ES_DL_Capítulo_07_Redes_Neuronales_Recurrentes.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_07_Redes_Neuronales_Recurrentes.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",
        "# Redes neuronales recurrentes (RNN)\n",
        "\n",
        "\n",
        "<br>\n",
        "\n",
        "\n",
        "💰 Únete a la comunidad de [Discord](https://discord.gg/wXjNPAc5BH)\n",
        "\n",
        "📚 Puedes leer nuestro libro en [Amazon](https://www.amazon.com/gp/product/B09HG18CYL)\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"
      ],
      "metadata": {
        "id": "6wUlLl3nUoTf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install yfinance\n",
        "!pip install ta"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "al7krzszJ-vs",
        "outputId": "1e129365-81fe-484a-f90a-4637f4825a6c"
      },
      "execution_count": 2,
      "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-0.1.85-py2.py3-none-any.whl (29 kB)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.28.1-py3-none-any.whl (62 kB)\n",
            "\u001b[K     |████████████████████████████████| 62 kB 513 kB/s \n",
            "\u001b[?25hRequirement already satisfied: appdirs>=1.4.4 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.4.4)\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: lxml>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from yfinance) (4.9.1)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.6)\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) (2022.5)\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: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\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: charset-normalizer<3,>=2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.1.1)\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",
            "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-0.1.85\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: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->ta) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->ta) (2022.5)\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=03d24cdca036921ed3f839671f6d6f56d781cc52b5230499566bd3259fb4ec86\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",
      "execution_count": 3,
      "metadata": {
        "id": "YefNbSyZjcOu"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import warnings\n",
        "import yfinance as yf\n",
        "import ta\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# El código aquí nos permitirá cambiar los gráficos a modo oscuro para aquellos que elijan programar en modo oscuro\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import matplotlib as mpl\n",
        "from matplotlib import cycler\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\")"
      ],
      "metadata": {
        "id": "lm_n7BhEUyRh"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Selección de características"
      ],
      "metadata": {
        "id": "tmKHKvaJVjZJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def feature_engineering(df):\n",
        "    \"\"\" Crear nuevas variables \"\"\"\n",
        "    \n",
        "    # Copiamos el data frame para evitar interferencias entre las variables\n",
        "    df_copy = df.copy()\n",
        "    \n",
        "    # Creamos la variable de retorno\n",
        "    df_copy[\"returns\"] = df_copy[\"close\"].pct_change(1)\n",
        "    df_copy[\"dummy\"] = np.round(df_copy[\"returns\"]+0.5)\n",
        "\n",
        "    # Creamos las 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",
        "    # Creamos las volatilidades\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",
        "    # Creamos el 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()"
      ],
      "metadata": {
        "id": "c9r_29R3kYkJ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preparación de los datos"
      ],
      "metadata": {
        "id": "weVyehMiKgtT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocessing_yf(symbol):\n",
        "  \n",
        "  #Importar los datos\n",
        "  df = yf.download(symbol).dropna()\n",
        "\n",
        "  #Renombrar\n",
        "  df.columns = [\"open\", \"high\", \"low\", \"close\", \"adj close\", \"volume\"]\n",
        "  df.index.name = \"time\"\n",
        "\n",
        "  #Eliminar la columna de cierre ajustado\n",
        "  del df[\"adj close\"]\n",
        "\n",
        "  return df"
      ],
      "metadata": {
        "id": "r1ka2CagTn-2"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = preprocessing_yf(\"BTC-USD\")\n",
        "dfc = feature_engineering(df)\n",
        "dfc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "CDK7aHysKomA",
        "outputId": "ba522194-23af-4f80-ddfc-59ccbea788f6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    open          high           low         close  \\\n",
              "time                                                                 \n",
              "2014-11-16    374.730011    390.799011    374.601990    387.881989   \n",
              "2014-11-17    388.348999    410.199005    377.502014    387.407990   \n",
              "2014-11-18    387.785004    392.402008    371.117004    375.197998   \n",
              "2014-11-19    373.895996    386.480988    373.895996    380.554993   \n",
              "2014-11-20    380.307007    382.024994    356.781006    357.839996   \n",
              "...                  ...           ...           ...           ...   \n",
              "2022-11-04  20208.769531  21209.560547  20188.019531  21147.230469   \n",
              "2022-11-05  21144.832031  21446.886719  21097.634766  21282.691406   \n",
              "2022-11-06  21285.056641  21345.376953  20920.191406  20926.486328   \n",
              "2022-11-07  20924.621094  21053.246094  20489.972656  20602.816406   \n",
              "2022-11-08  20620.449219  20662.337891  19448.533203  19793.365234   \n",
              "\n",
              "                 volume   returns  dummy        SMA 15        SMA 60  \\\n",
              "time                                                                   \n",
              "2014-11-16     11905600  0.031236    1.0    360.166400    373.194417   \n",
              "2014-11-17     41518800 -0.001222    0.0    364.308600    372.036884   \n",
              "2014-11-18     32222500 -0.031517    0.0    368.409666    371.419683   \n",
              "2014-11-19     18931800  0.014278    1.0    371.585933    371.093050   \n",
              "2014-11-20     25233200 -0.059689    0.0    374.923466    370.620567   \n",
              "...                 ...       ...    ...           ...           ...   \n",
              "2022-11-04  64072727950  0.046375    1.0  20059.938932  19737.478743   \n",
              "2022-11-05  37846047609  0.006406    1.0  20199.504948  19759.726400   \n",
              "2022-11-06  35082693210 -0.016737    0.0  20340.186458  19800.476790   \n",
              "2022-11-07  53510852236 -0.015467    0.0  20454.739583  19827.746159   \n",
              "2022-11-08  67305189376 -0.039288    0.0  20523.793490  19848.962533   \n",
              "\n",
              "              MSD 10    MSD 30        rsi  \n",
              "time                                       \n",
              "2014-11-16  0.059374  0.040284  52.526052  \n",
              "2014-11-17  0.059454  0.040680  55.885665  \n",
              "2014-11-18  0.058641  0.040524  55.714363  \n",
              "2014-11-19  0.060390  0.040934  51.348107  \n",
              "2014-11-20  0.058663  0.040894  53.085274  \n",
              "...              ...       ...        ...  \n",
              "2022-11-04  0.020275  0.014646  53.916137  \n",
              "2022-11-05  0.021785  0.016822  64.094144  \n",
              "2022-11-06  0.019439  0.016690  65.287438  \n",
              "2022-11-07  0.018553  0.016527  59.671529  \n",
              "2022-11-08  0.018887  0.016770  55.038707  \n",
              "\n",
              "[2915 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f3e2d53a-70fb-4f50-b711-c454d672c904\">\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>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>returns</th>\n",
              "      <th>dummy</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>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-11-16</th>\n",
              "      <td>374.730011</td>\n",
              "      <td>390.799011</td>\n",
              "      <td>374.601990</td>\n",
              "      <td>387.881989</td>\n",
              "      <td>11905600</td>\n",
              "      <td>0.031236</td>\n",
              "      <td>1.0</td>\n",
              "      <td>360.166400</td>\n",
              "      <td>373.194417</td>\n",
              "      <td>0.059374</td>\n",
              "      <td>0.040284</td>\n",
              "      <td>52.526052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-17</th>\n",
              "      <td>388.348999</td>\n",
              "      <td>410.199005</td>\n",
              "      <td>377.502014</td>\n",
              "      <td>387.407990</td>\n",
              "      <td>41518800</td>\n",
              "      <td>-0.001222</td>\n",
              "      <td>0.0</td>\n",
              "      <td>364.308600</td>\n",
              "      <td>372.036884</td>\n",
              "      <td>0.059454</td>\n",
              "      <td>0.040680</td>\n",
              "      <td>55.885665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-18</th>\n",
              "      <td>387.785004</td>\n",
              "      <td>392.402008</td>\n",
              "      <td>371.117004</td>\n",
              "      <td>375.197998</td>\n",
              "      <td>32222500</td>\n",
              "      <td>-0.031517</td>\n",
              "      <td>0.0</td>\n",
              "      <td>368.409666</td>\n",
              "      <td>371.419683</td>\n",
              "      <td>0.058641</td>\n",
              "      <td>0.040524</td>\n",
              "      <td>55.714363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-19</th>\n",
              "      <td>373.895996</td>\n",
              "      <td>386.480988</td>\n",
              "      <td>373.895996</td>\n",
              "      <td>380.554993</td>\n",
              "      <td>18931800</td>\n",
              "      <td>0.014278</td>\n",
              "      <td>1.0</td>\n",
              "      <td>371.585933</td>\n",
              "      <td>371.093050</td>\n",
              "      <td>0.060390</td>\n",
              "      <td>0.040934</td>\n",
              "      <td>51.348107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-20</th>\n",
              "      <td>380.307007</td>\n",
              "      <td>382.024994</td>\n",
              "      <td>356.781006</td>\n",
              "      <td>357.839996</td>\n",
              "      <td>25233200</td>\n",
              "      <td>-0.059689</td>\n",
              "      <td>0.0</td>\n",
              "      <td>374.923466</td>\n",
              "      <td>370.620567</td>\n",
              "      <td>0.058663</td>\n",
              "      <td>0.040894</td>\n",
              "      <td>53.085274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-11-04</th>\n",
              "      <td>20208.769531</td>\n",
              "      <td>21209.560547</td>\n",
              "      <td>20188.019531</td>\n",
              "      <td>21147.230469</td>\n",
              "      <td>64072727950</td>\n",
              "      <td>0.046375</td>\n",
              "      <td>1.0</td>\n",
              "      <td>20059.938932</td>\n",
              "      <td>19737.478743</td>\n",
              "      <td>0.020275</td>\n",
              "      <td>0.014646</td>\n",
              "      <td>53.916137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-11-05</th>\n",
              "      <td>21144.832031</td>\n",
              "      <td>21446.886719</td>\n",
              "      <td>21097.634766</td>\n",
              "      <td>21282.691406</td>\n",
              "      <td>37846047609</td>\n",
              "      <td>0.006406</td>\n",
              "      <td>1.0</td>\n",
              "      <td>20199.504948</td>\n",
              "      <td>19759.726400</td>\n",
              "      <td>0.021785</td>\n",
              "      <td>0.016822</td>\n",
              "      <td>64.094144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-11-06</th>\n",
              "      <td>21285.056641</td>\n",
              "      <td>21345.376953</td>\n",
              "      <td>20920.191406</td>\n",
              "      <td>20926.486328</td>\n",
              "      <td>35082693210</td>\n",
              "      <td>-0.016737</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20340.186458</td>\n",
              "      <td>19800.476790</td>\n",
              "      <td>0.019439</td>\n",
              "      <td>0.016690</td>\n",
              "      <td>65.287438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-11-07</th>\n",
              "      <td>20924.621094</td>\n",
              "      <td>21053.246094</td>\n",
              "      <td>20489.972656</td>\n",
              "      <td>20602.816406</td>\n",
              "      <td>53510852236</td>\n",
              "      <td>-0.015467</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20454.739583</td>\n",
              "      <td>19827.746159</td>\n",
              "      <td>0.018553</td>\n",
              "      <td>0.016527</td>\n",
              "      <td>59.671529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-11-08</th>\n",
              "      <td>20620.449219</td>\n",
              "      <td>20662.337891</td>\n",
              "      <td>19448.533203</td>\n",
              "      <td>19793.365234</td>\n",
              "      <td>67305189376</td>\n",
              "      <td>-0.039288</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20523.793490</td>\n",
              "      <td>19848.962533</td>\n",
              "      <td>0.018887</td>\n",
              "      <td>0.016770</td>\n",
              "      <td>55.038707</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2915 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3e2d53a-70fb-4f50-b711-c454d672c904')\"\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-f3e2d53a-70fb-4f50-b711-c454d672c904 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-f3e2d53a-70fb-4f50-b711-c454d672c904');\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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfc.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwMu-vklLFUE",
        "outputId": "384de5b5-c0d9-441a-aa50-46cb4d84d8b9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['open', 'high', 'low', 'close', 'volume', 'returns', 'dummy', 'SMA 15',\n",
              "       'SMA 60', 'MSD 10', 'MSD 30', 'rsi'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Porcentaje del conjunto de entrenamiento\n",
        "split = int(0.85*len(dfc))\n",
        "\n",
        "\n",
        "# Creación del conjunto de entrenamiento\n",
        "X_train = dfc[[\"SMA 15\", \"SMA 60\", \"MSD 10\", \"MSD 30\", \"rsi\"]].iloc[:split,:]\n",
        "y_train = dfc[[\"dummy\"]].iloc[:split,:]\n",
        "\n",
        "\n",
        "# Creación del conjunto de testing\n",
        "X_test = dfc[[\"SMA 15\", \"SMA 60\", \"MSD 10\", \"MSD 30\", \"rsi\"]].iloc[split:,:]\n",
        "y_test = dfc[[\"dummy\"]].iloc[split:,:]"
      ],
      "metadata": {
        "id": "BH7R2vzwKf7P"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ESTANDARIZACIÓN\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)"
      ],
      "metadata": {
        "id": "hlY5EsJmLRV2"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(X_train.values)\n",
        "plt.title(\"SIN Estandardización\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "s0BkrDq0LWXs",
        "outputId": "b201b463-9893-4535-a65b-68ee59763b58"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(X_train_sc, alpha=0.5)\n",
        "plt.title(\"CON Estandardización\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "xnZQ0AHvLXiX",
        "outputId": "dfd808ee-7ebd-44db-b7c3-00d0caf05fcd"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Transformar arrays 2-dimensionales en arrays de datos 3-dimensionales"
      ],
      "metadata": {
        "id": "cOlh69NRjubh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def X_3d_RNN(X_s, y_s, lag):\n",
        "\n",
        "    # Verificación simple de longitudes\n",
        "    if len(X_s) != len(y_s):\n",
        "        print(\"Warnings\")\n",
        "\n",
        "    # Creamos la variable X_train\n",
        "    X_train = []\n",
        "    for variable in range(0, X_s.shape[1]):\n",
        "        X = []\n",
        "        for i in range(lag, X_s.shape[0]):\n",
        "            X.append(X_s[i-lag:i, variable])\n",
        "        X_train.append(X)\n",
        "    X_train, np.array(X_train)\n",
        "    X_train = np.swapaxes(np.swapaxes(X_train, 0, 1), 1, 2)\n",
        "\n",
        "    # Creamos la variable y_train\n",
        "    y_train = []\n",
        "    for i in range(lag, y_s.shape[0]):\n",
        "        y_train.append(y_s[i, :].reshape(-1,1).transpose())\n",
        "    y_train = np.concatenate(y_train, axis=0)\n",
        "    return X_train, y_train"
      ],
      "metadata": {
        "id": "PtaNB8Wmjz2D"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lag = 15\n",
        "X_train_3d, y_train_3d = X_3d_RNN(X_train_sc, y_train.values, lag)\n",
        "X_test_3d, y_test_3d = X_3d_RNN(X_test_sc, y_test.values, lag)"
      ],
      "metadata": {
        "id": "hhEj4ohsFtJ7"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(X_train_3d[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "PzH9i5Mi_qfm",
        "outputId": "73bed89e-c403-4264-d1ca-c206d6587fa5"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           0         1         2         3         4\n",
              "0  -0.661377 -0.658601  1.274910  0.292430 -0.130347\n",
              "1  -0.661048 -0.658697  1.278858  0.316332  0.106348\n",
              "2  -0.660723 -0.658748  1.238677  0.306928  0.094279\n",
              "3  -0.660471 -0.658775  1.325062  0.331707 -0.213337\n",
              "4  -0.660206 -0.658814  1.239788  0.329271 -0.090948\n",
              "5  -0.660109 -0.658871  1.413665  0.414280 -0.615266\n",
              "6  -0.660100 -0.658941  1.426594  0.420832 -0.758070\n",
              "7  -0.660045 -0.659056 -0.092967  0.320454 -0.702559\n",
              "8  -0.659928 -0.659132  0.159154  0.362140 -0.325796\n",
              "9  -0.659856 -0.659180  0.089472  0.346947 -0.106788\n",
              "10 -0.659811 -0.659220 -0.136336  0.339782 -0.147644\n",
              "11 -0.659808 -0.659263 -0.214621  0.349646 -0.333312\n",
              "12 -0.660093 -0.659274 -0.209564  0.346610 -0.297792\n",
              "13 -0.660327 -0.659272 -0.255067  0.243945 -0.110886\n",
              "14 -0.660445 -0.659288 -0.274737  0.228665 -0.140720"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7b551dd3-326a-4aa5-b335-f253815c14b3\">\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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.661377</td>\n",
              "      <td>-0.658601</td>\n",
              "      <td>1.274910</td>\n",
              "      <td>0.292430</td>\n",
              "      <td>-0.130347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.661048</td>\n",
              "      <td>-0.658697</td>\n",
              "      <td>1.278858</td>\n",
              "      <td>0.316332</td>\n",
              "      <td>0.106348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.660723</td>\n",
              "      <td>-0.658748</td>\n",
              "      <td>1.238677</td>\n",
              "      <td>0.306928</td>\n",
              "      <td>0.094279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.660471</td>\n",
              "      <td>-0.658775</td>\n",
              "      <td>1.325062</td>\n",
              "      <td>0.331707</td>\n",
              "      <td>-0.213337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.660206</td>\n",
              "      <td>-0.658814</td>\n",
              "      <td>1.239788</td>\n",
              "      <td>0.329271</td>\n",
              "      <td>-0.090948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-0.660109</td>\n",
              "      <td>-0.658871</td>\n",
              "      <td>1.413665</td>\n",
              "      <td>0.414280</td>\n",
              "      <td>-0.615266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-0.660100</td>\n",
              "      <td>-0.658941</td>\n",
              "      <td>1.426594</td>\n",
              "      <td>0.420832</td>\n",
              "      <td>-0.758070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-0.660045</td>\n",
              "      <td>-0.659056</td>\n",
              "      <td>-0.092967</td>\n",
              "      <td>0.320454</td>\n",
              "      <td>-0.702559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-0.659928</td>\n",
              "      <td>-0.659132</td>\n",
              "      <td>0.159154</td>\n",
              "      <td>0.362140</td>\n",
              "      <td>-0.325796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-0.659856</td>\n",
              "      <td>-0.659180</td>\n",
              "      <td>0.089472</td>\n",
              "      <td>0.346947</td>\n",
              "      <td>-0.106788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>-0.659811</td>\n",
              "      <td>-0.659220</td>\n",
              "      <td>-0.136336</td>\n",
              "      <td>0.339782</td>\n",
              "      <td>-0.147644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>-0.659808</td>\n",
              "      <td>-0.659263</td>\n",
              "      <td>-0.214621</td>\n",
              "      <td>0.349646</td>\n",
              "      <td>-0.333312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>-0.660093</td>\n",
              "      <td>-0.659274</td>\n",
              "      <td>-0.209564</td>\n",
              "      <td>0.346610</td>\n",
              "      <td>-0.297792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>-0.660327</td>\n",
              "      <td>-0.659272</td>\n",
              "      <td>-0.255067</td>\n",
              "      <td>0.243945</td>\n",
              "      <td>-0.110886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>-0.660445</td>\n",
              "      <td>-0.659288</td>\n",
              "      <td>-0.274737</td>\n",
              "      <td>0.228665</td>\n",
              "      <td>-0.140720</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7b551dd3-326a-4aa5-b335-f253815c14b3')\"\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-7b551dd3-326a-4aa5-b335-f253815c14b3 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-7b551dd3-326a-4aa5-b335-f253815c14b3');\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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_3d[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rLG_ikj7_--_",
        "outputId": "5416356c-7763-4993-f629-bc99bbafc130"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.shape(X_train_3d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CpKQnyQcF-h-",
        "outputId": "8093ea6f-a87d-4a41-d71f-51dac1529abf"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2462, 15, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Red Neuronal Recurrente"
      ],
      "metadata": {
        "id": "cDd6TZQcLl2E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "# Librerías\n",
        "import tensorflow\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Dropout\n",
        "\n",
        "nb_hidden_layer = 3\n",
        "\n",
        "\n",
        "# Inicialización del modelo\n",
        "classifier = Sequential()\n",
        "\n",
        "# Añadimos la capa LSTM\n",
        "classifier.add(LSTM(units = 10, return_sequences = True,\n",
        "                    input_shape = (X_train_3d.shape[1],X_train_3d.shape[2],)))\n",
        "\n",
        "\n",
        "# Bucle para añadir capas LSTM\n",
        "for _ in range(nb_hidden_layer):\n",
        "    classifier.add(LSTM(units = 10, return_sequences = True))\n",
        "    classifier.add(Dropout(0.15))\n",
        "\n",
        "# Última capa LSTM con el parámetro return_sequences = False \n",
        "classifier.add(LSTM(units = 10, return_sequences = False))\n",
        "\n",
        "\n",
        "# Capa Densa de Salida\n",
        "classifier.add(Dense(1, activation=\"sigmoid\"))\n",
        "\n",
        "# Compilamos el modelo\n",
        "classifier.compile(loss=\"binary_crossentropy\", optimizer=\"adam\")\n",
        "\n",
        "\n",
        "# Entrenamiento y la parada temprana\n",
        "early_stop = EarlyStopping(verbose=1, patience = 5)\n",
        "\n",
        "classifier.fit(X_train_3d, y_train_3d, validation_data=(X_test_3d, y_test_3d), epochs=100, callbacks=[early_stop])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9I6M7IjewpFk",
        "outputId": "da256656-e855-497a-b1df-e4311ed8a768"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "77/77 [==============================] - 16s 66ms/step - loss: 0.6903 - val_loss: 0.6953\n",
            "Epoch 2/100\n",
            "77/77 [==============================] - 3s 35ms/step - loss: 0.6889 - val_loss: 0.6963\n",
            "Epoch 3/100\n",
            "77/77 [==============================] - 3s 36ms/step - loss: 0.6879 - val_loss: 0.6948\n",
            "Epoch 4/100\n",
            "77/77 [==============================] - 3s 35ms/step - loss: 0.6876 - val_loss: 0.6946\n",
            "Epoch 5/100\n",
            "77/77 [==============================] - 3s 38ms/step - loss: 0.6872 - val_loss: 0.6952\n",
            "Epoch 6/100\n",
            "77/77 [==============================] - 3s 36ms/step - loss: 0.6875 - val_loss: 0.6940\n",
            "Epoch 7/100\n",
            "77/77 [==============================] - 3s 36ms/step - loss: 0.6868 - val_loss: 0.6951\n",
            "Epoch 8/100\n",
            "77/77 [==============================] - 3s 36ms/step - loss: 0.6869 - val_loss: 0.6939\n",
            "Epoch 9/100\n",
            "77/77 [==============================] - 3s 35ms/step - loss: 0.6855 - val_loss: 0.6934\n",
            "Epoch 10/100\n",
            "77/77 [==============================] - 3s 36ms/step - loss: 0.6863 - val_loss: 0.6948\n",
            "Epoch 11/100\n",
            "77/77 [==============================] - 3s 36ms/step - loss: 0.6855 - val_loss: 0.6952\n",
            "Epoch 12/100\n",
            "77/77 [==============================] - 3s 36ms/step - loss: 0.6857 - val_loss: 0.6950\n",
            "Epoch 13/100\n",
            "77/77 [==============================] - 3s 35ms/step - loss: 0.6839 - val_loss: 0.6945\n",
            "Epoch 14/100\n",
            "77/77 [==============================] - 3s 35ms/step - loss: 0.6847 - val_loss: 0.6946\n",
            "Epoch 14: early stopping\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ffa3bad0bd0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(classifier.history.history).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "NO5rkcWaxVSv",
        "outputId": "c5982744-f247-4412-8771-fa5900db5587"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ffa3663bc90>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create predictions for the whole dataset\n",
        "y_pred_train = np.concatenate((np.zeros([lag,1]),classifier.predict(X_train_3d)),\n",
        "                                    axis=0)\n",
        "\n",
        "y_pred_test = np.concatenate((np.zeros([lag,1]),classifier.predict(X_test_3d)),\n",
        "                                    axis=0)\n",
        "\n",
        "\n",
        "dfc[\"prediction\"] = np.concatenate((y_pred_train,y_pred_test),\n",
        "                                    axis=0)\n",
        "\n",
        "dfc[\"prediction\"] = np.where(dfc[\"prediction\"] < 0.5, -1,1)\n",
        "\n",
        "# Compute the strategy\n",
        "dfc[\"strategy\"] =  dfc[\"prediction\"].shift(1) * dfc[\"returns\"]"
      ],
      "metadata": {
        "id": "d0Lvlum8MK0F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df5d67f0-562f-4b97-844d-569e437bde56"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "77/77 [==============================] - 3s 8ms/step\n",
            "14/14 [==============================] - 0s 7ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"X_train_3d: {np.shape(y_pred_train)}\")\n",
        "print(f\"X_test_3d: {np.shape(y_pred_test)}\")\n",
        "print(f\"X: {np.shape(dfc)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u8EBOsv7z-L1",
        "outputId": "377728bf-fe83-4351-e8c3-81c61af63e2a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train_3d: (2477, 1)\n",
            "X_test_3d: (438, 1)\n",
            "X: (2915, 14)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "2477+438"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pfzHF0if1Qen",
        "outputId": "8abb314b-08c9-478d-88da-4c3fb53a3cd7"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2915"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfc[\"strategy\"].iloc[split:].cumsum().plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "GwuyitiBMt-3",
        "outputId": "28057b76-8083-4759-da90-6173802006fc"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ffa37e84210>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Automatización"
      ],
      "metadata": {
        "id": "Ar6RQ-srOa3V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def feature_engineering(df):\n",
        "    \"\"\" Creamos nuevas 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",
        "    df_copy[\"dummy\"] = np.round(df_copy[\"returns\"]+0.5)\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",
        "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",
        "\n",
        "def X_3d_RNN(X_s, y_s, lag):\n",
        "\n",
        "    # Simple verification\n",
        "    if len(X_s) != len(y_s):\n",
        "        print(\"Warnings\")\n",
        "\n",
        "    # Create the X_train\n",
        "    X_train = []\n",
        "    for variable in range(0, X_s.shape[1]):\n",
        "        X = []\n",
        "        for i in range(lag, X_s.shape[0]):\n",
        "            X.append(X_s[i-lag:i, variable])\n",
        "        X_train.append(X)\n",
        "    X_train, np.array(X_train)\n",
        "    X_train = np.swapaxes(np.swapaxes(X_train, 0, 1), 1, 2)\n",
        "\n",
        "    # Create the y_train\n",
        "    y_train = []\n",
        "    for i in range(lag, y_s.shape[0]):\n",
        "        y_train.append(y_s[i, :].reshape(-1,1).transpose())\n",
        "    y_train = np.concatenate(y_train, axis=0)\n",
        "    return X_train, y_train\n",
        "\n",
        "\n",
        "import tensorflow\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Dropout\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "def RNN(symbol, nb_neurons=10, nb_hidden_layer=1, lag=15, nb_model=None):\n",
        "  \n",
        "  df = yf.download(symbol)[[\"Open\", \"High\", \"Low\", \"Adj Close\", \"Volume\"]]\n",
        "  df.columns = [\"open\", \"high\", \"low\", \"close\", \"volume\"]\n",
        "  dfc = feature_engineering(df)\n",
        "\n",
        "\n",
        "  # Percentage train set\n",
        "  split = int(0.80*len(dfc))\n",
        "  split_val = int(0.90*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[[\"dummy\"]].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[[\"dummy\"]].iloc[split:]\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",
        "  # Transform 2-dimensional data to 3-dimensional data\n",
        "  X_train_3d, y_train_3d = X_3d_RNN(X_train_sc, y_train.values, lag)\n",
        "  X_test_3d, y_test_3d = X_3d_RNN(X_test_sc, y_test.values, lag)\n",
        "\n",
        "  # INITIALIZATION OF THE MODEL\n",
        "  classifier = Sequential()\n",
        "\n",
        "  # ADD LSTM LAYER\n",
        "  classifier.add(LSTM(units = nb_neurons, return_sequences = True,\n",
        "                      input_shape = (X_train_3d.shape[1],X_train_3d.shape[2],)))\n",
        "\n",
        "\n",
        "  # LOOP WHICH ADD LSTM LAYER\n",
        "  for _ in range(nb_hidden_layer):\n",
        "      classifier.add(LSTM(units = nb_neurons, return_sequences = True))\n",
        "      classifier.add(Dropout(0.20))\n",
        "\n",
        "  # LAST LSTM LAYER BUT WITH return_sequences = False \n",
        "  classifier.add(LSTM(units = nb_neurons, return_sequences = False))\n",
        "\n",
        "\n",
        "  # OUTPUT DENSE LAYER\n",
        "  classifier.add(Dense(1, activation=\"sigmoid\"))\n",
        "\n",
        "  # COMPILE THE MODEL\n",
        "  classifier.compile(loss=\"binary_crossentropy\", optimizer=\"adam\")\n",
        "\n",
        "\n",
        "  # TRAINING\n",
        "  early_stop = EarlyStopping(verbose=1, patience = 5)\n",
        "  classifier.fit(X_train_3d, y_train_3d, validation_data=(X_test_3d, y_test_3d),\n",
        "                 epochs=1, callbacks=[early_stop])\n",
        "  \n",
        "  # Create predictions for the whole dataset\n",
        "  y_pred_train = np.concatenate((np.zeros([lag,1]),classifier.predict(X_train_3d)),\n",
        "                                      axis=0)\n",
        "\n",
        "  y_pred_test = np.concatenate((np.zeros([lag,1]),classifier.predict(X_test_3d)),\n",
        "                                      axis=0)\n",
        "\n",
        "  dfc[\"prediction\"] = np.concatenate((y_pred_train,y_pred_test),\n",
        "                                      axis=0)\n",
        "\n",
        "  dfc[\"prediction\"] = np.where(dfc[\"prediction\"] < 0.5, -1,1)\n",
        "\n",
        "  # Compute the strategy\n",
        "  dfc[\"strategy\"] = np.sign(dfc[\"prediction\"]) * dfc[\"returns\"]\n",
        "\n",
        "  if nb_model!=None:\n",
        "    classifier.save_weights(f\"Weights_RNN/RNN n°{nb_model}\")\n",
        "    return dfc[\"strategy\"].iloc[split+lag:split_val], dfc[\"strategy\"].iloc[split_val:]\n",
        "\n",
        "  else:\n",
        "      return dfc[\"strategy\"].iloc[split+lag:split_val], dfc[\"strategy\"].iloc[split_val:]"
      ],
      "metadata": {
        "id": "VWfaiP-ROeHy"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sr = RNN(\"BTC-USD\", nb_model=15)\n",
        "\n",
        "sr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0B7OLxOhpl-",
        "outputId": "52d719a2-bf6d-4783-e361-f7805f462f1f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "73/73 [==============================] - 9s 42ms/step - loss: 0.6873 - val_loss: 0.6944\n",
            "73/73 [==============================] - 2s 6ms/step\n",
            "18/18 [==============================] - 0s 6ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Date\n",
              " 2021-04-20   -0.013437\n",
              " 2021-04-21    0.045454\n",
              " 2021-04-22    0.039769\n",
              " 2021-04-23    0.012917\n",
              " 2021-04-24    0.020409\n",
              "                 ...   \n",
              " 2022-01-16    0.001471\n",
              " 2022-01-17    0.020024\n",
              " 2022-01-18   -0.002960\n",
              " 2022-01-19    0.014898\n",
              " 2022-01-20    0.025486\n",
              " Name: strategy, Length: 276, dtype: float64, Date\n",
              " 2022-01-21    0.103812\n",
              " 2022-01-22    0.039143\n",
              " 2022-01-23   -0.035585\n",
              " 2022-01-24   -0.010407\n",
              " 2022-01-25   -0.008176\n",
              "                 ...   \n",
              " 2022-11-04    0.046375\n",
              " 2022-11-05    0.006406\n",
              " 2022-11-06   -0.016737\n",
              " 2022-11-07   -0.015467\n",
              " 2022-11-08   -0.043424\n",
              " Name: strategy, Length: 292, dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Library to estimate the time until the end of the loop\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Empty lists to contain the returns\n",
        "returns_test = []\n",
        "returns_val = []\n",
        "\n",
        "\n",
        "# Compute the returns during the test and validation periods\n",
        "for i in tqdm(range(100)):\n",
        "  test, val = RNN(\"BTC-USD\", nb_model=i)\n",
        "  returns_test.append(test)\n",
        "  returns_val.append(val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FP3Y3TPghrOY",
        "outputId": "4975fe8f-14a2-45e0-9305-484e77d582cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/100 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 42ms/step - loss: 0.6891 - val_loss: 0.6920\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  1%|          | 1/100 [00:13<22:00, 13.34s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 42ms/step - loss: 0.6885 - val_loss: 0.6925\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  2%|▏         | 2/100 [00:25<20:51, 12.77s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 45ms/step - loss: 0.6900 - val_loss: 0.6944\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  3%|▎         | 3/100 [00:39<21:34, 13.35s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 8s 41ms/step - loss: 0.6891 - val_loss: 0.6918\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  4%|▍         | 4/100 [00:51<20:24, 12.75s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 43ms/step - loss: 0.6888 - val_loss: 0.6943\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  5%|▌         | 5/100 [01:03<19:37, 12.39s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 10s 43ms/step - loss: 0.6910 - val_loss: 0.6914\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  6%|▌         | 6/100 [01:15<19:25, 12.39s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 43ms/step - loss: 0.6908 - val_loss: 0.6934\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  7%|▋         | 7/100 [01:28<19:09, 12.36s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 52ms/step - loss: 0.6913 - val_loss: 0.6903\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  8%|▊         | 8/100 [01:42<19:56, 13.00s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 8s 42ms/step - loss: 0.6889 - val_loss: 0.6910\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  9%|▉         | 9/100 [01:53<18:56, 12.49s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 10s 44ms/step - loss: 0.6914 - val_loss: 0.6928\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 10/100 [02:08<19:36, 13.07s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 44ms/step - loss: 0.6925 - val_loss: 0.6899\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 11%|█         | 11/100 [02:20<19:03, 12.85s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 54ms/step - loss: 0.6904 - val_loss: 0.6934\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 12%|█▏        | 12/100 [02:33<18:58, 12.94s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 43ms/step - loss: 0.6913 - val_loss: 0.6927\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 13%|█▎        | 13/100 [02:45<18:11, 12.54s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 42ms/step - loss: 0.6898 - val_loss: 0.6914\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 14%|█▍        | 14/100 [02:59<18:37, 12.99s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 10s 43ms/step - loss: 0.6885 - val_loss: 0.6895\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 15%|█▌        | 15/100 [03:12<18:35, 13.12s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 10s 44ms/step - loss: 0.6917 - val_loss: 0.6946\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 16%|█▌        | 16/100 [03:25<18:15, 13.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 44ms/step - loss: 0.6895 - val_loss: 0.6923\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 17%|█▋        | 17/100 [03:39<18:29, 13.37s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 43ms/step - loss: 0.6925 - val_loss: 0.6957\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 18%|█▊        | 18/100 [03:52<18:13, 13.33s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 10s 43ms/step - loss: 0.6895 - val_loss: 0.6917\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 19%|█▉        | 19/100 [04:07<18:25, 13.65s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 43ms/step - loss: 0.6892 - val_loss: 0.6949\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 20/100 [04:19<17:41, 13.27s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 53ms/step - loss: 0.6918 - val_loss: 0.6905\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 21%|██        | 21/100 [04:31<17:04, 12.97s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 44ms/step - loss: 0.6900 - val_loss: 0.6930\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 22%|██▏       | 22/100 [04:43<16:23, 12.61s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 43ms/step - loss: 0.6879 - val_loss: 0.7001\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 23%|██▎       | 23/100 [04:57<16:41, 13.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 42ms/step - loss: 0.6898 - val_loss: 0.6930\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 24%|██▍       | 24/100 [05:09<16:10, 12.77s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 42ms/step - loss: 0.6888 - val_loss: 0.6957\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 25%|██▌       | 25/100 [05:23<16:17, 13.03s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 42ms/step - loss: 0.6921 - val_loss: 0.6918\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 26%|██▌       | 26/100 [05:35<15:43, 12.75s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 43ms/step - loss: 0.6900 - val_loss: 0.6917\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 27%|██▋       | 27/100 [05:47<15:05, 12.40s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 10s 43ms/step - loss: 0.6910 - val_loss: 0.6955\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 28%|██▊       | 28/100 [05:59<14:57, 12.46s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 42ms/step - loss: 0.6902 - val_loss: 0.6925\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 29%|██▉       | 29/100 [06:11<14:37, 12.36s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 8s 41ms/step - loss: 0.6894 - val_loss: 0.6910\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 30/100 [06:25<14:51, 12.74s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 43ms/step - loss: 0.6887 - val_loss: 0.6941\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 31%|███       | 31/100 [06:38<14:47, 12.86s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 43ms/step - loss: 0.6891 - val_loss: 0.6896\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 32%|███▏      | 32/100 [06:51<14:24, 12.71s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 43ms/step - loss: 0.6912 - val_loss: 0.6900\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 33%|███▎      | 33/100 [07:04<14:33, 13.03s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 8s 42ms/step - loss: 0.6892 - val_loss: 0.6926\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 34%|███▍      | 34/100 [07:18<14:30, 13.19s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 8s 43ms/step - loss: 0.6893 - val_loss: 0.6911\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 35%|███▌      | 35/100 [07:29<13:40, 12.63s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 10s 44ms/step - loss: 0.6891 - val_loss: 0.6969\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 36%|███▌      | 36/100 [07:42<13:31, 12.68s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 43ms/step - loss: 0.6902 - val_loss: 0.6909\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 37%|███▋      | 37/100 [07:56<13:42, 13.06s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 8s 42ms/step - loss: 0.6908 - val_loss: 0.6952\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 38%|███▊      | 38/100 [08:10<13:40, 13.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 8s 42ms/step - loss: 0.6927 - val_loss: 0.6909\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 39%|███▉      | 39/100 [08:21<12:57, 12.75s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 10s 44ms/step - loss: 0.6890 - val_loss: 0.7005\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 40/100 [08:35<13:08, 13.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 10s 47ms/step - loss: 0.6905 - val_loss: 0.6912\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 41%|████      | 41/100 [08:49<13:08, 13.37s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 8s 42ms/step - loss: 0.6908 - val_loss: 0.6910\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 42%|████▏     | 42/100 [09:01<12:28, 12.91s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 8s 42ms/step - loss: 0.6904 - val_loss: 0.6931\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 43%|████▎     | 43/100 [09:14<12:17, 12.94s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 43ms/step - loss: 0.6914 - val_loss: 0.6966\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 44%|████▍     | 44/100 [09:27<11:58, 12.83s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 10s 45ms/step - loss: 0.6907 - val_loss: 0.6903\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 45%|████▌     | 45/100 [09:41<12:06, 13.20s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 8s 42ms/step - loss: 0.6890 - val_loss: 0.7023\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 46%|████▌     | 46/100 [09:55<12:03, 13.40s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 43ms/step - loss: 0.6881 - val_loss: 0.6933\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 47%|████▋     | 47/100 [10:08<11:45, 13.30s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 42ms/step - loss: 0.6887 - val_loss: 0.6997\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 48%|████▊     | 48/100 [10:22<11:41, 13.48s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 9s 43ms/step - loss: 0.6891 - val_loss: 0.6926\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 49%|████▉     | 49/100 [10:35<11:31, 13.56s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 8s 42ms/step - loss: 0.6900 - val_loss: 0.6938\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 50/100 [10:47<10:56, 13.13s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 8s 43ms/step - loss: 0.6915 - val_loss: 0.6926\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 51%|█████     | 51/100 [11:00<10:41, 13.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - 10s 42ms/step - loss: 0.6964 - val_loss: 0.6980\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 52%|█████▏    | 52/100 [11:15<10:43, 13.40s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.6925"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 52%|█████▏    | 52/100 [11:24<10:32, 13.17s/it]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mget_attr\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   2689\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mc_api_util\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2690\u001b[0;31m         \u001b[0mpywrap_tf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_OperationGetAttrValueProto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_c_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2691\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpywrap_tf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidArgumentError\u001b[0m: Operation 'Slice_1/size' has no attr named '_read_only_resource_inputs'.",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-71e92be234f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Compute the returns during the test and validation periods\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m   \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRNN\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"BTC-USD\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_model\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m   \u001b[0mreturns_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m   \u001b[0mreturns_val\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-30-9ddc5b5b08ca>\u001b[0m in \u001b[0;36mRNN\u001b[0;34m(symbol, nb_neurons, nb_hidden_layer, lag, nb_model)\u001b[0m\n\u001b[1;32m    128\u001b[0m   \u001b[0mearly_stop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEarlyStopping\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatience\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m   classifier.fit(X_train_3d, y_train_3d, validation_data=(X_test_3d, y_test_3d),\n\u001b[0;32m--> 130\u001b[0;31m                  epochs=1, callbacks=[early_stop])\n\u001b[0m\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m   \u001b[0;31m# Create predictions for the whole dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1417\u001b[0m                 \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1418\u001b[0m                 \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1419\u001b[0;31m                 steps_per_execution=self._steps_per_execution)\n\u001b[0m\u001b[1;32m   1420\u001b[0m           val_logs = self.evaluate(\n\u001b[1;32m   1421\u001b[0m               \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_x\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/data_adapter.py\u001b[0m in \u001b[0;36mget_data_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1397\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"model\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_cluster_coordinator\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1398\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_ClusterCoordinatorDataHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1399\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mDataHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1400\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1401\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/data_adapter.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution, distribute)\u001b[0m\n\u001b[1;32m   1159\u001b[0m         \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1160\u001b[0m         \u001b[0mdistribution_strategy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistribute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_strategy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1161\u001b[0;31m         model=model)\n\u001b[0m\u001b[1;32m   1162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1163\u001b[0m     \u001b[0mstrategy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistribute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_strategy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/data_adapter.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)\u001b[0m\n\u001b[1;32m    324\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mflat_dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 326\u001b[0;31m     \u001b[0mindices_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindices_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflat_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslice_batch_indices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m     \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36mflat_map\u001b[0;34m(self, map_func, name)\u001b[0m\n\u001b[1;32m   2058\u001b[0m       \u001b[0mDataset\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mA\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2059\u001b[0m     \"\"\"\n\u001b[0;32m-> 2060\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mFlatMapDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2061\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2062\u001b[0m   def interleave(self,\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, input_dataset, map_func, name)\u001b[0m\n\u001b[1;32m   5278\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_input_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5279\u001b[0m     self._map_func = structured_function.StructuredFunctionWrapper(\n\u001b[0;32m-> 5280\u001b[0;31m         map_func, self._transformation_name(), dataset=input_dataset)\n\u001b[0m\u001b[1;32m   5281\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_func\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_structure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDatasetSpec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5282\u001b[0m       raise TypeError(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/structured_function.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, func, transformation_name, dataset, input_classes, input_shapes, input_types, input_structure, add_to_graph, use_legacy_function, defun_kwargs)\u001b[0m\n\u001b[1;32m    269\u001b[0m         \u001b[0mfn_factory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrace_tf_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefun_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    272\u001b[0m     \u001b[0;31m# There is no graph to add in eager mode.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m     \u001b[0madd_to_graph\u001b[0m \u001b[0;34m&=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mget_concrete_function\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3069\u001b[0m     \"\"\"\n\u001b[1;32m   3070\u001b[0m     graph_function = self._get_concrete_function_garbage_collected(\n\u001b[0;32m-> 3071\u001b[0;31m         *args, **kwargs)\n\u001b[0m\u001b[1;32m   3072\u001b[0m     \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_garbage_collector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3073\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_get_concrete_function_garbage_collected\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3034\u001b[0m       \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3035\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3036\u001b[0;31m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3037\u001b[0m       \u001b[0mseen_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3038\u001b[0m       captured = object_identity.ObjectIdentitySet(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   3290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3291\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_call_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3292\u001b[0;31m           \u001b[0mgraph_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3293\u001b[0m           self._function_cache.add(cache_key, cache_key_deletion_observer,\n\u001b[1;32m   3294\u001b[0m                                    graph_function)\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m   3138\u001b[0m             \u001b[0marg_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3139\u001b[0m             \u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3140\u001b[0;31m             capture_by_value=self._capture_by_value),\n\u001b[0m\u001b[1;32m   3141\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_attributes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3142\u001b[0m         \u001b[0mfunction_spec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction_spec\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes, acd_record_initial_resource_uses)\u001b[0m\n\u001b[1;32m   1200\u001b[0m         if x is not None)\n\u001b[1;32m   1201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1202\u001b[0;31m     \u001b[0mfunc_graph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1204\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0madd_control_dependencies\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/auto_control_deps.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, unused_type, unused_value, unused_traceback)\u001b[0m\n\u001b[1;32m    463\u001b[0m       \u001b[0;31m# Check for any resource inputs. If we find any, we update control_inputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m       \u001b[0;31m# and last_write_to_resource.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 465\u001b[0;31m       \u001b[0;32mfor\u001b[0m \u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresource_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_get_resource_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    466\u001b[0m         \u001b[0mis_read\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresource_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mResourceType\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mREAD_ONLY\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    467\u001b[0m         \u001b[0minput_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor_id\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/auto_control_deps.py\u001b[0m in \u001b[0;36m_get_resource_inputs\u001b[0;34m(op)\u001b[0m\n\u001b[1;32m    661\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_resource_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    662\u001b[0m   \u001b[0;34m\"\"\"Returns an iterable of resources touched by this `op`.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 663\u001b[0;31m   \u001b[0mreads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrites\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_read_write_resource_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    664\u001b[0m   \u001b[0msaturated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m   \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msaturated\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/auto_control_deps_utils.py\u001b[0m in \u001b[0;36mget_read_write_resource_inputs\u001b[0;34m(op)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m     \u001b[0mread_only_input_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_attr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mREAD_ONLY_RESOURCE_INPUTS_ATTR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;31m# Attr was not set. Add all resource inputs to `writes` and return.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mget_attr\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   2688\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2689\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mc_api_util\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2690\u001b[0;31m         \u001b[0mpywrap_tf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_OperationGetAttrValueProto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_c_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2691\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpywrap_tf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2692\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\" IT IS NOT A MONTE CARLO SIMULATION \"\"\"\n",
        "\n",
        "# Organize well all the data\n",
        "RNN_returns = pd.DataFrame(returns_test, index=[f\"RNN n°{i}\" for i in range(len(returns_test))]).transpose()\n",
        "\n",
        "# Adapt the size of the graph\n",
        "plt.figure(figsize=(15,8))\n",
        "\n",
        "# Plot the returns\n",
        "plt.plot(RNN_returns.cumsum(axis=0), alpha=0.6)\n",
        "\n",
        "# Plot break-even line\n",
        "plt.axhline(0, color=\"red\")\n",
        "\n",
        "# show the graph\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "IB78j0o4j2z4",
        "outputId": "eb3d37d0-f947-461c-9066-85d736b15a1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Crear un criterio"
      ],
      "metadata": {
        "id": "B5YXcySfPEJd"
      }
    },
    {
      "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": "LSXyw4mE66vv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create empty dataframe\n",
        "values = pd.DataFrame(index=[\"ret/dd\", \"ret\", \"dd\"])\n",
        "\n",
        "# Compute return and max drawdawn for each ANN\n",
        "for col in RNN_returns.columns:\n",
        "\n",
        "  # Compute the returns and the max drawdown for one ANN\n",
        "  ret, dd = RNN_returns[col].cumsum().iloc[-1], -np.min(drawdown_function(RNN_returns[col]))\n",
        "\n",
        "  # Create a column containing the ret/dd ratio, the returns and the drawdown\n",
        "  values[col] = ret/dd, ret, dd"
      ],
      "metadata": {
        "id": "KNGgNN4M7WGE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "values.transpose().sort_values(by=\"ret/dd\", ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Pvd2sEWj7nJq",
        "outputId": "75e1d128-e57c-4d75-caf0-cf0959496f5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d981ce8e-9d78-45ac-a0a4-11393f908e7e\">\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>ret/dd</th>\n",
              "      <th>ret</th>\n",
              "      <th>dd</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RNN n°31</th>\n",
              "      <td>19.943383</td>\n",
              "      <td>2.232328</td>\n",
              "      <td>0.111933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°34</th>\n",
              "      <td>14.942743</td>\n",
              "      <td>1.672590</td>\n",
              "      <td>0.111933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°3</th>\n",
              "      <td>9.720222</td>\n",
              "      <td>1.594739</td>\n",
              "      <td>0.164064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°23</th>\n",
              "      <td>9.110240</td>\n",
              "      <td>1.622201</td>\n",
              "      <td>0.178063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°36</th>\n",
              "      <td>7.818727</td>\n",
              "      <td>1.466737</td>\n",
              "      <td>0.187593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°13</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°27</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°43</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°28</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°29</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°33</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°14</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°21</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°1</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°11</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°10</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°9</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°8</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°7</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°44</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°5</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°40</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°41</th>\n",
              "      <td>7.813835</td>\n",
              "      <td>1.466127</td>\n",
              "      <td>0.187632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°38</th>\n",
              "      <td>7.448361</td>\n",
              "      <td>1.420072</td>\n",
              "      <td>0.190656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°26</th>\n",
              "      <td>7.346534</td>\n",
              "      <td>1.411330</td>\n",
              "      <td>0.192108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°48</th>\n",
              "      <td>7.280375</td>\n",
              "      <td>1.398567</td>\n",
              "      <td>0.192101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°50</th>\n",
              "      <td>7.190229</td>\n",
              "      <td>1.386936</td>\n",
              "      <td>0.192892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°18</th>\n",
              "      <td>7.111161</td>\n",
              "      <td>1.376681</td>\n",
              "      <td>0.193594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°20</th>\n",
              "      <td>4.752059</td>\n",
              "      <td>1.248205</td>\n",
              "      <td>0.262666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°24</th>\n",
              "      <td>4.278878</td>\n",
              "      <td>1.038030</td>\n",
              "      <td>0.242594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°6</th>\n",
              "      <td>3.498573</td>\n",
              "      <td>0.837703</td>\n",
              "      <td>0.239441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°32</th>\n",
              "      <td>2.431175</td>\n",
              "      <td>0.913949</td>\n",
              "      <td>0.375929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°25</th>\n",
              "      <td>2.181675</td>\n",
              "      <td>0.865164</td>\n",
              "      <td>0.396559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°16</th>\n",
              "      <td>2.067519</td>\n",
              "      <td>0.695109</td>\n",
              "      <td>0.336204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°49</th>\n",
              "      <td>2.035411</td>\n",
              "      <td>0.833992</td>\n",
              "      <td>0.409741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°12</th>\n",
              "      <td>1.328897</td>\n",
              "      <td>0.568748</td>\n",
              "      <td>0.427985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°17</th>\n",
              "      <td>1.288321</td>\n",
              "      <td>0.542820</td>\n",
              "      <td>0.421339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°2</th>\n",
              "      <td>-0.044507</td>\n",
              "      <td>-0.028565</td>\n",
              "      <td>0.641803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°19</th>\n",
              "      <td>-0.120400</td>\n",
              "      <td>-0.084500</td>\n",
              "      <td>0.701831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°46</th>\n",
              "      <td>-0.178467</td>\n",
              "      <td>-0.119894</td>\n",
              "      <td>0.671801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°15</th>\n",
              "      <td>-0.193798</td>\n",
              "      <td>-0.149274</td>\n",
              "      <td>0.770252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°42</th>\n",
              "      <td>-0.226357</td>\n",
              "      <td>-0.164525</td>\n",
              "      <td>0.726837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°47</th>\n",
              "      <td>-0.408704</td>\n",
              "      <td>-0.355697</td>\n",
              "      <td>0.870305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°0</th>\n",
              "      <td>-0.411073</td>\n",
              "      <td>-0.269530</td>\n",
              "      <td>0.655673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°22</th>\n",
              "      <td>-0.507494</td>\n",
              "      <td>-0.413985</td>\n",
              "      <td>0.815745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°4</th>\n",
              "      <td>-0.663268</td>\n",
              "      <td>-1.096623</td>\n",
              "      <td>1.653362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°37</th>\n",
              "      <td>-0.696433</td>\n",
              "      <td>-0.983714</td>\n",
              "      <td>1.412502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°39</th>\n",
              "      <td>-0.797300</td>\n",
              "      <td>-1.466127</td>\n",
              "      <td>1.838865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°45</th>\n",
              "      <td>-0.797300</td>\n",
              "      <td>-1.466127</td>\n",
              "      <td>1.838865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°35</th>\n",
              "      <td>-0.797300</td>\n",
              "      <td>-1.466127</td>\n",
              "      <td>1.838865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°51</th>\n",
              "      <td>-0.797300</td>\n",
              "      <td>-1.466127</td>\n",
              "      <td>1.838865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RNN n°30</th>\n",
              "      <td>-0.952741</td>\n",
              "      <td>-1.142019</td>\n",
              "      <td>1.198667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d981ce8e-9d78-45ac-a0a4-11393f908e7e')\"\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-d981ce8e-9d78-45ac-a0a4-11393f908e7e 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-d981ce8e-9d78-45ac-a0a4-11393f908e7e');\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",
              "  "
            ],
            "text/plain": [
              "             ret/dd       ret        dd\n",
              "RNN n°31  19.943383  2.232328  0.111933\n",
              "RNN n°34  14.942743  1.672590  0.111933\n",
              "RNN n°3    9.720222  1.594739  0.164064\n",
              "RNN n°23   9.110240  1.622201  0.178063\n",
              "RNN n°36   7.818727  1.466737  0.187593\n",
              "RNN n°13   7.813835  1.466127  0.187632\n",
              "RNN n°27   7.813835  1.466127  0.187632\n",
              "RNN n°43   7.813835  1.466127  0.187632\n",
              "RNN n°28   7.813835  1.466127  0.187632\n",
              "RNN n°29   7.813835  1.466127  0.187632\n",
              "RNN n°33   7.813835  1.466127  0.187632\n",
              "RNN n°14   7.813835  1.466127  0.187632\n",
              "RNN n°21   7.813835  1.466127  0.187632\n",
              "RNN n°1    7.813835  1.466127  0.187632\n",
              "RNN n°11   7.813835  1.466127  0.187632\n",
              "RNN n°10   7.813835  1.466127  0.187632\n",
              "RNN n°9    7.813835  1.466127  0.187632\n",
              "RNN n°8    7.813835  1.466127  0.187632\n",
              "RNN n°7    7.813835  1.466127  0.187632\n",
              "RNN n°44   7.813835  1.466127  0.187632\n",
              "RNN n°5    7.813835  1.466127  0.187632\n",
              "RNN n°40   7.813835  1.466127  0.187632\n",
              "RNN n°41   7.813835  1.466127  0.187632\n",
              "RNN n°38   7.448361  1.420072  0.190656\n",
              "RNN n°26   7.346534  1.411330  0.192108\n",
              "RNN n°48   7.280375  1.398567  0.192101\n",
              "RNN n°50   7.190229  1.386936  0.192892\n",
              "RNN n°18   7.111161  1.376681  0.193594\n",
              "RNN n°20   4.752059  1.248205  0.262666\n",
              "RNN n°24   4.278878  1.038030  0.242594\n",
              "RNN n°6    3.498573  0.837703  0.239441\n",
              "RNN n°32   2.431175  0.913949  0.375929\n",
              "RNN n°25   2.181675  0.865164  0.396559\n",
              "RNN n°16   2.067519  0.695109  0.336204\n",
              "RNN n°49   2.035411  0.833992  0.409741\n",
              "RNN n°12   1.328897  0.568748  0.427985\n",
              "RNN n°17   1.288321  0.542820  0.421339\n",
              "RNN n°2   -0.044507 -0.028565  0.641803\n",
              "RNN n°19  -0.120400 -0.084500  0.701831\n",
              "RNN n°46  -0.178467 -0.119894  0.671801\n",
              "RNN n°15  -0.193798 -0.149274  0.770252\n",
              "RNN n°42  -0.226357 -0.164525  0.726837\n",
              "RNN n°47  -0.408704 -0.355697  0.870305\n",
              "RNN n°0   -0.411073 -0.269530  0.655673\n",
              "RNN n°22  -0.507494 -0.413985  0.815745\n",
              "RNN n°4   -0.663268 -1.096623  1.653362\n",
              "RNN n°37  -0.696433 -0.983714  1.412502\n",
              "RNN n°39  -0.797300 -1.466127  1.838865\n",
              "RNN n°45  -0.797300 -1.466127  1.838865\n",
              "RNN n°35  -0.797300 -1.466127  1.838865\n",
              "RNN n°51  -0.797300 -1.466127  1.838865\n",
              "RNN n°30  -0.952741 -1.142019  1.198667"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RNN_models = list(values.transpose().sort_values(by=\"ret/dd\", ascending=False).index[0:3])"
      ],
      "metadata": {
        "id": "fQn4I6LS8gtm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RNN_models"
      ],
      "metadata": {
        "id": "cwa9i5LHRXJr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6b0ac51-9d6a-4971-f286-0f12c4d4e826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['RNN n°31', 'RNN n°34', 'RNN n°3']"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\" Use the 3best models on the test set to trade on the validation set\"\"\"\n",
        "\n",
        "# Organize the data \n",
        "RNN_val = pd.DataFrame(returns_val, index=[f\"RNN n°{i}\" for i in range(len(returns_val))]).transpose()\n",
        "\n",
        "# Adapt the size\n",
        "plt.figure(figsize=(15,8))\n",
        "\n",
        "# Plot the portfolio method containing the 10 best strategies\n",
        "plt.plot(RNN_val[RNN_models].cumsum(axis=0), alpha=0.6)\n",
        "\n",
        "plt.axhline(0, color=\"red\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "4iSUePucB3uA",
        "outputId": "6ec7db97-1e15-445c-f3ef-4e1d0e729064"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f8c094a5d50>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\" Use the 3best models on the test set to trade on the test set\"\"\"\n",
        "\n",
        "# Organize the data \n",
        "RNN_val = pd.DataFrame(returns_val, index=[f\"RNN n°{i}\" for i in range(len(returns_val))]).transpose()\n",
        "\n",
        "# Adapt the size\n",
        "plt.figure(figsize=(15,8))\n",
        "\n",
        "# Plot the portfolio method containing the 10 best strategies\n",
        "plt.plot(RNN_returns[RNN_models].cumsum(axis=0), alpha=0.6)\n",
        "\n",
        "plt.axhline(0, color=\"red\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "Uva1iVxy9OcR",
        "outputId": "6ffd3c9e-41d0-4b5c-a397-138c11277a67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f8c0de02790>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Técnicas de bagging"
      ],
      "metadata": {
        "id": "ZVixDG3Y4hwR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def RNN_weights():\n",
        "  # INITIALIZATION OF THE MODEL\n",
        "  classifier = Sequential()\n",
        "\n",
        "  # ADD LSTM LAYER\n",
        "  classifier.add(LSTM(units = 10, return_sequences = True,\n",
        "                      input_shape = (X_train_3d.shape[1],X_train_3d.shape[2],)))\n",
        "\n",
        "\n",
        "  # LOOP WHICH ADD LSTM LAYER\n",
        "  for _ in range(1):\n",
        "      classifier.add(LSTM(units = 10, return_sequences = True))\n",
        "      classifier.add(Dropout(0.50))\n",
        "\n",
        "  # LAST LSTM LAYER BUT WITH return_sequences = False \n",
        "  classifier.add(LSTM(units = 10, return_sequences = False))\n",
        "\n",
        "\n",
        "  # OUTPUT DENSE LAYER\n",
        "  classifier.add(Dense(1, activation=\"sigmoid\"))\n",
        "\n",
        "  # COMPILE THE MODEL\n",
        "  classifier.compile(loss=\"binary_crossentropy\", optimizer=\"adam\")\n",
        "\n",
        "  return classifier"
      ],
      "metadata": {
        "id": "d8a_rdPOOqg7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create empty dataframe\n",
        "prediction = pd.DataFrame(index = dfc.index)\n",
        "for nb in RNN_models:\n",
        "\n",
        "  # Create ANN structure\n",
        "  rnn = RNN_weights()\n",
        "\n",
        "  # Import trained weights\n",
        "  rnn.load_weights(f\"Weights_RNN/{nb}\")\n",
        "\n",
        "  # Create predictions for the whole dataset\n",
        "  \n",
        "  y_pred_train = np.concatenate((np.zeros([lag,1]),rnn.predict(X_train_3d)),\n",
        "                                    axis=0)\n",
        "\n",
        "  y_pred_test = np.concatenate((np.zeros([lag,1]),rnn.predict(X_test_3d)),\n",
        "                                    axis=0)\n",
        "\n",
        "\n",
        "  pre = np.concatenate((y_pred_train,y_pred_test),\n",
        "                                    axis=0)\n",
        "\n",
        "  prediction[f\"{nb}\"] = pre\n",
        "prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        },
        "id": "kzxvOQq0Pwpf",
        "outputId": "1eb63d3d-7743-4d12-e527-070bd17e3c7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.iter\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_1\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_2\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.decay\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.learning_rate\n",
            "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.iter\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_1\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_2\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.decay\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.learning_rate\n",
            "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.iter\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_1\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_2\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.decay\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.learning_rate\n",
            "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.iter\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_1\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_2\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.decay\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.learning_rate\n",
            "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.iter\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_1\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_2\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.decay\n",
            "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.learning_rate\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0476095e-d757-4f53-91a3-1cf249957fd3\">\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>RNN n°31</th>\n",
              "      <th>RNN n°34</th>\n",
              "      <th>RNN n°3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-11-16</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-17</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-18</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-19</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-20</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-12</th>\n",
              "      <td>0.492287</td>\n",
              "      <td>0.519987</td>\n",
              "      <td>0.497671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-13</th>\n",
              "      <td>0.492768</td>\n",
              "      <td>0.522389</td>\n",
              "      <td>0.497467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-14</th>\n",
              "      <td>0.493456</td>\n",
              "      <td>0.524009</td>\n",
              "      <td>0.497351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-15</th>\n",
              "      <td>0.494437</td>\n",
              "      <td>0.525106</td>\n",
              "      <td>0.497286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-16</th>\n",
              "      <td>0.495660</td>\n",
              "      <td>0.525887</td>\n",
              "      <td>0.497402</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2650 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0476095e-d757-4f53-91a3-1cf249957fd3')\"\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-0476095e-d757-4f53-91a3-1cf249957fd3 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-0476095e-d757-4f53-91a3-1cf249957fd3');\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",
              "  "
            ],
            "text/plain": [
              "            RNN n°31  RNN n°34   RNN n°3\n",
              "time                                    \n",
              "2014-11-16  0.000000  0.000000  0.000000\n",
              "2014-11-17  0.000000  0.000000  0.000000\n",
              "2014-11-18  0.000000  0.000000  0.000000\n",
              "2014-11-19  0.000000  0.000000  0.000000\n",
              "2014-11-20  0.000000  0.000000  0.000000\n",
              "...              ...       ...       ...\n",
              "2022-02-12  0.492287  0.519987  0.497671\n",
              "2022-02-13  0.492768  0.522389  0.497467\n",
              "2022-02-14  0.493456  0.524009  0.497351\n",
              "2022-02-15  0.494437  0.525106  0.497286\n",
              "2022-02-16  0.495660  0.525887  0.497402\n",
              "\n",
              "[2650 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction[\"Increase\"] = (np.sign(prediction[RNN_models]-0.5)==1).sum(axis=1)\n",
        "prediction[\"Decrease\"] = (np.sign(prediction[RNN_models]-0.5)==-1).sum(axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "Ub4ip1aRRERb",
        "outputId": "c9927ea8-6736-45f1-e7f8-02c215a8b5bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-594fa7f4-55de-4c67-8e2a-f0bd519f230d\">\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>RNN n°31</th>\n",
              "      <th>RNN n°34</th>\n",
              "      <th>RNN n°3</th>\n",
              "      <th>Increase</th>\n",
              "      <th>Decrease</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-11-16</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-17</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-18</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-19</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-20</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-12</th>\n",
              "      <td>0.492287</td>\n",
              "      <td>0.519987</td>\n",
              "      <td>0.497671</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-13</th>\n",
              "      <td>0.492768</td>\n",
              "      <td>0.522389</td>\n",
              "      <td>0.497467</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-14</th>\n",
              "      <td>0.493456</td>\n",
              "      <td>0.524009</td>\n",
              "      <td>0.497351</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-15</th>\n",
              "      <td>0.494437</td>\n",
              "      <td>0.525106</td>\n",
              "      <td>0.497286</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-16</th>\n",
              "      <td>0.495660</td>\n",
              "      <td>0.525887</td>\n",
              "      <td>0.497402</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2650 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-594fa7f4-55de-4c67-8e2a-f0bd519f230d')\"\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-594fa7f4-55de-4c67-8e2a-f0bd519f230d 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-594fa7f4-55de-4c67-8e2a-f0bd519f230d');\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",
              "  "
            ],
            "text/plain": [
              "            RNN n°31  RNN n°34   RNN n°3  Increase  Decrease\n",
              "time                                                        \n",
              "2014-11-16  0.000000  0.000000  0.000000         0         4\n",
              "2014-11-17  0.000000  0.000000  0.000000         0         4\n",
              "2014-11-18  0.000000  0.000000  0.000000         0         4\n",
              "2014-11-19  0.000000  0.000000  0.000000         0         4\n",
              "2014-11-20  0.000000  0.000000  0.000000         0         4\n",
              "...              ...       ...       ...       ...       ...\n",
              "2022-02-12  0.492287  0.519987  0.497671         1         2\n",
              "2022-02-13  0.492768  0.522389  0.497467         1         2\n",
              "2022-02-14  0.493456  0.524009  0.497351         1         2\n",
              "2022-02-15  0.494437  0.525106  0.497286         1         2\n",
              "2022-02-16  0.495660  0.525887  0.497402         1         2\n",
              "\n",
              "[2650 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Delete the rows containing only some 0\n",
        "prediction = prediction.iloc[lag:,:]\n",
        "prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "8ROHW34-utD0",
        "outputId": "d199386c-8669-4e13-8a1c-125e6439e0bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e912fee2-a28f-49d2-b1d9-9f9bbdfd03ae\">\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>RNN n°31</th>\n",
              "      <th>RNN n°34</th>\n",
              "      <th>RNN n°3</th>\n",
              "      <th>Increase</th>\n",
              "      <th>Decrease</th>\n",
              "      <th>returns</th>\n",
              "      <th>position</th>\n",
              "      <th>strategy</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-12-16</th>\n",
              "      <td>0.537507</td>\n",
              "      <td>0.535178</td>\n",
              "      <td>0.552034</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.052941</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.052941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-12-17</th>\n",
              "      <td>0.536451</td>\n",
              "      <td>0.535242</td>\n",
              "      <td>0.551838</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.022277</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.022277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-12-18</th>\n",
              "      <td>0.535062</td>\n",
              "      <td>0.535583</td>\n",
              "      <td>0.551387</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.026206</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.026206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-12-19</th>\n",
              "      <td>0.533163</td>\n",
              "      <td>0.535829</td>\n",
              "      <td>0.550555</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.020704</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.020704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-12-20</th>\n",
              "      <td>0.531547</td>\n",
              "      <td>0.536140</td>\n",
              "      <td>0.549744</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.038110</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.038110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-12</th>\n",
              "      <td>0.492287</td>\n",
              "      <td>0.519987</td>\n",
              "      <td>0.497671</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.003855</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.003855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-13</th>\n",
              "      <td>0.492768</td>\n",
              "      <td>0.522389</td>\n",
              "      <td>0.497467</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.001111</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.001111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-14</th>\n",
              "      <td>0.493456</td>\n",
              "      <td>0.524009</td>\n",
              "      <td>0.497351</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.009228</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.009228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-15</th>\n",
              "      <td>0.494437</td>\n",
              "      <td>0.525106</td>\n",
              "      <td>0.497286</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.046688</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.046688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-16</th>\n",
              "      <td>0.495660</td>\n",
              "      <td>0.525887</td>\n",
              "      <td>0.497402</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.009387</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.009387</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2620 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e912fee2-a28f-49d2-b1d9-9f9bbdfd03ae')\"\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-e912fee2-a28f-49d2-b1d9-9f9bbdfd03ae 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-e912fee2-a28f-49d2-b1d9-9f9bbdfd03ae');\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",
              "  "
            ],
            "text/plain": [
              "            RNN n°31  RNN n°34   RNN n°3  ...   returns  position  strategy\n",
              "time                                      ...                              \n",
              "2014-12-16  0.537507  0.535178  0.552034  ... -0.052941       1.0 -0.052941\n",
              "2014-12-17  0.536451  0.535242  0.551838  ... -0.022277       1.0 -0.022277\n",
              "2014-12-18  0.535062  0.535583  0.551387  ... -0.026206       1.0 -0.026206\n",
              "2014-12-19  0.533163  0.535829  0.550555  ...  0.020704       1.0  0.020704\n",
              "2014-12-20  0.531547  0.536140  0.549744  ...  0.038110       1.0  0.038110\n",
              "...              ...       ...       ...  ...       ...       ...       ...\n",
              "2022-02-12  0.492287  0.519987  0.497671  ... -0.003855      -1.0  0.003855\n",
              "2022-02-13  0.492768  0.522389  0.497467  ... -0.001111      -1.0  0.001111\n",
              "2022-02-14  0.493456  0.524009  0.497351  ...  0.009228      -1.0 -0.009228\n",
              "2022-02-15  0.494437  0.525106  0.497286  ...  0.046688      -1.0 -0.046688\n",
              "2022-02-16  0.495660  0.525887  0.497402  ... -0.009387      -1.0  0.009387\n",
              "\n",
              "[2620 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 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": "WNAqqSryZuws",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "1c3d7b1c-5f5d-4bda-a2d5-d48b9acd6125"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c0df96650>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RMc7p6u1suft"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}