Chapter_07_02_Linear_Regression_to_predict_stock_prices.ipynb

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      "name": "EN - ML Chapter 05: Linear Regression to predict stock prices.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ce076564"
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      "source": [
        " <p><img alt=\"Colaboratory logo\" height=\"45px\" src=\"https://www.quantreo.com/wp-content/uploads/2021/10/Original-on-Transparent.png\" align=\"left\" hspace=\"10px\" vspace=\"0px\"></p>\n",
        "\n",
        "\n",
        "# Linear Regression to predict stock prices\n",
        "\n",
        "</br>\n",
        "\n",
        "In this chapter, we will explain how the linear regression works theorically. Then, we will apply this algorithm to predict stock price and create a trading strategy\n",
        "\n",
        "### Summary\n",
        "* How a Liner Regression algorithm work theorically\n",
        "* Predict stock price using a Linear Regression\n",
        "* Create trading strategy \n",
        "\n",
        "</br>\n",
        "\n",
        "💰Join our community: https://discord.gg/wXjNPAc5BH\n",
        "\n",
        "📚Read our book: https://www.amazon.com/gp/product/B09HG18CYL \n",
        "\n",
        "🖥️Quantreo's YouTube channel: https://www.youtube.com/channel/UCp7jckfiEglNf_Gj62VR0pw"
      ],
      "id": "ce076564"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        "id": "96gJhLVz-9dw",
        "outputId": "6aa6887e-e30a-499f-a7eb-134d98e0385a"
      },
      "source": [
        "! pip install yfinance\n",
        "! pip install ta"
      ],
      "id": "96gJhLVz-9dw",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.66-py2.py3-none-any.whl (25 kB)\n",
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            "\u001b[K     |████████████████████████████████| 6.3 MB 7.0 MB/s \n",
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            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed lxml-4.6.4 yfinance-0.1.66\n",
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            "Building wheels for collected packages: ta\n",
            "  Building wheel for ta (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ta: filename=ta-0.7.0-py3-none-any.whl size=28718 sha256=2f24aaf9f2b0c388405c4e7c960c0950f637b12600f0d0333f7814fbe4b5a881\n",
            "  Stored in directory: /root/.cache/pip/wheels/5e/74/e0/72395003bd1d3c8f3f5860c2d180ff15699e47a2733d8ebd38\n",
            "Successfully built ta\n",
            "Installing collected packages: ta\n",
            "Successfully installed ta-0.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6e42806"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('seaborn')\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "import yfinance as yf\n",
        "import ta"
      ],
      "id": "e6e42806",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBqF1ghUdcTl"
      },
      "source": [
        "import matplotlib as mpl\n",
        "from matplotlib import cycler\n",
        "\n",
        "colors = cycler('color',\n",
        "                ['#669FEE', '#66EE91', '#9988DD',\n",
        "                 '#EECC55', '#88BB44', '#FFBBBB'])\n",
        "plt.rc('figure', facecolor='#313233')\n",
        "plt.rc('axes', facecolor=\"#313233\", edgecolor='none',\n",
        "       axisbelow=True, grid=True, prop_cycle=colors,\n",
        "       labelcolor='gray')\n",
        "plt.rc('grid', color='474A4A', linestyle='solid')\n",
        "plt.rc('xtick', color='gray')\n",
        "plt.rc('ytick', direction='out', color='gray')\n",
        "plt.rc('legend', facecolor=\"#313233\", edgecolor=\"#313233\")\n",
        "plt.rc(\"text\", color=\"#C9C9C9\")\n",
        "plt.rc('figure', facecolor='#313233')"
      ],
      "id": "QBqF1ghUdcTl",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0f5a9e7a"
      },
      "source": [
        "# How a Liner Regression algorithm works theorically\n",
        "Look the video and the attached PDF."
      ],
      "id": "0f5a9e7a"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fedb3480"
      },
      "source": [
        "# Predict stock prices using a Linear Regression\n"
      ],
      "id": "fedb3480"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2af6dea6"
      },
      "source": [
        "### Import the data from Yahoo (esiest choice)\n",
        "\n",
        "**IMPORTANT**: You can import the data from Yahoo or from MT5. But you need to have a Windows device to use MT5. To avoid this problem, you can see the video introduction video of the chapter MetaTrader 5 Live Trading! "
      ],
      "id": "2af6dea6"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "52084a45",
        "outputId": "3bec4b2d-1b55-4082-f6a3-d1af83854447"
      },
      "source": [
        "# Import the data\n",
        "df = yf.download(\"GOOG\")\n",
        "\n",
        "# Take adjusted close\n",
        "df = df[[\"Adj Close\"]]\n",
        "\n",
        "# Rename the column\n",
        "df.columns = [\"close\"]\n",
        "df"
      ],
      "id": "52084a45",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2004-08-19</th>\n",
              "      <td>49.982655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-08-20</th>\n",
              "      <td>53.952770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-08-23</th>\n",
              "      <td>54.495735</td>\n",
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              "    <tr>\n",
              "      <th>2004-08-24</th>\n",
              "      <td>52.239197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-08-25</th>\n",
              "      <td>52.802086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2021-11-10</th>\n",
              "      <td>2932.520020</td>\n",
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              "    <tr>\n",
              "      <th>2021-11-11</th>\n",
              "      <td>2934.959961</td>\n",
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              "    <tr>\n",
              "      <th>2021-11-12</th>\n",
              "      <td>2992.909912</td>\n",
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              "    <tr>\n",
              "      <th>2021-11-15</th>\n",
              "      <td>2987.760010</td>\n",
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              "                  close\n",
              "Date                   \n",
              "2004-08-19    49.982655\n",
              "2004-08-20    53.952770\n",
              "2004-08-23    54.495735\n",
              "2004-08-24    52.239197\n",
              "2004-08-25    52.802086\n",
              "...                 ...\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3c4388f9"
      },
      "source": [
        "### Features Engineering\n",
        "\n"
      ],
      "id": "3c4388f9"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5daca6ac"
      },
      "source": [
        "def feature_engineering(df):\n",
        "    \"\"\" Create new variables\"\"\"\n",
        "    \n",
        "    # We copy the dataframe to avoid interferences in the data\n",
        "    df_copy = df.dropna().copy()\n",
        "    \n",
        "    # Create the returns\n",
        "    df_copy[\"returns\"] = df_copy[\"close\"].pct_change(1)\n",
        "\n",
        "    # Create the SMAs\n",
        "    df_copy[\"SMA 15\"] = df_copy[[\"close\"]].rolling(15).mean().shift(1)\n",
        "    df_copy[\"SMA 60\"] = df_copy[[\"close\"]].rolling(60).mean().shift(1)\n",
        "    \n",
        "    # Create the volatilities\n",
        "    df_copy[\"MSD 10\"] = df_copy[[\"returns\"]].rolling(10).std().shift(1)\n",
        "    df_copy[\"MSD 30\"] = df_copy[[\"returns\"]].rolling(30).std().shift(1)\n",
        "    \n",
        "    # Create the Rsi\n",
        "    RSI = ta.momentum.RSIIndicator(df_copy[\"close\"], window= 14, fillna = False)\n",
        "    df_copy[\"rsi\"] = RSI.rsi()\n",
        "    \n",
        "    return df_copy.dropna()"
      ],
      "id": "5daca6ac",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "mvHxIPyjjt0h",
        "outputId": "93538a19-2d93-4191-8cf5-f854d6e3ded6"
      },
      "source": [
        "dfc = feature_engineering(df)\n",
        "dfc"
      ],
      "id": "mvHxIPyjjt0h",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>close</th>\n",
              "      <th>returns</th>\n",
              "      <th>SMA 15</th>\n",
              "      <th>SMA 60</th>\n",
              "      <th>MSD 10</th>\n",
              "      <th>MSD 30</th>\n",
              "      <th>rsi</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",
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              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2004-11-12</th>\n",
              "      <td>90.660187</td>\n",
              "      <td>-0.005573</td>\n",
              "      <td>91.001902</td>\n",
              "      <td>67.843456</td>\n",
              "      <td>0.045158</td>\n",
              "      <td>0.045223</td>\n",
              "      <td>60.962971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-11-15</th>\n",
              "      <td>92.089828</td>\n",
              "      <td>0.015769</td>\n",
              "      <td>91.319711</td>\n",
              "      <td>68.521415</td>\n",
              "      <td>0.045046</td>\n",
              "      <td>0.045295</td>\n",
              "      <td>62.484309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-11-16</th>\n",
              "      <td>85.947845</td>\n",
              "      <td>-0.066696</td>\n",
              "      <td>91.235693</td>\n",
              "      <td>69.157033</td>\n",
              "      <td>0.044225</td>\n",
              "      <td>0.045283</td>\n",
              "      <td>52.938988</td>\n",
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              "    <tr>\n",
              "      <th>2004-11-17</th>\n",
              "      <td>85.927917</td>\n",
              "      <td>-0.000232</td>\n",
              "      <td>90.928179</td>\n",
              "      <td>69.681234</td>\n",
              "      <td>0.048354</td>\n",
              "      <td>0.047390</td>\n",
              "      <td>52.910746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-11-18</th>\n",
              "      <td>83.457184</td>\n",
              "      <td>-0.028754</td>\n",
              "      <td>90.480856</td>\n",
              "      <td>70.242713</td>\n",
              "      <td>0.048425</td>\n",
              "      <td>0.047302</td>\n",
              "      <td>49.392430</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2021-11-10</th>\n",
              "      <td>2932.520020</td>\n",
              "      <td>-0.017571</td>\n",
              "      <td>2901.391341</td>\n",
              "      <td>2838.389164</td>\n",
              "      <td>0.019565</td>\n",
              "      <td>0.016162</td>\n",
              "      <td>55.682186</td>\n",
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              "    <tr>\n",
              "      <th>2021-11-11</th>\n",
              "      <td>2934.959961</td>\n",
              "      <td>0.000832</td>\n",
              "      <td>2907.006006</td>\n",
              "      <td>2841.497664</td>\n",
              "      <td>0.014452</td>\n",
              "      <td>0.016367</td>\n",
              "      <td>55.931997</td>\n",
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              "    <tr>\n",
              "      <th>2021-11-12</th>\n",
              "      <td>2992.909912</td>\n",
              "      <td>0.019745</td>\n",
              "      <td>2912.295996</td>\n",
              "      <td>2844.890332</td>\n",
              "      <td>0.014430</td>\n",
              "      <td>0.016207</td>\n",
              "      <td>61.484916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-15</th>\n",
              "      <td>2987.760010</td>\n",
              "      <td>-0.001721</td>\n",
              "      <td>2926.989990</td>\n",
              "      <td>2849.134330</td>\n",
              "      <td>0.015060</td>\n",
              "      <td>0.016038</td>\n",
              "      <td>60.752275</td>\n",
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              "    <tr>\n",
              "      <th>2021-11-16</th>\n",
              "      <td>2981.520020</td>\n",
              "      <td>-0.002089</td>\n",
              "      <td>2941.143327</td>\n",
              "      <td>2852.784664</td>\n",
              "      <td>0.010457</td>\n",
              "      <td>0.015475</td>\n",
              "      <td>59.822123</td>\n",
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              "<p>4283 rows × 7 columns</p>\n",
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            "text/plain": [
              "                  close   returns       SMA 15  ...    MSD 10    MSD 30        rsi\n",
              "Date                                            ...                               \n",
              "2004-11-12    90.660187 -0.005573    91.001902  ...  0.045158  0.045223  60.962971\n",
              "2004-11-15    92.089828  0.015769    91.319711  ...  0.045046  0.045295  62.484309\n",
              "2004-11-16    85.947845 -0.066696    91.235693  ...  0.044225  0.045283  52.938988\n",
              "2004-11-17    85.927917 -0.000232    90.928179  ...  0.048354  0.047390  52.910746\n",
              "2004-11-18    83.457184 -0.028754    90.480856  ...  0.048425  0.047302  49.392430\n",
              "...                 ...       ...          ...  ...       ...       ...        ...\n",
              "2021-11-10  2932.520020 -0.017571  2901.391341  ...  0.019565  0.016162  55.682186\n",
              "2021-11-11  2934.959961  0.000832  2907.006006  ...  0.014452  0.016367  55.931997\n",
              "2021-11-12  2992.909912  0.019745  2912.295996  ...  0.014430  0.016207  61.484916\n",
              "2021-11-15  2987.760010 -0.001721  2926.989990  ...  0.015060  0.016038  60.752275\n",
              "2021-11-16  2981.520020 -0.002089  2941.143327  ...  0.010457  0.015475  59.822123\n",
              "\n",
              "[4283 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64cf52d6"
      },
      "source": [
        "### Create different sets of data"
      ],
      "id": "64cf52d6"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07f40655",
        "outputId": "1a262ab0-333c-4286-d671-5ac57ecaff0c"
      },
      "source": [
        "dfc.columns"
      ],
      "id": "07f40655",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['close', 'returns', 'SMA 15', 'SMA 60', 'MSD 10', 'MSD 30', 'rsi'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ac6fbb79"
      },
      "source": [
        "# Percentage train set\n",
        "split = int(0.80*len(dfc))\n",
        "\n",
        "# Train set creation\n",
        "X_train = dfc[['SMA 15', 'SMA 60', 'MSD 10', 'MSD 30', 'rsi']].iloc[:split]\n",
        "y_train = dfc[[\"returns\"]].iloc[:split]\n",
        "\n",
        "\n",
        "# Test set creation\n",
        "X_test = dfc[['SMA 15', 'SMA 60', 'MSD 10', 'MSD 30', 'rsi']].iloc[split:]\n",
        "y_test = dfc[[\"returns\"]].iloc[split:]"
      ],
      "id": "ac6fbb79",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bFuKfe8GeRpf"
      },
      "source": [
        "### Linear Regression training"
      ],
      "id": "bFuKfe8GeRpf"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9b0f773d",
        "outputId": "058b0ec8-bc33-4858-be56-863dc981d242"
      },
      "source": [
        "# Import the class\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Initialize the class\n",
        "reg = LinearRegression()\n",
        "\n",
        "# Fit the model\n",
        "reg.fit(X_train, y_train)"
      ],
      "id": "9b0f773d",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kM9Jh_LOeYri"
      },
      "source": [
        "### Linear Regression predictions"
      ],
      "id": "kM9Jh_LOeYri"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "aafe0dfe",
        "outputId": "6af83c89-30b9-4802-f5a3-d07423b06432"
      },
      "source": [
        "# Create predictions for the whole dataset\n",
        "X = np.concatenate((X_train, X_test), axis=0)\n",
        "\n",
        "dfc[\"prediction\"] = reg.predict(X)\n",
        "\n",
        "# We verify that the algorithm doesn't predict only way (positive or negative)\n",
        "dfc[\"prediction\"].plot()"
      ],
      "id": "aafe0dfe",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8df7f17850>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wv3rJNaLeehZ"
      },
      "source": [
        "### Compute the profit "
      ],
      "id": "wv3rJNaLeehZ"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Y7YcURIBcH1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "68503d56-2aee-474f-d0ea-01a2316acf9a"
      },
      "source": [
        "# Compute the position\n",
        "dfc[\"position\"] = np.sign(dfc[\"prediction\"])\n",
        "\n",
        "# Compute the returns\n",
        "dfc[\"strategy\"] = dfc[\"returns\"] * dfc[\"position\"].shift(1)\n",
        "\n",
        "\n",
        "(dfc[\"strategy\"].iloc[split:].cumsum()*100).plot()"
      ],
      "id": "5Y7YcURIBcH1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8df7e67990>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTohgCOrnJan"
      },
      "source": [
        "def lin_reg_trading(symbol):\n",
        "\n",
        "  def feature_engineering(df):\n",
        "    \"\"\" Create new variables\"\"\"\n",
        "    \n",
        "    # We copy the dataframe to avoid interferences in the data\n",
        "    df_copy = df.dropna().copy()\n",
        "    \n",
        "    # Create the returns\n",
        "    df_copy[\"returns\"] = df_copy[\"close\"].pct_change(1)\n",
        "\n",
        "    # Create the SMAs\n",
        "    df_copy[\"SMA 15\"] = df_copy[[\"close\"]].rolling(15).mean().shift(1)\n",
        "    df_copy[\"SMA 60\"] = df_copy[[\"close\"]].rolling(60).mean().shift(1)\n",
        "    \n",
        "    # Create the volatilities\n",
        "    df_copy[\"MSD 10\"] = df_copy[[\"returns\"]].rolling(10).std().shift(1)\n",
        "    df_copy[\"MSD 30\"] = df_copy[[\"returns\"]].rolling(30).std().shift(1)\n",
        "    \n",
        "    # Create the Rsi\n",
        "    RSI = ta.momentum.RSIIndicator(df_copy[\"close\"], window= 14, fillna = False)\n",
        "    df_copy[\"rsi\"] = RSI.rsi()\n",
        "    \n",
        "    return df_copy.dropna()\n",
        "  \n",
        "\n",
        "  # Import the data\n",
        "  df = yf.download(symbol)\n",
        "\n",
        "  # Take adjusted close\n",
        "  df = df[[\"Adj Close\"]]\n",
        "\n",
        "  # Rename the column\n",
        "  df.columns = [\"close\"]\n",
        "\n",
        "  dfc = feature_engineering(df)\n",
        "\n",
        "  # Percentage train set\n",
        "  split = int(0.80*len(dfc))\n",
        "\n",
        "  # Train set creation\n",
        "  X_train = dfc[['SMA 15', 'SMA 60', 'MSD 10', 'MSD 30', 'rsi']].iloc[:split]\n",
        "  y_train = dfc[[\"returns\"]].iloc[:split]\n",
        "\n",
        "\n",
        "  # Test set creation\n",
        "  X_test = dfc[['SMA 15', 'SMA 60', 'MSD 10', 'MSD 30', 'rsi']].iloc[split:]\n",
        "  y_test = dfc[[\"returns\"]].iloc[split:]\n",
        "\n",
        "  # Import the class\n",
        "  from sklearn.linear_model import LinearRegression\n",
        "\n",
        "  # Initialize the class\n",
        "  reg = LinearRegression()\n",
        "\n",
        "  # Fit the model\n",
        "  reg.fit(X_train, y_train)\n",
        "\n",
        "  # Create predictions for the whole dataset\n",
        "  X = np.concatenate((X_train, X_test), axis=0)\n",
        "\n",
        "  dfc[\"prediction\"] = reg.predict(X)\n",
        "\n",
        "  # Compute the position\n",
        "  dfc[\"position\"] = np.sign(dfc[\"prediction\"])\n",
        "\n",
        "  # Compute the returns\n",
        "  dfc[\"strategy\"] = dfc[\"returns\"] * dfc[\"position\"].shift(1)\n",
        "\n",
        "\n",
        "  (dfc[\"strategy\"].iloc[split:].cumsum()*100).plot()"
      ],
      "id": "kTohgCOrnJan",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6_JMFn4sJ8L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "outputId": "0f05e45b-55b9-4515-f941-3d2da9291e4a"
      },
      "source": [
        "lin_reg_trading(\"ETH-EUR\")"
      ],
      "id": "c6_JMFn4sJ8L",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSeK4rtkGNcH"
      },
      "source": [
        ""
      ],
      "id": "QSeK4rtkGNcH",
      "execution_count": null,
      "outputs": []
    }
  ]
}