Short-Term Asset Direction Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  \n",
    "import pandas as pd  \n",
    "from pylab import plt\n",
    "import matplotlib as mlib\n",
    "plt.style.use('seaborn')\n",
    "%matplotlib inline\n",
    "\n",
    "# Preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, TimeSeriesSplit, cross_val_score\n",
    "\n",
    "# Classifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Metrics\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn.metrics import plot_confusion_matrix, auc, roc_curve, plot_roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('D:/CQF/0175.HK.csv', index_col=0, usecols=[0,5]) # 5-year stock price of Geely\n",
    "data.columns = ['price']  # Adj Close Price\n",
    "data = data.rename(columns={data.columns[0]: 'price'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-24</th>\n",
       "      <td>7.486081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-25</th>\n",
       "      <td>7.633774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-28</th>\n",
       "      <td>7.430698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-29</th>\n",
       "      <td>7.366083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-30</th>\n",
       "      <td>7.403007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price\n",
       "Date                \n",
       "2016-11-24  7.486081\n",
       "2016-11-25  7.633774\n",
       "2016-11-28  7.430698\n",
       "2016-11-29  7.366083\n",
       "2016-11-30  7.403007"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['price'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>Returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-24</th>\n",
       "      <td>7.486081</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-25</th>\n",
       "      <td>7.633774</td>\n",
       "      <td>0.019537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-28</th>\n",
       "      <td>7.430698</td>\n",
       "      <td>-0.026963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-29</th>\n",
       "      <td>7.366083</td>\n",
       "      <td>-0.008734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-30</th>\n",
       "      <td>7.403007</td>\n",
       "      <td>0.005000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price   Returns\n",
       "Date                          \n",
       "2016-11-24  7.486081       NaN\n",
       "2016-11-25  7.633774  0.019537\n",
       "2016-11-28  7.430698 -0.026963\n",
       "2016-11-29  7.366083 -0.008734\n",
       "2016-11-30  7.403007  0.005000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Returns'] = np.log(data).diff()\n",
    "\n",
    "data.head() # Add daily Log return to the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Returns'].plot(figsize=(12, 6)) # time series of daily log return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lags = 5 # include 5 past returns\n",
    "\n",
    "cols = []\n",
    "\n",
    "for lag in range(1, lags+1):\n",
    "    col = 'rt_%d' % lag\n",
    "    data[col] = data['Returns'].shift(lag)\n",
    "    cols.append(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>Returns</th>\n",
       "      <th>rt_1</th>\n",
       "      <th>rt_2</th>\n",
       "      <th>rt_3</th>\n",
       "      <th>rt_4</th>\n",
       "      <th>rt_5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-24</th>\n",
       "      <td>7.486081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-25</th>\n",
       "      <td>7.633774</td>\n",
       "      <td>0.019537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-28</th>\n",
       "      <td>7.430698</td>\n",
       "      <td>-0.026963</td>\n",
       "      <td>0.019537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-29</th>\n",
       "      <td>7.366083</td>\n",
       "      <td>-0.008734</td>\n",
       "      <td>-0.026963</td>\n",
       "      <td>0.019537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-30</th>\n",
       "      <td>7.403007</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>-0.008734</td>\n",
       "      <td>-0.026963</td>\n",
       "      <td>0.019537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price   Returns      rt_1      rt_2      rt_3  rt_4  rt_5\n",
       "Date                                                                    \n",
       "2016-11-24  7.486081       NaN       NaN       NaN       NaN   NaN   NaN\n",
       "2016-11-25  7.633774  0.019537       NaN       NaN       NaN   NaN   NaN\n",
       "2016-11-28  7.430698 -0.026963  0.019537       NaN       NaN   NaN   NaN\n",
       "2016-11-29  7.366083 -0.008734 -0.026963  0.019537       NaN   NaN   NaN\n",
       "2016-11-30  7.403007  0.005000 -0.008734 -0.026963  0.019537   NaN   NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>Returns</th>\n",
       "      <th>rt_1</th>\n",
       "      <th>rt_2</th>\n",
       "      <th>rt_3</th>\n",
       "      <th>rt_4</th>\n",
       "      <th>rt_5</th>\n",
       "      <th>return_sign</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-02</th>\n",
       "      <td>7.061471</td>\n",
       "      <td>-0.043485</td>\n",
       "      <td>-0.003748</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>-0.008734</td>\n",
       "      <td>-0.026963</td>\n",
       "      <td>0.019537</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-05</th>\n",
       "      <td>7.116855</td>\n",
       "      <td>0.007813</td>\n",
       "      <td>-0.043485</td>\n",
       "      <td>-0.003748</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>-0.008734</td>\n",
       "      <td>-0.026963</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-06</th>\n",
       "      <td>7.347621</td>\n",
       "      <td>0.031911</td>\n",
       "      <td>0.007813</td>\n",
       "      <td>-0.043485</td>\n",
       "      <td>-0.003748</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>-0.008734</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-07</th>\n",
       "      <td>7.661465</td>\n",
       "      <td>0.041827</td>\n",
       "      <td>0.031911</td>\n",
       "      <td>0.007813</td>\n",
       "      <td>-0.043485</td>\n",
       "      <td>-0.003748</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-08</th>\n",
       "      <td>7.827616</td>\n",
       "      <td>0.021455</td>\n",
       "      <td>0.041827</td>\n",
       "      <td>0.031911</td>\n",
       "      <td>0.007813</td>\n",
       "      <td>-0.043485</td>\n",
       "      <td>-0.003748</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price   Returns      rt_1      rt_2      rt_3      rt_4  \\\n",
       "Date                                                                     \n",
       "2016-12-02  7.061471 -0.043485 -0.003748  0.005000 -0.008734 -0.026963   \n",
       "2016-12-05  7.116855  0.007813 -0.043485 -0.003748  0.005000 -0.008734   \n",
       "2016-12-06  7.347621  0.031911  0.007813 -0.043485 -0.003748  0.005000   \n",
       "2016-12-07  7.661465  0.041827  0.031911  0.007813 -0.043485 -0.003748   \n",
       "2016-12-08  7.827616  0.021455  0.041827  0.031911  0.007813 -0.043485   \n",
       "\n",
       "                rt_5  return_sign  \n",
       "Date                               \n",
       "2016-12-02  0.019537         -1.0  \n",
       "2016-12-05 -0.026963          1.0  \n",
       "2016-12-06 -0.008734          1.0  \n",
       "2016-12-07  0.005000          1.0  \n",
       "2016-12-08 -0.003748          1.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(inplace=True)\n",
    "\n",
    "data['return_sign'] = np.sign(data['Returns'].values) \n",
    "# create return sign, if return > 0, its sign will be 1,\n",
    "# if return = 0, its sign will be 0, if return < 0, its sign will be -1\n",
    "data['return_sign'].replace(0.0,-1.0, inplace=True) # convert all zeros to -1 for the sake of classification\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a set of features, including momentum, moving average and exponential moving average\n",
    "def createFeatures(df): \n",
    "    df['momentum_1d']=df['price']-df['price'].shift(periods=1)\n",
    "    # df['momentum_2d']=df['price']-df['price'].shift(periods=2)\n",
    "    # df['momentum_3d']=df['price']-df['price'].shift(periods=3)\n",
    "    # df['momentum_4d']=df['price']-df['price'].shift(periods=4)\n",
    "    df['momentum_5d']=df['price']-df['price'].shift(periods=5)\n",
    "    df['momentum_10d']=df['price']-df['price'].shift(periods=10)\n",
    "    df['momentum_20d']=df['price']-df['price'].shift(periods=20)\n",
    "    df['momentum_50d']=df['price']-df['price'].shift(periods=50)\n",
    "    \n",
    "    df['MA_5d']=df['price'].rolling(5).mean() # Moving average\n",
    "    df['MA_10d']=df['price'].rolling(10).mean()\n",
    "    df['MA_15d']=df['price'].rolling(15).mean()\n",
    "    # df['MA_20d']=df['price'].rolling(20).mean()\n",
    "\n",
    "    df['EMA_5d']=df['price'].ewm(5, adjust=False).mean() # Exponential Moving average\n",
    "    df['EMA_10d']=df['price'].ewm(10, adjust=False).mean()\n",
    "    df['EMA_15d']=df['price'].ewm(15, adjust=False).mean()\n",
    "    # df['EMA_20d']=df['price'].ewm(20, adjust=False).mean()\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>Returns</th>\n",
       "      <th>rt_1</th>\n",
       "      <th>rt_2</th>\n",
       "      <th>rt_3</th>\n",
       "      <th>rt_4</th>\n",
       "      <th>rt_5</th>\n",
       "      <th>return_sign</th>\n",
       "      <th>momentum_1d</th>\n",
       "      <th>momentum_5d</th>\n",
       "      <th>momentum_10d</th>\n",
       "      <th>momentum_20d</th>\n",
       "      <th>momentum_50d</th>\n",
       "      <th>MA_5d</th>\n",
       "      <th>MA_10d</th>\n",
       "      <th>MA_15d</th>\n",
       "      <th>EMA_5d</th>\n",
       "      <th>EMA_10d</th>\n",
       "      <th>EMA_15d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-17</th>\n",
       "      <td>9.895290</td>\n",
       "      <td>-0.011132</td>\n",
       "      <td>-0.021899</td>\n",
       "      <td>0.009066</td>\n",
       "      <td>0.018383</td>\n",
       "      <td>0.067139</td>\n",
       "      <td>0.007968</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.110768</td>\n",
       "      <td>0.590765</td>\n",
       "      <td>0.895376</td>\n",
       "      <td>1.707677</td>\n",
       "      <td>2.833819</td>\n",
       "      <td>10.042980</td>\n",
       "      <td>9.676522</td>\n",
       "      <td>9.310064</td>\n",
       "      <td>9.591514</td>\n",
       "      <td>9.111556</td>\n",
       "      <td>8.756609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-20</th>\n",
       "      <td>10.282978</td>\n",
       "      <td>0.038431</td>\n",
       "      <td>-0.011132</td>\n",
       "      <td>-0.021899</td>\n",
       "      <td>0.009066</td>\n",
       "      <td>0.018383</td>\n",
       "      <td>0.067139</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.387688</td>\n",
       "      <td>0.332304</td>\n",
       "      <td>0.812300</td>\n",
       "      <td>1.873828</td>\n",
       "      <td>3.166123</td>\n",
       "      <td>10.109441</td>\n",
       "      <td>9.757752</td>\n",
       "      <td>9.423294</td>\n",
       "      <td>9.706758</td>\n",
       "      <td>9.218049</td>\n",
       "      <td>8.852007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-21</th>\n",
       "      <td>10.319901</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.038431</td>\n",
       "      <td>-0.011132</td>\n",
       "      <td>-0.021899</td>\n",
       "      <td>0.009066</td>\n",
       "      <td>0.018383</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.036923</td>\n",
       "      <td>0.184615</td>\n",
       "      <td>1.190757</td>\n",
       "      <td>1.873829</td>\n",
       "      <td>2.972280</td>\n",
       "      <td>10.146364</td>\n",
       "      <td>9.876828</td>\n",
       "      <td>9.543293</td>\n",
       "      <td>9.808949</td>\n",
       "      <td>9.318218</td>\n",
       "      <td>8.943750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-22</th>\n",
       "      <td>10.209133</td>\n",
       "      <td>-0.010791</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.038431</td>\n",
       "      <td>-0.011132</td>\n",
       "      <td>-0.021899</td>\n",
       "      <td>0.009066</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.110768</td>\n",
       "      <td>-0.018461</td>\n",
       "      <td>0.793838</td>\n",
       "      <td>1.735368</td>\n",
       "      <td>2.547668</td>\n",
       "      <td>10.142672</td>\n",
       "      <td>9.956212</td>\n",
       "      <td>9.658369</td>\n",
       "      <td>9.875646</td>\n",
       "      <td>9.399210</td>\n",
       "      <td>9.022837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-23</th>\n",
       "      <td>10.190671</td>\n",
       "      <td>-0.001810</td>\n",
       "      <td>-0.010791</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.038431</td>\n",
       "      <td>-0.011132</td>\n",
       "      <td>-0.021899</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.018462</td>\n",
       "      <td>0.184613</td>\n",
       "      <td>0.959991</td>\n",
       "      <td>2.012288</td>\n",
       "      <td>2.363055</td>\n",
       "      <td>10.179595</td>\n",
       "      <td>10.052211</td>\n",
       "      <td>9.784521</td>\n",
       "      <td>9.928150</td>\n",
       "      <td>9.471161</td>\n",
       "      <td>9.095826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                price   Returns      rt_1      rt_2      rt_3      rt_4  \\\n",
       "Date                                                                      \n",
       "2017-02-17   9.895290 -0.011132 -0.021899  0.009066  0.018383  0.067139   \n",
       "2017-02-20  10.282978  0.038431 -0.011132 -0.021899  0.009066  0.018383   \n",
       "2017-02-21  10.319901  0.003584  0.038431 -0.011132 -0.021899  0.009066   \n",
       "2017-02-22  10.209133 -0.010791  0.003584  0.038431 -0.011132 -0.021899   \n",
       "2017-02-23  10.190671 -0.001810 -0.010791  0.003584  0.038431 -0.011132   \n",
       "\n",
       "                rt_5  return_sign  momentum_1d  momentum_5d  momentum_10d  \\\n",
       "Date                                                                        \n",
       "2017-02-17  0.007968         -1.0    -0.110768     0.590765      0.895376   \n",
       "2017-02-20  0.067139          1.0     0.387688     0.332304      0.812300   \n",
       "2017-02-21  0.018383          1.0     0.036923     0.184615      1.190757   \n",
       "2017-02-22  0.009066         -1.0    -0.110768    -0.018461      0.793838   \n",
       "2017-02-23 -0.021899         -1.0    -0.018462     0.184613      0.959991   \n",
       "\n",
       "            momentum_20d  momentum_50d      MA_5d     MA_10d    MA_15d  \\\n",
       "Date                                                                     \n",
       "2017-02-17      1.707677      2.833819  10.042980   9.676522  9.310064   \n",
       "2017-02-20      1.873828      3.166123  10.109441   9.757752  9.423294   \n",
       "2017-02-21      1.873829      2.972280  10.146364   9.876828  9.543293   \n",
       "2017-02-22      1.735368      2.547668  10.142672   9.956212  9.658369   \n",
       "2017-02-23      2.012288      2.363055  10.179595  10.052211  9.784521   \n",
       "\n",
       "              EMA_5d   EMA_10d   EMA_15d  \n",
       "Date                                      \n",
       "2017-02-17  9.591514  9.111556  8.756609  \n",
       "2017-02-20  9.706758  9.218049  8.852007  \n",
       "2017-02-21  9.808949  9.318218  8.943750  \n",
       "2017-02-22  9.875646  9.399210  9.022837  \n",
       "2017-02-23  9.928150  9.471161  9.095826  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "createFeatures(data)\n",
    "data.dropna(inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Returns</th>\n",
       "      <th>rt_1</th>\n",
       "      <th>rt_2</th>\n",
       "      <th>rt_3</th>\n",
       "      <th>rt_4</th>\n",
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       "      <th>momentum_10d</th>\n",
       "      <th>momentum_20d</th>\n",
       "      <th>momentum_50d</th>\n",
       "      <th>MA_5d</th>\n",
       "      <th>MA_10d</th>\n",
       "      <th>MA_15d</th>\n",
       "      <th>EMA_5d</th>\n",
       "      <th>EMA_10d</th>\n",
       "      <th>EMA_15d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-17</th>\n",
       "      <td>-0.011132</td>\n",
       "      <td>-0.021899</td>\n",
       "      <td>0.009066</td>\n",
       "      <td>0.018383</td>\n",
       "      <td>0.067139</td>\n",
       "      <td>0.007968</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.110768</td>\n",
       "      <td>0.590765</td>\n",
       "      <td>0.895376</td>\n",
       "      <td>1.707677</td>\n",
       "      <td>2.833819</td>\n",
       "      <td>10.042980</td>\n",
       "      <td>9.676522</td>\n",
       "      <td>9.310064</td>\n",
       "      <td>9.591514</td>\n",
       "      <td>9.111556</td>\n",
       "      <td>8.756609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-20</th>\n",
       "      <td>0.038431</td>\n",
       "      <td>-0.011132</td>\n",
       "      <td>-0.021899</td>\n",
       "      <td>0.009066</td>\n",
       "      <td>0.018383</td>\n",
       "      <td>0.067139</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.387688</td>\n",
       "      <td>0.332304</td>\n",
       "      <td>0.812300</td>\n",
       "      <td>1.873828</td>\n",
       "      <td>3.166123</td>\n",
       "      <td>10.109441</td>\n",
       "      <td>9.757752</td>\n",
       "      <td>9.423294</td>\n",
       "      <td>9.706758</td>\n",
       "      <td>9.218049</td>\n",
       "      <td>8.852007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-21</th>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.038431</td>\n",
       "      <td>-0.011132</td>\n",
       "      <td>-0.021899</td>\n",
       "      <td>0.009066</td>\n",
       "      <td>0.018383</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.036923</td>\n",
       "      <td>0.184615</td>\n",
       "      <td>1.190757</td>\n",
       "      <td>1.873829</td>\n",
       "      <td>2.972280</td>\n",
       "      <td>10.146364</td>\n",
       "      <td>9.876828</td>\n",
       "      <td>9.543293</td>\n",
       "      <td>9.808949</td>\n",
       "      <td>9.318218</td>\n",
       "      <td>8.943750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-22</th>\n",
       "      <td>-0.010791</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.038431</td>\n",
       "      <td>-0.011132</td>\n",
       "      <td>-0.021899</td>\n",
       "      <td>0.009066</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.110768</td>\n",
       "      <td>-0.018461</td>\n",
       "      <td>0.793838</td>\n",
       "      <td>1.735368</td>\n",
       "      <td>2.547668</td>\n",
       "      <td>10.142672</td>\n",
       "      <td>9.956212</td>\n",
       "      <td>9.658369</td>\n",
       "      <td>9.875646</td>\n",
       "      <td>9.399210</td>\n",
       "      <td>9.022837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-23</th>\n",
       "      <td>-0.001810</td>\n",
       "      <td>-0.010791</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.038431</td>\n",
       "      <td>-0.011132</td>\n",
       "      <td>-0.021899</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.018462</td>\n",
       "      <td>0.184613</td>\n",
       "      <td>0.959991</td>\n",
       "      <td>2.012288</td>\n",
       "      <td>2.363055</td>\n",
       "      <td>10.179595</td>\n",
       "      <td>10.052211</td>\n",
       "      <td>9.784521</td>\n",
       "      <td>9.928150</td>\n",
       "      <td>9.471161</td>\n",
       "      <td>9.095826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-27</th>\n",
       "      <td>0.005115</td>\n",
       "      <td>-0.023650</td>\n",
       "      <td>0.070903</td>\n",
       "      <td>0.101783</td>\n",
       "      <td>-0.083699</td>\n",
       "      <td>0.062108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.058166</td>\n",
       "      <td>0.775525</td>\n",
       "      <td>-1.066346</td>\n",
       "      <td>-2.035752</td>\n",
       "      <td>-4.032730</td>\n",
       "      <td>10.989191</td>\n",
       "      <td>10.900006</td>\n",
       "      <td>11.546277</td>\n",
       "      <td>11.355296</td>\n",
       "      <td>11.932272</td>\n",
       "      <td>12.359207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-30</th>\n",
       "      <td>-0.041673</td>\n",
       "      <td>0.005115</td>\n",
       "      <td>-0.023650</td>\n",
       "      <td>0.070903</td>\n",
       "      <td>0.101783</td>\n",
       "      <td>-0.083699</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.465317</td>\n",
       "      <td>1.163286</td>\n",
       "      <td>-0.639810</td>\n",
       "      <td>-2.888832</td>\n",
       "      <td>-4.381719</td>\n",
       "      <td>11.221848</td>\n",
       "      <td>10.836025</td>\n",
       "      <td>11.406682</td>\n",
       "      <td>11.285231</td>\n",
       "      <td>11.841602</td>\n",
       "      <td>12.270188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>0.014085</td>\n",
       "      <td>-0.041673</td>\n",
       "      <td>0.005115</td>\n",
       "      <td>-0.023650</td>\n",
       "      <td>0.070903</td>\n",
       "      <td>0.101783</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.155106</td>\n",
       "      <td>0.271433</td>\n",
       "      <td>-0.329598</td>\n",
       "      <td>-2.578622</td>\n",
       "      <td>-3.955179</td>\n",
       "      <td>11.276135</td>\n",
       "      <td>10.803065</td>\n",
       "      <td>11.259332</td>\n",
       "      <td>11.252694</td>\n",
       "      <td>11.773275</td>\n",
       "      <td>12.196427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>-0.053873</td>\n",
       "      <td>0.014085</td>\n",
       "      <td>-0.041673</td>\n",
       "      <td>0.005115</td>\n",
       "      <td>-0.023650</td>\n",
       "      <td>0.070903</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.581643</td>\n",
       "      <td>-1.105122</td>\n",
       "      <td>0.058165</td>\n",
       "      <td>-3.160265</td>\n",
       "      <td>-3.877625</td>\n",
       "      <td>11.055110</td>\n",
       "      <td>10.808881</td>\n",
       "      <td>11.093887</td>\n",
       "      <td>11.128639</td>\n",
       "      <td>11.658284</td>\n",
       "      <td>12.090923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>0.003683</td>\n",
       "      <td>-0.053873</td>\n",
       "      <td>0.014085</td>\n",
       "      <td>-0.041673</td>\n",
       "      <td>0.005115</td>\n",
       "      <td>-0.023650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.038775</td>\n",
       "      <td>-0.794913</td>\n",
       "      <td>0.562255</td>\n",
       "      <td>-3.315371</td>\n",
       "      <td>-3.082713</td>\n",
       "      <td>10.896128</td>\n",
       "      <td>10.865107</td>\n",
       "      <td>10.969803</td>\n",
       "      <td>11.031723</td>\n",
       "      <td>11.557271</td>\n",
       "      <td>11.994437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>772 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Returns      rt_1      rt_2      rt_3      rt_4      rt_5  \\\n",
       "Date                                                                     \n",
       "2017-02-17 -0.011132 -0.021899  0.009066  0.018383  0.067139  0.007968   \n",
       "2017-02-20  0.038431 -0.011132 -0.021899  0.009066  0.018383  0.067139   \n",
       "2017-02-21  0.003584  0.038431 -0.011132 -0.021899  0.009066  0.018383   \n",
       "2017-02-22 -0.010791  0.003584  0.038431 -0.011132 -0.021899  0.009066   \n",
       "2017-02-23 -0.001810 -0.010791  0.003584  0.038431 -0.011132 -0.021899   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2020-03-27  0.005115 -0.023650  0.070903  0.101783 -0.083699  0.062108   \n",
       "2020-03-30 -0.041673  0.005115 -0.023650  0.070903  0.101783 -0.083699   \n",
       "2020-03-31  0.014085 -0.041673  0.005115 -0.023650  0.070903  0.101783   \n",
       "2020-04-01 -0.053873  0.014085 -0.041673  0.005115 -0.023650  0.070903   \n",
       "2020-04-02  0.003683 -0.053873  0.014085 -0.041673  0.005115 -0.023650   \n",
       "\n",
       "            return_sign  momentum_1d  momentum_5d  momentum_10d  momentum_20d  \\\n",
       "Date                                                                            \n",
       "2017-02-17         -1.0    -0.110768     0.590765      0.895376      1.707677   \n",
       "2017-02-20          1.0     0.387688     0.332304      0.812300      1.873828   \n",
       "2017-02-21          1.0     0.036923     0.184615      1.190757      1.873829   \n",
       "2017-02-22         -1.0    -0.110768    -0.018461      0.793838      1.735368   \n",
       "2017-02-23         -1.0    -0.018462     0.184613      0.959991      2.012288   \n",
       "...                 ...          ...          ...           ...           ...   \n",
       "2020-03-27          1.0     0.058166     0.775525     -1.066346     -2.035752   \n",
       "2020-03-30         -1.0    -0.465317     1.163286     -0.639810     -2.888832   \n",
       "2020-03-31          1.0     0.155106     0.271433     -0.329598     -2.578622   \n",
       "2020-04-01         -1.0    -0.581643    -1.105122      0.058165     -3.160265   \n",
       "2020-04-02          1.0     0.038775    -0.794913      0.562255     -3.315371   \n",
       "\n",
       "            momentum_50d      MA_5d     MA_10d     MA_15d     EMA_5d  \\\n",
       "Date                                                                   \n",
       "2017-02-17      2.833819  10.042980   9.676522   9.310064   9.591514   \n",
       "2017-02-20      3.166123  10.109441   9.757752   9.423294   9.706758   \n",
       "2017-02-21      2.972280  10.146364   9.876828   9.543293   9.808949   \n",
       "2017-02-22      2.547668  10.142672   9.956212   9.658369   9.875646   \n",
       "2017-02-23      2.363055  10.179595  10.052211   9.784521   9.928150   \n",
       "...                  ...        ...        ...        ...        ...   \n",
       "2020-03-27     -4.032730  10.989191  10.900006  11.546277  11.355296   \n",
       "2020-03-30     -4.381719  11.221848  10.836025  11.406682  11.285231   \n",
       "2020-03-31     -3.955179  11.276135  10.803065  11.259332  11.252694   \n",
       "2020-04-01     -3.877625  11.055110  10.808881  11.093887  11.128639   \n",
       "2020-04-02     -3.082713  10.896128  10.865107  10.969803  11.031723   \n",
       "\n",
       "              EMA_10d    EMA_15d  \n",
       "Date                              \n",
       "2017-02-17   9.111556   8.756609  \n",
       "2017-02-20   9.218049   8.852007  \n",
       "2017-02-21   9.318218   8.943750  \n",
       "2017-02-22   9.399210   9.022837  \n",
       "2017-02-23   9.471161   9.095826  \n",
       "...               ...        ...  \n",
       "2020-03-27  11.932272  12.359207  \n",
       "2020-03-30  11.841602  12.270188  \n",
       "2020-03-31  11.773275  12.196427  \n",
       "2020-04-01  11.658284  12.090923  \n",
       "2020-04-02  11.557271  11.994437  \n",
       "\n",
       "[772 rows x 18 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = data.drop(columns=\"price\",axis=1)\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature Correlation')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "corrmat = data2.drop(['Returns'],axis=1).corr()\n",
    "\n",
    "# Visualize feature correlation\n",
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "sns.heatmap(corrmat, annot=True, annot_kws={\"size\": 10}, fmt=\"0.2f\", linewidths=0.5, square=False, cbar=True, cmap='RdYlBu')\n",
    "ax.set_title('Feature Correlation', fontsize=12, color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt_1</th>\n",
       "      <th>rt_2</th>\n",
       "      <th>rt_3</th>\n",
       "      <th>rt_4</th>\n",
       "      <th>rt_5</th>\n",
       "      <th>momentum_1d</th>\n",
       "      <th>momentum_5d</th>\n",
       "      <th>momentum_10d</th>\n",
       "      <th>momentum_20d</th>\n",
       "      <th>momentum_50d</th>\n",
       "      <th>MA_5d</th>\n",
       "      <th>MA_10d</th>\n",
       "      <th>MA_15d</th>\n",
       "      <th>EMA_5d</th>\n",
       "      <th>EMA_10d</th>\n",
       "      <th>EMA_15d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-17</th>\n",
       "      <td>-0.021899</td>\n",
       "      <td>0.009066</td>\n",
       "      <td>0.018383</td>\n",
       "      <td>0.067139</td>\n",
       "      <td>0.007968</td>\n",
       "      <td>-0.110768</td>\n",
       "      <td>0.590765</td>\n",
       "      <td>0.895376</td>\n",
       "      <td>1.707677</td>\n",
       "      <td>2.833819</td>\n",
       "      <td>10.042980</td>\n",
       "      <td>9.676522</td>\n",
       "      <td>9.310064</td>\n",
       "      <td>9.591514</td>\n",
       "      <td>9.111556</td>\n",
       "      <td>8.756609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-20</th>\n",
       "      <td>-0.011132</td>\n",
       "      <td>-0.021899</td>\n",
       "      <td>0.009066</td>\n",
       "      <td>0.018383</td>\n",
       "      <td>0.067139</td>\n",
       "      <td>0.387688</td>\n",
       "      <td>0.332304</td>\n",
       "      <td>0.812300</td>\n",
       "      <td>1.873828</td>\n",
       "      <td>3.166123</td>\n",
       "      <td>10.109441</td>\n",
       "      <td>9.757752</td>\n",
       "      <td>9.423294</td>\n",
       "      <td>9.706758</td>\n",
       "      <td>9.218049</td>\n",
       "      <td>8.852007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-21</th>\n",
       "      <td>0.038431</td>\n",
       "      <td>-0.011132</td>\n",
       "      <td>-0.021899</td>\n",
       "      <td>0.009066</td>\n",
       "      <td>0.018383</td>\n",
       "      <td>0.036923</td>\n",
       "      <td>0.184615</td>\n",
       "      <td>1.190757</td>\n",
       "      <td>1.873829</td>\n",
       "      <td>2.972280</td>\n",
       "      <td>10.146364</td>\n",
       "      <td>9.876828</td>\n",
       "      <td>9.543293</td>\n",
       "      <td>9.808949</td>\n",
       "      <td>9.318218</td>\n",
       "      <td>8.943750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-22</th>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.038431</td>\n",
       "      <td>-0.011132</td>\n",
       "      <td>-0.021899</td>\n",
       "      <td>0.009066</td>\n",
       "      <td>-0.110768</td>\n",
       "      <td>-0.018461</td>\n",
       "      <td>0.793838</td>\n",
       "      <td>1.735368</td>\n",
       "      <td>2.547668</td>\n",
       "      <td>10.142672</td>\n",
       "      <td>9.956212</td>\n",
       "      <td>9.658369</td>\n",
       "      <td>9.875646</td>\n",
       "      <td>9.399210</td>\n",
       "      <td>9.022837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-23</th>\n",
       "      <td>-0.010791</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.038431</td>\n",
       "      <td>-0.011132</td>\n",
       "      <td>-0.021899</td>\n",
       "      <td>-0.018462</td>\n",
       "      <td>0.184613</td>\n",
       "      <td>0.959991</td>\n",
       "      <td>2.012288</td>\n",
       "      <td>2.363055</td>\n",
       "      <td>10.179595</td>\n",
       "      <td>10.052211</td>\n",
       "      <td>9.784521</td>\n",
       "      <td>9.928150</td>\n",
       "      <td>9.471161</td>\n",
       "      <td>9.095826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                rt_1      rt_2      rt_3      rt_4      rt_5  momentum_1d  \\\n",
       "Date                                                                        \n",
       "2017-02-17 -0.021899  0.009066  0.018383  0.067139  0.007968    -0.110768   \n",
       "2017-02-20 -0.011132 -0.021899  0.009066  0.018383  0.067139     0.387688   \n",
       "2017-02-21  0.038431 -0.011132 -0.021899  0.009066  0.018383     0.036923   \n",
       "2017-02-22  0.003584  0.038431 -0.011132 -0.021899  0.009066    -0.110768   \n",
       "2017-02-23 -0.010791  0.003584  0.038431 -0.011132 -0.021899    -0.018462   \n",
       "\n",
       "            momentum_5d  momentum_10d  momentum_20d  momentum_50d      MA_5d  \\\n",
       "Date                                                                           \n",
       "2017-02-17     0.590765      0.895376      1.707677      2.833819  10.042980   \n",
       "2017-02-20     0.332304      0.812300      1.873828      3.166123  10.109441   \n",
       "2017-02-21     0.184615      1.190757      1.873829      2.972280  10.146364   \n",
       "2017-02-22    -0.018461      0.793838      1.735368      2.547668  10.142672   \n",
       "2017-02-23     0.184613      0.959991      2.012288      2.363055  10.179595   \n",
       "\n",
       "               MA_10d    MA_15d    EMA_5d   EMA_10d   EMA_15d  \n",
       "Date                                                           \n",
       "2017-02-17   9.676522  9.310064  9.591514  9.111556  8.756609  \n",
       "2017-02-20   9.757752  9.423294  9.706758  9.218049  8.852007  \n",
       "2017-02-21   9.876828  9.543293  9.808949  9.318218  8.943750  \n",
       "2017-02-22   9.956212  9.658369  9.875646  9.399210  9.022837  \n",
       "2017-02-23  10.052211  9.784521  9.928150  9.471161  9.095826  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3 = data2.drop(['return_sign','Returns'],axis=1)\n",
    "data3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train and Test Size 617, 155\n"
     ]
    }
   ],
   "source": [
    "# Splitting the datasets into training and testing data.\n",
    "# Always keep shuffle = False for financial time series\n",
    "X_train, X_test, y_train, y_test = train_test_split(data3, data2['return_sign'], test_size=0.2, random_state=0, shuffle=False)\n",
    "\n",
    "# Output the train and test data size\n",
    "print(f\"Train and Test Size {len(X_train)}, {len(X_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scalar', StandardScaler()),\n",
       "                ('regressor',\n",
       "                 LogisticRegression(max_iter=100000, penalty='none',\n",
       "                                    solver='saga'))])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic classifier without penalty term\n",
    "original =  Pipeline([\n",
    "    ('scalar', StandardScaler()), # Standarize the training set\n",
    "    ('regressor', linear_model.LogisticRegression(solver='saga', penalty='none', max_iter=100000))\n",
    "])\n",
    "\n",
    "original.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scalar', StandardScaler()),\n",
       "                ('regressor',\n",
       "                 LogisticRegression(max_iter=100000, penalty='l1',\n",
       "                                    solver='saga'))])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lasso Logistic Regression\n",
    "lasso = Pipeline([\n",
    "    ('scalar', StandardScaler()),\n",
    "    ('regressor', linear_model.LogisticRegression(solver='saga', penalty='l1', max_iter=100000))\n",
    "])\n",
    "\n",
    "lasso.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scalar', StandardScaler()),\n",
       "                ('regressor',\n",
       "                 LogisticRegression(max_iter=100000, solver='saga'))])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ridge Logistic Regression\n",
    "ridge = Pipeline([\n",
    "    ('scalar', StandardScaler()),\n",
    "    ('regressor', linear_model.LogisticRegression(solver='saga', penalty='l2', max_iter=100000))\n",
    "])\n",
    "\n",
    "ridge.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.57310255, -0.33841461,  0.15160786,  0.23806262,  0.09446389,\n",
       "        52.90661194, -0.34926408,  0.31311821,  0.45307652,  1.86478302,\n",
       "        -4.77195538, -5.2879333 ,  0.91791203, -1.74109945,  3.73569932,\n",
       "         6.60958321]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original['regressor'].coef_ # Model coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4.06943471e-03, -4.50223381e-02,  9.65514339e-02,\n",
       "         0.00000000e+00,  0.00000000e+00,  2.23450534e+01,\n",
       "         0.00000000e+00,  0.00000000e+00, -1.35240107e-01,\n",
       "         2.70393883e-01,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso['regressor'].coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4.65345398e-01, -3.86444395e-01, -3.42763877e-01,\n",
       "        -3.72408733e-01, -9.71560001e-02,  7.46322170e+00,\n",
       "         1.11537216e+00, -1.90963545e-01,  2.67312609e-03,\n",
       "         2.09338716e-01, -2.41261801e-01, -1.08298863e-01,\n",
       "         6.74116123e-02, -3.27941453e-02,  1.85250205e-01,\n",
       "         2.67634963e-01]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge['regressor'].coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['rt_1', 'rt_2', 'rt_3', 'rt_4', 'rt_5', 'momentum_1d', 'momentum_5d',\n",
       "       'momentum_10d', 'momentum_20d', 'momentum_50d', 'MA_5d', 'MA_10d',\n",
       "       'MA_15d', 'EMA_5d', 'EMA_10d', 'EMA_15d'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.columns # Names of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt_1</th>\n",
       "      <th>rt_2</th>\n",
       "      <th>rt_3</th>\n",
       "      <th>rt_4</th>\n",
       "      <th>rt_5</th>\n",
       "      <th>momentum_1d</th>\n",
       "      <th>momentum_5d</th>\n",
       "      <th>momentum_10d</th>\n",
       "      <th>momentum_20d</th>\n",
       "      <th>momentum_50d</th>\n",
       "      <th>MA_5d</th>\n",
       "      <th>MA_10d</th>\n",
       "      <th>MA_15d</th>\n",
       "      <th>EMA_5d</th>\n",
       "      <th>EMA_10d</th>\n",
       "      <th>EMA_15d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Without penalty</th>\n",
       "      <td>-0.573103</td>\n",
       "      <td>-0.338415</td>\n",
       "      <td>0.151608</td>\n",
       "      <td>0.238063</td>\n",
       "      <td>0.094464</td>\n",
       "      <td>52.906612</td>\n",
       "      <td>-0.349264</td>\n",
       "      <td>0.313118</td>\n",
       "      <td>0.453077</td>\n",
       "      <td>1.864783</td>\n",
       "      <td>-4.771955</td>\n",
       "      <td>-5.287933</td>\n",
       "      <td>0.917912</td>\n",
       "      <td>-1.741099</td>\n",
       "      <td>3.735699</td>\n",
       "      <td>6.609583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso</th>\n",
       "      <td>-0.004069</td>\n",
       "      <td>-0.045022</td>\n",
       "      <td>0.096551</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.345053</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.135240</td>\n",
       "      <td>0.270394</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>-0.465345</td>\n",
       "      <td>-0.386444</td>\n",
       "      <td>-0.342764</td>\n",
       "      <td>-0.372409</td>\n",
       "      <td>-0.097156</td>\n",
       "      <td>7.463222</td>\n",
       "      <td>1.115372</td>\n",
       "      <td>-0.190964</td>\n",
       "      <td>0.002673</td>\n",
       "      <td>0.209339</td>\n",
       "      <td>-0.241262</td>\n",
       "      <td>-0.108299</td>\n",
       "      <td>0.067412</td>\n",
       "      <td>-0.032794</td>\n",
       "      <td>0.185250</td>\n",
       "      <td>0.267635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     rt_1      rt_2      rt_3      rt_4      rt_5  \\\n",
       "Without penalty -0.573103 -0.338415  0.151608  0.238063  0.094464   \n",
       "Lasso           -0.004069 -0.045022  0.096551  0.000000  0.000000   \n",
       "Ridge           -0.465345 -0.386444 -0.342764 -0.372409 -0.097156   \n",
       "\n",
       "                 momentum_1d  momentum_5d  momentum_10d  momentum_20d  \\\n",
       "Without penalty    52.906612    -0.349264      0.313118      0.453077   \n",
       "Lasso              22.345053     0.000000      0.000000     -0.135240   \n",
       "Ridge               7.463222     1.115372     -0.190964      0.002673   \n",
       "\n",
       "                 momentum_50d     MA_5d    MA_10d    MA_15d    EMA_5d  \\\n",
       "Without penalty      1.864783 -4.771955 -5.287933  0.917912 -1.741099   \n",
       "Lasso                0.270394  0.000000  0.000000  0.000000  0.000000   \n",
       "Ridge                0.209339 -0.241262 -0.108299  0.067412 -0.032794   \n",
       "\n",
       "                  EMA_10d   EMA_15d  \n",
       "Without penalty  3.735699  6.609583  \n",
       "Lasso            0.000000  0.000000  \n",
       "Ridge            0.185250  0.267635  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_coef = pd.DataFrame(original['regressor'].coef_.tolist(), index=['Without penalty'], columns=data3.columns)\n",
    "lasso_coef = pd.DataFrame(lasso['regressor'].coef_.tolist(), index=['Lasso'], columns=data3.columns)\n",
    "ridge_coef = pd.DataFrame(ridge['regressor'].coef_.tolist(), index=['Ridge'], columns=data3.columns)\n",
    "\n",
    "# Compare coefficients of 3 models\n",
    "df = pd.DataFrame(columns=data3.columns)\n",
    "df1 = df.append(original_coef, ignore_index=False)\n",
    "df2 = df1.append(lasso_coef, ignore_index=False)\n",
    "df3 = df2.append(ridge_coef, ignore_index=False)\n",
    "df3\n",
    "# Lasso shrinks a lot of coefficients to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data4 = data3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logistic_sigmoid(xb):\n",
    "    return (1 / (1 + np.exp(-xb)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Procedure RELIES X_Features, Y_Response variables to be existing\n",
    "def logistic_plot(FeatureName, FeatureCoefIdx, model):  \n",
    "    plt.clf() #clears the figure drawing space, nothing to do with classifier!\n",
    "    fig, ax = plt.subplots(figsize=(18,10))  #fig = plt.figure(figsize=(18,10))\n",
    "\n",
    "    X_min = data4[FeatureName].min(axis=0)\n",
    "    X_max = data4[FeatureName].max(axis=0)\n",
    "    # 2. Plot CALIBRATED sigmoid function -- with the correctly picked coeffient from Logistic Regression\n",
    "    X_Sim = np.linspace(X_min, X_max, 100) #fill in values for the range of Axe X\n",
    "    Y_Loss = logistic_sigmoid(X_Sim * model['regressor'].coef_[0,FeatureCoefIdx] + model['regressor'].intercept_) \n",
    "    # Y_Loss = logistic_sigmoid(X_Sim * logit.coef_[0,FeatureBetaIdx] + logit.intercept_).ravel() \n",
    "    ax.plot(X_Sim, Y_Loss, color='red', linewidth=3) # plot sigmoid in Red\n",
    "      \n",
    "    plt.ylabel('sigmoid', fontsize=22) # also ax.set_ylabel('Default Indicator')\n",
    "    plt.xlabel('Feature: ' + FeatureName, fontsize=22)\n",
    "    plt.xticks(np.linspace(X_min, X_max, num=10), fontsize=14)\n",
    "    plt.yticks([-1, 1], fontsize=14)\n",
    "    plt.ylim(-1.1, 1.1)\n",
    "    plt.xlim(X_min, X_max) #Axe X range\n",
    "    plt.legend(('Logistic Regression',),\n",
    "           loc=\"lower right\", fontsize=14)\n",
    "    #plt.show()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Feature: momentum_1d', ylabel='sigmoid'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_plot('momentum_1d', 5, lasso) # Logistic sigmoid of momentum_1d under lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Feature: momentum_50d', ylabel='sigmoid'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_plot('momentum_50d', 9, lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Feature: momentum_20d', ylabel='sigmoid'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_plot('momentum_20d', 8, lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Feature: momentum_1d', ylabel='sigmoid'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_plot('momentum_1d', 5, ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Feature: momentum_5d', ylabel='sigmoid'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_plot('momentum_5d', 6, ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Feature: EMA_15d', ylabel='sigmoid'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_plot('EMA_15d', 15, ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import VIF\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For each X, calculate VIF and save in dataframe\n",
    "def vif(X):\n",
    "    \n",
    "    # perform feature scaling\n",
    "    scaler = StandardScaler()\n",
    "    xs = scaler.fit_transform(X)\n",
    "    \n",
    "    # subsume into a dataframe\n",
    "    vif = pd.DataFrame()\n",
    "    vif[\"Features\"] = X.columns\n",
    "    vif[\"VIF Factor\"] = [variance_inflation_factor(xs, i) for i in range(xs.shape[1])]\n",
    "    \n",
    "    return vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF Factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rt_1</td>\n",
       "      <td>6.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rt_2</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rt_3</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rt_4</td>\n",
       "      <td>7.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rt_5</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>momentum_1d</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>momentum_5d</td>\n",
       "      <td>25.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>momentum_10d</td>\n",
       "      <td>16.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>momentum_20d</td>\n",
       "      <td>16.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>momentum_50d</td>\n",
       "      <td>11.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MA_5d</td>\n",
       "      <td>21406.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MA_10d</td>\n",
       "      <td>5790.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MA_15d</td>\n",
       "      <td>6854.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>EMA_5d</td>\n",
       "      <td>147892.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>EMA_10d</td>\n",
       "      <td>69064.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>EMA_15d</td>\n",
       "      <td>14332.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Features  VIF Factor\n",
       "0           rt_1        6.18\n",
       "1           rt_2        4.80\n",
       "2           rt_3        4.06\n",
       "3           rt_4        7.74\n",
       "4           rt_5        2.42\n",
       "5    momentum_1d        6.58\n",
       "6    momentum_5d       25.26\n",
       "7   momentum_10d       16.14\n",
       "8   momentum_20d       16.07\n",
       "9   momentum_50d       11.55\n",
       "10         MA_5d    21406.17\n",
       "11        MA_10d     5790.25\n",
       "12        MA_15d     6854.90\n",
       "13        EMA_5d   147892.47\n",
       "14       EMA_10d    69064.06\n",
       "15       EMA_15d    14332.67"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif(X_train).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF Factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rt_5</td>\n",
       "      <td>1.251785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>momentum_50d</td>\n",
       "      <td>1.765668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>momentum_20d</td>\n",
       "      <td>2.471240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>momentum_10d</td>\n",
       "      <td>3.328463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rt_2</td>\n",
       "      <td>3.921142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rt_4</td>\n",
       "      <td>3.933452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rt_1</td>\n",
       "      <td>3.946196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rt_3</td>\n",
       "      <td>3.949299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>momentum_1d</td>\n",
       "      <td>4.164979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>momentum_5d</td>\n",
       "      <td>18.376974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Features  VIF Factor\n",
       "4          rt_5    1.251785\n",
       "9  momentum_50d    1.765668\n",
       "8  momentum_20d    2.471240\n",
       "7  momentum_10d    3.328463\n",
       "1          rt_2    3.921142\n",
       "3          rt_4    3.933452\n",
       "0          rt_1    3.946196\n",
       "2          rt_3    3.949299\n",
       "5   momentum_1d    4.164979\n",
       "6   momentum_5d   18.376974"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop VIF score > 30\n",
    "newX = X_train.drop(['MA_5d', 'MA_10d', 'MA_15d', 'EMA_5d', 'EMA_10d', 'EMA_15d'],axis=1)\n",
    "# Scores in ascending values\n",
    "vif(newX).sort_values(by=\"VIF Factor\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_candidate = X_train[['rt_1', 'rt_2', 'rt_3', 'rt_4', 'rt_5', 'momentum_10d', 'momentum_20d', 'momentum_50d']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Selection\n",
    "from sklearn.feature_selection import f_regression, SelectKBest, SelectPercentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SelectKBest(k=6, score_func=<function f_regression at 0x0000017C3D309940>)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SelectKBest\n",
    "# selector1 = SelectPercentile(f_regression, percentile=25)\n",
    "method2 =  SelectKBest(f_regression, k=6) \n",
    "\n",
    "# Fit the model\n",
    "method2.fit(X_candidate,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-score: 0.006329 for feature rt_1\n",
      "F-score: 0.4136 for feature rt_2\n",
      "F-score: 0.451 for feature rt_3\n",
      "F-score: 0.8642 for feature rt_4\n",
      "F-score: 1.874 for feature rt_5\n",
      "F-score: 26.77 for feature momentum_10d\n",
      "F-score: 14.38 for feature momentum_20d\n",
      "F-score: 12.72 for feature momentum_50d\n"
     ]
    }
   ],
   "source": [
    "# Iterate the score\n",
    "for f, s in zip(X_candidate.columns, method2.scores_):\n",
    "    print(f'F-score: {s:0.4} for feature {f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter six features with highest score\n",
    "X_Kbest = X_train[['rt_5', 'momentum_10d', 'momentum_20d' ,'momentum_50d']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scalar', StandardScaler()),\n",
       "                ('regressor',\n",
       "                 LogisticRegression(max_iter=100000, penalty='l1',\n",
       "                                    solver='saga'))])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso2 = Pipeline([\n",
    "    ('scalar', StandardScaler()),\n",
    "    ('regressor', linear_model.LogisticRegression(solver='saga', penalty='l1', max_iter=100000))\n",
    "])\n",
    "\n",
    "lasso2.fit(X_Kbest, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scalar', StandardScaler()),\n",
       "                ('regressor',\n",
       "                 LogisticRegression(max_iter=100000, solver='saga'))])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge2 = Pipeline([\n",
    "    ('scalar', StandardScaler()),\n",
    "    ('regressor', linear_model.LogisticRegression(solver='saga', penalty='l2', max_iter=100000))\n",
    "])\n",
    "\n",
    "ridge2.fit(X_Kbest, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Feature: rt_5', ylabel='sigmoid'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_plot('rt_5',0,ridge2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Feature: momentum_10d', ylabel='sigmoid'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_plot('momentum_10d',1,ridge2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Feature: momentum_20d', ylabel='sigmoid'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_plot('momentum_20d',2,ridge2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Feature: momentum_50d', ylabel='sigmoid'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EEwKgC4QbAADsvV6vado5XXnR9r7o98HYuCEjl16677sfHc3UqvG278XamQBjXTt1ZMXKZGxvFl4F4FAi3AAAYGcXXdQ27Tx9IMCYWT519ILzF7T7yZOXX7nyov/x+GrLpQIwb8INAIBDzeWXZ3Ri43Rg0YQWA70vzjxzQbufOv74TK49ZSa06K840jbuzFFHDelEAKAh3AAAONhMTmZ065aZqSMDVRdjG9ZndMvmjExN7fPue0uXzlRatL0uZqaPrEvvuKsN8WQAYM+EGwAAXdPrZeSss2aWRx3sfbFhfcY2TWTk8sv3ffc7Ne08JZPrZjXtPOkkTTsBOKAINwAADkAjF5zfrjgyEFq0DTvH1q/PyMUX7fO+eyMjmVq56sr9Lta1y6Zq2glAxwg3AAD2h8suy9jEhoyuX5+csy1H/7i2AUZbjXH22Qva/dRJJw007JyZNjK5dl2mVq9JDj98SCcCAPufcAMAYDFMTmZ0y+aBaSOn77ziyNYtGen1pjdfOs/dTx1z7EzVRX/aSD/EWLM2OeaY4Z4PABzAhBsAAPui18vI9u3TlRZjG9ZntJ0yMrbh9IxumsjIjh37vvvDD29WGRkMLdbNTCPpnXCivhcA0BJuAADswsj55w30vbhy74uRiy/e5333RkcztWo8k2vX5fDrXjsXnbyqnTZySqbWrcvU8hXJ6OgQzwYADl7CDQDg0HXppU2Dzg2n7xRi9HtfjJ577oJ2P3XSskyu20Xfi/HV030vli07Nhdvv2AIJwQAhybhBgBw8LriioxummgDjPUZ3XB6O22kDTC2bV3Q7qeOPW6n1UYm1/VXHDml6XuxdL6dNACAfSHcAAC6q9fLyBlnzPS9WD8wbWTD+qbvxeTkvu/+iCMG+l60vS8G+14cf4K+FwBwABBuAAAHtJFzz2mCiv60kY3rd+57cckl+7zv3uhopsZXDyyZum7nvhcnL9f3AgA6QLgBAOxfF13UTBvphxaD00Y2rM/o+ectaPeTJy+ftWTqzHKpU+OrkyVLhnQiAMD+ItwAABbX5ZdndGLjzIojGzc0vS/aIGP0zO0L2v3U8cc3lRZtYDG5dl2m1q2b6Xtx1FFDOhEA4EAl3AAAFmZyMqNbt7RTR06f6XfRb+K5ZXNGpqb2efe9o46aNW1kZtWRqXXr0jvuakM8GQCgi4QbAMDu9XoZ2b69adq5ccNMv4v17XKpmyYysmPHvu/+sMMytXrNTLPOtvqi3/uit2yZpp0AwG4JNwCApmnnxg27btp58cX7vO/eyEimVq7aObRYd8p0H4yplauSsbEhng0AcKgRbgDAoaDftHPD6TNNOweqMBbatHPqpJMGpo6csnPvi/E1yRFHDOlEAACuTLgBAAeDSy/N2C9/sVO1RfNxO5XkzDMXtPupY48bWCa1v+rIukyuaZp45phjhnQiAADzJ9wAgC7YsSOjmzcNNOscWDJ144Zk65acuIDd9446aqbaYs3a6aadzaoj69K72vH6XgAAByzhBgAcCAZXHJldfbFxQ0Y3b8rI5OQ+7763ZEkmV6/J1Jp1mnYCAAcd4QYAXBWmpjJ6xraMbtgwM1Vk44a298X6Ba84ktHRTK4an1kudbrnRdP/YmrFSk07AYCDlnADAIah18vImWfOWi51w8yqIxMbM3LZZQs6xOSKlTPBxbp1TRVG+/nVb3L9nH3upUM6GQCAbhFuAMDe6PUycvbZTVgxWHGxccPMcqmXXLKgQ0ydtCyT65qwYnrFkTVrZ1YcOfLIXT95yZIkwg0A4NAk3ACApAkvzj2nrbrY0E4bGex9sSGjF124oENMnXBCJtee0vS76E8bWds271yzNlm6dEgnAwBwaBFuAHDIGDnv3OngYnq6yMYNGdvQ9L8YveD8Be1/5+VS186sOrJmbabWrk3v2OOGdCYAAAwSbgBw0Bg579yMbty4c3gxXYWxIaPnn7eg/U8dfUwbXrTTRdauy+SafvVFu1wqAABXOeEGAJ1xpcqLtupiWOFFb+nS6QadzdSRtgJj7bpMrl6b3oknWi4VAOAAJNwA4MDQ62XkvHMHel604UVbiTGU8OKoo2aadPbDi3Xrpj/uXf3qwgsAgA4SbgBw1dhVw85+z4uJjQvuedE78sgmuFi9ZqbXRbv6yOSademddJLwAgDgICTcAGA4er2MnHVWU3ExsbGZLjKxoa2+GM5qI3NVXky1/S+EFwAAhy7hBgB7Z2oqo9vPmAkr+tNFJtoeGBMbM3LxxQs6RG/p0iaoWL1GeAEAwF4TbgDQmJxMJiZy2Pd+krE2sGgqMNreF5smMnLZZQs6RG/p0TMrjaxeM9Ows51GoucFAAD7QrgBcKi4/PKMbt6UsYmN09UXYxMbp6eQjG6eSK64Iics4BBTxxzbVFz0A4vVzRKpU2vXWm0EAIBFI9wAOFhcfHHGNk3sHFwM3I5u3ZKRXm9Bh5g6/vhMru5XWgz0vVizJpNr1qZ3teOFFwAAXOWEGwAdMXLeuU2fi4mNbZ+LwY83ZPTMMxd+kGXLsmP1mkytbvpeNBUY/Qaea9I79riFHwMAAIZMuAFwIBhs1rlpog0x2p4XGzcOZ5nUkZFMLV/RVlusmQ4tpj8eX51lp6zIudsvGNJJAQDAVUO4AXBVGOx3MbFx5nZjW3mxaSIjl1++oEP0DjssU6tWt2HFmpkVR1Y3U0amVo0nRxwxpBMCAIADh3ADYAhGLrxgoNpiYqepI6ObJobS76J31FFNYNE26pxa0wQY/Y+nVqxMxsaGdEYAANAdwg2APelPGZmuuJhoQoxNEzMfn3vuwg8z3axzdVt1sa69bQIMy6QCAMDchBsAl16asc1tn4tNEzMhxqaJplHn5k0LnzLS73exek0zbWR8zU7BxdSaNekdc+yQTggAAA4twg3g4NbrZeSsszK2qa242LRxJsTY1Pa8OHP7wg9zxBGZHF/drjKyeqbnRf921Xhy+OFDOCEAAGA24QbQbZde2jTq3DTRVFr0Ky76t5smMnLJJQs+zNQJJ2RyvB9WzAoxxtekt2xZMjo6hBMCAADmS7gBHLh6vYyceebOVRcTEzNVFxMTGd1+xsIPMzaWqZWrmgqL8cGqi9XNlJHxcVNGAADgACbcAPafiy7K2OZNTY+L/m1bgTG6aSJjmzdl5NJLF3yYqWOObXpb9IOL8ZmKi6k1azK1fEVymB+HAADQVV7NA4vjiisyunVLRjdtaiou+rebNzXVF5snMnr22Qs+TL/qYmrV+EyPi/HVTdXFeFN90TvuakM4IQAA4EAl3ADmb7BJ56ZNGd080UwR2TyRbNuSE9dvyOjWLRmZmlrwoaaudnymxlc3/S3GZwKL/q2qCwAAwDsC4EpGLjh/JrTYtGm6Mefo5k17NV1kbC+P01uyZKbiYnx1JsfH2yVSV2dqfI1eFwAAwF4RbsCh5pJLMrZlUxNetEHFdJDRThkZveD8oRxq8uTlmeoHFuPjbYDRVGBMja/O1LKTrTACAAAsmHADDiaXX57RrVvawGKi6XOxuV9x0X581llDOdTUcVfL1Ph4JleNT1dZTK4az3E3uG7OOubqmVq5KjniiKEcCwAAYHeEG9AVgw06t2zaedpI//PtZ2Sk11vwoXpHHNGGFqtnpousWt0EGP3pIsceN/eTlx2bqe0XLHgMAAAAe0u4AQeCfnCxeXNbabG5nSbS3I5u3pzRM7YNpUHnTquLtKFFv9dFU32xOr2rXz0ZGRnCiQEAACw+4QYsth07ZoKLfoXFlk1NcNH/fFjBxchIppavaPpcrJwVXvQrMU5enoztbctPAACAA59wAxbisssyumVzxrZsbvpatIHFdHDRr7gYxlSRkZFMLTv5SsHF1KpVmWynjEwtX5EsWTKEEwMAAOgO4QbsykUXNaFFG1xMBxhbNs9UYZx55tAON3ny8kytWtVUWrS308HFqlWZWrEyOfzwoR0PAADgYCHc4NDT62Xk3HMyumVLE1Bs2dKEFv1VRtrwYvS8c4dzuJGRTM0OLlaON/0tVo4LLgAAABZIuMHBZXIyo2dsmwkotm7O2EB4Mbp5U8a2bsnIJZcM5XC90dFMrVg506CzH1z0Ky5WrmyCC1NFAAAAFo1wg+64+OKMbd3cVFrMDi/6FRjbtg6lMWeS9A4/PFMrVrVTRFZlakVzO7lyvAktxldnatnJyWG+jQAAAPYn78rY/6amMnLmmTPBxdYtyXln5phfnd70udi6pbl/SNNEkqS39OimIed0YLGqqb5YuaqZLrJiVbMc6ujo0I4JAADA4hBusLguumin0KK57VdbbM7otq1NtcWOHVd66lH7eMipk07K5IpVTXVFv8piZRtgrGo+7x17XDIysrBzAwAA4IAg3GDf7NiR0e1n7BxYbN3aBBZbt2a0H2hccP7QDtlbsmS6v8XkylUDvS5WzYQZK1YmRxwxtGMCAABw4BNusLNeLyNnn92sHLK1DSr6gcW2LTMVGNvPyEivN7TDTh1/fBNULF+RyZWrctS1TskFx1296XWxcpVpIgAAAOyScONQ0etl5ILz26qKNqBoKyzG+vf1p4hcfvnwDnv44U2FxYqVmVyxsq2umKmymGwfy9KlOz3vqGXH5tLtFwxtHAAAABy8hBsHgZELL9g5tNi2rb1tAoyxNrgY1vKnfVMnLWunh6xoAosVK9qmnCszubyZMtI78US9LQAAAFhUwo0DVa/XhBbbtjUVFf1Ki21bp0OL0W1bM7Z1a0Yuvmioh5467mpNULF85UCFxawA4+TlyZIlQz0uAAAA7AvhxlWt18vIeefOhBbbtjZBxRkDH2/bmrFt24YeWvSWLs3k8hWZWr6iCS2Wr2ybcrYBRvtYjj56qMcFAACAxSTcGJapqYycdVYTUJyxNaPbtmVs286BxegZ2zJ6xraMXHrpUA/dO/LIJrBom3FOLV/eBhcrpvtdTK1Ykd4xx5oiAgAAwEFHuLEnl13WLHm6betMtUUbUux03/YzMjI5OdRD9448MlMnr8jUihVt480V059PrVjZBBorVqR33NWEFgAAAByyDs1wo9fLyPnnNcFEP6Q444yZ4GLbtoxub+8/55zhH37p0ZlcvrwNKJa3VRcDH7f3Cy0AAABgzw7ecONf/iVH/uL0mbDijJ3/jVx22dAPOXX88Zk6uQ0oTp4dXqyY/rh3zLFDPzYAAAAcqg7ecOMP/iDDiBB6o6OZWnbyTDhx8vL2tg0sTj55OszIkUcO4YgAAADAfBy84cYe9JYubYKKttJicvny9E5e3q4YsjxTy9oqi6tfPRkb29/DBQAAAHbh4A037nWvXHK1E9sA4+RMndyGF22gkWOO2d8jBAAAAIbg4A03PvvZXLj9gv09CgAAAGCRje7vAQAAAAAshHADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOO2x3D5ZSnr+AffdqrS9ZwPMBAAAA9mi34UaSFybpJRkZuK838PFIdtYbuL+XRLgBAAAALKo9hRvPm+O+hyU5Ncm3knw6yelJrkiyMsm9ktw1yf8keevQRgkAAACwC7sNN2qtLxv8vJTy4DTBxjNrra+Z4ylvKqX8WZK3J3nfsAYJAAAAsCvzbSj67CQ/2EWwkSSptf59kv9N8pcLGRgAAADA3phvuFGS/HwvtlufZN38hwMAAAAwP/MNN7YmuWUpZZfTWUopS5PcJsmGhQwMAAAAYG/MN9z4ZJqKjPeXUo6b/WApZVmSTyQ5Ocn7Fz48AAAAgN3b02ops70syT2SPDTJfUsp/5NkU/vYuiS3SnJEkq8med2wBgkAAACwK/Oq3Ki1npvktmmWee0luVOSP2n/3S7JJUlenuTutdbLhjpSAAAAgDnMt3IjtdazkzyplPKXSW6RZFX70ESS/621XjHE8QEAAADs1rzDjb5a6+VJ/nuIYwEAAACYt92GG6WUP2o//Jda64UDn++VWuuH9nlkAAAAAHthT5UbH0jTW+P6SX4+8PneEm4AAAAAi2pP4cb704QZ5836HAAAAOCAsNtwo9b6qN19DgAAALC/7XND0SQppYwkOSHJZbXWi4YzJAAAAIC9t0/hRinlrkmekeT3kxzR3ndJki8leUut9d+HNkIAAACA3Rid7xNKKc9L8rkkd0qyJMnWJNuTHJnkXkn+tZTygmEOEgAAAGBX5hVutBUbL0pydpJHJTmu1jpea12R5Pgkj0lyTpLnl1JuP9SRAgAAAMxhvtNSnp7kiiR3r7V+d/CBWuuFSd5bSvlhkm8meWqSLw9jkAAAAAC7Mt9pKbdM8vXZwcag9rGvJbnVQgYGAAAAsDfmG24cneTMvdjuzDTTVAAAAAAW1XzDjfVJfqeUMrarDUophyX5nSQbFzIwAAAAgL0x33Dj00lWJ/nbUsrI7Afb+17fbvPphQ8PAAAAYPfm21D0lUn+KMmTktyxlPKpJKcn6SW5ZpL7J7l+kk1JXjW8YQIAAADMbV7hRq317FLK7ZJ8LMlNk9wgTbCRJP1Kju8leWitdW96cwAAAAAsyHwrN1Jr/VWSm5dSbpvktklWpQk2Nif5aq31K8MdIgAAAMCuzTvc6Ku1fjXJV4c4FgAAAIB526dwo5RyfJoeG0fubrta63/vy/4BAAAA9ta8wo1SylFJ3pvkAdnzSiu9+e4fAAAAYL7mGz68JMmDk1yR5EdJzs1MQ1EAAACAq9x8w437Jbkgye/UWn+2COMBAAAAmJc9TS2ZbUWS/xJsAAAAAAeK+YYbp6VZ+hUAAADggDDfcOOVSW5ZSnnQYgwGAAAAYL7m1XOj1vpPpZS/TvKRUsrjkvwgyVm72f7lCxwfAAAAwG7NdynYayZ5SpKRJHds/821WspIe79wAwAAAFhU810t5fVJ1iZZn+TfkmyPpWABAACA/Wi+4cZt0wQbN6y1XrQI4wEAAACYl/k2FB1J8l3BBgAAAHCgmG+48fUkNymljCzGYAAAAADma77hxv9LsirJ35dSjluE8QAAAADMy3x7bjw0yX8neXSSPy6l/CzJ2Ul2zLFtr9Z6twWODwAAAGC35htuPGvg4yVJbrSbba2iAgAAACy6+YYbd1iUUQAAAADso3mFG7XWryzWQAAAAAD2xXwbigIAAAAcUOZVuVFK+eJebNZL02D0vCS/SvLPtdZv78PYAAAAAPZovj03btvejrT/5tKb9dgzSymvrLU+Z76DAwAAANiT+U5LOT7J/yW5JMnzklwvyZFJjkpy3SR/naZi45dJfjfJA5L8KMmzSin3Hs6QAQAAAGbMt3LjxUlumuROtdavznrsl0n+tpTy5STfTPKHtdZnl1K+luT0JE9I8tmFDRcAAABgZ/Ot3Hhwkq/NEWxMq7X+X5IvJ3lE+/mZSb6R5Gb7OEYAAACAXZpvuHFCknP2YrsLk5w08Pm5SY6b57EAAAAA9mi+4cYvk9yhlHLirjYopZyQ5PZppqL0rU2ydb6DAwAAANiT+YYb70nTVPTzpZQbzX6wlHLDJJ9LcrUk72/vu3uSWyb51oJGCgAAADCH+TYUfXOaqow/SPL9UsrpSTakCUnWtv9Gknw+yWtLKSuT/FuSqSRvHM6QAQAAAGbMq3Kj1jqZ5P5J/izJaUmukeR2SW6TZF2SXyV5cpJ71lp3JFmWpmLjobXWbw5x3AAAAABJ5l+5kVprL8m7k7y7lLIsyZokS5JM1Fo3zdr2h0l+dxgDBQAAAJjLvMONQbXW7Um2D2ksAAAAAPO223CjlPL/2g/fVms9Z+DzvVJrffk+jwwAAABgL+ypcuOlSXpJPpHknIHPB43M+rzX3tdLItwAAAAAFtWewo0Xpwkpzpz1OQAAAMABYbfhRq31hbv7fFApZSzJCbXWM3e1DQAAAMCwzWsp2CQppZxcSnl+KeWmA/c9PsnZSbaVUn5VSrnHMAcJAAAAsCvzCjdKKauT/CDJC5Lcsr3vFknenOTYJGcluUaSTw+GHwAAAACLZb6VG89OsjzJR5P8R3vf49I0EH1lrfXkJHdLMpbkmcMaJAAAAMCuzDfcuFuSXyV5eK319Pa++6RpMvqmJKm1/meS/05y2yGNEQAAAGCX5htujCf5Xq21lySllJslOTnJT2qtWwa225Lk6sMZIgAAAMCuzTfcOCfJ1QY+v2d7+4VZ252S5Px9HBMAAADAXtvtUrBzOC3JbUop10myLcmfpJmS8un+BqWUP0hyiySfG9YgAQAAAHZlvpUbb0hyeJqQY3OSayf5fq31y0lSSvlMkk8kmUry+qGNEgAAAGAX5hVu1Fr/LcmDkkykWSHlP5L84cAm65KcmeQBtdbZU1UAAAAAhm6+01JSa/2nJP+0i4fvn+TXtdapBY0KAAAAYC/NO9zYnVrrL4e5PwAAAIA9mW/PDQAAAIADinADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6LTD9vcA9lYpZSTJfyT5WK31Xft7PAAAAMCBoROVG6WU0SR/l+Qu+3ssAAAAwIHlgK/cKKWMJ/lAkmsmOXf/jgYAAAA40HShcuOmSX6V5OZJztvPYwEAAGAebn3rW+Q73/nWouz7SU/687zznW/d43Y7duzIP//zJ+f9vNne/e535Na3vsVO/+50p9/PIx/50HzlK1+c9/72t5e97IV58Yuft7+HMRQHfOVGrfWzST6bJKWU/TwaAAAADhQvf/lrcthhS/a43Re+8B/5h394d/7wDx8wr+fN5frXv0Fe+crXTX9+3nnn5oMf/Ie84AX/Lx/4wMezevWafdrv/vDUp/71/h7C0Oz3cKOUcmSS1bt4eFut9YJ93feyZcfu61M5ALh+3eXadZvr112uXbe5ft3m+nWXa3fVOP74pYvytb7WtXb1VnJnxxxzREZHR6bHsK9jOfroI3LUUUfkete7xk733/SmN8gtb3nL/PCH38lNb3rqPu17fziY/v/v93AjyS2SfG0Xjz06yfv2dcfbt+9zLsJ+tmzZsa5fR7l23eb6dZdr122uX7e5ft3l2l11zj334l1+rU877Yd5y1vemF/8oub440/Iwx72x3nAAx48/fhHP/rBfPjDH8jFF1+ce97z3vnVr36Ze9zj3nnkI/8oD3nIw3LjG98kf/7nT8i2bVvz6le/PD/60Q8yNjaW29zmdnna056Rn/3sJ3n2s5+dpJkN8PGP/0te9rIXTj8vST7xiY/kox/9cM499+xc//o3yF/91bOybt0pVxrrRRddlh07Jq90LpOTkxkbG8sll1wx/dinP/2pfOAD/5Bzzz071772dfOUp/xlrn/9GyRJLrvs0rz+9a/Jl770hRx11NI89rGPy2te84p85CP/lJUrV+XWt75FHvnIx+TTn/5krnWt6+aNb3xrfvCD7+fNb/7b/OpXv8qqVeN5xCMembvf/V5JsstzX7p06W4fe9nLXpjJyck8//kvSZJ84xtfy7vf/facfvrpWblyZR7zmL/IHe945yTNVJ5b3OKW+eEPf5Af/OD/Mj6+Oo9//JPzu79763n/f1iMUGW/99yotX691jqyi3/v29/jAwAAYHGcfvpv8pSnPD43ucnN8p73fDCPeczj8ra3/V2++MUvJEk+//nP5V3vekee/OSn5+1vf0+2bNmc73///+bc1+tf/+ocdthY3vWu9+f1r39LTjvth3n/+9+TG93ot/KUp/xVrn71k/LpT/97Tj55+U7P++xn/znvfOfb8ud//vi8970fyrJlJ+dZz/rL9Hq9vTqHSy+9NG9+8xty+eU78nu/17zR//rXv5p3vevtecpTnp73vOeDudWtfi9Pecrjc+aZZyZJ3vCG1+aHP/x+Xve6N+VFL3pFPvjB92dycnKn/X7961/JW9/6rjz1qX+Vs846M894xlNzl7vcI+9//0fy6Ec/Nm94w2vy9a9/dbfnvqfHBn33u9/Jc57zjNz97vfK+973odz3vvfLi170nPzkJ6dNb/OP//je3PnOd8273/2BrF17Sl75ypdeadz7y4FQuQEAAMA+OOqtb8rS17wioxddeJUdc+roY3LxM56dS57w5AXv6zOf+adc61rXzuMe98Qkydq163L66b/Jhz70/tzxjnfOpz718TzwgQ/Jne501yTJc57zotz//vecc19btmzJta997axcuSpLlizJy172moyMjGTJkiU55phjMjo6mqtf/aQrPe+f//lTeeADH5K73OXuSZKnP/1v8r73vSsXXXRRjjnmmCtt/+Mf/yh3ucttkiS9Xi+XX355rnvd6+W1r31jVq5clST50Ifen0c84pG5zW1unyR55CMfk//932/ns5/95zz4wX+Uf//3f82rXvX63PCGN06SPO1pz8hf/dXOX8/73vd+Wbv2lCTJ3//923Kzm908D37ww5Ikq1evyfr1p+fjH/9wbn3r2+7y3Hf3dZntk5/8WG572zvkwQ/+o+lr8ZOfnJYPfegf89KXvipJcqtb/V7uec/7JEke9ajH5lGPeli2b9+eFStWzHlNrkr7vXIDAACAfXPU2950lQYbSTJ60YU56m1vGsq+Tj/99NzgBjfc6b4b3ejGWb/+9CTJr371i1zvejM9LI477risXbtuzn099rGPy5e+9F+5173unOc+92/yy1/+fJfb7jyGX6eU609/fswxx+RJT3ranMFGklznOiXvfe+H8u53fyCPf/yTc/TRx+ShD314bnazW0xvs379b/KOd7w1d7nLbab//fCH38/GjeuzYcPp2bFjx07ndcMb3uhKx1mxYtVO+/vmN/97p/194APvy8aNG/Z47nv7dVm//jc59dQb7HTfDW/4W1m//jfTn4+Pz/Q4Ofroo5Mkk5NXzPl1uqqp3AAAAOioSx7/5P1SuXHJ4xdetZEkhx9++JXum5ycmp7qMDY2lmTn6SG7mi5ym9vcPp/85L/ma1/7cr75zf/OK17x4nz729/Mc57zwt2OYb6rphx++OHTK6KsXbsuF198SV7ykudn5crx6aBmcnIyT3rS03LLW95qp+ceddRROfvss/pnMnBOcx+nb3JyMne+893yqEc9dqdtRkebeoXdnfvefl3muhZTU5OZmpqa/nyur9XeTt9ZbJ2q3Ki1nlJrfdf+HgcAAMCB4JInPDln/WZztp9x/lX276zfbB7KlJQkOeWUa+THPz5tp/t+/OMfTlcWXOMa18zPfvbT6ccuuujCTExMzLmvd77zrdm+fVvue9/75eUvf02e+czn5r/+6z+TZM5pGH1r1qzJL35Rpz+/5JJLct/73i2//vUv9+oc/uiP/jjXvOa18qpXvSRXXHFFu891OeOMbVm9es30v4985AP53ve+m/HxNVmyZElqnTmvwY/nHuO6bNy4Yaf9fetb/5PPfOaf93juu3ts0Lp118hPfvLjne477bQf7VX1y4GgU+EGAAAA3fOzn/003/zmf+/076KLLsz97vfA/PrXv8w73vGWbNiwPp/73GfzqU99fHq1lAc84CH51Kc+li9/+b9y+um/yStf+dJccsnFc4YV69f/Jq9//avz85//LBs2nJ4vf/mLKeV6SZqKiQsvvDAbNqyfDiD6HvSgh+UTn/hIvvzl/8rGjRvy2te+PMcdd7Wccso19+rcxsbG8vSn/01+/etf5VOf+niS5KEPfXg+8YmP5HOf+2w2bZrIe97zzvzrv/5L1q07JUuXLs0973mf/N3f/W1OO+1HOe20H+UNb3hNkl2HMPe//4Pyi1/8PG9/+5uzceOGfOlLX8jb3vZ3081Rd3fuu3ts0EMf+vB85StfzMc+9qFs3LghH/vYh/LVr34p97//g/bq67C/mZYCAADAonrHO958pfve9a7353rXOzWvec0b8+Y3vyEf+cgHsnz5ijzpSU/Lfe7zh0mSO9/5bpmY2JjXvvaVufzyy3Of+/xBVq4cz2GHXfmt7F//9bPzt3/76jz1qU/Ijh2X52Y3++284AUvS5Lc7Ga/nXXrTsmjHvWwvPWt797peXe96z2yffsZeeMbX5cLL7wgN7rRb+VVr/rb6Skfe+PGN75J7na3e+Q973lH7nznu+ZOd7przjnn7LznPX+fs87anrVrT8nLX/7aXOc6JUnyxCc+La997Svy9Kc/IUcffUwe8IAH5x3veMsup8isWLEyr3716/P2t785H/3oB3PiiVfPn/7p43K/+z1wj+e+u8cGXe96p+YFL3hZ3v3ut+dtb3tT1q5dlxe/+BX57d++1ZW2PRCNHCjzYxZBz5rV3WXN8e5y7brN9esu167bXL9uc/26y7U78H3ve9/NqlXjWb68WY3jiiuuyL3vfee8/OWvzd3udofOXr+vfvXLucUtbpmlS5cmSX760x/n8Y9/TL7wha/PGdwcbJYtO3bX84T20cH/VQMAAKCTvva1L+dHP/phnvGMZ2fp0qPz8Y9/OEuXHp0b3ODKq4t0yXvf+8584xtfzR//8aNz8cUX5S1veWNufevbHRLBxmLRcwMAAIAD0mMf+xdZu3Zdnv70J+ZRj3pY1q8/Pa973ZtyxBFH7O+hLcjzn//SbN26JY9+9MPztKc9MatWjedZz3re/h5Wp4mFAAAAOCAtXXp0nve8F+/vYQzdNa5xzbzxjW/b38M4qKjcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpwg0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADAAAA6DThBgAAANBpI71eb3+PAQAAAGCfqdwAAAAAOk24AQAAAHSacAMAAADoNOEGAAAA0GnCDQAAAKDThBsAAABApx22vwewL0opRyZ5dZKHpAlo/inJX9ZaL9zF9uuS/H2S30+yod32c3Ns94gkf1FrvfVijZ1GKeX/JXlikmOSfC7J42ut5+zhOddO8qMkx9Zarxi4/ylJnpxkZfv4X9Va/3uxxk6jlPLcJI+ttZ6ym23ulOSVSa6fZFOSV9da3z3HdtdO8sMk96y1fnlRBkxKKScneX2SuybpJflsmp+H5+5i+3sneUWSayepSZ49+LOzlPLzJNeZ9bSb1lq/P/TBk1LKSJKXJvmzJEuSvDvJM2utk3t43uFJvpfk47XWF7b3nZ5k3Rybv7fW+qdDHPYhr5RyRJLvJnlarfULe9j2xCQ/TXNd39fed1iS5yf5kyQnJvl2kifXWn+6mOM+1JVSrpfkzUluleSsJG+utb5mF9s+Oslzkown+b8kT6+1fnuO7Z6b5Hq11kcs2sBJKeWPknxw1t2frrX+4W6ec+skH6q1rp11/1OTPCXJSUn+I8mTaq1nDHfE9JVSHpXkvbt4eF2tdcOs7X8ryduT/Faan51/UWv9zsDjj0vyzDTX73+SPLHW+stFGDqtUsoJSd6U5J5JLknyj0meM9drlVLKHZO8Nsl1k3w/zc/O77SP9XZxiBfUWl+8q+N3tXLjBUnumOTeab5wt07yqrk2bF8MfjrNL6bfTvIPST5ZSrnGrO3ukOQdizhmWqWUpyX56ySPSXLbNP+h37SH56xJ80bsyFn3PyLJS5I8O8lNknw5yb+XUsaHPGwGlFKun+S5e9jmOmmu2T+luTYvTvKWUsp9Zm03kuRdSY5alMEy6ENJVie5S5qfnTdK8wb5Skoppyb5RJqfizdI8oEk/1xKuWb7+BFJrpkmNF458O+0xT2FQ9rTkzwqyYOS3C/Jw5I8Yy+e99wkp86677ez83X74ySXJ3nLkMZKpv8Y8+E030N74w1JTp5137OS/GmSP09z3SbS/J47ekjDZJZSypI0f3jZkOb31xOTPK+U8vA5tr1LkremCTdumORbST5XSjl21nYPS/LCRR04faemee0x+DPuUbvauJRyozS/70Zn3f9naQL+F6cJuSbTXNuuvn/qgo9m5+vWDww/OUewcXSa79NvJrl5kq8l+df+914p5W5p3jg/LcktklyY5j0hi+utaV5r3jbJI5I8Mslfzt6ofS/x70n+M831+5ck/1VKWd1usnLWv/+X5Jwk79vdwTtZuZHmRfm7BpKdtyV5wi62vUOSkuQ2tdYLkvyklHLnNG+sn9s+/wVp3hz/YrEHfqhrfyE8M81fpf69ve8ZSd5QShmttU7N8Zw/TPLOJFvm2OWjkry11vqJ9vNnl1IekOQ+aZJchqy9hu9O8p0ka3az6UOSfL/W+vL281+WUm6X5OFJPjOw3V8kGVuMsTKj/WVxpzR/NaztfU9N8rVSytJa68WznrImyZtqrW9uP39tW3F1qyS/TvNztZfkO7XWHVfJSfC0NH+x+EqSlFKemeaF9yt39YRSyo2TPDbJzwbvr7VuH9hmaZKXJ3lJrfW7wx/2oakNCD+UZGQvt79Hklsm2T7roUeluTafb7f78yRnJ7lNmheGDN94mgqZJ9ZaL0nz++sLSW6XK1cELE/zffnRJCmlvDBNEHmjJP/dVt68Kc11/NVVMnpOTfLDWuvWPW3Y/mX/tWl+r1191sNPSfLGWus/tNs+Jk24eJc0VRwMWfv9dkn/81LKk5KsTXLnOTZ/SJIdaSq2p0opT09yr/b+d6V5v/hftdZ/aff1wiQ/LKUsr7VuW9QTObTdM8kja62nJUkp5UNpihJmV749Icl3a63PbD9/dVvJ8cQ0lcLT37+llFVp3qs/YXbINVtXw42zkjyglPL+JFNJ7p+m5HMut0ryvTbY6Pt6mhcFfbdLU6Z9x8z9zcPwnJrmhcAn+3fUWv8rzYuAXblbmr+I/CLJl2Y99twks39A9TKrwoOhekqSi5N8JLuv3vhYmkR90E7Xpq3IeWGS2yf5yTAHyZWcl+aX/mCI20vzl6oj0lzTabXW/0j74q39K+aftNv9T7vJqUl+Ldi4arS/2Nck+erA3V9PsrqUsqbWunGO54wleU+aQPlxu9n9U9P8X3jt8EZMmtcZn0/zM+6i3W3Y/qXx7WkqaD406+E/T/Ljgc+n0gQmfs8tklrr6WneIPWrC38vzV8hnzjHth/of1xKOSpNsHFGZqrYjklyvSS/k+avl1197d0lpyb5+F5ue5c0v9+ulmba36BrZuZ3XmqtF5dSfpHkdyPcWHTtz8UXJHn+Lqau3yrJN/p/GK219kop30hzfd6V5v3iw9ug+edprvP69n4WT//r/vkkxye5e5pKqtl2+v5q/SDN9ZvtBWneJ8wOl6+kqz9g/zrJp5Kc2X7+4yR/uIttVybZPOu+bWnKZZIktdY7JtPzflhc10pyfpKbl1Jekeb6/HuaOVbnz/WEWuvjk6SUcvs5Hvvm4OellLunmeby1dnbsnDtlITnpPmFcofdbVtr/fms5y5P8tAkLxq4+x1pyrBVTS2yNuD9t1l3PzXJj3bX76add35amuqaZ9Vaf9M+dGqSyVLK55LcNE1Pjr+ptX5r6IMnaX5WJjv/PusHu6uTXCncSPO78sxa6z+2f528knZ60V8leV6t9dJhDZak1jo91bWUsqfNX53k32utX529ba31i7O2fWyanivfDFeFiSSr0kyz/MSuNmpL4PuB/sP7r2nankZ3aLdZ1IEy3WPoWknuXUp5cZoA/+Npqmsum719rfWB7fMeNcfudnq/0Faurk7Tv4HF97gkl6UJKuayMs1rj0Hb0kwlS5qKqTuleZ84mSZkvv1g3z4WxRPS9Nm4IM333xcz95S8nb6/Wusy6/urlLIyyaOT/EGtdVd9OKYdkOFGO0d19sn2bUuT9Eyk+QW/I8nfpuml8YdzbL80zTfGoMvS/AWSRbCH63dcmq/969K88L40yRuTvD+7Dqj29rjXbffz/lrr/y1kX4eqvfje+/s0TUF/1fap2dv9Hp0mkNycdrpQKeVP0rxgnLNBG/O3p+s3WMHWlm8+KE1l1O5sTTPP//eTvK6U8sta6yfTNIk9IU1VwKY0TS6/WEq5QftXT+ZpD9dvaXs7+Pus//GVfp+1Pw+fkWae8e48KE0VwD/s/UgZpna63n2yF305Sim/n+b35yv2puSeofiDNL+r3pamIfNTdrHdD5LcrN3+faWU38z+AwxXieukeX9zUZIHpgk63pjk2MxRebMHH0nyrLYa4Kdp/rhzcpLDhzZa5tRWTD0uzfTYXVWI7uk93oo0/dwelSbgeEqST5RSfrvWevbQB03ftdM0MX9Rmvd9b85M75NBH0nyb207gU+neT163zTv8Qc9Js2Uvr2ahnlAhhtpXox9bRePPTXJy5Lcvdb6jSQppTwwya9LKbeotf7vrO0vTVNqNuhKJdgM1e6u37PTlNI+vZ2O0m/Y9J1Syspa61x9NfaolHLDNOW/P8vuy6/Zvd1duz9PU172t/PZYSnlamn+4nXNJLduyzqXp/lBd89a6xXtnGQWbnfX79FpmzCVUv4qTaj05D2t3tD+1fF7Sb7Xfp89Oc20sj9KclT/r5OllCekCUD+JE3zNeZvd9fvb9rbI9KE+v2Pk1m/z9oXhe9O8rK9CJoekuQTc/Rc4SrQTmN4V5rvxfP2sO3t0zRc+0x2roBjEfVfV7a9af6hlPLXtdbL59hua5ow+PullN9N009KuHEVq7X+uJRyUq21P/XgB+3PxA+XUp46z7/avzTNX5L/L810sI+nqYCcs9KYobpZmmDqH3ezzaW5crg/+B7v7WlWyen3TPnTNJUefxrTMBdFKeVaaSqyT6m1TrT3PTbJf5ZSXjHY66TW+vm2l9sH0gSG/5cmCJk9k+Ihaf5wvceqjeQADTdqrV/PLhpwlVJumWb+4mkD2/+mlHJOmjdPs8ONTWmWBxq0InM3p2QI9nD9btt+ONjcrl9Stjb7cF1KKbdIM/fxR0nurbR63+3h2n0pTSf489rS2sOSHF5KuTDJqXM1+CmlnJQmdFqephSw30zt7mnKzr48q0z3c6WUlww0IWUednf9+kopL0qzrORTaq27XBmjbUR5TN15WeWfpFmdKu1fUqb/mtLOdf1ZmkZ87IM9fP+tSjN1YUWS/jJ2K9rb2T8316a5Tjcrpbykve+oJLcspfxOrfUe7T6PSPMi4v5DOwnm65Zp/sr1jwM/C5cmeXsp5Va11r9IklLKPdOEip9O8oi5mm8zPKVZce3m/UaErZ+keQF+XGamRacNMi6utf5g1rbXvSrGypUNBBt9P00zlWtZ5vE6sw19H95O61tSaz2nlPLtNKs7sLjukeRbtdbZrQUGbcrM78G+wfd4v52BP8i1f0z7QZr3iyyOmye5oB9stL6bZmrzuszqk1hrfXUp5fVJrl5r3VpKeXWS0/uPl6YZ/g3TVH/vlS4uZdT/T37j/h2llBVp/qI8Vxfqbya5Sdl5ybRbR5q+v3wvTcnYzQbuOzVNM7v1891Z2wPi39OkffestV44jEEyp0ekKZu+SfvvRWm+H2+SK/e16c97/WyaEOO2/RU6Wp9K88Kvv6+bt/c/Nla5WTSlWR3leUkeV2vd7fLLSR6cpgx70M3TvEhMKeXbpVmto7/v0TQ/l38Whq59gbchbbjUunWSzXM0E92UpjT7tzLzPfa9NN9bjx3Y7kZpKum+viiDZm98O821usnAv21pAsjnJ0kp5XfSBBsfT9PLwXzxxXf9JJ8qpQwuy3vzJNtrrWfO2vaJaZakz6xtf7qI42MXSin3L6Vsa1+D9N00yblpKmvms69XlVIeU2u9sA02Vrf7mt3cnuG7VZKv7GGbbyb5vbYyZ7D5b/893ubs/H5xJE1zX6sWLZ7NSY5vFwzou357++vBDUspDy2lvLnWuqMNNkaS3Ds7f3/dKs206tm9VXbpgKzc2J1a60Qp5V+TvKlNUi9NMwfya/0l7Eopy5Jc0r7R/UqaN83vK80SQPdO84V6zP4Y/6Gu1npBKeXtSd5YSjk3zZzItyX5p/784VnXb0/e0u7jcUmOK6Uc195/oaBjuGqtmwY/L6VsT3JFrfWXA/cNXrunp3mBd/ckF7UhZJJc3s51HOz/0P9ZtMk8yMVRSlmb5FVpvt/+ZeB6JM0L9slZ1+89SZ5eSnlZmuks90jTELbfxfqzSZ5RSvlRmhcKf5nkxPZ5LI63JXlFKWVDmuZor0gzlzzJlb7/fjn4xFLKpUnOnvV9fMMkp9dad7uSB8PXfv+dV9tlRmc9NpnkjFrrGe2LvfekmS/+rCTLBio8+s9n+L6Spvrife00vmulWXL5ZcmVrt9b0iyp/aQ0VaSPTPMHnIfvj4GTr6SpgHtnKeXlacLD1yR5TVthOHjt9mRTkheUUn6S5rXm36eZ5vDj3T+NIbhhko/OvnPW9ftEmu/LN5VS3pqm99dxaXo5JE2g/5z2dcqP0zS6XB49phbTN5N8P8l7Syl/maYS8R1J/rHWeuas6/ezNFP9vp7kO2laFxyddgp164bZebWwPepi5UbS/ML4dpJ/TvKFND987jfw+HfSNKtMrXUyTXOnk9OUxfxJkvtpeLdfPSPNX+7/KcmX00wnefTA49PXb3dKs0TU3dOUYP8qTRla/9+zhjpi9tbgtXtQmgD1C9n52vzL3E9lkd03zVzUJ2Tn67ElyTXabQZ/dv46zffXXZP8ME2A+MCBZr0vS/Oi/u1pfpFdN8md9tQ3gAV5TZplQj/Z/vtwdp43vFc/OwcsTyJM3D+2pF1qdA9ukKa68eZpXusMft9687xI2ml3905yRZJvZWZlr79rN5m+frXW/0nz++7xaV7P3DXJ3WaVZXMVaaek3C0zvTLemeb31CvaTfb2ey9pfsd9PM3rli+l+V33yCEOl13b1e+nwe+989Mscf97aa7176ep4u7/8exv07xWeW2a94A3STNFenb1FUPSVhbeK821+2Ka93tfyUw/xMHr9/00gdTL0zRkHk/zOnLwDy7zfp0y0uvtVW8OAAAAgANSVys3AAAAAJIINwAAAICOE24AAAAAnSbcAAAAADpNuAEAAAB0mnADABZRKWVkf4+Bq4ZrDQD7z2H7ewAAsBCllNsn+dJebv7oWuv7Fm80M0opV0vy4iTfSfKBq+KY7D+llLsk+askd9/fY9lXpZTbJPnqbjb5YK31EbOesyrJC5LcJcnKJBvS/H9/da31snkc+wtJ7pTkDrXWL89z6AAg3ADgoHFRkn/ewza/ugrG0feaJH+W5NFX4THZD9o3+J9Psn5/j2WBbtre/neS38zx+DcGPymlrE7yP0lWJ/lekv9L8vtpQr07llLuWmvdsXjDBYAZwg0ADhZnzv6r8n5m6ueh42C51v1w429qrd/Y7ZaNt6YJNp5Xa31pkpRSjk4TMt45yVOSvG4RxgkAV3Kw/DIGAGBhbppkKsn397RhKaUkuXeaaqiX9++vtV6U5DFJJpM8eVFGCQBzULkBwCGrlHK9JM9JM9f/6km2JPmXJC+ttZ4xx/Z3TfL4JL+T5KQklyb5WZL3J3lrrXWq3a438LT3llLem7aXQCnl9CTrkqyptU7M2v+70rwxnO4NUkp5X5JHJrldkr9O09vg/CTPHNhmr8+jlPLCND0SvlJrvf1efI2+3B772CRPase3JsnGJG+qtf5dKeXEJK9I8gdJlib5YZJn11q/Nsf+7pnkqUlumeSoND0aPpXkVbXWcwa2OyXN1IiPJnlWu/+7Jjk8TR+TZ9Rav1tKuV2SFyW5RZJzkvxn+9hZs457VJKnJ3lYkmunuXbfTPLKWutXZm37vjRf8xu143xykusluSDJf6SpVDh91tczSda11/4rtdbbl1IeleS9Sd5da33srGOsbr+G62utpwzc30vyrTTBwcvar+mxSU5L8txa63+WUm6U5JVJbt2ex9eT/GWtdZ+nxZRSDk9yapKftQHFntwtyUiSz/T/3/fVWjeUUv4vyW+XUk6ttf5k4DjXT/L8JHdIckyaaS1/s6/jBoA+lRsAHJLaoOK7SR6R5Iwkn0nzRvHJSf63lHLNWdv/TZo3tvdM8pM04cEvkvx2kjclee3A5h/MTH+P/2k/37bAIf99mn4Gn0vTX+T7+3IeC/DRJC9JEzh8Lck1k7yxlPLcND0a7p8mdPhVO84vllJuPLiDUsork/xrmhDm+0k+myYMeWaS77aBxmzXSPK/SW6fptnlpjRvjL9USvnzJP+VJtD5fJqw5NFJ/m3WcY9vx/yyJMuSfCFNj4g7t/t53C7O+SVJ3p2mCuFzaaoaHpHka6WUI9ttfpjkn9qPL0pzrf9zF/vbW8enCV4e3N7WNCHLv5VSHtPeV9Kc+2VpvvZfHRjTvrhhkiVJTi+lvLSU8tNSyiWllN+UUl7bfg0H3aC9PW0X+/tZe3uj/h2llFum+X54aJrr+Lkk109zba69gLEDgHADgENPKWVZko+kqQK4f631JrXWB6Z5o/XcNJUJHxjYflWSlyY5M8mptdY711ofWGu9eZIHtJs9rpSyJEna3h9fbu9/Z631EbXWny5w2MuT3KTWev8k1661fn++59F6c/v4n8zz+LdO8tu11rvWWu+S5Gnt/S9JUzFx3VrrfWqtN03yoTTVoX/af3Ip5b5pQowzktyi1nqHdqzXTPKuNCHGh+c47i3ThDfXrLXeL82b5W+lqWZ4R5pqhhu1X5cbtGO5ZSnlJgP7eFOSmyf5xyTXaMd5x3bfZyV5UynlBrmyeyW5b631Fu3+r5sm2Fqd5EFJUmv9VJreEknb96XW+rI9fC33pCS5MMm1aq1/2I79k2m+pu9K8r4kpR3TqUl+nmRtFrZSS7/fxj3TXNtfp6kIOSHNKjDfav+/9a1sb7fsYn/9+5cnSSlltB371ZI8rdZ681rrg9KEGp9PU80EAPtMuAHAwWJdKaW3m3+3H9j2MWnetL2p1tr/q3tqrb32jel3kvxuKeV324eWp5k68aJa604rrrRvbs9MU4Fw0uKdXv6p1rqxPWZ/GsB8zyO11jNrrT+rtW6Y5/HfWWv9/sDnHx34+FmDU0qSfKK9Hfxr/NPb26cN7qfWenmaqT6/SHKrdjnS2f6y1npJu/2ONNciad6Av3pgX9vSVAFMH7uUMp5mKsrmJI/r76fd/ntJXpimYqEfUAz6WK31MwPbn58mIEmaYGQxPafWenZ73F6Sj7X3X5hmStJk+9iFSf69fWwh1Q/9cOMraQKge7Uh1rXTVIhcN8nbB7Y/ur29eBf763+dj2lvfy9NMPU/tdY39jeqtV6a5v/xJQGABdBzA4CDxZ6Wgh2cFnKH9vZLu9j282mmm9wuzZux76UppZ/WVmlcJ03/jf7v08PnN+R5+cEc983rPBZ4/G/O+nz7wMffn/XYue3tkUlSSjkszZvbKzIzhWNarfWKUson0/TWuF1mAookuaDW+uNdHPuHs/s9zD52ktsmGUvyzcFgY8B/tLe3m+Oxb81x39b29ug5HhumXX29f9kGGoPObW8XMi3l6Un+LsmWWusF/TtrrWeWUv4kTXXI/UopK2utW9JM0UmS3pV3laTpxzF42//6fm72hrXWc0opX0vTUwUA9olwA4CDxXyWgl3T3v5Ls+jDHrfrv0F/SJqQ4wZppgGMtQ/33+CNZPGcvZvx7fV5DOv4tdZee8zJWut5s7ad/Yb36mmCn4n2L/Vz+U17u2LW/efM3nBg/2ft5rG+/rnff1aj19nm+hqdO8d9V7S3i1n92suVz3s+5zxvbUXMz3fx2Oa2QehtktwsTd+UfsBy1C522Q9a+s1JV7W3m3ax/enzGS8AzCbcAOBQ1A8lPp2ZN2lz+UGSlFKOTtND4xZp3qx9J80bvB+1938uTe+IYY1rLrMrFAa336vzWKAdC3huP/TZ3Zvw/rlcNsTjDu73R2maf+7KXGNbcGiwG7u91nNUpOxv/YqVpe3t5vZ2dhjVt6eeHLNdsedNAGDXhBsAHIq2pOkh8Npa69f3Yvu/ThNsfC7JQwbL9pPp1Tj2Vv9N61y/g+ezn2T+57G/nJXk8iQrSylH7qJ6ox8OLXRVmdn6b66/PXs51qvAMK/1oiql/F2a6pXHzbUMcpqGr0nSX764v0rKqbvY5fXb2x/Net6uGoeu3MX9ALBXNBQF4FD01fb2nnM9WEp5bynlO+0KH0nTVyNJ3jhHsHHzJCe2nw7+Xt3VX/37FRY7/cW7lDKWpj/GfMz3PPaLdsrD/6R5k3+/2Y+3U3769395yIfv9++441xLpZZS7llK+Vkp5a0LOMa8rnXrVgs43mL4/SR/mOQ+sx8opdwwTcPRs9KsXJPMNDG9b7sSyuD2a9vt19daf9Le/YWB7UdmbX9UmqV+AWCfCTcAOBS9M80qD39VStnpzXYp5VFJHpmZJUeTZGN7e59Z25bsvNTq4JvnfnXC1WYdu/+X7Cf33+S1bw5fnmR8kc8jpZSTSinXa9+AXpVe396+YXCZ1rYx61uTXCvJd2qt3x7mQdvVbT6TpvLg7aWU/rSKlFJOSfKWNEuv1gUcpn+tj531xr1/re9QSrnRwHFLkuct4HiL4R3t7ctLKdfr39ku//reNNNoXt2ubpNa62/SBBwlyYsHtj86zZKvY0le17+/1vqdJP+d5CZJXjzwf39JkrelWfUHAPaZaSkAHHJqrRPtm/8PJvlUKeW0NM0Ur53kxmmmE/xxu7Ro0rz5flSSJ7ZLyv40TRn976aZbvGbNG+eVyTp/6X6F+3tC9rlTV9fa/1GmhUpHpTkj5LctJTykzRNGlenWe7zwYt4HknypCQvSLPk5+339lgLVWv9dCnltWmm+PxvKeWraSoBbpXm3H+TZsnWxfBnaSpCHpnknqWU76R5DXS7JEekWcHlzQvY/9ntvxOTfL2U8p1a69Nqrb8opXwmTSj27VLKF9rj3T7N8qpHLOCYw/auJHdJ8sAkP2hXL7kozYo8x6b5v/m6Wc95YpJvJHlOKeUP0gREv5fme+NzaUKLQY9Ocx2em6bB64/TVCutSvK9zCxHCwDzpnIDgENSrfXjSW6Z5MNJTkpy7zR9ED6Z5Jbt4/1tf5DmjfDnk5yc5B5pVgD5YJKbJ3lTu+lgZcffp6nqOCzJ3ZPcsN3Xt9K8YfzPND0O7pomCPn97HpJ16Gcx/5Wa31GmqkPX04T6Nwryflp/vJ/s7bKYjGOuy3N1KIXJTkjyR3T9FD5fpLHJnlwrXVyAfvvJfnjNG/ub5Gd/x88NMnL0vT+uGua4OllSf4gyT4fc9jaBqYPTvIXaRqv/l6SO6UJ8v4syUNnf41qrb9O83/vfUmWpbme5yR5dpL711qvmLX9z9vt/z7N/9H7ZObr8n+Lc2YAHCpGer3FbAQOAAAAsLhUbgAAAACdpucGAMBBopRy/yT3n+fT3lFr/dqeNwOAA5dwAwDg4HHjJA+f53O+kJklcwGgk/TcAAAAADpNzw0AAACg04QbAAAAQKcJNwAAAIBOE24AAAAAnSbcAAAAADpNuAEAAAB02v8HwdSa8iRt85IAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_plot('momentum_50d',3,ridge2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Ktest = X_test[['rt_5', 'momentum_10d', 'momentum_20d' ,'momentum_50d']] # new test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict Probabilities\n",
    "probs = ridge2.predict_proba(X_Ktest)\n",
    "preds1 = probs[:, 0]\n",
    "preds2 = probs[:, 1]\n",
    "\n",
    "fpr1, tpr1, threshold1 = roc_curve(y_test, preds1, pos_label=-1)\n",
    "roc_auc1 = auc(fpr1, tpr1)\n",
    "\n",
    "fpr2, tpr2, threshold2 = roc_curve(y_test, preds2, pos_label=1)\n",
    "roc_auc2 = auc(fpr2, tpr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.legend.Legend at 0x17c3f6cbfa0>,\n",
       " <matplotlib.legend.Legend at 0x17c3f3eff70>)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curves\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2)\n",
    "\n",
    "ax[0].plot([0, 1], [0, 1], 'r--')\n",
    "ax[0].plot(fpr1, tpr1, 'cornflowerblue', label=f'AUC = {roc_auc1:0.2}')\n",
    "ax[0].set_title(\"ROC for Down Moves\")\n",
    "ax[0].set_xlabel('False Positive Rate')\n",
    "ax[0].set_ylabel('True Positive Rate')\n",
    "\n",
    "ax[1].plot([0, 1], [0, 1], 'r--')\n",
    "ax[1].plot(fpr2, tpr2, 'cornflowerblue', label=f'AUC = {roc_auc2:0.2}')\n",
    "ax[1].set_title(\"ROC for Up Moves\")\n",
    "ax[1].set_xlabel('False Positive Rate')\n",
    "ax[1].set_ylabel('True Positive Rate')\n",
    "\n",
    "# Define legend\n",
    "ax[0].legend(), ax[1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rog\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion matrix\n",
    "plot_confusion_matrix(ridge2, X_Ktest, y_test, cmap='Blues', values_format='.4g')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.grid(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54 26 45 30\n"
     ]
    }
   ],
   "source": [
    "tn, fp, fn, tp = confusion_matrix(y_test,ridge2.predict(X_Ktest)).ravel()\n",
    "print(tn, fp, fn, tp) # true negative, false positive, false negative, true positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.3.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.3.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cufflinks as cf\n",
    "cf.set_config_file(offline=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt_5</th>\n",
       "      <th>momentum_10d</th>\n",
       "      <th>momentum_20d</th>\n",
       "      <th>momentum_50d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-21</th>\n",
       "      <td>-0.005571</td>\n",
       "      <td>0.232658</td>\n",
       "      <td>-1.395944</td>\n",
       "      <td>-1.624811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-22</th>\n",
       "      <td>0.003717</td>\n",
       "      <td>0.639808</td>\n",
       "      <td>-0.697973</td>\n",
       "      <td>-0.452384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-23</th>\n",
       "      <td>0.005550</td>\n",
       "      <td>0.833691</td>\n",
       "      <td>-0.504091</td>\n",
       "      <td>-0.281127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-26</th>\n",
       "      <td>0.020092</td>\n",
       "      <td>0.756137</td>\n",
       "      <td>-0.562256</td>\n",
       "      <td>-0.348987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-27</th>\n",
       "      <td>0.003610</td>\n",
       "      <td>1.260228</td>\n",
       "      <td>-0.271434</td>\n",
       "      <td>-0.213270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-27</th>\n",
       "      <td>0.062108</td>\n",
       "      <td>-1.066346</td>\n",
       "      <td>-2.035752</td>\n",
       "      <td>-4.032730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-30</th>\n",
       "      <td>-0.083699</td>\n",
       "      <td>-0.639810</td>\n",
       "      <td>-2.888832</td>\n",
       "      <td>-4.381719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>0.101783</td>\n",
       "      <td>-0.329598</td>\n",
       "      <td>-2.578622</td>\n",
       "      <td>-3.955179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>0.070903</td>\n",
       "      <td>0.058165</td>\n",
       "      <td>-3.160265</td>\n",
       "      <td>-3.877625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>-0.023650</td>\n",
       "      <td>0.562255</td>\n",
       "      <td>-3.315371</td>\n",
       "      <td>-3.082713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                rt_5  momentum_10d  momentum_20d  momentum_50d\n",
       "Date                                                          \n",
       "2019-08-21 -0.005571      0.232658     -1.395944     -1.624811\n",
       "2019-08-22  0.003717      0.639808     -0.697973     -0.452384\n",
       "2019-08-23  0.005550      0.833691     -0.504091     -0.281127\n",
       "2019-08-26  0.020092      0.756137     -0.562256     -0.348987\n",
       "2019-08-27  0.003610      1.260228     -0.271434     -0.213270\n",
       "...              ...           ...           ...           ...\n",
       "2020-03-27  0.062108     -1.066346     -2.035752     -4.032730\n",
       "2020-03-30 -0.083699     -0.639810     -2.888832     -4.381719\n",
       "2020-03-31  0.101783     -0.329598     -2.578622     -3.955179\n",
       "2020-04-01  0.070903      0.058165     -3.160265     -3.877625\n",
       "2020-04-02 -0.023650      0.562255     -3.315371     -3.082713\n",
       "\n",
       "[155 rows x 4 columns]"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Ktest2 = X_Ktest.copy()\n",
    "X_Ktest2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt_5</th>\n",
       "      <th>momentum_10d</th>\n",
       "      <th>momentum_20d</th>\n",
       "      <th>momentum_50d</th>\n",
       "      <th>pred_sign</th>\n",
       "      <th>pred_d</th>\n",
       "      <th>pred_u</th>\n",
       "      <th>true_sign</th>\n",
       "      <th>cls</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-21</th>\n",
       "      <td>-0.005571</td>\n",
       "      <td>0.232658</td>\n",
       "      <td>-1.395944</td>\n",
       "      <td>-1.624811</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.523693</td>\n",
       "      <td>0.476307</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2019-08-22</th>\n",
       "      <td>0.003717</td>\n",
       "      <td>0.639808</td>\n",
       "      <td>-0.697973</td>\n",
       "      <td>-0.452384</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.507504</td>\n",
       "      <td>0.492496</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2019-08-23</th>\n",
       "      <td>0.005550</td>\n",
       "      <td>0.833691</td>\n",
       "      <td>-0.504091</td>\n",
       "      <td>-0.281127</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.496775</td>\n",
       "      <td>0.503225</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-26</th>\n",
       "      <td>0.020092</td>\n",
       "      <td>0.756137</td>\n",
       "      <td>-0.562256</td>\n",
       "      <td>-0.348987</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.536942</td>\n",
       "      <td>0.463058</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2019-08-27</th>\n",
       "      <td>0.003610</td>\n",
       "      <td>1.260228</td>\n",
       "      <td>-0.271434</td>\n",
       "      <td>-0.213270</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.461492</td>\n",
       "      <td>0.538508</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-27</th>\n",
       "      <td>0.062108</td>\n",
       "      <td>-1.066346</td>\n",
       "      <td>-2.035752</td>\n",
       "      <td>-4.032730</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.765557</td>\n",
       "      <td>0.234443</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-30</th>\n",
       "      <td>-0.083699</td>\n",
       "      <td>-0.639810</td>\n",
       "      <td>-2.888832</td>\n",
       "      <td>-4.381719</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.424276</td>\n",
       "      <td>0.575724</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>0.101783</td>\n",
       "      <td>-0.329598</td>\n",
       "      <td>-2.578622</td>\n",
       "      <td>-3.955179</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.792063</td>\n",
       "      <td>0.207937</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>0.070903</td>\n",
       "      <td>0.058165</td>\n",
       "      <td>-3.160265</td>\n",
       "      <td>-3.877625</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.716679</td>\n",
       "      <td>0.283321</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>-0.023650</td>\n",
       "      <td>0.562255</td>\n",
       "      <td>-3.315371</td>\n",
       "      <td>-3.082713</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.466312</td>\n",
       "      <td>0.533688</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                rt_5  momentum_10d  momentum_20d  momentum_50d  pred_sign  \\\n",
       "Date                                                                        \n",
       "2019-08-21 -0.005571      0.232658     -1.395944     -1.624811       -1.0   \n",
       "2019-08-22  0.003717      0.639808     -0.697973     -0.452384       -1.0   \n",
       "2019-08-23  0.005550      0.833691     -0.504091     -0.281127        1.0   \n",
       "2019-08-26  0.020092      0.756137     -0.562256     -0.348987       -1.0   \n",
       "2019-08-27  0.003610      1.260228     -0.271434     -0.213270        1.0   \n",
       "...              ...           ...           ...           ...        ...   \n",
       "2020-03-27  0.062108     -1.066346     -2.035752     -4.032730       -1.0   \n",
       "2020-03-30 -0.083699     -0.639810     -2.888832     -4.381719        1.0   \n",
       "2020-03-31  0.101783     -0.329598     -2.578622     -3.955179       -1.0   \n",
       "2020-04-01  0.070903      0.058165     -3.160265     -3.877625       -1.0   \n",
       "2020-04-02 -0.023650      0.562255     -3.315371     -3.082713        1.0   \n",
       "\n",
       "              pred_d    pred_u  true_sign  cls  \n",
       "Date                                            \n",
       "2019-08-21  0.523693  0.476307       -1.0  1.0  \n",
       "2019-08-22  0.507504  0.492496        1.0  0.0  \n",
       "2019-08-23  0.496775  0.503225        1.0  1.0  \n",
       "2019-08-26  0.536942  0.463058       -1.0  1.0  \n",
       "2019-08-27  0.461492  0.538508        1.0  1.0  \n",
       "...              ...       ...        ...  ...  \n",
       "2020-03-27  0.765557  0.234443        1.0  0.0  \n",
       "2020-03-30  0.424276  0.575724       -1.0  0.0  \n",
       "2020-03-31  0.792063  0.207937        1.0  0.0  \n",
       "2020-04-01  0.716679  0.283321       -1.0  1.0  \n",
       "2020-04-02  0.466312  0.533688        1.0  1.0  \n",
       "\n",
       "[155 rows x 9 columns]"
      ]
     },
     "execution_count": 496,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Ktest2['pred_sign'] = ridge2.predict(X_Ktest)\n",
    "X_Ktest2['pred_d'] = preds1 # predicted probabilities of down moves\n",
    "X_Ktest2['pred_u'] = preds2 # predicted probabilities of up moves\n",
    "X_Ktest2['true_sign'] = y_test\n",
    "X_Ktest2['cls'] = y_test\n",
    "for i in range(X_Ktest2.shape[0]):\n",
    "    if X_Ktest2.pred_sign[i] == X_Ktest2.true_sign[i]:\n",
    "        X_Ktest2['cls'][i] = 1 \n",
    "    else:\n",
    "        X_Ktest2['cls'][i] = 0    \n",
    "X_Ktest2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_d = X_Ktest2['true_sign'] == -1\n",
    "pic_d = X_Ktest2[['pred_d','cls']][idx_d]\n",
    "idx_u = X_Ktest2['true_sign'] == 1\n",
    "pic_u = X_Ktest2[['pred_u','cls']][idx_u]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rog\\anaconda3\\lib\\site-packages\\matplotlib\\collections.py:1120: UserWarning:\n",
      "\n",
      "Collection without array used. Make sure to specify the values to be colormapped via the `c` argument.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Transition Probabilities of Down Moves')"
      ]
     },
     "execution_count": 550,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot transition probabilities\n",
    "cl = np.where(pic_d.cls == 1, 'blue', 'red') # blue points for correct predictions, red points for false predictions\n",
    "fig, ax = plt.subplots()\n",
    "pic_1 = ax.scatter(pic_d.index, pic_d.pred_d, c=cl, label='correct')\n",
    "plt.ylabel('probability')\n",
    "plt.xticks([])\n",
    "legend1 = ax.legend(*pic_1.legend_elements(num = 2))\n",
    "plt.legend()\n",
    "plt.title('Transition Probabilities of Down Moves')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Transition Probabilities of Up Moves')"
      ]
     },
     "execution_count": 552,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl2 = np.where(pic_u.cls == 1, 'blue', 'red')\n",
    "pic_1 = plt.scatter(pic_u.index, pic_u.pred_u, c=cl2, label='incorrect')\n",
    "plt.xticks([])\n",
    "plt.ylabel('probability')\n",
    "plt.legend(loc='lower left')\n",
    "plt.title('Transition Probabilities of Up Moves')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model performance is not very impressive, feature selection and hyperparameter tuning need to be refined later ! ! !"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
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  "kernelspec": {
   "display_name": "Python 3.8.11 64-bit ('base': conda)",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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