(Beta)ShortTerm_StockPrice_Forecast_LSTM_sequence.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import datetime\n",
    "import pytz\n",
    "\n",
    "import yfinance as yf\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense, Dropout\n",
    "from keras.regularizers import l1_l2\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch stock data and earnings dates\n",
    "def fetch_stock_data_with_earnings(ticker, period):\n",
    "    try:\n",
    "        stock = yf.Ticker(ticker)\n",
    "\n",
    "        data = stock.history(period=period)\n",
    "        data.index = data.index.date\n",
    "\n",
    "        earnings = stock.earnings_dates\n",
    "        earnings.index = earnings.index.date\n",
    "        \n",
    "        return data, earnings\n",
    "    except Exception as e:\n",
    "        print(f\"Error fetching data for {ticker}: {e}\")\n",
    "        return None, None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch stock data\n",
    "ticker = \"TSLA\"\n",
    "period = \"1y\"\n",
    "\n",
    "data, earnings = fetch_stock_data_with_earnings(ticker, period)\n",
    "if data is None:\n",
    "    raise ValueError(\"Failed to fetch stock data.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge S&P 500 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch S&P 500 historical data\n",
    "sp500 = yf.Ticker(\"^GSPC\")\n",
    "sp500_data = sp500.history(period=period)\n",
    "sp500_data.index = sp500_data.index.date\n",
    "\n",
    "data_sp500 = sp500_data[['Close']] \n",
    "data_sp500.columns = ['SP500_Close']    # Rename the columns for clarity\n",
    "data = data.merge(data_sp500, how='left', left_index=True, right_index=True)\n",
    "\n",
    "data = data.drop(columns=['Dividends', 'Stock Splits']) # Drop unused columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>SP500_Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-02-16</th>\n",
       "      <td>202.059998</td>\n",
       "      <td>203.169998</td>\n",
       "      <td>197.399994</td>\n",
       "      <td>199.949997</td>\n",
       "      <td>111173600</td>\n",
       "      <td>5005.569824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-20</th>\n",
       "      <td>196.130005</td>\n",
       "      <td>198.600006</td>\n",
       "      <td>189.130005</td>\n",
       "      <td>193.759995</td>\n",
       "      <td>104545800</td>\n",
       "      <td>4975.509766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-21</th>\n",
       "      <td>193.360001</td>\n",
       "      <td>199.440002</td>\n",
       "      <td>191.949997</td>\n",
       "      <td>194.770004</td>\n",
       "      <td>103844000</td>\n",
       "      <td>4981.799805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-22</th>\n",
       "      <td>194.000000</td>\n",
       "      <td>198.320007</td>\n",
       "      <td>191.360001</td>\n",
       "      <td>197.410004</td>\n",
       "      <td>92739500</td>\n",
       "      <td>5087.029785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-23</th>\n",
       "      <td>195.309998</td>\n",
       "      <td>197.570007</td>\n",
       "      <td>191.500000</td>\n",
       "      <td>191.970001</td>\n",
       "      <td>78670300</td>\n",
       "      <td>5088.799805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close     Volume  \\\n",
       "2024-02-16  202.059998  203.169998  197.399994  199.949997  111173600   \n",
       "2024-02-20  196.130005  198.600006  189.130005  193.759995  104545800   \n",
       "2024-02-21  193.360001  199.440002  191.949997  194.770004  103844000   \n",
       "2024-02-22  194.000000  198.320007  191.360001  197.410004   92739500   \n",
       "2024-02-23  195.309998  197.570007  191.500000  191.970001   78670300   \n",
       "\n",
       "            SP500_Close  \n",
       "2024-02-16  5005.569824  \n",
       "2024-02-20  4975.509766  \n",
       "2024-02-21  4981.799805  \n",
       "2024-02-22  5087.029785  \n",
       "2024-02-23  5088.799805  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 7))  # Set figure size\n",
    "\n",
    "# Plot ticker 'Close' price with the first y-axis\n",
    "ax1 = plt.gca()\n",
    "line1 = ax1.plot(data.index, data['Close'], label='Ticker Close Price', color='blue')\n",
    "ax1.set_ylabel('Ticker Close Price', color='blue')\n",
    "ax1.tick_params(axis='y', labelcolor='blue')\n",
    "\n",
    "# Plot 'SP500_Close' with the second y-axis\n",
    "ax2 = ax1.twinx()\n",
    "line2 = ax2.plot(data.index, data['SP500_Close'], label='S&P 500 Close Price', color='red', linestyle='--')\n",
    "ax2.set_ylabel('S&P 500 Close Price', color='red')\n",
    "ax2.tick_params(axis='y', labelcolor='red')\n",
    "\n",
    "plt.title('Ticker Close Price vs. S&P 500 Close Price')\n",
    "plt.xlabel('Date')\n",
    "\n",
    "# Combine legends\n",
    "lines = line1 + line2\n",
    "labels = [l.get_label() for l in lines]\n",
    "plt.legend(lines, labels)\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After Hours.\n"
     ]
    }
   ],
   "source": [
    "# Function to check if the current time is within trading hours\n",
    "def is_trading_hours():\n",
    "    eastern = pytz.timezone('US/Eastern')\n",
    "    now_eastern = datetime.datetime.now(eastern)\n",
    "    \n",
    "    # Define market open and close times (9:30 AM to 4:00 PM)\n",
    "    market_open = now_eastern.replace(hour=9, minute=30, second=0, microsecond=0)\n",
    "    market_close = now_eastern.replace(hour=16, minute=0, second=0, microsecond=0)\n",
    "    \n",
    "    return market_open <= now_eastern <= market_close\n",
    "\n",
    "# Drop the last row if it's within trading hours (incomplete data)\n",
    "if is_trading_hours():\n",
    "    print('Trading Hours.')\n",
    "    data = data.drop(data.tail(1).index)\n",
    "else:\n",
    "    print('After Hours.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# EMA\n",
    "short_span, long_span = 10, 50\n",
    "\n",
    "data['EMA_Short'] = data['Close'].ewm(span=short_span, adjust=False).mean()\n",
    "data['EMA_Long'] = data['Close'].ewm(span=long_span, adjust=False).mean()\n",
    "\n",
    "# RSI\n",
    "delta = data['Close'].diff()\n",
    "gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()\n",
    "loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()\n",
    "rs = gain / loss\n",
    "data['RSI'] = 100 - (100 / (1 + rs))\n",
    "\n",
    "# MACD\n",
    "exp1 = data['Close'].ewm(span=12, adjust=False).mean()\n",
    "exp2 = data['Close'].ewm(span=26, adjust=False).mean()\n",
    "data['MACD'] = exp1 - exp2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EMA (Exponential Moving Average)\n",
    "\n",
    "- EMA is a type of moving average that places a greater weight and significance on the most recent data points. It is more responsive to new information compared to the SMA.\n",
    "- Usage in analysis: EMA is often used to identify the direction of the trend, with prices above the EMA suggesting an uptrend and prices below indicating a downtrend. Traders might use short-term EMAs to make quick, momentum-based decisions and longer-term EMAs for overall trend direction.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(data.index, data['Close'], label='Close Price')\n",
    "plt.plot(data.index, data['EMA_Short'], label='EMA Short')\n",
    "plt.plot(data.index, data['EMA_Long'], label='EMA Long')\n",
    "plt.title('EMA Short vs EMA Long')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RSI (Relative Strength Index)\n",
    "\n",
    "- RSI is a momentum oscillator that measures the speed and change of price movements on a scale of 0 to 100. It is used to identify overbought or oversold conditions in a security's price.\n",
    "- Usage in analysis: Values above 70 are considered overbought (suggesting a potential sell signal), while values below 30 are considered oversold (indicating a potential buy signal). RSI can also show divergence signals, where the price trend and RSI trend move in opposite directions, suggesting a potential reversal.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(data.index, data['RSI'], label='RSI')\n",
    "plt.title('Relative Strength Index (RSI) Over Time')\n",
    "plt.xlabel('Date')\n",
    "plt.axhline(70, linestyle='--', color='red')  # Overbought line\n",
    "plt.axhline(30, linestyle='--', color='green')  # Oversold line\n",
    "plt.ylabel('RSI')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MACD (Moving Average Convergence Divergence)\n",
    "\n",
    "- MACD is a trend-following momentum indicator that shows the relationship between two moving averages of a security's price. It consists of the MACD line (difference between two EMAs), a signal line (EMA of the MACD line), and a histogram (difference between MACD and its signal line).\n",
    "- Usage in analysis: MACD is used to identify changes in the strength, direction, momentum, and duration of a trend. A bullish crossover occurs when the MACD crosses above its signal line, suggesting a buy signal, while a bearish crossover (MACD falls below the signal line) suggests a sell signal. The divergence between the MACD and price action can also indicate potential market reversals.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(data.index, data['MACD'], label='MACD')\n",
    "plt.title('Moving Average Convergence Divergence (MACD)')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('MACD Value')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 251 entries, 2023-02-24 to 2024-02-23\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Open         251 non-null    float64\n",
      " 1   High         251 non-null    float64\n",
      " 2   Low          251 non-null    float64\n",
      " 3   Close        251 non-null    float64\n",
      " 4   Volume       251 non-null    int64  \n",
      " 5   SP500_Close  251 non-null    float64\n",
      " 6   EMA_Short    251 non-null    float64\n",
      " 7   EMA_Long     251 non-null    float64\n",
      " 8   RSI          238 non-null    float64\n",
      " 9   MACD         251 non-null    float64\n",
      "dtypes: float64(9), int64(1)\n",
      "memory usage: 29.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 12 entries, 2025-01-22 to 2022-04-20\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   EPS Estimate  9 non-null      float64\n",
      " 1   Reported EPS  8 non-null      float64\n",
      " 2   Surprise(%)   8 non-null      float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 384.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "earnings.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into features and labels\n",
    "features = data.drop(columns=['Close'])\n",
    "target = data[['Close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling\n",
    "feature_scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "features= features.bfill()\n",
    "scaled_features = feature_scaler.fit_transform(features)\n",
    "\n",
    "target_scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_target = target_scaler.fit_transform(target)\n",
    "\n",
    "\n",
    "# Convert scaled data back to DataFrame for further processing\n",
    "scaled_features_df = pd.DataFrame(scaled_features, index=data.index, columns=features.columns)\n",
    "scaled_target_df = pd.DataFrame(scaled_target, index=data.index, columns=target.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge earnings data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hd/pv9sg75d6b38kvxtq9c67bw40000gn/T/ipykernel_91290/2796264541.py:3: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  scaled_features_df = scaled_features_df.fillna(method='ffill').fillna(method='bfill')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>SP500_Close</th>\n",
       "      <th>EMA_Short</th>\n",
       "      <th>EMA_Long</th>\n",
       "      <th>RSI</th>\n",
       "      <th>MACD</th>\n",
       "      <th>EPS Estimate</th>\n",
       "      <th>Reported EPS</th>\n",
       "      <th>Surprise(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-02-24</th>\n",
       "      <td>0.304672</td>\n",
       "      <td>0.267920</td>\n",
       "      <td>0.294787</td>\n",
       "      <td>0.525683</td>\n",
       "      <td>0.092682</td>\n",
       "      <td>0.282480</td>\n",
       "      <td>0.264176</td>\n",
       "      <td>0.350206</td>\n",
       "      <td>0.386067</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-27</th>\n",
       "      <td>0.344421</td>\n",
       "      <td>0.352568</td>\n",
       "      <td>0.356471</td>\n",
       "      <td>0.653861</td>\n",
       "      <td>0.102576</td>\n",
       "      <td>0.299116</td>\n",
       "      <td>0.269681</td>\n",
       "      <td>0.350206</td>\n",
       "      <td>0.409591</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-28</th>\n",
       "      <td>0.404114</td>\n",
       "      <td>0.365608</td>\n",
       "      <td>0.374626</td>\n",
       "      <td>0.600113</td>\n",
       "      <td>0.092771</td>\n",
       "      <td>0.309757</td>\n",
       "      <td>0.273986</td>\n",
       "      <td>0.350206</td>\n",
       "      <td>0.423552</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-01</th>\n",
       "      <td>0.373570</td>\n",
       "      <td>0.336575</td>\n",
       "      <td>0.336493</td>\n",
       "      <td>0.625393</td>\n",
       "      <td>0.077556</td>\n",
       "      <td>0.313912</td>\n",
       "      <td>0.276618</td>\n",
       "      <td>0.350206</td>\n",
       "      <td>0.427630</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-02</th>\n",
       "      <td>0.237796</td>\n",
       "      <td>0.239680</td>\n",
       "      <td>0.245279</td>\n",
       "      <td>0.793441</td>\n",
       "      <td>0.101854</td>\n",
       "      <td>0.298943</td>\n",
       "      <td>0.273068</td>\n",
       "      <td>0.350206</td>\n",
       "      <td>0.404375</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-16</th>\n",
       "      <td>0.344630</td>\n",
       "      <td>0.307543</td>\n",
       "      <td>0.328327</td>\n",
       "      <td>0.313955</td>\n",
       "      <td>0.932500</td>\n",
       "      <td>0.247752</td>\n",
       "      <td>0.456542</td>\n",
       "      <td>0.568797</td>\n",
       "      <td>0.186607</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.71</td>\n",
       "      <td>-0.0439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-20</th>\n",
       "      <td>0.303278</td>\n",
       "      <td>0.274620</td>\n",
       "      <td>0.268028</td>\n",
       "      <td>0.268767</td>\n",
       "      <td>0.908121</td>\n",
       "      <td>0.249238</td>\n",
       "      <td>0.447401</td>\n",
       "      <td>0.495796</td>\n",
       "      <td>0.202909</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.71</td>\n",
       "      <td>-0.0439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-21</th>\n",
       "      <td>0.283961</td>\n",
       "      <td>0.280671</td>\n",
       "      <td>0.288589</td>\n",
       "      <td>0.263982</td>\n",
       "      <td>0.913223</td>\n",
       "      <td>0.252017</td>\n",
       "      <td>0.439135</td>\n",
       "      <td>0.549739</td>\n",
       "      <td>0.219979</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.71</td>\n",
       "      <td>-0.0439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-22</th>\n",
       "      <td>0.288424</td>\n",
       "      <td>0.272603</td>\n",
       "      <td>0.284287</td>\n",
       "      <td>0.188273</td>\n",
       "      <td>0.998565</td>\n",
       "      <td>0.258376</td>\n",
       "      <td>0.432545</td>\n",
       "      <td>0.558761</td>\n",
       "      <td>0.241023</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.71</td>\n",
       "      <td>-0.0439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-23</th>\n",
       "      <td>0.297559</td>\n",
       "      <td>0.267200</td>\n",
       "      <td>0.285308</td>\n",
       "      <td>0.092350</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.255160</td>\n",
       "      <td>0.423428</td>\n",
       "      <td>0.512103</td>\n",
       "      <td>0.247258</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.71</td>\n",
       "      <td>-0.0439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open      High       Low    Volume  SP500_Close  EMA_Short  \\\n",
       "2023-02-24  0.304672  0.267920  0.294787  0.525683     0.092682   0.282480   \n",
       "2023-02-27  0.344421  0.352568  0.356471  0.653861     0.102576   0.299116   \n",
       "2023-02-28  0.404114  0.365608  0.374626  0.600113     0.092771   0.309757   \n",
       "2023-03-01  0.373570  0.336575  0.336493  0.625393     0.077556   0.313912   \n",
       "2023-03-02  0.237796  0.239680  0.245279  0.793441     0.101854   0.298943   \n",
       "...              ...       ...       ...       ...          ...        ...   \n",
       "2024-02-16  0.344630  0.307543  0.328327  0.313955     0.932500   0.247752   \n",
       "2024-02-20  0.303278  0.274620  0.268028  0.268767     0.908121   0.249238   \n",
       "2024-02-21  0.283961  0.280671  0.288589  0.263982     0.913223   0.252017   \n",
       "2024-02-22  0.288424  0.272603  0.284287  0.188273     0.998565   0.258376   \n",
       "2024-02-23  0.297559  0.267200  0.285308  0.092350     1.000000   0.255160   \n",
       "\n",
       "            EMA_Long       RSI      MACD  EPS Estimate  Reported EPS  \\\n",
       "2023-02-24  0.264176  0.350206  0.386067          0.85          0.85   \n",
       "2023-02-27  0.269681  0.350206  0.409591          0.85          0.85   \n",
       "2023-02-28  0.273986  0.350206  0.423552          0.85          0.85   \n",
       "2023-03-01  0.276618  0.350206  0.427630          0.85          0.85   \n",
       "2023-03-02  0.273068  0.350206  0.404375          0.85          0.85   \n",
       "...              ...       ...       ...           ...           ...   \n",
       "2024-02-16  0.456542  0.568797  0.186607          0.74          0.71   \n",
       "2024-02-20  0.447401  0.495796  0.202909          0.74          0.71   \n",
       "2024-02-21  0.439135  0.549739  0.219979          0.74          0.71   \n",
       "2024-02-22  0.432545  0.558761  0.241023          0.74          0.71   \n",
       "2024-02-23  0.423428  0.512103  0.247258          0.74          0.71   \n",
       "\n",
       "            Surprise(%)  \n",
       "2023-02-24      -0.0004  \n",
       "2023-02-27      -0.0004  \n",
       "2023-02-28      -0.0004  \n",
       "2023-03-01      -0.0004  \n",
       "2023-03-02      -0.0004  \n",
       "...                 ...  \n",
       "2024-02-16      -0.0439  \n",
       "2024-02-20      -0.0439  \n",
       "2024-02-21      -0.0439  \n",
       "2024-02-22      -0.0439  \n",
       "2024-02-23      -0.0439  \n",
       "\n",
       "[251 rows x 12 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge earnings data\n",
    "scaled_features_df = scaled_features_df.merge(earnings, how='left', left_index=True, right_index=True)\n",
    "scaled_features_df = scaled_features_df.fillna(method='ffill').fillna(method='bfill')\n",
    "\n",
    "# Display the merged dataframe\n",
    "scaled_features_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 251 entries, 2023-02-24 to 2024-02-23\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Open          251 non-null    float64\n",
      " 1   High          251 non-null    float64\n",
      " 2   Low           251 non-null    float64\n",
      " 3   Volume        251 non-null    float64\n",
      " 4   SP500_Close   251 non-null    float64\n",
      " 5   EMA_Short     251 non-null    float64\n",
      " 6   EMA_Long      251 non-null    float64\n",
      " 7   RSI           251 non-null    float64\n",
      " 8   MACD          251 non-null    float64\n",
      " 9   EPS Estimate  251 non-null    float64\n",
      " 10  Reported EPS  251 non-null    float64\n",
      " 11  Surprise(%)   251 non-null    float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 33.6+ KB\n"
     ]
    }
   ],
   "source": [
    "scaled_features_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_sequences(features, target, n_steps):\n",
    "    X, y = [], []\n",
    "    for i in range(len(features) - n_steps):\n",
    "        seq_X, seq_y = features[i:i+n_steps], target[i+n_steps]\n",
    "        X.append(seq_X)\n",
    "        y.append(seq_y)\n",
    "    return np.array(X), np.array(y)\n",
    "\n",
    "n_steps = 5  # the last n days to predict the next day\n",
    "\n",
    "scaled_features_array = scaled_features_df.values  # Convert DataFrame to NumPy array for easier manipulation\n",
    "scaled_target_array = scaled_target_df.values.ravel()  # Flatten target array\n",
    "\n",
    "# Create sequences\n",
    "X, y = create_sequences(scaled_features_array, scaled_target_array, n_steps)\n",
    "\n",
    "# Note: \n",
    "# - X will have a shape of [samples, n_steps, features]\n",
    "# - y will have a shape of [samples,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training 196 196\n",
      "testing 50 50\n"
     ]
    }
   ],
   "source": [
    "test_size = 50 # Number of samples to be used for testing\n",
    "\n",
    "# The '- n_steps' adjustment is necessary to account for the creation of sequences\n",
    "train_test_split_index = len(scaled_features_df) - test_size - n_steps\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "training_features, test_features = X[:train_test_split_index], X[train_test_split_index:]\n",
    "training_target, test_target = y[:train_test_split_index], y[train_test_split_index:]\n",
    "\n",
    "print('training', len(training_features), len(training_target))\n",
    "print('testing', len(test_features), len(test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model parameters\n",
    "\n",
    "num_layers = 1\n",
    "size_layer = 32\n",
    "epochs = 30\n",
    "dropout_rate = 0.2\n",
    "dense_nums = 32\n",
    "reg_rate = 0.01\n",
    "batch_size = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TimeSeriesSplit for cross-validation\n",
    "tscv = TimeSeriesSplit(n_splits=5)\n",
    "\n",
    "# LSTM model with l1_l2 Regularization and Dropout\n",
    "def build_model(input_shape, num_layers=2, size_layer=128, dropout_rate=0.2, dense_nums=64, reg_rate=0.01):\n",
    "    model = Sequential()\n",
    "    reg = l1_l2(l1=reg_rate, l2=reg_rate)\n",
    "\n",
    "    for i in range(num_layers):\n",
    "        \n",
    "        if i == 0:  # If it's the first layer, specify the input shape\n",
    "            model.add(LSTM(size_layer, return_sequences=(num_layers > 1), kernel_regularizer=reg, input_shape=input_shape))\n",
    "        else:  # Subsequent layers will automatically infer the input shape\n",
    "            model.add(LSTM(size_layer, return_sequences=(i != num_layers - 1), kernel_regularizer=reg))\n",
    "\n",
    "        # Add dropout after each LSTM layer\n",
    "        model.add(Dropout(dropout_rate))\n",
    "\n",
    "        # Optionally, add a Dense layer with ReLU activation after each LSTM layer (except the last one)\n",
    "        if i < num_layers - 1:\n",
    "            model.add(Dense(dense_nums, activation='relu'))\n",
    "    \n",
    "    model.add(Dense(1, activation='linear'))\n",
    "    model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n",
    "    \n",
    "    return model\n",
    "\n",
    "# Setup early stopping to avoid overfitting\n",
    "early_stopping = EarlyStopping(\n",
    "    monitor='val_loss', patience=10, restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "8/8 [==============================] - 4s 149ms/step - loss: 1.8478 - mae: 0.1951 - val_loss: 1.7468 - val_mae: 0.1149\n",
      "Epoch 2/30\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 1.6849 - mae: 0.0822 - val_loss: 1.6168 - val_mae: 0.1301\n",
      "Epoch 3/30\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 1.5650 - mae: 0.1054 - val_loss: 1.4889 - val_mae: 0.1172\n",
      "Epoch 4/30\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 1.4343 - mae: 0.0800 - val_loss: 1.3726 - val_mae: 0.1105\n",
      "Epoch 5/30\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 1.3147 - mae: 0.0781 - val_loss: 1.2578 - val_mae: 0.1099\n",
      "Epoch 6/30\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 1.2057 - mae: 0.0866 - val_loss: 1.1474 - val_mae: 0.1111\n",
      "Epoch 7/30\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 1.0960 - mae: 0.0702 - val_loss: 1.0434 - val_mae: 0.1152\n",
      "Epoch 8/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.9942 - mae: 0.0659 - val_loss: 0.9461 - val_mae: 0.1128\n",
      "Epoch 9/30\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.8993 - mae: 0.0699 - val_loss: 0.8541 - val_mae: 0.1134\n",
      "Epoch 10/30\n",
      "8/8 [==============================] - 0s 13ms/step - loss: 0.8081 - mae: 0.0589 - val_loss: 0.7690 - val_mae: 0.1139\n",
      "Epoch 11/30\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.7252 - mae: 0.0646 - val_loss: 0.6889 - val_mae: 0.1147\n",
      "Epoch 12/30\n",
      "8/8 [==============================] - 0s 15ms/step - loss: 0.6490 - mae: 0.0690 - val_loss: 0.6148 - val_mae: 0.1137\n",
      "Epoch 13/30\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.5733 - mae: 0.0481 - val_loss: 0.5448 - val_mae: 0.1114\n",
      "Epoch 14/30\n",
      "8/8 [==============================] - 0s 24ms/step - loss: 0.5082 - mae: 0.0683 - val_loss: 0.4807 - val_mae: 0.1144\n",
      "Epoch 15/30\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 0.4437 - mae: 0.0590 - val_loss: 0.4213 - val_mae: 0.1209\n",
      "Epoch 16/30\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.3862 - mae: 0.0571 - val_loss: 0.3662 - val_mae: 0.1140\n",
      "Epoch 17/30\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.3334 - mae: 0.0592 - val_loss: 0.3158 - val_mae: 0.1124\n",
      "Epoch 18/30\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.2860 - mae: 0.0638 - val_loss: 0.2701 - val_mae: 0.1190\n",
      "Epoch 19/30\n",
      "8/8 [==============================] - 0s 14ms/step - loss: 0.2400 - mae: 0.0602 - val_loss: 0.2291 - val_mae: 0.1208\n",
      "Epoch 20/30\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.2004 - mae: 0.0592 - val_loss: 0.1924 - val_mae: 0.1185\n",
      "Epoch 21/30\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 0.1648 - mae: 0.0536 - val_loss: 0.1610 - val_mae: 0.1210\n",
      "Epoch 22/30\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 0.1354 - mae: 0.0549 - val_loss: 0.1330 - val_mae: 0.1199\n",
      "Epoch 23/30\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.1093 - mae: 0.0607 - val_loss: 0.1097 - val_mae: 0.1160\n",
      "Epoch 24/30\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.0868 - mae: 0.0559 - val_loss: 0.0891 - val_mae: 0.1172\n",
      "Epoch 25/30\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 0.0678 - mae: 0.0570 - val_loss: 0.0735 - val_mae: 0.1186\n",
      "Epoch 26/30\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 0.0550 - mae: 0.0624 - val_loss: 0.0614 - val_mae: 0.1170\n",
      "Epoch 27/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.0434 - mae: 0.0593 - val_loss: 0.0527 - val_mae: 0.1181\n",
      "Epoch 28/30\n",
      "8/8 [==============================] - 0s 29ms/step - loss: 0.0366 - mae: 0.0586 - val_loss: 0.0487 - val_mae: 0.1225\n",
      "Epoch 29/30\n",
      "8/8 [==============================] - 0s 15ms/step - loss: 0.0321 - mae: 0.0591 - val_loss: 0.0447 - val_mae: 0.1229\n",
      "Epoch 30/30\n",
      "8/8 [==============================] - 0s 16ms/step - loss: 0.0287 - mae: 0.0590 - val_loss: 0.0419 - val_mae: 0.1242\n",
      "Epoch 1/30\n",
      "14/14 [==============================] - 7s 77ms/step - loss: 1.7160 - mae: 0.1622 - val_loss: 1.6970 - val_mae: 0.3636\n",
      "Epoch 2/30\n",
      "14/14 [==============================] - 0s 14ms/step - loss: 1.4754 - mae: 0.1154 - val_loss: 1.5108 - val_mae: 0.3988\n",
      "Epoch 3/30\n",
      "14/14 [==============================] - 0s 20ms/step - loss: 1.2652 - mae: 0.0796 - val_loss: 1.3320 - val_mae: 0.4158\n",
      "Epoch 4/30\n",
      "14/14 [==============================] - 0s 14ms/step - loss: 1.0796 - mae: 0.0819 - val_loss: 1.1566 - val_mae: 0.4174\n",
      "Epoch 5/30\n",
      "14/14 [==============================] - 0s 17ms/step - loss: 0.9106 - mae: 0.0880 - val_loss: 0.9845 - val_mae: 0.4011\n",
      "Epoch 6/30\n",
      "14/14 [==============================] - 0s 17ms/step - loss: 0.7598 - mae: 0.0893 - val_loss: 0.8816 - val_mae: 0.4462\n",
      "Epoch 7/30\n",
      "14/14 [==============================] - 0s 15ms/step - loss: 0.6288 - mae: 0.0973 - val_loss: 0.6978 - val_mae: 0.3722\n",
      "Epoch 8/30\n",
      "14/14 [==============================] - 0s 24ms/step - loss: 0.5057 - mae: 0.0760 - val_loss: 0.6389 - val_mae: 0.4371\n",
      "Epoch 9/30\n",
      "14/14 [==============================] - 0s 17ms/step - loss: 0.4011 - mae: 0.0732 - val_loss: 0.5593 - val_mae: 0.4556\n",
      "Epoch 10/30\n",
      "14/14 [==============================] - 0s 15ms/step - loss: 0.3099 - mae: 0.0657 - val_loss: 0.4472 - val_mae: 0.4196\n",
      "Epoch 11/30\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.2363 - mae: 0.0770 - val_loss: 0.4097 - val_mae: 0.4544\n",
      "Epoch 12/30\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.1753 - mae: 0.0786 - val_loss: 0.3537 - val_mae: 0.4516\n",
      "Epoch 13/30\n",
      "14/14 [==============================] - 0s 14ms/step - loss: 0.1251 - mae: 0.0729 - val_loss: 0.3440 - val_mae: 0.4856\n",
      "Epoch 14/30\n",
      "14/14 [==============================] - 0s 21ms/step - loss: 0.0906 - mae: 0.0788 - val_loss: 0.3348 - val_mae: 0.5071\n",
      "Epoch 15/30\n",
      "14/14 [==============================] - 0s 34ms/step - loss: 0.0638 - mae: 0.0776 - val_loss: 0.3127 - val_mae: 0.5050\n",
      "Epoch 16/30\n",
      "14/14 [==============================] - 0s 15ms/step - loss: 0.0481 - mae: 0.0791 - val_loss: 0.3018 - val_mae: 0.5040\n",
      "Epoch 17/30\n",
      "14/14 [==============================] - 0s 18ms/step - loss: 0.0408 - mae: 0.0784 - val_loss: 0.2866 - val_mae: 0.4947\n",
      "Epoch 18/30\n",
      "14/14 [==============================] - 0s 24ms/step - loss: 0.0349 - mae: 0.0747 - val_loss: 0.2787 - val_mae: 0.4906\n",
      "Epoch 19/30\n",
      "14/14 [==============================] - 0s 14ms/step - loss: 0.0314 - mae: 0.0729 - val_loss: 0.2890 - val_mae: 0.5039\n",
      "Epoch 20/30\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0287 - mae: 0.0744 - val_loss: 0.2791 - val_mae: 0.4956\n",
      "Epoch 21/30\n",
      "14/14 [==============================] - 0s 13ms/step - loss: 0.0287 - mae: 0.0795 - val_loss: 0.2347 - val_mae: 0.4488\n",
      "Epoch 22/30\n",
      "14/14 [==============================] - 0s 15ms/step - loss: 0.0260 - mae: 0.0805 - val_loss: 0.3237 - val_mae: 0.5414\n",
      "Epoch 23/30\n",
      "14/14 [==============================] - 0s 14ms/step - loss: 0.0239 - mae: 0.0733 - val_loss: 0.2532 - val_mae: 0.4724\n",
      "Epoch 24/30\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0231 - mae: 0.0768 - val_loss: 0.2619 - val_mae: 0.4830\n",
      "Epoch 25/30\n",
      "14/14 [==============================] - 0s 14ms/step - loss: 0.0206 - mae: 0.0748 - val_loss: 0.2394 - val_mae: 0.4593\n",
      "Epoch 26/30\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0211 - mae: 0.0736 - val_loss: 0.3138 - val_mae: 0.5350\n",
      "Epoch 27/30\n",
      "14/14 [==============================] - 0s 11ms/step - loss: 0.0195 - mae: 0.0678 - val_loss: 0.2575 - val_mae: 0.4802\n",
      "Epoch 28/30\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0193 - mae: 0.0749 - val_loss: 0.2434 - val_mae: 0.4659\n",
      "Epoch 29/30\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0182 - mae: 0.0741 - val_loss: 0.2691 - val_mae: 0.4932\n",
      "Epoch 30/30\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0160 - mae: 0.0663 - val_loss: 0.2357 - val_mae: 0.4589\n",
      "Epoch 1/30\n",
      "20/20 [==============================] - 5s 63ms/step - loss: 1.7309 - mae: 0.1848 - val_loss: 1.5776 - val_mae: 0.2106\n",
      "Epoch 2/30\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 1.4149 - mae: 0.1150 - val_loss: 1.2588 - val_mae: 0.0653\n",
      "Epoch 3/30\n",
      "20/20 [==============================] - 0s 8ms/step - loss: 1.1527 - mae: 0.1038 - val_loss: 1.0181 - val_mae: 0.0778\n",
      "Epoch 4/30\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.9230 - mae: 0.0994 - val_loss: 0.8069 - val_mae: 0.0806\n",
      "Epoch 5/30\n",
      "20/20 [==============================] - 0s 8ms/step - loss: 0.7215 - mae: 0.0854 - val_loss: 0.6214 - val_mae: 0.0715\n",
      "Epoch 6/30\n",
      "20/20 [==============================] - 0s 8ms/step - loss: 0.5455 - mae: 0.0708 - val_loss: 0.4642 - val_mae: 0.0689\n",
      "Epoch 7/30\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.4064 - mae: 0.0829 - val_loss: 0.3361 - val_mae: 0.0687\n",
      "Epoch 8/30\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2944 - mae: 0.0892 - val_loss: 0.2396 - val_mae: 0.0740\n",
      "Epoch 9/30\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.2041 - mae: 0.0735 - val_loss: 0.1690 - val_mae: 0.0775\n",
      "Epoch 10/30\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.1447 - mae: 0.0798 - val_loss: 0.1207 - val_mae: 0.0735\n",
      "Epoch 11/30\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.1089 - mae: 0.0798 - val_loss: 0.0926 - val_mae: 0.0732\n",
      "Epoch 12/30\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.0822 - mae: 0.0657 - val_loss: 0.0792 - val_mae: 0.0834\n",
      "Epoch 13/30\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.0761 - mae: 0.0844 - val_loss: 0.0675 - val_mae: 0.0750\n",
      "Epoch 14/30\n",
      "20/20 [==============================] - 0s 9ms/step - loss: 0.0635 - mae: 0.0789 - val_loss: 0.0611 - val_mae: 0.0850\n",
      "Epoch 15/30\n",
      "20/20 [==============================] - 0s 8ms/step - loss: 0.0551 - mae: 0.0753 - val_loss: 0.0538 - val_mae: 0.0859\n",
      "Epoch 16/30\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.0522 - mae: 0.0833 - val_loss: 0.0518 - val_mae: 0.0953\n",
      "Epoch 17/30\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.0439 - mae: 0.0735 - val_loss: 0.0427 - val_mae: 0.0772\n",
      "Epoch 18/30\n",
      "20/20 [==============================] - 0s 10ms/step - loss: 0.0410 - mae: 0.0750 - val_loss: 0.0422 - val_mae: 0.0897\n",
      "Epoch 19/30\n",
      "20/20 [==============================] - 0s 9ms/step - loss: 0.0368 - mae: 0.0756 - val_loss: 0.0417 - val_mae: 0.1013\n",
      "Epoch 20/30\n",
      "20/20 [==============================] - 0s 9ms/step - loss: 0.0330 - mae: 0.0725 - val_loss: 0.0399 - val_mae: 0.1063\n",
      "Epoch 21/30\n",
      "20/20 [==============================] - 0s 8ms/step - loss: 0.0359 - mae: 0.0869 - val_loss: 0.0311 - val_mae: 0.0699\n",
      "Epoch 22/30\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.0342 - mae: 0.0835 - val_loss: 0.0351 - val_mae: 0.0986\n",
      "Epoch 23/30\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.0361 - mae: 0.0900 - val_loss: 0.0603 - val_mae: 0.1843\n",
      "Epoch 24/30\n",
      "20/20 [==============================] - 0s 9ms/step - loss: 0.0336 - mae: 0.0887 - val_loss: 0.0270 - val_mae: 0.0693\n",
      "Epoch 25/30\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.0254 - mae: 0.0677 - val_loss: 0.0255 - val_mae: 0.0715\n",
      "Epoch 26/30\n",
      "20/20 [==============================] - 0s 10ms/step - loss: 0.0264 - mae: 0.0771 - val_loss: 0.0401 - val_mae: 0.1351\n",
      "Epoch 27/30\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.0276 - mae: 0.0834 - val_loss: 0.0242 - val_mae: 0.0703\n",
      "Epoch 28/30\n",
      "20/20 [==============================] - 0s 10ms/step - loss: 0.0294 - mae: 0.0885 - val_loss: 0.0249 - val_mae: 0.0736\n",
      "Epoch 29/30\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.0284 - mae: 0.0861 - val_loss: 0.0318 - val_mae: 0.1056\n",
      "Epoch 30/30\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0218 - mae: 0.0687 - val_loss: 0.0242 - val_mae: 0.0789\n",
      "Epoch 1/30\n",
      "27/27 [==============================] - 5s 38ms/step - loss: 1.7652 - mae: 0.2949 - val_loss: 1.4723 - val_mae: 0.1394\n",
      "Epoch 2/30\n",
      "27/27 [==============================] - 0s 10ms/step - loss: 1.3305 - mae: 0.1567 - val_loss: 1.1367 - val_mae: 0.0897\n",
      "Epoch 3/30\n",
      "27/27 [==============================] - 0s 8ms/step - loss: 1.0136 - mae: 0.1232 - val_loss: 0.8571 - val_mae: 0.1090\n",
      "Epoch 4/30\n",
      "27/27 [==============================] - 0s 7ms/step - loss: 0.7483 - mae: 0.1008 - val_loss: 0.6238 - val_mae: 0.0894\n",
      "Epoch 5/30\n",
      "27/27 [==============================] - 0s 11ms/step - loss: 0.5387 - mae: 0.0961 - val_loss: 0.4440 - val_mae: 0.0897\n",
      "Epoch 6/30\n",
      "27/27 [==============================] - 0s 6ms/step - loss: 0.3807 - mae: 0.0971 - val_loss: 0.3096 - val_mae: 0.0874\n",
      "Epoch 7/30\n",
      "27/27 [==============================] - 0s 7ms/step - loss: 0.2646 - mae: 0.0966 - val_loss: 0.2144 - val_mae: 0.0890\n",
      "Epoch 8/30\n",
      "27/27 [==============================] - 0s 6ms/step - loss: 0.1868 - mae: 0.0905 - val_loss: 0.1601 - val_mae: 0.0905\n",
      "Epoch 9/30\n",
      "27/27 [==============================] - 0s 7ms/step - loss: 0.1455 - mae: 0.0958 - val_loss: 0.1280 - val_mae: 0.0884\n",
      "Epoch 10/30\n",
      "27/27 [==============================] - 0s 7ms/step - loss: 0.1210 - mae: 0.0916 - val_loss: 0.1075 - val_mae: 0.0860\n",
      "Epoch 11/30\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 0.0978 - mae: 0.0764 - val_loss: 0.0910 - val_mae: 0.0876\n",
      "Epoch 12/30\n",
      "27/27 [==============================] - 0s 10ms/step - loss: 0.0845 - mae: 0.0824 - val_loss: 0.0780 - val_mae: 0.0924\n",
      "Epoch 13/30\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 0.0718 - mae: 0.0787 - val_loss: 0.0705 - val_mae: 0.1035\n",
      "Epoch 14/30\n",
      "27/27 [==============================] - 0s 6ms/step - loss: 0.0631 - mae: 0.0811 - val_loss: 0.0595 - val_mae: 0.0936\n",
      "Epoch 15/30\n",
      "27/27 [==============================] - 0s 6ms/step - loss: 0.0556 - mae: 0.0829 - val_loss: 0.0519 - val_mae: 0.0949\n",
      "Epoch 16/30\n",
      "27/27 [==============================] - 0s 7ms/step - loss: 0.0481 - mae: 0.0825 - val_loss: 0.0468 - val_mae: 0.0894\n",
      "Epoch 17/30\n",
      "27/27 [==============================] - 0s 7ms/step - loss: 0.0439 - mae: 0.0861 - val_loss: 0.0447 - val_mae: 0.0954\n",
      "Epoch 18/30\n",
      "27/27 [==============================] - 0s 7ms/step - loss: 0.0431 - mae: 0.0916 - val_loss: 0.0396 - val_mae: 0.0929\n",
      "Epoch 19/30\n",
      "27/27 [==============================] - 0s 7ms/step - loss: 0.0378 - mae: 0.0845 - val_loss: 0.0411 - val_mae: 0.1009\n",
      "Epoch 20/30\n",
      "27/27 [==============================] - 0s 7ms/step - loss: 0.0349 - mae: 0.0856 - val_loss: 0.0346 - val_mae: 0.0929\n",
      "Epoch 21/30\n",
      "27/27 [==============================] - 0s 7ms/step - loss: 0.0312 - mae: 0.0830 - val_loss: 0.0327 - val_mae: 0.1007\n",
      "Epoch 22/30\n",
      "27/27 [==============================] - 0s 7ms/step - loss: 0.0310 - mae: 0.0850 - val_loss: 0.0324 - val_mae: 0.1040\n",
      "Epoch 23/30\n",
      "27/27 [==============================] - 0s 7ms/step - loss: 0.0303 - mae: 0.0908 - val_loss: 0.0314 - val_mae: 0.1048\n",
      "Epoch 24/30\n",
      "27/27 [==============================] - 0s 8ms/step - loss: 0.0303 - mae: 0.0884 - val_loss: 0.0314 - val_mae: 0.1042\n",
      "Epoch 25/30\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0310 - mae: 0.0905 - val_loss: 0.0316 - val_mae: 0.1118\n",
      "Epoch 26/30\n",
      "27/27 [==============================] - 0s 7ms/step - loss: 0.0292 - mae: 0.0916 - val_loss: 0.0288 - val_mae: 0.0971\n",
      "Epoch 27/30\n",
      "27/27 [==============================] - 0s 7ms/step - loss: 0.0251 - mae: 0.0834 - val_loss: 0.0264 - val_mae: 0.0947\n",
      "Epoch 28/30\n",
      "27/27 [==============================] - 0s 6ms/step - loss: 0.0251 - mae: 0.0856 - val_loss: 0.0301 - val_mae: 0.1035\n",
      "Epoch 29/30\n",
      "27/27 [==============================] - 0s 7ms/step - loss: 0.0249 - mae: 0.0854 - val_loss: 0.0289 - val_mae: 0.1105\n",
      "Epoch 30/30\n",
      "27/27 [==============================] - 0s 6ms/step - loss: 0.0290 - mae: 0.0986 - val_loss: 0.0285 - val_mae: 0.1113\n",
      "Epoch 1/30\n",
      "33/33 [==============================] - 5s 34ms/step - loss: 1.6856 - mae: 0.2354 - val_loss: 1.3654 - val_mae: 0.0526\n",
      "Epoch 2/30\n",
      "33/33 [==============================] - 0s 8ms/step - loss: 1.1911 - mae: 0.1358 - val_loss: 0.9660 - val_mae: 0.0515\n",
      "Epoch 3/30\n",
      "33/33 [==============================] - 0s 7ms/step - loss: 0.8219 - mae: 0.1171 - val_loss: 0.6455 - val_mae: 0.0531\n",
      "Epoch 4/30\n",
      "33/33 [==============================] - 0s 6ms/step - loss: 0.5380 - mae: 0.1125 - val_loss: 0.4032 - val_mae: 0.0531\n",
      "Epoch 5/30\n",
      "33/33 [==============================] - 0s 6ms/step - loss: 0.3288 - mae: 0.1002 - val_loss: 0.2402 - val_mae: 0.0635\n",
      "Epoch 6/30\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 0.1960 - mae: 0.0931 - val_loss: 0.1414 - val_mae: 0.0534\n",
      "Epoch 7/30\n",
      "33/33 [==============================] - 0s 7ms/step - loss: 0.1285 - mae: 0.0988 - val_loss: 0.0988 - val_mae: 0.0627\n",
      "Epoch 8/30\n",
      "33/33 [==============================] - 0s 6ms/step - loss: 0.0938 - mae: 0.0855 - val_loss: 0.0748 - val_mae: 0.0497\n",
      "Epoch 9/30\n",
      "33/33 [==============================] - 0s 6ms/step - loss: 0.0810 - mae: 0.1013 - val_loss: 0.0619 - val_mae: 0.0535\n",
      "Epoch 10/30\n",
      "33/33 [==============================] - 0s 7ms/step - loss: 0.0669 - mae: 0.0916 - val_loss: 0.0510 - val_mae: 0.0516\n",
      "Epoch 11/30\n",
      "33/33 [==============================] - 0s 5ms/step - loss: 0.0553 - mae: 0.0886 - val_loss: 0.0427 - val_mae: 0.0519\n",
      "Epoch 12/30\n",
      "33/33 [==============================] - 0s 5ms/step - loss: 0.0494 - mae: 0.0930 - val_loss: 0.0380 - val_mae: 0.0593\n",
      "Epoch 13/30\n",
      "33/33 [==============================] - 0s 5ms/step - loss: 0.0423 - mae: 0.0860 - val_loss: 0.0340 - val_mae: 0.0605\n",
      "Epoch 14/30\n",
      "33/33 [==============================] - 0s 5ms/step - loss: 0.0417 - mae: 0.0943 - val_loss: 0.0315 - val_mae: 0.0658\n",
      "Epoch 15/30\n",
      "33/33 [==============================] - 0s 5ms/step - loss: 0.0390 - mae: 0.0960 - val_loss: 0.0307 - val_mae: 0.0722\n",
      "Epoch 16/30\n",
      "33/33 [==============================] - 0s 5ms/step - loss: 0.0380 - mae: 0.1005 - val_loss: 0.0288 - val_mae: 0.0700\n",
      "Epoch 17/30\n",
      "33/33 [==============================] - 0s 5ms/step - loss: 0.0361 - mae: 0.0980 - val_loss: 0.0232 - val_mae: 0.0542\n",
      "Epoch 18/30\n",
      "33/33 [==============================] - 0s 5ms/step - loss: 0.0298 - mae: 0.0858 - val_loss: 0.0222 - val_mae: 0.0567\n",
      "Epoch 19/30\n",
      "33/33 [==============================] - 0s 5ms/step - loss: 0.0344 - mae: 0.1027 - val_loss: 0.0269 - val_mae: 0.0792\n",
      "Epoch 20/30\n",
      "33/33 [==============================] - 0s 5ms/step - loss: 0.0297 - mae: 0.0916 - val_loss: 0.0272 - val_mae: 0.0888\n",
      "Epoch 21/30\n",
      "33/33 [==============================] - 0s 7ms/step - loss: 0.0290 - mae: 0.0905 - val_loss: 0.0213 - val_mae: 0.0663\n",
      "Epoch 22/30\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 0.0301 - mae: 0.0939 - val_loss: 0.0199 - val_mae: 0.0562\n",
      "Epoch 23/30\n",
      "33/33 [==============================] - 0s 5ms/step - loss: 0.0271 - mae: 0.0882 - val_loss: 0.0173 - val_mae: 0.0529\n",
      "Epoch 24/30\n",
      "33/33 [==============================] - 0s 4ms/step - loss: 0.0260 - mae: 0.0876 - val_loss: 0.0239 - val_mae: 0.0867\n",
      "Epoch 25/30\n",
      "33/33 [==============================] - 0s 4ms/step - loss: 0.0261 - mae: 0.0919 - val_loss: 0.0168 - val_mae: 0.0537\n",
      "Epoch 26/30\n",
      "33/33 [==============================] - 0s 5ms/step - loss: 0.0260 - mae: 0.0915 - val_loss: 0.0173 - val_mae: 0.0581\n",
      "Epoch 27/30\n",
      "33/33 [==============================] - 0s 5ms/step - loss: 0.0256 - mae: 0.0887 - val_loss: 0.0173 - val_mae: 0.0623\n",
      "Epoch 28/30\n",
      "33/33 [==============================] - 0s 5ms/step - loss: 0.0235 - mae: 0.0883 - val_loss: 0.0166 - val_mae: 0.0607\n",
      "Epoch 29/30\n",
      "33/33 [==============================] - 0s 4ms/step - loss: 0.0248 - mae: 0.0952 - val_loss: 0.0161 - val_mae: 0.0607\n",
      "Epoch 30/30\n",
      "33/33 [==============================] - 0s 5ms/step - loss: 0.0246 - mae: 0.0939 - val_loss: 0.0169 - val_mae: 0.0632\n"
     ]
    }
   ],
   "source": [
    "best_val_loss = np.inf\n",
    "best_model = None\n",
    "best_history = None\n",
    "\n",
    "\n",
    "# Cross-validate using TimeSeriesSplit\n",
    "for train_index, val_index in tscv.split(training_features):\n",
    "    X_train, X_val = training_features[train_index], training_features[val_index]\n",
    "    y_train, y_val = training_target[train_index], training_target[val_index]\n",
    "\n",
    "    # Create and fit the LSTM model\n",
    "    model = build_model(input_shape=(n_steps, scaled_features_df.shape[1]),\n",
    "                        num_layers=num_layers,\n",
    "                        size_layer=size_layer,\n",
    "                        dropout_rate=dropout_rate,\n",
    "                        dense_nums=dense_nums,\n",
    "                        reg_rate=reg_rate,\n",
    "                        )\n",
    "\n",
    "    # Fit the model with Early Stopping\n",
    "    history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(\n",
    "        X_val, y_val), callbacks=[early_stopping], verbose=1)\n",
    "    \n",
    "    # Track the best model based on validation loss\n",
    "    val_loss = np.min(history.history['val_loss'])\n",
    "    if val_loss < best_val_loss:\n",
    "        best_val_loss = val_loss\n",
    "        best_model = model\n",
    "        best_history = history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Training and Validation Loss\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(best_history.history['loss'], label='Training Loss')\n",
    "plt.plot(best_history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Model Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "36/36 [==============================] - 0s 8ms/step - loss: 0.0261 - mae: 0.0991 - val_loss: 0.0132 - val_mae: 0.0398\n",
      "Epoch 2/30\n",
      "36/36 [==============================] - 0s 5ms/step - loss: 0.0253 - mae: 0.0945 - val_loss: 0.0129 - val_mae: 0.0390\n",
      "Epoch 3/30\n",
      "36/36 [==============================] - 0s 5ms/step - loss: 0.0251 - mae: 0.0954 - val_loss: 0.0149 - val_mae: 0.0544\n",
      "Epoch 4/30\n",
      "36/36 [==============================] - 0s 4ms/step - loss: 0.0223 - mae: 0.0886 - val_loss: 0.0124 - val_mae: 0.0427\n",
      "Epoch 5/30\n",
      "36/36 [==============================] - 0s 4ms/step - loss: 0.0197 - mae: 0.0824 - val_loss: 0.0140 - val_mae: 0.0529\n",
      "Epoch 6/30\n",
      "36/36 [==============================] - 0s 4ms/step - loss: 0.0220 - mae: 0.0882 - val_loss: 0.0120 - val_mae: 0.0395\n",
      "Epoch 7/30\n",
      "36/36 [==============================] - 0s 4ms/step - loss: 0.0208 - mae: 0.0899 - val_loss: 0.0122 - val_mae: 0.0466\n",
      "Epoch 8/30\n",
      "36/36 [==============================] - 0s 6ms/step - loss: 0.0233 - mae: 0.0914 - val_loss: 0.0138 - val_mae: 0.0521\n",
      "Epoch 9/30\n",
      "36/36 [==============================] - 0s 5ms/step - loss: 0.0221 - mae: 0.0917 - val_loss: 0.0138 - val_mae: 0.0499\n",
      "Epoch 10/30\n",
      "36/36 [==============================] - 0s 6ms/step - loss: 0.0232 - mae: 0.0939 - val_loss: 0.0116 - val_mae: 0.0407\n",
      "Epoch 11/30\n",
      "36/36 [==============================] - 0s 6ms/step - loss: 0.0195 - mae: 0.0842 - val_loss: 0.0120 - val_mae: 0.0434\n",
      "Epoch 12/30\n",
      "36/36 [==============================] - 0s 5ms/step - loss: 0.0224 - mae: 0.0933 - val_loss: 0.0137 - val_mae: 0.0525\n",
      "Epoch 13/30\n",
      "36/36 [==============================] - 0s 5ms/step - loss: 0.0198 - mae: 0.0842 - val_loss: 0.0114 - val_mae: 0.0395\n",
      "Epoch 14/30\n",
      "36/36 [==============================] - 0s 7ms/step - loss: 0.0209 - mae: 0.0883 - val_loss: 0.0113 - val_mae: 0.0410\n",
      "Epoch 15/30\n",
      "36/36 [==============================] - 0s 6ms/step - loss: 0.0211 - mae: 0.0911 - val_loss: 0.0188 - val_mae: 0.0876\n",
      "Epoch 16/30\n",
      "36/36 [==============================] - 0s 6ms/step - loss: 0.0209 - mae: 0.0895 - val_loss: 0.0116 - val_mae: 0.0423\n",
      "Epoch 17/30\n",
      "36/36 [==============================] - 0s 8ms/step - loss: 0.0186 - mae: 0.0814 - val_loss: 0.0110 - val_mae: 0.0406\n",
      "Epoch 18/30\n",
      "36/36 [==============================] - 0s 6ms/step - loss: 0.0193 - mae: 0.0844 - val_loss: 0.0129 - val_mae: 0.0512\n",
      "Epoch 19/30\n",
      "36/36 [==============================] - 0s 7ms/step - loss: 0.0178 - mae: 0.0828 - val_loss: 0.0162 - val_mae: 0.0796\n",
      "Epoch 20/30\n",
      "36/36 [==============================] - 0s 13ms/step - loss: 0.0200 - mae: 0.0893 - val_loss: 0.0133 - val_mae: 0.0592\n",
      "Epoch 21/30\n",
      "36/36 [==============================] - 0s 7ms/step - loss: 0.0196 - mae: 0.0883 - val_loss: 0.0114 - val_mae: 0.0432\n",
      "Epoch 22/30\n",
      "36/36 [==============================] - 0s 6ms/step - loss: 0.0217 - mae: 0.0886 - val_loss: 0.0143 - val_mae: 0.0617\n",
      "Epoch 23/30\n",
      "36/36 [==============================] - 0s 6ms/step - loss: 0.0232 - mae: 0.0947 - val_loss: 0.0123 - val_mae: 0.0444\n",
      "Epoch 24/30\n",
      "36/36 [==============================] - 0s 6ms/step - loss: 0.0203 - mae: 0.0872 - val_loss: 0.0114 - val_mae: 0.0431\n",
      "Epoch 25/30\n",
      "36/36 [==============================] - 0s 6ms/step - loss: 0.0216 - mae: 0.0934 - val_loss: 0.0105 - val_mae: 0.0401\n",
      "Epoch 26/30\n",
      "36/36 [==============================] - 0s 6ms/step - loss: 0.0173 - mae: 0.0805 - val_loss: 0.0160 - val_mae: 0.0797\n",
      "Epoch 27/30\n",
      "36/36 [==============================] - 0s 6ms/step - loss: 0.0206 - mae: 0.0886 - val_loss: 0.0136 - val_mae: 0.0608\n",
      "Epoch 28/30\n",
      "36/36 [==============================] - 0s 6ms/step - loss: 0.0193 - mae: 0.0819 - val_loss: 0.0107 - val_mae: 0.0407\n",
      "Epoch 29/30\n",
      "36/36 [==============================] - 0s 7ms/step - loss: 0.0194 - mae: 0.0860 - val_loss: 0.0114 - val_mae: 0.0470\n",
      "Epoch 30/30\n",
      "36/36 [==============================] - 0s 6ms/step - loss: 0.0199 - mae: 0.0878 - val_loss: 0.0129 - val_mae: 0.0547\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x1391b7950>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-train the best model on the entire training dataset (excluding the final validation set)\n",
    "best_model.fit(training_features, training_target, epochs=epochs, batch_size=batch_size, validation_split=0.1, callbacks=[early_stopping], verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_24\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_24 (LSTM)              (None, 32)                5760      \n",
      "                                                                 \n",
      " dropout_24 (Dropout)        (None, 32)                0         \n",
      "                                                                 \n",
      " dense_24 (Dense)            (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5793 (22.63 KB)\n",
      "Trainable params: 5793 (22.63 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Summary of the model\n",
    "best_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x139bf3100> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "2/2 [==============================] - 1s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the test data\n",
    "test_predictions = best_model.predict(test_features)\n",
    "\n",
    "# Inverse transform the predictions and actual values to their original scale\n",
    "predictions = target_scaler.inverse_transform(test_predictions)\n",
    "actual = target_scaler.inverse_transform(test_target.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "def plot_predictions(predictions, actual, title=f'Actual vs Predicted Prices ({ticker})'):\n",
    "    plt.figure(figsize=(14, 7))\n",
    "    plt.plot(actual, label='Actual Prices', marker='o')  # Use test_target's index for actual prices\n",
    "    plt.plot(predictions, label='Predicted Prices', linestyle='--', marker='o')  # Use the same index for predictions\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Date')\n",
    "    plt.ylabel('Price')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.xticks(rotation=45)  # Rotate date labels for better readability\n",
    "    plt.gcf().autofmt_xdate() \n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "plot_predictions(predictions, actual)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 74.78\n",
      "Mean Absolute Error (MAE): 6.52\n",
      "Root Mean Squared Error (RMSE): 8.65\n",
      "R-squared (R2): 0.89\n",
      "Adjusted R-squared: 0.87\n",
      "Mean Absolute Percentage Error (MAPE): 0.03\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error\n",
    "\n",
    "# Calculate evaluation metrics\n",
    "mse = mean_squared_error(actual, predictions)\n",
    "mae = mean_absolute_error(actual, predictions)\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "r2 = r2_score(actual, predictions)\n",
    "\n",
    "# Adjusted R-squared\n",
    "n = test_size  # Number of observations\n",
    "p = len(features.columns)   # Number of predictors\n",
    "adj_r2 = 1 - (1 - r2) * (n - 1) / (n - p - 1)\n",
    "\n",
    "mape = mean_absolute_percentage_error(actual, predictions)\n",
    "\n",
    "print(\"Mean Squared Error (MSE):\", round(mse, 2))\n",
    "print(\"Mean Absolute Error (MAE):\", round(mae, 2))\n",
    "print(\"Root Mean Squared Error (RMSE):\", round(rmse, 2))\n",
    "print(\"R-squared (R2):\", round(r2, 2))\n",
    "print(\"Adjusted R-squared:\", round(adj_r2, 2))\n",
    "print(\"Mean Absolute Percentage Error (MAPE):\", round(mape, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Squared Error (MSE) and Mean Absolute Error (MAE):\n",
    "\n",
    "- These metrics measure the average error between the predicted and actual values.\n",
    "- Lower values indicate better performance.\n",
    "- However, the absolute scale of these metrics is dependent on the scale of your target variable. Therefore, it's difficult to define a universally \"good\" threshold. It's more informative to compare these metrics across different models or variations of your LSTM model.\n",
    "\n",
    "### Root Mean Squared Error (RMSE):\n",
    "\n",
    "- RMSE is in the same units as the target variable, which can make it more interpretable.\n",
    "- Similar to MSE and MAE, lower values are desirable.\n",
    "- Like MSE and MAE, it's more useful for comparing models rather than establishing absolute thresholds.\n",
    "\n",
    "### R-squared (R2) and Adjusted R-squared:\n",
    "\n",
    "- R2 measures the proportion of the variance in the dependent variable that is predictable from the independent variables.\n",
    "- R2 ranges from 0 to 1, with higher values indicating better fit.\n",
    "- A value of 1 indicates that the model explains all the variability of the response data around its mean. However, a high R2 value doesn't necessarily mean a good model, as it may overfit.\n",
    "- Adjusted R-squared adjusts the R2 value based on the number of predictors in the model, providing a more accurate measure of model performance, especially with multiple variables.\n",
    "- Generally, R2 values closer to 1 and higher Adjusted R-squared values are desirable, but the interpretation depends on the context and the complexity of the model.\n",
    "\n",
    "### Mean Absolute Percentage Error (MAPE):\n",
    "\n",
    "- MAPE measures the average absolute percentage difference between predicted and actual values.\n",
    "- Lower values indicate better performance.\n",
    "- A MAPE of 0% indicates a perfect fit, but in practice, it's rare to achieve such a value.\n",
    "- It's common to interpret MAPE in the context of the variability of the target variable. For example, a MAPE below 10% might be considered good in some domains.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 38ms/step\n"
     ]
    }
   ],
   "source": [
    "n_future = 1\n",
    "\n",
    "# Prepare the last sequence from the scaled features\n",
    "last_sequence = scaled_features_array[-n_steps:]  # Get the last n_steps days\n",
    "future_predictions = []\n",
    "\n",
    "# Predict the future\n",
    "for i in range(n_future):\n",
    "    # Reshape the sequence for the model\n",
    "    last_sequence_reshaped = last_sequence.reshape((1, n_steps, scaled_features_df.shape[1]))\n",
    "    \n",
    "    # Predict the next day\n",
    "    next_price = best_model.predict(last_sequence_reshaped)\n",
    "    future_predictions.append(next_price[0])\n",
    "    \n",
    "    # Update the sequence with the predicted price\n",
    "    last_sequence = np.roll(last_sequence, -1, axis=0)\n",
    "    last_sequence[-1] = 0  # Reset the last day\n",
    "    last_sequence[-1, 0] = next_price  # Assuming 'Close' price is the first feature\n",
    "\n",
    "# Inverse transform the future predictions to the original scale\n",
    "future_predictions_inverse = target_scaler.inverse_transform(np.array(future_predictions).reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[198.19852]], dtype=float32)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_predictions_inverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "\n",
    "n_past = 20\n",
    "\n",
    "# Prepare the dates for plotting\n",
    "last_dates = data.index[-n_past:]\n",
    "future_dates = [last_dates[-1] + datetime.timedelta(days=i) for i in range(1, n_future + 1)]\n",
    "\n",
    "# Plot the last n_past days and the future predictions\n",
    "plt.figure(figsize=(14, 7))\n",
    "plt.plot(last_dates, data['Close'][-n_past:], label='Historical Actual Prices')\n",
    "plt.plot(future_dates, future_predictions_inverse, label='Future Predicted Prices', linestyle='--', marker='o')\n",
    "plt.title(f'Historical and Predicted Future Prices for the next {n_future} days')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "\n",
    "# Improve the date formatting\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=1))\n",
    "plt.gcf().autofmt_xdate()  # Rotation\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the refactored explanation to match your updated code:\n",
    "\n",
    "## Future Price Prediction Explanation\n",
    "\n",
    "The future price prediction in this model leverages a function that iteratively forecasts prices for a specified number of days ahead, utilizing the most recent input data. This approach is particularly pertinent for predicting stock prices where future values (like the next day's 'Open', 'High', 'Low') are unknown.\n",
    "\n",
    "### Preparing the Last Known Input\n",
    "\n",
    "The prediction process commences with the preparation of the last known input data, which is the most recent data point available from the dataset. This last input is crucial as it serves as the starting point for future predictions. The code snippet for this preparation is:\n",
    "\n",
    "```python\n",
    "last_known_input = scaled_features_df.iloc[-1:].values  # Use the last known input features\n",
    "```\n",
    "\n",
    "Here, `scaled_features_df.iloc[-1:]` fetches the last row of the scaled feature dataset, representing the most recent day's data.\n",
    "\n",
    "### Iterative Future Prediction\n",
    "\n",
    "The essence of the future price prediction lies in an iterative mechanism where each prediction is subsequently used as input for the next. This iterative forecasting is encapsulated in the `predict_future_days` function:\n",
    "\n",
    "```python\n",
    "def predict_future_days(model, last_input, feature_scaler, target_scaler, n_days):\n",
    "    future_predictions_scaled = []\n",
    "    input_sequence = last_input.copy()  # Copy the last known input sequence\n",
    "\n",
    "    for _ in range(n_days):\n",
    "        reshaped_input = input_sequence.reshape((1, 1, input_sequence.shape[1]))\n",
    "        next_day_prediction_scaled = model.predict(reshaped_input)\n",
    "        future_predictions_scaled.append(next_day_prediction_scaled[0, 0])\n",
    "        next_input_sequence = np.append(input_sequence[:, 1:], next_day_prediction_scaled, axis=1)\n",
    "        input_sequence = next_input_sequence\n",
    "\n",
    "    future_predictions_scaled = np.array(future_predictions_scaled).reshape(-1, 1)\n",
    "    future_predictions = target_scaler.inverse_transform(future_predictions_scaled)[:, 0]\n",
    "\n",
    "    return future_predictions\n",
    "```\n",
    "\n",
    "In this function:\n",
    "\n",
    "- `input_sequence` starts as the last known input, and with each iteration, the model predicts the next day's price.\n",
    "- The prediction (`next_day_prediction_scaled`) is appended to `future_predictions_scaled`, accumulating the future price predictions.\n",
    "- `input_sequence` is updated with the new prediction, effectively simulating the input for the next day's prediction.\n",
    "- After completing the predictions for the desired number of days, the `future_predictions` are inversely transformed to the original price scale using the `target_scaler`.\n",
    "\n",
    "### Conclusion\n",
    "\n",
    "This iterative prediction method allows for forecasting future stock prices based on historical data patterns. However, it's crucial to recognize that the accuracy of these predictions may diminish over time due to the cumulative effect of prediction errors and the absence of actual future inputs. This strategy presupposes that historical trends will persist, which may not always be the case in the volatile stock market environment.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ds-ml",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 "nbformat": 4,
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}