Final_Project/Neural_Network_Stock_Predictions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import yfinance as yf\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to pull in stock data\n",
    "def stock_data(ticker, start_date, end_date, drop_extra_cols = True):\n",
    "    try:\n",
    "        # get ticker object\n",
    "        ticker_data = yf.Ticker(ticker)\n",
    "        # fetch data between start date ane end date\n",
    "        df = ticker_data.history(start=start_date, end=end_date)\n",
    "        # Drop unnecessary columns\n",
    "        if len(df) > 0 and drop_extra_cols:\n",
    "            df = df.drop([\"Open\", \"High\", \"Low\", \"Volume\", \"Dividends\", \"Stock Splits\" ], axis=1)\n",
    "        return df\n",
    "    \n",
    "    except Exception as e:\n",
    "        print(f\"ERROR: An Error occurred while fetching and formatting data for {ticker} from yfinance  \\n DETAILS: {repr(e)}\")\n",
    "        raise\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Close</th>\n",
       "      <th>Daily Return</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>2011-01-04</th>\n",
       "      <td>22.230862</td>\n",
       "      <td>0.003932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>22.159628</td>\n",
       "      <td>-0.003204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>22.808588</td>\n",
       "      <td>0.029286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-07</th>\n",
       "      <td>22.634483</td>\n",
       "      <td>-0.007633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-10</th>\n",
       "      <td>22.333738</td>\n",
       "      <td>-0.013287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Close  Daily Return\n",
       "Date                               \n",
       "2011-01-04  22.230862      0.003932\n",
       "2011-01-05  22.159628     -0.003204\n",
       "2011-01-06  22.808588      0.029286\n",
       "2011-01-07  22.634483     -0.007633\n",
       "2011-01-10  22.333738     -0.013287"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get stock data \n",
    "# Calculate daily return and drop NaNs \n",
    "  \n",
    " \n",
    "window = 5\n",
    "start_date = \"2011-01-01\"\n",
    "end_date = \"2021-05-31\"\n",
    "ticker = \"MSFT\"\n",
    "ticker_df = stock_data(ticker, start_date, end_date, drop_extra_cols = True)\n",
    "\n",
    "ticker_df[\"Daily Return\"] = ticker_df[\"Close\"].pct_change().dropna()\n",
    "\n",
    "\n",
    "ticker_df.dropna(inplace=True)\n",
    "ticker_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function that returns numpy array for X and y\n",
    "def window_data(df, window, feature_col_number, target_col_number):\n",
    "    \n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(df) - window):\n",
    "        features = df.iloc[i : (i + window), feature_col_number]\n",
    "        target = df.iloc[(i + window), target_col_number]\n",
    "        X.append(features)\n",
    "        y.append(target)\n",
    "    return np.array(X), np.array(y).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the features (X) and target (y) data using the window_data() function.\n",
    "# Here we are using previous returns to predict future returns \n",
    "window_size = window\n",
    "\n",
    "feature_column = 1\n",
    "target_column = 1\n",
    "X, y = window_data(ticker_df, window_size, feature_column, target_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split the data into training data and testing data\n",
    "split = int(0.7 * len(X))\n",
    "X_train = X[: split]\n",
    "X_test = X[split:]\n",
    "y_train = y[: split]\n",
    "y_test = y[split:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale data between 0 and 1.\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "scaler.fit(y)\n",
    "y_train = scaler.transform(y_train)\n",
    "y_test = scaler.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the features into a vertical vector so they work for the LSTM model\n",
    "X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
    "X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the models\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the LSTM RNN model.\n",
    "model = Sequential()\n",
    "\n",
    "# Initial model setup\n",
    "#number of units should equal the window size\n",
    "number_units = window\n",
    "dropout_fraction = 0.2\n",
    "\n",
    "# Layer 1\n",
    "model.add(LSTM(\n",
    "    units=number_units,\n",
    "    return_sequences=True,\n",
    "    input_shape=(X_train.shape[1], 1))\n",
    "    )\n",
    "model.add(Dropout(dropout_fraction))\n",
    "\n",
    "# Layer 2\n",
    "model.add(LSTM(units=number_units, return_sequences=True))\n",
    "model.add(Dropout(dropout_fraction))\n",
    "\n",
    "# Layer 3\n",
    "model.add(LSTM(units=number_units, return_sequences=True))\n",
    "model.add(Dropout(dropout_fraction))\n",
    "\n",
    "# Layer 4\n",
    "model.add(LSTM(units=number_units))\n",
    "model.add(Dropout(dropout_fraction))\n",
    "\n",
    "# Output layer\n",
    "model.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer=\"adam\", loss=\"mean_squared_error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 5, 5)              140       \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 5, 5)              0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 5, 5)              220       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 5, 5)              0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 5, 5)              220       \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 5, 5)              0         \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 5)                 220       \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 5)                 0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 806\n",
      "Trainable params: 806\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Show the model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "21/21 [==============================] - 5s 7ms/step - loss: 0.2274\n",
      "Epoch 2/10\n",
      "21/21 [==============================] - 0s 7ms/step - loss: 0.1418\n",
      "Epoch 3/10\n",
      "21/21 [==============================] - 0s 7ms/step - loss: 0.0537\n",
      "Epoch 4/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0204\n",
      "Epoch 5/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0170\n",
      "Epoch 6/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0162\n",
      "Epoch 7/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0164\n",
      "Epoch 8/10\n",
      "21/21 [==============================] - 0s 7ms/step - loss: 0.0161\n",
      "Epoch 9/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0153\n",
      "Epoch 10/10\n",
      "21/21 [==============================] - 0s 7ms/step - loss: 0.0144\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1b36092db48>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "model.fit(X_train, y_train, epochs=10, shuffle=False, batch_size=90, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005190485622733831"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "model.evaluate(X_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions using the testing data X_test\n",
    "predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recover the original returns instead of the scaled version\n",
    "predicted_prices = scaler.inverse_transform(predicted)\n",
    "real_prices = scaler.inverse_transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Actual Return</th>\n",
       "      <th>Predicted Return</th>\n",
       "      <th>Actual Trading Signal</th>\n",
       "      <th>Predicted Trading Signal</th>\n",
       "      <th>Actual Entry/Exit</th>\n",
       "      <th>Predicted Entry/Exit</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>2018-04-20</th>\n",
       "      <td>-0.011549</td>\n",
       "      <td>-0.005030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-23</th>\n",
       "      <td>0.003684</td>\n",
       "      <td>-0.004607</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-24</th>\n",
       "      <td>-0.023388</td>\n",
       "      <td>-0.004809</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-25</th>\n",
       "      <td>-0.008698</td>\n",
       "      <td>-0.005515</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-26</th>\n",
       "      <td>0.021125</td>\n",
       "      <td>-0.005851</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-27</th>\n",
       "      <td>0.016550</td>\n",
       "      <td>-0.005973</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-30</th>\n",
       "      <td>-0.024004</td>\n",
       "      <td>-0.005722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01</th>\n",
       "      <td>0.015826</td>\n",
       "      <td>-0.005949</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-02</th>\n",
       "      <td>-0.015684</td>\n",
       "      <td>-0.005114</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-03</th>\n",
       "      <td>0.005988</td>\n",
       "      <td>-0.004644</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Actual Return  Predicted Return  Actual Trading Signal  \\\n",
       "Date                                                                 \n",
       "2018-04-20      -0.011549         -0.005030                    0.0   \n",
       "2018-04-23       0.003684         -0.004607                    1.0   \n",
       "2018-04-24      -0.023388         -0.004809                    0.0   \n",
       "2018-04-25      -0.008698         -0.005515                    0.0   \n",
       "2018-04-26       0.021125         -0.005851                    1.0   \n",
       "2018-04-27       0.016550         -0.005973                    1.0   \n",
       "2018-04-30      -0.024004         -0.005722                    0.0   \n",
       "2018-05-01       0.015826         -0.005949                    1.0   \n",
       "2018-05-02      -0.015684         -0.005114                    0.0   \n",
       "2018-05-03       0.005988         -0.004644                    1.0   \n",
       "\n",
       "            Predicted Trading Signal  Actual Entry/Exit  Predicted Entry/Exit  \n",
       "Date                                                                           \n",
       "2018-04-20                       0.0                0.0                   0.0  \n",
       "2018-04-23                       0.0                1.0                   0.0  \n",
       "2018-04-24                       0.0               -1.0                   0.0  \n",
       "2018-04-25                       0.0                0.0                   0.0  \n",
       "2018-04-26                       0.0                1.0                   0.0  \n",
       "2018-04-27                       0.0                0.0                   0.0  \n",
       "2018-04-30                       0.0               -1.0                   0.0  \n",
       "2018-05-01                       0.0                1.0                   0.0  \n",
       "2018-05-02                       0.0               -1.0                   0.0  \n",
       "2018-05-03                       0.0                1.0                   0.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create data frame with actual and predicted returns, trading signal, and entry/exit\n",
    "actual_trading_signal = np.where(\n",
    "    real_prices.ravel() > 0, 1.0, 0\n",
    ")\n",
    "predicted_trading_signal = np.where(\n",
    "    predicted_prices.ravel() > 0, 1.0, 0\n",
    ")\n",
    "stocks = pd.DataFrame({\n",
    "    \"Actual Return\": real_prices.ravel(),\n",
    "    \"Predicted Return\": predicted_prices.ravel(),\n",
    "    \"Actual Trading Signal\": actual_trading_signal,\n",
    "    \"Predicted Trading Signal\": predicted_trading_signal,\n",
    " }, index = ticker_df.index[-len(real_prices): ]) \n",
    "\n",
    "stocks[\"Actual Entry/Exit\"] = stocks[\"Actual Trading Signal\"].diff()\n",
    "stocks[\"Predicted Entry/Exit\"] = stocks[\"Predicted Trading Signal\"].diff()\n",
    "stocks = stocks.dropna()\n",
    "# Show the DataFrame's head\n",
    "stocks.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export to csv\n",
    "stocks.to_csv(\"lstm_daily_return.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key \"text.kerning_factor\" on line 4 in\n",
      "C:\\Users\\dredm\\anaconda3\\envs\\dev\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test_patch.mplstyle.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "http://github.com/matplotlib/matplotlib/blob/master/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b36f659b88>"
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     "execution_count": 19,
     "metadata": {},
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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot real returns vs predicted returns \n",
    "#Using previous returns to predict future returns\n",
    "stocks[[\"Actual Return\", \"Predicted Return\"]].plot(title=\"Actual Vs. Predicted Returns\", figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Reuse the data and functions to analyze rolling standard deviations ability to predict returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Close</th>\n",
       "      <th>Daily Return</th>\n",
       "      <th>Rolling Std Dev</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-10</th>\n",
       "      <td>22.333738</td>\n",
       "      <td>-0.013287</td>\n",
       "      <td>0.016596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-11</th>\n",
       "      <td>22.246687</td>\n",
       "      <td>-0.003898</td>\n",
       "      <td>0.016715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-12</th>\n",
       "      <td>22.594917</td>\n",
       "      <td>0.015653</td>\n",
       "      <td>0.017831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-13</th>\n",
       "      <td>22.309992</td>\n",
       "      <td>-0.012610</td>\n",
       "      <td>0.011824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-14</th>\n",
       "      <td>22.397053</td>\n",
       "      <td>0.003902</td>\n",
       "      <td>0.012145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Close  Daily Return  Rolling Std Dev\n",
       "Date                                                \n",
       "2011-01-10  22.333738     -0.013287         0.016596\n",
       "2011-01-11  22.246687     -0.003898         0.016715\n",
       "2011-01-12  22.594917      0.015653         0.017831\n",
       "2011-01-13  22.309992     -0.012610         0.011824\n",
       "2011-01-14  22.397053      0.003902         0.012145"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get stock data \n",
    "# Calculate daily return and rolling standard deviation\n",
    "window = 5\n",
    "start_date = \"2011-01-01\"\n",
    "end_date = \"2021-05-31\"\n",
    "ticker = \"MSFT\"\n",
    "ticker_df = stock_data(ticker, start_date, end_date, drop_extra_cols = True)\n",
    "\n",
    "ticker_df[\"Daily Return\"] = ticker_df[\"Close\"].pct_change().dropna()\n",
    "ticker_df[\"Rolling Std Dev\"] = ticker_df[\"Daily Return\"].rolling(window = window).std()\n",
    "ticker_df.dropna(inplace=True)\n",
    "ticker_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the features (X) and target (y) data using the window_data() function.\n",
    "# Here we are using rolling standard deviations of daily returns to predict future returns \n",
    "\n",
    "window_size = window\n",
    "\n",
    "feature_column = 2\n",
    "target_column = 1\n",
    "X, y = window_data(ticker_df, window_size, feature_column, target_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split the data into training data and testing data\n",
    "split = int(0.7 * len(X))\n",
    "X_train = X[: split]\n",
    "X_test = X[split:]\n",
    "y_train = y[: split]\n",
    "y_test = y[split:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale data between 0 and 1.\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "scaler.fit(y)\n",
    "y_train = scaler.transform(y_train)\n",
    "y_test = scaler.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the features into a vertical vector so they work for the LSTM model\n",
    "X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
    "X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the LSTM RNN model.\n",
    "model = Sequential()\n",
    "\n",
    "# Initial model setup\n",
    "#number of units should equal the window size\n",
    "number_units = window\n",
    "dropout_fraction = 0.2\n",
    "\n",
    "# Layer 1\n",
    "model.add(LSTM(\n",
    "    units=number_units,\n",
    "    return_sequences=True,\n",
    "    input_shape=(X_train.shape[1], 1))\n",
    "    )\n",
    "model.add(Dropout(dropout_fraction))\n",
    "\n",
    "# Layer 2\n",
    "model.add(LSTM(units=number_units, return_sequences=True))\n",
    "model.add(Dropout(dropout_fraction))\n",
    "\n",
    "# Layer 3\n",
    "model.add(LSTM(units=number_units, return_sequences=True))\n",
    "model.add(Dropout(dropout_fraction))\n",
    "\n",
    "# Layer 4\n",
    "model.add(LSTM(units=number_units))\n",
    "model.add(Dropout(dropout_fraction))\n",
    "\n",
    "# Output layer\n",
    "model.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer=\"adam\", loss=\"mean_squared_error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_4 (LSTM)                (None, 5, 5)              140       \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 5, 5)              0         \n",
      "_________________________________________________________________\n",
      "lstm_5 (LSTM)                (None, 5, 5)              220       \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 5, 5)              0         \n",
      "_________________________________________________________________\n",
      "lstm_6 (LSTM)                (None, 5, 5)              220       \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 5, 5)              0         \n",
      "_________________________________________________________________\n",
      "lstm_7 (LSTM)                (None, 5)                 220       \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 5)                 0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 806\n",
      "Trainable params: 806\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Show the model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "21/21 [==============================] - 4s 6ms/step - loss: 0.2251\n",
      "Epoch 2/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.1321\n",
      "Epoch 3/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0429\n",
      "Epoch 4/10\n",
      "21/21 [==============================] - 0s 7ms/step - loss: 0.0191\n",
      "Epoch 5/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0182\n",
      "Epoch 6/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0167\n",
      "Epoch 7/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0163\n",
      "Epoch 8/10\n",
      "21/21 [==============================] - 0s 7ms/step - loss: 0.0149\n",
      "Epoch 9/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0151\n",
      "Epoch 10/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1b373f77a08>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "model.fit(X_train, y_train, epochs=10, shuffle=False, batch_size=90, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005173258017748594"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "model.evaluate(X_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions using the testing data X_test\n",
    "predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recover the original returns instead of the scaled version\n",
    "predicted_prices = scaler.inverse_transform(predicted)\n",
    "real_prices = scaler.inverse_transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Actual Return</th>\n",
       "      <th>Predicted Return</th>\n",
       "      <th>Actual Trading Signal</th>\n",
       "      <th>Predicted Trading Signal</th>\n",
       "      <th>Actual Entry/Exit</th>\n",
       "      <th>Predicted Entry/Exit</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>2018-04-23</th>\n",
       "      <td>0.003684</td>\n",
       "      <td>-0.005534</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-24</th>\n",
       "      <td>-0.023388</td>\n",
       "      <td>-0.005805</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-25</th>\n",
       "      <td>-0.008698</td>\n",
       "      <td>-0.006087</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-26</th>\n",
       "      <td>0.021125</td>\n",
       "      <td>-0.006015</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-27</th>\n",
       "      <td>0.016550</td>\n",
       "      <td>-0.005887</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-30</th>\n",
       "      <td>-0.024004</td>\n",
       "      <td>-0.005888</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01</th>\n",
       "      <td>0.015826</td>\n",
       "      <td>-0.005693</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-02</th>\n",
       "      <td>-0.015684</td>\n",
       "      <td>-0.005294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-03</th>\n",
       "      <td>0.005988</td>\n",
       "      <td>-0.004477</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-04</th>\n",
       "      <td>0.011587</td>\n",
       "      <td>-0.004215</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Actual Return  Predicted Return  Actual Trading Signal  \\\n",
       "Date                                                                 \n",
       "2018-04-23       0.003684         -0.005534                    1.0   \n",
       "2018-04-24      -0.023388         -0.005805                    0.0   \n",
       "2018-04-25      -0.008698         -0.006087                    0.0   \n",
       "2018-04-26       0.021125         -0.006015                    1.0   \n",
       "2018-04-27       0.016550         -0.005887                    1.0   \n",
       "2018-04-30      -0.024004         -0.005888                    0.0   \n",
       "2018-05-01       0.015826         -0.005693                    1.0   \n",
       "2018-05-02      -0.015684         -0.005294                    0.0   \n",
       "2018-05-03       0.005988         -0.004477                    1.0   \n",
       "2018-05-04       0.011587         -0.004215                    1.0   \n",
       "\n",
       "            Predicted Trading Signal  Actual Entry/Exit  Predicted Entry/Exit  \n",
       "Date                                                                           \n",
       "2018-04-23                       0.0                1.0                   0.0  \n",
       "2018-04-24                       0.0               -1.0                   0.0  \n",
       "2018-04-25                       0.0                0.0                   0.0  \n",
       "2018-04-26                       0.0                1.0                   0.0  \n",
       "2018-04-27                       0.0                0.0                   0.0  \n",
       "2018-04-30                       0.0               -1.0                   0.0  \n",
       "2018-05-01                       0.0                1.0                   0.0  \n",
       "2018-05-02                       0.0               -1.0                   0.0  \n",
       "2018-05-03                       0.0                1.0                   0.0  \n",
       "2018-05-04                       0.0                0.0                   0.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame of Real and Predicted values \n",
    "\n",
    "actual_trading_signal = np.where(\n",
    "    real_prices.ravel() > 0, 1.0, 0\n",
    ")\n",
    "predicted_trading_signal = np.where(\n",
    "    predicted_prices.ravel() > 0, 1.0, 0\n",
    ")\n",
    "std_dev = pd.DataFrame({\n",
    "    \"Actual Return\": real_prices.ravel(),\n",
    "    \"Predicted Return\": predicted_prices.ravel(),\n",
    "    \"Actual Trading Signal\": actual_trading_signal,\n",
    "    \"Predicted Trading Signal\": predicted_trading_signal,\n",
    " }, index = ticker_df.index[-len(real_prices): ]) \n",
    "\n",
    "std_dev[\"Actual Entry/Exit\"] = std_dev[\"Actual Trading Signal\"].diff()\n",
    "std_dev[\"Predicted Entry/Exit\"] = std_dev[\"Predicted Trading Signal\"].diff()\n",
    "std_dev = std_dev.dropna()\n",
    "# Show the DataFrame's head\n",
    "std_dev.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b301d3c288>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot real returns vs predicted returns \n",
    "# Using rolling standard deviations of daily returns to predict future returns\n",
    "std_dev[[\"Actual Return\", \"Predicted Return\"]].plot(title=\"Actual Vs. Predicted Returns\", figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Export to csv\n",
    "std_dev.to_csv(\"lstm_standard_dev_and_returns.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use rolling standard deviations to predict closing prices and compare those daily returns to actual daily returns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the features (X) and target (y) data using the window_data() function.\n",
    "# Here we are using rolling standard deviations of daily returns to predict future prices \n",
    "\n",
    "window_size = window\n",
    "\n",
    "feature_column = 2\n",
    "target_column = 0\n",
    "X, y = window_data(ticker_df, window_size, feature_column, target_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Close</th>\n",
       "      <th>Daily Return</th>\n",
       "      <th>Rolling Std Dev</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-10</th>\n",
       "      <td>22.333738</td>\n",
       "      <td>-0.013287</td>\n",
       "      <td>0.016596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-11</th>\n",
       "      <td>22.246687</td>\n",
       "      <td>-0.003898</td>\n",
       "      <td>0.016715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-12</th>\n",
       "      <td>22.594917</td>\n",
       "      <td>0.015653</td>\n",
       "      <td>0.017831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-13</th>\n",
       "      <td>22.309992</td>\n",
       "      <td>-0.012610</td>\n",
       "      <td>0.011824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-14</th>\n",
       "      <td>22.397053</td>\n",
       "      <td>0.003902</td>\n",
       "      <td>0.012145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Close  Daily Return  Rolling Std Dev\n",
       "Date                                                \n",
       "2011-01-10  22.333738     -0.013287         0.016596\n",
       "2011-01-11  22.246687     -0.003898         0.016715\n",
       "2011-01-12  22.594917      0.015653         0.017831\n",
       "2011-01-13  22.309992     -0.012610         0.011824\n",
       "2011-01-14  22.397053      0.003902         0.012145"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get stock data \n",
    "#Calculate daily returns and rolling standard deviation\n",
    "\n",
    "window = 5\n",
    "start_date = \"2011-01-01\"\n",
    "end_date = \"2021-05-31\"\n",
    "ticker = \"MSFT\"\n",
    "ticker_df = stock_data(ticker, start_date, end_date, drop_extra_cols = True)\n",
    "\n",
    "ticker_df[\"Daily Return\"] = ticker_df[\"Close\"].pct_change().dropna()\n",
    "ticker_df[\"Rolling Std Dev\"] = ticker_df[\"Daily Return\"].rolling(window = window).std()\n",
    "ticker_df.dropna(inplace=True)\n",
    "ticker_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split the data into training data and testing data\n",
    "split = int(0.7 * len(X))\n",
    "X_train = X[: split]\n",
    "X_test = X[split:]\n",
    "y_train = y[: split]\n",
    "y_test = y[split:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale data between 0 and 1.\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "scaler.fit(y)\n",
    "y_train = scaler.transform(y_train)\n",
    "y_test = scaler.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the features into a vertical vector so they work for the LSTM model\n",
    "X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
    "X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the LSTM RNN model.\n",
    "model = Sequential()\n",
    "\n",
    "# Initial model setup\n",
    "#number of units should equal the window size\n",
    "number_units = window\n",
    "dropout_fraction = 0.2\n",
    "\n",
    "# Layer 1\n",
    "model.add(LSTM(\n",
    "    units=number_units,\n",
    "    return_sequences=True,\n",
    "    input_shape=(X_train.shape[1], 1))\n",
    "    )\n",
    "model.add(Dropout(dropout_fraction))\n",
    "\n",
    "# Layer 2\n",
    "model.add(LSTM(units=number_units, return_sequences=True))\n",
    "model.add(Dropout(dropout_fraction))\n",
    "\n",
    "# Layer 3\n",
    "model.add(LSTM(units=number_units, return_sequences=True))\n",
    "model.add(Dropout(dropout_fraction))\n",
    "\n",
    "# Layer 4\n",
    "model.add(LSTM(units=number_units))\n",
    "model.add(Dropout(dropout_fraction))\n",
    "\n",
    "# Output layer\n",
    "model.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer=\"adam\", loss=\"mean_squared_error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_8 (LSTM)                (None, 5, 5)              140       \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 5, 5)              0         \n",
      "_________________________________________________________________\n",
      "lstm_9 (LSTM)                (None, 5, 5)              220       \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 5, 5)              0         \n",
      "_________________________________________________________________\n",
      "lstm_10 (LSTM)               (None, 5, 5)              220       \n",
      "_________________________________________________________________\n",
      "dropout_10 (Dropout)         (None, 5, 5)              0         \n",
      "_________________________________________________________________\n",
      "lstm_11 (LSTM)               (None, 5)                 220       \n",
      "_________________________________________________________________\n",
      "dropout_11 (Dropout)         (None, 5)                 0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 806\n",
      "Trainable params: 806\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Show the model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "21/21 [==============================] - 5s 6ms/step - loss: 0.0074\n",
      "Epoch 2/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0055\n",
      "Epoch 3/10\n",
      "21/21 [==============================] - 0s 7ms/step - loss: 0.0062\n",
      "Epoch 4/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0066\n",
      "Epoch 5/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0064\n",
      "Epoch 6/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0064\n",
      "Epoch 7/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0065\n",
      "Epoch 8/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0063\n",
      "Epoch 9/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0063\n",
      "Epoch 10/10\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 0.0063\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1b305026348>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "model.fit(X_train, y_train, epochs=10, shuffle=False, batch_size=90, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26781320571899414"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "model.evaluate(X_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions using the testing data X_test\n",
    "predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recover the original returns instead of the scaled version\n",
    "predicted_prices = scaler.inverse_transform(predicted)\n",
    "real_prices = scaler.inverse_transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual Return</th>\n",
       "      <th>Predicted Return</th>\n",
       "      <th>Actual Trading Signal</th>\n",
       "      <th>Predicted Trading Signal</th>\n",
       "      <th>Actual Entry/Exit</th>\n",
       "      <th>Predicted Entry/Exit</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>2018-04-23</th>\n",
       "      <td>91.400871</td>\n",
       "      <td>41.782616</td>\n",
       "      <td>0.003684</td>\n",
       "      <td>-0.000067</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-24</th>\n",
       "      <td>89.263222</td>\n",
       "      <td>41.776630</td>\n",
       "      <td>-0.023388</td>\n",
       "      <td>-0.000143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-25</th>\n",
       "      <td>88.486771</td>\n",
       "      <td>41.769775</td>\n",
       "      <td>-0.008698</td>\n",
       "      <td>-0.000164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-26</th>\n",
       "      <td>90.356010</td>\n",
       "      <td>41.771500</td>\n",
       "      <td>0.021125</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-27</th>\n",
       "      <td>91.851402</td>\n",
       "      <td>41.774281</td>\n",
       "      <td>0.016550</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Actual  Predicted  Actual Return  Predicted Return  \\\n",
       "Date                                                                \n",
       "2018-04-23  91.400871  41.782616       0.003684         -0.000067   \n",
       "2018-04-24  89.263222  41.776630      -0.023388         -0.000143   \n",
       "2018-04-25  88.486771  41.769775      -0.008698         -0.000164   \n",
       "2018-04-26  90.356010  41.771500       0.021125          0.000041   \n",
       "2018-04-27  91.851402  41.774281       0.016550          0.000067   \n",
       "\n",
       "            Actual Trading Signal  Predicted Trading Signal  \\\n",
       "Date                                                          \n",
       "2018-04-23                    1.0                       0.0   \n",
       "2018-04-24                    0.0                       0.0   \n",
       "2018-04-25                    0.0                       0.0   \n",
       "2018-04-26                    1.0                       1.0   \n",
       "2018-04-27                    1.0                       1.0   \n",
       "\n",
       "            Actual Entry/Exit  Predicted Entry/Exit  \n",
       "Date                                                 \n",
       "2018-04-23                1.0                   0.0  \n",
       "2018-04-24               -1.0                   0.0  \n",
       "2018-04-25                0.0                   0.0  \n",
       "2018-04-26                1.0                   1.0  \n",
       "2018-04-27                0.0                   0.0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame of Real and Predicted values\n",
    "std_dev_prices = pd.DataFrame({\n",
    "    \"Actual\": real_prices.ravel(),\n",
    "    \"Predicted\": predicted_prices.ravel()\n",
    "}, index = ticker_df.index[-len(real_prices): ]) \n",
    "\n",
    "std_dev_prices[\"Actual Return\"] = std_dev_prices[\"Actual\"].pct_change()\n",
    "std_dev_prices[\"Predicted Return\"] = std_dev_prices[\"Predicted\"].pct_change()\n",
    "std_dev_prices[\"Actual Trading Signal\"] = np.where(\n",
    "   std_dev_prices[\"Actual Return\"] > 0, 1.0, 0 \n",
    ")\n",
    "\n",
    "std_dev_prices[\"Predicted Trading Signal\"] = np.where(\n",
    "    std_dev_prices[\"Predicted Return\"] > 0, 1.0, 0\n",
    ")\n",
    "std_dev_prices[\"Actual Entry/Exit\"] = std_dev_prices[\"Actual Trading Signal\"].diff()\n",
    "std_dev_prices[\"Predicted Entry/Exit\"] = std_dev_prices[\"Predicted Trading Signal\"].diff()\n",
    "std_dev_prices = std_dev_prices.dropna()\n",
    "\n",
    "# Show the DataFrame's head\n",
    "std_dev_prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b303436948>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot real returns vs predicted returns \n",
    "# Using rolling standard deviations of daily returns to predict future returns\n",
    "std_dev_prices[[\"Actual Return\", \"Predicted Return\"]].plot(title=\"Actual Vs. Predicted Returns\", figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Export to csv\n",
    "std_dev_prices.to_csv(\"lstm_standard_dev_prices_returns.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Stock</th>\n",
       "      <th>Entry Date</th>\n",
       "      <th>Exit Date</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Entry Share Price</th>\n",
       "      <th>Exit Share Price</th>\n",
       "      <th>Entry Portfolio Holding</th>\n",
       "      <th>Exit Portfolio Holding</th>\n",
       "      <th>Profit/Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Stock, Entry Date, Exit Date, Shares, Entry Share Price, Exit Share Price, Entry Portfolio Holding, Exit Portfolio Holding, Profit/Loss]\n",
       "Index: []"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize trade evaluation DataFrame with columns\n",
    "trade_evaluation_df = pd.DataFrame(\n",
    "    columns=[\n",
    "        'Stock', \n",
    "        'Entry Date', \n",
    "        'Exit Date', \n",
    "        'Shares', \n",
    "        'Entry Share Price', \n",
    "        'Exit Share Price', \n",
    "        'Entry Portfolio Holding', \n",
    "        'Exit Portfolio Holding', \n",
    "        'Profit/Loss']\n",
    ")\n",
    "trade_evaluation_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Stock</th>\n",
       "      <th>Entry Date</th>\n",
       "      <th>Exit Date</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Entry Share Price</th>\n",
       "      <th>Exit Share Price</th>\n",
       "      <th>Entry Portfolio Holding</th>\n",
       "      <th>Exit Portfolio Holding</th>\n",
       "      <th>Profit/Loss</th>\n",
       "      <th>Cumulative Profit/Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>2018-04-30</td>\n",
       "      <td>100</td>\n",
       "      <td>90.356010</td>\n",
       "      <td>89.646645</td>\n",
       "      <td>9035.601044</td>\n",
       "      <td>8964.664459</td>\n",
       "      <td>-70.936584</td>\n",
       "      <td>-70.936584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>2018-05-08</td>\n",
       "      <td>100</td>\n",
       "      <td>91.065353</td>\n",
       "      <td>91.841820</td>\n",
       "      <td>9106.535339</td>\n",
       "      <td>9184.181976</td>\n",
       "      <td>77.646637</td>\n",
       "      <td>6.710052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-05-17</td>\n",
       "      <td>2018-05-21</td>\n",
       "      <td>100</td>\n",
       "      <td>92.596115</td>\n",
       "      <td>93.963203</td>\n",
       "      <td>9259.611511</td>\n",
       "      <td>9396.320343</td>\n",
       "      <td>136.708832</td>\n",
       "      <td>143.418884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-05-24</td>\n",
       "      <td>2018-05-30</td>\n",
       "      <td>100</td>\n",
       "      <td>94.646744</td>\n",
       "      <td>95.262878</td>\n",
       "      <td>9464.674377</td>\n",
       "      <td>9526.287842</td>\n",
       "      <td>61.613464</td>\n",
       "      <td>205.032349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-05-31</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>100</td>\n",
       "      <td>95.156998</td>\n",
       "      <td>97.034332</td>\n",
       "      <td>9515.699768</td>\n",
       "      <td>9703.433228</td>\n",
       "      <td>187.733459</td>\n",
       "      <td>392.765808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>2018-06-11</td>\n",
       "      <td>100</td>\n",
       "      <td>98.670975</td>\n",
       "      <td>97.284630</td>\n",
       "      <td>9867.097473</td>\n",
       "      <td>9728.462982</td>\n",
       "      <td>-138.634491</td>\n",
       "      <td>254.131317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-06-13</td>\n",
       "      <td>2018-06-15</td>\n",
       "      <td>100</td>\n",
       "      <td>97.092094</td>\n",
       "      <td>96.398911</td>\n",
       "      <td>9709.209442</td>\n",
       "      <td>9639.891052</td>\n",
       "      <td>-69.318390</td>\n",
       "      <td>184.812927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-06-22</td>\n",
       "      <td>2018-07-05</td>\n",
       "      <td>100</td>\n",
       "      <td>96.668503</td>\n",
       "      <td>96.042717</td>\n",
       "      <td>9666.850281</td>\n",
       "      <td>9604.271698</td>\n",
       "      <td>-62.578583</td>\n",
       "      <td>122.234344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-07-06</td>\n",
       "      <td>2018-07-09</td>\n",
       "      <td>100</td>\n",
       "      <td>97.390549</td>\n",
       "      <td>98.054832</td>\n",
       "      <td>9739.054871</td>\n",
       "      <td>9805.483246</td>\n",
       "      <td>66.428375</td>\n",
       "      <td>188.662720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-07-19</td>\n",
       "      <td>2018-07-26</td>\n",
       "      <td>100</td>\n",
       "      <td>100.509819</td>\n",
       "      <td>105.535301</td>\n",
       "      <td>10050.981903</td>\n",
       "      <td>10553.530121</td>\n",
       "      <td>502.548218</td>\n",
       "      <td>691.210938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-07-27</td>\n",
       "      <td>2018-08-06</td>\n",
       "      <td>100</td>\n",
       "      <td>103.667595</td>\n",
       "      <td>104.100815</td>\n",
       "      <td>10366.759491</td>\n",
       "      <td>10410.081482</td>\n",
       "      <td>43.321991</td>\n",
       "      <td>734.532928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-08-16</td>\n",
       "      <td>2018-08-23</td>\n",
       "      <td>100</td>\n",
       "      <td>104.027878</td>\n",
       "      <td>103.950562</td>\n",
       "      <td>10402.787781</td>\n",
       "      <td>10395.056152</td>\n",
       "      <td>-7.731628</td>\n",
       "      <td>726.801300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-08-29</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>100</td>\n",
       "      <td>108.260895</td>\n",
       "      <td>108.560493</td>\n",
       "      <td>10826.089478</td>\n",
       "      <td>10856.049347</td>\n",
       "      <td>29.959869</td>\n",
       "      <td>756.761169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-09-06</td>\n",
       "      <td>2018-09-13</td>\n",
       "      <td>100</td>\n",
       "      <td>105.090958</td>\n",
       "      <td>109.121033</td>\n",
       "      <td>10509.095764</td>\n",
       "      <td>10912.103271</td>\n",
       "      <td>403.007507</td>\n",
       "      <td>1159.768677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-09-14</td>\n",
       "      <td>2018-09-18</td>\n",
       "      <td>100</td>\n",
       "      <td>109.565590</td>\n",
       "      <td>109.410957</td>\n",
       "      <td>10956.558990</td>\n",
       "      <td>10941.095734</td>\n",
       "      <td>-15.463257</td>\n",
       "      <td>1144.305420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>100</td>\n",
       "      <td>110.821953</td>\n",
       "      <td>110.532043</td>\n",
       "      <td>11082.195282</td>\n",
       "      <td>11053.204346</td>\n",
       "      <td>-28.990936</td>\n",
       "      <td>1115.314484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>2018-10-22</td>\n",
       "      <td>100</td>\n",
       "      <td>108.367210</td>\n",
       "      <td>105.951096</td>\n",
       "      <td>10836.721039</td>\n",
       "      <td>10595.109558</td>\n",
       "      <td>-241.611481</td>\n",
       "      <td>873.703003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-10-30</td>\n",
       "      <td>2018-11-02</td>\n",
       "      <td>100</td>\n",
       "      <td>100.249069</td>\n",
       "      <td>102.597549</td>\n",
       "      <td>10024.906921</td>\n",
       "      <td>10259.754944</td>\n",
       "      <td>234.848022</td>\n",
       "      <td>1108.551025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-11-13</td>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>100</td>\n",
       "      <td>103.351372</td>\n",
       "      <td>101.546028</td>\n",
       "      <td>10335.137177</td>\n",
       "      <td>10154.602814</td>\n",
       "      <td>-180.534363</td>\n",
       "      <td>928.016663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-11-23</td>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>100</td>\n",
       "      <td>100.041573</td>\n",
       "      <td>107.631798</td>\n",
       "      <td>10004.157257</td>\n",
       "      <td>10763.179779</td>\n",
       "      <td>759.022522</td>\n",
       "      <td>1687.039185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-12-03</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>100</td>\n",
       "      <td>108.796532</td>\n",
       "      <td>105.331413</td>\n",
       "      <td>10879.653168</td>\n",
       "      <td>10533.141327</td>\n",
       "      <td>-346.511841</td>\n",
       "      <td>1340.527344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>2018-12-10</td>\n",
       "      <td>100</td>\n",
       "      <td>101.740143</td>\n",
       "      <td>104.428749</td>\n",
       "      <td>10174.014282</td>\n",
       "      <td>10442.874908</td>\n",
       "      <td>268.860626</td>\n",
       "      <td>1609.387970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-12-11</td>\n",
       "      <td>2018-12-13</td>\n",
       "      <td>100</td>\n",
       "      <td>105.399368</td>\n",
       "      <td>106.234093</td>\n",
       "      <td>10539.936829</td>\n",
       "      <td>10623.409271</td>\n",
       "      <td>83.472443</td>\n",
       "      <td>1692.860413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-12-14</td>\n",
       "      <td>2018-12-18</td>\n",
       "      <td>100</td>\n",
       "      <td>102.914597</td>\n",
       "      <td>100.915115</td>\n",
       "      <td>10291.459656</td>\n",
       "      <td>10091.511536</td>\n",
       "      <td>-199.948120</td>\n",
       "      <td>1492.912292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>100</td>\n",
       "      <td>97.440315</td>\n",
       "      <td>98.935059</td>\n",
       "      <td>9744.031525</td>\n",
       "      <td>9893.505859</td>\n",
       "      <td>149.474335</td>\n",
       "      <td>1642.386627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>100</td>\n",
       "      <td>99.779510</td>\n",
       "      <td>99.051529</td>\n",
       "      <td>9977.951050</td>\n",
       "      <td>9905.152893</td>\n",
       "      <td>-72.798157</td>\n",
       "      <td>1569.588470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2019-01-22</td>\n",
       "      <td>2019-01-24</td>\n",
       "      <td>100</td>\n",
       "      <td>102.574875</td>\n",
       "      <td>103.079590</td>\n",
       "      <td>10257.487488</td>\n",
       "      <td>10307.958984</td>\n",
       "      <td>50.471497</td>\n",
       "      <td>1620.059967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2019-01-29</td>\n",
       "      <td>2019-01-30</td>\n",
       "      <td>100</td>\n",
       "      <td>99.915382</td>\n",
       "      <td>103.254318</td>\n",
       "      <td>9991.538239</td>\n",
       "      <td>10325.431824</td>\n",
       "      <td>333.893585</td>\n",
       "      <td>1953.953552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>2019-02-12</td>\n",
       "      <td>100</td>\n",
       "      <td>101.361595</td>\n",
       "      <td>103.749321</td>\n",
       "      <td>10136.159515</td>\n",
       "      <td>10374.932098</td>\n",
       "      <td>238.772583</td>\n",
       "      <td>2192.726135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2019-02-25</td>\n",
       "      <td>2019-02-26</td>\n",
       "      <td>100</td>\n",
       "      <td>108.773781</td>\n",
       "      <td>109.524368</td>\n",
       "      <td>10877.378082</td>\n",
       "      <td>10952.436829</td>\n",
       "      <td>75.058746</td>\n",
       "      <td>2267.784882</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>100</td>\n",
       "      <td>206.351974</td>\n",
       "      <td>206.044937</td>\n",
       "      <td>20635.197449</td>\n",
       "      <td>20604.493713</td>\n",
       "      <td>-30.703735</td>\n",
       "      <td>6703.185272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-07-16</td>\n",
       "      <td>2020-07-21</td>\n",
       "      <td>100</td>\n",
       "      <td>201.964462</td>\n",
       "      <td>206.748154</td>\n",
       "      <td>20196.446228</td>\n",
       "      <td>20674.815369</td>\n",
       "      <td>478.369141</td>\n",
       "      <td>7181.554413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-07-23</td>\n",
       "      <td>2020-07-31</td>\n",
       "      <td>100</td>\n",
       "      <td>200.597687</td>\n",
       "      <td>203.044006</td>\n",
       "      <td>20059.768677</td>\n",
       "      <td>20304.400635</td>\n",
       "      <td>244.631958</td>\n",
       "      <td>7426.186371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-08-07</td>\n",
       "      <td>2020-08-12</td>\n",
       "      <td>100</td>\n",
       "      <td>210.442368</td>\n",
       "      <td>207.183945</td>\n",
       "      <td>21044.236755</td>\n",
       "      <td>20718.394470</td>\n",
       "      <td>-325.842285</td>\n",
       "      <td>7100.344086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-08-18</td>\n",
       "      <td>2020-08-20</td>\n",
       "      <td>100</td>\n",
       "      <td>209.461884</td>\n",
       "      <td>213.035965</td>\n",
       "      <td>20946.188354</td>\n",
       "      <td>21303.596497</td>\n",
       "      <td>357.408142</td>\n",
       "      <td>7457.752228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-08-27</td>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>100</td>\n",
       "      <td>224.949631</td>\n",
       "      <td>229.983139</td>\n",
       "      <td>22494.963074</td>\n",
       "      <td>22998.313904</td>\n",
       "      <td>503.350830</td>\n",
       "      <td>7961.103058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-09-08</td>\n",
       "      <td>2020-09-17</td>\n",
       "      <td>100</td>\n",
       "      <td>201.201736</td>\n",
       "      <td>201.449951</td>\n",
       "      <td>20120.173645</td>\n",
       "      <td>20144.995117</td>\n",
       "      <td>24.821472</td>\n",
       "      <td>7985.924530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-09-25</td>\n",
       "      <td>2020-10-02</td>\n",
       "      <td>100</td>\n",
       "      <td>206.324615</td>\n",
       "      <td>204.706360</td>\n",
       "      <td>20632.461548</td>\n",
       "      <td>20470.635986</td>\n",
       "      <td>-161.825562</td>\n",
       "      <td>7824.098969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-10-08</td>\n",
       "      <td>2020-10-14</td>\n",
       "      <td>100</td>\n",
       "      <td>209.064774</td>\n",
       "      <td>219.270782</td>\n",
       "      <td>20906.477356</td>\n",
       "      <td>21927.078247</td>\n",
       "      <td>1020.600891</td>\n",
       "      <td>8844.699860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-10-21</td>\n",
       "      <td>2020-10-22</td>\n",
       "      <td>100</td>\n",
       "      <td>213.254395</td>\n",
       "      <td>213.343750</td>\n",
       "      <td>21325.439453</td>\n",
       "      <td>21334.375000</td>\n",
       "      <td>8.935547</td>\n",
       "      <td>8853.635406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-10-28</td>\n",
       "      <td>2020-11-06</td>\n",
       "      <td>100</td>\n",
       "      <td>201.221588</td>\n",
       "      <td>222.110214</td>\n",
       "      <td>20122.158813</td>\n",
       "      <td>22211.021423</td>\n",
       "      <td>2088.862610</td>\n",
       "      <td>10942.498016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-11-12</td>\n",
       "      <td>2020-11-18</td>\n",
       "      <td>100</td>\n",
       "      <td>213.889786</td>\n",
       "      <td>210.109802</td>\n",
       "      <td>21388.978577</td>\n",
       "      <td>21010.980225</td>\n",
       "      <td>-377.998352</td>\n",
       "      <td>10564.499664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>100</td>\n",
       "      <td>214.240707</td>\n",
       "      <td>213.086075</td>\n",
       "      <td>21424.070740</td>\n",
       "      <td>21308.607483</td>\n",
       "      <td>-115.463257</td>\n",
       "      <td>10449.036407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>2020-12-03</td>\n",
       "      <td>100</td>\n",
       "      <td>215.216232</td>\n",
       "      <td>213.255280</td>\n",
       "      <td>21521.623230</td>\n",
       "      <td>21325.527954</td>\n",
       "      <td>-196.095276</td>\n",
       "      <td>10252.941132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-12-10</td>\n",
       "      <td>2020-12-11</td>\n",
       "      <td>100</td>\n",
       "      <td>209.552383</td>\n",
       "      <td>212.279770</td>\n",
       "      <td>20955.238342</td>\n",
       "      <td>21227.976990</td>\n",
       "      <td>272.738647</td>\n",
       "      <td>10525.679779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>2020-12-21</td>\n",
       "      <td>100</td>\n",
       "      <td>213.215454</td>\n",
       "      <td>221.566895</td>\n",
       "      <td>21321.545410</td>\n",
       "      <td>22156.689453</td>\n",
       "      <td>835.144043</td>\n",
       "      <td>11360.823822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>100</td>\n",
       "      <td>223.925995</td>\n",
       "      <td>220.661072</td>\n",
       "      <td>22392.599487</td>\n",
       "      <td>22066.107178</td>\n",
       "      <td>-326.492310</td>\n",
       "      <td>11034.331512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>2021-01-08</td>\n",
       "      <td>100</td>\n",
       "      <td>217.286652</td>\n",
       "      <td>218.610550</td>\n",
       "      <td>21728.665161</td>\n",
       "      <td>21861.054993</td>\n",
       "      <td>132.389832</td>\n",
       "      <td>11166.721344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-01-11</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>100</td>\n",
       "      <td>216.490341</td>\n",
       "      <td>211.672577</td>\n",
       "      <td>21649.034119</td>\n",
       "      <td>21167.257690</td>\n",
       "      <td>-481.776428</td>\n",
       "      <td>10684.944916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-01-25</td>\n",
       "      <td>2021-01-28</td>\n",
       "      <td>100</td>\n",
       "      <td>228.474991</td>\n",
       "      <td>237.831787</td>\n",
       "      <td>22847.499084</td>\n",
       "      <td>23783.178711</td>\n",
       "      <td>935.679626</td>\n",
       "      <td>11620.624542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-02-04</td>\n",
       "      <td>2021-02-09</td>\n",
       "      <td>100</td>\n",
       "      <td>240.897629</td>\n",
       "      <td>242.649536</td>\n",
       "      <td>24089.762878</td>\n",
       "      <td>24264.953613</td>\n",
       "      <td>175.190735</td>\n",
       "      <td>11795.815277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-02-19</td>\n",
       "      <td>2021-03-11</td>\n",
       "      <td>100</td>\n",
       "      <td>240.414856</td>\n",
       "      <td>236.583710</td>\n",
       "      <td>24041.485596</td>\n",
       "      <td>23658.370972</td>\n",
       "      <td>-383.114624</td>\n",
       "      <td>11412.700653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-03-12</td>\n",
       "      <td>2021-03-15</td>\n",
       "      <td>100</td>\n",
       "      <td>235.206879</td>\n",
       "      <td>234.269043</td>\n",
       "      <td>23520.687866</td>\n",
       "      <td>23426.904297</td>\n",
       "      <td>-93.783569</td>\n",
       "      <td>11318.917084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-03-16</td>\n",
       "      <td>2021-03-17</td>\n",
       "      <td>100</td>\n",
       "      <td>237.162369</td>\n",
       "      <td>236.493912</td>\n",
       "      <td>23716.236877</td>\n",
       "      <td>23649.391174</td>\n",
       "      <td>-66.845703</td>\n",
       "      <td>11252.071381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-03-24</td>\n",
       "      <td>2021-03-30</td>\n",
       "      <td>100</td>\n",
       "      <td>234.917557</td>\n",
       "      <td>231.315872</td>\n",
       "      <td>23491.755676</td>\n",
       "      <td>23131.587219</td>\n",
       "      <td>-360.168457</td>\n",
       "      <td>10891.902924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-04-07</td>\n",
       "      <td>2021-04-13</td>\n",
       "      <td>100</td>\n",
       "      <td>249.324280</td>\n",
       "      <td>257.894501</td>\n",
       "      <td>24932.427979</td>\n",
       "      <td>25789.450073</td>\n",
       "      <td>857.022095</td>\n",
       "      <td>11748.925018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-04-20</td>\n",
       "      <td>2021-04-23</td>\n",
       "      <td>100</td>\n",
       "      <td>257.665039</td>\n",
       "      <td>260.548370</td>\n",
       "      <td>25766.503906</td>\n",
       "      <td>26054.837036</td>\n",
       "      <td>288.333130</td>\n",
       "      <td>12037.258148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>2021-04-27</td>\n",
       "      <td>100</td>\n",
       "      <td>260.947449</td>\n",
       "      <td>261.366486</td>\n",
       "      <td>26094.744873</td>\n",
       "      <td>26136.648560</td>\n",
       "      <td>41.903687</td>\n",
       "      <td>12079.161835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-04-29</td>\n",
       "      <td>2021-05-06</td>\n",
       "      <td>100</td>\n",
       "      <td>251.928268</td>\n",
       "      <td>249.154678</td>\n",
       "      <td>25192.826843</td>\n",
       "      <td>24915.467834</td>\n",
       "      <td>-277.359009</td>\n",
       "      <td>11801.802826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-05-13</td>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>100</td>\n",
       "      <td>242.470108</td>\n",
       "      <td>250.779999</td>\n",
       "      <td>24247.010803</td>\n",
       "      <td>25077.999878</td>\n",
       "      <td>830.989075</td>\n",
       "      <td>12632.791901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Stock Entry Date  Exit Date Shares  Entry Share Price  Exit Share Price  \\\n",
       "0    MSFT 2018-04-26 2018-04-30    100          90.356010         89.646645   \n",
       "1    MSFT 2018-05-01 2018-05-08    100          91.065353         91.841820   \n",
       "2    MSFT 2018-05-17 2018-05-21    100          92.596115         93.963203   \n",
       "3    MSFT 2018-05-24 2018-05-30    100          94.646744         95.262878   \n",
       "4    MSFT 2018-05-31 2018-06-01    100          95.156998         97.034332   \n",
       "5    MSFT 2018-06-06 2018-06-11    100          98.670975         97.284630   \n",
       "6    MSFT 2018-06-13 2018-06-15    100          97.092094         96.398911   \n",
       "7    MSFT 2018-06-22 2018-07-05    100          96.668503         96.042717   \n",
       "8    MSFT 2018-07-06 2018-07-09    100          97.390549         98.054832   \n",
       "9    MSFT 2018-07-19 2018-07-26    100         100.509819        105.535301   \n",
       "10   MSFT 2018-07-27 2018-08-06    100         103.667595        104.100815   \n",
       "11   MSFT 2018-08-16 2018-08-23    100         104.027878        103.950562   \n",
       "12   MSFT 2018-08-29 2018-08-31    100         108.260895        108.560493   \n",
       "13   MSFT 2018-09-06 2018-09-13    100         105.090958        109.121033   \n",
       "14   MSFT 2018-09-14 2018-09-18    100         109.565590        109.410957   \n",
       "15   MSFT 2018-09-24 2018-09-28    100         110.821953        110.532043   \n",
       "16   MSFT 2018-10-05 2018-10-22    100         108.367210        105.951096   \n",
       "17   MSFT 2018-10-30 2018-11-02    100         100.249069        102.597549   \n",
       "18   MSFT 2018-11-13 2018-11-19    100         103.351372        101.546028   \n",
       "19   MSFT 2018-11-23 2018-11-30    100         100.041573        107.631798   \n",
       "20   MSFT 2018-12-03 2018-12-04    100         108.796532        105.331413   \n",
       "21   MSFT 2018-12-07 2018-12-10    100         101.740143        104.428749   \n",
       "22   MSFT 2018-12-11 2018-12-13    100         105.399368        106.234093   \n",
       "23   MSFT 2018-12-14 2018-12-18    100         102.914597        100.915115   \n",
       "24   MSFT 2018-12-28 2019-01-04    100          97.440315         98.935059   \n",
       "25   MSFT 2019-01-11 2019-01-14    100          99.779510         99.051529   \n",
       "26   MSFT 2019-01-22 2019-01-24    100         102.574875        103.079590   \n",
       "27   MSFT 2019-01-29 2019-01-30    100          99.915382        103.254318   \n",
       "28   MSFT 2019-01-31 2019-02-12    100         101.361595        103.749321   \n",
       "29   MSFT 2019-02-25 2019-02-26    100         108.773781        109.524368   \n",
       "..    ...        ...        ...    ...                ...               ...   \n",
       "83   MSFT 2020-07-14 2020-07-15    100         206.351974        206.044937   \n",
       "84   MSFT 2020-07-16 2020-07-21    100         201.964462        206.748154   \n",
       "85   MSFT 2020-07-23 2020-07-31    100         200.597687        203.044006   \n",
       "86   MSFT 2020-08-07 2020-08-12    100         210.442368        207.183945   \n",
       "87   MSFT 2020-08-18 2020-08-20    100         209.461884        213.035965   \n",
       "88   MSFT 2020-08-27 2020-09-02    100         224.949631        229.983139   \n",
       "89   MSFT 2020-09-08 2020-09-17    100         201.201736        201.449951   \n",
       "90   MSFT 2020-09-25 2020-10-02    100         206.324615        204.706360   \n",
       "91   MSFT 2020-10-08 2020-10-14    100         209.064774        219.270782   \n",
       "92   MSFT 2020-10-21 2020-10-22    100         213.254395        213.343750   \n",
       "93   MSFT 2020-10-28 2020-11-06    100         201.221588        222.110214   \n",
       "94   MSFT 2020-11-12 2020-11-18    100         213.889786        210.109802   \n",
       "95   MSFT 2020-11-27 2020-11-30    100         214.240707        213.086075   \n",
       "96   MSFT 2020-12-01 2020-12-03    100         215.216232        213.255280   \n",
       "97   MSFT 2020-12-10 2020-12-11    100         209.552383        212.279770   \n",
       "98   MSFT 2020-12-14 2020-12-21    100         213.215454        221.566895   \n",
       "99   MSFT 2020-12-28 2020-12-30    100         223.925995        220.661072   \n",
       "100  MSFT 2021-01-07 2021-01-08    100         217.286652        218.610550   \n",
       "101  MSFT 2021-01-11 2021-01-15    100         216.490341        211.672577   \n",
       "102  MSFT 2021-01-25 2021-01-28    100         228.474991        237.831787   \n",
       "103  MSFT 2021-02-04 2021-02-09    100         240.897629        242.649536   \n",
       "104  MSFT 2021-02-19 2021-03-11    100         240.414856        236.583710   \n",
       "105  MSFT 2021-03-12 2021-03-15    100         235.206879        234.269043   \n",
       "106  MSFT 2021-03-16 2021-03-17    100         237.162369        236.493912   \n",
       "107  MSFT 2021-03-24 2021-03-30    100         234.917557        231.315872   \n",
       "108  MSFT 2021-04-07 2021-04-13    100         249.324280        257.894501   \n",
       "109  MSFT 2021-04-20 2021-04-23    100         257.665039        260.548370   \n",
       "110  MSFT 2021-04-26 2021-04-27    100         260.947449        261.366486   \n",
       "111  MSFT 2021-04-29 2021-05-06    100         251.928268        249.154678   \n",
       "112  MSFT 2021-05-13 2021-05-24    100         242.470108        250.779999   \n",
       "\n",
       "     Entry Portfolio Holding  Exit Portfolio Holding  Profit/Loss  \\\n",
       "0                9035.601044             8964.664459   -70.936584   \n",
       "1                9106.535339             9184.181976    77.646637   \n",
       "2                9259.611511             9396.320343   136.708832   \n",
       "3                9464.674377             9526.287842    61.613464   \n",
       "4                9515.699768             9703.433228   187.733459   \n",
       "5                9867.097473             9728.462982  -138.634491   \n",
       "6                9709.209442             9639.891052   -69.318390   \n",
       "7                9666.850281             9604.271698   -62.578583   \n",
       "8                9739.054871             9805.483246    66.428375   \n",
       "9               10050.981903            10553.530121   502.548218   \n",
       "10              10366.759491            10410.081482    43.321991   \n",
       "11              10402.787781            10395.056152    -7.731628   \n",
       "12              10826.089478            10856.049347    29.959869   \n",
       "13              10509.095764            10912.103271   403.007507   \n",
       "14              10956.558990            10941.095734   -15.463257   \n",
       "15              11082.195282            11053.204346   -28.990936   \n",
       "16              10836.721039            10595.109558  -241.611481   \n",
       "17              10024.906921            10259.754944   234.848022   \n",
       "18              10335.137177            10154.602814  -180.534363   \n",
       "19              10004.157257            10763.179779   759.022522   \n",
       "20              10879.653168            10533.141327  -346.511841   \n",
       "21              10174.014282            10442.874908   268.860626   \n",
       "22              10539.936829            10623.409271    83.472443   \n",
       "23              10291.459656            10091.511536  -199.948120   \n",
       "24               9744.031525             9893.505859   149.474335   \n",
       "25               9977.951050             9905.152893   -72.798157   \n",
       "26              10257.487488            10307.958984    50.471497   \n",
       "27               9991.538239            10325.431824   333.893585   \n",
       "28              10136.159515            10374.932098   238.772583   \n",
       "29              10877.378082            10952.436829    75.058746   \n",
       "..                       ...                     ...          ...   \n",
       "83              20635.197449            20604.493713   -30.703735   \n",
       "84              20196.446228            20674.815369   478.369141   \n",
       "85              20059.768677            20304.400635   244.631958   \n",
       "86              21044.236755            20718.394470  -325.842285   \n",
       "87              20946.188354            21303.596497   357.408142   \n",
       "88              22494.963074            22998.313904   503.350830   \n",
       "89              20120.173645            20144.995117    24.821472   \n",
       "90              20632.461548            20470.635986  -161.825562   \n",
       "91              20906.477356            21927.078247  1020.600891   \n",
       "92              21325.439453            21334.375000     8.935547   \n",
       "93              20122.158813            22211.021423  2088.862610   \n",
       "94              21388.978577            21010.980225  -377.998352   \n",
       "95              21424.070740            21308.607483  -115.463257   \n",
       "96              21521.623230            21325.527954  -196.095276   \n",
       "97              20955.238342            21227.976990   272.738647   \n",
       "98              21321.545410            22156.689453   835.144043   \n",
       "99              22392.599487            22066.107178  -326.492310   \n",
       "100             21728.665161            21861.054993   132.389832   \n",
       "101             21649.034119            21167.257690  -481.776428   \n",
       "102             22847.499084            23783.178711   935.679626   \n",
       "103             24089.762878            24264.953613   175.190735   \n",
       "104             24041.485596            23658.370972  -383.114624   \n",
       "105             23520.687866            23426.904297   -93.783569   \n",
       "106             23716.236877            23649.391174   -66.845703   \n",
       "107             23491.755676            23131.587219  -360.168457   \n",
       "108             24932.427979            25789.450073   857.022095   \n",
       "109             25766.503906            26054.837036   288.333130   \n",
       "110             26094.744873            26136.648560    41.903687   \n",
       "111             25192.826843            24915.467834  -277.359009   \n",
       "112             24247.010803            25077.999878   830.989075   \n",
       "\n",
       "     Cumulative Profit/Loss  \n",
       "0                -70.936584  \n",
       "1                  6.710052  \n",
       "2                143.418884  \n",
       "3                205.032349  \n",
       "4                392.765808  \n",
       "5                254.131317  \n",
       "6                184.812927  \n",
       "7                122.234344  \n",
       "8                188.662720  \n",
       "9                691.210938  \n",
       "10               734.532928  \n",
       "11               726.801300  \n",
       "12               756.761169  \n",
       "13              1159.768677  \n",
       "14              1144.305420  \n",
       "15              1115.314484  \n",
       "16               873.703003  \n",
       "17              1108.551025  \n",
       "18               928.016663  \n",
       "19              1687.039185  \n",
       "20              1340.527344  \n",
       "21              1609.387970  \n",
       "22              1692.860413  \n",
       "23              1492.912292  \n",
       "24              1642.386627  \n",
       "25              1569.588470  \n",
       "26              1620.059967  \n",
       "27              1953.953552  \n",
       "28              2192.726135  \n",
       "29              2267.784882  \n",
       "..                      ...  \n",
       "83              6703.185272  \n",
       "84              7181.554413  \n",
       "85              7426.186371  \n",
       "86              7100.344086  \n",
       "87              7457.752228  \n",
       "88              7961.103058  \n",
       "89              7985.924530  \n",
       "90              7824.098969  \n",
       "91              8844.699860  \n",
       "92              8853.635406  \n",
       "93             10942.498016  \n",
       "94             10564.499664  \n",
       "95             10449.036407  \n",
       "96             10252.941132  \n",
       "97             10525.679779  \n",
       "98             11360.823822  \n",
       "99             11034.331512  \n",
       "100            11166.721344  \n",
       "101            10684.944916  \n",
       "102            11620.624542  \n",
       "103            11795.815277  \n",
       "104            11412.700653  \n",
       "105            11318.917084  \n",
       "106            11252.071381  \n",
       "107            10891.902924  \n",
       "108            11748.925018  \n",
       "109            12037.258148  \n",
       "110            12079.161835  \n",
       "111            11801.802826  \n",
       "112            12632.791901  \n",
       "\n",
       "[113 rows x 10 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize iterative variables\n",
    "entry_date = ''\n",
    "exit_date = ''\n",
    "entry_portfolio_holding = 0\n",
    "exit_portfolio_holding = 0\n",
    "share_size = 100\n",
    "entry_share_price = 0\n",
    "exit_share_price = 0\n",
    "\n",
    "# Loop through signal DataFrame\n",
    "# If `Entry/Exit` is 1, set entry trade metrics\n",
    "# Else if `Entry/Exit` is -1, set exit trade metrics and calculate profit,\n",
    "# Then append the record to the trade evaluation DataFrame\n",
    "for index, row in std_dev_prices.iterrows():\n",
    "    if row['Predicted Entry/Exit'] == 1:\n",
    "        entry_date = index\n",
    "        entry_share_price = row['Actual']\n",
    "        entry_portfolio_holding = entry_share_price * share_size\n",
    "        \n",
    "\n",
    "    elif row['Predicted Entry/Exit'] == -1:\n",
    "        exit_date = index\n",
    "        exit_portfolio_holding = abs(row['Actual']) * share_size\n",
    "        exit_share_price = row['Actual']\n",
    "        profit_loss =  exit_portfolio_holding - entry_portfolio_holding\n",
    "        trade_evaluation_df = trade_evaluation_df.append(\n",
    "            {\n",
    "                'Stock': ticker,\n",
    "                'Entry Date': entry_date,\n",
    "                'Exit Date': exit_date,\n",
    "                'Shares': share_size,\n",
    "                'Entry Share Price': entry_share_price,\n",
    "                'Exit Share Price': exit_share_price,\n",
    "                'Entry Portfolio Holding': entry_portfolio_holding,\n",
    "                'Exit Portfolio Holding': exit_portfolio_holding,\n",
    "                'Profit/Loss': profit_loss\n",
    "            },\n",
    "            ignore_index=True)\n",
    "trade_evaluation_df[\"Cumulative Profit/Loss\"] = trade_evaluation_df[\"Profit/Loss\"].cumsum()\n",
    "# Print the DataFrame\n",
    "trade_evaluation_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Export to csv\n",
    "trade_evaluation_df.to_csv(\"lstm_trade_evaluation.csv\")"
   ]
  }
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