Stock_Algorithms/Basic_Machine_Learning_Predicts_Updates.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Simple Linear Regression for stock using scikit-learn\n"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import math\n",
        "\n",
        "import os\n",
        "import sys\n",
        "import platform\n",
        "\n",
        "import sklearn\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
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          "shell.execute_reply": "2022-10-01T20:05:58.077Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"numpy: \", np.__version__)\n",
        "print(\"pandas: \", pd.__version__)\n",
        "print(\"sklearn: \", sklearn.__version__)\n",
        "print(\"yfinance: \", yf.__version__)\n",
        "print(\"os system: \", os.name)\n",
        "print(\"Python Version:\", sys.version)\n",
        "print(\"Platform System: \", platform.system())"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "numpy:  1.19.5\n",
            "pandas:  1.1.5\n",
            "sklearn:  0.24.2\n",
            "yfinance:  0.1.63\n",
            "os system:  nt\n",
            "Python Version: 3.6.13 |Anaconda, Inc.| (default, Mar 16 2021, 11:37:27) [MSC v.1916 64 bit (AMD64)]\n",
            "Platform System:  Windows\n"
          ]
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
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        "execution": {
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          "iopub.execute_input": "2022-10-01T20:05:58.047Z",
          "iopub.status.idle": "2022-10-01T20:05:58.059Z",
          "shell.execute_reply": "2022-10-01T20:05:58.081Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock = 'AAPL'\n",
        "start = '2016-01-01' \n",
        "end = '2018-01-01'\n",
        "data = yf.download(stock, start, end)\n",
        "data.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "                 Open       High        Low      Close  Adj Close     Volume\nDate                                                                        \n2016-01-04  25.652500  26.342501  25.500000  26.337500  24.151493  270597600\n2016-01-05  26.437500  26.462500  25.602501  25.677500  23.546276  223164000\n2016-01-06  25.139999  25.592501  24.967501  25.174999  23.085480  273829600\n2016-01-07  24.670000  25.032499  24.107500  24.112499  22.111162  324377600\n2016-01-08  24.637501  24.777500  24.190001  24.240000  22.228090  283192000",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</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>2016-01-04</th>\n      <td>25.652500</td>\n      <td>26.342501</td>\n      <td>25.500000</td>\n      <td>26.337500</td>\n      <td>24.151493</td>\n      <td>270597600</td>\n    </tr>\n    <tr>\n      <th>2016-01-05</th>\n      <td>26.437500</td>\n      <td>26.462500</td>\n      <td>25.602501</td>\n      <td>25.677500</td>\n      <td>23.546276</td>\n      <td>223164000</td>\n    </tr>\n    <tr>\n      <th>2016-01-06</th>\n      <td>25.139999</td>\n      <td>25.592501</td>\n      <td>24.967501</td>\n      <td>25.174999</td>\n      <td>23.085480</td>\n      <td>273829600</td>\n    </tr>\n    <tr>\n      <th>2016-01-07</th>\n      <td>24.670000</td>\n      <td>25.032499</td>\n      <td>24.107500</td>\n      <td>24.112499</td>\n      <td>22.111162</td>\n      <td>324377600</td>\n    </tr>\n    <tr>\n      <th>2016-01-08</th>\n      <td>24.637501</td>\n      <td>24.777500</td>\n      <td>24.190001</td>\n      <td>24.240000</td>\n      <td>22.228090</td>\n      <td>283192000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-10-01T20:05:58.064Z",
          "iopub.execute_input": "2022-10-01T20:05:58.068Z",
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    },
    {
      "cell_type": "code",
      "source": [
        "df = data.reset_index()\n",
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "        Date       Open       High        Low      Close  Adj Close     Volume\n0 2016-01-04  25.652500  26.342501  25.500000  26.337500  24.151493  270597600\n1 2016-01-05  26.437500  26.462500  25.602501  25.677500  23.546276  223164000\n2 2016-01-06  25.139999  25.592501  24.967501  25.174999  23.085480  273829600\n3 2016-01-07  24.670000  25.032499  24.107500  24.112499  22.111162  324377600\n4 2016-01-08  24.637501  24.777500  24.190001  24.240000  22.228090  283192000",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Date</th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2016-01-04</td>\n      <td>25.652500</td>\n      <td>26.342501</td>\n      <td>25.500000</td>\n      <td>26.337500</td>\n      <td>24.151493</td>\n      <td>270597600</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2016-01-05</td>\n      <td>26.437500</td>\n      <td>26.462500</td>\n      <td>25.602501</td>\n      <td>25.677500</td>\n      <td>23.546276</td>\n      <td>223164000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2016-01-06</td>\n      <td>25.139999</td>\n      <td>25.592501</td>\n      <td>24.967501</td>\n      <td>25.174999</td>\n      <td>23.085480</td>\n      <td>273829600</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2016-01-07</td>\n      <td>24.670000</td>\n      <td>25.032499</td>\n      <td>24.107500</td>\n      <td>24.112499</td>\n      <td>22.111162</td>\n      <td>324377600</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2016-01-08</td>\n      <td>24.637501</td>\n      <td>24.777500</td>\n      <td>24.190001</td>\n      <td>24.240000</td>\n      <td>22.228090</td>\n      <td>283192000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
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        "execution": {
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    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(['Date','Close'], axis=1)\n",
        "y = df['Adj Close']"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-10-01T20:06:00.208Z",
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    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Split X and y into X_\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
        "                                   test_size=0.25, random_state=0)"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-10-01T20:06:00.230Z",
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        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "regression_model = LinearRegression()\n",
        "regression_model.fit(X_train, y_train)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "LinearRegression()"
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-10-01T20:06:00.246Z",
          "iopub.execute_input": "2022-10-01T20:06:00.249Z",
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          "shell.execute_reply": "2022-10-01T20:06:00.374Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "intercept = regression_model.intercept_\n",
        "\n",
        "print(\"The intercept for our model is {}\".format(intercept))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The intercept for our model is -1.637801005927031e-12\n"
          ]
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
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    },
    {
      "cell_type": "code",
      "source": [
        "regression_model.score(X_test, y_test)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "1.0"
          },
          "metadata": {}
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
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    },
    {
      "cell_type": "code",
      "source": [
        "y_predict = regression_model.predict(X_test)\n",
        "\n",
        "regression_model_mse = mean_squared_error(y_predict, y_test)\n",
        "\n",
        "regression_model_mse"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "2.4917115820171587e-25"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-10-01T20:06:00.309Z",
          "iopub.execute_input": "2022-10-01T20:06:00.313Z",
          "iopub.status.idle": "2022-10-01T20:06:00.323Z",
          "shell.execute_reply": "2022-10-01T20:06:00.380Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "math.sqrt(regression_model_mse)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "4.991704700818307e-13"
          },
          "metadata": {}
        }
      ],
      "execution_count": 11,
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        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-10-01T20:06:00.329Z",
          "iopub.execute_input": "2022-10-01T20:06:00.333Z",
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          "shell.execute_reply": "2022-10-01T20:06:00.382Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input the latest Open, High, Low, Close, Volume\n",
        "# predicts the next day price\n",
        "regression_model.predict([[167.81, 171.75, 165.19, 166.48, 37232900]])"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "array([166.48])"
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-10-01T20:06:00.348Z",
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