Tesla_Stock_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TESLA STOCKS ANALYSIS AND PREDICTION\n",
    "\n",
    "In this project, I have analysed tesla stocks from January 2021 till December 2023. The data has been gathered using YFinance API. This API offers various daily updated stock datasets. I have used python YFinance library for fetching the dataset but you can also go to their website \"https://finance.yahoo.com/\" and get required data.\n",
    "\n",
    "Furthermore, I have predicted the values for the month of January using 2 approached. First approach is using LSTM regression model and second approach is combining LSTM model along with Simple RNN model and predict the future stock prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: statsmodels in c:\\python312\\lib\\site-packages (0.14.1)\n",
      "Requirement already satisfied: numpy<2,>=1.18 in c:\\python312\\lib\\site-packages (from statsmodels) (1.26.2)\n",
      "Requirement already satisfied: scipy!=1.9.2,>=1.4 in c:\\python312\\lib\\site-packages (from statsmodels) (1.11.4)\n",
      "Requirement already satisfied: pandas!=2.1.0,>=1.0 in c:\\python312\\lib\\site-packages (from statsmodels) (2.1.4)\n",
      "Requirement already satisfied: patsy>=0.5.4 in c:\\python312\\lib\\site-packages (from statsmodels) (0.5.5)\n",
      "Requirement already satisfied: packaging>=21.3 in c:\\python312\\lib\\site-packages (from statsmodels) (23.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\python312\\lib\\site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\python312\\lib\\site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\python312\\lib\\site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2023.3)\n",
      "Requirement already satisfied: six in c:\\python312\\lib\\site-packages (from patsy>=0.5.4->statsmodels) (1.16.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.2.1 -> 23.3.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "# !pip install yfinance\n",
    "!pip install --user statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "87NNxK9kXiDs"
   },
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Fetch Tesla stock data\n",
    "tesla = yf.Ticker(\"TSLA\")\n",
    "\n",
    "# Get historical data (adjust the start and end dates as needed)\n",
    "df = tesla.history(start=\"2021-01-01\", end=\"2024-01-01\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "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>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-04 00:00:00-05:00</th>\n",
       "      <td>239.820007</td>\n",
       "      <td>248.163330</td>\n",
       "      <td>239.063339</td>\n",
       "      <td>243.256668</td>\n",
       "      <td>145914600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05 00:00:00-05:00</th>\n",
       "      <td>241.220001</td>\n",
       "      <td>246.946671</td>\n",
       "      <td>239.733337</td>\n",
       "      <td>245.036667</td>\n",
       "      <td>96735600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-06 00:00:00-05:00</th>\n",
       "      <td>252.830002</td>\n",
       "      <td>258.000000</td>\n",
       "      <td>249.699997</td>\n",
       "      <td>251.993332</td>\n",
       "      <td>134100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-07 00:00:00-05:00</th>\n",
       "      <td>259.209991</td>\n",
       "      <td>272.329987</td>\n",
       "      <td>258.399994</td>\n",
       "      <td>272.013336</td>\n",
       "      <td>154496700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-08 00:00:00-05:00</th>\n",
       "      <td>285.333344</td>\n",
       "      <td>294.829987</td>\n",
       "      <td>279.463318</td>\n",
       "      <td>293.339996</td>\n",
       "      <td>225166500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Open        High         Low       Close  \\\n",
       "Date                                                                        \n",
       "2021-01-04 00:00:00-05:00  239.820007  248.163330  239.063339  243.256668   \n",
       "2021-01-05 00:00:00-05:00  241.220001  246.946671  239.733337  245.036667   \n",
       "2021-01-06 00:00:00-05:00  252.830002  258.000000  249.699997  251.993332   \n",
       "2021-01-07 00:00:00-05:00  259.209991  272.329987  258.399994  272.013336   \n",
       "2021-01-08 00:00:00-05:00  285.333344  294.829987  279.463318  293.339996   \n",
       "\n",
       "                              Volume  Dividends  Stock Splits  \n",
       "Date                                                           \n",
       "2021-01-04 00:00:00-05:00  145914600        0.0           0.0  \n",
       "2021-01-05 00:00:00-05:00   96735600        0.0           0.0  \n",
       "2021-01-06 00:00:00-05:00  134100000        0.0           0.0  \n",
       "2021-01-07 00:00:00-05:00  154496700        0.0           0.0  \n",
       "2021-01-08 00:00:00-05:00  225166500        0.0           0.0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Open            0\n",
      "High            0\n",
      "Low             0\n",
      "Close           0\n",
      "Volume          0\n",
      "Dividends       0\n",
      "Stock Splits    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove duplicate rows\n",
    "df.drop_duplicates(inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series Plot\n",
    "\n",
    "In this plot, closing prices of the stocks are observed over a period of time. It seemed that TESLA stocks have faced extreme diversions throughout the time. Nonetheless, stock prices are decreased by the end of December."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(df.index, df['Close'], label='Tesla Closing Prices')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Closing Price')\n",
    "plt.title('Tesla Stock Prices Over Time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trend Analysis using Simple Moving Average\n",
    "\n",
    "Looking at the moving averages of stock, we can see that stocks see high variations on a short term basis (30 days), but after considering a wider perspective (100 or 200 days) stocks were relatively stable. Although, stock prices have faced a massive decline in the year 2023. Even when considering long term investments we can see that by the end of 2023 stocks have still not entirely touched their maximum price as they were in the beginning of 2023. Also, from November 2023 to December 2023 they have faced a slight decline in the market. Overall analysis suggests that it is riskier to invest in these stocks and also not a good time to invest presently since the stock prices can possibly decline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(df.index, df['Close'], label='Tesla Closing Prices')\n",
    "plt.plot(df.index, df['Close'].rolling(window=30).mean(), label='30-Day Moving Avg')\n",
    "plt.plot(df.index, df['Close'].rolling(window=100).mean(), label='100-Day Moving Avg')\n",
    "plt.plot(df.index, df['Close'].rolling(window=200).mean(), label='200-Day Moving Avg')\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Closing Price')\n",
    "plt.title('Tesla Stock Prices with Moving Averages')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Volatility Analysis with Bollinger Bands:\n",
    "\n",
    "Higher diversion in volatility indicates higher chances of future fluctuations in stock prices. If we see whether it be on a long run or a short run, price diversion was always high. Particularly, in the recent times, prices were dramatically declined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate daily returns\n",
    "df['Daily_Returns'] = df['Close'].pct_change()\n",
    "\n",
    "# Calculate 100-day volatility (standard deviation of returns)\n",
    "volatility = df['Daily_Returns'].rolling(window=100).std()\n",
    "\n",
    "# Plotting the volatility\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(volatility, color='blue')\n",
    "plt.title(f'100-day Volatility for TSLA')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seasonality Analysis\n",
    "\n",
    "When seasonal trend was observed on a monthly basis, a gradual decrease was seen in stock prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Resample the data to get monthly averages\n",
    "monthly_avg = data['Close'].resample('M').mean()\n",
    "\n",
    "# Extracting month and year for scatter plot\n",
    "monthly_avg.index = monthly_avg.index.strftime('%Y-%m')\n",
    "\n",
    "# Creating x and y values for the trendline\n",
    "x_values = np.arange(len(monthly_avg))\n",
    "y_values = monthly_avg.values\n",
    "\n",
    "# Calculate linear regression (trendline)\n",
    "coefficients = np.polyfit(x_values, y_values, 1)\n",
    "trendline = coefficients[0] * x_values + coefficients[1]\n",
    "\n",
    "# Plotting the scatter plot of monthly average stock prices with trendline\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(monthly_avg.index, monthly_avg.values, color='orange', label='Monthly Avg Prices')\n",
    "plt.plot(monthly_avg.index, trendline, color='blue', label='Trendline')\n",
    "plt.title(f'Monthly Average Stock Prices (Jan 2021 - Dec 2023) with Trendline')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Average Close Price')\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict prices for the month of January 2024 based on the historical data\n",
    "\n",
    "I have used LSTM model to predict the future prices for Tesla stocks. The reason I have used LSTM model is because of the ability of these model to capture long-term dependencies in sequential data. LSTMs have a memory cell that can maintain information for an extended period, allowing them to remember patterns or trends in historical stock prices. Since stock market data is generally unstable with highly fluctuating prices, LSTMs can do well with such data. They can handle various input types (e.g., multiple features like volume, opening price, etc.) and can predict a single value (next-day price) or multiple steps ahead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "22/22 [==============================] - 6s 51ms/step - loss: 0.0508\n",
      "Epoch 2/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0085\n",
      "Epoch 3/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0058\n",
      "Epoch 4/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0049\n",
      "Epoch 5/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0044\n",
      "Epoch 6/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0040\n",
      "Epoch 7/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.0038\n",
      "Epoch 8/100\n",
      "22/22 [==============================] - 1s 55ms/step - loss: 0.0035\n",
      "Epoch 9/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.0034\n",
      "Epoch 10/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.0032\n",
      "Epoch 11/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0030\n",
      "Epoch 12/100\n",
      "22/22 [==============================] - 1s 50ms/step - loss: 0.0029\n",
      "Epoch 13/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.0029\n",
      "Epoch 14/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0026\n",
      "Epoch 15/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0026\n",
      "Epoch 16/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0025\n",
      "Epoch 17/100\n",
      "22/22 [==============================] - 1s 50ms/step - loss: 0.0023\n",
      "Epoch 18/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0025\n",
      "Epoch 19/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0023\n",
      "Epoch 20/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0023\n",
      "Epoch 21/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0019\n",
      "Epoch 22/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0019\n",
      "Epoch 23/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0018\n",
      "Epoch 24/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0019\n",
      "Epoch 25/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0017\n",
      "Epoch 26/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.0017\n",
      "Epoch 27/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.0019\n",
      "Epoch 28/100\n",
      "22/22 [==============================] - 1s 50ms/step - loss: 0.0017\n",
      "Epoch 29/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0018\n",
      "Epoch 30/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0016\n",
      "Epoch 31/100\n",
      "22/22 [==============================] - 1s 54ms/step - loss: 0.0018\n",
      "Epoch 32/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0018\n",
      "Epoch 33/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0016\n",
      "Epoch 34/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0016\n",
      "Epoch 35/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0018\n",
      "Epoch 36/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0015\n",
      "Epoch 37/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0017\n",
      "Epoch 38/100\n",
      "22/22 [==============================] - 1s 50ms/step - loss: 0.0016\n",
      "Epoch 39/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0015\n",
      "Epoch 40/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.0016\n",
      "Epoch 41/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0017\n",
      "Epoch 42/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.0015\n",
      "Epoch 43/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.0016\n",
      "Epoch 44/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.0014\n",
      "Epoch 45/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0014\n",
      "Epoch 46/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0013\n",
      "Epoch 47/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0014\n",
      "Epoch 48/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0014\n",
      "Epoch 49/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.0014\n",
      "Epoch 50/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0014\n",
      "Epoch 51/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0013\n",
      "Epoch 52/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0013\n",
      "Epoch 53/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0013\n",
      "Epoch 54/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0012\n",
      "Epoch 55/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0012\n",
      "Epoch 56/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0012\n",
      "Epoch 57/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.0012\n",
      "Epoch 58/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0012\n",
      "Epoch 59/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0011\n",
      "Epoch 60/100\n",
      "22/22 [==============================] - 1s 50ms/step - loss: 0.0015\n",
      "Epoch 61/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0015\n",
      "Epoch 62/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0012\n",
      "Epoch 63/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0011\n",
      "Epoch 64/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0011\n",
      "Epoch 65/100\n",
      "22/22 [==============================] - 1s 55ms/step - loss: 0.0012\n",
      "Epoch 66/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.0011\n",
      "Epoch 67/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.0010\n",
      "Epoch 68/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0011\n",
      "Epoch 69/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0011\n",
      "Epoch 70/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0010\n",
      "Epoch 71/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0010\n",
      "Epoch 72/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0011\n",
      "Epoch 73/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0011\n",
      "Epoch 74/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0010\n",
      "Epoch 75/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0010\n",
      "Epoch 76/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0011\n",
      "Epoch 77/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0010\n",
      "Epoch 78/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0011\n",
      "Epoch 79/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0011\n",
      "Epoch 80/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0011\n",
      "Epoch 81/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0010\n",
      "Epoch 82/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0011\n",
      "Epoch 83/100\n",
      "22/22 [==============================] - 1s 50ms/step - loss: 0.0011\n",
      "Epoch 84/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 9.9390e-04\n",
      "Epoch 85/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0010\n",
      "Epoch 86/100\n",
      "22/22 [==============================] - 1s 50ms/step - loss: 0.0011\n",
      "Epoch 87/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0011\n",
      "Epoch 88/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0011\n",
      "Epoch 89/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0011\n",
      "Epoch 90/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0010\n",
      "Epoch 91/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0010\n",
      "Epoch 92/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0011\n",
      "Epoch 93/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 9.7973e-04\n",
      "Epoch 94/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.0011\n",
      "Epoch 95/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0010\n",
      "Epoch 96/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 9.8147e-04\n",
      "Epoch 97/100\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.0010\n",
      "Epoch 98/100\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 9.8532e-04\n",
      "Epoch 99/100\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.0011\n",
      "Epoch 100/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 1s 51ms/step - loss: 9.7402e-04\n",
      "1/1 [==============================] - 1s 1s/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 56ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "                        Date  Predicted Close\n",
      "0  2023-12-30 00:00:00-05:00       246.587448\n",
      "1  2023-12-31 00:00:00-05:00       244.537048\n",
      "2  2024-01-01 00:00:00-05:00       242.426132\n",
      "3  2024-01-02 00:00:00-05:00       240.024155\n",
      "4  2024-01-03 00:00:00-05:00       237.546432\n",
      "5  2024-01-04 00:00:00-05:00       235.078964\n",
      "6  2024-01-05 00:00:00-05:00       232.653717\n",
      "7  2024-01-06 00:00:00-05:00       230.289978\n",
      "8  2024-01-07 00:00:00-05:00       227.998978\n",
      "9  2024-01-08 00:00:00-05:00       225.785461\n",
      "10 2024-01-09 00:00:00-05:00       223.650085\n",
      "11 2024-01-10 00:00:00-05:00       221.591080\n",
      "12 2024-01-11 00:00:00-05:00       219.605316\n",
      "13 2024-01-12 00:00:00-05:00       217.688919\n",
      "14 2024-01-13 00:00:00-05:00       215.837753\n",
      "15 2024-01-14 00:00:00-05:00       214.047775\n",
      "16 2024-01-15 00:00:00-05:00       212.315247\n",
      "17 2024-01-16 00:00:00-05:00       210.636627\n",
      "18 2024-01-17 00:00:00-05:00       209.008835\n",
      "19 2024-01-18 00:00:00-05:00       207.429138\n",
      "20 2024-01-19 00:00:00-05:00       205.895325\n",
      "21 2024-01-20 00:00:00-05:00       204.405441\n",
      "22 2024-01-21 00:00:00-05:00       202.958008\n",
      "23 2024-01-22 00:00:00-05:00       201.551712\n",
      "24 2024-01-23 00:00:00-05:00       200.185562\n",
      "25 2024-01-24 00:00:00-05:00       198.858749\n",
      "26 2024-01-25 00:00:00-05:00       197.570679\n",
      "27 2024-01-26 00:00:00-05:00       196.320831\n",
      "28 2024-01-27 00:00:00-05:00       195.108749\n",
      "29 2024-01-28 00:00:00-05:00       193.934021\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "\n",
    "# Extract the 'Close' prices\n",
    "data = df['Close'].values.reshape(-1, 1)\n",
    "\n",
    "# Normalize the data\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_data = scaler.fit_transform(data)\n",
    "\n",
    "# Prepare the dataset\n",
    "def create_dataset(data, time_step=1):\n",
    "    X, y = [], []\n",
    "    for i in range(len(data) - time_step - 1):\n",
    "        X.append(data[i:(i + time_step), 0])\n",
    "        y.append(data[i + time_step, 0])\n",
    "    return np.array(X), np.array(y)\n",
    "\n",
    "time_steps = 60  # Number of time steps to consider for prediction\n",
    "X, y = create_dataset(scaled_data, time_step=time_steps)\n",
    "\n",
    "# Reshape data for LSTM (samples, time steps, features)\n",
    "X = X.reshape(X.shape[0], X.shape[1], 1)\n",
    "\n",
    "# Build LSTM model\n",
    "model = Sequential()\n",
    "model.add(LSTM(units=50, return_sequences=True, input_shape=(time_steps, 1)))\n",
    "model.add(LSTM(units=50))\n",
    "model.add(Dense(units=1))\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Fit the model\n",
    "model.fit(X, y, epochs=100, batch_size=32)\n",
    "\n",
    "# Predicting the next month stock prices\n",
    "last_month = scaled_data[-time_steps:]\n",
    "last_month = last_month.reshape(1, time_steps, 1)\n",
    "\n",
    "predicted_prices = []\n",
    "for i in range(30):  # Predicting 30 days (1 month) ahead\n",
    "    prediction = model.predict(last_month)\n",
    "    predicted_prices.append(prediction[0, 0])\n",
    "    last_month = np.append(last_month[:, 1:, :], prediction.reshape(1, 1, 1), axis=1)\n",
    "\n",
    "# Inverse transform the predicted prices to original scale\n",
    "predicted_prices = np.array(predicted_prices).reshape(-1, 1)\n",
    "predicted_prices = scaler.inverse_transform(predicted_prices)\n",
    "\n",
    "# Generate dates for the next month\n",
    "last_date = df.index[-1]  # Assuming the index contains date information\n",
    "predicted_dates = [last_date + pd.DateOffset(days=i) for i in range(1, 31)]\n",
    "\n",
    "# Create a DataFrame for predicted prices and dates\n",
    "predicted_df = pd.DataFrame({'Date': predicted_dates, 'Predicted Close': predicted_prices.flatten()})\n",
    "print(predicted_df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter historical data for December 2023\n",
    "december_2023_data = df[(df.index.month == 12) & (df.index.year == 2023)]\n",
    "\n",
    "# Plotting the historical data for December 2023\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(december_2023_data.index, december_2023_data['Close'], label='Historical Close Price (December 2023)', color='blue')\n",
    "# Plotting the predicted prices for the next month\n",
    "plt.plot(predicted_df['Date'], predicted_df['Predicted Close'], label='Predicted Close Price', color='red')\n",
    "plt.title('Actual vs Predicted Stock Prices')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Close Price')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding Percent Difference in the predicted prices for the month of January 2024\n",
    "\n",
    "A whopping difference of -21.35% is obtained after performing calculations for predicted prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted percent change for January 2023: -21.35%\n"
     ]
    }
   ],
   "source": [
    "if not predicted_df.empty:\n",
    "    # Calculate percent change\n",
    "    predicted_prices_jan_2023 = predicted_df['Predicted Close'].values\n",
    "    percent_change_jan_2023 = ((predicted_prices_jan_2023[-1] - predicted_prices_jan_2023[0]) / predicted_prices_jan_2023[0]) * 100\n",
    "\n",
    "    print(f\"Predicted percent change for January 2023: {percent_change_jan_2023:.2f}%\")\n",
    "else:\n",
    "    print(\"No data available for January 2023 in the predictions.\")\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}