btc_lagged_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BTC positive return prediction - Random Forest Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_ta as pta\n",
    "from pathlib import Path\n",
    "import os\n",
    "import requests\n",
    "import datetime\n",
    "\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-22</th>\n",
       "      <td>36471.589844</td>\n",
       "      <td>36688.812500</td>\n",
       "      <td>34349.250000</td>\n",
       "      <td>35030.250000</td>\n",
       "      <td>35030.250000</td>\n",
       "      <td>39714385405</td>\n",
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       "    <tr>\n",
       "      <th>2022-01-23</th>\n",
       "      <td>35047.359375</td>\n",
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       "      <td>34784.968750</td>\n",
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       "      <td>36276.804688</td>\n",
       "      <td>26017975951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-24</th>\n",
       "      <td>36275.734375</td>\n",
       "      <td>37247.519531</td>\n",
       "      <td>33184.058594</td>\n",
       "      <td>36654.328125</td>\n",
       "      <td>36654.328125</td>\n",
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       "    <tr>\n",
       "      <th>2022-01-25</th>\n",
       "      <td>36654.804688</td>\n",
       "      <td>37444.570313</td>\n",
       "      <td>35779.429688</td>\n",
       "      <td>36954.003906</td>\n",
       "      <td>36954.003906</td>\n",
       "      <td>26428189594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-26</th>\n",
       "      <td>36950.515625</td>\n",
       "      <td>38825.410156</td>\n",
       "      <td>36374.906250</td>\n",
       "      <td>36852.121094</td>\n",
       "      <td>36852.121094</td>\n",
       "      <td>31324598034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Open          High           Low         Close  \\\n",
       "Date                                                                 \n",
       "2022-01-22  36471.589844  36688.812500  34349.250000  35030.250000   \n",
       "2022-01-23  35047.359375  36433.312500  34784.968750  36276.804688   \n",
       "2022-01-24  36275.734375  37247.519531  33184.058594  36654.328125   \n",
       "2022-01-25  36654.804688  37444.570313  35779.429688  36954.003906   \n",
       "2022-01-26  36950.515625  38825.410156  36374.906250  36852.121094   \n",
       "\n",
       "               Adj Close       Volume  \n",
       "Date                                   \n",
       "2022-01-22  35030.250000  39714385405  \n",
       "2022-01-23  36276.804688  26017975951  \n",
       "2022-01-24  36654.328125  41856658597  \n",
       "2022-01-25  36954.003906  26428189594  \n",
       "2022-01-26  36852.121094  31324598034  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in BTC-USD data \n",
    "btc_price = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/BTC-USD.csv', parse_dates=True, index_col=\"Date\")\n",
    "btc_price.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct the dependent variable where if daily return is greater than 0, then 1, else, 0.\n",
    "btc_price['Daily_Return'] = btc_price['Adj Close'].pct_change()\n",
    "btc_price['Positive_Return'] = np.where(btc_price['Daily_Return'] > 0, 1.0, 0.0)\n",
    "btc_price = btc_price.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Daily_Return</th>\n",
       "      <th>Positive_Return</th>\n",
       "      <th>Lagged_Pos_Ret</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-22</th>\n",
       "      <td>36471.589844</td>\n",
       "      <td>36688.812500</td>\n",
       "      <td>34349.250000</td>\n",
       "      <td>35030.250000</td>\n",
       "      <td>35030.250000</td>\n",
       "      <td>39714385405</td>\n",
       "      <td>-0.039143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-23</th>\n",
       "      <td>35047.359375</td>\n",
       "      <td>36433.312500</td>\n",
       "      <td>34784.968750</td>\n",
       "      <td>36276.804688</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2022-01-24</th>\n",
       "      <td>36275.734375</td>\n",
       "      <td>37247.519531</td>\n",
       "      <td>33184.058594</td>\n",
       "      <td>36654.328125</td>\n",
       "      <td>36654.328125</td>\n",
       "      <td>41856658597</td>\n",
       "      <td>0.010407</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-25</th>\n",
       "      <td>36654.804688</td>\n",
       "      <td>37444.570313</td>\n",
       "      <td>35779.429688</td>\n",
       "      <td>36954.003906</td>\n",
       "      <td>36954.003906</td>\n",
       "      <td>26428189594</td>\n",
       "      <td>0.008176</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-26</th>\n",
       "      <td>36950.515625</td>\n",
       "      <td>38825.410156</td>\n",
       "      <td>36374.906250</td>\n",
       "      <td>36852.121094</td>\n",
       "      <td>36852.121094</td>\n",
       "      <td>31324598034</td>\n",
       "      <td>-0.002757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Open          High           Low         Close  \\\n",
       "Date                                                                 \n",
       "2022-01-22  36471.589844  36688.812500  34349.250000  35030.250000   \n",
       "2022-01-23  35047.359375  36433.312500  34784.968750  36276.804688   \n",
       "2022-01-24  36275.734375  37247.519531  33184.058594  36654.328125   \n",
       "2022-01-25  36654.804688  37444.570313  35779.429688  36954.003906   \n",
       "2022-01-26  36950.515625  38825.410156  36374.906250  36852.121094   \n",
       "\n",
       "               Adj Close       Volume  Daily_Return  Positive_Return  \\\n",
       "Date                                                                   \n",
       "2022-01-22  35030.250000  39714385405     -0.039143              0.0   \n",
       "2022-01-23  36276.804688  26017975951      0.035585              1.0   \n",
       "2022-01-24  36654.328125  41856658597      0.010407              1.0   \n",
       "2022-01-25  36954.003906  26428189594      0.008176              1.0   \n",
       "2022-01-26  36852.121094  31324598034     -0.002757              0.0   \n",
       "\n",
       "            Lagged_Pos_Ret  \n",
       "Date                        \n",
       "2022-01-22             0.0  \n",
       "2022-01-23             0.0  \n",
       "2022-01-24             1.0  \n",
       "2022-01-25             1.0  \n",
       "2022-01-26             1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create lagged y prediction variable\n",
    "btc_price['Lagged_Pos_Ret'] = btc_price['Positive_Return'].shift(1)\n",
    "btc_price.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "btc_price= btc_price.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate most dominant features using pandas-ta, RSI, TSI, Stochastic Oscilator (most dominant features identified in btc_dominant_features.ipynb)\n",
    "\n",
    "#Calculate RSI (Relative Strength Index)\n",
    "rsi_btc = pta.rsi(btc_price['Adj Close'],length=14).dropna()\n",
    "rsi_btc.to_csv('resources/dominant_features/rsi_btc.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate TSI (True Strength Index)\n",
    "tsi_btc = pta.tsi(btc_price['Adj Close'],length=14).dropna()\n",
    "tsi_btc = tsi_btc.drop(columns='TSIs_13_25_13')\n",
    "tsi_btc.to_csv('resources/dominant_features/tsi_btc.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate Stochastic Oscilator (STOCHd_14_3_3)\n",
    "stoch_btc = pta.stoch(btc_price['Adj Close'], btc_price['High'],btc_price['Low'],length=14).dropna()\n",
    "stoch_btc = stoch_btc.drop(columns='STOCHk_14_3_3')\n",
    "stoch_btc.to_csv('resources/dominant_features/stoch_btc.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RSI_14</th>\n",
       "      <th>TSI_13_25_13</th>\n",
       "      <th>STOCHd_14_3_3</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Daily_Return</th>\n",
       "      <th>Positive_Return</th>\n",
       "      <th>Lagged_Pos_Ret</th>\n",
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       "    <tr>\n",
       "      <th>2022-01-22</th>\n",
       "      <td>20.338766</td>\n",
       "      <td>-37.285541</td>\n",
       "      <td>-90.478249</td>\n",
       "      <td>35030.250000</td>\n",
       "      <td>-0.039143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2022-01-23</th>\n",
       "      <td>26.967368</td>\n",
       "      <td>-38.914563</td>\n",
       "      <td>-95.866913</td>\n",
       "      <td>36276.804688</td>\n",
       "      <td>0.035585</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2022-01-24</th>\n",
       "      <td>28.897024</td>\n",
       "      <td>-39.547892</td>\n",
       "      <td>-70.649850</td>\n",
       "      <td>36654.328125</td>\n",
       "      <td>0.010407</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2022-01-25</th>\n",
       "      <td>30.467542</td>\n",
       "      <td>-39.558198</td>\n",
       "      <td>-45.313349</td>\n",
       "      <td>36954.003906</td>\n",
       "      <td>0.008176</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-26</th>\n",
       "      <td>30.223127</td>\n",
       "      <td>-39.638065</td>\n",
       "      <td>-24.894554</td>\n",
       "      <td>36852.121094</td>\n",
       "      <td>-0.002757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "               RSI_14  TSI_13_25_13  STOCHd_14_3_3     Adj Close  \\\n",
       "Date                                                               \n",
       "2022-01-22  20.338766    -37.285541     -90.478249  35030.250000   \n",
       "2022-01-23  26.967368    -38.914563     -95.866913  36276.804688   \n",
       "2022-01-24  28.897024    -39.547892     -70.649850  36654.328125   \n",
       "2022-01-25  30.467542    -39.558198     -45.313349  36954.003906   \n",
       "2022-01-26  30.223127    -39.638065     -24.894554  36852.121094   \n",
       "\n",
       "            Daily_Return  Positive_Return  Lagged_Pos_Ret  \n",
       "Date                                                       \n",
       "2022-01-22     -0.039143              0.0             0.0  \n",
       "2022-01-23      0.035585              1.0             0.0  \n",
       "2022-01-24      0.010407              1.0             1.0  \n",
       "2022-01-25      0.008176              1.0             1.0  \n",
       "2022-01-26     -0.002757              0.0             1.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in RSI, TSI, STOCH. Concat data into dominant features dataframe.\n",
    "rsi_btc = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/dominant_features/rsi_btc.csv', parse_dates=True, index_col='Date')\n",
    "tsi_btc = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/dominant_features/tsi_btc.csv', parse_dates=True, index_col='Date')\n",
    "stoch_btc = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/dominant_features/stoch_btc.csv', parse_dates=True, index_col='Date')\n",
    "\n",
    "dominant_features = pd.concat([rsi_btc, tsi_btc, stoch_btc, btc_price], axis=1).dropna()\n",
    "dominant_features = dominant_features.drop(columns=['Open','High','Low','Close','Volume'])\n",
    "dominant_features.to_csv('resources/dominant_features/dominant_btc.csv')\n",
    "dominant_features.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select model features and timeframe for train data\n",
    "X = dominant_features[['RSI_14','STOCHd_14_3_3','TSI_13_25_13']]\n",
    "X_train = X[:'2021']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select prediction variable and timeframe for train data\n",
    "y = dominant_features['Lagged_Pos_Ret']\n",
    "y_train = y[:'2021']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define timeframe for testing data\n",
    "X_test = X['2022':]\n",
    "y_test = y['2022':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the StandardScaler instance\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the Standard Scaler with the training data\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Scale the training data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.641277 using {'criterion': 'entropy', 'max_depth': 10, 'n_estimators': 500}\n"
     ]
    }
   ],
   "source": [
    "# Perform the grid search for the optimal set of tree parameters\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "n_estimators = [20,500]\n",
    "max_depth= [5,10]\n",
    "num_folds = 10\n",
    "scoring = 'accuracy'\n",
    "\n",
    "criterion = [\"gini\",\"entropy\"]\n",
    "param_grid = dict(n_estimators=n_estimators, max_depth=max_depth, \\\n",
    "  criterion = criterion )\n",
    "\n",
    "model = RandomForestClassifier(n_jobs=-1)\n",
    "\n",
    "kfold = KFold(n_splits=num_folds, random_state=None)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, \\\n",
    "  scoring=scoring, cv=kfold)\n",
    "grid_result = grid.fit(X_train, y_train)\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_,\\\n",
    "  grid_result.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the model using optimized settings (also use if you want to run without optimizer above)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "model = RandomForestClassifier(n_jobs=-1)\n",
    "model = RandomForestClassifier(criterion='entropy', max_depth=10, n_estimators=500, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the model\n",
    "model = model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Making predictions using the testing data\n",
    "predictions = model.predict(X_test_scaled)\n",
    "\n",
    "# Calculating the confusion matrix\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "cm_df = pd.DataFrame(cm, index=[\"Actual 0\", \"Actual 1\"], columns=[\"Predicted 0\", \"Predicted 1\"])\n",
    "\n",
    "# Calculating the accuracy score\n",
    "acc_score = accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n"
     ]
    },
    {
     "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>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0           12            3\n",
       "Actual 1            6            5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score : 0.6538461538461539\n",
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.67      0.80      0.73        15\n",
      "         1.0       0.62      0.45      0.53        11\n",
      "\n",
      "    accuracy                           0.65        26\n",
      "   macro avg       0.65      0.63      0.63        26\n",
      "weighted avg       0.65      0.65      0.64        26\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Displaying results\n",
    "print(\"Confusion Matrix\")\n",
    "display(cm_df)\n",
    "print(f\"Accuracy Score : {acc_score}\")\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>Lagged_Pos_Ret</th>\n",
       "      <th>Predicted Value</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>2022-01-07</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-09</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-24</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-25</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-26</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Lagged_Pos_Ret  Predicted Value\n",
       "Date                                       \n",
       "2022-01-07             0.0              0.0\n",
       "2022-01-08             0.0              0.0\n",
       "2022-01-09             1.0              0.0\n",
       "2022-01-10             1.0              0.0\n",
       "2022-01-11             0.0              1.0\n",
       "2022-01-12             1.0              1.0\n",
       "2022-01-13             1.0              0.0\n",
       "2022-01-14             0.0              1.0\n",
       "2022-01-15             1.0              1.0\n",
       "2022-01-16             1.0              1.0\n",
       "2022-01-17             0.0              0.0\n",
       "2022-01-18             0.0              0.0\n",
       "2022-01-19             1.0              0.0\n",
       "2022-01-20             0.0              0.0\n",
       "2022-01-21             0.0              0.0\n",
       "2022-01-22             0.0              0.0\n",
       "2022-01-23             0.0              0.0\n",
       "2022-01-24             1.0              0.0\n",
       "2022-01-25             1.0              0.0\n",
       "2022-01-26             1.0              1.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assemble actual y data (Y_test) with predicted y data (from just above) into two columns in a dataframe\n",
    "Results = y_test.to_frame()\n",
    "Results[\"Predicted Value\"] = predictions\n",
    "Results[-20:]"
   ]
  }
 ],
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