Repository: JoinQuant/jqfactor_analyzer
Branch: master
Commit: 69e677dc0dd9
Files: 33
Total size: 23.5 MB

Directory structure:
gitextract_zj34mbsw/

├── .gitignore
├── LICENSE
├── MANiFEST.in
├── README.md
├── docs/
│   └── API文档.md
├── jqfactor_analyzer/
│   ├── __init__.py
│   ├── analyze.py
│   ├── attribution.py
│   ├── compat.py
│   ├── config.json
│   ├── data.py
│   ├── exceptions.py
│   ├── factor_cache.py
│   ├── performance.py
│   ├── plot_utils.py
│   ├── plotting.py
│   ├── prepare.py
│   ├── preprocess.py
│   ├── sample.py
│   ├── sample_data/
│   │   ├── VOL5.csv
│   │   ├── index_weight_info.csv
│   │   └── weight_info.csv
│   ├── utils.py
│   ├── version.py
│   └── when.py
├── requirements.txt
├── setup.cfg
├── setup.py
└── tests/
    ├── __init__.py
    ├── test_attribution.py
    ├── test_data.py
    ├── test_performance.py
    └── test_prepare.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
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.eggs/
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parts/
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var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
#  Usually these files are written by a python script from a template
#  before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
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.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3

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instance/
.webassets-cache

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.scrapy

# Sphinx documentation
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# PyBuilder
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# mkdocs documentation
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# mypy
.mypy_cache/


================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2019 JoinQuant

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: MANiFEST.in
================================================
include LICENSE
include *.txt
include jqfactor_analyzer/sample_data/*.csv
include jqfactor_analyzer/config.json


================================================
FILE: README.md
================================================
# jqfactor_analyzer
jqfactor_analyzer 是提供给用户配合 jqdatasdk 进行归因分析，因子数据缓存及单因子分析的开源工具。

### 安装
```pip install jqfactor_analyzer```
### 升级
```pip install -U jqfactor_analyzer```

### 具体使用方法
**详细用法请查看[API文档](https://github.com/JoinQuant/jqfactor_analyzer/blob/master/docs/API%E6%96%87%E6%A1%A3.md)**


## 归因分析使用示例

### 风格模型的基本概念
归因分析旨在通过对历史投资组合的收益进行分解，明确指出各个收益来源对组合的业绩贡献，能够更好地理解组合的表现是否符合预期，以及是否存在某一风格/行业暴露过高的风险。

多因子风险模型的基础理论认为，股票的收益是由一些共同的因子 (风格，行业和国家因子) 来驱动的，不能被这些因子解释的部分被称为股票的 “特异收益”， 而每只股票的特异收益之间是互不相关的。

(1) 风格因子，即影响股票收益的风格因素，如市值、成长、杠杆等。

(2) 行业因子，不同行业在不同时期可能优于或者差于其他行业，同一行业内的股票往往涨跌具有较强的关联性。

(3) 国家因子，表示股票市场整体涨落对投资组合的收益影响，对于任意投资组合，若他们投资的都是同一市场则其承担的国家因子和收益是相同的。

(4) 特异收益，即无法被多因子风险模型解释的部分，也就是影响个股收益的特殊因素，如公司经营能力、决策等。

根据上述多因子风险模型，股票的收益可以表达为 :

$$
R_i = \underbrace{1 \cdot f_c} _{\text{国家因子收益}} + \underbrace{\sum _{j=1}^{S} f _j^{style} \cdot X _{ij}^{style}} _{\text{风格因子收益}} + \underbrace{\sum _{j=1}^{I} f _j^{industry} \cdot X _{ij}^{industry}} _{\text{行业因子收益}} + \underbrace{u _i} _{\text{个股特异收益}}
$$

此公式可简化为:

$$
R_i = \underbrace{\sum_{j=1}^{K} f_j \cdot X_{ij}}_{\text{第 j 个因子 (含国家，风格和行业，总数为 K) 获得的收益}} + \underbrace{u_i} _{\text{个股特异收益}}
$$

其中：
- $R_i$ 是第 $i$ 只股票的收益
- $f_c$ 是国家因子的回报率
- $S$ 和 $I$ 分别是风格和行业因子的数量
- $f_j^{style}$ 是第 $j$ 个风格因子的回报率, $f_j^{industry}$ 是第 $j$ 个行业因子的回报率
- $X_{ij}^{style}$ 是第 $i$ 只股票在第 $j$ 个风格因子上的暴露, $X_{ij}^{industry}$ 是第 $i$ 只股票在第 $j$ 个行业因子上的暴露，因子暴露又称因子载荷/因子值 (通过<span style="color:red;">`jqdatasdk.get_factor_values`</span>可获取风格因子暴露及行业暴露哑变量)
- $u_i$ 是残差项，表示无法通过模型解释的部分 (即特异收益率)

根据上述公式，对市场上的股票 (一般采用中证全指作为股票池) 使用对数市值加权在横截面上进行加权最小二乘回归，可得到 :
- $f_j$ : 风格/行业因子和国家因子的回报率 ， 通过 <span style="color:red;">`jqdatasdk.get_factor_style_returns`</span> 获取
- $u_i$ : 回归残差 (无法被模型解释的部分，即特异收益率)， 通过<span style="color:red;"> `jqdatasdk.get_factor_specific_returns` </span>获取


### 使用上述已提供的数据进行归因分析 :
现已知你的投资组合 P 由权重 $w_n$ 构成，则投资组合第 j 个因子的暴露可表示为 :

$$
X^P_j = \sum_{i=1}^{n} w_i X_{ij}
$$

- $X^P_j$ 可通过 <span style="color:red;">`jqfactor_analyzer.AttributionAnalysis().exposure_portfolio` </span>获取


投资组合在第 j 个因子上获取到的收益率可以表示为 :

$$
R^P_j = X^P_j \cdot f_j
$$

- $R^P_j$ 可通过 <span style="color:red;">`jqfactor_analyzer.AttributionAnalysis().attr_daily_return` </span>获取

所以投资组合的收益率也可以被表示为 :

$$
R_P = \sum_{j=1}^{k} R^p_j \cdot f_j + \sum_{i-1}^{n} w_i u_i
$$

即理论上 $\sum_n w_n u_n$  就是投资组合的特异收益 (alpha) $R_s$ (您也可以直接获取个股特异收益率与权重相乘直接进行计算)，但现实中受到仓位，调仓时间，费用等其他因素的影响，此公式并非完全成立的，AttributionAnalysis 中是使用做差的方式来计算特异收益率，即:

$$
R_s = R_P - \sum_{j=1}^{k} R^p_j \cdot f_j
$$

### 以指数作为基准的归因分析
- jqdatasdk 已经根据指数权重计算好了指数的风格暴露 $X^B$，可通过<span style="color:red;">`jqdatasdk.get_index_style_exposure`</span> 获取

投资组合 P 相对于指数的第 j 个因子的暴露可表示为 :

$$
X^{P2B}_j = X^P_j -  X^B_j
$$

- $X^{P2B}_j$ 可通过<span style="color:red;"> `jqfactor_analyzer.AttributionAnalysis().get_exposure2bench(index_symbol)` </span>获取

投资组合在第 j 个因子上相对于指数获取到的收益率可以表示为 :

$$
R^{P2B}_j =  R^P_j  -  R^B_j = X^P_j \cdot f_j  - X^B_j \cdot f_j  = f_j \cdot X^{P2B}_j
$$

在 AttributionAnalysis 中，风格及行业因子部分，将指数的仓位和持仓的仓位进行了对齐；同时考虑了现金产生的收益 (国家因子在仓位对齐后不会产生暴露收益，现金收益为 0，现金相对于指数的收益即为：(-1) × 剩余仓位 × 指数收益)

所以投资组合相对于指数的收益可以被表示为:

$$
R_{P2B} = \sum_{j=1}^{k}  R^{P2B}_j  + R^{P2B}_s + 现金相对于指数的收益
$$

- $R_{P2B}$ 等可通过 <span style="color:red;">`jqfactor_analyzer.AttributionAnalysis().get_attr_daily_returns2bench(index_symbol)` </span>获取

### 累积收益的处理
上述 `attr_daily_return` 和  `get_attr_daily_returns2bench(index_symbol)` 获取到的均为单日收益率，在计算累积收益时需要考虑复利影响。

$$
N_t =  \prod_{t=1}^{n} (R^p_t+1)
$$
$$
Rcum^p_{jt}  = N_{t-1} \cdot R^P_{jt}
$$

其中 :

- $N_t$ 为投资组合在第 t 天盘后的净值
- $R^p_t$ 为投资组合在第 t 天的日度收益率
- $Rcum^p_{jt}$ 为投资组合 p 的第 j 个因子在 t 日的累积收益
- $R^P_{jt}$ 为投资组合 p 的第 j 个因子在 t 日的日收益率
- $N_t,  Rcum^p_{jt}$ 均可通过<span style="color:red;"> `jqfactor_analyzer.AttributionAnalysis().attr_returns`</span> 获取
- 相对于基准的累积收益算法类似, 可通过 <span style="color:red;">`jqfactor_analyzer.AttributionAnalysis().get_attr_returns2bench` </span>获取

### 导入模块并登陆 jqdatasdk


```python
import jqdatasdk
import jqfactor_analyzer as ja
# 获取 jqdatasdk 授权，输入用户名、密码，申请地址：https://www.joinquant.com/default/index/sdk
# 聚宽官网，使用方法参见：https://www.joinquant.com/help/api/doc?name=JQDatadoc
jqdatasdk.auth("账号", "密码")
```

### 处理权重信息
此处使用的是 jqfactor_analyzer 提供的示例文件
数据格式要求 :
- 权重数据, 一个 dataframe, index 为日期, columns 为标的代码 (可使用 jqdatasdk.normalize_code 转为支持的格式), values 为权重, 每日的权重和应该小于 1
- 组合的日度收益数据, 一个 series, index 为日期, values 为日收益率


```python
import os
import pandas as pd
weight_path = os.path.join(os.path.dirname(ja.__file__), 'sample_data', 'weight_info.csv')
weight_infos = pd.read_csv(weight_path, index_col=0)
daily_return = weight_infos.pop("return")
```


```python
weight_infos.head(5)
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    dataframe tbody tr th {
        vertical-align: top;
    }
    .dataframe thead th {
        text-align: right;
    }
</style>

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>000006.XSHE</th>
      <th>000008.XSHE</th>
      <th>000009.XSHE</th>
      <th>000012.XSHE</th>
      <th>000021.XSHE</th>
      <th>000025.XSHE</th>
      <th>000027.XSHE</th>
      <th>000028.XSHE</th>
      <th>000031.XSHE</th>
      <th>000032.XSHE</th>
      <th>...</th>
      <th>603883.XSHG</th>
      <th>603885.XSHG</th>
      <th>603888.XSHG</th>
      <th>603893.XSHG</th>
      <th>603927.XSHG</th>
      <th>603939.XSHG</th>
      <th>603979.XSHG</th>
      <th>603983.XSHG</th>
      <th>605117.XSHG</th>
      <th>605358.XSHG</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2020-01-02</th>
      <td>0.000873</td>
      <td>0.001244</td>
      <td>0.002934</td>
      <td>0.001219</td>
      <td>0.001614</td>
      <td>0.000433</td>
      <td>0.001274</td>
      <td>0.001181</td>
      <td>0.001471</td>
      <td>NaN</td>
      <td>...</td>
      <td>0.001294</td>
      <td>0.001536</td>
      <td>0.000781</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>0.001896</td>
      <td>NaN</td>
      <td>0.000482</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2020-01-03</th>
      <td>0.000897</td>
      <td>0.001247</td>
      <td>0.002679</td>
      <td>0.001203</td>
      <td>0.001708</td>
      <td>0.000432</td>
      <td>0.001293</td>
      <td>0.001195</td>
      <td>0.001463</td>
      <td>NaN</td>
      <td>...</td>
      <td>0.001298</td>
      <td>0.001505</td>
      <td>0.000824</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>0.001912</td>
      <td>NaN</td>
      <td>0.000466</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2020-01-06</th>
      <td>0.000879</td>
      <td>0.001216</td>
      <td>0.002926</td>
      <td>0.001225</td>
      <td>0.001613</td>
      <td>0.000434</td>
      <td>0.001278</td>
      <td>0.001228</td>
      <td>0.001429</td>
      <td>NaN</td>
      <td>...</td>
      <td>0.001238</td>
      <td>0.001534</td>
      <td>0.000767</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>0.001962</td>
      <td>NaN</td>
      <td>0.000488</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2020-01-07</th>
      <td>0.000883</td>
      <td>0.001241</td>
      <td>0.002591</td>
      <td>0.001220</td>
      <td>0.001536</td>
      <td>0.000439</td>
      <td>0.001294</td>
      <td>0.001195</td>
      <td>0.001488</td>
      <td>NaN</td>
      <td>...</td>
      <td>0.001267</td>
      <td>0.001575</td>
      <td>0.000764</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>0.001959</td>
      <td>NaN</td>
      <td>0.000468</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2020-01-08</th>
      <td>0.000877</td>
      <td>0.001231</td>
      <td>0.002758</td>
      <td>0.001205</td>
      <td>0.001528</td>
      <td>0.000429</td>
      <td>0.001270</td>
      <td>0.001208</td>
      <td>0.001448</td>
      <td>NaN</td>
      <td>...</td>
      <td>0.001277</td>
      <td>0.001554</td>
      <td>0.000749</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>0.001987</td>
      <td>NaN</td>
      <td>0.000474</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 818 columns</p>
</div>




```python
weight_infos.sum(axis=1).head(5)
```




    2020-01-02    0.752196
    2020-01-03    0.750206
    2020-01-06    0.752375
    2020-01-07    0.752054
    2020-01-08    0.748039
    dtype: float64



### 进行归因分析
**具体用法请查看[API文档](https://github.com/JoinQuant/jqfactor_analyzer/blob/master/docs/API%E6%96%87%E6%A1%A3.md), 此处仅作示例**


```python
An = ja.AttributionAnalysis(weight_infos, daily_return, style_type='style', industry='sw_l1', use_cn=True, show_data_progress=True)
```

    check/save factor cache : 100%|██████████| 54/54 [00:02<00:00, 25.75it/s]
    calc_style_exposure : 100%|██████████| 1087/1087 [00:27<00:00, 39.52it/s]
    calc_industry_exposure : 100%|██████████| 1087/1087 [00:19<00:00, 56.53it/s]



```python
An.exposure_portfolio.head(5) #查看暴露
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>size</th>
      <th>beta</th>
      <th>momentum</th>
      <th>residual_volatility</th>
      <th>non_linear_size</th>
      <th>book_to_price_ratio</th>
      <th>liquidity</th>
      <th>earnings_yield</th>
      <th>growth</th>
      <th>leverage</th>
      <th>...</th>
      <th>801050</th>
      <th>801040</th>
      <th>801780</th>
      <th>801970</th>
      <th>801120</th>
      <th>801790</th>
      <th>801760</th>
      <th>801890</th>
      <th>801960</th>
      <th>country</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2020-01-02</th>
      <td>-0.487816</td>
      <td>0.468947</td>
      <td>-0.048262</td>
      <td>0.104597</td>
      <td>0.976877</td>
      <td>-0.112042</td>
      <td>0.278131</td>
      <td>-0.311944</td>
      <td>-0.000541</td>
      <td>-0.356787</td>
      <td>...</td>
      <td>0.030234</td>
      <td>0.023728</td>
      <td>0.010499</td>
      <td>NaN</td>
      <td>0.017049</td>
      <td>0.032292</td>
      <td>0.042405</td>
      <td>0.027871</td>
      <td>NaN</td>
      <td>0.752196</td>
    </tr>
    <tr>
      <th>2020-01-03</th>
      <td>-0.485128</td>
      <td>0.461138</td>
      <td>-0.044422</td>
      <td>0.104270</td>
      <td>0.970710</td>
      <td>-0.110196</td>
      <td>0.271739</td>
      <td>-0.314469</td>
      <td>-0.002360</td>
      <td>-0.354623</td>
      <td>...</td>
      <td>0.030574</td>
      <td>0.023712</td>
      <td>0.010610</td>
      <td>NaN</td>
      <td>0.017071</td>
      <td>0.033261</td>
      <td>0.041491</td>
      <td>0.027631</td>
      <td>NaN</td>
      <td>0.750206</td>
    </tr>
    <tr>
      <th>2020-01-06</th>
      <td>-0.477658</td>
      <td>0.464642</td>
      <td>-0.034905</td>
      <td>0.116226</td>
      <td>0.958563</td>
      <td>-0.118501</td>
      <td>0.277993</td>
      <td>-0.320429</td>
      <td>-0.001766</td>
      <td>-0.352186</td>
      <td>...</td>
      <td>0.030807</td>
      <td>0.023681</td>
      <td>0.010619</td>
      <td>NaN</td>
      <td>0.016953</td>
      <td>0.033203</td>
      <td>0.042406</td>
      <td>0.027906</td>
      <td>NaN</td>
      <td>0.752375</td>
    </tr>
    <tr>
      <th>2020-01-07</th>
      <td>-0.474913</td>
      <td>0.456438</td>
      <td>-0.030596</td>
      <td>0.118867</td>
      <td>0.953152</td>
      <td>-0.117436</td>
      <td>0.274219</td>
      <td>-0.315071</td>
      <td>-0.000874</td>
      <td>-0.350100</td>
      <td>...</td>
      <td>0.030140</td>
      <td>0.024215</td>
      <td>0.010716</td>
      <td>NaN</td>
      <td>0.017240</td>
      <td>0.033022</td>
      <td>0.042867</td>
      <td>0.027853</td>
      <td>NaN</td>
      <td>0.752054</td>
    </tr>
    <tr>
      <th>2020-01-08</th>
      <td>-0.474413</td>
      <td>0.452745</td>
      <td>-0.026417</td>
      <td>0.123923</td>
      <td>0.951369</td>
      <td>-0.115294</td>
      <td>0.271193</td>
      <td>-0.305295</td>
      <td>-0.000920</td>
      <td>-0.345431</td>
      <td>...</td>
      <td>0.030176</td>
      <td>0.023694</td>
      <td>0.010671</td>
      <td>NaN</td>
      <td>0.017303</td>
      <td>0.032777</td>
      <td>0.040977</td>
      <td>0.027820</td>
      <td>NaN</td>
      <td>0.748039</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 43 columns</p>
</div>




```python
An.attr_daily_returns.head(5) #查看日度收益拆解
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>size</th>
      <th>beta</th>
      <th>momentum</th>
      <th>residual_volatility</th>
      <th>non_linear_size</th>
      <th>book_to_price_ratio</th>
      <th>liquidity</th>
      <th>earnings_yield</th>
      <th>growth</th>
      <th>leverage</th>
      <th>...</th>
      <th>801970</th>
      <th>801120</th>
      <th>801790</th>
      <th>801760</th>
      <th>801890</th>
      <th>801960</th>
      <th>country</th>
      <th>common_return</th>
      <th>specific_return</th>
      <th>total_return</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2020-01-02</th>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>0.000000</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2020-01-03</th>
      <td>0.000241</td>
      <td>-0.000144</td>
      <td>0.000130</td>
      <td>0.000090</td>
      <td>0.000955</td>
      <td>-0.000039</td>
      <td>-0.000045</td>
      <td>0.000174</td>
      <td>7.907650e-08</td>
      <td>0.000148</td>
      <td>...</td>
      <td>NaN</td>
      <td>-0.000168</td>
      <td>-0.000019</td>
      <td>0.000500</td>
      <td>-0.000050</td>
      <td>NaN</td>
      <td>0.000860</td>
      <td>0.003030</td>
      <td>-0.001083</td>
      <td>0.001948</td>
    </tr>
    <tr>
      <th>2020-01-06</th>
      <td>-0.000014</td>
      <td>0.000151</td>
      <td>0.000119</td>
      <td>0.000199</td>
      <td>0.002035</td>
      <td>-0.000017</td>
      <td>0.000025</td>
      <td>0.000573</td>
      <td>-1.457480e-07</td>
      <td>0.000160</td>
      <td>...</td>
      <td>NaN</td>
      <td>-0.000178</td>
      <td>-0.000145</td>
      <td>0.000286</td>
      <td>0.000015</td>
      <td>NaN</td>
      <td>0.000949</td>
      <td>0.004990</td>
      <td>0.002358</td>
      <td>0.007348</td>
    </tr>
    <tr>
      <th>2020-01-07</th>
      <td>0.000176</td>
      <td>0.001208</td>
      <td>0.000002</td>
      <td>0.000236</td>
      <td>0.001533</td>
      <td>0.000012</td>
      <td>-0.000213</td>
      <td>-0.000627</td>
      <td>8.726552e-07</td>
      <td>0.000250</td>
      <td>...</td>
      <td>NaN</td>
      <td>0.000077</td>
      <td>-0.000003</td>
      <td>0.000834</td>
      <td>-0.000008</td>
      <td>NaN</td>
      <td>0.006875</td>
      <td>0.009541</td>
      <td>-0.000621</td>
      <td>0.008920</td>
    </tr>
    <tr>
      <th>2020-01-08</th>
      <td>-0.000190</td>
      <td>-0.001919</td>
      <td>-0.000007</td>
      <td>0.000019</td>
      <td>0.000199</td>
      <td>0.000027</td>
      <td>-0.000134</td>
      <td>0.000400</td>
      <td>-8.393073e-09</td>
      <td>-0.000140</td>
      <td>...</td>
      <td>NaN</td>
      <td>0.000038</td>
      <td>-0.000384</td>
      <td>-0.000414</td>
      <td>0.000104</td>
      <td>NaN</td>
      <td>-0.009655</td>
      <td>-0.010019</td>
      <td>-0.000516</td>
      <td>-0.010535</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 46 columns</p>
</div>




```python
An.attr_returns.head(5)  #查看累积收益
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>size</th>
      <th>beta</th>
      <th>momentum</th>
      <th>residual_volatility</th>
      <th>non_linear_size</th>
      <th>book_to_price_ratio</th>
      <th>liquidity</th>
      <th>earnings_yield</th>
      <th>growth</th>
      <th>leverage</th>
      <th>...</th>
      <th>801970</th>
      <th>801120</th>
      <th>801790</th>
      <th>801760</th>
      <th>801890</th>
      <th>801960</th>
      <th>country</th>
      <th>common_return</th>
      <th>specific_return</th>
      <th>total_return</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2020-01-02</th>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2020-01-03</th>
      <td>0.000241</td>
      <td>-0.000144</td>
      <td>0.000130</td>
      <td>0.000090</td>
      <td>0.000955</td>
      <td>-0.000039</td>
      <td>-0.000045</td>
      <td>0.000174</td>
      <td>7.907650e-08</td>
      <td>0.000148</td>
      <td>...</td>
      <td>NaN</td>
      <td>-0.000168</td>
      <td>-0.000019</td>
      <td>0.000500</td>
      <td>-0.000050</td>
      <td>NaN</td>
      <td>0.000860</td>
      <td>0.003030</td>
      <td>-0.001083</td>
      <td>0.001948</td>
    </tr>
    <tr>
      <th>2020-01-06</th>
      <td>0.000227</td>
      <td>0.000007</td>
      <td>0.000249</td>
      <td>0.000290</td>
      <td>0.002994</td>
      <td>-0.000056</td>
      <td>-0.000020</td>
      <td>0.000748</td>
      <td>-6.695534e-08</td>
      <td>0.000308</td>
      <td>...</td>
      <td>NaN</td>
      <td>-0.000346</td>
      <td>-0.000164</td>
      <td>0.000787</td>
      <td>-0.000035</td>
      <td>NaN</td>
      <td>0.001812</td>
      <td>0.008030</td>
      <td>0.001280</td>
      <td>0.009310</td>
    </tr>
    <tr>
      <th>2020-01-07</th>
      <td>0.000405</td>
      <td>0.001226</td>
      <td>0.000252</td>
      <td>0.000528</td>
      <td>0.004541</td>
      <td>-0.000044</td>
      <td>-0.000234</td>
      <td>0.000115</td>
      <td>8.138242e-07</td>
      <td>0.000560</td>
      <td>...</td>
      <td>NaN</td>
      <td>-0.000268</td>
      <td>-0.000168</td>
      <td>0.001629</td>
      <td>-0.000043</td>
      <td>NaN</td>
      <td>0.008750</td>
      <td>0.017660</td>
      <td>0.000653</td>
      <td>0.018313</td>
    </tr>
    <tr>
      <th>2020-01-08</th>
      <td>0.000212</td>
      <td>-0.000728</td>
      <td>0.000245</td>
      <td>0.000547</td>
      <td>0.004744</td>
      <td>-0.000016</td>
      <td>-0.000371</td>
      <td>0.000522</td>
      <td>8.052775e-07</td>
      <td>0.000418</td>
      <td>...</td>
      <td>NaN</td>
      <td>-0.000229</td>
      <td>-0.000559</td>
      <td>0.001207</td>
      <td>0.000064</td>
      <td>NaN</td>
      <td>-0.001081</td>
      <td>0.007457</td>
      <td>0.000128</td>
      <td>0.007585</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 46 columns</p>
</div>




```python
An.get_attr_returns2bench('000905.XSHG').head(5)  #查看相对指数的累积收益
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>size</th>
      <th>beta</th>
      <th>momentum</th>
      <th>residual_volatility</th>
      <th>non_linear_size</th>
      <th>book_to_price_ratio</th>
      <th>liquidity</th>
      <th>earnings_yield</th>
      <th>growth</th>
      <th>leverage</th>
      <th>...</th>
      <th>801970</th>
      <th>801120</th>
      <th>801790</th>
      <th>801760</th>
      <th>801890</th>
      <th>801960</th>
      <th>common_return</th>
      <th>cash</th>
      <th>specific_return</th>
      <th>total_return</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2020-01-02</th>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>2020-01-03</th>
      <td>2.247752e-08</td>
      <td>5.274612e-07</td>
      <td>-1.010579e-06</td>
      <td>-1.780933e-07</td>
      <td>-5.018849e-09</td>
      <td>-1.576053e-07</td>
      <td>1.815168e-07</td>
      <td>3.067299e-07</td>
      <td>-8.676436e-08</td>
      <td>3.843589e-07</td>
      <td>...</td>
      <td>NaN</td>
      <td>-8.367921e-07</td>
      <td>2.211999e-07</td>
      <td>2.512287e-07</td>
      <td>-2.031997e-07</td>
      <td>NaN</td>
      <td>-0.000006</td>
      <td>-0.000670</td>
      <td>-0.000079</td>
      <td>-0.000755</td>
    </tr>
    <tr>
      <th>2020-01-06</th>
      <td>3.139000e-09</td>
      <td>-2.167887e-06</td>
      <td>1.005890e-06</td>
      <td>-9.837778e-06</td>
      <td>1.803920e-06</td>
      <td>3.592758e-07</td>
      <td>-3.082887e-07</td>
      <td>-4.489268e-06</td>
      <td>-1.570012e-07</td>
      <td>-7.565016e-07</td>
      <td>...</td>
      <td>NaN</td>
      <td>-4.620739e-06</td>
      <td>-2.607788e-06</td>
      <td>-8.734669e-06</td>
      <td>-1.166518e-07</td>
      <td>NaN</td>
      <td>-0.000063</td>
      <td>-0.003198</td>
      <td>-0.000234</td>
      <td>-0.003494</td>
    </tr>
    <tr>
      <th>2020-01-07</th>
      <td>-5.129552e-08</td>
      <td>-2.485408e-05</td>
      <td>9.140735e-07</td>
      <td>-2.227106e-05</td>
      <td>1.453669e-06</td>
      <td>-5.066033e-08</td>
      <td>4.500972e-06</td>
      <td>4.348111e-06</td>
      <td>1.794315e-07</td>
      <td>-3.707358e-06</td>
      <td>...</td>
      <td>NaN</td>
      <td>-1.876927e-06</td>
      <td>-2.703177e-06</td>
      <td>-3.476170e-05</td>
      <td>-2.429496e-07</td>
      <td>NaN</td>
      <td>-0.000095</td>
      <td>-0.006224</td>
      <td>-0.000283</td>
      <td>-0.006603</td>
    </tr>
    <tr>
      <th>2020-01-08</th>
      <td>-1.236180e-07</td>
      <td>4.020758e-05</td>
      <td>1.082783e-06</td>
      <td>-2.386474e-05</td>
      <td>1.502709e-06</td>
      <td>-1.806807e-06</td>
      <td>1.001751e-05</td>
      <td>-7.241071e-06</td>
      <td>1.893800e-07</td>
      <td>-1.425501e-06</td>
      <td>...</td>
      <td>NaN</td>
      <td>2.019730e-07</td>
      <td>-1.379156e-05</td>
      <td>-1.232299e-05</td>
      <td>1.799073e-06</td>
      <td>NaN</td>
      <td>-0.000087</td>
      <td>-0.002647</td>
      <td>-0.000427</td>
      <td>-0.003160</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 46 columns</p>
</div>




```python
An.plot_exposure(factors='style',index_symbol=None,figsize=(15,7))
```


![Img](https://image.joinquant.com/4e362f3cbce335f035e568d4a264ce7d)



```python
An.plot_returns(factors='style',index_symbol=None,figsize=(15,7))
```


![Img](https://image.joinquant.com/33494d30d39798500f5cdd7d560256d1)



```python
An.plot_exposure_and_returns(factors='style',index_symbol=None,show_factor_perf=False,figsize=(12,6))
```


![Img](https://image.joinquant.com/92359b7544f550d912bcaf602da10238)


## 因子数据本地缓存使用示例
**具体用法请查看[API文档](https://github.com/JoinQuant/jqfactor_analyzer/blob/master/docs/API%E6%96%87%E6%A1%A3.md), 此处仅作示例**

### 设置缓存目录


```python
from jqfactor_analyzer.factor_cache import set_cache_dir,get_cache_dir
# my_path = 'E:\\jqfactor_cache'
# set_cache_dir(my_path) #设置缓存目录为my_path
print(get_cache_dir()) #输出缓存目录
```

    C:\Users\wq\jqfactor_datacache\bundle


### 缓存/检查缓存和读取已缓存数据


```python
from jqfactor_analyzer.factor_cache import save_factor_values_by_group,get_factor_values_by_cache,get_factor_folder,get_cache_dir
# import jqdatasdk as jq
# jq.auth("账号",'密码') #登陆jqdatasdk来从服务端缓存数据

all_factors = jqdatasdk.get_all_factors()
factor_names = all_factors[all_factors.category=='growth'].factor.tolist()  #将聚宽因子库中的成长类因子作为一组因子
group_name = 'growth_factors' #因子组名定义为'growth_factors'
start_date = '2021-01-01'
end_date = '2021-06-01'
# 检查/缓存因子数据
factor_path = save_factor_values_by_group(start_date,end_date,factor_names=factor_names,group_name=group_name,overwrite=False,show_progress=True)
# factor_path = os.path.join(get_cache_dir(), get_factor_folder(factor_names,group_name=group_name)  #等同于save_factor_values_by_group返回的路径

```

    check/save factor cache : 100%|██████████| 6/6 [00:01<00:00,  5.87it/s]



```python
# 循环获取缓存的因子数据,并拼接
trade_days = jqdatasdk.get_trade_days(start_date,end_date)
factor_values = {}
for date in trade_days:
    factor_values[date] = get_factor_values_by_cache(date,codes=None,factor_names=factor_names,group_name=group_name, factor_path=factor_path)#这里实际只需要指定group_name,factor_names参数的其中一个,缓存时指定了group_name时,factor_names不生效
factor_values = pd.concat(factor_values)
factor_values.head(5)
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th></th>
      <th>financing_cash_growth_rate</th>
      <th>net_asset_growth_rate</th>
      <th>net_operate_cashflow_growth_rate</th>
      <th>net_profit_growth_rate</th>
      <th>np_parent_company_owners_growth_rate</th>
      <th>operating_revenue_growth_rate</th>
      <th>PEG</th>
      <th>total_asset_growth_rate</th>
      <th>total_profit_growth_rate</th>
    </tr>
    <tr>
      <th></th>
      <th>code</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th rowspan="5" valign="top">2021-01-04</th>
      <th>000001.XSHE</th>
      <td>4.218607</td>
      <td>0.245417</td>
      <td>-3.438636</td>
      <td>-0.036129</td>
      <td>-0.036129</td>
      <td>0.139493</td>
      <td>NaN</td>
      <td>0.172409</td>
      <td>-0.053686</td>
    </tr>
    <tr>
      <th>000002.XSHE</th>
      <td>-1.059306</td>
      <td>0.236022</td>
      <td>0.266020</td>
      <td>0.009771</td>
      <td>0.064828</td>
      <td>0.115457</td>
      <td>1.229423</td>
      <td>0.107217</td>
      <td>-0.013790</td>
    </tr>
    <tr>
      <th>000004.XSHE</th>
      <td>NaN</td>
      <td>11.430834</td>
      <td>-0.019530</td>
      <td>-3.350306</td>
      <td>-3.551808</td>
      <td>-0.328126</td>
      <td>NaN</td>
      <td>10.912087</td>
      <td>-3.888289</td>
    </tr>
    <tr>
      <th>000005.XSHE</th>
      <td>-1.014341</td>
      <td>0.052103</td>
      <td>-2.331018</td>
      <td>-0.480705</td>
      <td>-0.461062</td>
      <td>-0.700859</td>
      <td>NaN</td>
      <td>-0.040798</td>
      <td>-0.567470</td>
    </tr>
    <tr>
      <th>000006.XSHE</th>
      <td>-0.978757</td>
      <td>0.112236</td>
      <td>-1.509728</td>
      <td>0.083089</td>
      <td>0.044869</td>
      <td>0.170041</td>
      <td>1.931730</td>
      <td>-0.005611</td>
      <td>0.113066</td>
    </tr>
  </tbody>
</table>
</div>



## 单因子分析使用示例
**具体用法请查看[API文档](https://github.com/JoinQuant/jqfactor_analyzer/blob/master/docs/API%E6%96%87%E6%A1%A3.md), 此处仅作示例**
### 示例：5日平均换手率因子分析


```python
# 载入函数库
import pandas as pd
import jqfactor_analyzer as ja

# 获取5日平均换手率因子2018-01-01到2018-12-31之间的数据（示例用从库中直接调取）
# 聚宽因子库数据获取方法在下方
from jqfactor_analyzer.sample import VOL5
factor_data = VOL5

# 对因子进行分析
far = ja.analyze_factor(
    factor_data,  # factor_data 为因子值的 pandas.DataFrame
    quantiles=10,
    periods=(1, 10),
    industry='jq_l1',
    weight_method='avg',
    max_loss=0.1
)

# 获取整理后的因子的IC值
far.ic
```

    check/save price cache : 100%|██████████| 13/13 [00:00<00:00, 25.60it/s]
    load price info : 100%|██████████| 253/253 [00:06<00:00, 38.09it/s]
    load industry info : 100%|██████████| 243/243 [00:00<00:00, 331.46it/s]





<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>period_1</th>
      <th>period_10</th>
    </tr>
    <tr>
      <th>date</th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2018-01-02</th>
      <td>0.141204</td>
      <td>-0.058936</td>
    </tr>
    <tr>
      <th>2018-01-03</th>
      <td>0.082738</td>
      <td>-0.176327</td>
    </tr>
    <tr>
      <th>2018-01-04</th>
      <td>-0.183788</td>
      <td>-0.196901</td>
    </tr>
    <tr>
      <th>2018-01-05</th>
      <td>0.057023</td>
      <td>-0.180102</td>
    </tr>
    <tr>
      <th>2018-01-08</th>
      <td>-0.025403</td>
      <td>-0.187145</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>2018-12-24</th>
      <td>0.098161</td>
      <td>-0.198127</td>
    </tr>
    <tr>
      <th>2018-12-25</th>
      <td>-0.269072</td>
      <td>-0.166092</td>
    </tr>
    <tr>
      <th>2018-12-26</th>
      <td>-0.430034</td>
      <td>-0.117108</td>
    </tr>
    <tr>
      <th>2018-12-27</th>
      <td>-0.107514</td>
      <td>-0.040684</td>
    </tr>
    <tr>
      <th>2018-12-28</th>
      <td>-0.013224</td>
      <td>0.039446</td>
    </tr>
  </tbody>
</table>
<p>243 rows × 2 columns</p>
</div>




```python
# 生成统计图表
far.create_full_tear_sheet(
    demeaned=False, group_adjust=False, by_group=False,
    turnover_periods=None, avgretplot=(5, 15), std_bar=False
)
```

    分位数统计



<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>min</th>
      <th>max</th>
      <th>mean</th>
      <th>std</th>
      <th>count</th>
      <th>count %</th>
    </tr>
    <tr>
      <th>factor_quantile</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>0.00000</td>
      <td>0.30046</td>
      <td>0.072019</td>
      <td>0.056611</td>
      <td>7293</td>
      <td>10.054595</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0.08846</td>
      <td>0.49034</td>
      <td>0.198844</td>
      <td>0.066169</td>
      <td>7266</td>
      <td>10.017371</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0.14954</td>
      <td>0.65984</td>
      <td>0.309961</td>
      <td>0.089310</td>
      <td>7219</td>
      <td>9.952574</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0.22594</td>
      <td>0.80136</td>
      <td>0.423978</td>
      <td>0.111141</td>
      <td>7248</td>
      <td>9.992555</td>
    </tr>
    <tr>
      <th>5</th>
      <td>0.30904</td>
      <td>0.99400</td>
      <td>0.553684</td>
      <td>0.133578</td>
      <td>7280</td>
      <td>10.036672</td>
    </tr>
    <tr>
      <th>6</th>
      <td>0.38860</td>
      <td>1.23760</td>
      <td>0.696531</td>
      <td>0.166341</td>
      <td>7211</td>
      <td>9.941545</td>
    </tr>
    <tr>
      <th>7</th>
      <td>0.48394</td>
      <td>1.56502</td>
      <td>0.874488</td>
      <td>0.204828</td>
      <td>7240</td>
      <td>9.981526</td>
    </tr>
    <tr>
      <th>8</th>
      <td>0.61900</td>
      <td>2.09560</td>
      <td>1.132261</td>
      <td>0.265739</td>
      <td>7226</td>
      <td>9.962225</td>
    </tr>
    <tr>
      <th>9</th>
      <td>0.84984</td>
      <td>3.30790</td>
      <td>1.639863</td>
      <td>0.436992</td>
      <td>7261</td>
      <td>10.010478</td>
    </tr>
    <tr>
      <th>10</th>
      <td>1.23172</td>
      <td>40.47726</td>
      <td>4.276270</td>
      <td>3.640945</td>
      <td>7290</td>
      <td>10.050459</td>
    </tr>
  </tbody>
</table>
</div>


​
​    -------------------------
​
​    收益分析



<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>period_1</th>
      <th>period_10</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>Ann. alpha</th>
      <td>-0.087</td>
      <td>-0.060</td>
    </tr>
    <tr>
      <th>beta</th>
      <td>1.218</td>
      <td>1.238</td>
    </tr>
    <tr>
      <th>Mean Period Wise Return Top Quantile (bps)</th>
      <td>-20.913</td>
      <td>-18.530</td>
    </tr>
    <tr>
      <th>Mean Period Wise Return Bottom Quantile (bps)</th>
      <td>-6.156</td>
      <td>-6.452</td>
    </tr>
    <tr>
      <th>Mean Period Wise Spread (bps)</th>
      <td>-14.757</td>
      <td>-13.177</td>
    </tr>
  </tbody>
</table>
</div>



    <Figure size 640x480 with 0 Axes>



![Img](https://image.joinquant.com/5669a6a708055c73c4bc443677f21344)



    <Figure size 640x480 with 0 Axes>



![Img](https://image.joinquant.com/d336030c71a3cbf9d2ef56fdd8757ba5)



......(图片过多,此处内容演示已省略,请参考api说明使用)



![Img](https://image.joinquant.com/ce00c434033ac68259374438cb10ec06)


​
​    -------------------------
​
​    IC 分析



<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>period_1</th>
      <th>period_10</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>IC Mean</th>
      <td>-0.030</td>
      <td>-0.085</td>
    </tr>
    <tr>
      <th>IC Std.</th>
      <td>0.213</td>
      <td>0.176</td>
    </tr>
    <tr>
      <th>IR</th>
      <td>-0.140</td>
      <td>-0.487</td>
    </tr>
    <tr>
      <th>t-stat(IC)</th>
      <td>-2.180</td>
      <td>-7.587</td>
    </tr>
    <tr>
      <th>p-value(IC)</th>
      <td>0.030</td>
      <td>0.000</td>
    </tr>
    <tr>
      <th>IC Skew</th>
      <td>0.240</td>
      <td>0.091</td>
    </tr>
    <tr>
      <th>IC Kurtosis</th>
      <td>-0.420</td>
      <td>-0.485</td>
    </tr>
  </tbody>
</table>
</div>



    <Figure size 640x480 with 0 Axes>



![Img](https://image.joinquant.com/07aad6b961c5c38fa62b2d4601e49acb)



    <Figure size 640x480 with 0 Axes>



​
​    -------------------------
​
​    换手率分析



<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>period_1</th>
      <th>period_10</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>Quantile 1 Mean Turnover</th>
      <td>0.055</td>
      <td>0.222</td>
    </tr>
    <tr>
      <th>Quantile 2 Mean Turnover</th>
      <td>0.136</td>
      <td>0.447</td>
    </tr>
    <tr>
      <th>Quantile 3 Mean Turnover</th>
      <td>0.206</td>
      <td>0.599</td>
    </tr>
    <tr>
      <th>Quantile 4 Mean Turnover</th>
      <td>0.268</td>
      <td>0.680</td>
    </tr>
    <tr>
      <th>Quantile 5 Mean Turnover</th>
      <td>0.307</td>
      <td>0.730</td>
    </tr>
    <tr>
      <th>Quantile 6 Mean Turnover</th>
      <td>0.337</td>
      <td>0.742</td>
    </tr>
    <tr>
      <th>Quantile 7 Mean Turnover</th>
      <td>0.326</td>
      <td>0.735</td>
    </tr>
    <tr>
      <th>Quantile 8 Mean Turnover</th>
      <td>0.279</td>
      <td>0.708</td>
    </tr>
    <tr>
      <th>Quantile 9 Mean Turnover</th>
      <td>0.196</td>
      <td>0.593</td>
    </tr>
    <tr>
      <th>Quantile 10 Mean Turnover</th>
      <td>0.073</td>
      <td>0.283</td>
    </tr>
  </tbody>
</table>
</div>



<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>period_1</th>
      <th>period_10</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>Mean Factor Rank Autocorrelation</th>
      <td>0.991</td>
      <td>0.884</td>
    </tr>
  </tbody>
</table>
</div>


......(图片过多,此处内容演示已省略,请参考api说明使用)


###  获取聚宽因子库数据的方法
[聚宽因子库](https://www.joinquant.com/help/api/help#name:factor_values)包含数百个质量、情绪、风险等其他类目的因子

连接jqdatasdk获取数据包，数据接口需调用聚宽 [jqdatasdk](https://www.joinquant.com/help/api/doc?name=JQDatadoc) 接口获取金融数据 ([试用注册地址](https://www.joinquant.com/default/index/sdk))


```python
# 获取因子数据：以5日平均换手率为例，该数据可以直接用于因子分析
# 具体使用方法可以参照jqdatasdk的API文档
import jqdatasdk
jqdatasdk.auth('username', 'password')
# 获取聚宽因子库中的VOL5数据
factor_data=jqdatasdk.get_factor_values(
    securities=jqdatasdk.get_index_stocks('000300.XSHG'),
    factors=['VOL5'],
    start_date='2018-01-01',
    end_date='2018-12-31')['VOL5']
```

### 将自有因子值转换成 DataFrame 格式的数据
- index 为日期，格式为 pandas 日期通用的 DatetimeIndex

- columns 为股票代码，格式要求符合聚宽的代码定义规则（如：平安银行的股票代码为 000001.XSHE）

  - 如果是深交所上市的股票，在股票代码后面需要加入 .XSHE
  - 如果是上交所上市的股票，在股票代码后面需要加入 .XSHG
- 将 pandas.DataFrame 转换成满足格式要求数据格式

  首先要保证 index 为 DatetimeIndex 格式，一般是通过 pandas 提供的 pandas.to_datetime 函数进行转换，在转换前应确保 index 中的值都为合理的日期格式， 如 '2018-01-01' / '20180101'，之后再调用 pandas.to_datetime 进行转换；另外应确保 index 的日期是按照从小到大的顺序排列的，可以通过 sort_index 进行排序；最后请检查 columns 中的股票代码是否都满足聚宽的代码定义。

```python
import pandas as pd

sample_data = pd.DataFrame(
    [[0.84, 0.43, 2.33, 0.86, 0.96],
     [1.06, 0.51, 2.60, 0.90, 1.09],
     [1.12, 0.54, 2.68, 0.94, 1.12],
     [1.07, 0.64, 2.65, 1.33, 1.15],
     [1.21, 0.73, 2.97, 1.65, 1.19]],
    index=['2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05', '2018-01-08'],
    columns=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE']
)

print(sample_data)

factor_data = sample_data.copy()
# 将 index 转换为 DatetimeIndex
factor_data.index = pd.to_datetime(factor_data.index)
# 将 DataFrame 按照日期顺序排列
factor_data = factor_data.sort_index()
# 检查 columns 是否满足聚宽股票代码格式
if not sample_data.columns.astype(str).str.match('\d{6}\.XSH[EG]').all():
    print("有不满足聚宽股票代码格式的股票")
    print(sample_data.columns[~sample_data.columns.astype(str).str.match('\d{6}\.XSH[EG]')])

print(factor_data)
```
- 将键为日期，值为各股票因子值的 Series 的 dict 转换成 pandas.DataFrame，可以直接利用 pandas.DataFrame 生成
```python
sample_data = \
{'2018-01-02': pd.Seris([0.84, 0.43, 2.33, 0.86, 0.96],
                        index=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE']),
 '2018-01-03': pd.Seris([1.06, 0.51, 2.60, 0.90, 1.09],
                        index=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE']),
 '2018-01-04': pd.Seris([1.12, 0.54, 2.68, 0.94, 1.12],
                        index=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE']),
 '2018-01-05': pd.Seris([1.07, 0.64, 2.65, 1.33, 1.15],
                        index=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE']),
 '2018-01-08': pd.Seris([1.21, 0.73, 2.97, 1.65, 1.19],
                        index=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE'])}
import pandas as pd
# 直接调用 pd.DataFrame 将 dict 转换为 DataFrame
factor_data = pd.DataFrame(data).T
print(factor_data)
# 之后请按照 DataFrae 的方法转换成满足格式要求数据格式
```


================================================
FILE: docs/API文档.md
================================================
# **API文档**

## 一、因子缓存factor_cache模块

为了在本地进行分析时，为了提高数据获取的速度并避免反复从服务端获取数据，所以增加了本地数据缓存的方法。

注意缓存格式为pyarrow.feather格式，pyarrow库不同版本之间可能存在兼容问题，建议不要随意修改pyarrow库的版本，如果修改后产生大量缓存文件无法读取(提示已损坏)的情况，建议删除整个缓存目录后重新缓存。

### 1. 设置缓存目录

对于单因子分析和归因分析中使用到的市值/价格和风格因子等数据，默认会缓存到用户的主目录( `os.path.expanduser( '~/jqfactor_datacache/bundle')` )。 一般地，在 Unix 系统上可能是 `/home/username/jqfactor_datacache/bundle`，而在 Windows 系统上可能是 `C:\Users\username\jqfactor_datacache\bundle`。

您可以通过以下代码修改配置信息来设置为其他路径，设置过一次后后续都将沿用设置的这个路径，不用重复设置。

```python
from jqfactor_analyzer.factor_cache import set_cache_dir,get_cache_dir
set_cache_dir(my_path) #设置缓存目录为my_path
print(get_cache_dir()) #输出缓存目录
```

### 2. 缓存/检查缓存和读取已缓存数据

除过对单因子分析及归因分析依赖的数据进行缓存外，factor_cache还可以缓存自定义的因子组(仅限聚宽因子库中支持的因子)

```python
def save_factor_values_by_group(start_date,end_date,factor_names='prices',
				group_name=None,overwrite=False,cache_dir=None,show_progress=True):
    """将因子库数据按因子组储存到本地,根据factor_names因子列表(顺序无关)自动生成因子组的名称
    start_date : 开始时间
    end_date : 结束时间
    factor_names : 因子组所含因子的名称,除过因子库中支持的因子外，还支持指定为'prices'缓存价格数据
    group_name : 因子组名称，不指定时使用get_factor_folder自动生成因子组名(即缓存文件夹名)，如果指定则按照指定的名称生成文件夹名(使用get_factor_values_by_cache时,需要自行指定factor_path)
    overwrite  : 文件已存在时是否覆盖更新,默认为False即增量更新,文件已存在时跳过
    cache_dir : 缓存的路径，如果没有指定则使用配置信息中的路径,一般不用指定
    show_progress : 是否展示缓存进度,默认为True
    返回 : 因子组储存的路径 , 文件以天为单位储存为feather文件,每天一个feather文件,每月一个文件夹,columns为因子名称, index为当天在市的所有标的代码
    """
def get_factor_values_by_cache(date,codes=None,factor_names=None,group_name=None,
								factor_path=None):
    """从缓存的文件读取因子数据,文件不存在时返回空的dataframe
    date : 日期
    codes : 标的代码,默认为None获取当天在市的所有标的
    factor_names : 因子列表(顺序无关),当指定factor_path/group_name时失效
    group_name : 因子组名,如果缓存时指定了group_name,则获取时必须也指定group_name或factor_path
    factor_path : 可选参数,因子组的路径,一般不用指定
    返回:
    如果缓存文件存在，则返回当天的因子数据,index是标的代码,columns是因子名
    如果缓存文件不存在,则返回空的dataframe, 建议在使用get_factor_values_by_cache前,先运行save_factor_values_by_group检查时间区间内的缓存文件是否完整
    """
def get_factor_folder(factor_names,group_name=None):
    """获取因子组的文件夹名(文件夹位于get_cache_dir()获取的缓存目录下)
    factor_names : 因子储存时,如果未指定group_name,则根据因子列表(顺序无关)获取md5值生成因子组名(即储存的文件夹名)，使用此方法可以获取生成的文件夹名称
    group_name : 如果储存时指定了因子组名,则直接返回此因子组名
    """

```

**示例**

```python
from jqfactor_analyzer.factor_cache import save_factor_values_by_group,get_factor_values_by_cache,get_factor_folder,get_cache_dir
# import jqdatasdk as jq
# jq.auth("账号",'密码') #登陆jqdatasdk来从服务端缓存数据

all_factors = jq.get_all_factors()
factor_names = all_factors[all_factors.category=='growth'].factor.tolist()  #将聚宽因子库中的成长类因子作为一组因子
group_name = 'growth_factors' #因子组名定义为'growth_factors'
start_date = '2021-01-01'
end_date = '2021-06-01'
# 检查/缓存因子数据
factor_path = save_factor_values_by_group(start_date,end_date,factor_names=factor_names,group_name=group_name,overwrite=False,show_progress=True)
# factor_path = os.path.join(get_cache_dir(), get_factor_folder(factor_names,group_name=group_name)  #等同于save_factor_values_by_group返回的路径

# 循环获取缓存的因子数据,并拼接
trade_days = jq.get_trade_days(start_date,end_date)
factor_values = {}
for date in trade_days:
    factor_values[date] = get_factor_values_by_cache(date,codes=None,factor_names=factor_names,group_name=group_name, factor_path=factor_path)#这里实际只需要指定group_name,factor_names参数的其中一个,缓存时指定了group_name时,factor_names不生效
factor_values = pd.concat(factor_values)
```

## 二、归因分析模块

```python
from jqfactor_analyzer import AttributionAnalysis
AttributionAnalysis(weights,daily_return,style_type='style_pro',industry ='sw_l1',use_cn=True,show_data_progress=True)
```

**参数 :**

- `weights`:持仓权重信息，index是日期，columns是标的代码， value对应的是组合当天的仓位占比(单日仓位占比总和不为1时，剩余部分认为是当天的现金)
-  `daily_return`:Series,index是日期，values为当天组合的收益率
-  `style_type`:归因分析所使用的风格因子类型，可选'style'和'style_pro'中的一个
-  `industry`:归因分析所使用的行业分类，可选'sw_l1'和'jq_l1'中的一个
-  `use_cn`:绘图时是否使用中文
-  `show_data_progress`:是否展示数据获取进度(使用本地缓存,第一次运行时速度较慢,后续对于本地不存在的数据将增量缓存)

**示例**

```python
import pandas as pd
# position_weights.csv 是一个储存了组合权重信息的csv文件,index是日期,columns是股票代码
# position_daily_return.csv 是一个储存了组合日收益率的csv文件,index是日期,daily_return列是日收益
weights = pd.read_csv("position_weights.csv",index_col=0)
returns = pd.read_csv("position_daily_return.csv",index_col=0)['daily_return']

An =  AttributionAnalysis(weights , returns ,style_type='style_pro',industry ='sw_l1', show_data_progress=True )
```



### 1. 属性

- `style_exposure` : 组合的风格暴露
- `industry_exposure` : 组合的行业暴露
- `exposure_portfolio` : 组合的风格+行业及country暴露
- `attr_daily_returns` : 组合的\风格+行业及country日度归因收益率
- `attr_returns` : 组合的日度风格+行业及country累积归因收益率

### 2. 方法

#### (1) 获取组合相对于指数的暴露

```python
get_exposure2bench(index_symbol)
```

**参数 :**

- `index_symbol` : 基准指数, 可选`['000300.XSHG','000905.XSHG','000906.XSHG','000852.XSHG','932000.CSI','000985.XSHG']`中的一个

**返回 :**

- 一个dataframe,index为日期,columns为风格因子+行业因子+county , 其中country为股票总持仓占比

#### (2) 获取组合相对于指数的日度归因收益率

```python
get_attr_daily_returns2bench(index_symbol)
```

假设组合相对于指数的收益由以下部分构成 : 风格+行业暴露收益(common_return ) , 现金闲置收益(cash) ,策略本身的超额收益(specific_return)
**参数 :**

- `index_symbol` : 基准指数, 可选`['000300.XSHG','000905.XSHG','000906.XSHG','000852.XSHG','932000.CSI','000985.XSHG']`中的一个

**返回 :**

- 一个dataframe,index为日期,columns为`风格因子+行业因子+cash+common_return,specific_return,total_return`

  其中:
  cash是假设现金收益(0)相对指数带来的收益率
  common_return 为风格+行业总收益率
  specific_return 为特意收益率
  total_return 为组合相对于指数的总收益

#### (3) 获取相对于指数的累积归因收益率

```python
get_attr_returns2bench(index_symbol)
```

假设组合相对于指数的收益由以下部分构成 : 风格+行业暴露收益(common_return ) , 现金闲置收益(cash) ,策略本身的超额收益(specific_return)

**参数 :**

 `index_symbol` : 基准指数, 可选`['000300.XSHG','000905.XSHG','000906.XSHG','000852.XSHG','932000.CSI','000985.XSHG']`中的一个

**返回 :**

- 一个dataframe,index为日期,columns为`风格因子+行业因子+cash+common_return,specific_return,total_return`

  其中:
  cash是假设现金收益(0)相对指数带来的收益率
  common_return 为风格+行业总收益率
  specific_return 为特异收益率
  total_return 为组合相对于指数的总收益(减法超额)

### 3. 绘图方法

#### (1) 绘制风格暴露时序图

```python
plot_exposure(factors='style',index_symbol=None,figsize=(15,7))
```

绘制风格暴露时序

**参数**

- factors : 绘制的暴露类型 , 可选 'style'(所有风格因子) , 'industry'(所有行业因子),也可以传递一个list,list为exposure_portfolio中columns的一个或者多个
- index_symbol : 基准指数代码,指定时绘制相对于指数的暴露 , 默认None为组合本身的暴露
- figsize : 画布大小

#### (2) 绘制归因分析收益时序图

```python
plot_returns(factors='style',index_symbol=None,figsize=(15,7))
```

绘制归因分析收益时序

**参数**

- factors : 绘制的暴露类型 , 可选 'style'(所有风格因子) , 'industry'(所有行业因子),也可以传递一个list,list为exposure_portfolio中columns的一个或者多个
  同时也支持指定['common_return'(风格总收益),'specific_return'(特异收益),'total_return'(总收益)', 'country'(国家因子收益,当指定index_symbol时会用现金相对于指数的收益替代)]
- index_symbol : 基准指数代码,指定时绘制相对于指数的暴露 , 默认None为组合本身的暴露
- figsize : 画布大小

#### (3) 绘制暴露与收益对照图

```python
plot_exposure_and_returns(factors='style',index_symbol=None,show_factor_perf=False,figsize=(12,6))
```

将因子暴露与收益同时绘制在多个子图上

**参数**

-  factors : 绘制的暴露类型 , 可选 'style'(所有风格因子) , 'industry'(所有行业因子,也可以传递一个list,list为exposure_portfolio中columns的一个或者多个
  当指定index_symbol时,country会用现金相对于指数的收益替代)
- index_symbol : 基准指数代码,指定时绘制相对于指数的暴露及收益 , 默认None为组合本身的暴露和收益
- show_factor_perf : 是否同时绘制因子表现
- figsize : 画布大小,这里第一个参数是画布的宽度, 第二个参数为单个子图的高度

#### (4) 关闭中文图例显示

```python
plot_disable_chinese_label()
```

 画图时默认会从系统中查找中文字体显示以中文图例
 如果找不到中文字体则默认使用英文图例
 当找到中文字体但中文显示乱码时, 可调用此 API 关闭中文图例显示而使用英文



## 三、单因子分析模块

```python
from jqfactor_analyzer import analyze_factor
analyze_factor(factor, industry='jq_l1', quantiles=5, periods=(1, 5, 10), weight_method='avg', max_loss=0.25, allow_cache=True, show_data_progress=True )
```

单因子分析函数



**参数**

* `factor`: 因子值，

  pandas.DataFrame格式的数据

  - index为日期，格式为pandas日期通用的DatetimeIndex，转换方法见[将自有因子值转换成 DataFrame 格式的数据](#将自有因子值转换成-dataframe-格式的数据)
  - columns为股票代码，格式要求符合聚宽的代码定义规则（如：平安银行的股票代码为000001.XSHE）
      - 如果是深交所上市的股票，在股票代码后面需要加入.XSHE
      - 如果是上交所上市的股票，在股票代码后面需要加入.XSHG

  或 pd.Series格式的数据
  - index为日期和股票代码组成的MultiIndex

* `industry`: 行业分类, 默认为 `'jq_l1'`

  * `'sw_l1'`: 申万一级行业
  * `'sw_l2'`: 申万二级行业
  * `'sw_l3'`: 申万三级行业
  * `'jq_l1'`: 聚宽一级行业
  * `'jq_l2'`: 聚宽二级行业
  * `'zjw'`: 证监会行业

* `quantiles`: 分位数数量, 默认为 `5`

  `int`

  在因子分组中按照因子值大小平均分组的组数.

* `periods`: 调仓周期, 默认为 [1, 5, 10]

  `int` or `list[int]`

* `weight_method`: 基于分位数收益时的加权方法, 默认为 `'avg'`

  * `'avg'`: 等权重
  * `'mktcap'`：按总市值加权
  * `'ln_mktcap'`: 按总市值的对数加权
  * `'cmktcap'`: 按流通市值加权
  * `'ln_cmktcap'`: 按流通市值的对数加权

* `max_loss`: 因重复值或nan值太多而无效的因子值的最大占比, 默认为 0.25

  `float`

  允许的丢弃因子数据的最大百分比 (0.00 到 1.00),

  计算比较输入因子索引中的项目数和输出 DataFrame 索引中的项目数.

  因子数据本身存在缺陷 (例如 NaN),

  没有提供足够的价格数据来计算所有因子值的远期收益,

  或者因为分组失败, 因此可以部分地丢弃因子数据

* `allow_cache` : 是否允许对价格,市值等信息进行本地缓存(按天缓存,初次运行可能比较慢,但后续重新获取对应区间的数据将非常快,且分析时仅消耗较小的jqdatasdk流量)

* show_data_progress: 是否展示数据获取的进度信息



**示例**

```python
#载入函数库
import pandas as pd
import jqfactor_analyzer as ja

# 获取 jqdatasdk 授权
# 输入用户名、密码，申请地址：http://t.cn/EINDOxE
# 聚宽官网及金融终端，使用方法参见：http://t.cn/EINcS4j
import jqdatasdk
jqdatasdk.auth('username', 'password')

# 对因子进行分析
far = ja.analyze_factor(
    factor_data,  # factor_data 为因子值的 pandas.DataFrame
    quantiles=10,
    periods=(1, 10),
    industry='jq_l1',
    weight_method='avg',
    max_loss=0.1
)

# 生成统计图表
far.create_full_tear_sheet(
    demeaned=False, group_adjust=False, by_group=False,
    turnover_periods=None, avgretplot=(5, 15), std_bar=False
)
```







### 1. 绘制结果

#### 展示全部分析

```
far.create_full_tear_sheet(demeaned=False, group_adjust=False, by_group=False,
turnover_periods=None, avgretplot=(5, 15), std_bar=False)
```

**参数:**

- demeaned:
    - True: 使用超额收益计算 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
    - False: 不使用超额收益
- group_adjust:
    - True: 使用行业中性化后的收益计算 (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
    - False: 不使用行业中性化后的收益
- by_group:
    - True: 按行业展示
    - False: 不按行业展示
- turnover_periods: 调仓周期
- avgretplot: tuple 因子预测的天数-(计算过去的天数, 计算未来的天数)
- std_bar:
    - True: 显示标准差
    - False: 不显示标准差

#### 因子值特征分析

```
far.create_summary_tear_sheet(demeaned=False, group_adjust=False)
```

**参数:**

- demeaned:
    - True: 对每日因子收益去均值求得因子收益表
    - False: 因子收益表
- group_adjust:
    - True: 按行业对因子收益去均值后求得因子收益表
    - False: 因子收益表

#### 因子收益分析

```
far.create_returns_tear_sheet(demeaned=False, group_adjust=False, by_group=False)

```

**参数:**

- demeaned:
    - True: 使用超额收益计算 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
    - False: 不使用超额收益
- group_adjust:
    - True: 使用行业中性化后的收益计算 (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
    - False: 不使用行业中性化后的收益
- by_group:
    - True: 画各行业的各分位数平均收益图
    - False: 不画各行业的各分位数平均收益图

#### 因子 IC 分析

```
far.create_information_tear_sheet(group_adjust=False, by_group=False)

```

**参数:**

- group_adjust:
    - True: 使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
    - False: 不使用行业中性收益
- by_group:
    - True: 画按行业分组信息比率(IC)图
    - False: 画月度信息比率(IC)图

#### 因子换手率分析

```
far.create_turnover_tear_sheet(turnover_periods=None)

```

**参数:**

- turnover_periods: 调仓周期

#### 因子预测能力分析

```
far.create_event_returns_tear_sheet(avgretplot=(5, 15),demeaned=False, group_adjust=False,std_bar=False)

```

**参数:**

- avgretplot: tuple 因子预测的天数-(计算过去的天数, 计算未来的天数)
- demeaned:
    - True: 使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
    - False: 不使用超额收益
- group_adjust:
    - True: 使用行业中性化后的收益计算累积收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
    - False: 不使用行业中性化后的收益
- std_bar:
    - True: 显示标准差
    - False: 不显示标准差

#### 打印因子收益表

```
far.plot_returns_table(demeaned=False, group_adjust=False)

```

**参数：**

- demeaned:
    - True：使用超额收益计算 (基准收益被认为是每日所有股票收益按照weight列中权重的加权的均值)
    - False：不使用超额收益
- group_adjust:
    - True：使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
    - False：不使用行业中性收益

#### 打印换手率表

```
far.plot_turnover_table()

```

#### 打印信息比率（IC）相关表

```
far.plot_information_table(group_adjust=False, method='rank')

```

**参数：**

- group_adjust:
    - True：使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
    - False：不使用行业中性收益
- method：
    - 'rank'：用秩相关系数计算IC值
    - 'normal': 用相关系数计算IC值

#### 打印个分位数统计表

```
far.plot_quantile_statistics_table()

```

#### 画信息比率(IC)时间序列图

```
far.plot_ic_ts(group_adjust=False, method='rank')

```

**参数：**

- group_adjust:
    - True：使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
    - False：不使用行业中性收益
- method：
    - 'rank'：用秩相关系数计算IC值
    - 'normal': 用相关系数计算IC值

#### 画信息比率分布直方图

```
far.plot_ic_hist(group_adjust=False, method='rank')

```

**参数：**

- group_adjust:
    - True：使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
    - False：不使用行业中性收益
- method：
    - 'rank'：用秩相关系数计算IC值
    - 'normal': 用相关系数计算IC值

#### 画信息比率 qq 图

```
far.plot_ic_qq(group_adjust=False, method='rank', theoretical_dist='norm')

```

**参数：**

- group_adjust:
    - True：使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
    - False：不使用行业中性收益
- method：
    - 'rank'：用秩相关系数计算IC值
    - 'normal': 用相关系数计算IC值
- theoretical_dist：
    - 'norm'：正态分布
    - 't'：t分布

#### 画各分位数平均收益图

```
far.plot_quantile_returns_bar(by_group=False, demeaned=False, group_adjust=False)

```

**参数：**

- by_group：
    - True：各行业的各分位数平均收益图
    - False：各分位数平均收益图
- demeaned:
    - True：使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
    - False：不使用超额收益
- group_adjust:
    - True：使用行业中性化后的收益计算累积收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
    - False：不使用行业中性化后的收益

#### 画最高分位减最低分位收益图

```
far.plot_mean_quantile_returns_spread_time_series(demeaned=False, group_adjust=False, bandwidth=1)

```

**参数：**

- demeaned:
    - True：使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
    - False：不使用超额收益
- group_adjust:
    - True：使用行业中性化后的收益计算累积收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
    - False：不使用行业中性化后的收益
- bandwidth：n，加减n倍当日标准差

#### 画按行业分组信息比率(IC)图

```
far.plot_ic_by_group(group_adjust=False, method='rank')

```

**参数：**

- group_adjust:
    - True：使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
    - False：不使用行业中性收益
- method：
    - 'rank'：用秩相关系数计算IC值
    - 'normal': 用相关系数计算IC值

#### 画因子自相关图

```
far.plot_factor_auto_correlation(rank=True)

```

**参数：**

- rank：
    - True：用秩相关系数
    - False：用相关系数

#### 画最高最低分位换手率图

```
far.plot_top_bottom_quantile_turnover(periods=(1, 3, 9))

```

**参数：**

- periods：调仓周期

#### 画月度信息比率(IC)图

```
far.plot_monthly_ic_heatmap(group_adjust=False)

```

**参数：**

- group_adjust:
    - True：使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
    - False：不使用行业中性收益

#### 画按因子值加权多空组合每日累积收益图

```
far.plot_cumulative_returns(period=1, demeaned=False, group_adjust=False)

```

**参数：**

- periods：调仓周期
- demeaned:
    - True：对因子值加权组合每日收益的权重去均值 (每日权重 = 每日权重 - 每日权重的均值)，使组合转换为cash-neutral多空组合
    - False：不对权重去均值
- group_adjust:
    - True：对权重分行业去均值 (每日权重 = 每日权重 - 每日各行业权重的均值)，使组合转换为 industry-neutral 多空组合
    - False：不对权重分行业去均值

#### 画做多最大分位数做空最小分位数组合每日累积收益图

```
far.plot_top_down_cumulative_returns(period=1, demeaned=False, group_adjust=False)

```

**参数：**

- periods：指定调仓周期
- demeaned:
    - True：使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
    - False：不使用超额收益
- group_adjust:
    - True：使用行业中性化后的收益计算累积收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
    - False：不使用行业中性化后的收益

#### 画各分位数每日累积收益图

```
far.plot_cumulative_returns_by_quantile(period=(1, 3, 9), demeaned=False, group_adjust=False)

```

**参数：**

- periods：调仓周期
- demeaned:
    - True：使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
    - False：不使用超额收益
- group_adjust:
    - True：使用行业中性化后的收益计算累积收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
    - False：不使用行业中性化后的收益

#### 因子预测能力平均累计收益图

```
far.plot_quantile_average_cumulative_return(periods_before=5, periods_after=10, by_quantile=False, std_bar=False, demeaned=False, group_adjust=False)

```

**参数：**

- periods_before: 计算过去的天数
- periods_after: 计算未来的天数
- by_quantile：
    - True：各分位数分别显示因子预测能力平均累计收益图
    - False：不用各分位数分别显示因子预测能力平均累计收益图
- std_bar：
    - True：显示标准差
    - False：不显示标准差
- demeaned:
    - True: 使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
    - False: 不使用超额收益
- group_adjust:
    - True: 使用行业中性化后的收益计算累积收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
    - False: 不使用行业中性化后的收益

#### 画有效因子数量统计图

```
far.plot_events_distribution(num_days=1)

```

**参数：**

- num_days：统计间隔天数

#### 关闭中文图例显示

```
far.plot_disable_chinese_label()

```



### 2. 属性列表

用于访问因子分析的结果，大部分为惰性属性，在访问才会计算结果并返回



#### 查看因子值

```
far.factor_data
```

- 类型：pandas.Series
- index：为日期和股票代码的MultiIndex

#### 去除 nan/inf，整理后的因子值、forward_return 和分位数

```
far.clean_factor_data
```

- 类型：pandas.DataFrame index：为日期和股票代码的MultiIndex
- columns：根据period选择后的forward_return(如果调仓周期为1天，那么forward_return为[第二天的收盘价-今天的收盘价]/今天的收盘价)、因子值、行业分组、分位数数组、权重

#### 按分位数分组加权平均因子收益

```
far.mean_return_by_quantile
```

- 类型：pandas.DataFrame
- index：分位数分组
- columns：调仓周期

#### 按分位数分组加权因子收益标准差

```
far.mean_return_std_by_quantile
```

- 类型：pandas.DataFrame
- index：分位数分组
- columns：调仓周期

#### 按分位数及日期分组加权平均因子收益

```
far.mean_return_by_date
```

- 类型：pandas.DataFrame
- index：为日期和分位数的MultiIndex
- columns：调仓周期

#### 按分位数及日期分组加权因子收益标准差

```
far.mean_return_std_by_date
```

- 类型：pandas.DataFrame
- index：为日期和分位数的MultiIndex
- columns：调仓周期

#### 按分位数及行业分组加权平均因子收益

```
far.mean_return_by_group
```

- 类型：pandas.DataFrame
- index：为行业和分位数的MultiIndex
- columns：调仓周期

#### 按分位数及行业分组加权因子收益标准差

```
far.mean_return_std_by_group
```

- 类型：pandas.DataFrame
- index：为行业和分位数的MultiIndex
- columns：调仓周期

#### 最高分位数因子收益减最低分位数因子收益每日均值

```
far.mean_return_spread_by_quantile
```

- 类型：pandas.DataFrame
- index：日期
- columns：调仓周期

#### 最高分位数因子收益减最低分位数因子收益每日标准差

```
far.mean_return_spread_std_by_quantile
```

- 类型：pandas.DataFrame
- index：日期
- columns：调仓周期

#### 信息比率

```
far.ic
```

- 类型：pandas.DataFrame
- index：日期
- columns：调仓周期

#### 分行业信息比率

```
far.ic_by_group
```

- 类型：pandas.DataFrame
- index：行业
- columns：调仓周期

#### 月度信息比率

```
far.ic_monthly
```

- 类型：pandas.DataFrame
- index：月度
- columns：调仓周期表

#### 换手率

```
far.quantile_turnover
```

- 键：调仓周期
- 值: pandas.DataFrame 换手率
    - index：日期
    - columns：分位数分组

#### 计算按分位数分组加权因子收益和标准差

```
mean, std = far.calc_mean_return_by_quantile(by_date=True, by_group=False, demeaned=False, group_adjust=False)
```

**参数：**

- by_date：
    - True：按天计算收益
    - False：不按天计算收益
- by_group:
    - True: 按行业计算收益
    - False：不按行业计算收益
- demeaned:
    - True：使用超额收益计算各分位数收益，超额收益=收益-基准收益 (基准收益被认为是每日所有股票收益按照weight列中权重的加权的均值)
    - False：不使用超额收益
- group_adjust:
    - True：使用行业中性收益计算各分位数收益，行业中性收益=收益-行业收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
    - False：不使用行业中性收益

#### 计算按因子值加权多空组合每日收益

```
far.calc_factor_returns(demeaned=True, group_adjust=False)
```

权重 = 每日因子值 / 每日因子值的绝对值的和
正的权重代表买入, 负的权重代表卖出

**参数：**

- demeaned:
    - True: 对权重去均值 (每日权重 = 每日权重 - 每日权重的均值), 使组合转换为 cash-neutral 多空组合
    - False：不对权重去均值
- group_adjust:
    - True：对权重分行业去均值 (每日权重 = 每日权重 - 每日各行业权重的均值)，使组合转换为 industry-neutral 多空组合
    - False：不对权重分行业去均值

#### 计算两个分位数相减的因子收益和标准差

```
mean, std = far.compute_mean_returns_spread(upper_quant=None, lower_quant=None, by_date=False, by_group=False, demeaned=False, group_adjust=False)
```

**参数：**

- upper_quant：用upper_quant选择的分位数减去lower_quant选择的分位数，只能在已有的范围内选择
- lower_quant：用upper_quant选择的分位数减去lower_quant选择的分位数，只能在已有的范围内选择
- by_date：
    - True：按天计算两个分位数相减的因子收益和标准差
    - False：不按天计算两个分位数相减的因子收益和标准差
- by_group:
    - True: 分行业计算两个分位数相减的因子收益和标准差
    - False：不分行业计算两个分位数相减的因子收益和标准差
- demeaned:
    - True：使用超额收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
    - False：不使用超额收益
- group_adjust:
    - True：使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
    - False：不使用行业中性收益


#### 计算因子的 alpha 和 beta

```
far.calc_factor_alpha_beta(demeaned=True, group_adjust=False)
```

因子值加权组合每日收益 = beta * 市场组合每日收益 + alpha

因子值加权组合每日收益计算方法见 calc_factor_returns 函数

市场组合每日收益是每日所有股票收益按照weight列中权重加权的均值

结果中的 alpha 是年化 alpha

**参数：**

- demeaned:
    - True: 对因子值加权组合每日收益的权重去均值 (每日权重 = 每日权重 - 每日权重的均值)，使组合转换为cash-neutral多空组合
    - False：不对权重去均值
- group_adjust:
    - True：对权重分行业去均值 (每日权重 = 每日权重 - 每日各行业权重的均值)，使组合转换为 industry-neutral 多空组合
    - False：不对权重分行业去均值

#### 计算每日因子信息比率（IC值）

```
far.calc_factor_information_coefficient(group_adjust=False, by_group=False, method='rank')
```

**参数：**

- group_adjust:
    - True：使用行业中性收益计算 IC (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
    - False：不使用行业中性收益
- by_group:
    - True：分行业计算 IC
    - False：不分行业计算 IC
- method：
    - 'rank'：用秩相关系数计算IC值
    - 'normal'：用普通相关系数计算IC值

#### 计算因子信息比率均值（IC值均值）

```
far.calc_mean_information_coefficient(group_adjust=False, by_group=False, by_time=None, method='rank')
```

**参数：**

- group_adjust:
    - True：使用行业中性收益计算 IC (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
    - False：不使用行业中性收益
- by_group:
    - True：分行业计算 IC
    - False：不分行业计算 IC
- by_time：
    - 'Y'：按年求均值
    - 'M'：按月求均值
    - None：对所有日期求均值
- method：
    - 'rank'：用秩相关系数计算IC值
    - 'normal'：用普通相关系数计算IC值

#### 按照当天的分位数算分位数未来和过去的收益均值和标准差

```
far.calc_average_cumulative_return_by_quantile(periods_before=5, periods_after=15, demeaned=False, group_adjust=False)
```

**参数：**

- periods_before：计算过去的天数
- periods_after：计算未来的天数
- demeaned：
    - True：使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
    - False：不使用超额收益
- group_adjust：
    - True：使用行业中性化后的收益计算累积收益
    - False：不使用行业中性化后的收益

#### 计算指定调仓周期的各分位数每日累积收益

```
far.calc_cumulative_return_by_quantile(period=None, demeaned=False, group_adjust=False)
```

**参数：**

- period：指定调仓周期
- demeaned：
    - True：使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
    - False：不使用超额收益
- group_adjust：
    - True：使用行业中性化后的收益计算累积收益
    - False：不使用行业中性化后的收益

#### 计算指定调仓周期的按因子值加权多空组合每日累积收益

```
far.calc_cumulative_returns(period=5, demeaned=False, group_adjust=False)
```

当 period > 1 时，组合的累积收益计算方法为：

组合每日收益 = （从第0天开始每period天一调仓的组合每日收益 + 从第1天开始每period天一调仓的组合每日收益 + ... + 从第period-1天开始每period天一调仓的组合每日收益) / period

组合累积收益 = 组合每日收益的累积

**参数：**

- period：指定调仓周期
- demeaned:
    - True：对权重去均值 (每日权重 = 每日权重 - 每日权重的均值)，使组合转换为 cash-neutral 多空组合
    - False：不对权重去均值
- group_adjust:
    - True：对权重分行业去均值 (每日权重 = 每日权重 - 每日各行业权重的均值)，使组合转换为 industry-neutral 多空组合
    - False：不对权重分行业去均值

#### 计算指定调仓周期和前面定义好的加权方式计算多空组合每日累计收益

```
far.calc_top_down_cumulative_returns(period=5, demeaned=False, group_adjust=False)
```

**参数：**

- period：指定调仓周期
- demeaned:
    - True：使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
    - False：不使用超额收益
- group_adjust:
    - True：使用行业中性化后的收益计算累积收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
    - False：不使用行业中性化后的收益

#### 根据调仓周期确定滞后期的每天计算因子自相关性

```
far.calc_autocorrelation(rank=True)
```

**参数：**

- rank：
    - True：秩相关系数
    - False：普通相关系数

#### 滞后n天因子值自相关性

```
far.calc_autocorrelation_n_days_lag(n=9,rank=True)
```

**参数：**

- n：滞后n天到1天的因子值自相关性
- rank：
    - True：秩相关系数
    - False：普通相关系数

#### 各分位数滞后1天到n天的换手率均值

```
far.calc_quantile_turnover_mean_n_days_lag(n=10)
```

**参数：**

- n：滞后 1 天到 n 天的换手率

#### 滞后 0 - n 天因子收益信息比率(IC)的移动平均

```
far.calc_ic_mean_n_days_lag(n=10,group_adjust=False,by_group=False,method=None)
```

**参数：**

- n：滞后0-n天因子收益的信息比率(IC)的移动平均
- group_adjust:
    - True：使用行业中性收益计算 IC (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
    - False：不使用行业中性收益
- by_group：
    - True：分行业计算 IC
    - False：不分行业计算 IC
- method：
    - 'rank'：用秩相关系数计算IC值
    - 'normal'：用普通相关系数计算IC值



### 3. 获取聚宽因子库数据的方法

1. [聚宽因子库](https://www.joinquant.com/help/api/help?name=factor_values)包含数百个质量、情绪、风险等其他类目的因子

2. 连接jqdatasdk获取数据包，数据接口需调用聚宽 [`jqdatasdk`](https://github.com/JoinQuant/jqdatasdk/blob/master/README.md) 接口获取金融数据([试用注册地址](http://t.cn/EINDOxE))

    ```python
    # 获取因子数据：以5日平均换手率为例，该数据可以直接用于因子分析
    # 具体使用方法可以参照jqdatasdk的API文档
    import jqdatasdk
    jqdatasdk.auth('username', 'password')
    # 获取聚宽因子库中的VOL5数据
    factor_data=jqdatasdk.get_factor_values(
        securities=jqdatasdk.get_index_stocks('000300.XSHG'),
        factors=['VOL5'],
        start_date='2018-01-01',
        end_date='2018-12-31')['VOL5']
    ```



### 4. 将自有因子值转换成 DataFrame 格式的数据

- index 为日期，格式为 pandas 日期通用的 DatetimeIndex

- columns 为股票代码，格式要求符合聚宽的代码定义规则（如：平安银行的股票代码为 000001.XSHE）

    - 如果是深交所上市的股票，在股票代码后面需要加入.XSHE
    - 如果是上交所上市的股票，在股票代码后面需要加入.XSHG

- 将 pandas.DataFrame 转换成满足格式要求数据格式

    首先要保证 index 为 `DatetimeIndex` 格式

    一般是通过 pandas 提供的 [`pandas.to_datetime`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html) 函数进行转换, 在转换前应确保 index 中的值都为合理的日期格式, 如 `'2018-01-01'` / `'20180101'`, 之后再调用 `pandas.to_datetime` 进行转换

    另外应确保 index 的日期是按照从小到大的顺序排列的, 可以通过 [`sort_index`](https://pandas.pydata.org/pandas-docs/version/0.23.3/generated/pandas.DataFrame.sort_index.html) 进行排序

    最后请检查 columns 中的股票代码是否都满足聚宽的代码定义

    ```python
    import pandas as pd

    sample_data = pd.DataFrame(
        [[0.84, 0.43, 2.33, 0.86, 0.96],
         [1.06, 0.51, 2.60, 0.90, 1.09],
         [1.12, 0.54, 2.68, 0.94, 1.12],
         [1.07, 0.64, 2.65, 1.33, 1.15],
         [1.21, 0.73, 2.97, 1.65, 1.19]],
        index=['2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05', '2018-01-08'],
        columns=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE']
    )

    print(sample_data)

    factor_data = sample_data.copy()
    # 将 index 转换为 DatetimeIndex
    factor_data.index = pd.to_datetime(factor_data.index)
    # 将 DataFrame 按照日期顺序排列
    factor_data = factor_data.sort_index()
    # 检查 columns 是否满足聚宽股票代码格式
    if not sample_data.columns.astype(str).str.match('\d{6}\.XSH[EG]').all():
        print("有不满足聚宽股票代码格式的股票")
        print(sample_data.columns[~sample_data.columns.astype(str).str.match('\d{6}\.XSH[EG]')])

    print(factor_data)
    ```

- 将键为日期, 值为各股票因子值的 `Series` 的 `dict` 转换成 `pandas.DataFrame`

    可以直接利用 `pandas.DataFrame` 生成

    ```python
    sample_data = \
    {'2018-01-02': pd.Seris([0.84, 0.43, 2.33, 0.86, 0.96],
                            index=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE']),
     '2018-01-03': pd.Seris([1.06, 0.51, 2.60, 0.90, 1.09],
                            index=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE']),
     '2018-01-04': pd.Seris([1.12, 0.54, 2.68, 0.94, 1.12],
                            index=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE']),
     '2018-01-05': pd.Seris([1.07, 0.64, 2.65, 1.33, 1.15],
                            index=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE']),
     '2018-01-08': pd.Seris([1.21, 0.73, 2.97, 1.65, 1.19],
                            index=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE'])}

    import pandas as pd
    # 直接调用 pd.DataFrame 将 dict 转换为 DataFrame
    factor_data = pd.DataFrame(data).T

    print(factor_data)

    # 之后请按照 DataFrame 的方法转换成满足格式要求数据格式
    ```

## 四、数据处理函数

#### 1.  中性化

```python
from jqfactor_analyzer import neutralize
neutralize(data, how=None, date=None, axis=1, fillna=None, add_constant=False)
```

**参数 :**

- data: pd.Series/pd.DataFrame, 待中性化的序列, 序列的 index/columns 为股票的 code
- how: str list. 中性化使用的因子名称列表.
  默认为 ['jq_l1', 'market_cap'], 支持的中性化方法有:
  (1) 行业: sw_l1, sw_l2, sw_l3, jq_l1, jq_l2
  (2) mktcap(总市值), ln_mktcap(对数总市值), cmktcap(流通市值), ln_cmktcap(对数流通市值)
  (3)自定义的中性化数据: 支持同时传入额外的 Series 或者 DataFrame 用来进行中性化, index 必须是标的代码
  数列表。
- date: 日期, 将用 date 这天的相关变量数据对 series 进行中性化 (注意依赖数据的实际可用时间, 如市值数据当天盘中是无法获取到的)
- axis: 默认为 1. 仅在 data 为 pd.DataFrame 时生效. 表示沿哪个方向做中性化, 0 为对每列做中性化, 1 为对每行做中性化
- fillna: 缺失值填充方式, 默认为None, 表示不填充. 支持的值:
          'jq_l1': 聚宽一级行业
          'jq_l2': 聚宽二级行业
          'sw_l1': 申万一级行业
          'sw_l2': 申万二级行业
          'sw_l3': 申万三级行业 表示使用某行业分类的均值进行填充.
-  add_constant: 中性化时是否添加常数项, 默认为 False

**返回 :**

- 中性化后的因子数据



#### 2.  去极值

```python
from jqfactor_analyzer import winsorize
winsorize(data, scale=None, range=None, qrange=None, inclusive=True, inf2nan=True, axis=1)
```

**参数 :**

- data: pd.Series/pd.DataFrame/np.array, 待缩尾的序列
- scale: 标准差倍数，与 range，qrange 三选一，不可同时使用。会将位于 [mu - scale * sigma, mu + scale * sigma] 边界之外的值替换为边界值
- range: 列表， 缩尾的上下边界。与 scale，qrange 三选一，不可同时使用。
- qrange: 列表，缩尾的上下分位数边界，值应在 0 到 1 之间，如 [0.05, 0.95]。与 scale，range 三选一，不可同时使用。
- inclusive: 是否将位于边界之外的值替换为边界值，默认为 True。如果为 True，则将边界之外的值替换为边界值，否则则替换为 np.nan
- inf2nan: 是否将 np.inf 和 -np.inf 替换成 np.nan，默认为 True如果为 True，在缩尾之前会先将 np.inf 和 -np.inf 替换成 np.nan，缩尾的时候不会考虑 np.nan，否则 inf 被认为是在上界之上，-inf 被认为在下界之下
- axis: 在 data 为 pd.DataFrame 时使用，沿哪个方向做标准化，默认为 1。 0 为对每列做缩尾，1 为对每行做缩尾。

**返回 :**

- 去极值处理之后的因子数据



#### 3.  中位数去极值

```python
from jqfactor_analyzer import winsorize_med
winsorize_med(data, scale=1, inclusive=True, inf2nan=True, axis=1)
```

**参数 :**

- data: pd.Series/pd.DataFrame/np.array, 待缩尾的序列
- scale: 倍数，默认为 1.0。会将位于 [med - scale * distance, med + scale * distance] 边界之外的值替换为边界值/np.nan
- inclusive bool 是否将位于边界之外的值替换为边界值，默认为 True。 如果为 True，则将边界之外的值替换为边界值，否则则替换为 np.nan
- inf2nan: 是否将 np.inf 和 -np.inf 替换成 np.nan，默认为 True。如果为 True，在缩尾之前会先将 np.inf 和 -np.inf 替换成 np.nan，缩尾的时候不会考虑 np.nan，否则 inf 被认为是在上界之上，-inf 被认为在下界之下
- axis: 在 data 为 pd.DataFrame 时使用，沿哪个方向做标准化，默认为 1。0 为对每列做缩尾，1 为对每行做缩尾

**返回 :**

- 中位数去极值之后的因子数据



#### 4.  标准化(z-score)

```python
from jqfactor_analyzer import standardlize
standardlize(data, inf2nan=True, axis=1)
```

**参数 :**

- data: pd.Series/pd.DataFrame/np.array, 待标准化的序列
- inf2nan: 是否将 np.inf 和 -np.inf 替换成 np.nan。默认为 True
- axis=1: 在 data 为 pd.DataFrame 时使用，如果 series 为 pd.DataFrame，沿哪个方向做标准化。0 为对每列做标准化，1 为对每行做标准化

**返回 :**

- 标准化后的因子数据


================================================
FILE: jqfactor_analyzer/__init__.py
================================================
# -*- coding: utf-8 -*-

from .version import __version__
from .analyze import FactorAnalyzer
from .attribution import AttributionAnalysis
from .data import DataApi
from .preprocess import winsorize, winsorize_med, standardlize, neutralize
from .factor_cache import save_factor_values_by_group, get_factor_values_by_cache, get_cache_dir


def analyze_factor(
    factor, industry='jq_l1', quantiles=5, periods=(1, 5, 10),
    weight_method='avg', max_loss=0.25, allow_cache=True, show_data_progress=True
):
    """单因子分析

    输入:
        factor: pandas.DataFrame: 因子值, columns 为股票代码 (如 '000001.XSHE'),
                                          index 为 日期的 DatetimeIndex
                或 pandas.Series: 因子值, index 为日期和股票代码的 MultiIndex
        industry: 行业分类, 默认为 'jq_l1'
            - 'jq_l1': 聚宽一级行业
            - 'jq_l2': 聚宽二级行业
            - 'sw_l1': 申万一级行业
            - 'sw_l2': 申万二级行业
            - 'sw_l3': 申万三级行业
            - 'zjw': 证监会行业
        quantiles: 分位数数量, 默认为 5
        periods: 调仓周期, int 或 int 的 列表, 默认为 [1, 5, 10]
        weight_method: 计算分位数收益时的加权方法, 默认为 'avg'
            - 'avg': 等权重
            - 'mktcap': 按总市值加权
            - 'ln_mktcap': 按总市值的对数加权
            - 'cmktcap': 按流通市值加权
            - 'ln_cmktcap': 按流通市值的对数加权
        max_loss: 因重复值或nan值太多而无效的因子值的最大占比, 默认为 0.25
        allow_cache: 是否允许对价格,市值等信息进行本地缓存(按天缓存,初次运行可能比较慢,但后续重新获取对应区间的数据将非常快,且分析时仅消耗较小的jqdatasdk流量)
        show_data_progress: 是否展示数据获取的进度信息

    """

    dataapi = DataApi(industry=industry, weight_method=weight_method,
                      allow_cache=allow_cache, show_progress=show_data_progress)
    return FactorAnalyzer(factor,
                          quantiles=quantiles,
                          periods=periods,
                          max_loss=max_loss,
                          **dataapi.apis)


def attribution_analysis(
    weights, daily_return, style_type='style_pro', industry='sw_l1',
    use_cn=True, show_data_progress=True
):
    """归因分析

    用户需要提供的数据:
    1. 日度股票持仓权重 (加总不为 1 的剩余部分视为现金)
    2. 组合的的日度收益率 (使用 T 日持仓盘后的因子暴露与 T+1 日的收益进行归因分析)

    组合风格因子暴露 (含行业, country) = sum(组合权重 * 个股因子值), country 暴露为总的股票持仓权重
    组合风格收益率 (含行业, country) = sum(组合风格因子暴露 * factor_return)
    组合特异收益率 = 组合总收益率 - 组合风格收益率(含行业, country 或 cash)
    """
    return AttributionAnalysis(weights,
                               daily_return=daily_return,
                               style_type=style_type,
                               industry=industry,
                               use_cn=use_cn,
                               show_data_progress=show_data_progress)


================================================
FILE: jqfactor_analyzer/analyze.py
================================================
# -*- coding: utf-8 -*-

from __future__ import division, print_function

try:
    from collections import Iterable
except ImportError:
    from collections.abc import Iterable

import numpy as np
import pandas as pd
from fastcache import lru_cache
from cached_property import cached_property
from scipy.stats import spearmanr, pearsonr
from scipy import stats

from . import performance as pef, plotting as pl
from .prepare import (get_clean_factor_and_forward_returns, rate_of_return,
                      std_conversion)
from .plot_utils import _use_chinese
from .utils import convert_to_forward_returns_columns, ensure_tuple


class FactorAnalyzer(object):
    """单因子分析

        参数
        ----------
        factor :
            因子值
            pd.DataFrame / pd.Series
            一个 DataFrame, index 为日期, columns 为资产,
            values 为因子的值
            或一个 Series, index 为日期和资产的 MultiIndex,
            values 为因子的值
        prices :
            用于计算因子远期收益的价格数据
            pd.DataFrame
            index 为日期, columns 为资产
            价格数据必须覆盖因子分析时间段以及额外远期收益计算中的最大预期期数.
            或 function
            输入参数为 securities, start_date, end_date, count
            返回值为价格数据的 DataFrame
        groupby :
            分组数据, 默认为 None
            pd.DataFrame
            index 为日期, columns 为资产，为每个资产每天的分组.
            或 dict
            资产-分组映射的字典. 如果传递了dict，则假定分组映射在整个时间段内保持不变.
            或 function
            输入参数为 securities, start_date, end_date
            返回值为分组数据的 DataFrame 或 dict
        weights :
            权重数据, 默认为 1
            pd.DataFrame
            index 为日期, columns 为资产，为每个资产每天的权重.
            或 dict
            资产-权重映射的字典. 如果传递了dict，则假定权重映射在整个时间段内保持不变.
            或 function
            输入参数为 securities, start_date, end_date
            返回值为权重数据的 DataFrame 或 dict
        binning_by_group :
            bool
            如果为 True, 则对每个组分别计算分位数. 默认为 False
            适用于因子值范围在各个组上变化很大的情况.
            如果要分析分组(行业)中性的组合, 您最好设置为 True
        quantiles :
            int or sequence[float]
            默认为 None
            在因子分组中按照因子值大小平均分组的组数
            或分位数序列, 允许不均匀分组.
            例如 [0, .10, .5, .90, 1.] 或 [.05, .5, .95]
            'quantiles' 和 'bins' 有且只能有一个不为 None
        bins :
            int or sequence[float]
            默认为 None
            在因子分组中使用的等宽 (按照因子值) 区间的数量
            或边界值序列, 允许不均匀的区间宽度.
            例如 [-4, -2, -0.5, 0, 10]
            'quantiles' 和 'bins' 有且只能有一个不为 None
        periods :
            int or sequence[int]
            远期收益的期数, 默认为 (1, 5, 10)
        max_loss :
            float
            默认为 0.25
            允许的丢弃因子数据的最大百分比 (0.00 到 1.00),
            计算比较输入因子索引中的项目数和输出 DataFrame 索引中的项目数.
            因子数据本身存在缺陷 (例如 NaN),
            没有提供足够的价格数据来计算所有因子值的远期收益，
            或者因为分组失败, 因此可以部分地丢弃因子数据
            设置 max_loss = 0 以停止异常捕获.
        zero_aware :
            bool
            默认为 False
            如果为True，则分别为正负因子值计算分位数。
            适用于您的信号聚集并且零是正值和负值的分界线的情况.


    所有属性列表
    ----------
        factor_data:返回因子值
            - 类型: pandas.Series
            - index: 为日期和股票代码的MultiIndex
        clean_factor_data: 去除 nan/inf, 整理后的因子值、forward_return 和分位数
            - 类型: pandas.DataFrame
            - index: 为日期和股票代码的MultiIndex
            - columns: 根据period选择后的forward_return
                    (如果调仓周期为1天, 那么 forward_return 为
                        [第二天的收盘价-今天的收盘价]/今天的收盘价),
                    因子值、行业分组、分位数数组、权重
        mean_return_by_quantile: 按分位数分组加权平均因子收益
            - 类型: pandas.DataFrame
            - index: 分位数分组
            - columns: 调仓周期
        mean_return_std_by_quantile: 按分位数分组加权因子收益标准差
            - 类型: pandas.DataFrame
            - index: 分位数分组
            - columns: 调仓周期
        mean_return_by_date: 按分位数及日期分组加权平均因子收益
            - 类型: pandas.DataFrame
            - index: 为日期和分位数的MultiIndex
            - columns: 调仓周期
        mean_return_std_by_date: 按分位数及日期分组加权因子收益标准差
            - 类型: pandas.DataFrame
            - index: 为日期和分位数的MultiIndex
            - columns: 调仓周期
        mean_return_by_group: 按分位数及行业分组加权平均因子收益
            - 类型: pandas.DataFrame
            - index: 为行业和分位数的MultiIndex
            - columns: 调仓周期
        mean_return_std_by_group: 按分位数及行业分组加权因子收益标准差
            - 类型: pandas.DataFrame
            - index: 为行业和分位数的MultiIndex
            - columns: 调仓周期
        mean_return_spread_by_quantile: 最高分位数因子收益减最低分位数因子收益每日均值
            - 类型: pandas.DataFrame
            - index: 日期
            - columns: 调仓周期
        mean_return_spread_std_by_quantile: 最高分位数因子收益减最低分位数因子收益每日标准差
            - 类型: pandas.DataFrame
            - index: 日期
            - columns: 调仓周期
        cumulative_return_by_quantile:各分位数每日累积收益
            - 类型: pandas.DataFrame
            - index: 日期
            - columns: 调仓周期和分位数
        cumulative_returns: 按因子值加权多空组合每日累积收益
            - 类型: pandas.DataFrame
            - index: 日期
            - columns: 调仓周期
        top_down_cumulative_returns: 做多最高分位做空最低分位多空组合每日累计收益
            - 类型: pandas.DataFrame
            - index: 日期
            - columns: 调仓周期
        ic: 信息比率
            - 类型: pandas.DataFrame
            - index: 日期
            - columns: 调仓周期
        ic_by_group: 分行业信息比率
            - 类型: pandas.DataFrame
            - index: 行业
            - columns: 调仓周期
        ic_monthly: 月度信息比率
            - 类型: pandas.DataFrame
            - index: 月度
            - columns: 调仓周期表
        quantile_turnover: 换手率
            - 类型: dict
            - 键: 调仓周期
                - index: 日期
                - columns: 分位数分组

    所有方法列表
    ----------
        calc_mean_return_by_quantile:
            计算按分位数分组加权因子收益和标准差
        calc_factor_returns:
            计算按因子值加权多空组合每日收益
        compute_mean_returns_spread:
            计算两个分位数相减的因子收益和标准差
        calc_factor_alpha_beta:
            计算因子的 alpha 和 beta
        calc_factor_information_coefficient:
            计算每日因子信息比率 (IC值)
        calc_mean_information_coefficient:
            计算因子信息比率均值 (IC值均值)
        calc_average_cumulative_return_by_quantile:
            按照当天的分位数算分位数未来和过去的收益均值和标准差
        calc_cumulative_return_by_quantile:
            计算各分位数每日累积收益
        calc_cumulative_returns:
            计算按因子值加权多空组合每日累积收益
        calc_top_down_cumulative_returns:
            计算做多最高分位做空最低分位多空组合每日累计收益
        calc_autocorrelation:
            根据调仓周期确定滞后期的每天计算因子自相关性
        calc_autocorrelation_n_days_lag:
            后 1 - n 天因子值自相关性
        calc_quantile_turnover_mean_n_days_lag:
            各分位数 1 - n 天平均换手率
        calc_ic_mean_n_days_lag:
            滞后 0 - n 天 forward return 信息比率

        plot_returns_table: 打印因子收益表
        plot_turnover_table: 打印换手率表
        plot_information_table: 打印信息比率(IC)相关表
        plot_quantile_statistics_table: 打印各分位数统计表
        plot_ic_ts: 画信息比率(IC)时间序列图
        plot_ic_hist: 画信息比率分布直方图
        plot_ic_qq: 画信息比率 qq 图
        plot_quantile_returns_bar: 画各分位数平均收益图
        plot_quantile_returns_violin: 画各分位数收益分布图
        plot_mean_quantile_returns_spread_time_series: 画最高分位减最低分位收益图
        plot_ic_by_group: 画按行业分组信息比率(IC)图
        plot_factor_auto_correlation: 画因子自相关图
        plot_top_bottom_quantile_turnover: 画最高最低分位换手率图
        plot_monthly_ic_heatmap: 画月度信息比率(IC)图
        plot_cumulative_returns: 画按因子值加权组合每日累积收益图
        plot_top_down_cumulative_returns: 画做多最大分位数做空最小分位数组合每日累积收益图
        plot_cumulative_returns_by_quantile: 画各分位数每日累积收益图
        plot_quantile_average_cumulative_return: 因子预测能力平均累计收益图
        plot_events_distribution: 画有效因子数量统计图

        create_summary_tear_sheet: 因子值特征分析
        create_returns_tear_sheet: 因子收益分析
        create_information_tear_sheet: 因子 IC 分析
        create_turnover_tear_sheet: 因子换手率分析
        create_event_returns_tear_sheet: 因子预测能力分析
        create_full_tear_sheet: 全部分析

        plot_disable_chinese_label: 关闭中文图例显示
        """

    def __init__(self, factor, prices, groupby=None, weights=1.0,
                 quantiles=None, bins=None, periods=(1, 5, 10),
                 binning_by_group=False, max_loss=0.25, zero_aware=False):

        self.factor = factor
        self.prices = prices
        self.groupby = groupby
        self.weights = weights

        self._quantiles = quantiles
        self._bins = bins
        self._periods = ensure_tuple(periods)
        self._binning_by_group = binning_by_group
        self._max_loss = max_loss
        self._zero_aware = zero_aware

        self.__gen_clean_factor_and_forward_returns()

    def __gen_clean_factor_and_forward_returns(self):
        """格式化因子数据和定价数据"""

        factor_data = self.factor
        if isinstance(factor_data, pd.DataFrame):
            factor_data = factor_data.stack(dropna=False)

        stocks = list(factor_data.index.get_level_values(1).drop_duplicates())
        start_date = min(factor_data.index.get_level_values(0))
        end_date = max(factor_data.index.get_level_values(0))

        if hasattr(self.prices, "__call__"):
            prices = self.prices(securities=stocks,
                                 start_date=start_date,
                                 end_date=end_date,
                                 period=max(self._periods))
            prices = prices.loc[~prices.index.duplicated()]
        else:
            prices = self.prices
        self._prices = prices

        if hasattr(self.groupby, "__call__"):
            groupby = self.groupby(securities=stocks,
                                   start_date=start_date,
                                   end_date=end_date)
        else:
            groupby = self.groupby
        self._groupby = groupby

        if hasattr(self.weights, "__call__"):
            weights = self.weights(stocks,
                                   start_date=start_date,
                                   end_date=end_date)
        else:
            weights = self.weights
        self._weights = weights

        self._clean_factor_data = get_clean_factor_and_forward_returns(
            factor_data,
            prices,
            groupby=groupby,
            weights=weights,
            binning_by_group=self._binning_by_group,
            quantiles=self._quantiles,
            bins=self._bins,
            periods=self._periods,
            max_loss=self._max_loss,
            zero_aware=self._zero_aware
        )

    @property
    def clean_factor_data(self):
        return self._clean_factor_data

    @property
    def _factor_quantile(self):
        data = self.clean_factor_data
        if not data.empty:
            return max(data.factor_quantile)
        else:
            _quantiles = self._quantiles
            _bins = self._bins
            _zero_aware = self._zero_aware
            get_len = lambda x: len(x) - 1 if isinstance(x, Iterable) else int(x)
            if _quantiles is not None and _bins is None and not _zero_aware:
                return get_len(_quantiles)
            elif _quantiles is not None and _bins is None and _zero_aware:
                return int(_quantiles) // 2 * 2
            elif _bins is not None and _quantiles is None and not _zero_aware:
                return get_len(_bins)
            elif _bins is not None and _quantiles is None and _zero_aware:
                return int(_bins) // 2 * 2

    @lru_cache(16)
    def calc_mean_return_by_quantile(self, by_date=False, by_group=False,
                                     demeaned=False, group_adjust=False):
        """计算按分位数分组因子收益和标准差

        因子收益为收益按照 weight 列中权重的加权平均值

        参数:
        by_date:
        - True: 按天计算收益
        - False: 不按天计算收益
        by_group:
        - True: 按行业计算收益
        - False: 不按行业计算收益
        demeaned:
        - True: 使用超额收益计算各分位数收益，超额收益=收益-基准收益
                (基准收益被认为是每日所有股票收益按照weight列中权重的加权的均值)
        - False: 不使用超额收益
        group_adjust:
        - True: 使用行业中性收益计算各分位数收益，行业中性收益=收益-行业收益
                (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
        - False: 不使用行业中性收益
        """
        return pef.mean_return_by_quantile(self._clean_factor_data,
                                           by_date=by_date,
                                           by_group=by_group,
                                           demeaned=demeaned,
                                           group_adjust=group_adjust)

    @lru_cache(4)
    def calc_factor_returns(self, demeaned=True, group_adjust=False):
        """计算按因子值加权组合每日收益

        权重 = 每日因子值 / 每日因子值的绝对值的和
        正的权重代表买入, 负的权重代表卖出

        参数:
        demeaned:
        - True: 对权重去均值 (每日权重 = 每日权重 - 每日权重的均值), 使组合转换为 cash-neutral 多空组合
        - False: 不对权重去均值
        group_adjust:
        - True: 对权重分行业去均值 (每日权重 = 每日权重 - 每日各行业权重的均值)，
                使组合转换为 industry-neutral 多空组合
        - False: 不对权重分行业去均值
        """
        return pef.factor_returns(self._clean_factor_data,
                                  demeaned=demeaned,
                                  group_adjust=group_adjust)

    def compute_mean_returns_spread(self, upper_quant=None, lower_quant=None,
                                    by_date=True, by_group=False,
                                    demeaned=False, group_adjust=False):
        """计算两个分位数相减的因子收益和标准差

        参数:
        upper_quant: 用 upper_quant 选择的分位数减去 lower_quant 选择的分位数
        lower_quant: 用 upper_quant 选择的分位数减去 lower_quant 选择的分位数
        by_date:
        - True: 按天计算两个分位数相减的因子收益和标准差
        - False: 不按天计算两个分位数相减的因子收益和标准差
        by_group:
        - True: 分行业计算两个分位数相减的因子收益和标准差
        - False: 不分行业计算两个分位数相减的因子收益和标准差
        demeaned:
        - True: 使用超额收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
        - False: 不使用超额收益
        group_adjust:
        - True: 使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False: 不使用行业中性收益
        """
        upper_quant = upper_quant if upper_quant is not None else self._factor_quantile
        lower_quant = lower_quant if lower_quant is not None else 1
        if ((not 1 <= upper_quant <= self._factor_quantile) or
            (not 1 <= lower_quant <= self._factor_quantile)):
            raise ValueError("upper_quant 和 low_quant 的取值范围为 1 - %s 的整数"
                             % self._factor_quantile)
        mean, std = self.calc_mean_return_by_quantile(by_date=by_date, by_group=by_group,
                                                      demeaned=demeaned, group_adjust=group_adjust,)
        mean = mean.apply(rate_of_return, axis=0)
        std = std.apply(std_conversion, axis=0)
        return pef.compute_mean_returns_spread(mean_returns=mean,
                                               upper_quant=upper_quant,
                                               lower_quant=lower_quant,
                                               std_err=std)

    @lru_cache(4)
    def calc_factor_alpha_beta(self, demeaned=True, group_adjust=False):
        """计算因子的 alpha 和 beta

        因子值加权组合每日收益 = beta * 市场组合每日收益 + alpha

        因子值加权组合每日收益计算方法见 calc_factor_returns 函数
        市场组合每日收益是每日所有股票收益按照weight列中权重加权的均值
        结果中的 alpha 是年化 alpha

        参数:
        demeaned:
        详见 calc_factor_returns 中 demeaned 参数
        - True: 对因子值加权组合每日收益的权重去均值 (每日权重 = 每日权重 - 每日权重的均值),
                使组合转换为cash-neutral多空组合
        - False: 不对权重去均值
        group_adjust:
        详见 calc_factor_returns 中 group_adjust 参数
        - True: 对权重分行业去均值 (每日权重 = 每日权重 - 每日各行业权重的均值)，
                使组合转换为 industry-neutral 多空组合
        - False: 不对权重分行业去均值
        """
        return pef.factor_alpha_beta(self._clean_factor_data,
                                     demeaned=demeaned,
                                     group_adjust=group_adjust)

    @lru_cache(8)
    def calc_factor_information_coefficient(self, group_adjust=False, by_group=False, method=None):
        """计算每日因子信息比率 (IC值)

        参数:
        group_adjust:
        - True: 使用行业中性收益计算 IC (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False: 不使用行业中性收益
        by_group:
        - True: 分行业计算 IC
        - False: 不分行业计算 IC
        method:
        - 'rank': 用秩相关系数计算IC值
        - 'normal': 用普通相关系数计算IC值
        """
        if method is None:
            method = 'rank'
        if method not in ('rank', 'normal'):
            raise ValueError("`method` should be chosen from ('rank' | 'normal')")

        if method == 'rank':
            method = spearmanr
        elif method == 'normal':
            method = pearsonr
        return pef.factor_information_coefficient(self._clean_factor_data,
                                                  group_adjust=group_adjust,
                                                  by_group=by_group,
                                                  method=method)

    @lru_cache(16)
    def calc_mean_information_coefficient(self, group_adjust=False, by_group=False,
                                          by_time=None, method=None):
        """计算因子信息比率均值 (IC值均值)

        参数:
        group_adjust:
        - True: 使用行业中性收益计算 IC (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False: 不使用行业中性收益
        by_group:
        - True: 分行业计算 IC
        - False: 不分行业计算 IC
        by_time:
        - 'Y': 按年求均值
        - 'M': 按月求均值
        - None: 对所有日期求均值
        method:
        - 'rank': 用秩相关系数计算IC值
        - 'normal': 用普通相关系数计算IC值
        """
        if method is None:
            method = 'rank'
        if method not in ('rank', 'normal'):
            raise ValueError("`method` should be chosen from ('rank' | 'normal')")

        if method == 'rank':
            method = spearmanr
        elif method == 'normal':
            method = pearsonr
        return pef.mean_information_coefficient(
            self._clean_factor_data,
            group_adjust=group_adjust,
            by_group=by_group,
            by_time=by_time,
            method=method
        )

    @lru_cache(16)
    def calc_average_cumulative_return_by_quantile(self, periods_before, periods_after,
                                                   demeaned=False, group_adjust=False):
        """按照当天的分位数算分位数未来和过去的收益均值和标准差

        参数:
        periods_before: 计算过去的天数
        periods_after: 计算未来的天数
        demeaned:
        - True: 使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
        - False: 不使用超额收益
        group_adjust:
        - True: 使用行业中性化后的收益计算累积收益
                (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False: 不使用行业中性化后的收益
        """
        return pef.average_cumulative_return_by_quantile(
            self._clean_factor_data,
            prices=self._prices,
            periods_before=periods_before,
            periods_after=periods_after,
            demeaned=demeaned,
            group_adjust=group_adjust
        )

    @lru_cache(2)
    def calc_autocorrelation(self, rank=True):
        """根据调仓周期确定滞后期的每天计算因子自相关性

        当日因子值和滞后period天的因子值的自相关性

        参数:
        rank:
        - True: 秩相关系数
        - False: 普通相关系数
        """
        return pd.concat(
            [
                pef.factor_autocorrelation(self._clean_factor_data,
                                           period, rank=rank)
                for period in self._periods
            ],
            axis=1,
            keys=list(map(convert_to_forward_returns_columns, self._periods))
        )

    @lru_cache(None)
    def calc_quantile_turnover_mean_n_days_lag(self, n=10):
        """各分位数滞后1天到n天的换手率均值

        参数:
        n: 滞后 1 天到 n 天的换手率
        """
        quantile_factor = self._clean_factor_data['factor_quantile']

        quantile_turnover_rate = pd.concat(
            [pd.Series([pef.quantile_turnover(quantile_factor, q, p).mean()
                        for q in range(1, int(quantile_factor.max()) + 1)],
                       index=range(1, int(quantile_factor.max()) + 1),
                       name=p)
             for p in range(1, n + 1)],
            axis=1, keys='lag_' + pd.Index(range(1, n + 1)).astype(str)
        ).T
        quantile_turnover_rate.columns.name = 'factor_quantile'

        return quantile_turnover_rate

    @lru_cache(None)
    def calc_autocorrelation_n_days_lag(self, n=10, rank=False):
        """滞后1-n天因子值自相关性

        参数:
        n: 滞后1天到n天的因子值自相关性
        rank:
        - True: 秩相关系数
        - False: 普通相关系数
        """
        return pd.Series(
            [
                pef.factor_autocorrelation(self._clean_factor_data, p, rank=rank).mean()
                for p in range(1, n + 1)
            ],
            index='lag_' + pd.Index(range(1, n + 1)).astype(str)
        )

    @lru_cache(None)
    def _calc_ic_mean_n_day_lag(self, n, group_adjust=False, by_group=False, method=None):
        if method is None:
            method = 'rank'
        if method not in ('rank', 'normal'):
            raise ValueError("`method` should be chosen from ('rank' | 'normal')")

        if method == 'rank':
            method = spearmanr
        elif method == 'normal':
            method = pearsonr

        factor_data = self._clean_factor_data.copy()
        factor_value = factor_data['factor'].unstack('asset')

        factor_data['factor'] = factor_value.shift(n).stack(dropna=True)
        if factor_data.dropna().empty:
            return pd.Series(np.nan, index=pef.get_forward_returns_columns(factor_data.columns))
        ac = pef.factor_information_coefficient(
            factor_data.dropna(),
            group_adjust=group_adjust, by_group=by_group,
            method=method
        )
        return ac.mean(level=('group' if by_group else None))

    def calc_ic_mean_n_days_lag(self, n=10, group_adjust=False, by_group=False, method=None):
        """滞后 0 - n 天因子收益信息比率(IC)的均值

        滞后 n 天 IC 表示使用当日因子值和滞后 n 天的因子收益计算 IC

        参数:
        n: 滞后0-n天因子收益的信息比率(IC)的均值
        group_adjust:
        - True: 使用行业中性收益计算 IC (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False: 不使用行业中性收益
        by_group:
        - True: 分行业计算 IC
        - False: 不分行业计算 IC
        method:
        - 'rank': 用秩相关系数计算IC值
        - 'normal': 用普通相关系数计算IC值
        """
        ic_mean = [self.calc_factor_information_coefficient(
            group_adjust=group_adjust, by_group=by_group, method=method,
        ).mean(level=('group' if by_group else None))]

        for lag in range(1, n + 1):
            ic_mean.append(self._calc_ic_mean_n_day_lag(
                n=lag,
                group_adjust=group_adjust,
                by_group=by_group,
                method=method
            ))
        if not by_group:
            ic_mean = pd.concat(ic_mean, keys='lag_' + pd.Index(range(n + 1)).astype(str), axis=1)
            ic_mean = ic_mean.T
        else:
            ic_mean = pd.concat(ic_mean, keys='lag_' + pd.Index(range(n + 1)).astype(str), axis=0)
        return ic_mean

    @property
    def mean_return_by_quantile(self):
        """收益分析

        用来画分位数收益的柱状图

        返回 pandas.DataFrame, index 是 factor_quantile, 值是(1, 2, 3, 4, 5),
        column 是 period 的值 (1, 5, 10)
        """
        mean_ret_quantile, _ = self.calc_mean_return_by_quantile(
            by_date=False,
            by_group=False,
            demeaned=False,
            group_adjust=False,
        )
        mean_compret_quantile = mean_ret_quantile.apply(rate_of_return, axis=0)
        return mean_compret_quantile

    @property
    def mean_return_std_by_quantile(self):
        """收益分析

        用来画分位数收益的柱状图

        返回 pandas.DataFrame, index 是 factor_quantile, 值是(1, 2, 3, 4, 5),
        column 是 period 的值 (1, 5, 10)
        """
        _, mean_ret_std_quantile = self.calc_mean_return_by_quantile(
            by_date=False,
            by_group=False,
            demeaned=False,
            group_adjust=False,
        )
        mean_ret_std_quantile = mean_ret_std_quantile.apply(std_conversion, axis=0)
        return mean_ret_std_quantile

    @property
    def _mean_return_by_date(self):
        _mean_return_by_date, _ = self.calc_mean_return_by_quantile(
            by_date=True,
            by_group=False,
            demeaned=False,
            group_adjust=False,
        )
        return _mean_return_by_date

    @property
    def mean_return_by_date(self):
        mean_return_by_date = self._mean_return_by_date.apply(rate_of_return, axis=0)
        return mean_return_by_date

    @property
    def mean_return_std_by_date(self):
        _, std_quant_daily = self.calc_mean_return_by_quantile(
            by_date=True,
            demeaned=False,
            by_group=False,
            group_adjust=False,
        )
        mean_return_std_by_date = std_quant_daily.apply(std_conversion, axis=0)

        return mean_return_std_by_date

    @property
    def mean_return_by_group(self):
        """分行业的分位数收益

        返回值:
            MultiIndex 的 DataFrame
            index 分别是分位数、 行业名称,  column 是 period  (1, 5, 10)
        """
        mean_return_group, _ = self.calc_mean_return_by_quantile(
            by_date=False,
            by_group=True,
            demeaned=True,
            group_adjust=False,
        )
        mean_return_group = mean_return_group.apply(rate_of_return, axis=0)
        return mean_return_group

    @property
    def mean_return_std_by_group(self):
        _, mean_return_std_group = self.calc_mean_return_by_quantile(
            by_date=False,
            by_group=True,
            demeaned=True,
            group_adjust=False,
        )
        mean_return_std_group = mean_return_std_group.apply(std_conversion, axis=0)
        return mean_return_std_group

    @property
    def mean_return_spread_by_quantile(self):
        mean_return_spread_by_quantile, _ = self.compute_mean_returns_spread()
        return mean_return_spread_by_quantile

    @property
    def mean_return_spread_std_by_quantile(self):
        _, std_spread_quant = self.compute_mean_returns_spread()
        return std_spread_quant

    @lru_cache(5)
    def calc_cumulative_return_by_quantile(self, period=None, demeaned=False, group_adjust=False):
        """计算指定调仓周期的各分位数每日累积收益

        参数:
        period: 指定调仓周期
        demeaned:
        详见 calc_mean_return_by_quantile 中 demeaned 参数
        - True: 使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
        - False: 不使用超额收益
        group_adjust:
        详见 calc_mean_return_by_quantile 中 group_adjust 参数
        - True: 使用行业中性化后的收益计算累积收益
                (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False: 不使用行业中性化后的收益
        """
        if period is None:
            period = self._periods[0]
        period_col = convert_to_forward_returns_columns(period)

        factor_returns = self.calc_mean_return_by_quantile(
            by_date=True, demeaned=demeaned, group_adjust=group_adjust
        )[0][period_col].unstack('factor_quantile')

        cum_ret = factor_returns.apply(pef.cumulative_returns, period=period)

        return cum_ret

    @lru_cache(20)
    def calc_cumulative_returns(self, period=None,
                                demeaned=False, group_adjust=False):
        """计算指定调仓周期的按因子值加权组合每日累积收益

        当 period > 1 时，组合的累积收益计算方法为：
        组合每日收益 = （从第0天开始每period天一调仓的组合每日收益 +
                        从第1天开始每period天一调仓的组合每日收益 + ... +
                        从第period-1天开始每period天一调仓的组合每日收益) / period
        组合累积收益 = 组合每日收益的累积

        参数:
        period: 指定调仓周期
        demeaned:
        详见 calc_factor_returns 中 demeaned 参数
        - True: 对权重去均值 (每日权重 = 每日权重 - 每日权重的均值), 使组合转换为 cash-neutral 多空组合
        - False: 不对权重去均值
        group_adjust:
        详见 calc_factor_returns 中 group_adjust 参数
        - True: 对权重分行业去均值 (每日权重 = 每日权重 - 每日各行业权重的均值)，
                使组合转换为 industry-neutral 多空组合
        - False: 不对权重分行业去均值
        """
        if period is None:
            period = self._periods[0]
        period_col = convert_to_forward_returns_columns(period)
        factor_returns = self.calc_factor_returns(
            demeaned=demeaned, group_adjust=group_adjust
        )[period_col]

        return pef.cumulative_returns(factor_returns, period=period)

    @lru_cache(20)
    def calc_top_down_cumulative_returns(self, period=None,
                                         demeaned=False, group_adjust=False):
        """计算做多最大分位，做空最小分位组合每日累积收益

        参数:
        period: 指定调仓周期
        demeaned:
        详见 calc_mean_return_by_quantile 中 demeaned 参数
        - True: 使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
        - False: 不使用超额收益
        group_adjust:
        详见 calc_mean_return_by_quantile 中 group_adjust 参数
        - True: 使用行业中性化后的收益计算累积收益
                (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False: 不使用行业中性化后的收益
        """
        if period is None:
            period = self._periods[0]
        period_col = convert_to_forward_returns_columns(period)
        mean_returns, _ = self.calc_mean_return_by_quantile(
            by_date=True, by_group=False,
            demeaned=demeaned, group_adjust=group_adjust,
        )

        upper_quant = mean_returns[period_col].xs(self._factor_quantile,
                                                  level='factor_quantile')
        lower_quant = mean_returns[period_col].xs(1,
                                                  level='factor_quantile')
        return pef.cumulative_returns(upper_quant - lower_quant, period=period)

    @property
    def ic(self):
        """IC 分析, 日度 ic

        返回 DataFrame, index 是时间,  column 是 period 的值 (1, 5, 10)
        """
        return self.calc_factor_information_coefficient()

    @property
    def ic_by_group(self):
        """行业 ic"""
        return self.calc_mean_information_coefficient(by_group=True)

    @property
    def ic_monthly(self):
        ic_monthly = self.calc_mean_information_coefficient(group_adjust=False,
                                                            by_group=False,
                                                            by_time="M").copy()
        ic_monthly.index = ic_monthly.index.map(lambda x: x.strftime('%Y-%m'))
        return ic_monthly

    @cached_property
    def quantile_turnover(self):
        """换手率分析

        返回值一个 dict, key 是 period, value 是一个 DataFrame(index 是日期, column 是分位数)
        """

        quantile_factor = self._clean_factor_data['factor_quantile']

        quantile_turnover_rate = {
            convert_to_forward_returns_columns(p):
            pd.concat([pef.quantile_turnover(quantile_factor, q, p)
                       for q in range(1, int(quantile_factor.max()) + 1)],
                      axis=1)
            for p in self._periods
        }

        return quantile_turnover_rate

    @property
    def cumulative_return_by_quantile(self):
        return {
            convert_to_forward_returns_columns(p):
            self.calc_cumulative_return_by_quantile(period=p)
            for p in self._periods
        }

    @property
    def cumulative_returns(self):
        return pd.concat([self.calc_cumulative_returns(period=period)
                          for period in self._periods],
                         axis=1,
                         keys=list(map(convert_to_forward_returns_columns,
                                       self._periods)))

    @property
    def top_down_cumulative_returns(self):
        return pd.concat([self.calc_top_down_cumulative_returns(period=period)
                          for period in self._periods],
                         axis=1,
                         keys=list(map(convert_to_forward_returns_columns,
                                       self._periods)))

    def plot_returns_table(self, demeaned=False, group_adjust=False):
        """打印因子收益表

        参数:
        demeaned:
        - True: 使用超额收益计算 (基准收益被认为是每日所有股票收益按照weight列中权重的加权的均值)
        - False: 不使用超额收益
        group_adjust:
        - True: 使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
        - False: 不使用行业中性收益
        """
        mean_return_by_quantile = self.calc_mean_return_by_quantile(
            by_date=False, by_group=False,
            demeaned=demeaned, group_adjust=group_adjust,
        )[0].apply(rate_of_return, axis=0)

        mean_returns_spread, _ = self.compute_mean_returns_spread(
            upper_quant=self._factor_quantile,
            lower_quant=1,
            by_date=True,
            by_group=False,
            demeaned=demeaned,
            group_adjust=group_adjust,
        )

        pl.plot_returns_table(
            self.calc_factor_alpha_beta(demeaned=demeaned),
            mean_return_by_quantile,
            mean_returns_spread
        )

    def plot_turnover_table(self):
        """打印换手率表"""
        pl.plot_turnover_table(
            self.calc_autocorrelation(),
            self.quantile_turnover
        )

    def plot_information_table(self, group_adjust=False, method=None):
        """打印信息比率 (IC)相关表

        参数:
        group_adjust:
        - True：使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
        - False：不使用行业中性收益
        method：
        - 'rank'：用秩相关系数计算IC值
        - 'normal':用相关系数计算IC值
        """
        ic = self.calc_factor_information_coefficient(
            group_adjust=group_adjust,
            by_group=False,
            method=method
        )
        pl.plot_information_table(ic)

    def plot_quantile_statistics_table(self):
        """打印各分位数统计表"""
        pl.plot_quantile_statistics_table(self._clean_factor_data)

    def plot_ic_ts(self, group_adjust=False, method=None):
        """画信息比率(IC)时间序列图

        参数:
        group_adjust:
        - True: 使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
        - False: 不使用行业中性收益
        method:
        - 'rank': 用秩相关系数计算IC值
        - 'normal':用相关系数计算IC值
        """
        ic = self.calc_factor_information_coefficient(
            group_adjust=group_adjust, by_group=False, method=method
        )
        pl.plot_ic_ts(ic)

    def plot_ic_hist(self, group_adjust=False, method=None):
        """画信息比率分布直方图

        参数:
        group_adjust:
        - True: 使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
        - False: 不使用行业中性收益
        method:
        - 'rank': 用秩相关系数计算IC值
        - 'normal': 用相关系数计算IC值
        """
        ic = self.calc_factor_information_coefficient(
            group_adjust=group_adjust,
            by_group=False,
            method=method
        )
        pl.plot_ic_hist(ic)

    def plot_ic_qq(self, group_adjust=False, method=None, theoretical_dist=None):
        """画信息比率 qq 图

        参数:
        group_adjust:
        - True: 使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
        - False: 不使用行业中性收益
        method:
        - 'rank': 用秩相关系数计算IC值
        - 'normal': 用相关系数计算IC值
        theoretical_dist:
        - 'norm': 正态分布
        - 't': t 分布
        """
        theoretical_dist = 'norm' if theoretical_dist is None else theoretical_dist
        theoretical_dist = getattr(stats, theoretical_dist)
        ic = self.calc_factor_information_coefficient(
            group_adjust=group_adjust,
            by_group=False,
            method=method,
        )
        pl.plot_ic_qq(ic, theoretical_dist=theoretical_dist)

    def plot_quantile_returns_bar(self, by_group=False,
                                  demeaned=False, group_adjust=False):
        """画各分位数平均收益图

        参数:
        by_group:
        - True: 各行业的各分位数平均收益图
        - False: 各分位数平均收益图
        demeaned:
        - True: 使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
        - False: 不使用超额收益
        group_adjust:
        - True: 使用行业中性化后的收益计算累积收益
                (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False: 不使用行业中性化后的收益
        """
        mean_return_by_quantile = self.calc_mean_return_by_quantile(
            by_date=False, by_group=by_group,
            demeaned=demeaned, group_adjust=group_adjust,
        )[0].apply(rate_of_return, axis=0)

        pl.plot_quantile_returns_bar(
            mean_return_by_quantile, by_group=by_group, ylim_percentiles=None
        )

    def plot_quantile_returns_violin(self, demeaned=False, group_adjust=False,
                                     ylim_percentiles=(1, 99)):
        """画各分位数收益分布图

        参数:
        demeaned:
        - True: 使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
        - False: 不使用超额收益
        group_adjust:
        - True: 使用行业中性化后的收益计算累积收益
                (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False: 不使用行业中性化后的收益
        plot_quantile_returns_violin: 有效收益分位数(单位为百分之). 画图时y轴的范围为有效收益的最大/最小值.
                                      例如 (1, 99) 代表收益的从小到大排列的 1% 分位到 99% 分位为有效收益.
        """
        mean_return_by_date = self.calc_mean_return_by_quantile(
            by_date=True, by_group=False,
            demeaned=demeaned, group_adjust=group_adjust
        )[0].apply(rate_of_return, axis=0)

        pl.plot_quantile_returns_violin(mean_return_by_date,
                                        ylim_percentiles=ylim_percentiles)

    def plot_mean_quantile_returns_spread_time_series(
        self, demeaned=False, group_adjust=False, bandwidth=1
    ):
        """画最高分位减最低分位收益图

        参数:
        demeaned:
        - True: 使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
        - False: 不使用超额收益
        group_adjust:
        - True: 使用行业中性化后的收益计算累积收益
                (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False: 不使用行业中性化后的收益
        bandwidth: n, 加减 n 倍当日标准差
        """
        mean_returns_spread, mean_returns_spread_std = self.compute_mean_returns_spread(
            upper_quant=self._factor_quantile,
            lower_quant=1,
            by_date=True,
            by_group=False,
            demeaned=demeaned,
            group_adjust=group_adjust,
        )

        pl.plot_mean_quantile_returns_spread_time_series(
            mean_returns_spread, std_err=mean_returns_spread_std,
            bandwidth=bandwidth
        )

    def plot_ic_by_group(self, group_adjust=False, method=None):
        """画按行业分组信息比率(IC)图

        参数:
        group_adjust:
        - True: 使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
        - False: 不使用行业中性收益
        method:
        - 'rank': 用秩相关系数计算IC值
        - 'normal': 用相关系数计算IC值
        """
        ic_by_group = self.calc_mean_information_coefficient(
            group_adjust=group_adjust,
            by_group=True,
            method=method
        )
        pl.plot_ic_by_group(ic_by_group)

    def plot_factor_auto_correlation(self, periods=None, rank=True):
        """画因子自相关图

        参数:
        periods: 滞后周期
        rank:
        - True: 用秩相关系数
        - False: 用相关系数
        """
        if periods is None:
            periods = self._periods
        if not isinstance(periods, Iterable):
            periods = (periods,)
        periods = tuple(periods)
        for p in periods:
            if p in self._periods:
                pl.plot_factor_rank_auto_correlation(
                    self.calc_autocorrelation(rank=rank)[
                        convert_to_forward_returns_columns(p)
                    ],
                    period=p
                )

    def plot_top_bottom_quantile_turnover(self, periods=None):
        """画最高最低分位换手率图

        参数:
        periods: 调仓周期
        """
        quantile_turnover = self.quantile_turnover

        if periods is None:
            periods = self._periods
        if not isinstance(periods, Iterable):
            periods = (periods,)
        periods = tuple(periods)
        for p in periods:
            if p in self._periods:
                pl.plot_top_bottom_quantile_turnover(
                    quantile_turnover[convert_to_forward_returns_columns(p)],
                    period=p
                )

    def plot_monthly_ic_heatmap(self, group_adjust=False):
        """画月度信息比率(IC)图

        参数:
        group_adjust:
        - True: 使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
        - False: 不使用行业中性收益
        """
        ic_monthly = self.calc_mean_information_coefficient(
            group_adjust=group_adjust, by_group=False, by_time="M"
        )
        pl.plot_monthly_ic_heatmap(ic_monthly)

    def plot_cumulative_returns(self, period=None, demeaned=False,
                                group_adjust=False):
        """画按因子值加权组合每日累积收益图

        参数:
        periods: 调仓周期
        demeaned:
        详见 calc_factor_returns 中 demeaned 参数
        - True: 对因子值加权组合每日收益的权重去均值 (每日权重 = 每日权重 - 每日权重的均值),
                使组合转换为cash-neutral多空组合
        - False: 不对权重去均值
        group_adjust:
        详见 calc_factor_returns 中 group_adjust 参数
        - True: 对权重分行业去均值 (每日权重 = 每日权重 - 每日各行业权重的均值)，
                使组合转换为 industry-neutral 多空组合
        - False: 不对权重分行业去均值
        """
        if period is None:
            period = self._periods
        if not isinstance(period, Iterable):
            period = (period,)
        period = tuple(period)
        factor_returns = self.calc_factor_returns(demeaned=demeaned,
                                                  group_adjust=group_adjust)
        for p in period:
            if p in self._periods:
                pl.plot_cumulative_returns(
                    factor_returns[convert_to_forward_returns_columns(p)],
                    period=p
                )

    def plot_top_down_cumulative_returns(self, period=None, demeaned=False, group_adjust=False):
        """画做多最大分位数做空最小分位数组合每日累积收益图

        period: 指定调仓周期
        demeaned:
        详见 calc_mean_return_by_quantile 中 demeaned 参数
        - True: 使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
        - False: 不使用超额收益
        group_adjust:
        详见 calc_mean_return_by_quantile 中 group_adjust 参数
        - True: 使用行业中性化后的收益计算累积收益
                (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False: 不使用行业中性化后的收益
        """
        if period is None:
            period = self._periods
        if not isinstance(period, Iterable):
            period = (period, )
        period = tuple(period)
        for p in period:
            if p in self._periods:
                factor_return = self.calc_top_down_cumulative_returns(
                    period=p, demeaned=demeaned, group_adjust=group_adjust,
                )
                pl.plot_top_down_cumulative_returns(
                    factor_return, period=p
                )

    def plot_cumulative_returns_by_quantile(self, period=None, demeaned=False,
                                            group_adjust=False):
        """画各分位数每日累积收益图

        参数:
        period: 调仓周期
        demeaned:
        详见 calc_mean_return_by_quantile 中 demeaned 参数
        - True: 使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
        - False: 不使用超额收益
        group_adjust:
        详见 calc_mean_return_by_quantile 中 group_adjust 参数
        - True: 使用行业中性化后的收益计算累积收益
                (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False: 不使用行业中性化后的收益
        """
        if period is None:
            period = self._periods
        if not isinstance(period, Iterable):
            period = (period,)
        period = tuple(period)
        mean_return_by_date, _ = self.calc_mean_return_by_quantile(
            by_date=True, by_group=False, demeaned=demeaned, group_adjust=group_adjust,
        )
        for p in period:
            if p in self._periods:
                pl.plot_cumulative_returns_by_quantile(
                    mean_return_by_date[convert_to_forward_returns_columns(p)],
                    period=p
                )

    def plot_quantile_average_cumulative_return(self, periods_before=5, periods_after=10,
                                                by_quantile=False, std_bar=False,
                                                demeaned=False, group_adjust=False):
        """因子预测能力平均累计收益图

        参数:
        periods_before: 计算过去的天数
        periods_after: 计算未来的天数
        by_quantile: 是否各分位数分别显示因子预测能力平均累计收益图
        std_bar:
        - True: 显示标准差
        - False: 不显示标准差
        demeaned:
        详见 calc_mean_return_by_quantile 中 demeaned 参数
        - True: 使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
        - False: 不使用超额收益
        group_adjust:
        详见 calc_mean_return_by_quantile 中 group_adjust 参数
        - True: 使用行业中性化后的收益计算累积收益
                (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False: 不使用行业中性化后的收益
        """
        average_cumulative_return_by_q = self.calc_average_cumulative_return_by_quantile(
            periods_before=periods_before, periods_after=periods_after,
            demeaned=demeaned, group_adjust=group_adjust
        )
        pl.plot_quantile_average_cumulative_return(average_cumulative_return_by_q,
                                                   by_quantile=by_quantile,
                                                   std_bar=std_bar,
                                                   periods_before=periods_before,
                                                   periods_after=periods_after)

    def plot_events_distribution(self, num_days=5):
        """画有效因子数量统计图

        参数:
        num_days: 统计间隔天数
        """
        pl.plot_events_distribution(
            events=self._clean_factor_data['factor'],
            num_days=num_days,
            full_dates=pd.to_datetime(self.factor.index.get_level_values('date').unique())
        )

    def create_summary_tear_sheet(self, demeaned=False, group_adjust=False):
        """因子值特征分析

        参数:
        demeaned:
        - True: 对每日因子收益去均值求得因子收益表
        - False: 因子收益表
        group_adjust:
        - True: 按行业对因子收益去均值后求得因子收益表
        - False: 因子收益表
        """
        self.plot_quantile_statistics_table()
        self.plot_returns_table(demeaned=demeaned, group_adjust=group_adjust)
        self.plot_quantile_returns_bar(by_group=False, demeaned=demeaned, group_adjust=group_adjust)
        pl.plt.show()
        self.plot_information_table(group_adjust=group_adjust)
        self.plot_turnover_table()

    def create_returns_tear_sheet(self, demeaned=False, group_adjust=False, by_group=False):
        """因子值特征分析

        参数:
        demeaned:
        详见 calc_mean_return_by_quantile 中 demeaned 参数
        - True: 使用超额收益计算 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
        - False: 不使用超额收益
        group_adjust:
        详见 calc_mean_return_by_quantile 中 group_adjust 参数
        - True: 使用行业中性化后的收益计算 (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False: 不使用行业中性化后的收益
        by_group:
        - True: 画各行业的各分位数平均收益图
        - False: 不画各行业的各分位数平均收益图
        """
        self.plot_returns_table(demeaned=demeaned, group_adjust=group_adjust)
        self.plot_quantile_returns_bar(by_group=False,
                                       demeaned=demeaned,
                                       group_adjust=group_adjust)
        pl.plt.show()
        self.plot_cumulative_returns(
            period=None, demeaned=demeaned, group_adjust=group_adjust
        )
        pl.plt.show()
        self.plot_cumulative_returns_by_quantile(period=None,
                                                 demeaned=demeaned,
                                                 group_adjust=group_adjust)
        self.plot_top_down_cumulative_returns(period=None,
                                              demeaned=demeaned,
                                              group_adjust=group_adjust)
        pl.plt.show()
        self.plot_mean_quantile_returns_spread_time_series(
            demeaned=demeaned, group_adjust=group_adjust
        )
        pl.plt.show()
        if by_group:
            self.plot_quantile_returns_bar(by_group=True,
                                           demeaned=demeaned,
                                           group_adjust=group_adjust)
            pl.plt.show()

        self.plot_quantile_returns_violin(demeaned=demeaned,
                                          group_adjust=group_adjust)
        pl.plt.show()

    def create_information_tear_sheet(self, group_adjust=False, by_group=False):
        """因子 IC 分析

        参数:
        group_adjust:
        - True: 使用行业中性收益 (行业收益被认为是每日各个行业股票收益按照weight列中权重的加权的均值)
        - False: 不使用行业中性收益
        by_group:
        - True: 画按行业分组信息比率(IC)图
        - False: 画月度信息比率(IC)图
        """
        self.plot_ic_ts(group_adjust=group_adjust, method=None)
        pl.plt.show()
        self.plot_ic_qq(group_adjust=group_adjust)
        pl.plt.show()
        if by_group:
            self.plot_ic_by_group(group_adjust=group_adjust, method=None)
        else:
            self.plot_monthly_ic_heatmap(group_adjust=group_adjust)
        pl.plt.show()

    def create_turnover_tear_sheet(self, turnover_periods=None):
        """因子换手率分析

        参数:
        turnover_periods: 调仓周期
        """
        self.plot_turnover_table()
        self.plot_top_bottom_quantile_turnover(periods=turnover_periods)
        pl.plt.show()
        self.plot_factor_auto_correlation(periods=turnover_periods)
        pl.plt.show()

    def create_event_returns_tear_sheet(self, avgretplot=(5, 15),
                                        demeaned=False, group_adjust=False,
                                        std_bar=False):
        """因子预测能力分析

        参数:
        avgretplot: tuple 因子预测的天数
        -(计算过去的天数, 计算未来的天数)
        demeaned:
        详见 calc_mean_return_by_quantile 中 demeaned 参数
        - True: 使用超额收益计算累积收益 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
        - False: 不使用超额收益
        group_adjust:
        详见 calc_mean_return_by_quantile 中 group_adjust 参数
        - True: 使用行业中性化后的收益计算累积收益
                (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False: 不使用行业中性化后的收益
        std_bar:
        - True: 显示标准差
        - False: 不显示标准差
        """
        before, after = avgretplot
        self.plot_quantile_average_cumulative_return(
            periods_before=before, periods_after=after,
            by_quantile=False, std_bar=False,
            demeaned=demeaned, group_adjust=group_adjust
        )
        pl.plt.show()
        if std_bar:
            self.plot_quantile_average_cumulative_return(
                periods_before=before, periods_after=after,
                by_quantile=True, std_bar=True,
                demeaned=demeaned, group_adjust=group_adjust
            )
            pl.plt.show()

    def create_full_tear_sheet(self, demeaned=False, group_adjust=False, by_group=False,
                               turnover_periods=None, avgretplot=(5, 15), std_bar=False):
        """全部分析

        参数:
        demeaned:
        - True：使用超额收益计算 (基准收益被认为是每日所有股票收益按照weight列中权重加权的均值)
        - False：不使用超额收益
        group_adjust:
        - True：使用行业中性化后的收益计算
                (行业收益被认为是每日各个行业股票收益按照weight列中权重加权的均值)
        - False：不使用行业中性化后的收益
        by_group:
        - True: 按行业展示
        - False: 不按行业展示
        turnover_periods: 调仓周期
        avgretplot: tuple 因子预测的天数
        -(计算过去的天数, 计算未来的天数)
        std_bar:
        - True: 显示标准差
        - False: 不显示标准差
        """
        self.plot_quantile_statistics_table()
        print("\n-------------------------\n")
        self.plot_returns_table(demeaned=demeaned, group_adjust=group_adjust)
        self.plot_quantile_returns_bar(by_group=False,
                                       demeaned=demeaned,
                                       group_adjust=group_adjust)
        pl.plt.show()
        self.plot_cumulative_returns(period=None, demeaned=demeaned, group_adjust=group_adjust)
        pl.plt.show()
        self.plot_top_down_cumulative_returns(period=None,
                                              demeaned=demeaned,
                                              group_adjust=group_adjust)
        pl.plt.show()
        self.plot_cumulative_returns_by_quantile(period=None,
                                                 demeaned=demeaned,
                                                 group_adjust=group_adjust)
        self.plot_mean_quantile_returns_spread_time_series(demeaned=demeaned,
                                                           group_adjust=group_adjust)
        pl.plt.show()
        if by_group:
            self.plot_quantile_returns_bar(by_group=True,
                                           demeaned=demeaned,
                                           group_adjust=group_adjust)
            pl.plt.show()
        self.plot_quantile_returns_violin(demeaned=demeaned,
                                          group_adjust=group_adjust)
        pl.plt.show()
        print("\n-------------------------\n")
        self.plot_information_table(group_adjust=group_adjust)
        self.plot_ic_ts(group_adjust=group_adjust, method=None)
        pl.plt.show()
        self.plot_ic_qq(group_adjust=group_adjust)
        pl.plt.show()
        if by_group:
            self.plot_ic_by_group(group_adjust=group_adjust, method=None)
        else:
            self.plot_monthly_ic_heatmap(group_adjust=group_adjust)
        pl.plt.show()
        print("\n-------------------------\n")
        self.plot_turnover_table()
        self.plot_top_bottom_quantile_turnover(periods=turnover_periods)
        pl.plt.show()
        self.plot_factor_auto_correlation(periods=turnover_periods)
        pl.plt.show()
        print("\n-------------------------\n")
        before, after = avgretplot
        self.plot_quantile_average_cumulative_return(
            periods_before=before, periods_after=after,
            by_quantile=False, std_bar=False,
            demeaned=demeaned, group_adjust=group_adjust
        )
        pl.plt.show()
        if std_bar:
            self.plot_quantile_average_cumulative_return(
                periods_before=before, periods_after=after,
                by_quantile=True, std_bar=True,
                demeaned=demeaned, group_adjust=group_adjust
            )
            pl.plt.show()

    def plot_disable_chinese_label(self):
        """关闭中文图例显示

        画图时默认会从系统中查找中文字体显示以中文图例
        如果找不到中文字体则默认使用英文图例
        当找到中文字体但中文显示乱码时, 可调用此 API 关闭中文图例显示而使用英文
        """
        _use_chinese(False)


================================================
FILE: jqfactor_analyzer/attribution.py
================================================
import numpy as np
import pandas as pd
import datetime
from tqdm import tqdm
from functools import partial
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D

from jqfactor_analyzer.data import DataApi
from jqfactor_analyzer.factor_cache import save_factor_values_by_group, get_factor_values_by_cache
from jqfactor_analyzer.plot_utils import _use_chinese
from functools import lru_cache


dataapi = DataApi(allow_cache=True, show_progress=True)


def get_factor_style_returns(factors=None, start_date=None, end_date=None,
                             count=None, universe=None, industry='sw_l1'):
    if dataapi._api_name == 'jqdatasdk':
        func = dataapi.api.get_factor_style_returns
    else:
        import jqfactor
        func = jqfactor.get_factor_style_returns
    return func(factors=factors, start_date=start_date, end_date=end_date,
                count=count, universe=universe, industry=industry)


def get_price(security, start_date, end_date, fields):
    func = partial(dataapi.api.get_price, security=security,
                   start_date=start_date, end_date=end_date, fields=fields)
    if dataapi._api_name == 'jqdatasdk':
        return func()
    else:
        return func(pre_factor_ref_date=datetime.date.today())


def get_index_style_exposure(index, factors=None,
                             start_date=None, end_date=None, count=None):
    if dataapi._api_name == 'jqdatasdk':
        func = dataapi.api.get_index_style_exposure
    else:
        import jqfactor
        func = jqfactor.get_index_style_exposure
    return func(index=index, factors=factors,
                start_date=start_date, end_date=end_date, count=count)


class AttributionAnalysis():
    """归因分析

    用户需要提供的数据:
    1. 日度股票持仓权重 (加总不为 1 的剩余部分视为现金)
    2. 组合的的日度收益率 (使用 T 日持仓盘后的因子暴露与 T+1 日的收益进行归因分析)

    组合风格因子暴露 (含行业, country) = sum(组合权重 * 个股因子值), country 暴露为总的股票持仓权重
    组合风格收益率 (含行业, country) = sum(组合风格因子暴露 * factor_return)
    组合特异收益率 = 组合总收益率 - 组合风格收益率(含行业, country 或 cash)
    """

    def __init__(self, weights, daily_return,
                 style_type='style_pro', industry='sw_l1',
                 use_cn=True, show_data_progress=True):
        """
        参数
        ----------
            weights:
                持仓权重信息, index 是日期, columns 是标的代码, value 对应的是当天的仓位占比 (单日仓位占比总和不为 1 时, 剩余部分认为是当天的现金)
            daily_return:
                Series, index 是日期, values 为当天账户的收益率
            style_type:
                所选的风格因子类型, 'style' 和 'style_pro' 中的一个
            industry:
                行业分类, 可选: 'sw_l1' 或 'jq_l1'
            use_cn:
                绘图时是否使用中文
            show_data_progress:
                是否展示数据获取进度 (使用本地缓存, 第一次运行时速度较慢, 后续对于本地不存在的数据将增量缓存)

        所有属性列表
        ----------
            style_exposure:
                组合风格因子暴露
            industry_exposure:
                组合行业因子暴露
            exposure_portfolio:
                组合风格 / 行业及 country 的暴露
            attr_daily_return:
                组合归因日收益率
            attr_returns:
                组合归因累积收益汇总

        所有方法列表
        ----------
            get_exposure2bench(index_symbol):
                获取相对于指数的暴露
            get_attr_daily_returns2bench(index_symbol):
                获取相对于指数的日归因收益
            get_attr_returns2bench(index_symbol):
                获取相对于指数的累积归因收益

            plot_exposure(factors='style', index_symbol=None, use_cn=True, figsize=(15, 8))
                绘制风格或行业暴露, 当指定 index_symbol 时, 返回的是相对指数的暴露, 否则为组合自身的暴露
            plot_returns(factors='style', index_symbol=None, use_cn=True, figsize=(15, 8))
                绘制风格或者行业的暴露收益, 当指定 index_symbol 时, 返回的是相对指数的暴露收益, 否则为组合自身的暴露收益
            plot_exposure_and_returns(self, factors, index_symbol=None, use_cn=True, figsize=(12, 6))
                同时绘制暴露和收益信息
        """

        self.STYLE_TYPE_DICT = {
            'style': ['size', 'beta', 'momentum', 'residual_volatility', 'non_linear_size',
                      'book_to_price_ratio', 'liquidity', 'earnings_yield', 'growth', 'leverage'],
            'style_pro': ['btop', 'divyild', 'earnqlty', 'earnvar', 'earnyild', 'financial_leverage',
                          'invsqlty', 'liquidty', 'long_growth', 'ltrevrsl', 'market_beta', 'market_size',
                          'midcap', 'profit', 'relative_momentum', 'resvol']
        }
        weights.index = pd.to_datetime(weights.index)
        daily_return.index = pd.to_datetime(daily_return.index)
        weights.loc[weights.sum(axis=1) > 1] = weights.div(weights.sum(axis=1), axis=0)
        self.weights = weights.replace(0, np.nan)
        self.daily_return = daily_return
        self.style_factor_names = self.STYLE_TYPE_DICT[style_type]
        self.industry = industry
        self.industry_code = list(
            set(dataapi.api.get_industries(industry, date=weights.index[0]).index) |
            set(dataapi.api.get_industries(industry, date=weights.index[-1]).index)
        )
        self.style_type = style_type
        self.show_progress = show_data_progress
        self.factor_cache_directory = self.check_factor_values()

        # 当日收盘后的暴露
        self.style_exposure = self.calc_style_exposure()
        # 当日收盘后的暴露
        self.industry_exposure = self.calc_industry_exposure()
        # 当日收盘后的暴露
        self.exposure_portfolio = pd.concat([self.style_exposure, self.industry_exposure], axis=1)
        self.exposure_portfolio['country'] = self.weights.sum(axis=1)
        self.use_cn = use_cn
        if use_cn:
            _use_chinese(True)

        self._attr_daily_returns = None
        self._attr_returns = None
        self._factor_returns = None
        self._factor_cn_name = None

    def _get_factor_cn_name(self):
        """获取行业及风格因子的中文名称"""
        industry_info = dataapi.api.get_industries(self.industry).name
        factor_info = dataapi.api.get_all_factors()
        factor_info = factor_info[factor_info.category ==
                                  self.style_type].set_index("factor").factor_intro
        factor_info = pd.concat([industry_info, factor_info])
        factor_info['common_return'] = '因子收益'
        factor_info['specific_return'] = '特异收益'
        factor_info['total_return'] = '总收益'
        factor_info['cash'] = '现金'
        factor_info['country'] = 'country'
        self._factor_cn_name = factor_info
        return factor_info

    @property
    def factor_cn_name(self):
        if self._factor_cn_name is None:
            return self._get_factor_cn_name()
        else:
            return self._factor_cn_name

    def check_factor_values(self):
        """检查并缓存因子数据到本地"""
        start_date = self.weights.index[0]
        end_date = self.weights.index[-1]
        return save_factor_values_by_group(start_date, end_date,
                                           self.style_factor_names,
                                           show_progress=self.show_progress)

    def _get_style_exposure_daily(self, date, weight):
        weight = weight.dropna()
        resdaily = get_factor_values_by_cache(
            date,
            codes=weight.index,
            factor_names=self.style_factor_names,
            factor_path=self.factor_cache_directory).T
        resdaily = resdaily.mul(weight).sum(axis=1, min_count=1)
        resdaily.name = date
        return resdaily

    def calc_style_exposure(self):
        """计算组合的风格因子暴露
        返回: 一个 dataframe, index 为日期, columns 为风格因子名, values 为暴露值"""

        iters = self.weights.iterrows()

        if self.show_progress:
            iters = tqdm(iters, total=self.weights.shape[0], desc='calc_style_exposure ')
        results = []
        for date, weight in iters:
            results.append(self._get_style_exposure_daily(date, weight))
        return pd.DataFrame(results)

    def _get_industry_exposure_daily(self, date, weight):
        weight = weight.dropna()
        resdaily = pd.get_dummies(dataapi._get_cached_industry_one_day(
            str(date.date()), securities=weight.index, industry=self.industry))
        resdaily = resdaily.mul(weight, axis=0).sum(axis=0, min_count=1)
        resdaily.name = date
        return resdaily

    def calc_industry_exposure(self):
        """计算组合的行业因子暴露
        返回: 一个 dataframe, index 为日期, columns为风格因子名, values为暴露值"""
        iters = self.weights.iterrows()
        if self.show_progress:
            iters = tqdm(iters, total=self.weights.shape[0], desc='calc_industry_exposure ')
        results = []
        for date, weight in iters:
            results.append(self._get_industry_exposure_daily(date, weight))
        return pd.DataFrame(results).reindex(columns=self.industry_code, fill_value=0)

    @property
    def attr_daily_returns(self):
        if self._attr_daily_returns is None:
            return self.calc_attr_returns()[0]
        else:
            return self._attr_daily_returns

    @property
    def attr_returns(self):
        if self._attr_returns is None:
            return self.calc_attr_returns()[1]
        else:
            return self._attr_returns

    @property
    def factor_returns(self):
        if self._factor_returns is None:
            exposure_portfolio = self.exposure_portfolio.copy()
            self._factor_returns = get_factor_style_returns(
                exposure_portfolio.columns.tolist(),
                self.exposure_portfolio.index[0],
                dataapi.api.get_trade_days(self.exposure_portfolio.index[-1], count=2)[-1],
                industry=self.industry,
                universe='zzqz')
            return self._factor_returns
        else:
            return self._factor_returns

    @lru_cache()
    def _get_index_returns(self, index_symbol, start_date, end_date):
        index_return = get_price(index_symbol,
                                 start_date=start_date,
                                 end_date=end_date,
                                 fields='close')['close'].pct_change()
        return index_return

    @lru_cache()
    def _get_index_exposure(self, index_symbol):
        index_exposure = get_index_style_exposure(
            index_symbol,
            factors=self.style_exposure.columns.tolist() + self.industry_exposure.columns.tolist(),
            start_date=str(self.weights.index[0]),
            end_date=str(self.weights.index[-1]))
        index_exposure = index_exposure.mul(self.weights.sum(axis=1), axis=0)
        index_exposure['country'] = 1
        return index_exposure

    @lru_cache()
    def get_exposure2bench(self, index_symbol):
        """获取相对于指数的暴露"""
        index_exposure = self._get_index_exposure(index_symbol)
        return self.exposure_portfolio - index_exposure

    @lru_cache()
    def get_attr_daily_returns2bench(self, index_symbol):
        """获取相对于指数的日归因收益率
        返回: 一个 datafame, index 是日期, value 为对应日期的收益率值
        columns 为风格因子/行业因子/现金cash/因子总收益common_return(含风格,行业)/特异收益率 specific_return 及组合总收益率 total_return
        注意: 日收益率直接加总, 可能和实际的最终收益率不一致, 因为没考虑到资产的变动情况
        """
        exposure2bench = self.get_exposure2bench(index_symbol)
        exposure2bench = exposure2bench.reindex(self.factor_returns.index)

        index_return = self._get_index_returns(index_symbol,
                                               start_date=exposure2bench.index[0],
                                               end_date=exposure2bench.index[-1])
        daily_return = self.daily_return - index_return

        attr_daily_returns2bench = exposure2bench.shift()[1:].mul(self.factor_returns)
        # country 收益为 0, 无意义
        del attr_daily_returns2bench['country']
        attr_daily_returns2bench['common_return'] = attr_daily_returns2bench[self.style_exposure.columns.tolist() +
                                                                             self.industry_exposure.columns.tolist()].sum(axis=1)
        attr_daily_returns2bench['cash'] = index_return * exposure2bench.country.shift()
        attr_daily_returns2bench['specific_return'] = daily_return - \
            attr_daily_returns2bench['common_return'] - \
            attr_daily_returns2bench['cash']
        attr_daily_returns2bench['total_return'] = daily_return
        return attr_daily_returns2bench

    @lru_cache()
    def get_attr_returns2bench(self, index_symbol):
        """获取相对于指数的累积归因收益
        将超额收益分解成了:
        1.common_return (因子收益, 又可进一步拆分成风格和行业);
        2.cash (现金收益, 假设组合本身现金部分的收益为0, 则相对于指数的超额收益为"-1 * 指数收益");
              累积算法: (组合收益率 + 1).cumpord() - (日现金收益率+组合收益率 + 1).cumpord()
        3.specific_return: 残差, 无法被风格和行业因子解释的部分, 即为主动收益, 现金收益实际也可划分到主动收益中
        """
        index_return = self._get_index_returns(index_symbol,
                                               start_date=self.factor_returns.index[0],
                                               end_date=self.factor_returns.index[-1])

        attr_daily_returns2bench = self.get_attr_daily_returns2bench("000905.XSHG")
        # 假设持仓的现金用于购买指数时的净值
        position_with_cash_net = ((-attr_daily_returns2bench.cash + self.daily_return).fillna(0) + 1).cumprod()
        # 持仓本身的净值
        position_net = (self.daily_return.fillna(0) + 1).cumprod()
        # 假设指数满仓时的超额
        t_net = position_net - (index_return + 1).fillna(1).cumprod()
        # 假设指数调整仓位到和组合一致(风格暴露)的超额
        net = position_net - (index_return * self.weights.sum(axis=1).shift() + 1).fillna(1).cumprod()
        # 超额的暴露收益
        attr_returns2bench2 = attr_daily_returns2bench.mul(net.shift() + 1, axis=0).cumsum()
        # 现金的收益 = 持仓本身的净值 - 假设持仓的现金用于购买指数的净值
        attr_returns2bench2['cash'] = position_net - position_with_cash_net
        # 超额收益
        attr_returns2bench2['total_return'] = t_net
        # 风格 + 行业因子收益, 不含现金
        attr_returns2bench2['common_return'] = attr_returns2bench2[self.style_exposure.columns.tolist() +
                                                                   self.industry_exposure.columns.tolist()].sum(axis=1)
        attr_returns2bench2.loc[attr_returns2bench2.cash.isna(), 'common_return'] = np.nan
        # 除风格,现金以外的无法解释的收益
        attr_returns2bench2['specific_return'] = (
            attr_returns2bench2['total_return'] - attr_returns2bench2['common_return'] - attr_returns2bench2['cash']
        )
        return attr_returns2bench2

    def calc_attr_returns(self):
        """计算风格归因收益, country 收益率为国家收益 (这里的国家收益是用均衡大小市值后 (根号市值) 回归得到的"""
        self._attr_daily_returns = self.exposure_portfolio.reindex(
            self.factor_returns.index).shift(1).mul(self.factor_returns)
        self._attr_daily_returns['common_return'] = self._attr_daily_returns.sum(axis=1)
        self._attr_daily_returns['specific_return'] = self.daily_return - self._attr_daily_returns['common_return']
        self._attr_daily_returns['total_return'] = self.daily_return

        cum_return = (self._attr_daily_returns.total_return.fillna(0) + 1).cumprod()
        self._attr_returns = self._attr_daily_returns.mul(cum_return.shift(1), axis=0).cumsum()

        return self._attr_daily_returns, self._attr_returns

    def plot_data(self, data, title=None, figsize=(15, 8)):
        ax = data.plot(figsize=figsize, title=title)
        ax.legend(loc='upper left', bbox_to_anchor=(1, 1))
        plt.tight_layout(rect=[0, 0, 0.85, 1])
        plt.show()

    def plot_exposure(self, factors='style', index_symbol=None, figsize=(15, 7)):
        """绘制风格暴露
        factors: 绘制的暴露类型 , 可选 'style'(所有风格因子), 'industry'(所有行业因子), 也可以传递一个list, list 为 exposure_portfolio 中 columns 的一个或者多个
        index_symbol: 基准指数代码, 指定时绘制相对于指数的暴露, 默认 None 为组合本身的暴露
        figsize: 画布大小
        """
        exposure = self.exposure_portfolio if index_symbol is None else self.get_exposure2bench(index_symbol)
        if isinstance(factors, str):
            if factors == 'style':
                exposure = exposure[self.style_exposure.columns]
            elif factors == 'industry':
                exposure = exposure[self.industry_exposure.columns]
            else:
                exposure = exposure[[factors]]
        else:
            exposure = exposure[factors]

        if self.use_cn:
            exposure = exposure.rename(columns=self.factor_cn_name)
            title = '组合相对{}暴露'.format(index_symbol) if index_symbol else '组合暴露'
        else:
            title = 'exposure of {}'.format(index_symbol) if index_symbol else 'exposure'
        self.plot_data(exposure, title=title, figsize=figsize)

    def plot_returns(self, factors='style', index_symbol=None, figsize=(15, 7)):
        """绘制归因分析收益信息
        factors: 绘制的暴露类型, 可选 'style'(所有风格因子), 'industry'(所有行业因子), 也可以传递一个 list, list 为 exposure_portfolio 中 columns 的一个或者多个
                同时也支持指定 ['common_return'(风格总收益), 'specific_return'(特异收益), 'total_return'(总收益),
                               'country'(国家因子收益,当指定index_symbol时会用现金相对于指数的收益替代)]
        index_symbol: 基准指数代码, 指定时绘制相对于指数的暴露, 默认 None 为组合本身的暴露
        figsize: 画布大小
        """
        returns = self.attr_returns if index_symbol is None else self.get_attr_returns2bench(index_symbol)
        if isinstance(factors, str):
            if factors == 'style':
                returns = returns[self.style_exposure.columns]
            elif factors == 'industry':
                returns = returns[self.industry_exposure.columns]
            else:
                if index_symbol and factors == 'country':
                    factors = 'cash'
                if factors not in returns.columns:
                    raise ValueError("错误的因子名称: {}".format(factors))
                returns = returns[[factors]]
        else:
            if index_symbol and 'country' in factors:
                factors = [x if x != 'country' else 'cash' for x in factors]
            wrong_factors = [x for x in factors if x not in returns.columns]
            if wrong_factors:
                raise ValueError("错误的因子名称: {}".format(wrong_factors))
            returns = returns[factors]

        if self.use_cn:
            returns = returns.rename(columns=self.factor_cn_name)
            title = "累积归因收益 (相对{})".format(
                index_symbol) if index_symbol else "累积归因收益"
        else:
            title = 'cum return to {}  '.format(
                index_symbol) if index_symbol else "cum return"
        self.plot_data(returns, title=title, figsize=figsize)

    def plot_exposure_and_returns(self, factors='style', index_symbol=None, show_factor_perf=False, figsize=(12, 6)):
        """将因子暴露与收益同时绘制在多个子图上
        factors: 绘制的暴露类型, 可选 'style'(所有风格因子) , 'industry'(所有行业因子), 也可以传递一个 list, list为 exposure_portfolio 中 columns 的一个或者多个
                 当指定 index_symbol 时, country 会用现金相对于指数的收益替代)
        index_symbol: 基准指数代码,指定时绘制相对于指数的暴露及收益 , 默认None为组合本身的暴露和收益
        show_factor_perf: 是否同时绘制因子表现
        figsize: 画布大小, 这里第一个参数是画布的宽度, 第二个参数为单个子图的高度
        """
        if isinstance(factors, str):
            if factors == 'style':
                factors = self.style_exposure.columns
            elif factors == 'industry':
                factors = self.industry_exposure.columns
            else:
                factors = [factors]
        if index_symbol:
            exposure = self.get_exposure2bench(index_symbol).rename(columns={"country": "cash"})
            returns = self.get_attr_returns2bench(index_symbol)
        else:
            exposure = self.exposure_portfolio
            returns = self.attr_returns
        exposure, returns = exposure.align(returns, join='outer')
        if show_factor_perf:
            factor_performance = self.factor_returns.cumsum().reindex(exposure.index)

        num_factors = len(factors)
        # 每行最多两个子图
        ncols = 2 if num_factors > 1 else 1
        nrows = (num_factors + 1) // ncols if num_factors > 1 else 1

        fixed_width, base_height_per_row = figsize
        height_per_row = base_height_per_row if ncols == 1 else base_height_per_row / 2
        total_height = max(1, nrows) * height_per_row

        fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(fixed_width, total_height))
        axes = axes.flatten() if num_factors > 1 else [axes]

        # 删除多余的子图
        for j in range(len(factors), len(axes)):
            fig.delaxes(axes[j])

        for i, factor_name in enumerate(factors):
            if index_symbol and factor_name == 'country':
                factor_name = 'cash'
            if factor_name not in exposure.columns:
                raise ValueError("错误的因子名称: {}".format(factor_name))
            e = exposure[factor_name]
            r = returns[factor_name]

            ax1 = axes[i]
            e.plot(kind='area', stacked=False, alpha=0.5, ax=ax1, color='skyblue')

            ax2 = ax1.twinx()
            r.plot(ax=ax2, color='red')
            if factor_name != 'cash' and show_factor_perf:
                factor_performance[factor_name].plot(ax=ax2, color='blue')
            ax1.set_title(factor_name if not self.use_cn else self.factor_cn_name.get(factor_name))
        labels = ['暴露', '因子收益', '因子表现'] if self.use_cn else ['exposure', 'return', 'factor performance']
        fig.legend(labels[:1], loc='upper left')

        # 手动创建图例条目
        custom_lines = [Line2D([0], [0], color='red', lw=2),
                        Line2D([0], [0], color='blue', lw=2)]
        # 创建自定义图例
        fig.legend(custom_lines, labels[1:], loc='upper right',
                   bbox_to_anchor=(1, 1.02), bbox_transform=plt.gcf().transFigure)
        fig.suptitle('因子暴露与收益图' if self.use_cn else 'factor exposure and return', y=1.02)
        plt.tight_layout()
        plt.show()

    def plot_disable_chinese_label(self):
        """关闭中文图例显示

        画图时默认会从系统中查找中文字体显示以中文图例
        如果找不到中文字体则默认使用英文图例
        当找到中文字体但中文显示乱码时, 可调用此 API 关闭中文图例显示而使用英文
        """
        _use_chinese(False)
        self.use_cn = False


================================================
FILE: jqfactor_analyzer/compat.py
================================================
# -*- coding: utf-8 -*-

"""pandas库版本兼容模块"""

import warnings

import pandas as pd


# pandas
PD_VERSION = pd.__version__


def rolling_apply(
    x,
    window,
    func,
    min_periods=None,
    freq=None,
    center=False,
    args=None,
    kwargs=None
):
    if args is None:
        args = tuple()
    if kwargs is None:
        kwargs = dict()

    if PD_VERSION >= '0.23.0':
        return x.rolling(
            window, min_periods=min_periods, center=center
        ).apply(
            func, False, args=args, kwargs=kwargs
        )
    elif PD_VERSION >= '0.18.0':
        return x.rolling(
            window, min_periods=min_periods, center=center
        ).apply(
            func, args=args, kwargs=kwargs
        )
    else:
        return pd.rolling_apply(
            x,
            window,
            func,
            min_periods=min_periods,
            freq=freq,
            center=center,
            args=args,
            kwargs=kwargs
        )


def rolling_mean(x, window, min_periods=None, center=False):
    if PD_VERSION >= '0.18.0':
        return x.rolling(window, min_periods=min_periods, center=center).mean()
    else:
        return pd.rolling_mean(
            x, window, min_periods=min_periods, center=center
        )


def rolling_std(x, window, min_periods=None, center=False, ddof=1):
    if PD_VERSION >= '0.18.0':
        return x.rolling(
            window, min_periods=min_periods, center=center
        ).std(ddof=ddof)
    else:
        return pd.rolling_std(
            x, window, min_periods=min_periods, center=center, ddof=ddof
        )


# statsmodels
with warnings.catch_warnings():
    # 有的版本依赖的 pandas 库会有 deprecated warning
    warnings.simplefilter("ignore")
    import statsmodels
    from statsmodels.api import OLS, qqplot, ProbPlot
    from statsmodels.tools.tools import add_constant


================================================
FILE: jqfactor_analyzer/config.json
================================================
{"default_dir": "~/jqfactor_datacache/bundle", "user_dir": ""}


================================================
FILE: jqfactor_analyzer/data.py
================================================
# -*- coding: utf-8 -*-
import os
import numpy as np
import pandas as pd
from tqdm import tqdm
from fastcache import lru_cache
from functools import partial
import pyarrow.feather as feather

from .when import date2str, convert_date, today, now, Time, Date
from .factor_cache import save_factor_values_by_group, get_factor_values_by_cache, get_cache_dir


class DataApi(object):

    def __init__(self, price='close', fq='post',
                 industry='jq_l1', weight_method='avg', allow_cache=True, show_progress=True):
        """数据接口, 用于因子分析获取数据

        参数
        ----------
        price : 使用开盘价/收盘价计算收益 (请注意避免未来函数), 默认为 'close'
            - 'close': 使用当日收盘价和次日收盘价计算当日因子的远期收益
            - 'open' : 使用当日开盘价和次日开盘价计算当日因子的远期收益
        fq : 价格数据的复权方式, 默认为 'post'
            - 'post': 后复权
            - 'pre': 前复权
            - None: 不复权
        industry : 行业分类, 默认为 'jq_l1'
            - 'jq_l1': 聚宽一级行业
            - 'jq_l2': 聚宽二级行业
            - 'sw_l1': 申万一级行业
            - 'sw_l2': 申万二级行业
            - 'sw_l3': 申万三级行业
            - 'zjw': 证监会行业
        weight_method : 计算各分位收益时, 每只股票权重, 默认为 'avg'
            - 'avg': 等权重
            - 'mktcap': 按总市值加权
            - 'ln_mktcap': 按总市值的对数加权
            - 'cmktcap': 按流通市值加权
            - 'ln_cmktcap': 按流通市值的对数加权
        allow_cache : 是否允许将分析必须数据以文件的形式缓存至本地, 默认允许, 缓存开启时, 首次加载耗时较长
        show_progress : 是否展示数据获取进度

        使用示例
        ----------
        from jqfactor_analyzer import DataApi, FactorAnalyzer

        api = DataApi(fq='pre', industry='sw_l1', weight_method='ln_mktcap')
        api.auth('username', 'password')

        factor = FactorAnalyzer(factor_data,
                                price=api.get_prices,
                                groupby=api.get_groupby,
                                weights=api.get_weights)
        # 或者
        # factor = FactorAnalyzer(factor_data, **api.apis)


        方法列表
        ----------
        auth : 登陆 jqdatasdk
          参数 :
            username : jqdatasdk 用户名
            username : jqdatasdk 密码
          返回值 :
            None

        get_prices : 价格数据获取接口
          参数 :
            securities : 股票代码列表
            start_date : 开始日期
            end_date : 结束日期
            count : 交易日长度
            (start_date 和 count)
          返回值 :
            pd.DataFrame
            价格数据, columns 为股票代码, index 为日期

        get_groupby : 行业分类数据获取接口
          参数 :
            securities : 股票代码列表
            start_date : 开始日期
            end_date : 结束日期
          返回值 :
            dict
            行业分类, {股票代码 -> 行业分类名称}

        get_weights : 股票权重获取接口
          参数 :
            securities : 股票代码列表
            start_date : 开始日期
            end_date : 结束日期
          返回值 :
            pd.DataFrame
            权重数据, columns 为股票代码, index 为日期


        属性列表
        ----------
        apis : dict, {'prices': get_prices, 'groupby': get_groupby,
                      'weights': get_weights}

        """
        try:
            import jqdata
            self._api = jqdata.apis
            self._api_name = 'jqdata'
        except ImportError:
            import jqdatasdk
            self._api = jqdatasdk
            self._api_name = 'jqdatasdk'

        self.show_progress = show_progress
        valid_price = ('close', 'open')
        if price in valid_price:
            self.price = price
        else:
            ValueError("invalid 'price' parameter, "
                       "should be one of %s" % str(valid_price))

        valid_fq = ('post', 'pre', None)
        if fq in valid_fq:
            self.fq = fq
        else:
            raise ValueError("invalid 'fq' parameter, "
                             "should be one of %s" % str(valid_fq))

        valid_industry = ('sw_l1', 'sw_l2', 'sw_l3', 'jq_l1', 'jq_l2', 'zjw')
        if industry in valid_industry:
            self.industry = industry
        else:
            raise ValueError("invalid 'industry' parameter, "
                             "should be one of %s" % str(valid_industry))

        valid_weight_method = ('avg', 'mktcap', 'ln_mktcap', 'cmktcap', 'ln_cmktcap')
        if weight_method in valid_weight_method:
            self.weight_method = weight_method
        else:
            raise ValueError("invalid 'weight_method' parameter, "
                             "should be one of %s" % str(valid_weight_method))
        self.ini_cache_cfg(allow_cache)

    @lru_cache(10)
    def get_ind_record(self, industry):
        mapping = self.api.get_industries(industry).to_dict()['name']
        ind_record = self.api.get_history_industry(industry).set_index('stock')
        ind_record['industry_name'] = ind_record['code'].map(mapping)
        ind_record.end_date = ind_record.end_date.fillna(Date(2040, 1, 1))
        return ind_record

    def ini_cache_cfg(self, allow_cache):
        self.allow_cache = allow_cache

        if self._api_name != 'jqdatasdk':
            self.allow_cache = False
        self.allow_industry_cache = False

    def auth(self, username='', password=''):
        if self._api_name == 'jqdata':
            return
        if username:
            import jqdatasdk
            jqdatasdk.auth(username, password)

    @property
    def api(self):
        if not hasattr(self, "_api"):
            raise NotImplementedError('api not specified')
        if self.allow_cache:
            if not self._api.is_auth():
                raise Exception("Please run jqdatasdk.auth first")
            privilege = self._api.get_privilege()
            if 'GET_HISTORY_INDUSTRY' in privilege:
                self.allow_industry_cache = True
            else:
                self.allow_industry_cache = False
            if 'FACTOR_BASICS' in privilege or 'GET_FACTOR_VALUES' in privilege:
                self.mkt_cache_api = 'factor'
            else:
                self.mkt_cache_api = 'valuation'
        return self._api

    @lru_cache(2)
    def _get_trade_days(self, start_date=None, end_date=None):
        if start_date is not None:
            start_date = date2str(start_date)
        if end_date is not None:
            end_date = date2str(end_date)
        return list(self.api.get_trade_days(start_date=start_date,
                                            end_date=end_date))

    def _get_price(self, securities, start_date=None, end_date=None, count=None,
                   fields=None, skip_paused=False, fq='post', round=False):
        start_date = date2str(start_date) if start_date is not None else None
        end_date = date2str(end_date) if end_date is not None else None
        if self._api_name == 'jqdata':
            if 'panel' in self.api.get_price.__code__.co_varnames:
                get_price = partial(self.api.get_price,
                                    panel=False,
                                    pre_factor_ref_date=end_date)
            else:
                get_price = partial(self.api.get_price,
                                    pre_factor_ref_date=end_date)
        else:
            get_price = self.api.get_price
        p = get_price(
            securities, start_date=start_date, end_date=end_date, count=count,
            fields=fields, skip_paused=skip_paused, fq=fq, round=round
        )
        if hasattr(p, 'to_frame'):
            p = p.to_frame()
            p.index.names = ['time', 'code']
            p.reset_index(inplace=True)

        return p

    def _get_cached_price(self, securities, start_date=None, end_date=None, fq=None, overwrite=False):
        """获取缓存价格数据, 缓存文件中存储的数据是为未复权价格和后复权因子"""
        save_factor_values_by_group(start_date, end_date,
                                    factor_names='prices',
                                    overwrite=overwrite,
                                    show_progress=self.show_progress)
        trade_days = pd.to_datetime(self._get_trade_days(start_date, end_date))

        ret = []
        if self.show_progress:
            trade_days = tqdm(trade_days, desc="load price info : ")
        for day in trade_days:
            if day < today():
                p = get_factor_values_by_cache(
                    day, securities, factor_names='prices').reset_index()
            else:
                p = self.api.get_price(securities, start_date=day, end_date=day,
                                       skip_paused=False, round=False,
                                       fields=['open', 'close', 'factor'],
                                       fq='post', panel=False)
                p[['open', 'close']] = p[['open', 'close']].div(p['factor'], axis=0)
            p['time'] = day
            ret.append(p)
        ret = pd.concat(ret, ignore_index=True).sort_values(['code', 'time']).reset_index(drop=True)
        if fq == 'pre':
            # 前复权基准日期为最新一天
            latest_factor = self.api.get_price(securities,
                                               end_date=today(),
                                               count=1,
                                               skip_paused=False,
                                               round=False,
                                               fields=['factor'],
                                               fq='post',
                                               panel=False).set_index('code')
            ret = ret.set_index('code')
            ret.factor = ret.factor / latest_factor.factor
            ret = ret.reset_index().reindex(columns=['time', 'code', 'open', 'close', 'factor'])
        elif fq is None:
            ret.loc[ret['factor'].notna(), 'factor'] = 1.0
        ret[['open', 'close']] = ret[['open', 'close']].mul(ret['factor'], axis=0)
        return ret

    def get_prices(self, securities, start_date=None, end_date=None,
                   period=None):
        if period is not None:
            trade_days = self._get_trade_days(start_date=end_date)
            if len(trade_days):
                end_date = trade_days[:period + 1][-1]
        if self.allow_cache:
            p = self._get_cached_price(
                securities, start_date, end_date, fq=self.fq)
        else:
            p = self._get_price(
                fields=[self.price], securities=securities,
                start_date=start_date, end_date=end_date, round=False,
                fq=self.fq
            )
        p = p.set_index(['time', 'code'])[self.price].unstack('code').sort_index()
        return p

    def _get_industry(self, securities, start_date, end_date, industry='jq_l1'):
        trade_days = self._get_trade_days(start_date, end_date)
        industries = map(partial(self.api.get_industry, securities), trade_days)
        day_ind = zip(trade_days, industries)
        if self.show_progress:
            day_ind = tqdm(day_ind, desc='load industry info : ',
                           total=len(trade_days))
        industries = {
            d: {
                s: ind.get(s).get(industry, dict()).get('industry_name', 'NA')
                for s in securities
            }
            for d, ind in day_ind
        }
        return pd.DataFrame(industries).T.sort_index()

    def _get_cached_industry_one_day(self, date, securities=None, industry=None):
        date = convert_date(date)
        if self.allow_industry_cache:
            ind_record = self.get_ind_record(industry)
            if securities is not None:
                ind_record = ind_record[ind_record.index.isin(securities)]
            return ind_record[(ind_record.start_date <= date) & (date <= ind_record.end_date)].code
        else:
            ind_record = self.api.get_industry(securities, date, df=True)
            ind_record = ind_record[ind_record['type'] ==
                                    industry].set_index("code").industry_code
            return ind_record

    def _get_cached_industry(self, securities, start_date, end_date):
        ind_record = self.get_ind_record(self.industry)
        start_date = convert_date(start_date)
        end_date = convert_date(end_date)
        trade_days = self._get_trade_days(start_date, end_date)
        ind_record = ind_record[(ind_record.index.isin(securities))]
        if self.show_progress:
            trade_days = tqdm(trade_days, desc="load industry info : ")
        df_list = []
        for d in trade_days:
            rec = ind_record[(ind_record.start_date <= d) & (
                d <= ind_record.end_date)].industry_name
            rec.name = d
            df_list.append(rec)
        df = pd.DataFrame(df_list).reindex(columns=securities)
        return df.fillna('NA')

    def get_groupby(self, securities, start_date, end_date):
        if self.allow_industry_cache:
            return self._get_cached_industry(securities, start_date, end_date)
        else:
            return self._get_industry(securities=securities,
                                      start_date=start_date, end_date=end_date,
                                      industry=self.industry)

    def _get_cached_mkt_cap_by_valuation(self, securities, date, field, overwrite=False):
        """市值处理函数, 将获取的市值数据缓存到本地"""
        if not securities:
            return pd.Series(dtype='float64', name=date)

        query = self.api.query
        valuation = self.api.valuation
        cache_dir = os.path.join(get_cache_dir(), 'mkt_cap', date.strftime("%Y%m"))
        fp = os.path.join(cache_dir, date.strftime("%Y%m%d")) + '.feather'

        if os.path.exists(fp) and not overwrite:
            data = feather.read_feather(fp)
        else:
            if not os.path.exists(cache_dir):
                os.makedirs(cache_dir)
            codes = self.api.get_all_securities('stock').index.tolist()
            q = query(valuation.code,
                      valuation.market_cap,
                      valuation.circulating_market_cap).filter(
                          valuation.code.in_(codes))
            data = self.api.get_fundamentals(q, date=date2str(date))
            data[['market_cap', 'circulating_market_cap']] = data[
                ['market_cap', 'circulating_market_cap']] * (10 ** 8)
            if date < today() or (date == today() and now().time() >= Time(16, 30)):
                data.to_feather(fp)

        return data[data.code.isin(securities)].set_index('code')[field]

    def _get_market_cap(self, securities, start_date, end_date, ln=False, field='market_cap'):
        trade_days = self._get_trade_days(start_date, end_date)

        def get_mkt_cap(s, date, field):
            if not s:
                return pd.Series(dtype='float64', name=date)
            data = self.api.get_fundamentals(
                q, date=date2str(date)
            ).set_index('code')[field] * (10 ** 8)
            return data

        def get_mkt_cap_cache(s, date, field):
            cap = get_factor_values_by_cache(
                date, securities, factor_path=cache_dir).reindex(columns=[field])
            return cap[field]

        if self.allow_cache and len(trade_days) > 5:
            if self.mkt_cache_api == 'factor':
                desc = 'check/save cap cache :' if self.show_progress else False
                cache_dir = save_factor_values_by_group(start_date,
                                                        end_date,
                                                        factor_names=['market_cap', 'circulating_market_cap'],
                                                        group_name='mkt_cap',
                                                        show_progress=desc)
                market_api = get_mkt_cap_cache
            else:
                market_api = self._get_cached_mkt_cap_by_valuation
        else:
            market_api = get_mkt_cap
            query = self.api.query
            valuation = self.api.valuation

            if field == 'market_cap':
                q = query(valuation.code, valuation.market_cap).filter(
                    valuation.code.in_(securities))
            elif field == 'circulating_market_cap':
                q = query(valuation.code, valuation.circulating_market_cap).filter(
                    valuation.code.in_(securities))
            else:
                raise ValueError("不支持的字段 : {}".foramt(field))

        if self.show_progress:
            trade_days = tqdm(trade_days, desc="load cap info : ")

        market_cap = []
        for date in trade_days:
            cap = market_api(securities, date, field)
            cap.name = date
            market_cap.append(cap)
        market_cap = pd.concat(market_cap, axis=1).astype(float).reindex(index=securities)

        if ln:
            market_cap = np.log(market_cap)

        return market_cap.T

    def _get_circulating_market_cap(self, securities, start_date, end_date,
                                    ln=False):
        return self._get_market_cap(securities, start_date, end_date,
                                    ln=ln, field='circulating_market_cap')

    def _get_average_weights(self, securities, start_date, end_date):
        return {sec: 1.0 for sec in securities}

    def get_weights(self, securities, start_date, end_date):
        start_date = date2str(start_date)
        end_date = date2str(end_date)

        if self.weight_method == 'avg':
            weight_api = self._get_average_weights
        elif self.weight_method == 'mktcap':
            weight_api = partial(self._get_market_cap, ln=False)
        elif self.weight_method == 'ln_mktcap':
            weight_api = partial(self._get_market_cap, ln=True)
        elif self.weight_method == 'cmktcap':
            weight_api = partial(self._get_circulating_market_cap, ln=False)
        elif self.weight_method == 'ln_cmktcap':
            weight_api = partial(self._get_circulating_market_cap, ln=True)
        else:
            raise ValueError('invalid weight_method')

        return weight_api(securities=securities, start_date=start_date,
                          end_date=end_date)

    @property
    def apis(self):
        return dict(prices=self.get_prices,
                    groupby=self.get_groupby,
                    weights=self.get_weights)


================================================
FILE: jqfactor_analyzer/exceptions.py
================================================
# -*- coding: utf-8 -*-


from functools import wraps


def rethrow(exception, additional_message):
    """
    重新抛出当前作用域中的最后一个异常, 保留堆栈信息, 并且在报错信息中添加其他内容
    """
    e = exception
    m = additional_message
    if not e.args:
        e.args = (m,)
    else:
        e.args = (e.args[0] + m,) + e.args[1:]
    raise e


def non_unique_bin_edges_error(func):
    """
    捕获 pd.qcut 的异常, 添加提示信息并报错
    """
    message = u"""
    根据输入的 quantiles 计算时发生错误.
    这通常发生在输入包含太多相同值, 使得它们跨越多个分位.
    每天的因子值是按照分位数平均分组的, 相同的值不能跨越多个分位数.
    可能的解决方法:
    1. 减少分位数
    2. 调整因子减少重复值
    3. 尝试不同的股票池
    """

    @wraps(func)
    def dec(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except ValueError as e:
            if 'Bin edges must be unique' in str(e):
                rethrow(e, message)
            raise

    return dec


class MaxLossExceededError(Exception):
    pass


================================================
FILE: jqfactor_analyzer/factor_cache.py
================================================
import hashlib
from itertools import groupby
import pandas as pd
import os
import json
import functools
import logging
from .when import today, now, TimeDelta
from tqdm import tqdm


try:
    import jqdata
    api = jqdata.apis
    api_name = 'jqdata'
except ImportError:
    import jqdatasdk
    api = jqdatasdk
    api_name = 'jqdatasdk'


def get_cache_config():
    """获取缓存目录"""
    config_path = os.path.join(
        os.path.dirname(os.path.abspath(__file__)), 'config.json'
    )
    if not os.path.exists(config_path):
        return set_cache_dir("")
    else:
        with open(config_path, 'r') as conf:
            return json.load(conf)


def set_cache_dir(path):
    """设置缓存目录"""
    cfg = {'default_dir': '~/jqfactor_datacache/bundle',
           'user_dir': os.path.expanduser(path)}
    config_path = os.path.join(
        os.path.dirname(os.path.abspath(__file__)), 'config.json'
    )
    with open(config_path, 'w') as conf:
        json.dump(cfg, conf)
    get_cache_dir.cache_clear()
    return cfg


def get_factor_values(securities, factors=None, start_date=None, end_date=None, count=None):
    if api_name == 'jqdatasdk':
        func = api.get_factor_values
    else:
        from jqfactor import get_factor_values
        func = get_factor_values
    return func(securities, factors, start_date, end_date, count)


@functools.lru_cache()
def get_cache_dir():
    # 优先获取用户配置的缓存目录, 若无, 则使用默认目录
    cfg = get_cache_config()
    user_path = cfg.get('user_dir', "")
    if user_path != "":
        return os.path.expanduser(user_path)
    return os.path.expanduser(cfg['default_dir'])


def list_to_tuple_converter(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        # 将所有位置参数中的 list 转换为 tuple
        args = tuple(tuple(arg) if isinstance(
            arg, list) else arg for arg in args)

        # 将关键字参数中的 list 转换为 tuple
        kwargs = {k: tuple(v) if isinstance(v, list)
                  else v for k, v in kwargs.items()}

        return func(*args, **kwargs)
    return wrapper


@list_to_tuple_converter
@functools.lru_cache()
def get_factor_folder(factor_names, group_name=None):
    """获取因子组的文件夹
    factor_names : 因子名列表
    group_name : 因子组的名称, 如果指定则使用指定的名称作为文件夹名
                 否则用 jqfactor_cache_ + 因子名的 md5 值 (顺序无关) 作为文件夹名
    """
    if group_name:
        return group_name
    else:
        if factor_names == 'prices':
            return 'jqprice_cache'
        if isinstance(factor_names, str):
            factor_names = [factor_names]
        factor_names = sorted(factor_names)
        factor_names = ''.join(factor_names)
        hash_object = hashlib.md5(factor_names.encode())
        hash_hex = hash_object.hexdigest()
    return f"jqfactor_cache_{hash_hex}"


def get_date_miss_group(A, B):
    '''将A相比B缺失的部分按连续性进行分组'''
    group_values = []
    masks = [(x not in A) for x in B]
    for key, group in groupby(zip(B, masks), lambda x: x[1]):
        if key:
            group_values.append([item[0] for item in group])
    return group_values


def save_data_by_month(factor_names, start, end, month_path):
    """按时间段获取储存数据(不要跨月)
    """
    start = pd.to_datetime(start)
    end = pd.to_datetime(end)
    security_info = api.get_all_securities()
    security_info.start_date = pd.to_datetime(security_info.start_date)
    security_info.end_date = pd.to_datetime(security_info.end_date)

    month_value = {}
    stocks = security_info[(security_info.start_date <= end) & (
        security_info.end_date >= start)].index.tolist()
    if factor_names == 'prices':
        month_value = api.get_price(stocks, start_date=start, end_date=end,
                                    skip_paused=False, round=False,
                                    fields=['open', 'close', 'factor'],
                                    fq='post', panel=False)
        if month_value.empty:
            return 0
        month_value.set_index(['code', 'time'], inplace=True)
        month_value[['open', 'close']] = month_value[[
            'open', 'close']].div(month_value['factor'], axis=0)
    else:
        for factor in factor_names:
            month_value.update(get_factor_values(stocks,
                                                 start_date=start,
                                                 end_date=end,
                                                 factors=factor))
        if not month_value:
            return 0
        month_value = pd.concat(month_value).unstack(level=1).T
    month_value.index.names = ('code', 'date')

    for date, data in month_value.groupby(month_value.index.get_level_values(1)):
        data = data.reset_index(level=1, drop=True)
        data = data.reindex(security_info[(security_info.start_date <= date) & (
            security_info.end_date >= date)].index.tolist())
        # 数据未产生, 或者已经生产了但是全为 nan
        if data.isna().values.all():
            continue
        path = os.path.join(month_path, date.strftime("%Y%m%d") + ".feather")
        data.reset_index().to_feather(path)
    return month_value


def save_factor_values_by_group(start_date, end_date,
                                factor_names='prices', group_name=None,
                                overwrite=False, cache_dir=None, show_progress=True):
    """将因子库数据按因子组储存到本地
    start_date : 开始时间
    end_date : 结束时间
    factor_names : 因子组所含因子的名称,除过因子库中支持的因子外，还支持指定为'prices'缓存价格数据
    overwrite  : 文件已存在时是否覆盖更新
    返回 : 因子组储存的路径 , 文件以天为单位储存,每天一个feather文件,每月一个文件夹,columns第一列是因子名称, 而后是当天在市的所有标的代码
    """
    if cache_dir is None:
        cache_dir = get_cache_dir()

    start_date = pd.to_datetime(start_date).date()
    last_day = today() - TimeDelta(days=1) if now().hour > 8 else today() - TimeDelta(days=2)
    end_date = min(pd.to_datetime(end_date).date(), last_day)
    date_range = pd.date_range(start_date, end_date, freq='1M')
    _date = pd.to_datetime(end_date)
    if len(date_range) == 0 or date_range[-1] < _date:
        date_range = date_range.append(pd.Index([_date]))

    if show_progress:
        if isinstance(show_progress, str):
            desc = show_progress
        elif factor_names == 'prices':
            desc = 'check/save price cache '
        else:
            desc = 'check/save factor cache '
        date_range = tqdm(date_range, total=len(date_range), desc=desc)
    root_path = os.path.join(
        cache_dir, get_factor_folder(factor_names, group_name))

    for end in date_range:
        start = max(end.replace(day=1).date(), start_date)
        month_path = os.path.join(root_path, end.strftime("%Y%m"))
        if not os.path.exists(month_path):
            os.makedirs(month_path)
        elif not overwrite:
            dates = [x.split(".")[0] for x in os.listdir(month_path)]
            dates = pd.to_datetime(dates).date
            trade_days = api.get_trade_days(start, end)
            miss_group = get_date_miss_group(dates, trade_days)
            if miss_group:
                for group in miss_group:
                    save_data_by_month(
                        factor_names, group[0], group[-1], month_path)
            continue
        save_data_by_month(factor_names, start, end, month_path)

    return root_path


def get_factor_values_by_cache(date, codes=None, factor_names=None, group_name=None, factor_path=None):
    """从缓存的文件读取因子数据, 文件不存在时返回空的 DataFrame"""
    date = pd.to_datetime(date)
    if factor_path:
        path = os.path.join(factor_path,
                            date.strftime("%Y%m"),
                            date.strftime("%Y%m%d") + ".feather")
    elif group_name:
        path = os.path.join(get_cache_dir(),
                            group_name,
                            date.strftime("%Y%m"),
                            date.strftime("%Y%m%d") + ".feather")
    elif factor_names:
        path = os.path.join(get_cache_dir(),
                            get_factor_folder(factor_names),
                            date.strftime("%Y%m"),
                            date.strftime("%Y%m%d") + ".feather")
    else:
        raise ValueError("factor_names, factor_path 和 group_name 至少指定其中一个")
    # 数据未产生, 或者已经生产了但是全为 nan
    if not os.path.exists(path):
        factor_names = factor_names if factor_names != 'prices' else [
            'open', 'close', 'factor']
        data = pd.DataFrame(index=codes, columns=factor_names)
        data.index.name = 'code'
        return data

    try:
        data = pd.read_feather(path, use_threads=False).set_index('code')
    except Exception as e:
        if factor_names:
            logging.error("\n{} 缓存文件可能已损坏, 请重新下载".format(date))
            save_data_by_month(factor_names,
                               date, date,
                               os.path.join(factor_path, date.strftime("%Y%m")))
            data = get_factor_values_by_cache(
                date, codes, factor_names, factor_path)
        else:
            raise ValueError(
                "\n{} 缓存文件可能已损坏, 请重新下载 (指定 factor_names 时会自动下载) {} ".format(date, e))

    if codes is not None:
        data = data.reindex(codes)

    return data


================================================
FILE: jqfactor_analyzer/performance.py
================================================
# -*- coding: utf-8 -*-


import numpy as np
from scipy import stats
import pandas as pd
from statsmodels.api import OLS, add_constant

from .compat import rolling_apply
from .prepare import demean_forward_returns, common_start_returns
from .utils import get_forward_returns_columns


def factor_information_coefficient(
    factor_data, group_adjust=False, by_group=False, method=stats.spearmanr
):
    """
    通过因子值与因子远期收益计算信息系数(IC).

    参数
    ----------
    factor_data : pd.DataFrame - MultiIndex
        一个 DataFrame, index 为日期 (level 0) 和资产(level 1) 的 MultiIndex,
        values 包括因子的值, 各期因子远期收益, 因子分位数,
        因子分组(可选), 因子权重(可选)
    group_adjust : bool
        是否使用分组去均值后的因子远期收益计算 IC.
    by_group : bool
        是否分组计算 IC.
    Returns
    -------
    ic : pd.DataFrame
        因子信息系数(IC).
    """

    def src_ic(group):
        f = group['factor']
        _ic = group[get_forward_returns_columns(factor_data.columns)] \
            .apply(lambda x: method(x, f)[0])
        return _ic

    factor_data = factor_data.copy()

    grouper = [factor_data.index.get_level_values('date')]

    if group_adjust:
        factor_data = demean_forward_returns(factor_data, grouper + ['group'])
    if by_group:
        grouper.append('group')

    with np.errstate(divide='ignore', invalid='ignore'):
        ic = factor_data.groupby(grouper).apply(src_ic)

    return ic


def mean_information_coefficient(
    factor_data,
    group_adjust=False,
    by_group=False,
    by_time=None,
    method=stats.spearmanr
):
    """
    根据不同分组求因子 IC 均值.

    参数
    ----------
    factor_data : pd.DataFrame - MultiIndex
        一个 DataFrame, index 为日期 (level 0) 和资产(level 1) 的 MultiIndex,
        values 包括因子的值, 各期因子远期收益, 因子分位数,
        因子分组(可选), 因子权重(可选)
    group_adjust : bool
        是否使用分组去均值后的因子远期收益计算 IC.
    by_group : bool
        是否分组计算 IC.
    by_time : str (pd time_rule), optional
        根据相应的时间频率计算 IC 均值
        时间频率参见 http://pandas.pydata.org/pandas-docs/stable/timeseries.html

    返回值
    -------
    ic : pd.DataFrame
        根据不同分组求出的因子 IC 均值序列
    """

    ic = factor_information_coefficient(
        factor_data, group_adjust, by_group, method=method
    )

    grouper = []
    if by_time is not None:
        grouper.append(pd.Grouper(freq=by_time))
    if by_group:
        grouper.append('group')

    if len(grouper) == 0:
        ic = ic.mean()

    else:
        ic = (ic.reset_index().set_index('date').groupby(grouper).mean())

    return ic


def factor_returns(factor_data, demeaned=True, group_adjust=False):
    """
    计算按因子值加权的投资组合的收益
    权重为去均值的因子除以其绝对值之和 (实现总杠杆率为1).

    参数
    ----------
    factor_data : pd.DataFrame - MultiIndex
        一个 DataFrame, index 为日期 (level 0) 和资产(level 1) 的 MultiIndex,
        values 包括因子的值, 各期因子远期收益, 因子分位数,
        因子分组(可选), 因子权重(可选)
    demeaned : bool
        因子分析是否基于一个多空组合? 如果是 True, 则计算权重时因子值需要去均值
    group_adjust : bool
        因子分析是否基于一个分组(行业)中性的组合?
        如果是 True, 则计算权重时因子值需要根据分组和日期去均值

    返回值
    -------
    returns : pd.DataFrame
        每期零风险暴露的多空组合收益
    """

    def to_weights(group, is_long_short):
        if is_long_short:
            demeaned_vals = group - group.mean()
            return demeaned_vals / demeaned_vals.abs().sum()
        else:
            return group / group.abs().sum()

    grouper = [factor_data.index.get_level_values('date')]
    if group_adjust:
        grouper.append('group')

    weights = factor_data.groupby(grouper)['factor'] \
        .apply(to_weights, demeaned)

    if group_adjust:
        weights = weights.groupby(level='date').apply(to_weights, False)

    weighted_returns = \
        factor_data[get_forward_returns_columns(factor_data.columns)] \
        .multiply(weights, axis=0)

    returns = weighted_returns.groupby(level='date').sum()

    return returns


def factor_alpha_beta(factor_data, demeaned=True, group_adjust=False):
    """
    计算因子的alpha(超额收益),
    alpha t-统计量 (alpha 显著性）和 beta(市场暴露).
    使用每期平均远期收益作为自变量(视为市场组合收益)
    因子值加权平均的远期收益作为因变量(视为因子收益), 进行回归.

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        一个 DataFrame, index 为日期 (level 0) 和资产(level 1) 的 MultiIndex,
        values 包括因子的值, 各期因子远期收益, 因子分位数,
        因子分组(可选), 因子权重(可选)
    demeaned : bool
        因子分析是否基于一个多空组合? 如果是 True, 则计算权重时因子值需要去均值
    group_adjust : bool
        因子分析是否基于一个分组(行业)中性的组合?
        如果是 True, 则计算权重时因子值需要根据分组和日期去均值
    Returns
    -------
    alpha_beta : pd.Series
        一个包含 alpha, beta, a t-统计量(alpha) 的序列
    """

    returns = factor_returns(factor_data, demeaned, group_adjust)

    universe_ret = factor_data.groupby(level='date')[
        get_forward_returns_columns(factor_data.columns)] \
        .mean().loc[returns.index]

    if isinstance(returns, pd.Series):
        returns.name = universe_ret.columns.values[0]
        returns = pd.DataFrame(returns)

    alpha_beta = pd.DataFrame()
    for period in returns.columns.values:
        x = universe_ret[period].values
        y = returns[period].values
        x = add_constant(x)
        period_int = int(period.replace('period_', ''))

        reg_fit = OLS(y, x).fit()
        alpha, beta = reg_fit.params

        alpha_beta.loc['Ann. alpha', period] = \
            (1 + alpha) ** (250.0 / period_int) - 1
        alpha_beta.loc['beta', period] = beta

    return alpha_beta


def cumulative_returns(returns, period):
    """
    从'N 期'因子远期收益率构建累积收益
    当 'period' N 大于 1 时, 建立平均 N 个交错的投资组合 (在随后的时段 1,2,3，...，N 开始),
    每个 N 个周期重新调仓, 最后计算 N 个投资组合累积收益的均值。

    参数
    ----------
    returns: pd.Series
        N 期因子远期收益序列
    period: integer
        对应的因子远期收益时间跨度

    返回值
    -------
    pd.Series
        累积收益序列
    """

    returns = returns.fillna(0)

    if period == 1:
        return returns.add(1).cumprod()
    #
    # 构建 N 个交错的投资组合
    #

    def split_portfolio(ret, period):
        return pd.DataFrame(np.diag(ret))

    sub_portfolios = returns.groupby(
        np.arange(len(returns.index)) // period, axis=0
    ).apply(split_portfolio, period)
    sub_portfolios.index = returns.index

    #
    # 将 N 期收益转换为 1 期收益, 方便计算累积收益
    #

    def rate_of_returns(ret, period):
        return ((np.nansum(ret) + 1)**(1. / period)) - 1

    sub_portfolios = rolling_apply(
        sub_portfolios,
        window=period,
        func=rate_of_returns,
        min_periods=1,
        args=(period,)
    )
    sub_portfolios = sub_portfolios.add(1).cumprod()

    #
    # 求 N 个投资组合累积收益均值
    #
    return sub_portfolios.mean(axis=1)


def weighted_mean_return(factor_data, grouper):
    """计算(年化)加权平均/标准差"""
    forward_returns_columns = get_forward_returns_columns(factor_data.columns)

    def agg(values, weights):
        count = len(values)
        average = np.average(values, weights=weights, axis=0)
        # Fast and numerically precise
        variance = np.average(
            (values - average)**2, weights=weights, axis=0
        ) * count / max((count - 1), 1)
        return pd.Series(
            [average, np.sqrt(variance), count], index=['mean', 'std', 'count']
        )

    group_stats = factor_data.groupby(grouper)[
        forward_returns_columns.append(pd.Index(['weights']))] \
        .apply(lambda x: x[forward_returns_columns].apply(
            agg, weights=x['weights'].fillna(0.0).values
        ))

    mean_ret = group_stats.xs('mean', level=-1)

    std_error_ret = group_stats.xs('std', level=-1) \
        / np.sqrt(group_stats.xs('count', level=-1))

    return mean_ret, std_error_ret


def mean_return_by_quantile(
    factor_data,
    by_date=False,
    by_group=False,
    demeaned=True,
    group_adjust=False
):
    """
    计算各分位数的因子远期收益均值和标准差

    参数
    ----------
    factor_data : pd.DataFrame - MultiIndex
        一个 DataFrame, index 为日期 (level 0) 和资产(level 1) 的 MultiIndex,
        values 包括因子的值, 各期因子远期收益, 因子分位数,
        因子分组(可选), 因子权重(可选)
    by_date : bool
        如果为 True, 则按日期计算各分位数的因子远期收益均值
    by_group : bool
        如果为 True, 则分组计算各分位数的因子远期收益均值
    demeaned : bool
        是否按日期对因子远期收益去均值
    group_adjust : bool
        是否按日期和分组对因子远期收益去均值
    Returns
    -------
    mean_ret : pd.DataFrame
        各分位数因子远期收益均值
    std_error_ret : pd.DataFrame
        各分位数因子远期收益标准差
    """

    if group_adjust:
        grouper = [factor_data.index.get_level_values('date')] + ['group']
        factor_data = demean_forward_returns(factor_data, grouper)
    elif demeaned:
        factor_data = demean_forward_returns(factor_data)
    else:
        factor_data = factor_data.copy()

    grouper = ['factor_quantile']
    if by_date:
        grouper.append(factor_data.index.get_level_values('date'))

    if by_group:
        grouper.append('group')

    mean_ret, std_error_ret = weighted_mean_return(factor_data, grouper=grouper)

    return mean_ret, std_error_ret


def compute_mean_returns_spread(
    mean_returns, upper_quant, lower_quant, std_err=None
):
    """
    计算两个分位数的平均收益之差, 和(可选)计算此差异的标准差

    参数
    ----------
    mean_returns : pd.DataFrame
        各分位数因子远期收益均值
    upper_quant : int
        作为被减数的因子分位数
    lower_quant : int
        作为减数的因子分位数
    std_err : pd.DataFrame
        各分位数因子远期收益标准差

    返回值
    -------
    mean_return_difference : pd.Series
        每期两个分位数的平均收益之差
    joint_std_err : pd.Series
        每期两个分位数的平均收益标准差之差
    """
    if isinstance(mean_returns.index, pd.MultiIndex):
        mean_return_difference = mean_returns.xs(upper_quant,
                                                 level='factor_quantile') \
            - mean_returns.xs(lower_quant, level='factor_quantile')
    else:
        mean_return_difference = mean_returns.loc[
            upper_quant] - mean_returns.loc[lower_quant]

    if isinstance(std_err.index, pd.MultiIndex):
        std1 = std_err.xs(upper_quant, level='factor_quantile')
        std2 = std_err.xs(lower_quant, level='factor_quantile')
    else:
        std1 = std_err.loc[upper_quant]
        std2 = std_err.loc[lower_quant]
    joint_std_err = np.sqrt(std1**2 + std2**2)

    return mean_return_difference, joint_std_err


def quantile_turnover(quantile_factor, quantile, period=1):
    """
    计算当期在分位数中的因子不在上一期分位数中的比例

    Parameters
    ----------
    quantile_factor : pd.Series
        包含日期, 资产, 和因子分位数的 DataFrame.
    quantile : int
        对应的分位数
    period: int, optional
        对应的因子远期收益时间跨度
    Returns
    -------
    quant_turnover : pd.Series
        每期对饮分位数因子的换手率
    """

    quant_names = quantile_factor[quantile_factor == quantile]
    quant_name_sets = quant_names.groupby(
        level=['date']
    ).apply(lambda x: set(x.index.get_level_values('asset')))
    new_names = (quant_name_sets - quant_name_sets.shift(period)).dropna()
    quant_turnover = new_names.apply(lambda x: len(x)) / quant_name_sets.apply(
        lambda x: len(x)
    )
    quant_turnover.name = quantile
    return quant_turnover


def factor_autocorrelation(factor_data, period=1, rank=True):
    """
    计算指定时间跨度内平均因子排名/因子值的自相关性.
    该指标对于衡量因子的换手率非常有用.
    如果每个因子值在一个周期内随机变化，我们预计自相关为 0.

    参数
    ----------
    factor_data : pd.DataFrame - MultiIndex
        一个 DataFrame, index 为日期 (level 0) 和资产(level 1) 的 MultiIndex,
        values 包括因子的值, 各期因子远期收益, 因子分位数,
        因子分组(可选), 因子权重(可选)
    period: int, optional
        对应的因子远期收益时间跨度
    Returns
    -------
    autocorr : pd.Series
        滞后一期的因子自相关性
    """

    grouper = [factor_data.index.get_level_values('date')]

    if rank:
        ranks = factor_data.groupby(grouper)[['factor']].rank()
    else:
        ranks = factor_data[['factor']]
    asset_factor_rank = ranks.reset_index().pivot(
        index='date', columns='asset', values='factor'
    )

    autocorr = asset_factor_rank.corrwith(
        asset_factor_rank.shift(period), axis=1
    )
    autocorr.name = period
    return autocorr


def average_cumulative_return_by_quantile(
    factor_data,
    prices,
    periods_before=10,
    periods_after=15,
    demeaned=True,
    group_adjust=False,
    by_group=False
):
    """
    计算由 periods_before 到 periods_after 定义的周期范围内的因子分位数的平均累积收益率

    参数
    ----------
    factor_data : pd.DataFrame - MultiIndex
        一个 DataFrame, index 为日期 (level 0) 和资产(level 1) 的 MultiIndex,
        values 包括因子的值, 各期因子远期收益, 因子分位数,
        因子分组(可选), 因子权重(可选)
    prices : pd.DataFrame
        用于计算因子远期收益的价格数据
        columns 为资产, index 为 日期.
        价格数据必须覆盖因子分析时间段以及额外远期收益计算中的最大预期期数.
    periods_before : int, optional
        之前多少期
    periods_after  : int, optional
        之后多少期
    demeaned : bool, optional
        是否按日期对因子远期收益去均值
    group_adjust : bool
        是否按日期和分组对因子远期收益去均值
    by_group : bool
        如果为 True, 则分组计算各分位数的因子远期累积收益
    Returns
    -------
    cumulative returns and std deviation : pd.DataFrame
        一个 DataFrame, index 为分位数 (level 0) 和 'mean'/'std' (level 1) 的 MultiIndex
        columns 为取值范围从 -periods_before 到 periods_after 的整数
        如果 by_group=True, 则 index 会多出一个 'group' level
    """

    def cumulative_return(q_fact, demean_by):
        return common_start_returns(
            q_fact, prices, periods_before, periods_after, True, True, demean_by
        )

    def average_cumulative_return(q_fact, demean_by):
        q_returns = cumulative_return(q_fact, demean_by)
        return pd.DataFrame(
            {
                'mean': q_returns.mean(axis=1),
                'std': q_returns.std(axis=1)
            }
        ).T

    if by_group:

        returns_bygroup = []

        for group, g_data in factor_data.groupby('group'):
            g_fq = g_data['factor_quantile']
            if group_adjust:
                demean_by = g_fq  # demeans at group level
            elif demeaned:
                demean_by = factor_data['factor_quantile']  # demean by all
            else:
                demean_by = None
            #
            # Align cumulative return from different dates to the same index
            # then compute mean and std
            #
            avgcumret = g_fq.groupby(g_fq).apply(
                average_cumulative_return, demean_by
            )
            avgcumret['group'] = group
            avgcumret.set_index('group', append=True, inplace=True)
            returns_bygroup.append(avgcumret)

        return pd.concat(returns_bygroup, axis=0)

    else:

        if group_adjust:
            all_returns = []
            for group, g_data in factor_data.groupby('group'):
                g_fq = g_data['factor_quantile']
                avgcumret = g_fq.groupby(g_fq).apply(cumulative_return, g_fq)
                all_returns.append(avgcumret)
            q_returns = pd.concat(all_returns, axis=1)
            q_returns = pd.DataFrame(
                {
                    'mean': q_returns.mean(axis=1),
                    'std': q_returns.std(axis=1)
                }
            )
            return q_returns.unstack(level=1).stack(level=0)
        elif demeaned:
            fq = factor_data['factor_quantile']
            return fq.groupby(fq).apply(average_cumulative_return, fq)
        else:
            fq = factor_data['factor_quantile']
            return fq.groupby(fq).apply(average_cumulative_return, None)


================================================
FILE: jqfactor_analyzer/plot_utils.py
================================================
# -*- coding: utf-8 -*-


import sys
import subprocess
from functools import wraps

import matplotlib as mpl
import seaborn as sns
import pandas as pd


def customize(func):

    @wraps(func)
    def call_w_context(*args, **kwargs):

        if not PlotConfig.FONT_SETTED:
            _use_chinese(True)

        set_context = kwargs.pop('set_context', True)
        if set_context:
            with plotting_context(), axes_style():
                sns.despine(left=True)
                return func(*args, **kwargs)
        else:
            return func(*args, **kwargs)

    return call_w_context


def plotting_context(context='notebook', font_scale=1.5, rc=None):

    if rc is None:
        rc = {}

    rc_default = {'lines.linewidth': 1.5}

    for name, val in rc_default.items():
        rc.setdefault(name, val)

    return sns.plotting_context(context=context, font_scale=font_scale, rc=rc)


def axes_style(style='darkgrid', rc=None):

    if rc is None:
        rc = {}

    rc_default = {}

    for name, val in rc_default.items():
        rc.setdefault(name, val)

    return sns.axes_style(style=style, rc=rc)


def print_table(table, name=None, fmt=None):

    from IPython.display import display

    if isinstance(table, pd.Series):
        table = pd.DataFrame(table)

    if isinstance(table, pd.DataFrame):
        table.columns.name = name

    prev_option = pd.get_option('display.float_format')
    if fmt is not None:
        pd.set_option('display.float_format', lambda x: fmt.format(x))

    display(table)

    if fmt is not None:
        pd.set_option('display.float_format', prev_option)


class PlotConfig(object):
    FONT_SETTED = False
    USE_CHINESE_LABEL = False
    MPL_FONT_FAMILY = mpl.rcParams["font.family"]
    MPL_FONT = mpl.rcParams["font.sans-serif"]
    MPL_UNICODE_MINUS = mpl.rcParams["axes.unicode_minus"]


def get_chinese_font():
    if sys.platform.startswith('linux'):
        cmd = 'fc-list :lang=zh -f "%{family}\n"'
        output = subprocess.check_output(cmd, shell=True)
        if isinstance(output, bytes):
            output = output.decode("utf-8")
        zh_fonts = [
            f.split(',', 1)[0] for f in output.split('\n') if f.split(',', 1)[0]
        ]
        return zh_fonts

    return []


def _use_chinese(use=None):
    if use is None:
        return PlotConfig.USE_CHINESE_LABEL
    elif use:
        PlotConfig.USE_CHINESE_LABEL = use
        PlotConfig.FONT_SETTED = True
        _set_chinese_fonts()
    else:
        PlotConfig.USE_CHINESE_LABEL = use
        PlotConfig.FONT_SETTED = True
        _set_default_fonts()


def _set_chinese_fonts():
    default_chinese_font = ['SimHei', 'FangSong', 'STXihei', 'Hiragino Sans GB',
                            'Heiti SC', 'WenQuanYi Micro Hei']
    chinese_font = default_chinese_font + get_chinese_font()
    # 设置中文字体
    mpl.rc(
        "font", **{
            # seaborn 需要设置 sans-serif
            "sans-serif": chinese_font,
            "family": ','.join(chinese_font) + ',sans-serif'
        }
    )
    # 防止负号乱码
    mpl.rcParams["axes.unicode_minus"] = False


def _set_default_fonts():
    mpl.rc(
        "font", **{
            "sans-serif": PlotConfig.MPL_FONT,
            "family": PlotConfig.MPL_FONT_FAMILY
        }
    )
    mpl.rcParams["axes.unicode_minus"] = PlotConfig.MPL_UNICODE_MINUS


class _PlotLabels(object):

    def get(self, v):
        if _use_chinese():
            return getattr(self, v + "_CN")
        else:
            return getattr(self, v + "_EN")


class ICTS(_PlotLabels):
    TITLE_CN = "{} 天 IC"
    TITLE_EN = "{} Period Forward Return Information Coefficient (IC)"
    LEGEND_CN = ["IC", "1个月移动平均"]
    LEGEND_EN = ["IC", "1 month moving avg"]
    TEXT_CN = "均值 {:.3f} \n方差 {:.3f}"
    TEXT_EN = "Mean {:.3f} \nStd. {:.3f}"


ICTS = ICTS()


class ICHIST(_PlotLabels):
    TITLE_CN = "%s 天 IC 分布直方图"
    TITLE_EN = "%s Period IC"
    LEGEND_CN = "均值 {:.3f} \n方差 {:.3f}"
    LEGEND_EN = "Mean {:.3f} \nStd. {:.3f}"


ICHIST = ICHIST()


class ICQQ(_PlotLabels):
    NORM_CN = "正态"
    NORM_EN = "Normal"
    T_CN = "T"
    T_EN = "T"
    CUSTOM_CN = "自定义"
    CUSTOM_EN = "Theoretical"
    TITLE_CN = "{} 天 IC {}分布 Q-Q 图"
    TITLE_EN = "{} Period IC {} Dist. Q-Q"
    XLABEL_CN = "{} 分布分位数"
    XLABEL_EN = "{} Distribution Quantile"
    YLABEL_CN = "Observed Quantile"
    YLABEL_EN = "Observed Quantile"


ICQQ = ICQQ()


class QRETURNBAR(_PlotLabels):
    COLUMN_CN = "{} 天"
    COLUMN_EN = "{} Day"
    TITLE_CN = "各分位数平均收益"
    TITLE_EN = "Mean Period Wise Return By Factor Quantile"
    YLABEL_CN = "平均收益 (bps)"
    YLABEL_EN = "Mean Return (bps)"


QRETURNBAR = QRETURNBAR()


class QRETURNVIOLIN(_PlotLabels):
    LEGENDNAME_CN = "滞后天数"
    LEGENDNAME_EN = "forward periods"
    TITLE_CN = "各分位数收益分布图"
    TITLE_EN = "Period Wise Return By Factor Quantile"
    YLABEL_CN = "收益 (bps)"
    YLABEL_EN = "Return (bps)"


QRETURNVIOLIN = QRETURNVIOLIN()


class QRETURNTS(_PlotLabels):
    TITLE_CN = "最大分位收益减最小分位收益 ({} 天)"
    TITLE_EN = "Top Minus Bottom Quantile Mean Return ({} Period Forward Return)"
    LEGEND0_CN = "当日收益 (加减 {:.2f} 倍当日标准差)"
    LEGEND0_EN = "mean returns spread (+/- {:.2f} std)"
    LEGEND1_CN = "1 个月移动平均"
    LEGEND1_EN = "1 month moving avg"
    YLABEL_CN = "分位数平均收益差 (bps)"
    YLABEL_EN = "Difference In Quantile Mean Return (bps)"


QRETURNTS = QRETURNTS()


class ICGROUP(_PlotLabels):
    TITLE_CN = "分组 IC"
    TITLE_EN = "Information Coefficient By Group"


ICGROUP = ICGROUP()


class AUTOCORR(_PlotLabels):
    TITLE_CN = "因子自相关性 (滞后 {} 天)"
    TITLE_EN = "{} Period Factor Autocorrelation"
    YLABEL_CN = "自相关性"
    YLABEL_EN = "Autocorrelation Coefficient"
    TEXT_CN = "均值 {:.3f}"
    TEXT_EN = "Mean {:.3f}"


AUTOCORR = AUTOCORR()


class TBTURNOVER(_PlotLabels):
    TURNOVER_CN = "{:d} 分位换手率"
    TURNOVER_EN = "quantile {:d} turnover"
    TITLE_CN = "{} 天换手率"
    TITLE_EN = "{} Period Top and Bottom Quantile Turnover"
    YLABEL_CN = "分位数换手率"
    YLABEL_EN = "Proportion Of Names New To Quantile"


TBTURNOVER = TBTURNOVER()


class ICHEATMAP(_PlotLabels):
    TITLE_CN = "{} 天 IC 月度均值"
    TITLE_EN = "Monthly Mean {} Period IC"


ICHEATMAP = ICHEATMAP()


class CUMRET(_PlotLabels):
    YLABEL_CN = "累积收益"
    YLABEL_EN = "Cumulative Returns"
    TITLE_CN = "因子值加权多空组合累积收益 ({} 天平均)"
    TITLE_EN = """Factor Weighted Long/Short Portfolio Cumulative Return
                  ({} Fwd Period)"""


CUMRET = CUMRET()


class TDCUMRET(_PlotLabels):
    YLABEL_CN = "累积收益"
    YLABEL_EN = "Cumulative Returns"
    TITLE_CN = "做多最大分位做空最小分位组合累积收益 ({} 天平均)"
    TITLE_EN = """Long Top/Short Bottom Factor Portfolio Cumulative Return
                  ({} Fwd Period)"""


TDCUMRET = TDCUMRET()


class CUMRETQ(_PlotLabels):
    YLABEL_CN = "累积收益(对数轴)"
    YLABEL_EN = "Log Cumulative Returns"
    TITLE_CN = "分位数 {} 天 Forward Return 累积收益 (对数轴)"
    TITLE_EN = """Cumulative Return by Quantile
                  ({} Period Forward Return)"""


CUMRETQ = CUMRETQ()


class AVGCUMRET(_PlotLabels):
    TITLE_CN = "因子预测能力 (前 {} 天, 后 {} 天)"
    TITLE_EN = "Average Cumulative Returns by Quantile ({} days backword, {} days forward)"
    COLUMN_CN = "{} 分位"
    COLUMN_EN = "Quantile {}"
    XLABEL_CN = "天数"
    XLABEL_EN = "Periods"
    YLABEL_CN = "平均累积收益 (bps)"
    YLABEL_EN = "Mean Return (bps)"


AVGCUMRET = AVGCUMRET()


class EVENTSDIST(_PlotLabels):
    TITLE_CN = "因子数量随时间分布"
    TITLE_EN = "Distribution of events in time"
    XLABEL_CN = "日期"
    XLABEL_EN = "Date"
    YLABEL_CN = "因子数量"
    YLABEL_EN = "Number of events"


EVENTSDIST = EVENTSDIST()


class MISSIINGEVENTSDIST(_PlotLabels):
    TITLE_CN = "因子数量随时间分布"
    TITLE_EN = "Distribution of missing events in time"
    XLABEL_CN = "日期"
    XLABEL_EN = "Date"
    YLABEL_CN = "因子缺失率"
    YLABEL_EN = "Rate of missing events"


MISSIINGEVENTSDIST = MISSIINGEVENTSDIST()


================================================
FILE: jqfactor_analyzer/plotting.py
================================================
# -*- coding: utf-8 -*-


from __future__ import division, print_function

import pandas as pd
import numpy as np
from scipy import stats
from statsmodels.api import qqplot
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.ticker import ScalarFormatter
import seaborn as sns

from .compat import rolling_mean
from .plot_utils import (
    print_table, customize, ICTS, ICHIST, ICQQ, QRETURNBAR, QRETURNVIOLIN,
    QRETURNTS, ICGROUP, AUTOCORR, TBTURNOVER, ICHEATMAP, CUMRET, TDCUMRET,
    CUMRETQ, AVGCUMRET, EVENTSDIST, MISSIINGEVENTSDIST
)
from .performance import cumulative_returns
from .utils import (ignore_warning, convert_to_forward_returns_columns)


DECIMAL_TO_BPS = 10000


def plot_returns_table(alpha_beta, mean_ret_quantile, mean_ret_spread_quantile):
    returns_table = pd.DataFrame()
    returns_table = returns_table.append(alpha_beta)
    returns_table.loc["Mean Period Wise Return Top Quantile (bps)"] = \
        mean_ret_quantile.iloc[-1] * DECIMAL_TO_BPS
    returns_table.loc["Mean Period Wise Return Bottom Quantile (bps)"] = \
        mean_ret_quantile.iloc[0] * DECIMAL_TO_BPS
    returns_table.loc["Mean Period Wise Spread (bps)"] = \
        mean_ret_spread_quantile.mean() * DECIMAL_TO_BPS

    print("收益分析")
    print_table(returns_table.apply(lambda x: x.round(3)))


def plot_turnover_table(autocorrelation_data, quantile_turnover, return_df=False):
    turnover_table = pd.DataFrame()
    for period in sorted(quantile_turnover.keys()):
        for quantile, p_data in quantile_turnover[period].iteritems():
            turnover_table.loc["Quantile {} Mean Turnover ".format(quantile),
                               "{}".format(period)] = p_data.mean()
    auto_corr = pd.DataFrame()
    for period, p_data in autocorrelation_data.iteritems():
        auto_corr.loc["Mean Factor Rank Autocorrelation", "{}"
                      .format(period)] = p_data.mean()

    if return_df:
        return turnover_table.apply(lambda x: x.round(3)), auto_corr.apply(lambda x: x.round(3))
    else:
        print("换手率分析")
        print_table(turnover_table.apply(lambda x: x.round(3)))
        print_table(auto_corr.apply(lambda x: x.round(3)))


def plot_information_table(ic_data, return_df=False):
    ic_summary_table = pd.DataFrame()
    ic_summary_table["IC Mean"] = ic_data.mean()
    ic_summary_table["IC Std."] = ic_data.std()
    ic_summary_table["IR"] = ic_data.mean() / ic_data.std()
    t_stat, p_value = stats.ttest_1samp(ic_data, 0)
    ic_summary_table["t-stat(IC)"] = t_stat
    ic_summary_table["p-value(IC)"] = p_value
    ic_summary_table["IC Skew"] = stats.skew(ic_data)
    ic_summary_table["IC Kurtosis"] = stats.kurtosis(ic_data)

    if return_df:
        return ic_summary_table.apply(lambda x: x.round(3)).T
    else:
        print("IC 分析")
        print_table(ic_summary_table.apply(lambda x: x.round(3)).T)


def plot_quantile_statistics_table(factor_data, return_df=False):
    quantile_stats = factor_data.groupby('factor_quantile') \
        .agg(['min', 'max', 'mean', 'std', 'count'])['factor']
    quantile_stats['count %'] = quantile_stats['count'] \
        / quantile_stats['count'].sum() * 100.

    if return_df:
        return quantile_stats
    else:
        print("分位数统计")
        print_table(quantile_stats)


@customize
def plot_ic_ts(ic, ax=None):

    ic = ic.copy()

    num_plots = len(ic.columns)
    if ax is None:
        f, ax = plt.subplots(num_plots, 1, figsize=(18, num_plots * 7))
        ax = np.asarray([ax]).flatten()

    ymin, ymax = (None, None)
    for a, (period, ic) in zip(ax, ic.iteritems()):
        period_num = period.replace('period_', '')
        ic.plot(alpha=0.7, ax=a, lw=0.7, color='steelblue')
        rolling_mean(
            ic, window=22
        ).plot(
            ax=a, color='forestgreen', lw=2, alpha=0.8
        )

        a.axhline(0.0, linestyle='-', color='black', lw=1, alpha=0.8)
        a.set(ylabel='IC', xlabel="")
        a.set_title(ICTS.get("TITLE").format(period_num))
        a.legend(ICTS.get("LEGEND"), loc='upper right')
        a.text(
            .05,
            .95,
            ICTS.get("TEXT").format(ic.mean(), ic.std()),
            fontsize=16,
            bbox={
                'facecolor': 'white',
                'alpha': 1,
                'pad': 5
            },
            transform=a.transAxes,
            verticalalignment='top'
        )

        curr_ymin, curr_ymax = a.get_ylim()
        ymin = curr_ymin if ymin is None else min(ymin, curr_ymin)
        ymax = curr_ymax if ymax is None else max(ymax, curr_ymax)

    for a in ax:
        a.set_ylim([ymin, ymax])

    return ax


@ignore_warning(message='Using a non-tuple sequence for multidimensional indexing is deprecated',
                category=FutureWarning)
@customize
def plot_ic_hist(ic, ax=None):

    ic = ic.copy()

    num_plots = len(ic.columns)

    v_spaces = ((num_plots - 1) // 3) + 1

    if ax is None:
        f, ax = plt.subplots(v_spaces, 3, figsize=(18, v_spaces * 6))
        ax = ax.flatten()

    for a, (period, ic) in zip(ax, ic.iteritems()):
        period_num = period.replace('period_', '')
        sns.distplot(ic.replace(np.nan, 0.), norm_hist=True, ax=a)
        a.set_xlim([-1, 1])
        a.set(title=ICHIST.get("TITLE") % period_num, xlabel='IC')
        a.text(
            .05,
            .95,
            ICHIST.get("LEGEND").format(ic.mean(), ic.std()),
            fontsize=16,
            bbox={
                'facecolor': 'white',
                'alpha': 1,
                'pad': 5
            },
            transform=a.transAxes,
            verticalalignment='top'
        )
        a.axvline(ic.mean(), color='w', linestyle='dashed', linewidth=2)

    if num_plots < len(ax):
        for a in ax[num_plots:]:
            a.set_visible(False)

    return ax


@customize
def plot_ic_qq(ic, theoretical_dist=stats.norm, ax=None):

    ic = ic.copy()

    num_plots = len(ic.columns)

    v_spaces = ((num_plots - 1) // 3) + 1

    if ax is None:
        f, ax = plt.subplots(v_spaces, 3, figsize=(18, v_spaces * 6))
        ax = ax.flatten()

    if isinstance(theoretical_dist, stats.norm.__class__):
        dist_name = ICQQ.get("NORM")
    elif isinstance(theoretical_dist, stats.t.__class__):
        dist_name = ICQQ.get("T")
    else:
        dist_name = ICQQ.get("CUSTOM")

    for a, (period, ic) in zip(ax, ic.iteritems()):
        period_num = period.replace('period_', '')
        qqplot(
            ic.replace(np.nan, 0.).values,
            theoretical_dist,
            fit=True,
            line='45',
            ax=a
        )
        a.set(
            title=ICQQ.get("TITLE").format(period_num, dist_name),
            xlabel=ICQQ.get("XLABEL").format(dist_name),
            ylabel=ICQQ.get("YLABEL"),
        )

    if num_plots < len(ax):
        for a in ax[num_plots:]:
            a.set_visible(False)

    return ax


@customize
def plot_quantile_returns_bar(
    mean_ret_by_q, by_group=False, ylim_percentiles=None, ax=None
):
    mean_ret_by_q = mean_ret_by_q.copy()
    mean_ret_by_q.columns = mean_ret_by_q.columns.map(
        lambda x: QRETURNBAR.get("COLUMN").format(x.replace("period_", ""))
    )

    if ylim_percentiles is not None:
        ymin = (
            np.nanpercentile(mean_ret_by_q.values, ylim_percentiles[0]) *
            DECIMAL_TO_BPS
        )
        ymax = (
            np.nanpercentile(mean_ret_by_q.values, ylim_percentiles[1]) *
            DECIMAL_TO_BPS
        )
    else:
        ymin = None
        ymax = None

    if by_group:
        num_group = len(mean_ret_by_q.index.get_level_values('group').unique())

        if ax is None:
            v_spaces = ((num_group - 1) // 2) + 1
            f, ax = plt.subplots(
                v_spaces,
                2,
                sharex=False,
                sharey=True,
                figsize=(
                    max(
                        18,
                        mean_ret_by_q.index.get_level_values('factor_quantile')
                        .max()
                    ), 6 * v_spaces
                )
            )
            ax = ax.flatten()

        for a, (sc, cor) in zip(ax, mean_ret_by_q.groupby(level='group')):
            (
                cor.xs(sc, level='group').multiply(DECIMAL_TO_BPS).plot(
                    kind='bar', title=sc, ax=a
                )
            )

            a.set(xlabel='', ylabel=QRETURNBAR.get("YLABEL"), ylim=(ymin, ymax))

        if num_group < len(ax):
            for a in ax[num_group:]:
                a.set_visible(False)

        return ax

    else:
        if ax is None:
            f, ax = plt.subplots(
                1,
                1,
                figsize=(
                    max(
                        18,
                        mean_ret_by_q.index.get_level_values(
                            'factor_quantile'
                        ).max() // 2
                    ), 6
                )
            )

        mean_ret_by_q.multiply(DECIMAL_TO_BPS).plot(
            kind='bar', title=QRETURNBAR.get("TITLE"), ax=ax
        )
        ax.set(xlabel="", ylabel=QRETURNBAR.get("YLABEL"), ylim=(ymin, ymax))

        return ax


@customize
def plot_quantile_returns_violin(return_by_q, ylim_percentiles=None, ax=None):

    return_by_q = return_by_q.copy()

    if ylim_percentiles is not None:
        ymin = (
            np.nanpercentile(return_by_q.values, ylim_percentiles[0]) *
            DECIMAL_TO_BPS
        )
        ymax = (
            np.nanpercentile(return_by_q.values, ylim_percentiles[1]) *
            DECIMAL_TO_BPS
        )
    else:
        ymin = None
        ymax = None

    if ax is None:
        f, ax = plt.subplots(1, 1, figsize=(18, 6))

    unstacked_dr = (return_by_q.multiply(DECIMAL_TO_BPS))
    unstacked_dr.columns = unstacked_dr.columns.astype(str).str.replace(
        'period_', ''
    ).astype(int).set_names(QRETURNVIOLIN.get("LEGENDNAME"))
    unstacked_dr = unstacked_dr.stack()
    unstacked_dr.name = 'return'
    unstacked_dr = unstacked_dr.reset_index()

    sns.violinplot(
        data=unstacked_dr,
        x='factor_quantile',
        hue=QRETURNVIOLIN.get("LEGENDNAME"),
        y='return',
        orient='v',
        cut=0,
        inner='quartile',
        ax=ax
    )
    ax.set(
        xlabel='',
        ylabel=QRETURNVIOLIN.get("YLABEL"),
        title=QRETURNVIOLIN.get("TITLE"),
        ylim=(ymin, ymax)
    )

    ax.axhline(0.0, linestyle='-', color='black', lw=0.7, alpha=0.6)

    return ax


@customize
def plot_mean_quantile_returns_spread_time_series(
    mean_returns_spread, std_err=None, bandwidth=1, ax=None
):
    if isinstance(mean_returns_spread, pd.DataFrame):
        if ax is None:
            ax = [None for a in mean_returns_spread.columns]

        ymin, ymax = (None, None)
        for (i, a), (name, fr_column
                     ) in zip(enumerate(ax), mean_returns_spread.iteritems()):
            stdn = None if std_err is None else std_err[name]
            a = plot_mean_quantile_returns_spread_time_series(
                fr_column, std_err=stdn, bandwidth=bandwidth, ax=a
            )
            ax[i] = a
            curr_ymin, curr_ymax = a.get_ylim()
            ymin = curr_ymin if ymin is None else min(ymin, curr_ymin)
            ymax = curr_ymax if ymax is None else max(ymax, curr_ymax)

        for a in ax:
            a.set_ylim([ymin, ymax])

        return ax

    periods = mean_returns_spread.name
    title = QRETURNTS.get(
        "TITLE"
    ).format(periods.replace('period_', '') if periods is not None else '')

    if ax is None:
        f, ax = plt.subplots(figsize=(18, 6))

    mean_returns_spread_bps = mean_returns_spread * DECIMAL_TO_BPS

    mean_returns_spread_bps.plot(alpha=0.4, ax=ax, lw=0.7, color='forestgreen')
    rolling_mean(
        mean_returns_spread_bps, window=22
    ).plot(
        color='orangered', alpha=0.7, ax=ax
    )
    ax.legend(
        [QRETURNTS.get("LEGEND0").format(bandwidth),
         QRETURNTS.get("LEGEND1")],
        loc='upper right'
    )

    if std_err is not None:
        std_err_bps = std_err * DECIMAL_TO_BPS
        upper = mean_returns_spread_bps.values + (std_err_bps * bandwidth)
        lower = mean_returns_spread_bps.values - (std_err_bps * bandwidth)
        ax.fill_between(
            mean_returns_spread.index,
            lower,
            upper,
            alpha=0.3,
            color='steelblue'
        )

    ylim = np.nanpercentile(abs(mean_returns_spread_bps.values), 95)
    ax.set(
        ylabel=QRETURNTS.get("YLABEL"),
        xlabel="",
        title=title,
        ylim=(-ylim, ylim)
    )
    ax.axhline(0.0, linestyle='-', color='black', lw=1, alpha=0.8)

    return ax


@customize
def plot_ic_by_group(ic_group, ax=None):

    ic_group = ic_group.copy()
    ic_group.columns = ic_group.columns.astype(str).str.replace('period_', '')
    if ax is None:
        f, ax = plt.subplots(1, 1, figsize=(max(18, len(ic_group)), 6))
    ic_group.plot(kind='bar', ax=ax)
    ax.set(title=ICGROUP.get("TITLE"), xlabel="")
    ax.set_xticklabels(ic_group.index, rotation=45)

    return ax


@customize
def plot_factor_rank_auto_correlation(
    factor_autocorrelation, period=1, ax=None
):

    if ax is None:
        f, ax = plt.subplots(1, 1, figsize=(18, 6))

    factor_autocorrelation.plot(
        title=AUTOCORR.get("TITLE").format(period), ax=ax
    )
    ax.set(ylabel=AUTOCORR.get("YLABEL").format(period), xlabel="")
    ax.axhline(0.0, linestyle='-', color='black', lw=1)
    ax.text(
        .05,
        .95,
        AUTOCORR.get("TEXT").format(factor_autocorrelation.mean()),
        fontsize=16,
        bbox={
            'facecolor': 'white',
            'alpha': 1,
            'pad': 5
        },
        transform=ax.transAxes,
        verticalalignment='top'
    )

    return ax


@customize
def plot_top_bottom_quantile_turnover(quantile_turnover, period=1, ax=None):

    if ax is None:
        f, ax = plt.subplots(1, 1, figsize=(18, 6))

    max_quantile = quantile_turnover.columns.max()
    min_quantile = quantile_turnover.columns.min()
    turnover = pd.DataFrame()

    turnover[TBTURNOVER.get("TURNOVER").format(max_quantile)
             ] = quantile_turnover[max_quantile]
    turnover[TBTURNOVER.get("TURNOVER").format(min_quantile)
             ] = quantile_turnover[min_quantile]
    turnover.plot(
        title=TBTURNOVER.get("TITLE").format(period), ax=ax, alpha=0.6, lw=0.8
    )

    ax.set(ylabel=TBTURNOVER.get("YLABEL"), xlabel="")

    return ax


@customize
def plot_monthly_ic_heatmap(mean_monthly_ic, ax=None):

    mean_monthly_ic = mean_monthly_ic.copy()

    num_plots = len(mean_monthly_ic.columns)

    v_spaces = ((num_plots - 1) // 3) + 1

    if ax is None:
        f, ax = plt.subplots(v_spaces, 3, figsize=(18, v_spaces * 6))
        ax = ax.flatten()

    new_index_year = []
    new_index_month = []
    for date in mean_monthly_ic.index:
        new_index_year.append(date.year)
        new_index_month.append(date.month)

    mean_monthly_ic.index = pd.MultiIndex.from_arrays(
        [new_index_year, new_index_month], names=["year", "month"]
    )

    for a, (period, ic) in zip(ax, mean_monthly_ic.iteritems()):
        periods_num = period.replace('period_', '')

        sns.heatmap(
            ic.unstack(),
            annot=True,
            alpha=1.0,
            center=0.0,
            annot_kws={"size": 15},
            linewidths=0.01,
            linecolor='white',
            cmap=cm.RdYlGn,
            cbar=False,
            ax=a
        )
        a.set(ylabel='', xlabel='')
        a.set_title(ICHEATMAP.get("TITLE").format(periods_num))

    if num_plots < len(ax):
        for a in ax[num_plots:]:
            a.set_visible(False)

    return ax


@customize
def plot_cumulative_returns(factor_returns, period=1, overlap=True, ax=None):

    if ax is None:
        f, ax = plt.subplots(1, 1, figsize=(18, 6))

    overlapping_period = period if overlap else 1
    factor_returns = cumulative_returns(factor_returns, overlapping_period)

    factor_returns.plot(ax=ax, lw=3, color='forestgreen', alpha=0.6)
    ax.set(
        ylabel=CUMRET.get("YLABEL"),
        title=CUMRET.get("TITLE").format(period),
        xlabel=""
    )

    ax.axhline(1.0, linestyle='-', color='black', lw=1)

    return ax


@customize
def plot_top_down_cumulative_returns(factor_returns, period=1, ax=None):

    if ax is None:
        f, ax = plt.subplots(1, 1, figsize=(18, 6))

    factor_returns.plot(ax=ax, lw=3, color='forestgreen', alpha=0.6)

    ax.set(
        ylabel=TDCUMRET.get("YLABEL"),
        title=TDCUMRET.get("TITLE").format(period),
        xlabel=""
    )

    ax.axhline(1.0, linestyle='-', color='black', lw=1)

    return ax


@customize
def plot_cumulative_returns_by_quantile(
    quantile_returns, period=1, overlap=True, ax=None
):

    if ax is None:
        f, ax = plt.subplots(1, 1, figsize=(18, 6))

    ret_wide = quantile_returns.reset_index()\
        .pivot(index='date', columns='factor_quantile',
               values=convert_to_forward_returns_columns(period))

    overlapping_period = period if overlap else 1
    cum_ret = ret_wide.apply(cumulative_returns, args=(overlapping_period,))
    cum_ret = cum_ret.loc[:, ::-1]

    cum_ret.plot(lw=2, ax=ax, cmap=cm.RdYlGn_r)
    ax.legend()
    ymin, ymax = cum_ret.min().min(), cum_ret.max().max()
    ax.set(
        ylabel=CUMRETQ.get("YLABEL"),
        title=CUMRETQ.get("TITLE").format(period),
        xlabel='',
        ylim=(ymin, ymax)
    )
    ax.set_yscale('symlog', linthresh=1)
    ax.set_yticks(np.linspace(ymin, ymax, 8))
    ax.yaxis.set_major_formatter(ScalarFormatter())
    ax.axhline(1.0, linestyle='-', color='black', lw=1)

    return ax


@customize
def plot_quantile_average_cumulative_return(
    avg_cumulative_returns,
    by_quantile=False,
    std_bar=False,
    ax=None,
    periods_before='',
    periods_after=''
):

    avg_cumulative_returns = avg_cumulative_returns.multiply(DECIMAL_TO_BPS)
    quantiles = len(avg_cumulative_returns.index.levels[0].unique())
    palette = [cm.RdYlGn_r(i) for i in np.linspace(0, 1, quantiles)]

    if by_quantile:

        if ax is None:
            v_spaces = ((quantiles - 1) // 2) + 1
            f, ax = plt.subplots(
                v_spaces,
                2,
                sharex=False,
                sharey=False,
                figsize=(18, 6 * v_spaces)
            )
            ax = ax.flatten()

        for i, (quantile, q_ret) in enumerate(
            avg_cumulative_returns.groupby(level='factor_quantile')
        ):

            mean = q_ret.loc[(quantile, 'mean')]
            mean.name = AVGCUMRET.get("COLUMN").format(quantile)
            mean.plot(ax=ax[i], color=palette[i])
            ax[i].set_ylabel(AVGCUMRET.get("YLABEL"))

            if std_bar:
                std = q_ret.loc[(quantile, 'std')]
                ax[i].errorbar(
                    std.index,
                    mean,
                    yerr=std,
                    fmt='none',
                    ecolor=palette[i],
                    label=None
                )

            ax[i].axvline(x=0, color='k', linestyle='--')
            ax[i].legend()
            i += 1

    else:

        if ax is None:
            f, ax = plt.subplots(1, 1, figsize=(18, 6))

        for i, (quantile, q_ret) in enumerate(
            avg_cumulative_returns.groupby(level='factor_quantile')
        ):

            mean = q_ret.loc[(quantile, 'mean')]
            mean.name = AVGCUMRET.get("COLUMN").format(quantile)
            mean.plot(ax=ax, color=palette[i])

            if std_bar:
                std = q_ret.loc[(quantile, 'std')]
                ax.errorbar(
                    std.index,
                    mean,
                    yerr=std,
                    fmt='none',
                    ecolor=palette[i],
                    label=None
                )
            i += 1

        ax.axvline(x=0, color='k', linestyle='--')
        ax.legend()
        ax.set(
            title=AVGCUMRET.get("YLABEL").format(periods_before, periods_after),
            xlabel=AVGCUMRET.get("XLABEL"),
            ylabel=AVGCUMRET.get("YLABEL"),
        )

    return ax


@customize
def plot_events_distribution(events, num_days=5, full_dates=None, ax=None):

    if ax is None:
        f, ax = plt.subplots(1, 1, figsize=(18, 6))

    if full_dates is None:
        full_dates = events.index.get_level_values('date').unique()

    group = pd.Series(range(len(full_dates)), index=full_dates) // num_days
    grouper_label = group.drop_duplicates()
    grouper = group.reindex(events.index.get_level_values('date'))

    count = events.groupby(grouper.values).count()
    count = count.reindex(grouper_label.values, fill_value=0)
    count.index = grouper_label.index.map(lambda x: x.strftime('%Y-%m-%d'))
    count.plot(kind="bar", grid=False, ax=ax)

    def annotateBars(x, dt, ax=ax):
        color = 'black'
        vertalign = 'top'
        ax.text(
            x,
            count.loc[dt],
            "{:d}".format(count.loc[dt]),
            rotation=45,
            color=color,
            horizontalalignment='center',
            verticalalignment=vertalign,
            fontsize=15,
            weight='heavy'
        )

    [annotateBars(x, dt, ax=ax) for x, dt in enumerate(list(count.index))]
    ax.set(
        ylabel=EVENTSDIST.get("YLABEL"),
        title=EVENTSDIST.get("TITLE"),
        xlabel=EVENTSDIST.get("XLABEL"),
    )
    return ax


@customize
def plot_missing_events_distribution(
    events, num_days=5, full_dates=None, ax=None
):

    if ax is None:
        f, ax = plt.subplots(1, 1, figsize=(18, 6))

    if full_dates is None:
        full_dates = events.index.get_level_values('date').unique()

    daily_count = events.groupby(level='date').count()
    most_common_count = np.argmax(np.bincount(daily_count))
    daily_missing = daily_count / most_common_count - 1
    daily_missing = daily_missing.reindex(full_dates, fill_value=-1.0)

    grouper = pd.Series(range(len(full_dates)), index=full_dates) // num_days
    grouper_label = grouper.drop_duplicates()

    missing = daily_missing.groupby(grouper.values).mean()
    missing = missing.reindex(grouper_label.values, fill_value=-1.0)
    missing.index = grouper_label.index.map(lambda x: x.strftime('%Y-%m-%d'))
    missing.plot(kind="bar", grid=False, ax=ax)

    def annotateBars(x, dt, ax=ax):
        color = 'black'
        vertalign = 'top'
        ax.text(
            x,
            missing.loc[dt],
            "{:+.1f}%".format(missing.loc[dt] * 100),
            rotation=45,
            color=color,
            horizontalalignment='center',
            verticalalignment=vertalign,
            fontsize=15,
            weight='heavy'
        )

    [annotateBars(x, dt, ax=ax) for x, dt in enumerate(list(missing.index))]
    ax.set(
        ylabel=MISSIINGEVENTSDIST.get("YLABEL"),
        title=MISSIINGEVENTSDIST.get("TITLE"),
        xlabel=MISSIINGEVENTSDIST.get("XLABEL")
    )

    return ax


================================================
FILE: jqfactor_analyzer/prepare.py
================================================
# -*- coding: utf-8 -*-


from __future__ import division

import pandas as pd
import numpy as np

from .exceptions import MaxLossExceededError, non_unique_bin_edges_error
from .utils import get_forward_returns_columns


@non_unique_bin_edges_error
def quantize_factor(
    factor_data, quantiles=5, bins=None, by_group=False, no_raise=False, zero_aware=False,
):
    """
    计算每期因子分位数

    参数
    ----------
    factor_data : pd.DataFrame - MultiIndex
        一个 DataFrame, index 为日期 (level 0) 和资产(level 1) 的 MultiIndex,
        values 包括因子的值, 各期因子远期收益, 因子分位数,
        因子分组(可选), 因子权重(可选)
    quantiles : int or sequence[float]
        在因子分组中按照因子值大小平均分组的组数。
         或分位数序列, 允许不均匀分组
        例如 [0, .10, .5, .90, 1.] 或 [.05, .5, .95]
        'quantiles' 和 'bins' 有且只能有一个不为 None
    bins : int or sequence[float]
        在因子分组中使用的等宽 (按照因子值) 区间的数量
        或边界值序列, 允许不均匀的区间宽度
        例如 [-4, -2, -0.5, 0, 10]
        'quantiles' 和 'bins' 有且只能有一个不为 None
    by_group : bool
        如果是 True, 按照 group 分别计算分位数
    no_raise: bool, optional
        如果为 True，则不抛出任何异常，并且将抛出异常的值设置为 np.NaN
    zero_aware : bool, optional
        如果为True，则分别为正负因子值计算分位数。
        适用于您的信号聚集并且零是正值和负值的分界线的情况.

    返回值
    -------
    factor_quantile : pd.Series
        index 为日期 (level 0) 和资产(level 1) 的因子分位数
    """
    if not ((quantiles is not None and bins is None) or
            (quantiles is None and bins is not None)):
        raise ValueError('quantiles 和 bins 至少要输入一个')

    if zero_aware and not (isinstance(quantiles, int)
                           or isinstance(bins, int)):
        msg = ("只有 quantiles 或 bins 为 int 类型时， 'zero_aware' 才能为 True")
        raise ValueError(msg)

    def quantile_calc(x, _quantiles, _bins, _zero_aware, _no_raise):
        try:
            if _quantiles is not None and _bins is None and not _zero_aware:
                return pd.qcut(x, _quantiles, labels=False) + 1
            elif _quantiles is not None and _bins is None and _zero_aware:
                pos_quantiles = pd.qcut(x[x >= 0], _quantiles // 2,
                                        labels=False) + _quantiles // 2 + 1
                neg_quantiles = pd.qcut(x[x < 0], _quantiles // 2,
                                        labels=False) + 1
                return pd.concat([pos_quantiles, neg_quantiles]).sort_index()
            elif _bins is not None and _quantiles is None and not _zero_aware:
                return pd.cut(x, _bins, labels=False) + 1
            elif _bins is not None and _quantiles is None and _zero_aware:
                pos_bins = pd.cut(x[x >= 0], _bins // 2,
                                  labels=False) + _bins // 2 + 1
                neg_bins = pd.cut(x[x < 0], _bins // 2,
                                  labels=False) + 1
                return pd.concat([pos_bins, neg_bins]).sort_index()
        except Exception as e:
            if _no_raise:
                return pd.Series(index=x.index)
            raise e

    grouper = [factor_data.index.get_level_values('date')]
    if by_group:
        if 'group' not in factor_data.columns:
            raise ValueError('只有输入了 groupby 参数时 binning_by_group 才能为 True')
        grouper.append('group')

    factor_quantile = factor_data.groupby(grouper)['factor'] \
        .apply(quantile_calc, quantiles, bins, zero_aware, no_raise)
    factor_quantile.name = 'factor_quantile'

    return factor_quantile.dropna()


def compute_forward_returns(factor,
                            prices,
                            periods=(1, 5, 10)):
    """
    计算每个因子值对应的 N 期因子远期收益

    参数
    ----------
    factor : pd.Series - MultiIndex
        一个 Series, index 为日期 (level 0) 和资产(level 1) 的 MultiIndex,
        values 为因子值
    prices : pd.DataFrame
        用于计算因子远期收益的价格数据
        columns 为资产, index 为 日期.
        价格数据必须覆盖因子分析时间段以及额外远期收益计算中的最大预期期数.
    periods : sequence[int]
        远期收益的期数
    Returns
    -------
    forward_returns : pd.DataFrame - MultiIndex
        因子远期收益
        index 为日期 (level 0) 和资产(level 1) 的 MultiIndex
        column 为远期收益的期数
    """

    factor_dateindex = factor.index.levels[0]
    factor_dateindex = factor_dateindex.intersection(prices.index)

    if len(factor_dateindex) == 0:
        raise ValueError("Factor and prices indices don't match: make sure "
                         "they have the same convention in terms of datetimes "
                         "and symbol-names")

    prices = prices.filter(items=factor.index.levels[1])

    forward_returns = pd.DataFrame(
        index=pd.MultiIndex
        .from_product([prices.index, prices.columns], names=['date', 'asset'])
    )

    for period in periods:
        delta = prices.pct_change(period).shift(-period).reindex(factor_dateindex)
        forward_returns['period_{p}'.format(p=period)] = delta.stack()

    forward_returns.index = forward_returns.index.rename(['date', 'asset'])

    return forward_returns


def demean_forward_returns(factor_data, grouper=None):
    """
    根据相关分组为因子远期收益去均值.
    分组去均值包含了投资组合分组中性化约束的假设，因此允许跨组评估因子.

    Parameters
    ----------
    factor_data : pd.DataFrame - MultiIndex
        因子远期收益
        index 为日期 (level 0) 和资产(level 1) 的 MultiIndex
        column 为远期收益的期数
    grouper : list
        如果为 None, 则只根据日期去均值
        否则则根据列表中提供的组分组去均值

    返回值
    -------
    adjusted_forward_returns : pd.DataFrame - MultiIndex
        和 factor_data 相同形状的 DataFrame, 但每个收益都被分组去均值了
    """

    factor_data = factor_data.copy()

    if not grouper:
        grouper = factor_data.index.get_level_values('date')

    cols = get_forward_returns_columns(factor_data.columns)
    factor_data[cols] = factor_data.groupby(
        grouper, as_index=False
    )[cols.append(pd.Index(['weights']))].apply(
        lambda x: x[cols].subtract(
            np.average(x[cols], axis=0, weights=x['weights'].fillna(0.0).values),
            axis=1
        )
    )

    return factor_data


def get_clean_factor(factor,
                     forward_returns,
                     groupby=None,
                     weights=None,
                     binning_by_group=False,
                     quantiles=5,
                     bins=None,
                     max_loss=0.35,
                     zero_aware=False):
    """
    将因子值, 因子远期收益, 因子分组数据, 因子权重数据
    格式化为以时间和资产的 MultiIndex 作为索引的 DataFrame.

    参数
    ----------
    factor : pd.Series - MultiIndex
        一个 Series, index 为日期 (level 0) 和资产(level 1) 的 MultiIndex,
        values 为因子的值
    forward_returns : pd.DataFrame - MultiIndex
        一个 DataFrame, index 为日期 (level 0) 和资产(level 1) 的 MultiIndex,
        values 为因子的远期收益, columns 为因子远期收益的期数.
    groupby : pd.Series - MultiIndex or dict
        index 为日期和资产的 Series，为每个资产每天的分组，或资产-分组映射的字典.
        如果传递了dict，则假定分组映射在整个时间段内保持不变.
    weights : pd.Series - MultiIndex or dict
        index 为日期和资产的 Series，为每个资产每天的权重，或资产-权重映射的字典.
        如果传递了dict，则假定权重映射在整个时间段内保持不变.
    binning_by_group : bool
        如果为 True, 则对每个组分别计算分位数.
        适用于因子值范围在各个组上变化很大的情况.
        如果要分析分组(行业)中性的组合, 您最好设置为 True
    quantiles : int or sequence[float]
        在因子分组中按照因子值大小平均分组的组数。
         或分位数序列, 允许不均匀分组
        例如 [0, .10, .5, .90, 1.] 或 [.05, .5, .95]
        'quantiles' 和 'bins' 有且只能有一个不为 None
    bins : int or sequence[float]
        在因子分组中使用的等宽 (按照因子值) 区间的数量
        或边界值序列, 允许不均匀的区间宽度
        例如 [-4, -2, -0.5, 0, 10]
        'quantiles' 和 'bins' 有且只能有一个不为 None
    max_loss : float, optional
        允许的丢弃因子数据的最大百分比 (0.00 到 1.00),
        计算比较输入因子索引中的项目数和输出 DataFrame 索引中的项目数.
        因子数据本身存在缺陷 (例如 NaN),
        没有提供足够的价格数据来计算所有因子值的远期收益，
        或者因为分组失败, 因此可以部分地丢弃因子数据
        设置 max_loss = 0 以停止异常捕获.
    zero_aware : bool, optional
        如果为True，则分别为正负因子值计算分位数。
        适用于您的信号聚集并且零是正值和负值的分界线的情况.

    返回值
    -------
    merged_data : pd.DataFrame - MultiIndex
        一个 DataFrame, index 为日期 (level 0) 和资产(level 1) 的 MultiIndex,
        values 包括因子的值, 各期因子远期收益, 因子分位数,
        因子分组(可选), 因子权重(可选)
        - 各期因子远期收益的列名满足 'period_1', 'period_5' 的格式
    """

    initial_amount = float(len(factor.index))

    factor_copy = factor.copy()
    factor_copy.index = factor_copy.index.rename(['date', 'asset'])

    merged_data = forward_returns.copy()
    merged_data['factor'] = factor_copy

    if groupby is not None:
        if isinstance(groupby, dict):
            diff = set(factor_copy.index.get_level_values(
                'asset')) - set(groupby.keys())
            if len(diff) > 0:
                raise KeyError(
                    "Assets {} not in group mapping".format(
                        list(diff)))

            ss = pd.Series(groupby)
            groupby = pd.Series(index=factor_copy.index,
                                data=ss[factor_copy.index.get_level_values(
                                    'asset')].values)
        elif isinstance(groupby, pd.DataFrame):
            groupby = groupby.stack()
        merged_data['group'] = groupby

    if weights is not None:
        if isinstance(weights, dict):
            diff = set(factor_copy.index.get_level_values(
                'asset')) - set(weights.keys())
            if len(diff) > 0:
                raise KeyError(
                    "Assets {} not in weights mapping".format(
                        list(diff)))

            ww = pd.Series(weights)
            weights = pd.Series(index=factor_copy.index,
                                data=ww[factor_copy.index.get_level_values(
                                    'asset')].values)
        elif isinstance(weights, pd.DataFrame):
            weights = weights.stack()
        merged_data['weights'] = weights

    merged_data = merged_data.dropna()

    quantile_data = quantize_factor(
        merged_data,
        quantiles,
        bins,
        binning_by_group,
        True,
        zero_aware
    )

    merged_data['factor_quantile'] = quantile_data
    merged_data = merged_data.dropna()
    merged_data['factor_quantile'] = merged_data['factor_quantile'].astype(int)

    if 'weights' in merged_data.columns:
        merged_data['weights'] = merged_data.set_index(
            'factor_quantile', append=True
        ).groupby(level=['date', 'factor_quantile'])['weights'].apply(
            lambda s: s.divide(s.sum())
        ).reset_index('factor_quantile', drop=True)

    binning_amount = float(len(merged_data.index))

    tot_loss = (initial_amount - binning_amount) / initial_amount

    no_raise = True if max_loss == 0 else False
    if tot_loss > max_loss and not no_raise:
        message = ("max_loss (%.1f%%) 超过 %.1f%%"
                   % (tot_loss * 100, max_loss * 100))
        raise MaxLossExceededError(message)

    return merged_data


def get_clean_factor_and_forward_returns(factor,
                                         prices,
                                         groupby=None,
                                         weights=None,
                                         binning_by_group=False,
                                         quantiles=5,
                                         bins=None,
                                         periods=(1, 5, 10),
                                         max_loss=0.35,
                                         zero_aware=False):
    """
    将因子数据, 价格数据, 分组映射和权重映射格式化为
    由包含时间和资产的 MultiIndex 作为索引的 DataFrame

    参数
    ----------
    factor : pd.Series - MultiIndex
     一个 Series, index 为日期 (level 0) 和资产(level 1) 的 MultiIndex,
        values 为因子的值
    prices : pd.DataFrame
        用于计算因子远期收益的价格数据
        columns 为资产, index 为 日期.
        价格数据必须覆盖因子分析时间段以及额外远期收益计算中的最大预期期数.
    groupby : pd.Series - MultiIndex or dict
        index 为日期和资产的 Series，为每个资产每天的分组，或资产-分组映射的字典.
        如果传递了dict，则假定分组映射在整个时间段内保持不变.
    weights : pd.Series - MultiIndex or dict
        index 为日期和资产的 Series，为每个资产每天的权重，或资产-权重映射的字典.
        如果传递了dict，则假定权重映射在整个时间段内保持不变.
    binning_by_group : bool
        如果为 True, 则对每个组分别计算分位数.
        适用于因子值范围在各个组上变化很大的情况.
        如果要分析分组(行业)中性的组合, 您最好设置为 True
    quantiles : int or sequence[float]
        在因子分组中按照因子值大小平均分组的组数。
         或分位数序列, 允许不均匀分组
        例如 [0, .10, .5, .90, 1.] 或 [.05, .5, .95]
        'quantiles' 和 'bins' 有且只能有一个不为 None
    bins : int or sequence[float]
        在因子分组中使用的等宽 (按照因子值) 区间的数量
        或边界值序列, 允许不均匀的区间宽度
        例如 [-4, -2, -0.5, 0, 10]
        'quantiles' 和 'bins' 有且只能有一个不为 None
    periods : sequence[int]
        远期收益的期数
    max_loss : float, optional
        允许的丢弃因子数据的最大百分比 (0.00 到 1.00),
        计算比较输入因子索引中的项目数和输出 DataFrame 索引中的项目数.
        因子数据本身存在缺陷 (例如 NaN),
        没有提供足够的价格数据来计算所有因子值的远期收益，
        或者因为分组失败, 因此可以部分地丢弃因子数据
        设置 max_loss = 0 以停止异常捕获.
    zero_aware : bool, optional
        如果为True，则分别为正负因子值计算分位数。
        适用于您的信号聚集并且零是正值和负值的分界线的情况.

    返回值
    -------
    merged_data : pd.DataFrame - MultiIndex
        一个 DataFrame, index 为日期 (level 0) 和资产(level 1) 的 MultiIndex,
        values 包括因子的值, 各期因子远期收益, 因子分位数,
        因子分组(可选), 因子权重(可选)
        - 各期因子远期收益的列名满足 'period_1', 'period_5' 的格式
    """

    forward_returns = compute_forward_returns(factor, prices, periods)

    factor_data = get_clean_factor(factor, forward_returns, groupby=groupby,
                                   weights=weights,
                                   quantiles=quantiles, bins=bins,
                                   binning_by_group=binning_by_group,
                                   max_loss=max_loss, zero_aware=zero_aware)

    return factor_data


def common_start_returns(
    factor,
    prices,
    before,
    after,
    cumulative=False,
    mean_by_date=False,
    demean_by=None
):

    if cumulative:
        returns = prices
    else:
        returns = prices.pct_change(axis=0)

    all_returns = []

    for timestamp, df in factor.groupby(level='date'):

        equities = df.index.get_level_values('asset')

        try:
            day_zero_index = returns.index.get_loc(timestamp)
        except KeyError:
            continue

        starting_index = max(day_zero_index - before, 0)
        ending_index = min(day_zero_index + after + 1, len(returns.index))

        equities_slice = set(equities)
        if demean_by is not None:
            demean_equities = demean_by.loc[timestamp] \
                .index.get_level_values('asset')
            equities_slice |= set(demean_equities)

        series = returns.loc[returns.
                             index[starting_index:ending_index], equities_slice]
        series.index = range(
            starting_index - day_zero_index, ending_index - day_zero_index
        )

        if cumulative:
            series = (series / series.loc[0, :]) - 1

        if demean_by is not None:
            mean = series.loc[:, demean_equities].mean(axis=1)
            series = series.loc[:, equities]
            series = series.sub(mean, axis=0)

        if mean_by_date:
            series = series.mean(axis=1)

        all_returns.append(series)

    return pd.concat(all_returns, axis=1)


def rate_of_return(period_ret):
    """
    转换回报率为"每期"回报率：如果收益以稳定的速度增长, 则相当于每期的回报率
    """
    period = int(period_ret.name.replace('period_', ''))
    return period_ret.add(1).pow(1. / period).sub(1)


def std_conversion(period_std):
    """
    转换回报率标准差为"每期"回报率标准差
    """
    period_len = int(period_std.name.replace('period_', ''))
    return period_std / np.sqrt(period_len)


================================================
FILE: jqfactor_analyzer/preprocess.py
================================================
# encoding: utf-8

import warnings

import pandas as pd
import numpy as np
from scipy.stats.mstats import winsorize as spwinsorize
from decimal import Decimal
from .utils import ignore_warning

from .data import  DataApi,convert_date
from fastcache import lru_cache
from functools import partial
from statsmodels.api import OLS, add_constant as sm_add_constant



def winsorize(data, scale=None, range=None, qrange=None, inclusive=True, inf2nan=True, axis=1):

    if isinstance(data, pd.DataFrame):
        return data.apply(
            winsorize,
            axis,
            scale=scale,
            range=range,
            qrange=qrange,
            inclusive=inclusive,
            inf2nan=inf2nan
        )
    elif (isinstance(data, np.ndarray) and data.ndim > 1):
        return np.apply_along_axis(
            winsorize,
            axis,
            arr=data,
            scale=scale,
            range=range,
            qrange=qrange,
            inclusive=inclusive,
            inf2nan=inf2nan
        )

    if isinstance(data, pd.Series):
        v = data.values
    else:
        v = data

    if not np.isfinite(v).any():
        return data

    # 如果v是int arrary，无法给 array 赋值 np.nan，因为 np.nan 是个 float
    v = v.astype(float)

    if inf2nan:
        v[~np.isfinite(v)] = np.nan

    if qrange:
        if not ((0 <= qrange[0] <= 1) and (0 <= qrange[1] <= 1)):
            raise Exception(u'qrange 值应在 0 到 1 之间，如 [0.05, 0.95]')
        qrange = (Decimal(str(qrange[0])), 1 - Decimal(str(qrange[1])))

        if inclusive:
            v[~np.isnan(v)] = spwinsorize(v[~np.isnan(v)], qrange, inclusive=[True, True])
        else:
            # 如果v是int arrary，无法给 array 赋值 np.nan，因为 np.nan 是个 float
            v = v.astype(float)
            not_nan = v[~np.isnan(v)]
            not_nan[not_nan != spwinsorize(not_nan, qrange, inclusive=[True, True])] = np.nan
            v[~np.isnan(v)] = not_nan

    else:
        if range:
            range_ = (Decimal(str(range[0])) if not np.isnan(range[0]) else np.nan,
                      Decimal(str(range[1])) if not np.isnan(range[1]) else np.nan)
        else:
            mu = np.mean(data[np.isfinite(data)])
            sigma = np.std(data[np.isfinite(data)])
            range_ = (np.nanmin(v[v > mu - scale * sigma]),
                      np.nanmax(v[v < mu + scale * sigma]))

        if inclusive:
            not_nan = ~np.isnan(v)
            v[not_nan] = np.where(v[not_nan] < range_[0], range_[0], v[not_nan])
            not_nan = ~np.isnan(v)
            v[not_nan] = np.where(v[not_nan] > range_[1], range_[1], v[not_nan])
        else:
            not_nan = ~np.isnan(v)
            v_not_nan = v[not_nan]
            v[not_nan] = np.where(
                np.logical_and(v_not_nan >= range_[0], v_not_nan <= range_[1]), v_not_nan, np.nan
            )

    if isinstance(data, pd.Series):
        return pd.Series(v, index=data.index)
    else:
        return v


def winsorize_med(data, scale=1, inclusive=True, inf2nan=True, axis=1):

    if isinstance(data, pd.DataFrame):
        return data.apply(winsorize_med, axis, scale=scale, inclusive=inclusive, inf2nan=inf2nan)
    elif (isinstance(data, np.ndarray) and data.ndim > 1):
        return np.apply_along_axis(
            winsorize_med, axis, arr=data, scale=scale, inclusive=inclusive, inf2nan=inf2nan
        )

    if isinstance(data, pd.Series):
        v = data.values
    else:
        v = data

    if not np.isfinite(v).any():
        return data

    # 如果v是int arrary，无法给 array 赋值 np.nan，因为 np.nan 是个 float
    v = v.astype(float)

    if inf2nan:
        v[~np.isfinite(v)] = np.nan

    med = np.median(v[~np.isnan(v)])

    data_minus_med = v[~np.isnan(v)] - med
    median_absolute = np.median(np.abs(data_minus_med))

    if inclusive:
        not_nan = ~np.isnan(v)
        v[not_nan] = np.where(
            v[not_nan] > med + scale * median_absolute, med + scale * median_absolute, v[not_nan]
        )
        not_nan = ~np.isnan(v)
        v[not_nan] = np.where(
            v[not_nan] < med - scale * median_absolute, med - scale * median_absolute, v[not_nan]
        )
    else:
        # 如果v是int arrary，np.nan 会被转换成一个极小的数，比如 -2147483648
        v = v.astype(float)
        not_nan = ~np.isnan(v)
        v_not_nan = v[not_nan]
        v[not_nan] = np.where(
            np.logical_and(
                v_not_nan <= med + scale * median_absolute,
                v_not_nan >= med - scale * median_absolute
            ), v_not_nan, np.nan
        )

    if isinstance(data, pd.Series):
        return pd.Series(v, index=data.index)
    else:
        return v


@ignore_warning(message='Mean of empty slice', category=RuntimeWarning)
@ignore_warning(message='Degrees of freedom <= 0 for slice',
                category=RuntimeWarning)
@ignore_warning(message='invalid value encountered in true_divide',
                category=RuntimeWarning)
def standardlize(data, inf2nan=True, axis=1):
    if inf2nan:
        data = data.astype('float64')
        data[np.isinf(data)] = np.nan

    axis = min(data.ndim - 1, axis)

    if not np.any(np.isfinite(data)):
        return data

    mu = np.nanmean(np.where(~np.isinf(data), data, np.nan), axis=axis)
    std = np.nanstd(np.where(~np.isinf(data), data, np.nan), axis=axis)

    rep = np.tile if axis == 0 else np.repeat
    mu = np.asarray(rep(mu, data.shape[axis])).reshape(data.shape)
    std = np.asarray(rep(std, data.shape[axis])).reshape(data.shape)

    if isinstance(data, (pd.Series, pd.DataFrame)):
        data = data.where(np.isinf(data), (data - mu) / std)
    else:
        data = np.where(np.isinf(data), data, (data - mu) / std)
    return data


@lru_cache(3)
def cache_dataapi(allow_cache=True, show_progress=False):
    return DataApi(allow_cache=allow_cache, show_progress=show_progress)


def get_neu_basicdata(how, securities, date=None):
    """获取中性化的依赖数据
    返回: 一个 DataFrame, index 是股票代码
    """
    if isinstance(how, str):
        how = [how]

    if isinstance(how, (pd.Series, pd.DataFrame)):
        return how
    elif isinstance(how, (list, tuple)):
        how_datas = []
    else:
        raise ValueError("错误的 how 参数格式 : {}".format(how))

    dataapi = cache_dataapi()
    for how_name in how:
        if isinstance(how_name, pd.Series):
            how_datas.append(how_name.to_frame())
        elif isinstance(how_name, pd.DataFrame):
            how_datas.append(how_name)
        elif how_name in ['jq_l1', 'jq_l2', 'sw_l1', 'sw_l2', 'sw_l3', 'zjw']:
            industry_info = pd.get_dummies(dataapi._get_cached_industry_one_day(
                date, securities, industry=how_name)).reindex(securities, fill_value=0)
            how_datas.append(industry_info)
        elif how_name in ['mktcap', 'ln_mktcap', 'cmktcap', 'ln_cmktcap']:
            if how_name == 'mktcap':
                mkt_api = partial(dataapi._get_market_cap, ln=False)
            elif how_name == 'ln_mktcap':
                mkt_api = partial(dataapi._get_market_cap, ln=True)
            elif how_name == 'cmktcap':
                mkt_api = partial(dataapi._get_circulating_market_cap, ln=False)
            elif how_name == 'ln_cmktcap':
                mkt_api = partial(dataapi._get_circulating_market_cap, ln=True)

            market_info= mkt_api(securities=securities, start_date=date, end_date=date).T
            market_info.columns=[how_name]
            how_datas.append(market_info)
        else:
            raise ValueError("不支持的因子名称 : {} ".format(how_name))

    return pd.concat(how_datas,axis=1)


def neutralize(data, how=None, date=None, axis=1, fillna=None, add_constant=False):
    """中性化
    data: pd.Series/pd.DataFrame, 待中性化的序列, 序列的 index/columns 为股票的 code
    how: str list. 中性化使用的因子名称列表. 默认为 ['jq_l1', 'market_cap'], 支持的中性化方法有:
                1. 行业: sw_l1, sw_l2, sw_l3, jq_l1, jq_l2
                2. 市值因子: mktcap(总市值), ln_mktcap(对数总市值), cmktcap(流通市值), ln_cmktcap(对数流通市值)
                3. 自定义的中性化数据: 支持同时传入额外的 Series 或者 DataFrame 用来进行中性化, index 必须是标的代码
                以上三类参数可同时传入参数列表
    date: 日期, 将用 date 这天的相关变量数据对 series 进行中性化 (注意依赖数据的实际可用时间, 如市值数据当天盘中是无法获取到的)
    axis: 默认为 1. 仅在 data 为 pd.DataFrame 时生效. 表示沿哪个方向做中性化, 0 为对每列做中性化, 1 为对每行做中性化
    fillna: 缺失值填充方式, 默认为None, 表示不填充. 支持的值:
        'jq_l1': 聚宽一级行业
        'jq_l2': 聚宽二级行业
        'sw_l1': 申万一级行业
        'sw_l2': 申万二级行业
        'sw_l3': 申万三级行业 表示使用某行业分类的均值进行填充.
    add_constant: 中性化时是否添加常数项, 默认为 False
    """
    if data.dropna(how='all').empty:
        return data

    if how is None:
        how = ['jq_l1', 'mktcap']
    elif isinstance(how, str):
        how = [how]

    if isinstance(data, pd.Series) or axis == 0:
        securities = data.index.astype(str)
    else:
        securities = data.columns.astype(str)
    invalid_securities = securities[~(securities.str.endswith("XSHG") | securities.str.endswith("XSHE"))].tolist()
    if invalid_securities:
        raise ValueError('neutralize: 找不到股票: {sym:s}'.format(sym=str(invalid_securities)))

    exposure = get_neu_basicdata(how, securities.tolist(), date=date)

    with pd.option_context('mode.use_inf_as_null', True):
        exposure.dropna(axis=1, how='all', inplace=True)
        exposure.dropna(inplace=True)
        exposure = exposure.astype(np.float64)

    if exposure.empty:
        return data

    if fillna is not None:
        dataapi = cache_dataapi()
        ind = dataapi._get_cached_industry_one_day(date, securities)

    def valid_index(s):
        return s[np.isfinite(s)].index.intersection(exposure.index)

    def get_resid(s):
        valid_index_ = valid_index(s)
        if len(valid_index_) > 1:
            resid = OLS(
                s.loc[valid_index_].values,
                (sm_add_constant(exposure.loc[valid_index_].values) if add_constant
                 else exposure.loc[valid_index_].values),
                missing='drop'
            ).fit().resid
            resid = pd.Series(resid, index=valid_index_)
            resid = resid.reindex(s.index, fill_value=np.nan)
            if fillna is not None:
                resid = resid.groupby(ind.loc[s.index]).apply(lambda x: x.fillna(x.mean()))
        else:
            resid = pd.Series(np.nan, index=s.index)
        return resid

    if isinstance(data, pd.Series):
        return get_resid(data)
    else:
        return data.apply(get_resid, axis)


__all__ = [
    'neutralize',
    'winsorize',
    'winsorize_med',
    'standardlize',
]


================================================
FILE: jqfactor_analyzer/sample.py
================================================
# -*- coding: utf-8 -*-

import os
import pandas as pd


VOL5 = pd.read_csv(
    os.path.abspath(os.path.join(os.path.dirname(__file__),
                                 'sample_data',
                                 'VOL5.csv')),
    header=0, index_col=0, encoding='utf-8'
)

VOL5.index = pd.to_datetime(VOL5.index)
VOL5.index.set_names(['date'], inplace=True)
VOL5.columns.set_names(['asset'], inplace=True)


================================================
FILE: jqfactor_analyzer/sample_data/VOL5.csv
================================================
,000001.XSHE,000002.XSHE,000063.XSHE,000069.XSHE,000100.XSHE,000157.XSHE,000166.XSHE,000333.XSHE,000338.XSHE,000402.XSHE,000408.XSHE,000413.XSHE,000415.XSHE,000423.XSHE,000425.XSHE,000503.XSHE,000538.XSHE,000553.XSHE,000568.XSHE,000625.XSHE,000627.XSHE,000630.XSHE,000651.XSHE,000661.XSHE,000671.XSHE,000703.XSHE,000709.XSHE,000725.XSHE,000728.XSHE,000768.XSHE,000776.XSHE,000783.XSHE,000786.XSHE,000792.XSHE,000826.XSHE,000839.XSHE,000858.XSHE,000876.XSHE,000895.XSHE,000898.XSHE,000938.XSHE,000959.XSHE,000961.XSHE,000963.XSHE,000983.XSHE,001965.XSHE,001979.XSHE,002001.XSHE,002007.XSHE,002008.XSHE,002024.XSHE,002027.XSHE,002032.XSHE,002044.XSHE,002050.XSHE,002065.XSHE,002081.XSHE,002085.XSHE,002120.XSHE,002142.XSHE,002146.XSHE,002153.XSHE,002179.XSHE,002202.XSHE,002230.XSHE,002236.XSHE,002241.XSHE,002252.XSHE,002271.XSHE,002294.XSHE,002304.XSHE,002310.XSHE,002311.XSHE,002352.XSHE,002411.XSHE,002415.XSHE,002422.XSHE,002450.XSHE,002456.XSHE,002460.XSHE,002466.XSHE,002468.XSHE,002475.XSHE,002493.XSHE,002508.XSHE,002555.XSHE,002558.XSHE,002572.XSHE,002594.XSHE,002601.XSHE,002602.XSHE,002624.XSHE,002625.XSHE,002673.XSHE,002714.XSHE,002736.XSHE,002773.XSHE,002797.XSHE,002925.XSHE,300003.XSHE,300015.XSHE,300017.XSHE,300024.XSHE,300033.XSHE,300059.XSHE,300070.XSHE,300072.XSHE,300122.XSHE,300124.XSHE,300136.XSHE,300142.XSHE,300144.XSHE,300251.XSHE,300296.XSHE,300408.XSHE,300433.XSHE,600000.XSHG,600004.XSHG,600009.XSHG,600010.XSHG,600011.XSHG,600015.XSHG,600016.XSHG,600018.XSHG,600019.XSHG,600023.XSHG,600025.XSHG,600027.XSHG,600028.XSHG,600029.XSHG,600030.XSHG,600031.XSHG,600036.XSHG,600038.XSHG,600048.XSHG,600050.XSHG,600061.XSHG,600066.XSHG,600068.XSHG,600085.XSHG,600089.XSHG,600100.XSHG,600104.XSHG,600109.XSHG,600111.XSHG,600115.XSHG,600118.XSHG,600153.XSHG,600157.XSHG,600170.XSHG,600176.XSHG,600177.XSHG,600188.XSHG,600196.XSHG,600208.XSHG,600219.XSHG,600221.XSHG,600233.XSHG,600271.XSHG,600276.XSHG,600297.XSHG,600309.XSHG,600332.XSHG,600339.XSHG,600340.XSHG,600346.XSHG,600352.XSHG,600362.XSHG,600369.XSHG,600372.XSHG,600383.XSHG,600390.XSHG,600398.XSHG,600406.XSHG,600415.XSHG,600436.XSHG,600438.XSHG,600482.XSHG,600487.XSHG,600489.XSHG,600498.XSHG,600516.XSHG,600518.XSHG,600519.XSHG,600522.XSHG,600535.XSHG,600547.XSHG,600549.XSHG,600566.XSHG,600570.XSHG,600583.XSHG,600585.XSHG,600588.XSHG,600606.XSHG,600637.XSHG,600660.XSHG,600674.XSHG,600688.XSHG,600690.XSHG,600703.XSHG,600704.XSHG,600705.XSHG,600739.XSHG,600741.XSHG,600760.XSHG,600795.XSHG,600809.XSHG,600816.XSHG,600837.XSHG,600867.XSHG,600886.XSHG,600887.XSHG,600893.XSHG,600900.XSHG,600909.XSHG,600919.XSHG,600926.XSHG,600958.XSHG,600977.XSHG,600998.XSHG,600999.XSHG,601006.XSHG,601009.XSHG,601012.XSHG,601018.XSHG,601021.XSHG,601066.XSHG,601088.XSHG,601108.XSHG,601111.XSHG,601117.XSHG,601138.XSHG,601155.XSHG,601166.XSHG,601169.XSHG,601186.XSHG,601198.XSHG,601211.XSHG,601212.XSHG,601216.XSHG,601225.XSHG,601228.XSHG,601229.XSHG,601238.XSHG,601288.XSHG,601318.XSHG,601328.XSHG,601333.XSHG,601336.XSHG,601360.XSHG,601377.XSHG,601390.XSHG,601398.XSHG,601555.XSHG,601600.XSHG,601601.XSHG,601607.XSHG,601611.XSHG,601618.XSHG,601628.XSHG,601633.XSHG,601668.XSHG,601669.XSHG,601688.XSHG,601727.XSHG,601766.XSHG,601788.XSHG,601800.XSHG,601808.XSHG,601818.XSHG,601828.XSHG,601838.XSHG,601857.XSHG,601877.XSHG,601878.XSHG,601881.XSHG,601888.XSHG,601898.XSHG,601899.XSHG,601901.XSHG,601919.XSHG,601933.XSHG,601939.XSHG,601985.XSHG,601988.XSHG,601989.XSHG,601991.XSHG,601992.XSHG,601997.XSHG,601998.XSHG,603156.XSHG,603160.XSHG,603259.XSHG,603260.XSHG,603288.XSHG,603799.XSHG,603833.XSHG,603858.XSHG,603986.XSHG,603993.XSHG
2018-01-02,0.84028,0.4347,2.33086,0.858,0.9611,0.9985,0.38416,0.52398,1.79736,0.49858,1.86226,1.1847,0.46388,0.6936,5.15198,2.1828,0.33938,1.45414,0.85208,0.3978,1.77864,1.3369,0.77056,1.07982,1.17556,1.42408,0.615,2.12612,0.55996,0.37536,0.3947,0.4021,2.25696,3.45846,0.71692,2.46128,0.9331,0.39212,0.35118,0.53566,4.55528,0.2621,0.79312,0.64094,2.01166,14.19936,1.28788,2.63284,0.80862,1.978,0.74992,0.86948,0.4128,0.77224,1.61328,0.78144,0.9843,0.65384,1.21764,0.40562,2.03054,1.45732,0.5214,1.2682,2.1911,2.61724,1.04646,0.15232,0.8346,0.73358,0.38886,0.77678,0.38506,0.83976,0.87562,0.39006,0.71272,0.9053,1.17596,3.93732,1.26034,0.71682,0.61138,0.61632,0.56878,0.94678,1.08546,1.41972,1.38538,2.54218,0.20862,0.66094,0.0,0.4969,0.70612,0.23208,1.60094,3.33128,,0.60338,0.58258,4.65328,0.488,1.07936,1.58372,0.6688,0.92252,0.56742,0.58728,1.22886,1.41856,0.35924,0.71914,0.93554,1.08952,2.4766,0.08506,0.59726,0.47148,0.3048,0.0809,0.14718,0.2183,0.16162,0.31358,0.07914,32.84386,0.1588,0.17022,1.28674,0.9856,2.63472,0.25926,0.74294,1.06618,0.73686,0.28816,0.57938,0.5698,0.29808,0.72582,0.75656,0.19286,0.6054,3.27224,1.0138,0.35058,0.51928,0.0,0.17658,1.71238,0.33502,1.32988,1.40678,0.76248,1.33538,0.31456,0.2492,1.4297,0.29274,0.56968,0.0,0.0,1.9374,0.57348,2.1823,2.88488,3.08562,0.33598,0.2081,1.59288,1.59486,0.22168,0.90346,0.17836,0.5785,1.90326,0.33664,1.6873,0.89534,1.22174,6.94178,0.30932,0.50848,2.39696,0.23122,1.17238,3.2339,0.43846,1.45878,0.31292,1.22642,0.36608,3.21212,0.27142,0.5932,0.44762,0.2317,0.69556,1.02332,0.73268,0.43952,0.33336,0.76834,4.2974,0.21132,0.66124,0.39652,0.2543,0.43506,0.3433,0.83764,0.27286,0.25692,1.078,0.68476,1.67616,0.54352,1.45114,0.1442,0.37046,0.36134,0.40702,1.34948,0.1026,1.69562,,0.15924,12.07892,0.63724,0.78898,,2.29202,0.35484,0.25736,0.18202,0.34322,0.3702,7.23528,0.0,1.05966,3.24258,0.53608,0.15672,0.13682,1.0609,0.20132,1.30436,0.67464,1.2144,0.39046,0.14006,0.10354,0.35856,0.0,0.38064,0.35296,1.3935,0.10704,0.08376,0.16444,0.49904,0.10932,0.72374,0.1336,0.89904,0.37212,0.08742,0.19408,0.22748,,,0.02068,0.72248,5.8392,3.61866,0.41976,0.1473,3.61148,0.17492,0.50954,1.53626,1.3509,0.3996,0.09984,0.28406,0.08344,0.45368,1.12198,0.07118,,0.71844,,17.44342,0.07208,3.65378,1.6003,0.7078,0.0,1.29082
2018-01-03,1.05766,0.5105,2.59964,0.90086,1.0917,0.81928,0.39988,0.5489,1.83512,0.59372,2.11484,1.25386,0.56246,0.90208,4.47232,2.1196,0.37598,1.67316,0.89032,0.47504,1.4686,1.37514,0.90858,1.121,1.29548,1.47144,0.99712,2.21268,0.65348,0.38516,0.48426,0.43544,2.81396,3.74788,0.86608,2.88254,0.95756,0.41458,0.39066,0.60666,4.83484,0.34136,0.94074,0.72302,2.29346,14.93772,1.70328,2.56078,1.1334,2.13126,0.81202,0.95628,0.39746,0.90278,1.80192,0.91532,1.00854,0.67502,1.2484,0.37988,2.64702,1.55112,0.66578,1.35108,2.56846,2.6827,1.21268,0.13534,1.02876,0.78228,0.41758,1.05988,0.3596,0.96856,0.8753,0.42052,0.7248,0.8663,1.25564,4.9462,1.53504,0.68162,0.66472,0.68712,0.64566,0.8996,1.17142,1.63242,1.47196,3.30548,0.20924,0.70498,0.0,0.5861,0.74354,0.1939,1.63386,3.77238,,0.74318,0.661,5.06222,0.56868,1.28236,1.886,1.0788,0.9984,0.62782,0.62366,1.39674,1.33218,0.42586,0.84312,1.12186,1.07068,2.64002,0.10114,0.62424,0.53646,0.30444,0.0837,0.15062,0.24344,0.1815,0.3577,0.08014,29.66076,0.18524,0.2021,1.60112,1.09286,2.31088,0.2694,0.80716,1.1949,0.94154,0.31774,0.71164,0.69738,0.31538,0.82388,0.80176,0.21814,0.62478,3.43482,1.1519,0.37312,0.54474,0.0,0.19564,1.65084,0.3506,1.44904,1.18616,0.79904,1.48884,0.4005,0.2843,1.40192,0.33746,0.56876,0.0,0.0,1.57756,1.0066,2.34564,3.07524,3.09636,0.37558,0.23152,2.12492,1.88936,0.24546,0.9776,0.21148,0.65088,1.67946,0.39794,2.0467,1.08194,1.50398,7.09044,0.48872,0.54056,2.9654,0.24768,1.4746,3.3557,0.58458,1.54738,0.3601,1.42488,0.42278,4.82188,0.32414,0.60612,0.4311,0.36526,0.72456,1.0861,0.739,0.43622,0.36832,0.74284,4.6132,0.20842,0.70984,0.3882,0.29382,0.46254,0.40184,0.86178,0.33226,0.2834,1.3348,0.63388,1.55252,0.66138,1.54696,0.15544,0.42152,0.444,0.46794,1.3931,0.10958,1.80028,,0.19762,11.97822,0.81304,0.80974,,2.53212,0.385,0.29574,0.20416,0.41148,0.49032,6.8267,0.0,1.24604,3.06366,0.54454,0.17728,0.13834,1.23848,0.2074,1.42818,0.68266,2.12316,0.49452,0.14576,0.1134,0.40282,0.0,0.44566,0.40822,1.49064,0.11928,0.10464,0.21762,0.5027,0.12748,0.89122,0.15664,0.80018,0.39588,0.09746,0.19624,0.25248,,,0.0241,0.67068,6.32046,4.25122,0.53898,0.17064,4.39442,0.24304,0.6744,1.58332,1.41034,0.42828,0.10322,0.41202,0.12584,0.81884,1.14978,0.08212,,0.73792,,15.28784,0.08074,4.92154,1.3534,0.76572,0.0,1.5951
2018-01-04,1.11534,0.53802,2.6768,0.93602,1.12156,0.74116,0.39144,0.56728,1.68954,0.64188,1.70848,1.34768,0.58678,0.95486,3.6681,1.98944,0.39394,1.80878,1.00888,0.43796,1.48762,1.38646,0.94522,1.23496,1.53262,1.63424,1.20612,2.06164,0.69318,0.3898,0.48192,0.41952,3.10922,3.17254,0.86186,2.8597,1.00878,0.42816,0.4297,0.63464,4.61112,0.39494,1.23116,0.72922,2.25094,15.80046,1.8647,2.59354,1.2663,2.04256,0.7772,1.09962,0.33774,0.96504,1.63576,0.94612,0.97262,0.63576,1.32578,0.3539,2.9228,1.5483,0.77634,1.32276,2.7246,2.50034,1.26372,0.09864,1.09876,0.72154,0.46866,1.36916,0.33108,1.55812,0.74552,0.44526,0.88308,0.79172,1.27658,4.82582,1.6618,0.6829,0.68694,0.77386,0.68038,0.89168,1.1488,1.65272,1.21062,3.58632,0.20314,0.87984,1.29144,0.59262,0.75592,0.16604,1.4966,3.99882,,0.78204,0.73594,4.63964,0.59884,1.38478,1.98034,1.23046,0.9919,0.61932,0.59968,1.40732,1.26342,0.46872,0.89374,1.1421,1.00746,2.5104,0.09768,0.64336,0.54698,0.32152,0.0926,0.13242,0.23162,0.2191,0.36836,0.08862,26.5317,0.22012,0.27776,1.85468,1.16672,2.14976,0.25806,0.90776,1.26208,0.9476,0.29334,0.70602,0.7522,0.33828,0.8652,0.73384,0.22406,0.63278,3.33096,1.25636,0.37616,0.60172,0.0,0.21638,1.65734,0.31106,1.4597,1.18476,0.81196,1.44766,0.43704,0.3666,1.35174,0.373,0.64128,0.0,0.0,1.73186,1.17468,2.49032,3.2819,3.2554,0.35736,0.22542,2.44192,1.91716,0.23288,1.07234,0.21692,0.74566,1.56972,0.39684,2.09278,1.1174,1.5066,7.5976,0.52354,0.59546,2.92478,0.24818,1.54956,3.2405,0.6362,1.61992,0.67296,1.52084,0.44698,5.4938,0.3816,0.5942,0.42288,0.63138,0.76442,0.90214,1.02414,0.42674,0.38502,0.76738,4.86264,0.20708,0.9371,0.36382,0.28228,0.48582,0.44908,1.01588,0.37194,0.25762,1.49092,0.64826,1.66504,0.61026,1.59036,0.15218,0.40296,0.43132,0.47682,1.30858,0.1271,1.8086,,0.21274,11.58428,0.8874,0.85874,,2.65072,0.36306,0.26008,0.2,0.4003,0.49992,5.27678,0.0,1.22488,2.58954,0.59352,0.17934,0.1388,1.1562,0.19964,1.65462,0.82192,2.47686,0.52374,0.15044,0.12972,0.4085,0.0,0.54524,0.47982,1.67656,0.11536,0.11424,0.23422,0.4801,0.133,0.8859,0.17362,0.76408,0.31538,0.10166,0.3605,0.2579,,,0.03828,0.63888,6.34854,4.30762,0.56946,0.19576,4.9065,0.24704,0.76364,1.63266,1.43652,0.4351,0.1096,0.5024,0.16328,1.48824,1.291,0.08534,,0.7513,,14.01176,0.1085,5.67768,1.42076,0.73492,0.0,1.69766
2018-01-05,1.07482,0.63604,2.65488,1.33022,1.15164,0.8553,0.33714,0.5801,1.60712,1.0653,2.92862,1.37594,0.6153,0.95826,3.32256,1.48378,0.35034,1.97464,0.89484,0.44402,1.2678,1.00344,1.06268,1.22048,2.16148,1.40278,1.1757,2.28664,0.6812,0.40422,0.50518,0.4209,3.35958,2.91562,0.89258,3.22262,0.85348,0.50068,0.4268,0.59438,3.67308,0.41806,1.63172,0.66292,1.95722,12.85238,2.50684,2.44908,1.16736,1.96588,0.85222,1.04132,0.32168,1.07014,1.78802,0.96902,1.06424,0.5716,1.4078,0.3581,3.53586,1.59762,0.82418,1.26564,2.77574,2.3052,1.27816,0.07222,1.14996,0.73444,0.43692,1.74384,0.30164,1.79934,0.75448,0.47906,0.9207,0.84962,1.40968,4.87048,1.75354,0.70116,0.73196,0.79612,0.69714,0.81862,1.19248,1.56596,1.06802,3.61964,0.25654,0.85502,2.03988,0.5989,0.8669,0.09818,1.31478,4.49274,,0.95364,0.9793,4.5802,0.6045,1.97096,1.8378,1.25862,1.11706,0.68334,0.6228,1.40066,1.36206,0.47382,0.9192,1.06526,1.2971,2.3883,0.10278,0.6494,0.58014,0.29342,0.09666,0.14152,0.25194,0.283,0.34096,0.1066,22.67646,0.24302,0.33864,2.04916,1.31096,2.08876,0.26374,1.07786,1.55052,1.0214,0.29836,0.78396,1.09142,0.41234,0.94902,0.66746,0.23404,0.61634,2.71782,1.39428,0.40332,0.75668,0.0,0.29474,1.59222,0.3828,1.40218,1.25098,0.91164,1.1995,0.3695,0.39394,1.17014,0.4159,0.6597,0.0,0.0,1.74118,1.53878,2.35828,3.03256,2.86492,0.32558,0.23092,3.22972,1.91738,0.19042,1.22852,0.25726,0.8433,1.50792,0.41012,2.16686,1.04256,1.44268,8.02348,0.54796,0.46148,3.02254,0.24248,1.56148,2.63406,0.60112,1.87054,0.82262,1.62404,0.46742,8.90842,0.36188,0.62864,0.428,0.76208,0.83274,0.80036,1.15604,0.45798,0.4219,0.71292,4.76846,0.20458,0.87534,0.33484,0.2695,0.47784,0.45864,1.0204,0.4319,0.2713,1.3668,0.61656,1.62742,0.60776,1.41328,0.1345,0.39836,0.4982,0.48702,1.3774,0.13182,1.62002,,0.2404,9.87476,0.99876,0.90914,,3.17752,0.36772,0.24146,0.23018,0.40864,0.51022,3.91658,0.0,1.14068,2.87986,0.6057,0.18098,0.13486,1.08994,0.20232,1.45874,0.90868,6.13482,0.57508,0.17572,0.13504,0.39442,0.0,0.56826,0.5095,1.61238,0.13726,0.12896,0.25574,0.57802,0.1597,0.8903,0.17852,0.75472,0.27766,0.13086,0.60056,0.26176,,,0.0478,0.61788,5.48814,3.91656,0.54182,0.21316,4.62452,0.24314,0.85596,1.605,1.44288,0.41254,0.1171,0.60892,0.18296,1.91754,1.2914,0.09914,,0.8188,,12.43798,0.11992,6.3116,1.26644,0.7081,0.0,1.4986
2018-01-08,1.2138,0.72782,2.9691,1.65308,1.19424,1.10614,0.32478,0.59944,2.06526,1.40602,3.71946,1.4464,0.66502,0.974,3.63874,1.31088,0.38276,2.37676,0.87384,0.58874,1.30184,1.11862,1.20342,1.226,2.66724,1.17252,1.29826,2.56712,0.812,0.40982,0.66896,0.50552,3.67148,3.01254,0.84922,2.88894,0.77938,0.74468,0.43772,0.75916,3.03044,0.44516,2.06596,0.66474,2.18514,10.21266,3.19184,2.58026,1.16946,1.95656,0.93106,1.1299,0.279,1.07738,2.00198,0.98412,1.15344,0.37336,1.36006,0.48246,4.09308,1.69802,0.91782,1.42816,2.96372,2.15454,1.37118,0.05186,1.21402,0.79336,0.42248,1.8326,0.31368,1.92366,0.57968,0.51656,0.96656,0.87434,1.54196,5.34432,1.964,0.7057,0.77452,0.79862,0.84948,0.87762,0.95406,1.58636,1.12284,4.02386,0.29962,1.02982,2.73182,0.7121,1.22016,0.1016,1.40146,5.31862,,1.03888,1.12306,4.41692,0.6212,2.35972,2.17158,1.34262,1.06896,0.77082,0.64958,1.70728,1.05336,0.50988,1.04904,1.17986,1.20744,2.4239,0.11348,0.7269,0.66806,0.33006,0.0972,0.15332,0.29038,0.3123,0.43132,0.13034,18.7079,0.25042,0.394,2.80994,1.68716,2.35452,0.29568,1.13326,1.80876,1.07926,0.3487,0.85844,1.4103,0.4682,1.1243,0.73114,0.24852,0.71128,2.87156,1.67506,0.45498,0.87872,0.0,0.34224,1.68992,0.46004,1.79006,1.30602,1.0726,1.19672,0.46264,0.41882,1.238,0.4338,0.7297,0.0,0.91252,1.70166,2.09654,2.6732,3.10412,3.10826,0.3836,0.21476,3.80212,2.07922,0.20576,1.28926,0.3138,0.95708,1.44618,0.46574,2.50394,1.13858,1.33252,8.78772,0.54384,0.42354,3.31718,0.27356,1.70982,2.67462,0.57284,2.009,0.97582,1.94712,0.57728,12.53196,0.39312,0.70946,0.4736,0.94064,1.03328,0.84216,1.25278,0.51588,0.44602,0.79052,4.898,0.25102,0.90618,0.36566,0.33434,0.51246,0.46042,1.05086,0.43884,0.26618,1.4815,0.63586,1.65176,0.72846,1.42486,0.14158,0.50568,0.68362,0.5865,1.48194,0.13314,1.84384,,0.3362,10.58446,1.27982,0.91638,,3.65954,0.3973,0.28318,0.24812,0.53738,0.6136,3.7159,0.0,1.43476,3.0733,0.60002,0.1832,0.12918,1.12478,0.21204,1.68842,1.23664,8.18952,0.72652,0.19112,0.13516,0.51214,0.0,0.67922,0.5954,1.83014,0.25058,0.14022,0.32202,0.72766,0.18992,1.23658,0.18956,0.8924,0.29848,0.1525,0.73182,0.26998,,,0.05278,0.58198,5.7228,4.41246,0.60982,0.28548,4.95078,0.30998,0.8964,1.6359,1.43056,0.4268,0.12012,0.52208,0.23176,2.22366,1.36266,0.13468,,0.79002,,15.53688,0.12726,6.85224,1.3518,0.76486,0.0,1.67168
2018-01-09,1.12664,0.6856,3.00962,1.57334,1.54372,1.02654,0.33244,0.63666,2.05152,1.4525,4.06326,1.44724,0.59494,1.1033,3.61628,1.27978,0.46836,2.17984,0.8854,0.58024,1.42046,1.08758,1.2643,1.1479,2.75476,0.81222,1.29636,3.30368,0.76886,0.39602,0.65446,0.48412,3.33944,3.07766,0.79742,2.42028,0.81792,0.76754,0.44422,0.72608,2.90826,0.43208,2.04482,0.62902,2.01282,8.7251,3.039,2.6228,0.98716,2.20254,0.93484,1.15504,0.28068,1.22848,1.9115,0.96154,1.04156,0.35224,1.32898,0.49252,3.53758,1.6773,0.93538,1.3029,2.80922,2.00962,1.35218,0.05586,1.07,0.80516,0.47648,1.79906,0.32032,2.05484,0.57466,0.5415,0.9563,0.86906,1.5189,5.24418,2.0701,0.5764,0.71486,0.73758,0.86398,0.85494,0.83292,1.23644,1.06516,3.8682,0.30206,1.10726,3.06796,0.70496,1.3932,0.09656,1.14522,5.46738,,1.05622,1.18228,4.4657,0.63588,2.31976,2.23422,1.3405,1.0715,1.07218,0.67364,1.74302,1.0958,0.50946,0.96116,1.18748,1.17292,2.38252,0.10984,0.80478,0.70904,0.33232,0.09314,0.13548,0.28394,0.30622,0.42532,0.13242,16.95694,0.24916,0.37386,2.77654,1.69652,2.11144,0.2688,1.21608,1.74464,1.06468,0.34904,0.87042,1.40816,0.4827,1.15198,1.0199,0.25006,0.65906,3.14666,1.7309,0.46878,0.81968,0.0,0.35166,1.62356,0.45394,1.85374,1.33862,1.11226,1.116,0.48516,0.4197,1.1673,0.5219,0.74598,0.0,1.49022,1.6212,2.22974,2.75504,2.7298,2.90636,0.38066,0.23394,3.16514,2.54992,0.17794,1.30262,0.33198,1.09406,1.46196,0.44788,2.60618,1.20906,1.2897,9.1677,0.56938,0.4474,3.21696,0.3565,1.6287,3.09604,0.56152,1.93734,0.93398,1.67738,0.59576,12.93712,0.38546,0.6546,0.60542,0.91302,1.16734,0.84152,1.21792,0.46448,0.45714,0.66624,5.37368,0.24838,1.03704,0.41684,0.32402,0.62878,0.53182,1.08194,0.48396,0.25356,1.5065,0.56738,1.56504,0.67798,1.3623,0.13984,0.48526,0.72032,0.6531,1.34782,0.13706,2.3827,,0.3459,10.12042,1.18268,0.84698,,3.3283,0.3616,0.27206,0.23432,0.58446,0.60188,3.64012,0.0,1.27118,2.98054,0.55956,0.17628,0.09204,1.1254,0.17348,1.79674,1.36702,8.40242,0.681,0.18972,0.1077,0.51804,0.0,0.71374,0.704,1.87564,0.31016,0.13512,0.2882,0.76648,0.18424,1.22032,0.16548,0.81728,0.27564,0.14708,0.83408,0.25588,,,0.05102,0.51636,5.50434,4.5685,0.645,0.307,4.50862,0.3139,0.95114,1.7627,1.21938,0.39546,0.09988,0.58672,0.24838,2.296,1.2621,0.14184,,0.76452,,16.64768,0.13744,7.13782,1.31582,0.87206,0.0,1.6647
2018-01-10,1.06052,0.64582,2.65186,1.55354,1.64838,1.01236,0.3161,0.6431,2.03764,1.52062,4.24318,1.33,0.48666,0.96166,3.5543,1.32706,0.47356,2.08336,0.90992,0.5473,1.25842,1.0983,1.36228,1.15436,2.77176,0.50322,0.98092,3.78312,0.71942,0.4185,0.63488,0.46542,2.88624,3.35158,0.63342,2.12746,0.8484,0.81924,0.46718,0.72166,2.68844,0.37418,2.15956,0.50618,1.84274,7.41952,2.8117,2.3559,0.74846,2.3037,0.97738,1.0836,0.39608,1.1792,1.77336,1.24034,1.01216,0.31336,1.26536,0.55456,3.25196,1.73214,0.97916,1.19148,2.5618,1.65352,1.21238,0.0552,0.978,0.76122,0.51006,1.77582,0.3128,2.06582,0.5932,0.5381,0.93476,1.00152,1.44298,4.60692,1.92078,0.73274,0.68552,0.72776,0.87572,0.74158,0.9686,0.98582,0.96388,3.69192,0.31674,1.09876,3.39764,0.66282,1.42308,0.09074,1.09934,5.28452,,0.9293,1.2049,4.27738,0.60422,2.31528,2.0285,1.00038,0.98972,1.24388,0.68188,1.84416,1.09574,0.46622,0.82356,1.02596,1.02506,2.35898,0.14746,0.83018,0.62896,0.32362,0.09632,0.18118,0.31556,0.30048,0.41118,0.14154,16.43478,0.27774,0.42122,2.62818,1.69304,2.05764,0.27664,1.22068,1.6638,0.91532,0.31636,0.80508,1.36066,0.48984,1.15066,1.03788,0.28524,0.59178,3.56896,1.58776,0.46248,0.82138,0.0,0.35638,1.54376,0.4628,1.8645,1.23992,1.13808,1.0745,0.34462,0.38234,1.19658,0.53662,0.77424,0.0,1.79294,1.67464,2.1693,3.1406,2.3452,3.0184,0.35468,0.23174,2.67928,2.69286,0.18584,1.16236,0.30466,1.35744,1.49258,0.45256,2.38492,1.14396,1.07198,9.48804,0.40614,0.44054,2.9691,0.3607,1.44618,3.2725,0.42478,2.81086,1.15432,1.53238,0.65188,12.42912,0.33258,0.6327,0.60422,0.92088,1.24858,0.75044,1.23696,0.40914,0.4767,0.61286,6.43496,0.25048,1.07256,0.41958,0.33496,0.68122,0.53316,1.04336,0.47474,0.20804,1.22916,0.55406,1.55858,0.57616,1.38462,0.16048,0.46382,0.71614,0.67898,1.28522,0.13664,2.652,,0.3522,10.67422,1.06856,0.86486,,3.28364,0.66446,0.27546,0.24346,0.59262,0.56436,3.39806,0.0,1.14728,2.74456,0.55696,0.15626,0.09966,0.94706,0.25474,1.71314,1.39244,9.432,0.65034,0.19264,0.10024,0.52532,0.0,0.69508,0.72032,1.89606,0.326,0.11626,0.31702,0.90092,0.17912,1.2214,0.15012,0.82746,0.25216,0.14794,1.01562,0.42672,,,0.05926,0.49842,5.04506,4.06092,0.52442,0.31176,4.01432,0.2614,0.856,1.71596,1.23402,0.39578,0.11584,0.56744,0.22038,2.07788,1.2659,0.18168,,0.72932,,19.20256,0.1437,6.08586,1.31864,0.83752,0.0,1.48792
2018-01-11,1.01198,0.61974,2.74766,1.57184,1.66464,0.99302,0.29832,0.61486,2.07526,1.52532,4.6869,1.16434,0.50558,0.86294,3.54664,1.24718,0.4917,1.95314,0.77314,0.54132,1.10712,1.00224,1.37688,1.21358,2.65012,0.0,0.81268,3.954,0.69498,0.42424,0.64838,0.48246,2.8352,3.93058,0.51836,1.8909,0.79266,0.86406,0.48518,0.70282,2.84556,0.3329,2.05104,0.43962,1.76922,6.34452,2.81916,2.12732,0.64034,2.52296,1.21214,1.01794,0.44656,1.11192,1.70876,1.81438,0.9989,0.3496,1.19246,0.60232,3.09712,1.83358,0.96908,1.09492,3.42204,1.6382,1.16578,0.06808,0.9114,0.75412,0.48484,1.61238,0.31032,1.48546,0.57714,0.50874,0.80224,1.07228,1.53124,4.0866,1.71704,0.72462,0.65876,0.62214,0.90976,0.7043,1.39964,0.89582,0.88988,3.62028,0.33008,0.92586,2.45588,0.64428,1.3843,0.0839,0.94732,6.40222,,0.88082,1.16052,4.0981,0.64854,2.822,2.43298,0.89186,0.9904,1.33814,0.69774,2.73042,0.96728,0.44776,0.82436,0.99806,0.94122,2.41658,0.15454,0.7569,0.59718,0.31718,0.0879,0.20766,0.3314,0.24782,0.42002,0.14318,13.09992,0.25092,0.3487,2.51838,1.69016,2.15866,0.27894,1.11952,1.61508,0.92502,0.30308,0.788,1.41642,0.49142,1.1231,1.37764,0.28632,0.58322,3.55892,1.49324,0.44898,0.81458,0.0,0.339,1.53482,0.44616,1.853,1.1553,1.16692,1.00682,0.26042,0.3105,1.34202,0.48406,0.6617,0.0,2.04786,1.29842,2.2087,3.1062,2.22952,2.80046,0.34514,0.23802,2.51664,2.73548,0.17662,1.07714,0.30382,1.33686,1.47832,0.434,2.33284,1.12172,1.16924,8.54472,0.38238,0.40622,2.86038,0.41786,1.42016,3.21372,0.46602,4.0344,0.86516,1.87262,0.8263,12.77596,0.29544,0.61266,0.56278,0.72768,1.28576,0.72846,0.92228,0.41656,0.46242,0.60824,6.28138,0.23622,0.86296,0.40846,0.33654,0.634,0.4905,0.84428,0.48102,0.19366,1.0743,0.48038,1.2268,0.5681,1.44958,0.18542,0.45778,0.73262,0.86988,1.3394,0.11314,2.82772,,0.35342,11.14038,1.06176,0.77542,,3.63368,0.80106,0.27558,0.24894,0.67618,0.53802,3.36982,0.0,1.12686,2.5534,0.51768,0.14598,0.10546,0.95354,0.28004,1.49274,1.32702,11.04682,0.62238,0.1869,0.08258,0.53694,0.0,0.61662,0.6815,1.61626,0.3596,0.10788,0.302,0.96278,0.19416,1.24688,0.12872,0.7923,0.26304,0.13642,0.86908,0.4818,,,0.04822,0.53352,4.81554,3.9843,0.45784,0.30504,3.53446,0.2625,0.79148,1.53616,1.16352,0.39574,0.11786,0.55862,0.1997,1.5043,1.04432,0.22126,,0.71922,,19.47216,0.11634,5.55386,1.29428,0.8161,0.0,1.79768
2018-01-12,1.02896,0.55668,2.68112,1.2366,1.85132,0.7915,0.2736,0.57736,2.08692,1.3062,3.55854,1.16168,0.51258,0.91738,3.1348,1.32054,0.50002,2.0259,0.74446,0.54878,1.08194,1.0098,1.4013,1.2236,2.20778,0.0,0.76958,3.84078,0.72714,0.38766,0.62152,0.40638,2.6544,3.71398,0.50226,1.38474,0.83954,0.80172,0.47734,0.73458,2.73912,0.31044,1.47204,0.44502,1.80594,5.97948,2.26178,2.40736,0.66412,2.43178,1.24772,0.94202,0.47234,0.97102,1.53228,2.3868,0.9021,0.35556,1.28436,0.63842,2.6855,1.79262,0.90374,1.02398,3.94564,1.57796,1.14072,0.07988,0.94904,0.81928,0.47948,1.3098,0.29632,1.25606,0.58106,0.51496,0.75482,1.02168,1.4209,3.62902,1.62152,0.70032,0.58018,0.58676,0.99414,0.65774,1.56644,0.85756,1.04994,3.7023,0.2679,0.8901,1.91512,0.59024,1.33172,0.07724,1.05426,6.25102,,0.69606,0.90656,3.92764,0.67076,2.47016,2.61482,1.01848,0.82388,1.36108,0.67406,3.06416,0.79488,0.4442,0.78504,0.95246,0.62636,2.4576,0.14968,0.80932,0.5763,0.33574,0.08118,0.21994,0.31546,0.1868,0.44798,0.164,12.31066,0.24172,0.29996,2.3696,1.55102,1.96402,0.27966,1.16486,1.40652,0.82954,0.25872,0.7279,1.14388,0.4331,1.13758,1.49876,0.2824,0.54298,3.50918,1.38778,0.4357,0.73186,0.0,0.27434,1.55774,0.37346,1.79804,1.12214,1.00076,0.9841,0.18974,0.27574,1.33546,0.44022,0.706,0.0,2.42628,1.19022,2.14818,3.37554,2.12774,2.69514,0.33264,0.23024,1.87538,2.72018,0.1722,1.07438,0.26938,1.36184,1.41934,0.4207,2.21322,1.15264,1.11866,8.36784,0.3723,0.41578,2.69704,0.41608,1.52014,3.30116,0.5024,4.5783,0.77492,1.8562,1.08518,11.03568,0.32162,0.57552,0.55558,0.65502,1.29116,0.75184,0.80096,0.34844,0.41352,0.59414,7.64364,0.2275,0.87564,0.40876,0.3441,0.59584,0.51726,0.8336,0.42332,0.1725,0.93854,0.44856,1.06226,0.52694,1.2332,0.23212,0.46182,0.67568,1.0395,1.25936,0.10382,2.85804,,0.32756,13.01484,1.05332,0.68752,,3.61378,0.86592,0.26598,0.21582,0.73682,0.54288,3.1995,0.0,1.11278,1.9221,0.47186,0.15812,0.11224,1.04782,0.2909,1.3697,1.32138,8.68516,0.52964,0.1559,0.07792,0.5124,0.0,0.65138,0.66974,1.50388,0.36812,0.10276,0.2931,0.77554,0.16862,1.27794,0.11852,0.73994,0.24914,0.10284,0.70228,0.52382,,,0.04154,0.53402,4.54364,3.55388,0.43622,0.29406,3.09896,0.26118,0.69982,1.51756,1.15956,0.40676,0.1148,0.5591,0.18466,1.22254,0.97814,0.25468,,0.63044,,20.52746,0.10264,4.78688,1.51188,0.80948,0.0,1.85714
2018-01-15,1.1429,0.54152,2.96288,1.05068,2.17284,0.61118,0.24316,0.5771,1.85504,1.15314,3.2021,1.24352,0.57324,0.9527,2.65226,1.3036,0.4928,2.22574,0.8357,0.43876,1.34506,0.87424,1.46186,1.30402,1.94012,0.0,0.7165,4.28846,0.7137,0.4789,0.52762,0.36444,2.42548,3.80182,0.62532,1.31922,0.88846,0.64056,0.61514,0.69762,3.06492,0.27902,0.88034,0.47446,1.52242,5.86246,1.789,2.81918,0.70598,2.52868,1.5242,0.89686,0.62292,0.94946,1.45612,3.07224,0.78566,0.34068,1.29518,0.68262,2.28978,1.65372,0.97436,1.06736,4.3303,1.45802,1.01796,0.0738,0.9182,0.75584,0.4929,1.60208,0.30556,1.36738,0.57638,0.50742,0.72894,1.02748,1.3414,4.38254,1.92846,0.73026,0.57006,0.56464,0.98328,0.7258,1.64434,0.7908,1.07656,3.22612,0.21154,0.76508,1.47786,0.60174,1.06484,0.067,1.26062,5.99834,0.0077,0.59752,0.81294,3.9859,0.79506,2.38108,2.55984,1.0789,0.78684,1.39818,0.67906,3.0989,1.30044,0.45284,0.74898,0.94378,0.70558,2.37584,0.17098,0.97792,0.61136,0.30882,0.09136,0.27146,0.35316,0.18724,0.4134,0.16512,12.11436,0.2788,0.29934,1.76358,1.50042,1.76252,0.28108,1.45274,1.37108,0.81152,0.214,0.68984,0.96562,0.41352,1.0937,1.55956,0.2757,0.55394,3.34336,1.17974,0.47442,0.59398,0.0,0.25146,1.47924,0.3371,1.59832,1.23378,0.86606,0.94232,0.06562,0.26552,1.31122,0.4709,0.64536,0.0,1.94184,1.38456,1.89736,3.23638,2.07568,2.52938,0.30218,0.2472,1.58006,2.629,0.19522,1.3665,0.22972,1.38286,1.42296,0.41714,2.20934,1.1256,1.18874,8.6629,0.4043,0.4162,2.96734,0.42052,1.71158,3.2036,0.53122,4.89478,0.79422,1.65276,1.08594,9.33114,0.30082,0.55756,0.55222,0.65716,1.31058,0.89772,0.8489,0.32946,0.42138,0.488,8.64286,0.19332,0.98516,0.44846,0.31766,0.63192,0.55586,0.8878,0.5304,0.17586,0.83548,0.52628,1.09926,0.4595,1.19002,0.23656,0.37178,0.5911,1.15636,1.29126,0.10608,2.66592,,0.26244,13.1335,0.88706,0.69724,,3.4289,1.02222,0.27206,0.22636,0.77608,0.48364,3.05042,0.0,0.8585,1.76754,0.6039,0.1651,0.13394,1.19144,0.37918,1.23694,1.2924,7.99818,0.42854,0.15502,0.10106,0.44742,0.0,0.707,0.81922,1.3089,0.28494,0.13034,0.24082,0.75774,0.17352,1.12234,0.13248,0.62572,0.22868,0.09412,0.7335,0.62966,,,0.04392,0.51718,4.5256,3.04576,0.45002,0.25096,2.68216,0.22954,0.68548,1.53054,1.27744,0.4212,0.13106,0.60126,0.16128,1.04896,1.08276,0.2741,,0.6908,,18.46304,0.10106,4.90822,1.76668,0.8462,0.0,1.93496
2018-01-16,1.27296,0.64452,3.60626,1.4939,2.15794,0.64046,0.23444,0.52708,2.09512,1.46968,2.98476,1.20982,0.57266,0.97508,2.48638,1.22562,0.48076,2.55218,0.8186,0.44946,1.31794,0.85254,1.47786,1.32682,2.04528,0.0,0.68186,3.72756,0.72006,0.65532,0.54962,0.34842,2.52464,3.97558,0.71462,1.35148,0.87674,1.02452,0.69876,0.63472,3.1106,0.25364,1.26898,0.52084,1.37082,6.85294,2.48412,2.72794,0.7311,2.60318,1.82388,0.8574,0.70306,0.85132,1.35014,3.4734,0.7685,0.32098,1.25026,0.67678,2.43984,1.66014,0.9884,1.0434,4.7414,1.46748,1.17038,0.0665,0.9308,0.70824,0.48706,1.70262,0.30636,1.34104,0.53198,0.52508,0.68628,1.47276,1.71418,4.80532,1.97904,0.71168,0.7794,0.57116,0.96976,0.89052,1.57598,0.80062,1.07938,3.11012,0.23562,0.75364,1.34848,0.62,1.03826,0.07112,1.35086,6.04034,0.01094,0.64652,0.8344,3.71288,0.77328,2.32512,2.7479,1.1643,0.7669,1.19724,0.61832,3.73258,1.7102,0.42332,0.69864,1.01978,0.78642,2.92532,0.20562,1.06286,0.67018,0.2752,0.0981,0.31032,0.37608,0.19212,0.38574,0.18138,14.41368,0.283,0.33556,1.88934,1.68208,1.78448,0.30572,1.60066,1.67136,0.78922,0.22572,0.73528,0.88226,0.47448,1.0047,1.30822,0.3379,0.58008,2.83268,1.15504,0.49536,0.6607,0.0,0.2408,1.54708,0.40122,1.48438,1.27644,0.89646,0.92976,0.0,0.27756,1.3038,0.43858,0.68492,0.0,1.71934,1.39828,2.051,3.1455,2.0757,2.62448,0.30144,0.23346,1.8298,2.01672,0.28422,1.3474,0.23212,1.31258,1.28882,0.45156,2.57592,1.10062,1.22326,8.38688,0.40422,0.41752,2.91842,0.36432,1.83612,2.63842,0.6006,5.0981,0.81972,1.66152,1.13052,9.96806,0.31032,0.58816,0.38444,0.7509,1.44886,1.25122,0.86892,0.33686,0.45888,0.56368,8.77782,0.18122,1.03662,0.38612,0.33912,0.73232,0.46784,0.94544,0.48546,0.19328,0.83322,0.5759,1.3018,0.45924,1.15074,0.24992,0.41098,0.55552,1.1672,1.37726,0.09878,2.53298,,0.269,16.27028,1.05182,0.75238,,3.61232,1.1713,0.28016,0.22676,0.80396,0.48972,2.88798,0.0,0.72106,2.26574,0.76622,0.1378,0.1741,1.23984,0.45792,1.05524,1.31096,7.63266,0.41484,0.14918,0.117,0.42918,0.0,0.71946,0.86002,1.1474,0.2456,0.13026,0.21478,0.86486,0.16572,1.17142,0.13334,0.62746,0.24264,0.09536,0.70766,0.70328,,,0.04694,0.54434,4.7549,3.0973,0.53292,0.222,2.37384,0.2148,0.59936,1.63168,1.46652,0.47404,0.13666,0.5295,0.16104,1.05514,1.28468,0.28728,,0.67462,,17.84374,0.10436,4.90078,2.10498,0.72734,0.0,2.25322
2018-01-17,1.30288,0.71836,4.2951,1.86374,2.3346,0.69386,0.4122,0.51602,2.22106,1.60192,2.70676,1.27236,0.49922,1.01232,2.54586,1.05598,0.49642,2.83686,0.82502,0.51862,1.70652,0.77626,1.4383,1.28972,2.32394,0.0,0.66594,3.59798,0.95996,0.68732,0.88282,0.50922,2.56892,3.6568,0.75422,1.2869,0.9577,1.26282,0.71144,0.63514,3.2064,0.28814,1.64602,0.51526,1.3984,7.26286,3.42812,2.97766,0.6781,3.05484,2.04396,0.84152,0.62288,0.83042,1.34632,3.49302,0.80748,0.31276,1.4092,0.7285,3.05464,1.66924,0.92912,1.02412,5.38836,1.60788,1.29308,0.06796,0.85126,0.68288,0.46892,1.55644,0.3044,1.30534,0.50224,0.52478,0.67644,1.60468,2.01394,5.32264,1.98514,0.53554,0.80356,0.6504,0.9328,1.00088,1.42748,0.78896,1.11848,2.5798,0.22506,0.82512,1.23392,0.83172,1.05708,0.1862,1.42278,7.53154,0.01718,0.84194,0.8335,3.87668,0.8014,2.59362,3.44608,1.20338,0.78968,1.03502,0.67788,3.73266,1.85606,0.4269,0.80022,1.13678,0.806,3.25906,0.23156,1.07212,0.71886,0.26916,0.1013,0.36514,0.44452,0.19042,0.41842,0.19944,17.52512,0.25476,0.32638,2.04724,2.39046,1.8137,0.32774,1.66834,1.89612,1.06044,0.34516,0.74132,1.22382,0.57492,0.9408,1.30384,0.31974,0.84018,2.25642,1.10708,0.52148,0.7745,0.0,0.24258,2.08946,0.44426,1.44428,1.32776,0.94286,0.86338,0.0,0.28666,1.32158,0.41726,0.72256,0.0,1.72774,1.53866,2.03814,2.79798,2.23154,2.4862,0.45182,0.22564,2.04968,1.86728,0.32702,1.38004,0.28282,1.09944,1.5341,0.42108,2.68234,1.21182,1.2867,7.4463,0.40576,0.48598,3.05902,0.35398,2.05362,2.2827,0.68512,4.50606,0.71984,1.68792,1.18646,11.39982,0.35494,0.67482,0.35652,0.74806,1.51398,1.52266,1.05298,0.44448,0.63612,0.63262,8.15476,0.20716,1.09088,0.52968,0.48286,0.7578,0.43558,1.06012,0.50582,0.21888,1.25536,0.7512,1.7259,0.69498,1.38092,0.26078,0.58524,0.57156,1.61344,1.60232,0.09992,2.20064,,0.26064,21.17032,1.1856,0.80828,,4.02508,1.17922,0.33584,0.2486,1.0,0.6909,2.80684,0.0,0.70908,3.24812,1.0035,0.1335,0.20372,1.34792,0.49888,0.97978,1.2848,6.71352,0.61512,0.16386,0.1422,0.63794,0.0,0.73206,0.8628,1.19976,0.24274,0.15112,0.14992,1.1444,0.20732,1.6194,0.132,0.6395,0.36066,0.11968,0.643,0.70306,0.00942,,0.04848,0.51756,8.83754,5.2819,0.55764,0.22166,2.41804,0.29752,0.55352,1.76204,1.73762,0.53364,0.13952,0.51816,0.16558,1.035,1.59584,0.28102,,0.67128,,16.5104,0.1105,5.42828,2.15266,0.71808,0.0,2.4307
2018-01-18,1.38612,0.75058,4.24822,1.95626,2.42202,0.71624,0.4761,0.48696,2.39134,1.62506,2.13068,1.23032,0.3593,1.1602,2.41436,1.00336,0.44896,3.3796,0.95774,0.5281,1.91944,0.73638,1.41232,1.07838,2.4194,0.0,0.70088,3.5158,1.03336,0.67,1.06402,0.54816,2.31332,3.25492,0.77842,1.23054,1.04988,1.34708,0.6935,0.85184,2.65538,0.31102,1.70348,0.53654,1.39316,7.81288,3.6603,2.98234,0.68012,2.81832,1.85266,0.83568,0.62992,0.82464,1.35828,3.10636,0.79718,0.29638,1.48184,0.7355,3.519,1.3826,0.88258,1.15426,4.28846,1.47462,1.28216,0.05336,0.879,0.7563,0.46412,1.40922,0.31514,1.37752,0.46166,0.54546,0.85198,1.76916,2.0867,5.41656,1.96348,0.48522,0.8689,0.72174,0.842,0.98046,0.96432,0.76328,1.11392,2.7194,0.2055,0.79036,1.02176,0.8969,1.09258,0.37084,1.58908,6.66026,0.0249,0.86962,0.87064,4.58216,0.73494,2.17524,3.1463,1.16704,0.79282,0.9921,0.65224,2.99092,1.93124,0.4313,0.7347,1.14598,0.81988,3.14812,0.28406,1.11514,0.81488,0.2855,0.11266,0.40066,0.48454,0.21442,0.55206,0.20684,18.17298,0.28008,0.37702,2.06004,2.76192,1.75108,0.35396,1.7215,2.01552,1.18634,0.39048,0.75398,1.52254,0.63464,0.92712,0.99386,0.34624,0.92514,2.02248,1.15368,0.5042,0.8379,0.0,0.34756,2.22502,0.47658,1.35828,1.41646,1.0318,0.84672,0.0,0.2773,1.09152,0.42668,0.75552,0.0,1.78808,1.51038,2.01372,3.08444,2.71208,2.28216,0.50078,0.20814,2.105,1.86934,0.3633,1.37584,0.28284,1.06146,1.48116,0.4069,2.58434,1.2645,1.18508,7.02084,0.4275,0.49924,2.8793,0.28532,2.1655,2.00918,0.63376,3.26596,0.68278,1.34686,1.00298,11.15248,0.35734,0.6948,0.3892,0.78916,1.56826,1.6451,1.13942,0.4404,0.7596,0.76794,7.8073,0.20816,1.28922,0.58428,0.54434,0.76484,0.45282,1.2179,0.48184,0.24164,1.36686,0.94116,2.10958,0.7766,1.30574,0.25356,0.68524,0.61172,1.6501,1.56848,0.10188,2.03846,,0.27032,24.77796,1.26792,0.91438,,3.80738,1.3947,0.36318,0.35708,1.02436,0.81024,2.91592,0.0,0.68222,3.693,1.12252,0.13756,0.23142,1.52852,0.70544,0.9371,1.43498,5.88798,0.71972,0.24832,0.17112,0.69632,0.0,0.77054,0.90556,1.4521,0.26228,0.16288,0.1596,1.7488,0.2964,1.94158,0.12668,0.6989,0.3792,0.20192,0.65002,0.81242,0.01448,,0.0497,0.56726,10.97758,6.11432,0.60188,0.21452,2.2688,0.31932,0.54926,1.7839,1.97514,0.74786,0.17668,0.54584,0.19018,0.95554,1.74012,0.30228,,0.61686,,15.48396,0.11198,4.93354,2.2076,0.68388,0.0,2.1114
2018-01-19,1.53002,0.753,4.33608,2.23058,2.38366,0.85144,0.66312,0.50612,2.38696,1.58732,1.9662,1.1068,0.2506,1.22542,2.50198,0.88436,0.44018,3.48396,1.08474,0.56326,2.17232,0.69786,1.32966,0.94866,2.5395,0.0,0.77474,3.99418,1.32646,0.7204,1.2954,0.77992,2.36738,3.40256,0.77482,1.25892,1.08206,1.45358,0.71562,1.081,2.46234,0.33646,2.083,0.53394,1.44494,7.91154,3.98026,2.4774,0.63246,2.97418,1.86122,0.9105,0.65074,0.86134,1.49942,3.72372,0.85606,0.32802,1.52852,0.80472,3.79836,1.45828,0.93636,1.2376,3.63902,1.79168,1.3198,0.04762,0.85812,0.68544,0.46718,1.49854,0.2991,1.48898,0.46126,0.5291,0.9172,1.81482,2.15754,5.54156,1.95684,0.481,0.96982,0.73766,0.7231,0.90096,0.7577,0.83528,0.86162,2.72786,0.22944,0.78452,0.94718,1.29968,1.06176,0.5877,1.4644,8.62014,0.06222,0.9226,0.89326,5.07322,0.70006,2.60412,3.73396,1.02034,0.86814,0.97912,0.6607,3.1053,1.99788,0.4889,0.73412,1.12332,0.80136,3.18532,0.389,1.09094,0.82458,0.2707,0.1422,0.53224,0.61926,0.23986,0.84858,0.19096,18.0226,0.3332,0.42348,2.64172,3.2885,1.7909,0.38828,1.69438,2.11182,1.3324,0.52462,0.74448,1.71478,0.65984,1.18108,0.89084,0.37014,1.28484,1.9047,1.18518,0.56816,0.92768,0.0,0.40194,2.3187,0.56242,1.3821,1.28356,1.18436,0.87462,0.0,0.27566,1.00844,0.45846,0.71408,0.0,1.61602,1.70542,1.9001,3.13238,3.00868,2.20698,0.65152,0.21728,2.22484,2.26566,0.36642,1.4642,0.27906,0.94958,1.62436,0.41122,2.60252,1.1641,1.172,6.54086,0.4597,0.5071,2.89066,0.3399,1.95552,1.81794,0.61356,2.63882,0.71374,1.35134,0.71464,10.7532,0.34176,0.68532,0.3877,0.86496,1.58354,1.69894,1.16678,0.56196,0.91196,0.849,6.71688,0.23912,1.33666,0.71464,0.66284,0.81074,0.42422,1.29738,0.53362,0.26016,1.99908,1.46442,3.29164,1.10546,1.31064,0.23566,0.81794,0.72312,1.8375,1.7024,0.1032,1.94676,,0.32246,28.56178,1.3403,0.93028,,3.82694,1.89358,0.47424,0.41452,1.52988,1.01716,3.12126,0.0,0.72796,3.83972,1.56502,0.1162,0.24916,1.57858,0.98582,0.92538,1.47596,5.50914,1.01316,0.28526,0.18728,0.96004,0.0,0.7387,1.19086,1.56694,0.25542,0.16632,0.15466,2.06488,0.35504,2.19818,0.13456,0.7592,0.52584,0.24356,0.65122,1.03684,0.0237,,0.05946,0.57886,14.74318,8.57406,0.61786,0.21236,2.20762,0.44032,0.5804,1.89974,2.28952,0.84946,0.21982,0.6846,0.2211,0.88516,2.46826,0.35238,,0.58218,,14.46988,0.11184,5.17898,2.21592,1.16168,0.0,2.07592
2018-01-22,1.40606,0.68076,4.03584,2.28674,2.21596,0.87438,0.73188,0.53478,2.33564,1.44228,1.55238,1.0117,0.08702,1.48394,2.37322,0.9102,0.4678,3.38986,1.0377,0.60326,2.02942,0.5764,1.38362,0.82726,2.63832,0.0,0.77758,3.55012,1.3373,0.64178,1.35024,0.81994,2.21026,2.95548,0.63106,1.13196,1.11692,1.45904,0.59582,1.13506,1.93128,0.36846,2.30498,0.50986,1.42124,7.59488,4.0624,2.00558,0.61776,2.9846,1.73092,1.0374,0.62258,0.88376,1.5295,3.64112,0.91412,0.35846,1.6014,0.69352,3.55018,1.44852,0.8485,1.01464,3.30272,2.10918,1.36786,0.05846,0.87684,0.67658,0.4906,1.21796,0.29128,1.52786,0.42544,0.5205,0.88948,1.83092,2.19742,4.47588,1.57864,0.43472,1.11372,0.74116,0.66356,0.76928,0.61324,0.9177,0.89812,2.55716,0.25616,0.96572,0.86932,1.35842,0.9768,0.70502,1.3083,9.65324,0.09612,0.9409,0.83376,5.2306,0.75416,4.01274,4.591,1.01048,0.82362,0.9505,0.6582,3.702,1.5732,0.59038,1.0516,1.03182,0.72184,3.41012,0.5393,0.88914,0.71208,0.25916,0.14118,0.55652,0.62284,0.22474,1.01558,0.29186,17.40714,0.32166,0.4369,3.05456,3.2006,1.72978,0.4147,1.32282,1.92242,1.33166,0.5765,0.74874,1.72074,0.70798,1.25008,0.80952,0.3729,1.2898,1.65588,1.16336,0.49998,0.94366,0.0,0.42368,2.26256,0.58068,1.28594,1.12504,1.25362,0.81018,0.0,0.26762,0.94762,0.44892,0.74038,0.0,1.41274,1.46478,1.74398,2.987,2.9782,2.0325,0.67736,0.19724,2.11108,2.38568,0.3468,1.13466,0.29962,0.9651,1.69464,0.35862,2.46462,1.05258,1.05516,5.59648,0.42058,0.53044,2.49086,0.35958,1.58694,1.68852,0.54824,2.57752,0.5871,1.26996,0.65332,9.42492,0.38042,0.65016,0.35642,0.81748,1.5822,1.69958,1.07512,0.55034,0.88972,0.95208,5.61758,0.2506,1.0258,0.70402,0.67586,0.7734,0.3773,1.34032,0.41934,0.25848,2.20742,1.58534,3.67178,1.1629,2.10476,0.22146,0.85602,0.78006,1.88468,1.67156,0.09644,1.72516,,0.34008,30.09392,1.38618,0.84742,,3.71598,2.0956,0.49736,0.41098,2.1373,1.08696,2.90854,0.0,0.78934,3.79884,1.63318,0.10788,0.26614,1.43258,1.09458,0.77538,1.53014,4.94422,1.06782,0.28956,0.18274,1.0157,0.0,0.632,1.02402,1.49676,0.2287,0.14316,0.15216,2.20716,0.35494,2.17702,0.11202,0.69278,0.56748,0.26336,0.5278,1.10606,0.05448,,0.06034,0.56884,15.8664,12.09902,0.6022,0.2094,2.0296,0.4585,0.50538,2.2088,2.50294,0.89742,0.23022,0.6774,0.22912,0.82442,2.68462,0.3606,,0.50522,,13.26428,0.11484,4.51762,2.04274,1.34298,0.0,1.93704
2018-01-23,1.39948,0.61282,3.3531,2.05842,2.0085,0.87488,0.79966,0.5653,2.22694,1.13512,1.43178,0.91142,0.0,1.4222,2.48542,0.8996,0.41344,3.21116,0.99268,0.60926,2.06316,0.6502,1.38198,0.70248,2.80732,0.0,0.81268,3.57568,1.40192,0.51016,1.42626,0.86392,2.25468,2.62436,0.5757,1.10586,1.1267,1.1512,0.4707,1.22908,1.6657,0.37586,2.11616,0.46086,1.51958,6.09756,3.81764,1.79924,0.63152,2.67962,1.48608,1.2485,0.66758,0.81356,1.46214,3.70074,0.93196,0.3472,1.53022,0.71908,3.7568,1.33086,0.72796,0.96692,2.8605,2.23494,1.27182,0.06286,0.88866,0.6293,0.4939,1.0789,0.27122,1.34064,0.41992,0.525,0.88338,1.42306,1.81118,4.03298,1.53136,0.4537,1.00284,0.66604,0.6212,0.58816,0.6253,1.1136,0.94662,2.5972,0.24354,1.0659,0.7992,1.45386,0.81128,0.7917,1.24644,10.48438,0.12738,0.89728,0.74568,5.2706,0.76886,5.0563,5.16772,1.00938,0.82526,0.96316,0.80566,3.38634,1.11078,0.6414,1.3531,0.97068,0.6115,2.59176,0.64102,0.78712,0.7767,0.25542,0.13688,0.64076,0.69616,0.25852,1.20862,0.33992,14.3397,0.33578,0.47238,3.1572,3.40966,1.71566,0.43566,1.14936,1.81362,1.4914,0.6086,0.6772,1.78102,0.79112,1.29802,0.74546,0.33104,1.34156,1.89948,1.0754,0.47966,0.95216,0.0,0.4658,2.02006,0.53088,1.24434,0.96252,1.23732,0.79722,0.0,0.26312,0.86298,0.43106,0.68716,0.0,1.3199,1.39218,1.67686,2.80684,3.79788,1.93392,0.70196,0.1752,1.98342,2.54726,0.27872,1.07604,0.343,0.88864,1.84854,0.31604,2.06044,1.07046,0.97036,5.2552,0.35784,0.50516,2.3141,0.39554,1.56164,2.00524,0.47258,2.46766,0.6125,1.17664,0.65254,8.47296,0.4824,0.6325,0.33486,0.71464,1.42996,1.43462,1.07932,0.55256,0.8548,0.94508,4.8002,0.2681,0.73368,0.72198,0.71766,0.5995,0.384,1.23694,0.40454,0.2694,2.52474,1.78852,4.03812,1.23256,2.49062,0.20508,0.8593,0.81952,2.02788,1.82278,0.11558,1.04062,,0.34604,28.80586,1.2702,0.75218,,3.66928,2.2506,0.57618,0.4607,2.53308,1.16558,3.81772,0.0,0.86186,3.94422,1.62944,0.11672,0.32,1.5098,1.27032,0.66982,1.66388,4.39724,1.1878,0.32188,0.19914,1.06212,0.0,0.64424,0.87984,1.62442,0.22796,0.14404,0.14786,2.3371,0.38614,2.20948,0.11092,0.74964,0.60284,0.29286,0.52148,1.29748,0.28104,,0.06944,0.5508,16.61012,13.52308,0.48596,0.20918,2.01762,0.49308,0.49658,2.14426,2.7499,0.94058,0.2725,0.71802,0.2236,0.7051,2.89948,0.39056,,0.51532,,14.40864,0.10572,5.33176,1.72498,1.43894,0.0,1.66702
2018-01-24,1.3918,0.5573,2.866,1.8408,1.85392,0.8436,0.81602,0.53758,2.23278,1.02552,1.3394,0.90132,0.0,1.43726,2.45778,1.10926,0.39208,3.11582,0.97714,0.5404,1.7776,0.68564,1.2677,0.72144,3.02696,0.0,0.83612,3.54106,1.44182,0.47832,1.44888,0.81744,2.28352,2.43978,0.52674,1.05832,1.04956,0.9418,0.45082,1.30192,1.66218,0.32932,1.73376,0.45112,1.41822,4.9767,3.06314,1.819,0.7765,2.03608,1.33046,1.37616,0.6753,0.78346,1.61308,3.86828,0.79298,0.3279,1.39582,0.66526,3.22564,1.43198,0.6694,0.972,2.41494,2.19868,1.21894,0.06588,0.93698,0.63862,0.46568,1.00658,0.25406,1.1454,0.34766,0.51558,0.89262,1.25944,1.58774,3.1204,1.3264,0.45622,0.98314,0.54098,0.61208,0.80568,0.63078,1.25138,0.87616,2.58714,0.2393,1.5158,0.74818,1.66772,0.68402,0.82714,1.1254,10.50626,0.18032,0.88194,0.75986,6.3217,1.11828,6.60158,6.4514,1.21638,0.90618,1.03662,1.01994,4.76074,0.89022,1.01134,1.6623,0.8922,0.60212,2.44584,0.82072,0.81876,0.7718,0.25182,0.1413,0.67834,0.70738,0.28714,1.3243,0.36912,9.91762,0.3841,0.52854,3.10584,3.4284,1.71398,0.4502,1.0862,1.67344,1.32806,0.59162,0.70104,1.50162,0.72672,1.4766,0.74394,0.30562,1.32948,1.9214,1.05414,0.4533,0.96388,0.0,0.5373,1.38126,0.61798,1.14786,0.9707,1.24392,0.82828,0.0,0.25598,0.74536,0.44806,0.66422,0.0,1.34446,1.11602,1.55238,2.53068,3.94758,2.21242,0.68142,0.17102,1.7836,2.4622,0.25074,1.01414,0.30666,0.80616,1.62718,0.35692,2.09506,0.9454,0.96672,5.12994,0.36568,0.42968,2.16184,0.42008,1.37114,2.09258,0.46892,2.90402,0.76326,1.09912,0.64162,6.91064,0.5385,0.59602,0.33148,0.76674,1.28124,1.27998,1.21692,0.55688,0.88722,0.92712,4.1183,0.2513,0.5076,0.63596,0.70892,0.54888,0.39066,1.08636,0.38492,0.26384,2.46512,1.8572,3.90106,1.16732,3.26858,0.20622,0.88,0.79868,1.92296,1.802,0.11922,0.6544,,0.34292,25.82248,1.13338,0.67702,,3.92122,2.3573,0.6194,0.48654,2.8676,1.24762,7.09326,0.0,0.86794,3.30382,1.5601,0.13132,0.34416,1.56068,1.35556,0.71026,1.8793,4.08322,1.27382,0.3617,0.1926,1.0087,0.0,0.66304,0.817,1.51998,0.27028,0.13204,0.135,2.23026,0.3729,2.094,0.1305,0.75736,0.6097,0.29978,0.53176,1.28724,10.1702,,0.07084,0.52892,14.41526,13.15012,0.50294,0.19468,1.95922,0.51774,0.5971,1.61272,2.80208,1.00764,0.28842,0.87288,0.22788,0.73468,3.01324,0.3931,,0.51962,,14.58802,0.09268,5.16584,1.80798,1.48724,0.0,1.51176
2018-01-25,1.41804,0.57568,2.72796,1.96074,1.84964,0.95866,0.88406,0.55604,2.13022,1.0592,1.3928,1.0491,0.0,1.41292,2.7189,1.16964,0.40938,2.90644,0.89816,0.5474,1.65708,0.84402,1.26238,0.97452,3.28764,0.0,0.8528,3.62494,1.50038,0.63974,1.39812,0.82544,2.30828,2.20242,0.50766,1.19024,1.04504,0.9545,0.516,1.17366,1.7324,0.30618,1.69052,0.4256,1.52244,4.61166,3.113,1.89512,0.7586,2.1618,1.36664,1.47422,0.66684,0.7702,1.59516,4.0398,0.83548,0.29304,1.31116,0.66626,2.96892,1.67526,0.72856,0.97356,2.58052,2.33324,1.23658,0.09408,0.94642,0.54302,0.4709,1.025,0.25274,0.92336,0.34808,0.50644,0.8852,1.15058,1.491,3.08224,1.32852,0.49742,0.90244,0.49366,0.6762,1.09668,0.67668,1.30848,0.8887,2.49052,0.24026,2.08958,0.7802,1.82716,0.61162,0.79706,1.12674,11.21982,0.2443,0.95782,0.67036,6.57602,1.48292,7.93138,8.16262,1.48336,1.19742,1.10618,1.08536,5.25518,0.81044,1.27002,2.76008,0.94792,0.64498,2.51062,0.86726,0.81674,0.69984,0.32336,0.13326,0.68568,0.71806,0.3029,1.39394,0.45594,8.22716,0.42974,0.54656,3.66596,3.47254,1.77846,0.44806,1.3086,1.77174,1.58006,0.60184,0.747,1.29406,0.69552,1.61334,0.79612,0.31914,1.37282,1.98716,1.22178,0.58672,0.9949,0.0,0.53986,1.15474,0.65884,1.2746,0.97968,1.11002,0.95354,0.0,0.28584,0.82064,0.48714,0.6605,0.0,1.31406,1.07436,1.57896,2.02004,3.42322,2.81924,0.69904,0.2337,1.8353,2.69636,0.28532,1.05646,0.32898,0.83514,1.65488,0.44558,2.24388,1.30638,1.02486,4.9376,0.34026,0.41546,2.37504,0.43538,1.68056,2.31836,0.46804,3.31922,1.04132,1.11744,0.67436,6.88276,0.6342,0.7023,0.32318,0.78254,1.26384,1.20848,1.30632,0.56686,0.89642,0.80566,4.5575,0.29244,0.1974,0.61878,0.75474,0.68352,0.4039,0.95858,0.53814,0.24622,2.56464,1.81994,3.821,1.15106,4.04582,0.22342,0.8405,0.77388,2.04268,1.88188,0.15228,0.43804,,0.33968,23.31824,1.1866,0.58596,,4.232,2.17414,0.63198,0.40774,3.12486,1.25872,9.58086,0.0,0.93234,4.1094,1.55438,0.12962,0.33976,1.40558,1.23268,0.83662,1.83208,3.56606,1.31092,0.30858,0.1778,1.02776,0.0,0.66728,0.781,2.17628,0.25438,0.13574,0.1237,1.81466,0.30166,1.91346,0.15954,0.70556,0.646,0.26776,0.6103,1.23392,17.71664,,0.07668,0.48442,13.2367,12.9887,0.454,0.217,2.754,0.53638,0.66756,1.92524,2.91512,1.11314,0.26216,1.04778,0.21168,0.8392,3.48098,0.35986,,0.54544,,15.84078,0.09468,5.55498,1.70486,1.68664,0.0,1.46602
2018-01-26,1.35442,0.5471,3.03256,1.75864,1.93156,0.89594,0.77188,0.56218,2.12108,1.11234,1.5575,1.08218,0.0,1.29608,2.70514,1.13008,0.41612,2.83144,0.7784,0.498,1.41892,0.85874,1.3494,1.86514,3.37508,0.0,0.78394,3.0787,1.21768,0.61038,1.30164,0.63374,2.2829,1.94998,0.4673,1.17824,1.00818,1.00772,0.51734,0.99526,1.72754,0.30052,1.53454,0.39122,1.44462,4.37048,3.22938,1.9629,0.79046,2.03846,1.30982,1.43908,0.63538,0.75316,1.4609,3.11268,0.89312,0.23248,1.13846,0.60146,2.69992,1.55414,0.67586,0.9141,2.6468,2.0706,1.35904,0.09976,0.96382,0.51482,0.44588,0.8783,0.30514,0.68828,0.43082,0.48694,0.89464,1.27852,1.63512,3.33422,1.38794,0.46362,0.8743,0.48752,0.69524,1.20522,0.66258,1.23632,0.90834,2.4185,0.22514,2.26938,1.06798,1.53584,0.6096,0.64916,1.15378,8.98712,0.20698,0.98424,0.66576,6.0907,1.62836,7.85248,8.45208,1.53026,1.2071,1.23658,1.10888,5.365,0.79424,1.2942,3.37614,0.93294,0.64932,2.88256,0.90054,0.7876,0.72808,0.33484,0.09758,0.60116,0.65912,0.32038,1.30574,0.47962,7.1841,0.38488,0.52622,3.72984,3.24632,1.7108,0.43502,1.11758,1.71468,1.52634,0.51088,0.6914,1.50486,0.73488,1.34788,0.85688,0.3132,1.0978,2.04094,1.58096,0.5078,1.00786,0.0,0.52846,1.01336,0.69428,1.21586,0.9582,0.9563,0.91662,0.0,0.28446,0.80588,0.47896,0.6419,0.0,1.30944,0.79764,1.49846,1.72252,3.182,2.8785,0.55754,0.2182,1.78452,2.32278,0.44708,0.77698,0.33034,0.88378,1.4951,0.44324,2.48312,1.40092,1.21978,4.30776,0.37198,0.4044,2.37452,0.40498,1.7445,2.39602,0.46964,3.36734,1.05004,1.08536,0.70906,6.36406,0.69058,0.72604,0.29774,0.71284,1.26852,1.13556,1.321,0.48678,0.86312,0.7194,3.93088,0.27944,0.0,0.50282,0.6666,0.6812,0.38278,0.844,0.49336,0.2263,2.03122,1.41732,2.87764,0.89596,4.25546,0.2245,0.73534,0.69148,2.0892,1.87318,0.17394,0.36726,,0.29672,18.32924,1.31462,0.72816,,4.2926,1.87794,0.58122,0.40138,2.79066,1.10392,10.76566,0.0,0.8837,5.50484,1.31976,0.1317,0.42588,1.3265,1.06368,0.81834,1.90626,3.2367,1.10074,0.30958,0.17672,0.82072,0.0,0.69582,0.51058,2.2113,0.25366,0.13694,0.11668,1.7846,0.28044,1.70638,0.1605,0.68222,0.5385,0.26896,0.61758,1.08018,24.57386,,0.06948,0.451,9.93554,10.83556,0.4652,0.2125,2.88032,0.43126,0.70962,1.87906,3.21472,1.06834,0.24178,0.92832,0.18858,0.86072,3.13738,0.29504,,0.54624,,17.00192,0.10804,6.06668,1.50812,1.23926,0.0,1.50468
2018-01-29,1.3564,0.58652,3.7659,1.70682,2.0095,0.9085,0.76914,0.56484,2.20716,1.20248,1.9362,1.19808,0.0,1.05184,3.0419,1.00602,0.40264,2.69964,0.8426,0.47258,1.39562,0.9561,1.2495,2.08046,3.4643,0.0,0.9385,3.31256,1.1927,0.61186,1.37904,0.61102,2.36614,2.02726,0.45388,1.22282,1.08488,1.1349,0.51504,1.0241,1.9008,0.31338,1.71852,0.38888,1.6925,4.29608,3.29706,2.04564,0.88988,2.28614,1.2283,1.33558,0.60464,0.70346,1.46474,2.73538,0.88392,0.1924,1.07494,0.62336,2.65046,1.53406,0.63824,1.03598,2.5678,1.98312,1.63686,0.09586,0.94078,0.54484,0.43164,0.77732,0.34042,0.32422,0.42222,0.51358,0.94542,1.26828,1.61492,3.327,1.34952,0.52024,0.84654,0.50674,0.65456,1.31058,0.67122,1.21608,0.82498,2.56158,0.20286,2.7875,1.12478,1.45886,0.58398,0.58612,0.92702,7.89476,0.16538,1.03698,0.6987,7.4483,1.57462,6.81378,8.32546,1.62078,1.31864,1.4356,1.17886,5.0028,0.73398,1.29974,3.77002,0.91562,0.76008,2.97222,0.81604,0.85292,0.73448,0.36202,0.12046,0.60384,0.67664,0.34014,1.27814,0.40892,6.88064,0.4358,0.53734,3.59086,3.41644,1.88838,0.40974,1.1035,1.75702,1.60316,0.4968,0.71826,1.72956,0.73908,1.32928,0.90114,0.31396,1.087,1.96224,1.6933,0.51158,1.10656,0.0,0.5605,1.25232,0.73346,1.4626,1.16498,0.8208,0.96044,0.0,0.29552,0.84512,0.5129,0.6704,0.0,1.28268,0.75806,1.53692,1.65038,3.251,3.08804,0.56256,0.2207,1.75234,2.1434,0.39308,0.7311,0.30712,0.8906,1.37742,0.4636,3.29506,1.47768,1.53026,4.18292,0.38038,0.41618,2.87838,0.3811,1.87442,2.37796,0.48496,3.12324,1.16174,1.25578,0.7199,6.55242,0.68812,0.8534,0.32774,0.70996,1.18072,1.16532,1.43248,0.48238,0.91002,0.6505,3.84808,0.29262,0.0,0.51444,0.67914,0.7107,0.40776,0.7798,0.49758,0.2257,1.83036,1.40608,2.79632,0.9603,3.7833,0.25852,0.7434,0.62528,2.1104,1.8927,0.18562,0.34668,,0.32966,17.0875,1.31906,0.88834,,4.61206,1.79114,0.57482,0.42284,2.22436,1.08266,11.69234,0.0,0.94098,6.08944,1.4728,0.12956,0.43444,1.38226,0.99184,0.81228,1.70212,3.67294,1.14476,0.32684,0.16934,0.75648,0.0,0.69926,0.49584,2.3683,0.26294,0.13902,0.14172,1.71118,0.29176,1.75802,0.16984,0.77224,0.532,0.27864,0.66942,1.00386,30.58298,,0.06944,0.57192,8.6691,7.47226,0.44178,0.2515,3.36328,0.42108,0.84346,1.62968,3.21082,1.07198,0.22968,0.9748,0.19186,0.91284,4.56426,0.2594,,0.66214,,17.58266,0.10802,6.1314,1.54366,1.05916,0.0,1.42556
2018-01-30,1.20342,0.63998,3.84174,1.494,1.85628,0.87018,0.7959,0.5406,1.911,1.24094,2.10816,1.45924,0.0,0.97108,2.78562,0.94664,0.35204,2.50294,0.92686,0.45468,1.22846,0.92482,1.193,2.1754,3.3079,0.0,1.00794,2.9947,1.13288,0.55662,1.35758,0.59184,2.19742,1.97592,0.3789,1.23544,1.07958,1.11294,0.50814,1.04698,1.82988,0.32934,1.44782,0.3748,1.93404,4.18704,3.0584,2.01944,0.84604,2.20048,1.37468,1.06244,0.52752,0.6926,1.39788,2.6173,0.86222,0.18584,1.09616,0.55796,2.40226,1.5085,0.59742,1.0728,2.49006,2.08904,1.61992,0.09788,0.91834,0.54376,0.39594,0.7685,0.35658,0.28738,0.39718,0.47476,0.89316,1.31216,1.75674,3.63638,1.59994,0.51198,0.76558,0.52282,0.61072,1.36164,0.63954,0.97414,0.70614,2.84148,0.17236,2.85522,1.1908,1.38588,0.53744,0.56436,1.0594,6.98664,0.61894,1.03476,0.6562,8.9492,1.81472,6.20304,8.1607,1.55386,1.4523,1.54374,1.02208,5.1891,0.65994,1.3057,4.15084,0.8303,0.69988,2.94844,0.73774,0.81218,0.56888,0.37356,0.1578,0.54532,0.63068,0.31578,1.18118,0.3547,6.96728,0.48146,0.45956,3.4251,3.232,1.84888,0.36094,1.02728,1.59264,1.5518,0.48606,0.71746,1.88024,0.57416,1.35236,0.9293,0.28278,1.0121,1.6169,1.7536,0.47668,1.19178,0.0,0.51766,1.2833,0.72186,1.63762,1.17208,0.98222,0.90552,0.0,0.26144,0.87,0.46972,0.7027,0.0,1.2857,0.69712,1.3484,1.7635,2.70258,3.36766,0.54376,0.21682,1.59754,2.02534,0.34482,0.70588,0.24296,0.82146,1.21724,0.44262,3.56698,1.40368,1.47194,5.05252,0.43644,0.41994,2.82614,0.33462,1.83298,2.12334,0.46006,3.30942,1.18064,1.23782,0.72672,5.99882,0.5893,0.88546,0.31886,0.70264,1.09396,1.10514,1.4436,0.51132,0.9675,0.55756,3.75032,0.30792,0.0,0.51274,0.65444,0.78034,0.3711,0.77194,0.47606,0.1931,1.4821,1.263,2.4241,0.9672,3.60312,0.26104,0.72756,0.53728,2.07372,1.70214,0.17048,0.33406,,0.32348,15.90734,1.32148,0.87804,,5.00218,1.66628,0.51966,0.38902,1.8936,1.00656,11.3844,0.0,0.97078,5.81328,1.43152,0.11384,0.36008,1.18802,0.84172,0.7978,1.4207,5.04138,1.07828,0.30996,0.14288,0.71632,0.0,0.60396,0.50502,2.32234,0.24994,0.13006,0.15032,1.57122,0.27604,1.74044,0.18646,0.67056,0.50626,0.26828,0.65154,0.83264,35.64618,,0.06024,0.75924,7.76378,6.64306,0.44644,0.28054,3.55064,0.39162,0.90146,1.58098,2.88684,1.03166,0.18902,0.95186,0.17872,0.94004,4.96722,0.21484,,0.63772,,16.551,0.10306,5.36744,1.59684,0.9917,0.0,1.44338
2018-01-31,1.1037,0.71322,3.59282,1.37912,1.61452,0.83588,0.6758,0.51792,1.81012,1.2361,2.01766,1.61718,0.0,0.86426,2.60942,0.76556,0.32374,3.09054,0.92334,0.44898,1.15,0.95194,1.2063,2.22836,3.05882,0.0,1.0099,2.46828,0.92352,0.53586,1.07096,0.48762,1.9409,1.86364,0.39418,1.41454,1.1155,1.06834,0.46548,1.02156,1.6945,0.33694,1.36384,0.37642,2.0506,3.83152,3.18462,1.6761,0.74272,2.16708,1.42006,0.92562,0.52518,0.68316,1.12788,2.88912,0.88644,0.23896,0.99192,0.5682,2.75,1.16646,0.59876,1.00848,2.7633,2.08976,1.83222,0.0889,0.81658,0.51916,0.39196,0.75194,0.38162,0.27774,0.43612,0.41772,0.93726,1.14552,1.9381,3.74654,1.67874,0.53706,0.69942,0.55642,0.61282,1.11508,0.60036,0.84326,0.66728,3.35104,0.23546,2.51022,1.18306,1.02716,0.63334,0.54714,1.04814,5.80728,12.53386,0.9078,0.57098,8.1932,1.6533,5.23596,7.28058,1.52418,1.4095,1.53048,0.76308,3.87556,0.65092,1.03318,4.3694,0.76896,0.60528,2.38688,0.55132,0.75754,0.5064,0.39554,0.17026,0.4475,0.54412,0.28078,1.11738,0.32878,6.8508,0.46092,0.36856,3.28712,2.70252,1.81464,0.31134,0.95386,1.60468,1.45684,0.40694,0.70502,1.87774,0.5352,1.11998,0.90956,0.27276,0.84822,1.63572,1.71182,0.47436,1.12694,0.0,0.45156,1.262,0.87644,1.73208,1.11904,0.9742,0.91668,0.0,0.26424,0.80168,0.43204,0.67332,0.0,1.19458,0.68568,1.37232,1.90918,2.47928,3.19304,0.41978,0.21152,1.5874,1.95458,0.28586,1.06454,0.2468,0.81092,1.02672,0.39772,3.458,1.38848,1.4906,5.48764,0.42756,0.44716,2.4816,0.3056,1.81846,2.08194,0.39662,2.81746,0.9499,1.22908,0.69942,5.5677,0.55764,0.8477,0.28826,0.59474,1.09928,1.02234,1.18424,0.4579,0.79616,0.52582,3.79022,0.33106,0.0,0.4467,0.51132,0.73888,0.33302,0.80034,0.47006,0.1749,1.19244,1.05492,2.12156,0.82858,2.67906,0.24214,0.54358,0.59518,1.9336,1.483,0.16454,0.34282,,0.31776,14.54658,1.30968,0.875,,4.91926,1.36766,0.4492,0.3287,1.53564,0.7381,8.62016,0.0,0.96176,5.76732,1.3084,0.09352,0.3119,1.04874,0.70964,0.80912,1.19306,6.2111,0.8221,0.2593,0.1337,0.58254,0.0,0.5602,0.48706,2.26942,0.19198,0.1251,0.16214,1.4564,0.257,1.38934,0.17236,0.63786,0.40038,0.2377,0.6346,0.72616,28.86282,0.01102,0.05194,0.76564,6.48184,5.29154,0.49502,0.28416,3.69304,0.29718,0.86632,1.85862,2.72616,0.95622,0.1638,0.86184,0.1757,0.86214,5.30506,0.17986,,0.63162,,15.56376,0.10428,4.9773,1.98026,0.93798,0.0,1.45722
2018-02-01,1.06062,0.71062,4.32428,1.43624,1.71606,0.79836,0.63648,0.54854,1.73426,1.27484,2.07034,1.67808,0.0,0.7919,2.33656,1.03894,0.30036,3.03658,1.0346,0.4532,1.43244,0.90862,1.244,2.14266,2.96338,0.0,1.0761,2.84096,0.89454,0.3974,1.11108,0.47712,2.12322,2.1255,0.54836,1.42334,1.12844,1.08026,0.39752,1.06896,1.716,0.35578,1.37264,0.3971,2.18396,3.2443,3.11616,1.69072,0.84444,2.07388,1.55678,0.81298,0.52056,0.84096,1.28146,2.76532,0.89912,0.42916,1.07344,0.57988,2.89574,0.95078,0.62542,1.03208,2.8166,2.09306,1.83234,0.06304,0.85046,0.5632,0.36632,1.00628,0.43642,0.27276,0.5996,0.40122,0.85798,1.11422,2.0319,3.79544,1.758,0.58592,0.7003,0.5999,0.5766,0.86992,0.6418,0.79918,0.74834,3.5626,0.2987,2.06892,1.22826,0.93338,0.91,0.4564,1.04736,5.87442,19.52344,0.86892,0.60356,7.9346,1.57738,4.5243,6.36334,1.44598,1.2221,1.53616,0.70856,3.38022,0.73852,0.82294,3.78636,0.78784,0.56582,2.4597,0.54888,0.79224,0.5405,0.34782,0.18832,0.4754,0.5586,0.27248,1.03754,0.2854,6.67354,0.42708,0.33086,2.92226,2.62376,1.752,0.30202,0.82826,1.56864,1.16634,0.4355,0.6348,1.97432,0.57664,1.14028,0.90298,0.26834,0.78788,1.5481,1.6395,0.42294,1.12116,0.0,0.39348,1.26496,0.93154,1.665,1.04776,1.1582,0.94284,0.0,0.3525,0.9227,0.39096,0.7349,0.0,1.1259,0.85232,1.3837,2.111,2.77688,2.94846,0.3794,0.17882,1.6331,1.74366,0.18868,1.12078,0.26234,0.82294,1.13962,0.36784,3.56212,1.1284,1.6688,6.43576,0.48572,0.44802,2.59842,0.30504,1.52066,2.00746,0.54836,2.86428,0.75796,1.1883,0.77454,5.41572,0.54178,0.75476,0.28354,0.56202,1.0784,1.0736,1.1892,0.50546,0.69132,0.59038,3.30436,0.32262,0.0,0.64728,0.4673,0.65624,0.34216,0.84362,0.35628,0.19532,1.07734,1.10916,2.05224,0.88218,2.15804,0.23766,0.57968,0.61328,1.8471,1.46372,0.16242,0.32076,,0.3247,14.41836,1.23148,0.96712,,4.99746,1.4335,0.49892,0.3628,1.46024,0.77872,6.33256,0.0,0.90376,4.92276,1.2953,0.24686,0.31054,1.08222,0.77476,0.76012,1.0997,6.68666,0.79342,0.26208,0.13884,0.54856,0.0,0.52868,0.54728,1.74012,0.21394,0.1277,0.19816,1.44194,0.27262,1.4541,0.17078,0.6652,0.41836,0.2146,0.5948,0.74446,24.4768,0.01864,0.0467,0.7952,6.43564,4.98606,0.54842,0.28296,3.31426,0.27228,0.93626,1.68528,2.69574,0.93344,0.16862,0.7755,0.18512,1.02212,5.85482,0.18922,,0.68418,,14.45196,0.10016,4.6934,2.12184,0.85256,0.0,1.39956
2018-02-02,0.95936,0.7491,4.32086,1.46156,1.5156,0.76478,0.60696,0.51436,1.8331,1.18806,1.96198,1.76784,0.0,0.77898,2.21944,1.37906,0.2912,3.10608,1.08388,0.44542,1.51216,1.2095,1.1161,1.35072,2.99008,0.0,1.17826,2.8728,0.89954,0.37292,1.07808,0.44506,2.31534,1.92344,0.55986,1.47034,1.14768,0.96184,0.39458,1.3967,1.6261,0.3793,1.36774,0.4114,2.72942,2.9286,3.08404,1.67324,0.88108,2.10544,1.53534,0.86558,0.49992,0.90212,1.3672,2.63606,0.86126,0.39162,1.1012,0.56488,3.13482,1.01922,0.70084,1.063,2.842,2.18368,1.81352,0.06382,0.8749,0.59238,0.35906,1.02154,0.40718,0.28034,0.88648,0.40448,0.83124,1.23462,2.2132,3.55334,1.77332,0.675,0.81086,0.61992,0.59112,0.81812,0.69636,0.8794,0.75918,4.47706,0.33612,1.98694,1.18012,0.87412,0.997,0.4313,1.28878,6.11694,25.3652,0.88638,0.5852,8.43282,1.5983,4.57522,6.2452,1.64264,1.21856,1.4133,0.69812,3.56774,0.7183,0.83836,3.92346,0.8211,0.57712,2.20812,0.44454,0.87244,0.56106,0.37428,0.19976,0.45228,0.50336,0.25446,0.9949,0.27244,6.73654,0.41232,0.35646,2.42106,2.51984,1.6931,0.28146,0.9787,1.65378,1.1227,0.41492,0.64586,1.66664,0.49692,1.09738,0.84374,0.25068,0.77732,1.53756,1.3294,0.43318,1.0681,0.0,0.47458,1.3799,0.8908,1.89674,1.06818,1.25164,0.9887,0.0,0.36072,0.94982,0.3727,1.02202,0.0,1.10952,0.87024,1.4623,2.39224,2.89836,3.38768,0.36408,0.19158,1.62486,1.62644,0.0,1.18638,0.26396,0.79172,1.16788,0.3522,3.4234,1.0607,1.59008,7.33764,0.39236,0.49808,2.6387,0.33902,1.43514,1.97062,0.6038,2.83374,0.96508,1.21624,0.88574,5.43694,0.48632,0.74756,0.28142,0.5741,1.03866,1.10176,1.28768,0.46352,0.71322,0.6045,3.43194,0.32054,0.0,0.65348,0.45732,0.65578,0.34454,0.83912,0.37312,0.21922,1.00738,1.13062,1.95758,0.84098,2.2068,0.22734,0.56594,0.58216,1.52998,1.323,0.1453,0.2883,,0.38244,13.93608,1.01532,0.84132,,4.94508,1.2621,0.47306,0.33026,1.27854,0.7521,5.52292,0.0,1.0325,3.49634,1.2713,0.34004,0.21652,1.06044,0.69652,0.7144,0.97274,7.38116,0.76936,0.23382,0.13216,0.50564,0.0,0.51224,0.5084,1.79252,0.2086,0.14208,0.2029,1.25756,0.24544,1.4367,0.17482,0.62664,0.39262,0.17886,0.61276,0.67256,20.80848,0.0424,0.0521,0.86208,6.21278,4.75574,0.63108,0.33964,3.5396,0.25728,0.88734,1.66996,2.24414,1.00584,0.15284,0.7761,0.19188,0.95368,6.09232,0.18396,,0.70348,,13.51116,0.08898,4.01954,2.23658,0.8152,0.0,1.37528
2018-02-05,0.9879,0.73978,3.47592,1.41022,1.17246,0.67652,0.55686,0.54186,1.66582,1.12532,1.71952,1.6138,0.0,0.73786,1.91036,1.89806,0.26614,2.88382,1.14034,0.40122,1.35542,1.3964,1.11586,1.1953,2.72896,0.0,1.13212,2.35664,0.8113,0.33646,1.0228,0.39088,2.36666,1.76194,0.61918,1.41538,1.21192,0.90638,0.39978,1.5702,1.30558,0.41212,1.1398,0.43448,3.01242,2.6405,3.06384,1.52928,0.81922,1.65108,1.4217,0.87146,0.47902,0.96004,1.27102,2.35732,0.7772,0.3617,1.06156,0.67004,3.1089,0.85492,0.63698,1.04948,2.75748,1.92068,1.63604,0.0565,0.97118,0.52458,0.38608,0.98814,0.34972,0.2853,1.1395,0.37896,0.7603,1.29154,2.182,3.36196,1.78916,0.60266,0.70492,0.5639,0.61092,0.68934,0.72156,0.8851,0.61054,5.3298,0.47126,1.22208,1.19066,0.79248,1.2041,0.39682,1.53384,5.7101,29.29898,0.83018,0.56294,6.54752,1.5195,4.29912,5.88228,1.61058,1.11834,1.42654,0.62652,3.40886,0.66072,0.74108,3.66542,0.89878,0.44788,1.83886,0.44196,0.90656,0.57662,0.37868,0.17454,0.51822,0.50734,0.27564,1.00298,0.26138,6.18172,0.33734,0.33258,2.48748,2.431,1.55144,0.28712,0.88846,1.6859,1.08606,0.3787,0.58456,1.5132,0.44064,0.98164,0.76858,0.23196,0.70194,1.45162,1.34834,0.40452,1.2374,0.0,0.59202,1.11818,0.88706,1.87932,0.83686,1.26826,0.9319,0.0,0.38768,0.9627,0.33632,0.9682,0.0,1.04782,0.83184,1.40508,2.56072,3.06584,3.96028,0.32776,0.2037,1.64912,1.47498,0.05246,1.21246,0.24506,0.67442,1.50958,0.35702,2.48192,0.9878,1.23556,7.14836,0.3678,0.53704,2.2957,0.31262,1.33744,1.79758,0.60906,2.62502,0.97474,1.2093,0.91954,5.1767,0.44554,0.59888,0.2687,0.51798,1.05978,0.90644,1.18594,0.44862,0.64692,0.6131,3.17228,0.2875,0.05074,0.62502,0.42944,0.60728,0.29134,0.93012,0.344,0.21872,0.9851,1.24082,2.07048,0.6876,2.1382,0.1919,0.5347,0.554,1.45502,1.73224,0.13318,0.33616,,0.39852,12.97622,1.06504,0.73218,,4.7955,1.24384,0.54552,0.34456,1.1627,0.68598,4.72242,0.0,1.04004,2.81372,1.36124,0.40534,0.2207,0.95524,0.73982,0.6473,0.9368,7.95618,0.6808,0.21962,0.135,0.481,0.0,0.5569,0.44536,1.63894,0.19438,0.15038,0.17894,1.22262,0.22924,1.35182,0.16736,0.51688,0.3531,0.17758,0.57764,0.7271,17.8337,0.13738,0.0529,0.74076,5.9302,4.57544,0.67642,0.35874,3.44356,0.22598,0.78016,1.57178,2.16956,0.96418,0.1613,0.68744,0.17916,0.86034,5.35828,0.2317,,0.55472,,13.24482,0.08148,4.07578,2.17716,0.8108,0.0,1.37936
2018-02-06,1.16382,0.69686,3.32714,1.49966,1.21298,0.70498,0.5742,0.72132,1.81544,1.10406,2.08412,1.4693,0.0,0.8309,1.88616,1.79244,0.33564,2.9279,1.12902,0.47328,1.43654,1.57234,1.22894,1.14032,2.74052,0.0,1.2085,2.5387,0.90146,0.454,1.0958,0.44494,2.518,1.82808,0.7813,1.6569,1.30008,1.00128,0.53518,1.70444,1.6497,0.4537,1.18286,0.52888,3.01014,2.69306,3.22464,1.5987,0.87044,1.79536,1.2894,1.0137,0.43822,1.10454,1.69446,2.12436,0.79012,0.32852,1.06188,0.77036,3.09786,0.91012,0.77872,1.03766,3.29956,1.90216,1.74886,0.05736,1.01402,0.5812,0.43228,1.0544,0.3654,0.37104,1.29428,0.49158,0.76716,1.06866,2.11744,3.04642,1.59426,0.66892,0.7506,0.5995,0.6765,0.66452,0.77858,0.99034,0.69506,5.69742,0.5624,0.99022,1.19536,0.87916,1.333,0.37356,1.62724,6.09034,33.69208,0.88888,0.74768,5.65706,1.51426,4.49616,6.261,1.7119,1.25188,1.4069,0.64524,3.04024,0.73198,0.80398,3.62156,1.00206,0.51562,2.07534,0.53914,1.045,0.68842,0.43526,0.14818,0.62596,0.57374,0.31874,1.13388,0.32388,6.05694,0.30512,0.38226,2.55946,2.6171,1.7334,0.38904,1.14374,1.80158,1.14156,0.44904,0.66774,1.72296,0.49972,1.04448,0.82678,0.29602,0.78684,1.58334,1.5334,0.52744,1.2165,0.0,0.745,1.26352,0.91692,1.83492,0.90838,1.10866,1.00304,0.0,0.42904,1.06208,0.44642,0.94788,0.0,1.06474,0.88056,1.55446,2.60204,3.13718,4.40024,0.35054,0.23892,1.7735,1.58122,0.68832,1.32036,0.2803,0.76042,1.82388,0.52732,2.5658,1.2411,1.27838,6.22628,0.39094,0.59478,2.63312,0.32782,1.60292,1.74192,0.68324,2.82192,1.032,1.42396,1.00614,5.0875,0.51806,0.6275,0.28348,0.5948,1.18736,1.0197,1.3394,0.48698,0.67856,0.72304,3.9097,0.3093,0.63864,0.68686,0.48432,0.66422,0.32942,1.18218,0.40306,0.26776,1.05592,1.39812,2.3849,0.8218,2.29446,0.20156,0.59206,0.66876,1.41754,1.85324,0.16306,0.36942,,0.43708,13.19314,1.08422,0.75024,,4.46624,1.38668,0.6913,0.43224,1.26624,0.82446,4.79898,0.0,1.1166,2.75694,1.59632,0.44572,0.25828,1.17344,0.8988,0.75122,0.97946,7.8336,0.70968,0.32106,0.16714,0.55172,0.0,0.69144,0.43838,1.7437,0.25652,0.17602,0.19114,1.33698,0.29344,1.39814,0.18876,0.57476,0.40572,0.21904,0.60498,0.87264,15.72796,0.4175,0.05752,0.60562,6.40068,4.11556,0.78176,0.38128,3.78734,0.24358,0.78144,1.63306,2.42688,1.13306,0.20176,0.7307,0.19822,0.86426,5.39972,0.28118,,0.64992,,14.00656,0.09666,3.93514,2.3026,0.82174,0.0,1.36086
2018-02-07,1.35272,0.70798,3.32652,1.72722,1.17374,0.72626,0.57752,0.80916,1.8376,1.11502,2.09532,1.26014,0.0,0.90652,1.91814,1.59322,0.36376,2.07462,1.1713,0.49034,1.41142,1.66134,1.31036,1.1077,2.94124,0.0,1.32772,2.68716,0.83704,0.62194,1.11318,0.41278,2.842,1.8603,0.84984,1.58452,1.45074,1.04516,0.60996,1.89072,1.68114,0.50262,1.21442,0.55036,3.25798,2.60566,3.51756,1.6681,0.81236,1.89096,1.1456,1.03372,0.39776,1.14032,1.95214,1.34538,0.84146,0.23038,1.28172,0.87916,3.0971,0.9781,0.76024,1.14202,2.90476,1.97124,1.57428,0.06154,1.25174,0.59514,0.47622,1.23174,0.35714,0.41548,1.3167,0.56098,0.6977,0.90454,1.93698,3.29748,1.67852,0.68996,0.7903,0.5729,0.72902,0.61572,0.74052,1.0142,0.74388,6.2026,0.57804,0.79364,1.1889,0.84818,1.35232,0.27546,1.79762,5.72658,26.93108,0.88004,0.87522,5.77064,1.44238,4.11098,6.27308,1.64294,1.67596,1.42084,0.62856,3.00568,0.83686,0.80942,3.86346,1.17652,0.6334,2.30562,0.59826,1.13834,0.76862,0.44858,0.1312,0.74986,0.7031,0.37144,1.28062,0.35644,5.50614,0.27616,0.43558,2.79106,2.6593,1.83878,0.47028,1.45358,1.98942,1.18444,0.44756,0.68086,1.95362,0.50296,1.05348,0.82638,0.3158,0.79398,1.44246,1.70516,0.61138,1.27326,0.0,0.83832,1.36958,0.6984,1.99044,0.98732,1.14218,1.01082,0.0,0.42394,1.08518,0.49442,1.04406,0.0,1.04468,0.8362,1.68188,2.5973,3.49096,4.91446,0.33452,0.26618,2.0744,1.54886,1.14612,1.0214,0.26334,0.7902,1.91412,0.57814,2.61352,1.27178,1.2667,5.80036,0.38562,0.63918,2.77572,0.3385,1.6899,1.66944,0.68834,2.79408,1.11512,1.53564,1.17462,5.6969,0.50722,0.65082,0.31122,0.60126,1.23972,1.13082,1.36696,0.48024,0.69424,0.7407,4.29774,0.28716,0.99166,0.70016,0.50066,0.76978,0.3358,1.34522,0.44442,0.31132,0.98296,1.69472,2.74826,0.84954,2.58328,0.20338,0.60092,0.6241,1.46814,1.97358,0.16508,0.39544,,0.49504,11.57078,1.15754,0.71882,,4.50252,1.67016,0.77484,0.49818,1.14938,0.83164,4.6091,0.0,1.2616,2.44866,2.0432,0.49152,0.30046,1.32356,1.06576,0.7791,0.9578,7.91084,0.69004,0.41546,0.18646,0.57868,0.0,0.74672,0.4616,1.7549,0.2665,0.19496,0.19296,1.4247,0.30488,1.38752,0.18622,0.55224,0.39726,0.27302,0.54172,0.994,15.1602,3.17496,0.06388,0.5983,5.79508,3.6598,0.81816,0.44254,3.91384,0.22726,0.72048,1.68684,2.67894,1.17676,0.23776,0.69364,0.19522,0.86584,5.5692,0.33452,,0.63046,,15.18218,0.09916,3.76348,1.87392,0.83722,0.0,1.3197
2018-02-08,1.36834,0.69672,2.6318,1.62836,1.01752,0.62798,0.50936,0.8279,1.774,1.00604,1.97018,1.04404,0.0,0.87528,1.7159,1.1096,0.37696,1.78486,1.055,0.4619,0.96932,1.67316,1.36746,1.08648,2.8054,0.0,1.24112,2.21334,0.73538,0.65804,0.99816,0.35998,2.7028,1.3955,0.73772,1.38856,1.50302,1.0079,0.65748,1.86454,1.63778,0.51258,1.05252,0.52604,3.15236,2.4317,3.53546,1.58132,0.67268,1.90366,0.9428,1.0452,0.34264,0.93396,1.77276,1.11962,0.7998,0.0,1.1902,0.94782,2.9628,1.25748,0.70206,1.03084,2.84194,1.99664,1.572,0.06074,1.27332,0.56114,0.50148,0.98298,0.28854,0.41118,1.16696,0.6145,0.58658,0.69352,2.07864,3.27306,1.69878,0.63668,0.81136,0.51362,0.6981,0.67428,0.68048,1.03816,0.60156,6.5322,0.55774,0.75106,1.1108,0.74906,1.12498,0.23176,1.83078,4.49432,25.06914,0.89124,0.87688,5.35668,1.2201,4.41928,5.84524,1.59246,1.64642,1.34778,0.68238,3.19386,0.75906,0.85474,3.90852,1.1452,0.7293,2.29356,0.55262,1.1018,0.79104,0.4167,0.11594,0.7471,0.70216,0.36806,1.3073,0.37218,4.91466,0.23196,0.4609,2.6826,2.46788,1.70788,0.50682,1.39632,2.00596,1.05712,0.36942,0.76614,1.94962,0.41094,0.87044,0.75852,0.29926,0.75532,1.33364,1.62102,0.56038,1.22448,0.0,0.89466,1.3806,0.62978,2.09014,0.963,0.9429,0.90704,0.0,0.29874,0.88314,0.50188,0.9534,0.0,0.96926,0.60834,1.67666,2.63778,3.27034,5.09482,0.32074,0.23686,1.9572,1.27732,1.4292,0.83046,0.22202,0.70954,1.74336,0.53938,2.58012,1.2388,1.099,4.72364,0.2997,0.69144,2.56088,0.31048,1.98398,1.56468,0.4966,2.3506,1.0834,1.56838,1.32202,5.50886,0.40798,0.67008,0.31842,0.5602,1.1777,1.1878,1.1934,0.42126,0.67514,0.69764,3.91568,0.25936,1.23628,0.53982,0.48912,0.69818,0.31024,1.33444,0.43852,0.30512,0.87074,1.75054,2.75682,0.81962,2.4384,0.19234,0.54956,0.6151,1.39396,1.99662,0.13424,0.36468,,0.52508,9.94218,1.11468,0.70698,,4.70508,1.68742,0.746,0.52104,0.92252,0.72596,4.21788,0.0,1.3721,1.97064,2.16824,0.36128,0.31586,1.34468,1.0511,0.7796,0.95124,8.6615,0.61074,0.52562,0.1927,0.55546,0.0,0.75124,0.36272,1.29122,0.227,0.2001,0.15168,1.47132,0.26316,1.19012,0.15988,0.55,0.34874,0.3009,0.51898,0.9936,13.58084,13.28306,0.06496,0.53066,4.72324,3.14634,0.80072,0.46186,3.91352,0.19876,0.59414,1.47848,2.67114,0.94142,0.24592,0.5849,0.17172,0.59376,4.93892,0.3525,,0.56918,,14.69624,0.1015,3.6861,1.83054,0.7567,0.0,1.26754
2018-02-09,1.56322,0.72312,2.34106,1.66888,1.02834,0.68082,0.571,0.94692,1.87,1.03606,1.87098,0.99192,0.0,1.01366,1.86568,0.64436,0.41318,1.58792,1.0957,0.5418,0.9356,1.54106,1.53222,1.34138,2.90714,0.0,1.24594,2.3865,0.83624,0.71968,1.12378,0.42642,2.58326,1.31346,0.76742,1.46518,1.62966,1.08986,0.76314,1.61926,1.7335,0.54914,1.05362,0.59664,2.71134,2.60542,3.642,1.61336,0.6639,1.87966,1.02106,1.09124,0.34812,0.84268,1.7667,1.09536,0.78366,0.0,1.1043,1.05724,2.98924,1.31508,0.68514,1.0861,2.91724,1.9269,1.54394,0.04668,1.48482,0.52894,0.5302,1.09552,0.32278,0.46214,0.85504,0.6707,0.55532,0.24566,2.05638,3.66792,2.09278,0.60406,0.70966,0.51486,0.77628,0.66576,0.7776,1.06316,0.6426,6.44072,0.60588,0.68772,0.88438,0.8303,1.01546,0.23214,1.74566,4.1212,25.81498,0.8856,0.94156,5.53198,1.25884,4.6644,5.95658,1.51728,1.7357,1.42658,0.73012,2.8962,0.79118,0.88484,3.76756,1.18176,0.81932,2.13838,0.60774,1.25582,0.89706,0.48438,0.12148,0.76988,0.78246,0.39514,1.35338,0.4076,4.96534,0.258,0.47098,2.79306,2.76708,1.8776,0.58756,1.39708,2.1996,1.18026,0.41832,0.9194,2.14812,0.47088,0.91286,0.85488,0.32586,0.84772,1.32314,1.63284,0.62278,1.26144,0.0,0.89452,1.45808,0.64398,2.06118,1.0151,0.89364,1.02758,0.0,0.3082,0.9993,0.56388,0.74388,0.0,1.05326,0.55886,1.74354,2.65676,3.29982,5.16394,0.3511,0.25168,1.96236,1.44802,1.638,0.92548,0.25194,0.76094,1.82062,0.7012,2.64584,1.29836,1.00094,4.41058,0.39078,0.73946,2.50138,0.29216,2.28944,1.7411,0.50484,2.39842,0.91594,1.71406,1.3578,5.44226,0.46666,0.709,0.38212,0.59444,1.24654,1.34594,1.2503,0.50316,0.73972,0.7672,3.95496,0.30532,1.43462,0.62428,0.56394,0.71644,0.38776,1.55452,0.46986,0.35192,0.93152,1.82234,2.9886,0.94334,2.53048,0.20784,0.621,0.70456,1.5821,2.11196,0.18,0.37288,,0.5211,10.67794,1.16458,0.72562,,4.90656,1.84408,0.81914,0.59266,0.95726,0.82104,3.88236,0.0,1.40216,1.9379,2.18712,0.29618,0.35348,1.61876,1.22066,0.8741,1.02956,9.0925,0.69588,0.6414,0.2129,0.63762,0.0,0.82558,0.4497,1.08482,0.24264,0.19722,0.1667,1.63648,0.31506,1.39542,0.1661,0.65724,0.42878,0.34654,0.49462,1.09636,12.76306,19.98,0.06626,0.48482,4.91992,3.31752,0.7984,0.47058,3.98192,0.21496,0.61744,1.5779,2.99324,1.02944,0.27436,0.66844,0.17172,0.64834,5.08204,0.39828,,0.59144,,14.32962,0.11766,3.81742,1.75482,0.82684,0.0,1.25864
2018-02-12,1.4328,0.69326,2.26552,1.66458,1.01396,0.64882,0.54936,0.89024,1.77224,1.01498,1.7991,0.99238,0.0,0.99254,1.74972,0.0,0.41292,1.61418,0.95808,0.56368,0.91242,1.27546,1.50938,1.40656,2.8928,0.0,1.08244,2.45238,0.83734,0.74076,1.0889,0.4175,2.40282,1.34192,0.74328,1.47008,1.48884,0.97824,0.75332,1.33492,1.81244,0.53918,1.00192,0.55184,2.20306,2.64236,3.66054,1.57374,0.53344,1.96896,1.07108,1.07852,0.2713,0.80468,1.87148,1.09042,0.7728,0.0,1.07236,0.96234,3.05832,1.6705,0.72864,1.03888,3.10282,2.0795,1.42924,0.051,1.41322,0.50956,0.4952,1.05718,0.32084,0.4383,0.63134,0.67946,0.5662,0.0,2.19388,4.26918,2.54202,0.683,0.72826,0.53552,0.72312,0.76024,0.7604,0.94842,0.7127,5.841,0.50244,0.76062,0.74744,0.84796,0.90432,0.22442,1.7123,3.87714,27.90392,0.89176,1.06784,5.50018,1.24994,4.60384,6.03786,1.43934,1.72588,1.22542,0.7299,2.89674,0.82912,0.938,3.63128,1.06388,0.86082,2.29046,0.547,1.14732,0.87792,0.45996,0.11534,0.65082,0.73,0.34856,1.25684,0.38194,4.77242,0.27482,0.45288,2.55558,2.64702,1.7688,0.60052,1.44376,2.21586,1.0972,0.41648,1.04056,2.1139,0.42034,0.86752,0.85042,0.31028,0.8613,1.32244,1.4822,0.64654,0.95594,0.0,0.75238,1.43358,0.5993,1.86676,1.08684,0.8701,1.14474,0.0,0.27402,0.89104,0.56452,0.72286,0.0,1.05178,0.55708,1.72738,2.41878,2.88472,4.51478,0.32024,0.2298,1.88678,1.54594,1.73042,0.92816,0.25924,0.82494,1.53618,0.72566,2.85446,1.25318,1.04022,4.69792,0.40004,0.68402,2.34044,0.28242,2.29992,1.72468,0.5916,2.42824,0.79254,1.6064,1.4433,5.45596,0.46222,0.69106,0.3735,0.57846,1.15074,1.40514,1.31032,0.49524,0.73542,0.71016,4.24274,0.30348,1.57022,0.58328,0.58554,0.7788,0.42956,1.4227,0.50836,0.36198,0.86872,1.59108,2.52184,0.95502,2.3179,0.23146,0.61468,0.67036,1.57952,1.60954,0.186,0.3331,,0.4649,10.4068,1.06336,0.64422,,5.49084,1.65152,0.70676,0.56254,0.90474,0.88692,3.66654,0.0,1.2406,1.89568,1.81156,0.24388,0.3249,1.63382,1.116,0.92174,0.9615,8.2093,0.68246,0.65614,0.20894,0.66306,0.0,0.7605,0.47298,1.00902,0.23762,0.18202,0.15698,1.52462,0.2956,1.39528,0.16058,0.6412,0.47404,0.31878,0.47152,0.9957,11.9267,25.67754,0.06096,0.50526,4.87898,3.13102,0.72944,0.40958,3.66564,0.2095,0.57482,1.58302,2.83946,1.00836,0.25378,0.66202,0.17656,0.65062,4.59216,0.36504,0.49382,0.65778,,13.68092,0.11954,3.7651,1.90364,0.77452,0.0,1.20316
2018-02-13,1.28092,0.64276,2.25806,1.52854,0.95526,0.55878,0.44338,0.69482,1.55168,0.90188,1.20262,0.8859,0.0,0.95362,1.6423,0.0,0.34814,1.5551,0.87264,0.4817,0.85664,0.99452,1.38752,1.4548,2.81878,0.0,0.89696,2.34802,0.74288,0.6782,0.96434,0.31854,2.17224,1.23848,0.62368,1.21614,1.35284,0.8068,0.63264,1.17832,1.55356,0.5083,0.9726,0.44564,1.91588,2.56328,3.40876,1.54898,0.43994,1.84216,0.9631,0.9864,0.22694,0.6497,1.50368,0.978,0.67628,0.0,1.11488,0.92392,3.0364,1.84286,0.60592,0.99536,2.69138,1.89962,1.29234,0.04276,1.45266,0.42002,0.4515,0.9985,0.27536,0.37108,0.63864,0.55958,0.6275,0.0,2.19568,5.32984,2.94416,0.73952,0.68688,0.52344,0.64028,0.75756,0.68416,0.81996,0.62678,5.28448,0.42212,0.80042,0.6748,0.73554,0.81736,0.19886,1.6228,3.2512,28.70552,0.81576,0.97342,4.72542,1.00906,4.13876,5.51046,1.33128,1.45618,1.10122,0.80862,2.98348,0.77484,0.8644,3.3186,0.9211,0.83982,2.13596,0.41216,0.99314,0.78068,0.3965,0.10578,0.51688,0.63118,0.28128,1.11132,0.30886,4.08984,0.25404,0.40338,2.2158,2.37614,1.43438,0.54062,1.2837,2.14386,0.97056,0.30182,1.02274,1.75366,0.33524,0.70622,0.76142,0.24268,0.80224,1.1337,1.20042,0.61844,0.7734,0.0,0.61344,1.23374,0.54236,1.78086,1.01936,0.7372,1.19814,0.0,0.24044,0.79208,0.45762,0.67388,0.0,0.921,0.48542,1.52858,2.08732,2.57456,3.91582,0.27398,0.22342,1.72232,1.3522,1.24244,0.8009,0.21784,0.7737,1.39838,0.6122,2.44038,0.9374,0.99426,5.15298,0.31548,0.62864,1.98524,0.25754,1.99686,1.60294,0.62256,1.93356,0.61788,1.37308,1.94956,5.36756,0.3822,0.59396,0.34418,0.45712,0.99874,1.2664,1.18738,0.44292,0.71944,0.59044,4.00844,0.25352,1.13674,0.51634,0.51584,0.61882,0.4178,1.14168,0.49566,0.29788,0.76,1.39664,2.12514,0.7391,2.00876,0.22764,0.52374,0.53098,1.51482,1.42896,0.1517,0.31994,,0.41414,9.69676,0.98396,0.56528,,5.6641,1.40538,0.53118,0.45968,0.67804,0.74202,2.94068,0.0,1.11064,1.52554,1.56214,0.20734,0.29424,1.46734,0.92192,0.83436,0.93112,7.58234,0.62864,0.55526,0.18996,0.5859,0.0,0.66152,0.41644,0.79888,0.16536,0.1656,0.13278,1.3356,0.21044,1.25838,0.11598,0.53668,0.48074,0.2636,0.40564,0.81658,10.61716,31.17238,0.05424,0.46432,4.32614,2.66034,0.65566,0.36492,3.16714,0.17804,0.49524,1.35028,2.62242,0.781,0.21174,0.59432,0.15848,0.59098,4.1806,0.34816,4.13888,0.5934,,11.6076,0.10458,3.62456,1.84248,0.87716,0.0,1.08466
2018-02-14,0.98756,0.50228,2.1843,1.15594,0.94062,0.47986,0.32862,0.59608,1.2607,0.723,1.01368,0.78662,0.0,0.8148,1.43678,0.0,0.2888,1.45848,0.74934,0.46192,0.78944,0.78442,1.24322,1.36632,2.38448,0.0,0.72104,2.07742,0.71394,0.47506,0.81546,0.28832,1.7578,1.25306,0.49532,1.05154,1.09162,0.67586,0.52618,1.04286,1.34194,0.4639,0.81228,0.41578,1.53626,2.28298,2.67534,1.36762,0.39728,1.5682,0.85808,0.90926,0.20444,0.54352,1.21626,0.8864,0.53578,0.0,0.89088,0.73864,2.41072,1.84526,0.53824,0.81924,2.39704,1.63868,1.14012,0.038,1.26916,0.37084,0.39092,0.79234,0.25216,0.31412,0.74496,0.48444,0.56788,0.0,2.04816,5.97894,3.09882,0.65988,0.58292,0.48526,0.51238,0.81558,0.64596,0.75612,0.58078,4.51494,0.32838,0.73768,0.60616,0.62556,0.6831,0.16612,1.68346,2.70738,27.2595,0.72894,0.85944,3.77818,0.83124,3.56944,4.63024,1.08152,0.91766,0.91678,0.84714,2.90606,0.6189,0.74236,2.69004,0.6909,0.7588,1.91232,0.29248,0.84058,0.70382,0.3551,0.09318,0.34692,0.45032,0.20782,0.92284,0.248,3.49808,0.24146,0.32372,1.78802,2.0414,1.08802,0.4274,0.96614,1.7532,0.82256,0.27566,1.02272,1.45738,0.28262,0.61578,0.65348,0.20552,0.70424,0.9682,0.95734,0.51008,0.622,0.0,0.49866,1.09078,0.55706,1.62204,0.91528,0.56832,1.14616,0.0,0.20574,0.7239,0.38806,0.57056,0.0,0.80368,0.41074,1.19156,1.77596,2.06418,3.21712,0.24364,0.18284,1.30932,1.14408,0.91734,0.70258,0.194,0.70978,1.36964,0.57034,2.09492,0.82446,0.81508,5.14264,0.28714,0.53688,1.68954,0.21978,1.8719,1.3996,0.6101,1.55394,0.44916,1.17502,1.94368,4.32654,0.3262,0.51502,0.31546,0.40776,0.86118,1.09918,1.03914,0.36856,0.68598,0.51202,3.70832,0.21694,0.893,0.49696,0.45802,0.4845,0.39306,0.90592,0.46034,0.24766,0.6543,1.08926,1.6006,0.64496,1.69928,0.20256,0.45254,0.44002,1.24352,1.19814,0.1343,0.30386,,0.34468,8.64542,0.8385,0.49694,,5.01592,1.01578,0.38382,0.36914,0.5554,0.65094,2.49276,0.0,0.92676,1.27218,1.02784,0.16592,0.23588,1.264,0.69262,0.67436,0.85314,7.66762,0.5458,0.44854,0.1561,0.47624,0.0,0.5487,0.3641,0.6445,0.14098,0.1412,0.10914,1.047,0.17044,1.1361,0.09974,0.47752,0.5155,0.20062,0.39232,0.64042,9.11252,32.19862,0.04366,0.41842,3.73552,2.27958,0.5575,0.2892,2.80534,0.15728,0.45472,1.2925,2.16398,0.6634,0.1621,0.52146,0.14364,0.50934,3.47706,0.29876,12.04534,0.5379,,8.80836,0.09256,3.666,1.85488,1.11774,0.0,0.97628
2018-02-22,0.88478,0.44492,2.06086,1.00522,0.84226,0.44498,0.29428,0.53688,1.38524,0.70706,0.99292,0.78804,0.0,0.84658,1.35792,0.49468,0.2558,1.60456,0.70844,0.45026,0.831,0.71324,1.15766,1.27138,2.25568,0.0,0.7352,1.97994,0.73274,0.40946,0.79312,0.30302,1.58018,1.39672,0.4894,1.0818,0.95782,0.5618,0.43572,1.02818,1.3566,0.42368,0.82642,0.44374,1.39286,2.20068,2.50136,1.40332,0.3957,1.5017,0.81136,0.94528,0.22092,0.55618,1.27232,0.84332,0.48704,0.0,0.95424,0.63334,2.28492,1.69556,0.50218,0.73222,2.44008,1.6949,1.15998,0.03972,1.27974,0.34976,0.37024,0.75382,0.25168,0.31008,0.8606,0.41658,0.61354,0.0,1.80514,7.02544,3.40344,0.62236,0.563,0.46596,0.50078,0.69532,0.5836,0.75986,0.67116,4.10536,0.3001,0.67118,0.56102,0.61902,0.64166,0.1741,1.60788,2.53346,26.4402,0.69278,0.85262,3.3528,0.80834,2.73892,4.59186,0.99562,0.82318,0.84268,0.76704,2.75996,0.59846,0.69758,2.79318,0.6048,0.67142,1.63952,0.2445,0.79606,0.65244,0.37014,0.08128,0.27702,0.37264,0.19286,0.84352,0.1759,3.3985,0.23742,0.28754,1.83892,1.98286,1.0311,0.38938,0.86072,1.53228,0.82762,0.2626,0.89954,1.26598,0.2853,0.62098,0.63788,0.1763,0.70326,1.10772,0.96252,0.50936,0.57396,0.0,0.40354,1.02916,0.51236,1.5307,0.9092,0.5623,1.26742,0.0,0.20132,0.72736,0.41518,0.57066,0.0,0.79806,0.3536,1.02518,1.5292,2.1747,2.96656,0.24638,0.17598,1.20694,1.18812,0.86096,0.68458,0.18304,0.71308,1.42772,0.57144,1.93872,0.71948,0.68116,5.51968,0.292,0.4849,1.45774,0.20688,1.56292,1.53878,0.63386,1.54938,0.42352,1.06226,2.2753,4.03486,0.34478,0.4629,0.43698,0.37604,0.85086,0.9713,1.07196,0.35478,0.68166,0.45578,3.78538,0.22024,0.75968,0.42036,0.46748,0.44416,0.38426,0.91968,0.41648,0.21728,0.64948,0.95386,1.3635,0.60142,2.58822,0.20172,0.42508,0.37292,1.11064,0.9963,0.13016,0.3241,,0.29244,8.2327,0.8779,0.3464,,4.36082,0.8311,0.32008,0.27728,0.58032,0.67104,2.13274,0.0,0.77768,1.29834,0.84308,0.13896,0.2079,1.12618,0.59346,0.71624,0.78336,5.81558,0.55052,0.30904,0.13488,0.49574,0.0,0.47696,0.35044,0.60714,0.12362,0.12014,0.09338,0.80898,0.14366,1.17514,0.08992,0.39696,0.50276,0.1503,0.37746,0.54522,9.15948,25.7271,0.03876,0.42752,3.69282,2.22282,0.61222,0.23726,2.48004,0.1762,0.42462,1.31214,1.89726,0.59554,0.13198,0.4892,0.1376,0.73352,3.41694,0.2766,18.04806,0.55512,,8.53876,0.0942,3.49836,1.80654,1.2863,0.0,0.9715
2018-02-23,0.67066,0.3748,2.01368,0.82538,0.7879,0.31572,0.1663,0.40386,1.1064,0.66112,0.82554,0.7493,0.0,0.76364,1.06558,1.06728,0.2083,1.52164,0.56914,0.35738,0.80616,0.50706,1.02002,0.88602,1.81698,0.0,0.72296,1.65508,0.57018,0.35268,0.6071,0.21796,1.49064,1.42778,0.53262,0.90864,0.74716,0.44328,0.28596,0.94822,1.32922,0.3734,0.73076,0.38318,1.29366,2.65834,1.997,1.33064,0.33036,1.33028,0.7046,0.90086,0.22236,0.53782,1.13218,0.73324,0.4406,0.0,1.1094,0.50108,2.24586,1.69548,0.44094,0.61216,2.29606,1.68552,1.07212,0.03006,1.07278,0.31746,0.34246,0.68706,0.20266,0.24962,1.06436,0.36578,0.60842,0.0,1.42426,7.86856,3.3316,0.53842,0.51746,0.43174,0.35908,0.68258,0.43046,0.67338,0.61276,3.4351,0.2089,0.61744,0.69018,0.49092,0.65864,0.14718,1.4033,2.19828,23.43586,0.58858,0.80624,2.68324,0.58138,1.96752,3.76392,0.96074,0.62062,0.66924,0.72696,2.7456,0.49178,0.62022,2.49866,0.504,0.59558,1.5417,0.16584,0.53594,0.49678,0.29236,0.0636,0.23688,0.3324,0.16598,0.66912,0.11664,3.35668,0.22016,0.22566,1.62034,1.59706,0.73312,0.31062,0.70078,1.15776,0.63638,0.17418,0.7919,1.24714,0.2098,0.49662,0.49752,0.14346,0.54008,1.01996,0.81976,0.45646,0.41834,0.0,0.30002,0.73182,0.42096,1.3495,0.76184,0.45578,1.11074,0.0,0.17584,0.5356,0.36518,0.4932,0.0,0.645,0.33238,0.9457,1.17968,1.94222,2.4692,0.19536,0.14322,1.09674,0.9539,0.83468,0.5042,0.13632,0.66492,1.46092,0.43984,1.82458,0.5539,0.6607,5.1908,0.20582,0.37756,1.33368,0.14854,1.2157,1.29914,0.56096,1.28368,0.33638,0.77218,2.3767,3.8138,0.27582,0.38918,0.42274,0.30364,0.7272,0.7718,0.84166,0.26364,0.48668,0.36356,3.78808,0.16946,0.66086,0.30198,0.37446,0.4017,0.27738,0.69404,0.36982,0.15414,0.52428,0.79418,1.05002,0.45206,2.90124,0.17094,0.3213,0.25334,0.89746,0.99134,0.08606,0.2803,,0.23674,6.62482,0.81412,0.24808,,3.8705,0.57114,0.21146,0.19572,0.47352,0.56536,1.48634,0.0,0.62532,1.5369,0.69574,0.10058,0.15986,0.76874,0.43682,0.77882,0.6012,4.10198,0.43412,0.1919,0.1041,0.41816,0.0,0.33922,0.26196,0.50952,0.0976,0.09714,0.06726,0.59652,0.08474,0.97552,0.0651,0.27952,0.40542,0.13806,0.34642,0.4407,8.14018,21.805,0.02926,0.47058,3.12794,1.9029,0.5426,0.17694,1.98788,0.13766,0.34764,1.12006,1.48464,0.34488,0.09234,0.35202,0.11742,0.99208,2.97616,0.22644,22.29254,0.51972,,7.81058,0.081,3.6189,1.89994,1.38536,0.0,1.33966
2018-02-26,0.64902,0.4022,2.26916,0.85536,1.10428,0.34498,0.13518,0.36352,1.14684,0.66794,0.83178,0.79486,0.0,1.02688,1.11194,1.48186,0.20016,1.49164,0.4863,0.36172,0.8641,0.53516,0.94066,0.745,1.87842,0.0,0.77374,1.94988,0.62354,0.40648,0.58744,0.22262,1.5351,1.46214,0.57274,0.98562,0.6809,0.45112,0.25372,0.99992,1.46354,0.34276,0.77382,0.4179,1.32086,3.21518,1.95942,1.5494,0.3712,1.48208,0.74592,0.93782,0.25948,0.49508,1.01758,0.8126,0.66278,0.0,1.21302,0.44572,2.4466,1.69006,0.5241,0.5574,2.43022,2.54132,1.48532,0.02032,1.1241,0.30014,0.3091,0.74402,0.21704,0.47186,1.30288,0.4274,0.59996,0.0,1.45056,8.58986,3.17716,0.75094,0.53706,0.51952,0.35248,0.6785,0.43744,0.71256,0.74558,3.36456,0.18964,0.61888,0.8093,0.53596,0.86466,0.16192,1.45058,2.6085,21.88554,0.59386,0.73494,2.64866,0.62282,2.02908,3.7197,1.01906,0.66746,0.66498,0.85008,3.19452,0.47966,0.62276,2.54428,0.63318,0.70902,1.74804,0.15446,0.55834,0.60292,0.30286,0.06722,0.2506,0.31548,0.19662,0.66654,0.11788,3.717,0.20054,0.21314,1.59816,1.699,0.75276,0.29528,0.8449,1.16262,0.71814,0.17004,0.74332,1.34872,0.24762,0.50264,0.60726,0.14894,0.61604,1.17166,0.79352,0.50002,0.45876,0.0,0.28552,0.65658,0.45008,1.33118,0.70684,0.45086,1.08376,0.0,0.23246,0.56424,0.34648,0.4634,0.0,0.66524,0.36218,0.97352,1.21086,1.99908,2.49414,0.21512,0.15346,1.1695,0.94242,0.89518,0.485,0.13198,0.60884,1.43064,0.49074,1.8456,0.59364,0.75394,5.23076,0.18486,0.34488,1.47696,0.16688,1.23756,1.54212,0.46136,1.36644,0.34856,0.77868,2.61622,4.05148,0.28612,0.38684,0.4375,0.3101,0.67814,0.86216,0.81,0.28826,0.49328,0.37142,4.15184,0.20538,0.68068,0.32186,0.38658,0.33972,0.24034,0.66492,0.43564,0.13512,0.5669,0.83282,1.11166,0.4667,3.54214,0.14838,0.3067,0.22398,0.79828,1.10358,0.09378,0.29486,,0.22852,7.4594,0.82606,0.25332,,3.19358,0.53922,0.18892,0.18052,0.45346,0.54028,1.32912,0.0,0.68362,2.88904,0.68094,0.11014,0.16732,0.70674,0.41588,0.84406,0.5681,3.17112,0.47568,0.17682,0.0986,0.46854,0.02666,0.30876,0.28594,0.58044,0.10458,0.0943,0.14318,0.61856,0.0979,1.0524,0.06948,0.30168,0.35982,0.15864,0.34852,0.42216,8.55542,21.33086,0.02888,0.52658,3.57538,2.10936,0.5772,0.1706,1.99028,0.14906,0.34766,1.14562,1.443,0.37964,0.08998,0.3878,0.09448,1.1501,2.7774,0.21366,26.02982,0.57536,,7.96626,0.08258,3.91816,1.92442,1.57058,0.0,1.9237
2018-02-27,0.64758,0.4129,2.35406,0.88026,1.33854,0.35484,0.10592,0.4326,1.2614,0.6357,0.85894,0.873,0.0,0.9626,1.0641,2.17406,0.2045,1.4173,0.46282,0.39156,0.82024,0.55234,1.12172,0.75446,1.6991,0.0,0.7967,1.90748,0.61338,0.55766,0.54924,0.22034,1.53088,1.44914,0.61874,1.11552,0.68454,0.4872,0.24402,1.03412,1.58194,0.32578,0.77068,0.42744,1.46544,3.33728,2.17544,1.82832,0.43018,1.49082,0.79054,1.0113,0.2819,0.50014,1.10132,0.96164,0.74836,0.0,1.20252,0.40494,2.4328,1.9191,0.6703,0.60576,2.53526,2.81488,1.97598,0.01354,1.11604,0.38782,0.28204,0.80322,0.22784,0.55046,1.15746,0.50086,0.5007,0.0,1.5716,8.15224,2.91236,0.68278,0.52674,0.51288,0.37638,0.84166,0.59258,0.91018,0.82564,3.57752,0.22,0.75302,0.87904,0.52228,1.02878,0.16004,1.70494,2.6864,20.67908,0.61996,0.7113,3.76004,0.73756,2.138,3.758,1.06472,0.75594,0.63306,1.03174,3.42398,0.4715,0.68818,2.8121,1.06068,0.75912,2.10214,0.14996,0.50972,0.66268,0.30446,0.0652,0.23478,0.30872,0.23126,0.71526,0.12378,3.8344,0.19938,0.19826,1.72498,1.60016,0.8475,0.28716,0.99352,1.16096,0.70166,0.1637,0.68794,1.49856,0.2467,0.4707,0.62792,0.16016,0.59388,1.25726,0.77462,0.616,0.55964,0.0,0.27054,0.6252,0.46696,1.4155,0.69374,0.45416,1.01646,0.0,0.29118,0.62234,0.32764,0.45966,0.0,0.73244,0.3673,0.95634,1.45064,2.12168,2.47128,0.21556,0.3173,1.13802,1.05406,0.93386,0.5317,0.13326,0.60728,1.24336,0.58374,2.10192,0.60878,0.95988,4.7648,0.18354,0.3277,1.6681,0.16718,1.24174,1.6444,0.40322,1.60484,0.3991,0.94824,2.26982,3.83436,0.29992,0.40436,0.47402,0.3112,0.8357,0.95232,0.6979,0.32196,0.39452,0.35766,4.49164,0.21574,0.64686,0.31364,0.37814,0.35636,0.19128,0.68646,0.66178,0.1448,0.56914,0.81524,1.05338,0.49178,4.46598,0.12328,0.28892,0.22342,0.8028,1.2753,0.10902,0.31014,,0.24442,7.52858,0.82988,0.25102,,2.87394,0.51596,0.18424,0.16986,0.4253,0.52084,1.16752,0.0,0.76878,3.6428,0.6498,0.10962,0.16694,0.639,0.41676,0.85636,0.5818,2.03742,0.44578,0.17892,0.09266,0.49114,0.0489,0.2672,0.3069,0.71656,0.11352,0.08166,0.17152,0.57444,0.10456,1.01934,0.08648,0.30844,0.27892,0.1621,0.34294,0.40022,8.47358,19.0679,0.02742,0.5603,3.56028,2.12382,0.65258,0.163,2.04158,0.14766,0.37514,1.223,1.40064,0.41478,0.08892,0.44572,0.10014,1.19964,2.6116,0.17874,26.8984,0.69532,,8.3828,0.08242,4.00694,2.25788,1.53094,0.0,2.35928
2018-02-28,0.68896,0.46024,2.42966,0.8995,1.5034,0.35956,0.11648,0.46374,1.33542,0.6453,0.90916,1.03162,0.0,1.0053,1.12726,2.64226,0.23362,1.45656,0.50922,0.39134,0.85504,0.56728,1.1684,0.92216,1.6723,0.0,0.80246,1.98114,0.70046,0.73672,0.59708,0.24198,1.61894,1.50364,0.71542,1.29014,0.74886,0.51276,0.26194,0.96256,1.64218,0.31552,0.80538,0.42592,1.50444,3.4905,2.62394,2.03224,0.50086,1.80814,1.0135,1.13848,0.31244,0.54246,1.25538,1.10772,0.8982,0.0,1.32482,0.46064,2.55348,2.22074,0.82276,0.79584,2.76644,3.22978,2.2869,0.00874,1.31304,0.43128,0.43798,1.02916,0.23316,0.6328,1.10038,0.52282,0.6106,0.0,1.67388,8.29978,3.04102,0.79568,0.64468,0.54188,1.46468,0.9904,0.70226,1.02152,0.86336,3.54426,0.27936,1.01278,0.93534,0.8457,1.07458,0.18498,1.80986,3.2177,21.24214,0.7103,0.68776,4.61804,0.8581,2.35126,4.01504,1.41308,0.89038,0.67398,1.16136,3.5897,0.49018,0.78576,2.93414,1.28258,0.9022,2.56716,0.15514,0.50888,0.7055,0.34084,0.0691,0.24718,0.32804,0.27118,0.72878,0.12396,3.99406,0.19532,0.20536,1.78554,1.712,1.10698,0.30632,1.15336,1.26996,0.74138,0.14472,0.6223,1.57302,0.26024,0.48422,0.65464,0.16532,0.65612,1.61558,0.78292,0.81012,0.58786,0.0,0.2841,0.6456,0.3422,1.4006,0.65224,0.45144,1.07292,0.0,0.34294,0.69748,0.33486,0.42294,0.0,0.74436,0.3972,1.052,1.69264,2.3012,2.46102,0.24036,0.57282,1.2532,1.14326,0.92276,0.58022,0.13492,0.63444,1.17214,0.7624,2.31328,0.60874,1.1015,4.67064,0.17186,0.35584,1.85552,0.20722,1.20884,2.15314,0.44812,1.82678,0.41468,1.02032,2.51926,4.40352,0.31592,0.45272,0.4741,0.3262,0.86304,1.0545,0.72096,0.33892,0.35912,0.39908,4.99426,0.23022,0.68564,0.34224,0.4112,0.37744,0.17518,0.7884,0.8558,0.14694,0.66504,0.77186,1.09294,0.5173,4.80354,0.1371,0.30464,0.22326,0.8209,1.32418,0.11172,0.28734,,0.25226,8.22116,0.87468,0.2818,,3.07942,0.5038,0.20802,0.17784,0.4198,0.55626,1.06104,0.0,0.7911,3.99336,0.7552,0.10698,0.1761,0.66404,0.42666,0.86932,0.6316,0.0,0.47624,0.17426,0.09716,0.58336,0.15936,0.27718,0.31558,0.88028,0.1216,0.07838,0.20418,0.58574,0.1077,1.07916,0.09248,0.31378,0.22894,0.1675,0.32404,0.40468,9.10716,18.55644,0.03052,0.64314,4.02794,2.3827,0.67048,0.16994,1.98416,0.16348,0.3914,1.15188,1.43146,0.4155,0.09366,0.5722,0.0998,1.26992,2.4638,0.16962,24.51182,0.7891,,8.79898,0.08896,4.34998,2.32124,1.30122,0.0,2.81964
2018-03-01,0.64388,0.46718,2.39308,0.8384,1.55836,0.35678,0.1052,0.45944,1.22228,0.64556,0.9185,1.0627,0.0,0.95704,1.1948,2.14758,0.22894,1.30614,0.4879,0.42356,0.8778,0.48764,1.13864,1.10718,1.66092,0.0,0.73272,1.97146,0.73026,0.81918,0.5541,0.20794,1.61224,1.3592,0.72746,1.2872,0.72972,0.49602,0.28562,0.9523,2.79822,0.31888,0.73616,0.394,1.4629,3.54068,2.55464,2.11104,0.53056,1.86112,1.05002,1.06866,0.3135,0.54928,1.28182,1.45854,0.93962,0.0,1.46732,0.45436,2.64002,2.76772,0.83766,0.8671,2.85924,3.47328,2.4917,0.0,1.29668,0.42572,0.43652,1.15574,0.26248,0.65702,0.98096,0.59934,0.54346,0.0,1.65856,8.22816,3.07254,0.84924,0.6965,0.5319,2.4494,1.1796,0.78088,1.08218,0.82386,3.85842,0.28992,1.0796,1.19808,0.89524,1.0712,0.17306,2.09902,3.29422,20.79606,0.70238,0.68262,5.41376,1.08432,2.57684,4.28068,1.48576,0.92372,0.7121,1.21596,3.67908,0.62556,0.8019,2.55406,1.40884,0.93306,2.86076,0.14978,0.4655,0.69962,0.30326,0.07786,0.23168,0.3319,0.28988,0.6748,0.12698,4.00328,0.21984,0.17998,1.60272,1.62146,1.19538,0.27966,1.24396,1.32222,0.73028,0.13332,0.59464,1.71002,0.26428,0.54594,0.78764,0.15674,0.66786,1.54684,0.62962,0.85496,0.601,0.0,0.28328,0.65448,0.35632,1.35872,0.71676,0.41824,1.0685,0.0,0.38222,0.97844,0.28368,0.46634,0.0,0.70312,0.41526,1.05654,1.77352,2.39356,2.2267,0.23052,0.70484,1.40494,1.13062,0.80696,0.63462,0.13874,0.61006,1.20514,0.8463,2.34544,0.54452,1.25882,4.86748,0.16572,0.34634,1.94962,0.23436,1.02818,2.16572,0.42562,2.55006,0.43264,1.05038,2.36074,4.53036,0.34698,0.4548,0.344,0.33742,0.80726,1.07532,0.7075,0.32266,0.37222,0.36726,5.10938,0.23168,0.70374,0.34662,0.37306,0.4253,0.16052,0.73886,0.98382,0.14868,0.66544,0.665,1.03558,0.45816,4.1587,0.12392,0.27036,0.21512,0.81518,1.52378,0.11466,0.24866,,0.249,8.48646,0.76742,0.2969,,3.20294,0.45394,0.23262,0.18196,0.31758,0.48886,1.04004,0.0,0.8025,4.09788,0.74624,0.10332,0.16272,0.64182,0.37564,0.68076,0.63834,2.33992,0.4399,0.1705,0.0924,0.56374,2.25602,0.28342,0.31148,0.92296,0.13026,0.0677,0.2229,0.5578,0.13102,1.03396,0.09552,0.3157,0.22538,0.16614,0.2985,0.37268,8.73018,17.6261,0.02904,0.71346,4.06168,2.40734,0.66794,0.1634,1.8976,0.13736,0.38168,1.06668,1.41116,0.4027,0.09194,0.65902,0.09472,1.07538,2.12192,0.1512,21.94758,0.85582,,8.30456,0.07882,4.77628,2.25644,1.1915,0.0,3.0973
2018-03-02,0.60244,0.45446,2.31162,0.80794,1.5635,0.36672,0.1049,0.51734,1.24484,0.51486,1.00416,1.0635,0.0,0.87846,1.16622,1.57498,0.23308,1.15698,0.48388,0.47106,0.78262,0.52348,1.1374,1.27392,1.69706,0.0,0.62998,1.9417,0.72764,0.87566,0.50886,0.20102,1.38584,1.35856,0.68298,1.28862,0.76002,0.47738,0.28464,0.94312,3.64184,0.3059,0.82062,0.37554,1.3874,2.77868,2.58758,2.17148,0.59798,2.015,1.1259,0.97422,0.31322,0.53398,1.3233,1.68344,0.89156,0.02844,1.51222,0.47074,2.39256,3.01406,0.89172,0.89732,2.91916,3.89142,2.64134,0.0,1.45414,0.41438,0.4165,1.13362,0.26768,0.71058,0.67572,0.62782,0.57074,0.0,1.84056,7.62798,3.03466,0.90138,0.72188,0.52324,2.81444,1.29958,0.81904,1.04556,0.82286,4.04116,0.29588,1.14902,1.19954,0.9514,1.02066,0.1724,2.25618,3.29092,20.63402,0.7314,0.6467,5.9925,1.48936,2.7867,4.49476,1.57788,1.01102,0.79182,1.36206,3.55768,0.76672,0.80356,2.60502,1.49382,0.95104,2.95766,0.1386,0.4379,0.74854,0.27976,0.12012,0.21642,0.27142,0.3171,0.6782,0.12186,3.7476,0.25846,0.15676,1.6337,1.48184,1.2928,0.26256,1.33082,1.33998,0.74418,0.14064,0.5557,1.4914,0.28252,0.55386,0.85354,0.17752,0.68044,1.8431,0.63088,0.85956,0.59988,0.0,0.26174,0.69358,0.34918,1.33072,0.81354,0.37872,1.0664,0.0,0.39358,1.17306,0.2517,0.42984,0.0,0.69262,0.40996,0.96824,1.8704,2.4923,2.11696,0.24124,0.76464,1.38796,1.14152,0.79476,0.6493,0.14434,0.62442,1.11824,0.90296,2.35472,0.63206,1.33254,4.94356,0.14984,0.32696,1.99408,0.24448,1.23636,2.51858,0.4207,3.05268,0.41652,1.073,2.9373,4.24168,0.44316,0.48436,0.2978,0.33992,0.84516,1.10892,0.77432,0.34952,0.35952,0.38502,5.15804,0.29606,0.68534,0.3369,0.3678,0.3957,0.16954,0.7609,1.06516,0.15344,0.67328,0.62936,1.02852,0.44712,5.0067,0.13542,0.25736,0.20878,0.80384,1.39032,0.1095,0.2488,,0.24388,8.6776,0.74376,0.30912,,3.28214,0.43636,0.23722,0.17226,0.2729,0.45074,1.08728,0.0,0.80242,3.96754,0.74082,0.11026,0.15904,0.6465,0.32592,0.4907,0.63482,4.1228,0.40146,0.158,0.09048,0.58072,3.02968,0.30138,0.30788,0.92388,0.12062,0.05998,0.23132,0.46898,0.12394,0.91072,0.09848,0.29984,0.21946,0.12324,0.27532,0.33584,8.30962,19.58232,0.02696,0.68102,4.18078,2.36332,0.68124,0.14128,1.78324,0.14114,0.38326,0.99958,1.43904,0.4228,0.09516,0.67688,0.1046,0.74972,1.96464,0.1453,20.29626,0.91266,,8.11766,0.08204,4.8116,2.10874,1.10376,1.9778,2.93892
2018-03-05,0.56798,0.41998,1.90996,0.69568,1.33658,0.3129,0.09078,0.5041,1.23164,0.42252,0.86684,0.96704,0.0,0.59994,1.07832,1.1604,0.20914,0.9446,0.48116,0.4596,0.70064,0.47024,1.09798,1.23436,1.53252,0.0,0.56382,1.47584,0.63024,0.90352,0.42322,0.16748,1.49246,1.2013,0.62604,1.19396,0.70252,0.4228,0.26988,0.87436,4.27286,0.26428,0.81412,0.38626,1.16902,2.06578,2.44492,2.06996,0.66262,1.72332,1.05066,0.8637,0.32448,0.53084,1.22088,1.66948,0.71518,0.02844,1.50708,0.46352,2.10026,3.0127,0.9201,0.83766,2.7072,2.99262,2.23626,0.0,1.3879,0.39858,0.42522,1.09148,0.2905,0.51382,0.42084,0.52194,0.67384,0.0,1.5749,6.93274,2.84466,0.64266,0.64814,0.43452,3.10556,1.25508,0.78682,1.01772,0.81126,4.2477,0.30602,1.08694,1.22566,0.87634,0.78166,0.14922,2.30566,2.80958,19.30922,0.76428,0.5618,6.11502,1.56656,2.7028,4.06776,1.5168,0.9122,0.77282,1.26954,2.94338,0.75538,0.73244,2.38962,1.55424,0.77812,2.7778,0.12474,0.35358,0.64574,0.24986,0.13386,0.16532,0.24232,0.30676,0.65764,0.11624,3.43724,0.28876,0.14166,1.4367,1.19142,1.31436,0.22548,1.41088,1.20232,0.62274,0.11598,0.47422,1.3839,0.25524,0.49604,0.77042,0.1881,0.5336,1.7928,0.5667,0.9621,0.5054,0.0,0.23756,0.82502,0.28602,1.35146,0.7725,0.3492,1.05214,0.0,0.34324,1.22092,0.22044,0.40332,0.0,0.65454,0.3813,0.88364,2.13936,2.7066,1.85068,0.21542,0.8231,1.2702,1.02514,0.71222,0.58306,0.13354,0.72942,1.00268,0.92598,2.06648,0.58716,1.23046,5.65592,0.14236,0.31674,1.61706,0.25172,1.2188,2.38188,0.46736,3.3768,0.38332,1.10438,3.19454,3.74836,0.4571,0.46962,0.25176,0.28556,0.8403,0.89842,0.67022,0.3204,0.31588,0.38026,5.16234,0.28442,0.5617,0.29624,0.31322,0.39592,0.16756,0.75082,1.1057,0.14872,0.59662,0.49922,0.86684,0.3789,5.3005,0.12982,0.22632,0.20058,0.7241,1.2249,0.09226,0.20792,,0.22522,7.1043,0.66166,0.2733,,3.37546,0.3919,0.23382,0.15132,0.22338,0.35856,0.9962,0.0,0.7463,2.9706,0.67748,0.08594,0.13892,0.63098,0.2914,0.4199,0.61254,5.85402,0.31376,0.13828,0.0849,0.48616,3.77436,0.28672,0.27052,0.83828,0.10926,0.04942,0.15912,0.3721,0.10366,0.71046,0.09122,0.25894,0.18088,0.09226,0.24502,0.29286,6.88822,17.06532,0.0245,0.6075,3.49536,2.00324,0.6356,0.12042,1.5807,0.11782,0.35812,0.92998,1.39474,0.34594,0.08748,0.62582,0.10052,0.58594,1.60498,0.1348,18.74666,0.86482,,7.12608,0.07148,4.52216,1.8393,1.14912,3.48136,2.82266
2018-03-06,0.55192,0.4353,2.04826,0.78588,1.39784,0.33588,0.094,0.4149,1.10794,0.39778,0.8067,1.03606,0.0,0.58206,1.16586,0.65604,0.22992,1.17206,0.51652,0.44888,0.8578,0.48298,0.864,1.28866,1.65058,0.0,0.55792,1.72356,0.66056,0.85762,0.43634,0.1795,1.62758,1.23306,0.65276,0.98834,0.76374,0.36888,0.28334,0.82028,4.62866,0.25354,0.76956,0.41958,1.09958,2.22758,2.32658,2.14752,0.8189,1.71208,1.052,0.75628,0.31122,0.51398,1.17202,1.65622,0.67486,0.02844,1.47694,0.45106,2.19388,2.86752,0.91846,0.79906,3.09474,2.73718,1.9372,0.0,1.38778,0.45716,0.47132,1.06068,0.32424,0.47434,0.7278,0.43332,0.81394,0.0,1.44164,6.75084,2.69886,0.72702,0.6554,0.39114,3.5213,1.21464,0.74584,0.84004,0.89742,4.41468,0.3037,0.92714,1.33876,0.93794,0.80844,0.17336,2.75422,3.21142,19.07294,0.8622,0.56296,5.39882,1.57896,2.71884,4.08974,1.54906,0.8185,0.80768,1.00778,2.78258,0.81868,0.74242,2.0811,1.31988,0.68348,2.45322,0.10956,0.36688,0.54738,0.25546,0.16176,0.1506,0.231,0.33118,0.57188,0.11114,3.65072,0.34488,0.13884,1.25294,1.23658,1.42154,0.21612,1.3751,1.18084,0.69392,0.11436,0.45316,1.403,0.3027,0.54368,0.921,0.18154,0.5249,2.04848,0.56414,0.85608,0.44216,0.0,0.22166,0.8186,0.2405,1.36998,0.87942,0.37536,1.17256,0.0,0.44454,1.2713,0.24456,0.42138,0.0,0.60298,0.44198,0.95722,2.04046,2.8319,1.96244,0.22748,0.77662,1.29766,0.99918,0.6221,0.50048,0.13474,0.81652,0.97218,0.96706,1.90344,0.59386,1.14318,5.76256,0.14716,0.35172,1.59394,0.30166,1.21376,2.8613,0.47704,3.86142,0.34872,1.01476,3.52,4.18246,0.47982,0.46022,0.20464,0.28736,0.69776,0.87842,0.69878,0.27348,0.32796,0.3925,5.21984,0.2833,0.56138,0.27672,0.35472,0.42292,0.18586,0.7989,0.98934,0.13398,0.70018,0.49972,0.86036,0.36636,4.93318,0.15374,0.22998,0.18816,0.6675,1.03022,0.07638,0.1676,,0.21068,7.72588,0.64498,0.29606,,3.4311,0.35818,0.21674,0.159,0.25832,0.35842,1.0461,0.0,0.69184,2.69814,0.65838,0.08052,0.16864,0.6081,0.2515,0.39316,0.60256,7.0668,0.32908,0.12458,0.09194,0.50054,5.05214,0.28966,0.32448,0.74834,0.10396,0.04772,0.13002,0.34588,0.09384,0.71744,0.07712,0.25652,0.19332,0.0825,0.242,0.27056,6.52748,18.3412,0.02404,0.5795,4.09948,2.19234,0.5876,0.12944,1.62014,0.12846,0.35354,0.9594,1.47946,0.31358,0.08152,0.59248,0.10086,0.65244,1.53154,0.14386,17.25986,0.81216,,7.93264,0.067,4.77826,1.30684,1.19874,4.81436,2.72428
2018-03-07,0.57714,0.41222,1.95176,0.78958,1.33804,0.37374,0.09282,0.3804,1.0401,0.37514,0.79388,1.09382,0.0,0.6348,1.29464,0.8159,0.22332,1.1688,0.46894,0.44302,0.8797,0.47476,0.76824,1.19506,1.71212,0.0,0.55008,1.8153,0.57224,0.72108,0.41146,0.16252,1.56874,1.12206,0.61676,0.89014,0.73932,0.39294,0.2929,0.82066,4.8436,0.25536,0.74034,0.4125,1.0508,2.23356,1.9549,2.18978,0.87142,1.4698,0.87994,0.6623,0.28684,0.59026,1.07464,1.5808,0.5813,0.02844,1.8469,0.50088,2.15334,2.93716,0.86034,0.67652,3.07956,2.46212,1.67868,0.0,1.18,0.44178,0.33556,0.84404,0.33532,0.4272,0.67236,0.44428,1.00874,0.0,1.34656,6.86626,2.47896,0.81558,0.55992,0.38586,2.72954,0.99872,0.7505,0.73642,0.92828,4.39004,0.2537,0.66164,1.47574,0.6616,0.97066,0.16298,2.88864,2.97304,18.07084,0.863,0.54922,5.0445,1.56552,2.52914,3.76082,1.3655,0.72828,0.8096,0.85488,2.6201,0.9127,0.69498,1.9762,1.46952,0.50664,2.07086,0.1088,0.33872,0.51188,0.21902,0.17964,0.1537,0.23176,0.31914,0.52594,0.1315,4.50964,0.4161,0.12446,1.22674,1.1733,1.40416,0.24614,1.26614,1.12664,0.68468,0.11572,0.50638,1.43648,0.32666,0.56114,0.96376,0.21146,0.47046,1.91238,0.57294,0.71338,0.40496,0.0,0.2244,0.80032,0.22712,1.3704,0.9298,0.37292,1.16244,0.0,0.59782,1.24754,0.24002,0.42782,0.0,0.6087,0.5392,0.90124,2.1536,2.82424,1.95574,0.22254,0.56906,1.15694,1.03484,0.56086,0.48948,0.15142,0.92472,1.01932,0.8577,1.89224,0.64504,1.06166,5.72352,0.14298,0.32986,1.52492,0.30644,1.35954,2.6724,0.4255,4.07708,0.3981,1.0238,3.59094,3.83302,0.52746,0.4295,0.19548,0.27,0.64714,0.7958,0.75276,0.26456,0.37904,0.38334,4.69496,0.33124,0.5933,0.24114,0.35564,0.4352,0.2027,0.94214,0.85156,0.1356,0.68238,0.5533,0.93848,0.33406,4.76214,0.17044,0.21886,0.19704,0.76404,1.0377,0.07904,0.17304,,0.1947,7.42762,0.65496,0.29608,,3.2491,0.38906,0.19594,0.15778,0.25978,0.34418,0.969,0.0,0.6741,3.15624,0.61776,0.08312,0.18276,0.57274,0.26466,0.36596,0.59776,8.8932,0.33944,0.12948,0.10322,0.4591,5.70734,0.29368,0.35272,0.63352,0.10156,0.0497,0.11572,0.34248,0.09646,0.66794,0.07992,0.26112,0.19872,0.07582,0.24002,0.2782,6.08378,20.39568,0.02038,0.55748,3.99802,2.03772,0.5702,0.12148,1.5554,0.1201,0.33854,0.9271,1.72338,0.34108,0.08436,0.50272,0.13074,0.66092,1.77778,0.16498,13.99674,0.77886,,8.26798,0.06486,4.31504,1.12942,1.33278,5.74858,2.44734
2018-03-08,0.55382,0.4153,1.8513,0.8234,1.35934,0.3532,0.0919,0.4252,0.9805,0.30662,0.723,1.05806,0.0,0.70118,1.20606,1.0972,0.25804,1.17582,0.4604,0.3992,0.85966,0.47132,0.77458,1.1966,1.69948,0.0,0.57216,1.72898,0.5055,0.64666,0.38664,0.15824,1.74726,1.08498,0.64986,0.83318,0.81338,0.37816,0.27364,0.74202,3.75748,0.241,0.7197,0.5026,0.96422,2.46508,1.93674,2.0107,0.94954,1.41942,0.82178,0.64816,0.27624,0.67334,0.90986,1.26484,0.5366,0.02844,2.23024,0.52456,2.08668,2.56918,0.86582,0.64198,2.80938,2.0521,1.60338,0.0,1.19166,0.52864,0.33348,0.75566,0.32324,0.51836,0.70638,0.37154,1.31936,0.0,1.26976,6.77802,2.20504,1.51382,0.46304,0.35676,1.97726,0.90192,0.75046,0.60656,0.9096,3.7133,0.2279,0.59288,1.40628,0.57336,1.01616,0.15418,2.97946,2.89506,16.61874,0.88266,0.58014,4.37042,1.37002,2.09974,3.05032,1.43422,0.74472,0.8443,0.79822,2.46036,0.80748,0.60816,1.80748,1.43148,0.45218,1.81854,0.10004,0.33326,0.47336,0.21226,0.2689,0.14314,0.20764,0.29766,0.49866,0.14442,4.8709,0.56932,0.11198,1.10556,1.1085,1.29722,0.24636,1.23968,1.02498,0.65004,0.11332,0.50636,1.39064,0.40728,0.4988,0.8174,0.23156,0.40392,1.92154,0.56528,0.7015,0.4059,0.0,0.21472,0.77776,0.2018,1.32834,1.02674,0.35316,1.026,0.0,0.86584,0.9449,0.28978,0.47394,0.0,0.68112,0.61516,0.93376,2.13412,2.5874,1.98104,0.20124,0.4869,0.9269,1.08062,0.59038,0.4246,0.14316,1.06878,0.86498,0.80358,1.71898,0.65364,0.8979,5.02256,0.19812,0.31482,1.47076,0.33036,1.48744,2.7034,0.47286,3.43684,0.33108,0.9877,3.80928,3.64398,0.52378,0.41726,0.1702,0.24986,0.7304,0.73268,0.75394,0.26306,0.33514,0.47076,4.64722,0.40314,0.5817,0.24178,0.33392,0.53542,0.2338,1.2377,0.77316,0.14744,0.64732,0.5293,0.92814,0.31432,4.58788,0.23032,0.21194,0.1999,0.74918,0.85174,0.0746,0.16546,,0.18482,6.98852,0.64562,0.29194,,3.06068,0.35814,0.14618,0.14772,0.25362,0.31756,0.95384,0.0,0.61204,3.90548,0.57772,0.0871,0.19454,0.57816,0.26282,0.33898,0.5649,8.09392,0.32868,0.11592,0.1062,0.41852,4.0543,0.2881,0.49718,0.59554,0.10788,0.0498,0.10752,0.34086,0.07364,0.58954,0.0833,0.23782,0.16896,0.06848,0.20704,0.267,7.44026,20.82136,0.01894,0.47534,3.86482,1.90274,0.50182,0.11394,2.04082,0.12332,0.41162,0.94228,1.72414,0.3949,0.07936,0.4232,0.15604,0.89284,1.71522,0.15318,12.4368,0.7055,,8.52524,0.07106,4.08646,1.11184,1.41196,6.47632,2.4922
2018-03-09,0.587,0.41576,2.04738,0.90058,1.38374,0.36262,0.10052,0.43458,0.92568,0.30572,0.65452,1.08448,0.0,0.74514,1.2892,1.2978,0.24616,1.25852,0.5038,0.37738,0.89842,0.43772,0.74464,1.1358,1.68414,0.0,0.57564,2.10078,0.5869,0.65694,0.44676,0.20356,1.87184,1.01762,0.68698,0.95792,0.81282,0.38022,0.29912,0.78124,3.26094,0.23862,0.5921,0.63146,0.92146,2.64748,1.94894,1.9657,0.8869,1.64624,0.997,0.69628,0.27752,0.75426,0.94966,1.2825,0.59012,0.0,2.4263,0.5216,1.99582,2.71396,0.85506,0.68724,2.93804,1.78522,1.78856,0.0,0.93716,0.56812,0.35362,0.7055,0.3426,0.52,0.96248,0.38964,1.43464,0.0,1.23896,6.44922,1.9407,2.01246,0.48212,0.33124,1.9695,1.0331,1.0156,0.65262,0.95922,3.52498,0.22074,0.69418,1.45452,0.61624,1.15062,0.16938,3.15596,3.36418,16.80014,0.92938,0.62986,5.91714,1.2412,2.40184,3.81652,1.3773,0.78508,0.85836,0.8058,2.57514,0.74726,0.80246,1.85982,1.59712,0.55218,2.20708,0.10042,0.3507,0.43386,0.21186,0.25712,0.1308,0.19694,0.24986,0.5314,0.15072,4.92148,0.55312,0.1208,1.04114,1.26616,1.35182,0.24794,1.25746,1.0142,0.64638,0.10836,0.49774,1.41498,0.44136,0.55738,0.809,0.2164,0.44416,1.71554,0.53288,0.74506,0.46628,0.0,0.27848,0.77952,0.21796,1.33892,1.06576,0.37522,1.01276,0.0,1.0387,0.91728,0.32284,0.56586,0.0,0.69132,0.69816,0.90142,2.19504,2.44124,2.04042,0.21456,0.52588,0.8585,1.18594,0.53922,0.43894,0.14056,1.1309,0.84762,0.80326,1.9722,0.58182,0.90268,4.82062,0.29774,0.32148,1.67048,0.58192,1.33052,2.5403,0.48308,3.55974,0.32,0.98016,3.89972,3.71148,0.45718,0.42394,0.15752,0.21462,0.72832,0.78638,0.74306,0.25342,0.35162,0.47988,4.66172,0.34554,0.63498,0.24586,0.35454,0.57658,0.2477,1.38586,0.8384,0.12918,0.70922,0.52866,0.93928,0.352,3.40688,0.23712,0.24428,0.21686,0.70132,0.8122,0.0757,0.1551,,0.18384,7.64158,0.6684,0.28952,,2.82134,0.35552,0.13108,0.14372,0.2638,0.35156,1.00266,0.0,0.59386,4.13528,0.55814,0.09288,0.2012,0.55874,0.25626,0.34992,0.56244,7.91216,0.37862,0.11468,0.10772,0.45374,3.7736,0.25576,0.52092,0.6628,0.1239,0.05154,0.11086,0.35144,0.07914,0.72048,0.10582,0.2652,0.18312,0.06646,0.20146,0.2584,8.56048,18.77754,0.01902,0.51496,4.42926,2.20494,0.48712,0.11496,2.257,0.1362,0.5024,1.00862,1.643,0.4054,0.0749,0.43088,0.16346,1.01038,1.64256,0.14706,12.96502,0.7322,,8.7301,0.08282,3.56622,1.0955,1.60406,5.43994,2.30128
2018-03-12,0.64782,0.46374,2.25108,1.01338,1.45054,0.40148,0.12078,0.51536,1.03444,0.38058,0.7031,1.24858,0.0,0.80248,1.31312,1.66612,0.2546,1.4986,0.51268,0.43164,1.07232,0.49502,0.82654,1.4598,1.83698,0.0,0.58408,2.44798,0.71602,0.65952,0.52884,0.27358,1.9435,1.19334,0.83582,1.04496,0.82924,0.43916,0.31054,0.8244,3.07978,0.25926,0.59662,0.64762,1.03836,2.95984,2.30412,2.0093,0.8807,1.8437,1.19102,0.76894,0.24394,0.82406,1.17606,1.42284,0.62904,0.0,2.70834,0.60096,2.23564,3.03048,0.76832,0.826,3.37844,1.787,2.19042,0.0,1.04162,0.61174,0.40496,0.76006,0.35618,0.52804,1.16688,0.40588,1.4282,0.0,1.34018,7.44438,2.35528,2.32592,0.55264,0.3227,1.95742,1.20464,1.23426,0.6959,0.93758,3.48526,0.23876,0.86038,1.60166,0.73264,1.16994,0.20064,3.3312,3.94298,17.87444,1.00066,0.68382,7.3382,1.35136,2.6442,4.4784,1.59214,0.8826,0.94854,0.98042,2.81274,0.8493,0.9833,2.09774,1.70362,0.6385,2.57494,0.09744,0.40004,0.43662,0.23654,0.258,0.14156,0.2014,0.2457,0.5363,0.15572,5.20976,0.55992,0.1315,1.3205,1.51454,1.42352,0.27174,1.09468,1.14946,0.67794,0.12476,0.518,1.39822,0.45944,0.6666,0.8236,0.21334,0.5545,1.86654,0.65784,0.73728,0.56392,0.0,0.32276,0.77004,0.22592,1.3687,1.21056,0.4799,1.01876,0.0,1.15242,1.07452,0.34662,0.6601,0.0,0.71956,0.9143,1.11626,2.14456,2.30364,2.25372,0.2515,0.55052,0.86502,1.42456,0.55926,0.51226,0.15358,1.08422,0.94632,0.77446,2.12882,0.62154,0.93184,3.95812,0.37016,0.3257,1.97276,0.73632,1.3175,2.83506,0.4396,3.74588,0.38242,0.966,3.96372,3.81328,0.60688,0.47186,0.1676,0.2662,0.87676,0.9018,0.85798,0.273,0.3526,0.51568,4.37042,0.33998,0.7079,0.29364,0.37982,0.5936,0.27062,1.63848,0.7986,0.14266,0.85408,0.577,1.0544,0.43456,2.82158,0.25926,0.28312,0.2347,0.7754,0.86944,0.08054,0.1667,,0.20002,9.22204,0.75952,0.34708,,3.33558,0.38514,0.12354,0.16148,0.30908,0.40368,1.11524,0.0,0.63818,4.19504,0.58762,0.11166,0.21886,0.55386,0.26494,0.28854,0.59678,7.35548,0.4443,0.12674,0.12526,0.5478,3.68514,0.25984,0.60164,0.79924,0.1536,0.05676,0.1267,0.38744,0.09662,0.8599,0.1227,0.28362,0.22454,0.07634,0.26228,0.26026,9.61298,19.53526,0.02192,0.82158,5.2094,2.68604,0.51576,0.12616,2.55384,0.17278,0.53642,1.16978,1.87126,0.46798,0.07992,0.48358,0.1755,1.07096,1.73222,0.15374,14.03264,0.8629,,9.93392,0.09354,3.72412,1.13708,1.57528,4.89976,2.23188
2018-03-13,0.6398,0.38956,1.89728,0.85556,1.2862,0.37646,0.11512,0.57278,1.05988,0.37704,1.10878,1.16388,0.0,0.8034,1.21304,1.74628,0.22596,1.32934,0.47958,0.5046,0.95574,0.4518,0.82814,1.36896,1.56488,0.0,0.56816,2.14902,0.68726,0.557,0.49696,0.2715,1.83514,1.41336,0.78904,1.24026,0.7366,0.43964,0.2765,0.83528,3.18666,0.26284,0.61454,0.63424,0.88746,2.78538,2.00964,1.84424,0.72912,1.83936,1.2885,0.79504,0.2708,0.89384,1.11972,1.52694,0.65722,0.0,2.84198,0.59654,1.89628,3.03034,0.62638,0.83614,3.26716,1.79362,2.15806,0.0,1.05754,0.49604,0.39124,0.78278,0.30392,0.48644,0.8788,0.4112,1.40528,0.0,1.3178,7.73934,2.39466,2.33524,0.54296,0.31442,1.71438,1.15794,1.2485,0.67,0.8821,3.11922,0.21028,0.90662,1.52832,0.7244,1.0363,0.1759,2.64614,3.6301,16.68332,0.95182,0.70706,7.719,1.40424,2.62472,4.56456,1.56992,0.88782,0.88878,1.0113,2.5909,0.84032,0.99054,2.31326,1.70202,0.69028,2.6282,0.09596,0.3871,0.4298,0.25618,0.24202,0.13724,0.2082,0.22364,0.51902,0.15234,5.06768,0.52076,0.12154,1.29832,1.37698,1.34734,0.24798,0.95564,1.01856,0.59308,0.12506,0.53132,1.22324,0.44182,0.62888,0.7299,0.21922,0.56474,1.6722,0.65476,0.7284,0.5316,0.0,0.34346,0.7494,0.22222,1.2051,1.128,0.46682,0.84786,0.0,1.13474,1.10102,0.33208,0.6765,0.0,0.65576,1.05424,1.04054,2.27566,2.1156,1.95716,0.24414,0.47958,0.697,1.42406,0.57102,0.51554,0.1509,1.05288,0.92656,0.72512,1.97936,0.62368,0.84228,3.52646,0.36516,0.28432,1.86232,0.7722,1.29408,2.43114,0.41564,3.66498,0.3823,0.9588,3.8045,3.20086,0.70896,0.47026,0.16438,0.25572,0.8845,0.80226,0.90408,0.27078,0.35226,0.54798,3.51538,0.34786,0.70752,0.35228,0.33958,0.53308,0.2755,1.70256,0.73404,0.1399,0.76532,0.5534,1.07304,0.41242,2.49534,0.25418,0.25254,0.23174,0.70732,0.9182,0.10782,0.16898,,0.18244,8.34002,0.72156,0.4969,,3.14046,0.37458,0.12884,0.15754,0.27362,0.38482,1.16916,0.0,0.55154,4.31938,0.5446,0.13238,0.21616,0.54506,0.27406,0.27672,0.58444,8.5505,0.40834,0.1271,0.1145,0.56186,2.83634,0.25966,0.59102,0.79258,0.16726,0.0533,0.12946,0.41172,0.11512,0.79728,0.12478,0.2684,0.23622,0.0782,0.2669,0.24738,9.71196,18.2661,0.02092,0.93874,4.8487,2.48164,0.42282,0.1047,2.33404,0.17504,0.50908,1.07032,1.74256,0.49468,0.0796,0.47174,0.17122,0.96682,1.60318,0.14162,13.67268,0.88992,,9.62222,0.09346,2.72908,1.12774,1.5353,4.61022,2.3103
2018-03-14,0.54618,0.3747,1.8339,0.81554,1.26438,0.32972,0.10954,0.5926,1.09938,0.38426,1.31696,1.03676,0.0,0.72906,1.04716,1.41432,0.21424,1.28582,0.53788,0.545,0.9848,0.45222,0.85498,1.36826,1.3674,0.0,0.5513,2.0937,0.68166,0.64638,0.46636,0.27416,2.07142,1.53998,0.77752,1.27588,0.71366,0.41758,0.26884,0.87766,3.29158,0.24388,0.61406,0.66318,0.8867,2.7191,2.07894,1.70644,0.71214,1.84724,1.33194,0.74694,0.29186,0.78088,1.05,1.62804,0.70842,0.0,2.47756,0.49224,1.78816,2.78922,0.58674,0.95742,3.78892,1.7415,2.25964,0.0,1.0508,0.47498,0.3901,0.75836,0.2893,0.56918,0.79682,0.38306,1.22086,0.0,1.36558,7.71828,2.56946,2.1968,0.5331,0.2736,1.8006,1.18506,1.25358,0.67816,0.89062,3.22984,0.21528,0.95538,1.81248,0.80264,0.87274,0.16702,2.21426,3.53744,15.80636,0.94674,0.70958,8.0538,1.43072,2.72304,5.02728,1.51742,0.86968,1.00956,1.03182,2.63132,0.76506,1.04254,2.24016,1.42212,0.74882,2.73854,0.08456,0.3793,0.41852,0.31926,0.2567,0.1181,0.19622,0.22016,0.56178,0.12298,4.2727,0.48246,0.13264,1.3261,1.29762,1.21414,0.19184,0.98802,0.97058,0.5833,0.1166,0.45804,1.14252,0.4608,0.63048,0.7686,0.19218,0.55926,2.13746,0.65238,0.72336,0.53872,0.0,0.327,0.79766,0.23312,1.19384,1.11228,0.46434,0.93666,0.0,1.02212,1.11174,0.34508,0.76094,0.0,0.6392,1.0867,1.06822,2.07382,2.15172,2.0437,0.22808,0.4953,0.68516,1.42832,0.62874,0.50086,0.15106,0.9596,0.8565,0.71954,1.75988,0.5961,0.80866,3.4425,0.37362,0.28872,1.85064,0.94554,1.19182,2.82522,0.43614,3.6539,0.34218,1.12198,4.529,3.30702,0.70776,0.46954,0.16288,0.24428,0.88324,0.81216,0.91106,0.2801,0.27276,0.59498,3.60962,0.30054,0.59542,0.36554,0.31268,0.54326,0.28152,1.52796,0.76084,0.1319,0.70346,0.4574,0.9831,0.40598,2.29792,0.23554,0.2396,0.20644,0.54326,0.9425,0.1179,0.15718,,0.1912,7.84078,0.66156,0.53284,,3.19082,0.31576,0.11634,0.15866,0.26066,0.36058,1.2009,0.0,0.59088,3.97852,0.46204,0.13746,0.20202,0.53106,0.23184,0.30172,0.54734,8.58534,0.3573,0.1244,0.09968,0.5367,2.88404,0.24362,0.60774,0.93372,0.17526,0.05068,0.1228,0.39762,0.1104,0.75766,0.1239,0.25038,0.2316,0.093,0.28546,0.21576,10.54446,15.33836,0.02216,1.0226,4.53306,2.33784,0.3847,0.09538,2.5156,0.171,0.50982,1.02824,1.4552,0.47614,0.0706,0.47698,0.1479,0.9777,1.36516,0.11582,13.48686,0.88234,,9.64684,0.08874,2.14804,1.04046,1.53074,4.49454,2.59746
2018-03-15,0.60126,0.35436,1.89342,0.7511,1.20542,0.32544,0.10916,0.55692,1.13036,0.41378,1.42324,1.0793,0.0,0.66334,1.00126,1.57152,0.20422,1.21462,0.5949,0.573,1.3474,0.44892,0.92502,1.17392,1.26142,0.0,0.49132,2.17906,0.70952,0.70634,0.47,0.2857,1.85502,1.65228,0.78628,1.31438,0.70216,0.41908,0.26156,0.88354,3.36508,0.25458,0.63752,0.61798,0.94014,2.6358,2.07106,1.93672,0.68804,1.86532,1.43348,0.71624,0.3047,0.70226,1.0767,1.73576,0.7613,0.0,1.88008,0.44232,1.56156,2.68776,0.56262,1.08784,4.06926,1.73866,2.12002,0.0,0.92864,0.38148,0.38312,0.75508,0.2715,0.48686,1.09476,0.36536,0.97912,0.0,1.4111,7.35656,2.52632,1.49964,0.55346,0.27894,1.76322,1.2434,1.26458,0.69888,0.91478,3.62254,0.21262,0.99298,1.97632,0.855,0.84208,0.17348,1.7779,3.68676,16.7318,0.9671,0.62982,8.8003,1.49216,2.96894,5.81176,1.40754,0.79704,0.99098,1.08626,2.58228,0.71614,1.09254,2.42458,1.4371,0.75764,3.10904,0.08108,0.37526,0.39746,0.36446,0.1788,0.12346,0.20232,0.21594,0.56894,0.12496,4.15762,0.33918,0.13258,1.30514,1.29344,1.20472,0.17604,0.98712,0.97094,0.61874,0.11936,0.45982,1.00066,0.37476,0.61932,0.79222,0.1661,0.61216,2.25118,0.66976,0.73386,0.485,0.0,0.33062,0.86306,0.2265,1.16182,1.03572,0.5109,0.94174,0.0,0.78334,1.18264,0.33176,0.69194,0.0,0.57868,1.1242,1.01992,2.06546,2.0478,1.98772,0.2349,0.48582,0.68598,1.492,0.56564,0.53358,0.16308,0.92142,0.86124,0.71036,1.8155,0.61486,0.83296,3.6531,0.31954,0.32184,1.96546,0.95886,1.10248,2.7879,0.38626,3.78326,0.34306,1.1652,4.59026,3.24492,0.69782,0.50232,0.16212,0.24304,0.88516,0.83194,0.9306,0.29684,0.27674,0.55238,3.42848,0.22552,0.69186,0.3557,0.3169,0.415,0.253,1.31114,0.75686,0.1153,0.71728,0.46786,0.9806,0.4112,2.19752,0.17466,0.23844,0.1906,0.48056,0.91122,0.12704,0.16646,,0.1901,8.64986,0.63222,0.54894,,3.40762,0.31788,0.11318,0.155,0.25884,0.36618,1.25146,0.0,0.62292,3.28092,0.44162,0.13566,0.19326,0.51896,0.2188,0.32902,0.56168,8.17414,0.3606,0.12542,0.09384,0.56344,2.86004,0.26722,0.46424,1.10738,0.16224,0.05176,0.11822,0.37268,0.1136,0.77544,0.12692,0.25354,0.25026,0.1087,0.31378,0.20954,9.76656,15.34398,0.0235,1.04564,4.79048,2.44586,0.3538,0.09622,2.06934,0.1703,0.43156,1.2845,1.3539,0.4402,0.07112,0.49366,0.12906,0.7602,1.4755,0.10808,14.6439,0.9137,,10.2908,0.10298,2.31526,1.00894,1.6206,4.5331,2.67014
2018-03-16,0.60352,0.35694,1.66698,0.62812,1.20182,0.30006,0.10234,0.514,1.11134,0.4158,1.51344,0.9805,0.0,0.89104,0.88068,1.6073,0.20924,1.18608,0.55346,0.65706,1.46244,0.42364,1.01724,1.15152,1.18832,0.0,0.42236,1.83696,0.61166,0.62598,0.48226,0.23882,1.79122,1.69224,0.73004,1.3178,0.6809,0.38028,0.23536,0.79052,3.08644,0.22068,0.63384,0.5415,0.89728,2.437,2.01828,1.91494,0.72564,1.4913,1.322,0.6689,0.29554,0.65752,0.98418,1.49998,0.7397,0.0,1.59282,0.41424,1.45766,2.43548,0.4757,1.122,4.09602,1.91062,1.78978,0.0,0.91064,0.39806,0.37422,0.75698,0.26268,0.45298,1.04268,0.33712,0.93374,0.0,1.21232,7.37864,2.4926,1.03606,0.5014,0.25584,1.68226,1.06798,1.01838,0.66634,0.88122,3.58652,0.20968,0.96726,1.82388,0.75544,0.75626,0.1509,1.73914,3.1692,14.91102,0.95246,0.62934,7.29,1.32586,2.65048,5.77812,1.22604,0.6789,0.97626,1.00462,2.28504,0.66036,0.91222,2.20996,1.29136,0.65786,2.92842,0.08338,0.3349,0.37542,0.35092,0.16764,0.12726,0.20402,0.20638,0.46794,0.13174,3.84006,0.33986,0.13182,1.26556,1.16432,1.07744,0.1773,0.89312,0.94578,0.60286,0.11832,0.4339,0.87398,0.37706,0.53246,0.85852,0.16166,0.5771,2.26348,0.66424,0.66456,0.42288,0.0,0.27146,0.87064,0.20148,1.17304,0.9674,0.51566,0.89742,0.0,0.64218,1.06706,0.3282,0.57116,0.0,0.67006,1.08104,1.04566,1.95066,1.98844,1.99026,0.21434,0.40714,0.68164,1.39978,0.56064,0.5133,0.2598,0.86378,0.78446,0.67492,1.49578,0.59064,0.70408,3.37418,0.2379,0.33088,1.67818,0.8333,1.00636,2.80976,0.37896,3.89094,0.33904,1.23806,4.1199,3.14834,0.66442,0.47514,0.14694,0.2359,0.85916,0.7504,0.91,0.50108,0.25068,0.5369,3.1433,0.21108,0.65378,0.3547,0.28936,0.38502,0.23768,1.23174,0.60826,0.11716,0.63358,0.4468,0.88742,0.36538,1.92416,0.16772,0.20452,0.16766,0.47142,0.85768,0.1217,0.17518,,0.18114,7.68178,0.57954,0.59378,,3.5795,0.3304,0.118,0.15192,0.23604,0.34632,1.14426,0.0,0.6048,2.7524,0.50276,0.13088,0.19156,0.56052,0.21794,0.32172,0.61742,7.35888,0.30868,0.12466,0.09236,0.49358,2.77128,0.3081,0.45794,1.07058,0.14898,0.05176,0.1069,0.34454,0.1131,0.73456,0.10234,0.23248,0.23554,0.11816,0.29774,0.22012,8.56692,14.3506,0.02332,1.00202,3.99936,2.08238,0.34118,0.09032,1.92308,0.1495,0.34836,1.37212,1.41578,0.41864,0.07036,0.4626,0.1119,0.64676,1.44964,0.10684,13.40044,0.84618,,9.63576,0.08284,2.39566,1.23282,1.44584,4.53814,2.71384
2018-03-19,0.54912,0.30766,1.44768,0.5101,1.09378,0.24994,0.08882,0.43556,0.91084,0.34446,1.46454,0.79974,0.0,0.98634,0.80218,1.49012,0.20838,0.95668,0.50092,0.61652,1.41676,0.35594,0.92234,1.0234,1.07206,0.0,0.402,1.51024,0.4964,0.58778,0.40734,0.16852,1.60546,1.4783,0.60086,1.24578,0.66398,0.32804,0.21596,0.73034,2.76854,0.2236,0.56048,0.53972,0.97648,2.12484,1.48906,1.73198,0.71276,1.23738,1.14764,0.57428,0.32156,0.55964,0.80062,1.31104,0.69282,0.0,1.3067,0.31888,1.08342,1.95412,0.43122,1.0193,3.98516,1.85746,1.36124,0.0,0.7219,0.39682,0.3038,0.73114,0.2183,0.42908,1.19666,0.31444,0.902,0.0,1.07016,5.99948,1.90992,0.75918,0.39784,0.22478,1.51526,0.9197,0.83604,0.5882,0.72844,2.65656,0.1772,0.86704,1.89394,0.63896,0.7727,0.11748,1.5081,2.73412,13.4942,1.00628,0.62522,5.94558,1.13168,2.3184,5.91108,1.01332,0.60362,0.98734,0.83634,1.97378,0.61412,0.7922,1.95662,1.03778,0.56532,2.87994,0.0775,0.28366,0.33738,0.37244,0.15484,0.11412,0.18992,0.19088,0.42098,0.12952,3.50322,0.28646,0.13028,1.07348,0.9659,0.89908,0.15218,1.03142,0.80642,0.53992,0.0987,0.41104,0.78548,0.41676,0.44324,0.82624,0.14828,0.48494,2.06538,0.55968,0.53144,0.32002,0.0,0.22394,0.78944,0.17852,1.1538,1.06488,0.46728,0.7816,0.0,0.58116,0.8719,0.3681,0.4381,0.0,0.78264,0.89168,0.84162,1.75378,1.7424,1.7792,0.17744,0.40246,0.63834,1.15222,0.49282,0.44508,0.27976,0.81586,0.63576,0.63934,1.36138,0.51876,0.6041,2.7811,0.20022,0.32894,1.33298,0.93238,0.9172,2.53164,0.39708,3.5863,0.26914,1.24116,3.99294,3.05142,0.4996,0.53136,0.14826,0.1924,0.69776,0.62152,0.8436,0.52616,0.2322,0.4624,2.93774,0.19024,0.59194,0.31498,0.25054,0.41862,0.19794,1.07248,0.58014,0.09912,0.48256,0.39358,0.74706,0.29324,1.5854,0.15286,0.16478,0.1596,0.40854,0.78622,0.11404,0.18476,,0.2009,6.18634,0.53786,0.56596,,2.74398,0.29142,0.10782,0.13358,0.1926,0.2903,0.98442,0.0,0.59478,2.42972,0.45256,0.11618,0.1672,0.6235,0.19114,0.35456,0.63618,7.0387,0.23154,0.10712,0.07076,0.39232,2.46998,0.31058,0.41864,0.9769,0.11992,0.04802,0.08312,0.30488,0.10208,0.6202,0.0898,0.2134,0.19754,0.1124,0.23914,0.19864,7.04596,12.27324,0.02114,0.71862,3.2034,1.58482,0.31042,0.08774,1.73084,0.11222,0.31266,1.19554,1.15294,0.3691,0.05796,0.40236,0.09562,0.63662,1.30352,0.09292,11.73692,0.68632,,9.96988,0.07972,2.07244,1.24858,1.30712,4.56352,2.5927
2018-03-20,0.51294,0.32192,1.3479,0.518,0.97196,0.22932,0.07944,0.37268,0.88144,0.32232,1.04056,0.73326,0.0,1.17722,0.70866,1.39988,0.23842,0.90586,0.4658,0.54492,1.45278,0.36146,0.9125,1.1281,1.05976,0.0,0.35742,1.452,0.46562,0.58878,0.37418,0.15716,1.94018,1.20454,0.56828,1.17958,0.6683,0.30114,0.21482,0.63154,2.27866,0.2131,0.4508,0.6208,0.95256,1.90912,1.39438,1.52194,0.79224,1.12616,1.0001,0.58356,0.33126,0.54802,0.75154,1.16194,0.67556,0.0,1.40524,0.2993,1.04366,1.89388,0.41294,0.97914,3.77448,1.79906,1.28044,0.0,0.63644,0.549,0.26216,0.65844,0.23044,0.4418,1.24434,0.29824,0.87782,0.0,1.0636,5.3017,1.76832,0.84278,0.36218,0.2076,1.63614,0.92696,0.7728,0.59572,0.71142,2.0068,0.16704,0.75198,1.89372,0.57838,0.6878,0.10524,1.79656,2.44156,12.78512,1.04398,0.62552,5.59306,1.1047,2.0839,5.87236,0.97588,0.61094,1.29974,0.96322,1.84956,0.83696,0.75778,1.57438,0.99336,0.52584,3.0603,0.07432,0.2407,0.31786,0.35042,0.16082,0.09986,0.16492,0.15568,0.3787,0.1243,3.29652,0.30786,0.164,1.06736,0.9292,0.74068,0.15302,1.04152,0.79486,0.51926,0.08968,0.34084,0.72956,0.5084,0.42078,0.74678,0.13134,0.43692,2.00488,0.53576,0.49606,0.29766,0.0,0.2021,0.92544,0.17278,1.12136,1.3239,0.46366,0.78706,0.0,0.57702,0.75846,0.41896,0.41724,0.0,1.17256,0.75122,0.80654,1.45582,1.50408,1.67402,0.16882,0.41132,0.61728,1.07424,0.46638,0.45824,0.29062,0.84618,0.59614,0.55476,1.35322,0.48238,0.56704,2.6728,0.28552,0.3135,1.15988,1.05618,0.95594,2.4229,0.4367,3.17526,0.24828,1.16476,4.10486,2.97904,0.35232,0.51706,0.14756,0.22652,0.62554,0.62354,0.79448,0.51844,0.18526,0.42656,2.87488,0.1726,0.56226,0.26192,0.2397,0.51576,0.19032,0.92442,0.52968,0.09698,0.43764,0.3576,0.61612,0.27936,1.39514,0.20304,0.15422,0.15456,0.36592,0.6938,0.08838,0.20176,,0.20298,5.62502,0.51936,0.41472,,2.477,0.25686,0.09738,0.12008,0.16796,0.26408,0.77626,0.0,0.60612,2.0089,0.44876,0.09704,0.11788,0.6328,0.1504,0.32904,0.60724,5.42404,0.21542,0.09582,0.06764,0.32908,2.4562,0.29564,0.51936,1.05962,0.109,0.04676,0.08622,0.25912,0.09072,0.5963,0.0916,0.20516,0.1682,0.1133,0.2362,0.17216,6.47898,9.97252,0.02834,0.63222,2.7437,1.46084,0.32936,0.0801,1.65976,0.09584,0.31838,1.14844,1.0512,0.34904,0.05068,0.37506,0.08966,0.6686,1.23406,0.0834,10.55446,0.7111,,8.81958,0.07996,2.54584,1.31664,1.66666,4.04386,2.35076
2018-03-21,0.60864,0.3252,1.3889,0.49952,0.92136,0.22896,0.0953,0.34918,0.8653,0.31418,0.80772,0.70634,0.0,1.28562,0.65556,1.43856,0.25144,1.09568,0.37532,0.49924,1.392,0.36382,0.89382,1.16132,1.01342,0.0,0.33884,1.43796,0.56154,0.67398,0.42756,0.18364,2.02042,1.08868,0.58276,1.11302,0.66982,0.31446,0.18794,0.51382,2.23332,0.22358,0.41266,0.66098,0.89974,2.25714,1.27988,1.57272,0.79286,1.07726,1.00034,0.64704,0.33166,0.81728,0.79764,1.0696,0.66236,0.0,1.36278,0.33868,1.0229,2.0363,0.50314,1.04048,3.5303,2.01826,1.24734,0.0,0.68216,0.64742,0.23884,0.69954,0.2537,0.3372,1.68716,0.34376,0.88236,0.0,1.17428,4.52094,1.44756,1.00672,0.35754,0.21366,1.4321,0.9408,0.76796,0.64102,0.68252,1.34876,0.155,0.80442,1.67096,0.55144,0.66564,0.11588,2.0422,2.96062,12.25874,1.11162,0.68242,5.68672,1.13548,2.08872,5.82534,0.99252,0.6243,1.258,1.01178,1.63088,1.02762,0.71838,1.56408,0.99732,0.54646,3.23392,0.0751,0.2318,0.31466,0.29824,0.1495,0.1019,0.15536,0.14318,0.32418,0.13836,3.6852,0.29848,0.18448,1.02298,1.05508,0.72752,0.18424,1.26244,0.8196,0.5414,0.11496,0.40678,0.6841,0.524,0.446,0.70478,0.14094,0.51592,1.47608,0.48886,0.62846,0.28338,0.0,0.22172,0.97218,0.16378,1.04126,1.4736,0.52558,0.61912,0.0,0.5813,0.81472,0.46494,0.3245,0.0,1.36298,0.75962,0.77824,1.5903,1.40558,1.49788,0.19444,0.58984,0.58462,1.21036,0.4171,0.53806,0.35576,0.87538,1.0365,0.5417,1.42724,0.61912,0.62412,2.61144,0.3223,0.2966,1.16046,0.99762,1.22888,1.8687,0.45748,3.17678,0.26466,1.01518,3.21584,2.54632,0.29292,0.54806,0.15674,0.2888,0.71364,0.64742,0.77228,0.56516,0.17602,0.37882,2.99796,0.17506,0.58888,0.24072,0.27016,0.53114,0.18842,0.94272,0.5712,0.09768,0.51058,0.38512,0.5614,0.31672,1.46122,0.2116,0.18206,0.16312,0.35368,0.73734,0.08006,0.23708,,0.20006,7.04956,0.51108,0.37046,,2.50884,0.25394,0.1018,0.13414,0.44592,0.29686,0.90958,0.0,0.56508,1.7347,0.46108,0.08766,0.12,0.79024,0.15304,0.39284,0.89494,4.81794,0.24708,0.09594,0.0706,0.37066,2.0392,0.32442,0.56228,1.0374,0.11162,0.04844,0.0869,0.25494,0.09658,0.71652,0.09738,0.20862,0.18388,0.10404,0.2525,0.17272,5.67376,10.00704,0.0318,0.61268,3.5895,1.80412,0.39874,0.09554,1.45898,0.11214,0.3021,1.17384,1.0397,0.34756,0.05074,0.41068,0.091,0.63602,1.11664,0.09292,10.01406,0.76618,,8.33144,0.09194,3.0659,1.37574,1.7343,4.00458,2.14924
2018-03-22,0.58838,0.32182,1.30288,0.47992,0.97966,0.22818,0.09884,0.36878,0.81356,0.28306,0.72714,0.6328,0.0,1.3416,0.65032,1.25346,0.24726,1.09632,0.35152,0.4856,1.04804,0.34246,0.92242,1.13986,1.03578,0.0,0.32428,1.4184,0.5786,0.77968,0.45066,0.18814,2.15218,1.01102,0.50812,1.09718,0.64616,0.305,0.21834,0.45732,2.26832,0.21632,0.4177,0.65304,0.80864,2.09702,1.25862,1.3962,0.74538,1.12258,1.43458,0.6695,0.34276,0.86052,0.8087,0.94076,0.62558,0.0,1.3908,0.35056,1.1038,2.01934,0.55454,0.94636,3.27838,2.0622,1.24952,0.0,0.82832,0.72952,0.23458,0.68742,0.32282,0.31354,1.42314,0.36208,1.02222,0.0,1.1606,4.0931,1.32842,1.14838,0.367,0.216,1.51698,0.89232,0.77052,0.64036,0.649,0.56606,0.1489,0.76814,1.46322,0.52502,0.65166,0.11032,2.37598,2.94596,11.78518,1.17876,0.68992,5.184,1.08408,1.92618,5.43526,1.06332,0.60796,1.2751,1.02466,1.60666,1.1088,0.7021,1.39228,0.96736,0.56758,3.11308,0.07914,0.27652,0.3797,0.2558,0.15134,0.09992,0.14934,0.15134,0.30422,0.12914,3.5049,0.3131,0.19702,1.05394,1.2042,0.75286,0.20404,1.36598,0.87696,0.5295,0.12288,0.4212,0.6764,0.54522,0.4561,0.69692,0.16006,0.49634,1.24436,0.46514,0.63472,0.30236,0.0,0.22366,0.94292,0.15202,1.0184,1.52914,0.55324,0.5908,0.0,0.57478,0.8687,0.46474,0.31734,0.0,1.4529,0.84256,0.84412,1.51436,1.3098,1.39484,0.19422,0.69426,0.56022,1.16412,0.3933,0.72534,0.48286,0.83966,1.16896,0.58094,1.4828,0.71472,0.59312,2.6051,0.34438,0.26688,1.00282,1.09486,1.4689,1.6189,0.47874,3.26444,0.38898,0.98098,3.00388,2.45698,0.27742,0.51682,0.16272,0.32108,0.70814,0.70196,0.76046,0.57858,0.17724,0.36766,3.1356,0.17288,0.48672,0.2503,0.28164,0.58452,0.21036,1.09778,0.5841,0.1052,0.52062,0.39444,0.54098,0.32254,1.58076,0.21914,0.18964,0.17038,0.39944,0.73278,0.07522,0.27694,,0.19522,6.90116,0.54338,0.36984,,2.48674,0.29116,0.1071,0.14582,0.58994,0.30914,0.95888,0.0,0.5587,1.58462,0.50454,0.0871,0.12222,0.84116,0.16328,0.38838,1.12668,4.5158,0.25714,0.10432,0.07386,0.35804,1.90704,0.33652,0.59652,0.96372,0.13726,0.05156,0.0865,0.2798,0.10706,0.75502,0.09862,0.21282,0.17748,0.10032,0.33084,0.18682,4.91874,8.73384,0.03382,0.58552,3.63716,1.80544,0.41596,0.10296,1.36648,0.11402,0.32314,1.0298,1.10864,0.39126,0.05164,0.42016,0.0908,0.59598,1.03586,0.10722,8.79532,0.75524,,8.25862,0.07644,2.86264,1.29576,1.6703,4.16978,1.95028
2018-03-23,0.69036,0.35634,1.6179,0.5937,1.19078,0.40458,0.14592,0.48926,1.08514,0.36562,0.78334,0.90382,0.0,1.25186,0.90108,1.41942,0.26766,1.23724,0.44298,0.48336,1.07572,0.49658,0.97194,1.24776,1.1437,0.0,0.48782,1.77066,0.78292,1.21736,0.48378,0.29578,2.3609,1.06454,0.57778,1.18496,0.70738,0.5912,0.30122,0.56052,2.82968,0.29396,0.5268,0.72912,1.05576,2.35666,1.32734,1.64502,0.79972,1.46404,1.73362,0.71642,0.39978,0.93102,1.10404,1.1872,0.7611,0.0,1.40788,0.42072,1.23766,2.2209,0.65576,1.02784,3.64946,1.99268,1.62532,0.0,0.9159,0.81256,0.24162,0.78988,0.37802,0.31672,2.19692,0.4057,1.2637,0.0,1.48198,4.6699,1.534,1.17164,0.46064,0.26074,1.61052,0.93038,0.8991,0.63838,0.75408,0.0,0.20024,0.68488,1.689,0.69912,0.82834,0.14212,2.51124,3.5976,13.0514,1.3509,0.77478,5.30706,1.28596,1.98536,5.00348,1.26984,0.72104,1.39028,1.13596,1.9696,1.3192,0.76524,1.4229,1.22358,0.59794,3.37312,0.10072,0.36138,0.48572,0.32084,0.1718,0.131,0.19896,0.19628,0.43386,0.1438,4.11058,0.35034,0.23686,1.31902,1.4211,0.99696,0.25236,1.65044,1.07358,0.67852,0.18964,0.4615,0.78558,0.60248,0.60636,0.71438,0.19662,0.66158,1.37176,0.58618,0.7648,0.42002,0.0,0.27872,1.05928,0.20316,1.0964,1.75906,0.68228,0.78058,0.0,0.66078,1.1025,0.51108,0.3765,0.0,1.63658,1.0802,0.9525,1.63114,1.52506,1.69772,0.25516,0.8595,0.67526,1.42966,0.3769,0.9758,0.48368,0.95778,1.3204,0.73956,1.73196,1.43012,0.7546,3.26404,0.41356,0.29346,1.26754,1.20496,2.38354,1.79192,0.51688,3.11164,0.4854,1.09062,3.08864,2.8702,0.33946,0.6466,0.22776,0.41678,0.88184,0.89204,0.93766,0.46672,0.3444,0.42332,3.64728,0.21928,0.48998,0.32632,0.33986,0.67208,0.2572,1.23194,0.68362,0.14408,0.64922,0.61182,0.74784,0.34386,2.05016,0.24964,0.27662,0.23728,0.50166,0.87628,0.1354,0.31764,,0.23282,8.01164,0.64846,0.48752,,2.63026,0.39054,0.14502,0.18898,0.72472,0.4052,1.61034,0.0,0.71276,1.88426,0.563,0.09494,0.14864,0.98622,0.2332,0.47358,1.27276,5.00584,0.35106,0.1521,0.08798,0.45352,2.35488,0.36376,0.64694,1.36048,0.18706,0.06368,0.10008,0.44718,0.12684,0.83586,0.15412,0.27292,0.23182,0.12464,0.42644,0.24162,6.19352,9.5699,0.04102,0.6489,4.46914,2.43974,0.48464,0.14162,1.73862,0.15222,0.46534,1.08658,1.28708,0.5617,0.07412,0.54466,0.12006,0.70532,1.31318,0.1319,9.84112,0.85458,,9.34882,0.08584,3.26968,1.21504,1.91338,4.29624,2.12416
2018-03-26,0.75834,0.35038,1.74956,0.61602,1.19786,0.43928,0.14282,0.53998,1.1447,0.38468,0.77736,1.05426,0.0,1.15856,0.93864,1.47614,0.29028,1.2928,0.50272,0.46374,1.04338,0.54662,1.03474,1.26868,1.115,0.0,0.48566,1.86342,0.8082,1.64626,0.51338,0.32366,2.34862,1.11686,0.65116,1.2227,0.74346,0.72534,0.39954,0.54126,2.85824,0.31534,0.53024,0.76806,0.92304,2.52728,1.34522,1.99998,0.82812,1.50724,1.93256,0.7483,0.48776,1.02316,1.10632,1.18438,0.75532,0.0,1.3596,0.48788,1.21402,2.3567,0.88534,1.30608,3.64596,2.06888,1.75728,0.0,1.2208,0.87378,0.26866,0.83718,0.39494,0.29864,2.41884,0.42316,1.45958,0.0,1.53924,5.1667,1.7539,1.189,0.51982,0.34208,1.69796,0.95194,0.88734,0.66466,0.81342,0.0,0.21424,0.75164,1.41246,0.76216,0.87134,0.13956,2.65888,3.74584,12.69738,1.37748,0.7946,5.37506,1.35092,1.96768,4.82582,1.3965,0.73738,1.42884,1.24524,2.32982,1.47614,0.77492,1.35796,1.29544,0.631,3.20866,0.11462,0.41132,0.54752,0.30184,0.19026,0.15098,0.22758,0.18666,0.48322,0.13692,4.2495,0.35972,0.2578,1.26244,1.45492,1.00734,0.3089,1.85844,1.05302,0.73062,0.19842,0.4731,0.82496,0.60694,0.62858,0.69444,0.23518,0.7346,1.62318,0.58658,0.9882,0.48194,0.0,0.2899,1.1259,0.21926,1.00176,1.80732,0.73088,0.8165,0.0,0.72108,1.11574,0.5537,0.49118,0.0,1.7095,1.13066,1.15512,1.5883,1.67178,1.59388,0.26514,1.0665,0.68522,1.48398,0.38238,1.02764,0.49702,1.08454,1.44368,0.77366,1.86146,2.00322,0.81452,3.54736,0.44142,0.30554,1.34044,1.20586,3.16176,1.87648,0.5353,3.0755,0.52182,1.05242,3.09776,2.7076,0.3368,0.54394,0.22706,0.44128,0.90786,0.94508,1.0953,0.4542,0.38328,0.54376,4.47192,0.23456,0.48308,0.36904,0.36872,0.7359,0.28576,1.55796,0.7903,0.15744,0.67584,0.64416,0.79768,0.33152,2.00558,0.26686,0.28056,0.25218,0.58924,0.97536,0.1448,0.34136,,0.2183,8.16212,0.6154,0.49442,,2.76892,0.42062,0.17066,0.20356,0.77624,0.44116,2.14754,0.0,0.65922,1.9415,0.6079,0.09924,0.17246,1.0705,0.24966,0.46634,1.32046,5.13444,0.3828,0.15548,0.10654,0.47848,2.32712,0.43216,0.7131,1.41776,0.21228,0.06606,0.11696,0.45686,0.12964,0.86666,0.17262,0.27626,0.2489,0.12296,0.48768,0.2768,6.5858,9.57086,0.04368,0.6344,4.672,2.46916,0.56712,0.14522,1.8092,0.16458,0.51532,1.12908,1.49224,0.63988,0.08862,0.59332,0.12708,0.73566,1.38406,0.16512,9.6396,0.89854,,8.2204,0.09624,3.45672,1.24828,1.95496,4.185,2.08888
2018-03-27,0.79708,0.40978,1.90386,0.60284,1.21286,0.4537,0.15118,0.63216,1.19346,0.40552,1.00378,1.11284,0.0,0.98714,1.00632,1.62174,0.28308,1.30162,0.59156,0.45204,0.96814,0.54748,1.08778,1.27392,1.15848,0.0,0.52854,1.90584,0.84566,2.04734,0.55332,0.33694,2.06638,1.11232,0.70668,1.12828,0.9025,0.7856,0.51026,0.60096,3.24778,0.34522,0.56232,0.76056,0.9671,2.5266,1.46532,2.10324,1.19104,1.53554,2.28618,0.73102,0.49602,1.0724,1.13818,1.20224,0.74572,0.0,1.14452,0.6302,1.22254,2.52412,1.06928,1.51012,3.90622,2.19452,1.83188,0.0,1.32888,0.81964,0.29822,0.99758,0.44756,0.28198,2.74364,0.47128,1.49322,0.0,1.4865,5.91934,1.95468,1.12492,0.56052,0.4429,1.69712,0.97196,0.924,0.90562,0.74952,0.0,0.24276,0.96368,1.38638,0.80202,0.92736,0.1434,2.61816,3.99804,12.53664,1.4344,0.84016,5.71882,1.44954,2.26228,5.2471,1.71282,0.78424,1.32072,1.16414,2.5676,1.4263,0.83132,1.38672,1.46894,0.71554,3.21542,0.11936,0.46396,0.55918,0.3186,0.22062,0.17436,0.24308,0.18406,0.51258,0.13876,4.4147,0.33986,0.24064,1.39476,1.50962,1.015,0.3499,2.20042,1.08638,0.7904,0.20542,0.50786,0.83854,0.60204,0.65944,0.75564,0.27304,0.77768,1.74778,0.60184,1.20204,0.53302,0.0,0.29632,1.1154,0.22784,1.028,1.88414,0.80278,0.81078,0.0,0.65568,1.38076,0.595,0.58164,0.0,1.46892,1.17518,1.27146,1.8626,1.73908,1.6045,0.26566,1.20416,0.73614,1.58042,0.3769,1.10958,0.51028,1.11216,1.54448,0.86722,1.97396,2.45862,0.88706,3.61562,0.38348,0.33526,1.47256,1.32012,3.68544,2.20526,0.52622,3.60648,0.57756,1.1376,3.28122,2.74956,0.33746,0.58232,0.23444,0.40984,0.99746,1.00074,1.22476,0.47166,0.42576,0.66666,5.33828,0.25664,0.55172,0.37956,0.39194,0.7578,0.31182,1.7812,0.97384,0.1583,0.70594,0.65838,0.82954,0.32986,2.06594,0.35128,0.30926,0.28998,0.66076,1.01588,0.1506,0.44346,,0.22562,8.85998,0.70214,0.55332,,2.85052,0.4362,0.18064,0.21834,0.84752,0.47892,2.89118,0.0,0.68132,2.02786,0.6041,0.10476,0.1853,1.14068,0.26746,0.4824,1.39472,5.23014,0.4022,0.1549,0.11534,0.49774,2.227,0.55048,0.68194,1.47738,0.21822,0.06866,0.1099,0.44762,0.12634,0.91376,0.1789,0.27998,0.26638,0.11798,0.543,0.31268,6.63812,9.89438,0.03776,0.6507,4.87062,2.63192,0.63104,0.15476,1.96832,0.17124,0.53386,1.19096,1.71216,0.67576,0.1066,0.68168,0.13394,0.70948,1.44826,0.17568,10.47276,0.8304,,8.5883,0.10398,3.68672,1.19632,1.63072,4.63894,2.1362
2018-03-28,0.75616,0.42928,1.8483,0.59658,1.15522,0.46012,0.13304,0.74124,1.2023,0.41776,1.05516,1.18294,0.0,0.89102,1.10254,1.48826,0.30598,1.25378,0.7434,0.44014,0.95114,0.53336,1.28096,1.3369,1.16364,0.0,0.5481,1.88834,0.73276,2.13432,0.51784,0.3134,1.93072,1.04804,0.74098,1.08616,1.12346,0.77658,0.57196,0.61466,3.32008,0.3603,0.68446,0.80578,0.97408,2.18178,1.3968,2.00354,1.41054,1.52938,2.39874,0.74896,0.47908,0.89592,1.29092,1.2277,0.69208,0.0,1.17736,0.63316,1.21138,2.7021,1.09738,1.53096,3.61584,2.03762,1.82412,0.0,1.3701,0.78834,0.3252,1.19786,0.4695,0.33826,2.41676,0.48112,1.48006,0.0,1.4358,6.24628,1.9772,0.9677,0.60138,0.48152,1.71776,1.02708,0.89902,0.9342,1.1831,0.0,0.25548,1.07596,1.24466,0.73544,1.00352,0.1272,2.58142,3.4917,11.89324,1.42046,0.82066,5.55644,1.5903,2.27266,5.1415,2.27774,0.81528,1.31766,1.22554,2.62046,1.47676,0.93804,1.25176,1.73888,0.72282,3.03564,0.11866,0.53126,0.5687,0.31182,0.2356,0.17964,0.25096,0.18022,0.50748,0.1243,3.92298,0.41016,0.22966,1.55106,1.41264,1.03956,0.36956,2.09166,1.1028,0.7664,0.22156,0.48122,0.81238,0.62814,0.60696,0.79672,0.29266,0.71064,1.7003,0.63696,1.1652,0.55312,0.0,0.28052,1.12304,0.23754,1.02222,1.90102,0.81206,0.81156,0.0,0.66924,1.8594,0.6593,0.71562,0.0,1.41264,1.11394,1.29542,1.8562,1.72282,1.4969,0.24192,1.13206,0.7652,1.53788,0.35272,1.08008,0.45978,1.12248,1.20914,0.8971,1.92606,2.69604,0.90248,3.55458,0.38578,0.47344,1.4182,1.39438,3.77806,2.28432,0.53132,5.05052,0.57478,1.14024,3.36456,2.65114,0.3243,0.61572,0.2485,0.3415,1.04712,1.04098,1.2649,0.4331,0.4681,0.78496,5.47486,0.25754,0.72582,0.37988,0.39806,0.79192,0.31714,1.90988,1.08146,0.17288,0.63494,0.66262,0.88132,0.27418,1.95354,0.46408,0.29448,0.31306,0.7209,1.00638,0.14816,0.46998,,0.22152,7.53328,0.76414,0.584,,2.77294,0.43886,0.18182,0.19758,0.60378,0.46752,3.15194,0.0,0.70044,2.15746,0.60274,0.10908,0.18916,1.15052,0.27966,0.40038,1.19794,4.68518,0.368,0.1494,0.12592,0.43548,2.2026,0.61796,0.7238,1.45714,0.21436,0.07062,0.1107,0.45486,0.11978,0.79646,0.17882,0.28048,0.25982,0.11148,0.5486,0.30926,6.17504,9.75142,0.03538,0.58186,4.05024,2.45726,0.65128,0.14528,1.97328,0.15618,0.5579,1.23652,1.95492,0.67144,0.11326,0.63838,0.13616,0.77216,1.5846,0.1815,10.18402,0.78054,,8.49026,0.10284,3.87226,1.10128,1.47274,4.59842,2.03074
2018-03-29,0.7971,0.5865,1.92102,0.699,1.06158,0.48498,0.14236,0.75902,1.46936,0.51174,1.0902,1.19654,0.0,0.8423,1.1979,1.53802,0.30414,1.39746,0.8621,0.42828,0.92856,0.56656,1.28238,1.47408,1.4226,0.0,0.616,1.92862,0.757,2.1356,0.54406,0.32596,2.03196,0.98796,0.74586,1.0405,1.2259,0.7813,0.59238,0.66446,3.50366,0.39434,0.77418,0.82836,1.12526,2.13162,1.70628,1.94402,1.51788,1.4056,2.12008,0.92526,0.45006,0.9504,1.44338,1.30756,0.69928,0.0,1.1488,0.75126,1.3936,2.692,1.16734,1.61328,3.62878,2.09652,1.79698,0.0,1.37068,0.76858,0.36866,1.373,0.44822,0.32878,2.36792,0.48592,1.33832,0.0,1.47138,6.54636,1.97748,0.78024,0.56418,0.50752,1.56412,0.9836,0.92344,1.07326,1.32552,0.0,0.26622,1.07464,1.14226,0.83284,1.02598,0.13632,2.41772,3.65926,10.95182,1.44494,0.96784,5.60918,2.06508,2.4562,5.29968,2.53546,0.85258,1.33872,1.23722,2.59072,1.68014,1.03226,1.22184,2.01696,0.72896,2.8705,0.12588,0.53902,0.58994,0.35318,0.27282,0.19002,0.26238,0.1638,0.54612,0.11048,3.73698,0.51308,0.2405,1.78248,1.39948,1.11732,0.39218,2.03388,1.39416,0.77188,0.24852,0.4644,0.86898,0.66216,0.59432,0.82508,0.30762,0.80164,1.8229,0.70284,1.18828,0.5496,0.0,0.28074,1.18274,0.24312,1.15154,1.99344,0.80094,0.81692,0.0,0.6645,2.24808,0.6853,0.71332,0.0,1.3924,0.95648,1.4116,1.97878,1.84152,1.54716,0.25052,1.09578,0.95176,1.56844,0.35042,0.89012,0.33994,1.30358,1.05686,0.92188,1.86312,2.7586,0.9121,3.50176,0.38492,0.53966,1.37408,1.36734,3.79766,2.91874,0.53108,5.54788,0.47698,1.24054,3.25942,3.19136,0.31,0.72256,0.25768,0.3215,1.1197,1.32338,1.50096,0.41298,0.50864,0.83488,5.55484,0.24862,0.80528,0.38632,0.45214,0.81604,0.33394,1.86114,1.15768,0.17948,0.66728,0.66762,0.91734,0.28938,1.76952,0.49932,0.33702,0.33172,0.83898,1.0518,0.14962,0.46798,,0.25076,7.47238,0.86158,0.6685,,3.1984,0.44592,0.18652,0.19574,0.57876,0.50324,3.25732,0.0,0.79316,2.0645,0.57694,0.10336,0.19474,1.25384,0.29308,0.41736,1.15438,4.4487,0.3737,0.1421,0.14628,0.47246,2.32272,0.68134,0.79898,1.56124,0.18758,0.07114,0.11538,0.46344,0.10854,0.86866,0.17594,0.2889,0.28518,0.1114,0.4631,0.3127,5.9741,9.83106,0.03382,0.59986,4.38442,2.73654,0.75766,0.14516,2.0857,0.16318,0.52868,1.07922,2.20304,0.61128,0.1267,0.61506,0.13672,0.82768,1.67854,0.17928,10.01698,0.76586,,7.32888,0.11054,3.96632,1.0996,1.3636,4.39278,1.96728
2018-03-30,0.6702,0.60034,1.5636,0.60012,0.74178,0.31448,0.09306,0.65446,1.25222,0.46268,1.08524,0.96094,0.0,0.6841,0.99072,1.32852,0.3025,1.34408,0.83704,0.30218,0.76498,0.42178,1.15166,1.51982,1.37128,0.0,0.45954,1.59514,0.5455,1.9574,0.44972,0.2233,1.92974,0.9186,0.68818,0.84528,1.22218,0.49606,0.53726,0.5565,3.94106,0.34404,0.73886,0.8089,0.92246,1.8289,1.63814,1.66188,1.52744,1.2928,1.82394,1.02698,0.33188,1.02042,1.2131,1.1745,0.55342,0.0,1.07844,0.69606,1.3361,2.6388,1.26798,1.50434,3.32076,1.8661,1.64994,0.0,1.29098,0.75012,0.37064,1.4637,0.4093,0.29128,1.41724,0.39704,1.22188,0.0,1.20272,6.1837,1.76384,0.81946,0.48674,0.47694,1.31094,0.98904,0.77542,1.10322,1.24238,0.0,0.27228,1.13524,0.91236,0.67546,0.80528,0.1043,2.96888,3.07256,12.96526,1.50604,0.92408,6.19222,2.51318,2.50216,5.35598,2.56794,0.75834,1.29564,1.19828,2.31718,1.65184,1.09244,1.17146,2.01006,0.716,2.58078,0.1018,0.48446,0.46982,0.28746,0.24784,0.15304,0.22238,0.11326,0.43042,0.08094,2.94948,0.43606,0.20418,1.66048,1.23612,0.86112,0.34276,2.04808,1.23552,0.58616,0.19778,0.4264,0.86752,0.6268,0.47604,0.81698,0.28276,0.72302,1.59414,0.61524,1.32782,0.40914,0.0,0.22556,1.0503,0.18644,1.07546,1.93484,0.6896,0.60888,0.0,0.59208,2.58852,0.67386,0.68948,0.0,1.23188,0.73454,1.35428,1.90712,1.5376,1.02578,0.17984,1.13322,0.85154,1.32674,0.29916,0.6608,0.25554,1.2309,0.94546,0.96524,1.61958,2.12532,0.88688,2.88314,0.33244,0.51418,1.20514,1.40096,3.03542,2.8029,0.56516,6.05094,0.3709,1.06144,3.24662,2.74586,0.23154,0.63926,0.26492,0.22296,0.9482,1.3234,1.51932,0.31452,0.38984,0.80706,5.4896,0.20572,0.8021,0.31832,0.41748,0.91946,0.30858,1.74968,1.28788,0.15864,0.54066,0.45314,0.72622,0.26438,1.41498,0.52178,0.25922,0.27026,0.7537,0.94996,0.08916,0.46022,,0.20964,6.43184,0.88168,0.53762,,3.11604,0.34702,0.14318,0.16582,0.49696,0.40954,2.7615,0.0,0.71498,1.6335,0.4132,0.09626,0.16432,1.13476,0.2381,0.39606,1.0701,4.22444,0.27518,0.08806,0.13752,0.3862,1.8057,0.71862,0.8352,1.2036,0.143,0.05824,0.09618,0.31156,0.08254,0.73534,0.12334,0.21576,0.25182,0.08142,0.391,0.24502,4.80446,8.62492,0.02686,0.62526,3.83782,2.23208,0.73824,0.1131,1.771,0.13054,0.34598,0.89698,2.03364,0.4419,0.12218,0.67138,0.11524,0.77086,1.4335,0.14998,8.26528,0.78374,,6.90022,0.10292,3.4711,0.87994,1.1342,4.16752,1.66982
2018-04-02,0.63778,0.64014,1.55152,0.57162,0.70242,0.3219,0.09822,0.6683,1.40932,0.45852,1.39126,0.93596,0.0,0.68268,1.09956,1.77698,0.34674,1.36896,0.8778,0.33588,0.71758,0.3669,1.13542,1.54686,1.36792,0.0,0.46738,1.51952,0.60004,1.8882,0.48032,0.24206,1.93484,1.1387,0.65872,0.82476,1.24978,0.40744,0.47138,0.55522,5.12994,0.32342,0.82036,0.75294,0.91526,1.7249,1.72926,1.55496,1.55742,1.27458,2.01982,1.22674,0.2649,1.00248,1.30726,1.57616,0.54996,0.0,1.12496,0.63324,1.34872,3.09564,1.26438,1.5323,3.18612,2.32434,1.86422,0.0,1.03468,0.74552,0.35446,1.41512,0.46922,0.28494,0.8941,0.38014,1.17694,0.0,1.23148,6.5222,1.67786,0.73836,0.51192,0.3949,1.26112,1.02558,0.83104,1.10378,1.24108,0.0,0.29712,1.13856,2.23524,0.69876,0.7814,0.11982,3.23818,3.10552,13.28366,1.38092,0.90984,6.85656,3.3546,2.9943,5.30444,2.59062,0.77688,1.226,1.17444,1.99486,1.72754,1.09806,1.19322,2.2259,0.76272,2.59272,0.0956,0.4438,0.3954,0.26612,0.25098,0.14168,0.20148,0.11182,0.3769,0.07724,2.77036,0.45966,0.17824,1.69756,1.4116,1.00106,0.30122,1.9403,1.31022,0.5568,0.22004,0.46904,0.83262,0.6327,0.48856,1.2637,0.24884,1.03624,1.44446,0.56698,1.3807,0.37258,0.0,0.23506,0.95918,0.17724,1.1209,1.78548,0.64142,0.58728,0.0,0.51464,3.53488,0.60688,0.68772,0.0,1.10396,0.71696,1.19662,1.91528,1.41614,1.08334,0.19552,1.07142,0.82844,1.3636,0.29424,0.63554,0.2299,1.13806,0.87474,1.19576,1.52404,1.93834,0.9566,2.82556,0.33162,0.49154,1.29978,1.2964,2.8388,2.92326,0.55034,7.04074,0.37346,1.02296,3.13692,2.82674,0.28506,0.66228,0.28732,0.20514,0.96584,1.468,1.46098,0.29506,0.50912,0.725,5.9853,0.2097,0.8924,0.28378,0.48342,0.89094,0.28812,1.39576,1.40636,0.14658,0.56412,0.44402,0.70728,0.2966,1.59856,0.57428,0.29104,0.25412,0.68806,0.8393,0.0805,0.4442,,0.18214,6.84034,0.86814,0.53894,,3.01604,0.33378,0.12728,0.17396,0.53974,0.41662,2.60762,0.0,0.7891,1.37294,0.40452,0.09978,0.14748,1.02852,0.24266,0.37922,1.07222,4.08962,0.3303,0.09008,0.1301,0.45108,1.80106,0.71372,0.8179,1.17802,0.13418,0.0564,0.08412,0.2993,0.07382,0.86662,0.10904,0.21948,0.27078,0.08694,0.34268,0.2192,4.51548,8.41976,0.02378,0.74322,4.12756,2.4938,0.69824,0.09936,1.73902,0.13032,0.29662,0.86322,2.05098,0.37456,0.12402,0.73228,0.13032,0.74924,1.5119,0.11574,7.95138,1.03354,,7.18256,0.09676,3.57688,0.64728,1.23516,4.49082,1.53694
2018-04-03,0.61258,0.6099,1.46326,0.5844,0.64436,0.3243,0.09438,0.6183,1.53558,0.44986,1.77852,0.96528,0.0,0.676,1.0833,1.99136,0.33858,1.30688,0.93344,0.31276,0.71294,0.3739,1.06152,1.67232,1.265,0.00384,0.43868,1.54334,0.60028,1.73124,0.4836,0.23198,1.78604,1.67976,0.6835,0.79596,1.17342,0.3995,0.38202,0.51524,6.12242,0.3204,0.8581,0.67842,0.84508,1.71018,1.65678,1.4969,1.23866,1.26812,1.85014,1.62578,0.4055,0.90732,1.36934,1.6314,0.498,0.0,1.17042,0.48934,1.3125,3.09378,1.21278,1.4081,2.82326,2.21838,1.87154,0.0,1.01546,0.67542,0.36176,1.38602,0.48078,0.2589,0.47876,0.3519,1.20788,0.0,1.17874,6.25744,1.49744,0.6164,0.47494,0.29856,1.06578,1.08962,0.77526,0.86736,1.3317,0.0,0.29966,0.99974,3.05624,0.6864,0.95166,0.1149,3.04814,2.98456,13.04792,1.20878,0.8454,7.49426,3.58972,2.92608,4.77936,2.57904,0.74272,1.0511,1.26446,1.87164,1.65698,1.04748,1.13244,2.18216,0.76358,2.6223,0.08916,0.4209,0.4165,0.24644,0.24264,0.12514,0.205,0.14326,0.34934,0.07844,2.57346,0.4622,0.1644,1.59538,1.53224,1.02742,0.25244,1.84626,1.26446,0.5237,0.22396,0.55814,0.83286,0.7857,0.47406,1.36774,0.22742,1.49476,1.28238,0.51784,1.27232,0.33328,0.0,0.22546,1.0219,0.16234,1.07578,1.44148,0.5637,0.5665,0.0,0.49782,3.95908,0.56142,0.64392,0.0,0.98232,0.77782,1.05568,1.67494,1.31818,1.04148,0.19626,0.95412,0.82266,1.30302,0.2529,0.6592,0.21206,1.05118,0.8383,1.27472,1.56122,1.87122,0.97576,2.85742,0.30522,0.4799,1.32256,1.10112,2.89668,2.71072,0.55392,7.04586,0.36484,1.0606,2.91904,2.6348,0.40208,0.65352,0.3027,0.214,0.88368,1.49812,1.37528,0.2781,0.58314,0.59608,5.9067,0.18702,0.9171,0.27786,0.53542,0.85432,0.25576,1.22372,1.36078,0.15258,0.54002,0.4252,0.70096,0.30568,1.47318,0.45892,0.27968,0.22512,0.62368,0.81434,0.0748,0.35172,,0.16322,6.44218,0.80098,0.4679,,2.9092,0.3407,0.11686,0.16672,0.55054,0.39826,2.16768,0.0,0.78804,1.10108,0.41058,0.10618,0.1368,0.94804,0.23942,0.38766,1.01476,3.91584,0.34138,0.09026,0.12,0.4607,1.8468,0.66378,0.74844,1.07516,0.13272,0.05366,0.0787,0.2997,0.07086,0.91804,0.10358,0.23748,0.2658,0.10018,0.30318,0.19218,4.38076,8.03322,0.026,0.71984,4.07294,2.37372,0.6677,0.09556,1.6735,0.1271,0.27538,0.77724,1.82174,0.3324,0.11172,0.68244,0.14926,0.73518,1.49638,0.10644,6.71242,1.08568,,6.9769,0.1064,3.44042,0.49744,1.21426,4.2846,1.3242
2018-04-04,0.6721,0.64238,1.4653,0.5963,0.63326,0.3324,0.09394,0.56396,1.70186,0.4326,2.03858,0.9014,0.0,0.7653,0.97848,2.11048,0.32722,1.13942,0.9345,0.30722,0.71378,0.373,0.91316,1.74718,1.21712,1.1782,0.45826,1.49998,0.6225,1.8099,0.48528,0.22246,1.61178,1.97134,0.65518,0.80082,1.08822,0.42468,0.36938,0.51186,6.49686,0.31446,0.87272,0.70504,0.80224,1.6101,1.67092,1.5769,1.06732,1.26184,1.91056,1.90658,0.50824,0.84338,1.18826,1.62382,0.471,0.0,1.2663,0.4354,1.2786,2.83426,1.19102,1.25056,3.01728,2.46648,1.879,0.0,1.03058,0.79732,0.4071,1.32518,0.5246,0.178,0.4524,0.2849,1.32244,0.0,1.10622,6.00078,1.41902,0.5683,0.4398,0.2558,1.00626,1.03554,0.78606,0.76374,0.89496,0.0,0.3538,0.9301,3.692,0.70104,0.97912,0.11804,3.18818,2.96542,13.61464,1.15602,0.87368,7.71132,3.62804,2.90606,4.66908,2.21456,0.70386,1.08862,1.15756,1.80356,1.5036,0.94816,1.13202,1.9758,0.71766,2.5844,0.08684,0.38856,0.44148,0.23998,0.21524,0.1124,0.20202,0.165,0.34546,0.07906,2.45172,0.38254,0.14612,1.4283,1.62814,0.96704,0.21268,1.94456,1.24256,0.51686,0.22502,0.53488,0.841,1.0498,0.47496,1.42834,0.20764,1.7994,1.2772,0.49038,1.3382,0.32294,0.16738,0.21264,0.9745,0.15602,1.03632,1.39338,0.50546,0.54848,0.0,0.40816,3.94486,0.49318,0.53944,0.0,0.8973,0.79018,1.0069,1.49488,1.18936,1.04386,0.19258,0.92014,0.78028,1.15676,0.25432,0.6357,0.19802,1.0052,0.80516,1.44002,1.67694,1.7992,0.95456,2.81092,0.2796,0.38972,1.32322,1.0112,3.09926,2.73404,0.5679,5.90848,0.36366,1.14496,2.7873,2.6276,0.516,0.62114,0.29976,0.21786,0.82138,1.62922,1.36088,0.27376,0.62578,0.49976,6.66442,0.18674,0.96762,0.2972,0.55734,0.94248,0.24618,1.28256,1.39612,0.13646,0.53474,0.39332,0.6396,0.30904,1.3838,0.38836,0.28136,0.21774,0.60114,0.8375,0.07548,0.35484,,0.15168,6.30784,0.75222,0.44332,,2.72904,0.3429,0.11206,0.16592,0.56126,0.39806,1.86946,0.0,0.77546,1.00816,0.3926,0.12356,0.12342,0.81126,0.22176,0.3959,0.99772,4.47866,0.36606,0.08762,0.10652,0.46538,1.8116,0.62534,0.88214,1.00986,0.12606,0.04936,0.06602,0.27578,0.07194,0.97554,0.09292,0.2341,0.25994,0.10454,0.26504,0.18778,3.58386,7.04022,0.02472,0.77144,4.11056,2.32068,0.61926,0.089,1.65654,0.12576,0.2578,0.73474,1.62296,0.32406,0.1065,0.78086,0.14844,0.64112,1.34622,0.0899,9.50502,1.095,,7.68902,0.10622,3.11082,0.46604,1.33732,4.15464,1.20494
2018-04-09,0.64186,0.535,1.41088,0.48946,0.6443,0.34328,0.07958,0.57716,1.51178,0.36564,2.13156,0.90212,0.0,0.77804,0.8769,2.18154,0.31128,0.96336,0.81076,0.30948,0.70264,0.3553,0.79868,1.77638,0.8802,1.66786,0.415,1.47168,0.5571,1.83844,0.45124,0.2007,1.30618,2.11058,0.68132,0.84344,0.9397,0.49448,0.3262,0.4735,7.05766,0.299,0.80098,0.75354,0.67046,1.64328,1.3119,1.55232,1.13984,1.23526,1.84364,1.99246,0.5735,0.7124,1.06402,1.59288,0.43094,0.0,1.30902,0.33328,0.95054,3.1053,1.12276,1.17376,2.9304,2.42464,1.90074,0.0,0.95934,0.89176,0.37462,1.21356,0.53868,0.1573,0.66696,0.2472,1.43976,0.0,1.19814,5.64272,1.36712,0.60722,0.45568,0.21068,0.9578,1.37638,0.72562,0.69854,0.85948,0.0,0.3821,0.97922,3.97568,0.59986,1.03756,0.1068,3.13614,2.69528,12.97894,1.07894,0.8356,7.96632,3.58808,2.7518,4.5419,1.9183,0.75132,1.1437,1.15,1.87672,1.44312,0.92112,1.06568,1.72524,0.67086,2.69946,0.07518,0.35388,0.42394,0.16634,0.17116,0.0968,0.19238,0.22588,0.32264,0.07806,2.3843,0.25652,0.14762,1.20938,1.61176,0.88414,0.1737,1.99444,0.87904,0.50266,0.19516,0.54836,0.79668,1.15028,0.45694,1.4289,0.17978,1.90356,1.63942,0.41622,1.77018,0.28138,0.23944,0.21952,0.89098,0.15986,0.92506,1.18768,0.4821,0.53438,0.0,0.36498,3.85524,0.47068,0.57784,0.0,0.84606,0.81354,0.80252,1.48768,1.018,0.94316,0.17706,0.9379,0.56212,1.12288,0.26062,0.7079,0.19106,0.8593,0.85504,1.6415,1.68852,1.83478,1.0186,2.48796,0.26,0.3129,1.3594,1.05094,3.28578,2.34852,0.5817,5.57268,0.3475,1.14362,2.70048,1.93728,0.5425,0.53818,0.30008,0.20636,0.66156,1.37034,1.13722,0.2937,0.63674,0.43596,6.95948,0.19714,0.9316,0.28286,0.52124,1.00628,0.2193,1.34426,1.4316,0.13076,0.45598,0.34458,0.57304,0.28846,1.56328,0.44934,0.24202,0.19654,0.46352,0.79176,0.08082,0.3618,,0.11422,5.64158,0.63038,0.33678,,2.06192,0.30566,0.10618,0.16342,0.52102,0.36842,1.76222,0.0,0.67782,1.46778,0.3668,0.13984,0.10442,0.67346,0.20088,0.3949,0.87048,4.6143,0.36874,0.08644,0.08338,0.41314,1.7268,0.56612,0.93278,0.81784,0.11986,0.04424,0.0575,0.23842,0.08206,0.91558,0.0876,0.2232,0.24558,0.1012,0.26214,0.16382,4.41278,6.278,0.02426,0.7486,3.591,1.97164,0.54734,0.08044,1.5346,0.11374,0.26474,0.75736,1.32838,0.31878,0.0948,0.91452,0.15274,0.55686,1.1491,0.08812,10.12926,1.10142,,8.7222,0.10076,2.7253,0.3985,1.38654,3.9401,1.07826
2018-04-10,0.71738,0.51532,1.4501,0.48984,0.64714,0.35802,0.08274,0.59144,1.54412,0.34384,2.06124,0.94784,0.0,0.79176,0.95976,2.27742,0.29914,0.91634,0.75984,0.33668,0.71652,0.36976,0.824,1.7449,0.84086,1.96068,0.44504,1.53538,0.58376,1.77842,0.47716,0.1996,1.26,2.13842,0.74964,0.7892,0.88338,0.52646,0.30232,0.50684,6.64066,0.33778,0.82162,0.66506,0.6439,1.75144,1.33026,1.57178,1.10844,1.04466,1.74922,2.12524,0.67386,0.59876,1.09286,1.51408,0.54622,0.0,1.4975,0.36388,0.96438,2.92296,1.09668,1.10342,2.68326,2.47752,1.67536,0.0,0.95722,0.8548,0.3835,1.01172,0.5447,0.15692,0.7923,0.26194,1.4016,0.0,1.3199,5.51314,1.34254,0.4791,0.41808,0.21432,0.99458,1.51456,0.7034,0.6846,0.90792,0.0,0.35022,0.90878,4.22258,0.62736,1.04888,0.1118,2.35242,2.84792,9.60556,0.83518,0.82272,7.10456,3.1618,2.47346,4.09602,1.73752,0.74624,1.105,1.29256,1.80066,1.40638,0.81832,1.0422,1.45874,0.63284,3.7701,0.07908,0.40596,0.4564,0.12966,0.18326,0.10354,0.1871,0.27936,0.34044,0.07346,3.02684,0.27054,0.16022,1.22918,1.7306,0.93506,0.19594,1.90242,0.93344,0.52928,0.18856,0.55058,0.77388,1.21806,0.44236,1.36686,0.18858,2.04272,1.77422,0.41188,2.01126,0.30446,0.30772,0.21082,0.95068,0.39092,0.95046,1.0209,0.46116,0.6471,0.0,0.31712,3.59152,0.43748,0.72116,0.0,0.73532,0.81712,0.76518,1.47826,1.22088,1.03636,0.18736,0.84266,0.5301,1.17152,0.27092,0.70698,0.18942,0.81536,0.85676,1.57276,1.61372,1.91742,0.94582,2.49484,0.23552,0.30574,1.25242,0.8833,3.3763,2.29526,0.59024,5.17324,0.3793,1.28236,2.35964,1.9563,0.58518,0.51514,0.2492,0.22138,0.6642,1.29944,0.91204,0.33618,0.63796,0.41674,7.48484,0.18522,0.96526,0.2919,0.5303,0.8658,0.21022,1.40636,1.32948,0.11424,0.47452,0.37342,0.64352,0.28666,1.49674,0.42096,0.23998,0.24308,0.50026,0.7629,0.0884,0.37154,,0.12442,5.81498,0.5981,0.32228,,2.11946,0.30188,0.11124,0.1702,0.55088,0.37924,1.69426,0.0,0.61148,1.71668,0.40414,0.16098,0.1157,0.68512,0.20562,0.36862,0.87388,4.35902,0.39648,0.0993,0.09014,0.42604,1.94746,0.54462,0.91682,0.73562,0.12126,0.0497,0.06572,0.23768,0.08516,0.99252,0.08408,0.23586,0.23662,0.11054,0.26596,0.18544,4.40932,7.43378,0.02522,0.69514,3.71406,2.54188,0.51758,0.08048,1.49886,0.1121,0.30868,0.73338,1.46178,0.32326,0.08458,0.81206,0.14968,0.48584,1.18674,0.10314,11.4309,1.02576,,8.78196,0.10238,2.84466,0.37296,1.41188,3.734,1.21662
2018-04-11,0.83402,0.50592,1.47686,0.49034,0.5821,0.34804,0.07578,0.6073,1.34792,0.3498,1.79256,0.85802,0.0,0.76536,0.85784,1.72688,0.23216,0.92174,0.7263,0.29026,0.73872,0.41318,0.83274,1.66754,0.88152,2.22526,0.43274,1.55748,0.52698,1.48984,0.45722,0.20026,1.14336,1.90606,0.80608,0.73252,0.83584,0.4991,0.28854,0.48016,5.76408,0.34674,0.8481,0.6833,0.58392,1.77326,1.45784,1.42226,1.11764,1.02638,1.34638,2.11442,0.64086,0.56824,0.99856,1.15644,0.60012,0.0,1.45376,0.42878,1.12084,2.40412,0.94452,0.80204,2.35996,1.92864,1.35316,0.0,0.96286,0.86816,0.37106,1.01128,0.561,0.1427,0.86644,0.25564,1.27984,0.0,1.31924,4.70176,1.22786,0.49854,0.3234,0.19912,0.97742,1.84224,0.65638,0.83688,0.89264,0.0,0.35194,1.10176,3.1422,0.56032,1.16292,0.11872,1.8782,2.68988,8.4604,0.79038,0.76662,6.5352,2.4633,1.88466,3.68504,1.71968,0.6813,1.0778,1.2936,1.75972,1.21782,0.7615,0.97256,1.18696,0.59212,4.38864,0.08164,0.42156,0.48814,0.07934,0.16934,0.1156,0.19158,0.3246,0.3316,0.07326,3.07676,0.2707,0.18642,1.24836,1.634,0.94318,0.204,1.7587,0.93574,0.51724,0.16776,0.46372,0.821,1.28346,0.40176,0.95312,0.2146,1.85824,1.83966,0.4319,1.93704,0.30716,1.08756,0.19398,0.93762,0.45188,0.92938,1.3153,0.42214,0.68762,0.0,0.2775,2.83324,0.44482,0.7557,0.0,0.65196,1.18078,0.76032,1.4243,1.2279,1.03094,0.1673,0.73814,0.55116,1.09938,0.26092,0.71104,0.1841,0.77884,0.89672,1.3089,1.5293,1.92534,0.84254,2.28164,0.18996,0.2957,1.13888,0.7584,3.38534,2.23282,0.63818,4.34394,0.52978,1.2903,2.17546,1.9787,0.52702,0.5208,0.22952,0.24344,0.6792,1.26814,0.76818,0.35352,0.54452,0.47984,6.66254,0.18842,0.96428,0.33524,0.46076,0.8252,0.22008,1.4912,1.13596,0.10578,0.43478,0.41306,0.75116,0.24062,1.35328,0.4258,0.21382,0.27796,0.55406,0.7416,0.0894,0.38612,,0.13058,5.38354,0.65566,0.41374,,2.41548,0.30184,0.1135,0.14914,0.5033,0.36468,1.79896,0.0,0.53568,1.81562,0.50576,0.18894,0.12386,0.6695,0.22704,0.37396,0.85984,4.21164,0.34102,0.09578,0.09296,0.35378,2.09764,0.5012,1.0215,0.70754,0.11086,0.05196,0.07028,0.24994,0.08364,0.86816,0.07884,0.2322,0.2072,0.10866,0.3237,0.21846,4.6471,8.36848,0.02772,0.65828,3.44732,2.64754,0.48642,0.08552,1.57488,0.12222,0.33396,0.75978,1.55908,0.31492,0.09636,0.75864,0.13394,0.4452,1.23088,0.13448,11.38374,0.74424,,9.01978,0.1032,2.31656,0.40974,1.29474,2.88446,1.17814
2018-04-12,0.8674,0.486,1.41962,0.47646,0.5402,0.32688,0.07938,0.5885,1.17618,0.31014,1.25462,0.80474,0.0,0.74738,0.90552,1.37468,0.24574,1.44718,0.6172,0.40308,1.05844,0.38294,0.8147,1.5249,0.89204,2.38258,0.43694,1.40344,0.49194,1.29466,0.43866,0.19822,1.16092,1.41918,0.7469,0.68958,0.73622,0.44742,0.25504,0.44204,4.74126,0.40556,0.83332,0.70864,0.58274,1.8692,1.50746,1.30662,1.1017,0.91482,1.168,1.76234,0.45726,0.55864,0.84746,1.0059,0.63758,0.0,1.3717,0.4182,1.20224,2.13116,0.87028,0.76086,2.20496,1.82402,1.22134,0.0,0.91788,0.9644,0.32772,0.97406,0.49828,0.12826,0.89642,0.22786,1.32754,0.0,1.36658,4.35414,1.2407,0.54568,0.31408,0.18318,0.99862,1.79268,0.66856,0.87452,0.76756,0.0,0.47742,1.34302,2.82774,0.5337,1.08522,0.12702,1.71302,2.65574,9.08354,0.9064,0.71654,5.27362,2.2354,1.61216,3.40846,1.44768,0.59692,1.49272,1.10478,1.79398,1.35506,0.69412,1.02354,0.98912,0.51594,4.50044,0.08202,0.44338,0.48606,0.0338,0.12698,0.11358,0.17516,0.30232,0.31388,0.06748,3.35644,0.23156,0.19786,1.20562,1.51256,1.02524,0.20744,1.53916,0.92838,0.46584,0.15582,0.38348,0.77708,1.10656,0.37958,0.78948,0.21616,1.47346,1.91586,0.4404,1.93642,0.3079,1.50734,0.19758,0.72104,0.49394,0.9301,1.34464,0.42198,0.67054,0.0,0.24518,2.69588,0.4166,0.75998,0.0,0.68018,1.17358,0.77344,1.41594,1.20578,1.07466,0.16038,0.7012,0.48916,1.32648,0.26526,0.6601,0.19072,0.89986,0.8165,1.21076,1.39138,2.10252,0.74142,2.69978,0.1769,0.28054,0.99774,0.69672,3.39334,2.19284,0.62032,3.8744,0.6145,1.18822,1.7806,1.97152,0.488,0.48888,0.2032,0.22298,0.65384,1.26568,0.69014,0.44182,0.48224,0.47494,6.16816,0.18368,0.90008,0.43138,0.39174,0.82146,0.20958,1.45474,1.02022,0.10054,0.43254,0.43546,0.77344,0.21896,1.34074,0.43144,0.19674,0.3057,0.59324,0.73162,0.08388,0.37016,,0.12912,5.26476,0.66116,0.43244,,2.48888,0.28542,0.11558,0.13958,0.4675,0.34804,2.09378,0.0,0.51944,1.75258,0.54106,0.19568,0.12124,0.6585,0.2215,0.37566,0.851,4.0512,0.3148,0.09206,0.091,0.32508,2.0423,0.47226,1.0344,0.66034,0.09998,0.05278,0.06948,0.27288,0.0851,0.78696,0.06722,0.19526,0.19918,0.09552,0.34388,0.21252,6.61154,10.12828,0.02702,0.69912,3.42988,2.89852,0.5125,0.086,1.58514,0.13146,0.31402,0.84494,1.6539,0.30058,0.09706,0.73794,0.11246,0.42108,1.16212,0.14134,12.33236,0.64386,,9.35732,0.08734,2.33166,0.41044,1.24612,2.71748,1.22644
2018-04-13,0.83168,0.43806,1.32248,0.4554,0.52612,0.3008,0.07834,0.54726,1.29434,0.2883,1.10432,0.77312,0.0,0.63614,0.89916,1.30614,0.2421,1.62814,0.52274,0.41814,1.14178,0.3851,0.76336,1.41226,0.8606,1.37972,0.39334,1.3735,0.454,1.12908,0.41554,0.21338,1.19968,1.3562,0.71784,0.63344,0.59702,0.38374,0.19848,0.42628,4.35008,0.43746,0.75554,0.74168,0.60296,1.88058,1.58194,1.19774,1.11222,0.8216,0.94564,1.4985,0.34644,0.64954,0.8385,1.03368,0.7577,0.0,1.33964,0.42982,1.29132,2.14722,0.86178,0.85702,1.87888,1.45468,1.1155,0.0,0.79804,0.87078,0.25252,0.92656,0.4153,0.1275,0.87672,0.21138,1.40138,0.0,1.3026,4.35266,1.34068,0.62452,0.28558,0.16096,0.94866,1.77908,0.76392,0.91716,0.69692,0.0,0.51246,1.31758,2.29908,0.51822,1.01906,0.12312,1.60234,2.55058,8.48182,0.95838,0.65128,4.72078,2.17486,1.52698,3.31984,1.18872,0.56318,1.5723,1.1296,1.6886,1.47636,0.65592,1.02998,0.89514,0.47392,4.53152,0.07808,0.43874,0.43904,0.0,0.13192,0.11122,0.16188,0.27658,0.30586,0.06624,3.51208,0.23464,0.19662,1.19872,1.39686,1.02348,0.19602,1.36332,0.89024,0.42808,0.11286,0.35704,0.74242,0.82674,0.34378,0.69776,0.19886,1.25184,1.91752,0.43314,1.89162,0.312,1.65634,0.19852,0.69532,0.50348,0.99156,1.75462,0.46522,0.65822,0.0,0.24258,2.38992,0.4779,0.72848,0.0,0.687,1.17418,0.74126,1.375,1.22364,0.98118,0.15066,0.6763,0.44888,1.39354,0.28088,0.78628,0.17974,0.9772,0.70228,1.04882,1.26126,2.01596,0.69034,2.79684,0.15978,0.23382,0.96828,0.64598,3.05794,2.18764,0.67256,4.0371,0.61722,1.06024,1.56868,1.86226,0.41244,0.47598,0.18068,0.2124,0.56968,1.07956,0.61878,0.4628,0.43874,0.41516,5.13432,0.16908,0.67564,0.4538,0.33238,0.70294,0.20148,1.23686,0.80392,0.10228,0.41646,0.41352,0.76502,0.21528,1.31236,0.40676,0.18152,0.31178,0.5593,0.56996,0.0811,0.3465,,0.12774,5.21072,0.66718,0.43424,,2.57582,0.25916,0.10948,0.13446,0.42762,0.31604,2.21268,0.0,0.50966,1.56936,0.58764,0.18394,0.11918,0.61906,0.22124,0.39918,0.81062,3.19904,0.28386,0.08948,0.08932,0.30564,1.94458,0.4471,0.92898,0.60098,0.12016,0.0556,0.0707,0.26212,0.08618,0.72494,0.06124,0.18174,0.19112,0.0945,0.34614,0.20798,9.52508,11.1891,0.02474,0.65508,3.5381,2.90542,0.52242,0.0893,1.56398,0.13508,0.28772,0.77814,1.66428,0.28506,0.10056,0.73472,0.1195,0.39144,1.07426,0.13692,9.56,0.59286,,8.7486,0.07506,2.47392,0.36688,1.14516,2.68642,1.20948
2018-04-16,0.87332,0.44836,1.31036,0.5468,0.48218,0.29182,0.08036,0.55252,1.34134,0.30564,1.08298,0.75212,0.0,0.59454,0.96038,1.87094,0.25542,1.92116,0.53992,0.42998,1.18044,0.37686,0.7714,1.28698,1.12328,1.18716,0.40052,1.38394,0.47038,1.07774,0.42536,0.23182,1.35984,1.27024,0.60192,0.61612,0.58804,0.32824,0.18198,0.42368,4.30512,0.4825,0.76486,0.74464,0.64054,1.79198,1.70058,1.32118,1.0138,0.84096,0.81714,1.35542,0.28696,0.69622,0.87788,1.36502,0.79802,0.0,1.39176,0.55676,1.51248,2.16538,0.9349,0.85428,1.78252,1.3617,1.08056,0.0,0.86216,0.74412,0.23794,0.91812,0.39432,0.12202,0.60384,0.23144,1.3626,0.0,1.30792,4.28886,1.37544,0.89536,0.25034,0.1703,1.02002,1.49302,0.9049,0.90084,0.57956,0.0,0.52762,1.34506,2.14018,0.54566,0.92466,0.12732,1.7096,2.54954,8.45214,0.94754,0.54346,5.14798,2.12258,1.52484,3.3395,1.1781,0.38982,1.67394,1.09832,1.7757,1.51018,0.72426,1.14816,0.85478,0.50174,4.4607,0.08306,0.45692,0.47992,0.0,0.12036,0.1286,0.1745,0.23818,0.31442,0.06322,3.63344,0.2396,0.1917,1.09884,1.3701,1.11508,0.2578,1.38702,1.05594,0.43524,0.11132,0.3585,0.73388,0.74064,0.3367,0.76776,0.19954,1.18774,1.48266,0.4332,1.57456,0.3932,1.73574,0.18512,0.92426,0.52506,1.08252,1.9207,0.462,0.6662,0.0,0.23326,2.48302,0.50156,0.68266,0.0,0.7414,1.20232,1.19258,1.16366,1.32458,0.98344,0.1506,0.64774,0.52434,1.3707,0.28824,0.77542,0.17882,0.94248,0.63104,0.87826,1.4246,2.14176,0.64924,2.9857,0.16766,0.23718,0.96722,0.54782,2.92806,2.19028,0.69212,4.65676,0.67972,1.08968,1.84824,2.11776,0.40552,0.48816,0.18122,0.21224,0.57482,1.0408,0.57998,0.4677,0.425,0.40206,5.00458,0.1715,0.61048,0.49726,0.3336,0.65114,0.19296,1.01618,0.7119,0.09958,0.43676,0.45858,0.8065,0.20588,1.12862,0.32426,0.18292,0.33832,0.68688,0.6189,0.0786,0.33852,,0.14666,5.50228,0.63426,0.44872,,2.94562,0.2983,0.11698,0.12838,0.37286,0.32758,2.28828,0.0,0.57304,1.37184,0.62758,0.19096,0.1451,0.63632,0.2455,0.39252,0.8677,3.01294,0.27474,0.0927,0.10668,0.309,1.93328,0.44916,0.87164,0.62058,0.12716,0.06168,0.07996,0.27766,0.07418,0.7085,0.06444,0.17796,0.18726,0.08996,0.3527,0.26032,9.46434,11.68104,0.02418,0.75256,3.65344,3.00318,0.62198,0.09578,1.53482,0.14594,0.279,0.70768,2.00672,0.3048,0.11304,0.66726,0.12484,0.41242,1.15692,0.14274,10.52,0.56086,,8.01924,0.06982,2.624,0.36954,1.10982,2.8664,1.17268
2018-04-17,0.86278,0.4033,0.99766,0.5347,0.46222,0.28016,0.07926,0.5397,1.3034,0.30018,1.04442,0.6875,0.0,0.5629,0.83772,2.14308,0.24824,1.89978,0.51478,0.41158,1.17612,0.35974,0.75006,1.26444,1.13022,1.085,0.35794,1.3808,0.46446,1.10028,0.38756,0.2498,1.52928,1.18142,0.42346,0.63162,0.58442,0.30774,0.18484,0.35912,4.5611,0.46656,0.69412,0.90362,0.59048,1.63872,1.63734,1.3433,0.99396,1.21476,0.81908,1.20664,0.23244,0.69858,0.86892,1.5059,0.68098,0.0,1.21466,0.55938,1.42294,2.20626,0.96678,0.897,1.91274,1.59092,1.1766,0.0,0.93014,0.78166,0.2214,0.95084,0.39554,0.119,0.4706,0.42574,1.21906,0.0,1.19994,4.59244,1.45498,1.06548,0.28432,0.17034,1.03428,1.41414,1.04914,1.0701,0.51596,0.0,0.52462,1.4648,2.00644,0.5203,0.89,0.12028,1.66598,2.42906,8.82126,0.98584,0.47588,5.43284,2.26986,1.74158,3.48626,1.24542,0.61202,1.74298,0.89608,1.67568,1.5207,0.7321,1.17316,0.89914,0.52778,3.26928,0.08072,0.43202,0.53774,0.0,0.1001,0.12026,0.16932,0.19032,0.2734,0.06454,3.08026,0.22252,0.17546,0.8904,1.21916,1.07932,0.2602,1.37994,0.95426,0.45642,0.09984,0.35606,0.65842,0.74402,0.37782,0.7786,0.1774,1.13954,1.38934,0.36422,1.1721,0.39774,1.82442,0.19342,1.0531,0.30922,0.98764,2.08142,0.44616,0.60086,0.0,0.25394,2.57334,0.55206,0.55904,0.0,0.75254,1.30022,1.30866,1.07802,1.08496,0.90094,0.14008,0.6688,0.51164,1.28706,0.2812,0.7597,0.17512,0.96792,0.66288,0.81956,1.89358,2.31476,0.86874,2.99108,0.16968,0.24216,1.17172,0.65094,2.99062,2.15014,0.68856,4.91786,0.66426,1.00608,2.04764,1.88868,0.38692,0.50098,0.17566,0.19898,0.5393,1.10228,0.60166,0.42774,0.40124,0.39398,4.7891,0.17244,0.54152,0.5005,0.31438,0.60902,0.17508,0.83918,0.70964,0.10082,0.41394,0.42634,0.71118,0.20278,1.0884,0.31908,0.17966,0.29276,0.68788,0.6954,0.0715,0.33984,,0.13878,5.23274,0.5398,0.42666,,2.90162,0.29204,0.11854,0.11074,0.34354,0.3481,2.48994,0.0,0.55196,1.24016,0.59166,0.18304,0.13584,0.62274,0.22776,0.38334,0.88104,3.10376,0.25394,0.08254,0.09704,0.30268,1.79424,0.40404,0.86102,0.63108,0.17012,0.0594,0.07666,0.27982,0.07702,0.62806,0.0698,0.16324,0.18898,0.08334,0.35224,0.24442,9.96154,10.44208,0.0222,0.80492,3.38948,2.43726,0.7755,0.09668,1.50404,0.15268,0.2568,0.74058,1.8955,0.29536,0.10512,0.63234,0.13112,0.42454,0.99174,0.13376,10.19198,0.5845,,7.7812,0.0662,2.84604,0.3661,1.03796,3.30218,1.11076
2018-04-18,0.78948,0.37096,0.58022,0.51168,0.60174,0.27346,0.0798,0.5235,1.384,0.26978,1.1443,0.70624,0.0,0.51936,0.83834,2.3715,0.28582,1.9031,0.51538,0.42282,1.1971,0.3483,0.72214,1.15958,1.04744,0.99666,0.35674,1.7872,0.45148,1.25204,0.34808,0.22906,1.61794,1.1144,0.20978,0.65922,0.58372,0.27794,0.16786,0.37848,5.2299,0.49762,0.58942,0.91364,0.55676,1.52784,1.57606,1.23028,0.8838,1.35404,0.8792,1.50986,0.23674,0.7791,0.8294,1.72112,0.59752,0.0,1.08364,0.55418,1.28798,2.32206,0.97986,0.91984,2.1758,1.94422,1.22986,0.0,0.98816,0.7563,0.22676,1.03182,0.3308,0.13026,0.57434,0.59712,1.16434,0.0,1.37046,5.06056,1.60748,1.10408,0.3687,0.17328,0.99604,1.09904,1.1475,0.98308,0.54666,0.0,0.49844,1.2613,1.83286,0.5111,0.63794,0.09608,1.6969,2.36432,9.04346,1.11392,0.5239,5.7781,2.58048,1.83758,4.02916,1.12438,1.02356,1.80892,0.84062,1.8226,1.765,0.76152,1.38436,0.89428,0.55222,2.59244,0.07716,0.45894,0.59446,0.0,0.09104,0.105,0.16482,0.16096,0.30326,0.09204,2.95476,0.19962,0.15636,0.75306,1.1588,0.9441,0.26982,1.435,0.944,0.51622,0.09176,0.52474,0.62616,0.84224,0.44108,1.02956,0.1793,1.08922,1.28676,0.33114,1.185,0.38998,1.1749,0.18958,1.1649,0.25616,0.96716,1.89996,0.44918,0.58152,0.0,0.23664,2.76538,0.56478,0.47106,0.0,0.84508,1.10796,1.38158,1.09678,1.006,0.87002,0.12968,0.65282,0.53834,1.26652,0.2586,0.75808,0.1796,1.02248,1.10064,0.83386,2.59276,2.13436,1.48962,3.03776,0.17396,0.3009,1.36922,0.72598,2.58016,2.4818,0.68432,5.29988,0.49674,0.99696,2.25256,1.66798,0.45954,0.5229,0.17922,0.15842,0.49752,1.28236,0.62716,0.41796,0.37482,0.44586,4.90674,0.14854,0.45584,0.45662,0.33142,0.6353,0.16026,0.7425,0.80676,0.10304,0.40246,0.37182,0.5871,0.20922,1.09142,0.26546,0.19208,0.25922,0.63756,0.91266,0.0683,0.33016,,0.13512,5.00006,0.42848,0.41012,,3.0745,0.2866,0.1178,0.11304,0.35522,0.37476,2.17866,0.0,0.59732,1.15468,0.48396,0.26056,0.1264,0.63086,0.20216,0.3641,0.91782,3.68842,0.23838,0.08262,0.09514,0.29854,1.67708,0.42002,0.74286,0.62032,0.17714,0.0569,0.07248,0.30104,0.08014,0.67144,0.0711,0.1532,0.19264,0.08594,0.2913,0.22312,9.93962,10.54312,0.01924,0.79638,3.2635,2.17516,0.88112,0.0989,1.32772,0.13928,0.26496,0.73254,1.75582,0.3089,0.08472,0.6107,0.13218,0.39962,0.81034,0.10058,10.66546,0.93116,,7.44162,0.05948,3.2735,0.3384,1.00336,4.16068,1.06358
2018-04-19,0.75118,0.35472,0.35684,0.48708,0.61578,0.27748,0.0787,0.58054,1.35948,0.28294,1.15306,0.93204,0.0,0.50322,0.77076,2.46466,0.26548,1.47032,0.5432,0.31712,0.8645,0.48086,0.83796,1.0193,1.00266,1.01768,0.3584,2.01022,0.5797,1.53502,0.327,0.22712,1.59492,1.07078,0.09686,0.84066,0.66998,0.28802,0.18822,0.43306,5.79662,0.48116,0.54002,0.91104,0.60294,1.36984,1.54264,1.2233,0.7703,1.49588,0.89596,1.64156,0.24882,0.81024,0.7973,2.05178,0.58102,0.0,1.42198,0.58756,1.21992,2.45942,1.15758,0.98346,2.4653,2.11438,1.19164,0.0,0.9404,0.63554,0.23548,1.02306,0.33398,0.1484,0.67594,0.65258,1.02628,0.0,1.35344,5.08858,1.5237,1.09644,0.38528,0.19322,1.04338,1.00788,1.38422,0.974,0.54476,0.0,0.35904,1.11986,1.4731,0.53656,0.4663,0.08618,1.8069,2.31418,8.7416,1.01262,0.53884,6.17646,2.67816,1.85246,4.2022,1.15288,1.26102,1.48176,0.88664,1.71094,1.58834,0.75708,1.29148,0.85528,0.5304,2.2475,0.07554,0.42866,0.62774,0.0,0.09048,0.10178,0.15952,0.14598,0.36804,0.10118,2.50194,0.21132,0.15422,0.75192,1.13402,0.87444,0.27822,1.7285,0.94488,0.53286,0.0905,0.53142,0.62742,0.86114,0.45316,1.19632,0.17424,1.17754,1.56778,0.35244,1.40272,0.37762,0.8845,0.1846,1.29248,0.22188,1.10928,2.02958,0.42108,1.18136,0.0,0.24758,2.59072,0.56022,0.3962,0.0,1.17724,1.09098,1.42196,1.0788,1.09252,1.06524,0.12708,0.9056,0.61096,0.97636,0.2621,0.7094,0.16132,0.837,1.6428,0.85044,2.76954,1.75872,1.75686,2.62604,0.18654,0.32238,1.4095,0.75978,2.13108,2.53094,0.68412,5.59128,0.4399,1.02108,2.43874,1.61954,0.4263,0.57348,0.20354,0.15104,0.56338,1.48346,0.6368,0.33842,0.32144,0.45454,6.37838,0.15824,0.43946,0.34664,0.3369,0.63066,0.173,0.84412,1.16344,0.10258,0.40266,0.34434,0.5438,0.2122,1.1221,0.25124,0.19068,0.2361,0.6276,1.13346,0.07012,0.32092,,0.14974,4.9438,0.43504,0.43322,,3.10522,0.2794,0.11756,0.1205,0.37194,0.39622,1.68762,0.34216,0.69744,1.1435,0.4416,0.28018,0.12692,0.66446,0.1937,0.36542,0.9887,4.50168,0.24638,0.08074,0.09564,0.31684,2.66324,0.44912,0.71916,0.55382,0.23944,0.05402,0.07952,0.27752,0.07606,0.71066,0.0731,0.15306,0.18618,0.0834,0.31924,0.21948,7.9903,9.16746,0.01804,0.8316,3.20596,1.98864,0.8844,0.0999,1.26106,0.13274,0.2777,0.69208,1.73174,0.3185,0.07906,0.61534,0.12668,0.44034,0.7633,0.09022,10.02624,1.18408,,7.22536,0.0627,3.24252,0.34126,0.93116,4.722,1.02682
2018-04-20,0.7108,0.3348,0.21938,0.4839,0.6355,0.31728,0.0856,0.64028,1.14114,0.31406,1.11304,1.10572,0.0,0.55978,0.7749,2.44754,0.25842,1.30854,0.55894,0.36226,0.75336,0.50726,0.98176,0.9527,1.02426,1.03964,0.4115,2.27252,0.668,1.5101,0.33772,0.22506,1.57048,0.85662,0.0,0.94078,0.70712,0.31166,0.20254,0.43856,6.27292,0.46446,0.50578,0.77764,0.60186,1.36548,1.47088,1.24298,0.68062,1.60052,0.93604,2.16162,0.2461,0.68294,0.78986,2.3182,0.46846,0.0,1.30242,0.58884,1.11062,2.64192,1.1441,0.83136,2.61202,2.30642,1.36972,0.0,0.96592,0.54396,0.2513,0.97292,0.34362,0.14772,0.8063,0.71324,0.88022,0.0,1.42136,4.77284,1.34966,1.21736,0.44672,0.20836,1.13194,1.0514,1.32988,0.96532,0.575,0.0,0.2663,1.2615,1.79564,0.5326,0.3993,0.08836,1.71444,2.3994,9.53492,0.94898,0.57712,6.67596,3.02304,1.75046,4.4244,1.25608,1.3806,1.41398,0.82674,1.82406,1.50246,0.75434,1.38154,0.86048,0.6302,2.18006,0.07858,0.41726,0.66528,0.0919,0.07944,0.11538,0.1705,0.1585,0.40296,0.11174,2.44168,0.20162,0.15752,0.9359,1.15784,0.86266,0.29158,1.72194,0.95156,0.5812,0.0983,0.59536,0.66814,0.88626,0.53054,1.43222,0.18266,1.25718,1.71088,0.43276,1.44386,0.37288,0.6847,0.18658,1.30384,0.22742,1.17558,1.6321,0.39012,1.46254,0.0,0.28196,2.57244,0.49816,0.39372,0.0,1.40266,1.1303,1.48228,1.0036,1.06976,1.25664,0.14154,1.0037,0.68776,0.91592,0.23626,0.59876,0.16118,0.7528,1.84646,0.83042,2.90704,1.67546,2.09588,2.57024,0.19012,0.34462,1.49702,0.74722,1.97486,2.55948,0.59928,5.46954,0.42642,1.02384,2.5309,1.77576,0.4354,0.61576,0.2226,0.16112,0.63262,1.87382,0.6913,0.32334,0.3041,0.47432,6.87514,0.17036,0.41634,0.32136,0.3532,0.62762,0.17196,0.92996,1.27234,0.1058,0.42502,0.41918,0.5457,0.2268,1.2655,0.27366,0.20798,0.22202,0.65952,1.1946,0.08522,0.2932,,0.15824,4.99698,0.51688,0.53018,,3.11908,0.30954,0.13336,0.1376,0.42514,0.44028,1.52268,0.51646,0.70974,1.14178,0.42604,0.29112,0.13034,0.68976,0.21372,0.41246,1.0535,5.0224,0.26862,0.08448,0.09714,0.34166,3.30762,0.48688,0.59164,0.61994,0.2432,0.05542,0.08792,0.31768,0.08392,0.74194,0.088,0.18366,0.18754,0.08692,0.36114,0.23988,6.03196,8.71856,0.0189,0.8699,3.12308,2.0943,0.86892,0.10116,1.2496,0.1383,0.32532,0.79692,1.7258,0.38938,0.07552,0.56232,0.12664,0.59352,0.87028,0.09586,10.2216,1.99124,,8.41798,0.07126,3.29538,0.42834,0.81178,5.35572,1.03246
2018-04-23,0.66862,0.2676,0.0,0.41146,0.64936,0.29068,0.08948,0.59138,1.00342,0.25918,1.06296,1.44182,0.0,0.5421,0.72602,1.92776,0.27738,1.06194,0.49882,0.37362,0.7117,0.50504,1.06028,1.02106,0.7434,0.84796,0.39762,2.73868,0.69262,1.5808,0.29852,0.2151,1.46002,0.7779,0.0,0.93516,0.70222,0.29764,0.20492,0.43712,6.09034,0.43274,0.44614,0.71548,0.5905,1.3162,1.44858,1.21206,0.94932,1.96176,0.87292,2.3415,0.24426,0.71054,0.7493,2.14398,0.47596,0.02848,1.20312,0.45552,0.90698,2.38436,1.1968,0.74406,2.95764,2.3424,1.63532,0.0,0.85152,0.55252,0.247,0.91582,0.32396,0.13944,0.97302,0.74822,0.83262,0.0,1.5785,4.9287,1.34028,1.0214,0.6746,0.20032,1.1036,0.97322,1.21092,0.97548,0.57654,0.0,0.2399,1.3234,1.8591,0.47284,0.40322,0.07788,1.77386,2.34864,9.35726,0.97932,0.64334,6.48446,2.8886,1.58532,4.39016,1.18826,1.51358,1.3449,0.79672,1.99238,1.3978,0.60816,1.34972,0.99904,0.67914,2.45254,0.07464,0.44544,0.58876,0.15202,0.08444,0.10832,0.16044,0.13146,0.3809,0.11844,2.27444,0.18494,0.1449,0.98214,1.06072,0.76502,0.23214,1.85902,0.78182,0.92144,0.09586,0.65986,0.65394,0.96788,0.58112,1.64308,0.1816,1.1648,1.71278,0.40632,1.4174,0.2933,0.6907,0.17672,1.07456,0.2072,1.13036,1.47548,0.42554,1.48682,0.0,0.2933,2.72434,0.44878,0.4091,0.0,1.54168,1.10536,1.15206,0.97854,0.96592,1.31016,0.13734,1.03982,0.6317,0.85576,0.20036,0.55102,0.1649,0.70868,2.06634,0.81964,2.88974,1.57724,2.26294,2.42434,0.17142,0.34418,1.51506,0.81286,1.9366,2.31814,0.72824,4.79798,0.38064,0.85466,2.12688,1.51562,0.45454,0.6125,0.21478,0.15384,0.62874,1.96862,0.69142,0.30886,0.28354,0.5505,7.3518,0.15718,0.36988,0.26042,0.32736,0.5798,0.15766,0.96642,1.35164,0.1005,0.3945,0.3919,0.48068,0.22544,1.27744,0.27872,0.19686,0.19294,0.52768,1.2251,0.07542,0.26118,,0.13774,4.47272,0.53378,0.55492,,2.86034,0.26518,0.12278,0.13324,0.41862,0.42796,1.40256,0.62744,0.6703,0.8601,0.4364,0.28556,0.10518,0.6408,0.18626,0.38628,1.01376,5.587,0.25684,0.0771,0.08568,0.33116,3.4378,0.46606,0.5345,0.60312,0.24124,0.04886,0.0803,0.29766,0.08876,0.7357,0.08494,0.17344,0.1769,0.08538,0.355,0.18976,4.97094,7.93794,0.0178,0.8534,2.7231,1.90774,0.71648,0.09292,1.16408,0.12452,0.33394,0.7943,1.48072,0.39168,0.058,0.54582,0.11782,0.70456,0.75252,0.0862,8.2086,2.8036,,8.02902,0.08324,3.2193,0.48336,0.73726,5.5591,1.02026
2018-04-24,0.68744,0.31116,0.0,0.4305,0.67364,0.3162,0.0908,0.61036,1.0373,0.25324,1.06982,1.6857,0.0,0.50926,0.84612,1.64218,0.29218,1.18048,0.601,0.40546,0.75482,0.5381,1.11514,1.07472,0.79018,0.81202,0.42752,2.9892,0.77568,1.48176,0.31704,0.25342,1.31404,0.7406,0.0,0.94328,0.79136,0.30748,0.21216,0.51706,5.75194,0.45306,0.52914,0.57762,0.684,1.38282,1.80558,1.246,0.99694,2.08074,0.84278,2.676,0.3977,0.68776,0.6862,2.2041,0.48064,0.06138,1.09328,0.46642,1.0877,2.2354,1.12008,0.72238,3.17956,2.00572,1.53856,0.0,0.93036,0.46468,0.25098,0.95726,0.2974,0.1786,1.38834,0.58644,0.85212,0.0,1.72694,4.69558,1.3017,1.00804,0.70168,0.18554,1.16192,0.8858,1.13704,0.82364,0.54694,0.0,0.22408,1.38112,1.98714,0.47644,0.41914,0.07432,1.92596,2.2642,9.40804,0.95902,0.6659,6.1916,2.73644,1.36668,4.57408,1.2016,1.33822,1.40852,0.76734,2.08894,1.47648,0.55292,1.22322,1.03534,0.6561,2.76802,0.07292,0.45166,0.52406,0.19648,0.08842,0.12516,0.18578,0.12456,0.43826,0.1246,2.2096,0.21388,0.15504,1.08066,1.1402,1.0055,0.2379,1.7371,0.8832,1.13508,0.09694,0.7227,0.71292,0.92574,0.55448,1.8514,0.18556,1.0603,1.6223,0.47078,1.40118,0.32202,0.65834,0.16394,0.99584,0.2102,1.36824,1.37302,0.47718,1.56202,0.0,0.2676,2.64074,0.4078,0.40436,0.0,1.7682,1.04888,1.05504,0.97932,1.00692,1.52846,0.13936,0.99448,0.70004,0.82946,0.21776,0.59024,0.15932,0.66158,2.27186,0.73878,2.64872,1.33718,2.19256,2.5002,0.1718,0.36346,1.38908,0.76344,1.7944,2.13688,0.71632,4.48576,0.39336,0.94676,1.92742,1.79228,0.48712,0.73114,0.22806,0.1645,0.71898,1.9529,0.69584,0.3133,0.27318,0.585,6.93836,0.18258,0.4976,0.24438,0.37382,0.60826,0.16208,1.1613,1.30118,0.09658,0.39882,0.4204,0.54056,0.2287,1.2317,0.2669,0.19734,0.21108,0.53448,1.27698,0.07032,0.221,,0.15268,4.37778,0.58624,0.61604,,3.05982,0.2626,0.11924,0.1457,0.38584,0.4055,1.1139,0.70954,0.87444,0.73038,0.49484,0.28388,0.11394,0.6547,0.18738,0.38138,1.14804,5.39412,0.26848,0.08384,0.11066,0.3298,3.75088,0.52734,0.54286,0.6183,0.20256,0.04998,0.0786,0.29612,0.09372,0.85862,0.08128,0.1789,0.18792,0.08642,0.35842,0.19876,4.4231,8.16908,0.01982,0.88376,2.6331,1.84016,0.57042,0.09446,1.0442,0.11934,0.34028,0.7582,1.6892,0.43068,0.06296,0.52956,0.11454,0.70828,0.89088,0.0928,7.53738,3.24594,,8.27556,0.0907,3.12226,0.546,0.69764,5.17502,0.95706
2018-04-25,0.5993,0.32532,0.0,0.38482,0.56036,0.3019,0.09182,0.59394,0.92838,0.23254,0.91544,1.88514,0.0,0.4783,0.78834,1.65658,0.25002,1.20572,0.57322,0.38188,0.71418,0.50344,1.10912,1.43604,0.76132,0.79838,0.40834,2.71888,0.80608,1.29584,0.313,0.24684,1.16574,0.74938,0.0,0.9321,0.75724,0.30346,0.23976,0.47098,5.1227,0.44896,0.51354,0.71586,0.67684,1.3511,1.66528,1.446,1.09246,2.35892,0.74426,2.7049,0.43716,0.74938,0.68128,2.29228,0.46592,1.21574,1.10318,0.38068,1.02482,2.0285,1.05814,0.6307,3.08466,1.53588,1.386,0.0,0.91246,0.43636,0.23938,0.82422,0.29586,0.18152,1.30354,0.42974,0.99776,0.0,1.47962,4.26366,1.18564,0.9233,0.72628,0.16366,1.17276,0.8278,1.50928,0.8753,0.46406,0.0,0.20294,1.50654,1.9713,0.46402,0.39422,0.07586,2.11636,2.1291,9.29308,0.96048,0.7002,5.5524,2.38374,1.4231,3.98288,1.27916,0.9867,1.58604,0.75156,1.94358,1.34842,0.54146,0.95938,1.02378,0.58718,2.9243,0.06564,0.41626,0.42612,0.23066,0.09872,0.12706,0.21,0.10416,0.38282,0.0939,2.04666,0.22094,0.1506,1.23148,1.06422,0.9881,0.19834,1.61206,0.757,1.30834,0.09104,0.64974,0.64876,0.81022,0.46616,2.14418,0.14084,0.94968,1.4656,0.56158,1.26572,0.28746,0.60372,0.15464,0.92564,0.24308,1.3858,1.28514,0.44674,1.5234,0.0,0.32454,2.38764,0.42942,0.40962,0.0,1.94574,0.84672,0.92998,0.83394,0.98838,1.5357,0.1346,0.99838,0.59952,0.78682,0.23064,0.56974,0.14224,0.67932,1.92714,0.70254,2.17948,1.20926,1.93934,2.5667,0.1673,0.30396,1.25538,0.80256,1.70646,1.46238,0.73998,4.03154,0.38052,0.92016,1.91204,1.7369,0.41336,0.72426,0.20794,0.16806,0.67816,2.04328,0.6595,0.31338,0.25544,0.4357,6.33072,0.18288,0.46908,0.2537,0.34624,0.77786,0.15734,1.16402,1.27294,0.0976,0.393,0.42986,0.52712,0.22554,1.08416,0.25506,0.16198,0.19724,0.48252,1.07326,0.06762,0.19628,,0.1485,4.14812,0.69488,0.54688,,2.44254,0.2712,0.1111,0.14142,0.34238,0.36402,0.93746,0.80388,0.81302,0.77108,0.4813,0.18408,0.10092,0.6017,0.1726,0.39442,1.0951,4.63728,0.25994,0.07746,0.1029,0.3295,3.65842,0.48828,0.52046,0.57314,0.19178,0.04798,0.08412,0.24446,0.09362,0.7873,0.08544,0.16886,0.18574,0.07638,0.35046,0.1756,3.95124,6.84904,0.01862,0.86864,2.48006,1.69894,0.43734,0.08348,0.92036,0.1199,0.2951,0.65942,1.57968,0.41144,0.0574,0.49038,0.10596,0.70336,0.93492,0.08976,6.5426,3.1938,,7.7279,0.0972,2.70708,0.58796,0.64384,4.75214,0.89868
2018-04-26,0.60226,0.43978,0.0,0.41644,0.56882,0.30476,0.09092,0.56174,0.9034,0.22356,0.87558,1.83008,0.0,0.4746,0.79904,1.6156,0.2555,1.1533,0.67896,0.3777,0.70518,0.37432,2.14162,1.62428,0.78166,0.79596,0.41342,2.80518,0.7011,1.02588,0.32426,0.25028,1.11154,0.7291,0.0,0.81784,0.7112,0.3133,0.27036,0.40392,4.50132,0.4214,0.61674,0.74414,0.63236,1.3294,1.6306,1.56476,1.16494,2.45918,0.69732,2.68742,0.47022,0.7809,0.90198,2.2013,0.52142,1.75098,0.66918,0.3603,0.99972,1.85988,0.88414,0.57728,3.07228,1.55358,1.40898,0.0,1.23746,0.42458,0.22244,0.74374,0.31072,0.17112,1.34004,0.42584,1.50392,0.0,1.7548,4.3357,1.18396,0.83912,0.8332,0.17172,1.08218,0.82288,1.2769,0.95128,0.46612,0.0,0.18986,1.41428,1.83862,0.44968,0.36636,0.07964,2.18648,2.06234,9.07484,0.99752,0.7164,5.15664,2.15326,1.45376,3.93588,1.20256,0.79576,1.7185,0.7677,2.03754,1.394,0.6251,0.95648,1.05642,0.5608,2.92246,0.06682,0.4137,0.37526,0.26128,0.1014,0.13374,0.22844,0.10138,0.33148,0.09394,2.07538,0.2349,0.13764,1.3847,1.03096,1.08834,0.19012,1.2855,0.75562,1.39618,0.08924,0.64882,0.6438,0.76176,0.46806,2.24452,0.1308,0.80278,1.08908,0.59136,1.06594,0.30164,0.56356,0.15738,0.87994,0.32474,1.28952,1.23234,0.4342,0.91278,0.0,0.29558,2.19302,0.4722,0.4313,0.0,1.70128,0.75308,0.87562,0.7602,0.90666,1.32336,0.13514,0.76286,0.557,0.76254,0.22726,0.56138,0.14568,0.69408,1.52242,0.6619,2.28194,1.07992,2.03306,2.42092,0.16156,0.28938,1.94798,0.80256,1.54694,1.24408,0.71654,3.66204,0.32046,0.9131,1.82292,1.73424,0.38666,0.71252,0.1912,0.16532,0.68634,2.35584,0.65596,0.30722,0.2733,0.40618,4.62622,0.18552,0.41704,0.2626,0.34104,0.76644,0.13214,1.0609,0.98666,0.0999,0.37186,0.44428,0.501,0.21352,1.09926,0.23866,0.18372,0.1848,0.54758,0.87634,0.06906,0.1974,,0.13106,3.99546,0.7335,0.53328,,2.30284,0.30136,0.11066,0.13634,0.29678,0.33818,0.86804,0.52324,0.70534,0.76302,0.46184,0.15646,0.09402,0.62296,0.17608,0.5265,1.09392,3.79728,0.24898,0.0776,0.10224,0.31576,2.66236,0.452,0.50984,0.64614,0.13206,0.0512,0.0769,0.24878,0.09662,0.76038,0.09426,0.17396,0.18924,0.07752,0.28176,0.17602,3.77498,6.35892,0.01616,0.77896,2.43876,1.5662,0.39556,0.07574,0.82992,0.12312,0.29428,0.57938,1.53432,0.47168,0.05834,0.46748,0.1068,0.85084,0.95016,0.0927,6.0356,3.43714,,8.16056,0.10222,2.63736,0.66516,0.63664,4.3887,0.84986
2018-04-27,0.80926,0.46596,0.0,0.50364,0.5337,0.3015,0.0864,0.55328,0.79822,0.20724,0.95484,1.7134,0.0,0.41324,0.78846,1.54798,0.29878,1.23578,0.6983,0.31828,0.70432,0.32736,2.65294,1.76186,0.83072,0.77418,0.35784,2.68738,0.63894,0.94584,0.31168,0.24776,1.03892,0.66918,0.0,0.77082,0.67386,0.30036,0.30934,0.36794,3.92,0.42546,0.6879,0.84164,0.61148,1.2237,1.72058,1.69178,1.28758,2.61146,0.63116,2.4451,0.54034,0.97102,1.16956,2.0109,0.55028,2.18358,0.59398,0.34694,1.02744,1.47398,1.10346,0.6047,3.17552,1.59858,1.23426,0.0,1.51556,0.47284,0.26988,0.71102,0.32414,0.17264,1.39846,0.4019,1.74064,0.62296,1.87318,4.1401,1.18308,0.7262,0.8391,0.19648,1.0222,0.7961,1.20332,1.06106,0.43704,0.0,0.19676,1.38344,1.3511,0.4417,0.39528,0.0776,2.70148,1.92376,8.28492,1.1436,0.74402,4.7964,1.78502,1.50306,4.16092,1.41998,0.69428,1.83302,0.75944,1.96796,1.48526,0.71642,1.18862,1.08102,0.48878,2.90238,0.07042,0.41014,0.44728,0.21972,0.1045,0.13516,0.2304,0.0874,0.30744,0.08522,1.9069,0.23768,0.14514,1.25804,1.00188,1.13852,0.2073,1.36118,0.9245,1.44614,0.07934,0.59198,0.60878,0.8131,0.4437,2.18994,0.13436,0.83522,0.87724,0.59848,0.93654,0.30082,0.54506,0.15118,0.96814,0.3504,1.21466,1.25992,0.44262,0.6446,0.0,0.26782,2.15542,0.48402,0.42982,0.0,1.97976,0.6638,0.82676,0.78138,0.9234,1.17902,0.13408,0.77468,0.51952,0.68604,0.24922,0.5331,0.14528,0.92124,1.45278,0.62094,2.45402,0.95404,2.23474,2.25946,0.18086,0.31548,1.97454,0.83842,1.41074,0.99432,0.76722,3.38966,0.42808,0.9402,1.79222,1.7006,0.3514,0.94844,0.17398,0.16072,0.67032,2.23012,0.62518,0.3334,0.27056,0.42344,4.02412,0.1903,0.44798,0.27448,0.35104,1.01916,0.14544,1.58908,1.00208,0.1014,0.34226,0.38318,0.70566,0.19936,1.15638,0.23254,0.17708,0.18758,0.59288,0.84754,0.05726,0.21456,,0.13446,3.66272,0.6786,0.46482,,2.60702,0.3081,0.10874,0.12802,0.2326,0.34384,0.70518,0.3993,0.67844,0.71962,0.4659,0.1528,0.08492,0.99212,0.15636,0.5206,1.21446,3.74532,0.22756,0.07362,0.1043,0.31258,1.99004,0.44866,0.5246,0.59152,0.10518,0.04944,0.06958,0.22026,0.0911,0.82292,0.0834,0.15318,0.20814,0.06862,0.23998,0.15274,3.32914,5.35398,0.01616,0.80418,2.2441,1.30362,0.42484,0.07266,0.75288,0.11706,0.2732,0.4601,1.54038,0.47648,0.05348,0.42024,0.09548,0.79814,0.963,0.08946,5.28642,2.64266,,8.56534,0.10256,2.44954,0.63992,0.81024,4.10156,0.7837
2018-05-02,0.82348,0.5062,0.0,0.496,0.50174,0.33478,0.11134,0.55888,0.91756,0.2233,1.01702,1.45388,0.0,0.7869,0.84798,1.3181,0.39224,1.52618,0.75404,0.32716,0.72762,0.31398,2.7235,1.92808,0.88062,0.90162,0.33686,2.14824,0.59386,0.82918,0.31436,0.25202,0.97826,0.67566,0.0,0.76052,0.74426,0.33112,0.31424,0.39022,3.27938,0.4233,0.76328,0.88594,0.60898,1.14278,1.60632,2.01806,1.02988,2.40014,0.6924,2.2989,0.64824,0.995,1.20332,1.93272,0.53366,2.43974,0.62204,0.36554,1.02968,1.40594,0.98012,0.69168,2.78506,1.5631,0.95034,0.0,1.6307,0.51448,0.29612,0.77294,0.32632,0.17158,1.51342,0.41694,1.95342,0.86622,1.67156,3.78958,1.07244,0.6156,0.68166,0.20352,1.22646,1.0047,1.18666,1.1643,0.58828,0.0,0.19712,1.23796,1.14146,0.435,0.3632,0.0829,2.7488,1.79232,8.44002,1.13094,0.74318,4.00056,1.67878,1.55214,4.27404,1.78728,0.6991,2.09932,0.78108,1.69816,1.76154,0.73628,1.46694,0.93216,0.43854,2.64882,0.10682,0.37632,0.5064,0.22306,0.10958,0.17132,0.25402,0.08916,0.3746,0.08298,1.82512,0.32552,0.14034,1.25774,1.0384,1.1697,0.22562,1.10902,0.92382,1.13216,0.09924,0.54952,0.72188,0.73634,0.39542,1.95914,0.13952,0.91612,0.76662,0.70184,0.84974,0.31942,0.4712,0.16428,1.01794,0.3682,1.27408,1.48042,0.42696,0.63356,0.0,0.29212,2.13504,0.51574,0.44266,0.0,2.46304,0.693,0.65796,0.87576,0.9544,1.07386,0.17652,0.72334,0.49256,0.91102,0.32478,0.64434,0.14092,1.0181,1.3032,0.57954,2.52354,0.70818,2.26664,3.30772,0.28066,0.37534,1.99572,0.8523,1.15634,0.89072,0.67312,3.31576,0.51322,1.04872,1.75154,1.68764,0.37714,0.97674,0.21098,0.15256,0.64406,2.26902,0.66,0.3418,0.31612,0.47232,3.1539,0.20762,0.50514,0.29786,0.37254,1.15814,0.16028,1.93518,0.89196,0.12502,0.3468,0.36964,0.92032,0.2202,1.12068,0.23564,0.18088,0.22246,0.66932,0.8255,0.05328,0.2497,,0.13982,3.57718,0.67734,0.45246,,2.63242,0.331,0.1183,0.1492,0.21836,0.3619,0.61736,0.3637,0.6804,0.68746,0.46226,0.14234,0.08894,1.02248,0.16484,0.56162,1.20276,2.64036,0.23372,0.08198,0.09938,0.34924,1.83914,0.43868,0.57786,0.55756,0.11526,0.05166,0.06648,0.24784,0.09554,0.88134,0.0835,0.15114,0.23494,0.06822,0.33114,0.1533,3.18102,5.4153,0.01652,0.79796,2.24654,1.34298,0.60944,0.07874,0.78426,0.1394,0.26696,0.44788,1.4618,0.5107,0.0595,0.3606,0.09882,0.66554,1.55398,0.10048,5.19248,1.81762,,9.70184,0.1027,2.47576,0.61006,0.90624,3.80274,0.82518
2018-05-03,0.80224,0.52114,0.0,0.48198,0.53404,0.34598,0.1501,0.53816,0.86324,0.22778,1.03048,1.32102,0.0,0.99852,0.887,1.2619,0.47632,1.55212,0.70968,0.30812,0.626,0.29086,2.80678,1.97926,0.74016,0.99178,0.3019,2.14566,0.619,0.84142,0.32036,0.27276,0.83202,0.6452,0.0,0.73074,0.71796,0.31718,0.32322,0.30016,3.02996,0.40764,0.7254,0.9194,0.52476,1.07062,1.23458,2.18298,1.07374,2.09468,0.69886,2.02272,0.58958,1.05422,1.2978,1.83638,0.51642,2.6103,0.7141,0.34486,0.80758,1.3691,0.97122,0.70012,2.38104,1.61606,1.02536,0.0,1.52752,0.5386,0.3209,0.73548,0.34364,0.13352,1.35412,0.38314,2.06752,1.08764,1.48656,3.33456,0.9821,0.57624,0.63578,0.20578,1.25646,1.28442,1.18252,1.4024,0.62092,0.0,0.24292,1.18784,0.85242,0.45806,0.31422,0.09782,2.80456,1.88744,7.85916,1.12806,0.7657,4.02364,1.6312,1.56366,4.3604,1.74752,0.65506,2.29768,0.73914,1.68586,1.72748,0.7936,1.63018,0.8479,0.50662,2.52864,0.11508,0.34338,0.51246,0.2363,0.11766,0.17646,0.23648,0.09034,0.32748,0.07472,1.8415,0.393,0.13588,1.2986,1.0372,1.006,0.19184,1.16978,0.81326,0.93238,0.1142,0.50348,0.67946,0.6895,0.39174,1.79934,0.13356,1.0933,0.7393,0.77988,0.81328,0.29358,0.44612,0.16742,0.91906,0.36836,1.0405,1.47468,0.411,0.54246,0.0,0.3072,2.0701,0.50118,0.51972,0.0,2.54218,0.66082,0.5466,0.98972,0.97736,0.81928,0.32804,0.69326,0.38424,0.94854,0.35894,0.72752,0.14382,1.05532,1.13814,0.5607,2.52486,0.62258,2.28112,4.2556,0.30218,0.3579,2.03386,0.8837,1.0026,0.79888,0.7462,3.10536,0.4923,0.90842,1.73756,1.4491,0.38722,0.87378,0.21358,0.14508,0.5785,2.40244,0.79906,0.3859,0.30814,0.45042,3.24522,0.2161,0.41242,0.3139,0.37518,1.3053,0.16614,1.97888,0.95038,0.13066,0.39788,0.33052,0.90544,0.25276,1.08946,0.27014,0.2473,0.2275,0.63098,0.75448,0.05834,0.34302,,0.11722,4.1868,0.6764,0.43908,,2.18292,0.3265,0.1169,0.137,0.38316,0.38942,0.6411,0.3692,0.46826,0.66994,0.44224,0.1361,0.07156,0.9712,0.1509,0.58922,1.09144,1.94606,0.2735,0.07914,0.07084,0.39098,1.30326,0.38766,0.57476,0.48754,0.11576,0.05088,0.06496,0.2562,0.08908,0.96814,0.09424,0.13708,0.24672,0.06512,0.32898,0.13578,2.8995,4.91902,0.01582,0.7665,2.45128,1.7273,0.74,0.0782,0.81034,0.20418,0.25496,0.42662,1.13478,0.50832,0.05328,0.33458,0.09576,0.62994,1.58784,0.09202,4.63484,2.1785,,10.09382,0.09928,2.49044,0.56106,0.96126,4.10522,0.7421
2018-05-04,0.7999,0.52686,0.0,0.51414,0.50822,0.35296,0.15972,0.51902,0.87606,0.30212,1.03236,1.18994,0.0,1.08028,0.93974,0.91816,0.52548,1.6456,0.72314,0.29228,0.55408,0.28002,2.95154,1.86512,0.75336,1.05268,0.28994,2.05114,0.61528,0.84494,0.31032,0.2789,0.84396,0.58582,0.0,0.67196,0.73064,0.3198,0.29784,0.30282,2.57476,0.39662,0.82964,0.8093,0.51328,1.01374,1.26522,2.4226,1.07658,1.70014,0.68164,1.52498,0.54206,0.95658,1.33716,1.5124,0.51322,1.70882,0.72808,0.40532,0.73334,1.30244,0.9937,0.73706,2.16844,1.6232,1.0819,0.0,1.54488,0.5354,0.37696,0.85176,0.3141,0.1149,1.56228,0.3655,2.08538,1.40504,1.58088,3.2933,1.00432,0.75476,0.57276,0.21278,1.38846,1.63216,0.71326,1.49188,0.6292,0.0,0.30528,1.06074,0.7117,0.44798,0.3585,0.09992,2.77188,1.84872,7.23614,1.03308,0.70218,3.82242,1.49248,1.43762,4.41016,1.66158,0.57994,2.2176,0.70322,1.68118,1.70226,0.78098,1.70652,1.02548,0.53736,2.34116,0.11948,0.3361,0.54858,0.24896,0.11122,0.18376,0.20892,0.0994,0.33522,0.07366,1.86078,0.44342,0.12546,1.15976,1.05576,1.0356,0.20018,1.17026,0.81284,0.84834,0.12782,0.42776,0.68398,0.66938,0.4005,1.25532,0.13312,1.19832,0.78644,0.7442,0.74658,0.3228,0.73254,0.17352,0.86914,0.3214,0.95228,1.42052,0.42806,0.54282,0.0,0.25302,2.09818,0.43874,0.51674,0.0,2.62098,0.72752,0.47248,1.06602,1.0201,0.72528,0.40506,0.6437,0.36752,0.99492,0.372,0.79658,0.1448,0.98272,1.08782,0.54746,2.41254,0.60268,1.98712,4.69634,0.36722,0.3772,1.9795,0.96376,0.99912,0.77834,0.72602,2.96234,0.47878,0.9606,1.52644,1.47046,0.42434,0.83008,0.25536,0.14094,0.57042,2.11606,0.867,0.37762,0.29594,0.51108,3.0942,0.21428,0.4357,0.2976,0.37876,1.26716,0.1821,2.04432,0.83986,0.12806,0.39708,0.2966,0.9263,0.25636,1.166,0.27738,0.26302,0.27264,0.65138,0.75942,0.06158,0.37818,,0.11408,4.33092,0.57228,0.43824,,2.11492,0.28984,0.11866,0.13448,0.50958,0.40866,0.53456,0.35658,0.47576,0.66772,0.4593,0.12112,0.07062,0.9645,0.14788,0.58854,1.10942,1.41748,0.28158,0.07932,0.06514,0.4084,1.18142,0.4015,0.58262,0.48178,0.11504,0.05246,0.0502,0.25426,0.08544,1.03524,0.09642,0.13556,0.26824,0.06808,0.3242,0.13256,2.84662,4.81036,0.01584,0.7039,2.48512,1.85094,0.81834,0.08902,0.77482,0.2187,0.28398,0.38932,1.07638,0.51704,0.0515,0.35292,0.1064,0.63232,1.61124,0.09042,5.2125,2.25666,,10.43948,0.09298,2.67402,0.5313,0.9993,3.99562,0.6856
2018-05-07,0.81174,0.44372,0.0,0.50838,0.49616,0.38348,0.17496,0.5063,0.91926,0.33584,1.0459,1.13038,0.0,1.18016,1.09046,0.85308,0.7403,2.12084,0.70204,0.28298,0.57726,0.29818,2.01232,1.8465,0.78122,1.14754,0.27466,1.88324,0.62678,0.82752,0.30756,0.28456,0.95838,0.56924,0.0,0.6165,0.76276,0.31578,0.27308,0.32146,2.31666,0.40308,0.79924,0.86232,0.5959,0.96834,1.38,2.72902,1.09992,1.67956,0.82008,1.54152,0.5485,0.9533,1.15508,1.32698,0.46328,1.34078,0.74432,0.4411,0.72906,1.31366,0.99954,0.69316,1.94222,1.4751,1.08764,0.0,1.3108,0.59606,0.45574,0.91112,0.2955,0.11344,1.77022,0.34162,1.76216,1.603,1.33738,3.87716,1.29496,0.82372,0.51884,0.22578,1.48584,1.71274,0.701,1.67586,0.66854,0.0,0.34486,1.21992,0.59026,0.43192,0.37628,0.09986,3.0112,1.82198,6.8329,1.0608,0.69126,4.01282,1.63618,1.4648,4.49226,1.87754,0.584,2.25524,0.7165,1.5474,1.67914,0.75396,1.76846,1.06532,0.61804,2.42184,0.12604,0.35384,0.56918,0.2773,0.12494,0.1954,0.20408,0.10018,0.33256,0.06508,1.79202,0.49196,0.13834,1.05694,1.08052,0.97272,0.19708,1.51144,0.84558,0.85062,0.1287,0.38754,0.7349,0.74486,0.41046,1.01906,0.12652,1.25892,0.80938,0.7508,0.61548,0.35224,1.09964,0.169,0.87964,0.2459,1.08508,1.38046,0.42934,0.56692,0.0,0.26908,2.12004,0.39268,0.57346,0.0,2.8296,0.82368,0.45056,1.39274,1.09386,0.73888,0.46294,0.61174,0.35878,1.0852,0.41834,0.88608,0.1496,1.0429,1.16154,0.50104,2.19434,0.56766,1.68762,5.18208,0.43466,0.45768,1.25344,1.0274,0.98488,0.75142,0.8209,3.17944,0.49914,0.9551,1.68048,1.5553,0.42566,0.8152,0.29594,0.16358,0.53244,1.5729,0.90928,0.3836,0.29746,0.54962,3.14482,0.206,0.62386,0.30098,0.40132,1.40074,0.20908,2.40782,0.76754,0.12908,0.4026,0.28772,0.98298,0.2802,1.32784,0.28698,0.26138,0.29596,0.6158,0.80266,0.0575,0.4122,,0.12776,4.21412,0.5239,0.56436,,2.28152,0.28158,0.12728,0.14084,0.65758,0.4372,0.3604,0.3685,0.62452,0.73264,0.49088,0.1273,0.07246,0.96226,0.14806,0.47126,1.1431,0.8896,0.29854,0.06626,0.06412,0.42698,1.0739,0.43726,0.65898,0.3794,0.1236,0.05702,0.04664,0.24618,0.08434,1.10646,0.09928,0.1304,0.2953,0.06558,0.33954,0.13906,2.84644,4.82264,0.0205,0.75676,2.36876,1.96498,0.9277,0.1107,0.746,0.22908,0.2731,0.40808,1.08998,0.45672,0.04862,0.35942,0.11738,0.43652,1.88944,0.08398,6.3061,2.06352,,11.07662,0.09462,2.74244,0.70308,1.05692,3.61544,0.71298
2018-05-08,0.65374,0.50008,0.0,0.4711,0.50684,0.37722,0.17852,0.51238,0.98148,0.37712,0.85798,1.16378,0.0,1.365,1.14174,0.92096,0.81106,2.2307,0.70348,0.2825,0.77706,0.33418,1.57426,1.80576,0.76784,1.14918,0.27918,1.90154,0.66688,0.85336,0.3391,0.29948,0.9772,0.58048,0.42626,0.61734,0.78864,0.34194,0.24904,0.33988,2.18772,0.40088,0.74094,0.80386,0.68006,1.04064,1.50814,2.91852,1.01706,1.63156,0.83288,1.73362,0.48994,0.7926,1.05728,1.49464,0.52306,1.13054,0.98342,0.51722,0.76816,1.39258,0.78082,0.6863,1.77358,1.35436,1.17402,0.0,1.05966,0.58424,0.4516,0.9474,0.27678,0.19026,1.70828,0.35574,1.7096,1.20828,1.2663,4.585,1.44384,0.93516,0.5245,0.20638,1.55214,1.76582,0.73912,1.66554,0.73732,0.0,0.36014,1.33262,0.56968,0.43872,0.34958,0.10542,2.79694,1.9205,6.91548,0.88862,0.6584,3.82416,1.59024,1.53802,4.0551,1.60952,0.6494,2.33648,0.7456,1.71706,1.65062,0.74336,1.49372,1.09756,0.69344,3.76352,0.12986,0.40628,0.50612,0.26564,0.14174,0.21374,0.20658,0.1,0.32404,0.06376,1.84464,0.5225,0.14366,0.99916,1.1619,1.04562,0.1901,1.58894,0.73934,0.93876,0.12898,0.36638,0.76494,0.86494,0.39182,0.84376,0.13278,1.30122,0.87812,0.70504,0.67992,0.35454,1.37438,0.1689,0.80882,0.2153,1.07428,1.26228,0.3808,0.57266,0.0,0.31168,2.06998,0.35748,0.64802,0.0,2.67966,1.0026,0.49862,1.48486,1.22024,0.8463,0.4932,0.5219,0.38054,1.15386,0.44984,0.92408,0.1612,0.8863,1.18402,0.48396,1.94816,0.56462,1.25306,5.62064,0.4557,0.46498,1.16074,1.07296,1.02666,0.76002,0.77302,2.99124,0.38004,0.95838,1.8894,1.54728,0.43906,0.54804,0.30518,0.16728,0.54404,1.4217,0.91174,0.36994,0.27524,0.55178,3.20484,0.19838,0.67504,0.29112,0.40078,1.2337,0.1852,2.05694,0.68808,0.12768,0.42466,0.33342,0.81888,0.28248,1.39256,0.24438,0.26644,0.29144,0.56042,0.98344,0.06278,0.3978,,0.12474,4.29168,0.50872,0.61728,,2.09914,0.28766,0.12826,0.1569,0.75138,0.4154,0.263,0.40156,0.6804,0.82328,0.50036,0.12538,0.08056,0.69858,0.16792,0.41472,1.2027,0.0,0.31234,0.05252,0.07176,0.44216,1.08958,0.4942,0.75538,0.34578,0.13802,0.07064,0.0458,0.27198,0.0799,1.1127,0.1031,0.12186,0.28102,0.0622,0.34948,0.15628,3.53282,5.39612,0.02134,0.68988,2.45332,2.11902,0.89512,0.12226,0.80036,0.24344,0.26002,0.52844,1.25068,0.39212,0.05684,0.35126,0.11842,0.3372,1.96532,0.08482,7.37406,2.29912,0.0057,9.8402,0.10706,2.8277,0.72852,1.03428,3.1072,0.75114
2018-05-09,0.5872,0.50252,0.0,0.46006,0.52846,0.3505,0.15346,0.47066,0.8329,0.39836,0.81592,1.19658,0.0,1.05832,1.16608,0.98164,0.7539,2.14978,0.65424,0.22784,0.81776,0.36996,1.45678,1.57254,0.7173,1.01622,0.28378,1.889,0.66808,0.7777,0.37766,0.28964,0.91272,0.63006,0.67752,0.61634,0.71538,0.30366,0.24878,0.29734,2.11124,0.38974,0.69402,0.72826,0.7318,1.08426,1.60382,2.7023,1.0593,1.5048,0.77054,1.79628,0.37474,0.74612,1.00476,1.47152,0.5665,1.20532,0.92904,0.49402,0.82258,1.38106,0.72068,0.67112,1.81784,1.27778,1.15522,0.0,0.90618,0.53488,0.42248,0.8778,0.29384,0.19884,1.65152,0.2918,1.52738,1.12904,1.17622,5.17578,1.6418,0.95706,0.53534,0.23964,1.36812,1.6964,0.71912,1.52192,0.57346,0.0,0.37042,1.44672,0.56386,0.45852,0.3746,0.11154,2.66266,1.95096,6.78302,0.81406,0.60816,3.86556,1.58258,1.47796,3.71668,1.37118,0.6003,2.0401,0.6731,1.99918,1.34726,0.76694,1.15442,1.06124,0.67844,4.73318,0.0937,0.42642,0.48926,0.2639,0.13798,0.17656,0.1789,0.09428,0.26322,0.05824,1.85864,0.43286,0.15734,0.96424,1.1379,1.03752,0.16412,1.73822,0.73974,0.97416,0.1034,0.32166,0.65506,0.88286,0.38072,0.74952,0.1309,1.30458,0.87462,0.63706,0.60046,0.34476,1.49186,0.16392,0.73908,0.19088,0.98952,1.14686,0.34304,0.50088,0.0,0.28912,1.48634,0.31414,0.60482,0.0,2.1582,1.13534,0.55014,1.47006,1.66128,0.87388,0.48078,0.49886,0.40572,0.9146,0.42926,0.82552,0.1561,0.75446,1.23032,0.39238,1.64472,0.56184,1.09238,5.52114,0.42574,0.4046,1.07998,1.03346,0.98164,0.78236,0.7274,2.8087,0.27618,0.83226,2.04912,1.5615,0.37824,0.5205,0.27202,0.20616,0.55428,1.26558,1.049,0.3914,0.20792,0.44096,3.0825,0.17574,0.6814,0.27754,0.4003,1.13868,0.18226,1.75786,0.68728,0.09896,0.42094,0.32572,0.67494,0.26976,1.7966,0.24854,0.26284,0.27462,0.4576,0.92024,0.06522,0.38178,,0.13242,4.30724,0.49574,0.64244,,1.84184,0.25896,0.12116,0.14128,0.84446,0.38506,0.15128,0.38862,0.67656,0.86638,0.44354,0.13088,0.07218,0.65714,0.1572,0.3804,1.19726,0.0,0.29416,0.03234,0.06986,0.42922,0.99624,0.50384,0.7311,0.3414,0.12978,0.06944,0.05202,0.23636,0.07096,1.0699,0.1011,0.12292,0.25568,0.05894,0.277,0.15454,3.98944,5.33874,0.02688,0.63404,2.52242,2.24494,0.72962,0.1244,0.74724,0.23172,0.2529,0.55274,1.21262,0.32902,0.04916,0.34502,0.10838,0.31676,1.42208,0.0647,8.04394,2.51402,0.00966,9.14692,0.09442,2.79868,0.70694,1.01426,2.91062,0.69814
2018-05-10,0.50106,0.4793,0.0,0.47876,0.48132,0.32518,0.12236,0.44352,0.76054,0.39946,0.75844,1.1882,0.0,0.88674,1.0737,0.93034,0.7143,2.10832,0.66784,0.20294,0.8539,0.34168,1.33284,1.41282,0.74628,0.90438,0.30068,1.57136,0.53326,0.80122,0.36806,0.2192,0.84336,0.69714,0.86204,0.6013,0.696,0.30684,0.25224,0.28418,1.94124,0.39666,0.68388,0.74058,0.79236,1.06748,1.73712,2.55754,1.53308,1.28778,0.68604,1.67872,0.30884,0.76814,0.88902,1.43962,0.59442,1.19692,0.88446,0.42988,0.83904,1.33314,0.65758,0.6835,1.69532,1.15594,1.07668,0.0,0.83024,0.58954,0.41672,0.84714,0.26216,0.19866,1.65204,0.30022,1.53012,1.10736,1.2746,5.7536,1.73528,0.85502,0.58038,0.2592,1.38016,1.41644,0.8643,1.26304,0.51666,0.0,0.31768,1.39412,0.55236,0.39156,0.42648,0.10232,2.8164,1.69734,6.1598,0.90416,0.67114,3.50922,1.48654,1.5421,3.2185,1.31868,0.58968,2.14668,0.62758,1.99722,1.57456,0.72076,1.03926,1.0775,0.59014,4.85014,0.08044,0.49082,0.54042,0.2496,0.1353,0.16296,0.16982,0.0861,0.23202,0.05728,1.61734,0.40516,0.15412,0.89726,1.01366,0.96094,0.16416,1.71736,0.68502,0.96348,0.11748,0.35464,0.63642,0.9226,0.33722,0.6529,0.12218,1.116,0.85516,0.50558,0.56222,0.33386,1.62206,0.17036,0.66936,0.18478,1.11622,1.2031,0.30786,0.44346,0.0,0.28034,1.34898,0.43082,0.518,0.0,2.13122,1.1774,0.54372,1.61256,1.8908,0.9263,0.36118,0.4514,0.4164,0.86726,0.40846,0.7431,0.1533,0.75482,1.12004,0.38068,1.55948,0.53182,0.96884,4.89214,0.45926,0.40932,1.0993,1.00756,0.9363,0.74202,0.62904,2.84074,0.31476,0.76636,2.20058,1.51338,0.37692,0.49618,0.27168,0.22888,0.53564,1.10718,0.9546,0.37446,0.20646,0.42286,3.02464,0.1454,0.70184,0.27614,0.34466,1.0832,0.16808,1.60622,0.64928,0.10894,0.3347,0.33178,0.66166,0.21514,1.99982,0.2206,0.19436,0.26032,0.42174,0.8792,0.06764,0.31176,,0.14386,3.3374,0.42394,0.64388,,1.80242,0.24212,0.1286,0.14098,0.75396,0.33044,0.0,0.34284,0.79322,1.4254,0.42934,0.12354,0.06938,0.63296,0.154,0.33794,1.08834,0.0,0.23854,0.01178,0.07008,0.38708,0.95242,0.486,0.76852,0.35136,0.12032,0.06654,0.05422,0.20266,0.06814,0.92088,0.09988,0.1172,0.24936,0.05562,0.28846,0.14216,4.0042,5.11654,0.03106,0.59278,2.16014,1.8939,0.63102,0.13178,0.68026,0.17464,0.24342,0.52518,1.24418,0.3279,0.0445,0.32756,0.10938,0.30602,1.35464,0.0581,8.93004,1.95242,0.0113,9.7374,0.09808,2.3708,0.73544,1.00632,2.38656,0.6459
2018-05-11,0.50836,0.45724,0.0,0.47208,0.47648,0.33198,0.1122,0.40184,0.73374,0.33266,0.97638,1.13308,0.0,0.8401,1.03566,0.96822,0.77148,2.244,0.66784,0.2413,0.88318,0.3905,1.22622,1.29598,0.68464,0.77582,0.29696,1.4834,0.48838,0.7347,0.38368,0.20758,0.80338,0.80198,1.02154,0.63052,0.70842,0.3243,0.2554,0.3038,1.85844,0.38178,0.59992,0.73318,1.00656,1.1931,1.74336,2.35348,2.16582,1.219,0.74122,1.77868,0.31552,0.7511,0.93324,1.71876,0.57946,1.27446,0.87188,0.39916,0.85274,1.31476,0.57214,0.6826,1.62486,1.23024,0.9904,0.0,0.75124,0.68362,0.39582,0.70448,0.2767,0.238,1.5635,0.30716,1.52524,0.9515,1.23662,6.58342,2.10474,0.69232,0.56102,0.26916,1.28844,1.07512,1.02606,1.0893,0.59152,0.0,0.25604,1.4111,0.56514,0.38522,0.4391,0.09992,2.9588,1.7559,6.90662,0.89456,0.7547,3.52388,1.56508,1.5148,3.16504,1.44532,0.65192,2.2613,0.60286,2.01026,1.7524,0.74558,1.02584,0.90076,0.57658,5.3625,0.08026,0.52314,0.57612,0.24424,0.13232,0.1646,0.16234,0.07038,0.22882,0.054,1.5488,0.35838,0.15052,0.86896,0.99356,0.98548,0.17362,1.67022,0.64314,0.85438,0.11488,0.37542,0.6302,0.97894,0.315,0.60394,0.12506,1.0309,0.92368,0.44534,0.5483,0.32364,1.41124,0.15902,0.6415,0.1838,1.32556,1.256,0.2677,0.40484,0.0,0.28472,1.36576,0.56484,0.49322,0.0,2.08,1.10952,0.53732,1.69456,1.99916,1.23188,0.31032,0.3994,0.41782,0.87718,0.42448,0.70654,0.15766,0.82694,1.02076,0.37812,1.4267,0.49524,0.9498,4.65096,0.42498,0.41214,1.0456,1.07152,0.87864,0.96738,0.62904,2.787,0.32746,0.76768,2.10958,1.41058,0.36116,0.48974,0.22938,0.23394,0.53672,0.9836,0.97036,0.35708,0.20028,0.36734,2.96538,0.1487,0.72852,0.27864,0.33008,1.00476,0.17682,1.61096,0.62042,0.11484,0.31308,0.35586,0.64924,0.19752,2.14394,0.245,0.18476,0.22296,0.49454,0.95004,0.06498,0.29314,,0.1489,3.22194,0.40432,0.63594,,1.80698,0.24438,0.12472,0.14482,0.67026,0.2991,0.45766,0.3558,0.8569,1.57412,0.40678,0.12188,0.07368,0.63492,0.16596,0.3459,1.01474,0.0,0.22808,0.0,0.081,0.35928,0.9668,0.47974,0.8985,0.35614,0.12508,0.06474,0.07396,0.22284,0.07644,0.85352,0.09342,0.11448,0.2474,0.05688,0.30142,0.14712,4.12542,5.22014,0.03496,0.6048,2.16032,1.81916,0.56964,0.12712,0.71858,0.16064,0.2202,0.53816,1.45488,0.31886,0.04808,0.32046,0.09856,0.28766,1.31592,0.05776,8.5139,1.65346,0.0219,9.86012,0.11962,2.85516,0.84126,1.0328,2.11502,0.97898
2018-05-14,0.51604,0.44538,0.0,0.4761,0.49588,0.30376,0.09548,0.41228,0.79018,0.31406,1.08148,0.98306,0.0,0.80336,0.88666,0.95136,0.60316,1.91602,0.5826,0.23372,0.87868,0.38324,1.1398,1.277,0.65628,0.64558,0.2722,1.43536,0.43658,0.84146,0.40534,0.19208,0.66916,0.81288,1.20628,0.68714,0.64754,0.30994,0.2685,0.29038,1.84496,0.4023,0.5837,0.70616,0.97934,1.18318,1.64708,2.2138,2.70232,1.1778,0.82412,1.88304,0.27172,0.74016,0.96192,1.48916,0.56666,1.50116,0.78678,0.42016,0.79878,1.35196,0.53646,0.6794,1.96532,1.31376,0.97076,0.0,0.63632,0.65714,0.34524,0.60608,0.28602,0.23754,1.32606,0.36486,1.49228,0.93614,1.3271,6.41612,2.12176,0.63786,0.56366,0.26254,1.18896,1.05858,1.06752,0.86146,0.57626,0.0,0.22994,1.31992,0.66026,0.36324,0.5475,0.09146,3.14882,1.71608,6.51648,0.83842,0.76966,4.21592,1.37592,1.41176,3.0686,1.35128,0.6931,2.26482,0.60552,2.3199,1.7076,0.68426,0.93716,0.92648,0.56656,5.82194,0.07766,0.58734,0.58002,0.23086,0.1205,0.17596,0.16094,0.06486,0.23818,0.0548,1.46004,0.32298,0.14964,0.92546,1.02088,0.88584,0.2124,1.34948,0.55954,0.80626,0.11872,0.40028,0.57456,0.93094,0.27682,0.55874,0.12786,0.94846,0.90782,0.43714,0.54584,0.30048,1.104,0.1532,0.68134,0.18226,1.25538,1.2009,0.28026,0.36886,0.0,0.29022,1.3763,0.58838,0.424,0.0,1.98806,1.01434,0.54692,1.4696,2.24472,1.28622,0.27204,0.37278,0.37478,0.73208,0.41266,0.6272,0.15624,0.87658,0.83988,0.35744,1.474,0.4963,1.06896,4.58798,0.37922,0.35708,1.20374,1.14036,0.8808,1.03524,0.66484,2.58742,0.30042,0.72846,1.94684,1.29826,0.55052,0.50466,0.19686,0.2117,0.58226,1.19936,0.92146,0.35172,0.18046,0.36314,3.00332,0.15132,0.61816,0.26568,0.31292,1.01894,0.20238,1.504,0.63786,0.11534,0.28176,0.37272,0.6947,0.16762,2.1606,0.23678,0.17344,0.2302,0.61174,0.94168,0.06252,0.27174,,0.14376,3.0425,0.42734,0.50744,,1.58744,0.2345,0.12092,0.14524,0.63642,0.27472,0.66306,0.36118,0.72424,1.63632,0.4131,0.11422,0.07434,0.60802,0.17092,0.32054,0.96534,0.0,0.21732,0.0,0.08532,0.33536,0.9149,0.44562,0.93868,0.36648,0.12906,0.05878,0.085,0.22724,0.07908,0.75738,0.08446,0.10644,0.22308,0.05374,0.2987,0.1442,3.9392,6.22624,0.0312,0.56656,2.09602,1.68362,0.55542,0.11388,0.69484,0.14658,0.21666,0.51708,1.53938,0.3069,0.0532,0.34906,0.08146,0.27996,1.2126,0.06526,7.8914,1.50796,0.03604,8.45024,0.12314,3.30186,0.69814,1.0186,2.08952,1.16478
2018-05-15,0.43282,0.39376,0.0,0.42734,0.47688,0.38406,0.09504,0.36804,0.7301,0.29292,1.14604,1.50264,0.0,0.7016,1.08912,0.87012,0.54656,1.82562,0.56292,0.21914,0.78302,0.36716,0.96916,1.24254,0.60654,0.65808,0.26862,1.33106,0.37212,0.7808,0.40702,0.18192,0.71534,0.84708,0.91526,0.6851,0.62034,0.33908,0.28618,0.27826,1.92352,0.40636,0.64768,0.72348,0.93834,1.43964,1.46632,2.0138,3.03384,1.02616,0.87904,1.5502,0.30124,0.83516,0.8905,0.9984,0.49404,1.68596,0.51058,0.35738,0.69338,1.56642,0.4654,0.75702,2.00802,1.23086,0.86558,0.0,0.55288,0.65792,0.29612,0.6321,0.27224,0.1869,1.36786,0.3418,1.30194,0.81272,1.24408,6.08026,2.14526,0.48812,0.53288,0.27116,1.29362,1.19472,1.21578,0.91998,0.53092,0.0,0.19612,1.10422,0.64524,0.35308,0.6153,0.08076,3.19122,1.6403,7.12922,0.84956,0.77386,4.67326,1.2841,1.31222,3.08462,1.32602,0.73018,2.32364,0.73708,2.2201,1.6767,0.62922,0.87882,0.90834,0.48414,5.13992,0.07392,0.56824,0.53682,0.23312,0.12378,0.16002,0.15406,0.05922,0.26446,0.05536,2.611,0.33564,0.13292,0.90646,0.91504,0.9509,0.1956,1.18546,0.44488,0.73322,0.12004,0.3934,0.55242,0.89484,0.2673,0.51108,0.11928,0.7286,0.8582,0.4023,0.56502,0.2916,0.82348,0.14862,0.66502,0.17296,1.30868,1.27814,0.30012,0.33352,0.0,0.28402,1.38446,0.60842,0.56044,0.0,2.0275,1.0465,0.47606,1.49836,2.3219,1.22656,0.25708,0.2995,0.31184,0.662,0.41422,0.60658,0.14886,0.86864,0.76908,0.3297,1.58786,0.461,1.18332,5.03296,0.47426,0.31816,1.26058,1.15438,0.85386,1.0064,0.73516,2.52052,0.2817,0.68586,1.6548,1.19486,0.62388,0.5678,0.21202,0.19314,0.57176,1.14542,0.89284,0.3458,0.17558,0.34716,2.8117,0.15462,0.54592,0.23884,0.3098,1.09366,0.2482,1.35484,0.55818,0.12906,0.262,0.32758,0.71044,0.15298,2.06044,0.26942,0.16016,0.23732,0.62982,0.8369,0.0519,0.2564,,0.14276,2.93624,0.4209,0.4827,,1.2337,0.19346,0.11056,0.1267,0.6288,0.26826,0.85488,0.32452,0.72116,1.68716,0.39446,0.12168,0.0718,0.4889,0.1549,0.2826,0.72824,0.0,0.1985,0.0,0.07654,0.30418,0.9391,0.32552,0.92618,0.54222,0.14146,0.0404,0.08452,0.19006,0.08434,0.78276,0.13028,0.10016,0.22736,0.05596,0.2882,0.13086,3.34756,6.14776,0.02926,0.55824,1.95772,1.66038,0.53844,0.10738,0.6596,0.1637,0.2306,0.38404,1.36672,0.30406,0.04448,0.35328,0.08298,0.271,1.09818,0.06606,7.90814,1.41578,0.04756,8.22488,0.11476,3.34612,0.69778,1.12088,2.22636,1.23468
2018-05-16,0.44308,0.3864,0.0,0.41378,0.44748,0.40032,0.09162,0.3898,0.7645,0.2677,1.0682,1.86984,0.0,0.80732,1.06104,0.72956,0.61678,1.67974,0.6049,0.22372,0.74124,0.3347,0.89752,1.30492,0.5847,0.72218,0.24584,1.37422,0.37294,0.88148,0.38278,0.17716,0.73604,0.78904,0.8512,0.69142,0.64778,0.44816,0.3098,0.2714,1.98278,0.4315,0.66878,0.78072,0.88238,1.5586,1.37868,1.9871,3.54098,1.10496,0.88704,1.42444,0.34168,0.87402,0.93794,0.74574,0.52202,1.4823,0.55568,0.37374,0.62994,2.27716,0.49704,0.73982,1.9941,1.40102,0.82982,0.0,0.55572,0.8005,0.29902,0.66456,0.23562,0.1881,1.25658,0.34802,1.37556,0.7776,1.18728,6.06176,2.3461,0.49976,0.5003,0.25708,1.35216,1.21964,1.4476,0.97724,0.54532,0.0,0.24518,1.0289,0.6437,0.3375,0.65794,0.0734,3.56062,1.635,7.50422,0.91176,0.81302,4.7787,1.16782,1.33362,3.01058,1.22732,0.75758,2.50222,0.8698,1.86866,1.80668,0.59892,0.85136,0.91412,0.57628,4.33648,0.07752,0.55886,0.49106,0.20392,0.15102,0.15342,0.15544,0.06062,0.24084,0.05826,3.8108,0.43086,0.12596,0.93262,0.89636,0.94106,0.20742,1.17278,0.41774,0.69008,0.12458,0.40164,0.55622,1.05872,0.32222,0.55298,0.1164,0.6099,0.94818,0.37352,0.62704,0.28352,0.74042,0.14302,0.71268,0.18472,1.3706,1.32068,0.30062,0.31998,0.0,0.2786,1.40674,0.63514,0.6116,0.0,2.25118,1.28022,0.55542,1.5265,2.1172,1.25828,0.23992,0.31652,0.30372,0.70448,0.43952,0.6173,0.18062,1.06848,0.72914,0.39044,1.6562,0.54686,1.2307,4.62548,0.54048,0.3157,1.24396,1.26324,1.00568,1.05396,0.72608,2.41456,0.2882,0.7394,1.5837,1.08602,0.6612,0.63172,0.22378,0.16,0.61002,1.30094,0.69418,0.33744,0.17164,0.33194,3.6029,0.18302,0.5225,0.23996,0.31148,1.20496,0.25538,1.42922,0.59608,0.14156,0.2635,0.34666,0.68492,0.12552,2.31044,0.2806,0.15638,0.23768,0.67302,0.81446,0.05198,0.23152,,0.13474,2.90302,0.41324,0.46838,,1.16408,0.19642,0.11804,0.12846,0.63282,0.2677,0.97876,0.31666,0.71966,1.83356,0.43632,0.12122,0.07888,0.50276,0.15888,0.27842,0.76632,1.1536,0.22908,0.0,0.07758,0.2802,0.90472,0.34932,0.93312,0.5958,0.13958,0.0388,0.07762,0.2049,0.08568,0.76178,0.1617,0.09864,0.25162,0.05348,0.25366,0.13858,3.7796,6.13396,0.02456,0.5364,1.8798,1.53824,0.53462,0.1076,0.70138,0.16612,0.2707,0.3988,1.40812,0.33626,0.04544,0.36124,0.0909,0.29204,1.0952,0.06926,7.2875,1.3275,0.06258,8.27232,0.1373,3.35308,0.70738,2.89502,2.58788,1.29514
2018-05-17,0.44708,0.3897,0.0,0.34776,0.44604,0.38932,0.0812,0.39212,0.99086,0.30174,1.07176,1.95594,0.0,0.8269,1.00844,0.8912,0.6245,1.95102,0.557,0.243,0.72828,0.34174,0.85584,1.35376,0.55092,0.77714,0.21084,1.20504,0.3446,0.75374,0.35878,0.16078,0.72174,0.77102,0.82638,0.71002,0.64024,0.4582,0.29188,0.2808,1.82062,0.3857,0.64426,0.77536,0.82742,1.4971,1.27328,1.93848,3.23504,1.09782,1.01572,1.37482,0.352,0.80722,0.97858,0.48362,0.52094,1.4274,0.58728,0.38888,0.61634,2.67244,0.44378,0.73058,2.02434,1.34178,0.75494,0.0,0.51912,0.74178,0.27686,0.63826,0.22714,0.1969,1.11192,0.32926,1.28026,0.84248,1.03894,5.79254,2.38448,0.49064,0.4496,0.29876,1.21538,1.26696,1.32918,0.99008,0.5382,0.0,0.28116,1.01204,0.6149,0.32042,0.6986,0.06622,3.83206,1.55896,7.57576,0.75472,0.72888,4.64018,1.0342,1.0896,2.83442,1.22096,0.73232,2.1658,0.90146,1.78622,1.50104,0.65792,0.78694,0.8257,0.58764,4.37276,0.07926,0.4998,0.45456,0.22532,0.14596,0.1499,0.15034,0.06686,0.25318,0.0579,4.17094,0.37308,0.1193,0.91182,0.84656,0.94176,0.20434,1.11402,0.39924,0.63828,0.09402,0.33442,0.52406,1.05996,0.3178,0.59794,0.118,0.51568,0.92854,0.42098,0.65294,0.2757,0.596,0.12326,0.70932,0.1901,1.2599,1.22342,0.28214,0.30314,0.0,0.29402,1.3431,0.52926,0.59562,0.0,2.20614,2.17094,0.54578,1.45256,2.15808,1.17028,0.20654,0.32018,0.27636,0.673,0.47834,0.57954,0.18384,1.11242,0.7766,0.40808,1.52844,0.54452,1.15974,4.47608,0.55574,0.29854,1.0622,1.26228,1.0731,1.10598,0.73208,2.00062,0.38008,0.74508,1.40604,0.9911,0.6201,0.6091,0.24086,0.17854,0.6626,1.24692,0.59336,0.32036,0.16012,0.328,3.22852,0.1782,0.5777,0.21118,0.29692,1.1455,0.2679,1.39408,0.50526,0.12662,0.249,0.4269,0.67008,0.12508,2.72296,0.28888,0.14516,0.2433,0.6711,0.90948,0.04266,0.19934,,0.11864,2.69854,0.4423,0.44856,,1.00604,0.193,0.10452,0.12728,0.57784,0.25418,1.0782,0.37316,0.61282,1.71288,0.4152,0.12182,0.07892,0.499,0.1584,0.2925,0.75284,2.0015,0.22414,0.0,0.07666,0.23788,0.87184,0.36064,0.88926,0.5836,0.13838,0.03644,0.07836,0.19152,0.08704,0.69548,0.16416,0.09738,0.22388,0.04836,0.28418,0.13714,3.83014,6.06112,0.0194,0.48844,1.70902,1.28002,0.49814,0.10068,0.71904,0.15508,0.27168,0.41278,1.33014,0.30362,0.04538,0.3579,0.08472,0.2856,1.00964,0.06874,6.51242,1.07982,0.09868,7.98878,0.16886,3.47312,0.68074,3.69574,2.4892,1.30806
2018-05-18,0.42416,0.40802,0.0,0.32872,0.42386,0.3808,0.08278,0.4154,1.05938,0.35962,0.88072,2.12652,0.0,0.83476,1.02712,0.88806,0.54478,1.7934,0.53342,0.21834,0.86692,0.30218,0.83562,1.18764,0.54378,0.96644,0.20948,1.17146,0.33814,0.78238,0.34178,0.15352,0.83174,0.7545,0.80102,0.68434,0.63718,0.4651,0.2987,0.2431,1.70434,0.3821,0.64242,0.7045,1.00384,1.39122,1.2301,1.86898,2.78576,1.10082,1.03078,1.24494,0.33692,0.79026,0.88988,0.0,0.55706,1.20492,0.56216,0.37334,0.61984,2.85116,0.4956,0.78254,2.17282,1.17286,0.73556,0.0,0.62362,0.6265,0.26572,0.79658,0.20926,0.16526,1.05992,0.31452,1.11808,0.8453,1.00236,4.88548,2.0308,0.4947,0.4414,0.38338,1.1071,1.31344,1.16838,1.00806,0.53214,1.16846,0.27958,0.91554,0.57114,0.32192,0.73918,0.0631,3.6498,1.54194,6.98804,0.73104,0.6601,4.55434,0.94464,0.96194,2.9239,1.10982,0.6924,1.97708,0.96114,1.6037,1.17824,0.6562,0.7066,0.7528,0.66534,3.77708,0.07906,0.47944,0.43966,0.21844,0.14864,0.13986,0.16288,0.07756,0.24532,0.06022,4.51936,0.40042,0.15408,1.00734,0.87522,0.91674,0.20034,1.14772,0.39228,0.58704,0.08376,0.3451,0.55652,1.06032,0.33452,0.63198,0.12154,0.47964,0.76718,0.49134,0.65216,0.27142,0.59842,0.13788,0.79176,0.2015,1.25444,1.08642,0.2904,0.35258,0.0,0.3017,1.13592,0.41072,0.72902,0.0,2.1561,3.41006,0.5553,1.85038,2.21296,1.05714,0.20812,0.32464,0.28296,0.61694,0.46844,0.53516,0.19022,1.0739,0.9101,0.386,1.63222,0.55772,1.15606,4.39794,0.54218,0.2828,1.04934,1.07472,1.0272,0.8872,0.66826,1.91166,0.80902,0.66434,1.3544,0.9986,0.59324,0.60726,0.2617,0.2969,0.68956,1.30148,0.58996,0.33144,0.18434,0.33118,3.42472,0.18018,0.617,0.20246,0.31562,1.10764,0.27222,1.31036,0.47866,0.13542,0.26414,0.53394,0.72392,0.12762,2.74494,0.25226,0.13688,0.26844,0.60338,0.9122,0.04192,0.20646,,0.15068,2.60808,0.5026,0.52972,,0.9443,0.19238,0.1091,0.13044,0.58218,0.267,0.71422,0.39646,0.63498,2.01984,0.44778,0.12488,0.08004,0.52006,0.14762,0.3008,0.80686,3.22774,0.2217,0.0,0.06668,0.23268,0.86902,0.3794,0.83466,0.58692,0.14672,0.038,0.05884,0.17326,0.07968,0.69202,0.1719,0.10088,0.21958,0.04122,0.50308,0.13744,3.7372,5.93062,0.0322,0.5836,1.64268,1.1991,0.4906,0.12772,0.68852,0.15124,0.3256,0.4481,1.10936,0.29952,0.04412,0.34724,0.09146,0.28002,0.98668,0.07548,6.256,1.00618,0.1479,7.99634,0.16766,2.78156,0.57218,4.2184,2.3897,1.02314
2018-05-21,0.39024,0.42482,0.0,0.30422,0.45808,0.39654,0.08812,0.37002,1.0831,0.3994,0.75556,2.43568,0.0,0.874,1.07036,0.91216,0.52474,2.1322,0.47396,0.26406,0.9209,0.31958,0.79868,1.12244,0.55138,1.02196,0.25962,1.1352,0.34112,0.63522,0.32004,0.17534,0.87034,0.86786,0.90808,0.63278,0.61498,0.5173,0.27824,0.2449,1.93308,0.3636,0.64592,0.65868,1.13366,1.77586,1.30226,1.88098,2.47338,1.08336,0.8798,1.01632,0.34368,0.78594,0.85194,0.0,0.54544,0.96738,0.64594,0.31806,0.69816,3.37324,0.49934,0.77954,1.81422,1.15786,1.08022,0.0,0.83482,0.6372,0.26296,1.68602,0.20582,0.18042,1.13318,0.24246,0.98322,0.8616,0.94234,4.35424,1.96094,0.55986,0.42824,0.41302,1.04714,1.3238,1.15356,0.99316,0.54664,2.17474,0.28912,0.86706,0.44436,0.33542,0.9091,0.06658,3.26904,1.68538,7.69132,0.821,0.6634,3.75686,1.17684,1.0228,2.96554,1.38196,0.78788,1.85424,0.9389,1.50072,1.13284,0.67512,0.73378,0.85182,0.66616,4.3652,0.07878,0.43546,0.42346,0.29248,0.15256,0.11986,0.16402,0.08706,0.24942,0.06086,4.84894,0.42278,0.17584,0.97048,0.87332,1.01934,0.172,1.08938,0.4213,0.73332,0.0804,0.39548,0.58714,1.0755,0.36904,0.63746,0.12568,0.51048,0.6953,0.47874,0.67526,0.28812,0.64354,0.15202,0.88796,0.20172,1.28,1.08506,0.25834,0.41922,0.0,0.2921,1.05168,0.38152,0.87326,0.0,2.20168,4.76552,0.58654,2.08992,2.49138,1.10292,0.2265,0.3086,0.29784,0.72298,0.49148,0.51068,0.22314,0.99312,0.98178,0.38716,2.00058,0.5492,1.53678,4.25104,0.55934,0.24784,1.60662,0.96978,1.04008,0.82076,0.54666,1.90964,1.17194,0.71286,1.26916,1.03702,0.44782,0.57474,0.25782,0.36912,0.64884,1.22994,0.77732,0.34846,0.21674,0.32668,3.29862,0.2016,0.58058,0.2124,0.3353,0.9374,0.25566,1.10604,0.44658,0.13614,0.30162,0.55552,0.69676,0.14366,2.8154,0.25282,0.13768,0.2506,0.4296,0.8689,0.05804,0.20278,,0.15822,2.77934,0.48794,0.6033,,1.23684,0.18268,0.11126,0.12842,0.5174,0.28002,0.62304,0.40562,0.69908,2.32802,0.42732,0.1247,0.08184,0.524,0.14068,0.31224,0.78478,3.81784,0.2237,0.0,0.06398,0.24316,0.96878,0.39212,0.78506,0.62648,0.14576,0.04006,0.05292,0.16074,0.07792,0.70988,0.18206,0.12242,0.23162,0.0409,0.6385,0.13688,4.55338,5.3604,0.04116,0.60126,1.6821,1.1764,0.3801,0.1325,0.69094,0.16334,0.55768,0.4649,1.1647,0.36628,0.03874,0.31212,0.10508,0.27204,0.97424,0.0684,6.1022,0.99214,0.16758,9.3401,0.16576,2.30322,0.48898,4.68046,2.6689,0.86882
2018-05-22,0.37982,0.42696,0.0,0.29286,0.4521,0.29828,0.0864,0.3917,1.08622,0.43308,0.73388,2.06702,0.0,0.81424,0.82212,0.92228,0.50416,2.21788,0.43634,0.2746,0.84332,0.31208,0.92032,1.18768,0.61352,1.09438,0.26612,1.02454,0.33326,0.64556,0.28294,0.1638,0.80434,0.90024,0.95616,0.6315,0.58292,0.48752,0.2505,0.2444,1.84154,0.36476,0.69938,0.63064,1.18896,1.57526,1.37906,1.89082,2.65404,1.0506,0.83048,0.85226,0.33012,0.70156,0.7562,0.0,0.51808,0.68814,0.65978,0.34504,0.87636,3.76584,0.532,0.70106,1.59356,1.11152,1.26842,0.0,0.91428,0.67196,0.24772,2.02618,0.19284,0.15736,1.0309,0.22282,0.99696,0.90892,0.92378,4.31292,2.0463,0.49994,0.40766,0.44614,0.83026,1.19548,0.96684,0.85682,0.54824,2.92084,0.30826,0.89602,0.53488,0.31766,0.97064,0.06812,3.89316,1.70252,7.54984,0.8506,0.6757,3.1751,1.26318,0.95312,2.79504,1.60684,0.71064,1.6922,0.78906,1.52912,1.29434,0.62732,0.74608,0.84638,0.67106,4.26956,0.07342,0.3917,0.41422,0.31742,0.13324,0.11248,0.17756,0.08584,0.22042,0.06052,4.06562,0.38602,0.17788,1.02432,0.87094,0.87698,0.17544,1.01652,0.5827,0.70838,0.07852,0.41946,0.55418,0.92476,0.35582,0.62762,0.11202,0.50592,0.67208,0.48134,0.54402,0.2917,0.67764,0.16308,0.99156,0.22062,1.31258,1.04364,0.23312,0.44714,0.0,0.26154,1.02858,0.38682,0.77714,0.0,2.01366,5.59352,0.6293,2.25036,2.42984,1.09466,0.2262,0.28022,0.32954,0.72814,0.48804,0.52114,0.22248,1.02646,1.04276,0.39178,2.1839,0.54158,1.92588,3.82156,0.48344,0.21432,2.52868,0.91584,1.00344,0.80646,0.47122,1.82614,1.40188,0.67818,1.29488,1.33842,0.38622,0.46462,0.23218,0.4127,0.59706,1.2298,0.95972,0.32964,0.2269,0.30884,3.3884,0.1985,0.5611,0.22486,0.3264,0.84672,0.22868,0.9971,0.44098,0.1134,0.32404,0.59,0.64616,0.13782,2.80978,0.2894,0.13506,0.23598,0.40878,0.82188,0.06074,0.26276,,0.15618,3.06864,0.52862,0.58416,,2.03032,0.18442,0.11372,0.12526,0.47106,0.27824,0.55846,0.44658,0.6951,2.43796,0.41132,0.11488,0.08136,0.50704,0.13866,0.35002,0.83208,5.013,0.22976,0.0,0.06256,0.25352,0.87352,0.40354,0.72466,0.45432,0.12764,0.03994,0.05314,0.15424,0.07046,0.67818,0.13906,0.13162,0.22616,0.03572,0.70696,0.1271,6.16966,6.46304,0.04628,0.60656,1.8302,1.04844,0.3395,0.13384,0.6271,0.13514,0.6234,0.62278,1.35394,0.35884,0.04176,0.3048,0.0985,0.35992,0.96924,0.06076,5.57696,0.89356,0.2001,9.5641,0.15748,2.35716,0.491,4.84902,2.62878,0.91288
2018-05-23,0.4108,0.4393,0.0,0.30782,0.49234,0.2709,0.08472,0.38466,1.08158,0.4465,0.9277,1.76912,0.0,0.71434,0.7455,0.96466,0.48074,2.3635,0.40726,0.302,0.80834,0.34664,0.91698,1.2294,0.7603,1.14408,0.28004,0.9729,0.3025,0.5719,0.2732,0.17584,0.86264,1.0397,0.89402,0.61362,0.54776,0.41916,0.2295,0.24716,2.07626,0.3448,0.71042,0.63004,1.4027,1.60854,1.4167,2.24246,2.23984,0.97884,0.91626,0.83214,0.30164,0.67324,0.70818,0.0,0.43182,0.6283,0.63364,0.31902,0.90424,3.29822,0.5577,0.67266,1.54258,1.02332,1.37798,0.0,1.1096,0.60196,0.2407,2.70986,0.19176,0.16064,1.13202,0.22064,0.94728,0.94188,1.0565,3.98612,1.8708,0.52244,0.36156,0.45384,0.68,1.05568,0.7993,0.7486,0.58992,3.76972,0.25568,1.14982,0.55116,0.32182,1.0293,0.0668,4.09008,1.73108,8.57428,0.86942,0.72884,3.45336,1.29052,0.97684,2.8831,1.71028,0.71788,1.55498,0.73432,1.5604,1.19682,0.6238,0.74026,0.93272,0.56128,4.75966,0.07172,0.41316,0.44856,0.35012,0.18208,0.1161,0.18856,0.0889,0.22624,0.0781,3.66556,0.54464,0.19244,1.08386,0.876,0.88002,0.17078,0.90186,0.66048,0.73334,0.0715,0.42568,0.53054,0.82848,0.29884,0.66472,0.11736,0.48756,0.65622,0.50826,0.5185,0.33582,0.68616,0.17156,1.0521,0.22724,1.7129,0.93868,0.22368,0.46442,0.0,0.26694,0.9942,0.3873,1.23882,0.0,1.95282,5.9947,0.56654,2.38956,2.58804,1.10266,0.22364,0.31096,0.34436,0.69696,0.45202,0.47452,0.19308,0.98748,1.0695,0.38626,2.34818,0.44516,2.20234,3.65238,0.41748,0.2103,3.06194,0.84856,0.83602,0.77254,0.47752,2.08796,1.53358,0.80588,1.28432,1.47278,0.37936,0.38478,0.25182,0.45244,0.56288,1.16058,1.10626,0.3077,0.26636,0.34328,2.77548,0.2742,0.57866,0.23044,0.32416,0.81992,0.342,0.95322,0.37254,0.1019,0.34024,0.6068,0.65768,0.13792,2.64084,0.28264,0.13424,0.24592,0.44316,0.8351,0.0592,0.27978,,0.20464,3.17394,0.55884,0.586,,2.29176,0.19376,0.1075,0.1235,0.42642,0.2722,0.55696,0.45678,0.9696,2.5062,0.41694,0.12246,0.0812,0.50882,0.14378,0.3517,0.77472,4.54178,0.19966,0.0,0.06946,0.2494,0.91228,0.37708,0.7753,0.40484,0.12986,0.0407,0.05478,0.14478,0.06648,0.69166,0.1253,0.13172,0.19998,0.0369,0.79786,0.1194,6.53162,7.09464,0.05212,0.61498,1.8601,1.019,0.33918,0.14492,0.58466,0.12334,0.62296,0.62506,1.44228,0.3647,0.04262,0.32606,0.17032,0.35426,1.00576,0.05898,5.50276,0.86822,0.23272,11.5114,0.14744,2.34714,0.54674,3.79624,2.38578,0.94216
2018-05-24,0.42162,0.3973,0.0,0.29186,0.4961,0.25768,0.08656,0.38258,0.89142,0.42408,1.00648,1.65876,0.0,0.83544,0.7198,0.97514,0.43172,1.97706,0.39292,0.29604,0.82006,0.36018,0.92182,1.20392,0.81002,1.1472,0.27662,1.0379,0.29654,0.63336,0.30262,0.17464,1.0899,1.01104,0.8732,0.58126,0.55834,0.41938,0.24508,0.248,2.2177,0.3526,0.7522,0.5767,1.43128,2.16062,1.30504,2.5598,2.11158,1.00136,1.38248,0.83938,0.28034,0.65494,0.69182,0.0,0.39764,0.69042,0.61108,0.36058,0.90212,3.57536,0.59676,0.6684,1.4461,1.20366,1.5164,0.0,1.25806,0.68368,0.2353,3.83904,0.19688,0.14778,1.15872,0.2401,0.95426,0.77656,1.07984,3.93594,1.80676,0.54992,0.3624,0.41702,0.63556,0.925,0.73922,0.69668,0.61228,4.09774,0.2359,1.08662,0.56042,0.32712,1.04766,0.06328,3.96248,1.757,9.06206,0.93704,0.78886,4.30302,1.36722,0.93814,2.86138,1.86212,0.79648,1.62866,0.7405,1.50504,1.18642,0.53674,0.79644,1.0069,0.54038,4.63202,0.06942,0.4044,0.41926,0.33198,0.23968,0.11194,0.19092,0.08482,0.20824,0.08868,3.72366,0.7067,0.19352,1.02142,0.86684,0.93408,0.17706,0.9182,0.69732,0.78668,0.0626,0.44678,0.51634,0.83066,0.29988,0.6509,0.11764,0.4851,0.64962,0.47226,0.49478,0.34654,0.86816,0.17326,1.1931,0.22432,1.82152,0.93144,0.22462,0.4747,0.0,0.24576,1.06494,0.37664,1.41834,0.0,1.84954,5.45002,0.5574,2.2171,2.51842,1.19278,0.23308,0.32528,0.39272,0.7038,0.44004,0.48538,0.19166,0.97102,1.06634,0.51836,2.44006,0.44886,2.46628,3.65038,0.3786,0.21332,3.35648,0.93428,0.76898,0.69928,0.48918,2.68894,1.4563,0.81842,1.19572,1.52258,0.3856,0.36484,0.21862,0.4171,0.4706,1.1118,1.20464,0.29846,0.2841,0.3437,2.78662,0.31448,0.50444,0.30874,0.3569,0.84356,0.39216,1.01184,0.40286,0.10116,0.3529,0.52144,0.65676,0.13114,2.15412,0.2899,0.12962,0.2354,0.4515,0.86136,0.0625,0.29956,,0.21276,3.28372,0.50304,0.58448,,2.52224,0.20104,0.11026,0.1196,0.41718,0.27926,0.55048,0.412,1.01018,2.5583,0.42826,0.11964,0.0897,0.50212,0.14486,0.3283,0.75788,4.243,0.19174,0.0,0.07668,0.25478,0.92532,0.34308,0.83592,0.40118,0.13342,0.04016,0.05252,0.14782,0.06482,0.66462,0.11468,0.13166,0.19764,0.03714,0.75116,0.1285,6.7602,7.56616,0.05348,0.64718,1.99618,1.03768,0.31948,0.14486,0.5599,0.12236,0.64766,0.69804,1.60072,0.36244,0.04502,0.33922,0.1933,0.3498,0.9873,0.05502,5.34704,0.87722,0.30826,10.78518,0.12524,2.16802,0.57862,3.3183,2.44952,0.90582
2018-05-25,0.42236,0.36904,0.0,0.29854,0.51424,0.2469,0.08418,0.38794,0.92492,0.36672,0.97168,1.62386,0.0,0.97798,0.64,1.40216,0.43968,1.96702,0.40906,0.29636,0.68004,0.34832,0.87146,1.22932,0.86694,1.0039,0.27124,1.18278,0.29102,0.65284,0.31306,0.16962,1.20356,0.9874,0.87,0.60236,0.57454,0.38844,0.2515,0.24294,2.4653,0.34126,0.80382,0.6215,1.0642,2.3301,1.27778,2.58218,2.0732,1.0214,1.97564,0.89198,0.29854,0.66996,0.71684,0.0,0.4208,0.67692,0.58738,0.35476,0.86816,3.67158,0.54798,0.91544,1.3892,1.38424,1.71424,0.0,1.25572,0.7953,0.25486,3.58836,0.2338,0.16296,1.0204,0.28824,1.03112,0.72428,1.00896,3.93886,1.80888,0.52862,0.35426,0.31674,0.75986,0.8996,0.71434,0.59434,0.54528,3.29168,0.23424,1.0719,0.57748,0.3155,1.0239,0.063,4.79212,1.7317,9.08914,0.94032,0.81034,4.32168,1.37234,0.9524,2.68296,2.07884,0.85932,1.63322,0.64694,1.72206,1.2717,0.57476,0.9058,1.4342,0.4274,4.72368,0.0675,0.43842,0.41332,0.34722,0.2701,0.11154,0.17354,0.08252,0.21072,0.09062,3.52418,0.85594,0.1675,0.93214,0.80748,0.89418,0.16324,0.91852,0.71988,0.84362,0.0674,0.39744,0.44264,0.81038,0.29032,0.6595,0.11858,0.50968,0.69458,0.40194,0.507,0.37076,0.88982,0.16444,1.19314,0.20966,1.65628,1.20336,0.25954,0.52062,0.0,0.24096,1.14672,0.42188,1.38214,0.0,1.84248,4.53636,0.56424,1.78562,2.49114,1.00434,0.21172,0.33702,0.4032,0.74858,0.42754,0.48218,0.19004,1.0587,1.06036,0.5594,2.39246,0.46736,2.72914,3.90478,0.38538,0.20894,3.55114,0.98272,0.83892,0.69254,0.51178,2.8088,1.07412,0.79418,1.2367,1.56914,0.40726,0.35096,0.21528,0.32016,0.46098,1.15822,1.18514,0.30584,0.26584,0.3655,2.61238,0.33708,0.46008,0.42738,0.36734,0.85944,0.43556,1.01104,0.42094,0.105,0.34664,0.4897,0.60334,0.13116,2.0824,0.2988,0.13144,0.21398,0.41048,0.83092,0.06904,0.30062,,0.17506,3.17236,0.46096,0.50102,,2.62502,0.19688,0.11264,0.11808,0.4028,0.2681,0.56566,0.34482,0.89854,2.35952,0.40804,0.11482,0.0878,0.46292,0.14246,0.2938,0.68148,3.56824,0.19986,0.0,0.07932,0.28582,0.92838,0.30952,0.8002,0.40998,0.11686,0.03702,0.05462,0.1652,0.06526,0.63946,0.13412,0.13452,0.18462,0.03854,0.5471,0.14268,7.68346,7.85554,0.04074,0.55366,1.93936,0.98896,0.32718,0.118,0.59644,0.12054,0.6047,0.78498,1.69752,0.34892,0.04394,0.3504,0.19674,0.36208,1.00462,0.04894,5.2978,0.90338,0.73518,11.03984,0.10554,2.2455,0.55416,3.57538,2.53268,0.88998
2018-05-28,0.39974,0.33244,0.0,0.28222,0.44214,0.2225,0.07478,0.4188,0.95904,0.31432,0.98618,1.39044,0.0,1.0271,0.55066,1.43696,0.5113,1.6101,0.50648,0.25994,0.61518,0.3162,0.84718,1.24478,0.88294,0.99614,0.22846,1.17116,0.30348,0.6981,0.30234,0.14474,1.21792,0.8156,0.6906,0.61492,0.66028,0.33848,0.29918,0.21746,2.3842,0.30492,0.78154,0.66416,0.84842,2.03418,1.12088,2.52564,1.9846,0.97834,2.20108,0.85028,0.29272,0.71234,0.6861,1.3474,0.4018,0.63712,0.56624,0.34388,0.79252,3.60312,0.5106,0.95786,1.2484,1.4206,1.31398,0.0,1.06998,0.87692,0.29352,2.62082,0.22896,0.1577,0.99898,0.34834,1.15686,0.65878,0.94104,3.87086,1.58952,0.84682,0.3447,0.26664,0.9161,0.80352,0.81732,0.5162,0.52004,2.678,0.22312,1.18762,0.62524,0.29588,0.79922,0.05532,5.79836,1.55622,8.50006,0.84248,0.84632,4.1519,1.1254,0.8367,2.56656,1.7495,0.79498,1.65792,0.59028,1.59624,1.35216,0.67408,0.93278,1.3361,0.37302,4.17284,0.06394,0.45644,0.41802,0.56632,0.27868,0.1133,0.16794,0.07174,0.17756,0.0879,3.37134,0.88744,0.14052,0.88184,0.70348,0.75278,0.18164,0.96756,0.6974,0.64744,0.06248,0.31184,0.37666,0.94424,0.25396,0.64858,0.11774,0.45436,0.68402,0.3847,0.51046,0.35604,0.8554,0.14998,1.035,0.19808,1.51134,1.36976,0.26838,0.469,0.0,0.22994,1.04828,0.48488,1.44798,0.0,1.91606,3.66796,0.51074,1.73158,2.19124,0.80978,0.17776,0.36544,0.38664,0.8075,0.44564,0.47216,0.14988,1.30726,1.03656,0.58784,1.86168,0.5216,2.45004,3.72948,0.4013,0.23084,3.06222,1.05474,0.8192,0.6352,0.53244,2.8351,0.80908,0.71754,1.16012,1.46292,0.39768,0.37142,0.21282,0.25894,0.48244,0.96738,1.0227,0.2756,0.23758,0.34286,3.09858,0.31762,0.55154,0.45136,0.38842,0.97712,0.46696,1.07832,0.3879,0.10356,0.30938,0.47988,0.5444,0.1136,1.87666,0.33398,0.12078,0.21382,0.40318,0.86092,0.05428,0.32362,,0.1526,2.9988,0.4363,0.41402,,2.39,0.19478,0.11276,0.11178,0.452,0.24332,0.59264,0.34294,0.81728,2.10018,0.38804,0.11182,0.08052,0.41748,0.13836,0.29906,0.63898,3.45302,0.18918,0.0,0.07368,0.25066,0.78864,0.27686,0.7698,0.3664,0.10558,0.03322,0.04924,0.15208,0.06348,0.59272,0.1477,0.11666,0.1639,0.0366,0.45524,0.13212,7.52488,7.3428,0.03558,0.5973,1.88006,0.8904,0.33082,0.10718,0.61082,0.09948,0.40098,0.79294,1.5363,0.32016,0.04232,0.36874,0.20168,0.35952,0.79964,0.04562,4.95748,0.80936,1.10704,9.69868,0.12064,2.0623,0.57498,3.90114,2.26338,0.78234
2018-05-29,0.43464,0.3614,0.0,0.33304,0.43184,0.21614,0.07478,0.37652,0.98362,0.25668,1.10364,1.24836,0.0,1.27044,0.51886,1.45842,0.59204,1.52718,0.5413,0.2547,0.5982,0.31296,0.69694,1.25172,0.9669,0.9435,0.25692,1.18548,0.2948,0.6621,0.33306,0.14754,1.22992,0.7599,0.6365,0.58014,0.74268,0.31766,0.35204,0.23076,2.34026,0.3165,0.74704,0.84762,0.79124,1.93582,1.10094,2.53432,1.95964,0.96428,2.45928,0.91548,0.28536,0.87436,0.63958,2.79514,0.40152,0.61302,0.6252,0.32834,0.71782,3.53284,0.45706,0.91904,1.19456,1.53394,1.11406,0.0,1.18102,0.96806,0.34734,2.07518,0.24024,0.15606,1.25752,0.37594,1.46664,0.89314,0.963,3.75512,1.414,0.97218,0.36454,0.21346,0.96358,0.73952,0.979,0.5442,0.54926,2.17354,0.21546,1.14926,0.65454,0.29824,0.78558,0.056,5.64854,1.6818,8.29132,0.93678,1.00776,4.19024,0.97958,0.79776,2.71618,1.58184,0.66784,1.82084,0.5807,1.49154,1.27404,0.73946,0.8616,1.31508,0.38716,3.9729,0.0646,0.55064,0.50532,0.69248,0.30614,0.11448,0.15176,0.07152,0.19328,0.0848,2.95634,0.93346,0.1338,0.8521,0.71346,0.7151,0.1928,0.91598,0.70752,0.61482,0.06304,0.28602,0.40348,1.13098,0.24654,0.64118,0.13312,0.44048,0.65478,0.36818,0.49416,0.39338,0.79862,0.1393,0.94752,0.16578,1.46906,1.51348,0.28796,0.47226,0.0,0.2107,1.04312,0.5562,1.42806,0.0,2.21748,2.86858,0.62986,1.48626,2.25576,0.74468,0.1626,0.347,0.4086,0.95574,0.47858,0.40764,0.14704,1.81062,1.01636,0.59156,1.4442,0.4933,2.11134,3.46024,0.4463,0.26878,2.19526,1.2849,0.76552,0.85436,0.61564,2.85832,0.61316,0.83352,1.06608,1.61118,0.41634,0.38546,0.23078,0.2224,0.4987,0.9194,0.84582,0.27978,0.25228,0.36654,2.96024,0.3138,0.63658,0.44142,0.39952,1.21578,0.4858,1.39876,0.3517,0.11778,0.30782,0.46936,0.57184,0.1224,2.06626,0.3357,0.1176,0.22434,0.39526,0.83478,0.10114,0.29254,,0.1489,2.6446,0.38308,0.39414,,2.39036,0.22376,0.1175,0.11386,0.47348,0.23906,0.55422,0.347,0.83148,1.98994,0.45994,0.1072,0.08252,0.4542,0.15682,0.3068,0.63012,3.1295,0.19594,0.0,0.0823,0.24018,0.7859,0.31488,0.84832,0.38702,0.09382,0.03768,0.04898,0.16424,0.07042,0.52572,0.14986,0.12624,0.18518,0.04512,0.38064,0.1549,7.22572,5.9645,0.03228,0.60712,1.9477,0.92838,0.46136,0.102,0.6311,0.09078,0.34944,0.75652,1.56598,0.4149,0.0435,0.38304,0.20766,0.33828,0.85674,0.05784,5.61734,0.7903,1.8627,9.22276,0.14412,1.78158,0.64554,4.33322,2.1977,0.64034
2018-05-30,0.45018,0.38342,0.0,0.35276,0.43138,0.27168,0.0903,0.3916,1.07644,0.2427,0.9794,1.22982,0.0,1.42804,0.58006,1.50094,0.55852,1.42916,0.56502,0.26604,0.63064,0.35404,0.71176,1.36878,0.90062,0.87968,0.31326,1.29538,0.35974,0.65518,0.36798,0.16794,1.22062,0.64404,0.6366,0.62446,0.78116,0.3793,0.47344,0.23734,2.07154,0.33276,0.71202,0.88654,0.6198,1.9827,1.02862,2.14092,2.18592,0.91446,2.67904,0.92854,0.30506,0.9654,0.5951,4.17158,0.45302,0.60076,0.60366,0.36516,0.69934,3.7441,0.3774,0.92082,1.2705,1.50982,1.12484,0.0,1.05332,1.02626,0.39068,1.23852,0.25078,0.27732,1.28562,0.3956,1.59076,0.90358,0.95074,3.90324,1.347,1.22408,0.39514,0.1925,1.03546,0.82,1.02902,0.55144,0.59348,1.66946,0.2205,0.90794,0.67504,0.34882,0.70254,0.0611,6.05438,1.947,7.8957,0.9428,1.19586,3.94074,1.04032,0.79804,2.98012,1.4928,0.5445,2.03326,0.5922,1.53474,1.3507,1.11774,0.91056,1.26734,0.43936,3.57736,0.07246,0.56758,0.51586,0.76376,0.24424,0.11794,0.14466,0.0712,0.20134,0.06954,2.40764,0.74924,0.11386,0.7708,0.7733,0.72544,0.19698,0.83424,0.65064,0.60752,0.07482,0.2597,0.43564,1.15864,0.31248,0.57954,0.12924,0.5143,0.65436,0.34168,0.4993,0.3938,0.7778,0.14336,0.89858,0.1535,0.99498,1.5831,0.2843,0.53774,0.0,0.3464,1.152,0.59904,0.97898,0.0,2.26236,2.30468,0.58846,1.42296,2.16282,0.78932,0.17594,0.27382,0.40002,1.08246,0.5771,0.42322,0.17562,2.20804,1.01438,0.66446,1.6035,0.77384,1.964,3.3116,0.4747,0.28292,1.9251,1.29002,1.19818,0.8792,0.67418,2.81802,0.56702,0.67086,0.96684,1.5521,0.39334,0.39542,0.22622,0.20128,0.55924,0.98576,0.75018,0.29974,0.22718,0.35828,2.96256,0.22554,0.66488,0.45376,0.43942,1.26492,0.39414,1.62532,0.36428,0.13,0.33276,0.4859,0.62902,0.15572,1.78664,0.34218,0.1351,0.20234,0.3596,0.87322,0.1258,0.30502,,0.09596,2.93866,0.35444,0.47832,,2.25804,0.23058,0.13006,0.12626,0.5134,0.27932,0.66936,0.36068,0.57682,1.96136,0.43124,0.11042,0.07698,0.43974,0.1667,0.36608,0.60114,3.06806,0.24592,0.0,0.07436,0.26844,0.8071,0.31068,0.89532,0.41574,0.09568,0.03684,0.05108,0.17174,0.07866,0.53192,0.15062,0.1423,0.2089,0.0469,0.30896,0.15642,8.5937,6.83674,0.02894,0.6354,2.422,1.2006,0.49016,0.1007,0.6993,0.1025,0.3458,0.81288,1.54994,0.45524,0.04364,0.41352,0.14302,0.37622,0.85838,0.06026,6.661,0.7714,15.73136,7.41198,0.15804,1.82256,0.6259,4.1423,2.2135,0.66244
2018-05-31,0.48502,0.479,0.0,0.39928,0.47564,0.3362,0.1007,0.47488,1.1848,0.2155,0.97444,1.2192,0.0,1.65232,0.60582,1.40304,0.62702,1.463,0.6548,0.26434,0.60662,0.37326,0.9104,1.3727,0.89102,0.80426,0.35324,1.62518,0.38998,0.67,0.34972,0.19732,1.04466,0.64568,0.55616,0.63902,0.8478,0.39352,0.59092,0.23172,2.04638,0.35004,0.67966,0.97468,0.58134,1.49674,1.19876,1.79246,2.2947,0.8963,2.68654,1.26852,0.3114,1.06702,0.61146,5.07814,0.46078,0.48514,0.94766,0.36854,0.69574,3.62752,0.3777,0.92888,1.4265,1.39092,1.00348,0.0,1.083,0.99094,0.45138,0.0,0.2624,0.43978,1.47232,0.41008,1.7112,0.93506,0.96274,3.5224,1.06084,1.52414,0.40356,0.18798,1.11568,0.87864,1.08818,0.62214,0.6068,1.60574,0.2247,1.6909,0.68136,0.37542,0.68366,0.0665,6.00386,2.03912,8.46512,0.95222,1.27838,3.1114,1.01614,0.83444,3.1584,1.3872,0.3868,2.00882,0.62352,1.57386,1.40806,1.58758,1.04342,1.23904,0.4803,3.6196,0.09402,0.77732,0.538,0.8508,0.22228,0.13158,0.1639,0.0746,0.22998,0.06708,2.06486,0.6576,0.1253,0.79228,0.82258,0.7059,0.21324,0.82658,0.66502,0.55572,0.08586,0.26794,0.46702,1.1923,0.35402,0.50944,0.15362,0.54456,0.6952,0.33432,0.48896,0.43816,0.69678,0.25542,0.8494,0.1696,0.8575,1.63436,0.29284,0.56682,0.0,0.57712,1.24228,0.65542,0.88268,0.0,2.50796,2.2122,0.61128,1.3868,2.22064,0.73778,0.19528,0.25104,0.38286,1.18536,0.63842,0.37594,0.22084,2.42354,1.0148,0.59444,1.56956,0.93686,1.7668,3.09842,0.52528,0.3489,1.75426,1.17772,1.48264,0.89474,0.6746,2.3924,0.57584,0.73168,1.2102,1.67106,0.39636,0.4282,0.23232,0.1978,0.83148,0.99122,0.70234,0.30674,0.23058,0.39312,2.94518,0.18586,0.79242,0.44286,0.4391,1.32104,0.36436,2.27834,0.3293,0.16826,0.33888,0.50716,0.6861,0.18174,2.50774,0.35784,0.15876,0.21966,0.39424,0.80374,0.15298,0.31476,,0.0878,3.02176,0.4127,0.46824,,2.2777,0.25984,0.16464,0.13988,0.52872,0.32318,0.74502,0.37018,0.55014,1.78072,0.44364,0.12234,0.07336,0.48106,0.1828,0.41786,0.62378,2.98078,0.32226,0.0,0.07078,0.27428,0.82292,0.34766,0.8586,0.42346,0.11288,0.03792,0.04854,0.19804,0.08648,0.55974,0.16374,0.15352,0.23274,0.05124,0.31374,0.1655,12.10148,8.45574,0.03046,0.60498,2.54994,1.25272,0.58754,0.09884,0.80732,0.10642,0.35736,0.994,1.49264,0.48178,0.04978,0.409,0.12392,0.39302,0.87782,0.06772,8.0214,0.8398,23.82136,7.17782,0.18052,2.03196,0.71504,4.28184,2.29674,0.6822
2018-06-01,0.48546,0.53062,0.0,0.40214,0.47746,0.40844,0.1098,0.46954,1.26858,0.24244,1.04918,1.09744,0.0,1.63674,0.72118,1.1157,0.65214,1.45322,0.67174,0.2582,0.57484,0.3772,0.96362,1.5423,0.88026,0.78126,0.38962,1.55182,0.41796,0.73994,0.3725,0.23636,0.92016,0.59346,0.50302,0.61864,0.8631,0.41738,0.63512,0.27728,1.76222,0.37246,0.64032,1.03226,0.60308,1.33124,1.27502,1.63224,2.29188,0.89062,2.34564,1.30232,0.36322,1.22106,0.56194,5.7502,0.41804,0.51238,1.24266,0.38964,0.75922,3.74624,0.43734,0.78196,1.81396,1.26844,0.82114,0.0,0.99668,0.9503,0.43114,0.0,0.2213,0.4873,1.52962,0.35886,1.8271,1.42628,1.16642,3.40626,0.76176,1.71106,0.4273,0.18658,1.21286,0.83444,1.14516,0.71936,0.59722,1.68882,0.24844,2.16634,0.6751,0.44288,0.6358,0.07034,5.6023,2.71028,8.70766,0.993,1.32698,2.9699,0.97814,0.80514,3.17792,1.13846,0.20876,2.37914,0.63784,1.39152,1.60698,1.72098,0.96328,0.80514,0.52516,3.5603,0.09354,0.83502,0.573,0.88376,0.18886,0.13564,0.16946,0.06736,0.24128,0.06922,1.89114,0.48828,0.11834,0.79172,0.84574,0.7435,0.2173,0.80652,0.76816,0.49114,0.10838,0.32552,0.65312,1.2676,0.36006,0.4497,0.15522,0.53938,0.67052,0.37048,0.4886,0.46584,0.92862,0.307,0.89784,0.17882,0.99546,1.45958,0.26768,0.4756,0.0,0.74818,1.33486,0.65488,0.80988,0.0,2.81208,2.06754,0.63414,1.3025,2.35372,0.7179,0.26456,0.23416,0.38734,1.18246,0.6746,0.39736,0.24172,2.6632,1.16142,0.67126,1.47284,0.955,1.48074,3.08648,0.57176,0.3643,1.5999,1.20028,1.55398,0.86382,0.7024,2.23914,0.55638,0.7652,1.17334,1.67052,0.40064,0.46124,0.22378,0.17716,0.90384,0.92866,0.68484,0.2953,0.22036,0.41726,3.12046,0.1585,0.83948,0.35636,0.4712,1.46696,0.3047,2.49368,0.33956,0.1558,0.3817,0.44758,0.74558,0.2093,2.84472,0.3568,0.19158,0.2245,0.46678,0.9922,0.1617,0.29746,,0.09804,3.72902,0.4101,0.45756,,2.4572,0.26288,0.15832,0.1356,0.55978,0.34154,0.7593,0.37602,0.71066,1.58762,0.44346,0.15052,0.07244,0.49104,0.17932,0.419,0.64058,2.843,0.38078,0.0,0.07042,0.25962,0.77586,0.34822,0.85418,0.4001,0.12166,0.04016,0.0484,0.1817,0.08696,0.58388,0.19142,0.14864,0.2744,0.04932,0.29532,0.14762,13.75402,9.8462,0.02816,0.66664,3.30606,1.46562,0.62856,0.1026,0.81352,0.11894,0.32872,0.99622,1.40206,0.51122,0.05244,0.39448,0.11856,0.371,0.8488,0.07662,8.46992,0.84152,29.5978,7.3101,0.2013,1.91906,0.85656,3.8636,2.46926,0.67488
2018-06-04,0.48822,0.64066,0.0,0.41848,0.49746,0.42706,0.11108,0.53502,1.4959,0.25322,1.03546,1.07352,0.0,1.55116,0.71798,1.1994,0.55714,1.61156,0.69684,0.24084,0.60954,0.36888,1.13898,1.56726,0.89254,0.6804,0.38234,1.54196,0.3849,0.68052,0.36356,0.24032,0.87858,0.58256,0.45206,0.5565,0.91974,0.4069,0.64608,0.33392,1.54736,0.37214,0.70848,1.11024,0.61764,1.18278,1.57896,1.51488,2.22628,0.88312,2.24916,1.44558,0.4327,1.1689,0.5962,4.98204,0.44698,0.54756,1.26316,0.40402,0.91312,3.91822,0.4677,1.051,1.96146,1.10844,0.79574,0.0,1.0697,0.90578,0.40514,0.0,0.22456,0.52064,1.55748,0.30512,1.7436,1.29288,1.1676,3.23802,0.54348,1.51186,0.39292,0.18112,1.3338,0.84374,1.12788,0.90734,0.58434,1.51654,0.35056,1.8942,0.68018,0.73406,0.62224,0.0702,5.09248,2.97912,8.23814,1.06118,1.32058,2.81108,0.89562,0.79528,3.13156,1.1518,0.5342,2.50348,0.66768,1.2668,1.73488,1.81784,1.19302,0.66278,0.55052,2.99164,0.09198,0.9885,0.59592,0.629,0.18424,0.12902,0.17102,0.06546,0.28356,0.07116,1.8296,0.44764,0.11474,0.75166,0.82682,0.79754,0.20066,0.76292,0.934,0.4805,0.12204,0.41484,0.68316,1.159,0.49222,0.44616,0.1515,0.52558,0.64796,0.3505,0.43178,0.48204,0.92954,0.3432,0.89734,0.1796,1.08488,1.25792,0.25342,0.45142,0.0,0.86784,1.54856,0.64208,0.6269,0.05326,2.67606,1.70864,0.65808,1.15796,2.24596,0.70016,0.2912,0.23512,0.44318,1.02942,0.6579,0.4245,0.25606,2.54986,1.06532,0.84146,1.42858,0.90224,1.29684,2.99084,0.65026,0.41364,1.33838,1.16878,1.5591,0.86028,0.73648,2.30604,0.56462,0.83254,1.16524,1.80086,0.35056,0.44944,0.22086,0.16178,1.00978,0.97926,0.63858,0.29514,0.2117,0.43714,2.70936,0.1459,0.83874,0.33754,0.4529,1.52666,0.27712,2.58528,0.327,0.15184,0.38102,0.43382,0.80552,0.22768,3.39654,0.32406,0.18922,0.23034,0.50222,0.84874,0.17354,0.30432,,0.1057,3.76584,0.40044,0.4355,,2.7592,0.26372,0.16388,0.1318,0.47964,0.3372,0.8042,0.3614,0.82992,1.4723,0.48514,0.16962,0.07896,0.56488,0.17988,0.41904,0.70594,2.88442,0.39752,0.0,0.07536,0.25844,0.82658,0.37502,0.85236,0.37492,0.11504,0.0445,0.04752,0.20194,0.08562,0.59622,0.18314,0.1426,0.2852,0.0491,0.22568,0.162,14.41306,10.85908,0.02334,0.62812,3.5534,1.55062,0.66472,0.10556,0.82212,0.11944,0.3176,1.10464,1.49642,0.50408,0.0542,0.34528,0.09986,0.36198,0.89274,0.08084,9.03168,0.8039,36.14562,9.31536,0.20788,2.27488,0.89078,3.25808,2.58846,0.70368
2018-06-05,0.4611,0.6511,0.0,0.37904,0.58062,0.43552,0.11068,0.59652,1.60934,0.24944,0.85398,1.08818,0.0,1.36378,0.72116,1.27398,0.47004,1.7524,0.72952,0.25042,0.62588,0.3663,1.35408,1.5161,0.8074,0.62144,0.34978,1.70602,0.39496,0.72132,0.3279,0.2371,0.94888,0.5789,0.38784,0.5418,0.95444,0.39492,0.63664,0.3243,1.5397,0.39754,0.64368,0.95174,0.58944,1.13214,1.52358,1.40562,1.92214,1.25912,2.1202,1.46376,0.46384,1.0723,0.65422,4.38792,0.47688,0.5266,1.33202,0.3975,0.82428,3.72838,0.46626,1.2646,2.18822,1.16772,1.04098,0.0,0.9893,0.83882,0.36604,0.0,0.2199,0.55096,1.28692,0.30934,1.49182,0.90638,1.41756,3.22058,0.28102,1.47186,0.54234,0.18052,1.39628,0.80896,1.4156,0.93354,0.55344,1.5341,0.39378,1.71396,0.58334,0.99404,0.50282,0.06408,4.61712,2.94842,7.81482,0.9868,1.1681,2.78386,0.8996,0.83132,3.09696,1.12124,0.709,2.45704,0.6422,1.63148,1.66676,1.93986,1.336,0.72078,0.52452,3.81594,0.09322,1.01072,0.59226,0.54348,0.18666,0.11982,0.1751,0.06176,0.26246,0.07846,1.7728,0.44172,0.124,0.79528,0.77364,0.82508,0.19024,0.82732,0.85856,0.44424,0.14036,0.47108,0.65488,1.01736,0.529,0.43502,0.13798,0.49064,0.68088,0.3945,0.44236,0.46892,1.03506,0.35218,0.904,0.19738,1.07566,1.13702,0.23718,0.40144,0.0,0.94968,1.48284,0.63626,0.55708,1.30452,2.3475,1.5441,0.53656,1.17114,2.4018,0.67804,0.30474,0.24368,0.41468,0.87012,0.59636,0.43402,0.2685,2.20974,0.9333,1.00398,1.52082,0.94644,1.1772,2.9905,0.61892,0.43244,1.369,0.91494,1.60872,0.64616,0.67894,2.30764,0.56758,0.7596,1.2639,1.3637,0.30346,0.46006,0.2206,0.16108,1.06664,1.19154,0.64034,0.28886,0.1893,0.43344,2.75144,0.14526,0.85434,0.35814,0.47222,1.32548,0.26426,2.4069,0.33652,0.14498,0.33834,0.47782,0.79294,0.22992,3.3383,0.29392,0.18786,0.23876,0.49872,0.76676,0.13192,0.3343,,0.1084,3.68538,0.4191,0.4082,,2.22992,0.24118,0.20594,0.12598,0.44282,0.32062,0.84634,0.32764,0.89292,1.44558,0.43772,0.18838,0.07282,0.61078,0.15428,0.41024,0.78432,2.47724,0.40228,0.0,0.0639,0.2399,0.83322,0.37278,0.80094,0.33262,0.11744,0.04556,0.04586,0.19932,0.0823,0.57548,0.19482,0.12562,0.25918,0.0445,0.2164,0.1407,14.10058,11.35752,0.0225,0.9136,3.33396,1.4491,0.59068,0.1064,0.82816,0.11978,0.31418,1.18476,1.26108,0.48854,0.0487,0.30944,0.08932,0.29204,0.78168,0.0748,8.08284,0.80536,40.47726,10.53066,0.20866,2.46154,0.79888,2.67886,2.71286,0.70338
2018-06-06,0.41314,0.64452,0.0,0.36468,0.55212,0.3758,0.0937,0.59352,1.64478,0.2343,0.76696,1.0005,0.0,1.16946,0.66328,1.1941,0.39966,1.79708,0.74212,0.21854,0.6301,0.3579,1.38812,1.27424,0.78764,0.5788,0.28098,1.57618,0.34248,0.74848,0.29338,0.20008,0.88758,0.51518,0.32982,0.47156,0.96158,0.28608,0.53868,0.32936,1.47182,0.37738,0.64618,0.92348,0.51932,0.90134,1.57838,1.28608,1.61164,1.38454,1.97692,1.46252,0.44904,0.99534,0.80762,3.50288,0.42472,0.571,1.6481,0.37012,0.82168,3.5437,0.48406,1.28862,2.17614,1.17154,0.9993,0.0,1.06704,0.74456,0.35564,0.0,0.21592,0.57618,1.20826,0.2999,1.30232,0.73408,1.4389,3.27622,0.0,1.2966,0.70002,0.18026,1.42602,0.72604,1.36938,1.03584,0.4852,1.41434,0.39068,1.53304,0.51834,1.02808,0.47682,0.0558,3.90618,3.27878,6.67874,0.91436,0.91658,2.39786,0.78132,0.72314,2.76602,1.36474,0.87622,2.39506,0.58616,1.74836,1.62874,1.71888,1.33298,0.72656,0.4939,4.6466,0.08094,1.09364,0.61578,0.47964,0.18344,0.11164,0.164,0.0606,0.26702,0.06984,1.68142,0.41176,0.12368,1.04742,0.66774,0.79578,0.19082,0.9641,0.85642,0.36394,0.15368,0.49954,0.61442,0.91024,0.46956,0.39302,0.12652,0.4044,0.60364,0.48742,0.38736,0.43254,1.03068,0.3569,0.86656,0.2274,1.07054,1.06592,0.23084,0.33036,0.0,1.0829,1.448,0.6577,0.49718,2.25578,2.14698,1.47716,0.51898,1.00066,2.38728,0.79174,0.30366,0.23884,0.39814,0.74204,0.51382,0.41978,0.25854,1.845,1.90636,0.95328,1.22596,0.69972,1.16442,2.76884,0.59044,0.45126,1.24218,0.86052,1.258,0.6055,0.59494,2.07004,0.48432,0.83444,1.29728,1.26274,0.30288,0.43182,0.19552,0.14498,1.09216,1.10214,0.58326,0.24868,0.17082,0.41408,2.997,0.12586,0.84448,0.33764,0.45172,1.20476,0.27798,2.09234,0.3196,0.14662,0.307,0.44734,0.70546,0.20984,3.68228,0.27786,0.17134,0.23334,0.51178,1.7712,0.11112,0.37584,,0.11314,3.36326,0.49732,0.2814,,2.31516,0.22312,0.2267,0.1031,0.41374,0.27642,0.79058,0.29606,0.95816,1.29552,0.44884,0.18884,0.07248,0.61066,0.13508,0.36158,0.79512,2.24466,0.37894,0.0,0.06256,0.22934,0.80188,0.36806,0.70574,0.2949,0.10646,0.04534,0.04154,0.18334,0.09162,0.57166,0.16512,0.1201,0.23256,0.04284,0.20046,0.14102,12.48508,10.29886,0.02134,0.99702,3.14684,1.16612,0.56086,0.1044,1.08218,0.1132,0.28548,1.1738,1.17174,0.51762,0.04684,0.25514,0.07496,0.25204,0.68792,0.07234,7.04842,0.84654,31.53092,10.30556,0.2121,2.534,0.78062,2.24094,2.62906,0.6619
2018-06-07,0.44522,0.62222,0.0,0.377,0.49234,0.31474,0.0878,0.5234,1.73704,0.2683,0.67462,0.9564,0.0,0.8573,0.64582,1.10488,0.33378,1.78464,0.68488,0.19522,0.71564,0.35552,1.22748,1.16362,0.84686,0.55322,0.2909,1.24454,0.34656,0.66648,0.31934,0.22006,0.87846,0.49594,0.33202,0.44504,0.86934,0.26116,0.4286,0.36864,1.27032,0.44036,0.70138,0.88388,0.5679,0.8261,1.51352,1.23156,1.49726,1.59176,1.56258,1.12782,0.4343,1.02878,0.89298,3.05004,0.49,0.60484,1.56558,0.33584,0.83878,3.45456,0.4735,1.27536,2.14724,1.09784,1.05144,0.0,0.96936,0.7271,0.31782,0.0,0.21512,0.48278,0.94636,0.25526,1.12608,0.6033,1.41964,3.37854,0.0,1.25324,0.78074,0.17538,1.3716,0.66406,1.39844,1.01628,0.45948,1.37146,0.43876,0.59876,0.50928,1.1269,0.42024,0.05202,3.6926,4.05534,5.51206,0.8414,0.9626,2.22196,0.7385,0.68442,2.57488,1.317,1.00308,2.31138,0.603,1.75952,1.54846,1.57874,1.12256,0.8896,0.48364,4.65164,0.06516,0.98216,0.60084,0.50254,0.16388,0.10718,0.15308,0.0572,0.31096,0.0614,1.7136,0.49044,0.10662,1.10302,0.74592,0.7422,0.18806,0.89172,0.91614,0.3492,0.16318,0.47944,0.60058,0.88258,0.43696,0.38656,0.10034,0.39924,0.56112,0.50402,0.36482,0.39886,0.9928,0.27514,0.88084,0.21788,1.1223,1.05118,0.2388,0.29772,0.0,1.05382,1.2638,0.66826,0.3908,2.93292,1.97084,1.41512,0.56766,0.95758,2.43608,0.9718,0.31646,0.22596,0.40504,0.66118,0.44694,0.43548,0.2254,1.64536,2.25646,0.90292,1.366,0.5599,1.17302,2.86152,0.5605,0.39922,1.22962,0.76124,1.02988,0.57554,0.562,1.9656,0.4245,0.82206,1.07182,1.16162,0.32836,0.42608,0.19352,0.1402,0.91234,1.16664,0.52242,0.23072,0.17478,0.38734,3.17082,0.11882,0.74022,0.28272,0.51234,1.1608,0.3018,1.41834,0.28912,0.1245,0.34194,0.47104,0.69384,0.22214,3.02196,0.24908,0.16324,0.22554,0.50714,2.05478,0.0883,0.38798,,0.1236,3.6904,0.46878,0.26336,,2.56156,0.2062,0.26078,0.08588,0.47518,0.2317,0.76638,0.327,1.03714,1.14838,0.4858,0.20024,0.06838,0.6256,0.11752,0.29962,0.91766,2.78924,0.49618,0.0,0.06064,0.268,0.89256,0.38426,0.73692,0.2697,0.08192,0.05346,0.04024,0.16562,0.09678,0.60698,0.13648,0.1131,0.22726,0.03966,0.174,0.15602,10.11032,9.46354,0.018,1.08016,3.7131,1.2537,0.50022,0.11362,1.183,0.12332,0.255,1.00708,1.20626,0.53384,0.04058,0.24238,0.0666,0.26232,0.69916,0.076,5.93094,0.76596,26.90406,10.55506,0.2047,2.42322,0.68562,1.88808,2.44714,0.65514
2018-06-08,0.50372,0.595,0.0,0.36484,0.49804,0.25596,0.0887,0.56058,1.76226,0.24502,0.54198,1.07208,0.0,0.7427,0.59712,1.0543,0.29448,1.80392,0.65108,0.23282,0.8359,0.38076,1.30732,1.14074,0.91058,0.71618,0.2816,1.20224,0.34698,0.58838,0.29988,0.28052,0.82196,0.52196,0.36316,0.66648,0.85906,0.26194,0.39602,0.3783,1.2583,0.43122,0.6988,0.79684,0.59122,0.89926,1.44978,1.2922,1.37614,1.81578,1.43012,1.0661,0.3986,0.8879,0.99384,3.02708,0.5189,0.70186,1.3698,0.3179,0.79236,3.27364,0.44502,1.11784,1.7237,1.12808,1.2574,0.0,1.03378,0.68116,0.33654,0.0,0.25852,0.45306,0.9256,0.26352,0.98394,0.0,1.30182,3.28708,0.0,1.1735,0.89148,0.22862,1.18988,0.66972,1.40346,1.0069,0.47034,1.19334,0.46776,0.0,0.5837,1.14212,0.42864,0.05002,3.54154,3.77032,5.00188,0.76438,0.9367,2.24018,0.73196,0.7889,2.8,1.23668,1.08452,1.91986,0.60388,1.8012,1.29132,1.48492,1.10808,0.99384,0.5391,5.08672,0.07144,0.95536,0.57038,0.5051,0.18006,0.11946,0.1764,0.0647,0.32528,0.06256,1.82268,0.5349,0.1052,1.13462,0.76502,0.71152,0.20094,0.8227,0.84362,0.37352,0.17122,0.43726,0.46934,0.71488,0.4896,0.40518,0.10566,0.36924,0.58636,0.48334,0.34972,0.369,0.81194,0.24252,0.88558,0.21894,1.01566,0.92386,0.248,0.29224,0.0,0.99728,1.13268,0.69864,0.37698,3.43708,1.57844,1.51166,0.54348,1.03772,2.34274,1.12988,0.29536,0.2277,0.38038,0.62624,0.45434,0.41948,0.21406,1.33682,2.21332,0.82894,1.55298,0.5313,1.3418,2.5999,0.56798,0.39728,1.27082,0.6717,0.9327,0.5888,0.50156,1.93634,0.41712,0.85978,1.06376,1.28564,0.36464,0.41268,0.1949,0.13978,0.94226,1.18426,0.53622,0.2856,0.19082,0.3639,3.22004,0.13324,0.72928,0.2989,0.5946,1.16608,0.31204,1.22232,0.28096,0.1334,0.34236,0.64976,0.73344,0.21658,2.96422,0.24042,0.14642,0.22646,0.5331,2.00754,0.0871,0.4221,,0.12232,3.22092,0.47472,0.27226,0.02992,2.45104,0.23746,0.3134,0.10164,0.4962,0.22962,0.76626,0.33204,0.91766,1.28734,0.62424,0.20044,0.06876,0.65726,0.13254,0.3149,0.98612,3.40058,0.53554,0.0,0.06264,0.28596,0.99832,0.40842,0.72916,0.35362,0.08304,0.0587,0.04568,0.18702,0.14524,0.59064,0.06544,0.13892,0.19318,0.04734,0.17998,0.1759,8.71914,9.0668,0.0185,1.00448,3.23754,1.12188,0.48586,0.12394,1.25284,0.11916,0.31128,0.9994,1.2961,0.70916,0.04046,0.28806,0.06854,0.32686,0.72828,0.07354,5.55654,0.76072,24.07558,9.97364,0.2576,2.3996,0.6721,1.75278,2.3591,0.62476
2018-06-11,0.51866,0.54092,0.0,0.36014,0.48418,0.2366,0.10044,0.59004,1.51318,0.22656,0.46608,1.01154,0.0,0.67698,0.60962,0.94082,0.29076,2.06452,0.5777,0.27112,0.78398,0.3807,1.25888,1.12138,1.04148,0.79682,0.30448,1.17,0.35854,0.56222,0.28976,0.29584,0.82494,0.49198,0.37242,0.88446,0.83844,0.27338,0.36594,0.36782,1.18212,0.48536,0.636,0.76118,0.5683,0.95092,1.31772,1.21176,1.29764,1.91518,1.3407,0.88368,0.38366,0.9322,1.12272,2.86162,0.48954,0.65842,1.3683,0.27816,0.6525,2.72018,0.39726,0.76484,1.5351,1.20206,1.32868,0.0,0.95284,0.62144,0.33582,0.0,0.31614,0.44282,0.75422,0.26602,1.01702,0.0,1.38984,3.12222,0.0,1.03016,1.02704,0.24498,0.96564,0.63406,1.27006,0.8593,0.45278,1.29874,0.3921,0.0,0.5257,0.88226,0.38006,0.0505,3.72386,3.85134,5.026,0.7313,0.88798,2.03392,0.71568,0.79386,2.78468,1.09066,0.62278,1.91828,0.61848,1.85364,1.19658,1.37828,0.81354,1.02044,0.51858,5.3554,0.07274,0.784,0.56744,0.48728,0.17576,0.15134,0.1791,0.06488,0.31854,0.0605,1.66066,0.52724,0.10044,1.10688,0.76462,0.70528,0.1956,0.76614,0.74114,0.35292,0.17348,0.37102,0.45074,0.6885,0.35322,0.38536,0.1078,0.36428,0.5677,0.46434,0.33688,0.3475,0.77292,0.22048,0.88328,0.2217,0.99182,1.0146,0.25272,0.27876,0.0,0.93966,0.92592,0.79368,0.34334,3.97898,1.45532,1.47946,0.57378,0.92232,2.46104,1.22132,0.26932,0.20764,0.33742,0.6327,0.45128,0.39044,0.20476,1.24338,2.90774,0.62762,1.5891,0.53632,1.3317,2.5397,0.4921,0.33796,1.27224,0.6324,0.93986,0.57736,0.56702,1.62762,0.31302,0.8583,1.02154,1.19998,0.39244,0.45294,0.18998,0.1374,0.84918,1.25032,0.5428,0.30552,0.21186,0.3585,3.08052,0.14036,0.67722,0.30348,0.58474,1.12016,0.29568,1.05004,0.27424,0.1412,0.37762,0.6479,0.75802,0.21726,2.7406,0.22298,0.14752,0.2444,0.52926,2.33252,0.0852,0.38282,,0.11934,3.90524,0.45748,0.2879,0.0645,2.28204,0.2362,0.33824,0.1063,0.57312,0.24046,0.77818,0.3244,0.81622,1.4386,0.68704,0.19108,0.06458,0.59336,0.12936,0.2818,0.95704,3.38734,0.55036,0.0,0.05662,0.29188,0.93768,0.37932,0.6932,0.39426,0.08374,0.05594,0.04984,0.1715,0.15932,0.57594,0.03416,0.1434,0.1814,0.04838,0.17434,0.16876,8.09456,8.06352,0.0199,0.9394,3.13616,1.13144,0.53118,0.13186,1.35172,0.12042,0.30018,1.0727,1.20968,0.73974,0.03742,0.29458,0.0725,0.32536,0.69528,0.07104,5.05532,0.77002,21.9244,8.0178,0.27944,2.16238,0.654,1.8062,2.30366,0.58478
2018-06-12,0.54856,0.5186,0.0,0.3465,0.40802,0.27322,0.09844,0.64384,2.60768,0.21894,0.44846,0.9077,0.0,0.63628,0.85762,0.88132,0.29442,2.09146,0.5742,0.26856,0.78662,0.39756,1.25122,1.04088,1.0159,0.7377,0.30014,1.01426,0.33834,0.5067,0.28616,0.29762,0.74536,0.44454,0.40606,0.93642,0.87286,0.27062,0.4039,0.39488,1.09642,0.43754,0.64168,0.74464,0.58468,0.93164,1.30482,1.2627,1.3062,1.6817,1.30486,0.85932,0.423,0.99788,1.18338,2.31478,0.45004,0.69566,1.33934,0.26052,0.64824,2.59696,0.40684,0.57628,1.34832,1.15418,1.12492,0.0,0.8813,0.5387,0.36734,0.0,0.35874,0.50038,0.90742,0.25136,0.97452,0.0,1.1704,2.95134,0.0,0.98922,0.9039,0.23926,1.07344,0.58586,0.87962,0.82276,0.43684,1.28456,0.3109,0.0,0.52646,0.62232,0.3554,0.0506,3.68208,4.0404,5.19438,0.67856,0.89164,1.84862,0.6906,0.78814,2.81888,1.06346,0.55462,1.77336,0.64056,1.5046,1.07266,1.45038,0.84512,0.93882,0.56552,4.58886,0.07088,0.83344,0.61152,0.45716,0.15148,0.17374,0.17496,0.06654,0.32742,0.0543,1.59472,0.51082,0.09484,1.09654,0.75772,1.10286,0.18968,0.71894,0.68816,0.36518,0.16004,0.33874,0.45096,0.67302,0.31924,0.53504,0.11588,0.35136,0.56146,0.47816,0.3244,0.34042,0.75028,0.22402,0.86688,0.21144,1.02244,1.0043,0.24042,0.31018,0.0,0.925,0.94972,0.7565,0.33906,3.16004,1.51438,1.47374,0.5587,0.86474,2.20352,1.2996,0.2539,0.22448,0.30576,0.66418,0.44532,0.37254,0.18782,1.08668,3.3135,0.50334,1.53614,0.5388,1.27726,2.66956,0.47802,0.34218,1.10284,0.6795,0.93608,0.55226,0.59194,1.60904,0.28712,0.97414,0.90294,1.1495,0.39104,0.51444,0.17416,0.13326,0.87316,1.06408,0.49866,0.31886,0.2128,0.38192,2.88772,0.13646,0.71832,0.29174,0.57736,1.09398,0.30146,1.05286,0.2442,0.15774,0.37852,0.59252,0.77698,0.2153,2.76308,0.17904,0.14674,0.276,0.55342,2.62652,0.0796,0.36792,,0.12652,4.2868,0.44996,0.29066,0.32378,2.22544,0.2372,0.33284,0.1222,0.6253,0.2341,0.77304,0.31492,0.82216,1.48194,0.67976,0.17326,0.0601,0.57092,0.12534,0.28518,0.99194,3.43598,0.55666,0.0,0.05438,0.29906,0.90068,0.38262,0.6692,0.43934,0.07718,0.0519,0.05018,0.16652,0.16456,0.54524,0.0,0.13992,0.19152,0.04642,0.16802,0.1714,7.49496,7.3705,0.01888,0.645,3.19018,1.16744,0.55046,0.14374,1.41714,0.11752,0.30338,0.93806,1.1669,0.66694,0.03608,0.29604,0.075,0.31962,0.6699,0.06304,4.92982,0.74428,20.12706,6.82872,0.29468,2.02106,0.67512,1.64334,2.30834,0.5733
2018-06-13,0.52734,0.53808,0.00842,0.36224,0.39158,0.27398,0.10128,0.67528,3.10352,0.22468,0.47266,0.87652,0.0,0.62446,0.9812,0.91792,0.30244,2.14908,0.53686,0.2994,0.76182,0.31666,1.25784,0.98908,1.02324,0.74266,0.33544,0.99064,0.367,0.44668,0.28452,0.31518,0.76632,0.45854,0.39632,0.94976,0.8759,0.26846,0.42878,0.42658,1.11488,0.47274,0.69932,0.74312,0.60642,0.89444,1.39096,1.25394,1.48918,1.8627,1.21262,0.83342,0.47034,1.00782,1.16604,2.12312,0.44458,0.6643,1.06582,0.2443,0.8029,2.47636,0.43056,0.51584,1.2897,1.15162,1.12916,0.0,0.88232,0.52014,0.3527,0.0,0.36588,0.42078,1.01308,0.229,0.93808,0.0,1.05792,2.75072,0.0,0.86392,0.78084,0.21804,1.13452,0.62096,0.83424,0.72432,0.40844,1.34796,0.25334,0.0,0.53728,0.58166,0.33726,0.04684,3.56398,3.63696,4.8841,0.67474,0.94328,1.80368,0.63492,0.80128,2.7224,0.81954,0.50748,1.59836,0.64094,1.3787,0.9987,1.3507,0.8111,0.90804,0.58758,3.68596,0.07442,0.79164,0.60228,0.4601,0.13412,0.18046,0.17726,0.06318,0.32522,0.05556,1.41064,0.4877,0.094,0.86818,0.74372,1.20222,0.18212,0.53616,0.71704,0.38004,0.14174,0.30734,0.4249,0.65624,0.3382,0.53126,0.11714,0.35244,0.5748,0.39164,0.32686,0.35116,0.85008,0.21862,0.84774,0.18122,1.02226,0.97032,0.25288,0.31462,0.0,0.68946,0.81692,0.7122,0.3343,2.65764,1.50746,1.40948,0.63268,0.85444,1.9587,1.1345,0.26296,0.22346,0.31132,0.67442,0.42908,0.35528,0.16456,0.94124,2.39858,0.48228,1.56378,0.5238,1.14562,2.75912,0.45394,0.32034,1.00722,0.66994,0.88888,0.5291,0.5977,1.53672,0.25606,0.9503,1.02514,1.44562,0.39142,0.60818,0.18156,0.12478,0.86158,1.00302,0.52066,0.3329,0.22784,0.41628,2.58082,0.14486,0.73206,0.3227,0.5566,1.10512,0.31326,1.0518,0.22006,0.17112,0.35906,0.59698,0.82462,0.20472,2.4323,0.1515,0.15714,0.31896,0.54704,1.68736,0.07954,0.31552,,0.13314,4.3224,0.36622,0.33286,11.65258,2.29374,0.2459,0.31986,0.12974,0.61952,0.2287,0.69402,0.3571,0.78292,1.5197,0.68106,0.176,0.05894,0.56518,0.12658,0.27208,1.01144,3.58734,0.5464,0.0,0.055,0.285,0.88318,0.3859,0.6063,0.45294,0.07282,0.05204,0.05106,0.17686,0.15064,0.51242,0.0,0.1371,0.2026,0.04698,0.14982,0.16672,7.01398,7.18698,0.01598,0.5495,2.91498,1.1146,0.5694,0.13674,1.10324,0.10722,0.29666,0.85906,1.1646,0.54412,0.03624,0.3062,0.07132,0.31724,0.70542,0.05944,5.04108,0.68108,18.37974,6.60188,0.30436,1.7332,0.6664,1.62536,2.54118,0.47408
2018-06-14,0.48118,0.48868,0.01088,0.32924,0.40258,0.2783,0.0936,0.7034,3.44222,0.2027,0.42026,0.8385,0.0,0.56772,1.04066,0.88456,0.29084,2.18862,0.58358,0.30718,0.68488,0.27314,1.27242,0.98956,0.9166,0.7349,0.389,0.94738,0.34806,0.44014,0.23292,0.28786,0.76134,0.45948,0.39066,1.04712,0.8894,0.26936,0.44284,0.42772,1.20604,0.4999,0.64606,0.7242,0.59982,0.85196,1.3362,1.22062,1.55554,1.80786,1.1675,0.82276,0.50476,0.90432,1.02424,1.9304,0.43618,0.60802,0.79396,0.23314,0.76038,2.20026,0.43508,0.43974,1.14882,1.1563,1.06974,0.0,0.85504,0.45642,0.3515,0.0,0.38256,0.37756,1.42144,0.22566,0.90114,0.0,1.10162,2.72682,0.0,0.62066,0.7443,0.19334,1.15396,0.61096,0.7829,0.67678,0.39936,1.30932,0.15456,0.0,0.55716,0.49712,0.31646,0.0435,3.46968,3.27684,4.78494,0.7691,0.74764,2.28098,0.58304,0.79378,2.73362,0.7435,0.47678,1.54154,0.58684,1.33092,1.04484,1.09998,0.81268,0.71134,0.54832,3.48664,0.0703,0.7741,0.63432,0.45692,0.10626,0.1751,0.17052,0.06124,0.30578,0.05954,1.26276,0.33808,0.09674,0.82382,0.58546,1.29978,0.16748,0.55696,0.63776,0.36808,0.13394,0.3085,0.43076,0.6265,0.32614,0.51252,0.12418,0.33142,0.85096,0.3699,0.34232,0.34724,0.86392,0.18268,0.7896,0.15506,1.02948,0.89244,0.2399,0.31052,0.0,0.5152,0.77482,0.66908,0.33256,2.48694,1.5238,1.12588,0.59742,0.78782,1.64844,0.95604,0.23212,0.27822,0.30164,0.6528,0.40384,0.3232,0.15112,0.94418,2.10062,0.63266,1.37276,0.51616,1.13784,2.60794,0.46012,0.31554,0.95338,0.69702,0.93766,0.68448,0.58824,1.47256,0.23502,0.92006,1.06776,1.46106,0.33328,0.644,0.20388,0.12024,0.85942,1.05756,0.5395,0.32588,0.24718,0.44156,2.36632,0.14352,0.75282,0.32154,0.5074,1.24704,0.3145,1.05742,0.21634,0.1903,0.31278,0.55238,0.81888,0.17462,2.33116,0.13146,0.14692,0.34494,0.54382,1.4487,0.07308,0.32622,,0.13494,4.25354,0.35904,0.35568,17.8747,1.9605,0.24588,0.26658,0.131,0.702,0.2258,0.68302,0.30826,0.76938,1.50674,0.63494,0.1604,0.06238,0.57482,0.12906,0.29806,1.04962,2.98924,0.38314,0.0,0.0533,0.24754,0.76784,0.40852,0.5156,0.4801,0.07014,0.04698,0.05102,0.17492,0.13966,0.45714,0.0,0.13888,0.1973,0.04626,0.14008,0.15308,5.62874,5.90508,0.01586,0.43912,2.33732,0.98408,0.51836,0.12826,0.91088,0.101,0.29728,0.82624,1.08308,0.46944,0.03736,0.31066,0.07024,0.30014,0.64118,0.04896,4.4014,0.62648,19.44662,5.98278,0.29604,1.72936,0.75978,1.4866,2.6525,0.44092
2018-06-15,0.49858,0.55244,0.01586,0.43802,0.39624,0.27646,0.08234,0.68674,3.6981,0.20752,0.60098,0.97306,0.0,0.55744,1.10476,0.90302,0.28242,2.22884,0.6077,0.26792,0.56796,0.27706,1.1772,0.79364,0.93302,0.58508,0.50696,0.93972,0.3417,0.44498,0.24164,0.22072,0.72142,0.45698,0.36292,0.85822,0.88088,0.30258,0.4331,0.41552,1.29606,0.79904,0.71262,0.71266,0.7589,0.83478,1.55176,1.15922,1.57104,1.62678,1.18608,0.75596,0.5073,0.88622,0.94802,1.68666,0.42432,0.48796,0.7949,0.2576,0.9097,2.19874,0.47714,0.409,1.07344,1.11292,0.91406,0.0,0.75442,0.50304,0.33416,0.0,0.33846,0.38898,1.3088,0.22406,0.89286,0.0,1.03996,2.7968,0.0,0.53942,0.62424,0.16452,1.15414,0.60988,0.76918,0.60872,0.39592,1.24968,0.06878,0.0,0.48148,0.56824,0.33828,0.03884,3.02142,3.42694,4.97018,0.75004,0.66538,2.41004,0.59596,0.7417,2.41348,0.85874,0.71866,1.49688,0.61514,1.363,1.0173,1.12366,0.78614,0.63724,0.45236,3.59864,0.06514,0.73082,0.64198,0.49182,0.08704,0.1709,0.15244,0.0704,0.31688,0.05516,1.1946,0.31744,0.0944,0.89626,0.54212,1.38456,0.16684,0.6747,0.67616,0.33812,0.10874,0.2699,0.56242,0.66892,0.27776,0.48708,0.12932,0.34132,0.9371,0.41102,0.33986,0.33768,0.84816,0.17214,0.68226,0.15616,1.09796,0.90168,0.24638,0.31332,0.0,0.4625,0.7939,0.59912,0.30512,2.3099,1.59924,0.87724,0.67474,0.75364,1.63552,0.9044,0.20112,0.29238,0.49514,0.66252,0.37206,0.34612,0.14648,1.01858,2.10106,0.74314,1.29412,0.6026,1.16264,2.46156,0.42562,0.31036,1.09164,0.64912,1.09448,0.8161,0.62672,1.69062,0.22614,0.88352,0.97192,1.54926,0.29184,0.67524,0.20604,0.13042,0.80874,0.99702,0.648,0.27282,0.24424,0.48328,2.46362,0.13548,0.72946,0.30506,0.58428,1.05998,0.31188,1.07292,0.19498,0.20194,0.30242,0.37084,0.7813,0.16444,2.35564,0.14298,0.15632,0.34966,0.5419,1.39192,0.06236,0.32104,,0.1417,4.36966,0.35996,0.37472,21.6711,1.99614,0.24906,0.23618,0.18158,0.7565,0.2116,0.81996,0.31954,0.80994,1.35322,0.48468,0.17106,0.0647,0.5909,0.12192,0.29088,1.06796,2.28974,0.31248,0.0,0.05904,0.21952,0.74556,0.40622,0.45962,0.47042,0.0684,0.0454,0.04564,0.1949,0.09642,0.47918,0.0,0.12576,0.20746,0.04554,0.13192,0.1485,4.75366,4.84838,0.01418,0.47126,2.6791,1.02564,0.50506,0.13134,0.91752,0.09986,0.2687,0.75898,1.1376,0.24366,0.03898,0.28308,0.06462,0.25142,0.65014,0.04564,4.2216,0.60858,18.96866,5.83912,0.26402,1.70206,0.64052,1.28658,2.58984,0.47966
2018-06-19,0.59696,0.56766,0.02114,0.54406,0.57476,0.39016,0.09068,0.6896,4.12786,0.28292,0.83202,1.237,0.0,0.63774,1.3041,1.06616,0.31288,1.95878,0.62596,0.3211,0.78588,0.41358,1.1921,0.70044,0.90598,0.61008,0.66214,1.3115,0.44994,0.53396,0.37172,0.26682,0.83218,0.61224,0.46216,0.838,0.86114,0.37806,0.46302,0.45402,1.59672,0.926,0.773,0.66094,0.98568,1.09442,1.71326,1.33342,1.78612,1.73232,1.383,0.87384,0.51134,0.90968,0.83996,1.69846,0.52788,0.50356,0.81354,0.33476,0.99806,2.33026,0.54856,0.5279,1.5161,1.09014,1.09712,0.0,0.84828,0.56836,0.34374,0.0,0.27402,0.41992,1.2161,0.26646,0.93176,0.0,1.03598,3.34346,0.0,0.6765,0.589,0.19434,1.2506,0.7156,0.98646,0.63436,0.5311,1.43544,0.0,0.5191,0.62896,0.75056,0.387,0.05792,2.22828,3.94772,5.68182,0.75328,0.72838,2.8989,0.82208,1.00788,2.60674,1.08032,0.6462,1.40696,0.69916,1.49938,1.14708,1.25246,0.89336,0.8177,0.51346,3.7107,0.08292,0.71826,0.86638,0.62804,0.08916,0.16904,0.18232,0.1095,0.35526,0.06626,1.60342,0.32932,0.14168,1.22286,0.75952,1.79156,0.2471,0.81694,0.75762,0.49084,0.12578,0.2581,0.73486,0.72316,0.38748,0.5674,0.18848,0.53482,1.18042,0.6089,0.47284,0.41078,0.9638,0.22272,0.79004,0.2602,1.14956,0.983,0.42224,0.45762,0.0,0.59238,0.87428,0.5369,0.27928,2.6043,2.01488,0.88028,0.93946,0.98728,1.77112,1.38534,0.25978,0.34322,0.76318,0.83762,0.35334,0.67138,0.20508,1.1595,1.77528,0.9629,1.61812,0.75342,1.35398,2.90366,0.45504,0.34802,1.39736,0.69248,1.26614,1.03968,0.55176,2.05932,0.28234,1.0525,1.12698,2.42188,0.3272,0.86002,0.23874,0.26694,0.92902,1.15412,0.87078,0.3389,0.31996,0.65454,2.64308,0.18814,0.77022,0.36718,0.69212,1.034,0.34748,1.20438,0.27954,0.26206,0.36392,0.44442,0.8155,0.21556,2.68582,0.17652,0.21316,0.37176,0.5868,1.31732,0.08036,0.40426,,0.22644,4.31978,0.46846,0.55608,25.39302,2.22408,0.39884,0.24562,0.21978,0.83064,0.28168,0.84806,0.40292,1.09396,1.35782,0.62926,0.1723,0.09342,0.73222,0.2145,0.38164,1.3533,2.41616,0.3808,0.0,0.08506,0.29418,0.99682,0.53804,0.50982,0.78454,0.08884,0.07198,0.05638,0.3585,0.11674,0.62086,0.0,0.17848,0.26972,0.07104,0.18258,0.23112,5.00112,5.49684,0.01606,0.47662,3.0951,1.21222,0.54988,0.15144,1.1148,0.19658,0.41596,0.65352,1.54204,0.1773,0.06506,0.4011,0.0862,0.37182,1.27,0.08174,4.60324,0.69824,17.58976,6.47778,0.23334,1.91738,0.62738,1.31594,2.82684,0.60882
2018-06-20,0.57952,0.54604,0.03272,0.57024,0.60134,0.36328,0.10132,0.59922,3.16408,0.30098,0.9895,1.39384,0.0,0.6559,1.12038,1.36398,0.29322,1.81808,0.58442,0.32336,0.80008,0.44446,1.02252,0.72782,0.92158,0.67082,0.72496,1.44796,0.50466,0.59294,0.42288,0.30336,0.82056,0.68424,0.49924,1.06802,0.75734,0.39542,0.40764,0.4668,1.72868,1.23026,0.76602,0.6282,1.06574,1.23066,1.71696,1.35316,1.71884,1.73872,1.47592,0.88274,0.49084,0.88316,0.77842,1.71952,0.5494,0.46588,0.81048,0.33326,1.00482,2.19406,0.55504,0.51234,1.85686,1.0217,1.25548,0.0,0.95114,0.58874,0.3456,0.0,0.23918,0.36686,0.91238,0.25572,0.92276,0.0,1.01972,3.44774,0.5643,0.76668,0.55492,0.21614,1.03672,0.77922,0.93604,0.56186,0.54998,1.4565,0.0,0.95014,0.56712,0.84996,0.40698,0.06338,2.6756,4.08186,7.17266,1.04746,0.84068,3.01086,0.87264,1.19912,2.5866,1.12712,0.53956,1.47628,0.7693,1.7496,1.32198,1.1608,0.77244,0.90136,0.48466,3.77578,0.08494,0.67772,0.92522,0.73968,0.09188,0.16386,0.18832,0.11328,0.36284,0.06648,1.65762,0.30094,0.13632,1.18072,0.8457,1.49524,0.2522,0.96486,0.7986,0.4996,0.13144,0.2406,1.0086,0.68964,0.41028,0.6891,0.19364,0.60284,1.20518,0.62926,0.55576,0.40168,1.02266,0.22886,0.83628,0.28304,1.1302,0.98132,0.4345,0.45964,0.0,0.58702,0.82128,0.58472,0.23564,2.46196,1.85946,0.83528,0.96724,1.14338,1.80328,1.34272,0.29094,0.3864,0.81268,0.84812,0.3718,0.68426,0.22694,1.14254,1.8507,1.01014,1.7017,0.80064,1.43244,2.81562,0.43828,0.33298,1.60752,0.68548,1.35612,1.10606,0.53982,2.17948,0.36196,0.97408,1.26278,2.61452,0.3358,0.91294,0.24708,0.28338,0.84938,1.12542,0.96422,0.39072,0.34718,0.65556,2.9009,0.20098,0.6773,0.41334,0.706,1.03222,0.34496,1.15812,0.33434,0.25928,0.4225,0.43334,0.85656,0.26066,2.5095,0.19266,0.24158,0.3383,0.54916,1.20674,0.08156,0.3969,0.01528,0.21964,4.11138,0.45318,0.6067,28.681,2.44496,0.40658,0.22002,0.21986,0.8432,0.34444,0.84014,0.4503,1.05376,1.27936,0.67708,0.17498,0.09826,0.72846,0.22564,0.3749,1.24114,2.24744,0.404,0.0,0.08652,0.31694,1.0731,0.52226,0.47286,0.81102,0.10836,0.07562,0.05902,0.3817,0.12522,0.82416,0.0,0.19476,0.277,0.08396,0.20416,0.24856,4.72856,5.5873,0.01746,0.44394,3.6336,1.38738,0.54538,0.15138,1.13662,0.29694,0.3804,0.64994,1.54914,0.14186,0.06956,0.44286,0.09774,0.40974,1.29676,0.08978,4.96072,0.71354,17.58766,6.56316,0.23548,2.15924,0.66996,1.38616,2.76746,0.64334
2018-06-21,0.61558,0.58208,0.04426,0.61512,0.6626,0.37844,0.10356,0.58262,2.9448,0.32002,1.3022,1.71738,0.0,0.6572,1.13486,1.58176,0.28058,1.76256,0.5596,0.31076,0.80698,0.49974,0.99704,0.79884,0.9553,0.80588,0.833,1.61038,0.5067,0.647,0.43918,0.31014,0.88616,0.7199,0.60146,1.40498,0.7462,0.40348,0.37676,0.47212,1.7865,1.62726,0.75638,0.5859,1.23076,1.48212,1.72882,1.92952,1.5748,1.59644,1.52792,0.85076,0.45962,0.90622,0.68794,1.73052,0.54698,0.50244,0.78856,0.35106,0.89286,2.12928,0.51524,0.51936,2.00238,0.93688,1.24012,0.0,0.9655,0.60582,0.36078,0.0,0.24076,0.33338,0.59082,0.27712,1.06408,0.0,1.31538,3.49076,1.07014,0.94396,0.6224,0.2329,0.89608,0.73742,0.92284,0.60736,0.59804,1.42646,0.0,1.30336,0.61762,0.88018,0.4571,0.06928,2.52234,4.1901,9.09602,1.26442,0.86758,3.1633,0.95362,1.3969,2.79532,1.19252,0.41948,1.53518,0.7964,1.82664,1.33718,1.24526,0.77952,0.95802,0.45906,4.12886,0.08756,1.48006,0.92014,1.01878,0.0997,0.16556,0.19104,0.11414,0.37084,0.07342,1.67884,0.29828,0.13298,1.20836,1.00028,1.61822,0.25692,1.10562,0.8535,0.53766,0.14304,0.25092,1.12982,0.71772,0.40238,0.83074,0.2061,0.67732,1.27474,0.6704,0.58348,0.4086,1.38674,0.22394,0.92318,0.29138,1.24556,1.02222,0.43468,0.5092,0.0,0.60534,0.89696,0.60266,0.21304,2.48122,1.97632,0.7178,0.97514,1.29142,1.96468,1.31898,0.28508,0.50326,0.91918,0.8541,0.34766,0.66884,0.24622,1.2553,1.91254,1.02916,1.73438,0.86484,1.47146,2.89402,0.43524,0.33924,1.68804,0.72406,1.40692,1.16276,0.5319,2.37998,0.39802,1.01404,1.22698,2.7316,0.33794,0.97206,0.22596,0.29428,0.82542,1.16022,1.01176,0.41512,0.35672,0.64966,3.08788,0.21452,0.62984,0.48954,0.72946,1.02624,0.29834,1.20146,0.41754,0.25258,0.4544,0.43616,0.8947,0.29678,2.3425,0.20676,0.25268,0.35626,0.53576,1.14776,0.0899,0.38708,0.02902,0.22462,4.34794,0.46604,0.58204,20.8919,2.70414,0.4016,0.21434,0.23678,0.91536,0.3886,0.87742,0.45082,1.14658,1.23724,0.672,0.1622,0.10064,0.7555,0.232,0.40268,1.22662,2.18728,0.44302,0.0,0.08674,0.32904,1.09084,0.53474,0.4788,0.85712,0.12094,0.07872,0.06254,0.40376,0.13802,0.91128,0.0,0.20318,0.27922,0.09576,0.21896,0.25556,4.47826,5.44028,0.01862,0.41466,4.24808,1.6147,0.6121,0.16972,1.15174,0.36062,0.37526,0.75678,1.56346,0.13598,0.07164,0.45746,0.10054,0.43616,1.46256,0.09546,4.7509,0.70604,18.66154,6.4993,0.22704,2.35996,0.66252,1.42352,2.71702,0.68336
2018-06-22,0.59128,0.59994,0.08986,0.61152,0.75302,0.39364,0.1046,0.5837,2.59518,0.30882,1.4578,1.83552,0.0,0.62852,1.18134,1.62414,0.26058,1.7098,0.45346,0.31202,0.79168,0.52166,0.94648,0.78628,0.97212,0.8561,0.83288,1.73258,0.52748,0.68964,0.4459,0.30936,0.9318,0.7813,0.69272,1.50762,0.73728,0.38822,0.34516,0.46526,1.74554,1.8273,0.71912,0.5253,1.29252,1.62922,1.79316,2.26552,1.40944,1.52684,1.49594,0.82356,0.44708,0.90054,0.73528,1.66532,0.49668,0.49368,0.83122,0.35116,0.8906,2.0973,0.49694,0.55908,2.1263,0.9343,1.28144,0.0,1.0022,0.60882,0.3417,0.0,0.22186,0.34506,0.05852,0.2787,1.07438,0.0,1.42232,3.90256,1.4164,2.04582,0.6016,0.25264,0.79684,0.78506,0.8526,0.59896,0.63728,1.46848,0.0,1.53784,0.58644,0.99182,0.49844,0.07078,2.51338,3.95522,10.85176,1.26434,0.89222,2.8526,0.9799,1.5199,2.92036,1.25874,0.32332,1.55634,0.84902,1.92366,1.29912,1.28528,0.84756,0.98574,0.46374,4.30164,0.09288,1.8378,0.9081,1.0932,0.10924,0.18698,0.1904,0.111,0.3486,0.07278,1.68604,0.3253,0.13448,1.21464,1.03502,1.76064,0.24514,1.14886,0.87784,0.57078,0.14888,0.23982,1.16794,0.6824,0.41358,0.8841,0.19874,0.70978,1.04938,0.70864,0.57754,0.405,1.88122,0.23614,0.9323,0.30874,1.27578,1.0917,0.422,0.54944,0.0,0.62824,0.94152,0.62594,0.1927,2.22418,1.89024,0.70838,0.98516,1.63752,1.99758,1.30252,0.2894,0.54198,0.98546,0.85514,0.32054,0.71046,0.24576,1.18854,1.9294,0.93826,1.74998,0.86754,1.49926,2.86202,0.40352,0.32762,1.70388,0.75352,1.3371,1.0973,0.50914,2.4693,0.40578,1.01372,1.26514,2.87622,0.33944,0.94544,0.19234,0.29054,0.77956,1.05072,1.02152,0.42584,0.30998,0.58886,3.09342,0.2168,0.59254,0.53718,0.71698,0.79516,0.26438,1.11486,0.45486,0.25182,0.45862,0.44706,0.94222,0.32,2.30848,0.20104,0.2622,0.34184,0.51248,1.05808,0.09642,0.361,0.07588,0.21612,4.08374,0.47262,0.63126,17.81096,2.76828,0.39138,0.20458,0.23914,0.77966,0.40072,0.8562,0.46416,1.10232,1.16016,0.64602,0.17332,0.09748,0.694,0.22666,0.39082,1.0713,2.14128,0.45814,0.0,0.08558,0.34522,1.10064,0.46952,0.48098,0.87292,0.1234,0.07736,0.06548,0.43208,0.13768,0.93326,0.0,0.20692,0.27366,0.09888,0.22698,0.24892,4.3156,5.59772,0.0186,0.40048,4.49902,1.69104,0.64898,0.17464,1.11374,0.4173,0.3539,0.7468,1.52898,0.13042,0.06854,0.47588,0.1074,0.44672,1.59284,0.09656,5.12874,0.69462,17.03334,6.92624,0.22014,2.6578,0.52936,1.46222,2.73668,0.75056
2018-06-25,0.58504,0.58298,3.29262,0.51282,0.77326,0.39634,0.10486,0.57152,2.42868,0.3033,1.36584,1.5724,0.0,0.59538,1.19168,1.535,0.2521,1.57108,0.43048,0.31536,0.79032,0.48938,0.96742,0.7992,0.89826,0.85812,0.74546,1.72552,0.50956,0.64738,0.43602,0.29106,0.95494,0.764,0.73652,1.5389,0.80362,0.32166,0.3423,0.45046,1.65294,1.67104,0.64704,0.5713,1.12798,1.7924,1.85482,2.4411,1.34048,1.4764,1.37806,0.91068,0.43344,0.99706,0.80916,1.47822,0.49156,0.46768,0.7989,0.31484,0.76128,2.02488,0.42838,0.58024,2.15082,0.97266,1.24656,0.0,1.00708,0.5171,0.35424,0.0,0.235,0.30778,0.0,0.26218,1.0066,0.0,1.49676,4.44584,1.81792,5.26966,0.63066,0.23636,0.79732,0.72246,0.77128,0.63084,0.6514,1.70126,0.0,1.72788,0.56108,0.92894,0.47754,0.06898,2.67154,3.68096,11.23738,1.41832,0.98884,2.72628,0.90492,1.56424,2.88614,1.20004,0.0,1.54768,0.8679,1.9276,1.34578,1.19998,0.88984,1.21482,0.47234,3.90112,0.09596,2.01958,0.92612,1.11602,0.10538,0.17152,0.18904,0.0939,0.32314,0.07168,1.6249,0.29336,0.1347,1.5038,1.03104,1.84498,0.2512,1.06124,0.92626,0.5833,0.14992,0.26676,1.06698,0.61464,0.41374,0.89244,0.1918,0.69626,0.95476,0.81856,0.57968,0.41498,2.01386,0.22482,0.95258,0.29438,1.19996,1.11136,0.39476,0.5628,0.0,0.57194,0.83538,0.67348,0.1457,2.29936,1.7698,0.79386,0.9388,1.6201,1.88162,1.20946,0.2895,0.56934,0.85942,0.88312,0.3209,0.65088,0.23624,1.08084,1.7857,0.88634,1.67704,0.74508,1.31216,2.80004,0.37702,0.32958,1.47156,0.79688,1.12508,0.94586,0.46486,2.28696,0.41156,1.00234,1.341,2.88762,0.3326,0.92738,0.18542,0.26526,0.8779,1.05284,0.88232,0.42588,0.28706,0.51656,2.69216,0.2087,0.65248,0.54696,0.5492,0.76536,0.24834,1.11992,0.46532,0.24524,0.41696,0.42944,0.92554,0.33518,1.90674,0.17104,0.25218,0.36448,0.46834,0.96952,0.09546,0.3714,0.17104,0.21008,3.82544,0.6181,0.5931,16.05002,3.01254,0.37174,0.20128,0.17566,0.6727,0.43354,0.67402,0.43158,1.07856,1.0932,0.6558,0.14292,0.09482,0.6831,0.22524,0.37512,1.05468,2.19482,0.4502,0.0,0.0776,0.35044,1.03056,0.45878,0.4732,0.82036,0.11422,0.07452,0.06354,0.40022,0.12888,0.91092,0.0,0.19962,0.2608,0.0979,0.22168,0.23894,3.98096,5.42596,0.01844,0.3474,4.20812,1.63566,0.67668,0.16042,0.97394,0.44626,0.32822,0.70456,1.41458,0.13196,0.06572,0.4588,0.10634,0.44066,1.62786,0.09314,5.62802,0.6307,17.42192,6.8156,0.20246,2.90364,0.56058,1.42512,2.66466,0.73132
2018-06-26,0.53358,0.58122,5.06268,0.46208,0.64144,0.28104,0.08792,0.4812,2.11406,0.24594,1.3488,1.39118,0.0,0.63032,1.10828,1.34964,0.21456,1.327,0.484,0.23998,0.53166,0.35982,0.81968,0.83644,1.00914,0.74908,0.61926,1.41782,0.41778,0.65784,0.33458,0.24596,1.11804,0.63244,0.69994,1.41546,0.75588,0.2287,0.29794,0.43884,1.47986,1.66222,0.86374,0.52074,0.9714,1.59146,1.94978,2.38812,1.05068,1.25016,1.10304,0.8394,0.37886,0.94054,0.78098,1.36276,0.43596,0.40906,0.75364,0.30594,0.8323,1.95822,0.38338,0.65034,1.8944,0.86786,0.9826,0.0,1.24078,0.41036,0.35744,0.0,0.25444,0.27694,0.0,0.21694,0.9147,0.0,1.48166,4.14728,2.0631,6.19962,0.56306,0.19604,0.71904,0.6111,0.57584,0.59804,0.55366,1.61734,0.0,1.36192,0.4015,0.8336,0.46116,0.05056,2.7822,3.08516,11.2484,1.42632,0.95178,2.57034,0.73886,1.44082,2.81542,1.07052,0.0,1.462,0.79914,1.7511,1.14746,1.08098,0.82406,1.20578,0.42792,3.59398,0.08274,2.12362,0.73702,1.02414,0.1078,0.15918,0.16408,0.05114,0.28906,0.06484,1.2531,0.27362,0.0842,1.35418,0.84456,1.64786,0.19468,1.07958,0.9418,0.46,0.13122,0.2808,0.90812,0.49026,0.31248,0.85074,0.13986,0.5788,0.7584,0.68126,0.4945,0.46032,2.01398,0.1607,0.91876,0.198,1.18154,0.97036,0.22608,0.45266,0.0,0.38286,0.71836,0.63306,0.12034,1.75558,1.4747,0.66742,0.70078,1.44462,1.48914,0.65812,0.24172,0.53768,0.65122,0.73856,0.29236,0.33982,0.1716,0.88246,1.5542,0.74058,1.35172,0.57394,1.15974,2.34146,0.34096,0.3079,1.2073,0.68776,0.8818,0.74362,0.39252,2.05146,0.3564,0.8935,1.40132,2.31128,0.27534,0.75174,0.1735,0.12526,0.82178,0.81328,0.6591,0.33496,0.18474,0.3467,2.61144,0.1621,0.65212,0.50462,0.45566,0.68544,0.2181,0.9647,0.42266,0.19312,0.32578,0.36666,0.89066,0.2905,1.16048,0.14266,0.20996,0.35724,0.42338,0.84752,0.07202,0.3196,0.67466,0.13348,3.34792,0.62232,0.4172,14.32818,3.32062,0.24578,0.17978,0.14686,0.52646,0.35728,0.53892,0.33152,0.8602,0.93058,0.44816,0.12472,0.072,0.57964,0.14354,0.27718,0.73198,1.91088,0.38148,0.0,0.0574,0.30008,0.80814,0.33634,0.45454,0.52574,0.09386,0.04766,0.04798,0.2747,0.1024,0.81308,0.0,0.14846,0.22168,0.07672,0.17206,0.16804,2.9923,4.47882,0.01522,0.36462,3.60636,1.46288,0.59314,0.13388,0.68842,0.38812,0.24946,0.61268,1.10498,0.10968,0.04832,0.37402,0.08906,0.33172,1.1156,0.05888,5.36266,0.53572,16.83726,6.09614,0.198,2.70802,0.61768,1.21182,2.77282,0.61224
2018-06-27,0.56638,0.63866,6.3377,0.5109,0.63692,0.26552,0.07632,0.5217,2.03616,0.23516,1.57594,1.36446,0.0,0.6326,1.15832,1.00348,0.21234,1.24406,0.54608,0.24182,0.47468,0.30834,0.8944,0.8006,1.10392,0.84594,0.5923,1.30466,0.37916,0.65298,0.30426,0.20608,1.28898,0.56828,0.66368,1.21872,0.8398,0.21164,0.2928,0.4296,1.4486,1.49214,1.27446,0.54314,0.88072,1.56774,2.17938,2.57278,1.01644,1.2058,1.03988,0.85316,0.3735,0.88792,0.9083,1.22878,0.47448,0.39688,0.74266,0.32674,0.86874,1.96338,0.38554,0.70808,1.57502,0.88998,0.75436,0.0,1.32168,0.41752,0.33932,0.0,0.2534,0.24534,0.0,0.22818,0.93954,0.0,1.58286,3.98978,1.70206,6.83444,0.63126,0.19344,0.78412,0.57038,0.61954,0.78306,0.62142,1.6451,0.0,1.0374,0.38796,0.79836,0.56994,0.04396,1.80556,2.87342,10.2679,1.14042,0.83182,2.73014,0.66058,1.2533,2.69874,1.0114,0.0,1.3005,0.73622,1.42364,1.42854,1.03478,0.77446,1.2306,0.40174,3.36592,0.087,2.13032,0.5884,0.95178,0.11058,0.15588,0.17776,0.0458,0.314,0.066,1.18396,0.2944,0.1056,1.51834,0.77558,1.6958,0.20226,0.96378,1.0419,0.42482,0.1265,0.31134,0.69068,0.51686,0.289,0.5818,0.141,0.55034,0.7802,0.69214,0.41234,0.50706,2.02864,0.13788,1.0122,0.1653,1.1227,1.02576,0.21878,0.44982,0.0,0.34904,0.659,0.57654,0.11686,1.86624,1.55188,0.87202,0.75632,1.4464,1.36472,0.61436,0.20946,0.49958,0.64306,0.6935,0.28834,0.31872,0.14686,0.9105,1.28622,0.77316,1.23006,0.50508,1.04162,2.07578,0.32596,0.32614,0.97794,0.61056,0.80572,0.68342,0.33124,1.95088,0.30826,0.8667,1.38994,2.2464,0.26886,0.63888,0.17584,0.11056,0.9516,0.80406,0.62176,0.26422,0.14372,0.3261,2.35454,0.1519,0.7417,0.4679,0.42396,0.72696,0.21036,1.00126,0.39606,0.19194,0.26646,0.40226,0.83184,0.24954,1.06274,0.1362,0.1883,0.38614,0.43124,0.8691,0.07142,0.32576,12.47134,0.13652,3.21766,0.70746,0.3666,12.51704,3.49196,0.25638,0.17038,0.14348,0.44882,0.29526,0.62426,0.27978,0.81042,0.90674,0.39406,0.12704,0.0787,0.57112,0.15326,0.25336,0.6761,1.83354,0.35808,0.0,0.06164,0.2839,0.72188,0.33538,0.4876,0.45656,0.08048,0.04176,0.04792,0.27172,0.09128,0.64892,0.0,0.1426,0.2114,0.07912,0.16682,0.18152,2.68202,4.34578,0.01732,0.40668,2.86672,1.28656,0.65808,0.1241,0.5752,0.31608,0.28882,0.55048,1.17804,0.1027,0.05198,0.34792,0.07702,0.30566,1.19186,0.05828,5.10538,0.49504,15.46422,5.6153,0.18374,2.44706,0.58098,1.13278,3.09912,0.5658
2018-06-28,0.5979,0.57374,7.45272,0.46354,0.5787,0.24934,0.07516,0.5383,1.76482,0.23342,1.7669,1.05542,0.0,0.60296,1.0709,0.79028,0.22096,1.08364,0.67624,0.22472,0.41376,0.264,0.90284,0.74512,1.06816,0.73344,0.47736,1.23548,0.41466,0.62918,0.31734,0.22998,1.55598,0.51486,0.58868,0.92536,0.8749,0.20508,0.26782,0.43178,1.42556,1.1784,1.35212,0.56878,0.70542,1.37924,2.17882,2.11614,0.88904,1.16624,0.95888,0.91598,0.4828,0.81688,0.87906,1.13498,0.56094,0.31716,0.78182,0.30936,0.80822,1.90782,0.37646,0.71036,1.40238,0.89286,0.6674,0.0,1.3344,0.42906,0.34938,0.0,0.2442,0.2231,0.0,0.23744,0.79236,0.0,1.4856,4.00548,1.43382,6.92336,0.63172,0.18542,0.96156,0.57326,0.68762,0.91994,0.59524,1.55626,0.0,0.78032,0.30338,0.95312,0.56336,0.04668,1.51164,2.99398,9.0505,0.96606,0.74858,2.9306,0.62044,1.1036,2.68158,0.9213,0.0,1.22708,0.72964,1.26078,1.41672,1.32872,0.7829,1.22656,0.4072,2.86954,0.09206,1.6797,0.57302,0.67538,0.10688,0.16064,0.19292,0.04104,0.3077,0.05934,1.12112,0.27598,0.11638,1.52308,0.70486,1.51544,0.21422,0.84856,1.00904,0.4086,0.13796,0.38992,0.6265,0.48656,0.27842,0.41462,0.14556,0.61958,0.72424,0.66552,0.39118,0.50946,1.64454,0.1235,1.1676,0.14456,0.98108,0.99882,0.18578,0.42994,0.0,0.2921,0.5614,0.59684,0.11194,1.78428,1.39624,1.00196,0.66558,1.38128,1.2225,0.54462,0.23488,0.38036,0.586,0.66772,0.28764,0.30886,0.12388,0.87206,1.18268,0.81262,1.07096,0.46058,1.1459,1.86586,0.32486,0.3447,0.8964,0.53166,0.71464,0.60682,0.32656,1.7738,0.30622,0.7771,1.30382,1.8171,0.24384,0.48482,0.17798,0.10026,0.97598,0.72474,0.58828,0.22844,0.16172,0.34016,1.96436,0.13438,0.79052,0.3792,0.44116,0.7298,0.19914,0.95316,0.33026,0.18612,0.28596,0.40412,0.76246,0.24866,0.9982,0.12164,0.1839,0.35268,0.43062,0.88448,0.07088,0.34488,20.36986,0.1172,3.25222,0.75226,0.3457,10.5231,3.11798,0.25868,0.17174,0.12672,0.3804,0.27134,0.60194,0.2288,0.69736,0.91632,0.39154,0.13804,0.0791,0.5952,0.15438,0.23448,0.65812,1.60468,0.358,0.0,0.06308,0.31832,0.63246,0.34744,0.52186,0.4103,0.07776,0.0366,0.04372,0.25414,0.08904,0.68458,0.0,0.12754,0.25878,0.06718,0.15884,0.18722,2.6493,4.0601,0.02034,0.40482,2.25486,1.38728,0.63318,0.10562,0.51626,0.46146,0.28582,0.60374,1.213,0.09734,0.0534,0.31654,0.07686,0.27732,1.05444,0.0549,4.7079,0.45052,13.4684,5.40304,0.17722,2.39928,0.61828,1.05986,2.94996,0.52018
2018-06-29,0.59994,0.5549,8.63614,0.48836,0.48792,0.23296,0.07788,0.51608,1.6965,0.245,1.55744,1.12742,0.0,0.6186,1.07734,0.98118,0.25068,1.05844,0.72076,0.2449,0.46456,0.2591,0.9125,1.06102,1.0638,0.7399,0.4199,1.28106,0.42414,0.65664,0.33248,0.24606,1.62806,0.48006,0.54582,0.84352,0.8647,0.20212,0.2643,0.41824,1.85024,1.0171,1.4406,0.62004,0.69554,1.44758,2.21432,2.06566,0.8695,1.19018,0.95212,0.98412,0.49532,0.83672,0.91576,1.2465,0.5681,0.35962,0.79392,0.3189,0.83528,2.1207,0.38456,0.77742,1.53106,0.98192,0.67294,0.0,1.56722,0.44964,0.36482,0.0,0.2522,0.23818,0.0,0.2566,0.90582,0.0,1.57294,3.88722,1.3916,6.04048,0.648,0.17472,1.04102,0.54368,0.71642,0.99394,0.62074,1.60604,0.0,0.70976,0.3175,0.96128,0.59102,0.05726,1.17694,3.03642,8.95832,1.07864,0.78344,3.13002,0.7457,1.10296,2.95788,0.92412,0.0,1.37722,0.7232,1.26436,1.51862,1.50754,0.81346,1.3152,0.427,2.88124,0.08812,1.4475,0.54932,0.55086,0.10292,0.13676,0.2028,0.03946,0.31622,0.06092,1.2453,0.26892,0.11252,1.5664,0.72824,1.44234,0.22266,0.86566,1.02084,0.40596,0.14512,0.43762,0.59482,0.5117,0.3032,0.36704,0.1558,0.63124,0.72322,0.6447,0.39746,0.50144,1.20668,0.116,1.24848,0.12408,0.96248,0.99028,0.18032,0.41448,0.0,0.27572,0.54242,0.57572,0.11626,1.80346,1.4696,1.11894,0.6302,1.15346,1.30304,0.5379,0.24148,0.30296,0.54494,0.72342,0.27682,0.26788,0.11906,0.88248,1.11172,0.75376,0.99204,0.484,1.23712,1.91962,0.32682,0.35118,0.94746,0.51824,0.72952,0.52208,0.36844,1.95634,0.32582,0.75968,1.37508,1.65702,0.2304,0.44712,0.21018,0.1038,0.99432,0.76812,0.60258,0.23256,0.16664,0.35844,2.06082,0.13816,0.78574,0.35684,0.4644,0.7387,0.1874,1.01088,0.307,0.15696,0.28804,0.40762,0.71856,0.25092,0.99118,0.13168,0.19616,0.3315,0.4143,0.88196,0.06728,0.33584,26.61864,0.11074,3.46244,0.78292,0.26474,9.66838,3.26558,0.26152,0.16832,0.12892,0.37388,0.25492,0.58764,0.22886,0.7368,0.977,0.42192,0.14536,0.08108,0.62838,0.15114,0.25514,0.65762,1.73224,0.34992,0.0,0.06348,0.31654,0.65514,0.34782,0.50736,0.41408,0.07672,0.0342,0.03972,0.22012,0.11716,0.76306,0.0,0.1202,0.29184,0.06508,0.16344,0.1976,2.97026,4.20614,0.02258,0.40764,1.85844,1.4811,0.65432,0.10502,0.54234,0.55106,0.2969,0.64576,1.24212,0.10122,0.05482,0.31338,0.07744,0.2742,0.96806,0.05798,4.51274,0.49844,12.93864,5.50408,0.17044,2.32562,0.6967,1.1909,3.32338,0.50442
2018-07-02,0.61396,0.60232,6.87938,0.53404,0.47448,0.2379,0.07988,0.60742,1.52206,0.28368,1.38588,1.24596,0.0,0.65788,1.04796,1.06518,0.2694,1.41886,0.7546,0.27936,0.4943,0.28744,0.95158,1.51874,1.10962,0.69054,0.39844,1.31962,0.4529,0.6874,0.37934,0.2575,1.73956,0.47448,0.53428,0.85356,0.8097,0.2144,0.26646,0.41902,2.01122,0.97644,1.54518,0.60076,0.70874,1.22418,2.30076,2.07462,0.86978,1.44814,0.92062,0.98804,0.50644,0.82338,0.99654,1.40096,0.5764,0.39722,0.7656,0.35094,0.88596,2.2482,0.39972,0.81096,1.68982,0.99126,0.67498,0.0,2.27004,0.48622,0.34346,0.0,0.24222,0.24706,0.0,0.2692,1.03346,0.0,1.67372,3.45206,1.19952,3.05394,0.67278,0.1835,1.07856,0.55618,0.81516,1.08122,0.62836,1.44822,0.0,0.62352,0.34366,0.97228,0.60168,0.06232,0.79244,2.96116,9.56226,1.1002,0.7746,3.21546,0.82798,1.07088,3.12048,0.93394,0.0,1.63718,0.72096,1.28492,1.59264,1.75232,0.88052,1.20762,0.456,2.76728,0.087,1.30828,0.4845,0.4986,0.1303,0.14238,0.21284,0.0393,0.32324,0.06312,1.30598,0.37166,0.11616,1.22212,0.77756,1.44288,0.21812,0.87584,1.12862,0.40304,0.15058,0.60048,0.56738,0.5563,0.31438,0.35104,0.16102,0.66518,0.74048,0.50262,0.39258,0.53636,1.07384,0.13208,1.36198,0.12822,0.99332,0.9892,0.186,0.419,0.0,0.27768,0.54964,0.54252,0.12244,1.66964,1.5244,1.10628,0.70594,1.16342,1.3392,0.6044,0.24242,0.2653,0.52918,0.73668,0.2387,0.28998,0.1172,0.96016,1.33792,0.6593,1.01912,0.53066,1.41998,1.92652,0.3388,0.35854,0.99848,0.47924,0.7697,0.51426,0.40134,2.09716,0.30024,0.80226,1.4115,1.30096,0.20774,0.43956,0.23208,0.11546,0.85592,0.78392,0.62432,0.24108,0.17066,0.3679,2.3062,0.15916,0.7672,0.32314,0.46932,0.85438,0.23006,0.98034,0.31242,0.14482,0.29052,0.42592,0.71772,0.2417,1.03802,0.16126,0.1994,0.3093,0.39734,0.89894,0.07416,0.29248,31.4252,0.11588,3.65898,0.672,0.25766,9.78222,3.45892,0.2801,0.16198,0.14924,0.35998,0.2071,0.60976,0.24056,0.77112,1.14256,0.41418,0.15128,0.0861,0.68242,0.15198,0.26192,0.63204,1.76442,0.35414,0.0,0.0642,0.31104,0.67358,0.37638,0.5006,0.43558,0.0852,0.03348,0.0446,0.23726,0.1437,0.8501,0.0,0.12262,0.30274,0.06422,0.1521,0.22066,3.04812,4.23552,0.02352,0.40402,1.71532,1.53534,0.64182,0.10648,0.57376,0.60396,0.319,0.78506,1.29164,0.09366,0.0592,0.31612,0.08342,0.30386,0.91642,0.06168,3.8555,0.56792,12.78862,5.51304,0.15946,2.1268,0.72472,1.27482,3.64496,0.4931
2018-07-03,0.63152,0.5937,6.81438,0.51328,0.43304,0.24498,0.07962,0.75382,1.56714,0.31478,1.0877,1.2984,0.0,0.55466,1.16516,1.13366,0.28226,1.72642,0.86568,0.27838,0.532,0.29178,1.05994,1.78122,0.83534,0.71378,0.36792,1.34104,0.45186,0.7347,0.40546,0.25486,1.62544,0.55266,0.51612,0.96682,0.87674,0.20216,0.26904,0.3836,2.19442,0.889,1.25652,0.67726,0.7952,1.1329,2.271,2.1686,0.95594,1.77918,1.23184,1.0681,0.51714,0.91448,1.2198,1.74076,0.56572,0.43138,1.11304,0.31862,0.74312,2.44048,0.44026,0.6698,1.79,1.14998,0.7283,0.0,2.452,0.59174,0.42534,0.0,0.23562,0.23456,0.0,0.32706,1.13904,0.0,1.76538,3.56576,1.27928,2.2645,0.71498,0.1771,1.0825,0.56756,1.06916,1.17252,0.6793,1.2487,0.0,0.59106,0.37184,0.98966,0.60442,0.06746,0.3749,2.81578,10.04804,1.1318,0.88112,3.28164,0.86912,1.0333,3.23246,0.9526,0.0,1.75328,0.78102,1.35842,1.66556,1.8836,1.0362,1.15948,0.50018,2.83148,0.08678,1.3781,0.54568,0.46898,0.14636,0.13298,0.2157,0.04084,0.33298,0.06126,1.27984,0.4181,0.11994,1.4669,0.79176,1.36696,0.22156,0.91404,1.08172,0.41296,0.17552,0.65928,0.61538,0.66838,0.31704,0.317,0.16396,0.69486,0.79936,0.63046,0.41668,0.48664,1.0504,0.13482,1.46602,0.1216,0.95986,1.09836,0.1833,0.40248,0.0,0.2935,0.63574,0.64708,0.12438,1.56998,1.44354,1.15996,0.65436,1.15486,1.52214,0.62248,0.24158,0.33734,0.48272,0.70466,0.28246,0.29696,0.1259,1.02144,1.38636,0.60648,1.112,0.56458,1.5794,1.94556,0.37072,0.41626,1.0495,0.51782,0.7543,0.51794,0.4398,2.15872,0.32186,0.8297,1.4092,0.86458,0.1978,0.47084,0.2295,0.12508,0.78718,0.80994,0.64164,0.24854,0.17518,0.37758,2.8344,0.15568,0.81666,0.2801,0.4551,0.90256,0.22818,1.1518,0.40348,0.14924,0.28216,0.41872,0.67002,0.2423,1.3167,0.17882,0.19788,0.28494,0.3837,0.84682,0.07178,0.2846,35.03814,0.11622,3.48002,0.73774,0.24022,9.35312,2.92802,0.28358,0.14894,0.14566,0.34886,0.20692,0.6003,0.23414,0.73108,1.14578,0.37456,0.1641,0.09092,0.72512,0.14914,0.26576,0.6639,2.03268,0.34548,0.0,0.06566,0.30614,0.66592,0.41044,0.49916,0.42436,0.09756,0.0332,0.04854,0.23684,0.19166,0.87644,0.0,0.12448,0.29702,0.064,0.14984,0.22584,3.1563,4.23362,0.0242,0.37108,1.78694,1.50716,0.75416,0.10818,0.54492,0.65588,0.27112,0.8127,1.2984,0.08674,0.05564,0.33132,0.07888,0.33854,0.86018,0.07028,3.53648,0.66298,12.50264,5.85766,0.16506,2.10898,0.6828,1.36404,3.74744,0.49956
2018-07-04,0.59262,0.50672,8.2979,0.46836,0.39858,0.241,0.08032,0.80438,1.50844,0.39428,0.62372,1.19982,0.0,0.47618,1.23208,1.10268,0.26098,1.86216,0.86634,0.28218,0.53472,0.31768,1.00124,1.82392,0.67648,0.55696,0.35524,1.35216,0.43884,0.76472,0.42086,0.2544,1.50246,0.56362,0.52042,0.95932,0.78484,0.19326,0.2498,0.37422,2.28834,0.8655,0.82956,0.6402,0.79784,1.03156,2.01016,1.9569,1.18922,1.99442,1.15392,1.00614,0.48226,0.86094,1.05066,1.9626,0.53584,0.50168,1.33514,0.32524,0.70828,2.55384,0.44202,0.65078,1.85728,1.15288,0.78116,0.0,2.76796,0.60034,0.45878,0.0,0.2619,0.22934,0.0,0.36052,1.02566,0.0,1.72746,3.55572,1.25898,1.83512,0.65334,0.15654,1.09636,0.52512,1.02966,1.02402,0.62288,1.16884,0.0,0.59386,0.426,0.9976,0.5301,0.0676,0.32226,2.71758,10.26074,1.12624,0.85118,3.23518,0.91494,1.02852,3.42582,1.086,0.0,1.80344,0.8017,1.51844,1.18612,1.8359,1.19776,1.09754,0.5014,2.81008,0.07936,1.36714,0.64546,0.46966,0.15418,0.12324,0.20154,0.0381,0.30472,0.05988,1.27084,0.48242,0.09796,1.42012,0.77088,1.43538,0.21448,0.88618,0.96844,0.40886,0.18372,0.6779,0.63018,0.59714,0.32086,0.29694,0.15328,0.71046,0.75498,0.6044,0.41986,0.50664,1.05072,0.1373,1.46886,0.12536,0.9567,0.9534,0.1706,0.38228,0.0,0.28306,0.6987,0.63272,0.16842,1.32878,1.34228,0.93862,0.58038,1.02542,1.70506,0.64392,0.23842,0.36336,0.47158,0.69526,0.26622,0.35232,0.12764,0.91306,1.30054,0.51074,1.18428,0.80856,1.64974,1.9806,0.35566,0.38552,1.10784,0.53688,1.0111,0.48624,0.4598,2.15994,0.29338,0.78732,1.367,0.5934,0.20794,0.471,0.22984,0.12714,0.66832,0.8216,0.63206,0.25126,0.17594,0.3593,3.0681,0.15486,0.73322,0.28292,0.43346,0.74352,0.2315,1.04614,0.43126,0.147,0.28412,0.37484,0.62728,0.23754,1.35954,0.19084,0.20046,0.24938,0.35508,0.71864,0.06842,0.2956,29.01486,0.10906,3.42756,0.73384,0.23762,9.06122,2.5494,0.25838,0.147,0.13792,0.33812,0.20716,0.69254,0.22752,0.70198,1.10926,0.36356,0.16334,0.08946,0.69234,0.13136,0.25896,0.63472,2.21026,0.33104,0.0,0.06046,0.30476,0.65058,0.39286,0.43136,0.52848,0.09656,0.0334,0.04798,0.22734,0.21784,0.8747,0.0,0.11576,0.28722,0.05026,0.13094,0.20452,3.25336,3.92496,0.02102,0.33382,1.8452,1.55888,0.71668,0.10868,0.62114,0.68834,0.23186,0.84592,1.24164,0.1135,0.05026,0.3165,0.09402,0.35988,0.75352,0.06928,3.06868,0.73618,12.80022,6.30772,0.15434,2.15492,0.64268,1.35814,3.62708,0.48856
2018-07-05,0.5606,0.46652,8.60528,0.52154,0.43286,0.25628,0.0754,0.8882,1.50742,0.47094,0.0,1.23636,0.0,0.48674,1.29408,1.10314,0.26388,1.9665,0.8783,0.3073,0.54132,0.38242,1.00338,1.93648,0.6037,0.71446,0.36182,1.3206,0.38824,0.75098,0.41208,0.21918,1.2035,0.59966,0.5466,0.98906,0.78282,0.21778,0.25088,0.33398,2.36314,0.82696,0.81238,0.66982,0.87452,1.01834,1.85122,1.95534,1.25278,2.11146,1.20418,0.97718,0.34996,0.83486,1.03152,2.10494,0.5408,0.57966,1.48802,0.33102,0.72594,2.62618,0.43908,0.92554,1.90228,1.41788,0.8448,0.0,3.18582,0.59192,0.43774,0.0,0.2643,0.25934,0.0,0.3803,1.07782,0.0,1.60906,3.53606,1.24776,1.7145,0.55276,0.17966,0.96642,0.46798,0.93642,0.89808,0.61314,1.19682,0.0,0.5531,0.64946,0.84646,0.51918,0.06038,0.31536,2.41012,9.97798,1.08848,0.84474,2.97654,0.92138,0.98054,3.32696,1.20652,0.0,1.8275,0.77852,1.49088,1.13502,1.45466,1.27952,1.04074,0.51046,2.7458,0.06922,0.98882,0.68098,0.49148,0.15996,0.12314,0.1872,0.04728,0.28956,0.06144,1.33328,0.53482,0.09932,1.54318,0.70476,1.44258,0.21994,0.91888,0.97054,0.38744,0.16518,0.663,0.57542,0.58774,0.3301,0.29956,0.15236,0.62438,0.80106,0.70344,0.42284,0.5327,1.03658,0.15206,1.39648,0.12588,1.0149,0.95296,0.19942,0.3725,0.0,0.31338,0.77968,0.62954,0.18792,1.27952,1.2877,0.84946,0.62754,1.04528,1.73534,0.80866,0.19802,0.3591,0.4086,0.70536,0.31262,0.42866,0.13216,0.89766,1.27788,0.46916,1.38024,0.86496,1.46246,2.19448,0.33164,0.3796,1.10708,0.54374,1.11522,0.57348,0.50606,2.14356,0.2783,0.75778,1.34644,0.50834,0.23124,0.4753,0.2529,0.1308,0.62252,0.88958,0.65952,0.27258,0.12698,0.31442,3.2499,0.15524,0.7147,0.27026,0.37962,0.6706,0.2506,1.05748,0.42436,0.17052,0.22486,0.35776,0.67154,0.22418,1.4488,0.2163,0.19406,0.24204,0.3166,0.75932,0.06556,0.29424,26.32136,0.1178,3.15126,0.77952,0.30288,9.1471,2.515,0.28028,0.2068,0.1361,0.29664,0.18984,0.7608,0.23878,0.73894,1.14814,0.3996,0.1586,0.09688,0.65772,0.1357,0.25542,0.6631,2.2545,0.29216,0.0,0.06252,0.26312,0.74204,0.3884,0.37404,0.64258,0.09488,0.03848,0.0628,0.23392,0.21836,0.76794,0.0,0.11246,0.2434,0.05346,0.12342,0.22314,3.2439,3.91084,0.01728,0.31806,1.7673,1.38112,0.68678,0.1152,0.81546,0.52884,0.2459,0.72514,1.24138,0.11724,0.05044,0.32178,0.09584,0.37778,0.75928,0.07344,2.98508,0.76534,12.38706,6.61952,0.14164,2.06162,0.57638,1.29738,3.9422,0.55936
2018-07-06,0.59706,0.4653,8.5568,0.5563,0.43432,0.29334,0.07444,0.94812,1.5198,0.55428,0.0,1.17462,0.0,0.48436,1.5914,0.8888,0.2448,2.08724,0.96226,0.33988,0.53886,0.44348,1.00402,1.77542,0.60056,0.77796,0.37042,1.29498,0.40008,0.76248,0.41544,0.21354,1.24508,0.5921,0.55934,0.93284,0.83666,0.22544,0.27244,0.31466,2.12572,0.7422,0.78078,0.71918,0.83616,0.88476,1.88056,1.80074,1.31322,2.25152,1.22504,0.98982,0.3248,0.77784,1.02918,2.21864,0.54444,0.5603,1.50008,0.34998,0.73528,2.57792,0.45848,1.05406,1.80192,2.22066,0.79888,0.0,3.45194,0.64826,0.44016,0.0,0.2647,0.24258,0.0,0.44888,1.01412,0.0,1.57528,3.92702,1.2851,1.7168,0.5608,0.17552,0.9813,0.4498,0.90738,0.96676,0.60056,1.19604,0.0,0.59028,0.74532,0.8435,0.48388,0.04962,0.30594,2.41548,8.99326,0.95656,0.84572,2.87734,0.8406,0.95516,3.27476,1.68928,0.0,1.81732,0.7431,1.56194,1.04374,1.29996,1.37622,0.99196,0.56488,3.13894,0.07142,0.87072,0.68866,0.48354,0.16252,0.13624,0.1931,0.04752,0.27772,0.0593,1.36326,0.56668,0.09598,1.53974,0.75664,1.44132,0.23276,0.8537,1.034,0.39176,0.165,0.68344,0.60342,0.57456,0.32076,0.28528,0.1457,0.68182,0.84874,0.70254,0.43714,0.58202,0.81772,0.1683,1.44878,0.1355,1.07076,0.94262,0.21222,0.39322,0.0,0.30686,0.87808,0.70956,0.24876,1.2822,1.20658,0.82606,0.69624,1.06032,1.71724,0.79552,0.20474,0.36728,0.40204,0.694,0.33098,0.44692,0.1352,0.88828,1.3556,0.47388,1.55158,0.91402,1.40562,2.23686,0.32754,0.39748,1.04606,0.57118,1.15368,0.5615,0.4933,3.02522,0.26196,0.80738,1.38408,0.32002,0.27676,0.5108,0.23812,0.1269,0.63636,0.89562,0.68538,0.30358,0.16848,0.29754,3.66854,0.16024,0.75536,0.25868,0.36588,0.70952,0.26282,1.13706,0.44814,0.1759,0.23888,0.3655,0.63972,0.2201,1.45498,0.28392,0.19274,0.24292,0.32326,0.76856,0.07082,0.31832,25.26846,0.1277,3.25206,0.7647,0.32364,8.75154,2.4589,0.27654,0.26406,0.1417,0.28708,0.19824,0.82986,0.27898,0.68862,1.0944,0.37388,0.14492,0.10328,0.6908,0.14304,0.23292,0.71194,2.14292,0.2954,0.0,0.06714,0.28014,0.78618,0.42616,0.38336,0.64618,0.10508,0.0417,0.07516,0.23892,0.19178,0.76582,0.0,0.1143,0.2107,0.05926,0.1209,0.21576,3.00924,3.81358,0.01566,0.32486,1.87498,1.3221,0.681,0.11402,0.82642,0.4534,0.24484,0.68074,1.25226,0.11374,0.05184,0.31908,0.0944,0.45322,0.74114,0.07626,2.94894,0.77444,13.70806,6.38242,0.1472,1.98918,0.52234,1.18948,3.94668,0.5739
2018-07-09,0.60806,0.3744,8.07638,0.53698,0.41834,0.28396,0.06894,0.88452,1.51444,0.57208,0.0,1.05794,0.0,0.46964,1.58522,0.76264,0.23412,1.7651,1.00338,0.31556,0.5409,0.42926,0.99712,1.63618,0.50342,0.87832,0.3584,1.29956,0.3763,0.78876,0.36952,0.21716,1.17778,0.57126,0.55532,0.86762,0.86768,0.2314,0.2654,0.31354,2.02262,0.6822,0.69446,0.72956,0.80052,0.85364,1.57386,1.70906,1.38472,2.25068,1.27622,1.0134,0.30294,0.717,0.88598,2.12062,0.58648,0.52398,1.63866,0.34484,0.69418,2.57594,0.52474,1.14564,1.71336,2.62554,0.77398,0.0,3.46734,0.68504,0.44986,0.0,0.26706,0.26506,0.0,0.50458,0.89928,0.0,1.6506,4.2919,1.3154,1.667,0.59496,0.17596,0.96668,0.43598,0.83854,0.97316,0.62014,1.17502,0.0,0.67892,0.84936,0.7906,0.53504,0.0444,0.27458,2.23876,7.74286,0.84126,0.82896,2.82984,0.7829,0.91662,3.2156,1.84932,0.0,1.75916,0.86366,1.56734,0.96866,1.15438,1.36474,0.9199,0.69608,3.3003,0.07106,0.88006,0.6922,0.46088,0.15056,0.15114,0.19126,0.0427,0.27468,0.05656,1.34938,0.5078,0.0895,1.56784,0.72704,1.28174,0.24166,0.83964,0.89218,0.39984,0.16654,0.58922,0.56762,0.60798,0.3043,0.26762,0.13834,0.67718,0.81566,0.67708,0.42428,0.55066,0.60732,0.16034,1.60592,0.12978,1.09902,0.92732,0.20554,0.38002,0.0,0.31634,0.89646,0.73418,0.2711,1.31286,1.30284,0.73004,0.6262,1.05476,1.86338,0.69408,0.19952,0.3731,0.35726,0.63476,0.36004,0.446,0.12874,0.82948,1.0825,0.4981,1.59098,0.88974,1.23434,2.52578,0.3355,0.40228,1.01552,0.61334,1.14856,0.55008,0.47208,3.29346,0.26462,0.78612,1.41004,0.2652,0.28848,0.50606,0.24296,0.12244,0.68124,0.93928,0.68912,0.30252,0.17018,0.30976,3.7578,0.14998,0.75996,0.25572,0.33748,0.6271,0.23452,1.1662,0.4568,0.17074,0.23728,0.40124,0.59486,0.22448,1.43346,0.2875,0.17386,0.22938,0.32058,0.75592,0.0636,0.33594,24.06306,0.11648,3.022,0.7728,0.34904,8.0051,2.05238,0.25298,0.29034,0.11872,0.27412,0.20262,0.81454,0.27566,0.6717,0.89794,0.36304,0.1516,0.10752,0.6531,0.14824,0.23554,0.71438,2.01792,0.28568,0.0,0.06966,0.27652,0.75878,0.4086,0.41976,0.59904,0.10438,0.04082,0.0779,0.2161,0.19444,0.70082,0.0,0.10664,0.1861,0.05832,0.11758,0.20404,2.91938,3.84312,0.01498,0.30418,1.64156,1.26732,0.67112,0.10766,0.80886,0.43688,0.22064,0.57908,1.26946,0.11352,0.05314,0.30942,0.08568,0.46148,0.7146,0.07746,2.9138,0.70634,13.30772,6.74068,0.1586,2.06346,0.5346,1.13612,3.85684,0.5897
2018-07-10,0.56402,0.34132,7.5709,0.51852,0.39272,0.2688,0.06328,0.77432,1.48758,0.57142,0.0,1.12128,0.0,0.41586,1.36304,0.63698,0.27336,1.4703,0.81772,0.30278,0.51596,0.43576,0.85648,1.57626,0.52756,0.95646,0.37116,1.2161,0.36052,0.6732,0.32314,0.2097,1.38334,0.50648,0.51908,0.71384,0.76394,0.24136,0.24314,0.31818,1.7346,0.68214,0.69684,0.6299,0.66726,0.80918,1.34898,1.52828,1.35272,1.9771,0.95408,0.87446,0.32574,0.60528,0.66698,1.687,0.60202,0.4773,1.34132,0.32148,0.74012,2.35032,0.5457,1.31204,1.47814,2.99374,0.69344,0.0,3.3378,0.66532,0.35046,0.0,0.26492,0.24286,0.0,0.4726,0.80498,0.0,1.62376,4.45876,1.28182,1.46102,0.5325,0.17238,1.04282,0.4141,0.65248,0.92578,0.53806,1.3137,0.0,0.87842,0.97258,0.74384,0.5833,0.0362,0.29406,2.16916,7.32242,0.7759,0.69158,2.5403,0.69908,0.82436,2.88112,1.88796,0.37656,1.7037,0.7538,1.5524,1.07636,0.98186,1.25132,0.94894,0.69826,3.09756,0.06272,0.8164,0.63706,0.45424,0.12954,0.1477,0.17392,0.0404,0.27002,0.0508,1.3566,0.48704,0.08422,1.31118,0.68364,1.15714,0.20948,0.68354,0.88426,0.4373,0.14042,0.55868,0.53306,0.51568,0.28982,0.2348,0.12826,0.60166,0.78236,0.52694,0.35978,0.49862,0.39332,0.15968,1.665,0.11724,1.09428,0.73874,0.18644,0.37438,0.0,0.30082,0.77786,0.61244,0.28972,1.24022,1.2759,0.70484,0.84932,1.12082,1.90042,0.64706,0.19592,0.27578,0.39664,0.63278,0.32464,0.41114,0.12118,0.8325,1.0216,0.5874,1.5278,0.92844,1.01304,2.68182,0.29602,0.31658,0.92084,0.60658,1.23208,0.60018,0.45012,3.29452,0.23128,0.73372,1.3713,0.2565,0.28786,0.40022,0.23958,0.11322,0.63556,0.84436,0.6542,0.30086,0.1694,0.27774,3.2763,0.14078,0.69322,0.25706,0.34514,0.56774,0.21958,0.95856,0.33432,0.15526,0.23226,0.39564,0.56232,0.21218,1.12874,0.26304,0.15688,0.20732,0.31626,0.85194,0.06458,0.34284,24.28214,0.11398,3.11826,0.6615,0.3533,7.57348,2.08738,0.21374,0.2988,0.10618,0.25688,0.19044,0.81308,0.2829,0.63896,0.83746,0.36054,0.14248,0.09666,0.58044,0.14726,0.2242,0.67034,1.67466,0.28546,0.0,0.06282,0.26608,0.77574,0.34882,0.36626,0.56152,0.09178,0.04022,0.08452,0.17554,0.14856,0.67928,0.0,0.09838,0.16674,0.05124,0.13192,0.18628,2.91398,3.94662,0.01592,0.29466,1.50176,1.2324,0.5414,0.10196,0.81034,0.39176,0.1732,0.54064,1.1629,0.1151,0.05382,0.25596,0.08252,0.48784,0.68698,0.06664,2.62296,0.54864,12.85758,6.76426,0.14146,2.10076,0.49388,1.03542,3.54172,0.73984
2018-07-11,0.58034,0.34876,5.76394,0.50228,0.39822,0.27752,0.06482,0.70238,1.473,0.51748,0.0,1.17492,0.0,0.44288,1.19152,0.95146,0.28044,1.3617,0.7261,0.3096,0.60898,0.43904,0.77622,1.75606,0.51676,1.04658,0.3661,1.23468,0.4348,0.68246,0.30852,0.22762,1.58186,0.56338,0.52238,0.72774,0.73414,0.23826,0.22002,0.30008,1.75494,0.61494,0.6702,0.62848,0.6636,0.76836,1.3662,1.49392,0.99214,1.78932,0.95196,0.83268,0.35778,0.64468,0.7555,1.6938,0.6378,0.4027,1.10434,0.2829,0.7575,2.30786,0.5484,1.36216,1.448,3.13522,0.64744,0.0,3.12182,0.65096,0.29118,0.0,0.23138,0.25664,0.0,0.448,0.8446,0.0,1.66052,5.15704,1.4993,1.25122,0.53744,0.18834,1.1376,0.5328,0.64478,1.02306,0.5158,1.55478,0.0,1.2316,0.99368,0.7439,0.73026,0.03824,0.32338,2.22136,6.86464,0.83604,0.73072,2.64192,0.6997,0.89762,2.83546,1.8065,0.59976,1.76598,0.71874,1.49648,1.23122,1.03754,1.18898,0.94368,0.77436,2.97468,0.06324,0.76926,0.57516,0.41474,0.11374,0.16168,0.16846,0.04042,0.26196,0.04746,1.45362,0.43594,0.08062,1.32358,0.71696,0.90514,0.20434,0.7423,0.86732,0.47684,0.14772,0.53116,0.4471,0.53758,0.28442,0.235,0.12084,0.62816,0.78076,0.55618,0.39886,0.44294,0.16698,0.1632,1.79924,0.11552,1.11608,0.69948,0.175,0.3945,0.0,0.32656,0.74804,0.61396,0.24834,1.27448,1.29292,0.71722,0.88744,1.13844,1.9604,0.67804,0.2133,0.2443,0.4149,0.66506,0.3302,0.33506,0.12756,0.94896,0.99748,0.6797,1.56452,0.74188,1.04382,2.8426,0.29046,0.29476,0.89388,0.75958,1.02812,0.6004,0.465,3.30958,0.21902,0.73876,1.46492,0.26588,0.332,0.3619,0.23178,0.10538,0.6485,0.83912,0.66436,0.3101,0.2022,0.25622,3.25612,0.14196,0.65568,0.24702,0.38428,0.68354,0.1954,0.90242,0.3351,0.139,0.23972,0.42084,0.57356,0.24406,1.09614,0.25898,0.16214,0.19792,0.31724,0.88246,0.06642,0.29292,22.15722,0.11762,3.26056,0.6298,0.35224,7.54684,1.9753,0.21274,0.32906,0.10258,0.27676,0.19258,0.62618,0.29694,0.63602,0.85226,0.3345,0.14468,0.09174,0.572,0.15426,0.22586,0.68502,1.5562,0.30388,0.0,0.06494,0.31032,0.82982,0.32672,0.43854,0.47444,0.09444,0.03852,0.14402,0.15738,0.11912,0.70288,0.0,0.09596,0.17936,0.04956,0.13346,0.18176,2.89278,4.41012,0.0157,0.32254,1.57842,1.274,0.51038,0.09814,0.8345,0.37762,0.21778,0.55452,1.23636,0.08888,0.0563,0.2673,0.07006,0.49308,0.69306,0.06126,2.69572,0.49276,12.72734,6.93526,0.13018,2.73636,0.5484,1.02944,3.41252,0.94672
2018-07-12,0.61582,0.37284,4.5259,0.4376,0.48122,0.27442,0.06934,0.6389,1.6869,0.46216,0.0,1.2446,0.0,0.47062,1.10218,1.15404,0.31602,1.38534,0.65066,0.30752,0.6616,0.39314,0.74958,1.8089,0.53286,0.90846,0.36434,1.7517,0.45412,0.71388,0.31458,0.25486,1.63588,0.563,0.60184,0.81514,0.71018,0.2184,0.2279,0.30512,1.90506,0.58826,0.5845,0.57068,0.59922,0.77556,1.43782,1.49072,1.05624,1.8039,0.94328,0.83628,0.44724,0.70768,0.83382,1.69684,0.5968,0.33144,0.9745,0.31628,0.77844,2.4733,0.58566,1.17078,1.69166,3.18244,0.7642,0.0,3.00578,0.7072,0.27372,0.0,0.25648,0.22954,0.0,0.4524,0.9001,0.0,1.80578,5.56312,1.6315,1.2994,0.6625,0.18462,1.31194,0.56766,0.78344,1.07586,0.5402,2.01604,0.0,1.69406,0.84012,0.8583,0.8029,0.0391,0.37708,2.27436,7.87876,0.91562,0.81408,2.75224,0.75168,1.00974,3.04214,1.81782,0.75026,1.87342,0.77026,1.63222,1.30428,1.18368,1.18418,1.02474,0.95562,3.2312,0.06552,0.73082,0.56022,0.41602,0.1126,0.17072,0.18,0.03016,0.27602,0.04612,1.60062,0.43796,0.07754,1.3228,0.79612,0.89248,0.20178,0.76446,0.86498,0.5722,0.16178,0.48322,0.48974,0.59484,0.28394,0.2387,0.13266,0.6274,0.75768,0.5326,0.42284,0.43078,0.0,0.16994,1.95206,0.12276,1.11886,0.78628,0.18226,0.42542,0.0,0.31322,0.85532,0.6088,0.2421,1.14752,1.448,0.69412,0.89082,1.09088,2.15642,0.58386,0.2276,0.24748,0.46116,0.68232,0.27996,0.28624,0.13084,1.004,0.9849,0.68846,1.934,0.70984,1.27718,2.97514,0.33318,0.2837,1.35342,0.8745,1.0099,0.54796,0.4249,3.5372,0.20636,0.78366,1.69464,0.25018,0.35088,0.37934,0.21812,0.1024,0.67782,0.94406,0.6303,0.32366,0.23972,0.26084,3.45778,0.14602,0.69038,0.25352,0.44862,0.80534,0.18366,0.93284,0.3782,0.10876,0.2789,0.45312,0.57876,0.25524,1.08734,0.25626,0.17072,0.19508,0.34502,0.86654,0.07274,0.28046,22.10508,0.12456,3.77232,0.62202,0.31478,9.12618,2.23376,0.1974,0.28986,0.11906,0.30066,0.2107,0.5635,0.31276,0.55708,0.76498,0.29142,0.13888,0.08676,0.59032,0.16498,0.21976,0.66398,1.7207,0.34684,0.0,0.06694,0.35386,0.7798,0.30168,0.52266,0.3429,0.1211,0.03664,0.17406,0.1489,0.11888,0.80034,0.0,0.10172,0.19798,0.04588,0.16516,0.16916,2.95318,5.05938,0.01654,0.3322,1.72588,1.33922,0.51318,0.08728,0.69046,0.3816,0.21992,0.62454,1.32816,0.08746,0.06018,0.27428,0.06962,0.49114,0.65538,0.05996,3.02736,0.52302,14.21888,7.84606,0.14376,3.15974,0.66856,1.11818,3.66454,0.98702
2018-07-13,0.57098,0.38532,5.91684,0.38126,0.51288,0.23006,0.06314,0.58896,1.65892,0.40176,0.0,1.25476,0.0,0.47862,0.6839,1.42442,0.353,1.45576,0.61016,0.26032,0.60378,0.33732,0.7096,1.85606,0.53536,0.84068,0.37754,1.84888,0.40038,0.7052,0.30356,0.22842,1.52838,0.52622,0.60816,0.82226,0.68062,0.29656,0.2021,0.35186,1.79472,0.58936,0.6079,0.54342,0.58502,0.68718,1.3716,1.54226,1.10548,1.72354,0.90446,0.81824,0.4716,0.7576,0.76544,1.47092,0.55462,0.29756,0.90766,0.28194,0.81166,2.20328,0.5595,0.99828,1.63946,2.50852,0.7601,0.0,3.031,0.70972,0.2822,0.0,0.29196,0.19808,0.0,0.38286,1.00334,0.0,1.7162,5.2575,1.61132,1.30318,0.66618,0.19466,1.45324,0.5735,0.76786,0.94758,0.51468,2.12718,0.0,1.85508,0.73364,0.79006,0.85216,0.0361,0.48022,2.0079,7.98144,0.98548,0.81762,2.5119,0.70556,0.93566,2.75908,1.39886,0.85488,1.87514,0.71532,1.60652,1.36968,1.3198,1.08342,1.02968,1.05162,2.85492,0.06014,0.70492,0.603,0.40376,0.1088,0.15428,0.15278,0.0262,0.2665,0.04202,1.47418,0.41634,0.07356,1.4396,0.69082,0.72128,0.18296,0.84774,0.82842,0.57776,0.15132,0.46716,0.45414,0.6436,0.267,0.27,0.13436,0.52048,0.73126,0.55568,0.3968,0.37752,0.0,0.1566,1.91058,0.10686,1.01112,0.93142,0.18428,0.4124,0.0,0.28986,0.7247,0.56938,0.17808,1.42056,1.52258,0.61582,0.83612,1.11478,2.16812,0.59544,0.20222,0.22416,0.45046,0.6497,0.2893,0.29432,0.12736,1.09154,0.8997,0.6419,2.0825,0.6499,1.2952,3.07392,0.3603,0.25942,1.69982,0.91854,0.92786,0.59188,0.42774,2.43752,0.19056,0.72938,1.77854,0.25096,0.35218,0.32384,0.19538,0.10376,0.62902,0.88484,0.56684,0.27286,0.1922,0.27216,3.27722,0.13092,0.75816,0.22918,0.42336,0.84466,0.15998,0.82912,0.38622,0.1024,0.24508,0.42898,0.53266,0.25728,1.01232,0.1937,0.14592,0.17716,0.33026,0.90672,0.07188,0.2581,22.52006,0.1305,3.6364,0.61022,0.34618,9.01436,2.48532,0.1856,0.2423,0.10938,0.26856,0.18888,0.51352,0.25732,0.51528,0.68802,0.3019,0.12804,0.07584,0.51084,0.18144,0.20444,0.54752,1.63654,0.31726,0.0,0.06348,0.32098,0.72486,0.24034,0.53512,0.30618,0.11606,0.03044,0.18994,0.14818,0.12168,0.6917,0.0,0.09388,0.20958,0.03628,0.1672,0.16118,2.82666,4.99238,0.01536,0.40718,1.53476,1.30528,0.52856,0.07846,0.66504,0.32436,0.21568,0.57524,1.3142,0.08264,0.06398,0.25416,0.06498,0.41008,0.59474,0.0544,3.56332,0.46896,13.27564,8.37528,0.15594,3.4989,0.66738,1.24524,3.91394,1.11186
2018-07-16,0.4871,0.39068,6.18902,0.36492,0.51492,0.22146,0.0666,0.57938,1.81078,0.34704,0.0,1.45572,0.0,0.48266,0.63654,1.49856,0.37308,1.87694,0.56234,0.29448,0.57344,0.3291,0.6078,1.68652,0.52052,0.80328,0.36276,1.91722,0.37674,0.6842,0.26842,0.20484,1.51416,0.75592,0.64128,0.8577,0.61896,0.30604,0.17806,0.37458,1.98664,0.55328,0.67434,0.51442,0.56332,0.67904,1.31832,1.45116,1.28474,1.5089,0.8961,0.73218,0.47968,0.8066,0.7456,1.50812,0.47692,0.26252,0.71834,0.27848,0.80868,2.2968,0.50194,0.88368,1.55014,2.16342,0.7359,0.0,2.59572,0.68046,0.28102,0.0,0.31398,0.16006,0.0,0.32678,1.07794,0.0,1.52754,5.0383,1.59244,1.17596,0.60852,0.20868,1.4581,0.56792,0.7409,0.83758,0.60628,2.32544,0.0,2.0602,0.60774,0.7767,0.84212,0.03348,0.51952,2.01216,8.12622,1.03022,0.89576,2.50648,0.80022,0.88232,2.68654,1.30914,0.98778,1.79122,0.5521,1.54132,1.34192,1.57936,1.25386,0.97786,1.0521,3.07582,0.05462,0.64956,0.66516,0.38784,0.1072,0.13232,0.13788,0.026,0.24902,0.04162,1.39984,0.40648,0.07004,1.5428,0.63664,0.69894,0.1667,0.85652,0.80554,0.61724,0.14128,0.40168,0.43662,0.56764,0.24698,0.31258,0.13624,0.46526,0.707,0.64434,0.4646,0.39366,0.0,0.16326,1.68446,0.11046,1.0368,0.9561,0.18116,0.43084,0.0,0.27304,0.7877,0.52724,0.16934,1.48176,1.41126,0.57426,0.8465,1.08808,2.02276,0.5786,0.1909,0.20894,0.45088,0.61864,0.2554,0.27082,0.1221,1.13424,0.89724,0.59752,2.32256,0.62242,1.50486,2.94882,0.33406,0.24652,1.98952,0.88538,0.85854,0.64604,0.43234,2.07902,0.16556,0.7489,1.85886,0.24706,0.35078,0.33926,0.16408,0.1144,0.5617,0.80476,0.50228,0.2472,0.22912,0.25786,3.09642,0.1094,0.73374,0.2418,0.413,0.84686,0.16512,0.7469,0.4037,0.09928,0.23638,0.35646,0.4932,0.24938,0.92008,0.17378,0.13858,0.17018,0.29078,0.86484,0.07896,0.25532,21.99816,0.13032,3.53528,0.62772,0.37138,9.20968,2.72588,0.1719,0.21014,0.11274,0.25046,0.18032,0.50708,0.26002,0.46354,0.6933,0.31416,0.11138,0.0664,0.46208,0.18416,0.1877,0.4931,1.67896,0.29984,0.0,0.06038,0.31838,0.6782,0.2465,0.50856,0.29902,0.11812,0.02684,0.199,0.1339,0.09508,0.64698,0.0,0.09094,0.2164,0.03086,0.1593,0.16072,2.80324,4.56382,0.0149,0.41792,1.44498,1.26052,0.54832,0.07576,0.61994,0.26128,0.2251,0.58268,1.29204,0.07834,0.05984,0.27394,0.06546,0.37486,0.52598,0.04642,3.99014,0.4569,12.35178,8.24096,0.13918,3.59846,0.65326,1.24238,4.02196,1.13622
2018-07-17,0.42636,0.37516,7.4406,0.32216,0.54842,0.22572,0.06478,0.57066,1.55204,0.29982,0.0,1.60406,0.04312,0.4986,0.57568,1.54406,0.3231,1.89144,0.56408,0.30274,0.55318,0.2937,0.57882,1.48216,0.46024,0.71608,0.33514,1.9669,0.38094,0.66816,0.25426,0.2036,1.13834,0.76346,0.66334,0.96206,0.60284,0.297,0.16558,0.35384,2.10154,0.4862,0.6825,0.5215,0.54628,0.67552,1.23434,1.386,1.50682,1.46076,0.90134,0.74906,0.44634,0.84192,0.70754,1.50694,0.44202,0.25632,0.57564,0.27096,0.71168,2.7338,0.42554,0.6761,1.59658,1.80088,0.72836,0.0,2.49492,0.62898,0.26674,0.0,0.29386,0.19682,0.0,0.29528,1.02136,0.0,1.44714,4.90666,1.6579,1.13198,0.61694,0.2049,1.34724,0.54656,0.62346,0.74872,0.6467,2.25174,0.0,2.01232,0.6114,0.7493,0.80722,0.03256,0.55154,1.92542,7.73218,1.05476,0.95662,2.5792,0.81644,0.85572,2.72636,1.2172,0.75954,1.76564,0.5279,1.50638,1.11178,1.68606,1.26384,0.8693,1.13208,3.0895,0.05556,0.55764,0.60882,0.35966,0.10876,0.1216,0.13182,0.0272,0.21942,0.04134,1.36538,0.36448,0.0722,1.4772,0.64372,0.67638,0.16308,0.95912,0.73086,0.56788,0.13916,0.35426,0.40254,0.55226,0.2398,0.33418,0.14052,0.43314,0.63618,0.62222,0.4911,0.41124,0.0,0.15874,1.4703,0.11932,1.10672,0.9959,0.18654,0.4244,0.0,0.25738,0.82326,0.5406,0.16632,1.70716,1.54404,0.50944,0.57592,0.98678,1.81126,0.54898,0.17726,0.21008,0.38714,0.59718,0.25736,0.25662,0.11614,1.12588,0.8056,0.49508,2.37074,0.54072,1.68992,2.89326,0.31172,0.24462,2.1503,0.87074,0.72496,0.5796,0.4327,1.965,0.15556,0.66416,2.0965,0.2227,0.34146,0.32828,0.14624,0.12452,0.53326,0.85602,0.55814,0.2294,0.24336,0.25766,2.92776,0.10056,0.66938,0.32032,0.4139,0.84982,0.1649,0.7627,0.41502,0.09624,0.22578,0.32474,0.53576,0.24576,0.89438,0.17182,0.13518,0.16994,0.2509,0.91782,0.07668,0.20588,20.56914,0.1283,3.27176,0.59552,0.4177,9.13024,2.75566,0.16594,0.19558,0.1215,0.24508,0.1711,0.48138,0.24852,0.44152,0.63042,0.29238,0.12324,0.062,0.42964,0.18558,0.19868,0.46502,1.68772,0.28678,0.0,0.05838,0.31068,0.62838,0.25248,0.49794,0.29752,0.12024,0.02318,0.20284,0.12678,0.08544,0.59494,0.0,0.08884,0.22178,0.03324,0.1486,0.1553,2.52814,4.30174,0.01694,0.44836,1.42542,1.22754,0.5313,0.07238,0.59062,0.25538,0.23014,0.57288,1.31954,0.07352,0.05518,0.2792,0.06132,0.41552,0.44742,0.04538,3.98728,0.46912,11.48778,7.8154,0.14816,3.57134,0.72754,1.225,4.18468,1.0403
2018-07-18,0.38838,0.40082,8.31874,0.29908,0.55256,0.21006,0.06072,0.5644,1.44082,0.27262,0.0,1.8271,0.78604,0.47282,0.58472,1.26824,0.321,1.85092,0.60058,0.3055,0.43814,0.26482,0.63776,1.33338,0.4415,0.65676,0.31006,1.96048,0.2995,0.59148,0.2252,0.18592,0.85934,0.70704,0.77452,1.01202,0.5896,0.29828,0.16948,0.37966,1.97352,0.44092,0.77948,0.52036,0.499,0.66642,1.21558,1.39352,1.57914,1.4722,0.88138,0.767,0.4111,0.87532,0.64406,1.34314,0.37872,0.26584,0.5418,0.27792,0.68826,3.18052,0.38232,0.6474,1.53778,1.66634,0.75472,0.0,2.5175,0.59816,0.2659,0.0,0.30472,0.25026,0.0,0.26578,1.02328,0.0,1.32552,4.38084,1.53332,1.11586,0.60924,0.19694,1.23414,0.4377,0.60144,0.60686,0.63638,2.04764,0.0,1.68912,0.61714,0.70332,0.63158,0.0289,0.66148,1.6597,8.3744,1.01978,0.93584,2.41202,0.7968,0.7909,2.6353,1.42192,0.70752,1.6841,0.53812,1.43296,1.0291,1.82304,1.21664,0.92632,1.18866,2.99552,0.0582,0.54034,0.57506,0.33152,0.16622,0.10598,0.12676,0.02616,0.2232,0.06632,2.34414,0.582,0.07216,1.42196,0.62856,0.66578,0.1646,0.96794,0.74824,0.54016,0.12528,0.37744,0.50268,0.51146,0.2387,0.38174,0.1435,0.36126,0.59992,0.53072,0.45962,0.40762,0.0,0.15906,1.2105,0.12018,1.15406,1.06398,0.18542,0.39612,0.0,0.21138,0.8047,0.5629,0.16832,1.96406,1.49936,0.49112,0.50434,0.96464,1.64532,0.47742,0.15704,0.19214,0.33798,0.5683,0.23742,0.25404,0.1073,1.02778,0.74384,0.37142,2.3802,0.50154,1.70794,3.02558,0.31246,0.245,2.2096,0.7584,0.63446,0.56758,0.38404,1.88112,0.14072,0.63048,2.39518,0.21104,0.28222,0.35008,0.15416,0.12184,0.48668,0.96772,0.51894,0.22,0.2301,0.29106,2.65346,0.18092,0.67148,0.31608,0.39452,0.78168,0.25988,0.76686,0.3646,0.11274,0.2109,0.29024,0.54702,0.2089,0.83044,0.14486,0.1181,0.16704,0.23556,0.94722,0.07824,0.19764,20.20008,0.12324,3.07796,0.6066,0.44004,8.8244,2.9579,0.15824,0.17168,0.12028,0.21622,0.17652,0.44858,0.2457,0.4088,0.58606,0.29926,0.12066,0.06014,0.42792,0.17776,0.2137,0.5022,1.6241,0.2701,0.0,0.05424,0.26332,0.56202,0.27126,0.43648,0.28344,0.11488,0.02358,0.17796,0.12312,0.08464,0.54532,0.0,0.08672,0.20336,0.03164,0.14634,0.14978,2.39468,3.8336,0.01818,0.4248,1.24768,1.06034,0.61108,0.0723,0.51742,0.22916,0.18244,0.52078,1.21148,0.09778,0.05592,0.26888,0.10254,0.4264,0.39996,0.04238,3.75194,0.43776,11.38894,7.63106,0.15318,2.99442,0.6996,1.1498,4.3126,0.90078
2018-07-19,0.30928,0.42608,9.40358,0.26708,0.42348,0.18284,0.05374,0.50944,1.08714,0.24006,0.0,1.7953,1.39758,0.42716,0.55426,1.0708,0.26856,1.78598,0.57054,0.28488,0.40564,0.246,0.58788,1.25836,0.40846,0.57936,0.29796,1.4285,0.26386,0.63376,0.1874,0.15702,0.83632,0.97208,0.76896,0.88348,0.5166,0.29226,0.15324,0.45122,1.745,0.41314,0.80008,0.5031,0.46012,0.57442,1.18598,1.26998,1.61968,1.30924,0.82218,0.63836,0.3562,0.9084,0.59036,1.17568,0.30734,0.25854,0.7065,0.22306,0.61606,3.08414,0.34872,0.67206,1.26926,1.69128,0.5697,0.0,2.5802,0.508,0.2677,0.0,0.2696,0.29844,0.0,0.2391,0.99634,0.0,1.12546,3.93086,1.38622,1.54538,0.47026,0.17512,1.0158,0.4319,0.58294,0.5035,0.62278,1.6013,0.0,1.43386,0.57232,0.51688,0.54966,0.0265,0.65182,1.42374,8.8371,0.96024,0.87022,2.85764,0.6908,0.6851,2.43126,1.57002,0.65194,1.80622,0.59902,1.26992,1.02926,1.75044,1.127,1.08398,1.05896,2.66394,0.05724,0.54814,0.55862,0.32394,0.20046,0.08152,0.10692,0.02762,0.28408,0.07314,2.56014,0.65216,0.05718,1.61024,0.5573,0.68086,0.1574,1.12718,0.7263,0.45518,0.11636,0.36818,0.7668,0.4244,0.21796,0.3957,0.12102,0.30468,0.54072,0.58348,0.44316,0.39038,0.0,0.13894,1.04644,0.11752,1.12072,1.07052,0.1605,0.35256,0.0,0.40728,0.76546,0.50132,0.18752,2.16858,1.36166,0.41988,0.43424,0.89318,1.41552,0.4014,0.131,0.20292,0.28502,0.49816,0.21738,0.2242,0.09722,1.00574,0.68878,0.29366,2.17084,0.4598,1.7576,2.72572,0.29094,0.20982,1.78782,0.72012,0.55482,0.53098,0.40468,1.66734,0.1382,0.64448,2.32068,0.19004,0.26342,0.30046,0.15816,0.11422,0.40806,0.85286,0.5123,0.20588,0.19454,0.29756,2.34238,0.1885,0.6294,0.37656,0.32842,0.69228,0.31424,0.68606,0.31682,0.12002,0.16484,0.26294,0.46898,0.18352,0.71542,0.13396,0.10622,0.1576,0.20114,0.9599,0.06492,0.20316,17.21612,0.10552,2.29502,0.62458,0.44736,6.55618,2.79358,0.13306,0.13934,0.13216,0.1773,0.15932,0.41574,0.22058,0.40108,0.5169,0.28942,0.12016,0.05826,0.43826,0.15202,0.19564,0.59674,1.40002,0.21764,0.0,0.04646,0.21358,0.53862,0.27554,0.35562,0.26558,0.08516,0.02196,0.15236,0.12698,0.08082,0.44538,0.0,0.07714,0.18218,0.03384,0.11384,0.12854,2.1457,3.04016,0.01788,0.42292,0.97808,0.85848,0.58916,0.06512,0.47926,0.19502,0.16898,0.40632,1.08164,0.10196,0.04914,0.25146,0.10906,0.41644,0.32078,0.0342,3.27584,0.37398,10.16534,6.49936,0.14024,2.55868,0.681,0.99792,3.76922,0.87462
2018-07-20,0.44334,0.4678,7.81996,0.26706,0.48616,0.19968,0.06652,0.56392,1.10052,0.21318,0.0,1.75836,1.783,0.3883,0.55958,0.82258,0.22884,1.57258,0.54056,0.3069,0.6137,0.24202,0.67484,1.14574,0.41218,0.52486,0.27418,1.31656,0.30876,0.63804,0.22666,0.19962,0.76218,1.16452,0.82926,0.85662,0.50342,0.20288,0.14554,0.42186,1.83348,0.38978,0.84294,0.45352,0.42104,0.55508,1.34268,1.10398,1.491,1.31924,0.78262,1.6027,0.34328,0.9238,0.5467,1.3671,0.31428,0.28416,0.87314,0.32546,0.64776,3.58574,0.38802,0.68454,1.25466,1.62782,0.50888,0.0,2.25638,0.44322,0.27508,0.0,0.22354,0.33734,0.0,0.24188,0.8601,0.0,1.04144,3.49468,1.31634,1.56554,0.41216,0.17774,0.83788,0.38244,0.79348,0.47764,0.6292,1.42906,0.0,1.25368,0.66968,0.56264,0.49672,0.03406,0.55006,1.71392,9.67316,0.86948,0.85236,3.29048,0.73718,0.71536,2.94072,1.52598,0.62428,1.75378,0.72756,1.07964,0.88012,1.61398,1.04896,1.15118,0.93324,2.38076,0.07022,0.50126,0.53466,0.3488,0.27818,0.11174,0.1421,0.03286,0.3169,0.11748,3.00162,0.81262,0.0642,1.67268,0.75726,0.6969,0.23144,1.19884,0.82272,0.47748,0.13276,0.326,0.89426,0.34482,0.22746,0.37566,0.11666,0.38592,0.50134,0.63154,0.44556,0.40224,0.0,0.14426,1.08756,0.13922,1.20196,0.9496,0.17658,0.35536,0.021,0.52896,0.9351,0.46758,0.21034,1.94144,1.32356,0.38836,0.4404,0.84844,1.37072,0.39628,0.1677,0.23674,0.27192,0.71134,0.20012,0.22082,0.09762,0.93778,0.7007,0.29066,2.03868,0.45096,1.8185,2.55444,0.25266,0.21244,1.47414,0.64282,0.59632,0.48254,0.38222,1.92458,0.13494,0.69916,2.44928,0.20524,0.24758,0.30224,0.16664,0.10836,0.47882,0.8404,0.54612,0.4047,0.22544,0.32912,2.0393,0.27734,0.51022,0.55574,0.40916,0.65538,0.39012,0.67804,0.34132,0.12132,0.22462,0.40496,0.72274,0.1888,0.68118,0.12138,0.15222,0.17964,0.31846,0.98284,0.06162,0.24846,16.90464,0.10208,2.61656,0.69678,0.4484,6.05218,2.99976,0.19438,0.1789,0.14916,0.2361,0.22424,0.38386,0.2396,0.45666,0.5377,0.3282,0.12478,0.07984,0.563,0.1593,0.20162,0.92968,1.41474,0.26818,0.0,0.06224,0.27614,0.57304,0.335,0.32234,0.26284,0.08604,0.02966,0.13904,0.14888,0.08714,0.647,0.0,0.08568,0.18814,0.03794,0.1064,0.16308,2.1278,4.00548,0.0199,0.3781,1.16932,1.01826,0.54914,0.06784,0.51486,0.25276,0.21016,0.39466,1.42718,0.1121,0.05454,0.26368,0.20008,0.44376,0.61998,0.04466,2.473,0.38078,8.89584,5.70732,0.12438,2.05922,0.68928,0.80388,3.26836,0.73642
2018-07-23,0.57052,0.49354,7.56408,0.26928,0.6359,0.24028,0.06978,0.5709,1.00002,0.23838,0.0,1.57182,2.0282,0.3622,0.63052,0.80004,0.26324,1.1537,0.58174,0.29382,0.74222,0.286,0.75864,1.02816,0.525,0.48386,0.3384,1.37098,0.3644,0.8134,0.29078,0.22786,0.84082,1.04506,0.89366,0.86278,0.55344,0.18658,0.1759,0.40254,2.10648,0.4455,0.83976,0.66914,0.49928,0.611,1.5732,1.1923,2.28334,1.48602,0.70698,1.96286,0.36862,1.0503,0.5326,1.5623,0.37578,0.3106,0.8932,0.36896,0.71864,3.91404,0.49412,0.68718,1.37374,1.7655,0.54822,0.0,2.4856,0.5233,0.2582,0.0,0.1973,0.3531,0.0,0.27802,1.30334,0.0,1.07432,3.26162,1.29492,1.59502,0.46952,0.17478,0.85742,0.38334,0.8477,0.49812,0.50078,1.2518,0.0,1.02928,0.71134,0.62532,0.512,0.0429,0.52492,1.9097,11.07022,1.11168,0.92526,3.47488,0.70142,0.82398,3.30332,1.60374,0.65242,1.47482,0.79624,1.05562,0.78376,1.4406,0.87556,1.40418,0.97292,1.8355,0.08236,0.49778,0.55436,0.44348,0.30306,0.14032,0.16568,0.03864,0.34522,0.12416,3.28818,0.84318,0.07036,1.75944,0.91264,0.8436,0.2862,1.59696,0.97994,0.51532,0.1547,0.32804,1.35948,0.413,0.29908,0.34714,0.1115,0.49322,0.60098,0.65104,0.48174,0.3942,0.0,0.17288,1.17284,0.15614,1.27136,1.2117,0.1754,0.46402,0.07216,0.56068,1.2002,0.60692,0.22254,1.94474,1.51536,0.41306,0.39086,0.97736,1.46148,0.50666,0.20338,0.35794,0.31558,1.1416,0.22658,0.24256,0.11758,1.08578,0.6386,0.34698,2.14298,0.499,1.9272,2.4851,0.29728,0.218,1.29292,0.77192,0.65144,0.4682,0.4787,2.10622,0.16228,0.85424,2.51176,0.23136,0.2541,0.25192,0.16968,0.09384,0.5237,0.89848,0.66146,0.5624,0.38268,0.3365,2.65134,0.31802,0.52798,0.85018,0.51392,0.99632,0.39794,0.7287,0.401,0.12632,0.27844,0.535,0.96466,0.20798,0.73554,0.13874,0.19914,0.20158,0.40926,1.00652,0.0649,0.29448,17.10746,0.11364,4.07092,0.7403,0.60066,5.28944,3.19484,0.28094,0.21596,0.2452,0.28286,0.2527,0.47178,0.2836,0.5132,0.60068,0.46788,0.12628,0.09886,0.6318,0.16586,0.25536,1.17236,1.33748,0.3256,0.0,0.07848,0.30178,0.82872,0.34558,0.40718,0.3573,0.10914,0.03384,0.1378,0.27556,0.16554,0.81904,0.0,0.12906,0.21306,0.07366,0.11532,0.17796,2.12352,10.09572,0.02154,0.3779,1.6493,1.23024,0.64986,0.07882,0.68296,0.31784,0.26348,0.443,1.65332,0.1367,0.06108,0.30694,0.22978,0.58558,0.83552,0.05172,1.87608,0.42774,9.7492,5.67434,0.14074,1.99604,0.69854,1.05598,3.46264,0.75684
2018-07-24,0.69288,0.60036,6.1053,0.34266,0.80458,0.3916,0.09068,0.67062,1.59182,0.29884,0.0,1.45778,2.2372,0.39888,0.917,0.82776,0.31018,1.22198,0.65848,0.35088,1.05448,0.41578,0.8841,2.93078,0.67094,0.77788,0.47518,1.77898,0.4834,0.9178,0.41362,0.36658,1.18124,1.08814,1.13476,0.92208,0.65338,0.21662,0.20128,0.49828,2.29712,0.5998,0.93752,0.77144,0.65612,0.8897,1.80094,1.3533,2.89056,1.59274,0.74848,2.19476,0.37842,1.08722,0.55282,1.72168,0.46344,0.32382,1.05762,0.45886,0.87012,3.69532,0.6653,0.76768,1.56874,1.8793,0.66282,0.0,3.06816,0.56748,0.27248,0.0,0.20204,0.3675,0.0,0.33214,1.44032,0.0,1.01824,3.07852,1.20878,2.23498,0.49514,0.35586,1.07038,0.45096,0.97968,0.56628,0.47634,1.40136,0.0,0.91598,0.7286,0.75652,0.5137,0.0602,0.47444,2.44926,11.57816,1.23862,1.01298,3.73806,0.79714,1.50684,3.86872,1.94284,0.69804,2.85898,0.88344,1.10966,1.21428,1.50484,0.8673,1.68336,1.12998,1.89332,0.09852,0.5284,0.68156,0.60256,0.39112,0.16896,0.19918,0.04484,0.43322,0.13866,3.89664,1.05618,0.07418,1.94706,1.10106,1.31774,0.32548,1.72056,1.19152,0.70344,0.20034,0.4161,1.98686,0.48918,0.41818,0.36846,0.11978,0.65846,0.88954,0.70374,0.54486,0.44122,0.0,0.31262,1.3417,0.2127,1.45884,1.39218,0.19138,0.64052,0.39546,0.62492,1.40788,0.64992,0.23496,1.91612,1.48968,0.581,0.53606,1.47176,1.83564,0.70302,0.29296,0.46514,0.40124,1.47658,0.21936,0.28254,0.15614,1.12054,0.92272,0.36024,2.3029,0.6308,1.98636,2.682,0.33476,0.24028,1.36402,0.8068,0.8236,0.6142,0.59948,2.27362,0.2174,1.15986,2.29616,0.30676,0.28756,0.30738,0.18106,0.09278,0.72478,0.96274,0.71156,0.62848,0.63344,0.36992,3.02688,0.36296,0.5999,0.95826,0.70252,1.09454,0.43432,0.81914,0.40802,0.14736,0.38008,0.65952,1.25168,0.25674,0.84772,0.1511,0.25014,0.22958,0.50328,1.15738,0.0755,0.32478,22.14744,0.14082,5.53576,0.8105,0.82854,6.43228,3.62632,0.36626,0.25804,0.48232,0.35976,0.31542,0.70094,0.41398,0.69798,0.78282,0.75414,0.14798,0.1146,0.71616,0.1805,0.30448,1.39808,1.54414,0.75404,0.0,0.09046,0.3881,1.30656,0.43278,0.54006,0.58336,0.16972,0.04148,0.16442,0.47598,0.30118,1.02162,0.0,0.22434,0.2753,0.15828,0.13366,0.21786,2.72766,16.24336,0.02052,0.37812,2.24504,1.7938,0.7061,0.10138,0.94528,0.36092,0.33082,0.52442,1.81414,0.17552,0.07122,0.38924,0.25068,0.72166,1.12258,0.06214,2.07278,0.52516,10.2375,5.76614,0.14744,2.00264,0.78064,1.185,3.3755,1.0365
2018-07-25,0.70324,0.58894,5.14892,0.36078,0.86654,0.49778,0.09868,0.68708,1.73402,0.30966,0.0,1.36834,1.6533,0.40542,1.11276,0.7656,0.31344,1.26226,0.63886,0.37592,1.15438,0.48534,0.88958,3.6742,0.69454,0.7707,0.60996,1.87616,0.53886,0.9854,0.43704,0.40994,1.26822,1.13712,1.0471,0.91612,0.6897,0.22236,0.2021,0.54382,3.03252,0.8065,0.90664,0.85008,0.789,0.99716,1.77346,1.35636,3.46666,1.59424,0.7097,2.52276,0.39282,1.0565,0.55882,1.78752,0.48738,0.29128,1.12814,0.46008,0.8877,3.26588,0.77486,0.71906,1.71538,1.96868,0.76232,0.0,3.21454,0.58904,0.28024,0.0,0.2162,0.33488,0.0,0.3823,1.475,0.0,1.05496,3.22422,1.34804,2.4792,0.51552,0.41386,1.11378,0.56862,1.102,0.62582,0.51126,1.39798,0.0,0.98536,0.73578,0.84424,0.52268,0.06472,0.33874,2.65226,10.97862,1.2711,1.07172,3.67674,0.81642,1.7995,3.7785,1.72146,0.63602,3.61474,0.8613,1.14608,1.21936,1.52512,0.86422,1.6155,1.12812,1.84962,0.09636,0.5167,0.74878,0.80142,0.38036,0.17614,0.2034,0.04952,0.4534,0.12134,3.09462,0.8632,0.08682,1.97706,1.10046,1.55174,0.33498,1.73988,1.15328,0.74506,0.21264,0.44086,2.10918,0.52556,0.43784,0.34144,0.1202,0.69634,0.9882,0.72478,0.55996,0.45896,0.0,0.36744,1.33772,0.23804,1.52454,1.35244,0.1944,0.71316,0.79466,0.65898,1.4888,0.60738,0.22492,1.78762,1.6029,0.58724,0.55124,1.70068,1.93382,0.80664,0.31198,0.51348,0.41486,1.57748,0.22598,0.3058,0.1655,1.12186,1.26782,0.4438,2.3489,0.81492,2.0618,2.57228,0.3412,0.23586,1.38858,0.75478,0.9892,0.66758,0.66412,2.30298,0.23128,1.25968,1.97416,0.3237,0.29646,0.3107,0.1723,0.1062,0.80326,0.85126,0.7303,0.6617,0.75398,0.3511,3.27558,0.28692,0.61188,1.0445,0.75848,1.16034,0.35674,0.87278,0.43712,0.14578,0.41226,0.70266,1.32,0.26806,0.86958,0.18726,0.26268,0.2385,0.52598,1.2101,0.07426,0.33288,24.80544,0.14116,5.98736,0.77076,0.87748,6.8588,3.47948,0.38296,0.25958,0.59184,0.38848,0.312,0.9825,0.44766,0.7544,0.81272,0.8288,0.15456,0.11706,0.70592,0.18372,0.31064,1.41166,1.5975,1.01016,0.0,0.09744,0.40646,1.49854,0.42738,0.59484,0.6739,0.19344,0.04068,0.16184,0.54892,0.3565,1.0989,0.0,0.25316,0.29274,0.26142,0.13932,0.22016,2.8251,20.81432,0.01952,0.37216,2.49088,2.02564,0.68396,0.11202,1.01426,0.37586,0.35082,0.62492,1.85014,0.15824,0.0695,0.40958,0.21288,0.78224,1.17508,0.06396,2.12146,0.78824,9.6437,5.54416,0.14322,2.16856,0.78272,1.24878,3.11226,1.16992
2018-07-26,0.74658,0.56386,5.05812,0.37652,0.93968,0.5421,0.10476,0.74362,1.8527,0.33428,0.0,1.419,1.25684,0.40788,1.1984,0.70482,0.30406,1.27824,0.61904,0.38796,1.1804,0.53272,0.90512,3.85754,0.7603,0.77964,0.66918,1.95716,0.55622,0.96188,0.46122,0.42162,1.394,0.98108,1.01172,1.42308,0.72052,0.21924,0.20152,0.47306,3.51728,0.87684,0.90106,0.90126,0.84032,1.20964,1.73798,1.3531,3.87826,1.72616,0.63366,2.73538,0.3594,0.96134,0.51756,1.94288,0.53076,0.3174,0.84964,0.52256,0.96266,3.31426,0.86906,0.63014,1.70864,1.78924,0.8716,0.0,3.06144,0.59002,0.27852,0.0,0.26296,0.29422,0.0,0.3708,1.41544,0.0,1.1257,3.1333,1.64874,2.11666,0.56352,0.44978,1.18086,0.58324,1.45816,0.58686,0.49354,1.59632,0.0,1.01384,0.76328,0.9379,0.50254,0.0677,0.2706,2.75392,10.8962,1.25108,1.05054,3.0803,0.85388,1.9047,3.9214,1.4438,0.61628,3.79232,0.7863,1.34062,1.13338,1.54476,0.95262,1.4618,1.1594,1.95442,0.09412,0.48046,0.79866,0.88918,0.37136,0.1865,0.21284,0.04968,0.40406,0.11936,3.06724,0.82844,0.0904,1.81888,1.16328,1.54918,0.34112,1.65594,1.1648,0.74744,0.21178,0.4962,1.96726,0.51856,0.46754,0.311,0.12066,0.7294,1.00824,0.65992,0.56012,0.47368,0.0,0.4449,1.2962,0.24518,1.5658,1.23954,0.209,0.7168,1.1273,0.45164,1.57282,0.61614,0.18756,1.63224,1.59462,0.6585,0.61834,1.83412,1.9584,0.84924,0.33948,0.54062,0.44094,1.67058,0.23784,0.31668,0.19446,1.0539,1.52172,0.47328,2.20138,0.86728,1.90572,3.1086,0.3166,0.2438,1.40352,0.71126,1.01688,0.69688,0.64796,2.33224,0.25126,1.30314,1.94472,0.35742,0.29418,0.31686,0.1671,0.11166,0.85796,0.82816,0.76764,0.67366,0.83786,0.32408,3.61336,0.28394,0.56518,1.06476,0.81676,1.1869,0.33432,0.8891,0.43042,0.1375,0.4459,0.72776,1.36196,0.26962,0.8431,0.19326,0.2565,0.22726,0.54208,1.1955,0.08422,0.31322,26.82236,0.14218,6.29274,0.80606,1.00368,7.1065,3.55836,0.40264,0.26528,0.62042,0.3995,0.31254,1.12684,0.54466,0.8235,0.8951,0.90868,0.15548,0.11834,0.6651,0.1844,0.34534,1.33056,1.81694,1.10898,0.0,0.1042,0.4323,1.52886,0.44174,0.61688,0.73218,0.2016,0.03858,0.17054,0.57444,0.37146,1.15114,0.0,0.26844,0.28116,0.32304,0.14906,0.23816,2.85148,23.8512,0.019,0.38654,2.59708,2.09422,0.70182,0.12138,1.06892,0.38838,0.38598,0.64196,1.86954,0.15662,0.07224,0.44174,0.21648,0.8305,1.23906,0.07088,2.18556,0.96812,8.85154,6.50318,0.14056,2.32744,0.74454,1.32144,3.44702,1.25976
2018-07-27,0.60224,0.5047,4.87866,0.35776,0.83626,0.53558,0.09562,0.67326,1.87438,0.32324,0.0,1.39976,1.01864,0.38298,1.26362,0.70216,0.29626,1.216,0.59278,0.36674,0.97488,0.54298,0.80164,3.92934,0.71452,0.79378,0.72806,1.85962,0.52422,0.9141,0.4019,0.39806,1.3785,0.85438,0.9278,1.63234,0.67484,0.21416,0.2007,0.54278,3.42406,0.95748,0.75466,0.89794,0.86632,1.28464,1.44498,1.32408,4.19612,1.85014,0.5839,1.7876,0.31632,1.00416,0.50114,1.80862,0.55416,0.39542,0.71378,0.43104,0.84952,2.85734,0.86424,0.63246,1.62214,1.85654,0.88534,0.0,3.01768,0.57066,0.27106,0.0,0.25086,0.29294,0.0,0.3597,1.46868,0.0,1.11238,2.8826,1.55534,1.92142,0.54098,0.46688,1.15512,0.60462,2.30688,0.5295,0.45814,1.6753,0.0,0.90406,0.71796,0.87848,0.52168,0.06006,0.23158,2.67952,9.65784,1.26024,0.99782,2.59958,0.79388,1.90604,3.28534,1.39584,0.59446,4.0375,0.69696,1.3851,1.4869,1.49704,0.93934,1.34052,1.1274,1.80442,0.07906,0.4616,0.83166,1.02172,0.31346,0.15434,0.179,0.0481,0.37594,0.0779,2.84864,0.65974,0.08764,1.64596,0.97666,1.59386,0.27302,1.51912,1.0117,0.71172,0.20906,0.4944,1.97112,0.4987,0.46642,0.3086,0.1375,0.67196,0.99072,0.59394,0.55996,0.45756,0.0,0.47202,1.18646,0.23482,1.4927,1.1552,0.18488,0.7019,1.36636,0.3429,1.45398,0.58132,0.2156,1.57704,1.4826,0.7003,0.57648,1.84842,1.87848,0.81126,0.35124,0.54576,0.4148,1.46652,0.22818,0.27896,0.19756,1.02098,1.75084,0.4764,2.04428,0.8789,1.82262,3.12992,0.2982,0.22846,1.32932,0.69954,0.97432,0.70064,0.67216,2.01762,0.26276,1.29626,1.61028,0.33266,0.2779,0.31076,0.16392,0.10936,0.85178,0.76534,0.8505,0.49548,0.8531,0.28644,3.6083,0.21204,0.543,0.97928,0.73912,1.79794,0.26492,0.84354,0.35946,0.12856,0.40618,0.58272,1.13912,0.24256,0.84044,0.19678,0.20958,0.20222,0.4156,1.2318,0.0895,0.26496,24.50432,0.12928,5.91812,0.81174,1.00292,6.80676,2.96166,0.346,0.21644,0.73208,0.33826,0.24058,1.1853,0.55482,0.76276,0.9309,0.88896,0.1514,0.09762,0.52936,0.15492,0.37752,1.05204,1.81862,1.10104,0.0,0.08608,0.3931,1.47424,0.39476,0.60862,0.76866,0.20732,0.03066,0.1742,0.57,0.39502,0.97074,0.0,0.26876,0.2766,0.39114,0.14694,0.2059,2.78678,25.60322,0.01736,0.37054,2.45038,1.94908,0.69368,0.12642,1.02244,0.33566,0.35332,0.63376,1.4838,0.15004,0.06126,0.47102,0.13348,0.83272,0.99152,0.05802,2.44078,0.99942,8.0971,6.28892,0.16248,2.34242,0.74936,1.27606,3.18388,1.26788
2018-07-30,0.47698,0.4638,4.51432,0.3478,0.71018,0.52322,0.09242,0.64464,1.81806,0.29982,0.0,1.41622,0.88286,0.3539,1.26586,0.6564,0.2471,1.11412,0.53204,0.35282,0.85704,0.52306,0.72158,4.29264,0.6213,0.83522,0.73946,1.71124,0.49992,0.74382,0.35158,0.39648,1.3032,0.7872,0.80844,1.69292,0.622,0.21222,0.18594,0.59324,2.94436,1.00908,0.68922,0.68644,0.87066,1.223,1.12526,1.15754,3.49768,1.79404,0.6258,1.56514,0.3266,0.9076,0.51726,1.51116,0.54782,0.3911,0.78446,0.437,0.75268,2.38914,0.80338,0.59526,1.54762,1.92924,0.87696,0.0,2.87854,0.45742,0.28544,0.0,0.30632,0.29572,0.0,0.3761,1.16952,0.0,1.08944,2.92352,1.6044,2.06382,0.44232,0.50656,1.07424,0.62628,3.1744,0.53322,0.46514,1.64984,0.0,0.8096,0.7558,0.85508,0.4472,0.05412,0.23504,2.60476,9.23166,1.09106,0.86688,2.6189,0.7295,1.8662,2.8824,1.2316,0.5247,4.8327,0.62844,1.33394,1.80248,1.38204,0.93316,1.1315,0.99202,1.93132,0.07096,0.46448,0.8069,1.14824,0.3278,0.13032,0.16342,0.04994,0.40312,0.08556,2.69962,0.68234,0.08532,1.44686,0.8421,1.52406,0.2206,1.15916,0.80208,0.61986,0.2052,0.51846,1.76908,0.43768,0.42984,0.34068,0.13352,0.59518,0.90828,0.49536,0.5068,0.4304,0.0,0.45536,1.08084,0.22414,1.32902,0.99522,0.19038,0.60968,1.43826,0.30386,1.17946,0.45456,0.24574,1.43508,1.28318,0.8711,0.54358,1.96804,1.64716,0.75268,0.34286,0.44446,0.34604,1.17882,0.2056,0.2809,0.18654,0.87842,1.9546,0.43028,1.6607,0.8793,1.61504,2.99778,0.2439,0.23834,1.19146,0.59922,1.00064,0.68174,0.65544,1.74006,0.2795,1.24968,1.39958,0.31452,0.26232,0.30788,0.1817,0.11182,0.8218,0.77492,0.7805,0.35178,0.73148,0.26372,2.95074,0.2139,0.472,0.78666,0.64332,2.26136,0.25332,0.81814,0.27838,0.14184,0.35602,0.49204,0.95322,0.21798,0.79974,0.18452,0.17496,0.2043,0.33808,1.29138,0.08086,0.21842,23.88376,0.11088,4.59246,0.72638,0.8634,6.59926,2.38096,0.27664,0.18762,0.76376,0.30092,0.21288,1.14852,0.52242,0.686,0.8524,0.7699,0.14582,0.08056,0.4503,0.13674,0.34592,0.85918,1.82676,1.25256,0.0,0.06966,0.37726,1.25748,0.3548,0.57476,0.85736,0.1991,0.02848,0.18308,0.49418,0.36738,0.80454,0.0,0.30122,0.2445,0.42092,0.15632,0.18622,2.67456,21.39502,0.01672,0.36092,2.1555,1.7557,0.5677,0.12576,0.88794,0.27364,0.3108,0.53568,1.21268,0.13936,0.05658,0.43986,0.1394,0.77952,0.8178,0.05292,2.6419,1.04318,6.94998,5.74762,0.15348,2.156,0.74862,1.01914,2.75546,1.27458
2018-07-31,0.39478,0.36536,4.19144,0.27752,0.54876,0.39322,0.07146,0.509,1.27238,0.24888,0.0,1.24262,0.70286,0.28148,1.05606,0.60674,0.1818,0.97124,0.41052,0.2896,0.5346,0.41184,0.56564,2.46748,0.51746,0.52486,0.63386,1.23342,0.37516,0.6762,0.22084,0.256,0.98086,0.77472,0.54156,1.5043,0.50096,0.1867,0.17576,0.50396,2.8256,0.97594,0.59574,0.5629,0.78218,0.92978,1.0635,0.98582,2.861,1.73924,0.62994,1.49172,0.33052,1.44452,0.72242,1.2546,0.45122,0.42568,0.66874,0.39218,0.62508,2.12144,0.67712,0.49908,1.3087,1.85034,0.75604,0.0,2.25194,0.41024,0.27886,0.0,0.32378,0.27696,0.0,0.34162,1.12596,0.0,1.10522,2.66312,1.59404,1.65498,0.40358,0.3489,0.81036,0.68014,4.20902,0.4665,0.42074,1.38658,0.0,0.69778,0.5973,0.68252,0.3961,0.04066,0.24578,2.03856,8.19474,0.91086,0.7555,2.25346,0.5945,1.20384,2.26404,0.8412,0.38876,3.64372,0.68082,1.22024,1.40326,1.27768,0.8345,0.80444,0.77298,1.7225,0.058,0.39278,0.7756,1.05322,0.25234,0.10946,0.14058,0.0457,0.31088,0.076,2.1917,0.48602,0.08642,1.15298,0.64462,1.2904,0.18702,0.9212,0.6026,0.42776,0.21802,0.42854,1.22862,0.33806,0.31566,0.37266,0.12108,0.4194,0.62512,0.42012,0.42888,0.37432,0.0,0.32378,0.87736,0.15922,1.01962,0.76252,0.18608,0.43452,1.18794,0.27764,0.98408,0.3845,0.25534,1.25522,1.11188,0.89218,0.40866,1.58614,1.1518,0.57834,0.25778,0.42034,0.25994,0.9736,0.19922,0.24526,0.14836,0.77472,1.74168,0.37466,1.37422,0.7499,1.4415,2.5946,0.17036,0.20472,0.99754,0.48998,0.91946,0.5338,0.52014,1.38382,0.27754,0.96592,1.25818,0.25132,0.22192,0.26086,0.18408,0.1127,0.61432,0.64114,0.71494,0.30956,0.52624,0.22298,2.5256,0.18158,0.35092,0.71356,0.43412,2.57426,0.2349,0.71444,0.2466,0.12162,0.26976,0.4287,0.65334,0.18636,0.63062,0.18348,0.13448,0.17806,0.64084,1.01124,0.06786,0.2121,18.68756,0.07252,3.28402,0.63504,0.59282,4.88366,1.92444,0.21206,0.14672,0.57554,0.21786,0.15658,0.94068,0.3924,0.47824,0.6823,0.54312,0.10548,0.06786,0.34394,0.12376,0.29752,0.69168,1.53916,0.8588,0.0,0.05984,0.28832,0.82666,0.2778,0.4946,0.72088,0.14202,0.02366,0.16782,0.31324,0.2714,0.60724,0.0,0.22626,0.18248,0.37524,0.15584,0.1604,2.00824,18.03232,0.01738,0.3228,1.56148,1.1962,0.52814,0.11006,0.60568,0.2003,0.25368,0.42604,1.01012,0.10348,0.05262,0.35616,0.12858,0.57652,0.63502,0.04864,2.65162,0.98284,6.29526,5.2327,0.1366,2.05576,0.65618,0.79838,2.36828,1.02682
2018-08-01,0.41806,0.43096,3.9653,0.32276,0.57294,0.34904,0.06824,0.48616,1.24248,0.26088,0.0,1.12878,0.66082,0.29108,0.96042,0.64426,0.18676,1.02788,0.41782,0.25972,0.45024,0.36356,0.56458,1.80736,0.6191,0.51186,0.55804,1.2475,0.33752,0.65486,0.2204,0.2571,0.9717,0.7579,0.53838,1.43674,0.51434,0.21306,0.22824,0.54504,2.1627,0.83856,0.60372,0.49048,0.74994,0.95148,1.21294,0.96362,2.37704,2.12156,0.6985,1.23872,0.31058,1.82022,0.74946,1.1877,0.50298,0.45148,0.58944,0.44308,0.68582,2.11104,0.65672,0.51506,1.14548,1.95084,0.62812,0.0,2.21516,0.4421,0.27202,0.0,0.32576,0.28572,0.0,0.33906,1.02462,0.0,1.2312,2.4659,1.39322,1.62938,0.43966,0.34958,0.7671,0.72082,5.40022,0.6419,0.3848,1.44308,0.0,0.56578,0.54504,0.61448,0.41854,0.0423,0.22208,1.9045,7.39082,0.93334,0.68096,2.17862,0.55582,0.9658,2.35586,0.82462,0.34642,2.91304,0.7341,1.15742,1.37792,1.19406,0.8192,0.81634,0.71642,1.7974,0.06146,0.40534,0.80618,0.9257,0.22786,0.11464,0.14916,0.05334,0.32806,0.0796,2.05154,0.47146,0.07976,1.08664,0.67334,1.28918,0.19734,0.94156,0.73314,0.41564,0.24056,0.41462,1.22638,0.29844,0.3186,0.43826,0.12478,0.42126,0.5589,0.4054,0.42032,0.36542,0.0,0.28608,0.90386,0.14378,0.98328,0.75504,0.1886,0.42796,0.8951,0.25388,1.04546,0.35238,0.26284,1.28352,0.9929,0.94002,0.43334,1.48224,1.03214,0.49748,0.25852,0.44738,0.3186,0.96592,0.1932,0.23712,0.15134,0.8654,1.51718,0.29218,1.389,0.55868,1.69732,3.2014,0.19894,0.21028,0.95536,0.50228,0.75536,0.52258,0.50928,1.25252,0.30646,0.9947,1.18798,0.25816,0.23996,0.25206,0.18584,0.11216,0.62134,0.65328,0.79616,0.31908,0.43068,0.28214,2.46548,0.19124,0.32268,0.65834,0.40928,2.59882,0.24254,0.67804,0.24164,0.11592,0.25084,0.42462,0.59506,0.19992,0.67566,0.14782,0.13568,0.18212,0.77942,0.88006,0.07006,0.2347,15.60574,0.07084,3.06298,0.6246,0.5945,4.15408,2.25192,0.2289,0.1548,0.59656,0.19486,0.16368,0.6856,0.36654,0.45676,0.64616,0.51798,0.10186,0.07068,0.37244,0.12778,0.3259,0.75486,1.43864,0.63478,0.0,0.05794,0.28424,0.78888,0.2759,0.4426,0.68142,0.14412,0.02684,0.15812,0.30148,0.25998,0.6057,0.0,0.22304,0.17826,0.35842,0.16628,0.16626,2.58858,15.90334,0.01848,0.34984,1.4039,1.07674,0.45808,0.10596,0.48778,0.18912,0.29246,0.32704,1.05604,0.10416,0.05646,0.34896,0.13116,0.53484,0.65938,0.07882,2.97766,0.83794,6.45248,4.95358,0.13676,2.11106,0.70818,0.7525,2.73418,0.96782
2018-08-02,0.42948,0.4912,3.58686,0.34248,0.55846,0.38482,0.07048,0.55474,1.5552,0.2474,0.0,1.16748,0.7317,0.33452,1.0288,0.9155,0.21018,1.07288,0.5732,0.27598,0.46766,0.37104,0.7169,1.841,0.63132,0.5792,0.56516,1.25804,0.36606,0.72508,0.24018,0.30588,0.87008,0.8144,0.53866,1.0272,0.58402,0.24008,0.26542,0.61936,1.75202,0.8221,0.60478,0.48202,0.77752,0.8531,1.31842,1.02044,2.10034,2.3634,0.78424,1.2059,0.32062,2.2918,0.84902,1.10654,0.55186,0.43816,0.71044,0.42116,0.6734,2.07678,0.73456,0.53386,1.3221,2.25226,0.61606,0.0,2.30612,0.47666,0.31212,0.0,0.29814,0.30136,0.0,0.43228,1.01454,0.0,1.27516,2.52982,1.21718,1.61718,0.48496,0.40328,0.77686,0.8719,5.94382,0.78962,0.48352,1.39706,0.0,0.4151,0.58492,0.60224,0.4477,0.04618,0.2293,2.18218,6.03042,1.04756,0.72106,2.17638,0.63006,0.9567,2.41782,0.92334,0.35708,2.83464,0.84574,1.01624,1.42352,1.29866,0.80292,0.86322,0.67288,1.67336,0.06806,0.41406,0.74278,0.88638,0.26308,0.11732,0.15566,0.05904,0.34504,0.083,1.82546,0.46182,0.0864,0.95232,0.6561,1.46068,0.20832,1.11368,0.75786,0.4531,0.25816,0.43496,1.27636,0.3354,0.32744,0.50178,0.13368,0.45556,0.58744,0.3488,0.45538,0.3515,0.0,0.23834,0.9247,0.16716,1.01514,0.7719,0.1856,0.5083,0.7455,0.28562,0.9612,0.38068,0.27964,1.36854,1.04292,1.06892,0.41032,1.63804,1.00802,0.51778,0.26386,0.47276,0.30766,0.95686,0.20376,0.29758,0.15354,0.89398,1.37978,0.29836,1.2973,0.54376,2.03352,3.29706,0.21336,0.26112,0.9806,0.50438,0.82258,0.5425,0.51448,1.33948,0.33758,1.0452,1.09778,0.2674,0.27158,0.28088,0.18732,0.11792,0.62876,0.71562,0.85068,0.31948,0.44286,0.32618,2.6458,0.20574,0.53238,0.62052,0.38962,2.60966,0.22436,0.78454,0.3176,0.14018,0.27846,0.43602,0.61184,0.20884,0.96608,0.14054,0.14702,0.2018,0.87524,0.8079,0.06486,0.24854,15.09966,0.07242,3.10922,0.50894,0.48416,4.19648,2.2053,0.24138,0.16726,0.67802,0.22426,0.1678,0.59844,0.30162,0.41534,0.69154,0.47044,0.10552,0.07424,0.40276,0.13884,0.3804,0.80476,1.41294,0.61058,0.0,0.05424,0.2986,0.9002,0.26606,0.44914,0.67886,0.15354,0.02942,0.173,0.32378,0.28214,0.62084,0.0,0.25418,0.19356,0.3556,0.17452,0.16226,2.91618,15.23458,0.0195,0.34718,1.4713,1.12116,0.48728,0.1133,0.48986,0.19416,0.29936,0.32386,1.08136,0.11222,0.0606,0.36548,0.13454,0.56382,0.69186,0.08526,3.49322,0.70338,6.66952,4.20084,0.15366,2.17722,0.80528,0.84646,2.55722,0.9405
2018-08-03,0.42498,0.49598,3.42922,0.34144,0.54778,0.38544,0.06534,0.6865,1.5592,0.23366,0.0445,1.1223,0.7064,0.40336,0.97542,0.90318,0.2366,1.13544,0.67162,0.282,0.48146,0.35138,0.88558,1.85952,0.61146,0.68976,0.50756,1.23414,0.35522,0.7126,0.23824,0.30688,0.922,0.80396,0.55684,0.78108,0.63678,0.27622,0.3352,0.58846,1.69706,0.73992,0.66212,0.51028,0.90258,0.98962,1.42212,1.02316,2.0659,2.4744,0.85524,1.1946,0.33066,2.41736,0.91558,1.05164,0.57126,0.36734,0.85528,0.42084,0.67526,2.15784,0.71128,0.47992,1.3666,2.96356,0.62648,0.0,2.45654,0.4998,0.29728,0.0,0.30562,0.30076,0.0,0.58148,0.91564,0.0,1.27224,2.59644,1.21858,1.907,0.53358,0.45106,0.89788,0.90144,5.4447,0.94882,0.58846,1.32642,0.0,0.57912,0.5291,0.53422,0.63922,0.04538,0.23594,1.98588,5.518,1.09962,0.7854,2.11034,0.61612,0.92604,2.46252,0.87868,0.3589,2.71184,0.90716,0.97262,1.12728,1.29386,0.79096,1.03426,0.73308,1.62386,0.07154,0.46128,0.7368,0.75558,0.27932,0.11762,0.15684,0.0589,0.34844,0.0959,1.55808,0.5017,0.08918,1.12036,0.63634,1.54056,0.20732,1.15992,0.75928,0.4316,0.24406,0.4882,1.21338,0.3877,0.30532,0.52308,0.13384,0.42438,0.56438,0.41208,0.44026,0.36446,0.0,0.2057,1.01312,0.15996,1.0563,0.97856,0.18796,0.52384,0.58152,0.2623,1.01162,0.41858,0.23394,1.40084,1.03196,1.32934,0.42832,1.67116,1.027,0.55004,0.21378,0.45868,0.31686,0.9206,0.187,0.39362,0.14682,0.8898,1.17134,0.3019,1.44938,0.4982,2.30488,3.38698,0.21698,0.30318,0.97228,0.5068,0.86002,0.522,0.50124,1.26724,0.36658,1.0105,1.03202,0.26282,0.28312,0.30924,0.19898,0.11894,0.6603,0.81262,0.74686,0.30334,0.43132,0.34996,2.55014,0.19758,0.73538,0.57662,0.37546,2.10786,0.24674,0.843,0.33836,0.15816,0.2563,0.42676,0.589,0.20734,1.19398,0.18434,0.1481,0.21684,0.91392,0.69654,0.05744,0.2304,13.96138,0.08044,2.8142,0.51084,0.4713,4.1482,2.29284,0.24674,0.16444,0.63986,0.21998,0.17654,0.53514,0.29536,0.48182,0.6265,0.47196,0.10034,0.08386,0.40792,0.1354,0.34934,0.8141,1.39066,0.57986,0.0,0.05776,0.2707,0.9209,0.26524,0.53042,0.63724,0.1485,0.03,0.16838,0.31832,0.26664,0.58902,0.0,0.26044,0.16892,0.33604,0.17424,0.16078,2.96938,13.5602,0.0224,0.34104,1.3862,1.00028,0.59998,0.18446,0.49466,0.17264,0.3138,0.31482,1.14412,0.10726,0.06124,0.32666,0.13206,0.55272,0.67012,0.08808,3.28806,0.66362,6.86024,4.0281,0.1303,2.1606,0.84914,0.84812,2.982,0.9548
2018-08-06,0.40724,0.48748,3.3721,0.34176,0.54064,0.37626,0.0613,0.76068,1.56224,0.22466,0.62418,1.0923,0.74642,0.45956,0.96496,1.21382,0.2336,1.1085,0.7365,0.29204,0.48638,0.33616,0.97822,1.8663,0.57384,0.83138,0.45618,1.19964,0.3483,0.74436,0.25854,0.28978,0.96426,0.82866,0.64356,0.70956,0.66752,0.28694,0.38626,0.53988,1.96814,0.61938,0.63178,0.64816,0.9385,1.05984,1.58802,1.0365,1.99612,2.60698,0.8028,1.1644,0.2991,2.65732,0.967,1.04178,0.55524,0.3687,0.85512,0.36982,0.6858,2.18,0.66874,0.51034,1.45476,2.91896,0.64022,0.0,2.2906,0.53982,0.29282,0.0,0.2694,0.30784,0.0,0.59472,0.79742,0.0,1.2678,2.85042,1.4145,2.06854,0.53544,0.5421,0.9774,0.8956,4.4341,0.97784,0.61984,1.27628,0.0,0.60834,0.4482,0.48986,0.79464,0.04636,0.2646,1.98128,4.1059,1.01276,0.81622,1.74288,0.60704,0.9026,2.52738,0.91272,0.35592,2.25442,1.01564,1.00272,0.91588,1.34304,0.71954,1.09174,0.77648,1.63274,0.06966,0.46338,0.89324,0.58004,0.25658,0.11768,0.15296,0.05804,0.29854,0.08906,1.46906,0.4438,0.09496,1.15456,0.6525,1.64966,0.20756,1.17016,0.78002,0.46924,0.23362,0.50474,1.03002,0.40084,0.29286,0.5037,0.1386,0.41432,0.5459,0.43014,0.39544,0.3848,0.0,0.1973,1.08416,0.16702,1.0241,0.965,0.2544,0.51232,0.5387,0.27272,1.01726,0.54218,0.19334,1.4591,0.9888,1.2736,0.44572,1.75894,1.19478,0.5359,0.20862,0.4623,0.33488,0.80202,0.2057,0.3882,0.14852,0.9923,1.09492,0.30648,1.42166,0.44682,2.24832,3.52028,0.22342,0.30808,0.94524,0.73482,0.86206,0.53748,0.4427,1.18808,0.3638,0.92078,1.02688,0.25728,0.28388,0.40312,0.20422,0.11512,0.6819,0.76192,0.73422,0.2951,0.3883,0.35444,2.5674,0.1708,0.8145,0.56632,0.42328,1.40404,0.26492,0.95462,0.32062,0.1597,0.25278,0.40468,0.59714,0.22242,1.2899,0.21358,0.14578,0.19428,0.97366,0.9004,0.05972,0.23002,12.91522,0.08006,2.74342,0.51482,0.43864,4.36966,2.5136,0.24422,0.15654,0.59646,0.38476,0.18398,0.54398,0.28072,0.50462,0.6216,0.49644,0.0978,0.09358,0.41594,0.136,0.3525,0.8509,1.3879,0.3988,0.0,0.05528,0.26806,0.89618,0.27388,0.52616,0.47132,0.1313,0.03168,0.16802,0.29732,0.23088,0.7025,0.0,0.19972,0.17948,0.32464,0.16004,0.15396,3.1498,13.58154,0.02346,0.34746,1.29226,0.9822,0.69302,0.2058,0.50058,0.1711,0.3225,0.28878,1.12546,0.0991,0.06046,0.30478,0.1055,0.48782,0.66572,0.0882,3.34058,0.58404,6.73248,4.50684,0.14552,2.54842,0.83582,0.87596,2.9687,1.0566
2018-08-07,0.40378,0.50746,3.47924,0.39032,0.58304,0.42784,0.06642,0.82796,1.58476,0.21902,1.02432,1.07534,0.81688,0.4859,1.22062,1.92272,0.27626,1.07002,0.8036,0.32192,0.5384,0.37986,1.12986,1.9767,0.54044,0.93716,0.45624,1.26622,0.36386,0.7548,0.31296,0.32914,1.0208,0.82704,0.75418,0.7487,0.73488,0.31498,0.39276,0.57214,2.00138,0.53232,0.59152,0.72186,1.07492,1.20638,1.4657,1.07792,1.9537,2.64288,0.76628,1.22404,0.2996,2.23772,0.78544,1.10754,0.60304,0.36394,0.96302,0.42474,0.69868,2.40884,0.69362,0.53084,1.60396,3.06018,0.6726,0.0,2.29396,0.52504,0.3432,0.0,0.30756,0.33586,0.0,0.63848,0.81146,0.0,1.41222,3.25174,1.50268,2.05068,0.54944,0.59314,1.0441,0.84178,3.1802,1.14162,0.68652,1.38688,0.0,0.7481,0.46474,0.56836,0.8844,0.04496,0.3052,2.1396,4.2694,1.08934,0.8192,1.76738,0.6335,1.03694,2.77348,0.96588,0.37936,2.12722,0.96992,0.9588,0.90074,1.33762,0.7679,1.50296,0.7873,1.7399,0.0722,0.49062,0.90024,0.58388,0.26174,0.12684,0.1516,0.05844,0.3161,0.09202,1.44174,0.50982,0.10442,1.37248,0.6998,1.8116,0.22362,1.20678,0.79762,0.52298,0.18264,0.5231,1.19618,0.43242,0.32314,0.4785,0.13974,0.4588,0.58088,0.50192,0.39312,0.3392,0.0,0.2434,1.11542,0.17088,1.11022,1.07692,0.2869,0.5815,0.54978,0.2532,1.04588,0.6052,0.16474,1.51734,1.0481,1.52524,0.45692,1.94766,1.29864,0.63616,0.22896,0.38732,0.36034,0.70984,0.20706,0.39288,0.1541,1.11188,1.09874,0.33106,1.47054,0.42918,2.33708,3.7591,0.23892,0.33736,0.90782,0.91588,0.8412,0.54028,0.4824,1.3984,0.37396,0.98998,1.1279,0.26376,0.2997,0.44716,0.20262,0.13136,0.74318,0.81564,0.73454,0.28898,0.38268,0.36128,2.5932,0.18038,0.88698,0.54178,0.47164,1.04946,0.26214,1.02818,0.32576,0.1667,0.25874,0.38156,0.59458,0.21104,1.35748,0.23406,0.15286,0.19398,0.68766,0.92354,0.0995,0.2251,12.67728,0.09284,2.891,0.5831,0.59954,4.658,2.49388,0.24758,0.16484,0.754,0.51018,0.19428,0.63084,0.31824,0.60268,0.64854,0.50082,0.10694,0.09638,0.47154,0.12914,0.37932,0.93892,1.44638,0.39308,0.0,0.06194,0.2784,0.93886,0.2767,0.55068,0.43828,0.16832,0.03164,0.15498,0.3707,0.27998,0.86878,0.0153,0.27536,0.1846,0.38652,0.17394,0.14292,3.2758,13.1825,0.0258,0.36164,1.3667,1.02604,0.85382,0.24364,0.62218,0.1812,0.33862,0.30364,1.18474,0.10228,0.05762,0.31342,0.10482,0.51806,0.63236,0.08186,3.18142,0.53724,7.54838,5.0875,0.15606,3.24232,0.90928,0.94512,3.24244,1.08618
2018-08-08,0.3764,0.4533,3.41984,0.43514,0.52352,0.41498,0.06436,0.86364,1.46144,0.19716,1.44394,1.0273,0.85206,0.49026,1.31214,2.20648,0.26806,0.96518,0.85934,0.30226,0.53482,0.38084,1.12206,1.88936,0.40148,1.00684,0.4884,1.12404,0.36158,0.72728,0.31048,0.29624,1.06544,0.96894,0.79996,0.72044,0.72218,0.28904,0.33832,0.5227,2.15546,0.5316,0.5018,0.74628,1.10094,1.16152,1.32968,0.97248,1.87612,2.3217,0.87618,1.19152,0.28282,1.98368,0.74926,1.05276,0.58632,0.40768,0.94856,0.44914,0.61242,2.27296,0.6156,0.49,1.66982,2.9786,0.63934,0.0,2.09142,0.46446,0.37386,0.0,0.30988,0.31252,0.0,0.67374,0.81308,0.0,1.2997,3.32392,1.5747,1.90268,0.46472,0.56568,1.07044,0.7274,1.76976,1.35256,0.73708,1.30472,0.0,0.83328,0.4523,0.5543,0.86114,0.04428,0.35902,2.15444,4.23356,1.05628,0.8191,1.68264,0.64378,1.08814,2.749,1.10822,0.36556,2.18946,0.92472,0.9206,0.82566,1.2104,0.73492,1.66446,0.72822,1.61158,0.06702,0.46896,0.86448,0.64264,0.26476,0.1191,0.13844,0.04894,0.30244,0.08772,1.31046,0.54332,0.1122,1.44292,0.66678,1.8064,0.19896,1.23638,0.68302,0.53396,0.14498,0.47574,1.08602,0.44904,0.31382,0.44004,0.14376,0.42692,0.54466,0.53064,0.36138,0.2851,0.0,0.25604,1.08096,0.15536,1.09634,1.08654,0.33744,0.5497,0.52782,0.24686,0.9887,0.62038,0.19776,1.44482,0.9833,2.14384,0.41182,1.84282,1.20142,0.63554,0.22782,0.3118,0.28306,0.67172,0.2355,0.42922,0.14182,1.0221,1.09558,0.48144,1.3518,0.47274,2.1182,3.17702,0.18726,0.34444,0.81644,0.9651,0.92418,0.51338,0.46238,1.5321,0.6607,1.03608,1.1377,0.23538,0.2687,0.43228,0.2086,0.14456,0.67922,0.77688,0.63302,0.25504,0.37008,0.30438,2.57226,0.16942,0.97416,0.60334,0.46698,1.01706,0.26286,1.0698,0.29518,0.1739,0.2509,0.35072,0.55598,0.1841,1.3128,0.25304,0.1422,0.18586,0.59196,0.96064,0.11172,0.20694,12.57342,0.0972,2.67436,0.60902,0.60688,4.68312,2.19566,0.22072,0.14588,0.81034,0.54618,0.18156,0.71552,0.34106,0.60684,0.6399,0.48194,0.08984,0.08972,0.4436,0.1232,0.33962,0.87766,1.63218,0.366,0.0,0.05872,0.26926,0.8861,0.2764,0.5892,0.53926,0.17616,0.02858,0.1725,0.35314,0.27614,0.89624,0.12758,0.28616,0.17708,0.36838,0.35852,0.1328,2.57306,11.9278,0.03328,0.32762,1.31284,0.92788,0.95568,0.2563,0.7068,0.20306,0.29012,0.32164,1.1122,0.12396,0.05258,0.37062,0.10154,0.58158,0.58044,0.05052,3.29336,0.4239,7.198,5.27138,0.15578,3.45228,0.9429,0.92686,2.89004,1.01856
2018-08-09,0.36188,0.49828,3.86222,0.46586,0.56642,0.3997,0.0639,0.7712,1.0262,0.19776,1.72682,0.9149,0.74836,0.49732,1.46648,2.12276,0.27738,0.8665,0.79086,0.28708,0.577,0.37036,1.05602,1.719,0.32624,1.119,0.46698,1.21084,0.33988,0.65664,0.27364,0.25432,1.04322,0.84716,0.84872,0.6539,0.6846,0.2749,0.32082,0.48202,2.47344,0.5167,0.4521,0.77518,1.1524,1.14472,1.38942,0.90046,1.7389,2.0762,0.87576,1.17604,0.28128,1.7506,0.68466,1.1404,0.55564,0.42836,0.84218,0.45772,0.63802,2.3949,0.53464,0.4628,1.72598,3.2542,0.55912,0.0,1.79272,0.4423,0.35306,0.0,0.32536,0.27708,0.0,0.69298,0.84436,0.0,1.2693,3.32918,1.49912,2.04552,0.4236,0.52234,1.06138,0.61116,0.65004,1.42544,0.67892,1.39178,0.0,0.8502,0.38792,0.59048,0.83662,0.04776,0.39046,1.94928,3.99354,1.10188,0.85174,1.761,0.60728,1.13004,2.71944,1.03234,0.35316,2.03612,0.84234,1.01352,0.79806,1.0514,0.7101,1.67112,0.7089,1.6191,0.06218,0.47978,0.87856,0.64586,0.23274,0.11356,0.13096,0.0439,0.26962,0.08558,1.27646,0.50898,0.11926,1.53292,0.65466,1.7267,0.18268,1.0803,0.70928,0.54956,0.12658,0.42186,0.96154,0.41582,0.30738,0.40556,0.13782,0.38366,0.51374,0.5633,0.3252,0.2764,0.0,0.24762,1.00846,0.11942,1.1015,1.14,0.3451,0.5664,0.43598,0.21258,1.00418,0.64018,0.20752,1.33048,0.942,2.2118,0.38998,1.74186,1.14842,0.65686,0.24054,0.26232,0.28188,0.66848,0.24226,0.37476,0.12832,0.99936,1.06348,0.54858,1.48956,0.4217,2.0952,2.62084,0.17896,0.31178,0.8176,1.00602,0.84424,0.48132,0.48732,1.57682,0.7058,1.0097,1.31222,0.211,0.22048,0.42928,0.2128,0.15256,0.67886,0.81618,0.58466,0.22854,0.3268,0.26658,2.2436,0.15588,0.84162,0.6107,0.51178,1.03392,0.2536,1.03326,0.24284,0.15814,0.2173,0.32088,0.5432,0.17594,1.12082,0.26224,0.13228,0.18646,0.53532,0.98146,0.11894,0.2183,11.52812,0.104,2.4849,0.65958,0.60476,4.55632,2.18852,0.21606,0.13328,0.80268,0.53046,0.1793,0.73364,0.34574,0.59626,0.5437,0.50332,0.08156,0.08396,0.4415,0.11292,0.27742,0.87094,1.63584,0.32352,0.0,0.05886,0.24382,0.94518,0.27514,0.63162,0.57682,0.17816,0.02756,0.17678,0.31816,0.27342,0.92312,0.18002,0.26952,0.164,0.35724,0.43964,0.12516,2.09898,10.8583,0.03504,0.32364,1.21202,0.8411,0.9984,0.25804,0.70016,0.19296,0.26146,0.31678,1.05314,0.11994,0.0477,0.36198,0.09504,0.57472,0.53432,0.04164,2.83988,0.43304,8.12246,5.1491,0.14618,3.64648,1.01494,0.8523,2.92452,1.03862
2018-08-10,0.35578,0.67432,4.5201,0.53708,0.5968,0.40258,0.06488,0.64158,0.85546,0.22956,1.8896,0.95532,0.76216,0.4862,1.54328,2.24652,0.27858,0.84282,0.796,0.26864,0.62218,0.36992,0.91134,1.67942,0.42254,1.17262,0.46018,1.2625,0.35282,0.66248,0.2609,0.23808,1.05886,0.80326,0.85678,0.74182,0.6576,0.26174,0.26266,0.4405,2.82774,0.4987,0.47662,0.7987,1.06056,0.99472,1.54538,0.909,1.66788,1.98136,0.86198,1.12436,0.27218,1.83232,0.72688,1.13514,0.53394,0.41608,0.74418,0.48972,0.70788,2.37574,0.53802,0.47558,2.11906,2.53566,0.53682,0.0,1.57174,0.42226,0.33962,0.0,0.3315,0.23592,0.0,0.60874,0.94584,0.0,1.26286,3.5009,1.5399,1.88384,0.3917,0.47296,1.04382,0.60422,0.0,1.48914,0.69134,1.4649,0.0,0.72772,0.38134,0.65294,0.6631,0.0513,0.42196,1.98364,3.97888,1.13218,0.82662,1.88156,0.66448,1.2025,2.73492,1.11658,0.39952,2.0006,0.77784,1.0318,0.77848,1.01982,0.7683,1.55932,0.66294,1.62286,0.05946,0.45518,0.80124,0.62982,0.27842,0.11038,0.12794,0.0422,0.25216,0.11726,1.3006,0.69792,0.12044,1.3558,0.64622,1.70686,0.1801,1.03378,0.80594,0.59522,0.12402,0.36916,0.94012,0.3873,0.3156,0.38196,0.11412,0.38994,0.50792,0.49816,0.3316,0.26684,0.0,0.24382,0.9074,0.1204,1.05614,1.01978,0.35694,0.56804,0.40212,0.22178,0.97082,0.68386,0.23476,1.03622,0.93834,2.00384,0.41706,1.69726,1.08614,0.6384,0.24736,0.24632,0.3224,0.69718,0.26404,0.29574,0.13112,1.03756,1.00554,0.57266,1.47036,0.48172,1.9378,2.39754,0.17932,0.27434,0.90708,1.05752,0.77326,0.49068,0.54772,1.83956,0.7663,1.03022,1.55076,0.2377,0.20916,0.4575,0.20862,0.16164,0.58788,0.7514,0.7098,0.21496,0.36966,0.24374,2.30552,0.20254,0.68176,0.61098,0.5549,0.96626,0.29164,1.05746,0.23372,0.14016,0.21162,0.3298,0.54356,0.16704,0.97872,0.24442,0.12772,0.18278,0.5125,0.94746,0.12762,0.24978,11.42656,0.09574,2.447,0.5848,0.6046,4.27542,2.45966,0.20886,0.14278,0.81402,0.56498,0.17428,0.73506,0.33962,0.57212,0.71016,0.48862,0.07708,0.07374,0.43908,0.11338,0.28408,0.8843,1.66312,0.30528,0.0,0.05842,0.24486,0.9354,0.2628,0.57088,0.6404,0.18164,0.02746,0.18732,0.33134,0.2792,0.97934,0.20918,0.27194,0.15804,0.34552,0.53616,0.12076,1.96188,10.05422,0.03346,0.30766,1.17322,0.94626,0.96474,0.18908,0.70724,0.19314,0.24092,0.33694,0.9835,0.1311,0.04642,0.36994,0.11082,0.58378,0.53264,0.03718,2.96336,0.47216,8.73352,5.49238,0.15318,3.79826,1.16532,1.1214,2.76104,1.02942
2018-08-13,0.38658,0.71008,5.49226,0.52038,0.60378,0.40736,0.07662,0.55522,0.78254,0.23116,1.53964,0.9261,0.74192,0.43748,1.649,1.99062,0.29406,0.86828,0.7859,0.24226,0.60888,0.36592,0.81986,1.37556,0.4562,1.13586,0.46378,1.36732,0.34028,0.64104,0.23892,0.23342,0.97522,0.8379,0.83426,0.72924,0.71476,0.2479,0.21534,0.45696,3.06368,0.51064,0.45314,0.66002,0.97752,0.90972,1.52244,1.06422,1.448,1.85626,0.84626,1.05288,0.23438,1.6783,0.71372,1.33168,0.51784,0.39556,0.76454,0.51322,0.68908,2.67114,0.55782,0.49916,2.3331,2.3345,0.49938,0.0,1.429,0.3753,0.35982,0.0,0.30374,0.20332,0.0,0.59114,0.80834,0.0,1.25024,3.19302,1.26694,1.65242,0.4306,0.35584,0.98226,0.58798,0.84716,1.5289,0.7942,1.65094,0.0,0.73844,0.44052,0.65196,0.50724,0.04924,0.35066,1.84676,4.09572,1.12358,0.71722,2.0001,0.75194,1.27932,2.77796,1.06048,0.46414,1.91552,0.72648,1.12426,0.72426,0.90966,0.88788,1.46632,0.65204,1.49758,0.0598,0.45284,0.59384,0.6408,0.30702,0.10674,0.13202,0.03694,0.25824,0.12376,1.28318,0.74698,0.1192,1.36446,0.63892,1.61246,0.18802,0.97186,0.81406,0.58938,0.11746,0.30842,0.94894,0.3534,0.31266,0.38158,0.10884,0.37056,0.5014,0.48572,0.34576,0.24458,0.0,0.23426,0.81212,0.09814,1.04082,0.88866,0.31942,0.54348,0.3788,0.2023,1.12418,0.55398,0.27052,0.75628,0.88346,1.96368,0.41652,1.52944,0.92272,0.64806,0.23414,0.22838,0.31014,0.70858,0.26858,0.30832,0.12554,0.8432,0.913,0.60336,1.86786,0.61284,2.3074,2.28536,0.16776,0.25734,1.16696,0.79952,0.80226,0.45502,0.55954,2.12626,0.77216,0.9796,2.01402,0.23598,0.19978,0.38968,0.17862,0.16038,0.54716,0.80034,0.77312,0.23768,0.38316,0.25152,2.30318,0.2039,0.62824,0.55696,0.53334,0.83404,0.28506,0.93134,0.22698,0.13026,0.21182,0.32358,0.50242,0.15004,0.93558,0.20324,0.12658,0.18628,0.5026,0.6468,0.13084,0.26438,11.86818,0.09864,2.37016,0.5923,0.61574,4.34418,2.37706,0.21434,0.14396,0.83324,0.40782,0.16478,0.69642,0.34166,0.55966,1.2253,0.46558,0.07752,0.06396,0.44552,0.11346,0.30622,0.82312,1.6932,0.29134,0.0,0.06444,0.23374,0.95266,0.2481,0.51602,0.6676,0.18182,0.02548,0.18692,0.3243,0.28884,0.94118,0.23642,0.3065,0.152,0.32764,0.60302,0.123,1.74198,9.33956,0.03286,0.29358,1.12126,0.9666,0.90168,0.17364,0.68024,0.1877,0.2143,0.33424,1.07982,0.1294,0.04562,0.38966,0.11032,0.57546,0.50516,0.0384,2.91404,0.5258,8.31448,5.08618,0.12732,3.65356,1.2016,1.10064,2.92236,0.8889
2018-08-14,0.35628,0.71732,6.24602,0.47976,0.61954,0.3328,0.07462,0.49074,0.73606,0.21372,1.35204,0.99254,0.72464,0.4059,1.45814,1.3908,0.2883,0.83796,0.77924,0.20622,0.54828,0.32698,0.68232,1.11906,0.46738,1.16118,0.43894,1.3249,0.30004,0.58046,0.20642,0.1899,0.90024,0.79754,0.7433,0.747,0.7237,0.21678,0.19368,0.43658,3.06928,0.48398,0.46802,0.58692,0.86328,0.7809,1.47662,1.0491,1.31018,1.7828,0.78942,0.83692,0.22418,1.57552,0.79306,1.32408,0.46722,0.3648,0.63192,0.42008,0.69568,2.61882,0.4848,0.47216,2.27186,2.07068,0.50958,0.0,1.2883,0.3762,0.32594,0.0,0.23516,0.13596,0.0,0.54296,0.68416,0.0,1.24376,2.81912,1.10842,1.74582,0.43352,0.31136,0.93826,0.53124,1.30436,1.47784,0.8185,1.55672,0.0,0.64864,0.4705,0.5764,0.40872,0.05046,0.29978,1.69958,3.74416,1.15174,0.64144,1.91682,0.76126,1.1437,2.47688,1.05718,0.5053,1.90348,0.635,1.15518,0.63038,0.81504,0.87918,1.07256,0.5741,1.35544,0.0556,0.41356,0.51324,0.60194,0.29168,0.08818,0.11918,0.03256,0.26272,0.12218,1.16646,0.68546,0.10094,1.17576,0.55018,1.27936,0.16398,0.9001,0.83934,0.5632,0.10364,0.2943,0.72318,0.31482,0.29002,0.34044,0.09994,0.3351,0.50468,0.4387,0.3244,0.23684,0.0,0.17646,0.74466,0.09004,1.02536,0.79248,0.30828,0.46132,0.3386,0.1848,1.11526,0.50446,0.30942,0.50764,0.8484,1.80316,0.37496,1.30148,0.86546,0.58,0.22994,0.20282,0.2728,0.69112,0.27278,0.33022,0.11404,0.68856,0.80584,0.62116,1.98812,0.64184,2.30784,2.06448,0.1552,0.22966,1.23452,0.66564,0.78054,0.45362,0.5152,1.97014,0.90024,0.9108,2.0898,0.23252,0.18758,0.37924,0.1636,0.12884,0.53446,0.75194,0.72432,0.23222,0.36092,0.25516,2.13694,0.19488,0.63976,0.46392,0.49094,0.72552,0.27204,0.81724,0.22914,0.13138,0.1954,0.2782,0.44668,0.13548,0.85278,0.16758,0.10924,0.18764,0.43316,0.58098,0.09824,0.29276,10.92478,0.0887,2.0095,0.53228,0.45928,4.33264,2.40978,0.20092,0.12484,0.70368,0.29524,0.15016,0.62046,0.31414,0.48606,1.50644,0.40456,0.06854,0.05934,0.39878,0.10342,0.35212,0.69912,1.78638,0.25916,0.0,0.05934,0.2114,0.85902,0.2223,0.429,0.765,0.1466,0.02332,0.19418,0.24574,0.21476,0.78998,0.24706,0.22622,0.1467,0.2488,0.63522,0.11834,1.65744,7.3024,0.02848,0.26396,0.99834,0.89704,0.74826,0.14664,0.54974,0.17286,0.17922,0.36454,0.9577,0.15876,0.04292,0.3725,0.11374,0.5579,0.44086,0.0355,2.81958,0.546,7.20464,4.39294,0.11468,2.9877,1.10922,1.06722,2.98376,0.77544
2018-08-15,0.36694,0.73004,6.8674,0.36546,0.62702,0.29412,0.07354,0.48334,0.74488,0.2046,1.04924,0.953,0.6568,0.44376,1.38134,1.1996,0.32438,0.81554,0.79304,0.21786,0.52538,0.30742,0.6558,1.09196,0.465,1.22184,0.3726,1.36652,0.30324,0.56268,0.20858,0.1901,0.83722,0.64326,0.76556,0.72914,0.70804,0.2071,0.28784,0.39132,3.12858,0.4658,0.48784,0.5374,0.89668,0.69806,1.41566,1.0704,1.31502,1.68228,0.61604,0.80228,0.22176,1.58298,0.91934,1.32516,0.41852,0.30316,0.65706,0.35592,0.72304,2.72664,0.48924,0.52562,2.36216,2.02736,0.56894,0.0,1.2402,0.52866,0.32926,0.0,0.2044,0.11404,0.0,0.49784,0.71972,0.0,1.28472,2.78104,1.03928,1.87816,0.46506,0.29686,0.81768,0.4928,1.73034,1.13014,0.86374,1.52974,0.0,0.68158,0.50306,0.555,0.40062,0.04848,0.25052,1.62096,3.6274,1.15134,0.65756,2.03282,0.78194,1.07886,2.33202,0.89754,0.95708,1.86164,0.6334,1.31856,0.64534,0.8094,0.89712,0.98316,0.69532,1.3025,0.05654,0.3871,0.43592,0.51602,0.27638,0.08334,0.11746,0.03092,0.22986,0.11886,1.17548,0.6266,0.09542,1.10098,0.5529,1.12392,0.17388,0.79688,0.82922,0.54066,0.10206,0.32506,0.69372,0.31694,0.28694,0.28794,0.09908,0.3253,0.49574,0.41576,0.343,0.24822,0.0,0.14854,0.83132,0.08876,0.97558,0.873,0.28328,0.46422,0.30126,0.1892,1.0974,0.5477,0.2926,0.22976,0.89984,1.2756,0.3454,1.39218,0.83472,0.57494,0.24222,0.19068,0.257,0.65858,0.23902,0.2969,0.11836,0.65624,0.72746,0.5138,2.09672,0.60368,2.2981,1.87748,0.15838,0.23812,1.31152,0.63886,0.67402,0.47276,0.72906,1.9327,0.6752,0.83262,2.252,0.22602,0.1803,0.39974,0.1572,0.10584,0.5452,0.82378,0.7215,0.22706,0.37846,0.24464,2.06868,0.19784,0.59496,0.38146,0.5238,1.04174,0.27498,0.75926,0.23514,0.11738,0.19478,0.2686,0.46728,0.13794,0.83868,0.16142,0.10574,0.1854,0.39162,0.64738,0.08084,0.30408,9.62308,0.08812,1.97942,0.53418,0.41012,4.22894,2.28236,0.2016,0.1325,0.6338,0.27194,0.14844,0.5127,0.28508,0.47654,1.71886,0.38024,0.06768,0.06166,0.43602,0.10006,0.3988,0.69036,1.68958,0.25884,0.0,0.06192,0.2062,0.78142,0.2167,0.3899,0.84578,0.14164,0.02376,0.22172,0.23096,0.1949,0.70534,0.15692,0.20934,0.14406,0.2199,0.49152,0.119,1.60244,6.7342,0.02116,0.27688,0.97588,0.87744,0.65766,0.13656,0.46126,0.15524,0.19148,0.38932,0.93108,0.13904,0.04444,0.3004,0.1085,0.50016,0.4256,0.03744,2.46508,0.55894,7.10648,4.175,0.11298,2.5715,1.11236,1.10912,3.02414,0.72212
2018-08-16,0.3473,0.6465,7.1665,0.31734,0.55092,0.24068,0.07572,0.47154,0.72342,0.1813,0.87774,0.85896,0.5564,0.47736,1.14984,1.31184,0.33496,0.75426,0.81602,0.2116,0.43268,0.39092,0.57284,1.00488,0.44712,1.17674,0.34404,1.19344,0.30986,0.5026,0.23512,0.19354,0.74764,0.61262,0.72878,0.6964,0.73126,0.20348,0.39134,0.3507,2.89964,0.41756,0.47578,0.46188,0.82654,0.59924,1.22376,1.00426,1.17414,1.50968,0.62674,0.95108,0.19562,1.5154,0.91932,1.13048,0.39648,0.2706,0.73552,0.32264,0.79498,2.53028,0.38958,0.49856,2.19976,1.4972,0.56584,0.0,1.24074,0.51672,0.39404,0.0,0.17398,0.10726,0.0,0.4289,0.70728,0.0,1.2296,2.69494,1.0199,1.8048,0.43472,0.27106,0.7105,0.43636,2.06428,0.98182,0.86226,1.27168,0.0,0.88486,0.53536,0.96002,0.49334,0.04308,0.2251,1.7651,3.78682,1.04662,0.5877,2.20928,0.7239,1.01224,2.21294,0.79734,1.07274,1.676,0.54948,1.28802,0.60432,0.7149,0.96026,0.96146,0.69128,1.1638,0.06126,0.34748,0.45212,0.44886,0.2527,0.07594,0.1176,0.02986,0.22508,0.11156,0.98404,0.62092,0.0833,1.10886,0.54582,1.00444,0.17192,0.61768,0.78866,0.8637,0.10494,0.3175,0.76634,0.30172,0.24922,0.2578,0.10022,0.32646,0.442,0.3844,0.32566,0.23632,0.0,0.13082,0.86336,0.08372,0.83086,0.78074,0.27658,0.37004,0.28236,0.17854,1.05656,0.51676,0.28282,0.0,0.83824,1.17588,0.32784,1.3834,0.7981,0.52466,0.239,0.16336,0.24406,0.6088,0.25472,0.276,0.1063,0.58644,0.64444,0.4151,2.1561,0.57932,2.11642,1.73932,0.13854,0.27866,1.38194,0.57746,0.62982,0.43574,0.76334,1.76538,0.65862,0.7185,2.17282,0.21632,0.16656,0.40712,0.14516,0.09142,0.51032,0.77862,0.68404,0.23286,0.37592,0.23034,1.77214,0.19914,0.5962,0.32548,0.50562,1.05046,0.27038,0.67654,0.20152,0.1116,0.20276,0.24704,0.4121,0.1461,0.73506,0.13598,0.13746,0.17834,0.34608,0.6677,0.07168,0.29634,9.19374,0.08042,2.06568,0.54786,0.38244,3.74182,2.21228,0.18494,0.1337,0.59822,0.28242,0.1501,0.4404,0.24602,0.45324,1.98606,0.3364,0.05568,0.06274,0.40642,0.10474,0.44284,0.64492,1.52654,0.2531,0.0,0.0653,0.22148,0.5965,0.1979,0.31166,0.89304,0.12972,0.02252,0.2053,0.21464,0.17116,0.67472,0.127,0.19948,0.1475,0.20198,0.42904,0.11818,1.60296,6.29438,0.01928,0.24308,1.06626,1.04992,0.60876,0.12558,0.42096,0.15838,0.17628,0.37246,0.92836,0.1366,0.04348,0.26556,0.09814,0.44508,0.39238,0.03516,3.17916,0.50392,5.36098,4.0149,0.11886,2.46022,1.00464,1.1396,2.79758,0.57614
2018-08-17,0.37356,0.48654,6.8937,0.25246,0.51888,0.23586,0.07942,0.48258,0.74568,0.15576,0.76532,0.7889,0.53394,0.4954,1.11892,1.79982,0.35904,0.74376,0.7749,0.24424,0.35684,0.44564,0.55622,1.1384,0.33564,1.1549,0.33238,1.14334,0.3092,0.48396,0.2805,0.19878,0.7118,0.63936,0.7213,0.63368,0.75608,0.1932,0.52686,0.33662,2.7886,0.43872,0.38374,0.50444,0.8463,0.5285,0.9947,0.98576,1.08938,1.18684,0.58768,1.36318,0.19118,1.76736,0.8944,1.07012,0.39566,0.2957,0.6677,0.32896,0.82528,2.6793,0.35448,0.4748,1.84916,1.4642,0.59424,0.0,1.17866,0.53684,0.4643,0.0,0.16486,0.11362,0.0,0.38062,0.9703,0.0,1.24068,2.64912,0.99404,1.7687,0.5346,0.28738,0.5889,0.43142,2.52142,0.88612,0.78122,1.11818,0.0,1.15976,0.60304,1.31966,0.57926,0.04106,0.25626,1.773,3.87442,1.06706,0.7099,2.3034,0.66406,0.96636,2.13258,0.72918,1.11348,1.70604,0.50754,1.39972,0.62562,0.67868,0.9347,1.0058,0.66364,1.1193,0.06262,0.35314,0.4922,0.4322,0.19084,0.08048,0.12058,0.03116,0.2258,0.06426,0.91564,0.43472,0.0777,1.15594,0.56674,0.92862,0.17074,0.54698,0.72494,1.0147,0.10576,0.3019,0.72876,0.29648,0.24258,0.31978,0.10108,0.34488,0.43754,0.38382,0.3138,0.22822,0.0,0.13522,0.90462,0.0756,0.78414,1.03752,0.28002,0.3597,0.28176,0.1677,1.10816,0.55422,0.26764,0.34566,0.84674,1.18784,0.27834,1.65926,0.74402,0.51408,0.26368,0.18596,0.19448,0.59992,0.26322,0.27786,0.10616,0.56354,0.81484,0.4069,2.14964,0.52976,2.11696,1.80414,0.14026,0.3149,1.37246,0.52146,0.62844,0.41342,0.7193,1.59492,0.62428,0.6991,2.05004,0.18954,0.147,0.40712,0.14338,0.0832,0.50188,0.78882,0.5958,0.2421,0.37774,0.23152,1.6565,0.1645,0.6002,0.28038,0.51768,1.11728,0.2462,0.61778,0.19936,0.1235,0.22134,0.22932,0.40216,0.15548,0.6431,0.11178,0.15464,0.17792,0.33646,0.95384,0.06278,0.27922,8.99454,0.07804,2.15066,0.57946,0.34982,3.67014,1.81326,0.18494,0.12208,0.58104,0.27554,0.16248,0.45428,0.22556,0.42332,1.92222,0.32764,0.05996,0.06526,0.40686,0.10536,0.44926,0.64044,1.41738,0.26972,0.0,0.06414,0.23538,0.59658,0.2247,0.33846,0.90026,0.1206,0.02338,0.199,0.19824,0.15938,0.65504,0.12036,0.2436,0.17244,0.18428,0.37674,0.11746,1.59204,6.25062,0.0198,0.32176,1.15516,1.21774,0.61526,0.12442,0.4009,0.1666,0.17328,0.36076,0.91364,0.13336,0.04332,0.25512,0.07624,0.42224,0.3777,0.03542,3.89542,0.43576,5.0888,4.50616,0.11506,2.46024,0.96356,0.92264,2.55584,0.52826
2018-08-20,0.35758,0.4838,6.44254,0.2518,0.4855,0.22898,0.06798,0.50544,0.69712,0.1605,0.76734,0.79342,0.50774,0.55112,0.96348,2.06586,0.53572,0.71474,0.7769,0.26894,0.33526,0.43702,0.57304,1.27366,0.2811,1.08408,0.31816,1.03602,0.31732,0.41126,0.30998,0.1913,0.73488,0.72222,0.7476,0.63718,0.71694,0.20666,0.64476,0.31562,2.5676,0.43368,0.38112,0.58626,0.82164,0.47146,0.943,0.78914,1.4769,1.16128,0.59972,1.62978,0.21606,1.7851,0.85968,0.83094,0.41716,0.34478,0.82198,0.37728,0.8937,2.49694,0.32358,0.46736,1.53388,1.59278,0.62696,0.0,1.27784,0.58616,0.47608,0.0,0.2158,0.13424,0.0,0.3674,1.07614,0.0,1.21314,2.59098,1.04354,1.96764,0.61328,0.27282,0.54726,0.43468,1.92478,0.81876,0.80738,0.96428,0.0,1.55866,0.60536,1.67286,0.69698,0.04226,0.26378,1.77952,5.0616,1.1229,0.85014,2.4744,0.57414,0.86296,2.03442,0.68872,1.33198,1.67994,0.45124,1.3421,0.62566,0.76006,0.83856,1.04646,0.64388,0.98656,0.06906,0.44506,0.59112,0.3957,0.16306,0.0916,0.12186,0.03118,0.21522,0.04862,0.83962,0.41116,0.0745,1.22034,0.54298,0.87492,0.16122,0.53898,0.7287,1.18316,0.1038,0.33842,0.70698,0.37458,0.23372,0.33382,0.10418,0.35412,0.42114,0.39174,0.29614,0.22404,0.0,0.13122,0.99288,0.07634,0.74022,1.09332,0.26488,0.36812,0.29506,0.18454,1.0235,0.63466,0.24326,0.55212,0.83686,1.18326,0.25266,1.5219,0.71016,0.4598,0.27068,0.1712,0.23778,0.58576,0.2568,0.25454,0.11532,0.66382,0.87918,0.38388,2.03168,0.3745,1.85834,1.67914,0.15324,0.33764,1.30504,0.52064,0.48492,0.37896,0.75014,1.40256,0.6817,0.72284,1.66104,0.1754,0.1517,0.43718,0.17188,0.08068,0.50062,0.73774,0.5104,0.21266,0.37906,0.23846,1.48472,0.165,0.6438,0.23208,0.51486,1.2288,0.23926,0.65274,0.18294,0.141,0.22528,0.21192,0.38242,0.15112,0.54046,0.10582,0.16036,0.17734,0.28454,1.04706,0.05714,0.2501,8.46466,0.07398,2.14526,0.5695,0.35242,3.35882,1.74176,0.17448,0.1229,0.54814,0.29882,0.19264,0.43202,0.20968,0.4024,1.45758,0.32586,0.05768,0.06168,0.40824,0.10226,0.43446,0.72816,1.3413,0.26316,0.07896,0.05918,0.25376,0.62592,0.25072,0.37714,0.98006,0.11962,0.02322,0.19176,0.1972,0.1429,0.613,0.11452,0.21786,0.17718,0.16978,0.33668,0.11264,1.48566,5.65156,0.02052,0.34886,1.27322,1.38268,0.63628,0.11048,0.38896,0.17128,0.18104,0.38898,0.77776,0.1432,0.0402,0.21836,0.06628,0.415,0.37514,0.03156,4.16444,0.4533,4.96882,4.45944,0.12474,2.56052,0.94392,0.94276,2.35908,0.5013
2018-08-21,0.45474,0.48566,6.0695,0.24798,0.41584,0.23714,0.07404,0.53092,0.63188,0.18488,0.8078,0.67604,0.47548,0.65186,0.91194,2.12834,0.60718,0.78814,0.8378,0.2871,0.40746,0.4318,0.68186,1.44288,0.26948,1.08184,0.32236,1.03654,0.35224,0.44616,0.33088,0.20276,0.83814,0.78942,0.80502,0.5844,0.74912,0.20738,0.72122,0.32512,2.606,0.41828,0.35386,0.65318,0.7525,0.42306,0.92384,0.751,1.62636,1.32956,0.64936,1.85452,0.18442,1.99078,0.75714,0.83276,0.45184,0.35188,0.91788,0.4516,0.90266,2.36304,0.36412,0.54812,1.54282,2.54816,0.65822,0.0,1.35652,0.6154,0.4777,0.0,0.25032,0.1708,0.0,0.44206,1.16286,0.0,1.09368,2.72286,1.11772,1.85932,0.63712,0.24676,0.5479,0.42914,1.71904,0.72194,0.8363,1.12898,0.0,1.89514,0.57254,1.90646,0.73432,0.04114,0.28014,1.80648,5.32224,1.11834,0.99432,2.6832,0.54774,0.89216,2.13582,0.66964,1.5495,1.9126,0.47004,1.53014,0.68544,0.79316,0.84552,1.0865,0.65664,0.9516,0.07184,0.51784,0.62366,0.384,0.16444,0.1081,0.13104,0.03216,0.19996,0.04216,0.84012,0.4161,0.0738,1.52914,0.6037,0.8481,0.1713,0.61624,0.7012,1.29628,0.10788,0.34406,0.76672,0.40874,0.2264,0.34508,0.105,0.36876,0.3717,0.48406,0.30358,0.2217,0.0,0.13572,1.12832,0.07748,0.65156,1.16908,0.2445,0.40282,0.32168,0.2002,1.04044,0.68394,0.24452,0.70162,0.8606,0.99978,0.25402,1.55534,0.67986,0.4248,0.26212,0.19836,0.25072,0.55718,0.25176,0.20544,0.11958,0.75168,1.02372,0.39042,2.1724,0.35532,1.89202,1.63836,0.16526,0.38628,1.44074,0.5617,0.47408,0.3265,0.77188,1.4602,0.5506,0.6839,1.6617,0.15842,0.1405,0.58772,0.17876,0.08834,0.48364,1.0689,0.50932,0.2128,0.4028,0.23816,1.73084,0.16608,0.67654,0.25204,0.53412,1.36468,0.2439,0.7594,0.17768,0.13254,0.23782,0.2394,0.39832,0.15746,0.58114,0.11264,0.16946,0.17894,0.28258,1.088,0.0506,0.2807,8.33956,0.07496,2.2631,0.6968,0.35872,3.18754,1.65546,0.18308,0.12862,0.60914,0.31826,0.22798,0.41244,0.20288,0.38884,1.19076,0.34496,0.05536,0.06328,0.46878,0.10708,0.37394,0.93786,1.26516,0.27952,0.14432,0.05894,0.30532,0.84198,0.2965,0.42594,0.8621,0.12298,0.02662,0.19798,0.21516,0.14854,0.71234,0.10352,0.22458,0.17724,0.19078,0.29838,0.117,1.43624,6.11336,0.02022,0.43022,1.42142,1.54962,0.69204,0.09796,0.39942,0.17572,0.19522,0.40704,0.80328,0.11626,0.04072,0.22356,0.05462,0.37958,0.39338,0.03354,4.54494,0.46068,6.27626,4.58042,0.1405,2.87136,0.96734,0.9566,2.16448,0.5261
2018-08-22,0.49108,0.47522,5.55434,0.28706,0.37848,0.23862,0.07172,0.46936,0.5408,0.17792,0.86744,0.61348,0.47126,0.59252,0.80664,2.14884,0.61562,0.75156,0.77044,0.266,0.40782,0.41962,0.64134,1.74682,0.2564,0.93892,0.31066,0.9257,0.32996,0.41338,0.31336,0.18156,0.84176,0.79936,0.708,0.55938,0.71784,0.20572,0.67568,0.30774,2.35874,0.39986,0.35414,0.74024,0.7128,0.46374,0.90476,0.80624,1.5871,1.2526,0.61676,1.8686,0.17262,2.00854,0.66314,0.74192,0.4566,0.3749,0.92532,0.47898,0.85478,2.24748,0.38636,0.49008,1.51294,2.72012,0.78738,0.0,1.26354,0.47184,0.43842,0.0,0.25276,0.1655,0.0,0.42096,1.10496,0.0,0.9766,2.50914,1.10988,1.67632,0.70858,0.22386,0.62542,0.39814,1.51814,0.6174,0.82236,1.18384,0.0,1.96782,0.5688,2.05036,0.73066,0.0371,0.27812,1.7577,5.46916,1.09222,0.9479,2.50136,0.48796,0.81232,2.0259,0.60632,1.23824,1.9525,0.42124,1.49884,0.63434,0.86312,0.79772,1.02236,0.51782,0.93152,0.06986,0.5778,0.61292,0.33202,0.16072,0.11444,0.12674,0.03082,0.18348,0.0374,0.7593,0.40578,0.06904,1.4981,0.55028,0.80906,0.15368,0.57266,0.65774,1.348,0.10702,0.30362,0.7223,0.39666,0.20312,0.33588,0.0915,0.35288,0.3546,0.4657,0.279,0.20772,0.0,0.12984,1.04426,0.08304,0.59404,1.08418,0.21356,0.36502,0.31298,0.19176,1.05688,0.64484,0.25618,0.8335,0.91644,0.87054,0.24422,1.43502,0.6573,0.39028,0.23472,0.20842,0.24946,0.48748,0.25632,0.19102,0.11014,0.74616,1.00542,0.34896,2.13482,0.31536,1.76954,1.5658,0.1586,0.36348,1.44748,0.56604,0.48314,0.26406,0.5359,1.38132,0.4965,0.5839,1.51702,0.1485,0.1355,0.6457,0.17488,0.09742,0.43058,1.13294,0.47874,0.18616,0.40308,0.2233,1.56958,0.1662,0.65756,0.2244,0.45548,1.02082,0.21296,0.73354,0.16808,0.13722,0.2281,0.23066,0.3454,0.16058,0.51186,0.09706,0.16438,0.17276,0.2699,0.9533,0.04926,0.27676,7.95018,0.06586,2.11302,0.6702,0.30544,2.9541,1.66532,0.16744,0.12546,0.56594,0.30094,0.22958,0.39264,0.2469,0.38804,0.95812,0.34014,0.05042,0.05784,0.54782,0.1047,0.3103,0.91196,1.15884,0.27566,0.17852,0.05312,0.30526,0.87012,0.29142,0.52914,0.64394,0.09324,0.02558,0.14866,0.20606,0.14126,0.69876,0.09388,0.21602,0.16116,0.18382,0.25152,0.10786,1.33458,5.67446,0.02064,0.43984,1.46074,1.61868,0.67922,0.09432,0.36124,0.1458,0.16204,0.35934,0.72504,0.10458,0.03504,0.2247,0.05502,0.31216,0.37454,0.0289,4.8489,0.54232,7.78768,4.41992,0.13738,2.86314,0.89252,0.89888,2.00466,0.52812
2018-08-23,0.50166,0.48606,5.90334,0.29494,0.37566,0.26494,0.07058,0.47412,0.56258,0.17678,0.92362,0.61038,0.45036,0.50658,0.75072,1.92828,0.59814,0.7531,0.68812,0.32052,0.3882,0.28432,0.65694,1.88232,0.25748,0.84034,0.27328,0.91068,0.30098,0.4237,0.28422,0.15514,0.8746,0.86906,0.65008,0.51984,0.6498,0.19382,0.61978,0.32738,2.41862,0.39396,0.35926,0.73744,0.70622,0.46182,0.85738,0.93768,1.53618,1.16698,0.52558,1.67378,0.1659,1.94264,0.66004,0.75382,0.46678,0.38012,0.8241,0.51176,0.70822,2.4279,0.43682,0.49784,1.47156,2.7033,0.97076,0.0,1.16982,0.45862,0.36008,0.0,0.25978,0.2184,0.0,0.36258,1.04764,0.0,0.9372,2.40756,1.05744,1.42518,0.74718,0.20666,1.0679,0.34102,1.45294,0.64692,0.83866,1.16594,0.0,1.92918,0.56924,1.79756,0.66038,0.03272,0.2583,1.45562,5.11996,1.07016,0.96002,2.1696,0.46478,0.8742,2.07418,0.64192,1.32906,2.07764,0.41916,1.48732,0.69036,0.9263,0.64128,0.9209,0.48432,0.90888,0.06362,0.61658,0.57986,0.3065,0.18198,0.13128,0.1196,0.02882,0.18444,0.04388,0.70176,0.40062,0.0699,1.35552,0.53652,0.76556,0.14764,0.6083,0.60334,1.06656,0.09802,0.27978,0.5847,0.39676,0.19348,0.34144,0.08126,0.32278,0.35344,0.43016,0.26412,0.19982,0.0,0.12166,0.9913,0.07946,0.60444,1.05832,0.18556,0.33312,0.28176,0.2096,1.0368,0.63404,0.24432,1.02672,1.20664,0.8476,0.22194,1.36046,0.61432,0.33914,0.2046,0.2158,0.2306,0.45108,0.22754,0.19854,0.10652,0.74984,1.01116,0.34922,2.04934,0.30544,1.82332,1.5465,0.1564,0.3116,1.40416,0.5753,0.44666,0.25816,0.44922,1.87868,0.48268,0.61138,1.55916,0.13272,0.12956,0.68812,0.16444,0.10884,0.4009,1.3915,0.42576,0.16882,0.41168,0.22862,1.59654,0.1517,0.61362,0.20946,0.41404,0.92452,0.19026,0.69466,0.1612,0.13064,0.20798,0.23342,0.34256,0.1569,0.48144,0.08572,0.13056,0.16664,0.27826,0.86192,0.04272,0.27048,7.87244,0.06208,1.8588,0.60094,0.30056,3.20016,1.58088,0.15896,0.12044,0.5504,0.27582,0.2263,0.37282,0.25152,0.37362,0.6786,0.32136,0.04804,0.05204,0.5997,0.09348,0.25172,0.90176,1.13244,0.25396,0.20756,0.04524,0.30326,0.86554,0.29382,0.59442,0.5477,0.08346,0.02592,0.13466,0.21,0.1308,0.67504,0.08176,0.20824,0.15436,0.17574,0.28066,0.10254,1.22952,5.35678,0.01974,0.47706,1.37432,1.4957,0.6387,0.09348,0.30532,0.13056,0.1578,0.39,0.6277,0.0965,0.03196,0.21692,0.04968,0.28046,0.37726,0.02752,4.2598,0.57812,9.62084,4.2482,0.12156,2.76602,0.79874,0.7707,2.14454,0.52104
2018-08-24,0.78014,0.45502,6.036,0.29156,0.36796,0.28788,0.07304,0.44786,0.49436,0.2425,0.92022,0.61036,0.4093,0.46826,0.68118,1.34994,0.59414,0.71372,0.68052,0.34128,0.44614,0.23298,0.64004,1.81342,0.25764,0.76694,0.28326,0.88674,0.30108,0.39778,0.262,0.15176,0.85796,0.8399,0.63232,0.47682,0.6102,0.1781,0.50752,0.3231,2.42688,0.39622,0.36606,0.70928,0.65454,0.43934,0.89768,1.00352,1.64452,1.13492,0.50186,1.39504,0.14962,1.53074,0.58048,0.81334,0.4394,0.34948,0.81642,0.55394,0.60166,2.24104,0.45972,0.50498,1.47572,2.65302,1.03882,0.0,1.16092,0.42134,0.31834,0.0,0.2645,0.24264,0.0,0.34318,0.71176,0.0,0.9669,2.27234,1.02492,2.04684,0.75414,0.14922,1.37496,0.2912,1.30606,0.59136,0.87528,1.25624,0.0,1.73604,0.54166,1.67778,0.51852,0.0322,0.19532,1.36018,5.06842,0.94364,0.77422,1.96994,0.4345,0.93588,2.12144,0.6122,1.32216,2.02712,0.41068,1.34234,0.73714,0.92126,0.55326,0.8103,0.46122,0.87612,0.07584,0.63166,0.53596,0.26798,0.17164,0.21132,0.14688,0.0268,0.18622,0.04938,0.72582,0.36278,0.07142,1.23826,0.56282,0.72742,0.16906,0.58988,0.52048,0.90724,0.09668,0.28182,0.53844,0.41714,0.18642,0.26676,0.07948,0.2989,0.36412,0.40578,0.26246,0.19378,0.0,0.11722,0.91796,0.08608,0.57006,0.83764,0.1603,0.30032,0.28246,0.27992,0.94016,0.55498,0.2478,0.77126,1.35316,0.81112,0.19442,1.04492,0.58946,0.32172,0.17982,0.1702,0.21886,0.43404,0.20016,0.2577,0.0991,0.70136,0.79526,0.3142,2.00192,0.28016,2.00478,1.70128,0.13596,0.27898,1.36388,0.56378,0.42676,0.2459,0.42152,2.3216,0.52104,0.57282,1.47192,0.12022,0.15032,0.6971,0.1532,0.12686,0.367,1.50186,0.35316,0.16462,0.37264,0.2558,1.74586,0.14164,0.63532,0.22716,0.38008,0.80612,0.14466,0.63418,0.13896,0.12272,0.20188,0.33334,0.4418,0.162,0.4484,0.07148,0.12728,0.1625,0.4269,0.56382,0.03968,0.27102,7.86944,0.06098,1.83682,0.55466,0.30072,3.08098,1.5069,0.19584,0.15262,0.51432,0.2773,0.22882,0.34882,0.25844,0.33708,0.57096,0.3727,0.04046,0.0547,0.64054,0.12654,0.25838,0.89334,1.10154,0.24048,0.22278,0.04754,0.32176,0.96816,0.2872,0.574,0.52258,0.08084,0.02958,0.13092,0.20344,0.12338,0.7406,0.0669,0.14892,0.1401,0.16832,0.28548,0.1312,1.12834,9.02958,0.01846,0.4096,1.40718,1.28386,0.53438,0.0897,0.27334,0.12704,0.14808,0.3777,0.69458,0.08784,0.03608,0.21882,0.05138,0.28552,0.55942,0.03824,3.49208,0.6487,9.914,3.31784,0.11256,2.73798,0.79972,0.72426,2.5494,0.5213
2018-08-27,0.95918,0.47936,6.37948,0.37104,0.50226,0.3163,0.0787,0.47264,0.54598,0.2612,0.7949,0.68896,0.39136,0.50558,0.68394,1.36438,0.48166,0.77048,0.7089,0.34948,0.49492,0.23372,0.69896,1.7451,0.3712,0.83846,0.27836,1.18546,0.3201,0.50164,0.23558,0.19334,0.8576,0.66772,0.59082,0.47802,0.6361,0.1595,0.4334,0.33738,2.49976,0.41486,0.41044,0.6236,0.64904,0.48326,0.98026,1.14392,1.66844,1.099,0.5114,1.23478,0.13622,1.56526,0.57024,0.93756,0.43038,0.37212,0.62374,0.5031,0.58108,2.2007,0.57758,0.55942,1.618,2.93564,1.15876,0.0,1.247,0.39134,0.36564,0.06594,0.20994,0.27198,0.0,0.34078,0.73294,0.0,1.0272,2.28144,1.12158,2.47334,0.71066,0.12416,1.63634,0.43194,1.4244,0.63396,0.7706,1.23038,0.0,1.53302,0.52694,1.5386,0.42268,0.03656,0.1999,1.7057,4.18602,0.88884,0.72452,1.84844,0.45386,1.0973,2.30314,0.69354,1.1223,2.10514,0.3895,1.44026,0.789,0.86678,0.54634,0.83182,0.44276,0.9823,0.0732,0.55554,0.56966,0.29362,0.15998,0.25286,0.15538,0.0274,0.1952,0.06166,0.80088,0.34934,0.09736,1.35252,0.63878,0.77846,0.18276,0.7493,0.56146,0.79364,0.1037,0.28232,0.49172,0.34964,0.20758,0.22566,0.08224,0.31764,0.3937,0.49046,0.27802,0.19906,0.0,0.12204,0.89988,0.10082,0.65216,1.21488,0.14618,0.34694,0.33462,0.37606,0.9326,0.53812,0.24722,0.7719,1.5931,0.85556,0.257,1.10884,0.62266,0.34444,0.18192,0.19924,0.2077,0.43588,0.1907,0.30254,0.09218,0.69162,0.8347,0.2953,2.157,0.32476,2.35184,1.87404,0.12692,0.27764,1.47116,0.5466,0.48534,0.27436,0.35886,2.5345,0.53886,0.5857,1.4873,0.1498,0.1541,0.71818,0.13486,0.15184,0.40546,1.70002,0.39416,0.186,0.36854,0.26848,1.98122,0.14354,0.62684,0.3777,0.3888,0.80484,0.1271,0.62868,0.17346,0.10154,0.20952,0.40288,0.52396,0.169,0.49318,0.07874,0.13336,0.17282,0.56554,0.56508,0.03732,0.28026,8.252,0.06888,1.9957,0.6634,0.28076,3.41204,1.8541,0.2248,0.1809,0.52458,0.27258,0.22004,0.3964,0.29916,0.33532,0.49378,0.3822,0.05074,0.058,0.66338,0.14174,0.238,0.8585,1.1438,0.2412,0.16818,0.05092,0.32918,1.01092,0.31272,0.58816,0.45274,0.07782,0.03408,0.1369,0.21094,0.12814,0.81872,0.06052,0.1369,0.14096,0.17044,0.31666,0.16016,1.25642,10.79806,0.01736,0.3757,1.42196,1.15448,0.57802,0.10582,0.36056,0.13584,0.14048,0.48026,0.72936,0.09942,0.03934,0.22708,0.05834,0.31538,0.67396,0.05016,3.09062,0.69238,11.1815,3.21968,0.12324,2.63912,0.86814,0.65468,2.89446,0.55546
2018-08-28,0.96214,0.44894,6.82632,0.43296,0.57708,0.32136,0.07256,0.46024,0.57232,0.26296,0.67634,0.82914,0.36494,0.54482,0.63584,1.4223,0.47846,0.9127,0.78872,0.35506,0.4287,0.2688,0.63954,1.7205,0.43456,1.0468,0.2694,1.30956,0.2985,0.56498,0.20064,0.1886,0.71062,0.61528,0.55842,0.47596,0.65484,0.1489,0.3851,0.38094,2.48412,0.42098,0.39646,0.59398,0.67116,0.51558,0.92942,1.17884,1.77384,1.01766,0.45874,1.08076,0.14184,1.35398,0.5604,0.90866,0.40232,0.41092,0.66616,0.4606,0.55102,2.26462,0.63462,0.53902,1.58414,2.31494,1.21888,0.0,1.18866,0.37408,0.3827,1.43258,0.17276,0.2806,0.0,0.26494,0.7115,0.0,0.95798,2.33308,1.12714,2.65384,0.68296,0.16848,1.87558,0.6949,1.39942,0.6337,0.73984,1.00806,0.0,1.50612,0.63256,1.41164,0.38836,0.03526,0.19812,1.84176,4.65732,0.8112,0.67958,1.60192,0.42836,1.11376,2.15054,0.73732,1.05522,1.8179,0.38638,1.3246,0.88468,0.882,0.49886,0.82756,0.40444,1.03598,0.06952,0.49152,0.62102,0.2927,0.15822,0.2629,0.15672,0.02744,0.20182,0.07128,0.77164,0.32876,0.11626,1.20998,0.61326,0.76138,0.18262,0.83966,0.52082,0.74152,0.10384,0.35568,0.40792,0.34224,0.31868,0.2042,0.086,0.2944,0.4747,0.4633,0.3302,0.2232,0.0,0.12258,0.82312,0.11292,0.68882,1.44374,0.13142,0.43368,0.35698,0.39908,0.94412,0.49548,0.23656,0.84226,1.66844,0.96218,0.2525,1.33054,0.68854,0.37932,0.17132,0.22968,0.19844,0.46942,0.1787,0.44864,0.09196,0.67476,0.8048,0.32068,2.13698,0.32324,2.52192,1.95064,0.11468,0.24618,1.50628,0.46466,0.48702,0.3319,0.35722,2.61006,0.50512,0.64104,1.34734,0.17832,0.16102,0.61798,0.12978,0.16692,0.40026,1.4922,0.40144,0.1771,0.3501,0.2486,2.0141,0.14336,0.63336,0.41548,0.37958,0.73528,0.11772,0.548,0.22278,0.1002,0.19878,0.4058,0.52958,0.17112,0.5673,0.0789,0.13092,0.1634,0.58492,0.54718,0.03752,0.23374,8.24606,0.08164,1.91968,0.61858,0.25976,3.34934,1.90368,0.21498,0.18518,0.45484,0.2681,0.1939,0.42754,0.30724,0.34132,0.46624,0.40354,0.06446,0.05422,0.6243,0.1455,0.23742,0.66048,1.09566,0.21742,0.12144,0.04884,0.28652,1.08608,0.32882,0.58144,0.43646,0.07708,0.03286,0.13322,0.2019,0.11554,0.74988,0.05792,0.12144,0.1377,0.14874,0.30472,0.15948,1.72978,11.03502,0.01696,0.3291,1.3294,1.01578,0.52954,0.1198,0.3967,0.13108,0.13542,0.45214,0.74234,0.09686,0.03912,0.23302,0.06218,0.32224,0.78904,0.05726,2.85346,0.70038,10.36106,3.0983,0.11814,2.63462,0.88758,0.75154,2.80832,0.62798
2018-08-29,0.95276,0.42204,7.33466,0.44122,0.62184,0.31308,0.07182,0.46818,0.62096,0.28088,0.84266,0.96132,0.35148,0.60642,0.60852,1.44032,0.46086,1.16,0.83004,0.3642,0.4196,0.31562,0.66736,1.39778,0.51702,1.1383,0.25314,1.55248,0.30136,0.57176,0.19428,0.1938,0.62128,0.95812,0.5478,0.58558,0.70466,0.14462,0.33882,0.40002,2.58584,0.43356,0.3817,0.49082,0.58042,0.43618,0.9783,1.15534,1.83906,1.01132,0.43504,1.9574,0.14772,1.2462,0.45696,0.91046,0.42238,0.45948,0.68826,0.43206,0.56868,2.3696,0.63888,0.57068,2.1455,2.47102,1.12324,0.0,1.20988,0.39858,0.40344,2.03504,0.1967,0.30782,0.0,0.25794,0.70716,0.0,0.96182,2.3683,1.11056,2.75586,0.57886,0.17852,1.9111,0.8272,1.91528,0.92298,0.74034,0.93984,0.0,1.47672,1.13976,1.42374,0.36446,0.0358,0.31918,1.889,4.41008,0.8084,0.71898,1.58684,0.41396,1.10806,2.20176,0.72434,1.06012,1.69556,0.42114,1.18208,0.88666,1.31068,0.51984,0.80608,0.4657,0.9976,0.06836,0.4693,0.6509,0.30284,0.15402,0.26838,0.15498,0.02706,0.21394,0.07454,0.73906,0.32564,0.12858,1.25618,0.6458,0.71444,0.18826,0.8743,0.54676,0.69656,0.10976,0.3535,0.35432,0.3403,0.37998,0.19738,0.0907,0.3088,0.49536,0.48996,0.34592,0.24244,0.0,0.12334,0.75886,0.11024,0.649,1.50314,0.14544,0.50458,0.4087,0.4325,0.87338,0.48254,0.26058,0.86452,1.77638,0.97624,0.3398,1.5239,0.69242,0.4032,0.16874,0.24828,0.23474,0.59296,0.161,0.52062,0.08974,0.6963,0.80098,0.33036,2.17346,0.30966,2.63816,2.00016,0.12026,0.2569,1.5211,0.39574,0.46496,0.3427,0.33772,2.64926,0.48028,0.61918,1.26722,0.26458,0.1548,0.58584,0.11822,0.16742,0.41092,1.39802,0.43618,0.18574,0.33898,0.24092,2.05136,0.14624,0.61852,0.43382,0.37896,0.69092,0.11854,0.55368,0.25832,0.10432,0.19802,0.41446,0.53818,0.1598,0.6839,0.10436,0.13338,0.24284,0.59968,0.56362,0.03702,0.21068,8.57554,0.08934,1.9191,0.58468,0.26838,3.38578,1.96776,0.25762,0.17902,0.41714,0.2755,0.18648,0.47452,0.24248,0.3007,0.47618,0.42958,0.08864,0.05842,0.48344,0.15032,0.32826,0.66256,1.93072,0.204,0.10094,0.04834,0.28354,1.3089,0.33978,0.53638,0.45692,0.07514,0.0325,0.1341,0.19602,0.106,0.75982,0.05426,0.11138,0.15336,0.13162,0.29816,0.1723,1.83488,11.54542,0.01466,0.3041,1.3301,0.94862,0.51396,0.12284,0.46326,0.1336,0.13316,0.4555,0.75166,0.09954,0.0419,0.24618,0.06932,0.34352,0.89494,0.0626,2.52994,0.59918,9.08018,3.09676,0.12994,2.60408,0.8359,0.80898,2.77692,0.6329
2018-08-30,0.98372,0.3691,6.62056,0.45894,0.62184,0.31196,0.06382,0.44414,0.65276,0.29814,0.84408,0.9808,0.36208,0.62532,0.7085,1.43506,0.47846,1.41928,0.85092,0.30602,0.41882,0.33994,0.69666,1.44852,0.54586,1.18868,0.3032,1.8558,0.31002,0.56974,0.19656,0.18798,0.5493,1.1457,0.51952,0.62666,0.71032,0.13734,0.26852,0.39194,2.6814,0.43822,0.35592,0.50338,0.5491,0.428,1.00916,1.08136,1.94364,1.03696,0.41702,2.27432,0.14784,1.15624,0.4335,0.95312,0.4298,0.58226,0.83524,0.41484,0.5839,2.18242,0.6318,0.58238,2.47152,2.42878,0.94894,0.0,1.2759,0.3934,0.4887,2.51564,0.17834,0.27644,0.0,0.29246,0.72756,0.0,0.9229,2.23376,1.14558,2.99092,0.57428,0.20546,1.53358,0.90634,2.07538,0.93192,0.75378,0.89952,0.0,1.28344,2.13198,1.29268,0.31416,0.03346,0.34386,1.8609,4.18444,0.74638,0.70166,1.5604,0.4319,1.03622,1.86618,0.70618,0.89602,1.51248,0.40156,1.02782,0.76908,1.473,0.54308,0.7927,0.47866,1.0431,0.07056,0.48982,0.62388,0.3272,0.14086,0.28522,0.1578,0.02618,0.23346,0.07756,0.75692,0.3378,0.12814,1.2261,0.58498,0.75668,0.18352,0.87962,0.52704,0.6521,0.11172,0.3281,0.31322,0.33482,0.40412,0.19364,0.08966,0.315,0.49956,0.48738,0.36412,0.3131,0.0,0.13068,0.75916,0.10726,0.63028,1.61606,0.18092,0.54296,0.46514,0.4576,0.81442,0.48332,0.34008,0.87182,1.58496,0.88444,0.38388,1.6975,0.69938,0.43746,0.16124,0.257,0.29246,0.66672,0.14268,0.5748,0.08822,0.67756,0.82226,0.33728,2.46412,0.30182,2.6254,2.06928,0.12628,0.26714,1.48612,0.36612,0.46962,0.33222,0.33816,2.11338,0.45362,0.55634,1.02396,0.3007,0.15302,0.61102,0.11132,0.17024,0.41666,1.17644,0.4571,0.18196,0.32928,0.22628,2.1673,0.15374,0.59622,0.46596,0.34808,0.6852,0.1193,0.56674,0.28698,0.10258,0.1827,0.42508,0.5516,0.14252,0.78146,0.12158,0.12524,0.25642,0.59824,0.58312,0.03858,0.1873,7.97386,0.09836,1.8739,0.55362,0.28906,3.0443,1.964,0.26926,0.17776,0.40568,0.2903,0.17674,0.48534,0.23996,0.29596,0.45238,0.47002,0.10278,0.05846,0.4285,0.15714,0.33902,0.66642,2.56436,0.20984,0.09042,0.05042,0.3194,1.35902,0.3291,0.479,0.48166,0.0792,0.03312,0.13522,0.17972,0.09812,0.69946,0.05376,0.10238,0.20332,0.12352,0.2407,0.1863,1.87874,11.74632,0.01522,0.28268,1.38904,0.84114,0.5528,0.12406,0.49896,0.12416,0.13218,0.4521,0.75808,0.09846,0.04268,0.2504,0.08084,0.38278,1.01488,0.06434,2.30888,0.58408,7.744,3.8128,0.13142,2.40512,0.85988,0.81066,2.62282,0.66196
2018-08-31,0.78228,0.40096,6.67716,0.5377,0.69556,0.32174,0.0706,0.49366,0.89756,0.24872,1.01346,1.0042,0.38176,0.6335,0.7091,1.43498,0.4727,1.78292,0.93926,0.27946,0.40654,0.3427,0.94254,1.32948,0.71598,1.14632,0.36538,1.90648,0.30158,0.59458,0.19178,0.1909,0.52138,1.43404,0.50444,0.70958,0.74182,0.15394,0.25424,0.41054,2.48142,0.40576,0.39974,0.4769,0.48852,0.43956,1.12792,0.9904,1.8364,1.05696,0.44852,2.26908,0.16906,1.01296,0.44246,0.8848,0.4331,0.62892,0.90574,0.36006,0.60852,2.11096,0.6483,0.61516,2.67176,2.43786,0.86332,0.0,1.29824,0.44638,0.52036,3.18254,0.17968,0.2671,0.0,0.30594,0.81182,0.0,0.87224,2.25374,1.24238,2.47468,0.49548,0.22016,1.3495,1.20038,2.077,1.15052,0.73338,0.80112,0.0,1.50884,2.69816,1.14128,0.30096,0.03386,0.3589,1.83324,4.01492,0.91392,0.71004,1.5347,0.4484,0.92848,1.74312,0.705,0.87008,1.44512,0.4607,1.0211,0.6027,1.5154,0.5614,0.78724,0.49062,1.1051,0.07416,0.45992,0.65384,0.37358,0.14328,0.29818,0.15234,0.0266,0.2531,0.08094,0.75228,0.34228,0.14054,1.31172,0.54174,0.74526,0.18164,0.92526,0.62718,0.67172,0.11776,0.35174,0.33292,0.31316,0.43924,0.1756,0.1127,0.32782,0.4777,0.55268,0.3836,0.3329,0.0,0.13942,0.80852,0.11306,0.5991,2.2737,0.2082,0.60468,0.49654,0.40756,0.80356,0.54268,0.38828,0.92286,1.4354,0.83968,0.47158,1.85278,0.76858,0.4468,0.15402,0.26994,0.3365,0.65408,0.15694,0.60652,0.09402,0.68066,0.99938,0.47816,2.52812,0.30702,2.37852,1.84906,0.15238,0.3195,1.44324,0.3689,0.47436,0.33202,0.31124,1.6713,0.4104,0.54298,1.07786,0.35388,0.13998,0.66118,0.12116,0.17416,0.47426,1.09928,0.4806,0.19418,0.3644,0.1989,2.13738,0.18914,0.5527,0.53336,0.34436,0.93936,0.12266,1.11398,0.30144,0.13216,0.17636,0.40388,0.50788,0.14036,0.83154,0.13512,0.1177,0.27412,0.46968,0.70248,0.04056,0.19298,7.3199,0.11196,1.73004,0.58128,0.3001,2.95254,2.38748,0.25654,0.1924,0.43188,0.35856,0.17102,0.4977,0.23272,0.29728,0.41356,0.4581,0.11802,0.05794,0.419,0.14534,0.33326,0.65442,2.84672,0.21924,0.10568,0.0537,0.29652,1.32384,0.32966,0.49408,0.47228,0.10522,0.03134,0.14146,0.1962,0.1044,0.61694,0.06188,0.11364,0.22674,0.12956,0.19292,0.18516,1.97868,8.35574,0.01666,0.28032,1.29482,0.76534,0.61148,0.12418,0.53326,0.11602,0.15638,0.4654,0.7158,0.11502,0.0464,0.25694,0.0947,0.38914,0.93568,0.05922,2.2004,0.52214,7.12892,4.19082,0.14276,2.31088,0.8096,0.8169,2.16644,0.67616
2018-09-03,0.60678,0.38104,6.70368,0.49636,0.61952,0.28958,0.06638,0.44522,1.02362,0.22328,1.12284,0.94156,0.3654,0.55424,0.66142,1.15436,0.39696,1.88538,0.8738,0.24866,0.38222,0.34894,0.93502,1.28522,0.70476,0.9995,0.4566,1.60596,0.25864,0.57276,0.1737,0.15056,0.4464,1.95024,0.4575,0.71332,0.67222,0.1655,0.21398,0.3949,2.24856,0.37914,0.39768,0.42968,0.50628,0.39446,1.08908,0.9134,1.5199,0.96146,0.5231,2.28046,0.17882,0.80004,0.42972,0.80376,0.40222,0.62358,0.99474,0.3191,0.5683,2.09628,0.58264,0.53108,2.62814,2.09226,0.73552,0.0,1.1382,0.44458,0.44628,3.47108,0.21614,0.28784,0.0,0.27576,0.73404,0.0,1.063,2.06138,1.09158,1.93522,0.53834,0.21986,1.13964,1.28866,1.91818,1.25426,0.67764,0.76618,0.0,1.55612,2.97696,1.02404,0.23864,0.02822,0.33734,1.39394,3.56434,0.9911,0.62144,1.30176,0.38986,0.77026,1.54296,0.66474,0.776,1.37004,0.45638,0.97224,0.50514,1.48852,0.51206,1.00246,0.4835,0.9875,0.06778,0.43834,0.50088,0.31648,0.14318,0.28678,0.14492,0.03144,0.24788,0.07474,0.75014,0.33448,0.11526,1.0891,0.4427,0.66366,0.1574,0.80454,0.56714,0.63868,0.1087,0.29948,0.34246,0.31512,0.48868,0.18732,0.10608,0.29318,0.4201,0.48668,0.3682,0.35666,0.0,0.14294,0.7011,0.10394,0.53816,2.19654,0.21088,0.573,0.4567,0.34504,0.80934,0.4684,0.38486,0.92248,1.29086,0.75964,0.45928,1.7945,0.76222,0.43396,0.1584,0.31832,0.30994,0.63544,0.1742,0.60274,0.0897,0.54382,1.10836,0.75122,2.34806,0.25012,2.17306,1.66168,0.1479,0.2911,1.21088,0.39074,0.4327,0.30478,0.31594,1.69994,0.3764,0.4468,1.09644,0.34702,0.11988,0.65762,0.12126,0.17698,0.41332,0.94146,0.4615,0.1816,0.39346,0.19102,2.23928,0.18348,0.4853,0.4675,0.29118,1.22924,0.11938,1.25892,0.3404,0.13802,0.154,0.35562,0.50948,0.13882,0.82194,0.1338,0.1021,0.2928,0.34514,0.72466,0.03936,0.18248,6.13296,0.10554,1.48116,0.47004,0.30028,2.38826,2.12566,0.21682,0.16958,0.38256,0.41058,0.14438,0.47868,0.19748,0.28688,0.37996,0.43322,0.12168,0.0529,0.37062,0.13192,0.35886,0.61278,3.49138,0.21492,0.10238,0.04712,0.26532,1.27498,0.27622,0.43628,0.42268,0.12934,0.02632,0.14594,0.1831,0.10124,0.51034,0.05886,0.10146,0.29274,0.11872,0.14266,0.1595,1.87294,6.518,0.01542,0.34328,1.1479,0.6576,0.51488,0.12566,0.44866,0.09766,0.1657,0.36314,0.6484,0.09106,0.04178,0.2561,0.09616,0.41794,0.90838,0.04824,2.29002,0.44298,6.07988,4.36316,0.12426,2.28748,0.73054,0.76214,1.85382,0.63308
2018-09-04,0.60754,0.39948,6.22206,0.49224,0.53598,0.28968,0.07038,0.41848,1.06646,0.21198,1.1833,0.87112,0.5054,0.42408,0.65506,1.12604,0.32834,1.92152,0.74606,0.23996,0.42036,0.30042,0.94262,1.29042,0.70912,0.74398,0.53672,1.42586,0.26374,0.68156,0.1698,0.1416,0.46036,2.33106,0.44868,0.71938,0.56888,0.18166,0.2018,0.32768,2.12842,0.35932,0.4118,0.41162,0.47504,0.36936,1.19686,0.90694,1.3942,0.78404,0.56748,2.3574,0.18874,0.78014,0.41468,1.38704,0.38964,0.5853,0.87258,0.29918,0.58264,2.22192,0.63468,0.53422,2.7441,2.07534,0.68372,0.0,1.05418,0.43144,0.43844,2.40322,0.23086,0.30814,0.0,0.27566,0.7089,0.0,1.10648,1.92276,1.24982,1.74148,0.61432,0.17462,0.96406,1.13614,1.93378,1.33944,0.63978,0.82634,0.0,1.35956,3.29676,1.05644,0.24004,0.03006,0.3186,1.29184,2.75468,1.06102,0.62026,1.2838,0.38422,0.75822,1.7311,0.64324,0.5866,1.41592,0.43422,0.95346,0.38386,1.46646,0.66148,1.60462,0.49206,1.3714,0.06902,0.4432,0.37672,0.3022,0.14622,0.30672,0.15806,0.03306,0.23442,0.07876,0.7664,0.335,0.1054,0.9261,0.46592,0.6406,0.1535,0.93672,0.55788,0.57544,0.11714,0.19226,0.37978,0.29382,0.44402,0.22736,0.10202,0.3201,0.33862,0.409,0.45106,0.34288,0.0,0.1445,0.66552,0.09566,0.469,2.0968,0.20994,0.46822,0.4191,0.32552,0.93874,0.4712,0.3633,0.89762,1.24302,0.76738,0.49226,1.54358,0.6946,0.37322,0.17652,0.35278,0.3188,0.68736,0.19658,0.51214,0.09006,0.47638,1.21884,0.9562,2.25752,0.22258,2.0858,1.58588,0.1529,0.27368,0.9791,0.40578,0.38954,0.26522,0.33812,1.89464,0.41294,0.37366,1.37142,0.32902,0.12194,0.63448,0.1215,0.1891,0.39278,0.93798,0.47326,0.19358,0.3757,0.20592,2.51772,0.1839,0.40876,0.45152,0.28798,1.38114,0.11982,1.3195,0.37328,0.1382,0.15352,0.38128,0.51912,0.1328,0.773,0.12662,0.10252,0.30992,0.3574,0.77442,0.04238,0.16772,6.12316,0.09452,1.4801,0.3635,0.32562,2.25466,2.0337,0.22552,0.18382,0.35708,0.45906,0.14908,0.46318,0.18706,0.2438,0.35418,0.43952,0.11796,0.05686,0.41372,0.13704,0.39404,0.68256,4.69316,0.2235,0.10462,0.04766,0.2698,1.01474,0.24934,0.4077,0.42056,0.13076,0.02604,0.15112,0.17224,0.10272,0.5029,0.06738,0.09862,0.34702,0.11396,0.14372,0.16458,1.44356,6.34986,0.01638,0.34418,1.17364,0.68146,0.5283,0.11796,0.41482,0.09714,0.1645,0.33374,0.59778,0.09018,0.04304,0.31008,0.1001,0.41222,0.91424,0.04632,2.16152,0.58578,6.06602,4.61786,0.11548,1.9741,0.63514,0.64576,1.99072,0.54606
2018-09-05,0.63986,0.45208,6.36306,0.48608,0.50768,0.32488,0.07394,0.41732,1.12988,0.20934,1.04884,0.78694,0.56544,0.36354,0.67078,1.25798,0.32868,1.75842,0.71254,0.25036,0.42888,0.2606,1.02726,1.3172,0.74208,0.66846,0.61122,1.18922,0.29456,0.7757,0.17178,0.2151,0.50034,2.17412,0.43286,0.6345,0.55562,0.18522,0.21826,0.30458,2.32072,0.30572,0.40488,0.41428,0.4681,0.36166,1.1953,0.84372,1.3903,0.75904,0.59398,1.5164,0.18884,0.72998,0.45608,1.58058,0.36122,0.54276,0.81494,0.30112,0.55704,2.16788,0.6914,0.5018,2.26178,1.9069,0.64022,0.0,0.98998,0.4369,0.44234,2.24236,0.20906,0.313,0.0,0.31564,0.72794,0.0,1.14624,1.97802,1.30796,1.76182,0.65368,0.17194,0.9173,1.21832,1.36704,1.36604,0.62474,0.82156,0.0,1.33424,3.0157,0.99552,0.25066,0.03042,0.18208,1.31614,3.26794,1.07116,0.58298,1.29704,0.38568,0.79102,1.80378,0.6893,0.50334,1.43502,0.42838,0.97196,0.39018,0.94144,0.7239,1.98788,0.58648,1.4884,0.07782,0.41648,0.3683,0.29048,0.13924,0.3203,0.18428,0.03684,0.24262,0.08294,0.8333,0.3245,0.09872,0.84634,0.47304,0.6903,0.16304,0.98274,0.5707,0.7423,0.11452,0.18384,0.41576,0.2894,0.427,0.23714,0.1033,0.32334,0.32524,0.37398,0.53236,0.35006,0.0,0.13802,0.6755,0.097,0.49346,2.14676,0.21604,0.41838,0.37812,0.31882,1.0094,0.48698,0.34438,0.89014,1.118,1.04482,0.45892,1.333,0.68254,0.34724,0.18434,0.39668,0.30276,0.62168,0.2102,0.45114,0.09462,0.4272,1.24596,1.14,2.43474,0.22812,2.08534,1.5308,0.14588,0.27618,0.93952,0.41772,0.38314,0.26736,0.34214,2.00818,0.46068,0.42048,1.50746,0.26582,0.12566,0.63252,0.12156,0.19416,0.39424,0.97266,0.4481,0.20922,0.37194,0.22718,2.68862,0.17254,0.41168,0.471,0.2924,1.59478,0.14278,1.37716,0.40322,0.12902,0.15598,0.4837,0.56198,0.13456,0.70364,0.09948,0.10234,0.23468,0.36016,0.82796,0.0426,0.1732,5.90724,0.09112,1.52146,0.37588,0.33892,2.14552,1.93928,0.20858,0.20012,0.3756,0.48736,0.15844,0.4313,0.18876,0.24124,0.35494,0.44758,0.1095,0.05708,0.4559,0.14148,0.33214,0.66094,5.185,0.23194,0.10656,0.05112,0.27734,0.77534,0.25244,0.33142,0.3902,0.13978,0.02802,0.15512,0.16974,0.10514,0.50442,0.08834,0.09808,0.38778,0.11526,0.16224,0.17668,1.42264,6.17764,0.0181,0.35026,1.1288,0.69376,0.54804,0.1157,0.37656,0.09508,0.16868,0.36378,0.70232,0.08738,0.04474,0.32876,0.11142,0.41776,0.91522,0.05254,2.0779,0.63444,5.65352,4.70428,0.11074,1.927,0.61124,0.59594,2.07876,0.54572
2018-09-06,0.60688,0.4705,6.12776,0.4974,0.48054,0.3221,0.07138,0.4186,1.11976,0.20992,1.05242,0.75366,0.58416,0.3462,0.6044,1.35878,0.33602,1.56584,0.8236,0.24126,0.42246,0.23546,1.12832,1.14542,0.75212,0.6261,0.59824,0.82602,0.26008,0.83148,0.15248,0.21844,0.54714,2.08318,0.43708,0.648,0.62388,0.20342,0.22362,0.27992,2.0247,0.27552,0.41258,0.4096,0.44682,0.3585,1.27238,0.76708,1.39334,0.7133,0.6343,1.62392,0.19196,0.7386,0.42546,1.66012,0.30116,0.41468,0.68928,0.26408,0.56804,2.11078,0.77762,0.48376,2.09172,1.87112,0.6312,0.0,0.87656,0.46326,0.39746,2.08074,0.20254,0.33008,0.0,0.31016,0.70054,0.0,1.24084,2.30358,1.43148,1.57744,0.60804,0.14836,0.97062,1.26784,1.10238,1.64618,0.5471,0.85112,0.0,1.40784,2.23188,0.90756,0.2816,0.03064,0.1979,1.28368,3.36334,1.07956,0.5541,1.2865,0.35266,0.72882,1.87508,0.70568,0.49022,1.41836,0.4576,0.99518,0.4203,0.79296,0.74512,2.46968,0.92476,1.45026,0.07206,0.35902,0.3475,0.2731,0.1243,0.3095,0.1866,0.03874,0.2235,0.08088,1.0329,0.29014,0.09866,0.85078,0.47314,0.63952,0.16046,1.03604,0.65674,0.97518,0.1065,0.21362,0.41788,0.33178,0.42054,0.23696,0.1067,0.29478,0.31116,0.39704,0.7953,0.27946,0.0,0.12712,0.6558,0.09904,0.50824,2.12032,0.20924,0.42242,0.33114,0.35812,1.07908,0.47348,0.27616,0.90638,1.06248,1.27816,0.4577,1.15188,0.66414,0.3235,0.17432,0.40716,0.26774,0.55218,0.22178,0.4397,0.09348,0.44906,1.2907,1.37268,2.0755,0.30666,1.93444,1.43042,0.14804,0.28984,0.89334,0.40534,0.53754,0.29048,0.33788,2.35456,0.46924,0.44528,1.62594,0.23356,0.12226,0.55662,0.11824,0.18184,0.37226,0.88376,0.45768,0.21296,0.34814,0.2384,2.7466,0.16102,0.4344,0.56446,0.28548,1.77832,0.16514,1.55148,0.42976,0.12692,0.15232,0.47828,0.55768,0.13524,0.63458,0.09086,0.10318,0.20916,0.36688,0.8445,0.03876,0.19952,5.67238,0.0829,1.45556,0.37198,0.29246,2.74184,1.9772,0.20688,0.1958,0.3837,0.46314,0.15068,0.45782,0.17964,0.23522,0.37896,0.41456,0.10916,0.05848,0.4557,0.13526,0.38172,0.61106,6.6456,0.21208,0.10416,0.04866,0.1967,0.69332,0.24468,0.3088,0.32442,0.1414,0.02606,0.178,0.18532,0.1067,0.51126,0.0938,0.09652,0.34884,0.13996,0.20368,0.1641,1.39932,5.5846,0.01908,0.35968,0.99862,0.717,0.50224,0.11558,0.42732,0.0918,0.16608,0.32498,0.71918,0.09086,0.04486,0.33746,0.11414,0.37246,0.81862,0.05092,1.93808,0.6866,5.71806,3.75856,0.11316,1.9399,0.66658,0.61676,1.98518,0.52536
2018-09-07,0.54842,0.45132,5.9217,0.45584,0.39746,0.29686,0.06248,0.36088,0.93228,0.21648,0.90506,0.7279,0.5909,0.3542,0.58998,1.37602,0.35374,1.30502,0.75566,0.22362,0.3686,0.21922,0.96498,1.1863,0.65734,0.58702,0.54496,0.80448,0.25646,0.94432,0.13216,0.21394,0.58386,1.95024,0.41934,0.6022,0.61238,0.19344,0.2041,0.26454,2.16878,0.25148,0.42896,0.37334,0.50196,0.32738,1.129,0.75926,1.461,0.69324,0.60658,1.892,0.18878,0.7962,0.44022,1.8895,0.30178,0.34838,0.7139,0.21394,0.55142,2.60814,0.85834,0.43324,2.12954,2.01088,0.7396,0.0,0.8485,0.45344,0.39698,1.70006,0.17842,0.3531,0.0,0.3615,0.6877,0.0,1.28086,2.45286,1.43742,1.48588,0.5817,0.14726,0.93494,1.11814,0.94912,1.61892,0.55036,0.84002,0.0,1.11454,1.75782,0.90526,0.31124,0.02846,0.21084,1.26572,3.87434,1.06096,0.56928,1.41304,0.35556,0.7613,2.08308,0.7938,0.5085,1.38518,0.40372,1.01382,0.4866,0.85716,0.80282,2.94036,1.08508,1.43784,0.0519,0.33954,0.33004,0.2423,0.12142,0.2333,0.16468,0.03902,0.21156,0.08042,1.10772,0.25376,0.08818,0.75842,0.4751,0.68268,0.15428,1.0775,0.6197,1.07274,0.10064,0.17892,0.45052,0.32994,0.44908,0.24972,0.08474,0.27726,0.31736,0.3489,0.9575,0.25372,0.0,0.12128,0.60256,0.0939,0.61408,1.52806,0.19634,0.40644,0.28652,0.40894,1.30608,0.42194,0.20574,0.94026,1.07716,1.46118,0.4041,0.94528,0.62246,0.3204,0.17602,0.43316,0.23502,0.54164,0.20566,0.35946,0.08734,0.42986,1.14182,1.38542,2.1193,0.45792,2.01252,1.39162,0.12406,0.2425,0.95698,0.45406,0.78812,0.304,0.37712,2.73812,0.45728,0.4365,1.75452,0.18776,0.1169,0.4611,0.0979,0.1609,0.30982,0.88272,0.45698,0.2012,0.32454,0.24768,2.81618,0.12968,0.4478,0.52348,0.26326,1.65322,0.16652,1.1542,0.46496,0.09666,0.1472,0.41744,0.51016,0.13788,0.58052,0.0914,0.09262,0.2105,0.35922,0.7279,0.03604,0.19352,6.47172,0.10648,1.50364,0.3266,0.27638,3.02738,1.60242,0.18218,0.1522,0.3802,0.39576,0.13714,0.59064,0.16902,0.64668,0.41242,0.37054,0.1065,0.05138,0.45082,0.11536,0.38066,0.5867,7.99966,0.21042,0.10002,0.03908,0.17798,0.59406,0.2322,0.26544,0.27484,0.12034,0.0243,0.1882,0.1801,0.09772,0.49108,0.09444,0.0796,0.34942,0.14984,0.22204,0.14012,1.36444,5.23944,0.01766,0.35156,1.0257,0.72762,0.51378,0.1282,0.4659,0.08542,0.1498,0.33002,0.78238,0.07582,0.03624,0.3362,0.10562,0.3515,0.74978,0.05412,1.85472,0.73946,6.3169,3.53702,0.09854,1.88186,0.59844,0.59404,2.34388,0.50392
2018-09-10,0.53972,0.44664,5.5415,0.43618,0.35004,0.28364,0.06686,0.29224,0.78248,0.2169,0.8811,0.6669,0.59064,0.34932,0.5779,1.37482,0.37696,1.24672,0.76682,0.24058,0.33796,0.20052,0.92156,1.17398,0.7165,0.59192,0.47414,0.80902,0.2677,0.95408,0.15444,0.22806,0.71584,1.70078,0.42894,0.60698,0.60586,0.18008,0.18536,0.23798,2.02324,0.22306,0.44766,0.37996,0.44024,0.31576,1.07662,0.72968,1.441,0.84284,0.5003,1.95374,0.16168,0.8038,0.4342,1.91214,0.29634,0.29324,0.69314,0.19524,0.51366,2.57962,0.8389,0.4197,2.20238,1.97862,0.82468,0.0,0.80382,0.44332,0.3941,1.57932,0.13686,0.31062,0.0,0.38694,0.64456,0.0,1.4312,2.77814,1.6758,1.38848,0.79324,0.14942,0.97074,0.91902,0.90302,1.6104,0.54484,0.85872,0.0,0.86184,1.56288,0.90584,0.37386,0.03118,0.20566,1.4095,3.97872,0.96134,0.5341,1.44412,0.37298,0.9971,2.84868,0.85486,0.53502,1.55416,0.3846,1.19054,0.51182,0.84144,0.85202,2.9729,1.08416,1.65686,0.05014,0.2931,0.3299,0.24694,0.10852,0.20038,0.15154,0.03714,0.19786,0.08646,1.13488,0.2221,0.09102,0.73178,0.5227,0.67612,0.15458,1.0661,0.69386,1.09994,0.11072,0.20046,0.4145,0.3019,0.53022,0.23658,0.07958,0.3062,0.4603,0.31254,1.03036,0.2099,0.0,0.11202,0.6378,0.08776,0.59844,1.20636,0.20406,0.37992,0.24576,0.42182,1.33578,0.43034,0.18266,0.89932,0.98304,1.5314,0.39358,0.8959,0.6007,0.33336,0.18056,0.38906,0.23314,0.51882,0.1786,0.68544,0.08976,0.49688,0.91492,1.23124,1.86646,0.52336,1.89508,1.35138,0.10928,0.22822,0.87614,0.42396,0.86374,0.36352,0.34096,2.57664,0.4082,0.54008,1.74318,0.17468,0.13022,0.4184,0.0968,0.1407,0.31672,0.90384,0.4179,0.19172,0.32504,0.23786,2.63758,0.12574,0.43466,0.46566,0.29086,1.29974,0.17184,1.02332,0.44656,0.08754,0.16866,0.41212,0.41712,0.14506,0.55818,0.10388,0.09958,0.17164,0.3375,0.62116,0.0356,0.18828,7.49232,0.10894,1.75488,0.31002,0.24862,3.02846,1.63204,0.17456,0.13768,0.3785,0.3224,0.14754,0.67746,0.15286,0.79954,0.4417,0.34898,0.09566,0.05152,0.45074,0.10822,0.38728,0.5656,8.19816,0.22074,0.0947,0.0389,0.20674,0.57864,0.21662,0.2777,0.25836,0.0939,0.0244,0.18748,0.1727,0.08734,0.51346,0.09486,0.07856,0.33896,0.15228,0.2225,0.1298,1.31666,4.9338,0.01748,0.32844,1.0269,0.8163,0.50016,0.1191,0.49252,0.13042,0.147,0.33842,0.77232,0.07024,0.03666,0.33724,0.10486,0.29794,0.67814,0.05824,1.82046,0.76698,6.23696,3.62812,0.09228,1.73048,0.76246,0.59154,2.78226,0.50566
2018-09-11,0.47176,0.43032,5.15574,0.39908,0.35914,0.27044,0.06142,0.21684,0.78768,0.22202,0.87716,0.58726,0.45862,0.34272,0.54794,1.24068,0.37386,1.08604,0.75076,0.25264,0.3207,0.19748,0.90194,1.09896,0.68968,0.66654,0.449,0.79514,0.26424,0.84042,0.14766,0.22228,1.04448,1.52612,0.366,0.68118,0.61012,0.18686,0.1675,0.36572,1.87998,0.22992,0.43588,0.35464,0.5006,0.2951,0.96404,0.63608,1.37102,0.85154,0.44954,1.91778,0.15728,0.74708,0.4215,1.31998,0.29418,0.27448,0.86752,0.1718,0.48616,2.3706,0.76728,0.36946,1.9906,1.75432,0.88962,0.0,0.88456,0.4495,0.36086,1.69288,0.11838,0.2655,0.0,0.36742,0.62704,0.0,1.69186,2.6869,1.4902,1.19398,0.77362,0.19634,0.98032,0.80114,0.84262,1.63248,0.50218,0.85368,0.0,0.74404,1.22178,0.83512,0.38634,0.0309,0.26102,1.31178,3.88214,0.87768,0.41462,1.33616,0.35688,1.02388,3.17964,0.88668,0.57026,1.5275,0.36402,1.25178,0.51748,0.88774,0.7076,2.494,1.12552,1.19562,0.05252,0.32022,0.32496,0.25888,0.112,0.17018,0.14762,0.0415,0.27318,0.08226,1.12928,0.26692,0.09046,0.69326,0.46806,0.70768,0.1552,0.84864,0.70144,1.1226,0.09354,0.20506,0.42826,0.29596,0.68628,0.20212,0.07974,0.26938,0.47652,0.30638,0.97212,0.2056,0.0,0.10998,0.74874,0.0874,0.63716,1.0138,0.2304,0.37654,0.22548,0.44514,1.1455,0.40116,0.19138,1.111,0.8586,1.69276,0.37974,0.93792,0.82478,0.31526,0.16324,0.32024,0.22454,0.43386,0.14596,0.7475,0.08586,0.51142,0.69608,1.1187,1.69568,0.54934,1.7799,1.34458,0.11476,0.22496,0.8196,0.42872,0.99472,0.35086,0.30284,2.28304,0.4861,0.75752,1.43294,0.17108,0.12424,0.39896,0.09504,0.11836,0.2944,0.82382,0.3995,0.18344,0.32836,0.22812,2.2025,0.12114,0.39858,0.52882,0.26834,1.1368,0.17404,1.0196,0.40458,0.08572,0.17104,0.38506,0.3772,0.15674,0.50442,0.10704,0.08902,0.15846,0.28858,0.5597,0.03232,0.18188,6.91004,0.11246,1.75866,0.3217,0.22422,2.96322,1.53674,0.16864,0.11998,0.36244,0.29304,0.13382,0.68182,0.14554,0.90426,0.43042,0.3207,0.0852,0.0466,0.39326,0.10138,0.3466,0.46552,7.66372,0.21136,0.08858,0.0363,0.1925,0.5659,0.20104,0.25986,0.24662,0.08618,0.0242,0.19114,0.16902,0.07816,0.46802,0.09578,0.07496,0.31354,0.15158,0.2836,0.13546,1.27278,4.83162,0.0166,0.30058,0.9385,0.72786,0.43108,0.11176,0.50422,0.13794,0.14656,0.32822,0.74346,0.06224,0.03448,0.27142,0.12364,0.2718,0.57154,0.0567,1.78958,0.61204,6.5771,3.24392,0.0895,1.63562,0.85574,0.63436,2.90538,0.48702
2018-09-12,0.40018,0.38238,4.34154,0.37302,0.33282,0.21998,0.05838,0.14396,0.75766,0.2098,0.75576,0.5552,0.43814,0.34526,0.49138,1.02198,0.37916,1.02512,0.75778,0.24866,0.3255,0.17806,0.82742,1.10672,0.64634,0.77568,0.42938,0.7612,0.2709,0.88316,0.13146,0.15022,1.21936,1.41008,0.35582,0.65902,0.59668,0.17928,0.14234,0.41234,1.52652,0.21414,0.44616,0.52212,0.50966,0.28122,0.91778,0.60822,1.4962,0.85016,0.42642,1.96594,0.15648,0.7012,0.4192,1.1541,0.27118,0.22724,0.98744,0.14662,0.48478,2.33678,0.7249,0.35616,1.7718,2.51494,0.88488,0.0,0.89684,0.46142,0.3598,1.51656,0.1062,0.25528,0.0,0.50782,0.77978,0.0,1.78066,3.0302,1.62446,1.00386,0.77968,0.18532,1.01266,0.58992,0.79396,1.43446,0.45492,0.80074,0.0,0.6219,1.23922,0.84068,0.39592,0.03136,0.29366,1.26834,3.17602,0.96658,0.49534,1.72232,0.35196,0.98106,3.274,0.90254,0.55372,1.67424,0.43478,1.27992,0.53006,0.92896,0.70108,2.20254,0.98756,1.12288,0.04808,0.29394,0.2816,0.269,0.1168,0.1581,0.14076,0.03976,0.28052,0.08476,1.16574,0.35008,0.09278,0.65328,0.42602,0.70142,0.14264,0.91782,0.66024,1.01702,0.0867,0.20004,0.399,0.27624,0.75596,0.19056,0.07204,0.2575,0.46196,0.33354,1.00148,0.20206,0.0,0.11584,0.79102,0.08058,0.60704,1.06268,0.23162,0.34586,0.20066,0.4296,1.17698,0.50832,0.17522,1.1532,0.82974,1.50792,0.34276,0.94002,0.8548,0.2931,0.16264,0.41376,0.2093,0.42634,0.13524,0.79334,0.08834,0.55382,0.62976,1.00162,1.37944,0.55778,1.69394,1.25906,0.10746,0.24434,0.72774,0.44042,1.03002,0.33294,0.38304,2.269,0.48482,0.7714,1.31926,0.1509,0.12876,0.35684,0.08706,0.11552,0.27414,0.78886,0.38976,0.2058,0.31994,0.24286,2.27754,0.11808,0.42698,0.661,0.27026,0.99686,0.15818,0.987,0.44796,0.0891,0.16902,0.30772,0.31898,0.17026,0.47876,0.10718,0.08616,0.1625,0.25572,0.49528,0.03188,0.19324,6.8349,0.10812,1.77146,0.31192,0.21224,2.86332,1.45102,0.15456,0.1001,0.33656,0.30572,0.12658,0.72038,0.18408,1.03056,0.38178,0.31608,0.07204,0.0413,0.37998,0.1016,0.3255,0.42588,6.75218,0.20824,0.08754,0.03506,0.18286,0.53368,0.17648,0.27944,0.22268,0.07972,0.0225,0.1932,0.16184,0.0741,0.45536,0.0909,0.07,0.29028,0.15348,0.30118,0.12544,1.2328,5.01918,0.01656,0.27634,1.0025,0.70432,0.4076,0.1001,0.49232,0.1393,0.1404,0.2759,0.6575,0.05778,0.0334,0.2307,0.11508,0.23654,0.49596,0.04968,1.81892,0.5721,7.61076,2.99866,0.08856,1.71278,0.93526,0.62002,2.89022,0.5138
2018-09-13,0.43372,0.40162,4.75992,0.32854,0.32502,0.1885,0.06682,0.06278,0.7243,0.19624,0.68644,0.58542,0.4302,0.35352,0.42704,0.88318,0.36574,1.03228,0.669,0.25246,0.32676,0.17654,0.71,1.34524,0.6478,0.73944,0.41264,0.73994,0.31478,0.91844,0.13402,0.14774,1.28874,1.27678,0.35298,0.59902,0.53748,0.15468,0.14452,0.42814,1.48594,0.20854,0.4302,0.80708,0.50304,0.26294,0.79134,0.61026,1.88434,0.93554,0.4439,1.9147,0.18142,0.711,0.4424,1.0828,0.27772,0.2199,1.02378,0.1524,0.43182,2.5596,0.68158,0.37486,1.58188,3.38112,0.9043,0.0,0.91906,0.55424,0.37184,1.4984,0.10156,0.24332,0.0,0.59452,1.11512,0.0,2.01366,3.05864,1.57032,1.14496,0.89938,0.17132,0.95304,0.54204,0.82978,1.11656,0.46986,0.7278,0.0,0.4916,1.27808,0.94436,0.35302,0.03436,0.37234,1.32822,3.13394,1.5732,0.64408,1.65164,0.37034,0.9522,3.34036,0.94016,0.56356,1.99328,0.45362,1.42804,0.54998,0.90134,0.86838,1.99368,0.6544,1.27072,0.06152,0.28658,0.29018,0.28664,0.12944,0.15174,0.17286,0.04132,0.27318,0.09026,0.99948,0.37068,0.1103,0.72174,0.44326,0.82196,0.14854,0.94488,0.59304,0.7758,0.08892,0.21654,0.4222,0.26922,0.98686,0.17946,0.07042,0.27652,0.46026,0.3357,0.8384,0.20724,0.0,0.12414,0.80284,0.08446,0.58758,1.17104,0.21452,0.31464,0.19312,0.40846,1.2132,0.74168,0.16728,1.09748,0.92948,1.63558,0.3329,0.79694,0.971,0.29618,0.1772,0.49564,0.19316,0.50402,0.12488,0.82572,0.08722,0.65484,0.56466,0.97366,1.40254,0.4993,1.94124,1.24066,0.10556,0.25284,0.68742,0.4746,0.97124,0.31844,0.6071,2.0935,0.5309,0.77404,1.30044,0.16736,0.1524,0.39904,0.1008,0.12352,0.30596,0.7852,0.38714,0.2351,0.30938,0.24314,2.27136,0.1297,0.45542,0.5894,0.31518,0.98992,0.14314,0.82896,0.46824,0.0925,0.1815,0.34264,0.31586,0.18138,0.4598,0.10964,0.08742,0.22788,0.24638,0.53322,0.04032,0.17558,6.4857,0.10692,1.7782,0.32306,0.2078,2.21928,1.37958,0.15248,0.10806,0.28684,0.33988,0.15292,0.75518,0.18136,1.07836,0.34574,0.34078,0.06336,0.04112,0.395,0.11092,0.28772,0.47958,5.18794,0.234,0.0906,0.0372,0.19792,0.54476,0.18836,0.34974,0.20838,0.07566,0.02254,0.16706,0.15386,0.0719,0.46276,0.09402,0.07366,0.30968,0.13018,0.35806,0.14192,1.23456,5.48754,0.02012,0.25716,1.07376,0.66864,0.39796,0.0951,0.44648,0.14022,0.15222,0.28688,0.66896,0.04836,0.03522,0.24162,0.12792,0.23484,0.49776,0.05868,1.90224,0.47766,8.37674,3.00494,0.08678,1.90226,0.9014,0.64124,3.29912,0.5339
2018-09-14,0.41166,0.4016,4.60138,0.3024,0.31688,0.1752,0.06726,0.0,0.77724,0.1794,0.77096,0.5751,0.40438,0.31746,0.46622,0.82234,0.3366,0.9979,0.71396,0.3238,0.30932,0.18694,0.68786,1.31034,0.65014,0.7587,0.5197,0.69064,0.3103,0.94244,0.1302,0.14832,1.41236,1.3225,0.4137,0.59288,0.57554,0.13832,0.13654,0.44636,1.26294,0.22716,0.36332,0.93254,0.44854,0.28232,0.85644,0.5627,1.77982,0.93026,0.4676,1.77618,0.16532,0.59518,0.4854,0.92176,0.26174,0.24692,0.91418,0.15344,0.43288,2.29036,0.69696,0.39908,1.35284,3.45828,0.82702,0.0,0.89216,0.59654,0.36508,1.45948,0.1103,0.23402,0.0,0.5786,1.1687,0.0,2.44518,3.39492,1.70608,1.27714,1.05336,0.16628,0.92846,0.4468,0.95386,1.0171,0.53394,0.6772,0.0,0.43762,1.27264,0.86296,0.3356,0.0349,0.44194,1.32836,2.92862,1.63712,0.67484,1.4402,0.3544,0.9217,3.49392,0.92708,0.8146,1.93806,0.50434,1.50284,0.5155,0.94364,0.84084,1.65874,0.7281,1.6842,0.06682,0.25872,0.2847,0.3144,0.13364,0.14104,0.1694,0.04128,0.26862,0.08512,0.89606,0.49198,0.10968,0.71254,0.41702,0.92096,0.12706,0.96906,0.56342,0.64198,0.08424,0.2617,0.37094,0.26968,1.02784,0.16696,0.08622,0.27952,0.56114,0.3055,0.744,0.20852,0.0,0.12348,0.81166,0.10348,0.50298,1.05062,0.20998,0.29128,0.17958,0.3729,1.34752,0.7823,0.16332,1.0522,0.91896,1.5946,0.34394,0.81058,0.97112,0.2909,0.1689,0.58204,0.17984,0.54386,0.1225,0.88812,0.08846,0.68726,0.69254,1.1548,1.2265,0.3677,1.98294,1.23346,0.11124,0.2714,0.59046,0.41288,0.73596,0.39894,0.67546,1.9851,0.55516,0.79202,1.28486,0.16144,0.1524,0.43538,0.1121,0.12798,0.3651,0.74406,0.35634,0.2604,0.26116,0.23868,2.28598,0.12004,0.5181,0.56964,0.32974,0.94362,0.1325,0.76186,0.49728,0.08552,0.18228,0.34416,0.49058,0.1764,0.48124,0.09632,0.08802,0.22212,0.21718,0.58976,0.0428,0.17028,5.67994,0.07544,1.89212,0.318,0.19256,1.9871,1.39244,0.14728,0.10386,0.23812,0.3312,0.16222,0.62566,0.17692,0.77668,0.35076,0.34134,0.0705,0.04328,0.40676,0.1065,0.26522,0.47858,3.94362,0.24792,0.07584,0.03586,0.20018,0.60584,0.17492,0.34926,0.20404,0.07264,0.02194,0.19478,0.13806,0.06628,0.46268,0.09802,0.08966,0.29556,0.11126,0.42458,0.13816,1.24864,5.35988,0.02542,0.2446,1.22018,0.6284,0.35066,0.0779,0.45106,0.13794,0.15178,0.2581,0.55114,0.0438,0.03634,0.27648,0.13402,0.26498,0.49258,0.05494,2.15932,0.43276,7.64824,2.76376,0.08732,2.18028,0.87158,0.66564,3.08332,0.5793
2018-09-17,0.3977,0.35634,4.25622,0.28094,0.31656,0.1759,0.06288,0.0,0.77054,0.1652,0.91196,0.56578,0.42158,0.29614,0.43754,0.78242,0.3654,0.86664,0.74462,0.32604,0.3142,0.18632,0.65376,1.2296,0.54794,0.76828,0.5431,0.73442,0.36238,0.92868,0.10002,0.13612,1.30634,1.35052,0.39936,0.60596,0.59638,0.12588,0.13624,0.47772,1.20398,0.2386,0.28584,0.9934,0.42884,0.29554,0.81314,0.48924,1.60172,0.71638,0.44756,1.69686,0.16012,0.47446,0.48924,0.90592,0.258,0.23968,0.84542,0.14954,0.441,2.13276,0.69284,0.3944,1.19914,3.42846,0.73534,0.0,1.0708,0.5948,0.38822,1.55484,0.11128,0.21702,0.0,0.57826,1.16984,0.0,2.1621,3.19968,1.48766,1.23406,0.85222,0.16126,0.84682,0.4031,0.78992,0.92082,0.59044,0.61852,0.0,0.39432,1.22352,0.89832,0.34502,0.03286,0.46272,1.22698,2.86008,1.86028,0.6824,1.3005,0.35038,0.6378,2.76816,0.98816,0.88244,1.61682,0.5522,1.18406,0.47358,1.0892,0.79352,1.37206,0.72174,1.71752,0.0659,0.25298,0.27484,0.35598,0.15058,0.13868,0.16964,0.03708,0.26056,0.08034,0.77694,0.51644,0.10556,0.63822,0.34678,0.90542,0.1172,0.9542,0.472,0.58538,0.07474,0.26872,0.39518,0.2813,0.97758,0.15948,0.08442,0.2617,0.43878,0.27368,0.70404,0.2248,0.0,0.1162,0.76318,0.10894,0.48598,1.01226,0.20416,0.26274,0.17892,0.3274,1.43924,0.8615,0.1603,1.13144,0.8929,1.65176,0.30864,0.74282,1.00192,0.25396,0.139,0.64724,0.16678,0.58202,0.1086,0.54276,0.08874,0.62476,0.7643,1.11368,1.20938,0.35652,1.87442,1.27076,0.1072,0.284,0.58004,0.38684,0.7631,0.3619,0.73656,1.84172,0.58364,0.68362,1.20994,0.15462,0.1362,0.4618,0.09616,0.1302,0.37682,0.67864,0.35784,0.26058,0.21772,0.2214,2.27554,0.12054,0.54736,0.5895,0.30316,0.95124,0.13504,0.70568,0.50356,0.08264,0.16226,0.33934,0.51416,0.16088,0.5016,0.08732,0.07536,0.22776,0.2076,0.55708,0.04208,0.15596,4.7196,0.06788,1.79132,0.29072,0.19666,2.04946,1.1603,0.14052,0.10204,0.2304,0.33,0.15476,0.50078,0.1775,0.66966,0.30904,0.32634,0.0656,0.03902,0.39684,0.10322,0.26,0.47112,3.36166,0.24268,0.07314,0.03416,0.17452,0.54804,0.18382,0.41534,0.24092,0.0717,0.02074,0.19684,0.1317,0.07582,0.4238,0.10272,0.09186,0.2509,0.11744,0.48942,0.14246,1.26334,5.30978,0.02724,0.22564,1.42098,0.54294,0.37376,0.07442,0.42274,0.10356,0.1421,0.22594,0.51074,0.04294,0.03436,0.2731,0.14994,0.25744,0.50214,0.05742,1.9739,0.41032,7.24228,2.52472,0.09102,2.10176,0.74114,0.71484,2.6279,0.5581
2018-09-18,0.44592,0.36586,4.53644,0.283,0.3054,0.19496,0.07444,0.0,0.7267,0.16512,1.02028,0.588,0.4411,0.32296,0.49738,0.70504,0.44134,0.85796,0.81654,0.31868,0.32068,0.20916,0.72202,1.26006,0.56172,0.6328,0.5611,0.79778,0.41606,0.89628,0.10426,0.15566,1.11504,1.30116,0.46194,0.5184,0.62848,0.10456,0.14518,0.38416,1.18216,0.23528,0.27078,1.09368,0.35154,0.33032,0.84306,0.70792,1.53632,0.71252,0.46698,1.79118,0.17728,0.46372,0.52742,0.90732,0.2908,0.22958,0.7059,0.16038,0.44228,2.20852,0.65666,0.43552,1.29724,3.7514,0.63938,0.0,1.11678,0.5981,0.43046,1.4798,0.11322,0.21814,0.0,0.6582,1.26544,0.0,2.00146,3.40742,1.53602,1.3385,0.8713,0.10932,0.8722,0.44244,0.60824,0.82,0.64806,0.5715,0.0,0.4104,1.17,1.02276,0.34356,0.03618,0.4031,1.34312,3.0762,1.97844,0.77718,1.37234,0.4091,0.54426,2.42426,1.0681,0.9205,1.57108,0.61968,1.10552,0.46468,1.10532,0.83702,1.36796,0.7448,1.9037,0.06376,0.20714,0.30622,0.37898,0.1506,0.1871,0.16944,0.03368,0.2018,0.08054,0.80732,0.5252,0.10462,0.66506,0.37338,0.93244,0.12348,0.91206,0.4678,0.56854,0.08378,0.27724,0.55014,0.2811,0.90992,0.14868,0.08586,0.3167,0.41956,0.2947,0.67856,0.23686,0.0,0.1419,0.70508,0.11744,0.45442,1.07452,0.18548,0.2582,0.181,0.34648,1.54998,0.9847,0.13672,0.97966,0.93844,1.45428,0.29236,0.54864,0.79902,0.29628,0.14154,0.71354,0.1595,0.64084,0.1273,0.58848,0.09714,0.64264,0.89452,1.05582,1.31916,0.32728,2.04748,1.3284,0.0903,0.31324,0.60724,0.39082,0.65454,0.3656,0.79892,1.96914,0.49594,0.55596,1.36678,0.15126,0.13546,0.49674,0.1084,0.12472,0.41392,0.70782,0.37276,0.26266,0.2047,0.22458,2.31292,0.13828,0.66374,0.52178,0.32524,0.96644,0.1539,0.6828,0.48978,0.0842,0.17406,0.36472,0.6608,0.16844,0.52234,0.08824,0.08392,0.23666,0.22402,0.60124,0.04484,0.1652,5.03564,0.0629,1.92402,0.2862,0.27118,2.12676,1.1712,0.137,0.10614,0.37028,0.33194,0.17518,0.48672,0.17664,0.6528,0.31132,0.31052,0.0715,0.04224,0.4501,0.10186,0.29312,0.5527,3.24798,0.27384,0.131,0.03562,0.2004,0.55756,0.17684,0.5006,0.27496,0.08864,0.01924,0.18684,0.1997,0.12482,0.47574,0.10086,0.14086,0.24896,0.16676,0.45384,0.1425,1.31886,5.07188,0.0279,0.24074,1.9502,0.61996,0.4118,0.0807,0.4509,0.11016,0.14342,0.28198,0.51428,0.04718,0.03522,0.29294,0.14364,0.30164,0.52968,0.06022,1.97736,0.51866,6.69916,2.76892,0.09538,2.23748,0.736,0.6928,2.43944,0.57822
2018-09-19,0.52358,0.57256,4.83754,0.48232,0.32474,0.22182,0.08596,0.0,0.83568,0.23202,1.13296,0.65346,0.45896,0.3491,0.57086,0.7059,0.36876,0.93884,1.03354,0.33464,0.3629,0.25302,0.91128,1.35876,0.75634,0.60352,0.58218,0.91654,0.4341,0.82312,0.1185,0.1889,1.0944,1.352,0.54516,0.53266,0.74426,0.1257,0.15988,0.34518,1.23434,0.24316,0.34892,1.07288,0.37502,0.39892,1.3111,0.8535,1.39442,0.75038,0.55524,1.92454,0.19614,0.54314,0.77582,0.99182,0.34978,0.26728,0.76946,0.20284,0.58876,2.30398,0.70448,0.4965,1.4706,3.05202,0.64002,0.0,1.28796,0.61862,0.45528,1.6541,0.11914,0.26278,0.02628,0.59602,1.22664,0.0,2.13888,3.1302,1.44784,2.42932,1.06284,0.11502,0.96844,0.52524,0.4827,0.96138,0.77792,0.64014,0.0,0.46948,0.95368,1.06546,0.37906,0.03934,0.3837,1.45796,3.37048,2.01646,0.71854,1.04968,0.48192,0.61622,2.62264,1.13084,1.0748,1.43904,0.60216,1.23766,0.55246,1.13012,0.82646,1.38958,0.81302,1.8597,0.06522,0.2633,0.41526,0.41094,0.1756,0.23014,0.17048,0.0361,0.2042,0.07824,0.84984,0.528,0.11028,0.803,0.49774,0.94748,0.14316,0.84124,0.63042,0.55204,0.0987,0.30218,0.67118,0.30978,1.06944,0.15632,0.1061,0.40428,0.49546,0.3124,0.63516,0.28826,0.0,0.15984,0.6968,0.15098,0.5303,1.02638,0.2079,0.29096,0.2802,0.46438,1.62858,0.98098,0.11686,0.99556,0.99422,1.55858,0.37376,0.56616,0.84372,0.40442,0.14106,0.61574,0.2613,0.6683,0.14344,0.62048,0.10614,0.7269,0.97822,1.10396,1.41722,0.34734,2.28864,1.48612,0.09686,0.3245,0.66652,0.37372,0.6582,0.44846,0.83806,2.06938,0.549,0.57754,1.5281,0.19656,0.14276,0.57034,0.14896,0.131,0.5475,0.78448,0.4107,0.2678,0.22866,0.23806,2.19436,0.17818,0.74984,0.42008,0.37362,0.95354,0.17184,0.76082,0.41506,0.08296,0.19366,0.41448,0.82952,0.18254,0.53918,0.09554,0.10382,0.29812,0.2927,0.68,0.04948,0.16938,5.36252,0.07988,2.08874,0.3549,0.32744,2.38084,1.74238,0.14934,0.12166,0.5426,0.31768,0.19824,0.46134,0.16076,0.63288,0.38298,0.2919,0.08122,0.05202,0.5238,0.10274,0.40768,0.66524,3.2995,0.31332,0.16056,0.04126,0.21796,0.62834,0.20752,0.54112,0.43532,0.0985,0.02158,0.20968,0.25344,0.1526,0.60672,0.1008,0.16974,0.26326,0.19808,0.50574,0.13916,1.4127,5.21022,0.03268,0.25414,2.09598,0.68058,0.45164,0.09514,0.6054,0.11912,0.16606,0.34186,0.60556,0.0624,0.03794,0.31812,0.14368,0.35672,0.60912,0.06992,2.247,0.58128,6.08546,3.00122,0.10212,2.23942,0.8859,0.74186,2.76352,0.59308
2018-09-20,0.4794,0.58348,4.35804,0.50498,0.34066,0.22308,0.08294,0.0,0.8664,0.2358,1.12804,0.67696,0.53716,0.32668,0.5716,0.78928,0.30418,0.91704,1.06,0.33406,0.35498,0.27398,0.9551,1.07614,0.7639,0.82428,0.62156,0.91728,0.41814,0.76056,0.1165,0.1921,1.0281,1.47274,0.58938,0.5315,0.72176,0.14284,0.17334,0.34542,1.1025,0.26188,0.35582,0.815,0.38062,0.43004,1.67526,0.90074,0.9189,0.71332,0.5134,1.6607,0.17858,0.52302,0.90292,0.8963,0.33928,0.28502,0.7506,0.23948,0.64624,1.99836,0.65902,0.51388,1.45044,2.22702,0.59338,0.0,1.3053,0.52624,0.4124,1.7315,0.13948,0.28188,0.35608,0.55028,0.91372,0.0,2.03418,3.31282,1.57294,2.58756,1.03512,0.14694,1.1504,0.46332,0.2827,1.05948,0.82448,0.66118,0.0,0.47476,0.71782,1.00544,0.42072,0.03928,0.25894,1.75866,3.34856,1.45962,0.55042,0.96846,0.46472,0.61822,2.50638,1.20302,1.1167,1.0683,0.57714,1.18978,0.50602,1.18814,0.64676,1.18416,0.7601,1.56772,0.05588,0.27024,0.43592,0.42832,0.17904,0.24398,0.13942,0.03226,0.1906,0.0705,0.82204,0.53864,0.09464,0.72742,0.53008,0.81572,0.1412,0.74336,0.666,0.5069,0.10576,0.2757,0.72458,0.25946,0.91954,0.14706,0.10402,0.411,0.73324,0.28824,0.55752,0.29174,0.0,0.163,0.67468,0.15534,0.5196,0.87942,0.21716,0.30754,0.29436,0.4742,1.53898,0.77664,0.12256,1.03704,0.83096,1.28492,0.37704,0.68984,0.70878,0.44898,0.13058,0.55684,0.29088,0.77312,0.14352,0.5739,0.10974,0.63314,0.95222,0.94166,1.29008,0.37146,2.11776,1.51848,0.0827,0.29236,0.60206,0.3345,0.62558,0.59638,0.65792,1.96656,0.51366,0.63322,1.46582,0.19242,0.12572,0.53908,0.14126,0.12684,0.68252,0.76028,0.39116,0.25436,0.22634,0.26236,2.03684,0.17446,0.7424,0.399,0.38378,0.90158,0.1713,0.74592,0.37754,0.07836,0.1892,0.38844,0.88558,0.17688,0.53548,0.09528,0.1078,0.28488,0.293,0.65392,0.04318,0.15792,5.36918,0.09242,2.17298,0.35614,0.37604,2.28764,1.83974,0.15112,0.12296,0.64806,0.30078,0.18054,0.41344,0.16156,0.62794,0.38444,0.29604,0.0805,0.05634,0.50166,0.11862,0.42626,0.628,3.41366,0.29968,0.17028,0.0437,0.20568,0.64174,0.20236,0.52892,0.54226,0.102,0.02176,0.21876,0.27122,0.1644,0.66308,0.09626,0.17286,0.23982,0.21996,0.47082,0.13424,1.43392,5.14252,0.02886,0.2726,2.19206,0.71852,0.49408,0.10638,0.64258,0.11874,0.14628,0.3226,0.68808,0.06888,0.04312,0.29346,0.13476,0.37644,0.61236,0.06528,2.4032,0.66294,4.76638,3.08766,0.10222,2.38038,0.95936,0.7098,2.33864,0.65042
2018-09-21,0.59644,0.73362,4.41056,0.63056,0.45092,0.22948,0.0917,0.0,0.9466,0.27698,1.17172,0.76172,0.59514,0.39206,0.55624,1.1043,0.24448,0.95742,1.0982,0.57592,0.44522,0.29994,1.07234,1.1712,0.91758,0.95844,0.55962,1.05268,0.4781,0.71332,0.15182,0.24844,1.0297,1.39776,0.59452,0.54186,0.74076,0.16998,0.20418,0.32504,1.08782,0.27874,0.39758,0.82998,0.4715,0.45998,1.91482,1.0361,0.92754,0.7965,0.60322,1.54616,0.20278,0.63818,0.96736,0.89896,0.35534,0.31438,0.81226,0.32438,0.71304,1.7762,0.58364,0.62764,1.47964,2.18738,0.6052,0.0,1.45016,0.5159,0.41002,1.81346,0.14492,0.31982,0.38306,0.59824,0.82992,0.0,1.66778,3.99448,1.81092,3.0551,0.97642,0.18494,1.24982,0.4341,0.0,1.01372,0.92444,0.73272,0.0,0.5065,0.65932,1.1796,0.43922,0.04714,0.16328,2.07572,3.12704,1.35,0.52846,1.1059,0.4987,0.67686,2.58968,1.1838,0.91474,1.15652,0.5612,1.18986,0.52452,1.08936,0.676,1.31334,0.56962,1.02434,0.07092,0.31448,0.4872,0.44968,0.18842,0.31258,0.18166,0.034,0.19536,0.07074,0.98036,0.4875,0.10604,1.2561,0.70806,0.70148,0.19964,0.77332,0.7487,0.5798,0.12894,0.2792,0.81688,0.251,1.0112,0.15186,0.12014,0.50492,0.73768,0.44238,0.5131,0.33388,0.0,0.1738,0.65624,0.15192,0.68876,1.03764,0.24542,0.34192,0.39208,0.6054,1.21402,0.73926,0.14874,1.1026,0.84508,1.16116,0.43852,0.78174,0.80176,0.57136,0.1681,0.47784,0.36732,0.84976,0.1687,0.58574,0.115,0.66888,0.8838,0.68448,1.3925,0.41322,2.08728,2.03932,0.0878,0.2951,0.67896,0.34422,0.69512,0.62876,0.59918,1.81148,0.52158,0.67918,1.48606,0.23626,0.13514,0.56958,0.15064,0.12726,0.72686,0.84502,0.45604,0.24408,0.2398,0.38156,1.8802,0.19848,0.72428,0.45256,0.50292,0.98782,0.19474,0.78544,0.34106,0.09062,0.24714,0.5467,0.88446,0.18502,0.57516,0.1149,0.15776,0.38138,0.43858,0.6638,0.04754,0.17082,6.52576,0.1322,2.4954,0.5417,0.44954,2.46744,2.1526,0.22096,0.16264,0.75552,0.32276,0.21828,0.4546,0.19036,0.69572,0.40076,0.37482,0.11788,0.09018,0.54818,0.1659,0.47744,0.68744,3.63434,0.3303,0.19388,0.063,0.23018,0.66848,0.27622,0.61168,0.80938,0.10952,0.02742,0.26102,0.32492,0.18266,0.9222,0.09366,0.19314,0.26266,0.24286,0.45316,0.17066,1.61236,7.43506,0.02828,0.3015,2.42392,0.90412,0.52244,0.12502,0.69052,0.13324,0.15158,0.4579,1.02478,0.10904,0.06082,0.28052,0.13406,0.38256,0.80052,0.07924,3.13876,0.64904,5.23016,3.26172,0.11694,2.55496,1.03296,0.77576,2.56932,0.73332
2018-09-25,0.65,0.86822,4.27658,0.72888,0.46596,0.21982,0.09418,0.0,0.94466,0.31114,0.9789,0.77766,0.6029,0.4049,0.55634,1.2858,0.1342,1.00944,1.13298,0.68444,0.47624,0.32938,1.14766,1.1531,1.04844,0.99934,0.52412,0.95074,0.4326,0.75632,0.1804,0.25082,1.06756,1.18596,0.63888,0.45946,0.7195,0.19166,0.22212,0.30146,1.09368,0.28662,0.52538,0.86596,0.50838,0.47446,2.23488,1.10342,0.96214,0.796,0.64978,1.47428,0.22426,0.68938,0.95114,0.80822,0.33626,0.34772,0.915,0.3504,0.77808,1.8272,0.57336,0.67296,1.63904,2.19472,0.58042,0.0,1.2812,0.51476,0.38464,1.68802,0.14012,0.37006,0.38306,0.6,0.79762,0.0,1.55198,5.17262,1.94186,3.4304,0.9131,0.18956,1.2984,0.40128,0.0,1.00848,0.95466,0.81658,0.0,0.54482,0.94014,1.11392,0.46942,0.04852,0.13858,2.10454,2.85638,1.05648,0.5277,1.09068,0.50886,0.7031,2.51276,1.01498,0.86018,1.22614,0.50364,1.30584,0.52384,0.92704,0.69916,1.47324,0.60082,0.62114,0.07472,0.31714,0.49978,0.41382,0.18274,0.34536,0.19144,0.03518,0.19652,0.06766,1.09882,0.49294,0.11516,1.50128,0.76642,0.69422,0.23506,0.8073,0.8732,0.56946,0.13066,0.28468,0.8495,0.22822,1.0588,0.15436,0.1349,0.50546,0.78494,0.51838,0.53992,0.34514,0.0,0.18476,0.66946,0.15402,0.7562,1.09118,0.24158,0.39514,0.43214,0.83022,0.96382,0.67124,0.2624,0.96832,0.82056,1.1906,0.51376,0.82986,0.75596,0.63212,0.17338,0.43058,0.48188,0.9015,0.17718,0.59432,0.11946,0.67924,0.79864,0.67246,1.42586,0.47292,2.25974,2.159,0.09468,0.29752,0.66792,0.34988,0.73018,0.66264,0.58452,1.90722,0.56494,0.70112,1.48874,0.25924,0.1451,0.53814,0.14924,0.12432,0.75626,0.81406,0.47648,0.24762,0.21258,0.40394,2.19998,0.20586,0.7069,0.43126,0.51484,1.02186,0.18602,0.76258,0.33174,0.08704,0.25414,0.5882,0.9185,0.18268,0.55188,0.12312,0.16872,0.41062,0.48486,0.71254,0.05004,0.17058,6.58932,0.14948,2.35954,0.69232,0.47668,2.41494,2.90418,0.24846,0.17644,0.81644,0.3277,0.23262,0.43362,0.20156,0.67914,0.41102,0.4009,0.14652,0.10486,0.60514,0.17586,0.4677,0.67442,3.58058,0.33156,0.20836,0.07252,0.22448,0.69478,0.28862,0.561,0.93604,0.11898,0.02844,0.29104,0.34526,0.17516,0.99098,0.09038,0.20206,0.25188,0.25294,0.45566,0.17626,1.64578,8.36188,0.03088,0.3005,2.42238,0.96858,0.49836,0.13262,0.75752,0.11894,0.15114,0.50632,1.19086,0.12702,0.067,0.30318,0.11628,0.38804,0.90028,0.07844,3.49706,0.57652,5.45626,2.92176,0.11934,2.80396,0.98672,0.81264,2.62438,0.77266
2018-09-26,0.69236,0.95906,4.14342,0.73932,0.48124,0.21236,0.09968,0.0,1.0743,0.31936,0.79758,0.80108,0.77042,0.46072,0.55458,1.4138,0.0,1.04348,1.27208,0.74494,0.53114,0.38074,1.1574,1.14598,1.1041,1.05206,0.48644,0.9543,0.40592,0.76596,0.20584,0.26792,1.01876,1.08434,0.64526,0.45814,0.81128,0.23684,0.25236,0.31058,1.08338,0.30184,0.56774,0.92096,0.56194,0.50562,2.38202,0.98184,1.07222,0.892,0.6814,1.47016,0.2306,0.80756,0.98648,0.86648,0.30304,0.36536,1.1315,0.39796,0.85118,1.64318,0.60672,0.89096,1.60588,2.26196,0.62218,0.0,1.23018,0.5589,0.4291,1.69338,0.14868,0.46374,0.38306,0.68832,0.7499,0.0,1.49058,5.80182,1.94496,4.11412,0.86228,0.21476,1.29336,0.38184,0.0,1.14184,1.00994,0.88654,0.0,0.52592,1.05542,1.04624,0.59002,0.05034,0.12308,2.52996,2.74728,1.0611,0.5702,1.12968,0.51484,0.74838,2.5853,0.93128,0.91634,1.24754,0.50824,1.40272,0.51002,0.87736,0.6741,1.45894,0.62312,0.3227,0.08502,0.3914,0.5249,0.45662,0.1984,0.3605,0.20428,0.03808,0.19562,0.06754,1.1423,0.45802,0.14144,1.64448,0.86648,0.67294,0.26898,0.83474,0.94964,0.5801,0.1349,0.35054,0.666,0.27308,1.16364,0.15244,0.1686,0.51946,0.87136,0.53084,0.53576,0.33908,0.0,0.15914,0.70578,0.1592,0.82216,1.15004,0.25158,0.45232,0.45738,1.0693,0.95328,0.66144,0.38526,0.94268,0.88426,1.18112,0.5551,0.93718,0.7858,0.6979,0.17508,0.3955,0.52028,0.94958,0.19304,0.55336,0.11948,0.79694,0.91404,0.63094,1.48712,0.59678,2.4612,2.38542,0.11626,0.35232,0.74026,0.34312,0.8604,0.74932,0.54908,1.8801,0.63256,0.70978,1.42462,0.2638,0.15394,0.50724,0.14244,0.14158,0.7901,0.8232,0.4779,0.25324,0.22138,0.4527,2.31556,0.20546,0.77812,0.46342,0.54894,1.16626,0.17554,0.806,0.34732,0.0931,0.25946,0.61636,0.90254,0.16882,0.63942,0.15228,0.1852,0.45926,0.5323,0.90172,0.0591,0.1753,6.77172,0.17166,2.35544,0.80782,0.45396,2.63884,3.20408,0.29858,0.21522,0.75374,0.33558,0.25904,0.46164,0.21108,0.67178,0.43146,0.45188,0.16692,0.12468,0.65554,0.1991,0.44554,0.67018,3.37842,0.3363,0.17368,0.08142,0.21182,0.70234,0.32852,0.54936,1.00034,0.11382,0.03602,0.30472,0.29878,0.14178,1.1283,0.08616,0.17034,0.25408,0.21728,0.45382,0.18922,1.64678,8.97382,0.03378,0.3336,2.31056,1.01008,0.52028,0.14208,0.7817,0.10612,0.15194,0.52124,1.39564,0.14594,0.07678,0.32184,0.12006,0.37062,1.1298,0.08876,3.9627,0.4945,5.72836,2.78214,0.14792,2.775,0.92688,0.91212,2.67578,0.78254
2018-09-27,0.65144,0.77942,4.07802,0.55996,0.47658,0.20218,0.1022,0.0,0.98132,0.25294,0.6963,0.75934,0.80548,0.44986,0.51974,1.43586,0.0,1.38186,1.12772,0.76326,0.48356,0.36862,1.0062,1.03326,0.8828,1.00562,0.66226,0.90414,0.35786,0.75204,0.21044,0.23758,0.91196,0.99802,0.74256,0.46884,0.70852,0.23376,0.24998,0.31158,0.94452,0.32392,0.4932,0.86564,0.59774,0.47152,1.96898,0.87274,1.53916,0.92658,0.62946,1.2766,0.2356,0.74662,0.75998,0.83102,0.24986,0.35778,1.13354,0.39316,0.70714,1.56982,0.57118,0.94312,1.5253,2.04864,0.6439,0.0,1.09078,0.52304,0.42724,1.57724,0.13896,0.42618,0.35678,0.65488,0.71664,0.0,1.3523,6.9067,2.04558,3.6386,0.72858,0.24314,1.17554,0.33754,0.0,1.00998,0.89216,0.82906,0.0,0.46032,1.27478,0.95698,0.61342,0.04946,0.13938,2.59398,2.71518,0.93708,0.5993,1.17136,0.50842,0.70322,2.24194,0.87486,0.81824,1.37646,0.4614,1.73428,0.53926,0.86506,0.65858,1.77158,0.6792,0.22508,0.08902,0.34816,0.47942,0.44516,0.20426,0.34186,0.20184,0.03824,0.17186,0.08488,1.1087,0.3971,0.13924,1.56824,0.80434,0.61994,0.26604,0.78376,0.83228,0.5281,0.12342,0.33052,0.58142,0.26196,1.02808,0.14604,0.17264,0.44706,0.83458,0.4955,0.51414,0.27536,0.0,0.13326,0.70322,0.1392,0.79748,1.15136,0.25298,0.4597,0.3842,1.19312,0.82406,0.60086,0.447,0.73792,0.81886,1.00466,0.4934,0.91766,0.72174,0.65926,0.16664,0.37052,0.44436,0.95452,0.18674,0.48676,0.11434,0.75832,0.95182,0.5392,1.43556,0.64728,2.3255,2.36412,0.12314,0.34004,0.71986,0.34342,0.89734,0.7273,0.46434,1.71134,0.56442,0.65344,1.54282,0.23316,0.1529,0.4527,0.12526,0.15426,0.68174,0.71248,0.47018,0.2292,0.19676,0.43052,2.35666,0.17742,0.7332,0.43,0.52252,1.22766,0.22998,0.7054,0.36014,0.09768,0.24898,0.58462,0.82496,0.15044,0.671,0.16336,0.17776,0.4127,0.5366,0.90516,0.06048,0.15788,6.36134,0.17382,2.31648,0.77602,0.44974,2.67096,2.7994,0.30782,0.21768,0.68332,0.31768,0.24668,0.48352,0.1903,0.61382,0.38738,0.47344,0.18392,0.12906,0.61656,0.20012,0.33602,0.64808,3.41626,0.31066,0.18468,0.07794,0.19584,0.6483,0.33122,0.5485,0.91106,0.11658,0.03568,0.2899,0.2634,0.1227,1.1338,0.07628,0.15634,0.23694,0.20242,0.3887,0.19684,1.59004,8.85404,0.03098,0.31968,2.28634,1.0133,0.52186,0.1468,0.68184,0.10134,0.13926,0.5141,1.35528,0.14252,0.07582,0.31902,0.12614,0.34046,1.1364,0.08714,3.94414,0.41082,5.64872,2.64248,0.14712,2.80642,0.70886,1.56718,2.33466,0.74072
2018-09-28,0.82498,0.81264,3.67868,0.564,0.48432,0.21494,0.1035,0.0,1.00432,0.24796,0.72048,0.70616,0.74958,0.48696,0.50632,1.38802,0.0,1.5278,1.0697,0.82518,0.52106,0.39176,0.9861,1.0478,0.9009,0.78616,0.77306,0.91862,0.3487,0.71672,0.23326,0.23752,0.90986,0.8268,0.75008,0.44026,0.74522,0.25422,0.2448,0.34914,0.97062,0.35304,0.51234,0.84692,0.6281,0.54378,1.73554,0.9962,1.79314,0.94078,0.69526,1.23398,0.25124,0.71948,0.6607,0.86558,0.26944,0.32426,1.182,0.43416,0.70356,1.64216,0.57582,1.00262,1.53868,2.05958,0.63444,0.0,1.10832,0.50878,0.43508,1.50278,0.13378,0.40008,0.02698,0.63674,0.73606,0.0,1.28462,7.16838,1.90966,3.79524,0.68022,0.23418,1.008,0.38184,0.0,0.986,0.88312,0.93974,0.0,0.58234,1.2446,0.9194,0.63966,0.05252,0.1754,2.265,2.7927,0.90086,0.68654,1.21288,0.52056,0.70546,2.38574,0.82332,0.83658,1.49238,0.49638,1.98786,0.55138,0.84604,0.72518,2.11676,0.77904,0.23802,0.10406,0.43448,0.46886,0.94012,0.21428,0.37348,0.22258,0.04386,0.20328,0.08944,1.1193,0.3822,0.14312,1.56772,0.83276,0.61634,0.29216,0.84694,0.88638,0.51898,0.1171,0.37112,0.5398,0.27228,1.02078,0.15744,0.21736,0.4495,0.84412,0.50256,0.53466,0.2866,0.0,0.1318,0.72162,0.1642,0.86756,1.20932,0.24904,0.46682,0.38522,1.31458,0.92612,0.59728,0.4683,0.86158,0.83478,0.93396,0.50752,0.87248,0.7567,0.63956,0.16822,0.3377,0.44456,0.84114,0.18734,0.49188,0.11234,0.76242,1.0729,0.52748,1.55892,0.66566,2.2855,2.38284,0.16714,0.35208,0.74744,0.33112,0.88382,0.82032,0.4415,1.75888,0.54414,0.61144,1.72668,0.23056,0.15974,0.4641,0.13282,0.16542,0.55554,0.7773,0.4837,0.23764,0.2525,0.44726,2.42948,0.19794,0.73854,0.42476,0.51444,1.18306,0.26358,0.71106,0.36436,0.10402,0.26848,0.6995,0.89866,0.14442,0.71026,0.15994,0.19106,0.38744,0.59318,1.04404,0.06662,0.20126,6.28722,0.17554,2.20214,0.81144,0.47304,2.61056,2.87124,0.35188,0.24108,0.67232,0.2998,0.2604,0.47048,0.19542,0.61902,0.44632,0.49332,0.20904,0.138,0.67576,0.20234,0.3057,0.7389,2.89168,0.31132,0.19012,0.08182,0.19982,0.6478,0.37064,0.51956,0.87354,0.11824,0.03972,0.3288,0.2579,0.12386,1.1743,0.07562,0.152,0.24356,0.20226,0.35132,0.20982,1.70478,9.3673,0.03238,0.31216,2.41152,1.02072,0.4968,0.1556,0.65862,0.1018,0.1556,0.61928,1.43614,0.14398,0.08104,0.33474,0.12324,0.3465,1.2543,0.09684,3.8299,0.30376,6.08956,2.89948,0.14592,2.52372,0.58554,1.86716,2.3461,0.73552
2018-10-08,0.83218,0.80722,3.67038,0.44706,0.42386,0.23278,0.10164,0.0,1.00406,0.23068,0.5586,0.66074,0.75506,0.47312,0.51356,1.34336,0.0,1.59428,1.01084,0.55538,0.51732,0.39962,0.95074,0.91816,0.74548,0.87578,0.7907,0.9289,0.30924,0.70096,0.22384,0.1959,0.8786,0.76432,0.73858,0.47368,0.76606,0.26258,0.2441,0.41436,1.11328,0.3487,0.52842,0.7281,0.59124,0.58436,1.5466,1.027,1.87618,1.01832,0.67658,1.21148,0.2592,0.65518,0.59982,0.8045,0.29738,0.27548,1.20122,0.3892,0.66014,1.64932,0.57938,1.02756,1.52982,2.35618,0.6387,0.0,1.16576,0.48668,0.46346,1.46424,0.1377,0.35934,0.0,0.76408,0.72414,0.0,1.32592,6.71236,1.55876,3.60586,0.67518,0.21984,0.9564,0.46078,0.0,1.08104,0.79376,1.1194,0.0,0.53764,1.21458,0.76702,0.68366,0.05008,0.19908,2.0548,2.73604,0.88624,0.70878,1.23508,0.54022,0.67228,2.10294,0.83754,0.795,1.56508,0.4748,2.02444,0.57286,0.8343,0.76366,2.23212,0.80654,0.2653,0.10718,0.4667,0.563,1.28148,0.20652,0.35942,0.21854,0.04982,0.24614,0.09756,1.13818,0.34888,0.18476,1.15074,0.76428,0.61744,0.27714,0.78528,0.87942,0.47258,0.1103,0.3676,0.50308,0.27392,0.9751,0.16572,0.25168,0.38686,0.99854,0.41812,0.54296,0.27234,0.0,0.131,0.79616,0.16922,0.77306,1.07384,0.27744,0.46976,0.3161,1.32632,0.92812,0.64414,0.47226,0.85328,0.84244,1.0246,0.47018,0.84058,0.71008,0.5769,0.15868,0.3054,0.40608,0.80268,0.1806,0.44828,0.11738,0.77622,1.04026,0.53172,1.5536,0.79486,2.30582,1.96448,0.20422,0.41596,0.7122,0.31932,0.99252,0.95822,0.44114,1.70948,0.53036,0.6412,1.597,0.21798,0.16584,0.45342,0.13944,0.17504,0.51242,0.73486,0.49544,0.237,0.2578,0.36016,2.53956,0.20062,0.7545,0.54814,0.44934,1.03732,0.2947,0.694,0.37378,0.11524,0.23156,0.6151,0.8193,0.1479,0.76908,0.15318,0.16504,0.30676,0.52798,1.05474,0.07312,0.18536,5.3714,0.1474,1.90486,0.72572,0.4452,2.52482,2.73758,0.33876,0.24552,0.65382,0.28506,0.2453,0.4263,0.18776,0.54786,0.43936,0.45594,0.17858,0.11532,0.73448,0.19084,0.28918,0.72998,2.66274,0.2876,0.20206,0.07374,0.19638,0.72898,0.332,0.4755,0.69722,0.11352,0.03888,0.26782,0.23496,0.1183,0.99352,0.08248,0.13918,0.23252,0.19398,0.3267,0.1967,1.5841,7.49828,0.03536,0.30676,2.24554,0.9699,0.4857,0.15736,0.61138,0.09828,0.16648,0.5626,1.2886,0.11746,0.07418,0.34002,0.12384,0.3451,1.17486,0.0926,3.04404,0.3393,6.06778,3.18752,0.14818,2.40706,0.54274,2.00002,2.1606,0.65696
2018-10-09,0.84232,0.74954,3.59008,0.3568,0.40482,0.24716,0.09808,0.0,1.06886,0.19732,0.47994,0.69432,0.75494,0.4919,0.51532,1.21392,0.0,1.68126,0.98208,0.44556,0.48352,0.41146,0.9319,0.99152,0.57522,0.81792,0.79818,0.91088,0.28366,0.69202,0.21144,0.19468,0.84668,0.76858,0.718,0.5089,0.7761,0.25112,0.28318,0.43806,1.0997,0.34168,0.41058,0.61142,0.66872,0.58168,1.23166,1.02674,2.13006,1.24212,0.63826,1.26132,0.25724,0.61448,0.60866,0.8106,0.33724,0.25136,1.06826,0.43354,0.60654,1.76094,0.5769,1.0949,1.2588,2.41324,0.68436,0.0,1.2209,0.47318,0.48884,1.42914,0.13728,0.31888,0.0,0.90268,0.81358,0.0,1.37128,5.83746,1.4756,3.43884,0.77244,0.21884,0.90742,0.50606,0.0,1.08844,0.68722,0.99192,0.0,0.6151,0.86654,0.70388,0.63926,0.05174,0.197,1.94484,2.78396,0.95772,0.72416,1.21982,0.5205,0.62124,2.10704,0.92868,0.7848,1.88428,0.44866,1.96694,0.58688,0.84174,0.79022,2.20424,0.86544,0.29548,0.11236,0.54312,0.61992,1.4501,0.2043,0.34856,0.2151,0.05018,0.26002,0.114,1.02076,0.31982,0.20376,1.00146,0.75166,0.6517,0.26686,0.7661,0.75714,0.44962,0.1058,0.3681,0.46212,0.2883,0.86728,0.15866,0.25164,0.36262,1.0438,0.40226,0.49764,0.25122,0.0,0.1216,0.81002,0.17152,0.86722,1.06516,0.28798,0.44962,0.26552,1.20632,1.04886,0.65804,0.3746,0.95612,0.84636,0.97694,0.38,0.8054,0.68972,0.56416,0.16746,0.28204,0.30438,0.74594,0.1778,0.458,0.11342,0.8226,1.06056,0.54946,1.5643,1.03996,2.30114,1.81652,0.226,0.43122,0.70652,0.32944,1.21104,1.0323,0.40446,1.83182,0.5338,0.66888,1.77532,0.2019,0.17526,0.44642,0.15102,0.18262,0.4866,0.76664,0.47946,0.23406,0.25872,0.35352,2.63304,0.20094,0.81188,0.60424,0.4461,1.01796,0.329,0.72244,0.3778,0.12668,0.2231,0.5824,0.78806,0.14656,0.8589,0.13664,0.16006,0.27738,0.51648,1.09876,0.08126,0.20414,5.09786,0.15036,1.98648,0.6203,0.4333,2.41812,2.00028,0.35436,0.24844,0.65428,0.27352,0.23126,0.47934,0.18498,0.5956,0.45706,0.4682,0.17362,0.1076,0.71752,0.1973,0.3366,0.7322,2.51302,0.2873,0.20228,0.06898,0.19454,0.75428,0.3306,0.4213,0.54448,0.10568,0.03884,0.2448,0.22456,0.11608,0.97472,0.0871,0.1368,0.22908,0.18526,0.32848,0.19462,1.56344,6.65006,0.04,0.30952,2.13662,0.89302,0.47808,0.16314,0.65782,0.09566,0.17932,0.53896,1.1811,0.11048,0.07372,0.33644,0.13098,0.32866,1.0824,0.0944,2.70128,0.36232,5.9487,3.31754,0.15624,2.33848,0.60046,2.02564,2.57814,0.6169
2018-10-10,0.78462,0.66552,3.06758,0.34286,0.3624,0.2328,0.08068,0.0,0.97096,0.1822,0.62796,0.63508,0.58344,0.44758,0.4443,1.11766,0.0,1.7687,0.90896,0.39054,0.40696,0.36046,0.87508,0.95668,0.49146,0.82828,0.79164,0.8498,0.24054,0.62074,0.20946,0.1655,0.77844,0.80232,0.67032,0.48732,0.7664,0.20818,0.28322,0.3904,1.19596,0.31886,0.38596,0.50352,0.70438,0.52078,1.08326,0.90808,2.22066,1.2227,0.59304,1.08384,0.24354,0.51564,0.51622,0.68696,0.35638,0.2353,0.85648,0.41054,0.52474,1.77872,0.51584,1.28992,1.21862,1.9763,0.63798,0.0,1.1487,0.42144,0.51492,1.33232,0.15332,0.2247,0.0,0.81706,0.7409,0.0,1.28008,6.31914,1.7682,2.93778,0.7583,0.21732,0.78298,0.50482,0.0,0.96006,0.66714,1.24702,0.0,0.62086,0.6992,0.58424,0.53948,0.04612,0.19728,1.3593,2.7805,0.87956,0.64866,1.1392,0.4584,0.5425,2.0941,0.88884,0.70884,1.82664,0.3595,1.99918,0.60222,0.78764,0.75458,2.29702,0.80476,0.29238,0.10308,0.47136,0.58762,1.57526,0.18608,0.29078,0.19078,0.04464,0.24638,0.11056,0.94252,0.27808,0.17836,0.83534,0.65612,0.60328,0.23204,0.72062,0.66012,0.39014,0.09194,0.3116,0.46286,0.22834,0.65454,0.16074,0.24206,0.30338,1.03446,0.37202,0.4397,0.23928,0.0,0.12366,0.70614,0.15822,1.03364,0.96742,0.29484,0.40946,0.23428,1.041,1.0036,0.5629,0.27192,0.86286,0.72334,0.95172,0.32778,0.64552,0.61056,0.56704,0.15868,0.24162,0.262,0.672,0.14726,0.42122,0.10544,0.7619,0.9572,0.57062,1.3433,1.28138,1.88936,1.4999,0.22726,0.4166,0.56798,0.31512,1.43716,1.03596,0.37818,1.74076,0.46316,0.63064,1.72312,0.18872,0.16526,0.4296,0.14004,0.17124,0.44824,0.69242,0.44216,0.21876,0.23124,0.32708,2.49224,0.19112,0.87906,0.57298,0.39428,0.94018,0.3291,0.6507,0.35684,0.12384,0.1965,0.5077,0.68012,0.13488,0.82448,0.10448,0.15848,0.22222,0.46318,0.96176,0.07386,0.19118,4.52486,0.14954,1.73896,0.50182,0.41108,1.99314,1.69876,0.3203,0.2078,0.72372,0.21704,0.18606,0.45924,0.17562,0.60336,0.41726,0.4451,0.17612,0.0908,0.65738,0.17712,0.39374,0.66742,2.22942,0.24558,0.21094,0.06246,0.18478,0.73578,0.29384,0.36552,0.45222,0.09598,0.03092,0.24734,0.21754,0.11296,0.83396,0.11544,0.13918,0.19606,0.19866,0.30582,0.16982,1.54932,5.60836,0.0393,0.30034,1.8279,0.77482,0.44438,0.1549,0.78006,0.08744,0.17482,0.51208,1.01628,0.08988,0.06526,0.31932,0.11082,0.30418,0.83742,0.07958,2.22332,0.2925,5.53046,3.12088,0.12672,2.51814,0.59666,1.93374,2.66278,0.58988
2018-10-11,0.91448,0.71994,3.04706,0.37726,0.4582,0.29872,0.09164,0.0,1.13924,0.25368,0.728,1.13818,0.57726,0.49612,0.5789,1.20072,0.0,1.84082,0.99618,0.44888,0.54934,0.50292,0.94452,1.30712,0.58142,0.97104,0.63362,1.13082,0.32682,0.65312,0.25844,0.23288,0.8203,1.03336,0.67424,0.65548,0.89346,0.24868,0.31244,0.4437,1.46196,0.33114,0.47248,0.49858,0.772,0.65128,1.23154,1.01244,1.90646,1.44298,0.70374,1.26586,0.26432,0.59636,0.51746,0.7639,0.47988,0.26298,0.74406,0.43008,0.57244,1.86734,0.5317,1.48558,1.4014,2.09522,0.7956,0.0,1.28956,0.48182,0.57338,1.45248,0.17676,0.25886,0.0,0.89156,0.8124,0.0,1.3598,5.92788,1.87894,2.76882,0.73518,0.22886,0.81182,0.55824,0.0,1.11268,0.76702,1.50584,0.0,0.70208,0.5314,0.73122,0.51282,0.05886,0.23388,1.5081,3.30532,0.90724,0.67918,1.23354,0.57284,0.66704,2.4895,1.09866,0.72022,1.76824,0.4117,1.76108,0.598,0.74758,0.82736,2.2,0.72302,0.30582,0.11368,0.53278,0.61288,1.7813,0.1714,0.30582,0.21188,0.05116,0.27422,0.09958,1.1639,0.28842,0.19266,0.96046,0.76526,0.69162,0.2527,0.7398,0.67474,0.45324,0.1278,0.3742,0.52188,0.26234,0.7321,0.20114,0.2444,0.44236,1.20874,0.42972,0.5434,0.3177,0.0,0.16752,0.81112,0.20344,1.17808,0.9782,0.35136,0.51872,0.28634,0.91566,1.11174,0.60574,0.2273,1.14344,0.71738,1.09278,0.35412,0.79108,0.7471,0.67106,0.22398,0.23824,0.31398,0.78076,0.16356,0.52466,0.14376,0.78552,1.02322,0.579,1.54124,1.77768,1.9396,1.78686,0.26248,0.4979,0.7204,0.35938,1.83398,1.17708,0.39202,2.00222,0.52574,0.68712,1.5616,0.21068,0.20162,0.464,0.15218,0.1645,0.50994,0.79586,0.60136,0.24392,0.2804,0.36466,2.51646,0.24092,0.98624,0.64924,0.50846,0.92372,0.30474,0.7307,0.38488,0.15996,0.24768,0.5767,0.7376,0.15928,0.99212,0.12396,0.1992,0.24864,0.49,1.01028,0.0903,0.22012,5.01498,0.16364,1.95604,0.53426,0.43794,2.38592,1.80706,0.34074,0.22788,0.78772,0.24098,0.2215,0.52856,0.24704,0.68224,0.53694,0.48652,0.19016,0.09206,0.75372,0.19992,0.4746,0.64676,2.17598,0.31922,0.20908,0.07042,0.29336,0.87754,0.3005,0.38582,0.54522,0.10782,0.03268,0.2597,0.25432,0.13788,0.80656,0.13926,0.16062,0.25922,0.2,0.34748,0.18308,1.9751,6.0562,0.04576,0.33674,2.19962,0.89598,0.46444,0.16792,0.94528,0.12176,0.22126,0.55156,1.10418,0.11008,0.07612,0.37932,0.11082,0.35316,1.03976,0.08116,2.21326,0.31906,6.01062,3.61766,0.1338,2.71116,0.62158,1.53156,2.98296,0.7101
2018-10-12,0.84536,0.68868,3.34852,0.37748,0.52362,0.339,0.09976,0.0,1.17042,0.27294,0.80002,1.32944,0.57408,0.50392,0.657,1.73792,0.0,1.94974,1.1207,0.44532,0.96328,0.53662,0.98104,1.90474,0.62664,1.05876,0.64618,1.31106,0.36862,0.7367,0.25648,0.28614,1.00656,1.10282,0.80016,1.06354,0.96602,0.24014,0.32424,0.45416,1.67592,0.30512,0.49152,0.54056,0.84296,0.59726,1.27326,0.92534,1.81304,1.51608,0.67672,1.29444,0.27872,0.63036,0.49122,1.05866,0.5257,0.29376,0.7305,0.41382,0.59176,2.00264,0.55278,1.53534,1.79326,2.18002,0.85078,0.0,1.55072,0.51396,0.6504,2.04146,0.18856,0.27666,0.0,0.99196,0.8517,0.0,1.56714,6.1207,1.98422,2.78228,0.72402,0.2435,0.80762,0.57382,0.0,1.11296,0.8587,1.5585,0.0,0.76758,0.62036,0.7601,0.53766,0.0636,0.20636,1.67758,4.6413,0.93844,0.6677,1.78902,0.76648,0.8139,2.69802,1.46986,0.65962,1.87524,0.40386,1.64154,0.6468,0.75806,0.76856,1.95506,0.73964,0.3451,0.10494,0.45768,0.67118,1.41372,0.15284,0.28578,0.20326,0.05142,0.27998,0.10344,1.27532,0.28174,0.19968,1.05604,0.77988,0.73976,0.26292,0.73142,0.60306,0.48296,0.14344,0.34714,0.54558,0.27554,0.75802,0.2355,0.24478,0.5198,1.06482,0.46392,0.59058,0.3373,0.0,0.19006,0.8316,0.1922,1.30272,0.95862,0.4157,0.5899,0.3009,0.85952,1.05176,0.65736,0.187,1.0169,0.70186,1.36196,0.44628,1.03796,0.87832,0.69928,0.28412,0.2664,0.32362,0.76984,0.17626,0.54886,0.1763,0.83658,1.0679,0.57766,1.48854,2.18202,1.93714,1.95002,0.26004,0.52876,0.7899,0.38512,2.14844,1.02924,0.40448,2.01498,0.56872,0.7526,1.46418,0.21516,0.21906,0.44914,0.15766,0.1623,0.60012,0.75594,0.68586,0.24684,0.29286,0.34996,2.55252,0.23994,1.03062,0.70578,0.5666,0.98706,0.29982,0.78586,0.3978,0.18358,0.24292,0.49442,0.67666,0.1793,1.0451,0.18388,0.20548,0.23964,0.48962,0.9092,0.1031,0.19186,5.27392,0.17902,2.48064,0.54308,0.49718,2.8779,1.79642,0.31536,0.20812,0.86256,0.26138,0.231,0.56698,0.29426,0.7351,0.5485,0.46952,0.21306,0.08752,0.7602,0.1942,0.5608,0.59528,2.3167,0.35638,0.21972,0.06782,0.40462,0.90272,0.28322,0.38182,0.63568,0.11912,0.03218,0.26184,0.26196,0.14438,0.75762,0.15472,0.17522,0.31852,0.20418,0.3597,0.1737,2.14354,5.52382,0.04796,0.34894,2.17514,0.98672,0.49368,0.16336,1.0915,0.14964,0.22644,0.4704,1.07046,0.1234,0.07156,0.39738,0.111,0.38056,1.11762,0.07792,2.25236,0.34484,5.83796,3.71494,0.15246,3.26272,0.83482,1.40148,3.26504,0.72668
2018-10-15,0.81232,0.56674,3.1345,0.38766,0.50646,0.32038,0.09582,0.0,1.1028,0.2575,0.8088,1.3433,0.55118,0.48084,0.63324,2.1444,0.0,2.10326,1.12862,0.52956,1.03746,0.5216,0.90072,2.52582,0.66672,1.02756,0.64428,1.22776,0.35958,0.68688,0.26194,0.2734,1.02662,1.10712,0.82312,1.0812,0.90576,0.22186,0.3741,0.41484,1.57538,0.27146,0.43854,0.51984,0.85562,0.53202,1.16224,0.82274,1.8039,1.41606,0.60948,1.24032,0.28758,0.64714,0.47198,1.1677,0.49294,0.3574,0.72494,0.41754,0.53808,2.0124,0.53162,1.48894,1.86124,1.67822,0.84146,0.0,1.50566,0.50158,0.61606,2.34054,0.19712,0.26408,0.0,0.83138,0.82132,0.0,1.69768,5.85268,1.98948,2.77068,0.71828,0.24498,0.70822,0.5793,0.0,1.14114,0.83074,1.41526,0.0,0.83126,0.6022,0.71998,0.47684,0.0615,0.1891,1.57,5.3806,0.88248,0.66362,1.949,0.7484,0.81018,2.65066,1.5177,0.64612,1.77056,0.59696,1.87046,0.72144,0.73384,0.67066,1.82282,0.70718,0.34278,0.08502,0.4034,0.5429,1.12686,0.14828,0.25048,0.17496,0.0513,0.23468,0.09528,1.1937,0.2373,0.15008,0.97998,0.70758,0.72244,0.23642,0.76262,0.51544,0.43938,0.13092,0.30742,0.51234,0.30206,0.65854,0.22544,0.22356,0.51098,0.83466,0.39618,0.56684,0.31528,0.0,0.18658,0.82414,0.18458,1.3419,0.9364,0.39546,0.5676,0.27716,0.84672,1.05818,0.58882,0.16694,0.9685,0.61636,1.41454,0.43286,1.1185,0.8551,0.66746,0.28288,0.29612,0.31384,0.73062,0.16456,0.55678,0.1761,0.79376,1.06732,0.52314,1.39104,2.22414,1.84524,1.8761,0.22792,0.45372,0.77214,0.3712,2.16484,0.8294,0.41018,1.92316,0.54314,0.70584,1.44646,0.18592,0.20452,0.41012,0.13456,0.15636,0.79588,0.70376,0.65352,0.29628,0.5454,0.33762,2.41176,0.22128,0.98346,0.53526,0.57154,0.93964,0.2633,0.7768,0.3742,0.18224,0.22572,0.42294,0.60782,0.18398,0.94332,0.1978,0.22552,0.21124,0.43884,0.88018,0.09752,0.18568,5.24814,0.18124,2.51884,0.45588,0.50674,2.83544,1.64258,0.27478,0.20632,0.894,0.2397,0.2122,0.56462,0.31704,0.7243,0.5077,0.43368,0.20632,0.08066,0.65152,0.1736,0.56236,0.54392,2.07676,0.33648,0.21424,0.05938,0.44192,0.73608,0.26558,0.34298,0.6157,0.12416,0.0272,0.34372,0.2362,0.13812,0.70378,0.16474,0.15944,0.35742,0.20288,0.31848,0.15642,2.14856,5.05996,0.04454,0.34976,1.99496,0.90568,0.5122,0.15724,1.14012,0.15304,0.20778,0.4318,0.93832,0.12096,0.06292,0.36944,0.10892,0.35226,1.05994,0.08204,2.01014,0.27956,5.4718,3.39664,0.1745,3.09768,1.01152,1.27924,3.2385,0.69176
2018-10-16,0.8511,0.51916,3.02822,0.42074,0.51532,0.31518,0.09888,0.0,1.07662,0.29338,0.86554,1.37006,0.5565,0.59382,0.64596,2.53772,0.0,2.58084,1.09546,0.56214,1.11962,0.53876,0.87758,2.99246,0.9431,1.10708,0.6643,1.27764,0.39558,0.62642,0.31366,0.28456,1.08222,1.0638,0.85512,1.13814,0.87326,0.2381,0.3464,0.41206,1.88792,0.26664,0.50686,0.55484,0.80804,0.51186,1.1332,0.7982,1.78218,1.2789,0.65606,1.13686,0.28784,0.6697,0.4277,1.52538,0.4643,0.6277,0.7403,0.39226,0.5286,1.91738,0.64868,1.42358,2.43354,1.56768,0.83454,0.0,1.60802,0.5341,0.59652,2.6286,0.19628,0.26352,0.0,0.68596,0.78568,0.0,1.67542,5.90488,2.0197,3.15268,0.55712,0.26226,0.7472,0.60212,0.0,1.07758,0.87116,1.48628,0.0,0.72324,0.61672,0.76746,0.44846,0.05986,0.2308,1.7032,6.08864,0.84028,0.6719,2.15758,0.78872,0.86996,2.72396,1.5433,0.85232,1.51154,0.74082,2.06106,1.0229,0.69014,0.62096,2.03226,0.65142,0.32428,0.07944,0.33928,0.48348,1.02318,0.14424,0.25256,0.18004,0.0514,0.23084,0.08388,1.20908,0.23952,0.13208,0.89656,0.70562,0.698,0.25156,0.78894,0.5394,0.42846,0.1377,0.34532,0.6028,0.34732,0.65408,0.23116,0.22856,0.55506,0.791,0.33852,0.56324,0.33056,0.0,0.19184,1.06662,0.19328,1.28594,0.92384,0.39464,0.59906,0.28776,1.0486,1.20852,0.55294,0.15786,1.03716,0.6813,1.35634,0.48702,1.19334,0.94806,0.66608,0.30602,0.31284,0.33926,0.72528,0.16638,0.5674,0.18086,0.87794,1.1004,0.43418,1.5598,2.09626,1.6554,1.9578,0.34886,0.4317,0.8225,0.3864,1.99672,0.76536,0.41166,1.85726,0.49366,0.7178,1.3671,0.214,0.21042,0.4427,0.13004,0.15448,0.82506,0.68404,0.70464,0.31186,0.821,0.35984,1.97118,0.23098,0.94646,0.5811,0.63278,0.89984,0.22594,0.79572,0.36006,0.18962,0.23256,0.42662,0.63886,0.19452,0.87858,0.23234,0.23578,0.20862,0.4617,0.8317,0.0982,0.23684,5.70866,0.16872,2.64936,0.43524,0.5012,2.94834,1.99624,0.26242,0.20338,0.92798,0.25754,0.222,0.52754,0.3275,0.68386,0.48598,0.42132,0.19372,0.08052,0.66956,0.16932,0.52366,0.57354,2.0315,0.34182,0.21246,0.05596,0.48244,0.68624,0.28018,0.35198,0.67474,0.12742,0.0281,0.36654,0.2374,0.14328,0.70886,0.17454,0.15938,0.40256,0.19752,0.24754,0.15466,2.17702,5.0745,0.0394,0.34268,1.98588,0.97516,0.52938,0.15422,1.20672,0.16586,0.20666,0.39994,0.98034,0.11796,0.0608,0.35772,0.10624,0.3707,1.0965,0.08348,1.9233,0.23314,5.44588,3.4487,0.17108,3.2277,1.00352,1.21342,2.81152,0.70436
2018-10-17,0.89254,0.49234,3.30372,0.43248,0.55268,0.31914,0.1021,0.0,1.09392,0.2982,0.72074,1.49606,0.55212,0.76072,0.65184,2.78876,0.0,2.63232,0.96926,0.5852,1.14468,0.5799,0.87298,3.205,1.0322,1.10958,0.6752,1.38624,0.4428,0.66172,0.33162,0.34188,1.16412,0.98268,0.90192,1.20238,0.79042,0.24684,0.3271,0.4513,2.101,0.25572,0.54496,0.5413,0.78676,0.49964,1.10394,0.85896,1.78082,1.26596,0.68276,1.16022,0.2624,0.63166,0.44712,1.8756,0.4443,0.78324,0.68682,0.38772,0.51312,2.15376,0.7825,1.08314,2.92078,1.5925,0.82152,0.0,1.99262,0.55064,0.51248,2.84348,0.16704,0.27614,0.0,0.62736,0.83918,0.0,1.77756,5.38626,1.86794,3.33324,0.5526,0.24938,0.79758,0.64748,0.0,1.06222,0.90978,1.2224,0.0,0.73424,0.62256,0.81386,0.40148,0.0646,0.26128,1.86848,6.51258,0.83212,0.71268,2.4046,0.85158,1.09302,2.96078,1.65042,1.0179,1.70408,0.85336,2.16096,1.26116,0.6808,0.69134,2.6168,0.68394,0.34246,0.08542,0.3647,0.48332,0.88784,0.1507,0.26942,0.19712,0.0538,0.22712,0.08528,1.41116,0.2411,0.14638,0.90258,0.71708,0.6888,0.27858,0.8642,0.53744,0.43916,0.14644,0.3319,0.60896,0.37892,0.66336,0.2577,0.22006,0.58352,0.7343,0.33092,0.60214,0.3452,0.0,0.18818,1.1501,0.2123,1.08922,0.92944,0.3679,0.64472,0.44158,1.10278,1.28296,0.5592,0.14552,1.0703,0.75372,1.42192,0.52586,1.29406,1.01314,0.5793,0.33198,0.32888,0.36212,0.72276,0.16258,0.60096,0.18446,0.99016,0.9818,0.40212,1.8341,1.91232,1.7088,2.10328,0.45478,0.39982,0.90156,0.3853,1.78574,0.7203,0.40324,1.94054,0.51384,0.6641,1.52882,0.25622,0.22446,0.42198,0.13516,0.14484,0.85474,0.91968,0.73116,0.32332,1.16306,0.35366,1.9409,0.23646,0.80892,0.6021,0.70188,0.82716,0.22006,0.8848,0.35844,0.2116,0.2416,0.44222,0.64494,0.21802,0.85,0.23616,0.23274,0.22002,0.4822,0.76306,0.09744,0.26108,5.90884,0.15744,2.92536,0.45864,0.47508,3.12652,2.32326,0.27006,0.2177,0.8317,0.35052,0.23846,0.5251,0.33598,0.68062,0.50714,0.43168,0.1685,0.08458,0.70254,0.18086,0.52338,0.66114,2.19124,0.37532,0.2043,0.05908,0.5215,0.64668,0.29636,0.37064,0.6916,0.12222,0.02948,0.367,0.23596,0.14196,0.70294,0.14826,0.14942,0.45804,0.18008,0.25292,0.16816,2.20202,5.10106,0.04074,0.34348,2.21018,1.15234,0.72572,0.15426,1.0748,0.1812,0.21094,0.40276,1.0163,0.12416,0.06316,0.34414,0.1155,0.37788,1.10108,0.09124,1.95414,0.24004,5.2859,3.65548,0.18026,4.11666,1.06102,1.23738,2.8389,0.74576
2018-10-18,0.77676,0.46524,2.9602,0.37464,0.48848,0.26054,0.0821,0.0,0.9305,0.23292,0.64146,1.14976,0.4905,0.76324,0.53324,2.8832,0.0,2.45988,0.84564,0.49956,1.04016,0.47328,0.77954,3.06482,1.05974,1.00752,0.64322,1.13416,0.38212,0.6041,0.362,0.29882,1.09602,0.6973,0.78868,1.06718,0.6428,0.20894,0.31462,0.45238,2.00422,0.21806,0.43654,0.51106,0.70938,0.3594,0.89886,0.72586,1.69424,0.9708,0.55062,0.9353,0.22698,0.68206,0.45272,1.94256,0.3254,0.79318,0.64904,0.3632,0.4525,2.11318,0.76672,0.8228,2.92594,1.4452,0.66804,0.0,2.2672,0.51388,0.45062,3.3044,0.15144,0.23612,0.0,0.51014,0.72492,0.0,1.75634,4.8139,1.60614,3.13332,0.56598,0.26962,0.78194,0.62118,0.0,0.8812,0.84684,0.95132,0.0,0.65746,0.54948,0.66574,0.35936,0.05642,0.19996,1.5977,6.6157,0.81418,0.76354,2.42636,0.71386,1.0823,2.9472,1.52716,1.09848,1.83542,0.83898,2.34042,1.4804,0.68456,0.61802,3.4607,0.71084,0.34532,0.07356,0.34192,0.51194,0.7818,0.14488,0.23802,0.17526,0.04528,0.21182,0.07912,1.28888,0.23022,0.13624,0.71142,0.58868,0.71608,0.2746,0.81506,0.48456,0.34882,0.1129,0.29676,0.572,0.34974,0.50494,0.2231,0.2092,0.45276,0.56174,0.2779,0.49176,0.27786,0.0,0.1598,0.99822,0.16804,0.95834,0.81096,0.31614,0.57764,0.42928,1.10314,1.26752,0.5514,0.16876,0.93646,0.71476,1.3191,0.49348,1.38264,0.87802,0.4608,0.2862,0.29814,0.29124,0.57496,0.14068,0.52884,0.14048,0.95734,0.82214,0.36512,1.64758,1.5354,1.5191,1.78374,0.41428,0.33706,0.71604,0.32552,1.47424,0.56054,0.37872,1.72522,0.44176,0.63892,1.59434,0.22838,0.18858,0.38712,0.12552,0.13464,0.8375,0.85,0.5758,0.3034,1.26244,0.321,1.73272,0.18872,0.68164,0.56888,0.62076,0.70808,0.19738,0.85728,0.30276,0.17916,0.1906,0.34712,0.52064,0.20318,0.64268,0.19952,0.19074,0.19244,0.42586,0.67902,0.07714,0.24642,5.38688,0.14828,2.70462,0.39482,0.4422,2.53064,2.2525,0.24404,0.20046,0.76044,0.34214,0.20888,0.4871,0.26462,0.63768,0.432,0.3645,0.13466,0.07636,0.60242,0.163,0.5305,0.64088,1.87588,0.31686,0.1893,0.0526,0.43828,0.50716,0.28872,0.31304,0.5517,0.11006,0.02664,0.36794,0.21472,0.1173,0.6165,0.14118,0.14694,0.41788,0.18682,0.21578,0.14962,1.9462,3.87028,0.04172,0.33892,1.69126,1.03808,0.94636,0.15856,0.91768,0.17578,0.18578,0.37714,0.9655,0.1089,0.05472,0.25896,0.10998,0.30866,0.83668,0.08892,1.9185,0.21608,4.28978,3.39358,0.16834,4.27058,1.09074,0.9815,2.50726,0.6123
2018-10-19,0.84278,0.51226,3.02372,0.4278,0.47018,0.2375,0.08224,0.0,0.9342,0.25618,0.5211,1.11554,0.47446,0.87614,0.52472,2.60256,0.0,2.4032,0.7661,0.46198,0.76142,0.43992,0.76456,2.86618,1.20254,2.0018,0.5267,1.09312,0.39488,0.65708,0.50254,0.29592,1.03224,0.62912,0.6948,0.75588,0.64144,0.2259,0.31368,0.4446,1.62508,0.2297,0.41924,0.52508,0.6574,0.31202,0.85254,0.67836,1.76514,1.22852,0.55838,1.0008,0.22434,1.10312,0.6545,1.78708,0.33028,0.85,0.69132,0.38454,0.41914,2.0539,0.81442,0.72892,2.8221,1.55534,0.6885,0.0,2.33936,0.51646,0.4026,3.01916,0.13826,0.22218,0.0,0.52604,0.76858,0.0,1.69778,4.37854,1.57894,3.08556,0.6549,0.38928,0.82148,0.60056,0.0,0.88076,0.83068,0.83356,0.0,0.52786,0.52924,0.67164,0.37198,0.05856,0.1961,1.65268,5.7959,0.9465,0.81926,2.19528,0.58366,1.47614,3.29478,1.32488,1.20692,1.95362,0.88096,2.67078,2.01192,0.66168,0.68682,4.18038,0.6986,0.33908,0.08436,0.34942,0.5577,0.69988,0.15006,0.24422,0.1904,0.04704,0.19348,0.07342,1.31696,0.2364,0.1367,0.66304,0.66316,0.74354,0.28484,0.8258,0.51068,0.34048,0.11386,0.33686,0.62852,0.35362,0.45952,0.19778,0.19424,0.42642,0.54862,0.27564,0.47872,0.26196,0.0,0.14358,1.01168,0.1673,0.86648,0.85608,0.3623,0.7916,0.43152,1.0742,1.36798,0.58466,0.20052,0.87484,0.77124,1.09004,0.3992,1.28528,0.88524,0.42666,0.32974,0.30108,0.2985,0.55392,0.14296,0.60028,0.11382,0.98914,0.78542,0.32982,1.8001,1.2166,1.5473,1.72448,0.38894,0.37166,0.6956,0.45816,1.2426,0.50618,0.3724,1.84766,0.45864,0.57818,1.7748,0.24486,0.17702,0.3903,0.12628,0.12486,0.8051,0.8793,0.57616,0.30902,1.42128,0.33368,2.03568,0.18078,0.67996,0.62572,0.63268,0.9087,0.19282,0.85914,0.3156,0.18218,0.19018,0.35168,0.47196,0.23116,0.67138,0.1941,0.19348,0.2095,0.44274,0.67204,0.06764,0.25048,5.81816,0.13808,2.70062,0.40342,0.3465,2.30624,2.29072,0.26662,0.22496,0.65428,0.34768,0.23074,0.46178,0.25764,0.66342,0.42662,0.36134,0.09666,0.0818,0.66966,0.17164,0.4849,0.71632,1.77594,0.3644,0.18204,0.05878,0.42188,0.46702,0.34828,0.32714,0.45334,0.10092,0.02872,0.3478,0.21176,0.10598,0.66852,0.16058,0.155,0.40818,0.17042,0.20334,0.1621,1.7806,3.70756,0.04378,0.3442,1.59866,1.0188,1.04204,0.15918,0.8212,0.18806,0.18614,0.40728,1.0242,0.10034,0.05782,0.23488,0.10636,0.28008,0.69198,0.09006,1.8659,0.22446,4.45988,3.80456,0.15396,4.12484,0.9159,0.92368,2.61876,0.53612
2018-10-22,0.98756,0.62812,3.41602,0.4661,0.54368,0.28072,0.13208,0.0,1.00638,0.3053,0.76552,1.3676,0.65294,0.96246,0.67362,2.23284,0.0,2.41952,0.8018,0.42718,1.0428,0.49962,0.89022,2.79984,1.25744,2.55124,0.5388,1.32748,0.5703,0.7453,0.79854,0.42436,1.09142,0.63508,0.77976,0.82286,0.7412,0.26578,0.25876,0.5048,1.36622,0.25576,0.5101,0.62906,0.7478,0.34356,1.08306,0.82872,2.0305,2.29098,0.63278,1.10874,0.27964,1.3512,0.9658,1.91456,0.38726,0.9992,0.77522,0.4751,0.45068,2.223,0.89176,0.77294,3.23036,1.97486,0.75102,0.0,2.68404,0.56614,0.41134,3.53146,0.13594,0.29044,0.0,0.74268,0.93056,0.0,1.94354,4.76872,1.78394,3.55566,0.809,0.41952,0.92936,0.56922,0.0,0.84442,0.95944,0.95166,0.0,0.5396,0.5916,0.91262,0.439,0.08142,0.2113,2.44098,6.00642,1.10104,0.9409,2.34802,0.72358,2.18734,4.33542,1.51476,1.35052,2.18106,0.84444,3.00124,2.25562,0.85094,0.80118,4.90136,0.7962,0.38788,0.10786,0.42274,0.64974,0.7364,0.1887,0.2925,0.23804,0.04816,0.24376,0.08034,1.81654,0.25856,0.16458,0.80532,1.07278,0.82254,0.33242,0.82078,0.59938,0.44402,0.14798,0.40408,0.78718,0.37284,0.59094,0.20728,0.1783,0.6037,0.661,0.33558,0.5684,0.31474,0.0,0.16528,1.06852,0.20906,0.90766,0.96478,0.37472,1.02872,0.50568,1.1071,1.5642,0.76098,0.28694,1.43974,0.89992,0.99758,0.442,1.29242,1.095,0.5217,0.44264,0.33326,0.35754,0.93578,0.17796,0.69682,0.12884,1.1644,0.86722,0.37768,2.12704,1.1788,1.74274,2.03816,2.81406,0.43136,0.81716,0.53434,1.25112,0.54856,0.46658,2.38002,0.50772,0.66236,1.93904,0.27804,0.21136,0.42458,0.15286,0.1335,0.6759,0.99556,0.65992,0.35064,2.03102,0.34626,2.1983,0.2105,0.77476,0.71464,0.79986,1.1477,0.21108,1.01134,0.36444,0.19636,0.31628,0.46482,0.61966,0.28158,0.80272,0.2094,0.26868,0.25786,0.54908,0.758,0.07526,0.2699,6.60518,0.1536,3.17356,0.52664,0.35782,3.1121,2.5461,0.31904,0.24074,0.64286,0.4279,0.42712,0.5166,0.2811,0.75756,0.51856,0.44484,0.11,0.09894,0.86852,0.20986,0.5336,0.89236,2.11464,0.48492,0.19224,0.0715,0.5242,0.55776,0.44012,0.36206,0.4918,0.11266,0.04466,0.31292,0.24924,0.13052,0.97156,0.17082,0.1958,0.47924,0.18442,0.24854,0.20696,2.12624,4.8761,0.04814,0.3743,2.14362,1.38104,1.11742,0.1636,0.86754,0.2624,0.20658,0.47242,1.23518,0.12012,0.07186,0.28212,0.11128,0.33512,0.87112,0.09852,2.27478,0.28526,5.0178,4.79236,0.13698,4.7392,0.80694,1.0605,2.91478,0.6549
2018-10-23,1.0115,0.65122,3.75418,0.46304,0.56248,0.28954,0.2014,0.0,1.0371,0.30294,0.72538,1.58744,0.7565,0.89366,0.72012,2.0001,0.0,2.09246,0.93308,0.40928,1.18082,0.4854,0.99276,2.6304,1.02898,2.7448,0.56572,1.44904,0.89724,0.79156,1.11282,0.69732,1.15616,0.6212,0.93416,0.83356,0.94664,0.26098,0.26982,0.55168,0.87268,0.25856,0.50834,0.66198,0.79874,0.41126,1.18516,0.91012,2.22784,2.89778,0.60512,1.25734,0.30242,1.57884,1.08718,1.64612,0.41698,0.87896,0.88324,0.48114,0.42564,2.09362,0.77208,0.81936,3.11674,2.78604,0.74924,0.0,2.93836,0.56746,0.59784,4.35928,0.1452,0.32256,0.0,0.93282,0.97464,0.0,2.32404,4.94218,1.87348,3.60386,0.8978,0.45598,0.9507,0.57946,0.0,0.92818,1.04818,1.04166,0.0,0.59054,0.6557,1.32,0.557,0.14298,0.19556,3.43274,5.88862,1.1533,1.06962,2.34278,0.74856,2.73622,5.04864,2.1127,1.28884,2.29014,0.85402,3.19474,2.16178,0.93484,0.83,5.30836,0.74898,0.41286,0.12406,0.44234,0.69088,0.77392,0.28476,0.30526,0.25832,0.05024,0.27754,0.07474,2.15868,0.31178,0.16588,0.89968,1.61176,0.88558,0.33402,0.83568,0.57224,0.50234,0.21072,0.41016,0.84776,0.35702,0.63278,0.20944,0.17282,0.86724,0.70842,0.35694,0.61574,0.30352,0.0,0.18106,0.85458,0.22534,0.93978,1.01426,0.39026,1.24372,0.56802,1.03458,1.41854,0.87288,0.36256,1.83502,0.90346,1.02618,0.43272,1.33582,1.10652,0.5237,0.68404,0.32608,0.33888,1.24652,0.19194,0.73166,0.13754,1.22924,0.96014,0.39418,2.11046,1.24444,1.82736,2.13674,4.04852,0.58534,0.85584,0.52542,1.39206,0.54226,0.57586,2.38558,0.51834,0.7656,1.84722,0.25164,0.21932,0.43158,0.15754,0.13604,0.76164,1.05114,0.64758,0.51994,2.20832,0.31006,2.15862,0.20792,0.9042,1.2641,1.12158,1.2582,0.23,1.08152,0.35688,0.20034,0.5306,0.50642,0.63814,0.47994,0.79916,0.22216,0.40846,0.28464,0.54126,0.76332,0.075,0.21142,11.65014,0.18456,5.50266,0.57834,0.38698,3.46444,2.2664,0.33214,0.26338,0.6184,0.57952,0.61068,0.60692,0.28576,0.8066,0.62958,0.4653,0.10928,0.10178,0.9771,0.23174,0.5647,0.92402,2.25966,1.33422,0.21226,0.07914,0.73462,0.60918,0.4658,0.35904,0.47708,0.1223,0.0518,0.30394,0.28012,0.14052,1.38276,0.17112,0.20984,0.67166,0.20896,0.26252,0.22146,2.28414,5.2169,0.04774,0.38346,3.1509,1.88216,1.2117,0.15512,0.7902,0.34516,0.20266,0.55872,1.3139,0.12628,0.07794,0.29186,0.11372,0.34878,0.89514,0.09682,2.34168,0.31412,5.71794,5.34446,0.13502,5.2341,0.80488,1.23722,3.0702,0.70196
2018-10-24,1.06724,0.69362,3.61786,0.44086,0.54958,0.29,0.2466,0.0,0.98696,0.30732,0.63554,1.6476,0.91116,0.7646,0.73512,1.9012,0.0,1.9606,1.06454,0.39498,1.40922,0.44274,1.00296,2.82368,0.9372,2.93302,0.54768,1.40982,1.19132,0.7672,1.37628,0.87734,1.12452,0.56432,0.9564,0.80274,1.04048,0.26022,0.28776,0.5555,0.36334,0.26012,0.49792,0.67782,0.8048,0.42482,1.29862,0.94976,2.20118,3.25298,0.58736,1.39202,0.32344,1.69782,1.25534,1.38532,0.4046,0.76702,0.93074,0.53396,0.41848,1.88264,0.6391,0.75104,2.8327,3.12566,0.74838,0.0,2.77962,0.58712,0.76388,4.7656,0.14896,0.33662,0.0,0.99818,0.93506,0.0,2.5508,4.41748,1.8133,3.563,0.9315,0.4875,0.90748,0.53656,0.0,0.93758,1.03814,1.06726,0.0,0.5711,0.6751,1.7014,0.66488,0.18318,0.22264,4.5902,5.86608,1.1554,1.11684,2.16622,0.70562,2.86064,5.3113,2.227,1.151,2.47502,0.79456,3.25246,1.97416,1.02764,0.78218,5.10936,0.70118,0.42198,0.14728,0.4268,0.73572,0.7686,0.33254,0.34104,0.27624,0.04814,0.29408,0.07368,2.46018,0.3781,0.16364,1.00098,2.0109,0.9774,0.33082,0.77036,0.59316,0.56192,0.25556,0.46414,0.86302,0.33768,0.60938,0.178,0.1689,1.11038,0.71974,0.37246,0.59228,0.28188,0.0,0.18716,0.76074,0.21778,0.96042,1.0198,0.40618,1.12528,0.45054,1.06036,1.43584,0.9355,0.49276,1.99274,0.87224,0.9883,0.40588,1.43226,1.04748,0.5645,1.05648,0.31302,0.3354,1.44536,0.19078,0.74556,0.14164,1.14484,1.00454,0.39266,1.9298,1.42952,1.67968,2.016,4.70412,0.63794,0.81422,0.55596,1.54968,0.4996,0.61742,2.37188,0.60988,0.82394,1.72104,0.21512,0.21866,0.48396,0.1579,0.14342,0.73314,0.89924,0.65698,0.58244,2.11378,0.2985,2.07612,0.20162,0.87976,1.72884,1.346,1.34748,0.24964,1.33528,0.34944,0.17768,0.72736,0.71828,0.97612,0.61034,0.7868,0.26456,0.49848,0.26718,0.63366,0.74682,0.07354,0.2052,17.52468,0.1843,6.09492,0.6102,0.4211,3.45544,2.08806,0.36468,0.30222,0.67526,0.74576,0.731,0.63188,0.2827,0.78834,0.70726,0.54066,0.1098,0.10846,0.98618,0.24872,0.55732,0.90184,2.11292,1.99268,0.2199,0.08482,0.9564,0.63064,0.49166,0.33104,0.48928,0.12954,0.05968,0.32482,0.29356,0.1617,1.71894,0.17012,0.23528,0.82338,0.22228,0.2786,0.24046,2.29664,6.98826,0.04954,0.32922,3.86504,2.19678,1.04534,0.1643,0.82274,0.40194,0.20312,0.56368,1.40114,0.11934,0.08534,0.28972,0.1175,0.35182,1.21854,0.10476,2.23436,0.29842,6.03154,5.61304,0.1358,4.60582,0.7595,1.23876,3.0731,0.6867
2018-10-25,1.1469,0.76664,3.76528,0.45504,0.53674,0.28642,0.29218,0.0,0.9493,0.33648,0.57802,1.65854,1.00394,0.72024,0.72634,1.89078,0.0,1.84788,1.154,0.4947,1.6676,0.42516,0.99876,2.7049,0.86012,2.75658,0.52306,1.41908,1.55486,0.77988,1.47348,1.1657,1.21108,0.57132,0.967,0.79136,1.10002,0.25896,0.30062,0.53196,0.0,0.25598,0.58238,0.6413,0.7763,0.43034,1.59384,1.00334,2.4197,3.57734,0.62502,1.5117,0.33358,1.55982,1.3584,1.28414,0.40046,0.73382,1.03516,0.56966,0.47944,1.72842,0.59282,0.76394,2.78176,3.28126,0.68998,0.0,2.52938,0.5608,0.8332,4.57932,0.17166,0.37294,0.0,1.0506,0.93656,0.0,2.67162,4.29108,1.88526,4.05232,0.93276,0.4493,0.9003,0.48394,0.0,1.04496,1.0854,1.39154,0.0,0.55566,0.68524,2.08948,0.68352,0.2187,0.2343,6.31508,5.43238,1.1276,1.05766,2.01096,0.7159,3.16578,5.52742,2.2841,1.047,2.51502,0.83326,3.1763,1.7282,1.02434,0.79248,4.30402,0.68966,0.42506,0.15758,0.40514,0.73242,0.71718,0.34992,0.3656,0.28284,0.04896,0.3036,0.0749,3.34546,0.36282,0.15818,1.02378,2.33788,0.90726,0.32926,0.7563,0.72526,0.6163,0.34964,0.50078,0.89646,0.34162,0.61338,0.16478,0.1694,1.4097,0.77232,0.35204,0.56976,0.27324,0.0,0.18006,0.81354,0.21942,0.96236,1.02102,0.37362,1.00122,0.41768,1.1576,1.46316,0.91064,0.59322,2.20842,0.91238,1.01626,0.4183,1.2819,1.04324,0.56064,1.51114,0.31826,0.39104,1.60692,0.19142,0.77708,0.1469,1.16746,1.10892,0.42362,2.03634,1.56112,1.67888,2.00566,5.38262,0.67522,0.83972,0.5888,1.66806,0.49708,0.6347,2.57368,0.6529,0.99998,1.59658,0.25734,0.22756,0.48718,0.1452,0.13346,0.74036,0.89982,0.65216,0.65524,2.13536,0.29112,2.06964,0.19942,0.87992,2.03706,1.56412,1.34276,0.2637,1.47854,0.34506,0.17896,1.60044,0.84144,1.24568,0.72008,0.82224,0.29262,0.58246,0.25464,0.69052,0.69346,0.08082,0.20656,21.4654,0.17916,6.56764,0.61992,0.40614,3.50312,2.43648,0.38452,0.30558,0.65242,0.96374,0.81042,0.7495,0.28846,0.79146,0.76842,0.57538,0.1234,0.11152,1.01076,0.26882,0.5338,0.94,2.12236,2.54174,0.21708,0.09004,1.21828,0.62266,0.51528,0.31532,0.4947,0.1289,0.06536,0.3427,0.28342,0.16992,1.93782,0.15942,0.21872,0.93796,0.20552,0.26086,0.24872,2.26728,8.32774,0.04192,0.3203,5.00706,2.7722,0.80816,0.15046,0.90624,0.4462,0.18346,0.54912,1.42674,0.11164,0.08576,0.30498,0.11348,0.37408,1.40768,0.10914,1.95322,0.29928,6.1494,5.49324,0.13344,4.5882,0.95398,1.28974,2.70784,0.70518
2018-10-26,1.05562,0.76046,3.52272,0.46946,0.5129,0.26606,0.31448,0.0,0.85038,0.35026,0.54956,1.95846,1.07254,0.58388,0.68776,1.66436,0.0,1.73624,1.24378,0.57856,1.69358,0.48202,0.93566,2.63586,0.74052,1.58,0.52892,1.32648,1.8475,0.64012,1.48384,1.33192,1.15226,0.54732,0.9133,0.81656,1.02766,0.2322,0.28378,0.54844,0.57884,0.24556,0.637,0.89474,0.88978,0.45588,1.74922,0.98798,2.45498,3.45158,0.58124,1.40886,0.31116,1.10238,1.21448,1.11152,0.32444,0.69354,1.11808,0.54742,0.55844,1.5561,0.60926,0.75802,2.52944,3.30774,0.65476,0.0,2.20298,0.51626,0.9237,4.47284,0.1747,0.376,0.0,0.96416,0.86468,0.0,2.59806,3.99906,1.80172,4.0434,0.87504,0.38824,0.80988,0.43558,0.0,1.02346,1.00974,1.4093,0.0,0.5027,0.59076,2.32204,0.61222,0.23684,0.2405,7.38782,5.39952,1.00884,0.98136,1.75394,0.64868,2.91386,5.30506,2.1352,0.92612,2.35596,0.75782,2.97364,1.2628,0.92954,0.7126,3.79936,0.61746,0.40384,0.14846,0.38958,0.77446,0.74842,0.3494,0.3431,0.26184,0.04216,0.30154,0.06902,3.78848,0.33972,0.15732,0.97722,2.39936,0.85632,0.30296,0.7813,0.8106,0.59364,0.47696,0.51452,0.90956,0.31694,0.59454,0.1438,0.14692,1.54818,0.8054,0.32608,0.52936,0.25658,0.0,0.18794,0.74854,0.22436,1.00046,0.94248,0.33276,0.6379,0.39594,1.2262,1.4392,0.85428,0.66124,2.2675,0.86016,0.97754,0.46108,1.162,0.88848,0.58022,1.92776,0.28816,0.4353,1.75046,0.16826,0.7419,0.14962,1.16348,1.0038,0.43468,1.8488,1.5978,1.5447,2.10674,6.00446,0.64198,0.79026,0.41616,1.6752,0.48846,0.64916,2.68556,0.59278,1.0473,1.38028,0.26146,0.2156,0.61932,0.13502,0.15746,0.68342,0.85976,0.56758,0.6701,2.03254,0.3107,1.60188,0.19588,0.91226,2.1643,1.66428,1.94466,0.24894,1.50734,0.29048,0.17364,2.35822,0.8434,1.32674,0.747,0.71836,0.29796,0.61328,0.24416,0.65316,0.61758,0.0742,0.21122,23.97234,0.17564,6.4371,0.62368,0.41464,3.34412,2.54996,0.36066,0.29422,0.62338,1.0668,0.82928,0.82538,0.24652,0.84754,0.80894,0.56152,0.12116,0.10584,0.94772,0.26632,0.54832,0.87792,1.9838,2.92836,0.20986,0.0864,1.41758,0.66412,0.49204,0.28614,0.48482,0.13736,0.06734,0.33748,0.29106,0.18214,2.08076,0.12738,0.19872,0.98114,0.19186,0.23652,0.23608,2.22356,8.6506,0.03752,0.32358,5.8138,3.06578,0.7755,0.17964,0.8879,0.4519,0.1727,0.52632,1.33912,0.10618,0.0811,0.30644,0.12664,0.37094,1.49602,0.10848,2.19436,0.2767,6.25064,4.8172,0.1704,4.43596,1.12,1.29522,1.95408,0.75836
2018-10-29,0.93286,0.68028,3.1063,0.51502,0.4258,0.21978,0.27074,0.302,0.70068,0.3209,0.28584,2.32188,0.88062,0.55544,0.55188,1.43054,0.0,1.62032,1.33698,0.50178,1.3813,0.42428,0.96324,2.33594,0.70596,0.73682,0.47574,1.07342,1.91542,0.50494,1.24584,1.38438,1.02338,0.44094,0.79534,0.69776,0.9929,0.18984,0.31184,0.49544,0.95034,0.21716,0.58004,0.9005,0.82558,0.4203,1.56462,0.8185,2.28498,2.61254,0.5198,1.44654,0.24384,0.87582,1.01862,0.91788,0.25752,0.4749,1.08272,0.46806,0.57894,1.31266,0.63054,0.87736,2.01032,2.98974,0.63898,0.0,1.7529,0.51752,0.98318,3.78984,0.15668,0.46048,0.0,0.76498,0.86532,0.0,2.13596,3.28976,1.57132,3.5777,0.68926,0.36246,0.69826,0.41022,0.0,0.99816,0.83518,1.26154,0.0,0.54812,0.51408,2.19046,0.55968,0.2273,0.22262,7.1916,4.48588,0.87576,0.92326,1.3444,0.48386,2.60418,4.50412,1.91038,0.74126,2.12488,0.60436,2.6687,0.94904,0.755,0.62288,3.03892,0.49812,0.35128,0.14012,0.40098,0.85178,0.66538,0.32398,0.29454,0.22932,0.03562,0.24944,0.06324,3.57758,0.36972,0.13956,0.79498,2.1115,0.787,0.31402,0.72218,0.80556,0.50084,0.47928,0.60194,0.76542,0.31732,0.44734,0.12438,0.13514,1.5339,0.6841,0.317,0.42506,0.20848,0.0,0.16248,0.65344,0.17734,1.20912,0.92954,0.43584,0.3188,0.32724,1.11964,1.29466,0.72252,0.61798,1.89644,0.78342,0.91312,0.44448,1.25902,0.67346,0.4802,2.0076,0.2342,0.37982,1.41948,0.21164,0.73998,0.12938,1.2031,1.06526,0.3624,1.63778,1.52952,1.30654,1.83158,4.27722,0.60372,0.62762,0.33484,1.59316,0.39372,0.54174,2.50802,0.5366,1.00174,1.26406,0.25262,0.17964,0.6934,0.12508,0.16152,0.64526,0.82164,0.41674,0.60992,1.22526,0.32854,1.47224,0.16216,0.99136,2.15476,1.5554,2.01392,0.23784,1.47892,0.22692,0.1695,2.49884,0.75042,1.24744,0.7044,0.72666,0.27384,0.53238,0.19742,0.57008,0.45826,0.06046,0.19032,24.17598,0.16038,5.89372,0.52428,0.381,2.4557,2.40694,0.34718,0.27344,0.54554,1.05418,0.67696,0.80452,0.18572,0.83714,0.75536,0.52498,0.11374,0.0937,0.87378,0.2732,0.62408,0.81754,1.64882,2.99492,0.18834,0.07966,1.6618,0.55626,0.45404,0.27818,0.40128,0.12412,0.05962,0.3243,0.27544,0.18432,1.85476,0.09912,0.16042,0.91722,0.17234,0.18622,0.21222,1.81644,7.51438,0.0325,0.26416,5.57358,2.84372,0.77688,0.17886,0.82726,0.38684,0.14786,0.47708,1.20878,0.08428,0.07348,0.24504,0.12356,0.30646,1.36564,0.09318,2.2468,0.22886,5.51754,3.90206,0.18862,4.1274,1.1463,1.11122,1.19614,0.61894
2018-10-30,0.92114,0.69946,3.11934,0.56276,0.40686,0.21184,0.26434,0.50388,0.65246,0.32566,0.27606,2.52344,0.89466,0.53972,0.51628,1.29124,0.0,1.54864,1.2649,0.52914,1.43416,0.4197,0.8998,2.23578,0.80874,0.36034,0.45724,1.0604,1.98566,0.4283,1.09438,1.40654,0.89332,0.40148,0.73536,0.6365,0.99702,0.18816,0.29004,0.49226,1.44534,0.20006,0.61412,0.96678,0.80044,0.37266,1.56198,0.7338,2.62796,2.24638,0.53038,1.88858,0.2611,0.73056,0.98446,0.92082,0.23112,0.33326,1.00722,0.57912,0.63904,1.37708,0.66534,0.9136,1.74582,2.49928,0.6425,0.0,1.39588,0.50902,1.08136,2.97378,0.15804,0.48244,0.0,0.60478,0.85448,0.0,1.90304,2.95546,1.5379,3.16996,0.67826,0.31718,0.72334,0.39896,0.0,1.02606,0.80212,1.1619,0.0,0.48982,0.4318,2.13076,0.44348,0.2141,0.21558,7.62314,4.49182,0.90668,0.84582,1.34542,0.45202,2.95848,4.62246,1.27918,0.58544,2.09262,0.508,2.66484,0.90772,0.70368,0.64546,2.9568,0.52126,0.34742,0.13656,0.60464,0.98436,0.72534,0.25916,0.29074,0.22896,0.03334,0.2215,0.06582,4.13744,0.33116,0.1351,0.71014,1.93144,0.74764,0.32852,0.62856,0.87292,0.48548,0.5181,0.74558,0.63864,0.28782,0.42768,0.11348,0.15658,1.60978,0.6907,0.30892,0.36646,0.21418,0.0,0.15274,0.61696,0.1633,1.22356,1.0646,0.49638,0.0,0.30004,0.99206,1.28984,0.6675,0.65396,1.4924,0.95828,0.92542,0.45904,1.29956,0.61094,0.46714,2.31534,0.2163,0.4722,1.44812,0.35192,0.7615,0.11594,1.13256,0.97214,0.33872,2.08958,1.37174,1.3923,1.71912,3.45718,0.73314,0.6151,0.34252,1.3729,0.38386,0.4487,2.64274,0.50274,0.94848,1.2886,0.26304,0.17468,0.70774,0.13548,0.1634,0.54498,0.85792,0.35188,0.51538,0.92144,0.38766,1.46882,0.15328,0.92462,1.80378,1.4743,2.03164,0.2274,1.50362,0.20976,0.1617,2.84684,0.78788,1.55846,0.63662,0.91204,0.2464,0.48874,0.19116,0.61176,0.44584,0.05584,0.19958,23.25788,0.13428,3.97858,0.47816,0.37206,2.12668,2.62246,0.3632,0.28938,0.5028,1.13554,0.59342,0.7677,0.17742,0.83826,0.73212,0.56634,0.12132,0.09626,0.8917,0.2844,0.6422,0.8403,1.5912,2.60388,0.16904,0.07574,1.86148,0.5251,0.45756,0.30238,0.40502,0.1406,0.05774,0.36422,0.25932,0.19576,1.76214,0.08836,0.15352,0.85918,0.14894,0.18058,0.22404,1.75718,7.7899,0.0327,0.24962,5.73754,3.06302,0.71862,0.1983,0.77134,0.36442,0.14472,0.44956,1.15764,0.0894,0.07518,0.2397,0.12488,0.2929,1.49068,0.09466,2.2457,0.22434,4.9831,2.9543,0.23504,3.70384,1.20006,1.00066,0.5836,0.62952
2018-10-31,0.8423,0.7102,3.21724,0.63346,0.4272,0.2229,0.25164,0.6512,0.81302,0.32372,0.3001,2.67174,0.89458,0.50456,0.57834,1.26118,0.0,1.58986,1.17666,0.55028,1.34516,0.47122,0.9211,2.02726,0.83178,0.0,0.4951,1.03774,1.9268,0.4182,0.95018,1.32974,0.92934,0.43918,0.87336,0.6608,1.00834,0.20382,0.30908,0.508,1.76414,0.19906,0.67572,0.98258,0.79914,0.3972,1.49478,0.675,2.70122,2.07274,0.67398,1.92106,0.28498,0.7174,0.8803,0.94356,0.27038,0.31588,1.06764,0.55114,0.6733,1.41358,0.69006,0.96518,1.61068,2.4361,0.6664,0.0,1.42626,0.52102,1.03418,2.65034,0.17638,0.47794,0.0,0.56188,0.89508,0.0,1.75076,3.0392,1.5638,2.94714,0.6449,0.29774,0.77836,0.49164,0.0,1.13594,0.72614,1.10276,0.0,0.52612,0.4338,1.92682,0.35784,0.19896,0.1642,7.29116,4.73016,0.9465,0.80136,1.43306,0.45854,3.14136,4.31782,1.14212,0.53394,1.76854,0.49868,2.6035,0.93184,0.61862,0.66214,2.79854,0.54136,0.34192,0.10992,0.8334,1.01132,0.92666,0.21944,0.24622,0.20878,0.0336,0.2259,0.06312,4.06738,0.30152,0.12504,0.69706,1.71406,0.6885,0.33146,0.60374,0.89916,0.44062,0.57304,0.81164,0.7133,0.29554,0.45532,0.11514,0.16836,1.5389,1.24384,0.33924,0.38736,0.24066,0.0,0.16602,0.6311,0.1734,1.23918,1.11354,0.54946,0.0,0.29452,0.88106,1.20636,0.6467,0.62456,1.38462,1.13388,0.85588,0.49062,1.21508,0.72102,0.56276,2.29786,0.2352,0.5376,1.49912,0.43816,0.72332,0.11836,1.1661,1.0296,0.27498,2.2571,1.1749,1.861,1.99044,3.30054,0.76622,0.65158,0.31886,1.15424,0.7002,0.46844,2.59508,0.3917,1.02796,1.26032,0.28114,0.17722,0.69352,0.14622,0.18524,0.56016,0.89218,0.31054,0.49524,0.8394,0.43852,1.44372,0.15736,0.9859,1.51358,1.38302,2.29124,0.2347,1.23272,0.20296,0.17424,2.9399,0.64384,1.36914,0.58082,1.08964,0.2477,0.44566,0.23022,0.79718,0.51776,0.06274,0.23486,21.35774,0.13374,4.02674,0.5407,0.4197,2.26632,2.57634,0.33124,0.25674,0.64174,1.00954,0.5324,0.81124,0.19898,0.85914,0.74814,0.49594,0.14398,0.08928,0.88658,0.27816,0.65512,0.82758,1.61608,2.15188,0.21048,0.06744,1.88822,0.59234,0.43934,0.33638,0.74316,0.1804,0.05296,0.38052,0.28808,0.23646,1.69902,0.091,0.19058,0.7514,0.17722,0.15408,0.21622,1.92384,7.10202,0.03104,0.30022,5.99256,3.13046,0.76162,0.1906,0.72866,0.3348,0.14484,0.6357,1.03358,0.13638,0.0688,0.28836,0.1313,0.35586,1.34354,0.08196,2.3348,0.39284,5.28728,2.3488,0.25576,3.64122,1.34098,1.05686,0.0,0.76366
2018-11-01,0.82572,0.6454,3.46034,0.69316,0.45908,0.25906,0.26616,0.80442,0.94964,0.3283,0.3296,2.79776,0.95048,0.5426,0.61454,1.21174,0.0,1.56126,1.1525,0.4999,1.22424,0.48862,0.9857,2.10212,0.92178,0.0,0.5524,1.24552,1.85302,0.46282,1.01806,1.37034,0.99084,0.45756,0.91918,0.9279,1.1921,0.20874,0.47796,0.54756,2.4019,0.21656,0.752,1.04624,0.84844,0.45348,1.41214,0.73306,2.6205,2.15756,0.79282,2.17278,0.27986,0.80124,0.76774,1.00246,0.3306,0.3116,1.09476,0.56556,0.72356,1.70186,0.72634,1.05376,2.43272,2.8718,0.77138,0.0,1.65504,0.57604,1.12588,2.50582,0.17308,0.46286,0.0,0.65296,0.92794,0.0,1.69684,3.25176,1.67278,2.81238,0.60938,0.28976,0.78188,0.77034,0.0,1.0964,0.72294,0.8976,0.0,0.66306,0.48784,1.82554,0.44216,0.26184,0.17024,6.6701,6.3143,1.0053,0.79496,1.59188,0.57974,3.39614,4.399,1.2083,0.53896,1.65098,0.47914,2.66916,1.01386,0.5915,0.77264,2.89452,0.52132,0.38248,0.1004,1.02638,1.00704,0.99394,0.2109,0.23196,0.21974,0.0366,0.22448,0.0663,3.42434,0.30348,0.12776,0.8976,1.79608,0.71422,0.3435,0.64954,0.85972,0.46206,0.61908,0.88422,0.83254,0.30964,0.50936,0.13048,0.18552,1.58376,1.57026,0.40362,0.41548,0.2917,0.0,0.17718,0.682,0.18534,1.27568,1.25296,0.60164,0.21102,0.30738,0.87164,1.17202,0.67812,0.57272,1.32892,1.3284,0.77864,0.55046,1.19352,0.80642,0.6426,2.2168,0.28918,0.60844,1.67074,0.5127,0.79124,0.12844,1.19332,1.1831,0.27068,2.45408,1.22236,2.02492,2.21496,3.36244,0.8021,0.7546,0.34158,1.15458,0.88662,0.47854,2.63102,0.3827,1.01844,1.39602,0.26498,0.19274,0.76036,0.1605,0.21856,0.56972,0.90992,0.28138,0.5105,0.88462,0.4952,1.6277,0.16094,1.1023,1.40958,1.41642,2.61472,0.24274,1.12188,0.23062,0.19934,2.43324,0.5786,1.2647,0.5736,1.60124,0.24522,0.47154,0.24788,0.95226,0.80474,0.06358,0.25502,20.86902,0.13,4.27776,0.64486,0.47288,2.55028,2.41286,0.32946,0.26556,0.73702,0.95802,0.57792,0.7711,0.22588,0.8965,0.72582,0.52352,0.14178,0.09026,0.87802,0.27368,0.64058,0.8681,1.77522,1.95412,0.2367,0.06554,1.88404,0.61398,0.44688,0.3725,1.23276,0.20618,0.05354,0.37392,0.3088,0.29192,1.8607,0.09718,0.24072,0.76164,0.18818,0.16442,0.21376,2.21242,6.60746,0.03424,0.3073,6.2552,3.1401,0.77578,0.18942,0.71012,0.33112,0.14806,0.68468,0.9981,0.16992,0.07048,0.33784,0.14634,0.433,1.3043,0.08106,2.63874,0.54564,5.42734,1.96166,0.26958,3.71828,1.33394,1.10928,1.1297,0.89372
2018-11-02,0.93206,0.62946,4.20464,0.6577,0.4977,0.28932,0.27236,1.03404,1.21376,0.34012,0.42852,2.82076,1.01608,0.73072,0.6664,1.38164,0.0,1.84056,1.16592,0.43828,1.35668,0.44558,1.131,2.03768,0.90432,0.51912,0.54144,1.45734,1.77584,0.53384,1.02464,1.3729,1.07778,0.57394,1.08232,1.01172,1.383,0.22826,0.64576,0.5889,2.36764,0.2178,0.7435,0.85392,0.82488,0.49232,1.57826,0.88048,2.67716,2.72272,0.96036,2.57072,0.29498,0.93528,0.88868,1.24264,0.3928,0.3301,1.07428,0.65106,0.69272,1.9806,0.71628,1.30458,3.10614,3.74784,0.96352,0.0,2.22584,0.73952,1.14756,2.70626,0.18284,0.50284,0.0,0.93284,1.10344,0.0,1.9111,4.16728,2.12342,3.0659,0.71382,0.23426,0.9756,1.0975,0.0,1.21156,0.83218,1.0433,0.0,0.94568,0.59982,1.72148,0.50048,0.29922,0.22644,6.19868,7.15664,1.14944,0.94914,1.91378,0.68756,3.59936,4.76064,1.6262,0.65256,1.74316,0.66308,2.83506,1.03998,0.71778,0.8688,3.53524,0.67854,0.50158,0.11482,1.23752,1.0819,0.98194,0.21748,0.26658,0.2527,0.05204,0.22832,0.07836,3.18608,0.32598,0.13724,1.44784,1.85208,0.73742,0.41372,0.62388,0.8628,0.52128,0.57014,0.97704,0.84236,0.37182,0.61588,0.14966,0.2061,1.60986,1.75998,0.64106,0.4388,0.36138,0.0,0.18212,0.8913,0.2071,1.298,1.43084,0.59432,0.36904,0.40196,0.93994,1.18878,0.75474,0.60664,1.60642,1.50248,0.91932,0.55466,1.33282,1.02482,0.78598,1.93738,0.3248,0.59954,1.85014,0.58806,0.80528,0.1468,1.23558,1.67978,0.3043,2.75272,1.42268,2.19754,2.4418,3.69604,0.85596,0.9545,0.45062,1.24882,1.03706,0.49888,2.69022,0.45806,1.10652,1.52886,0.24978,0.22296,0.70816,0.1912,0.2267,0.66218,1.09658,0.28324,0.56198,0.89806,0.54436,1.75724,0.16466,1.1402,1.36018,1.46444,1.85428,0.26934,1.21602,0.29832,0.24726,1.90838,0.64276,1.46028,0.59482,1.84498,0.22822,0.48158,0.24922,1.24258,1.24888,0.07454,0.33994,20.21176,0.13844,4.81954,0.91104,0.5655,3.0069,2.3935,0.39368,0.28308,0.84264,1.0318,0.63056,0.80606,0.26674,0.83684,0.73478,0.58228,0.18242,0.10666,0.94754,0.2908,0.70476,1.01414,2.08728,1.7522,0.3037,0.07602,1.78438,0.57834,0.46998,0.40968,1.60536,0.23116,0.05862,0.4031,0.32684,0.3379,1.87306,0.10502,0.27724,0.87846,0.20844,0.18456,0.22972,2.51808,7.19732,0.04044,0.34568,6.36854,3.17514,0.81676,0.16614,0.79716,0.33184,0.2247,0.7504,1.1561,0.19762,0.07608,0.37762,0.15458,0.47086,1.40018,0.08916,2.66086,0.58466,5.53918,1.61804,0.24872,4.30948,1.28274,1.1922,2.22178,1.04722
2018-11-05,0.89714,0.62222,4.28364,0.57842,0.52252,0.29762,0.29598,0.8372,1.3777,0.35004,0.4509,2.41382,1.09274,0.72062,0.67654,1.5424,0.0,1.96682,1.00426,0.44372,1.3628,0.44966,0.97502,1.98552,0.79082,0.8142,0.54034,1.589,1.69838,0.59898,1.08068,1.37006,1.07996,0.65658,1.172,1.10326,1.42414,0.2369,0.68344,0.59038,2.352,0.24928,0.74172,0.77984,0.8405,0.52002,1.57982,1.13992,2.73566,2.90518,1.03374,2.7372,0.29836,0.91972,0.8685,1.39652,0.44658,0.37408,1.00794,0.68872,0.67562,2.05548,0.65984,1.43516,3.48096,4.50414,0.99198,0.0,2.4724,0.74032,1.1582,3.9271,0.20114,0.37336,0.0,1.04822,0.9519,0.0,2.31574,4.76664,2.41398,2.92506,0.91188,0.20908,1.06508,1.2575,0.0,1.21456,0.90366,1.20688,0.0,0.98998,0.79194,1.87954,0.54426,0.31884,0.25564,6.54216,8.61354,1.216,0.9673,2.1852,0.78464,3.44418,4.9641,2.04864,0.79964,1.76404,0.77064,3.0271,1.08306,0.79622,0.94368,4.64808,0.73226,0.58294,0.10182,1.24916,1.02206,1.00902,0.20328,0.28166,0.2378,0.06596,0.2242,0.086,3.51172,0.28616,0.13006,1.59284,1.84794,0.74672,0.36266,0.69466,0.84482,0.52886,0.60278,0.95874,0.85928,0.31976,0.84014,0.23536,0.20982,1.66384,1.92164,0.63806,0.51786,0.39964,0.0,0.21756,0.98164,0.22138,1.02406,1.33672,0.48822,0.49808,0.4408,1.1059,1.16054,0.73948,0.64746,1.65896,1.54752,1.06062,0.5474,1.14186,1.0945,0.84482,1.95258,0.40798,0.6063,2.204,0.54618,0.75842,0.15556,1.0857,1.6813,0.36966,2.85604,1.50704,2.37718,2.62422,3.66248,0.89806,1.11628,0.53624,1.31068,1.13186,0.52306,2.47414,0.54764,1.09256,1.479,0.26168,0.26286,0.63244,0.18422,0.22922,0.6235,1.1064,0.3487,0.62946,1.01656,0.5848,1.67526,0.1655,1.0013,1.5867,1.50632,1.6001,0.28936,1.0971,0.3371,0.2628,1.91788,0.66464,1.51836,0.64976,1.94412,0.23842,0.51034,0.25464,1.40294,1.51398,0.08612,0.35734,22.1701,0.12878,5.34008,1.01074,0.67954,3.25692,2.29484,0.35884,0.25552,0.86998,1.08552,0.63914,0.82556,0.30088,0.70852,0.77668,0.5421,0.17924,0.1083,0.82398,0.25192,0.63012,0.98902,2.15634,1.77344,0.31232,0.07628,1.5309,0.57894,0.4522,0.378,2.02498,0.24888,0.05744,0.38588,0.31354,0.33964,2.01032,0.11422,0.29328,0.93994,0.20646,0.22234,0.2235,2.93856,7.56968,0.04094,0.54356,7.2544,3.43324,0.72738,0.16542,0.77296,0.35776,0.25036,0.7499,1.15454,0.20908,0.0744,0.4048,0.15814,0.49416,1.40268,0.08552,2.6599,0.6506,5.66136,1.20372,0.22428,4.83682,1.29476,1.33738,3.04668,1.15528
2018-11-06,0.8079,0.58474,4.4059,0.50984,0.49838,0.30032,0.26006,0.7382,1.38422,0.35522,0.44886,2.04242,1.00764,0.7235,0.67974,1.54734,0.0,1.89544,0.9585,0.4236,1.25794,0.42516,0.9165,1.78616,0.70046,1.12358,0.49656,1.54006,1.408,0.5874,0.94584,1.1726,1.07536,0.7831,1.06982,1.22198,1.2835,0.29208,0.69918,0.53774,2.56568,0.2518,0.6603,0.65248,0.86186,0.51882,1.51,1.26188,2.18508,2.77792,1.0561,2.48074,0.24668,0.83474,0.8124,1.38042,0.4409,0.36696,1.00588,0.5878,0.6384,1.94422,0.59682,1.46754,3.63394,4.58058,0.99192,0.0,2.3988,0.74582,0.90342,4.3227,0.19078,0.34026,0.0,1.08268,0.87158,0.0308,2.28242,4.94892,2.5107,2.96074,0.9867,0.20792,1.0274,1.32814,0.83582,1.10714,0.83216,1.24566,0.0,1.17236,0.85884,1.65164,0.6416,0.33296,0.28152,5.69912,8.72328,1.12648,0.92638,2.11898,0.783,2.88548,4.15732,2.17688,0.82308,2.02924,0.7751,2.96982,1.08888,0.76476,0.90236,4.46366,0.6789,0.60262,0.0914,1.12536,0.85662,0.86934,0.16554,0.2562,0.2162,0.07994,0.20552,0.08406,2.96044,0.2736,0.13078,1.63542,1.61832,0.7206,0.32692,0.70004,0.7913,0.50804,0.54286,0.7963,0.8527,0.33438,0.90814,0.37644,0.18204,1.44732,1.96588,0.6687,0.52808,0.41786,0.0,0.27894,0.94262,0.24042,0.92644,1.18514,0.47086,0.58138,0.41684,1.08176,1.04306,0.69862,0.58094,1.57192,1.40526,1.02558,0.54542,0.9779,1.08064,0.84752,1.59526,0.4185,0.52974,2.1033,0.40848,0.6813,0.1626,0.9659,2.146,0.38742,2.36194,1.51718,2.2327,2.64238,3.5106,0.64518,1.17176,0.5641,1.2986,1.2239,0.51806,2.33862,0.61264,1.00372,1.37986,0.26862,0.28972,0.57684,0.16812,0.22934,0.58542,1.03502,0.35298,0.6187,0.96538,0.56058,1.6082,0.15516,0.92142,1.4723,1.31758,1.51854,0.31476,0.98866,0.3456,0.299,1.56296,0.58664,1.20054,0.56706,1.97542,0.22676,0.472,0.22614,1.42372,2.0047,0.08826,0.37254,19.61094,0.12824,5.07236,1.06882,0.71542,3.51088,1.99648,0.32184,0.21958,0.87318,0.92812,0.5959,0.82164,0.48134,0.62988,0.71864,0.48226,0.176,0.105,0.66076,0.2225,0.58916,0.93968,2.3162,1.51964,0.31128,0.07676,1.20194,0.5509,0.42612,0.34304,2.1585,0.24774,0.0538,0.37026,0.2919,0.33058,1.76386,0.11862,0.2907,0.82932,0.2055,0.2311,0.19662,2.97326,7.03812,0.04374,0.57256,6.95426,2.9364,0.72894,0.14366,0.75584,0.31938,0.25106,0.7131,1.08716,0.2048,0.06754,0.4005,0.152,0.4806,1.22258,0.07632,2.7214,0.69748,6.19202,1.27568,0.17252,4.92478,1.21362,1.3069,3.95168,1.11424
2018-11-07,0.76514,0.55918,5.26234,0.45216,0.49764,0.29466,0.24142,0.69818,1.24698,0.4132,0.50776,2.11168,0.95066,0.74594,0.65324,1.5224,0.0,1.9351,0.87844,0.39868,1.18448,0.42686,0.8516,1.60652,0.6939,1.40666,0.47508,1.56612,1.25074,0.61988,0.89794,1.11022,0.96696,0.83088,1.0384,1.30142,1.16698,0.30824,0.69406,0.48896,2.7157,0.26206,0.56772,0.61982,0.89388,0.53978,1.45106,1.52566,2.09988,2.79014,1.03374,2.49338,0.20666,0.74952,0.7536,1.4443,0.426,0.35776,0.94692,0.57548,0.64816,1.99884,0.59926,1.48992,3.674,4.50808,1.00414,0.0,2.14786,0.73676,0.78356,4.5131,0.16148,0.33182,0.0,1.10632,0.83478,0.04088,2.2409,5.5109,2.69856,3.02352,1.05008,0.18878,0.9888,1.33724,1.49962,1.01222,0.84682,1.35358,0.83202,1.1892,1.2319,1.56566,0.72258,0.3348,0.29144,5.2041,8.79484,1.07072,0.86956,2.11302,0.78762,2.61858,4.10828,2.2446,0.84184,2.36156,0.7773,2.99866,1.17496,0.78106,0.8653,4.4636,0.72278,0.62106,0.09078,1.0108,0.79202,0.6782,0.16188,0.25032,0.20404,0.09034,0.1743,0.0896,2.90204,0.27924,0.12564,1.70064,1.54016,0.6911,0.31566,0.80484,0.7402,0.5644,0.48808,0.7162,0.748,0.34034,0.93262,0.4518,0.1768,1.35434,1.62146,0.65326,0.5471,0.44868,0.0,0.29606,0.91352,0.2486,0.88892,1.17788,0.43698,0.73142,0.40768,1.08482,0.99266,0.64486,0.52838,1.59358,1.30904,1.01474,0.55982,0.89386,1.02872,0.80838,1.3528,0.44188,0.47852,2.2108,0.33586,0.6322,0.17956,0.84064,2.22912,0.46128,2.49018,1.54942,2.0869,2.6341,3.23048,0.5516,1.49556,0.5845,1.38224,1.13596,0.9413,2.4172,0.57256,0.88214,1.38532,0.25758,0.30748,0.54556,0.1672,0.22726,0.52904,0.93192,0.34162,0.60922,0.91716,0.53686,1.62384,0.151,0.8241,1.41724,1.21102,1.16068,0.30368,0.9237,0.37164,0.29984,1.42546,0.53004,1.09498,0.52748,2.16192,0.18788,0.46178,0.1856,1.22116,2.2089,0.08838,0.36,16.71308,0.12642,4.4154,1.07428,0.69396,3.52184,1.88654,0.31202,0.20768,0.6662,0.86122,0.56616,0.88372,0.55816,0.64624,0.65252,0.4669,0.17932,0.10102,0.6105,0.20788,0.55198,0.92298,2.4362,1.42734,0.25042,0.07686,1.04352,0.50508,0.41588,0.31262,1.96388,0.22564,0.05206,0.3517,0.24328,0.28248,1.59228,0.11782,0.23314,0.8067,0.15852,0.21964,0.17214,2.89692,6.12892,0.04334,0.56486,6.39534,2.6573,0.67268,0.1363,0.79812,0.30452,0.25952,0.49134,1.09316,0.16826,0.06362,0.38634,0.1624,0.43318,1.03712,0.07586,2.69464,0.59556,6.40228,1.26976,0.14056,6.17358,1.09218,1.29976,4.7243,1.3128
2018-11-08,0.67792,0.54484,5.5357,0.3912,0.436,0.25326,0.19418,0.62636,1.11686,0.4195,0.5048,1.9268,0.87584,0.71062,0.61328,1.40838,0.0,1.87954,0.74762,0.3427,1.0947,0.39164,0.73592,1.40444,0.58878,1.57506,0.42002,1.29118,1.10254,0.56572,0.72012,0.87462,0.79638,0.8074,1.0808,1.05598,0.92616,0.32296,0.54402,0.42356,2.44082,0.24788,0.38574,0.54118,0.85804,0.48704,1.25666,1.51844,2.01182,2.56598,0.99578,2.40866,0.18584,0.6415,0.74172,1.32812,0.36516,0.33476,0.80276,0.59402,0.52006,1.67892,0.63662,1.42922,2.73728,3.96284,0.91354,0.0,1.94334,0.66806,0.62002,4.58692,0.13208,0.3239,0.0,0.9536,0.73684,0.04088,2.13832,5.3857,2.61002,2.71388,1.05214,0.17252,0.8945,1.20684,1.79186,0.85634,0.90486,1.33722,1.2946,1.09064,1.80332,1.3432,0.70242,0.30444,0.27444,4.50474,7.08874,1.02092,0.79374,1.89146,0.62876,2.27074,3.90054,2.08138,0.78858,2.50434,0.71592,2.84354,1.09796,0.8225,0.69588,4.22004,0.86564,0.57918,0.08828,0.8272,0.73722,0.59514,0.15432,0.23216,0.17712,0.0932,0.15252,0.08236,2.73678,0.29942,0.11346,1.52068,1.25162,0.58764,0.29594,0.85756,0.63746,0.52826,0.4068,0.66546,0.56932,0.30902,0.87222,0.52278,0.15776,1.10916,1.34124,0.60898,0.53428,0.41152,0.0,0.28602,0.82166,0.24054,0.81682,1.04054,0.38884,0.60606,0.46162,0.96886,0.86208,0.55146,0.4734,1.48154,1.10518,1.06846,0.49036,0.78902,0.94344,0.73976,1.16572,0.42168,0.37636,2.06738,0.27398,0.51126,0.17556,0.72996,2.13982,0.45898,2.27344,1.36874,2.01084,2.44308,2.79956,0.44916,1.47514,0.52924,1.25332,1.0333,1.01044,2.2145,0.53832,0.71282,1.23088,0.22202,0.28312,0.4923,0.15348,0.20158,0.44254,0.88748,0.30202,0.5235,0.78348,0.48898,1.58458,0.14196,0.70166,1.32172,1.06904,0.86112,0.2832,0.82312,0.35522,0.273,1.24168,0.47474,0.98472,0.4589,1.83554,0.1837,0.39004,0.16118,1.07178,2.05822,0.0817,0.33674,14.60354,0.12716,3.83204,0.98054,0.67018,3.11194,1.5074,0.2778,0.1823,0.53466,0.74342,0.4663,0.8286,0.54802,0.54852,0.57452,0.39586,0.1838,0.09468,0.57636,0.18262,0.5537,0.83312,2.22528,1.21388,0.2232,0.0715,0.91158,0.46936,0.3742,0.26974,1.55592,0.20492,0.04552,0.34618,0.20334,0.22534,1.28398,0.10572,0.17914,0.72098,0.13756,0.20492,0.16196,2.46534,5.458,0.04014,0.5755,5.60632,2.30834,0.61236,0.1232,0.72878,0.2632,0.26172,0.41276,1.079,0.13278,0.05676,0.34048,0.16626,0.34324,0.88772,0.06642,2.44982,0.46568,6.19666,1.19018,0.12294,6.5207,0.872,1.18508,4.16458,1.2722
2018-11-09,0.60994,0.48656,5.09894,0.33096,0.34602,0.2301,0.16266,0.47078,0.87604,0.36802,0.47556,1.51224,0.76128,0.50474,0.52452,1.2287,0.0,1.57404,0.6002,0.29816,0.80158,0.34474,0.55054,1.12788,0.51726,1.1993,0.39582,1.0419,1.17898,0.47016,0.58834,0.75654,0.6146,0.67296,0.95804,0.91796,0.69034,0.30974,0.39834,0.3229,2.19264,0.2224,0.30394,0.39578,0.77266,0.44148,0.86262,1.394,1.8494,1.79064,0.88286,2.6976,0.16738,0.54102,0.57676,1.11952,0.32364,0.27498,0.79382,0.62584,0.4301,1.32686,0.62816,1.16296,2.01528,2.90844,0.67726,0.0,1.5364,0.51344,0.46362,4.65236,0.12618,0.30414,0.0,0.65536,0.59956,0.04088,1.79238,4.32438,2.14044,2.48592,0.93346,0.13584,0.67074,0.9526,2.41104,0.64398,0.91822,1.33342,1.84144,0.80074,2.0412,1.30638,0.68298,0.27938,0.22786,3.90324,5.86322,0.82974,0.60918,1.52056,0.5107,1.95904,3.386,1.6833,0.6547,2.53648,0.52902,2.7554,0.97774,0.725,0.55224,3.51002,0.77642,0.4675,0.07248,0.62018,0.54458,0.5112,0.14418,0.1992,0.14642,0.08006,0.12542,0.07546,2.57092,0.30562,0.09344,0.97804,1.0572,0.50992,0.29332,0.78458,0.48094,0.46086,0.34988,0.52998,0.4736,0.2356,0.74448,0.5666,0.13198,0.9636,1.11168,0.37458,0.46276,0.34502,0.0,0.27222,0.57604,0.21286,0.68244,0.8026,0.3383,0.58172,0.3886,0.825,0.92698,0.4161,0.36834,1.15792,0.91166,0.87722,0.41988,0.54406,0.7424,0.56036,1.06426,0.39172,0.32208,1.8669,0.2166,0.41298,0.16022,0.57012,1.67778,0.42232,2.06998,1.1692,2.0155,1.96108,2.23988,0.34596,1.43114,0.42516,1.14698,0.89982,0.98948,1.82764,0.45312,0.56272,1.02332,0.20586,0.26666,0.40412,0.13994,0.17716,0.2956,0.71026,0.24804,0.44246,0.68248,0.41798,1.44132,0.12036,0.5322,1.20872,0.88602,0.68378,0.26042,0.60958,0.29068,0.27034,1.0979,0.40312,0.82208,0.3883,1.76276,0.14578,0.34526,0.13932,0.90998,1.72864,0.07086,0.24532,12.99448,0.1051,3.04202,0.691,0.6342,2.47432,1.1102,0.24822,0.15316,0.44396,0.59472,0.3736,0.72768,0.51888,0.44186,0.46752,0.45726,0.15286,0.09438,0.46018,0.16058,0.48412,0.6533,1.86112,1.03656,0.15226,0.07664,1.00874,0.45962,0.3343,0.22242,1.16972,0.17416,0.03408,0.31294,0.15748,0.18362,1.06488,0.10558,0.1425,0.53484,0.11392,0.18328,0.15118,2.06366,4.43676,0.03372,0.53454,4.8735,2.0468,0.44216,0.10934,0.60026,0.24054,0.17874,0.32006,1.22958,0.1049,0.0537,0.28706,0.15654,0.28988,0.80142,0.06124,2.09008,0.44476,5.45278,1.0141,0.0977,5.91032,0.73224,1.0512,3.51538,1.08432
2018-11-12,0.56666,0.44602,5.28732,0.31766,0.34078,0.28448,0.14048,0.43126,0.75658,0.34132,0.54402,1.50996,0.75708,0.43588,0.50568,1.4457,0.0,1.38874,0.60516,0.31894,0.7428,0.35604,0.49268,0.99038,0.52414,1.04538,0.367,1.06196,1.26668,0.41498,0.52184,0.6443,0.54838,0.63556,1.01216,1.01414,0.58716,0.31666,0.3442,0.30668,2.2065,0.19156,0.27938,0.42078,0.71388,0.46748,0.7248,1.25108,1.79174,1.52508,0.8317,2.76192,0.14672,0.51852,0.47494,0.97208,0.3008,0.2905,0.95486,0.62528,0.40084,1.36462,0.58762,1.15974,1.71914,2.70274,0.61448,0.0,1.43768,0.49658,0.39282,4.33728,0.12252,0.30354,0.0,0.56734,0.5826,0.04088,1.44206,3.9333,2.05294,2.47346,0.7728,0.12746,0.59188,0.91006,2.84652,0.59948,0.9596,1.19768,2.15614,0.71494,2.0295,1.1378,0.62668,0.27494,0.20594,3.2704,4.75666,0.75488,0.54886,1.3634,0.44602,2.03578,3.26332,1.64796,0.59056,2.69796,0.44492,2.63094,0.95214,0.64834,0.5024,2.8587,0.82722,0.41102,0.0796,0.56868,0.4469,0.49262,0.16404,0.17346,0.14926,0.06494,0.11208,0.07002,1.9658,0.35902,0.09038,0.87522,1.0255,0.48602,0.31812,0.71188,0.42008,0.47534,0.31194,0.50044,0.4587,0.22726,0.624,0.5038,0.1179,0.78922,1.01758,0.3308,0.38824,0.3226,0.0,0.24366,0.444,0.1974,0.60114,0.75592,0.32664,0.58302,0.37288,0.7003,1.01048,0.37018,0.34208,0.92378,0.89138,0.7451,0.38084,0.45496,0.73012,0.50454,0.99096,0.31176,0.34634,1.69506,0.19154,0.37812,0.16752,0.4983,1.73142,0.39282,2.1164,0.90844,1.97314,1.70616,2.04192,0.3337,1.62456,0.36518,1.12464,0.86994,0.99196,2.20544,0.39028,0.51446,1.15554,0.1859,0.2451,0.36318,0.14984,0.163,0.25974,0.60124,0.19216,0.34226,0.56488,0.36828,1.3847,0.12302,0.47468,0.94544,0.79784,0.67348,0.2698,0.53364,0.2605,0.26062,0.95876,0.36922,0.73368,0.33618,1.67978,0.1488,0.30238,0.14336,0.76984,1.77846,0.06564,0.23318,10.50896,0.12422,2.5846,0.59892,0.60388,2.23512,1.02238,0.2575,0.14818,0.4263,0.56426,0.34802,0.71086,0.50918,0.48432,0.4499,0.46358,0.14582,0.09522,0.44382,0.14918,0.54338,0.64778,1.75754,0.9955,0.14162,0.07674,1.09144,0.46742,0.3946,0.2314,0.86662,0.16928,0.02824,0.31914,0.14244,0.17712,0.92024,0.09616,0.13096,0.44732,0.11184,0.15192,0.14278,1.71846,4.01432,0.03232,0.3851,3.94112,1.8078,0.43118,0.09876,0.59292,0.21486,0.15702,0.27942,1.2264,0.09984,0.04858,0.27306,0.166,0.29212,0.72648,0.06172,1.76188,0.4395,5.3207,0.94468,0.08654,5.64524,0.60266,0.9698,3.21118,1.04364
2018-11-13,0.60588,0.45272,5.31968,0.3369,0.47896,0.34088,0.16608,0.43278,0.82468,0.31918,0.62402,1.96344,0.95104,0.41938,0.5954,2.14822,0.0,1.5937,0.69408,0.32836,0.74128,0.51804,0.49974,1.13334,0.50842,0.93818,0.41944,1.21834,1.55922,0.4529,0.57222,0.79276,0.57976,0.57866,1.2566,1.23614,0.60972,0.32678,0.34634,0.32176,1.91802,0.21222,0.26748,0.49204,0.738,0.50454,0.72044,1.37216,1.87564,1.59252,0.88026,3.30472,0.1554,0.5717,0.5349,0.99086,0.34786,0.34656,1.09012,0.6324,0.40696,1.56446,0.65822,1.42956,1.61306,2.80538,0.72686,0.0,1.60312,0.5944,0.43884,4.72118,0.15692,0.30904,0.0,0.58286,0.63198,0.01008,1.72368,3.99094,2.06898,2.60798,0.97406,0.12556,0.59348,1.03974,2.48148,0.66094,1.04756,1.2388,2.44718,0.64478,2.12092,1.36786,0.54452,0.2798,0.18772,4.28952,4.3405,0.81278,0.65544,1.6878,0.55362,2.1096,3.74106,1.93196,0.69904,2.46214,0.47906,2.76776,0.87106,0.72846,1.02404,3.06384,0.95442,0.45552,0.08122,0.56966,0.4797,0.695,0.18422,0.17702,0.1622,0.05444,0.12124,0.0742,1.76876,0.36024,0.08906,1.00912,1.16968,0.52468,0.33028,0.77028,0.36766,0.50354,0.3604,0.5452,0.48138,0.2114,0.67514,0.41286,0.11806,0.99698,1.03034,0.36776,0.44316,0.3349,0.0,0.21358,0.55354,0.18086,0.62498,0.75196,0.3295,0.74668,0.49882,0.79374,1.0928,0.37206,0.3526,0.9399,0.94228,0.82132,0.3739,0.44508,0.80856,0.57012,1.15678,0.33232,0.39902,1.94982,0.20666,0.41034,0.21336,0.59774,1.3878,0.389,2.29432,0.7229,2.17616,1.7619,2.27358,0.36692,1.79234,0.3246,1.21216,0.83038,1.02812,2.72004,0.33912,0.52428,1.3161,0.17084,0.24326,0.40214,0.1613,0.164,0.28784,0.59684,0.22654,0.34154,0.60844,0.38034,1.43584,0.14872,0.5757,1.18972,0.90374,0.66888,0.27444,0.60776,0.27348,0.24028,1.15118,0.40436,0.82316,0.36778,1.7774,0.18246,0.29376,0.1472,0.7743,1.35734,0.07272,0.24252,11.04432,0.12756,2.85854,0.65016,0.69426,2.1473,1.09412,0.26094,0.14226,0.48108,0.69112,0.35324,0.92074,0.4464,0.53288,0.61434,0.47086,0.15882,0.09778,0.46892,0.1469,0.67338,0.68174,1.6833,1.12908,0.16484,0.07958,1.3439,0.56276,0.4324,0.24962,0.84428,0.1947,0.02912,0.30354,0.14672,0.20886,0.99022,0.10546,0.14468,0.49192,0.11546,0.14644,0.15496,1.77138,4.22932,0.03204,0.4215,4.04796,2.02624,0.42802,0.10874,0.61468,0.2603,0.17336,0.27114,1.25508,0.11414,0.04932,0.31172,0.18956,0.39232,0.8251,0.0686,1.88708,0.42752,4.78502,0.94314,0.0988,5.845,0.55652,1.10434,2.97266,1.11958
2018-11-14,0.60456,0.48924,4.61306,0.32594,0.50432,0.38464,0.18002,0.38144,0.83788,0.26212,0.6639,1.87764,1.01268,0.42592,0.62264,2.36528,0.0,1.75776,0.71288,0.35048,0.74038,0.56456,0.50338,1.11598,0.61848,0.82246,0.45432,1.35006,1.6995,0.42202,0.54496,0.83406,0.61358,0.52728,1.22202,1.287,0.59152,0.3639,0.31234,0.33944,1.87396,0.23438,0.27842,0.50322,0.73578,0.5328,0.83916,1.18344,1.95532,1.46608,0.81086,3.5172,0.14038,0.54866,0.5007,0.88844,0.3463,0.36122,1.1752,0.64842,0.41806,1.36058,0.62102,1.4887,1.57938,2.79884,0.85506,0.0,1.62382,0.62316,0.48712,4.97576,0.1703,0.31692,0.0,0.57354,0.59904,0.0,1.7758,3.53948,1.9165,2.5405,0.9897,0.14526,0.61956,1.04268,2.18488,0.66068,1.10468,1.10882,1.93432,0.73274,1.96934,1.43892,0.4586,0.28168,0.1836,4.88928,4.25166,0.79756,0.66658,1.75062,0.62886,2.15554,3.75404,1.96458,0.73338,2.09272,0.48708,2.78776,0.69894,0.76312,1.33362,2.8992,0.8919,0.45962,0.07918,0.5177,0.48608,1.00698,0.17632,0.16732,0.17228,0.04842,0.13612,0.08852,1.6362,0.36506,0.1158,1.24718,1.1888,0.52018,0.32892,0.796,0.41242,0.43974,0.36868,0.59274,0.5383,0.195,0.68702,0.4144,0.10604,1.08946,0.90518,0.53978,0.43582,0.31372,0.0,0.20612,0.5533,0.17188,0.66564,0.7137,0.46882,0.88262,0.73896,0.822,1.075,0.3677,0.33486,0.90332,0.95596,1.05484,0.36302,0.4347,0.8871,0.5288,1.32458,0.29316,0.39826,1.86184,0.22222,0.49178,0.22476,0.60418,1.26692,0.34806,2.19044,0.47324,1.9111,1.5529,2.41152,0.37274,1.56668,0.29244,1.13714,0.67412,0.62466,2.85144,0.50984,0.4983,1.30604,0.1741,0.23974,0.39818,0.1649,0.14304,0.3103,0.5961,0.2305,0.39812,0.65604,0.3716,1.52902,0.15154,0.59058,1.31876,0.91432,0.66074,0.2766,0.60054,0.27354,0.26718,1.30048,0.39836,0.8342,0.37316,1.53444,0.20008,0.2879,0.14308,0.7108,1.15442,0.09896,0.2713,10.8973,0.12934,2.98066,0.76208,0.75296,2.04814,1.34844,0.2615,0.13444,0.47428,0.87202,0.35504,0.94318,0.43848,0.49152,0.72624,0.4702,0.1408,0.10632,0.48058,0.13912,0.74972,0.74316,1.6946,1.18994,0.16928,0.08138,1.4634,0.56994,0.47786,0.27122,0.748,0.20782,0.02872,0.29964,0.14596,0.2267,0.94132,0.11352,0.14628,0.49128,0.12136,0.20372,0.16058,1.9039,4.25562,0.03348,0.43096,3.99308,2.08984,0.43418,0.12018,0.5712,0.28546,0.20872,0.29648,1.36642,0.11494,0.0531,0.30314,0.18314,0.43296,0.8768,0.07132,2.12284,0.4093,4.02706,0.89432,0.10456,4.62264,0.62628,1.1008,3.86032,0.7998
2018-11-15,0.61708,0.42962,4.36006,0.33124,0.53706,0.44096,0.2111,0.35306,0.90042,0.22786,0.9321,1.99214,1.02994,0.46348,0.64054,2.61326,0.0,1.86218,0.72086,0.35836,0.8429,0.62494,0.5661,1.16442,0.56988,0.75392,0.4481,1.5378,1.75868,0.51316,0.61262,0.9076,0.65556,0.54636,1.1537,1.4313,0.57568,0.38774,0.28288,0.32378,2.01808,0.23196,0.28488,0.68518,0.79224,0.5438,0.82782,1.12096,1.88744,1.44086,0.73834,3.46058,0.15794,0.53014,0.4524,0.86954,0.33408,0.3901,1.21876,0.60426,0.4264,1.32544,0.54122,1.50448,1.90204,3.0332,0.93184,0.0,1.44882,0.69362,0.48222,4.99906,0.18682,0.3253,0.0,0.57144,0.64738,0.19694,1.90896,3.49022,1.92788,2.5035,1.06782,0.15342,0.65164,0.99146,2.29846,0.72686,0.97734,1.10314,1.78678,0.79478,1.47886,1.59804,0.41378,0.23942,0.2112,5.47436,5.14536,0.90494,0.69986,1.88606,0.7411,2.52372,4.14656,2.02548,0.78902,1.79186,0.48632,2.89906,0.59812,0.69992,1.53634,2.9839,0.72698,0.4867,0.07676,0.50962,0.46648,1.06916,0.1696,0.16704,0.18946,0.044,0.13704,0.10226,1.62926,0.35906,0.12384,1.30354,1.29108,0.5426,0.3168,0.8168,0.39268,0.51964,0.42408,0.53798,0.54672,0.205,0.75576,0.47412,0.12844,1.25486,0.88428,0.5622,0.51178,0.31518,0.0,0.23506,0.53346,0.17444,0.72922,0.7233,0.62668,0.92992,0.76414,0.93158,1.15038,0.42176,0.3269,0.861,0.94096,1.06056,0.38332,0.43096,0.95046,0.5574,1.33688,0.39394,0.39516,2.0237,0.2352,0.51878,0.24524,0.59994,1.17708,0.3385,2.25746,0.22934,1.90574,1.63066,2.4697,0.36808,1.5715,0.28698,1.09628,0.6335,0.5444,2.9988,0.58266,0.45724,1.36798,0.18332,0.25498,0.39628,0.16314,0.13312,0.30748,0.59572,0.24504,0.58284,0.65224,0.39376,1.80794,0.15466,0.56108,1.34816,0.88954,0.66832,0.25748,0.60842,0.2986,0.27014,1.47348,0.41826,0.86158,0.44122,1.40086,0.20138,0.28322,0.13322,0.6637,1.07134,0.11268,0.28976,11.80956,0.11672,3.1337,0.7836,0.8133,2.47912,1.34978,0.2613,0.1303,0.53894,0.95468,0.41178,0.96008,0.52992,0.53394,0.79044,0.4682,0.13016,0.11012,0.47978,0.13758,0.73922,0.79452,2.4286,1.28388,0.18248,0.08126,1.5173,0.59246,0.5195,0.28268,0.79856,0.2282,0.03412,0.29726,0.14984,0.247,0.98446,0.12196,0.15956,0.5256,0.13474,0.21446,0.16624,2.01164,4.61678,0.03442,0.4325,4.06646,2.40704,0.4768,0.13136,0.56038,0.34008,0.21816,0.3054,1.35252,0.12934,0.05672,0.32416,0.1697,0.46336,0.90124,0.07402,2.32626,0.42608,3.91338,1.0512,0.109,4.03958,0.74932,1.23334,4.45574,0.7285
2018-11-16,0.55072,0.4557,4.7462,0.42198,0.62962,0.47442,0.27096,0.37232,1.00756,0.23608,1.06948,2.28128,1.20498,0.55238,0.708,2.80114,0.0,1.99394,0.67984,0.41836,0.98604,0.68698,0.6189,1.23692,0.62908,0.77476,0.4501,1.73412,1.82184,0.58782,0.77698,1.03648,0.73884,0.6004,1.21768,1.55508,0.60212,0.40278,0.26354,0.32238,2.24214,0.24162,0.28312,1.0687,0.86992,0.57284,0.90218,1.16774,2.12672,2.03426,0.74786,3.02498,0.14302,0.48074,0.48292,0.90136,0.31494,0.45628,1.12038,0.51388,0.4668,1.48226,0.45238,1.58748,2.26472,3.48786,1.1646,0.0,1.38598,0.81138,0.51634,4.87516,0.1741,0.32518,0.0,0.62258,0.68176,2.62842,2.17372,4.12416,2.21102,2.37036,1.15998,0.16604,0.76676,1.04706,2.33194,0.81546,0.9186,0.9788,1.50776,0.8588,1.91642,1.90798,0.40658,0.25476,0.26178,7.6436,5.92194,1.32718,0.73618,2.07782,0.87816,2.96792,4.65802,2.12216,0.867,1.67084,0.52902,3.15844,0.62768,0.69746,1.70064,2.93536,0.73094,0.55206,0.07378,0.56622,0.49104,1.13714,0.1767,0.17012,0.19688,0.06694,0.1446,0.1015,1.586,0.37436,0.1348,1.3682,1.64674,0.6062,0.24932,0.97302,0.42212,0.58016,0.50822,0.59836,0.56416,0.23112,0.88062,0.78734,0.17074,1.71748,0.95342,0.57734,0.5804,0.3484,0.0,0.30628,0.61208,0.18884,0.7941,0.90976,0.77034,0.9807,0.79894,1.12864,1.0149,0.51424,0.3365,0.94472,1.03122,1.2053,0.39624,0.45916,1.00774,0.62412,1.5346,0.47184,0.44914,2.60366,0.2758,0.5608,0.29376,0.6441,1.27676,0.37544,2.38898,0.0,1.9606,1.78566,2.49516,0.37542,1.58764,0.30356,1.1231,0.712,0.5932,3.3336,0.6179,0.55022,1.53362,0.26918,0.37296,0.39404,0.15024,0.13406,0.35308,0.6805,0.28618,0.81946,0.792,0.40952,1.92616,0.18334,0.6029,1.52488,1.06572,0.76964,0.26718,0.65308,0.334,0.21778,1.93404,0.4631,0.88756,0.57886,1.39146,0.21418,0.34698,0.14354,0.56884,1.0931,0.12556,0.32648,13.35524,0.11766,4.3231,0.78836,0.8509,2.85958,1.5139,0.21478,0.12502,0.55238,1.11146,0.47716,1.0502,0.619,0.57732,0.97428,0.34672,0.13996,0.09494,0.49366,0.13164,0.79454,0.87468,2.83702,1.55104,0.20242,0.06122,1.52778,0.60396,0.53656,0.30892,0.90394,0.25342,0.0396,0.35346,0.20398,0.2583,1.10054,0.1272,0.17928,0.64736,0.14636,0.2232,0.15294,2.15556,5.34798,0.03596,0.49044,5.83912,3.04632,0.51124,0.14102,0.59072,0.41042,0.23464,0.33128,1.04296,0.14226,0.054,0.35208,0.17836,0.50028,0.8878,0.07012,2.568,0.4843,4.38532,1.20038,0.12258,4.4382,0.87812,1.3928,5.21272,0.75062
2018-11-19,0.60204,0.49766,4.60332,0.49296,0.65192,0.4433,0.3277,0.37046,1.0414,0.25338,1.04706,2.37604,1.2554,0.64328,0.73188,2.73922,0.0,2.0663,0.64198,0.41446,1.12322,0.68584,0.6479,1.20334,0.80144,0.82044,0.54666,1.63978,2.4145,0.6132,0.8717,1.239,1.00294,0.61914,1.24882,1.4664,0.5944,0.39834,0.24204,0.33698,2.1948,0.30658,0.35446,1.1369,0.9647,0.5759,1.1798,1.07132,2.02026,2.08494,0.7273,2.85434,0.15264,0.48,0.54842,0.90618,0.28194,0.42454,0.94204,0.49712,0.62366,1.35012,0.46132,1.26848,2.3843,3.14834,1.18746,0.0,1.4409,0.81614,0.518,4.42732,0.16824,0.31594,0.0,0.6015,0.74042,3.59374,2.31698,4.10046,2.13926,2.3315,1.22022,0.17526,0.80078,1.06598,2.3631,0.87234,0.969,0.95958,1.4519,0.93508,2.2516,2.18032,0.49686,0.28934,0.28466,9.523,6.225,1.51458,0.698,2.04978,0.91194,2.96682,4.59072,2.01258,0.88058,1.4056,0.5212,3.03678,0.59732,0.6859,1.77362,2.7653,0.72114,0.55316,0.06978,0.54468,0.49914,1.13388,0.16986,0.1884,0.2064,0.08296,0.16712,0.1056,1.88616,0.38218,0.13448,1.36524,1.97682,0.61638,0.23176,1.0332,0.4828,0.60742,0.57716,0.55356,0.57764,0.25334,0.86912,0.95656,0.2007,2.08438,1.14334,0.63124,0.69206,0.37492,0.0,0.35934,0.70602,0.20912,0.8498,1.0947,0.8499,0.96388,0.80834,1.13658,0.89046,0.53988,0.29254,1.01438,1.01816,1.25384,0.57766,0.48198,0.99402,0.67502,1.70302,0.51438,0.5033,2.84824,0.28312,0.57162,0.32182,0.77946,1.22876,0.38852,2.2352,0.0,1.8158,2.05556,2.8477,0.32504,1.32716,0.29478,1.05194,0.8105,0.5974,2.90884,0.58474,0.73516,1.33284,0.32432,0.44896,0.4236,0.14442,0.14396,0.37984,0.6924,0.31616,0.93114,0.83388,0.39018,1.99512,0.1953,0.67796,1.65962,1.23848,0.72328,0.237,0.6782,0.3421,0.20354,2.31298,0.52524,1.08438,0.68584,1.42602,0.2088,0.4047,0.13096,0.64516,0.8909,0.14036,0.33552,15.7576,0.09632,5.07838,0.79136,0.89222,3.06178,1.85066,0.21232,0.13542,0.57318,1.18036,0.52402,1.08338,0.64718,0.51226,1.03378,0.33556,0.14934,0.09238,0.50272,0.13696,0.7594,0.846,2.99032,1.89662,0.2167,0.06016,1.58246,0.63574,0.47086,0.3037,0.9157,0.26794,0.04386,0.37702,0.23814,0.27938,1.23848,0.14696,0.18258,0.76928,0.15728,0.2295,0.14458,2.12866,7.01184,0.03742,0.49828,7.62136,3.5822,0.51566,0.15326,0.58128,0.4873,0.23796,0.4011,1.05556,0.14844,0.05534,0.36936,0.177,0.7584,1.04228,0.0737,2.50422,0.47848,5.43328,1.246,0.12564,4.14254,0.89048,1.40848,5.75034,0.71966
2018-11-20,0.59754,0.50194,4.69988,0.54574,0.57038,0.41834,0.32228,0.35148,1.02332,0.29282,1.07332,2.1632,1.15166,0.64142,0.67798,2.47008,0.0,2.03058,0.49588,0.40576,1.06834,0.66698,0.66744,1.13158,0.8201,0.7813,0.57562,1.55038,2.52832,0.71418,0.8735,1.2105,1.15524,0.68954,1.13994,1.26606,0.51792,0.3899,0.21464,0.31946,2.19826,0.3659,0.35714,1.17426,0.9563,0.58538,1.23126,0.90784,1.9892,2.18192,0.6806,2.31612,0.14562,0.41216,0.48862,0.86444,0.26348,0.39324,0.80092,0.48372,0.73754,1.15038,0.47636,0.9432,2.5265,3.87412,1.13466,0.0,1.44376,0.76248,0.42552,3.751,0.13566,0.30718,0.0,0.72914,0.68844,4.28942,2.2996,4.34708,2.11888,2.272,0.95112,0.19058,0.8315,0.985,2.23082,0.88462,1.08694,0.9181,1.40638,0.98444,2.30966,2.05388,0.54148,0.26114,0.26764,9.02364,6.52128,1.52492,0.57428,1.76646,0.92102,3.05844,4.43656,1.73828,0.80726,1.28544,0.54042,2.96254,0.61298,0.59128,1.324,2.33948,0.61526,0.52414,0.07062,0.49002,0.44872,0.99082,0.1719,0.18926,0.20008,0.08934,0.1874,0.11324,2.08118,0.41168,0.13244,1.24518,1.96542,0.59216,0.21604,1.03226,0.52776,0.60948,0.54504,0.52978,0.65506,0.25838,0.83176,0.9993,0.21484,2.00268,1.14132,0.61998,0.90756,0.36844,0.0,0.36598,0.68984,0.21442,0.8285,1.09888,0.88272,0.9144,0.69556,1.07466,0.83038,0.52156,0.29744,0.96764,0.93574,1.12784,0.65178,0.73238,0.97698,0.70496,1.61474,0.59882,0.47416,2.7468,0.27304,0.54718,0.3006,0.7475,1.1652,0.42954,2.1047,0.0,1.83906,2.1214,2.70412,0.26218,1.17358,0.28836,1.20116,0.84454,0.5322,2.34616,0.5654,0.7526,1.18982,0.35096,0.45612,0.4219,0.13664,0.1367,0.36434,0.71108,0.30134,0.9302,0.84316,0.35204,2.08344,0.18682,0.61794,1.33296,1.17336,0.61516,0.22252,0.5683,0.37416,0.18678,2.29608,0.53184,1.07696,0.7001,1.24202,0.17372,0.40616,0.13194,0.61566,0.82352,0.13916,0.3366,15.86472,0.0864,4.9783,0.71998,0.88848,2.99862,1.8592,0.20778,0.13532,0.607,1.1109,0.52618,1.15054,0.5674,0.47438,0.94296,0.32024,0.17102,0.09162,0.51578,0.137,0.6749,0.84064,2.99754,1.86134,0.21236,0.0562,1.41056,0.4717,0.4632,0.29926,0.90394,0.25426,0.04662,0.38912,0.2636,0.26828,1.2795,0.14044,0.18612,0.76256,0.17046,0.22258,0.13226,2.01568,7.27086,0.03458,0.49436,7.92462,3.54844,0.4812,0.15008,0.71502,0.46496,0.22932,0.47922,1.08704,0.14322,0.05356,0.36936,0.1659,1.00274,1.02302,0.07244,2.64578,0.4612,5.40184,1.46808,0.11286,4.00214,0.91854,1.32036,6.03692,0.70222
2018-11-21,0.58496,0.49428,4.61344,0.56268,0.53278,0.39266,0.31444,0.36698,0.9575,0.30244,1.02876,2.05276,1.04168,0.60334,0.6366,2.42022,0.0,1.89348,0.49382,0.38182,1.02168,0.62936,0.63284,1.24002,0.8672,0.773,0.55268,1.42036,2.55144,0.72194,0.86036,1.18886,1.33748,0.67236,1.05848,1.31722,0.50556,0.33158,0.21692,0.4894,2.03172,0.3592,0.35038,1.25432,0.9162,0.57206,1.20464,0.88596,2.28624,2.14272,0.6663,2.0587,0.17626,0.36886,0.47746,1.0358,0.26556,0.35788,0.71062,0.45564,0.83446,1.16678,0.46988,0.86306,2.5271,4.09178,1.03364,0.0,1.43946,0.7129,0.38352,3.16052,0.12726,0.28648,0.0,0.74806,0.70632,4.82102,2.26288,4.21136,1.9906,2.32694,0.88446,0.19972,0.81694,0.96366,2.04382,0.86216,1.07286,0.87728,1.29778,0.88446,2.19338,1.95832,0.5764,0.24716,0.29528,8.703,6.09426,1.67894,0.53808,1.60894,0.8725,3.01622,4.27412,1.64684,0.82854,1.6327,0.51162,3.13976,0.83542,0.51738,1.0844,2.27906,0.60166,0.5053,0.06922,0.43652,0.3823,0.7675,0.166,0.18418,0.1878,0.08414,0.21596,0.09932,2.05574,0.38966,0.11766,0.93834,2.03394,0.60726,0.19544,0.93364,0.49792,0.59588,0.56994,0.48692,0.62538,0.24868,0.78282,0.9895,0.21662,2.04468,1.11654,0.47926,0.9952,0.356,0.0,0.36354,0.77016,0.21372,0.76262,1.2479,0.7713,0.73268,0.47422,1.0363,0.84926,0.52594,0.29512,0.89526,0.8256,1.0261,0.80194,1.0863,0.88972,0.68544,1.46936,0.64004,0.54038,2.74654,0.25342,0.45148,0.2889,0.76078,1.2727,0.41978,1.90706,0.0,1.78278,2.08206,2.5323,0.23884,1.08866,0.28992,1.2437,0.79244,0.44934,2.11706,0.52492,0.78416,1.12686,0.37252,0.45142,0.42456,0.13074,0.13234,0.35228,0.67904,0.31362,0.89596,0.78496,0.36312,2.02716,0.18358,0.5865,1.1105,1.17682,0.56454,0.20906,0.59244,0.36432,0.14172,2.23596,0.54842,1.08728,0.7007,1.39266,0.17442,0.39034,0.1333,0.57718,0.83254,0.11434,0.3036,16.74084,0.0844,4.95808,0.52486,0.80062,2.88056,1.8733,0.19632,0.13588,0.67698,0.9775,0.50566,1.20892,0.49932,0.49362,0.82512,0.29094,0.16862,0.08094,0.48096,0.13188,0.61992,0.72956,2.8023,1.86112,0.22204,0.05036,1.30862,0.48208,0.40772,0.29428,0.937,0.23956,0.04768,0.38772,0.26764,0.24708,1.27068,0.13136,0.22488,0.7924,0.1668,0.19598,0.11932,1.79402,7.17228,0.03196,0.45332,8.28992,3.55914,0.4593,0.1444,0.7496,0.44318,0.18906,0.45316,0.92292,0.1397,0.04716,0.3604,0.1548,1.10268,0.99,0.06544,2.40584,0.42824,5.53794,1.43584,0.1029,3.81004,0.8164,1.28316,5.0031,0.6795
2018-11-22,0.53824,0.50296,4.29456,0.53922,0.52566,0.35126,0.28226,0.37004,0.83798,0.29702,0.7977,1.90164,0.96742,0.51468,0.60286,2.56752,0.0,1.81154,0.46812,0.39714,0.83048,0.56716,0.54294,1.29016,0.89682,0.74414,0.54072,1.23134,2.5424,0.6228,0.792,1.14898,1.40028,0.81516,1.0112,1.2215,0.4909,0.28888,0.19456,0.58818,1.69878,0.35756,0.37256,1.14466,0.81032,0.63346,1.24652,1.43718,2.4039,2.03668,0.63012,1.99196,0.1568,0.39032,0.56608,1.0298,0.29512,0.34722,0.652,0.41548,0.87202,1.15462,0.42164,0.79446,2.50702,3.86496,0.95374,0.0,1.34184,0.66384,0.36296,2.98596,0.11208,0.25226,0.0,0.73162,0.63302,5.06692,2.01706,4.27032,1.86804,2.26666,0.75356,0.20144,0.87674,0.91254,1.8277,0.80114,1.12986,0.7505,1.15756,0.82436,2.18146,1.78294,0.59294,0.23104,0.28748,8.18286,5.48012,1.58204,0.47324,1.42766,0.84558,2.4903,3.70842,1.61222,0.7911,1.67452,0.53562,3.07462,1.13426,0.49106,0.95804,2.13174,0.57866,0.46374,0.06738,0.42312,0.351,0.69776,0.15634,0.17282,0.16764,0.08198,0.22596,0.08796,1.958,0.37336,0.11296,0.93734,1.89862,0.5742,0.18784,0.89856,0.5138,0.47232,0.50058,0.5081,0.64548,0.23716,0.69444,0.87352,0.20094,2.20542,1.0382,0.51418,0.92116,0.34164,0.0,0.33712,0.77794,0.20718,0.66168,1.28838,0.64514,0.66986,0.40108,0.8774,0.79682,0.47186,0.29156,0.84896,0.78366,1.01132,0.81916,1.24098,0.89576,0.62856,1.41986,0.5138,0.5302,2.6303,0.22992,0.39318,0.2678,0.79476,1.24582,0.38902,1.74402,0.0,1.5752,1.91056,2.48574,0.22352,1.0046,0.29012,1.253,0.73576,0.4006,1.90018,0.44672,0.77556,0.96832,0.4095,0.4735,0.39532,0.12184,0.12598,0.35544,0.62292,0.31742,0.72506,0.74452,0.3361,1.55218,0.1729,0.55278,0.95654,1.11822,0.52042,0.21826,0.54812,0.32828,0.12796,2.08574,0.52176,1.05446,0.64846,1.44332,0.15618,0.38732,0.13048,0.5448,0.83682,0.10546,0.30698,15.29568,0.08126,4.61432,0.54552,0.71654,2.44126,1.98604,0.18824,0.13096,0.62306,0.89256,0.42654,1.16152,0.4838,0.438,0.76226,0.28564,0.16334,0.07306,0.47046,0.1293,0.59918,0.66796,2.09868,1.77704,0.21326,0.04728,1.1994,0.49152,0.39484,0.30344,0.8833,0.21458,0.04296,0.3985,0.2658,0.22814,1.15852,0.12522,0.22624,0.74844,0.14696,0.18552,0.10664,1.67956,6.74904,0.02944,0.42974,8.17,3.23504,0.41152,0.12636,0.7178,0.38032,0.16512,0.4335,0.85082,0.12012,0.0415,0.34002,0.14872,1.15438,0.98058,0.0592,2.24098,0.3621,5.45872,1.24234,0.09376,3.63388,0.76806,1.2171,4.54226,0.62132
2018-11-23,0.4964,0.46192,3.61562,0.44812,0.52838,0.3386,0.2365,0.34468,0.72088,0.28432,0.72526,1.84468,0.86182,0.44632,0.61814,2.98952,0.49706,1.73972,0.46796,0.3829,0.71728,0.57252,0.47586,1.35732,0.81982,0.71842,0.55648,1.0885,2.35014,0.62054,0.65042,1.03784,1.37728,0.94008,0.9498,1.17948,0.46004,0.28274,0.19774,0.64348,1.52874,0.36256,0.39364,0.83218,0.7665,0.64792,1.14526,2.14212,2.23034,1.5447,0.74276,1.81904,0.1616,0.48286,0.52198,1.03276,0.36168,0.29158,0.60272,0.383,0.89168,1.04434,0.41286,0.72344,2.41596,3.60512,0.83088,0.0,1.2282,0.5712,0.31758,2.92296,0.12436,0.21902,0.0,0.67948,0.62738,3.53556,1.78922,4.04288,1.67182,2.06194,0.64816,0.2407,0.8427,0.9238,1.54016,0.73868,1.0982,0.69642,1.05796,0.86732,1.6044,1.42432,0.6086,0.19486,0.2346,6.4245,4.75038,1.26386,0.42686,1.36684,0.81218,2.15174,3.22354,1.57138,0.85632,1.64992,0.56066,2.72826,1.19588,0.49402,0.94466,2.23802,0.52074,0.4284,0.06264,0.38982,0.28456,0.67904,0.13662,0.1603,0.15072,0.06198,0.23702,0.09568,1.99122,0.34384,0.10154,0.89296,1.60446,0.52608,0.1712,0.8537,0.47504,0.4505,0.48132,0.4822,0.64606,0.22828,0.62848,0.57382,0.1684,1.9799,1.123,0.51964,0.9439,0.32312,0.0,0.2783,0.7254,0.20458,0.60474,1.25784,0.58698,0.59986,0.3931,0.66028,0.71228,0.36708,0.28838,0.81732,0.72404,1.0479,0.86172,1.52288,0.86804,0.60086,1.23224,0.4575,0.4738,2.20664,0.18872,0.38998,0.23774,0.92118,1.1274,0.36664,1.54096,0.0,1.36726,2.03648,2.41644,0.20948,0.8767,0.28938,1.24314,0.73378,0.29186,1.70418,0.39418,0.68102,0.81734,0.34982,0.38094,0.37646,0.12154,0.1202,0.32952,0.53338,0.30446,0.53128,0.65666,0.30608,1.45044,0.15898,0.54528,0.81652,0.9723,0.42352,0.20818,0.47284,0.31714,0.12626,1.73674,0.4733,0.97782,0.53022,1.43194,0.16054,0.3441,0.12918,0.51316,0.73382,0.09996,0.29482,14.23346,0.07938,3.58064,0.52432,0.65856,2.40968,1.89636,0.18178,0.12412,0.64726,0.75856,0.35562,1.17076,0.45502,0.40184,0.63332,0.26658,0.14932,0.06602,0.42476,0.11524,0.53714,0.57444,1.81164,1.62488,0.21276,0.04358,1.0163,0.4921,0.36958,0.33642,0.86902,0.20354,0.03808,0.36012,0.22696,0.22572,1.06702,0.12024,0.23008,0.65848,0.14792,0.1794,0.09476,1.65594,6.1485,0.02806,0.3798,6.86522,2.7979,0.37624,0.1231,0.74494,0.3177,0.15768,0.41234,0.79414,0.11898,0.03702,0.34918,0.13692,1.22896,0.92258,0.0539,2.09336,0.305,5.06844,1.15756,0.0815,3.49846,0.6987,1.15462,4.0561,0.60858
2018-11-26,0.39806,0.38998,3.4392,0.36068,0.48412,0.30682,0.17782,0.32288,0.62978,0.24046,0.74318,1.55592,0.75102,0.32782,0.59214,2.97954,0.65822,1.69178,0.4028,0.3387,0.56122,0.56286,0.4221,1.36056,0.65074,0.77696,0.4511,0.97584,1.60818,0.5835,0.51232,0.81708,1.1574,1.07886,0.76708,1.1017,0.3799,0.27466,0.22028,0.62962,1.41384,0.29408,0.31462,0.72924,0.6323,0.59626,0.88222,2.61418,2.24936,1.28244,0.75484,1.6005,0.15466,0.46956,0.42818,0.96024,0.41006,0.26466,0.52008,0.31544,0.7364,0.9375,0.38378,0.67468,2.19592,3.35418,0.79678,0.0,1.02988,0.51506,0.26158,2.7466,0.1202,0.188,0.0,0.59852,0.56086,3.05488,1.684,3.75518,1.4996,1.85228,0.52278,0.2738,0.8043,0.8066,1.4225,0.59878,0.95852,0.64538,0.9497,0.74642,1.14276,1.0684,0.5553,0.14142,0.21222,4.55702,4.35096,1.13656,0.43602,1.2386,0.73786,1.89842,2.89994,1.35042,0.81396,1.64198,0.57926,2.69196,1.40226,0.43122,0.825,2.02518,0.49816,0.39606,0.05766,0.36582,0.2279,0.62886,0.13412,0.12724,0.13228,0.04472,0.23776,0.09214,1.61512,0.30436,0.09966,0.96954,1.2211,0.46326,0.14914,0.9401,0.4094,0.37348,0.44314,0.46116,0.60516,0.1905,0.52556,0.40434,0.14008,1.69432,0.88786,0.5217,0.85446,0.36406,0.0,0.23138,0.64538,0.19548,0.57358,1.05628,0.51136,0.57082,0.36302,0.55882,0.74436,0.30924,0.27372,0.71732,0.64346,1.14278,0.72044,1.77404,0.78306,0.54468,0.9722,0.46944,0.36972,1.8695,0.19344,0.33752,0.1999,0.77954,0.94338,0.30732,1.40166,0.1974,1.33052,1.7082,1.77398,0.18232,0.8398,0.28184,1.1932,0.5879,0.23684,1.5964,0.4198,0.49172,0.81368,0.2898,0.29084,0.32354,0.11862,0.11692,0.30738,0.51116,0.2884,0.44424,0.59572,0.2744,1.36666,0.14196,0.4272,0.68728,0.75988,0.42404,0.2072,0.41504,0.30074,0.12012,1.3903,0.40172,0.73038,0.41334,1.32492,0.13874,0.28066,0.13408,0.39822,0.63846,0.0826,0.30582,11.51318,0.07234,2.64994,0.548,0.53546,2.04998,1.55598,0.15548,0.10972,0.60884,0.70752,0.28072,1.15318,0.40952,0.43092,0.50574,0.2455,0.13344,0.05868,0.39934,0.10438,0.44698,0.495,1.5679,1.25656,0.20166,0.03694,0.81054,0.45426,0.34768,0.33718,0.81826,0.1806,0.03272,0.30602,0.19432,0.18772,0.87082,0.09776,0.2242,0.55086,0.13358,0.18456,0.08776,1.57574,4.26058,0.02632,0.32426,5.23892,2.27918,0.3406,0.10546,0.73048,0.23244,0.14406,0.33882,0.70978,0.10126,0.03528,0.32572,0.1111,1.01988,0.768,0.04398,2.0619,0.30148,3.63158,1.118,0.06874,3.23214,0.7097,1.0567,3.40562,0.56212
2018-11-27,0.36308,0.33198,3.14388,0.26402,0.43314,0.27346,0.13368,0.28764,0.50344,0.17838,0.68258,1.24984,0.6194,0.28566,0.5271,2.92888,0.76412,1.4605,0.39088,0.29032,0.49928,0.41104,0.3749,1.16042,0.56348,0.71314,0.36038,0.77752,1.1784,0.41216,0.39588,0.64446,0.9663,1.01834,0.6388,0.968,0.33762,0.21116,0.23568,0.6286,1.17174,0.20964,0.29252,0.67802,0.51574,0.54334,0.75268,2.8113,2.12888,0.963,0.72068,1.3507,0.14214,0.47138,0.41382,0.87786,0.383,0.22936,0.4193,0.3054,0.61196,0.90314,0.29544,0.60184,2.02956,2.26192,0.6971,0.0,0.79816,0.4279,0.25678,2.68052,0.13368,0.19162,0.0,0.42412,0.53998,2.79252,1.40448,3.21574,1.3493,1.54686,0.51506,0.27506,0.70426,0.71612,1.26972,0.51846,0.9339,0.53386,0.92178,0.63612,1.02028,0.8243,0.5,0.11246,0.21542,3.70978,5.02068,1.19848,0.45538,1.11814,0.59502,1.60252,2.51112,1.18054,0.7533,1.57788,0.55114,2.71408,1.46684,0.4328,0.72676,1.91444,0.50066,0.3504,0.05282,0.3284,0.2353,0.55592,0.12362,0.11142,0.11388,0.0317,0.22108,0.07912,1.35096,0.27738,0.09278,0.971,0.99434,0.40258,0.14908,0.94568,0.3595,0.33328,0.41446,0.41504,0.5247,0.156,0.39498,0.33178,0.12684,1.52946,0.73804,0.4994,0.60228,0.36532,0.0,0.19366,0.56906,0.17964,0.53194,1.02706,0.42026,0.4232,0.33638,0.4526,0.71902,0.2815,0.22054,0.67736,0.5663,1.19232,0.63198,1.62156,0.67354,0.41082,0.74512,0.40754,0.3229,1.57672,0.16862,0.2974,0.16806,0.69102,0.88308,0.26658,1.22616,0.31212,1.0833,1.42926,1.4685,0.17164,0.86254,0.28744,0.9054,0.4645,0.26148,1.5545,0.39546,0.47092,0.78118,0.25478,0.25128,0.26552,0.11786,0.12562,0.28946,0.42746,0.25182,0.3934,0.49554,0.25938,1.21512,0.13084,0.36792,0.58998,0.6522,0.45268,0.19214,0.37586,0.23418,0.12512,1.13052,0.36224,0.6143,0.34422,1.24898,0.13358,0.23046,0.13426,0.35234,0.69464,0.0697,0.2784,10.02252,0.07042,2.148,0.54016,0.39254,1.78174,1.39628,0.14028,0.0995,0.51266,0.63494,0.22166,0.87644,0.37296,0.43764,0.4069,0.22404,0.08728,0.04984,0.35942,0.09642,0.36208,0.42998,1.3507,1.02904,0.17918,0.03122,0.7659,0.50514,0.29972,0.34378,0.73754,0.14732,0.02594,0.26084,0.1917,0.16384,0.66294,0.08188,0.2007,0.47632,0.10956,0.1827,0.08212,1.44614,3.5535,0.0245,0.24298,4.32144,1.92814,0.3173,0.09282,0.54528,0.18508,0.12086,0.27886,0.57718,0.0824,0.03212,0.30452,0.09342,0.7106,0.78966,0.03636,1.63034,0.27704,3.4272,0.84282,0.06404,2.8954,0.66834,0.95488,2.95572,0.46202
2018-11-28,0.34812,0.29174,3.19048,0.23738,0.49074,0.2453,0.12842,0.26264,0.4844,0.15346,0.66402,1.17836,0.65104,0.2555,0.53574,2.98238,0.86766,1.34634,0.36884,0.2767,0.46312,0.33972,0.3915,1.08748,0.37354,0.6753,0.32134,0.86232,1.2922,0.39278,0.39226,0.70804,0.77194,1.05796,0.68668,1.50592,0.31364,0.29038,0.24214,0.42208,1.15934,0.19104,0.29788,0.5786,0.4524,0.51446,0.68618,3.01228,1.66204,0.97936,0.68056,1.37326,0.1451,0.49464,0.49032,0.68402,0.37348,0.27422,0.39338,0.29808,0.47256,0.84836,0.2902,0.6919,2.0297,2.12868,0.62868,0.0,0.78542,0.42624,0.24142,2.76158,0.1409,0.18568,0.03,0.41556,0.51784,2.62318,1.40948,3.15656,1.33832,1.34796,0.49992,0.2537,0.6699,0.66288,1.3014,0.56634,0.98096,0.48806,0.82456,0.58674,0.96368,0.81982,0.49716,0.12336,0.26,3.76298,6.09028,1.06922,0.45692,1.32406,0.58918,1.57844,2.58164,1.17274,0.8241,1.1885,0.73656,2.80084,1.30892,0.44522,0.7185,2.06904,0.51522,0.32946,0.05448,0.31808,0.25896,0.42534,0.11358,0.10778,0.10614,0.0306,0.18434,0.07328,1.15268,0.26108,0.07354,0.91996,0.90366,0.35046,0.14592,1.06432,0.34806,0.3591,0.39898,0.39382,0.47298,0.1574,0.3883,0.28366,0.12604,1.52186,0.68712,0.4559,0.54124,0.36584,0.0,0.17084,0.48208,0.1635,0.49238,0.84862,0.38006,0.37688,0.38456,0.42302,0.77266,0.29888,0.2108,0.65762,0.56958,1.13682,0.45864,1.62104,0.611,0.38928,0.7345,0.40694,0.25898,1.50208,0.1595,0.31424,0.15496,0.68568,1.01534,0.25416,1.31512,0.4386,1.14964,1.35386,1.81292,0.17264,1.02046,0.27654,0.78034,0.45972,0.26284,1.64528,0.25606,0.45158,0.75824,0.24232,0.41634,0.25932,0.10952,0.12382,0.28116,0.4601,0.2363,0.3598,0.49364,0.23074,1.2962,0.12344,0.34758,0.5756,0.5984,0.57446,0.18552,0.30434,0.21308,0.132,1.08122,0.34456,0.53944,0.33802,1.23,0.1044,0.2358,0.1364,0.34676,0.89372,0.06148,0.24724,10.64682,0.05786,2.0431,0.52316,0.3761,1.72022,1.13644,0.13162,0.09188,0.4207,0.65656,0.21716,0.68036,0.35072,0.37704,0.43768,0.22384,0.09202,0.04872,0.3643,0.0961,0.45082,0.4354,1.3203,1.02216,0.16422,0.0297,0.81556,0.43612,0.30016,0.30924,0.75658,0.13194,0.0228,0.2354,0.19382,0.1599,0.66896,0.0753,0.15174,0.4548,0.0993,0.14166,0.0872,1.32504,3.35244,0.02234,0.29064,4.43676,1.99564,0.39172,0.08228,0.51322,0.18156,0.15262,0.26966,0.56644,0.0835,0.0318,0.29754,0.08146,0.5782,0.79404,0.03604,1.601,0.26266,3.09994,0.84018,0.06866,2.87676,0.67166,0.95142,2.891,0.42826
2018-11-29,0.34636,0.31086,3.9719,0.2966,0.50802,0.232,0.13006,0.28336,0.49874,0.15924,0.68772,1.16998,0.71442,0.28758,0.54378,2.80896,1.059,1.2692,0.3847,0.25186,0.48904,0.34856,0.41504,0.92972,0.36856,0.75646,0.31544,0.92104,1.3409,0.36494,0.3726,0.693,0.78744,0.91472,0.73594,2.15144,0.3765,0.3639,0.25088,0.32798,1.2016,0.18248,0.31594,0.56538,0.45848,0.42218,0.63294,2.61372,1.62,1.03526,0.71404,1.31576,0.14548,0.51316,0.43342,0.67074,0.37686,0.2881,0.38054,0.31662,0.46706,0.83298,0.33614,0.74482,1.70154,2.32564,0.64238,0.0,0.84636,0.454,0.26444,2.82064,0.15726,0.23032,5.87626,0.4498,0.54404,3.54222,1.5474,3.21084,1.45296,1.28792,0.55222,0.23946,0.60286,0.68934,1.39192,0.57966,0.98608,0.46732,0.79806,0.58256,1.11854,0.82082,0.58948,0.12292,0.31792,3.8825,5.9588,1.10052,0.4736,1.45154,0.5518,1.5975,2.53466,1.18352,0.88072,1.12442,0.8182,2.98316,1.02784,0.4914,0.67948,2.37562,0.53148,0.3359,0.05924,0.33584,0.2801,0.47018,0.12614,0.10676,0.10938,0.02998,0.1847,0.07334,1.07808,0.27426,0.06612,0.88388,0.90632,0.37154,0.15776,1.08776,0.37764,0.3814,0.40534,0.37642,0.487,0.14714,0.39234,0.26382,0.11278,1.26666,0.74416,0.40592,0.56886,0.38426,0.0,0.16898,0.47294,0.16466,0.48684,0.79506,0.36506,0.43268,0.37368,0.47834,0.8042,0.30302,0.20338,0.73884,0.64396,1.24144,0.4334,1.61022,0.52786,0.44816,0.69122,0.44074,0.2727,1.42302,0.15196,0.35898,0.15712,0.65462,1.11408,0.25202,1.36164,0.75246,1.43352,1.36982,1.99092,0.21062,1.1477,0.28418,1.13598,0.48216,0.27018,1.71958,0.2226,0.49316,0.74896,0.23266,0.49886,0.26008,0.11608,0.1231,0.26652,0.47862,0.24218,0.3849,0.50784,0.22584,1.32642,0.14882,0.49158,0.58102,0.59668,0.65248,0.18782,0.3246,0.21592,0.1467,1.047,0.34038,0.51902,0.31826,1.2062,0.11642,0.22358,0.14778,0.36254,0.9546,0.0586,0.233,12.67908,0.05534,2.09564,0.46586,0.3842,1.84282,1.10594,0.13906,0.0925,0.42318,0.69968,0.23602,0.73856,0.2639,0.37174,0.47838,0.24088,0.09168,0.05248,0.35376,0.09586,0.4689,0.41838,1.2789,1.00118,0.16682,0.02986,0.88208,0.37576,0.28652,0.31658,1.09482,0.14714,0.02144,0.21874,0.24884,0.1847,0.73182,0.07322,0.15016,0.45878,0.10906,0.13214,0.0862,1.30264,3.4465,0.02268,0.30542,4.90892,2.01616,0.43386,0.08634,0.63392,0.17974,0.17156,0.26572,0.58932,0.10608,0.0324,0.29142,0.09516,0.54586,0.7711,0.03726,1.52672,0.29712,3.16416,0.83212,0.07486,3.08398,0.59122,1.03034,2.89098,0.47896
2018-11-30,0.33406,0.28948,4.0812,0.295,0.43476,0.21724,0.1273,0.28194,0.45958,0.14444,0.63908,0.93146,0.64012,0.271,0.45378,2.26358,0.71672,1.18986,0.43352,0.201,0.42578,0.2804,0.47134,0.768,0.34806,0.78234,0.28142,0.84848,1.32948,0.30818,0.33448,0.7341,0.7408,0.83892,0.71006,2.31556,0.51396,0.41164,0.26956,0.26288,1.04944,0.14946,0.29388,0.54318,0.38386,0.3734,0.5997,1.99558,1.4717,1.02644,0.57244,1.26376,0.15638,0.44994,0.42244,0.59218,0.3388,0.26906,0.41802,0.28176,0.40764,0.94138,0.32392,0.73884,1.45782,2.34818,0.54768,0.0,0.7868,0.44274,0.34378,2.71306,0.14748,0.25754,9.03254,0.43416,0.46206,3.23656,1.56778,3.74624,1.96188,1.19652,0.5405,0.1962,0.51862,0.56364,1.2588,0.55408,0.9764,0.39104,0.6925,0.48274,1.1937,0.77996,0.74304,0.11774,0.33336,3.64222,5.8121,1.05072,0.471,1.40646,0.52102,1.4151,2.37848,1.14924,0.75786,0.98404,0.86662,2.97988,0.9544,0.4502,0.58206,2.27206,0.55124,0.29966,0.0691,0.2992,0.33584,0.55158,0.1415,0.09402,0.10342,0.024,0.18486,0.06184,0.9522,0.3316,0.06396,0.85316,0.83706,0.34152,0.15088,1.04074,0.37132,0.3355,0.37044,0.36116,0.46102,0.1419,0.3199,0.19358,0.10708,1.13328,0.54966,0.37214,0.5069,0.36518,0.0,0.15258,0.44272,0.15072,0.43324,0.62956,0.27422,0.57742,0.31828,0.43184,0.81688,0.32546,0.17772,0.67584,0.84636,1.17974,0.37588,1.43222,0.69422,0.41214,0.63474,0.43092,0.25206,1.27118,0.1535,0.34696,0.13824,0.50338,1.16066,0.23152,1.28724,0.9153,1.3384,1.08796,2.03704,0.2566,1.12416,0.25668,1.16772,0.37462,0.269,1.52058,0.2243,0.46944,0.71578,0.21812,0.51928,0.27494,0.1066,0.12404,0.25262,0.41488,0.22422,0.36754,0.49488,0.20704,1.23682,0.15912,0.66552,0.52452,0.52532,0.64992,0.18318,0.34676,0.18962,0.15478,0.9636,0.31304,0.44158,0.31974,1.26592,0.10306,0.19634,0.14998,0.32486,0.99776,0.05076,0.22374,13.78134,0.05672,1.93868,0.45666,0.33336,1.51284,1.06372,0.1566,0.09706,0.38258,0.68852,0.2369,0.66786,0.20148,0.34758,0.42886,0.2637,0.08048,0.05104,0.34948,0.10456,0.48254,0.3785,1.20672,0.89174,0.15414,0.02774,0.87598,0.32762,0.25878,0.25812,1.36744,0.14122,0.01844,0.20194,0.28422,0.17862,0.71618,0.0634,0.14248,0.46988,0.09824,0.11998,0.09128,1.13398,3.15608,0.02146,0.28472,5.02104,1.97904,0.44798,0.07338,0.58252,0.164,0.17808,0.24982,0.53054,0.10788,0.03062,0.29026,0.08546,0.46794,0.6806,0.03342,1.3928,0.2714,2.85412,0.74534,0.08766,3.20516,0.57236,1.03736,2.69956,0.49806
2018-12-03,0.39782,0.36504,4.8313,0.3342,0.496,0.26466,0.14968,0.40198,0.63384,0.17716,0.86224,1.06664,0.66584,0.35932,0.50026,2.19974,0.72906,1.30332,0.57836,0.26374,0.49684,0.30498,0.6876,0.75816,0.45158,0.76024,0.29016,1.01336,1.55976,0.37832,0.44134,0.92678,0.82862,0.76734,0.84112,2.56044,0.70634,0.5968,0.34716,0.27886,1.24238,0.15668,0.3511,0.64042,0.48862,0.41236,0.793,1.98542,1.62088,1.43724,0.6036,1.8108,0.1743,0.47488,0.60142,0.64728,0.35518,0.2939,0.56108,0.35436,0.49892,1.12418,0.35504,0.8579,1.60406,3.27618,0.69024,0.0,0.86852,0.5289,0.43372,2.63706,0.1855,0.30686,10.94778,0.63432,0.58402,3.46068,1.82416,4.89548,2.74164,1.23118,0.6738,0.18776,0.62948,0.61988,1.17346,0.71108,1.17172,0.39964,0.65164,0.55626,1.45086,0.8777,0.87706,0.1379,0.39602,3.95246,6.4631,1.06924,0.54182,1.60958,0.6479,1.5835,2.88096,1.23954,0.77904,1.16054,0.99036,3.7308,0.81674,0.60544,0.64378,2.44062,0.62068,0.36102,0.0895,0.44184,0.44066,0.65098,0.14492,0.12492,0.12934,0.02882,0.20134,0.06462,1.05926,0.36976,0.0815,0.9145,1.08042,0.45592,0.1911,1.0058,0.4903,0.40756,0.37734,0.3943,0.55512,0.20112,0.40422,0.1978,0.13982,1.24668,0.6276,0.42068,0.55636,0.339,0.0,0.15194,0.54414,0.16816,0.50674,0.84168,0.29362,0.65104,0.395,0.47826,0.81902,0.40484,0.25592,0.8853,1.12374,1.40252,0.37956,1.39892,0.89618,0.46116,0.70702,0.44824,0.28084,1.77786,0.25012,0.4068,0.15654,0.65974,1.33796,0.2822,1.63588,0.91804,1.62172,1.36772,2.3304,0.35896,1.40838,0.26142,1.36252,0.46898,0.30182,1.74138,0.22518,0.54384,0.74224,0.2411,0.5644,0.37226,0.11098,0.11988,0.36608,0.5105,0.37816,0.4184,0.58112,0.26406,1.31558,0.18174,0.92526,0.5441,0.63558,0.85176,0.1889,0.47186,0.21276,0.17046,0.97526,0.36034,0.50022,0.42594,1.40932,0.137,0.24022,0.17292,0.39062,1.14322,0.05934,0.2747,16.96758,0.06378,2.3668,0.5519,0.43356,2.09894,1.43562,0.19868,0.12134,0.47228,0.71756,0.33076,0.71974,0.24116,0.34384,0.51468,0.3289,0.0906,0.06056,0.48832,0.12384,0.61552,0.45866,1.3559,0.96696,0.1798,0.03776,0.9661,0.34544,0.31142,0.27138,1.82162,0.16534,0.02438,0.23486,0.34542,0.23246,0.88642,0.07278,0.18018,0.5626,0.12166,0.13532,0.10754,1.42138,3.55218,0.02604,0.36664,5.8062,2.2611,0.53084,0.0818,0.61166,0.19638,0.27916,0.3223,0.67374,0.13302,0.03398,0.33928,0.10314,0.44324,0.73536,0.04246,1.58414,0.30596,3.71366,0.7595,0.1134,3.67832,0.59034,1.28248,3.16042,0.58112
2018-12-04,0.37468,0.4304,4.8388,0.38116,0.50786,0.30554,0.15292,0.41764,0.75246,0.18232,0.96594,1.0969,0.68526,0.37012,0.52546,1.94822,0.71064,1.3568,0.61602,0.27888,0.4888,0.31566,0.71848,0.7902,0.54452,0.80134,0.32512,1.0847,1.60034,0.44468,0.47146,0.9697,0.92692,0.71098,0.82814,2.65114,0.76656,0.67334,0.35928,0.28462,1.31148,0.16244,0.43404,0.67074,0.5305,0.40594,0.97338,1.92066,1.70032,1.5223,0.5832,1.97954,0.20998,0.50518,0.6188,0.66242,0.36498,0.30208,0.65622,0.3484,0.55174,1.12048,0.38114,0.90016,1.50366,3.3965,0.75718,0.0,0.96062,0.62032,0.4498,2.56312,0.19108,0.27564,11.93584,0.63706,0.7621,3.45676,2.03472,5.11352,3.0185,1.22726,0.66214,0.17284,0.64476,0.63526,1.14782,0.73714,1.10012,0.58078,0.53292,0.57524,1.54076,0.8794,1.00486,0.1362,0.4499,4.12874,5.373,1.04696,0.58318,1.68894,0.6652,1.44698,2.92656,1.3079,0.78588,1.15916,0.98502,3.7618,0.73086,0.64504,0.70112,2.4736,0.64696,0.39678,0.09358,0.48064,0.44612,0.71106,0.14918,0.12958,0.13768,0.03048,0.22998,0.06524,0.98818,0.39046,0.08946,1.05754,1.14314,0.47834,0.18974,0.97696,0.62184,0.3999,0.38104,0.40568,0.58188,0.23176,0.42628,0.18534,0.1453,1.23714,0.661,0.4852,0.54928,0.32284,0.0,0.15286,0.54136,0.17704,0.50876,0.91128,0.28984,0.6978,0.44328,0.55134,1.1241,0.43322,0.29684,1.2358,1.22012,1.49162,0.39658,1.36376,0.98748,0.51358,0.68282,0.41818,0.30352,1.99146,0.33688,0.41488,0.15904,0.72708,1.30118,0.29138,1.8304,1.07298,1.61832,1.44252,2.66214,0.37684,1.35972,0.25618,1.73572,0.48612,0.36204,1.68418,0.26376,0.59008,0.71134,0.24834,0.58398,0.40288,0.1087,0.10612,0.41192,0.52486,0.43238,0.41896,0.59472,0.32692,1.81006,0.18448,1.01904,0.5553,0.63436,0.9578,0.2072,0.53414,0.23572,0.15702,0.96532,0.37868,0.49702,0.45896,1.48028,0.15006,0.24466,0.18826,0.38776,1.20626,0.06476,0.3181,18.39486,0.06224,2.52186,0.64204,0.49622,2.4109,1.80932,0.21816,0.13208,0.5092,0.68852,0.35058,0.72398,0.24326,0.32354,0.57044,0.34242,0.09592,0.06244,0.52282,0.13184,0.64504,0.44882,1.36134,1.00828,0.1935,0.04,0.88476,0.35426,0.32696,0.26278,1.98788,0.18354,0.02446,0.2609,0.42884,0.2741,1.0136,0.08132,0.19832,0.57356,0.13782,0.142,0.11844,1.6679,3.59406,0.02796,0.41312,6.07302,2.4024,0.60212,0.08446,0.68596,0.20312,0.30264,0.33224,0.7309,0.1366,0.03414,0.32406,0.11506,0.52104,0.6208,0.04946,1.71632,0.36816,4.1902,0.70128,0.12488,3.77524,0.59602,1.62472,3.09576,0.61378
2018-12-05,0.37978,0.43472,4.68892,0.37674,0.43686,0.33,0.15048,0.43026,0.78388,0.1797,1.03544,1.0629,0.61618,0.3857,0.49604,1.77092,0.76942,1.31836,0.69778,0.27492,0.47914,0.33802,0.75744,0.78138,0.57308,0.7925,0.3298,0.9148,1.32476,0.46876,0.4476,0.85326,0.93264,0.71048,0.80042,1.97778,0.8421,0.66106,0.36348,0.29494,1.26736,0.15068,0.49526,0.86244,0.58864,0.38376,0.95736,1.83956,1.83498,1.50942,0.58028,1.96582,0.20936,0.5334,0.57238,0.6396,0.38088,0.2579,0.71158,0.34852,0.58538,1.14654,0.4456,0.80114,1.41622,3.1914,0.7589,0.0,0.96446,0.77822,0.45982,2.87318,0.23718,0.29014,13.73624,0.60026,0.85754,3.99484,2.19852,5.97686,3.48092,1.24432,0.66818,0.17386,0.65698,0.68418,1.14702,0.67706,1.16192,0.64218,0.53876,0.75134,1.72562,0.79372,1.20024,0.11702,0.443,3.75056,4.15928,1.10202,0.65274,1.50204,0.67162,1.29966,2.7546,1.37424,0.64814,1.2122,0.82794,3.65574,0.8025,0.6186,0.65156,2.28082,0.68914,0.41776,0.09866,0.5031,0.42592,0.75128,0.17168,0.13436,0.14682,0.03026,0.2296,0.06536,1.00402,0.41026,0.08978,1.09568,1.09404,0.53214,0.18698,0.86456,0.6472,0.37258,0.35702,0.48666,0.62886,0.25374,0.4319,0.1772,0.14664,1.10656,0.70522,0.5133,0.52832,0.32706,0.0,0.15428,0.55344,0.18964,0.51454,1.01458,0.28504,0.72874,0.43414,0.51264,1.2806,0.42544,0.2972,1.56394,1.32026,1.56938,0.56658,1.08376,1.0666,0.53494,0.59186,0.3857,0.33472,1.92688,0.38484,0.48796,0.16132,0.85132,1.35238,0.30816,1.7325,1.19774,1.57868,1.46602,2.55296,0.3989,1.24316,0.34458,2.00208,0.56922,0.42086,1.52028,0.27926,0.61468,0.7203,0.24158,0.4232,0.40022,0.11264,0.10222,0.453,0.48522,0.4552,0.40246,0.5655,0.33598,1.78116,0.19772,1.21658,0.52146,0.59928,1.0006,0.22474,0.57982,0.24366,0.15692,0.85088,0.4009,0.49216,0.4411,1.32858,0.15986,0.22686,0.18892,0.4002,1.16994,0.06936,0.33856,17.2195,0.06678,2.47638,0.67758,0.53126,2.86038,1.73324,0.22726,0.1352,0.53582,0.62872,0.35212,0.76426,0.26432,0.30768,0.52766,0.342,0.08868,0.06338,0.52994,0.13514,0.5366,0.48158,1.32632,0.9017,0.19714,0.04126,0.7691,0.375,0.33454,0.27812,1.9971,0.1842,0.0243,0.33624,0.46448,0.28318,1.09702,0.08946,0.21512,0.54706,0.14002,0.14544,0.12078,1.79462,3.60836,0.02772,0.4174,5.49018,2.19424,0.54458,0.08592,0.73816,0.20264,0.27286,0.54814,0.74584,0.13048,0.03392,0.31686,0.11932,0.69592,0.60328,0.05014,1.73306,0.43862,4.3981,0.67388,0.13,4.37012,0.56098,1.90226,2.95426,0.71958
2018-12-06,0.39068,0.4037,4.61188,0.31636,0.4147,0.35606,0.1406,0.41892,0.78726,0.16686,0.99716,1.02174,0.5881,0.39042,0.46976,1.82514,0.68696,1.32732,0.73316,0.27154,0.4444,0.32982,0.88486,0.93718,0.55734,0.74018,0.33018,0.86952,1.17558,0.48332,0.43198,0.80266,0.83448,0.69604,0.77588,1.329,0.8717,0.66166,0.38422,0.30746,1.2925,0.14996,0.48516,1.23816,0.59076,0.40414,0.95188,2.03396,2.01792,1.5803,0.5409,2.07934,0.23858,0.5022,0.55976,0.6059,0.38284,0.23312,0.70308,0.3388,0.5505,1.20902,0.44276,0.8107,1.38098,3.67598,0.77262,0.0,0.91244,0.93254,0.44664,2.8509,0.25096,0.26864,9.3793,0.66678,1.22476,3.18042,2.57108,6.266,3.72996,1.19414,0.69612,0.18038,0.6482,0.71124,1.02834,0.70458,1.21606,0.6547,0.45156,0.85038,1.64102,0.71712,1.22734,0.11936,0.48714,3.40484,4.29132,1.08982,0.73508,1.41412,0.67252,1.2402,2.65698,1.3279,0.5594,1.33882,0.74414,4.29634,0.8487,0.57576,0.65044,1.9386,0.7233,0.48004,0.10074,0.5031,0.41582,0.6947,0.17986,0.13904,0.1525,0.03022,0.2149,0.06292,0.94828,0.41028,0.09856,1.10302,1.0843,0.52976,0.1846,0.89318,0.62368,0.37978,0.35204,0.49604,0.59748,0.2972,0.4783,0.17586,0.15672,1.0105,0.6445,0.55278,0.55364,0.3181,0.0,0.15452,0.58954,0.19262,0.52052,1.37082,0.275,0.6751,0.44496,0.45088,1.28268,0.54296,0.29284,1.69758,1.48262,1.4308,0.59816,0.94666,1.09492,0.47976,0.52758,0.35684,0.34706,1.91322,0.42384,0.55146,0.15358,0.95476,1.57608,0.3195,1.82764,1.14284,2.00794,1.6538,2.3775,0.38726,1.19872,0.44746,1.81416,0.55188,0.52098,1.40724,0.29518,0.60684,0.73468,0.2173,0.31726,0.4029,0.1182,0.1008,0.51024,0.52266,0.45488,0.35988,0.56206,0.3442,1.76908,0.18378,1.20798,0.49806,0.56444,1.06438,0.224,0.7184,0.27222,0.18088,0.75702,0.46708,0.47864,0.43284,1.37364,0.16052,0.21422,0.1946,0.38846,1.27148,0.07098,0.34434,16.59828,0.07222,2.31074,0.72802,0.51686,2.89958,1.55436,0.24378,0.14798,0.50522,0.5668,0.336,0.70618,0.25992,0.3077,0.47866,0.3398,0.08464,0.06836,0.56672,0.1422,0.52056,0.48878,1.30404,0.84872,0.18868,0.04358,0.67322,0.38574,0.33382,0.33686,1.67138,0.16406,0.02526,0.33784,0.44632,0.26014,1.17288,0.0916,0.21108,0.54076,0.13456,0.1394,0.13892,1.92436,3.5002,0.02728,0.44488,4.85428,1.98072,0.51428,0.08398,0.66006,0.19724,0.30128,0.6215,0.80226,0.10892,0.03542,0.30806,0.10574,0.75372,0.61912,0.0533,1.6716,0.54416,5.30606,0.68006,0.13026,4.59612,0.56224,2.00024,2.77426,0.69434
2018-12-07,0.36102,0.42068,4.44208,0.30846,0.4626,0.36394,0.12016,0.42216,0.76268,0.15652,0.94034,0.95698,0.55468,0.38352,0.45198,2.19552,0.6365,1.26696,0.7009,0.25626,0.41832,0.31216,0.83744,1.08018,0.5318,0.69276,0.33502,0.8099,1.04554,0.4833,0.41612,0.6838,0.95914,0.59216,0.75286,1.07294,0.73894,0.64682,0.36818,0.30164,1.25982,0.17434,0.4985,1.61878,0.61106,0.38076,0.94562,2.07676,2.20972,1.44078,0.4896,1.9907,0.24108,0.44792,0.54946,0.54338,0.38154,0.27,0.65424,0.31308,0.56186,0.9693,0.44122,0.7833,1.26088,3.43724,0.7427,0.0044,1.00142,1.22148,0.38636,2.78486,0.28082,0.23004,7.0705,0.64068,1.64662,2.95178,2.6628,5.55142,3.38034,1.1104,0.65736,0.16722,0.60822,0.75762,0.88204,0.69492,1.28036,0.75174,0.4506,0.92692,2.72836,0.63194,1.14154,0.11004,0.59252,3.08924,4.30174,1.28524,0.77064,1.26142,0.60194,1.20222,2.44292,1.41208,0.51862,1.49196,0.63112,4.4198,1.00998,0.5567,0.7411,1.71836,0.71698,0.49046,0.08792,0.5035,0.36862,0.53454,0.17586,0.1311,0.14478,0.02798,0.19714,0.0643,0.8843,0.36784,0.09212,1.05196,1.03582,0.54878,0.18954,0.87964,0.63958,0.37716,0.31464,0.47844,0.74168,0.31686,0.49688,0.18838,0.1477,0.8591,0.601,0.52626,0.53394,0.30464,0.0,0.14096,0.57408,0.19658,0.54414,1.76796,0.27364,0.47276,0.43702,0.43032,1.2972,0.74298,0.28496,1.7076,1.39488,1.33306,0.67214,0.90128,0.88302,0.44492,0.46202,0.32646,0.38866,1.80338,0.44644,0.57014,0.1413,1.00072,1.67558,0.34168,1.7809,1.11952,2.13094,1.5918,2.22778,0.35198,1.17942,0.56604,1.82754,0.5156,0.7414,1.38054,0.27762,0.60722,0.66992,0.19686,0.26698,0.36852,0.1237,0.08154,0.52552,0.52298,0.46344,0.31162,0.51504,0.3427,1.82878,0.17048,1.08484,0.40954,0.55392,1.2358,0.25342,0.69918,0.27406,0.16012,0.65162,0.45962,0.43928,0.39762,1.4646,0.17286,0.20568,0.1824,0.37418,1.28354,0.06906,0.328,15.16706,0.07118,2.10788,0.71772,0.5221,2.86066,1.45324,0.21486,0.13234,0.55488,0.52164,0.31816,0.68774,0.25596,0.33476,0.44992,0.29406,0.09182,0.06526,0.55216,0.1299,0.47864,0.4797,1.1654,0.78606,0.19672,0.04346,0.58668,0.36338,0.35296,0.54154,1.33378,0.15278,0.02428,0.33786,0.41096,0.28642,1.13588,0.08782,0.25758,0.48518,0.14064,0.13286,0.13152,1.87,3.12554,0.0263,0.43046,4.13076,1.68058,0.50064,0.0858,0.61978,0.1838,0.2929,0.6861,0.78886,0.09326,0.03222,0.27368,0.1006,0.92812,0.57106,0.05344,1.5677,0.56288,5.56396,0.65796,0.11646,4.1544,0.50072,2.13076,2.63424,0.59516
2018-12-10,0.29472,0.35202,3.59544,0.27482,0.58258,0.31898,0.08928,0.3668,0.57988,0.13626,0.73278,1.09676,0.50904,0.31244,0.3663,2.25572,0.54892,1.07472,0.56832,0.22068,0.33768,0.253,0.68632,1.14798,0.52116,0.56678,0.31026,0.64068,0.79078,0.45982,0.33802,0.47102,0.9625,0.49542,0.64542,0.79684,0.58012,0.49156,0.26196,0.24162,1.01472,0.15674,0.43672,1.97668,0.49586,0.33562,0.72878,1.74954,2.04052,1.10656,0.40274,1.44274,0.24726,0.38636,0.37188,0.45298,0.30374,0.22942,0.53362,0.30336,0.49214,0.87264,0.47434,0.75818,1.02914,2.52152,0.55324,0.00874,0.95438,1.29358,0.3265,2.7002,0.28746,0.18492,5.72622,0.47198,1.70394,2.60684,2.57264,4.55494,2.81352,0.90326,0.49446,0.12898,0.44742,0.98232,0.72232,0.55888,1.14242,0.7287,0.42972,1.17446,3.37374,0.5101,1.04996,0.08818,0.62964,2.5905,3.4629,2.11828,0.69324,0.99778,0.46772,0.95262,1.94044,1.3487,0.43474,1.37134,0.47476,3.78428,0.93752,0.6936,0.8615,1.35894,0.59104,0.41132,0.06862,0.38524,0.26434,0.41622,0.15768,0.09702,0.12222,0.02242,0.16136,0.05686,0.91122,0.34692,0.0765,0.84166,0.79716,0.44202,0.1485,0.78204,0.51518,0.31514,0.26728,0.4266,0.73592,0.27778,0.4165,0.17758,0.11928,0.68116,0.4982,0.43334,0.46754,0.25388,0.06448,0.12742,0.45332,0.17548,0.4032,1.69364,0.22368,0.3774,0.35386,0.36518,1.22858,0.74938,0.20322,1.519,1.2495,1.0498,0.62996,0.67464,0.69048,0.3725,0.35252,0.27086,0.38464,1.1979,0.33082,0.66284,0.11692,0.85222,1.68194,0.33668,1.43418,1.1247,1.9647,1.31188,1.9658,0.25842,0.95306,0.59534,1.86124,0.40822,0.81302,1.1936,0.25052,0.55936,0.57256,0.15966,0.22142,0.28388,0.11648,0.06742,0.43144,0.43456,0.28936,0.217,0.40752,0.2768,2.12898,0.153,0.92112,0.31796,0.43712,1.13676,0.24274,0.61434,0.2607,0.14472,0.50416,0.40844,0.36966,0.27696,1.62296,0.14846,0.14916,0.16626,0.33194,1.2085,0.05632,0.2767,12.76036,0.06488,1.56038,0.57166,0.40724,2.31646,0.96846,0.1748,0.1095,0.57678,0.40884,0.2235,0.6191,0.20254,0.285,0.3695,0.22414,0.08236,0.05286,0.43634,0.11228,0.34202,0.39984,0.98082,0.59892,0.18318,0.03296,0.45528,0.31488,0.33192,0.63956,0.88164,0.12066,0.01804,0.32092,0.38064,0.25276,0.97704,0.07742,0.28076,0.3731,0.12102,0.10388,0.11506,1.53252,2.54128,0.02038,0.36088,3.2072,1.27076,0.43002,0.07552,0.66992,0.14704,0.20048,0.80126,0.61324,0.06662,0.02762,0.2242,0.08582,1.02988,0.4713,0.04718,1.26952,0.47402,4.82318,0.56144,0.09342,3.85874,0.5007,1.98662,2.01756,0.5015
2018-12-11,0.26726,0.33632,3.4164,0.2246,0.57914,0.26846,0.08326,0.38434,0.44024,0.12454,0.64024,1.0912,0.48368,0.30258,0.3235,2.19622,0.55676,1.07812,0.53114,0.20962,0.2896,0.22804,0.64282,1.17224,0.49702,0.5408,0.2603,0.54816,0.75592,0.38784,0.30182,0.4569,0.84494,0.45194,0.61094,0.77832,0.52484,0.44484,0.22918,0.22472,0.86308,0.13002,0.36982,2.13968,0.44384,0.30432,0.55816,1.58924,1.87546,1.25998,0.40864,1.47384,0.23458,0.35246,0.43166,0.4349,0.28064,0.2014,0.4612,0.29098,0.60064,0.89672,0.49026,0.71624,0.86324,2.36542,0.47386,0.00964,1.0287,1.24262,0.33208,2.75328,0.30898,0.1734,5.4548,0.44766,1.60536,2.52108,2.29142,4.23978,2.66042,0.73888,0.41686,0.1216,0.42022,1.08792,0.668,0.4846,1.1305,0.52384,0.40166,1.37988,3.76582,0.45554,1.10594,0.08516,0.62994,2.09434,3.48828,2.4334,0.67332,0.85526,0.43712,0.89128,1.74068,1.26938,0.46286,1.35562,0.4557,3.77198,0.86032,0.9148,0.90244,1.18318,0.5473,0.37502,0.06606,0.37138,0.25862,0.35366,0.14172,0.08066,0.11704,0.02078,0.11878,0.05254,0.99386,0.33962,0.0704,0.59256,0.67208,0.416,0.13916,0.74762,0.42646,0.29536,0.2489,0.42226,0.6876,0.27768,0.39708,0.16906,0.10528,0.61032,0.44122,0.32902,0.43486,0.2239,0.57412,0.13156,0.41574,0.15714,0.34832,1.63968,0.20306,0.32044,0.30178,0.27904,0.89074,0.77614,0.16192,1.16324,1.23218,0.93476,0.60022,0.636,0.5953,0.31406,0.32226,0.23784,0.4123,0.92212,0.25738,0.75452,0.11102,0.89964,1.6775,0.38664,1.20344,1.17714,2.0486,1.20458,1.6098,0.2343,1.01598,0.59728,1.70734,0.36042,0.76284,1.1424,0.21822,0.51612,0.5419,0.13822,0.20086,0.26806,0.12448,0.06114,0.37202,0.40526,0.22752,0.17354,0.355,0.22714,1.6348,0.1433,0.8807,0.28376,0.39388,1.1033,0.22744,0.57994,0.2318,0.14584,0.40904,0.36442,0.34352,0.21842,1.78274,0.15048,0.13062,0.14624,0.2968,1.10072,0.04962,0.21432,10.71042,0.05752,1.2373,0.4291,0.33726,2.01876,0.52592,0.15564,0.09826,0.57112,0.35964,0.18796,0.61106,0.19014,0.25734,0.30048,0.20332,0.07798,0.05052,0.41004,0.1023,0.33396,0.36708,0.8563,0.52726,0.17334,0.03032,0.42186,0.29766,0.31126,0.6582,0.76342,0.09276,0.0169,0.3003,0.28568,0.2106,0.83116,0.06928,0.29246,0.33958,0.10272,0.08414,0.09946,1.24246,2.152,0.01732,0.32638,2.59574,1.01172,0.35774,0.06472,0.64268,0.13252,0.18512,0.85928,0.53486,0.05926,0.02672,0.20318,0.07444,0.9874,0.42736,0.04094,1.12896,0.40136,4.19766,0.52878,0.08162,3.78452,0.51378,1.69922,1.81426,0.44424
2018-12-12,0.2422,0.3523,3.32056,0.23134,0.59276,0.23826,0.07788,0.40594,0.3886,0.15338,0.7246,1.08906,0.50086,0.29674,0.3199,2.15334,0.47314,1.12962,0.4604,0.21648,0.27054,0.1938,0.58844,1.20498,0.51708,0.58744,0.22482,0.4785,0.72224,0.34464,0.27888,0.44042,0.80716,0.4004,0.55602,0.7087,0.44102,0.37564,0.21404,0.21136,0.77104,0.1165,0.35492,2.0318,0.36286,0.2812,0.60244,1.4503,1.66988,1.19136,0.37826,1.39522,0.23046,0.29682,0.45394,0.3976,0.25206,0.193,0.39348,0.28124,0.68612,0.89582,0.50738,0.67058,0.7563,2.3117,0.43752,0.011,1.0065,1.06164,0.30132,2.38564,0.28874,0.15184,4.21804,0.47796,1.54364,1.97676,2.0727,3.40364,2.4023,0.7101,0.40276,0.10778,0.37746,1.07926,0.5632,0.50478,1.08664,0.44238,0.34334,1.27242,4.35012,0.4357,0.97824,0.08178,0.59086,2.40814,3.52696,2.54468,0.58674,0.73812,0.38834,0.84278,1.6077,1.33284,0.47156,1.29444,0.42772,3.73516,0.73544,1.07696,0.94016,1.0363,0.4905,0.34984,0.05678,0.38126,0.24886,0.28484,0.1251,0.07166,0.11798,0.02024,0.10736,0.04834,1.01106,0.38906,0.06866,0.47816,0.61886,0.41408,0.13332,0.75456,0.444,0.27192,0.22564,0.34856,0.64654,0.25442,0.3725,0.16378,0.09954,0.60332,0.35478,0.27588,0.3767,0.20436,0.92484,0.1244,0.37514,0.14314,0.29788,1.539,0.19536,0.29138,0.22602,0.25564,0.68668,0.79002,0.14498,0.82302,1.11666,0.77312,0.49996,0.67142,0.5384,0.2459,0.29358,0.21844,0.43896,0.78172,0.23048,0.73914,0.09832,0.7595,1.4148,0.41746,1.13118,1.11396,2.0114,1.11446,1.1885,0.1998,0.98394,0.51742,1.61984,0.24202,0.72262,1.03618,0.19898,0.47862,0.46112,0.13724,0.17332,0.25772,0.12778,0.06284,0.33644,0.38414,0.1903,0.1419,0.3257,0.21388,1.57176,0.12976,0.70916,0.2553,0.37828,0.98014,0.22458,0.5246,0.21284,0.16752,0.3672,0.32448,0.33002,0.237,1.89298,0.15322,0.1392,0.13426,0.27132,0.84822,0.0469,0.23076,10.46336,0.05236,1.0613,0.36876,0.2776,1.59666,0.49008,0.14984,0.09096,0.55598,0.32696,0.17412,0.56098,0.17012,0.25652,0.26694,0.20584,0.07428,0.04706,0.39844,0.1027,0.28728,0.3116,0.77116,0.47242,0.16952,0.0279,0.36692,0.2517,0.3024,0.6935,0.71338,0.0863,0.0156,0.23486,0.25392,0.20546,0.70244,0.0601,0.30278,0.31542,0.10394,0.06952,0.08848,1.0968,1.8674,0.0165,0.33252,2.38376,0.9067,0.33762,0.05644,0.56808,0.10972,0.16918,0.71454,0.52496,0.06038,0.02588,0.18346,0.06574,0.84464,0.39332,0.0383,1.25242,0.3835,3.76354,0.45308,0.07496,3.03136,0.56166,1.45258,2.14166,0.35282
2018-12-13,0.2669,0.43496,2.62616,0.30244,0.6061,0.23254,0.0917,0.56766,0.42944,0.20402,0.81314,1.18636,0.44266,0.29562,0.46148,2.04954,0.47896,1.29136,0.4687,0.26378,0.28276,0.19518,0.71764,1.2096,0.6582,0.63904,0.23216,0.4939,0.70586,0.32676,0.28024,0.41464,0.96766,0.38558,0.56676,0.62066,0.4044,0.319,0.20892,0.2253,0.67942,0.11604,0.4274,1.79188,0.38062,0.26424,0.77906,1.168,1.4394,1.15538,0.38742,1.32036,0.26224,0.3075,0.53628,0.4001,0.2458,0.1852,0.41102,0.29192,0.81916,0.81522,0.53492,0.68362,0.7196,1.78652,0.4062,0.01562,1.49104,0.87346,0.31132,2.46588,0.33564,0.14174,3.29632,0.43642,1.2983,1.99016,1.67934,3.05976,2.29174,0.65728,0.37048,0.10344,0.96278,1.04016,0.55064,0.8392,1.02574,0.43574,0.35658,1.35228,5.75614,0.45508,1.02962,0.07794,0.49988,3.39838,3.23612,2.67988,0.55204,0.81758,0.3744,0.78046,1.72306,1.3997,0.55466,1.15678,0.45876,3.03106,0.80512,1.1468,0.96196,1.09978,0.58384,0.29994,0.05406,0.53448,0.28242,0.34208,0.12072,0.07438,0.13326,0.0232,0.12098,0.0481,1.17426,0.45568,0.06522,0.44228,0.68226,0.53828,0.1506,0.72224,0.50704,0.2619,0.2138,0.37456,0.8442,0.28306,0.41878,0.17644,0.10664,0.66578,0.4535,0.2318,0.2914,0.22696,1.1534,0.15872,0.43898,0.14744,0.30216,1.22534,0.22206,0.3794,0.21012,0.28868,0.69452,0.71428,0.14632,0.7717,0.94926,0.82668,0.53224,0.77644,0.64584,0.26162,0.3142,0.20258,0.49772,0.70708,0.22802,0.70968,0.10046,0.77666,1.12704,0.44012,1.06178,1.0058,1.434,0.94836,1.07034,0.204,1.10962,0.41812,1.47834,0.22384,0.67668,1.12754,0.2075,0.57532,0.42398,0.15172,0.16782,0.2728,0.14228,0.08512,0.42216,0.36224,0.18466,0.13808,0.3242,0.21822,1.64788,0.13376,0.67072,0.25194,0.40444,0.86126,0.23826,0.43348,0.17668,0.16234,0.37442,0.2942,0.35358,0.28888,1.9395,0.17302,0.17128,0.13704,0.28562,0.71162,0.0636,0.24738,9.4349,0.05128,1.07986,0.33706,0.35138,1.60742,0.6072,0.14408,0.08264,0.82962,0.3359,0.17946,0.52306,0.19046,0.2688,0.3031,0.1994,0.08152,0.04258,0.4348,0.10972,0.31236,0.33466,0.68632,0.4723,0.25932,0.02678,0.36324,0.2645,0.29934,0.68606,0.9608,0.12206,0.01502,0.25096,0.34496,0.28174,0.63648,0.0593,0.39074,0.3196,0.16024,0.06818,0.0813,0.99358,1.81014,0.01682,0.31426,2.46862,0.9757,0.3441,0.0554,0.56126,0.10798,0.1305,0.68786,0.58586,0.0782,0.02554,0.204,0.06346,0.83524,0.4014,0.04092,1.46848,0.41296,2.85618,0.43946,0.08572,2.6888,0.82284,1.25442,2.09198,0.37302
2018-12-14,0.28862,0.44242,2.50782,0.36424,0.59506,0.22146,0.11394,0.59788,0.5131,0.23368,1.05822,1.32286,0.46726,0.31654,0.54132,1.76776,0.4583,1.52548,0.46106,0.35958,0.29222,0.20794,0.79162,1.28796,0.72226,0.6821,0.21202,0.53896,0.7107,0.32722,0.29994,0.42412,0.98502,0.42018,0.70486,0.65516,0.41826,0.28128,0.1994,0.24732,0.7174,0.10544,0.44232,1.5711,0.35778,0.26854,0.89448,1.15894,2.21216,1.21656,0.392,1.61976,0.31802,0.35794,0.56724,0.44818,0.2318,0.23024,0.47508,0.30638,0.89266,0.90072,0.63606,0.70832,1.17714,1.76156,0.44392,0.02108,1.96392,0.63156,0.29712,2.70092,0.4324,0.1734,2.99682,0.45154,0.99846,2.16588,1.75624,3.15076,2.34848,0.79788,0.53084,0.11142,1.30546,1.0534,0.59226,1.00328,0.96716,0.34638,0.3663,1.36502,5.9833,0.48122,1.036,0.08202,0.42654,3.96686,4.08304,2.99044,0.54584,0.8708,0.3916,0.7647,1.84084,1.46128,0.64764,1.11772,0.4615,3.70798,0.8172,1.17978,0.86348,1.32134,0.56702,0.31536,0.06374,0.69914,0.28572,0.45884,0.14532,0.0887,0.19816,0.03068,0.14754,0.05058,1.21576,0.51178,0.08058,0.46056,0.71154,0.60112,0.1596,0.7152,0.51186,0.2596,0.2183,0.36836,0.7661,0.30278,0.44218,0.2121,0.1283,0.72336,0.68252,0.25532,0.30562,0.25534,1.29372,0.18062,0.56294,0.16034,0.27488,0.99834,0.24078,0.48218,0.22406,0.31148,0.76316,0.54746,0.1593,0.80292,0.91994,0.90484,0.5505,0.76598,0.86164,0.29448,0.33456,0.21936,0.50872,0.71448,0.23514,0.72296,0.11338,0.80486,1.0933,0.36264,1.1502,1.06904,1.44534,0.96974,1.00312,0.20036,1.21448,0.32354,1.50276,0.27704,0.56442,1.1848,0.21694,0.61266,0.45706,0.17742,0.20928,0.30938,0.1612,0.12698,0.46134,0.3947,0.20672,0.1608,0.42102,0.22358,1.76226,0.1399,0.86852,0.4542,0.45272,0.687,0.2184,0.45314,0.17672,0.17722,0.41304,0.29884,0.38772,0.41844,1.942,0.23262,0.24184,0.13312,0.31702,0.70386,0.09472,0.29964,9.66886,0.05232,1.1655,0.34788,0.39268,2.12556,0.60966,0.15042,0.09784,0.86662,0.3764,0.22596,0.54018,0.22242,0.24608,0.46272,0.22504,0.08336,0.04556,0.5235,0.12448,0.30638,0.33842,0.91792,0.48542,0.31404,0.02794,0.3901,0.2792,0.29964,0.52318,1.11228,0.15264,0.0172,0.25756,0.4183,0.30856,0.65992,0.0672,0.40042,0.34092,0.20154,0.07112,0.09322,1.08534,2.02592,0.02166,0.35506,2.86324,1.2599,0.4172,0.05226,0.60394,0.1179,0.12618,0.69516,0.7185,0.0849,0.03014,0.22914,0.06554,0.70354,0.48636,0.04388,1.82044,0.46788,2.97716,0.45418,0.10106,2.68602,0.91182,1.07754,2.2042,0.49928
2018-12-17,0.29752,0.45208,2.55562,0.37198,0.42654,0.22416,0.12226,0.5844,0.62064,0.24304,1.02338,1.03076,0.43504,0.35954,0.575,1.54934,0.47716,1.52662,0.48422,0.38362,0.24578,0.22204,0.77432,1.3666,0.62778,0.74906,0.20724,0.55416,0.66462,0.29262,0.26762,0.42376,0.90084,0.42276,0.74912,0.6339,0.38932,0.28768,0.203,0.25298,0.71404,0.10942,0.50152,1.21006,0.3516,0.26084,0.94204,1.1634,2.42596,1.18978,0.37378,1.79874,0.3107,0.41434,0.55542,0.4501,0.26106,0.30186,0.5093,0.25704,0.88914,0.79278,0.60542,0.60704,1.30018,1.7082,0.43932,0.02248,2.11852,0.47562,0.30782,2.68554,0.44888,0.19662,3.6144,0.41896,0.84278,2.26132,1.62988,3.18454,2.4193,0.9081,0.7236,0.12586,1.46734,0.81392,0.71962,1.0389,1.01524,0.42628,0.35352,1.07164,6.16062,0.45904,1.067,0.07844,0.35682,4.79674,4.6083,2.19282,0.54954,0.90146,0.39406,0.77724,1.83434,1.4614,0.75738,1.0415,0.4616,3.73202,0.92068,1.00592,0.71962,1.4439,0.54034,0.33902,0.06646,0.75724,0.32886,0.5002,0.17042,0.10082,0.21314,0.0326,0.15084,0.0587,0.96776,0.5406,0.07564,0.47254,0.68024,0.71696,0.1639,0.70878,0.50856,0.24588,0.218,0.37358,0.74468,0.39564,0.44106,0.24318,0.15156,0.69236,0.67056,0.25558,0.30316,0.2594,1.34232,0.2803,0.61548,0.1649,0.27292,0.91384,0.25292,0.50446,0.23912,0.31194,0.82454,0.57638,0.16058,0.80238,0.84106,0.81798,0.58222,0.7769,0.9282,0.283,0.33308,0.20156,0.52956,0.74722,0.26662,0.60476,0.1119,0.84124,0.95948,0.2843,1.10914,0.99024,1.31676,0.94072,0.9076,0.20076,1.14154,0.28346,1.34682,0.27344,0.4988,1.12186,0.21656,0.57778,0.49006,0.1895,0.21044,0.30362,0.18288,0.16322,0.44704,0.3817,0.23826,0.16876,0.43894,0.22982,1.43406,0.14358,0.87168,0.54902,0.45714,0.57164,0.23738,0.44764,0.17442,0.17648,0.41088,0.32182,0.36688,0.47356,1.8389,0.23906,0.27856,0.13094,0.28614,0.7069,0.1021,0.29098,9.38034,0.0534,1.14584,0.34788,0.4258,2.3546,0.607,0.1604,0.10682,0.8921,0.385,0.25506,0.49604,0.23196,0.27944,0.59822,0.2439,0.0825,0.05098,0.56366,0.13824,0.30342,0.32102,0.96368,0.48624,0.32928,0.02802,0.39066,0.27072,0.2515,0.44198,1.13654,0.1689,0.01774,0.24062,0.4812,0.32498,0.6451,0.07752,0.37768,0.35416,0.225,0.06288,0.09482,1.1211,2.00112,0.02176,0.37366,2.79524,1.25904,0.40946,0.05704,0.49374,0.1156,0.1245,0.52002,0.68312,0.10034,0.03,0.24506,0.06472,0.67724,0.56346,0.04112,1.92612,0.69636,2.97522,0.45952,0.10702,2.50184,0.93338,1.14014,2.40944,0.53906
2018-12-18,0.32532,0.44796,2.37312,0.3947,0.42364,0.24384,0.12424,0.602,0.71858,0.2763,0.99798,0.9617,0.44146,0.42444,0.60388,1.5308,0.44118,1.54462,0.49848,0.41802,0.2266,0.23128,0.80926,1.44424,0.63874,0.7305,0.2239,0.60876,0.6016,0.30004,0.2771,0.3713,1.01512,0.43184,0.77744,0.53846,0.38752,0.31568,0.21426,0.2607,0.78808,0.13324,0.51932,1.02298,0.38682,0.25712,1.00618,1.11122,2.47708,1.0662,0.36078,1.73784,0.36974,0.4801,0.46106,0.51644,0.31198,0.33564,0.54918,0.26388,0.78562,0.78354,0.88014,0.6004,1.38102,1.61016,0.43112,0.02672,2.17964,0.44684,0.30062,2.59666,0.44078,0.24708,3.91202,0.41074,0.77564,2.2579,1.66868,3.36538,2.72396,1.07424,0.78284,0.12138,1.60942,0.6902,0.78512,1.21212,0.98002,0.44636,0.3499,0.82726,6.3122,0.4719,0.9991,0.07716,0.30366,5.49774,4.49902,2.02226,0.51072,0.955,0.39978,0.9484,1.97996,1.5948,0.70758,1.04046,0.45476,3.59868,0.91832,0.77274,0.74078,1.55736,0.56228,0.34602,0.06804,0.7696,0.33864,0.50046,0.17776,0.12274,0.22532,0.03374,0.1572,0.07106,0.79652,0.57888,0.07996,0.49154,0.7596,0.80402,0.18206,0.7191,0.51192,0.25766,0.2514,0.35142,0.89832,0.42264,0.47668,0.28178,0.17216,0.71182,0.67934,0.2661,0.32758,0.2819,0.94912,0.2908,0.65178,0.18496,0.27746,0.8744,0.28716,0.53638,0.2468,0.31926,0.86334,0.57692,0.15958,0.8153,0.7937,0.81296,0.68588,0.82938,0.98632,0.2888,0.34694,0.22662,0.51404,0.74562,0.26822,0.58672,0.11252,0.74736,0.9478,0.16916,1.04932,0.88656,1.15182,0.94636,0.8748,0.20538,1.0049,0.25946,1.29834,0.31134,0.49052,1.1505,0.21472,0.57698,0.49724,0.21076,0.19988,0.3354,0.21246,0.1728,0.49474,0.44502,0.26904,0.18482,0.51488,0.21622,1.4453,0.15222,0.86054,0.63648,0.46792,0.50578,0.2971,0.4744,0.17484,0.19788,0.41902,0.34596,0.35238,0.50748,1.69174,0.2266,0.29988,0.15478,0.31196,0.67088,0.11558,0.34818,9.73632,0.05672,1.26984,0.36812,0.47656,2.34168,0.61096,0.17094,0.1118,0.94972,0.42282,0.2881,0.48188,0.2301,0.27692,0.78086,0.26234,0.08514,0.05572,0.5844,0.14598,0.31016,0.3407,1.01222,0.49512,0.34556,0.03076,0.3866,0.28112,0.25422,0.46246,1.18542,0.1854,0.01928,0.24048,0.56228,0.34718,0.69418,0.08292,0.379,0.38098,0.24032,0.0702,0.1052,1.1568,1.98666,0.02296,0.38006,2.90964,1.3239,0.43352,0.0685,0.49784,0.1162,0.13172,0.47346,0.69884,0.11476,0.03188,0.26776,0.06222,0.72634,0.58422,0.04332,1.97756,0.75078,2.80522,0.4552,0.1239,2.64122,0.99594,1.13512,2.4885,0.59106
2018-12-19,0.3523,0.408,2.22944,0.41286,0.3876,0.2533,0.11844,0.57792,0.81046,0.27306,0.82324,0.85908,0.37786,0.49778,0.5841,1.48934,0.45018,1.51072,0.5411,0.4483,0.22058,0.22822,0.82656,1.53066,0.60556,0.70964,0.24132,0.62938,0.58038,0.30762,0.26616,0.3404,1.0285,0.41698,0.7872,0.5073,0.42406,0.34116,0.23884,0.28514,0.81726,0.1361,0.4812,1.15938,0.43644,0.2605,0.9498,1.08402,2.72432,1.10608,0.36614,1.72786,0.39392,0.54332,0.42954,0.53302,0.32104,0.34252,0.74534,0.27968,0.75796,0.69306,0.97788,0.66462,1.42918,1.50218,0.41308,0.06804,2.29404,0.5198,0.31284,2.44964,0.44984,0.28496,3.97262,0.34888,0.78674,2.3818,1.6457,3.03552,2.70722,1.09714,0.82038,0.11656,1.73566,0.72418,0.84206,1.23582,0.94876,0.4534,0.3338,0.82756,6.0069,0.44958,1.043,0.07204,0.2935,5.25708,4.45442,2.3373,0.6953,0.95164,0.3919,1.01032,2.05388,1.39482,0.6749,1.0204,0.44236,3.60788,0.91608,0.61906,0.74614,1.56324,0.54456,0.35682,0.06886,0.76184,0.36834,0.49568,0.1882,0.12578,0.21952,0.03454,0.1623,0.08174,0.7092,0.51958,0.0949,0.5233,0.75706,0.83068,0.2429,0.67412,0.46526,0.2541,0.26234,0.32166,0.93284,0.51404,0.46384,0.28946,0.21516,0.6738,0.66512,0.32206,0.3508,0.29146,0.69564,0.30282,0.66348,0.19202,0.31008,1.02314,0.31504,0.5254,0.23756,0.32482,0.79184,0.59962,0.14756,0.88548,0.82732,0.81968,0.6728,0.92334,0.97198,0.31284,0.35408,0.26962,0.44952,0.75124,0.25556,0.58132,0.11252,0.76472,0.97258,0.07924,1.01114,0.86318,1.12786,0.94558,0.90084,0.2146,1.00076,0.25872,1.23544,0.36248,0.48408,1.21094,0.26104,0.55836,0.52764,0.20298,0.19866,0.36998,0.2328,0.17212,0.5146,0.46152,0.29948,0.19044,0.54508,0.2553,1.41028,0.1597,0.84002,0.6821,0.45768,0.53996,0.32512,0.52296,0.19522,0.19316,0.39072,0.35322,0.34204,0.47864,1.52138,0.2146,0.28298,0.18322,0.35506,0.7462,0.1143,0.34006,8.98738,0.05866,1.2737,0.3923,0.48984,2.3496,0.58352,0.17526,0.11804,0.98828,0.39228,0.28958,0.50294,0.2188,0.2801,0.86046,0.2656,0.08648,0.0593,0.60806,0.14842,0.31752,0.3564,0.9984,0.48272,0.34826,0.03344,0.38888,0.30074,0.25588,0.46904,1.17136,0.18688,0.02108,0.22838,0.60094,0.35224,0.74772,0.08108,0.36884,0.36962,0.24002,0.10134,0.11354,1.13096,1.92236,0.02486,0.32202,2.66838,1.23344,0.40136,0.072,0.47534,0.1113,0.13862,0.41834,0.7154,0.11198,0.03276,0.27582,0.06256,0.68574,0.57558,0.04472,2.10246,0.74164,2.86324,0.4379,0.12298,2.5689,1.02938,1.0838,2.00838,0.55574
2018-12-20,0.37552,0.36018,2.11996,0.3493,0.3448,0.2464,0.11092,0.41494,0.8012,0.2556,0.6854,0.70988,0.35732,0.5694,0.45212,1.32708,0.3993,1.3003,0.5108,0.4031,0.21572,0.2157,0.58826,1.56756,0.47372,0.63854,0.22822,0.56734,0.59526,0.34434,0.25614,0.3401,0.84936,0.4271,0.72764,0.46798,0.39874,0.38788,0.22776,0.26376,0.79562,0.12844,0.39988,1.09678,0.41452,0.25398,0.84984,0.97802,2.69118,1.14932,0.33688,1.6137,0.3875,0.6612,0.34818,0.54218,0.31276,0.59362,0.79692,0.33966,0.67166,0.63486,1.04756,0.5637,1.50062,1.21034,0.36508,0.2666,1.91596,0.50876,0.3077,2.1669,0.41018,0.29056,3.93844,0.29494,0.7338,2.3233,1.69656,2.66688,2.70344,1.13002,0.82428,0.12672,1.22436,0.72818,0.86168,0.9129,0.95448,0.4455,0.3069,0.6975,4.87,0.43038,0.96136,0.06736,0.25666,5.34658,4.29618,2.36494,0.7252,0.80664,0.34134,0.99936,1.93958,1.26188,0.59244,0.94232,0.39432,3.59274,0.78546,0.57226,0.72788,1.41728,0.40284,0.34886,0.07618,0.60454,0.34268,0.4071,0.18718,0.13044,0.2187,0.02972,0.1586,0.08932,0.45616,0.482,0.0944,0.49884,0.66046,0.79596,0.2879,0.56594,0.43024,0.22496,0.26514,0.25686,0.80818,0.56056,0.35654,0.28356,0.2272,0.63716,0.52838,0.30426,0.34944,0.2557,0.60604,0.27634,0.53254,0.20958,0.29008,1.04,0.28798,0.43664,0.20944,0.29038,0.76992,0.68668,0.1348,0.73284,0.80512,0.69436,0.62368,1.07696,0.82628,0.26804,0.32516,0.27506,0.37698,0.71866,0.22716,0.5653,0.1065,0.67586,1.00038,0.0,0.8171,0.87326,1.06052,0.8249,0.81904,0.19318,0.80472,0.23982,1.19978,0.35976,0.45478,1.08484,0.25456,0.44534,0.50686,0.17978,0.18434,0.34556,0.23468,0.15896,0.3929,0.45176,0.297,0.2035,0.52478,0.26638,1.33992,0.15276,0.80586,0.77492,0.41692,0.56392,0.32188,0.50246,0.21232,0.17946,0.3569,0.34378,0.39526,0.4428,1.3648,0.18396,0.28204,0.17862,0.36046,0.82244,0.10046,0.33372,8.76284,0.06062,1.25534,0.35838,0.4242,2.22756,0.46226,0.18978,0.13146,0.8549,0.36934,0.27486,0.47276,0.1849,0.27942,0.84446,0.29824,0.0838,0.06756,0.61252,0.16042,0.2928,0.3109,0.94774,0.44044,0.27322,0.03994,0.39168,0.26452,0.2378,0.39682,0.85806,0.15566,0.02086,0.1869,0.60434,0.2805,0.72484,0.0763,0.29428,0.36224,0.20442,0.10892,0.12118,1.07038,1.79654,0.02414,0.33228,2.39876,1.15012,0.37032,0.07584,0.47942,0.10442,0.13694,0.38296,0.73302,0.09526,0.03758,0.25258,0.0556,0.61224,0.53832,0.04676,1.9195,0.5897,2.60028,0.38534,0.1142,2.41584,0.75286,1.05728,1.78776,0.52572
2018-12-21,0.43074,0.4212,2.0343,0.31878,0.293,0.24934,0.10418,0.3945,0.79828,0.2664,0.49366,0.55478,0.3469,0.70362,0.40564,1.2891,0.37546,1.08418,0.59,0.32316,0.22458,0.20986,0.55218,1.47064,0.5287,0.58574,0.21708,0.74162,0.57852,0.36288,0.26716,0.31868,0.72774,0.39692,0.5676,0.40406,0.4102,0.4211,0.25302,0.2758,0.84908,0.11776,0.44404,1.0302,0.42668,0.24542,0.91878,0.85264,1.74614,1.2114,0.40804,1.70908,0.35212,0.64794,0.35652,0.78038,0.3772,0.7902,0.75276,0.38444,0.71264,0.6022,1.28772,0.53006,1.131,1.33486,0.33726,1.54552,1.47532,0.49242,0.34896,1.92686,0.29974,0.29422,3.88056,0.29736,0.7529,2.1461,1.51838,2.58836,2.85394,1.03736,0.7296,0.1512,1.04494,0.97818,1.088,0.8694,0.97252,0.43262,0.30468,1.05906,3.63298,0.3988,0.90464,0.06388,0.19628,5.70298,3.41218,2.2396,0.77264,0.75298,0.30938,1.07186,1.96406,1.01406,0.5158,0.85086,0.38028,3.00468,0.552,0.57768,0.79384,1.22294,0.40166,0.33636,0.07384,0.47156,0.33942,0.29412,0.1586,0.12666,0.18058,0.02546,0.14294,0.09854,0.34228,0.43178,0.08506,0.52608,0.7072,0.8042,0.3342,0.58728,0.51674,0.22244,0.28162,0.27392,0.74208,0.55292,0.32524,0.24132,0.22816,0.63468,0.31082,0.31754,0.32222,0.24598,0.57572,0.2725,0.4317,0.2026,0.29982,0.89876,0.28532,0.31928,0.18918,0.27478,0.67702,0.85584,0.12118,0.73134,0.6764,0.61848,0.6011,1.19968,0.6836,0.24256,0.31162,0.30726,0.3918,0.8207,0.1856,0.5313,0.09742,0.65434,0.99054,0.0,0.67304,0.80722,0.91798,0.7924,0.82294,0.20286,0.6971,0.23294,1.1606,0.32948,0.41028,0.9991,0.24296,0.47016,0.52442,0.17792,0.142,0.34476,0.25196,0.125,0.34496,0.4155,0.26002,0.18824,0.4324,0.29618,1.22352,0.14738,0.58392,0.62714,0.41634,0.57056,0.3523,0.50474,0.2389,0.18908,0.3153,0.39838,0.41536,0.34356,1.33092,0.11384,0.23716,0.1877,0.38564,0.87542,0.08216,0.30666,7.9746,0.05782,1.22404,0.37314,0.37196,1.7032,0.50168,0.20162,0.12852,0.88422,0.34092,0.23626,0.43802,0.13872,0.2811,0.71046,0.30908,0.07754,0.07564,0.61038,0.16274,0.29496,0.33922,0.71288,0.42182,0.20588,0.04936,0.37636,0.24476,0.2194,0.37418,0.73418,0.11882,0.02098,0.17054,0.56652,0.21088,0.8177,0.07158,0.23812,0.36358,0.15826,0.127,0.12304,0.98196,1.55994,0.02012,0.33072,1.92996,0.8787,0.30596,0.07388,0.50614,0.09722,0.13658,0.43852,0.69496,0.0868,0.04068,0.24032,0.05208,0.60724,0.49642,0.04848,1.61992,0.53904,2.3717,0.36096,0.11568,2.51524,0.6611,0.9645,1.65154,0.42856
2018-12-24,0.4235,0.45654,2.31448,0.30458,0.26576,0.23464,0.09456,0.352,0.70074,0.26084,0.4152,0.4662,0.33138,0.70324,0.39338,1.32774,0.34842,0.99502,0.57536,0.29214,0.20346,0.18852,0.50968,1.42784,0.55654,0.487,0.20354,0.72626,0.58306,0.35316,0.27088,0.3348,0.67718,0.3487,0.4475,0.41576,0.41076,0.41222,0.27524,0.28222,0.91624,0.11418,0.40602,0.98554,0.41154,0.22548,0.95692,0.68436,1.47696,1.22826,0.39158,1.5845,0.39738,0.6118,0.36684,0.84388,0.36906,0.80974,0.7008,0.40754,0.73438,0.63692,1.32898,0.52318,1.0484,1.32822,0.32634,2.37092,1.33918,0.51896,0.31308,1.72458,0.2517,0.27486,3.13652,0.28596,0.76636,2.01774,1.40846,2.30812,2.87454,1.05498,0.60054,0.1432,0.9603,1.13184,1.09204,0.83664,0.86506,0.30354,0.27544,1.23164,2.99576,0.38556,0.97492,0.06148,0.15428,5.51148,2.8637,2.30122,0.76308,0.74692,0.3111,1.09432,1.93068,0.8646,0.41466,0.88906,0.40798,3.35972,0.42916,0.47096,0.83058,1.08448,0.46626,0.34322,0.076,0.40764,0.29306,0.25654,0.13322,0.11944,0.16998,0.0231,0.13572,0.09282,0.33252,0.3711,0.09474,0.50978,0.69974,0.77592,0.36702,0.553,0.55666,0.22642,0.3087,0.25986,0.70714,0.4643,0.30942,0.19868,0.21584,0.64896,0.30138,0.30322,0.30016,0.2218,0.58082,0.17716,0.382,0.18902,0.28294,0.89562,0.2656,0.29148,0.15998,0.27424,0.67956,0.8613,0.10698,0.6946,0.64502,0.62084,0.59658,1.1752,0.62688,0.23436,0.31016,0.31372,0.36082,0.77114,0.15106,0.48326,0.09764,0.62524,1.02248,0.0,0.85062,0.8086,1.1625,0.8028,0.83224,0.19364,0.80586,0.21064,1.13796,0.32278,0.38462,0.97718,0.22414,0.45208,0.47614,0.1726,0.13826,0.35458,0.23202,0.08216,0.30154,0.40034,0.22296,0.1781,0.40284,0.2855,1.11432,0.15008,0.50828,0.59646,0.41794,0.55882,0.33536,0.4645,0.23844,0.18942,0.29856,0.41578,0.40566,0.3039,1.18216,0.09798,0.22886,0.1755,0.37662,0.8846,0.07754,0.32202,9.74246,0.05338,1.23172,0.3509,0.3325,1.42346,0.48394,0.20198,0.1229,0.78542,0.30496,0.21742,0.42566,0.12892,0.22732,0.563,0.3241,0.073,0.07686,0.54514,0.15574,0.33974,0.3606,0.61922,0.40344,0.1828,0.0513,0.38692,0.2379,0.21374,0.33626,0.6421,0.09942,0.02222,0.16262,0.49556,0.17198,0.83256,0.06208,0.22288,0.34488,0.13546,0.1245,0.12794,0.97892,1.5092,0.0193,0.3224,1.9476,0.89336,0.279,0.0702,0.50294,0.0885,0.13164,0.44264,0.68976,0.10742,0.0421,0.26856,0.05094,0.4981,0.42702,0.05198,1.5581,0.33038,2.17592,0.3607,0.10844,2.36236,0.58858,0.83228,1.44174,0.36702
2018-12-25,0.42918,0.45926,2.73996,0.311,0.28782,0.2291,0.09786,0.31034,0.66366,0.23886,0.41976,0.50086,0.32606,0.65894,0.43414,1.4434,0.34352,0.93756,0.57692,0.28176,0.21786,0.20584,0.5169,1.48752,0.50234,0.49336,0.21902,0.78912,0.67392,0.3847,0.28636,0.39126,0.56588,0.37618,0.44506,0.51014,0.41818,0.36962,0.27546,0.30034,1.00522,0.11268,0.3896,1.06952,0.43932,0.26918,0.94388,0.669,1.43162,1.14986,0.43216,1.44514,0.41558,0.5567,0.39046,0.83156,0.33544,0.84148,0.66842,0.42166,0.73474,0.64188,1.15066,0.51856,1.08342,1.54036,0.40758,3.14544,1.26126,0.54064,0.3053,1.99364,0.23428,0.24332,2.50976,0.2708,0.80398,2.19624,1.68098,2.1035,2.59536,1.0179,0.60258,0.14044,0.92556,1.38738,1.1526,0.71454,0.902,0.3167,0.25468,1.39216,2.88424,0.467,0.98434,0.07176,0.15502,5.51488,2.83462,2.21884,0.77776,0.85806,0.37842,1.05182,2.02494,0.92722,0.47774,0.98278,0.43764,4.0397,0.45962,0.43608,0.83266,1.13908,0.51594,0.3724,0.08694,0.38062,0.25146,0.32072,0.12086,0.11884,0.18786,0.02328,0.15522,0.08474,0.32112,0.30856,0.09372,0.59702,0.5473,0.7361,0.39952,0.50556,0.54512,0.25076,0.30876,0.27966,0.60876,0.44248,0.3021,0.1761,0.22794,0.7239,0.38236,0.34502,0.27938,0.2034,0.68506,0.1624,0.46732,0.18616,0.32764,0.93294,0.25802,0.30756,0.16536,0.299,0.74508,0.88698,0.10904,0.76502,0.61936,0.68732,0.50262,1.33908,0.7468,0.25174,0.34096,0.31772,0.32746,0.85384,0.15162,0.452,0.11934,0.64874,1.11476,0.0,1.15204,0.8114,1.4603,1.03086,0.93434,0.20538,0.99494,0.23128,1.09454,0.3251,0.39694,1.00388,0.27088,0.4626,0.5051,0.16436,0.16838,0.33404,0.21488,0.07294,0.26868,0.3971,0.20872,0.19362,0.37756,0.3086,0.99126,0.14984,0.50524,0.67776,0.5027,0.61198,0.29032,0.46072,0.26032,0.17062,0.34422,0.48382,0.4471,0.34872,1.22236,0.0923,0.26038,0.16732,0.37422,0.91734,0.0775,0.3195,11.51026,0.0567,1.2782,0.39354,0.31984,1.47956,0.46374,0.21326,0.14714,0.78462,0.30688,0.21802,0.43424,0.1477,0.25056,0.45528,0.34368,0.07018,0.08646,0.56956,0.17142,0.3509,0.3701,0.73232,0.43072,0.18456,0.0564,0.41242,0.25962,0.2245,0.30656,0.73108,0.09508,0.02442,0.16708,0.4238,0.15562,0.86654,0.06452,0.21602,0.42664,0.1394,0.13136,0.13372,1.01534,1.67782,0.02058,0.33082,2.12154,0.94966,0.27154,0.0732,0.49812,0.10304,0.13434,0.47502,0.74078,0.10328,0.04874,0.26996,0.0574,0.4094,0.48048,0.05702,1.5996,0.39356,2.35702,0.39102,0.09736,2.38408,0.52596,0.92766,1.52764,0.3586
2018-12-26,0.40858,0.44512,2.9004,0.32432,0.30016,0.21892,0.10308,0.3002,0.60182,0.20976,0.42202,0.49848,0.33842,0.58646,0.4536,1.4482,0.3038,0.86246,0.52062,0.27022,0.23358,0.21672,0.49634,1.2738,0.48418,0.44316,0.21042,0.89602,0.67248,0.39008,0.29368,0.40588,0.57092,0.38598,0.45146,0.56748,0.36562,0.34632,0.25314,0.2758,1.05526,0.12508,0.35648,0.92984,0.41072,0.26566,0.89962,0.70218,1.21698,1.07602,0.42624,1.35742,0.4312,0.5249,0.36852,0.86404,0.3204,0.8642,0.45798,0.42638,0.64248,0.72132,1.05194,0.40172,1.12042,1.59166,0.4443,3.96758,1.18978,0.45954,0.29154,2.4879,0.20744,0.21348,2.251,0.26368,0.75342,2.05108,1.8267,2.028,2.42946,0.9789,0.5967,0.13822,0.93722,1.44416,1.21758,0.7062,0.839,0.3208,0.24466,1.40654,2.7746,0.48462,0.95132,0.07284,0.14064,5.4401,3.08486,1.87656,0.62086,0.89816,0.4125,0.98596,1.96046,0.94854,0.4784,0.94472,0.45226,4.31364,0.50536,0.50678,0.95892,1.21066,0.54574,0.38088,0.0919,0.35794,0.23252,0.32494,0.10146,0.11958,0.1947,0.02244,0.15218,0.0837,0.26018,0.29116,0.08036,0.60298,0.43738,0.69392,0.3624,0.51056,0.54322,0.2681,0.3111,0.28198,0.59468,0.35166,0.43164,0.16928,0.23898,0.70944,0.38504,0.31896,0.25418,0.18978,0.72898,0.16054,0.50024,0.18312,0.30906,0.74264,0.22114,0.31944,0.16868,0.31318,0.78918,0.84454,0.11074,0.69568,0.56364,0.6337,0.44664,1.17688,0.91274,0.2681,0.33728,0.297,0.32598,0.88726,0.1414,0.47754,0.12512,0.64358,1.00374,0.0,1.28458,0.88998,1.57636,1.1619,1.01218,0.19288,1.13574,0.2294,1.14698,0.2742,0.37894,1.0417,0.22892,0.45678,0.49218,0.15274,0.18664,0.33568,0.20136,0.07452,0.24784,0.41394,0.17854,0.20342,0.33966,0.27402,1.02042,0.1435,0.55716,0.6743,0.52226,0.53926,0.2571,0.41262,0.24922,0.16134,0.35556,0.5327,0.43124,0.3639,1.32562,0.08558,0.25998,0.15474,0.34994,0.94352,0.07794,0.30416,11.9172,0.06148,1.21668,0.37116,0.32614,1.355,0.4035,0.21454,0.15896,0.75172,0.3113,0.22936,0.39948,0.1449,0.2386,0.3823,0.355,0.06352,0.0902,0.54782,0.17714,0.3636,0.3615,0.7977,0.43856,0.17776,0.05576,0.41344,0.25624,0.2181,0.24764,0.72838,0.08992,0.02394,0.16146,0.37938,0.13578,0.81214,0.07046,0.20686,0.47042,0.13124,0.10758,0.131,1.04208,1.6832,0.01802,0.35398,2.1224,0.93982,0.29248,0.08492,0.53482,0.1162,0.12686,0.49572,0.71724,0.10284,0.05216,0.27354,0.07258,0.41762,0.46876,0.05846,1.3222,0.4883,2.179,0.3905,0.09412,2.38676,0.45198,0.88902,1.69768,0.34436
2018-12-27,0.366,0.43282,2.97146,0.39112,0.324,0.1964,0.10888,0.30702,0.62826,0.18354,0.43468,0.50762,0.37478,0.57568,0.47686,1.53314,0.28604,0.91886,0.52072,0.29434,0.2067,0.21618,0.50154,1.12732,0.50662,0.48664,0.20962,0.98028,0.60652,0.36514,0.2943,0.37832,0.57926,0.36778,0.49808,0.63026,0.3405,0.30194,0.24914,0.26952,1.13594,0.12224,0.38624,0.91156,0.40198,0.29084,0.83306,0.71194,1.15276,1.0556,0.44152,1.64244,0.45772,0.41442,0.40274,0.87272,0.33662,0.75412,0.5057,0.3665,0.66026,0.76758,1.05254,0.40288,1.3043,1.75694,0.49132,4.74342,1.38832,0.43584,0.29458,2.74418,0.20006,0.21586,3.25964,0.25968,0.72624,2.055,1.96508,2.0777,2.2555,1.00716,0.62132,0.11802,0.89602,1.6706,1.2028,0.85672,0.83276,0.33124,0.2512,1.43228,2.6196,0.46036,0.88368,0.0705,0.1695,4.52172,3.275,1.82682,0.57866,1.4584,0.45332,1.04014,1.86678,1.05052,0.53856,1.04496,0.56954,4.56956,0.51032,0.51434,1.0449,1.27382,0.60874,0.4169,0.08996,0.38868,0.20992,0.41596,0.09786,0.11658,0.19548,0.02376,0.1648,0.08732,0.25312,0.2858,0.15488,0.62096,0.28338,0.6071,0.32598,0.5062,0.51706,0.28736,0.3166,0.3151,0.51222,0.25572,0.47636,0.15412,0.27916,0.642,0.40598,0.34564,0.2455,0.21868,1.08784,0.16354,0.56694,0.16708,0.30418,0.6916,0.23316,0.30806,0.17444,0.3131,0.8302,0.78494,0.108,0.73206,0.50362,0.90338,0.43384,0.992,1.03654,0.27714,0.31406,0.38242,0.32644,0.93108,0.16596,0.4513,0.1324,0.61818,0.92136,0.0,1.46556,0.9232,1.6597,1.22888,1.1985,0.19694,1.2458,0.26556,1.20054,0.30194,0.3869,1.12034,0.25212,0.44034,0.50924,0.1561,0.22168,0.3893,0.21098,0.0783,0.26578,0.64726,0.18084,0.19662,0.33332,0.28566,1.24268,0.16378,0.59086,0.60368,0.54056,0.52852,0.2645,0.39902,0.25146,0.16272,0.3366,0.5998,0.36632,0.36306,1.39292,0.09416,0.25104,0.15128,0.35592,0.87594,0.07074,0.30542,12.0041,0.06514,1.23102,0.44324,0.30326,1.2585,0.39824,0.1995,0.1655,0.65286,0.28442,0.26294,0.4058,0.14618,0.2204,0.38324,0.35844,0.06134,0.08882,0.53766,0.16818,0.3643,0.41638,0.85462,0.42888,0.1695,0.04956,0.37716,0.32418,0.22286,0.25238,0.75518,0.08476,0.02704,0.16492,0.27312,0.12324,0.84358,0.07056,0.19812,0.48404,0.11328,0.12436,0.12596,1.07504,1.59872,0.02424,0.32296,1.9916,0.88618,0.30726,0.09186,0.53138,0.1168,0.1227,0.51998,0.63164,0.10748,0.05606,0.31956,0.1017,0.40876,0.4811,0.05372,1.36078,0.62482,2.11606,0.41,0.09834,2.47114,0.51166,0.87102,1.99958,0.32876
2018-12-28,0.3166,0.37414,3.02828,0.45836,0.35016,0.17766,0.1112,0.29264,0.62384,0.14788,0.4251,0.5245,0.3701,0.4801,0.45518,1.50482,0.2735,0.88024,0.44816,0.32526,0.18666,0.21578,0.4684,1.03412,0.4333,0.48122,0.21514,0.78666,0.619,0.33056,0.28178,0.3997,0.57992,0.45872,0.53418,0.64384,0.33826,0.29008,0.2387,0.2679,1.04416,0.12166,0.35756,0.80744,0.3573,0.3361,0.72348,0.61228,1.05034,1.00626,0.38242,1.51376,0.4857,0.42106,0.38492,0.62614,0.27306,0.63954,0.53208,0.33864,0.5746,0.77144,0.79758,0.4842,1.34706,1.61532,0.48966,4.16754,1.49248,0.35822,0.29158,2.7522,0.2151,0.24598,3.39508,0.22422,0.58048,2.22464,2.03386,1.79344,1.85936,0.96862,0.61918,0.08822,0.77906,1.53058,0.96004,0.9311,0.7965,0.32302,0.26148,1.13014,2.50032,0.48268,0.87228,0.07516,0.1799,3.92092,3.42128,1.86434,0.57694,1.7549,0.47136,0.98154,1.80298,1.09384,0.56552,1.01816,0.62874,4.60608,0.53284,0.57072,1.08286,1.26548,0.59042,0.47024,0.0946,0.37968,0.27026,0.42824,0.10676,0.12172,0.20448,0.02316,0.16578,0.08068,0.24734,0.2928,0.2081,0.57186,0.10296,0.60588,0.30054,0.52398,0.4322,0.30128,0.32934,0.28994,0.49024,0.23926,0.49942,0.1455,0.31064,0.63282,0.40178,0.32748,0.34978,0.26882,1.38714,0.15522,0.56122,0.15616,0.28822,0.70856,0.23148,0.31286,0.1811,0.30762,0.84844,0.61512,0.10622,0.6864,0.55906,0.93016,0.37568,0.97496,1.04688,0.27116,0.3504,0.35846,0.28212,0.78396,0.18446,0.45798,0.13452,0.57078,0.90788,0.2857,1.5555,0.94814,1.8352,1.222,1.21088,0.24954,1.29696,0.25622,1.12528,0.29496,0.3684,1.395,0.26848,0.37154,0.50204,0.14554,0.23716,0.3755,0.21086,0.07968,0.30884,1.02388,0.1748,0.2012,0.32536,0.2707,1.39012,0.20564,0.65024,0.6205,0.5299,0.60824,0.28506,0.46614,0.2418,0.16308,0.36836,0.5702,0.34142,0.38262,1.2023,0.0911,0.25206,0.17158,0.33246,0.86004,0.0588,0.31074,12.2123,0.0641,1.2614,0.43146,0.29716,1.26376,0.35232,0.19938,0.17682,0.55924,0.27848,0.28854,0.3635,0.14552,0.2164,0.37726,0.37524,0.05422,0.08364,0.57014,0.17812,0.34162,0.39798,0.88304,0.47338,0.18578,0.04284,0.39072,0.3547,0.22734,0.25954,0.72144,0.0849,0.02752,0.1552,0.24748,0.1297,0.77954,0.06834,0.20744,0.54608,0.12372,0.11824,0.13144,1.08596,1.61694,0.02468,0.34324,2.11526,0.91102,0.33916,0.09686,0.46748,0.14274,0.11414,0.44556,0.59106,0.11486,0.05562,0.31508,0.11998,0.35632,0.48988,0.05496,1.42872,0.82098,1.79812,0.41262,0.08614,2.28338,0.51548,0.88028,2.10136,0.30096


================================================
FILE: jqfactor_analyzer/sample_data/index_weight_info.csv
================================================
[File too large to display: 11.4 MB]

================================================
FILE: jqfactor_analyzer/sample_data/weight_info.csv
================================================
[File too large to display: 11.3 MB]

================================================
FILE: jqfactor_analyzer/utils.py
================================================
# -*- coding: utf-8 -*-


import re
import six
import warnings
from functools import wraps
try:
    from collections import Iterable
except ImportError:
    from collections.abc import Iterable

import pandas as pd


def get_forward_returns_columns(columns):
    syntax = re.compile("^period_\\d+$")
    return columns[columns.astype('str').str.contains(syntax, regex=True)]


def convert_to_forward_returns_columns(period):
    try:
        return 'period_{:d}'.format(period)
    except ValueError:
        return period


def ignore_warning(message='', category=Warning, module='', lineno=0, append=False):
    """过滤 warnings"""
    def decorator(func):
        @wraps(func)
        def func_wrapper(*args, **kwargs):
            with warnings.catch_warnings():
                warnings.filterwarnings('ignore', message=message, category=category,
                                        module=module, lineno=lineno, append=append)
                return func(*args, **kwargs)
        return func_wrapper

    return decorator


def ensure_tuple(x):
    if isinstance(x, six.string_types) or not isinstance(x, Iterable):
        return (x,)
    else:
        return tuple(x)


================================================
FILE: jqfactor_analyzer/version.py
================================================
# -*- coding: utf-8 -*-


__version__ = '1.1.0'


================================================
FILE: jqfactor_analyzer/when.py
================================================
# -*- coding: utf-8 -*-

import six
import datetime

import pandas as pd


DateTime = datetime.datetime
Date = datetime.date
Time = datetime.time
TimeDelta = datetime.timedelta

today = datetime.date.today
now = datetime.datetime.now


def date2str(date, format='%Y-%m-%d'):
    return pd.to_datetime(date).strftime(format)


def convert_date(date):
    if isinstance(date, six.string_types):
        if ':' in date:
            date = date[:10]
        return datetime.datetime.strptime(date, '%Y-%m-%d').date()
    elif isinstance(date, datetime.datetime):
        return date.date()
    elif isinstance(date, datetime.date):
        return date
    raise Exception("date 必须是datetime.date, datetime.datetime或者如下格式的字符串:'2015-01-05'")


================================================
FILE: requirements.txt
================================================
six
fastcache>=1.0.2
SQLAlchemy>=1.2.8
cached_property>=1.5.1
statsmodels
scipy
numpy>=1.15.0
pandas>=1.0.0
matplotlib
seaborn
jqdatasdk
pyarrow


================================================
FILE: setup.cfg
================================================
[bdist_wheel]
universal=1


================================================
FILE: setup.py
================================================
#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import print_function

from os.path import join as path_join, dirname as path_dirname

from setuptools import setup, find_packages

try:
    # for pip >= 10
    from pip._internal.req import parse_requirements
except ImportError:
    # for pip <= 9.0.3
    from pip.req import parse_requirements

try:
    requirements = [str(ir.req) for ir in parse_requirements("requirements.txt", session=False)]
except AttributeError:
    requirements = [str(ir.requirement) for ir in parse_requirements("requirements.txt", session=False)]


def get_version():
    scope = {}
    with open(path_join(path_dirname(__file__), "jqfactor_analyzer", "version.py")) as fp:
        exec(fp.read(), scope)
    return scope.get('__version__', '1.0')


def get_long_description():
    with open(path_join(path_dirname(__file__), 'README.md'), 'rb') as fp:
        long_desc = fp.read()

    long_desc = long_desc.replace(
        u'docs/API文档.md'.encode('utf-8'),
        u'https://github.com/JoinQuant/jqfactor_analyzer/blob/master/docs/API%E6%96%87%E6%A1%A3.md'.encode('utf-8'),
    )

    return long_desc.decode('utf-8')


setup_args = dict(
    name='jqfactor_analyzer',
    version=get_version(),
    packages=find_packages(exclude=("tests", "tests.*")),
    author='JoinQuant',
    author_email='xlx@joinquant.com',
    maintainer="",
    maintainer_email="",
    url='https://www.joinquant.com',
    description='JoinQuant single factor analyzer',
    long_description=get_long_description(),
    long_description_content_type='text/markdown',
    zip_safe=False,
    platforms=["all"],
    license='Apache License v2',
    classifiers=[
        'Programming Language :: Python',
        'Operating System :: Microsoft :: Windows',
        'Operating System :: Unix',
        'Programming Language :: Python :: 2.7',
        'Programming Language :: Python :: 3.4',
        'Programming Language :: Python :: 3.5',
        'Programming Language :: Python :: 3.6',
        'Programming Language :: Python :: 3.7',
    ],
    install_requires=requirements,
    include_package_data=True,
    package_data={'jqfactor_analyzer': ['jqfactor_analyzer/sample_data/*.csv', 'jqfactor_analyzer/config.json']},
)


def main():
    setup(**setup_args)


if __name__ == "__main__":
    main()


================================================
FILE: tests/__init__.py
================================================


================================================
FILE: tests/test_attribution.py
================================================
import os
import datetime
import pandas as pd
from functools import partial

from jqfactor_analyzer import AttributionAnalysis, DataApi

try:
    import jqdata
except:
    # 使用 sdk 进行测试时可能需要先登陆
    import jqdatasdk

weights = pd.read_csv(
    os.path.join(os.getcwd(), "jqfactor_analyzer/sample_data/weight_info.csv"), index_col=0)
returns = weights.pop("return")
index_weights = pd.read_csv(
    os.path.join(os.getcwd(), "jqfactor_analyzer/sample_data/index_weight_info.csv"), index_col=0)
index_returns = index_weights.pop("return")

dataapi = DataApi(allow_cache=True, show_progress=True)
w2 = index_weights.div(index_weights.sum(axis=1), axis=0) * 0.1
r2 = dataapi.api.get_price('000905.XSHG',
                           start_date='2020-01-01',
                           end_date='2024-07-01',
                           fields='close',
                           fq=None)['close'].pct_change() * 0.1
An = AttributionAnalysis(w2, r2, style_type='style' )
df = An.get_attr_returns2bench("000905.XSHG")


def test_get_attr_returns2bench():
    assert df.shape == (1088, 46)
    assert set(df.columns) == set([
        'beta', 'book_to_price_ratio', 'earnings_yield', 'growth', 'leverage',
        'liquidity', 'momentum', 'non_linear_size', 'residual_volatility',
        'size', '801750', '801160', '801200', '801780', '801050', '801040',
        '801960', '801170', '801760', '801790', '801720', '801130', '801080',
        '801110', '801890', '801140', '801120', '801180', '801880', '801030',
        '801770', '801740', '801730', '801950', '801010', '801230', '801710',
        '801970', '801210', '801150', '801020', '801980', 'common_return',
        'cash', 'specific_return', 'total_return']
    )


def test_net():
    func = partial(dataapi.api.get_price,
                   '000905.XSHG',
                   start_date='2020-01-01',
                   end_date='2024-07-01',
                   fields='close')
    if dataapi._api_name == 'jqdata':
        index_return = func(pre_factor_ref_date=datetime.date.today())['close'].pct_change()[1:]
    else:
        index_return = func()['close'].pct_change()[1:]
    index_net = (index_return.fillna(0) + 1).cumprod()
    assert len(index_net) == 1087


================================================
FILE: tests/test_data.py
================================================
import os
import shutil

from jqfactor_analyzer.data import DataApi
from jqfactor_analyzer.preprocess import *
from jqfactor_analyzer.factor_cache import *


try:
    import jqdata
except:
    # 使用 sdk 进行测试时可能需要先登陆
    import jqdatasdk


def test_preprocess():
    api = DataApi(weight_method='mktcap')
    codes = api._api.get_all_securities('stock').index.tolist()
    start_date = '2024-07-05'
    end_date = '2024-07-15'
    df = api.apis['prices'](codes, start_date, end_date).dropna(how='all', axis=1)

    w_df = winsorize(df, scale=1)
    assert all(df.max() >= w_df.max())

    wm_df = winsorize_med(df, scale=1)
    assert not wm_df.equals(w_df)

    s_df = standardlize(df)
    assert set(s_df.std(axis=1).round()) == {1.0}

    n_df = neutralize(df, how='sw_l3', date='2024-07-10')
    assert n_df.shape == (7, 5111)


def test_cache():
    # api1 不开启缓存, api2 开启缓存
    api1 = DataApi(weight_method='mktcap', allow_cache=False)
    api2 = DataApi(weight_method='mktcap')
    codes = api1._api.get_all_securities('stock').index.tolist()
    start_date = '2024-07-01'
    end_date = '2024-07-10'

    df1 = api1.apis['weights'](codes, start_date, end_date)
    df2 = api2.apis['weights'](codes, start_date, end_date)
    for code in codes:
        assert (df1[code] - df2[code]).abs().sum() < 1e-3

    api1.weight_method = api2.weight_method = 'cmktcap'
    df1 = api1.apis['weights'](codes, start_date, end_date)
    df2 = api2.apis['weights'](codes, start_date, end_date)
    for code in codes:
        assert (df1[code] - df2[code]).abs().sum() < 1e-3

    df1 = api1.apis['prices'](codes, start_date, end_date)
    df2 = api2.apis['prices'](codes, start_date, end_date)
    assert df1.equals(df2)

    # 非后复权的 price 存在微量差异
    api1.fq = 'pre'
    api2.fq = 'pre'
    df1 = api1.apis['prices'](codes, start_date, end_date)  # 无缓存
    df2 = api2.apis['prices'](codes, start_date, end_date)  # 有缓存
    for code in codes:
        diff = (df1[code] - df2[code]).abs().sum()
        assert diff < 1e-12

    api1.fq = None
    api1.price = 'open'
    api2.fq = None
    api2.price = 'open'
    df1 = api1.apis['prices'](codes, start_date, end_date)
    df2 = api2.apis['prices'](codes, start_date, end_date)
    for code in codes:
        diff = (df1[code] - df2[code]).abs().sum()
        assert diff < 1e-12

    df1 = api1.apis['groupby'](codes, start_date, end_date)
    df2 = api2.apis['groupby'](codes, start_date, end_date)
    assert df1.equals(df2)

    # 删除缓存文件
    cache_path = get_cache_dir()
    if os.path.exists(cache_path):
        shutil.rmtree(cache_path)


================================================
FILE: tests/test_performance.py
================================================
# -*- coding: utf-8 -*-


import pytest
import pandas as pd
from numpy import nan, float64

from jqfactor_analyzer.prepare import get_clean_factor_and_forward_returns
from jqfactor_analyzer.performance import (
    factor_information_coefficient,
    factor_autocorrelation,
    mean_information_coefficient,
    quantile_turnover,
    factor_returns, factor_alpha_beta,
    average_cumulative_return_by_quantile
)
from jqfactor_analyzer.utils import get_forward_returns_columns


dr = pd.date_range(start='2015-1-1', end='2015-1-2')
dr.name = 'date'
tickers = ['A', 'B', 'C', 'D']
factor = pd.DataFrame(index=dr,
                      columns=tickers,
                      data=[[1, 2, 3, 4],
                            [4, 3, 2, 1]]).stack()
factor.index = factor.index.set_names(['date', 'asset'])
factor.name = 'factor'
factor_data = pd.DataFrame()
factor_data['factor'] = factor
factor_data['group'] = pd.Series(index=factor.index,
                                 data=[1, 1, 2, 2, 1, 1, 2, 2],)
factor_data['weights'] = pd.Series(range(8), index=factor.index,
                                   dtype=float64) + 1


@pytest.mark.parametrize(
    ('factor_data', 'forward_returns', 'group_adjust',
     'by_group', 'expected_ix', 'expected_ic_val'),
    [(factor_data, [4, 3, 2, 1, 1, 2, 3, 4], False, False, dr, [-1., -1.]),
     (factor_data, [1, 2, 3, 4, 4, 3, 2, 1], False, False, dr, [1., 1.]),
     (factor_data, [1, 2, 3, 4, 4, 3, 2, 1], False, True,
      pd.MultiIndex.from_product([dr, [1, 2]], names=['date', 'group']),
      [1., 1., 1., 1.]),
     (factor_data, [1, 2, 3, 4, 4, 3, 2, 1], True, True,
      pd.MultiIndex.from_product([dr, [1, 2]], names=['date', 'group']),
      [1., 1., 1., 1.])]
)
def test_information_coefficient(factor_data,
                                 forward_returns,
                                 group_adjust,
                                 by_group,
                                 expected_ix,
                                 expected_ic_val):

    factor_data = factor_data.copy()
    factor_data['period_1'] = pd.Series(index=factor_data.index,
                                        data=forward_returns)

    ic = factor_information_coefficient(factor_data=factor_data,
                                        group_adjust=group_adjust,
                                        by_group=by_group)

    expected_ic_df = pd.DataFrame(index=expected_ix,
                                  columns=pd.Index(['period_1'], dtype='object'),
                                  data=expected_ic_val)

    pd.testing.assert_frame_equal(ic, expected_ic_df)

@pytest.mark.parametrize(
    (
        'factor_data', 'forward_returns', 'group_adjust',
        'by_group', 'by_time', 'expected_ix', 'expected_ic_val'
    ), [
        (factor_data, [4, 3, 2, 1, 1, 2, 3, 4], False, False, 'D',
         dr, [-1., -1.]),
        (factor_data, [1, 2, 3, 4, 4, 3, 2, 1], False, False, 'W',
         pd.DatetimeIndex(['2015-01-04'], name='date', freq='W-SUN'), [1.]),
        (factor_data, [1, 2, 3, 4, 4, 3, 2, 1], False, True, None,
         pd.Int64Index([1, 2], name='group'), [1., 1.]),
        (factor_data, [1, 2, 3, 4, 4, 3, 2, 1], False, True, 'W',
         pd.MultiIndex.from_product(
             [pd.DatetimeIndex(['2015-01-04'], name='date', freq='W-SUN'),
              [1, 2]],
             names=['date', 'group']
         ),
         [1., 1.])
    ]
)
def test_mean_information_coefficient(factor_data,
                                      forward_returns,
                                      group_adjust,
                                      by_group,
                                      by_time,
                                      expected_ix,
                                      expected_ic_val):

    factor_data = factor_data.copy()
    factor_data['period_1'] = pd.Series(index=factor_data.index,
                                        data=forward_returns)

    ic = mean_information_coefficient(factor_data,
                                      group_adjust=group_adjust,
                                      by_group=by_group,
                                      by_time=by_time)

    expected_ic_df = pd.DataFrame(index=expected_ix,
                                  columns=pd.Index(['period_1']),
                                  data=expected_ic_val)

    pd.testing.assert_frame_equal(ic, expected_ic_df,
                                  check_index_type=False,
                                  check_column_type=False)


@pytest.mark.parametrize(
    ('quantile_values', 'test_quantile', 'expected_vals'),
    [([[1.0, 2.0, 3.0, 4.0],
       [4.0, 3.0, 2.0, 1.0],
       [1.0, 2.0, 3.0, 4.0],
       [1.0, 2.0, 3.0, 4.0]],
      4.0,
      [nan, 1.0, 1.0, 0.0]),
     ([[1.0, 2.0, 3.0, 4.0],
       [1.0, 2.0, 3.0, 4.0],
       [1.0, 2.0, 3.0, 4.0],
       [1.0, 2.0, 3.0, 4.0]],
      3.0,
      [nan, 0.0, 0.0, 0.0]),
     ([[1.0, 2.0, 3.0, 4.0],
       [4.0, 3.0, 2.0, 1.0],
       [1.0, 2.0, 3.0, 4.0],
       [4.0, 3.0, 2.0, 1.0]],
      2.0,
      [nan, 1.0, 1.0, 1.0])]
)
def test_quantile_turnover(quantile_values, test_quantile,
                           expected_vals):

    dr = pd.date_range(start='2015-1-1', end='2015-1-4')
    dr.name = 'date'
    tickers = ['A', 'B', 'C', 'D']

    quantized_test_factor = pd.Series(
        pd.DataFrame(index=dr, columns=tickers, data=quantile_values).stack()
    )
    quantized_test_factor.index = quantized_test_factor.index.set_names(
        ['date', 'asset']
    )

    to = quantile_turnover(quantized_test_factor, test_quantile)

    expected = pd.Series(
        index=quantized_test_factor.index.levels[0], data=expected_vals)
    expected.name = test_quantile

    pd.testing.assert_series_equal(to, expected)


@pytest.mark.parametrize(
    ('factor_data', 'factor_vals', 'fwd_return_vals',
     'group_adjust', 'expected_vals'),
    [(factor_data, [1, 2, 3, 4, 4, 3, 2, 1], [4, 3, 2, 1, 1, 2, 3, 4],
      False, [-1.25000, -1.25000]),
     (factor_data, [1, 1, 1, 1, 1, 1, 1, 1], [4, 3, 2, 1, 1, 2, 3, 4],
      False, [0.0, 0.0]),
     (factor_data, [1, 2, 3, 4, 4, 3, 2, 1], [4, 3, 2, 1, 1, 2, 3, 4],
      True, [-0.5, -0.5]),
     (factor_data, [1, 2, 3, 4, 1, 2, 3, 4], [1, 4, 1, 2, 1, 2, 2, 1],
      True, [1.0, 0.0]),
     (factor_data, [1, 1, 1, 1, 1, 1, 1, 1], [4, 3, 2, 1, 1, 2, 3, 4],
      True, [0.0, 0.0])]
)
def test_factor_returns(factor_data,
                        factor_vals,
                        fwd_return_vals,
                        group_adjust,
                        expected_vals):

    factor_data = factor_data.copy()
    factor_data['period_1'] = fwd_return_vals
    factor_data['factor'] = factor_vals

    factor_returns_s = factor_returns(factor_data=factor_data,
                                      demeaned=True,
                                      group_adjust=group_adjust)

    expected = pd.DataFrame(
        index=dr,
        data=expected_vals,
        columns=get_forward_returns_columns(factor_data.columns)
    )

    pd.testing.assert_frame_equal(factor_returns_s, expected)


@pytest.mark.parametrize(
    ('factor_data', 'fwd_return_vals', 'alpha', 'beta'),
    [(factor_data, [1, 2, 3, 4, 1, 1, 1, 1], -1, 5. / 6.)]
)
def test_factor_alpha_beta(factor_data, fwd_return_vals, alpha, beta):

    factor_data = factor_data.copy()
    factor_data['period_1'] = fwd_return_vals

    ab = factor_alpha_beta(factor_data=factor_data)

    expected = pd.DataFrame(columns=['period_1'],
                            index=['Ann. alpha', 'beta'],
                            data=[alpha, beta])

    pd.testing.assert_frame_equal(ab, expected)

@pytest.mark.parametrize(
    ('factor_values', 'end_date', 'period', 'expected_vals'),
    [([[1.0, 2.0, 3.0, 4.0],
       [1.0, 2.0, 3.0, 4.0],
       [1.0, 2.0, 3.0, 4.0],
       [1.0, 2.0, 3.0, 4.0]],
      '2015-1-4', 1,
      [nan, 1.0, 1.0, 1.0]),
     ([[4.0, 3.0, 2.0, 1.0],
       [1.0, 2.0, 3.0, 4.0],
       [4.0, 3.0, 2.0, 1.0],
       [1.0, 2.0, 3.0, 4.0]],
      '2015-1-4', 1,
      [nan, -1.0, -1.0, -1.0]),
     ([[1.0, 2.0, 3.0, 4.0],
       [2.0, 1.0, 4.0, 3.0],
       [4.0, 3.0, 2.0, 1.0],
       [1.0, 2.0, 3.0, 4.0],
       [2.0, 1.0, 4.0, 3.0],
       [4.0, 3.0, 2.0, 1.0],
       [2.0, 1.0, 4.0, 3.0],
       [4.0, 3.0, 2.0, 1.0],
       [1.0, 2.0, 3.0, 4.0],
       [2.0, 1.0, 4.0, 3.0],
       [2.0, 1.0, 4.0, 3.0],
       [4.0, 3.0, 2.0, 1.0]],
      '2015-1-12', 3,
      [nan, nan, nan, 1.0, 1.0, 1.0, 0.6, -0.6, -1.0, 1.0, -0.6, -1.0])]
)
def test_factor_autocorrelation(factor_values,
                                end_date,
                                period,
                                expected_vals):
    dr = pd.date_range(start='2015-1-1', end=end_date)
    dr.name = 'date'
    tickers = ['A', 'B', 'C', 'D']
    factor = pd.DataFrame(index=dr,
                          columns=tickers,
                          data=factor_values).stack()
    factor.index = factor.index.set_names(['date', 'asset'])

    factor_df = pd.DataFrame()
    factor_df['factor'] = factor

    fa = factor_autocorrelation(factor_df, period)
    expected = pd.Series(index=dr, data=expected_vals)
    expected.name = period

    pd.testing.assert_series_equal(fa, expected)

@pytest.mark.parametrize(
    ('before', 'after', 'demeaned', 'quantiles', 'expected_vals'),
    [(1, 2, False, 4,
      [[1.00, 0.0, -0.50, -0.75],
       [0.0, 0.0, 0.0, 0.0],
       [0.00, 0.00, 0.00, 0.00],
       [0.0, 0.0, 0.0, 0.0],
       [-0.20, 0.0, 0.25, 0.5625],
       [0.0, 0.0, 0.0, 0.0],
       [-0.3333333, 0.0, 0.50, 1.25],
       [0.0, 0.0, 0.0, 0.0]]),
     (1, 2, True, 4,
      [[0.8833333, 0.0, -0.5625, -1.015625],
       [0.0, 0.0, 0.0, 0.0],
       [-0.1166667, 0.0, -0.0625, -0.265625],
       [0.0, 0.0, 0.0, 0.0],
       [-0.3166667, 0.0, 0.1875, 0.296875],
       [0.0, 0.0, 0.0, 0.0],
       [-0.4500000, 0.0, 0.4375, 0.984375],
       [0.0, 0.0, 0.0, 0.0]]),
     (3, 0, False, 4,
      [[7.0, 3.0, 1.0, 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],
       [-0.488, -0.36, -0.2, 0.0],
       [0.0, 0.0, 0.0, 0.0],
       [-0.703704, -0.55555555, -0.333333333, 0.0],
       [0.0, 0.0, 0.0, 0.0]]),
     (0, 3, True, 4,
      [[0.0, -0.5625, -1.015625, -1.488281],
       [0.0, 0.0, 0.0, 0.0],
       [0.0, -0.0625, -0.265625, -0.613281],
       [0.0, 0.0, 0.0, 0.0],
       [0.0, 0.1875, 0.296875, 0.339844],
       [0.0, 0.0, 0.0, 0.0],
       [0.0, 0.4375, 0.984375, 1.761719],
       [0.0, 0.0, 0.0, 0.0]]),
     (3, 3, False, 2,
      [[3.5, 1.5, 0.5, 0.0, -0.25, -0.375, -0.4375],
       [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
       [-0.595852, -0.457778, -0.266667, 0.0, 0.375, 0.90625, 1.664062],
       [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]),
     (3, 3, True, 2,
      [[2.047926, 0.978888, 0.383333, 0.0, -0.3125, -0.640625, -1.050781],
       [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
       [-2.047926, -0.978888, -0.383333, 0.0, 0.3125, 0.640625, 1.050781],
       [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])]
)
def test_average_cumulative_return_by_quantile(before, after,
                                               demeaned, quantiles,
                                               expected_vals):
    dr = pd.date_range(start='2015-1-15', end='2015-2-1')
    dr.name = 'date'
    tickers = ['A', 'B', 'C', 'D']
    r1, r2, r3, r4 = (1.25, 1.50, 1.00, 0.50)
    data = [[r1**i, r2**i, r3**i, r4**i] for i in range(1, 19)]
    prices = pd.DataFrame(index=dr, columns=tickers, data=data)
    dr2 = pd.date_range(start='2015-1-21', end='2015-1-26')
    dr2.name = 'date'
    factor = pd.DataFrame(
        index=dr2, columns=tickers, data=[
            [3, 4, 2, 1],
            [3, 4, 2, 1],
            [3, 4, 2, 1],
            [3, 4, 2, 1],
            [3, 4, 2, 1],
            [3, 4, 2, 1]]).stack()

    factor_data = get_clean_factor_and_forward_returns(
        factor, prices, quantiles=quantiles, periods=range(0, after + 1)
    )

    avgrt = average_cumulative_return_by_quantile(
        factor_data, prices, before, after, demeaned)
    arrays = []
    for q in range(1, quantiles + 1):
        arrays.append((q, 'mean'))
        arrays.append((q, 'std'))
    index = pd.MultiIndex.from_tuples(arrays, names=['factor_quantile', None])
    expected = pd.DataFrame(
        index=index, columns=range(-before, after + 1), data=expected_vals)
    pd.testing.assert_frame_equal(avgrt, expected)

@pytest.mark.parametrize(
    ('before', 'after', 'demeaned', 'quantiles', 'expected_vals'),
    [(0, 2, False, 4,
      [[0.0, -0.50, -0.75],
       [0.0, 0.0, 0.0],
       [0.0, 0.0, 0.0],
       [0.0, 0.0, 0.0],
       [0.0, 0.25, 0.5625],
       [0.0, 0.0, 0.0],
       [0.0, 0.50, 1.25],
       [0.0, 0.0, 0.0]]),
     (0, 3, True, 4,
      [[0.0, -0.5625, -1.015625, -1.488281],
       [0.0, 0.0, 0.0, 0.0],
       [0.0, -0.0625, -0.265625, -0.613281],
       [0.0, 0.0, 0.0, 0.0],
       [0.0, 0.1875, 0.296875, 0.339844],
       [0.0, 0.0, 0.0, 0.0],
       [0.0, 0.4375, 0.984375, 1.761719],
       [0.0, 0.0, 0.0, 0.0]]),
     (0, 3, False, 2,
      [[0.0, -0.25, -0.375, -0.4375],
       [0.0, 0.0, 0.0, 0.0],
       [0.0, 0.375, 0.90625, 1.664062],
       [0.0, 0.0, 0.0, 0.0]]),
     (0, 3, True, 2,
      [[0.0, -0.3125, -0.640625, -1.050781],
       [0.0, 0.0, 0.0, 0.0],
       [0.0, 0.3125, 0.640625, 1.050781],
       [0.0, 0.0, 0.0, 0.0]])]
)
def test_average_cumulative_return_by_quantile_2(before, after,
                                                 demeaned, quantiles,
                                                 expected_vals):
    """Test varying factor asset universe

    at different dates there might be different assets
    """
    dr = pd.date_range(start='2015-1-15', end='2015-1-25')
    dr.name = 'date'
    tickers = ['A', 'B', 'C', 'D', 'E', 'F']
    r1, r2, r3, r4 = (1.25, 1.50, 1.00, 0.50)
    data = [[r1**i, r2**i, r3**i, r4**i, r2**i, r3**i]
            for i in range(1, 12)]
    prices = pd.DataFrame(index=dr, columns=tickers, data=data)
    dr2 = pd.date_range(start='2015-1-18', end='2015-1-21')
    dr2.name = 'date'
    factor = pd.DataFrame(index=dr2, columns=tickers,
                          data=[[3, 4, 2, 1, nan, nan],
                                [3, 4, 2, 1, nan, nan],
                                [3, nan, nan, 1, 4, 2],
                                [3, nan, nan, 1, 4, 2]]).stack()

    factor_data = get_clean_factor_and_forward_returns(
        factor, prices, quantiles=quantiles, periods=range(0, after + 1),
    )

    avgrt = average_cumulative_return_by_quantile(
        factor_data, prices, before, after, demeaned
    )
    arrays = []
    for q in range(1, quantiles + 1):
        arrays.append((q, 'mean'))
        arrays.append((q, 'std'))
    index = pd.MultiIndex.from_tuples(arrays, names=['factor_quantile', None])
    expected = pd.DataFrame(
        index=index, columns=range(-before, after + 1), data=expected_vals
    )
    pd.testing.assert_frame_equal(avgrt, expected)


================================================
FILE: tests/test_prepare.py
================================================
# -*- coding: utf-8 -*-


import pytest
import pandas as pd
from numpy import nan

from jqfactor_analyzer.prepare import (
    quantize_factor, compute_forward_returns, common_start_returns
)


dr = pd.date_range(start='2015-1-1', end='2015-1-2')
dr.name = 'date'
tickers = ['A', 'B', 'C', 'D']
factor = pd.DataFrame(
    index=dr, columns=tickers, data=[[1, 2, 3, 4], [4, 3, 2, 1]]
).stack()
factor.index = factor.index.set_names(['date', 'asset'])
factor.name = 'factor'
factor_data = pd.DataFrame()
factor_data['factor'] = factor
factor_data['group'] = pd.Series(
    index=factor.index,
    data=[1, 1, 2, 2, 1, 1, 2, 2],
)


def test_compute_forward_returns():
    dr = pd.date_range(start='2015-1-1', end='2015-1-3')
    prices = pd.DataFrame(
        index=dr, columns=['A', 'B'], data=[[1, 1], [1, 2], [2, 1]]
    )

    fp = compute_forward_returns(factor, prices, periods=[1, 2])

    ix = pd.MultiIndex.from_product([dr, ['A', 'B']], names=['date', 'asset'])
    expected = pd.DataFrame(index=ix, columns=['period_1', 'period_2'])
    expected['period_1'] = [0., 1., 1., -0.5, nan, nan]
    expected['period_2'] = [1., 0., nan, nan, nan, nan]

    pd.testing.assert_frame_equal(fp, expected)


@pytest.mark.parametrize(
    ('factor', 'quantiles', 'bins', 'by_group', 'expected_vals'), [
        (factor_data, 4, None, False, [1, 2, 3, 4, 4, 3, 2, 1]),
        (factor_data, 2, None, False, [1, 1, 2, 2, 2, 2, 1, 1]),
        (factor_data, 2, None, True, [1, 2, 1, 2, 2, 1, 2, 1]),
        (
            factor_data, [0, .25, .5, .75, 1.], None, False,
            [1, 2, 3, 4, 4, 3, 2, 1]
        ),
        (factor_data, [0, .5, .75, 1.], None, False, [1, 1, 2, 3, 3, 2, 1, 1]),
        (factor_data, [0, .25, .5, 1.], None, False, [1, 2, 3, 3, 3, 3, 2, 1]),
        (factor_data, [0, .5, 1.], None, False, [1, 1, 2, 2, 2, 2, 1, 1]),
        (
            factor_data, [.25, .5, .75], None, False,
            [nan, 1, 2, nan, nan, 2, 1, nan]
        ), (factor_data, [0, .5, 1.], None, True, [1, 2, 1, 2, 2, 1, 2, 1]),
        (factor_data, [.5, 1.], None, True, [nan, 1, nan, 1, 1, nan, 1, nan]),
        (factor_data, [0, 1.], None, True, [1, 1, 1, 1, 1, 1, 1, 1]),
        (factor_data, None, 4, False, [1, 2, 3, 4, 4, 3, 2, 1]),
        (factor_data, None, 2, False, [1, 1, 2, 2, 2, 2, 1, 1]),
        (factor_data, None, 3, False, [1, 1, 2, 3, 3, 2, 1, 1]),
        (factor_data, None, 8, False, [1, 3, 6, 8, 8, 6, 3, 1]),
        (factor_data, None, [0, 1, 2, 3, 5], False, [1, 2, 3, 4, 4, 3, 2, 1]),
        (factor_data, None, [1, 2, 3], False, [nan, 1, 2, nan, nan, 2, 1, nan]),
        (factor_data, None, [0, 2, 5], False, [1, 1, 2, 2, 2, 2, 1, 1]),
        (factor_data, None, [0.5, 2.5, 4.5], False, [1, 1, 2, 2, 2, 2, 1, 1]),
        (factor_data, None, [0.5, 2.5], True, [1, 1, nan, nan, nan, nan, 1, 1]),
        (factor_data, None, 2, True, [1, 2, 1, 2, 2, 1, 2, 1])
    ]
)
def test_quantize_factor(factor, quantiles, bins, by_group, expected_vals):
    quantized_factor = quantize_factor(
        factor, quantiles=quantiles, bins=bins, by_group=by_group
    )
    expected = pd.Series(
        index=factor.index, data=expected_vals, name='factor_quantile'
    ).dropna()
    pd.testing.assert_series_equal(quantized_factor, expected)


@pytest.mark.parametrize(
    ('before', 'after', 'mean_by_date', 'demeaned', 'expected_vals'), [
        (
            2, 3, False, False, [
                [0.075, 0.241868], [0.075, 0.241868], [0.075, 0.241868],
                [0.075, 0.241868], [0.075, 0.241868], [0.075, 0.241868]
            ]
        ),
        (
            3, 2, False, True, [
                [0.0, 0.241868], [0.0, 0.241868], [0.0, 0.241868],
                [0.0, 0.241868], [0.0, 0.241868], [0.0, 0.241868]
            ]
        ),
        (
            3, 5, True, False, [
                [0.075, 0.0], [0.075, 0.0], [0.075, 0.0], [0.075, 0.0],
                [0.075, 0.0], [0.075, 0.0], [0.075, 0.0], [0.075, 0.0],
                [0.075, 0.0]
            ]
        ),
        (
            1, 4, True, True,
            [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]]
        ),
        (
            6, 6, False, False, [
                [0.075, 0.243614], [0.075, 0.242861], [0.075, 0.242301],
                [0.075, 0.241868], [0.075, 0.241868], [0.075, 0.241868],
                [0.075, 0.241868], [0.075, 0.241868], [0.075, 0.241868],
                [0.075, 0.241868], [0.075, 0.241868], [0.075, 0.242301],
                [0.075, 0.242861]
            ]
        ),
        (
            6, 6, False, True, [
                [0.0, 0.243614], [0.0, 0.242861], [0.0, 0.242301],
                [0.0, 0.241868], [0.0, 0.241868], [0.0, 0.241868],
                [0.0, 0.241868], [0.0, 0.241868], [0.0, 0.241868],
                [0.0, 0.241868], [0.0, 0.241868], [0.0, 0.242301],
                [0.0, 0.242861]
            ]
        ),
        (
            6, 6, True, False, [
                [0.075, 0.0], [0.075, 0.0], [0.075, 0.0], [0.075, 0.0],
                [0.075, 0.0], [0.075, 0.0], [0.075, 0.0], [0.075, 0.0],
                [0.075, 0.0], [0.075, 0.0], [0.075, 0.0], [0.075, 0.0],
                [0.075, 0.0]
            ]
        ),
        (
            6, 6, True, True, [
                [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.]
            ]
        )
    ]
)
def test_common_start_returns(
    before, after, mean_by_date, demeaned, expected_vals
):
    dr = pd.date_range(start='2015-1-17', end='2015-2-2')
    dr.name = 'date'
    tickers = ['A', 'B', 'C', 'D']
    r1, r2, r3, r4 = (1.20, 1.40, 0.90, 0.80)
    prices = pd.DataFrame(
        index=dr,
        columns=tickers,
        data=[
            [r1**1, r2**1, r3**1, r4**1], [r1**2, r2**2, r3**2, r4**2],
            [r1**3, r2**3, r3**3, r4**3], [r1**4, r2**4, r3**4, r4**4],
            [r1**5, r2**5, r3**5, r4**5], [r1**6, r2**6, r3**6, r4**6],
            [r1**7, r2**7, r3**7, r4**7], [r1**8, r2**8, r3**8, r4**8],
            [r1**9, r2**9, r3**9, r4**9], [r1**10, r2**10, r3**10, r4**10],
            [r1**11, r2**11, r3**11, r4**11], [r1**12, r2**12, r3**12, r4**12],
            [r1**13, r2**13, r3**13, r4**13], [r1**14, r2**14, r3**14, r4**14],
            [r1**15, r2**15, r3**15, r4**15], [r1**16, r2**16, r3**16, r4**16],
            [r1**17, r2**17, r3**17, r4**17]
        ]
    )
    dr2 = pd.date_range(start='2015-1-21', end='2015-1-29')
    factor = pd.DataFrame(
        index=dr2,
        columns=tickers,
        data=[
            [3, 4, 2, 1], [3, 4, 2, 1], [3, 4, 2, 1], [3, 4, 2,
                                                       1], [3, 4, 2, 1],
            [3, 4, 2, 1], [3, 4, 2, 1], [3, 4, 2, 1], [3, 4, 2, 1]
        ]
    ).stack()
    factor.index = factor.index.set_names(['date', 'asset'])
    factor.name = 'factor'

    cmrt = common_start_returns(
        factor, prices, before, after, False, mean_by_date,
        factor if demeaned else None
    )
    cmrt = pd.DataFrame({'mean': cmrt.mean(axis=1), 'std': cmrt.std(axis=1)})
    expected = pd.DataFrame(
        index=range(-before, after + 1),
        columns=['mean', 'std'],
        data=expected_vals
    )
    pd.testing.assert_frame_equal(cmrt, expected)