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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
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FILE CONTENTS
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FILE: Final Project.ipynb
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{
"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",
"<class 'pandas.core.frame.DataFrame'>\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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
gitextract_fejt_ku7/ ├── Final Project.ipynb └── README.md
Condensed preview — 2 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,648K chars).
[
{
"path": "Final Project.ipynb",
"chars": 1632454,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 1. Import Libraries\"\n ]\n },\n "
},
{
"path": "README.md",
"chars": 1475,
"preview": "# Stanford Project: Predicting stock prices using a LSTM-Network\n\nPredicting stock prices using a LSTM-Network\n\nIntroduc"
}
]
About this extraction
This page contains the full source code of the dduemig/Stanford-Project-Predicting-stock-prices-using-a-LSTM-Network GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 2 files (1.6 MB), approximately 1.1M tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.