eth_lagged_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ETH positive return prediction - Random Forest Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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>2561.145264</td>\n",
       "      <td>2615.247314</td>\n",
       "      <td>2330.247314</td>\n",
       "      <td>2405.181152</td>\n",
       "      <td>2405.181152</td>\n",
       "      <td>27369692036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-23</th>\n",
       "      <td>2406.924316</td>\n",
       "      <td>2542.144775</td>\n",
       "      <td>2381.515137</td>\n",
       "      <td>2535.039063</td>\n",
       "      <td>2535.039063</td>\n",
       "      <td>16481489511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-24</th>\n",
       "      <td>2535.891113</td>\n",
       "      <td>2537.208496</td>\n",
       "      <td>2172.301270</td>\n",
       "      <td>2440.352295</td>\n",
       "      <td>2440.352295</td>\n",
       "      <td>28220804648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-25</th>\n",
       "      <td>2440.393555</td>\n",
       "      <td>2498.507080</td>\n",
       "      <td>2359.384766</td>\n",
       "      <td>2455.935059</td>\n",
       "      <td>2455.935059</td>\n",
       "      <td>16179776932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-26</th>\n",
       "      <td>2455.579102</td>\n",
       "      <td>2705.784180</td>\n",
       "      <td>2417.683105</td>\n",
       "      <td>2468.030273</td>\n",
       "      <td>2468.030273</td>\n",
       "      <td>21229909340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2022-01-22  2561.145264  2615.247314  2330.247314  2405.181152  2405.181152   \n",
       "2022-01-23  2406.924316  2542.144775  2381.515137  2535.039063  2535.039063   \n",
       "2022-01-24  2535.891113  2537.208496  2172.301270  2440.352295  2440.352295   \n",
       "2022-01-25  2440.393555  2498.507080  2359.384766  2455.935059  2455.935059   \n",
       "2022-01-26  2455.579102  2705.784180  2417.683105  2468.030273  2468.030273   \n",
       "\n",
       "                 Volume  \n",
       "Date                     \n",
       "2022-01-22  27369692036  \n",
       "2022-01-23  16481489511  \n",
       "2022-01-24  28220804648  \n",
       "2022-01-25  16179776932  \n",
       "2022-01-26  21229909340  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in ETH-USD data \n",
    "eth_price = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/ETH-USD.csv', parse_dates=True, index_col=\"Date\")\n",
    "eth_price.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct the dependent variable where if daily return is greater than 0, then 1, else, 0.\n",
    "eth_price['Daily_Return'] = eth_price['Adj Close'].pct_change()\n",
    "eth_price['Positive_Return'] = np.where(eth_price['Daily_Return'] > 0, 1.0, 0.0)\n",
    "eth_price = eth_price.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-10</th>\n",
       "      <td>320.670990</td>\n",
       "      <td>324.717987</td>\n",
       "      <td>294.541992</td>\n",
       "      <td>299.252991</td>\n",
       "      <td>299.252991</td>\n",
       "      <td>885985984</td>\n",
       "      <td>-0.067411</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-11</th>\n",
       "      <td>298.585999</td>\n",
       "      <td>319.453003</td>\n",
       "      <td>298.191986</td>\n",
       "      <td>314.681000</td>\n",
       "      <td>314.681000</td>\n",
       "      <td>842300992</td>\n",
       "      <td>0.051555</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-12</th>\n",
       "      <td>314.690002</td>\n",
       "      <td>319.153015</td>\n",
       "      <td>298.513000</td>\n",
       "      <td>307.907990</td>\n",
       "      <td>307.907990</td>\n",
       "      <td>1613479936</td>\n",
       "      <td>-0.021523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13</th>\n",
       "      <td>307.024994</td>\n",
       "      <td>328.415009</td>\n",
       "      <td>307.024994</td>\n",
       "      <td>316.716003</td>\n",
       "      <td>316.716003</td>\n",
       "      <td>1041889984</td>\n",
       "      <td>0.028606</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-14</th>\n",
       "      <td>316.763000</td>\n",
       "      <td>340.177002</td>\n",
       "      <td>316.763000</td>\n",
       "      <td>337.631012</td>\n",
       "      <td>337.631012</td>\n",
       "      <td>1069680000</td>\n",
       "      <td>0.066037</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2017-11-10  320.670990  324.717987  294.541992  299.252991  299.252991   \n",
       "2017-11-11  298.585999  319.453003  298.191986  314.681000  314.681000   \n",
       "2017-11-12  314.690002  319.153015  298.513000  307.907990  307.907990   \n",
       "2017-11-13  307.024994  328.415009  307.024994  316.716003  316.716003   \n",
       "2017-11-14  316.763000  340.177002  316.763000  337.631012  337.631012   \n",
       "\n",
       "                Volume  Daily_Return  Positive_Return  Lagged_Pos_Ret  \n",
       "Date                                                                   \n",
       "2017-11-10   885985984     -0.067411              0.0             NaN  \n",
       "2017-11-11   842300992      0.051555              1.0             0.0  \n",
       "2017-11-12  1613479936     -0.021523              0.0             1.0  \n",
       "2017-11-13  1041889984      0.028606              1.0             0.0  \n",
       "2017-11-14  1069680000      0.066037              1.0             1.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create lagged y prediction variable\n",
    "eth_price['Lagged_Pos_Ret'] = eth_price['Positive_Return'].shift(1)\n",
    "eth_price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "eth_price= eth_price.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate most dominant features using pandas-ta, RSI, TSI, Stochastic Oscilator (most dominant features identified in eth_dominant_features.ipynb)\n",
    "\n",
    "#Calculate RSI (Relative Strength Index)\n",
    "rsi_eth = pta.rsi(eth_price['Adj Close'],length=14).dropna()\n",
    "rsi_eth.to_csv('resources/dominant_features/rsi_eth.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate TSI (True Strength Index)\n",
    "tsi_eth = pta.tsi(eth_price['Adj Close'],length=14).dropna()\n",
    "tsi_eth = tsi_eth.drop(columns='TSIs_13_25_13')\n",
    "tsi_eth.to_csv('resources/dominant_features/tsi_eth.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate Stochastic Oscilator (STOCHd_14_3_3)\n",
    "stoch_eth = pta.stoch(eth_price['Adj Close'], eth_price['High'],eth_price['Low'],length=14).dropna()\n",
    "stoch_eth = stoch_eth.drop(columns='STOCHk_14_3_3')\n",
    "stoch_eth.to_csv('resources/dominant_features/stoch_eth.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-22</th>\n",
       "      <td>21.615801</td>\n",
       "      <td>-35.923640</td>\n",
       "      <td>-79.705025</td>\n",
       "      <td>2405.181152</td>\n",
       "      <td>-0.059716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-23</th>\n",
       "      <td>27.552694</td>\n",
       "      <td>-37.557619</td>\n",
       "      <td>-86.240001</td>\n",
       "      <td>2535.039063</td>\n",
       "      <td>0.053991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-24</th>\n",
       "      <td>26.005982</td>\n",
       "      <td>-39.388232</td>\n",
       "      <td>-65.578150</td>\n",
       "      <td>2440.352295</td>\n",
       "      <td>-0.037351</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-25</th>\n",
       "      <td>26.734906</td>\n",
       "      <td>-40.595574</td>\n",
       "      <td>-43.529697</td>\n",
       "      <td>2455.935059</td>\n",
       "      <td>0.006385</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-26</th>\n",
       "      <td>27.333284</td>\n",
       "      <td>-41.386144</td>\n",
       "      <td>-27.641320</td>\n",
       "      <td>2468.030273</td>\n",
       "      <td>0.004925</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               RSI_14  TSI_13_25_13  STOCHd_14_3_3    Adj Close  Daily_Return  \\\n",
       "Date                                                                            \n",
       "2022-01-22  21.615801    -35.923640     -79.705025  2405.181152     -0.059716   \n",
       "2022-01-23  27.552694    -37.557619     -86.240001  2535.039063      0.053991   \n",
       "2022-01-24  26.005982    -39.388232     -65.578150  2440.352295     -0.037351   \n",
       "2022-01-25  26.734906    -40.595574     -43.529697  2455.935059      0.006385   \n",
       "2022-01-26  27.333284    -41.386144     -27.641320  2468.030273      0.004925   \n",
       "\n",
       "            Positive_Return  Lagged_Pos_Ret  \n",
       "Date                                         \n",
       "2022-01-22              0.0             0.0  \n",
       "2022-01-23              1.0             0.0  \n",
       "2022-01-24              0.0             1.0  \n",
       "2022-01-25              1.0             0.0  \n",
       "2022-01-26              1.0             1.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in RSI, TSI, STOCH. Concat data into dominant features dataframe.\n",
    "rsi_eth = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/dominant_features/rsi_eth.csv', parse_dates=True, index_col='Date')\n",
    "tsi_eth = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/dominant_features/tsi_eth.csv', parse_dates=True, index_col='Date')\n",
    "stoch_eth = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/dominant_features/stoch_eth.csv', parse_dates=True, index_col='Date')\n",
    "\n",
    "dominant_features = pd.concat([rsi_eth, tsi_eth, stoch_eth, eth_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_eth.csv')\n",
    "dominant_features.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 22,
   "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define timeframe for testing data\n",
    "X_test = X['2022':]\n",
    "y_test = y['2022':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.635677 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": 35,
   "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": 36,
   "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": 37,
   "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>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0           13            2\n",
       "Actual 1           10            1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score : 0.5384615384615384\n",
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.57      0.87      0.68        15\n",
      "         1.0       0.33      0.09      0.14        11\n",
      "\n",
      "    accuracy                           0.54        26\n",
      "   macro avg       0.45      0.48      0.41        26\n",
      "weighted avg       0.47      0.54      0.46        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": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>0.0</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>0.0</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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-17</th>\n",
       "      <td>1.0</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>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-26</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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             0.0              0.0\n",
       "2022-01-10             1.0              0.0\n",
       "2022-01-11             0.0              0.0\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              0.0\n",
       "2022-01-16             1.0              0.0\n",
       "2022-01-17             1.0              0.0\n",
       "2022-01-18             0.0              0.0\n",
       "2022-01-19             0.0              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             0.0              0.0\n",
       "2022-01-26             1.0              0.0"
      ]
     },
     "execution_count": 38,
     "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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