Split_Data.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Split Data Into Training And Test Sets"
      ],
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      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# fetch yahoo data\n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
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        }
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    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2018-08-27'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# Only keep close columns \n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Adj Close</th>\n      <th>Close</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Open</th>\n      <th>Volume</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-02</th>\n      <td>3.95</td>\n      <td>3.95</td>\n      <td>3.98</td>\n      <td>3.84</td>\n      <td>3.85</td>\n      <td>20548400</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>3.98</td>\n      <td>22887200</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.01</td>\n      <td>42398300</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.19</td>\n      <td>42932100</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.23</td>\n      <td>30678700</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "            Adj Close  Close  High   Low  Open    Volume\nDate                                                    \n2014-01-02       3.95   3.95  3.98  3.84  3.85  20548400\n2014-01-03       4.00   4.00  4.00  3.88  3.98  22887200\n2014-01-06       4.13   4.13  4.18  3.99  4.01  42398300\n2014-01-07       4.18   4.18  4.25  4.11  4.19  42932100\n2014-01-08       4.18   4.18  4.26  4.14  4.23  30678700"
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      "execution_count": 2,
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      "cell_type": "code",
      "source": [
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,0)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,0)\n",
        "dataset['Returns'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
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        "execution": {
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          "iopub.status.busy": "2020-08-23T00:39:32.516Z",
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          "iopub.status.idle": "2020-08-23T00:39:32.540Z"
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "dataset.tail()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Adj Close</th>\n      <th>Close</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Open</th>\n      <th>Volume</th>\n      <th>Increase_Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Returns</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2018-08-20</th>\n      <td>19.980000</td>\n      <td>19.980000</td>\n      <td>20.08</td>\n      <td>19.350000</td>\n      <td>19.790001</td>\n      <td>62983200</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.010622</td>\n    </tr>\n    <tr>\n      <th>2018-08-21</th>\n      <td>20.400000</td>\n      <td>20.400000</td>\n      <td>20.42</td>\n      <td>19.860001</td>\n      <td>19.980000</td>\n      <td>55629000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.021021</td>\n    </tr>\n    <tr>\n      <th>2018-08-22</th>\n      <td>20.900000</td>\n      <td>20.900000</td>\n      <td>20.92</td>\n      <td>20.209999</td>\n      <td>20.280001</td>\n      <td>62002700</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.024510</td>\n    </tr>\n    <tr>\n      <th>2018-08-23</th>\n      <td>22.290001</td>\n      <td>22.290001</td>\n      <td>22.32</td>\n      <td>21.139999</td>\n      <td>21.190001</td>\n      <td>113444100</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.066507</td>\n    </tr>\n    <tr>\n      <th>2018-08-24</th>\n      <td>23.980000</td>\n      <td>23.980000</td>\n      <td>24.00</td>\n      <td>22.670000</td>\n      <td>22.910000</td>\n      <td>164328200</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.075819</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "            Adj Close      Close   High        Low       Open     Volume  \\\nDate                                                                       \n2018-08-20  19.980000  19.980000  20.08  19.350000  19.790001   62983200   \n2018-08-21  20.400000  20.400000  20.42  19.860001  19.980000   55629000   \n2018-08-22  20.900000  20.900000  20.92  20.209999  20.280001   62002700   \n2018-08-23  22.290001  22.290001  22.32  21.139999  21.190001  113444100   \n2018-08-24  23.980000  23.980000  24.00  22.670000  22.910000  164328200   \n\n            Increase_Decrease  Buy_Sell_on_Open  Buy_Sell   Returns  \nDate                                                                 \n2018-08-20                  0                 1         1  0.010622  \n2018-08-21                  1                 1         1  0.021021  \n2018-08-22                  1                 1         1  0.024510  \n2018-08-23                  1                 1         1  0.066507  \n2018-08-24                  0                 0         0  0.075819  "
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      "execution_count": 4,
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    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
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    {
      "cell_type": "code",
      "source": [
        "X = dataset.drop(['Adj Close'], axis=1)\n",
        "Y = dataset['Adj Close']"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
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      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, \n",
        "                                                    Y, \n",
        "                                                    test_size=0.1, \n",
        "                                                    random_state=1)"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
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      "cell_type": "code",
      "source": [
        "# Create standardizer\n",
        "standardizer = StandardScaler()\n",
        "\n",
        "# Fit standardizer to training set\n",
        "standardizer.fit(X_train)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": "StandardScaler(copy=True, with_mean=True, with_std=True)"
          },
          "metadata": {}
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      "execution_count": 8,
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      "cell_type": "code",
      "source": [
        "# Apply to both training and test sets\n",
        "X_train_std = standardizer.transform(X_train)\n",
        "X_test_std = standardizer.transform(X_test)"
      ],
      "outputs": [],
      "execution_count": 9,
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