Neural_Networks_part.ipynb

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      "name": "python"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ymf4JbdXX5Hm",
        "outputId": "f7aaac18-6954-45e2-b16c-649070e21a4d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[name: \"/device:CPU:0\"\n",
            "device_type: \"CPU\"\n",
            "memory_limit: 268435456\n",
            "locality {\n",
            "}\n",
            "incarnation: 1993217540940559742\n",
            "xla_global_id: -1\n",
            "]\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.python.client import device_lib\n",
        "print(device_lib.list_local_devices())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pydrive.auth import GoogleAuth \n",
        "from pydrive.drive import GoogleDrive \n",
        "from google.colab import auth \n",
        "from oauth2client.client import GoogleCredentials \n",
        "  \n",
        "  \n",
        "# Authenticate and create the PyDrive client. \n",
        "auth.authenticate_user() \n",
        "gauth = GoogleAuth() \n",
        "gauth.credentials = GoogleCredentials.get_application_default() \n",
        "drive = GoogleDrive(gauth)"
      ],
      "metadata": {
        "id": "XLcm01ssX_jZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#This part linkes the dataset in the drive to the google colab file\n",
        "#oldlink = 'https://drive.google.com/file/d/1TxMpCYlXbuakmrVVacn6IRT6kOVEP-Cf/view?usp=sharing'\n",
        "link = 'https://drive.google.com/file/d/1TH1vQRhqoRNfuKZgVsMVnfkrjS60ChXL/view?usp=sharing'\n",
        "id = link.split('/')[-2]\n",
        "downloaded = drive.CreateFile({'id' : id})\n",
        "downloaded.GetContentFile('StockPrices.csv')"
      ],
      "metadata": {
        "id": "vTpmU3LhYR4E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "df = pd.read_csv('StockPrices.csv')\n",
        "df[\"DailyRet\"] = 0\n",
        "df[\"20DayRet\"] = 0\n",
        "df[\"20DayVol\"] = 0\n",
        "df[\"Z20DayRet\"] = 0\n",
        "df['Z20DayVol'] = 0\n",
        "df['Date']= pd.to_datetime(df['Date'])\n",
        "df_close = df[['Date', 'Index', 'Close']] \n",
        "df_close.info()\n",
        "df_close = df_close.pivot_table(index = 'Date', columns = 'Index', values = 'Close').dropna(axis=1)\n",
        "stocks_tickers = df_close.columns"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "1r2SEEHaYY3Q",
        "outputId": "053096b3-0a79-471e-dec9-fc9078042d2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 199746 entries, 0 to 199745\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Non-Null Count   Dtype         \n",
            "---  ------  --------------   -----         \n",
            " 0   Date    199746 non-null  datetime64[ns]\n",
            " 1   Index   199746 non-null  object        \n",
            " 2   Close   199422 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(1), object(1)\n",
            "memory usage: 4.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Saving Trading Days\n",
        "dates = df_close.index \n",
        "\n",
        "#Saving Stocks\n",
        "stocks = df_close.columns"
      ],
      "metadata": {
        "id": "3HfpZuXjYi-J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
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        "id": "8uDqpXOnwONW",
        "outputId": "aed0c8b8-c4c4-4d53-feed-fc628ed8a6d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Date', 'Index', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adjusted',\n",
              "       'DailyRet', '20DayRet', '20DayVol', 'Z20DayRet', 'Z20DayVol'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "raw_df = df.set_index(['Date' , 'Index']).unstack(level = 1).stack(level = 0).unstack()\n",
        "#raw_df = raw_df.dropna(axis = 1)\n",
        "for stock in stocks_tickers:\n",
        "  #Calculate Daily Returns\n",
        "  raw_df.loc[:, (stock, 'DailyRet')] = raw_df[stock][\"Close\"].pct_change()\n",
        "  #Calculate 20-Day Returns\n",
        "  raw_df.loc[:, (stock, '20DayRet')] = raw_df[stock][\"Close\"].pct_change(20)\n",
        "  #Calculate Rolling 20 day volatility \n",
        "  roller = raw_df[stock][\"DailyRet\"].rolling(20)\n",
        "  raw_df.loc[:, (stock, '20DayVol')] = roller.std(ddof=0)\n",
        "  \n",
        "  '''Normalize data with the rolling 252'''\n",
        "  #Normalize 20 Day Returns \n",
        "  rolling_year_ret = raw_df[stock][\"20DayRet\"].rolling(252)\n",
        "  raw_df.loc[:, (stock, 'Z20DayRet')] = (rolling_year_ret.mean().shift(1) - raw_df[stock]['20DayRet']) / rolling_year_ret.std(ddof=0).shift(1)\n",
        "  #Normalize 20 Day Volatility\n",
        "  rolling_year_vol = raw_df[stock][\"20DayVol\"].rolling(252)\n",
        "  raw_df.loc[:, (stock, 'Z20DayVol')] = (rolling_year_vol.mean().shift(1) - raw_df[stock]['20DayVol']) / rolling_year_vol.std(ddof=0).shift(1) \n",
        "\n",
        "raw_df.head(5)  "
      ],
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              "Index        ACC.NS                                                            \\\n",
              "           20DayRet 20DayVol     Adjusted        Close  DailyRet         High   \n",
              "Date                                                                            \n",
              "2012-08-16      NaN      NaN  1118.372803  1338.449951       NaN  1358.050049   \n",
              "2012-08-17      NaN      NaN  1108.262207  1326.349976 -0.009040  1338.449951   \n",
              "2012-08-21      NaN      NaN  1134.583008  1357.849976  0.023749  1363.000000   \n",
              "2012-08-22      NaN      NaN  1137.256836  1361.050049  0.002357  1370.800049   \n",
              "2012-08-23      NaN      NaN  1125.308228  1346.750000 -0.010507  1367.900024   \n",
              "\n",
              "Index                                                     ... ZYDUSLIFE.NS  \\\n",
              "                    Low         Open    Volume Z20DayRet  ...     20DayVol   \n",
              "Date                                                      ...                \n",
              "2012-08-16  1335.099976  1356.000000  124774.0       NaN  ...          NaN   \n",
              "2012-08-17  1320.150024  1335.150024  190538.0       NaN  ...          NaN   \n",
              "2012-08-21  1320.000000  1330.000000  175534.0       NaN  ...          NaN   \n",
              "2012-08-22  1345.199951  1351.500000  180195.0       NaN  ...          NaN   \n",
              "2012-08-23  1340.000000  1366.000000  139318.0       NaN  ...          NaN   \n",
              "\n",
              "Index                                                                 \\\n",
              "              Adjusted       Close  DailyRet        High         Low   \n",
              "Date                                                                   \n",
              "2012-08-16  153.041046  172.830002       NaN  173.940002  170.199997   \n",
              "2012-08-17  155.095383  175.149994  0.013424  175.949997  173.000000   \n",
              "2012-08-21  152.810791  172.570007 -0.014730  177.800003  170.199997   \n",
              "2012-08-22  155.449600  175.550003  0.017268  177.970001  173.000000   \n",
              "2012-08-23  154.245285  174.190002 -0.007747  177.539993  173.059998   \n",
              "\n",
              "Index                                                 \n",
              "                  Open    Volume Z20DayRet Z20DayVol  \n",
              "Date                                                  \n",
              "2012-08-16  173.809998  465130.0       NaN       NaN  \n",
              "2012-08-17  173.000000   77795.0       NaN       NaN  \n",
              "2012-08-21  175.399994  291750.0       NaN       NaN  \n",
              "2012-08-22  173.000000  226630.0       NaN       NaN  \n",
              "2012-08-23  176.000000  114420.0       NaN       NaN  \n",
              "\n",
              "[5 rows x 891 columns]"
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              "      <th>2012-08-16</th>\n",
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              "      <td>1108.262207</td>\n",
              "      <td>1326.349976</td>\n",
              "      <td>-0.009040</td>\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#DROP ROWS WITH NA vals \n",
        "full_feature_dataset = raw_df.dropna(axis=0)\n",
        "#full_feature_dataset = raw_df"
      ],
      "metadata": {
        "id": "1wIx4kFMUGUG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zBJKZUV1vtLs",
        "outputId": "e520b1ba-bce1-43c8-f36f-913fe2f347d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2012-08-16', '2012-08-17', '2012-08-21', '2012-08-22',\n",
              "               '2012-08-23', '2012-08-24', '2012-08-27', '2012-08-28',\n",
              "               '2012-08-29', '2012-08-30',\n",
              "               ...\n",
              "               '2022-07-29', '2022-08-01', '2022-08-02', '2022-08-03',\n",
              "               '2022-08-04', '2022-08-05', '2022-08-08', '2022-08-10',\n",
              "               '2022-08-11', '2022-08-12'],\n",
              "              dtype='datetime64[ns]', name='Date', length=2466, freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_features = raw_df[[]].columns.remove_unused_levels()"
      ],
      "metadata": {
        "id": "08tnLaLoaV6o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Principal Component Analysis"
      ],
      "metadata": {
        "id": "qCnrTKUmcjfn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "full_feature_dataset.head()"
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          "height": 449
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        "id": "eCsmNV7NckMq",
        "outputId": "823fe452-997a-4527-afea-b8330b356b86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index         ACC.NS                                                        \\\n",
              "            20DayRet  20DayVol    Adjusted        Close  DailyRet     High   \n",
              "Date                                                                         \n",
              "2013-09-23  0.122714  0.024518  952.846985  1111.150024 -0.010772  1138.00   \n",
              "2013-09-24  0.096307  0.024759  941.999268  1098.500000 -0.011385  1125.00   \n",
              "2013-09-25  0.161487  0.020091  939.040649  1095.050049 -0.003141  1106.75   \n",
              "2013-09-26  0.159416  0.020120  939.255127  1095.300049  0.000228  1117.75   \n",
              "2013-09-27  0.147696  0.020078  942.899475  1099.550049  0.003880  1115.00   \n",
              "\n",
              "Index                                                     ... ZYDUSLIFE.NS  \\\n",
              "                    Low         Open    Volume Z20DayRet  ...     20DayVol   \n",
              "Date                                                      ...                \n",
              "2013-09-23  1090.300049  1110.150024  390593.0 -2.061994  ...     0.008589   \n",
              "2013-09-24  1086.150024  1100.150024  183976.0 -1.660285  ...     0.008368   \n",
              "2013-09-25  1076.300049  1090.099976  261395.0 -2.581359  ...     0.008414   \n",
              "2013-09-26  1082.349976  1090.000000  382724.0 -2.511170  ...     0.008176   \n",
              "2013-09-27  1093.099976  1100.000000  173613.0 -2.309323  ...     0.008424   \n",
              "\n",
              "Index                                                                 \\\n",
              "              Adjusted       Close  DailyRet        High         Low   \n",
              "Date                                                                   \n",
              "2013-09-23  122.460777  136.940002 -0.000365  138.759995  133.740005   \n",
              "2013-09-24  122.577042  137.070007  0.000949  137.770004  135.399994   \n",
              "2013-09-25  122.147797  136.589996 -0.003502  138.440002  135.339996   \n",
              "2013-09-26  122.478668  136.960007  0.002709  137.570007  135.300003   \n",
              "2013-09-27  123.954208  138.610001  0.012047  139.800003  135.929993   \n",
              "\n",
              "Index                                                  \n",
              "                  Open     Volume Z20DayRet Z20DayVol  \n",
              "Date                                                   \n",
              "2013-09-23  136.600006   270455.0 -0.884901  1.381906  \n",
              "2013-09-24  136.419998  1447475.0 -1.015956  1.423397  \n",
              "2013-09-25  138.440002    57360.0 -0.987478  1.401759  \n",
              "2013-09-26  136.800003   148420.0 -1.137849  1.444183  \n",
              "2013-09-27  137.699997   139545.0 -1.245373  1.374823  \n",
              "\n",
              "[5 rows x 891 columns]"
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              "      <th colspan=\"10\" halign=\"left\">ACC.NS</th>\n",
              "      <th>...</th>\n",
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              "    <tr>\n",
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              "      <th>20DayRet</th>\n",
              "      <th>20DayVol</th>\n",
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              "      <th>DailyRet</th>\n",
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              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Z20DayRet</th>\n",
              "      <th>...</th>\n",
              "      <th>20DayVol</th>\n",
              "      <th>Adjusted</th>\n",
              "      <th>Close</th>\n",
              "      <th>DailyRet</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
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              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-09-23</th>\n",
              "      <td>0.122714</td>\n",
              "      <td>0.024518</td>\n",
              "      <td>952.846985</td>\n",
              "      <td>1111.150024</td>\n",
              "      <td>-0.010772</td>\n",
              "      <td>1138.00</td>\n",
              "      <td>1090.300049</td>\n",
              "      <td>1110.150024</td>\n",
              "      <td>390593.0</td>\n",
              "      <td>-2.061994</td>\n",
              "      <td>...</td>\n",
              "      <td>0.008589</td>\n",
              "      <td>122.460777</td>\n",
              "      <td>136.940002</td>\n",
              "      <td>-0.000365</td>\n",
              "      <td>138.759995</td>\n",
              "      <td>133.740005</td>\n",
              "      <td>136.600006</td>\n",
              "      <td>270455.0</td>\n",
              "      <td>-0.884901</td>\n",
              "      <td>1.381906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-09-24</th>\n",
              "      <td>0.096307</td>\n",
              "      <td>0.024759</td>\n",
              "      <td>941.999268</td>\n",
              "      <td>1098.500000</td>\n",
              "      <td>-0.011385</td>\n",
              "      <td>1125.00</td>\n",
              "      <td>1086.150024</td>\n",
              "      <td>1100.150024</td>\n",
              "      <td>183976.0</td>\n",
              "      <td>-1.660285</td>\n",
              "      <td>...</td>\n",
              "      <td>0.008368</td>\n",
              "      <td>122.577042</td>\n",
              "      <td>137.070007</td>\n",
              "      <td>0.000949</td>\n",
              "      <td>137.770004</td>\n",
              "      <td>135.399994</td>\n",
              "      <td>136.419998</td>\n",
              "      <td>1447475.0</td>\n",
              "      <td>-1.015956</td>\n",
              "      <td>1.423397</td>\n",
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              "    <tr>\n",
              "      <th>2013-09-25</th>\n",
              "      <td>0.161487</td>\n",
              "      <td>0.020091</td>\n",
              "      <td>939.040649</td>\n",
              "      <td>1095.050049</td>\n",
              "      <td>-0.003141</td>\n",
              "      <td>1106.75</td>\n",
              "      <td>1076.300049</td>\n",
              "      <td>1090.099976</td>\n",
              "      <td>261395.0</td>\n",
              "      <td>-2.581359</td>\n",
              "      <td>...</td>\n",
              "      <td>0.008414</td>\n",
              "      <td>122.147797</td>\n",
              "      <td>136.589996</td>\n",
              "      <td>-0.003502</td>\n",
              "      <td>138.440002</td>\n",
              "      <td>135.339996</td>\n",
              "      <td>138.440002</td>\n",
              "      <td>57360.0</td>\n",
              "      <td>-0.987478</td>\n",
              "      <td>1.401759</td>\n",
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              "    <tr>\n",
              "      <th>2013-09-26</th>\n",
              "      <td>0.159416</td>\n",
              "      <td>0.020120</td>\n",
              "      <td>939.255127</td>\n",
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              "      <td>0.000228</td>\n",
              "      <td>1117.75</td>\n",
              "      <td>1082.349976</td>\n",
              "      <td>1090.000000</td>\n",
              "      <td>382724.0</td>\n",
              "      <td>-2.511170</td>\n",
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              "      <td>0.008176</td>\n",
              "      <td>122.478668</td>\n",
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              "      <td>135.300003</td>\n",
              "      <td>136.800003</td>\n",
              "      <td>148420.0</td>\n",
              "      <td>-1.137849</td>\n",
              "      <td>1.444183</td>\n",
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              "    <tr>\n",
              "      <th>2013-09-27</th>\n",
              "      <td>0.147696</td>\n",
              "      <td>0.020078</td>\n",
              "      <td>942.899475</td>\n",
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              "      <td>0.012047</td>\n",
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              "      <td>137.699997</td>\n",
              "      <td>139545.0</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_feature_dataset = full_feature_dataset.to_numpy()\n",
        "full_feature_dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8VzUrGL6cnVB",
        "outputId": "9ba75331-7a5b-4290-c109-0ececa156928"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2192, 891)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "emV_Ki3Lc5mB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "closing_prices = df_close.iloc[:full_feature_dataset.shape[0],:]"
      ],
      "metadata": {
        "id": "GK-5AMf4dmhl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "#Division of data\n",
        "# df_close = df_close.to_numpy()\n",
        "array_train, array_test = train_test_split(closing_prices, shuffle = False, test_size = .2)\n",
        "PCA_train, PCA_test = train_test_split(full_feature_dataset, shuffle = False, test_size = .2)"
      ],
      "metadata": {
        "id": "PS6QdZw-dpzH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(PCA_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8GuyhA6qiWnx",
        "outputId": "2f1a9fa3-ee61-4862-886d-dfb17ac5d2c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1753, 891)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(PCA_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHAVFBbph9ps",
        "outputId": "62e24baf-fb2e-4119-a790-05d86fd5a4f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(439, 891)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(array_train.shape)\n",
        "print(PCA_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zak54hFKdsey",
        "outputId": "b7e767c3-d08a-494b-fb21-d1eb5f1ded62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1753, 81)\n",
            "(1753, 891)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pc3 = PCA()\n",
        "temp = pc3.fit_transform(PCA_train)"
      ],
      "metadata": {
        "id": "Z35DFQZodv4W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(np.cumsum(pc3.explained_variance_ratio_))\n",
        "plt.xlabel('Num Components')\n",
        "plt.ylabel('Cumulative Explained Variance');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "GoJw2rObd1IY",
        "outputId": "fa810311-ec97-497e-d6c2-7f2dc387d2d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Data with only closing prices\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scl = MinMaxScaler()\n",
        "scale = MinMaxScaler()\n",
        "array_train = scl.fit_transform(array_train)\n",
        "array_test  = scale.fit_transform(array_test) #[0,1]\n",
        "\n",
        "#Full features dataset; reducing dimensionality here\n",
        "from sklearn.decomposition import PCA\n",
        "pcTrain_scl = MinMaxScaler()\n",
        "pcTest_scl  = MinMaxScaler()\n",
        "pcaTrain = PCA(n_components = 81)\n",
        "pcaTest = PCA(n_components = 81)\n",
        "\n",
        "PCA_train = pcTrain_scl.fit_transform(PCA_train)\n",
        "PCA_train = pcaTrain.fit_transform(PCA_train)\n",
        "\n",
        "PCA_test = pcTest_scl.fit_transform(PCA_test)\n",
        "PCA_test = pcaTest.fit_transform(PCA_test)"
      ],
      "metadata": {
        "id": "85xnCYomd4_u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(array_train.shape)\n",
        "print(PCA_train.shape)\n",
        "print(array_test.shape)\n",
        "print(PCA_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLtJCU6Gd-tA",
        "outputId": "4201ca71-56d0-4ed9-d5d1-9f7bb1093e1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1753, 81)\n",
            "(1753, 81)\n",
            "(439, 81)\n",
            "(439, 81)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get the data and splits in input X and output Y, by spliting in lookback number of past days as input X \n",
        "#and horizon number of coming days as Y.\n",
        "def processData(data, lookback, horizon,num_companies,jump=1):\n",
        "    X,Y = [],[]\n",
        "    for i in range(0,len(data) -lookback - horizon +1, jump):\n",
        "        X.append(data[i:(i+lookback)])\n",
        "        Y.append(data[(i+lookback):(i+lookback+horizon)])\n",
        "    return np.array(X),np.array(Y)"
      ],
      "metadata": {
        "id": "hjCQgSLyebPH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_companies = df_close.shape[1]\n",
        "lookback = 252 #How many days of the past can the model see\n",
        "horizon = 22 #How many days into the future are we trying to predict"
      ],
      "metadata": {
        "id": "5vhR15Nle6Jo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Formation of lookback dataframes and forward dataframes for training, and testing dataset\n",
        "# to calculate accuracy\n",
        "\n",
        "#Testing Data\n",
        "X_test, void = processData(PCA_test,lookback,horizon,num_companies,horizon)\n",
        "void, y_test = processData(array_test,lookback,horizon,num_companies,horizon) \n",
        "y_test = np.array([list(a.ravel()) for a in y_test])\n",
        "\n",
        "#Training/Validation Data\n",
        "X_PCA, void = processData(PCA_train,lookback,horizon,num_companies)\n",
        "void, y = processData(array_train,lookback,horizon,num_companies)\n",
        "y = np.array([list(x.ravel()) for x in y])\n",
        "\n"
      ],
      "metadata": {
        "id": "2HEAkDZte9So"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_validate, y_train, y_validate = train_test_split(X_PCA, y, test_size=0.20, train_size=0.80, random_state=1)"
      ],
      "metadata": {
        "id": "a8MwcVNmjuuU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "1782/22"
      ],
      "metadata": {
        "id": "xZI_7AMHmtTg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfe4e6f3-2301-467c-dd86-522e9e8408e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "81.0"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_validate.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_validate.shape)\n",
        "print(y_test.shape)\n",
        "#(Sample,Timestep,Features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6VJrOXWHtDHK",
        "outputId": "f72d0c4d-8a6e-4795-8c65-ddf1bfc87d06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1184, 252, 81)\n",
            "(296, 252, 81)\n",
            "(8, 252, 81)\n",
            "(1184, 1782)\n",
            "(296, 1782)\n",
            "(8, 1782)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Architecture + Training "
      ],
      "metadata": {
        "id": "RLdPqFabt34h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential, load_model\n",
        "import tensorflow as tf\n",
        "from keras.layers import LSTM,Dense,Activation,Attention,Dropout\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.regularizers import Regularizer\n",
        "from keras import optimizers\n",
        "\n",
        "#Model Architecture - Two LSTM layers with x neurons & y epochs\n",
        "num_neurons_L1 = 800\n",
        "num_neurons_L2 = 600\n",
        "num_neurons_dense1 = 200\n",
        "EPOCHS = 10\n",
        "\n",
        "#Model\n",
        "model = Sequential()\n",
        "\n",
        "model.add(LSTM(num_neurons_L1,input_shape=(lookback,num_companies), return_sequences=True))\n",
        "model.add(LSTM(num_neurons_L2,input_shape=(num_neurons_L1,1)))\n",
        "model.add(Dense(horizon * num_companies, activation = 'relu'))\n",
        "model.add(Dense(horizon * num_companies, activation = 'sigmoid'))\n",
        "\n",
        "model.compile(loss='mean_squared_error', optimizer='adam', metrics = ['accuracy'])\n",
        "#model.compile(optimizer='adam',loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "dQYO8wKCtYzY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train,y_train,epochs=EPOCHS, validation_data=(X_validate,y_validate), shuffle=False,batch_size=256, verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZAeQsQDtxC9",
        "outputId": "9716e7dc-0733-4875-f0a6-728c25d47267"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "5/5 - 287s - loss: 0.0508 - accuracy: 0.0025 - val_loss: 0.0286 - val_accuracy: 0.0068 - 287s/epoch - 57s/step\n",
            "Epoch 2/10\n",
            "5/5 - 271s - loss: 0.0227 - accuracy: 0.0093 - val_loss: 0.0170 - val_accuracy: 0.0101 - 271s/epoch - 54s/step\n",
            "Epoch 3/10\n",
            "5/5 - 263s - loss: 0.0149 - accuracy: 0.0127 - val_loss: 0.0109 - val_accuracy: 0.0135 - 263s/epoch - 53s/step\n",
            "Epoch 4/10\n",
            "5/5 - 275s - loss: 0.0100 - accuracy: 0.0068 - val_loss: 0.0083 - val_accuracy: 0.0135 - 275s/epoch - 55s/step\n",
            "Epoch 5/10\n",
            "5/5 - 285s - loss: 0.0076 - accuracy: 0.0068 - val_loss: 0.0065 - val_accuracy: 0.0068 - 285s/epoch - 57s/step\n",
            "Epoch 6/10\n",
            "5/5 - 286s - loss: 0.0059 - accuracy: 0.0051 - val_loss: 0.0054 - val_accuracy: 0.0169 - 286s/epoch - 57s/step\n",
            "Epoch 7/10\n",
            "5/5 - 275s - loss: 0.0050 - accuracy: 0.0110 - val_loss: 0.0045 - val_accuracy: 0.0169 - 275s/epoch - 55s/step\n",
            "Epoch 8/10\n",
            "5/5 - 275s - loss: 0.0044 - accuracy: 0.0101 - val_loss: 0.0041 - val_accuracy: 0.0068 - 275s/epoch - 55s/step\n",
            "Epoch 9/10\n",
            "5/5 - 285s - loss: 0.0039 - accuracy: 0.0127 - val_loss: 0.0037 - val_accuracy: 0.0068 - 285s/epoch - 57s/step\n",
            "Epoch 10/10\n",
            "5/5 - 264s - loss: 0.0036 - accuracy: 0.0076 - val_loss: 0.0034 - val_accuracy: 0.0034 - 264s/epoch - 53s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (15,10))\n",
        "\n",
        "plt.plot(history.history['loss'], label='loss')\n",
        "plt.plot(history.history['val_loss'], label='val_loss')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "Vq-NxqmjuBkE",
        "outputId": "bc523b8b-677e-41ef-c519-85ab405345d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (15,10))\n",
        "\n",
        "plt.plot(history.history['accuracy'], label='accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='val_accuracy')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "jvaHNTrjthWY",
        "outputId": "083f63c8-ee92-4378-ff3c-cc88de584edb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predicting** **Results**"
      ],
      "metadata": {
        "id": "2KvHgZJHtu6H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_test, void = processData(PCA_test,lookback,horizon,num_companies,horizon)\n",
        "void, y_test = processData(array_test,lookback,horizon,num_companies,horizon) \n",
        "y_test = np.array([list(a.ravel()) for a in y_test])\n",
        "\n",
        "\n",
        "Xt = model.predict(X_test)\n",
        "Xt.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72Hhbk6BtnEo",
        "outputId": "4c423b9d-9d20-40f5-9e4e-f6885481173e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 1782)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Un-scaling the data and returning data to original shape**"
      ],
      "metadata": {
        "id": "izQsEvLJ6LIc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Aux function 1\n",
        "def do_inverse_transform(output_result,num_companies):\n",
        "    #transform to input shape\n",
        "    original_matrix_format = []\n",
        "    for result in output_result:\n",
        "        #do inverse transform\n",
        "        original_matrix_format.append(scl.inverse_transform([result[x:x+num_companies] for x in range(0, len(result), num_companies)]))\n",
        "    original_matrix_format = np.array(original_matrix_format)\n",
        "    \n",
        "    #restore to original shape\n",
        "    for i in range(len(original_matrix_format)):\n",
        "        output_result[i] = original_matrix_format[i].ravel()\n",
        "\n",
        "    return output_result"
      ],
      "metadata": {
        "id": "o1t85sAYt2dd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Aux function 2\n",
        "def prediction_by_step_by_company(raw_model_output, num_companies):\n",
        "    matrix_prediction = []\n",
        "    for i in range(0,num_companies):\n",
        "        matrix_prediction.append([[lista[j] for j in range(i,len(lista),num_companies)] for lista in raw_model_output])\n",
        "    return np.array(matrix_prediction)"
      ],
      "metadata": {
        "id": "Gno9VL0F6XXA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Aux function 3\n",
        "def target_by_company(raw_model_output, num_companies):\n",
        "    matrix_target = [[] for x in range(num_companies)]\n",
        "    \n",
        "    for output in raw_model_output:\n",
        "        for i in range (num_companies):\n",
        "            for j in range(0,len(output),num_companies):\n",
        "                matrix_target[i].append(output[i+j])\n",
        "    return np.array(matrix_target)"
      ],
      "metadata": {
        "id": "11-C-dAu6dtB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Xt = do_inverse_transform(Xt, num_companies)\n",
        "predictions = prediction_by_step_by_company(Xt, num_companies)\n",
        "predictions.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qPIQu65-6htX",
        "outputId": "ed4a3074-9457-4a8c-8b0b-5201fa0ce980"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(81, 8, 22)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Yt = do_inverse_transform(y_test, num_companies)\n",
        "\n",
        "actuals = target_by_company(Yt, num_companies)\n",
        "actuals.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KZT4E726luY",
        "outputId": "890d5a92-edb0-4af8-d2f4-c63ee5293d77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(81, 176)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Prediction Results on Test Data - Plot**"
      ],
      "metadata": {
        "id": "8GDfbIvP62Nc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (15,10))\n",
        "\n",
        "companies_to_show = [2] \n",
        "colors = ['r','g','c','m','y','k','w','b']\n",
        "companies_names = ['A', 'AAPL', 'ABC', 'ABMD']\n",
        " \n",
        "for id_company in companies_to_show:\n",
        "    for i in range(0,len(predictions[id_company])):\n",
        "        plt.plot([x + i*horizon for x in range(len(predictions[id_company][i]))], predictions[id_company][i], color=colors[id_company])\n",
        "    plt.plot(0,predictions[id_company][0][0] ,color=colors[id_company], label='predict_{}'.format(companies_names[id_company])) #only to place the label\n",
        "\n",
        "   \n",
        "\n",
        "for id_company in companies_to_show:\n",
        "    plt.plot(actuals[id_company], label='target_{}'.format(companies_names[id_company]))\n",
        "\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "OOH_3q3y6x3K",
        "outputId": "4da47103-6c97-4ef0-ab47-798ab2efe570"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_PCA = np.concatenate((PCA_train,PCA_test),axis = 0)"
      ],
      "metadata": {
        "id": "Lf9lGtll7CoU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_all,void = processData(full_PCA,lookback,horizon,num_companies,horizon)\n",
        "void,y_all = processData(scl.fit_transform(df_close),lookback,horizon,num_companies,horizon)\n",
        "y_all = np.array([list(a.ravel()) for a in y_all])"
      ],
      "metadata": {
        "id": "qtnEbHpf7KBd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Xt = model.predict(X_all)\n",
        "Xt.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pQNzj4mf7Nii",
        "outputId": "17bc74d5-fd81-4bac-ee1f-456f780ce569"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(88, 1782)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Xt = do_inverse_transform(Xt, num_companies)\n",
        "\n",
        "predictions = prediction_by_step_by_company(Xt, num_companies)\n",
        "predictions.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Io786diN7Rv6",
        "outputId": "e307337e-01d5-4b6a-d40e-744287d074a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(81, 88, 22)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Yt = do_inverse_transform(y_all, num_companies)\n",
        "\n",
        "actuals = target_by_company(Yt, num_companies)\n",
        "actuals.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8xCj1gf67ihu",
        "outputId": "60c7a4bc-0453-4afa-edd6-fde1ff74eb69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(81, 2200)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Prediction Results on Full Data - Plot**"
      ],
      "metadata": {
        "id": "ffrqlJbW7w_T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (40,10))\n",
        "\n",
        "companies_to_show = [2] #Type a number between 0:4 to see 4 different companies\n",
        "colors = ['r','g','c','m','y','k','w','b']\n",
        "companies_names = ['A', 'B', 'C', 'D']\n",
        "\n",
        "    \n",
        "for id_company in companies_to_show:\n",
        "    for i in range(0,len(predictions[id_company])):\n",
        "        plt.plot([x + i*horizon for x in range(len(predictions[id_company][i]))], predictions[id_company][i], color=colors[id_company])\n",
        "    plt.plot(0,predictions[id_company][0][0] ,color=colors[id_company], label='predict_{}'.format(companies_names[id_company])) #only to place the label\n",
        "\n",
        "   \n",
        "\n",
        "for id_company in companies_to_show:\n",
        "    plt.plot(actuals[id_company], label='target_{}'.format(companies_names[id_company]))\n",
        "\n",
        "plt.legend(loc='best')\n",
        "plt.show()\n",
        "\n",
        "##QQ-plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186
        },
        "id": "RYFhFz4A7nZt",
        "outputId": "0c081ef0-7cbc-48a6-ff04-651074d5f86e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x720 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Stock Prices Predictions**"
      ],
      "metadata": {
        "id": "AJSmkvC-8A7l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_prices = np.zeros((predictions.shape[1]*predictions.shape[2],predictions.shape[0]))\n",
        "# actual_prices = []\n",
        "\n",
        "for i in range(predictions.shape[0]):\n",
        "  counter = 0\n",
        "  for j in range(predictions.shape[1]):\n",
        "    for z in range(predictions.shape[2]):\n",
        "      predicted_prices[counter, i] = predictions[i,j,z]\n",
        "      counter+=1\n",
        "\n",
        "print(counter)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WTnggrux76QR",
        "outputId": "d675485c-608e-4e5c-97a9-6563b7aca552"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1936\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "actuals_prices = actuals[:,:1936].T"
      ],
      "metadata": {
        "id": "MCEt9Y9B8K0E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(predicted_prices.shape)\n",
        "print(actuals_prices.shape)\n",
        "print(dates.shape)\n",
        "#(lookback+1, )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OEB0dNPM8a4x",
        "outputId": "7d8fc554-b5f8-4e3b-a3ce-a06a0dc915b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1936, 81)\n",
            "(1936, 81)\n",
            "(2462,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(2462-lookback+1)-1936"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aM_Z9qN-8e8L",
        "outputId": "d0b4b2a3-093a-4462-d5bd-288ada59a04e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "275"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Need to remove certain days because we predict into the future. But, we aren't able to test these predicted days\n",
        "# against actual days in our porfolio optimization\n",
        "\n",
        "actual_prices = pd.DataFrame(data = actuals_prices, columns = stocks,index = dates[lookback+1:2463-274])\n",
        "predicted_prices = pd.DataFrame(data = predicted_prices,columns=stocks, index = dates[lookback+1:2463-274])"
      ],
      "metadata": {
        "id": "BC3VWHBx8yfZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "actual_prices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "id": "BR6u7n3G9QsI",
        "outputId": "4edae9b8-cd24-417f-a731-25a30c8449a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index            ACC.NS  ADANIENT.NS  ADANIPORTS.NS  AMBUJACEM.NS  \\\n",
              "Date                                                                \n",
              "2013-08-28   942.799988    22.692371     123.500000    159.500000   \n",
              "2013-08-29   944.700012    22.439468     122.800003    160.100006   \n",
              "2013-08-30   958.049988    23.113878     127.750000    164.449997   \n",
              "2013-09-02   964.099976    21.688419     125.949997    171.600006   \n",
              "2013-09-03   952.000000    21.328222     130.399994    170.100006   \n",
              "...                 ...          ...            ...           ...   \n",
              "2021-07-02  1996.000000  1490.250000     703.099976    340.299988   \n",
              "2021-07-05  1987.250000  1422.050049     710.400024    339.250000   \n",
              "2021-07-06  1982.750000  1394.349976     710.049988    340.299988   \n",
              "2021-07-07  2022.849976  1414.800049     712.700012    351.850006   \n",
              "2021-07-08  2036.550049  1425.650024     720.099976    357.450012   \n",
              "\n",
              "Index       APOLLOHOSP.NS  ASIANPAINT.NS  AXISBANK.NS  BAJAJ-AUTO.NS  \\\n",
              "Date                                                                   \n",
              "2013-08-28     883.349976     410.000000   175.529999    1727.750000   \n",
              "2013-08-29     858.650024     392.000000   167.589996    1727.750000   \n",
              "2013-08-30     863.299988     403.200012   170.149994    1740.449951   \n",
              "2013-09-02     864.549988     420.950012   166.589996    1837.000000   \n",
              "2013-09-03     856.099976     422.399994   172.860001    1856.099976   \n",
              "...                   ...            ...          ...            ...   \n",
              "2021-07-02    3678.449951    3021.600098   746.450012    4204.549805   \n",
              "2021-07-05    3733.199951    3005.000000   750.650024    4175.350098   \n",
              "2021-07-06    3718.100098    3014.800049   760.349976    4201.500000   \n",
              "2021-07-07    3706.250000    3002.500000   758.349976    4195.399902   \n",
              "2021-07-08    3715.600098    3040.100098   762.900024    4188.250000   \n",
              "\n",
              "Index       BAJAJFINSV.NS  BAJAJHLDNG.NS  ...  TATASTEEL.NS       TCS.NS  \\\n",
              "Date                                      ...                              \n",
              "2013-08-28     576.650024     799.700012  ...     25.943018   919.400024   \n",
              "2013-08-29     588.750000     787.099976  ...     26.581352   953.950012   \n",
              "2013-08-30     595.900024     810.250000  ...     26.519424   973.549988   \n",
              "2013-09-02     590.099976     804.799988  ...     26.104982  1016.599976   \n",
              "2013-09-03     590.250000     802.849976  ...     27.572199  1028.425049   \n",
              "...                   ...            ...  ...           ...          ...   \n",
              "2021-07-02   11816.450195    3617.750000  ...    116.355003  3341.500000   \n",
              "2021-07-05   11750.450195    3677.000000  ...    113.599998  3325.449951   \n",
              "2021-07-06   11923.099609    3750.750000  ...    115.684998  3321.000000   \n",
              "2021-07-07   12090.799805    3862.149902  ...    116.724998  3262.300049   \n",
              "2021-07-08   12374.150391    3785.600098  ...    121.864998  3274.949951   \n",
              "\n",
              "Index          TECHM.NS     TITAN.NS  TORNTPHARM.NS  ULTRACEMCO.NS  \\\n",
              "Date                                                                 \n",
              "2013-08-28   340.725006   221.699997     201.449997    1550.699951   \n",
              "2013-08-29   342.562500   212.350006     199.100006    1463.699951   \n",
              "2013-08-30   341.387512   216.899994     200.000000    1447.949951   \n",
              "2013-09-02   343.750000   224.649994     203.800003    1474.849976   \n",
              "2013-09-03   343.762512   230.600006     204.074997    1498.400024   \n",
              "...                 ...          ...            ...            ...   \n",
              "2021-07-02  1085.150024  1740.300049    1462.099976    6707.250000   \n",
              "2021-07-05  1089.400024  1754.750000    1475.175049    6719.899902   \n",
              "2021-07-06  1074.650024  1749.900024    1469.425049    6719.850098   \n",
              "2021-07-07  1049.550049  1762.800049    1465.949951    6933.049805   \n",
              "2021-07-08  1045.900024  1727.250000    1474.775024    6925.100098   \n",
              "\n",
              "Index           UPL.NS     VEDL.NS    WIPRO.NS  ZYDUSLIFE.NS  \n",
              "Date                                                          \n",
              "2013-08-28   94.933327  168.350006  170.606293    131.240005  \n",
              "2013-08-29   94.900002  169.149994  176.793793    130.369995  \n",
              "2013-08-30   90.933327  192.449997  177.543793    131.019989  \n",
              "2013-09-02   88.933327  188.750000  181.350052    130.809998  \n",
              "2013-09-03   90.699997  188.800003  181.668793    130.149994  \n",
              "...                ...         ...         ...           ...  \n",
              "2021-07-02  791.500000  262.899994  539.349976    639.200012  \n",
              "2021-07-05  799.349976  262.049988  538.599976    639.349976  \n",
              "2021-07-06  807.599976  272.450012  536.349976    643.950012  \n",
              "2021-07-07  803.299988  270.600006  532.599976    637.450012  \n",
              "2021-07-08  816.900024  272.250000  532.150024    638.099976  \n",
              "\n",
              "[1936 rows x 81 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th>Index</th>\n",
              "      <th>ACC.NS</th>\n",
              "      <th>ADANIENT.NS</th>\n",
              "      <th>ADANIPORTS.NS</th>\n",
              "      <th>AMBUJACEM.NS</th>\n",
              "      <th>APOLLOHOSP.NS</th>\n",
              "      <th>ASIANPAINT.NS</th>\n",
              "      <th>AXISBANK.NS</th>\n",
              "      <th>BAJAJ-AUTO.NS</th>\n",
              "      <th>BAJAJFINSV.NS</th>\n",
              "      <th>BAJAJHLDNG.NS</th>\n",
              "      <th>...</th>\n",
              "      <th>TATASTEEL.NS</th>\n",
              "      <th>TCS.NS</th>\n",
              "      <th>TECHM.NS</th>\n",
              "      <th>TITAN.NS</th>\n",
              "      <th>TORNTPHARM.NS</th>\n",
              "      <th>ULTRACEMCO.NS</th>\n",
              "      <th>UPL.NS</th>\n",
              "      <th>VEDL.NS</th>\n",
              "      <th>WIPRO.NS</th>\n",
              "      <th>ZYDUSLIFE.NS</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-08-28</th>\n",
              "      <td>942.799988</td>\n",
              "      <td>22.692371</td>\n",
              "      <td>123.500000</td>\n",
              "      <td>159.500000</td>\n",
              "      <td>883.349976</td>\n",
              "      <td>410.000000</td>\n",
              "      <td>175.529999</td>\n",
              "      <td>1727.750000</td>\n",
              "      <td>576.650024</td>\n",
              "      <td>799.700012</td>\n",
              "      <td>...</td>\n",
              "      <td>25.943018</td>\n",
              "      <td>919.400024</td>\n",
              "      <td>340.725006</td>\n",
              "      <td>221.699997</td>\n",
              "      <td>201.449997</td>\n",
              "      <td>1550.699951</td>\n",
              "      <td>94.933327</td>\n",
              "      <td>168.350006</td>\n",
              "      <td>170.606293</td>\n",
              "      <td>131.240005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-08-29</th>\n",
              "      <td>944.700012</td>\n",
              "      <td>22.439468</td>\n",
              "      <td>122.800003</td>\n",
              "      <td>160.100006</td>\n",
              "      <td>858.650024</td>\n",
              "      <td>392.000000</td>\n",
              "      <td>167.589996</td>\n",
              "      <td>1727.750000</td>\n",
              "      <td>588.750000</td>\n",
              "      <td>787.099976</td>\n",
              "      <td>...</td>\n",
              "      <td>26.581352</td>\n",
              "      <td>953.950012</td>\n",
              "      <td>342.562500</td>\n",
              "      <td>212.350006</td>\n",
              "      <td>199.100006</td>\n",
              "      <td>1463.699951</td>\n",
              "      <td>94.900002</td>\n",
              "      <td>169.149994</td>\n",
              "      <td>176.793793</td>\n",
              "      <td>130.369995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-08-30</th>\n",
              "      <td>958.049988</td>\n",
              "      <td>23.113878</td>\n",
              "      <td>127.750000</td>\n",
              "      <td>164.449997</td>\n",
              "      <td>863.299988</td>\n",
              "      <td>403.200012</td>\n",
              "      <td>170.149994</td>\n",
              "      <td>1740.449951</td>\n",
              "      <td>595.900024</td>\n",
              "      <td>810.250000</td>\n",
              "      <td>...</td>\n",
              "      <td>26.519424</td>\n",
              "      <td>973.549988</td>\n",
              "      <td>341.387512</td>\n",
              "      <td>216.899994</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>1447.949951</td>\n",
              "      <td>90.933327</td>\n",
              "      <td>192.449997</td>\n",
              "      <td>177.543793</td>\n",
              "      <td>131.019989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-09-02</th>\n",
              "      <td>964.099976</td>\n",
              "      <td>21.688419</td>\n",
              "      <td>125.949997</td>\n",
              "      <td>171.600006</td>\n",
              "      <td>864.549988</td>\n",
              "      <td>420.950012</td>\n",
              "      <td>166.589996</td>\n",
              "      <td>1837.000000</td>\n",
              "      <td>590.099976</td>\n",
              "      <td>804.799988</td>\n",
              "      <td>...</td>\n",
              "      <td>26.104982</td>\n",
              "      <td>1016.599976</td>\n",
              "      <td>343.750000</td>\n",
              "      <td>224.649994</td>\n",
              "      <td>203.800003</td>\n",
              "      <td>1474.849976</td>\n",
              "      <td>88.933327</td>\n",
              "      <td>188.750000</td>\n",
              "      <td>181.350052</td>\n",
              "      <td>130.809998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-09-03</th>\n",
              "      <td>952.000000</td>\n",
              "      <td>21.328222</td>\n",
              "      <td>130.399994</td>\n",
              "      <td>170.100006</td>\n",
              "      <td>856.099976</td>\n",
              "      <td>422.399994</td>\n",
              "      <td>172.860001</td>\n",
              "      <td>1856.099976</td>\n",
              "      <td>590.250000</td>\n",
              "      <td>802.849976</td>\n",
              "      <td>...</td>\n",
              "      <td>27.572199</td>\n",
              "      <td>1028.425049</td>\n",
              "      <td>343.762512</td>\n",
              "      <td>230.600006</td>\n",
              "      <td>204.074997</td>\n",
              "      <td>1498.400024</td>\n",
              "      <td>90.699997</td>\n",
              "      <td>188.800003</td>\n",
              "      <td>181.668793</td>\n",
              "      <td>130.149994</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>2021-07-02</th>\n",
              "      <td>1996.000000</td>\n",
              "      <td>1490.250000</td>\n",
              "      <td>703.099976</td>\n",
              "      <td>340.299988</td>\n",
              "      <td>3678.449951</td>\n",
              "      <td>3021.600098</td>\n",
              "      <td>746.450012</td>\n",
              "      <td>4204.549805</td>\n",
              "      <td>11816.450195</td>\n",
              "      <td>3617.750000</td>\n",
              "      <td>...</td>\n",
              "      <td>116.355003</td>\n",
              "      <td>3341.500000</td>\n",
              "      <td>1085.150024</td>\n",
              "      <td>1740.300049</td>\n",
              "      <td>1462.099976</td>\n",
              "      <td>6707.250000</td>\n",
              "      <td>791.500000</td>\n",
              "      <td>262.899994</td>\n",
              "      <td>539.349976</td>\n",
              "      <td>639.200012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-05</th>\n",
              "      <td>1987.250000</td>\n",
              "      <td>1422.050049</td>\n",
              "      <td>710.400024</td>\n",
              "      <td>339.250000</td>\n",
              "      <td>3733.199951</td>\n",
              "      <td>3005.000000</td>\n",
              "      <td>750.650024</td>\n",
              "      <td>4175.350098</td>\n",
              "      <td>11750.450195</td>\n",
              "      <td>3677.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>113.599998</td>\n",
              "      <td>3325.449951</td>\n",
              "      <td>1089.400024</td>\n",
              "      <td>1754.750000</td>\n",
              "      <td>1475.175049</td>\n",
              "      <td>6719.899902</td>\n",
              "      <td>799.349976</td>\n",
              "      <td>262.049988</td>\n",
              "      <td>538.599976</td>\n",
              "      <td>639.349976</td>\n",
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              "    <tr>\n",
              "      <th>2021-07-06</th>\n",
              "      <td>1982.750000</td>\n",
              "      <td>1394.349976</td>\n",
              "      <td>710.049988</td>\n",
              "      <td>340.299988</td>\n",
              "      <td>3718.100098</td>\n",
              "      <td>3014.800049</td>\n",
              "      <td>760.349976</td>\n",
              "      <td>4201.500000</td>\n",
              "      <td>11923.099609</td>\n",
              "      <td>3750.750000</td>\n",
              "      <td>...</td>\n",
              "      <td>115.684998</td>\n",
              "      <td>3321.000000</td>\n",
              "      <td>1074.650024</td>\n",
              "      <td>1749.900024</td>\n",
              "      <td>1469.425049</td>\n",
              "      <td>6719.850098</td>\n",
              "      <td>807.599976</td>\n",
              "      <td>272.450012</td>\n",
              "      <td>536.349976</td>\n",
              "      <td>643.950012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-07</th>\n",
              "      <td>2022.849976</td>\n",
              "      <td>1414.800049</td>\n",
              "      <td>712.700012</td>\n",
              "      <td>351.850006</td>\n",
              "      <td>3706.250000</td>\n",
              "      <td>3002.500000</td>\n",
              "      <td>758.349976</td>\n",
              "      <td>4195.399902</td>\n",
              "      <td>12090.799805</td>\n",
              "      <td>3862.149902</td>\n",
              "      <td>...</td>\n",
              "      <td>116.724998</td>\n",
              "      <td>3262.300049</td>\n",
              "      <td>1049.550049</td>\n",
              "      <td>1762.800049</td>\n",
              "      <td>1465.949951</td>\n",
              "      <td>6933.049805</td>\n",
              "      <td>803.299988</td>\n",
              "      <td>270.600006</td>\n",
              "      <td>532.599976</td>\n",
              "      <td>637.450012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-08</th>\n",
              "      <td>2036.550049</td>\n",
              "      <td>1425.650024</td>\n",
              "      <td>720.099976</td>\n",
              "      <td>357.450012</td>\n",
              "      <td>3715.600098</td>\n",
              "      <td>3040.100098</td>\n",
              "      <td>762.900024</td>\n",
              "      <td>4188.250000</td>\n",
              "      <td>12374.150391</td>\n",
              "      <td>3785.600098</td>\n",
              "      <td>...</td>\n",
              "      <td>121.864998</td>\n",
              "      <td>3274.949951</td>\n",
              "      <td>1045.900024</td>\n",
              "      <td>1727.250000</td>\n",
              "      <td>1474.775024</td>\n",
              "      <td>6925.100098</td>\n",
              "      <td>816.900024</td>\n",
              "      <td>272.250000</td>\n",
              "      <td>532.150024</td>\n",
              "      <td>638.099976</td>\n",
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          "metadata": {},
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    {
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        "actual_prices.to_csv('y_dataset.csv')"
      ],
      "metadata": {
        "id": "wlcXdnXni_KU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "write.csv(actual_prices, 'y_dataset.csv', row.names = FALSE)"
      ],
      "metadata": {
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          "height": 130
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        "outputId": "f7015b3e-5656-440c-a802-2907a9797e35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-61-95a1195ba5d0>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    write.csv(actual_prices, 'y_dataset.csv', row.names = FALSE)\u001b[0m\n\u001b[0m                                             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m keyword can't be an expression\n"
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          "data": {
            "text/plain": [
              "Index            ACC.NS  ADANIENT.NS  ADANIPORTS.NS  AMBUJACEM.NS  \\\n",
              "Date                                                                \n",
              "2013-08-28  1168.125122   209.581070     175.747040    185.140915   \n",
              "2013-08-29  1175.395630   224.744308     205.586090    198.565857   \n",
              "2013-08-30  1140.923096   195.316467     203.242599    185.812592   \n",
              "2013-09-02  1181.454346   203.409683     191.983185    183.757202   \n",
              "2013-09-03  1192.129883   237.603455     202.512848    188.241058   \n",
              "...                 ...          ...            ...           ...   \n",
              "2021-07-02  2078.775146  2215.869141     769.798157    299.970551   \n",
              "2021-07-05  2098.907227  2185.454834     751.567139    298.743347   \n",
              "2021-07-06  2051.858643  2257.159668     739.074951    305.737000   \n",
              "2021-07-07  2067.032227  2137.535889     773.167175    300.114899   \n",
              "2021-07-08  2040.571533  2224.961914     765.568481    296.762817   \n",
              "\n",
              "Index       APOLLOHOSP.NS  ASIANPAINT.NS  AXISBANK.NS  BAJAJ-AUTO.NS  \\\n",
              "Date                                                                   \n",
              "2013-08-28    2067.877441     537.469299   219.744843    2075.252197   \n",
              "2013-08-29    2039.834961     524.020203   201.000854    2052.678711   \n",
              "2013-08-30    2145.557617     533.543335   211.596786    2005.678345   \n",
              "2013-09-02    2139.608398     592.816040   212.488510    2004.932251   \n",
              "2013-09-03    2121.836182     600.856079   213.546753    2141.566406   \n",
              "...                   ...            ...          ...            ...   \n",
              "2021-07-02    3966.531494    2674.658936   700.500122    3491.274414   \n",
              "2021-07-05    4131.488770    2515.637939   721.402039    3438.084961   \n",
              "2021-07-06    3964.316650    2591.021240   715.036804    3408.195312   \n",
              "2021-07-07    4091.453857    2657.047363   721.907104    3419.894043   \n",
              "2021-07-08    4156.616699    2623.510254   722.153809    3510.491943   \n",
              "\n",
              "Index       BAJAJFINSV.NS  BAJAJHLDNG.NS  ...  TATASTEEL.NS       TCS.NS  \\\n",
              "Date                                      ...                              \n",
              "2013-08-28     686.764343     975.657227  ...     46.432476  1317.770386   \n",
              "2013-08-29     673.573608     999.273315  ...     46.113880  1392.945923   \n",
              "2013-08-30     656.498901    1006.159180  ...     49.312710  1433.477905   \n",
              "2013-09-02     707.256897    1029.058960  ...     44.516720  1344.568359   \n",
              "2013-09-03     694.083801     990.226257  ...     49.546108  1463.139160   \n",
              "...                   ...            ...  ...           ...          ...   \n",
              "2021-07-02   14888.556641    5123.076172  ...    106.518883  3563.668945   \n",
              "2021-07-05   14816.315430    5012.432617  ...     98.701790  3521.311768   \n",
              "2021-07-06   15507.294922    4899.542480  ...    104.207703  3563.727295   \n",
              "2021-07-07   14751.158203    5053.649414  ...    108.895897  3515.599609   \n",
              "2021-07-08   14874.522461    5071.690430  ...    101.285339  3507.332031   \n",
              "\n",
              "Index          TECHM.NS     TITAN.NS  TORNTPHARM.NS  ULTRACEMCO.NS  \\\n",
              "Date                                                                 \n",
              "2013-08-28   643.795227   259.062469     289.266510    1926.259766   \n",
              "2013-08-29   628.795532   273.228668     262.963165    1997.330688   \n",
              "2013-08-30   649.845459   240.078918     302.842133    2041.852295   \n",
              "2013-09-02   685.222351   254.153702     276.111816    1977.654663   \n",
              "2013-09-03   673.501770   241.339355     270.000885    2023.073242   \n",
              "...                 ...          ...            ...            ...   \n",
              "2021-07-02  1561.046143  2032.026978    1353.126953    6823.322754   \n",
              "2021-07-05  1574.577759  2091.114014    1410.262329    7074.271484   \n",
              "2021-07-06  1538.662598  2054.698486    1393.890503    6988.169922   \n",
              "2021-07-07  1566.373413  2085.078125    1364.733276    6660.829590   \n",
              "2021-07-08  1522.909546  2056.469482    1394.851440    6729.637695   \n",
              "\n",
              "Index           UPL.NS     VEDL.NS    WIPRO.NS  ZYDUSLIFE.NS  \n",
              "Date                                                          \n",
              "2013-08-28  105.283455  204.429138  351.913483    154.291000  \n",
              "2013-08-29  115.039162  210.431747  354.935699    159.598343  \n",
              "2013-08-30  111.624405  207.678650  336.121124    153.781937  \n",
              "2013-09-02  110.424736  208.623199  349.751465    156.916840  \n",
              "2013-09-03  111.343498  199.976059  347.657288    161.402191  \n",
              "...                ...         ...         ...           ...  \n",
              "2021-07-02  676.778931  255.888763  573.370239    389.155762  \n",
              "2021-07-05  646.670532  253.453568  571.571228    385.373138  \n",
              "2021-07-06  684.049072  246.847198  596.337463    387.896576  \n",
              "2021-07-07  676.673096  260.196716  589.367188    409.213379  \n",
              "2021-07-08  674.801514  264.279968  571.602417    385.006897  \n",
              "\n",
              "[1936 rows x 81 columns]"
            ],
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Index</th>\n",
              "      <th>ACC.NS</th>\n",
              "      <th>ADANIENT.NS</th>\n",
              "      <th>ADANIPORTS.NS</th>\n",
              "      <th>AMBUJACEM.NS</th>\n",
              "      <th>APOLLOHOSP.NS</th>\n",
              "      <th>ASIANPAINT.NS</th>\n",
              "      <th>AXISBANK.NS</th>\n",
              "      <th>BAJAJ-AUTO.NS</th>\n",
              "      <th>BAJAJFINSV.NS</th>\n",
              "      <th>BAJAJHLDNG.NS</th>\n",
              "      <th>...</th>\n",
              "      <th>TATASTEEL.NS</th>\n",
              "      <th>TCS.NS</th>\n",
              "      <th>TECHM.NS</th>\n",
              "      <th>TITAN.NS</th>\n",
              "      <th>TORNTPHARM.NS</th>\n",
              "      <th>ULTRACEMCO.NS</th>\n",
              "      <th>UPL.NS</th>\n",
              "      <th>VEDL.NS</th>\n",
              "      <th>WIPRO.NS</th>\n",
              "      <th>ZYDUSLIFE.NS</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-08-28</th>\n",
              "      <td>1168.125122</td>\n",
              "      <td>209.581070</td>\n",
              "      <td>175.747040</td>\n",
              "      <td>185.140915</td>\n",
              "      <td>2067.877441</td>\n",
              "      <td>537.469299</td>\n",
              "      <td>219.744843</td>\n",
              "      <td>2075.252197</td>\n",
              "      <td>686.764343</td>\n",
              "      <td>975.657227</td>\n",
              "      <td>...</td>\n",
              "      <td>46.432476</td>\n",
              "      <td>1317.770386</td>\n",
              "      <td>643.795227</td>\n",
              "      <td>259.062469</td>\n",
              "      <td>289.266510</td>\n",
              "      <td>1926.259766</td>\n",
              "      <td>105.283455</td>\n",
              "      <td>204.429138</td>\n",
              "      <td>351.913483</td>\n",
              "      <td>154.291000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-08-29</th>\n",
              "      <td>1175.395630</td>\n",
              "      <td>224.744308</td>\n",
              "      <td>205.586090</td>\n",
              "      <td>198.565857</td>\n",
              "      <td>2039.834961</td>\n",
              "      <td>524.020203</td>\n",
              "      <td>201.000854</td>\n",
              "      <td>2052.678711</td>\n",
              "      <td>673.573608</td>\n",
              "      <td>999.273315</td>\n",
              "      <td>...</td>\n",
              "      <td>46.113880</td>\n",
              "      <td>1392.945923</td>\n",
              "      <td>628.795532</td>\n",
              "      <td>273.228668</td>\n",
              "      <td>262.963165</td>\n",
              "      <td>1997.330688</td>\n",
              "      <td>115.039162</td>\n",
              "      <td>210.431747</td>\n",
              "      <td>354.935699</td>\n",
              "      <td>159.598343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-08-30</th>\n",
              "      <td>1140.923096</td>\n",
              "      <td>195.316467</td>\n",
              "      <td>203.242599</td>\n",
              "      <td>185.812592</td>\n",
              "      <td>2145.557617</td>\n",
              "      <td>533.543335</td>\n",
              "      <td>211.596786</td>\n",
              "      <td>2005.678345</td>\n",
              "      <td>656.498901</td>\n",
              "      <td>1006.159180</td>\n",
              "      <td>...</td>\n",
              "      <td>49.312710</td>\n",
              "      <td>1433.477905</td>\n",
              "      <td>649.845459</td>\n",
              "      <td>240.078918</td>\n",
              "      <td>302.842133</td>\n",
              "      <td>2041.852295</td>\n",
              "      <td>111.624405</td>\n",
              "      <td>207.678650</td>\n",
              "      <td>336.121124</td>\n",
              "      <td>153.781937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-09-02</th>\n",
              "      <td>1181.454346</td>\n",
              "      <td>203.409683</td>\n",
              "      <td>191.983185</td>\n",
              "      <td>183.757202</td>\n",
              "      <td>2139.608398</td>\n",
              "      <td>592.816040</td>\n",
              "      <td>212.488510</td>\n",
              "      <td>2004.932251</td>\n",
              "      <td>707.256897</td>\n",
              "      <td>1029.058960</td>\n",
              "      <td>...</td>\n",
              "      <td>44.516720</td>\n",
              "      <td>1344.568359</td>\n",
              "      <td>685.222351</td>\n",
              "      <td>254.153702</td>\n",
              "      <td>276.111816</td>\n",
              "      <td>1977.654663</td>\n",
              "      <td>110.424736</td>\n",
              "      <td>208.623199</td>\n",
              "      <td>349.751465</td>\n",
              "      <td>156.916840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-09-03</th>\n",
              "      <td>1192.129883</td>\n",
              "      <td>237.603455</td>\n",
              "      <td>202.512848</td>\n",
              "      <td>188.241058</td>\n",
              "      <td>2121.836182</td>\n",
              "      <td>600.856079</td>\n",
              "      <td>213.546753</td>\n",
              "      <td>2141.566406</td>\n",
              "      <td>694.083801</td>\n",
              "      <td>990.226257</td>\n",
              "      <td>...</td>\n",
              "      <td>49.546108</td>\n",
              "      <td>1463.139160</td>\n",
              "      <td>673.501770</td>\n",
              "      <td>241.339355</td>\n",
              "      <td>270.000885</td>\n",
              "      <td>2023.073242</td>\n",
              "      <td>111.343498</td>\n",
              "      <td>199.976059</td>\n",
              "      <td>347.657288</td>\n",
              "      <td>161.402191</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>2021-07-02</th>\n",
              "      <td>2078.775146</td>\n",
              "      <td>2215.869141</td>\n",
              "      <td>769.798157</td>\n",
              "      <td>299.970551</td>\n",
              "      <td>3966.531494</td>\n",
              "      <td>2674.658936</td>\n",
              "      <td>700.500122</td>\n",
              "      <td>3491.274414</td>\n",
              "      <td>14888.556641</td>\n",
              "      <td>5123.076172</td>\n",
              "      <td>...</td>\n",
              "      <td>106.518883</td>\n",
              "      <td>3563.668945</td>\n",
              "      <td>1561.046143</td>\n",
              "      <td>2032.026978</td>\n",
              "      <td>1353.126953</td>\n",
              "      <td>6823.322754</td>\n",
              "      <td>676.778931</td>\n",
              "      <td>255.888763</td>\n",
              "      <td>573.370239</td>\n",
              "      <td>389.155762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-05</th>\n",
              "      <td>2098.907227</td>\n",
              "      <td>2185.454834</td>\n",
              "      <td>751.567139</td>\n",
              "      <td>298.743347</td>\n",
              "      <td>4131.488770</td>\n",
              "      <td>2515.637939</td>\n",
              "      <td>721.402039</td>\n",
              "      <td>3438.084961</td>\n",
              "      <td>14816.315430</td>\n",
              "      <td>5012.432617</td>\n",
              "      <td>...</td>\n",
              "      <td>98.701790</td>\n",
              "      <td>3521.311768</td>\n",
              "      <td>1574.577759</td>\n",
              "      <td>2091.114014</td>\n",
              "      <td>1410.262329</td>\n",
              "      <td>7074.271484</td>\n",
              "      <td>646.670532</td>\n",
              "      <td>253.453568</td>\n",
              "      <td>571.571228</td>\n",
              "      <td>385.373138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-06</th>\n",
              "      <td>2051.858643</td>\n",
              "      <td>2257.159668</td>\n",
              "      <td>739.074951</td>\n",
              "      <td>305.737000</td>\n",
              "      <td>3964.316650</td>\n",
              "      <td>2591.021240</td>\n",
              "      <td>715.036804</td>\n",
              "      <td>3408.195312</td>\n",
              "      <td>15507.294922</td>\n",
              "      <td>4899.542480</td>\n",
              "      <td>...</td>\n",
              "      <td>104.207703</td>\n",
              "      <td>3563.727295</td>\n",
              "      <td>1538.662598</td>\n",
              "      <td>2054.698486</td>\n",
              "      <td>1393.890503</td>\n",
              "      <td>6988.169922</td>\n",
              "      <td>684.049072</td>\n",
              "      <td>246.847198</td>\n",
              "      <td>596.337463</td>\n",
              "      <td>387.896576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-07</th>\n",
              "      <td>2067.032227</td>\n",
              "      <td>2137.535889</td>\n",
              "      <td>773.167175</td>\n",
              "      <td>300.114899</td>\n",
              "      <td>4091.453857</td>\n",
              "      <td>2657.047363</td>\n",
              "      <td>721.907104</td>\n",
              "      <td>3419.894043</td>\n",
              "      <td>14751.158203</td>\n",
              "      <td>5053.649414</td>\n",
              "      <td>...</td>\n",
              "      <td>108.895897</td>\n",
              "      <td>3515.599609</td>\n",
              "      <td>1566.373413</td>\n",
              "      <td>2085.078125</td>\n",
              "      <td>1364.733276</td>\n",
              "      <td>6660.829590</td>\n",
              "      <td>676.673096</td>\n",
              "      <td>260.196716</td>\n",
              "      <td>589.367188</td>\n",
              "      <td>409.213379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-08</th>\n",
              "      <td>2040.571533</td>\n",
              "      <td>2224.961914</td>\n",
              "      <td>765.568481</td>\n",
              "      <td>296.762817</td>\n",
              "      <td>4156.616699</td>\n",
              "      <td>2623.510254</td>\n",
              "      <td>722.153809</td>\n",
              "      <td>3510.491943</td>\n",
              "      <td>14874.522461</td>\n",
              "      <td>5071.690430</td>\n",
              "      <td>...</td>\n",
              "      <td>101.285339</td>\n",
              "      <td>3507.332031</td>\n",
              "      <td>1522.909546</td>\n",
              "      <td>2056.469482</td>\n",
              "      <td>1394.851440</td>\n",
              "      <td>6729.637695</td>\n",
              "      <td>674.801514</td>\n",
              "      <td>264.279968</td>\n",
              "      <td>571.602417</td>\n",
              "      <td>385.006897</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1936 rows × 81 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c6ef834-20c6-42e8-b6e1-d17e32b48d5d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2c6ef834-20c6-42e8-b6e1-d17e32b48d5d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2c6ef834-20c6-42e8-b6e1-d17e32b48d5d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5apMnJ49fas",
        "outputId": "77f8c9bc-d9ab-4227-e43d-fcf09ab8dddc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_prices.to_csv('PCA_Predicted_Prices1.csv')\n",
        "!cp PCA_Predicted_Prices1.csv \"drive/My Drive/Machine Learning Project/ML Section Exports\""
      ],
      "metadata": {
        "id": "gL48JDxF9nUh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "actual_prices.to_csv('PCA_Actual_Prices1.csv')\n",
        "!cp PCA_Actual_Prices1.csv \"drive/My Drive/Machine Learning Project/ML Section Exports\""
      ],
      "metadata": {
        "id": "Ny7CWuQT9xqU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RV2Yae3r93e4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}