ES_DL_Capítulo_06_Backtesting_Vectorizado.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "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-dl/blob/main/ES_DL_Cap%C3%ADtulo_06_Backtesting_Vectorizado.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0gX2s9FqJ6H"
      },
      "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",
        "# BackTesting Vectorizado\n",
        "El backtesting es una parte esencial del trading. En efecto, es importante comprender el comportamiento de nuestra estrategia en el pasado para poder considerar los del futuro.\n",
        "\n",
        "### Contenido\n",
        "\n",
        "* Sortino Beta Alpha\n",
        "* Drawdown \n",
        "* Crear la función BackTest para evaluar la estrategia en las secciones posteriores\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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dBbjGbqrtFrj"
      },
      "source": [
        "### Entorno"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tjz98gQfs9T-",
        "outputId": "2effa66c-1d00-4827-ea99-9c12e0a279d1"
      },
      "source": [
        "!pip install yfinance    \n",
        "!pip install ta"
      ],
      "execution_count": 1,
      "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-0.1.77-py2.py3-none-any.whl (28 kB)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.11)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.28.1-py3-none-any.whl (62 kB)\n",
            "\u001b[K     |████████████████████████████████| 62 kB 1.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.6)\n",
            "Requirement already satisfied: appdirs>=1.4.4 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.4.4)\n",
            "Requirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from yfinance) (4.9.1)\n",
            "Requirement 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) (2022.4)\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: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2022.9.24)\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: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.1.1)\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-0.1.77\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting ta\n",
            "  Downloading ta-0.10.2.tar.gz (25 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) (2022.4)\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-0.10.2-py3-none-any.whl size=29104 sha256=1a98a4771d5aa3f750081b5c50b29840b1474fc823ad8dcb13d7fae5155527d0\n",
            "  Stored in directory: /root/.cache/pip/wheels/31/31/f1/f2ff471bbc5b84a4b973698ceecdd453ae043971791adc3431\n",
            "Successfully built ta\n",
            "Installing collected packages: ta\n",
            "Successfully installed ta-0.10.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TflLwK15qGcx"
      },
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import ta\n",
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\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')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hKMvnPIwslSn"
      },
      "source": [
        "# Sección 1: Sortino Beta Alpha"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MvSQJefks3OP"
      },
      "source": [
        "###### Sortino\n",
        "El índice de Sortino es una estadística similar al índice de Sharpe con la misma interpretación. Sin embargo, el cálculo es ligeramente diferente. En efecto, en el índice de Sortino sólo se tiene en cuenta la volatilidad de las ganancias negativas porque es lo que tememos y no las ganancias alcistas que nos hacen ganar dinero. <br>\n",
        "<p align=\"center\">\n",
        "$Sortino = \\frac{\\mu }{\\sigma_{down}}$\n",
        "<p> <br>\n",
        "\n",
        "* SortinoRatio < 0 : Inversión no rentable porque las ganancias son negativas.\n",
        "* SortinoRatio < 1: Inversión rentable, pero el riesgo de la inversión es mayor que la rentabilidad.\n",
        "* SortinoRatio > 1: Inversión muy rentable porque el riesgo es menor que el rendimiento.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m1Fuei6Ssmrk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0b56302-2e98-4d96-a1a4-6f1d32aa5344"
      },
      "source": [
        "# Preparar los datos \n",
        "f = yf.download(\"GOOG\", end=\"2021-01-01\")\n",
        "return_serie = f[\"Adj Close\"].pct_change(1).dropna()\n",
        "return_serie.name = \"return\"\n",
        "\n",
        "# Calcular el índice de Sortino\n",
        "mean = np.mean(return_serie) #252\n",
        "vol = np.std(return_serie[return_serie<0]) #np.sqrt(252)\n",
        "sortino = np.sqrt(252)*mean/vol\n",
        "\n",
        "print(f\"Sortino: {'%.3f' % sortino}\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Sortino: 1.232\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2WSR_5vIAgR"
      },
      "source": [
        "###### Beta\n",
        "La beta es un estadístico que indica la relación entre las variaciones de nuestra cartera y las del mercado que está representado por un índice (por ejemplo el SP500). <br>\n",
        "\n",
        "<p align=\"center\">\n",
        "$Beta = \\frac{Cov(r_{M},r_{PF}) }{Var(r_{M})}$\n",
        "<p> <br>\n",
        "\n",
        "\n",
        "\n",
        "* abs(Beta) < 1: Es bueno porque significa que la cartera tiene una variación menor que el índice. (Si beta=0,9 significa que si el índice varía en 1 la cartera varía en 0,9)\n",
        "* abs(Beta) > 1: No es bueno porque significa que slau cartera tiene una variación mayor que el índice. (Si beta=1,1 significa que si el índice varía de 1 la cartera varía en 1,1)\n",
        "\n",
        "*abs(-x) = x = abs(x) (abs da sólo el valor de la variable no el signo)*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-lUE91pbHhwv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "outputId": "ba45cc32-20ca-4822-9de2-ceb37c5132c3"
      },
      "source": [
        "# Necesitamos calcular la covarianza entre el mercado y la cartera \n",
        "sp500 = yf.download(\"^GSPC\")[\"Adj Close\"].pct_change(1)\n",
        "sp500.name = \"SP500\"\n",
        "\n",
        "# Los concatenamos para hacer las covarianzas\n",
        "val = pd.concat((return_serie, sp500), axis=1).dropna()\n",
        "val"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             return     SP500\n",
              "Date                                         \n",
              "2004-08-20 00:00:00-04:00  0.079430  0.006525\n",
              "2004-08-23 00:00:00-04:00  0.010064 -0.002431\n",
              "2004-08-24 00:00:00-04:00 -0.041408  0.000465\n",
              "2004-08-25 00:00:00-04:00  0.010775  0.008000\n",
              "2004-08-26 00:00:00-04:00  0.018019  0.000118\n",
              "...                             ...       ...\n",
              "2020-12-24 00:00:00-05:00  0.003735  0.003537\n",
              "2020-12-28 00:00:00-05:00  0.021416  0.008723\n",
              "2020-12-29 00:00:00-05:00 -0.009780 -0.002227\n",
              "2020-12-30 00:00:00-05:00 -0.010917  0.001342\n",
              "2020-12-31 00:00:00-05:00  0.007105  0.006439\n",
              "\n",
              "[4121 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4c4a89d8-be00-4771-847a-357c51d19586\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>return</th>\n",
              "      <th>SP500</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2004-08-20 00:00:00-04:00</th>\n",
              "      <td>0.079430</td>\n",
              "      <td>0.006525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-08-23 00:00:00-04:00</th>\n",
              "      <td>0.010064</td>\n",
              "      <td>-0.002431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-08-24 00:00:00-04:00</th>\n",
              "      <td>-0.041408</td>\n",
              "      <td>0.000465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-08-25 00:00:00-04:00</th>\n",
              "      <td>0.010775</td>\n",
              "      <td>0.008000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-08-26 00:00:00-04:00</th>\n",
              "      <td>0.018019</td>\n",
              "      <td>0.000118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24 00:00:00-05:00</th>\n",
              "      <td>0.003735</td>\n",
              "      <td>0.003537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28 00:00:00-05:00</th>\n",
              "      <td>0.021416</td>\n",
              "      <td>0.008723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29 00:00:00-05:00</th>\n",
              "      <td>-0.009780</td>\n",
              "      <td>-0.002227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30 00:00:00-05:00</th>\n",
              "      <td>-0.010917</td>\n",
              "      <td>0.001342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31 00:00:00-05:00</th>\n",
              "      <td>0.007105</td>\n",
              "      <td>0.006439</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4121 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c4a89d8-be00-4771-847a-357c51d19586')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4c4a89d8-be00-4771-847a-357c51d19586 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4c4a89d8-be00-4771-847a-357c51d19586');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculamos la matriz de covarianzas\n",
        "cov_var_mat = np.cov(val.values, rowvar=False)\n",
        "cov_var_mat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6ylQu7qZWpB",
        "outputId": "2213236d-6d59-4d83-862b-38947adfe58e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00037086, 0.00014733],\n",
              "       [0.00014733, 0.00015267]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculamos beta \n",
        "cov = cov_var_mat[0][1]\n",
        "var = cov_var_mat[1][1]\n",
        "\n",
        "beta = cov/var\n",
        "\n",
        "print(f\"Beta: {'%.3f' % beta}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ta752DUTZIFQ",
        "outputId": "e4f0ae9c-1b71-4ade-ee8e-b81522098d52"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Beta: 0.965\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ecanIMv7KEg3"
      },
      "source": [
        "###### **Alpha**\n",
        "El alfa es una estadístico que indica si la cartera supera al mercado en términos de rentabilidad del riesgo. <br>\n",
        "\n",
        "<p align=\"center\">\n",
        "$Alpha = r_{PF} - \\beta*(r_{PF} - r_{f}) - r_{f}$\n",
        "<p> <br>\n",
        "\n",
        "\n",
        "* alfa > 0: La cartera supera al mercado en términos de rentabilidad del riesgo\n",
        "* alfa < 0: la cartera tiene un rendimiento inferior al del mercado en términos de riesgo-rendimiento\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3m9gBHCInQT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89da7e66-da84-491b-f268-a3b7ec81963f"
      },
      "source": [
        "# Calculamos alpha \n",
        "alpha = (252 * mean * (1-beta))*100\n",
        "print(f\"Alpha: {'%.1f' % alpha} %\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Alpha: 0.9 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLkdFSptKhTk"
      },
      "source": [
        "# Sección 2: Drawdown\n",
        "\n",
        "El drawdown (reducción) es una medida del riesgo de la estrategia. Una estadística interesante para conocer el rendimiento del algoritmo es el drawdown máximo. En efecto, representa la pérdida máxima de una estrategia durante un período de tiempo que nos permite comprender si la estrategia es arriesgada y, por tanto, elegirla en función de nuestro nivel de aversión al riesgo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYIRBGgEKUPD"
      },
      "source": [
        "def drawdown_function(serie):\n",
        "\n",
        "  # Calculamos la suma de los rendimientos\n",
        "  cum = serie.dropna().cumsum() + 1\n",
        "\n",
        "  # Calculamos el máximo de la suma en el período (máximo acumulado) # (1,3,5,3,1) --> (1,3,5,5,5)\n",
        "  running_max = np.maximum.accumulate(cum)\n",
        "\n",
        "  # Calculamos el drawdown\n",
        "  drawdown = cum/running_max - 1\n",
        "  return drawdown"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculamos el drawdown\n",
        "drawdown = drawdown_function(return_serie)\n",
        "drawdown"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zjC9SSZRgdM6",
        "outputId": "657be774-923e-4ccf-9e50-247c7178814a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2004-08-20 00:00:00-04:00    0.000000\n",
              "2004-08-23 00:00:00-04:00    0.000000\n",
              "2004-08-24 00:00:00-04:00   -0.038006\n",
              "2004-08-25 00:00:00-04:00   -0.028116\n",
              "2004-08-26 00:00:00-04:00   -0.011578\n",
              "                               ...   \n",
              "2020-12-24 00:00:00-05:00   -0.009246\n",
              "2020-12-28 00:00:00-05:00   -0.005249\n",
              "2020-12-29 00:00:00-05:00   -0.007074\n",
              "2020-12-30 00:00:00-05:00   -0.009112\n",
              "2020-12-31 00:00:00-05:00   -0.007786\n",
              "Name: return, Length: 4121, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVF7wHtoLA9l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "5e08eda8-4d94-4677-8f19-29bc5949427d"
      },
      "source": [
        "# Adaptar el tamaño de la figura\n",
        "plt.figure(figsize=(15,8))\n",
        "\n",
        "# Representar el drawdown\n",
        "plt.fill_between(drawdown.index, drawdown*100, 0,\n",
        "                 drawdown, color=\"#CE5757\", alpha=0.65)\n",
        "\n",
        "# Añadimos el título\n",
        "plt.title(\"Drawdown\")\n",
        "\n",
        "# Etiqueta del eje de la y\n",
        "plt.ylabel(\"Drawdown en %\")\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82ms70tbMQvx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a8fc5df-3c1c-4394-d7e1-3a8e3978d3e6"
      },
      "source": [
        "# Cálculo del drawdown máximo\n",
        "max_drawdown = -np.min(drawdown)*100\n",
        "print(f\"Max drawdown: {'%.1f' % max_drawdown} %\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max drawdown: 28.8 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pY-8oU31MiNr"
      },
      "source": [
        "# Sección 3: Crear la función BackTest para evaluar la estrategia en las secciones posteriores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "62IR-tyILvXa"
      },
      "source": [
        "def BackTest(serie, annualiazed_scalar=252):\n",
        "\n",
        "  # Importar el benchmark\n",
        "  sp500 = yf.download(\"^GSPC\")[\"Adj Close\"].pct_change(1)\n",
        "  \n",
        "  # Cambiar el nombre\n",
        "  sp500.name = \"SP500\"\n",
        "\n",
        "  # Concatenar los retornos y el sp500\n",
        "  val = pd.concat((serie,sp500), axis=1).dropna()\n",
        "  # Calcular el drawdown\n",
        "  drawdown = drawdown_function(serie)*100\n",
        "  \n",
        "  # Calcular el max drawdown\n",
        "  max_drawdown = -np.min(drawdown)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  # Put a subplots\n",
        "  fig, (cum, dra) = plt.subplots(1,2, figsize=(20,6))\n",
        "  \n",
        "  # Put a Suptitle\n",
        "  fig.suptitle(\"Backtesting\", size=20)\n",
        "\n",
        "  # Returns cumsum chart\n",
        "  cum.plot(serie.cumsum()*100, color=\"#39B3C7\")\n",
        "\n",
        "  # SP500 cumsum chart\n",
        "  cum.plot(val[\"SP500\"].cumsum()*100, color=\"#B85A0F\")\n",
        "\n",
        "  # Put a legend\n",
        "  cum.legend([\"Portfolio\", \"SP500\"])\n",
        "  \n",
        "  # Set individual title\n",
        "  cum.set_title(\"Cumulative Return\", size=13)\n",
        "\n",
        "  cum.set_ylabel(\"Cumulative Return %\", size=11)\n",
        "\n",
        "  # Put the drawdown\n",
        "  dra.fill_between(drawdown.index,0,drawdown, color=\"#C73954\", alpha=0.65)\n",
        "\n",
        "  # Set individual title\n",
        "  dra.set_title(\"Drawdown\", size=13)\n",
        "\n",
        "  dra.set_ylabel(\"drawdown en %\", size=11)\n",
        "\n",
        "  # Plot the graph\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "  # Calcular el índice sortino\n",
        "  sortino = np.sqrt(annualiazed_scalar) * serie.mean()/serie.loc[serie<0].std()\n",
        "\n",
        "  # Calcular el índice  beta\n",
        "  beta = np.cov(val[[\"return\", \"SP500\"]].values,rowvar=False)[0][1] / np.var(val[\"SP500\"].values)\n",
        "\n",
        "  # Calcular el índice  alpha\n",
        "  alpha = annualiazed_scalar * (serie.mean() - beta*serie.mean())\n",
        "\n",
        "  # Imprimir los estadísticos\n",
        "  print(f\"Sortino: {np.round(sortino,3)}\")\n",
        "  print(f\"Beta: {np.round(beta,3)}\")\n",
        "  print(f\"Alpha: {np.round(alpha*100,3)} %\")\n",
        "  print(f\"MaxDrawdown: {np.round(max_drawdown,3)} %\")"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Aplicación"
      ],
      "metadata": {
        "id": "z9wA_sSuzFOX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocessing_yf(symbol):\n",
        "  \n",
        "  #Import the data\n",
        "  df = yf.download(symbol).dropna()\n",
        "\n",
        "  #Rename\n",
        "  df.columns = [\"open\", \"high\", \"low\", \"close\", \"adj close\", \"volume\"]\n",
        "  df.index.name = \"time\"\n",
        "\n",
        "  # Remove adj close\n",
        "  del df[\"adj close\"]\n",
        "\n",
        "  return df\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.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().shift(1)\n",
        "    \n",
        "    return df_copy.dropna()\n",
        "\n",
        "\n",
        "import tensorflow\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "import yfinance as yf\n",
        "import ta\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "\n",
        "\n",
        "df = preprocessing_yf(\"CADGBP=X\")\n",
        "dfc = feature_engineering(df)\n",
        "\n",
        "# Percentage train set\n",
        "split = int(0.85*len(dfc))\n",
        "split_val = int(0.95*len(dfc))\n",
        "\n",
        "# Train set creation\n",
        "X_train = dfc[['SMA 15', 'SMA 60',\n",
        "      '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',\n",
        "      'MSD 10', 'MSD 30', 'rsi']].iloc[split:,:]\n",
        "y_test = dfc[[\"returns\"]].iloc[split:]\n",
        "\n",
        "\n",
        "\n",
        "# STANDARDISATION\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "\n",
        "X_train_sc = sc.fit_transform(X_train)\n",
        "X_test_sc = sc.transform(X_test)\n",
        "\n",
        "def ANN_weights():\n",
        "  nb_hidden_layer=2\n",
        "  # INTIALIZATION SEQUENTIAL MODEL\n",
        "  regressor = Sequential()\n",
        "\n",
        "  # FIRST LAYER\n",
        "  regressor.add(Dense(25, input_shape=(X_train_sc.shape[1],), activation=\"relu\"))\n",
        "\n",
        "  # ADD HIDDEN LAYER\n",
        "  for i in range(1,nb_hidden_layer):\n",
        "    regressor.add(Dense(int(25/i), activation=\"relu\"))\n",
        "\n",
        "  # OUTPUT LAYER DENSE\n",
        "  regressor.add(Dense(1, activation=\"linear\"))\n",
        "\n",
        "  # COMPILE THE MODEL\n",
        "  regressor.compile(loss=\"mse\", optimizer=\"adam\")\n",
        "\n",
        "  return regressor\n",
        "\n",
        "ANN_models = [\"ANN n17\", \"ANN n38\", \"ANN n66\"]\n",
        "\n",
        "# Create empty dataframe\n",
        "prediction = pd.DataFrame(index = dfc.index)\n",
        "for nb in ANN_models:\n",
        "\n",
        "  # Create ANN structure\n",
        "  ann = ANN_weights()\n",
        "\n",
        "  # Import trained weights\n",
        "  ann.load_weights(f\"{nb}\")\n",
        "\n",
        "  # Create predictions for the whole dataset\n",
        "  pre = ann.predict(np.concatenate((X_train_sc,X_test_sc),\n",
        "                                        axis=0))\n",
        "  prediction[f\"{nb}\"] = pre\n",
        "\n",
        "prediction[\"Increase\"] = (np.sign(prediction)==1).sum(axis=1)\n",
        "prediction[\"Decrease\"] = (np.sign(prediction)==-1).sum(axis=1)\n",
        "\n",
        "# Put the daily return of the asset\n",
        "prediction[\"returns\"] = dfc[\"returns\"]\n",
        "\n",
        "# Compute the position doing a bagging on the best ANN\n",
        "prediction[\"position\"] = np.nan\n",
        "prediction.loc[prediction[\"Increase\"] > prediction[\"Decrease\"], \"position\"] = 1\n",
        "prediction.loc[prediction[\"Increase\"] < prediction[\"Decrease\"], \"position\"] = -1\n",
        "\n",
        "# When you will run again the code you can have different results\n",
        "# Compute and plot the strategy return on the validation test\n",
        "# Very important to put the shift at he position,Don't forget!\n",
        "prediction[\"strategy\"] = prediction[\"returns\"] * prediction[\"position\"].shift(1)\n",
        "split, split_val = int(0.85*len(dfc)), int(0.95*len(dfc))\n",
        "prediction[\"strategy\"].iloc[split_val:].cumsum().plot()"
      ],
      "metadata": {
        "id": "-X7O27PTzDNr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "466ba7ad-3455-4ee0-f752-f5c67a180277"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "152/152 [==============================] - 1s 1ms/step\n",
            "152/152 [==============================] - 0s 2ms/step\n",
            "152/152 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f02c00d7b90>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5i9X9w_vgi_C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "b3813392-4f00-418b-cead-ba3c37feac4d"
      },
      "source": [
        "prediction[\"return\"] = prediction[\"strategy\"]\n",
        "\n",
        "BackTest(prediction[\"return\"].iloc[split_val:])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x432 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sortino: 0.663\n",
            "Beta: nan\n",
            "Alpha: nan %\n",
            "MaxDrawdown: 6.985 %\n"
          ]
        }
      ]
    }
  ]
}