PFE_lstm.ipynb

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      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "sns.set_style('white')\n",
        "\n",
        "import statsmodels.api as sm\n",
        "import statsmodels.tsa.stattools as ts\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import time"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install yfinance"
      ],
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.87-py2.py3-none-any.whl (29 kB)\n",
            "Requirement already satisfied: appdirs>=1.4.4 in /usr/local/lib/python3.8/dist-packages (from yfinance) (1.4.4)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.28.1-py3-none-any.whl (62 kB)\n",
            "\u001b[K     |████████████████████████████████| 62 kB 409 kB/s \n",
            "\u001b[?25hRequirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.8/dist-packages (from yfinance) (0.0.11)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.8/dist-packages (from yfinance) (1.3.5)\n",
            "Requirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.8/dist-packages (from yfinance) (4.9.1)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.8/dist-packages (from yfinance) (1.21.6)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.24.0->yfinance) (2022.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.26->yfinance) (2022.9.24)\n",
            "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.26->yfinance) (2.1.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
            "Installing collected packages: requests, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "Successfully installed requests-2.28.1 yfinance-0.1.87\n"
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      "source": [
        "import yfinance\n",
        "\n",
        "PFE = yfinance.download (tickers = \"PFE\", start = \"2012-01-03\", \n",
        "                              end = \"2022-10-15\", interval = \"1d\")"
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            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
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    {
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      "source": [
        "PFE.reset_index(inplace = True)\n",
        "PFE['Date'] = PFE['Date'].apply(lambda x: str(x)[:10])\n",
        "PFE.head()"
      ],
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      "execution_count": 4,
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              "         Date       Open       High        Low      Close  Adj Close    Volume\n",
              "0  2012-01-03  20.740038  20.872866  20.721064  20.844402  14.024997  53124340\n",
              "1  2012-01-04  20.796965  20.825426  20.635674  20.654650  13.897316  31912485\n",
              "2  2012-01-05  20.607210  20.607210  20.294119  20.493359  13.788795  52764821\n",
              "3  2012-01-06  20.607210  20.664137  20.455408  20.464895  13.769644  31613571\n",
              "4  2012-01-09  20.455408  20.721064  20.407970  20.702087  13.929235  41786041"
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        "PFE.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']\n",
        "PFE['ret'] = PFE['Adj Close'].pct_change()\n",
        "data = PFE[['Date', 'Adj Close']]\n",
        "data.head()"
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              "         Date  Adj Close\n",
              "0  2012-01-03  14.024997\n",
              "1  2012-01-04  13.897316\n",
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    {
      "cell_type": "code",
      "source": [
        "data.set_index('Date', inplace = True)\n",
        "import math"
      ],
      "metadata": {
        "id": "48C4IU7VGEOB"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "close_prices = data['Adj Close']\n",
        "values = close_prices.values\n",
        "training_data_len = math.ceil(len(values)* 0.8)\n",
        "\n",
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "scaled_data = scaler.fit_transform(values.reshape(-1,1))\n",
        "train_data = scaled_data[0: training_data_len, :]\n",
        "\n",
        "x_train = []\n",
        "y_train = []\n",
        "\n",
        "for i in range(60, len(train_data)):\n",
        "    x_train.append(train_data[i-60:i, 0])\n",
        "    y_train.append(train_data[i, 0])\n",
        "    \n",
        "x_train, y_train = np.array(x_train), np.array(y_train)\n",
        "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))"
      ],
      "metadata": {
        "id": "QcKVA2CsGEPf"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "test_data = scaled_data[training_data_len-60: , : ]\n",
        "x_test = []\n",
        "y_test = values[training_data_len:]\n",
        "\n",
        "for i in range(60, len(test_data)):\n",
        "  x_test.append(test_data[i-60:i, 0])\n",
        "\n",
        "x_test = np.array(x_test)\n",
        "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))"
      ],
      "metadata": {
        "id": "Et45RD70GER8"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "def mape(actual, pred): \n",
        "    actual, pred = np.array(actual), np.array(pred)\n",
        "    return np.mean(np.abs((actual - pred) / actual)) * 100"
      ],
      "metadata": {
        "id": "MoBTAgeWGEUM"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tscv = TimeSeriesSplit(max_train_size=None, n_splits=3)\n",
        "print(tscv)\n",
        "\n",
        "mape_lst = []\n",
        "\n",
        "for train_index, valid_index in tscv.split(x_train):\n",
        "  X_train, X_valid = x_train[train_index], x_train[valid_index]\n",
        "  Y_train, Y_valid = y_train[train_index], y_train[valid_index]\n",
        "  model = keras.Sequential()\n",
        "  model.add(layers.LSTM(100, return_sequences=True, input_shape=(x_train.shape[1], 1)))\n",
        "  model.add(layers.LSTM(100, return_sequences=False))\n",
        "  model.add(layers.Dense(25))\n",
        "  model.add(layers.Dense(1))\n",
        "  model.summary()\n",
        "  model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "  model.fit(X_train, Y_train, batch_size= 64, epochs=3)\n",
        "  predictions = model.predict(X_valid)\n",
        "  #predictions = scaler.inverse_transform(predictions)\n",
        "  metrics = mape(predictions, Y_valid)\n",
        "  print(metrics)\n",
        "  mape_lst.append(metrics)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NsjzNu5WGEWj",
        "outputId": "22cc3c4c-d9c4-4a06-f114-8a4653aa4132"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None)\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 60, 100)           40800     \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 100)               80400     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 25)                2525      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 26        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 123,751\n",
            "Trainable params: 123,751\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/3\n",
            "9/9 [==============================] - 11s 332ms/step - loss: 0.0036\n",
            "Epoch 2/3\n",
            "9/9 [==============================] - 3s 338ms/step - loss: 8.4968e-04\n",
            "Epoch 3/3\n",
            "9/9 [==============================] - 2s 180ms/step - loss: 3.4084e-04\n",
            "17/17 [==============================] - 1s 32ms/step\n",
            "23.191254834069518\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_2 (LSTM)               (None, 60, 100)           40800     \n",
            "                                                                 \n",
            " lstm_3 (LSTM)               (None, 100)               80400     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 25)                2525      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 26        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 123,751\n",
            "Trainable params: 123,751\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/3\n",
            "17/17 [==============================] - 7s 186ms/step - loss: 0.0053\n",
            "Epoch 2/3\n",
            "17/17 [==============================] - 3s 190ms/step - loss: 6.6182e-04\n",
            "Epoch 3/3\n",
            "17/17 [==============================] - 3s 189ms/step - loss: 1.9291e-04\n",
            "17/17 [==============================] - 1s 31ms/step\n",
            "14.474626368166613\n",
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_4 (LSTM)               (None, 60, 100)           40800     \n",
            "                                                                 \n",
            " lstm_5 (LSTM)               (None, 100)               80400     \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 25)                2525      \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 26        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 123,751\n",
            "Trainable params: 123,751\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/3\n",
            "25/25 [==============================] - 9s 194ms/step - loss: 0.0049\n",
            "Epoch 2/3\n",
            "25/25 [==============================] - 5s 197ms/step - loss: 3.3946e-04\n",
            "Epoch 3/3\n",
            "25/25 [==============================] - 5s 197ms/step - loss: 1.4810e-04\n",
            "17/17 [==============================] - 2s 57ms/step\n",
            "14.789361478725874\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential()\n",
        "model.add(layers.LSTM(100, return_sequences=True, input_shape=(x_train.shape[1], 1)))\n",
        "model.add(layers.LSTM(100, return_sequences=False))\n",
        "model.add(layers.Dense(25))\n",
        "model.add(layers.Dense(1))\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-i6vTQb6GEZQ",
        "outputId": "97a82052-9581-453b-cdf0-ef19f4689a93"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_6 (LSTM)               (None, 60, 100)           40800     \n",
            "                                                                 \n",
            " lstm_7 (LSTM)               (None, 100)               80400     \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 25)                2525      \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 1)                 26        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 123,751\n",
            "Trainable params: 123,751\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "model.fit(x_train, y_train, batch_size= 1, epochs=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j622MOvyGEbm",
        "outputId": "ab00ca6d-df33-4c19-ef2f-e3b37810eab0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "2112/2112 [==============================] - 80s 36ms/step - loss: 5.0157e-04\n",
            "Epoch 2/3\n",
            "1986/2112 [===========================>..] - ETA: 4s - loss: 2.0826e-04"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(x_test)\n",
        "predictions = scaler.inverse_transform(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rsLBqwoGGEd8",
        "outputId": "4fc39f8a-5ef7-4e83-dfbf-87fa9eb585a1"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17/17 [==============================] - 1s 32ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mape(predictions, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSfAxgUeGEgT",
        "outputId": "2c52d0c2-884b-4f0a-a3cc-af6c421926a2"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21.174946984069194"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3Lz0c7iMGEip"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lR6epqAPGEkp"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}