Repository: dduemig/Stanford-Project-Predicting-stock-prices-using-a-LSTM-Network Branch: master Commit: f80d42ee11dd Files: 2 Total size: 1.6 MB Directory structure: gitextract_fejt_ku7/ ├── Final Project.ipynb └── README.md ================================================ FILE CONTENTS ================================================ ================================================ FILE: Final Project.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Import Libraries" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "#Standard libraries\n", "import pandas as pd\n", "import seaborn as sns\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import time \n", "\n", "# library for sampling\n", "from scipy.stats import uniform\n", "\n", "# libraries for Data Download\n", "import datetime\n", "from pandas_datareader import data as pdr\n", "import fix_yahoo_finance as yf\n", "\n", "# sklearn\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.model_selection import TimeSeriesSplit\n", "from sklearn.model_selection import RandomizedSearchCV\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn import metrics\n", "from sklearn.metrics import make_scorer\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.model_selection import train_test_split\n", "from sklearn import linear_model\n", "\n", "# Keras\n", "import keras\n", "from keras.models import Sequential\n", "from keras.layers import LSTM\n", "from keras.layers import Dense\n", "from keras.layers import Dropout\n", "from keras.wrappers.scikit_learn import KerasClassifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Create Classes" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Define a callback class\n", "# Resets the states after each epoch (after going through a full time series)\n", "class ModelStateReset(keras.callbacks.Callback):\n", " def on_epoch_end(self, epoch, logs={}):\n", " self.model.reset_states()\n", "reset=ModelStateReset()\n", "\n", "# Different Approach\n", "#class modLSTM(LSTM):\n", "# def call(self, x, mask=None):\n", "# if self.stateful: \n", "# self.reset_states()\n", "# return super(modLSTM, self).call(x, mask)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Write Functions" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Function to create an LSTM model, required for KerasClassifier\n", "def create_shallow_LSTM(epochs=1, \n", " LSTM_units=1,\n", " num_samples=1, \n", " look_back=1,\n", " num_features=None, \n", " dropout_rate=0,\n", " recurrent_dropout=0,\n", " verbose=0):\n", " \n", " model=Sequential()\n", " \n", " model.add(LSTM(units=LSTM_units, \n", " batch_input_shape=(num_samples, look_back, num_features), \n", " stateful=True, \n", " recurrent_dropout=recurrent_dropout)) \n", " \n", " model.add(Dropout(dropout_rate))\n", " \n", " model.add(Dense(1, activation='sigmoid', kernel_initializer=keras.initializers.he_normal(seed=1)))\n", "\n", " model.compile(loss='binary_crossentropy', optimizer=\"adam\", metrics=['accuracy'])\n", "\n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4.1 Import Raw Data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[*********************100%***********************] 1 of 1 downloaded\n" ] }, { "data": { "text/plain": [ "(9875, 6)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Imports data\n", "start_sp=datetime.datetime(1980, 1, 1) \n", "end_sp=datetime.datetime(2019, 2, 28)\n", "\n", "yf.pdr_override() \n", "sp500=pdr.get_data_yahoo('^GSPC', \n", " start_sp,\n", " end_sp)\n", "sp500.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4.2 Create Features" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Compute the logarithmic returns using the Closing price \n", "sp500['Log_Ret_1d']=np.log(sp500['Close'] / sp500['Close'].shift(1))\n", "\n", "# Compute logarithmic returns using the pandas rolling mean function\n", "sp500['Log_Ret_1w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=5).sum()\n", "sp500['Log_Ret_2w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=10).sum()\n", "sp500['Log_Ret_3w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=15).sum()\n", "sp500['Log_Ret_4w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=20).sum()\n", "sp500['Log_Ret_8w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=40).sum()\n", "sp500['Log_Ret_12w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=60).sum()\n", "sp500['Log_Ret_16w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=80).sum()\n", "sp500['Log_Ret_20w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=100).sum()\n", "sp500['Log_Ret_24w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=120).sum()\n", "sp500['Log_Ret_28w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=140).sum()\n", "sp500['Log_Ret_32w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=160).sum()\n", "sp500['Log_Ret_36w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=180).sum()\n", "sp500['Log_Ret_40w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=200).sum()\n", "sp500['Log_Ret_44w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=220).sum()\n", "sp500['Log_Ret_48w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=240).sum()\n", "sp500['Log_Ret_52w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=260).sum()\n", "sp500['Log_Ret_56w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=280).sum()\n", "sp500['Log_Ret_60w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=300).sum()\n", "sp500['Log_Ret_64w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=320).sum()\n", "sp500['Log_Ret_68w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=340).sum()\n", "sp500['Log_Ret_72w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=360).sum()\n", "sp500['Log_Ret_76w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=380).sum()\n", "sp500['Log_Ret_80w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=400).sum()\n", "\n", "# Compute Volatility using the pandas rolling standard deviation function\n", "sp500['Vol_1w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=5).std()*np.sqrt(5)\n", "sp500['Vol_2w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=10).std()*np.sqrt(10)\n", "sp500['Vol_3w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=15).std()*np.sqrt(15)\n", "sp500['Vol_4w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=20).std()*np.sqrt(20)\n", "sp500['Vol_8w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=40).std()*np.sqrt(40)\n", "sp500['Vol_12w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=60).std()*np.sqrt(60)\n", "sp500['Vol_16w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=80).std()*np.sqrt(80)\n", "sp500['Vol_20w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=100).std()*np.sqrt(100)\n", "sp500['Vol_24w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=120).std()*np.sqrt(120)\n", "sp500['Vol_28w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=140).std()*np.sqrt(140)\n", "sp500['Vol_32w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=160).std()*np.sqrt(160)\n", "sp500['Vol_36w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=180).std()*np.sqrt(180)\n", "sp500['Vol_40w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=200).std()*np.sqrt(200)\n", "sp500['Vol_44w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=220).std()*np.sqrt(220)\n", "sp500['Vol_48w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=240).std()*np.sqrt(240)\n", "sp500['Vol_52w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=260).std()*np.sqrt(260)\n", "sp500['Vol_56w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=280).std()*np.sqrt(280)\n", "sp500['Vol_60w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=300).std()*np.sqrt(300)\n", "sp500['Vol_64w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=320).std()*np.sqrt(320)\n", "sp500['Vol_68w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=340).std()*np.sqrt(340)\n", "sp500['Vol_72w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=360).std()*np.sqrt(360)\n", "sp500['Vol_76w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=380).std()*np.sqrt(380)\n", "sp500['Vol_80w']=pd.Series(sp500['Log_Ret_1d']).rolling(window=400).std()*np.sqrt(400)\n", "\n", "# Compute Volumes using the pandas rolling mean function\n", "sp500['Volume_1w']=pd.Series(sp500['Volume']).rolling(window=5).mean()\n", "sp500['Volume_2w']=pd.Series(sp500['Volume']).rolling(window=10).mean()\n", "sp500['Volume_3w']=pd.Series(sp500['Volume']).rolling(window=15).mean()\n", "sp500['Volume_4w']=pd.Series(sp500['Volume']).rolling(window=20).mean()\n", "sp500['Volume_8w']=pd.Series(sp500['Volume']).rolling(window=40).mean()\n", "sp500['Volume_12w']=pd.Series(sp500['Volume']).rolling(window=60).mean()\n", "sp500['Volume_16w']=pd.Series(sp500['Volume']).rolling(window=80).mean()\n", "sp500['Volume_20w']=pd.Series(sp500['Volume']).rolling(window=100).mean()\n", "sp500['Volume_24w']=pd.Series(sp500['Volume']).rolling(window=120).mean()\n", "sp500['Volume_28w']=pd.Series(sp500['Volume']).rolling(window=140).mean()\n", "sp500['Volume_32w']=pd.Series(sp500['Volume']).rolling(window=160).mean()\n", "sp500['Volume_36w']=pd.Series(sp500['Volume']).rolling(window=180).mean()\n", "sp500['Volume_40w']=pd.Series(sp500['Volume']).rolling(window=200).mean()\n", "sp500['Volume_44w']=pd.Series(sp500['Volume']).rolling(window=220).mean()\n", "sp500['Volume_48w']=pd.Series(sp500['Volume']).rolling(window=240).mean()\n", "sp500['Volume_52w']=pd.Series(sp500['Volume']).rolling(window=260).mean()\n", "sp500['Volume_56w']=pd.Series(sp500['Volume']).rolling(window=280).mean()\n", "sp500['Volume_60w']=pd.Series(sp500['Volume']).rolling(window=300).mean()\n", "sp500['Volume_64w']=pd.Series(sp500['Volume']).rolling(window=320).mean()\n", "sp500['Volume_68w']=pd.Series(sp500['Volume']).rolling(window=340).mean()\n", "sp500['Volume_72w']=pd.Series(sp500['Volume']).rolling(window=360).mean()\n", "sp500['Volume_76w']=pd.Series(sp500['Volume']).rolling(window=380).mean()\n", "sp500['Volume_80w']=pd.Series(sp500['Volume']).rolling(window=400).mean()\n", "\n", "# Label data: Up (Down) if the the 1 month (≈ 21 trading days) logarithmic return increased (decreased)\n", "sp500['Return_Label']=pd.Series(sp500['Log_Ret_1d']).shift(-21).rolling(window=21).sum()\n", "sp500['Label']=np.where(sp500['Return_Label'] > 0, 1, 0)\n", "\n", "# Drop NA´s\n", "sp500=sp500.dropna(\"index\")\n", "sp500=sp500.drop(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume', \"Return_Label\"], axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4.3 Extract Basic Information " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rows, Columns:\n", "(9454, 71)\n", "\n", "\n", "Columns:\n", "Index(['Log_Ret_1d', 'Log_Ret_1w', 'Log_Ret_2w', 'Log_Ret_3w', 'Log_Ret_4w',\n", " 'Log_Ret_8w', 'Log_Ret_12w', 'Log_Ret_16w', 'Log_Ret_20w',\n", " 'Log_Ret_24w', 'Log_Ret_28w', 'Log_Ret_32w', 'Log_Ret_36w',\n", " 'Log_Ret_40w', 'Log_Ret_44w', 'Log_Ret_48w', 'Log_Ret_52w',\n", " 'Log_Ret_56w', 'Log_Ret_60w', 'Log_Ret_64w', 'Log_Ret_68w',\n", " 'Log_Ret_72w', 'Log_Ret_76w', 'Log_Ret_80w', 'Vol_1w', 'Vol_2w',\n", " 'Vol_3w', 'Vol_4w', 'Vol_8w', 'Vol_12w', 'Vol_16w', 'Vol_20w',\n", " 'Vol_24w', 'Vol_28w', 'Vol_32w', 'Vol_36w', 'Vol_40w', 'Vol_44w',\n", " 'Vol_48w', 'Vol_52w', 'Vol_56w', 'Vol_60w', 'Vol_64w', 'Vol_68w',\n", " 'Vol_72w', 'Vol_76w', 'Vol_80w', 'Volume_1w', 'Volume_2w', 'Volume_3w',\n", " 'Volume_4w', 'Volume_8w', 'Volume_12w', 'Volume_16w', 'Volume_20w',\n", " 'Volume_24w', 'Volume_28w', 'Volume_32w', 'Volume_36w', 'Volume_40w',\n", " 'Volume_44w', 'Volume_48w', 'Volume_52w', 'Volume_56w', 'Volume_60w',\n", " 'Volume_64w', 'Volume_68w', 'Volume_72w', 'Volume_76w', 'Volume_80w',\n", " 'Label'],\n", " dtype='object')\n", "\n", "\n", "Info:\n", "\n", "DatetimeIndex: 9454 entries, 1981-07-31 to 2019-01-28\n", "Data columns (total 71 columns):\n", "Log_Ret_1d 9454 non-null float64\n", "Log_Ret_1w 9454 non-null float64\n", "Log_Ret_2w 9454 non-null float64\n", "Log_Ret_3w 9454 non-null float64\n", "Log_Ret_4w 9454 non-null float64\n", "Log_Ret_8w 9454 non-null float64\n", "Log_Ret_12w 9454 non-null float64\n", "Log_Ret_16w 9454 non-null float64\n", "Log_Ret_20w 9454 non-null float64\n", "Log_Ret_24w 9454 non-null float64\n", "Log_Ret_28w 9454 non-null float64\n", "Log_Ret_32w 9454 non-null float64\n", "Log_Ret_36w 9454 non-null float64\n", "Log_Ret_40w 9454 non-null float64\n", "Log_Ret_44w 9454 non-null float64\n", "Log_Ret_48w 9454 non-null float64\n", "Log_Ret_52w 9454 non-null float64\n", "Log_Ret_56w 9454 non-null float64\n", "Log_Ret_60w 9454 non-null float64\n", "Log_Ret_64w 9454 non-null float64\n", "Log_Ret_68w 9454 non-null float64\n", "Log_Ret_72w 9454 non-null float64\n", "Log_Ret_76w 9454 non-null float64\n", "Log_Ret_80w 9454 non-null float64\n", "Vol_1w 9454 non-null float64\n", "Vol_2w 9454 non-null float64\n", "Vol_3w 9454 non-null float64\n", "Vol_4w 9454 non-null float64\n", "Vol_8w 9454 non-null float64\n", "Vol_12w 9454 non-null float64\n", "Vol_16w 9454 non-null float64\n", "Vol_20w 9454 non-null float64\n", "Vol_24w 9454 non-null float64\n", "Vol_28w 9454 non-null float64\n", "Vol_32w 9454 non-null float64\n", "Vol_36w 9454 non-null float64\n", "Vol_40w 9454 non-null float64\n", "Vol_44w 9454 non-null float64\n", "Vol_48w 9454 non-null float64\n", "Vol_52w 9454 non-null float64\n", "Vol_56w 9454 non-null float64\n", "Vol_60w 9454 non-null float64\n", "Vol_64w 9454 non-null float64\n", "Vol_68w 9454 non-null float64\n", "Vol_72w 9454 non-null float64\n", "Vol_76w 9454 non-null float64\n", "Vol_80w 9454 non-null float64\n", "Volume_1w 9454 non-null float64\n", "Volume_2w 9454 non-null float64\n", "Volume_3w 9454 non-null float64\n", "Volume_4w 9454 non-null float64\n", "Volume_8w 9454 non-null float64\n", "Volume_12w 9454 non-null float64\n", "Volume_16w 9454 non-null float64\n", "Volume_20w 9454 non-null float64\n", "Volume_24w 9454 non-null float64\n", "Volume_28w 9454 non-null float64\n", "Volume_32w 9454 non-null float64\n", "Volume_36w 9454 non-null float64\n", "Volume_40w 9454 non-null float64\n", "Volume_44w 9454 non-null float64\n", "Volume_48w 9454 non-null float64\n", "Volume_52w 9454 non-null float64\n", "Volume_56w 9454 non-null float64\n", "Volume_60w 9454 non-null float64\n", "Volume_64w 9454 non-null float64\n", "Volume_68w 9454 non-null float64\n", "Volume_72w 9454 non-null float64\n", "Volume_76w 9454 non-null float64\n", "Volume_80w 9454 non-null float64\n", "Label 9454 non-null int64\n", "dtypes: float64(70), int64(1)\n", "memory usage: 5.2 MB\n", "None\n", "\n", "\n", "Non-NA:\n", "Log_Ret_1d 9454\n", "Log_Ret_1w 9454\n", "Log_Ret_2w 9454\n", "Log_Ret_3w 9454\n", "Log_Ret_4w 9454\n", "Log_Ret_8w 9454\n", "Log_Ret_12w 9454\n", "Log_Ret_16w 9454\n", "Log_Ret_20w 9454\n", "Log_Ret_24w 9454\n", "Log_Ret_28w 9454\n", "Log_Ret_32w 9454\n", "Log_Ret_36w 9454\n", "Log_Ret_40w 9454\n", "Log_Ret_44w 9454\n", "Log_Ret_48w 9454\n", "Log_Ret_52w 9454\n", "Log_Ret_56w 9454\n", "Log_Ret_60w 9454\n", "Log_Ret_64w 9454\n", "Log_Ret_68w 9454\n", "Log_Ret_72w 9454\n", "Log_Ret_76w 9454\n", "Log_Ret_80w 9454\n", "Vol_1w 9454\n", "Vol_2w 9454\n", "Vol_3w 9454\n", "Vol_4w 9454\n", "Vol_8w 9454\n", "Vol_12w 9454\n", " ... \n", "Vol_60w 9454\n", "Vol_64w 9454\n", "Vol_68w 9454\n", "Vol_72w 9454\n", "Vol_76w 9454\n", "Vol_80w 9454\n", "Volume_1w 9454\n", "Volume_2w 9454\n", "Volume_3w 9454\n", "Volume_4w 9454\n", "Volume_8w 9454\n", "Volume_12w 9454\n", "Volume_16w 9454\n", "Volume_20w 9454\n", "Volume_24w 9454\n", "Volume_28w 9454\n", "Volume_32w 9454\n", "Volume_36w 9454\n", "Volume_40w 9454\n", "Volume_44w 9454\n", "Volume_48w 9454\n", "Volume_52w 9454\n", "Volume_56w 9454\n", "Volume_60w 9454\n", "Volume_64w 9454\n", "Volume_68w 9454\n", "Volume_72w 9454\n", "Volume_76w 9454\n", "Volume_80w 9454\n", "Label 9454\n", "Length: 71, dtype: int64\n", "\n", "\n", "Head\n", " Log_Ret_1d Log_Ret_1w Log_Ret_2w Log_Ret_3w Log_Ret_4w \\\n", "Date \n", "1981-07-31 0.006975 0.018969 0.001223 0.011910 0.017569 \n", "1981-08-03 -0.003367 0.004455 0.013580 0.006459 0.024124 \n", "1981-08-04 0.005350 0.015673 0.021887 0.011732 0.022667 \n", "1981-08-05 0.011294 0.026813 0.042655 0.018563 0.033338 \n", "1981-08-06 -0.000226 0.020027 0.040307 0.017492 0.025503 \n", "\n", " Log_Ret_8w Log_Ret_12w Log_Ret_16w Log_Ret_20w Log_Ret_24w \\\n", "Date \n", "1981-07-31 -0.000306 0.001070 -0.022582 0.003520 0.012683 \n", "1981-08-03 -0.013247 -0.009079 -0.028931 0.004070 0.009549 \n", "1981-08-04 -0.008048 -0.003652 -0.026257 -0.015206 0.022667 \n", "1981-08-05 0.005290 0.022564 -0.013774 -0.003311 0.039905 \n", "1981-08-06 0.002415 0.014581 -0.003838 -0.015263 0.043609 \n", "\n", " ... Volume_48w Volume_52w Volume_56w Volume_60w \\\n", "Date ... \n", "1981-07-31 ... 4.800596e+07 4.795673e+07 4.766939e+07 4.717517e+07 \n", "1981-08-03 ... 4.799646e+07 4.790835e+07 4.768893e+07 4.715470e+07 \n", "1981-08-04 ... 4.798354e+07 4.788362e+07 4.769511e+07 4.715020e+07 \n", "1981-08-05 ... 4.799821e+07 4.792927e+07 4.772293e+07 4.720257e+07 \n", "1981-08-06 ... 4.797262e+07 4.799012e+07 4.774779e+07 4.723613e+07 \n", "\n", " Volume_64w Volume_68w Volume_72w Volume_76w Volume_80w \\\n", "Date \n", "1981-07-31 46354531.25 4.569368e+07 4.540333e+07 4.563068e+07 45952075.0 \n", "1981-08-03 46389093.75 4.562300e+07 4.540144e+07 4.560037e+07 45949675.0 \n", "1981-08-04 46416781.25 4.560165e+07 4.540325e+07 4.553079e+07 45922125.0 \n", "1981-08-05 46499125.00 4.565591e+07 4.544658e+07 4.555100e+07 45960025.0 \n", "1981-08-06 46565437.50 4.571426e+07 4.546814e+07 4.557468e+07 45978950.0 \n", "\n", " Label \n", "Date \n", "1981-07-31 0 \n", "1981-08-03 0 \n", "1981-08-04 0 \n", "1981-08-05 0 \n", "1981-08-06 0 \n", "\n", "[5 rows x 71 columns]\n", "\n", "\n", "Tail\n", " Log_Ret_1d Log_Ret_1w Log_Ret_2w Log_Ret_3w Log_Ret_4w \\\n", "Date \n", "2019-01-22 -0.014258 0.019285 0.032114 0.057515 0.064913 \n", "2019-01-23 0.002200 0.010821 0.024666 0.051259 0.087916 \n", "2019-01-24 0.001375 0.009976 0.021951 0.051366 0.116778 \n", "2019-01-25 0.008453 0.010867 0.025896 0.084888 0.076827 \n", "2019-01-28 -0.007878 -0.010108 0.018164 0.043250 0.060423 \n", "\n", " Log_Ret_8w Log_Ret_12w Log_Ret_16w Log_Ret_20w Log_Ret_24w \\\n", "Date \n", "2019-01-22 -0.003409 -0.040124 -0.101976 -0.095500 -0.062462 \n", "2019-01-23 -0.004247 -0.006573 -0.096481 -0.093569 -0.065134 \n", "2019-01-24 0.003704 -0.023652 -0.097866 -0.097879 -0.062718 \n", "2019-01-25 -0.003256 0.002281 -0.089406 -0.084986 -0.059180 \n", "2019-01-28 -0.014390 0.000984 -0.100918 -0.092999 -0.071691 \n", "\n", " ... Volume_48w Volume_52w Volume_56w Volume_60w \\\n", "Date ... \n", "2019-01-22 ... 3.601485e+09 3.629041e+09 3.599487e+09 3.587928e+09 \n", "2019-01-23 ... 3.596106e+09 3.628588e+09 3.600307e+09 3.586275e+09 \n", "2019-01-24 ... 3.588305e+09 3.628038e+09 3.601526e+09 3.586096e+09 \n", "2019-01-25 ... 3.580530e+09 3.628702e+09 3.602449e+09 3.587466e+09 \n", "2019-01-28 ... 3.578684e+09 3.628852e+09 3.602700e+09 3.587370e+09 \n", "\n", " Volume_64w Volume_68w Volume_72w Volume_76w \\\n", "Date \n", "2019-01-22 3.582160e+09 3.559243e+09 3.530446e+09 3.520048e+09 \n", "2019-01-23 3.582736e+09 3.559011e+09 3.531507e+09 3.520774e+09 \n", "2019-01-24 3.583622e+09 3.554835e+09 3.532314e+09 3.521433e+09 \n", "2019-01-25 3.586429e+09 3.556658e+09 3.533421e+09 3.523418e+09 \n", "2019-01-28 3.588690e+09 3.557728e+09 3.535712e+09 3.525004e+09 \n", "\n", " Volume_80w Label \n", "Date \n", "2019-01-22 3.508435e+09 1 \n", "2019-01-23 3.508233e+09 1 \n", "2019-01-24 3.507829e+09 1 \n", "2019-01-25 3.508694e+09 1 \n", "2019-01-28 3.504530e+09 1 \n", "\n", "[5 rows x 71 columns]\n", "\n", "\n", "Summary statistics:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Log_Ret_1d Log_Ret_1w Log_Ret_2w Log_Ret_3w Log_Ret_4w \\\n", "count 9454.000000 9454.000000 9454.000000 9454.000000 9454.000000 \n", "mean 0.000319 0.001596 0.003191 0.004772 0.006342 \n", "std 0.011115 0.023453 0.031869 0.038598 0.044166 \n", "min -0.228997 -0.319214 -0.377868 -0.365360 -0.350374 \n", "25% -0.004497 -0.010109 -0.012040 -0.013111 -0.015094 \n", "50% 0.000531 0.003047 0.005691 0.008145 0.010847 \n", "75% 0.005554 0.014624 0.020748 0.026528 0.031437 \n", "max 0.109572 0.174887 0.195882 0.199030 0.211030 \n", "\n", " Log_Ret_8w Log_Ret_12w Log_Ret_16w Log_Ret_20w Log_Ret_24w \\\n", "count 9454.000000 9454.000000 9454.000000 9454.000000 9454.000000 \n", "mean 0.012641 0.019043 0.025468 0.031968 0.038525 \n", "std 0.061672 0.075067 0.086494 0.097284 0.108054 \n", "min -0.474377 -0.532590 -0.534617 -0.535123 -0.605207 \n", "25% -0.014885 -0.013295 -0.013128 -0.010956 -0.009207 \n", "50% 0.018085 0.026825 0.033895 0.040462 0.050485 \n", "75% 0.049131 0.063615 0.077685 0.090158 0.101540 \n", "max 0.289631 0.328761 0.325124 0.377440 0.421288 \n", "\n", " ... Volume_48w Volume_52w Volume_56w Volume_60w \\\n", "count ... 9.454000e+03 9.454000e+03 9.454000e+03 9.454000e+03 \n", "mean ... 1.642717e+09 1.639007e+09 1.635288e+09 1.631596e+09 \n", "std ... 1.691191e+09 1.689579e+09 1.687964e+09 1.686398e+09 \n", "min ... 4.668229e+07 4.653250e+07 4.665168e+07 4.691313e+07 \n", "25% ... 1.756674e+08 1.761194e+08 1.760642e+08 1.754544e+08 \n", "50% ... 8.858820e+08 8.861620e+08 8.787942e+08 8.702093e+08 \n", "75% ... 3.402212e+09 3.392543e+09 3.391066e+09 3.388343e+09 \n", "max ... 6.128274e+09 6.073674e+09 5.995085e+09 5.949259e+09 \n", "\n", " Volume_64w Volume_68w Volume_72w Volume_76w Volume_80w \\\n", "count 9.454000e+03 9.454000e+03 9.454000e+03 9.454000e+03 9.454000e+03 \n", "mean 1.627909e+09 1.624251e+09 1.620625e+09 1.617043e+09 1.613479e+09 \n", "std 1.684841e+09 1.683312e+09 1.681791e+09 1.680278e+09 1.678765e+09 \n", "min 4.635453e+07 4.560165e+07 4.540144e+07 4.529534e+07 4.521465e+07 \n", "25% 1.745719e+08 1.736218e+08 1.729558e+08 1.730130e+08 1.727768e+08 \n", "50% 8.689982e+08 8.589569e+08 8.484074e+08 8.447265e+08 8.424810e+08 \n", "75% 3.389822e+09 3.391340e+09 3.392891e+09 3.392742e+09 3.388016e+09 \n", "max 5.871179e+09 5.828395e+09 5.766422e+09 5.691528e+09 5.615789e+09 \n", "\n", " Label \n", "count 9454.000000 \n", "mean 0.626296 \n", "std 0.483812 \n", "min 0.000000 \n", "25% 0.000000 \n", "50% 1.000000 \n", "75% 1.000000 \n", "max 1.000000 \n", "\n", "[8 rows x 71 columns]\n", "\n", "\n" ] } ], "source": [ "# Show rows and columns\n", "print(\"Rows, Columns:\");print(sp500.shape);print(\"\\n\")\n", "\n", "# Describe DataFrame columns\n", "print(\"Columns:\");print(sp500.columns);print(\"\\n\")\n", "\n", "# Show info on DataFrame\n", "print(\"Info:\");print(sp500.info()); print(\"\\n\")\n", "\n", "# Count Non-NA values\n", "print(\"Non-NA:\");print(sp500.count()); print(\"\\n\")\n", "\n", "# Show head\n", "print(\"Head\");print(sp500.head()); print(\"\\n\")\n", "\n", "# Show tail\n", "print(\"Tail\");print(sp500.tail());print(\"\\n\")\n", "\n", "# Show summary statistics\n", "print(\"Summary statistics:\");print(sp500.describe());print(\"\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4.4 Plot Data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the logarithmic returns\n", "sp500.iloc[:,1:24].plot(subplots=True, color='blue', figsize=(20, 20));" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the Volatilities\n", "sp500.iloc[:,24:47].plot(subplots=True, color='blue',figsize=(20, 20));" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the Volumes\n", "sp500.iloc[:,47:70].plot(subplots=True, color='blue', figsize=(20, 20));" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot correlation matrix\n", "\n", "focus_cols=sp500.iloc[:,24:47].columns \n", "\n", "corr=sp500.iloc[:,24:70].corr().filter(focus_cols).drop(focus_cols)\n", "\n", "mask=np.zeros_like(corr); mask[np.triu_indices_from(mask)]=True # we use mask to plot only part of the matrix\n", "\n", "heat_fig, (ax)=plt.subplots(1, 1, figsize=(9,6))\n", "\n", "heat=sns.heatmap(corr, \n", " ax=ax, \n", " mask=mask, \n", " vmax=.5, \n", " square=True, \n", " linewidths=.2, \n", " cmap=\"Blues_r\")\n", "\n", "heat_fig.subplots_adjust(top=.93)\n", "\n", "heat_fig.suptitle('Volatility vs. Volume', fontsize=14, fontweight='bold')\n", "\n", "plt.savefig('heat1.eps', dpi=200, format='eps');" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot correlation matrix\n", "\n", "focus_cols=sp500.iloc[:,24:47].columns \n", "\n", "corr=sp500.iloc[:,1:47].corr().filter(focus_cols).drop(focus_cols)\n", "\n", "mask=np.zeros_like(corr); mask[np.triu_indices_from(mask)]=True # we use mask to plot only part of the matrix\n", "\n", "heat_fig, (ax)=plt.subplots(1, 1, figsize=(9,6))\n", "\n", "heat=sns.heatmap(corr, \n", " ax=ax, \n", " mask=mask, \n", " vmax=.5, \n", " square=True, \n", " linewidths=.2, \n", " cmap=\"Blues_r\")\n", "\n", "heat_fig.subplots_adjust(top=.93)\n", "\n", "heat_fig.suptitle('Volatility vs. Return', fontsize=14, fontweight='bold')\n", "\n", "plt.savefig('heat2.eps', dpi=200, format='eps');" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot correlation matrix\n", "\n", "focus_cols=sp500.iloc[:,47:70].columns \n", "\n", "corr=sp500.iloc[:, np.r_[1:24, 47:70]].corr().filter(focus_cols).drop(focus_cols)\n", "\n", "mask=np.zeros_like(corr); mask[np.triu_indices_from(mask)]=True # we use mask to plot only part of the matrix\n", "\n", "heat_fig, (ax)=plt.subplots(1, 1, figsize=(9,6))\n", "\n", "heat=sns.heatmap(corr, \n", " ax=ax, \n", " mask=mask, \n", " vmax=.5, \n", " square=True, \n", " linewidths=.2, \n", " cmap=\"Blues_r\")\n", "\n", "heat_fig.subplots_adjust(top=.93)\n", "\n", "heat_fig.suptitle('Volume vs. Return', fontsize=14, fontweight='bold')\n", "\n", "plt.savefig('heat3.eps', dpi=200, format='eps');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4.5 Separate Test Data & Generate Model Sets for Baseline and LSTM Models " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Model Set 1: Volatility\n", "\n", "# Baseline\n", "X_train_1, X_test_1, y_train_1, y_test_1=train_test_split(sp500.iloc[:,24:47], sp500.iloc[:,70], test_size=0.1 ,shuffle=False, stratify=None)\n", "# LSTM \n", "# Input arrays should be shaped as (samples or batch, time_steps or look_back, num_features):\n", "X_train_1_lstm=X_train_1.values.reshape(X_train_1.shape[0], 1, X_train_1.shape[1])\n", "X_test_1_lstm=X_test_1.values.reshape(X_test_1.shape[0], 1, X_test_1.shape[1])\n", "\n", "# Model Set 2: Return\n", "X_train_2, X_test_2, y_train_2, y_test_2=train_test_split(sp500.iloc[:,1:24], sp500.iloc[:,70], test_size=0.1 ,shuffle=False, stratify=None)\n", "# LSTM \n", "# Input arrays should be shaped as (samples or batch, time_steps or look_back, num_features):\n", "X_train_2_lstm=X_train_2.values.reshape(X_train_2.shape[0], 1, X_train_2.shape[1])\n", "X_test_2_lstm=X_test_2.values.reshape(X_test_2.shape[0], 1, X_test_2.shape[1])\n", "\n", "# Model Set 3: Volume\n", "X_train_3, X_test_3, y_train_3, y_test_3=train_test_split(sp500.iloc[:,47:70], sp500.iloc[:,70], test_size=0.1 ,shuffle=False, stratify=None)\n", "# LSTM \n", "# Input arrays should be shaped as (samples or batch, time_steps or look_back, num_features):\n", "X_train_3_lstm=X_train_3.values.reshape(X_train_3.shape[0], 1, X_train_3.shape[1])\n", "X_test_3_lstm=X_test_3.values.reshape(X_test_3.shape[0], 1, X_test_3.shape[1])\n", "\n", "# Model Set 4: Volatility and Return\n", "X_train_4, X_test_4, y_train_4, y_test_4=train_test_split(sp500.iloc[:,1:47], sp500.iloc[:,70], test_size=0.1 ,shuffle=False, stratify=None)\n", "# LSTM \n", "# Input arrays should be shaped as (samples or batch, time_steps or look_back, num_features):\n", "X_train_4_lstm=X_train_4.values.reshape(X_train_4.shape[0], 1, X_train_4.shape[1])\n", "X_test_4_lstm=X_test_4.values.reshape(X_test_4.shape[0], 1, X_test_4.shape[1])\n", "\n", "# Model Set 5: Volatility and Volume\n", "X_train_5, X_test_5, y_train_5, y_test_5=train_test_split(sp500.iloc[:,24:70], sp500.iloc[:,70], test_size=0.1 ,shuffle=False, stratify=None)\n", "# LSTM \n", "# Input arrays should be shaped as (samples or batch, time_steps or look_back, num_features):\n", "X_train_5_lstm=X_train_5.values.reshape(X_train_5.shape[0], 1, X_train_5.shape[1])\n", "X_test_5_lstm=X_test_5.values.reshape(X_test_5.shape[0], 1, X_test_5.shape[1])\n", "\n", "# Model Set 6: Return and Volume\n", "X_train_6, X_test_6, y_train_6, y_test_6=train_test_split(pd.concat([sp500.iloc[:,1:24], sp500.iloc[:,47:70]], axis=1), sp500.iloc[:,70], test_size=0.1 ,shuffle=False, stratify=None)\n", "# LSTM \n", "# Input arrays should be shaped as (samples or batch, time_steps or look_back, num_features):\n", "X_train_6_lstm=X_train_6.values.reshape(X_train_6.shape[0], 1, X_train_6.shape[1])\n", "X_test_6_lstm=X_test_6.values.reshape(X_test_6.shape[0], 1, X_test_6.shape[1])\n", "\n", "# Model Set 7: Volatility, Return and Volume\n", "X_train_7, X_test_7, y_train_7, y_test_7=train_test_split(sp500.iloc[:,1:70], sp500.iloc[:,70], test_size=0.1 ,shuffle=False, stratify=None)\n", "# LSTM \n", "# Input arrays should be shaped as (samples or batch, time_steps or look_back, num_features):\n", "X_train_7_lstm=X_train_7.values.reshape(X_train_7.shape[0], 1, X_train_7.shape[1])\n", "X_test_7_lstm=X_test_7.values.reshape(X_test_7.shape[0], 1, X_test_7.shape[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4.6 Show Label Distribution" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "train set increase bias = 0.6243535496003761%\n", "test set increase bias = 0.6437632135306554%\n" ] } ], "source": [ "print(\"train set increase bias = \"+ str(np.mean(y_train_7==1))+\"%\")\n", "\n", "print(\"test set increase bias = \" + str(np.mean(y_test_7==1))+\"%\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5. Workflow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Walk Forward Cross-Validation\n", "\n", "Time Series cross-validator provides train/dev indices to split time series data samples that are observed at fixed time intervals, in train/dev sets. In each split, dev indices must be higher than before, and thus shuffling in cross validator is inappropriate. The following graph illustrates how the time series split works:\n", "\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Time Series Split \n", "dev_size=0.1 \n", "n_splits=int((1//dev_size)-1) # using // for integer division\n", "tscv=TimeSeriesSplit(n_splits=n_splits) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scaling (Standardization/Normalization)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The splitting of the data set during cross-validation should be done before doing any preprocessing. Any process that extracts knowledge from the dataset should only ever be applied to the training portion of the data set, so any cross-validation should be the “outermost loop” in our processing.
\n", "\n", "The Pipeline class is a class that allows “gluing” together multiple processing steps into a single scikit-learn estimator. The Pipeline class itself has fit, predict and score methods and behaves just like any other model in scikit-learn. The most common use-case of the pipeline class is in chaining preprocessing steps (like scaling of the data) together with a supervised model like a classifier.
\n", "\n", "For each split in the Walk-Forward CV, the Scaler is refit with only the Sub training splits, not leaking any information of the test split into the parameter search, as illustrated below. \n", "\n", "\n", "\n", "\n", "**The preprocessing includes the following steps:**
\n", "**Step 0**: The data is split into TRAINING data and VALIDATION data according to the cv parameter that we specified in the GridSearchCV or RandomizedSearchCV
\n", "**Step 1**: the scaler is fitted on the TRAINING data
\n", "**Step 2**: the scaler transforms TRAINING data
\n", "**Step 3**: the models are fitted/trained using the transformed TRAINING data
\n", "**Step 4**: the scaler is used to transform the VALIDATION data
\n", "**Step 5**: the trained models predict using the transformed VALIDATION data
\n", "**Step 6**: the scaler is fitted on the TRAINING and VALIDATION data
\n", "**Step 7**: the scaler transforms TRAINING and VALIDATION data
\n", "**Step 8**: the model is fitted/trained using the transformed TRAINING and VALIDATION data and the best found parameters during Walk-Forward CV
\n", "**Step 9**: the scaler transforms TEST data
\n", "**Step 10**: the trained model predicts using the transformed TEST data
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6 Regularization \n", "\n", "Regularization adds a penalty on the different parameters of the **baseline** model to reduce the freedom of the model. Hence, the model will be less likely to fit the noise of the training data and will improve the generalization abilities of the mode\n", "\n", "\n", "### 6.1 L2 Regularization (Ridge penalisation)\n", "\n", "The L2 regularization adds a penalty equal to the sum of the squared value of the coefficients.\n", "\n", "$$ J = \\frac{1}{m} \\sum_{i=1}^m \\mathcal{L}(a_{i}, y_{i}) + \\lambda\\sum_{i=1}^m\\beta_{i}^2\\tag{1}$$\n", "\n", "The λ parameter is a scalar that should be learned as well, using walk-forward-cross-validation.\n", "\n", "L2 regularization will force the **parameters to be relatively small**, the bigger the penalization, the smaller (and the more robust) the coefficients are.\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "### 6.2 L1 Regularization (Lasso penalisation)\n", "\n", "The L1 regularization adds a penalty equal to the sum of the absolute value of the coefficients. \n", "\n", "$$ J = \\frac{1}{m} \\sum_{i=1}^m \\mathcal{L}(a_{i}, y_{i}) + \\lambda\\sum_{i=1}^m|\\beta_{i}|\\tag{2}$$\n", "\n", "The λ parameter is a scalar that should be learned as well, using walk-forward-cross-validation.\n", "\n", "L1 regularization will **shrink some parameters to zero**. Hence some variables will not play any role in the model, L1 regression can be seen as a way to select features in a model. \n", "\n", "\n", "\n", "\n", "\n", "### 6.3 Elastic Net \n", "\n", "Elastic-net is a **mix of both L1 and L2 regularizations**. A penalty is applied to the sum of the absolute values and to the sum of the squared values:\n", "\n", "$$ J = \\frac{1}{m} \\sum_{i=1}^m \\mathcal{L}(a_{i}, y_{i}) + \\lambda((1-\\alpha)\\sum_{i=1}^m\\beta{i}^2+\\alpha\\sum_{i=1}^m|\\beta_{i}|)\\tag{3}$$ \n", "\n", "Lambda is a shared penalization parameter while alpha sets the ratio between L1 and L2 regularization in the Elastic Net Regularization. Hence, we expect a hybrid behavior between L1 and L2 regularization.\n", "\n", "Though coefficients are cut, the cut is less abrupt than the cut with lasso penalization alone. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Configuration Baseline Models" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Standardized Data\n", "steps_b=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), \n", " ('logistic', linear_model.SGDClassifier(loss=\"log\", shuffle=False, early_stopping=False, tol=1e-3, random_state=1))]\n", "\n", "#Normalized Data\n", "#steps_b=[('scaler', MinMaxScaler(feature_range=(0, 1), copy=True)), \n", "# ('logistic', linear_model.SGDClassifier(loss=\"log\", shuffle=False, early_stopping=False, tol=1e-3, random_state=1))]\n", "\n", "pipeline_b=Pipeline(steps_b) # Using a pipeline we glue together the Scaler & the Classifier\n", "# This ensure that during cross validation the Scaler is fitted to only the training folds\n", "\n", "# Penalties\n", "penalty_b=['l1', 'l2', 'elasticnet']\n", "\n", "# Evaluation Metric\n", "scoring_b={'AUC': 'roc_auc', 'accuracy': make_scorer(accuracy_score)} #multiple evaluation metrics\n", "metric_b='accuracy' #scorer is used to find the best parameters for refitting the estimator at the end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Configuration LSTM Models" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Batch_input_shape=[1, 1, Z] -> (batch size, time steps, number of features) \n", "# Data set inputs(trainX)=[X, 1, Z] -> (samples, time steps, number of features) \n", "\n", "# number of samples\n", "num_samples=1 \n", "# time_steps\n", "look_back=1\n", "\n", "\n", "# Evaluation Metric\n", "scoring_lstm='accuracy' " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 6. Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model 1: Volatility" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Baseline " ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loss function: log\n", "accuracy of the best model: 0.5448412698412698\n", "\n", "\n", "Best hyperparameters:\n", "Number of iterations: 8\n", "Penalty: l2\n", "Alpha: 0.0014\n", "l1_ratio: 0\n", "Total number of features: 23\n", "Number of selected features: 23\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Model specific Parameter \n", "\n", "# Number of iterations\n", "iterations_1_b=[8] \n", "\n", "\n", "# Grid Search\n", "\n", "# Regularization \n", "alpha_g_1_b=[0.0011, 0.0013, 0.0014] #0.0011, 0.0012, 0.0013\n", "l1_ratio_g_1_b=[0, 0.2, 0.4, 0.6, 0.8, 1] \n", "\n", "# Create hyperparameter options\n", "hyperparameters_g_1_b={'logistic__alpha':alpha_g_1_b, \n", " 'logistic__l1_ratio':l1_ratio_g_1_b, \n", " 'logistic__penalty':penalty_b, \n", " 'logistic__max_iter':iterations_1_b}\n", "\n", "# Create grid search \n", "search_g_1_b=GridSearchCV(estimator=pipeline_b, \n", " param_grid=hyperparameters_g_1_b, \n", " cv=tscv, \n", " verbose=0, \n", " n_jobs=-1, \n", " scoring=scoring_b, \n", " refit=metric_b, \n", " return_train_score=False)\n", "# Setting refit='Accuracy', refits an estimator on the whole dataset with the parameter setting that has the best cross-validated mean Accuracy score. \n", "# For multiple metric evaluation, this needs to be a string denoting the scorer is used to find the best parameters for refitting the estimator at the end\n", "# If return_train_score=True training results of CV will be saved as well \n", "\n", "# Fit grid search\n", "tuned_model_1_b=search_g_1_b.fit(X_train_1, y_train_1)\n", "#search_g_1_b.cv_results_\n", "\n", "\n", "# Random Search\n", "\n", "# Create regularization hyperparameter distribution using uniform distribution\n", "#alpha_r_1_b=uniform(loc=0.00006, scale=0.002) \n", "#l1_ratio_r_1_b=uniform(loc=0, scale=1) \n", "\n", "# Create hyperparameter options\n", "#hyperparameters_r_1_b={'logistic__alpha':alpha_r_1_b, 'logistic__l1_ratio':l1_ratio_r_1_b, 'logistic__penalty':penalty_b,'logistic__max_iter':iterations_1_b}\n", "\n", "# Create randomized search \n", "#search_r_1_b=RandomizedSearchCV(pipeline_b, \n", "# hyperparameters_r_1_b, \n", "# n_iter=10, \n", "# random_state=1, \n", "# cv=tscv, \n", "# verbose=0, \n", "# n_jobs=-1, \n", "# scoring=scoring_b, \n", "# refit=metric_b, \n", "# return_train_score=True)\n", "# Setting refit='Accuracy', refits an estimator on the whole dataset with the parameter setting that has the best cross-validated Accuracy score.\n", "# For multiple metric evaluation, this needs to be a string denoting the scorer is used to find the best parameters for refitting the estimator at the end\n", "# If return_train_score=True training results of CV will be saved as well \n", "\n", "# Fit randomized search\n", "#tuned_model_1_b=search_r_1_b.fit(X_train_1, y_train_1)\n", "\n", "\n", "\n", "# View Cost function\n", "print('Loss function:', tuned_model_1_b.best_estimator_.get_params()['logistic__loss'])\n", "\n", "# View Accuracy \n", "print(metric_b +' of the best model: ', tuned_model_1_b.best_score_);print(\"\\n\")\n", "# best_score_ Mean cross-validated score of the best_estimator\n", "\n", "# View best hyperparameters\n", "print(\"Best hyperparameters:\")\n", "print('Number of iterations:', tuned_model_1_b.best_estimator_.get_params()['logistic__max_iter'])\n", "print('Penalty:', tuned_model_1_b.best_estimator_.get_params()['logistic__penalty'])\n", "print('Alpha:', tuned_model_1_b.best_estimator_.get_params()['logistic__alpha'])\n", "print('l1_ratio:', tuned_model_1_b.best_estimator_.get_params()['logistic__l1_ratio'])\n", "\n", "# Find the number of nonzero coefficients (selected features)\n", "print(\"Total number of features:\", len(tuned_model_1_b.best_estimator_.steps[1][1].coef_[0][:]))\n", "print(\"Number of selected features:\", np.count_nonzero(tuned_model_1_b.best_estimator_.steps[1][1].coef_[0][:]))\n", "\n", "# Gridsearch table\n", "plt.title('Gridsearch')\n", "pvt_1_b=pd.pivot_table(pd.DataFrame(tuned_model_1_b.cv_results_), values='mean_test_accuracy', index='param_logistic__l1_ratio', columns='param_logistic__alpha')\n", "ax_1_b=sns.heatmap(pvt_1_b, cmap=\"Blues\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.6501057082452432\n", "Precision: 0.6565315315315315\n", "Recall: 0.9573070607553367\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make predictions\n", "y_pred_1_b=tuned_model_1_b.predict(X_test_1)\n", "\n", "# create confustion matrix\n", "fig, ax=plt.subplots()\n", "sns.heatmap(pd.DataFrame(metrics.confusion_matrix(y_test_1, y_pred_1_b)), annot=True, cmap=\"Blues\" ,fmt='g')\n", "plt.title('Confusion matrix'); plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n", "ax.xaxis.set_ticklabels(['Down', 'Up']); ax.yaxis.set_ticklabels(['Down', 'Up'])\n", "\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test_1, y_pred_1_b))\n", "print(\"Precision:\",metrics.precision_score(y_test_1, y_pred_1_b))\n", "print(\"Recall:\",metrics.recall_score(y_test_1, y_pred_1_b))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ROC Curve" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_proba_1_b=tuned_model_1_b.predict_proba(X_test_1)[:, 1]\n", "fpr, tpr, _=metrics.roc_curve(y_test_1, y_proba_1_b)\n", "auc=metrics.roc_auc_score(y_test_1, y_proba_1_b)\n", "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n", "plt.legend(loc=4)\n", "plt.plot([0, 1], [0, 1], linestyle='--') # plot no skill\n", "plt.title('ROC-Curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LSTM Model " ] }, { "cell_type": "code", "execution_count": 720, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "accuracy of the best model: 0.6101851851851852\n", "\n", "\n", "Best hyperparameters:\n", "epochs: 1\n", "batch_size: 1\n", "dropout_rate: 0.1\n", "recurrent_dropout: 0.1\n", "\n", "\n", "Running Time: 241.70626878738403\n" ] } ], "source": [ "start=time.time()\n", "\n", "# number of epochs\n", "epochs=1\n", "# number of units \n", "LSTM_units_1_lstm=195\n", "# numer of features\n", "num_features_1_lstm=X_train_1.shape[1]\n", "# Regularization\n", "dropout_rate=0.1\n", "recurrent_dropout=0.1 # 0.21\n", "# print\n", "verbose=0\n", "\n", "#hyperparameter\n", "batch_size=[1] \n", "\n", "# hyperparameter\n", "hyperparameter_1_lstm={'batch_size':batch_size}\n", "\n", "\n", "# create Classifier\n", "clf_1_lstm=KerasClassifier(build_fn=create_shallow_LSTM, \n", " epochs=epochs, \n", " LSTM_units=LSTM_units_1_lstm, \n", " num_samples=num_samples, \n", " look_back=look_back, \n", " num_features=num_features_1_lstm, \n", " dropout_rate=dropout_rate,\n", " recurrent_dropout=recurrent_dropout,\n", " verbose=verbose)\n", "# Gridsearch\n", "search_1_lstm=GridSearchCV(estimator=clf_1_lstm, \n", " param_grid=hyperparameter_1_lstm, \n", " n_jobs=-1, \n", " cv=tscv, \n", " scoring=scoring_lstm, # accuracy\n", " refit=True, \n", " return_train_score=False)\n", "\n", "# Fit model\n", "tuned_model_1_lstm=search_1_lstm.fit(X_train_1_lstm, y_train_1, shuffle=False, callbacks=[reset])\n", "\n", "\n", "\n", "print(\"\\n\")\n", "\n", "# View Accuracy \n", "print(scoring_lstm +' of the best model: ', tuned_model_1_lstm.best_score_)\n", "# best_score_ Mean cross-validated score of the best_estimator\n", "\n", "print(\"\\n\")\n", "\n", "# View best hyperparameters\n", "print(\"Best hyperparameters:\")\n", "print('epochs:', tuned_model_1_lstm.best_estimator_.get_params()['epochs'])\n", "print('batch_size:', tuned_model_1_lstm.best_estimator_.get_params()['batch_size'])\n", "print('dropout_rate:', tuned_model_1_lstm.best_estimator_.get_params()['dropout_rate'])\n", "print('recurrent_dropout:', tuned_model_1_lstm.best_estimator_.get_params()['recurrent_dropout'])\n", "\n", "end=time.time()\n", "print(\"\\n\")\n", "print(\"Running Time:\", end - start)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 721, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.6437632135306554\n", "Precision: 0.6437632135306554\n", "Recall: 1.0\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make predictions\n", "y_pred_1_lstm=tuned_model_1_lstm.predict(X_test_1_lstm)\n", "\n", "# create confustion matrix\n", "fig, ax=plt.subplots()\n", "sns.heatmap(pd.DataFrame(metrics.confusion_matrix(y_test_1, y_pred_1_lstm)), annot=True, cmap=\"Blues\" ,fmt='g')\n", "plt.title('Confusion matrix'); plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n", "ax.xaxis.set_ticklabels(['Down', 'Up']); ax.yaxis.set_ticklabels(['Down', 'Up'])\n", "\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test_1, y_pred_1_lstm))\n", "print(\"Precision:\",metrics.precision_score(y_test_1, y_pred_1_lstm))\n", "print(\"Recall:\",metrics.recall_score(y_test_1, y_pred_1_lstm))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ROC Curve" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_proba_1_lstm=tuned_model_1_lstm.predict_proba(X_test_1_lstm)[:, 1]\n", "fpr, tpr, _=metrics.roc_curve(y_test_1, y_proba_1_lstm)\n", "auc=metrics.roc_auc_score(y_test_1, y_proba_1_lstm)\n", "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n", "plt.legend(loc=4)\n", "plt.plot([0, 1], [0, 1], linestyle='--') # plot no skill\n", "plt.title('ROC-Curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model 2: Return" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Baseline" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loss function: log\n", "accuracy of the best model: 0.5543650793650794\n", "\n", "\n", "Best hyperparameters:\n", "Number of iterations: 8\n", "Penalty: elasticnet\n", "Alpha: 0.0013\n", "l1_ratio: 0.4\n", "Total number of features: 23\n", "Number of selected features: 10\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Model specific Parameter \n", "\n", "# Number of iterations\n", "iterations_2_b=[8] \n", "\n", "\n", "# Grid Search\n", "\n", "# Regularization \n", "alpha_g_2_b=[0.0011, 0.0012, 0.0013]\n", "l1_ratio_g_2_b=[0, 0.2, 0.4, 0.6, 0.8, 1] \n", "\n", "# Create hyperparameter options\n", "hyperparameters_g_2_b={'logistic__alpha':alpha_g_2_b, \n", " 'logistic__l1_ratio':l1_ratio_g_2_b, \n", " 'logistic__penalty':penalty_b, \n", " 'logistic__max_iter':iterations_2_b}\n", "\n", "# Create grid search \n", "search_g_2_b=GridSearchCV(estimator=pipeline_b, \n", " param_grid=hyperparameters_g_2_b, \n", " cv=tscv, \n", " verbose=0, \n", " n_jobs=-1, \n", " scoring=scoring_b, \n", " refit=metric_b, \n", " return_train_score=False)\n", "# Setting refit='Accuracy', refits an estimator on the whole dataset with the parameter setting that has the best cross-validated mean Accuracy score. \n", "# For multiple metric evaluation, this needs to be a string denoting the scorer is used to find the best parameters for refitting the estimator at the end\n", "# If return_train_score=True training results of CV will be saved as well \n", "\n", "# Fit grid search\n", "tuned_model_2_b=search_g_2_b.fit(X_train_2, y_train_2)\n", "#search_g_2_b.cv_results_\n", "\n", "\n", "# Random Search\n", "\n", "# Create regularization hyperparameter distribution using uniform distribution\n", "#alpha_r_2_b=uniform(loc=0.00006, scale=0.002) #loc=0.00006, scale=0.002\n", "#l1_ratio_r_2_b=uniform(loc=0, scale=1) \n", "\n", "# Create hyperparameter options\n", "#hyperparameters_r_2_b={'logistic__alpha':alpha_r_2_b, 'logistic__l1_ratio':l1_ratio_r_2_b, 'logistic__penalty':penalty_b,'logistic__max_iter':iterations_2_b}\n", "\n", "# Create randomized search \n", "#search_r_2_b=RandomizedSearchCV(pipeline_b, hyperparameters_r_2_b, n_iter=10, random_state=1, cv=tscv, verbose=0, n_jobs=-1, scoring=scoring_b, refit=metric_b, return_train_score=False)\n", "# Setting refit='Accuracy', refits an estimator on the whole dataset with the parameter setting that has the best cross-validated Accuracy score.\n", "\n", "# Fit randomized search\n", "#tuned_model_2_b=search_r_2_b.fit(X_train_2, y_train_2)\n", "\n", "\n", "\n", "# View Cost function\n", "print('Loss function:', tuned_model_2_b.best_estimator_.get_params()['logistic__loss'])\n", "\n", "# View Accuracy \n", "print(metric_b +' of the best model: ', tuned_model_2_b.best_score_);print(\"\\n\")\n", "# best_score_ Mean cross-validated score of the best_estimator\n", "\n", "# View best hyperparameters\n", "print(\"Best hyperparameters:\")\n", "print('Number of iterations:', tuned_model_2_b.best_estimator_.get_params()['logistic__max_iter'])\n", "print('Penalty:', tuned_model_2_b.best_estimator_.get_params()['logistic__penalty'])\n", "print('Alpha:', tuned_model_2_b.best_estimator_.get_params()['logistic__alpha'])\n", "print('l1_ratio:', tuned_model_2_b.best_estimator_.get_params()['logistic__l1_ratio'])\n", "\n", "# Find the number of nonzero coefficients (selected features)\n", "print(\"Total number of features:\", len(tuned_model_2_b.best_estimator_.steps[1][1].coef_[0][:]))\n", "print(\"Number of selected features:\", np.count_nonzero(tuned_model_2_b.best_estimator_.steps[1][1].coef_[0][:]))\n", "\n", "# Gridsearch table\n", "plt.title('Gridsearch')\n", "pvt_2_b=pd.pivot_table(pd.DataFrame(tuned_model_2_b.cv_results_), values='mean_test_accuracy', index='param_logistic__l1_ratio', columns='param_logistic__alpha')\n", "ax_2_b=sns.heatmap(pvt_2_b, cmap=\"Blues\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.6606765327695561\n", "Precision: 0.6607142857142857\n", "Recall: 0.9720853858784894\n" ] }, { "data": { 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make predictions\n", "y_pred_2_b=tuned_model_2_b.predict(X_test_2)\n", "\n", "# create confustion matrix\n", "fig, ax=plt.subplots()\n", "sns.heatmap(pd.DataFrame(metrics.confusion_matrix(y_test_2, y_pred_2_b)), annot=True, cmap=\"Blues\" ,fmt='g')\n", "plt.title('Confusion matrix'); plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n", "ax.xaxis.set_ticklabels(['Down', 'Up']); ax.yaxis.set_ticklabels(['Down', 'Up'])\n", "\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test_2, y_pred_2_b))\n", "print(\"Precision:\",metrics.precision_score(y_test_2, y_pred_2_b))\n", "print(\"Recall:\",metrics.recall_score(y_test_2, y_pred_2_b))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ROC Curve" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_proba_2_b=tuned_model_2_b.predict_proba(X_test_2)[:, 1]\n", "fpr, tpr, _=metrics.roc_curve(y_test_2, y_proba_2_b)\n", "auc=metrics.roc_auc_score(y_test_2, y_proba_2_b)\n", "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n", "plt.legend(loc=4)\n", "plt.plot([0, 1], [0, 1], linestyle='--') # plot no skill\n", "plt.title('ROC-Curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LSTM" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "accuracy of the best model: 0.5562169312169312\n", "\n", "\n", "Best hyperparameters:\n", "epochs: 1\n", "batch_size: 1\n", "dropout_rate: 0.0\n", "recurrent_dropout: 0.4\n", "\n", "\n", "Running Time: 203.3864288330078\n" ] } ], "source": [ "start=time.time()\n", "\n", "\n", "# number of epochs\n", "epochs=1\n", "# number of units \n", "LSTM_units_2_lstm=180\n", "# number of samples\n", "num_samples=1 \n", "# time_steps\n", "look_back=1\n", "# numer of features\n", "num_features_2_lstm=X_train_2.shape[1]\n", "# Regularization\n", "dropout_rate=0.\n", "recurrent_dropout=0.4\n", "# print\n", "verbose=0\n", "\n", "#hyperparameter\n", "batch_size=[1] \n", "\n", "# hyperparameter\n", "hyperparameter_2_lstm={'batch_size':batch_size}\n", "\n", "\n", "# create Classifier\n", "clf_2_lstm=KerasClassifier(build_fn=create_shallow_LSTM, \n", " epochs=epochs, \n", " LSTM_units=LSTM_units_2_lstm, \n", " num_samples=num_samples, \n", " look_back=look_back, \n", " num_features=num_features_2_lstm, \n", " dropout_rate=dropout_rate,\n", " recurrent_dropout=recurrent_dropout,\n", " verbose=verbose)\n", "# Gridsearch\n", "search_2_lstm=GridSearchCV(estimator=clf_2_lstm, \n", " param_grid=hyperparameter_2_lstm, \n", " n_jobs=-1, \n", " cv=tscv, \n", " scoring=scoring_lstm, # accuracy\n", " refit=True, \n", " return_train_score=False)\n", "\n", "# Fit model\n", "tuned_model_2_lstm=search_2_lstm.fit(X_train_2_lstm, y_train_2, shuffle=False, callbacks=[reset])\n", "\n", "\n", "\n", "print(\"\\n\")\n", "\n", "# View Accuracy \n", "print(scoring_lstm +' of the best model: ', tuned_model_2_lstm.best_score_)\n", "# best_score_ Mean cross-validated score of the best_estimator\n", "\n", "print(\"\\n\")\n", "\n", "# View best hyperparameters\n", "print(\"Best hyperparameters:\")\n", "print('epochs:', tuned_model_2_lstm.best_estimator_.get_params()['epochs'])\n", "print('batch_size:', tuned_model_2_lstm.best_estimator_.get_params()['batch_size'])\n", "print('dropout_rate:', tuned_model_2_lstm.best_estimator_.get_params()['dropout_rate'])\n", "print('recurrent_dropout:', tuned_model_2_lstm.best_estimator_.get_params()['recurrent_dropout'])\n", "\n", "end=time.time()\n", "print(\"\\n\")\n", "print(\"Running Time:\", end - start)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.6501057082452432\n", "Precision: 0.6551339285714286\n", "Recall: 0.9638752052545156\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make predictions\n", "y_pred_2_lstm=tuned_model_2_lstm.predict(X_test_2_lstm)\n", "\n", "# create confustion matrix\n", "fig, ax=plt.subplots()\n", "sns.heatmap(pd.DataFrame(metrics.confusion_matrix(y_test_2, y_pred_2_lstm)), annot=True, cmap=\"Blues\" ,fmt='g')\n", "plt.title('Confusion matrix'); plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n", "ax.xaxis.set_ticklabels(['Down', 'Up']); ax.yaxis.set_ticklabels(['Down', 'Up'])\n", "\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test_2, y_pred_2_lstm))\n", "print(\"Precision:\",metrics.precision_score(y_test_2, y_pred_2_lstm))\n", "print(\"Recall:\",metrics.recall_score(y_test_2, y_pred_2_lstm))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ROC Curve" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_proba_2_lstm=tuned_model_2_lstm.predict_proba(X_test_2_lstm)[:, 1]\n", "fpr, tpr, _=metrics.roc_curve(y_test_2, y_proba_2_lstm)\n", "auc=metrics.roc_auc_score(y_test_2, y_proba_2_lstm)\n", "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n", "plt.legend(loc=4)\n", "plt.plot([0, 1], [0, 1], linestyle='--') # plot no skill\n", "plt.title('ROC-Curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model 3: Trading Volume " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Baseline" ] }, { "cell_type": "code", "execution_count": 599, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loss function: log\n", "accuracy of the best model: 0.5521164021164021\n", "\n", "\n", "Best hyperparameters:\n", "Number of iterations: 20\n", "Penalty: l1\n", "Alpha: 1.836230045470262e-05\n", "l1_ratio: 0.9168613345297285\n", "Total number of features: 23\n", "Number of selected features: 6\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Model specific Parameter \n", "\n", "# Number of iterations\n", "#iterations_3_b=[20] \n", "\n", "\n", "# Grid Search\n", "\n", "# Regularization \n", "#alpha_g_3_b=[0.00007, 0.00008, 0.00009] \n", "#l1_ratio_g_3_b=[0., 0.2, 0.4, 0.6, 0.8, 1] \n", "\n", "# Create hyperparameter options\n", "#hyperparameters_g_3_b={'logistic__alpha':alpha_g_3_b, \n", "# 'logistic__l1_ratio':l1_ratio_g_3_b, \n", "# 'logistic__penalty':penalty_b, \n", "# 'logistic__max_iter':iterations_3_b}\n", "\n", "# Create grid search \n", "#search_g_3_b=GridSearchCV(estimator=pipeline_b, \n", "# param_grid=hyperparameters_g_3_b, \n", "# cv=tscv, \n", "# verbose=0, \n", "# n_jobs=-1, \n", "# scoring=scoring_b, \n", "# refit=metric_b, \n", "# return_train_score=False)\n", "# Setting refit='Accuracy', refits an estimator on the whole dataset with the parameter setting that has the best cross-validated mean Accuracy score. \n", "# For multiple metric evaluation, this needs to be a string denoting the scorer is used to find the best parameters for refitting the estimator at the end\n", "# If return_train_score=True training results of CV will be saved as well \n", "\n", "# Fit grid search\n", "#tuned_model_3_b=search_g_3_b.fit(X_train_3, y_train_3)\n", "#search_g_3_b.cv_results_\n", "\n", "\n", "# Random Search\n", "\n", "# Create regularization hyperparameter distribution using uniform distribution\n", "alpha_r_3_b=uniform(loc=0.00001, scale=0.0001) \n", "l1_ratio_r_3_b=uniform(loc=0, scale=1) \n", "\n", "# Create hyperparameter options\n", "hyperparameters_r_3_b={'logistic__alpha':alpha_r_3_b, 'logistic__l1_ratio':l1_ratio_r_3_b, 'logistic__penalty':penalty_b,'logistic__max_iter':iterations_3_b}\n", "\n", "# Create randomized search \n", "search_r_3_b=RandomizedSearchCV(pipeline_b, hyperparameters_r_3_b, n_iter=20, random_state=1, cv=tscv, verbose=0, n_jobs=-1, scoring=scoring_b, refit=metric_b, return_train_score=False)\n", "# Setting refit='Accuracy', refits an estimator on the whole dataset with the parameter setting that has the best cross-validated Accuracy score.\n", "\n", "# Fit randomized search\n", "tuned_model_3_b=search_r_3_b.fit(X_train_3, y_train_3)\n", "\n", "\n", "\n", "# View Cost function\n", "print('Loss function:', tuned_model_3_b.best_estimator_.get_params()['logistic__loss'])\n", "\n", "# View Accuracy \n", "print(metric_b +' of the best model: ', tuned_model_3_b.best_score_);print(\"\\n\")\n", "# best_score_ Mean cross-validated score of the best_estimator\n", "\n", "# View best hyperparameters\n", "print(\"Best hyperparameters:\")\n", "print('Number of iterations:', tuned_model_3_b.best_estimator_.get_params()['logistic__max_iter'])\n", "print('Penalty:', tuned_model_3_b.best_estimator_.get_params()['logistic__penalty'])\n", "print('Alpha:', tuned_model_3_b.best_estimator_.get_params()['logistic__alpha'])\n", "print('l1_ratio:', tuned_model_3_b.best_estimator_.get_params()['logistic__l1_ratio'])\n", "\n", "# Find the number of nonzero coefficients (selected features)\n", "print(\"Total number of features:\", len(tuned_model_3_b.best_estimator_.steps[1][1].coef_[0][:]))\n", "print(\"Number of selected features:\", np.count_nonzero(tuned_model_3_b.best_estimator_.steps[1][1].coef_[0][:]))\n", "\n", "# Gridsearch table\n", "plt.title('Gridsearch')\n", "pvt_3_b=pd.pivot_table(pd.DataFrame(tuned_model_3_b.cv_results_), values='mean_test_accuracy', index='param_logistic__l1_ratio', columns='param_logistic__alpha')\n", "ax_3_b=sns.heatmap(pvt_3_b, cmap=\"Blues\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 600, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.6395348837209303\n", "Precision: 0.6434689507494646\n", "Recall: 0.986863711001642\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make predictions\n", "y_pred_3_b=tuned_model_3_b.predict(X_test_3)\n", "\n", "# create confustion matrix\n", "fig, ax=plt.subplots()\n", "sns.heatmap(pd.DataFrame(metrics.confusion_matrix(y_test_3, y_pred_3_b)), annot=True, cmap=\"Blues\" ,fmt='g')\n", "plt.title('Confusion matrix'); plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n", "ax.xaxis.set_ticklabels(['Down', 'Up']); ax.yaxis.set_ticklabels(['Down', 'Up'])\n", "\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test_3, y_pred_3_b))\n", "print(\"Precision:\",metrics.precision_score(y_test_3, y_pred_3_b))\n", "print(\"Recall:\",metrics.recall_score(y_test_3, y_pred_3_b))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ROC Curve" ] }, { "cell_type": "code", "execution_count": 590, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_proba_3_b=tuned_model_3_b.predict_proba(X_test_3)[:, 1]\n", "fpr, tpr, _=metrics.roc_curve(y_test_3, y_proba_3_b)\n", "auc=metrics.roc_auc_score(y_test_3, y_proba_3_b)\n", "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n", "plt.legend(loc=4)\n", "plt.plot([0, 1], [0, 1], linestyle='--') # plot no skill\n", "plt.title('ROC-Curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LSTM" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "accuracy of the best model: 0.56494708994709\n", "\n", "\n", "Best hyperparameters:\n", "epochs: 1\n", "batch_size: 1\n", "dropout_rate: 0.0\n", "recurrent_dropout: 0.4\n", "\n", "\n", "Running Time: 188.86053204536438\n" ] } ], "source": [ "start=time.time()\n", "\n", "\n", "# number of epochs\n", "epochs=1\n", "# number of units \n", "LSTM_units_3_lstm=180\n", "# number of samples\n", "num_samples=1 \n", "# time_steps\n", "look_back=1\n", "# numer of features\n", "num_features_3_lstm=X_train_3.shape[1]\n", "# Regularization\n", "dropout_rate=0.\n", "recurrent_dropout=0.4\n", "# print\n", "verbose=0\n", "\n", "#hyperparameter\n", "batch_size=[1] \n", "\n", "# hyperparameter\n", "hyperparameter_3_lstm={'batch_size':batch_size}\n", "\n", "\n", "# create Classifier\n", "clf_3_lstm=KerasClassifier(build_fn=create_shallow_LSTM, \n", " epochs=epochs, \n", " LSTM_units=LSTM_units_3_lstm, \n", " num_samples=num_samples, \n", " look_back=look_back, \n", " num_features=num_features_3_lstm, \n", " dropout_rate=dropout_rate,\n", " recurrent_dropout=recurrent_dropout,\n", " verbose=verbose)\n", "# Gridsearch\n", "search_3_lstm=GridSearchCV(estimator=clf_3_lstm, \n", " param_grid=hyperparameter_3_lstm, \n", " n_jobs=-1, \n", " cv=tscv, \n", " scoring=scoring_lstm, # accuracy\n", " refit=True, \n", " return_train_score=False)\n", "\n", "# Fit model\n", "tuned_model_3_lstm=search_3_lstm.fit(X_train_3_lstm, y_train_3, shuffle=False, callbacks=[reset])\n", "\n", "\n", "\n", "print(\"\\n\")\n", "\n", "# View Accuracy \n", "print(scoring_lstm +' of the best model: ', tuned_model_3_lstm.best_score_)\n", "# best_score_ Mean cross-validated score of the best_estimator\n", "\n", "print(\"\\n\")\n", "\n", "# View best hyperparameters\n", "print(\"Best hyperparameters:\")\n", "print('epochs:', tuned_model_3_lstm.best_estimator_.get_params()['epochs'])\n", "print('batch_size:', tuned_model_3_lstm.best_estimator_.get_params()['batch_size'])\n", "print('dropout_rate:', tuned_model_3_lstm.best_estimator_.get_params()['dropout_rate'])\n", "print('recurrent_dropout:', tuned_model_3_lstm.best_estimator_.get_params()['recurrent_dropout'])\n", "\n", "end=time.time()\n", "print(\"\\n\")\n", "print(\"Running Time:\", end - start)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.6511627906976745\n", "Precision: 0.6511375947995667\n", "Recall: 0.986863711001642\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make predictions\n", "y_pred_3_lstm=tuned_model_3_lstm.predict(X_test_3_lstm)\n", "\n", "# create confustion matrix\n", "fig, ax=plt.subplots()\n", "sns.heatmap(pd.DataFrame(metrics.confusion_matrix(y_test_3, y_pred_3_lstm)), annot=True, cmap=\"Blues\" ,fmt='g')\n", "plt.title('Confusion matrix'); plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n", "ax.xaxis.set_ticklabels(['Down', 'Up']); ax.yaxis.set_ticklabels(['Down', 'Up'])\n", "\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test_3, y_pred_3_lstm))\n", "print(\"Precision:\",metrics.precision_score(y_test_3, y_pred_3_lstm))\n", "print(\"Recall:\",metrics.recall_score(y_test_3, y_pred_3_lstm))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ROC Curve" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_proba_3_lstm=tuned_model_3_lstm.predict_proba(X_test_3_lstm)[:, 1]\n", "fpr, tpr, _=metrics.roc_curve(y_test_3, y_proba_3_lstm)\n", "auc=metrics.roc_auc_score(y_test_3, y_proba_3_lstm)\n", "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n", "plt.legend(loc=4)\n", "plt.plot([0, 1], [0, 1], linestyle='--') # plot no skill\n", "plt.title('ROC-Curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model 4: Volatility + Return" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loss function: log\n", "accuracy of the best model: 0.5206349206349207\n", "\n", "\n", "Best hyperparameters:\n", "Number of iterations: 8\n", "Penalty: elasticnet\n", "Alpha: 0.0012\n", "l1_ratio: 0.6\n", "Total number of features: 46\n", "Number of selected features: 19\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Model specific Parameter \n", "\n", "# Number of iterations\n", "iterations_4_b=[8] \n", "\n", "\n", "# Grid Search\n", "\n", "# Regularization \n", "alpha_g_4_b=[0.0011, 0.0012, 0.0013]\n", "l1_ratio_g_4_b=[0, 0.2, 0.4, 0.6, 0.8, 1] \n", "\n", "# Create hyperparameter options\n", "hyperparameters_g_4_b={'logistic__alpha':alpha_g_4_b, \n", " 'logistic__l1_ratio':l1_ratio_g_4_b, \n", " 'logistic__penalty':penalty_b, \n", " 'logistic__max_iter':iterations_4_b}\n", "\n", "# Create grid search \n", "search_g_4_b=GridSearchCV(estimator=pipeline_b, \n", " param_grid=hyperparameters_g_4_b, \n", " cv=tscv, \n", " verbose=0, \n", " n_jobs=-1, \n", " scoring=scoring_b, \n", " refit=metric_b, \n", " return_train_score=False)\n", "# Setting refit='Accuracy', refits an estimator on the whole dataset with the parameter setting that has the best cross-validated mean Accuracy score. \n", "# For multiple metric evaluation, this needs to be a string denoting the scorer is used to find the best parameters for refitting the estimator at the end\n", "# If return_train_score=True training results of CV will be saved as well \n", "\n", "# Fit grid search\n", "tuned_model_4_b=search_g_4_b.fit(X_train_4, y_train_4)\n", "#search_g_4_b.cv_results_\n", "\n", "\n", "# Random Search\n", "\n", "# Create regularization hyperparameter distribution using uniform distribution\n", "#alpha_r_4_b=uniform(loc=0.00006, scale=0.002) #loc=0.00006, scale=0.002\n", "#l1_ratio_r_4_b=uniform(loc=0, scale=1) \n", "\n", "# Create hyperparameter options\n", "#hyperparameters_r_4_b={'logistic__alpha':alpha_r_4_b, 'logistic__l1_ratio':l1_ratio_r_4_b, 'logistic__penalty':penalty_b,'logistic__max_iter':iterations_4_b}\n", "\n", "# Create randomized search \n", "#search_r_4_b=RandomizedSearchCV(pipeline_b, hyperparameters_r_4_b, n_iter=10, random_state=1, cv=tscv, verbose=0, n_jobs=-1, scoring=scoring_b, refit=metric_b, return_train_score=False)\n", "# Setting refit='Accuracy', refits an estimator on the whole dataset with the parameter setting that has the best cross-validated Accuracy score.\n", "\n", "# Fit randomized search\n", "#tuned_model_4_b=search_r_4_b.fit(X_train_4, y_train_4)\n", "\n", "\n", "\n", "# View Cost function\n", "print('Loss function:', tuned_model_4_b.best_estimator_.get_params()['logistic__loss'])\n", "\n", "# View Accuracy \n", "print(metric_b +' of the best model: ', tuned_model_4_b.best_score_);print(\"\\n\")\n", "# best_score_ Mean cross-validated score of the best_estimator\n", "\n", "# View best hyperparameters\n", "print(\"Best hyperparameters:\")\n", "print('Number of iterations:', tuned_model_4_b.best_estimator_.get_params()['logistic__max_iter'])\n", "print('Penalty:', tuned_model_4_b.best_estimator_.get_params()['logistic__penalty'])\n", "print('Alpha:', tuned_model_4_b.best_estimator_.get_params()['logistic__alpha'])\n", "print('l1_ratio:', tuned_model_4_b.best_estimator_.get_params()['logistic__l1_ratio'])\n", "\n", "# Find the number of nonzero coefficients (selected features)\n", "print(\"Total number of features:\", len(tuned_model_4_b.best_estimator_.steps[1][1].coef_[0][:]))\n", "print(\"Number of selected features:\", np.count_nonzero(tuned_model_4_b.best_estimator_.steps[1][1].coef_[0][:]))\n", "\n", "# Gridsearch table\n", "plt.title('Gridsearch')\n", "pvt_4_b=pd.pivot_table(pd.DataFrame(tuned_model_4_b.cv_results_), values='mean_test_accuracy', index='param_logistic__l1_ratio', columns='param_logistic__alpha')\n", "ax_4_b=sns.heatmap(pvt_4_b, cmap=\"Blues\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.6004228329809725\n", "Precision: 0.6501950585175552\n", "Recall: 0.8210180623973727\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make predictions\n", "y_pred_4_b=tuned_model_4_b.predict(X_test_4)\n", "\n", "# create confustion matrix\n", "fig, ax=plt.subplots()\n", "sns.heatmap(pd.DataFrame(metrics.confusion_matrix(y_test_4, y_pred_4_b)), annot=True, cmap=\"Blues\" ,fmt='g')\n", "plt.title('Confusion matrix'); plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n", "ax.xaxis.set_ticklabels(['Down', 'Up']); ax.yaxis.set_ticklabels(['Down', 'Up'])\n", "\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test_4, y_pred_4_b))\n", "print(\"Precision:\",metrics.precision_score(y_test_4, y_pred_4_b))\n", "print(\"Recall:\",metrics.recall_score(y_test_4, y_pred_4_b))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ROC Curve" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_proba_4_b=tuned_model_4_b.predict_proba(X_test_4)[:, 1]\n", "fpr, tpr, _=metrics.roc_curve(y_test_4, y_proba_4_b)\n", "auc=metrics.roc_auc_score(y_test_4, y_proba_4_b)\n", "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n", "plt.legend(loc=4)\n", "plt.plot([0, 1], [0, 1], linestyle='--') # plot no skill\n", "plt.title('ROC-Curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LSTM" ] }, { "cell_type": "code", "execution_count": 197, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "accuracy of the best model: 0.5441798941798942\n", "\n", "\n", "Best hyperparameters:\n", "epochs: 1\n", "batch_size: 1\n", "dropout_rate: 0.0\n", "recurrent_dropout: 0.4\n", "\n", "\n", "Running Time: 244.36903405189514\n" ] } ], "source": [ "start=time.time()\n", "\n", "\n", "# number of epochs\n", "epochs=1\n", "# number of units \n", "LSTM_units_4_lstm=200\n", "# number of samples\n", "num_samples=1 \n", "# time_steps\n", "look_back=1\n", "# numer of features\n", "num_features_4_lstm=X_train_4.shape[1]\n", "# Regularization\n", "dropout_rate=0.\n", "recurrent_dropout=0.4\n", "# print\n", "verbose=0\n", "\n", "#hyperparameter\n", "batch_size=[1] \n", "\n", "# hyperparameter\n", "hyperparameter_4_lstm={'batch_size':batch_size}\n", "\n", "\n", "# create Classifier\n", "clf_4_lstm=KerasClassifier(build_fn=create_shallow_LSTM, \n", " epochs=epochs, \n", " LSTM_units=LSTM_units_4_lstm, \n", " num_samples=num_samples, \n", " look_back=look_back, \n", " num_features=num_features_4_lstm, \n", " dropout_rate=dropout_rate,\n", " recurrent_dropout=recurrent_dropout,\n", " verbose=verbose)\n", "# Gridsearch\n", "search_4_lstm=GridSearchCV(estimator=clf_4_lstm, \n", " param_grid=hyperparameter_4_lstm, \n", " n_jobs=-1, \n", " cv=tscv, \n", " scoring=scoring_lstm, # accuracy\n", " refit=True, \n", " return_train_score=False)\n", "\n", "# Fit model\n", "tuned_model_4_lstm=search_4_lstm.fit(X_train_4_lstm, y_train_4, shuffle=False, callbacks=[reset])\n", "\n", "\n", "\n", "print(\"\\n\")\n", "\n", "# View Accuracy \n", "print(scoring_lstm +' of the best model: ', tuned_model_4_lstm.best_score_)\n", "# best_score_ Mean cross-validated score of the best_estimator\n", "\n", "print(\"\\n\")\n", "\n", "# View best hyperparameters\n", "print(\"Best hyperparameters:\")\n", "print('epochs:', tuned_model_4_lstm.best_estimator_.get_params()['epochs'])\n", "print('batch_size:', tuned_model_4_lstm.best_estimator_.get_params()['batch_size'])\n", "print('dropout_rate:', tuned_model_4_lstm.best_estimator_.get_params()['dropout_rate'])\n", "print('recurrent_dropout:', tuned_model_4_lstm.best_estimator_.get_params()['recurrent_dropout'])\n", "\n", "end=time.time()\n", "print(\"\\n\")\n", "print(\"Running Time:\", end - start)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 198, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.6331923890063424\n", "Precision: 0.6714659685863874\n", "Recall: 0.8423645320197044\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make predictions\n", "y_pred_4_lstm=tuned_model_4_lstm.predict(X_test_4_lstm)\n", "\n", "# create confustion matrix\n", "fig, ax=plt.subplots()\n", "sns.heatmap(pd.DataFrame(metrics.confusion_matrix(y_test_4, y_pred_4_lstm)), annot=True, cmap=\"Blues\" ,fmt='g')\n", "plt.title('Confusion matrix'); plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n", "ax.xaxis.set_ticklabels(['Down', 'Up']); ax.yaxis.set_ticklabels(['Down', 'Up'])\n", "\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test_4, y_pred_4_lstm))\n", "print(\"Precision:\",metrics.precision_score(y_test_4, y_pred_4_lstm))\n", "print(\"Recall:\",metrics.recall_score(y_test_4, y_pred_4_lstm))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ROC Curve" ] }, { "cell_type": "code", "execution_count": 199, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_proba_4_lstm=tuned_model_4_lstm.predict_proba(X_test_4_lstm)[:, 1]\n", "fpr, tpr, _=metrics.roc_curve(y_test_4, y_proba_4_lstm)\n", "auc=metrics.roc_auc_score(y_test_4, y_proba_4_lstm)\n", "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n", "plt.legend(loc=4)\n", "plt.plot([0, 1], [0, 1], linestyle='--') # plot no skill\n", "plt.title('ROC-Curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model 5: Volatility + Return" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Baseline " ] }, { "cell_type": "code", "execution_count": 478, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loss function: log\n", "accuracy of the best model: 0.5551587301587302\n", "\n", "\n", "Best hyperparameters:\n", "Number of iterations: 10\n", "Penalty: elasticnet\n", "Alpha: 0.0005\n", "l1_ratio: 0.6\n", "Total number of features: 46\n", "Number of selected features: 15\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Model specific Parameter \n", "\n", "# Number of iterations\n", "iterations_5_b=[10] \n", "\n", "\n", "# Grid Search\n", "\n", "# Regularization \n", "alpha_g_5_b=[0.0001, 0.0003, 0.0005]\n", "l1_ratio_g_5_b=[0, 0.2, 0.4, 0.6, 0.8, 1] \n", "\n", "# Create hyperparameter options\n", "hyperparameters_g_5_b={'logistic__alpha':alpha_g_5_b, \n", " 'logistic__l1_ratio':l1_ratio_g_5_b, \n", " 'logistic__penalty':penalty_b, \n", " 'logistic__max_iter':iterations_5_b}\n", "\n", "# Create grid search \n", "search_g_5_b=GridSearchCV(estimator=pipeline_b, \n", " param_grid=hyperparameters_g_5_b, \n", " cv=tscv, \n", " verbose=0, \n", " n_jobs=-1, \n", " scoring=scoring_b, \n", " refit=metric_b, \n", " return_train_score=False)\n", "# Setting refit='Accuracy', refits an estimator on the whole dataset with the parameter setting that has the best cross-validated mean Accuracy score. \n", "# For multiple metric evaluation, this needs to be a string denoting the scorer is used to find the best parameters for refitting the estimator at the end\n", "# If return_train_score=True training results of CV will be saved as well \n", "\n", "# Fit grid search\n", "tuned_model_5_b=search_g_5_b.fit(X_train_5, y_train_5)\n", "#search_g_5_b.cv_results_\n", "\n", "\n", "# Random Search\n", "\n", "# Create regularization hyperparameter distribution using uniform distribution\n", "#alpha_r_5_b=uniform(loc=0.00006, scale=0.002) #loc=0.00006, scale=0.002\n", "#l1_ratio_r_5_b=uniform(loc=0, scale=1) \n", "\n", "# Create hyperparameter options\n", "#hyperparameters_r_5_b={'logistic__alpha':alpha_r_5_b, 'logistic__l1_ratio':l1_ratio_r_5_b, 'logistic__penalty':penalty_b,'logistic__max_iter':iterations_5_b}\n", "\n", "# Create randomized search \n", "#search_r_5_b=RandomizedSearchCV(pipeline_b, hyperparameters_r_5_b, n_iter=10, random_state=1, cv=tscv, verbose=0, n_jobs=-1, scoring=scoring_b, refit=metric_b, return_train_score=False)\n", "# Setting refit='Accuracy', refits an estimator on the whole dataset with the parameter setting that has the best cross-validated Accuracy score.\n", "\n", "# Fit randomized search\n", "#tuned_model_5_b=search_r_4_b.fit(X_train_5, y_train_5)\n", "\n", "\n", "\n", "# View Cost function\n", "print('Loss function:', tuned_model_5_b.best_estimator_.get_params()['logistic__loss'])\n", "\n", "# View Accuracy \n", "print(metric_b +' of the best model: ', tuned_model_5_b.best_score_);print(\"\\n\")\n", "# best_score_ Mean cross-validated score of the best_estimator\n", "\n", "# View best hyperparameters\n", "print(\"Best hyperparameters:\")\n", "print('Number of iterations:', tuned_model_5_b.best_estimator_.get_params()['logistic__max_iter'])\n", "print('Penalty:', tuned_model_5_b.best_estimator_.get_params()['logistic__penalty'])\n", "print('Alpha:', tuned_model_5_b.best_estimator_.get_params()['logistic__alpha'])\n", "print('l1_ratio:', tuned_model_5_b.best_estimator_.get_params()['logistic__l1_ratio'])\n", "\n", "# Find the number of nonzero coefficients (selected features)\n", "print(\"Total number of features:\", len(tuned_model_5_b.best_estimator_.steps[1][1].coef_[0][:]))\n", "print(\"Number of selected features:\", np.count_nonzero(tuned_model_5_b.best_estimator_.steps[1][1].coef_[0][:]))\n", "\n", "# Gridsearch table\n", "plt.title('Gridsearch')\n", "pvt_5_b=pd.pivot_table(pd.DataFrame(tuned_model_5_b.cv_results_), values='mean_test_accuracy', index='param_logistic__l1_ratio', columns='param_logistic__alpha')\n", "ax_5_b=sns.heatmap(pvt_5_b, cmap=\"Blues\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 479, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.5909090909090909\n", "Precision: 0.629976580796253\n", "Recall: 0.8834154351395731\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make predictions\n", "y_pred_5_b=tuned_model_5_b.predict(X_test_5)\n", "\n", "# create confustion matrix\n", "fig, ax=plt.subplots()\n", "sns.heatmap(pd.DataFrame(metrics.confusion_matrix(y_test_5, y_pred_5_b)), annot=True, cmap=\"Blues\" ,fmt='g')\n", "plt.title('Confusion matrix'); plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n", "ax.xaxis.set_ticklabels(['Down', 'Up']); ax.yaxis.set_ticklabels(['Down', 'Up'])\n", "\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test_5, y_pred_5_b))\n", "print(\"Precision:\",metrics.precision_score(y_test_5, y_pred_5_b))\n", "print(\"Recall:\",metrics.recall_score(y_test_5, y_pred_5_b))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ROC Curve" ] }, { "cell_type": "code", "execution_count": 480, "metadata": {}, "outputs": [ { "data": { "image/png": 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dmFCQvhuqn+UU+kp43in01bBNkS/xtALG5tRDh4W7McEsJ8cp8PXFeOg1DrreDq36FPsy92C3FTChycLdmGB1YCPMvQe2LXPOLm3Z26uXWbCXDxbuxgSjH6bCvAehYmXoNxE63ej1WaYW7OWDhbsxwah2JLToBX1fhBpne/0yOxmp/LBwNyYYZGXC1y84ty994pQKfYHVhSmP7CQmYwLd9pXw1vnwzT/gyB6n0Ncpyg12W+JYftjI3ZhAlXkEFv8NVv7Lqbc+9D/OVIyX3E9MspOSyh+vwl1EEoBXgDDgHVV9zkObgcB4QIF1qjrEh/00pvxJS4Y1k53ljZc+CZVrePUyTycm2Yi9/Ck23EUkDJgI9AaSgdUikqiqSW5tWgKPAuepaoqINCytDhsT0o6lwIZPIe5m5ySke9dBzXCvX24nJplc3ozcuwIbVXUzgIjMBPoBSW5tbgcmqmoKgKru83VHjQl5v8x1rmOacQCizof6LUsc7LYaxngT7o2BHW73k4H4fG1aAYjIMpypm/GquiD/hkRkJDASIDLSfvCMAeDwXpj/oFOa9+wOzkU06rcs0Cx/ca/8cqdhLNgNeBfuns6MyH+4viLQErgYiAC+EZH2qpp60otUJwGTAOLi4k79kL8xoSYnGyYnQNpO1ra6lxfSe5M99xiwvEDT/MW98rNpGOPOm3BPBpq43Y8Adnlos0JVTwBbROQ3nLBf7ZNeGhNq0nZCjXCoEMaSZmNJ3FaJT36qBqRbeBuf8CbcVwMtRSQa2AncAORfCfMpMBh4X0Tq40zTbPZlR40JCTk5sPptTiwax9RqN7Oo2tWs3FIbsPA2vlVsuKtqloiMAhbizKe/p6obRORpYI2qJrqe6yMiSUA28KCqHizNjhsTdPb/DomjYccKNlTuwqz0dtSpZqFuSodoCc5284W4uDhds2aNX97bmDL3/RSY9yCZFSrzzpkjeSvtXGLCa9lJReaUicj3qhpXXDs7Q9UYH/O0qqVd5jH6VDqXUak3cuBwLeKja9lJRaZUWbgb4wPugb5yyyEq8yfP1ZsPwMyaN7Ohckc2VO5I8zowxqZgTBmwcDfGB9wLcw1vvJsHjr9GrYytcM5w+l/Vzata68b4koW7MSWQf+olaXc655xVkSlNPoZVb0PtJjD0Y2hxqR97acozC3djvJA/zPOfUBQTXpPrW2TC8qkQfwf0fAIqV/dLX40BC3djipW/GFfuv/06NWZIh+qw4WM49zancdd1p3RlJGNKi4W7McXweGk6VacWzMSxTiXH6IucejAW7CZAWLgb44WTLk13eI9TvfHXzyC8Ewz7xGOhL2P8ycLdmFORkw3vJcDh3dD7aeh2N4TZfyMTeOyn0hgv1Mve79SFqRAGff8BtaOgfgt/d8uYQtkFso0pwowVW2i7fTov7b8N1rzrPNiilwW7CXg2cjfGg+krt7Nm9XJu3DeBwZX+YFf982nUKsHf3TLGaxbuxlBwHXuz7R/xbMUpZIadyXcd/o8e/e+ys0xNULFwN+VGUZepy39SUtWzWrGnci+a3vg6Pao3KLM+GuMrFu4mJHkK8qIuU3d+VDUeq/opMY1qQe+ngO7AnWXQU2NKh4W7CRn5KzPCyUFe6EUxti6DxDGwZxPUu8U5QcmmYEyQs3A3Qauoei9eXd3oeDp8Od5ZBVMnCoYnQrOLSrfTxpQRC3cTlIqs9+JtrfTDe2DtdOg+Ci55DM6oVlrdNabMWbiboOSx3os3Mg46hb663g4NWsF9P0H1hqXUS2P8x8LdBK2T6r0UR9UJ9XkPwfE0aHaJcyKSBbsJUXaGqgk601duz5tf90r6bpg5BGbf4lxE446v7QxTE/Js5G6CTu6UjFcXmM7JhsmXO4W++jwD8X+xQl+mXLCfchNUckftxU7JpG6Hmo1dhb5edFbD1GteZv00xt9sWsYElWJH7TnZ8N3r8HpXWJ1b6OtSC3ZT7tjI3QSF3DXtSbvTCx+1702CxFGw83tolQBt+pZ9R40JEBbuJijkBntMeE3Po/bV78L8h6FKTbjuXWh/nZ1laso1C3cTcDzVhckN9ll3dD+5cW6pgAatod01kPAcVKtfhr01JjBZuJuAkRvqnurCFBix/3kUlvzdOWDa+2mIOt/5MsYAFu4mgLjPqRdZRmDLN5A4GlK2wLm3WaEvYzywcDcBwX2JY4Gpl1zH0+CLJ+H796FONNw0F6IvLNN+GhMsLNyN37kXASvyxKTDe+GnD6HHaLj4MTjjzDLqoTHBx6twF5EE4BUgDHhHVZ8rpN0A4CPgXFVd47NempDjqfa6xyJgGQdg/X8g/g5Xoa+f7YCpMV4oNtxFJAyYCPQGkoHVIpKoqkn52tUA7gFWlkZHTWjwdNDU4xy7Kvw8G+Y/BJmHofmlTj0YC3ZjvOLNyL0rsFFVNwOIyEygH5CUr93fgBeAsT7toQkpXh00TUuGz8bAHwuhcRz0e90KfRlzirwJ98bADrf7yUC8ewMR6Qw0UdXPRKTQcBeRkcBIgMjIU6jBbYKa+xRMoevVc2Vnwft94cg+uOxZZzqmQlgZ9taY0OBNuHtaY6Z5T4pUAF4GRhS3IVWdBEwCiIuL02KamxCQ/4pJhZ5hmrINakU4FRuv/KdT6KtudNl21pgQ4k24JwNN3O5HALvc7tcA2gNLxVlrfDaQKCJX20HV8iv/3HqhV0zKzoIVbzgnJPV+2hmpN7+kjHtrTOjxJtxXAy1FJBrYCdwADMl9UlXTgLyjXCKyFBhrwV4+eTpgWujc+p71TqGvXT9C677Q9uoy7q0xoavYcFfVLBEZBSzEWQr5nqpuEJGngTWqmljanTSBzdOyxmLPMl31Nix4BKrUhgGToV1/O8vUGB/yap27qs4D5uV77MlC2l58+t0ywcS9YmOxoZ5bKqBhjFO58bJnoVq9su2wMeWAnaFqfKLIFTAAf2bA4meclS99noGo85wvY0ypsCsxmRKbvnI7g/61nKTd6UU33LwU3ujuHDjN+tMZvRtjSpWN3I3X8tdZzz+/XsCxVFj0V/jxA6jbHG6eD017lFV3jSnXLNxNkQo7WJr7b5Hz6xn7Yf3HcN59cPEjUKlqmfTZGGPhbtx4ugJSsTVg8juyzyn01e0vUL+lq9CXHTA1pqxZuJs87qtecnkV6ODMo//0ISx42Dl42rIP1GtuwW6Mn1i4m7wRe7F1XwqTugM+ux82fgERXZ1CX/Wal05njTFesXAvRzxNu4AXB0aLklvoK+MAXP6Cc9k7K/RljN9ZuJcT+Qt4ufN66sXdoS1QO9Ip9HX1q85l7+o09WWXjTGnwcI9BBV1YLTQAl7eys6C5a/BkmedQl/d7oRmF5d8e8aYUmHhHmIKG6GXaHSe3+6fnEJfu9dBmyuh3TWn211jTCmxcA8BXl+P9HSsnAQLH4WqdWHgVIjp57ttG2N8zsI9BJxS4a5TlVvo66x20GEgXPZ3OLNu8a8zxviVhXsAKmxVS2FKvISxKJlHYPHfoEJFJ9Ct0JcxQcXC3U+KCvD8p/kXp9BL15XUxq9g7n2QtsO5MlLu6N0YEzQs3MtAcaf15+fzqRVvHUuBhY/D2n9DvZauQl8+/GvAGFNmLNxLWamuXvG1jAOQNAfOHwMXPQyVqvi7R8aYErJwL0Xuwe7z1Su+cngvrJ8N3e/+X6EvO2BqTNCzcC8lAR/sqrBuBix4FE4cg1YJTj0YC3ZjQoKFu48UdiGLgAz2lG3w2X2waTE06QZXv2aFvowJMRbuPuBpXj0g59TBKR8w5Uo4egiu+AfE3QoV7GqLxoQaC/cSCKpReq6Dm6BOlFPoq99E53btAO2rMea0WbifgtxQP+XLzflT9glY9gp8/Tz0/ptT6Cv6Qn/3yhhTyizcvZR/6iVgw9zdrrVOoa89P0PMNdD+Wn/3yBhTRizcvZQ7DRPQUy/uVrwFCx+DavVh0DRoe5W/e2SMKUMW7l6YvnI7K7ccIj66buAHe26pgPBY6DgYLnsGqtbxd6+MMWXMwr0Y7tMxPq3f4muZh+HLp6BiZafQV9MezpcxplyyNXBFCPgTkXL98SW80R1Wv+OM3FX93SNjjJ/ZyN2D/KtiAjbYjx5y5tXXzYD6reHWRdCkq797ZYwJABbu+QTVqpijh+CXz+DCh+DCsc6UjDHG4GW4i0gC8AoQBryjqs/le34McBuQBewHblHVbT7uq08VVk894Efrh/fATx9Cj9FQvwXc/7MdMDXGFFBsuItIGDAR6A0kA6tFJFFVk9ya/QjEqepREfkL8AIwqDQ67AuFleHNvR+Qo3VV+HGaU289OxPa9HXqwViwG2M88Gbk3hXYqKqbAURkJtAPyAt3VV3i1n4FMNSXnfS1oFuznrIV5t4Lm5dC0/Pgqlet0JcxpkjehHtjYIfb/WQgvoj2twLzPT0hIiOBkQCRkf4N1aBYsw6uQl9XwdEU6PsSdLnZCn0ZY4rlTbh7unimx7V2IjIUiAMu8vS8qk4CJgHExcXZer2inFTo6w2oGw21IvzdK2NMkPBmCJgMNHG7HwHsyt9IRHoBjwNXq2qmb7pXDmWfgK8nwBvdYNUk57HoCyzYjTGnxJuR+2qgpYhEAzuBG4Ah7g1EpDPwLyBBVff5vJflxc4fIHE07F0P7a+D9gP83SNjTJAqNtxVNUtERgELcZZCvqeqG0TkaWCNqiYCE4DqwEciArBdVa8uxX6HnhVvOickVT8LbpgBba7wd4+MMUHMq3XuqjoPmJfvsSfdbvfycb9KjXsRsICQW+irUWfoPAx6Pw1Va/u7V8aYIBdyZ6gWdnJSrtyTlPxeBOx4Onw5DipWgYRnIbKb82WMMT4Qcmvq5qzdSdLu9EKfj4+u6//17b8vcg6Yfv8+VAizQl/GGJ8L+pF7/pF60u50YsJrMuuO7n7sVSEyDsKCR+DnD6FBWxg4FSLi/N0rY0wICspwdw/0/NczjQmv6f8pl8IcT4XfF8BFj8AFD0DFM/zdI2NMiArKcM+deokJrxm4tWBype9yCn2dd69TMuC+n+2AqTGm1AVluAOBO/WSSxV+mAKLnnBOTGp7lavQlwW7Mab0Bd0B1dyljAHt0GanHszceyG8I/xlmRX6MsaUqaAbuefOtQfsvHp2FkzpB8dS4Mp/wjk3WaEvY0yZC7pwhwCt6HjgD6gT7RT66v+mc7tWgP4CMsaEPBtSnq6sP2Hpc64LVL/tPBZ1vgW7McavgnLkHjCSv4fEUbAvCTpcDx0G+rtHxhgDWLiX3PI3YNHjUP1sGDwLWif4u0fGGJPHwv1U5Rb6atzFOVja+ymoUsvfvTLGmJNYuHvreBp88SRUrAqXPweR8c6XMcYEIDug6o3f5sPEePhhqlMywAp9GWMCnI3ci5JxAOY/DOtnQ8N2cMO/nekYY4wJcBbuRTmeBn98ARc/Buffb4W+jDFBw8I9v7Rk+GkWnD/GKRlw/892wNQYE3Qs3HPl5MD3k+GLcaDZEHONE+4W7MaYIGThDnBwEyTeA9u+heiL4KpXoG60v3tljDElZuGenQVTr3Hm169+HToPddaxG2NMECu/4b7/N6jb3Cn0de2/nEJfNcP93SvjYydOnCA5OZnjx4/7uyvGnJIqVaoQERFBpUqVSvT68hfuWZnwzYvOV++/Qfe7oGkPf/fKlJLk5GRq1KhBVFQUYn+RmSChqhw8eJDk5GSio0s2RVy+wn3HaqfQ1/5fIfYG6HiDv3tkStnx48ct2E3QERHq1avH/v37S7yN8hPu373mXPKuZmO4cTa07O3vHpkyYsFugtHp/tyGfrjn5DhXQoroCnG3QK/xUKWmv3tljDGlKnRryxxLhTl3w4KHnfuR8XDlSxbsxi8++eQTRIRff/0VgKVLl3LllVee1GbEiBHMnj0bcA4EP/LII7Rs2ZL27dvTtWtX5s+f79V7ZWZmMmjQIFq0aEF8fDxbt2712C4qKooOHTrQqVMn4uLi8h5/8MEHadOmDbGxsfTv35/U1NS8Pt1000106NCBtm3b8uyzz560vezsbDp37nzSft1666107NiR2NhYBgwYwJEjRwDYvn07l1xyCZ07dyY2NpZ58+adtK3t27dTvXp1/vGPf+Q9tmDBAlq3bk2LFi147rm4N5OJAAAPIklEQVTn8h5fvHgx55xzDu3bt+emm24iKysLcOat77nnHlq0aEFsbCw//PADAGvXrqV79+60a9eO2NhYZs2albetCy64gE6dOtGpUycaNWrENddcA8CECRPyHm/fvj1hYWEcOuRcy/nll1+mXbt2tG/fnsGDB+cdvC9sWykpKfTv35/Y2Fi6du3K+vXri/w8S0xV/fLVpUsXLYmBb32nA9/6ruhGSXNVJ7RSHV9H9Yvxqjk5JXovE/ySkpL83QVVVb3++uv1/PPP13Hjxqmq6pIlS7Rv374ntbnpppv0o48+UlXVhx9+WIcPH67Hjx9XVdU9e/borFmzvHqviRMn6h133KGqqjNmzNCBAwd6bNe0aVPdv39/gccXLlyoJ06cUFXVhx56SB966CFVVf33v/+tgwYNUlXVjIwMbdq0qW7ZsiXvdS+++KIOHjz4pP1KS0vLu33//ffrs88+q6qqt99+u77xxhuqqrphwwZt2rTpSX249tprdcCAATphwgRVVc3KytJmzZrppk2bNDMzU2NjY3XDhg2anZ2tERER+ttvv6mq6hNPPKHvvPOOqqp+/vnnmpCQoDk5Obp8+XLt2rWrqqr+9ttv+vvvv6uq6s6dO/Xss8/WlJSUAt+Ha6+9VqdMmVLg8cTERL3kkktUVTU5OVmjoqL06NGjqup8zpMnTy5yW2PHjtXx48erquovv/yiPXv2LNA+l6efX2CNepGxoTUtc2Q/zBsLSZ/C2R1gyCxo1MnfvTIB4qm5G0jale7TbcY0qsm4q9oV2ebIkSMsW7aMJUuWcPXVVzN+/Pgi2x89epS3336bLVu2ULlyZQDOOussBg707kpfc+bMyXuPAQMGMGrUKFTV6zncPn365N3u1q1b3l8TIkJGRgZZWVkcO3aMM844g5o1nb+Ek5OT+fzzz3n88cd56aWX8l6f+7yqcuzYsbw+iAjp6c5nkZaWRqNGjfJe8+mnn9KsWTOqVauW99iqVato0aIFzZo1A+CGG25gzpw5NGjQgMqVK9OqVSsAevfuzbPPPsutt97KnDlzGD58OCJCt27dSE1NZffu3XltARo1akTDhg3Zv38/tWvXznv88OHDLF68mMmTJxf4/syYMYPBgwfn3c/9flSqVImjR4+etC+etpWUlMSjjz4KQJs2bdi6dSt79+7lrLPOKupjOWWhNS2TmQ6bl0DPJ+D2JRbsJiB8+umnJCQk0KpVK+rWrZs3PVCYjRs3EhkZmReM+Q0aNCjvz333r6lTpwKwc+dOmjRpAkDFihWpVasWBw8eLLAdEaFPnz506dKFSZMmeXyv9957j8svvxxwflFUq1aN8PBwIiMjGTt2LHXr1gXgvvvu44UXXqBChYKRcvPNN3P22Wfz66+/Mnr0aADGjx/PtGnTiIiI4IorruC1114DICMjg+eff55x48adtA33fQKIiIhg586d1K9fnxMnTrBmzRoAZs+ezY4dO4p8jbtVq1bx559/0rx585Me/+STT7j00ksLfAZHjx5lwYIFXHfddQA0btyYsWPHEhkZSXh4OLVq1Trpl6OnbXXs2JGPP/447/23bdtGcnKyh+/+6Qn+kXvqDvhpJlww1lXoawNUruHvXpkAVNwIu7TMmDGD++67D3BGnDNmzCgw357Lm9G1+xyxJ+rhegOetrts2TIaNWrEvn376N27N23atOHCCy/Me/7vf/87FStW5MYbbwScIAoLC2PXrl2kpKRwwQUX0KtXL5KSkmjYsCFdunRh6dKlBd5n8uTJZGdnM3r0aGbNmsXNN9/MjBkzGDFiBA888ADLly9n2LBhrF+/nnHjxnH//fdTvXp1r/ZJRJg5cyb3338/mZmZ9OnTh4oVK3r1fdi9ezfDhg1jypQpBX4pzZgxg9tuu63A6+fOnct5552X90stJSWFOXPmsGXLFmrXrs3111/PtGnTGDp0aKHbeuSRR7j33nvp1KkTHTp0oHPnznl99iWvtigiCcArQBjwjqo+l+/5ysBUoAtwEBikqlt929V8cnJgzbvw5XjQHGh3rRPuFuwmgBw8eJDFixezfv16RITs7GxEhOHDh5OSknJS20OHDlG/fn1atGjB9u3bOXz4MDVqFPx5HjRoEL/99luBx8eMGcPw4cOJiIhgx44dREREkJWVRVpaWl4YucudPmjYsCH9+/dn1apVeeE+ZcoUPvvsM7766qu8QJw+fToJCQlUqlSJhg0bct5557FmzRp+/PFHEhMTmTdvHsePHyc9PZ2hQ4cybdq0vPcKCwtj0KBBTJgwgZtvvpl3332XBQsWANC9e3eOHz/OgQMHWLlyJbNnz+ahhx4iNTWVChUqUKVKFbp06ZI3IgdnGii3/927d+ebb74BYNGiRfz+++8Aed8HT69JT0+nb9++PPPMM3Tr1q3AZ7Zq1So++eSTAt+zmTNnnjQl8+WXXxIdHU2DBg0AuPbaa/nuu+/ywt3TtmrWrJk3RaOqREdHl/hEpSIVNymPE+ibgGbAGcA6ICZfm7uAt1y3bwBmFbfd0zmgeu/rs1TfTVAdV1N1Sj/VQ1tLtC0T+vx9QPWtt97SkSNHnvTYhRdeqEuXLtWoqKi8/m3dulUjIyM1NTVVVVUffPBBHTFihGZmZqqq6q5du/SDDz7w6j1ff/31kw6oXn/99QXaHDlyRNPT0/Nud+/eXefPn6+qqvPnz9e2bdvqvn37TnrNc889pyNGjNCcnBw9cuSItm3bVtetW3dSG/cDxTk5OfrHH3/k3X7ggQf0gQceUFXVhISEvAOPSUlJGh4erjn5Fj6MGzcu74DqiRMnNDo6Wjdv3px3QHX9+vWqqrp3715VVT1+/Lj27NlTv/rqK1VV/eyzz046oHruueeqqmpmZqb27NlTX375ZY/fvzfffFOHDx9e4PHU1FStU6eOHjlyJO+xFStWaExMjGZkZGhOTo4OHz5cX3311SK3lZKSkve5Tpo0SYcNG+axH7nfm/zw8oCqN+HeHVjodv9R4NF8bRYC3V23KwIHAClquyUN9xve/Eb3Pt1C9dkmqj9Ms5Uwpkj+DveLLrooLzRzvfLKK3rnnXfqt99+q/Hx8dqxY0eNi4vTRYsW5bXJzMzUBx98UJs3b67t2rXTrl276oIFC7x6z2PHjumAAQO0efPmeu655+qmTZtU1VkZcvnll6uq6qZNmzQ2NlZjY2M1JiZGn3nmmbzXN2/eXCMiIrRjx47asWPHvF8Uhw8f1gEDBmhMTIy2bdtWX3jhhQLv7R7u2dnZ2qNHD23fvr22a9dOhwwZkrd6ZsOGDdqjRw+NjY3Vjh076sKFCwtsyz3cVZ3VLy1bttRmzZqd1N+xY8dqmzZttFWrVicFdk5Ojt51113arFkzbd++va5evVpVVT/44AOtWLFi3v517NhRf/zxx7zXefrMVFUnT56ct1rI3ZNPPqmtW7fWdu3a6dChQ/NWOBW2re+++05btGihrVu31v79++uhQ4cKbDPX6YS7aDHXAxWRAUCCqt7muj8MiFfVUW5t1rvaJLvub3K1OZBvWyOBkQCRkZFdtm3bdqp/aPDU3A00ObKOW67sCTXOPuXXm/Lll19+oW3btv7uhjEl4unnV0S+V9W4Ql6Sx5s5d09HePL/RvCmDao6CZgEEBcXV6KrTDsHxfxzYMwYY4KFN0shk4EmbvcjgF2FtRGRikAt4JAvOmiMMebUeRPuq4GWIhItImfgHDBNzNcmEbjJdXsAsFiLm+8xpozYj6IJRqf7c1tsuKtqFjAK56DpL8CHqrpBRJ4Wkatdzd4F6onIRmAM8Mhp9coYH6lSpQoHDx60gDdBRV313KtUqVLibRR7QLW0xMXFae5ZZcaUFrsSkwlWhV2JyZcHVI0JWpUqVSqdE0SMCXChVVvGGGMMYOFujDEhycLdGGNCkN8OqIrIfuDUT1F11McpcVCe2D6XD7bP5cPp7HNTVW1QXCO/hfvpEJE13hwtDiW2z+WD7XP5UBb7bNMyxhgTgizcjTEmBAVruHu+Jlhos30uH2yfy4dS3+egnHM3xhhTtGAduRtjjCmChbsxxoSggA53EUkQkd9EZKOIFKg0KSKVRWSW6/mVIhJV9r30LS/2eYyIJInITyLylYg09Uc/fam4fXZrN0BEVESCftmcN/ssIgNdn/UGEZle1n30NS9+tiNFZImI/Oj6+b7CH/30FRF5T0T2ua5U5+l5EZFXXd+Pn0TkHJ92wJtr8fnji1K6MHcgf3m5z5cAZ7pu/6U87LOrXQ3gv8AKIM7f/S6Dz7kl8CNQx3W/ob/7XQb7PAn4i+t2DLDV3/0+zX2+EDgHWF/I81cA83GuZNcNWOnL9w/kkXtXYKOqblbVP4GZQL98bfoBU1y3ZwOXioinS/4Fi2L3WVWXqOpR190VOFfGCmbefM4AfwNeAEKhdq83+3w7MFFVUwBUdV8Z99HXvNlnBWq6btei4BXfgoqq/peir0jXD5iqjhVAbREJ99X7B3K4NwZ2uN1Pdj3msY06FxVJA+qVSe9Khzf77O5WnN/8wazYfRaRzkATVf2sLDtWirz5nFsBrURkmYisEJGEMutd6fBmn8cDQ0UkGZgHjC6brvnNqf5/PyWBXM/dZxfmDiJe74+IDAXigItKtUelr8h9FpEKwMvAiLLqUBnw5nOuiDM1czHOX2ffiEh7VU0t5b6VFm/2eTDwvqq+KCLdgQ9c+5xT+t3zi1LNr0AeuZfHC3N7s8+ISC/gceBqVc0so76VluL2uQbQHlgqIltx5iYTg/ygqrc/23NU9YSqbgF+wwn7YOXNPt8KfAigqsuBKjgFtkKVV//fSyqQw708Xpi72H12TVH8CyfYg30eForZZ1VNU9X6qhqlqlE4xxmuVtVgvkajNz/bn+IcPEdE6uNM02wu0176ljf7vB24FEBE2uKE+/4y7WXZSgSGu1bNdAPSVHW3z7bu7yPKxRxtvgL4Heco++Oux57G+c8Nzof/EbARWAU083efy2CfvwT2AmtdX4n+7nNp73O+tksJ8tUyXn7OArwEJAE/Azf4u89lsM8xwDKclTRrgT7+7vNp7u8MYDdwAmeUfitwJ3Cn22c80fX9+NnXP9dWfsAYY0JQIE/LGGOMKSELd2OMCUEW7sYYE4Is3I0xJgRZuBtjTAiycDfGmBBk4W6MMSHo/wFxg+HAOM+HJAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_proba_5_b=tuned_model_5_b.predict_proba(X_test_5)[:, 1]\n", "fpr, tpr, _=metrics.roc_curve(y_test_5, y_proba_5_b)\n", "auc=metrics.roc_auc_score(y_test_5, y_proba_5_b)\n", "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n", "plt.legend(loc=4)\n", "plt.plot([0, 1], [0, 1], linestyle='--') # plot no skill\n", "plt.title('ROC-Curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LSTM" ] }, { "cell_type": "code", "execution_count": 389, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "accuracy of the best model: 0.5933862433862434\n", "\n", "\n", "Best hyperparameters:\n", "epochs: 1\n", "batch_size: 1\n", "dropout_rate: 0.0\n", "recurrent_dropout: 0.3\n", "\n", "\n", "Running Time: 303.28468012809753\n" ] } ], "source": [ "start=time.time()\n", "\n", "\n", "# number of epochs\n", "epochs=1\n", "# number of units \n", "LSTM_units_5_lstm=190\n", "# number of samples\n", "num_samples=1 \n", "# time_steps\n", "look_back=1\n", "# numer of features\n", "num_features_5_lstm=X_train_5.shape[1]\n", "# Regularization\n", "dropout_rate=0.\n", "recurrent_dropout=0.3\n", "# print\n", "verbose=0\n", "\n", "#hyperparameter\n", "batch_size=[1] \n", "\n", "# hyperparameter\n", "hyperparameter_5_lstm={'batch_size':batch_size}\n", "\n", "\n", "# create Classifier\n", "clf_5_lstm=KerasClassifier(build_fn=create_shallow_LSTM, \n", " epochs=epochs, \n", " LSTM_units=LSTM_units_5_lstm, \n", " num_samples=num_samples, \n", " look_back=look_back, \n", " num_features=num_features_5_lstm, \n", " dropout_rate=dropout_rate,\n", " recurrent_dropout=recurrent_dropout,\n", " verbose=verbose)\n", "# Gridsearch\n", "search_5_lstm=GridSearchCV(estimator=clf_5_lstm, \n", " param_grid=hyperparameter_5_lstm, \n", " n_jobs=-1, \n", " cv=tscv, \n", " scoring=scoring_lstm, # accuracy\n", " refit=True, \n", " return_train_score=False)\n", "\n", "# Fit model\n", "tuned_model_5_lstm=search_5_lstm.fit(X_train_5_lstm, y_train_5, shuffle=False, callbacks=[reset])\n", "\n", "\n", "\n", "print(\"\\n\")\n", "\n", "# View Accuracy \n", "print(scoring_lstm +' of the best model: ', tuned_model_5_lstm.best_score_)\n", "# best_score_ Mean cross-validated score of the best_estimator\n", "\n", "print(\"\\n\")\n", "\n", "# View best hyperparameters\n", "print(\"Best hyperparameters:\")\n", "print('epochs:', tuned_model_5_lstm.best_estimator_.get_params()['epochs'])\n", "print('batch_size:', tuned_model_5_lstm.best_estimator_.get_params()['batch_size'])\n", "print('dropout_rate:', tuned_model_5_lstm.best_estimator_.get_params()['dropout_rate'])\n", "print('recurrent_dropout:', tuned_model_5_lstm.best_estimator_.get_params()['recurrent_dropout'])\n", "\n", "end=time.time()\n", "print(\"\\n\")\n", "print(\"Running Time:\", end - start)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 390, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.6596194503171248\n", "Precision: 0.662883087400681\n", "Recall: 0.9589490968801314\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make predictions\n", "y_pred_5_lstm=tuned_model_5_lstm.predict(X_test_5_lstm)\n", "\n", "# create confustion matrix\n", "fig, ax=plt.subplots()\n", "sns.heatmap(pd.DataFrame(metrics.confusion_matrix(y_test_5, y_pred_5_lstm)), annot=True, cmap=\"Blues\" ,fmt='g')\n", "plt.title('Confusion matrix'); plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n", "ax.xaxis.set_ticklabels(['Down', 'Up']); ax.yaxis.set_ticklabels(['Down', 'Up'])\n", "\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test_5, y_pred_5_lstm))\n", "print(\"Precision:\",metrics.precision_score(y_test_5, y_pred_5_lstm))\n", "print(\"Recall:\",metrics.recall_score(y_test_5, y_pred_5_lstm))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ROC Curce" ] }, { "cell_type": "code", "execution_count": 393, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_proba_5_lstm=tuned_model_5_lstm.predict_proba(X_test_5_lstm)[:, 1]\n", "fpr, tpr, _=metrics.roc_curve(y_test_5, y_proba_5_lstm)\n", "auc=metrics.roc_auc_score(y_test_5, y_proba_5_lstm)\n", "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n", "plt.legend(loc=4)\n", "plt.plot([0, 1], [0, 1], linestyle='--') # plot no skill\n", "plt.title('ROC-Curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model 6: Trading Volume + Return" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Baseline" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loss function: log\n", "accuracy of the best model: 0.5419312169312169\n", "\n", "\n", "Best hyperparameters:\n", "Number of iterations: 8\n", "Penalty: l1\n", "Alpha: 0.0013\n", "l1_ratio: 0\n", "Total number of features: 46\n", "Number of selected features: 6\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Model specific Parameter \n", "\n", "# Number of iterations\n", "iterations_6_b=[8] \n", "\n", "\n", "# Grid Search\n", "\n", "# Regularization \n", "alpha_g_6_b=[0.0011, 0.0012, 0.0013]\n", "l1_ratio_g_6_b=[0, 0.2, 0.4, 0.6, 0.8, 1] \n", "\n", "# Create hyperparameter options\n", "hyperparameters_g_6_b={'logistic__alpha':alpha_g_6_b, \n", " 'logistic__l1_ratio':l1_ratio_g_6_b, \n", " 'logistic__penalty':penalty_b, \n", " 'logistic__max_iter':iterations_6_b}\n", "\n", "# Create grid search \n", "search_g_6_b=GridSearchCV(estimator=pipeline_b, \n", " param_grid=hyperparameters_g_6_b, \n", " cv=tscv, \n", " verbose=0, \n", " n_jobs=-1, \n", " scoring=scoring_b, \n", " refit=metric_b, \n", " return_train_score=False)\n", "# Setting refit='Accuracy', refits an estimator on the whole dataset with the parameter setting that has the best cross-validated mean Accuracy score. \n", "# For multiple metric evaluation, this needs to be a string denoting the scorer is used to find the best parameters for refitting the estimator at the end\n", "# If return_train_score=True training results of CV will be saved as well \n", "\n", "# Fit grid search\n", "tuned_model_6_b=search_g_6_b.fit(X_train_6, y_train_6)\n", "#search_g_6_b.cv_results_\n", "\n", "\n", "# Random Search\n", "\n", "# Create regularization hyperparameter distribution using uniform distribution\n", "#alpha_r_6_b=uniform(loc=0.00006, scale=0.002) #loc=0.00006, scale=0.002\n", "#l1_ratio_r_6_b=uniform(loc=0, scale=1) \n", "\n", "# Create hyperparameter options\n", "#hyperparameters_r_6_b={'logistic__alpha':alpha_r_6_b, 'logistic__l1_ratio':l1_ratio_r_6_b, 'logistic__penalty':penalty_b,'logistic__max_iter':iterations_6_b}\n", "\n", "# Create randomized search \n", "#search_r_6_b=RandomizedSearchCV(pipeline_b, hyperparameters_r_6_b, n_iter=10, random_state=1, cv=tscv, verbose=0, n_jobs=-1, scoring=scoring_b, refit=metric_b, return_train_score=False)\n", "# Setting refit='Accuracy', refits an estimator on the whole dataset with the parameter setting that has the best cross-validated Accuracy score.\n", "\n", "# Fit randomized search\n", "#tuned_model_6_b=search_r_4_b.fit(X_train_6, y_train_6)\n", "\n", "\n", "\n", "# View Cost function\n", "print('Loss function:', tuned_model_6_b.best_estimator_.get_params()['logistic__loss'])\n", "\n", "# View Accuracy \n", "print(metric_b +' of the best model: ', tuned_model_6_b.best_score_);print(\"\\n\")\n", "# best_score_ Mean cross-validated score of the best_estimator\n", "\n", "# View best hyperparameters\n", "print(\"Best hyperparameters:\")\n", "print('Number of iterations:', tuned_model_6_b.best_estimator_.get_params()['logistic__max_iter'])\n", "print('Penalty:', tuned_model_6_b.best_estimator_.get_params()['logistic__penalty'])\n", "print('Alpha:', tuned_model_6_b.best_estimator_.get_params()['logistic__alpha'])\n", "print('l1_ratio:', tuned_model_6_b.best_estimator_.get_params()['logistic__l1_ratio'])\n", "\n", "# Find the number of nonzero coefficients (selected features)\n", "print(\"Total number of features:\", len(tuned_model_6_b.best_estimator_.steps[1][1].coef_[0][:]))\n", "print(\"Number of selected features:\", np.count_nonzero(tuned_model_6_b.best_estimator_.steps[1][1].coef_[0][:]))\n", "\n", "# Gridsearch table\n", "plt.title('Gridsearch')\n", "pvt_6_b=pd.pivot_table(pd.DataFrame(tuned_model_6_b.cv_results_), values='mean_test_accuracy', index='param_logistic__l1_ratio', columns='param_logistic__alpha')\n", "ax_6_b=sns.heatmap(pvt_6_b, cmap=\"Blues\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.6553911205073996\n", "Precision: 0.6556655665566556\n", "Recall: 0.9786535303776683\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make predictions\n", "y_pred_6_b=tuned_model_6_b.predict(X_test_6)\n", "\n", "# create confustion matrix\n", "fig, ax=plt.subplots()\n", "sns.heatmap(pd.DataFrame(metrics.confusion_matrix(y_test_6, y_pred_6_b)), annot=True, cmap=\"Blues\" ,fmt='g')\n", "plt.title('Confusion matrix'); plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n", "ax.xaxis.set_ticklabels(['Down', 'Up']); ax.yaxis.set_ticklabels(['Down', 'Up'])\n", "\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test_6, y_pred_6_b))\n", "print(\"Precision:\",metrics.precision_score(y_test_6, y_pred_6_b))\n", "print(\"Recall:\",metrics.recall_score(y_test_6, y_pred_6_b))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ROC Curce" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_proba_6_b=tuned_model_6_b.predict_proba(X_test_6)[:, 1]\n", "fpr, tpr, _=metrics.roc_curve(y_test_6, y_proba_6_b)\n", "auc=metrics.roc_auc_score(y_test_6, y_proba_6_b)\n", "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n", "plt.legend(loc=4)\n", "plt.plot([0, 1], [0, 1], linestyle='--') # plot no skill\n", "plt.title('ROC-Curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LSTM" ] }, { "cell_type": "code", "execution_count": 301, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "accuracy of the best model: 0.49563492063492065\n", "\n", "\n", "Best hyperparameters:\n", "epochs: 1\n", "batch_size: 1\n", "dropout_rate: 0.0\n", "recurrent_dropout: 0.5\n", "\n", "\n", "Running Time: 674.0648419857025\n" ] } ], "source": [ "start=time.time()\n", "\n", "\n", "# number of epochs\n", "epochs=1\n", "# number of units \n", "LSTM_units_6_lstm=400\n", "# number of samples\n", "num_samples=1 \n", "# time_steps\n", "look_back=1\n", "# numer of features\n", "num_features_6_lstm=X_train_6.shape[1]\n", "# Regularization\n", "dropout_rate=0.\n", "recurrent_dropout=0.5\n", "# print\n", "verbose=0\n", "\n", "#hyperparameter\n", "batch_size=[1] \n", "\n", "# hyperparameter\n", "hyperparameter_6_lstm={'batch_size':batch_size}\n", "\n", "\n", "# create Classifier\n", "clf_6_lstm=KerasClassifier(build_fn=create_shallow_LSTM, \n", " epochs=epochs, \n", " LSTM_units=LSTM_units_6_lstm, \n", " num_samples=num_samples, \n", " look_back=look_back, \n", " num_features=num_features_6_lstm, \n", " dropout_rate=dropout_rate,\n", " recurrent_dropout=recurrent_dropout,\n", " verbose=verbose)\n", "# Gridsearch\n", "search_6_lstm=GridSearchCV(estimator=clf_6_lstm, \n", " param_grid=hyperparameter_6_lstm, \n", " n_jobs=-1, \n", " cv=tscv, \n", " scoring=scoring_lstm, # accuracy\n", " refit=True, \n", " return_train_score=False)\n", "\n", "# Fit model\n", "tuned_model_6_lstm=search_6_lstm.fit(X_train_6_lstm, y_train_6, shuffle=False, callbacks=[reset])\n", "\n", "\n", "\n", "print(\"\\n\")\n", "\n", "# View Accuracy \n", "print(scoring_lstm +' of the best model: ', tuned_model_6_lstm.best_score_)\n", "# best_score_ Mean cross-validated score of the best_estimator\n", "\n", "print(\"\\n\")\n", "\n", "# View best hyperparameters\n", "print(\"Best hyperparameters:\")\n", "print('epochs:', tuned_model_6_lstm.best_estimator_.get_params()['epochs'])\n", "print('batch_size:', tuned_model_6_lstm.best_estimator_.get_params()['batch_size'])\n", "print('dropout_rate:', tuned_model_6_lstm.best_estimator_.get_params()['dropout_rate'])\n", "print('recurrent_dropout:', tuned_model_6_lstm.best_estimator_.get_params()['recurrent_dropout'])\n", "\n", "end=time.time()\n", "print(\"\\n\")\n", "print(\"Running Time:\", end - start)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 302, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.638477801268499\n", "Precision: 0.6471885336273429\n", "Recall: 0.9638752052545156\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make predictions\n", "y_pred_6_lstm=tuned_model_6_lstm.predict(X_test_6_lstm)\n", "\n", "# create confustion matrix\n", "fig, ax=plt.subplots()\n", "sns.heatmap(pd.DataFrame(metrics.confusion_matrix(y_test_6, y_pred_6_lstm)), annot=True, cmap=\"Blues\" ,fmt='g')\n", "plt.title('Confusion matrix'); plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n", "ax.xaxis.set_ticklabels(['Down', 'Up']); ax.yaxis.set_ticklabels(['Down', 'Up'])\n", "\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test_6, y_pred_6_lstm))\n", "print(\"Precision:\",metrics.precision_score(y_test_6, y_pred_6_lstm))\n", "print(\"Recall:\",metrics.recall_score(y_test_6, y_pred_6_lstm))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ROC Curve" ] }, { "cell_type": "code", "execution_count": 303, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_proba_6_b=tuned_model_6_b.predict_proba(X_test_6)[:, 1]\n", "fpr, tpr, _=metrics.roc_curve(y_test_6, y_proba_6_b)\n", "auc=metrics.roc_auc_score(y_test_6, y_proba_6_b)\n", "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n", "plt.legend(loc=4)\n", "plt.plot([0, 1], [0, 1], linestyle='--') # plot no skill\n", "plt.title('ROC-Curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model 7: Volatility, Return and Trading Volume " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Baseline" ] }, { "cell_type": "code", "execution_count": 295, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loss function: log\n", "accuracy of the best model: 0.5227513227513227\n", "\n", "\n", "Best hyperparameters:\n", "Number of iterations: 10\n", "Penalty: l1\n", "Alpha: 0.0019\n", "l1_ratio: 0\n", "Total number of features: 69\n", "Number of selected features: 11\n" ] }, { "data": { "image/png": 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U6uvI/xd9rG9m1jLqGflHxIyI2CO3zcg3Vab50uui84GxEbETcCPdI/dqdT8aETsC+6ft43Wcr4cia/heWOkQsEmt+mZmg0UD0zusAMbk3o8mW8P8bRHxQu7tpWTz9F11Dyype0uq83T69xVJ/0U2vfTD3PlWSBoGbAysqtbBIiP/vwcWA/eVbIvwPL+ZtZEhUuGthoXAeEnjJK0NHA/MyxeQNCL3diKwNL2+HjhU0qaSNgUOBa6XNEzSFqnuWsBRZLGZ1HbXXUHHAjfVSsNT5G6fhcDiiLiz9ICkLxeob2Y2KDTqNv+IWC1pGlkgHwrMjIglkqYDiyJiHnCKpInAarJR+uRUd5Wks8liL8D0tG842Y/AWqnNG+lOq/894EeSlqe2jq/VxyLB/1i6F3Ip/YBVF3MxMxtMGvmQV0T8HPh5yb5/zb0+AzijQt2ZwMySfX8Edq9Q/jXguHr6VzP4R0TVeSMzs3bRQRmdC13wfYTyV427snru1PBemZkNgE5K71Bk2ueopvfCzKwFFLiQ2zaKTPs8WaQhSXdFxL5975KZ2cDopGmfRqZ3WLdWAUm7NfB8ZmYNVc8TvoNd07J6StqtZNsdmCdp12o/AvlHppfddGUDu2dmVp3z+TfGIuBuei70vjlwPtkPxcHlKqVHpGcAfOyyh5wm2sz6jef8G+PDwD8A30j3uyLpNxFxUBPPaWbWax0U+xs67ZPPY0FEXAkcCRwi6QpJW+MFX8yshTVyMZdW18iR/4rSHRHxB+CzknYhy1i3QQPPZ2bWUJ726Z2Ko/qIeFDSwcCGDTyfmVlDdU7ob+y0T1WR+T2AJD84ZmYtx7d69k4938aeDTyvmVlDDFHxbbArPO0jaRzwTMoeh6T1gK0i4olU5ONl6mxHtrbkKLJpoZXAvIg4s4/9NjNruHa4kFtUPSP/K4A1ufdvpX0ARMTifGFJp5EtWizgXrLc1AJmSzq9tx02M2uWTpr2qeeC77C0Cj0AEfFGWqGmkinADhHxZn6npPOBJcA5dfXUzKzJOmjgX9fI/7m06gwAko4Gnq9Sfg0wssz+EfT8C8LMrCV45F/eycDlki4im755CjixSvlTgQWSlqWyAFsD2wDTetFXM7OmGvwhvbjCwT8ifg3sI2kDQBHxSo3y10nalmx1+VFk3+sKYGFEvNWHPpuZNYUf8sqR9LGIuEzS50r2AxAR51eqGxFryJK7mZm1vE6626fIyH94+rfc07nO1WNmbaODBv6FVvL6Tnp5Y0TckT8m6a+a0iszswHQSdM+9dzt862C+8zMBiUv5pIjaV9gP+AdJfP+GwFDm9UxgPeOGF67kPXJqI2qPaphjfDaat/ZPFi0wy2cRRWZ81+bLBXzMHrO+/8eOLYZnTIzGwj9lumyBRSZ878VuFXSDyLiSQBJQ4ANurJ0mpm1g6EddLdPPT90X5O0kaThwKPAY5L+sUn9MjPrd52U1bOe4L99Gul/APg52dO6f5bJ08xssOqk9A71BP+1JK1FFvyvTgnbfJ+/mbWNRo78JU2Q9Jik5eUyGUuaLOk5SQ+m7aTcsUmSlqVtUtq3vqT/lvRLSUsknVOkrUrqye3zHeAJ4CHgNknvIrvoa2bWFho1oJc0FLgYOISU1kbSvIh4tKTo3IiYVlJ3M+BMYA+yAfZ9kuYBrwP/HhE3p4zKCyQdHhHXVmqrmsIj/4i4MCJGRcQRaUnGJ4GDitY3M2t1w6TCWw17Acsj4vGUCn8O2cJWRRwG3BARqyLiReAGYEJE/CkiboYspT5wPzC6Vx+UPuT2yamY28fMbDBp4FT+KLqzGUM2+t+7TLljJB0A/Ar4bEQ8VaHuqJ791CbA3wL/UaOtioqM/PO5fcptZmZtYYhUeJM0VdKi3DY111S5n5HSa6TzgbERsRNwIzCrSF1Jw4DZwIUR8XiNtioqnNsnIs6qVdbMbDCrZ+QfETOAGRUOrwDG5N6PJlvDPF//hdzbS4Fzc3UPLKl7S+79DGBZRHyzQFsV1bOA+4Vldr8MLIqIq4u2Y2bWqhp4//5CYLykccDTwPHAR/IFJI2IiGfS24nA0vT6euCrkjZN7w8Fzkh1/g3YGDipYFsV1XO3z7rAdnQv2n4M2Vq8UyQdFBGn1tGWmVnLaVRWz4hYLWkaWSAfCsyMiCWSppMNmOcBp6SlcVcDq4DJqe4qSWeT/YAATE/7RgNfAn4J3J+eNbgoIr5bqa1qFFHsVn1JNwGHRsTq9H4Y8AuyW5keiYjtCzVUh68sWO7nCJrMid2az4nd+sfJ+47tc+SuJ+Z86f3bDOonveoZ+Y8iu/j7cno/HBgZEW9Jer3hPTMz62fqoFV86wn+XwcelHQL2dXoA8jmpYaTXV02MxvU2iFnT1H1LOD+PUk/J3t4QcA/RUTX1WsneDOzQc/Bv7I9gf3T67couXWpGkkbAeOBx9NTa2ZmLaUdErYVVTi9Q0oi9BmydM6Pkl1d/lqV8pdJ2iK9PozszqBzyaaOjqtS7+0HJxZeM6do98zM+qyTUjrXM/I/AtglItYASJoFPEC6/7SMnSPi+fT6TGD/iHgi/SAsoPuW0R7yD074bh8z609ezKWyTXKvN67VdprqAVgD/C9A+kGod7rJzKzpPPIv72vAA5Jupvtun0qjfoCzgJslXQzcAVwh6WrgYOC6XvbXzKxpOmjKv667fWan2zz3JAv+p0XEb6uU/7Gk+4FPAtumc+0LzI6I6/vUazOzJhji+/y7SdqtZNeK9O9ISSMj4v5KdSNiOXBaH/pnZtZvPPLv6bwqx4JsGqcuko6KiGvqrWdm1kzD2mEyv6AiKZ0LrdYl6ZCIuKHgefcEHPzNrKV45N8755ItN/Y2SduRLV02iuyvhJXAvIg4s4HnNTNriEZl9RwM6r3Vs5oe35qk08jWrRRwL1l6UgGzy61kb2Y20KTi22DXyJF/6QNZU4AdIuLN/E5J55M97XtOA89tZtZnjRwNt7pmftY1wMgy+0ekY2ZmLUXZ2ryFtsGukSP/J0renwoskLSM7pXotwa2AaY18LxmZg0xtA2CelH1rOE7FDgSGJuvFxHnp38/lC8fEddJ2pYsBfQosvn+FcDCiHirzz03M2uwzgn99Y385wOvAY9QcNomJYG7uxf9MjPrdx008K8r+I+OiJ2a1hMzswHWDnP5RdVzwfdaSYc2rSdmZgNsSB3bYFfPyP9u4CpJQ4A3yabHIiI2ql7NzGxw6KSHvOoJ/ueRZeV8JCL6ZZGVdYe1w+9ra9tivXUGugttb/GzfxjoLlhBnTTtU0/wXwYs7q/Ab2bW3zppuFlP8H8GuEXStcDrXTu7bvU0MxvsPPIv7zdpWzttZmZtpXNCf30reZ3VzI6YmQ20Dhr41/WE7zuALwI7AOt27Y+IuhdzMTNrRZ2U3qGe6xuXA78ExpEtzv4EWZpmM7O2oDr+G+zqCf6bR8T3gDcj4taI+ASwT5P6ZWbW7xqZz1/SBEmPSVpebg0TSZMlPSfpwbSdlDs2SdKytE3K7d9d0iOpzQuVrlBL2kzSDan8DZI2rdW/eoJ/V17+ZyQdKWlXYHQd9c3MWtoQVHirJiXCvBg4HNgeOEHS9mWKzo2IXdL23VR3M+BMYG+yxJhn5oL5t4GpwPi0TUj7TwcWRMR4YEF6X+OzFvdvkjYGPg98Afgu8Nk66puZtbQGjvz3ApZHxOMR8QbZqoZHF+zGYcANEbEqIl4kWx53gqQRwEYRcVd63uqHwAdSnaOBWen1rNz+igoF//QrNj4iXo6IxRFxUETsHhHzCn4YM7OWV0/wlzRV0qLcNjXX1Ci61zGBLJ39qDKnPEbSw5KulDSmRt1R6XW5NreKiGcA0r9b1vqshYJ/yr8/sUhZM7PBaqhUeIuIGRGxR26bkWuq3N8GpdkR5gNjU7bkG+keuVeqW6TNwuqZ9rlT0kWS9pe0W9fW2xObmbWaBt7tswIYk3s/GliZLxARL0REV7aES4Hda9RdQc/rrPk2f5emhUj/Plurg/U84btf+nd6vv+A7/M3s7bQwNv8FwLjJY0DngaOBz7S81wa0TVVQzazsjS9vh74au4i76HAGRGxStIrkvYB7gFOBL6VyswDJgHnpH+vrtXBep7wPahoWTOzwahR9+9HxGpJ08gC+VBgZkQskTQdWJSul54iaSKwGlgFTE51V0k6m+7nqKZHxKr0+v8BPwDWA65NG2RB/8eSpgD/CxxXq4+qJ0mnpCP58yd8p1eu0Tfn3fq4M4g22Xs2Hz7QXWh7TuncP04/+N19jty3/WpV4ZhzwLabDeonvepJ73AJsD5wENltnscC9zapX2Zm/a6TFnOp54LvfhFxIvBiSvK2Lz0vStQkaYt6ypuZ9SfVsQ129QT/V9O/f5I0kuyJ33GVCks6XNJvJP2PpF0lLQHukbRC0vv70Gczs6YYIhXeBrt6gv81kjYBvg7cR5bYbU6V8l8DjgD+kewe1ikR8W7gEOAblSrlH5y4e/7sOrpnZtY3nTTyr+dWz38nu9K8P3AXcDtZnolK1kTEUgBJf4qIuwEiYmlaBL6s9KDEDPAFXzPrZ+0Q1QuqJ/jPAl4BLkzvTyDLLfHhCuVfkvQpYCPgRUmfBX4M/A3g2x/MrOW0Q6rmouoJ/u+JiJ1z72+W9FCV8pOAfwbWkD2kcALZPa9PAp+st6NmZs02pHNif13B/wFJ+3RN30jaG7ijUuGIeAr4VG7XBWkzM2tNHRT867nguzdZfp8nJD1BNu//vrSwwMP1nFTSUfWUNzPrD520klc9I/8JtYsUtidwTQPbMzPrsza4g7OwenL7PFlv45K2I1tkYBRZEriVwLyIOLPetszMmq2DYn9d0z51kXQa2XMAIksDsTC9nl1uPUszswHXQTf61zPtU68pwA4R8WZ+p6TzgSVkWejMzFpGOzy5W1TTRv5kt3iOLLN/RDpmZtZSOmjg39SR/6nAAknL6F6PcmtgG2BaE89rZtY77RDVC2pa8I+I6yRtS7aK/Siyr3UFsDCtCWxm1lLa4RbOopo58ici1gB3N/McZmaN0kFT/s0N/mZmg4mDv5lZB/K0j5lZB+qkkX9dC7j3t/V2nda6nWsT7/jrQwe6C23v5edfHugudISXZ3+8z6F76co/Fo457x05fFD/VHjkb2bWZVCH8/o4+JuZJZ7zNzPrQF7MxcysEzn4m5l1Hk/7mJl1oE661dPB38ws6aDY7+BvZva2Dor+zcznb2Y2qAyRCm+1SJog6TFJy6utXijpWEkhaY/0fm1J35f0iKSHJB2Y9m8o6cHc9rykb6ZjkyU9lzt2Uq3+eeRvZpY0auAvaShwMXAIKZW9pHkR8WhJuQ2BU4B7crs/CRARO0raErhW0p4R8QqwS67ufcBPc/XmRkThtVI88jcz69K4pbz2ApZHxOMR8QbZeuZHlyl3NvB14LXcvu2BBQAR8SzwErBHj25K44EtgdsLf7YSDv5mZonq+U+aKmlRbpuaa2oU3SsYQjb6H9XjXNKuwJiIuKakGw8BR0saJmkcsDswpqTMCWQj/XwuomMkPSzpSkml5f+Mp33MzJJ6bvWMiBnAjEpNlavSfR4NAS4AJpcpNxN4L7AIeBK4E1hdUuZ44OO59/OB2RHxuqSTgVnAwdX67+BvZpY0ML3DCnqO1kcDK3PvNwT+ErhF2S/OO4F5kiZGxCLgs10FJd0JLMu93xkYFhH3de2LiBdybV8KnFurg572MTN7W8Mm/RcC4yWNk7Q22Uh9XtfBiHg5IraIiLERMZZsuduJEbFI0vqShgNIOgRYXXKh+ARgdo9eSyNybycCS2t10CN/M7OkUU/4RsRqSdOA64GhwMyIWCJpOrAoIuZVqb4lcL2kNcDT9JzeAfgwcETJvlMkTSSbHlpF+emkHryYS4fzYi7N58Vc+kcjFnNZ+dIbhWPOyE3WHtSPhDV95C9pK7Kr3AGsjIjfNfucZma94dw+DSBpF+ASYGOyP10ARkt6Cfh0RNxfod5UYCrAsNEHMmyLHZrVRTOzHpzVszF+AHwqIvJPriFpH+D7wM7lKuVvn/K0j5n1J4/8G2N4aeAHiIi7u65km5m1Egf/xrhW0n8DP6T7SbcxwInAdU08r5lZr3japwEi4hTFHqTPAAAHtElEQVRJh5PlsxhFdmPsCuDiiPh5s85rZtZrnRP7m3u3T0RcC1zbzHOYmTVKB8X+gXnCtyQBkplZS5CKb4PdQD3h2wZfnZm1myKLtLSLgcrt88YAndfMzBi44H/WAJ3XzKwiT/s0gKSHKx0CtmrWec3Mesu3ejbGVsBhwIsl+0W2OIGZWUtphxF9Uc0M/tcAG0TEg6UHJN3SxPOamfWKg38DRMSUKsc+0qzzmpn1lqd9zMw6kEf+ZmYdqINiv4O/mdnbOij6O/ibmSWdNOff0mv4DkaSpqYFaaxJ/B03n7/j9jdQT/i2Myetaz5/x83n77jNOfibmXUgB38zsw7k4N94nidtPn/HzefvuM35gq+ZWQfyyN/MrAM5+JuZdSAH/xKSJkh6TNJySaeXOb6OpLnp+D2SxuaOnZH2PybpsNz+mZKelbS4pK2dJd0l6RFJ8yVt1MzP1ooa/X1LGiPpZklLJS2R9Jn++zStpT+/W0nHpX1rJO3RH5/P+igivKUNGAr8GvgLYG3gIWD7kjKfBi5Jr48H5qbX26fy6wDjUjtD07EDgN2AxSVtLQTel15/Ajh7oL+Dwf59AyOA3VKZDYFflbbZCVt/f7fAe4H3ALcAewz05/dWe/PIv6e9gOUR8XhEvAHMAY4uKXM0MCu9vhJ4vySl/XMi4vWI+A2wPLVHRNwGrCpzvvcAt6XXNwDHNPLDDAIN/74j4pmIuB8gIl4BlgKj+uGztJp+/W4jYmlEPNb0T2UN4+Df0yjgqdz7Ffx54Hi7TESsBl4GNi9Yt9RiYGJ6fRwwple9Hrya+n2naYxdgXsa2OfBwt+tVeXg31O5rE6l98JWKlOkbqlPAP9f0n1kf0a/UbOH7aVp37ekDYCfAKdGxO973cPBy9+tVeXg39MKeo6+RwMrK5WRNAzYmGxKp0jdHiLilxFxaETsDswmm1vtJE35viWtRRacLo+Inzal563P361V5eDf00JgvKRxktYmuwg2r6TMPGBSen0scFNERNp/fLqDYhwwHri32skkbZn+HQL8M3BJwz7J4NDw7zvNWX8PWBoR5/fLp2hN/m6tuoG+4txqG3AE2V0Mvwa+lPZNByam1+sCV5BdBLsX+Itc3S+leo8Bh+f2zwaeAd4kG1VNSfs/k871K+Ac0hPXnbQ1+vsG/ppsiuJh4MG0HTHQn7Pdv1vgg+n/7deB3wHXD/Tn91Z9c3oHM7MO5GkfM7MO5OBvZtaBHPzNzDqQg7+ZWQdy8Dcz60AO/mZmHcjB3/qVpFt6m/JX0smSTqxy/EBJ+xUt3wiS/tCIMmb9bdhAd8Bak6RhkSX7ahkRUesJ6AOBPwB3Fixv1rE88m9jksZK+qWkWZIelnSlpPUl/aukhZIWS5qRHtvvGpV/VdKtwGck/W1a5OMBSTdK2iqV+3Jq8xeSnpD0IUlfT4vSXJfyvxTp3wmpzmJJ5+b2T5H0q9SfSyVdlDvvF9LrUyQ9mj7XnJRl8mTgs5IelLR/Sflt0md4SNL9kt5dx/e4gaQFqd4jkkpTI3f91XGbpKtSvy5JaTu6jn8lnfvu3PdY9vs16w8O/u3vPcCMiNgJ+D3ZAh4XRcSeEfGXwHrAUbnym0TE+yLiPOB/gH0iYleyfPBfzJV7N3AkWe73y4CbI2JH4NW0vypJI4FzgYOBXYA9JX0g7f8XYB/gEGC7Ck2cDuyaPtfJEfEEWW6kCyJil4i4vaT85cDFEbEzsB9Zuo2iXgM+GBG7AQcB53X9YJbYC/g8sCPZ9/OhtH84cHc6923AJ9P+at+vWVN52qf9PRURd6TXlwGnAL+R9EVgfWAzYAkwP5WZm6s7GpgraQTZalC/yR27NiLelPQI2SpP16X9jwBjC/RrT+CWiHgOQNLlZCueAdwaEavS/iuAbcvUfxi4XNLPgJ9VO5GkDYFREXEVQES8VqB/PZoAvirpAGANWW77rYDflpS7NyIeT+ecTZYL50qyVN3XpDL3kf2oQfXv16ypPPJvf6XJmwL4T+DYNFK/lCzBV5c/5l5/i+yvhB2BT5WUex0gItYAb0Z3kqg1FBtUlBs5V9tf6kjgYmB34L6UkrjecxX1UeAdwO4RsQtZ4rJ1y5Qr911Dz+/nLbq/n2rfr1lTOfi3v60l7Zten0A21QDwvLJFOY6tUndj4On0elKVcr1xD/A+SVtIGpr6ditZdsn3Sdo0BfQ/W9oyzaWPiYibyaZKNgE2AF4hWxSnh8gWHFkh6QOp/jqS1q+jrxsDz6a/dA4C3lWh3F4phfIQ4O/o/q6rtdus79esKgf/9rcUmCTpYbIpnm+TjfYfIZsuWVil7peBKyTdDjzfyE5FxDPAGcDNZIuF3x8RV0fE08BXyX4cbgQeJVteMG8ocFmacnqAbJ7/JbKpqw92XfAtqfNx4JT0PdwJvLOO7l4O7CFpEdlfAb+sUO4ustTci8mmcK6q0e6XadL3a1aLUzq3sXQHzDXpwu6gIWmDiPhDGvlfBczsmq9vVZIOBL4QEUfVKmvWCjzyt1b0ZUkP0j2CrnpB18zq55G/NYWkq4BxJbtPi4jrB6I/pSTtCPyoZPcY4KmSfa9HxN790yuz/uPgb2bWgTztY2bWgRz8zcw6kIO/mVkHcvA3M+tA/wcYf/9hMsupoQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Model specific Parameter \n", "\n", "# Number of iterations\n", "iterations_7_b=[10] \n", "\n", "\n", "# Grid Search\n", "\n", "# Regularization \n", "alpha_g_7_b=[0.0019, 0.002, 0.0021] \n", "l1_ratio_g_7_b=[0, 0.2, 0.4, 0.6, 0.8, 1] \n", "\n", "# Create hyperparameter options\n", "hyperparameters_g_7_b={'logistic__alpha':alpha_g_7_b, \n", " 'logistic__l1_ratio':l1_ratio_g_7_b, \n", " 'logistic__penalty':penalty_b, \n", " 'logistic__max_iter':iterations_7_b}\n", "\n", "# Create grid search \n", "search_g_7_b=GridSearchCV(estimator=pipeline_b, \n", " param_grid=hyperparameters_g_7_b, \n", " cv=tscv, \n", " verbose=0, \n", " n_jobs=-1, \n", " scoring=scoring_b, \n", " refit=metric_b, \n", " return_train_score=False)\n", "# Setting refit='Accuracy', refits an estimator on the whole dataset with the parameter setting that has the best cross-validated mean Accuracy score. \n", "# For multiple metric evaluation, this needs to be a string denoting the scorer is used to find the best parameters for refitting the estimator at the end\n", "# If return_train_score=True training results of CV will be saved as well \n", "\n", "# Fit grid search\n", "tuned_model_7_b=search_g_7_b.fit(X_train_7, y_train_7)\n", "#search_g_7_b.cv_results_\n", "\n", "\n", "# Random Search\n", "\n", "# Create regularization hyperparameter distribution using uniform distribution\n", "#alpha_r_7_b=uniform(loc=0.00006, scale=0.002) #loc=0.00006, scale=0.002\n", "#l1_ratio_r_7_b=uniform(loc=0, scale=1) \n", "\n", "# Create hyperparameter options\n", "#hyperparameters_r_7_b={'logistic__alpha':alpha_r_7_b, 'logistic__l1_ratio':l1_ratio_r_7_b, 'logistic__penalty':penalty_b,'logistic__max_iter':iterations_7_b}\n", "\n", "# Create randomized search \n", "#search_r_7_b=RandomizedSearchCV(pipeline_b, hyperparameters_r_7_b, n_iter=10, random_state=1, cv=tscv, verbose=0, n_jobs=-1, scoring=scoring_b, refit=metric_b, return_train_score=False)\n", "# Setting refit='Accuracy', refits an estimator on the whole dataset with the parameter setting that has the best cross-validated Accuracy score.\n", "\n", "# Fit randomized search\n", "#tuned_model_7_b=search_r_4_b.fit(X_train_7, y_train_7)\n", "\n", "\n", "\n", "# View Cost function\n", "print('Loss function:', tuned_model_7_b.best_estimator_.get_params()['logistic__loss'])\n", "\n", "# View Accuracy \n", "print(metric_b +' of the best model: ', tuned_model_7_b.best_score_);print(\"\\n\")\n", "# best_score_ Mean cross-validated score of the best_estimator\n", "\n", "# View best hyperparameters\n", "print(\"Best hyperparameters:\")\n", "print('Number of iterations:', tuned_model_7_b.best_estimator_.get_params()['logistic__max_iter'])\n", "print('Penalty:', tuned_model_7_b.best_estimator_.get_params()['logistic__penalty'])\n", "print('Alpha:', tuned_model_7_b.best_estimator_.get_params()['logistic__alpha'])\n", "print('l1_ratio:', tuned_model_7_b.best_estimator_.get_params()['logistic__l1_ratio'])\n", "\n", "# Find the number of nonzero coefficients (selected features)\n", "print(\"Total number of features:\", len(tuned_model_7_b.best_estimator_.steps[1][1].coef_[0][:]))\n", "print(\"Number of selected features:\", np.count_nonzero(tuned_model_7_b.best_estimator_.steps[1][1].coef_[0][:]))\n", "\n", "# Gridsearch table\n", "plt.title('Gridsearch')\n", "pvt_7_b=pd.pivot_table(pd.DataFrame(tuned_model_7_b.cv_results_), values='mean_test_accuracy', index='param_logistic__l1_ratio', columns='param_logistic__alpha')\n", "ax_7_b=sns.heatmap(pvt_7_b, cmap=\"Blues\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 296, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.6617336152219874\n", "Precision: 0.656554712892741\n", "Recall: 0.9950738916256158\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make predictions\n", "y_pred_7_b=tuned_model_7_b.predict(X_test_7)\n", "\n", "# create confustion matrix\n", "fig, ax=plt.subplots()\n", "sns.heatmap(pd.DataFrame(metrics.confusion_matrix(y_test_7, y_pred_7_b)), annot=True, cmap=\"Blues\" ,fmt='g')\n", "plt.title('Confusion matrix'); plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n", "ax.xaxis.set_ticklabels(['Down', 'Up']); ax.yaxis.set_ticklabels(['Down', 'Up'])\n", "\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test_7, y_pred_7_b))\n", "print(\"Precision:\",metrics.precision_score(y_test_7, y_pred_7_b))\n", "print(\"Recall:\",metrics.recall_score(y_test_7, y_pred_7_b))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ROC Curve" ] }, { "cell_type": "code", "execution_count": 297, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_proba_7_b=tuned_model_7_b.predict_proba(X_test_7)[:, 1]\n", "fpr, tpr, _=metrics.roc_curve(y_test_7, y_proba_7_b)\n", "auc=metrics.roc_auc_score(y_test_7, y_proba_7_b)\n", "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n", "plt.legend(loc=4)\n", "plt.plot([0, 1], [0, 1], linestyle='--') # plot no skill\n", "plt.title('ROC-Curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LSTM" ] }, { "cell_type": "code", "execution_count": 244, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "accuracy of the best model: 0.5313492063492063\n", "\n", "\n", "Best hyperparameters:\n", "epochs: 1\n", "batch_size: 1\n", "dropout_rate: 0.0\n", "recurrent_dropout: 0.4\n", "\n", "\n", "Running Time: 288.78774094581604\n" ] } ], "source": [ "start=time.time()\n", "\n", "\n", "# number of epochs\n", "epochs=1\n", "# number of units \n", "LSTM_units_7_lstm=220\n", "# numer of features\n", "num_features_7_lstm=X_train_7.shape[1]\n", "# Regularization\n", "dropout_rate=0.\n", "recurrent_dropout=0.4\n", "# print\n", "verbose=0\n", "\n", "#hyperparameter\n", "batch_size=[1] \n", "\n", "# hyperparameter\n", "hyperparameter_7_lstm={'batch_size':batch_size}\n", "\n", "\n", "# create Classifier\n", "clf_7_lstm=KerasClassifier(build_fn=create_shallow_LSTM, \n", " epochs=epochs, \n", " LSTM_units=LSTM_units_7_lstm, \n", " num_samples=num_samples, \n", " look_back=look_back, \n", " num_features=num_features_7_lstm, \n", " dropout_rate=dropout_rate,\n", " recurrent_dropout=recurrent_dropout,\n", " verbose=verbose)\n", "# Gridsearch\n", "search_7_lstm=GridSearchCV(estimator=clf_7_lstm, \n", " param_grid=hyperparameter_7_lstm, \n", " n_jobs=-1, \n", " cv=tscv, \n", " scoring=scoring_lstm, # accuracy\n", " refit=True, \n", " return_train_score=False)\n", "\n", "# Fit model\n", "tuned_model_7_lstm=search_7_lstm.fit(X_train_7_lstm, y_train_7, shuffle=False, callbacks=[reset])\n", "\n", "\n", "\n", "print(\"\\n\")\n", "\n", "# View Accuracy \n", "print(scoring_lstm +' of the best model: ', tuned_model_7_lstm.best_score_)\n", "# best_score_ Mean cross-validated score of the best_estimator\n", "\n", "print(\"\\n\")\n", "\n", "# View best hyperparameters\n", "print(\"Best hyperparameters:\")\n", "print('epochs:', tuned_model_7_lstm.best_estimator_.get_params()['epochs'])\n", "print('batch_size:', tuned_model_7_lstm.best_estimator_.get_params()['batch_size'])\n", "print('dropout_rate:', tuned_model_7_lstm.best_estimator_.get_params()['dropout_rate'])\n", "print('recurrent_dropout:', tuned_model_7_lstm.best_estimator_.get_params()['recurrent_dropout'])\n", "\n", "end=time.time()\n", "print(\"\\n\")\n", "print(\"Running Time:\", end - start)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 245, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.6553911205073996\n", "Precision: 0.6539717083786725\n", "Recall: 0.986863711001642\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make predictions\n", "y_pred_7_lstm=tuned_model_7_lstm.predict(X_test_7_lstm)\n", "\n", "# create confustion matrix\n", "fig, ax=plt.subplots()\n", "sns.heatmap(pd.DataFrame(metrics.confusion_matrix(y_test_7, y_pred_7_lstm)), annot=True, cmap=\"Blues\" ,fmt='g')\n", "plt.title('Confusion matrix'); plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n", "ax.xaxis.set_ticklabels(['Down', 'Up']); ax.yaxis.set_ticklabels(['Down', 'Up'])\n", "\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test_7, y_pred_7_lstm))\n", "print(\"Precision:\",metrics.precision_score(y_test_7, y_pred_7_lstm))\n", "print(\"Recall:\",metrics.recall_score(y_test_7, y_pred_7_lstm))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ROC Curve" ] }, { "cell_type": "code", "execution_count": 247, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_proba_7_b=tuned_model_7_b.predict_proba(X_test_7)[:, 1]\n", "fpr, tpr, _=metrics.roc_curve(y_test_7, y_proba_7_b)\n", "auc=metrics.roc_auc_score(y_test_7, y_proba_7_b)\n", "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n", "plt.legend(loc=4)\n", "plt.plot([0, 1], [0, 1], linestyle='--') # plot no skill\n", "plt.title('ROC-Curve')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: README.md ================================================ # Stanford Project: Predicting stock prices using a LSTM-Network Predicting stock prices using a LSTM-Network Introduction: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how investment decisions are made on a broad scale. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train LSTM-networks with time series price-volume data and compare their out-of-sample return predictability with the performance of simple logistic regressions (our baseline models). Methods: • Long short-term memory (LSTM) recurrent neural network Programming language: Python