlGAkzZJM7+AIpbkZpKUk0eMbUbdzpZmT0Gq1KAqU5qYzPD7JwNCYuhUuw2HFPx2kKNtNKBxhfKbG0mxXtoIsF1qNBntqCnWtPWQ502jt9RKd+f29tDSPBIOe/qFRugaGWFScw5ETCl7/fzXX8e3P/bn6d0VR8Pv9DA4OMjg4yPj4OBaLBafTidPpfMf/b7q7u2lpaWH58uWknjBHCAQCfPzjH2d8fJwXXnhhzm3nk7//+79n/fr15Obm4vV6+e53v8uOHTs4cuQIeXl5Cz28k0gYNA+ZyAkhhLhYnNMw6K18Pnj++fiWsueeg7Gx47fp9XD11cdXDZWWnrtxvY9kDiHEhemtYdB8haLD4TA+nw+v14vP58NkMqnBkNlsfl+DoUAoxK83PM+G7fto7BmkLC+D9l6vWpfnmmXlTAVCHDrWSTgaVVe5KIpCut1KdVE2+xvbGZ5pjT4bhiSZjFQX5ZCUYGT7/gb19S6vLmZ3XSuLinLQaDSYjPo5K0dWVRbhHR7DaTVT395DtjNVXbUDsKI8H71eT493mL7BYSoLstUVSCajgZWVhYyOT3G0rZsEo54sp01tdV5ZkIXDaubNulYCoTBJCXqSE00Mjk6SZkmmqiCHwbFxmjritXJcNguhUITJ6QCLS/LQaDQ0tvfinwmS8jMcDAyNkeO2YzUnMeGfpu2Ec1eel8nQ+CRFWS7a+wZxppnnrFBaXpYPwHQwRENH30nbudauqMQ/HeBwSxfBUITlZXlq8e2URCPVBRm09Q/jHZlQz+0bR1vQabUsLsnFZk7ipb3HizCvqihkT0MbGXYLWU47Wp12znaupaV5tPV6qSjIotc7TJolhSMtx1flVhVmq9286lt7KMx2c6yrH4gXrF5UnINBr6eho1ddmTW71cyemkJ5fhb9vhHaer2Yk0wkmox4h+OrwZaX55NgMLCnvpVINEamM42t//UNrOa3rxUYDAbVYGh4eJiEhAQ1GLJarWqNoZ6eHo4dO8by5cuxWq3q40OhEHfccQf9/f386U9/Ii0t7W1fa6F9/OMfZ+fOnfh8PpxOJ5dffjn/+q//SmVl5UIPbV4SBs1DJnJCCCEuFgsaBp0oHI4Xnn7mmXg41NAw9/bi4uPB0NVXg9G4MON8j2QOIcSF6cQwaLZI9DsVio5Go2owNDg4iMFgUIOh1NTUsxoMRSIRjhw5wvT0NMuWLaPLO8KGl3bz+HOvMu6fJsdtZ3TCz8RUgNSUJJaV5dE1MKSGK6W56XT0DRJTFBYX52IyGdl9pJnoTHetxSW5HGnppiQnHUtyIigx9s60Eof4iqEuj4/CTBctPR6KslxzgqE1i0qIRKMMj03S2uulKs9NXadHvf2apWVMTAc53NxJNKao3bcAMh1WKguz2VPfpm6Zml3Vk2Qykm03Y05JYd9MuKLTaijJzaCps59FRTlotfHW9bPbp0xGA+l2K9FYPKjo7POi0+vVVTSW5CQc1hScVgsDQ6NMTAfQoGFoLB7UpNtTyXLaCEejHGnpIsOexrh/msnpeDet5eX5mIxGjrR2MeEPUJDlotc7PNNS3sSqykI6B3zHz31OOi09HhRFIddlxWZJ5lBrH7GZj+Cz7zXLZSPbZUNRYrxZ16aeu1WVhdS19VBdmMPg2ATOmZBs1urqYianAiQY9Bxq7mRVZdGcFUyzXeCauwcYGptkdXWxugLKaNBzzbJyuj1DNHX2qz/r2bbxxdlu8jIcvHqoiWAo/v/GkpI8DjV3kmZOpjw/k/v+8hauWHLqX+ZEo1GGh4fVcCgWi+FwONDpdPT397N8+fI5YU84HOauu+6ivb2dl19++bzsyHUhkzBoHjKRE0IIcbEIhUJ8//vfB+DrX/86xvMlZGltjYdCzzwTrzkUDh+/LSUlXoR63br4nwuoCLXMIYS4MMViMUKhkLoiCE69UPTsB1yv14vX60Wn0+FyuXC5XHNWPpyJQCDAwYMH0ev1LFmyBIPBoN42FQjy5I69PL/rEH968ygQX9mh02rxjoxTVZhNcmIC7b1eBmc6iWW50pjwx0OjTEcaY5N+ujzD6nasomw34/4pCrPc9PtGSExIoK3Xo3aQWlSUi06nIRqNcaS1Ww2SZj9SXrOsnEAwzJHWLqaDYSry3DTMBEOpyYksK8ujsbOfgaH4StHZcGK20HVqShIv761T3+PsKpuyvAzMSSd351pVWUhnv4/CLBct3R7yMhxzVu2sKC9Ao9Ew7p/mWFf/nO1aWo2GyxeVEAyFOdzchYJCYdbxVTRZLhsl2W6OtvXgG53AZDTgslnoGhgiwWhgWWkesVhMLepsSU4iMcGAZ3iM8vxMUpMT6RoYon+mbpDDmkIoFEGr0eC2JjEVijA8MY1/ZpteljONsckpsp2pKBod04EQ3pFxdTtXYZYLo0FPSqKJI61d5LkdtPV553SgSzDo6egbpH9olKqibOpnVjjptFquWVbO4OiE+v6XleVxYCZky890Upzl5o2jzWqNpdkuceYk08x2OgM7Dxz/IufeP7+Br91de+oX81soisL4+DhtbW34fD40Gg1paWnY7XZCoRCFhYV8+tOfpr6+nm3btuFyuc74tcT8JAyah0zkhBBCiHNoYgK2bo2HQ88+Cx7P3NsrK48HQ1ddBadRiPJckzmEEBemaDRKIBBQQ40z7RgWi8UYGRnB4/EwODiIoihqMGSz2U4rGJqYmODgwYOkpaVRWVn5jo892trNb/74CodbutSCykkmIy5bKuOT05TmpuMZGiMcjdIzs0rGmpKEOTkRly0V/1SAwdFxtFoNgyPHV8m4banodToON3fhsluY8AfUWjRLS/NIMhlp7OhjeNxPYZaLvsGR+NYuUwKXVRXTNzikdqQqSLfR6RlBURSKs504Ui3sbWwnHD1em+bgsU4y7KlYEg0kJyWz/1in+h5XVhbS1NlPZUF8G1O63XrSKplgMExMUTjc0jWnRhLEt3NNBYLUt/UwOR2cs0omNSWJVZWFNHb0qedndruWTquluigbe6p5TlA1O96CTCcOq5loNMb+mTbwGk28s1q3Z4iKgiwGZoK12Tbwep2WXJcNnVZDOBKm1zeG3ZLMwEi85lGCUU9lQRZGg4H69h6i0RhplhR1hVO6PZXiE7rAWZKTMBn1eEfi27kuqyxCq9XwZl0rMUXBYTUTiUQZnZwi05lGQZaTrr4hur3x7WEZDivj/mmi0RiLinOJxqLUt/WqQVS8oPUYBZlOkk0JaDSw4Qf3YdC/t6YQ/f39NDQ0sGTJEhITE/H5fOzZs4e7776bpKQkjEYjjz/+ODfccMNZa1kvjpMwaB4ykRNCCCEWSCwGBw7Eaww99xy88cbcItTJyXNXDZ1nBRllDiHEhemee+4hHA5TW1vLNddcM2cFzplSFIXR0VE8Hg9er5doNIrT6cTlcmG329+x9fbQ0BCHDx8mNzeXwsLCUw6mJqcCbNmxl98+/yqKglo8WqfVUJ6fhVarRafV0NDeS36mU90elGDUU12Ui16nVcMHly2Vjr54C/osZxqFWW7a+7z0eIexJCeRkphAn28EvU7LqsoiYrGYun3MabUQU2IMjcVbqzvTLLT2evDMrAhypaUw4Q9i0GvJS7cTjkbp8oyoxazzMxwMj/spzc1gcipAJBqla8Cn1vmpyM8EjYZkk5HDzV2U5GbQ0NFLbGbr2+WLikHR0NIzgG90Qg1uIL6V7KqlZXT2+9TtZSduj6osyCLDYWXH/gZ11c1scJRut5Kf4UCv0/HqoSb1vF9eXczBY50sKs5hdGIKmyX5pO5cY/5pkhLi411enj/n9uo8F9GYQq9vnPHpIJV56dR3xsdmNOi5akkZvYPD6s/rxPdTlptBtssW384Vjm/nml0BZU81U5LjJqoo7Jl5Pa1GQ2luBq29HhYV5RAMhwmFo+q50Ou0aut5R6qZ5u5+Us3J6rWQnJjA8//xjxRmuU/pmnw7Ho+Huro6Fi9ejMPhUI9Ho1E+85nP8PLLL3PFFVewbds2UlJS2LZtG6UXSX3B84WEQfOQiZwQQoiLRSwWo2GmPk9FRcWF983a8HB81dBzz8WLUb911VBFxfFg6OqrF3zVkMwhhLgwbd++nd/97nc8+eSThMNhbr31Vmpra1m7du1ptcV+O7NbYrxeLx6Ph1AohMPhwOVy4XA40Ov16n17enpoamqisrKSjIyMM37Nw81dPP78q2ze9iZVM3V5Zq1ZVIKiKHT0DzIwNMaysnwOzKxqMRkNrFlUQp9vlKbOPrQaDZWF2er2ouqiHJxWM68ebCIcjWLQ6SjIcnGsq59MZxq5bjvTwRCHZlYoJSYYcdtTGZzZujY2Mc1UMKh2GktJNJJoNJBsMqDVaBieCJCQYMQzHA+ObKkpZNitpCSZqG/vwWQwEo3FGB6Pr6IpynbjsqXS6x2ia2CILGe8zs/EVACdVsuaxSVMB0Psa4hvH8vPcOIZHmM6GKIg00WO286R1i5GZoprl+ak094/SLIpgfL8TCKxGAebOtRgqLIwi5ZuD5UFWURnjh1t61FXlS0uyWVyKoDDaqautYeS3HQ1uIF4MATQ4x2mxztMaZaDY73x4ttarYYrF5fg8Y1wrCcevlTlp1PXMaCOvSTXzRtHWpiYitcxmg2qZrdzmRIM7HibYuBabbwY+BtH5wZV3d5hsl02mjr6KMvPnNNZLV4T6njntJ/+3V382QdXn97F+BZer5cjR46wePFinE6nejwWi3HfffexdetWtm/fTl5eHqFQiJ07d3LttdeelZBWHCdh0DxkIieEEOJicd4UkD4bYjE4ePD4qqG3tq5PSoIPfOB4OFRQcM6HKHMIIS5skUiEV199lQ0bNrBlyxYmJye5+eabqa2t5YMf/CCJiYnv+TUURWFyclJdMTQ9PY3dbsfpdDIxMUF/fz9LlizBZrOdhXcEE1MBntyxl/997tWTtk9pNBquW1nJ8Ngkh5u7iCnKnO1Tuel2SnMz2F3XwoQ/Hj7M1pKxpiRRlpeBwaDn1YPHV8ksL8vncEsXi4pzCYXCGPR6DjZ3zrxePEwKBMNYzck0tPeQl+6grr0XiK9aKc60o9EoDI5NMz4VoCDTpa5aMSeZWFqax+DoBI0dfSQY9WQ6bLT3xQs2Ly7JxZqSxBtHmglFoqQkJpBmSaHbM4TblkpRtouRcT8NM53ILMlJJJkMjE5Msag4h0AwwsDQiFpjyZ4a//3ptqWSmGCk3zfCdCisBkduWyopSSacVjMtPR4Meh2TUwE1qCnMcuGwmhmd8HOsa4Act52hsQl1BVRlrgutTk9Tl4dwNEpBppPewRFC4QhuWyolOS6OdQ7gnRlPjjOVvqEJ9Doti4pzMeh1vFnXohYDr8jPoqmrj5KcdMzJiYRCYY60Hg+qqgtz8AyPUpTtprPfhzPNzOET2sQvK81Do9UQDEeoa+2ZswIJ4NM11/GdE9rIn4nBwUEOHz7MokWL5tQBisVi/OM//iNPPfUU27Zto6io6D29jnh3EgbNQyZyQgghLhYXVRj0ViMj8Kc/HV811N8/9/aysuPB0DXXgMn0vg9J5hBCXDyi0Si7du1i48aNbN68maGhIW666SZqamq48cYbz9q/p36/n4GBAbq6uohEIlitVjIyMnC5XGe96P/R1m4ef+4VNm/by+R0QA12IB58LCvPp66lRy16vLqqiN11rRgNehYV52JJNrHthDbos8FSRUEWSQlGDEY9bxxunnN718AQuel2mrsHKM5Jn7Pq5PLqYkbHxpmYDtLrG5vTaQxgWXEWY5NTtHtGUJR4WHFgJpzIddspzcvgzbo2xv1zO5FZkhOpyM/CoJ+7nWtRUQ517T0sKspFUWLEFIWjrfGW91qNhrL8TKaDIdy2VJo6+0i3WefU+aksyCbRFA+FBnyj5KY7aOmJr1hNMhlZWprP5HSAIy1dGA16Mhxp6vaq0rwMMmxW9je1MzEVIMGgI82czMDwOJbkRKoLs5kOhdUVWuYkEylJJjzDYywqygGgf3AY72h8RVSyyUiC0UCSKYEsZxp9vlGmgyF8M8GRzZJMcqKJDIeVwZFxxv3TxBRFDbJcaZZ4BzPgcHMn9lQzgVBYrQlVVZhNakoSjZ19DI9Nkp/p5IX/+BopSWf+u9Tn83Ho0CGqq6txu49vM4vFYvzzP/8z//d//3debQe7//77+frXv86Xv/xlHnjggYUezlknYdA8ZCInhBDiYnFRh0EnUhQ4dOj4qqHXX4eZjkAAJCbCtdfCDTfE/1RWxr+iPstkDiHExSkWi7F37142bNjA5s2b6evr40Mf+hA1NTWsW7fuPf3/HgqFOHjwIABlZWWMjIzg9XoZHx/HarWqBahNZzHQ9k8HePbVA/zvC6+xdyYMmg1KYjGF6sJs7FYzu440E5qpQ7O0NI9DzV1ku2xkOtNAUebUvVlRXkBz9wAV+Vl4hkdxWC1qHR6Ay6qKCIUjxGLxAs8rKwrY23C889faFZVMTQepa+vBHwiyuqqY3XXx4MiakkhZlpPm3kGGJ+NhxaqKAvY0tL9tJ7LZoKo0NwNLciIGvY5dR44HVauri2nr9VCUlU57n5f8TOdJQVUkGsM/HaSho/ekoOra5RX4p4Mcae0iGIrMqTvkTDOzpDiPw61deIfjRZ2Xlxewv7Edo0FPrsOCLdXMm43HV93MBlnxzmmJRGMxNRgCWFycS//QCEVZbjr6B7EmJ9I4U5wboCzHhSkhgXA0RkN7L5WF2dS1xYMujQYury5Ru8AFQiHK8zLVFVKO1BQWFedwrHuAXu8IWo2Gktx0mjr70Wm1LC8v4Dv3/DmLi8+8u+fQ0BCHDh2isrKS9PR09biiKPzrv/4rjzzyCC+//DKVlZVn/Bpn0549e/joRz+KxWLhuuuukzDoUiETOSGEEBeLSyYMeqvR0bmrhvr65t6elXU8GLr+ejiheOV7IXMIIS5+sViMQ4cOqcFQW1sb119/Pbfddhu33HILVqv1lAs++/1+Dhw4gNlsprq6ek5R6UAgoLarHx0dxWKx4HK5cLvdZ2W72qyG9l6eeGk3T+/cR59vBICCLBeeoVESDAbK8jIIhcPUt/ep3aVKctLp941SmptOMBIhEo7S3uedW+AZSE40cbS1m4JMJ83dA2rdnZXl+fj9frxjfobGp6guyqa+PV4A2mQ0cMWSUvq8I+qqnBNb2JfmuEkxGTjc2ktkZnvUoqJsjrT2kOVMI8dtR4E5wc6K8gIaO/uoKshmaHwSa0oi+xo71NtXVRQSCIXjndNaOlleXjCnxtI1y8oJhiM0dfQxOjnFZZVFvDkTDKUkmlizqJiWXi/tvfHtarNb7TQaDYuKcnCkmdmxr17dzrWkOJtDLT3kuO1kOtPQ67S8duiY+nqXVxdztLWHysIsfKMTuNIsczqjXV5dzOR0EINex+HmTpaW5LKv6XiwtLwkGzQ6OgZ8DI/752wNTDIZuXJxGZ39gxyb2X534u2VBVlkOtPYub9B/Xn+6z1/zl/ddt3pXFZzDA8Pc/DgQSoqKubUwVIUhR/+8Ic8+OCDvPzyyyxatOiMX+NsmpycZPny5Tz44IN897vfZenSpRIGXSpkIieEEOJiccmGQSdSFKirgxdfjP/ZsQMCgeO3azSwfDnceGM8HFqzBs5wa4bMIYS4tCiKQn19PRs2bGDTpk00NDSwdu1aamtrufXWW7Hb7W8bDI2MjHDo0CGysrIoLi5+xwApFAqpwdDw8DApKSm43W5cLtdZ+3d9OhjiudcPsmX7Ho60dKttyl1pFqIxBVeahaTEBDzDY0xNB9UCzhl2KynJidgtKbT0DGDU65kKBBmdjG/dKsp247Sa8Y1O0NLjIdNhZXTCz1QwjEaj4YpFJYSjUfY1tBGNKeSm2xkam8Q/HSQv3UFeuoNjPQMM+EaBeBFl78gYBr2OokwnU9NB2vqPdxrLS7czNOanOMdNLKYwFQjS7R0mOBNkFWe70Wq1WM1J1Lf1kOFIo7N/UH38srJ8Eox6ejwzBZ5zM2jv8xKORDHodVy5uJSRST+HjsULZJfnZdLSEw+6irPdZDltHGzuYGxmBVNFfibHugawJJtwpyZhTEjgaHuf2vmssjCL1h4vlQVZhCMRFIU5ndGqirIJBsPYUlOob+shP8NFXfvxOkCrKgvRabX0Do7EW9nnZ9LY2YeixLe+LSvOwh+K0NQ1gKLA4uIctU5QfqaTwkwXexvbGJ8Z7+LiXA63dKlb7fKznPzky3eccsD5ViMjIxw4cIDy8nIyMzPV44qi8MADD/CTn/yEl156iWXLlp3R878f7rrrLmw2G//+7//O2rVrJQy6lMhETgghxMVCwqB5BALwyivHw6HDh+fenpwM1113PBwqKTnlLWUyhxDi0qUoCs3NzWowdOjQIa666ipqa2tZv349brdb/UDd3d3NsWPHKCsrIzs7+7ReJxwOMzg4iMfjYXh4mMTERDUYSklJOeMP7Sdq7fHw+xdf59lXD6DRatW6NymJCWS77KSmJOIdHcc3Mk6aJYWugXhnsNSUJCryM5kOBjnc0k1KUgKpyUn0eIcBKM1Nx6iJ0e4ZxR8IxVvUJyXQNziCzZJCRUEmE1MBDs90IktNSSLJZGRodILq4lyisRieoVEGZlrUp5mTSDAasCQnkmwy0u0ZIhKJMjpT7NqakojLlorNYqajP75ySafVMDgSr6uT6UgjN93O2OQ0DR292FPj52+27s7KigISDAb2NrQRDEdwWM0oisLQ2CTp9lSKc9x4hsZo7o7XDXJazURnAqhFRTmEoxE6+4cYmYjX6bFZkgENbnsqSaYEBoZGmZoOqre70ixYkhNxWM20zawyCoUjarCW47KR6bIxNjlFY0cfrjTLnNtXVhRi1OvY19ROMBTBaTUTCIWYmAqSmpxAntvGmD9Ip2dYfb1gOMJ0IMii4lwUoLV7gLGZukG56Xae/4+vkZqSdEbX0ejoKPv376esrIysrCz1uKIo/Pd//zf3338/L7zwApdddtkZPf/74fe//z3f+9732LNnDyaTScKgS41M5IQQQlwsJAw6Bf398S1ls+GQ1zv39ry841vKPvhBSEt726eSOYQQAuIfdtvb29m4cSObNm1iz549rFmzhttuu436+noaGxv5wx/+gOM9blGNRCL4fD68Xi+Dg4MkJCSowZDFYnnPwVA4EuVPbx7hty+8zqsHGinNzeDoTB0anVbDZdXFRCMx6tp6CIRClOVlUj/TGSzdnkpFQRZNHX30+UbQaTVk2i10D46TYDSwuCgHU4KBVw42AvECzSU5GTR09FFRkElSQgKhcJgjMy3t9TotxdnpjPmnyUt30DkwSGpykrqVzKDTUZ6fhSnBwPDYBB19g2Q6LHQPxoMjo17HiooCgqEIh1u60KChIMvFsa5484H8TCcFGU4ONXcyPO7HoNOpt6ckmqguykKj0bDrSIv6evmZTlp6PFQX5aDTaQkEQzR29M25fSoQICVBz9D4NGmpZrUzmkGnoyw/kySTEd/oBF39PgqyjndOMxr0rKwoZDoQ4nBLJxqNZk5ntfwMJ/lZTg4fi49Xr9NSmOXmWFc/yYkJVBdmo9NpeX2moLdOqyHPbaet30eeMxWjQU8UDW19PvX24px0JvwBctPtdA74+NU/fY4lpXlndO2Mjo5y4MABSkpK5gSeiqLwi1/8gm9+85s899xzXHHFFWf0/O+H7u5uVq5cyYsvvsiSJUsAJAy61MhETgghxMUiFArxjW98A4Dvfe97Z70zzUUnFouvFJoNhl55BUKh47drtXDZZcfDocsuA4NBvVnmEEKIt1IUhe7ubp544gl++MMf4vV6qays5I477qCmpoa8vLyzsponGo0yNDSkBkN6vV4tPn06dYzeTt/gMH/Yups/bN1Ft2doTieyBIOeq5aW0Ts4ooYhqyoK2TNTULk4y0GKUU9d9yDhme1YswWXs1w2sl1p6LTHg4vZ2xs7+mbq5oyRZjazr7F9zu2hSAStRsPh5k6WlxfOKVh99dIyQpEIDe29jPunKct20tRzfIXTZZVFtPV56ej3zRmPTquluiheQHvb3vrj27Fm3k9hlgt7ago6nZY3jhyv47OqspCWbg9leRn0Dg6TbrfOqTt0WVUR4XCUaCzK4ZbueQtSTwdDNLT3MjEVmFOXKM2czMqKApq6+tVVWJdVFfFmXSs6rZZFxTnYU1N4eW8ds5/uZzvBFWQ6caVZ0Gq1cwpoLyvN5VjXANkOC6P+AC6rmSPtx7tyfvfzH+Xu9WtP/QI5wdjYGPv376e4uJicnBz1uKIoPProo/zjP/4jTz/9NNdee+0ZPf/7ZcuWLdx+++1zandFo1E0Gg1arZZgMDjntgudhEHzkImcEEIIIQCYmoKdO+GFF+LhUH393NvNZli7Fj70Ibj+esYzM0m1WmUOIYSYY3JyktraWoaGhvjlL3/JG2+8waZNm9i5cyeLFy+mpqaGmpqad60ddKpisRjDw8N4PB4GBwfRaDRqMJSWloZWq31Pz73rSDP/+/xrPPfaIUKRyJwCxDluG+V5mexrbGd4po15eY6Txu7BmTo0mZgSjOzY36A+5+XVxeypb2NRUTYKGkxG/ZxOZZdXF+MdGcdpNVPf0UtVQfacgspXLSkjEo2qdXNOHI9Br+Pa5eUMDI3NtJFXqMpzU9cZXwWan26nMMvJG3WtTAXi4X+8AHQrDquZ4mw3Br2WVw4eb1G/urqYA02dLCrOYToQwpyUMGe8l1UV4RuZQEeUbt84i0tyefOE22fH2zngo983Oif4MRr0XLOsnL7BUerb46uwTuxUVpaXQbbLxuuHjzEdDM/cHu/MNjtejVbDrhOCteXlBRxp6WJRcS6hUASjQcf+EzqVVRdmMz45RaJRR4dnmNUV+fzkr/8Cp9OJ4YQvPE7F+Pg4+/bto7CwkLy846uKFEXht7/9Lffddx9PPvkkH/jAB07rec+FiYkJOjs75xy7++67KS8v56tf/SrV1dULNLL3h4RB85AwSAghhBDz6umBrVvjwdDWrTA0NOfm8YwMUvv7ZQ4hhJgjGo3ywx/+kHvvvRez2QzEPxz7fD62bNnCxo0befnllykvL6empoba2lrKy8vPWjA0OjqKx+PB6/WiKApOpxOXy4Xdbn9PwdDIhJ9nXtnPY8++om4PW1FewL7GdnRaLUWZDkxGDUfbvcRmPnbOtlgvynZjT01BUZQ5K2iWleXT7RmiONtNt3cYl9XCgWMd6u3LywvQAIFQiLq2XrXl/ezH2muXVzAdCHGktYvpYHhOJzK3LZWqomwa2nvpnylIXZRpp61/CINOR2mOi+SkRPbUt6vjrSrKprG9j7K8DBJNRsLhMHVtfURj8c5olQVZjPunyXbZaOv1YE81c6xrQL19UXEOJqOBcf8UTZ39lOVl0NbrVVdIXb20nEAozOHmToLhCCU5bjpnCmJnOtIozUmnuTfe8h2gMMtF72B8211VYXz71eHmLoLhCAD5GQ68I+PkZThJMiUwNj5J39CoGnTluO3otFrS7am09w2i1+sYnZjCPx0EoDwvg//3xQ8zPTnO5OQkaWlp6vViMpne8XqYmJhg37595Ofnk5+frx5XFIUnnniCe++9lw0bNnDTTTed4hW28GSb2CVGwiAhhBAXi1gsRldXvBBnbm7ue5r0i7eIxeDgwXi9oa1b4ZVXGA8GSQWZQwghTouiKIyMjPDUU0+xceNGtm7dSkFBATU1Ndx+++1UVVWdlX+/FUVhbGxMDYYikQgOhwOXy4XD4TjjLTCKonCkpZsnd+zliZd2MzQW7zSW57TSNzKBJTmRkpx0wtEoR5q71M5dpbkZDAyNUpKbznQgTDgSpstzvPNXSU46RoOeRJORoy3dZLls9HqH1Rb3y8vzSTAYaO314B0epyDTGe92FgiRZDKyqrKQ4XE/R2a6Z+Wl2/HNdCqrLszGnJJIU0efuoLJZU1hYipAsslIpsPKVDCCZ3SciZmC1JmONBQUclx2RiYmmZgKEAiG1QLOGfZUbJYkAoEAHZ4xbJYUorGo+vxVhVmkWVKob+9heGwSW2oKWo0W3+gEKYkmlpTmMhUIcmBm1U5qShImoxHvyDjVRdmYDHoGhkbpninIbU42YU5MRKfTkuW00e2Nf0HRO3N7kslIlstOWkoSIxN+uj1DuG2pdA7Et8YlJhhYUpJPOBLlSEsXOp2OZx/4B8ry4l2/pqenGRwcxOv1Mjo6SkpKCi6XC6fTeVKx8snJSfbu3UteXh4FBQVzro8tW7bwmc98ht///vesX7/+jK6xhSJh0CVGwiAhhBAXCykgfQ5NTzP+4ouk1tbKHEII8Z6MjY3xzDPPsHHjRl544QUyMjLUFUPLli07a8HQ+Pi42rI+EAiowZDT6USv15/R845PTPKz3z/NGw1dNPX41KAky5WGfypIhsNKcqKJodFxRien1U5a6fZULMmJ2Cwp8U5aGohGY8eDpXQ7mU4bQ2MTHOsawGE1o9HA4MgEGo2GyyoL0et0vFnXSjgaVTuNDQyNkeO2k5NuwzcyqRaMnu1kNjkVoCI/kwn/NGP+aXoH4ytwEhMMWBKNJJuMGAwG+nxj2FLNc4KUioIsDHodTR39TIdCuKxmumdW8DjTzFQUZNE9MER736BaULq5ewCdVsvi4lzMKSZePdBETFHQaTWU5sYLaOdnOnHZLITDUTUY0migqjCHbs8Q5XkZeIfHMCcnqm3iAZaV5RGLKWg0Go60dLK4OI8Dx45ve7pyaSmRcJRjXf2MTPhZUVHIvob2mfORyE/+5hPcfOX8Ld5nu9gNDg7i8/lISEjA6XSqW8n2799PdnY2RUVFcx73zDPPcPfdd/Ob3/yGD3/4w2d0TYn3h4RB85AwSAghxMVCwqBzS+YQQoizbXJykj/+8Y9s3LiR5557DpvNxm233UZtbS2rVq06KwVtFUVhcnJSDYb8fj92ux23231adWOmp6fZv38/ZrOZ6upquj3D/N+f3uC51w8yORVQgxZLciIum4U0cwpjk1P0DQ5jSz3eot6cZKKyMJtwOMKRlm4MBh1uW3xbE0Bxtptst43DzV0Mj/tJMOjJTXfQ3D2AJTmRysIsNGjUgsl6nZbinHSauwZYVBTfWhWJxWZqCIFWo6GiIIupQBCXzUJTRz85brvayQygLMsBKIxPh+kfHmdJSS6HmrvU519emsvg8CgdnlEUYEVFgRq0FGa5KMxysbuuRV1lNFtA2mZJpjQ3A6NBz84Djerrra4uZl9DG4uKc4nGYiQaDSfVURocncCRmkJDRx+VBVkn1VEKRyN4hsbo6PexuqqY3XUt6nivXV6Bb3SCIy3dxBSFv7rtWv71no+e0s85Go0yPDysrhoKh8OkpKRQWFg4Z4XZCy+8wB133MH//M//8LGPfeyUnlucOxIGzUMmckIIIS4WEgadWzKHEEK8n6ampnjhhRfYuHEjzz77LMnJyaxfv57a2lrWrFlzxqt53srv96vB0MTEBGlpaWowlJCQMO9jJicn2b9/Py6Xi7KysjlbiKLRGK8eauL3L+7i5T1HyXHbaZjpOqbXaVlRXgAaDc3d/YyOT1FZmM3RmSAmzZzEsrJ8Ovp98RVDwNKSXA42d8U7aRVlY0s1s31fvVrnZ7ZuUWGWC0dqClqtdk5QsqqykJYeD2W5GfQOjpDhsM4p8Hx5dTHBcIRoNMqR1m5WVxXPefzSogymAkF6fBNMBcMsLszkcFv8/dhTU1heXkBDey89M9u1VlcXsftoK0a9juriHFKTk9i2r/6E8cSDodLcdFLNSWjQzBnP8rJ8umbqKPV4h3FYzRw8YcXPsrJ8NBpmtnt1U12YQ117j1pH6colpUQiMerbe5iYClCWl0Frj4dINIY9NYUPrKrih1/6S4yG07t+pqam2LNnDzabjYSEBAYHB+nu7uZ//ud/KCsr4/e//z0/+9nPuOOOO85K/StxdkkYNA+ZyAkhhLhYSBh0bskcQghxrgQCAf70pz+xadMmnnzySQwGA7feeiu33347V1111Wl3gXo709PTajA0NjZGamoqbrd7TkHh0dFRDhw4oNaLeacP/sNjkzy5cy9/2PoGR1q656yg0Wo0XLuigpFxP0dauojGFLWFOsRX2BRlu9l9tJVxf3z72WznsDRLMmW5GRgNBnYemNupbF9jO4uKcohEoyQmGE/q/DUy7ifNnEx9ew8VhVnsqTte0PrKJaVEozG6vcP0eofVoAnincqq8pyMjE/TOTgGwKKibI629aIoCmV5GaTbrexvbGdiKr4iqLIwi6aOfuypKRRmuYhEohxq6VILSpfkuPEMj1OSm04gFGY6EKJvcEStk5SXbiclKZEkUwL1bT1YLUmMTUwzOR1//or8TNIsKXQODNLrHcFptRCJRRkZ92M06FlZUUAkGlPPqTPNzPP/8Y+k262nfV3s3bsXl8tFaWkpGo0GRVHo7Ozkm9/8Jps2bQLgiiuuoKamhrvvvhu73X5aryHeXxIGzUMmckIIIS4WEgadWzKHEEIshHA4zLZt29i4cSNbtmwhGo1y6623Ultby9q1azEajWfldYLBoBoMjYyMYDabSU5OxuPxUFZWRnZ29mk9X0N7L797cRebt+1heHxyTkv4NHMyKysLae7qp6M/XqdndVURu+taMeh1VBflYDUnsX1fPbOfaGdbrJfkpJNmjv++m23ZDrC0NI8+3wiFmS66vcPYLckcbeshFos/wZKSXPR6HYFQmLrWHsrzM2nt8ahBzZUzLeGPtHQxFQiR7bDiHZ0gFIniSE0h22lhwDfOwGi8zlGm08qEP0A4EqW6KIeYEqOtx6vWUXKlWYjGYrjSLCQnmujzjRCORBgcmQDiBaQzHVZSU5LoHPAxORXAkpyobrdLsyRTmZ/FyMQU9e09JBj1ZDrS1O10VYXZOK1m3qxvZSoQQqfVUJKbQWNHHy6bheLsdP7+jptZXV1yWj+32SDI6XSetAps165d3H777fzgBz/gtttu4+mnn2bLli08/PDD5ObmntbriPeXhEHzkImcEEKIi4WEQeeWzCGEEAstEonw6quv8sQTT7Blyxb8fj+33HILNTU1XH/99e/aHvxUhUIhjh07Rn9/PxqNhuTkZHXF0OzvnVN+rnCEbfvq+N3zr/Py3nqisRjLyvLV4snFOW5yXHb2N3UwNhOkLCrKob69F6s5iZKcdKKxGAePdarBTVleBn2DI5TmZhAMRQiGQ3R7jnciy890kmxKIMmUQF1bN/ZUM6MTfnUFT1VhFlZzMu29g/T5RtTgZmhsEpPRQGmOk1AwRGNPPKhKTUkiyWSk3zdKcbYLo17D8JifgZF4MJSYYMBtSyUQipCf4aBvcBiDwUBrjwcg3to+L4PEBCNTgSBNnX2U5mXS0N4LxFdNXbGkdCao6mY6GGZpaZ66XcxtS2VRUXx7WL9vFIhvh9tT34bJaKC6KAdLsomX9x7fnvav9/w5f3Xb2tP6WQUCAfbu3Yvdbqe8vHxOELRnzx5qamr4zne+w5e+9CXZGnaekzBoHjKRE0IIcbGQMOjckjmEEOJ8Eo1G2bVrFxs2bGDLli0MDw9z0003UVNTww033HDGvxNmtwO1t7ezZMkSzGYzPp8Pj8fD0NAQiYmJuFwuXC4XZrP5tEIB7/AYW3bsZcNLb1LXFi/wXJLjptszTCwWo7ooB6Nex9G2HiangwDkuO2MTk6RYbdiTUlicGyckTH/nBU4qSlJ2FNT6PYMMR0Kodfp8A6Px2+3WSjOcjM6OU19ew/mJBNWczLdnnhB68UluaSZk3mzroXpYBiDTosrLYVe3zjpdisFGQ4isRh76uPby7QaDZUFWbT2eijNTWd8fBJQaPeMqu9zeVk+gVCYJFMCR1q6WFScy96G49vTrl5aRjgSpaN/kIGhsTmrphITjFy5pJRe77Bae+nE2ysKsshyWHnt8DGmg/Hwa2VFIXsb2ki3W8nPcFCam873v/jx0/rZBAIB9u3bR1paGhUVFXMee+DAAW699Va+8Y1v8Hd/93cLGgQ99NBDPPTQQ3R0dABQVVXFv/zLv7Bu3boFG9P5SMKgechETgghxMUiGAxy3333AfD//t//e9vCn+LskDmEEOJ8FYvF2LNnDxs2bGDz5s309/dzww03UFNTw7p16zCbzaf0PIqicOzYMQYGBli+fPlJj4tEIgwNDeHxePD5fBiNRjUYSk1NPeWQQFEUDh7rZOPLb7Jtbz0d/fGtT06rBTTxj7BFWW5Gxv2M+acZGBoF4p3KHFYzaZZkgsEIHf1eHGkWOma2TpmMBhaX5KIoCnVtPYTDUYqyXTR2xlvOZznTKMvPoKG9T11hM9s5LDHBQJ47jUSDjgOt/epYV1UUsqehjcqCLBITjBj0Wt44OrcgdeeAj0x7Kp39PpypSTR0D6q3r6kuJhKNMTkdoKGjj8sqi+Zsb7tuZSUT/mmOtvYQCIVZVpbHgab4iqAMh5XK/CwaOvro88W3j1XkZ3KsawCjQU9VYTZ6vZb9jR2EwhEAFhfnsulHf0tiwqlvHwwGg+zbt4/U1FQqKyvn/ByPHDnCzTffzN/93d/xta99bcFXBD399NPodDqKi4sBePTRR/nRj37EgQMHqKqqWtCxnU8kDJqHTOSEEEIIcSZkDiGEuBDEYjEOHjzIxo0b2bRpEx0dHXzwgx+kpqaGW2655W1Dm1gsRn19PaOjoyxfvpykpKR3fJ3ZFuQej4fBwUF0Op0aDKWlpZ1yaBAIhXlh12E2vrybjn6furUqwagnx21Hp9WSmpzEse5+MuxWdbWMVqNhRUUBOq2Wbu8Qvd6ROVurTEYDVywuxTM8Sl1bfDvWbGcvmFlh40zjtUNN6gqbqjw3dZ0edYWNXq/j1YNN6lgvqyriaGs3VQXZ+ANBkkxG9s4UyAZYXp7PdDCEyaCnvr2XLLuF9oERZj+Ur6osRKfT0tnno39olNKcdDoGfITCEZJMRlZVFDIyOcXhmbb2WS4b45NTTEwFqCjIIjU5kY5+nxqO2VKT0Wm16LQ68jMcjPmnePSbnyfLZTulcw/xLYF79+7FYrFQVVU15+dWX1/PzTffzOc//3m+9a1vLXgQ9HZsNhs/+tGP+PSnP73QQzlvSBg0D5nICSGEEOJMyBxCCHGhURSFuro6NmzYwKZNm2hqamLt2rXU1tZy6623YrPZ0Gg0TE5O0tjYSCQSYdmyZae90jQWizEyMoLH48Hr9aLRaHA6nbjdbtLS0tBqtaf0PP2+UTa+/CYbX95NYoKRQzOhCMSDlFhMIRgOc7S1R62ZM2vtigr80yHq23rwB4Jztla5baksLsmlvq1HLdA828ksMcFAnstKotHAgdY+9fmWleVx8FgX5fmZJJsSiMSiHG7uUgtSl+dn4p8OkOW00TngI8mUQK/3eN2ikpx00izJjE9O0djZj82cRCAUZmomeFpRnk+C0cCBpg6mg2GsKUmYEowMDI2SbrdSmOXEPx3iUHM83Eow6sly2uj3jVJVmM10MEg0GlNXPel1Wv7w/b/m8kWnXjA6FAqxb98+kpOTqa6unvNzOnbsGOvWreNTn/oU3//+98/LICgajfLEE09w1113ceDAASorKxd6SOcNCYPmIRM5IYQQFwtFUfD54sUtHQ7HeTlRu5jIHEIIcSGb3QK2ceNGNm7cyOHDh7n66qv5wAc+wGOPPcYtt9zCt7/9bfR6/Xt+nZGREbUzWTQaVYMhm82GTqc7pec40NTB//3pDZ7auY+K/Cw12IHjNXd6B0fo9gyxurqI3TNbtxKMBq5aUoZneJSjrfG6RCcWrC7PzyTdlsr+pg7G/dMA5LvT6B0ax56aQn6Gk1AoTH1H35yW75PTQQoyXUwHg4yM+5kKhNS6RU6rhWxXGnq9jrq2Hgx6PckmI30zW9HyM53kuh0c6+pnYGgUg06Lw5JE/8gkJqOeysJsUkwmdh5sVN/j4uIcDrd0U5GfSXJiAhqNZk74taqykK6BIQoynXR7hvjiR2/grluuOeWfUzgcZt++fSQmJrJo0aI5QVBrayvr1q3jox/9KD/+8Y9POcw7V44cOcKaNWsIBAKkpKTw29/+lptvvnmhh3VeWfAw6MEHH+RHP/oR/f39VFVV8cADD3D11Ve/7f137NjBfffdR11dHZmZmXzlK1/hnnvumfe+v//97/mLv/gLampq2LJlyymPSSZyQgghLhZSQPrckjmEEOJioSgKbW1t/PKXv+SBBx4gEAhwxRVX8OEPf5jbbruNzMzMs/IFg6IojI2NqcFQKBTC4XDgdrtxOBynFAxNB4Ns3X2UJ/70Btv3N7C8LH/O1qxrl5cTCIap7+hlwh+gqiibpo4+ItF4W/eqomw6+wZpm6krVJDpxDM8TjQaJc9tRafV0z88flJLeKfVrLZ6j8YUdWtWSmIC+ZlOUhJNdHuH8Y2Ok+2yq9vbUhITWF5ewPCYn6Nt3Wg08e5oh1u6ASjPyyDLZWP30Ra1SHZJpo3mvmHslmQKslwkJSTMCYYury7mwLFOqouyCYbCJCUkzKk79ImbruSHf/2Xp/xzCYfD7N+/n4SEBBYvXjwn7Ono6GDdunWsX7+e//iP/zjvgiCIr2jq6upidHSUjRs38stf/pIdO3bIyqATLGgY9Ic//IE777yTBx98kCuvvJKf//zn/PKXv6S+vp7c3NyT7t/e3k51dTWf+cxn+NznPsdrr73GF77wBX73u9/xkY98ZM59Ozs7ufLKKyksLMRms0kYJIQQ4pIkYdC5JXMIIcTFpKmpieuvv55bbrmFr371q2zZsoXNmzfz+uuvs2rVKm677TZqa2vJzc09a8HQxMSEGgxNT09jt9vVYMhgMLzrcwwMjfLkjn38Yesumjr7Kc1Jp9MzRDAUxqjXcXl1MVPBMPsb24kpCpmONKYCQUYnpyjNTceRaqZvaFQtOJ2caMSakkwoEqEoy41vdJxoTKF95najXkdJbjomo5FQJEJDWy/l+ZkcnemEBvFVSoFwmMb2PiamAmpnL4gHS0tKc2ns6Fe7l81uX0swGqguzMaSksj2ffXMfnIvybTT3DdEfroNm8WMVqedE35VFWYxNDZJfoaTPt8I6XYrf/j+X2M0nNqKrkgkwv79+zEYDCxZsmRO2NPb28sNN9zADTfcwEMPPXReBkHzuf766ykqKuLnP//5Qg/lvLGgYdDq1atZvnw5Dz30kHqsoqKC2tpa7r///pPu/9WvfpWnnnqKhoYG9dg999zDoUOH2LVrl3osGo1y7bXXcvfdd/PKK68wOjoqYZAQQohLkoRB55bMIYQQF5Ph4WF++9vf8sUvflENexRFoa+vj82bN7Np0yZeeeUVFi9eTG1tLTU1NRQVFZ21LcmTk5N4vV48Hg9+vx+bzYbb7cbpdGI0vnMnLEVRONLSzdOv7Of3L+5ieHwSp9WMRqvBOzyOzZJMeUEmwWCEfY3xICXJZMRtS6XHO0yey4pWq0VvMFLfHi8urdVoqCrKJhAMk2ZJprGjj+JsN/tntpcBXLmkhGg0vkqoo983py6RUa/jmmUVeEfHOdrSTUxRWFVVyJ66eDBUkpNOTrqNfQ0djM2sQlpcnMPRth7SzMmU5KQTi8U4cKyTcCQKQHpaChNTQXLcNrRaLRNTQcb80+r2tixnGs/+9CvxLmynYDYI0uv1LFmyZM7KrIGBAW688UauuuoqfvnLX57Sqq3zxQc/+EFycnJ45JFHFnoo540FC4NCoRBJSUk88cQT3H777erxL3/5yxw8eJAdO3ac9JhrrrmGZcuW8dOf/lQ9tnnzZj760Y8yNTWlJsXf/OY3OXz4MJs3b+ZTn/rUu4ZBwWCQYDCo/n18fJycnByZyAkhhLjgSRh0bkkYJIS4lCiKwuDgIFu2bGHjxo1s27aN8vJyNRgqLy8/a8HQ1NSUGgxNTEyQlpamdiZ7t2LWoXCEbXvreHlvPX/YuotwJIpRr6Mgy0VTZz+FWS4caRaUaJQ9J3b+KsunvX+Q0twMPEOjOK0W9jQcr8mzZlHxTJexePC0srKI3SfULVq7vILpYIimzn5GJ6dYVprPgWMdANgsySwrzafT46OlO759bLZzmKIoVBVmYzIaaOjoZWwyHuyk21MJhiLYLMk40iwM+EYIhCJ4hscAMBn1OC1JpKYkMREI4Rme4Kn/9/dUFWaf0jmORqPs378frVbL0qVL54Q9Xq+XdevWsXz5ch577LHzOgj6+te/zrp168jJyWFiYoLf//73/Nu//RvPP/88H/rQhxZ6eOeN91b56z3w+XxEo1Hcbvec4263m4GBgXkfMzAwMO/9I5EIPp+PjIwMXnvtNX71q19x8ODBUx7L/fffz7e//e3Tfg9CCCGEEEIIcanSaDS4XC4++9nP8pnPfIaRkRGefPJJNm7cyA9/+EMKCwupqanh9ttvp7Ky8j1tKUpKSiI/P5/8/HwCgQBer5eBgQGamppITU1Vg6HExMSTHms06LlxzRJuXLOEr961nqd27GNvQzubt+8BoK3Xiz3VzP6mTgrT00hIMJKaYuaNuniws/toC5dXFzMwPMbq6mJaugcozHKz68jx4OfyqiIURaE4201Lj4eqomxeOdhENBZDp9VyzbJypoIhDDod4WiUlCQTu+tamZwOUJDpJMuZRr9vlFA4AkBH3yCJJiOgYXVVEeP+KaaCYQaGxhiZ8NM1MERxbjpWs45MZxr1bT2U5GZwpKWbbt84AH9TczmhsUF6ezXvupoqGo1y4MABNBrNSUGQz+dj/fr1LFq0iEcfffS8DoIAPB4Pd955J/39/aSmprJ48WIJguaxYGHQrLcmxYqivGN6PN/9Z49PTExwxx138Itf/AKHw3HKY/ja177Gfffdp/59dmWQEEIIIYQQQoh3p9FosNls3H333dx9992MjY3x9NNPs3HjRtauXUtWVpa6Ymjp0qXvKRgymUzk5uaSm5tLMBhkcHAQj8dDc3MzZrNZDYbmWw1rs6TwqfXX8qn11/Llj9/Ehpd209TVz9bdRwBoGxjhsqoi6tp7uKyyiDH/FEkJRnWrV0ffIIuLcwmGQqwoL+Boazd56Q4ONHcRDM22hC8gwajHZklhcHQchzWFo63dDI/7sSQncllRDqFIhK6BeI0gz/AYCtA14GNRcQ5GnZ5QJMyRmU5nu+taWVySS2w6xOXVxbT1eslJt7PvhFVMVywuIRKJUp6XQWNnP/9w56185rZr8Hq99Pb20tDQgNVqxeVy4XQ654Rm0WiUgwcPoigKy5cvnxP2jIyMqNv/Hn/88ffcSe5c+NWvfrXQQ7ggLNhPcrYy/FtXAXm93pNW/8xKT0+f9/56vR673U5dXR0dHR2sX79evT0WiwGg1+tpamqiqKjopOdNSEh416WFQgghhBBCCCFOTWpqKnfccQd33HEHExMT/PGPf2Tjxo2sW7cOh8OhFp9etWrVewqGEhISyM7OJjs7m3A4rAZDra2tJCcn43K5cLvdJCcnn7SwoCQ3na/dXcPwyAj/+6SVg51DDE8E2dvQRiym8GZ9K+X5mQxP+Lm8upjOfh+mBAMtPR6mAvEyI4WZLmypKeh0Who6+rBZkukdHGZgaAyNRsOysnzMySb21MU7ewWCIXxjEzR19pPlTCM33Q6grjI60tLNiooCWnsHWVVZiH86SEpiAm/OtIzv9gxxeXUx4/5pVlYWUt/WQ3VhNq8fblbf1ydvvpovf/wmNBoNBQUFFBQUEAgEGBwcxOv1cuzYMVJSUnC5XDgcDpqbm4lGoycFQWNjY9TU1JCZmckf/vCHd63TJC4sC15AesWKFTz44IPqscrKSmpqat62gPTTTz9NfX29euzzn/88Bw8eZNeuXQQCAVpaWuY85p/+6Z+YmJjgpz/9KaWlpad0Act+fyGEEBeLYDDI5z73OQB+/vOfy5cf7zOZQwghxDubmpri+eefZ9OmTTzzzDOYzWbWr19PbW0ta9asOWtbkCKRiBp++Hw+TCaTumLIYrGowdDIyAgHDx6kqKiI3Nxc/NMBnnv9EBteepOuAR8jE361GLMrzUKWMw29Xk9Dew9arZbUlES6PcMA5KY7KMx00tzjodc7jFajobIwm6Ot3SQmGKkqzMKSnMjLe49/nl1dVcTuunjoZEkyodfr5gQ7l1UW0TngoyDTSe/gCA6rmQMnFKxeUpKHXq8lFI5wtLWbpaX5PPFvXyYx4e0/954Ymvl8PrRaLdnZ2aSnp6vnZmJigpqaGiwWC0899RQmk+ms/FzE+eO8aC3/s5/9jDVr1vDwww/zi1/8grq6OvLy8vja175Gb28vjz32GHC8tfznPvc5PvOZz7Br1y7uueeeeVvLzzqVAtJvJRM5IYQQQpwJmUMIIcSpCwQCbN26lU2bNvHUU09hMBhYv349t99+O1deeeUptZI/FdFoFJ/Ph9frZXBwEIPBgMvlwmQy0dLSQllZGdnZJxdZ7veNsHnbXja8vJsezzBOm0VtOZ+SmMCysnzG/NNqZ7ClpXkcPNYJQEmOm7wMJ2/WtTHuj3cGm+0s5rCaKc52YzQY2HngeKfsFeUFHGntprowm2gsRlRRaGjrJTqz26Ukx41OqyU1JYmmzn6Sk0xMnNA5rLoom8e+/XncNuu7npNYLMaRI0eYmpoiLy+P4eFhBgcH+fnPf47BYKCxsRGHw8Fzzz1HUlLSezr/4vy0oGEQwIMPPsgPf/hD+vv7qa6u5t///d+55pprgHiQ09HRwfbt29X779ixg7/927+lrq6OzMxMvvrVr3LPPfe87fNLGCSEEEKIc0XmEEJceh588EF+9KMf0d/fT1VVFQ888ABXX331Qg/rghMOh9m2bRsbNmzgySefJBqNcuutt1JbW8vatWvP2halWCzG0NAQXV1dDA8Po9frSU9Px+12Y7Va592ypigKdW09PPHSbrZs34tvdILl5fnsb+wAwGpOYlVFIW19g7T2xDuDzQY/ep2W6qIcbJZkXjnQRDgabwm/pCSPwy1d5Gc4cKWlEgqHqWvrITTTMj4v3U44GiPHZWN4fJLRyen42McmAbCZUyjJcxMORzna2o1er2PLj//ulDqHxWIxjh49ytTUFCtWrFBDt1gsxqZNm/iHf/gHRkZGSE5Opqamhr/8y7/khhtueM/nXpxfFjwMOh/JRE4IIcTFQlEUpqbi30gmJSWdtRa/Yn4yhxDi0jK70+HBBx/kyiuv5Oc//zm//OUvqa+vJzc3d6GHd8GKRCK88sorPPHEEzz55JNMTU1xyy23UFNTwwc/+MH3vGXJ4/Fw9OhRqqqqMBgMeL1evF4viqLgdDpxu93YbLZ5g6FINMprh47xh627eGHXYQKhMKuritk903ksN91OaU46h1t78M60fK8uyqahvY/kxATK8zIBhcMt3QRmCk5nOdOYDobIctowGvT0+UZQFIWBofjjk0xGSnMzMBkNdPQP4hudoCQ3g4b2XgAsyYk8+NW/4rqVle/63hVF4ejRo0xOTrJixYo5IVsgEODjH/844+PjPPfcczQ0NLB582YSExP5zne+857OuTj/SBg0D5nICSGEuFj4/X5SUlIAmJycnLezijh7ZA4hxKVl9erVLF++nIceekg9VlFRQW1t7bw1UMXpi0ajvP7662zcuJHNmzczOjrKTTfdRE1NDTfccMNpb2Hq7++noaGBRYsW4XQ61eOKojA6OqoGQ5FIBKfTicvlwm63z1vLaGJqmhffOMz/bX2D1w43oygKlQVZNHcPEI5EqSzIwmZOpqlrgMHReLt3t81CJBojwaAnN92Bb2yC6WCYXm+87lCCQU9xTjqJCUYCwTANHT1UFWZzuKVbfd1rl1cwHQjS0NHHxFSAb332I3ym9gPv+t4VRaGuro7x8XFWrlw5JwgKBoPccccdeDwetm7dSlpa2mmdV3HhkTBoHjKRE0IIcbGQMOjckjmEEJeOUChEUlISTzzxBLfffrt6/Mtf/jIHDx5kx44dCzi6i1MsFuPNN99Ug6GBgQE+9KEPUVtby0033YTZbH7Hx/f09HDs2DGWLFmC3W5/2/spisL4+DherxePx0MoFMLhcKjdt+Zrr947OMKW7Xv442sH1bpBTqsFNArj/gBVhVnEYgpjk1O0z9QdMuh0FOW4icViWM3JHOvsJz/TqT4e4MolpUSjMTzDY7T3DarbzwCMeh1f+PMb+Ps7bnnXlb+KolBfX8/o6CgrV66c01AiHA7zyU9+ks7OTl566aV3PDfnwv3338+mTZtobGwkMTGRK664gh/84AeUlZUt6LguNhIGzUMmckIIIS4WEgadWzKHEOLS0dfXR1ZWFq+99hpXXHGFevz73/8+jz76KE1NTQs4uotfLBbj4MGDbNiwgU2bNtHZ2cn1119PTU0NN998M6mpqXMCkq6uLlpbW1m6dOlprXpRFIXJyUk1GJqensZut+NyuXA6nScVuVYUhaOt3Wx46U32N7WrdYX0Oi2luRn0+0YpzU1naGwCS3IS+0/oDHZ5dTHTwRBarZa61m6WlRWo288ArllWTigcobXHy+DoOGtXVPDotz6P/l06sCmKQkNDA8PDw6xcuXLONrtIJMKnP/1pGhoaePnll3G5XKd8bt4vN910Ex//+MdZtWoVkUiEb3zjGxw5coT6+nqZx5xFEgbNQyZyQgghLhYSBp1bMocQ4tIxGwa9/vrrrFmzRj3+ve99j9/85jc0NjYu4OguLbN1cGaDoWPHjnHddddRW1vLLbfcwk9/+lNSUlL43Oc+R2pq6nt6Lb/frwZDk5OT2Gw2tWX9W4tcR6JRXjnQyKZtexgYGp3TMv7y6mJ6B4fJctro7PeRm25nd12revtllYUogH86SH17L2V5GbT3eglFomg0Gm68fDEP/N2dmJMS3/XcNDY2MjQ0dFIQFI1Gueeee9i/fz/btm0jPT39PZ2b98vg4CAul4sdO3aozabEe3fy+jYhhBBCCCGEEO/I4XCg0+kYGBiYc9zr9eJ2uxdoVJcmjUbDokWLWLRoEd/61rdoampi48aNPPzww3zhC19Ap9Nx7733EgwGURTlPTVTSE5OpqCggIKCAqanp/F4PPT19dHY2IjValWDIZPJhF6n47qVVVy3sgr/dIDnXj/Epm17CITC6lavbs8wS0pyGfNPs6qqkKaOftJtqRxq6SY4U2C6qjAbqzmJYChMR78Pp9XMdz73Z6cUBDU1NeHz+eYNgr70pS/x5ptvsn379vM2CAIYG4sX0rbZbAs8kouLrAyah3yrJ4QQ4mIhK4POLZlDCHFpWb16NStWrODBBx9Uj1VWVlJTUyMFpBeYoih85Stf4dFHH+UTn/gEu3btYt++faxZs4ba2lpuu+02MjIyzlqXzUAgoBafHh0dxWKx4HK5cLvdJCbODW08w2M8uWMfm7a9SSAYotszrHYWy3HbcNtSiUbj280STQmYk01qgenKgix+8jefYHFJ3ru+/+bmZjweDytXrpwzhlgsxt/+7d/y0ksvsW3bNvLy3vm5FpKiKNTU1DAyMsIrr7yy0MO5qMjKICGEEEIIIYQ4A/fddx933nknK1euZM2aNTz88MN0dXVxzz33LPTQLnktLS0888wzvPbaa5SUlKAoCl1dXWzatIlNmzbxla98hcsuu4zbbruN2tpacnJy3lMwZDKZyM3NJTc3l1AopAZDLS0tpKSkqMFQcnIyblsqn739A3z29g/Q1NnP5u172LxtD+P+aQD2NrQDYLMks6g4l8GRcXoBrVbDP9x56ykFQS0tLQwMDMwbBH31q1/lhRdeYPv27ed1EARw7733cvjwYV599dX39Dzbt2/nuuuuY2RkBKvVenYGd4GTlUHzkG/1hBBCXCwCgQB33nknAL/5zW/mLBEXZ5/MIYS49Dz44IP88Ic/pL+/n+rqav793/9d6pqcJyKRyLydvxRFoa+vj82bN7Nx40ZeffVVlixZQm1tLTU1NRQWFp61FUPhcJjBwUG8Xi9DQ0MkJiaqwVBKSor6OrFYjH0N7Tzx8m6efeUAo5NTLC/LVwtMu2wWvnLnev7ixive4dXiWlpa6O3tZeXKlXNWBMdiMf75n/+Z//u//2P79u2UlJSclff4fvnSl77Eli1b2LlzJwUFBaf12LVr17J06VIeeOABIN79b3h4GLfbfdZ+thc6CYPmIRM5IYQQQpwJmUMIIcSFRVEUvF4vW7ZsYePGjWzfvp2Kigo1GCorKztr4UEkEsHn8+H1evH5fBiNRtxuNy6XC4vFor5OMBzmlQON/GHrG7z05lGC4Qifqf0A3/rsR971Ndra2uju7mbFihXqNvHZ9/mv//qvPPLII2zbto2Kioqz8p7eD4qi8KUvfYnNmzefcWj11jBInEy70AMQQgghhBBCCCEWgkajwe1287nPfY4XXniB/v5+vvzlL7N3716uuOIKLrvsMr773e9y9OhRYrHYe3otvV5Peno6ixcv5tprr6W0tJRgMMj+/ft59dVXaWpqYmRkBKNez/WXLeIX3/gMB/73fh7++v/Hv/x/t7/r87e3t9PV1TVvEPSDH/yAX/3qV2zduvW8DoIAvvjFL/L444/z29/+FrPZzMDAAAMDA0xPT5/S4z/1qU+xY8cOfvrTn6LRaNBoNDzyyCNoNBpGR0cBeOSRR7BarTzzzDOUlZWRlJTEn/3Zn+H3+3n00UfJz88nLS2NL33pS0SjUfW5Q6EQX/nKV8jKyiI5OZnVq1ezffv29+EsvP9kZdA85Fs9IYQQQpwJmUMIIcTFY3R0lKeffppNmzbxwgsvkJ2dTU1NDbW1tSxZsgSt9uysrYjFYgwPD6t1hjQajdqVLC0t7ZRep6Ojg46ODlasWIHZbFaPK4rCAw88wE9+8hNeeuklli1bdlbG/H56u5VYv/71r/nUpz71ro8fGxtj3bp1VFdX853vfAeAuro6rr/+erVm0COPPMJnP/tZ1q5dyw9+8AMmJib48Ic/zIoVK7BarXzzm9+kra2Nj3zkIzz22GN87GMfA+ATn/gEHR0d/Nu//RuZmZls3ryZf/qnf+LIkSPn/ba7t5IwaB4ykRNCCHGxkG5i55bMIYQQ4uI0MTHBs88+y6ZNm3juuedwOBzcdttt3H777axcufKsBkOjo6N4vV48Hg+KouB0OnG5XNjt9nlfp6uri9bWVlasWDHnd4+iKPz3f/83999/Py+88AKXXXbZWRnjheCt28TeWkD6kUce4e6776alpYWioiIA7rnnHn7zm9/g8XjUudNNN91Efn4+P/vZz2htbaWkpISenh4yMzPV17r++uu57LLL+P73v3/O3+d7Id3EhBBCCCGEEEKId2A2m/n4xz/Oxz/+caampnj++efZuHEjtbW1WCwW1q9fT21tLZdffjk6ne6MX0er1WKz2bDZbJSVlTE2NobH46GxsZFwOKwGQw6HA51OR3d3N62trSxfvvykIOgXv/gF3/ve93juuecuqSDoVCUlJalBEIDb7SY/P3/OFju3243X6wVg//79KIpCaWnpnOcJBoPY7fZzM+izSMIgIYQQQgghhLiE7Ny5kx/96Efs27eP/v5+Nm/eTG1t7UIP64KRlJTEhz/8YT784Q8TCATYunUrGzdu5GMf+xgJCQmsX7+e22+/nSuvvHLebmanSqPRYLVasVqtlJaWMjExgcfjoaWlhaNHj5KcnIzf72fp0qWkpqaqj1MUhUcffZR/+Zd/4ZlnnuGKK969A9mlyGAwzPm7RqOZ99hsrahYLIZOp2Pfvn0nBX4nBkgXCgmDhBBCCCGEEOIS4vf7WbJkCXfffTcf+ci7d6gSb89kMrF+/XrWr19PKBRi27ZtbNiwgbvuugtFUbj11lupra3l2muvxWg0nvHraDQaLBYLFouF4uJi2traaG9vx2QyceDAAex2O319fSxfvpyXXnqJr371qzz11FNcc801Z/HdXjiMRuOcws9nw7Jly4hGo3i9Xq6++uqz+twLQcIgIYQQQgghhLiErFu3jnXr1i30MC46RqORG2+8kRtvvJGHHnqInTt3smHDBj7/+c8zPT3NrbfeSk1NDR/4wAcwmUxn/Dr9/f10dnayfPlybDYbfr8fr9fLP/zDP7B7924g3pHrfO8a9n7Kz89n9+7ddHR0kJKS8p47wQGUlpbyiU98gk9+8pP85Cc/YdmyZfh8Pl5++WUWLVrEzTfffBZGfu5Ia3khhBBCCCGEEOIs0uv1fOADH+DBBx+ku7ubJ598EpvNxn333UdBQQF/9Vd/xZNPPsnU1NRpPW9/fz+NjY0sXboUm80GQHJyMgUFBXzxi1/EaDRy9913s3//frKzs/nyl7/8fry9897f//3fo9PpqKysxOl00tXVdVae99e//jWf/OQn+bu/+zvKysq47bbb2L17Nzk5OWfl+c8l6SY2D+kEIoQQ4mIh3cTOLZlDCCEuNBqNRmoGnUOxWIw333yTDRs2sHnzZjweDzfccAO1tbXcdNNN71h7xuPxUFdXx5IlS04qWPzMM89w99138/jjj3P77bcD8eBocHCQxYsXv6/vSVyYZGWQEEIIcRHT6XTcfPPN3Hzzze+pu4kQQggh3jutVsvll1/Oj3/8Y5qbm9mxYwdlZWV873vfIz8/n4997GP87ne/Y2xsjBPXbQwMDHD06FEWLVp0UhD0/PPPc/fdd/PrX/9aDYIAMjIyFiQI2rlzJ+vXryczMxONRsOWLVvO+RjEu5MwSAghhLiImUwmnn32WZ599tn3VJ9ACCGEEGeXVqtlxYoV3H///TQ2NrJ7926WLVvGAw88QH5+Pn/2Z3/GY489xi9/+Utuu+02qqurcTqdc57j5Zdf5pOf/CQ///nP+fM///MFeidzzRYo/6//+q+FHop4B1JAWgghhBBCCCGEWEAajYZFixaxaNEivv3tb9PY2MiGDRv40Y9+RFtbGxUVFTzzzDOsX78ep9OJRqNh586d/MVf/AX/+Z//ySc+8Qk0Gs1Cvw1ACpRfKCQMEkIIIYQQQohLyOTkJC0tLerf29vbOXjwIDabjdzc3AUcmYB4MFRRUcHq1av5wQ9+wE9/+lOmp6d5/PHHue+++7jiiitYsmQJ//M//8OPf/xjPvWpT503QZC4cEgYJIQQQlzE/H4/LpcLAK/XKwWkhRBCsHfvXq677jr17/fddx8Ad911F4888sgCjUq81fPPP8/DDz/MX/7lXwLwla98ha6uLjZu3Mj999/PRz7yET73uc9JECTOiHQTm4d0AhFCCHGxkG5i55bMIYQQQpwL0WgUrVZ73gdB0q3u/CUrg4QQQgghhBBCiAuIdAgV75V0ExNCCCGEEEIIIYS4hMjKICGEEEIIIYQQQpwVUqD8wiBhkBBCCCGEEEIIIc4KKVB+YZAwSAghhBBCCCGEEGfF2rVrkT5V5z+pGSSEEEJcxLRaLddeey3XXnstWq382hdCCHHhuv/++1m1ahVmsxmXy0VtbS1NTU0LPSwhLkgyKxRCCCEuYomJiWzfvp3t27eTmJi40MMRQgghztiOHTv44he/yBtvvMHWrVuJRCLccMMN+P3+hR6aEBccjSLrt04yPj5OamoqY2NjWCyWhR6OEEIIIS4QMocQQohzZ3BwEJfLxY4dO7jmmmsWejhCXFBkZZAQQgghhBBCiAvO2NgYADabbYFHIsSFR8IgIYQQ4iLm9/txOp04nU5ZRi+EEOKioSgK9913H1dddRXV1dULPRwhLjjSTUwIIYS4yPl8voUeghBCCHFW3XvvvRw+fJhXX311oYcixAVJwiAhhBBCCCGEEBeML33pSzz11FPs3LmT7OzshR6OEBck2SYmhBBCCCGEEOK8pygK9957L5s2beLll1+moKBgoYd00XrwwQcpKCjAZDKxYsUKXnnllYUekjjLJAwSQgghhBBCCHHe++IXv8jjjz/Ob3/7W8xmMwMDAwwMDDA9Pb3QQ7uo/OEPf+Bv/uZv+MY3vsGBAwe4+uqrWbduHV1dXQs9NHEWSWv5eUhbWCGEEBcLv99PSkoKAJOTkyQnJy/wiC5uMocQQlyoQqEQRqNxoYfxjjQazbzHf/3rX/OpT33q3A7mIrZ69WqWL1/OQw89pB6rqKigtraW+++/fwFHJs4mqRkkhBBCCCGEEJeYtWvXUl1djdFo5LHHHqOqqoodO3Ys9LDekaxjeP+FQiH27dvHP/7jP845fsMNN/D6668v0KjE+0HCICGEEOIiptVqWblypfrfQgghxKxHH32Uz3/+87z22msStAgg3oE0Go3idrvnHHe73QwMDCzQqMT7QcKgecz+Qzg+Pr7AIxFCCCHeu5deegmAcDhMOBxe4NFc3GbnDvKhSghxISguLuaHP/zhQg9DnIfeuiVPUZS33aYnLkwSBs1jYmICgJycnAUeiRBCCCEuRENDQ6Smpi70MIQQ4h3NrhwVYpbD4UCn0520Csjr9Z60Wkhc2CQMmkdmZibd3d2YzWZJP9/G+Pg4OTk5dHd3S4HM95mc63NHzvW5I+f63JLzfe6MjY2Rm5uLzWZb6KEIIcS7kqYC4q2MRiMrVqxg69at3H777erxrVu3UlNTs4AjE2ebhEHz0Gq1ZGdnL/QwLggWi0U+WJwjcq7PHTnX546c63NLzve5I/WZhBBCXKjuu+8+7rzzTlauXMmaNWt4+OGH6erq4p577lnooYmzSGYqQgghhBBCCCHEOfDQQw+xePFi9QuaNWvW8Nxzzy30sOb42Mc+xgMPPMB3vvMdli5dys6dO/njH/9IXl7eQg9NnEWyMkgIIYQQQgghhDgHsrOz+bd/+zeKi4uBeEe3mpoaDhw4QFVV1QKP7rgvfOELfOELX1joYYj3kYRB4owkJCTwzW9+k4SEhIUeykVPzvW5I+f63JFzfW7J+T535FwLIS4U27dvX+ghXJLWr18/5+/f+973eOihh3jjjTfOqzBIXPw0ivQ+FUIIIYQQQgghzqloNMoTTzzBXXfdxYEDB6isrFzoIYlLiKwMEkIIIYQQQgghzpEjR46wZs0aAoEAKSkpbN68WYIgcc7JyiAhhBBCCCGEEOIcCYVCdHV1MTo6ysaNG/nlL3/Jjh07JBAS55SEQUIIIYQQQgghxAK5/vrrKSoq4uc///lCD0VcQqS1vBBCCCGEEEIIsUAURSEYDC70MMQlRsIg8bYefPBBCgoKMJlMrFixgldeeeUd779jxw5WrFiByWSisLCQn/3sZ+dopBe+0znX27dvR6PRnPSnsbHxHI74wrRz507Wr19PZmYmGo2GLVu2vOtj5Lo+M6d7ruW6PnP3338/q1atwmw243K5qK2tpamp6V0fJ9f26TuTcy3XthBCiBN9/etf55VXXqGjo4MjR47wjW98g+3bt/OJT3xioYcmLjESBol5/eEPf+Bv/uZv+MY3vsGBAwe4+uqrWbduHV1dXfPev729nZtvvpmrr76aAwcO8PWvf52//uu/ZuPGjed45Bee0z3Xs5qamujv71f/lJSUnKMRX7j8fj9Llizhv/7rv07p/nJdn7nTPdez5Lo+fTt27OCLX/wib7zxBlu3biUSiXDDDTfg9/vf9jFybZ+ZMznXs+TaFkIIAeDxeLjzzjspKyvjgx/8ILt37+b555/nQx/60EIPTVxipGaQmNfq1atZvnw5Dz30kHqsoqKC2tpa7r///pPu/9WvfpWnnnqKhoYG9dg999zDoUOH2LVr1zkZ84XqdM/19u3bue666xgZGcFqtZ7DkV5cNBoNmzdvpra29m3vI9f12XEq51qu67NncHAQl8vFjh07uOaaa+a9j1zbZ8epnGu5toUQQghxPpKVQeIkoVCIffv2ccMNN8w5fsMNN/D666/P+5hdu3addP8bb7yRvXv3Eg6H37exXujO5FzPWrZsGRkZGXzwgx9k27Zt7+cwL1lyXZ97cl2/d2NjYwDYbLa3vY9c22fHqZzrWXJtCyGEEOJ8ImGQOInP5yMajeJ2u+ccd7vdDAwMzPuYgYGBee8fiUTw+Xzv21gvdGdyrjMyMnj44YfZuHEjmzZtUpeY7ty581wM+ZIi1/W5I9f12aEoCvfddx9XXXUV1dXVb3s/ubbfu1M913JtCyGEEOJ8pF/oAYjzl0ajmfN3RVFOOvZu95/vuDjZ6ZzrsrIyysrK1L+vWbOG7u5ufvzjH7/tNgVx5uS6Pjfkuj477r33Xg4fPsyrr9lfxxUAAARFSURBVL76rveVa/u9OdVzLde2EEIIIc5HsjJInMThcKDT6U5ameL1ek/6JnlWenr6vPfX6/XY7fb3bawXujM51/O5/PLLaW5uPtvDu+TJdb2w5Lo+PV/60pd46qmn2LZtG9nZ2e94X7m235vTOdfzkWtbCCGEEAtNwiBxEqPRyIoVK9i6deuc41u3buWKK66Y9zFr1qw56f4vvvgiK1euxGAwvG9jvdCdybmez4EDB8jIyDjbw7vkyXW9sOS6PjWKonDvvfeyadMmXn75ZQoKCt71MXJtn5kzOdfzkWtbCCGEEAtNtomJed13333ceeedrFy5kjVr1vDwww/T1dXFPffcA8DXvvY1ent7eeyxx4B4F5r/+q//4r777uMzn/kMu3bt4le/+hW/+93vFvJtXBBO91w/8MAD5OfnU1VVRSgU4vHHH2fjxo3SEvoUTE5O0tLSov69vb2dgwcPYrPZyM3Nlev6LDrdcy3X9Zn74he/yG9/+1uefPJJzGazuuInNTWVxMREQP7NPlvO5FzLtS2EEEKI85IixNv47//+byUvL08xGo3K8uXLlR07dqi33XXXXcq111475/7bt29Xli1bphiNRiU/P1956KGHzvGIL1ync65/8IMfKEVFRYrJZFLS0tKUq666Snn22WcXYNQXnm3btinASX/uuusuRVHkuj6bTvdcy3V95uY7z4Dy61//Wr2PXNtnx5mca7m2hRBCCHE+0ijKTMVIIYQQQgghhBBCCHHRk5pBQgghhBBCCCGEEJcQCYOEEEIIIYQQQgghLiESBgkhhBBCCCGEEEJcQiQMEkIIIYQQQgghhLiESBgkhBBCCCGEEEIIcQmRMEgIIYQQQgghhBDiEiJhkBBCCCGEEEIIIcQlRMIgIYQQQgghhBBCiEuIhEFCCCGEEEIIIYQQlxAJg4QQQgghhBBCCCEuIRIGCSHEexAKhRZ6CEIIIYQQQghxWvQLPQAhhLiQrF27lurqaoxGI4899hhVVVXs2LFjoYclhBBCCCGEEKdMwiAhhDhNjz76KJ///Od57bXXUBRloYcjhBBCCCGEEKdFo8gnGSGEOGVr165lbGyMAwcOLPRQhBBCCCGEEOKMSM0gIYQ4TStXrlzoIQghhBBCCCHEGZMwSAghTlNycvJCD0EIIYQQQgghzpiEQUIIIYQQQgghhBCXEAmDhBBCCCGEEEIIIS4hEgYJIYQQQgghhBBCXEKkm5gQQgghhBBCCCHEJURWBgkhhBBCCCGEEEJcQiQMEkIIIYQQQgghhLiESBgkhBBCCCGEEEIIcQmRMEgIIYQQQgghhBDiEiJhkBBCCCGEEEIIIcQlRMIgIYQQQgghhBBCiEuIhEFCCCGEEEIIIYQQlxAJg4QQQgghhBBCCCEuIRIGCSGEEEIIIYQQQlxCJAwSQgghhBBCCCGEuIRIGCSEEEIIIYQQQghxCfn/AZdFVuomI6FJAAAAAElFTkSuQmCC",
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(15, 4))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122, projection=\"3d\")\n",
"\n",
"ax1.text(X0, 0.06, \"Bond Price\")\n",
"ax1.plot([X0, X0], [0, Price], \"k--\")\n",
"ax1.plot([-0.5, X0], [Price, Price], \"k--\")\n",
"ax1.plot(r, V[:, 0], color=\"red\", label=\"Barrier curve\")\n",
"ax1.set_xlim(-0.4, 2.5)\n",
"ax1.set_ylim(0.025, 0.12)\n",
"ax1.set_xlabel(\"r\")\n",
"ax1.set_ylabel(\"P\")\n",
"ax1.legend(loc=\"upper right\")\n",
"ax1.set_title(\"Vasicek bond price at t=0\")\n",
"\n",
"X_plt, Y_plt = np.meshgrid(T_array, r[700:-200]) # I consider [700:-200] to remove lateral boundary effects\n",
"ax2.plot_surface(Y_plt, X_plt, V[700:-200])\n",
"ax2.set_title(\"Vasicek bond price surface\")\n",
"ax2.set_xlabel(\"r\")\n",
"ax2.set_ylabel(\"time\")\n",
"ax2.set_zlabel(\"V\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Tracking the OU process\n",
"\n",
"In this section I consider a **state space model** with a state process $\\{X_t, 0\\leq t \\leq T\\}$ following an OU process and an observation (or measurement) process $\\{Y_t, 0\\leq t \\leq T\\}$. \n",
"\n",
"In order to estimate the true state process, I will make use of the **Kalman filter**. I have alread presented the topic in the notebook **5.1** and showed its applications in the notebooks **5.2** and **5.3**.\n",
"\n",
"In order to create an observation process, let's add some noise to the true state process we have simulated at the beginning of the book. "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"sig_eta = std_resid\n",
"var_eta = sig_eta**2 # error of the true state process\n",
"sig_eps = 0.1\n",
"var_eps = sig_eps**2 # error of the measurement"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(seed=42)\n",
"eps = ss.norm.rvs(loc=0, scale=sig_eps, size=N) # additional gaussian noise\n",
"eps[0] = 0\n",
"Y_1 = X_1 + eps # process + noise = measurement process"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot true process vs measurement process"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(13, 4))\n",
"plt.plot(T_vec, X_1, linewidth=0.5, alpha=1, label=\"True process\", color=\"#1f77b4\")\n",
"plt.plot(T_vec, theta * np.ones_like(T_vec), label=\"Long term mean\", color=\"#d62728\")\n",
"plt.plot(T_vec, Y_1, linewidth=0.3, alpha=0.5, label=\"Measurement process = true + noise\", color=\"#1f77b4\")\n",
"plt.legend()\n",
"plt.title(\"OU process plus some noise\")\n",
"plt.xlabel(\"time\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gaussian linear state space model:\n",
"\n",
"**State equation:**\n",
"\n",
"$$ x_k = \\alpha + \\beta x_{k-1} + \\eta_k \\quad \\text{with} \\quad \\eta_{k} \\sim \\mathcal{N}(0,\\sigma_{\\eta}^2). $$\n",
"\n",
"**Measurment equation:**\n",
"\n",
"$$ y_k= \\; x_k + \\epsilon_k \\quad \\text{with} \\quad \\epsilon_k \\sim \\mathcal{N}\\bigg(0,\\sigma_{\\epsilon}^2 \\bigg).$$\n",
"\n",
"### Kalman filter:\n",
"\n",
"Let us identify the Kalman filter elements. You can find them in the notebook **5.1** or in the [wiki page](https://en.wikipedia.org/wiki/Kalman_filter#Details).\n",
"In order to get $F_k$, we need to rewrite the state equation as\n",
"\n",
"$$\n",
"\\biggl(\\begin{array}{c} 1 \\\\ x_k \\end{array} \\biggr) = \n",
"\\biggl(\\begin{array}{cc} 1 & 0\\\\ \\alpha & \\beta \\end{array}\\biggr)\n",
"\\biggl(\\begin{array}{c} 1 \\\\ x_{k-1} \\end{array}\\biggr) +\n",
"\\biggl(\\begin{array}{c} 0\\\\ \\eta_k \\end{array}\\biggr) \\quad \\text{with} \\quad\n",
"\\eta_k \\sim \\mathcal{N}(0, \\sigma^2_{\\eta}) $$\n",
"\n",
"such that $F_k = \\biggl(\\begin{array}{cc} 1 & 0\\\\ \\alpha & \\beta \\end{array}\\biggr) $\n",
"and then ignore the first line. The matrix $ H_k=1$. \n",
"\n",
"##### Predict step:\n",
"$$ \\hat x_{k \\mid k-1} = \\alpha + \\beta \\, \\hat x_{k-1 \\mid k-1} \\quad \\quad \\text{and} \\quad \\quad P_{k \\mid k-1} = \\beta^2 \\, P_{k-1 \\mid k-1} + \\sigma_{\\eta}^2. $$\n",
"\n",
"##### Auxiliary variables:\n",
"$$ \n",
"\\begin{aligned}\n",
"\\tilde r_k &= y_k - \\hat x_{k \\mid k-1} &\\quad \\quad \\text{(pre-fit residual)} \\\\\n",
"S_k &= P_{k \\mid k-1} + \\sigma_{\\epsilon}^2 &\\quad \\quad \\text{(conditional innovation covariance)} \\\\\n",
"K_k &= \\frac{P_{k \\mid k-1}}{S_k} &\\quad \\quad \\text{(Kalman Gain)}\n",
"\\end{aligned}\n",
"$$\n",
"\n",
"##### Update step:\n",
"$$ \\hat x_{k \\mid k} = \\hat x_{k \\mid k-1} + K_k \\, \\tilde r_k \\quad \\text{and} \\quad \n",
"P_{k \\mid k} = P_{k \\mid k-1} \\biggl( 1- K_k \\biggr)\n",
"$$\n",
"\n",
"##### Log-likelihood:\n",
" \n",
"The log-likelihood function is:\n",
"\n",
"$$ \\log L \\big( \\alpha,\\beta, \\sigma_{\\eta}, \\sigma_{\\epsilon} \\mid y_k, y_{k-1}, ..., y_1, y_0 \\big) = -\\frac{1}{2} \n",
"\\sum_{k=1}^N \\biggl( \\log 2\\pi + \\log S_k + \\frac{\\tilde r_k^2}{S_k} \\biggr)\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def Kalman(Y, x0, P0, alpha, beta, var_eta, var_eps):\n",
" \"\"\"Kalman filter algorithm for the OU process.\"\"\"\n",
"\n",
" N = len(Y) # length of measurements\n",
" xs = np.zeros_like(Y) # Initialization\n",
" Ps = np.zeros_like(Y)\n",
"\n",
" x = x0\n",
" P = P0 # initial values of h and P\n",
" log_2pi = np.log(2 * np.pi)\n",
" loglikelihood = 0 # initialize log-likelihood\n",
"\n",
" for k in range(N):\n",
" # Prediction step\n",
" x_p = alpha + beta * x # predicted h\n",
" P_p = beta**2 * P + var_eta # predicted P\n",
"\n",
" # auxiliary variables\n",
" r = Y[k] - x_p # residual\n",
" S = P_p + var_eps\n",
" KG = P_p / S # Kalman Gain\n",
"\n",
" # Update step\n",
" x = x_p + KG * r\n",
" P = P_p * (1 - KG)\n",
"\n",
" loglikelihood += -0.5 * (log_2pi + np.log(S) + (r**2 / S))\n",
" xs[k] = x\n",
" Ps[k] = P\n",
"\n",
" return xs, Ps, loglikelihood"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Train and test split"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"skip_data = 1000\n",
"training_size = 5000\n",
"train = Y_1[skip_data : skip_data + training_size]\n",
"test = Y_1[skip_data + training_size :]\n",
"\n",
"## Initial guess for teh parameters\n",
"guess_beta, guess_alpha, _, _, _ = ss.linregress(train[1:], train[:-1])\n",
"guess_var_eps = np.var(train[:-1] - guess_beta * train[1:] - guess_alpha, ddof=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### MLE estimation"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
" success: True\n",
" status: 0\n",
" fun: -4213.924989882811\n",
" x: [ 4.328e-04 9.992e-01 5.628e-05 1.006e-02]\n",
" nit: 25\n",
" jac: [-9.095e-05 9.095e-05 5.548e-03 -1.637e-03]\n",
" nfev: 230\n",
" njev: 46\n",
" hess_inv: <4x4 LbfgsInvHessProduct with dtype=float64>\n"
]
}
],
"source": [
"def minus_likelihood(c):\n",
" \"\"\"Returns the negative log-likelihood\"\"\"\n",
" _, _, loglik = Kalman(train, X0, 10, c[0], c[1], c[2], c[3])\n",
" return -loglik\n",
"\n",
"\n",
"result = minimize(\n",
" minus_likelihood,\n",
" x0=[guess_alpha, guess_beta, 0.01, guess_var_eps],\n",
" method=\"L-BFGS-B\",\n",
" bounds=[[-1, 1], [1e-15, 1], [1e-15, 1], [1e-15, 1]],\n",
" tol=1e-12,\n",
")\n",
"kalman_params = result.x\n",
"alpha_KF = kalman_params[0]\n",
"beta_KF = kalman_params[1]\n",
"var_eta_KF = kalman_params[2]\n",
"var_eps_KF = kalman_params[3]\n",
"print(result)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The estimated parameters are very good! \n",
"Below we can see the error of the estimation:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Error in estimation of alpha = 33.1%\n",
"Error in estimation of beta = 0.0115%\n",
"Error in estimation of var eta = 8.89%\n",
"Error in estimation of var eps = 0.61%\n"
]
}
],
"source": [
"print(f\"Error in estimation of alpha = {(np.abs(alpha-alpha_KF)/alpha *100).round(2)}%\")\n",
"print(f\"Error in estimation of beta = {(np.abs(beta-beta_KF)/beta *100).round(4)}%\")\n",
"print(f\"Error in estimation of var eta = {(np.abs(var_eta-var_eta_KF)/var_eta *100).round(2)}%\")\n",
"print(f\"Error in estimation of var eps = {(np.abs(var_eps-var_eps_KF)/var_eps *100).round(2)}%\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Kalman smoother\n",
"\n",
"Let's implement the [RTS](https://en.wikipedia.org/wiki/Kalman_filter#Rauch%E2%80%93Tung%E2%80%93Striebel) smoother.\n",
"\n",
"In addition to the smooth state and variance process, here I also estimate the smooth covariance $P_{k-1,k \\mid N}$ (called `Cs_smooth` in the code).\n",
"\n",
"$$ \\hat x_{k\\mid N} = \\mathbb{E}\\biggl[x_k \\bigg| y_N,...,y_0\\biggr] \\quad \\text{and} \\quad \n",
"P_{k-1,k \\mid N} = \\mathbb{E}\\biggl[ \\bigl(x_k-\\hat x_{k\\mid N}\\bigr) \\bigl(x_{k-1}-\\hat x_{k-1\\mid N}\\bigr) \\,\\bigg | \\, y_N,...,y_0\\biggr] $$\n",
"\n",
"The formulas to obtain $P_{k-1,k \\mid N}$ are long and can be found in [3] or in the Appendix of [7]. \n",
"Here I just write the python code:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"def Smoother(Y, x0, P0, alpha, beta, var_eta, var_eps):\n",
" \"\"\"Kalman smoother\"\"\"\n",
"\n",
" xs, Ps, _ = Kalman(Y, x0, P0, alpha, beta, var_eta, var_eps)\n",
"\n",
" xs_smooth = np.zeros_like(xs)\n",
" Ps_smooth = np.zeros_like(Ps)\n",
" Cs_smooth = np.zeros_like(Ps)\n",
" C = np.zeros_like(Ps)\n",
" xs_smooth[-1] = xs[-1]\n",
" Ps_smooth[-1] = Ps[-1]\n",
" K = (beta**2 * Ps[-2] + var_eta) / (beta**2 * Ps[-2] + var_eta + var_eps)\n",
" Cs_smooth[-1] = Ps[-1]\n",
" Cs_smooth[-2] = beta * (1 - K) * Ps[-2]\n",
"\n",
" for k in range(len(xs) - 2, -1, -1):\n",
" C[k] = beta * Ps[k] / (beta**2 * Ps[k] + var_eta)\n",
" xs_smooth[k] = xs[k] + C[k] * (xs_smooth[k + 1] - (alpha + xs[k] * beta))\n",
" Ps_smooth[k] = Ps[k] + C[k] ** 2 * (Ps_smooth[k + 1] - (beta**2 * Ps[k] + var_eta))\n",
" if k == (len(xs) - 2):\n",
" continue\n",
" Cs_smooth[k] = C[k] * Ps[k + 1] + C[k] * C[k + 1] * (\n",
" Cs_smooth[k + 1] - beta * Ps[k + 1]\n",
" ) # covariance x(k) and x(k+1)\n",
" return xs_smooth, Ps_smooth, Cs_smooth"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"x_tmp, P_tmp, _ = Kalman(train, 1, 10, alpha_KF, beta_KF, var_eta_KF, var_eps_KF) # to get initial values for KF\n",
"xs, Ps, _ = Kalman(test, x_tmp[-1], P_tmp[-1], alpha_KF, beta_KF, var_eta_KF, var_eps_KF)\n",
"x_smooth, P_smooth, _ = Smoother(test, x_tmp[-1], P_tmp[-1], alpha_KF, beta_KF, var_eta_KF, var_eps_KF)\n",
"V_up = xs + np.sqrt(Ps) # error up bound\n",
"V_down = xs - np.sqrt(Ps) # error down bound"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 5))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"ax1.plot(\n",
" T_vec[skip_data + training_size :], xs, linewidth=0.5, alpha=1, label=\"Optimal state estimate\", color=\"#1f77b4\"\n",
")\n",
"ax1.plot(T_vec[skip_data + training_size :], theta * np.ones_like(xs), label=\"Long term mean\", color=\"#d62728\")\n",
"ax1.plot(\n",
" T_vec[skip_data + training_size :],\n",
" Y_1[skip_data + training_size :],\n",
" linewidth=0.6,\n",
" alpha=0.5,\n",
" label=\"Process + noise\",\n",
" color=\"#1f77b4\",\n",
")\n",
"ax1.plot(\n",
" T_vec[skip_data + training_size :],\n",
" x_smooth,\n",
" linewidth=0.5,\n",
" alpha=1,\n",
" label=\"Smoothed state estimate\",\n",
" color=\"purple\",\n",
")\n",
"ax1.fill_between(\n",
" x=T_vec[skip_data + training_size :],\n",
" y1=V_up,\n",
" y2=V_down,\n",
" alpha=0.7,\n",
" linewidth=2,\n",
" color=\"seagreen\",\n",
" label=\"Kalman error: $\\pm$ 1 std dev \",\n",
")\n",
"ax1.legend()\n",
"ax1.set_title(\"State estimation of the OU process\")\n",
"ax1.set_xlabel(\"time\")\n",
"ax2.plot(T_vec[skip_data + training_size :], xs, linewidth=1, alpha=1, label=\"Optimal state estimator\", color=\"blue\")\n",
"ax2.plot(T_vec[skip_data + training_size :], theta * np.ones_like(xs), label=\"Long term mean\", color=\"#d62728\")\n",
"ax2.plot(\n",
" T_vec[skip_data + training_size :],\n",
" Y_1[skip_data + training_size :],\n",
" linewidth=0.5,\n",
" alpha=0.8,\n",
" label=\"Process + noise\",\n",
" color=\"#1f77b4\",\n",
")\n",
"ax2.plot(\n",
" T_vec[skip_data + training_size :], x_smooth, linewidth=1, alpha=1, label=\"Smoothed state estimate\", color=\"purple\"\n",
")\n",
"ax2.fill_between(\n",
" x=T_vec[skip_data + training_size :],\n",
" y1=V_up,\n",
" y2=V_down,\n",
" alpha=0.7,\n",
" linewidth=2,\n",
" color=\"seagreen\",\n",
" label=\"Kalman error: $\\pm$ 1 std dev \",\n",
")\n",
"ax2.set_xlim(1.99, 2.15)\n",
"ax2.set_ylim(0.2, 0.65)\n",
"ax2.legend()\n",
"ax2.set_title(\"Zoom\")\n",
"ax2.set_xlabel(\"time\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's have a look at the errors:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test set, mean linear error Kalman: 0.020994726339122482\n",
"Average standard deviation of the estimate: 0.026797960608008427\n",
"Test set, MSE Kalman: 0.0007025316744007705\n",
"Test set, MSE Smoother: 0.00036413798567487364\n"
]
}
],
"source": [
"print(\"Test set, mean linear error Kalman: \", np.linalg.norm(xs - X_1[skip_data + training_size :], 1) / len(xs))\n",
"print(\"Average standard deviation of the estimate: \", np.mean(np.sqrt(Ps)))\n",
"print(\"Test set, MSE Kalman: \", np.linalg.norm(xs - X_1[skip_data + training_size :], 2) ** 2 / len(xs))\n",
"print(\"Test set, MSE Smoother: \", np.linalg.norm(x_smooth - X_1[skip_data + training_size :], 2) ** 2 / len(x_smooth))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking at the mean linear error value, we can see that the average distance of the estimated process from the true hidden process is smaller that the average standard deviation. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Iterative approach for parameters estimation\n",
"\n",
"This method is described by Shumway and Stoffer (1982) in [7] and is an off-line calculation that\n",
"makes use of smoother estimators for the Kalman Filter. \n",
"The Shumway and Stoffer algorithm has been widely tested (for instance in [3]).\n",
"\n",
"Let's recall the formulas for this iterative algorithm (taken from [3]). \n",
"Let's start with an initial guess \n",
"$(\\alpha, \\beta, \\sigma_{\\eta}^2, \\sigma_{\\epsilon}^2)$ and then compute the new parameters:\n",
"\n",
"$$ \\alpha = \\frac{(AC-BD)}{NA - D^2}, \\quad \\beta = \\frac{NB-CD}{NA - D^2}, \\quad \\sigma_{\\epsilon}^2 = \\frac{1}{N+1} \\sum_{k=0}^N y_k^2 - 2 y_k \\hat x_{k\\mid N} + P_{k\\mid N} + \\hat x_{k\\mid N}^2, $$\n",
"$$\\sigma_{\\eta}^2 = \\frac{1}{N} \\sum_{k=1}^{N} \\biggl[ P_{k \\mid N} + \\hat x_{k\\mid N}^2 + \\alpha^2 + \\beta^2 P_{k-1 \\mid N} + \\beta^2 \\hat x_{k-1\\mid N}^2 - 2 \\alpha \\hat x_{k\\mid N} + 2 \\alpha \\beta \\hat x_{k-1\\mid N} \n",
"-2 \\beta P_{k-1,k \\mid N} -2 \\beta \\hat x_{k\\mid N} \\hat x_{k-1\\mid N} \\biggr].$$\n",
"\n",
"Very long formulas... where I introduced:\n",
"\n",
"$$ A = \\sum_{k=1}^{N} \\bigl[ P_{k-1 \\mid N} + \\hat x_{k-1\\mid N}^2 \\bigr], \\quad \n",
"B = \\sum_{k=1}^{N} \\bigl[ P_{k-1, k \\mid N} + \\hat x_{k-1\\mid N} \\hat x_{k\\mid N} \\bigr], \\quad \n",
"C = \\sum_{k=1}^{N} \\hat x_{k\\mid N}, \\quad\n",
"D = \\sum_{k=1}^{N} \\hat x_{k-1 \\mid N} $$\n",
"\n",
"The iteration runs until the improvements (measured by relative errors) are smaller than a specified error."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"N_max = 1000 # number of iterations\n",
"err = 0.001 # error in the parameters\n",
"NN = len(train)\n",
"alpha_SS = guess_alpha # initial guess\n",
"beta_SS = guess_beta # initial guess\n",
"var_eps_SS = guess_var_eps # initial guess\n",
"var_eta_SS = 0.1 # initial guess\n",
"x_start = 1 # initial guess\n",
"P_start = 10 # initial guess"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of iteration: 217\n"
]
}
],
"source": [
"for i in range(N_max):\n",
" a_old = alpha_SS\n",
" b_old = beta_SS\n",
" eta_old = var_eta_SS\n",
" eps_old = var_eps_SS\n",
"\n",
" x_sm, P_sm, C_sm = Smoother(train, x_start, P_start, alpha_SS, beta_SS, var_eta_SS, var_eps_SS)\n",
"\n",
" AA = np.sum(P_sm[:-1] + x_sm[:-1] ** 2) # A\n",
" BB = np.sum(C_sm[:-1] + x_sm[:-1] * x_sm[1:]) # B\n",
" CC = np.sum(x_sm[1:]) # C\n",
" DD = np.sum(x_sm[:-1]) # D\n",
"\n",
" alpha_SS = (AA * CC - BB * DD) / (NN * AA - DD**2)\n",
" beta_SS = (NN * BB - CC * DD) / (NN * AA - DD**2)\n",
" var_eta_SS = (\n",
" np.sum(\n",
" P_sm[1:]\n",
" + x_sm[1:] ** 2\n",
" + alpha_SS**2\n",
" + P_sm[:-1] * beta_SS**2\n",
" + (x_sm[:-1] * beta_SS) ** 2\n",
" - 2 * alpha_SS * x_sm[1:]\n",
" + 2 * alpha_SS * beta_SS * x_sm[:-1]\n",
" - 2 * beta_SS * C_sm[:-1]\n",
" - 2 * beta_SS * x_sm[1:] * x_sm[:-1]\n",
" )\n",
" / NN\n",
" )\n",
" var_eps_SS = np.sum(train**2 - 2 * train * x_sm + P_sm + x_sm**2) / (NN + 1)\n",
"\n",
" if (\n",
" (np.abs(a_old - alpha_SS) / a_old < err)\n",
" and (np.abs(b_old - beta_SS) / b_old < err) # iterate until there is no improvement\n",
" and (np.abs(eta_old - var_eta_SS) / eta_old < err)\n",
" and (np.abs(eps_old - var_eps_SS) / eps_old < err)\n",
" ):\n",
" print(\"Number of iteration: \", i)\n",
" break\n",
"if i == N_max - 1:\n",
" print(\"Maximum number of iterations reached \", i + 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Comparison of the estimated values with the values obtained from the true state process:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Value of estimated alpha = 0.0004416375091507386 vs real alpha = 0.0003251627987039818\n",
"Value of estimated beta = 0.9992319104078352 vs real beta = 0.999358630299211\n",
"Value of estimated var_eta = 5.859834334726142e-05 vs real var_eta = 6.177659604006022e-05\n",
"Value of estimated var_eps = 0.01005151049145717 vs real var_eps = 0.010000000000000002\n"
]
}
],
"source": [
"print(f\"Value of estimated alpha = {alpha_SS} vs real alpha = {alpha}\")\n",
"print(f\"Value of estimated beta = {beta_SS} vs real beta = {beta}\")\n",
"print(f\"Value of estimated var_eta = {var_eta_SS} vs real var_eta = {var_eta}\")\n",
"print(f\"Value of estimated var_eps = {var_eps_SS} vs real var_eps = {var_eps}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### We can see that this algorithm works very well. \n",
"It is a good alternative to the numerical maximization of the loglikelihood function used in the previous section."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Trading strategy \n",
"\n",
"Let us assume that our OU process describes the dynamics of an asset, or a portfolio of assets. \n",
"We can implement a trading strategy that takes advantage from the mean reversion. This strategy is better known as [pair trading](https://en.wikipedia.org/wiki/Pairs_trade) (or, more in general, statistical arbitrage).\n",
"\n",
"The mechanics of the strategy (taken from [8]) is the following: \n",
"- set two bands above and below the asymptotic mean (usually at one asymptotic standard deviation) for opening the position.\n",
" - buy if the lower band is crossed\n",
" - sell if the upper band is crossed\n",
"- set two (smaller) bands to close the position (usually at 1/10 of the standard deviation).\n",
"\n",
"Now let us simulate again the process:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"N = 1000 # time steps\n",
"paths = 5000 # simulated paths\n",
"X0 = 0 # initial position\n",
"kappa = 10\n",
"theta = 0\n",
"sigma = 2\n",
"T = 1 # terminal time\n",
"std_asy = np.sqrt(sigma**2 / (2 * kappa)) # open position\n",
"std_10 = std_asy / 10 # close position"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(seed=41)\n",
"OU = OU_process(sigma=sigma, theta=theta, kappa=kappa) # creates the OU object\n",
"X = OU.path(X0=X0, T=T, N=N, paths=paths) # path simulation\n",
"process = 0 # the process for the plot\n",
"T_vec, dt = np.linspace(0, T, N, retstep=True)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"def strategy(X, mean=0, std_open=0.5, std_close=0.05, TC=0):\n",
" \"\"\"Implementation of the strategy.\n",
" - std_open = levels for opening the position.\n",
" - std_close = levels for closing the position.\n",
" - TC = Transaction costs\n",
" Returns:\n",
" - status: at each time says if we are long=1, short=-1 or we have no open positions = 0\n",
" - cash: the cumulative amount of cash gained by the strategy.\n",
" At terminal time if there is an open position, it is closed.\n",
" \"\"\"\n",
" status = np.zeros_like(X)\n",
" cash = np.zeros_like(X)\n",
" cash[0] = X0\n",
" for i, x in enumerate(X):\n",
" if i == 0:\n",
" continue\n",
" if (status[i - 1] == 1) and (x >= mean - std_close):\n",
" status[i] = 0\n",
" cash[i] += x * (1 + TC)\n",
" elif (status[i - 1] == -1) and (x <= mean + std_close):\n",
" status[i] = 0\n",
" cash[i] -= x * (1 + TC)\n",
" elif (status[i - 1] == 0) and (x >= mean + std_open):\n",
" status[i] = -1\n",
" cash[i] += x * (1 + TC)\n",
" elif (status[i - 1] == 0) and (x <= mean - std_open):\n",
" status[i] = 1\n",
" cash[i] -= x * (1 + TC)\n",
" else:\n",
" status[i] = status[i - 1]\n",
"\n",
" if status[-1] == 1:\n",
" cash[-1] += x * (1 + TC)\n",
" if status[-1] == -1:\n",
" cash[-1] -= x * (1 + TC)\n",
"\n",
" return status, cash.cumsum()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"status, cash = strategy(X[:, process], mean=theta, std_open=std_asy, std_close=std_10, TC=0)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"PnL = [] # Profit and loss for this strategy\n",
"for i in range(paths):\n",
" PnL.append(strategy(X[:, i], mean=theta, std_open=std_asy, std_close=std_10, TC=0)[1][-1])\n",
"PnL = np.array(PnL)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 10))\n",
"ax1 = fig.add_subplot(221)\n",
"ax2 = fig.add_subplot(223)\n",
"ax3 = fig.add_subplot(222)\n",
"ax4 = fig.add_subplot(224)\n",
"ax1.plot(T_vec, X[:, process], linewidth=0.5)\n",
"ax1.plot(T_vec, (theta + std_asy) * np.ones_like(T_vec), label=\"open short position\", color=\"sandybrown\")\n",
"ax1.plot(T_vec, (theta - std_asy) * np.ones_like(T_vec), label=\"open long position\", color=\"chocolate\")\n",
"ax1.plot(T_vec, (theta + std_10) * np.ones_like(T_vec), label=\"close short position\", color=\"gray\")\n",
"ax1.plot(T_vec, (theta - std_10) * np.ones_like(T_vec), label=\"close long position\", color=\"rosybrown\")\n",
"ax1.plot(T_vec, theta * np.ones_like(T_vec), label=\"Long term mean\", color=\"black\")\n",
"ax1.legend()\n",
"ax1.set_title(f\"OU process: process {process}\")\n",
"ax1.set_xlabel(\"T\")\n",
"\n",
"ax2.plot(T_vec, status, linestyle=\"dashed\", color=\"grey\")\n",
"ax2.set_title(\"Strategy: 1=LONG, -1=SHORT, 0=No open positions \")\n",
"\n",
"ax3.hist(PnL, density=True, bins=100, facecolor=\"LightBlue\", label=\"frequencies\")\n",
"x = np.linspace(PnL.min(), PnL.max(), 100)\n",
"ax3.plot(x, ss.norm.pdf(x, loc=PnL.mean(), scale=PnL.std()), color=\"r\", label=\"Normal density\")\n",
"SR = PnL.mean() / PnL.std()\n",
"ax3.legend()\n",
"ax3.set_title(f\"PnL distribution. Sharpe ratio SR = {SR.round(2)}\")\n",
"\n",
"ax4.plot(T_vec, cash)\n",
"ax4.set_title(\"Cumulative amount of cash in the portfolio\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The histogram is computed considering the $PnL$ of each generated path. \n",
"The obtained distribution is a multimodal distribution where each peak is roughly a multiple of the band size.\n",
"\n",
"From the *Cumulative amount of cash* plot, we can see that we make money when we open the position. When the position is closed, a small amount of money is lost. \n",
"At terminal time the position must be closed. \n",
"\n",
"The **sharpe ratio** is defined as $SR = \\frac{\\mathbb{E}[PnL]}{\\text{Std}[PnL]}$. There are alternative definitions involving net returns and log-returns, however here it makes more sense to consider the $PnL$ (when the initial value is 0, returns are not defined)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## First time to exit the strip\n",
"\n",
"Let us assume the process at time $t=0$ is contained in the strip formed by the two asymptotic standard deviations i.e. \n",
"\n",
"$$\\theta - \\sqrt{\\frac{\\sigma^2}{2\\kappa}} \\leq \\, X_0 \\leq \\, \\theta + \\sqrt{\\frac{\\sigma^2}{2\\kappa}}.$$ \n",
"\n",
"What is the expected time to exit this strip? \n",
"\n",
"We can estimate the time using the **Monte Carlo** simulated data, by simply taking the average of the first times the process touches the above or below lines. (since argmax return zero if it can find a True value, check if there are zeros and if there are, replace them with a high value)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Are there paths that never exit the strip? False\n"
]
}
],
"source": [
"T_to_asy = np.argmax(np.logical_or(X <= -std_asy, X >= std_asy), axis=0) * dt # first exit time\n",
"print(\"Are there paths that never exit the strip?\", (T_to_asy == 0).any()) # argmax returns 0 if it can't find"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The expected time is 0.07165985985985986 with standard error 0.0008597119982512018\n"
]
}
],
"source": [
"print(f\"The expected time is {T_to_asy.mean()} with standard error {ss.sem(T_to_asy)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ODE approach\n",
"\n",
"The expected time $U$ is the solution of the following differential equation:\n",
"\n",
"$$ \\kappa (\\theta - x) \\frac{dU}{dx} + \\frac{1}{2} \\sigma^2 \\frac{d^2 U}{dx^2} = -1$$\n",
"\n",
"with boundary conditions:\n",
"\n",
"$$ U(B_1) = U(B_2) = 0. $$\n",
"\n",
"Here I called $B_1,B_2$ the levels of the asymptiotic standard deviation. \n",
"This formula comes from [9], Chapter 10.9.\n",
"\n",
"#### Discretization\n",
"\n",
"This is a boudary value problem, that can be solved by a finite difference method. The discretization scheme is very similatr to the scheme I used for the Vasicek PDE:\n",
"\n",
"$$\n",
" + \\max \\biggl( \\kappa(\\theta-x_i),\\, 0 \\biggr) \\frac{U_{i+1} - U_{i}}{ \\Delta x} \n",
" + \\min \\biggl( \\kappa(\\theta-x_i),\\, 0 \\biggr) \\frac{U_{i} - U_{i-1}}{ \\Delta x} \n",
" + \\frac{1}{2} \\sigma^2 \\frac{U_{i+1} + U_{i-1} - 2 U_{i}}{\\Delta x^2} \n",
" + 1 = 0. \n",
"$$\n",
"\n",
"Again, let's call $\\text{max}_i = \\max \\bigl( \\kappa(\\theta-x_i),\\, 0 \\bigr)$ and $\\text{min}_i = \\min \\bigl( \\kappa(\\theta-x_i),\\, 0 \\bigr)$. \n",
"We can rewrite the equation above as:\n",
"\n",
"$$ \n",
"-1 = \\; U_{i-1} \\underbrace{\\biggl( - \\frac{\\text{min}_i}{\\Delta x} + \\frac{1}{2} \n",
"\\frac{\\sigma^2}{\\Delta x^2} \\biggr)}_{a} \n",
" + U_{i} \\underbrace{\\biggl( \\frac{( \\text{min}_i - \\text{max}_i )}{\\Delta x} + \\frac{\\sigma^2}{\\Delta x^2} \\biggr) }_{b}\n",
" + U_{i+1} \\underbrace{\\biggl( \\frac{\\text{max}_i}{\\Delta x} + \\frac{1}{2} \n",
"\\frac{\\sigma^2}{\\Delta x^2} \\biggr)}_{c}\n",
"$$\n",
"\n",
"That in matrix form becomes \n",
"\n",
"$$ \\mathcal{D}\\, U = -1 $$\n",
"\n",
"where $\\mathcal{D}$ is the usual tridiagonal matrix. "
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"Nspace = 100000 # space steps\n",
"x_max = theta + std_asy\n",
"x_min = theta - std_asy\n",
"x, dx = np.linspace(x_min, x_max, Nspace, retstep=True) # space discretization\n",
"\n",
"U = np.zeros(Nspace) # grid initialization\n",
"constant_term = -np.ones(Nspace - 2) # -1\n",
"\n",
"# construction of the tri-diagonal matrix D\n",
"sig2 = sigma * sigma\n",
"dxx = dx * dx\n",
"max_part = np.maximum(kappa * (theta - x), 0) # upwind positive part\n",
"min_part = np.minimum(kappa * (theta - x), 0) # upwind negative part\n",
"\n",
"a = -min_part / dx + 0.5 * sig2 / dxx\n",
"b = (min_part - max_part) / dx - sig2 / dxx\n",
"c = max_part / dx + 0.5 * sig2 / dxx\n",
"\n",
"aa = a[1:]\n",
"cc = c[:-1] # upper and lower diagonals\n",
"D = sparse.diags([aa, b, cc], [-1, 0, 1], shape=(Nspace - 2, Nspace - 2)).tocsc() # matrix D\n",
"U[1:-1] = spsolve(D, constant_term)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot expected time"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12, 5))\n",
"plt.plot(x, U, label=\"Expected time curve\")\n",
"plt.plot([X0, X0], [0, 0.06], \"k--\", label=\"starting point in the simulation\")\n",
"plt.plot([x_max, x_max], [0, 0.06], \"g--\", label=\"$B_2$\")\n",
"plt.plot([x_min, x_min], [0, 0.06], \"g--\", label=\"$B_1$\")\n",
"plt.legend(loc=\"upper right\")\n",
"plt.xlabel(\"Starting point, X0\")\n",
"plt.title(\"Expected time to exit the strip\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The expected time starting at X_0=0,computed by ODE is 0.059574670418834914\n"
]
}
],
"source": [
"expected_t_ode = np.interp(X0, x, U)\n",
"print(f\"The expected time starting at X_0={X0},computed by ODE is {expected_t_ode}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Distribution of the first exit time from the strip\n",
"\n",
"If we call $\\tau$ the first exit time, let us indicate with $P(t',x; T) = \\mathbb{P}\\big(\\tau"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12, 5))\n",
"plt.xlabel(\"Time\")\n",
"plt.title(\"Distribution of the first exit time from the strip\")\n",
"plt.hist(T_to_asy, density=True, bins=100, facecolor=\"LightBlue\", label=\"histogram from the MC simulation\")\n",
"plt.plot(T_array[:-1], distribution, label=\"Density from the PDE method\")\n",
"plt.axvline(expected_t_ode, label=\"expected time\")\n",
"plt.xlim([0, 0.4])\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Expected value from density tau=0.05957056936631608 corresponds to PDE tau=0.059574670418834914\n"
]
}
],
"source": [
"exp_t_integral = distribution @ T_array[:-1] * Dt # integral of t*density(t)*dt\n",
"print(f\"Expected value from density tau={exp_t_integral} corresponds to PDE tau={expected_t_ode}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"[1] Thijs van den Berg, [Calibrating the Ornstein-Uhlenbeck(Vasicek) model](https://www.statisticshowto.com/wp-content/uploads/2016/01/Calibrating-the-Ornstein.pdf)\n",
"\n",
"[2] Brigo and Mercurio, *Interest Rate Models - Theory and Practice: With Smile, Inflation and Credit*\n",
"\n",
"[3] Robert J. Elliott , John Van Der Hoek & William P. Malcolm, *Pairs trading*, Quantitative Finance, (2007)\n",
"\n",
"[4] Finch, S., [Ornstein–Uhlenbeck process](https://oeis.org/A249417/a249417.pdf), (2004).\n",
"\n",
"[5] Alexander Lipton, Vadim Kaushansky, [On the First Hitting Time Density of an\n",
"Ornstein-Uhlenbeck Process](https://arxiv.org/pdf/1810.02390.pdf), (2018)\n",
"\n",
"[6] L. M. Ricciardi and S. Sato, First-passage-time density and moments of the Ornstein-Uhlenbeck process, J. Appl. Probab. 25 (1988)\n",
"\n",
"[7] Shumway, R.H. and Stoffer, D.S., *An approach to time series smoothing and forecasting using the EM algorithm*, Journal of Time Series, (1982)\n",
"\n",
"[8] Cartea, A., Jaimungal, S., and Peñalva, J., *Algorithmic and high frequency trading*. Cambridge University Press, (2015)\n",
"\n",
"[9] Paul Wilmott, *Paul Wilmott on quantitative finance*, (2006)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: 7.1 Classical MVO.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Mean-Variance Optimization\n",
"\n",
"In this notebook I implement the classical **mean-variance optimization** (MVO) algorithm that finds the optimal weights of a portfolio. \n",
"For a theoretical background see [modern portfolio theory](https://en.wikipedia.org/wiki/Modern_portfolio_theory).\n",
"\n",
"\n",
"## Contents\n",
" - [Loading data and parameter definition](#sec1) \n",
" - [Some mathematics](#sec2)\n",
" - [Optimization with scipy.optimize](#sec3)\n",
" - [Optimize the Sharpe ratio](#sec3.1)\n",
" - [Optimal weights between stocks and bond](#sec3.2)\n",
" - [Optimization with cvxpy](#sec4) \n",
" - [Probability density of the tangency portfolio](#sec5) \n",
" - [Short positions - closed formula](#sec6) "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import cvxpy as cp\n",
"import scipy.stats as ss\n",
"import seaborn as sns\n",
"import warnings\n",
"from scipy.optimize import Bounds, LinearConstraint, minimize\n",
"from IPython.display import display\n",
"from FMNM.portfolio_optimization import optimal_weights"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Loading data and parameter definition\n",
"\n",
"In the following I define the investment horizon of **1 month** and the risk free monthly return **Rf**. \n",
"I also load the time series of daily prices from *filename*, and print the names of the stocks considered in the analysis. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"filename = \"data/stocks_data.csv\"\n",
"investment_horizon = 252 / 12 # 252 days / 12 months = 1 month\n",
"Rf = 0.01 / 12 # annual rate / 12 months"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are NO NaNs\n"
]
},
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
AAPL
\n",
"
AMZN
\n",
"
GOOGL
\n",
"
JPM
\n",
"
GLD
\n",
"
DIS
\n",
"
VNO
\n",
"
FB
\n",
"
UBS
\n",
"
KO
\n",
"
MCD
\n",
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^GSPC
\n",
"
GM
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"
\n",
" \n",
" \n",
" \n",
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\n",
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"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [AAPL, AMZN, GOOGL, JPM, GLD, DIS, VNO, FB, UBS, KO, MCD, ^GSPC, GM]\n",
"Index: []"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv(filename, index_col=\"Date\", parse_dates=True)\n",
"stocks = data.columns\n",
"N = len(stocks)\n",
"print(\"There are NaNs\") if data.isna().any(axis=1).any(axis=0) else print(\"There are NO NaNs\")\n",
"pd.DataFrame(columns=stocks)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the number of stocks is small, it could be useful to plot the normalized time series and the correlation matrix of the log-returns."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"log_ret = np.log(data / data.shift())[1:] # compute log-returns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### plots"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(16, 6))\n",
"ax1 = fig.add_subplot(121)\n",
"ax2 = fig.add_subplot(122)\n",
"for stk in stocks:\n",
" ax1.plot(data[stk] / data[stk][0], label=stk)\n",
"ax1.legend()\n",
"ax1.xaxis.set_tick_params(rotation=45)\n",
"sns.heatmap(log_ret.corr(), annot=True, cmap=\"YlGnBu\", ax=ax2)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Some mathematics\n",
"\n",
"Let us indicate the stock price at time $t$ with $S_t$. \n",
"We can now recall some important definitions:\n",
"\n",
"#### Log returns\n",
"\n",
"$$ L_t := \\log \\frac{S_t}{S_{t-1}} $$\n",
"\n",
"#### Linear returns\n",
"\n",
"$$ R_t := \\frac{S_t - S_{t-1}}{S_{t-1}} = \\frac{S_t}{S_{t-1}} -1 $$\n",
"\n",
"There are two important properties to remember:\n",
"\n",
"1) Thanks to the properties of logarithms, for $t_0 = 0 < t_1 < ... < t_n = T$ we can write:\n",
"\n",
"$$ \n",
"\\begin{aligned}\n",
"L_T &= \\log \\frac{S_T}{S_{0}} \\\\\n",
"&= \\log \\bigg( \\frac{S_{t_1}}{S_{t_0}} \\cdot \\frac{S_{t_2}}{S_{t_1}} \\cdot \\frac{S_{t_3}}{S_{t_2}} ....\\frac{S_{t_n}}{S_{t_{n-1}}} \\bigg) \\\\\n",
"&= \\log \\frac{S_{t_1}}{S_{0}} + \\log \\frac{S_{t_2}}{S_{t_1}} + ... + \\log \\frac{S_{T}}{S_{t_{n-1}}} \\\\\n",
"&= \\sum_{i=1}^n \\log \\frac{S_{t_{i}}}{S_{t_{i-1}}} = \\sum_{i=1}^n L_{t_i}. \n",
"\\end{aligned}\n",
"$$\n",
"\n",
"2) If we have a **portfolio** $P_t$ with $N$ stocks, we can prove that the linear return of the portfolio at time $t$ before rebalancing (with weights selected at time $t-1$) is:\n",
"\n",
"$$ R_t^P = \\sum_{i=1}^N w_{t-1}^i R_t^i $$\n",
"\n",
"Let us consider an investor with a total capital $C$ in cash at time $t=0$. \n",
"At time $t \\geq 0$ he decides to buy $\\alpha_t^i$ shares of the stock $S_t^i$. Of course the conditions \n",
"\n",
"$$ P_t = \\sum_{i=1}^N \\alpha_t^i \\, S_t^i \\quad \\text{and} \\quad P_0 = C $$\n",
"\n",
"must hold. It convenient to define the **relative weights** of the portfolio:\n",
"\n",
"$$ w_t^i := \\frac{\\alpha_t^i \\, S_t^i}{P_t} $$\n",
"\n",
"such that for each $t\\geq0$ \n",
"\n",
"$$ \\sum_{i=1}^N w_t^i = 1.$$ \n",
"\n",
"At this point it is easy to prove the initial expression:\n",
"\n",
"$$ \n",
"\\begin{aligned}\n",
"\\sum_{i=1}^N w^i_{t-1} R_t^i &= \\sum_{i=1}^N w_{t-1}^i \\biggl( \\frac{S^i_t}{S^i_{t-1}} -1 \\biggr) \n",
"= \\sum_{i=1}^N \\frac{\\alpha_{t-1}^i \\, S_{t-1}^i}{P_{t-1}} \\frac{S^i_t}{S^i_{t-1}} - 1 \\\\ \n",
"&= \\frac{ \\sum_{i=1}^N \\alpha_{t-1}^i \\, S_{t}^i}{P_{t-1}} - 1 \\\\\n",
"&= \\frac{P_t}{P_{t-1}} - 1 = R^P_t\n",
"\\end{aligned}\n",
"$$\n",
"\n",
"where in $\\sum_{i=1}^N \\alpha_{t-1}^i \\, S_{t}^i = P_t$ the values $\\alpha_{t-1}^i$ are the number of shares selected at time $t-1$ i.e. before the rebalancing. \n",
"\n",
"### Why did I recall these formulas?\n",
"\n",
"Simply because\n",
"\n",
"$$ L_t^P \\neq \\sum_{i=1}^N w_{t-1}^i L_t^i \\quad \\text{and} \\quad R_T \\neq \\sum_{i=1}^n R_{t_i} $$\n",
"\n",
"therefore **DO NOT USE THEM** with the equal sign!! \n",
"\n",
"Ok... if the time interval is short...we can use the first order Taylor approximation of the logarithmic function\n",
"$\\log(1+x) \\approx x$ such that:\n",
"\n",
"$$ L_t = \\log(1 + R_t ) = \\log(1+ \\frac{S_t-S_{0}}{S_{0}}) \\underset{t\\to 0}{\\approx} R_t $$\n",
"\n",
"and linear returns are not so different from the log-returns. \n",
"But if we consider monthly or annual returns, the difference becomes significant!\n",
"\n",
"\n",
"#### 1) We use log-returns to estimate the monthly mean and covariance matrix.\n",
"\n",
"We can assume that the daily log-returns of $S^i$ are i.i.d. with mean $\\mu^i$ and standard deviation $\\sigma^i$. \n",
"Later, we will also assume that the log-returns are normally distributed, but the reality is that this assumption is wrong! \n",
"If we try to test for normality using Shapiro-Wilk (here below), we can see that for each time series this assumption is rejacted. \n",
"Well, this is a well known fact. I didn't waste time writing my Lévy processes notebooks :) \n",
"\n",
"We can calculate the monthly mean and covariance matrix from the daily mean and covariance matrix:\n",
"\n",
"$$ \\mathbb{E}[L^j_T] = \\mathbb{E}\\biggl[\\sum_{i=1}^n L^j_{t_i}\\biggr] = \\sum_{i=1}^n \\mathbb{E}[L^j_{t_i}] = n \\mu^j $$\n",
"\n",
"$$ \n",
"\\begin{aligned}\n",
"\\text{COV}\\biggl[ L^j_T, L^k_T\\biggr] &= \\text{COV} \\biggl[ \\sum_{i=1}^n L^j_{t_i}, \\sum_{i=1}^n L^k_{t_i} \\biggr] \\\\\n",
"&= \\sum_{i=1}^n \\text{COV} \\biggl[ L^j_{t_i}, L^k_{t_i} \\biggr] = n\\, \\rho^{j,k} \\sigma^j \\sigma^k = n \\, \\Sigma^{j,k}\n",
"\\end{aligned}\n",
"$$\n",
"\n",
"where I used the i.i.d. property of log-returns such that $\\text{COV} \\biggl[ L^j_{t_i}, L^k_{t_h} \\biggr] = 0 $ for $i\\neq h$. \n",
"The term $\\rho^{j,k}$ is the correlation coefficient between the daily log-returns $L^j$ and $L^k$, and $\\Sigma^{j,k}$ the daily covariance."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normality test fails with log-returns. Pvalues:\n",
" [4.249113268271071e-13, 1.0231917002556656e-07, 1.1263771626567401e-13, 4.623055004137136e-16, 2.722110210851003e-11, 1.9630867349301187e-16, 4.913086496815297e-16, 4.408671778338702e-12, 7.546176293023567e-16, 8.741717134777387e-15, 8.642707697237299e-23, 5.612456645324092e-20, 3.866022695660394e-14]\n"
]
}
],
"source": [
"pvalues = []\n",
"for stk in stocks:\n",
" pvalues.append(ss.shapiro(log_ret[stk]).pvalue)\n",
"print(\"Normality test fails with log-returns. Pvalues:\\n\", pvalues)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2) We use monthly linear returns to compute the monthly portfolio linear return.\n",
"\n",
"Once we obtained the monthly log-returns, we need to convert them into linear returns.\n",
"For this purpose, we can assume that log-returns are normally distributed. It follows that the prices are log-normally distributed and linear returns as well: \n",
"\n",
"$$ R_t = e^{L_t} - 1 \\sim \\text{lognormal} $$\n",
"\n",
"The formulas for the mean and covariance of the multivariate distribution can be found on [wiki](https://en.wikipedia.org/wiki/Log-normal_distribution#Multivariate_log-normal). \n",
"Let us call $\\mu_T$ and $\\Sigma_T$ the mean and covariance of the monthly log-returns. The monthly linear returns have: \n",
"\n",
"$$ \\mathbb{E}[L^j_T] = e^{\\mu_T + \\frac{1}{2} \\Sigma_T^{j,j} } - 1 $$\n",
"\n",
"$$ \n",
"\\text{COV} \\biggl[ L^j_T, L^k_T\\biggr] = e^{\\mu_T^j + \\mu_T^k + \\frac{1}{2}(\\Sigma_T^{j,j} + \\Sigma_T^{k,k}) }\n",
"\\biggl( e^{\\Sigma_T^{j,k}} - 1 \\biggr)\n",
"$$\n",
"\n",
"This topic is also discussed in detail in [1] (see equation 6.162). "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# log-return monthly mean and covariance\n",
"mu_log = investment_horizon * log_ret.mean().values\n",
"cov_log = investment_horizon * log_ret.cov().values\n",
"\n",
"# linear return monthly mean and covariance\n",
"MU = np.exp((mu_log + 0.5 * np.diag(cov_log))) - 1\n",
"COV = np.diag(MU + 1) @ (np.exp(cov_log) - 1) @ np.diag(MU + 1) # COV written in matrix form"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Variance of the portfolio\n",
"\n",
"The expected return of the portfolio is simply the weighted sum of the expected returns of each stock (here I use the subscript $i$ to simplify the notation)\n",
"\n",
"$$ \\mathbb{E}[ R^P ] = \\sum_{i=1}^N w_i \\, \\mathbb{E}[R_i]. $$\n",
"\n",
"But the variance involves all the covariance terms:\n",
"\n",
"$$ \\text{VAR}[ R^P ] = \\text{VAR}\\biggl[ \\sum_{i=1}^N w_i \\,R_i \\biggr] = \\sum_{i=1}^N w_i^2 \\, \\text{VAR}[R_i] + \\sum_{i,j=1\\\\ \\, i\\not=j}^N w_i w_j \\, \\text{COV}[R_i, R_j] . $$\n",
"\n",
"There are $N$ variance terms and $\\frac{N(N-1)}{2}$ covariance terms."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Optimization with scipy.optimize\n",
"\n",
"Let us write the optimization problem in matrix form. \n",
"Let $\\boldsymbol{\\mu}$ and $\\mathbf \\Sigma$ be the expected returns vector and the covariance matrix. Let $\\mathbf w$ be the vector of weights. With $\\mu^P$ I indicate the target portfolio return. \n",
"Then the optimization problem can be written as:\n",
"\n",
"$$ \\min_{\\mathbf w} \\; \\mathbf w^T \\mathbf \\Sigma \\, \\mathbf w \\quad \\text{subject to } \\quad \\boldsymbol \\mu^T \\mathbf w = \\mu^P, \\quad \\mathbf w^T \\mathbf 1 = 1 \\quad \\text{and} \\quad \\boldsymbol w \\geq 0. $$\n",
"\n",
"Here I use `scipy.optimize.minimize` to solve the optimization problem. \n",
"It can be useful to read the doc for the [Linear constraint](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.LinearConstraint.html).\n",
"It is convenient to write the two linear equality constraints in a compact form: \n",
"\n",
"$$ \\begin{pmatrix} 1 & 1 & ... & 1 \\\\ \\mu_1 & \\mu_2 & ... & \\mu_N \\end{pmatrix} \\cdot \n",
"\\begin{pmatrix} w_1 \\\\ w_2 \\\\ \\vdots \\\\ w_N \\end{pmatrix} = \\bigg( \\begin{array}{c} 1 \\\\ \\mu^P \\end{array} \\bigg)\n",
"$$\n",
"\n",
"The condition $\\boldsymbol w \\geq 0$ is for an investor that is not allowed to take short positions."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def optimizer(MU, COV, target_mu, OnlyLong=True):\n",
" \"\"\"Finds optimal weights for a fixed target portfolio return\"\"\"\n",
"\n",
" N = len(MU)\n",
" if OnlyLong == True:\n",
" bounds = Bounds(0, 1)\n",
" A = np.vstack((np.ones(N), MU))\n",
" B = np.array([1, target_mu])\n",
" linear_constraint = LinearConstraint(A, B, B)\n",
"\n",
" weights = np.ones(N)\n",
" x0 = weights / np.sum(weights) # Create x0, the initial guess for the weights\n",
"\n",
" # Define the objective function\n",
" quadratic_form = lambda w: (w.T @ COV @ w)\n",
" if OnlyLong:\n",
" res = minimize(quadratic_form, x0=x0, method=\"trust-constr\", constraints=linear_constraint, bounds=bounds)\n",
" else:\n",
" res = minimize(quadratic_form, x0=x0, method=\"trust-constr\", constraints=linear_constraint)\n",
" return res.x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now I compute the optimal weights for several values of target expected return $\\mu^P$. With these weights we can compute the standard deviation $\\sigma^P$ of the corresponding portfolio. \n",
"The curve of all the points $(\\sigma^P,\\mu^P)$ is called **efficient frontier**.\n",
"\n",
"The Sharpe ratio is a performance measure defined as\n",
"\n",
"$$SR = \\frac{\\mu^P - Rf}{\\sigma^P}$$\n",
"\n",
"where $Rf$ is the risk free rate. The line with a slope equal to the maximum Sharpe ratio is called **capital market line** (CML). \n",
"The point on the efficient frontier with maximum Sharpe ratio is called **tangent portfolio**."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"samples = 200\n",
"means = np.linspace(0, np.max(MU), samples) # vector of target expected returns\n",
"stds = np.zeros_like(means)\n",
"sharpe_ratio = np.zeros_like(means)\n",
"\n",
"for i, mn in enumerate(means):\n",
" w_opt = optimizer(MU, COV, mn) # optimal weights\n",
" stds[i] = np.sqrt(w_opt @ COV @ w_opt)\n",
" sharpe_ratio[i] = (mn - Rf) / stds[i]\n",
"\n",
"ind_SR = np.argmax(sharpe_ratio) # index of the maximum Sharpe Ratio\n",
"max_SR = sharpe_ratio[ind_SR] # maximum Sharpe ratio\n",
"\n",
"y = np.linspace(0, stds.max(), samples)\n",
"CML = Rf + max_SR * y # capital market line"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
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],
"text/plain": [
" AAPL AMZN GOOGL JPM GLD DIS VNO FB UBS KO \\\n",
"0 0.4856 0.0494 0.0002 0.0001 0.464 0.0002 0.0 0.0001 0.0003 0.0 \n",
"\n",
" MCD ^GSPC GM \n",
"0 0.0 0.0 0.0001 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(pd.DataFrame([dict(zip(data.columns, optimizer(MU, COV, means[ind_SR]).round(4)))]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Optimize the Sharpe ratio\n",
"\n",
"Most of the time, we are not interested in the efficient frontier, but we just want to find the tangent portfolio.\n",
"An alternative is to maximize the Sharpe ratio i.e. solve the following problem:\n",
"\n",
"$$ \\max_{\\mathbf w} \\; \\frac{\\mathbf w^T \\boldsymbol \\mu - Rf }{ \\sqrt{\\mathbf w^T \\mathbf \\Sigma \\, \\mathbf w} } \\quad \\text{subject to } \\quad \\mathbf w^T \\mathbf 1 = 1 \\quad \\text{and} \\quad \\boldsymbol w \\geq 0. $$\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"weights = np.ones(N)\n",
"x0 = weights / np.sum(weights) # initial guess\n",
"\n",
"# Define the objective function (the negative Sharpe ratio)\n",
"sharpe_fun = lambda w: -(MU @ w - Rf) / np.sqrt(w.T @ COV @ w)\n",
"bounds = Bounds(0, 1)\n",
"linear_constraint = LinearConstraint(np.ones(N, dtype=int), 1, 1)\n",
"res = minimize(sharpe_fun, x0=x0, method=\"trust-constr\", constraints=linear_constraint, bounds=bounds)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"weights = \n"
]
},
{
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"text/plain": [
" AAPL AMZN GOOGL JPM GLD DIS VNO FB UBS KO MCD ^GSPC \\\n",
"0 0.4879 0.0486 0.0 0.0 0.4635 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" GM \n",
"0 0.0 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Max Sharpe ratio = 0.5609210027002335\n",
"optimal portfolio at sigma= 0.07327567641496409 and mean=0.04193519922155284\n"
]
}
],
"source": [
"w_sr = res.x\n",
"print(\"weights = \")\n",
"display(pd.DataFrame([dict(zip(data.columns, w_sr.round(4)))]))\n",
"print(\"Max Sharpe ratio = \", -sharpe_fun(w_sr))\n",
"print(\"optimal portfolio at sigma= {} and mean={}\".format(np.sqrt(w_sr @ COV @ w_sr), MU @ w_sr))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The weights are a bit different than before because here the maximum Sharpe ratio is more accurate. Before it was computed by grid search."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Optimal weights between stocks and bond\n",
"\n",
"Once we have computed the tangency portfolio, it is optimal to stay on the CML. \n",
"Let us call $R^P$ the return of a portfolio on the CML that allocates a fraction $w$ of the initial capital to the tangency portfolio (with return $R^T$) and $1-w$ to the risk-free asset $R_f$, i.e.\n",
"\n",
"$$ R^P = w R^T + (1-w) R_f = R_f + w (R^T-R_f) $$\n",
"\n",
"such that \n",
"\n",
"$$ \\mathbb{E}[R^P] = R_f + w (\\mathbb{E}[R^T] - R_f) \\quad \\text{and} \\quad \n",
"\\text{VAR}[R^P] = w^2 \\text{VAR}[R^T]. $$\n",
"\n",
"From these equations, given a desired level of expected return or variance, it is possible to calculate the optimal balance between the stocks portfolio (the tangency portfolio) and a risk free (a bond) asset.\n",
"\n",
"The following function does it. Let us give a value of expected return as input and see how it works:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"`gtol` termination condition is satisfied.\n",
"\n"
]
},
{
"data": {
"text/plain": [
"{'Sharpe Ratio': 0.5609210027002335,\n",
" 'stock weights': array([0.4879, 0.0486, 0. , 0. , 0.4635, 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0. , 0. ]),\n",
" 'stock portfolio': {'std': 0.073276, 'mean': 0.041935},\n",
" 'Bond + Stock weights': {'Bond': 0.5337, 'Stock': 0.4663},\n",
" 'Total portfolio': {'std': 0.03417, 'mean': 0.02}}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"optimal_weights(MU, COV, Rf=Rf, w_max=1, desired_mean=0.02, desired_std=None)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Compute the optimal weights for a portfolio containing a risk free asset and stocks.\n",
" MU = vector of mean\n",
" COV = covariance matrix\n",
" Rf = risk free return\n",
" w_max = maximum weight bound for the stock portfolio\n",
" desired_mean = desired mean of the portfolio\n",
" desired_std = desired standard deviation of the portfolio\n",
" \n"
]
}
],
"source": [
"print(optimal_weights.__doc__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this function I also introduced the possibility to give an upperbound to the weights:\n",
"\n",
"$$0 \\leq \\boldsymbol w \\leq w_{max}$$\n",
"\n",
"Let us try it. We can see that although there is more diversification, the introduction of this bound reduced the Sharpe ratio."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"`gtol` termination condition is satisfied.\n",
"\n"
]
},
{
"data": {
"text/plain": [
"{'Sharpe Ratio': 0.46269725666071015,\n",
" 'stock weights': array([2.000e-01, 2.000e-01, 1.981e-01, 1.000e-04, 2.000e-01, 1.400e-03,\n",
" 0.000e+00, 4.000e-04, 1.997e-01, 1.000e-04, 0.000e+00, 1.000e-04,\n",
" 1.000e-04]),\n",
" 'stock portfolio': {'std': 0.080421, 'mean': 0.038044},\n",
" 'Bond + Stock weights': {'Bond': 0.4849, 'Stock': 0.5151},\n",
" 'Total portfolio': {'std': 0.041424, 'mean': 0.02}}"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"bounded_porf = optimal_weights(MU, COV, Rf=Rf, w_max=0.2, desired_mean=0.02, desired_std=None)\n",
"display(bounded_porf)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bounded Weights = \n"
]
},
{
"data": {
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"\n",
" ^GSPC GM \n",
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(\"Bounded Weights = \")\n",
"display(pd.DataFrame([dict(zip(data.columns, bounded_porf[\"stock weights\"].round(4)))]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Optimization with cvxpy\n",
"\n",
"The code of this section follows closely the example given in the [cvxpy website](https://colab.research.google.com/github/cvxgrp/cvx_short_course/blob/master/applications/portfolio_optimization.ipynb#scrollTo=--osR2je4bDI). \n",
"CVXPY uses a better solver, which as we will see it is much faster. It is also very easy to use.\n",
"\n",
"Here I also present an alternative and equivalent formulation of the problem:\n",
"\n",
"$$ \\max_{\\mathbf w} \\; \\boldsymbol \\mu^T \\mathbf w - \\lambda \\, \\mathbf w^T \\mathbf \\Sigma \\, \\mathbf w \\quad \\text{subject to } \\quad \\mathbf w^T \\mathbf 1 = 1 \\quad \\text{and} \\quad \\boldsymbol w \\geq 0. $$\n",
"\n",
"where $\\lambda > 0$ represents the risk aversion coefficient of the investor. \n",
"The choice of a positive lambda follows the common requirement to describe a risk-averse investor. \n",
"For $\\lambda \\to 0$, the investor tends to be more **risk-neutral**. For $\\lambda \\to \\infty$ the investor is very risk-averse and the optimal portfolio converges to the **minimum variance portfolio** i.e. the portfolio with the least variance. \n",
"\n",
"From a mathematical point of view, this formulation corresponds to a problem where we want to maximize the expected return $ \\boldsymbol \\mu^T \\mathbf w$ for a fixed variance level $v = \\mathbf w^T \\mathbf \\Sigma \\, \\mathbf w$ plus the other constraints. \n",
"We can introduce the **Lagrange multiplier** $\\lambda$ such that the objective function becomes $\\boldsymbol \\mu^T \\mathbf w - \\lambda \\, (\\mathbf w^T \\mathbf \\Sigma \\, \\mathbf w - v)$, which is equivalent to the initial problem (since $v$ is just a constant and does not affect the weights)."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"w = cp.Variable(N) # weights\n",
"gamma = cp.Parameter(nonneg=True)\n",
"ret = MU @ w # portfolio return\n",
"risk = cp.quad_form(w, COV) # portfolio variance\n",
"objective = cp.Maximize(ret - gamma * risk) # objective function\n",
"constraints = [cp.sum(w) == 1, w >= 0]\n",
"prob = cp.Problem(objective, constraints)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now I replicate the results of the previous section.\n",
"\n",
"I run compute the optimal portfolio for seveal values of risk aversion $\\gamma$, that is equivalent to optimize for several values of portfolio standard deviations. The resulting curve is again the **efficient frontier**, where each point corresponds to a value of $\\gamma$. "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# Compute efficient frontier.\n",
"samples = 500\n",
"std_values = np.zeros(samples)\n",
"mean_values = np.zeros(samples)\n",
"sharpe_ratio = np.zeros(samples)\n",
"gamma_vals = np.logspace(0, 4, num=samples) # several levels of risk aversion\n",
"portfolios = []\n",
"for i in range(samples):\n",
" gamma.value = gamma_vals[i]\n",
" prob.solve()\n",
" std_values[i] = cp.sqrt(risk).value\n",
" mean_values[i] = ret.value\n",
" sharpe_ratio[i] = (mean_values[i] - Rf) / std_values[i]\n",
" portfolios.append(w.value)\n",
"\n",
"ind_SR = np.argmax(sharpe_ratio) # index of the maximum Sharpe Ratio\n",
"max_SR = sharpe_ratio[ind_SR] # maximum Sharpe ratio\n",
"y = np.linspace(0, std_values.max(), samples)\n",
"CML = Rf + max_SR * y # capital market line"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot: "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"
"
],
"text/plain": [
" AAPL AMZN GOOGL JPM GLD DIS VNO FB UBS KO MCD ^GSPC \\\n",
"0 0.48551 0.04903 -0.0 -0.0 0.46546 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 \n",
"\n",
" GM \n",
"0 -0.0 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"gamma= 3.8474265772585037\n",
"Sharpe Ratio = 0.5609197942924549\n",
"Standard Deviation and mean of the portfolio 0.073067 0.041818\n"
]
}
],
"source": [
"print(\"Weights:\")\n",
"display(pd.DataFrame([dict(zip(data.columns, portfolios[ind_SR].round(5)))]))\n",
"print(\"gamma=\", gamma_vals[ind_SR])\n",
"print(\"Sharpe Ratio = \", max_SR)\n",
"print(\"Standard Deviation and mean of the portfolio\", std_values[ind_SR].round(6), mean_values[ind_SR].round(6))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Probability density of the tangency portfolio\n",
"\n",
"What if we want to compute some probabilities? For instance the probability of losing money e.g. $\\mathbb{P}(R^P < x)$ ?\n",
"\n",
"Well, we do not have a closed form for the density of the portfolio. Since each $R^i$ is log-normal, then $\\sum_i w_i R^i$ is not a log-normal. \n",
"You can see here [wiki-related distribution](https://en.wikipedia.org/wiki/Log-normal_distribution#Related_distributions) that the sum of independent LN random variables is approximately log-normal, but not exactly log-normal. In our case the variables are correlated.\n",
"\n",
"We can use Monte Carlo to simulate many LN linear returns and compute the empirical density of the portfolio.\n",
"It is convenient, in order to obtain a smooth density, to work with the **Gaussian Kernel density estimation** [KDE](Kernel density estimation). \n",
"\n",
"Let us generate some multivariate log-normal (LN) random variables $R_T = \\frac{S_0e^{X_T}-S_0}{S_0}$, starting from the knowledge of the multivariate normal random variables $X_T$. We previously computed the monthly mean and covariance of $X_T$."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(seed=42)\n",
"LN_ret = np.exp(ss.multivariate_normal.rvs(mean=mu_log, cov=cov_log, size=50000)) - 1"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"def plot_prob(LN_ret, w, lower_value):\n",
" \"\"\"Probability density of the optimal portfolio\n",
" LN_ret = simulated log-returns. MxN matrix\n",
" w = weights vector Nx1 vector\n",
" lower_value = the maximum loss we are considering\n",
" \"\"\"\n",
" if LN_ret.shape[1] != w.shape[0]:\n",
" raise ValueError\n",
"\n",
" MU_simul = LN_ret.mean(axis=0) # mean of the linear returns (equal to MU)\n",
" COV_simul = np.cov(LN_ret, rowvar=False) # covariance matrix (equal to COV)\n",
" Opt_portfolios = LN_ret @ w # portfolio linear returns\n",
" mu_port = MU_simul @ w # portfolio mean\n",
" sig_port = np.sqrt(w @ COV_simul @ w) # portfolio standard deviation\n",
"\n",
" x = np.linspace(Opt_portfolios.min(), Opt_portfolios.max(), 500)\n",
" kde = ss.gaussian_kde(Opt_portfolios)\n",
" normal = ss.norm(loc=mu_port, scale=sig_port)\n",
"\n",
" fig = plt.figure(figsize=(15, 5))\n",
" plt.plot(\n",
" x,\n",
" kde.evaluate(x),\n",
" color=\"salmon\",\n",
" label=\"KDE, loss prob={:.4f}\".format(kde.integrate_box_1d(-np.inf, lower_value)),\n",
" )\n",
" plt.plot(x, normal.pdf(x), label=\"Normal, loss prob={:.4f}\".format(normal.cdf(lower_value)))\n",
" plt.axvline(x=lower_value, color=\"grey\", linestyle=\"--\", lw=2, label=\"Maximum loss\")\n",
" plt.axvline(x=mu_port, color=\"green\", linestyle=\"--\", lw=1, label=\"Portfolio mean return\")\n",
" plt.axvline(x=mu_port - sig_port, color=\"orange\", linestyle=\"--\", lw=1, label=r\"$\\pm$ standard deviation\")\n",
" plt.axvline(x=mu_port + sig_port, color=\"orange\", linestyle=\"--\", lw=1)\n",
" plt.title(\"Probability density of the portfolio returns\")\n",
" plt.xlabel(\"Return\")\n",
" plt.ylabel(\"Density\")\n",
" plt.legend(loc=\"upper right\")\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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WXLt27alWDi1dujRt2rThm2++oU2bNvz66698/PHHZr2w0uLp6YlOp+POnTtmyRlVVYmMjDT1uMltj35ux48fT5cuXdI8ply5cpnWk9nPppWVFRqNhoiIiFTHPVqU4ck2lFY7zKrsvu+enp5Zuoa0PHld7u7upp5y6c1DFxAQkGm9dnZ2qSa+h/STac96fzt27EjHjh1JSkriwIEDTJ48md69e1OiRIlnmtNQCCGEeFrSQ0wIIYTIAc2aNePhw4esW7fObP9PP/1k+vxx27dvN5s4Xq/Xs2LFCkqVKmWaBFtRlFSJnpMnT6Y77GrNmjUkJiaa3sfGxrJhwwZeeumlTBMymXmyB8iTatSoQb169ZgyZQpLly4lJCQER0fHDOt0dHSkdu3a6cb9uPbt26OqKjdu3KBmzZqpXkFBQc90fW5ubnTr1o3hw4cTFRWV4SqcT/Y0e9Kbb77JyZMn6d+/P1qtlsGDB2d6/kc/H0uWLDHbv3r1auLi4lL9/Fgqs1gzU65cOcqUKcOJEyfSvO81a9bE2dk503oy+9l0dHSkTp06rFmzxixeg8HAkiVLKFq0KGXLls30PJn9nD4pu+97s2bN2LFjR6qVVX/66SccHBxMq9ZmhYODA02aNOHYsWNUrlw5zWfwKIme0fWXKFGCf/75x2zV23v37rFv374sx5QVtra2NGrUiClTpgBw7NixHD2fEEIIkR7pISaEEELkgH79+vHNN9/Qv39/rly5QlBQEHv37uWzzz6jbdu2NG/e3Ox4Ly8vmjZtyocffmhaZfLcuXMsX77cdEz79u2ZNGkSEyZMoFGjRpw/f55PPvmEgIAAdDpdqhi0Wi0tWrRgzJgxGAwGpkyZQkxMDB9//PEzX1+pUqWwt7dn6dKlVKhQAScnJwoXLmy2guSbb75Jjx49UBTFNJwuM5MmTaJ169a0aNGCt99+G71ez5QpU3B0dDStsglQv359hgwZwoABAzh8+DANGzbE0dGRiIgI9u7dS1BQkGkuM0u9/PLLVKpUiZo1a+Lt7c3Vq1eZOXMmxYsXN1sl8klBQUGsWbOGuXPnUqNGDTQajVmvwhYtWhAYGMjOnTt59dVX8fHxyTSWFi1a0KpVK959911iYmKoX7++abXDatWq0bdv3yxdm6WxWmLevHm0adOGVq1aERISQpEiRYiKiiI0NJSjR4+yatWqTOuw5Gdz8uTJtGjRgiZNmvDOO+9gY2PDnDlzOH36NMuWLbOox9KjxOhXX31F//79sba2ply5cukm7bL7vk+YMIGNGzfSpEkTPvroIzw8PFi6dCmbNm1i6tSpppU7s+qrr76iQYMGvPTSSwwdOpQSJUoQGxvLxYsX2bBhg9nKmem10759+zJv3jxeffVVBg8ezL1795g6dWqq4d/Z4aOPPuL69es0a9aMokWL8uDBA7766iusra1p1KhRtp9PCCGEsEhezugvhBBC5FePVq47dOhQhsdltMLfvXv31DfeeEP18/NTrays1OLFi6vjx49XExMTzY4D1OHDh6tz5sxRS5UqpVpbW6vly5dXly5danZcUlKS+s4776hFihRR7ezs1OrVq6vr1q1Ld1W4KVOmqB9//LFatGhR1cbGRq1WrZq6ZcuWNK8zq6tMqqqqLlu2TC1fvrxqbW2danW9R/Ha2tqqrVu3Tv8GpuHXX39VK1eurNrY2KjFihVTP//8c9Nqdk/68ccf1Tp16qiOjo6qvb29WqpUKbVfv37q4cOHTcc0atRIrVixYqqyT17n9OnT1Xr16qleXl6mcw8aNEi9cuWK6Zi07ldUVJTarVs31c3NTVUUJc04J06caFq11FIJCQnqu+++qxYvXly1trZW/fz81KFDh6r3799PdR2WrjKZXqyPfmamTZuWqkxaz/bEiRNq9+7dVR8fH9Xa2lr19fVVmzZtqn777bcZnj8rP5uqqqp79uxRmzZtanq+devWVTds2GB2TGZtdfz48WrhwoVVjUajAurOnTtVVU3/vll63y1ZZVJVVfXUqVPqyy+/rLq6uqo2NjZqlSpV0lz1MS2P/t+QlrCwMHXgwIFqkSJFVGtra9Xb21utV6+e+umnn5odl1E7XbRokVqhQgXVzs5ODQwMVFesWJHu/0/S+tl41C7v3Lljtv/JdrJx40a1TZs2apEiRVQbGxvVx8dHbdu2rbpnzx6L7oMQQgiRExRVVdVczcAJIYQQokDYsGEDHTp0YNOmTabJvguqmjVroigKhw4dyutQ8tSVK1cICAhg2rRpvPPOO3kdjhBCCCEKMBkyKYQQQohsdfbsWa5evcrbb79N1apVadOmTV6HlCdiYmI4ffo0Gzdu5MiRI6xduzavQxJCCCGEEP+ShJgQQgghstWwYcP466+/qF69OosWLcqW1f+eR0ePHqVJkyZ4enoyYcIEOnXqlNchCSGEEEKIf8mQSSGEEEIIIYQQQghRoGjyOgAhhBBCCCGEEEIIIXKTJMSEEEIIIYQQQgghRIEiCTEhhBBCCCGEEEIIUaA815PqGwwGbt68ibOzc4GdsFcIIYQQQgghhBBCGKmqSmxsLIULF0ajSb8f2HOdELt58yb+/v55HYYQQgghhBBCCCGEyEeuXbtG0aJF0/38uU6IOTs7A8aLdHFxyeNohBCWmj17NrGxsTg7OzNixIi8Dkc8EnUc/mgEzXeDR9W8jkbksuORx2m0oBG7B+ymqm/VvA5H5DZp/0IIIYR4QcTExODv72/KGaXnuU6IPRom6eLiIgkxIZ4j5cuXJz4+HgcHB2m7+YoflGwJHn4gz6XA8Uvxo2XFlvh5+km7LJCk/QshhBDixZLZ1FqKqqpqLsWS7WJiYnB1dSU6Olq+vAshhBBCCCGEEEIUcJbmimSVSSGEEEYGPaTEGLeiwNEb9MQkxaCX518wSfsXQgghRAEjCTEhhBBGD07AKlfjVhQ4J26dwPVzV07ckudfIEn7F0IIIUQB81zPISaEEEIIIYQQQuQUVVXR6XTo9dKDVoj8QqvVYmVllekcYZmRhJgQItctWrSIuLg4HB0d6d+/f16HI4QQQgghRCrJyclEREQQHx+f16EIIZ7g4OCAn58fNjY2T12HJMSEELnu3r17xMbGkpiYmNehCCGEEEIIkYrBYCAsLAytVkvhwoWxsbF55t4oQohnp6oqycnJ3Llzh7CwMMqUKYNG83SzgUlCTAghhBBCCCGEeExycjIGgwF/f38cHBzyOhwhxGPs7e2xtrbm6tWrJCcnY2dn91T1SEJMCCGEkVsQdLkNNm55HYnIA0E+Qdx+5zZudm55HYrIC9L+hRAiTU/b80QIkbOyo21KQkwIIYSRxhrsvPM6CpFHrLXWeDvK8y+wpP0LIYQQooCRdLcQQgij2Euwu4NxKwqcS1GX6LCsA5ei5PkXSNL+hRBCCFHASEJMCCGEUUo03Nhg3IoCJzopmg3/bCA6SZ5/gSTtXwghRA5auHAhbm5ueR1Gtps4cSJVq1bN6zDEU5KEmBBCCCGEEEII8YIICQmhU6dOZvt++eUX7OzsmDp1KmBM5CiKgqIoWFlZ4eXlRcOGDZk5cyZJSUlmZRs3bmw69vHXG2+8kVuXVKCoqsrEiRMpXLgw9vb2NG7cmDNnzmRabvXq1QQGBmJra0tgYCBr1641+3zy5MnUqlULZ2dnfHx86NSpE+fPnzc7Jq3nrCgK06ZNMx2TlJTEyJEj8fLywtHRkQ4dOnD9+vXsufhcJgkxIYQQQgghhBDiBfX999/Tp08fZs+ezbhx40z7K1asSEREBOHh4ezcuZNXXnmFyZMnU69ePWJjY83qGDx4MBEREWavR8m1F1FKSkqenXvq1KnMmDGD2bNnc+jQIXx9fWnRokWqZ/K4/fv306NHD/r27cuJEyfo27cv3bt35+DBg6Zjdu/ezfDhwzlw4ADbtm1Dp9PRsmVL4uLiTMc8+Yx//PFHFEWha9eupmNGjx7N2rVrWb58OXv37uXhw4e0b98evV6fMzckB0lCTAghhMinVJ0Ow7Uw9If3o9+8Dt2qRegWfoPuuy9J+XY6Kd99iW7hHHQrFqL/fS36fbswXDyHGvMAVVXzOnwhhBDihaKqKmpyUt68nvL3+tSpUxkxYgQ///wzr732mtlnVlZW+Pr6UrhwYYKCghg5ciS7d+/m9OnTTJkyxexYBwcHfH19zV4uLi5PfS8B5s6dS6lSpbCxsaFcuXIsXrzY7POJEydSrFgxbG1tKVy4MKNGjTJ9NmfOHMqUKYOdnR2FChWiW7du6Z7n0XDNdevWUbZsWezs7GjRogXXrl0zO1fVqlX58ccfKVmyJLa2tqiqSnh4OB07dsTJyQkXFxe6d+/OrVu3Up1j3rx5+Pv74+DgwCuvvMKDBw+e6p6oqsrMmTN5//336dKlC5UqVWLRokXEx8fz888/p1tu5syZtGjRgvHjx1O+fHnGjx9Ps2bNmDlzpumYzZs3ExISQsWKFalSpQoLFiwgPDycI0eOmI558hmvX7+eJk2aULJkSQCio6P54YcfmD59Os2bN6datWosWbKEU6dO8ccffzzVNeclWWVSCJHrGjVqRHJyMjY2NnkdinicfRGoNt24FXlGjYvFcPYk6rlTqOFXQJfxXyjVJ7Ymzq4o/iXQlCyDUro8iqt7hvUUcS7C9JbTKeIsz79AkvYvhBCZS0lGN/l/eXJqq/GfgY1tlsq89957fPPNN2zcuJHmzZtbVKZ8+fK0adOGNWvW8Omnnz5NqBZZu3Ytb775JjNnzqR58+Zs3LiRAQMGULRoUZo0acIvv/zCl19+yfLly6lYsSKRkZGcOHECgMOHDzNq1CgWL15MvXr1iIqKYs+ePRmeLz4+nv/7v/9j0aJF2NjYMGzYMHr27Mlff/1lOubixYusXLmS1atXo9VqAejUqROOjo7s3r0bnU7HsGHD6NGjB7t27UpVbsOGDcTExDBo0CCGDx/O0qVLAVi6dCmvv/56hvHNmzePPn36EBYWRmRkJC1btjR9ZmtrS6NGjdi3b1+69ezfv5+33nrLbF+rVq3MEmJPio42zhvq4eGR5ue3bt1i06ZNLFq0yLTvyJEjpKSkmMVXuHBhKlWqxL59+2jVqlWG15nfSEJMCJHratSokdchiLTYF4IKY/I6igJJVVXU8MsYDu5BPXcaHv8rsKMTil9RFK9C4OaO4ugEtnag0YDBAEmJqPFxEPMA9X4U6p1IuHsHYqNRz55Af9b45RG/omgqVkETVB3FxS1VDIWcCjEmWJ5/gSXtXwghXii///4769evZ/v27TRt2jRLZcuXL8/WrVvN9s2ZM4fvv//ebN8333xD//79nyq+L774gpCQEIYNGwbAmDFjOHDgAF988QVNmjQhPDwcX19fmjdvjrW1NcWKFaN27doAhIeH4+joSPv27XF2dqZ48eJUq1Ytw/OlpKQwe/Zs6tSpA8CiRYuoUKECf//9t6ne5ORkFi9ejLe3NwDbtm3j5MmThIWF4e/vD8DixYupWLEihw4dolatWgAkJiayaNEiihYtCsDXX39Nu3btmD59Or6+vnTo0MF03vQUKlQIgMjISLP3j39+9erVdMtHRkamWeZRfU9SVZUxY8bQoEEDKlWqlOYxixYtwtnZmS5dupidx8bGBnd38z+0ZnSu/EwSYkIIIYyS70PkH+DbHGwy7k0kso/h6mUM2zehXrti2qcU9kepWBVNmQrg5YOiKFmqU01OQr15HfXqJdSL51BvhEPEdQwR1zFs/w2lTAU0teqjlCpnqvt+wn3+uPwHzUs2x91enn+BI+1fCCEyZ21j7KmVR+fOisqVK3P37l0++ugj00TqllJVNdV3jz59+vD++++b7fPx8clSTI8LDQ1lyJAhZvvq16/PV199BcArr7zCzJkzKVmyJK1bt6Zt27a8/PLLWFlZ0aJFC4oXL276rHXr1nTu3BkHB4d0z2dlZUXNmjVN78uXL4+bmxuhoaGmhFjx4sVNybBHMfr7+5uSYQCBgYGmco8SYsWKFTMlwwCCg4MxGAycP38eX19fnJ2ds3T/gVT3P61n8ixlRowYwcmTJ9m7d2+69f3444/06dMHOzu7TOO1JL78SOYQE0IIYfQwDPZ2N25FjlOj7xvn/lr4jTEZZmWFpnpdrIaOxWrwaLT1GqN4F3qqLxeKjS2aEqXQNmqJ1aBRWL09EU27bijFS4Kqov5zFv3S+ei+nY7h5BFUg4GwB2F0/6U7YQ/k+RdI0v6FECJTiqKg2NjmzSuL3weKFCnC7t27iYiIoHXr1hlOyP6k0NBQAgICzPa5urpSunRps9ezziGWUQLH39+f8+fP880332Bvb8+wYcNo2LAhKSkpODs7c/ToUZYtW4afnx8fffQRVapUyXTerrTu4eP7HB0d043Hkv1P1vlou3TpUpycnDJ8PRpe6evrC5Cqt9Xt27dT9QB7nK+vr8VlRo4cya+//srOnTvNEnmP27NnD+fPn08175yvry/Jycncv38/S/HlV5IQE0LkutjYWGJiYrL0i1mIF4Wqquj/3otuzjTUc6dA0aCpEYzVqP+hffkVFB/fbD+n4uiEtmYwViHDsRr+Lpo6DY3zkNyOQL/2Z3TffoEh7EK2n1cIIYQQeadYsWLs3r2b27dv07JlS2JiYjItc+7cOTZv3my2qmBOqFChQqreSfv27aNChQqm9/b29nTo0IFZs2axa9cu9u/fz6lTpwBjj6/mzZszdepUTp48yZUrV9ixY0e659PpdBw+fNj0/vz58zx48IDy5cunWyYwMJDw8HCzyffPnj1LdHS0WZzh4eHcvHnT9H7//v1oNBrKli0LQIcOHTh+/HiGrw4dOgAQEBCAr68v27ZtM9WXnJzM7t27qVevXrqxBgcHm5UB2Lp1q1kZVVUZMWIEa9asYceOHamSno/74YcfqFGjBlWqVDHbX6NGDaytrc3OFRERwenTpzOML7+SIZNCiFw3f/58YmNjcXZ2ZswYmbNGFBzqw1j065ejXjwHgOJfAm37big+fjl+bp3ewJV7cdx4oHDLqzq3a5Th1qWrPIyIRH/DQOTNs2ANUzecolwhG9wdbXB3sMHTyYaSXk6U9HbEzlqb43EKIYQQIvsULVqUXbt20aRJE1q2bMmWLVtwdXUFjEmiyMhIDAYD9+7dY9euXXz66adUrVqVsWPHmtUTHx+fqgeSra1tqrmkLDV27Fi6d+9O9erVadasGRs2bGDNmjWmlQoXLlyIXq+nTp06ODg4sHjxYuzt7SlevDgbN27k8uXLNGzYEHd3d3777TcMBgPlypVL93zW1taMHDmSWbNmYW1tzYgRI6hbt65puGRamjdvTuXKlenTpw8zZ840TarfqFEjs+GXdnZ29O/fny+++IKYmBhGjRpF9+7dTb29sjJkUlEURo8ezWeffUaZMmUoU6YMn332GQ4ODvTu3dt0XL9+/ShSpAiTJ08G4M0336Rhw4ZMmTKFjh07sn79ev744w+zpOPw4cP5+eefWb9+Pc7Ozqbn6erqir29vem4mJgYVq1axfTp01PF5+rqyqBBg3j77bfx9PTEw8ODd955h6CgIIsXbshPJCEmhBBC5ALD1cvoV/0EcbHG4ZEtXkZTqx6Kkv2dtVVV5dKdOPZfvsfZm9GcvRnDuchYknSGNI42zv+RpDwEa9gd9oADl1NP2qpRoJiHA6V9nKlWzI3gUp4EFXHFWiudzYUQQoj87NHwySZNmtCiRQvThPlnzpzBz88PrVaLq6srgYGBjB8/nqFDh2Jra76i5fz585k/f77ZvlatWrF582YAGjduTIkSJVi4cKFFMXXq1ImvvvqKadOmMWrUKAICAliwYAGNGzcGwM3Njc8//5wxY8ag1+sJCgpiw4YNeHp64ubmxpo1a5g4cSKJiYmUKVOGZcuWUbFixXTP5+DgwLvvvkvv3r25fv06DRo04Mcff8wwRkVRWLduHSNHjqRhw4ZoNBpat27N119/bXZc6dKl6dKlC23btiUqKoq2bdsyZ84ci+5DWsaNG0dCQgLDhg3j/v371KlTh61bt5ol1cLDw9Fo/vsOVq9ePZYvX84HH3zAhx9+SKlSpVixYoXZZP5z584FMN3jRxYsWEBISIjp/fLly1FVlV69eqUZ35dffomVlRXdu3cnISGBZs2asXDhQtPKnM8TRVXVVCu1Py9iYmJwdXUlOjr6mccvCyFyz4wZM6SHWH4UHQr7+kC9peBaIfPjhcX0h/7CsHmdcVVIb1+sur2a7b3CYhNT+POfu+y5cIc//7nDzejEVMc42mjx93DAx8WOQs62FHKxw8nOCiuNQuS9U8w/9SYDk3thpxblvo0z0X4B3NZpuXj7IdEJKWnWVyvAg4ZlvGlX2Y9CLplPuiryKWn/QghhJjExkbCwMAICAiyaVLygK1GiBBMnTjRLrOQXCxcuZPTo0ZnOMSaeLxm1UUtzRdJDTAghhJFrBWhzNK+jeKGoqgHD1o0YDuwGQKlYFW2H7ig2tpmUtIxOb2DPhbusOXaDbWcjSUz5rweYjZWGWiXcqervRqCfKxULu1DMwwGNJr1JYEvyfscOqGeOo9/yKzy8DBGn0DRuhTKkGffidVy4FUtoZCx/h93jYFgUD+JT2HX+DrvO32HSprPUKuHBy5X9aBPkh5dT9lyjyCXS/oUQQjylc+fO4ezsTL9+/fI6FCGyRBJiQgghRA5Q9Tr061egnjImGTTN2qKp3zRblqS+8SCBRfuusObode4+TDbtD/BypEk5H14q60XdAE/sbbLWdV1RFJRK1VBKl0e/aTXq6WMYdv6OEvYPXp17413ai3qlvRjUIACDQSU0MoZ9F++x+UwkR67e5++wKP4Oi+LjDWdpG+RH/3olqF7M7blchlsIIYQQlilfvrxpsnshnicyZFIIketkyGQ+FXUMttaFlgfAo1peR/NcU5MS0a9ahHrpH9Bo0HbogaZKzcwLZuLk9QfM3xPGb6ci0BuMv749HG3oUKUwnasVoXJR16dOPh2LOEbdH+pyYNABqvlVQ1VV1BOH0f+2BlKSwd4BbadeaMoGpln+xoMEfjsZwYaTNzl5Pdq0P6iIKyH1StChamGZbyw/k/YvhBBmZMikEPmbDJkUQgiRjVQwJBu34qmpcQ/RL52PGnEdrG3Qdu+PpnT6S3pb4tCVKKZvPc+By1GmffVKeTKgfgCNy3lnS6JJRSVZn4z67/NXFAWlai0U/xLo1yxFvXkN/bIfUZu0RvNSs1SJtyJu9gxuWJLBDUty+kY0i/ZdYf2Jm5y6Ec3bq04wc/s/jGpahs7VimAlibF8SNq/EEIIIQoWSYgJIYQQ2USNf4hu8bdwKwIcnND2HoSmSLGnru9cZAzTNp9n+7nbAFhpFF6uUphBDQKoVMQ1u8LOkOLpjXbgCAyb12M4vA/Dzt9Rb91A27FnunOhVSriyrRXqjC+bQWW/R3Ogr+ucC0qgbG/nGTurku82bwM7SsXRpvufGZCCCGEEELkLEmICSGEENlATYhHt3ieMRnm5IJV/6EoXj5PVVdkdCJTN59j7fEbqCpoNQrda/ozsmlpCrvZZ3PkmVO0VmjbdUXxK4J+0xrUsyfR3b2NVY8BKB5e6ZbzcLRheJPSDKwfwE/7r/Dt7ktcvhvHm8uPM3/PZT7uUIkaxd1z8UqEEEIIIYQwkoSYEEII8YxMybDIm+DojFX/N54qGabTG1i47wpfbvuHuGQ9AO2C/Hi7ZVlKejtld9hZpqleF7wLoV+5CG5Hovv+K7Q9B6IpFpBhOXsbLa83KkWfusVZtM+YGDt9I4auc/fRrUZR3m1dHm9nWZVSCCGEEELkHplUXwiR6+7evYvBYECj0eDllX7vEpHLdAnw8DI4lQSr3O+F9LxSExPQL56HevMaODoZe4Z5+2a5nqPh93l/7WlCI2IAqF7MjYkdKlK5qFs2R5y2hJQELt+/TEn3kthbZ/z81Zho9Mt/NM6TprVC26knmkqWT8R+92ESUzefY+Xh6wA421rxTqty9K1bHI0Mo8wb0v6FEMKMTKovRP6WHZPqS0JMCCGEeEpqYgL6Jd+h3ggHB0djMszHL0t1JCTrmbL5HIv2X0FVwdXemvfalKdHTf98nRxSk5OMk+2fPwOApmkbNA1ST7afkWPh9/lo/RlO3TCuSlknwIMvXqmCv4dDjsQshBBCWEoSYkLkb9mREJNlnoQQQhjFXYWDrxm3IlNqUqJxNckb4WDvgFW/N7KcDDt5/QHtv97Dwn3GZFjX6kXZ8XYjetUuluvJsKsPrvLar69x9YFlz1+xsUXbPQRNnYYAGHb8jn7DSlS93uJzVivmzrrh9ZnUsSIONloOhkXRauafLDlwlef473XPJ2n/Qggh8tiuXbtQFIUHDx5YXCYkJIROnTrlWEx5pXHjxowePTqvw3jhSUJMCCGEUdI9uPSDcSsyZEqGXb/6XzKsUGGLy+v0Br7efoEuc/Zx6U4cPs62LBpYm+ndq+DplDdzad1LuMcPx37gXoLlz1/RaNC27oimTWdQFNRjf6P/eT5qYoLFdWg1Cn2DS7D5zYbUDvAgPlnPB+tO0+/Hv7kdm/g0lyKehrR/IYR4YYSEhKAoCp9//rnZ/nXr1mWpJ7d4sdy/f5++ffvi6uqKq6srffv2zTT5qKoqEydOpHDhwtjb29O4cWPOnDljdszrr79OqVKlsLe3x9vbm44dO3Lu3LlUdW3atIk6depgb2+Pl5cXXbp0MftcUZRUr2+//faZrzsjkhATQuS6U6dOcfToUU6dOpXXoQiRZWpyEvqff0C9dgXs7LHq+zqKbxGLy0dGJ9LzuwNM3/YPOoNKuyA/toxuSKOy3jkXdA7T1m6AtudAsLZBvXwB3Y+zUR9EZamOYp4OLB9cl4/aB2JrpWHPhbu0m7WXfZfu5lDUQgghxIvLzs6OKVOmcP/+/WytNzk5OVvrK2jy8v717t2b48ePs3nzZjZv3szx48fp27dvhmWmTp3KjBkzmD17NocOHcLX15cWLVoQGxtrOqZGjRosWLCA0NBQtmzZgqqqtGzZEv1jowZWr15N3759GTBgACdOnOCvv/6id+/eqc63YMECIiIiTK/+/ftn3w1IgyTEhBC5btu2bWzYsIFt27bldShCZIkpGRZ+GWzt0PZ9HcWvqMXl9128S7tZezh89T7OtlZ82aMKs3tXw93RJgejzh2asoFYDRgOTi5wJxLdD7Mw3LyWtTo0CgMbBLBpVAPKFXLmTmwSr35/kFnbL2AwyBBKIYQQeUtVVeKTdXnyyupUAs2bN8fX15fJkydneNzq1aupWLEitra2lChRgunTp5t9XqJECT799FNCQkJwdXVl8ODBLFy4EDc3NzZu3Ei5cuVwcHCgW7duxMXFsWjRIkqUKIG7uzsjR440S4osWbKEmjVr4uzsjK+vL7179+b27dtZuq7MJCUlMWrUKHx8fLCzs6NBgwYcOnTI9Pn9+/fp06cP3t7e2NvbU6ZMGRYsWAAYk1UjRozAz88POzs7SpQokeH9ezRc8+OPP8bHxwcXFxdef/11s6RX48aNGTFiBGPGjMHLy4sWLVoAsHv3bmrXro2trS1+fn6899576HQ6s/p1Oh0jRozAzc0NT09PPvjgg6eeUiI0NJTNmzfz/fffExwcTHBwMPPnz2fjxo2cP38+zTKqqjJz5kzef/99unTpQqVKlVi0aBHx8fH8/PPPpuOGDBlCw4YNKVGiBNWrV+fTTz/l2rVrXLlyxXQdb775JtOmTeONN96gbNmylCtXjm7duqU6p5ubG76+vqaXvX3OLvRjlaO1CyGEEC8INSXZuLLi1UumZJimsL9FZQ0GlW//vMQXW85jUKGCnwvfvlqd4p6OORx17lL8imL12ih0P/8AtyPQL/gGuvZBUz4oS/WU9nFm3fD6fLT+NKuOXGfGtn84dCWKr3pWw+MFSB4KIYR4PiWk6An8aEuenPvsJ61wsLH8n+9arZbPPvuM3r17M2rUKIoWTf0HvCNHjtC9e3cmTpxIjx492LdvH8OGDcPT05OQkBDTcdOmTePDDz/kgw8+AGDv3r3Ex8cza9Ysli9fTmxsLF26dKFLly64ubnx22+/cfnyZbp27UqDBg3o0aMHYEw4TZo0iXLlynH79m3eeustQkJC+O23357t5jxm3LhxrF69mkWLFlG8eHGmTp1Kq1atuHjxIh4eHnz44YecPXuW33//HS8vLy5evEhCgnGqh1mzZvHrr7+ycuVKihUrxrVr17h2LeM/7m3fvh07Ozt27tzJlStXGDBgAF5eXvzf//2f6ZhFixYxdOhQ/vrrL1RV5caNG7Rt25aQkBB++uknzp07x+DBg7Gzs2PixIlm5QYNGsTBgwc5fPgwQ4YMoXjx4gwePBiAN954gyVLlmQY39mzZylWrBj79+/H1dWVOnXqmD6rW7curq6u7Nu3j3LlyqUqGxYWRmRkJC1btjTts7W1pVGjRuzbt4/XX389VZm4uDgWLFhAQEAA/v7G78lHjx7lxo0baDQaqlWrRmRkJFWrVuWLL76gYsWKZuVHjBjBa6+9RkBAAIMGDWLIkCFoNDnXj0sSYkIIIYzsCkHge8atMKOmpBiTYWEXwcYWbZ/BaIoUs6jswyQdb604zraztwDoVqMon3aqhJ21NidDzrJCjoV4r/57FHJ8tuevuLpjNXAE+l8Wo148h37FItQW7dAEN87SvCX2NlqmvVKFOiU9+WDdKfZcuEunb/7i+/41KVvI+ZliFGmQ9i+EEC+czp07U7VqVSZMmMAPP/yQ6vMZM2bQrFkzPvzwQwDKli3L2bNnmTZtmllCrGnTprzzzjum93v37iUlJYW5c+dSqlQpALp168bixYu5desWTk5OBAYG0qRJE3bu3GlKiA0cONBUR8mSJZk1axa1a9fm4cOHODk5PfP1xsXFMXfuXBYuXEibNm0AmD9/Ptu2beOHH35g7NixhIeHU61aNWrWrAkYe8A9Eh4eTpkyZWjQoAGKolC8ePFMz2ljY8OPP/6Ig4MDFStW5JNPPmHs2LFMmjTJlMgpXbo0U6dONZV5//338ff3Z/bs2SiKQvny5bl58ybvvvsuH330kamcv78/X375JYqiUK5cOU6dOsWXX35pSoh98sknZs8lLYULG+e4jYyMxMfHJ9XnPj4+REZGpln20f5Chcy/GxQqVIirV80X4ZkzZw7jxo0jLi6O8uXLs23bNmxsjH/EvHz5MgATJ05kxowZpp6IjRo14p9//sHDwwOASZMm0axZM+zt7dm+fTtvv/02d+/eNSVic4IkxIQQQhg5FIGqGXerL4hUXQr6FQtQL18AaxtjMsy/hEVlbzxIYNDCQ5yLjMXGSsMnHSrSo5Z/vpzQtohLESY3z57nr9jaoe01EMPv6zAc3odh20bUe3fRtu2Cos1aIrBbjaIEFXFl8E+HCY+Kp8ucfczqVZWm5SVxk62k/QshRKbsrbWc/aRVnp37aUyZMoWmTZvy9ttvp/osNDSUjh07mu2rX78+M2fORK/Xo/33d/aj5NHjHBwcTMkwMCZJSpQoYZbYKlSokNmQyGPHjjFx4kSOHz9OVFQUBoMBMCaiAgMDn+r6Hnfp0iVSUlKoX7++aZ+1tTW1a9cmNDQUgKFDh9K1a1eOHj1Ky5Yt6dSpE/Xq1QOMQyBbtGhBuXLlaN26Ne3btzfrHZWWKlWq4ODgYHofHBzMw4cPuXbtmimh9uT9Cw0NJTg42Oz7YP369Xn48CHXr1+nWDHjH13r1q1rdkxwcDDTp083PRsfH580k1zpSev7p6qqmX4vffLztMr06dOHFi1aEBERwRdffEH37t3566+/sLOzMz3n999/n65duwLGucKKFi3KqlWrTD3NHk98Va1aFTAm/XIyISZziAkhhDBKiYVbu4xbAfybDFu5CPXS+f+SYcUCLCp74toDOn3zF+ciY/FysmXl68H0rF0sXybDAGKTYtl1ZRexSdnz/BWNFk3bLmhadQQU1KMHjCtzZmEFykfK+RqHUNYJ8OBhko5Biw4z/8/LTz2PhkiDtH8hhMiUoig42Fjlyetpvz80bNiQVq1a8b///S/VZ2klNtL63eromHqKB2tr61T3Jq19j5IhcXFxtGzZEicnJ5YsWcKhQ4dYu3YtkH0TzT+KPaMETps2bbh69SqjR4/m5s2bNGvWzNTLqnr16oSFhTFp0iQSEhLo3r17mvNcWeLxGJ68fxnd96w85zfeeAMnJ6cMX+Hh4QD4+vpy69atVHXcuXMnVQ+wR3x9fQFS9SC7fft2qjKurq6UKVOGhg0b8ssvv3Du3DnT8/Xz8wMwS3ra2tpSsmRJU3xpqVu3LjExMWnGnV0kISaEEMIo9gJsb2Lciv+GSV4IBStrtL0HoSle0qKyv5+KoMd3+7kTm0R5X2fWj6hPVX+3nA34GV2IukCTRU24EJV9z19RFLR1G6LtOcC4AmXYBXQ/zEK9fy/LdXk42rB4UB161fZHVeH/fgvlf2tPo9Mbsi3eAk3avxBCvLA+//xzNmzYwL59+8z2BwYGsnfvXrN9+/bto2zZsqbeYdnl3Llz3L17l88//5yXXnqJ8uXLZ/uE+qVLl8bGxsbsmlJSUjh8+DAVKlQw7fP29iYkJIQlS5Ywc+ZMvvvuO9NnLi4u9OjRg/nz57NixQpWr15NVFT6K2efOHHCNAcZwIEDB3ByckpzzrZHAgMD2bdvn1nycd++fTg7O1OkyH8rlx84cMCs3IEDByhTpozp2XzyySccP348w9ejIZPBwcFER0fz999/m+o7ePAg0dHRph5yTwoICMDX19dsIbTk5GR2796dbplHVFUlKSkJMK5CaWtrazZ5f0pKCleuXMlwWOqxY8ews7PDzc0tw3M9CxkyKYQQQjxBTU76b84waxtjMqxEaYvK/rg3jE82ngWgSTlvZvWqhrOddSalXmyachVRBo4wTrZ/9za6779C23MAGn/Lets9YmOl4bPOQZTxcebTTWdZ9nc49x4mMatXtXw3J5sQQgiRXwQFBdGnTx++/vprs/1vv/02tWrVYtKkSfTo0YP9+/cze/Zs5syZk+0xFCtWDBsbG77++mveeOMNTp8+zaRJk7L1HI6OjgwdOpSxY8fi4eFBsWLFmDp1KvHx8QwaNAiAjz76iBo1alCxYkWSkpLYuHGjKVn25Zdf4ufnR9WqVdFoNKxatQpfX98MEzLJyckMGjSIDz74gKtXrzJhwgRGjBiR4UTww4YNY+bMmYwcOZIRI0Zw/vx5JkyYwJgxY8zKXbt2jTFjxvD6669z9OhRvv76a7NVQLMyZLJChQq0bt2awYMHM2/ePMC4OmT79u3NJtQvX748kydPpnPnziiKwujRo/nss88oU6YMZcqU4bPPPsPBwYHevXsDxvnBVqxYQcuWLfH29ubGjRtMmTIFe3t72rZtCxiTjG+88QYTJkzA39+f4sWLM23aNABeeeUVADZs2EBkZCTBwcHY29uzc+dO3n//fYYMGYKtra1F1/g0JCEmhBBCPEZNSkT/8w+o4Zf/nUD/NTTFMu8ZpqoqM7b9w9c7LgLQP7g4H7YPxEornbEBFN8iWA1+E92yHyHiOvpF30LHHmiCqmetHkVhYIMACrvZMWr5cbaevUW/H/5mfv+auNoX7MSjEEIIkZ5JkyaxcuVKs33Vq1dn5cqVfPTRR0yaNAk/Pz8++eQTswn1s4u3tzcLFy7kf//7H7NmzaJ69ep88cUXdOjQIcNyiqKwYMECi2P6/PPPMRgM9O3bl9jYWGrWrMmWLVtwd3cHjJPgjx8/nitXrmBvb89LL73E8uXLAXBycmLKlClcuHABrVZLrVq1+O233zJMbjVr1sw0VDApKYmePXuarRSZliJFivDbb78xduxYqlSpgoeHhymp9rh+/fqRkJBA7dq10Wq1jBw5kiFDhlh0H9KydOlSRo0aZZoXrUOHDsyePdvsmPPnzxMdHW16P27cOBISEhg2bBj379+nTp06bN26FWdn4wJHdnZ27Nmzh5kzZ3L//n0KFSpEw4YN2bdvn1mybtq0aVhZWdG3b18SEhKoU6cOO3bsMD0Xa2tr5syZw5gxYzAYDJQsWZJPPvmE4cOHP/X1WkJRn+MJOGJiYnB1dSU6OhoXF5e8DkcIYaEZM2YQGxuLs7MzY8aMyetwxCNRR2FzDWh9BDyylqR4UaiJCcZ5rq5fBVs7tK8OQVM08xWG9AaVj9afZulB4zwIY1uVY1jjUvl2vrC0HI04So3vanBkyBGq++Xc81eTk9CvWYp6/gwAmkYt0TRq+VT36sDlewxedJjYJB3lCjmzaGBtfF3tsjvkgkHavxBCmElMTCQsLIyAgADs7OR3S164cuUKZcqU4ezZs5QpUyavw0klJCSEBw8esG7durwOpUDKqI1amiuSP1sLIXKdk5MTzs7O2bK8sshGGmuwL2LcFkBqQjz6Jd8Zk2F29mj7vm5RMixZZ+DN5cdYejAcRYH/61yJ4U1KP1fJMABrjTVFnItgncPPX7GxRds9BE1wYwAMu7eiX/szqk6X5brqlvRk5RvB+Djbcv5WLN2+3ce1qPhsjriAKODtXwghRP6zefNmhgwZki+TYeLFID3EhBBCFHhqfBy6Jd9BxHWwd8Cq7+sofulPhvpIYoqeYUuPsuPcbay1Cl/2qEr7yoVzIeIXg+HIfvSb1oBqQPEvgbbnABSHrCfKr0XF0/eHg1y5F09hVzt+HlyXEl6pV8QSQgghLCU9xERmpIdY3nrue4hNnDgRRVHMXo+W9hRCCCFygxr3EN1P3xqTYQ5OWPUfanEy7I0lR9hx7jZ21hq+719LkmFZpKkRjLbPa2Brh3rtCrrvZ6Heu5Plevw9HFjxejClvB25GZ1Ij+/2c/H2wxyIWAghhBDCaOHChZIMe87l+ZDJihUrEhERYXqdOnUqr0MSQoiC6cEpWFvUuC0g1Iex6BbNhVs3wdEZq5ChKIUyT2olpugZsvgIu87fwc5aw48htWhU1jsXIs45p26douiMopy6lbvPX1OqHFaDRoKbB9y/h+77rzBcvZTlegq52LF8SDDlCjlzKyaJnt8d4J9bsTkQ8QuqALZ/IYQQQhRseZ4Qs7KywtfX1/Ty9n6+/0EhhBDPLUMKJNwwbgsANTYa3aI5cCcSnF2wChmG4p15L+XEFD2DfzrMn//cwd5ay4KQ2tQr5ZULEeesFEMKN2JvkJIHz1/x9sVq0CiUIsUgMQH9T/MwnDic5Xq8nW1ZNqQugX4u3H1oTIpdkKSYZQpY+xdCCCGEyPOE2IULFyhcuDABAQH07NmTy5cvp3tsUlISMTExZi8hxPNnw4YNrFq1ig0bNuR1KKKAUqPvo1s4B+7eBhc3rEKGo3j5ZFouSWdMhu25cNeYDBtQi+BSnrkQ8YtPcXJG238YSmBlMOjRr1uGftcWsjrVqYejDT8PrkNQEVei4pLp8/1BrtyNy6GohRBCCCHE8ypPE2J16tThp59+YsuWLcyfP5/IyEjq1avHvXv30jx+8uTJuLq6ml7+/v65HLEQIjtcuHCBs2fPcuHChbwORRRA6oMoYzIs6i64eRh7hnlk3sNLpzcwatkxUzJs4YBa1C0pybDspFhbo+3WF039JsDTr0Dp5mDDTwNrU66QM7djk+jz/UGu35fVJ4UQQgghxH/yNCHWpk0bunbtSlBQEM2bN2fTpk0ALFq0KM3jx48fT3R0tOl17dq13AxXCCHEc06NumtMhj2IAndPYzLMPfOklsGgMu6Xk2w5cwsbrYbv+9ekjiTDcoSiaNA2b4+2/SugaFBPHUW/eB5qfNZ6ebk72rDktTqU9HLkxoME+nx/kFsxiTkUtRBCCCGEeN7k+ZDJxzk6OhIUFJRurxFbW1tcXFzMXkIIIbKJcxlottO4fQGpd28bk2HR98HT2zhM0tU983KqyoRfz7Dm2A20GoXZvatRv/TzP2fYk8p4lGFn/52U8cgfz19To+5/K1CGX0b3wyzUqLtZqsPb2Zalg+vg72HP1Xvx9Pn+IFFxyTkU8XPuBW//QgghhBBPylcJsaSkJEJDQ/Hz88vrUIQQouCxdoZCjY3bF4x6J9I4gX5sNHgXMvYMc3G1qOy0LedZfOAqigLTX6lCy4qZT7z/PHK2daZxicY42+af568pVQ6rgSPB1R2i7qL7cTZq5M0s1eHnas/Pr9XFz9WOi7cfMmDhIeKTszYEs0B4gdu/EEKInFeiRAlmzpyZ12FY7HmLV+SMPE2IvfPOO+zevZuwsDAOHjxIt27diImJoX///nkZlhBCFEzxN+D4eOP2BaLevY1u0Vx4GAuF/LDqPwzFybIexj/uDWPOrksATOpYiU7ViuRkqHnqRswNxv8xnhsx+ev5Kz6+WL02Cgr5QVwsuoXfYAhPfwGetPh7OLB4UB3cHKw5ce0Bw5YeJUVvyKGIn1MvaPsXQoiCKCQkBEVReOONN1J9NmzYMBRFISQkJFvPeejQIYYMGZKtdQqR0/I0IXb9+nV69epFuXLl6NKlCzY2Nhw4cIDixYvnZVhCCFEwJd6Cs58bty8I9f49dD99C3EPwbcwVv2Gojg6WVR208kIJm06C8DYVuV4te6L/bvpVtwtPv/rc27F5b/nrzi5GIe4FguApET0i+dh+Odsluoo7ePEjyG1sLPWsOv8Hd5bfSrLK1i+0F7A9i+EEAWZv78/y5cvJyEhwbQvMTGRZcuWUaxYsWw/n7e3Nw4ODtlerxA5KU8TYsuXL+fmzZskJydz48YNVq9eTWBgYF6GJIQQ4gWhRt83JsMeDZN89XUUB0eLyh68fI+3VhxHVaFv3eIMa1wqh6MVmVHs7NG+OgSlTAXQ6dAvX4Dh1NEs1VG9mDtz+lRHq1FYffQ6Uzafz6FohRBCiLxVvXp1ihUrxpo1a0z71qxZg7+/P9WqVTM7dvPmzTRo0AA3Nzc8PT1p3749ly5dMn3+008/4eTkZDbX98iRIylbtixxccZFb54cgqgoCvPmzaN9+/Y4ODhQoUIF9u/fz8WLF2ncuDGOjo4EBwebnSckJIROnTqZxTZ69GgaN25set+4cWNGjhzJ6NGjcXd3p1ChQnz33XfExcUxYMAAnJ2dKVWqFL///nuW7ld4eDgdO3bEyckJFxcXunfvzq1b//2R6MSJEzRp0gRnZ2dcXFyoUaMGhw8fBuDq1au8/PLLuLu74+joSMWKFfntt9+ydH6RN6zyOgAhhBAiu6kPY4zJsAdR4OGFVd83LO4Z9s+tWAb/dJhkvYGWgYWY2KEiiqLkcMTCEoq1DdoeA9D/ugL15BH0a38GVUVTuYbFdTQtX4jJXYIY98tJvt19CV8XW0LqB+Rg1EIIIV40+/fvZ//+/Zke5+fnR69evcz2LVu2jIiIiEzLBgcHExwc/NQxAgwYMIAFCxbQp08fAH788UcGDhzIrl27zI6Li4tjzJgxBAUFERcXx0cffUTnzp05fvw4Go2Gfv36sXHjRvr06cO+ffv4448/mDdvHn/99ReOjun/sXHSpEnMmDGDGTNm8O6779K7d29KlizJ+PHjKVasGAMHDmTEiBFZTl4tWrSIcePG8ffff7NixQqGDh3KunXr6Ny5M//73//48ssv6du3L+Hh4Rb1WlNVlU6dOuHo6Mju3bvR6XQMGzaMHj16mO5Vnz59qFatGnPnzkWr1XL8+HGsra0BGD58OMnJyfz55584Ojpy9uxZnJws+94p8pYkxIQQQrxQ1IR4dD/Ng6i74OqOVb83UJwtmzPsVkwiIT/+TUyijhrF3ZnVqxpajSTD8hNFq0XbqSd6rRXqsYPo1y0zJsWq1LS4ju41/bkTm8S0Lef5ZONZink60LR8oRyMWgghxIskKSmJ2NjYTI9zdU29gE98fLxFZZOSkp4qtsf17duX8ePHc+XKFRRF4a+//mL58uWpEmJdu3Y1e//DDz/g4+PD2bNnqVSpEgDz5s2jcuXKjBo1ijVr1jBhwgRq1aqV4fkHDBhA9+7dAXj33XcJDg7mww8/pFWrVgC8+eabDBgwIMvXVaVKFT744AMAxo8fz+eff46XlxeDBw8G4KOPPmLu3LmcPHmSunXrZlrfH3/8wcmTJwkLC8Pf3x+AxYsXU7FiRQ4dOkStWrUIDw9n7NixlC9fHoAyZf5blTk8PJyuXbsSFBQEQMmSJbN8TSJvSEJMCJHrKlWqRGJiInZ2dnkdinicrSeUGmTcPqdUXQr65T/CnUhwdjEmw1zdLSobn6xj0KJD3IxOpJS3I9/3q4mdtTaHI84/PO09GVRtEJ72+f/5K4oG7cvdMCgKhqMH0K9bbkyKVc34i/njhjUuRfi9eFYcvsaIn4+x6o1gKha2bOXRF9IL0P6FECK32Nra4uyc+aq8afVOcnBwsKisra3tU8X2OC8vL9q1a8eiRYtQVZV27drh5eWV6rhLly7x4YcfcuDAAe7evYvBYFx4Jjw83JQQc3d354cffqBVq1bUq1eP9957L9PzV65c2fTfhQoZ//D0KGn0aF9iYiIxMTG4uFj2x8sn69VqtXh6eqaqF+D27dsW1RcaGoq/v78pGQYQGBiIm5sboaGh1KpVizFjxvDaa6+xePFimjdvziuvvEKpUsYpNUaNGsXQoUPZunUrzZs3p2vXrmYxivxLEmJCiFzXsmXLvA5BpMWxONT5Pq+jeGqqwYB+zc+o4WFga4dVnyEoHqm/9KXFYFAZs+IEp2/E4OFow4KQ2rg72uRwxPlLcbfifN/h+Xn+iqJB074rKAqGI/vRr18BqGiq1rawvMKnnStx/UE8f128x6CFh1k3vD6+rgU0Uf+ct38hhMhNzzKc8ckhlDnt0bBEgG+++SbNY15++WX8/f2ZP38+hQsXxmAwUKlSJZKTk82O+/PPP9Fqtdy8eZO4uLhMk1iPhhQCpukn0tr3KAGn0WhSLXiTkpKSYb2P6smo3syoqprm9BiP7584cSK9e/dm06ZN/P7770yYMIHly5fTuXNnXnvtNVq1asWmTZvYunUrkydPZvr06YwcOdKi84u8k6eT6gshhMhHdAnw4Ixx+5xRVRXDll9RQ0+CVou25wCUQn4Wl/9i63k2n4nERqvhu741KOZZ8FZJSkhJ4MztMySkPD/PX1E0aNp1RVOzHqCi/3UlhrMnLC5vrdUwp08NSvs4ERmTyKBFh4hL0uVcwPnZc9z+hRBCpK9169YkJyeTnJxsGqr4uHv37hEaGsoHH3xAs2bNqFChAvfv30913L59+5g6dSobNmzAxcUlR5I93t7eqeZXO378eLaf50mBgYGEh4dz7do1076zZ88SHR1NhQoVTPvKli3LW2+9xdatW+nSpQsLFiwwfebv788bb7zBmjVrePvtt5k/f36Oxy2enSTEhBBCGMWEwm+VjNvnjGH/Lgx/7wFA26kXmhKlLS676vA15uwyrnA0pVsQNUt45EiM+V3o3VAqza1E6N3n6/krioKmbReU6nVBVdGvXorhkuWrR7raW7MgpBaejjacuRnDm8uPYzComRd80TzH7V8IIUT6tFotoaGhhIaGotWmngrC3d0dT09PvvvuOy5evMiOHTsYM2aM2TGxsbH07duXkSNH0qZNG37++WdWrlzJqlWrsjXWpk2bcvjwYX766ScuXLjAhAkTOH36dLaeIy3NmzencuXK9OnTh6NHj/L333/Tr18/GjVqRM2aNUlISGDEiBHs2rWLq1ev8tdff3Ho0CFTsmz06NFs2bKFsLAwjh49yo4dO8wSaSL/koSYEEKI55rh7AkM2zYCoGnxMppK1TIp8Z+/w6L439pTAIxsWprO1YrmSIwiZymKgrZdV5SKVcCgR79iIYZrYRaX9/dw4Lt+NbGx0vBH6C2+/OOfHIxWCCGEyF0uLi7pDm/UaDQsX76cI0eOUKlSJd566y2mTZtmdsybb76Jo6Mjn332GQAVK1ZkypQpvPHGG9y4cSPb4mzVqhUffvgh48aNo1atWsTGxtKvX79sqz89iqKwbt063N3dadiwIc2bN6dkyZKsWLECMCYV7927R79+/Shbtizdu3enTZs2fPzxxwDo9XqGDx9OhQoVaN26NeXKlWPOnDk5Hrd4dor65CDd50hMTAyurq5ER0dnaRI+IUTemj17NrGxsTg7O5vmNBD5QNRR2FwDWh8Bj+p5HY1F1Mgb6H6cDSnJaGq/hKZ1xzTngEjLzQcJvPz1Xu7FJdM2yJfZvaqjKcArSh6NOEqN72pwZMgRqvs9H8//Sapeh375AtSL58DOHquQYSiFCltcfvWR67y9yjjk8pve1WlX2fJht8+957D9CyFETkpMTCQsLIyAgABZCEqIfCijNmpprkh6iAkhct2jeQyenKhTiKxQ42LRLV8AKckopcqiafWyxcmwxBQ9Q5cc4V5cMhX8XJj+StUCnQx7UShaK7Td+6P4l4DEBHSLv0N9EGVx+a41ivJagwAA3ll1gjM3o3MoUiGEEEIIkdckISaEEOJfCmhsjNt8TtXr0K/8CaLvg4cX2q59UTSp58VIs6yq8tH605y4Ho2bgzXf9a2BvY1lZV9kCgo2WhuU5+D5Z0SxtkHb+zUo5AdxseiWfo+aEG9x+ffalOelMl4kpOgZ8tMR7j1MysFo85Pnp/0LIYQQQmQHSYgJIYQw8qgGPZOM23xMVVUMv61FDb8MtnZY9RqIYm/5qpBLD4az8vB1NAp83asa/h4Fb0XJtFTzq0bSB0lU88vfz98Sip09Vr0Hg7Mr3L2FfuVCVJ1lq0daaTXM7lWdAC9HbjxIYOjSo6ToLVu2/bn2nLR/IYQQQojsIgkxIYQQzxXDkf0Yjh4AFLRdX0XxKmRx2cNXovh4wxkAxrUuz0tlvHMoSpHXFBdXrPq8Bja2qFcuod+wEkunTXV1sGZ+vxo42Vrxd1gU07ZYvmqlEEIIIYR4PkhCTAghhFF0KPxe3bjNpww3r2HYvA4ATbO2aMpYvqT1rZjEf3v7qLQL8uP1hiVzKMrnU+idUKrPq07onfz7/LNKKVQYbff+oGhQTx7BsGuLxWVL+zjzxStVAPjuz8tsPh2RU2HmD89B+xdCCCGEyE6SEBNCCGGkT4D7x4zbfEhNiEe/6ifQ61HKV0JTv4nFZZN0xkn078QmUa6QM1O7VbZ4Av6CIkGXwLHIYyTo8ufzf1qaUuXQtu8GgOHPbRhOHrG4bOtKvgz5N3H6zqqTXL7zMEdizBfyefsXQgghhMhukhATQgiR76mqin79cngQBe6eaDv2zFJC6+MNZzka/gAXOyvm9a2Bo61VDkYr8htN9TpoGjQDQP/rSgw3wi0uO65VOWoHePAwScfQJUeJT7ZsLjIhhBBCCJG/SUJMCCFEvmfYtwv1/BnQarF6pR+Knb3FZdcdu8HPB8NRFPiqVzVKeDnmYKQiv9I0bY1SriLodeiXL0CNjbaonHGS/Wp4O9ty/lYs7689bfFcZEIIIYQQIv+ShJgQQoh8zRB+GcP23wDQtO6E4lfU4rIXbz/kf2tPATCqaRmalPPJkRhF/qcoGrSde4O3LzyMQb9iIaouxaKyPi52zO5VDa1GYe2xGyw5aHkPMyGEEEIIkT9JQkwIkevat29Pt27daN++fV6HIh7nFAANVhq3+YQaH4f+lyWgGlCCqqGpEWxx2YRkPcOXHiU+WU9wSU9GNSuTg5E+/wLcAljZbSUBbvnn+Wc3xdYOq54DwN4B9UY4+g2rLO7tVaekJ++1Lg/AJxvOcPzagxyMNA/kw/YvhBDi+RAZGUmLFi1wdHTEzc3NojITJ06katWqpvchISF06tQpR+ITIj2SEBNC5LqyZctSsWJFypYtm9ehiMfZuEOxV4zbfEBVVfQbVkFsNHh6o23/ShbnDTvD+VuxeDnZ8lWvqmg1Mol+Rtzt3Xml4iu42+eP559TFA8vtK/0+2/lyQN/Wlz2tZcCaF3RlxS9yrAlR4iKS87BSHNZPmv/Qgghnl5ISAiKoqAoCtbW1pQsWZJ33nmHuLi4Z6r3ySTWI19++SUREREcP36cf/7556nq/uqrr1i4cOEzxVcQXLlyBUVROH78eF6H8kKQhJgQQgijhFsQOsO4zQcMRw6gnjsFGi1WXV9FsbG1uOzaY9dZfuiacd6wnlXxcbbLwUhfDLce3mLG/hncepg/nn9O0gSUQdOqAwCGbRsxXL1sUTlFUZj6SmUCvBy5GZ3IuF9OvDjzieWz9i+EEOLZtG7dmoiICC5fvsynn37KnDlzeOedd56qLlVV0enSX1Tm0qVL1KhRgzJlyuDj83TTU7i6ulrcu+xFlZJi2VQO2SU5+QX6w95TkoSYEEIIo4QbcOxt4zaPqXciMWxZD4CmWdsszxv2/trTALzZrAz1S3vlSIwvmhuxN3h769vciM37558bNLUboARVB9WA/pefUB/GWFTOxc6ab3pXx8ZKwx+ht1m470rOBppb8lH7F0II8exsbW3x9fXF39+f3r1706dPH9atWwdAUlISo0aNwsfHBzs7Oxo0aMChQ4dMZXft2oWiKGzZsoWaNWtia2vL4sWL+fjjjzlx4oSp99nChQspUaIEq1ev5qeffkJRFEJCQgAIDw+nY8eOODk54eLiQvfu3bl1K/0/ujw5ZDKzGNNSokQJPv30U/r164eTkxPFixdn/fr13LlzxxRLUFAQhw8fNiu3b98+GjZsiL29Pf7+/owaNcqsN92SJUuoWbMmzs7O+Pr60rt3b27fvp3qfm3fvp2aNWvi4OBAvXr1OH/+fLqxPurptXLlSho3boydnR1LliwBYMGCBVSoUAE7OzvKly/PnDlzTOUCAoxTG1SrVg1FUWjcuDEAjRs3ZvTo0Wbn6NSpk+l5PH5/QkJCcHV1ZfDgwSxcuBA3Nze2bNlChQoVcHJyMiVTCwJJiAkhct3Nmze5du0aN2/ezOtQRD6k6lLQrV4KuhSUUmXRBDe0uOzj84bVK+XJyKYyb5hIm6IoaNt3+3eS/Vj0vyxGNegtKhtY2IX321YAYPJv5zh9w7IVK4UQQrwYImIjOBpx1OwVdj8MgERdYqrPjkYcNZU9f/d8qs+iEqIAuBN3J9VnF+5dyJaY7e3tTT2Qxo0bx+rVq1m0aBFHjx6ldOnStGrViqioKLMy48aNY/LkyYSGhtKyZUvefvttKlasSEREBBEREfTo0YNDhw7RunVrunfvTkREBF999RWqqtKpUyeioqLYvXs327Zt49KlS/To0cPieC2N8Ulffvkl9evX59ixY7Rr146+ffvSr18/Xn31VVM9/fr1M/XwPnXqFK1ataJLly6cPHmSFStWsHfvXkaMGGGqMzk5mUmTJnHixAnWrVtHWFiYWaLpkffff5/p06dz+PBhrKysGDhwYKbX+e677zJq1ChCQ0Np1aoV8+fP5/333+f//u//CA0N5bPPPuPDDz9k0aJFAPz9998A/PHHH0RERLBmzRpLbykA06ZNo1KlShw5coQPP/wQgPj4eL744gsWL17Mn3/+SXh4+FP3JnzeWOV1AEKIgmf58uXExsbi7OzMmDFj8jockc8Y/tgEt26CgxPaTr1QFMv/djPx1//mDZvZU+YNExlTbGyx6t4f3fyZqFeNq5lqW7xsUdl+wcXZe/Eu287eYuSyY2wc2QBHW/laJYQQBcG8I/P4ePfHZvv6BPVhSZclXI+5To3vaqQqo04wJmBC1odw4PoBs88Wd17Mq5VfZeWZlYz4fYTZZy1LtWTLq1ueKd6///6bn3/+mWbNmhEXF8fcuXNZuHAhbdq0AWD+/Pls27aNH374gbFjx5rKffLJJ7Ro0cL03snJCSsrK3x9fU377O3tsbW1xd7e3rR/27ZtnDx5krCwMPz9/Y3XuHgxFStW5NChQ9SqVSvDeLMS45Patm3L66+/DsBHH33E3LlzqVWrFq+88gpgTEAFBwdz69YtfH19mTZtGr179zb1ripTpgyzZs2iUaNGzJ07Fzs7O7PEVsmSJZk1axa1a9fm4cOHODk5mT77v//7Pxo1agTAe++9R7t27UhMTMTOLv2pO0aPHk2XLl1M7ydNmsT06dNN+wICAjh79izz5s2jf//+eHt7A+Dp6Wn2HCzVtGlTs2TX3r17SUlJ4dtvv6VUqVIAjBgxgk8++STLdT+P5JubEEKIfMNw6TyGg3sA0HbsgeLkYnHZ9cdvsOLwNTQKzJJ5w4SFFC8ftB17ol+1CMO+XShFi6OpUDnzcorCtG6VafPVHsLuxvHR+jNM714lFyIWQgiR116v8TodynUw2+duZ1yUpKhLUY4MOZJu2YUdFxKXYj65fQm3EgB0r9idYH/zFbWdbZyfKsaNGzfi5OSETqcjJSWFjh078vXXX3Pp0iVSUlKoX7++6Vhra2tq165NaGioWR01a9Z8qnOHhobi7+9vSoYBBAYG4ubmRmhoaKYJsazE+KTKlf/7HV6oUCEAgoKCUu27ffs2vr6+HDlyhIsXL7J06VLTMaqqYjAYCAsLo0KFChw7doyJEydy/PhxoqKiMBgMgHFYaGBgYJrn9vPzM52nWLFi6cb7+D2+c+cO165dY9CgQQwePNi0X6fT4erqmuF1WyqtZ+rg4GBKhoEx9seHhL7IJCEmhBDCyNoVirxs3OYBNSEe/frlAGhq1UdTNjCTEv+5FhXPB//OGzaiaRnqybxhWeZq68rLZV/G1TZvnn9e0gRWRg1uhGH/bvTrlqP4+KF4emdazs3Bhq96VqPnd/tZffQ6Dcp40rma5fPd5St53P6FEOJ54ufsh5+zX5qf2VnZUd2verply3mVS/czb0dvvB0z//1jiSZNmjB37lysra0pXLgw1tbWAKa5oZ5cuVtV1VT7HB0dn+rcadWV0f60jrM0xic9us7Hy6e171FSy2Aw8PrrrzNq1KhUdRUrVoy4uDhatmxJy5YtWbJkCd7e3oSHh9OqVatUk9JndJ70PH6PHx07f/586tSpY3acVqvNsB6NRpNqoZ+0JulP65k+HjcYY39hFg3KhMwhJoQQwsi5FDT61bjNA/rf1kBsDHh6o2nR3uJyOr2Bt1YcJzZJR43i7oxqWjoHo3xxlfIoxa+9fqWUR948/7ymadYOpVhJSE5Ct3IhanKSReVqB3jwZrOyAHyw9jRhd59tSfs8k8ftXwghRPZydHSkdOnSFC9e3CzhUbp0aWxsbNi7d69pX0pKCocPH6ZChQoZ1mljY4Nen/l8m4GBgYSHh3Pt2jXTvrNnzxIdHZ3pOZ41xqyqXr06Z86coXTp0qleNjY2nDt3jrt37/L555/z0ksvUb58+RzrPVWoUCGKFCnC5cuXU8XyaDJ9GxsbgFTPwdvb22wifL1ez+nTp3MkzheJJMSEEEIYGVIg8Y5xm9unPn0M9fQxUDRoO/dGsbaxuOw3Oy9x+Op9nG2tmNmjKlZa+dX2NFL0KdyJu0OKPveff36gaLVou/UFJ2e4HYl+02qL/zo6omlp6gR4EJesZ+SyoyTpLJucP1/Jw/YvhBAi9zg6OjJ06FDGjh3L5s2bOXv2LIMHDyY+Pp5BgwZlWLZEiRKEhYVx/Phx7t69S1JS2n88at68OZUrV6ZPnz4cPXqUv//+m379+tGoUSOLhmE+S4xZ9e6777J//36GDx/O8ePHuXDhAr/++isjR44EjL3EbGxs+Prrr7l8+TK//vorkyZNytYYHjdx4kQmT57MV199xT///MOpU6dYsGABM2bMAMDHxwd7e3s2b97MrVu3iI42LuzTtGlTNm3axKZNmzh37hzDhg3jwYMHORbni0L+1SCEEMLowSlY42Pc5iI1Nhr9ptUAaF5qhqZI+vMsPOnI1fvM2mFcfWlSp0r4ezjkSIwFwanbp/D5wodTt3P3+ecnirOLMSmmaFBPHkE9kfHy7o9oNQoze1bFzcGa0zdimLY5/WXW8608av9CCCFy3+eff07Xrl3p27cv1atX5+LFi2zZsgV3d/cMy3Xt2pXWrVvTpEkTvL29WbZsWZrHKYrCunXrcHd3p2HDhjRv3pySJUuyYsWKHI8xqypXrszu3bu5cOECL730EtWqVePDDz80zQHm7e3NwoULWbVqFYGBgXz++ed88cUX2RrD41577TW+//57Fi5cSFBQEI0aNWLhwoWmHmJWVlbMmjWLefPmUbhwYTp27AjAwIED6d+/vynxGBAQQJMmTXIszheFoj7Hg0NjYmJwdXUlOjoaFxfLJ14WQuStGTNmyCqT+VHUUdhcA1ofAY/0577ITqqqol86H/XSeRS/omgHjULJZI6ER2ITU2g7aw/XohLoVLUwM3tWy+FoX2xHI45S47saHBlyJMO5TwoC/Z4/MOz4HaxtsBoyGsWrkEXl/jh7i9d+OgzAz6/Veb7mssuD9i+EEPlZYmIiYWFhBAQEZLhKoBAib2TURi3NFUkPMSGEEHnGcGQ/6qXzoLUyDpW0MBkGMGH9Ga5FJVDU3Z5POlXKwShFQaOp3xQloAykJKP7ZTGqzrJhhM0DC9G7jrGH49urThAdL8MPhRBCCCHyK0mICSGEyBPqvTsYtm4AQNO8HYq3Zb1wANYfv8GaYzfQKDCzR1Vc7KwzLySEhRSNcS47HJzgVoTp59QSH7SrQICXIxHRiXy4XiazFUIIIYTIryQhJoQQItepBj36dcsgJRkloDSaOg0sLnstKp4P1hoTDSOblqFmCY+cClMUYIqzC9rOvQAwHPoLQ6hlc2s52FjxZY+qaDUKv564yfrjN3IyTCGEEEII8ZSs8joAIUTBM3z48LwOQaTFrQq8Eg1axxw/leGvXajXr4KtHdqOPVEUy/4+ozeovL3yBLFJOmoUd2dk09I5HGnBUaVQFaLfi8bROuef//NCU7o8ar3GGPbtQv/rChS/IihumSdgq/q7MbJpaWb+cYEP1p2mVgkPCrvZ50LEzyAX278QQgghRH4gPcSEELnO1tbW9BL5iEYL1i7GbQ5Sb0dg2LUFAG3rTiiulq8W9OPeMP6+EoWjjZYvu1fFSiu/xrKLVqPFxdYFbQ4//+eNpmkblCLFIDEB/ZqlqAa9ReVGNClNVX83YhN1vL3yBAZDPl/DKJfavxBCCCFEfiH/khBCCGEUcwF2tDJuc4iq16NftxwMepSygShValpc9p9bsUzbeh6AD9oHUszTIafCLJAu3LtAqyWtuHAv557/80jRWqHt+irY2qFeu4Jh11aLyllpNXzZoyr21lr2X77Hj3+F5XCkzygX2r8QQgghRH4iCTEhhBBGuliI3Grc5hDDXztQI66DnT3a9q+gKIpF5VL0BsasPE6yzkDjct70rOWfYzEWVLHJsWy9tJXY5Jx7/s8rxd0TbftuABj2bMcQdtGicgFejnzYPhCAqZvPcy4yJsdifGa50P6FEEIIIfITSYgJIXLd/v372bVrF/v378/rUEQuUm/dxLB7GwDaNp1RnF0sLvv1joucvhGDm4M1U7tWtjiRJkR20VSqhlKtDqCiX/czakK8ReV61fanWXkfkvUGRi8/TpLOsiGXQgghhBAiZ0lCTAiR6/bv38/u3bslIVaAqHo9unXLjEMly1VECapucdkT1x7wzU5jj5xJHSvh42KXU2EKkSFt647g4QUx0eg3rUZVM58XTFEUPu9aGU9HG85FxjJruwxJFEIIIYTIDyQhJoQQIscZ9vwBkTfB3gFt+24W9/BKTNEzZuVx9AaV9pX9eLlK4RyOVIj0KTa2aLv0AUWDeuY46qmjFpXzdrbl006VAJi76xInrj3IwSiFEEIIIYQlJCEmhBDCyMEfas42brORGnnDmBDj36GSTpYPlZy6+TyX7sTh42zLpI6VsjUuYc7fxZ/ZbWbj7yLzs2VEU6QYmkYtAdD/tgb1QZRF5doE+dGhSmEMKry96gSJKfls6GQOtX8hhBDiWTVu3JjRo0fnm7qzO57sqi8n79OLShJiQgghjOy8oexw4zabqHrdv0MlDSgVglAqVbO47P5L/63MN6VrZdwdbbItLpGat6M3w2sPx9sx+57/i0rzUlMU/xKQlIh+7c+oBoNF5T7uUBFvZ1su3n7Il3/8k7NBZlUOtH8hhBAvJkm8ZK81a9YwadKkLJVJ6xk8TT0FnSTEhBBCGCVFQdgS4zabGP78A25FgIMj2nZdLR4q+TBJxzurTgDGScmblPfJtphE2qISolhycglRCdn3/F9UikaLtnNvsLFFDQ/D8NcOi8q5O9rwWecgAOb/eZkjV+/nZJhZkwPtXwghRP7RuHFjFi5cmNdh5Ink5OS8DiFDHh4eODs755t6ChJJiAkhhDCKuwL7+xq32UCNuI5hz3YAtG27oDha/gt6yu/nuPEggaLu9rzfLjBb4hEZu/LgCn3X9uXKgyt5HcpzQXH3RNumMwCGXVsw3LxmUbkWgYXoUr0IBhXeWXWChOR8MnQym9u/EEKI59svv/xCUFAQ9vb2eHp60rx5c+Li4ggJCWH37t189dVXKIqCoihcuXKFzZs306BBA9zc3PD09KR9+/ZcunTJrM7GjRszatQoxo0bh4eHB76+vkycONHsmLi4OPr164eTkxN+fn5Mnz7d7HNLzzNixAjGjBmDl5cXLVq0sKjutFhSRlVVpk6dSsmSJbG3t6dKlSr88ssvAMybN48iRYpgeKI3eYcOHejfv78p3sd7e2V2jek9gyfrSUpKYtSoUfj4+GBnZ0eDBg04dOhQqnuV2TN5kUlCTAghRLZTdf8OlVQNKIFV0FSsanHZ/ZfusfjAVcA4VNLJ1iqHohTi2ShVaqIEVgaDAf2apajJSRaVm9C+IoVcbAm7G8cXW8/ncJRCCCGyXUIERB01fz00TvOAPjH1Z1GPLcIScz71Z4965ybeSf1ZTO6vThwREUGvXr0YOHAgoaGh7Nq1iy5duqCqKl999RXBwcEMHjyYiIgIIiIi8Pf3Jy4ujjFjxnDo0CG2b9+ORqOhc+fOqRJBixYtwtHRkYMHDzJ16lQ++eQTtm3bZvp87Nix7Ny5k7Vr17J161Z27drFkSNHTJ9n5TxWVlb89ddfzJs3z6K602JJmQ8++IAFCxYwd+5czpw5w1tvvcWrr77K7t27eeWVV7h79y47d+40HX///n22bNlCnz590jxnZteY3jN40rhx41i9ejWLFi3i6NGjlC5dmlatWhEVZd4bPLNn8iKTf2UIIYTIdoY/t8HtSHBwQtu2i8Xl4pN1vLv6JAC9ahejfmmvnApRiGemKAra9q+gu3YF7t3BsG0j2nZdMy3n6mDN510rM2DBIX78K4xWFX2pHeCR8wELIYTIHhfmwemPzfeV6AP1lkD8ddhcI3WZ3qpxuz8E7h0w/yx4MQS8CuEr4fAI8898W0LTLVkO8bPPPuOzzz4zvU9ISODAgQOMGPFf/b///jsvvfRSqrIRERHodDq6dOlC8eLFAQgKCjJ9bmNjg4ODA76+vqZ9Xbua//774Ycf8PHx4ezZs1Sq9N/CSJUrV2bChAkAlClThtmzZ7N9+3ZatGjBw4cP+eGHH/jpp59MvboWLVpE0aJFs3ye0qVLM3XqVNN7S+p+kiVl4uLimDFjBjt27CA4OBiAkiVLsnfvXubNm8fPP/9M69at+fnnn2nWrBkAq1atwsPDw/T+SZldo6ura5rP4HFxcXHMnTuXhQsX0qZNGwDmz5/Ptm3b+OGHHxg7dqzp2IyeyYtOEmJCCCGyleHmNQx7jXMqadt1RXF0srjstC3nCY+Kp7CrHf9rWz6nQhQi2yj2Dmg79UK/eB6Gw/tQyldCU6pcpuWalPOhR01/Vhy+xjurTrB59Es42MjXMiGEeC6UeR2KdjDfZ+Nu3DoUhdYZ9DoKXgi6OPN9jiWM22LdwSvY/DOrp5sT6o033qB79+6m93369KFr16506fLfHyqLFCmSZtkqVarQrFkzgoKCaNWqFS1btqRbt264u7une75Lly7x4YcfcuDAAe7evWvqzRQeHp4qIfY4Pz8/bt++baojOTnZlFgC47xY5cr993vV0vPUrFkzVXyZ1Z3WNWVW5uzZsyQmJqZKHiUnJ1OtmnExqT59+jBkyBDmzJmDra0tS5cupWfPnmi12nTPa8k1ZuTSpUukpKRQv3590z5ra2tq165NaGio2bEZPZMXnXzzEkLkOj8/P1xdXXFwcMjrUMTjrBzBs65x+5RUnQ79o6GSFauiCayceaF/Hb4SxcJ9VwCY3LUyznbWTx2HyDpHa0fqFq2Lo/XTP/+CSlOyLGqt+hgO/YX+1xUoQ8ei2NlnWu799hXYc+EO4VHxTN18nokdKuZCtOnIhvYvhBAFhr2f8ZUWrR14VE+/rEsGfzSx88621X49PDzw8Piv97G9vT0+Pj6ULl0607JarZZt27axb98+tm7dytdff83777/PwYMHCQgISLPMyy+/jL+/P/Pnz6dw4cIYDAYqVaqUakJ7a2vz73eKopgSPqqqZhqbpedxdDT/fWZJ3U+ypMyj2Ddt2pQqwWhra2uK2WAwsGnTJmrVqsWePXuYMWNGunVaeo2WxP7kglaqqqbal9EzedHJHGJCiFzXq1cvBg0aRK9evfI6FPE4l3LQan/GX9QyYdi1Be7cAkcntG07W1wuMUXPuF9OoqrwSo2iNCqbPV8GheXKeZVj/6D9lPN6+udfkGmatwN3T4iJRr9lvUVlXOyMQycBFu2/wpGrebjCYza0fyGEEC8ORVGoX78+H3/8MceOHcPGxoa1a9cCxiGTev1/i8Lcu3eP0NBQPvjgA5o1a0aFChW4fz/rKymXLl0aa2trDhz4b0jp/fv3+eeff575PJnV/bRlAgMDsbW1JTw8nNKlS5u9Hs3rZW9vT5cuXVi6dCnLli2jbNmy1KiRxrDaLFzjk88grdhtbGzYu3evaV9KSgqHDx+mQoUK6ZYraKSHmBBCiGxhuBGOYZ9xwlBtu24oDpYPlfxy2z9cvhuHj7MtH8iqkuI5pNjYGodOLvgG9fghDOUroSmX+bCGhmW96VajKL8cuc64X06yadRL2FmnPYRCCCGEsNTDhw95+PCh6f3y5csBiIyMNO3z8PDAxsYmVdmDBw+yfft2WrZsiY+PDwcPHuTOnTumREqJEiU4ePAgV65cwcnJCQ8PDzw9Pfnuu+/w8/MjPDyc9957L8sxOzk5MWjQIMaOHYunpyeFChXi/fffR6Mx9uNxd3d/6vNkVvfTlnF2duadd97hrbfewmAw0KBBA2JiYti3bx9OTk6mlST79OnDyy+/zJkzZ3j11VfTPael15jWM3ico6MjQ4cOZezYsXh4eFCsWDGmTp1KfHw8gwYNsuieFQTSQ0wIIYRR1FH4WTFfCclCqi7l36GSKkpQNTQVgjIv9K/j1x4wf89lAD7rHISrgwyVzAtHI46ifKxwNCLrz18YaYoFoKnXCAD9hl9Q4x9mUsLow3aBeDvbculOHLN3XMzJENP3DO1fCCFE/vPFF1/g5+eX4Wvfvn1plnVxceHPP/+kbdu2lC1blg8++IDp06ebJmd/55130Gq1BAYG4u3tTXh4OMuXL+fIkSNUqlSJt956i2nTpj1V3NOmTaNhw4Z06NCB5s2b06BBA1NvKo1G80znyajuZykzadIkPvroIyZPnkyFChVo1aoVGzZsMBte2rRpUzw8PDh//jy9e/dO93yWXmNaz+BJn3/+OV27dqVv375Ur16dixcvsmXLlgzngitoFPVpBtPmEzExMbi6uhIdHY2Li0tehyOEEM+3qKPGVZFaH8l47os06LdtwLBvFzg5YzV0LIqDZfMQJen0tJ+1lwu3H9KpamFm9qz2FIGL7HA04ig1vqvBkSFHqO6Xtecv/qPqUtB99yXcuYVSsQpW3fpZVG7z6QjeWHIUrUbh1xH1qVjYNYcjfcIztH8hhHgRJSYmEhYWRkBAAHZ2dnkdjhDiCRm1UUtzRdJDTAiR65YtW8YPP/zAsmXL8joUkQ0M169i2L8bAG37bhYnwwC+3n6RC7cf4uVky4SX83BCcSGyiWJljVWnXqBoUM+cwHD6mEXlWlfyo22QL3qDyrhfTqLTF4zJbIUQQggh8kq+SYhNnjwZRVEYPXp0XocihMhhERERXL9+nYiIiLwORTwjVadDv37Fv0Mlq1s0Z9Ijp29EM3f3JQA+7VQRd8fUc1gI8TxSCvujadgcAP1va1BjYywqN7FDRVztrTlzM4bv/h1GLIQQQgghcka+SIgdOnSI7777jsqVK+d1KEIIIbLA8Oc2uPvvqpKtO1lcLlln4J1VJ9AbVNoF+dG6UjpLlwvxnNK81Bx8i0BCPPqNqyxaut3H2Y6P2hsXlZj5xwUu3bFsDjIhhBBCCJF1eZ4Qe/jwIX369GH+/PkyuZsQQuQl10B4+YJxawE14jqGvTsA0LbtmqWhkvN2X+JcZCwejjZ83FGGSuYHgd6BXBh5gUBvWeUzOyhaLVade4FWi/rPWdQThywq16V6ERqV9SZZZ+C91ScxGHJpqtcstn8hhBBCiOddnifEhg8fTrt27WjevHmmxyYlJRETE2P2EkIIkU20duBc2rjNhKrXo/t1BagGlMDKaAIt7+F78fZDvv53Jb0JLwfi5WT71CGL7GNnZUdpj9LYWcnEwdlF8fFD06Q1APrN61Gj72deRlH4v86VcLTRcujKfZYcvJrTYRplof0LIURB8hyvQSfECy072maeJsSWL1/O0aNHmTx5skXHT548GVdXV9PL398/hyMUQogC5GEY7HvVuM2E4a+dEHkT7B3Qtuli8SkMBpX/rTlFst5A43LedKhS+FkiFtko7H4Yr655lbD7mT9/YTlNcGOUosUhKRH9+hUWfXkr6u7Au23KAzDl93Ncvx+f02Fmqf0LIURBYG1tDUB8fC78P1gIkWWP2uajtvo0rLIrmKy6du0ab775Jlu3brV4Gdvx48czZswY0/uYmBhJigkhRHZJvg9XlkL5MUBAuoepdyIx/LkVAG3rTihOzhafYvmha/x9JQoHGy2fdqqEoijPGrXIJvcT77P01FLGBI8hIIPnL7JG0WjQduqF7tvpqGEXUI8eRKlRN9Nyr9YpzoYTNzl05T7/W3uaRQNq5Wx7sbD9CyFEQaHVanFzc+P27dsAODg4yPcWIfIBVVWJj4/n9u3buLm5odVqn7quPEuIHTlyhNu3b1OjRg3TPr1ez59//sns2bNJSkpKdWG2trbY2srQGiGEyCuqwWBcVVKvRylTASWousVlb8UkMvm3UADeaVmOou4OORWmEPmK4umNpmkbDFt/Rb9tA0qZ8igubhmW0WgUPu9amTZf7eHPf+6w+ugNutUomjsBCyGEAMDX1xfAlBQTQuQfbm5upjb6tPIsIdasWTNOnTpltm/AgAGUL1+ed99995myfEIIIXKG4eAe1BvhYGuHtn23LP2ldML6M8Qm6aji70b/eiVyLkgh8iFNnZdQz55AvX4V/abVaHsOzLT9lPJ24q3mZZmy+RyTNp6lcTlvmXNPCCFykaIo+Pn54ePjQ0pKSl6HI4T4l7W1dbbkjPIsIebs7EylSpXM9jk6OuLp6ZlqvxBCiLyn3ruDYcdvAGhbvpxpD5fHbTkTyeYzkVhpFD7vEoRWI0MORMGiaDRoO3RHN2+GcdXJ08cs6mE5+KUANpy4ydmIGCZtPMtXPavlQrRCCCEep9VqpcOGEC+gPF9lUghR8AQHB9OoUSOCg4PzOhTxOHs/qDTBuH2CqhrQb1gJOh1KQBmUanUsrjYmMYWP1p8GYEjDklTwc8m2kEX28XPyY0KjCfg5pX7+Inso3r5oGrYAQP/7WtS42EzLWGk1TO4ShEaB9cdvsvufOzkTXAbtXwghhBDiRaSoz/E6sjExMbi6uhIdHY2Li/wDSwghcor+0D4Mv60Gaxushr6D4u5pcdn3155i6cFwSng6sHl0Q+ys5S+souBS9Xp082fCrZsoFatg1a2fReU+3nCGBX9dwd/Dnq2jG2FvI+1ICCGEECItluaKpIeYEEIIo5QYuLnFuH2M+iAKwx8bAdA0a5ulZNihK1EsPRgOwGddgiQZlo/FJMWw5eIWYpJiMj9YPDVFq8WqYw9QNKhnTmA4dyrzQsDbLcvh52rHtagEZm7/J/sDS6f9CyGEEEK8qCQhJoQQwij2Iuxqbdz+S1VV9JtWQ3ISin8JNLXrW1xdkk7Pe6tPAtCjpj/1Snlle8gi+1yMukjrpa25GHUx84PFM1H8iqKp1xgA/abVqAnxmZZxsrViUkfjHKvf7wnj7M1sTlyl0f6FEEIIIV5kkhATQuS6pKQk00vkb+qZ46gXz4FWi7ZDDxTF8l8b3+y8xKU7cXg52fK/thVyMEohnj+axi3B0xsexqLf+qtFZZoHFqJNJV/0BpXxa0+hNzy3s14IIYQQQuQ5SYgJIXLdN998w+eff84333yT16GIDKgJ8eg3rwdA81JzFC8fi8v+cyuWubuMPU0mdgjE1cE6R2IU4nmlWFmj7dADUFCPH8Jw6bxF5SZ2qIizrRUnrj1g8f4rORqjEEIIIcSLTBJiQggh0mT4YxPExYKXD5r6TS0vZ1AZv+YUKXqVZuV9aBckq9YJkRZNsQDTMGT9ptWoKSmZlinkYse41uUAmLblPBHRCTkaoxBCCCHEi0oSYkIIIYw0tuBUCjS2GMIvYzh6AABt+24oVlYWV7Pi8DWOXL2Po42WSZ0qoShKTkUsspGt1pZS7qWw1drmdSgFiqZpG3B2gfv3MOzdblGZPnWKU72YG3HJeiasP5NNgfzX/oUQQgghCgJJiAkhhDByqwgdLqI6l0O/8RcAlGq10RQvZXEVdx8m8fnv5wAY07Ichd3scyRUkf0q+lTk4qiLVPSpmNehFCiKrR3a1p0BMOzdgXr3VqZlNBqFyV0qY6VR2Hr2FptPRz57IP+2f9zk+QshhBCiYJCEmBBCCDOGv3bBnVvg4IS2xctZKvvZplCiE1II9HOhf3DxnAlQiBeMUiEIpUwFMOjRb1yNqmY+WX45X2eGNCwJwMRfzxCbmPlwSyGEEEII8R9JiAkhhDC6fxJ1lReGA0sB0LbqgGLvYHHxfZfusubYDRQFPusShJVWfsU8T07eOon3NG9O3jqZ16EUOIqioG3bBaysUa9eQj15xKJyo5qVobinA5ExiXyxxbJJ+dN1/ySs9jZuhRBCCCEKAPnXihBCCABUQwpKyj0wpKCUKosSVN3iskk6PR+sOw3Aq3WKU9XfLYeiFDlFZ9BxN/4uOoMur0MpkBQ3DzSNWwKg3/oranxcpmXsrLX8X6cgAH46cJWj4fefPgBVB0l3jVshhBBCiAJAEmJCCCEAUC+EGv9Dq0XbtmuWJsP/bvdlLt+Jw8vJlndalcuhCIV4sWnqNgIfX4iPQ//HRovKNCjjRZdqRVBV+N+aU6ToDTkcpRBCCCHEi0ESYkIIIVAT4tEf2A2ApkYwioeXxWWv3I3j650XAfiwfQVc7a1zJEYhXnSKVou2fTcA1GN/Y7h62aJy77ergLuDNeciY/l+T1hOhiiEEEII8cKQhJgQQggMO36HxAQANEE1LC6nqiofrj9Nss5Ag9JedKhSOKdCFKJA0PgHoFSvC4B+02pUfeZDGD2dbHm/XSAAM//4h6v3Mh9uKYQQQghR0ElCTAiR63r27MnAgQPp2bNnXociAPXmNQyH90OyO4aglShuFSwuu/FkBHsu3MXGSsOkTpWyNMxS5C9lPcuyb+A+ynqWzetQCjxt83bg4AR3IjHs321Rma7Vi1CvlCdJOgMfrj9j0UqVZpzLQot9xq0QQgghRAEgCTEhRK4rXLgw/v7+FC4svYnymqoa0P+2BlBRKtVBE/QKWDtZVDYmMYVPNp4FYHjj0gR4OeZgpCKnOdk4EewfjJONZc9f5BzF3gFtqw4AGP78AzU688nyFUXh006VsNFq+POfO/x2KjJrJ7V2Au9gi9u/EEIIIcTzThJiQghRgKnHD6HeCAcbW7QNqsORMRB/3aKyX2w5z53YJEp6OfJG45I5HKnIaddjrjNmyxiux1j2/EXOUoKqoxQrCSnJFk+wX9LbiaGNSwHw8YYzxCamWH7C+OtZav9CCCGEEM87SYgJIUQBpSbEo/9jEwCaRi1RrBLh/JeQeDvTsieuPWDxgasAfNqpErZW2hyNVeS823G3+fLAl9yOy/z5i5ynKAraNp1AUVBPH8dw5ZJF5YY2LkUJTwduxyYxfes/lp8w8bbF7V8IIYQQ4kUgCTEhRK77559/OHPmDP/8k4V/rIlsZ9i5GeLjwLsQmjovWVxOpzfwv7WnUFXoXK0I9UpbviKlEMJyim8RNDWCAdBvXotq0Gdaxs5ayycdKwHw0/4rnL4RnaMxCiGEEEI8ryQhJoTIdRs3buSXX35h40bLhgGJ7KdG3sRweB8A2rZdULSW9/BafOAqZ27G4GJnxf/aWj4BvxAi6zRNWoO9A9yKwHDkgEVlGpb15uUqhTGo8P7aU+gNWZxgXwghhBCiAJCEmBBCFDCqqqLfsg5UFaViVTQlSltcNjI60TQM69025fF2ts2hKIUQAIqDI5ombQAw7PgdNf6hReU+bFcBZ1srTlyP5ueDV3MyRCGEEEKI55IkxIQQooBRz59GvXIJrKzQNm/33we2XlBmmHGbjk82nuFhko5qxdzoVatYLkQrcouXgxfDag7Dy0GGwOY3mhp1oVBhSEzAsGOzRWV8XOwY27ocAFM3n+d2bGLGBSxo/0IIIYQQLxJJiAkhRAGi6nTot24AQBPcGMXN478PHYtBrW+M2zTsPHeb305FotUo/F+nIDQaJTdCFrmkmGsxvmn3DcVcJdGZ3ygajXGCfcBw5ABqhGUrQfapU5zKRV2JTdLx6cbQjA/OpP0LIYQQQrxoJCEmhBAFiOHvvXD/Hjg5o2nQ1PxDXTxEHTVun5CQrOejX08DMLB+CQILu+RGuCIXxafEczTiKPEpqZ+/yHua4qVQKlUDVPSb16Gqmc8LZkpeK/DriZvsuXAn/YMzaP9CCCGEEC8iSYgJIUQBocY9xPDnNgC0Tdui2Dwx/1fMOdhcw7h9wtc7LnAtKoHCrnaMbl42N8IVuezc3XPU+K4G5+6mfv4if9C2aA/WNqjhYainj1lUJqioK/2CSwDw0fozJKaks1JlBu1fCCGEEOJFJAkxIYQoIAy7tkBSIvgWQala0+Jy/9yK5bs/LwMwsUNFHG2tcipEIUQGFBc3NC81A0C/bQNqcpJF5ca0LIuPsy1hd+P4dvelnAxRCCGEEOK5IQkxIYQoANTbERiO7AdA26ojimLZ//5VVeWDtafRGVSaVyhEy4q+ORmmECITmuBG4O4JsTEY9u6wqIyLnTUfvRwIwJydlwi7G5eTIQohhBBCPBckISaEEC84VVXRb/31/9u77/isyvv/469z7jt7ksneey9ZgoACCgq4t2jratX2Z6n9qrVWO6y1rbS11r33Km5FQBkiIAJhhr1HEpKQve/7XL8/7hBAAoRAcie538/H4/aQc65z530/zjkx+Zzrug4Yg9WjD3b7TjXe94MVe1m28yBhQS4entKzDlOKSE1Y7iBcEyYD4Cyej8k9WKP9LuzTgnO6JlLudXjwo3U1moNMREREpClTQUxE6l1wcHDVS+qe2boRs20zuFy4xl10/IaWDe4o3xI4WFTOX77wPZnuV+O70LpZeH3EFT+xLZuo4CjsGvYeFP+xuvXG6tAZvB68cz+r2T6WxZ+m9iLYbbNoaxafrkn7UYOjr38RERGRps4yjfgWYX5+PjExMeTl5REdrSeeiYj8mPF68Tz9d8jOxB4xBtf4yTXe9/8+WM17y/fSvXkUn/5iJEEu/aEs0lCYjP14np0BxuC66U7sdh1rtN9/vt7C43M2kxgVwtzpo4kJC6rjpCIiIiL1q6a1Iv11IyLShDkrlkB2JoRHYo8aV+P9lu04yHvL9wLwyCW9VQwTaWCs5JbYA4cB4P3qY4xxarTfbaM70jExgsyCMh6fvakuI4qIiIg0aPoLR0SkiTJlpTgLZgNgjzkfKzTsxDvkpcLnvSjPXsfvPloLwDVD2jCoXVxdR5UGIDUzlV5P9SI1M9XfUaSG7LHnQ0gopO3FrFpeo31C3C7+PLU3AK8v3cXqPbm+DZXXP3k6/iIiIhIYVBATEWminMXzobgI4hOxBw49+Q7eUshL5aMV29icUUh8RDD3XtC9znNKw1DqKSU1M5VST6m/o0gNWRFR2OeMB8D7zReYspoduxGdE7hkQCuMgQc+WovXMVXXP14dfxEREQkMKoiJSL2bPXs2n3zyCbNnz/Z3lCbLFBbgLFkAgOvciVguV433feeH3QA8cGEPYsP14AORhsweOhLiEqCwAGfRNzXe77eTehAd6mbdvnxeX7Kz7gKKiIiINFAqiIlIvVu3bh0pKSmsW7fO31GaLGfhHKgox2rZBqtH3xrtc+gZK2Ueh2Ed47hkQKu6jCgiZ4DlcuOa4HtYhrNkASYnu0b7JUaFcO9EXw/Qf8zeTHZRWZ1lFBEREWmIVBATEWlizMEs32T6gD3uIizLqtF+S7ZlAeC2Lf58cZ8a7yci/mV17YXVsQt4PXjnflbj/a45qy0D2sZSWObh+W+312FCERERkYZHBTERkSbGO28WOA5Wp27YHTrXaJ/CMg+//6aUW3Y+yISzhtM5KbKOU0pD07FZRz6++mM6Nuvo7yhyiizLwjVhKlgWJnUNzs5tNdrPti3+fHFvbAveWudmXefXIFLHX0RERAKDCmIiIk2ISduLWZcCgGvchTXeb8bszWzNdbMlaCw3nzewruJJAxYbGsuUblOIDY31dxSpBSu5Bfag4QB4v/oI4zg12q9Xyxh+cnYH8p1I7lzQmlIrqi5jioiIiDQYKoiJiDQh3q8/B8DqMwCrec3mAFu3L49XFu8g0Z3DK0O+JtSTWZcRpYFKL0zn0W8fJb0w3d9RpJbssedDSCik78es+qHG+/1qfFd6NivmQvsVXvl6SR0mFBEREWk4VBATEWkinO2bMds2g+3CNXZijfbxOoYHPlyLY+DynjYd9j0CJfvrOKk0RPsL9vPbb37L/gId/8bKCo/EHj0BAO83X2DKSmu0X2SIm9+Njeb/WrzGlz+sZOuBwrqMKSIiItIgqCAmItIEGGNwvv4CAHvwcKxm8TXa761lu1m9N4+oEDe3jupQlxFFpB7YQ86G+EQoKsT5dm6N9xveKQEAj2N48KN1VU+dFREREWmqVBATEWkCTOoazP49EByCfc64Gu1zoKCUv83aCMBvLuhGXERIXUYUkXpgudy4JkwBwFm6EHMwq2b7VT5VNsRts2R7Nh+t2ldnGUVEREQaAhXEREQaOeP14v2msnfY8NFYETWbFPvPn22goNRD39YxXDe0XV1GFJF6ZHXpgdWpK3i9eOd8dkr7Xn1WW8D38yGvuKIu4omIiIg0CCqIiUi969KlCz179qRLly7+jtIkOCnfw8EsCI/EHj66Rvt8uyWTT1bvx7bgL5f0wWVbEBwLbS73LSXgxIbGcnnPy/WUySbAsixcE6aCZWM2rsXZsfXkO1Ve/xcP6UWXpEiyi8p57KuNdZ5VRERExF8s04gnicjPzycmJoa8vDyio6P9HUdEpN6Z8jI8/3kUCguwL7gY19BRJ92ntMLLBf9ayM7sYn5ydnsemtyrHpKKSH3zfjET54fvILkl7tt+hWXX7D7o99uzueq5pQD87+cjGNSuWV3GFBERETmjalorUg8xEZFGzFn6LRQWQGwc9uDhNdrnqfnb2JldTHJ0CNPHdz28wVsOxXt9Swk45d5y9ubvpVzHv8mwx5wPoWGQsR+TsuzEjY+4/od2jOeKQa0BeODDtVR4nXpIKyIiIlK/VBATEWmkTHERzuJ5ALjOnYjlcp90n22ZhTwzfxsAD0/uRVRo0OGNeevgoza+pQScdQfW0eafbVh3QMe/qbDCI3xFMcD7zZeY0pLjN/7R9X//pB40Cw9iY3oBL3+3oz7iioiIiNQrFcRERBopZ9HXUFYKyS2xevc/aXtjDA9+tI5yr8OYbolc0Lt53YcUEb+yB4+AhCQoLsRZOLfG+8VFBHP/pB4A/HPOFvblnqCYJiIiItII1aogtmOH7hSKSO0999xzzJgxg+eee87fURotk5eDs+w7AFznTcKyTv7j/MOUfSzelk1okM2fpvbGsqy6jikifma5XLgmTAHA+f5bzMGsGu97xaDWDOkQR0mFl4c/WV9XEUVERET8olYFsc6dOzN27FjeeOMNSktLz3QmEWniCgsLKSgooLCw0N9RGi3vgtng9WC164jVuftJ2+cWl/PI5xsA+OV5XWgTF17XEUWkgbC79PD9nHC8eGd/UuP9LMvikYt747Yt5qRmMHt9eh2mFBEREalftSqIrV69mgEDBvDrX/+a5s2bc/vtt7Ns2UkmaxURkTPCZGVgVv0AgH3epBr19Hps1kayi8rpmhzJraM61nVEEWlgXBOmgGVjNq3H2b65xvt1SY7itnN8PzMe/mQ9RWWeuoooIiIiUq9qVRDr3bs3M2bMYN++fbz88sukp6czcuRIevXqxYwZM8jMzDzTOUVEpJJ33iwwBqtbL+w2HU7afvnOg7y9bA8Aj1zShyDXcX70N+sPV5X6lhJw+jfvT+kDpfRv3t/fUaQOWInJ2GedDYD3q48xjvfoBie4/n9xbhfaxIWxP6+Uf3+9pe7DioiIiNSD05pU3+12c8kll/Dee+/x2GOPsW3bNu655x5at27NtGnTSEtLO1M5RUQEcPbtxqSuASxc5048afsKr8MDH/qeGnfV4Dac1T7u+I0tG1whvqUEHNuyCXGHYOv4N1n2mAkQFg4H0nFWfn/0xhNc/2HBLv44pTcALy7aQer+/PqIKyIiIlKnTuu33uXLl3PHHXfQokULZsyYwT333MO2bdv45ptv2LdvH1OnTj3h/k8//TR9+/YlOjqa6Ohohg8fzpdffnk6kUREmjTnmy8AsPoNwkpqcdL2Ly7awaaMAuIigrlv4knmGsvfDHPH+JYScDZnb2bMK2PYnK3j31RZYeHYY84HwJk3C1N6xJMjT3L9j+2exKQ+zfE6hgc+WovjmHpILCIiIlJ3alUQmzFjBn369GHEiBHs37+f1157jV27dvHnP/+ZDh06cPbZZ/Pss8+ycuXKE75P69at+etf/8ry5ctZvnw55557LlOnTmX9ej3JSETkx5ztmzHbt4DtwlX5R+2J7DlYzL/m+v64/e2kHjSLCD7xDp5COLDAt5SAU1heyIJdCygs1/FvyuzBwyExGYqLcBbMObyhBtf/7y/qRWSIm5Tdubz9w+56SCsiIiJSd2pVEHv66ae59tpr2b17Nx999BEXXXQRtn30W7Vt25YXX3zxhO8zefJkJk2aRNeuXenatSuPPPIIkZGRLF26tDaxRESaLGMMzte+3mH24BFYsScY+ljZ/qFP1lNa4TCsYxyXDWxVHzFFpIGzbJdvgn3AWfYtJrPmT45sHhPKryd0BeCxLzeSWVBWJxlFRERE6kOtCmJz5szh3nvvpXnz5ketN8awe7fvjmFwcDA33nhjjd/T6/XyzjvvUFRUxPDhw6ttU1ZWRn5+/lEvEZFAYDasxezfA0HB2Oecd9L2s9al883GAwS5LP58cZ8aPYlSRAKD3bk7Vrde4Dh4P5+JMTUf/jhteHt6t4omv9TDX77YUIcpRUREROpWrQpinTp1Iisr65j1Bw8epEOHkz/x7Ehr164lMjKSkJAQfvazn/Hhhx/Ss2fPats++uijxMTEVL3atGlTm/giIo2Kcbx4K+cOs4ePxoqIOmH7wjIPD3/qG3r+89Gd6JwUWecZRaRxcV1wMQQFY3Ztw6xZUfP9bIu/XNIHy4IPU/bx3dZjfx8UERERaQxqVRA73p3EwsJCQkNDT+m9unXrxqpVq1i6dCk///nPufHGG0lNTa227f33309eXl7Va8+ePaecXUT8b/z48UyePJnx48f7O0qjYFYth+xMCAvHHjHmpO0fn72JjPwy2sWHc8fYzjX/RuFtYcjzvqUEnLYxbXl+8vO0jdHxDwRWbBz2Ob6fwd7Zn2LshBpf/31bxzJtWDsAfvfROkorvHWaVURERKQuWOYU+slPnz4dgH//+9/ceuuthIeHV23zer18//33uFwuvvvuu1oHGjduHJ06deLZZ589adv8/HxiYmLIy8sjOjq61t9TRKShMp4KPP95FPLzsCdMwTV89Anbr9uXx5QnF+EYeP3mIYzqklhPSUWksTFeD55nZ0BmBvbAYbgmX1HjffNLKxj3+AIOFJRx97gu3D2uax0mFREREam5mtaKTqmHWEpKCikpKRhjWLt2bdXXKSkpbNy4kX79+vHKK6+cVnBjDGVlmqRVRATA+WEx5OdBdCz2WSNO2NbrGH774VocA1P6tTz1YlhpFmx9wbeUgJNVnMULK18gq1jHP1BYLjeuCy8DwFk1D+eHx2p8/UeHBvH7yb4pLp6at40dWUV1llNERESkLrhPpfG8efMA+MlPfsK///3v0+6V9dvf/paJEyfSpk0bCgoKeOedd5g/fz6zZs06rfcVEWkKTGkJzrdzAXCNmYDlDjph+zeW7mLN3jyiQt387qIep/4Ni3fDslshbiCEJtQmsjRiu/N2c+untzKwxUASwnX8A4XdrhNO/7MwGz7D3nIfpsO5WDW8/i/s04L3uu5l4eZMHvxoHa/fPEQP8BAREZFGo1ZziL388stnZIhiRkYGN9xwA926deO8887j+++/Z9asWZpXSKSJy8rK4sCBA9U+nEMOcxbPh5JiSEjC6jf4hG0z8kv5+1ebAPi/C7qTFHVq8zmKSOByjbsIgkMAcNasrPF+lmXxp6m9CHHbLNqaxSer99dVRBEREZEzrsY9xC699FJeeeUVoqOjufTSS0/YdubMmTV6zxdffLGm315EmpDXXnuNgoICoqKiquYmlKOZ/DycJQsAcJ07Cct2nbD9Hz9NpbDMQ/82sVw3RJOii0jNWRGRuIaNhp0v4axYjN3vUqz4mg25bhcfwS/O7cw/Zm/mT5+lMqZrEjHhJ+7NKiIiItIQ1LiHWExMTFU3+JiYmBO+RETk9HjnfQmeCqy2HbC69z5h23mbDvD52jRctsVfLumDbWvIkoicGqtbL98/vF68n76PMU6N9731nI50Sowgq7Ccv8/eWEcJRURERM6sGvcQe/nll6v9t4iInFkmfT9m1XIA7PGTTzgnT0m5l99/vA6An4xoT8+WpzGc3R0JSaN9Swk4kcGRjG43mshgHf9AZAVFYeJGwN4IzK5tOCu+xzV4eI32DXG7+PPFfbjm+aW8+f1uLh3YmoFtm9VxYhEREZHTU6s5xEpKSiguLq76eteuXfzrX/9i9uzZZyyYiEig8s75FDBYvfpjt253wrb/+WYLew6W0DImlF+N73p63zi6K4yb71tKwOka35X5N82na7yOf0CK7op1wXfY51wHgDPnU0x+bo13H94pnssGtsYYuP9/a6nw1ryHmYiIiIg/1KogNnXqVF577TUAcnNzGTJkCI8//jhTp07l6aefPqMBRUQCibN1I2b7ZrBduM6bdMK2m9ILeG7hdgAentKLiJBTenDwsYwD3jLfUgKOYxzKPGU4Ov6BqfL6t88agdW6HZSX4f3sA4wxNX6LBy7sQVxEMJsyDv9sEhEREWmoalUQW7lyJaNGjQLggw8+oHnz5uzatYvXXnuNJ5544owGFBEJFMZxKnuHgT1kJFaz+OO29TqG+2auweMYxvdMZkKv5qcfIGcVvBvqW0rAWZW+itBHQlmVvsrfUcQfKq9/K28NrilXgsuF2bIBs7bmT52MiwjmwYt6APDvr7ewI6uojsKKiIiInL5aFcSKi4uJiooCYPbs2Vx66aXYts2wYcPYtWvXGQ0oIhIozKof4EA6hIZhnzPuhG3f/H4XKbtziQxx86epJ550X0TkVFiJzbHPGQ+A98sPT2no5MX9WzGqSwLlHocHPlx7Sj3MREREROpTrQpinTt35qOPPmLPnj189dVXTJgwAYADBw4QHX0aEzqLiAQoU16Gd94sAOxzxmOFhR+3bVpeCX+btQmAey/oRvOY0HrJKCKBwz77XKyWbaC0BO/H79a4sGVZFo9c3IfQIJvF27L5YMXeOk4qIiIiUju1Koj9/ve/55577qF9+/YMHTqU4cN9TyGaPXs2AwYMOKMBRUQCgbNkARTmQ2wc9llnH7edMYYHP1pPYZmHgW1juW7oiSfdFxGpDcvlwnXJNeB2Y7Zvxvnhuxrv2zY+nLvH+R7O8MgXG8gqLKurmCIiIiK1VquC2OWXX87u3btZvnw5s2bNqlp/3nnn8c9//vOMhRMRCQSmMB/nu3kAuM6bhOU+/uT4s9alM3dDBkEui79e1hfbtuorpogEGCshGXvcRQA4cz7DZGfWeN+bR3agR4tocosr+PNnqXUVUURERKTWLNOIJ3fIz88nJiaGvLw8DdUUaUQKCgowxmBZVtV8hIHM++n7OCuXYrVqi+vmX2JZ1Re58koqGDdjAZkFZfzi3M78ekK3MxykHMoOQEgSuILP7HtLg1fuLedA0QGSIpII1vEPPMe5/o1x8L7xHGb7Ft/PqJ/ehWW7avSWq/fkcslT3+EYePWnQxjdNbGu0ouIiIhUqWmtqFY9xIqKinjwwQcZMWIEnTt3pmPHjke9REROJCoqiujoaBXDAJO2F2fl9wDY4ycftxgG8NisjWQWlNExMYI7x3Y+82FcwRDeWsWwABXsCqZ1dGsVwwLVca5/y7JxTbkKQkIx+3bjfPt1jd+yX5tYbhrRAYAHPlxLcbnnjEYWEREROR3HH5dzArfccgsLFizghhtuoEWLFif8A05ERKpnjIP3i5mAwerdH7vd8W8oLNtxkLe+3w3AXy7pQ2hQzXponJLC7ZByLwx4DCJ1cyPQbM/Zzr1z7+WxcY/RsZmOf8A5wfVvxTTDNelSvB++hbNgDlaHLthtO9TobX89oStfrU9nb04J/567hfsn9aiL9CIiIiKnrFYFsS+//JLPP/+cs88+/sTPIiJyYmbNSszeXRAUjGv85OO2K/N4uX/mGgCuPqsNwzrG102g8lzY8wH0ur9u3l8atNzSXD5I/YD7R+r4B6STXP9Wn4FY2zZh1qzAO/NNrNunn/BpuIdEhLj508W9+Okry3lh0Q4m92tJ71YxZzi8iIiIyKmr1ZDJZs2aERcXd6aziEiAWLFiBUuWLGHFihX+juI3prQE75zPALDPGY8VHXvctk/N28a2zCISIkO4f6J6V4hI/bMsC9ekSyEuAfJy8H7yHjWdhvbc7slc2LcFXsdw/8y1eJ1GO32tiIiINCG1Koj96U9/4ve//z3FxcVnOo+IBIAFCxYwe/ZsFixY4O8ofuMsmA1FBRCfiD3snOO223qggKfmbwXg4Sk9iQkPqq+IIiJHsUJCcV92PdguzMa1OMsX13jfhyb3JCrUzdp9ebyyeGfdhRQRERGpoVoVxB5//HG++uorkpOT6dOnDwMHDjzqJSIix2cOpON8vwgA1wUXY7mrH73uOIb7/reWCq/hvO5JXNinRX3GFBE5htWyDfb4iwBwvvoEk76/RvslRYXy28r5wx6fvYm9ObqpKiIiIv5VqznELr744jMcQ0QkMBhj8H75IRgHq3tv7M7dj9v2rWW7Wb4rh4hgF3+8uHfdP8AkrCX0+4tvKQGnZVRL/nLuX2gZpeMfkE7h+reHjsLs2ILZnIrng9dw33o3VkjoSfe7anAbPly5j2U7D/L7j9fz4o2D9WAmERER8RvL1HQCiAYoPz+fmJgY8vLyiI6O9nccEamhGTNmUFBQQFRUFNOnT/d3nHrlrF+F94PXwe3Gfcf/YTWrfoL8jPxSxj2+gIIyDw9N7slPzq7ZE91EROqDKS7E88wMKMjD6tkX1+XTalTc2nqggEn/XkS51+HJawdwUV8VYEVEROTMqmmtqFZDJgFyc3N54YUXuP/++zl48CAAK1euZN++fbV9SxGRJs2Ul+Gd/QkA9tnnHrcYBvDQx+spKPPQr00s04a3r5+A5bmw9xPfUgJObmkun2z6hNzSXH9HEX84xevfCo/EdcU033xiqWtwltRsTsjOSVHcMbYTAA9/sp6covJaBhYRERE5PbUqiK1Zs4auXbvy2GOP8Y9//IPc3FwAPvzwQ+6/X49rFxGpjvPt15CfB7Fx2Gefe9x2s9enM2t9Om7b4q+X9sFl19OQosLtsHCqbykBZ3vOdqa+M5XtOTr+AakW17/dpj32BVMBcOZ+jrNzW432+/mYTnRJiiSrsJw/fZZaq7giIiIip6tWBbHp06dz0003sWXLFkJDD88ZMXHiRBYuXHjGwomINBUmOxNn8XwAXOdPxQqq/mmReSUV/O6jdQDcek5HerTQcHARabjswSOw+g4C4+D94DVMft5J9wlxu3js8r5YFsxM2ce8jQfqIamIiIjI0WpVEPvhhx+4/fbbj1nfqlUr0tPTTzuUiEhTYozBO+sjcLxYnbtjdet13LaPfJ7KgYIyOiZE8P/O61J/IUVEasGyLFwXXQ7JLaCoEO/7r2K8npPuN7BtM35aOTfibz9cS0FpRV1HFRERETlKrQpioaGh5OfnH7N+06ZNJCYmnnYoEZGmxGxej9m6EWwXrgsuPu7E099uyeS95XuxLPjb5X0JDXLVc1IRkVNnBQXjvvImCAnF7N2F94sPqckzm+6Z0I22ceGk5ZXy1y831n1QERERkSPUqiA2depU/vjHP1JR4bubZ1kWu3fv5r777uOyyy47owFFpOmJj48nMTGR+PjjTyrfVJiKCryzPgbAHj4aK776mwZFZR7u+99aAG4c3p7B7ePqLWMVVyjE9PQtJeCEukPpmdiTULeOf0A6zevfikvAddn1gIVZuRRn2aKT7hMW7OKvl/UB4M3vd7N0e3atvreIiIhIbVimJrfwfiQ/P59Jkyaxfv16CgoKaNmyJenp6QwfPpwvvviCiIiIushabY6aPEpTRMRfvAtm48z/CqJicN91L1ZwSLXtHvp4Ha8u2UXrZmF8dfc5RIS46zmpiMjp8y6ehzPnM7BsXNffit2x60n3uX/mGt5etof28eF8+f/OISxYvWNFRESk9mpaK6rVX1zR0dEsWrSIefPmsWLFChzHYeDAgYwbN67WgUVEmhqTexBn0dcAuCZMPm4xbNmOg7y6ZBcAf720r4phItJo2cPHYA6kY1Yvx/v+a1i3/L/j9ow95P5JPZi3MZOd2cX8c+5mfjupRz2lFRERkUB2ykMmHcfhpZde4qKLLuIXv/gFr776KosWLWL//v01mi9CRCRQeL/6BDwerPadsHr1r7ZNaYWXe/+3BoCrz2rDyC4J9ZjwR3JWwXvRvqUEnFXpq4h+NJpV6av8HUX84Qxd/4cm2bdat4PSEjxvv4gpLTnhPtGhQTxySW8AXvh2O6v35J5WBhEREZGaOKWCmDGGKVOmcMstt7Bv3z769OlDr1692LVrFzfddBOXXHJJXeUUEWlUnG2bMBvX+oYNTbzkuBPp/3POZnZkFdE8OpTfXujnXhHGAU+BbykBxzEOBeUFODr+gekMXv+WOwjXVTdBdAxkZ+J97+RPnjyvRzJT+7fEMfB/H6yh3KPzUEREROrWKRXEXnnlFRYuXMjXX39NSkoKb7/9Nu+88w6rV69m7ty5fPPNN7z22mt1lVVEmoiZM2fyxhtvMHPmTH9HqRPG68H75YcA2ENGYiW1qLbd6j25PP/tdgAeuaQ30aFB9ZZRRKQuWZHRuK+5GYJDMDu24P30/ZOOJHhoci/iI4LZlFHAf+dtraekIiIiEqhOqSD29ttv89vf/paxY8ces+3cc8/lvvvu48033zxj4USkadq5cyfbtm1j586d/o5SJ5yl30J2JkREYo+ZUG2bMo+X33ywGsfAxf1bcl6P5HpOKSJSt6zmrXBdMQ0sG7N6ue8BIycQFxHMw1N6AfDfeVvZmJ5fHzFFREQkQJ1SQWzNmjVccMEFx90+ceJEVq9efdqhREQaK5Ofh7NwDgCucRdhhYZV2+6/87axOaOQ+Ihgfj+5V31GFBGpN3bn7rguugwAZ+EcnJXfn7D9RX1bML5nMh7H8H8frMHj1dBJERERqRunVBA7ePAgycnH78WQnJxMTk7OaYcSEWmsvHM/hfIyrNbtsPoNqrbNhrR8nqocDvTHqb2Jiwiuz4jHF90dLljhW0rA6Z7QnRW3raB7go5/QKrD698eOAx7lO9J5N7PPsDZuvG4bS3L4s8X9yYq1M2avXk8/+2OM55HREREBE6xIOb1enG73cfd7nK58HhOPGmqiEhT5ezahlmbAli4Jl2KZR37I9bjdXy9HhzD+b2SmdSnef0HPR53OMQN9C0l4IQHhTOwxUDCg3T8A1IdX//22Auw+g4C4+B9/zVM+r7jtk2ODuXBi3oCvgePbM4oqJNMIiIiEtiOX92qhjGGm266iZCQkGq3l5WVnZFQIiKNjXG8eL+onEh/0DCsFq2rbff0/G2s3ZdHdKibP03tfdynT/pF0W5IfQx63gsRbf2dRurZ7rzdPLboMe4deS9tY3T8A04dX/+WZeGaciXegjzMjq143nwB9y2/xIppVm37Kwa1Zta6dL7ZeIBfv7eamXeMIMh1SvdxRURERE7olH6zuPHGG0lKSiImJqbaV1JSEtOmTaurrCIiDZbzw2I4kAZh4djnTqy2zfr9eTzxzRbAN1QyKTq0PiOeXFkWbHnKt5SAk1WcxVPLnyKrWMc/INXD9W+53LiuvAmSmkNhPp43n8eUFFff1rJ49NI+xIQFsXZfHk/P31ZnuURERCQwnVIPsZdffrmucoiINFqmqABn3iwA7HMnYYVHHNOm3OPw6/dWU+H1DZWc2r9lfccUEfE7KzQM97W34HnxCcjMwPvOS7iuvx0rKOiYtsnRofxhSi/ufncVT3y9hfN6JNGrZYwfUouIiEhTpL7nIiKnyTv3CygrhRatsQcOrbbNE19vYWN6AXERwTxySZ+GNVRSRKQeWTHNcF93G4SEYnbvwDvzDYxT/dMkp/ZvyYTKp07++r3VlHv01EkRERE5M1QQExE5Dc7eXZhVywBwTbwEyz72x+qqPbk8vcA33OeRi3uTEFn9PIwiIoHCSm6B6+qfgsuN2bgO54uZGGOObWdZPHJJH5qFB7ExvYAnK4edi4iIiJwuFcREpN4NHDiQYcOGMXDgQH9HOS3GcXC+mAmA1f8s7Dbtj2lTWuHl1++twusYpvRrycQ+Leo55SkITYJuv/ItJeAkRSTxq2G/IilCxz8g+eH6t9t3wnXpdYCFs2IJzsK51bZLjArhTxf3BuC/87exZm9uvWUUERGRpssy1d2OayTy8/OJiYkhLy+P6Ohof8cRkQDjrFiK97P3ISQU9133YUVGHdPmkc9Tef7bHSRGhTDnV+cQGx7sh6QiIg2X94fvqm4uuCZfgT1wWLXt7nxrJZ+vSaNLUiSf/XIkIW5XfcYUERGRRqKmtSL1EBMRqQVTUoz3688BsMecX20xbNmOg7ywaAcAj13Wp+EXwyoKIXOJbykBp7C8kCV7llBYruMfkPx4/bvOOht71DgAvJ99gLNpXbXt/jS1NwmRwWw5UMg/52jopIiIiJweFcRERGrB+eZLKCmGpObYQ84+ZntxuYfffLAaY+CKQa05t3uyH1KeooLNMGeEbykBZ3P2Zka8NILN2Tr+AcnP17899gKs/kPAGLwfvI6zZ8cxbQ49lATguYXbWLk7p75jioiISBOigpiIyCkyaXtxViwBDk2kf+ywnb9+uZFd2cW0jAnlwck96zuiiEijYlkWrsmXY3XpAR4P3rdfwmRmHNPu/F7NuWRAKxwD97y/mtIKrx/SioiISFOggpiI1LsZM2bwhz/8gRkzZvg7yikzxuD98kMwBqt3f+z2nY9p893WLF5bsguAv13ej+jQoPqOKSLS6Fi2C9cV07Bat4OSYjxvPIfJzzum3cOTe5EUFcL2zCL+/tUmPyQVERGRpkAFMRGRU2DWrMDs2QlBwbjGTz5me35pBf/3wRoArh/WlpFdEuo5oYhI42UFBeO65qcQnwj5uXjefA5TWnJUm5jwIB67rC8ALy7aweKtWf6IKiIiIo2cCmIiIjVkykrxzvkMAPuc8VjRsce0+dOnqezLLaFtXDj3T+xRzwlPk+WGkATfUgKO23aTEJ6A29bxD0gN6Pq3wiNxX38bREbDgXS877yE8VQc1WZs9ySuGdIW8A2dzCupqO6tRERERI5LBTERkRpy5s+GogKIS8Aeds4x22etS+P9FXuxLPjHFf2ICPH/H5anpFlfuCzTt5SA0ze5L5m/yaRvso5/QGpg178VG4f7ulshJBSzazvemW9hHOeoNr+7sAft48PZn1fKQx9X/2RKERERkeNRQUxEpAbMgXSc778FKifSdx9d7MrIL+W+mWsB+NnoTgzpEFfvGUVEmhKreUtcV/0EXC7MhjU4sz7CGFO1PSLEzYyr+uOyLT5atZ9PV+/3Y1oRERFpbFQQExE5CWMM3lkfgnGwuvXC7tz9qO2OY7jn/dXkFlfQq2U0vxrX1U9JT1Pueviks28pAWf9gfV0fqIz6w/o+AekBnr92x0647rkWsDC+eE7nEVfH7V9YNtm3DnW93CTBz5cS1peSTXvIiIiInIsFcRERE7CpK7B7NgKLjeu86ces/21JTv5dksWIW6bf1/dn2B3I/3R6pRB4TbfUgJOmbeMbTnbKPPq+AekBnz92736Y1/g+9nrfPMlTsqyo7b/4tzO9GsdQ36ph3veX43jmOreRkREROQojfSvNhGR+mHKy/DO/hgAe+S5WM3ij9q+JaOAR7/cCMBvJ/Wgc1JUvWcUEWnqXENHYZ99LgDeT9/H2ZxatS3IZTPjqv6EBtl8tzWbVxbv9FNKERERaUxUEBMROQHn268hPw9i46r+GDuk3OPw/95ZRZnHYXTXRKYNb+enlCIiTZ993iSsfoPBOHjffw1n766qbZ0SI3ngwp4A/HXWRrZkFPgrpoiIiDQSfi2IPfroo5x11llERUWRlJTExRdfzKZNm/wZSUSkisnOxFkyHwDX+VOwgoKO2j5jzmZS0/JpFh7E3y/vi2VZfkgpIhIYLMvCNflKrM7dwVOB960XMFkHqrZfP7QtY7olHnGzwuvHtCIiItLQ+bUgtmDBAu68806WLl3KnDlz8Hg8TJgwgaKiIn/GEpE6dumll3Lddddx6aWX+jvKcfkm0v8IvF6sTt2wuvU+avv327N5duE2AB69tC9J0aF+SHmGRXWGMbN8Swk4neM6M+u6WXSO0/EPSI3k+rdcLlxXTMNq2QZKivG8+TymuNC3zbL42+V9aRYeRGpaPv/4SjdZRURE5Pgsc+Tzq/0sMzOTpKQkFixYwDnnnHPS9vn5+cTExJCXl0d0dHQ9JBSRQOFsWo/3nZfAduH++T1YCUlV2/JKKpj072/Zl1vClYNb87fL+/kxqYhI4DFFhXhefAJysrHadsQ17XYslxuA2evTue31FQC8+tMhjO6a6M+oIiIiUs9qWitqUHOI5eXlARAXF1ft9rKyMvLz8496iYicacZT4esdBtjDzzmqGGaM4bcz17Ivt4R28eH8fnIvP6WsAyVpsOZh31ICTlpBGg/Pf5i0Ah3/gNTIrn8rIhL3NT+FkFDM7u14P/uAQ/d4J/Rqzg3DfHM6/vq91WQVNrwnZ4qIiIj/NZiCmDGG6dOnM3LkSHr37l1tm0cffZSYmJiqV5s2beo5pYgEAue7+ZB7EKKisc8Zf9S2d3/Yw+dr03DbFk9cPYDIELd/QtaFkjRY94dG8wexnFlphWn8YcEfSCvU8Q9IjfD6txKb47r8BrAszKofcJYsqNr2wIU96JYcRVZhGb95fzUNaECEiIiINBANpiB21113sWbNGt5+++3jtrn//vvJy8ureu3Zs6ceE4rImbJz5062bt3Kzp07/R3lGCb3IM6iuQC4JkzBCg6p2rb1QAEPf7oegN+c341+bWL9EVFERCrZnbtjnz8VAGfOZzibUwEIDXLxxDUDCHbbzNuUycvf7fRjShEREWmIGkRB7Be/+AWffPIJ8+bNo3Xr1sdtFxISQnR09FEvEWl8Zs6cyZtvvsnMmTP9HeUY3tmfgMeD1a4TVq/+VetLK7zc9VYKpRUOo7okcOuojv4LKSIiVewhI7EHDQcM3v+9gcnw9XLr1jyK313YA4C/frmR9fvz/JhSREREGhq/FsSMMdx1113MnDmTb775hg4dOvgzjogEOGfbJsyGtWDZuCZegmVZVdv++uVGNqYXEB8RzONX9sO2rRO8k4iI1BfLsrAnXoLVoTOUl+F5+0VMUQEANwxrx7geyZR7HX75dgrF5R4/pxUREZGGwq8FsTvvvJM33niDt956i6ioKNLT00lPT6ekpMSfsUQkABmvB++XHwJgDzkbK7lF1ba5qRm8sngnAP+4sh9JUaH+iFj3gptB++t8Swk4zUKbcV2f62gWquMfkBr59W+5XLiuuBHiEiAvB++7r2A8HizL4m+X9yU5OoRtmUX86bNUf0cVERGRBsKvBbGnn36avLw8xowZQ4sWLape7777rj9jiUgAcpZ+C9mZEBGJPeb8qvXpeaX85oPVANwysgNjuyUd7y0av8gOMOIN31ICTodmHXjj0jfo0EzHPyA1gevfCgvHfc3NvidP7tmJ93PfkyfjIoKZcWV/LAveXraHL9Y2ngcHiIiISN3x+5DJ6l433XSTP2OJSIAx+Xk4C+cA4Bp3IVZoGABex/Crd1eRU1xB71bR/OaCbv6MWfe8pVCw1beUgFPqKWXrwa2UenT8A1ITuf6thCRcV0wDyz7qyZNnd07gZ6M7AXDvB2vYnV3sz5giIiLSADSISfVFRPzJO/dTKC/Dat0Oq9/gqvVPfrOVJduzCQ928cTVAwhxu/yYsh7kpcKnXXxLCTipmal0+U8XUjN1/ANSE7r+7U7dsM+fAlQ+eXLLBgCmj+/KoHbNKCjzcOdbKynzeP0ZU0RERPxMBTERCWjOrm2YtSmAVTmRvu/H4ndbs/jX15sB+PPFvemYGOnHlCIicirsISOxBw4DDN4PXsdkphPksvnPNQOIDQ9i7b48Hv1io79jioiIiB+pICYiAcs4XrxfVE6kP2gYVss2ABzIL+X/vZOCMXDV4DZcOrC1P2OKiMgpsiwLe9IlWO06VT558iVMcREtY8OYcWU/AF5ZvJMvNZ+YiIhIwFJBTEQClvPDYjiQBmHh2OdOBMDjdfjF2ylkFZbTvXkUf5jay88pRUSkNiyXG9eV0yA2DnKy8b7/Ksbr5dzuydw+uiMA/6f5xERERAKWCmIiEpBMYT7OvFkA2GMnYoVHAPCvuVv4fsdBIoJdPHXdQEKDmvi8YSIiTZgVHul78mRwCGbnNpwvP8QYwz0Tumk+MRERkQBnGWOMv0PUVn5+PjExMeTl5REdHe3vOCLSiHhmvoFZm4LVsg2um3+JZdss2JzJTS8vwxh44poBTOnX0t8xRUTkDHA2p+J9+yXAYE+8BNeQkezPLWHSE9+SW1zBTSPa8/AU9QgWERFpCmpaK1IPMREJOM6OLVUT6dsXXoZl26TllfCrd1dhDFw/rK2KYSIiTYjdtSf2uAsBcGZ9jLN9s+YTExERCXAqiIlIQDFeD97P/weAPXg4dss2VHgdfvl2CgeLyunVMprfXdjTzyn9JH8TfDXct5SAsylrE8NfHM6mLB3/gBQA1789YgxWv8FgHLzvv4bJzjxmPrHtmYV+TikiIiL1RQUxEQkozuL5kJ0JEZHY500C4B9fbeKHnTlEhbgDe94wTxFkL/UtJeAUVRSxdO9Siip0/ANSAFz/lmXhuuhyrNbtoLQEz9svYkpLuGdCN4a0j6OgzMPP3lhBcbnH31FFRESkHqggJiL1bv78+Xz11VfMnz+/Xr+vycnGWTgHANeEKVihYXyxNo1nF24H4G+X96VdfES9ZhIRkfpjuYNwXXUTRMdCdibeD17HbRmevG4ASVEhbM4o5N7/raURT7ErIiIiNaSCmIjUu5UrV7J06VJWrlxZr9/XO+sj8Hiw2nfC6jOQLRkF/Ob91QDcfk5HJvZpUa95RESk/lmR0biv/gkEBWO2bcL56lOSokL573UDcdsWn67ez8vf7fR3TBEREaljKoiJSEBwNq7DbE4F28Y16TIKyjzc/voKisq9jOgUz2/O7+bviCIiUk+sFq1xXXwNAM6yb/EuWcBZ7eN44MIeAPzliw0s23HQnxFFRESkjqkgJiJNnikv8/UOA+zhYzDxSdzz3mq2ZxXRMiaU/1wzALdLPw6JaA/DX/ctJeC0j23P65e8TvvY9v6OIv4QgNe/3bMv9vjJADizP8FZv4qbRrRnSr+WeBzDnW+t5EB+qZ9TioiISF3RX4Ai0uQ5C+dCXg7ENMMePZ6nF2xjdmoGwS6bp68fRHxkiL8jNgwhcdDhet9SAk5cWBzX972euDAd/4AUoNe/PXw09pBRAHg/fAuzezt/vawP3ZKjyCwo4863VlLhdfycUkREROqCCmIi0qSZA2k4S+YD4Jp4Md/uyOMfszcB8MepvejXJtZ/4Rqa0kzY/F/fUgJOZlEm/132XzKLdPwDUoBe/5ZlYZ8/BatHH/B68b7zMmF5WTx9/UCiQtz8sDOHR7/Y6O+YIiIiUgdUEBORJss4Dt5P3gPHwerWi32JHfnlOykYA9cMacPVQ9r6O2LDUrwHlt/lW0rA2ZO/h7u+vIs9+Tr+ASmAr3/LtnFdch1Wm/ZQWoLnjefpEFzB41f2A+Cl73Ywc+Ve/4YUERGRM04FMRFpspxlizD7dkNIKOXjL+Znb6wgt7iCfm1ieXhKL3/HExGRBsIKCsJ19U8hPhHyc/G89gzj20Vw19jOANw3cy2r9uT6N6SIiIicUSqIiUiTZHIP4nzzJQDWeRfym692sn5/PvERwTx93UBC3C4/JxQRkYbECo/APe1nENMMDmbheeNZfjWqNeN6JFHucbj99eWaZF9ERKQJUUFMRJocYwzez96HinKsdh35b14Cn69JI8hl8fT1g2gZG+bviCIi0gBZ0bG4b7gdIqIgIw3z9ovMuKQnXZIiycgv47bXV1Ba4fV3TBERETkDVBATkXrXvn17OnXqRPv27evk/c2aFZhtm8HlZk63cfxz7hYA/jS1N0M6BNYT1E6JOwqaT/AtJeBEBUcxodMEooJ1/AOSrv8qVnyirygWGobZu4vwj17nuWv7ExMWxKo9uTzw4TqMMf6OKSIiIqfJMo34/+j5+fnExMSQl5dHdHS0v+OISANgigrw/PdvUFLMpsEXcMVSLyUVXm4a0V7zhomISI05e3fhfe0ZX2/jjl1ZctYUbnotBa9j+N2FPbhlVEd/RxQREZFq1LRWpB5iItKkeGd9BCXFZCe24ba1NiUVXkZ1SeB3F/bwd7SGz/FCRb5vKQHH63jJL8vHq+MfmHT9H8Nu3Q7XdbdAUDBm+2aGL/uYBy7oCsBfvtjAws2Zfk4oIiIip0MFMRFpMpzNqZh1qyjH5i5PP/bnltIhIYInrxmI26UfdyeVuxrej/EtJeCszlhNzF9jWJ2h4x+QdP1Xy27XCdf1t0JwCGbHFm7YPocrBrTAMXDXWyvZkVXk74giIiJSS/oLUUSaBFNWivfzDzAG/tBsNMv3FxMV6ub5aYOJCQ/ydzwREWmk7LYdcV3nK4qxaysPFy9lYJsY8ks9/PSVH8gpKvd3RBEREakFFcREpN69+uqrPPXUU7z66qtn7D2d2Z9Cfh4vBXfnvf0WtgX/uWYAnZMiz9j3EBGRwGS37YDr+tsgOITg3dt4yr2aVjGh7Mgq4vY3VlDm0VBTERGRxkYFMRGpd9nZ2WRmZpKdnX1G3s/ZsgFn5VJmeRN5LL8lAL+d1IMx3ZLOyPuLiIjYbdrjmvYzCA0jPn07z4WvJyrExbIdB7n/f2v15EkREZFGRgUxEWnUTEkx3k/eY5UTzT2e3hhg2vB23Dyyg7+jiYhIE2O3aov7J3dBVDRdc3fxRMQmXBbMTNnHE19v9Xc8EREROQUqiIlIo+b9cia788u5vaI/ZY7Fud2T+P1FPbEsy9/RGp/YPnDpAd9SAk6fpD4cuOcAfZJ0/AOSrv8as5Ka+4picQmMLNnNHyJ2APDPuZv5KGWfn9OJiIhITakgJiKNlpO6mpw1a7m1vB8HHTe9Wkbzn2sG6ImStWUHQWiibykBJ8gVRGJEIkEuHf+ApOv/lFjN4n1FseSWXOXdwS0hvkLY/32whmU7Dvo5nYiIiNSE/moUkUbJFBZQ/On/uLO8D9tNBC1jQnnpprOICHH7O1rjVbANFkzxLSXgbDu4jSlvT2HbQR3/gKTr/5RZkVG4b7oDq20HfmNt4nx3FuVeh9tfX86OrCJ/xxMREZGTUEFMRBodYwyeT9/ngfx2LHOaERni4qWfnEVydKi/ozVuFXmw71PfUgJOXlken27+lLwyHf+ApOu/VqzQMFzX34araw/+7l5HXzufnOIKfvLyMrIKy/wdT0RERE5ABTERaXTM6uX8a30xH3ub47LgqesG0b15tL9jiYhIALKCgnFd9RPC+w3g2eDVtLZK2JldzE9f+YGiMo+/44mIiMhxqCAmIo2Kycvh1Y+X8l+P7ymSj1zSh3O6Jvo5lYiIBDLL5cJ18dUkjRjBS8GraEY5a/bm8bM3VlDucfwdT0RERKqhgpiINBrGOHzy2if8scRXDPvVeZ25ekhbP6cSEREBy7JxTZhCp/PP4/mQ1YTh5dstWfzm/VU4jvF3PBEREfkRzT4tIvVu9OjRlJeXExwcfEr7ffvJN9yzLw6DxQ0DkvjluK51lDBAhbWCAY/7lhJwWkW14vEJj9MqSsc/IOn6P2NcI8YyMCKKJ2fO4fay3ny8Oo2EiCAenNLH39FERETkCJYxptHessrPzycmJoa8vDyiozV/kEhTtjplE9e8u4liXFzYJpgnfj4Ol235O5aIiEi1nC0bmPnmF9xT2h2A+8d15PZxPfycSkREpOmraa1IQyZFpMHbtv8gN72/kWJcjIgq5/HbxqoYVhfKc2D3+76lBJyckhzeX/8+OSU6/gFJ1/8ZZ3fpwaW3XM69YbsAeHTudv63aJOfU4mIiMghKoiJSIOWkV/KtGe/I8dx09tdxLN3nkdokEZ714nCHbDoSt9SAs6O3B1c+cGV7MjV8Q9Iuv7rhN26HT+74zJ+Gp4BwL2fbWbu0o1+TiUiIiKggpiI+EFBQQH5+fkUFBScsF1OUTk3PLWAfWU27a1iXr6uH1GxGh4tIiKNh5WYzAP/7xKmhufiweaOj7bw7eK1/o4lIiIS8FQQE5F69/zzz/PPf/6T559//rht8koqmPb8YjbnekiijFdHRpDYo1s9phQRETkzXDHN+Pv0qYyPKKIcm9s+2cGyhcv9HUtERCSgqSAmIg1OUZmHn7y8jLXpRcRRzmtt02l7wfn+jiUiIlJrwZGR/OeeKYyKKqMEFz/9Yh+r537n71giIiIBSwUxEWlQSsq93PzqD6zcnUsMFbwSuZ5u11yF5XL5O1rT5wqDZgN8Swk4Ye4wBjQfQJhbxz8g6fqvF6FhoTz764s4K8pDIW5unJtJ6hdzaMQPfRcREWm0VBATkQajzOPltteXs3T7QSLx8FLIKnpPmYjVLN7f0QJDTA+YuNK3lIDTI7EHK29fSY9EHf+ApOu/3oSHBvPSryfRLxpyCWLawgK2zfwYYxx/RxMREQkoKoiJSINQ4XW4880Uvt2SRRheXgheTb/+XbH7DvJ3NBERkTMqKjSIV+8eT/domyxCuGG5l91vv4Pxev0dTUREJGCoICYifufxOtz9zirmbsggxHJ4NngNg5JDcV14mb+jBZaDKfBOiG8pASclLYWQP4eQkqbjH5B0/de72PBg3vjluXSMdpFmQrlubTC7Xn8NU1Hu72giIiIBQQUxEfErj9fhV++t5vO1aQRZ8FTQGoaHFuG+chpWcIi/4wUYA065bykBx2Ao95ZjdPwDlK5/f0iIDOGtO8fQLtrNXhPGtZui2fnSy5jSEn9HExERafJUEBMRv/rF2yl8uno/QTY8EbSWc1wHcV14GVZic39HExERqXPNY0J5987RdIgNZr8J5dqdCWx74XlMUYG/o4mIiDRpKoiJiN/kFFfw5bp0gl0WT0ZsZpwrE2vgMOx+g/0dTUREpN40jwnl3TtG0bFZCGkmlOv3tWLb889j8nL8HU1ERKTJUkFMROrdoQE5ZR4vwW6bp1ukc65nLyS3xHXBxf6MJiIi4hdJ0aG8c8dIOseHkm5CuS69HVufew6TleHvaCIiIk2SCmIiUq9Kyr2sD+/Hh6W9mOftzgt9HM7JXg/BIbivmIYVFOTviIErugdMWudbSsDpkdCDdT9fR48EHf+ApOu/QUiKCuXtn42ka0I4GYRyXXYXNr/wImb/Hn9HExERaXJUEBORelNc7uGnr/zANztLKQ+K5Ilx7Ri+8RsAXFOvwopP9HPCAOcOg9hevqUEnLCgMHol9SIsSMc/IOn6bzASo0J462cj6JYUwQFCuC63O+teehVn5zZ/RxMREWlSVBATkXqRV1LBtBeXsWR7NpEhbl69rAuDl8wEwB4yErtnPz8nFIp2wfe3+JYScHbl7uKWT25hV66Of0DS9d+gJESG8PbtI+jRPJIsQriusDc/vPYOzuZUf0cTERFpMvxaEFu4cCGTJ0+mZcuWWJbFRx995M84IlJHDuSXctWzS1i+K4eoUDev3dCf/os+gLJSrLYdsCdM9ndEASjLhm0v+pYScLJLsnkx5UWyS3T8A5Ku/wYnLiKYd24fweC2seQTxI3FfZj/5kc4a1b4O5qIiEiT4NeCWFFREf369ePJJ5/0ZwwRqUO7sou4/JklbEwvIDEqhHdvG4r9zXusysphfWg0rituxHK5/R1TRESkwYkJC+L1W4YxpmsCpbi4vawPn74/F+/ieRhjTv4GIiIiclx+/St04sSJTJw40Z8RRKQOpe7PZ9pLy8gqLKNdfDiv/3QordYt4t9pWRQQRpQdTv/IKH/HFBERabDCgl08N+0spr+3is/WpHF3RS/yv1zJtbk52BdcjGVrBhQREZHaaFT/By0rKyM/P/+ol4g0TMt2HOSq55aQVVhGjxbRvP+z4bTO2o4z/6vDjVwu/wUUERFpJILdNv++egDXDW2LweLBiu48vXgv3ndfxpSX+TueiIhIo9SoCmKPPvooMTExVa82bdr4O5KIVOPrDRnc8OL3FJR6GNI+jnduG0Zi8UG8M9/yNQgK9m9AqV5oMvS8z7eUgJMckcx9Z99HcoSOf0DS9d/guWyLP1/cmzvHdgLgH55O/HFdBeWvPI0p1E1iERGRU2WZBjIBgWVZfPjhh1x88cXHbVNWVkZZ2eG7YPn5+bRp04a8vDyio6PrIaWInMyb3+/i9x+vx+sYxvVI4slrBxJSWoTnhX9Dfi5Wh848kVVCQUEBUVFRTJ8+3d+RRUREGpUXvt3Onz/fAMA4O5MZ8XuIvPpG7FZt/ZxMRETE//Lz84mJiTlprahR9RALCQkhOjr6qJeINAyOY/jLFxt44MN1eB3DFYNa88z1gwjBi/edlyA/F+ITcV1xo7+jyvFUFEDGfN9SAk5BWQHzd86noEzHPyDp+m9UbhnVkSevHUCwy2Kuk8j1WZ048OJzOCnL/B1NRESk0WhUBTERaZhKyr3c+dZKnlu4HYBfj+/K3y7vi8sG70dvY/bvgbBw3NfeghUW7ue0clwFW+Drsb6lBJwtB7cw9tWxbDmo4x+QdP03Ohf1bclbtw4jNszNGhPD5SX92fLRp3i//BDj9fo7noiISIPn14JYYWEhq1atYtWqVQDs2LGDVatWsXv3bn/GEpFTkFlQxtXPL+XLdekEu2z+fXV/fnFeFyzLwvlmFiZ1DdguXFfdhBWX4O+4IiIiTcbg9nHMvONs2saFsdeEcWXZIL5fshbvK//F5B70dzwREZEGza8FseXLlzNgwAAGDBgAwPTp0xkwYAC///3v/RlLRGpoS0YBlzz1Hav35BIbHsQbtwxlav9WAHh/+A5n0dcAuKZcid2ukz+jioiINEkdEyP58I6zGdA2ljyCuLF8AB/tLMXzzOM461L8HU9ERKTBcvvzm48ZM4YGMqe/iJyib7dkcsebKyko9dA+PpyXbjqLjomRADjrV+N88SEA9ugJ2P0G+zOqiIhIkxYfGcLbtw7j7ndWMWt9OvdU9GJT4S5+/cEbuLduxDXxEqyQUH/HFBERaVA0h5iInBJjDM8t3MaNLy2joNTD4HbNmHnH2YeLYTu24P3wTcBgDxqOPXrCMe8RGRlJVFQUkZGR9ZxeTsgOgrBWvqUEnCA7iFZRrQjS8Q9Muv4bvdAgF/+9biB3jPH1yH7e047by/uRt2qVr7fY1o1+TigiItKwWKYRd9Gq6aM0ReTMKCn3cu//1vDJ6v0AXDm4NX+c2pvQIBcAJm0vnleegvIyrJ59cV12A5aturuIiEh9+mT1fv7vg9WUVjh0cJXyjHsVHe1irD4DcZ0/BSsiyt8RRURE6kxNa0X6S1VEamRvTjGXPb2YT1bvx21b/HFqLx67rO/hYljWATxvPu8rhrXvjOuS61QMExER8YMp/Vrywc9G0DImlB3eUC7zDGW+E49ZuxLPf/+Gk7IMYxx/xxQREfEr/bUqIie1eFsWU578jtS0fOIjgnnjlqFMG94ey7IAMNmZeF59GooKoXlL3xMl3X6dolBqI3ctfNjat5SAszZjLa1ntGZtho5/QNL13+T0bhXDJ78YyVntm1HotbitrB/PhvbGFBfj/eRdvC88gbNru79jioiI+I0KYiJyXMYYXly0gxteXMbBonJ6t4rmk1+MZFjH+MNtDmb5imGF+ZDUHPf1t2OFhvkxtdSaUwEl+3xLCTgVTgX7CvZRoeMfmHT9N0kJkSG8ecswrhnSFgP8IyeJO2LGkxcUjtm/B+8r/8Xz3quYnGx/RxUREal36sIhItXKK67gNx+sZnZqBgCXDGjFo5f2qRoiCWBysn3FsII8SEzGPe1nWBEnnyj/008/pbS0lNDQUCZPnlxnn0FERCTQBbttHr20D31axfDwp+uZm+Hl4pjR/KdrHr23LMFsWINn03rsQcOwR56HFR3j78giIiL1QgUxETnGqj253PnmSvbllhDssvntpO7cOOLwEEkAk3vQVwzLz4WEJNzTfl7jSXq3bNlCQUEBUVGa1FdERKQ+XDu0LX1bx3DHmyvZfbCYq9aF88A507j2wBLYvhnnh+9wVn6PPXgE9sixWJF6YJWIiDRtGjIpIlUODZG84pnF7MstoW1cOP/7+QhuOrvD0cWwzHQ8L/0H8nIgPtFXDItUcUtERKQh690qhs9+OZLzeyVT7nV4aN4e7nadRfHVt2G1aQ9eD873C/H8+y94Z3+KKSr0d2QREZE6ox5iIgL4hkje88Fq5lQOkZzUpzl/vawv0aFBR7Vz9u3G++bzUFLsGyZ5w+1YUbqL3CREdYHz5vmWEnC6xHVh3o3z6BKn4x+QdP0HjOjQIJ65fhAvfbeTR7/YwOdr0kjdH8G/r76B3mUZOPNmYfbtxlkyH2f5YuwhI7FHjMYKP/mUCCIiIo2JZYwx/g5RW/n5+cTExJCXl0d0tP4gF6mt77dnM/291VVDJH93UQ9uGNbuqF5hAM72zXjfeRkqyrFatcV17S1Y4RGn/P1mzJhRNWRy+vTpZ+pjiIiIyClYuTuHu95cyf68Uty2xfQJXbltVEfsbRvxzv8K0vb6GgaH+Apjw89RYUxERBq8mtaKNGRSJICVebw8+sUGrn5+KftyS2gXH87MO0YwbXj7Y4thqavxvvWCrxjWoQuuaT+rVTFMGrDifbDqft9SAs6+/H3cP/d+9uXr+AckXf8BaWDbZnz+y1FM7N0cj2P426xNXPvC9+xPbI/71rtxXfUTaN4SystwFn3tG0r59ReY4iJ/RxcRETltKoiJBKgNaflMffI7nl24HWPgqsFt+PyXo+jd6uinSxlj8C76Gu/7r4HXi9Wjj69nWHCIn5JLnSnNgNS/+pYScDKKMvjrd38lo0jHPyDp+g9YzSKCeeq6gfz98r5EBLtYtuMgE//1LR+v2o/VrRfu26ZXUxh7RIUxERFp9DSHmEiA8TqGFxdt5x9fbabc6xAfEcyjl/ZhQq/mx7Q1Xg/ezz7ArPoBAPuss7EvmIplu+o7toiIiNQRy7K4YnAbhnaI51fvrWLFrhzufncVX288wJ+n9iame2+sbr0wm9bjXfAVpO/HWfQ1zrJFlUMpR6vXuIiINDoqiIkEkD0Hi7nn/dV8v+MgAON6JPHopX1JjDq2t5cpLsT73quYXdvBsrAvuBjXkJH1HVlERETqSdv4cN69bRhPzd/Gv7/ewqer9/P99mz+fHFvJvRqjnWiwtjQUdgjxmCFhvn7Y4iIiNSICmIiAcDrGF7+bgePz95MSYWX8GAXv7+oJ1ed1eaYucIATNpePO+/BjnZEBKK6/IbsDt390NyERERqU9ul80vz+vCOV0Tmf7eKrZnFnHb6yu4qG8LHp7Si4TIkOoLY9/OxfnhO+xR52EPGYnlDjr5NxMREfEjFcREmrgNafnc9781rN6bB8DQDnH87fK+tIuvfmiDs/J7vF/MBK8HYuNwX3MzVtKxwylPR+/evSktLSU0NPSMvq+cppB46HSzbykBJz4snpsH3Ex8mI5/QNL1Lz/Sv00sX/xyFP/+egvPLdzOZ2vS+G5rFg9P6cWUfi2xLOuIwtg6vN/Mgsx0nDmf4Xz/La4xF2D1G4xla8piERFpmCxjjPF3iNqq6aM0RQJRaYWX/87bytPzt+FxDFEhbn57YQ+uGtwG266mV1hFBd4v/lc1X5jVpQeuS67FCguv7+giIiLSgKzdm8dvPljNxvQCAM7rnsSfL+lNi5jDwyON42DWrMA7bxbk5/pWJjbHdd5ErK69qu2RLiIiUhdqWitSQUykCVq24yD3z1zDtkzf058m9EzmTxf3Jjm6+h5ZJjMDz//egIz9vvnCxl6APfJcLEt3dQOKpwQKt0NkR3BrDphAU1JRwvac7XRs1pGwIB3/gKPrX06i3OPw7IJt/OebrZR7HSJD3Nw9rgs3jWiP23X49wXjqcBZ9h3Ooq+hpBgAq0177HEXYbft4K/4IiISQFQQEwlAB/JLefTLjXyYsg+AhMgQ/jS1Fxf0bl79XGHG4CxfjDP7U/BUQHgkrsuuw+7Ytb6jS0NwcCXMGgQXrIC4gf5OI/VsZdpKBj03iBW3rWBgCx3/gKPrX2poS0YB//e/NaTszgWgW3IUf5zai6Edjx5ua0pLcL6bh7N0oe93DMDq1gvXuZPO+FQMIiIiR6pprUhziIk0ARVeh1e+28m/5m6mqNyLZcHVZ7Xhvgt6EBNe/aS2pqgA78fvYrZsAMDq1BXX1KuxomLqM7qIiIg0Il2So/jfz0bw3vI9PDZrI5syCrjquaVcMqAV90/qTlKUrze6FRqG67xJ2EPOxpk/GydlGWbTejybU7H6DcY15nysmGZ+/jQiIhLIVBATaeQWbcni4U/Xs/VAIQD92sTyxym96Ncm9rj7OBvX4v3sAygqBJcbe9yF2ENH1tsQySeffJKCggKioqK466676uV7ioiIyJlh2xZXD2nL+b2a8/fZm3h72W4+TNnH3NQMfjW+K9OGt6saRmlFxeCafAX28HPwfvMlZsNazKof8KxNwR4yEnvUeZqvVERE/EIFMZFGald2EX/9ciNfrksHID4imHsndufyga2rnTQfwBQV4v3yQ8z6Vb4VSS1wX3odVnKLekrtU15eXvUSERGRxqlZRDB/uaQPVw1uw4Mfr2PN3jz++Fkqb36/i/sm9mBcj6SqKRushGTcV96Es3cXztzPMLu24yyZj7NyKfbIc7GHjsIKCvbzJxIRkUCigphII5NdWMZ/vtnKm9/vosJrcNkWNwxrx6/GdyUm7DjDI43BrF+F98sPobgILBv77LHYo8djuavfRwKRBXawbykBx8Ii2BWMpeMfoHT9S+31axPLh3eczTs/7Obx2ZvZllnEra8tZ2iHOB64sAd9W8dWtbVbt8O68Q7M1o14534OB9Jwvv4CZ9kiXKPPx+p/FpbL5b8PIyIiAUOT6os0EiXlXl5ctJ1nFmynsMwDwOiuidw/qTvdmx///Dc52b5eYZVzhZHcAveUq7BatqmP2NWaMWNG1ZDJ6dOn+y2HiIiInFn5pRU8PX8bLy7aQbnHAeDi/i255/xutG529NBI4ziYtSvxzpsFeTm+lbFxuEaNw+o3WIUxERGpFT1lUqSJ8Hgd/rdyLzPmbCYjvwyA3q2iuX9iD87unHDc/Yynwvd0p0Vfg8cDtgt71Hm+uTpc/u0cqoKYiIhI07Yvt4THv9rEzMonXwe7bW4c3o6fje5EfGTIUW2Nx+N76vWir33zm4KvMDbyPKz+g/3+e4uIiDQuKoiJNHIer8Ona/bzn6+3sj2rCIDWzcL4zfndmNy35XHnCQNwtm7E+8VMyMkGwOrQGdekS7ESkusl+8moINZA5W2AxdfBiDchpoe/00g925C5getmXsebl75Jj0Qd/4Cj61/qyLp9eTzy+QaWbPf9ThIe7OLGEe25bVRHmkUcPWeYqSjHWb4E57tvDhfGoqKxh47CHjQcKzSsvuOLiEgjVNNakW63iDQw1RXCmoUHcefYztwwvB0h7uMPHzB5OXi/+hizYa1vRWQ0rvOnYPXqXzWprchxeUsgJ8W3lIBT4ikhJT2FEo+Of0DS9S91pHerGN66dSjzN2fyzzmbWbM3j6fnb+O1xTv56cgO3DKyIzHhvvlMraBgXMNHYw8e7iuMLZ4PBfk4cz/HWTgXe+Aw31OxY+P8+6FERKRJUEFMpIE4XiHs1nM6Mm14eyJDjn+5mrJSnEXf4CxdCJ4K36T5Q0dijzkfKyS0vj6CiIiIyDEsy2JstyTGdE3k6w0HmDFnM6lp+fznm6288p2vMHbTiPZVPcaqCmNnnY1ZtxLv4gWQmY6zdAHO0oVYXbpjDx6B1bk7lm37+dOJiEhjpYKYiJ+VVnh5f8VeXvh2O7uyi4FTKIR5vTgrl+LMnw3FvqEFVtuOvuGRyS3qJb+IiIhITViWxbieyZzXI4mv1mfwr7mb2ZhewL+/3sJzC7dz1VltuGVUh6rJ9y23G6v/EKx+Z2G2bsRZugCzfQtmywa8WzZATDPsgUOx+w5SrzERETllKoiJ+MnBonJeW7KT15bs4mBROQCx4UHcVpNCmDGYTet8jyvPzvStjEvANf4irG69NTxSREREGizLsrigd3Mm9Exm1vp0/jtvK+v35/PK4p28vnQXU/q15PbRHaueom1ZFlaXHthdemCyM33DKVctg7wcnHmzcObNwmrb0VcY69kXKyz8JAlEREQ0qb5IvduVXcQL3+7g/RV7KK3wPY68dbMwbhnZgSsGtyHiBIUwAGfnNpx5szC7t/tWhEdgj57gm2y2kTyefPPmzVRUVBAUFETXrl39HUcOKc+B9LnQfBwEN/N3GqlnOSU5zN0+l3Edx9EsTMc/4Oj6Fz8yxrBoaxbPLNjGd1uzq9aP6ZbIzSM7MLJzwjE3+0xFBSZ1Nc7q5ZgdW4HKP2lcLqyuvbD7DvQNqXQH1eMnERGRhkBPmRRpQBzHsHBLJq8v2cU3mw5w6Krr0yqG287pyMTezXG7TjwHhrNzK86C2Zid23wr3G7sYaOxR56recJERESkSVi7N49nFmzjy3VpOJW/L3VKjGDa8PZcNqh1tT3oTX4uztqVOGtWwIH0wxuCQ3w9y3r29RXHgkPq6VOIiIg/qSAm0gDkFpfz/vK9vPH9rqr5wcB3x/O2czoyvGP8CYc3GmMwO7f5CmG7Kgthtgt7wBDsUedhxeguvpxBJRmw801ofx2EJfs7jdSzjMIM3lz7Jtf1uY7kSB3/gKPrXxqYnVlFvPzdDj5YsZeici8AkSFuLhvYimkj2tMpMbLa/Uz6fpw1y3HWr4L8vMMb3EFYnbtj9+iD1bUnVmhYPXwKERHxBxXERPzEGMOqPbm8vWw3H6/aT5nHNywyKtTNFYPacP2wtnQ8zi9xR76H2bYJZ9HXmF2VQyNdLuwBQ309wlQIk7pwcCXMGgQXrIC4gf5OI/VsZdpKBj03iBW3rWBgCx3/gKPrXxqogtIKZq7cx6tLdrI9s6hq/YhO8Vx1VhvO79Wc0KBjp4wwxmD27/ENq9ywFnIOD8XE5cLq2NVXHOvWGys8oj4+ioiI1JOa1oo0qb7IGXKgoJQPV+7j/RV72XqgsGp9jxbRTBvejqn9WxIefOJLzlRUYNauwLt0IWRm+FY2wULY/v378Xq9uFwuWrZs6e84IiIi0kBFhQZx44j2TBvejkVbs3h18S6+3pjB4m3ZLN6WTXSom6n9W3HVWW3o3Sqmaj/LsrBatYVWbbHHXQQZ+3FS1/iKY1kZh59UaX2A1b4jVvc+2N16NZnftURE5ORUEBM5DeUeh282ZvD+8r3M35yJt3Kyi9Agm0m9W3DdsLYMbNvspE99NIUFOD98h7N8MRRX3v0MDvENjRwxBis6to4/Sf165513KCgoICoqiunTp/s7joiIiDRwlmUxqksio7oksjenmPeX7+WDFXvZl1vC60t38frSXfRsEc2Vg1tzYd+WJEaFHLUvzVvhat4K17kTMZkZOBvW4KSugYz9mB1bMTu24nz5IVaL1ljde2N37w2JzfXkbhGRJkwFMZFT5HUMS7dn8+nq/cxan05ucUXVtkHtmnHFoNZc2LcFUaEnfqqRMQazdxfOiiWYdSng9c2PQUwz7CEjsQcO1fwWIiIiIj/Sulk4vxrflV+e14XF27J494c9zF6fQWpaPg9/msofP0vl7M4JTOnXkvN7Nyf6R7+TWYnJuBLH4zpnPOZgFs6m9ZiN6zC7d2DS9mLS9uLMmwXN4rG798bq3hurdXss+8QPQBIRkcZFBTGRGnAcw4rdOXy2ej+fr00nq7CsaltydAiXDmzN5YNaH3eC1yOZkmKcNStwVi496klIVut22MPOwerRB8s+di4MkToXFAOtJvuWEnBiQmKY3HUyMSE6/gFJ1780Qi77cK+x3OJyPkrZx4er9rN6Ty7fbsni2y1ZPPDROs7tlsSU/i0Z2y2JsOCjf8ey4hJwDR8Nw0djigowm1NxNq7DbNsMOdk4SxbAkgUQHonVrSd29z5YHbpgBZ34xqeIiDR8mlRf5DgqvA7fbz/InNR0ZqdmkJZXWrUtNjyIib1bMLlfC4Z2iMdln2RIpDGY3TtwVi7FpK4Gj8e3we3G6tUfe9Bw7Dbt6/DTNCwzZszQkEkRERGpEzuzivh09X4+Xr3/qHldQ4NszumSyPm9mnNejyRiw4OP+x6mvMz3gKON6zCbU6G05PDGoGCsjl2wOnXH7twNq1l8XX4cERE5RZpUX6QWCss8LNiUyZzUdL7ZeID8Uk/VtqgQN+N7JTO5X0tGdk4gyHXybvMmO9PXG2ztyqOfbpTcAnvgMOy+gzQsUhoOpwLKcyE4Fmzd+Q40Fd4KcktziQ2NJcil4x9wdP1LE9I+IYJfnNeFu87tzIa0Aj5ZvZ/P1uxnb04Js1MzmJ2agcu2GNYxjvN7NWdcj2Raxh79+5gVHILVoy92j74Yrxezeztm4zqcjesgPxezaT1m03ocgPhE7M7dsTp3x2rXSb3HREQaCfUQk4BmjGHLgUIWbMpk4ZZMvt9+kHKvU7U9ITKY87onM75nMiO7JFT7WO9j3rOoEGf9KsyaFZh9uw9vCArG6u3rDWa1bBPQk7Sqh1gDdXAlzBoEF6yAuIH+TiP1bGXaSgY9N4gVt61gYAsd/4Cj61+aOGMMqWn5fLU+g9nr09mYXnDU9q7JkYzplsSYrokMbh9HsLv6G5/GGEjfh7N1E2brRsyenWAO/+6I7cJq1cZXGGvfCatNe6zgkGrfS0RE6oZ6iIkcR15xBYu2ZrFws68IduRQSIAOCRFM6Okrgg1o2+ykwyEBTEUFZvN6nDUrMFs3glP5i5FlY3Xq6usJ1q2XfiESERER8QPLsujVMoZeLWOYPr4rO7OKmJ2azlfrM0jZncPmjEI2ZxTy3MLtRAS7GNE5gdFdEzm7cwLt48OrbmRalgUtWuNq0RpGnYcpLcHs2FJZINsA+XmYPTt9hbJFX4Nt+55c2a4jVqu2WC3bQMzJn0AuIiJ1TwUxafLySir4YcdBlmzPZun2bFLT8jmyX2SI22Zox3hGd01kdNdEOiVG1OiXFFNRgdm6ASd1NWbzBig/PNG+1aI1Vt9B2L0HYEVG1cXHEhEREZFaap8QwW3ndOK2czqRW1zOwi1ZzN90gIWbM8kqLGdOagZzUjMAaB4dyohO8QzrFM+ITvG0bhZe9T5WaNjhoZXGQE42Ztc2nF3bMTu3QV4OZt/uo0cNhIX7RgsceiW3gNhmWJaeYikiUp9UEJMmJyO/lJW7clixK4elO7JZv//oAhhA56RIRndN5JyuiQztEFejoZAApqIcs2UDTuoa3wSrFeWHN8Y0w+4z0NcbLDH5DH4iEREREakrseHBTOnXkin9WuI4vqGV8zcd4NstWaTsziU9v5SZKfuYmbIPgDZxYZzVPo5B7ZoxsG0zuiZH4bIt3w3VuASsuATsAUMBMLkHMbu2+3qN7d+DyUiDkmLMtk2YbZsOhwgK9v3+mJiMlZiMldgcKyHJVyjT08dFROqECmLSqJV5vKzfn0/K7lxW7s5h1e5c9uWWHNOuY0IEQzvGM7xTPMM6xJEUHVrj72HKSjFbN/l6gm3ZcGwRrGdfrJ79fN3g1f1dREREpNGybYverWLo3SqGu87tQmmFlxW7cli8LYsl27JZvTePPQdL2HNwHzNX+gpkkSFuBrSNZWDbZgxs14z+bWKJCfNNrG/FxmHFxkG/wQAYjweTsR+zfy9m/25M2l7IOgAV5Zj9e2D/Ho66j2vZvqJYs3jf0yybxWPFHfHvkJr/TisiIkfTpPrSaJR7HLYcKCB1fz7r9+ezZm8u6/blHzUJPoBtQbfm0QxoG8vQDnEM6xhP8ikUwABMTjbO5lTM5lRfd3fHe3hjbBx2j75YvfoF/OT4tVVWdnh4aUiI5lVrMBwveIvAFQG6Gx1wvI6XoooiIoIicOn4Bx5d/yI1UljmYfnOg6zclcPK3bmk7M6hqNx7VBvLgk6JkfRuGU3vVjH0bBlNr5YxVUWyHzOOFw5mYQ5kYDLTfa8DGXAwC7yeavepEhrmK5jF+F7ExGLFxPmWsc0gIlJDMUUk4NS0VqSCmDRIBaUVbEgrIHV/HusrC2BbDhRQ4T32dI2LCGZg21gGtG3GgLax9GsdS0TIqXV+NBXlvq7s2zbjbEmFzIwffZME7O59fEWwFq1VBBMRERERvI5hU3oBK3bnkLIrhxW7c9iVXVxt2zZxYfRuGUOvygJZt+ZRtIgJPe7vlcY4UJCPycmGg9mYHN+LQ8viopMHdLkri2TNqpZWTLOqIhrRsVhuDRoSkaZFBTFpFHKKytmaWciWjEK2Hihky4ECth4oPObJj4dEh7qr7rL1bhXNwLbNaBsXfsoFKlNRgdm7C7NzK2bnNsy+XeA94u6eZfueBtS1J3bXnljxiafzMUUah/wtsPwuGPwkRHfxdxqpZ1uyt3DXl3fx5MQn6RKv4x9wdP2LnDGZBWWs25fne1Xe3N2bc+yUHuAbbtk5KZKuyZF0SYqiS3IkXZKjaHmCQtkhpqzUN2l/Xi4mNwfyDmLycivX5UBBPsdMpHsMCyKjsGJifUWy6GZH9ziLbQYhJ88iItKQ1LRWpNsBUueKyjzsyi5mV3YRuw4Wsyu7mO2ZhWzLLCSrsPy4+7WICaVXy2h6toimZ+XdtNbNwk69+HXoiT/7dvsmM92/B7Nvz7Fd0KNisDp0xu7SA6tzd6zQsNp8XJHGy1MA6bN9Swk4BeUFzN42m4JyHf+ApOtf5IxJjAphbPckxnZPqlqXW1xO6v78qgLZ+v357MwqorDMw6o9uazak3vUe0QEu+iUFEm7+Ag6xIfTLj6C9gm+ZXxEMJZl+eYPS2qBldSi2hzG64WCvMpima9IZnJzIL9ymZcLngoozMcU5sO+3VRbPgsO+dGwzGaHC2gxzSAyGsvWsEwRaXxUEJPTVlrhZX9uCftzS9mfV8L+3BJ2ZxdXFb+yCstOuH+r2DA6JUXSpfLVufIVGx58SjmMcSAvF5OZ4XtlZUBmBibrAJRWc1cuMhqrQyfs9p2x2nf2TUyqu1/1YsmSJZSVlRESEsLw4cP9HUdERESkTsWGBzOicwIjOidUrSv3OOzMLmJLRiGbM3yjJDZnFLAjq4iici9r9uaxZm/eMe8VFeKmXUI47eIiaN0sjJaxh16htIoNIyYsyFcwc7ng0KT+1TDGQHGRrzdZ3uEiWdXXeTm+YZnlZXAgHXMgvfqCmW37hl4eGooZXTl/2ZHzmgWd2u/1IiL1QQUxOS5jDIVlHjILysgsKCOjoIz9uSWk5ZawL7eUtLwS0vJKOVh0/F5ehzQLD6JtfATt4sJpX3mXq0tyJJ0SI2s035cxxvc/46JCTH5eVfdw8nIw+ZX/487NOfoJkEdyubGat/RNgt+yDVab9r7HYqsA5hdLliyhoKCAqKgoFcREREQkIAW7bbomR9E1OYoLOdzLq8LrsDOriO1ZRezKLmJndjE7s4rYlV3M/rwSCso8rNuXz7p9+dW+b3iwq6pI1io2lJYxYTSPCSUxKoTEqBCSokKJiwjGZVu+SfcjIqFlm2rfy5SXQX7uUcWyowpo+XngOJB7EJN7EHZRfdEsPPLoXmWHimXRMb7vHx6BFawHLYlI/VJBLMCUebzkFVeQU1xBbnE5OcXlZBaWk1VQRmahr/CVVbnMLCijzOOc/E05/D/eFjG+/+m2jQ+nbVw47eMjaBsffsxTdYzj+HptFebgZBZDie9liot8Ra+iAigqhMICTFEhFBWA5yRP2QFwuSA+ESsxGSshGSshCSsxGRKSsFw63UVERESkYQty2XRJjqJLctQx20orvOzNKWZHlm86kv25pb6RGpWjNLIKyyku97L1gG9+3uOxLYiPDCExMoSkaN/yUMEsLiKY2PBgmoUH0Sw8mNjIOCLjk6q9kWwcxzcs88i5y3Irh2dWrqO8DIoLMcWFkLa3+oIZgDsIDhXHwiN8xbrwCN+TNENCfUNEQ0IhNPTor0NCIChYN7pF5JT5vULw1FNP8fe//520tDR69erFv/71L0aNGuXvWA2W1zEUlXsoLPVQVOahoMz378Iy3yuvuILcknJyiisqC1/l5FYWv3JLKij+0WOhayIqxE1C5f8gW1UWvVrEhtEiOoSWkW5ahlpEU+H7n92hwlZJGhwsgn3FmNJiPMWH1lcWv0pLOc79oxMLCvZN/HlEN2wrJvbwXaZmcVh6XLxI7YS38U2oHV79XWJp2tpEt+HJiU/SJlrHPyDp+hdpFEKDXHROiqJz0rHFMvAVzNLyfEWyfbklldOalJCRX8aByhve2UVlOIaqG+CpaSf/vkEui5iwI4pkh5YRvmVUqJvIkGSiEloR1TqIyBA3kSFuokLdRJhy3AV5h+cwO3JYZn6eb1im1+Obz+zQ+srvW+O/FizbVxhzB0GQ72UFBR/1NZVfW0FBh9dXLq2qr91Hbat+vVt/b4g0EX4tiL377rvcfffdPPXUU5x99tk8++yzTJw4kdTUVNq2bevPaH5RWObhb7M2HlXgqnpVrqtNQevHbAtiw4KIDXUTG2KTEOYiIdQiMcQiIciQ6PaSYFeQQDkJppRQTx6UlUJpKSa3BNJLfV+Xlfq6SAO1ThUcAmHhvjtBYWEQFo4VHukrekVEQkTUEf+OVFdqkboUmghd7/R3CvGTxIhE7hyi4x+wdP2LNAmhQS46JETQISHiuG08XoeDxeUcyD88QuTI18Gi8qqb6jnF5ZR5HCq8hqzCspPODXw8YUEuX9Es1E1USDMiQhIJD3YR2tzlW9oQbjmEGg/hxkOoU06Yt4wwTxlhTjlh3nLfvz2lBFf4XiFlJQSXlxCMg5vK0Sccnjf4eMW0WtySP5Zt+wpkPyqYVRXb3O7DBTd3ENahr12VhTW3y7fe5a7ct/JVtd3t28fl8q2zbd+/bVfl8vDX6hknUnt+LYjNmDGDm2++mVtuuQWAf/3rX3z11Vc8/fTTPProo/6M5hfG8fLakl01ahtkQaQbIt2GSBsiXA4RtiHG9hLr8hJreYilghhTRqxTSqxTSoynhGaeEiJNObbB9/+LEiD3JLlOFsayKrsrh/oKWmHhRy2PLHYRFnF4fVi4b7JPEWkYyg7C/i+g5SQIqX4CXmm6DpYc5IstXzCpyyTiwnT8A46uf5GA4XbZJEWFkhQVWqP2JeVecoqPLpLlFFeQW1S5LCmvunlfcMSyoLSiavqVkgovJRVeDhScakHNBkIrX8fnsiDYZRPssgh2WYS4INiGYMu3DLENwRiCLd8rCAe38RXS3MbBZbwEVS5djoPbeHE7HtyOF5fjIcjx4DrUHoMbg6vcEITBZRncOLgow0UpFgYXYGMqX5X/to74N+DCYB31b9/yyLaH1tuYH7UBKr+2bNv3crl8T/t0ubBsF5b7iMKZy121zVdUs48orlUuDxXYqi2+uY/Zx3IdWu86XLg7Ymkduc7t/lEbFfOkYfBbQay8vJwVK1Zw3333HbV+woQJLF68uNp9ysrKKCs7/EM0P7/6iSQbq3Dj5Q73DiLxEml5iKhcHvu1hxDriDKVU/mqqUM/d1xuCA729dIKDvZ1Kw4OripuWSGhh8fsh4YeLnqFhmKFhFWN3yc4RD/MRJqCop2w5Aa4YIX+IA5AO3N3csOHN7DithUqiAUiXf8ichxhwS7Cgn2T9J+qco9TNdqloKzCVzAr9VBU7qGk3Etxua9QVlK5LC73UlrhpbjcQ0mFQ0m55/D6ci/FFV7KKhzKPF6cI/4c8hoo8TiU1GDKYR+78tX0WYcKZ1XLo9dTuc46Zp2DhfeIbb7tHGefw+9tsKzq9zk22wnWHOfPyxP91XnCbWf6/WrR17A236s2n8m3nznJ3kdrGWJ4+cEraty+qfBbQSwrKwuv10tycvJR65OTk0lPT692n0cffZQ//OEP9RHPL+zgIH4Vm+nrJuuq7EbrDq+mC60LXJVdb6vWH+5ei8vt665bWeg6XOyq/Lqy8KWx7yIiIiIiUleC3TZx7mDiIoLP+Ht7vA7lXodyj0OZ58dLL+Ue3/ayisPtDm2r8Bq8jqHCcfB6DRWOwes4eLymcpvjW+etbOMYPF6Dp7KNx/nxv337OA44xlS+wHF8//Yag+OAOfTvI7Yd085UtnPMUUW/2jJVpZt67MBwJsalnpGxrVJTZaZ2w6EbO79Pqv/jnkXGmOP2Nrr//vuZPn161df5+fm0adN0Jn+13EEE3dN0C34iIiIiIiJngttl43bZhJ/5WluDclThrLLY5nUMBjAGMGAwGEPlOl8l6dB242tw1NdHtq1sXu22I9+Ho7Yd0faIf1f3Pkcyjtc3B7XXAcdTmf/Qmx4a8nToTY9Y/6M3O5StOuaI/x6z7QRFthNtq937mhq97ynnMeaktUJzwrTVCwsOOoXWTYffCmIJCQm4XK5jeoMdOHDgmF5jh4SEhBASoknVRUREREREpOmzbQu7Pnt3iQQQvw2cDg4OZtCgQcyZM+eo9XPmzGHEiBF+SiUi9aFFixa0bt2aFi1a+DuKHMkdAfHDfEsJOBFBEQxrPYyIIB3/gKTrX0RERAKMZU7U37COvfvuu9xwww0888wzDB8+nOeee47nn3+e9evX065du5Pun5+fT0xMDHl5eURHR9dDYhERERERERERaahqWivy6xxiV111FdnZ2fzxj38kLS2N3r1788UXX9SoGCYiIiIiIiIiIlIbfn/W7B133MHOnTspKytjxYoVnHPOOf6OJCISmA6uhLcs31ICzsq0lVh/sFiZpuMfkHT9i4iISIDxe0FMRERERERERESkPvl1yKSIBKa3336b4uJiwsPDueaaa/wdR0RERERERAKMCmIiUu/S0tIoKCggKirK31FEREREREQkAGnIpIiIiIiIiIiIBBT1EBMREZ+YnjB5C4S39ncS8YOeiT3Z8osttI7W8Q9Iuv5FREQkwKggJiIiPq5QiOrs7xTiJ6HuUDrH6fgHLF3/IiIiEmA0ZFJERHwKd8Di631LCTg7cnZw/czr2ZGj4x+QdP2LiIhIgFFBTEREfMpzYOebvqUEnJzSHN5c+yY5pTr+AUnXv4iIiAQYFcRERERERERERCSgqCAmIiIiIiIiIiIBpVFPqm+MASA/P9/PSUTkVJSWllJaWkpQUJCu34YkvxCKK5duHZdAU1hQCKW+ZX6Ejn/A0fUvIiIiTcShvzEP1YyOxzIna9GA7d27lzZt2vg7hoiIiIiIiIiINCB79uyhdevWx93eqAtijuOwf/9+oqKisCzL33FqLD8/nzZt2rBnzx6io6P9HUekxnTuSmOm81caK5270ljp3JXGSueuNFY6d32MMRQUFNCyZUts+/gzhTXqIZO2bZ+w2tfQRUdHB/RJKo2Xzl1pzHT+SmOlc1caK5270ljp3JXGSucuxMTEnLSNJtUXEREREREREZGAooKYiIiIiIiIiIgEFBXE/CAkJISHHnqIkJAQf0cROSU6d6Ux0/krjZXOXWmsdO5KY6VzVxornbunplFPqi8iIiIiIiIiInKq1ENMREREREREREQCigpiIiIiIiIiIiISUFQQExERERERERGRgKKCmIiIiIiIiIiIBBQVxOpJTk4ON9xwAzExMcTExHDDDTeQm5t73PYVFRXce++99OnTh4iICFq2bMm0adPYv39//YUW4dTPXYCZM2dy/vnnk5CQgGVZrFq1ql6ySmB76qmn6NChA6GhoQwaNIhvv/32hO0XLFjAoEGDCA0NpWPHjjzzzDP1lFTkaKdy7qalpXHttdfSrVs3bNvm7rvvrr+gItU4lfN35syZjB8/nsTERKKjoxk+fDhfffVVPaYVOexUzt1FixZx9tlnEx8fT1hYGN27d+ef//xnPaYVOexUf+c95LvvvsPtdtO/f/+6DdiIqCBWT6699lpWrVrFrFmzmDVrFqtWreKGG244bvvi4mJWrlzJgw8+yMqVK5k5cyabN29mypQp9Zha5NTPXYCioiLOPvts/vrXv9ZTSgl07777LnfffTcPPPAAKSkpjBo1iokTJ7J79+5q2+/YsYNJkyYxatQoUlJS+O1vf8svf/lL/ve//9Vzcgl0p3rulpWVkZiYyAMPPEC/fv3qOa3I0U71/F24cCHjx4/niy++YMWKFYwdO5bJkyeTkpJSz8kl0J3quRsREcFdd93FwoUL2bBhA7/73e/43e9+x3PPPVfPySXQneq5e0heXh7Tpk3jvPPOq6ekjYNljDH+DtHUbdiwgZ49e7J06VKGDh0KwNKlSxk+fDgbN26kW7duNXqfH374gSFDhrBr1y7atm1bl5FFgNM/d3fu3EmHDh1ISUnRnQipU0OHDmXgwIE8/fTTVet69OjBxRdfzKOPPnpM+3vvvZdPPvmEDRs2VK372c9+xurVq1myZEm9ZBaBUz93jzRmzBj69+/Pv/71rzpOKVK90zl/D+nVqxdXXXUVv//97+sqpsgxzsS5e+mllxIREcHrr79eVzFFjlHbc/fqq6+mS5cuuFwuPvroI43gqaQeYvVgyZIlxMTEVBUUAIYNG0ZMTAyLFy+u8fvk5eVhWRaxsbF1kFLkWGfq3BWpS+Xl5axYsYIJEyYctX7ChAnHPU+XLFlyTPvzzz+f5cuXU1FRUWdZRY5Um3NXpKE4E+ev4zgUFBQQFxdXFxFFqnUmzt2UlBQWL17M6NGj6yKiSLVqe+6+/PLLbNu2jYceeqiuIzY6bn8HCATp6ekkJSUdsz4pKYn09PQavUdpaSn33Xcf1157LdHR0Wc6oki1zsS5K1LXsrKy8Hq9JCcnH7U+OTn5uOdpenp6te09Hg9ZWVm0aNGizvKKHFKbc1ekoTgT5+/jjz9OUVERV155ZV1EFKnW6Zy7rVu3JjMzE4/Hw8MPP8wtt9xSl1FFjlKbc3fLli3cd999fPvtt7jdKv/8mHqInYaHH34Yy7JO+Fq+fDkAlmUds78xptr1P1ZRUcHVV1+N4zg89dRTZ/xzSOCpr3NXpD79+Jw82XlaXfvq1ovUtVM9d0Uaktqev2+//TYPP/ww7777brU330TqWm3O3W+//Zbly5fzzDPP8K9//Yu33367LiOKVKum567X6+Xaa6/lD3/4A127dq2veI2KSoSn4a677uLqq68+YZv27duzZs0aMjIyjtmWmZl5THX3xyoqKrjyyivZsWMH33zzjXqHyRlRH+euSH1JSEjA5XIdc2fswIEDxz1PmzdvXm17t9tNfHx8nWUVOVJtzl2RhuJ0zt93332Xm2++mffff59x48bVZUyRY5zOuduhQwcA+vTpQ0ZGBg8//DDXXHNNnWUVOdKpnrsFBQUsX76clJQU7rrrLsA3VN0Yg9vtZvbs2Zx77rn1kr2hUkHsNCQkJJCQkHDSdsOHDycvL49ly5YxZMgQAL7//nvy8vIYMWLEcfc7VAzbsmUL8+bN0x9pcsbU9bkrUp+Cg4MZNGgQc+bM4ZJLLqlaP2fOHKZOnVrtPsOHD+fTTz89at3s2bMZPHgwQUFBdZpX5JDanLsiDUVtz9+3336bn/70p7z99ttceOGF9RFV5Chn6mevMYaysrK6iChSrVM9d6Ojo1m7du1R65566im++eYbPvjgg6oCb0AzUi8uuOAC07dvX7NkyRKzZMkS06dPH3PRRRcd1aZbt25m5syZxhhjKioqzJQpU0zr1q3NqlWrTFpaWtWrrKzMHx9BAtSpnrvGGJOdnW1SUlLM559/bgDzzjvvmJSUFJOWllbf8SVAvPPOOyYoKMi8+OKLJjU11dx9990mIiLC7Ny50xhjzH333WduuOGGqvbbt2834eHh5le/+pVJTU01L774ogkKCjIffPCBvz6CBKhTPXeNMSYlJcWkpKSYQYMGmWuvvdakpKSY9evX+yO+BLhTPX/feust43a7zX//+9+jfrfNzc3110eQAHWq5+6TTz5pPvnkE7N582azefNm89JLL5no6GjzwAMP+OsjSICqze8NR3rooYdMv3796iltw6eCWD3Jzs421113nYmKijJRUVHmuuuuMzk5OUe1AczLL79sjDFmx44dBqj2NW/evHrPL4HrVM9dY4x5+eWXqz13H3rooXrNLoHlv//9r2nXrp0JDg42AwcONAsWLKjaduONN5rRo0cf1X7+/PlmwIABJjg42LRv3948/fTT9ZxYxOdUz93qfr62a9eufkOLVDqV83f06NHVnr833nhj/QeXgHcq5+4TTzxhevXqZcLDw010dLQZMGCAeeqpp4zX6/VDcgl0p/p7w5FUEDuaZUzlLMIiIiIiIiIiIiIBQE+ZFBERERERERGRgKKCmIiIiIiIiIiIBBQVxEREREREREREJKCoICYiIiIiIiIiIgFFBTEREREREREREQkoKoiJiIiIiIiIiEhAUUFMREREREREREQCigpiIiIiIiIiIiISUFQQExERERERERGRgKKCmIiIiEg9u+mmm7AsC8uycLvdtG3blp///Ofk5OTUaP+dO3diWRarVq2q26AiIiIiTZQKYiIiIiJ+cMEFF5CWlsbOnTt54YUX+PTTT7njjjvqPUd5eXm9f08RERERf1NBTERERMQPQkJCaN68Oa1bt2bChAlcddVVzJ49u2r7yy+/TI8ePQgNDaV79+489dRTVds6dOgAwIABA7AsizFjxgAwZswY7r777qO+z8UXX8xNN91U9XX79u3585//zE033URMTAy33norr7zyCrGxsXz11Vf06NGDyMjIqoKdiIiISFOkgpiIiIiIn23fvp1Zs2YRFBQEwPPPP88DDzzAI488woYNG/jLX/7Cgw8+yKuvvgrAsmXLAJg7dy5paWnMnDnzlL7f3//+d3r37s2KFSt48MEHASguLuYf//gHr7/+OgsXLmT37t3cc889Z/BTioiIiDQcbn8HEBEREQlEn332GZGRkXi9XkpLSwGYMWMGAH/60594/PHHufTSSwFfj7DU1FSeffZZbrzxRhITEwGIj4+nefPmp/y9zz333KOKXYsWLaKiooJnnnmGTp06AXDXXXfxxz/+8bQ+o4iIiEhDpYKYiIiIiB+MHTuWp59+muLiYl544QU2b97ML37xCzIzM9mzZw8333wzt956a1V7j8dDTEzMGfnegwcPPmZdeHh4VTEMoEWLFhw4cOCMfD8RERGRhkYFMRERERE/iIiIoHPnzgA88cQTjB07lj/84Q/cddddgG/Y5NChQ4/ax+VynfA9bdvGGHPUuoqKimq/948dGq55iGVZx7yXiIiISFOhOcREREREGoCHHnqIf/zjH3i9Xlq1asX27dvp3LnzUa9Dk+kHBwcD4PV6j3qPxMTEoybC93q9rFu3rv4+hIiIiEgjoR5iIiIiIg3AmDFj6NWrF3/5y194+OGH+eUvf0l0dDQTJ06krKyM5cuXk5OTw/Tp00lKSiIsLIxZs2bRunVrQkNDiYmJ4dxzz2X69Ol8/vnndOrUiX/+85/k5ub6+6OJiIiINDjqISYiIiLSQEyfPp3nn3+e888/nxdeeIFXXnmFPn36MHr0aF555ZWqHmJut5snnniCZ599lpYtWzJ16lQAfvrTn3LjjTcybdo0Ro8eTYcOHRg7dqw/P5KIiIhIg2QZTQ4hIiIiIiIiIiIBRD3EREREREREREQkoKggJiIiIiIiIiIiAUUFMRERERERERERCSgqiImIiIiIiIiISEBRQUxERERERERERAKKCmIiIiIiIiIiIhJQVBATEREREREREZGAooKYiIiIiIiIiIgEFBXEREREREREREQkoKggJiIiIiIiIiIiAUUFMRERERERERERCSj/HxQxT0HhHUIsAAAAAElFTkSuQmCC",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_prob(LN_ret=LN_ret, w=w_sr, lower_value=-0.09)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Comment: \n",
"\n",
"- We can see that the two curves are not so different. This is because 1 month is still a small interval of time, and therefore the linear returns are approximately distributed like the log-returns (Normally distributed) and the sum of Normal distributed random variables is Normal. \n",
"For a bigger time interval e.g. 1 year, the two curves become very very different.\n",
"\n",
"- The loss probability in the plot is the probability of losing more than the Maximum loss value i.e. $\\mathbb{P}(R^P < -0.9)$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Short positions - closed formula\n",
"\n",
"If are allowed to take short positions, we can remove the bound $\\boldsymbol w \\geq 0$ and the problem becomes:\n",
"\n",
"$$ \\min_{\\mathbf w} \\; \\mathbf w^T \\mathbf \\Sigma \\, \\mathbf w \\quad \\text{subject to } \\quad \\boldsymbol \\mu^T \\mathbf w = \\mu^P, \\quad \\mathbf w^T \\mathbf 1 = 1. $$\n",
"\n",
"Following [3] we can compute the weights and the efficient frontier in closed form.\n",
"\n",
"We can define the following new variables: \n",
"\n",
"$$ \\boldsymbol{\\Omega} = \\boldsymbol{\\Sigma}^{-1}, \\quad A = \\boldsymbol{1}^T \\boldsymbol{\\Omega} \\boldsymbol{\\mu}, \n",
"\\quad B = \\boldsymbol{\\mu}^T \\boldsymbol{\\Omega} \\boldsymbol{\\mu}, \\quad C = \\boldsymbol{1}^T \\boldsymbol{\\Omega} \\boldsymbol{1}, \\quad D = B\\,C - A^2. \n",
"$$\n",
"\n",
"The weights have the following **closed expression**:\n",
"\n",
"$$ \\boldsymbol{w} = \\frac{\\mu^P \\boldsymbol{\\Omega} \\bigl(C \\boldsymbol{\\mu} - A \\bigr) + \n",
"\\boldsymbol{\\Omega} \\bigl( B-A\\boldsymbol{\\mu} \\bigr) }{D}. $$\n",
"\n",
"The **efficient frontier** is a **parabola** with the following equation:\n",
"\n",
"$$ \\bigl(\\sigma^P \\bigr)^2 = \\frac{ C \\bigl(\\mu^P \\bigr)^2 - 2A \\mu^P + B}{D}. $$\n",
"\n",
"It is good to check that the covariance matrix is full rank before the inversion."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The rank of the covariance matrix is: 13\n",
"The shape of the covariance matrix is: (13, 13)\n"
]
}
],
"source": [
"print(\"The rank of the covariance matrix is:\", np.linalg.matrix_rank(COV))\n",
"print(\"The shape of the covariance matrix is:\", COV.shape)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"Omega = np.linalg.inv(COV)\n",
"A = np.ones(N) @ Omega @ MU\n",
"B = MU @ Omega @ MU\n",
"C = Omega.sum()\n",
"D = B * C - A**2"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"target_mu = 0.1\n",
"weights_th = (target_mu * Omega @ (C * MU - A) + Omega @ (B - MU * A)) / D # theoretical weights"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Theoretical weights:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
" AAPL AMZN GOOGL JPM GLD DIS VNO FB UBS \\\n",
"0 1.2458 0.3685 0.5282 0.5597 0.9733 0.1442 -0.2223 -0.272 0.4089 \n",
"\n",
" KO MCD ^GSPC GM \n",
"0 0.1547 -0.1562 -2.9257 0.193 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(pd.DataFrame([dict(zip(data.columns, optimizer(MU, COV, target_mu, False).round(4)))]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Let us compute the efficient frontier"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"samples = 50\n",
"means = np.linspace(-0.1, 0.17, samples) # vector of target expected returns\n",
"stds = np.zeros_like(means)\n",
"\n",
"for i, mn in enumerate(means):\n",
" w_opt = optimizer(MU, COV, mn, OnlyLong=False) # optimal weights\n",
" stds[i] = np.sqrt(w_opt @ COV @ w_opt) # std of the portfolio"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(15, 6))\n",
"y = np.linspace(-0.1, 0.2, 400)\n",
"x = np.linspace(0, 0.23, samples)\n",
"y, x = np.meshgrid(x, y)\n",
"CS = plt.contour(y, x, y**2 - (C * x**2 - 2 * A * x + B) / D, [0], colors=\"k\", alpha=0.7)\n",
"plt.clabel(CS, inline=1, fontsize=10)\n",
"CS.collections[0].set_label(\"Theoretical efficient frontier\")\n",
"plt.scatter(stds, means, linewidths=0.1, color=\"green\", label=\"Numerical efficient Frontier\")\n",
"for i in range(N):\n",
" plt.plot(cp.sqrt(COV[i, i]).value, MU[i], \"o\", color=\"goldenrod\")\n",
" plt.annotate(f\"{data.columns[i]}\", (cp.sqrt(COV[i, i]).value, MU[i]))\n",
"plt.xlim([0.03, 0.19])\n",
"plt.legend(loc=\"upper left\")\n",
"plt.title(\"Efficient frontier - Short positions allowed\")\n",
"plt.xlabel(\"Standard deviation\")\n",
"plt.ylabel(\"Return\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"[1] Attilio Meucci (2005) *Risk and asset allocation*. \n",
"\n",
"[2] D. Ruppert, D. Matteson (2015) *Statistics and Data analysis for financial engineering*\n",
"\n",
"[3] Robert Merton (1970) *An analytical derivation of the efficient portfolio frontier*"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: A.1 Solution of linear equations.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Numerical algorithms by examples\n",
"\n",
"## Contents\n",
" - [LU decomposition](#sec1)\n",
" - [Jacobi method](#sec2)\n",
" - [Gauss-Seidel method](#sec3)\n",
" - [SOR method](#sec4)\n",
" - [Thomas algorithm](#sec5)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy as scp\n",
"from scipy.linalg import solve_triangular\n",
"from FMNM.Solvers import Thomas\n",
"\n",
"from IPython.display import display\n",
"import sympy\n",
"\n",
"sympy.init_printing()\n",
"\n",
"\n",
"def display_matrix(m):\n",
" display(sympy.Matrix(m))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The problem we want to solve in this notebook is very simple:\n",
"$$ A x = b$$\n",
"where A is a $n\\times n$ square matrix and b is a vector of dimension $n$. **Our goal is to find the $x$!**\n",
"\n",
"It is well known that the solution of this problem is simply:\n",
"$$ x = A^{-1} b $$\n",
"however, inverting a matrix is not (in general) a good idea. \n",
"Matrix inversion is a slow operation and there are plenty of algorithms that permit to solve the matrix equation with no matrix inversion.\n",
"\n",
"I will review the main methods by examples. \n",
"Let us choose the following values of $A$,$x$ and $b$: "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix A:\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAHgAAABkCAYAAABNcPQyAAAACXBIWXMAAA7EAAAOxAGVKw4bAAALDUlEQVR4Ae2dX47VNhTG71Q8V3Qq9b2XHVBYAdMd0LICYAdFPMEbgh0AKyiwA2AFCHYAC6hUGHUF/X6ZOHJynXvtxP/mYkueOE7iz+d89rFjn5s5efTo0fXNZvNR0RXePH78+A/XhZZXhwbEz2fVZOuqja6dXLEuPFOam+3wxT5p6So18NRRq9+Vd5t8m+DnYrxKQlWv64qfbEF0flXnp7XW2a7rkrTkoleeKb5S+nyuDF17Mb2mPLJ2CJ7eNzrvAR/0mTd0/Kr4QPkjxY8einfyXjgQarBIE367OKT72+M+tBDAfq38d1ZeiiRD53OisFzlnyv/J9cFO8/uwXb+KK2CaE0A0fW7oDSm4SN5iqmFpTEREBor80bxiXBnW7auxwpPhXPfLkznEHxVkXqkCjdVMHp1WVV6tulse/G9CFYJkDkVkt57T/mvFQ+2JN2zJnwSVvbJXi+fawJ6V8K8V0xJ8Eb4Q4cyylNeN6HS0Qv7B/PggSMt5rMKNabR3E4LoyV3oCbziI7XJMuOkjPJ92EGZ8eizNzXZfsS3JkKETlnEqfE78O8TNdQ8m3J/XbSuLFojI/JgvB2eqjywH0SAuplolXwnHlkTNzoupn8hGAH3SsMhgMa0s+KWAzG4KS4Kp91ABTNjPSb0ox79GoI3yFA+cmC8JCZtwmvsddUxLcHm/uHo4AgF9AgwKGAsATE8rrwrBcQTCZ4DB1JgzBo3OZVhB4EZtKGNSMQ2MSgsJhgoTC5ooWzQJI0CIOZ+jA8KM3MkmEjqZlEKGHRe8Gm54JJo2Y+0r1nKp08CAvMMx3BDwqLCBYQiv2i45zpDqrEwpshedsLv7CI/Y+pbIYFGhdvDMjLhMvI/FLnWJYcgTcY1+vSQexggiUUQrOClGV2KRzGO9erihEupZIxiaMhSHVh7KU3g5t8iBAGAWth1gK6DN8/QQRLOICu6Wha8UbppL1IeDcUXSSeIqTwk4yHKhdMXgHPwbGD8uhNEN3Vwb4WO93XY6tyd+rhg+VNsICYVN3UcdSilbe4dflUUPe8ECY9ZhroPcFj0rSQuXNholCWA1GuK9AAkuFbgDRwwqIefOXi2f1/eyGZVL1TejqxYfBPOdFiiZQ4rKQp/Vdf48GS7Jdg8VXKZ1nyluLQg5RmmGLoWDQuBtaGhkQY8C9O/f56Eayi3irSkhFsGpKYSAOCEhVZvTENC7NIa/5VeYuENmUfOqp8GvRd3ceEyu5BOTYbTPXQL3LOrWyZ+5xHL4IlnMtEOgtMkSl8esrQg1NgzJUpbBSc2lLMwW962Rev9XuPwbM1aBeq1kAjuGp61leuEbxeh1WX0Aiump71lWsEr9dh1SU0gqumZ33lGsHrdVh1CY3gqulZX7lG8HodVl2CN8FaUWGzYRSUd8wOdyNZL+uJ11JlL1xJ5/Ot6mB2sdhdYV04i9O9GnFJbDYaHiqawHnQOngIwWaxnZ5s9kOTO5/3Ci7idF8Su2d0x0VWdQpyug8huIjzuQTFq2K00SAhczndF8OWjOzcuTxZ2N3ydrr3HoNVaKlwJuBSTvclsdnBW+0WdRkIxmuCPeG5vV/GpVShJHYUp/sQE73pzQYKzel8PrcX283qVSf2a5MElV0SO4rTfUgPhtgizudT9qR4yLVnt9Nbkp3nxO4b2Cqne2+CBVbM+dzBVjan+5LY0jkOjQxNjMcMFzTqIKd7CP5RkWCOF2d+f3ldSu02O6qJhC7mdJ8TW1jMopc63f9ilObVgwVW0vnc1HXTC53N6X4AVqIAdhSnewj+rxfEHG25TJrVI9ds9ZQbJHyyiY6pgDAwV7md7jv43NjCQ9drnO7/MXrz6sG6uYjzuamkBGZSVcLpflMCW5iMu1Gc7n1fk4o5n0vYrYQt4nRfElsyR3G69yJYghZzPpegxZzuS2JL51Gc7r0IlqAbSNZhtCZMfuog3GJO9yWx0avwmdvMLbZ4qd53DPYqrN1UnwYawfVxErVGjeCo6qyvsEZwfZxErVEjOKo66yusEVwfJ1FrBMHm9edr1JJbYSU1wM5T90oLwVtFdmi6dWUdW7j8GjiTCN0XEZqJvvxk7pXAi2CtqLDne0/RtaO0FyDGReGy2TAK1EUR69PCHg34LlWiYLo8mw6u4tj5WPwdCVeBk7xiTvemHpKP/Vk7/K285NukAK7B9iX4pnA6D0Nbwj6NvTe/OnBcjpJlJoA0tGxO99RcysVKsJuFLzY6II9zYtJ18hjYvgQj1I6Pbl8BrqX+tG4pp3v4hEh6a0cuGQoMVTS01GE1ti/BH2Yk4acVq3Y7ZsqtIluy4UWC1bhlV0j5O43dvh4jHQvbd5K100NVAcakJzGEqbgM3iWZX+BhkTtEwfbtwSPhJDDjUvDXx0eFBJ4IEy9DTGM2p3th4YuGswO9+I7iv4qMu0G/8NP9S0IU7EUEq7b03umscokQvs9A7PAPovoGhn8wbqX22Ohbnu994BJuCGeYSCrN5/3vKu5Ytovbo/yNgu1lou3qSih675mOKRVrQ26Eld3pXphGwVgq8+sCU69XSvD9SnOPyY9ytMpdjR1MsCRgbMgxgzykLOqQw+neJSs/64RczGjKsBp7CcHMLM17aUrhurLVmos43Qv3vBfOHF2yYs2ih5jYQQT3pgOh9gkdW2B6Cb1lGk7JUJ1SriYxDLmwTV1cPcxcW3uMgh1EsGpsTFK2HizMkk73LM+6ein/FJPXp5TzkCjYoQSb1pyzB3dO91LoEKTYLF98Fw6zZPyThzcGpdHBn4p8SiFZiIUd+pqEOYTcuZWt6AJL0JJO9xvhM4Nnxa7bX5WADA184j/l0NDpMQb2ifUv3vlhV8oxJTr5rUC3BsQji0JYvpNQE+0useVWq4FGcLXUxKlYIziOHqstpRFcLTVxKtYIjqPHaktpBFdLTZyKNYLj6LHaUhrB1VITp2KN4Dh6rLYUb4K1KoLbyigorzmfjzRS30nIWnRx53Ojvr6x3dcR54OsISc2HUjCPbQE5DzIHyyEYLNFSE9mzZqdluRffBeGK+AvnHyx3wWsvJzYbHKMGrHOj+6L7yM9S0CzVTjKz3GSE1tY380X3wfuJDTWg+1KYtZQABv33NUO9t6TrKzanAe7I0VPPRzn7457JTc2e+63JS8+aYy9JuB8YPamTd7sMWQM3giohPN5V3lhY5q9BZuVeMGFEtjC/H6++C5ht+IFH6jsDgmFsfndl7FY9NwzxaDJpbeJlqDZnc8ljAm8EhlBTV6uYzFsyYyLMvMNxmMc/GjowV981zOLAz0qqfO5hOzcTxbXcMWDFWDTqfhdMn5pTLjMLzm9f1Xh1YNVeCnnc1osq2WlTHMR7L5NYpKH30ORJz2w9kBvZtKFuT4YfCdZ+EObhQ67UDwMAQ4aF+wCDqQhmA+Bs7hgB16XsBzk07pHirBvXJEuhi15IJDGhXkeBeUhL0R3uh9ddJz4EozzuUuJtKJkzt/CpOyd8pX/jXwdjclyiLYuqzA2E0oijdhlvWgAO3pxSexlovVgMedzV6WV17XwmWups3Nh03i7ZUlbIBHOnIQh00W8fWuX9vaLpjXpCdOLMQ+YbCYAO2akKznBH2HxHkw9zPiDqfqg/GcJ4EZFlsAWJkMRmw328Hhws0HPdRNTHU+8CR5J206q1oBNsK+JrlqgVrl5DTSC53VzFFcawUdB47wQjeB53RzFlUbwUdA4L4S90MEi9vROtqySLSZMwdp5uAbEz2c9tZ17EoJ5YR75/Vg3e71MW/e3ZH4NDF8fcEH/Dx/JpF05sGX3AAAAAElFTkSuQmCC",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}2 & 5 & 8 & 7\\\\5 & 2 & 2 & 8\\\\7 & 5 & 6 & 6\\\\5 & 4 & 4 & 8\\end{matrix}\\right]$"
],
"text/plain": [
"⎡2 5 8 7⎤\n",
"⎢ ⎥\n",
"⎢5 2 2 8⎥\n",
"⎢ ⎥\n",
"⎢7 5 6 6⎥\n",
"⎢ ⎥\n",
"⎣5 4 4 8⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Vector x:\n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1\\\\2\\\\3\\\\4\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1⎤\n",
"⎢ ⎥\n",
"⎢2⎥\n",
"⎢ ⎥\n",
"⎢3⎥\n",
"⎢ ⎥\n",
"⎣4⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Vector b:\n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}64\\\\47\\\\59\\\\57\\end{matrix}\\right]$"
],
"text/plain": [
"⎡64⎤\n",
"⎢ ⎥\n",
"⎢47⎥\n",
"⎢ ⎥\n",
"⎢59⎥\n",
"⎢ ⎥\n",
"⎣57⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"A = np.array([[2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]])\n",
"x = np.array([[1], [2], [3], [4]])\n",
"b = A @ x\n",
"print(\"Matrix A:\")\n",
"display_matrix(A)\n",
"print(\"Vector x:\")\n",
"display_matrix(x)\n",
"print(\"Vector b:\")\n",
"display_matrix(b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The matrix A of this example is invertible! \n",
"We can check it is a full rank matrix and calculate (just for curiosity) its determinant."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rank: 4\n",
"Determinant: 194.0\n"
]
}
],
"source": [
"print(\"Rank:\", np.linalg.matrix_rank(A))\n",
"print(\"Determinant:\", np.linalg.det(A).round(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first method I'm presenting, is forbidden!! \n",
"So... read it and then forget it."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The inverse of A is:\n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}-0.08 & 0.19 & 0.25 & -0.3\\\\-0.27 & -0.9 & -0.2 & 1.28\\\\0.27 & 0.4 & 0.2 & -0.78\\\\0.05 & 0.13 & -0.15 & 0.06\\end{matrix}\\right]$"
],
"text/plain": [
"⎡-0.08 0.19 0.25 -0.3 ⎤\n",
"⎢ ⎥\n",
"⎢-0.27 -0.9 -0.2 1.28 ⎥\n",
"⎢ ⎥\n",
"⎢0.27 0.4 0.2 -0.78⎥\n",
"⎢ ⎥\n",
"⎣0.05 0.13 -0.15 0.06 ⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The value of x, given by matrix multiplication 'x = inv(A) @ b' is:\n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0\\\\2.0\\\\3.0\\\\4.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0⎤\n",
"⎢ ⎥\n",
"⎢2.0⎥\n",
"⎢ ⎥\n",
"⎢3.0⎥\n",
"⎢ ⎥\n",
"⎣4.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"inv_A = scp.linalg.inv(A)\n",
"print(\"The inverse of A is:\")\n",
"display_matrix(inv_A.round(2))\n",
"print(\"The value of x, given by matrix multiplication 'x = inv(A) @ b' is:\")\n",
"display_matrix((inv_A @ b).round(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## LU decomposition\n",
"\n",
"For more info about the theory of the LU decomposition, have a look at the wiki page [link](https://en.wikipedia.org/wiki/LU_decomposition)\n",
"\n",
"Let us have a look at the LU decomposition. \n",
"The matrix A is decomposed into the product of three matrices:\n",
"$$ A = P L U $$\n",
"where L and U are lower and upper triangular matrices, and P is a permutation matrix, which, reorders the rows of L@U. \n",
"\n",
"If you are not familiar with these concepts, they will be immediately clear after seeing them in practice. \n",
"Scipy offers 3 modules:\n",
"- [scipy.linalg.lu](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.lu.html)\n",
"- [scipy.linalg.lu_factor](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.lu_factor.html)\n",
"- [scipy.linalg.lu_solve](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.lu_solve.html)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Lower triangular matrix:\n"
]
},
{
"data": {
"image/png": 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ffAbxpdd92obndvGBr9hCWwKjAzDg6COvRPzHgZjcwH0jGdHV8rlxMvEnwQn4cNUa26ysK6CCHbXZNfFAvqNtGTxDOjOK01j57eKDMYQs3BAoDAF0GnbxQWF1ZsU1BGYhoHoHn5WyRTIEDIHNETAF37wKrACGwHoImIKvh62lbAhsjoAp+OZVYAUwBNZDwP2b7D2+vvVzeguau8iiH27PhoAhsCEC0M/3yP4iVAQqOP+3Dp2qkvtyvpBcRjcEakGgW8jjE/j/eVxgzinYqVAAAAAASUVORK5CYII=",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0 & 0 & 0 & 0\\\\0.29 & 1.0 & 0 & 0\\\\0.71 & 0.12 & 1.0 & 0\\\\0.71 & -0.44 & -0.46 & 1.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0 0 0 0 ⎤\n",
"⎢ ⎥\n",
"⎢0.29 1.0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢0.71 0.12 1.0 0 ⎥\n",
"⎢ ⎥\n",
"⎣0.71 -0.44 -0.46 1.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Upper triangular matrix:\n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}7.0 & 5.0 & 6.0 & 6.0\\\\0 & 3.57 & 6.29 & 5.29\\\\0 & 0 & -1.04 & 3.08\\\\0 & 0 & 0 & 7.46\\end{matrix}\\right]$"
],
"text/plain": [
"⎡7.0 5.0 6.0 6.0 ⎤\n",
"⎢ ⎥\n",
"⎢ 0 3.57 6.29 5.29⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 -1.04 3.08⎥\n",
"⎢ ⎥\n",
"⎣ 0 0 0 7.46⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Permutation matrix:\n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}0 & 1.0 & 0 & 0\\\\0 & 0 & 0 & 1.0\\\\1.0 & 0 & 0 & 0\\\\0 & 0 & 1.0 & 0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡ 0 1.0 0 0 ⎤\n",
"⎢ ⎥\n",
"⎢ 0 0 0 1.0⎥\n",
"⎢ ⎥\n",
"⎢1.0 0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎣ 0 0 1.0 0 ⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Have we obtained the correct decomposition? True\n"
]
}
],
"source": [
"P, L, U = scp.linalg.lu(A)\n",
"print(\"Lower triangular matrix:\")\n",
"display_matrix(L.round(2))\n",
"print(\"Upper triangular matrix:\")\n",
"display_matrix(U.round(2))\n",
"print(\"Permutation matrix:\")\n",
"display_matrix(P)\n",
"print(\"Have we obtained the correct decomposition?\", np.allclose(P @ L @ U, A))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ok... But what is the Permutation matrix? \n",
"For more information on this subject have a look at the [wiki link](https://en.wikipedia.org/wiki/Permutation_matrix)\n",
"\n",
"We can see that the product L@U is almost equal to A, but the rows have a different order. For this reason we need to multiply it by P which produces the desired permutation."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}7.0 & 5.0 & 6.0 & 6.0\\\\2.0 & 5.0 & 8.0 & 7.0\\\\5.0 & 4.0 & 4.0 & 8.0\\\\5.0 & 2.0 & 2.0 & 8.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡7.0 5.0 6.0 6.0⎤\n",
"⎢ ⎥\n",
"⎢2.0 5.0 8.0 7.0⎥\n",
"⎢ ⎥\n",
"⎢5.0 4.0 4.0 8.0⎥\n",
"⎢ ⎥\n",
"⎣5.0 2.0 2.0 8.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_matrix(L @ U)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's have a deeper look. \n",
"The coordinates of the ones in the matrix P indicate the relation between the rows of A and the rows of L@U: \n",
"- (0,1) The first row of A corresponds to the second row of L@U; \n",
"- (1,3) the second row of A correspond to the fourth of L@U; \n",
"- (2,0) the third row of A corresponds to the first of L@U;\n",
"- (3,2) the fourth of A corresponds to the third of L@U. \n",
"\n",
"This is clear by looking at the columns of the matrix ```np.where(P==1)```."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}0 & 1 & 2 & 3\\\\1 & 3 & 0 & 2\\end{matrix}\\right]$"
],
"text/plain": [
"⎡0 1 2 3⎤\n",
"⎢ ⎥\n",
"⎣1 3 0 2⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"After the permutation the resulting matrix is equal to A:\n"
]
},
{
"data": {
"image/png": 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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}2.0 & 5.0 & 8.0 & 7.0\\\\5.0 & 2.0 & 2.0 & 8.0\\\\7.0 & 5.0 & 6.0 & 6.0\\\\5.0 & 4.0 & 4.0 & 8.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡2.0 5.0 8.0 7.0⎤\n",
"⎢ ⎥\n",
"⎢5.0 2.0 2.0 8.0⎥\n",
"⎢ ⎥\n",
"⎢7.0 5.0 6.0 6.0⎥\n",
"⎢ ⎥\n",
"⎣5.0 4.0 4.0 8.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_matrix(np.where(P == 1))\n",
"print(\"After the permutation the resulting matrix is equal to A:\")\n",
"display_matrix((L @ U)[[1, 3, 0, 2]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The previous code is useful to obtain the decomposition. \n",
"Now we can solve the problem \n",
"\n",
"$$ PLU x = b $$\n",
"\n",
"using both forward (for L) and backward (for U) substitution. \n",
"Since permutation matrices are orthogonal matrices (i.e., $PP^T = I$) the inverse matrix exists and can be written as \n",
"$$ P^{-1} = P^T $$\n",
"and there is no need to perform a matrix inversion.\n",
"\n",
"The method **solve_triangular** performs the forward/backward substitution (it is a wrapper of the LAPACK function *trtrs*). \n",
"We can write:\n",
"\n",
"$$ L y = P^T b $$\n",
"and\n",
"$$ U x = y $$\n",
"\n",
"and solve it:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0\\\\2.0\\\\3.0\\\\4.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0⎤\n",
"⎢ ⎥\n",
"⎢2.0⎥\n",
"⎢ ⎥\n",
"⎢3.0⎥\n",
"⎢ ⎥\n",
"⎣4.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"y = solve_triangular(L, P.T @ b, lower=True)\n",
"x_solution = solve_triangular(U, y) # by default it considers upper triangular matricies as input\n",
"display_matrix(x_solution.round(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Another LU solver\n",
"A faster method to solve the linear equation using the LU decomposition is to use the **lu_factor** method (it is a wrapper of the LAPACK function getrf). \n",
"It returns two matrices\n",
"- The first, LU, is a matrix from which it is possible to derive easily the L and U matrices\n",
"- The second, piv, corrsponds to the pivot indices of the permutation matrix P: \n",
" In the matrix P, changing the row i with the row piv[i] (for all $0\\leq i\\leq 3$), produces the identity matrix. The same permutation transforms A into L@U. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Construction of the matrix L:\n"
]
},
{
"data": {
"image/png": 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ffAbxpdd92obndvGBr9hCWwKjAzDg6COvRPzHgZjcwH0jGdHV8rlxMvEnwQn4cNUa26ysK6CCHbXZNfFAvqNtGTxDOjOK01j57eKDMYQs3BAoDAF0GnbxQWF1ZsU1BGYhoHoHn5WyRTIEDIHNETAF37wKrACGwHoImIKvh62lbAhsjoAp+OZVYAUwBNZDwP2b7D2+vvVzeguau8iiH27PhoAhsCEC0M/3yP4iVAQqOP+3Dp2qkvtyvpBcRjcEakGgW8jjE/j/eVxgzinYqVAAAAAASUVORK5CYII=",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0 & 0 & 0 & 0\\\\0.29 & 1.0 & 0 & 0\\\\0.71 & 0.12 & 1.0 & 0\\\\0.71 & -0.44 & -0.46 & 1.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0 0 0 0 ⎤\n",
"⎢ ⎥\n",
"⎢0.29 1.0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢0.71 0.12 1.0 0 ⎥\n",
"⎢ ⎥\n",
"⎣0.71 -0.44 -0.46 1.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Construction of the matrix U:\n"
]
},
{
"data": {
"image/png": 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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}7.0 & 5.0 & 6.0 & 6.0\\\\0 & 3.57 & 6.29 & 5.29\\\\0 & 0 & -1.04 & 3.08\\\\0 & 0 & 0 & 7.46\\end{matrix}\\right]$"
],
"text/plain": [
"⎡7.0 5.0 6.0 6.0 ⎤\n",
"⎢ ⎥\n",
"⎢ 0 3.57 6.29 5.29⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 -1.04 3.08⎥\n",
"⎢ ⎥\n",
"⎣ 0 0 0 7.46⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"piv vector:\n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}2\\\\2\\\\3\\\\3\\end{matrix}\\right]$"
],
"text/plain": [
"⎡2⎤\n",
"⎢ ⎥\n",
"⎢2⎥\n",
"⎢ ⎥\n",
"⎢3⎥\n",
"⎢ ⎥\n",
"⎣3⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"LU, piv = scp.linalg.lu_factor(A)\n",
"print(\"Construction of the matrix L:\")\n",
"display_matrix((np.tril(LU, k=-1) + np.eye(4)).round(2))\n",
"print(\"Construction of the matrix U:\")\n",
"display_matrix(np.triu(LU).round(2))\n",
"print(\"piv vector:\")\n",
"display_matrix(piv)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Product of L@U:\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAALgAAABkCAYAAAA1z/qTAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAPiklEQVR4Ae2dX5LcthHGZ1N6TtlKVd69voGkPYHlGzjxCWzfIC4/SW8q+waWTxDbN5B1Av25gZz3VEWeygWS74dlUyAIDsEZckDOdldxAQIY4OOHRqNJgtirJ0+ePNjtdm905OTXp0+f/i2X4WnOwBoYkH6+E47rHBblXd2LMn5QnMKx/B6feNwZWCED32cwfa60L0iPFfxHaXxWoZXOCHms42fF9/xwSyLMD3S8jTHr/COd31eYvea4rMfXy4D673mKTmkk9RQ8LRef48b8yNH8OM4jvlf6x2lieq4yNtr+o7xPdXyvtHMo2Eu1g0KbkhNHHt4G438rYg/govYN7D+VZtdjadkw+u25eW/xRBgs7Sz4YwtuDefCGyX+piOnjFj2b3M/itN0gfj5zxT+SrpClOyNws915Oql2FzyvqmIgUpbYADLvkk/GKhcNexqm9nzFx3fKk4f7BRyzoGROCgqWw07wNR+VfylCg5Q/JqONODJC0rbyYxOlP+1Tj+KyymO1ed3zAy9uqOfzxF9q7aOulnW72pjR5GxdkG5GzIwDqNGYQXYgVsV/58awsaCVwMFcDG+GciLk1Gu3HRKvY9Vh7kM8W/WEq+GXbzgRzLrdPxMpTPrlRiFatjpvDXgL1JwAe1ZaKXhTz/jQgoEN8bchLi4WSHy1yo1sWM8mOmKXKkMgTWxA6c6/nsZUkaTRDh+FU8mSnzvEut8f7TREwsIa3A1VM1fdIAfHzw3q7QtKb829kcC87twYMW/1GE3ib8oLXZZWswWWQF2oFTHf5SCCzjW256IcCGHxJT3kBUqUaRDbYzlUX/7iFOdj4K/U8hUf0hRamM3Xh4JZ2tMFP9Dx1c6ejNrRERt7ECpjr/IRYlI24lUlAO/+ZBixD8piWNVFxNhRZH31oDiuEbg5wb3VFkEuzCacjBTdnxwAf5Zx09RmWOvYRHsgImwVcU/WcGFHb/KfGeuZUxyvrf9xqwMU++5hWu4VkcwYIdkDdhzXPPojwGACzAka8AOtqr4j1Fw7uwPkdchXApkltMsUpxvaTkS4nJHx9X+Cx0oxJAYhl5+TexR28ZfD58SBgdn9Pvc9Vnakrwbbgur4J+k4CINYiD1EOjcheAO5DrDLPic7k7aPlYO3KmEtnVNb9OM5LwmdtrOYTeIYwpaEzsYq+OfpOACbFNisQVveoKH/fbbJikED/WXlzBTB0xcx1j8uerPvfHjERodMCY1sXOPkDMM8MbjwzH8NbHDa3X8UxXcrElWIUU4byv/p6PjEuicm6T3CnFvgihOXX/X8dVtymJ/WT/TuZnU+T+a1tq3m+DRsSrswsNTkt8Utk+swKm0Dm+k6VgVdvgVpur4pz4mZDpHuXkD2RNdEFaFafN1L/N2YRNvPm+Ux00l4Wc6H3MRMlWVJ4FHB+2akuOaMAN9orR2oBLXsSrsXKUw8QQoxd/hTfmrxL4G/FfRBw+fiqgxnw7MLs7AqhmQHvNSj5n7aqqLsuoLc3DOQMqAK3jKiJ9fFAOu4BfVnX4xKQOu4Ckjfn5RDLiCX1R3+sWkDLiCp4z4+UUx4Ap+Ud3pF5My4AqeMuLnF8WAK/hFdadfTMrAnVBwvdF6kF640li/cZ2m+/l8DMCvjq/her5ap9VUvBalUQb7bIqVgaznYK+O4rUkKmuLhliL4hv/FPRVoxzfRUVRltFvMqPyu4q8Y1hYA1Rtw6giBRdBWDpAtlsVNKTZxj1jyzZ3Ks8KQ9/4RyRMlN7WHOISBWcGOvRNZmimMu8sqEM3cmucWK5sBjNgzf05FX+RgqthLC+fqrWihrHeLGphzfHBbduacp0OURor4OggRng7cBRfQja58U/DW2fpcUMOS4xf6jio4CvgfScMvb5VWnANFS6Ov9QHZ7TxFXrqSzE6S3xZ1l3nXBmW3fIBc1qvklcjNbHjxvUUZAIzNbEDM7usWum9WWngmk7GX6rgYZqRIu4HgIwpKAMEnz0Vm7rIX6vUxI6CfCHe+a405pgZ1da3H+KtJvadMPcstNLA/uwQ6CjvZPz3osoGowLFSMpJeDqh/Jx1DuWVF3dMrg7S+AhhURGOzW38I8z8AwKUhC+h2AsFnxWrjsL3lEfprSh/Fby3gBQRJlyTs24YVWrBY5whLrAoN4DHbhRMefe9Sj4klHTGh9LTY9TPxj8/6AAvBzfIWIhDUh27MGJcbF8UrB+YBw1KdDHVsUdYLAp+jhKZBf/RCi6E3FxiYfjPEKfKYhvQAEwYN7fxjxEq7FhvjAOWG1cRo8L9UPt9q86PlUV5j0EJL7i53+Ia5pJR/Cj4n5vWLBxtXCDx//jWcch1iet4H58kcRulPBc/t+D/r3rjH/GLW8Xg5IkVfHPDaZyP7Wy1Nt7PuWHUX02ZUPBJ0pB+vyF79LcqZ65Jzg2xNLvZHK1vagG1v8mNf5rrZDrvuIC6HnxvrDncDbpYtXkXtlSYcQ4Nuk75ufCj4P9taraw01B8okYBycfJZkV2io9ZQaqwqTWujrhZ8DmnrbSNR0qwgRTnhbaF/22cmIlXwS5cYOYR7D7FpDQMAopu/KVF7LwKdmvcwuZarnXeuxYrMxAei//fVl+xBRfIB/rRjcKORVFaycjEX0fRUmEDG9/4J2VF5+IZZeBlGIqREwbAmGGoyXuM2fq+2II3Pz4Zf5GCNyTTGBYlbKRjodK+UTyMTIXkr20DmoA3ZlsYN7HxjzAzU4bX8gl+fHNcr+DarZT3GDKDEQl6chv98HdJ/EXPwQXlhQ4sCcSm0k7xAorFgfTXaSGdY615g3WjkJtKws4GNjqfXcCjI904B0uy+o1/hJtdrXgtzw1lbP06i62Utzrek45ER1DuV0l6OF0Sv2/8k2Pc0zbNgAYMhpiZ+6rIRdn01Tr4O82AK/id7v7Lv3hX8Mvv4zt9ha7gd7r7L//iXcEvv4/v9BW6gt/p7r/8i0fBeW7NQpj4OevlX7lf4SUzwBve8IklCn6tg9WBY+saVMTFGdgEAyxCC188uYuyif5ykMcyUKzgeivEYquOKI21J8wALs7AKhkoXYsC+JcotELWFSC2gIY1JkWi39vnSqxFYU0za0TCgqGiCmYqpDYZrCwSC35aSbWOvYSlfhnxhp58F+Vw3llLE+Vlo6dwX2zB1TI3oXsdKAcgWY/8UI2TNioqx/4erxTydQqfubHslhVxNWYAVkYW33M4drF1vGDE6HM7MCoYF5ZZj8qp3E9RcNZtf6zjSgcfPQC4VLlZ/II7w6AI0vyW83Az0CQvHqhdWypb1JbKO/YipvqFGu4wbKmwQjK26ml+OJ+D+ykKngVRmMi6ZnNt4p+wfJIPUZkRFhe1w+zDoCwamA0gx358z+CGVt246FwKzmOb3HN287/JP4d8KSW3LRhK23PspUz1y2HAqm5cNOUmcyflCNO1QPO5/rUONtPMWWZl3YryS6xzsT9s9U4NhQPXZJI75NinstwtL/6qb1w0xYKjqFvdPIfBaF+9dHvh8JkNvEMuTckAPtzKgVwpyWaxc1nCj4tnsyZP0ZgRDxpF5SOzcF+s4AK62c1zRBZ37UZyYG/GP8xmS8qWsaPgPC3BQFTZuKhYwQd6EB96bNuInO9t1dko5bn4IiKCw+dLR1bu2I8kjp813FfduKhIwQV0k5vnCDfTO48n7WZ2Unfpd+aa5NwQSzuq7jEgW8YeXVv1jYtKbzLZ1yJnzYIFVmeM+VTHbuAScXVUFAVnLxde7MTyQCfMPKTz1X2610tc1rHHbBTGxSkGAONiRqL9pdLgnHcgNoO3eUnkZO5LFfy5AOWUgBsGQIwJimSv6eOyvOZfbOMfYQZbD5/S/yBdITdAY+LYxxjK5Itbbuo5MCS5WY4B0OubpKqTuS9yUdToljfPSTgLp8G6xBnqBKzN2jYtiiFafEvYMSBVNy4q3heFkSiwZsWZWnBZeq/rVe6d0rGOnYVMOqdjsOJ7HdxU3ugYfY6uMrOJMPAcnOtg5kGYJlkfE7aAVujYAy3z/RGnuIO8lo9d3N5iqzm5V13hwYLCq2IFn++SvSZnYFkGYgUvdVGWReS1OwMLMeAKvhCxXu06GHAFX0c/OIqFGHAFX4hYr3YdDLiCr6MfHMVCDLiCL0SsV7sOBlzB19EPjmIhBlzBFyLWq10HA67g6+gHR7EQA8UKrrdDvHLtiNJYv3HdSfQTZ2BFDJSuJgTyZjf+aQahraOxpb+soxlb5tt2lcraakjW0Zxt06ItY4c84WcNUrxFBOe9tSiUHZJTuC+24GqcxTIslMKSA5KFSqvf+KdREFZD8ukXR1iiK+xvFLdFVzodFpVjbw8WZTEozrZpkdpidtwk9ohN3/gnImOJKJY3XdmINWewst74oEjJam78s2Xsu4Y7jEMqvvFPysgJ51jpdyKbWScWFtuX3EOwpjnnyrxS+tKbFm0ZO1z7xj+wIClyFW6LTv6LIvOJFBY7J6nip2XAFq9ltnz7SsWxGyP9ECPgG/+IBD6gWESk2EOfpYWnQsrPWeeAJWP1cxgde44VpYk/3/in4WbMig5QeFyyiEe5uYGzJytDFZnyDll/fufYh9hTurjGwNieNNxTMOMNGhXlmczCffFTFAHd8sY/RpqF3FxiXcKnapZ4ZLj0xj8prE1hF8e+8Y96kGfLZxERzneZ+ORDrkuMI+d7W75ZGMdujCShOOYJlG/8IxLshi2haN7ThvD7Cou29FU5c01yboilOfbhbsIl6biB4pT3Jzxdgb/BG/S5uC91UXj7Zx2qaCvBignMmE/Fkwx83lTMCo7tj5H+bvK5MDJVsnF/a7kVH9t2jnYc+2S22zeYPIbdpz9XGkbhLBv/lCo4G/8w6lJhBJYoJ34jgySVRTf+scaE/YHi7HDVsSZKQ+kPuSFU4dhhYaKIaxR7rzBn2KgNgzmmOydzX7RtRAOS19TtG0HF2W+bNQafKB5GqUJAs2sUu1WhvK3onD1HqIORu1NI2X/p+EzxsRmAnxwlqhuCX+jIkflY+WHgNngc+1Es538kTjGAuCn0cdARSipub4dtP5pZ9aapnyUOV/docExUkJsy1hRwg4bgWmD5WuUmUfmMWKaf15wngsJTx41CbswIF1Vu1Y+g3Cg5pKbSDizHnlJz+rk4ZQMoXsv/pDCeKTuLrZbkvsiCn36pXoMzcD4GNGAwZsGCl/rg50PnLTkDMzLgCj4jmV7V+hhwBV9fnziiGRlwBZ+RTK9qfQy4gq+vTxzRjAzEjwn5KCCtmgVJ7Zu/NNPPnYHaDEg/eb9yPYQDBee5dfsCJyl4lnUWSZt+6gxMYYAXSYPyf+t0M7KuqVpQAAAAAElFTkSuQmCC",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}7.0 & 5.0 & 6.0 & 6.0\\\\2.0 & 5.0 & 8.0 & 7.0\\\\5.0 & 4.0 & 4.0 & 8.0\\\\5.0 & 2.0 & 2.0 & 8.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡7.0 5.0 6.0 6.0⎤\n",
"⎢ ⎥\n",
"⎢2.0 5.0 8.0 7.0⎥\n",
"⎢ ⎥\n",
"⎢5.0 4.0 4.0 8.0⎥\n",
"⎢ ⎥\n",
"⎣5.0 2.0 2.0 8.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The matrix A after the permutations indicated by piv:\n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}7 & 5 & 6 & 6\\\\2 & 5 & 8 & 7\\\\5 & 4 & 4 & 8\\\\5 & 2 & 2 & 8\\end{matrix}\\right]$"
],
"text/plain": [
"⎡7 5 6 6⎤\n",
"⎢ ⎥\n",
"⎢2 5 8 7⎥\n",
"⎢ ⎥\n",
"⎢5 4 4 8⎥\n",
"⎢ ⎥\n",
"⎣5 2 2 8⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Permutation of A in order to obtain L@U:\n",
"temp_A = list(A)\n",
"for i, j in enumerate(piv):\n",
" temp_A[i], temp_A[j] = temp_A[j], temp_A[i] # swap row i with row j=piv[i]\n",
"\n",
"print(\"Product of L@U:\")\n",
"display_matrix(L @ U)\n",
"print(\"The matrix A after the permutations indicated by piv:\")\n",
"display_matrix(np.array(temp_A))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Solution\n",
"\n",
"The solution of the linear system of equation can be obtained by feeding the output of lu_factor into the method **lu_solve** (which is a wrapper of the LAPACK function getrf)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The value of x is:\n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0\\\\2.0\\\\3.0\\\\4.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0⎤\n",
"⎢ ⎥\n",
"⎢2.0⎥\n",
"⎢ ⎥\n",
"⎢3.0⎥\n",
"⎢ ⎥\n",
"⎣4.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = scp.linalg.lu_solve((LU, piv), b)\n",
"print(\"The value of x is:\")\n",
"display_matrix(x.round(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Jacobi method\n",
"\n",
"This is an iterative method. \n",
"The idea is to write the matrix $A = D + R$ as the sum of a diagonal matrix D and a \"reminder\" R (a matrix with only zeros in the diagonal).\n",
"\n",
"$$ (D+R) x = b $$\n",
"\n",
"$$ Dx = b - Rx$$\n",
"\n",
"Using this idea, we can start with a guess solution $x^0$ and then iterate until $||x^{n+1}-x^n|| < \\epsilon$.\n",
"\n",
"$$ x^{n+1} = D^{-1} (b - Rx^{n})$$\n",
"\n",
"The Jacobi and Gauss–Seidel methods converge if the matrix A is strictly diagonally dominant:\n",
"\n",
"$$ |a_{i,i}| > \\sum_{i \\not= j} |a_{i,j}| $$\n",
"where $a_{i,j}$ are the entries of A.\n",
"\n",
"For more info on the Jacobi method have a look at the wiki page [link](https://en.wikipedia.org/wiki/Jacobi_method).\n",
"\n",
"#### New example:\n",
"Since the matrix A in the previous example is not diagonally dominant, let us define a new A, and therefore a new b as well. I like x=[1,2,3,4]."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix A with Rank: 4\n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}10 & 5 & 2 & 1\\\\2 & 15 & 2 & 3\\\\1 & 8 & 13 & 1\\\\2 & 3 & 1 & 8\\end{matrix}\\right]$"
],
"text/plain": [
"⎡10 5 2 1⎤\n",
"⎢ ⎥\n",
"⎢2 15 2 3⎥\n",
"⎢ ⎥\n",
"⎢1 8 13 1⎥\n",
"⎢ ⎥\n",
"⎣2 3 1 8⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"b:\n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}30\\\\50\\\\60\\\\43\\end{matrix}\\right]$"
],
"text/plain": [
"⎡30⎤\n",
"⎢ ⎥\n",
"⎢50⎥\n",
"⎢ ⎥\n",
"⎢60⎥\n",
"⎢ ⎥\n",
"⎣43⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"D:\n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}10.0 & 0 & 0 & 0\\\\0 & 15.0 & 0 & 0\\\\0 & 0 & 13.0 & 0\\\\0 & 0 & 0 & 8.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡10.0 0 0 0 ⎤\n",
"⎢ ⎥\n",
"⎢ 0 15.0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 13.0 0 ⎥\n",
"⎢ ⎥\n",
"⎣ 0 0 0 8.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"R:\n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}0 & 5.0 & 2.0 & 1.0\\\\2.0 & 0 & 2.0 & 3.0\\\\1.0 & 8.0 & 0 & 1.0\\\\2.0 & 3.0 & 1.0 & 0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡ 0 5.0 2.0 1.0⎤\n",
"⎢ ⎥\n",
"⎢2.0 0 2.0 3.0⎥\n",
"⎢ ⎥\n",
"⎢1.0 8.0 0 1.0⎥\n",
"⎢ ⎥\n",
"⎣2.0 3.0 1.0 0 ⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"A = np.array([[10, 5, 2, 1], [2, 15, 2, 3], [1, 8, 13, 1], [2, 3, 1, 8]])\n",
"print(\"Matrix A with Rank: \", np.linalg.matrix_rank(A))\n",
"display_matrix(A)\n",
"x = np.array([[1], [2], [3], [4]])\n",
"print(\"b:\")\n",
"b = A @ x\n",
"display_matrix(b)\n",
"\n",
"D = np.eye(A.shape[0]) * np.diag(A) # diagonal\n",
"D_inv = np.eye(A.shape[0]) * 1 / np.diag(A) # inverse of D\n",
"R = A - D # remainder\n",
"print(\"D:\")\n",
"display_matrix(D)\n",
"print(\"R:\")\n",
"display_matrix(R)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Comment\n",
"Notice that we do not need to invert the matrix D. \n",
"Since D is a diagonal matrix, we can just invert the elements of the diagonal. This is a much more efficient method."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Convergence in 56 iterations:\n",
"x = \n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0\\\\2.0\\\\3.0\\\\4.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0⎤\n",
"⎢ ⎥\n",
"⎢2.0⎥\n",
"⎢ ⎥\n",
"⎢3.0⎥\n",
"⎢ ⎥\n",
"⎣4.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x0 = np.ones_like(b) # initial guess\n",
"eps = 1e-10 # tolerance error\n",
"N_max = 100 # max number of iterations\n",
"\n",
"for i in range(1, N_max + 1):\n",
" x_new = D_inv @ (b - R @ x0)\n",
" if scp.linalg.norm(x_new - x0) < eps:\n",
" print(\"Convergence in {} iterations:\".format(i))\n",
" break\n",
" x0 = x_new\n",
" if i == N_max:\n",
" print(\"Fail to converge in {} iterations\".format(i))\n",
"print(\"x = \")\n",
"display_matrix(x_new.round(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Gauss-Seidel method\n",
"\n",
"This method is very similar to the Jacobi method. It is again an iterative method. \n",
"The idea is to write the matrix $A = L + U$ as the sum of a lower triangular matrix L and a strictly upper triangular matrix U.\n",
"\n",
"$$ (L+U) x = b $$\n",
"\n",
"$$ Lx = b - Ux$$\n",
"\n",
"Like in the Jacobi method we look for an approximate solution $x^{n+1}$, and iterate until $||x^{n+1}-x^n|| < \\epsilon$.\n",
"\n",
"$$ x^{n+1} = L^{-1} (b - Ux^{n})$$\n",
"\n",
"Again, given the specific form of the matrices L and U, we don't need to invert any matrix. \n",
"We will just use the forward substitution.\n",
"\n",
"For more info on the Gauss-Seidel method have a look at the wiki page [link](https://en.wikipedia.org/wiki/Gauss%E2%80%93Seidel_method).\n",
"\n",
"Let us consider the same A,x,b as in the previous example:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"U = np.triu(A, k=1) # Matrix U\n",
"L = np.tril(A) # Matrix L"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Convergence in 15 iterations:\n",
"x = \n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0\\\\2.0\\\\3.0\\\\4.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0⎤\n",
"⎢ ⎥\n",
"⎢2.0⎥\n",
"⎢ ⎥\n",
"⎢3.0⎥\n",
"⎢ ⎥\n",
"⎣4.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x0 = np.ones_like(b) # initial guess\n",
"eps = 1e-10 # tolerance error\n",
"N_max = 100 # max number of iterations\n",
"\n",
"for i in range(1, N_max + 1):\n",
" x_new = solve_triangular(L, (b - U @ x0), lower=True)\n",
" if scp.linalg.norm(x_new - x0) < eps:\n",
" print(\"Convergence in {} iterations:\".format(i))\n",
" break\n",
" x0 = x_new\n",
" if i == N_max:\n",
" print(\"Fail to converge in {} iterations\".format(i))\n",
"print(\"x = \")\n",
"display_matrix(x_new.round(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Comment:\n",
"The Gauss-Seidel algorithm converges much faster than the Jacobi method (15 iterations agains 56)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## SOR (successive over-relaxation) \n",
"\n",
"\n",
"This is another improvement of the previous methods. \n",
"Let's write the matrix $A = L + U + D$ as the sum of a strictly lower triangular matrix L, a strictly upper triangular matrix U and a diagonal matrix D.\n",
"\n",
"$$ (L+U+D) x = b $$\n",
"\n",
"$$ w (L+U+D) x = w b $$\n",
"\n",
"$$ w L x = w b - w D x - w U x \\pm D x $$\n",
"\n",
"$$ (w L + D) x = w b - \\bigl( w U + (w - 1) D \\bigr) x $$\n",
"\n",
"As in the previous methods, given the specific form of the matrices, we don't need to invert any matrix. The iterative method is the following:\n",
"\n",
"$$ x^{n+1} = (w L + D)^{-1} \\biggl( w b - \\bigl( w U + (w - 1) D \\bigr) x^n \\biggr) $$\n",
"\n",
"For more info on the SOR method have a look at the wiki page [link](https://en.wikipedia.org/wiki/Successive_over-relaxation)."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"D = np.eye(A.shape[0]) * np.diag(A) # diagonal\n",
"U = np.triu(A, k=1) # Strict U\n",
"L = np.tril(A, k=-1) # Strict L"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Convergence in 34 iterations:\n",
"x = \n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0\\\\2.0\\\\3.0\\\\4.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0⎤\n",
"⎢ ⎥\n",
"⎢2.0⎥\n",
"⎢ ⎥\n",
"⎢3.0⎥\n",
"⎢ ⎥\n",
"⎣4.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x0 = np.ones_like(b) # initial guess\n",
"eps = 1e-10 # tolerance error\n",
"N_max = 100 # max number of iterations\n",
"w = 1.4 # relaxation factor\n",
"\n",
"for i in range(1, N_max + 1):\n",
" x_new = solve_triangular((w * L + D), (w * b - w * U @ x0 - (w - 1) * D @ x0), lower=True)\n",
" if scp.linalg.norm(x_new - x0) < eps:\n",
" print(\"Convergence in {} iterations:\".format(i))\n",
" break\n",
" x0 = x_new\n",
" if i == N_max:\n",
" print(\"Fail to converge in {} iterations\".format(i))\n",
"print(\"x = \")\n",
"display_matrix(x_new.round(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By changing the *relaxation parameter* we can see how the number of iterations changes. \n",
"The relaxation must satisfy $0\n",
"## Thomas algorithm\n",
"\n",
"The Thomas algorithm is a simplified form of Gaussian elimination that is used to solve tridiagonal systems of equations. \n",
"Have a look at the wiki page: [TDMA](https://en.wikipedia.org/wiki/Tridiagonal_matrix_algorithm)\n",
"\n",
"The idea is simply to transform the matrix A into a triagonal matrix and then use backward substitution.\n",
"\n",
"Here we consider the single coefficients of the linear system instead of performing matrix operations like in the previous algorithms. \n",
"\n",
"$$ a_i x_{i-1} + b_i x_{i} + c_i x_{i+1} = d_i $$\n",
"\n",
"The algorithm we implement is the following (taken from wikipedia):\n",
"\n",
"$$ w_i = \\frac{a_i}{b_{i-1}} $$\n",
"\n",
"$$ b_i = b_i - w_i c_{i-1} $$\n",
"\n",
"$$ d_i = d_i - w_i d_{i-1} $$\n",
"\n",
"and then we perform backward substitution:\n",
"\n",
"$$ x_n = \\frac{d_n}{b_n} $$\n",
"\n",
"$$ x_i = \\frac{d_i - c_i x_{i+1}}{b_i} $$\n",
"\n",
"Let us introduce a new matrix A wich is tridiagonal. Of course we like to keep the ```x = [1,2,3,4]``` "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix A with Rank: 4\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAJcAAABkCAYAAACLmaLOAAAACXBIWXMAAA7EAAAOxAGVKw4bAAALc0lEQVR4Ae2dXZLcNBSFJ1SeqRCqeKfZwYSsILCDwA6AHUDxlLxRsAPICvjZAWEFIdlBWABVwBQr4HyOb5dGLbdtWbZk+t4qj/Vn6eroWJIl3ek7T548ub66unqpKyW/PH369JNUhIc5AuLGa6FwSCGhuDt3g4jv5CZxKH+EHnc7AhEC30Z+vB/reowjJNf3Ytv/kkyq17WuV1TYRP57ct//v9bZ6rnmXdj9EOevMIJOyBWnS/r1MMPoz7oeyH2TSqRwY/Rfiv9A17cKq0nc31Q+ZDKC4UYevLm18bc13JbqE/Zcgwj3DfNMCf7W9aGu5DhLBkrL/O0b3X/p/TTkS/k/1lWLYOiN8GKgA7qhY/LlUNzmIl2awq2EPlPJRSN0E3sV+qXcNNKJKO5zBd7TvSMWCeS+6f3fy8t4XENeSYdmP0ykW1O4ldLnrcItTQPa0BNm/UKej6S0DUdhnLvfvLgt4VakHUuT6yMxxYagkDQ2HBLvcopAa7gV0efuaT3zQib2Svfzcl/+lHX1yuldXcwZmXOleovlhc3IoTXcSupTsucy4pybJNcaFin3JwH3na6v5ObiI6OFnrQ13IrpU5JcU95Xeo3NRSTiS/VIerkZpp/r4iNjD1IFtzPATNKnJLlScy3Tz94G1r1aEQh2ENEYImtKa7gV06cYuYKeITX0WZhN7DdrTOn1qy7WkIbEdBuKXzW8NdxK6lOMXH0LMNSkegLruYjfWlj0TRGo00lgVp/US7/WcCuiT2lysS1EY8byQAEsZN7EERv4f1C5bEHFwmS+BtljPfC3hlsRfXLIZZM5642OYKkR2cj8W/du45IIuek1PtX1Gf4Kwob8rYm7/OwyIE2s2kufpnArpc/kdS4VCJsR+3z/WWHMoZjThLvj9FJsVD/UnQk890fyVxl+0FEX+hjBeCmYtL6vsBo9qYpOSlO4ScPF+twJDgt+QEMkq+2BjsBEBMQh9kkZLe7kDIsTi/Fkl46Ak+vSGbBi/Z1cK4J76Vk7uS6dASvW38m1IriXnrWT69IZsGL9nVwrgnvpWTu5Lp0BK9bfybUiuJee9UWSS6vH13HDKwyrpUMc7v58BCbvLaaK6BuDI8MIpyHYs/tK4VX2EVECUfmQh73QIcPd5oxkW8RSOi0ybs4mVw8Ge0hHW8ReGTOA3fQ4i8rm9MUkw12l4yVAICH7qVWNZFvDEmCk02Ij3WxyqXxY/QWKmEghei02Luk13rHwLe4qlxMO3REauQcNd3tdWjOSbQ3LIka6S+ZcHL15rYakxwiFHsvnLyEi4+7WsOQlTU1tXih8snHzEnJBIs5KDZ2Jikk3DvHlpmgNS8huU4ewVexIFvGjcnc0xUACkaobghLR3ZeY4lPMTySvEyT9uq5fpXOylq/EakayLWEpXaZ0CventNqSnuskfykGsWgo+4I8SdNIAAC2aiTbQVQRSyPO0IiEflMIeFWUXCqUiTz/6pL/UtisSL89GMm2jCW9/ahArrf7VHYffSiVQA3GGXXmYEPDZeqxlsKYT7RgJHtVGcvUXMvayXq1c8bN71niIj2XwGD+wr+APK55WQGt3aVj60ayVbEUPjYcpoY+C7OJ/dnmhVz/9insfvaBOFLKYEaGccexx5K7iR4g1rX3s5NgIIVJurdSulf7EGkIS75eDyE4vdt6LuKH5E+LWNRzCYxrZfRQ93gCD+HOda9Wfo17k0ayjWHJfI+XMBbMzSYbNy9ZioDZKPFcwJhNoCnDQlvNSb1NOHnTrJs33TojWel33F2Qu6qRrMpvCkvpwwvIbstjXeH/tv1UID4yIMfu2eRSxr/qAhTmCLFUGVoEBGRHbJHvxHBXaVo0km0OS2FIL7XIuNmNYqGiSzEE9PLS2bhRbDFEPaMkAosm9MkcPdAR6BFwcjkVVkPAybUatJ6xk8s5sBoCTq7VoPWMIRf7RCwotrqi7q20LwTYGuoWqCHXQRcr7LZvJKeLI5CNAAvY3Y6ND4vZGPqDYwg4ucYQ8vhsBJbsLXaFarl/keFktuYzHpSOHLH5OngEP/uO546OBMnXcap8TpWwH5o03lU8UxbbYEdn/Oz3bab3mI7SZ1AWkUsFLzacHNSsbAQNYo3U5Sw/5Lr1w6Nli0znRpmKGTXe7dNxMuGot9wcZeKw4ye6jj+Ymi4pP1R5T9JxrITsYVEKsEF5q3EUdqMwKh0fwRnTY7X4Xk9eglj4v/hhbxbHr+IHI12QA9L8eKYQ8P1c6SCUifVYq+o9Q0fTK3nPJpdyK2I4mdSqbCC/ntH88etElTm2xMvK1QmNbu493JcMi3xyhj9uYPW189XEr9Z1W2ET7i+U5ks1DGem6DGsgZgrNtPDxvWQnvRSt/4lgsKsF2tW77AeWeRSJe+FmQy4m1g3k66YukFyGuYfuTmSTW/G3KUF8kuVcZGuvKzd/5SQO/VSj2eycYrcYdGIY71ASu0pBEw9VzxMjcEQbg1CA9FQVU7Lzq2cdL/WxTFs5mjo/PvcPGqlzyXXFH3tHPuUtKumUePQa/Ei0GMx3Bx08U9UbJiRt02RjhhE8PPJvCB8APAvqprXGzRzyXVuH9J6tXOGk5S9iagh+OrCwprPes7PM7k3M7hn8jfTw44BIl0ZxnlJumWUsfS147PIpUpSQSTVMBZmE/s3Kev9ZRi8ZfrWNxK9GLoyRDYn0pHhkEXWWGxYbFLvUNkscvUZ2PAS5ofbei5bk4njN/OrcSAPa3E3caEKg/z0BKZvnKS2n7U5hkB7WWvrM7v8JeRi2+LDRImzDCcTzxcL6knFouVhIFMarvpLMKAbLwRfuvGLYZi3qvexOtnkUqX5+mrtV2GPFQsczK9O5ijSn7kYyxE1h2/76En1ngzlrM0dRboykeeF+ELumHTHdIUd53Q8W9Qiu0VVkIoyp6GiTOAf6qr2T9RUdlKkJ3MXtkzCD5FqG9fSJzTeBUOWGCA5ZLclkyu5mVfZx4ec221cT9URpULRc7y0nd3iInKFmbrbEQCBkFzZw6JD6QiMIeDkGkPI47MRcHJlQ+cPjiHg5BpDyOOzEXByZUPnD44h4OQaQ8jjsxFwcmVD5w+OIeDkGkPI47MRcHJlQ+cPjiHg5BpDyOOzEbib/WT/oJb72VtE2FvkjBQ2gjU3g9HlRHakJ/ugg4ayJxVbKUB4secZmrDhn7Ufu4hcUoAzR2xUcy7qqlfIfim2GYK1rmeP26ihLBhvKIsNibOHRQHC7vdejGKb1lNYcuZsiqHsJtzq25aOI5ZZhsTZ5FKpHAXhqEgs2AlO/jXR+OEV/HvRc4WqZ2fJ9GaxIfEScnHWKDwfZTWx4ZD4FmQveraAlelAB8GvZ3C+jLmWCfPryQa5WXOuqEArOL6nTlfGaVb170XPVUHIyFy4FTEkzu25jDg3Z3QPGX8m2apRe9FzVRByMhfBmE7YqVh6LEaA1DRoMPtccg1mGETY2esgqEnnXvTcFDyRi/P6dB7ZhsS55ErNtazy1luw7lVb9qJnbZxulS9iFTEkziKXCrfhMDX0WZhN7G8pvqVnL3puicnEshgGFxsSZ5GrV7B5o9id6Tmx3ddNpheSzoF1QetAjgUqjA5jsiHxEnKxRWEGmkcF5Higa/KviYYPruTei54rVX9etj2pWNQ9DDwJ+SYZ5GaTS4Xvwih2L3oGDWkfGDZ3DaI2c/KluNiQOGudK6givRR7UA91ZwLP/ZH8sz5Z9cza0ryewoweFuGTH6FxGYZuGcp2MSv/Ubn8tDRbPc90Dz+KZm1cu1Hsyg11admLjEeL6+xh8dJA8/rOR8DJNR8zf2IiAk6uiUB5svkIOLnmY+ZPTETAyTURKE82H4FwKYL/bhznwNEL1jxcHIETBMSN1wo8nET0AZCLtZTjjxdFCavvD0b6uLctBMw4J6nVf+wOgUnYwWePAAAAAElFTkSuQmCC",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}10 & 5 & 0 & 0\\\\2 & 15 & 2 & 0\\\\0 & 8 & 13 & 1\\\\0 & 0 & 1 & 8\\end{matrix}\\right]$"
],
"text/plain": [
"⎡10 5 0 0⎤\n",
"⎢ ⎥\n",
"⎢2 15 2 0⎥\n",
"⎢ ⎥\n",
"⎢0 8 13 1⎥\n",
"⎢ ⎥\n",
"⎣0 0 1 8⎦"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"b:\n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}20\\\\38\\\\59\\\\35\\end{matrix}\\right]$"
],
"text/plain": [
"⎡20⎤\n",
"⎢ ⎥\n",
"⎢38⎥\n",
"⎢ ⎥\n",
"⎢59⎥\n",
"⎢ ⎥\n",
"⎣35⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"A = np.array([[10, 5, 0, 0], [2, 15, 2, 0], [0, 8, 13, 1], [0, 0, 1, 8]])\n",
"print(\"Matrix A with Rank: \", np.linalg.matrix_rank(A))\n",
"display_matrix(A)\n",
"print(\"b:\")\n",
"x = np.array([[1], [2], [3], [4]])\n",
"b = A @ x\n",
"display_matrix(b)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"## TDMA: Tri-Diagonal-Matrix-Algorithm solver\n",
"def TDMA(A, B):\n",
" N = len(B) # number of equations\n",
" a = A.diagonal(offset=-1).copy() # need to copy because the array is not writable\n",
" b = A.diagonal(offset=0).copy() # see A.diagonal(offset=0).flags\n",
" c = A.diagonal(offset=1).copy()\n",
" d = B.copy()\n",
" x = np.ones_like(d) # initialize x\n",
"\n",
" # Overwright coefficients\n",
" for i in range(1, N):\n",
" w = a[i - 1] / b[i - 1]\n",
" b[i] = b[i] - w * c[i - 1]\n",
" d[i] = d[i] - w * d[i - 1]\n",
"\n",
" # backward substitution\n",
" x[-1] = d[-1] / b[-1]\n",
" for i in range(N - 2, -1, -1):\n",
" x[i] = (d[i] - c[i] * x[i + 1]) / b[i]\n",
"\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x= \n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1\\\\2\\\\3\\\\4\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1⎤\n",
"⎢ ⎥\n",
"⎢2⎥\n",
"⎢ ⎥\n",
"⎢3⎥\n",
"⎢ ⎥\n",
"⎣4⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_sol = TDMA(A, b)\n",
"print(\"x= \")\n",
"display_matrix(x_sol)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Ok, I know the previous code is not very efficient! \n",
"I just wanted to prove that the algorithms work. \n",
"\n",
"In order to show how slow is python code compared to routines that call optimized C/fortran, I wrote a wrapper for the LAPACK function **dgtsv** which implements the Thomas algorithm. I called this function *\"Thomas\"*. \n",
"You can see that it is about **8 times much faster** than the function I wrote above.\n",
"\n",
"On this topic, I wrote an entire notebook about code optimization and speed up. (notebook **A2**)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Solution of Ax=b with the LAPACK function dgtsv. \n",
" x= \n"
]
},
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left[\\begin{matrix}1.0\\\\2.0\\\\3.0\\\\4.0\\end{matrix}\\right]$"
],
"text/plain": [
"⎡1.0⎤\n",
"⎢ ⎥\n",
"⎢2.0⎥\n",
"⎢ ⎥\n",
"⎢3.0⎥\n",
"⎢ ⎥\n",
"⎣4.0⎦"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(\"Solution of Ax=b with the LAPACK function dgtsv. \\n x= \")\n",
"display_matrix(Thomas(A, b))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"15.2 µs ± 188 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"TDMA(A, b)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.4 µs ± 4.48 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"Thomas(A, b)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: A.2 Optimize and speed up the code. (SOR algorithm, Cython and C).ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How to optimize the code?\n",
"\n",
"In this notebook I want to show how to write efficient code and how cython and C code can help to improve the speed. \n",
"I decided to consider as example the SOR algorithm. Here we can see how the algorithm presented in the Notebook **A.1 Solution of linear equations** can be modified for our specific needs (i.e. solving PDEs). \n",
"\n",
"Again, if you are curious about the SOR and want to know more, have a look at the wiki page [link](https://en.wikipedia.org/wiki/Successive_over-relaxation).\n",
"\n",
"## Contents\n",
" - [Python implelentation](#sec1)\n",
" - [Cython](#sec2)\n",
" - [C code](#sec3)\n",
" - [BS python vs C](#sec3.1) "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import subprocess\n",
"import numpy as np\n",
"import scipy as scp\n",
"from scipy.linalg import norm\n",
"from FMNM.Solvers import SOR, SOR2\n",
"\n",
"%load_ext cython\n",
"import cython"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"N = 3000\n",
"aa = 2\n",
"bb = 10\n",
"cc = 5\n",
"A = np.diag(aa * np.ones(N - 1), -1) + np.diag(bb * np.ones(N), 0) + np.diag(cc * np.ones(N - 1), 1)\n",
"x = 2 * np.ones(N)\n",
"b = A @ x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we use a tridiagonal matrix A \n",
"\n",
"$$ \\left(\n",
"\\begin{array}{ccccc}\n",
"bb & cc & 0 & \\cdots & 0 \\\\\n",
"aa & bb & cc & 0 & 0 \\\\\n",
"0 & \\ddots & \\ddots & \\ddots & 0 \\\\\n",
"\\vdots & 0 & aa & bb & cc \\\\\n",
"0 & 0 & 0 & aa & bb \\\\\n",
"\\end{array}\n",
"\\right) $$\n",
"\n",
"with equal elements in the three diagonals: \n",
"\n",
"$$ aa = 2, \\quad bb = 10, \\quad cc = 5 $$\n",
"\n",
"This is the case of the Black-Scholes equation (in log-variables). \n",
"The matrix A is quite big because we want to test the performances of the algorithms.\n",
"\n",
"The linear system is always the same: \n",
"\n",
"$$ A x = b$$\n",
"\n",
"For simplicity I chose $x = [2,...,2]$. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Python implementation\n",
"\n",
"I wrote two functions to implement the SOR algorithm with the aim of solving PDEs. \n",
" - ```SOR``` uses matrix multiplications. The code is the same presented in the notebook **A1**: First it creates the matrices D,U,L (if A is sparse, it is converted into a numpy.array). Then it iterates the solutions until convergence. \n",
" - ```SOR2``` iterates over all components of $x$ . It does not perform matrix multiplications but it considers each component of $x$ for the computations. \n",
"The algorithm is the following: \n",
"\n",
"```python\n",
" x0 = np.ones_like(b, dtype=np.float64) # initial guess\n",
" x_new = np.ones_like(x0) # new solution\n",
" \n",
" for k in range(1,N_max+1): # iteration until convergence\n",
" for i in range(N): # iteration over all the rows\n",
" S = 0\n",
" for j in range(N): # iteration over the columns\n",
" if j != i:\n",
" S += A[i,j]*x_new[j]\n",
" x_new[i] = (1-w)*x_new[i] + (w/A[i,i]) * (b[i] - S) \n",
" \n",
" if norm(x_new - x0) < eps: # check convergence\n",
" return x_new\n",
" x0 = x_new.copy() # updates the solution \n",
" if k==N_max:\n",
" print(\"Fail to converge in {} iterations\".format(k))\n",
"```\n",
"This algorithm is taken from the SOR wiki [page](https://en.wikipedia.org/wiki/Successive_over-relaxation) and it is equivalent to the algorithm presented in the notebook **A1**.\n",
"\n",
"Let us see how fast they are: (well... how **slow** they are... be ready to wait about 6 minutes)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 979 ms, sys: 279 ms, total: 1.26 s\n",
"Wall time: 869 ms\n"
]
},
{
"data": {
"text/plain": [
"array([2., 2., 2., ..., 2., 2., 2.])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"SOR(A, b)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 59.9 s, sys: 597 ms, total: 1min\n",
"Wall time: 1min\n"
]
},
{
"data": {
"text/plain": [
"array([2., 2., 2., ..., 2., 2., 2.])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"SOR2(A, b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TOO BAD!\n",
"\n",
"The second algorithm is very bad. There is an immediate improvement to do: \n",
"We are working with a **tridiagonal matrix**. It means that all the elements not on the three diagonals are zero. The first piece of code to modify is OBVIOUSLY this:\n",
"```python\n",
"for j in range(N): # iteration over the columns\n",
" if j != i:\n",
" S += A[i,j]*x_new[j]\n",
"``` \n",
"There is no need to sum zero elements. \n",
"Let us consider the new function:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def SOR3(A, b, w=1, eps=1e-10, N_max=100):\n",
" N = len(b)\n",
" x0 = np.ones_like(b, dtype=np.float64) # initial guess\n",
" x_new = np.ones_like(x0) # new solution\n",
" for k in range(1, N_max + 1):\n",
" for i in range(N):\n",
" if i == 0: # new code start\n",
" S = A[0, 1] * x_new[1]\n",
" elif i == N - 1:\n",
" S = A[N - 1, N - 2] * x_new[N - 2]\n",
" else:\n",
" S = A[i, i - 1] * x_new[i - 1] + A[i, i + 1] * x_new[i + 1]\n",
" # new code end\n",
" x_new[i] = (1 - w) * x_new[i] + (w / A[i, i]) * (b[i] - S)\n",
" if norm(x_new - x0) < eps:\n",
" return x_new\n",
" x0 = x_new.copy()\n",
" if k == N_max:\n",
" print(\"Fail to converge in {} iterations\".format(k))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"83.5 ms ± 1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"SOR3(A, b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### OK ... it was easy!\n",
"\n",
"But wait a second... if all the elements in the three diagonals are equal, do we really need a matrix? \n",
"Of course, we can use sparse matrices to save space in memory. But do we really need any kind of matrix? \n",
"The same algorithm can be written considering just the three values $aa,bb,cc$. \n",
"\n",
"**In the following algorithm, even if the gain in speed is not so much, we save a lot of space in memory!!** "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def SOR4(aa, bb, cc, b, w=1, eps=1e-10, N_max=100):\n",
" N = len(b)\n",
" x0 = np.ones_like(b, dtype=np.float64) # initial guess\n",
" x_new = np.ones_like(x0) # new solution\n",
" for k in range(1, N_max + 1):\n",
" for i in range(N):\n",
" if i == 0:\n",
" S = cc * x_new[1]\n",
" elif i == N - 1:\n",
" S = aa * x_new[N - 2]\n",
" else:\n",
" S = aa * x_new[i - 1] + cc * x_new[i + 1]\n",
" x_new[i] = (1 - w) * x_new[i] + (w / bb) * (b[i] - S)\n",
" if norm(x_new - x0) < eps:\n",
" return x_new\n",
" x0 = x_new.copy()\n",
" if k == N_max:\n",
" print(\"Fail to converge in {} iterations\".format(k))\n",
" return x_new"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"59 ms ± 242 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"SOR4(aa, bb, cc, b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Cython\n",
"\n",
"\n",
"For those who are not familiar with Cython, I suggest to read this introduction [link](https://cython.readthedocs.io/en/latest/src/userguide/numpy_tutorial.html).\n",
"\n",
"Cython, basically, consists in adding types to the python variables. \n",
"\n",
"Let's see what happens to the speed when we add types to the previous pure python function (SOR4)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"%%cython --compile-args=-mcpu=apple-m2 --compile-args=-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION --compile-args=-w --force\n",
"import numpy as np\n",
"from scipy.linalg import norm\n",
"cimport numpy as np \n",
"cimport cython\n",
"\n",
"@cython.boundscheck(False) # turn off bounds-checking for entire function\n",
"@cython.wraparound(False) # turn off negative index wrapping for entire function\n",
"def SOR_cy(np.float64_t aa, \n",
" np.float64_t bb, np.float64_t cc, \n",
" np.ndarray[np.float64_t , ndim=1] b, \n",
" double w=1, double eps=1e-10, int N_max = 100):\n",
" \n",
" cdef unsigned int N = b.size\n",
" cdef np.ndarray[np.float64_t , ndim=1] x0 = np.ones(N, dtype=np.float64) # initial guess\n",
" cdef np.ndarray[np.float64_t , ndim=1] x_new = np.ones(N, dtype=np.float64) # new solution\n",
" cdef unsigned int i, k\n",
" cdef np.float64_t S\n",
" \n",
" for k in range(1,N_max+1):\n",
" for i in range(N):\n",
" if (i==0):\n",
" S = cc * x_new[1]\n",
" elif (i==N-1):\n",
" S = aa * x_new[N-2]\n",
" else:\n",
" S = aa * x_new[i-1] + cc * x_new[i+1]\n",
" x_new[i] = (1-w)*x_new[i] + (w/bb) * (b[i] - S) \n",
" if norm(x_new - x0) < eps:\n",
" return x_new\n",
" x0 = x_new.copy()\n",
" if k==N_max:\n",
" print(\"Fail to converge in {} iterations\".format(k))\n",
" return x_new"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.06 ms ± 1.09 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"SOR_cy(aa, bb, cc, b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### About 100 times faster!!!\n",
"\n",
"That's good.\n",
"\n",
"So... those who are not familiar with Cython maybe are confused about the new type `np.float64_t`. We wrote: \n",
"```python\n",
"import numpy as np\n",
"cimport numpy as np \n",
"``` \n",
"The first line imports numpy module in the python space. \n",
"It only gives access to Numpy’s pure-Python API and it occurs at runtime.\n",
"\n",
"The second line gives access to the Numpy’s C API defined in the `__init__.pxd` file ([link to the file](https://github.com/cython/cython/blob/master/Cython/Includes/numpy/__init__.pxd)) during compile time. \n",
"\n",
"Even if they are both named `np`, they are automatically recognized.\n",
"In `__init__.pdx` it is defined:\n",
"```\n",
"ctypedef double npy_float64\n",
"ctypedef npy_float64 float64_t\n",
"``` \n",
"The `np.float64_t` represents the type `double` in C."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Memoryviews\n",
"\n",
"Let us re-write the previous code using the faster [memoryviews](https://cython.readthedocs.io/en/latest/src/userguide/memoryviews.html). \n",
"I suggest to the reader to have a fast look at the memoryviews manual in the link. There are no difficult concepts and the notation is not so different from the notation used in the previous function. \n",
"\n",
"Memoryviews is another tool to help speed up the algorithm.\n",
"\n",
"I have to admit that when I was writing the new code I realized that using the function `norm` is not the optimal way. (I got an error because `norm` only accepts ndarrays... so, thanks memoryviews :) ). \n",
"Well, the `norm` function computes a square root, which still requires some computations. \n",
"We can define our own function `distance2` (which is the square of the distance) that is compared with the square of the tolerance parameter `eps * eps`. This is another improvement of the algorithm."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"%%cython --compile-args=-O2 --compile-args=-mcpu=apple-m2 --compile-args=-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION --compile-args=-w --force\n",
"import numpy as np\n",
"cimport numpy as np\n",
"cimport cython\n",
"\n",
"cdef double distance2(double[:] a, double[:] b, unsigned int N):\n",
" cdef double dist = 0\n",
" cdef unsigned int i \n",
" for i in range(N):\n",
" dist += (a[i] - b[i]) * (a[i] - b[i])\n",
" return dist\n",
"\n",
"@cython.boundscheck(False)\n",
"@cython.wraparound(False)\n",
"def SOR_cy2(double aa, \n",
" double bb, double cc, \n",
" double[:] b, \n",
" double w=1, double eps=1e-10, int N_max = 200):\n",
" \n",
" cdef unsigned int N = b.size \n",
" cdef double[:] x0 = np.ones(N, dtype=np.float64) # initial guess\n",
" cdef double[:] x_new = np.ones(N, dtype=np.float64) # new solution\n",
" cdef unsigned int i, k\n",
" cdef double S\n",
" \n",
" for k in range(1,N_max+1):\n",
" for i in range(N):\n",
" if (i==0):\n",
" S = cc * x_new[1]\n",
" elif (i==N-1):\n",
" S = aa * x_new[N-2]\n",
" else:\n",
" S = aa * x_new[i-1] + cc * x_new[i+1]\n",
" x_new[i] = (1-w)*x_new[i] + (w/bb) * (b[i] - S) \n",
" if distance2(x_new, x0, N) < eps*eps:\n",
" return np.asarray(x_new)\n",
" x0[:] = x_new\n",
" if k==N_max:\n",
" print(\"Fail to converge in {} iterations\".format(k))\n",
" return np.asarray(x_new)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.06 ms ± 5.02 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"SOR_cy2(aa, bb, cc, b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Good job!! Another improvement!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## C code\n",
"\n",
"The last improvement is to write the function in C code and call it from python. \n",
"Inside the folder `src/C` you can find the header file `SOR.h` and the implementation file `SOR.c` (you will find also the `mainSOR.c` if you want to test the SOR algorithm directly in C). \n",
"I will call the function `SOR_abc` declared in the header `SOR.h`. \n",
"First it is declared as extern, and then it is called inside `SOR_c` with a cast to ``."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"%%cython -I src/C --compile-args=-O2 --compile-args=-mcpu=apple-m2 --compile-args=-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION --compile-args=-w --force\n",
"#\n",
"# The %%cython directive must be the first keyword in the cell\n",
"\n",
"cdef extern from \"SOR.c\":\n",
" pass\n",
"cdef extern from \"SOR.h\":\n",
" double* SOR_abc(double, double, double, double *, int, double, double, int)\n",
"\n",
"import numpy as np\n",
"cimport cython\n",
"\n",
"@cython.boundscheck(False)\n",
"@cython.wraparound(False)\n",
"def SOR_c(double aa, double bb, double cc, B, double w=1, double eps=1e-10, int N_max = 200): \n",
"\n",
" if not B.flags['C_CONTIGUOUS']:\n",
" B = np.ascontiguousarray(B) # Makes a contiguous copy of the numpy array\n",
" \n",
" cdef double[::1] arr_memview = B \n",
" cdef double[::1] x = SOR_abc(aa, bb, cc, \n",
" &arr_memview[0], arr_memview.shape[0], \n",
" w, eps, N_max)\n",
" return np.asarray(x)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"738 µs ± 3.53 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"SOR_c(aa, bb, cc, b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Well... it looks like that using Cython with memoryviews has the same performances as wrapping a C function.\n",
"\n",
"For this reason, I used the cython version as solver in the class `BS_pricer`. \n",
"We already compared some performances in the notebook **1.2 - BS PDE**, and we saw that the SOR algorithm is slow compared to the LU or Thomas algorithms. \n",
"Just for curiosity, let us compare the speed of the python PDE_price method implemented with cython SOR algorithm, and a pricer with same SOR algorithm fully implemented in C."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from FMNM.Parameters import Option_param\n",
"from FMNM.Processes import Diffusion_process\n",
"from FMNM.BS_pricer import BS_pricer\n",
"\n",
"opt_param = Option_param(S0=100, K=100, T=1, exercise=\"European\", payoff=\"call\")\n",
"diff_param = Diffusion_process(r=0.1, sig=0.2)\n",
"BS = BS_pricer(opt_param, diff_param)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## BS python vs C"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the command `make` to compile the C [code](./FMNM/C/PDE_solver.c):"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"clang -Wall -Werror -O2 -c -o BS_SOR_main.o BS_SOR_main.c\n",
"clang -Wall -Werror -O2 -c -o SOR.o SOR.c\n",
"clang -Wall -Werror -O2 -c -o PDE_solver.o PDE_solver.c\n",
"clang -Wall -Werror -O2 -o BS_sor BS_SOR_main.o SOR.o PDE_solver.o -lm \n",
" \n",
"Compilation completed!\n",
" \n"
]
},
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.system(\"cd ./src/C/ && make\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python program with Cython SOR method:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Price: 13.269170 Time: 7.370949\n"
]
}
],
"source": [
"print(\"Price: {0:.6f} Time: {1:.6f}\".format(*BS.PDE_price((3000, 2000), Time=True, solver=\"SOR\")))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pure C program:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The price is: 13.269139 \n",
" \n",
"CPU times: user 821 µs, sys: 5.9 ms, total: 6.72 ms\n",
"Wall time: 5.56 s\n"
]
}
],
"source": [
"%%time\n",
"result = subprocess.run(\"./src/C/BS_sor\", stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n",
"print(result.stdout.decode(\"utf-8\"))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: CITATION.cff
================================================
# This CITATION.cff file was generated with cffinit.
# https://bit.ly/cffinit
cff-version: 1.2.0
title: Financial Models Numerical Methods
message: '"If you use software from this repo, please cite it as below."'
type: software
authors:
- given-names: >-
Nicola Cantarutti
repository-code: 'https://github.com/cantaro86/Financial-Models-Numerical-Methods'
date-released: 2019-06-09
================================================
FILE: Dockerfile
================================================
# Start from this image
FROM python:3.11.4-slim
# Set the working directory in the container
WORKDIR /workspace
# COPY . /workspace
# Install Git, gcc, g++, cmake
RUN apt-get update && \
apt-get install -y \
git \
gcc \
g++ \
cmake \
build-essential \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
# Clone the GitHub repository
RUN git clone https://github.com/cantaro86/Financial-Models-Numerical-Methods.git .
# Install requirments
RUN pip install --upgrade pip
RUN pip install --no-cache-dir --requirement requirements.txt
# Old style jupyter notebooks
RUN pip install nbclassic
# Install local package
RUN pip install .
# Expose the Jupyter Notebook port
EXPOSE 8888
# Start Jupyter Notebook server
CMD ["jupyter", "nbclassic", "--ip=0.0.0.0", "--port=8888", "--no-browser", "--allow-root", "--NotebookApp.token=''"]
#################################################################
# 1. BUILD
# docker build -t fmnm .
# 2. CREATE CONTAINER
# docker run --rm -d -p 8888:8888 --name Numeric_Finance fmnm
# 3. OPEN IN BROWSER
# http://localhost:8888/lab
# or
# http://localhost:8888/lab
# OR
# 1. docker-compose up --build -d --remove-orphans
# 2. docker-compose down --rmi all --volumes --remove-orphans
================================================
FILE: LICENSE
================================================
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.
================================================
FILE: README.md
================================================
Financial-Models-Numerical-Methods
==================================
This is a collection of [Jupyter notebooks](https://jupyter.org/) based on different topics in the area of quantitative finance.
### Is this a tutorial?
Almost! :)
This is just a collection of topics and algorithms that in my opinion are interesting.
It contains several topics that are not so popular nowadays, but that can be very powerful.
Usually, topics such as PDE methods, Lévy processes, Fourier methods or Kalman filter are not very popular among practitioners, who prefers to work with more standard tools.
The aim of these notebooks is to present these interesting topics, by showing their practical application through an interactive python implementation.
### Who are these notebooks for?
Not for absolute beginners.
These topics require a basic knowledge in stochastic calculus, financial mathematics and statistics. A basic knowledge of python programming is also necessary.
In these notebooks I will not explain what is a call option, or what is a stochastic process, or a partial differential equation.
However, every time I will introduce a concept, I will also add a link to the corresponding wiki page or to a reference manual.
In this way, the reader will be able to immediately understand what I am talking about.
These notes are for students in science, economics or finance who have followed at least one undergraduate course in financial mathematics and statistics.
Self-taught students or practicioners should have read at least an introductiory book on financial mathematics.
### Why is it worth to read these notes?
First of all, this is not a book!
Every notebook is (almost) independent from the others. Therefore you can select only the notebook you are interested in!
```diff
- Every notebook contains python code ready to use!
```
It is not easy to find on internet examples of financial models implemented in python which are ready to use and well documented.
I think that beginners in quantitative finance will find these notebooks very useful!
Moreover, Jupyter notebooks are interactive i.e. you can run the code inside the notebook.
This is probably the best way to study!
If you open a notebook with Github or [NBviewer](https://nbviewer.ipython.org), sometimes mathematical formulas are not displayed correctly.
For this reason, I suggest you to clone/download the repository.
### Is this series of notebooks complete?
**No!**
I will upload more notebooks from time to time.
At the moment, I'm interested in the areas of stochastic processes, Kalman Filter, statistics and much more. I will add more interesting notebooks on these topics in the future.
If you have any kind of questions, or if you find some errors, or you have suggestions for improvements, feel free to contact me.
### Contents
1.1) **Black-Scholes numerical methods**
*(lognormal distribution, change of measure, Monte Carlo, Binomial method)*.
1.2) **SDE simulation and statistics**
*(paths generation, Confidence intervals, Hypothesys testing, Geometric Brownian motion, Cox-Ingersoll-Ross process, Euler Maruyama method, parameters estimation)*
1.3) **Fourier inversion methods**
*(inversion formula, numerical inversion, option pricing, FFT, Lewis formula)*
1.4) **SDE, Heston model**
*(correlated Brownian motions, Heston paths, Heston distribution, characteristic function, option pricing)*
1.5) **SDE, Lévy processes**
*(Merton, Variance Gamma, NIG, path generation, parameter estimation)*
2.1) **The Black-Scholes PDE**
*(PDE discretization, Implicit method, sparse matrix tutorial)*
2.2) **Exotic options**
*(Binary options, Barrier options, Asian options)*
2.3) **American options**
*(PDE, Early exercise, Binomial method, Longstaff-Schwartz, Perpetual put)*
3.1) **Merton Jump-Diffusion PIDE**
*(Implicit-Explicit discretization, discrete convolution, model limitations, Monte Carlo, Fourier inversion, semi-closed formula )*
3.2) **Variance Gamma PIDE**
*(approximated jump-diffusion PIDE, Monte Carlo, Fourier inversion, Comparison with Black-Scholes)*
3.3) **Normal Inverse Gaussian PIDE**
*(approximated jump-diffusion PIDE, Monte Carlo, Fourier inversion, properties of the Lévy measure)*
4.1) **Pricing with transaction costs**
*(Davis-Panas-Zariphopoulou model, singular control problem, HJB variational inequality, indifference pricing, binomial tree, performances)*
4.2) **Volatility smile and model calibration**
*(Volatility smile, root finder methods, calibration methods)*
5.1) **Linear regression and Kalman filter**
*(market data cleaning, Linear regression methods, Kalman filter design, choice of parameters)*
5.2) **Kalman auto-correlation tracking - AR(1) process**
*(Autoregressive process, estimation methods, Kalman filter, Kalman smoother, variable autocorrelation tracking)*
5.3) **Volatility tracking**
*(Heston simulation, hypothesis testing, distribution fitting, estimation methods, GARCH(1,1), Kalman filter, Kalman smoother)*
6.1) **Ornstein-Uhlenbeck process and applications**
*(parameters estimation, hitting time, Vasicek PDE, Kalman filter, trading strategy)*
7.1) **Classical MVO**
*(mean variance optimization, quadratic programming, only long and long-short, closed formula)*
A.1) **Appendix: Linear equations**
*(LU, Jacobi, Gauss-Seidel, SOR, Thomas)*
A.2) **Appendix: Code optimization**
*(cython, C code)*
A.3) **Appendix: Review of Lévy processes theory**
*(basic and important definitions, derivation of the pricing PIDE)*
## How to run the notebooks
**Virtual environment:**
Here I explain how to create a virtual environment with [Anaconda](https://www.anaconda.com/distribution/) and with the python module [venv](https://docs.python.org/3.7/tutorial/venv.html).
- Option 1:
You can recreate my tested conda virtual environment with:
```bash
conda env create -f environment.yml
pip install -e .
```
The first line recreates the virtual environment and installs all the packages.
With the second line we just install the local package `FMNM`.
- Option 2:
If you want to create a new environment with the latest python version, you can do:
```bash
conda create -n FMNM python
conda activate FMNM
PACKAGES=$(tr '\n' ' ' < list_of_packages.txt | sed "s/arch/arch-py/g")
conda install ${PACKAGES[@]}
pip install -e .
```
where in the third line we replace the package name `arch` with the `arch-py`, which is the name used by conda.
- Option 3:
If you prefer to create a `venv` that uses python 3.11.4, you can do it as follows:
```bash
python3.11.4 -m venv --prompt FMNM python-venv
source python-venv/bin/activate
python3 -m pip install --upgrade pip
pip install --requirement requirements.txt
pip install -e .
```
- Option 4:
If you prefer to use the python version already installed in your system, you just need to run
```bash
pip install --requirement list_of_packages.txt
pip install -e .
```
and then enter in the shell `jupyter-notebook` or `jupyter-lab`:
However, if you are using old versions, there could be compatibility problems.
**Docker:**
Here we run the notebooks with jupyterlab:
- Option 1:
You can use docker-compose to build a container:
```bash
docker-compose up --build -d
```
And then stop the container with
```bash
docker-compose down
```
And open the browser at `http://localhost:8888/lab`
- Option 2:
Alternatively, you can
```bash
docker build -t fmnm .
docker run --rm -d -p 8888:8888 --name Numeric_Finance fmnm
```
### Enjoy!
================================================
FILE: data/historical_data.csv
================================================
Date,AAPL,EXO.MI,FB,GOOGL,UBS,^GSPC,^IXIC,^TNX,^VIX
2014-05-12,77.32666015625,29.93533706665039,59.83000183105469,538.4299926757812,15.106929779052734,1896.6500244140625,4143.85986328125,2.6559998989105225,12.229999542236328
2014-05-13,77.4479751586914,29.621427536010742,59.83000183105469,541.5399780273438,15.0921630859375,1897.449951171875,4130.169921875,2.618000030517578,12.130000114440918
2014-05-14,77.46231842041016,29.630937576293945,59.22999954223633,534.4099731445312,15.084779739379883,1888.530029296875,4100.6298828125,2.5429999828338623,12.170000076293945
2014-05-15,76.80360412597656,28.546531677246094,57.91999816894531,529.1199951171875,14.730364799499512,1870.8499755859375,4069.2900390625,2.502000093460083,13.170000076293945
2014-05-16,77.93710327148438,29.621427536010742,58.02000045776367,528.2999877929688,14.700828552246094,1877.8599853515625,4090.590087890625,2.5179998874664307,12.4399995803833
2014-05-19,78.860595703125,29.621427536010742,59.209999084472656,538.8300170898438,14.612227439880371,1885.0799560546875,4125.81005859375,2.5360000133514404,12.420000076293945
2014-05-20,78.8762435913086,29.545326232910156,58.560001373291016,540.3900146484375,14.560539245605469,1872.8299560546875,4096.89013671875,2.509000062942505,12.960000038146973
2014-05-21,79.08495330810547,29.849721908569336,60.4900016784668,549.7000122070312,14.715596199035645,1888.030029296875,4131.5400390625,2.5369999408721924,11.90999984741211
2014-05-22,79.21016693115234,29.193370819091797,60.52000045776367,555.4500122070312,14.715596199035645,1892.489990234375,4154.33984375,2.555000066757202,12.029999732971191
2014-05-23,80.10496520996094,29.535818099975586,61.349998474121094,563.7999877929688,14.86326789855957,1900.530029296875,4185.81005859375,2.5360000133514404,11.359999656677246
2014-05-26,,29.916311264038086,,,,,,,
2014-05-27,81.60498046875,29.95435905456543,63.47999954223633,574.8699951171875,14.907571792602539,1911.9100341796875,4237.06982421875,2.5179998874664307,11.510000228881836
2014-05-28,81.39368438720703,29.89728546142578,63.5099983215332,570.4500122070312,15.010941505432129,1909.780029296875,4225.080078125,2.437999963760376,11.680000305175781
2014-05-29,82.8767318725586,29.773622512817383,63.83000183105469,570.5599975585938,14.944486618041992,1920.030029296875,4247.9501953125,2.447000026702881,11.569999694824219
2014-05-30,82.56629943847656,29.963871002197266,63.29999923706055,571.6500244140625,14.86326789855957,1923.5699462890625,4242.6201171875,2.4570000171661377,11.399999618530273
2014-06-02,81.9989013671875,29.916311264038086,63.08000183105469,564.3400268554688,14.855888366699219,1924.969970703125,4237.2001953125,2.5339999198913574,11.579999923706055
2014-06-03,83.158447265625,29.640451431274414,62.869998931884766,554.510009765625,14.759897232055664,1924.239990234375,4234.080078125,2.5929999351501465,11.869999885559082
2014-06-04,84.10804748535156,29.307518005371094,63.34000015258789,553.760009765625,14.715596199035645,1927.8800048828125,4251.64013671875,2.6059999465942383,12.079999923706055
2014-06-05,84.43806457519531,29.859233856201172,63.189998626708984,564.9299926757812,14.7968168258667,1940.4599609375,4296.22998046875,2.5840001106262207,11.680000305175781
2014-06-06,84.20587921142578,30.201679229736328,62.5,566.030029296875,14.97402572631836,1949.43994140625,4321.39990234375,2.5969998836517334,10.729999542236328
2014-06-09,85.55329895019531,30.31583023071289,62.880001068115234,570.72998046875,14.97402572631836,1951.27001953125,4336.240234375,2.61299991607666,11.149999618530273
2014-06-10,86.05547332763672,30.49656105041504,65.7699966430664,568.2999877929688,15.055245399475098,1950.7900390625,4338.0,2.634999990463257,10.989999771118164
2014-06-11,85.69939422607422,30.31583023071289,65.77999877929688,567.5,14.74513053894043,1943.8900146484375,4331.93017578125,2.640000104904175,11.600000381469727
2014-06-12,84.2658920288086,30.27777671813965,64.29000091552734,559.5,14.523621559143066,1930.1099853515625,4297.6298828125,2.5859999656677246,12.5600004196167
2014-06-13,83.34368896484375,29.745086669921875,64.5,560.3499755859375,14.339030265808105,1936.1600341796875,4310.64990234375,2.6040000915527344,12.180000305175781
2014-06-16,84.1837158203125,29.421667098999023,64.19000244140625,552.2999877929688,14.257808685302734,1937.780029296875,4321.10009765625,2.5969998836517334,12.649999618530273
2014-06-17,84.07414245605469,29.35508155822754,64.4000015258789,550.6199951171875,14.339030265808105,1941.989990234375,4337.22998046875,2.6549999713897705,12.0600004196167
2014-06-18,84.16544342041016,29.421667098999023,65.5999984741211,560.6599731445312,14.457170486450195,1956.97998046875,4362.83984375,2.61299991607666,10.609999656677246
2014-06-19,83.87326049804688,29.288494110107422,64.33999633789062,564.989990234375,14.18397331237793,1959.47998046875,4359.330078125,2.621999979019165,10.619999885559082
2014-06-20,83.00587463378906,29.3265438079834,64.5,566.52001953125,14.065835952758789,1962.8699951171875,4368.0400390625,2.624000072479248,10.850000381469727
2014-06-23,82.93281555175781,28.879362106323242,65.37000274658203,574.2899780273438,14.014150619506836,1962.6099853515625,4368.68017578125,2.622999906539917,10.979999542236328
2014-06-24,82.4306411743164,28.610088348388672,65.72000122070312,572.5399780273438,13.726186752319336,1949.97998046875,4350.35009765625,2.5859999656677246,12.130000114440918
2014-06-25,82.50369262695312,28.427370071411133,67.44000244140625,585.9299926757812,13.977231979370117,1959.530029296875,4379.759765625,2.559000015258789,11.59000015258789
2014-06-26,82.99674224853516,28.773576736450195,67.12999725341797,584.77001953125,13.637585639953613,1957.219970703125,4379.0498046875,2.5250000953674316,11.630000114440918
2014-06-27,83.98284149169922,28.571622848510742,67.5999984741211,585.6900024414062,13.541597366333008,1960.9599609375,4397.93017578125,2.5320000648498535,11.260000228881836
2014-06-30,84.8502197265625,28.840896606445312,67.29000091552734,584.6699829101562,13.526829719543457,1960.22998046875,4408.18017578125,2.5160000324249268,11.569999694824219
2014-07-01,85.38893127441406,28.85051155090332,68.05999755859375,591.489990234375,13.674504280090332,1973.3199462890625,4458.64990234375,2.562999963760376,11.149999618530273
2014-07-02,85.35242462158203,28.985145568847656,66.44999694824219,590.780029296875,13.79263973236084,1974.6199951171875,4457.72998046875,2.628000020980835,10.819999694824219
2014-07-03,85.8545913696289,29.408287048339844,66.29000091552734,593.0800170898438,13.903395652770996,1985.43994140625,4485.93017578125,2.6480000019073486,10.319999694824219
2014-07-04,,28.792810440063477,,,,,,,
2014-07-07,87.62593078613281,28.63894271850586,65.29000091552734,590.760009765625,13.681886672973633,1977.6500244140625,4451.52978515625,2.617000102996826,11.329999923706055
2014-07-08,87.05982971191406,28.321584701538086,62.7599983215332,578.4000244140625,13.482527732849121,1963.7099609375,4391.4599609375,2.565000057220459,11.979999542236328
2014-07-09,87.0963363647461,28.475454330444336,64.97000122070312,583.3599853515625,13.578516006469727,1972.8299560546875,4419.02978515625,2.546999931335449,11.649999618530273
2014-07-10,86.77678680419922,27.792659759521484,64.87000274658203,580.0399780273438,13.445610046386719,1964.6800537109375,4396.2001953125,2.5320000648498535,12.59000015258789
2014-07-11,86.94114685058594,27.802274703979492,66.33999633789062,586.6500244140625,13.445610046386719,1967.5699462890625,4415.490234375,2.5199999809265137,12.079999923706055
2014-07-14,88.06417083740234,27.792659759521484,67.9000015258789,594.260009765625,13.630199432373047,1977.0999755859375,4440.419921875,2.5490000247955322,11.819999694824219
2014-07-15,87.0324478149414,27.696491241455078,67.16999816894531,593.0599975585938,13.681886672973633,1973.280029296875,4416.39013671875,2.5490000247955322,11.960000038146973
2014-07-16,86.5394058227539,28.600475311279297,67.66000366210938,590.6199951171875,13.822175025939941,1981.5699462890625,4425.97021484375,2.5380001068115234,11.0
2014-07-17,84.99632263183594,28.54277229309082,66.41000366210938,580.8200073242188,13.526829719543457,1958.1199951171875,4363.4501953125,2.4749999046325684,14.539999961853027
2014-07-18,86.21981811523438,28.523536682128906,68.41999816894531,605.1099853515625,13.526829719543457,1978.219970703125,4432.14990234375,2.4839999675750732,12.0600004196167
2014-07-21,85.77244567871094,27.879209518432617,69.4000015258789,598.4400024414062,13.512063026428223,1973.6300048828125,4424.7001953125,2.4739999771118164,12.8100004196167
2014-07-22,86.4845962524414,28.54277229309082,69.2699966430664,603.5700073242188,13.416074752807617,1983.530029296875,4456.02001953125,2.4660000801086426,12.239999771118164
2014-07-23,88.73986053466797,28.427370071411133,71.29000091552734,605.1900024414062,13.467761039733887,1987.010009765625,4473.7001953125,2.4639999866485596,11.520000457763672
2014-07-24,88.59375762939453,28.812040328979492,74.9800033569336,603.010009765625,13.644969940185547,1987.97998046875,4472.10986328125,2.509000062942505,11.84000015258789
2014-07-25,89.1781234741211,28.6485595703125,75.19000244140625,598.0800170898438,13.563749313354492,1978.3399658203125,4449.56005859375,2.4690001010894775,12.6899995803833
2014-07-28,90.4107437133789,28.465837478637695,74.91999816894531,599.02001953125,13.526829719543457,1978.9100341796875,4444.91015625,2.490999937057495,12.5600004196167
2014-07-29,89.8263931274414,28.590856552124023,73.70999908447266,593.9500122070312,13.224100112915039,1969.949951171875,4442.7001953125,2.4619998931884766,13.279999732971191
2014-07-30,89.61637878417969,28.05231285095215,74.68000030517578,595.4400024414062,13.054275512695312,1970.0699462890625,4462.89990234375,2.553999900817871,13.329999923706055
2014-07-31,87.2880859375,27.600322723388672,72.6500015258789,579.5499877929688,12.67771053314209,1930.6700439453125,4369.77001953125,2.555999994277954,16.950000762939453
2014-08-01,87.77200317382812,27.5041561126709,72.36000061035156,573.5999755859375,12.685096740722656,1925.1500244140625,4352.64013671875,2.505000114440918,17.030000686645508
2014-08-04,87.27896118164062,27.5041561126709,73.51000213623047,582.27001953125,12.69986343383789,1938.989990234375,4383.89013671875,2.490999937057495,15.119999885559082
2014-08-05,86.8498306274414,26.830974578857422,72.69000244140625,573.1400146484375,12.470968246459961,1920.2099609375,4352.83984375,2.4830000400543213,16.8700008392334
2014-08-06,86.70374298095703,26.215496063232422,72.47000122070312,574.489990234375,12.507888793945312,1920.239990234375,4355.0498046875,2.4739999771118164,16.3700008392334
2014-08-07,86.69457244873047,26.003929138183594,73.16999816894531,571.8099975585938,12.426667213439941,1909.5699462890625,4334.97021484375,2.4240000247955322,16.65999984741211
2014-08-08,86.93313598632812,26.378984451293945,73.05999755859375,577.9400024414062,12.862302780151367,1931.5899658203125,4370.89990234375,2.4149999618530273,15.770000457763672
2014-08-11,88.08013153076172,26.44630241394043,73.44000244140625,577.25,12.773696899414062,1936.9200439453125,4401.330078125,2.4200000762939453,14.229999542236328
2014-08-12,88.0617904663086,26.840595245361328,72.83000183105469,572.1199951171875,12.788466453552246,1933.75,4389.25,2.441999912261963,14.130000114440918
2014-08-13,89.22712707519531,27.023311614990234,73.7699966430664,584.5599975585938,12.899219512939453,1946.719970703125,4434.1298828125,2.4130001068115234,12.899999618530273
2014-08-14,89.4657211303711,26.8886775970459,74.30000305175781,584.6500244140625,13.046894073486328,1955.1800537109375,4453.0,2.4000000953674316,12.420000076293945
2014-08-15,89.90617370605469,,73.62999725341797,583.7100219726562,12.973054885864258,1955.06005859375,4464.93017578125,2.3450000286102295,13.149999618530273
2014-08-18,90.98892974853516,27.311817169189453,74.58999633789062,592.7000122070312,13.061660766601562,1971.739990234375,4508.31005859375,2.38700008392334,12.319999694824219
2014-08-19,92.24603271484375,27.27334976196289,75.29000091552734,597.1099853515625,13.07642650604248,1981.5999755859375,4527.509765625,2.4049999713897705,12.210000038146973
2014-08-20,92.28274536132812,27.4176025390625,74.80999755859375,595.4099731445312,13.032124519348145,1986.510009765625,4526.47998046875,2.4260001182556152,11.779999732971191
2014-08-21,92.29190063476562,27.869592666625977,74.56999969482422,592.4199829101562,13.24625301361084,1992.3699951171875,4532.10009765625,2.4070000648498535,11.760000228881836
2014-08-22,92.97093200683594,27.88882827758789,74.56999969482422,592.5399780273438,13.054275512695312,1988.4000244140625,4538.5498046875,2.4030001163482666,11.470000267028809
2014-08-25,93.17279052734375,28.504304885864258,75.0199966430664,590.5700073242188,13.179800033569336,1997.9200439453125,4557.35009765625,2.38700008392334,11.699999809265137
2014-08-26,92.57636260986328,28.812040328979492,75.95999908447266,588.1199951171875,13.261018753051758,2000.02001953125,4570.64013671875,2.3910000324249268,11.630000114440918
2014-08-27,93.71417236328125,29.13901710510254,74.62999725341797,583.0,13.305320739746094,2000.1199951171875,4569.6201171875,2.3610000610351562,11.779999732971191
2014-08-28,93.82431030273438,28.917827606201172,73.86000061035156,580.3200073242188,13.238869667053223,1996.739990234375,4557.7001953125,2.3340001106262207,12.050000190734863
2014-08-29,94.05368041992188,29.119781494140625,74.81999969482422,582.3599853515625,13.24625301361084,2003.3699951171875,4580.27001953125,2.3429999351501465,12.09000015258789
2014-09-01,,29.052461624145508,,,,,,,
2014-09-02,94.78778076171875,29.187101364135742,76.68000030517578,588.6300048828125,13.179800033569336,2002.280029296875,4598.18994140625,2.4189999103546143,12.25
2014-09-03,90.78704833984375,29.581390380859375,75.83000183105469,589.52001953125,13.194567680358887,2000.719970703125,4572.56005859375,2.4100000858306885,12.359999656677246
2014-09-04,90.03462219238281,30.485374450683594,75.94999694824219,593.1400146484375,13.172416687011719,1997.6500244140625,4562.2900390625,2.447999954223633,12.640000343322754
2014-09-05,90.81458282470703,29.72564697265625,77.26000213623047,597.780029296875,13.113346099853516,2007.7099609375,4582.89990234375,2.4609999656677246,12.09000015258789
2014-09-08,90.25483703613281,29.764110565185547,77.88999938964844,601.6300048828125,13.002593040466309,2001.5400390625,4592.2900390625,2.4690001010894775,12.65999984741211
2014-09-09,89.91532135009766,29.66794204711914,76.66999816894531,591.969970703125,12.943524360656738,1988.43994140625,4552.2900390625,2.5,13.5
2014-09-10,92.67729949951172,29.764110565185547,77.43000030517578,593.4199829101562,13.002593040466309,1995.68994140625,4586.52001953125,2.5339999198913574,12.880000114440918
2014-09-11,93.0718765258789,29.812192916870117,77.91999816894531,591.1099853515625,12.95828914642334,1997.449951171875,4591.81005859375,2.5309998989105225,12.800000190734863
2014-09-12,93.28291320800781,29.812192916870117,77.4800033569336,584.9000244140625,13.039509773254395,1985.5400390625,4567.60009765625,2.614000082015991,13.3100004196167
2014-09-15,93.25537872314453,29.69679069519043,74.58000183105469,581.6400146484375,12.928754806518555,1984.1300048828125,4518.89990234375,2.5910000801086426,14.119999885559082
2014-09-16,92.54883575439453,29.812192916870117,76.08000183105469,588.780029296875,12.95828914642334,1998.97998046875,4552.759765625,2.5889999866485596,12.729999542236328
2014-09-17,93.2094955444336,29.812192916870117,76.43000030517578,593.2899780273438,12.980441093444824,2001.5699462890625,4562.18994140625,2.5999999046325684,12.649999618530273
2014-09-18,93.40218353271484,29.783344268798828,77.0,597.27001953125,13.113346099853516,2011.3599853515625,4593.43017578125,2.628999948501587,12.029999732971191
2014-09-19,92.64059448242188,29.802576065063477,77.91000366210938,605.4000244140625,13.054275512695312,2010.4000244140625,4579.7900390625,2.5869998931884766,12.109999656677246
2014-09-22,92.73234558105469,29.783344268798828,76.80000305175781,597.27001953125,12.840152740478516,1994.2900390625,4527.68994140625,2.565999984741211,13.6899995803833
2014-09-23,94.18216705322266,29.629474639892578,78.29000091552734,591.1799926757812,12.773696899414062,1982.77001953125,4508.68994140625,2.5350000858306885,14.930000305175781
2014-09-24,93.36549377441406,29.56215476989746,78.54000091552734,598.4199829101562,13.00997543334961,1998.300048828125,4555.22021484375,2.569000005722046,13.270000457763672
2014-09-25,89.80522155761719,29.292884826660156,77.22000122070312,585.25,12.862302780151367,1965.989990234375,4466.75,2.510999917984009,15.640000343322754
2014-09-26,92.4478759765625,29.687175750732422,78.79000091552734,587.9000244140625,13.032124519348145,1982.8499755859375,4512.18994140625,2.5350000858306885,14.850000381469727
2014-09-29,91.86064147949219,29.206335067749023,79.0,587.8099975585938,12.943524360656738,1977.800048828125,4505.85009765625,2.490999937057495,15.979999542236328
2014-09-30,92.4478759765625,29.56215476989746,79.04000091552734,588.4099731445312,12.825384140014648,1972.2900390625,4493.39013671875,2.507999897003174,16.309999465942383
2014-10-01,91.00727081298828,29.167865753173828,76.55000305175781,579.6300048828125,12.79584789276123,1946.1600341796875,4422.08984375,2.4030001163482666,16.709999084472656
2014-10-02,91.6679458618164,28.148483276367188,77.08000183105469,580.8800048828125,12.69986343383789,1946.1700439453125,4430.2001953125,2.437999963760376,16.15999984741211
2014-10-03,91.41102600097656,28.475454330444336,77.44000244140625,586.25,12.404516220092773,1967.9000244140625,4475.6201171875,2.447000026702881,14.550000190734863
2014-10-06,91.41102600097656,28.485071182250977,77.55999755859375,587.780029296875,12.478352546691895,1964.8199462890625,4454.7998046875,2.424999952316284,15.460000038146973
2014-10-07,90.6126937866211,27.658023834228516,76.29000091552734,574.0999755859375,12.219924926757812,1935.0999755859375,4385.2001953125,2.3499999046325684,17.200000762939453
2014-10-08,92.49378204345703,27.20603370666504,77.5199966430664,583.739990234375,12.434051513671875,1968.8900146484375,4468.58984375,2.3299999237060547,15.109999656677246
2014-10-09,92.6956558227539,27.004077911376953,75.91000366210938,570.8099975585938,11.946731567382812,1928.2099609375,4378.33984375,2.3269999027252197,18.760000228881836
2014-10-10,92.42955780029297,27.04254722595215,72.91000366210938,555.1900024414062,11.747371673583984,1906.1300048828125,4276.240234375,2.306999921798706,21.239999771118164
2014-10-13,91.58535766601562,27.1964168548584,72.98999786376953,544.75,11.73998737335205,1874.739990234375,4213.66015625,2.2860000133514404,24.639999389648438
2014-10-14,90.6126937866211,27.148332595825195,73.58999633789062,548.6900024414062,11.78429126739502,1877.699951171875,4227.169921875,2.2060000896453857,22.790000915527344
2014-10-15,89.50241088867188,26.494384765625,73.20999908447266,540.72998046875,11.599699020385742,1862.489990234375,4215.31982421875,2.0899999141693115,25.270000457763672
2014-10-16,88.32788848876953,26.330900192260742,72.62999725341797,536.9199829101562,11.363422393798828,1862.760009765625,4217.39013671875,2.1530001163482666,25.200000762939453
2014-10-17,89.62169647216797,27.234882354736328,75.94999694824219,522.969970703125,11.695687294006348,1886.760009765625,4258.43994140625,2.1989998817443848,21.989999771118164
2014-10-20,91.53948211669922,26.946378707885742,76.94999694824219,532.3800048828125,11.850743293762207,1904.010009765625,4316.06982421875,2.180999994277954,18.56999969482422
2014-10-21,94.02617645263672,27.57147216796875,78.69000244140625,538.030029296875,12.064868927001953,1941.280029296875,4419.47998046875,2.2079999446868896,16.079999923706055
2014-10-22,94.50331115722656,27.523387908935547,78.37000274658203,542.6900024414062,11.954115867614746,1927.1099853515625,4382.85009765625,2.2290000915527344,17.8700008392334
2014-10-23,96.19168853759766,27.70610809326172,80.04000091552734,553.6500244140625,12.064868927001953,1950.8199462890625,4452.7900390625,2.2750000953674316,16.530000686645508
2014-10-24,96.54954528808594,27.773426055908203,80.66999816894531,548.9000244140625,12.146090507507324,1964.5799560546875,4483.72021484375,2.2730000019073486,16.110000610351562
2014-10-27,96.44861602783203,27.379133224487305,80.27999877929688,549.8800048828125,11.983649253845215,1961.6300048828125,4485.93017578125,2.256999969482422,16.040000915527344
2014-10-28,97.94429779052734,28.186948776245117,80.7699966430664,558.9400024414062,12.840152740478516,1985.050048828125,4564.2900390625,2.2839999198913574,14.390000343322754
2014-10-29,98.4948501586914,30.168018341064453,75.86000061035156,558.4500122070312,12.640795707702637,1982.300048828125,4549.22998046875,2.322999954223633,15.149999618530273
2014-10-30,98.16451263427734,31.71632957458496,74.11000061035156,560.27001953125,12.618642807006836,1994.6500244140625,4566.14013671875,2.305000066757202,14.520000457763672
2014-10-31,99.10047149658203,33.41851043701172,74.98999786376953,567.8699951171875,12.83276653289795,2018.050048828125,4630.740234375,2.3350000381469727,14.029999732971191
2014-11-03,100.3851089477539,32.033687591552734,73.87999725341797,563.77001953125,12.67771053314209,2017.81005859375,4638.91015625,2.3480000495910645,14.729999542236328
2014-11-04,99.65104675292969,31.71632957458496,75.76000213623047,564.1900024414062,12.662943840026855,2012.0999755859375,4623.64013671875,2.3420000076293945,14.890000343322754
2014-11-05,99.88961791992188,32.1202392578125,74.83000183105469,555.9500122070312,12.722013473510742,2023.5699462890625,4620.72021484375,2.3499999046325684,14.170000076293945
2014-11-06,100.17529296875,31.60092544555664,75.26000213623047,551.6900024414062,12.626026153564453,2031.2099609375,4638.47021484375,2.375,13.670000076293945
2014-11-07,100.46098327636719,31.649009704589844,75.5999984741211,551.8200073242188,12.596488952636719,2031.9200439453125,4632.52978515625,2.312000036239624,13.119999885559082
2014-11-10,100.29510498046875,31.927898406982422,75.0,558.22998046875,12.63340950012207,2038.260009765625,4651.6201171875,2.3589999675750732,12.670000076293945
2014-11-11,101.09687042236328,32.65877914428711,74.61000061035156,561.2899780273438,12.79584789276123,2039.6800537109375,4660.56005859375,2.359999895095825,12.920000076293945
2014-11-12,102.52532196044922,32.25487518310547,74.72000122070312,558.25,12.83276653289795,2038.25,4675.14013671875,2.3589999675750732,13.020000457763672
2014-11-13,103.97219848632812,32.72608947753906,74.25,556.4400024414062,12.950905799865723,2039.3299560546875,4680.14013671875,2.3469998836517334,13.789999961853027
2014-11-14,105.22554016113281,32.69724655151367,74.87999725341797,555.1900024414062,12.950905799865723,2039.8199462890625,4688.5400390625,2.319999933242798,13.3100004196167
2014-11-17,105.05043029785156,32.98575210571289,74.23999786376953,546.6400146484375,12.884454727172852,2041.3199462890625,4671.0,2.3399999141693115,13.989999771118164
2014-11-18,106.41436767578125,33.274253845214844,74.33999633789062,544.510009765625,12.936139106750488,2051.800048828125,4702.43994140625,2.322000026702881,13.859999656677246
2014-11-19,105.67709350585938,32.85111618041992,73.33000183105469,547.2000122070312,12.921370506286621,2048.719970703125,4675.7099609375,2.3510000705718994,13.960000038146973
2014-11-20,107.1884765625,32.75495147705078,73.5999984741211,543.760009765625,12.788466453552246,2052.75,4701.8701171875,2.3350000381469727,13.579999923706055
2014-11-21,107.33594512939453,33.72624969482422,73.75,545.8900146484375,12.884454727172852,2063.5,4712.97021484375,2.315000057220459,12.899999618530273
2014-11-24,109.32652282714844,33.73586654663086,74.01000213623047,547.47998046875,159.08055114746094,2069.409912109375,4754.89013671875,2.309999942779541,12.619999885559082
2014-11-25,108.37732696533203,34.197471618652344,75.62999725341797,549.22998046875,159.08055114746094,2067.030029296875,4758.25,2.259999990463257,12.25
2014-11-26,109.66753387451172,34.03398895263672,77.62000274658203,547.72998046875,159.08055114746094,2072.830078125,4787.31982421875,2.2339999675750732,12.069999694824219
2014-11-27,,34.52444839477539,,,,,,,
2014-11-28,109.60302734375,34.42827224731445,77.69999694824219,549.0800170898438,159.08055114746094,2067.56005859375,4791.6298828125,2.194000005722046,13.329999923706055
2014-12-01,106.04573822021484,34.31287384033203,75.0999984741211,539.6500244140625,,2053.43994140625,4727.35009765625,2.2179999351501465,14.15999984741211
2014-12-02,105.64022827148438,34.31287384033203,75.45999908447266,538.5900268554688,13.290553092956543,2066.550048828125,4755.81005859375,2.2850000858306885,12.850000381469727
2014-12-03,106.83828735351562,34.64946365356445,74.87999725341797,536.969970703125,13.408692359924316,2074.330078125,4774.47021484375,2.2869999408721924,12.5
2014-12-04,106.43280792236328,33.56275939941406,75.23999786376953,542.5800170898438,13.416074752807617,2071.919921875,4769.43994140625,2.256999969482422,12.380000114440918
2014-12-05,105.98120880126953,34.88988494873047,76.36000061035156,528.0800170898438,13.452991485595703,2075.3701171875,4780.759765625,2.306999921798706,11.890000343322754
2014-12-08,103.58513641357422,34.7552490234375,76.5199966430664,530.72998046875,13.320089340209961,2060.31005859375,4740.68994140625,2.256999969482422,14.210000038146973
2014-12-09,105.17022705078125,33.649314880371094,76.83999633789062,536.1099853515625,13.275784492492676,2059.820068359375,4766.47021484375,2.2200000286102295,15.350000381469727
2014-12-10,103.17041778564453,33.2646369934082,76.18000030517578,528.0399780273438,13.07642650604248,2026.1400146484375,4684.02001953125,2.1689999103546143,18.530000686645508
2014-12-11,102.86629486083984,32.41835403442383,77.7300033569336,532.1099853515625,13.105963706970215,2035.3299560546875,4708.16015625,2.177999973297119,20.079999923706055
2014-12-12,101.12451934814453,31.3701229095459,77.83000183105469,521.510009765625,12.847535133361816,2002.3299560546875,4653.60009765625,2.1029999256134033,21.079999923706055
2014-12-15,99.74217224121094,31.139320373535156,76.98999786376953,515.8400268554688,12.685096740722656,1989.6300048828125,4605.16015625,2.115999937057495,20.420000076293945
2014-12-16,98.37821197509766,31.97597885131836,74.69000244140625,498.1600036621094,12.714629173278809,1972.739990234375,4547.830078125,2.071000099182129,23.56999969482422
2014-12-17,100.82962036132812,31.75479507446289,76.11000061035156,506.45001220703125,12.825384140014648,2012.8900146484375,4644.31005859375,2.1480000019073486,19.440000534057617
2014-12-18,103.81551361083984,32.43759536743164,78.4000015258789,514.6199951171875,12.995208740234375,2061.22998046875,4748.39990234375,2.2039999961853027,16.809999465942383
2014-12-19,103.01375579833984,33.158851623535156,79.87999725341797,520.0399780273438,12.995208740234375,2070.64990234375,4765.3798828125,2.1760001182556152,16.489999771118164
2014-12-22,104.08277130126953,33.11076736450195,81.44999694824219,532.2999877929688,12.943524360656738,2078.5400390625,4781.419921875,2.1619999408721924,15.25
2014-12-23,103.71414947509766,33.17808532714844,80.61000061035156,538.77001953125,12.921370506286621,2082.169921875,4765.419921875,2.256999969482422,14.800000190734863
2014-12-24,103.2257080078125,,80.7699966430664,536.9299926757812,13.002593040466309,2081.8798828125,4773.47021484375,2.2639999389648438,14.369999885559082
2014-12-26,105.05043029785156,,80.77999877929688,541.52001953125,13.046894073486328,2088.77001953125,4806.85986328125,2.25,14.5
2014-12-29,104.97673034667969,33.072303771972656,80.0199966430664,537.3099975585938,12.781082153320312,2090.570068359375,4806.91015625,2.2070000171661377,15.0600004196167
2014-12-30,103.69571685791016,32.73571014404297,79.22000122070312,535.280029296875,12.751547813415527,2080.35009765625,4777.43994140625,2.190000057220459,15.920000076293945
2014-12-31,101.72354125976562,,78.0199966430664,530.6599731445312,12.58910846710205,2058.89990234375,4736.0498046875,2.1700000762939453,19.200000762939453
2015-01-02,100.75589752197266,33.11076736450195,78.44999694824219,529.5499877929688,12.618642807006836,2058.199951171875,4726.81005859375,2.122999906539917,17.790000915527344
2015-01-05,97.91742706298828,32.08176803588867,77.19000244140625,519.4600219726562,12.256844520568848,2020.5799560546875,4652.56982421875,2.0390000343322754,19.920000076293945
2015-01-06,97.92666625976562,31.87981414794922,76.1500015258789,506.6400146484375,12.116555213928223,2002.6099853515625,4592.740234375,1.9630000591278076,21.1200008392334
2015-01-07,99.2998046875,32.11061096191406,76.1500015258789,505.1499938964844,12.323297500610352,2025.9000244140625,4650.47021484375,1.9539999961853027,19.309999465942383
2015-01-08,103.11512756347656,32.83188247680664,78.18000030517578,506.9100036621094,12.29376220703125,2062.139892578125,4736.18994140625,2.0160000324249268,17.010000228881836
2015-01-09,103.2257080078125,32.1202392578125,77.73999786376953,500.7200012207031,12.212541580200195,2044.81005859375,4704.06982421875,1.9709999561309814,17.549999237060547
2015-01-12,100.68215942382812,32.84150314331055,76.72000122070312,497.05999755859375,12.094403266906738,2028.260009765625,4664.7099609375,1.9119999408721924,19.600000381469727
2015-01-13,101.57608032226562,33.120384216308594,76.44999694824219,501.79998779296875,12.249459266662598,2023.030029296875,4661.5,1.8899999856948853,20.559999465942383
2015-01-14,101.18904113769531,32.37027359008789,76.27999877929688,505.92999267578125,12.16823959350586,2011.27001953125,4639.31982421875,1.8370000123977661,21.479999542236328
2015-01-15,98.4427261352539,33.1300048828125,74.05000305175781,504.010009765625,12.205159187316895,1992.6700439453125,4570.81982421875,1.774999976158142,22.389999389648438
2015-01-16,97.67781066894531,33.505062103271484,75.18000030517578,510.4599914550781,11.983649253845215,2019.4200439453125,4634.3798828125,1.815000057220459,20.950000762939453
2015-01-19,,33.158851623535156,,,,,,,
2015-01-20,100.1937255859375,33.39927291870117,76.23999786376953,509.94000244140625,12.227309226989746,2022.550048828125,4654.85009765625,1.8070000410079956,19.889999389648438
2015-01-21,100.95864868164062,33.524295806884766,76.73999786376953,520.3900146484375,12.39713191986084,2032.1199951171875,4667.419921875,1.8530000448226929,18.850000381469727
2015-01-22,103.58513641357422,34.22632598876953,77.6500015258789,537.2999877929688,12.729395866394043,2063.14990234375,4750.39990234375,1.8960000276565552,16.399999618530273
2015-01-23,104.11962890625,34.197471618652344,77.83000183105469,541.9500122070312,12.80323314666748,2051.820068359375,4757.8798828125,1.8170000314712524,16.65999984741211
2015-01-26,104.23023223876953,35.05337142944336,77.5,536.719970703125,12.73677921295166,2057.090087890625,4771.759765625,1.8279999494552612,15.520000457763672
2015-01-27,100.58080291748047,34.41865539550781,75.77999877929688,521.1900024414062,12.670327186584473,2029.550048828125,4681.5,1.8250000476837158,17.219999313354492
2015-01-28,106.26691436767578,34.60137939453125,76.23999786376953,512.4299926757812,12.256844520568848,2002.1600341796875,4637.990234375,1.7239999771118164,20.440000534057617
2015-01-29,109.57537841796875,35.00529098510742,78.0,513.22998046875,12.441436767578125,2021.25,4683.41015625,1.7510000467300415,18.760000228881836
2015-01-30,107.97183227539062,34.832183837890625,75.91000366210938,537.5499877929688,12.315914154052734,1994.989990234375,4635.240234375,1.6749999523162842,20.969999313354492
2015-02-02,109.32652282714844,34.3994255065918,74.98999786376953,532.2000122070312,12.463586807250977,2020.8499755859375,4676.68994140625,1.6729999780654907,19.43000030517578
2015-02-03,109.34498596191406,34.995662689208984,75.4000015258789,533.2999877929688,12.685096740722656,2050.030029296875,4727.740234375,1.7799999713897705,17.329999923706055
2015-02-04,110.18359375,35.120689392089844,75.62999725341797,526.0999755859375,12.877069473266602,2041.510009765625,4716.7001953125,1.7970000505447388,18.329999923706055
2015-02-05,110.97004699707031,35.149539947509766,75.61000061035156,529.8300170898438,12.877069473266602,2062.52001953125,4765.10009765625,1.815000057220459,16.850000381469727
2015-02-06,110.03556823730469,35.02451705932617,74.47000122070312,533.8800048828125,12.825384140014648,2055.469970703125,4744.39990234375,1.937999963760376,17.290000915527344
2015-02-09,110.76651000976562,34.21670913696289,74.44000244140625,529.280029296875,12.80323314666748,2046.739990234375,4726.009765625,1.9479999542236328,18.549999237060547
2015-02-10,112.89449310302734,34.80332946777344,75.19000244140625,540.1599731445312,12.500503540039062,2068.590087890625,4787.64013671875,1.9910000562667847,17.229999542236328
2015-02-11,115.54060363769531,34.48598098754883,76.51000213623047,538.0,12.212541580200195,2068.530029296875,4801.18017578125,1.9880000352859497,16.959999084472656
2015-02-12,117.0024185180664,36.226627349853516,76.2300033569336,546.010009765625,12.537421226501465,2088.47998046875,4857.60986328125,1.9859999418258667,15.34000015258789
2015-02-13,117.57605743408203,36.082374572753906,75.73999786376953,551.1599731445312,12.692479133605957,2096.989990234375,4893.83984375,2.0209999084472656,14.6899995803833
2015-02-16,,36.17854309082031,,,,,,,
2015-02-17,118.26997375488281,36.43819808959961,75.5999984741211,545.010009765625,12.83276653289795,2100.340087890625,4899.27001953125,2.1449999809265137,15.800000190734863
2015-02-18,119.09342956542969,37.005592346191406,76.70999908447266,542.6500244140625,12.847535133361816,2099.679931640625,4906.35986328125,2.065999984741211,15.449999809265137
2015-02-19,118.84358215332031,37.851871490478516,79.41999816894531,546.4500122070312,12.80323314666748,2097.449951171875,4924.7001953125,2.11299991607666,15.289999961853027
2015-02-20,119.81507110595703,37.457584381103516,79.9000015258789,541.7999877929688,13.024742126464844,2110.300048828125,4955.97021484375,2.132999897003174,14.300000190734863
2015-02-23,123.0533218383789,37.63068771362305,78.83999633789062,535.0,12.825384140014648,2109.659912109375,4960.97021484375,2.059000015258789,14.5600004196167
2015-02-24,122.28540802001953,37.986507415771484,78.44999694824219,538.6500244140625,12.818001747131348,2115.47998046875,4968.1201171875,1.9880000352859497,13.6899995803833
2015-02-25,119.15816497802734,38.18845748901367,79.55999755859375,547.3300170898438,12.906604766845703,2113.860107421875,4967.14013671875,1.968999981880188,13.84000015258789
2015-02-26,120.66629028320312,38.32309341430664,80.41000366210938,559.2899780273438,12.884454727172852,2110.739990234375,4987.89013671875,2.0160000324249268,13.90999984741211
2015-02-27,118.85285186767578,38.2653923034668,78.97000122070312,562.6300048828125,12.91398811340332,2104.5,4963.52978515625,2.002000093460083,13.34000015258789
2015-03-02,119.43573760986328,38.19807815551758,79.75,575.02001953125,12.906604766845703,2117.389892578125,5008.10009765625,2.0840001106262207,13.039999961853027
2015-03-03,119.68553161621094,37.32294845581055,79.5999984741211,578.7899780273438,12.91398811340332,2107.780029296875,4979.89990234375,2.121999979019165,13.859999656677246
2015-03-04,118.9268798828125,37.93842315673828,80.9000015258789,578.3300170898438,13.00997543334961,2098.530029296875,4967.14013671875,2.122999906539917,14.229999542236328
2015-03-05,116.9561767578125,37.74608612060547,81.20999908447266,581.4299926757812,12.980441093444824,2101.0400390625,4982.81005859375,2.111999988555908,14.039999961853027
2015-03-06,117.1319580078125,37.457584381103516,80.01000213623047,572.9000244140625,12.950905799865723,2071.260009765625,4927.3701171875,2.240000009536743,15.199999809265137
2015-03-09,117.63157653808594,37.371028900146484,79.44000244140625,574.0999755859375,12.906604766845703,2079.429931640625,4942.43994140625,2.194999933242798,15.0600004196167
2015-03-10,115.1982650756836,37.082523345947266,77.55000305175781,559.8499755859375,12.74416446685791,2044.1600341796875,4859.7900390625,2.125999927520752,16.690000534057617
2015-03-11,113.09803771972656,37.68838119506836,77.56999969482422,555.6900024414062,12.685096740722656,2040.239990234375,4849.93994140625,2.1089999675750732,16.8700008392334
2015-03-12,115.14275360107422,37.63068771362305,78.93000030517578,561.1699829101562,12.818001747131348,2065.949951171875,4893.2900390625,2.0959999561309814,15.420000076293945
2015-03-13,114.34705352783203,38.20769500732422,78.05000305175781,553.0,12.995208740234375,2053.39990234375,4871.759765625,2.111999988555908,16.0
2015-03-16,115.60536193847656,39.96757507324219,78.06999969482422,561.6400146484375,13.224100112915039,2081.18994140625,4929.509765625,2.0980000495910645,15.609999656677246
2015-03-17,117.53904724121094,39.25592803955078,79.36000061035156,557.6099853515625,13.12811279296875,2074.280029296875,4937.43017578125,2.058000087738037,15.65999984741211
2015-03-18,118.86211395263672,39.74639129638672,80.91000366210938,566.1599731445312,13.489913940429688,2099.5,4982.830078125,1.9509999752044678,13.970000267028809
2015-03-19,117.96466064453125,39.91948699951172,82.75,563.6699829101562,13.393924713134766,2089.27001953125,4992.3798828125,1.9769999980926514,14.069999694824219
2015-03-20,116.48431396484375,39.861793518066406,83.80000305175781,564.9500122070312,13.807409286499023,2108.10009765625,5026.419921875,1.9299999475479126,13.020000457763672
2015-03-23,117.69631958007812,39.313629150390625,84.43000030517578,565.3699951171875,14.058452606201172,2104.419921875,5010.97021484375,1.9149999618530273,13.40999984741211
2015-03-24,117.21525573730469,40.12144470214844,85.30999755859375,577.5399780273438,14.132288932800293,2091.5,4994.72998046875,1.878000020980835,13.619999885559082
2015-03-25,114.15276336669922,40.08298110961914,82.91999816894531,567.0,14.043685913085938,2061.050048828125,4876.52001953125,1.9199999570846558,15.4399995803833
2015-03-26,114.94845581054688,39.49635314941406,83.01000213623047,563.6400146484375,14.058452606201172,2056.14990234375,4863.35986328125,2.006999969482422,15.800000190734863
2015-03-27,114.0324935913086,39.82332229614258,83.30000305175781,557.5499877929688,14.028916358947754,2061.02001953125,4891.22021484375,1.9479999542236328,15.069999694824219
2015-03-30,116.91915893554688,40.554195404052734,83.19999694824219,561.1400146484375,14.147056579589844,2086.239990234375,4947.43994140625,1.9630000591278076,14.510000228881836
2015-03-31,115.12425231933594,40.66960144042969,82.22000122070312,554.7000122070312,13.859095573425293,2067.889892578125,4900.8798828125,1.934000015258789,15.289999961853027
2015-04-01,114.95770263671875,40.56382369995117,81.66999816894531,549.489990234375,14.132288932800293,2059.68994140625,4880.22998046875,1.8680000305175781,15.109999656677246
2015-04-02,115.94767761230469,40.534969329833984,81.55999755859375,541.3099975585938,14.265193939208984,2066.9599609375,4886.93994140625,1.9040000438690186,14.670000076293945
2015-04-06,117.82585906982422,,82.44000244140625,543.9500122070312,14.346414566040039,2080.6201171875,4917.31982421875,1.9040000438690186,14.739999771118164
2015-04-07,116.5860824584961,40.92926025390625,82.31999969482422,544.8599853515625,14.36118221282959,2076.330078125,4910.22998046875,1.8930000066757202,14.779999732971191
2015-04-08,116.20674133300781,40.84270477294922,82.27999877929688,548.8400268554688,14.2356595993042,2081.89990234375,4950.81982421875,1.8949999809265137,13.979999542236328
2015-04-09,117.09495544433594,41.862091064453125,82.16999816894531,548.02001953125,14.294730186462402,2091.179931640625,4974.56005859375,1.9579999446868896,13.09000015258789
2015-04-10,117.59456634521484,41.612056732177734,82.04000091552734,548.5399780273438,14.302114486694336,2102.06005859375,4995.97998046875,1.9509999752044678,12.579999923706055
2015-04-13,117.36326599121094,41.554351806640625,83.01000213623047,548.6400146484375,14.250426292419434,2092.429931640625,4988.25,1.9390000104904175,13.9399995803833
2015-04-14,116.8543930053711,41.698604583740234,83.5199966430664,539.780029296875,14.375948905944824,2095.840087890625,4977.2900390625,1.9040000438690186,13.670000076293945
2015-04-15,117.29849243164062,41.43894958496094,82.70999908447266,541.0399780273438,14.560539245605469,2106.6298828125,5011.02001953125,1.899999976158142,12.84000015258789
2015-04-16,116.7341079711914,41.554351806640625,82.30999755859375,543.52001953125,14.686060905456543,2104.989990234375,5007.7900390625,1.878000020980835,12.600000381469727
2015-04-17,115.4203109741211,40.72730255126953,80.77999877929688,532.739990234375,14.641759872436523,2081.179931640625,4931.81005859375,1.850000023841858,13.890000343322754
2015-04-20,118.05719757080078,40.861942291259766,83.08999633789062,544.530029296875,14.649144172668457,2100.39990234375,4994.60009765625,1.8949999809265137,13.300000190734863
2015-04-21,117.41876983642578,41.89093780517578,83.62000274658203,542.9199829101562,14.730364799499512,2097.2900390625,5014.10009765625,1.9160000085830688,13.25
2015-04-22,119.00090026855469,41.83324432373047,84.62999725341797,549.1799926757812,14.604843139648438,2107.9599609375,5035.169921875,1.972000002861023,12.710000038146973
2015-04-23,119.97236633300781,41.265846252441406,82.41000366210938,557.4600219726562,14.907571792602539,2112.929931640625,5056.06005859375,1.9470000267028809,12.479999542236328
2015-04-24,120.5367660522461,41.458187103271484,81.52999877929688,573.6599731445312,14.92972183227539,2117.68994140625,5092.080078125,1.9170000553131104,12.289999961853027
2015-04-27,122.72950744628906,42.07366180419922,81.91000366210938,566.1199951171875,14.848502159118652,2108.919921875,5060.25,1.9240000247955322,13.119999885559082
2015-04-28,120.79582977294922,41.19853210449219,80.68000030517578,564.3699951171875,14.767282485961914,2114.760009765625,5055.419921875,1.9730000495910645,12.40999984741211
2015-04-29,119.01940155029297,39.756004333496094,80.47000122070312,561.3900146484375,14.841117858886719,2106.85009765625,5023.64013671875,2.0350000858306885,13.390000343322754
2015-04-30,115.79039764404297,39.72715759277344,78.7699966430664,548.77001953125,14.818965911865234,2085.510009765625,4941.419921875,2.0460000038146973,14.550000190734863
2015-05-01,119.30621337890625,,78.98999786376953,551.1599731445312,14.855888366699219,2108.2900390625,5005.39013671875,2.117000102996826,12.699999809265137
2015-05-04,119.07491302490234,40.333011627197266,78.80999755859375,552.8400268554688,15.033093452453613,2114.489990234375,5016.93017578125,2.134999990463257,12.850000381469727
2015-05-05,116.39179992675781,39.38094711303711,77.55999755859375,543.0399780273438,15.778841018676758,2089.4599609375,4939.330078125,2.1760001182556152,14.3100004196167
2015-05-06,115.66088104248047,39.448272705078125,78.0999984741211,535.0800170898438,15.513028144836426,2080.14990234375,4919.64013671875,2.240000009536743,15.149999618530273
2015-05-07,116.37626647949219,40.14067840576172,78.43000030517578,542.0399780273438,15.727151870727539,2088.0,4945.5400390625,2.184000015258789,15.130000114440918
2015-05-08,118.56890106201172,40.73692321777344,78.51000213623047,548.9500122070312,15.793607711791992,2116.10009765625,5003.5498046875,2.1500000953674316,12.859999656677246
2015-05-11,117.36109161376953,40.785003662109375,78.01000213623047,545.780029296875,16.048845291137695,2105.330078125,4993.56982421875,2.2739999294281006,13.850000381469727
2015-05-12,116.94300842285156,40.02527618408203,77.45999908447266,538.72998046875,16.071565628051758,2099.1201171875,4976.18994140625,2.259999990463257,13.859999656677246
2015-05-13,117.07307434082031,39.707923889160156,78.44000244140625,539.489990234375,15.95796012878418,2098.47998046875,4981.68994140625,2.2820000648498535,13.760000228881836
2015-05-14,119.80455780029297,40.458030700683594,81.37000274658203,549.2000122070312,16.086711883544922,2121.10009765625,5050.7998046875,2.239000082015991,12.739999771118164
2015-05-15,119.6373291015625,40.409950256347656,80.41999816894531,546.489990234375,16.079137802124023,2122.72998046875,5048.2900390625,2.1410000324249268,12.380000114440918
2015-05-18,120.95661926269531,41.1312141418457,80.87999725341797,546.6699829101562,16.048845291137695,2129.199951171875,5078.43994140625,2.2279999256134033,12.729999542236328
2015-05-19,120.84515380859375,42.13136672973633,80.62999725341797,549.280029296875,16.003402709960938,2127.830078125,5070.02978515625,2.26200008392334,12.850000381469727
2015-05-20,120.83580780029297,42.97764587402344,80.55000305175781,552.510009765625,16.63202667236328,2125.85009765625,5071.740234375,2.250999927520752,12.880000114440918
2015-05-21,122.07154083251953,42.97764587402344,80.4800033569336,556.8099975585938,16.57900619506836,2130.820068359375,5090.7900390625,2.184999942779541,12.109999656677246
2015-05-22,123.13996124267578,43.093048095703125,80.54000091552734,554.52001953125,16.37451934814453,2126.06005859375,5089.35986328125,2.2149999141693115,12.130000114440918
2015-05-25,,41.862091064453125,,,,,,,
2015-05-26,120.4270248413086,42.51603698730469,79.33000183105469,547.1900024414062,16.154876708984375,2104.199951171875,5032.75,2.13700008392334,14.0600004196167
2015-05-27,122.6753921508789,43.68928909301758,80.55000305175781,554.25,16.48812484741211,2123.47998046875,5106.58984375,2.134999990463257,13.270000457763672
2015-05-28,122.433837890625,43.5835075378418,80.1500015258789,554.1799926757812,16.58658218383789,2120.7900390625,5097.97998046875,2.130000114440918,13.3100004196167
2015-05-29,121.04025268554688,43.5835075378418,79.19000244140625,545.3200073242188,16.344221115112305,2107.389892578125,5070.02978515625,2.0950000286102295,13.84000015258789
2015-06-01,121.28179931640625,43.19883346557617,80.29000091552734,549.2100219726562,16.306352615356445,2111.72998046875,5082.93017578125,2.191999912261963,13.970000267028809
2015-06-02,120.7429428100586,43.36231994628906,80.44000244140625,553.9500122070312,16.601730346679688,2109.60009765625,5076.52001953125,2.2660000324249268,14.239999771118164
2015-06-03,120.89158630371094,43.06419372558594,82.44000244140625,555.2899780273438,16.685041427612305,2114.070068359375,5099.22998046875,2.365999937057495,13.65999984741211
2015-06-04,120.18548583984375,42.852630615234375,82.05000305175781,551.6900024414062,16.624452590942383,2095.840087890625,5059.1201171875,2.306999921798706,14.710000038146973
2015-06-05,119.52582550048828,41.83324432373047,82.13999938964844,549.530029296875,16.351795196533203,2092.830078125,5068.4599609375,2.4019999504089355,14.210000038146973
2015-06-08,118.73612976074219,41.36201477050781,80.66999816894531,543.47998046875,16.344221115112305,2079.280029296875,5021.6298828125,2.38100004196167,15.289999961853027
2015-06-09,118.38306427001953,41.554351806640625,80.66999816894531,542.1599731445312,16.26848602294922,2080.14990234375,5013.8701171875,2.4170000553131104,14.470000267028809
2015-06-10,119.7395248413086,42.27561569213867,82.16000366210938,552.5999755859375,16.685041427612305,2105.199951171875,5076.68994140625,2.4779999256134033,13.220000267028809
2015-06-11,119.4700927734375,41.71784210205078,81.83000183105469,550.0399780273438,16.52599334716797,2108.860107421875,5082.509765625,2.382999897003174,12.850000381469727
2015-06-12,118.15081024169922,40.9484977722168,81.52999877929688,547.469970703125,16.472976684570312,2094.110107421875,5051.10009765625,2.384999990463257,13.779999732971191
2015-06-15,117.91854858398438,40.159912109375,80.70999908447266,543.0,16.3593692779541,2084.429931640625,5029.97021484375,2.3580000400543213,15.390000343322754
2015-06-16,118.5503158569336,40.554195404052734,81.05999755859375,544.8699951171875,16.238187789916992,2096.2900390625,5055.5498046875,2.318000078201294,14.8100004196167
2015-06-17,118.2715835571289,40.21760940551758,81.79000091552734,546.5999755859375,16.086711883544922,2100.43994140625,5064.8798828125,2.305999994277954,14.5
2015-06-18,118.8104476928711,40.71768569946289,82.91000366210938,556.1799926757812,16.09428596496582,2121.239990234375,5132.9501953125,2.3510000705718994,13.1899995803833
2015-06-19,117.6212387084961,41.05427551269531,82.51000213623047,557.52001953125,16.223041534423828,2109.989990234375,5117.0,2.2669999599456787,13.960000038146973
2015-06-22,118.55960083007812,42.460243225097656,84.73999786376953,559.6799926757812,16.495695114135742,2122.85009765625,5153.97021484375,2.359999895095825,12.739999771118164
2015-06-23,118.02072143554688,42.69295883178711,87.87999725341797,563.3900146484375,16.571435928344727,2124.199951171875,5160.08984375,2.4089999198913574,12.109999656677246
2015-06-24,119.02413940429688,42.605690002441406,88.86000061035156,558.5700073242188,16.427534103393555,2108.580078125,5122.41015625,2.371000051498413,13.260000228881836
2015-06-25,118.45740509033203,43.15837478637695,87.9800033569336,557.9500122070312,16.48812484741211,2102.31005859375,5112.18994140625,2.3929998874664307,14.010000228881836
2015-06-26,117.76060485839844,43.48805618286133,88.01000213623047,553.0599975585938,16.609304428100586,2101.489990234375,5080.509765625,2.4760000705718994,14.020000457763672
2015-06-29,115.69804382324219,41.88816452026367,85.80000305175781,541.25,16.132152557373047,2057.639892578125,4958.47021484375,2.3310000896453857,18.850000381469727
2015-06-30,116.53419494628906,41.51969909667969,85.7699966430664,540.0399780273438,16.056421279907227,2063.110107421875,4986.8701171875,2.3350000381469727,18.229999542236328
2015-07-01,117.6212387084961,42.91597366333008,86.91000366210938,543.2999877929688,16.306352615356445,2077.419921875,5013.1201171875,2.4179999828338623,16.09000015258789
2015-07-02,117.47256469726562,42.392372131347656,87.29000091552734,547.3400268554688,16.563859939575195,2076.780029296875,5009.2099609375,2.3929998874664307,16.790000915527344
2015-07-03,,42.188743591308594,,,,,,,
2015-07-06,117.06379699707031,40.676124572753906,87.55000305175781,545.6199951171875,15.920090675354004,2068.760009765625,4991.93994140625,2.2780001163482666,17.010000228881836
2015-07-07,116.77577209472656,40.24947738647461,87.22000122070312,550.030029296875,16.03369903564453,2081.340087890625,4997.4599609375,2.2309999465942383,16.09000015258789
2015-07-08,113.8770523071289,40.967010498046875,85.6500015258789,541.7000122070312,15.609562873840332,2046.6800537109375,4909.759765625,2.2060000896453857,19.65999984741211
2015-07-09,111.55435943603516,42.082088470458984,85.87999725341797,544.6500244140625,16.048845291137695,2051.31005859375,4922.39990234375,2.3010001182556152,19.969999313354492
2015-07-10,114.53669738769531,43.03232955932617,87.94999694824219,556.1099853515625,16.556289672851562,2076.6201171875,4997.7001953125,2.4170000553131104,16.829999923706055
2015-07-13,116.7479019165039,43.265037536621094,90.0999984741211,571.72998046875,16.7001895904541,2099.60009765625,5071.509765625,2.430000066757202,13.899999618530273
2015-07-14,116.70144653320312,43.13899612426758,89.68000030517578,584.1799926757812,16.81379508972168,2108.949951171875,5104.89013671875,2.3989999294281006,13.369999885559082
2015-07-15,117.82563781738281,43.94378662109375,89.76000213623047,583.9600219726562,17.033435821533203,2107.39990234375,5098.93994140625,2.3499999046325684,13.229999542236328
2015-07-16,119.39576721191406,45.09754943847656,90.8499984741211,601.780029296875,17.14704132080078,2124.2900390625,5163.18017578125,2.3519999980926514,12.109999656677246
2015-07-17,120.4270248413086,44.806663513183594,94.97000122070312,699.6199951171875,17.101598739624023,2126.639892578125,5210.14013671875,2.3489999771118164,11.949999809265137
2015-07-20,122.70327758789062,45.12673568725586,97.91000366210938,692.8400268554688,17.0561580657959,2128.280029296875,5218.85986328125,2.371999979019165,12.25
2015-07-21,121.4769058227539,44.399513244628906,98.38999938964844,695.3499755859375,17.025861740112305,2119.2099609375,5208.1201171875,2.3399999141693115,12.220000267028809
2015-07-22,116.33909606933594,44.21518325805664,97.04000091552734,695.0999755859375,16.828941345214844,2114.14990234375,5171.77001953125,2.322000026702881,12.119999885559082
2015-07-23,116.28337097167969,44.147308349609375,95.44000244140625,674.72998046875,17.071306228637695,2102.14990234375,5146.41015625,2.2769999504089355,12.640000343322754
2015-07-24,115.670166015625,44.1957893371582,96.94999694824219,654.77001953125,16.904682159423828,2079.64990234375,5088.6298828125,2.2709999084472656,13.739999771118164
2015-07-27,114.06285858154297,42.751136779785156,94.16999816894531,658.27001953125,16.647172927856445,2067.639892578125,5039.77978515625,2.2279999256134033,15.600000381469727
2015-07-28,114.62960052490234,43.63350296020508,95.29000091552734,659.6599731445312,17.0561580657959,2093.25,5089.2099609375,2.25,13.4399995803833
2015-07-29,114.26725769042969,43.09050369262695,96.98999786376953,661.4299926757812,17.283370971679688,2108.570068359375,5111.72998046875,2.2790000438690186,12.5
2015-07-30,113.69123077392578,43.866119384765625,95.20999908447266,664.5599975585938,17.412124633789062,2108.6298828125,5128.77978515625,2.2679998874664307,12.130000114440918
2015-07-31,112.69710540771484,44.46738815307617,94.01000213623047,657.5,17.46514129638672,2103.840087890625,5128.27978515625,2.2049999237060547,12.119999885559082
2015-08-03,110.03997039794922,44.99089050292969,94.13999938964844,664.719970703125,17.510583877563477,2098.0400390625,5115.3798828125,2.1500000953674316,12.5600004196167
2015-08-04,106.50947570800781,44.76787567138672,94.05999755859375,661.280029296875,17.23792839050293,2093.320068359375,5105.5498046875,2.2109999656677246,13.0
2015-08-05,107.2155532836914,45.7473030090332,96.44000244140625,673.2899780273438,17.25307846069336,2099.840087890625,5139.93994140625,2.2679998874664307,12.510000228881836
2015-08-06,107.44889068603516,45.049068450927734,95.12000274658203,670.1500244140625,17.32880973815918,2083.56005859375,5056.43994140625,2.2339999675750732,13.770000457763672
2015-08-07,107.81285858154297,44.370323181152344,94.30000305175781,664.3900146484375,16.995567321777344,2077.570068359375,5043.5400390625,2.174999952316284,13.390000343322754
2015-08-10,111.73265075683594,44.76787567138672,94.1500015258789,663.1400146484375,17.20005989074707,2104.179931640625,5101.7998046875,2.23799991607666,12.229999542236328
2015-08-11,105.91830444335938,44.108524322509766,93.62000274658203,690.2999877929688,16.950124740600586,2084.070068359375,5036.7900390625,2.13700008392334,13.710000038146973
2015-08-12,107.55155181884766,42.81901168823242,94.19000244140625,691.469970703125,16.74563217163086,2086.050048828125,5044.39013671875,2.130000114440918,13.609999656677246
2015-08-13,107.4675521850586,43.63350296020508,93.43000030517578,686.510009765625,16.760778427124023,2083.389892578125,5033.56005859375,2.188999891281128,13.489999771118164
2015-08-14,108.22349548339844,43.67228317260742,94.41999816894531,689.3699951171875,16.730485916137695,2091.5400390625,5048.240234375,2.196000099182129,12.829999923706055
2015-08-17,109.34343719482422,43.934085845947266,93.93000030517578,694.1099853515625,16.889535903930664,2102.43994140625,5091.7001953125,2.1500000953674316,13.020000457763672
2015-08-18,108.72746276855469,43.85641860961914,95.16999816894531,688.72998046875,16.81379508972168,2096.919921875,5059.35009765625,2.196000099182129,13.789999961853027
2015-08-19,107.33688354492188,42.50872802734375,95.30999755859375,694.0399780273438,16.7001895904541,2079.610107421875,5019.0498046875,2.128999948501587,15.25
2015-08-20,105.13433074951172,41.180328369140625,90.55999755859375,679.47998046875,16.238187789916992,2035.72998046875,4877.490234375,2.0840001106262207,19.139999389648438
2015-08-21,98.70401763916016,39.7840576171875,86.05999755859375,644.030029296875,15.761039733886719,1970.8900146484375,4706.0400390625,2.053999900817871,28.030000686645508
2015-08-24,96.24016571044922,37.61207580566406,82.08999633789062,618.1099853515625,15.450514793395996,1893.2099609375,4526.25,1.996999979019165,40.7400016784668
2015-08-25,96.81878662109375,39.706485748291016,83.0,612.469970703125,15.70044994354248,1867.6099853515625,4506.490234375,2.132999897003174,36.02000045776367
2015-08-26,102.37183380126953,39.561038970947266,87.19000244140625,659.739990234375,15.851924896240234,1940.510009765625,4697.5400390625,2.171999931335449,30.31999969482422
2015-08-27,105.38633728027344,40.501583099365234,89.7300033569336,667.9600219726562,15.753464698791504,1987.6600341796875,4812.7099609375,2.1679999828338623,26.100000381469727
2015-08-28,105.73163604736328,40.12342834472656,91.01000213623047,659.6900024414062,15.814056396484375,1988.8699951171875,4828.31982421875,2.186000108718872,26.049999237060547
2015-08-31,105.23699951171875,39.72587585449219,89.43000030517578,647.8200073242188,15.670157432556152,1972.1800537109375,4776.509765625,2.200000047683716,28.43000030517578
2015-09-01,100.53324890136719,39.00835037231445,87.2300033569336,629.5599975585938,15.336910247802734,1913.8499755859375,4636.10009765625,2.171999931335449,31.399999618530273
2015-09-02,104.84502410888672,39.13440704345703,89.88999938964844,644.9099731445312,15.465662002563477,1948.8599853515625,4749.97998046875,2.193000078201294,26.09000015258789
2015-09-03,103.0064468383789,40.11372756958008,88.1500015258789,637.0499877929688,15.458089828491211,1951.1300048828125,4733.5,2.1679999828338623,25.610000610351562
2015-09-04,101.9798355102539,39.115013122558594,88.26000213623047,628.9600219726562,15.193005561828613,1921.219970703125,4683.919921875,2.128000020980835,27.799999237060547
2015-09-07,,39.55134963989258,,,,,,,
2015-09-08,104.8170166015625,40.13312530517578,89.52999877929688,643.8800048828125,15.503531455993652,1969.4100341796875,4811.93017578125,2.194000005722046,24.899999618530273
2015-09-09,102.8011245727539,40.53067398071289,90.44000244140625,643.4099731445312,15.511103630065918,1942.0400390625,4756.52978515625,2.180999994277954,26.229999542236328
2015-09-10,105.05967712402344,40.239784240722656,91.9800033569336,651.0800170898438,15.632283210754395,1952.2900390625,4796.25,2.2219998836517334,24.3700008392334
2015-09-11,106.59026336669922,39.75497055053711,92.05000305175781,655.2999877929688,15.723172187805176,1961.050048828125,4822.33984375,2.183000087738037,23.200000762939453
2015-09-14,107.61686706542969,39.502864837646484,92.30999755859375,652.469970703125,15.420221328735352,1953.030029296875,4805.759765625,2.180000066757202,24.25
2015-09-15,108.52214813232422,40.16221618652344,92.9000015258789,665.0700073242188,15.246021270751953,1978.0899658203125,4860.52001953125,2.2809998989105225,22.540000915527344
2015-09-16,108.64347839355469,40.210697174072266,93.44999694824219,665.52001953125,15.223301887512207,1995.31005859375,4889.240234375,2.302999973297119,21.350000381469727
2015-09-17,106.31961059570312,40.72460174560547,94.33999633789062,671.6699829101562,15.01123332977295,1990.199951171875,4893.9501953125,2.2170000076293945,21.139999389648438
2015-09-18,105.8809585571289,39.59013366699219,94.4000015258789,660.9199829101562,14.76574420928955,1958.030029296875,4827.22998046875,2.130000114440918,22.280000686645508
2015-09-21,107.52354431152344,40.142826080322266,95.55000305175781,666.97998046875,14.78876781463623,1966.969970703125,4828.9501953125,2.2139999866485596,20.139999389648438
2015-09-22,105.83430480957031,37.59268569946289,92.95999908447266,653.2000122070312,14.420392036437988,1942.739990234375,4756.72021484375,2.125,22.440000534057617
2015-09-23,106.69291687011719,37.86418151855469,93.97000122070312,653.2899780273438,14.082714080810547,1938.760009765625,4752.740234375,2.1440000534057617,22.1299991607666
2015-09-24,107.32756805419922,36.128536224365234,94.41000366210938,654.9099731445312,13.913871765136719,1932.239990234375,4734.47998046875,2.119999885559082,23.469999313354492
2015-09-25,107.05689239501953,37.515113830566406,92.7699966430664,640.1500244140625,14.113409996032715,1931.3399658203125,4686.5,2.1679999828338623,23.6200008392334
2015-09-28,104.93833923339844,36.34185791015625,89.20999908447266,624.25,13.837129592895508,1881.77001953125,4543.97021484375,2.0950000286102295,27.6299991607666
2015-09-29,101.78384399414062,35.9249153137207,86.66999816894531,622.6099853515625,14.02899169921875,1884.0899658203125,4517.31982421875,2.053999900817871,26.829999923706055
2015-09-30,102.94112396240234,37.79630661010742,89.9000015258789,638.3699951171875,14.213179588317871,1920.030029296875,4620.16015625,2.059999942779541,24.5
2015-10-01,102.26915740966797,37.54420471191406,90.94999694824219,642.0,14.35132122039795,1923.8199462890625,4627.080078125,2.0420000553131104,22.549999237060547
2015-10-02,103.01578521728516,38.252037048339844,92.06999969482422,656.989990234375,14.758068084716797,1951.3599853515625,4707.77978515625,1.9889999628067017,20.940000534057617
2015-10-05,103.38910675048828,39.2022819519043,94.01000213623047,671.6799926757812,15.049699783325195,1987.050048828125,4781.259765625,2.055999994277954,19.540000915527344
2015-10-06,103.88373565673828,39.43498992919922,92.80000305175781,671.6400146484375,15.172492027282715,1979.9200439453125,4748.35986328125,2.0350000858306885,19.399999618530273
2015-10-07,103.38910675048828,39.61922073364258,92.4000015258789,670.0,15.325982093811035,1995.8299560546875,4791.14990234375,2.062000036239624,18.399999618530273
2015-10-08,102.1944808959961,39.97798156738281,92.47000122070312,667.0,15.264585494995117,2013.4300537109375,4810.7900390625,2.1080000400543213,17.420000076293945
2015-10-09,104.63971710205078,40.385231018066406,93.23999786376953,671.239990234375,15.18784236907959,2014.8900146484375,4830.47021484375,2.0989999771118164,17.079999923706055
2015-10-12,104.15436553955078,40.58885192871094,94.26000213623047,676.4299926757812,15.157142639160156,2017.4599609375,4838.64013671875,2.0899999141693115,16.170000076293945
2015-10-13,104.33171081542969,40.69551086425781,94.12000274658203,683.1699829101562,14.988302230834961,2003.68994140625,4796.60986328125,2.055000066757202,17.670000076293945
2015-10-14,102.85713195800781,40.49188995361328,94.06999969482422,680.4099731445312,15.095746040344238,1994.239990234375,4782.85009765625,1.9809999465942383,18.030000686645508
2015-10-15,104.39704895019531,42.179054260253906,95.95999908447266,693.02001953125,15.379704475402832,2023.8599853515625,4870.10009765625,2.0230000019073486,16.049999237060547
2015-10-16,103.63176727294922,42.615386962890625,97.54000091552734,695.3200073242188,15.740403175354004,2033.1099853515625,4886.68994140625,2.0230000019073486,15.050000190734863
2015-10-19,104.27571105957031,43.84682083129883,98.47000122070312,699.9500122070312,15.502494812011719,2033.6600341796875,4905.47021484375,2.0280001163482666,14.979999542236328
2015-10-20,106.17964172363281,42.94506072998047,97.0,680.0,15.379704475402832,2030.77001953125,4880.97021484375,2.071000099182129,15.75
2015-10-21,106.17029571533203,42.59599304199219,97.11000061035156,671.7999877929688,15.164816856384277,2018.93994140625,4840.1201171875,2.0299999713897705,16.700000762939453
2015-10-22,107.794189453125,43.507450103759766,99.66999816894531,681.1400146484375,15.49482250213623,2052.510009765625,4920.0498046875,2.0250000953674316,14.449999809265137
2015-10-23,111.1353530883789,44.1957893371582,102.19000244140625,719.3300170898438,15.771100044250488,2075.14990234375,5031.85986328125,2.0829999446868896,14.460000038146973
2015-10-26,107.5888671875,44.089229583740234,103.7699966430664,731.1199951171875,15.763428688049316,2071.179931640625,5034.7001953125,2.058000087738037,15.289999961853027
2015-10-27,106.9075698852539,43.323219299316406,103.69999694824219,732.8200073242188,15.579238891601562,2065.889892578125,5030.14990234375,2.0280001163482666,15.430000305175781
2015-10-28,111.31266784667969,43.62380599975586,104.19999694824219,736.9199829101562,15.594588279724121,2090.35009765625,5095.68994140625,2.0920000076293945,14.329999923706055
2015-10-29,112.48859405517578,42.97414779663086,104.87999725341797,744.8499755859375,15.325982093811035,2089.409912109375,5074.27001953125,2.1730000972747803,14.609999656677246
2015-10-30,111.52732849121094,43.77885055541992,101.97000122070312,737.3900146484375,15.372028350830078,2079.360107421875,5053.75,2.1510000228881836,15.069999694824219
2015-11-02,113.09524536132812,43.49775314331055,103.30999755859375,747.739990234375,15.663661003112793,2104.050048828125,5127.14990234375,2.187000036239624,14.149999618530273
2015-11-03,114.39250183105469,43.49775314331055,102.58000183105469,748.8200073242188,14.750391960144043,2109.7900390625,5145.1298828125,2.2200000286102295,14.539999961853027
2015-11-04,113.86053466796875,43.42018127441406,103.94000244140625,755.3099975585938,14.78876781463623,2102.31005859375,5142.47998046875,2.2300000190734863,15.510000228881836
2015-11-05,113.33565521240234,43.29412841796875,108.76000213623047,760.6699829101562,14.811789512634277,2099.929931640625,5127.740234375,2.244999885559082,15.050000190734863
2015-11-06,113.46687316894531,44.34123611450195,107.0999984741211,761.5999755859375,15.149467468261719,2099.199951171875,5147.1201171875,2.3329999446868896,14.329999923706055
2015-11-09,113.00760650634766,43.10989761352539,106.48999786376953,754.77001953125,15.026677131652832,2078.580078125,5095.2998046875,2.3420000076293945,16.520000457763672
2015-11-10,109.44595336914062,44.0019645690918,107.91000366210938,758.260009765625,14.888534545898438,2081.719970703125,5083.240234375,2.322000026702881,15.289999961853027
2015-11-11,108.82733917236328,43.478355407714844,109.01000213623047,765.25,14.949930191040039,2075.0,5067.02001953125,2.3380000591278076,16.059999465942383
2015-11-12,108.46180725097656,40.676124572753906,108.0199966430664,756.530029296875,14.62760066986084,2045.969970703125,5005.080078125,2.319000005722046,18.3700008392334
2015-11-13,105.29380798339844,40.2300910949707,103.94999694824219,740.0700073242188,14.5585298538208,2023.0400390625,4927.8798828125,2.2799999713897705,20.079999923706055
2015-11-16,107.01840209960938,39.929500579833984,104.04000091552734,750.4199829101562,14.489459037780762,2053.18994140625,4984.6201171875,2.2730000019073486,18.15999984741211
2015-11-17,106.55913543701172,40.424015045166016,105.12999725341797,745.97998046875,14.704347610473633,2050.43994140625,4986.02001953125,2.260999917984009,18.84000015258789
2015-11-18,109.93334197998047,39.37681198120117,107.7699966430664,760.010009765625,14.957605361938477,2083.580078125,5075.2001953125,2.2690000534057617,16.850000381469727
2015-11-19,111.32989501953125,39.37681198120117,106.26000213623047,759.9400024414062,15.126444816589355,2081.239990234375,5073.64013671875,2.247999906539917,16.989999771118164
2015-11-20,111.8172607421875,39.561038970947266,107.31999969482422,777.0,14.949930191040039,2089.169921875,5104.919921875,2.26200008392334,15.470000267028809
2015-11-23,110.36449432373047,40.00707244873047,106.94999694824219,776.7000122070312,14.727370262145996,2086.590087890625,5102.47998046875,2.25,15.619999885559082
2015-11-24,111.42359924316406,38.998653411865234,105.73999786376953,769.6300048828125,14.73504638671875,2089.139892578125,5102.81005859375,2.243000030517578,15.930000305175781
2015-11-25,110.62692260742188,39.79375076293945,105.41000366210938,769.260009765625,14.758068084716797,2088.8701171875,5116.14013671875,2.2320001125335693,15.1899995803833
2015-11-26,,40.53067398071289,,,,,,,
2015-11-27,110.42073059082031,40.32705307006836,105.44999694824219,771.969970703125,14.719696998596191,2090.110107421875,5127.52001953125,2.2249999046325684,15.119999885559082
2015-11-30,110.87999725341797,41.1318473815918,104.23999786376953,762.8499755859375,14.704347610473633,2080.409912109375,5108.669921875,2.2179999351501465,16.1299991607666
2015-12-01,109.98018646240234,40.899139404296875,107.12000274658203,783.7899780273438,14.857837677001953,2102.6298828125,5156.31005859375,2.1549999713897705,14.670000076293945
2015-12-02,108.98667907714844,41.12215042114258,106.06999969482422,777.8499755859375,14.834813117980957,2079.510009765625,5123.22021484375,2.177999973297119,15.90999984741211
2015-12-03,107.97441864013672,40.239784240722656,104.37999725341797,768.2000122070312,14.842488288879395,2049.6201171875,5037.52978515625,2.3299999237060547,18.110000610351562
2015-12-04,111.56419372558594,40.27857208251953,106.18000030517578,779.2100219726562,15.072722434997559,2091.68994140625,5142.27001953125,2.2750000953674316,14.8100004196167
2015-12-07,110.86124420166016,40.83126449584961,105.61000061035156,772.989990234375,14.857837677001953,2077.070068359375,5101.81005859375,2.2249999046325684,15.84000015258789
2015-12-08,110.81437683105469,39.91011428833008,106.48999786376953,775.1400146484375,14.60457992553711,2063.590087890625,5098.240234375,2.23799991607666,17.600000381469727
2015-12-09,108.36808013916016,39.929500579833984,104.5999984741211,762.5499877929688,14.589229583740234,2047.6199951171875,5022.8701171875,2.2079999446868896,19.610000610351562
2015-12-10,108.88358306884766,39.648311614990234,105.41999816894531,760.0399780273438,14.474113464355469,2052.22998046875,5045.169921875,2.239000082015991,19.34000015258789
2015-12-11,106.08112335205078,39.115013122558594,102.12000274658203,750.4199829101562,14.082714080810547,2012.3699951171875,4933.47021484375,2.1389999389648438,24.389999389648438
2015-12-14,105.4250259399414,38.16476821899414,104.66000366210938,762.5399780273438,14.052013397216797,2021.93994140625,4952.22998046875,2.2249999046325684,22.729999542236328
2015-12-15,103.55984497070312,39.54164505004883,104.55000305175781,760.0900268554688,14.343647003173828,2043.4100341796875,4995.35986328125,2.2679998874664307,20.950000762939453
2015-12-16,104.35651397705078,39.77436447143555,106.79000091552734,776.5900268554688,14.704347610473633,2073.070068359375,5071.1298828125,2.2869999408721924,17.860000610351562
2015-12-17,102.1445541381836,40.210697174072266,106.22000122070312,769.8300170898438,14.742718696594238,2041.8900146484375,5002.5498046875,2.23799991607666,18.940000534057617
2015-12-18,99.37958526611328,39.80344772338867,104.04000091552734,756.8499755859375,14.681323051452637,2005.550048828125,4923.080078125,2.1989998817443848,20.700000762939453
2015-12-21,100.5980453491211,39.59013366699219,104.7699966430664,760.7999877929688,14.704347610473633,2021.1500244140625,4968.919921875,2.197000026702881,18.700000762939453
2015-12-22,100.50433349609375,39.09561538696289,105.51000213623047,767.1300048828125,14.880860328674316,2038.969970703125,5001.10986328125,2.239000082015991,16.600000381469727
2015-12-23,101.79776000976562,40.11372756958008,104.62999725341797,768.510009765625,15.095746040344238,2064.2900390625,5045.93017578125,2.2639999389648438,15.569999694824219
2015-12-24,101.25414276123047,,105.0199966430664,765.8400268554688,15.103424072265625,2060.989990234375,5048.490234375,2.243000030517578,15.739999771118164
2015-12-28,100.12003326416016,39.90040969848633,105.93000030517578,782.239990234375,15.157142639160156,2056.5,5040.990234375,2.2249999046325684,16.90999984741211
2015-12-29,101.91960906982422,40.98640441894531,107.26000213623047,793.9600219726562,15.279934883117676,2078.360107421875,5107.93994140625,2.306999921798706,16.079999923706055
2015-12-30,100.58867645263672,40.81187057495117,106.22000122070312,790.2999877929688,15.11877155303955,2063.360107421875,5065.85009765625,2.302999973297119,17.290000915527344
2015-12-31,98.65788269042969,40.81187057495117,104.66000366210938,778.010009765625,14.865514755249023,2043.93994140625,5007.41015625,2.2690000534057617,18.209999084472656
2016-01-04,98.74224853515625,39.0665283203125,102.22000122070312,759.4400024414062,14.681323051452637,2012.6600341796875,4903.08984375,2.244999885559082,20.700000762939453
2016-01-05,96.26781463623047,39.2701530456543,102.7300033569336,761.530029296875,14.596905708312988,2016.7099609375,4891.43017578125,2.247999906539917,19.34000015258789
2016-01-06,94.3838882446289,37.49571990966797,102.97000122070312,759.3300170898438,14.412715911865234,1990.260009765625,4835.759765625,2.177000045776367,20.59000015258789
2016-01-07,90.40046691894531,37.04969024658203,97.91999816894531,741.0,14.044340133666992,1943.0899658203125,4689.43017578125,2.1530001163482666,24.989999771118164
2016-01-08,90.87847900390625,35.81825637817383,97.33000183105469,730.9099731445312,13.330611228942871,1922.030029296875,4643.6298828125,2.130000114440918,27.010000228881836
2016-01-11,92.35000610351562,35.48857879638672,97.51000213623047,733.0700073242188,13.491775512695312,1923.6700439453125,4637.990234375,2.1579999923706055,24.299999237060547
2016-01-12,93.69029998779297,35.42070388793945,99.37000274658203,745.3400268554688,13.560846328735352,1938.6800537109375,4685.919921875,2.1019999980926514,22.469999313354492
2016-01-13,91.281494140625,35.4304084777832,95.44000244140625,719.5700073242188,13.261540412902832,1890.280029296875,4526.06005859375,2.065999984741211,25.219999313354492
2016-01-14,93.27791595458984,32.822086334228516,98.37000274658203,731.3900146484375,13.36898422241211,1921.8399658203125,4615.0,2.0980000495910645,23.950000762939453
2016-01-15,91.03781127929688,30.999176025390625,94.97000122070312,710.489990234375,12.755023956298828,1880.3299560546875,4488.419921875,2.0329999923706055,27.020000457763672
2016-01-18,,30.465879440307617,,,,,,,
2016-01-19,90.5972900390625,31.04766273498535,95.26000213623047,719.0800170898438,12.94688606262207,1881.3299560546875,4476.9501953125,2.0350000858306885,26.049999237060547
2016-01-20,90.71913146972656,28.836896896362305,94.3499984741211,718.5599975585938,12.609208106994629,1859.3299560546875,4471.68994140625,1.9839999675750732,27.59000015258789
2016-01-21,90.25987243652344,30.10711669921875,94.16000366210938,726.6699829101562,12.51711368560791,1868.989990234375,4472.06005859375,2.0190000534057617,26.690000534057617
2016-01-22,95.0587387084961,30.068330764770508,97.94000244140625,745.4600219726562,12.785720825195312,1906.9000244140625,4591.18017578125,2.0480000972747803,22.34000015258789
2016-01-25,93.20293426513672,29.564119338989258,97.01000213623047,733.6199951171875,12.394319534301758,1877.0799560546875,4518.490234375,2.0220000743865967,24.149999618530273
2016-01-26,93.71843719482422,30.204078674316406,97.33999633789062,733.7899780273438,12.578509330749512,1903.6300048828125,4567.669921875,1.99399995803833,22.5
2016-01-27,87.56050872802734,29.690174102783203,94.44999694824219,717.5800170898438,12.44036865234375,1882.949951171875,4468.169921875,2.000999927520752,23.110000610351562
2016-01-28,88.18848419189453,28.245420455932617,109.11000061035156,748.2999877929688,12.54013729095459,1893.3599853515625,4506.68017578125,1.9850000143051147,22.420000076293945
2016-01-29,91.23464965820312,29.021127700805664,112.20999908447266,761.3499755859375,12.770371437072754,1940.239990234375,4613.9501953125,1.930999994277954,20.200000762939453
2016-02-01,90.38172149658203,28.85628890991211,115.08999633789062,770.77001953125,12.678278923034668,1939.3800048828125,4620.3701171875,1.965999960899353,19.979999542236328
2016-02-02,88.55402374267578,27.556982040405273,114.61000061035156,780.9099731445312,11.68826675415039,1903.030029296875,4516.9501953125,1.8639999628067017,21.979999542236328
2016-02-03,90.30672454833984,26.868539810180664,112.69000244140625,749.3800048828125,11.857105255126953,1912.530029296875,4504.240234375,1.88100004196167,21.649999618530273
2016-02-04,91.03235626220703,26.955806732177734,110.48999786376953,730.030029296875,11.826409339904785,1915.449951171875,4509.56005859375,1.8639999628067017,21.84000015258789
2016-02-05,88.60105895996094,26.40311622619629,104.06999969482422,703.760009765625,11.70361614227295,1880.050048828125,4363.14013671875,1.8480000495910645,23.3799991607666
2016-02-08,89.53398895263672,23.920856475830078,99.75,704.1599731445312,11.327566146850586,1853.43994140625,4283.75,1.7350000143051147,26.0
2016-02-09,89.51514434814453,22.893043518066406,99.54000091552734,701.02001953125,10.882445335388184,1852.2099609375,4268.759765625,1.7289999723434448,26.540000915527344
2016-02-10,88.83665466308594,24.589900970458984,101.0,706.8499755859375,11.266169548034668,1851.8599853515625,4283.58984375,1.7050000429153442,26.290000915527344
2016-02-11,88.29949188232422,24.008121490478516,101.91000366210938,706.3599853515625,10.951515197753906,1829.0799560546875,4266.83984375,1.6440000534057617,28.139999389648438
2016-02-12,88.57278442382812,25.27834129333496,102.01000213623047,706.8900146484375,11.557801246643066,1864.780029296875,4337.509765625,1.7480000257492065,25.399999618530273
2016-02-15,,26.054048538208008,,,,,,,
2016-02-16,91.0700454711914,26.180099487304688,101.61000061035156,717.6400146484375,11.442683219909668,1895.5799560546875,4435.9599609375,1.777999997138977,24.110000610351562
2016-02-17,92.46475219726562,27.30487632751465,105.19999694824219,731.969970703125,11.695941925048828,1926.8199462890625,4534.06005859375,1.819000005722046,22.309999465942383
2016-02-18,90.71195983886719,27.004287719726562,103.47000122070312,717.510009765625,11.519427299499512,1917.8299560546875,4487.5400390625,1.7589999437332153,21.639999389648438
2016-02-19,90.504638671875,26.87823486328125,104.56999969482422,722.1099853515625,11.419660568237305,1917.780029296875,4504.43017578125,1.7480000257492065,20.530000686645508
2016-02-22,91.29621887207031,27.634550094604492,107.16000366210938,729.0499877929688,11.68059253692627,1945.5,4570.60986328125,1.7660000324249268,19.3799991607666
2016-02-23,89.23242950439453,26.790969848632812,105.45999908447266,717.2899780273438,11.396636962890625,1921.27001953125,4503.580078125,1.7450000047683716,20.979999542236328
2016-02-24,90.56116485595703,27.149734497070312,106.87999725341797,720.9000244140625,11.496404647827148,1929.800048828125,4542.60986328125,1.7419999837875366,20.719999313354492
2016-02-25,91.18312072753906,28.00301170349121,108.06999969482422,729.1199951171875,11.71129035949707,1951.699951171875,4582.2001953125,1.6970000267028809,19.110000610351562
2016-02-26,91.32449340820312,29.428373336791992,107.91999816894531,724.8599853515625,11.741989135742188,1948.050048828125,4590.47021484375,1.7619999647140503,19.809999465942383
2016-02-29,91.11715698242188,29.583513259887695,106.91999816894531,717.219970703125,11.68059253692627,1932.22998046875,4557.9501953125,1.7400000095367432,20.549999237060547
2016-03-01,94.73583984375,30.029544830322266,109.81999969482422,742.1699829101562,12.141063690185547,1978.3499755859375,4689.60009765625,1.8339999914169312,17.700000762939453
2016-03-02,94.94316101074219,30.174989700317383,109.94999694824219,739.47998046875,12.478741645812988,1986.449951171875,4703.419921875,1.8480000495910645,17.09000015258789
2016-03-03,95.64993286132812,30.174989700317383,109.58000183105469,731.5900268554688,12.678278923034668,1993.4000244140625,4707.419921875,1.8300000429153442,16.700000762939453
2016-03-04,97.07290649414062,29.88409996032715,108.38999938964844,730.219970703125,12.67060375213623,1999.989990234375,4717.02001953125,1.8830000162124634,16.860000610351562
2016-03-07,95.99861907958984,29.205358505249023,105.7300033569336,712.7999877929688,12.716650009155273,2001.760009765625,4708.25,1.9019999504089355,17.350000381469727
2016-03-08,95.20703125,28.788415908813477,105.93000030517578,713.530029296875,12.47106647491455,1979.260009765625,4648.81982421875,1.8320000171661377,18.670000076293945
2016-03-09,95.29185485839844,28.788415908813477,107.51000213623047,725.4099731445312,12.51711368560791,1989.260009765625,4674.3798828125,1.8919999599456787,18.34000015258789
2016-03-10,95.33894348144531,28.264812469482422,107.31999969482422,732.1699829101562,12.524786949157715,1989.5699462890625,4662.16015625,1.9290000200271606,18.049999237060547
2016-03-11,96.36614227294922,29.97136878967285,109.41000366210938,744.8699951171875,13.023630142211914,2022.18994140625,4748.47021484375,1.9769999980926514,16.5
2016-03-14,96.61114501953125,30.10711669921875,109.88999938964844,750.239990234375,12.96990966796875,2019.6400146484375,4750.27978515625,1.9630000591278076,16.920000076293945
2016-03-15,98.55241394042969,29.680477142333984,110.66999816894531,750.5700073242188,12.916187286376953,2015.9300537109375,4728.669921875,1.9589999914169312,16.84000015258789
2016-03-16,99.8623046875,29.88409996032715,112.18000030517578,757.3599853515625,12.31757640838623,2027.219970703125,4763.97021484375,1.937999963760376,14.989999771118164
2016-03-17,99.70211029052734,29.4671573638916,111.0199966430664,758.47998046875,12.716650009155273,2040.5899658203125,4774.990234375,1.902999997138977,14.4399995803833
2016-03-18,99.81517791748047,29.816226959228516,111.44999694824219,755.4099731445312,12.831768035888672,2049.580078125,4795.64990234375,1.871000051498413,14.020000457763672
2016-03-21,99.8057632446289,30.94099998474121,111.8499984741211,762.1599731445312,12.96990966796875,2051.60009765625,4808.8701171875,1.9229999780654907,13.789999961853027
2016-03-22,100.56906127929688,30.795555114746094,112.25,760.0499877929688,12.885489463806152,2049.800048828125,4821.66015625,1.934999942779541,14.170000076293945
2016-03-23,100.01307678222656,30.281648635864258,112.54000091552734,757.5599975585938,12.547810554504395,2036.7099609375,4768.85986328125,1.875,14.9399995803833
2016-03-24,99.57960510253906,29.438068389892578,113.05000305175781,754.8400268554688,12.463391304016113,2035.93994140625,4773.5,1.899999976158142,14.739999771118164
2016-03-28,99.12726593017578,,113.69000244140625,753.280029296875,12.509438514709473,2037.050048828125,4766.7900390625,1.8700000047683716,15.239999771118164
2016-03-29,101.47373962402344,29.816226959228516,116.13999938964844,765.8900146484375,12.524786949157715,2055.010009765625,4846.6201171875,1.8140000104904175,13.819999694824219
2016-03-30,103.2453842163086,31.52277946472168,114.69999694824219,768.3400268554688,12.54013729095459,2063.949951171875,4869.2900390625,1.8300000429153442,13.5600004196167
2016-03-31,102.70824432373047,30.5434513092041,114.0999984741211,762.9000244140625,12.29455280303955,2059.739990234375,4869.85009765625,1.7860000133514404,13.949999809265137
2016-04-01,103.65060424804688,29.97136878967285,116.05999755859375,769.6699829101562,12.13338851928711,2072.780029296875,4914.5400390625,1.7920000553131104,13.100000381469727
2016-04-04,104.7154769897461,29.74835205078125,112.55000305175781,765.1199951171875,11.926177978515625,2066.1298828125,4891.7998046875,1.7790000438690186,14.119999885559082
2016-04-05,103.48098754882812,28.27450942993164,112.22000122070312,758.5700073242188,11.50407886505127,2045.1700439453125,4843.93017578125,1.7269999980926514,15.420000076293945
2016-04-06,104.56468200683594,28.9532527923584,113.70999908447266,768.0700073242188,11.70361614227295,2066.659912109375,4920.72021484375,1.7549999952316284,14.09000015258789
2016-04-07,102.28417205810547,28.119367599487305,113.63999938964844,760.1199951171875,11.473381042480469,2041.9100341796875,4848.3701171875,1.690999984741211,16.15999984741211
2016-04-08,102.39727020263672,29.282930374145508,110.62999725341797,759.469970703125,11.642219543457031,2047.5999755859375,4850.68994140625,1.7200000286102295,15.359999656677246
2016-04-11,102.73651885986328,30.55314826965332,108.98999786376953,757.5399780273438,11.68059253692627,2041.989990234375,4833.39990234375,1.7239999771118164,16.260000228881836
2016-04-12,104.0746841430664,29.244140625,110.61000061035156,764.3200073242188,11.849431991577148,2061.719970703125,4872.08984375,1.781000018119812,14.850000381469727
2016-04-13,105.58246612548828,30.717985153198242,110.51000213623047,771.9099731445312,12.256180763244629,2082.419921875,4947.419921875,1.7619999647140503,13.84000015258789
2016-04-14,105.63898468017578,30.524057388305664,110.83999633789062,775.3900146484375,12.355948448181152,2082.780029296875,4945.89013671875,1.781000018119812,13.720000267028809
2016-04-15,103.51866912841797,29.83561897277832,109.63999938964844,780.0,12.256180763244629,2080.72998046875,4938.22021484375,1.7519999742507935,13.619999885559082
2016-04-18,101.28527069091797,30.427093505859375,110.44999694824219,787.6799926757812,12.417346000671387,2094.340087890625,4960.02001953125,1.7730000019073486,13.350000381469727
2016-04-19,100.74813079833984,31.144622802734375,112.29000091552734,776.25,12.678278923034668,2100.800048828125,4940.330078125,1.7829999923706055,13.239999771118164
2016-04-20,100.95545196533203,32.07547378540039,112.41999816894531,774.9199829101562,12.854791641235352,2102.39990234375,4948.1298828125,1.8539999723434448,13.279999732971191
2016-04-21,99.8623046875,32.02699279785156,113.44000244140625,780.0,12.639904975891113,2091.47998046875,4945.89013671875,1.8700000047683716,13.949999809265137
2016-04-22,99.58902740478516,31.842761993408203,110.55999755859375,737.77001953125,12.74734878540039,2091.580078125,4906.22998046875,1.8880000114440918,13.220000267028809
2016-04-25,99.02359771728516,31.7554931640625,110.0999984741211,742.2100219726562,12.770371437072754,2087.7900390625,4895.7900390625,1.9019999504089355,14.079999923706055
2016-04-26,98.33567810058594,32.2693977355957,108.76000213623047,725.3699951171875,13.062004089355469,2091.699951171875,4888.27978515625,1.930999994277954,13.960000038146973
2016-04-27,92.18204498291016,32.5602912902832,108.88999938964844,721.4600219726562,13.138750076293945,2095.14990234375,4863.14013671875,1.8600000143051147,13.770000457763672
2016-04-28,89.36436462402344,32.72512435913086,116.7300033569336,705.0599975585938,13.169445991516113,2075.81005859375,4805.2900390625,1.8380000591278076,15.220000267028809
2016-04-29,88.33718872070312,31.803974151611328,117.58000183105469,707.8800048828125,13.253866195678711,2065.300048828125,4775.35986328125,1.819000005722046,15.699999809265137
2016-05-02,88.24295043945312,32.395450592041016,118.56999969482422,714.4099731445312,13.223166465759277,2081.429931640625,4817.58984375,1.8650000095367432,14.680000305175781
2016-05-03,89.6941909790039,31.7554931640625,117.43000030517578,708.4400024414062,12.210132598876953,2063.3701171875,4763.22021484375,1.7999999523162842,15.600000381469727
2016-05-04,88.7612533569336,31.629440307617188,118.05999755859375,711.3699951171875,12.04129409790039,2051.1201171875,4725.64013671875,1.784000039100647,16.049999237060547
2016-05-05,88.4009780883789,31.87184715270996,117.80999755859375,714.7100219726562,11.972223281860352,2050.6298828125,4717.08984375,1.746999979019165,15.90999984741211
2016-05-06,87.907958984375,31.212495803833008,119.48999786376953,725.1799926757812,11.972223281860352,2057.139892578125,4736.16015625,1.7790000438690186,14.720000267028809
2016-05-09,87.9743423461914,30.853734970092773,119.23999786376953,729.1300048828125,12.03361988067627,2058.68994140625,4750.2099609375,1.7599999904632568,14.569999694824219
2016-05-10,88.5716323852539,31.79427719116211,120.5,739.3800048828125,12.271530151367188,2084.389892578125,4809.8798828125,1.7599999904632568,13.630000114440918
2016-05-11,87.70886993408203,30.999176025390625,119.5199966430664,730.5499877929688,12.614398956298828,2064.4599609375,4760.68994140625,1.7369999885559082,14.6899995803833
2016-05-12,85.65148162841797,30.931304931640625,120.27999877929688,728.0700073242188,12.669261932373047,2064.110107421875,4737.330078125,1.7569999694824219,14.40999984741211
2016-05-13,85.82213592529297,31.154319763183594,119.80999755859375,724.8300170898438,12.424487113952637,2046.6099853515625,4717.68017578125,1.7050000429153442,15.039999961853027
2016-05-16,89.00775909423828,30.301040649414062,118.66999816894531,730.2999877929688,12.356963157653809,2066.659912109375,4775.4599609375,1.753000020980835,14.680000305175781
2016-05-17,88.63799285888672,29.447765350341797,117.3499984741211,720.1900024414062,12.179710388183594,2047.2099609375,4715.72998046875,1.7589999437332153,15.569999694824219
2016-05-18,89.6524658203125,30.55314826965332,117.6500015258789,721.780029296875,12.407604217529297,2047.6300048828125,4739.1201171875,1.8799999952316284,15.949999809265137
2016-05-19,89.3111572265625,30.591930389404297,116.80999755859375,715.3099975585938,12.45824909210205,2040.0400390625,4712.52978515625,1.8450000286102295,16.329999923706055
2016-05-20,90.27822875976562,30.708288192749023,117.3499984741211,721.7100219726562,12.517332077026367,2052.320068359375,4769.56005859375,1.8489999771118164,15.199999809265137
2016-05-23,91.42542266845703,30.058631896972656,115.97000122070312,717.25,12.534213066101074,2048.0400390625,4765.77978515625,1.8380000591278076,15.819999694824219
2016-05-24,92.81913757324219,31.377336502075195,117.69999694824219,733.030029296875,12.711464881896973,2076.06005859375,4861.06005859375,1.8589999675750732,14.420000076293945
2016-05-25,94.44985961914062,33.04510498046875,117.88999938964844,738.0999755859375,13.006884574890137,2090.5400390625,4894.89013671875,1.8700000047683716,13.899999618530273
2016-05-26,95.19886779785156,32.822086334228516,119.47000122070312,736.9299926757812,13.074408531188965,2090.10009765625,4901.77001953125,1.8229999542236328,13.430000305175781
2016-05-27,95.14198303222656,33.09358596801758,119.37999725341797,747.5999755859375,13.032205581665039,2099.06005859375,4933.5,1.8509999513626099,13.119999885559082
2016-05-30,,33.1905517578125,,,,,,,
2016-05-31,94.67741394042969,33.054805755615234,118.80999755859375,748.8499755859375,12.990002632141113,2096.949951171875,4948.0498046875,1.8339999914169312,14.1899995803833
2016-06-01,93.35006713867188,32.59906768798828,118.77999877929688,748.4600219726562,12.947799682617188,2099.330078125,4952.25,1.8459999561309814,14.199999809265137
2016-06-02,92.64846801757812,32.5602912902832,118.93000030517578,744.27001953125,12.770548820495605,2105.260009765625,4971.35986328125,1.8109999895095825,13.630000114440918
2016-06-03,92.83809661865234,31.260982513427734,118.47000122070312,735.8599853515625,12.492011070251465,2099.1298828125,4942.52001953125,1.7039999961853027,13.470000267028809
2016-06-06,93.51124572753906,31.629440307617188,118.79000091552734,730.0599975585938,12.584856986999512,2109.409912109375,4968.7099609375,1.7230000495910645,13.649999618530273
2016-06-07,93.89048767089844,32.72512435913086,117.76000213623047,731.0900268554688,12.72834587097168,2112.1298828125,4961.75,1.7130000591278076,14.050000190734863
2016-06-08,93.80516052246094,32.36635971069336,118.38999938964844,742.9299926757812,12.762107849121094,2119.1201171875,4974.64013671875,1.7059999704360962,14.079999923706055
2016-06-09,94.47831726074219,32.03668975830078,118.55999755859375,742.52001953125,12.449807167053223,2115.47998046875,4958.6201171875,1.6799999475479126,14.640000343322754
2016-06-10,93.70087432861328,30.378612518310547,116.62000274658203,733.1900024414062,12.095303535461426,2096.070068359375,4894.5498046875,1.6390000581741333,17.030000686645508
2016-06-13,92.28819274902344,29.767744064331055,113.94999694824219,731.8800048828125,11.791444778442383,2079.06005859375,4848.43994140625,1.6160000562667847,20.969999313354492
2016-06-14,92.4019546508789,29.224748611450195,114.94000244140625,733.25,11.63951587677002,2075.320068359375,4843.5498046875,1.6109999418258667,20.5
2016-06-15,92.09856414794922,29.670780181884766,114.5999984741211,732.1900024414062,11.774563789367676,2071.5,4834.93017578125,1.5959999561309814,20.139999389648438
2016-06-16,92.4873046875,29.855009078979492,114.38999938964844,724.25,11.909611701965332,2077.989990234375,4844.919921875,1.5640000104904175,19.3700008392334
2016-06-17,90.38251495361328,30.688894271850586,113.0199966430664,704.25,12.416045188903809,2071.219970703125,4800.33984375,1.6180000305175781,19.40999984741211
2016-06-20,90.16444396972656,32.6596565246582,113.37000274658203,706.1300048828125,12.711464881896973,2083.25,4837.2099609375,1.6699999570846558,18.3700008392334
2016-06-21,90.93241882324219,32.69887161254883,114.37999725341797,708.8800048828125,13.032205581665039,2088.89990234375,4843.759765625,1.6970000267028809,18.479999542236328
2016-06-22,90.59107971191406,32.75769805908203,113.91000366210938,710.469970703125,13.074408531188965,2085.449951171875,4833.31982421875,1.684999942779541,21.170000076293945
2016-06-23,91.112548828125,34.6794319152832,115.08000183105469,714.8699951171875,13.589282035827637,2113.320068359375,4910.0400390625,1.7389999628067017,17.25
2016-06-24,88.55268859863281,31.18893814086914,112.08000183105469,685.2000122070312,11.774563789367676,2037.4100341796875,4707.97998046875,1.5789999961853027,25.760000228881836
2016-06-27,87.26325988769531,30.051586151123047,108.97000122070312,681.1400146484375,10.812342643737793,2000.5400390625,4594.43994140625,1.4600000381469727,23.850000381469727
2016-06-28,88.7328109741211,31.522302627563477,112.69999694824219,691.260009765625,10.702615737915039,2036.0899658203125,4691.8701171875,1.4609999656677246,18.75
2016-06-29,89.50077056884766,31.639957427978516,114.16000366210938,695.1900024414062,11.006474494934082,2070.77001953125,4779.25,1.4769999980926514,16.639999389648438
2016-06-30,90.63848876953125,32.33609390258789,114.27999877929688,703.530029296875,10.938949584960938,2098.860107421875,4842.669921875,1.4880000352859497,15.630000114440918
2016-07-01,90.91344451904297,32.56160354614258,114.19000244140625,710.25,11.116201400756836,2102.949951171875,4862.56982421875,1.4559999704360962,14.770000457763672
2016-07-04,,31.228158950805664,,,,,,,
2016-07-05,90.06015014648438,30.237878799438477,114.19999694824219,704.8900146484375,10.432517051696777,2088.550048828125,4822.89990234375,1.3669999837875366,15.579999923706055
2016-07-06,90.57213592529297,29.94373321533203,116.69999694824219,708.969970703125,10.466279029846191,2099.72998046875,4859.16015625,1.3849999904632568,14.960000038146973
2016-07-07,90.96085357666016,30.394752502441406,115.8499984741211,707.260009765625,10.322789192199707,2097.89990234375,4876.81005859375,1.3869999647140503,14.760000228881836
2016-07-08,91.66244506835938,31.826248168945312,117.23999786376953,717.780029296875,10.339670181274414,2129.89990234375,4956.759765625,1.3660000562667847,13.199999809265137
2016-07-11,91.94688415527344,32.47336196899414,117.87000274658203,727.2000122070312,10.559125900268555,2137.159912109375,4988.64013671875,1.434000015258789,13.539999961853027
2016-07-12,92.36404418945312,33.610713958740234,117.93000030517578,732.510009765625,10.820782661437988,2152.139892578125,5022.81982421875,1.5130000114440918,13.550000190734863
2016-07-13,91.84259796142578,32.83613967895508,116.77999877929688,729.47998046875,10.905187606811523,2152.429931640625,5005.72998046875,1.468000054359436,13.039999961853027
2016-07-14,93.6629409790039,33.571495056152344,117.29000091552734,735.7999877929688,11.158404350280762,2163.75,5034.06005859375,1.531000018119812,12.819999694824219
2016-07-15,93.65345001220703,32.963600158691406,116.86000061035156,735.6300048828125,11.116201400756836,2161.739990234375,5029.58984375,1.593999981880188,12.670000076293945
2016-07-18,94.64896392822266,33.345985412597656,119.37000274658203,753.2000122070312,11.158404350280762,2166.889892578125,5055.77978515625,1.5870000123977661,12.4399995803833
2016-07-19,94.6868896484375,33.071449279785156,120.61000061035156,753.4099731445312,10.99803352355957,2163.780029296875,5036.3701171875,1.5579999685287476,11.970000267028809
2016-07-20,94.77222442626953,33.483253479003906,121.91999816894531,757.0800170898438,11.217487335205078,2173.02001953125,5089.93017578125,1.5800000429153442,11.770000457763672
2016-07-21,94.26973724365234,33.336177825927734,120.61000061035156,754.4099731445312,11.14996337890625,2165.169921875,5073.89990234375,1.565000057220459,12.739999771118164
2016-07-22,93.53968811035156,33.130279541015625,121.0,759.280029296875,11.217487335205078,2175.030029296875,5100.16015625,1.5700000524520874,12.020000457763672
2016-07-25,92.28819274902344,32.855743408203125,121.62999725341797,757.52001953125,11.369417190551758,2168.47998046875,5097.6298828125,1.5709999799728394,12.869999885559082
2016-07-26,91.6529541015625,33.29696273803711,121.22000122070312,757.6500244140625,11.394740104675293,2169.179931640625,5110.0498046875,1.562999963760376,13.050000190734863
2016-07-27,97.6070327758789,34.12055969238281,123.33999633789062,761.969970703125,11.52978801727295,2166.580078125,5139.81005859375,1.5149999856948853,12.829999923706055
2016-07-28,98.92488861083984,32.97340774536133,125.0,765.8400268554688,11.445382118225098,2170.06005859375,5154.97998046875,1.5110000371932983,12.720000267028809
2016-07-29,98.80166625976562,34.14997863769531,123.94000244140625,791.3400268554688,11.631074905395508,2173.60009765625,5162.1298828125,1.4579999446868896,11.869999885559082
2016-08-01,100.54615783691406,33.689151763916016,124.30999755859375,800.9400024414062,11.386299133300781,2170.840087890625,5184.2001953125,1.496000051498413,12.4399995803833
2016-08-02,99.05764770507812,32.267459869384766,123.08999633789062,800.1199951171875,10.947389602661133,2157.030029296875,5137.72998046875,1.5369999408721924,13.369999885559082
2016-08-03,100.29964447021484,33.2479362487793,122.51000213623047,798.9199829101562,11.07399845123291,2163.7900390625,5159.740234375,1.5420000553131104,12.859999656677246
2016-08-04,100.91925811767578,33.54207992553711,124.36000061035156,797.25,11.166845321655273,2164.25,5166.25,1.503000020980835,12.420000076293945
2016-08-05,102.4539566040039,34.63040542602539,125.1500015258789,806.9299926757812,11.360977172851562,2182.8701171875,5221.1201171875,1.5820000171661377,11.390000343322754
2016-08-08,103.30235290527344,34.8068962097168,125.26000213623047,805.22998046875,11.436943054199219,2180.889892578125,5213.14013671875,1.5829999446868896,11.5
2016-08-09,103.72177124023438,34.9539680480957,125.05999755859375,807.47998046875,11.52978801727295,2181.739990234375,5225.47998046875,1.5449999570846558,11.65999984741211
2016-08-10,102.94963836669922,35.1402587890625,124.87999725341797,808.489990234375,11.555109977722168,2175.489990234375,5204.580078125,1.5089999437332153,12.050000190734863
2016-08-11,102.88292694091797,35.66971206665039,124.9000015258789,808.2000122070312,11.631074905395508,2185.7900390625,5228.39990234375,1.5729999542236328,11.680000305175781
2016-08-12,103.12122344970703,36.13053894042969,124.87999725341797,807.0499877929688,11.698598861694336,2184.050048828125,5232.89013671875,1.5149999856948853,11.550000190734863
2016-08-15,104.36043548583984,,123.9000015258789,805.9600219726562,11.656396865844727,2190.14990234375,5262.02001953125,1.5529999732971191,11.8100004196167
2016-08-16,104.26510620117188,34.98337936401367,123.30000305175781,801.1900024414062,11.580431938171387,2178.14990234375,5227.10986328125,1.5759999752044678,12.640000343322754
2016-08-17,104.11260223388672,34.277435302734375,124.37000274658203,805.4199829101562,11.571990966796875,2182.219970703125,5228.66015625,1.5609999895095825,12.1899995803833
2016-08-18,103.97914123535156,34.993186950683594,123.91000366210938,802.75,11.52978801727295,2187.02001953125,5240.14990234375,1.5360000133514404,11.430000305175781
2016-08-19,104.24605560302734,34.551971435546875,123.55999755859375,799.6500244140625,11.360977172851562,2183.8701171875,5238.3798828125,1.5779999494552612,11.34000015258789
2016-08-22,103.435791015625,34.50294876098633,124.1500015258789,796.9500122070312,11.386299133300781,2182.639892578125,5244.60009765625,1.5390000343322754,12.270000457763672
2016-08-23,103.75989532470703,35.40498733520508,124.37000274658203,796.5900268554688,11.681717872619629,2186.89990234375,5260.080078125,1.5529999732971191,12.380000114440918
2016-08-24,102.9782485961914,35.77756881713867,123.4800033569336,793.5999755859375,11.892732620239258,2175.43994140625,5217.68994140625,1.559000015258789,13.449999809265137
2016-08-25,102.53975677490234,35.277523040771484,123.88999938964844,791.2999877929688,11.858969688415527,2172.469970703125,5212.2001953125,1.5770000219345093,13.630000114440918
2016-08-26,101.939208984375,35.85600280761719,124.95999908447266,793.219970703125,11.842087745666504,2169.0400390625,5218.919921875,1.6349999904632568,13.649999618530273
2016-08-29,101.8248291015625,35.30693435668945,126.54000091552734,795.8200073242188,11.901172637939453,2180.3798828125,5232.330078125,1.565999984741211,12.9399995803833
2016-08-30,101.04318237304688,35.93444061279297,125.83999633789062,791.9199829101562,12.053102493286133,2176.1201171875,5222.990234375,1.5700000524520874,13.119999885559082
2016-08-31,101.13849639892578,36.13053894042969,126.12000274658203,789.8499755859375,12.1965913772583,2170.949951171875,5213.22021484375,1.5679999589920044,13.420000076293945
2016-09-01,101.7390365600586,35.875614166259766,126.16999816894531,791.4000244140625,12.205032348632812,2170.860107421875,5227.2099609375,1.5700000524520874,13.479999542236328
2016-09-02,102.6922836303711,36.542335510253906,126.51000213623047,796.8699951171875,12.416045188903809,2179.97998046875,5249.89990234375,1.5959999561309814,11.979999542236328
2016-09-05,,36.22858428955078,,,,,,,
2016-09-06,102.6636734008789,35.83639144897461,129.72999572753906,808.02001953125,12.27255630493164,2186.47998046875,5275.91015625,1.5429999828338623,12.020000457763672
2016-09-07,103.29280853271484,36.71882247924805,131.0500030517578,807.989990234375,12.306318283081055,2186.159912109375,5283.93017578125,1.5410000085830688,11.9399995803833
2016-09-08,100.58560943603516,36.356048583984375,130.27000427246094,802.8400268554688,12.390724182128906,2181.300048828125,5259.47998046875,1.6160000562667847,12.510000228881836
2016-09-09,98.307373046875,35.56186294555664,127.0999984741211,788.47998046875,12.390724182128906,2127.81005859375,5125.91015625,1.6720000505447388,17.5
2016-09-12,100.50936126708984,34.5911865234375,128.69000244140625,798.8200073242188,12.382283210754395,2159.0400390625,5211.89013671875,1.6720000505447388,15.15999984741211
2016-09-13,102.90198516845703,34.4343147277832,127.20999908447266,788.719970703125,12.112186431884766,2127.02001953125,5155.25,1.7339999675750732,17.850000381469727
2016-09-14,106.5433578491211,34.8068962097168,127.7699966430664,790.4600219726562,11.96025562286377,2125.77001953125,5173.77001953125,1.6890000104904175,18.139999389648438
2016-09-15,110.1656494140625,35.17947769165039,128.35000610351562,801.22998046875,12.036221504211426,2147.260009765625,5249.68994140625,1.7029999494552612,16.299999237060547
2016-09-16,109.54605102539062,33.79700469970703,129.07000732421875,797.969970703125,11.622634887695312,2139.159912109375,5244.56982421875,1.7009999752044678,15.369999885559082
2016-09-19,108.26871490478516,34.5911865234375,128.64999389648438,795.3900146484375,11.504467010498047,2139.1201171875,5235.02978515625,1.6959999799728394,15.529999732971191
2016-09-20,108.25917053222656,34.62060546875,128.63999938964844,799.780029296875,11.403179168701172,2139.760009765625,5241.35009765625,1.687999963760376,15.920000076293945
2016-09-21,108.24011993408203,35.59127426147461,129.94000244140625,805.030029296875,11.673276901245117,2163.1201171875,5295.18017578125,1.6679999828338623,13.300000190734863
2016-09-22,109.26009368896484,36.05209732055664,130.0800018310547,815.9500122070312,11.791444778442383,2177.179931640625,5339.52001953125,1.6319999694824219,12.020000457763672
2016-09-23,107.43939971923828,36.003074645996094,127.95999908447266,814.9600219726562,11.656396865844727,2164.68994140625,5305.75,1.6150000095367432,12.289999961853027
2016-09-26,107.60144805908203,35.101036071777344,127.30999755859375,802.6500244140625,11.352537155151367,2146.10009765625,5257.490234375,1.5889999866485596,14.5
2016-09-27,107.80162048339844,34.699039459228516,128.69000244140625,810.72998046875,11.335655212402344,2159.929931640625,5305.7099609375,1.555999994277954,13.100000381469727
2016-09-28,108.62141418457031,35.09123229980469,129.22999572753906,810.0599975585938,11.45382308959961,2171.3701171875,5318.5498046875,1.5670000314712524,12.390000343322754
2016-09-29,106.93418884277344,35.06182098388672,128.08999633789062,802.6400146484375,11.023356437683105,2151.1298828125,5269.14990234375,1.5570000410079956,14.020000457763672
2016-09-30,107.76350402832031,35.34615707397461,128.27000427246094,804.0599975585938,11.496026039123535,2168.27001953125,5312.0,1.6080000400543213,13.289999961853027
2016-10-03,107.25828552246094,35.06182098388672,128.77000427246094,800.3800048828125,11.41162109375,2161.199951171875,5300.8701171875,1.621999979019165,13.569999694824219
2016-10-04,107.71583557128906,35.77756881713867,128.19000244140625,802.7899780273438,11.673276901245117,2150.489990234375,5289.66015625,1.6829999685287476,13.630000114440918
2016-10-05,107.76350402832031,36.0913200378418,128.47000122070312,801.22998046875,11.901172637939453,2159.72998046875,5316.02001953125,1.715999960899353,12.989999771118164
2016-10-06,108.56421661376953,36.150146484375,128.74000549316406,803.0800170898438,11.757682800292969,2160.77001953125,5306.85009765625,1.7419999837875366,12.84000015258789
2016-10-07,108.72626495361328,36.07170867919922,128.99000549316406,800.7100219726562,11.740802764892578,2153.739990234375,5292.39990234375,1.7359999418258667,13.479999542236328
2016-10-10,110.62321472167969,36.93452835083008,130.24000549316406,814.1699829101562,11.757682800292969,2163.659912109375,5328.669921875,,13.380000114440918
2016-10-11,110.86151885986328,36.44429016113281,128.8800048828125,809.5700073242188,11.622634887695312,2136.72998046875,5246.7900390625,1.7589999437332153,15.359999656677246
2016-10-12,111.85289764404297,36.27760696411133,129.0500030517578,811.77001953125,11.546669960021973,2139.179931640625,5239.02001953125,1.777999997138977,15.90999984741211
2016-10-13,111.50971984863281,36.25799560546875,127.81999969482422,804.0800170898438,11.268131256103516,2132.550048828125,5213.330078125,1.7380000352859497,16.690000534057617
2016-10-14,112.12931060791016,36.47370529174805,127.87999725341797,804.5999755859375,11.327214241027832,2132.97998046875,5214.16015625,1.7940000295639038,16.1200008392334
2016-10-17,112.05306243896484,36.55214309692383,127.54000091552734,806.8400268554688,11.268131256103516,2126.5,5199.81982421875,1.7660000324249268,16.209999084472656
2016-10-18,111.97681427001953,36.640384674072266,128.57000732421875,821.489990234375,11.403179168701172,2139.60009765625,5243.83984375,1.7489999532699585,15.279999732971191
2016-10-19,111.64317321777344,36.30702209472656,130.11000061035156,826.8400268554688,11.571990966796875,2144.2900390625,5246.41015625,1.7519999742507935,14.40999984741211
2016-10-20,111.58598327636719,36.47370529174805,130.0,821.6300048828125,11.512907028198242,2141.340087890625,5241.830078125,1.746999979019165,13.75
2016-10-21,111.14749145507812,36.699214935302734,132.07000732421875,824.0599975585938,11.538228988647461,2141.159912109375,5257.39990234375,1.7400000095367432,13.34000015258789
2016-10-24,112.14838409423828,37.51300811767578,133.27999877929688,835.739990234375,11.69015884399414,2151.330078125,5309.830078125,1.7630000114440918,13.020000457763672
2016-10-25,112.72034454345703,37.40515899658203,132.2899932861328,828.5499877929688,11.647955894470215,2143.159912109375,5283.39990234375,1.7580000162124634,13.460000038146973
2016-10-26,110.18472290039062,37.4345703125,131.0399932861328,822.0999755859375,11.622634887695312,2139.429931640625,5250.27001953125,1.7899999618530273,14.239999771118164
2016-10-27,109.12663269042969,37.97383499145508,129.69000244140625,817.3499755859375,11.799885749816895,2133.0400390625,5215.97021484375,1.843000054359436,15.359999656677246
2016-10-28,108.40216827392578,37.66987991333008,131.2899932861328,819.5599975585938,12.120625495910645,2126.409912109375,5190.10009765625,1.8450000286102295,16.190000534057617
2016-10-31,108.23058319091797,37.954219818115234,130.99000549316406,809.9000244140625,11.875849723815918,2126.14990234375,5189.14013671875,1.8339999914169312,17.059999465942383
2016-11-01,106.27644348144531,37.66987991333008,129.5,805.47998046875,11.80832576751709,2111.719970703125,5153.580078125,1.8200000524520874,18.559999465942383
2016-11-02,106.37175750732422,35.99327087402344,127.16999816894531,788.4199829101562,11.622634887695312,2097.93994140625,5105.56982421875,1.7990000247955322,19.31999969482422
2016-11-03,105.23159790039062,35.02260208129883,120.0,782.1900024414062,11.462264060974121,2088.659912109375,5058.41015625,1.812000036239624,22.079999923706055
2016-11-04,104.28305053710938,34.7774772644043,120.75,781.0999755859375,11.403179168701172,2085.179931640625,5046.3701171875,1.7829999923706055,22.510000228881836
2016-11-07,105.78732299804688,36.01287841796875,122.1500015258789,802.030029296875,11.943374633789062,2131.52001953125,5166.169921875,1.8279999494552612,18.709999084472656
2016-11-08,106.41010284423828,35.98347091674805,124.22000122070312,811.97998046875,11.943374633789062,2139.56005859375,5193.490234375,1.8619999885559082,18.739999771118164
2016-11-09,106.2376480102539,35.50303268432617,123.18000030517578,805.5900268554688,12.188150405883789,2163.260009765625,5251.06982421875,2.072000026702881,14.380000114440918
2016-11-10,103.27700805664062,36.081512451171875,120.80000305175781,780.2899780273438,13.251660346984863,2167.47998046875,5208.7998046875,2.117000102996826,14.739999771118164
2016-11-11,103.89022064208984,36.46389389038086,119.0199966430664,771.75,13.34450626373291,2164.449951171875,5237.10986328125,,14.170000076293945
2016-11-14,101.28409576416016,37.64046859741211,115.08000183105469,753.219970703125,13.800294876098633,2164.199951171875,5218.39990234375,2.2219998836517334,14.479999542236328
2016-11-15,102.62548828125,37.41495895385742,117.19999694824219,775.1599731445312,13.783415794372559,2180.389892578125,5275.6201171875,2.23799991607666,13.369999885559082
2016-11-16,105.38489532470703,37.03257369995117,116.33999633789062,779.97998046875,13.420470237731934,2176.93994140625,5294.580078125,2.2230000495910645,13.720000267028809
2016-11-17,105.3465576171875,37.189453125,117.79000091552734,786.1599731445312,13.49643611907959,2187.1201171875,5333.97021484375,2.2760000228881836,13.350000381469727
2016-11-18,105.45197296142578,36.78745651245117,117.0199966430664,775.969970703125,13.412030220031738,2181.89990234375,5321.509765625,2.3350000381469727,12.850000381469727
2016-11-21,107.05205535888672,37.307106018066406,121.7699966430664,784.7999877929688,13.293862342834473,2198.179931640625,5368.85986328125,2.3389999866485596,12.420000076293945
2016-11-22,107.1191177368164,38.1993408203125,121.47000122070312,785.0,13.319184303283691,2202.93994140625,5386.35009765625,2.321000099182129,12.40999984741211
2016-11-23,106.5729751586914,39.04255294799805,120.83999633789062,779.0,13.184135437011719,2204.719970703125,5380.68017578125,2.3570001125335693,12.430000305175781
2016-11-24,,38.611141204833984,,,,,,,
2016-11-25,107.10953521728516,38.31700134277344,120.37999725341797,780.22998046875,13.31074333190918,2213.35009765625,5398.919921875,2.371999979019165,12.34000015258789
2016-11-28,106.89875030517578,37.709102630615234,120.41000366210938,785.7899780273438,12.9562406539917,2201.719970703125,5368.81005859375,2.319999933242798,13.149999618530273
2016-11-29,106.79336547851562,38.73860168457031,120.87000274658203,789.4400024414062,13.091290473937988,2204.659912109375,5379.919921875,2.302000045776367,12.899999618530273
2016-11-30,105.8927001953125,38.83665084838867,118.41999816894531,775.8800048828125,13.378268241882324,2198.81005859375,5323.68017578125,2.368000030517578,13.329999923706055
2016-12-01,104.90583038330078,38.84645462036133,115.0999984741211,764.3300170898438,13.471114158630371,2191.080078125,5251.10986328125,2.440999984741211,14.069999694824219
2016-12-02,105.29867553710938,37.84636688232422,115.4000015258789,764.4600219726562,13.217897415161133,2191.949951171875,5255.64990234375,2.390000104904175,14.119999885559082
2016-12-05,104.541748046875,38.28758239746094,117.43000030517578,778.219970703125,13.614602088928223,2204.7099609375,5308.89013671875,2.38700008392334,12.140000343322754
2016-12-06,105.3465576171875,38.73860168457031,117.30999755859375,776.1799926757812,14.374252319335938,2212.22998046875,5333.0,2.3959999084472656,11.789999961853027
2016-12-07,106.38135528564453,39.16020584106445,117.94999694824219,791.469970703125,14.661231994628906,2241.35009765625,5393.759765625,2.3469998836517334,12.220000267028809
2016-12-08,107.42572021484375,39.40532302856445,118.91000366210938,795.1699829101562,14.602145195007324,2246.18994140625,5417.35986328125,2.38700008392334,12.640000343322754
2016-12-09,109.17911529541016,38.758209228515625,119.68000030517578,809.4500122070312,14.163240432739258,2259.530029296875,5444.5,2.4639999866485596,11.75
2016-12-12,108.55630493164062,38.86606216430664,117.7699966430664,807.9000244140625,13.960665702819824,2256.9599609375,5412.5400390625,2.4790000915527344,12.640000343322754
2016-12-13,110.36719512939453,39.41513442993164,120.30999755859375,815.3400268554688,14.154799461364746,2271.719970703125,5463.830078125,2.4809999465942383,12.720000267028809
2016-12-14,110.36719512939453,39.660247802734375,120.20999908447266,817.8900146484375,13.808736801147461,2253.280029296875,5436.669921875,2.5250000953674316,13.1899995803833
2016-12-15,110.97080993652344,40.630916595458984,120.56999969482422,815.6500244140625,13.682126998901367,2262.030029296875,5456.85009765625,2.5799999237060547,12.789999961853027
2016-12-16,111.11453247070312,40.51326370239258,119.87000274658203,809.8400268554688,13.530198097229004,2258.070068359375,5437.16015625,2.5969998836517334,12.199999809265137
2016-12-19,111.7564697265625,40.22892761230469,119.23999786376953,812.5,13.563959121704102,2262.530029296875,5457.43994140625,2.5420000553131104,11.710000038146973
2016-12-20,112.0534896850586,40.375999450683594,119.08999633789062,815.2000122070312,13.64836597442627,2270.760009765625,5483.93994140625,2.568000078201294,11.449999809265137
2016-12-21,112.15888977050781,40.395606994628906,119.04000091552734,812.2000122070312,13.530198097229004,2265.179931640625,5471.43017578125,2.5460000038146973,11.270000457763672
2016-12-22,111.42113494873047,40.101463317871094,117.4000015258789,809.6799926757812,13.412030220031738,2260.9599609375,5447.419921875,2.552999973297119,11.430000305175781
2016-12-23,111.64148712158203,40.92506408691406,117.2699966430664,807.7999877929688,13.276981353759766,2263.7900390625,5462.68994140625,2.5429999828338623,11.4399995803833
2016-12-27,112.35051727294922,41.1015510559082,118.01000213623047,809.9299926757812,13.336065292358398,2268.8798828125,5487.43994140625,2.562999963760376,11.989999771118164
2016-12-28,111.87145233154297,40.375999450683594,116.91999816894531,804.5700073242188,13.133491516113281,2249.919921875,5438.56005859375,2.50600004196167,12.949999809265137
2016-12-29,111.84270477294922,39.9151725769043,116.3499984741211,802.8800048828125,13.065967559814453,2249.260009765625,5432.08984375,2.4769999980926514,13.369999885559082
2016-12-30,110.97080993652344,40.17990493774414,115.05000305175781,792.4500122070312,13.226337432861328,2238.830078125,5383.1201171875,2.446000099182129,14.039999961853027
2017-01-02,,41.248619079589844,,,,,,,
2017-01-03,111.2869873046875,41.258426666259766,116.86000061035156,808.010009765625,13.791854858398438,2257.830078125,5429.080078125,2.450000047683716,12.850000381469727
2017-01-04,111.16243743896484,40.88584518432617,118.69000244140625,807.77001953125,14.239204406738281,2270.75,5477.0,2.4519999027252197,11.850000381469727
2017-01-05,111.72771453857422,41.0721321105957,120.66999816894531,813.02001953125,14.180119514465332,2269.0,5487.93994140625,2.368000030517578,11.670000076293945
2017-01-06,112.97330474853516,42.49382781982422,123.41000366210938,825.2100219726562,14.163240432739258,2276.97998046875,5521.06005859375,2.4179999828338623,11.319999694824219
2017-01-09,114.0080795288086,42.09183120727539,124.9000015258789,827.1799926757812,13.89314079284668,2268.89990234375,5531.81982421875,2.375999927520752,11.5600004196167
2017-01-10,114.123046875,43.10171890258789,124.3499984741211,826.010009765625,13.901581764221191,2268.89990234375,5551.81982421875,2.378999948501587,11.489999771118164
2017-01-11,114.73627471923828,43.26839828491211,126.08999633789062,829.8599853515625,14.154799461364746,2275.320068359375,5563.64990234375,2.369999885559082,11.260000228881836
2017-01-12,114.25719451904297,39.219032287597656,126.62000274658203,829.530029296875,14.070393562316895,2270.43994140625,5547.490234375,2.3610000610351562,11.539999961853027
2017-01-13,114.05598449707031,41.85651397705078,128.33999633789062,830.9400024414062,14.39957332611084,2274.639892578125,5574.1201171875,2.380000114440918,11.229999542236328
2017-01-16,,39.80732345581055,,,,,,,
2017-01-17,114.97579193115234,40.425018310546875,127.87000274658203,827.4600219726562,14.129477500915527,2267.889892578125,5538.72998046875,2.3269999027252197,11.869999885559082
2017-01-18,114.96622467041016,40.209312438964844,127.91999816894531,829.02001953125,13.926904678344727,2271.889892578125,5555.64990234375,2.3889999389648438,12.479999542236328
2017-01-19,114.76500701904297,41.09174346923828,127.55000305175781,824.3699951171875,14.070393562316895,2263.68994140625,5540.080078125,2.4590001106262207,12.779999732971191
2017-01-20,114.97579193115234,40.8270149230957,127.04000091552734,828.1699829101562,14.036630630493164,2271.31005859375,5555.330078125,2.4670000076293945,11.539999961853027
2017-01-23,115.05244445800781,40.80740737915039,128.92999267578125,844.4299926757812,13.994427680969238,2265.199951171875,5552.93994140625,2.4030001163482666,11.770000457763672
2017-01-24,114.94705963134766,42.719337463378906,129.3699951171875,849.530029296875,14.22232437133789,2280.070068359375,5600.9599609375,2.4709999561309814,11.069999694824219
2017-01-25,116.77708435058594,42.33694839477539,131.47999572753906,858.4500122070312,14.559944152832031,2298.3701171875,5656.33984375,2.5230000019073486,10.8100004196167
2017-01-26,116.83456420898438,43.11152648925781,132.77999877929688,856.97998046875,14.272966384887695,2296.679931640625,5655.18017578125,2.507999897003174,10.630000114440918
2017-01-27,116.84415435791016,43.13113784790039,132.17999267578125,845.030029296875,13.758091926574707,2294.68994140625,5660.77978515625,2.4809999465942383,10.579999923706055
2017-01-30,116.53755950927734,41.532958984375,130.97999572753906,823.8300170898438,13.741212844848633,2280.89990234375,5613.7099609375,2.4830000400543213,11.880000114440918
2017-01-31,116.26925659179688,41.33686447143555,130.32000732421875,820.1900024414062,13.597723007202148,2278.8701171875,5614.7900390625,2.4509999752044678,11.989999771118164
2017-02-01,123.35944366455078,41.8369026184082,133.22999572753906,815.239990234375,13.547078132629395,2279.550048828125,5642.64990234375,2.4739999771118164,11.8100004196167
2017-02-02,123.14866638183594,42.582069396972656,130.83999633789062,818.260009765625,13.378268241882324,2280.85009765625,5636.2001953125,2.4700000286102295,11.930000305175781
2017-02-03,123.6756362915039,42.425193786621094,130.97999572753906,820.1300048828125,13.639925003051758,2297.419921875,5666.77001953125,2.490999937057495,10.970000267028809
2017-02-06,124.83497619628906,41.768272399902344,132.05999755859375,821.6199951171875,13.428911209106445,2292.56005859375,5663.5498046875,2.4130001068115234,11.369999885559082
2017-02-07,126.02304077148438,42.28792953491211,131.83999633789062,829.22998046875,13.395150184631348,2293.080078125,5674.22021484375,2.3889999389648438,11.289999961853027
2017-02-08,126.51168823242188,42.748748779296875,134.1999969482422,829.8800048828125,13.403589248657227,2294.669921875,5682.4501953125,2.3510000705718994,11.449999809265137
2017-02-09,127.42585754394531,42.827186584472656,134.13999938964844,830.0599975585938,13.487994194030762,2307.8701171875,5715.18017578125,2.3949999809265137,10.880000114440918
2017-02-10,127.13717651367188,42.44480514526367,134.19000244140625,834.8499755859375,13.420470237731934,2316.10009765625,5734.1298828125,2.4089999198913574,10.850000381469727
2017-02-13,128.2630615234375,43.11152648925781,134.0500030517578,838.9600219726562,13.369827270507812,2328.25,5763.9599609375,2.434000015258789,11.069999694824219
2017-02-14,129.9278106689453,44.44497299194336,133.85000610351562,840.030029296875,13.403589248657227,2337.580078125,5782.56982421875,2.4700000286102295,10.739999771118164
2017-02-15,130.39932250976562,44.974430084228516,133.44000244140625,837.3200073242188,13.538640022277832,2349.25,5819.43994140625,2.502000093460083,11.970000267028809
2017-02-16,130.2454071044922,44.70970153808594,133.83999633789062,842.1699829101562,13.487994194030762,2347.219970703125,5814.89990234375,2.450000047683716,11.760000228881836
2017-02-17,130.60142517089844,44.2586784362793,133.52999877929688,846.5499877929688,13.378268241882324,2351.159912109375,5838.580078125,2.424999952316284,11.489999771118164
2017-02-20,,44.631263732910156,,,,,,,
2017-02-21,131.54446411132812,44.69989776611328,133.72000122070312,849.27001953125,13.369827270507812,2365.3798828125,5865.9501953125,2.427000045776367,11.569999694824219
2017-02-22,131.93899536132812,44.199859619140625,136.1199951171875,851.3599853515625,13.125051498413086,2362.820068359375,5860.6298828125,2.4179999828338623,11.739999771118164
2017-02-23,131.380859375,44.003761291503906,135.36000061035156,851.0,13.091290473937988,2363.81005859375,5835.509765625,2.388000011444092,11.710000038146973
2017-02-24,131.50599670410156,43.0625,135.44000244140625,847.8099975585938,12.930919647216797,2367.340087890625,5845.31005859375,2.316999912261963,11.470000267028809
2017-02-27,131.76577758789062,44.81755065917969,136.41000366210938,849.6699829101562,12.981561660766602,2369.75,5861.89990234375,2.36899995803833,12.09000015258789
2017-02-28,131.82351684570312,43.856689453125,135.5399932861328,844.9299926757812,12.973121643066406,2363.639892578125,5825.43994140625,2.3580000400543213,12.920000076293945
2017-03-01,134.51791381835938,44.64106750488281,137.4199981689453,856.75,13.547078132629395,2395.9599609375,5904.02978515625,2.4630000591278076,12.539999961853027
2017-03-02,133.71922302246094,44.91559982299805,136.75999450683594,849.8499755859375,13.34450626373291,2381.919921875,5861.22021484375,2.489000082015991,11.8100004196167
2017-03-03,134.50828552246094,44.92540740966797,137.1699981689453,849.0800170898438,13.504877090454102,2383.1201171875,5870.75,2.492000102996826,10.960000038146973
2017-03-06,134.08486938476562,44.16063690185547,137.4199981689453,847.27001953125,13.336065292358398,2375.31005859375,5849.18017578125,2.49399995803833,11.239999771118164
2017-03-07,134.25808715820312,44.69989776611328,137.3000030517578,851.1500244140625,13.226337432861328,2368.389892578125,5833.93017578125,2.510999917984009,11.449999809265137
2017-03-08,133.75770568847656,45.1705207824707,137.72000122070312,853.6400146484375,13.141932487487793,2362.97998046875,5837.5498046875,2.552000045776367,11.859999656677246
2017-03-09,133.4497833251953,45.101890563964844,138.24000549316406,857.8400268554688,13.403589248657227,2364.8701171875,5838.81005859375,2.5980000495910645,12.300000190734863
2017-03-10,133.89244079589844,45.278377532958984,138.7899932861328,861.4099731445312,13.521757125854492,2372.60009765625,5861.72998046875,2.5820000171661377,11.65999984741211
2017-03-13,133.95016479492188,45.46466827392578,139.60000610351562,864.5800170898438,13.445792198181152,2373.469970703125,5875.77978515625,2.6080000400543213,11.350000381469727
2017-03-14,133.74807739257812,45.101890563964844,139.32000732421875,865.9099731445312,13.369827270507812,2365.449951171875,5856.81982421875,2.5950000286102295,12.300000190734863
2017-03-15,135.1626739501953,45.72939682006836,139.72000122070312,868.3900146484375,13.454232215881348,2385.260009765625,5900.0498046875,2.507999897003174,11.630000114440918
2017-03-16,135.38397216796875,46.788307189941406,139.99000549316406,870.0,13.72433090209961,2381.3798828125,5900.759765625,2.5220000743865967,11.210000038146973
2017-03-17,134.71041870117188,47.07264709472656,139.83999633789062,872.3699951171875,13.538640022277832,2378.25,5901.0,2.500999927520752,11.279999732971191
2017-03-20,136.1249542236328,46.76870346069336,139.94000244140625,867.9099731445312,13.327624320983887,2373.469970703125,5901.52978515625,2.4730000495910645,11.34000015258789
2017-03-21,134.56600952148438,45.641151428222656,138.50999450683594,850.1400146484375,13.243219375610352,2344.02001953125,5793.830078125,2.436000108718872,12.470000267028809
2017-03-22,136.08644104003906,45.425453186035156,139.58999633789062,849.7999877929688,13.217897415161133,2348.449951171875,5821.64013671875,2.3959999084472656,12.8100004196167
2017-03-23,135.6053009033203,46.219635009765625,139.52999877929688,839.6500244140625,13.167254447937012,2345.9599609375,5817.68994140625,2.4179999828338623,13.119999885559082
2017-03-24,135.3358612060547,46.13138961791992,140.33999633789062,835.1400146484375,13.19257640838623,2343.97998046875,5828.740234375,2.4000000953674316,12.960000038146973
2017-03-27,135.5668182373047,46.180416107177734,140.32000732421875,838.510009765625,13.327624320983887,2341.590087890625,5840.3701171875,2.372999906539917,12.5
2017-03-28,138.3766632080078,47.05303955078125,141.75999450683594,840.6300048828125,13.412030220031738,2358.570068359375,5875.14013671875,2.4089999198913574,11.529999732971191
2017-03-29,138.68463134765625,47.062843322753906,142.64999389648438,849.8699951171875,13.378268241882324,2361.1298828125,5897.5498046875,2.385999917984009,11.420000076293945
2017-03-30,138.50177001953125,47.062843322753906,142.41000366210938,849.47998046875,13.471114158630371,2368.06005859375,5914.33984375,2.4159998893737793,11.539999961853027
2017-03-31,138.24195861816406,47.53347396850586,142.0500030517578,847.7999877929688,13.462674140930176,2362.719970703125,5911.740234375,2.3959999084472656,12.369999885559082
2017-04-03,138.28045654296875,47.013816833496094,142.27999877929688,856.75,13.336065292358398,2358.840087890625,5894.68017578125,2.3499999046325684,12.380000114440918
2017-04-04,139.3101043701172,47.58249282836914,141.72999572753906,852.5700073242188,13.260100364685059,2360.159912109375,5898.60986328125,2.3499999046325684,11.789999961853027
2017-04-05,138.58839416503906,47.02362823486328,141.85000610351562,848.9099731445312,13.074408531188965,2352.949951171875,5864.47998046875,2.3570001125335693,12.890000343322754
2017-04-06,138.24195861816406,46.670650482177734,141.1699981689453,845.0999755859375,13.150373458862305,2357.489990234375,5878.9501953125,2.3429999351501465,12.390000343322754
2017-04-07,137.93402099609375,47.062843322753906,140.77999877929688,842.0999755859375,13.125051498413086,2355.5400390625,5877.81005859375,2.372999906539917,12.869999885559082
2017-04-10,137.77044677734375,47.41581344604492,141.0399932861328,841.7000122070312,13.209457397460938,2357.159912109375,5880.93017578125,2.3610000610351562,14.050000190734863
2017-04-11,136.28854370117188,47.38640213012695,139.9199981689453,839.8800048828125,13.209457397460938,2353.780029296875,5866.77001953125,2.2980000972747803,15.069999694824219
2017-04-12,136.45208740234375,47.435420989990234,139.5800018310547,841.4600219726562,12.990002632141113,2344.929931640625,5836.16015625,2.2960000038146973,15.770000457763672
2017-04-13,135.7304229736328,46.945186614990234,139.38999938964844,840.1799926757812,12.821192741394043,2328.949951171875,5805.14990234375,2.2320001125335693,15.960000038146973
2017-04-17,136.48097229003906,,141.4199981689453,855.1300048828125,13.032205581665039,2349.010009765625,5856.7900390625,2.252000093460083,14.65999984741211
2017-04-18,135.87474060058594,46.366703033447266,140.9600067138672,853.989990234375,12.829631805419922,2342.18994140625,5849.47021484375,2.178999900817871,14.420000076293945
2017-04-19,135.3743438720703,46.9745979309082,142.27000427246094,856.510009765625,12.939359664916992,2338.169921875,5863.02978515625,2.2019999027252197,14.930000305175781
2017-04-20,137.0679931640625,46.71967697143555,143.8000030517578,860.0800170898438,13.141932487487793,2355.840087890625,5916.77978515625,2.240999937057495,14.149999618530273
2017-04-21,136.9043731689453,46.327484130859375,143.67999267578125,858.9500122070312,13.133491516113281,2348.68994140625,5910.52001953125,2.236999988555908,14.630000114440918
2017-04-24,138.22268676757812,49.26891326904297,145.47000122070312,878.9299926757812,13.901581764221191,2374.14990234375,5983.81982421875,2.2730000019073486,10.84000015258789
2017-04-25,139.0791473388672,48.5335578918457,146.49000549316406,888.8400268554688,14.028190612792969,2388.610107421875,6025.490234375,2.3269999027252197,10.760000228881836
2017-04-26,138.2611846923828,49.95524597167969,146.55999755859375,889.1400146484375,14.129477500915527,2387.449951171875,6025.22998046875,2.311000108718872,10.850000381469727
2017-04-27,138.36708068847656,50.59255599975586,147.6999969482422,891.4400024414062,14.1210355758667,2388.77001953125,6048.93994140625,2.2960000038146973,10.359999656677246
2017-04-28,138.23233032226562,50.54353332519531,150.25,924.52001953125,14.357373237609863,2384.199951171875,6047.60986328125,2.2820000648498535,10.819999694824219
2017-05-01,141.0518341064453,,152.4600067138672,932.8200073242188,14.458656311035156,2388.330078125,6091.60009765625,2.325000047683716,10.109999656677246
2017-05-02,141.94676208496094,50.59255599975586,152.77999877929688,937.0900268554688,14.593708038330078,2391.169921875,6095.3701171875,2.2960000038146973,10.59000015258789
2017-05-03,141.5137481689453,50.69060516357422,151.8000030517578,948.4500122070312,14.686552047729492,2388.1298828125,6072.5498046875,2.309000015258789,10.680000305175781
2017-05-04,141.00372314453125,51.671077728271484,150.85000610351562,954.719970703125,14.863804817199707,2389.52001953125,6075.33984375,2.3559999465942383,10.460000038146973
2017-05-05,143.34207153320312,52.70058059692383,150.24000549316406,950.280029296875,14.897565841674805,2399.2900390625,6100.759765625,2.3519999980926514,10.569999694824219
2017-05-08,147.2393341064453,52.406436920166016,151.05999755859375,958.6900024414062,14.903685569763184,2399.3798828125,6102.66015625,2.375999927520752,9.770000457763672
2017-05-09,148.182373046875,52.65155792236328,150.47999572753906,956.7100219726562,14.67641544342041,2396.919921875,6120.58984375,2.4070000648498535,9.960000038146973
2017-05-10,147.4799041748047,52.65155792236328,150.2899932861328,954.8400268554688,14.798791885375977,2399.6298828125,6129.14013671875,2.4140000343322754,10.210000038146973
2017-05-11,148.75534057617188,52.21034240722656,150.0399932861328,955.8900146484375,14.597744941711426,2394.43994140625,6115.9599609375,2.4000000953674316,10.600000381469727
2017-05-12,150.8328399658203,52.74959945678711,150.3300018310547,955.1400146484375,14.658933639526367,2390.89990234375,6121.22998046875,2.3350000381469727,10.399999618530273
2017-05-15,150.44631958007812,53.435935974121094,150.19000244140625,959.219970703125,14.230616569519043,2402.320068359375,6149.669921875,2.3380000591278076,10.420000076293945
2017-05-16,150.2240753173828,53.04374694824219,149.77999877929688,964.6099853515625,14.422922134399414,2400.669921875,6169.8701171875,2.3289999961853027,10.649999618530273
2017-05-17,145.18020629882812,51.27888870239258,144.85000610351562,942.1699829101562,14.073274612426758,2357.030029296875,6011.240234375,2.2160000801086426,15.59000015258789
2017-05-18,147.3929443359375,51.13182067871094,147.66000366210938,950.5,14.047050476074219,2365.719970703125,6055.1298828125,2.2330000400543213,14.65999984741211
2017-05-19,147.89540100097656,51.6220588684082,148.05999755859375,954.6500244140625,14.309286117553711,2381.72998046875,6083.7001953125,2.244999885559082,12.039999961853027
2017-05-22,148.79400634765625,50.396461486816406,148.24000549316406,964.0700073242188,14.387955665588379,2394.02001953125,6133.6201171875,2.253999948501587,10.930000305175781
2017-05-23,148.61041259765625,51.18083953857422,148.07000732421875,970.5499877929688,14.405438423156738,2398.419921875,6138.7099609375,2.2850000858306885,10.720000267028809
2017-05-24,148.1659393310547,50.396461486816406,150.0399932861328,977.6099853515625,14.195650100708008,2404.389892578125,6163.02001953125,2.2660000324249268,10.020000457763672
2017-05-25,148.6780242919922,50.102317810058594,151.9600067138672,991.8599853515625,14.178168296813965,2415.070068359375,6205.259765625,2.255000114440918,9.989999771118164
2017-05-26,148.4268341064453,50.396461486816406,152.1300048828125,993.27001953125,14.178168296813965,2415.820068359375,6210.18994140625,2.25,9.8100004196167
2017-05-29,,49.563053131103516,,,,,,,
2017-05-30,148.4847869873047,48.474727630615234,152.3800048828125,996.1699829101562,13.98586368560791,2412.909912109375,6203.18994140625,2.2170000076293945,10.380000114440918
2017-05-31,147.6055145263672,48.68062973022461,151.4600067138672,987.0900268554688,13.889710426330566,2411.800048828125,6198.52001953125,2.196000099182129,10.40999984741211
2017-06-01,148.01133728027344,49.21989059448242,151.52999877929688,988.2899780273438,14.073274612426758,2430.06005859375,6246.830078125,2.2170000076293945,9.890000343322754
2017-06-02,150.20474243164062,49.759151458740234,153.61000061035156,996.1199951171875,14.047050476074219,2439.070068359375,6305.7998046875,2.1589999198913574,9.75
2017-06-05,148.7360382080078,49.170867919921875,153.6300048828125,1003.8800048828125,13.942157745361328,2436.10009765625,6295.68017578125,2.181999921798706,10.069999694824219
2017-06-06,149.23849487304688,48.5335578918457,152.80999755859375,996.6799926757812,13.968379974365234,2429.330078125,6275.06005859375,2.1470000743865967,10.449999809265137
2017-06-07,150.12744140625,48.04331970214844,153.1199951171875,1001.5,14.047050476074219,2433.139892578125,6297.3798828125,2.177999973297119,10.390000343322754
2017-06-08,149.76028442382812,48.543357849121094,154.7100067138672,1004.280029296875,13.863486289978027,2433.7900390625,6321.759765625,2.194000005722046,10.15999984741211
2017-06-09,143.9530487060547,49.170867919921875,149.60000610351562,970.1199951171875,14.020829200744629,2431.77001953125,6207.919921875,2.1989998817443848,10.699999809265137
2017-06-12,140.51316833496094,48.29824447631836,148.44000244140625,961.8099975585938,13.950898170471191,2429.389892578125,6175.4599609375,2.2130000591278076,11.460000038146973
2017-06-13,141.64369201660156,49.072818756103516,150.67999267578125,970.5,14.265579223632812,2440.35009765625,6220.3701171875,2.2070000171661377,10.420000076293945
2017-06-14,140.26193237304688,48.435516357421875,150.25,967.9299926757812,14.143203735351562,2437.919921875,6194.89013671875,2.138000011444092,10.640000343322754
2017-06-15,139.42129516601562,48.83750534057617,149.8000030517578,960.1799926757812,13.98586368560791,2432.4599609375,6165.5,2.1619999408721924,10.899999618530273
2017-06-16,137.46946716308594,50.05329132080078,150.63999938964844,958.6199951171875,14.186909675598145,2433.14990234375,6151.759765625,2.1570000648498535,10.380000114440918
2017-06-19,141.4021453857422,50.49755859375,152.8699951171875,975.219970703125,14.239357948303223,2453.4599609375,6239.009765625,2.190000057220459,10.369999885559082
2017-06-20,140.11703491210938,50.00393295288086,152.25,968.989990234375,14.003345489501953,2437.030029296875,6188.02978515625,2.1530001163482666,10.859999656677246
2017-06-21,140.947998046875,50.15201950073242,153.91000366210938,978.5900268554688,14.099496841430664,2435.610107421875,6233.9501953125,2.1549999713897705,10.75
2017-06-22,140.7161102294922,50.54691696166992,153.39999389648438,976.6199951171875,14.10823917388916,2434.5,6236.68994140625,2.1530001163482666,10.479999542236328
2017-06-23,141.34417724609375,49.954566955566406,155.07000732421875,986.0900268554688,14.038310050964355,2438.300048828125,6265.25,2.1440000534057617,10.020000457763672
2017-06-26,140.89968872070312,49.80648422241211,153.58999633789062,972.0900268554688,14.195650100708008,2439.070068359375,6247.14990234375,2.13700008392334,9.899999618530273
2017-06-27,138.8801727294922,48.04918670654297,150.5800018310547,948.0900268554688,14.501591682434082,2419.3798828125,6146.6201171875,2.197999954223633,11.0600004196167
2017-06-28,140.9093475341797,48.87847137451172,153.24000549316406,961.010009765625,14.903685569763184,2440.68994140625,6234.41015625,2.2209999561309814,10.029999732971191
2017-06-29,138.83187866210938,47.269264221191406,151.0399932861328,937.8200073242188,14.956132888793945,2419.699951171875,6144.35009765625,2.2669999599456787,11.4399995803833
2017-06-30,139.1604461669922,46.78551483154297,150.97999572753906,929.6799926757812,14.842496871948242,2423.409912109375,6140.419921875,2.302000045776367,11.180000305175781
2017-07-03,138.657958984375,48.207149505615234,148.42999267578125,919.4600219726562,15.139697074890137,2429.010009765625,6110.06005859375,2.3459999561309814,11.220000267028809
2017-07-04,,47.71352767944336,,,,,,,
2017-07-05,139.22805786132812,47.417354583740234,150.33999633789062,932.260009765625,15.174662590026855,2432.5400390625,6150.85986328125,2.3340001106262207,11.069999694824219
2017-07-06,137.91397094726562,48.09855270385742,148.82000732421875,927.6900024414062,15.130955696105957,2409.75,6089.4599609375,2.369999885559082,12.539999961853027
2017-07-07,139.3150177001953,48.019569396972656,151.44000244140625,940.8099975585938,15.340742111206055,2425.179931640625,6153.080078125,2.3929998874664307,11.1899995803833
2017-07-10,140.1653289794922,48.3552360534668,153.5,951.0,15.349485397338867,2427.429931640625,6176.39013671875,2.371000051498413,11.109999656677246
2017-07-11,140.61949157714844,49.273372650146484,155.27000427246094,953.530029296875,15.305778503417969,2425.530029296875,6193.2998046875,2.361999988555908,10.890000343322754
2017-07-12,140.8223876953125,50.00393295288086,158.89999389648438,967.6599731445312,15.262072563171387,2443.25,6261.169921875,2.3269999027252197,10.300000190734863
2017-07-13,142.78387451171875,50.6456413269043,159.25999450683594,968.8499755859375,15.305778503417969,2447.830078125,6274.43994140625,2.3480000495910645,9.899999618530273
2017-07-14,144.01101684570312,50.102657318115234,159.97000122070312,976.9099731445312,15.349485397338867,2459.27001953125,6312.47021484375,2.319000005722046,9.510000228881836
2017-07-17,144.5134735107422,50.2507438659668,159.72999572753906,975.9600219726562,15.288296699523926,2459.139892578125,6314.43017578125,2.309000015258789,9.819999694824219
2017-07-18,145.01596069335938,49.55967330932617,162.86000061035156,986.9500122070312,15.428153991699219,2460.610107421875,6344.31005859375,2.263000011444092,9.890000343322754
2017-07-19,145.92420959472656,50.300106048583984,164.13999938964844,992.77001953125,15.533050537109375,2473.830078125,6385.0400390625,2.2679998874664307,9.789999961853027
2017-07-20,145.26715087890625,49.20426559448242,164.52999877929688,992.1900024414062,15.559273719787598,2473.449951171875,6390.0,2.2660000324249268,9.579999923706055
2017-07-21,145.19952392578125,48.37498092651367,164.42999267578125,993.8400268554688,15.436896324157715,2472.5400390625,6387.75,2.2320001125335693,9.359999656677246
2017-07-24,146.9581298828125,48.93770980834961,166.0,998.3099975585938,15.786542892456055,2469.909912109375,6410.81005859375,2.253999948501587,9.430000305175781
2017-07-25,147.58621215820312,48.799495697021484,165.27999877929688,969.030029296875,15.891437530517578,2477.1298828125,6412.169921875,2.3259999752044678,9.430000305175781
2017-07-26,148.2819061279297,49.11540985107422,165.61000061035156,965.3099975585938,15.83025074005127,2477.830078125,6422.75,2.2820000648498535,9.600000381469727
2017-07-27,145.47976684570312,49.8558464050293,170.44000244140625,952.510009765625,15.716612815856934,2475.419921875,6382.18994140625,2.309999942779541,10.109999656677246
2017-07-28,144.45553588867188,49.60903549194336,172.4499969482422,958.3300170898438,15.314520835876465,2472.10009765625,6374.68017578125,2.2890000343322754,10.289999961853027
2017-07-31,143.71148681640625,49.954566955566406,169.25,945.5,15.23585033416748,2470.300048828125,6348.1201171875,2.2920000553131104,10.260000228881836
2017-08-01,144.98696899414062,50.54691696166992,169.86000061035156,946.5599975585938,15.36696720123291,2476.35009765625,6362.93994140625,2.250999927520752,10.09000015258789
2017-08-02,151.8377227783203,50.398826599121094,169.3000030517578,947.6400146484375,15.288296699523926,2477.570068359375,6362.64990234375,2.26200008392334,10.279999732971191
2017-08-03,150.32069396972656,50.84309387207031,168.58999633789062,940.2999877929688,15.262072563171387,2472.159912109375,6340.33984375,2.2279999256134033,10.4399995803833
2017-08-04,151.11302185058594,51.33671569824219,169.6199951171875,945.7899780273438,15.323263168334961,2476.830078125,6351.56005859375,2.2669999599456787,10.029999732971191
2017-08-07,153.4513702392578,51.53416061401367,171.97999572753906,945.75,15.279555320739746,2480.909912109375,6383.77001953125,2.256999969482422,9.930000305175781
2017-08-08,154.6785125732422,51.53416061401367,171.22999572753906,944.1900024414062,15.061025619506836,2474.919921875,6370.4599609375,2.2829999923706055,10.960000038146973
2017-08-09,155.6254425048828,50.991180419921875,171.17999267578125,940.0800170898438,15.130955696105957,2474.02001953125,6352.330078125,2.240999937057495,11.109999656677246
2017-08-10,150.6685028076172,50.84309387207031,167.39999389648438,923.5900268554688,14.903685569763184,2438.2099609375,6216.8701171875,2.2119998931884766,16.040000915527344
2017-08-11,152.76380920410156,49.36222457885742,168.0800018310547,930.0900268554688,14.903685569763184,2441.320068359375,6256.56005859375,2.188999891281128,15.510000228881836
2017-08-14,155.0628204345703,51.4354362487793,170.75,938.9299926757812,14.964875221252441,2465.840087890625,6340.22998046875,2.2190001010894775,12.329999923706055
2017-08-15,156.7604217529297,,171.0,938.0800170898438,14.947391510009766,2464.610107421875,6333.009765625,2.2660000324249268,12.039999961853027
2017-08-16,156.1298828125,52.52140808105469,170.0,944.27001953125,14.929908752441406,2468.110107421875,6345.10986328125,2.2260000705718994,11.739999771118164
2017-08-17,153.13241577148438,51.58352279663086,166.91000366210938,927.6599731445312,14.641449928283691,2430.010009765625,6221.91015625,2.1989998817443848,15.550000190734863
2017-08-18,152.783203125,51.58352279663086,167.41000366210938,926.1799926757812,14.711379051208496,2425.550048828125,6216.52978515625,2.194000005722046,14.260000228881836
2017-08-21,152.50192260742188,51.879695892333984,167.77999877929688,920.8699951171875,14.650191307067871,2428.3701171875,6213.1298828125,2.180000066757202,13.1899995803833
2017-08-22,154.99490356445312,51.879695892333984,169.63999938964844,940.4000244140625,14.641449928283691,2452.510009765625,6297.47998046875,2.2149999141693115,11.350000381469727
2017-08-23,155.18893432617188,52.62013244628906,168.7100067138672,942.5800170898438,14.554038047790527,2444.0400390625,6278.41015625,2.1710000038146973,12.25
2017-08-24,154.5001983642578,53.31119918823242,167.74000549316406,936.8900146484375,14.589003562927246,2438.969970703125,6271.330078125,2.194000005722046,12.229999542236328
2017-08-25,155.07252502441406,52.669490814208984,166.32000732421875,930.5,14.693897247314453,2443.050048828125,6265.64013671875,2.1689999103546143,11.279999732971191
2017-08-28,156.63430786132812,52.37331771850586,167.24000549316406,928.1300048828125,14.597744941711426,2444.239990234375,6283.02001953125,2.1589999198913574,11.319999694824219
2017-08-29,158.0312042236328,50.991180419921875,168.0500030517578,935.75,14.46662712097168,2446.300048828125,6301.89013671875,2.135999917984009,11.699999809265137
2017-08-30,158.45802307128906,50.991180419921875,169.9199981689453,943.6300048828125,14.431662559509277,2457.590087890625,6368.31005859375,2.1429998874664307,11.220000267028809
2017-08-31,159.08856201171875,53.21247863769531,171.97000122070312,955.239990234375,14.370474815368652,2471.64990234375,6428.66015625,2.121000051498413,10.59000015258789
2017-09-01,159.1370391845703,53.60737228393555,172.02000427246094,951.989990234375,14.352992057800293,2476.550048828125,6435.330078125,2.1570000648498535,10.130000114440918
2017-09-04,,52.42267990112305,,,,,,,
2017-09-05,157.22604370117188,52.32395553588867,170.72000122070312,941.47998046875,14.12572193145752,2457.85009765625,6375.56982421875,2.069999933242798,12.229999542236328
2017-09-06,157.06112670898438,52.768218994140625,172.08999633789062,942.02001953125,14.25683879852295,2465.5400390625,6393.31005859375,2.1080000400543213,11.630000114440918
2017-09-07,156.43060302734375,50.892452239990234,173.2100067138672,949.8900146484375,14.204392433166504,2465.10009765625,6397.8701171875,2.061000108718872,11.550000190734863
2017-09-08,153.8793487548828,50.74436569213867,170.9499969482422,941.4099731445312,14.335509300231934,2461.429931640625,6360.18994140625,2.061000108718872,12.119999885559082
2017-09-11,156.66342163085938,52.866939544677734,173.50999450683594,943.2899780273438,14.405438423156738,2488.110107421875,6432.259765625,2.125,10.729999542236328
2017-09-12,156.04258728027344,52.768218994140625,172.9600067138672,946.6500244140625,14.606486320495605,2496.47998046875,6454.27978515625,2.1710000038146973,10.579999923706055
2017-09-13,154.86878967285156,52.52140808105469,173.0500030517578,950.4400024414062,14.562779426574707,2498.3701171875,6460.18994140625,2.194999933242798,10.5
2017-09-14,153.5398406982422,52.866939544677734,170.9600067138672,940.1300048828125,14.536556243896484,2495.6201171875,6429.080078125,2.197000026702881,10.4399995803833
2017-09-15,155.09193420410156,52.52140808105469,171.63999938964844,935.2899780273438,14.597744941711426,2500.22998046875,6448.47021484375,2.2019999027252197,10.170000076293945
2017-09-18,153.9181365966797,53.21247863769531,170.00999450683594,929.75,14.693897247314453,2503.8701171875,6454.64013671875,2.2290000915527344,10.149999618530273
2017-09-19,153.9763641357422,53.16311264038086,172.52000427246094,936.8599853515625,14.807533264160156,2506.64990234375,6461.31982421875,2.243000030517578,10.180000305175781
2017-09-20,151.3960418701172,53.70610046386719,172.1699981689453,947.5399780273438,14.763825416564941,2508.239990234375,6456.0400390625,2.2769999504089355,9.779999732971191
2017-09-21,148.79629516601562,53.85418701171875,171.11000061035156,947.5499877929688,14.825013160705566,2500.60009765625,6422.68994140625,2.2780001163482666,9.670000076293945
2017-09-22,147.34121704101562,54.29844665527344,170.5399932861328,943.260009765625,14.842496871948242,2502.219970703125,6426.919921875,2.26200008392334,9.59000015258789
2017-09-25,146.04132080078125,53.2618408203125,162.8699951171875,934.280029296875,14.667675018310547,2496.659912109375,6370.58984375,2.2200000286102295,10.210000038146973
2017-09-26,148.55377197265625,53.11375045776367,164.2100067138672,937.4299926757812,14.580263137817383,2496.840087890625,6380.16015625,2.2290000915527344,10.170000076293945
2017-09-27,149.6111297607422,53.31119918823242,167.67999267578125,959.9000244140625,14.798791885375977,2507.0400390625,6453.259765625,2.309000015258789,9.869999885559082
2017-09-28,148.6895751953125,53.31119918823242,168.72999572753906,964.8099975585938,14.842496871948242,2510.06005859375,6453.4501953125,2.309000015258789,9.550000190734863
2017-09-29,149.5044403076172,52.965667724609375,170.8699951171875,973.719970703125,14.991097450256348,2519.360107421875,6495.9599609375,2.3259999752044678,9.510000228881836
2017-10-02,149.20372009277344,53.80482482910156,169.47000122070312,967.469970703125,14.964875221252441,2529.1201171875,6516.72021484375,2.3369998931884766,9.449999809265137
2017-10-03,149.8536376953125,54.347808837890625,169.9600067138672,972.0800170898438,15.026062965393066,2534.580078125,6531.7099609375,2.3340001106262207,9.510000228881836
2017-10-04,148.8835906982422,52.965667724609375,168.4199981689453,966.780029296875,14.816274642944336,2537.739990234375,6534.6298828125,2.3299999237060547,9.630000114440918
2017-10-05,150.7364044189453,55.137603759765625,171.24000549316406,985.1900024414062,15.008578300476074,2552.070068359375,6585.35986328125,2.3499999046325684,9.1899995803833
2017-10-06,150.64906311035156,55.236328125,172.22999572753906,993.6400146484375,14.956132888793945,2549.330078125,6590.18017578125,2.369999885559082,9.649999618530273
2017-10-09,151.1728973388672,55.28569030761719,172.5,992.3099975585938,14.886202812194824,2544.72998046875,6579.72998046875,2.3610000610351562,10.329999923706055
2017-10-10,151.2311248779297,54.5946159362793,171.58999633789062,987.7999877929688,15.043542861938477,2550.639892578125,6587.25,2.3450000286102295,10.079999923706055
2017-10-11,151.8616485595703,55.137603759765625,172.74000549316406,1005.6500244140625,14.999836921691895,2555.239990234375,6603.5498046875,2.3450000286102295,9.850000381469727
2017-10-12,151.32814025878906,54.19972610473633,172.5500030517578,1005.6500244140625,15.017321586608887,2550.929931640625,6591.509765625,2.322999954223633,9.90999984741211
2017-10-13,152.2884979248047,53.656734466552734,173.74000549316406,1007.8699951171875,15.104732513427734,2553.169921875,6605.7998046875,2.2799999713897705,9.609999656677246
2017-10-16,155.09193420410156,53.558013916015625,174.52000427246094,1009.3499755859375,15.104732513427734,2557.639892578125,6624.0,2.306999921798706,9.90999984741211
2017-10-17,155.6642608642578,52.81757736206055,176.11000061035156,1011.0,15.069766998291016,2559.360107421875,6623.66015625,2.2980000972747803,10.3100004196167
2017-10-18,154.97552490234375,52.866939544677734,176.02999877929688,1012.739990234375,15.087250709533691,2561.260009765625,6624.22021484375,2.3389999866485596,10.069999694824219
2017-10-19,151.3087158203125,51.682247161865234,174.55999755859375,1001.8400268554688,15.130955696105957,2562.10009765625,6605.06982421875,2.321000099182129,10.050000190734863
2017-10-20,151.5706329345703,51.33671569824219,174.97999572753906,1005.0700073242188,15.23585033416748,2575.2099609375,6629.0498046875,2.38100004196167,9.970000267028809
2017-10-23,151.49302673339844,51.48480224609375,171.27000427246094,985.5399780273438,15.148436546325684,2564.97998046875,6586.830078125,2.375999927520752,11.069999694824219
2017-10-24,152.3951873779297,52.91630172729492,171.8000030517578,988.489990234375,15.218366622924805,2569.1298828125,6598.43017578125,2.4059998989105225,11.15999984741211
2017-10-25,151.72584533691406,51.92905807495117,170.60000610351562,991.4600219726562,15.104732513427734,2557.14990234375,6563.89013671875,2.444000005722046,11.229999542236328
2017-10-26,152.69590759277344,53.01502990722656,170.6300048828125,991.4199829101562,14.964875221252441,2560.39990234375,6556.77001953125,2.4519999027252197,11.300000190734863
2017-10-27,158.1669921875,53.11375045776367,177.8800048828125,1033.6700439453125,14.868721961975098,2581.070068359375,6701.259765625,2.427999973297119,9.800000190734863
2017-10-30,161.72711181640625,53.40992736816406,179.8699951171875,1033.1300048828125,14.790050506591797,2572.830078125,6698.9599609375,2.369999885559082,10.5
2017-10-31,163.9775848388672,54.347808837890625,180.05999755859375,1033.0400390625,14.868721961975098,2575.260009765625,6727.669921875,2.375999927520752,10.180000305175781
2017-11-01,161.8920135498047,55.137603759765625,182.66000366210938,1042.5999755859375,14.772567749023438,2579.360107421875,6716.52978515625,2.375999927520752,10.199999809265137
2017-11-02,163.07546997070312,55.236328125,178.9199981689453,1042.969970703125,15.087250709533691,2579.85009765625,6714.93994140625,2.3469998836517334,9.930000305175781
2017-11-03,167.333984375,55.77931213378906,178.9199981689453,1049.989990234375,14.929908752441406,2587.840087890625,6764.43994140625,2.3429999351501465,9.140000343322754
2017-11-06,169.03158569335938,55.335052490234375,180.1699981689453,1042.6800537109375,14.903685569763184,2591.1298828125,6786.43994140625,2.319999933242798,9.399999618530273
2017-11-07,169.57479858398438,55.236328125,180.25,1052.3900146484375,14.772567749023438,2590.639892578125,6767.77978515625,2.306999921798706,9.890000343322754
2017-11-08,170.96197509765625,53.75545883178711,179.55999755859375,1058.2900390625,14.816274642944336,2594.3798828125,6789.1201171875,2.325000047683716,9.779999732971191
2017-11-09,170.61279296875,53.11375045776367,179.3000030517578,1047.719970703125,14.798791885375977,2584.6201171875,6750.0498046875,2.3310000896453857,10.5
2017-11-10,170.048095703125,53.2618408203125,178.4600067138672,1044.1500244140625,14.956132888793945,2582.300048828125,6750.93994140625,2.4000000953674316,11.289999961853027
2017-11-13,169.36663818359375,53.21247863769531,178.77000427246094,1041.199951171875,14.868721961975098,2584.840087890625,6757.60009765625,2.4000000953674316,11.5
2017-11-14,166.80621337890625,52.81757736206055,178.07000732421875,1041.6400146484375,14.868721961975098,2578.8701171875,6737.8701171875,2.38100004196167,11.59000015258789
2017-11-15,164.60601806640625,51.53416061401367,177.9499969482422,1036.4100341796875,14.842496871948242,2564.6201171875,6706.2099609375,2.3350000381469727,13.130000114440918
2017-11-16,166.57257080078125,51.48480224609375,179.58999633789062,1048.469970703125,14.903685569763184,2585.639892578125,6793.2900390625,2.3610000610351562,11.760000228881836
2017-11-17,165.64768981933594,52.52140808105469,179.0,1035.8900146484375,14.991097450256348,2578.85009765625,6782.7900390625,2.3540000915527344,11.430000305175781
2017-11-20,165.48220825195312,52.866939544677734,178.74000549316406,1034.6600341796875,14.947391510009766,2582.139892578125,6790.7099609375,2.371999979019165,10.649999618530273
2017-11-21,168.55857849121094,52.91630172729492,181.86000061035156,1050.300048828125,14.956132888793945,2599.030029296875,6862.47998046875,2.36299991607666,9.729999542236328
2017-11-22,170.3304443359375,52.42267990112305,180.8699951171875,1051.9200439453125,15.03480339050293,2597.080078125,6867.35986328125,2.322000026702881,9.880000114440918
2017-11-23,,52.472042083740234,,,,,,,
2017-11-24,170.34017944335938,51.879695892333984,182.77999877929688,1056.52001953125,15.087250709533691,2602.419921875,6889.16015625,2.3399999141693115,9.670000076293945
2017-11-27,169.4834442138672,51.4354362487793,183.02999877929688,1072.010009765625,14.982355117797852,2601.419921875,6878.52001953125,2.328000068664551,9.869999885559082
2017-11-28,168.49046325683594,51.33671569824219,182.4199981689453,1063.2900390625,15.130955696105957,2627.0400390625,6912.35986328125,2.3380000591278076,10.029999732971191
2017-11-29,164.99545288085938,50.84309387207031,175.1300048828125,1037.3800048828125,15.104732513427734,2626.070068359375,6824.39013671875,2.375999927520752,10.699999809265137
2017-11-30,167.302734375,50.2507438659668,177.17999267578125,1036.1700439453125,15.017321586608887,2647.580078125,6873.97021484375,2.4170000553131104,11.279999732971191
2017-12-01,166.52389526367188,49.24375534057617,175.10000610351562,1025.0699462890625,15.314520835876465,2642.219970703125,6847.58984375,2.361999988555908,11.430000305175781
2017-12-04,165.30697631835938,50.00393295288086,171.47000122070312,1011.8699951171875,15.174662590026855,2639.43994140625,6775.3701171875,2.378999948501587,11.680000305175781
2017-12-05,165.15118408203125,49.90520477294922,172.8300018310547,1019.5999755859375,14.947391510009766,2629.570068359375,6762.2099609375,2.3559999465942383,11.329999923706055
2017-12-06,164.5378875732422,49.707759857177734,176.05999755859375,1032.719970703125,14.833755493164062,2629.27001953125,6776.3798828125,2.3299999237060547,11.020000457763672
2017-12-07,164.8396759033203,49.65839767456055,180.13999938964844,1044.5699462890625,14.89494514465332,2636.97998046875,6812.83984375,2.375999927520752,10.15999984741211
2017-12-08,164.88836669921875,50.59627914428711,179.0,1049.3800048828125,15.279555320739746,2651.5,6840.080078125,2.382999897003174,9.579999923706055
2017-12-11,168.1010284423828,50.94181442260742,179.0399932861328,1051.969970703125,15.559273719787598,2659.989990234375,6875.080078125,2.384999990463257,9.34000015258789
2017-12-12,167.15667724609375,51.33671569824219,176.9600067138672,1048.77001953125,15.611720085144043,2664.110107421875,6862.31982421875,2.4030001163482666,9.920000076293945
2017-12-13,167.7116241455078,51.33671569824219,178.3000030517578,1051.3900146484375,15.63794231414795,2662.85009765625,6875.7998046875,2.3489999771118164,10.180000305175781
2017-12-14,167.66294860839844,50.892452239990234,178.38999938964844,1057.469970703125,15.769061088562012,2652.010009765625,6856.52978515625,2.3459999561309814,10.489999771118164
2017-12-15,169.36663818359375,49.8558464050293,180.17999267578125,1072.0,15.83025074005127,2675.81005859375,6936.580078125,2.3550000190734863,9.420000076293945
2017-12-18,171.75181579589844,50.892452239990234,180.82000732421875,1085.0899658203125,16.057519912719727,2690.159912109375,6994.759765625,2.3919999599456787,9.529999732971191
2017-12-19,169.92153930664062,50.59627914428711,179.50999450683594,1079.780029296875,16.066259384155273,2681.469970703125,6963.85009765625,2.4630000591278076,10.029999732971191
2017-12-20,169.73658752441406,50.54691696166992,177.88999938964844,1073.56005859375,15.9176607131958,2679.25,6960.9599609375,2.496999979019165,9.720000267028809
2017-12-21,170.37911987304688,50.84309387207031,177.4499969482422,1070.8499755859375,16.083742141723633,2684.570068359375,6965.35986328125,2.4809999465942383,9.619999885559082
2017-12-22,170.37911987304688,50.74436569213867,177.1999969482422,1068.8599853515625,16.00507164001465,2683.340087890625,6959.9599609375,2.484999895095825,9.899999618530273
2017-12-26,166.0565948486328,,175.99000549316406,1065.8499755859375,15.95262336730957,2680.5,6936.25,2.4670000076293945,10.25
2017-12-27,166.0858154296875,50.54691696166992,177.6199951171875,1060.199951171875,15.865211486816406,2682.6201171875,6939.33984375,2.4140000343322754,10.470000267028809
2017-12-28,166.5531005859375,50.695003509521484,177.9199981689453,1055.949951171875,16.057519912719727,2687.5400390625,6950.16015625,2.431999921798706,10.180000305175781
2017-12-29,164.75205993652344,50.44819259643555,176.4600067138672,1053.4000244140625,16.075000762939453,2673.610107421875,6903.39013671875,2.4049999713897705,11.039999961853027
2018-01-02,167.70188903808594,50.59627914428711,181.4199981689453,1073.2099609375,16.171154022216797,2695.81005859375,7006.89990234375,2.4649999141693115,9.770000457763672
2018-01-03,167.67266845703125,51.089900970458984,184.6699981689453,1091.52001953125,16.424650192260742,2713.06005859375,7065.52978515625,2.447000026702881,9.149999618530273
2018-01-04,168.45150756835938,54.5946159362793,184.3300018310547,1095.760009765625,16.56450843811035,2723.989990234375,7077.91015625,2.453000068664551,9.220000267028809
2018-01-05,170.369384765625,56.37165832519531,186.85000610351562,1110.2900390625,16.424650192260742,2743.14990234375,7136.56005859375,2.4760000705718994,9.220000267028809
2018-01-08,169.73658752441406,56.91464614868164,188.27999877929688,1114.2099609375,16.50331687927246,2747.7099609375,7157.39013671875,2.4800000190734863,9.520000457763672
2018-01-09,169.7171173095703,58.099334716796875,187.8699951171875,1112.7900390625,16.54702377319336,2751.2900390625,7163.580078125,2.5460000038146973,10.079999923706055
2018-01-10,169.67816162109375,58.49423599243164,187.83999633789062,1110.1400146484375,16.625696182250977,2748.22998046875,7153.56982421875,2.549999952316284,9.819999694824219
2018-01-11,170.6419677734375,59.4321174621582,187.77000427246094,1112.050048828125,16.870447158813477,2767.56005859375,7211.77978515625,2.5309998989105225,9.880000114440918
2018-01-12,172.4040985107422,60.32063674926758,179.3699951171875,1130.6500244140625,17.12394142150879,2786.239990234375,7261.06005859375,2.552000045776367,10.15999984741211
2018-01-15,,60.66617202758789,,,,,,,
2018-01-16,171.52789306640625,59.92573928833008,178.38999938964844,1130.699951171875,16.975341796875,2776.419921875,7223.68994140625,2.5439999103546143,11.65999984741211
2018-01-17,174.3608856201172,59.92573928833008,177.60000610351562,1139.0999755859375,17.027786254882812,2802.56005859375,7298.27978515625,2.578000068664551,11.90999984741211
2018-01-18,174.51663208007812,60.468727111816406,179.8000030517578,1135.969970703125,17.351211547851562,2798.030029296875,7296.0498046875,2.6110000610351562,12.220000267028809
2018-01-19,173.7378387451172,61.20915603637695,181.2899932861328,1143.5,17.6221866607666,2810.300048828125,7336.3798828125,2.63700008392334,11.270000457763672
2018-01-22,172.3164520263672,61.554691314697266,185.3699951171875,1164.1600341796875,17.718339920043945,2832.969970703125,7408.02978515625,2.6649999618530273,11.029999732971191
2018-01-23,172.3553924560547,62.14704132080078,189.35000610351562,1176.1700439453125,17.866941452026367,2839.1298828125,7460.2900390625,2.624000072479248,11.100000381469727
2018-01-24,169.61001586914062,61.99895095825195,186.5500030517578,1171.2900390625,18.04176139831543,2837.5400390625,7415.06005859375,2.6540000438690186,11.470000267028809
2018-01-25,166.5823211669922,62.19640350341797,187.47999572753906,1182.1400146484375,18.024282455444336,2839.25,7411.16015625,2.621000051498413,11.579999923706055
2018-01-26,166.9717254638672,63.33173370361328,190.0,1187.56005859375,18.260290145874023,2872.8701171875,7505.77001953125,2.6619999408721924,11.079999923706055
2018-01-29,163.5156707763672,63.18364715576172,185.97999572753906,1186.47998046875,17.94561195373535,2853.530029296875,7466.509765625,2.6989998817443848,13.84000015258789
2018-01-30,162.55186462402344,62.01869583129883,187.1199951171875,1177.3699951171875,17.797012329101562,2822.429931640625,7402.47998046875,2.7260000705718994,14.789999961853027
2018-01-31,162.99969482421875,61.426353454589844,186.88999938964844,1182.219970703125,17.76204490661621,2823.81005859375,7411.47998046875,2.7200000286102295,13.539999961853027
2018-02-01,163.34042358398438,62.59130096435547,193.08999633789062,1181.5899658203125,18.06798553466797,2821.97998046875,7385.85986328125,2.7730000019073486,13.470000267028809
2018-02-02,156.25306701660156,62.058189392089844,190.27999877929688,1119.199951171875,17.604703903198242,2762.1298828125,7240.9501953125,2.8540000915527344,17.309999465942383
2018-02-05,152.3491668701172,60.02445983886719,181.25999450683594,1062.3900146484375,16.739330291748047,2648.93994140625,6967.52978515625,2.7939999103546143,37.31999969482422
2018-02-06,158.7161102294922,57.49711990356445,185.30999755859375,1084.4300537109375,16.791776657104492,2695.139892578125,7115.8798828125,2.7679998874664307,29.979999542236328
2018-02-07,155.3184356689453,60.83400344848633,180.17999267578125,1055.4100341796875,16.739330291748047,2681.659912109375,7051.97998046875,2.8440001010894775,27.729999542236328
2018-02-08,151.0446319580078,58.36589050292969,171.5800018310547,1007.7100219726562,16.171154022216797,2581.0,6777.16015625,2.8510000705718994,33.459999084472656
2018-02-09,152.89210510253906,57.24043273925781,176.11000061035156,1046.27001953125,16.25856590270996,2619.550048828125,6874.490234375,2.8289999961853027,29.059999465942383
2018-02-12,159.0504150390625,57.5366096496582,176.41000366210938,1054.56005859375,16.354717254638672,2656.0,6981.9599609375,2.8570001125335693,25.610000610351562
2018-02-13,160.64378356933594,57.04298782348633,173.14999389648438,1054.1400146484375,16.27604866027832,2662.93994140625,7013.509765625,2.8399999141693115,24.969999313354492
2018-02-14,163.6055908203125,58.95824432373047,179.52000427246094,1072.699951171875,16.69562530517578,2698.6298828125,7143.6201171875,2.9130001068115234,19.260000228881836
2018-02-15,169.09921264648438,59.5900764465332,179.9600067138672,1091.3599853515625,16.774293899536133,2731.199951171875,7256.43017578125,2.8910000324249268,19.1299991607666
2018-02-16,168.5518035888672,60.2219123840332,177.36000061035156,1095.5,16.791776657104492,2732.219970703125,7239.47021484375,2.877000093460083,19.459999084472656
2018-02-19,,59.53084182739258,,,,,,,
2018-02-20,167.98484802246094,59.80727005004883,176.00999450683594,1103.5899658203125,16.765554428100586,2716.260009765625,7234.31005859375,2.8929998874664307,20.600000381469727
2018-02-21,167.222412109375,60.41936111450195,177.91000366210938,1113.75,16.81800079345703,2701.330078125,7218.22998046875,2.943000078201294,20.020000457763672
2018-02-22,168.62022399902344,58.76079177856445,178.99000549316406,1109.9000244140625,16.80051612854004,2703.9599609375,7210.08984375,2.9170000553131104,18.719999313354492
2018-02-23,171.552734375,58.83977127075195,183.2899932861328,1128.0899658203125,16.975341796875,2747.300048828125,7337.39013671875,2.871000051498413,16.489999771118164
2018-02-26,174.9447021484375,59.51109313964844,184.92999267578125,1143.699951171875,17.036529541015625,2779.60009765625,7421.4599609375,2.8589999675750732,15.800000190734863
2018-02-27,174.37774658203125,59.55058670043945,181.4600067138672,1117.510009765625,16.887929916381836,2744.280029296875,7330.35009765625,2.9079999923706055,18.59000015258789
2018-02-28,174.11383056640625,59.13594436645508,178.32000732421875,1103.9200439453125,16.581987380981445,2713.830078125,7273.009765625,2.868000030517578,19.850000381469727
2018-03-01,171.06399536132812,57.85252380371094,175.94000244140625,1071.4100341796875,16.337238311767578,2677.669921875,7180.56005859375,2.803999900817871,22.469999313354492
2018-03-02,172.2467803955078,54.33793640136719,176.6199951171875,1084.1400146484375,16.188636779785156,2691.25,7257.8701171875,2.8570001125335693,19.59000015258789
2018-03-05,172.84307861328125,55.60160827636719,180.39999389648438,1094.760009765625,16.066259384155273,2720.93994140625,7330.7099609375,2.88100004196167,18.729999542236328
2018-03-06,172.69644165039062,56.76655960083008,179.77999877929688,1100.9000244140625,16.241085052490234,2728.1201171875,7372.009765625,2.877000093460083,18.360000610351562
2018-03-07,171.0933380126953,58.16844177246094,183.7100067138672,1115.0400390625,16.127450942993164,2726.800048828125,7396.64990234375,2.882999897003174,17.760000228881836
2018-03-08,172.9603729248047,59.0372200012207,182.33999633789062,1129.3800048828125,16.092483520507812,2738.969970703125,7427.9501953125,2.865999937057495,16.540000915527344
2018-03-09,175.93199157714844,60.00471878051758,185.22999572753906,1160.8399658203125,16.075000762939453,2786.570068359375,7560.81005859375,2.8940000534057617,14.640000343322754
2018-03-12,177.63284301757812,60.18242263793945,184.75999450683594,1165.9300537109375,16.214860916137695,2783.02001953125,7588.31982421875,2.869999885559082,15.779999732971191
2018-03-13,175.92221069335938,59.55058670043945,181.8800048828125,1139.9100341796875,15.935141563415527,2765.31005859375,7511.009765625,2.8480000495910645,16.350000381469727
2018-03-14,174.42662048339844,59.0372200012207,184.19000244140625,1148.8900146484375,15.908919334411621,2749.47998046875,7496.81005859375,2.816999912261963,17.229999542236328
2018-03-15,174.6318817138672,60.20216751098633,183.86000061035156,1150.6099853515625,16.075000762939453,2747.330078125,7481.740234375,2.8259999752044678,16.59000015258789
2018-03-16,174.01608276367188,60.241661071777344,185.08999633789062,1134.4200439453125,16.127450942993164,2752.010009765625,7481.990234375,2.8480000495910645,15.800000190734863
2018-03-19,171.35723876953125,58.83977127075195,172.55999755859375,1100.0699462890625,15.970108985900879,2712.919921875,7344.240234375,2.8450000286102295,19.020000457763672
2018-03-20,171.29861450195312,59.70854568481445,168.14999389648438,1095.800048828125,16.0400390625,2716.93994140625,7364.2998046875,2.878999948501587,18.200000762939453
2018-03-21,167.4178924560547,60.02445983886719,169.38999938964844,1094.0,15.8389892578125,2711.929931640625,7345.2900390625,2.9070000648498535,17.860000610351562
2018-03-22,165.0523223876953,57.59584045410156,164.88999938964844,1053.1500244140625,15.15717887878418,2643.68994140625,7166.68017578125,2.8320000171661377,23.34000015258789
2018-03-23,161.2302703857422,56.92451477050781,159.38999938964844,1026.550048828125,15.069766998291016,2588.260009765625,6992.669921875,2.8299999237060547,24.8700008392334
2018-03-26,168.8841552734375,55.68059158325195,160.05999755859375,1054.0899658203125,15.401931762695312,2658.550048828125,7220.5400390625,2.8429999351501465,21.030000686645508
2018-03-27,164.55380249023438,57.02323913574219,152.22000122070312,1006.9400024414062,15.27081298828125,2612.6201171875,7008.81005859375,2.7860000133514404,22.5
2018-03-28,162.73562622070312,57.08247375488281,153.02999877929688,1005.1799926757812,15.375709533691406,2605.0,6949.22998046875,2.7750000953674316,22.8700008392334
2018-03-29,164.00637817382812,56.9837532043457,159.7899932861328,1037.1400146484375,15.436896324157715,2640.8701171875,7063.4501953125,2.740999937057495,19.969999313354492
2018-04-02,162.93112182617188,,155.38999938964844,1012.6300048828125,15.183403015136719,2581.8798828125,6870.1201171875,2.7320001125335693,23.6200008392334
2018-04-03,164.60267639160156,57.47737503051758,156.11000061035156,1018.6799926757812,15.104732513427734,2614.449951171875,6941.27978515625,2.7839999198913574,21.100000381469727
2018-04-04,167.750244140625,56.17420959472656,155.10000610351562,1029.7099609375,14.929908752441406,2644.68994140625,7042.10986328125,2.7880001068115234,20.059999465942383
2018-04-05,168.91348266601562,58.44487380981445,159.33999633789062,1032.6400146484375,15.130955696105957,2662.840087890625,7076.5498046875,2.8320000171661377,18.940000534057617
2018-04-06,164.59291076660156,58.20793533325195,157.1999969482422,1009.9500122070312,14.938651084899902,2604.469970703125,6915.10986328125,2.7750000953674316,21.489999771118164
2018-04-09,166.225341796875,58.58308792114258,157.92999267578125,1020.0900268554688,15.061025619506836,2613.159912109375,6950.33984375,2.7860000133514404,21.770000457763672
2018-04-10,169.35337829589844,59.39262771606445,165.0399932861328,1036.5,15.244590759277344,2656.8701171875,7094.2998046875,2.796999931335449,20.469999313354492
2018-04-11,168.5615692138672,57.49711990356445,166.32000732421875,1025.06005859375,15.139697074890137,2642.18994140625,7069.02978515625,2.7899999618530273,20.239999771118164
2018-04-12,170.22335815429688,58.7213020324707,163.8699951171875,1037.2900390625,15.323263168334961,2663.989990234375,7140.25,2.8340001106262207,18.489999771118164
2018-04-13,170.800048828125,58.44487380981445,164.52000427246094,1036.0400390625,15.25333309173584,2656.300048828125,7106.64990234375,2.8289999961853027,17.40999984741211
2018-04-16,171.86558532714844,58.42512893676758,164.8300018310547,1046.0999755859375,15.314520835876465,2677.840087890625,7156.27978515625,2.8320000171661377,16.559999465942383
2018-04-17,174.2311248779297,60.00471878051758,168.66000366210938,1079.3599853515625,15.305778503417969,2706.389892578125,7281.10009765625,2.813999891281128,15.25
2018-04-18,173.8401336669922,60.55757522583008,166.36000061035156,1075.3900146484375,15.244590759277344,2708.639892578125,7295.240234375,2.867000102996826,15.600000381469727
2018-04-19,168.91348266601562,60.241661071777344,168.10000610351562,1089.449951171875,15.428153991699219,2693.1298828125,7238.06005859375,2.9140000343322754,15.960000038146973
2018-04-20,161.99270629882812,60.45885467529297,166.27999877929688,1077.3199462890625,15.428153991699219,2670.139892578125,7146.1298828125,2.9509999752044678,16.8799991607666
2018-04-23,161.5235137939453,61.13017654418945,165.83999633789062,1073.81005859375,14.973612785339355,2670.2900390625,7128.60009765625,2.9730000495910645,16.34000015258789
2018-04-24,159.27525329589844,61.91997528076172,159.69000244140625,1022.6400146484375,14.755085945129395,2634.56005859375,7007.35009765625,2.9830000400543213,18.020000457763672
2018-04-25,159.96926879882812,60.61681365966797,159.69000244140625,1022.989990234375,14.658933639526367,2639.39990234375,7003.740234375,3.0239999294281006,17.84000015258789
2018-04-26,160.52647399902344,61.70277786254883,174.16000366210938,1043.31005859375,14.781309127807617,2666.93994140625,7118.68017578125,2.990000009536743,16.239999771118164
2018-04-27,158.66920471191406,60.79451370239258,173.58999633789062,1031.449951171875,14.702637672424316,2669.909912109375,7119.7998046875,2.9570000171661377,15.40999984741211
2018-04-30,161.5430450439453,60.755027770996094,172.0,1018.5800170898438,14.685155868530273,2648.050048828125,7066.27001953125,2.936000108718872,15.930000305175781
2018-05-01,165.29669189453125,,173.86000061035156,1040.75,14.580263137817383,2654.800048828125,7130.7001953125,2.9760000705718994,15.489999771118164
2018-05-02,172.5986785888672,62.1174201965332,176.07000732421875,1026.050048828125,14.440403938293457,2635.669921875,7100.89990234375,2.9639999866485596,15.970000267028809
2018-05-03,172.91148376464844,60.89324188232422,174.02000427246094,1026.300048828125,14.440403938293457,2629.72998046875,7088.14990234375,2.946000099182129,15.899999618530273
2018-05-04,179.69541931152344,62.808494567871094,176.61000061035156,1051.0,14.510334014892578,2663.419921875,7209.6201171875,2.944000005722046,14.770000457763672
2018-05-07,180.99546813964844,64.24987030029297,177.97000122070312,1059.4599609375,14.53762435913086,2672.6298828125,7265.2099609375,2.950000047683716,14.75
2018-05-08,181.86546325683594,64.48680877685547,178.9199981689453,1058.5899658203125,15.154026985168457,2671.919921875,7266.89990234375,2.9690001010894775,14.710000038146973
2018-05-09,183.14599609375,64.3091049194336,182.66000366210938,1088.949951171875,15.381621360778809,2697.7900390625,7339.91015625,3.003999948501587,13.420000076293945
2018-05-10,185.76571655273438,63.91420364379883,185.52999877929688,1105.469970703125,15.419554710388184,2723.070068359375,7404.97021484375,2.9709999561309814,13.229999542236328
2018-05-11,185.0592041015625,64.32884979248047,186.99000549316406,1103.3800048828125,15.4954195022583,2727.719970703125,7402.8798828125,2.9709999561309814,12.649999618530273
2018-05-14,184.62744140625,63.835227966308594,186.63999938964844,1106.5999755859375,15.429038047790527,2730.1298828125,7411.31982421875,2.994999885559082,12.930000305175781
2018-05-15,182.949462890625,64.58553314208984,184.32000732421875,1084.8699951171875,15.362656593322754,2711.449951171875,7351.6298828125,3.0799999237060547,14.630000114440918
2018-05-16,184.6568603515625,63.53905487060547,183.1999969482422,1084.0899658203125,15.248858451843262,2722.4599609375,7398.2998046875,3.0950000286102295,13.420000076293945
2018-05-17,183.48916625976562,63.3810920715332,183.75999450683594,1081.260009765625,15.239375114440918,2720.1298828125,7382.47021484375,3.1089999675750732,13.430000305175781
2018-05-18,182.82188415527344,63.084922790527344,182.67999267578125,1069.6400146484375,15.14454460144043,2712.969970703125,7354.33984375,3.066999912261963,13.420000076293945
2018-05-21,184.11717224121094,63.30211639404297,184.49000549316406,1084.010009765625,15.201443672180176,2733.010009765625,7394.0400390625,3.065000057220459,13.079999923706055
2018-05-22,183.65597534179688,64.07217407226562,183.8000030517578,1075.31005859375,15.4954195022583,2724.43994140625,7378.4599609375,3.065000057220459,13.220000267028809
2018-05-23,184.83352661132812,62.847984313964844,186.89999389648438,1085.9599609375,15.315239906311035,2733.2900390625,7425.9599609375,3.003000020980835,12.579999923706055
2018-05-24,184.62744140625,62.23589324951172,185.92999267578125,1085.449951171875,15.14454460144043,2727.760009765625,7424.43017578125,2.9809999465942383,12.529999732971191
2018-05-25,185.0493927001953,62.13716506958008,184.9199981689453,1084.0799560546875,15.059196472167969,2721.330078125,7433.85009765625,2.930999994277954,13.220000267028809
2018-05-28,,60.81425857543945,,,,,,,
2018-05-29,184.38211059570312,59.4321174621582,185.74000549316406,1068.0699462890625,14.423827171325684,2689.860107421875,7396.58984375,2.7679998874664307,17.020000457763672
2018-05-30,183.98960876464844,60.123191833496094,187.6699981689453,1077.469970703125,14.594523429870605,2724.010009765625,7462.4501953125,2.8420000076293945,14.9399995803833
2018-05-31,183.37139892578125,61.20915603637695,191.77999877929688,1100.0,14.433310508728027,2705.27001953125,7442.1201171875,2.822000026702881,15.430000305175781
2018-06-01,186.67831420898438,62.19640350341797,193.99000549316406,1135.0,14.81263542175293,2734.6201171875,7554.330078125,2.8949999809265137,13.460000038146973
2018-06-04,188.2385711669922,60.65629959106445,193.27999877929688,1153.0400390625,14.831602096557617,2746.8701171875,7606.4599609375,2.937000036239624,12.739999771118164
2018-06-05,189.69082641601562,59.70854568481445,192.94000244140625,1151.02001953125,14.490208625793457,2748.800048828125,7637.85986328125,2.9189999103546143,12.399999618530273
2018-06-06,190.34829711914062,59.609825134277344,191.33999633789062,1146.949951171875,14.67987060546875,2772.35009765625,7689.240234375,2.9749999046325684,11.640000343322754
2018-06-07,189.83802795410156,59.37287902832031,188.17999267578125,1134.4200439453125,14.74625301361084,2770.3701171875,7635.06982421875,2.933000087738037,12.130000114440918
2018-06-08,188.1109619140625,58.14870071411133,189.10000610351562,1132.7099609375,14.651421546936035,2779.030029296875,7645.509765625,2.937000036239624,12.180000305175781
2018-06-11,187.6497802734375,59.45186233520508,191.5399932861328,1140.9000244140625,15.087645530700684,2782.0,7659.93017578125,2.9570000171661377,12.350000381469727
2018-06-12,188.6800994873047,59.17543411254883,192.39999389648438,1148.18994140625,15.059196472167969,2786.85009765625,7703.7900390625,2.9570000171661377,12.34000015258789
2018-06-13,187.1296844482422,59.9059944152832,192.41000366210938,1144.22998046875,15.011780738830566,2775.6298828125,7695.7001953125,2.9769999980926514,12.9399995803833
2018-06-14,187.22784423828125,61.031455993652344,196.80999755859375,1160.1099853515625,14.983331680297852,2782.489990234375,7761.0400390625,2.946000099182129,12.119999885559082
2018-06-15,185.30450439453125,60.51808547973633,195.85000610351562,1159.27001953125,14.74625301361084,2779.659912109375,7746.3798828125,2.9240000247955322,11.979999542236328
2018-06-18,185.20639038085938,60.23014450073242,198.30999755859375,1183.5799560546875,14.490208625793457,2773.75,7747.02978515625,2.9260001182556152,12.3100004196167
2018-06-19,182.21348571777344,60.25,197.49000549316406,1178.68994140625,14.376411437988281,2762.590087890625,7725.58984375,2.8929998874664307,13.350000381469727
2018-06-20,183.00833129882812,59.276947021484375,202.0,1184.0699462890625,14.518658638000488,2767.320068359375,7781.509765625,2.927999973297119,12.789999961853027
2018-06-21,181.98780822753906,57.76771545410156,201.5,1169.43994140625,14.404860496520996,2749.760009765625,7712.9501953125,2.8970000743865967,14.640000343322754
2018-06-22,181.45790100097656,58.32374954223633,201.74000549316406,1169.2900390625,14.784185409545898,2754.8798828125,7692.81982421875,2.9000000953674316,13.770000457763672
2018-06-25,178.75938415527344,56.059906005859375,196.35000610351562,1139.280029296875,14.452276229858398,2717.070068359375,7532.009765625,2.875,17.329999923706055
2018-06-26,180.9770965576172,56.417354583740234,199.0,1132.6199951171875,14.53762435913086,2723.06005859375,7561.6298828125,2.880000114440918,15.920000076293945
2018-06-27,180.712158203125,57.052818298339844,195.83999633789062,1116.93994140625,14.366928100585938,2699.6298828125,7445.080078125,2.8269999027252197,17.90999984741211
2018-06-28,182.02703857421875,55.424442291259766,196.22999572753906,1126.780029296875,14.423827171325684,2716.31005859375,7503.68017578125,2.8469998836517334,16.850000381469727
2018-06-29,181.6443634033203,57.29111862182617,194.32000732421875,1129.18994140625,14.547107696533203,2718.3701171875,7510.2998046875,2.8489999771118164,16.09000015258789
2018-07-02,183.6755828857422,56.31806564331055,197.36000061035156,1142.1099853515625,14.395378112792969,2726.7099609375,7567.68994140625,2.865999937057495,15.600000381469727
2018-07-03,180.4766387939453,57.07267761230469,192.72999572753906,1116.280029296875,14.35744571685791,2713.219970703125,7502.669921875,2.8380000591278076,16.139999389648438
2018-07-04,,55.901039123535156,,,,,,,
2018-07-05,181.92892456054688,56.99324417114258,198.4499969482422,1141.2900390625,14.423827171325684,2736.610107421875,7586.43017578125,2.8399999141693115,14.970000267028809
2018-07-06,184.45079040527344,57.052818298339844,203.22999572753906,1155.0799560546875,14.575556755065918,2759.820068359375,7688.39013671875,2.8310000896453857,13.369999885559082
2018-07-09,187.01194763183594,57.29111862182617,204.74000549316406,1167.280029296875,14.594523429870605,2784.169921875,7756.2001953125,2.859999895095825,12.6899995803833
2018-07-10,186.7862548828125,58.105308532714844,203.5399932861328,1167.1400146484375,14.4996919631958,2793.840087890625,7759.2001953125,2.872999906539917,12.640000343322754
2018-07-11,184.3625030517578,56.19891357421875,202.5399932861328,1171.4599609375,14.20571517944336,2774.02001953125,7716.60986328125,2.8420000076293945,13.630000114440918
2018-07-12,187.45350646972656,56.81452178955078,206.9199981689453,1201.260009765625,14.224681854248047,2798.2900390625,7823.919921875,2.8529999256134033,12.579999923706055
2018-07-13,187.74789428710938,57.35069274902344,207.32000732421875,1204.4200439453125,14.224681854248047,2801.31005859375,7825.97998046875,2.8310000896453857,12.180000305175781
2018-07-16,187.3357696533203,57.19182586669922,207.22999572753906,1196.510009765625,14.433310508728027,2798.429931640625,7805.72021484375,2.8580000400543213,12.829999923706055
2018-07-17,187.8656463623047,57.827293395996094,209.99000549316406,1213.0799560546875,14.404860496520996,2809.550048828125,7855.1201171875,2.864000082015991,12.0600004196167
2018-07-18,186.8353271484375,57.946441650390625,209.36000061035156,1212.9100341796875,14.604005813598633,2815.6201171875,7854.43994140625,2.875,12.100000381469727
2018-07-19,188.28761291503906,57.33083724975586,208.08999633789062,1199.0999755859375,14.53762435913086,2804.489990234375,7825.2998046875,2.8469998836517334,12.869999885559082
2018-07-20,187.8558349609375,56.19891357421875,209.94000244140625,1197.8800048828125,14.585040092468262,2801.830078125,7820.2001953125,2.8949999809265137,12.859999656677246
2018-07-23,188.0226593017578,54.371952056884766,210.91000366210938,1211.0,14.717803955078125,2806.97998046875,7841.8701171875,2.9649999141693115,12.619999885559082
2018-07-24,189.3866424560547,55.205997467041016,214.6699981689453,1258.1500244140625,15.22040843963623,2820.39990234375,7840.77001953125,2.9489998817443848,12.40999984741211
2018-07-25,191.17257690429688,53.279747009277344,217.5,1275.93994140625,15.229891777038574,2846.070068359375,7932.240234375,2.936000108718872,12.289999961853027
2018-07-26,190.57398986816406,55.345008850097656,176.25999450683594,1285.5,15.239375114440918,2837.43994140625,7852.18017578125,2.9749999046325684,12.140000343322754
2018-07-27,187.40444946289062,55.205997467041016,174.88999938964844,1252.8900146484375,15.305756568908691,2818.820068359375,7737.419921875,2.9600000381469727,13.029999732971191
2018-07-30,186.35447692871094,55.30529022216797,171.05999755859375,1230.0400390625,15.46696949005127,2802.60009765625,7630.0,2.9749999046325684,14.260000228881836
2018-07-31,186.7273712158203,55.88117980957031,172.5800018310547,1227.219970703125,15.571285247802734,2816.2900390625,7671.7900390625,2.9639999866485596,12.829999923706055
2018-08-01,197.7274932861328,55.662742614746094,171.64999389648438,1232.989990234375,15.53335189819336,2813.360107421875,7707.2900390625,3.003000020980835,13.149999618530273
2018-08-02,203.50721740722656,54.47124481201172,176.3699951171875,1241.1300048828125,15.296273231506348,2827.219970703125,7802.68994140625,2.9860000610351562,12.1899995803833
2018-08-03,204.0959930419922,55.84146499633789,177.77999877929688,1238.1600341796875,15.334206581115723,2840.35009765625,7812.009765625,2.953000068664551,11.640000343322754
2018-08-06,205.15579223632812,55.623023986816406,185.69000244140625,1237.6700439453125,15.106612205505371,2850.39990234375,7859.68017578125,2.937999963760376,11.270000457763672
2018-08-07,203.23248291015625,56.139339447021484,183.80999755859375,1255.8399658203125,15.154026985168457,2858.449951171875,7883.66015625,2.9730000495910645,10.930000305175781
2018-08-08,203.36985778808594,55.82160568237305,185.17999267578125,1261.3299560546875,15.286791801452637,2857.699951171875,7888.330078125,2.9709999561309814,10.850000381469727
2018-08-09,204.96932983398438,55.40458297729492,183.08999633789062,1264.4599609375,15.191960334777832,2853.580078125,7891.77978515625,2.934999942779541,11.270000457763672
2018-08-10,204.3588104248047,54.252803802490234,180.25999450683594,1252.510009765625,14.803152084350586,2833.280029296875,7839.10986328125,2.8570001125335693,13.15999984741211
2018-08-13,205.6783447265625,53.99464797973633,180.0500030517578,1248.6400146484375,14.651421546936035,2821.929931640625,7819.7099609375,2.877000093460083,14.779999732971191
2018-08-14,206.54489135742188,54.41166687011719,181.11000061035156,1258.1400146484375,14.74625301361084,2839.9599609375,7870.89013671875,2.8949999809265137,13.3100004196167
2018-08-15,207.0274200439453,,179.52999877929688,1232.219970703125,14.67987060546875,2818.3701171875,7774.1201171875,2.8519999980926514,14.640000343322754
2018-08-16,210.06033325195312,54.034358978271484,174.6999969482422,1224.06005859375,14.660904884338379,2840.68994140625,7806.52001953125,2.871000051498413,13.449999809265137
2018-08-17,214.25523376464844,53.875492095947266,173.8000030517578,1215.8499755859375,14.670388221740723,2850.1298828125,7816.330078125,2.872999906539917,12.640000343322754
2018-08-20,212.16763305664062,54.94784164428711,172.5,1221.949951171875,14.736769676208496,2857.050048828125,7821.009765625,2.822999954223633,12.489999771118164
2018-08-21,211.7540283203125,54.90812301635742,172.6199951171875,1217.4100341796875,14.916949272155762,2862.9599609375,7859.169921875,2.8440001010894775,12.859999656677246
2018-08-22,211.76390075683594,54.888267517089844,173.63999938964844,1221.75,14.907466888427734,2861.820068359375,7889.10009765625,2.822999954223633,12.25
2018-08-23,212.1971893310547,54.90812301635742,172.89999389648438,1221.1600341796875,14.708320617675781,2856.97998046875,7878.4599609375,2.821000099182129,12.40999984741211
2018-08-24,212.85694885253906,55.503875732421875,174.64999389648438,1236.75,14.784185409545898,2874.68994140625,7945.97998046875,2.8259999752044678,11.989999771118164
2018-08-27,214.6097412109375,56.00033187866211,177.4600067138672,1256.27001953125,14.992815017700195,2896.739990234375,8017.89990234375,2.8480000495910645,12.15999984741211
2018-08-28,216.3428497314453,56.04004669189453,176.25999450683594,1245.8599853515625,14.964365005493164,2897.52001953125,8030.0400390625,2.884000062942505,12.5
2018-08-29,219.57272338867188,56.536502838134766,175.89999389648438,1264.6500244140625,15.07816219329834,2914.0400390625,8109.68994140625,2.884000062942505,12.25
2018-08-30,221.5913848876953,56.59607696533203,177.63999938964844,1254.43994140625,14.945399284362793,2901.1298828125,8088.35986328125,2.859999895095825,13.529999732971191
2018-08-31,224.1516571044922,55.58330535888672,175.72999572753906,1231.800048828125,14.850567817687988,2901.52001953125,8109.5400390625,2.8529999256134033,12.859999656677246
2018-09-03,,55.70245361328125,,,,,,,
2018-09-04,224.87049865722656,56.07976150512695,171.16000366210938,1211.31005859375,14.841084480285645,2896.719970703125,8091.25,2.9019999504089355,13.15999984741211
2018-09-05,223.4032745361328,55.146427154541016,167.17999267578125,1199.0999755859375,15.049713134765625,2888.60009765625,7995.169921875,2.9019999504089355,13.90999984741211
2018-09-06,219.69090270996094,55.00741958618164,162.52999877929688,1183.989990234375,14.983331680297852,2878.050048828125,7922.72998046875,2.878999948501587,14.649999618530273
2018-09-07,217.9184112548828,53.89535140991211,163.0399932861328,1177.5899658203125,14.850567817687988,2871.679931640625,7902.5400390625,2.941999912261963,14.880000114440918
2018-09-10,214.99375915527344,54.82869338989258,164.17999267578125,1175.06005859375,14.736769676208496,2877.1298828125,7924.16015625,2.937000036239624,14.15999984741211
2018-09-11,220.429443359375,54.371952056884766,165.94000244140625,1189.989990234375,14.67987060546875,2887.889892578125,7972.47021484375,2.9769999980926514,13.220000267028809
2018-09-12,217.69192504882812,55.30529022216797,162.0,1171.5999755859375,14.670388221740723,2888.919921875,7954.22998046875,2.9630000591278076,13.140000343322754
2018-09-13,222.9503173828125,54.649967193603516,161.36000061035156,1182.1400146484375,14.774702072143555,2904.179931640625,8013.7099609375,2.9630000591278076,12.369999885559082
2018-09-14,220.41957092285156,54.649967193603516,162.32000732421875,1177.97998046875,14.81263542175293,2904.97998046875,8010.0400390625,2.99399995803833,12.069999694824219
2018-09-17,214.5506591796875,55.00741958618164,160.5800018310547,1159.8299560546875,14.850567817687988,2888.800048828125,7895.7900390625,3.000999927520752,13.680000305175781
2018-09-18,214.90516662597656,55.38472366333008,160.3000030517578,1167.1099853515625,14.89798355102539,2904.31005859375,7956.10986328125,3.0480000972747803,12.789999961853027
2018-09-19,215.03314208984375,56.55636215209961,163.05999755859375,1174.27001953125,15.14454460144043,2907.949951171875,7950.0400390625,3.0829999446868896,11.75
2018-09-20,216.66781616210938,56.973388671875,166.02000427246094,1191.5699462890625,15.789396286010742,2930.75,8028.22998046875,3.078000068664551,11.800000190734863
2018-09-21,214.3340301513672,57.54927444458008,162.92999267578125,1172.1199951171875,15.656633377075195,2929.669921875,7986.9599609375,3.068000078201294,11.680000305175781
2018-09-24,217.4161834716797,57.39040756225586,165.41000366210938,1179.56005859375,15.552318572998047,2919.3701171875,7993.25,3.078000068664551,12.199999809265137
2018-09-25,218.7947998046875,58.24431610107422,164.91000366210938,1193.8900146484375,15.485936164855957,2915.56005859375,8007.47021484375,3.1019999980926514,12.420000076293945
2018-09-26,217.05184936523438,58.7606315612793,166.9499969482422,1194.06005859375,15.476452827453613,2905.969970703125,7990.3701171875,3.061000108718872,12.890000343322754
2018-09-27,221.51260375976562,58.66134262084961,168.83999633789062,1207.3599853515625,15.286791801452637,2914.0,8041.97021484375,3.055999994277954,12.40999984741211
2018-09-28,222.29054260253906,57.4102668762207,164.4600067138672,1207.0799560546875,14.916949272155762,2913.97998046875,8046.35009765625,3.055999994277954,12.119999885559082
2018-10-01,223.78732299804688,57.787574768066406,162.44000244140625,1208.530029296875,14.765218734741211,2924.590087890625,8037.2998046875,3.0799999237060547,12.0
2018-10-02,225.77645874023438,57.56913375854492,159.3300018310547,1207.6400146484375,14.594523429870605,2923.429931640625,7999.5498046875,3.055999994277954,12.050000190734863
2018-10-03,228.52381896972656,58.26417541503906,162.42999267578125,1211.530029296875,14.632455825805664,2925.510009765625,8025.08984375,3.1610000133514404,11.609999656677246
2018-10-04,224.5061798095703,57.588993072509766,158.85000610351562,1177.0699462890625,14.670388221740723,2901.610107421875,7879.509765625,3.197000026702881,14.220000267028809
2018-10-05,220.8627166748047,56.8542366027832,157.3300018310547,1167.8299560546875,14.338479042053223,2885.570068359375,7788.4501953125,3.2249999046325684,14.819999694824219
2018-10-08,220.3506622314453,55.205997467041016,157.25,1155.9200439453125,14.14881706237793,2884.429931640625,7735.9501953125,3.2330000400543213,15.6899995803833
2018-10-09,223.4032745361328,54.90812301635742,157.89999389648438,1145.1700439453125,14.072951316833496,2880.340087890625,7738.02001953125,3.2079999446868896,15.949999809265137
2018-10-10,213.05389404296875,53.279747009277344,151.3800048828125,1092.1600341796875,13.797941207885742,2785.679931640625,7422.0498046875,3.2249999046325684,22.959999084472656
2018-10-11,211.17306518554688,51.4726448059082,153.35000610351562,1090.739990234375,13.380683898925781,2728.3701171875,7329.06005859375,3.132999897003174,24.979999542236328
2018-10-12,218.71603393554688,51.01590347290039,153.74000549316406,1120.5400390625,13.532414436340332,2767.1298828125,7496.89013671875,3.1410000324249268,21.309999465942383
2018-10-15,214.03860473632812,50.24142837524414,153.52000427246094,1102.43994140625,13.48499870300293,2750.7900390625,7430.740234375,3.1630001068115234,21.299999237060547
2018-10-16,218.7554168701172,51.81023406982422,158.77999877929688,1133.0799560546875,13.579829216003418,2809.919921875,7645.490234375,3.1559998989105225,17.6200008392334
2018-10-17,217.81008911132812,49.94355773925781,159.4199981689453,1127.5899658203125,13.551380157470703,2809.2099609375,7642.7001953125,3.178999900817871,17.399999618530273
2018-10-18,212.71908569335938,49.685401916503906,154.9199981689453,1097.9100341796875,13.134122848510742,2768.780029296875,7485.14013671875,3.174999952316284,20.059999465942383
2018-10-19,215.95880126953125,48.89107131958008,154.0500030517578,1105.1800537109375,13.124639511108398,2767.780029296875,7449.02978515625,3.197999954223633,19.889999389648438
2018-10-22,217.2783203125,49.19887161254883,154.77999877929688,1111.3699951171875,13.058258056640625,2755.8798828125,7468.6298828125,3.196000099182129,19.639999389648438
2018-10-23,219.3265380859375,48.910926818847656,154.38999938964844,1114.9100341796875,12.916010856628418,2740.68994140625,7437.5400390625,3.1659998893737793,20.709999084472656
2018-10-24,211.80328369140625,48.48397445678711,146.0399932861328,1057.1199951171875,12.527202606201172,2656.10009765625,7108.39990234375,3.124000072479248,25.229999542236328
2018-10-25,216.4413299560547,49.179012298583984,150.9499969482422,1103.5899658203125,12.640999794006348,2705.570068359375,7318.33984375,3.135999917984009,24.219999313354492
2018-10-26,212.9947967529297,49.149227142333984,145.3699951171875,1083.75,12.517719268798828,2658.68994140625,7167.2099609375,3.0769999027252197,24.15999984741211
2018-10-29,208.99685668945312,49.82440948486328,142.08999633789062,1034.72998046875,12.56513500213623,2641.25,7050.2900390625,3.0869998931884766,24.700000762939453
2018-10-30,210.0406494140625,49.48681640625,146.22000122070312,1049.510009765625,12.953943252563477,2682.6298828125,7161.64990234375,3.109999895095825,23.350000381469727
2018-10-31,215.51568603515625,49.685401916503906,151.7899932861328,1090.5799560546875,13.181537628173828,2711.739990234375,7305.89990234375,3.1589999198913574,21.229999542236328
2018-11-01,218.8243408203125,50.1619987487793,151.75,1085.97998046875,13.456548690795898,2740.3701171875,7434.06005859375,3.1440000534057617,19.34000015258789
2018-11-02,204.3095703125,51.035762786865234,150.35000610351562,1071.489990234375,13.5039644241333,2723.06005859375,7356.990234375,3.2139999866485596,19.510000228881836
2018-11-05,198.50958251953125,50.618736267089844,148.67999267578125,1055.72998046875,13.380683898925781,2738.31005859375,7328.85009765625,3.2009999752044678,19.959999084472656
2018-11-06,200.65626525878906,50.479732513427734,149.94000244140625,1069.5699462890625,13.361717224121094,2755.449951171875,7375.9599609375,3.2139999866485596,19.90999984741211
2018-11-07,206.7418212890625,51.17477035522461,151.52999877929688,1108.239990234375,13.693626403808594,2813.889892578125,7570.75,3.2130000591278076,16.360000610351562
2018-11-08,206.02049255371094,51.35349655151367,147.8699951171875,1094.6300048828125,13.41861629486084,2806.830078125,7530.8798828125,3.2339999675750732,16.719999313354492
2018-11-09,202.048095703125,50.797462463378906,144.9600067138672,1077.02001953125,13.276369094848633,2781.010009765625,7406.89990234375,3.188999891281128,17.360000610351562
2018-11-12,191.87008666992188,50.00313186645508,141.5500030517578,1049.3599853515625,12.925494194030762,2726.219970703125,7200.8701171875,3.186000108718872,20.450000762939453
2018-11-13,189.9530792236328,51.49250411987305,142.16000366210938,1047.969970703125,12.934977531433105,2722.179931640625,7200.8701171875,3.1449999809265137,20.020000457763672
2018-11-14,184.58740234375,51.591793060302734,144.22000122070312,1054.5799560546875,12.991875648498535,2701.580078125,7136.39013671875,3.119999885559082,21.25
2018-11-15,189.14279174804688,50.69817352294922,143.85000610351562,1071.050048828125,13.02032470703125,2730.199951171875,7259.02978515625,3.118000030517578,19.979999542236328
2018-11-16,191.23768615722656,50.40029525756836,139.52999877929688,1068.27001953125,12.991875648498535,2736.27001953125,7247.8701171875,3.0739998817443848,18.139999389648438
2018-11-19,183.65853881835938,50.261287689208984,131.5500030517578,1027.4200439453125,12.944459915161133,2690.72998046875,7028.47998046875,3.056999921798706,20.100000381469727
2018-11-20,174.8837127685547,48.5534782409668,132.42999267578125,1030.449951171875,12.612550735473633,2641.889892578125,6908.81982421875,3.0480000972747803,22.479999542236328
2018-11-21,174.6860809326172,49.0797233581543,134.82000732421875,1043.4300537109375,12.897045135498047,2649.929931640625,6972.25,3.061000108718872,20.799999237060547
2018-11-22,,48.742130279541016,,,,,,,
2018-11-23,170.24925231933594,49.24851608276367,131.72999572753906,1030.0999755859375,12.764281272888184,2632.56005859375,6938.97998046875,3.053999900817871,21.520000457763672
2018-11-26,172.55166625976562,50.837181091308594,136.3800048828125,1055.93994140625,13.08670711517334,2673.449951171875,7081.85009765625,3.072000026702881,18.899999618530273
2018-11-27,172.17616271972656,50.75774383544922,135.0,1052.280029296875,13.058258056640625,2682.169921875,7082.7001953125,3.055000066757202,19.020000457763672
2018-11-28,178.7967987060547,50.44001388549805,136.75999450683594,1091.7900390625,13.115156173706055,2743.7900390625,7291.58984375,3.0439999103546143,18.489999771118164
2018-11-29,177.4232635498047,51.49250411987305,138.67999267578125,1094.5799560546875,12.953943252563477,2737.800048828125,7273.080078125,3.0350000858306885,18.790000915527344
2018-11-30,176.4647674560547,51.53221893310547,140.61000061035156,1109.6500244140625,12.830662727355957,2760.169921875,7330.5400390625,3.013000011444092,18.06999969482422
2018-12-03,182.630859375,53.49818801879883,141.08999633789062,1116.3599853515625,13.02032470703125,2790.3701171875,7441.509765625,2.992000102996826,16.440000534057617
2018-12-04,174.59715270996094,52.72371292114258,137.92999267578125,1062.469970703125,12.527202606201172,2700.06005859375,7158.43017578125,2.9240000247955322,20.739999771118164
2018-12-05,,51.90952682495117,,,,,,,
2018-12-06,172.65048217773438,49.64568328857422,139.6300048828125,1078.0799560546875,12.185811042785645,2695.949951171875,7188.259765625,2.875999927520752,21.190000534057617
2018-12-07,166.49427795410156,50.042850494384766,137.4199981689453,1046.5799560546875,11.920283317565918,2633.080078125,6969.25,2.8499999046325684,23.229999542236328
2018-12-10,167.59112548828125,48.821563720703125,141.85000610351562,1053.1800537109375,11.740103721618652,2637.719970703125,7020.52001953125,2.8559999465942383,22.639999389648438
2018-12-11,166.6326141357422,49.24851608276367,142.0800018310547,1061.6500244140625,11.607339859008789,2636.780029296875,7031.830078125,2.878999948501587,21.760000228881836
2018-12-12,167.0970458984375,50.22157669067383,144.5,1073.72998046875,11.986665725708008,2651.070068359375,7098.31005859375,2.9059998989105225,21.459999084472656
2018-12-13,168.9251251220703,49.983272552490234,145.00999450683594,1073.5400390625,11.90131664276123,2650.5400390625,7070.330078125,2.9110000133514404,20.649999618530273
2018-12-14,163.51991271972656,49.09958267211914,144.05999755859375,1051.7099609375,11.730620384216309,2599.949951171875,6910.66015625,2.8910000324249268,21.6299991607666
2018-12-17,161.9981689453125,48.622982025146484,140.19000244140625,1025.6500244140625,11.616823196411133,2545.93994140625,6753.72998046875,2.8570001125335693,24.520000457763672
2018-12-18,164.10293579101562,48.59319305419922,143.66000366210938,1043.4100341796875,11.654755592346191,2546.159912109375,6783.91015625,2.825000047683716,25.579999923706055
2018-12-19,158.9842987060547,49.119441986083984,133.24000549316406,1035.4599609375,11.588374137878418,2506.9599609375,6636.830078125,2.7780001163482666,25.579999923706055
2018-12-20,154.97239685058594,48.235748291015625,133.39999389648438,1023.5800170898438,11.446126937866211,2467.419921875,6528.41015625,2.7890000343322754,28.3799991607666
2018-12-21,148.9446258544922,46.935028076171875,124.94999694824219,991.25,11.256464958190918,2416.6201171875,6332.990234375,2.7920000553131104,30.110000610351562
2018-12-24,145.09083557128906,,124.05999755859375,984.6699829101562,11.199565887451172,2351.10009765625,6192.919921875,2.749000072479248,36.06999969482422
2018-12-26,155.308349609375,,134.17999267578125,1047.8499755859375,11.56940746307373,2467.699951171875,6554.35986328125,2.796999931335449,30.40999984741211
2018-12-27,154.30044555664062,45.63431167602539,134.52000427246094,1052.9000244140625,11.48405933380127,2488.830078125,6579.490234375,2.743000030517578,29.959999084472656
2018-12-28,154.37948608398438,46.92509841918945,133.1999969482422,1046.6800537109375,11.759069442749023,2485.739990234375,6584.52001953125,2.7360000610351562,28.34000015258789
2018-12-31,155.87161254882812,,131.08999633789062,1044.9599609375,11.740103721618652,2506.85009765625,6635.27978515625,2.686000108718872,25.420000076293945
2019-01-02,156.0494842529297,46.49814987182617,135.67999267578125,1054.6800537109375,11.90131664276123,2510.030029296875,6665.93994140625,2.6610000133514404,23.219999313354492
2019-01-03,140.50579833984375,46.07119369506836,131.74000549316406,1025.469970703125,11.749587059020996,2447.889892578125,6463.5,2.553999900817871,25.450000762939453
2019-01-04,146.50389099121094,48.27546310424805,137.9499969482422,1078.0699462890625,12.1763277053833,2531.93994140625,6738.85986328125,2.6589999198913574,21.3799991607666
2019-01-07,146.1778106689453,48.791778564453125,138.0500030517578,1075.9200439453125,12.29960823059082,2549.68994140625,6823.47021484375,2.681999921798706,21.399999618530273
2019-01-08,148.96438598632812,49.625823974609375,142.52999877929688,1085.3699951171875,12.328057289123535,2574.409912109375,6897.0,2.7160000801086426,20.469999313354492
2019-01-09,151.4940643310547,51.035762786865234,144.22999572753906,1081.6500244140625,12.290124893188477,2584.9599609375,6957.080078125,2.7279999256134033,19.979999542236328
2019-01-10,151.978271484375,51.790374755859375,144.1999969482422,1078.8299560546875,12.42288875579834,2596.639892578125,6986.06982421875,2.7309999465942383,19.5
2019-01-11,150.4861602783203,51.92938232421875,143.8000030517578,1064.469970703125,12.498753547668457,2596.260009765625,6971.47998046875,2.7009999752044678,18.190000534057617
2019-01-14,148.22328186035156,51.63151168823242,145.38999938964844,1051.510009765625,12.574618339538574,2582.610107421875,6905.919921875,2.7100000381469727,19.06999969482422
2019-01-15,151.25692749023438,52.127967834472656,148.9499969482422,1086.510009765625,12.536685943603516,2610.300048828125,7023.830078125,2.7109999656677246,18.600000381469727
2019-01-16,153.1047821044922,53.2598876953125,147.5399932861328,1089.510009765625,12.8401460647583,2616.10009765625,7034.68994140625,2.7309999465942383,19.040000915527344
2019-01-17,154.01388549804688,53.57762145996094,148.3000030517578,1099.1199951171875,12.792730331420898,2635.9599609375,7084.4599609375,2.749000072479248,18.059999465942383
2019-01-18,154.96249389648438,54.66982650756836,150.0399932861328,1107.300048828125,12.906527519226074,2670.7099609375,7157.22998046875,2.7839999198913574,17.799999237060547
2019-01-21,,54.649967193603516,,,,,,,
2019-01-22,151.4842071533203,54.21308517456055,147.57000732421875,1078.6300048828125,12.271158218383789,2632.89990234375,7020.35986328125,2.7300000190734863,20.799999237060547
2019-01-23,152.0968475341797,53.29960632324219,144.3000030517578,1084.4100341796875,12.375473022460938,2638.699951171875,7025.77001953125,2.755000114440918,19.520000457763672
2019-01-24,150.89129638671875,54.19322967529297,145.8300018310547,1084.0,12.394438743591309,2642.330078125,7073.4599609375,2.7119998931884766,18.889999389648438
2019-01-25,155.891357421875,54.35209655761719,149.00999450683594,1101.510009765625,12.42288875579834,2664.760009765625,7164.85986328125,2.753000020980835,17.420000076293945
2019-01-28,154.4486541748047,53.41875457763672,147.47000122070312,1079.8599853515625,12.365989685058594,2643.85009765625,7085.68017578125,2.74399995803833,18.8700008392334
2019-01-29,152.84783935546875,53.67691421508789,144.19000244140625,1070.06005859375,12.337540626525879,2640.0,7028.2900390625,2.7119998931884766,19.1299991607666
2019-01-30,163.2926483154297,54.610252380371094,150.4199981689453,1097.989990234375,12.517719268798828,2681.050048828125,7183.080078125,2.694999933242798,17.65999984741211
2019-01-31,164.46856689453125,55.40458297729492,166.69000244140625,1125.8900146484375,12.290124893188477,2704.10009765625,7281.740234375,2.634999990463257,16.56999969482422
2019-02-01,164.547607421875,55.563446044921875,165.7100067138672,1118.6199951171875,12.204776763916016,2706.530029296875,7263.8701171875,2.690999984741211,16.139999389648438
2019-02-04,169.22158813476562,55.166282653808594,169.25,1141.4200439453125,12.166844367980957,2724.8701171875,7347.5400390625,2.7239999771118164,15.729999542236328
2019-02-05,172.1168670654297,56.774803161621094,171.16000366210938,1151.8699951171875,12.347023963928223,2737.699951171875,7402.080078125,2.7019999027252197,15.569999694824219
2019-02-06,172.17616271972656,56.675514221191406,170.49000549316406,1122.8900146484375,12.318573951721191,2731.610107421875,7375.27978515625,2.7019999027252197,15.380000114440918
2019-02-07,168.91526794433594,53.99464797973633,166.3800048828125,1105.9100341796875,12.109946250915527,2706.050048828125,7288.35009765625,2.6519999504089355,16.3700008392334
2019-02-08,169.1137237548828,53.458473205566406,167.3300018310547,1102.3800048828125,12.024598121643066,2707.8798828125,7298.2001953125,2.631999969482422,15.720000267028809
2019-02-11,168.14117431640625,53.9350700378418,165.7899932861328,1102.1199951171875,12.034080505371094,2709.800048828125,7307.89990234375,2.6610000133514404,15.970000267028809
2019-02-12,169.59007263183594,54.23294448852539,165.0399932861328,1127.5799560546875,12.157361030578613,2744.72998046875,7414.6201171875,2.684000015258789,15.430000305175781
2019-02-13,168.88546752929688,55.30529022216797,164.07000732421875,1128.6300048828125,12.242709159851074,2753.030029296875,7420.3798828125,2.7079999446868896,15.649999618530273
2019-02-14,169.5007781982422,54.66982650756836,163.9499969482422,1129.199951171875,12.024598121643066,2745.72998046875,7426.9501953125,2.6570000648498535,16.219999313354492
2019-02-15,169.1236572265625,55.980472564697266,162.5,1119.6300048828125,12.280641555786133,2775.60009765625,7472.41015625,2.6659998893737793,14.90999984741211
2019-02-18,,55.96061325073242,,,,,,,
2019-02-19,169.6297607421875,55.60316467285156,162.2899932861328,1126.510009765625,12.252192497253418,2779.760009765625,7486.77001953125,2.6470000743865967,14.880000114440918
2019-02-20,170.72140502929688,56.536502838134766,162.55999755859375,1120.5899658203125,11.81596851348877,2784.699951171875,7489.06982421875,2.6540000438690186,14.020000457763672
2019-02-21,169.7587890625,55.32514953613281,160.0399932861328,1104.2099609375,11.69268798828125,2774.8798828125,7459.7099609375,2.687999963760376,14.460000038146973
2019-02-22,171.6542510986328,55.424442291259766,161.88999938964844,1116.56005859375,11.702171325683594,2792.669921875,7527.5400390625,2.6549999713897705,13.510000228881836
2019-02-25,172.90467834472656,55.205997467041016,164.6199951171875,1117.3299560546875,11.81596851348877,2796.110107421875,7554.4599609375,2.6730000972747803,14.850000381469727
2019-02-26,173.00392150878906,54.59039306640625,164.1300048828125,1122.010009765625,11.958215713500977,2793.89990234375,7549.2998046875,2.635999917984009,15.170000076293945
2019-02-27,173.539794921875,53.91521072387695,162.80999755859375,1122.8900146484375,11.996148109436035,2792.3798828125,7554.509765625,2.693000078201294,14.699999809265137
2019-02-28,171.8328857421875,53.71662902832031,161.4499969482422,1126.550048828125,12.015114784240723,2784.489990234375,7532.52978515625,2.7109999656677246,14.779999732971191
2019-03-01,173.6390380859375,53.59748077392578,162.27999877929688,1148.52001953125,12.053047180175781,2803.68994140625,7595.35009765625,2.755000114440918,13.569999694824219
2019-03-04,174.51235961914062,53.65705490112305,167.3699951171875,1153.4200439453125,11.977182388305664,2792.81005859375,7577.56982421875,2.7219998836517334,14.630000114440918
2019-03-05,174.1947784423828,54.252803802490234,171.25999450683594,1169.18994140625,11.844417572021484,2789.64990234375,7576.35986328125,2.7219998836517334,14.739999771118164
2019-03-06,173.19247436523438,55.32514953613281,172.50999450683594,1164.93994140625,11.721137046813965,2771.449951171875,7505.919921875,2.691999912261963,15.739999771118164
2019-03-07,171.18783569335938,54.05421829223633,169.1300048828125,1150.8499755859375,11.389228820800781,2748.929931640625,7421.4599609375,2.635999917984009,16.59000015258789
2019-03-08,171.59471130371094,53.557762145996094,169.60000610351562,1149.969970703125,11.313364028930664,2743.070068359375,7408.14013671875,2.625,16.049999237060547
2019-03-11,177.5391387939453,54.23294448852539,172.07000732421875,1179.260009765625,11.474576950073242,2783.300048828125,7558.06005859375,2.6429998874664307,14.329999923706055
2019-03-12,179.53387451171875,53.59748077392578,171.9199981689453,1197.25,11.48405933380127,2791.52001953125,7591.02978515625,2.6050000190734863,13.770000457763672
2019-03-13,180.32777404785156,54.173370361328125,173.3699951171875,1199.06005859375,11.616823196411133,2810.919921875,7643.41015625,2.6110000610351562,13.40999984741211
2019-03-14,182.3323974609375,54.292518615722656,170.1699981689453,1192.530029296875,11.645272254943848,2808.47998046875,7630.91015625,2.630000114440918,13.5
2019-03-15,184.7042236328125,55.205997467041016,165.97999572753906,1190.300048828125,11.597856521606445,2822.47998046875,7688.52978515625,2.5929999351501465,12.880000114440918
2019-03-18,186.58978271484375,55.48401641845703,160.47000122070312,1188.550048828125,11.863384246826172,2832.93994140625,7714.47998046875,2.6019999980926514,13.100000381469727
2019-03-19,185.11111450195312,57.588993072509766,161.57000732421875,1202.4599609375,11.920283317565918,2832.570068359375,7723.9501953125,2.614000082015991,13.5600004196167
2019-03-20,186.7287139892578,56.973388671875,165.44000244140625,1226.4300537109375,11.683204650878906,2824.22998046875,7728.97021484375,2.5350000858306885,13.90999984741211
2019-03-21,193.60598754882812,57.39040756225586,166.0800018310547,1236.1300048828125,11.607339859008789,2854.8798828125,7838.9599609375,2.5369999408721924,13.630000114440918
2019-03-22,189.5967254638672,55.76203155517578,164.33999633789062,1207.6500244140625,11.265948295593262,2800.7099609375,7642.669921875,2.4549999237060547,16.479999542236328
2019-03-25,187.30429077148438,56.79466247558594,166.2899932861328,1197.3800048828125,11.237499237060547,2798.360107421875,7637.5400390625,2.4200000762939453,16.329999923706055
2019-03-26,185.36912536621094,57.469844818115234,167.67999267578125,1189.8399658203125,11.30388069152832,2818.4599609375,7691.52001953125,2.4140000343322754,14.680000305175781
2019-03-27,187.03634643554688,58.343605041503906,165.8699951171875,1178.010009765625,11.398712158203125,2805.3701171875,7643.3798828125,2.374000072479248,15.149999618530273
2019-03-28,187.28443908691406,57.62871170043945,165.5500030517578,1172.27001953125,11.370262145996094,2815.43994140625,7669.169921875,2.3889999389648438,14.430000305175781
2019-03-29,188.5050811767578,57.48970413208008,166.69000244140625,1176.8900146484375,11.48405933380127,2834.39990234375,7729.31982421875,2.4140000343322754,13.710000038146973
2019-04-01,189.7852783203125,59.137939453125,168.6999969482422,1198.97998046875,11.759069442749023,2867.18994140625,7828.91015625,2.496999979019165,13.399999618530273
2019-04-02,192.5441436767578,58.78049087524414,174.1999969482422,1205.5400390625,11.768552780151367,2867.239990234375,7848.68994140625,2.4809999465942383,13.359999656677246
2019-04-03,193.86402893066406,59.674110412597656,173.5399932861328,1210.81005859375,11.882349967956543,2873.39990234375,7895.5498046875,2.5169999599456787,13.739999771118164
2019-04-04,194.20143127441406,59.634395599365234,176.02000427246094,1219.449951171875,11.939249992370605,2879.389892578125,7891.77978515625,2.51200008392334,13.579999923706055
2019-04-05,195.50146484375,59.89255142211914,175.72000122070312,1211.449951171875,11.910799980163574,2892.739990234375,7938.68994140625,2.500999927520752,12.819999694824219
2019-04-08,198.577880859375,60.15071105957031,174.92999267578125,1208.280029296875,11.882349967956543,2895.77001953125,7953.8798828125,2.5190000534057617,13.180000305175781
2019-04-09,197.98245239257812,59.197509765625,177.5800018310547,1202.68994140625,11.872867584228516,2878.199951171875,7909.27978515625,2.499000072479248,14.279999732971191
2019-04-10,199.0939178466797,58.7606315612793,177.82000732421875,1206.449951171875,11.825451850891113,2888.2099609375,7964.240234375,2.4769999980926514,13.300000190734863
2019-04-11,197.43663024902344,58.87977981567383,177.50999450683594,1209.5899658203125,12.062530517578125,2888.320068359375,7947.35986328125,2.503999948501587,13.020000457763672
2019-04-12,197.35723876953125,59.475528717041016,179.10000610351562,1222.72998046875,12.185811042785645,2907.409912109375,7984.16015625,2.559999942779541,12.010000228881836
2019-04-15,197.71449279785156,59.93227005004883,179.64999389648438,1226.530029296875,12.252192497253418,2905.580078125,7976.009765625,2.552999973297119,12.319999694824219
2019-04-16,197.73434448242188,60.269859313964844,178.8699951171875,1231.9100341796875,12.441854476928711,2907.06005859375,8000.22998046875,2.5920000076293945,12.180000305175781
2019-04-17,201.5848388671875,60.110992431640625,178.77999877929688,1240.1400146484375,12.640999794006348,2900.449951171875,7996.080078125,2.5920000076293945,12.600000381469727
2019-04-18,202.30929565429688,59.8726921081543,178.27999877929688,1241.469970703125,12.574618339538574,2905.030029296875,7998.06005859375,2.559999942779541,12.09000015258789
2019-04-22,202.9741973876953,,181.44000244140625,1253.760009765625,12.574618339538574,2907.969970703125,8015.27001953125,2.5899999141693115,12.420000076293945
2019-04-23,205.90174865722656,59.554962158203125,183.77999877929688,1270.5899658203125,12.413405418395996,2933.679931640625,8120.81982421875,2.569999933242798,12.279999732971191
2019-04-24,205.5841827392578,58.99892807006836,182.5800018310547,1260.050048828125,12.489270210266113,2927.25,8102.009765625,2.5220000743865967,13.140000343322754
2019-04-25,203.71849060058594,58.84006118774414,193.25999450683594,1267.3399658203125,12.612550735473633,2926.169921875,8118.68017578125,2.5339999198913574,13.25
2019-04-26,202.74594116210938,59.634395599365234,191.49000549316406,1277.4200439453125,12.603067398071289,2939.8798828125,8146.39990234375,2.505000114440918,12.729999542236328
2019-04-29,203.0535888671875,59.57482147216797,194.77999877929688,1296.199951171875,12.830662727355957,2943.030029296875,8161.85009765625,2.5360000133514404,13.109999656677246
2019-04-30,199.14353942871094,58.939353942871094,193.39999389648438,1198.9599609375,12.745314598083496,2945.830078125,8095.39013671875,2.509000062942505,13.119999885559082
2019-05-01,208.9186248779297,,193.02999877929688,1173.3199462890625,12.593584060668945,2923.72998046875,8049.64013671875,2.510999917984009,14.800000190734863
2019-05-02,207.55905151367188,58.00601577758789,192.52999877929688,1166.510009765625,12.688416481018066,2917.52001953125,8036.77001953125,2.552000045776367,14.420000076293945
2019-05-03,210.13926696777344,58.7606315612793,195.47000122070312,1189.550048828125,12.640999794006348,2945.639892578125,8164.0,2.5309998989105225,12.869999885559082
2019-05-06,206.89413452148438,56.695369720458984,193.8800048828125,1193.4599609375,12.5600004196167,2932.469970703125,8123.2900390625,2.5,15.4399995803833
2019-05-07,201.31689453125,56.953529357910156,189.77000427246094,1178.8599853515625,12.220000267028809,2884.050048828125,7963.759765625,2.447999954223633,19.31999969482422
2019-05-08,201.35658264160156,56.893951416015625,189.5399932861328,1170.780029296875,12.270000457763672,2879.419921875,7943.31982421875,2.4820001125335693,19.399999618530273
2019-05-09,199.19317626953125,55.364864349365234,188.64999389648438,1167.969970703125,12.119999885559082,2870.719970703125,7910.58984375,2.4570000171661377,19.100000381469727
2019-05-10,196.43365478515625,55.2457160949707,188.33999633789062,1167.6400146484375,12.130000114440918,2881.39990234375,7916.93994140625,2.4549999237060547,16.040000915527344
2019-05-13,185.0170440673828,54.82869338989258,181.5399932861328,1136.5899658203125,11.789999961853027,2811.8701171875,7647.02001953125,2.4049999713897705,20.549999237060547
2019-05-14,187.94590759277344,55.901039123535156,180.72999572753906,1124.8599853515625,11.8100004196167,2834.409912109375,7734.490234375,2.4189999103546143,18.059999465942383
2019-05-15,190.19735717773438,56.4769287109375,186.27000427246094,1170.800048828125,11.789999961853027,2850.9599609375,7822.14990234375,2.378999948501587,16.440000534057617
2019-05-16,189.36053466796875,57.84714889526367,186.99000549316406,1184.5,11.989999771118164,2876.320068359375,7898.0498046875,2.4049999713897705,15.289999961853027
2019-05-17,188.2846221923828,57.64856719970703,185.3000030517578,1168.780029296875,11.960000038146973,2859.530029296875,7816.27978515625,2.3929998874664307,15.960000038146973
2019-05-20,182.39698791503906,56.71522903442383,182.72000122070312,1144.6600341796875,12.0,2840.22998046875,7702.3798828125,2.4159998893737793,16.309999465942383
2019-05-21,185.89370727539062,56.81452178955078,184.82000732421875,1154.43994140625,12.039999961853027,2864.360107421875,7785.72021484375,2.4260001182556152,14.949999809265137
2019-05-22,182.08816528320312,56.735084533691406,185.32000732421875,1155.8499755859375,11.90999984741211,2856.27001953125,7750.83984375,2.3929998874664307,14.75
2019-05-23,178.97998046875,55.364864349365234,180.8699951171875,1145.3399658203125,11.770000457763672,2822.239990234375,7628.27978515625,2.2960000038146973,16.920000076293945
2019-05-24,178.29258728027344,56.11948013305664,181.05999755859375,1138.6099853515625,11.90999984741211,2826.06005859375,7637.009765625,2.3239998817443848,15.850000381469727
2019-05-27,,59.554962158203125,,,,,,,
2019-05-28,177.55538940429688,59.37623596191406,184.30999755859375,1139.56005859375,11.65999984741211,2802.389892578125,7607.35009765625,2.2679998874664307,17.5
2019-05-29,176.70860290527344,58.28403091430664,182.19000244140625,1119.93994140625,11.619999885559082,2783.02001953125,7547.31005859375,2.2360000610351562,17.899999618530273
2019-05-30,177.6251220703125,57.29111862182617,183.00999450683594,1121.4100341796875,11.569999694824219,2788.860107421875,7567.72021484375,2.2269999980926514,17.299999237060547
2019-05-31,174.4073486328125,55.82160568237305,177.47000122070312,1106.5,11.430000305175781,2752.06005859375,7453.14990234375,2.1419999599456787,18.709999084472656
2019-06-03,172.64404296875,55.22585678100586,164.14999389648438,1038.739990234375,11.470000267028809,2744.449951171875,7333.02001953125,2.0810000896453857,18.860000610351562
2019-06-04,178.96005249023438,57.48970413208008,167.5,1054.489990234375,11.770000457763672,2803.27001953125,7527.1201171875,2.11899995803833,16.969999313354492
2019-06-05,181.84906005859375,57.4102668762207,168.1699981689453,1044.6400146484375,11.75,2826.14990234375,7575.47998046875,2.122999906539917,16.09000015258789
2019-06-06,184.51893615722656,57.4102668762207,168.3300018310547,1047.760009765625,11.770000457763672,2843.489990234375,7615.5498046875,2.124000072479248,15.930000305175781
2019-06-07,189.43026733398438,58.303890228271484,173.35000610351562,1068.3699951171875,11.800000190734863,2873.340087890625,7742.10009765625,2.0840001106262207,16.299999237060547
2019-06-10,191.85107421875,58.581905364990234,174.82000732421875,1082.760009765625,11.859999656677246,2886.72998046875,7823.169921875,2.1429998874664307,15.9399995803833
2019-06-11,194.0726318359375,59.29680633544922,178.10000610351562,1081.0400390625,11.920000076293945,2885.719970703125,7822.56982421875,2.140000104904175,15.989999771118164
2019-06-12,193.45498657226562,59.0187873840332,175.0399932861328,1079.0999755859375,11.739999771118164,2879.840087890625,7792.72021484375,2.127000093460083,15.90999984741211
2019-06-13,193.4151153564453,59.435813903808594,177.47000122070312,1091.010009765625,11.6899995803833,2891.639892578125,7837.1298828125,2.0910000801086426,15.819999694824219
2019-06-14,192.01046752929688,58.78049087524414,181.3300018310547,1086.300048828125,11.539999961853027,2886.97998046875,7796.66015625,2.0929999351501465,15.279999732971191
2019-06-17,193.15611267089844,59.03864669799805,189.00999450683594,1093.8900146484375,11.479999542236328,2889.669921875,7845.02001953125,2.0859999656677246,15.350000381469727
2019-06-18,197.6988525390625,60.42872619628906,188.47000122070312,1105.239990234375,11.670000076293945,2917.75,7953.8798828125,2.059999942779541,15.149999618530273
2019-06-19,197.1210479736328,60.25,187.47999572753906,1104.510009765625,11.760000228881836,2926.4599609375,7987.31982421875,2.0290000438690186,14.329999923706055
2019-06-20,198.7050323486328,60.42872619628906,189.52999877929688,1113.199951171875,11.760000228881836,2954.179931640625,8051.33984375,2.000999927520752,14.75
2019-06-21,198.02760314941406,60.25,191.13999938964844,1125.3699951171875,11.640000343322754,2950.4599609375,8031.7099609375,2.068000078201294,15.399999618530273
2019-06-24,197.828369140625,60.63999938964844,192.60000610351562,1116.699951171875,11.65999984741211,2945.35009765625,8005.7001953125,2.0209999084472656,15.260000228881836
2019-06-25,194.8297576904297,60.34000015258789,188.83999633789062,1087.5799560546875,11.579999923706055,2917.3798828125,7884.72021484375,1.99399995803833,16.280000686645508
2019-06-26,199.0437469482422,60.040000915527344,187.66000366210938,1080.3199462890625,11.680000305175781,2913.780029296875,7909.97021484375,2.0490000247955322,16.209999084472656
2019-06-27,198.98397827148438,61.400001525878906,189.5,1076.6300048828125,11.84000015258789,2924.919921875,7967.759765625,2.005000114440918,15.819999694824219
2019-06-28,197.1708526611328,61.599998474121094,193.0,1082.800048828125,11.850000381469727,2941.760009765625,8006.240234375,2.0,15.079999923706055
2019-07-01,200.78712463378906,62.08000183105469,193.0,1100.0,11.899999618530273,2964.330078125,8091.16015625,2.0339999198913574,14.0600004196167
2019-07-02,201.962646484375,62.599998474121094,195.0,1112.5999755859375,11.930000305175781,2973.010009765625,8109.08984375,1.9759999513626099,12.930000305175781
2019-07-03,203.63629150390625,63.939998626708984,197.1999969482422,1122.989990234375,11.989999771118164,2995.820068359375,8170.22998046875,1.9529999494552612,12.569999694824219
2019-07-04,,64.4000015258789,,,,,,,
2019-07-05,203.45697021484375,63.7599983215332,196.39999389648438,1132.6700439453125,12.140000343322754,2990.409912109375,8161.7900390625,2.0480000972747803,13.279999732971191
2019-07-08,199.26290893554688,63.900001525878906,195.75999450683594,1116.7900390625,11.960000038146973,2975.949951171875,8098.3798828125,2.0339999198913574,13.960000038146973
2019-07-09,200.47830200195312,63.2599983215332,199.2100067138672,1124.2900390625,11.880000114440918,2979.6298828125,8141.72998046875,2.053999900817871,14.09000015258789
2019-07-10,202.46075439453125,63.84000015258789,202.72999572753906,1140.9100341796875,12.020000457763672,2993.070068359375,8202.5302734375,2.061000108718872,13.029999732971191
2019-07-11,200.98635864257812,63.220001220703125,201.22999572753906,1144.0799560546875,12.15999984741211,2999.909912109375,8196.0400390625,2.119999885559082,12.930000305175781
2019-07-12,202.53050231933594,63.2599983215332,204.8699951171875,1145.3399658203125,12.220000267028809,3013.77001953125,8244.1396484375,2.1059999465942383,12.390000343322754
2019-07-15,204.4332733154297,63.599998474121094,203.91000366210938,1150.510009765625,12.149999618530273,3014.300048828125,8258.1904296875,2.0920000076293945,12.680000305175781
2019-07-16,203.7259521484375,63.34000015258789,203.83999633789062,1153.4599609375,12.109999656677246,3004.0400390625,8222.7998046875,2.121999979019165,12.859999656677246
2019-07-17,202.58030700683594,62.97999954223633,201.8000030517578,1146.739990234375,12.039999961853027,2984.419921875,8185.2099609375,2.061000108718872,13.970000267028809
2019-07-18,204.88156127929688,62.939998626708984,200.77999877929688,1147.239990234375,12.130000114440918,2995.110107421875,8207.240234375,2.0380001068115234,13.529999732971191
2019-07-19,201.82318115234375,62.15999984741211,198.36000061035156,1131.550048828125,11.970000267028809,2976.610107421875,8146.490234375,2.0480000972747803,14.449999809265137
2019-07-22,206.43565368652344,62.5,202.32000732421875,1139.2099609375,12.109999656677246,2985.030029296875,8204.1396484375,2.0429999828338623,13.529999732971191
2019-07-23,208.0495147705078,63.400001525878906,202.36000061035156,1148.050048828125,12.289999961853027,3005.469970703125,8251.400390625,2.0739998817443848,12.609999656677246
2019-07-24,207.8801727294922,64.9000015258789,204.66000366210938,1139.72998046875,11.84000015258789,3019.56005859375,8321.5,2.049999952316284,12.069999694824219
2019-07-25,206.23641967773438,63.68000030517578,200.7100067138672,1135.93994140625,11.460000038146973,3003.669921875,8238.5400390625,2.0739998817443848,12.739999771118164
2019-07-26,206.9536895751953,64.0,199.75,1245.219970703125,11.380000114440918,3025.860107421875,8330.2099609375,2.0810000896453857,12.15999984741211
2019-07-29,208.88633728027344,63.959999084472656,195.94000244140625,1241.8399658203125,11.359999656677246,3020.969970703125,8293.330078125,2.055000066757202,12.829999923706055
2019-07-30,207.98974609375,61.880001068115234,197.0399932861328,1228.0,11.25,3013.179931640625,8273.6103515625,2.061000108718872,13.9399995803833
2019-07-31,212.23362731933594,62.959999084472656,194.22999572753906,1218.199951171875,11.170000076293945,2980.3798828125,8175.419921875,2.0209999084472656,16.1200008392334
2019-08-01,207.6410675048828,63.91999816894531,192.72999572753906,1211.780029296875,10.859999656677246,2953.56005859375,8111.1201171875,1.8940000534057617,17.8700008392334
2019-08-02,203.24777221679688,61.2400016784668,189.02000427246094,1196.3199462890625,11.119999885559082,2932.050048828125,8004.06982421875,1.8550000190734863,17.610000610351562
2019-08-05,192.60818481445312,59.880001068115234,181.72999572753906,1154.75,10.890000343322754,2844.739990234375,7726.0400390625,1.7350000143051147,24.59000015258789
2019-08-06,196.25433349609375,59.31999969482422,184.50999450683594,1171.0799560546875,10.989999771118164,2881.77001953125,7833.27001953125,1.7389999628067017,20.170000076293945
2019-08-07,198.28660583496094,59.15999984741211,185.14999389648438,1175.9100341796875,10.8100004196167,2883.97998046875,7862.830078125,1.684000015258789,19.489999771118164
2019-08-08,202.6599884033203,60.040000915527344,190.16000366210938,1206.18994140625,10.869999885559082,2938.090087890625,8039.16015625,1.715999960899353,16.90999984741211
2019-08-09,200.99000549316406,58.720001220703125,187.85000610351562,1188.9000244140625,10.880000114440918,2918.64990234375,7959.14013671875,1.7339999675750732,17.969999313354492
2019-08-12,200.47999572753906,58.81999969482422,185.3699951171875,1174.5,10.640000343322754,2882.699951171875,7863.41015625,1.6390000581741333,21.09000015258789
2019-08-13,208.97000122070312,59.380001068115234,188.4499969482422,1196.72998046875,10.630000114440918,2926.320068359375,8016.35986328125,1.6799999475479126,17.520000457763672
2019-08-14,202.75,57.86000061035156,179.7100067138672,1164.25,10.15999984741211,2840.60009765625,7773.93994140625,1.5809999704360962,22.100000381469727
2019-08-15,201.74000549316406,,182.58999633789062,1169.3199462890625,10.15999984741211,2847.60009765625,7766.6201171875,1.5290000438690186,21.18000030517578
2019-08-16,206.5,57.15999984741211,183.6999969482422,1179.2099609375,10.399999618530273,2888.679931640625,7895.990234375,1.5390000343322754,18.469999313354492
2019-08-19,210.35000610351562,58.560001373291016,186.1699981689453,1200.43994140625,10.479999542236328,2923.64990234375,8002.81005859375,1.5980000495910645,16.8799991607666
2019-08-20,210.36000061035156,58.41999816894531,183.80999755859375,1183.530029296875,10.40999984741211,2900.510009765625,7948.56005859375,1.5609999895095825,17.5
2019-08-21,212.63999938964844,60.79999923706055,183.5500030517578,1191.5799560546875,10.470000267028809,2924.429931640625,8020.2099609375,1.5770000219345093,15.800000190734863
2019-08-22,212.4600067138672,60.08000183105469,182.0399932861328,1191.52001953125,10.630000114440918,2922.949951171875,7991.39013671875,1.6100000143051147,16.68000030517578
2019-08-23,202.63999938964844,58.880001068115234,177.75,1153.5799560546875,10.479999542236328,2847.110107421875,7751.77001953125,1.527999997138977,19.8700008392334
2019-08-26,206.49000549316406,59.459999084472656,180.36000061035156,1171.1800537109375,10.59000015258789,2878.3798828125,7853.740234375,1.5449999570846558,19.31999969482422
2019-08-27,204.16000366210938,60.08000183105469,181.3000030517578,1170.8199462890625,10.479999542236328,2869.159912109375,7826.9501953125,1.4900000095367432,20.309999465942383
2019-08-28,205.52999877929688,59.86000061035156,181.75999450683594,1173.75,10.510000228881836,2887.93994140625,7856.8798828125,1.465999960899353,19.350000381469727
2019-08-29,209.00999450683594,60.70000076293945,185.57000732421875,1194.239990234375,10.579999923706055,2924.580078125,7973.39013671875,1.5160000324249268,17.8799991607666
2019-08-30,208.74000549316406,61.5,185.6699981689453,1190.530029296875,10.600000381469727,2926.4599609375,7962.8798828125,1.50600004196167,18.979999542236328
2019-09-02,,61.84000015258789,,,,,,,
2019-09-03,205.6999969482422,61.619998931884766,182.38999938964844,1169.550048828125,10.479999542236328,2906.27001953125,7874.16015625,1.465999960899353,19.65999984741211
2019-09-04,209.19000244140625,62.2599983215332,187.13999938964844,1182.27001953125,10.59000015258789,2937.780029296875,7976.8798828125,1.4589999914169312,17.329999923706055
2019-09-05,213.27999877929688,63.939998626708984,190.89999389648438,1212.18994140625,10.8100004196167,2976.0,8116.830078125,1.565000057220459,16.270000457763672
2019-09-06,213.25999450683594,64.5199966430664,187.49000549316406,1206.3199462890625,10.880000114440918,2978.7099609375,8103.06982421875,1.5499999523162842,15.0
2019-09-09,214.1699981689453,64.76000213623047,188.75999450683594,1205.27001953125,11.140000343322754,2978.429931640625,8087.43994140625,1.621999979019165,15.270000457763672
2019-09-10,216.6999969482422,63.65999984741211,186.1699981689453,1205.699951171875,11.489999771118164,2979.389892578125,8084.16015625,1.7020000219345093,15.199999809265137
2019-09-11,223.58999633789062,63.15999984741211,188.49000549316406,1220.0,11.579999923706055,3000.929931640625,8169.68017578125,1.7330000400543213,14.609999656677246
2019-09-12,223.08999633789062,63.119998931884766,187.47000122070312,1234.969970703125,11.529999732971191,3009.570068359375,8194.4697265625,1.7910000085830688,14.220000267028809
2019-09-13,218.75,63.31999969482422,187.19000244140625,1240.030029296875,11.729999542236328,3007.389892578125,8176.7099609375,1.902999997138977,13.739999771118164
2019-09-16,219.89999389648438,63.2599983215332,186.22000122070312,1231.6300048828125,11.579999923706055,2997.9599609375,8153.5400390625,1.840999960899353,14.670000076293945
2019-09-17,220.6999969482422,62.560001373291016,188.0800018310547,1229.8800048828125,11.4399995803833,3005.699951171875,8186.02001953125,1.812000036239624,14.4399995803833
2019-09-18,222.77000427246094,63.119998931884766,188.13999938964844,1232.6500244140625,11.4399995803833,3006.72998046875,8177.39013671875,1.7860000133514404,13.949999809265137
2019-09-19,220.9600067138672,63.599998474121094,190.13999938964844,1238.75,11.520000457763672,3006.7900390625,8182.8798828125,1.7740000486373901,14.050000190734863
2019-09-20,217.72999572753906,63.13999938964844,189.92999267578125,1229.8399658203125,11.449999809265137,2992.070068359375,8117.669921875,1.7549999952316284,15.319999694824219
2019-09-23,218.72000122070312,61.040000915527344,186.82000732421875,1234.68994140625,11.350000381469727,2991.780029296875,8112.4599609375,1.7079999446868896,14.90999984741211
2019-09-24,217.67999267578125,60.400001525878906,181.27999877929688,1218.3299560546875,11.229999542236328,2966.60009765625,7993.6298828125,1.6349999904632568,17.049999237060547
2019-09-25,221.02999877929688,60.15999984741211,182.8000030517578,1245.93994140625,11.229999542236328,2984.8701171875,8077.3798828125,1.7319999933242798,15.960000038146973
2019-09-26,219.88999938964844,61.20000076293945,180.11000061035156,1242.2900390625,11.210000038146973,2977.6201171875,8030.66015625,1.684999942779541,16.06999969482422
2019-09-27,218.82000732421875,61.52000045776367,177.10000610351562,1225.949951171875,11.319999694824219,2961.7900390625,7939.6298828125,1.6749999523162842,17.219999313354492
2019-09-30,223.97000122070312,61.47999954223633,178.0800018310547,1221.1400146484375,11.3100004196167,2976.739990234375,7999.33984375,1.6749999523162842,16.239999771118164
2019-10-01,224.58999633789062,60.439998626708984,175.80999755859375,1206.0,11.140000343322754,2940.25,7908.68017578125,1.6440000534057617,18.559999465942383
2019-10-02,218.9600067138672,58.34000015258789,174.60000610351562,1177.9200439453125,10.729999542236328,2887.610107421875,7785.25,1.5959999561309814,20.559999465942383
2019-10-03,220.82000732421875,58.34000015258789,179.3800048828125,1189.4300537109375,10.630000114440918,2910.6298828125,7872.259765625,1.5360000133514404,19.1200008392334
2019-10-04,227.00999450683594,58.619998931884766,180.4499969482422,1210.9599609375,10.729999542236328,2952.010009765625,7982.47021484375,1.5149999856948853,17.040000915527344
2019-10-07,227.05999755859375,58.91999816894531,179.67999267578125,1208.25,10.680000305175781,2938.7900390625,7956.2900390625,1.5529999732971191,17.860000610351562
2019-10-08,224.39999389648438,58.2400016784668,177.75,1190.1300048828125,10.59000015258789,2893.06005859375,7823.77978515625,1.5369999408721924,20.280000686645508
2019-10-09,227.02999877929688,58.540000915527344,179.85000610351562,1202.4000244140625,10.579999923706055,2919.39990234375,7903.740234375,1.5889999866485596,18.639999389648438
2019-10-10,230.08999633789062,59.52000045776367,180.02999877929688,1209.469970703125,10.779999732971191,2938.1298828125,7950.77978515625,1.6579999923706055,17.56999969482422
2019-10-11,236.2100067138672,61.15999984741211,184.19000244140625,1215.7099609375,11.100000381469727,2970.27001953125,8057.0400390625,1.752000093460083,15.579999923706055
2019-10-14,235.8699951171875,61.13999938964844,183.27999877929688,1217.77001953125,11.050000190734863,2966.14990234375,8048.64990234375,1.7330000400543213,14.569999694824219
2019-10-15,235.32000732421875,61.7599983215332,188.88999938964844,1242.239990234375,11.260000228881836,2995.679931640625,8148.7099609375,1.7709999084472656,13.539999961853027
2019-10-16,234.3699951171875,62.220001220703125,189.5500030517578,1243.0,11.180000305175781,2989.68994140625,8124.18017578125,1.747999906539917,13.680000305175781
2019-10-17,235.27999877929688,62.02000045776367,190.38999938964844,1252.800048828125,11.260000228881836,2997.949951171875,8156.85009765625,1.7569999694824219,13.789999961853027
2019-10-18,236.41000366210938,61.599998474121094,185.85000610351562,1244.4100341796875,11.350000381469727,2986.199951171875,8089.5400390625,1.746999979019165,14.25
2019-10-21,240.50999450683594,62.959999084472656,189.75999450683594,1244.280029296875,11.539999961853027,3006.719970703125,8162.990234375,1.7920000553131104,14.0
2019-10-22,239.9600067138672,62.2599983215332,182.33999633789062,1241.199951171875,11.5,2995.989990234375,8104.2998046875,1.7680000066757202,14.460000038146973
2019-10-23,243.17999267578125,61.36000061035156,186.14999389648438,1257.6300048828125,11.640000343322754,3004.52001953125,8119.7900390625,1.7589999437332153,14.010000228881836
2019-10-24,243.5800018310547,61.65999984741211,186.3800048828125,1259.1099853515625,11.6899995803833,3010.2900390625,8185.7998046875,1.7660000324249268,13.710000038146973
2019-10-25,246.5800018310547,62.13999938964844,187.88999938964844,1264.300048828125,11.770000457763672,3022.550048828125,8243.1201171875,1.8009999990463257,12.649999618530273
2019-10-28,249.0500030517578,62.939998626708984,189.39999389648438,1288.97998046875,11.949999809265137,3039.419921875,8325.990234375,1.8530000448226929,13.109999656677246
2019-10-29,243.2899932861328,62.15999984741211,189.30999755859375,1260.6600341796875,12.079999923706055,3036.889892578125,8276.849609375,1.8350000381469727,13.199999809265137
2019-10-30,243.25999450683594,65.0199966430664,188.25,1260.699951171875,11.84000015258789,3046.77001953125,8303.98046875,1.7979999780654907,12.329999923706055
2019-10-31,248.75999450683594,68.72000122070312,191.64999389648438,1258.800048828125,11.770000457763672,3037.56005859375,8292.3603515625,1.690999984741211,13.220000267028809
2019-11-01,255.82000732421875,69.45999908447266,193.6199951171875,1272.25,11.960000038146973,3066.909912109375,8386.400390625,1.7280000448226929,12.300000190734863
================================================
FILE: data/spy-options-exp-2020-07-10-weekly-show-all-stacked-07-05-2020.csv
================================================
Strike,Last,% From Last,Bid,Midpoint,Ask,Change,%Chg,IV,Volume,Open Int,Type,Time
155,154.98,-50.36%,157.68,157.9,158.12,154.98,unch,235.16%,2,1,Call,06/22/20
160,0,-48.76%,152.68,152.9,153.12,0,unch,225.46%,0,0,Call,N/A
165,0,-47.15%,147.68,147.9,148.12,0,unch,216.04%,0,0,Call,N/A
170,0,-45.55%,142.68,142.9,143.12,0,unch,206.90%,0,0,Call,N/A
175,0,-43.95%,137.68,137.94,138.19,0,unch,199.79%,0,0,Call,N/A
180,0,-42.35%,132.68,132.9,133.12,0,unch,189.37%,0,0,Call,N/A
185,0,-40.75%,127.68,127.9,128.12,0,unch,180.95%,0,0,Call,N/A
190,0,-39.15%,122.68,122.91,123.14,0,unch,173.20%,0,0,Call,N/A
195,0,-37.55%,117.68,117.91,118.13,0,unch,164.95%,0,0,Call,N/A
200,102.05,-35.94%,112.68,112.9,113.12,-0.34,-0.33%,156.89%,10,30,Call,06/29/20
205,114.33,-34.34%,107.68,107.91,108.13,114.33,unch,149.44%,0,0,Call,N/A
210,0,-32.74%,102.68,102.91,103.13,0,unch,141.93%,0,0,Call,N/A
215,96.3,-31.14%,97.69,97.91,98.14,96.3,unch,134.96%,0,1,Call,N/A
220,0,-29.54%,92.69,92.91,93.13,0,unch,127.55%,0,0,Call,N/A
225,0,-27.94%,87.69,87.92,88.15,0,unch,120.81%,0,0,Call,N/A
230,0,-26.34%,82.7,82.93,83.15,0,unch,113.99%,0,0,Call,N/A
235,75,-24.73%,77.7,77.92,78.14,3,+4.17%,106.96%,1,4,Call,07/01/20
240,67.13,-23.13%,72.71,72.93,73.15,67.13,unch,100.50%,1,1,Call,06/15/20
245,59.24,-21.53%,67.71,67.93,68.15,59.24,unch,93.82%,2,2,Call,06/24/20
250,55.06,-19.93%,62.72,62.95,63.18,1.91,+3.59%,87.76%,2,6,Call,06/30/20
255,60.2,-18.33%,57.73,57.95,58.17,4.7,+8.47%,116.85%,2,5,Call,07/02/20
260,0,-16.73%,52.74,52.96,53.18,0,unch,74.97%,0,0,Call,N/A
265,0,-15.13%,47.75,47.97,48.19,0,unch,68.75%,0,0,Call,N/A
270,43.96,-13.53%,42.78,42.99,43.2,3.71,+9.22%,77.88%,1,22,Call,07/02/20
271,0,-13.21%,41.78,41.99,42.2,0,unch,61.46%,0,0,Call,N/A
272,41.72,-12.88%,40.77,40.99,41.21,41.72,unch,71.77%,1,0,Call,07/02/20
273,0,-12.56%,39.78,40,40.23,0,unch,59.18%,0,0,Call,N/A
274,38.86,-12.24%,38.78,39,39.22,2.12,+5.77%,55.03%,2,1,Call,07/02/20
275,35.75,-11.92%,37.79,38,38.22,4.75,+15.32%,56.61%,2,39,Call,07/01/20
276,26.17,-11.60%,36.8,37.02,37.24,-0.17,-0.65%,55.59%,1,26,Call,06/29/20
277,28.07,-11.28%,35.8,36.02,36.23,-5.56,-16.53%,54.21%,2,2,Call,06/25/20
278,29.71,-10.96%,34.81,35.03,35.25,1.04,+3.63%,53.18%,8,8,Call,06/25/20
279,32.84,-10.64%,33.81,34.03,34.24,4.07,+14.15%,51.81%,20,15,Call,07/01/20
280,34.42,-10.32%,32.82,33.04,33.25,2.57,+8.07%,68.02%,7,190,Call,07/02/20
281,21.8,-10.00%,31.82,32.04,32.26,-0.21,-0.95%,49.46%,19,13,Call,06/29/20
282,33.06,-9.68%,30.83,31.05,31.26,11.93,+56.46%,71.03%,1,11,Call,07/02/20
283,27.42,-9.36%,29.84,30.06,30.27,5.67,+26.07%,47.10%,1,7,Call,07/01/20
284,29.79,-9.04%,28.85,29.07,29.28,5.77,+24.02%,55.21%,5,11,Call,07/02/20
285,28.77,-8.72%,27.87,28.09,28.3,2.77,+10.65%,53.52%,21,76,Call,07/02/20
286,27.61,-8.40%,26.88,27.09,27.3,7.15,+34.95%,50.27%,1,45,Call,07/02/20
287,24.88,-8.08%,25.9,26.11,26.32,7.08,+39.78%,42.64%,25,67,Call,07/01/20
288,24.79,-7.76%,24.91,25.12,25.33,1.16,+4.91%,36.43%,1,63,Call,07/02/20
289,25.5,-7.44%,23.94,24.15,24.35,6.23,+32.33%,54.60%,10,124,Call,07/02/20
290,22.53,-7.12%,22.97,23.17,23.37,0.23,+1.03%,28.70%,130,1051,Call,07/02/20
291,18.72,-6.80%,21.99,22.2,22.4,3.06,+19.54%,38.36%,11,75,Call,06/30/20
292,23.05,-6.48%,21.02,21.23,21.43,5.35,+30.23%,54.29%,1,224,Call,07/02/20
293,20.76,-6.16%,20.06,20.27,20.47,2.7,+14.95%,41.54%,40,802,Call,07/02/20
294,20.39,-5.84%,19.1,19.3,19.5,2.74,+15.52%,45.59%,12,581,Call,07/02/20
295,18.3,-5.52%,18.15,18.34,18.53,1.88,+11.45%,33.98%,74,749,Call,07/02/20
296,17.46,-5.20%,17.21,17.4,17.59,1.79,+11.42%,34.16%,26,362,Call,07/02/20
297,16.13,-4.88%,16.26,16.45,16.64,1.24,+8.33%,29.34%,3,368,Call,07/02/20
298,14.96,-4.56%,15.34,15.53,15.71,0.53,+3.67%,26.05%,50,660,Call,07/02/20
299,13.77,-4.24%,14.41,14.59,14.78,0.9,+6.99%,22.40%,63,1027,Call,07/02/20
300,13.4,-3.92%,13.5,13.68,13.86,1.64,+13.95%,27.65%,405,1256,Call,07/02/20
301,13.9,-3.60%,12.65,12.78,12.9,2.95,+26.94%,37.50%,36,964,Call,07/02/20
302,11.3,-3.28%,11.75,11.88,12.01,0.43,+3.96%,23.74%,128,1257,Call,07/02/20
303,10.93,-2.96%,10.88,11,11.12,1.39,+14.57%,27.12%,349,2706,Call,07/02/20
304,9.93,-2.64%,10.02,10.13,10.23,1.17,+13.36%,25.41%,372,3194,Call,07/02/20
305,9.24,-2.32%,9.23,9.29,9.34,1.15,+14.22%,25.77%,519,3455,Call,07/02/20
306,8.33,-2.00%,8.4,8.46,8.51,1.12,+15.53%,24.51%,2830,2371,Call,07/02/20
307,7.58,-1.68%,7.59,7.65,7.71,1.2,+18.81%,24.15%,361,1624,Call,07/02/20
308,6.8,-1.35%,6.81,6.86,6.9,1,+17.24%,23.45%,643,1666,Call,07/02/20
309,6.08,-1.03%,6.06,6.1,6.13,0.96,+18.75%,22.95%,582,1776,Call,07/02/20
310,5.41,-0.71%,5.35,5.38,5.41,0.95,+21.30%,22.55%,9307,5297,Call,07/02/20
311,4.73,-0.39%,4.65,4.69,4.72,0.86,+22.22%,21.91%,1896,5228,Call,07/02/20
312,4.06,-0.07%,4.01,4.04,4.07,0.76,+23.03%,21.13%,8491,5568,Call,07/02/20
313,3.43,+0.25%,3.4,3.44,3.47,0.56,+19.51%,20.35%,3735,4812,Call,07/02/20
314,2.9,+0.57%,2.85,2.88,2.9,0.49,+20.33%,19.88%,9866,4792,Call,07/02/20
315,2.35,+0.89%,2.34,2.37,2.4,0.37,+18.69%,19.04%,20733,15901,Call,07/02/20
316,1.91,+1.21%,1.89,1.92,1.95,0.25,+15.06%,18.54%,7446,3717,Call,07/02/20
317,1.56,+1.53%,1.51,1.54,1.56,0.19,+13.87%,18.28%,6818,4313,Call,07/02/20
318,1.19,+1.85%,1.18,1.2,1.22,0.09,+8.18%,17.59%,5410,2882,Call,07/02/20
319,0.92,+2.17%,0.92,0.94,0.95,0,unch,17.23%,6387,2321,Call,07/02/20
320,0.71,+2.49%,0.71,0.72,0.72,-0.03,-4.05%,17.01%,42618,11969,Call,07/02/20
321,0.52,+2.81%,0.52,0.54,0.55,-0.06,-10.34%,16.61%,4372,1821,Call,07/02/20
322,0.38,+3.13%,0.39,0.4,0.41,-0.09,-19.15%,16.35%,3498,7159,Call,07/02/20
323,0.3,+3.45%,0.29,0.3,0.31,-0.09,-23.08%,16.50%,2905,1624,Call,07/02/20
324,0.23,+3.77%,0.21,0.22,0.23,-0.09,-28.13%,16.56%,2713,3120,Call,07/02/20
325,0.17,+4.09%,0.16,0.17,0.18,-0.09,-34.62%,16.52%,40153,24108,Call,07/02/20
326,0.13,+4.41%,0.12,0.13,0.13,-0.07,-35.00%,16.63%,2639,1842,Call,07/02/20
327,0.09,+4.73%,0.09,0.1,0.1,-0.09,-50.00%,16.47%,1376,2215,Call,07/02/20
328,0.07,+5.05%,0.07,0.08,0.08,-0.08,-53.33%,16.67%,1387,1777,Call,07/02/20
329,0.04,+5.37%,0.05,0.06,0.06,-0.1,-71.43%,16.14%,1241,1279,Call,07/02/20
330,0.04,+5.69%,0.04,0.05,0.05,-0.06,-60.00%,16.94%,5149,6136,Call,07/02/20
331,0.03,+6.01%,0.03,0.04,0.04,-0.07,-70.00%,17.07%,1948,3155,Call,07/02/20
332,0.02,+6.33%,0.02,0.03,0.03,-0.08,-80.00%,16.97%,779,194,Call,07/02/20
333,0.03,+6.65%,0.02,0.03,0.03,-0.03,-50.00%,18.60%,1016,2309,Call,07/02/20
334,0.02,+6.97%,0.01,0.02,0.02,-0.03,-60.00%,18.43%,737,134,Call,07/02/20
335,0.02,+7.29%,0.01,0.02,0.02,-0.03,-60.00%,19.15%,1676,3255,Call,07/02/20
336,0.02,+7.61%,0.01,0.02,0.02,-0.03,-60.00%,19.86%,499,916,Call,07/02/20
337,0.01,+7.93%,0,0.01,0.01,-0.04,-80.00%,19.08%,283,737,Call,07/02/20
338,0.01,+8.25%,0,0.01,0.01,-0.02,-66.67%,19.75%,46,599,Call,07/02/20
339,0.02,+8.57%,0,0.01,0.01,-0.02,-50.00%,21.98%,31,180,Call,07/02/20
340,0.01,+8.89%,0.01,0.02,0.02,-0.02,-66.67%,21.06%,244,2124,Call,07/02/20
341,0.01,+9.21%,0,0.01,0.01,-0.01,-50.00%,21.71%,217,712,Call,07/02/20
342,0.02,+9.53%,0,0.01,0.01,-0.01,-33.33%,24.05%,5,396,Call,07/02/20
343,0.01,+9.85%,0,0.01,0.01,-0.01,-50.00%,23.01%,1,618,Call,07/02/20
344,0.01,+10.18%,0,0.01,0.01,-0.01,-50.00%,23.65%,21,1003,Call,07/02/20
345,0.01,+10.50%,0,0.01,0.01,-0.01,-50.00%,24.28%,182,5391,Call,07/02/20
346,0.01,+10.82%,0,0.01,0.01,-0.01,-50.00%,24.91%,21,851,Call,07/02/20
347,0.01,+11.14%,0,0.01,0.01,-0.01,-50.00%,25.54%,19,645,Call,07/02/20
348,0.02,+11.46%,0,0.01,0.01,0,unch,0.00%,25,857,Call,07/01/20
349,0.01,+11.78%,0,0.01,0.01,-0.01,-50.00%,26.79%,3,776,Call,07/02/20
350,0.01,+12.10%,0,0.01,0.01,0,unch,27.41%,1,1679,Call,07/02/20
351,0.01,+12.42%,0,0.01,0.01,0,unch,0.00%,1,37,Call,07/01/20
352,0.02,+12.74%,0,0.01,0.01,0.01,+100.00%,0.00%,22,308,Call,07/01/20
353,0.01,+13.06%,0,0.01,0.01,-0.01,-50.00%,0.00%,23,114,Call,07/01/20
355,0.01,+13.70%,0,0.01,0.01,-0.02,-66.67%,0.00%,108,1311,Call,06/24/20
360,0.01,+15.30%,0,0.01,0.01,-0.01,-50.00%,0.00%,1,2252,Call,06/29/20
365,0.01,+16.90%,0,0.01,0.01,0,unch,0.00%,514,1722,Call,06/26/20
370,0.01,+18.50%,0,0.01,0.01,0,unch,0.00%,20,679,Call,06/24/20
375,0.02,+20.10%,0,0.01,0.01,-0.01,-33.33%,0.00%,7,485,Call,06/19/20
380,0.01,+21.71%,0,0.01,0.01,0,unch,0.00%,2292,3141,Call,06/26/20
385,0.03,+23.31%,0,0.01,0.01,0.01,+50.00%,0.00%,4,27,Call,06/16/20
390,0.01,+24.91%,0,0.01,0.01,0,unch,0.00%,5,143,Call,06/19/20
155,0.01,-50.36%,0,0.01,0.01,-0.01,-50.00%,0.00%,10,637,Put,07/01/20
160,0.01,-48.76%,0,0.01,0.01,-0.01,-50.00%,0.00%,3280,3809,Put,07/01/20
165,0.01,-47.15%,0,0.01,0.01,-0.01,-50.00%,0.00%,5340,5350,Put,07/01/20
170,0.01,-45.55%,0,0.01,0.01,-0.01,-50.00%,0.00%,65,772,Put,07/01/20
175,0.02,-43.95%,0,0.01,0.01,-0.03,-60.00%,0.00%,10,333,Put,06/29/20
180,0.01,-42.35%,0.01,0.02,0.02,-0.01,-50.00%,123.03%,100,194,Put,07/01/20
185,0.01,-40.75%,0.01,0.02,0.02,-0.01,-50.00%,113.39%,4409,760,Put,07/02/20
190,0.01,-39.15%,0.01,0.02,0.02,-0.01,-50.00%,107.95%,1,1171,Put,07/02/20
195,0.01,-37.55%,0.01,0.02,0.02,-0.01,-50.00%,102.65%,32,374,Put,07/02/20
200,0.02,-35.94%,0.01,0.02,0.02,0,unch,103.48%,103,3511,Put,07/02/20
205,0.02,-34.34%,0.01,0.02,0.02,0,unch,98.14%,1,1989,Put,07/02/20
210,0.01,-32.74%,0.01,0.02,0.02,-0.01,-50.00%,87.46%,1,2426,Put,07/02/20
215,0.02,-31.14%,0.01,0.02,0.02,-0.01,-33.33%,87.81%,100,1206,Put,07/02/20
220,0.02,-29.54%,0.01,0.02,0.02,-0.01,-33.33%,82.80%,349,994,Put,07/02/20
225,0.03,-27.94%,0.02,0.03,0.03,-0.01,-25.00%,81.00%,98,1284,Put,07/02/20
230,0.03,-26.34%,0.02,0.03,0.03,-0.01,-25.00%,76.01%,45,1611,Put,07/02/20
235,0.03,-24.73%,0.02,0.03,0.03,-0.02,-40.00%,71.12%,62,1344,Put,07/02/20
240,0.03,-23.13%,0.03,0.04,0.04,-0.02,-40.00%,66.30%,254,1531,Put,07/02/20
245,0.04,-21.53%,0.03,0.04,0.04,-0.03,-42.86%,63.47%,28,1940,Put,07/02/20
250,0.05,-19.93%,0.04,0.05,0.05,-0.03,-37.50%,60.17%,160,15442,Put,07/02/20
255,0.06,-18.33%,0.05,0.06,0.06,-0.03,-33.33%,56.55%,224,1765,Put,07/02/20
260,0.06,-16.73%,0.06,0.07,0.07,-0.04,-40.00%,51.70%,267,2815,Put,07/02/20
265,0.08,-15.13%,0.08,0.08,0.09,-0.04,-33.33%,48.65%,335,27523,Put,07/02/20
270,0.09,-13.53%,0.09,0.1,0.1,-0.06,-40.00%,44.46%,968,3044,Put,07/02/20
271,0.1,-13.21%,0.1,0.11,0.11,-0.08,-44.44%,44.11%,2,43,Put,07/02/20
272,0.1,-12.88%,0.1,0.11,0.11,-0.07,-41.18%,43.11%,22,1653,Put,07/02/20
273,0.11,-12.56%,0.1,0.11,0.11,-0.07,-38.89%,42.69%,28,1395,Put,07/02/20
274,0.1,-12.24%,0.11,0.11,0.12,-0.09,-47.37%,41.12%,135,1392,Put,07/02/20
275,0.12,-11.92%,0.11,0.11,0.12,-0.07,-36.84%,41.21%,2429,8750,Put,07/02/20
276,0.12,-11.60%,0.12,0.13,0.13,-0.08,-40.00%,40.19%,317,1995,Put,07/02/20
277,0.13,-11.28%,0.12,0.13,0.13,-0.1,-43.48%,39.66%,21,1301,Put,07/02/20
278,0.13,-10.96%,0.13,0.14,0.14,-0.08,-38.10%,38.64%,26,1196,Put,07/02/20
279,0.13,-10.64%,0.13,0.14,0.14,-0.1,-43.48%,37.62%,51,1003,Put,07/02/20
280,0.13,-10.32%,0.14,0.15,0.15,-0.11,-45.83%,36.60%,3094,19575,Put,07/02/20
281,0.15,-10.00%,0.15,0.16,0.16,-0.12,-44.44%,36.39%,85,772,Put,07/02/20
282,0.16,-9.68%,0.15,0.16,0.17,-0.12,-42.86%,35.73%,62,1726,Put,07/02/20
283,0.16,-9.36%,0.16,0.17,0.18,-0.14,-46.67%,34.69%,177,2486,Put,07/02/20
284,0.19,-9.04%,0.17,0.18,0.18,-0.13,-40.63%,34.64%,78,967,Put,07/02/20
285,0.19,-8.72%,0.18,0.19,0.2,-0.16,-45.71%,33.57%,3402,10898,Put,07/02/20
286,0.19,-8.40%,0.19,0.2,0.21,-0.17,-47.22%,32.50%,11058,12455,Put,07/02/20
287,0.22,-8.08%,0.21,0.22,0.22,-0.18,-45.00%,32.27%,452,1543,Put,07/02/20
288,0.23,-7.76%,0.23,0.24,0.24,-0.24,-51.06%,31.44%,549,1221,Put,07/02/20
289,0.26,-7.44%,0.24,0.25,0.26,-0.26,-50.00%,31.06%,6548,5594,Put,07/02/20
290,0.28,-7.12%,0.27,0.28,0.28,-0.29,-50.88%,30.38%,35643,31369,Put,07/02/20
291,0.3,-6.80%,0.29,0.3,0.31,-0.31,-50.82%,29.66%,1760,1856,Put,07/02/20
292,0.33,-6.48%,0.32,0.33,0.34,-0.33,-50.00%,29.09%,1769,1477,Put,07/02/20
293,0.36,-6.16%,0.35,0.36,0.37,-0.4,-52.63%,28.46%,986,1251,Put,07/02/20
294,0.4,-5.84%,0.39,0.4,0.41,-0.43,-51.81%,27.94%,4227,2062,Put,07/02/20
295,0.44,-5.52%,0.44,0.45,0.46,-0.48,-52.17%,27.34%,11132,10965,Put,07/02/20
296,0.5,-5.20%,0.49,0.5,0.51,-0.52,-50.98%,26.94%,1827,5776,Put,07/02/20
297,0.57,-4.88%,0.55,0.56,0.57,-0.55,-49.11%,26.57%,2413,5367,Put,07/02/20
298,0.61,-4.56%,0.61,0.63,0.64,-0.61,-50.00%,25.74%,1952,1605,Put,07/02/20
299,0.7,-4.24%,0.68,0.7,0.71,-0.69,-49.64%,25.41%,9437,2247,Put,07/02/20
300,0.78,-3.92%,0.77,0.79,0.8,-0.74,-48.68%,24.84%,18078,47109,Put,07/02/20
301,0.87,-3.60%,0.86,0.88,0.9,-0.88,-50.29%,24.28%,12127,10957,Put,07/02/20
302,1,-3.28%,0.96,0.98,1,-0.84,-45.65%,23.95%,4226,4486,Put,07/02/20
303,1.1,-2.96%,1.08,1.1,1.12,-0.93,-45.81%,23.24%,3867,6195,Put,07/02/20
304,1.23,-2.64%,1.22,1.23,1.25,-1.02,-45.33%,22.66%,10320,4532,Put,07/02/20
305,1.35,-2.32%,1.37,1.39,1.41,-1.12,-45.34%,21.88%,10779,6137,Put,07/02/20
306,1.59,-2.00%,1.54,1.56,1.58,-1.14,-41.76%,21.80%,2288,2697,Put,07/02/20
307,1.72,-1.68%,1.73,1.75,1.77,-1.25,-42.09%,20.81%,4480,2506,Put,07/02/20
308,1.94,-1.35%,1.94,1.96,1.98,-1.36,-41.21%,20.25%,10227,2888,Put,07/02/20
309,2.2,-1.03%,2.19,2.21,2.23,-1.52,-40.86%,19.75%,22270,3661,Put,07/02/20
310,2.46,-0.71%,2.45,2.48,2.5,-1.64,-40.00%,19.06%,40115,9546,Put,07/02/20
311,2.75,-0.39%,2.75,2.78,2.81,-1.7,-38.20%,18.33%,3894,4171,Put,07/02/20
312,3.11,-0.07%,3.1,3.14,3.17,-1.8,-36.66%,17.75%,6479,1612,Put,07/02/20
313,3.49,+0.25%,3.49,3.53,3.56,-1.92,-35.49%,17.02%,6651,813,Put,07/02/20
314,3.95,+0.57%,3.93,3.97,4.01,-2.14,-35.14%,16.45%,6984,514,Put,07/02/20
315,4.45,+0.89%,4.41,4.46,4.5,-2.28,-33.88%,15.78%,13969,2358,Put,07/02/20
316,5.01,+1.21%,4.96,5.01,5.05,-1.42,-22.08%,15.10%,1250,285,Put,07/02/20
317,6.36,+1.53%,5.57,5.62,5.67,-1.6,-20.10%,19.09%,1741,200,Put,07/02/20
318,7.1,+1.85%,6.24,6.29,6.34,-0.91,-11.36%,19.19%,822,317,Put,07/02/20
319,7.51,+2.17%,6.89,7.03,7.16,-1.94,-20.53%,16.62%,282,533,Put,07/02/20
320,7.75,+2.49%,7.65,7.82,7.98,-2.53,-24.61%,9.59%,2450,2740,Put,07/02/20
321,9.31,+2.81%,8.45,8.64,8.82,-1.68,-15.29%,17.76%,208,288,Put,07/02/20
322,10.7,+3.13%,9.31,9.5,9.7,-0.58,-5.14%,22.50%,181,160,Put,07/02/20
323,9.6,+3.45%,10.21,10.41,10.6,-7.68,-44.44%,0.00%,9,52,Put,07/02/20
324,9.43,+3.77%,11.13,11.33,11.53,-7.6,-44.63%,0.00%,5,37,Put,07/02/20
325,11.97,+4.09%,12.07,12.28,12.48,-2.8,-18.96%,4.35%,72,74,Put,07/02/20
326,11,+4.41%,13.02,13.23,13.44,-4.54,-29.21%,0.00%,1,37,Put,07/02/20
327,14.55,+4.73%,13.99,14.2,14.41,-9.54,-39.60%,10.14%,1,23,Put,07/02/20
328,17.94,+5.05%,14.96,15.18,15.39,-0.36,-1.97%,10.66%,10,43,Put,07/01/20
329,14.73,+5.37%,15.95,16.17,16.38,-4.87,-24.85%,0.00%,3,6,Put,07/02/20
330,15.98,+5.69%,16.93,17.15,17.36,-4.11,-20.46%,0.00%,29,90,Put,07/02/20
331,16.95,+6.01%,17.91,18.13,18.34,-11.09,-39.55%,0.00%,1,24,Put,07/02/20
332,17.23,+6.33%,18.92,19.14,19.35,0.7,+4.23%,0.00%,1,1,Put,07/02/20
333,24.88,+6.65%,19.91,20.13,20.34,-8.33,-25.08%,11.12%,8,23,Put,06/30/20
334,20.47,+6.97%,20.9,21.12,21.34,20.47,unch,22.62%,8,0,Put,07/02/20
335,26.77,+7.29%,21.9,22.12,22.33,-4.57,-14.58%,10.76%,7,7,Put,06/30/20
336,25.98,+7.61%,22.88,23.1,23.33,25.98,unch,10.17%,1,1,Put,06/16/20
337,0,+7.93%,23.89,24.11,24.33,0,unch,10.01%,0,0,Put,N/A
338,0,+8.25%,24.89,25.11,25.32,0,unch,9.28%,0,0,Put,N/A
339,33.4,+8.57%,25.89,26.11,26.33,33.4,unch,8.83%,0,2,Put,N/A
340,26.3,+8.89%,26.89,27.11,27.33,-3.1,-10.54%,22.55%,5,39,Put,07/02/20
341,23.05,+9.21%,27.89,28.11,28.33,23.05,unch,6.85%,0,50,Put,N/A
342,25.1,+9.53%,28.89,29.11,29.33,25.1,unch,5.46%,0,1,Put,N/A
343,0,+9.85%,29.89,30.11,30.33,0,unch,3.75%,0,0,Put,N/A
344,30.38,+10.18%,30.89,31.11,31.33,30.38,unch,22.99%,2,0,Put,07/02/20
345,0,+10.50%,31.89,32.11,32.33,0,unch,31.45%,0,0,Put,N/A
346,0,+10.82%,32.89,33.11,33.33,0,unch,31.56%,0,0,Put,N/A
347,0,+11.14%,33.89,34.11,34.33,0,unch,31.61%,0,0,Put,N/A
348,32.84,+11.46%,34.89,35.11,35.33,-10.52,-24.26%,0.19%,5,5,Put,07/02/20
349,0,+11.78%,35.89,36.11,36.33,0,unch,31.60%,0,0,Put,N/A
350,44.65,+12.10%,36.89,37.11,37.33,6.3,+16.43%,31.52%,15,15,Put,06/24/20
351,0,+12.42%,37.89,38.11,38.33,0,unch,31.39%,0,0,Put,N/A
352,0,+12.74%,38.89,39.11,39.33,0,unch,31.21%,0,0,Put,N/A
353,0,+13.06%,39.89,40.11,40.33,0,unch,30.98%,0,0,Put,N/A
355,0,+13.70%,41.89,42.11,42.33,0,unch,30.34%,0,0,Put,N/A
360,50.03,+15.30%,46.89,47.11,47.33,50.03,unch,27.54%,2,1,Put,06/22/20
365,0,+16.90%,51.89,52.11,52.33,0,unch,22.44%,0,0,Put,N/A
370,0,+18.50%,56.89,57.11,57.33,0,unch,13.87%,0,0,Put,N/A
375,62,+20.10%,61.9,62.11,62.33,62,unch,0.25%,2,0,Put,06/23/20
380,0,+21.71%,66.89,67.11,67.33,0,unch,0.00%,0,0,Put,N/A
385,75.19,+23.31%,71.9,72.12,72.33,2.19,+3.00%,0.00%,6,8,Put,06/18/20
390,77.95,+24.91%,76.88,77.1,77.33,-3.78,-4.62%,74.04%,3,5,Put,07/02/20
================================================
FILE: data/spy-options-exp-2021-01-15-weekly-show-all-stacked-07-05-2020.csv
================================================
Symbol,Strike,Last,% From Last,Bid,Midpoint,Ask,Change,%Chg,IV,Volume,Open Int,Type,DTE,Exp Date,Time,Avg IV
SPY|20210115|25.00C,25,288.7,-91.99%,287.3,287.91,288.52,0.19,+0.07%,159.39%,1,2,Call,197,01/15/21,06/23/20,0.00%
SPY|20210115|30.00C,30,280.95,-90.39%,282.3,282.91,283.52,4.35,+1.57%,145.44%,2,1,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|35.00C,35,0,-88.79%,277.31,277.92,278.53,0,unch,134.22%,0,0,Call,197,01/15/21,N/A,0.00%
SPY|20210115|40.00C,40,270.95,-87.19%,272.31,272.92,273.53,-2.53,-0.93%,124.15%,2,1,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|45.00C,45,0,-85.59%,267.32,267.93,268.54,0,unch,115.72%,0,0,Call,197,01/15/21,N/A,0.00%
SPY|20210115|50.00C,50,269.6,-83.99%,262.33,262.94,263.55,269.6,unch,108.18%,0,1,Call,197,01/15/21,N/A,0.00%
SPY|20210115|55.00C,55,251.8,-82.38%,257.33,257.95,258.56,-1.2,-0.47%,101.15%,1,1,Call,197,01/15/21,06/24/20,0.00%
SPY|20210115|60.00C,60,0,-80.78%,252.35,252.96,253.57,0,unch,95.12%,0,0,Call,197,01/15/21,N/A,0.00%
SPY|20210115|65.00C,65,0,-79.18%,247.36,247.97,248.58,0,unch,89.34%,0,0,Call,197,01/15/21,N/A,0.00%
SPY|20210115|70.00C,70,162.5,-77.58%,242.37,242.98,243.59,162.5,unch,83.92%,0,0,Call,197,01/15/21,N/A,0.00%
SPY|20210115|75.00C,75,234.84,-75.98%,237.38,237.99,238.6,-2.53,-1.07%,78.80%,1,1,Call,197,01/15/21,06/24/20,0.00%
SPY|20210115|80.00C,80,0,-74.38%,232.39,233.01,233.62,0,unch,74.17%,0,0,Call,197,01/15/21,N/A,0.00%
SPY|20210115|85.00C,85,171.02,-72.78%,227.41,228.02,228.63,171.02,unch,69.73%,0,0,Call,197,01/15/21,N/A,0.00%
SPY|20210115|90.00C,90,221,-71.18%,222.42,223.04,223.65,221,unch,65.42%,3,2,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|95.00C,95,216.44,-69.57%,217.44,218.05,218.66,216.44,unch,61.16%,1100,267,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|100.00C,100,210.44,-67.97%,212.46,213.08,213.69,-1.6,-0.75%,57.67%,5,13,Call,197,01/15/21,06/22/20,0.00%
SPY|20210115|105.00C,105,205.47,-66.37%,207.48,208.1,208.71,-1.56,-0.75%,53.81%,5,5,Call,197,01/15/21,06/22/20,0.00%
SPY|20210115|110.00C,110,201.45,-64.77%,202.5,203.12,203.73,201.45,unch,49.76%,1004,268,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|115.00C,115,197,-63.17%,197.53,198.15,198.77,197,unch,47.34%,600,132,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|120.00C,120,192.03,-61.57%,192.56,193.18,193.79,192.03,unch,43.57%,2200,485,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|125.00C,125,186.57,-59.97%,187.59,188.2,188.82,186.57,unch,40.19%,5500,5,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|130.00C,130,177.22,-58.36%,182.62,183.24,183.85,3.32,+1.91%,35.91%,1699,1700,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|135.00C,135,176.56,-56.76%,177.65,178.27,178.89,176.56,unch,32.21%,11000,2344,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|140.00C,140,171.85,-55.16%,172.7,173.32,173.93,-2.01,-1.16%,34.14%,7700,1668,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|145.00C,145,166.86,-53.56%,167.74,168.36,168.98,166.86,unch,34.24%,6600,1,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|150.00C,150,161.43,-51.96%,162.79,163.41,164.02,161.43,unch,33.83%,600,15,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|155.00C,155,158.25,-50.36%,157.85,158.47,159.08,7.1,+4.70%,3.89%,10,1408,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|160.00C,160,151.4,-48.76%,152.92,153.54,154.15,151.4,unch,34.96%,2,908,Call,197,01/15/21,06/19/20,0.00%
SPY|20210115|165.00C,165,149.12,-47.15%,147.99,148.61,149.22,2.06,+1.40%,44.02%,3,827,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|170.00C,170,152.01,-45.55%,143.07,143.69,144.3,152.01,unch,34.61%,0,112,Call,197,01/15/21,N/A,0.00%
SPY|20210115|175.00C,175,133.61,-43.95%,138.19,138.8,139.41,-3.5,-2.55%,34.89%,4,1296,Call,197,01/15/21,06/22/20,0.00%
SPY|20210115|180.00C,180,126.75,-42.35%,133.3,133.92,134.53,126.75,unch,34.72%,0,838,Call,197,01/15/21,N/A,0.00%
SPY|20210115|185.00C,185,99.77,-40.75%,128.29,128.99,129.68,99.77,unch,33.65%,0,1212,Call,197,01/15/21,N/A,0.00%
SPY|20210115|190.00C,190,114.5,-39.15%,123.63,124.25,124.86,114.5,unch,34.72%,0,1172,Call,197,01/15/21,N/A,0.00%
SPY|20210115|191.00C,191,114.51,-38.83%,122.67,123.28,123.89,114.51,unch,34.67%,0,1070,Call,197,01/15/21,N/A,0.00%
SPY|20210115|192.00C,192,97.22,-38.51%,121.7,122.32,122.93,97.22,unch,34.61%,0,557,Call,197,01/15/21,N/A,0.00%
SPY|20210115|193.00C,193,96.39,-38.19%,120.74,121.35,121.97,96.39,unch,34.58%,0,294,Call,197,01/15/21,N/A,0.00%
SPY|20210115|194.00C,194,118.67,-37.87%,119.78,120.39,121,118.67,unch,34.50%,0,82,Call,197,01/15/21,N/A,0.00%
SPY|20210115|195.00C,195,100.45,-37.55%,118.82,119.43,120.04,100.45,unch,34.45%,0,34,Call,197,01/15/21,N/A,0.00%
SPY|20210115|196.00C,196,0,-37.23%,117.87,118.48,119.09,0,unch,34.47%,0,0,Call,197,01/15/21,N/A,0.00%
SPY|20210115|197.00C,197,65.39,-36.91%,116.92,117.54,118.16,65.39,unch,34.56%,0,9,Call,197,01/15/21,N/A,0.00%
SPY|20210115|198.00C,198,64.11,-36.59%,115.96,116.58,117.2,64.11,unch,34.48%,0,1,Call,197,01/15/21,N/A,0.00%
SPY|20210115|199.00C,199,93,-36.26%,115.01,115.63,116.25,93,unch,34.46%,0,2,Call,197,01/15/21,N/A,0.00%
SPY|20210115|200.00C,200,115.83,-35.94%,114.06,114.68,115.3,7.07,+6.50%,40.59%,1,851,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|201.00C,201,67,-35.62%,113.12,113.74,114.35,67,unch,34.42%,0,229,Call,197,01/15/21,N/A,0.00%
SPY|20210115|202.00C,202,83,-35.30%,112.17,112.79,113.41,83,unch,34.40%,0,301,Call,197,01/15/21,N/A,0.00%
SPY|20210115|203.00C,203,63.81,-34.98%,111.22,111.84,112.46,63.81,unch,34.34%,0,252,Call,197,01/15/21,N/A,0.00%
SPY|20210115|204.00C,204,65.29,-34.66%,110.27,110.89,111.51,65.29,unch,34.26%,0,220,Call,197,01/15/21,N/A,0.00%
SPY|20210115|205.00C,205,110.55,-34.34%,109.32,109.94,110.56,27.37,+32.90%,37.33%,3,296,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|206.00C,206,115.45,-34.02%,108.38,109,109.62,115.45,unch,34.15%,0,566,Call,197,01/15/21,N/A,0.00%
SPY|20210115|207.00C,207,62.56,-33.70%,107.44,108.06,108.67,62.56,unch,34.09%,0,267,Call,197,01/15/21,N/A,0.00%
SPY|20210115|208.00C,208,95.67,-33.38%,106.5,107.12,107.73,95.67,unch,34.04%,2,267,Call,197,01/15/21,06/15/20,0.00%
SPY|20210115|209.00C,209,51.92,-33.06%,105.56,106.18,106.79,51.92,unch,33.98%,0,251,Call,197,01/15/21,N/A,0.00%
SPY|20210115|210.00C,210,102.89,-32.74%,104.63,105.25,105.86,2.09,+2.07%,33.96%,41,245,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|211.00C,211,78.12,-32.42%,103.7,104.32,104.93,78.12,unch,33.93%,0,5,Call,197,01/15/21,N/A,0.00%
SPY|20210115|212.00C,212,78.98,-32.10%,103.1,103.35,103.59,78.98,unch,33.70%,0,3,Call,197,01/15/21,N/A,0.00%
SPY|20210115|213.00C,213,57.84,-31.78%,102.17,102.41,102.66,57.84,unch,33.66%,0,5,Call,197,01/15/21,N/A,0.00%
SPY|20210115|214.00C,214,76.33,-31.46%,100.91,101.53,102.14,76.33,unch,33.79%,0,1,Call,197,01/15/21,N/A,0.00%
SPY|20210115|215.00C,215,91,-31.14%,100.31,100.56,100.8,91,unch,33.55%,0,16,Call,197,01/15/21,N/A,0.00%
SPY|20210115|216.00C,216,0,-30.82%,99.39,99.63,99.87,0,unch,33.50%,0,1,Call,197,01/15/21,N/A,0.00%
SPY|20210115|217.00C,217,96.43,-30.50%,98.49,98.72,98.94,96.43,unch,33.49%,0,10,Call,197,01/15/21,N/A,0.00%
SPY|20210115|218.00C,218,86.37,-30.18%,97.57,97.79,98.02,86.37,unch,33.44%,1,51,Call,197,01/15/21,06/15/20,0.00%
SPY|20210115|219.00C,219,94.49,-29.86%,96.65,96.88,97.1,94.49,unch,33.39%,0,2,Call,197,01/15/21,N/A,0.00%
SPY|20210115|220.00C,220,93.9,-29.54%,95.73,95.96,96.18,-1.79,-1.87%,33.33%,3,191,Call,197,01/15/21,06/19/20,0.00%
SPY|20210115|221.00C,221,94.28,-29.22%,94.79,95.03,95.27,94.28,unch,33.24%,2,1018,Call,197,01/15/21,06/16/20,0.00%
SPY|20210115|222.00C,222,68.44,-28.90%,93.88,94.12,94.35,68.44,unch,33.18%,0,99,Call,197,01/15/21,N/A,0.00%
SPY|20210115|223.00C,223,44.5,-28.58%,92.96,93.2,93.44,44.5,unch,33.11%,0,168,Call,197,01/15/21,N/A,0.00%
SPY|20210115|224.00C,224,90.24,-28.26%,92.05,92.29,92.53,90.24,unch,33.06%,1,195,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|225.00C,225,89.59,-27.94%,91.15,91.39,91.62,-3.52,-3.78%,33.01%,1,529,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|226.00C,226,67.1,-27.62%,90.24,90.48,90.72,67.1,unch,32.95%,0,276,Call,197,01/15/21,N/A,0.00%
SPY|20210115|227.00C,227,61.8,-27.30%,89.33,89.57,89.8,61.8,unch,32.86%,0,294,Call,197,01/15/21,N/A,0.00%
SPY|20210115|228.00C,228,87.2,-26.98%,88.45,88.68,88.91,87.2,unch,32.85%,1,297,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|229.00C,229,79.2,-26.66%,87.53,87.77,88,79.2,unch,32.74%,0,241,Call,197,01/15/21,N/A,0.00%
SPY|20210115|230.00C,230,81.5,-26.34%,86.63,86.87,87.1,-7.02,-7.93%,32.67%,100,581,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|231.00C,231,79.54,-26.02%,85.73,85.97,86.2,18.15,+29.57%,32.59%,20,484,Call,197,01/15/21,06/25/20,0.00%
SPY|20210115|232.00C,232,69.67,-25.70%,84.86,85.09,85.31,69.67,unch,32.56%,0,21,Call,197,01/15/21,N/A,0.00%
SPY|20210115|233.00C,233,90.07,-25.38%,83.97,84.2,84.42,90.07,unch,32.50%,0,37,Call,197,01/15/21,N/A,0.00%
SPY|20210115|234.00C,234,56.37,-25.06%,83.06,83.3,83.53,56.37,unch,32.40%,0,16,Call,197,01/15/21,N/A,0.00%
SPY|20210115|235.00C,235,83.2,-24.73%,82.17,82.4,82.63,5.93,+7.67%,34.38%,7,274,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|236.00C,236,56.04,-24.41%,81.31,81.54,81.76,56.04,unch,32.29%,0,132,Call,197,01/15/21,N/A,0.00%
SPY|20210115|237.00C,237,86.58,-24.09%,80.43,80.66,80.88,86.58,unch,32.22%,0,35,Call,197,01/15/21,N/A,0.00%
SPY|20210115|238.00C,238,81.24,-23.77%,79.52,79.76,79.99,81.24,unch,32.10%,2,1513,Call,197,01/15/21,06/16/20,0.00%
SPY|20210115|239.00C,239,77.79,-23.45%,78.64,78.87,79.1,77.79,unch,32.01%,1,325,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|240.00C,240,78.35,-23.13%,77.77,78,78.22,4.16,+5.61%,32.79%,1,1432,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|241.00C,241,70.74,-22.81%,76.89,77.12,77.35,-4.36,-5.81%,31.85%,10,1063,Call,197,01/15/21,06/25/20,0.00%
SPY|20210115|242.00C,242,83.05,-22.49%,76.02,76.25,76.47,83.05,unch,31.77%,0,1413,Call,197,01/15/21,N/A,0.00%
SPY|20210115|243.00C,243,79.29,-22.17%,75.17,75.39,75.61,79.29,unch,31.72%,0,994,Call,197,01/15/21,N/A,0.00%
SPY|20210115|244.00C,244,73.2,-21.85%,74.28,74.51,74.73,-3.46,-4.51%,31.60%,1,1084,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|245.00C,245,71.74,-21.53%,73.42,73.65,73.87,-2.1,-2.84%,31.52%,1,1533,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|246.00C,246,66.59,-21.21%,72.55,72.78,73.01,66.59,unch,31.43%,0,691,Call,197,01/15/21,N/A,0.00%
SPY|20210115|247.00C,247,66.84,-20.89%,71.69,71.91,72.14,66.84,unch,31.34%,0,249,Call,197,01/15/21,N/A,0.00%
SPY|20210115|248.00C,248,72.73,-20.57%,70.86,71.08,71.29,72.73,unch,31.29%,2,590,Call,197,01/15/21,06/16/20,0.00%
SPY|20210115|249.00C,249,60.19,-20.25%,69.99,70.21,70.43,60.19,unch,31.18%,1,792,Call,197,01/15/21,06/15/20,0.00%
SPY|20210115|250.00C,250,68.66,-19.93%,69.13,69.35,69.58,0.34,+0.50%,29.63%,7,5871,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|251.00C,251,62.94,-19.61%,68.28,68.51,68.73,14.63,+30.28%,31.00%,20,837,Call,197,01/15/21,06/25/20,0.00%
SPY|20210115|252.00C,252,60.5,-19.29%,67.31,67.7,68.09,-12.28,-16.87%,30.99%,2,566,Call,197,01/15/21,06/25/20,0.00%
SPY|20210115|253.00C,253,67.25,-18.97%,66.46,66.85,67.25,1.25,+1.89%,31.66%,2,814,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|254.00C,254,46.13,-18.65%,65.61,66.01,66.4,46.13,unch,30.79%,0,1482,Call,197,01/15/21,N/A,0.00%
SPY|20210115|255.00C,255,56.5,-18.33%,64.77,65.16,65.56,-3.14,-5.26%,30.69%,1,3238,Call,197,01/15/21,06/26/20,0.00%
SPY|20210115|256.00C,256,66.2,-18.01%,63.94,64.33,64.72,6.3,+10.52%,30.60%,3,780,Call,197,01/15/21,06/16/20,0.00%
SPY|20210115|257.00C,257,66.67,-17.69%,63.1,63.5,63.89,66.67,unch,30.50%,0,357,Call,197,01/15/21,N/A,0.00%
SPY|20210115|258.00C,258,61.4,-17.37%,62.27,62.67,63.06,61.4,unch,30.41%,0,696,Call,197,01/15/21,N/A,0.00%
SPY|20210115|259.00C,259,60.18,-17.05%,61.44,61.83,62.23,-6.82,-10.18%,30.30%,4,889,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|260.00C,260,61.75,-16.73%,60.61,61,61.4,2.95,+5.02%,31.48%,2,4110,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|261.00C,261,60.33,-16.41%,59.79,60.19,60.58,60.33,unch,30.10%,1,1274,Call,197,01/15/21,06/18/20,0.00%
SPY|20210115|262.00C,262,53.96,-16.09%,58.97,59.36,59.75,-5.32,-8.97%,29.99%,14,888,Call,197,01/15/21,06/25/20,0.00%
SPY|20210115|263.00C,263,53.08,-15.77%,58.15,58.54,58.93,-10.36,-16.33%,29.88%,27,518,Call,197,01/15/21,06/25/20,0.00%
SPY|20210115|264.00C,264,51.74,-15.45%,57.33,57.72,58.12,51.74,unch,29.78%,0,579,Call,197,01/15/21,N/A,0.00%
SPY|20210115|265.00C,265,50.15,-15.13%,56.51,56.91,57.3,-2.33,-4.44%,29.66%,10,3633,Call,197,01/15/21,06/29/20,0.00%
SPY|20210115|266.00C,266,49.42,-14.81%,55.7,56.1,56.5,0.99,+2.04%,29.56%,1,1532,Call,197,01/15/21,06/26/20,0.00%
SPY|20210115|267.00C,267,48.94,-14.49%,54.9,55.3,55.69,48.94,unch,29.45%,0,771,Call,197,01/15/21,N/A,0.00%
SPY|20210115|268.00C,268,56.14,-14.17%,54.09,54.49,54.88,7.45,+15.30%,29.34%,5,205,Call,197,01/15/21,06/23/20,0.00%
SPY|20210115|269.00C,269,60.6,-13.85%,53.29,53.69,54.08,60.6,unch,29.23%,0,570,Call,197,01/15/21,N/A,0.00%
SPY|20210115|270.00C,270,52.23,-13.53%,52.5,52.89,53.29,1.14,+2.23%,28.10%,174,5458,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|271.00C,271,52.59,-13.21%,51.71,52.1,52.49,2.98,+6.01%,29.75%,6,747,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|272.00C,272,48.68,-12.88%,50.91,51.31,51.7,48.68,unch,28.89%,0,2914,Call,197,01/15/21,N/A,0.00%
SPY|20210115|273.00C,273,52.23,-12.56%,50.13,50.52,50.91,6.36,+13.87%,28.78%,1,1481,Call,197,01/15/21,06/23/20,0.00%
SPY|20210115|274.00C,274,51.47,-12.24%,49.34,49.73,50.12,51.47,unch,28.66%,3,718,Call,197,01/15/21,06/16/20,0.00%
SPY|20210115|275.00C,275,50.71,-11.92%,48.55,48.95,49.34,3.28,+6.92%,31.05%,4,4943,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|276.00C,276,47.07,-11.60%,47.77,48.16,48.55,4.64,+10.94%,28.41%,2,683,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|277.00C,277,46.45,-11.28%,47,47.39,47.77,3.38,+7.85%,28.29%,1,696,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|278.00C,278,46.68,-10.96%,46.22,46.61,47,0.07,+0.15%,28.17%,30,313,Call,197,01/15/21,06/22/20,0.00%
SPY|20210115|279.00C,279,44.24,-10.64%,45.45,45.84,46.23,44.24,unch,28.05%,0,646,Call,197,01/15/21,N/A,0.00%
SPY|20210115|280.00C,280,45.78,-10.32%,44.69,45.08,45.46,1.78,+4.05%,28.89%,25,5540,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|281.00C,281,40.8,-10.00%,43.92,44.31,44.7,2.6,+6.81%,27.80%,1,14518,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|282.00C,282,39.17,-9.68%,43.17,43.55,43.93,-5.7,-12.70%,27.67%,22,1293,Call,197,01/15/21,06/25/20,0.00%
SPY|20210115|283.00C,283,38.49,-9.36%,42.41,42.8,43.18,-0.66,-1.69%,27.55%,19,699,Call,197,01/15/21,06/25/20,0.00%
SPY|20210115|284.00C,284,40.8,-9.04%,41.65,42.04,42.42,2.9,+7.65%,27.41%,4,1613,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|285.00C,285,41.7,-8.72%,40.9,41.29,41.67,1.21,+2.99%,27.82%,3,5933,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|286.00C,286,40.7,-8.40%,40.16,40.54,40.92,3,+7.96%,27.36%,1,1697,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|287.00C,287,40.02,-8.08%,39.41,39.79,40.17,4.62,+13.05%,27.30%,7,5652,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|288.00C,288,35.72,-7.76%,38.67,39.05,39.43,35.72,unch,26.88%,8,1358,Call,197,01/15/21,06/15/20,0.00%
SPY|20210115|289.00C,289,35.55,-7.44%,37.94,38.32,38.69,-3.82,-9.70%,26.74%,6,854,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|290.00C,290,37.89,-7.12%,37.2,37.58,37.95,1.88,+5.22%,26.99%,24,5208,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|291.00C,291,30.64,-6.80%,36.48,36.85,37.22,-4.79,-13.52%,26.46%,26,1245,Call,197,01/15/21,06/26/20,0.00%
SPY|20210115|292.00C,292,29.34,-6.48%,35.75,36.12,36.49,-8.66,-22.79%,26.32%,44,1195,Call,197,01/15/21,06/29/20,0.00%
SPY|20210115|293.00C,293,31.7,-6.16%,35.03,35.4,35.77,2.81,+9.73%,26.18%,2,504,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|294.00C,294,28.06,-5.84%,34.31,34.68,35.05,-4.41,-13.58%,26.03%,35,3206,Call,197,01/15/21,06/29/20,0.00%
SPY|20210115|295.00C,295,34.6,-5.52%,33.6,33.97,34.34,4.05,+13.26%,26.65%,4,5174,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|296.00C,296,33.39,-5.20%,32.89,33.25,33.62,6.59,+24.59%,25.90%,1,1238,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|297.00C,297,28.24,-4.88%,32.19,32.56,32.92,1.66,+6.25%,25.60%,44,858,Call,197,01/15/21,06/29/20,0.00%
SPY|20210115|298.00C,298,30.08,-4.56%,31.49,31.85,32.21,2.65,+9.66%,25.45%,1,974,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|299.00C,299,26.85,-4.24%,30.8,31.16,31.52,1.9,+7.62%,25.31%,23,1462,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|300.00C,300,30.25,-3.92%,30.11,30.47,30.82,1.35,+4.67%,24.91%,151,21219,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|301.00C,301,29,-3.60%,29.42,29.78,30.13,0.8,+2.84%,25.01%,4,1346,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|302.00C,302,27.79,-3.28%,28.74,29.08,29.41,2.21,+8.64%,24.84%,13,1318,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|303.00C,303,27.51,-2.96%,28.07,28.34,28.6,3.12,+12.79%,24.61%,21,1484,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|304.00C,304,27.64,-2.64%,27.4,27.74,28.09,0.84,+3.13%,24.44%,1,1583,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|305.00C,305,26.92,-2.32%,26.73,27.08,27.42,0.54,+2.05%,24.23%,37,17840,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|306.00C,306,26.43,-2.00%,26.07,26.42,26.76,1.32,+5.26%,24.27%,25,1537,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|307.00C,307,26,-1.68%,25.42,25.76,26.1,1.7,+7.00%,24.37%,1,1089,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|308.00C,308,25.43,-1.35%,24.77,25.11,25.45,2.35,+10.18%,24.30%,92,5794,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|309.00C,309,24.7,-1.03%,24.12,24.46,24.8,1.9,+8.33%,24.05%,22,1677,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|310.00C,310,23.25,-0.71%,23.48,23.81,24.15,0.17,+0.74%,22.99%,316,7160,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|311.00C,311,23.85,-0.39%,22.85,23.19,23.52,1.42,+6.33%,24.20%,8,1857,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|312.00C,312,22.25,-0.07%,22.22,22.56,22.89,0.88,+4.12%,22.97%,5,1277,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|313.00C,313,22.07,+0.25%,21.59,21.93,22.27,1.02,+4.85%,23.30%,6,2370,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|314.00C,314,21.78,+0.57%,20.97,21.31,21.64,1.12,+5.42%,23.50%,23,2073,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|315.00C,315,20.2,+0.89%,20.36,20.7,21.03,0.26,+1.30%,22.28%,315,4091,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|316.00C,316,20.41,+1.21%,19.75,20.09,20.42,1.06,+5.48%,23.01%,43,1772,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|317.00C,317,20.27,+1.53%,19.15,19.49,19.82,1.67,+8.98%,23.34%,1,781,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|318.00C,318,18.33,+1.85%,18.55,18.89,19.23,2.9,+18.79%,22.32%,103,944,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|319.00C,319,14.74,+2.17%,17.96,18.3,18.64,-0.56,-3.66%,22.15%,1,2157,Call,197,01/15/21,06/26/20,0.00%
SPY|20210115|320.00C,320,17.38,+2.49%,17.38,17.69,18,0.18,+1.05%,21.61%,345,6161,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|321.00C,321,18.16,+2.81%,16.81,17.15,17.48,1.68,+10.19%,22.92%,147,976,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|322.00C,322,17.46,+3.13%,16.25,16.59,16.92,1.52,+9.54%,22.61%,22,2065,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|323.00C,323,16.84,+3.45%,15.7,16.03,16.36,3.32,+24.56%,22.37%,40,1914,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|324.00C,324,16.03,+3.77%,15.15,15.48,15.81,1.08,+7.22%,21.92%,63,1106,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|325.00C,325,14.91,+4.09%,14.64,14.96,15.27,0.71,+5.00%,21.12%,187,5697,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|326.00C,326,14.6,+4.41%,14.09,14.42,14.74,2.11,+16.89%,21.19%,133,2409,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|327.00C,327,13.31,+4.73%,13.57,13.89,14.21,2.25,+20.34%,20.82%,1,2028,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|328.00C,328,13.85,+5.05%,13.06,13.38,13.7,3.24,+30.54%,21.18%,20,1102,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|329.00C,329,12.61,+5.37%,12.56,12.88,13.19,0.51,+4.21%,20.20%,65,1046,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|330.00C,330,12.32,+5.69%,12.07,12.38,12.69,0.48,+4.05%,20.26%,246,11312,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|331.00C,331,12.05,+6.01%,11.59,11.9,12.2,3.21,+36.31%,20.34%,44,1257,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|332.00C,332,11.06,+6.33%,11.11,11.42,11.73,1.01,+10.05%,19.59%,5,1561,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|333.00C,333,10.58,+6.65%,10.65,10.96,11.26,0.38,+3.73%,19.41%,10,1305,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|334.00C,334,9.92,+6.97%,10.2,10.5,10.8,1.14,+12.98%,19.68%,3,661,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|335.00C,335,10.54,+7.29%,9.76,10.06,10.35,1.23,+13.21%,20.08%,12,3322,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|336.00C,336,10.17,+7.61%,9.33,9.63,9.92,2.41,+31.06%,20.01%,68,873,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|337.00C,337,9.35,+7.93%,8.91,9.2,9.49,1.8,+23.84%,19.38%,26,797,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|338.00C,338,8.85,+8.25%,8.5,8.69,8.88,0.57,+6.88%,19.13%,2,2151,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|339.00C,339,6,+8.57%,8.1,8.39,8.68,-1.26,-17.36%,18.90%,10,362,Call,197,01/15/21,06/26/20,0.00%
SPY|20210115|340.00C,340,7.8,+8.89%,7.72,8,8.28,0.1,+1.30%,18.50%,103,13738,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|341.00C,341,7.75,+9.21%,7.35,7.63,7.9,1.72,+28.52%,18.76%,1,866,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|342.00C,342,7.2,+9.53%,6.99,7.26,7.53,0.15,+2.13%,18.38%,6,1125,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|343.00C,343,5.79,+9.85%,6.64,6.91,7.18,0.79,+15.80%,18.32%,94,909,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|344.00C,344,5.31,+10.18%,6.3,6.56,6.83,0.52,+10.86%,18.17%,6,323,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|345.00C,345,6.05,+10.50%,6.17,6.34,6.5,0.1,+1.68%,17.79%,37,5803,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|346.00C,346,6.56,+10.82%,5.67,5.93,6.18,2.16,+49.09%,18.76%,33,941,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|347.00C,347,6.09,+11.14%,5.37,5.63,5.88,1.64,+36.85%,18.42%,3,374,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|348.00C,348,4.15,+11.46%,5.09,5.34,5.58,0.18,+4.53%,17.67%,1,316,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|349.00C,349,5.61,+11.78%,4.82,5.06,5.3,-0.81,-12.62%,18.33%,60,498,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|350.00C,350,4.56,+12.10%,4.55,4.78,5,0.24,+5.56%,17.09%,228,14940,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|351.00C,351,4.66,+12.42%,4.3,4.53,4.76,0.91,+24.27%,17.51%,816,250,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|352.00C,352,4.35,+12.74%,4.06,4.29,4.51,0.89,+25.72%,17.31%,399,323,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|353.00C,353,3.67,+13.06%,3.84,4.06,4.28,0.47,+14.69%,17.11%,1,152,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|354.00C,354,2.84,+13.38%,3.63,3.84,4.06,-0.03,-1.05%,17.03%,1,265,Call,197,01/15/21,06/29/20,0.00%
SPY|20210115|355.00C,355,3.68,+13.70%,3.42,3.63,3.84,0.48,+15.00%,17.01%,38,604,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|356.00C,356,3.52,+14.02%,3.23,3.44,3.64,0.55,+18.52%,16.99%,2,425,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|357.00C,357,3.22,+14.34%,3.05,3.22,3.4,0.2,+6.62%,16.72%,2,472,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|358.00C,358,2.72,+14.66%,2.88,3.08,3.27,-1.15,-29.72%,16.70%,6,454,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|359.00C,359,2.55,+14.98%,2.72,2.92,3.11,0.53,+26.24%,16.64%,2,210,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|360.00C,360,2.69,+15.30%,2.57,2.76,2.94,0.09,+3.46%,16.45%,135,9137,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|361.00C,361,2.07,+15.62%,2.43,2.61,2.79,-1.34,-39.30%,16.52%,6,797,Call,197,01/15/21,06/26/20,0.00%
SPY|20210115|362.00C,362,2.53,+15.94%,2.29,2.47,2.65,0.69,+37.50%,16.59%,2,1088,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|363.00C,363,2.4,+16.26%,2.17,2.34,2.52,0.36,+17.65%,16.55%,20,246,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|364.00C,364,1.95,+16.58%,2.05,2.22,2.4,0.36,+22.64%,16.40%,1,400,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|365.00C,365,2.04,+16.90%,1.94,2.11,2.28,0.24,+13.33%,16.21%,8,18035,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|366.00C,366,2.05,+17.22%,1.84,2.01,2.17,0.15,+7.89%,16.45%,4,189,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|367.00C,367,2.05,+17.54%,1.74,1.9,2.07,0.49,+31.41%,16.65%,5,358,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|368.00C,368,1.89,+17.86%,1.65,1.81,1.97,0.25,+15.24%,16.49%,2,3527,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|369.00C,369,1.74,+18.18%,1.56,1.72,1.88,0.19,+12.26%,16.34%,14,133,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|370.00C,370,1.71,+18.50%,1.48,1.64,1.79,0.23,+15.54%,16.46%,31,2198,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|371.00C,371,1.45,+18.82%,1.4,1.56,1.71,0.31,+27.19%,16.27%,2,722,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|375.00C,375,1.31,+20.10%,1.15,1.29,1.42,0.12,+10.08%,16.35%,509,2403,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|380.00C,380,1.03,+21.71%,0.99,1.03,1.06,0.04,+4.04%,16.37%,107,2760,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|385.00C,385,0.86,+23.31%,0.79,0.82,0.85,0.1,+13.16%,16.61%,2,1096,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|390.00C,390,0.72,+24.91%,0.64,0.68,0.71,0.09,+14.29%,16.84%,14,5969,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|395.00C,395,0.6,+26.51%,0.52,0.55,0.59,0.06,+11.11%,17.05%,2,817,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|400.00C,400,0.46,+28.11%,0.45,0.46,0.47,0.01,+2.22%,17.01%,1211,14828,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|405.00C,405,0.44,+29.71%,0.36,0.4,0.43,0.07,+18.92%,17.59%,208,2129,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|410.00C,410,0.36,+31.31%,0.33,0.35,0.37,0.02,+5.88%,17.72%,88,1327,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|415.00C,415,0.29,+32.91%,0.26,0.29,0.32,-0.01,-3.33%,17.81%,30,2310,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|420.00C,420,0.25,+34.52%,0.24,0.25,0.25,-0.03,-10.71%,18.07%,292,45522,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|425.00C,425,0.23,+36.12%,0.19,0.22,0.25,0.01,+4.55%,18.47%,4,2500,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|430.00C,430,0.21,+37.72%,0.17,0.2,0.22,0.01,+5.00%,18.85%,8,131,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|435.00C,435,0.19,+39.32%,0.14,0.17,0.2,0,unch,19.18%,43,270,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|440.00C,440,0.15,+40.92%,0.12,0.15,0.18,0.01,+7.14%,19.18%,47,2174,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|445.00C,445,0.15,+42.52%,0.11,0.14,0.16,0.03,+25.00%,19.73%,12,139,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|450.00C,450,0.13,+44.12%,0.09,0.12,0.15,0.01,+8.33%,19.93%,14,3770,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|455.00C,455,0.12,+45.73%,0.08,0.11,0.14,0.03,+33.33%,20.27%,2,278,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|460.00C,460,0.11,+47.33%,0.07,0.1,0.12,0.01,+10.00%,20.58%,72,1279,Call,197,01/15/21,07/02/20,0.00%
SPY|20210115|465.00C,465,0.09,+48.93%,0.06,0.08,0.11,0.02,+28.57%,20.49%,8,142,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|470.00C,470,0.07,+50.53%,0.05,0.08,0.11,-0.01,-12.50%,20.85%,6,198,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|475.00C,475,0.07,+52.13%,0.04,0.07,0.1,-0.02,-22.22%,21.03%,30,30,Call,197,01/15/21,06/26/20,0.00%
SPY|20210115|480.00C,480,0.06,+53.73%,0.03,0.06,0.09,-0.02,-25.00%,21.16%,1,104,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|485.00C,485,0.06,+55.33%,0.03,0.06,0.08,0,unch,21.43%,1,2,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|490.00C,490,0.03,+56.94%,0.02,0.05,0.08,-0.02,-40.00%,21.67%,1,23,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|495.00C,495,0.04,+58.54%,0.02,0.05,0.07,0,unch,21.89%,1,1372,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|500.00C,500,0.04,+60.14%,0.03,0.05,0.07,0.01,+33.33%,22.54%,1,2,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|505.00C,505,0.03,+61.74%,0.01,0.03,0.06,-0.01,-25.00%,22.22%,3,178,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|510.00C,510,0.06,+63.34%,0,0.03,0.06,0.01,+20.00%,0.00%,3,198,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|515.00C,515,0.02,+64.94%,0.02,0.04,0.06,0,unch,23.31%,5,41,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|520.00C,520,0.02,+66.54%,0,0.03,0.05,-0.02,-50.00%,0.00%,5,35,Call,197,01/15/21,07/01/20,0.00%
SPY|20210115|525.00C,525,0.03,+68.15%,0,0.03,0.05,-0.01,-25.00%,0.00%,5,81,Call,197,01/15/21,06/25/20,0.00%
SPY|20210115|530.00C,530,0.05,+69.75%,0,0.03,0.05,0.01,+25.00%,0.00%,6,81,Call,197,01/15/21,06/25/20,0.00%
SPY|20210115|535.00C,535,0.01,+71.35%,0,0.03,0.05,-0.02,-66.67%,0.00%,1,1,Call,197,01/15/21,06/30/20,0.00%
SPY|20210115|540.00C,540,0,+72.95%,0,0.02,0.04,0,unch,0.00%,0,0,Call,197,01/15/21,N/A,0.00%
SPY|20210115|545.00C,545,0.03,+74.55%,0,0.02,0.04,0.03,unch,0.00%,1,1,Call,197,01/15/21,06/25/20,0.00%
SPY|20210115|550.00C,550,0.05,+76.15%,0,0.02,0.04,0.02,+66.67%,0.00%,45,93,Call,197,01/15/21,06/29/20,0.00%
SPY|20210115|555.00C,555,0.04,+77.75%,0,0.02,0.04,0,unch,0.00%,11,133,Call,197,01/15/21,06/29/20,0.00%
SPY|20210115|25.00P,25,0.04,-91.99%,0.03,0.04,0.04,0.01,+33.33%,119.34%,72,15455,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|30.00P,30,0.04,-90.39%,0.03,0.04,0.05,0,unch,110.46%,5,1905,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|35.00P,35,0.05,-88.79%,0.02,0.04,0.06,0,unch,103.04%,8,368,Put,197,01/15/21,06/30/20,0.00%
SPY|20210115|40.00P,40,0.06,-87.19%,0.04,0.06,0.08,0.01,+20.00%,100.45%,1,342,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|45.00P,45,0.08,-85.59%,0.06,0.08,0.09,0,unch,97.40%,7,1883,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|50.00P,50,0.13,-83.99%,0.11,0.11,0.12,-0.01,-7.14%,96.95%,1,6442,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|55.00P,55,0.14,-82.38%,0.09,0.12,0.14,0,unch,92.68%,3,1412,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|60.00P,60,0.12,-80.78%,0.11,0.14,0.16,-0.02,-14.29%,86.61%,2,1592,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|65.00P,65,0.2,-79.18%,0.13,0.16,0.18,0.02,+11.11%,84.85%,19,2294,Put,197,01/15/21,06/26/20,0.00%
SPY|20210115|70.00P,70,0.19,-77.58%,0.17,0.19,0.2,-0.01,-5.00%,82.85%,2,6239,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|75.00P,75,0.16,-75.98%,0.16,0.19,0.22,-0.11,-40.74%,77.57%,160,2115,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|80.00P,80,0.25,-74.38%,0.19,0.22,0.24,0.02,+8.70%,78.21%,2000,1136,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|85.00P,85,0.27,-72.78%,0.21,0.24,0.26,-0.04,-12.90%,75.58%,3,1094,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|90.00P,90,0.28,-71.18%,0.24,0.27,0.29,-0.03,-9.68%,72.73%,20,926,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|95.00P,95,0.38,-69.57%,0.26,0.29,0.32,0.01,+2.70%,70.03%,1,672,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|100.00P,100,0.34,-67.97%,0.32,0.33,0.35,-0.05,-12.82%,68.57%,662,17291,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|105.00P,105,0.4,-66.37%,0.35,0.37,0.39,-0.02,-4.76%,67.26%,7,851,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|110.00P,110,0.42,-64.77%,0.36,0.39,0.42,-0.04,-8.70%,65.00%,1,1328,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|115.00P,115,0.49,-63.17%,0.4,0.43,0.46,-0.13,-20.97%,62.62%,1,714,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|120.00P,120,0.52,-61.57%,0.45,0.48,0.51,-0.03,-5.45%,61.80%,7,1536,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|125.00P,125,0.64,-59.97%,0.49,0.53,0.56,-0.02,-3.03%,59.45%,21,2870,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|130.00P,130,0.59,-58.36%,0.55,0.58,0.61,-0.11,-15.71%,58.11%,1,1108,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|135.00P,135,0.61,-56.76%,0.6,0.64,0.67,-0.08,-11.59%,56.10%,2,649,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|140.00P,140,0.69,-55.16%,0.67,0.7,0.73,-0.25,-26.60%,54.93%,3,1894,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|145.00P,145,0.93,-53.56%,0.74,0.77,0.8,-0.13,-12.26%,53.70%,94,1922,Put,197,01/15/21,06/30/20,0.00%
SPY|20210115|150.00P,150,0.82,-51.96%,0.82,0.85,0.87,-0.14,-14.58%,52.10%,354,16040,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|155.00P,155,0.91,-50.36%,0.9,0.93,0.96,-0.12,-11.65%,50.91%,7,5660,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|160.00P,160,1.02,-48.76%,1,1.03,1.06,-0.11,-9.73%,49.84%,60,5589,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|165.00P,165,1.16,-47.15%,1.1,1.14,1.17,-0.09,-7.20%,48.94%,1,2938,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|170.00P,170,1.24,-45.55%,1.23,1.27,1.3,-0.23,-15.65%,47.53%,14,4673,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|175.00P,175,1.41,-43.95%,1.31,1.44,1.57,-0.1,-6.62%,46.70%,11,7553,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|180.00P,180,1.59,-42.35%,1.48,1.61,1.74,-0.24,-13.11%,45.83%,30,3756,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|185.00P,185,3.12,-40.75%,1.68,1.81,1.94,0.82,+35.65%,45.09%,1,3694,Put,197,01/15/21,06/24/20,0.00%
SPY|20210115|190.00P,190,1.95,-39.15%,1.93,2.06,2.18,-0.36,-15.58%,43.85%,62,5897,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|191.00P,191,2.71,-38.83%,1.95,2.09,2.23,-0.56,-17.13%,44.13%,2,2054,Put,197,01/15/21,06/30/20,0.00%
SPY|20210115|192.00P,192,2.2,-38.51%,2,2.14,2.28,-0.7,-24.14%,44.24%,1,1084,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|193.00P,193,4.07,-38.19%,2.06,2.2,2.33,4.07,unch,43.83%,14,4573,Put,197,01/15/21,06/15/20,0.00%
SPY|20210115|194.00P,194,3.02,-37.87%,2.11,2.25,2.39,-1.12,-27.05%,43.69%,1,957,Put,197,01/15/21,06/30/20,0.00%
SPY|20210115|195.00P,195,2.18,-37.55%,2.16,2.3,2.44,-0.76,-25.85%,42.99%,214,6392,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|196.00P,196,2.31,-37.23%,2.22,2.36,2.5,-1.09,-32.06%,43.17%,8,3823,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|197.00P,197,3.63,-36.91%,2.27,2.42,2.56,-0.25,-6.44%,43.23%,1,429,Put,197,01/15/21,06/25/20,0.00%
SPY|20210115|198.00P,198,4.59,-36.59%,2.33,2.48,2.62,4.59,unch,43.09%,7,501,Put,197,01/15/21,06/15/20,0.00%
SPY|20210115|199.00P,199,2.48,-36.26%,2.39,2.54,2.68,-1.38,-35.75%,42.72%,1,497,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|200.00P,200,2.75,-35.94%,2.55,2.65,2.74,-0.13,-4.51%,43.39%,1710,40386,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|201.00P,201,4.26,-35.62%,2.51,2.66,2.81,0.41,+10.65%,42.66%,3,571,Put,197,01/15/21,06/17/20,0.00%
SPY|20210115|202.00P,202,3.55,-35.30%,2.58,2.73,2.88,-0.8,-18.39%,42.54%,5,335,Put,197,01/15/21,06/23/20,0.00%
SPY|20210115|203.00P,203,4.46,-34.98%,2.64,2.79,2.94,0.06,+1.36%,42.37%,1,1516,Put,197,01/15/21,06/24/20,0.00%
SPY|20210115|204.00P,204,3.97,-34.66%,2.71,2.86,3.01,-0.69,-14.81%,42.24%,1,705,Put,197,01/15/21,06/29/20,0.00%
SPY|20210115|205.00P,205,2.86,-34.34%,2.77,2.93,3.08,-0.39,-12.00%,41.86%,34,1914,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|206.00P,206,4.7,-34.02%,2.84,3,3.16,-0.72,-13.28%,41.97%,1,522,Put,197,01/15/21,06/18/20,0.00%
SPY|20210115|207.00P,207,5.48,-33.70%,2.91,3.07,3.23,5.48,unch,41.82%,9,349,Put,197,01/15/21,06/15/20,0.00%
SPY|20210115|208.00P,208,4.87,-33.38%,2.99,3.15,3.3,0.85,+21.14%,41.69%,1,565,Put,197,01/15/21,06/24/20,0.00%
SPY|20210115|209.00P,209,4,-33.06%,3.06,3.22,3.38,-0.93,-18.86%,41.56%,1,473,Put,197,01/15/21,06/23/20,0.00%
SPY|20210115|210.00P,210,3.22,-32.74%,3.14,3.3,3.46,-0.43,-11.78%,41.17%,9,19535,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|211.00P,211,3.45,-32.42%,3.21,3.38,3.54,-1.46,-29.74%,41.53%,30,1018,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|212.00P,212,5.33,-32.10%,3.29,3.46,3.62,-0.07,-1.30%,41.15%,1,5258,Put,197,01/15/21,06/24/20,0.00%
SPY|20210115|213.00P,213,3.95,-31.78%,3.37,3.54,3.71,-0.42,-9.61%,41.03%,1,3284,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|214.00P,214,3.54,-31.46%,3.45,3.63,3.8,-0.8,-18.43%,40.64%,11,739,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|215.00P,215,3.6,-31.14%,3.54,3.71,3.88,-0.46,-11.33%,40.44%,11,9839,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|216.00P,216,6.57,-30.82%,3.62,3.8,3.97,6.57,unch,40.63%,25,1830,Put,197,01/15/21,06/15/20,0.00%
SPY|20210115|217.00P,217,6.19,-30.50%,3.71,3.89,4.06,-0.55,-8.16%,40.51%,40,1017,Put,197,01/15/21,06/24/20,0.00%
SPY|20210115|218.00P,218,3.75,-30.18%,3.8,3.98,4.16,-0.55,-12.79%,39.72%,20,1003,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|219.00P,219,5,-29.86%,3.89,4.07,4.25,-1.23,-19.74%,40.25%,1,1406,Put,197,01/15/21,06/30/20,0.00%
SPY|20210115|220.00P,220,4.47,-29.54%,3.98,4.17,4.35,-0.08,-1.76%,40.95%,551,14319,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|221.00P,221,6.73,-29.22%,4.07,4.26,4.44,-0.53,-7.30%,39.98%,43,3146,Put,197,01/15/21,06/24/20,0.00%
SPY|20210115|222.00P,222,4.25,-28.90%,4.17,4.36,4.54,-0.4,-8.60%,39.57%,3,3618,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|223.00P,223,6.85,-28.58%,4.27,4.46,4.64,-0.15,-2.14%,39.73%,9,1072,Put,197,01/15/21,06/26/20,0.00%
SPY|20210115|224.00P,224,4.29,-28.26%,4.37,4.56,4.74,-2.82,-39.66%,38.90%,4,1674,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|225.00P,225,4.8,-27.94%,4.47,4.66,4.85,-0.15,-3.03%,39.83%,86,14795,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|226.00P,226,5.14,-27.62%,4.57,4.77,4.96,-1.64,-24.19%,39.34%,2,2531,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|227.00P,227,6.67,-27.30%,4.68,4.87,5.06,0.84,+14.41%,39.21%,1,1107,Put,197,01/15/21,06/29/20,0.00%
SPY|20210115|228.00P,228,7.77,-26.98%,4.78,4.98,5.17,-0.43,-5.24%,39.07%,43,1380,Put,197,01/15/21,06/24/20,0.00%
SPY|20210115|229.00P,229,7.13,-26.66%,4.89,5.09,5.29,-0.92,-11.43%,38.95%,1,1288,Put,197,01/15/21,06/29/20,0.00%
SPY|20210115|230.00P,230,5.15,-26.34%,5,5.2,5.4,-0.51,-9.01%,38.70%,70,14256,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|231.00P,231,5.25,-26.02%,5.12,5.32,5.52,-2.3,-30.46%,38.54%,1,1372,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|232.00P,232,8.44,-25.70%,5.23,5.43,5.63,0.08,+0.96%,38.56%,110,1626,Put,197,01/15/21,06/24/20,0.00%
SPY|20210115|233.00P,233,7.6,-25.38%,5.35,5.55,5.75,-0.89,-10.48%,38.43%,2,4739,Put,197,01/15/21,06/29/20,0.00%
SPY|20210115|234.00P,234,5.6,-25.06%,5.47,5.67,5.87,-3.28,-36.94%,38.14%,2,1960,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|235.00P,235,5.63,-24.73%,5.59,5.8,6,-0.48,-7.86%,37.81%,6,14484,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|236.00P,236,6.64,-24.41%,5.71,5.92,6.12,-0.18,-2.64%,38.04%,10,2146,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|237.00P,237,8.83,-24.09%,5.84,6.05,6.25,0.83,+10.38%,37.91%,148,3689,Put,197,01/15/21,06/24/20,0.00%
SPY|20210115|238.00P,238,8.92,-23.77%,5.96,6.17,6.38,1.52,+20.54%,37.78%,141,2577,Put,197,01/15/21,06/24/20,0.00%
SPY|20210115|239.00P,239,6.2,-23.45%,6.09,6.31,6.52,-2.09,-25.21%,37.44%,4,2421,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|240.00P,240,6.57,-23.13%,6.22,6.44,6.65,-0.3,-4.37%,37.79%,83,14968,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|241.00P,241,9.72,-22.81%,6.36,6.57,6.78,-0.28,-2.80%,37.39%,1,2021,Put,197,01/15/21,06/29/20,0.00%
SPY|20210115|242.00P,242,8.02,-22.49%,6.49,6.71,6.92,-1.54,-16.11%,37.25%,1,1941,Put,197,01/15/21,06/30/20,0.00%
SPY|20210115|243.00P,243,10.35,-22.17%,6.63,6.85,7.07,0.49,+4.97%,37.13%,1,2497,Put,197,01/15/21,06/29/20,0.00%
SPY|20210115|244.00P,244,7.41,-21.85%,6.77,6.99,7.21,-3.36,-31.20%,37.00%,7,3699,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|245.00P,245,7.45,-21.53%,6.91,7.14,7.36,-0.34,-4.36%,37.47%,28,24112,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|246.00P,246,10,-21.21%,7.06,7.28,7.5,-0.92,-8.42%,36.74%,6,2380,Put,197,01/15/21,06/29/20,0.00%
SPY|20210115|247.00P,247,7.23,-20.89%,7.21,7.43,7.65,-0.59,-7.54%,36.23%,19,2082,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|248.00P,248,7.96,-20.57%,7.36,7.59,7.81,-2.11,-20.95%,36.48%,10,1528,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|249.00P,249,8.24,-20.25%,7.51,7.73,7.96,-2.66,-24.40%,36.35%,2,1902,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|250.00P,250,8.35,-19.93%,7.66,7.89,8.12,-0.08,-0.95%,37.03%,334,40028,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|251.00P,251,7.91,-19.61%,7.82,8.05,8.28,-2.98,-27.36%,35.83%,1,1570,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|252.00P,252,10.9,-19.29%,7.98,8.21,8.44,-1.47,-11.88%,35.95%,11,1437,Put,197,01/15/21,06/25/20,0.00%
SPY|20210115|253.00P,253,12.36,-18.97%,8.14,8.37,8.6,0.35,+2.91%,35.81%,84,1613,Put,197,01/15/21,06/24/20,0.00%
SPY|20210115|254.00P,254,12.63,-18.65%,8.31,8.54,8.77,0.05,+0.40%,35.69%,1,2154,Put,197,01/15/21,06/26/20,0.00%
SPY|20210115|255.00P,255,8.87,-18.33%,8.47,8.71,8.94,-0.69,-7.22%,35.83%,7,20741,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|256.00P,256,12.37,-18.01%,8.64,8.88,9.12,-0.1,-0.80%,35.42%,2,1405,Put,197,01/15/21,06/25/20,0.00%
SPY|20210115|257.00P,257,13.24,-17.69%,8.81,9.05,9.29,0.41,+3.20%,35.28%,82,1690,Put,197,01/15/21,06/24/20,0.00%
SPY|20210115|258.00P,258,13.45,-17.37%,8.99,9.23,9.47,1.01,+8.12%,35.16%,10,1720,Put,197,01/15/21,06/26/20,0.00%
SPY|20210115|259.00P,259,9.13,-17.05%,9.19,9.42,9.65,-4.55,-33.26%,34.57%,1,1589,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|260.00P,260,10,-16.73%,9.35,9.59,9.83,-0.19,-1.86%,35.54%,266,41136,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|261.00P,261,9.52,-16.41%,9.53,9.77,10.02,-4.63,-32.72%,34.35%,3,1358,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|262.00P,262,10.28,-16.09%,9.71,9.96,10.2,-2.85,-21.71%,35.12%,1,3663,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|263.00P,263,10.31,-15.77%,9.9,10.15,10.39,-4.28,-29.34%,34.73%,1,1018,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|264.00P,264,11.35,-15.45%,10.09,10.34,10.59,-0.75,-6.20%,34.34%,22,1468,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|265.00P,265,10.83,-15.13%,10.29,10.54,10.78,-0.47,-4.16%,34.65%,308,17673,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|266.00P,266,15.5,-14.81%,10.48,10.73,10.98,0.13,+0.85%,34.07%,12,902,Put,197,01/15/21,06/29/20,0.00%
SPY|20210115|267.00P,267,10.79,-14.49%,10.68,10.93,11.19,-1.98,-15.51%,33.72%,19,1375,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|268.00P,268,12.24,-14.17%,10.88,11.14,11.39,-2.23,-15.41%,33.79%,2,1306,Put,197,01/15/21,06/30/20,0.00%
SPY|20210115|269.00P,269,10.99,-13.85%,11.09,11.34,11.6,-1.12,-9.25%,33.14%,2,1121,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|270.00P,270,11.9,-13.53%,11.29,11.55,11.81,-0.41,-3.33%,34.02%,139,18126,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|271.00P,271,12.46,-13.21%,11.5,11.76,12.02,-4.13,-24.89%,33.37%,2,1160,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|272.00P,272,12,-12.88%,11.71,11.98,12.24,-0.85,-6.61%,33.27%,1,1669,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|273.00P,273,12.97,-12.56%,11.93,12.2,12.46,-5.03,-27.94%,33.10%,45,1365,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|274.00P,274,15.62,-12.24%,12.14,12.41,12.68,-1.84,-10.54%,32.95%,7,2224,Put,197,01/15/21,06/30/20,0.00%
SPY|20210115|275.00P,275,12.66,-11.92%,12.36,12.64,12.91,-0.77,-5.73%,32.84%,177,20883,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|276.00P,276,13.54,-11.60%,12.59,12.87,13.14,-4.42,-24.61%,32.67%,5,2521,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|277.00P,277,12.75,-11.28%,12.82,13.09,13.37,-4.3,-25.22%,32.06%,302,1692,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|278.00P,278,15.87,-10.96%,13.04,13.33,13.61,-2.22,-12.27%,32.38%,3,1632,Put,197,01/15/21,06/30/20,0.00%
SPY|20210115|279.00P,279,14.4,-10.64%,13.29,13.57,13.85,-3.07,-17.57%,32.25%,9,2792,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|280.00P,280,14.16,-10.32%,13.61,13.85,14.09,-0.22,-1.53%,32.57%,211,183468,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|281.00P,281,19.4,-10.00%,13.76,14.05,14.34,1,+5.43%,31.96%,73,6768,Put,197,01/15/21,06/24/20,0.00%
SPY|20210115|282.00P,282,14.17,-9.68%,14,14.29,14.58,-0.83,-5.53%,31.65%,5,2131,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|283.00P,283,14.16,-9.36%,14.25,14.55,14.84,-5.74,-28.84%,31.17%,12,1746,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|284.00P,284,14.58,-9.04%,14.49,14.79,15.09,-0.99,-6.36%,31.25%,43,3529,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|285.00P,285,14.68,-8.72%,14.75,15.05,15.35,-1.47,-9.10%,30.90%,44,14568,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|286.00P,286,16.37,-8.40%,15,15.31,15.61,-3.91,-19.28%,31.22%,15,2760,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|287.00P,287,20.3,-8.08%,15.27,15.57,15.87,-0.81,-3.84%,31.07%,3,1891,Put,197,01/15/21,06/29/20,0.00%
SPY|20210115|288.00P,288,20.5,-7.76%,15.54,15.84,16.14,-1.08,-5.00%,30.93%,16,1881,Put,197,01/15/21,06/29/20,0.00%
SPY|20210115|289.00P,289,14.98,-7.44%,15.8,16.11,16.42,-4.66,-23.73%,29.38%,1,3173,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|290.00P,290,16.77,-7.12%,16.07,16.38,16.7,-0.28,-1.64%,31.10%,234,24889,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|291.00P,291,15.95,-6.80%,16.35,16.67,16.98,-2.08,-11.54%,29.61%,1,2322,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|292.00P,292,17.98,-6.48%,16.63,16.95,17.26,-5.52,-23.49%,30.33%,6,2715,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|293.00P,293,16.9,-6.16%,16.92,17.24,17.55,-1.3,-7.14%,29.78%,2,2048,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|294.00P,294,20.42,-5.84%,17.2,17.52,17.84,-3.88,-15.97%,30.03%,76,5542,Put,197,01/15/21,06/30/20,0.00%
SPY|20210115|295.00P,295,18.36,-5.52%,17.49,17.81,18.14,-0.19,-1.02%,30.53%,48,11010,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|296.00P,296,19.34,-5.20%,17.78,18.11,18.44,-2.33,-10.75%,29.72%,30,2223,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|297.00P,297,18.39,-4.88%,18.08,18.41,18.74,-1.29,-6.55%,29.55%,25,2222,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|298.00P,298,18.48,-4.56%,18.39,18.72,19.05,-2.34,-11.24%,29.14%,40,2544,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|299.00P,299,18.51,-4.24%,18.7,19.03,19.36,-1.99,-9.71%,28.66%,15,1974,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|300.00P,300,19.9,-3.92%,19.25,19.46,19.67,-0.2,-1.00%,29.75%,321,21674,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|301.00P,301,20.65,-3.60%,19.35,19.67,19.99,-2.67,-11.45%,28.97%,89,1629,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|302.00P,302,21.1,-3.28%,19.68,20,20.32,-2.37,-10.10%,28.82%,28,4285,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|303.00P,303,20.95,-2.96%,19.97,20.31,20.65,-0.7,-3.23%,29.37%,44,1804,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|304.00P,304,20.19,-2.64%,20.3,20.64,20.98,-1.46,-6.74%,27.98%,30,7383,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|305.00P,305,21.55,-2.32%,20.68,21,21.32,-0.45,-2.05%,28.98%,244,14073,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|306.00P,306,21.05,-2.00%,21.02,21.35,21.67,-1.25,-5.61%,27.87%,8,2195,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|307.00P,307,21.42,-1.68%,21.35,21.69,22.02,-1.13,-5.01%,27.74%,1,2247,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|308.00P,308,21.46,-1.35%,21.73,22.06,22.38,-1.64,-7.10%,27.24%,2,5752,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|309.00P,309,21.73,-1.03%,22.09,22.42,22.74,-1.52,-6.54%,26.98%,1,1701,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|310.00P,310,22.79,-0.71%,22.6,22.85,23.1,-1.28,-5.32%,27.60%,366,7995,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|311.00P,311,22.73,-0.39%,22.82,23.15,23.48,-1.52,-6.27%,26.97%,2,3440,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|312.00P,312,23.01,-0.07%,23.16,23.51,23.86,-1.52,-6.20%,26.71%,8,1568,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|313.00P,313,23.6,+0.25%,23.58,23.92,24.25,-4.11,-14.83%,26.79%,9,1234,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|314.00P,314,23.83,+0.57%,23.95,24.3,24.64,-1.69,-6.62%,26.46%,34,815,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|315.00P,315,24.4,+0.89%,24.4,24.72,25.04,-1.32,-5.13%,26.50%,318,12435,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|316.00P,316,24,+1.21%,24.71,25.08,25.44,-8.85,-26.94%,25.47%,5,3907,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|317.00P,317,24.81,+1.53%,25.12,25.49,25.85,-6.16,-19.89%,25.76%,2,1416,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|318.00P,318,24.6,+1.85%,25.58,25.92,26.27,-3.4,-12.14%,24.93%,10,2335,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|319.00P,319,28.23,+2.17%,25.96,26.33,26.7,-0.94,-3.22%,26.21%,40,2084,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|320.00P,320,27.43,+2.49%,26.43,26.78,27.13,-0.47,-1.68%,26.79%,393,16504,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|321.00P,321,33.97,+2.81%,26.83,27.2,27.56,-1.03,-2.94%,25.91%,5,3283,Put,197,01/15/21,06/26/20,0.00%
SPY|20210115|322.00P,322,29.91,+3.13%,27.23,27.62,28.01,-2.66,-8.17%,25.74%,55,1469,Put,197,01/15/21,06/23/20,0.00%
SPY|20210115|323.00P,323,33.32,+3.45%,27.67,28.07,28.46,2.32,+7.48%,25.59%,8,1486,Put,197,01/15/21,06/19/20,0.00%
SPY|20210115|324.00P,324,35.98,+3.77%,28.12,28.52,28.92,2.53,+7.56%,25.44%,20,936,Put,197,01/15/21,06/29/20,0.00%
SPY|20210115|325.00P,325,28.13,+4.09%,28.58,28.99,29.39,-1.82,-6.08%,24.36%,26,13700,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|326.00P,326,34.36,+4.41%,29.05,29.46,29.87,34.36,unch,25.15%,10,2154,Put,197,01/15/21,06/19/20,0.00%
SPY|20210115|327.00P,327,29.94,+4.73%,29.53,29.94,30.35,-7.49,-20.01%,25.01%,14,697,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|328.00P,328,33.62,+5.05%,30.02,30.44,30.85,-1.26,-3.61%,24.88%,36,3552,Put,197,01/15/21,06/19/20,0.00%
SPY|20210115|329.00P,329,33.1,+5.37%,30.52,30.94,31.35,-7.65,-18.77%,24.74%,1,510,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|330.00P,330,31.5,+5.69%,31.03,31.45,31.86,-1.5,-4.55%,24.67%,21,14371,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|331.00P,331,36.47,+6.01%,31.54,31.97,32.39,36.47,unch,24.48%,499,1984,Put,197,01/15/21,06/19/20,0.00%
SPY|20210115|332.00P,332,33.78,+6.33%,32.07,32.5,32.92,-6.92,-17.00%,24.35%,2,684,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|333.00P,333,39.9,+6.65%,32.61,33.04,33.46,0.9,+2.31%,24.23%,2,672,Put,197,01/15/21,06/25/20,0.00%
SPY|20210115|334.00P,334,30.49,+6.97%,33.16,33.58,34.01,30.49,unch,24.11%,0,790,Put,197,01/15/21,N/A,0.00%
SPY|20210115|335.00P,335,33.5,+7.29%,33.71,34.14,34.57,-2,-5.63%,23.26%,1,3648,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|336.00P,336,42.51,+7.61%,34.28,34.71,35.14,6.78,+18.98%,23.87%,1,1083,Put,197,01/15/21,06/24/20,0.00%
SPY|20210115|337.00P,337,38.83,+7.93%,34.86,35.29,35.72,8.5,+28.03%,23.76%,1,634,Put,197,01/15/21,06/22/20,0.00%
SPY|20210115|338.00P,338,40.75,+8.25%,35.45,35.88,36.31,3.58,+9.63%,23.66%,7,1421,Put,197,01/15/21,06/30/20,0.00%
SPY|20210115|339.00P,339,44.11,+8.57%,36.06,36.49,36.91,-1.39,-3.05%,23.56%,2,17,Put,197,01/15/21,06/29/20,0.00%
SPY|20210115|340.00P,340,37.2,+8.89%,36.67,37.1,37.52,-1.3,-3.38%,23.58%,1,8831,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|341.00P,341,41.57,+9.21%,37.3,37.72,38.15,-3.43,-7.62%,23.38%,51,961,Put,197,01/15/21,06/30/20,0.00%
SPY|20210115|342.00P,342,0,+9.53%,37.94,38.36,38.79,0,unch,23.30%,0,61,Put,197,01/15/21,N/A,0.00%
SPY|20210115|343.00P,343,0,+9.85%,38.59,39.02,39.44,0,unch,23.22%,0,86,Put,197,01/15/21,N/A,0.00%
SPY|20210115|344.00P,344,95.12,+10.18%,39.5,39.73,39.95,95.12,unch,23.21%,0,3,Put,197,01/15/21,N/A,0.00%
SPY|20210115|345.00P,345,45.48,+10.50%,40.18,40.41,40.63,-7.52,-14.19%,23.15%,1,2245,Put,197,01/15/21,06/30/20,0.00%
SPY|20210115|346.00P,346,36.44,+10.82%,40.86,41.08,41.3,36.44,unch,23.08%,0,29,Put,197,01/15/21,N/A,0.00%
SPY|20210115|347.00P,347,51.82,+11.14%,41.57,41.8,42.03,51.82,unch,23.06%,0,6,Put,197,01/15/21,N/A,0.00%
SPY|20210115|348.00P,348,64.65,+11.46%,42.28,42.5,42.72,64.65,unch,23.01%,0,121,Put,197,01/15/21,N/A,0.00%
SPY|20210115|349.00P,349,0,+11.78%,43,43.24,43.47,0,unch,22.99%,0,2,Put,197,01/15/21,N/A,0.00%
SPY|20210115|350.00P,350,43.46,+12.10%,43.75,43.98,44.21,-3.01,-6.48%,22.32%,30,601,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|351.00P,351,55.6,+12.42%,44.49,44.73,44.96,55.6,unch,22.96%,0,56,Put,197,01/15/21,N/A,0.00%
SPY|20210115|352.00P,352,47.58,+12.74%,45.26,45.5,45.73,-1.27,-2.60%,22.97%,225,225,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|353.00P,353,54.75,+13.06%,46.03,46.27,46.5,2.98,+5.76%,22.97%,1,34,Put,197,01/15/21,06/29/20,0.00%
SPY|20210115|354.00P,354,28.81,+13.38%,46.81,47.04,47.27,28.81,unch,22.97%,0,1,Put,197,01/15/21,N/A,0.00%
SPY|20210115|355.00P,355,47.93,+13.70%,47.61,47.84,48.07,-3.77,-7.29%,23.12%,25,34,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|356.00P,356,108,+14.02%,48.43,48.67,48.9,108,unch,23.05%,0,200,Put,197,01/15/21,N/A,0.00%
SPY|20210115|357.00P,357,48,+14.34%,49.25,49.49,49.73,-2.2,-4.38%,21.02%,1,1,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|358.00P,358,52.03,+14.66%,50.08,50.32,50.56,-2.48,-4.55%,23.15%,1,2,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|359.00P,359,0,+14.98%,50.92,51.14,51.37,0,unch,23.19%,0,1,Put,197,01/15/21,N/A,0.00%
SPY|20210115|360.00P,360,51.24,+15.30%,51.77,52,52.22,-3.81,-6.92%,22.19%,50,77,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|361.00P,361,102.58,+15.62%,52.21,52.83,53.44,102.58,unch,23.30%,0,9,Put,197,01/15/21,N/A,0.00%
SPY|20210115|362.00P,362,103.63,+15.94%,53.08,53.7,54.31,103.63,unch,23.38%,0,8,Put,197,01/15/21,N/A,0.00%
SPY|20210115|363.00P,363,58.4,+16.26%,53.95,54.57,55.18,58.4,unch,23.47%,1,1,Put,197,01/15/21,06/18/20,0.00%
SPY|20210115|364.00P,364,0,+16.58%,54.83,55.45,56.07,0,unch,23.57%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|365.00P,365,83.98,+16.90%,55.72,56.34,56.96,83.98,unch,23.67%,0,10,Put,197,01/15/21,N/A,0.00%
SPY|20210115|366.00P,366,59.9,+17.22%,56.61,57.23,57.85,-9.69,-13.92%,23.77%,2,4,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|367.00P,367,0,+17.54%,57.51,58.13,58.75,0,unch,23.88%,0,147,Put,197,01/15/21,N/A,0.00%
SPY|20210115|368.00P,368,64.97,+17.86%,58.42,59.04,59.66,64.97,unch,24.01%,0,1,Put,197,01/15/21,N/A,0.00%
SPY|20210115|369.00P,369,88.53,+18.18%,59.33,59.95,60.57,88.53,unch,24.13%,0,9,Put,197,01/15/21,N/A,0.00%
SPY|20210115|370.00P,370,63.05,+18.50%,60.24,60.86,61.48,-5.83,-8.46%,24.25%,7,185,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|371.00P,371,63.95,+18.82%,61.19,61.8,62.41,-5.85,-8.38%,24.41%,7,643,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|375.00P,375,124.2,+20.10%,64.91,65.52,66.13,124.2,unch,24.96%,0,11,Put,197,01/15/21,N/A,0.00%
SPY|20210115|380.00P,380,76.58,+21.71%,69.64,70.26,70.87,6.78,+9.71%,25.73%,1,75,Put,197,01/15/21,06/30/20,0.00%
SPY|20210115|385.00P,385,74.44,+23.31%,74.44,75.06,75.67,-13.29,-15.15%,25.50%,1,3,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|390.00P,390,88.16,+24.91%,79.28,79.9,80.51,88.16,unch,27.42%,1,4,Put,197,01/15/21,06/15/20,0.00%
SPY|20210115|395.00P,395,87.03,+26.51%,84.16,84.78,85.39,-6.04,-6.49%,28.31%,1,3,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|400.00P,400,87.88,+28.11%,89.06,89.68,90.29,-4.05,-4.41%,25.65%,1,76,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|405.00P,405,153.71,+29.71%,93.98,94.6,95.21,153.71,unch,30.12%,0,981,Put,197,01/15/21,N/A,0.00%
SPY|20210115|410.00P,410,99.2,+31.31%,98.91,99.53,100.15,99.2,unch,31.03%,4,105,Put,197,01/15/21,06/23/20,0.00%
SPY|20210115|415.00P,415,0,+32.91%,103.86,104.48,105.09,0,unch,31.94%,0,181,Put,197,01/15/21,N/A,0.00%
SPY|20210115|420.00P,420,108.55,+34.52%,108.83,109.44,110.05,-11.45,-9.54%,32.86%,1,617,Put,197,01/15/21,06/16/20,0.00%
SPY|20210115|425.00P,425,116.61,+36.12%,113.79,114.4,115.01,116.61,unch,33.76%,2,2,Put,197,01/15/21,07/01/20,0.00%
SPY|20210115|430.00P,430,0,+37.72%,118.75,119.36,119.97,0,unch,34.63%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|435.00P,435,0,+39.32%,123.72,124.33,124.94,0,unch,35.51%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|440.00P,440,0,+40.92%,128.69,129.3,129.91,0,unch,36.38%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|445.00P,445,0,+42.52%,133.66,134.27,134.88,0,unch,37.23%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|450.00P,450,140.75,+44.12%,138.64,139.25,139.86,-0.37,-0.26%,41.04%,165,8,Put,197,01/15/21,07/02/20,0.00%
SPY|20210115|455.00P,455,0,+45.73%,143.61,144.23,144.84,0,unch,38.91%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|460.00P,460,141.46,+47.33%,148.59,149.2,149.81,141.46,unch,39.73%,0,7,Put,197,01/15/21,N/A,0.00%
SPY|20210115|465.00P,465,0,+48.93%,153.57,154.19,154.8,0,unch,40.55%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|470.00P,470,0,+50.53%,158.55,159.17,159.78,0,unch,41.35%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|475.00P,475,0,+52.13%,163.53,164.15,164.77,0,unch,42.15%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|480.00P,480,0,+53.73%,168.52,169.14,169.75,0,unch,42.94%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|485.00P,485,0,+55.33%,173.5,174.12,174.73,0,unch,43.71%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|490.00P,490,0,+56.94%,178.48,179.1,179.71,0,unch,44.47%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|495.00P,495,0,+58.54%,183.47,184.08,184.7,0,unch,45.24%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|500.00P,500,0,+60.14%,188.45,189.07,189.68,0,unch,45.97%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|505.00P,505,0,+61.74%,193.44,194.06,194.67,0,unch,46.72%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|510.00P,510,0,+63.34%,198.42,199.04,199.65,0,unch,47.44%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|515.00P,515,0,+64.94%,203.41,204.02,204.64,0,unch,48.17%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|520.00P,520,0,+66.54%,208.4,209.02,209.63,0,unch,48.89%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|525.00P,525,0,+68.15%,213.38,214,214.61,0,unch,49.58%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|530.00P,530,0,+69.75%,218.37,218.99,219.6,0,unch,50.29%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|535.00P,535,0,+71.35%,223.36,223.98,224.59,0,unch,50.99%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|540.00P,540,0,+72.95%,228.35,228.97,229.58,0,unch,51.68%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|545.00P,545,0,+74.55%,233.33,233.95,234.56,0,unch,52.33%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|550.00P,550,0,+76.15%,238.32,238.94,239.55,0,unch,53.01%,0,0,Put,197,01/15/21,N/A,0.00%
SPY|20210115|555.00P,555,252,+77.75%,243.31,243.93,244.54,252,unch,53.68%,1,1,Put,197,01/15/21,06/24/20,0.00%
================================================
FILE: data/stocks_data.csv
================================================
Date,AAPL,AMZN,GOOGL,JPM,GLD,DIS,VNO,FB,UBS,KO,MCD,^GSPC,GM
2019-08-14,50.10308837890625,1762.9599609375,1164.25,100.32390594482422,142.75,132.05880737304688,54.555301666259766,179.7100067138672,8.827184677124023,50.48763656616211,208.76168823242188,2840.60009765625,35.99055480957031
2019-08-15,49.85350036621094,1776.1199951171875,1169.3199462890625,100.70681762695312,143.6999969482422,132.61546325683594,54.67313003540039,182.58999633789062,8.827184677124023,51.32607650756836,210.4878692626953,2847.60009765625,35.29377746582031
2019-08-16,51.02977752685547,1792.5699462890625,1179.2099609375,103.11919403076172,142.77999877929688,134.39480590820312,55.516075134277344,183.6999969482422,9.035699844360352,51.840576171875,210.68072509765625,2888.679931640625,35.80668258666992
2019-08-19,51.98118209838867,1816.1199951171875,1200.43994140625,104.04776763916016,141.11000061035156,134.48426818847656,55.64297103881836,186.1699981689453,9.105205535888672,52.10735321044922,210.97967529296875,2923.64990234375,36.15507507324219
2019-08-20,51.9836540222168,1801.3800048828125,1183.530029296875,102.72669982910156,142.2100067138672,134.32522583007812,54.691253662109375,183.80999755859375,9.044387817382812,51.335609436035156,210.68072509765625,2900.510009765625,35.767974853515625
2019-08-21,52.547080993652344,1823.5400390625,1191.5799560546875,103.01387786865234,141.75999450683594,134.9514617919922,54.50998306274414,183.5500030517578,9.096517562866211,51.54521179199219,212.84088134765625,2924.429931640625,35.92280578613281
2019-08-22,52.50259780883789,1804.6600341796875,1191.52001953125,104.07647705078125,141.39999389648438,135.26956176757812,54.564369201660156,182.0399932861328,9.235528945922852,51.91679763793945,211.66436767578125,2922.949951171875,36.05829620361328
2019-08-23,50.075904846191406,1749.6199951171875,1153.5799560546875,101.49179077148438,144.1699981689453,130.88583374023438,53.30449295043945,177.75,9.105205535888672,51.20222091674805,207.006591796875,2847.110107421875,34.89699935913086
2019-08-26,51.027305603027344,1768.8699951171875,1171.1800537109375,102.30549621582031,144.19000244140625,133.80831909179688,53.893646240234375,180.36000061035156,9.200775146484375,51.96443557739258,209.17637634277344,2878.3798828125,35.08087158203125
2019-08-27,50.45151901245117,1761.8299560546875,1170.8199462890625,101.2237548828125,145.57000732421875,133.6890411376953,53.69424057006836,181.3000030517578,9.105205535888672,52.13593673706055,208.34703063964844,2869.159912109375,34.732479095458984
2019-08-28,50.79007339477539,1764.25,1173.75,102.23848724365234,145.16000366210938,135.7367706298828,53.911766052246094,181.75999450683594,9.131270408630371,52.50751876831055,210.29501342773438,2887.93994140625,35.26474380493164
2019-08-29,51.65003967285156,1786.4000244140625,1194.239990234375,104.55513000488281,144.1199951171875,137.01907348632812,54.62781524658203,185.57000732421875,9.192086219787598,52.45035171508789,212.6769256591797,2924.580078125,35.71958541870117
2019-08-30,51.58332443237305,1776.2900390625,1190.530029296875,105.16779327392578,143.75,136.44253540039062,54.8090934753418,185.6699981689453,9.209465026855469,52.440826416015625,211.3100128173828,2926.4599609375,35.89377975463867
2019-09-03,50.83208084106445,1789.8399658203125,1169.550048828125,103.9233169555664,145.75,135.49819946289062,55.47981643676758,182.38999938964844,9.105205535888672,52.68854904174805,210.49566650390625,2906.27001953125,35.71958541870117
2019-09-04,51.694522857666016,1800.6199951171875,1182.27001953125,105.17735290527344,146.66000366210938,137.06878662109375,55.76079559326172,187.13999938964844,9.200775146484375,53.1363525390625,211.82383728027344,2937.780029296875,37.006690979003906
2019-09-05,52.70523452758789,1840.719970703125,1212.18994140625,107.5705795288086,143.13999938964844,138.01312255859375,56.123348236083984,190.89999389648438,9.391915321350098,52.51704788208008,212.78358459472656,2976.0,37.85708236694336
2019-09-06,52.700286865234375,1833.510009765625,1206.3199462890625,107.80033111572266,141.9199981689453,138.71890258789062,56.875648498535156,187.49000549316406,9.45273208618164,52.62185287475586,213.3070526123047,2978.7099609375,37.90595245361328
2019-09-09,52.9251708984375,1831.3499755859375,1205.27001953125,110.47117614746094,141.38999938964844,138.00318908691406,56.93909454345703,188.75999450683594,9.678625106811523,51.94538116455078,210.62171936035156,2978.429931640625,38.687923431396484
2019-09-10,53.55037307739258,1820.550048828125,1205.699951171875,111.87838745117188,140.17999267578125,134.98129272460938,57.70045852661133,186.1699981689453,9.982709884643555,51.831050872802734,203.27330017089844,2979.389892578125,38.687923431396484
2019-09-11,55.25301742553711,1822.989990234375,1220.0,112.18472290039062,141.02999877929688,135.37890625,58.235225677490234,188.49000549316406,10.060904502868652,52.18357849121094,203.7774200439453,3000.929931640625,38.57062530517578
2019-09-12,55.129459381103516,1843.550048828125,1234.969970703125,112.87397003173828,141.32000732421875,136.6811065673828,58.12645721435547,187.47000122070312,10.017463684082031,52.50751876831055,205.66783142089844,3009.570068359375,38.18941879272461
2019-09-13,54.05696487426758,1839.3399658203125,1240.030029296875,115.09487915039062,140.14999389648438,137.19801330566406,57.52824783325195,187.19000244140625,10.191226959228516,52.07563400268555,203.3993377685547,3007.389892578125,37.98414993286133
2019-09-16,54.34115219116211,1807.8399658203125,1231.6300048828125,114.0705795288086,141.32000732421875,134.99124145507812,57.92705535888672,186.22000122070312,10.060904502868652,51.78771209716797,201.06298828125,2997.9599609375,36.371337890625
2019-09-17,54.53884506225586,1822.550048828125,1229.8800048828125,113.50577545166016,141.60000610351562,135.49819946289062,57.40135955810547,188.0800018310547,9.93927001953125,52.05644607543945,203.43812561035156,3005.699951171875,37.426998138427734
2019-09-18,55.050384521484375,1817.4599609375,1232.6500244140625,114.64495086669922,140.7100067138672,135.9852752685547,57.02973556518555,188.13999938964844,9.93927001953125,52.046844482421875,204.00038146972656,3006.72998046875,37.31947708129883
2019-09-19,54.60310363769531,1821.5,1238.75,114.28117370605469,141.27999877929688,132.50611877441406,56.90283966064453,190.13999938964844,10.008776664733887,52.32516860961914,204.08766174316406,3006.7900390625,36.928489685058594
2019-09-20,53.804901123046875,1794.1600341796875,1229.8399658203125,113.8216781616211,142.9499969482422,131.4822540283203,56.75782012939453,189.92999267578125,9.947957992553711,51.73972702026367,202.99217224121094,2992.070068359375,36.527732849121094
2019-09-23,54.049556732177734,1785.300048828125,1234.68994140625,113.80253601074219,143.75,131.67112731933594,56.966285705566406,186.82000732421875,9.861076354980469,51.96046447753906,205.0764617919922,2991.780029296875,36.400665283203125
2019-09-24,53.79254913330078,1741.6099853515625,1218.3299560546875,112.31874084472656,144.50999450683594,131.1840362548828,56.34994888305664,181.27999877929688,9.756817817687988,52.1140251159668,205.5418243408203,2966.60009765625,35.941253662109375
2019-09-25,54.62039566040039,1768.3299560546875,1245.93994140625,112.96012115478516,141.8300018310547,132.29736328125,57.41947937011719,182.8000030517578,9.756817817687988,51.989253997802734,206.1331787109375,2984.8701171875,36.27359390258789
2019-09-26,54.33868408203125,1739.8399658203125,1242.2900390625,112.03155517578125,141.7899932861328,130.48822021484375,57.72764587402344,180.11000061035156,9.739442825317383,52.20040512084961,206.10411071777344,2977.6201171875,36.762325286865234
2019-09-27,54.07426834106445,1725.449951171875,1225.949951171875,112.69207763671875,141.05999755859375,129.18601989746094,58.063011169433594,177.10000610351562,9.83501148223877,52.12362289428711,206.64698791503906,2961.7900390625,36.57660675048828
2019-09-30,55.346920013427734,1735.9100341796875,1221.1400146484375,112.66336822509766,138.8699951171875,129.5438690185547,57.70952224731445,178.0800018310547,9.826324462890625,52.248390197753906,208.14962768554688,2976.739990234375,36.63525390625
2019-10-01,55.5001335144043,1735.6500244140625,1206.0,110.61476135253906,139.6300048828125,128.77845764160156,56.88471603393555,175.80999755859375,9.678625106811523,52.4499397277832,202.63348388671875,2940.25,35.2961311340332
2019-10-02,54.108863830566406,1713.22998046875,1177.9200439453125,108.41299438476562,141.25999450683594,128.3708953857422,56.875648498535156,174.60000610351562,9.322409629821777,50.94314193725586,199.96749877929688,2887.610107421875,33.89836120605469
2019-10-03,54.56849670410156,1724.4200439453125,1189.4300537109375,108.25861358642578,141.89999389648438,127.38678741455078,57.49199295043945,179.3800048828125,9.235528945922852,51.672542572021484,203.6126251220703,2910.6298828125,34.19160079956055
2019-10-04,56.0981559753418,1739.6500244140625,1210.9599609375,110.60346221923828,141.89999389648438,129.49417114257812,58.044891357421875,180.4499969482422,9.322409629821777,52.344364166259766,205.2218780517578,2952.010009765625,34.12317657470703
2019-10-07,56.11051559448242,1732.6600341796875,1208.25,110.36222076416016,140.69000244140625,130.1204071044922,57.62794876098633,179.67999267578125,9.278968811035156,51.70133972167969,205.44485473632812,2938.7900390625,33.9667854309082
2019-10-08,55.45317840576172,1705.510009765625,1190.1300048828125,107.90157318115234,141.9199981689453,127.70488739013672,56.93003845214844,177.75,9.200775146484375,51.42301559448242,204.65963745117188,2893.06005859375,33.11639404296875
2019-10-09,56.10309982299805,1721.989990234375,1202.4000244140625,108.68318176269531,142.0500030517578,128.5597686767578,56.61279296875,179.85000610351562,9.192086219787598,51.6629524230957,206.32705688476562,2919.39990234375,33.37052917480469
2019-10-10,56.859283447265625,1720.260009765625,1209.469970703125,110.20781707763672,140.80999755859375,128.5697021484375,56.368072509765625,180.02999877929688,9.365850448608398,51.49979019165039,205.2897491455078,2938.1298828125,33.87881088256836
2019-10-11,58.37164306640625,1731.9200439453125,1215.7099609375,112.07018280029297,140.02999877929688,129.24566650390625,56.77595138549805,184.19000244140625,9.64387321472168,51.1542854309082,202.63348388671875,2970.27001953125,34.7682991027832
2019-10-14,58.287620544433594,1736.4300537109375,1217.77001953125,112.36932373046875,140.58999633789062,128.92755126953125,56.368072509765625,183.27999877929688,9.600432395935059,51.1542854309082,202.01303100585938,2966.14990234375,34.69987869262695
2019-10-15,58.151710510253906,1767.3800048828125,1242.239990234375,115.7563247680664,139.61000061035156,128.98719787597656,57.51918411254883,188.88999938964844,9.782882690429688,51.35582733154297,200.88848876953125,2995.679931640625,35.4427490234375
2019-10-16,57.91694641113281,1777.4300537109375,1243.0,115.48613739013672,140.41000366210938,130.08065795898438,57.05692672729492,189.5500030517578,9.713377952575684,51.336639404296875,201.9354705810547,2989.68994140625,35.8239631652832
2019-10-17,58.14181900024414,1787.47998046875,1252.800048828125,116.13265991210938,140.61000061035156,131.58164978027344,56.77595138549805,190.38999938964844,9.782882690429688,51.62455749511719,200.52980041503906,2997.949951171875,35.374324798583984
2019-10-18,58.42106628417969,1757.510009765625,1244.4100341796875,116.3353042602539,140.4600067138672,130.1104736328125,56.5765380859375,185.85000610351562,9.861076354980469,52.574703216552734,202.12936401367188,2986.199951171875,35.35477828979492
2019-10-21,59.43424606323242,1785.6600341796875,1244.280029296875,119.22053527832031,139.7899932861328,129.48422241210938,57.44667053222656,189.75999450683594,10.026152610778809,52.046844482421875,203.43812561035156,3006.719970703125,35.12018966674805
2019-10-22,59.298336029052734,1765.72998046875,1241.199951171875,120.41706848144531,140.1999969482422,131.61148071289062,57.69139862060547,182.33999633789062,9.991398811340332,51.6821403503418,193.181396484375,2995.989990234375,35.49162292480469
2019-10-23,60.09404754638672,1762.1700439453125,1257.6300048828125,120.73551177978516,140.52999877929688,130.3490447998047,58.65216064453125,186.14999389648438,10.11303424835205,52.440338134765625,193.1232147216797,3004.52001953125,35.78486633300781
2019-10-24,60.192893981933594,1780.780029296875,1259.1099853515625,120.64866638183594,141.52999877929688,129.48422241210938,58.479949951171875,186.3800048828125,10.156474113464355,52.41154861450195,190.0306854248047,3010.2900390625,35.01266860961914
2019-10-25,60.93425369262695,1761.3299560546875,1264.300048828125,121.61361694335938,141.86000061035156,130.1204071044922,58.262420654296875,187.88999938964844,10.225979804992676,51.58616638183594,188.6637725830078,3022.550048828125,35.91193389892578
2019-10-28,61.54462814331055,1777.0799560546875,1288.97998046875,122.0768051147461,140.63999938964844,129.75262451171875,58.044891357421875,189.39999389648438,10.382366180419922,51.41341018676758,185.9202423095703,3039.419921875,35.814186096191406
2019-10-29,60.12123107910156,1762.7099609375,1260.6600341796875,121.9996109008789,140.25,128.7088623046875,59.05097579956055,189.30999755859375,10.49531364440918,51.259857177734375,186.7345733642578,3036.889892578125,37.34879684448242
2019-10-30,60.11381530761719,1779.989990234375,1260.699951171875,121.32413482666016,141.02000427246094,128.8281707763672,60.464927673339844,188.25,10.286797523498535,51.768516540527344,190.87411499023438,3046.77001953125,37.05556106567383
2019-10-31,61.47296142578125,1776.6600341796875,1258.800048828125,120.54251098632812,142.42999267578125,129.14625549316406,59.48604202270508,191.64999389648438,10.225979804992676,52.23879623413086,190.68991088867188,3037.56005859375,36.32246398925781
2019-11-01,63.21761703491211,1791.43994140625,1272.25,123.32160186767578,142.55999755859375,131.9593963623047,59.48604202270508,193.6199951171875,10.3910551071167,51.73012924194336,188.01425170898438,3066.909912109375,37.11420822143555
2019-11-04,63.63276672363281,1804.6600341796875,1289.6099853515625,124.30585479736328,142.14999389648438,132.12838745117188,60.218505859375,194.72000122070312,10.712517738342285,51.00072479248047,182.89556884765625,3078.27001953125,37.52474594116211
2019-11-05,63.541343688964844,1801.7099609375,1291.43994140625,124.57604217529297,139.85000610351562,130.66712951660156,59.843116760253906,194.32000732421875,10.747269630432129,50.30970764160156,186.30799865722656,3074.6201171875,37.368350982666016
2019-11-06,63.568519592285156,1795.77001953125,1291.010009765625,124.76904296875,140.4499969482422,130.48822021484375,59.751556396484375,191.5500030517578,10.747269630432129,50.67441177368164,188.2469024658203,3076.780029296875,37.5540657043457
2019-11-07,64.30218505859375,1788.199951171875,1306.93994140625,125.44450378417969,138.27000427246094,132.16815185546875,59.46772384643555,190.4199981689453,10.877593040466309,50.18494415283203,187.18052673339844,3085.179931640625,37.62248992919922
2019-11-08,64.47816467285156,1785.8800048828125,1309.0,125.81118774414062,137.38999938964844,137.1383819580078,60.218505859375,190.83999633789062,10.782022476196289,50.1081657409668,187.69432067871094,3093.080078125,37.798431396484375
2019-11-11,64.98876953125,1771.6500244140625,1298.280029296875,125.36730194091797,137.05999755859375,135.92564392089844,60.154422760009766,189.61000061035156,10.773333549499512,49.75305938720703,186.7539520263672,3087.010009765625,37.84730529785156
2019-11-12,64.92926788330078,1778.0,1297.2099609375,124.63394927978516,137.42999267578125,137.7546844482422,59.22052001953125,194.47000122070312,10.712517738342285,49.628292083740234,187.37440490722656,3091.840087890625,37.72023391723633
2019-11-13,65.55139923095703,1753.1099853515625,1296.1800537109375,123.97776794433594,137.97999572753906,147.83428955078125,59.39447784423828,193.19000244140625,10.556130409240723,50.30011749267578,189.0418701171875,3094.0400390625,36.35179138183594
2019-11-14,65.09781646728516,1754.5999755859375,1309.1500244140625,124.09356689453125,138.55999755859375,146.2736358642578,59.85227966308594,193.14999389648438,10.556130409240723,50.511260986328125,188.0723876953125,3096.6298828125,35.9705810546875
2019-11-15,65.87113952636719,1739.489990234375,1333.5400390625,124.990966796875,138.2100067138672,143.80841064453125,59.870582580566406,195.10000610351562,10.608259201049805,50.54964065551758,188.0433349609375,3120.4599609375,36.05854797363281
2019-11-18,66.2032699584961,1752.530029296875,1319.8399658203125,126.04277801513672,138.6199951171875,146.77066040039062,59.59590530395508,197.39999389648438,10.608259201049805,50.8951530456543,188.34384155273438,3122.030029296875,35.79463577270508
2019-11-19,66.00250244140625,1752.7900390625,1312.5899658203125,126.00416564941406,138.69000244140625,147.49632263183594,59.08317184448242,199.32000732421875,10.547442436218262,50.94314193725586,187.52952575683594,3120.179931640625,35.56005096435547
2019-11-20,65.23414611816406,1745.530029296875,1301.8599853515625,125.08747863769531,138.75999450683594,146.05494689941406,58.56128692626953,197.50999450683594,10.46924877166748,51.07750701904297,188.1984405517578,3108.4599609375,34.48483657836914
2019-11-21,64.94166564941406,1734.7099609375,1300.1400146484375,125.376953125,138.0,146.02511596679688,57.88375473022461,197.92999267578125,10.486624717712402,50.827972412109375,186.4728240966797,3103.5400390625,33.888587951660156
2019-11-22,64.88465118408203,1745.719970703125,1293.6700439453125,126.20682525634766,137.74000549316406,147.40684509277344,57.76472473144531,198.82000732421875,10.616948127746582,50.8951530456543,187.2386932373047,3110.2900390625,34.533714294433594
2019-11-25,66.02232360839844,1773.8399658203125,1305.6400146484375,126.88229370117188,137.0800018310547,148.7985076904297,58.20420455932617,199.7899932861328,10.738581657409668,51.07750701904297,186.02688598632812,3133.639892578125,35.00289535522461
2019-11-26,65.50679016113281,1796.93994140625,1313.0,127.05596923828125,137.74000549316406,150.7368927001953,58.94583511352539,198.97000122070312,10.599571228027344,51.73012924194336,188.0821075439453,3140.52001953125,35.10063934326172
2019-11-27,66.38668060302734,1818.510009765625,1312.1300048828125,127.43230438232422,137.00999450683594,150.57785034179688,59.02824401855469,202.0,10.651700019836426,51.778114318847656,190.30213928222656,3153.6298828125,35.32545471191406
2019-11-29,66.24044799804688,1800.800048828125,1304.0899658203125,127.14281463623047,137.86000061035156,150.67726135253906,59.11980438232422,201.63999938964844,10.556130409240723,51.63308334350586,189.74600219726562,3140.97998046875,35.1886100769043
2019-12-02,65.47456359863281,1781.5999755859375,1288.8599853515625,126.86298370361328,137.7899932861328,149.72296142578125,58.02109146118164,199.6999969482422,10.477936744689941,51.97149658203125,190.428955078125,3113.8701171875,35.07131576538086
2019-12-03,64.3071517944336,1769.9599609375,1294.739990234375,125.23220825195312,139.11000061035156,147.69512939453125,58.80850601196289,198.82000732421875,10.312861442565918,52.019840240478516,188.41912841796875,3093.199951171875,34.72920227050781
2019-12-04,64.87474060058594,1760.68994140625,1318.93994140625,127.70249938964844,138.9199981689453,147.39691162109375,59.10149002075195,198.7100067138672,10.382366180419922,52.49363327026367,189.5801544189453,3112.760009765625,34.99311447143555
2019-12-05,65.82652282714844,1740.47998046875,1326.9599609375,128.39727783203125,139.0,146.56190490722656,59.00992965698242,199.36000061035156,10.417119979858398,52.39693832397461,189.4825897216797,3117.429931640625,34.88444519042969
2019-12-06,67.09803009033203,1751.5999755859375,1339.3900146484375,130.30789184570312,137.6199951171875,146.78060913085938,60.02623748779297,201.0500030517578,10.451871871948242,52.619327545166016,190.59483337402344,3145.909912109375,35.111671447753906
2019-12-09,66.15865325927734,1749.510009765625,1342.989990234375,129.69996643066406,137.5800018310547,145.33924865722656,60.38330841064453,201.33999633789062,10.443184852600098,52.280906677246094,189.94113159179688,3135.9599609375,34.9536018371582
2019-12-10,66.54531860351562,1739.2099609375,1342.8900146484375,129.7868194580078,137.97000122070312,145.22988891601562,61.02423858642578,200.8699951171875,10.373677253723145,51.99083709716797,190.20457458496094,3132.52001953125,34.68685531616211
2019-12-11,67.1129150390625,1748.719970703125,1344.25,129.47801208496094,138.9199981689453,146.7110137939453,60.072017669677734,202.25999450683594,10.347614288330078,52.16488265991211,189.98016357421875,3141.6298828125,34.81528854370117
2019-12-12,67.28392791748047,1760.3299560546875,1348.489990234375,133.1834716796875,138.42999267578125,146.8800048828125,59.550132751464844,196.75,10.564818382263184,52.348594665527344,191.5314483642578,3168.570068359375,35.664920806884766
2019-12-13,68.19853973388672,1760.93994140625,1346.8699951171875,132.015869140625,139.0500030517578,146.3800048828125,59.760711669921875,194.11000061035156,10.660387992858887,52.619327545166016,192.3217315673828,3168.800048828125,35.2203483581543
2019-12-16,69.36595153808594,1769.2099609375,1360.699951171875,132.52728271484375,139.0399932861328,148.4600067138672,59.870582580566406,197.9199981689453,10.816776275634766,52.619327545166016,193.06326293945312,3191.449951171875,35.6748046875
2019-12-17,69.50227355957031,1790.6600341796875,1354.8900146484375,133.3378448486328,139.00999450683594,147.72999572753906,59.51350402832031,198.38999938964844,10.912345886230469,52.619327545166016,191.73635864257812,3192.52001953125,35.87239456176758
2019-12-18,69.33621215820312,1784.030029296875,1351.9100341796875,133.2027587890625,139.02000427246094,146.25999450683594,60.61220932006836,202.5,10.808087348937988,52.12620544433594,190.86802673339844,3191.139892578125,36.8603401184082
2019-12-19,69.40560913085938,1792.280029296875,1356.43994140625,132.53695678710938,139.3800048828125,146.14999389648438,61.59189987182617,206.05999755859375,10.816776275634766,52.52263641357422,192.26319885253906,3205.3701171875,36.81093978881836
2019-12-20,69.26185607910156,1786.5,1351.219970703125,132.43080139160156,139.52000427246094,146.8800048828125,61.26229476928711,206.3000030517578,10.764646530151367,53.1511344909668,192.3412628173828,3221.219970703125,36.801063537597656
2019-12-23,70.39208984375,1793.0,1350.6300048828125,132.39219665527344,139.9499969482422,144.67999267578125,61.35384750366211,206.17999267578125,10.799399375915527,53.093116760253906,191.4241180419922,3224.010009765625,36.326847076416016
2019-12-24,70.4590072631836,1789.2099609375,1344.4300537109375,132.75889587402344,141.27000427246094,145.2899932861328,61.53696060180664,205.1199951171875,10.790711402893066,52.89973449707031,191.88272094726562,3223.3798828125,36.13914108276367
2019-12-26,71.85694122314453,1868.77001953125,1362.469970703125,134.16770935058594,142.3800048828125,145.6999969482422,62.17787551879883,207.7899932861328,10.842840194702148,53.19947814941406,192.26319885253906,3239.909912109375,36.04034423828125
2019-12-27,71.82967376708984,1869.800048828125,1354.6400146484375,134.26422119140625,142.3300018310547,145.75,62.11189270019531,208.10000610351562,10.86021614074707,53.518558502197266,193.34617614746094,3240.02001953125,36.119380950927734
2019-12-30,72.25599670410156,1846.8900146484375,1339.7099609375,133.77207946777344,142.6300048828125,143.77000427246094,61.876224517822266,204.41000366210938,10.929720878601074,53.44120407104492,192.1168670654297,3221.2900390625,36.000823974609375
2019-12-31,72.783935546875,1847.8399658203125,1339.3900146484375,134.5150909423828,142.89999389648438,144.6300048828125,62.68691635131836,205.25,10.929720878601074,53.518558502197266,192.79981994628906,3230.780029296875,36.15889358520508
2020-01-02,74.4446029663086,1898.010009765625,1368.6800537109375,136.14588928222656,143.9499969482422,148.1999969482422,61.744258880615234,209.77999877929688,11.225120544433594,53.17047119140625,195.90240478515625,3257.85009765625,36.92949676513672
2020-01-03,73.72084045410156,1874.969970703125,1361.52001953125,134.3492431640625,145.86000061035156,146.5,62.225013732910156,208.6699981689453,11.042667388916016,52.880393981933594,195.20970153808594,3234.85009765625,35.88227081298828
2020-01-06,74.30826568603516,1902.8800048828125,1397.81005859375,134.242431640625,147.38999938964844,145.64999389648438,63.035701751708984,212.60000610351562,11.016603469848633,52.861053466796875,197.40492248535156,3246.280029296875,35.408058166503906
2020-01-07,73.95879364013672,1906.8599853515625,1395.1099853515625,131.96022033691406,147.97000122070312,145.6999969482422,62.27214050292969,213.05999755859375,11.2424955368042,52.454959869384766,197.6976318359375,3237.179931640625,34.726375579833984
2020-01-08,75.14852142333984,1891.969970703125,1405.0400390625,132.98963928222656,146.86000061035156,145.39999389648438,62.14017105102539,215.22000122070312,11.3901948928833,52.55164337158203,200.8977813720703,3253.050048828125,34.232398986816406
2020-01-09,76.7447280883789,1901.050048828125,1419.7900390625,133.4752197265625,146.02999877929688,144.8300018310547,62.319278717041016,218.3000030517578,11.416259765625,53.508888244628906,203.27838134765625,3274.699951171875,34.65721893310547
2020-01-10,76.91822052001953,1883.1600341796875,1428.9599609375,132.14474487304688,146.91000366210938,144.6199951171875,62.62092590332031,218.05999755859375,11.27724838256836,53.69260025024414,202.2246856689453,3265.35009765625,34.232398986816406
2020-01-13,78.56153106689453,1891.300048828125,1440.030029296875,133.24212646484375,145.82000732421875,143.8800048828125,63.16767501831055,221.91000366210938,11.372819900512695,54.27275085449219,201.48316955566406,3288.1298828125,34.55842208862305
2020-01-14,77.50070190429688,1869.43994140625,1430.5899658203125,134.79598999023438,145.69000244140625,145.1999969482422,63.09225845336914,219.05999755859375,11.416259765625,54.14704895019531,202.27346801757812,3283.14990234375,34.726375579833984
2020-01-15,77.16856384277344,1862.02001953125,1439.199951171875,132.77598571777344,146.5399932861328,144.32000732421875,63.177093505859375,221.14999389648438,11.364130020141602,54.82388687133789,204.66383361816406,3289.2900390625,34.726375579833984
2020-01-16,78.13522338867188,1877.93994140625,1450.1600341796875,133.29067993164062,146.30999755859375,145.1199951171875,63.41276168823242,221.77000427246094,11.468388557434082,54.939918518066406,205.717529296875,3316.81005859375,35.121551513671875
2020-01-17,79.000244140625,1864.719970703125,1479.52001953125,134.21328735351562,146.5800018310547,144.3300018310547,63.34677505493164,222.13999938964844,11.416259765625,55.055946350097656,206.82000732421875,3329.6201171875,35.17094802856445
2020-01-21,78.46488189697266,1892.0,1482.25,132.8925323486328,146.74000549316406,143.55999755859375,63.893516540527344,221.44000244140625,10.868904113769531,55.27833557128906,206.01998901367188,3320.7900390625,34.568302154541016
2020-01-22,78.74495697021484,1887.4599609375,1483.8699951171875,132.7371368408203,146.7899932861328,144.00999450683594,63.74270248413086,221.32000732421875,11.146925926208496,55.70378112792969,206.2931671142578,3321.75,34.48926544189453
2020-01-23,79.12418365478516,1884.5799560546875,1484.68994140625,132.60116577148438,147.1199951171875,142.1999969482422,64.6759262084961,219.75999450683594,11.129549980163574,55.91650390625,208.2249755859375,3325.5400390625,34.45962905883789
2020-01-24,78.89614868164062,1861.6400146484375,1466.1700439453125,129.3089599609375,147.97999572753906,140.0800018310547,64.06676483154297,217.94000244140625,10.99053955078125,55.77145767211914,206.09803771972656,3295.469970703125,33.8964958190918
2020-01-27,76.57618713378906,1828.3399658203125,1431.72998046875,128.22128295898438,148.99000549316406,135.89999389648438,62.98169708251953,214.8699951171875,10.825464248657227,55.57807922363281,204.24427795410156,3243.6298828125,33.00734329223633
2020-01-28,78.74247741699219,1853.25,1450.5,130.55203247070312,147.66000366210938,138.3699951171875,63.20062255859375,217.7899932861328,10.816776275634766,55.12363052368164,205.2687225341797,3276.239990234375,33.23456954956055
2020-01-29,80.3907470703125,1858.0,1456.699951171875,130.35780334472656,148.4600067138672,136.05999755859375,63.30531692504883,223.22999572753906,10.877593040466309,55.12363052368164,209.2201385498047,3273.39990234375,33.19505310058594
2020-01-30,80.27424621582031,1870.6800537109375,1454.25,131.9699249267578,148.47000122070312,137.80999755859375,63.181575775146484,209.52999877929688,10.921032905578613,56.91241455078125,210.91778564453125,3283.659912109375,32.92831039428711
2020-01-31,76.7149887084961,2008.719970703125,1432.780029296875,128.541748046875,149.3300018310547,138.30999755859375,62.60096740722656,201.91000366210938,10.747269630432129,56.4676399230957,208.76158142089844,3225.52001953125,32.98758316040039
2020-02-03,76.50431060791016,2004.199951171875,1482.5999755859375,129.5226287841797,148.36000061035156,141.32000732421875,62.115543365478516,204.19000244140625,10.712517738342285,56.64168167114258,209.94212341308594,3248.919921875,33.31360626220703
2020-02-04,79.02999877929688,2049.669921875,1445.4100341796875,131.38722229003906,146.42999267578125,144.72999572753906,62.95314407348633,209.8300018310547,10.825464248657227,56.8930778503418,209.39576721191406,3297.590087890625,33.955772399902344
2020-02-05,79.6744384765625,2039.8699951171875,1446.050048828125,133.62088012695312,146.61000061035156,141.3699951171875,63.27676010131836,210.11000061035156,11.08611011505127,56.902748107910156,209.15184020996094,3334.68994140625,34.607818603515625
2020-02-06,80.60638427734375,2050.22998046875,1475.969970703125,133.6403045654297,147.39999389648438,142.6999969482422,63.47664260864258,210.85000610351562,11.181678771972656,57.03812026977539,207.6883544921875,3345.780029296875,33.916255950927734
2020-02-07,79.51072692871094,2079.280029296875,1479.1099853515625,133.21299743652344,147.7899932861328,141.02000427246094,63.14350509643555,212.3300018310547,11.25118350982666,57.23149490356445,206.45904541015625,3327.7099609375,33.224693298339844
2020-02-10,79.88835906982422,2133.909912109375,1508.6600341796875,133.76657104492188,148.1699981689453,142.58999633789062,63.0292854309082,213.05999755859375,11.34675407409668,57.77296829223633,208.0200958251953,3352.090087890625,33.83721923828125
2020-02-11,79.4063720703125,2150.800048828125,1510.06005859375,134.01905822753906,147.66000366210938,141.00999450683594,63.90495681762695,207.19000244140625,11.442323684692383,57.618263244628906,210.478759765625,3357.75,34.58805847167969
2020-02-12,81.29209899902344,2160.0,1518.6300048828125,134.01905822753906,147.5399932861328,141.85000610351562,64.1143569946289,210.75999450683594,11.642151832580566,57.444217681884766,212.1666259765625,3379.449951171875,35.06227493286133
2020-02-13,80.71321868896484,2149.8701171875,1513.3900146484375,133.90252685546875,148.3800048828125,140.89999389648438,64.4379653930664,213.13999938964844,11.47707748413086,57.63759994506836,212.1276092529297,3373.93994140625,34.86468505859375
2020-02-14,80.73308563232422,2134.8701171875,1518.72998046875,133.49464416503906,149.0,139.5399932861328,64.61882019042969,214.17999267578125,11.398883819580078,57.96635437011719,211.80563354492188,3380.159912109375,34.34107208251953
2020-02-18,79.25482177734375,2155.669921875,1519.43994140625,131.72714233398438,150.91000366210938,139.13999938964844,64.51412200927734,217.8000030517578,11.416259765625,57.56024932861328,210.8885040283203,3370.2900390625,33.99528884887695
2020-02-19,80.40264892578125,2170.219970703125,1524.8699951171875,133.52378845214844,151.7899932861328,141.3000030517578,61.27793884277344,217.49000549316406,11.424946784973145,57.79230880737305,210.3811798095703,3386.14990234375,34.499141693115234
2020-02-20,79.57780456542969,2153.10009765625,1516.989990234375,133.52378845214844,152.41000366210938,140.3699951171875,61.18276596069336,214.5800018310547,11.529206275939941,57.74396514892578,209.8445587158203,3373.22998046875,34.86468505859375
2020-02-21,77.77655792236328,2095.969970703125,1483.4599609375,131.89222717285156,154.6999969482422,138.97000122070312,60.77347946166992,210.17999267578125,11.2424955368042,58.14039993286133,210.6153106689453,3337.75,34.232398986816406
2020-02-24,74.08214569091797,2009.2900390625,1419.8599853515625,128.34751892089844,156.08999633789062,133.00999450683594,59.36479187011719,200.72000122070312,10.643012046813965,56.70936965942383,208.32252502441406,3225.889892578125,32.691200256347656
2020-02-25,71.57282257080078,1972.739990234375,1386.3199462890625,122.61773681640625,153.3000030517578,128.19000244140625,56.071510314941406,196.77000427246094,10.269420623779297,55.90682601928711,206.93710327148438,3128.2099609375,31.36734962463379
2020-02-26,72.70822143554688,1979.5899658203125,1390.469970703125,122.98675537109375,153.97000122070312,123.36000061035156,54.72944641113281,197.1999969482422,10.234667778015137,55.69411087036133,204.9857940673828,3116.389892578125,31.614336013793945
2020-02-27,67.9554214477539,1884.300048828125,1314.949951171875,117.86878967285156,154.0,118.04000091552734,51.807373046875,189.75,9.800259590148926,53.112457275390625,196.1072998046875,2978.760009765625,30.339881896972656
2020-02-28,67.91566467285156,1883.75,1339.25,112.76052856445312,148.3800048828125,117.6500015258789,50.998329162597656,192.47000122070312,9.556990623474121,51.7201042175293,190.62905883789062,2954.219970703125,30.132413864135742
2020-03-02,74.23866271972656,1953.949951171875,1386.3199462890625,118.01445770263672,149.1999969482422,119.9800033569336,52.90196228027344,196.44000244140625,9.782882690429688,54.069698333740234,198.85623168945312,3090.22998046875,31.04132652282715
2020-03-03,71.88089752197266,1908.989990234375,1337.719970703125,113.58600616455078,153.88999938964844,116.44999694824219,51.912071228027344,185.88999938964844,9.470108985900879,54.2050666809082,195.87167358398438,3003.3701171875,30.15217399597168
2020-03-04,75.2150650024414,1975.8299560546875,1381.5999755859375,116.39263916015625,154.16000366210938,119.18000030517578,53.263648986816406,191.75999450683594,9.661248207092285,56.970428466796875,203.24472045898438,3130.1201171875,31.150001525878906
2020-03-05,72.77530670166016,1924.030029296875,1314.760009765625,110.68226623535156,157.49000549316406,113.9800033569336,50.474822998046875,185.1699981689453,9.183398246765137,54.86256408691406,194.703369140625,3023.93994140625,30.100000381469727
2020-03-06,71.80884552001953,1901.0899658203125,1295.739990234375,104.96217346191406,157.5500030517578,115.2699966430664,49.82759475708008,181.08999633789062,9.08782958984375,53.43153762817383,195.23353576660156,2972.3701171875,28.690000534057617
2020-03-09,66.12933349609375,1800.6099853515625,1215.7900390625,90.74449920654297,157.80999755859375,104.3499984741211,44.76393127441406,169.5,8.053936958312988,50.14403533935547,183.45237731933594,2746.56005859375,24.690000534057617
2020-03-10,70.89207458496094,1891.8199462890625,1275.1700439453125,97.7950668334961,154.47999572753906,111.45999908447266,47.9810676574707,178.19000244140625,8.705549240112305,51.88447570800781,196.2152862548828,2882.22998046875,27.1200008392334
2020-03-11,68.4299545288086,1820.8599853515625,1210.9000244140625,93.1917953491211,153.92999267578125,105.51000213623047,45.163692474365234,170.24000549316406,8.236387252807617,50.48245620727539,184.8170166015625,2741.3798828125,26.0
2020-03-12,61.672176361083984,1676.6099853515625,1111.550048828125,85.5099868774414,147.7899932861328,91.80999755859375,40.604488372802734,154.47000122070312,7.202495098114014,45.599552154541016,167.0274658203125,2480.639892578125,23.040000915527344
2020-03-13,69.0610122680664,1785.0,1214.27001953125,100.91246795654297,143.27999877929688,102.5199966430664,42.09884262084961,170.27999877929688,7.8801727294921875,47.277225494384766,173.89979553222656,2711.02001953125,24.709999084472656
2020-03-16,60.1765251159668,1689.1500244140625,1073.0,85.81103515625,141.63999938964844,95.01000213623047,31.8668155670166,146.00999450683594,6.6464524269104,44.14621353149414,146.2926025390625,2386.1298828125,21.0
2020-03-17,62.82249069213867,1807.8399658203125,1118.06005859375,91.05526733398438,143.55999755859375,93.52999877929688,33.62767791748047,149.4199981689453,7.272000312805176,46.01897048950195,144.92794799804688,2529.18994140625,20.31999969482422
2020-03-18,61.28459930419922,1830.0,1091.18994140625,81.4699935913086,140.6999969482422,88.80000305175781,29.59197425842285,146.9600067138672,7.0287322998046875,43.74631118774414,134.79615783691406,2398.10009765625,16.799999237060547
2020-03-19,60.815032958984375,1880.9300537109375,1111.6700439453125,82.83931732177734,138.0399932861328,94.93000030517578,31.31476402282715,153.1300048828125,7.384946823120117,40.800628662109375,146.77366638183594,2409.389892578125,17.709999084472656
2020-03-20,56.95415496826172,1846.0899658203125,1068.2099609375,81.09124755859375,140.11000061035156,85.9800033569336,28.230876922607422,149.72999572753906,7.089549541473389,37.35749435424805,145.7821044921875,2304.919921875,18.139999389648438
2020-03-23,55.74421691894531,1902.8299560546875,1054.1300048828125,76.75018310546875,146.3000030517578,85.76000213623047,29.135103225708008,148.10000610351562,6.863656520843506,36.635704040527344,134.59982299804688,2237.39990234375,17.600000381469727
2020-03-24,61.33677291870117,1940.0999755859375,1130.010009765625,85.8790283203125,153.39999389648438,98.12000274658203,32.266578674316406,160.97999572753906,7.784603118896484,38.47919464111328,158.9966278076172,2447.330078125,21.110000610351562
2020-03-25,60.99888610839844,1885.8399658203125,1101.6199951171875,89.08383178710938,151.3000030517578,100.7300033569336,32.885257720947266,156.2100067138672,8.00180721282959,40.58604049682617,160.00784301757812,2475.56005859375,21.489999771118164
2020-03-26,64.20883178710938,1955.489990234375,1162.9200439453125,95.28949737548828,153.24000549316406,105.36000061035156,35.25528335571289,163.33999633789062,8.575226783752441,43.20009231567383,164.29815673828125,2630.070068359375,22.559999465942383
2020-03-27,61.550437927246094,1900.0999755859375,1110.260009765625,88.50113677978516,152.25,96.4000015258789,35.883480072021484,156.7899932861328,8.13213062286377,41.75651168823242,161.01905822753906,2541.469970703125,21.3799991607666
2020-03-30,63.30696105957031,1963.949951171875,1146.31005859375,90.80277252197266,152.9199981689453,99.80000305175781,35.788299560546875,165.9499969482422,8.27114200592041,43.892616271972656,165.06393432617188,2626.64990234375,21.31999969482422
2020-03-31,63.17776870727539,1949.719970703125,1161.949951171875,87.43286895751953,148.0500030517578,96.5999984741211,34.465274810791016,166.8000030517578,8.045248985290527,43.161075592041016,162.33462524414062,2584.590087890625,20.780000686645508
2020-04-01,59.853538513183594,1907.699951171875,1102.0999755859375,81.92642974853516,149.4499969482422,94.91999816894531,32.038143157958984,159.60000610351562,7.732473373413086,41.08348846435547,155.28555297851562,2470.5,19.260000228881836
2020-04-02,60.852294921875,1918.8299560546875,1117.030029296875,84.98556518554688,151.89999389648438,96.97000122070312,29.848966598510742,158.19000244140625,7.819355487823486,42.86845397949219,158.55484008789062,2526.89990234375,18.190000534057617
2020-04-03,59.97776794433594,1906.5899658203125,1092.699951171875,82.47357940673828,152.64999389648438,93.87999725341797,30.35342788696289,154.17999267578125,7.680344581604004,42.75141143798828,157.40615844726562,2488.64990234375,18.040000915527344
2020-04-06,65.21006774902344,1997.5899658203125,1183.18994140625,87.78211212158203,156.8800048828125,99.58000183105469,31.981033325195312,165.5500030517578,8.305893898010254,45.521522521972656,173.81143188476562,2663.679931640625,19.549999237060547
2020-04-07,64.45479583740234,2011.5999755859375,1182.56005859375,88.93997955322266,156.0399932861328,101.23999786376953,35.50275421142578,168.8300018310547,8.331958770751953,45.365455627441406,172.3878936767578,2659.409912109375,21.299999237060547
2020-04-08,66.10448455810547,2043.0,1207.0,92.53133392333984,154.64999389648438,101.06999969482422,38.5295295715332,174.27999877929688,8.331958770751953,46.643218994140625,174.2532501220703,2749.97998046875,23.1299991607666
2020-04-09,66.58150482177734,2042.760009765625,1206.5699462890625,100.83265686035156,158.69000244140625,104.5,41.384979248046875,175.19000244140625,8.731614112854004,47.79418182373047,180.3499755859375,2789.820068359375,24.059999465942383
2020-04-13,67.88833618164062,2168.8701171875,1210.4100341796875,96.34837341308594,161.41000366210938,103.5,40.061954498291016,174.7899932861328,8.436216354370117,45.775123596191406,176.83526611328125,2761.6298828125,23.010000228881836
2020-04-14,71.31690979003906,2283.320068359375,1265.22998046875,93.70882415771484,162.67999267578125,106.02999877929688,42.079803466796875,178.1699981689453,8.601290702819824,47.71615219116211,180.63470458984375,2846.06005859375,22.979999542236328
2020-04-15,70.66598510742188,2307.679931640625,1257.300048828125,89.08716583251953,161.85000610351562,103.37000274658203,40.39508819580078,176.97000122070312,8.166882514953613,46.43838882446289,174.5968475341797,2783.360107421875,21.65999984741211
2020-04-16,71.22747039794922,2408.18994140625,1257.4300537109375,85.69206237792969,161.7100067138672,102.0199966430664,38.35820388793945,176.25,7.984431266784668,45.940940856933594,176.22657775878906,2799.550048828125,20.8700008392334
2020-04-17,70.26101684570312,2375.0,1279.0,93.39482879638672,158.57000732421875,106.62999725341797,40.02388000488281,179.24000549316406,8.375398635864258,46.8773193359375,182.70623779296875,2874.56005859375,22.479999542236328
2020-04-20,68.80261993408203,2393.610107421875,1261.1500244140625,89.98990631103516,159.6999969482422,102.26000213623047,36.31180191040039,178.24000549316406,8.401463508605957,45.38496017456055,178.33737182617188,2823.159912109375,22.3799991607666
2020-04-21,66.67591094970703,2328.1201171875,1212.1600341796875,87.37980651855469,158.61000061035156,100.54000091552734,34.3320198059082,170.8000030517578,8.036561012268066,44.263267517089844,174.3415985107422,2736.56005859375,21.239999771118164
2020-04-22,68.5964126586914,2363.489990234375,1258.4100341796875,87.66435241699219,161.72999572753906,100.98999786376953,35.41708755493164,182.27999877929688,8.019183158874512,44.555885314941406,183.0792999267578,2799.31005859375,21.299999237060547
2020-04-23,68.33058166503906,2399.449951171875,1271.1700439453125,87.71342468261719,163.33999633789062,101.0,35.6740837097168,185.1300048828125,8.106064796447754,43.96089172363281,178.7202606201172,2797.800048828125,21.520000457763672
2020-04-24,70.30325317382812,2410.219970703125,1276.5999755859375,89.00865936279297,162.63999938964844,101.19000244140625,35.950111389160156,190.07000732421875,8.175570487976074,44.312034606933594,180.66416931152344,2836.739990234375,21.950000762939453
2020-04-27,70.35294342041016,2376.0,1270.8599853515625,92.8453369140625,161.55999755859375,106.05999755859375,38.158321380615234,187.5,8.497032165527344,45.62881088256836,182.50006103515625,2878.47998046875,22.450000762939453
2020-04-28,69.21255493164062,2314.080078125,1232.5899658203125,93.50276184082031,160.83999633789062,106.20999908447266,39.5574951171875,182.91000366210938,8.974883079528809,45.58980178833008,182.539306640625,2863.389892578125,22.18000030517578
2020-04-29,71.4858627319336,2372.7099609375,1342.1800537109375,96.0245590209961,161.72999572753906,112.25,42.15595245361328,194.19000244140625,9.548301696777344,45.960445404052734,184.3948516845703,2939.510009765625,23.780000686645508
2020-04-30,72.99393463134766,2474.0,1346.699951171875,93.96395111083984,158.8000030517578,108.1500015258789,41.708595275878906,204.7100067138672,9.348474502563477,44.760711669921875,184.13958740234375,2912.429931640625,22.290000915527344
2020-05-01,71.81878662109375,2286.0400390625,1317.3199462890625,91.50102233886719,159.77999877929688,105.5,39.97629165649414,202.27000427246094,9.02701187133789,44.477848052978516,179.32896423339844,2830.7099609375,20.899999618530273
2020-05-04,72.83493041992188,2315.989990234375,1322.9000244140625,90.4118423461914,160.33999633789062,103.18000030517578,37.99651336669922,205.25999450683594,8.827184677124023,44.02916717529297,178.55335998535156,2842.739990234375,20.75
2020-05-05,73.9281005859375,2317.800048828125,1349.02001953125,90.27447509765625,161.02000427246094,101.05999755859375,36.97806930541992,207.07000732421875,8.705522537231445,44.28277587890625,175.97132873535156,2868.43994140625,21.260000228881836
2020-05-06,74.69084167480469,2351.260009765625,1345.4300537109375,88.57691955566406,158.9499969482422,100.87999725341797,34.236839294433594,208.47000122070312,9.358681678771973,43.64876937866211,173.74273681640625,2848.419921875,21.889999389648438
2020-05-07,75.46350860595703,2367.610107421875,1369.280029296875,89.4992904663086,161.38999938964844,105.56999969482422,35.87396240234375,211.25999450683594,9.543905258178711,43.50245666503906,177.81703186035156,2881.18994140625,22.440000534057617
2020-05-08,77.25967407226562,2379.610107421875,1384.3399658203125,90.96133422851562,160.4199981689453,109.16000366210938,38.17966079711914,212.35000610351562,9.699883460998535,44.975303649902344,177.92503356933594,2929.800048828125,23.93000030517578
2020-05-11,78.47537231445312,2409.0,1403.5899658203125,88.28254699707031,159.4199981689453,107.7699966430664,35.593017578125,213.17999267578125,9.504910469055176,44.41933059692383,177.5814208984375,2930.18994140625,22.799999237060547
2020-05-12,77.57853698730469,2356.949951171875,1375.1800537109375,85.3976821899414,160.0399932861328,104.55999755859375,34.42079162597656,210.10000610351562,9.348933219909668,43.71704864501953,173.320556640625,2870.1201171875,22.559999465942383
2020-05-13,76.64185333251953,2367.919921875,1348.3299560546875,82.45394897460938,161.5800018310547,102.91999816894531,31.291627883911133,205.10000610351562,9.202702522277832,42.858699798583984,169.6684112548828,2820.0,21.459999084472656
2020-05-14,77.11268615722656,2388.85009765625,1356.8599853515625,85.87849426269531,163.00999450683594,105.91000366210938,33.917022705078125,206.80999755859375,9.124713897705078,42.624610900878906,172.211181640625,2852.5,22.309999465942383
2020-05-15,76.65679168701172,2409.780029296875,1373.06005859375,84.28888702392578,163.92999267578125,109.05000305175781,34.21734619140625,210.8800048828125,9.027228355407715,42.1954345703125,170.6403350830078,2863.699951171875,22.6299991607666
2020-05-18,78.4629135131836,2426.260009765625,1385.1800537109375,88.7535400390625,162.69000244140625,116.8499984741211,36.68773651123047,213.19000244140625,9.70963191986084,43.86335754394531,176.55056762695312,2953.909912109375,24.809999465942383
2020-05-19,78.009521484375,2449.330078125,1374.4000244140625,87.00692749023438,164.25999450683594,114.37000274658203,36.2905387878418,216.8800048828125,9.59264850616455,43.443939208984375,176.29531860351562,2922.93994140625,24.690000534057617
2020-05-20,79.52666473388672,2497.93994140625,1409.1600341796875,89.61703491210938,164.64999389648438,119.91999816894531,35.49613571166992,229.97000122070312,9.836363792419434,44.760711669921875,180.74270629882812,2971.610107421875,25.479999542236328
2020-05-21,78.93375396728516,2446.739990234375,1406.75,88.47879028320312,162.25,117.83000183105469,34.6339225769043,231.38999938964844,9.768123626708984,44.05842971801758,181.70481872558594,2948.510009765625,25.799999237060547
2020-05-22,79.44196319580078,2436.8798828125,1413.239990234375,87.79192352294922,163.2100067138672,118.0199966430664,33.0838737487793,234.91000366210938,9.573150634765625,43.921875,181.0470428466797,2955.449951171875,25.979999542236328
2020-05-26,78.90386199951172,2421.860107421875,1421.3699951171875,94.02281951904297,160.88999938964844,120.94999694824219,36.50366973876953,232.1999969482422,10.128823280334473,44.95579528808594,181.46920776367188,2991.77001953125,27.399999618530273
2020-05-27,79.24764251708984,2410.389892578125,1420.280029296875,99.4687271118164,161.17999267578125,121.52999877929688,38.26685333251953,229.13999938964844,10.567511558532715,45.58004379272461,184.29669189453125,3036.1298828125,27.790000915527344
2020-05-28,79.28252410888672,2401.10009765625,1418.239990234375,97.98705291748047,161.72000122070312,116.75,36.716800689697266,225.4600067138672,10.47977352142334,45.931182861328125,185.2882537841797,3029.72998046875,26.68000030517578
2020-05-29,79.2052993774414,2442.3701171875,1433.52001953125,95.48487854003906,162.91000366210938,117.30000305175781,35.07956314086914,225.08999633789062,10.4310302734375,45.531272888183594,184.14183044433594,3044.31005859375,25.8799991607666
2020-06-01,80.17935943603516,2471.0400390625,1434.8699951171875,96.75067901611328,163.66000366210938,118.7699966430664,36.93962097167969,231.91000366210938,10.587007522583008,45.83364486694336,185.2190704345703,3055.72998046875,26.940000534057617
2020-06-02,80.55054473876953,2472.409912109375,1442.31005859375,97.07449340820312,162.60000610351562,118.75,38.27653503417969,232.72000122070312,10.840472221374512,45.74586486816406,185.39697265625,3080.820068359375,27.40999984741211
2020-06-03,80.99398040771484,2478.39990234375,1439.25,102.31433868408203,159.60000610351562,122.18000030517578,40.010658264160156,230.16000366210938,11.308406829833984,46.72125244140625,191.0303192138672,3122.8701171875,29.06999969482422
2020-06-04,80.29644775390625,2460.60009765625,1414.300048828125,104.44363403320312,161.27999877929688,123.69000244140625,40.02034378051758,226.2899932861328,11.318155288696289,46.740753173828125,190.98092651367188,3112.35009765625,29.15999984741211
2020-06-05,82.5833740234375,2483.0,1440.02001953125,109.143798828125,158.00999450683594,124.81999969482422,42.393856048583984,230.77000427246094,11.366898536682129,47.8819694519043,194.8551025390625,3193.929931640625,30.610000610351562
2020-06-08,83.07164001464844,2524.06005859375,1448.0400390625,111.32215881347656,159.72000122070312,127.27999877929688,44.50579833984375,231.39999389648438,11.649608612060547,48.62326431274414,200.28091430664062,3232.389892578125,30.68000030517578
2020-06-09,85.69487762451172,2600.860107421875,1452.0799560546875,108.46674346923828,161.25999450683594,123.88999938964844,43.69202423095703,238.6699981689453,11.415641784667969,47.79418182373047,197.18751525878906,3207.179931640625,29.860000610351562
2020-06-10,87.89958953857422,2647.449951171875,1464.699951171875,104.07075500488281,163.57000732421875,122.18000030517578,40.969749450683594,236.72999572753906,11.201171875,47.42353439331055,193.51100158691406,3190.139892578125,28.75
2020-06-11,83.67949676513672,2557.9599609375,1401.9000244140625,95.38674926757812,162.38999938964844,112.63999938964844,37.64683151245117,224.42999267578125,10.421280860900879,44.41933059692383,185.3179168701172,3002.10009765625,26.5
2020-06-12,84.40194702148438,2545.02001953125,1412.9200439453125,97.99686431884766,162.6199951171875,115.48999786376953,38.915931701660156,228.5800018310547,10.606505393981934,44.881927490234375,186.95851135253906,3041.31005859375,27.959999084472656
2020-06-15,85.44575500488281,2572.679931640625,1420.739990234375,99.35098266601562,162.3300018310547,117.08000183105469,38.94499588012695,232.5,10.664997100830078,45.57090377807617,187.27476501464844,3066.590087890625,27.770000457763672
2020-06-16,87.71025848388672,2615.27001953125,1446.469970703125,100.14579010009766,162.25,118.44000244140625,40.11722183227539,235.64999389648438,10.820975303649902,46.033504486083984,188.09507751464844,3124.739990234375,27.780000686645508
2020-06-17,87.58819580078125,2640.97998046875,1452.5400390625,97.61418151855469,162.55999755859375,117.6500015258789,38.71249008178711,235.52999877929688,10.781981468200684,45.84649658203125,188.5595703125,3113.489990234375,27.15999984741211
2020-06-18,87.62307739257812,2653.97998046875,1434.1199951171875,97.08431243896484,162.24000549316406,118.37000274658203,38.78999328613281,235.94000244140625,10.801478385925293,46.25004196166992,187.27476501464844,3115.340087890625,27.09000015258789
2020-06-19,87.1223373413086,2675.010009765625,1424.6400146484375,95.97549438476562,164.02999877929688,114.3499984741211,37.92778015136719,238.7899932861328,10.548013687133789,45.29531478881836,184.37901306152344,3097.739990234375,26.59000015258789
2020-06-22,89.40178680419922,2713.820068359375,1450.6600341796875,94.93537902832031,165.08999633789062,115.91999816894531,37.93746566772461,239.22000122070312,10.781981468200684,45.01972198486328,185.2685089111328,3117.860107421875,26.3700008392334
2020-06-23,91.31005096435547,2764.409912109375,1463.97998046875,96.09325408935547,166.47999572753906,116.58999633789062,37.92778015136719,242.24000549316406,10.976953506469727,44.921295166015625,184.4383087158203,3131.2900390625,26.25
2020-06-24,89.6982421875,2734.39990234375,1432.699951171875,92.88458251953125,165.89999389648438,112.06999969482422,36.600547790527344,234.02000427246094,10.684494972229004,43.9468879699707,182.1355438232422,3050.330078125,25.280000686645508
2020-06-25,90.8890380859375,2754.580078125,1441.0999755859375,96.12268829345703,165.8000030517578,111.36000061035156,36.72649002075195,235.67999267578125,11.113433837890625,44.222476959228516,180.62344360351562,3083.760009765625,25.229999542236328
2020-06-26,88.09640502929688,2692.8701171875,1362.5400390625,90.85340118408203,166.5399932861328,109.0999984741211,35.49613571166992,216.0800018310547,10.937958717346191,42.88389587402344,177.6387481689453,3009.050048828125,24.459999084472656
2020-06-29,90.1267318725586,2680.3798828125,1397.1700439453125,91.2557144165039,166.6300048828125,111.5199966430664,37.375572204589844,220.63999938964844,11.093936920166016,43.66145706176758,180.6629638671875,3053.239990234375,25.299999237060547
2020-06-30,90.87906646728516,2758.820068359375,1418.050048828125,92.29582977294922,167.3699951171875,111.51000213623047,37.017120361328125,227.07000732421875,11.24991512298584,43.97641372680664,182.31346130371094,3100.2900390625,25.299999237060547
2020-07-01,90.7071762084961,2878.699951171875,1442.0,91.51083374023438,166.6199951171875,113.01000213623047,37.42401123046875,237.5500030517578,11.12318229675293,44.11421203613281,182.50123596191406,3115.860107421875,24.959999084472656
2020-07-02,90.7071762084961,2890.300048828125,1469.9300537109375,91.8080825805664,166.97999572753906,112.18000030517578,36.929931640625,233.4199981689453,11.357149124145508,44.17326736450195,181.37457275390625,3130.010009765625,25.239999771118164
2020-07-06,93.13361358642578,3057.0400390625,1499.6500244140625,94.12657165527344,167.97999572753906,114.43000030517578,36.51335906982422,240.27999877929688,11.630111694335938,44.517757415771484,186.29632568359375,3179.719970703125,25.729999542236328
2020-07-07,92.84463500976562,3000.1201171875,1489.9200439453125,91.47120666503906,169.0399932861328,113.62999725341797,34.69205093383789,240.86000061035156,11.415641784667969,44.49806594848633,183.64767456054688,3145.320068359375,25.1299991607666
2020-07-08,95.00699615478516,3081.110107421875,1503.5999755859375,92.44219970703125,170.08999633789062,116.66000366210938,34.74048614501953,243.5800018310547,11.571619033813477,44.36027526855469,183.6773223876953,3169.93994140625,24.940000534057617
2020-07-09,95.41555786132812,3182.6298828125,1518.6600341796875,90.44076538085938,169.6300048828125,116.80999755859375,33.829833984375,244.5,11.376646995544434,43.218544006347656,182.1750946044922,3152.050048828125,23.420000076293945
2020-07-10,95.58246612548828,3200.0,1539.010009765625,95.3848876953125,169.19000244140625,119.33999633789062,34.87611770629883,245.07000732421875,11.630111694335938,44.43901824951172,182.71865844726562,3185.0400390625,24.389999389648438
2020-07-13,95.14152526855469,3104.0,1512.22998046875,96.75220489501953,169.39999389648438,116.22000122070312,35.65114212036133,239.0,11.503378868103027,44.53744125366211,182.75819396972656,3155.219970703125,24.860000610351562
2020-07-14,96.7159652709961,3084.0,1520.8599853515625,97.30705261230469,170.19000244140625,118.66000366210938,35.52519989013672,239.72999572753906,11.854329109191895,45.147674560546875,188.49038696289062,3197.52001953125,25.639999389648438
2020-07-15,97.38111114501953,3008.8701171875,1516.8800048828125,98.8130874633789,170.33999633789062,120.9000015258789,36.95899963378906,240.27999877929688,11.766592025756836,45.66933059692383,189.52810668945312,3226.56005859375,26.8799991607666
2020-07-16,96.18284606933594,2999.89990234375,1514.9200439453125,99.09050750732422,168.72999572753906,119.43000030517578,35.91271209716797,240.92999267578125,11.766592025756836,45.42326736450195,188.6880340576172,3215.570068359375,26.850000381469727
2020-07-17,95.98853302001953,2961.969970703125,1516.8499755859375,97.25752258300781,170.1199951171875,118.6500015258789,35.44770050048828,242.02999877929688,11.766592025756836,46.08271789550781,189.24148559570312,3224.72998046875,26.450000762939453
2020-07-20,98.01139068603516,3196.840087890625,1563.8399658203125,96.40542602539062,170.94000244140625,117.79000091552734,33.9073371887207,245.4199981689453,11.717849731445312,45.39373779296875,189.36997985839844,3251.840087890625,26.010000228881836
2020-07-21,96.65866088867188,3138.2900390625,1555.9200439453125,98.49602508544922,173.0,118.62000274658203,34.35297393798828,241.75,12.00055980682373,46.45673370361328,190.72396850585938,3257.300048828125,26.149999618530273
2020-07-22,96.9301986694336,3099.909912109375,1564.8499755859375,97.78264617919922,175.6300048828125,119.02999877929688,35.11831283569336,239.8699951171875,12.049302101135254,47.716575622558594,196.29803466796875,3276.02001953125,26.299999237060547
2020-07-23,92.5182876586914,2986.550048828125,1516.75,98.06997680664062,177.17999267578125,118.12000274658203,34.55641555786133,232.60000610351562,11.854329109191895,47.51972579956055,195.24053955078125,3235.659912109375,26.760000228881836
2020-07-24,92.28909301757812,3008.909912109375,1508.2099609375,97.37641143798828,178.6999969482422,117.61000061035156,33.917022705078125,230.7100067138672,11.864078521728516,47.72642135620117,196.39686584472656,3215.6298828125,26.25
2020-07-27,94.47636413574219,3055.2099609375,1529.4300537109375,96.00910186767578,182.22999572753906,116.30999755859375,33.674827575683594,233.5,11.99081039428711,47.716575622558594,198.89727783203125,3239.409912109375,25.700000762939453
2020-07-28,92.92435455322266,3000.330078125,1503.6500244140625,96.42523956298828,183.75,116.18000030517578,35.07956314086914,230.1199951171875,11.77634048461914,47.42129898071289,193.94586181640625,3218.43994140625,26.329999923706055
2020-07-29,94.70555877685547,3033.530029296875,1523.510009765625,98.76354217529297,185.1300048828125,115.61000061035156,35.098934173583984,233.2899932861328,11.678853988647461,47.26382064819336,193.9162139892578,3258.43994140625,25.889999389648438
2020-07-30,95.85151672363281,3051.8798828125,1538.3699951171875,96.12799072265625,183.75999450683594,115.66000366210938,34.13984680175781,234.5,11.444887161254883,46.93901443481445,193.1255645751953,3246.219970703125,25.200000762939453
2020-07-31,105.8860855102539,3164.679931640625,1487.949951171875,95.7514877319336,185.42999267578125,116.94000244140625,33.44232177734375,253.6699981689453,11.386395454406738,46.4961051940918,192.00875854492188,3271.1201171875,24.889999389648438
2020-08-03,108.55415344238281,3111.889892578125,1482.760009765625,95.2164535522461,185.63999938964844,116.3499984741211,32.95793151855469,251.9600067138672,11.542373657226562,45.57090377807617,192.1273651123047,3294.610107421875,25.639999389648438
2020-08-04,109.27909851074219,3138.830078125,1473.300048828125,94.67151641845703,189.58999633789062,117.29000091552734,34.905181884765625,249.8300018310547,11.698351860046387,45.95476150512695,197.0293731689453,3306.510009765625,25.799999237060547
2020-08-05,109.6751937866211,3205.030029296875,1479.0899658203125,96.31624603271484,191.35000610351562,127.61000061035156,34.11077880859375,249.1199951171875,11.903072357177734,46.476417541503906,196.9305419921875,3327.77001953125,26.329999923706055
2020-08-06,113.50167846679688,3225.0,1504.949951171875,96.3459701538086,193.88999938964844,130.82000732421875,34.6339225769043,265.2799987792969,11.932318687438965,46.732322692871094,200.80471801757812,3349.159912109375,26.6200008392334
2020-08-07,110.92113494873047,3167.4599609375,1498.3699951171875,98.46629333496094,190.80999755859375,129.92999267578125,34.958431243896484,268.44000244140625,11.883575439453125,47.04728317260742,202.2081298828125,3351.280029296875,26.719999313354492
2020-08-10,112.53335571289062,3148.159912109375,1496.8199462890625,99.71471405029297,190.14999389648438,128.7899932861328,36.56130599975586,263.0,11.971312522888184,46.968544006347656,201.73373413085938,3360.469970703125,27.979999542236328
2020-08-11,109.1866226196289,3080.669921875,1480.5400390625,102.865478515625,179.94000244140625,130.49000549316406,36.44330596923828,256.1300048828125,12.010307312011719,47.17523956298828,202.6034393310547,3333.68994140625,28.520000457763672
2020-08-12,112.81536865234375,3162.239990234375,1507.239990234375,101.99356842041016,179.10000610351562,131.7899932861328,36.19746398925781,259.8900146484375,12.205280303955078,47.667362213134766,203.6115264892578,3380.35009765625,28.030000686645508
2020-08-13,114.81192016601562,3161.02001953125,1516.6500244140625,101.4288101196289,183.3300018310547,130.9600067138672,34.791259765625,261.29998779296875,12.098045349121094,47.6181526184082,204.07601928710938,3373.429931640625,27.549999237060547
2020-08-14,114.70960235595703,3148.02001953125,1504.6300048828125,101.46844482421875,182.5399932861328,130.52999877929688,34.98793411254883,261.239990234375,12.020055770874023,47.68704605102539,204.60971069335938,3372.85009765625,27.860000610351562
2020-08-17,114.41011047363281,3182.409912109375,1516.239990234375,98.79326629638672,186.5,129.3699951171875,34.59458923339844,261.1600036621094,11.981061935424805,47.450828552246094,206.23052978515625,3381.989990234375,30.010000228881836
2020-08-18,115.36347198486328,3312.489990234375,1555.780029296875,97.41604614257812,188.17999267578125,128.9199981689453,34.102909088134766,262.3399963378906,11.834833145141602,47.65752029418945,207.8612518310547,3389.780029296875,29.84000015258789
2020-08-19,115.50821685791016,3260.47998046875,1544.6099853515625,97.64393615722656,182.24000549316406,127.7699966430664,33.64072799682617,262.5899963378906,11.84458065032959,46.624053955078125,207.0607147216797,3374.85009765625,29.219999313354492
2020-08-20,118.07129669189453,3297.3701171875,1576.25,96.47478485107422,183.5,128.1199951171875,34.142242431640625,269.010009765625,11.7275972366333,46.6043701171875,207.42640686035156,3385.510009765625,28.829999923706055
2020-08-21,124.15579986572266,3284.719970703125,1575.5699462890625,96.42523956298828,182.02999877929688,127.44000244140625,34.86992645263672,267.010009765625,11.7275972366333,46.53547286987305,209.0966339111328,3397.159912109375,28.559999465942383
2020-08-24,125.64073944091797,3307.4599609375,1585.1500244140625,99.14004516601562,181.0,130.69000244140625,36.354801177978516,271.3900146484375,11.961565017700195,47.21460723876953,210.13436889648438,3431.280029296875,30.309999465942383
2020-08-25,124.61001586914062,3346.489990234375,1605.8499755859375,99.57600402832031,181.22000122070312,129.7899932861328,36.50230407714844,280.82000732421875,11.922569274902344,47.15555191040039,210.16400146484375,3443.6201171875,29.690000534057617
2020-08-26,126.30459594726562,3441.85009765625,1644.1300048828125,98.17896270751953,183.36000061035156,132.17999267578125,35.16493606567383,303.9100036621094,12.06879997253418,47.401615142822266,211.2610321044922,3478.72998046875,29.479999542236328
2020-08-27,124.7947006225586,3400.0,1628.52001953125,101.40898895263672,181.24000549316406,133.72999572753906,36.266300201416016,293.2200012207031,11.981061935424805,47.47051239013672,209.7093963623047,3484.550048828125,29.690000534057617
2020-08-28,124.5925521850586,3401.800048828125,1639.4300537109375,101.82512664794922,184.38999938964844,135.5399932861328,36.54164123535156,293.6600036621094,12.049302101135254,49.045318603515625,212.39759826660156,3508.010009765625,30.020000457763672
2020-08-31,128.8177490234375,3450.9599609375,1629.530029296875,99.26885223388672,184.8300018310547,131.8699951171875,35.23377227783203,293.20001220703125,11.84458065032959,48.75004196166992,212.2584228515625,3500.31005859375,29.6299991607666
2020-09-01,133.9488983154297,3499.1201171875,1655.0799560546875,99.21930694580078,185.0500030517578,133.5500030517578,34.486419677734375,295.44000244140625,11.883575439453125,48.34649658203125,211.43331909179688,3526.64990234375,29.790000915527344
2020-09-02,131.17369079589844,3531.449951171875,1717.3900146484375,100.7154312133789,182.6199951171875,135.38999938964844,35.135433197021484,302.5,12.11754322052002,50.3838996887207,214.95240783691406,3580.840087890625,30.959999084472656
2020-09-03,120.67180633544922,3368.0,1629.510009765625,100.39836883544922,181.13999938964844,133.24000549316406,35.64678192138672,291.1199951171875,11.922569274902344,49.685081481933594,212.53677368164062,3455.06005859375,29.479999542236328
2020-09-04,120.75167083740234,3294.6201171875,1581.2099609375,102.56822967529297,181.63999938964844,131.99000549316406,36.16796112060547,282.7300109863281,11.981061935424805,50.236263275146484,210.47898864746094,3426.9599609375,30.0
2020-09-08,112.62569427490234,3149.840087890625,1523.5999755859375,99.00133514404297,181.2899932861328,134.1999969482422,34.9485969543457,271.1600036621094,11.717849731445312,49.025634765625,212.3180694580078,3331.840087890625,32.380001068115234
2020-09-09,117.1179428100586,3268.610107421875,1547.22998046875,99.9426040649414,183.0500030517578,133.36000061035156,34.12257766723633,273.7200012207031,12.010307312011719,49.3996467590332,214.43548583984375,3398.9599609375,31.950000762939453
2020-09-10,113.29454040527344,3175.110107421875,1526.050048828125,98.91216278076172,182.4600067138672,133.22000122070312,33.139217376708984,268.0899963378906,11.903072357177734,49.21263885498047,215.95645141601562,3339.18994140625,30.170000076293945
2020-09-11,111.8071060180664,3116.219970703125,1515.760009765625,100.14076232910156,182.4499969482422,131.75,33.060546875,266.6099853515625,11.94206714630127,50.255950927734375,216.71194458007812,3340.969970703125,30.459999084472656
2020-09-14,115.16131591796875,3102.969970703125,1508.8299560546875,101.52789306640625,183.88999938964844,131.25,34.69292068481445,266.1499938964844,12.205280303955078,50.31547927856445,219.25682067871094,3383.5400390625,31.18000030517578
2020-09-15,115.34101104736328,3156.1298828125,1535.1199951171875,98.36721801757812,183.4499969482422,131.24000549316406,35.518943786621094,272.4200134277344,11.981061935424805,50.652835845947266,221.05612182617188,3401.199951171875,31.579999923706055
2020-09-16,111.93688201904297,3078.10009765625,1512.0899658203125,98.78335571289062,183.97000122070312,132.08999633789062,37.180824279785156,263.5199890136719,12.03955364227295,50.394859313964844,223.48170471191406,3385.489990234375,31.790000915527344
2020-09-17,110.14996337890625,3008.72998046875,1487.0400390625,97.65383911132812,182.9600067138672,130.22000122070312,36.75798034667969,254.82000732421875,11.961565017700195,50.1567268371582,221.264892578125,3357.010009765625,31.920000076293945
2020-09-18,106.65599060058594,2954.909912109375,1451.0899658203125,97.44577026367188,183.1999969482422,128.6300048828125,34.85026168823242,252.52999877929688,11.7275972366333,50.0575065612793,218.96853637695312,3319.469970703125,31.5
2020-09-21,109.89041137695312,2960.469970703125,1430.1400146484375,94.4337158203125,179.52000427246094,125.41000366210938,32.77537536621094,248.14999389648438,11.084188461303711,48.70808410644531,215.13134765625,3281.06005859375,30.0
2020-09-22,111.617431640625,3128.989990234375,1459.8199462890625,93.40327453613281,178.64999389648438,127.20999908447266,32.93271255493164,254.75,10.947707176208496,49.273651123046875,215.13134765625,3315.570068359375,29.440000534057617
2020-09-23,106.93550872802734,2999.860107421875,1409.3900146484375,91.88734436035156,174.7899932861328,123.27999877929688,31.70351219177246,249.02000427246094,10.635750770568848,47.834930419921875,213.69985961914062,3236.919921875,29.34000015258789
2020-09-24,108.03361511230469,3019.7900390625,1422.8599853515625,91.8080825805664,175.44000244140625,122.48999786376953,32.0378532409668,249.52999877929688,10.509018898010254,48.32112121582031,214.84304809570312,3246.590087890625,29.110000610351562
2020-09-25,112.08662414550781,3095.1298828125,1439.06005859375,92.61063385009766,174.94000244140625,124.0,32.893375396728516,254.82000732421875,10.245806694030762,48.340965270996094,216.89088439941406,3298.4599609375,29.0
2020-09-28,114.76200866699219,3174.050048828125,1458.6600341796875,95.27590942382812,176.6999969482422,125.98999786376953,34.181575775146484,256.82000732421875,10.791728973388672,48.8966064453125,218.95858764648438,3351.60009765625,29.440000534057617
2020-09-29,113.89350128173828,3144.8798828125,1466.02001953125,94.47335052490234,178.19000244140625,125.4000015258789,33.237552642822266,261.7900085449219,10.733238220214844,48.539405822753906,217.38792419433594,3335.469970703125,28.739999771118164
2020-09-30,115.61054229736328,3148.72998046875,1465.5999755859375,95.3848876953125,177.1199951171875,124.08000183105469,33.1490478515625,261.8999938964844,10.869717597961426,48.985904693603516,218.19314575195312,3363.0,29.59000015258789
2020-10-01,116.58885955810547,3221.260009765625,1487.9000244140625,96.07846069335938,178.6999969482422,123.30999755859375,34.07341003417969,266.6300048828125,10.937958717346191,48.797386169433594,218.2925567626953,3380.800048828125,30.3799991607666
2020-10-02,112.82534790039062,3125.0,1455.5999755859375,96.98999786376953,178.5399932861328,122.55000305175781,35.11576843261719,259.94000244140625,11.05494213104248,48.97598648071289,221.35435485839844,3348.419921875,30.459999084472656
2020-10-05,116.29935455322266,3199.199951171875,1482.8299560546875,99.04000091552734,179.41000366210938,123.37000274658203,34.97809600830078,264.6499938964844,11.279160499572754,48.99583053588867,224.73426818847656,3408.60009765625,30.959999084472656
2020-10-06,112.9651107788086,3099.9599609375,1451.02001953125,98.0199966430664,177.3000030517578,120.93000030517578,34.673255920410156,258.6600036621094,11.288908958435059,48.55924987792969,222.76596069335938,3360.969970703125,30.399999618530273
2020-10-07,114.88180541992188,3195.68994140625,1459.1400146484375,99.7300033569336,177.22000122070312,122.91000366210938,35.07643127441406,258.1199951171875,11.454635620117188,49.17443084716797,225.141845703125,3419.43994140625,31.6200008392334
2020-10-08,114.77198791503906,3190.550048828125,1483.4300537109375,101.77999877929688,177.85000610351562,123.08999633789062,36.059791564941406,263.760009765625,11.483881950378418,50.06742477416992,224.4658660888672,3446.830078125,32.209999084472656
2020-10-09,116.76854705810547,3286.64990234375,1510.449951171875,101.19999694824219,181.0800018310547,124.9800033569336,34.928932189941406,264.45001220703125,11.376646995544434,50.414703369140625,223.50160217285156,3477.139892578125,32.15999984741211
2020-10-12,124.18575286865234,3442.929931640625,1564.5899658203125,102.44000244140625,180.55999755859375,124.97000122070312,34.909263610839844,275.75,11.513128280639648,50.69252395629883,224.77403259277344,3534.219970703125,32.209999084472656
2020-10-13,120.89143371582031,3443.6298828125,1567.0699462890625,100.77999877929688,177.72000122070312,128.9600067138672,33.42439270019531,276.1400146484375,11.34740161895752,49.82929611206055,226.0067138671875,3511.929931640625,31.8700008392334
2020-10-14,120.98127746582031,3363.7099609375,1563.43994140625,100.22000122070312,178.27000427246094,126.58999633789062,33.414554595947266,271.82000732421875,11.22066879272461,49.730072021484375,226.2751007080078,3488.669921875,31.670000076293945
2020-10-15,120.50210571289062,3338.64990234375,1555.469970703125,101.72000122070312,178.9199981689453,127.36000061035156,33.59156036376953,266.7200012207031,11.152427673339844,49.6010856628418,228.28317260742188,3483.340087890625,32.59000015258789
2020-10-16,118.81501007080078,3272.7099609375,1567.699951171875,101.51000213623047,178.3000030517578,126.80999755859375,33.444053649902344,265.92999267578125,11.327903747558594,49.640769958496094,228.0147705078125,3483.81005859375,33.45000076293945
2020-10-19,115.7802505493164,3207.2099609375,1529.949951171875,99.80000305175781,178.38999938964844,124.2300033569336,32.04768753051758,261.3999938964844,11.454635620117188,49.23395919799805,224.66468811035156,3426.919921875,33.349998474121094
2020-10-20,117.3076171875,3217.010009765625,1551.0799560546875,100.37000274658203,179.25,124.94999694824219,32.283695220947266,267.55999755859375,12.059050559997559,49.85905838012695,226.10610961914062,3443.1201171875,35.599998474121094
2020-10-21,116.6687240600586,3184.93994140625,1585.989990234375,99.37000274658203,180.60000610351562,126.62999725341797,31.605175018310547,278.7300109863281,11.912821769714355,49.6010856628418,226.8417510986328,3435.56005859375,35.77000045776367
2020-10-22,115.55064392089844,3176.39990234375,1606.6600341796875,102.87999725341797,178.8300018310547,127.55999755859375,33.139217376708984,278.1199951171875,12.127290725708008,50.28571701049805,227.7960662841797,3453.489990234375,37.40999984741211
2020-10-23,114.84187316894531,3204.39990234375,1632.97998046875,103.80999755859375,178.63999938964844,128.35000610351562,33.50305938720703,284.7900085449219,12.127290725708008,50.12696075439453,227.35867309570312,3465.389892578125,36.83000183105469
2020-10-26,114.85185241699219,3207.0400390625,1584.2900390625,101.23999786376953,178.5500030517578,124.05999755859375,32.59836959838867,277.1099853515625,12.078548431396484,49.412559509277344,222.9449005126953,3400.969970703125,35.81999969482422
2020-10-27,116.39917755126953,3286.330078125,1598.8800048828125,99.33000183105469,179.02000427246094,123.30999755859375,31.68384552001953,283.2900085449219,11.786088943481445,49.511783599853516,221.652587890625,3390.679931640625,34.88999938964844
2020-10-28,111.00847625732422,3162.780029296875,1510.800048828125,96.54000091552734,176.1300048828125,118.47000122070312,29.8646297454834,267.6700134277344,11.29865837097168,47.586875915527344,213.4215087890625,3271.030029296875,34.09000015258789
2020-10-29,115.12138366699219,3211.010009765625,1556.8800048828125,97.16999816894531,175.39999389648438,121.54000091552734,29.697458267211914,280.8299865722656,11.29865837097168,47.64640808105469,213.67996215820312,3310.110107421875,34.88999938964844
2020-10-30,108.67251586914062,3036.14990234375,1616.1099853515625,98.04000091552734,176.1999969482422,121.25,30.218637466430664,263.1099853515625,11.396143913269043,47.686100006103516,211.74148559570312,3269.9599609375,34.529998779296875
2020-11-02,108.5826644897461,3004.47998046875,1624.3199462890625,100.25,177.91000366210938,120.12999725341797,31.959184646606445,261.3599853515625,11.639859199523926,48.24174118041992,211.30409240722656,3310.239990234375,34.54999923706055
2020-11-03,110.24979400634766,3048.409912109375,1645.6600341796875,103.41000366210938,178.9199981689453,124.0199966430664,33.463722229003906,265.29998779296875,12.410000801086426,48.966060638427734,215.51904296875,3369.159912109375,35.349998474121094
2020-11-04,114.75202178955078,3241.159912109375,1745.8499755859375,100.25,178.82000732421875,125.06999969482422,31.113494873046875,287.3800048828125,12.254022598266602,48.78746032714844,213.60043334960938,3443.43994140625,35.2400016784668
2020-11-05,118.82499694824219,3322.0,1762.5,104.3499984741211,182.92999267578125,126.95999908447266,31.32000160217285,294.67999267578125,12.66346549987793,49.05535888671875,215.0319366455078,3510.449951171875,37.13999938964844
2020-11-06,118.69000244140625,3311.3701171875,1759.72998046875,102.95999908447266,183.19000244140625,127.45999908447266,30.3700008392334,293.4100036621094,12.731706619262695,49.065284729003906,215.28045654296875,3509.43994140625,37.470001220703125
2020-11-09,116.31999969482422,3143.739990234375,1761.4200439453125,116.9000015258789,175.0800018310547,142.58999633789062,38.689998626708984,278.7699890136719,13.18989086151123,52.1610107421875,211.96018981933594,3550.5,38.959999084472656
2020-11-10,115.97000122070312,3035.02001953125,1737.719970703125,116.5199966430664,175.66000366210938,142.11000061035156,39.02000045776367,272.42999267578125,13.336121559143066,53.56003952026367,212.05960083007812,3545.530029296875,41.08000183105469
2020-11-11,119.48999786376953,3137.389892578125,1747.22998046875,114.77999877929688,174.89999389648438,137.82000732421875,37.470001220703125,276.4800109863281,13.30687427520752,53.16315460205078,216.73184204101562,3572.659912109375,40.560001373291016
2020-11-12,119.20999908447266,3110.280029296875,1742.8199462890625,113.37000274658203,175.9600067138672,135.52000427246094,36.029998779296875,275.0799865722656,13.131400108337402,52.59758758544922,211.8110809326172,3537.010009765625,39.31999969482422
2020-11-13,119.26000213623047,3128.81005859375,1772.260009765625,114.08000183105469,177.16000366210938,138.36000061035156,38.5099983215332,276.95001220703125,13.326372146606445,53.03416442871094,212.01983642578125,3585.14990234375,41.189998626708984
2020-11-16,120.30000305175781,3131.06005859375,1774.030029296875,117.30000305175781,177.14999389648438,144.6699981689453,39.93000030517578,278.9599914550781,13.901541709899902,53.43104934692383,215.44944763183594,3626.909912109375,42.130001068115234
2020-11-17,119.38999938964844,3135.659912109375,1761.6600341796875,116.11000061035156,176.5,144.5,41.4900016784668,275.0,14.13550853729248,53.26237487792969,214.73370361328125,3609.530029296875,41.97999954223633
2020-11-18,118.02999877929688,3105.4599609375,1740.6400146484375,115.25,175.49000549316406,143.89999389648438,39.790000915527344,271.9700012207031,13.891792297363281,52.20069885253906,214.24661254882812,3567.7900390625,42.720001220703125
2020-11-19,118.63999938964844,3117.02001953125,1758.5699462890625,115.55999755859375,175.16000366210938,141.72000122070312,39.84000015258789,272.94000244140625,14.10626220703125,52.71665573120117,213.83901977539062,3581.8701171875,42.81999969482422
2020-11-20,117.33999633789062,3099.39990234375,1736.3800048828125,114.56999969482422,175.69000244140625,141.07000732421875,39.93000030517578,269.70001220703125,14.05751895904541,52.260231018066406,212.82504272460938,3557.5400390625,43.040000915527344
2020-11-23,113.8499984741211,3098.389892578125,1727.56005859375,117.87000274658203,172.22999572753906,145.97999572753906,41.02000045776367,268.42999267578125,14.194000244140625,52.27015686035156,215.71786499023438,3577.590087890625,44.77000045776367
2020-11-24,115.16999816894531,3118.06005859375,1763.9000244140625,123.31999969482422,169.58999633789062,151.49000549316406,41.84000015258789,276.9200134277344,14.760000228881836,52.80595397949219,218.41184997558594,3635.409912109375,46.459999084472656
2020-11-25,116.02999877929688,3185.070068359375,1764.1300048828125,122.02999877929688,169.50999450683594,149.08999633789062,40.619998931884766,275.5899963378906,14.619999885559082,52.518211364746094,218.0440216064453,3629.64990234375,45.459999084472656
2020-11-27,116.58999633789062,3195.340087890625,1787.02001953125,121.22000122070312,167.7899932861328,147.1300048828125,40.33000183105469,277.80999755859375,14.550000190734863,52.290000915527344,217.04000854492188,3638.35009765625,45.060001373291016
2020-11-30,119.05000305175781,3168.0400390625,1754.4000244140625,117.87999725341797,166.6699981689453,148.00999450683594,38.90999984741211,276.9700012207031,14.140000343322754,51.599998474121094,217.44000244140625,3621.6298828125,43.84000015258789
2020-12-01,122.72000122070312,3220.080078125,1795.3599853515625,119.73999786376953,170.17999267578125,149.44000244140625,39.29999923706055,286.54998779296875,14.5,52.040000915527344,216.13999938964844,3662.449951171875,44.68000030517578
2020-12-02,123.08000183105469,3203.530029296875,1824.969970703125,122.04000091552734,171.4600067138672,153.61000061035156,39.720001220703125,287.5199890136719,14.539999961853027,52.11000061035156,210.86000061035156,3669.010009765625,44.58000183105469
2020-12-03,122.94000244140625,3186.72998046875,1821.8399658203125,121.23999786376953,172.80999755859375,153.24000549316406,40.290000915527344,281.8500061035156,14.5,52.779998779296875,211.50999450683594,3666.719970703125,44.09000015258789
2020-12-04,122.25,3162.580078125,1823.760009765625,122.33999633789062,172.32000732421875,154.13999938964844,41.630001068115234,279.70001220703125,14.4399995803833,53.849998474121094,210.74000549316406,3699.1201171875,44.400001525878906
2020-12-07,123.75,3158.0,1817.030029296875,121.87999725341797,174.88999938964844,153.67999267578125,40.58000183105469,285.5799865722656,14.470000267028809,52.9900016784668,208.88999938964844,3691.9599609375,44.310001373291016
2020-12-08,124.37999725341797,3177.2900390625,1811.3299560546875,122.0,175.5,153.72000122070312,40.029998779296875,283.3999938964844,14.430000305175781,53.18000030517578,208.38999938964844,3702.25,43.79999923706055
2020-12-09,121.77999877929688,3104.199951171875,1777.8599853515625,121.05000305175781,172.5,154.42999267578125,39.68000030517578,277.9200134277344,14.4399995803833,53.33000183105469,208.69000244140625,3672.820068359375,44.43000030517578
2020-12-10,123.23999786376953,3101.489990234375,1767.6500244140625,120.2699966430664,172.1699981689453,154.69000244140625,39.709999084472656,277.1199951171875,14.15999984741211,53.04999923706055,208.0399932861328,3668.10009765625,42.869998931884766
2020-12-11,122.41000366210938,3116.419921875,1774.800048828125,119.55999755859375,172.49000549316406,175.72000122070312,38.79999923706055,273.54998779296875,13.960000038146973,53.349998474121094,207.75999450683594,3663.4599609375,41.91999816894531
2020-12-14,121.77999877929688,3156.969970703125,1752.260009765625,118.30000305175781,171.5399932861328,169.3000030517578,38.08000183105469,274.19000244140625,13.9399995803833,53.27000045776367,211.9199981689453,3647.489990234375,41.619998931884766
2020-12-15,127.87999725341797,3165.1201171875,1761.0799560546875,120.31999969482422,173.94000244140625,173.94000244140625,38.779998779296875,275.54998779296875,14.300000190734863,53.84000015258789,214.86000061035156,3694.6201171875,41.65999984741211
2020-12-16,127.80999755859375,3240.9599609375,1757.18994140625,120.66999816894531,174.89999389648438,173.1199951171875,38.209999084472656,275.6700134277344,14.210000038146973,53.060001373291016,213.8000030517578,3701.169921875,41.41999816894531
2020-12-17,128.6999969482422,3236.080078125,1740.510009765625,119.66999816894531,176.74000549316406,173.5500030517578,38.380001068115234,274.4800109863281,14.109999656677246,53.27000045776367,214.25,3722.47998046875,42.029998779296875
2020-12-18,126.66000366210938,3201.64990234375,1726.219970703125,119.08000183105469,176.44000244140625,172.88999938964844,36.709999084472656,276.3999938964844,14.0600004196167,53.7400016784668,215.0800018310547,3709.409912109375,41.0099983215332
2020-12-21,128.22999572753906,3206.179931640625,1734.56005859375,123.55000305175781,175.8800048828125,170.69000244140625,36.18000030517578,272.7900085449219,13.800000190734863,52.810001373291016,211.6699981689453,3694.919921875,41.209999084472656
2020-12-22,131.8800048828125,3206.52001953125,1720.219970703125,121.66999816894531,174.49000549316406,170.4499969482422,35.40999984741211,267.0899963378906,13.680000305175781,52.7599983215332,211.9199981689453,3687.260009765625,40.900001525878906
2020-12-23,130.9600067138672,3185.27001953125,1728.22998046875,125.06999969482422,175.64999389648438,173.5500030517578,36.470001220703125,268.1099853515625,14.119999885559082,53.08000183105469,212.02000427246094,3690.010009765625,42.45000076293945
2020-12-24,131.97000122070312,3172.68994140625,1734.1600341796875,124.5199966430664,176.35000610351562,173.72999572753906,36.150001525878906,267.3999938964844,14.069999694824219,53.439998626708984,211.38999938964844,3703.06005859375,41.58000183105469
================================================
FILE: docker-compose.yml
================================================
version: '3.8'
services:
jupyterlab:
image: fmnm
build: .
ports:
- "8888:8888"
volumes:
- .:/workspace
command: ["jupyter", "nbclassic", "--ip=0.0.0.0", "--port=8888", "--no-browser", "--allow-root", "--NotebookApp.token=''"]
container_name: Numeric_Finance
================================================
FILE: environment.yml
================================================
name: FMNM
channels:
- conda-forge
- defaults
dependencies:
- anyio=3.7.1=pyhd8ed1ab_0
- appnope=0.1.3=pyhd8ed1ab_0
- arch-py=6.1.0=py311hb49d859_0
- argon2-cffi=21.3.0=pyhd8ed1ab_0
- argon2-cffi-bindings=21.2.0=py311he2be06e_3
- asttokens=2.2.1=pyhd8ed1ab_0
- async-lru=2.0.3=pyhd8ed1ab_0
- attrs=23.1.0=pyh71513ae_1
- babel=2.12.1=pyhd8ed1ab_1
- backcall=0.2.0=pyh9f0ad1d_0
- backports=1.0=pyhd8ed1ab_3
- backports.functools_lru_cache=1.6.5=pyhd8ed1ab_0
- beautifulsoup4=4.12.2=pyha770c72_0
- black=23.7.0=py311h267d04e_1
- black-jupyter=23.7.0=hd8ed1ab_1
- bleach=6.0.0=pyhd8ed1ab_0
- brotli=1.0.9=h1a8c8d9_9
- brotli-bin=1.0.9=h1a8c8d9_9
- brotli-python=1.0.9=py311ha397e9f_9
- bzip2=1.0.8=h3422bc3_4
- ca-certificates=2023.7.22=hf0a4a13_0
- certifi=2023.7.22=pyhd8ed1ab_0
- cffi=1.15.1=py311hae827db_3
- charset-normalizer=3.2.0=pyhd8ed1ab_0
- click=8.1.6=unix_pyh707e725_0
- comm=0.1.3=pyhd8ed1ab_0
- contourpy=1.1.0=py311he4fd1f5_0
- cvxpy=1.3.2=py311ha1ab1f8_0
- cvxpy-base=1.3.2=py311h9e438b8_0
- cycler=0.11.0=pyhd8ed1ab_0
- cython=3.0.0=py311ha891d26_0
- debugpy=1.6.7=py311ha397e9f_0
- decorator=5.1.1=pyhd8ed1ab_0
- defusedxml=0.7.1=pyhd8ed1ab_0
- ecos=2.0.11=py311h4add359_0
- entrypoints=0.4=pyhd8ed1ab_0
- exceptiongroup=1.1.2=pyhd8ed1ab_0
- executing=1.2.0=pyhd8ed1ab_0
- flake8=6.1.0=pyhd8ed1ab_0
- flit-core=3.9.0=pyhd8ed1ab_0
- fonttools=4.41.1=py311heffc1b2_0
- freetype=2.12.1=hd633e50_1
- gmp=6.2.1=h9f76cd9_0
- gmpy2=2.1.2=py311h2ba9262_1
- idna=3.4=pyhd8ed1ab_0
- importlib-metadata=6.8.0=pyha770c72_0
- importlib_metadata=6.8.0=hd8ed1ab_0
- importlib_resources=6.0.0=pyhd8ed1ab_1
- ipykernel=6.24.0=pyh5fb750a_0
- ipython=8.14.0=pyhd1c38e8_0
- ipywidgets=8.0.7=pyhd8ed1ab_0
- jedi=0.18.2=pyhd8ed1ab_0
- jinja2=3.1.2=pyhd8ed1ab_1
- joblib=1.3.0=pyhd8ed1ab_1
- json5=0.9.14=pyhd8ed1ab_0
- jsonschema=4.18.4=pyhd8ed1ab_0
- jsonschema-specifications=2023.7.1=pyhd8ed1ab_0
- jupyter=1.0.0=py311h267d04e_8
- jupyter-lsp=2.2.0=pyhd8ed1ab_0
- jupyter_client=8.3.0=pyhd8ed1ab_0
- jupyter_console=6.6.3=pyhd8ed1ab_0
- jupyter_core=5.3.1=py311h267d04e_0
- jupyter_events=0.6.3=pyhd8ed1ab_0
- jupyter_server=2.7.0=pyhd8ed1ab_0
- jupyter_server_terminals=0.4.4=pyhd8ed1ab_1
- jupyterlab=4.0.3=pyhd8ed1ab_0
- jupyterlab_pygments=0.2.2=pyhd8ed1ab_0
- jupyterlab_server=2.24.0=pyhd8ed1ab_0
- jupyterlab_widgets=3.0.8=pyhd8ed1ab_0
- kiwisolver=1.4.4=py311hd6ee22a_1
- lcms2=2.15=hd835a16_1
- lerc=4.0.0=h9a09cb3_0
- libblas=3.9.0=17_osxarm64_openblas
- libbrotlicommon=1.0.9=h1a8c8d9_9
- libbrotlidec=1.0.9=h1a8c8d9_9
- libbrotlienc=1.0.9=h1a8c8d9_9
- libcblas=3.9.0=17_osxarm64_openblas
- libcxx=16.0.6=h4653b0c_0
- libdeflate=1.18=h1a8c8d9_0
- libexpat=2.5.0=hb7217d7_1
- libffi=3.4.2=h3422bc3_5
- libgfortran=5.0.0=12_3_0_hd922786_1
- libgfortran5=12.3.0=ha3a6a3e_1
- libjpeg-turbo=2.1.5.1=h1a8c8d9_0
- liblapack=3.9.0=17_osxarm64_openblas
- libopenblas=0.3.23=openmp_hc731615_0
- libosqp=0.6.3=h13dd4ca_0
- libpng=1.6.39=h76d750c_0
- libqdldl=0.1.5=hb7217d7_1
- libsodium=1.0.18=h27ca646_1
- libsqlite=3.42.0=hb31c410_0
- libtiff=4.5.1=h23a1a89_0
- libwebp-base=1.3.1=hb547adb_0
- libxcb=1.15=hf346824_0
- libzlib=1.2.13=h53f4e23_5
- llvm-openmp=16.0.6=h1c12783_0
- markupsafe=2.1.3=py311heffc1b2_0
- matplotlib=3.7.2=py311ha1ab1f8_0
- matplotlib-base=3.7.2=py311h3bc9839_0
- matplotlib-inline=0.1.6=pyhd8ed1ab_0
- mccabe=0.7.0=pyhd8ed1ab_0
- mistune=3.0.0=pyhd8ed1ab_0
- mpc=1.3.1=h91ba8db_0
- mpfr=4.2.0=he09a6ba_0
- mpmath=1.3.0=pyhd8ed1ab_0
- munkres=1.1.4=pyh9f0ad1d_0
- mypy_extensions=1.0.0=pyha770c72_0
- nbclient=0.8.0=pyhd8ed1ab_0
- nbconvert=7.7.2=pyhd8ed1ab_0
- nbconvert-core=7.7.2=pyhd8ed1ab_0
- nbconvert-pandoc=7.7.2=pyhd8ed1ab_0
- nbformat=5.9.1=pyhd8ed1ab_0
- ncurses=6.4=h7ea286d_0
- nest-asyncio=1.5.6=pyhd8ed1ab_0
- notebook=7.0.0=pyhd8ed1ab_0
- notebook-shim=0.2.3=pyhd8ed1ab_0
- numpy=1.25.1=py311hb8f3215_0
- openjpeg=2.5.0=hbc2ba62_2
- openssl=3.1.2=h53f4e23_0
- osqp=0.6.3=py311h9e438b8_1
- overrides=7.3.1=pyhd8ed1ab_0
- packaging=23.1=pyhd8ed1ab_0
- pandas=2.0.3=py311h9e438b8_1
- pandoc=3.1.3=hce30654_0
- pandocfilters=1.5.0=pyhd8ed1ab_0
- parso=0.8.3=pyhd8ed1ab_0
- pathspec=0.11.2=pyhd8ed1ab_0
- patsy=0.5.3=pyhd8ed1ab_0
- pexpect=4.8.0=pyh1a96a4e_2
- pickleshare=0.7.5=py_1003
- pillow=10.0.0=py311h095fde6_0
- pip=23.2.1=pyhd8ed1ab_0
- pkgutil-resolve-name=1.3.10=pyhd8ed1ab_0
- platformdirs=3.9.1=pyhd8ed1ab_0
- pooch=1.7.0=pyha770c72_3
- prometheus_client=0.17.1=pyhd8ed1ab_0
- prompt-toolkit=3.0.39=pyha770c72_0
- prompt_toolkit=3.0.39=hd8ed1ab_0
- psutil=5.9.5=py311he2be06e_0
- pthread-stubs=0.4=h27ca646_1001
- ptyprocess=0.7.0=pyhd3deb0d_0
- pure_eval=0.2.2=pyhd8ed1ab_0
- pycodestyle=2.11.0=pyhd8ed1ab_0
- pycparser=2.21=pyhd8ed1ab_0
- pyflakes=3.1.0=pyhd8ed1ab_0
- pygments=2.15.1=pyhd8ed1ab_0
- pyobjc-core=9.2=py311hb702dc4_0
- pyobjc-framework-cocoa=9.2=py311hb702dc4_0
- pyparsing=3.0.9=pyhd8ed1ab_0
- pysocks=1.7.1=pyha2e5f31_6
- python=3.11.4=h47c9636_0_cpython
- python-dateutil=2.8.2=pyhd8ed1ab_0
- python-fastjsonschema=2.18.0=pyhd8ed1ab_0
- python-json-logger=2.0.7=pyhd8ed1ab_0
- python-tzdata=2023.3=pyhd8ed1ab_0
- python_abi=3.11=3_cp311
- pytz=2023.3=pyhd8ed1ab_0
- pyyaml=6.0=py311he2be06e_5
- pyzmq=25.1.0=py311hb1af645_0
- qdldl-python=0.1.5.post2=py311h4eec4a9_0
- readline=8.2=h92ec313_1
- referencing=0.30.0=pyhd8ed1ab_0
- requests=2.31.0=pyhd8ed1ab_0
- rfc3339-validator=0.1.4=pyhd8ed1ab_0
- rfc3986-validator=0.1.1=pyh9f0ad1d_0
- rpds-py=0.9.2=py311h0563b04_0
- scikit-learn=1.3.0=py311hf0b18b8_0
- scipy=1.11.1=py311h93d07a4_0
- scs=3.2.3=py311h0dc035f_0
- seaborn=0.12.2=hd8ed1ab_0
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\begin{thebibliography}{}
\bibitem[Applebaum, 2009]{Applebaum}
Applebaum, D. (2009).
\newblock {\em Lévy Processes and Stochastic Calculus}.
\newblock Cambridge University Press; 2nd edition.
\bibitem[Barndorff-Nielsen, 1997]{BN97}
Barndorff-Nielsen (1997).
\newblock Processes of {N}ormal inverse {G}aussian type.
\newblock {\em Finance and Stochastics}, 2:41--68.
\bibitem[Black and Scholes, 1973]{BS73}
Black, F. and Scholes, M. (1973).
\newblock The pricing of options and corporate liabilities.
\newblock {\em The Journal of Political Economy}, 81(3):637--654.
\bibitem[Carr et~al., 2002]{CGMY02}
Carr, P., Geman, H., D.B., M., and M., Y. (2002).
\newblock The fine structure of asset returns: An empirical investigation.
\newblock {\em Journal of Business}, 75(2):305--333.
\bibitem[Cont et~al., 1997]{BoPoCo97}
Cont, R., Potters, M., and Bouchaud, J. (1997).
\newblock Scaling in stock market data: stable laws and beyond.
\newblock {\em Scale invariance and beyond, Springer}.
\bibitem[Cont and Tankov, 2003]{Cont}
Cont, R. and Tankov, P. (2003).
\newblock {\em Financial Modelling with Jump Processes}.
\newblock Chapman and Hall/CRC; 1 edition.
\bibitem[Cont and Voltchkova, 2005a]{CoVo05b}
Cont, R. and Voltchkova, E. (2005a).
\newblock A finite difference scheme for option pricing in jump diffusion and
exponential {L}\'evy models.
\newblock {\em SIAM Journal of numerical analysis}, 43(4):1596--1626.
\bibitem[Cont and Voltchkova, 2005b]{CoVo05}
Cont, R. and Voltchkova, E. (2005b).
\newblock Integro-differential equations for option prices in exponential
{L}èvy models.
\newblock {\em Finance and Stochastics}, 9:299--325.
\bibitem[Eberlein and Keller, 1995]{EbKe95}
Eberlein, E. and Keller, U. (1995).
\newblock Hyperbolic distributions in finance.
\newblock {\em Bernoulli}, 1(3):281--299.
\bibitem[Kabasinskas et~al., 2009]{alpha09}
Kabasinskas, A., Rachev, S., Sakalauskas, L., Wei, S., and Belovas, I. (2009).
\newblock Alpha-stable paradigm in financial markets.
\newblock {\em Journal of Computational Analysis and Applications},
11(4):641--669.
\bibitem[Kou, 2002]{Kou02}
Kou, S. (2002).
\newblock A jump-diffusion model for option pricing.
\newblock {\em Management Science}, 48(8):1086--1101.
\bibitem[Madan et~al., 1998]{MCC98}
Madan, D., Carr, P., and Chang, E. (1998).
\newblock The {V}ariance {G}amma process and option pricing.
\newblock {\em European Finance Review}, 2:79–105.
\bibitem[Madan and Seneta, 1990]{MaSe90}
Madan, D. and Seneta, E. (1990).
\newblock The {V}ariance {G}amma {(V.G.)} model for share market returns.
\newblock {\em The journal of Business}, 63(4):511--524.
\bibitem[Mandelbrot, 1963]{Ma63}
Mandelbrot, B. (1963).
\newblock Modeling financial data with stable distributions.
\newblock {\em Journal of Business}, XXXVI(1):392--417.
\bibitem[Merton, 1976]{Me76}
Merton, R. (1976).
\newblock Option pricing when underlying stock returns are discontinuous.
\newblock {\em Journal of Financial Economics}, 3:125--144.
\bibitem[Papapantoleon, ]{papapa}
Papapantoleon, A.
\newblock An introduction to lévy processes with applications in finance.
\newblock {\em Available in Arxiv}.
\bibitem[Sato, 1999]{Sato}
Sato, K.~I. (1999).
\newblock {\em Lévy processes and infinitely divisible distributions}.
\newblock Cambridge University Press.
\bibitem[Schoutens, 2003]{Schoutens}
Schoutens, W. (2003).
\newblock {\em L\'evy processes in finance}.
\newblock Wiley, First Edition.
\end{thebibliography}
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\title{\textsc{An introduction to Lévy processes and PIDEs}\\ \small with applications to Merton and Variance Gamma processes}
\author{ Nicola Cantarutti }
\begin{document}
\maketitle
\begin{abstract}
In this brief introduction I have tried to summarize all the most important concepts regarding Lévy's processes.
The goal is to introduce the formalism and the tools necessary to derive the partial integro-differential equation (PIDE)
for pricing financial derivatives, when the underlying stock price is modeled by the exponential of a Lévy process.
These arguments are not simple and the reader is required to have a good knowledge of the basics of stochastic calculus and financial mathematics.
However, a complete list of references and tutorials for beginners will be presented.\\
At the end of the tutorial I will make a thorough presentation of the cases of the Merton and Variance Gamma process. I will also present the Black-Scholes PDE, which is a
special case when the Lévy process under consideration is the Brownian motion.
\end{abstract}
\tableofcontents
\newpage
Over the past thirty years, a lot of research has been done on processes with jumps and their applications to financial derivatives.
Let us start with the list of references.
\noindent
A good tutorial for beginners is \cite{papapa}. I really suggest to read this tutorial because it is very clear, compact, and gives a good overview of the theory and applications
of Lévy processes. The article can be found on the Arxiv web page: \url{https://arxiv.org/abs/0804.0482}.
If you are looking for some more challenging references I suggest
\cite{Sato}, which is a reference book for the theory of Lévy processes, and
\cite{Applebaum} that gives more emphasis on stochastic calculus and stochastic differential equations (SDEs). These two books are very rigorous mathematical books.
Comprehensive guides on applications of Lévy processes in finance are the books of
\cite{Cont} and \cite{Schoutens}. These books are also accessible to beginners.
Among the most popular Lévy processes applied to finance, it is worth to mention:
\begin{itemize}
\item[-] the Merton jump-diffusion model \cite{Me76}
\item[-] the Kou jump-diffusion model \cite{Kou02}
\item[-] the $\alpha$-stable \cite{Ma63}, \cite{BoPoCo97}, \cite{alpha09}
\item[-] the Variance-Gamma (VG) \cite{MaSe90}, \cite{MCC98}
\item[-] the Normal-Inverse-Gaussian (NIG) \cite{BN97}
\item[-] hyperbolic Lévy processes \cite{EbKe95}
\item[-] Carr-Geman-Madan-Yor (CGMY) model \cite{CGMY02}
\end{itemize}
We begin with the most important definitions concerning the theory of Lévy processes and the stochastic calculus applied to jump processes.
These definitions are very quite abstract. I encourage the reader to have a look at \cite{papapa} for more practical examples, or to read
the first three chapters of \cite{Cont}. Sometimes it is enough to have a look at wikipedia, in order to clarify the ideas.
Here, I prefer to give a very short presentation of the main concepts, and dedicate more space to the second part of the tutorial.
In the second part we focus on the derivation of the PIDE for option pricing and on the exponential Lévy models used in this tutorial:
the \emph{Merton} and \emph{Variance Gamma} models.
In this part of the tutorial, I will present all the calculation step by step.
\section{Basic definitions}
Let $\{X_t\}_{t \ge 0}$ be a stochastic process defined on a probability space $(\Omega,\mathcal{F},\{\mathcal{F}_t\}_{t \ge 0}),\PP)$,
where $\mathcal{F}_t$ is the filtration to which the process $\{X_t\}_{t \ge 0}$ is adapted and represents the accumulated ``information'' up to time $t$.\\
\begin{Definition}\label{LevyDef}
We say that $\{X_t\}_{t \ge 0}$ is a \textbf{Lévy Process} if:
\begin{itemize}
\item[(\textbf{L1})] $X_{t=0} = 0$.
\item[(\textbf{L2})] $\{X_t\}_{t \ge 0}$ has independent increments i.e. $X_t - X_s$ is independent of $\mathcal{F}_s$ for any $0 \leq s < t$.
\item[(\textbf{L3})] $\{X_t\}_{t \ge 0}$ has stationary increments i.e. for any $s,t \geq 0$, the distribution of $X_{t+s} - X_t$ does not depend on $t$.
\item[(\textbf{L4})] $\{X_t\}_{t \ge 0}$ is stochastically continuous i.e. for every $\epsilon > 0 $ and $t \ge 0$ $$\lim_{h\to 0} \PP(|X_{t+h}-X_t| > \epsilon)=0. $$
\end{itemize}
\end{Definition}
It can be proven that Lévy processes have ``cádlág''
paths, i.e. paths which are right-continuous and have left limits.
%Lévy processes are intrinsically connected with infinitely divisible distributions. In particular the Lévy-Khintchine formula
%for infinitely divisible random variables, is an essential tool for the classification of the Lévy processes by the form of their
%characteristic functions.
\begin{Definition} \label{chf}
Let $X: \Omega \to \R$ be a random variable.\\
The \textbf{Characteristic function} $\phi_X:\R \to \C$ of $X$, is defined by
\begin{align}
\phi_{X}(u) &= \E [e^{iuX}] \nonumber \\
&= \int_{\Omega} e^{iuX} \PP(d\omega) \nonumber \\
&= \int_{\R} e^{iux} f_X dx.
\end{align}
for each $u \in \R$. Where $\omega \in \Omega$, and we indicated with $f_X = \frac{d\PP}{dx}$ the \textbf{probability density function} (pdf) of $X$.
\end{Definition}
For each $n \in \N$ , if $\E\bigl[ |X^{n}| \bigr] < \infty$, then
\begin{equation}\label{moments}
\E\bigl[ X^{n} \bigr] = i^{-n}\frac{\partial^n}{\partial u^{n}} \phi_X(u) \biggr|_{u=0} .
\end{equation}
With this property it is straightforward to compute the moments of all orders, as long as we know the analytic form
of the characteristic function.
\subsection{Lévy-Khintchine representation}
We now present a beautiful formula, first established by Paul Lévy and A.Ya. Khintchine in the 1930s
which gives a characterization of every infinitely divisible random variable.\\
\begin{Definition} \label{Levy_measure}
Let $\nu(dx)$ be a measure on $\R$. We say it is a \textbf{Lévy measure} if it satisfies
\begin{equation}
\nu (\{ 0 \} ) = 0,
\end{equation}
\begin{equation} \label{Levy_m}
\int_{\R} \min\{1, x^2\} \nu(dx) < \infty.
\end{equation}
\end{Definition}
The characteristic function of a Lévy processes at a fixed time $t\geq 0$ has the following \textbf{Lévy Khintchine representation}:
\begin{Theorem}
Let $X_t$ be the random value of a Lévy process at time $t\geq0$. Then there exist $b\in R$, $\sigma>0$
and a Lévy measure $\nu$ on $\R$, such that $\forall u \in \R$:
\begin{align}
\phi_{X_t}(u) &= \mathbb{E} [e^{iuX_t}] \\
&= e^{t \eta(u)} \nonumber \\
&= \exp \left[ t \left( ibu - \frac{1}{2} \sigma^2 u^2 + \int_{\R}
( e^{iux} -1 -iux \mathbbm{1}_{(|x|<1)}(x) ) \nu(dx) \right) \right]. \nonumber
\end{align}
\end{Theorem}
A proof can be found in \cite{Applebaum} (Theorem 1.2.14).\\
\begin{itemize}
\item We call the map $\eta : \R \to \C$, the \textbf{Lévy symbol}.
\item The triplet $(b, \sigma, \nu)$ is called \textbf{Lévy triplet}, and completely characterizes the Lévy process.
\end{itemize}
\subsection{Random measures}\label{random_measures}
A convenient tool for analyzing the jumps of a Lévy process is the random
measure of the jumps of the process.
The jump process $\{\Delta X_t\}_{t \ge 0}$ associated to the Lévy process $\{X_t\}_{t \ge 0}$ is
defined, for each $t \geq 0$ , by
\begin{equation}\label{jump}
\Delta X_t = X_t - X_{t^-}
\end{equation}
where $X_{t^-} = \lim_{s\uparrow t} X_s $.\\
\begin{Definition}
Consider an open set $A \subseteq \R \backslash \{ 0 \})$.
We define the \textbf{random measure} of the jumps of the process $\{X_t\}_{t \ge 0}$ by
\begin{align}
N^X(t,A)(\omega) &= \# \{ s \in [0,t] \, : \; \Delta X_s(\omega) \in A \} \\
&= \sum_{0 \leq s \leq t} \mathbbm{1}_A(\Delta X_s(\omega)) . \nonumber
\end{align}
\end{Definition}
\noindent
For each $\omega \in \Omega$ and for each $0 \leq t < \infty$, the map
$$ A \to N^X(t,A)(w) $$
is a counting measure i.e. it counts the number of jumps of the process $\{X_t\}_{t\geq0}$ with size in $A$ up to time $t$.\\
We say that $A$ is \emph{bounded below} if $0 \not \in \bar A$ i.e. zero does not belong to the closure of $A$.
\begin{itemize}
\item For each $A$ bounded below, the process $\bigl \{ N^X(t,A)(\omega) \bigr \}_{t\geq 0}$ is a \textbf{Poisson process} with intensity
\begin{equation}\label{Expect_N}
\nu(A) = \mathbb{E}[N^X(1,A) ]
\end{equation}
\item If $A_1, ..., A_m$ are disjoint subsets of $\R \backslash \{ 0 \})$ and bounded below and $t_1, ..., t_m$ are distinct non-negative times, then
the random variables $N(t_1,A_1), ..., N(t_m,A_m)$ are independent.
\end{itemize}
A random measure satisfying the properties above is called \textbf{Poisson random measure}.\\
If $A$ is not bounded below, it is possible to have $\nu(A) = \infty$.
We can also define the \textbf{Compensated Poisson random measure}. For each $t \geq 0$ and $A$ bounded below, let us define:
\begin{equation}
\tilde{N}(t,A) = N(t,A) - t\nu(A).
\end{equation}
This is a martingale-valued measure, i.e. for each $A$ the process $\bigl \{ \tilde{N}(t,A) \bigr \}_{t\geq 0} $ is a martingale.
\noindent
Now we can define the integration with respect to a random measure:
\begin{Definition} \label{Poisson_int}
Let $N$ be the Poisson random measure associated to a Lévy process $\{X_t\}_{t \geq 0}$, and let $f:\R \to \R$ be a measurable
function. For any $A$ bounded below, we define the \textbf{Poisson integral} of $f$ as
\begin{equation}
\int_A f(x) N(t,dx) = \sum_{x\in A} f(x) N(t,\{x\}).
\end{equation}
\end{Definition}
\noindent
Since $N(t,\{x\}) \neq 0 \Leftrightarrow \Delta X_s=x$ for at least one $s\in [0,t]$, we have
\begin{equation}
\int_A f(x) N(t,dx) = \sum_{0 \leq s \leq t} f(\Delta X_s) \mathbbm{1}_A(\Delta X_s).
\end{equation}
We can also define in the same way the \textbf{compensated Poisson integral}
\begin{equation}
\int_A f(x) \tilde{N}(t,dx) := \int_A f(x) N(t,dx) - t \int_A f(x) \mu(dx),
\end{equation}
We can further define:
\begin{equation}
\int_{|x|<1} f(x) \tilde N(t,dx) := \lim_{\epsilon \to 0} \int_{\epsilon < |x| < 1} f(x) \tilde N(t,dx),
\end{equation}
that represents the compensated sum of small jumps.
\subsection{Lévy-It\={o} decomposition} \label{LevyIto_sec}
The following is a fundamental theorem which decomposes a general Lévy process in a superposition
of independent processes: a drift term, a Brownian motion, a Poisson process with ``big jumps'' and a compensated Poisson process with ``small jumps''.
\begin{Theorem}
Given a Lévy process $\{X_t\}_{t \ge 0}$ , there exist $b\in \R$, a Brownian motion $W$ with variance $\sigma^2$, and an
independent Poisson random measure $N$ on $[0,\infty) \times \R$ such that
\begin{equation}\label{Levy_Ito}
X_t = bt + \sigma W_t + \int_{|x|<1} x \tilde{N}(t,dx) + \int_{|x|\geq1} x N(t,dx).
\end{equation}
This is called \textbf{Lévy-It\={o} decomposition}.
\end{Theorem}
For a proof the reader can look at Theorem 2.4.16 in \cite{Applebaum}.\\
A lot of information on the features of a Lévy process can be derived from the integrability conditions of its Lévy measure.\\
Let $\{X_t\}_{t\geq0}$ be a Lévy process with Lévy measure $\nu$. Then
\begin{enumerate}
\item For all $t\geq0$, $X_t$ has finite p-moment i.e.
$\E[|X_t|^p]<\infty$ for $p\geq0$ if and only if $\int_{|x| \geq 1} |x|^p \nu(dx) <\infty$.
\item For all $t\geq0$, $X_t$ has finite exponential p-moment i.e. $\E[\exp(pX_t)]<\infty$ for $p\in \R$ if and only if
$\int_{|x| \geq 1} e^{xp} \nu(dx) <\infty$.
\end{enumerate}
The majority of Lévy processes used in finance have finite moments.
For practical reasons, it makes sense to assume finite mean and variance of the price process.
In this tutorial we will model the 1-dimensional dynamics of the prices with the exponential of a Lévy process,
i.e. $S_t = S_0 e^{X_t}$. Let us introduce the important assumption:
\begin{center}
\begin{riquadro}{12cm}
\textbf{Assumption}:\\
We consider only Lévy processes with finite exponential second moment.\\
Therefore it follows that:
\begin{equation}\label{AssumptionEM}
\E\bigl[ S_t^2 \bigr] < \infty \quad \Leftrightarrow \quad \int_{|x| \geq 1} e^{2x} \nu(dx) <\infty
\end{equation}
\end{riquadro}
\end{center}
The existence of the exponential 2-moment implies that $\{X_t\}_{t \geq 0}$ has finite p-moment for all $p \in \N$.
If we assume that $\{X_t\}_{t \geq 0}$ has finite first moment, we can simplify the Lévy-It\={o} decomposition,
by adding and subtracting the finite term $\int_{|x| \geq 1} x t\, \nu(dx)$
in (\ref{Levy_Ito}).
The decomposition has now the form:
\begin{equation}\label{Levy_Ito2}
X_t = \biggl( b+\int_{|x| \geq 1} x \nu(dx) \biggr)t + \sigma W_t + \int_{\R} x \tilde{N}(t,dx).
\end{equation}
Let us recall the definition of the \emph{total variation} $TV(f)$ of a function $f : [a,b] \to \R$
\begin{equation}
TV(f) = \sup_P \sum_{i=1}^n |f(t_i) - f(t_{i-1})|,
\end{equation}
where the supremum is taken over all $P$, the finite partitions $a=t_0 < t_1 < ... < t_n = b$ of the interval $[a,b]$.
A Lévy process is said to be of finite variation if its paths are of finite variation with probability 1.
The variation of any Lévy processes is related with the behavior of the Lévy measure in the region $|x|<1$.\\
A Lévy process $\{X_t\}_{t \geq 0}$ with triplet $(b,\sigma,\nu)$ is of \textbf{finite variation} if and only if
\begin{equation}
\sigma = 0 \quad \mbox{ and } \quad \int_{|x| < 1} |x| \nu(dx).
\end{equation}
\noindent
If we assume that $\{X_t\}_{t \geq 0}$ has finite variation, the Lévy-It\={o} decomposition (\ref{Levy_Ito})
becomes
\begin{equation}\label{Levy_Ito3}
X_t = \biggl( b-\int_{|x| < 1} x \nu(dx) \biggr)t + \int_{\R} x N(t,dx).
\end{equation}
\subsection{The It\={o} formula and infinitesimal generator}
Let us express the Lévy It\=o decomposition in the differential form:
\begin{equation}\label{Levy_Ito22}
dX_t = b\,dt + \sigma \, dW_t + \int_{|z|<1} z \tilde{N}(dt,dz) + \int_{|z|\geq1} z N(dt,dz).
\end{equation}
Now we can introduce the most important formula in stochastic calculus: the \textbf{It\={o}'s formula}.
\begin{Theorem}
If $X_t$ is the Lévy process with dynamics as in (\ref{Levy_Ito22}), for each $f \in C^2(\R^n)$ we have
\begin{align} \label{Ito_form}
df(X_t) &= \frac{\partial f}{\partial x}(X_{t^-}) b dt + \frac{\partial f}{\partial x}(X_{t^-}) \sigma dW_t
+ \frac{1}{2} \frac{\partial^2 f}{\partial x^2}(X_{t^-}) \sigma^2 dt \\ \nonumber
&+ \int_{|z|\geq 1} \bigl[ f\bigl( X_{t^-} + z \bigr) - f( X_{t^-} ) \bigr] N(dt,dz) \\ \nonumber
&+ \int_{|z|< 1} \bigl[ f\bigl( X_{t^-} + z \bigr) - f(X_{t^-}) \bigr] \tilde N(dt,dz) \\ \nonumber
&+ \int_{|z|< 1} \bigl[ f\bigl( X_{t^-} + z \bigr) - f(X_{t^-}) - \frac{\partial f}{\partial x}(X_{t^-}) z \bigr] \nu(dz)dt. \nonumber
\end{align}
\end{Theorem}
The terms in the first line are the same of the well known diffusion case. The other terms comes from the discontinuous part of the process.\\
\noindent
A Lévy process is a \textbf{Markov process}.
To be precise, a Lévy process is a \textbf{time homogeneous}, \textbf{translation invariant} Markov process. For more information on these topics, have a look at \cite{Applebaum}.\\
We can define the \textbf{infinitesimal generator} $\LL^X : f \to \LL^X f$ of the Lévy process $X$ with triplet $(b,\sigma,\nu)$:
\begin{align}\label{genLevy}
(\LL^X f)(x) =& \; \lim_{t\to0} \frac{\E \bigl[ f(x + X_t) \bigr] - f(x) }{t} \\
=& \; b \frac{\partial f}{\partial x}(x) +
\frac{1}{2} \sigma^2 \frac{\partial^2 f}{\partial x^2}(x)\\
& + \int_{\R} \left( f(x+z) - f(x) - z \frac{\partial f}{\partial x}(x)
\mathbbm{1}_{\{ |z|<1 \}}(z) \right) \nu(dz). \nonumber
\end{align}
This works for functions twice continuously differentiable, and with nice behavior at infinity (usually they are required to have compact support, or to vanish at infinity or even
polynomial growth).
If the Lévy process $\{X_t\}_{t \geq 0}$ has finite first moment i.e. with Lévy-It\=o decomposition (\ref{Levy_Ito2}), the generator has form:
\begin{align}\label{genLevy2}
(\LL^X f)(x) =& \; b \frac{\partial f}{\partial x}(x) +
\frac{1}{2} \sigma^2 \frac{\partial^2 f}{\partial x^2}(x)\\ \nonumber
& + \int_{\R} \left( f(x+z) - f(x) - z \frac{\partial f}{\partial x}(x) \right) \nu(dz).
\end{align}
\section{Exponential Lévy models}\label{Section_ELM}
If we indicate with $S_t$ the stock price,
the name \textbf{exponential Lévy model} comes from the expression:
\begin{equation}\label{ELM}
S_t = S_0 e^{X_t} ,
\end{equation}
where $X_t$ is a one dimensional Lévy process with triplet $(b,\sigma,\nu)$.
\subsection{Exponential Lévy SDE}
In order to obtain an SDE for $S_t$ in (\ref{ELM}), we apply the It\={o} formula (\ref{Ito_form}), and consider
the dynamics (\ref{Levy_Ito22}) for $X_t$:
\begin{align*}
d S_t \; &= S_0 e^{X_{t^-}} b dt \; + \; S_0 e^{X_{t^-}} \sigma dW_t \; + \; \frac{1}{2}S_0 e^{X_{t^-}}\sigma^2 dt \\ \nonumber
&+ \int_{|x|\geq 1} (S_0 e^{X_{t^-}+x} - S_0 e^{X_{t^-}}) N(dt,dx) \\ \nonumber
&+ \int_{|x|< 1} (S_0 e^{X_{t^-}+x} - S_0 e^{X_{t^-}}) \tilde N(dt,dx) \\ \nonumber
&+ \int_{|x|< 1} (S_0 e^{X_{t^-}+x} - S_0 e^{X_{t^-}} - x S_0 e^{X_{t^-}}) \nu(dx) dt. \nonumber
\end{align*}
After some substitutions we get:
\begin{align}
\frac{d S_t}{S_{t^-}} \; &= (b + \frac{1}{2}\sigma^2 ) dt + \sigma dW_t \\ \nonumber
&+ \int_{|x|< 1} ( e^{x} - x - 1) \nu(dx) dt \\ \nonumber
&+ \int_{|x|\geq 1} (e^{x} - 1) N(dt,dx) + \int_{|x|< 1} (e^{x} - 1) \tilde N(dt,dx). \nonumber
\end{align}
Thanks to the assumption (\ref{AssumptionEM}) we can simplify this equation.
First we look at the integrability conditions:
\begin{itemize}
\item $\int_{|x|\geq 1} e^{x} \nu(dx) < \infty$ by (\ref{AssumptionEM}).
\item $\int_{|x|\geq 1} 1\; \nu(dx) < \infty$ by definition (\ref{Levy_measure}) of $\nu$.
\end{itemize}
We can add and subtract $\pm \int_{|x|\geq 1} ( e^{x} - 1) \nu(dx) dt $ and obtain the final form
\begin{align} \label{exp_sde}
\frac{d S_t}{S_{t^-}} \; &= \left(b + \frac{1}{2}\sigma^2 + \int_{\R} ( e^{x} - 1 -x\mathbbm{1}_{|x|<1}) \nu(dx) \right) dt \\ \nonumber
&+ \sigma dW_t \; + \int_{\R} (e^{x} - 1) \tilde N(dt,dx). \nonumber
\end{align}
If we set
\begin{equation}\label{mu}
\mu := b + \frac{1}{2}\sigma^2 + \int_{\R} ( e^{x} - 1 -x\mathbbm{1}_{|x|<1}) \nu(dx)
\end{equation}
The SDE for $S_t$ becomes:
\begin{equation}\label{exp_sde2}
d S_t = \; \mu S_{t^-} dt + \sigma S_{t^-} dW_t \; + \int_{\R} S_{t^-} (e^{x} - 1) \tilde N(dt,dx).
\end{equation}
The same equation can be derived quickly by considering the Lévy-It\={o} form (\ref{Levy_Ito2}) for $X_t$:
\begin{align*}
d S_t \; =& \; S_0 e^{X_{t^-}} \biggl( b + \int_{|x|\geq 1}x \nu(dx) \biggr) dt \; + \; S_0 e^{X_{t^-}} \sigma dW_t \; + \; \frac{1}{2}S_0 e^{X_{t^-}}\sigma^2 dt \\ \nonumber
&+ \int_{\R} (S_0 e^{X_{t^-}+x} - S_0 e^{X_{t^-}}) \tilde N(dt,dx) + \int_{\R} (S_0 e^{X_{t^-}+x} - S_0 e^{X_{t^-}} - x S_0 e^{X_{t^-}}) \nu(dx) dt \\ \nonumber
=& \; S_{t^-} \biggl[ \mu dt + \sigma dW_t \; + \int_{\R} (e^{x} - 1) \tilde N(dt,dx) \biggr].\\
\end{align*}
\subsection{The Merton Model}\label{Merton_section}
The first jump-diffusion model for the log-prices is the \emph{Merton model}, presented in
\cite{Me76}. In the same paper the author also obtains a closed form solution for the price of an European vanilla option.
The Merton model describes the log-price evolution with a Lévy process with a nonzero diffusion
component and a finite activity jump process with normal distributed jumps.
\begin{equation}\label{MertonM}
X_t = \bar b t + \sigma W_t + \sum_{i=1}^{N_t} Y_i,
\end{equation}
where $N_t$ is a Poisson random variable counting the jumps of $X_t$ in $[0,t]$, and $Y_i \sim \mathcal{N}(\alpha, \xi^2)$ is the size of the jumps.\\
Using the Poisson integral notation (Def. \ref{Poisson_int}), the process becomes
\begin{equation*}
X_t = \bar b t + \sigma W_t + \int_{\R} x N(t,dx)
\end{equation*}
The previous equation corresponds to the Lévy-It\={o} decomposition (\ref{Levy_Ito3}) with an additional Brownian motion term,
and with
$$\bar b = b - \int_{|x|<1} x \nu(dx).$$
The Lévy measure of a finite activity Lévy process, can be factorized in the activity $\lambda$ of the Poisson process and
the pdf of the jump size:
\begin{align}\label{Merton_measure}
\nu(dx) &= \lambda f_Y(dx), \\
&= \frac{\lambda}{\xi \sqrt{2\pi}} e^{- \frac{(x-\alpha)^2}{2\xi^2}} dx.
\end{align}
such that $\int_{\R} \nu(dx) = \lambda$.\\
Since $\int_{|x|<1} x \nu(dx)$ is finite, the jump process has \textbf{finite variation}. However,
the Merton process has infinite variation because $\sigma >0$.
The Lévy exponent has the following form:
\begin{equation}
\eta(u) = i\bar b u - \frac{1}{2} \sigma^2 u^2 + \lambda \biggl( e^{i\alpha u -\frac{\xi^2 u^2}{2} }-1 \biggr).
\end{equation}
\newline
Using the formula for the moments (\ref{moments}) we obtain:
\begin{align}\label{Merton_moments}
\E[X_t] &= t(\bar b+\lambda \alpha). \\ \nonumber
\mbox{Var}[X_t] &= t(\sigma^2 + \lambda \xi^2 + \lambda \alpha^2). \\ \nonumber
\mbox{Skew}[X_t] &= \frac{t\lambda (3\xi^2 \alpha + \alpha^3)}{\bigl(\mbox{Var}[X_t])^{3/2}}. \\ \nonumber
\mbox{Kurt}[X_t] &= \frac{t \lambda (3\xi^3 +6\alpha^2\xi^2 +\alpha^4)}{\bigl(\mbox{Var}[X_t]\bigr)^2}. \nonumber
\end{align} \newline
\subsection{The Variance Gamma process}\label{VG_section}
The \emph{variance gamma} process is a pure jump Lévy process with infinite activity.
The first presentation with applications in finance is due to \cite{MaSe90}.
The model presented in their paper is however a symmetric VG model,
where there is only an additional parameter which controls the kurtosis, while the skewness is still not considered.\\
The non-symmetric VG process is described in \cite{MCC98} where a closed form solution for European vanilla options is also presented.\\
If we consider a Brownian motion with drift $X_t = \theta t + \bar\sigma W_t$ and substitute the time variable with the gamma random variable
$T_t \sim \Gamma(t,\kappa t)$,
we obtain the \textbf{variance gamma} process:
\begin{equation}\label{VG_process}
X_{T_t} = \theta T_t + \bar\sigma W_{T_t} .
\end{equation}
It depends on three parameters:
\begin{itemize}
\item $\bar\sigma$, the volatility of the Brownian motion
\item $\kappa$, the variance of the Gamma process
\item $\theta$, the drift of the Brownian motion
\end{itemize}
The VG is a process with \textbf{finite variation}.
The pdf of $X_t$ can be computed conditioning on the realization of $T_t$:
\begin{align}\label{VG_density}
f_{X_t}(x) &= \int_y f_{X_t,T_t}(x,y) dy = \int_y f_{X_t|T_t}(x|y) f_{T_t}(y) dy \\ \nonumber
&= \int_0^{\infty} \frac{1}{\bar\sigma \sqrt{2\pi y}} e^{-\frac{(x -\theta y)^2}{2\bar\sigma^2 y}}
\frac{y^{\frac{t}{\kappa} -1}}{\kappa^{\frac{t}{\kappa}} \Gamma(\frac{t}{\kappa})}
e^{-\frac{y}{\kappa}} \, dy \\ \nonumber
&= \frac{2 \exp(\frac{\theta x}{\bar\sigma^2})}{\kappa^{\frac{t}{\kappa}} \sqrt{2\pi}\bar\sigma \Gamma(\frac{t}{\kappa}) }
\biggl( \frac{x^2}{2\frac{\bar\sigma^2}{\kappa} + \theta^2} \biggr)^{\frac{t}{2\kappa}-\frac{1}{4}}
K_{\frac{t}{\kappa}-\frac{1}{2}}
\biggl( \frac{1}{\bar\sigma^2} \sqrt{x^2 \bigl(\frac{2\bar\sigma^2}{\kappa}+\theta^2 \bigr)} \biggr),
\end{align}
where the function $K$ is a modified Bessel function of the second kind (see \cite{MCC98} for explicit computations).\\
The characteristic function can be obtained from the composition of the Gamma moment generating function and the Normal characteristic functions:
\begin{align*}
\phi_{X_t}(u) &= \biggl( 1- \kappa \bigl( i u\theta -\frac{1}{2}\bar\sigma^2 u^2 \bigr) \biggr)^{-\frac{t}{\kappa}} \\
&= \biggl( 1-i\theta \kappa u + \frac{1}{2} \bar\sigma^2 \kappa u^2 \biggr)^{-\frac{t}{\kappa}}.
\end{align*}
I will not prove the previous formula, but the interested reader can consult \cite{Applebaum} (Proposition 1.3.17 and Example 1.3.31) or \cite{Cont} (Theorem 4.2).\\
The VG Lévy measure is
\begin{equation}\label{VG_measure}
\nu^{X_t}(dx) = \frac{e^{\frac{\theta x}{\bar\sigma^2}}}{\kappa|x|} \exp
\left( - \frac{\sqrt{\frac{2}{\kappa} + \frac{\theta^2}{\bar\sigma^2}}}{\bar\sigma} |x|\right) dx,
\end{equation}
and the Lévy exponent is
\begin{equation}
\eta(u) = -\frac{1}{\kappa} \log(1-i\theta \kappa u + \frac{1}{2} \bar\sigma^2 \kappa u^2).
\end{equation}
Using the formula for the moments (\ref{moments}) we obtain:
\begin{align}\label{VG_cumulants}
\E[X_t] &= t\theta. \\ \nonumber
\mbox{Var}[X_t] &= t(\bar\sigma^2 + \theta^2 \kappa). \\ \nonumber
\mbox{Skew}[X_t] &= \frac{t (2\theta^3\kappa^2 + 3 \bar\sigma^2 \theta \kappa)}{\bigl(\mbox{Var}[X_t])^{3/2}}. \\ \nonumber
\mbox{Kurt}[X_t] &= \frac{t (3\bar\sigma^4 \kappa + 12\bar\sigma^2 \theta^2 \kappa^2 +6\theta^4\kappa^3)}{\bigl(\mbox{Var}[X_t]\bigr)^2}.\nonumber
\end{align}
\\
The Lévy-It\={o} decomposition (\ref{Levy_Ito3}) for any pure jump finite variation process,
can be written as
\begin{equation}\label{Levy_Ito4}
X_t = \bar b t + \int_{\R} x N(t,dx)
\end{equation}
with $\bar b = b - \int_{|x|<1} x \nu(dx)$.
\noindent Let us consider the process (\ref{VG_process}). We can take its expectation
$$\E[X_{T_t}] = \theta \E[T_t] + \bar\sigma \E[W_{T_t}] = \theta t,$$
which must correspond to the expectation of (\ref{Levy_Ito4}). Using (\ref{Expect_N}) we obtain
\begin{align}
\E[X_t] &= \bar b t + \E \biggl[ \int_{\R} x N(t,dx)\biggr] \\ \nonumber
&= t \biggl( \bar b + \int_{\R} x \, \nu(dx) \biggr), \nonumber
\end{align}
and therefore $ \bar b = \theta - \int_{\R} x \nu(dx) $.\\
Let us compute this integral using the explicit formula (\ref{VG_measure}) for the Lévy measure.
Let us call $$A = \frac{\theta}{\bar\sigma^2} \hspace{2em} \mbox{and} \hspace{2em}
B=\frac{|\theta|}{\bar\sigma^2}\sqrt{1+\frac{2\bar\sigma^2}{\kappa \theta^2}}$$
with $A0$, the integrals in the SDE are split in two domains: $\{|z|<\epsilon\}$ and $\{|z|\geq \epsilon\}$.
The integrand on the domain $\{ |z|<\epsilon \}$, is approximated by the Taylor expansion
$e^z-1-z = \frac{z^2}{2} + \mathcal{O}(z^3)$ such that
\begin{align}\label{log_sde_inf_act}\nonumber
dL_t =& \biggl( r -\frac{1}{2}\sigma^2 - \int_{\R} \bigl( e^z-1-z \bigr) \nu(dz) \biggr)dt + \sigma dW + + \int_{\R} z \tilde N(dt,dz) \\ \nonumber
=& \biggl( r - \frac{1}{2}\sigma^2 -\int_{|z|<\epsilon} (e^z-1-z) \nu(dz) -\int_{|z|\geq \epsilon} (e^z-1-z) \nu(dz) \biggr) dt\\ \nonumber
&+ \sigma dW_t + \underbrace{\int_{|z|< \epsilon} z \tilde N(dt,dz)}_{\sigma_{\epsilon} dW_t} + \int_{|z| \geq \epsilon} z \tilde N(dt,dz) \\
=& \biggl( r - \frac{1}{2} (\sigma^2 + \sigma_{\epsilon}^2) - w_{\epsilon} + \lambda_{\epsilon} \theta_{\epsilon} \biggr) dt + \bigl( \sigma+\sigma_{\epsilon}\bigr) dW_t
+ \int_{|z|\geq \epsilon} z \tilde N(dt,dz) ,
\end{align}
where we defined the new parameters
\begin{align}\label{sig_eps}
& \sigma_{\epsilon}^2 := \int_{|z| < \epsilon} z^2 \nu(dz), \quad \quad w_{\epsilon} := \int_{|z| \geq \epsilon} (e^z-1) \nu(dz), \\ \nonumber
& \lambda_{\epsilon} := \int_{|z| \geq \epsilon} \nu(dz), \quad \quad \theta_{\epsilon} := \frac{1}{\lambda_{\epsilon}} \int_{|z| \geq \epsilon} z \nu(dz) .
\end{align}
The process $\int_{|z|\geq \epsilon} z \tilde N(dt,dz)$ is a compensated Poisson process with finite activity $\lambda_{\epsilon}$.
For the VG process, where $\sigma = 0$, the approximated dynamics is thus
\begin{equation}\label{log_sde_VG}
dL_t = \biggl( r - \frac{1}{2} \sigma_{\epsilon}^2 - w_{\epsilon} + \lambda_{\epsilon} \theta_{\epsilon} \biggr) dt
+ \sigma_{\epsilon} dW_t + \int_{|z|\geq \epsilon} z \tilde N(dt,dz),
\end{equation}
or the equivalent
\begin{equation}
dL_t = \biggl( r - \frac{1}{2} \sigma_{\epsilon}^2 - w_{\epsilon} \biggr) dt
+ \sigma_{\epsilon} dW_t + \int_{|z|\geq \epsilon} z N(dt,dz),
\end{equation}
where the parameters are obtained from the Lévy measure (\ref{VG_measure}).
The \textbf{approximated VG PIDE} is:
\begin{align}\label{VG_JD}
& \frac{\partial V(t,x)}{\partial t} +
\bigl( r-\frac{1}{2}\sigma_{\epsilon}^2 - w_{\epsilon} \bigr) \frac{\partial V(t,x)}{\partial x}
+ \frac{1}{2}\sigma_{\epsilon}^2 \frac{\partial^2 V(t,x)}{\partial x^2} \\ \nonumber
&+ \int_{|z| \geq \epsilon} V(t,x+z) \nu(dz) = (\lambda_{\epsilon} + r) V(t,x).
\end{align}
It has the same ``jump-diffusion'' form of the Merton PIDE, except for the truncation in the integral.
\bibliographystyle{apalike}
\bibliography{/home/nicola/Documenti/ISEG/Bibliografia/trans_cost.bib,/home/nicola/Documenti/ISEG/Bibliografia/book.bib,/home/nicola/Documenti/ISEG/Bibliografia/fin_math.bib,/home/nicola/Documenti/ISEG/Bibliografia/viscosity.bib,/home/nicola/Documenti/ISEG/Bibliografia/num_meth.bib,/home/nicola/Documenti/ISEG/Bibliografia/phd_thesis.bib,/home/nicola/Documenti/ISEG/Bibliografia/Levy.bib,/home/nicola/Documenti/ISEG/Bibliografia/control.bib}
\end{document}
================================================
FILE: list_of_packages.txt
================================================
arch
cvxpy
cython
jupyter
matplotlib
numpy
pandas
scikit-learn
scipy
seaborn
statsmodels
sympy
================================================
FILE: pyproject.toml
================================================
[build-system]
requires = ["setuptools", "setuptools-scm", "numpy", "cython"]
build-backend = "setuptools.build_meta"
[project]
name = "FMNM"
version = "1.0.0"
authors = [{ name = "cantaro86", email = "nicolacantarutti@gmail.com" }]
description = "Library for the code used in the FMNM notebooks."
readme = "README.md"
requires-python = ">=3.8"
dynamic = ["dependencies"]
[tool.setuptools.dynamic]
dependencies = {file = ["list_of_packages.txt"]}
[tool.setuptools.packages.find]
where = ["src"]
namespaces = false # prevent folders without __init__.py from being scanned
[project.urls]
"Homepage" = "https://github.com/cantaro86/Financial-Models-Numerical-Methods"
================================================
FILE: requirements.txt
================================================
anyio==3.7.1
appnope==0.1.3
arch==6.1.0
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
arrow==1.2.3
asttokens==2.2.1
async-lru==2.0.3
attrs==23.1.0
Babel==2.12.1
backcall==0.2.0
beautifulsoup4==4.12.2
black==23.7.0
bleach==6.0.0
certifi==2023.7.22
cffi==1.15.1
charset-normalizer==3.2.0
click==8.1.6
comm==0.1.3
contourpy==1.1.0
cvxpy==1.3.2
cycler==0.11.0
Cython==3.0.0
debugpy==1.6.7
decorator==5.1.1
defusedxml==0.7.1
ecos==2.0.12
executing==1.2.0
fastjsonschema==2.18.0
flake8==6.0.0
fonttools==4.41.1
fqdn==1.5.1
idna==3.4
ipykernel==6.24.0
ipython==8.14.0
ipython-genutils==0.2.0
ipywidgets==8.0.7
isoduration==20.11.0
jedi==0.18.2
Jinja2==3.1.2
joblib==1.3.1
json5==0.9.14
jsonpointer==2.4
jsonschema==4.18.4
jsonschema-specifications==2023.7.1
jupyter==1.0.0
jupyter-console==6.6.3
jupyter-events==0.6.3
jupyter-lsp==2.2.0
jupyter_client==8.3.0
jupyter_core==5.3.1
jupyter_server==2.7.0
jupyter_server_terminals==0.4.4
jupyterlab==4.0.3
jupyterlab-pygments==0.2.2
jupyterlab-widgets==3.0.8
jupyterlab_server==2.24.0
kiwisolver==1.4.4
MarkupSafe==2.1.3
matplotlib==3.7.2
matplotlib-inline==0.1.6
mccabe==0.7.0
mistune==3.0.1
mpmath==1.3.0
mypy-extensions==1.0.0
nbclient==0.8.0
nbconvert==7.7.2
nbformat==5.9.1
nest-asyncio==1.5.6
notebook==7.0.0
notebook_shim==0.2.3
numpy==1.25.1
osqp==0.6.3
overrides==7.3.1
packaging==23.1
pandas==2.0.3
pandocfilters==1.5.0
parso==0.8.3
pathspec==0.11.1
patsy==0.5.3
pexpect==4.8.0
pickleshare==0.7.5
Pillow==10.0.0
platformdirs==3.9.1
prometheus-client==0.17.1
prompt-toolkit==3.0.39
psutil==5.9.5
ptyprocess==0.7.0
pure-eval==0.2.2
pycodestyle==2.10.0
pycparser==2.21
pyflakes==3.0.1
Pygments==2.15.1
pyparsing==3.0.9
python-dateutil==2.8.2
python-json-logger==2.0.7
pytz==2023.3
PyYAML==6.0.1
pyzmq==25.1.0
qdldl==0.1.7.post0
qtconsole==5.4.3
QtPy==2.3.1
referencing==0.30.0
requests==2.31.0
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rpds-py==0.9.2
scikit-learn==1.3.0
scipy==1.11.1
scs==3.2.3
seaborn==0.12.2
Send2Trash==1.8.2
six==1.16.0
sniffio==1.3.0
soupsieve==2.4.1
stack-data==0.6.2
statsmodels==0.14.0
sympy==1.12
terminado==0.17.1
threadpoolctl==3.2.0
tinycss2==1.2.1
tokenize-rt==5.1.0
tornado==6.3.2
traitlets==5.9.0
tzdata==2023.3
uri-template==1.3.0
urllib3==2.0.4
wcwidth==0.2.6
webcolors==1.13
webencodings==0.5.1
websocket-client==1.6.1
widgetsnbextension==4.0.8
================================================
FILE: setup.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 29 17:49:22 2023
@author: cantaro86
"""
from setuptools import Extension, setup
from Cython.Build import build_ext, cythonize
import numpy
extensions = [
Extension(
"src/FMNM/cython/*",
["src/FMNM/cython/*.pyx"],
include_dirs=[numpy.get_include()],
define_macros=[("NPY_NO_DEPRECATED_API", "NPY_1_7_API_VERSION")],
)
]
setup(
name="fmnm_cython",
cmdclass={"build_ext": build_ext},
ext_modules=cythonize(extensions, language_level="3"),
)
================================================
FILE: src/C/BS_SOR_main.c
================================================
#include
#include
#include
#include "SOR.h"
#include "PDE_solver.h"
#include
int main()
{
double r = 0.1;
double sig = 0.2;
double S = 100.0;
double K = 100.0;
double T = 1.;
int Nspace = 3000; // space steps
int Ntime = 2000; // time steps
double w = 1.68; // relaxation parameter
printf("The price is: %f \n ", PDE_SOR(Nspace,Ntime,S,K,T,sig,r,w) );
return 0;
}
================================================
FILE: src/C/Makefile
================================================
PROGRAM_NAME=BS_sor
OBJECTS=BS_SOR_main.o SOR.o PDE_solver.o
CXX=clang #gcc
CXXFLAGS +=-Wall -Werror -O2
LIBS=-lm
$(PROGRAM_NAME):$(OBJECTS)
$(CXX) $(CXXFLAGS) -o $(PROGRAM_NAME) $(OBJECTS) $(LIBS)
@echo " "
@echo "Compilation completed!"
@echo " "
BS_SOR_main.o:SOR.h PDE_solver.h BS_SOR_main.c
$(CXX) $(CXXFLAGS) -c -o BS_SOR_main.o BS_SOR_main.c
SOR.o:SOR.h SOR.c
$(CXX) $(CXXFLAGS) -c -o SOR.o SOR.c
PDE_solver.o:SOR.h SOR.c PDE_solver.h PDE_solver.c
$(CXX) $(CXXFLAGS) -c -o PDE_solver.o PDE_solver.c
clean:
rm -f *.o
rm -f *~
rm -f core
cat:
cat Makefile
all: $(PROGRAM_NAME) clean
================================================
FILE: src/C/PDE_solver.c
================================================
#include "PDE_solver.h"
double PDE_SOR(int Ns, int Nt, double S, double K, double T, double sig, double r, double w)
{
const double eps = 1e-10;
const int N_max = 600;
double S_max = 3*K;
double S_min = K/3;
double x_max = log(S_max);
double x_min = log(S_min);
double dx = (x_max - x_min)/(Ns-1);
double dt = T/Nt;
double sig2 = sig*sig;
double dxx = dx * dx;
double aa = ( (dt/2) * ( (r-0.5*sig2)/dx - sig2/dxx ) );
double bb = ( 1 + dt * ( sig2/dxx + r ) );
double cc = (-(dt/2) * ( (r-0.5*sig2)/dx + sig2/dxx ) );
// array allocations
double *x = calloc(Ns, sizeof(double) );
double *x_old = calloc(Ns-2, sizeof(double) );
double *x_new = calloc(Ns-2, sizeof(double) );
double *help_ptr = calloc(Ns-2, sizeof(double) );
double *temp;
for (unsigned int i=0; i=0; --k)
{
x_old[Ns-3] -= cc * ( S_max - K * exp( -r*(T-k*dt) ) ); // offset
x_new = SOR_aabbcc(aa, bb, cc, x_old, help_ptr, x_new, Ns-2, w, eps, N_max); //SOR solver
// x_new = SOR_abc(aa, bb, cc, x_old, Ns-2, w, eps, N_max); //SOR solver
if (k != 0) // swap the pointers (we don't need to allocate new memory)
{
temp = x_old;
x_old = x_new;
x_new = temp;
}
}
free(help_ptr);
free(x_old);
// x_new is the solution!!
// binary search: Search for the points for the interpolation
int low = 1;
int high = Ns-2;
int mid;
double result = -1;
if (S > x[high] || S < x[low])
{
printf("error: Price S out of grid.\n");
free(x_new);
free(x);
return result;
}
while ( (low+1) != high)
{
mid = (low + high) / 2;
if ( fabs(x[mid]-S)< 1e-10 )
{
result = x_new[mid-1];
free(x_new);
free(x);
return result;
}
else if ( x[mid] < S)
{
low = mid;
}
else
{
high = mid;
}
}
// linear interpolation
result = x_new[low-1] + (S - x[low]) * (x_new[high-1] - x_new[low-1]) / (x[high] - x[low]) ;
free(x_new);
free(x);
return result;
}
================================================
FILE: src/C/PDE_solver.h
================================================
#ifndef PDE_SOLVER_H
#define PDE_SOLVER_H
#include
#include
#include
#include "SOR.h"
#include
double PDE_SOR(int Ns, int Nt, double S, double K, double T, double sig, double r, double w);
#endif
================================================
FILE: src/C/SOR.c
================================================
#include "SOR.h"
double dist_square(double* a, double* b)
{
double dist = 0;
for (int i=0; i
#include
#include
static const int N = 4;
void print_matrix(double arr[N][N]);
void print_array(double* arr);
double* SOR(double A[N][N], double* b, double w, double eps, int N_max);
double* SOR_trid(double A[N][N], double* b, double w, double eps, int N_max);
double* SOR_abc(double aa, double bb, double cc, double* b, int NN, double w, double eps, int N_max);
double* SOR_aabbcc(double aa, double bb, double cc, double *b, double *x0, double *x1,
int NN, double w, double eps, int N_max);
double dist_square(double* a, double* b);
double dist_square2(double* a, double* b, int NN);
void print_array_2(double* arr, int NN);
#endif
================================================
FILE: src/C/mainSOR.c
================================================
#include "SOR.h"
/*
GENERAL MATRIX (DIAGONAL DOMINANT)
double A[4][4] = { {10, 5, 2, 1},
{2, 15, 2, 3},
{1, 8, 13, 1},
{2, 3, 1, 8} };
double b[4] = {30, 50, 60, 43};
double* x = SOR(A,b, w,eps, N_max);
TRIDIAGONAL MATRIX
double A[4][4] = { {10, 5, 0, 0},
{2, 15, 2, 0},
{0, 8, 13, 1},
{0, 0, 1, 8} };
double b[4] = {20, 38, 59, 35};
double* x = SOR_trid(A, b, w, eps, N_max);
*/
int main()
{
// TRIDIAGONAL WITH CONSTANT aa,bb,cc
double A[4][4] = { {10, 5, 0, 0},
{2, 10, 5, 0},
{0, 2, 10, 5},
{0, 0, 2, 10} };
double b[4] = {20, 37, 54, 46};
double aa=2, bb=10, cc=5;
const double w = 1;
const double eps = 1e-10;
const int N_max = 100;
printf("Matrix A: \n");
print_matrix(A);
printf("Vector b: \n");
print_array(b);
// double* x = SOR_abc(aa, bb, cc, b, N, w, eps, N_max);
double* x0 = calloc(N, sizeof(double) );
double* x1 = calloc(N, sizeof(double) );
double* x = SOR_aabbcc(aa, bb, cc, b, x0, x1, N, w, eps, N_max);
printf("Solution x: \n");
print_array(x);
free(x0);
free(x1);
return 0;
}
================================================
FILE: src/FMNM/BS_pricer.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 13 10:18:39 2019
@author: cantaro86
"""
import numpy as np
import scipy as scp
from scipy.sparse.linalg import spsolve
from scipy import sparse
from scipy.sparse.linalg import splu
import matplotlib.pyplot as plt
from matplotlib import cm
from time import time
import scipy.stats as ss
from FMNM.Solvers import Thomas
from FMNM.cython.solvers import SOR
from FMNM.CF import cf_normal
from FMNM.probabilities import Q1, Q2
from functools import partial
from FMNM.FFT import fft_Lewis, IV_from_Lewis
class BS_pricer:
"""
Closed Formula.
Monte Carlo.
Finite-difference Black-Scholes PDE:
df/dt + r df/dx + 1/2 sigma^2 d^f/dx^2 -rf = 0
"""
def __init__(self, Option_info, Process_info):
"""
Option_info: of type Option_param. It contains (S0,K,T)
i.e. current price, strike, maturity in years
Process_info: of type Diffusion_process. It contains (r, mu, sig) i.e.
interest rate, drift coefficient, diffusion coefficient
"""
self.r = Process_info.r # interest rate
self.sig = Process_info.sig # diffusion coefficient
self.S0 = Option_info.S0 # current price
self.K = Option_info.K # strike
self.T = Option_info.T # maturity in years
self.exp_RV = Process_info.exp_RV # function to generate solution of GBM
self.price = 0
self.S_vec = None
self.price_vec = None
self.mesh = None
self.exercise = Option_info.exercise
self.payoff = Option_info.payoff
def payoff_f(self, S):
if self.payoff == "call":
Payoff = np.maximum(S - self.K, 0)
elif self.payoff == "put":
Payoff = np.maximum(self.K - S, 0)
return Payoff
@staticmethod
def BlackScholes(payoff="call", S0=100.0, K=100.0, T=1.0, r=0.1, sigma=0.2):
"""Black Scholes closed formula:
payoff: call or put.
S0: float. initial stock/index level.
K: float strike price.
T: float maturity (in year fractions).
r: float constant risk-free short rate.
sigma: volatility factor in diffusion term."""
d1 = (np.log(S0 / K) + (r + sigma**2 / 2) * T) / (sigma * np.sqrt(T))
d2 = (np.log(S0 / K) + (r - sigma**2 / 2) * T) / (sigma * np.sqrt(T))
if payoff == "call":
return S0 * ss.norm.cdf(d1) - K * np.exp(-r * T) * ss.norm.cdf(d2)
elif payoff == "put":
return K * np.exp(-r * T) * ss.norm.cdf(-d2) - S0 * ss.norm.cdf(-d1)
else:
raise ValueError("invalid type. Set 'call' or 'put'")
@staticmethod
def vega(sigma, S0, K, T, r):
"""BS vega: derivative of the price with respect to the volatility"""
d1 = (np.log(S0 / K) + (r + sigma**2 / 2) * T) / (sigma * np.sqrt(T))
return S0 * np.sqrt(T) * ss.norm.pdf(d1)
def closed_formula(self):
"""
Black Scholes closed formula:
"""
d1 = (np.log(self.S0 / self.K) + (self.r + self.sig**2 / 2) * self.T) / (self.sig * np.sqrt(self.T))
d2 = (np.log(self.S0 / self.K) + (self.r - self.sig**2 / 2) * self.T) / (self.sig * np.sqrt(self.T))
if self.payoff == "call":
return self.S0 * ss.norm.cdf(d1) - self.K * np.exp(-self.r * self.T) * ss.norm.cdf(d2)
elif self.payoff == "put":
return self.K * np.exp(-self.r * self.T) * ss.norm.cdf(-d2) - self.S0 * ss.norm.cdf(-d1)
else:
raise ValueError("invalid type. Set 'call' or 'put'")
def Fourier_inversion(self):
"""
Price obtained by inversion of the characteristic function
"""
k = np.log(self.K / self.S0)
cf_GBM = partial(
cf_normal,
mu=(self.r - 0.5 * self.sig**2) * self.T,
sig=self.sig * np.sqrt(self.T),
) # function binding
if self.payoff == "call":
call = self.S0 * Q1(k, cf_GBM, np.inf) - self.K * np.exp(-self.r * self.T) * Q2(
k, cf_GBM, np.inf
) # pricing function
return call
elif self.payoff == "put":
put = self.K * np.exp(-self.r * self.T) * (1 - Q2(k, cf_GBM, np.inf)) - self.S0 * (
1 - Q1(k, cf_GBM, np.inf)
) # pricing function
return put
else:
raise ValueError("invalid type. Set 'call' or 'put'")
def FFT(self, K):
"""
FFT method. It returns a vector of prices.
K is an array of strikes
"""
K = np.array(K)
cf_GBM = partial(
cf_normal,
mu=(self.r - 0.5 * self.sig**2) * self.T,
sig=self.sig * np.sqrt(self.T),
) # function binding
if self.payoff == "call":
return fft_Lewis(K, self.S0, self.r, self.T, cf_GBM, interp="cubic")
elif self.payoff == "put": # put-call parity
return (
fft_Lewis(K, self.S0, self.r, self.T, cf_GBM, interp="cubic") - self.S0 + K * np.exp(-self.r * self.T)
)
else:
raise ValueError("invalid type. Set 'call' or 'put'")
def IV_Lewis(self):
"""Implied Volatility from the Lewis formula"""
cf_GBM = partial(
cf_normal,
mu=(self.r - 0.5 * self.sig**2) * self.T,
sig=self.sig * np.sqrt(self.T),
) # function binding
if self.payoff == "call":
return IV_from_Lewis(self.K, self.S0, self.T, self.r, cf_GBM)
elif self.payoff == "put":
raise NotImplementedError
else:
raise ValueError("invalid type. Set 'call' or 'put'")
def MC(self, N, Err=False, Time=False):
"""
BS Monte Carlo
Err = return Standard Error if True
Time = return execution time if True
"""
t_init = time()
S_T = self.exp_RV(self.S0, self.T, N)
PayOff = self.payoff_f(S_T)
V = scp.mean(np.exp(-self.r * self.T) * PayOff, axis=0)
if Err is True:
if Time is True:
elapsed = time() - t_init
return V, ss.sem(np.exp(-self.r * self.T) * self.payoff_f(S_T)), elapsed
else:
return V, ss.sem(np.exp(-self.r * self.T) * self.payoff_f(S_T))
else:
if Time is True:
elapsed = time() - t_init
return V, elapsed
else:
return V
def PDE_price(self, steps, Time=False, solver="splu"):
"""
steps = tuple with number of space steps and time steps
payoff = "call" or "put"
exercise = "European" or "American"
Time = Boolean. Execution time.
Solver = spsolve or splu or Thomas or SOR
"""
t_init = time()
Nspace = steps[0]
Ntime = steps[1]
S_max = 6 * float(self.K)
S_min = float(self.K) / 6
x_max = np.log(S_max)
x_min = np.log(S_min)
x0 = np.log(self.S0) # current log-price
x, dx = np.linspace(x_min, x_max, Nspace, retstep=True)
t, dt = np.linspace(0, self.T, Ntime, retstep=True)
self.S_vec = np.exp(x) # vector of S
Payoff = self.payoff_f(self.S_vec)
V = np.zeros((Nspace, Ntime))
if self.payoff == "call":
V[:, -1] = Payoff
V[-1, :] = np.exp(x_max) - self.K * np.exp(-self.r * t[::-1])
V[0, :] = 0
else:
V[:, -1] = Payoff
V[-1, :] = 0
V[0, :] = Payoff[0] * np.exp(-self.r * t[::-1]) # Instead of Payoff[0] I could use K
# For s to 0, the limiting value is e^(-rT)(K-s)
sig2 = self.sig**2
dxx = dx**2
a = (dt / 2) * ((self.r - 0.5 * sig2) / dx - sig2 / dxx)
b = 1 + dt * (sig2 / dxx + self.r)
c = -(dt / 2) * ((self.r - 0.5 * sig2) / dx + sig2 / dxx)
D = sparse.diags([a, b, c], [-1, 0, 1], shape=(Nspace - 2, Nspace - 2)).tocsc()
offset = np.zeros(Nspace - 2)
if solver == "spsolve":
if self.exercise == "European":
for i in range(Ntime - 2, -1, -1):
offset[0] = a * V[0, i]
offset[-1] = c * V[-1, i]
V[1:-1, i] = spsolve(D, (V[1:-1, i + 1] - offset))
elif self.exercise == "American":
for i in range(Ntime - 2, -1, -1):
offset[0] = a * V[0, i]
offset[-1] = c * V[-1, i]
V[1:-1, i] = np.maximum(spsolve(D, (V[1:-1, i + 1] - offset)), Payoff[1:-1])
elif solver == "Thomas":
if self.exercise == "European":
for i in range(Ntime - 2, -1, -1):
offset[0] = a * V[0, i]
offset[-1] = c * V[-1, i]
V[1:-1, i] = Thomas(D, (V[1:-1, i + 1] - offset))
elif self.exercise == "American":
for i in range(Ntime - 2, -1, -1):
offset[0] = a * V[0, i]
offset[-1] = c * V[-1, i]
V[1:-1, i] = np.maximum(Thomas(D, (V[1:-1, i + 1] - offset)), Payoff[1:-1])
elif solver == "SOR":
if self.exercise == "European":
for i in range(Ntime - 2, -1, -1):
offset[0] = a * V[0, i]
offset[-1] = c * V[-1, i]
V[1:-1, i] = SOR(a, b, c, (V[1:-1, i + 1] - offset), w=1.68, eps=1e-10, N_max=600)
elif self.exercise == "American":
for i in range(Ntime - 2, -1, -1):
offset[0] = a * V[0, i]
offset[-1] = c * V[-1, i]
V[1:-1, i] = np.maximum(
SOR(
a,
b,
c,
(V[1:-1, i + 1] - offset),
w=1.68,
eps=1e-10,
N_max=600,
),
Payoff[1:-1],
)
elif solver == "splu":
DD = splu(D)
if self.exercise == "European":
for i in range(Ntime - 2, -1, -1):
offset[0] = a * V[0, i]
offset[-1] = c * V[-1, i]
V[1:-1, i] = DD.solve(V[1:-1, i + 1] - offset)
elif self.exercise == "American":
for i in range(Ntime - 2, -1, -1):
offset[0] = a * V[0, i]
offset[-1] = c * V[-1, i]
V[1:-1, i] = np.maximum(DD.solve(V[1:-1, i + 1] - offset), Payoff[1:-1])
else:
raise ValueError("Solver is splu, spsolve, SOR or Thomas")
self.price = np.interp(x0, x, V[:, 0])
self.price_vec = V[:, 0]
self.mesh = V
if Time is True:
elapsed = time() - t_init
return self.price, elapsed
else:
return self.price
def plot(self, axis=None):
if type(self.S_vec) != np.ndarray or type(self.price_vec) != np.ndarray:
self.PDE_price((7000, 5000))
# print("run the PDE_price method")
# return
plt.plot(self.S_vec, self.payoff_f(self.S_vec), color="blue", label="Payoff")
plt.plot(self.S_vec, self.price_vec, color="red", label="BS curve")
if type(axis) == list:
plt.axis(axis)
plt.xlabel("S")
plt.ylabel("price")
plt.title(f"{self.exercise} - Black Scholes price")
plt.legend()
plt.show()
def mesh_plt(self):
if type(self.S_vec) != np.ndarray or type(self.mesh) != np.ndarray:
self.PDE_price((7000, 5000))
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
X, Y = np.meshgrid(np.linspace(0, self.T, self.mesh.shape[1]), self.S_vec)
ax.plot_surface(Y, X, self.mesh, cmap=cm.ocean)
ax.set_title(f"{self.exercise} - BS price surface")
ax.set_xlabel("S")
ax.set_ylabel("t")
ax.set_zlabel("V")
ax.view_init(30, -100) # this function rotates the 3d plot
plt.show()
def LSM(self, N=10000, paths=10000, order=2):
"""
Longstaff-Schwartz Method for pricing American options
N = number of time steps
paths = number of generated paths
order = order of the polynomial for the regression
"""
if self.payoff != "put":
raise ValueError("invalid type. Set 'call' or 'put'")
dt = self.T / (N - 1) # time interval
df = np.exp(-self.r * dt) # discount factor per time time interval
X0 = np.zeros((paths, 1))
increments = ss.norm.rvs(
loc=(self.r - self.sig**2 / 2) * dt,
scale=np.sqrt(dt) * self.sig,
size=(paths, N - 1),
)
X = np.concatenate((X0, increments), axis=1).cumsum(1)
S = self.S0 * np.exp(X)
H = np.maximum(self.K - S, 0) # intrinsic values for put option
V = np.zeros_like(H) # value matrix
V[:, -1] = H[:, -1]
# Valuation by LS Method
for t in range(N - 2, 0, -1):
good_paths = H[:, t] > 0
rg = np.polyfit(S[good_paths, t], V[good_paths, t + 1] * df, 2) # polynomial regression
C = np.polyval(rg, S[good_paths, t]) # evaluation of regression
exercise = np.zeros(len(good_paths), dtype=bool)
exercise[good_paths] = H[good_paths, t] > C
V[exercise, t] = H[exercise, t]
V[exercise, t + 1 :] = 0
discount_path = V[:, t] == 0
V[discount_path, t] = V[discount_path, t + 1] * df
V0 = np.mean(V[:, 1]) * df #
return V0
================================================
FILE: src/FMNM/CF.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 7 17:57:19 2019
@author: cantaro86
"""
import numpy as np
def cf_normal(u, mu=1, sig=2):
"""
Characteristic function of a Normal random variable
"""
return np.exp(1j * u * mu - 0.5 * u**2 * sig**2)
def cf_gamma(u, a=1, b=2):
"""
Characteristic function of a Gamma random variable
- shape: a
- scale: b
"""
return (1 - b * u * 1j) ** (-a)
def cf_poisson(u, lam=1):
"""
Characteristic function of a Poisson random variable
- rate: lam
"""
return np.exp(lam * (np.exp(1j * u) - 1))
def cf_mert(u, t=1, mu=1, sig=2, lam=0.8, muJ=0, sigJ=0.5):
"""
Characteristic function of a Merton random variable at time t
mu: drift
sig: diffusion coefficient
lam: jump activity
muJ: jump mean size
sigJ: jump size standard deviation
"""
return np.exp(
t * (1j * u * mu - 0.5 * u**2 * sig**2 + lam * (np.exp(1j * u * muJ - 0.5 * u**2 * sigJ**2) - 1))
)
def cf_VG(u, t=1, mu=0, theta=-0.1, sigma=0.2, kappa=0.1):
"""
Characteristic function of a Variance Gamma random variable at time t
mu: additional drift
theta: Brownian motion drift
sigma: Brownian motion diffusion
kappa: Gamma process variance
"""
return np.exp(t * (1j * mu * u - np.log(1 - 1j * theta * kappa * u + 0.5 * kappa * sigma**2 * u**2) / kappa))
def cf_NIG(u, t=1, mu=0, theta=-0.1, sigma=0.2, kappa=0.1):
"""
Characteristic function of a Normal Inverse Gaussian random variable at time t
mu: additional drift
theta: Brownian motion drift
sigma: Brownian motion diffusion
kappa: Inverse Gaussian process variance
"""
return np.exp(
t * (1j * mu * u + 1 / kappa - np.sqrt(1 - 2j * theta * kappa * u + kappa * sigma**2 * u**2) / kappa)
)
def cf_Heston(u, t, v0, mu, kappa, theta, sigma, rho):
"""
Heston characteristic function as proposed in the original paper of Heston (1993)
"""
xi = kappa - sigma * rho * u * 1j
d = np.sqrt(xi**2 + sigma**2 * (u**2 + 1j * u))
g1 = (xi + d) / (xi - d)
cf = np.exp(
1j * u * mu * t
+ (kappa * theta) / (sigma**2) * ((xi + d) * t - 2 * np.log((1 - g1 * np.exp(d * t)) / (1 - g1)))
+ (v0 / sigma**2) * (xi + d) * (1 - np.exp(d * t)) / (1 - g1 * np.exp(d * t))
)
return cf
def cf_Heston_good(u, t, v0, mu, kappa, theta, sigma, rho):
"""
Heston characteristic function as proposed by Schoutens (2004)
"""
xi = kappa - sigma * rho * u * 1j
d = np.sqrt(xi**2 + sigma**2 * (u**2 + 1j * u))
g1 = (xi + d) / (xi - d)
g2 = 1 / g1
cf = np.exp(
1j * u * mu * t
+ (kappa * theta) / (sigma**2) * ((xi - d) * t - 2 * np.log((1 - g2 * np.exp(-d * t)) / (1 - g2)))
+ (v0 / sigma**2) * (xi - d) * (1 - np.exp(-d * t)) / (1 - g2 * np.exp(-d * t))
)
return cf
================================================
FILE: src/FMNM/FFT.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 18 20:13:17 2020
@author: cantaro86
"""
import numpy as np
from scipy.fftpack import ifft
from scipy.interpolate import interp1d
from scipy.integrate import quad
from scipy.optimize import fsolve
def fft_Lewis(K, S0, r, T, cf, interp="cubic"):
"""
K = vector of strike
S = spot price scalar
cf = characteristic function
interp can be cubic or linear
"""
N = 2**15 # FFT more efficient for N power of 2
B = 500 # integration limit
dx = B / N
x = np.arange(N) * dx # the final value B is excluded
weight = np.arange(N) # Simpson weights
weight = 3 + (-1) ** (weight + 1)
weight[0] = 1
weight[N - 1] = 1
dk = 2 * np.pi / B
b = N * dk / 2
ks = -b + dk * np.arange(N)
integrand = np.exp(-1j * b * np.arange(N) * dx) * cf(x - 0.5j) * 1 / (x**2 + 0.25) * weight * dx / 3
integral_value = np.real(ifft(integrand) * N)
if interp == "linear":
spline_lin = interp1d(ks, integral_value, kind="linear")
prices = S0 - np.sqrt(S0 * K) * np.exp(-r * T) / np.pi * spline_lin(np.log(S0 / K))
elif interp == "cubic":
spline_cub = interp1d(ks, integral_value, kind="cubic")
prices = S0 - np.sqrt(S0 * K) * np.exp(-r * T) / np.pi * spline_cub(np.log(S0 / K))
return prices
def IV_from_Lewis(K, S0, T, r, cf, disp=False):
"""Implied Volatility from the Lewis formula
K = strike; S0 = spot stock; T = time to maturity; r = interest rate
cf = characteristic function"""
k = np.log(S0 / K)
def obj_fun(sig):
integrand = (
lambda u: np.real(
np.exp(u * k * 1j)
* (cf(u - 0.5j) - np.exp(1j * u * r * T + 0.5 * r * T) * np.exp(-0.5 * T * (u**2 + 0.25) * sig**2))
)
* 1
/ (u**2 + 0.25)
)
int_value = quad(integrand, 1e-15, 2000, limit=2000, full_output=1)[0]
return int_value
X0 = [0.2, 1, 2, 4, 0.0001] # set of initial guess points
for x0 in X0:
x, _, solved, msg = fsolve(
obj_fun,
[
x0,
],
full_output=True,
xtol=1e-4,
)
if solved == 1:
return x[0]
if disp is True:
print("Strike", K, msg)
return -1
================================================
FILE: src/FMNM/Heston_pricer.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 19 12:13:10 2020
@author: cantaro86
"""
from time import time
import numpy as np
import scipy as scp
import scipy.stats as ss
from FMNM.CF import cf_Heston_good
from FMNM.cython.heston import Heston_paths
from FMNM.probabilities import Q1, Q2
from functools import partial
from FMNM.FFT import fft_Lewis, IV_from_Lewis
class Heston_pricer:
"""
Class to price the options with the Heston model by:
- Fourier-inversion.
- Monte Carlo.
"""
def __init__(self, Option_info, Process_info):
"""
Process_info: of type VG_process. It contains the interest rate r
and the VG parameters (sigma, theta, kappa)
Option_info: of type Option_param. It contains (S0,K,T) i.e. current price,
strike, maturity in years
"""
self.r = Process_info.mu # interest rate
self.sigma = Process_info.sigma # Heston parameter
self.theta = Process_info.theta # Heston parameter
self.kappa = Process_info.kappa # Heston parameter
self.rho = Process_info.rho # Heston parameter
self.S0 = Option_info.S0 # current price
self.v0 = Option_info.v0 # spot variance
self.K = Option_info.K # strike
self.T = Option_info.T # maturity in years
self.exercise = Option_info.exercise
self.payoff = Option_info.payoff
def payoff_f(self, S):
if self.payoff == "call":
Payoff = np.maximum(S - self.K, 0)
elif self.payoff == "put":
Payoff = np.maximum(self.K - S, 0)
return Payoff
def MC(self, N, paths, Err=False, Time=False):
"""
Heston Monte Carlo
N = time steps
paths = number of simulated paths
Err = return Standard Error if True
Time = return execution time if True
"""
t_init = time()
S_T, _ = Heston_paths(
N=N,
paths=paths,
T=self.T,
S0=self.S0,
v0=self.v0,
mu=self.r,
rho=self.rho,
kappa=self.kappa,
theta=self.theta,
sigma=self.sigma,
)
S_T = S_T.reshape((paths, 1))
DiscountedPayoff = np.exp(-self.r * self.T) * self.payoff_f(S_T)
V = scp.mean(DiscountedPayoff, axis=0)
std_err = ss.sem(DiscountedPayoff)
if Err is True:
if Time is True:
elapsed = time() - t_init
return V, std_err, elapsed
else:
return V, std_err
else:
if Time is True:
elapsed = time() - t_init
return V, elapsed
else:
return V
def Fourier_inversion(self):
"""
Price obtained by inversion of the characteristic function
"""
k = np.log(self.K / self.S0) # log moneyness
cf_H_b_good = partial(
cf_Heston_good,
t=self.T,
v0=self.v0,
mu=self.r,
theta=self.theta,
sigma=self.sigma,
kappa=self.kappa,
rho=self.rho,
)
limit_max = 2000 # right limit in the integration
if self.payoff == "call":
call = self.S0 * Q1(k, cf_H_b_good, limit_max) - self.K * np.exp(-self.r * self.T) * Q2(
k, cf_H_b_good, limit_max
)
return call
elif self.payoff == "put":
put = self.K * np.exp(-self.r * self.T) * (1 - Q2(k, cf_H_b_good, limit_max)) - self.S0 * (
1 - Q1(k, cf_H_b_good, limit_max)
)
return put
else:
raise ValueError("invalid type. Set 'call' or 'put'")
def FFT(self, K):
"""
FFT method. It returns a vector of prices.
K is an array of strikes
"""
K = np.array(K)
cf_H_b_good = partial(
cf_Heston_good,
t=self.T,
v0=self.v0,
mu=self.r,
theta=self.theta,
sigma=self.sigma,
kappa=self.kappa,
rho=self.rho,
)
if self.payoff == "call":
return fft_Lewis(K, self.S0, self.r, self.T, cf_H_b_good, interp="cubic")
elif self.payoff == "put": # put-call parity
return (
fft_Lewis(K, self.S0, self.r, self.T, cf_H_b_good, interp="cubic")
- self.S0
+ K * np.exp(-self.r * self.T)
)
else:
raise ValueError("invalid type. Set 'call' or 'put'")
def IV_Lewis(self):
"""Implied Volatility from the Lewis formula"""
cf_H_b_good = partial(
cf_Heston_good,
t=self.T,
v0=self.v0,
mu=self.r,
theta=self.theta,
sigma=self.sigma,
kappa=self.kappa,
rho=self.rho,
)
if self.payoff == "call":
return IV_from_Lewis(self.K, self.S0, self.T, self.r, cf_H_b_good)
elif self.payoff == "put":
raise NotImplementedError
else:
raise ValueError("invalid type. Set 'call' or 'put'")
================================================
FILE: src/FMNM/Kalman_filter.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 5 10:43:12 2019
@author: cantaro86
"""
import numpy as np
from scipy.optimize import minimize
import scipy.stats as ss
import matplotlib.pyplot as plt
class Kalman_regression:
"""Kalman Filter algorithm for the linear regression beta estimation.
Alpha is assumed constant.
INPUT:
X = predictor variable. ndarray, Series or DataFrame.
Y = response variable.
alpha0 = constant alpha. The regression intercept.
beta0 = initial beta.
var_eta = variance of process error
var_eps = variance of measurement error
P0 = initial covariance of beta
"""
def __init__(self, X, Y, alpha0=None, beta0=None, var_eta=None, var_eps=None, P0=10):
self.alpha0 = alpha0
self.beta0 = beta0
self.var_eta = var_eta
self.var_eps = var_eps
self.P0 = P0
self.X = np.asarray(X)
self.Y = np.asarray(Y)
self.loglikelihood = None
self.R2_pre_fit = None
self.R2_post_fit = None
self.betas = None
self.Ps = None
if (self.alpha0 is None) or (self.beta0 is None) or (self.var_eps is None):
self.alpha0, self.beta0, self.var_eps = self.get_OLS_params()
print("alpha0, beta0 and var_eps initialized by OLS")
#################### enforce X and Y to be numpy arrays ######################
# @property
# def X(self):
# return self._X
# @X.setter
# def X(self, value):
# if not isinstance(value, np.ndarray):
# raise TypeError('X must be a numpy array')
# self._X = value
#
# @property
# def Y(self):
# return self._Y
# @Y.setter
# def Y(self, value):
# if not isinstance(value, np.ndarray):
# raise TypeError('Y must be a numpy array')
# self._Y = value
###############################################################################
def get_OLS_params(self):
"""Returns the OLS alpha, beta and sigma^2 (variance of epsilon)
Y = alpha + beta * X + epsilon
"""
beta, alpha, _, _, _ = ss.linregress(self.X, self.Y)
resid = self.Y - beta * self.X - alpha
sig2 = resid.var(ddof=2)
return alpha, beta, sig2
def set_OLS_params(self):
self.alpha0, self.beta0, self.var_eps = self.get_OLS_params()
def run(self, X=None, Y=None, var_eta=None, var_eps=None):
"""
Run the Kalman Filter
"""
if (X is None) and (Y is None):
X = self.X
Y = self.Y
X = np.asarray(X)
Y = np.asarray(Y)
N = len(X)
if len(Y) != N:
raise ValueError("Y and X must have same length")
if var_eta is not None:
self.var_eta = var_eta
if var_eps is not None:
self.var_eps = var_eps
if self.var_eta is None:
raise ValueError("var_eta is None")
betas = np.zeros_like(X)
Ps = np.zeros_like(X)
res_pre = np.zeros_like(X) # pre-fit residuals
Y = Y - self.alpha0 # re-define Y
P = self.P0
beta = self.beta0
log_2pi = np.log(2 * np.pi)
loglikelihood = 0
for k in range(N):
# Prediction
beta_p = beta # predicted beta
P_p = P + self.var_eta # predicted P
# ausiliary variables
r = Y[k] - beta_p * X[k]
S = P_p * X[k] ** 2 + self.var_eps
KG = X[k] * P_p / S # Kalman gain
# Update
beta = beta_p + KG * r
P = P_p * (1 - KG * X[k])
loglikelihood += 0.5 * (-log_2pi - np.log(S) - (r**2 / S))
betas[k] = beta
Ps[k] = P
res_pre[k] = r
res_post = Y - X * betas # post fit residuals
sqr_err = Y - np.mean(Y)
R2_pre = 1 - (res_pre @ res_pre) / (sqr_err @ sqr_err)
R2_post = 1 - (res_post @ res_post) / (sqr_err @ sqr_err)
self.loglikelihood = loglikelihood
self.R2_post_fit = R2_post
self.R2_pre_fit = R2_pre
self.betas = betas
self.Ps = Ps
def calibrate_MLE(self):
"""Returns the result of the MLE calibration for the Beta Kalman filter,
using the L-BFGS-B method.
The calibrated parameters are var_eta and var_eps.
X, Y = Series, array, or DataFrame for the regression
alpha_tr = initial alpha
beta_tr = initial beta
var_eps_ols = initial guess for the errors
"""
def minus_likelihood(c):
"""Function to minimize in order to calibrate the kalman parameters:
var_eta and var_eps."""
self.var_eps = c[0]
self.var_eta = c[1]
self.run()
return -1 * self.loglikelihood
result = minimize(
minus_likelihood,
x0=[self.var_eps, self.var_eps],
method="L-BFGS-B",
bounds=[[1e-15, None], [1e-15, None]],
tol=1e-6,
)
if result.success is True:
self.beta0 = self.betas[-1]
self.P0 = self.Ps[-1]
self.var_eps = result.x[0]
self.var_eta = result.x[1]
print("Optimization converged successfully")
print("var_eps = {}, var_eta = {}".format(result.x[0], result.x[1]))
def calibrate_R2(self, mode="pre-fit"):
"""Returns the result of the R2 calibration for the Beta Kalman filter,
using the L-BFGS-B method.
The calibrated parameters is var_eta
"""
def minus_R2(c):
"""Function to minimize in order to calibrate the kalman parameters:
var_eta and var_eps."""
self.var_eta = c
self.run()
if mode == "pre-fit":
return -1 * self.R2_pre_fit
elif mode == "post-fit":
return -1 * self.R2_post_fit
result = minimize(
minus_R2,
x0=[self.var_eps],
method="L-BFGS-B",
bounds=[[1e-15, 1]],
tol=1e-6,
)
if result.success is True:
self.beta0 = self.betas[-1]
self.P0 = self.Ps[-1]
self.var_eta = result.x[0]
print("Optimization converged successfully")
print("var_eta = {}".format(result.x[0]))
def RTS_smoother(self, X, Y):
"""
Kalman smoother for the beta estimation.
It uses the Rauch-Tung-Striebel (RTS) algorithm.
"""
self.run(X, Y)
betas, Ps = self.betas, self.Ps
betas_smooth = np.zeros_like(betas)
Ps_smooth = np.zeros_like(Ps)
betas_smooth[-1] = betas[-1]
Ps_smooth[-1] = Ps[-1]
for k in range(len(X) - 2, -1, -1):
C = Ps[k] / (Ps[k] + self.var_eta)
betas_smooth[k] = betas[k] + C * (betas_smooth[k + 1] - betas[k])
Ps_smooth[k] = Ps[k] + C**2 * (Ps_smooth[k + 1] - (Ps[k] + self.var_eta))
return betas_smooth, Ps_smooth
def rolling_regression_test(X, Y, rolling_window, training_size):
"""Rolling regression in the test set"""
rolling_beta = []
for i in range(len(X) - training_size):
beta_temp, _, _, _, _ = ss.linregress(
X[1 + i + training_size - rolling_window : 1 + i + training_size],
Y[1 + i + training_size - rolling_window : 1 + i + training_size],
)
rolling_beta.append(beta_temp)
return rolling_beta
def plot_betas(X, Y, true_rho, rho_err, var_eta=None, training_size=250, rolling_window=50):
"""
This function performs all the calculations necessary for the plot of:
- Kalman beta
- Rolling beta
- Smoothed beta
Input:
X, Y: predictor and response variables
true_rho: (an array) the true value of the autocorrelation coefficient
rho_err: (an array) rho with model error
var_eta: If None, MLE estimator is used
training_size: size of the training set
rolling window: for the computation of the rolling regression
"""
X_train = X[:training_size]
X_test = X[training_size:]
Y_train = Y[:training_size]
Y_test = Y[training_size:]
# beta_tr, alpha_tr, _ ,_ ,_ = ss.linregress(X_train, Y_train)
# resid_tr = Y_train - beta_tr * X_train - alpha_tr
# var_eps = resid_tr.var(ddof=2)
KR = Kalman_regression(X_train, Y_train)
var_eps = KR.var_eps
if var_eta is None:
KR.calibrate_MLE()
var_eta, var_eps = KR.var_eta, KR.var_eps
if var_eta < 1e-8:
print(" MLE FAILED. var_eta set equal to var_eps")
var_eta = var_eps
else:
print("MLE parameters")
print("var_eta = ", var_eta)
print("var_eps = ", var_eps)
KR.run(X_train, Y_train, var_eps=var_eps, var_eta=var_eta)
KR.beta0, KR.P0 = KR.betas[-1], KR.Ps[-1]
KR.run(X_test, Y_test)
# Kalman
betas_KF, Ps_KF = KR.betas, KR.Ps
# Rolling betas
rolling_beta = rolling_regression_test(X, Y, rolling_window, training_size)
# Smoother
betas_smooth, Ps_smooth = KR.RTS_smoother(X_test, Y_test)
plt.figure(figsize=(16, 6))
plt.plot(betas_KF, color="royalblue", label="Kalman filter betas")
plt.plot(
rolling_beta,
color="orange",
label="Rolling beta, window={}".format(rolling_window),
)
plt.plot(betas_smooth, label="RTS smoother", color="maroon")
plt.plot(
rho_err[training_size + 1 :],
color="springgreen",
marker="o",
linestyle="None",
label="rho with model error",
)
plt.plot(true_rho[training_size + 1 :], color="black", alpha=1, label="True rho")
plt.fill_between(
x=range(len(betas_KF)),
y1=betas_KF + np.sqrt(Ps_KF),
y2=betas_KF - np.sqrt(Ps_KF),
alpha=0.5,
linewidth=2,
color="seagreen",
label="Kalman Std Dev: $\pm 1 \sigma$",
)
plt.legend()
plt.title("Kalman results")
print(
"MSE Rolling regression: ",
np.mean((np.array(rolling_beta) - true_rho[training_size + 1 :]) ** 2),
)
print("MSE Kalman Filter: ", np.mean((betas_KF - true_rho[training_size + 1 :]) ** 2))
print(
"MSE RTS Smoother: ",
np.mean((betas_smooth - true_rho[training_size + 1 :]) ** 2),
)
================================================
FILE: src/FMNM/Merton_pricer.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 11 09:47:49 2019
@author: cantaro86
"""
from scipy import sparse
from scipy.sparse.linalg import splu
from time import time
import numpy as np
import scipy as scp
import scipy.stats as ss
from scipy import signal
import matplotlib.pyplot as plt
from matplotlib import cm
from FMNM.BS_pricer import BS_pricer
from math import factorial
from FMNM.CF import cf_mert
from FMNM.probabilities import Q1, Q2
from functools import partial
from FMNM.FFT import fft_Lewis, IV_from_Lewis
class Merton_pricer:
"""
Closed Formula.
Monte Carlo.
Finite-difference PIDE: Explicit-implicit scheme
0 = dV/dt + (r -(1/2)sig^2 -m) dV/dx + (1/2)sig^2 d^V/dx^2
+ \int[ V(x+y) nu(dy) ] -(r+lam)V
"""
def __init__(self, Option_info, Process_info):
"""
Process_info: of type Merton_process. It contains (r, sig, lam, muJ, sigJ) i.e.
interest rate, diffusion coefficient, jump activity and jump distribution params
Option_info: of type Option_param. It contains (S0,K,T) i.e. current price,
strike, maturity in years
"""
self.r = Process_info.r # interest rate
self.sig = Process_info.sig # diffusion coefficient
self.lam = Process_info.lam # jump activity
self.muJ = Process_info.muJ # jump mean
self.sigJ = Process_info.sigJ # jump std
self.exp_RV = Process_info.exp_RV # function to generate exponential Merton Random Variables
self.S0 = Option_info.S0 # current price
self.K = Option_info.K # strike
self.T = Option_info.T # maturity in years
self.price = 0
self.S_vec = None
self.price_vec = None
self.mesh = None
self.exercise = Option_info.exercise
self.payoff = Option_info.payoff
def payoff_f(self, S):
if self.payoff == "call":
Payoff = np.maximum(S - self.K, 0)
elif self.payoff == "put":
Payoff = np.maximum(self.K - S, 0)
return Payoff
def closed_formula(self):
"""
Merton closed formula.
"""
m = self.lam * (np.exp(self.muJ + (self.sigJ**2) / 2) - 1) # coefficient m
lam2 = self.lam * np.exp(self.muJ + (self.sigJ**2) / 2)
tot = 0
for i in range(18):
tot += (np.exp(-lam2 * self.T) * (lam2 * self.T) ** i / factorial(i)) * BS_pricer.BlackScholes(
self.payoff,
self.S0,
self.K,
self.T,
self.r - m + i * (self.muJ + 0.5 * self.sigJ**2) / self.T,
np.sqrt(self.sig**2 + (i * self.sigJ**2) / self.T),
)
return tot
def Fourier_inversion(self):
"""
Price obtained by inversion of the characteristic function
"""
k = np.log(self.K / self.S0) # log moneyness
m = self.lam * (np.exp(self.muJ + (self.sigJ**2) / 2) - 1) # coefficient m
cf_Mert = partial(
cf_mert,
t=self.T,
mu=(self.r - 0.5 * self.sig**2 - m),
sig=self.sig,
lam=self.lam,
muJ=self.muJ,
sigJ=self.sigJ,
)
if self.payoff == "call":
call = self.S0 * Q1(k, cf_Mert, np.inf) - self.K * np.exp(-self.r * self.T) * Q2(
k, cf_Mert, np.inf
) # pricing function
return call
elif self.payoff == "put":
put = self.K * np.exp(-self.r * self.T) * (1 - Q2(k, cf_Mert, np.inf)) - self.S0 * (
1 - Q1(k, cf_Mert, np.inf)
) # pricing function
return put
else:
raise ValueError("invalid type. Set 'call' or 'put'")
def FFT(self, K):
"""
FFT method. It returns a vector of prices.
K is an array of strikes
"""
K = np.array(K)
m = self.lam * (np.exp(self.muJ + (self.sigJ**2) / 2) - 1) # coefficient m
cf_Mert = partial(
cf_mert,
t=self.T,
mu=(self.r - 0.5 * self.sig**2 - m),
sig=self.sig,
lam=self.lam,
muJ=self.muJ,
sigJ=self.sigJ,
)
if self.payoff == "call":
return fft_Lewis(K, self.S0, self.r, self.T, cf_Mert, interp="cubic")
elif self.payoff == "put": # put-call parity
return (
fft_Lewis(K, self.S0, self.r, self.T, cf_Mert, interp="cubic") - self.S0 + K * np.exp(-self.r * self.T)
)
else:
raise ValueError("invalid type. Set 'call' or 'put'")
def IV_Lewis(self):
"""Implied Volatility from the Lewis formula"""
m = self.lam * (np.exp(self.muJ + (self.sigJ**2) / 2) - 1) # coefficient m
cf_Mert = partial(
cf_mert,
t=self.T,
mu=(self.r - 0.5 * self.sig**2 - m),
sig=self.sig,
lam=self.lam,
muJ=self.muJ,
sigJ=self.sigJ,
)
if self.payoff == "call":
return IV_from_Lewis(self.K, self.S0, self.T, self.r, cf_Mert)
elif self.payoff == "put":
raise NotImplementedError
else:
raise ValueError("invalid type. Set 'call' or 'put'")
def MC(self, N, Err=False, Time=False):
"""
Merton Monte Carlo
Err = return Standard Error if True
Time = return execution time if True
"""
t_init = time()
S_T = self.exp_RV(self.S0, self.T, N)
V = scp.mean(np.exp(-self.r * self.T) * self.payoff_f(S_T), axis=0)
if Err is True:
if Time is True:
elapsed = time() - t_init
return V, ss.sem(np.exp(-self.r * self.T) * self.payoff_f(S_T)), elapsed
else:
return V, ss.sem(np.exp(-self.r * self.T) * self.payoff_f(S_T))
else:
if Time is True:
elapsed = time() - t_init
return V, elapsed
else:
return V
def PIDE_price(self, steps, Time=False):
"""
steps = tuple with number of space steps and time steps
payoff = "call" or "put"
exercise = "European" or "American"
Time = Boolean. Execution time.
"""
t_init = time()
Nspace = steps[0]
Ntime = steps[1]
S_max = 6 * float(self.K)
S_min = float(self.K) / 6
x_max = np.log(S_max)
x_min = np.log(S_min)
dev_X = np.sqrt(self.lam * self.sigJ**2 + self.lam * self.muJ**2)
dx = (x_max - x_min) / (Nspace - 1)
extraP = int(np.floor(5 * dev_X / dx)) # extra points beyond the B.C.
x = np.linspace(x_min - extraP * dx, x_max + extraP * dx, Nspace + 2 * extraP) # space discretization
t, dt = np.linspace(0, self.T, Ntime, retstep=True) # time discretization
Payoff = self.payoff_f(np.exp(x))
offset = np.zeros(Nspace - 2)
V = np.zeros((Nspace + 2 * extraP, Ntime)) # grid initialization
if self.payoff == "call":
V[:, -1] = Payoff # terminal conditions
V[-extraP - 1 :, :] = np.exp(x[-extraP - 1 :]).reshape(extraP + 1, 1) * np.ones(
(extraP + 1, Ntime)
) - self.K * np.exp(-self.r * t[::-1]) * np.ones(
(extraP + 1, Ntime)
) # boundary condition
V[: extraP + 1, :] = 0
else:
V[:, -1] = Payoff
V[-extraP - 1 :, :] = 0
V[: extraP + 1, :] = self.K * np.exp(-self.r * t[::-1]) * np.ones((extraP + 1, Ntime))
cdf = ss.norm.cdf(
[np.linspace(-(extraP + 1 + 0.5) * dx, (extraP + 1 + 0.5) * dx, 2 * (extraP + 2))],
loc=self.muJ,
scale=self.sigJ,
)[0]
nu = self.lam * (cdf[1:] - cdf[:-1])
lam_appr = sum(nu)
m_appr = np.array([np.exp(i * dx) - 1 for i in range(-(extraP + 1), extraP + 2)]) @ nu
sig2 = self.sig**2
dxx = dx**2
a = (dt / 2) * ((self.r - m_appr - 0.5 * sig2) / dx - sig2 / dxx)
b = 1 + dt * (sig2 / dxx + self.r + lam_appr)
c = -(dt / 2) * ((self.r - m_appr - 0.5 * sig2) / dx + sig2 / dxx)
D = sparse.diags([a, b, c], [-1, 0, 1], shape=(Nspace - 2, Nspace - 2)).tocsc()
DD = splu(D)
if self.exercise == "European":
for i in range(Ntime - 2, -1, -1):
offset[0] = a * V[extraP, i]
offset[-1] = c * V[-1 - extraP, i]
V_jump = V[extraP + 1 : -extraP - 1, i + 1] + dt * signal.convolve(
V[:, i + 1], nu[::-1], mode="valid", method="fft"
)
V[extraP + 1 : -extraP - 1, i] = DD.solve(V_jump - offset)
elif self.exercise == "American":
for i in range(Ntime - 2, -1, -1):
offset[0] = a * V[extraP, i]
offset[-1] = c * V[-1 - extraP, i]
V_jump = V[extraP + 1 : -extraP - 1, i + 1] + dt * signal.convolve(
V[:, i + 1], nu[::-1], mode="valid", method="fft"
)
V[extraP + 1 : -extraP - 1, i] = np.maximum(DD.solve(V_jump - offset), Payoff[extraP + 1 : -extraP - 1])
X0 = np.log(self.S0) # current log-price
self.S_vec = np.exp(x[extraP + 1 : -extraP - 1]) # vector of S
self.price = np.interp(X0, x, V[:, 0])
self.price_vec = V[extraP + 1 : -extraP - 1, 0]
self.mesh = V[extraP + 1 : -extraP - 1, :]
if Time is True:
elapsed = time() - t_init
return self.price, elapsed
else:
return self.price
def plot(self, axis=None):
if type(self.S_vec) != np.ndarray or type(self.price_vec) != np.ndarray:
self.PIDE_price((5000, 4000))
plt.plot(self.S_vec, self.payoff_f(self.S_vec), color="blue", label="Payoff")
plt.plot(self.S_vec, self.price_vec, color="red", label="Merton curve")
if type(axis) == list:
plt.axis(axis)
plt.xlabel("S")
plt.ylabel("price")
plt.title("Merton price")
plt.legend(loc="upper left")
plt.show()
def mesh_plt(self):
if type(self.S_vec) != np.ndarray or type(self.mesh) != np.ndarray:
self.PDE_price((7000, 5000))
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
X, Y = np.meshgrid(np.linspace(0, self.T, self.mesh.shape[1]), self.S_vec)
ax.plot_surface(Y, X, self.mesh, cmap=cm.ocean)
ax.set_title("Merton price surface")
ax.set_xlabel("S")
ax.set_ylabel("t")
ax.set_zlabel("V")
ax.view_init(30, -100) # this function rotates the 3d plot
plt.show()
================================================
FILE: src/FMNM/NIG_pricer.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 1 12:47:00 2019
@author: cantaro86
"""
from scipy import sparse
from scipy.sparse.linalg import splu
from time import time
import numpy as np
import scipy as scp
from scipy import signal
from scipy.integrate import quad
import scipy.stats as ss
import scipy.special as scps
import matplotlib.pyplot as plt
from matplotlib import cm
from FMNM.CF import cf_NIG
from FMNM.probabilities import Q1, Q2
from functools import partial
class NIG_pricer:
"""
Closed Formula.
Monte Carlo.
Finite-difference PIDE: Explicit-implicit scheme, with Brownian approximation
0 = dV/dt + (r -(1/2)sig^2 -w) dV/dx + (1/2)sig^2 d^V/dx^2
+ \int[ V(x+y) nu(dy) ] -(r+lam)V
"""
def __init__(self, Option_info, Process_info):
"""
Process_info: of type NIG_process. It contains the interest rate r
and the NIG parameters (sigma, theta, kappa)
Option_info: of type Option_param.
It contains (S0,K,T) i.e. current price, strike, maturity in years
"""
self.r = Process_info.r # interest rate
self.sigma = Process_info.sigma # NIG parameter
self.theta = Process_info.theta # NIG parameter
self.kappa = Process_info.kappa # NIG parameter
self.exp_RV = Process_info.exp_RV # function to generate exponential NIG Random Variables
self.S0 = Option_info.S0 # current price
self.K = Option_info.K # strike
self.T = Option_info.T # maturity in years
self.price = 0
self.S_vec = None
self.price_vec = None
self.mesh = None
self.exercise = Option_info.exercise
self.payoff = Option_info.payoff
def payoff_f(self, S):
if self.payoff == "call":
Payoff = np.maximum(S - self.K, 0)
elif self.payoff == "put":
Payoff = np.maximum(self.K - S, 0)
return Payoff
def Fourier_inversion(self):
"""
Price obtained by inversion of the characteristic function
"""
k = np.log(self.K / self.S0) # log moneyness
w = (
1 - np.sqrt(1 - 2 * self.theta * self.kappa - self.kappa * self.sigma**2)
) / self.kappa # martingale correction
cf_NIG_b = partial(
cf_NIG,
t=self.T,
mu=(self.r - w),
theta=self.theta,
sigma=self.sigma,
kappa=self.kappa,
)
if self.payoff == "call":
call = self.S0 * Q1(k, cf_NIG_b, np.inf) - self.K * np.exp(-self.r * self.T) * Q2(
k, cf_NIG_b, np.inf
) # pricing function
return call
elif self.payoff == "put":
put = self.K * np.exp(-self.r * self.T) * (1 - Q2(k, cf_NIG_b, np.inf)) - self.S0 * (
1 - Q1(k, cf_NIG_b, np.inf)
) # pricing function
return put
else:
raise ValueError("invalid type. Set 'call' or 'put'")
def MC(self, N, Err=False, Time=False):
"""
NIG Monte Carlo
Err = return Standard Error if True
Time = return execution time if True
"""
t_init = time()
S_T = self.exp_RV(self.S0, self.T, N)
V = scp.mean(np.exp(-self.r * self.T) * self.payoff_f(S_T))
if Err is True:
if Time is True:
elapsed = time() - t_init
return V, ss.sem(np.exp(-self.r * self.T) * self.payoff_f(S_T)), elapsed
else:
return V, ss.sem(np.exp(-self.r * self.T) * self.payoff_f(S_T))
else:
if Time is True:
elapsed = time() - t_init
return V, elapsed
else:
return V
def NIG_measure(self, x):
A = self.theta / (self.sigma**2)
B = np.sqrt(self.theta**2 + self.sigma**2 / self.kappa) / self.sigma**2
C = np.sqrt(self.theta**2 + self.sigma**2 / self.kappa) / (np.pi * self.sigma * np.sqrt(self.kappa))
return C / np.abs(x) * np.exp(A * (x)) * scps.kv(1, B * np.abs(x))
def PIDE_price(self, steps, Time=False):
"""
steps = tuple with number of space steps and time steps
payoff = "call" or "put"
exercise = "European" or "American"
Time = Boolean. Execution time.
"""
t_init = time()
Nspace = steps[0]
Ntime = steps[1]
S_max = 2000 * float(self.K)
S_min = float(self.K) / 2000
x_max = np.log(S_max)
x_min = np.log(S_min)
dev_X = np.sqrt(self.sigma**2 + self.theta**2 * self.kappa) # std dev NIG process
dx = (x_max - x_min) / (Nspace - 1)
extraP = int(np.floor(7 * dev_X / dx)) # extra points beyond the B.C.
x = np.linspace(x_min - extraP * dx, x_max + extraP * dx, Nspace + 2 * extraP) # space discretization
t, dt = np.linspace(0, self.T, Ntime, retstep=True) # time discretization
Payoff = self.payoff_f(np.exp(x))
offset = np.zeros(Nspace - 2)
V = np.zeros((Nspace + 2 * extraP, Ntime)) # grid initialization
if self.payoff == "call":
V[:, -1] = Payoff # terminal conditions
V[-extraP - 1 :, :] = np.exp(x[-extraP - 1 :]).reshape(extraP + 1, 1) * np.ones(
(extraP + 1, Ntime)
) - self.K * np.exp(-self.r * t[::-1]) * np.ones(
(extraP + 1, Ntime)
) # boundary condition
V[: extraP + 1, :] = 0
else:
V[:, -1] = Payoff
V[-extraP - 1 :, :] = 0
V[: extraP + 1, :] = self.K * np.exp(-self.r * t[::-1]) * np.ones((extraP + 1, Ntime))
eps = 1.5 * dx # the cutoff near 0
lam = (
quad(self.NIG_measure, -(extraP + 1.5) * dx, -eps)[0] + quad(self.NIG_measure, eps, (extraP + 1.5) * dx)[0]
) # approximated intensity
def int_w(y):
return (np.exp(y) - 1) * self.NIG_measure(y)
def int_s(y):
return y**2 * self.NIG_measure(y)
w = quad(int_w, -(extraP + 1.5) * dx, -eps)[0] + quad(int_w, eps, (extraP + 1.5) * dx)[0] # is the approx of w
sig2 = quad(int_s, -eps, eps, points=0)[0] # the small jumps variance
dxx = dx * dx
a = (dt / 2) * ((self.r - w - 0.5 * sig2) / dx - sig2 / dxx)
b = 1 + dt * (sig2 / dxx + self.r + lam)
c = -(dt / 2) * ((self.r - w - 0.5 * sig2) / dx + sig2 / dxx)
D = sparse.diags([a, b, c], [-1, 0, 1], shape=(Nspace - 2, Nspace - 2)).tocsc()
DD = splu(D)
nu = np.zeros(2 * extraP + 3) # Lévy measure vector
x_med = extraP + 1 # middle point in nu vector
x_nu = np.linspace(-(extraP + 1 + 0.5) * dx, (extraP + 1 + 0.5) * dx, 2 * (extraP + 2)) # integration domain
for i in range(len(nu)):
if (i == x_med) or (i == x_med - 1) or (i == x_med + 1):
continue
nu[i] = quad(self.NIG_measure, x_nu[i], x_nu[i + 1])[0]
if self.exercise == "European":
# Backward iteration
for i in range(Ntime - 2, -1, -1):
offset[0] = a * V[extraP, i]
offset[-1] = c * V[-1 - extraP, i]
V_jump = V[extraP + 1 : -extraP - 1, i + 1] + dt * signal.convolve(
V[:, i + 1], nu[::-1], mode="valid", method="auto"
)
V[extraP + 1 : -extraP - 1, i] = DD.solve(V_jump - offset)
elif self.exercise == "American":
for i in range(Ntime - 2, -1, -1):
offset[0] = a * V[extraP, i]
offset[-1] = c * V[-1 - extraP, i]
V_jump = V[extraP + 1 : -extraP - 1, i + 1] + dt * signal.convolve(
V[:, i + 1], nu[::-1], mode="valid", method="auto"
)
V[extraP + 1 : -extraP - 1, i] = np.maximum(DD.solve(V_jump - offset), Payoff[extraP + 1 : -extraP - 1])
X0 = np.log(self.S0) # current log-price
self.S_vec = np.exp(x[extraP + 1 : -extraP - 1]) # vector of S
self.price = np.interp(X0, x, V[:, 0])
self.price_vec = V[extraP + 1 : -extraP - 1, 0]
self.mesh = V[extraP + 1 : -extraP - 1, :]
if Time is True:
elapsed = time() - t_init
return self.price, elapsed
else:
return self.price
def plot(self, axis=None):
if type(self.S_vec) != np.ndarray or type(self.price_vec) != np.ndarray:
self.PIDE_price((5000, 4000))
plt.plot(self.S_vec, self.payoff_f(self.S_vec), color="blue", label="Payoff")
plt.plot(self.S_vec, self.price_vec, color="red", label="NIG curve")
if type(axis) == list:
plt.axis(axis)
plt.xlabel("S")
plt.ylabel("price")
plt.title("NIG price")
plt.legend(loc="best")
plt.show()
def mesh_plt(self):
if type(self.S_vec) != np.ndarray or type(self.mesh) != np.ndarray:
self.PDE_price((7000, 5000))
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
X, Y = np.meshgrid(np.linspace(0, self.T, self.mesh.shape[1]), self.S_vec)
ax.plot_surface(Y, X, self.mesh, cmap=cm.ocean)
ax.set_title("NIG price surface")
ax.set_xlabel("S")
ax.set_ylabel("t")
ax.set_zlabel("V")
ax.view_init(30, -100) # this function rotates the 3d plot
plt.show()
================================================
FILE: src/FMNM/Parameters.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 10 09:56:25 2019
@author: cantaro86
"""
class Option_param:
"""
Option class wants the option parameters:
S0 = current stock price
K = Strike price
T = time to maturity
v0 = (optional) spot variance
exercise = European or American
"""
def __init__(self, S0=15, K=15, T=1, v0=0.04, payoff="call", exercise="European"):
self.S0 = S0
self.v0 = v0
self.K = K
self.T = T
if exercise == "European" or exercise == "American":
self.exercise = exercise
else:
raise ValueError("invalid type. Set 'European' or 'American'")
if payoff == "call" or payoff == "put":
self.payoff = payoff
else:
raise ValueError("invalid type. Set 'call' or 'put'")
================================================
FILE: src/FMNM/Processes.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 27 17:06:01 2019
@author: cantaro86
"""
import numpy as np
import scipy.stats as ss
from FMNM.probabilities import VG_pdf
from scipy.optimize import minimize
from statsmodels.tools.numdiff import approx_hess
import pandas as pd
class Diffusion_process:
"""
Class for the diffusion process:
r = risk free constant rate
sig = constant diffusion coefficient
mu = constant drift
"""
def __init__(self, r=0.1, sig=0.2, mu=0.1):
self.r = r
self.mu = mu
if sig <= 0:
raise ValueError("sig must be positive")
else:
self.sig = sig
def exp_RV(self, S0, T, N):
W = ss.norm.rvs((self.r - 0.5 * self.sig**2) * T, np.sqrt(T) * self.sig, N)
S_T = S0 * np.exp(W)
return S_T.reshape((N, 1))
class Merton_process:
"""
Class for the Merton process:
r = risk free constant rate
sig = constant diffusion coefficient
lam = jump activity
muJ = jump mean
sigJ = jump standard deviation
"""
def __init__(self, r=0.1, sig=0.2, lam=0.8, muJ=0, sigJ=0.5):
self.r = r
self.lam = lam
self.muJ = muJ
if sig < 0 or sigJ < 0:
raise ValueError("sig and sigJ must be positive")
else:
self.sig = sig
self.sigJ = sigJ
# moments
self.var = self.sig**2 + self.lam * self.sigJ**2 + self.lam * self.muJ**2
self.skew = self.lam * (3 * self.sigJ**2 * self.muJ + self.muJ**3) / self.var ** (1.5)
self.kurt = self.lam * (3 * self.sigJ**3 + 6 * self.sigJ**2 * self.muJ**2 + self.muJ**4) / self.var**2
def exp_RV(self, S0, T, N):
m = self.lam * (np.exp(self.muJ + (self.sigJ**2) / 2) - 1) # coefficient m
W = ss.norm.rvs(0, 1, N) # The normal RV vector
P = ss.poisson.rvs(self.lam * T, size=N) # Poisson random vector (number of jumps)
Jumps = np.asarray([ss.norm.rvs(self.muJ, self.sigJ, ind).sum() for ind in P]) # Jumps vector
S_T = S0 * np.exp(
(self.r - 0.5 * self.sig**2 - m) * T + np.sqrt(T) * self.sig * W + Jumps
) # Martingale exponential Merton
return S_T.reshape((N, 1))
class VG_process:
"""
Class for the Variance Gamma process:
r = risk free constant rate
Using the representation of Brownian subordination, the parameters are:
theta = drift of the Brownian motion
sigma = standard deviation of the Brownian motion
kappa = variance of the of the Gamma process
"""
def __init__(self, r=0.1, sigma=0.2, theta=-0.1, kappa=0.1):
self.r = r
self.c = self.r
self.theta = theta
self.kappa = kappa
if sigma < 0:
raise ValueError("sigma must be positive")
else:
self.sigma = sigma
# moments
self.mean = self.c + self.theta
self.var = self.sigma**2 + self.theta**2 * self.kappa
self.skew = (2 * self.theta**3 * self.kappa**2 + 3 * self.sigma**2 * self.theta * self.kappa) / (
self.var ** (1.5)
)
self.kurt = (
3 * self.sigma**4 * self.kappa
+ 12 * self.sigma**2 * self.theta**2 * self.kappa**2
+ 6 * self.theta**4 * self.kappa**3
) / (self.var**2)
def exp_RV(self, S0, T, N):
w = -np.log(1 - self.theta * self.kappa - self.kappa / 2 * self.sigma**2) / self.kappa # coefficient w
rho = 1 / self.kappa
G = ss.gamma(rho * T).rvs(N) / rho # The gamma RV
Norm = ss.norm.rvs(0, 1, N) # The normal RV
VG = self.theta * G + self.sigma * np.sqrt(G) * Norm # VG process at final time G
S_T = S0 * np.exp((self.r - w) * T + VG) # Martingale exponential VG
return S_T.reshape((N, 1))
def path(self, T=1, N=10000, paths=1):
"""
Creates Variance Gamma paths
N = number of time points (time steps are N-1)
paths = number of generated paths
"""
dt = T / (N - 1) # time interval
X0 = np.zeros((paths, 1))
G = ss.gamma(dt / self.kappa, scale=self.kappa).rvs(size=(paths, N - 1)) # The gamma RV
Norm = ss.norm.rvs(loc=0, scale=1, size=(paths, N - 1)) # The normal RV
increments = self.c * dt + self.theta * G + self.sigma * np.sqrt(G) * Norm
X = np.concatenate((X0, increments), axis=1).cumsum(1)
return X
def fit_from_data(self, data, dt=1, method="Nelder-Mead"):
"""
Fit the 4 parameters of the VG process using MM (method of moments),
Nelder-Mead, L-BFGS-B.
data (array): datapoints
dt (float): is the increment time
Returns (c, theta, sigma, kappa)
"""
X = data
sigma_mm = np.std(X) / np.sqrt(dt)
kappa_mm = dt * ss.kurtosis(X) / 3
theta_mm = np.sqrt(dt) * ss.skew(X) * sigma_mm / (3 * kappa_mm)
c_mm = np.mean(X) / dt - theta_mm
def log_likely(x, data, T):
return (-1) * np.sum(np.log(VG_pdf(data, T, x[0], x[1], x[2], x[3])))
if method == "L-BFGS-B":
if theta_mm < 0:
result = minimize(
log_likely,
x0=[c_mm, theta_mm, sigma_mm, kappa_mm],
method="L-BFGS-B",
args=(X, dt),
tol=1e-8,
bounds=[[-0.5, 0.5], [-0.6, -1e-15], [1e-15, 1], [1e-15, 2]],
)
else:
result = minimize(
log_likely,
x0=[c_mm, theta_mm, sigma_mm, kappa_mm],
method="L-BFGS-B",
args=(X, dt),
tol=1e-8,
bounds=[[-0.5, 0.5], [1e-15, 0.6], [1e-15, 1], [1e-15, 2]],
)
print(result.message)
elif method == "Nelder-Mead":
result = minimize(
log_likely,
x0=[c_mm, theta_mm, sigma_mm, kappa_mm],
method="Nelder-Mead",
args=(X, dt),
options={"disp": False, "maxfev": 3000},
tol=1e-8,
)
print(result.message)
elif "MM":
self.c, self.theta, self.sigma, self.kappa = (
c_mm,
theta_mm,
sigma_mm,
kappa_mm,
)
return
self.c, self.theta, self.sigma, self.kappa = result.x
class Heston_process:
"""
Class for the Heston process:
r = risk free constant rate
rho = correlation between stock noise and variance noise
theta = long term mean of the variance process
sigma = volatility coefficient of the variance process
kappa = mean reversion coefficient for the variance process
"""
def __init__(self, mu=0.1, rho=0, sigma=0.2, theta=-0.1, kappa=0.1):
self.mu = mu
if np.abs(rho) > 1:
raise ValueError("|rho| must be <=1")
self.rho = rho
if theta < 0 or sigma < 0 or kappa < 0:
raise ValueError("sigma,theta,kappa must be positive")
else:
self.theta = theta
self.sigma = sigma
self.kappa = kappa
def path(self, S0, v0, N, T=1):
"""
Produces one path of the Heston process.
N = number of time steps
T = Time in years
Returns two arrays S (price) and v (variance).
"""
MU = np.array([0, 0])
COV = np.matrix([[1, self.rho], [self.rho, 1]])
W = ss.multivariate_normal.rvs(mean=MU, cov=COV, size=N - 1)
W_S = W[:, 0] # Stock Brownian motion: W_1
W_v = W[:, 1] # Variance Brownian motion: W_2
# Initialize vectors
T_vec, dt = np.linspace(0, T, N, retstep=True)
dt_sq = np.sqrt(dt)
X0 = np.log(S0)
v = np.zeros(N)
v[0] = v0
X = np.zeros(N)
X[0] = X0
# Generate paths
for t in range(0, N - 1):
v_sq = np.sqrt(v[t])
v[t + 1] = np.abs(v[t] + self.kappa * (self.theta - v[t]) * dt + self.sigma * v_sq * dt_sq * W_v[t])
X[t + 1] = X[t] + (self.mu - 0.5 * v[t]) * dt + v_sq * dt_sq * W_S[t]
return np.exp(X), v
class NIG_process:
"""
Class for the Normal Inverse Gaussian process:
r = risk free constant rate
Using the representation of Brownian subordination, the parameters are:
theta = drift of the Brownian motion
sigma = standard deviation of the Brownian motion
kappa = variance of the of the Gamma process
"""
def __init__(self, r=0.1, sigma=0.2, theta=-0.1, kappa=0.1):
self.r = r
self.theta = theta
if sigma < 0 or kappa < 0:
raise ValueError("sigma and kappa must be positive")
else:
self.sigma = sigma
self.kappa = kappa
# moments
self.var = self.sigma**2 + self.theta**2 * self.kappa
self.skew = (3 * self.theta**3 * self.kappa**2 + 3 * self.sigma**2 * self.theta * self.kappa) / (
self.var ** (1.5)
)
self.kurt = (
3 * self.sigma**4 * self.kappa
+ 18 * self.sigma**2 * self.theta**2 * self.kappa**2
+ 15 * self.theta**4 * self.kappa**3
) / (self.var**2)
def exp_RV(self, S0, T, N):
lam = T**2 / self.kappa # scale for the IG process
mu_s = T / lam # scaled mean
w = (1 - np.sqrt(1 - 2 * self.theta * self.kappa - self.kappa * self.sigma**2)) / self.kappa
IG = ss.invgauss.rvs(mu=mu_s, scale=lam, size=N) # The IG RV
Norm = ss.norm.rvs(0, 1, N) # The normal RV
X = self.theta * IG + self.sigma * np.sqrt(IG) * Norm # NIG random vector
S_T = S0 * np.exp((self.r - w) * T + X) # exponential dynamics
return S_T.reshape((N, 1))
class GARCH:
"""
Class for the GARCH(1,1) process. Variance process:
V(t) = omega + alpha R^2(t-1) + beta V(t-1)
VL: Unconditional variance >=0
alpha: coefficient > 0
beta: coefficient > 0
gamma = 1 - alpha - beta
omega = gamma*VL
"""
def __init__(self, VL=0.04, alpha=0.08, beta=0.9):
if VL < 0 or alpha <= 0 or beta <= 0:
raise ValueError("VL>=0, alpha>0 and beta>0")
else:
self.VL = VL
self.alpha = alpha
self.beta = beta
self.gamma = 1 - self.alpha - self.beta
self.omega = self.gamma * self.VL
def path(self, N=1000):
"""
Generates a path with N points.
Returns the return process R and the variance process var
"""
eps = ss.norm.rvs(loc=0, scale=1, size=N)
R = np.zeros_like(eps)
var = np.zeros_like(eps)
for i in range(N):
var[i] = self.omega + self.alpha * R[i - 1] ** 2 + self.beta * var[i - 1]
R[i] = np.sqrt(var[i]) * eps[i]
return R, var
def fit_from_data(self, data, disp=True):
"""
MLE estimator for the GARCH
"""
# Automatic re-scaling:
# 1. the solver has problems with positive derivative in linesearch.
# 2. the log has overflows using small values
n = np.floor(np.log10(np.abs(data.mean())))
R = data / 10**n
# initial guesses
a0 = 0.05
b0 = 0.9
g0 = 1 - a0 - b0
w0 = g0 * np.var(R)
# bounds and constraint
bounds = ((0, None), (0, 1), (0, 1))
def sum_small_1(x):
return 1 - x[1] - x[2]
cons = {"fun": sum_small_1, "type": "ineq"}
def log_likely(x):
var = R[0] ** 2 # initial variance
N = len(R)
log_lik = 0
for i in range(1, N):
var = x[0] + x[1] * R[i - 1] ** 2 + x[2] * var # variance update
log_lik += -np.log(var) - (R[i] ** 2 / var)
return (-1) * log_lik
result = minimize(
log_likely,
x0=[w0, a0, b0],
method="SLSQP",
bounds=bounds,
constraints=cons,
tol=1e-8,
options={"maxiter": 150},
)
print(result.message)
self.omega = result.x[0] * 10 ** (2 * n)
self.alpha, self.beta = result.x[1:]
self.gamma = 1 - self.alpha - self.beta
self.VL = self.omega / self.gamma
if disp is True:
hess = approx_hess(result.x, log_likely) # hessian by finite differences
se = np.sqrt(np.diag(np.linalg.inv(hess))) # standard error
cv = ss.norm.ppf(1.0 - 0.05 / 2.0) # alpha=0.05
p_val = ss.norm.sf(np.abs(result.x / se)) # survival function
df = pd.DataFrame(index=["omega", "alpha", "beta"])
df["Params"] = result.x
df["SE"] = se
df["P-val"] = p_val
df["95% CI lower"] = result.x - cv * se
df["95% CI upper"] = result.x + cv * se
df.loc["omega", ["Params", "SE", "95% CI lower", "95% CI upper"]] *= 10 ** (2 * n)
print(df)
def log_likelihood(self, R, last_var=True):
"""
Computes the log-likelihood and optionally returns the last value
of the variance
"""
var = R[0] ** 2 # initial variance
N = len(R)
log_lik = 0
log_2pi = np.log(2 * np.pi)
for i in range(1, N):
var = self.omega + self.alpha * R[i - 1] ** 2 + self.beta * var # variance update
log_lik += 0.5 * (-log_2pi - np.log(var) - (R[i] ** 2 / var))
if last_var is True:
return log_lik, var
else:
return log_lik
def generate_var(self, R, R0, var0):
"""
generate the variance process.
R (array): return array
R0: initial value of the returns
var0: initial value of the variance
"""
N = len(R)
var = np.zeros(N)
var[0] = self.omega + self.alpha * (R0**2) + self.beta * var0
for i in range(1, N):
var[i] = self.omega + self.alpha * R[i - 1] ** 2 + self.beta * var[i - 1]
return var
class OU_process:
"""
Class for the OU process:
theta = long term mean
sigma = diffusion coefficient
kappa = mean reversion coefficient
"""
def __init__(self, sigma=0.2, theta=-0.1, kappa=0.1):
self.theta = theta
if sigma < 0 or kappa < 0:
raise ValueError("sigma,theta,kappa must be positive")
else:
self.sigma = sigma
self.kappa = kappa
def path(self, X0=0, T=1, N=10000, paths=1):
"""
Produces a matrix of OU process: X[N, paths]
X0 = starting point
N = number of time points (there are N-1 time steps)
T = Time in years
paths = number of paths
"""
dt = T / (N - 1)
X = np.zeros((N, paths))
X[0, :] = X0
W = ss.norm.rvs(loc=0, scale=1, size=(N - 1, paths))
std_dt = np.sqrt(self.sigma**2 / (2 * self.kappa) * (1 - np.exp(-2 * self.kappa * dt)))
for t in range(0, N - 1):
X[t + 1, :] = self.theta + np.exp(-self.kappa * dt) * (X[t, :] - self.theta) + std_dt * W[t, :]
return X
================================================
FILE: src/FMNM/Solvers.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 27 11:13:45 2019
@author: cantaro86
"""
import numpy as np
from scipy import sparse
from scipy.linalg import norm, solve_triangular
from scipy.linalg.lapack import get_lapack_funcs
from scipy.linalg.misc import LinAlgError
def Thomas(A, b):
"""
Solver for the linear equation Ax=b using the Thomas algorithm.
It is a wrapper of the LAPACK function dgtsv.
"""
D = A.diagonal(0)
L = A.diagonal(-1)
U = A.diagonal(1)
if len(A.shape) != 2 or A.shape[0] != A.shape[1]:
raise ValueError("expected square matrix")
if A.shape[0] != b.shape[0]:
raise ValueError("incompatible dimensions")
(dgtsv,) = get_lapack_funcs(("gtsv",))
du2, d, du, x, info = dgtsv(L, D, U, b)
if info == 0:
return x
if info > 0:
raise LinAlgError("singular matrix: resolution failed at diagonal %d" % (info - 1))
def SOR(A, b, w=1, eps=1e-10, N_max=100):
"""
Solver for the linear equation Ax=b using the SOR algorithm.
A = L + D + U
Arguments:
L = Strict Lower triangular matrix
D = Diagonal
U = Strict Upper triangular matrix
w = Relaxation coefficient
eps = tollerance
N_max = Max number of iterations
"""
x0 = b.copy() # initial guess
if sparse.issparse(A):
D = sparse.diags(A.diagonal()) # diagonal
U = sparse.triu(A, k=1) # Strict U
L = sparse.tril(A, k=-1) # Strict L
DD = (w * L + D).toarray()
else:
D = np.eye(A.shape[0]) * np.diag(A) # diagonal
U = np.triu(A, k=1) # Strict U
L = np.tril(A, k=-1) # Strict L
DD = w * L + D
for i in range(1, N_max + 1):
x_new = solve_triangular(DD, (w * b - w * U @ x0 - (w - 1) * D @ x0), lower=True)
if norm(x_new - x0) < eps:
return x_new
x0 = x_new
if i == N_max:
raise ValueError("Fail to converge in {} iterations".format(i))
def SOR2(A, b, w=1, eps=1e-10, N_max=100):
"""
Solver for the linear equation Ax=b using the SOR algorithm.
It uses the coefficients and not the matrix multiplication.
"""
N = len(b)
x0 = np.ones_like(b, dtype=np.float64) # initial guess
x_new = np.ones_like(x0) # new solution
for k in range(1, N_max + 1):
for i in range(N):
S = 0
for j in range(N):
if j != i:
S += A[i, j] * x_new[j]
x_new[i] = (1 - w) * x_new[i] + (w / A[i, i]) * (b[i] - S)
if norm(x_new - x0) < eps:
return x_new
x0 = x_new.copy()
if k == N_max:
print("Fail to converge in {} iterations".format(k))
================================================
FILE: src/FMNM/TC_pricer.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 10 09:56:25 2019
@author: cantaro86
"""
from time import time
import numpy as np
import numpy.matlib
import FMNM.cost_utils as cost
class TC_pricer:
"""
Solver for the option pricing model of Davis-Panas-Zariphopoulou.
"""
def __init__(self, Option_info, Process_info, cost_b=0, cost_s=0, gamma=0.001):
"""
Option_info: of type Option_param. It contains (S0,K,T)
i.e. current price, strike, maturity in years
Process_info: of type Diffusion_process.
It contains (r,mu, sig) i.e. interest rate, drift coefficient, diffusion coeff
cost_b: (lambda in the paper) BUY cost
cost_s: (mu in the paper) SELL cost
gamma: risk avversion coefficient
"""
if Option_info.payoff == "put":
raise ValueError("Not implemented for Put Options")
self.r = Process_info.r # interest rate
self.mu = Process_info.mu # drift coefficient
self.sig = Process_info.sig # diffusion coefficient
self.S0 = Option_info.S0 # current price
self.K = Option_info.K # strike
self.T = Option_info.T # maturity in years
self.cost_b = cost_b # (lambda in the paper) BUY cost
self.cost_s = cost_s # (mu in the paper) SELL cost
self.gamma = gamma # risk avversion coefficient
def price(self, N=500, TYPE="writer", Time=False):
"""
N = number of time steps
TYPE writer or buyer
Time: Boolean
"""
t = time() # measures run time
np.seterr(all="ignore") # ignore Warning for overflows
x0 = np.log(self.S0) # current log-price
T_vec, dt = np.linspace(0, self.T, N + 1, retstep=True) # vector of time steps and time steps
delta = np.exp(-self.r * (self.T - T_vec)) # discount factor
dx = self.sig * np.sqrt(dt) # space step1
dy = dx # space step2
M = int(np.floor(N / 2))
y = np.linspace(-M * dy, M * dy, 2 * M + 1)
N_y = len(y) # dim of vector y
med = np.where(y == 0)[0].item() # point where y==0
def F(xx, ll, nn):
return np.exp(self.gamma * (1 + self.cost_b) * np.exp(xx) * ll / delta[nn])
def G(xx, mm, nn):
return np.exp(-self.gamma * (1 - self.cost_s) * np.exp(xx) * mm / delta[nn])
for portfolio in ["no_opt", TYPE]:
# interates on the zero option and writer/buyer portfolios
# Tree nodes at time N
x = np.array([x0 + (self.mu - 0.5 * self.sig**2) * dt * N + (2 * i - N) * dx for i in range(N + 1)])
# Terminal conditions
if portfolio == "no_opt":
Q = np.exp(-self.gamma * cost.no_opt(x, y, self.cost_b, self.cost_s))
elif portfolio == "writer":
Q = np.exp(-self.gamma * cost.writer(x, y, self.cost_b, self.cost_s, self.K))
elif portfolio == "buyer":
Q = np.exp(-self.gamma * cost.buyer(x, y, self.cost_b, self.cost_s, self.K))
else:
raise ValueError("TYPE can be only writer or buyer")
for k in range(N - 1, -1, -1):
# expectation term
Q_new = (Q[:-1, :] + Q[1:, :]) / 2
# create the logprice vector at time k
x = np.array([x0 + (self.mu - 0.5 * self.sig**2) * dt * k + (2 * i - k) * dx for i in range(k + 1)])
# buy term
Buy = np.copy(Q_new)
Buy[:, :-1] = np.matlib.repmat(F(x, dy, k), N_y - 1, 1).T * Q_new[:, 1:]
# sell term
Sell = np.copy(Q_new)
Sell[:, 1:] = np.matlib.repmat(G(x, dy, k), N_y - 1, 1).T * Q_new[:, :-1]
# update the Q(:,:,k)
Q = np.minimum(np.minimum(Buy, Sell), Q_new)
if portfolio == "no_opt":
Q_no = Q[0, med]
else:
Q_yes = Q[0, med]
if TYPE == "writer":
price = (delta[0] / self.gamma) * np.log(Q_yes / Q_no)
else:
price = (delta[0] / self.gamma) * np.log(Q_no / Q_yes)
if Time is True:
elapsed = time() - t
return price, elapsed
else:
return price
================================================
FILE: src/FMNM/VG_pricer.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 12 18:47:05 2019
@author: cantaro86
"""
from scipy import sparse
from scipy.sparse.linalg import splu
from time import time
import numpy as np
import scipy as scp
from scipy import signal
from scipy.integrate import quad
import scipy.stats as ss
import scipy.special as scps
import matplotlib.pyplot as plt
from matplotlib import cm
from FMNM.CF import cf_VG
from FMNM.probabilities import Q1, Q2
from functools import partial
from FMNM.FFT import fft_Lewis, IV_from_Lewis
class VG_pricer:
"""
Closed Formula.
Monte Carlo.
Finite-difference PIDE: Explicit-implicit scheme, with Brownian approximation
0 = dV/dt + (r -(1/2)sig^2 -w) dV/dx + (1/2)sig^2 d^V/dx^2
+ \int[ V(x+y) nu(dy) ] -(r+lam)V
"""
def __init__(self, Option_info, Process_info):
"""
Process_info: of type VG_process.
It contains the interest rate r and the VG parameters (sigma, theta, kappa)
Option_info: of type Option_param.
It contains (S0,K,T) i.e. current price, strike, maturity in years
"""
self.r = Process_info.r # interest rate
self.sigma = Process_info.sigma # VG parameter
self.theta = Process_info.theta # VG parameter
self.kappa = Process_info.kappa # VG parameter
self.exp_RV = Process_info.exp_RV # function to generate exponential VG Random Variables
self.w = -np.log(1 - self.theta * self.kappa - self.kappa / 2 * self.sigma**2) / self.kappa # coefficient w
self.S0 = Option_info.S0 # current price
self.K = Option_info.K # strike
self.T = Option_info.T # maturity in years
self.price = 0
self.S_vec = None
self.price_vec = None
self.mesh = None
self.exercise = Option_info.exercise
self.payoff = Option_info.payoff
def payoff_f(self, S):
if self.payoff == "call":
Payoff = np.maximum(S - self.K, 0)
elif self.payoff == "put":
Payoff = np.maximum(self.K - S, 0)
return Payoff
def closed_formula(self):
"""
VG closed formula. Put is obtained by put/call parity.
"""
def Psy(a, b, g):
f = lambda u: ss.norm.cdf(a / np.sqrt(u) + b * np.sqrt(u)) * u ** (g - 1) * np.exp(-u) / scps.gamma(g)
result = quad(f, 0, np.inf)
return result[0]
# Ugly parameters
xi = -self.theta / self.sigma**2
s = self.sigma / np.sqrt(1 + ((self.theta / self.sigma) ** 2) * (self.kappa / 2))
alpha = xi * s
c1 = self.kappa / 2 * (alpha + s) ** 2
c2 = self.kappa / 2 * alpha**2
d = 1 / s * (np.log(self.S0 / self.K) + self.r * self.T + self.T / self.kappa * np.log((1 - c1) / (1 - c2)))
# Closed formula
call = self.S0 * Psy(
d * np.sqrt((1 - c1) / self.kappa),
(alpha + s) * np.sqrt(self.kappa / (1 - c1)),
self.T / self.kappa,
) - self.K * np.exp(-self.r * self.T) * Psy(
d * np.sqrt((1 - c2) / self.kappa),
(alpha) * np.sqrt(self.kappa / (1 - c2)),
self.T / self.kappa,
)
if self.payoff == "call":
return call
elif self.payoff == "put":
return call - self.S0 + self.K * np.exp(-self.r * self.T)
else:
raise ValueError("invalid type. Set 'call' or 'put'")
def Fourier_inversion(self):
"""
Price obtained by inversion of the characteristic function
"""
k = np.log(self.K / self.S0) # log moneyness
cf_VG_b = partial(
cf_VG,
t=self.T,
mu=(self.r - self.w),
theta=self.theta,
sigma=self.sigma,
kappa=self.kappa,
)
right_lim = 5000 # using np.inf may create warnings
if self.payoff == "call":
call = self.S0 * Q1(k, cf_VG_b, right_lim) - self.K * np.exp(-self.r * self.T) * Q2(
k, cf_VG_b, right_lim
) # pricing function
return call
elif self.payoff == "put":
put = self.K * np.exp(-self.r * self.T) * (1 - Q2(k, cf_VG_b, right_lim)) - self.S0 * (
1 - Q1(k, cf_VG_b, right_lim)
) # pricing function
return put
else:
raise ValueError("invalid type. Set 'call' or 'put'")
def MC(self, N, Err=False, Time=False):
"""
Variance Gamma Monte Carlo
Err = return Standard Error if True
Time = return execution time if True
"""
t_init = time()
S_T = self.exp_RV(self.S0, self.T, N)
V = scp.mean(np.exp(-self.r * self.T) * self.payoff_f(S_T), axis=0)
if Err is True:
if Time is True:
elapsed = time() - t_init
return V, ss.sem(np.exp(-self.r * self.T) * self.payoff_f(S_T)), elapsed
else:
return V, ss.sem(np.exp(-self.r * self.T) * self.payoff_f(S_T))
else:
if Time is True:
elapsed = time() - t_init
return V, elapsed
else:
return V
def FFT(self, K):
"""
FFT method. It returns a vector of prices.
K is an array of strikes
"""
K = np.array(K)
cf_VG_b = partial(
cf_VG,
t=self.T,
mu=(self.r - self.w),
theta=self.theta,
sigma=self.sigma,
kappa=self.kappa,
)
if self.payoff == "call":
return fft_Lewis(K, self.S0, self.r, self.T, cf_VG_b, interp="cubic")
elif self.payoff == "put": # put-call parity
return (
fft_Lewis(K, self.S0, self.r, self.T, cf_VG_b, interp="cubic") - self.S0 + K * np.exp(-self.r * self.T)
)
else:
raise ValueError("invalid type. Set 'call' or 'put'")
def IV_Lewis(self):
"""Implied Volatility from the Lewis formula"""
cf_VG_b = partial(
cf_VG,
t=self.T,
mu=(self.r - self.w),
theta=self.theta,
sigma=self.sigma,
kappa=self.kappa,
)
if self.payoff == "call":
return IV_from_Lewis(self.K, self.S0, self.T, self.r, cf_VG_b)
elif self.payoff == "put":
raise NotImplementedError
else:
raise ValueError("invalid type. Set 'call' or 'put'")
def PIDE_price(self, steps, Time=False):
"""
steps = tuple with number of space steps and time steps
payoff = "call" or "put"
exercise = "European" or "American"
Time = Boolean. Execution time.
"""
t_init = time()
Nspace = steps[0]
Ntime = steps[1]
S_max = 6 * float(self.K)
S_min = float(self.K) / 6
x_max = np.log(S_max)
x_min = np.log(S_min)
dev_X = np.sqrt(self.sigma**2 + self.theta**2 * self.kappa) # std dev VG process
dx = (x_max - x_min) / (Nspace - 1)
extraP = int(np.floor(5 * dev_X / dx)) # extra points beyond the B.C.
x = np.linspace(x_min - extraP * dx, x_max + extraP * dx, Nspace + 2 * extraP) # space discretization
t, dt = np.linspace(0, self.T, Ntime, retstep=True) # time discretization
Payoff = self.payoff_f(np.exp(x))
offset = np.zeros(Nspace - 2)
V = np.zeros((Nspace + 2 * extraP, Ntime)) # grid initialization
if self.payoff == "call":
V[:, -1] = Payoff # terminal conditions
V[-extraP - 1 :, :] = np.exp(x[-extraP - 1 :]).reshape(extraP + 1, 1) * np.ones(
(extraP + 1, Ntime)
) - self.K * np.exp(-self.r * t[::-1]) * np.ones(
(extraP + 1, Ntime)
) # boundary condition
V[: extraP + 1, :] = 0
else:
V[:, -1] = Payoff
V[-extraP - 1 :, :] = 0
V[: extraP + 1, :] = self.K * np.exp(-self.r * t[::-1]) * np.ones((extraP + 1, Ntime))
A = self.theta / (self.sigma**2)
B = np.sqrt(self.theta**2 + 2 * self.sigma**2 / self.kappa) / self.sigma**2
def levy_m(y):
"""Levy measure VG"""
return np.exp(A * y - B * np.abs(y)) / (self.kappa * np.abs(y))
eps = 1.5 * dx # the cutoff near 0
lam = (
quad(levy_m, -(extraP + 1.5) * dx, -eps)[0] + quad(levy_m, eps, (extraP + 1.5) * dx)[0]
) # approximated intensity
def int_w(y):
"""integrator"""
return (np.exp(y) - 1) * levy_m(y)
int_s = lambda y: np.abs(y) * np.exp(A * y - B * np.abs(y)) / self.kappa # avoid division by zero
w = (
quad(int_w, -(extraP + 1.5) * dx, -eps)[0] + quad(int_w, eps, (extraP + 1.5) * dx)[0]
) # is the approx of omega
sig2 = quad(int_s, -eps, eps)[0] # the small jumps variance
dxx = dx * dx
a = (dt / 2) * ((self.r - w - 0.5 * sig2) / dx - sig2 / dxx)
b = 1 + dt * (sig2 / dxx + self.r + lam)
c = -(dt / 2) * ((self.r - w - 0.5 * sig2) / dx + sig2 / dxx)
D = sparse.diags([a, b, c], [-1, 0, 1], shape=(Nspace - 2, Nspace - 2)).tocsc()
DD = splu(D)
nu = np.zeros(2 * extraP + 3) # Lévy measure vector
x_med = extraP + 1 # middle point in nu vector
x_nu = np.linspace(-(extraP + 1 + 0.5) * dx, (extraP + 1 + 0.5) * dx, 2 * (extraP + 2)) # integration domain
for i in range(len(nu)):
if (i == x_med) or (i == x_med - 1) or (i == x_med + 1):
continue
nu[i] = quad(levy_m, x_nu[i], x_nu[i + 1])[0]
if self.exercise == "European":
# Backward iteration
for i in range(Ntime - 2, -1, -1):
offset[0] = a * V[extraP, i]
offset[-1] = c * V[-1 - extraP, i]
V_jump = V[extraP + 1 : -extraP - 1, i + 1] + dt * signal.convolve(
V[:, i + 1], nu[::-1], mode="valid", method="auto"
)
V[extraP + 1 : -extraP - 1, i] = DD.solve(V_jump - offset)
elif self.exercise == "American":
for i in range(Ntime - 2, -1, -1):
offset[0] = a * V[extraP, i]
offset[-1] = c * V[-1 - extraP, i]
V_jump = V[extraP + 1 : -extraP - 1, i + 1] + dt * signal.convolve(
V[:, i + 1], nu[::-1], mode="valid", method="auto"
)
V[extraP + 1 : -extraP - 1, i] = np.maximum(DD.solve(V_jump - offset), Payoff[extraP + 1 : -extraP - 1])
X0 = np.log(self.S0) # current log-price
self.S_vec = np.exp(x[extraP + 1 : -extraP - 1]) # vector of S
self.price = np.interp(X0, x, V[:, 0])
self.price_vec = V[extraP + 1 : -extraP - 1, 0]
self.mesh = V[extraP + 1 : -extraP - 1, :]
if Time is True:
elapsed = time() - t_init
return self.price, elapsed
else:
return self.price
def plot(self, axis=None):
if type(self.S_vec) != np.ndarray or type(self.price_vec) != np.ndarray:
self.PIDE_price((5000, 4000))
plt.plot(self.S_vec, self.payoff_f(self.S_vec), color="blue", label="Payoff")
plt.plot(self.S_vec, self.price_vec, color="red", label="VG curve")
if type(axis) == list:
plt.axis(axis)
plt.xlabel("S")
plt.ylabel("price")
plt.title("VG price")
plt.legend(loc="upper left")
plt.show()
def mesh_plt(self):
if type(self.S_vec) != np.ndarray or type(self.mesh) != np.ndarray:
self.PDE_price((7000, 5000))
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
X, Y = np.meshgrid(np.linspace(0, self.T, self.mesh.shape[1]), self.S_vec)
ax.plot_surface(Y, X, self.mesh, cmap=cm.ocean)
ax.set_title("VG price surface")
ax.set_xlabel("S")
ax.set_ylabel("t")
ax.set_zlabel("V")
ax.view_init(30, -100) # this function rotates the 3d plot
plt.show()
def closed_formula_wrong(self):
"""
VG closed formula. This implementation seems correct, BUT IT DOES NOT WORK!!
Here I use the closed formula of Carr,Madan,Chang 1998.
With scps.kv, a modified Bessel function of second kind.
You can try to run it, but the output is slightly different from expected.
"""
def Phi(alpha, beta, gamm, x, y):
f = lambda u: u ** (alpha - 1) * (1 - u) ** (gamm - alpha - 1) * (1 - u * x) ** (-beta) * np.exp(u * y)
result = quad(f, 0.00000001, 0.99999999)
return (scps.gamma(gamm) / (scps.gamma(alpha) * scps.gamma(gamm - alpha))) * result[0]
def Psy(a, b, g):
c = np.abs(a) * np.sqrt(2 + b**2)
u = b / np.sqrt(2 + b**2)
value = (
(c ** (g + 0.5) * np.exp(np.sign(a) * c) * (1 + u) ** g)
/ (np.sqrt(2 * np.pi) * g * scps.gamma(g))
* scps.kv(g + 0.5, c)
* Phi(g, 1 - g, 1 + g, (1 + u) / 2, -np.sign(a) * c * (1 + u))
- np.sign(a)
* (c ** (g + 0.5) * np.exp(np.sign(a) * c) * (1 + u) ** (1 + g))
/ (np.sqrt(2 * np.pi) * (g + 1) * scps.gamma(g))
* scps.kv(g - 0.5, c)
* Phi(g + 1, 1 - g, 2 + g, (1 + u) / 2, -np.sign(a) * c * (1 + u))
+ np.sign(a)
* (c ** (g + 0.5) * np.exp(np.sign(a) * c) * (1 + u) ** (1 + g))
/ (np.sqrt(2 * np.pi) * (g + 1) * scps.gamma(g))
* scps.kv(g - 0.5, c)
* Phi(g, 1 - g, 1 + g, (1 + u) / 2, -np.sign(a) * c * (1 + u))
)
return value
# Ugly parameters
xi = -self.theta / self.sigma**2
s = self.sigma / np.sqrt(1 + ((self.theta / self.sigma) ** 2) * (self.kappa / 2))
alpha = xi * s
c1 = self.kappa / 2 * (alpha + s) ** 2
c2 = self.kappa / 2 * alpha**2
d = 1 / s * (np.log(self.S0 / self.K) + self.r * self.T + self.T / self.kappa * np.log((1 - c1) / (1 - c2)))
# Closed formula
call = self.S0 * Psy(
d * np.sqrt((1 - c1) / self.kappa),
(alpha + s) * np.sqrt(self.kappa / (1 - c1)),
self.T / self.kappa,
) - self.K * np.exp(-self.r * self.T) * Psy(
d * np.sqrt((1 - c2) / self.kappa),
(alpha) * np.sqrt(self.kappa / (1 - c2)),
self.T / self.kappa,
)
return call
================================================
FILE: src/FMNM/__init__.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 5 16:35:17 2023
@author: cantaro86
"""
__all__ = [
"cython",
"BS_pricer",
"CF",
"cost_utils",
"FFT",
"Heston_pricer",
"Kalman_filter",
"Merton_pricer",
"NIG_pricer",
"Parameters",
"portfolio_optimization",
"probabilities",
"Processes",
"Solvers",
"TC_pricer",
"VG_pricer",
]
from FMNM import *
================================================
FILE: src/FMNM/cost_utils.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 10 09:56:25 2019
@author: cantaro86
"""
import numpy as np
def no_opt(x, y, cost_b, cost_s):
cost = np.zeros((len(x), len(y)))
for i in range(len(y)):
if y[i] <= 0:
cost[:, i] = (1 + cost_b) * y[i] * np.exp(x)
else:
cost[:, i] = (1 - cost_s) * y[i] * np.exp(x)
return cost
def writer(x, y, cost_b, cost_s, K):
cost = np.zeros((len(x), len(y)))
for i in range(len(x)):
for j in range(len(y)):
if y[j] < 0 and (1 + cost_b) * np.exp(x[i]) <= K:
cost[i][j] = (1 + cost_b) * y[j] * np.exp(x[i])
elif y[j] >= 0 and (1 + cost_b) * np.exp(x[i]) <= K:
cost[i][j] = (1 - cost_s) * y[j] * np.exp(x[i])
elif y[j] - 1 >= 0 and (1 + cost_b) * np.exp(x[i]) > K:
cost[i][j] = ((1 - cost_s) * (y[j] - 1) * np.exp(x[i])) + K
elif y[j] - 1 < 0 and (1 + cost_b) * np.exp(x[i]) > K:
cost[i][j] = ((1 + cost_b) * (y[j] - 1) * np.exp(x[i])) + K
return cost
def buyer(x, y, cost_b, cost_s, K):
cost = np.zeros((len(x), len(y)))
for i in range(len(x)):
for j in range(len(y)):
if y[j] < 0 and (1 + cost_b) * np.exp(x[i]) <= K:
cost[i][j] = (1 + cost_b) * y[j] * np.exp(x[i])
elif y[j] >= 0 and (1 + cost_b) * np.exp(x[i]) <= K:
cost[i][j] = (1 - cost_s) * y[j] * np.exp(x[i])
elif y[j] + 1 >= 0 and (1 + cost_b) * np.exp(x[i]) > K:
cost[i][j] = ((1 - cost_s) * (y[j] + 1) * np.exp(x[i])) - K
elif y[j] + 1 < 0 and (1 + cost_b) * np.exp(x[i]) > K:
cost[i][j] = ((1 + cost_b) * (y[j] + 1) * np.exp(x[i])) - K
return cost
================================================
FILE: src/FMNM/cython/__init__.py
================================================
================================================
FILE: src/FMNM/cython/heston.pyx
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 15 17:46:10 2019
@author: cantaro86
"""
import numpy as np
import scipy.stats as ss
cimport numpy as np
cimport cython
from libc.math cimport isnan
cdef extern from "math.h":
double sqrt(double m)
double exp(double m)
double log(double m)
double fabs(double m)
@cython.boundscheck(False) # turn off bounds-checking for entire function
@cython.wraparound(False) # turn off negative index wrapping for entire function
cpdef Heston_paths(int N, int paths, double T, double S0, double v0,
double mu, double rho, double kappa, double theta, double sigma ):
"""
Generates random values of stock S and variance v at maturity T.
This function uses the "abs" method for the variance.
OUTPUT:
Two arrays of size equal to "paths".
int N = time steps
int paths = number of paths
double T = maturity
double S0 = spot price
double v0 = spot variance
double mu = drift
double rho = correlation coefficient
double kappa = mean reversion coefficient
double theta = long-term variance
double sigma = Vol of Vol - Volatility of instantaneous variance
"""
cdef double dt = T/(N-1)
cdef double dt_sq = sqrt(dt)
assert(2*kappa * theta > sigma**2) # Feller condition
cdef double[:] W_S # declaration Brownian motion for S
cdef double[:] W_v # declaration Brownian motion for v
# Initialize
cdef double[:] v_T = np.zeros(paths) # values of v at T
cdef double[:] S_T = np.zeros(paths) # values of S at T
cdef double[:] v = np.zeros(N)
cdef double[:] S = np.zeros(N)
cdef int t, path
for path in range(paths):
# Generate random Brownian Motions
W_S_arr = np.random.normal(loc=0, scale=1, size=N-1 )
W_v_arr = rho * W_S_arr + sqrt(1-rho**2) * np.random.normal(loc=0, scale=1, size=N-1 )
W_S = W_S_arr
W_v = W_v_arr
S[0] = S0 # stock at 0
v[0] = v0 # variance at 0
for t in range(0,N-1):
v[t+1] = fabs( v[t] + kappa*(theta - v[t])*dt + sigma * sqrt(v[t]) * dt_sq * W_v[t] )
S[t+1] = S[t] * exp( (mu - 0.5*v[t])*dt + sqrt(v[t]) * dt_sq * W_S[t] )
S_T[path] = S[N-1]
v_T[path] = v[N-1]
return np.asarray(S_T), np.asarray(v_T)
cpdef Heston_paths_log(int N, int paths, double T, double S0, double v0,
double mu, double rho, double kappa, double theta, double sigma ):
"""
Generates random values of stock S and variance v at maturity T.
This function uses the log-variables. NaN and abnormal numbers are ignored.
OUTPUT:
Two arrays of size smaller or equal of "paths".
INPUT:
int N = time steps
int paths = number of paths
double T = maturity
double S0 = spot price
double v0 = spot variance
double mu = drift
double rho = correlation coefficient
double kappa = mean reversion coefficient
double theta = long-term variance
double sigma = Vol of Vol - Volatility of instantaneous variance
"""
cdef double dt = T/(N-1)
cdef double dt_sq = sqrt(dt)
cdef double X0 = log(S0) # log price
cdef double Y0 = log(v0) # log-variance
assert(2*kappa * theta > sigma**2) # Feller condition
cdef double std_asy = sqrt( theta * sigma**2 /(2*kappa) )
cdef double[:] W_S # declaration Brownian motion for S
cdef double[:] W_v # declaration Brownian motion for v
# Initialize
cdef double[:] Y_T = np.zeros(paths)
cdef double[:] X_T = np.zeros(paths)
cdef double[:] Y = np.zeros(N)
cdef double[:] X = np.zeros(N)
cdef int t, path
cdef double v, v_sq
cdef double up_bound = log( (theta + 10*std_asy) ) # mean + 10 standard deviations
cdef int warning = 0
cdef int counter = 0
# Generate paths
for path in range(paths):
# Generate random Brownian Motions
W_S_arr = np.random.normal(loc=0, scale=1, size=N-1 )
W_v_arr = rho * W_S_arr + sqrt(1-rho**2) * np.random.normal(loc=0, scale=1, size=N-1 )
W_S = W_S_arr
W_v = W_v_arr
X[0] = X0 # log-stock
Y[0] = Y0 # log-variance
for t in range(0,N-1):
v = exp(Y[t]) # variance
v_sq = sqrt(v) # square root of variance
Y[t+1] = Y[t] + (1/v)*( kappa*(theta - v) - 0.5*sigma**2 )*dt + sigma * (1/v_sq) * dt_sq * W_v[t]
X[t+1] = X[t] + (mu - 0.5*v)*dt + v_sq * dt_sq * W_S[t]
if ( Y[-1] > up_bound or isnan(Y[-1]) ):
warning = 1
counter += 1
X_T[path] = 10000
Y_T[path] = 10000
continue
X_T[path] = X[-1]
Y_T[path] = Y[-1]
if (warning==1):
print("WARNING. ", counter, " paths have been removed because of the overflow.")
print("SOLUTION: Use a bigger value N.")
Y_arr = np.asarray(Y_T)
Y_good = Y_arr[ Y_arr < up_bound ]
X_good = np.asarray(X_T)[ Y_arr < up_bound ]
return np.exp(X_good), np.exp(Y_good)
================================================
FILE: src/FMNM/cython/solvers.pyx
================================================
"""
Created on Mon Jul 29 11:13:45 2019
@author: cantaro86
"""
import numpy as np
from scipy.linalg import norm
cimport numpy as np
cimport cython
cdef np.float64_t distance2(np.float64_t[:] a, np.float64_t[:] b, unsigned int N):
cdef np.float64_t dist = 0
cdef unsigned int i
for i in range(N):
dist += (a[i] - b[i]) * (a[i] - b[i])
return dist
@cython.boundscheck(False)
@cython.wraparound(False)
def SOR(np.float64_t aa,
np.float64_t bb, np.float64_t cc,
np.float64_t[:] b,
np.float64_t w=1, np.float64_t eps=1e-10, unsigned int N_max = 500):
cdef unsigned int N = b.size
cdef np.float64_t[:] x0 = np.ones(N, dtype=np.float64) # initial guess
cdef np.float64_t[:] x_new = np.ones(N, dtype=np.float64) # new solution
cdef unsigned int i, k
cdef np.float64_t S
for k in range(1,N_max+1):
for i in range(N):
if (i==0):
S = cc * x_new[1]
elif (i==N-1):
S = aa * x_new[N-2]
else:
S = aa * x_new[i-1] + cc * x_new[i+1]
x_new[i] = (1-w)*x_new[i] + (w/bb) * (b[i] - S)
if distance2(x_new, x0, N) < eps*eps:
return x_new
x0[:] = x_new
if k==N_max:
print("Fail to converge in {} iterations".format(k))
return x_new
@cython.boundscheck(False)
@cython.wraparound(False)
def PSOR(np.float64_t aa,
np.float64_t bb, np.float64_t cc,
np.float64_t[:] B, np.float64_t[:] C,
np.float64_t w=1, np.float64_t eps=1e-10, unsigned int N_max = 500):
cdef unsigned int N = B.size
cdef np.float64_t[:] x0 = np.ones(N, dtype=np.float64) # initial guess
cdef np.float64_t[:] x_new = np.ones(N, dtype=np.float64) # new solution
cdef unsigned int i, k
cdef np.float64_t S
for k in range(1,N_max+1):
for i in range(N):
if (i==0):
S = cc * x_new[1]
elif (i==N-1):
S = aa * x_new[N-2]
else:
S = aa * x_new[i-1] + cc * x_new[i+1]
x_new[i] = (1-w)*x_new[i] + (w/bb) * (B[i] - S)
x_new[i] = x_new[i] if (x_new[i] > C[i]) else C[i]
if distance2(x_new, x0, N) < eps*eps:
print("Convergence after {} iterations".format(k))
return x_new
x0[:] = x_new
if k==N_max:
print("Fail to converge in {} iterations".format(k))
return x_new
================================================
FILE: src/FMNM/portfolio_optimization.py
================================================
import numpy as np
from scipy.optimize import minimize, Bounds, LinearConstraint
def optimal_weights(MU, COV, Rf=0, w_max=1, desired_mean=None, desired_std=None):
"""
Compute the optimal weights for a portfolio containing a risk free asset and stocks.
MU = vector of mean
COV = covariance matrix
Rf = risk free return
w_max = maximum weight bound for the stock portfolio
desired_mean = desired mean of the portfolio
desired_std = desired standard deviation of the portfolio
"""
if (desired_mean is not None) and (desired_std is not None):
raise ValueError("One among desired_mean and desired_std must be None")
if ((desired_mean is not None) or (desired_std is not None)) and Rf == 0:
raise ValueError("We just optimize the Sharpe ratio, no computation of efficient frontier")
N = len(MU)
bounds = Bounds(0, w_max)
linear_constraint = LinearConstraint(np.ones(N, dtype=int), 1, 1)
weights = np.ones(N)
x0 = weights / np.sum(weights) # initial guess
def sharpe_fun(w):
return -(MU @ w - Rf) / np.sqrt(w.T @ COV @ w)
res = minimize(
sharpe_fun,
x0=x0,
method="trust-constr",
constraints=linear_constraint,
bounds=bounds,
)
print(res.message + "\n")
w_sr = res.x
std_stock_portf = np.sqrt(w_sr @ COV @ w_sr)
mean_stock_portf = MU @ w_sr
stock_port_results = {
"Sharpe Ratio": -sharpe_fun(w_sr),
"stock weights": w_sr.round(4),
"stock portfolio": {
"std": std_stock_portf.round(6),
"mean": mean_stock_portf.round(6),
},
}
if (desired_mean is None) and (desired_std is None):
return stock_port_results
elif (desired_mean is None) and (desired_std is not None):
w_stock = desired_std / std_stock_portf
if desired_std > std_stock_portf:
print(
"The risk you take is higher than the tangency portfolio risk \
==> SHORT POSTION"
)
tot_port_mean = Rf + w_stock * (mean_stock_portf - Rf)
return {
**stock_port_results,
"Bond + Stock weights": {
"Bond": (1 - w_stock).round(4),
"Stock": w_stock.round(4),
},
"Total portfolio": {"std": desired_std, "mean": tot_port_mean.round(6)},
}
elif (desired_mean is not None) and (desired_std is None):
w_stock = (desired_mean - Rf) / (mean_stock_portf - Rf)
if desired_mean > mean_stock_portf:
print(
"The return you want is higher than the tangency portfolio return \
==> SHORT POSTION"
)
tot_port_std = w_stock * std_stock_portf
return {
**stock_port_results,
"Bond + Stock weights": {
"Bond": (1 - w_stock).round(4),
"Stock": w_stock.round(4),
},
"Total portfolio": {"std": tot_port_std.round(6), "mean": desired_mean},
}
================================================
FILE: src/FMNM/probabilities.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 7 18:33:39 2019
@author: cantaro86
"""
import numpy as np
from scipy.integrate import quad
from functools import partial
from FMNM.CF import cf_Heston_good
import scipy.special as scps
from math import factorial
def Q1(k, cf, right_lim):
"""
P(X