Main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sqlalchemy import create_engine , text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"mysql+mysqlconnector://root:root@localhost:3307/stocks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataFromDB = pd.read_sql('SELECT * FROM stocks.stocks_data s where s.stock_mstr_key = 1;', con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>stock_mstr_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004-08-09</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.46</td>\n",
       "      <td>290847200.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004-08-10</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.48</td>\n",
       "      <td>351036000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004-08-11</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.47</td>\n",
       "      <td>322392000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004-08-12</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.46</td>\n",
       "      <td>226200800.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-08-13</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.47</td>\n",
       "      <td>328048000.0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4978</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>224.37</td>\n",
       "      <td>224.48</td>\n",
       "      <td>217.02</td>\n",
       "      <td>218.36</td>\n",
       "      <td>218.36</td>\n",
       "      <td>61877900.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4979</th>\n",
       "      <td>2024-08-02</td>\n",
       "      <td>219.15</td>\n",
       "      <td>225.60</td>\n",
       "      <td>217.71</td>\n",
       "      <td>219.86</td>\n",
       "      <td>219.86</td>\n",
       "      <td>98480100.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4980</th>\n",
       "      <td>2024-08-05</td>\n",
       "      <td>199.09</td>\n",
       "      <td>213.50</td>\n",
       "      <td>196.00</td>\n",
       "      <td>209.27</td>\n",
       "      <td>209.27</td>\n",
       "      <td>119548600.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4981</th>\n",
       "      <td>2024-08-06</td>\n",
       "      <td>205.30</td>\n",
       "      <td>209.99</td>\n",
       "      <td>201.07</td>\n",
       "      <td>207.23</td>\n",
       "      <td>207.23</td>\n",
       "      <td>69548700.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4982</th>\n",
       "      <td>2024-08-07</td>\n",
       "      <td>206.89</td>\n",
       "      <td>212.81</td>\n",
       "      <td>206.41</td>\n",
       "      <td>212.77</td>\n",
       "      <td>212.77</td>\n",
       "      <td>11590779.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4983 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date    Open    High     Low   Close  Adj Close       Volume  \\\n",
       "0     2004-08-09    0.53    0.54    0.53    0.54       0.46  290847200.0   \n",
       "1     2004-08-10    0.54    0.56    0.54    0.56       0.48  351036000.0   \n",
       "2     2004-08-11    0.56    0.56    0.54    0.55       0.47  322392000.0   \n",
       "3     2004-08-12    0.54    0.55    0.54    0.54       0.46  226200800.0   \n",
       "4     2004-08-13    0.55    0.56    0.54    0.55       0.47  328048000.0   \n",
       "...          ...     ...     ...     ...     ...        ...          ...   \n",
       "4978  2024-08-01  224.37  224.48  217.02  218.36     218.36   61877900.0   \n",
       "4979  2024-08-02  219.15  225.60  217.71  219.86     219.86   98480100.0   \n",
       "4980  2024-08-05  199.09  213.50  196.00  209.27     209.27  119548600.0   \n",
       "4981  2024-08-06  205.30  209.99  201.07  207.23     207.23   69548700.0   \n",
       "4982  2024-08-07  206.89  212.81  206.41  212.77     212.77   11590779.0   \n",
       "\n",
       "      stock_mstr_key  \n",
       "0                  1  \n",
       "1                  1  \n",
       "2                  1  \n",
       "3                  1  \n",
       "4                  1  \n",
       "...              ...  \n",
       "4978               1  \n",
       "4979               1  \n",
       "4980               1  \n",
       "4981               1  \n",
       "4982               1  \n",
       "\n",
       "[4983 rows x 8 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFromDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Date': dtype('O'),\n",
       " 'Open': dtype('float64'),\n",
       " 'High': dtype('float64'),\n",
       " 'Low': dtype('float64'),\n",
       " 'Close': dtype('float64'),\n",
       " 'Adj Close': dtype('float64'),\n",
       " 'Volume': dtype('float64'),\n",
       " 'stock_mstr_key': dtype('int64')}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(DataFromDB.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataFromDB[['Date_2', 'Open_2', 'High_2', 'Low_2', 'Close_2', 'Adj_Close_2', 'Volume_2']] = DataFromDB[['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']].shift(1)\n",
    "DataFromDB[['Date_3', 'Open_3', 'High_3', 'Low_3', 'Close_3', 'Adj_Close_3', 'Volume_3']] = DataFromDB[['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']].shift(2)\n",
    "DataFromDB[['Date_4', 'Open_4', 'High_4', 'Low_4', 'Close_4', 'Adj_Close_4', 'Volume_4']] = DataFromDB[['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']].shift(3)\n",
    "DataFromDB[['Date_5', 'Open_5', 'High_5', 'Low_5', 'Close_5', 'Adj_Close_5', 'Volume_5']] = DataFromDB[['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']].shift(4)\n",
    "DataFromDB[['Date_6', 'Open_6', 'High_6', 'Low_6', 'Close_6', 'Adj_Close_6', 'Volume_6']] = DataFromDB[['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']].shift(5)\n",
    "DataFromDB[['Date_target', 'Open_target', 'High_target', 'Low_target', 'Close_target', 'Adj_Close_target', 'Volume_target']] = DataFromDB[['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume',]].shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataFromDB.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataFromDB.to_csv(\"./train_data.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('./train_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
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       "\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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>stock_mstr_key</th>\n",
       "      <th>Date_2</th>\n",
       "      <th>Open_2</th>\n",
       "      <th>...</th>\n",
       "      <th>Close_6</th>\n",
       "      <th>Adj_Close_6</th>\n",
       "      <th>Volume_6</th>\n",
       "      <th>Date_target</th>\n",
       "      <th>Open_target</th>\n",
       "      <th>High_target</th>\n",
       "      <th>Low_target</th>\n",
       "      <th>Close_target</th>\n",
       "      <th>Adj_Close_target</th>\n",
       "      <th>Volume_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004-08-16</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.46</td>\n",
       "      <td>435674400.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-08-13</td>\n",
       "      <td>0.55</td>\n",
       "      <td>...</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.46</td>\n",
       "      <td>290847200.0</td>\n",
       "      <td>2004-08-17</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.47</td>\n",
       "      <td>323019200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004-08-17</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.47</td>\n",
       "      <td>323019200.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-08-16</td>\n",
       "      <td>0.55</td>\n",
       "      <td>...</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.48</td>\n",
       "      <td>351036000.0</td>\n",
       "      <td>2004-08-18</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.48</td>\n",
       "      <td>364655200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004-08-18</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.48</td>\n",
       "      <td>364655200.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-08-17</td>\n",
       "      <td>0.55</td>\n",
       "      <td>...</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.47</td>\n",
       "      <td>322392000.0</td>\n",
       "      <td>2004-08-19</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.46</td>\n",
       "      <td>388920000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004-08-19</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.46</td>\n",
       "      <td>388920000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-08-18</td>\n",
       "      <td>0.54</td>\n",
       "      <td>...</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.46</td>\n",
       "      <td>226200800.0</td>\n",
       "      <td>2004-08-20</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.46</td>\n",
       "      <td>316780800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-08-20</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.46</td>\n",
       "      <td>316780800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-08-19</td>\n",
       "      <td>0.56</td>\n",
       "      <td>...</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.47</td>\n",
       "      <td>328048000.0</td>\n",
       "      <td>2004-08-23</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.47</td>\n",
       "      <td>254660000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Open  High   Low  Close  Adj Close       Volume  \\\n",
       "0  2004-08-16  0.55  0.57  0.55   0.55       0.46  435674400.0   \n",
       "1  2004-08-17  0.55  0.56  0.54   0.55       0.47  323019200.0   \n",
       "2  2004-08-18  0.54  0.57  0.54   0.57       0.48  364655200.0   \n",
       "3  2004-08-19  0.56  0.57  0.54   0.55       0.46  388920000.0   \n",
       "4  2004-08-20  0.55  0.55  0.54   0.55       0.46  316780800.0   \n",
       "\n",
       "   stock_mstr_key      Date_2  Open_2  ...  Close_6  Adj_Close_6     Volume_6  \\\n",
       "0               1  2004-08-13    0.55  ...     0.54         0.46  290847200.0   \n",
       "1               1  2004-08-16    0.55  ...     0.56         0.48  351036000.0   \n",
       "2               1  2004-08-17    0.55  ...     0.55         0.47  322392000.0   \n",
       "3               1  2004-08-18    0.54  ...     0.54         0.46  226200800.0   \n",
       "4               1  2004-08-19    0.56  ...     0.55         0.47  328048000.0   \n",
       "\n",
       "   Date_target  Open_target High_target  Low_target  Close_target  \\\n",
       "0   2004-08-17         0.55        0.56        0.54          0.55   \n",
       "1   2004-08-18         0.54        0.57        0.54          0.57   \n",
       "2   2004-08-19         0.56        0.57        0.54          0.55   \n",
       "3   2004-08-20         0.55        0.55        0.54          0.55   \n",
       "4   2004-08-23         0.55        0.56        0.55          0.56   \n",
       "\n",
       "   Adj_Close_target  Volume_target  \n",
       "0              0.47    323019200.0  \n",
       "1              0.48    364655200.0  \n",
       "2              0.46    388920000.0  \n",
       "3              0.46    316780800.0  \n",
       "4              0.47    254660000.0  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume',\n",
       "       'stock_mstr_key', 'Date_2', 'Open_2', 'High_2', 'Low_2', 'Close_2',\n",
       "       'Adj_Close_2', 'Volume_2', 'Date_3', 'Open_3', 'High_3', 'Low_3',\n",
       "       'Close_3', 'Adj_Close_3', 'Volume_3', 'Date_4', 'Open_4', 'High_4',\n",
       "       'Low_4', 'Close_4', 'Adj_Close_4', 'Volume_4', 'Date_5', 'Open_5',\n",
       "       'High_5', 'Low_5', 'Close_5', 'Adj_Close_5', 'Volume_5', 'Date_6',\n",
       "       'Open_6', 'High_6', 'Low_6', 'Close_6', 'Adj_Close_6', 'Volume_6',\n",
       "       'Date_target', 'Open_target', 'High_target', 'Low_target',\n",
       "       'Close_target', 'Adj_Close_target', 'Volume_target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop(['Date_2','Date_3','Date_4','Date_5','Date_6','Volume','Volume_2','Volume_3','Volume_4','Volume_5','Volume_6','stock_mstr_key'],axis=1,inplace=True,errors='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Open_2</th>\n",
       "      <th>High_2</th>\n",
       "      <th>Low_2</th>\n",
       "      <th>Close_2</th>\n",
       "      <th>...</th>\n",
       "      <th>Low_6</th>\n",
       "      <th>Close_6</th>\n",
       "      <th>Adj_Close_6</th>\n",
       "      <th>Date_target</th>\n",
       "      <th>Open_target</th>\n",
       "      <th>High_target</th>\n",
       "      <th>Low_target</th>\n",
       "      <th>Close_target</th>\n",
       "      <th>Adj_Close_target</th>\n",
       "      <th>Volume_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004-08-16</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>...</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2004-08-17</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.47</td>\n",
       "      <td>323019200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004-08-17</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>...</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2004-08-18</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.48</td>\n",
       "      <td>364655200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004-08-18</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>...</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2004-08-19</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.46</td>\n",
       "      <td>388920000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004-08-19</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>...</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2004-08-20</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.46</td>\n",
       "      <td>316780800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-08-20</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>...</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2004-08-23</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.47</td>\n",
       "      <td>254660000.0</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",
       "      <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>4972</th>\n",
       "      <td>2024-07-31</td>\n",
       "      <td>221.44</td>\n",
       "      <td>223.82</td>\n",
       "      <td>220.63</td>\n",
       "      <td>222.08</td>\n",
       "      <td>222.08</td>\n",
       "      <td>219.19</td>\n",
       "      <td>220.33</td>\n",
       "      <td>216.12</td>\n",
       "      <td>218.80</td>\n",
       "      <td>...</td>\n",
       "      <td>217.13</td>\n",
       "      <td>218.54</td>\n",
       "      <td>218.54</td>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>224.37</td>\n",
       "      <td>224.48</td>\n",
       "      <td>217.02</td>\n",
       "      <td>218.36</td>\n",
       "      <td>218.36</td>\n",
       "      <td>61877900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4973</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>224.37</td>\n",
       "      <td>224.48</td>\n",
       "      <td>217.02</td>\n",
       "      <td>218.36</td>\n",
       "      <td>218.36</td>\n",
       "      <td>221.44</td>\n",
       "      <td>223.82</td>\n",
       "      <td>220.63</td>\n",
       "      <td>222.08</td>\n",
       "      <td>...</td>\n",
       "      <td>214.62</td>\n",
       "      <td>217.49</td>\n",
       "      <td>217.49</td>\n",
       "      <td>2024-08-02</td>\n",
       "      <td>219.15</td>\n",
       "      <td>225.60</td>\n",
       "      <td>217.71</td>\n",
       "      <td>219.86</td>\n",
       "      <td>219.86</td>\n",
       "      <td>98480100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4974</th>\n",
       "      <td>2024-08-02</td>\n",
       "      <td>219.15</td>\n",
       "      <td>225.60</td>\n",
       "      <td>217.71</td>\n",
       "      <td>219.86</td>\n",
       "      <td>219.86</td>\n",
       "      <td>224.37</td>\n",
       "      <td>224.48</td>\n",
       "      <td>217.02</td>\n",
       "      <td>218.36</td>\n",
       "      <td>...</td>\n",
       "      <td>216.01</td>\n",
       "      <td>217.96</td>\n",
       "      <td>217.96</td>\n",
       "      <td>2024-08-05</td>\n",
       "      <td>199.09</td>\n",
       "      <td>213.50</td>\n",
       "      <td>196.00</td>\n",
       "      <td>209.27</td>\n",
       "      <td>209.27</td>\n",
       "      <td>119548600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4975</th>\n",
       "      <td>2024-08-05</td>\n",
       "      <td>199.09</td>\n",
       "      <td>213.50</td>\n",
       "      <td>196.00</td>\n",
       "      <td>209.27</td>\n",
       "      <td>209.27</td>\n",
       "      <td>219.15</td>\n",
       "      <td>225.60</td>\n",
       "      <td>217.71</td>\n",
       "      <td>219.86</td>\n",
       "      <td>...</td>\n",
       "      <td>215.75</td>\n",
       "      <td>218.24</td>\n",
       "      <td>218.24</td>\n",
       "      <td>2024-08-06</td>\n",
       "      <td>205.30</td>\n",
       "      <td>209.99</td>\n",
       "      <td>201.07</td>\n",
       "      <td>207.23</td>\n",
       "      <td>207.23</td>\n",
       "      <td>69548700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4976</th>\n",
       "      <td>2024-08-06</td>\n",
       "      <td>205.30</td>\n",
       "      <td>209.99</td>\n",
       "      <td>201.07</td>\n",
       "      <td>207.23</td>\n",
       "      <td>207.23</td>\n",
       "      <td>199.09</td>\n",
       "      <td>213.50</td>\n",
       "      <td>196.00</td>\n",
       "      <td>209.27</td>\n",
       "      <td>...</td>\n",
       "      <td>216.12</td>\n",
       "      <td>218.80</td>\n",
       "      <td>218.80</td>\n",
       "      <td>2024-08-07</td>\n",
       "      <td>206.89</td>\n",
       "      <td>212.81</td>\n",
       "      <td>206.41</td>\n",
       "      <td>212.77</td>\n",
       "      <td>212.77</td>\n",
       "      <td>11590779.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4977 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date    Open    High     Low   Close  Adj Close  Open_2  High_2  \\\n",
       "0     2004-08-16    0.55    0.57    0.55    0.55       0.46    0.55    0.56   \n",
       "1     2004-08-17    0.55    0.56    0.54    0.55       0.47    0.55    0.57   \n",
       "2     2004-08-18    0.54    0.57    0.54    0.57       0.48    0.55    0.56   \n",
       "3     2004-08-19    0.56    0.57    0.54    0.55       0.46    0.54    0.57   \n",
       "4     2004-08-20    0.55    0.55    0.54    0.55       0.46    0.56    0.57   \n",
       "...          ...     ...     ...     ...     ...        ...     ...     ...   \n",
       "4972  2024-07-31  221.44  223.82  220.63  222.08     222.08  219.19  220.33   \n",
       "4973  2024-08-01  224.37  224.48  217.02  218.36     218.36  221.44  223.82   \n",
       "4974  2024-08-02  219.15  225.60  217.71  219.86     219.86  224.37  224.48   \n",
       "4975  2024-08-05  199.09  213.50  196.00  209.27     209.27  219.15  225.60   \n",
       "4976  2024-08-06  205.30  209.99  201.07  207.23     207.23  199.09  213.50   \n",
       "\n",
       "       Low_2  Close_2  ...   Low_6  Close_6  Adj_Close_6  Date_target  \\\n",
       "0       0.54     0.55  ...    0.53     0.54         0.46   2004-08-17   \n",
       "1       0.55     0.55  ...    0.54     0.56         0.48   2004-08-18   \n",
       "2       0.54     0.55  ...    0.54     0.55         0.47   2004-08-19   \n",
       "3       0.54     0.57  ...    0.54     0.54         0.46   2004-08-20   \n",
       "4       0.54     0.55  ...    0.54     0.55         0.47   2004-08-23   \n",
       "...      ...      ...  ...     ...      ...          ...          ...   \n",
       "4972  216.12   218.80  ...  217.13   218.54       218.54   2024-08-01   \n",
       "4973  220.63   222.08  ...  214.62   217.49       217.49   2024-08-02   \n",
       "4974  217.02   218.36  ...  216.01   217.96       217.96   2024-08-05   \n",
       "4975  217.71   219.86  ...  215.75   218.24       218.24   2024-08-06   \n",
       "4976  196.00   209.27  ...  216.12   218.80       218.80   2024-08-07   \n",
       "\n",
       "      Open_target  High_target  Low_target  Close_target  Adj_Close_target  \\\n",
       "0            0.55         0.56        0.54          0.55              0.47   \n",
       "1            0.54         0.57        0.54          0.57              0.48   \n",
       "2            0.56         0.57        0.54          0.55              0.46   \n",
       "3            0.55         0.55        0.54          0.55              0.46   \n",
       "4            0.55         0.56        0.55          0.56              0.47   \n",
       "...           ...          ...         ...           ...               ...   \n",
       "4972       224.37       224.48      217.02        218.36            218.36   \n",
       "4973       219.15       225.60      217.71        219.86            219.86   \n",
       "4974       199.09       213.50      196.00        209.27            209.27   \n",
       "4975       205.30       209.99      201.07        207.23            207.23   \n",
       "4976       206.89       212.81      206.41        212.77            212.77   \n",
       "\n",
       "      Volume_target  \n",
       "0       323019200.0  \n",
       "1       364655200.0  \n",
       "2       388920000.0  \n",
       "3       316780800.0  \n",
       "4       254660000.0  \n",
       "...             ...  \n",
       "4972     61877900.0  \n",
       "4973     98480100.0  \n",
       "4974    119548600.0  \n",
       "4975     69548700.0  \n",
       "4976     11590779.0  \n",
       "\n",
       "[4977 rows x 38 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "# Encoder model\n",
    "class Encoder(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, num_layers):\n",
    "        super(Encoder, self).__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.num_layers = num_layers\n",
    "        self.rnn = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x: (batch_size, seq_length, input_dim)\n",
    "        outputs, (hidden, cell) = self.rnn(x)\n",
    "        return hidden, cell\n",
    "\n",
    "# Decoder model\n",
    "class Decoder(nn.Module):\n",
    "    def __init__(self, output_dim, hidden_dim, num_layers):\n",
    "        super(Decoder, self).__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.num_layers = num_layers\n",
    "        self.rnn = nn.LSTM(output_dim, hidden_dim, num_layers, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "\n",
    "    def forward(self, x, hidden, cell):\n",
    "        # x: (batch_size, seq_length, output_dim)\n",
    "        outputs, (hidden, cell) = self.rnn(x, (hidden, cell))\n",
    "        predictions = self.fc(outputs)\n",
    "        return predictions, hidden, cell\n",
    "\n",
    "# Full Encoder-Decoder model\n",
    "class EncoderDecoder(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim, hidden_dim, num_layers):\n",
    "        super(EncoderDecoder, self).__init__()\n",
    "        self.encoder = Encoder(input_dim, hidden_dim, num_layers)\n",
    "        self.decoder = Decoder(output_dim, hidden_dim, num_layers)\n",
    "\n",
    "    def forward(self, src, trg):\n",
    "        # src: source input (batch_size, seq_length, input_dim)\n",
    "        # trg: target input (batch_size, seq_length, output_dim)\n",
    "        hidden, cell = self.encoder(src)\n",
    "        output, hidden, cell = self.decoder(trg, hidden, cell)\n",
    "        return output\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/500], Train Loss: 66055888.0000, Val Loss: 68965384.0000\n",
      "Epoch [11/500], Train Loss: 66055888.0000, Val Loss: 68965384.0000\n",
      "Epoch [21/500], Train Loss: 66055888.0000, Val Loss: 68965376.0000\n",
      "Epoch [31/500], Train Loss: 66055888.0000, Val Loss: 68965368.0000\n",
      "Epoch [41/500], Train Loss: 66055880.0000, Val Loss: 68965368.0000\n",
      "Epoch [51/500], Train Loss: 66055880.0000, Val Loss: 68965368.0000\n",
      "Epoch [61/500], Train Loss: 66055876.0000, Val Loss: 68965368.0000\n",
      "Epoch [71/500], Train Loss: 66055876.0000, Val Loss: 68965368.0000\n",
      "Epoch [81/500], Train Loss: 66055876.0000, Val Loss: 68965368.0000\n",
      "Epoch [91/500], Train Loss: 66055876.0000, Val Loss: 68965360.0000\n",
      "Epoch [101/500], Train Loss: 66055876.0000, Val Loss: 68965360.0000\n",
      "Epoch [111/500], Train Loss: 66055876.0000, Val Loss: 68965360.0000\n",
      "Epoch [121/500], Train Loss: 66055876.0000, Val Loss: 68965360.0000\n",
      "Epoch [131/500], Train Loss: 66055876.0000, Val Loss: 68965360.0000\n",
      "Epoch [141/500], Train Loss: 66055872.0000, Val Loss: 68965360.0000\n",
      "Epoch [151/500], Train Loss: 66055872.0000, Val Loss: 68965360.0000\n",
      "Epoch [161/500], Train Loss: 66055872.0000, Val Loss: 68965360.0000\n",
      "Epoch [171/500], Train Loss: 66055872.0000, Val Loss: 68965360.0000\n",
      "Epoch [181/500], Train Loss: 66055872.0000, Val Loss: 68965360.0000\n",
      "Epoch [191/500], Train Loss: 66055872.0000, Val Loss: 68965352.0000\n",
      "Epoch [201/500], Train Loss: 66055864.0000, Val Loss: 68965352.0000\n",
      "Epoch [211/500], Train Loss: 66055864.0000, Val Loss: 68965352.0000\n",
      "Epoch [221/500], Train Loss: 66055860.0000, Val Loss: 68965352.0000\n",
      "Epoch [231/500], Train Loss: 66055860.0000, Val Loss: 68965352.0000\n",
      "Epoch [241/500], Train Loss: 66055860.0000, Val Loss: 68965352.0000\n",
      "Epoch [251/500], Train Loss: 66055860.0000, Val Loss: 68965352.0000\n",
      "Epoch [261/500], Train Loss: 66055860.0000, Val Loss: 68965352.0000\n",
      "Epoch [271/500], Train Loss: 66055860.0000, Val Loss: 68965352.0000\n",
      "Epoch [281/500], Train Loss: 66055860.0000, Val Loss: 68965352.0000\n",
      "Epoch [291/500], Train Loss: 66055860.0000, Val Loss: 68965352.0000\n",
      "Epoch [301/500], Train Loss: 66055860.0000, Val Loss: 68965352.0000\n",
      "Epoch [311/500], Train Loss: 66055860.0000, Val Loss: 68965344.0000\n",
      "Epoch [321/500], Train Loss: 66055860.0000, Val Loss: 68965344.0000\n",
      "Epoch [331/500], Train Loss: 66055856.0000, Val Loss: 68965344.0000\n",
      "Epoch [341/500], Train Loss: 66055856.0000, Val Loss: 68965344.0000\n",
      "Epoch [351/500], Train Loss: 66055856.0000, Val Loss: 68965344.0000\n",
      "Epoch [361/500], Train Loss: 66055856.0000, Val Loss: 68965344.0000\n",
      "Epoch [371/500], Train Loss: 66055856.0000, Val Loss: 68965344.0000\n",
      "Epoch [381/500], Train Loss: 66055856.0000, Val Loss: 68965336.0000\n",
      "Epoch [391/500], Train Loss: 66055856.0000, Val Loss: 68965336.0000\n",
      "Epoch [401/500], Train Loss: 66055856.0000, Val Loss: 68965336.0000\n",
      "Epoch [411/500], Train Loss: 66055856.0000, Val Loss: 68965336.0000\n",
      "Epoch [421/500], Train Loss: 66055848.0000, Val Loss: 68965336.0000\n",
      "Epoch [431/500], Train Loss: 66055844.0000, Val Loss: 68965336.0000\n",
      "Epoch [441/500], Train Loss: 66055844.0000, Val Loss: 68965336.0000\n",
      "Epoch [451/500], Train Loss: 66055844.0000, Val Loss: 68965336.0000\n",
      "Epoch [461/500], Train Loss: 66055844.0000, Val Loss: 68965336.0000\n",
      "Epoch [471/500], Train Loss: 66055844.0000, Val Loss: 68965336.0000\n",
      "Epoch [481/500], Train Loss: 66055844.0000, Val Loss: 68965336.0000\n",
      "Epoch [491/500], Train Loss: 66055844.0000, Val Loss: 68965336.0000\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Source input\n",
    "X = train_df.drop(['Date', 'Date_target', 'Open_target', 'High_target', 'Low_target', \n",
    "                   'Close_target', 'Adj_Close_target', 'Volume_target'], axis=1).values.astype(np.float32)\n",
    "\n",
    "# Target output\n",
    "y = train_df[['Open_target', 'High_target', 'Low_target', 'Close_target', \n",
    "              'Adj_Close_target', 'Volume_target']].values.astype(np.float32)\n",
    "\n",
    "# Split the data into training and validation sets (80% train, 20% validation)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Convert to PyTorch tensors\n",
    "X_train = torch.tensor(X_train)\n",
    "X_val = torch.tensor(X_val)\n",
    "y_train = torch.tensor(y_train)\n",
    "y_val = torch.tensor(y_val)\n",
    "\n",
    "\n",
    "# Hyperparameters\n",
    "input_dim = X_train.size(-1)  # Assuming input_dim is the feature size of X_train\n",
    "output_dim = y_train.size(-1)  # Assuming output_dim is the feature size of y_train\n",
    "hidden_dim = 50  # Hidden state size for the LSTM\n",
    "num_layers = 2   # Number of LSTM layers\n",
    "learning_rate = 0.01\n",
    "num_epochs = 500\n",
    "\n",
    "# Instantiate the model\n",
    "model = EncoderDecoder(input_dim=input_dim, output_dim=output_dim, hidden_dim=hidden_dim, num_layers=num_layers)\n",
    "\n",
    "# Loss function and optimizer\n",
    "criterion = nn.L1Loss()  # You can switch to nn.MSELoss() or nn.CrossEntropyLoss() if needed\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# Training and validation loop\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()  # Set model to training mode\n",
    "    \n",
    "    # Zero the gradients\n",
    "    optimizer.zero_grad()\n",
    "    \n",
    "    # Forward pass on the training data\n",
    "    outputs = model(X_train, y_train)  # src = X_train, trg = y_train\n",
    "    \n",
    "    # Compute training loss\n",
    "    loss = criterion(outputs, y_train)\n",
    "    \n",
    "    # Backward pass\n",
    "    loss.backward()\n",
    "    \n",
    "    # Update parameters\n",
    "    optimizer.step()\n",
    "    \n",
    "    # Validation phase\n",
    "    model.eval()  # Set model to evaluation mode\n",
    "    with torch.no_grad():  # No gradient calculation for validation\n",
    "        val_outputs = model(X_val, y_val)\n",
    "        val_loss = criterion(val_outputs, y_val)\n",
    "    \n",
    "    # Print loss for every 10 epochs\n",
    "    if epoch % 10 == 0:\n",
    "        print(f'Epoch [{epoch+1}/{num_epochs}], Train Loss: {loss.item():.4f}, Val Loss: {val_loss.item():.4f}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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