model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "qurl = \"https://yahoo-finance15.p.rapidapi.com/api/yahoo/hi/history/AAPL/15m\"\n",
    "\n",
    "querystring = {\n",
    "    \"diffandsplits\": \"false\"\n",
    "}\n",
    "\n",
    "headers = {\n",
    "    \"X-RapidAPI-Key\": os.environ.get('RAPIDAPI_KEY'),\n",
    "    \"X-RapidAPI-Host\": \"yahoo-finance15.p.rapidapi.com\",\n",
    "}\n",
    "\n",
    "response = requests.get(qurl, headers = headers, params = querystring)\n",
    "data = response.json().get('items')\n",
    "df = pd.DataFrame(data.values())\n",
    "df['time'] = pd.to_datetime(df.get('date_utc'), unit='s')\n",
    "df.drop(['date_utc'], axis=1, inplace=True)\n",
    "df.drop(['date'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'postgresql://postgres-ag:postgres-abhijith@localhost/postgres-db'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psql_url = os.environ.get('DB_HOST')\n",
    "psql_user = os.environ.get('POSTGRES_USER')\n",
    "psql_pw = os.environ.get('POSTGRES_PASSWORD')\n",
    "psql_db = os.environ.get('POSTGRES_DB')\n",
    "\n",
    "conn_str = f'postgresql://{psql_user}:{psql_pw}@{psql_url}/{psql_db}'\n",
    "conn_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "573"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_sql('stock_data', conn_str, if_exists='replace')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding time as an index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</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>2023-09-13 13:30:00</th>\n",
       "      <td>176.51</td>\n",
       "      <td>177.30</td>\n",
       "      <td>175.33</td>\n",
       "      <td>175.83</td>\n",
       "      <td>8946266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-13 13:45:00</th>\n",
       "      <td>175.84</td>\n",
       "      <td>175.95</td>\n",
       "      <td>174.59</td>\n",
       "      <td>175.81</td>\n",
       "      <td>5431528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-13 14:00:00</th>\n",
       "      <td>175.82</td>\n",
       "      <td>175.90</td>\n",
       "      <td>174.66</td>\n",
       "      <td>174.68</td>\n",
       "      <td>3103405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-13 14:15:00</th>\n",
       "      <td>174.67</td>\n",
       "      <td>175.29</td>\n",
       "      <td>174.07</td>\n",
       "      <td>175.19</td>\n",
       "      <td>4285646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-13 14:30:00</th>\n",
       "      <td>175.19</td>\n",
       "      <td>175.50</td>\n",
       "      <td>175.00</td>\n",
       "      <td>175.09</td>\n",
       "      <td>2730312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       open    high     low   close   volume\n",
       "time                                                        \n",
       "2023-09-13 13:30:00  176.51  177.30  175.33  175.83  8946266\n",
       "2023-09-13 13:45:00  175.84  175.95  174.59  175.81  5431528\n",
       "2023-09-13 14:00:00  175.82  175.90  174.66  174.68  3103405\n",
       "2023-09-13 14:15:00  174.67  175.29  174.07  175.19  4285646\n",
       "2023-09-13 14:30:00  175.19  175.50  175.00  175.09  2730312"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('time', inplace=True)\n",
    "df.sort_index(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -1.0693366798173336\n",
      "p-value: 0.7271439354426359\n"
     ]
    }
   ],
   "source": [
    "result = adfuller(df['high'])\n",
    "\n",
    "print('ADF Statistic:', result[0])\n",
    "print('p-value:', result[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# High p-value Warning\n",
    "If the p value is higher than 0.05, one needs to difference it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['high_diff'] = df['high'] - df['high'].shift(1)\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test train split\n",
    "\n",
    "The train set is of 80 % of the last one month's AAPL stock data with the interval of 15 miutes\n",
    "\n",
    "While the test set is the remaining 20 %\n",
    "\n",
    "\n",
    "**They've not been shuffled to maintain chronological significance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size = int(0.8 * len(data))\n",
    "train, test = df['high'][:train_size], df['high'][train_size:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARIMA model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "model = ARIMA(train, order=(1, 1, 1))\n",
    "model_fit = model.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "forecast = model_fit.forecast(steps=len(test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Error control and check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>high</td>       <th>  No. Observations:  </th>    <td>458</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(1, 1, 1)</td>  <th>  Log Likelihood     </th> <td>-205.382</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Fri, 13 Oct 2023</td> <th>  AIC                </th>  <td>416.765</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>02:22:49</td>     <th>  BIC                </th>  <td>429.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th>  <td>421.639</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 458</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    0.2188</td> <td>    7.125</td> <td>    0.031</td> <td> 0.976</td> <td>  -13.745</td> <td>   14.183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.2251</td> <td>    7.119</td> <td>   -0.032</td> <td> 0.975</td> <td>  -14.179</td> <td>   13.728</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.1438</td> <td>    0.003</td> <td>   52.999</td> <td> 0.000</td> <td>    0.139</td> <td>    0.149</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>12149.51</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.99</td> <th>  Prob(JB):          </th>   <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.81</td> <th>  Skew:              </th>   <td>1.95</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.20</td> <th>  Kurtosis:          </th>   <td>27.96</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &       high       & \\textbf{  No. Observations:  } &    458      \\\\\n",
       "\\textbf{Model:}                  &  ARIMA(1, 1, 1)  & \\textbf{  Log Likelihood     } &  -205.382   \\\\\n",
       "\\textbf{Date:}                   & Fri, 13 Oct 2023 & \\textbf{  AIC                } &  416.765    \\\\\n",
       "\\textbf{Time:}                   &     02:22:49     & \\textbf{  BIC                } &  429.139    \\\\\n",
       "\\textbf{Sample:}                 &        0         & \\textbf{  HQIC               } &  421.639    \\\\\n",
       "\\textbf{}                        &       - 458      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{ar.L1}  &       0.2188  &        7.125     &     0.031  &         0.976        &      -13.745    &       14.183     \\\\\n",
       "\\textbf{ma.L1}  &      -0.2251  &        7.119     &    -0.032  &         0.975        &      -14.179    &       13.728     \\\\\n",
       "\\textbf{sigma2} &       0.1438  &        0.003     &    52.999  &         0.000        &        0.139    &        0.149     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.00 & \\textbf{  Jarque-Bera (JB):  } & 12149.51  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.99 & \\textbf{  Prob(JB):          } &   0.00    \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 0.81 & \\textbf{  Skew:              } &   1.95    \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.20 & \\textbf{  Kurtosis:          } &  27.96    \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                   high   No. Observations:                  458\n",
       "Model:                 ARIMA(1, 1, 1)   Log Likelihood                -205.382\n",
       "Date:                Fri, 13 Oct 2023   AIC                            416.765\n",
       "Time:                        02:22:49   BIC                            429.139\n",
       "Sample:                             0   HQIC                           421.639\n",
       "                                - 458                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.2188      7.125      0.031      0.976     -13.745      14.183\n",
       "ma.L1         -0.2251      7.119     -0.032      0.975     -14.179      13.728\n",
       "sigma2         0.1438      0.003     52.999      0.000       0.139       0.149\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):             12149.51\n",
       "Prob(Q):                              0.99   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.81   Skew:                             1.95\n",
       "Prob(H) (two-sided):                  0.20   Kurtosis:                        27.96\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast = pd.DataFrame(forecast)\n",
    "model_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(df.index[train_size:], test, label='Actual High Diff')\n",
    "plt.plot(df.index[train_size:], forecast, label='Forecasted High Diff')\n",
    "plt.legend()\n",
    "plt.title('ARIMA Forecast for High Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since ARIMA model isn't working, we'll be trying out LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM\n",
    "\n",
    "The `train_test_split` function is called with a 80-20 split!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_time_steps = 6\n",
    "high_prices = df['high']\n",
    "x = []\n",
    "y = []\n",
    "\n",
    "for i in range(num_time_steps, len(high_prices)):\n",
    "    x.append(high_prices[i - num_time_steps:i])\n",
    "    y.append(high_prices.iloc[i])\n",
    "\n",
    "x = np.array(x)\n",
    "y = np.array(y)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 64)                16896     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 16961 (66.25 KB)\n",
      "Trainable params: 16961 (66.25 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.LSTM(64, input_shape=(num_time_steps, 1)))\n",
    "model.add(tf.keras.layers.Dense(1))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "29/29 - 3s - loss: 30609.8457 - 3s/epoch - 99ms/step\n",
      "Epoch 2/2000\n",
      "29/29 - 0s - loss: 30196.3984 - 130ms/epoch - 4ms/step\n",
      "Epoch 3/2000\n",
      "29/29 - 0s - loss: 29376.5840 - 135ms/epoch - 5ms/step\n",
      "Epoch 4/2000\n",
      "29/29 - 0s - loss: 28435.6934 - 131ms/epoch - 5ms/step\n",
      "Epoch 5/2000\n",
      "29/29 - 0s - loss: 27722.0703 - 132ms/epoch - 5ms/step\n",
      "Epoch 6/2000\n",
      "29/29 - 0s - loss: 27149.2363 - 129ms/epoch - 4ms/step\n",
      "Epoch 7/2000\n",
      "29/29 - 0s - loss: 26587.7129 - 135ms/epoch - 5ms/step\n",
      "Epoch 8/2000\n",
      "29/29 - 0s - loss: 26117.3594 - 137ms/epoch - 5ms/step\n",
      "Epoch 9/2000\n",
      "29/29 - 0s - loss: 25702.5996 - 135ms/epoch - 5ms/step\n",
      "Epoch 10/2000\n",
      "29/29 - 0s - loss: 25305.7637 - 126ms/epoch - 4ms/step\n",
      "Epoch 11/2000\n",
      "29/29 - 0s - loss: 24921.9023 - 127ms/epoch - 4ms/step\n",
      "Epoch 12/2000\n",
      "29/29 - 0s - loss: 24533.9473 - 127ms/epoch - 4ms/step\n",
      "Epoch 13/2000\n",
      "29/29 - 0s - loss: 24119.4375 - 135ms/epoch - 5ms/step\n",
      "Epoch 14/2000\n",
      "29/29 - 0s - loss: 23630.9648 - 136ms/epoch - 5ms/step\n",
      "Epoch 15/2000\n",
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      "Epoch 1875/2000\n",
      "29/29 - 0s - loss: 0.2822 - 118ms/epoch - 4ms/step\n",
      "Epoch 1876/2000\n",
      "29/29 - 0s - loss: 0.3158 - 117ms/epoch - 4ms/step\n",
      "Epoch 1877/2000\n",
      "29/29 - 0s - loss: 0.3976 - 116ms/epoch - 4ms/step\n",
      "Epoch 1878/2000\n",
      "29/29 - 0s - loss: 0.2550 - 126ms/epoch - 4ms/step\n",
      "Epoch 1879/2000\n",
      "29/29 - 0s - loss: 0.2459 - 127ms/epoch - 4ms/step\n",
      "Epoch 1880/2000\n",
      "29/29 - 0s - loss: 0.2300 - 122ms/epoch - 4ms/step\n",
      "Epoch 1881/2000\n",
      "29/29 - 0s - loss: 0.3142 - 115ms/epoch - 4ms/step\n",
      "Epoch 1882/2000\n",
      "29/29 - 0s - loss: 0.2591 - 117ms/epoch - 4ms/step\n",
      "Epoch 1883/2000\n",
      "29/29 - 0s - loss: 0.2583 - 120ms/epoch - 4ms/step\n",
      "Epoch 1884/2000\n",
      "29/29 - 0s - loss: 0.2403 - 117ms/epoch - 4ms/step\n",
      "Epoch 1885/2000\n",
      "29/29 - 0s - loss: 0.2618 - 119ms/epoch - 4ms/step\n",
      "Epoch 1886/2000\n",
      "29/29 - 0s - loss: 0.3952 - 121ms/epoch - 4ms/step\n",
      "Epoch 1887/2000\n",
      "29/29 - 0s - loss: 0.2823 - 118ms/epoch - 4ms/step\n",
      "Epoch 1888/2000\n",
      "29/29 - 0s - loss: 0.2745 - 118ms/epoch - 4ms/step\n",
      "Epoch 1889/2000\n",
      "29/29 - 0s - loss: 0.2867 - 118ms/epoch - 4ms/step\n",
      "Epoch 1890/2000\n",
      "29/29 - 0s - loss: 0.2727 - 123ms/epoch - 4ms/step\n",
      "Epoch 1891/2000\n",
      "29/29 - 0s - loss: 0.5279 - 122ms/epoch - 4ms/step\n",
      "Epoch 1892/2000\n",
      "29/29 - 0s - loss: 0.2540 - 123ms/epoch - 4ms/step\n",
      "Epoch 1893/2000\n",
      "29/29 - 0s - loss: 0.4405 - 128ms/epoch - 4ms/step\n",
      "Epoch 1894/2000\n",
      "29/29 - 0s - loss: 0.3472 - 123ms/epoch - 4ms/step\n",
      "Epoch 1895/2000\n",
      "29/29 - 0s - loss: 0.2511 - 116ms/epoch - 4ms/step\n",
      "Epoch 1896/2000\n",
      "29/29 - 0s - loss: 0.3931 - 118ms/epoch - 4ms/step\n",
      "Epoch 1897/2000\n",
      "29/29 - 0s - loss: 0.5808 - 124ms/epoch - 4ms/step\n",
      "Epoch 1898/2000\n",
      "29/29 - 0s - loss: 0.3045 - 117ms/epoch - 4ms/step\n",
      "Epoch 1899/2000\n",
      "29/29 - 0s - loss: 0.2327 - 116ms/epoch - 4ms/step\n",
      "Epoch 1900/2000\n",
      "29/29 - 0s - loss: 0.2270 - 118ms/epoch - 4ms/step\n",
      "Epoch 1901/2000\n",
      "29/29 - 0s - loss: 0.2616 - 118ms/epoch - 4ms/step\n",
      "Epoch 1902/2000\n",
      "29/29 - 0s - loss: 0.3772 - 120ms/epoch - 4ms/step\n",
      "Epoch 1903/2000\n",
      "29/29 - 0s - loss: 0.4863 - 123ms/epoch - 4ms/step\n",
      "Epoch 1904/2000\n",
      "29/29 - 0s - loss: 0.3450 - 121ms/epoch - 4ms/step\n",
      "Epoch 1905/2000\n",
      "29/29 - 0s - loss: 0.2469 - 118ms/epoch - 4ms/step\n",
      "Epoch 1906/2000\n",
      "29/29 - 0s - loss: 0.2488 - 115ms/epoch - 4ms/step\n",
      "Epoch 1907/2000\n",
      "29/29 - 0s - loss: 0.2595 - 120ms/epoch - 4ms/step\n",
      "Epoch 1908/2000\n",
      "29/29 - 0s - loss: 0.2314 - 120ms/epoch - 4ms/step\n",
      "Epoch 1909/2000\n",
      "29/29 - 0s - loss: 0.2271 - 116ms/epoch - 4ms/step\n",
      "Epoch 1910/2000\n",
      "29/29 - 0s - loss: 0.2476 - 117ms/epoch - 4ms/step\n",
      "Epoch 1911/2000\n",
      "29/29 - 0s - loss: 0.4145 - 124ms/epoch - 4ms/step\n",
      "Epoch 1912/2000\n",
      "29/29 - 0s - loss: 0.2599 - 120ms/epoch - 4ms/step\n",
      "Epoch 1913/2000\n",
      "29/29 - 0s - loss: 0.2939 - 117ms/epoch - 4ms/step\n",
      "Epoch 1914/2000\n",
      "29/29 - 0s - loss: 0.2439 - 116ms/epoch - 4ms/step\n",
      "Epoch 1915/2000\n",
      "29/29 - 0s - loss: 0.3614 - 117ms/epoch - 4ms/step\n",
      "Epoch 1916/2000\n",
      "29/29 - 0s - loss: 0.2500 - 128ms/epoch - 4ms/step\n",
      "Epoch 1917/2000\n",
      "29/29 - 0s - loss: 0.2428 - 138ms/epoch - 5ms/step\n",
      "Epoch 1918/2000\n",
      "29/29 - 0s - loss: 0.2907 - 142ms/epoch - 5ms/step\n",
      "Epoch 1919/2000\n",
      "29/29 - 0s - loss: 0.2928 - 139ms/epoch - 5ms/step\n",
      "Epoch 1920/2000\n",
      "29/29 - 0s - loss: 0.5257 - 123ms/epoch - 4ms/step\n",
      "Epoch 1921/2000\n",
      "29/29 - 0s - loss: 0.2476 - 134ms/epoch - 5ms/step\n",
      "Epoch 1922/2000\n",
      "29/29 - 0s - loss: 0.2583 - 125ms/epoch - 4ms/step\n",
      "Epoch 1923/2000\n",
      "29/29 - 0s - loss: 0.3879 - 126ms/epoch - 4ms/step\n",
      "Epoch 1924/2000\n",
      "29/29 - 0s - loss: 0.3210 - 123ms/epoch - 4ms/step\n",
      "Epoch 1925/2000\n",
      "29/29 - 0s - loss: 0.4085 - 117ms/epoch - 4ms/step\n",
      "Epoch 1926/2000\n",
      "29/29 - 0s - loss: 0.2929 - 119ms/epoch - 4ms/step\n",
      "Epoch 1927/2000\n",
      "29/29 - 0s - loss: 0.2767 - 118ms/epoch - 4ms/step\n",
      "Epoch 1928/2000\n",
      "29/29 - 0s - loss: 0.5778 - 115ms/epoch - 4ms/step\n",
      "Epoch 1929/2000\n",
      "29/29 - 0s - loss: 0.2591 - 122ms/epoch - 4ms/step\n",
      "Epoch 1930/2000\n",
      "29/29 - 0s - loss: 0.2541 - 119ms/epoch - 4ms/step\n",
      "Epoch 1931/2000\n",
      "29/29 - 0s - loss: 0.3562 - 119ms/epoch - 4ms/step\n",
      "Epoch 1932/2000\n",
      "29/29 - 0s - loss: 0.2852 - 122ms/epoch - 4ms/step\n",
      "Epoch 1933/2000\n",
      "29/29 - 0s - loss: 0.2760 - 119ms/epoch - 4ms/step\n",
      "Epoch 1934/2000\n",
      "29/29 - 0s - loss: 0.3376 - 120ms/epoch - 4ms/step\n",
      "Epoch 1935/2000\n",
      "29/29 - 0s - loss: 0.2808 - 126ms/epoch - 4ms/step\n",
      "Epoch 1936/2000\n",
      "29/29 - 0s - loss: 0.3289 - 120ms/epoch - 4ms/step\n",
      "Epoch 1937/2000\n",
      "29/29 - 0s - loss: 0.2657 - 117ms/epoch - 4ms/step\n",
      "Epoch 1938/2000\n",
      "29/29 - 0s - loss: 0.2239 - 117ms/epoch - 4ms/step\n",
      "Epoch 1939/2000\n",
      "29/29 - 0s - loss: 0.3392 - 117ms/epoch - 4ms/step\n",
      "Epoch 1940/2000\n",
      "29/29 - 0s - loss: 0.3299 - 119ms/epoch - 4ms/step\n",
      "Epoch 1941/2000\n",
      "29/29 - 0s - loss: 0.4535 - 117ms/epoch - 4ms/step\n",
      "Epoch 1942/2000\n",
      "29/29 - 0s - loss: 0.2669 - 116ms/epoch - 4ms/step\n",
      "Epoch 1943/2000\n",
      "29/29 - 0s - loss: 0.2394 - 119ms/epoch - 4ms/step\n",
      "Epoch 1944/2000\n",
      "29/29 - 0s - loss: 0.2740 - 116ms/epoch - 4ms/step\n",
      "Epoch 1945/2000\n",
      "29/29 - 0s - loss: 0.2925 - 122ms/epoch - 4ms/step\n",
      "Epoch 1946/2000\n",
      "29/29 - 0s - loss: 0.2464 - 118ms/epoch - 4ms/step\n",
      "Epoch 1947/2000\n",
      "29/29 - 0s - loss: 0.2642 - 115ms/epoch - 4ms/step\n",
      "Epoch 1948/2000\n",
      "29/29 - 0s - loss: 0.2383 - 117ms/epoch - 4ms/step\n",
      "Epoch 1949/2000\n",
      "29/29 - 0s - loss: 0.2949 - 124ms/epoch - 4ms/step\n",
      "Epoch 1950/2000\n",
      "29/29 - 0s - loss: 0.2800 - 117ms/epoch - 4ms/step\n",
      "Epoch 1951/2000\n",
      "29/29 - 0s - loss: 0.5158 - 118ms/epoch - 4ms/step\n",
      "Epoch 1952/2000\n",
      "29/29 - 0s - loss: 0.3432 - 118ms/epoch - 4ms/step\n",
      "Epoch 1953/2000\n",
      "29/29 - 0s - loss: 0.2218 - 117ms/epoch - 4ms/step\n",
      "Epoch 1954/2000\n",
      "29/29 - 0s - loss: 0.2323 - 115ms/epoch - 4ms/step\n",
      "Epoch 1955/2000\n",
      "29/29 - 0s - loss: 0.3583 - 117ms/epoch - 4ms/step\n",
      "Epoch 1956/2000\n",
      "29/29 - 0s - loss: 0.5187 - 117ms/epoch - 4ms/step\n",
      "Epoch 1957/2000\n",
      "29/29 - 0s - loss: 0.2811 - 121ms/epoch - 4ms/step\n",
      "Epoch 1958/2000\n",
      "29/29 - 0s - loss: 0.2767 - 125ms/epoch - 4ms/step\n",
      "Epoch 1959/2000\n",
      "29/29 - 0s - loss: 0.2647 - 117ms/epoch - 4ms/step\n",
      "Epoch 1960/2000\n",
      "29/29 - 0s - loss: 0.3255 - 117ms/epoch - 4ms/step\n",
      "Epoch 1961/2000\n",
      "29/29 - 0s - loss: 0.3099 - 114ms/epoch - 4ms/step\n",
      "Epoch 1962/2000\n",
      "29/29 - 0s - loss: 0.2430 - 123ms/epoch - 4ms/step\n",
      "Epoch 1963/2000\n",
      "29/29 - 0s - loss: 0.2254 - 119ms/epoch - 4ms/step\n",
      "Epoch 1964/2000\n",
      "29/29 - 0s - loss: 0.3970 - 117ms/epoch - 4ms/step\n",
      "Epoch 1965/2000\n",
      "29/29 - 0s - loss: 0.2307 - 123ms/epoch - 4ms/step\n",
      "Epoch 1966/2000\n",
      "29/29 - 0s - loss: 0.3251 - 122ms/epoch - 4ms/step\n",
      "Epoch 1967/2000\n",
      "29/29 - 0s - loss: 0.2828 - 119ms/epoch - 4ms/step\n",
      "Epoch 1968/2000\n",
      "29/29 - 0s - loss: 0.2309 - 118ms/epoch - 4ms/step\n",
      "Epoch 1969/2000\n",
      "29/29 - 0s - loss: 0.3875 - 117ms/epoch - 4ms/step\n",
      "Epoch 1970/2000\n",
      "29/29 - 0s - loss: 0.2651 - 121ms/epoch - 4ms/step\n",
      "Epoch 1971/2000\n",
      "29/29 - 0s - loss: 0.2351 - 123ms/epoch - 4ms/step\n",
      "Epoch 1972/2000\n",
      "29/29 - 0s - loss: 0.2301 - 128ms/epoch - 4ms/step\n",
      "Epoch 1973/2000\n",
      "29/29 - 0s - loss: 0.3039 - 127ms/epoch - 4ms/step\n",
      "Epoch 1974/2000\n",
      "29/29 - 0s - loss: 0.3040 - 118ms/epoch - 4ms/step\n",
      "Epoch 1975/2000\n",
      "29/29 - 0s - loss: 0.2778 - 118ms/epoch - 4ms/step\n",
      "Epoch 1976/2000\n",
      "29/29 - 0s - loss: 0.2371 - 117ms/epoch - 4ms/step\n",
      "Epoch 1977/2000\n",
      "29/29 - 0s - loss: 0.2303 - 128ms/epoch - 4ms/step\n",
      "Epoch 1978/2000\n",
      "29/29 - 0s - loss: 0.2624 - 117ms/epoch - 4ms/step\n",
      "Epoch 1979/2000\n",
      "29/29 - 0s - loss: 0.2604 - 116ms/epoch - 4ms/step\n",
      "Epoch 1980/2000\n",
      "29/29 - 0s - loss: 0.2981 - 114ms/epoch - 4ms/step\n",
      "Epoch 1981/2000\n",
      "29/29 - 0s - loss: 0.2690 - 117ms/epoch - 4ms/step\n",
      "Epoch 1982/2000\n",
      "29/29 - 0s - loss: 0.2503 - 118ms/epoch - 4ms/step\n",
      "Epoch 1983/2000\n",
      "29/29 - 0s - loss: 0.3157 - 118ms/epoch - 4ms/step\n",
      "Epoch 1984/2000\n",
      "29/29 - 0s - loss: 0.3113 - 118ms/epoch - 4ms/step\n",
      "Epoch 1985/2000\n",
      "29/29 - 0s - loss: 0.3743 - 118ms/epoch - 4ms/step\n",
      "Epoch 1986/2000\n",
      "29/29 - 0s - loss: 0.2220 - 121ms/epoch - 4ms/step\n",
      "Epoch 1987/2000\n",
      "29/29 - 0s - loss: 0.2382 - 116ms/epoch - 4ms/step\n",
      "Epoch 1988/2000\n",
      "29/29 - 0s - loss: 0.3366 - 118ms/epoch - 4ms/step\n",
      "Epoch 1989/2000\n",
      "29/29 - 0s - loss: 0.4404 - 117ms/epoch - 4ms/step\n",
      "Epoch 1990/2000\n",
      "29/29 - 0s - loss: 0.4001 - 120ms/epoch - 4ms/step\n",
      "Epoch 1991/2000\n",
      "29/29 - 0s - loss: 0.2479 - 118ms/epoch - 4ms/step\n",
      "Epoch 1992/2000\n",
      "29/29 - 0s - loss: 0.3059 - 122ms/epoch - 4ms/step\n",
      "Epoch 1993/2000\n",
      "29/29 - 0s - loss: 0.2477 - 124ms/epoch - 4ms/step\n",
      "Epoch 1994/2000\n",
      "29/29 - 0s - loss: 0.2640 - 119ms/epoch - 4ms/step\n",
      "Epoch 1995/2000\n",
      "29/29 - 0s - loss: 0.2491 - 115ms/epoch - 4ms/step\n",
      "Epoch 1996/2000\n",
      "29/29 - 0s - loss: 0.3003 - 156ms/epoch - 5ms/step\n",
      "Epoch 1997/2000\n",
      "29/29 - 0s - loss: 0.3191 - 127ms/epoch - 4ms/step\n",
      "Epoch 1998/2000\n",
      "29/29 - 0s - loss: 0.2990 - 122ms/epoch - 4ms/step\n",
      "Epoch 1999/2000\n",
      "29/29 - 0s - loss: 0.2614 - 121ms/epoch - 4ms/step\n",
      "Epoch 2000/2000\n",
      "29/29 - 0s - loss: 0.2433 - 117ms/epoch - 4ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x187d8f66ed0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, epochs=2000, batch_size=16, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Error validation for LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 1s 4ms/step\n",
      "Root Mean Squared Error: 0.39\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(f'Root Mean Squared Error: {rmse:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_axis = [df.index[num_time_steps] + pd.Timedelta(minutes=15 * i) for i in range(len(y_test))]\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(x_axis, y_test, label='Actual High')\n",
    "plt.plot(x_axis, y_pred, label='Predicted High')\n",
    "plt.legend()\n",
    "plt.title('LSTM plot for high values')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('High Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = ('model_weights.h5')\n",
    "model.save_weights(filename)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}