ES_TA_Capítulo_08_bis_Aplicación.ipynb

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  "metadata": {
    "colab": {
      "name": "ES - TA Capítulo 08 bis: Aplicación",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/joanby/trading-algoritmico-a-z-con-python/blob/main/ES_TA_Cap%C3%ADtulo_08_bis_Aplicaci%C3%B3n.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "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\">\n",
        "<img alt=\"Colaboratory logo\" height=\"45px\" src=\"https://static-881c.kxcdn.com/wp-content/uploads/2016/03/frogamesLogoFull4.png\" align=\"left\" hspace=\"10px\" vspace=\"0px\"></p>\n",
        "\n",
        "#  Machine Learning con estrategia Intra Diaria\n",
        "\n",
        "\n",
        "<br>\n",
        "\n",
        "\n",
        "💰 Únete a la comunidad de [Discord](https://discord.gg/z3dx5XpkX4)\n",
        "\n",
        "📚 Puedes leer nuestro libro en [Amazon](https://www.amazon.es/Python-para-finanzas-trading-algor%C3%ADtmico-ebook/dp/B0BT4ZS9Q3/)\n",
        "\n",
        "🖥️ El canal de [YouTube de Quantreo's](https://www.youtube.com/channel/UCp7jckfiEglNf_Gj62VR0pw) (en inglés) y el de [Frogames](https://www.youtube.com/channel/UCMUxXNYrVCv6-bQakhomvBg) en español\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "K29LE8BuZEK0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LXUd0NqAqE60",
        "outputId": "88e306b0-6b32-40bd-8d1b-9eccb75bbcf5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.72-py2.py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.6)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.28.0-py3-none-any.whl (62 kB)\n",
            "\u001b[K     |████████████████████████████████| 62 kB 901 kB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
            "Collecting lxml>=4.5.1\n",
            "  Downloading lxml-4.9.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.4 MB 37.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.15.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.0.12)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2022.6.15)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Installing collected packages: requests, lxml, 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",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.28.0 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed lxml-4.9.0 requests-2.28.0 yfinance-0.1.72\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting ta\n",
            "  Downloading ta-0.10.1.tar.gz (24 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from ta) (1.21.6)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from ta) (1.3.5)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->ta) (2022.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->ta) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->ta) (1.15.0)\n",
            "Building wheels for collected packages: ta\n",
            "  Building wheel for ta (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ta: filename=ta-0.10.1-py3-none-any.whl size=28985 sha256=282b08c09667aebae990b18eb0f8a62ea26fa6a0a94652ffaa2855f81184ca32\n",
            "  Stored in directory: /root/.cache/pip/wheels/bc/2a/c2/a56e77d07edc16a1fa7fb012667e55cb0643cfa65996bddecc\n",
            "Successfully built ta\n",
            "Installing collected packages: ta\n",
            "Successfully installed ta-0.10.1\n"
          ]
        }
      ],
      "source": [
        "! pip install yfinance\n",
        "! pip install ta"
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "417TEZu3qNDE"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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')"
      ],
      "metadata": {
        "id": "HKULuUYdqQVA"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Funciones"
      ],
      "metadata": {
        "id": "1MfZfPVzZOah"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def lin_reg_trading(symbol):\n",
        "  def preprocessing_min(name):\n",
        "\n",
        "    # Import the data\n",
        "    df = pd.read_csv(name, delimiter=\"\\t\", index_col=[\"<DATE>\",\"<TIME>\"] , parse_dates=True).dropna()\n",
        "\n",
        "    # Delete the two last columns\n",
        "    df = df.iloc[:,:-2]\n",
        "\n",
        "    # Rename\n",
        "    df.columns = [\"open\", \"high\", \"low\", \"close\", \"volume\"]\n",
        "    #df.index.name = \"time\"\n",
        "    return df\n",
        "\n",
        "  def feature_engineering(df):\n",
        "    \"\"\" Crear nuevas 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 = preprocessing_min(symbol)\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",
        "  return dfc[\"strategy\"][dfc[\"strategy\"]<0.50]"
      ],
      "metadata": {
        "id": "hAlAYU5_qQ44"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Aplicación"
      ],
      "metadata": {
        "id": "uE4x4MbmZTcg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " lin_reg_trading(\"ETHUSD_H1.csv\").cumsum().plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "id": "CSWkn7qqqwq3",
        "outputId": "08ed2bc6-7591-49cd-c883-d4e1ed66a9c1"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f451633b150>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# List of assets\n",
        "listnames = [\"BTCUSD_H1.csv\", \"ETHUSD_H1.csv\"]\n",
        "returns = pd.DataFrame()\n",
        "\n",
        "# Compute the return of each strategy\n",
        "for name in listnames:\n",
        "  returns[name] = lin_reg_trading(name)\n",
        "\n",
        "# Plot the results\n",
        "(returns.dropna().sum(axis=1)/returns.shape[1]).cumsum().plot(figsize=(20,8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "9imejAmKrqob",
        "outputId": "ad0bbc99-4459-42ed-cb96-451fa9465757"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f451627b850>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pf = returns.dropna().sum(axis=1)/returns.shape[1]"
      ],
      "metadata": {
        "id": "cJdnrrivt4c3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def drawdown_function(serie):\n",
        "\n",
        "  # We compute Cumsum of the returns\n",
        "  cum = serie.dropna().cumsum() + 1\n",
        "\n",
        "  # We compute max of the cumsum on the period (accumulate max) # (1,3,5,3,1) --> (1,3,5,5,5)\n",
        "  running_max = np.maximum.accumulate(cum)\n",
        "\n",
        "  # We compute drawdown\n",
        "  drawdown = cum/running_max - 1\n",
        "  return drawdown"
      ],
      "metadata": {
        "id": "GwGOVgSkuny-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(pf.dropna().cumsum().iloc[-1]) / -np.min(drawdown_function(pf.dropna()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1amHBnZXurSr",
        "outputId": "725652d0-1b46-4104-b6d0-1534c00722fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.63760692141075"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "HP2UDwl1uySH"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}