{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PREDICTING FUTURE BEHAVIOUR OF S&P 500 STOCK MARKET INDEX\n",
    "# A machine learning-based approach leveraging MarketPsych sentiment indicators\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Index\n",
    "* [1. Data loading and some EDA](#1.-Data-loading-and-some-EDA)\n",
    "    * [Sentiment indicators](#Sentiment-indicators)\n",
    "    * [Market data](#Market-data)\n",
    "* [2. Data preparation](#2.-Data-preparation)\n",
    "    * [Missing values](#Missing-values)\n",
    "    * [Transformations](#Transformations)\n",
    "    * [Smoothing](#Smoothing)\n",
    "    * [Feature Engineering](#Feature-Engineering)\n",
    "        * [PCA](#PCA)\n",
    "        * [Technical indicators](#Technical-indicators)\n",
    "        * [New variables (crossovers)](#New-variables-(crossovers))\n",
    "    * [Labeling target variable](#Labeling-target-variable)\n",
    "* [3. Model](#3.-Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "import mlfinlab as mlfin\n",
    "import pandas as pd\n",
    "import pandas_datareader as pdr\n",
    "import pandas_profiling as pf\n",
    "import numpy as np\n",
    "from yahoo_finance import Share\n",
    "from datetime import datetime, timedelta, date\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import missingno as msno\n",
    "from scipy import stats\n",
    "import pywt\n",
    "from ta.momentum import RSIIndicator, StochasticOscillator, WilliamsRIndicator, ROCIndicator\n",
    "from ta.trend import MACD, ADXIndicator\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc, roc_auc_score, accuracy_score\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import TimeSeriesSplit, GridSearchCV, cross_val_score\n",
    "from xgboost import XGBClassifier, plot_tree\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs\n",
    "from plotly.subplots import make_subplots\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "from plotly.validators.scatter.marker import SymbolValidator\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, HBox, Label\n",
    "import plotly.graph_objs as go\n",
    "import warnings\n",
    "import sys\n",
    "import os\n",
    "\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files/Graphviz/bin/'\n",
    "sns.set_context('notebook', rc={\"axes.titlesize\":14,\"axes.labelsize\":13})\n",
    "sns.set_style('white')\n",
    "%matplotlib inline\n",
    "bbva = ['#004481','#2DCCCD', '#D8BE75','#1973B8', '#5BBEFF', '#F7893B', '#02A5A5', '#48AE64', '#F8CD51', '#F78BE8'];\n",
    "sns.set_palette(bbva);\n",
    "sns.color_palette(bbva)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data loading and some EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(data='countries', data_type='News_Social', asset_code_id='US'):\n",
    "    \"\"\"\n",
    "    This function loads the sentiment indicators downloaded from Thomsom Reuters, \n",
    "    i.e. the MarketPsych Sentiment Indicators related to countries, companies and currencies.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data: str\n",
    "        One of 'countries', 'companies', 'currencies'.\n",
    "    data_type: str\n",
    "        Whether to filter info by data source. One of 'News', 'Social', 'News_Social'.\n",
    "    asset_code_id: str\n",
    "        Code of the asset / data. One of 'US', 'US500', 'USD'.\n",
    "        \n",
    "    Return\n",
    "    ------\n",
    "    Pandas dataframe.\n",
    "    \"\"\"\n",
    "    dirs = os.listdir('./')\n",
    "    if data == 'countries':\n",
    "        dim, file = 'COU', 'COU_CARGA_INICIAL.csv'\n",
    "        sufix = '_USA'\n",
    "    elif data == 'currencies':\n",
    "        dim, file = 'CUR', 'CUR_CARGA_INICIAL.csv'\n",
    "        sufix = '_USD'\n",
    "    elif data == 'companies':\n",
    "        dim, file = 'CMPNY', 'CMPNY_GRP_CARGA_INICIAL.csv'\n",
    "        sufix = '_US500'\n",
    "        \n",
    "    dirs = [dire_x for dire_x in os.listdir('./') if dim in dire_x]\n",
    "    dataset = pd.read_csv(file, sep=';')\n",
    "    dataset = dataset[dataset.date <= '2020-03-30']\n",
    "    \n",
    "    for dire in dirs:\n",
    "        if (dire != file):\n",
    "            new_month = pd.read_csv(dire, delimiter='\\t')\n",
    "            if len(new_month.columns) == 1:\n",
    "                new_month = pd.read_csv(dire, delimiter=';')\n",
    "            if 'date' not in new_month.columns:\n",
    "                new_month['date'] = new_month.id.apply(lambda x: x[3:13])\n",
    "            if 'asset_code_id' not in new_month.columns:\n",
    "                new_month['asset_code_id'] = new_month.assetCode\n",
    "            if 'data_type' not in new_month.columns:\n",
    "                new_month['data_type'] = new_month.dataType\n",
    "            if 'id_refinitiv' not in new_month.columns:\n",
    "                new_month['id_refinitiv'] = new_month.id\n",
    "            if 'system_version' not in new_month.columns:\n",
    "                new_month['system_version'] = new_month.systemVersion\n",
    "            if 'date_audit_laod' not in new_month.columns:\n",
    "                new_month['date_audit_laod'] = 'NA'\n",
    "            if 'process_audit_load' not in new_month.columns:\n",
    "                new_month['process_audit_load'] = 'NA'\n",
    "            new_month = new_month[dataset.columns]\n",
    "            dataset = pd.concat([dataset, new_month], ignore_index=True)\n",
    "\n",
    "    dataset = dataset[(dataset.data_type == data_type) & \n",
    "                       (dataset.asset_code_id == asset_code_id) &\n",
    "                       (dataset.date >= '2000-01-01')].sort_values(by='date')\n",
    "    \n",
    "    dataset['Date'] = pd.to_datetime(dataset.date)\n",
    "    dataset.set_index('Date', inplace=True)\n",
    "    dataset.drop(['date', 'asset_code_id', 'data_type', 'id_refinitiv',\n",
    "                  'system_version', 'date_audit_laod', 'process_audit_load'], axis=1, inplace=True)\n",
    "    dataset.columns = [col + sufix for col in dataset.columns]\n",
    "    \n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Countries info\n",
    "countries = load_data(data='countries', data_type='News_Social', asset_code_id='US')\n",
    "#pf.ProfileReport(countries, explorative=True, minimal=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>stockIndexBuzz_USA</th>\n",
       "      <th>bondBuzz_USA</th>\n",
       "      <th>ratesBuzz_USA</th>\n",
       "      <th>stockIndexSentiment_USA</th>\n",
       "      <th>stockIndexOptimism_USA</th>\n",
       "      <th>stockIndexTrust_USA</th>\n",
       "      <th>stockIndexFear_USA</th>\n",
       "      <th>stockIndexStress_USA</th>\n",
       "      <th>stockIndexSurprise_USA</th>\n",
       "      <th>stockIndexUncertainty_USA</th>\n",
       "      <th>...</th>\n",
       "      <th>bondUncertainty_USA</th>\n",
       "      <th>bondDefault_USA</th>\n",
       "      <th>bondPriceDirection_USA</th>\n",
       "      <th>bondPriceForecast_USA</th>\n",
       "      <th>bondVolatility_USA</th>\n",
       "      <th>centralBank_USA</th>\n",
       "      <th>debtDefault_USA</th>\n",
       "      <th>interestRates_USA</th>\n",
       "      <th>interestRatesForecast_USA</th>\n",
       "      <th>monetaryPolicyLooseVsTight_USA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7548.000000</td>\n",
       "      <td>7548.000000</td>\n",
       "      <td>7548.000000</td>\n",
       "      <td>7548.000000</td>\n",
       "      <td>7548.000000</td>\n",
       "      <td>7546.000000</td>\n",
       "      <td>7521.000000</td>\n",
       "      <td>7548.000000</td>\n",
       "      <td>7455.000000</td>\n",
       "      <td>7548.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7540.000000</td>\n",
       "      <td>7487.000000</td>\n",
       "      <td>7543.000000</td>\n",
       "      <td>7215.000000</td>\n",
       "      <td>7538.000000</td>\n",
       "      <td>7548.000000</td>\n",
       "      <td>7548.000000</td>\n",
       "      <td>7416.000000</td>\n",
       "      <td>6770.000000</td>\n",
       "      <td>7432.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7766.818243</td>\n",
       "      <td>5018.980896</td>\n",
       "      <td>3059.338103</td>\n",
       "      <td>-0.045077</td>\n",
       "      <td>-0.004602</td>\n",
       "      <td>0.003662</td>\n",
       "      <td>0.011027</td>\n",
       "      <td>0.046969</td>\n",
       "      <td>0.004845</td>\n",
       "      <td>0.029227</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020278</td>\n",
       "      <td>0.018076</td>\n",
       "      <td>0.008856</td>\n",
       "      <td>0.000788</td>\n",
       "      <td>0.028208</td>\n",
       "      <td>0.582781</td>\n",
       "      <td>0.253980</td>\n",
       "      <td>0.026239</td>\n",
       "      <td>0.007460</td>\n",
       "      <td>0.016272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7423.268580</td>\n",
       "      <td>5703.682036</td>\n",
       "      <td>3620.876817</td>\n",
       "      <td>0.146176</td>\n",
       "      <td>0.026762</td>\n",
       "      <td>0.005205</td>\n",
       "      <td>0.007044</td>\n",
       "      <td>0.015133</td>\n",
       "      <td>0.004011</td>\n",
       "      <td>0.027715</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008642</td>\n",
       "      <td>0.012289</td>\n",
       "      <td>0.030095</td>\n",
       "      <td>0.004918</td>\n",
       "      <td>0.011702</td>\n",
       "      <td>0.131323</td>\n",
       "      <td>0.132086</td>\n",
       "      <td>0.038675</td>\n",
       "      <td>0.012789</td>\n",
       "      <td>0.031317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>59.200000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>-0.466583</td>\n",
       "      <td>-0.212583</td>\n",
       "      <td>-0.035411</td>\n",
       "      <td>-0.004999</td>\n",
       "      <td>0.007194</td>\n",
       "      <td>-0.009065</td>\n",
       "      <td>0.001890</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014634</td>\n",
       "      <td>-0.118110</td>\n",
       "      <td>-0.142631</td>\n",
       "      <td>-0.054225</td>\n",
       "      <td>-0.007491</td>\n",
       "      <td>0.056878</td>\n",
       "      <td>0.013831</td>\n",
       "      <td>-0.156575</td>\n",
       "      <td>-0.080899</td>\n",
       "      <td>-0.094737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2221.575000</td>\n",
       "      <td>1746.250000</td>\n",
       "      <td>634.875000</td>\n",
       "      <td>-0.154254</td>\n",
       "      <td>-0.019818</td>\n",
       "      <td>0.000708</td>\n",
       "      <td>0.006011</td>\n",
       "      <td>0.036367</td>\n",
       "      <td>0.002423</td>\n",
       "      <td>0.016069</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014702</td>\n",
       "      <td>0.010157</td>\n",
       "      <td>-0.006501</td>\n",
       "      <td>-0.001321</td>\n",
       "      <td>0.020393</td>\n",
       "      <td>0.498940</td>\n",
       "      <td>0.150913</td>\n",
       "      <td>0.004769</td>\n",
       "      <td>0.000681</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5814.000000</td>\n",
       "      <td>3864.050000</td>\n",
       "      <td>1903.000000</td>\n",
       "      <td>-0.042142</td>\n",
       "      <td>-0.004615</td>\n",
       "      <td>0.002594</td>\n",
       "      <td>0.009419</td>\n",
       "      <td>0.044996</td>\n",
       "      <td>0.003920</td>\n",
       "      <td>0.021073</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018928</td>\n",
       "      <td>0.015456</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>0.000647</td>\n",
       "      <td>0.025982</td>\n",
       "      <td>0.595217</td>\n",
       "      <td>0.235606</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.003730</td>\n",
       "      <td>0.006530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11281.950000</td>\n",
       "      <td>6928.450000</td>\n",
       "      <td>4178.750000</td>\n",
       "      <td>0.066406</td>\n",
       "      <td>0.010433</td>\n",
       "      <td>0.005566</td>\n",
       "      <td>0.014252</td>\n",
       "      <td>0.055582</td>\n",
       "      <td>0.006144</td>\n",
       "      <td>0.029007</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024224</td>\n",
       "      <td>0.022533</td>\n",
       "      <td>0.023477</td>\n",
       "      <td>0.002868</td>\n",
       "      <td>0.033606</td>\n",
       "      <td>0.678474</td>\n",
       "      <td>0.339387</td>\n",
       "      <td>0.036436</td>\n",
       "      <td>0.010460</td>\n",
       "      <td>0.019464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>134839.000000</td>\n",
       "      <td>126062.900000</td>\n",
       "      <td>45542.500000</td>\n",
       "      <td>0.390025</td>\n",
       "      <td>0.231104</td>\n",
       "      <td>0.045873</td>\n",
       "      <td>0.068737</td>\n",
       "      <td>0.158899</td>\n",
       "      <td>0.072478</td>\n",
       "      <td>0.267216</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098859</td>\n",
       "      <td>0.125559</td>\n",
       "      <td>0.189027</td>\n",
       "      <td>0.045312</td>\n",
       "      <td>0.153226</td>\n",
       "      <td>0.892727</td>\n",
       "      <td>0.835544</td>\n",
       "      <td>0.257800</td>\n",
       "      <td>0.103896</td>\n",
       "      <td>0.234619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       stockIndexBuzz_USA   bondBuzz_USA  ratesBuzz_USA  \\\n",
       "count         7548.000000    7548.000000    7548.000000   \n",
       "mean          7766.818243    5018.980896    3059.338103   \n",
       "std           7423.268580    5703.682036    3620.876817   \n",
       "min             59.200000      38.000000      25.000000   \n",
       "25%           2221.575000    1746.250000     634.875000   \n",
       "50%           5814.000000    3864.050000    1903.000000   \n",
       "75%          11281.950000    6928.450000    4178.750000   \n",
       "max         134839.000000  126062.900000   45542.500000   \n",
       "\n",
       "       stockIndexSentiment_USA  stockIndexOptimism_USA  stockIndexTrust_USA  \\\n",
       "count              7548.000000             7548.000000          7546.000000   \n",
       "mean                 -0.045077               -0.004602             0.003662   \n",
       "std                   0.146176                0.026762             0.005205   \n",
       "min                  -0.466583               -0.212583            -0.035411   \n",
       "25%                  -0.154254               -0.019818             0.000708   \n",
       "50%                  -0.042142               -0.004615             0.002594   \n",
       "75%                   0.066406                0.010433             0.005566   \n",
       "max                   0.390025                0.231104             0.045873   \n",
       "\n",
       "       stockIndexFear_USA  stockIndexStress_USA  stockIndexSurprise_USA  \\\n",
       "count         7521.000000           7548.000000             7455.000000   \n",
       "mean             0.011027              0.046969                0.004845   \n",
       "std              0.007044              0.015133                0.004011   \n",
       "min             -0.004999              0.007194               -0.009065   \n",
       "25%              0.006011              0.036367                0.002423   \n",
       "50%              0.009419              0.044996                0.003920   \n",
       "75%              0.014252              0.055582                0.006144   \n",
       "max              0.068737              0.158899                0.072478   \n",
       "\n",
       "       stockIndexUncertainty_USA  ...  bondUncertainty_USA  bondDefault_USA  \\\n",
       "count                7548.000000  ...          7540.000000      7487.000000   \n",
       "mean                    0.029227  ...             0.020278         0.018076   \n",
       "std                     0.027715  ...             0.008642         0.012289   \n",
       "min                     0.001890  ...            -0.014634        -0.118110   \n",
       "25%                     0.016069  ...             0.014702         0.010157   \n",
       "50%                     0.021073  ...             0.018928         0.015456   \n",
       "75%                     0.029007  ...             0.024224         0.022533   \n",
       "max                     0.267216  ...             0.098859         0.125559   \n",
       "\n",
       "       bondPriceDirection_USA  bondPriceForecast_USA  bondVolatility_USA  \\\n",
       "count             7543.000000            7215.000000         7538.000000   \n",
       "mean                 0.008856               0.000788            0.028208   \n",
       "std                  0.030095               0.004918            0.011702   \n",
       "min                 -0.142631              -0.054225           -0.007491   \n",
       "25%                 -0.006501              -0.001321            0.020393   \n",
       "50%                  0.006400               0.000647            0.025982   \n",
       "75%                  0.023477               0.002868            0.033606   \n",
       "max                  0.189027               0.045312            0.153226   \n",
       "\n",
       "       centralBank_USA  debtDefault_USA  interestRates_USA  \\\n",
       "count      7548.000000      7548.000000        7416.000000   \n",
       "mean          0.582781         0.253980           0.026239   \n",
       "std           0.131323         0.132086           0.038675   \n",
       "min           0.056878         0.013831          -0.156575   \n",
       "25%           0.498940         0.150913           0.004769   \n",
       "50%           0.595217         0.235606           0.015038   \n",
       "75%           0.678474         0.339387           0.036436   \n",
       "max           0.892727         0.835544           0.257800   \n",
       "\n",
       "       interestRatesForecast_USA  monetaryPolicyLooseVsTight_USA  \n",
       "count                6770.000000                     7432.000000  \n",
       "mean                    0.007460                        0.016272  \n",
       "std                     0.012789                        0.031317  \n",
       "min                    -0.080899                       -0.094737  \n",
       "25%                     0.000681                        0.000000  \n",
       "50%                     0.003730                        0.006530  \n",
       "75%                     0.010460                        0.019464  \n",
       "max                     0.103896                        0.234619  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Currencies info\n",
    "currencies = load_data(data='currencies', data_type='News_Social', asset_code_id='USD')\n",
    "#pf.ProfileReport(currencies, minimal=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buzz_USD</th>\n",
       "      <th>sentiment_USD</th>\n",
       "      <th>optimism_USD</th>\n",
       "      <th>joy_USD</th>\n",
       "      <th>loveHate_USD</th>\n",
       "      <th>trust_USD</th>\n",
       "      <th>anger_USD</th>\n",
       "      <th>conflict_USD</th>\n",
       "      <th>fear_USD</th>\n",
       "      <th>gloom_USD</th>\n",
       "      <th>...</th>\n",
       "      <th>emotionVsFact_USD</th>\n",
       "      <th>marketRisk_USD</th>\n",
       "      <th>longShort_USD</th>\n",
       "      <th>longShortForecast_USD</th>\n",
       "      <th>priceDirection_USD</th>\n",
       "      <th>priceForecast_USD</th>\n",
       "      <th>volatility_USD</th>\n",
       "      <th>carryTrade_USD</th>\n",
       "      <th>currencyPegInstability_USD</th>\n",
       "      <th>priceMomentum_USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7548.000000</td>\n",
       "      <td>7547.000000</td>\n",
       "      <td>7527.000000</td>\n",
       "      <td>6838.000000</td>\n",
       "      <td>6917.000000</td>\n",
       "      <td>7462.000000</td>\n",
       "      <td>6926.000000</td>\n",
       "      <td>7526.000000</td>\n",
       "      <td>7335.000000</td>\n",
       "      <td>7481.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7548.000000</td>\n",
       "      <td>7544.000000</td>\n",
       "      <td>7459.000000</td>\n",
       "      <td>6982.000000</td>\n",
       "      <td>7545.000000</td>\n",
       "      <td>7502.000000</td>\n",
       "      <td>7471.000000</td>\n",
       "      <td>2034.000000</td>\n",
       "      <td>4162.000000</td>\n",
       "      <td>7443.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6291.483148</td>\n",
       "      <td>-0.048832</td>\n",
       "      <td>-0.008621</td>\n",
       "      <td>0.003236</td>\n",
       "      <td>0.002251</td>\n",
       "      <td>0.002421</td>\n",
       "      <td>0.002856</td>\n",
       "      <td>0.002425</td>\n",
       "      <td>0.008548</td>\n",
       "      <td>0.021687</td>\n",
       "      <td>...</td>\n",
       "      <td>0.248426</td>\n",
       "      <td>0.035269</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.019455</td>\n",
       "      <td>0.000849</td>\n",
       "      <td>0.025825</td>\n",
       "      <td>0.000723</td>\n",
       "      <td>0.000541</td>\n",
       "      <td>0.005659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5878.626096</td>\n",
       "      <td>0.076155</td>\n",
       "      <td>0.025536</td>\n",
       "      <td>0.003636</td>\n",
       "      <td>0.003705</td>\n",
       "      <td>0.005628</td>\n",
       "      <td>0.003006</td>\n",
       "      <td>0.013154</td>\n",
       "      <td>0.006322</td>\n",
       "      <td>0.010245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.068880</td>\n",
       "      <td>0.025429</td>\n",
       "      <td>0.008083</td>\n",
       "      <td>0.003613</td>\n",
       "      <td>0.045363</td>\n",
       "      <td>0.010810</td>\n",
       "      <td>0.011157</td>\n",
       "      <td>0.001348</td>\n",
       "      <td>0.002789</td>\n",
       "      <td>0.008824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.405405</td>\n",
       "      <td>-0.238095</td>\n",
       "      <td>-0.009083</td>\n",
       "      <td>-0.031250</td>\n",
       "      <td>-0.140000</td>\n",
       "      <td>-0.005576</td>\n",
       "      <td>-0.177778</td>\n",
       "      <td>-0.044444</td>\n",
       "      <td>-0.088889</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.104673</td>\n",
       "      <td>-0.154762</td>\n",
       "      <td>-0.111111</td>\n",
       "      <td>-0.111111</td>\n",
       "      <td>-0.195946</td>\n",
       "      <td>-0.120690</td>\n",
       "      <td>-0.009539</td>\n",
       "      <td>-0.000789</td>\n",
       "      <td>-0.025316</td>\n",
       "      <td>-0.060606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1364.325000</td>\n",
       "      <td>-0.097635</td>\n",
       "      <td>-0.020774</td>\n",
       "      <td>0.001253</td>\n",
       "      <td>0.000934</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001248</td>\n",
       "      <td>-0.003665</td>\n",
       "      <td>0.004441</td>\n",
       "      <td>0.015184</td>\n",
       "      <td>...</td>\n",
       "      <td>0.207735</td>\n",
       "      <td>0.021048</td>\n",
       "      <td>-0.001442</td>\n",
       "      <td>-0.000481</td>\n",
       "      <td>-0.010270</td>\n",
       "      <td>-0.003578</td>\n",
       "      <td>0.019311</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>-0.000149</td>\n",
       "      <td>0.000765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4119.500000</td>\n",
       "      <td>-0.047602</td>\n",
       "      <td>-0.007243</td>\n",
       "      <td>0.002205</td>\n",
       "      <td>0.001759</td>\n",
       "      <td>0.001826</td>\n",
       "      <td>0.002090</td>\n",
       "      <td>0.001981</td>\n",
       "      <td>0.007153</td>\n",
       "      <td>0.019908</td>\n",
       "      <td>...</td>\n",
       "      <td>0.243211</td>\n",
       "      <td>0.035822</td>\n",
       "      <td>0.002030</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>0.018441</td>\n",
       "      <td>0.001145</td>\n",
       "      <td>0.025046</td>\n",
       "      <td>0.000292</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.005420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10248.975000</td>\n",
       "      <td>-0.000846</td>\n",
       "      <td>0.005401</td>\n",
       "      <td>0.003954</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>0.004299</td>\n",
       "      <td>0.003479</td>\n",
       "      <td>0.007852</td>\n",
       "      <td>0.011070</td>\n",
       "      <td>0.025938</td>\n",
       "      <td>...</td>\n",
       "      <td>0.281525</td>\n",
       "      <td>0.050797</td>\n",
       "      <td>0.005407</td>\n",
       "      <td>0.001226</td>\n",
       "      <td>0.047655</td>\n",
       "      <td>0.005700</td>\n",
       "      <td>0.030945</td>\n",
       "      <td>0.000736</td>\n",
       "      <td>0.000728</td>\n",
       "      <td>0.010466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46081.300000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.242424</td>\n",
       "      <td>0.061958</td>\n",
       "      <td>0.134658</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.073132</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.099196</td>\n",
       "      <td>0.121131</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.018123</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           buzz_USD  sentiment_USD  optimism_USD      joy_USD  loveHate_USD  \\\n",
       "count   7548.000000    7547.000000   7527.000000  6838.000000   6917.000000   \n",
       "mean    6291.483148      -0.048832     -0.008621     0.003236      0.002251   \n",
       "std     5878.626096       0.076155      0.025536     0.003636      0.003705   \n",
       "min        1.000000      -0.405405     -0.238095    -0.009083     -0.031250   \n",
       "25%     1364.325000      -0.097635     -0.020774     0.001253      0.000934   \n",
       "50%     4119.500000      -0.047602     -0.007243     0.002205      0.001759   \n",
       "75%    10248.975000      -0.000846      0.005401     0.003954      0.002925   \n",
       "max    46081.300000       0.500000      0.242424     0.061958      0.134658   \n",
       "\n",
       "         trust_USD    anger_USD  conflict_USD     fear_USD    gloom_USD  ...  \\\n",
       "count  7462.000000  6926.000000   7526.000000  7335.000000  7481.000000  ...   \n",
       "mean      0.002421     0.002856      0.002425     0.008548     0.021687  ...   \n",
       "std       0.005628     0.003006      0.013154     0.006322     0.010245  ...   \n",
       "min      -0.140000    -0.005576     -0.177778    -0.044444    -0.088889  ...   \n",
       "25%       0.000000     0.001248     -0.003665     0.004441     0.015184  ...   \n",
       "50%       0.001826     0.002090      0.001981     0.007153     0.019908  ...   \n",
       "75%       0.004299     0.003479      0.007852     0.011070     0.025938  ...   \n",
       "max       0.055556     0.073132      0.208333     0.099196     0.121131  ...   \n",
       "\n",
       "       emotionVsFact_USD  marketRisk_USD  longShort_USD  \\\n",
       "count        7548.000000     7544.000000    7459.000000   \n",
       "mean            0.248426        0.035269       0.002033   \n",
       "std             0.068880        0.025429       0.008083   \n",
       "min            -0.104673       -0.154762      -0.111111   \n",
       "25%             0.207735        0.021048      -0.001442   \n",
       "50%             0.243211        0.035822       0.002030   \n",
       "75%             0.281525        0.050797       0.005407   \n",
       "max             1.000000        0.200000       0.173913   \n",
       "\n",
       "       longShortForecast_USD  priceDirection_USD  priceForecast_USD  \\\n",
       "count            6982.000000         7545.000000        7502.000000   \n",
       "mean                0.000402            0.019455           0.000849   \n",
       "std                 0.003613            0.045363           0.010810   \n",
       "min                -0.111111           -0.195946          -0.120690   \n",
       "25%                -0.000481           -0.010270          -0.003578   \n",
       "50%                 0.000331            0.018441           0.001145   \n",
       "75%                 0.001226            0.047655           0.005700   \n",
       "max                 0.133333            0.400000           0.400000   \n",
       "\n",
       "       volatility_USD  carryTrade_USD  currencyPegInstability_USD  \\\n",
       "count     7471.000000     2034.000000                 4162.000000   \n",
       "mean         0.025825        0.000723                    0.000541   \n",
       "std          0.011157        0.001348                    0.002789   \n",
       "min         -0.009539       -0.000789                   -0.025316   \n",
       "25%          0.019311        0.000121                   -0.000149   \n",
       "50%          0.025046        0.000292                    0.000148   \n",
       "75%          0.030945        0.000736                    0.000728   \n",
       "max          0.500000        0.018123                    0.047619   \n",
       "\n",
       "       priceMomentum_USD  \n",
       "count        7443.000000  \n",
       "mean            0.005659  \n",
       "std             0.008824  \n",
       "min            -0.060606  \n",
       "25%             0.000765  \n",
       "50%             0.005420  \n",
       "75%             0.010466  \n",
       "max             0.100000  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "currencies.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "companies = load_data(data='companies', data_type='News_Social', asset_code_id='MPTRXUS500')\n",
    "#pf.ProfileReport(companies, minimal=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Market data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-11-30</th>\n",
       "      <td>1410.589966</td>\n",
       "      <td>1386.949951</td>\n",
       "      <td>1407.829956</td>\n",
       "      <td>1388.910034</td>\n",
       "      <td>9.515000e+08</td>\n",
       "      <td>1388.910034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-01</th>\n",
       "      <td>1400.119995</td>\n",
       "      <td>1387.380005</td>\n",
       "      <td>1388.910034</td>\n",
       "      <td>1397.719971</td>\n",
       "      <td>8.840000e+08</td>\n",
       "      <td>1397.719971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-02</th>\n",
       "      <td>1409.040039</td>\n",
       "      <td>1397.719971</td>\n",
       "      <td>1397.719971</td>\n",
       "      <td>1409.040039</td>\n",
       "      <td>9.007000e+08</td>\n",
       "      <td>1409.040039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-03</th>\n",
       "      <td>1447.420044</td>\n",
       "      <td>1409.040039</td>\n",
       "      <td>1409.040039</td>\n",
       "      <td>1433.300049</td>\n",
       "      <td>1.006400e+09</td>\n",
       "      <td>1433.300049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   High          Low         Open        Close        Volume  \\\n",
       "Date                                                                           \n",
       "1999-11-30  1410.589966  1386.949951  1407.829956  1388.910034  9.515000e+08   \n",
       "1999-12-01  1400.119995  1387.380005  1388.910034  1397.719971  8.840000e+08   \n",
       "1999-12-02  1409.040039  1397.719971  1397.719971  1409.040039  9.007000e+08   \n",
       "1999-12-03  1447.420044  1409.040039  1409.040039  1433.300049  1.006400e+09   \n",
       "1999-12-04          NaN          NaN          NaN          NaN           NaN   \n",
       "\n",
       "              Adj Close  \n",
       "Date                     \n",
       "1999-11-30  1388.910034  \n",
       "1999-12-01  1397.719971  \n",
       "1999-12-02  1409.040039  \n",
       "1999-12-03  1433.300049  \n",
       "1999-12-04          NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Later on we will adapt dates to the data loaded before\n",
    "start_date = datetime(1999, 11, 30)\n",
    "end_date = datetime(2020, 8, 31)\n",
    "\n",
    "# SP500 Yahoo Finance\n",
    "sp500_yahoo = pdr.get_data_yahoo(symbols='^GSPC', start=start_date, end=end_date)\n",
    "sp500_yahoo = sp500_yahoo.asfreq('D', method=None) # generating extra days so that we don't have date jumps\n",
    "display(sp500_yahoo.head())\n",
    "\n",
    "# Adding return and volatility just for plotting (as this has to be calculated separately in train and test)\n",
    "original_columns = list(sp500_yahoo.columns)\n",
    "original_columns.remove('Volume') # unrealiable volume data. It will not be used\n",
    "sp500_yahoo['Daily Return'] = sp500_yahoo['Adj Close'].pct_change(periods=1)*100\n",
    "sp500_yahoo['Daily Volatility'] = sp500_yahoo['Daily Return'].ewm(span=30).std() # exponential moving std\n",
    "sp500_yahoo['Daily Expected Return'] = sp500_yahoo['Daily Return'].ewm(span=30).mean()\n",
    "\n",
    "def daterange(start_date, end_date):\n",
    "    for n in range(int ((end_date - start_date).days) + 1):\n",
    "        yield start_date + timedelta(n)\n",
    "\n",
    "weekend = [6, 7]\n",
    "weekdays = []\n",
    "for dt in daterange(start_date, end_date):\n",
    "    if dt.isoweekday() not in weekend:\n",
    "        weekdays.append(dt.strftime('%Y-%m-%d'))\n",
    "\n",
    "# We'll take only weekdays and we'll delete weekends (as markets are closed during these days)\n",
    "sp500_yahoo = sp500_yahoo[sp500_yahoo.index.isin(weekdays)]\n",
    "\n",
    "# Expected Volatility of SP500\n",
    "#vix = pdr.get_data_yahoo(symbols='^VIX', start=start_date, end=end_date)\n",
    "#vix = vix.asfreq('D', method='ffill')\n",
    "#display(vix.head())\n",
    "\n",
    "# añadimos vix a sp500\n",
    "#sp500['VIX Close'] = vix.Close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting price, volatility and return\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('Daily Volatility [%]')\n",
    "ax1.plot(sp500_yahoo['Daily Volatility'], label='Daily Volatility', color='lightgrey')\n",
    "ax1.legend(loc='upper left')\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "ax2.set_ylabel('S&P 500 Close Price')  # we already handled the x-label with ax1\n",
    "ax2.plot(sp500_yahoo['Close'], color=bbva[0], label='S&P 500 Close Price')\n",
    "ax2.legend(loc='upper right')\n",
    "\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "plt.show()\n",
    "\n",
    "_, ax = plt.subplots()\n",
    "ax.plot(sp500_yahoo['Daily Return'], color=bbva[0], label='Daily Return');\n",
    "#ax.fill_between(sp500_yahoo.index, \n",
    "#                sp500_yahoo['Daily Mean Return'] + sp500_yahoo['Daily Volatility'], \n",
    "#                sp500_yahoo['Daily Mean Return'] - sp500_yahoo['Daily Volatility'],\n",
    "#                color='lightgrey', label='Daily Volatility');\n",
    "ax.plot(sp500_yahoo['Daily Expected Return'] + sp500_yahoo['Daily Volatility'], color='lightgrey')\n",
    "ax.plot(sp500_yahoo['Daily Expected Return'] - sp500_yahoo['Daily Volatility'], color='lightgrey', label='Volatility Bands')\n",
    "ax.plot(sp500_yahoo['Daily Expected Return'], color=bbva[1], label='Daily Expected Return')\n",
    "ax.legend(loc='upper right')\n",
    "ax.set_ylabel('Daily Return [%]');\n",
    "ax.set_xlabel('Date');\n",
    "#plt.xticks(rotation=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "92737a74566544cf9c3426dbb1160e55",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Select(description='View', options=('Close', 'Daily Return', 'Daily Volatility'), value='Close'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bed5e674f0e647a28999c3ee690a4eda",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating events with cusum filter (just for plotting)\n",
    "cusum_events = mlfin.filters.cusum_filter(sp500_yahoo['Adj Close'], \n",
    "                                          threshold=0.1) #threshold abs(change)\n",
    "\n",
    "# interactive plot\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "#configure_plotly_browser_state()\n",
    "\n",
    "# creating widgets\n",
    "dependent=widgets.Select(options=['Close', 'Daily Return', 'Daily Volatility'],\n",
    "                         value='Close', description='View', disabled=False)\n",
    "dataframe=widgets.RadioButtons(options=['Companies', 'Countries', 'Currencies'], \n",
    "                               value='Companies', description='Indices', disabled=False)\n",
    "sentiment1=widgets.Dropdown(options=companies.columns,\n",
    "                            value='sentiment_US500', description='Comp. Value', disabled=False)\n",
    "sentiment2=widgets.Dropdown(options=countries.columns,\n",
    "                            value='stockIndexSentiment_USA', description='Count. Value', disabled=False)\n",
    "sentiment3=widgets.Dropdown(options=currencies.columns,\n",
    "                            value='sentiment_USD', description='Curr. Value',\n",
    "                            disabled=False, layout={'positioning': 'right'})\n",
    "\n",
    "# setting the ui for our widgets\n",
    "ui = widgets.HBox([dependent, dataframe, widgets.VBox([sentiment1, sentiment2, sentiment3])])\n",
    "\n",
    "#@interact\n",
    "def plot_sentiment_index(dependent, dataframe, sentiment1, sentiment2, sentiment3):\n",
    "    \n",
    "    if dataframe == 'Companies':\n",
    "        sentiment = sentiment1\n",
    "        df = companies\n",
    "    elif dataframe == 'Countries':\n",
    "        sentiment = sentiment2\n",
    "        df = countries\n",
    "    elif dataframe == 'Currencies':\n",
    "        sentiment = sentiment3\n",
    "        df = currencies\n",
    "    \n",
    "    figura = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "\n",
    "    figura.add_trace(go.Scatter(y=sp500_yahoo[dependent].fillna('ffill'),\n",
    "                                x=sp500_yahoo.index,\n",
    "                                mode='lines',\n",
    "                                name='S&P 500 '+ dependent),\n",
    "                     secondary_y=True,)\n",
    "    figura.add_trace(\n",
    "        go.Scatter(y=df[sentiment],\n",
    "                   x=df.index,\n",
    "                   mode='lines',\n",
    "                   name=sentiment + ' Index',\n",
    "                   visible='legendonly'),\n",
    "                   secondary_y=False,)\n",
    "    figura.add_trace(\n",
    "        go.Scatter(y=df[sentiment].ewm(span=365).mean(),\n",
    "                   x=df.index,\n",
    "                   mode='lines',\n",
    "                   name='EWMA 1y ' + sentiment[:5] + '. Index'),\n",
    "                   secondary_y=False,)\n",
    "    figura.add_trace(\n",
    "        go.Scatter(y=df[sentiment].ewm(span=180).mean(),\n",
    "                   x=df.index,\n",
    "                   mode='lines',\n",
    "                   name='EWMA 6m ' + sentiment[:5] + '. Index'),\n",
    "                   secondary_y=False,)\n",
    "    figura.add_trace(\n",
    "        go.Scatter(y=df[sentiment].ewm(span=90).mean(),\n",
    "                   x=df.index,\n",
    "                   mode='lines',\n",
    "                   name='EWMA 3m ' + sentiment[:5] + '. Index'),\n",
    "                   secondary_y=False,)\n",
    "    figura.add_trace(\n",
    "        go.Scatter(y=df[sentiment].ewm(span=30).mean(),\n",
    "                   x=df.index,\n",
    "                   mode='lines',\n",
    "                   name='EWMA 1m ' + sentiment[:5] + '. Index',\n",
    "                   visible='legendonly'),\n",
    "                   secondary_y=False,)\n",
    "    figura.add_trace(go.Scatter(y=sp500_yahoo['Adj Close'][cusum_events],\n",
    "                                x=cusum_events,\n",
    "                                mode='markers',\n",
    "                                name='S&P 500 Index CUSUM Events'),\n",
    "                     secondary_y=True,)\n",
    "    figura.add_trace(\n",
    "        go.Scatter(y=[0],\n",
    "                   x=['2001-09-11'],\n",
    "                   mode='markers',\n",
    "                   name='Sept 11 Attacks',\n",
    "                   marker=dict(size=15),\n",
    "                   marker_symbol=17),\n",
    "                   secondary_y=False,)\n",
    "    figura.add_trace(\n",
    "        go.Scatter(y=[0],\n",
    "                   x=['2002-10-09'],\n",
    "                   mode='markers',\n",
    "                   name='Dot-Com Bubble Burst',\n",
    "                   marker=dict(size=15),\n",
    "                   marker_symbol=17),\n",
    "                   secondary_y=False,)\n",
    "    figura.add_trace(\n",
    "        go.Scatter(y=[0],\n",
    "                   x=['2008-09-15'],\n",
    "                   mode='markers',\n",
    "                   name='Lehman Brothers Collapse',\n",
    "                   marker=dict(size=15),\n",
    "                   marker_symbol=17),\n",
    "                   secondary_y=False,)\n",
    "    figura.add_trace(\n",
    "        go.Scatter(y=[0],\n",
    "                   x=['2018-12-22'],\n",
    "                   mode='markers',\n",
    "                   name='U.S. Federal Government Shutdown',\n",
    "                   marker=dict(size=15),\n",
    "                   marker_symbol=17),\n",
    "                   secondary_y=False,)\n",
    "    figura.add_trace(\n",
    "        go.Scatter(y=[0],\n",
    "                   x=['2020-01-20'],\n",
    "                   mode='markers',\n",
    "                   name='1st COVID-19 Case USA',\n",
    "                   marker=dict(size=15),\n",
    "                   marker_symbol=17),\n",
    "                   secondary_y=False,)\n",
    "    \"\"\"   \n",
    "    # to use after results are generated\n",
    "    dataset = sp500_yahoo.merge(test_labels,\n",
    "                            left_index=True,\n",
    "                            right_index=True,\n",
    "                            how='left')\n",
    "    figura.add_trace(\n",
    "        go.Scatter(y=dataset[(dataset.bin == 1) & (dataset.side == 1)]['Adj Close'],\n",
    "                   x=dataset[(dataset.bin == 1) & (dataset.side == 1)].index,\n",
    "                   mode='markers',\n",
    "                   name='Buy',\n",
    "                   marker=dict(size=8, color='#008000'),\n",
    "                   marker_symbol=5),\n",
    "                   secondary_y=True,)\n",
    "    figura.add_trace(\n",
    "        go.Scatter(y=dataset[(dataset.bin == 1) & (dataset.side == -1)]['Adj Close'],\n",
    "                   x=dataset[(dataset.bin == 1) & (dataset.side == -1)].index,\n",
    "                   mode='markers',\n",
    "                   name='Sell',\n",
    "                   marker=dict(size=8, color='#FF0000'),\n",
    "                   marker_symbol=6),\n",
    "                   secondary_y=True,)\n",
    "    \"\"\"\n",
    "\n",
    "    figura.update_layout(\n",
    "        title_text='S&P 500 Index vs Sentiment Indices | Indicator: {}'.format(sentiment),\n",
    "        colorway = bbva)\n",
    "\n",
    "    figura.update_xaxes(rangeslider_visible=True)\n",
    "    figura.update_yaxes(title_text=\"<b>Sentiment Index</b>\", secondary_y=False)\n",
    "    figura.update_yaxes(title_text=\"<b>S&P 500 Close Price</b>\", secondary_y=True)\n",
    "\n",
    "    figura.update_xaxes(\n",
    "        rangeslider_visible=True,\n",
    "        rangeselector=dict(\n",
    "            dict(font = dict(color = \"black\")),\n",
    "            buttons=list([\n",
    "                dict(count=1, label=\"1m\", step=\"month\", stepmode=\"backward\"),\n",
    "                dict(count=6, label=\"6m\", step=\"month\", stepmode=\"backward\"),\n",
    "                dict(count=1, label=\"YTD\", step=\"year\", stepmode=\"todate\"),\n",
    "                dict(count=1, label=\"1y\", step=\"year\", stepmode=\"backward\"),\n",
    "                dict(count=3, label=\"3y\", step=\"year\", stepmode=\"backward\"),\n",
    "                dict(count=5, label=\"5y\", step=\"year\", stepmode=\"backward\"),\n",
    "                dict(step=\"all\"),\n",
    "            ])\n",
    "        )\n",
    "    )\n",
    "\n",
    "    figura.update_layout(template='simple_white', hovermode='x')\n",
    "    iplot(figura)\n",
    "    \n",
    "out = widgets.interactive_output(plot_sentiment_index, {'dependent': dependent, 'dataframe': dataframe, \n",
    "                                                        'sentiment1': sentiment1, 'sentiment2': sentiment2,\n",
    "                                                        'sentiment3': sentiment3})\n",
    "display(ui, out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We'll only take the original columns (remember that we created three new ones just for plotting)\n",
    "sp500_yahoo = sp500_yahoo[original_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buzz_US500</th>\n",
       "      <th>sentiment_US500</th>\n",
       "      <th>optimism_US500</th>\n",
       "      <th>joy_US500</th>\n",
       "      <th>loveHate_US500</th>\n",
       "      <th>trust_US500</th>\n",
       "      <th>anger_US500</th>\n",
       "      <th>conflict_US500</th>\n",
       "      <th>fear_US500</th>\n",
       "      <th>gloom_US500</th>\n",
       "      <th>...</th>\n",
       "      <th>bondUncertainty_USA</th>\n",
       "      <th>bondDefault_USA</th>\n",
       "      <th>bondPriceDirection_USA</th>\n",
       "      <th>bondPriceForecast_USA</th>\n",
       "      <th>bondVolatility_USA</th>\n",
       "      <th>centralBank_USA</th>\n",
       "      <th>debtDefault_USA</th>\n",
       "      <th>interestRates_USA</th>\n",
       "      <th>interestRatesForecast_USA</th>\n",
       "      <th>monetaryPolicyLooseVsTight_USA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>33223.8</td>\n",
       "      <td>0.023402</td>\n",
       "      <td>0.016494</td>\n",
       "      <td>0.023959</td>\n",
       "      <td>0.009060</td>\n",
       "      <td>0.001211</td>\n",
       "      <td>0.011212</td>\n",
       "      <td>0.011513</td>\n",
       "      <td>0.006727</td>\n",
       "      <td>0.027932</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006748</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.053981</td>\n",
       "      <td>0.026991</td>\n",
       "      <td>0.014845</td>\n",
       "      <td>0.677966</td>\n",
       "      <td>0.220339</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>36635.9</td>\n",
       "      <td>0.008134</td>\n",
       "      <td>0.014508</td>\n",
       "      <td>0.015217</td>\n",
       "      <td>0.010864</td>\n",
       "      <td>0.000583</td>\n",
       "      <td>0.010099</td>\n",
       "      <td>0.015354</td>\n",
       "      <td>0.007206</td>\n",
       "      <td>0.027487</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.014085</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.028169</td>\n",
       "      <td>0.840909</td>\n",
       "      <td>0.102273</td>\n",
       "      <td>0.005682</td>\n",
       "      <td>0.011364</td>\n",
       "      <td>0.005682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>57064.8</td>\n",
       "      <td>0.017612</td>\n",
       "      <td>0.014641</td>\n",
       "      <td>0.016096</td>\n",
       "      <td>0.008429</td>\n",
       "      <td>0.000771</td>\n",
       "      <td>0.010471</td>\n",
       "      <td>0.014816</td>\n",
       "      <td>0.006983</td>\n",
       "      <td>0.032209</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009392</td>\n",
       "      <td>0.000648</td>\n",
       "      <td>-0.089708</td>\n",
       "      <td>-0.010687</td>\n",
       "      <td>0.069564</td>\n",
       "      <td>0.779070</td>\n",
       "      <td>0.085271</td>\n",
       "      <td>0.042636</td>\n",
       "      <td>0.011628</td>\n",
       "      <td>-0.011628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>79355.1</td>\n",
       "      <td>0.000825</td>\n",
       "      <td>0.006994</td>\n",
       "      <td>0.015147</td>\n",
       "      <td>0.009514</td>\n",
       "      <td>0.001709</td>\n",
       "      <td>0.010907</td>\n",
       "      <td>0.012847</td>\n",
       "      <td>0.007145</td>\n",
       "      <td>0.032575</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006594</td>\n",
       "      <td>0.004396</td>\n",
       "      <td>-0.023380</td>\n",
       "      <td>-0.001998</td>\n",
       "      <td>0.078094</td>\n",
       "      <td>0.702671</td>\n",
       "      <td>0.118467</td>\n",
       "      <td>0.118467</td>\n",
       "      <td>0.015099</td>\n",
       "      <td>-0.004646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>91858.2</td>\n",
       "      <td>-0.013466</td>\n",
       "      <td>0.001110</td>\n",
       "      <td>0.014114</td>\n",
       "      <td>0.008225</td>\n",
       "      <td>0.000631</td>\n",
       "      <td>0.010941</td>\n",
       "      <td>0.011757</td>\n",
       "      <td>0.007327</td>\n",
       "      <td>0.031995</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007030</td>\n",
       "      <td>0.003409</td>\n",
       "      <td>0.008308</td>\n",
       "      <td>-0.001278</td>\n",
       "      <td>0.060716</td>\n",
       "      <td>0.735938</td>\n",
       "      <td>0.115625</td>\n",
       "      <td>0.095312</td>\n",
       "      <td>0.010156</td>\n",
       "      <td>-0.007813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            buzz_US500  sentiment_US500  optimism_US500  joy_US500  \\\n",
       "Date                                                                 \n",
       "2000-01-01     33223.8         0.023402        0.016494   0.023959   \n",
       "2000-01-02     36635.9         0.008134        0.014508   0.015217   \n",
       "2000-01-03     57064.8         0.017612        0.014641   0.016096   \n",
       "2000-01-04     79355.1         0.000825        0.006994   0.015147   \n",
       "2000-01-05     91858.2        -0.013466        0.001110   0.014114   \n",
       "\n",
       "            loveHate_US500  trust_US500  anger_US500  conflict_US500  \\\n",
       "Date                                                                   \n",
       "2000-01-01        0.009060     0.001211     0.011212        0.011513   \n",
       "2000-01-02        0.010864     0.000583     0.010099        0.015354   \n",
       "2000-01-03        0.008429     0.000771     0.010471        0.014816   \n",
       "2000-01-04        0.009514     0.001709     0.010907        0.012847   \n",
       "2000-01-05        0.008225     0.000631     0.010941        0.011757   \n",
       "\n",
       "            fear_US500  gloom_US500  ...  bondUncertainty_USA  \\\n",
       "Date                                 ...                        \n",
       "2000-01-01    0.006727     0.027932  ...             0.006748   \n",
       "2000-01-02    0.007206     0.027487  ...             0.023474   \n",
       "2000-01-03    0.006983     0.032209  ...             0.009392   \n",
       "2000-01-04    0.007145     0.032575  ...             0.006594   \n",
       "2000-01-05    0.007327     0.031995  ...             0.007030   \n",
       "\n",
       "            bondDefault_USA  bondPriceDirection_USA  bondPriceForecast_USA  \\\n",
       "Date                                                                         \n",
       "2000-01-01              NaN               -0.053981               0.026991   \n",
       "2000-01-02              NaN               -0.014085                    NaN   \n",
       "2000-01-03         0.000648               -0.089708              -0.010687   \n",
       "2000-01-04         0.004396               -0.023380              -0.001998   \n",
       "2000-01-05         0.003409                0.008308              -0.001278   \n",
       "\n",
       "            bondVolatility_USA  centralBank_USA  debtDefault_USA  \\\n",
       "Date                                                               \n",
       "2000-01-01            0.014845         0.677966         0.220339   \n",
       "2000-01-02            0.028169         0.840909         0.102273   \n",
       "2000-01-03            0.069564         0.779070         0.085271   \n",
       "2000-01-04            0.078094         0.702671         0.118467   \n",
       "2000-01-05            0.060716         0.735938         0.115625   \n",
       "\n",
       "            interestRates_USA  interestRatesForecast_USA  \\\n",
       "Date                                                       \n",
       "2000-01-01           0.016949                        NaN   \n",
       "2000-01-02           0.005682                   0.011364   \n",
       "2000-01-03           0.042636                   0.011628   \n",
       "2000-01-04           0.118467                   0.015099   \n",
       "2000-01-05           0.095312                   0.010156   \n",
       "\n",
       "            monetaryPolicyLooseVsTight_USA  \n",
       "Date                                        \n",
       "2000-01-01                             NaN  \n",
       "2000-01-02                        0.005682  \n",
       "2000-01-03                       -0.011628  \n",
       "2000-01-04                       -0.004646  \n",
       "2000-01-05                       -0.007813  \n",
       "\n",
       "[5 rows x 90 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging the datasets\n",
    "sentiments = companies.merge(currencies.merge(countries, \n",
    "                                              left_index=True, right_index=True), left_index=True, right_index=True)\n",
    "sentiments.drop_duplicates(inplace=True)\n",
    "sentiments.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We'll calculate a weighted average on Mondays adding info from the weekend\n",
    "mondays_weekends = []\n",
    "for dt in daterange(start_date, end_date):\n",
    "    if dt.isoweekday() in [1, 6, 7]:\n",
    "        mondays_weekends.append(dt.strftime('%Y-%m-%d'))\n",
    "\n",
    "senti_mon = sentiments.reindex(pd.DatetimeIndex(mondays_weekends))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1999-12-04 00:00:00')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First day was Saturday (this will be useful for allocating weights)\n",
    "senti_mon.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buzz_US500</th>\n",
       "      <th>sentiment_US500</th>\n",
       "      <th>optimism_US500</th>\n",
       "      <th>joy_US500</th>\n",
       "      <th>loveHate_US500</th>\n",
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       "      <th>conflict_US500</th>\n",
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       "      <th>bondPriceDirection_USA</th>\n",
       "      <th>bondPriceForecast_USA</th>\n",
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       "      <th>debtDefault_USA</th>\n",
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       "      <th>interestRatesForecast_USA</th>\n",
       "      <th>monetaryPolicyLooseVsTight_USA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>49709.522</td>\n",
       "      <td>0.016222</td>\n",
       "      <td>0.014834</td>\n",
       "      <td>0.016846</td>\n",
       "      <td>0.009040</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>0.010478</td>\n",
       "      <td>0.014538</td>\n",
       "      <td>0.007001</td>\n",
       "      <td>0.030657</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012173</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.068784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.053891</td>\n",
       "      <td>0.780542</td>\n",
       "      <td>0.105220</td>\n",
       "      <td>0.031424</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>79355.100</td>\n",
       "      <td>0.000825</td>\n",
       "      <td>0.006994</td>\n",
       "      <td>0.015147</td>\n",
       "      <td>0.009514</td>\n",
       "      <td>0.001709</td>\n",
       "      <td>0.010907</td>\n",
       "      <td>0.012847</td>\n",
       "      <td>0.007145</td>\n",
       "      <td>0.032575</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006594</td>\n",
       "      <td>0.004396</td>\n",
       "      <td>-0.023380</td>\n",
       "      <td>-0.001998</td>\n",
       "      <td>0.078094</td>\n",
       "      <td>0.702671</td>\n",
       "      <td>0.118467</td>\n",
       "      <td>0.118467</td>\n",
       "      <td>0.015099</td>\n",
       "      <td>-0.004646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>91858.200</td>\n",
       "      <td>-0.013466</td>\n",
       "      <td>0.001110</td>\n",
       "      <td>0.014114</td>\n",
       "      <td>0.008225</td>\n",
       "      <td>0.000631</td>\n",
       "      <td>0.010941</td>\n",
       "      <td>0.011757</td>\n",
       "      <td>0.007327</td>\n",
       "      <td>0.031995</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007030</td>\n",
       "      <td>0.003409</td>\n",
       "      <td>0.008308</td>\n",
       "      <td>-0.001278</td>\n",
       "      <td>0.060716</td>\n",
       "      <td>0.735938</td>\n",
       "      <td>0.115625</td>\n",
       "      <td>0.095312</td>\n",
       "      <td>0.010156</td>\n",
       "      <td>-0.007813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>105962.800</td>\n",
       "      <td>-0.003497</td>\n",
       "      <td>0.005271</td>\n",
       "      <td>0.015430</td>\n",
       "      <td>0.007866</td>\n",
       "      <td>0.001687</td>\n",
       "      <td>0.011820</td>\n",
       "      <td>0.012259</td>\n",
       "      <td>0.006304</td>\n",
       "      <td>0.028685</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010203</td>\n",
       "      <td>0.003601</td>\n",
       "      <td>0.062421</td>\n",
       "      <td>0.003601</td>\n",
       "      <td>0.063502</td>\n",
       "      <td>0.694239</td>\n",
       "      <td>0.134417</td>\n",
       "      <td>0.050222</td>\n",
       "      <td>0.020679</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>122253.000</td>\n",
       "      <td>-0.007431</td>\n",
       "      <td>0.004474</td>\n",
       "      <td>0.015607</td>\n",
       "      <td>0.008642</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>0.010294</td>\n",
       "      <td>0.010519</td>\n",
       "      <td>0.007210</td>\n",
       "      <td>0.032101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015878</td>\n",
       "      <td>0.001732</td>\n",
       "      <td>0.016455</td>\n",
       "      <td>0.002598</td>\n",
       "      <td>0.059469</td>\n",
       "      <td>0.765446</td>\n",
       "      <td>0.094966</td>\n",
       "      <td>0.068650</td>\n",
       "      <td>0.019451</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            buzz_US500  sentiment_US500  optimism_US500  joy_US500  \\\n",
       "Date                                                                 \n",
       "2000-01-03   49709.522         0.016222        0.014834   0.016846   \n",
       "2000-01-04   79355.100         0.000825        0.006994   0.015147   \n",
       "2000-01-05   91858.200        -0.013466        0.001110   0.014114   \n",
       "2000-01-06  105962.800        -0.003497        0.005271   0.015430   \n",
       "2000-01-07  122253.000        -0.007431        0.004474   0.015607   \n",
       "\n",
       "            loveHate_US500  trust_US500  anger_US500  conflict_US500  \\\n",
       "Date                                                                   \n",
       "2000-01-03        0.009040     0.000782     0.010478        0.014538   \n",
       "2000-01-04        0.009514     0.001709     0.010907        0.012847   \n",
       "2000-01-05        0.008225     0.000631     0.010941        0.011757   \n",
       "2000-01-06        0.007866     0.001687     0.011820        0.012259   \n",
       "2000-01-07        0.008642     0.001127     0.010294        0.010519   \n",
       "\n",
       "            fear_US500  gloom_US500  ...  bondUncertainty_USA  \\\n",
       "Date                                 ...                        \n",
       "2000-01-03    0.007001     0.030657  ...             0.012173   \n",
       "2000-01-04    0.007145     0.032575  ...             0.006594   \n",
       "2000-01-05    0.007327     0.031995  ...             0.007030   \n",
       "2000-01-06    0.006304     0.028685  ...             0.010203   \n",
       "2000-01-07    0.007210     0.032101  ...             0.015878   \n",
       "\n",
       "            bondDefault_USA  bondPriceDirection_USA  bondPriceForecast_USA  \\\n",
       "Date                                                                         \n",
       "2000-01-03              NaN               -0.068784                    NaN   \n",
       "2000-01-04         0.004396               -0.023380              -0.001998   \n",
       "2000-01-05         0.003409                0.008308              -0.001278   \n",
       "2000-01-06         0.003601                0.062421               0.003601   \n",
       "2000-01-07         0.001732                0.016455               0.002598   \n",
       "\n",
       "            bondVolatility_USA  centralBank_USA  debtDefault_USA  \\\n",
       "Date                                                               \n",
       "2000-01-03            0.053891         0.780542         0.105220   \n",
       "2000-01-04            0.078094         0.702671         0.118467   \n",
       "2000-01-05            0.060716         0.735938         0.115625   \n",
       "2000-01-06            0.063502         0.694239         0.134417   \n",
       "2000-01-07            0.059469         0.765446         0.094966   \n",
       "\n",
       "            interestRates_USA  interestRatesForecast_USA  \\\n",
       "Date                                                       \n",
       "2000-01-03           0.031424                        NaN   \n",
       "2000-01-04           0.118467                   0.015099   \n",
       "2000-01-05           0.095312                   0.010156   \n",
       "2000-01-06           0.050222                   0.020679   \n",
       "2000-01-07           0.068650                   0.019451   \n",
       "\n",
       "            monetaryPolicyLooseVsTight_USA  \n",
       "Date                                        \n",
       "2000-01-03                             NaN  \n",
       "2000-01-04                       -0.004646  \n",
       "2000-01-05                       -0.007813  \n",
       "2000-01-06                        0.000000  \n",
       "2000-01-07                        0.000000  \n",
       "\n",
       "[5 rows x 90 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Continuation:\n",
    "for col in senti_mon.columns:\n",
    "    senti_mon[col] = \\\n",
    "        senti_mon[col].rolling(3).apply(lambda x: np.average(x, weights=[0.12, 0.22, 0.66])) \n",
    "        # damos mayor peso a los lunes (mon 2/3, sun 2/9, sat 1/9)\n",
    "        \n",
    "# Substituting indices corresponding to senti_mon\n",
    "sentiments[sentiments.index.isin(mondays_weekends)] = senti_mon\n",
    "\n",
    "del senti_mon\n",
    "\n",
    "# Deleting weekends\n",
    "sentiments = sentiments[sentiments.index.isin(weekdays)]\n",
    "sentiments.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting train and test periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting into train and test sets\n",
    "\n",
    "train_start, train_end = '2001-08-31', '2016-08-31'\n",
    "test_start, test_end = '2016-09-01', '2020-08-31'\n",
    "\n",
    "sentiments_test = sentiments[test_start:test_end]\n",
    "sentiments_train = sentiments[train_start:train_end]\n",
    "sp500_test = sp500_yahoo[test_start:test_end]\n",
    "sp500_train = sp500_yahoo[train_start:train_end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79%\n",
      "21%\n"
     ]
    }
   ],
   "source": [
    "# Train and test ratios\n",
    "print(\"{0:.0%}\".format(len(sp500_train)/len(sp500_yahoo[train_start:])))\n",
    "print(\"{0:.0%}\".format(len(sp500_test)/len(sp500_yahoo[train_start:])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We'll plot the rate of variables with missing values over time\n",
    "sentiments_train['year'] = sentiments_train.index.year\n",
    "sentiments_train['missing'] = sentiments_train.isnull().sum(axis=1)\n",
    "\n",
    "((1-(sentiments_train.groupby('year').count().min(axis=1)/\n",
    "    sentiments_train.groupby('year').count()['bondBuzz_USA']))*100).plot(legend=False, marker='o');\n",
    "((1-(sentiments_train.groupby('year').count().mean(axis=1)/\n",
    "    sentiments_train.groupby('year').count()['bondBuzz_USA']))*100).plot(legend=False, marker='o');\n",
    "plt.title('Max. and mean missing-value rates per year');\n",
    "plt.ylabel('Missing rate [%]');\n",
    "plt.xlabel('Date');\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(sentiments_train.groupby('year').max().index, \n",
    "         list(sentiments_train.groupby('year').max()['missing']), marker='o');\n",
    "plt.plot(sentiments_train.groupby('year').mean().index, \n",
    "         list(sentiments_train.groupby('year').mean()['missing']), marker='o');\n",
    "plt.title('Max. and mean number of variables with missing values per year');\n",
    "plt.ylabel('Number of variables');\n",
    "plt.xlabel('Date');\n",
    "\n",
    "sentiments_train.drop(['year', 'missing'], axis=1, inplace=True)\n",
    "\n",
    "# y axis represents the percentage of variables with missing values for every year \n",
    "# disclaimer: these are not missing value rates, see section Data Loading for checking those"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['carryTrade_USD', 'currencyPegInstability_USD']\n"
     ]
    }
   ],
   "source": [
    "# Dropping cols with a missing rate larger than 20%\n",
    "missing_rate = (sentiments_train.isnull().sum() / len(sentiments_train))*100\n",
    "drop_cols = [col for col in missing_rate.index if missing_rate[col] >= 20]\n",
    "print(drop_cols)\n",
    "sentiments_train.drop(drop_cols, axis=1, inplace=True)\n",
    "sentiments_test.drop(drop_cols, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputing missing values for the rest of variables\n",
    "# We'll perform a forward filling since we're dealing with news\n",
    "\n",
    "sentiments_train.fillna(method='ffill', inplace=True) \n",
    "sentiments_test.fillna(method='ffill', inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#msno.matrix(sp500_yahoo[['Close']])\n",
    "\n",
    "# We'll interpolate over the days were there is no data (be it due to the closing of markets in festive days or\n",
    "# just because of an absence of data due to system errors)\n",
    "\n",
    "sp500_train.interpolate(method='spline', order=3, limit_direction='forward', \n",
    "                        axis=0, inplace=True) \n",
    "sp500_test.interpolate(method='spline', order=3, limit_direction='forward', \n",
    "                        axis=0, inplace=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lambda:  0.2749559707497384\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We'll apply yeo johnson to balance dispersion amongst varaibles\n",
    "# in this chunk we'll only apply it to the sentiments dataset. Later on we'll apply it to the financial variables\n",
    "#example\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(211)\n",
    "x = currencies.buzz_USD\n",
    "prob = stats.probplot(x, dist=stats.norm, plot=ax1)\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_title('Probplot against normal distribution')\n",
    "\n",
    "ax2 = fig.add_subplot(212)\n",
    "xt, l = stats.yeojohnson(x)\n",
    "prob = stats.probplot(xt, dist=stats.norm, plot=ax2)\n",
    "ax2.set_title('Probplot after Box-Cox transformation')\n",
    "plt.show()\n",
    "print('Lambda: ', l)\n",
    "\n",
    "fig, ax = plt.subplots(2,1)\n",
    "ax[0].hist(x, bins=30);\n",
    "ax[1].hist(xt, bins=30);\n",
    "ax[0].set_title('Original and transformed variable');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We use the same lambdas for test dataset\n",
    "\n",
    "def apply_transformation(data_train, data_test, transformation):\n",
    "    \"\"\"\n",
    "    Applies dispersion and scale transformations on data split in train and test.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data_train: pandas dataframe\n",
    "        Train set.\n",
    "    data_test: pandas dataframe\n",
    "        Test set.\n",
    "    transformation: str\n",
    "        One of 'dispersion', 'scale' or 'dispersion_and_scale'.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    Tuple with train set and test set.\n",
    "    \"\"\" \n",
    "    \n",
    "    data_train = data_train.dropna()\n",
    "    data_test = data_test.dropna()\n",
    "    \n",
    "    index_train = data_train.index\n",
    "    index_test = data_test.index\n",
    "    \n",
    "    if transformation == 'dispersion':\n",
    "        for col in data_train.columns:\n",
    "            data_train[col], fitted_lambda = stats.yeojohnson(data_train[col])\n",
    "            data_test[col] = stats.yeojohnson(data_test[col], fitted_lambda)\n",
    "            \n",
    "    elif transformation == 'scale':\n",
    "        scaler = StandardScaler().fit(data_train)\n",
    "        std_train = scaler.transform(data_train)\n",
    "        std_test = scaler.transform(data_test)\n",
    "        data_train = pd.DataFrame(std_train, columns=data_train.columns)\n",
    "        data_test = pd.DataFrame(std_test, columns=data_test.columns)\n",
    "        data_train['Date'] = index_train\n",
    "        data_test['Date'] = index_test\n",
    "        data_train.set_index('Date', inplace=True)\n",
    "        data_test.set_index('Date', inplace=True)\n",
    "        \n",
    "    elif transformation == 'dispersion_and_scale':\n",
    "        for col in data_train.columns:\n",
    "            data_train[col], fitted_lambda = stats.yeojohnson(data_train[col])\n",
    "            data_test[col] = stats.yeojohnson(data_test[col], fitted_lambda)\n",
    "        scaler = StandardScaler().fit(data_train)\n",
    "        std_train = scaler.transform(data_train)\n",
    "        std_test = scaler.transform(data_test)\n",
    "        data_train = pd.DataFrame(std_train, columns=data_train.columns)\n",
    "        data_test = pd.DataFrame(std_test, columns=data_test.columns)\n",
    "        data_train['Date'] = index_train\n",
    "        data_test['Date'] = index_test\n",
    "        data_train.set_index('Date', inplace=True)\n",
    "        data_test.set_index('Date', inplace=True)\n",
    "    \n",
    "    return data_train, data_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.904373699855421"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking linear correlation before transformation\n",
    "sentiments_train.optimism_US500.ewm(90).mean().corr(sp500_yahoo.Close, method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buzz_US500</th>\n",
       "      <th>sentiment_US500</th>\n",
       "      <th>optimism_US500</th>\n",
       "      <th>joy_US500</th>\n",
       "      <th>loveHate_US500</th>\n",
       "      <th>trust_US500</th>\n",
       "      <th>anger_US500</th>\n",
       "      <th>conflict_US500</th>\n",
       "      <th>fear_US500</th>\n",
       "      <th>gloom_US500</th>\n",
       "      <th>...</th>\n",
       "      <th>bondUncertainty_USA</th>\n",
       "      <th>bondDefault_USA</th>\n",
       "      <th>bondPriceDirection_USA</th>\n",
       "      <th>bondPriceForecast_USA</th>\n",
       "      <th>bondVolatility_USA</th>\n",
       "      <th>centralBank_USA</th>\n",
       "      <th>debtDefault_USA</th>\n",
       "      <th>interestRates_USA</th>\n",
       "      <th>interestRatesForecast_USA</th>\n",
       "      <th>monetaryPolicyLooseVsTight_USA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "      <td>3.910000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.803878e-16</td>\n",
       "      <td>-4.974424e-16</td>\n",
       "      <td>-5.251554e-17</td>\n",
       "      <td>-1.275252e-15</td>\n",
       "      <td>2.198156e-16</td>\n",
       "      <td>-4.195280e-17</td>\n",
       "      <td>1.171271e-15</td>\n",
       "      <td>9.881269e-18</td>\n",
       "      <td>1.374348e-15</td>\n",
       "      <td>6.720399e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.112884e-16</td>\n",
       "      <td>4.515002e-16</td>\n",
       "      <td>-2.040851e-19</td>\n",
       "      <td>1.891070e-17</td>\n",
       "      <td>6.554291e-16</td>\n",
       "      <td>-1.608120e-16</td>\n",
       "      <td>1.097218e-15</td>\n",
       "      <td>2.304494e-16</td>\n",
       "      <td>5.224579e-18</td>\n",
       "      <td>-3.448790e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "      <td>1.000128e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.064271e+00</td>\n",
       "      <td>-3.836744e+00</td>\n",
       "      <td>-4.284522e+00</td>\n",
       "      <td>-3.013036e+00</td>\n",
       "      <td>-4.381088e+00</td>\n",
       "      <td>-6.928434e+00</td>\n",
       "      <td>-2.586858e+00</td>\n",
       "      <td>-4.609965e+00</td>\n",
       "      <td>-3.552208e+00</td>\n",
       "      <td>-2.670639e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.163638e+00</td>\n",
       "      <td>-8.062726e+00</td>\n",
       "      <td>-6.847632e+00</td>\n",
       "      <td>-6.703713e+00</td>\n",
       "      <td>-4.173702e+00</td>\n",
       "      <td>-3.048743e+00</td>\n",
       "      <td>-2.270924e+00</td>\n",
       "      <td>-9.733690e+00</td>\n",
       "      <td>-1.687397e+01</td>\n",
       "      <td>-9.594321e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-8.493998e-01</td>\n",
       "      <td>-6.976976e-01</td>\n",
       "      <td>-6.582821e-01</td>\n",
       "      <td>-7.451503e-01</td>\n",
       "      <td>-6.698294e-01</td>\n",
       "      <td>-6.259648e-01</td>\n",
       "      <td>-7.581757e-01</td>\n",
       "      <td>-6.460022e-01</td>\n",
       "      <td>-6.559033e-01</td>\n",
       "      <td>-7.677362e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.686969e-01</td>\n",
       "      <td>-6.951505e-01</td>\n",
       "      <td>-4.799653e-01</td>\n",
       "      <td>-4.732337e-01</td>\n",
       "      <td>-7.013733e-01</td>\n",
       "      <td>-6.927469e-01</td>\n",
       "      <td>-7.495620e-01</td>\n",
       "      <td>-5.990254e-01</td>\n",
       "      <td>-4.879330e-01</td>\n",
       "      <td>-6.143743e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.815230e-02</td>\n",
       "      <td>-7.880494e-02</td>\n",
       "      <td>-1.911985e-02</td>\n",
       "      <td>-7.581875e-02</td>\n",
       "      <td>2.395684e-03</td>\n",
       "      <td>-3.272897e-02</td>\n",
       "      <td>-8.365807e-02</td>\n",
       "      <td>1.615158e-02</td>\n",
       "      <td>1.833695e-02</td>\n",
       "      <td>-2.910251e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.556898e-03</td>\n",
       "      <td>-4.066074e-02</td>\n",
       "      <td>-4.698007e-02</td>\n",
       "      <td>-5.073219e-02</td>\n",
       "      <td>-3.039403e-02</td>\n",
       "      <td>4.215866e-03</td>\n",
       "      <td>4.375376e-03</td>\n",
       "      <td>-1.634409e-01</td>\n",
       "      <td>-2.458350e-01</td>\n",
       "      <td>-2.346027e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.401832e-01</td>\n",
       "      <td>7.451540e-01</td>\n",
       "      <td>6.455215e-01</td>\n",
       "      <td>7.962038e-01</td>\n",
       "      <td>6.810202e-01</td>\n",
       "      <td>5.983299e-01</td>\n",
       "      <td>8.139464e-01</td>\n",
       "      <td>6.483610e-01</td>\n",
       "      <td>6.645912e-01</td>\n",
       "      <td>7.996133e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>6.920137e-01</td>\n",
       "      <td>6.463170e-01</td>\n",
       "      <td>5.109784e-01</td>\n",
       "      <td>4.678733e-01</td>\n",
       "      <td>7.241609e-01</td>\n",
       "      <td>6.946301e-01</td>\n",
       "      <td>7.372704e-01</td>\n",
       "      <td>4.860234e-01</td>\n",
       "      <td>2.338346e-01</td>\n",
       "      <td>6.134070e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.942192e+00</td>\n",
       "      <td>3.112603e+00</td>\n",
       "      <td>3.886110e+00</td>\n",
       "      <td>3.362441e+00</td>\n",
       "      <td>4.069919e+00</td>\n",
       "      <td>5.643069e+00</td>\n",
       "      <td>2.700716e+00</td>\n",
       "      <td>4.335172e+00</td>\n",
       "      <td>3.704308e+00</td>\n",
       "      <td>3.576385e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>3.779286e+00</td>\n",
       "      <td>2.888767e+00</td>\n",
       "      <td>4.780882e+00</td>\n",
       "      <td>5.367399e+00</td>\n",
       "      <td>2.825161e+00</td>\n",
       "      <td>2.955895e+00</td>\n",
       "      <td>3.014466e+00</td>\n",
       "      <td>2.787403e+00</td>\n",
       "      <td>5.097762e+00</td>\n",
       "      <td>2.267225e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         buzz_US500  sentiment_US500  optimism_US500     joy_US500  \\\n",
       "count  3.910000e+03     3.910000e+03    3.910000e+03  3.910000e+03   \n",
       "mean   6.803878e-16    -4.974424e-16   -5.251554e-17 -1.275252e-15   \n",
       "std    1.000128e+00     1.000128e+00    1.000128e+00  1.000128e+00   \n",
       "min   -3.064271e+00    -3.836744e+00   -4.284522e+00 -3.013036e+00   \n",
       "25%   -8.493998e-01    -6.976976e-01   -6.582821e-01 -7.451503e-01   \n",
       "50%    3.815230e-02    -7.880494e-02   -1.911985e-02 -7.581875e-02   \n",
       "75%    7.401832e-01     7.451540e-01    6.455215e-01  7.962038e-01   \n",
       "max    2.942192e+00     3.112603e+00    3.886110e+00  3.362441e+00   \n",
       "\n",
       "       loveHate_US500   trust_US500   anger_US500  conflict_US500  \\\n",
       "count    3.910000e+03  3.910000e+03  3.910000e+03    3.910000e+03   \n",
       "mean     2.198156e-16 -4.195280e-17  1.171271e-15    9.881269e-18   \n",
       "std      1.000128e+00  1.000128e+00  1.000128e+00    1.000128e+00   \n",
       "min     -4.381088e+00 -6.928434e+00 -2.586858e+00   -4.609965e+00   \n",
       "25%     -6.698294e-01 -6.259648e-01 -7.581757e-01   -6.460022e-01   \n",
       "50%      2.395684e-03 -3.272897e-02 -8.365807e-02    1.615158e-02   \n",
       "75%      6.810202e-01  5.983299e-01  8.139464e-01    6.483610e-01   \n",
       "max      4.069919e+00  5.643069e+00  2.700716e+00    4.335172e+00   \n",
       "\n",
       "         fear_US500   gloom_US500  ...  bondUncertainty_USA  bondDefault_USA  \\\n",
       "count  3.910000e+03  3.910000e+03  ...         3.910000e+03     3.910000e+03   \n",
       "mean   1.374348e-15  6.720399e-16  ...        -3.112884e-16     4.515002e-16   \n",
       "std    1.000128e+00  1.000128e+00  ...         1.000128e+00     1.000128e+00   \n",
       "min   -3.552208e+00 -2.670639e+00  ...        -3.163638e+00    -8.062726e+00   \n",
       "25%   -6.559033e-01 -7.677362e-01  ...        -6.686969e-01    -6.951505e-01   \n",
       "50%    1.833695e-02 -2.910251e-02  ...        -6.556898e-03    -4.066074e-02   \n",
       "75%    6.645912e-01  7.996133e-01  ...         6.920137e-01     6.463170e-01   \n",
       "max    3.704308e+00  3.576385e+00  ...         3.779286e+00     2.888767e+00   \n",
       "\n",
       "       bondPriceDirection_USA  bondPriceForecast_USA  bondVolatility_USA  \\\n",
       "count            3.910000e+03           3.910000e+03        3.910000e+03   \n",
       "mean            -2.040851e-19           1.891070e-17        6.554291e-16   \n",
       "std              1.000128e+00           1.000128e+00        1.000128e+00   \n",
       "min             -6.847632e+00          -6.703713e+00       -4.173702e+00   \n",
       "25%             -4.799653e-01          -4.732337e-01       -7.013733e-01   \n",
       "50%             -4.698007e-02          -5.073219e-02       -3.039403e-02   \n",
       "75%              5.109784e-01           4.678733e-01        7.241609e-01   \n",
       "max              4.780882e+00           5.367399e+00        2.825161e+00   \n",
       "\n",
       "       centralBank_USA  debtDefault_USA  interestRates_USA  \\\n",
       "count     3.910000e+03     3.910000e+03       3.910000e+03   \n",
       "mean     -1.608120e-16     1.097218e-15       2.304494e-16   \n",
       "std       1.000128e+00     1.000128e+00       1.000128e+00   \n",
       "min      -3.048743e+00    -2.270924e+00      -9.733690e+00   \n",
       "25%      -6.927469e-01    -7.495620e-01      -5.990254e-01   \n",
       "50%       4.215866e-03     4.375376e-03      -1.634409e-01   \n",
       "75%       6.946301e-01     7.372704e-01       4.860234e-01   \n",
       "max       2.955895e+00     3.014466e+00       2.787403e+00   \n",
       "\n",
       "       interestRatesForecast_USA  monetaryPolicyLooseVsTight_USA  \n",
       "count               3.910000e+03                    3.910000e+03  \n",
       "mean                5.224579e-18                   -3.448790e-16  \n",
       "std                 1.000128e+00                    1.000128e+00  \n",
       "min                -1.687397e+01                   -9.594321e+00  \n",
       "25%                -4.879330e-01                   -6.143743e-01  \n",
       "50%                -2.458350e-01                   -2.346027e-01  \n",
       "75%                 2.338346e-01                    6.134070e-01  \n",
       "max                 5.097762e+00                    2.267225e+00  \n",
       "\n",
       "[8 rows x 88 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We'll also apply standardization to even scales. This will make distributions more comparable\n",
    "sentiments_train, sentiments_test = apply_transformation(sentiments_train, sentiments_test, 'dispersion_and_scale')\n",
    "\n",
    "display(sentiments_train.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9131197876553399"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking linear correlation after transformation\n",
    "sentiments_train.optimism_US500.ewm(90).mean().corr(sp500_yahoo.Close, method='pearson') \n",
    "# correlation has improved a little"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Smoothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# testing fast fourier transform\n",
    "def filter_signal(signal, threshold=1e8):\n",
    "    \"\"\"\n",
    "    Performs a Fast Fourier Transform over a signal and returns filtered data.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    signal: numpy array\n",
    "    threshold: double\n",
    "    \"\"\"\n",
    "    fourier = np.fft.rfft(signal)\n",
    "    frequencies = np.fft.rfftfreq(signal.size, d=1.)\n",
    "    fourier[frequencies > threshold] = 0\n",
    "    return np.fft.irfft(fourier)\n",
    "\n",
    "span = 500\n",
    "signal = np.array(sentiments_train.sentiment_US500[1:])\n",
    "threshold = 0.1\n",
    "filtered = filter_signal(signal, threshold=threshold)\n",
    "#plt.figure(figsize=(15, 10))\n",
    "plt.plot(signal[-span:], label='Raw')\n",
    "#plt.plot(signal[-100:], 'bo', label='Raw')\n",
    "plt.plot(filtered[-span:], label='Filtered')\n",
    "plt.plot(np.array(pd.Series(signal).ewm(22).mean())[-span:], label='22-day EWM')\n",
    "#plt.plot(np.array(sp500_train['Adj Close'][-span:]/max(sp500_train['Adj Close'][-span:])) - 0.8,\n",
    "#         label='Adj Close Price (scaled)', color='gray')\n",
    "#plt.plot(filtered[-500:], 'ro', label='Filtered')\n",
    "plt.legend()\n",
    "plt.title(\"FFT Denoising with threshold = {} cycles per day\".format(threshold), size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('Sentiment Index')\n",
    "#ax1.plot(sentiments_train['sentiment_US500'][:300], label='Transf. EWMA Sentiment US500', color=bbva[2])\n",
    "ax1.plot(sentiments['sentiment_US500'][train_start:train_end][:300], label='Raw Sentiment US500', color=bbva[2])\n",
    "ax1.plot(sentiments['sentiment_US500'][train_start:train_end].ewm(22).mean()[:300], label='EWMA Sentiment US500', \n",
    "         color=bbva[1])\n",
    "ax1.legend(loc='lower left')\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "ax2.set_ylabel('S&P 500 Close Price')  # we already handled the x-label with ax1\n",
    "ax2.plot(sp500_train['Adj Close'][:300], color=bbva[0], label='S&P 500 Close Price')\n",
    "ax2.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for selecting smoothing technique\n",
    "def select_smoothing(data, technique, span0=22):\n",
    "    \"\"\"\n",
    "    Performs a smoothing (EWMA) or filtering (FFT) technique over data.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data: pandas dataframe column\n",
    "    technique: str\n",
    "        One of 'fft' for Fast Fourier Transform or 'ewma' for Exponentially Weighted Moving Average.\n",
    "        Calls function filter_signal() for FFT.\n",
    "    span0: int\n",
    "        Decay in terms of span.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    Pandas dataframe column.\n",
    "    \"\"\"\n",
    "    if technique == 'fft':\n",
    "        filtered = list(filter_signal(data, threshold=threshold))\n",
    "        filtered.append(0)\n",
    "    elif technique == 'ewma':\n",
    "        filtered = data.ewm(span=span0).mean()\n",
    "    return filtered\n",
    "\n",
    "# we'll be using the ewma\n",
    "for col in sentiments_train.columns:\n",
    "    sentiments_train[col] = select_smoothing(sentiments_train[col], 'ewma')\n",
    "    sentiments_test[col] = select_smoothing(sentiments_test[col], 'ewma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\"\"\"\n",
    "def apply_pca(data, scale=False):\n",
    "    # Separating out the features\n",
    "    # already separated\n",
    "    data = data.dropna()\n",
    "    x = data\n",
    "    \n",
    "    # Standardizing the features\n",
    "    if scale is True:\n",
    "        x = StandardScaler().fit_transform(x) #already transformed\n",
    "    pca = PCA(n_components=3)\n",
    "    x_pca = pca.fit_transform(x)\n",
    "    pDf = pd.DataFrame(data = x_pca, \n",
    "                       columns = ['pc1', 'pc2', 'pc3'])\n",
    "\n",
    "    data.reset_index(inplace=True)\n",
    "    data['pc1'] = pDf.pc1\n",
    "    data['pc2'] = pDf.pc2\n",
    "    data['pc3'] = pDf.pc3\n",
    "    data.set_index('Date', inplace=True)\n",
    "\n",
    "    print(pca.explained_variance_ratio_)\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "    plt.xlabel('Number of Components')\n",
    "    plt.ylabel('Variance (%)') \n",
    "    plt.title('Explained Variance')\n",
    "    plt.show()\n",
    "\n",
    "    return data\n",
    "\n",
    "sentiments_train = apply_pca(sentiments_train, scale=False)\n",
    "sentiments_test = apply_pca(sentiments_test, scale=False)\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Technical indicators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[TA Library Documentation](https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We'll add some technical indicators used in trading for evaluating momemtum and trends\n",
    "def add_technical_indicators(data):\n",
    "    \"\"\"\n",
    "    Adds technical indicators widely used by traders when checking for bearish or bullish signals.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data: pandas dataframe\n",
    "    \"\"\"\n",
    "    \n",
    "    # data cannot contain nans before fft\n",
    "    data.dropna(inplace=True)\n",
    "    #d = 10 # horizonte de predicción (aquí hacia atrás)\n",
    "    #fft_close = filter_signal(data['Adj Close'])\n",
    "    #data['FFT Discrete Close'] = [np.sign(fft_close[i] - fft_close[i - d])\n",
    "    #                              if (i - d > 0) and (i < len(fft_close)) else np.nan\n",
    "    #                              for i in range(0, len(fft_close) + 1)]\n",
    "    #\n",
    "    data['ROC'] = ROCIndicator(data['Adj Close'], 10).roc()\n",
    "    data['RSI'] = RSIIndicator(data['Adj Close'], 10).rsi()\n",
    "    data['Stoch'] = StochasticOscillator(high=data['High'], \n",
    "                                         low=data['Low'], \n",
    "                                         close=data['Close'], \n",
    "                                         n=10).stoch()\n",
    "    data['Williams'] = WilliamsRIndicator(high=data['High'], \n",
    "                                          low=data['Low'], \n",
    "                                          close=data['Close'],  \n",
    "                                          lbp=10).wr()\n",
    "    data['MACD'] = MACD(data['Close'], \n",
    "                        n_slow = 22, \n",
    "                        n_fast = 8, \n",
    "                        n_sign = 5).macd()\n",
    "    data['ADX'] = ADXIndicator(high=data['High'], \n",
    "                               low=data['Low'], \n",
    "                               close=data['Close'], n=10).adx()\n",
    "    \n",
    "    data['Close/Open'] = [1 if data.Close[x] > data.Open[x] else 0\n",
    "                          for x in range(0, len(data))]\n",
    "    \n",
    "    data['Daily Return'] = data['Adj Close'].pct_change(periods=1)*100\n",
    "    data['Daily Volatility'] = data['Daily Return'].ewm(span=22).std() # exponential moving std\n",
    "    \n",
    "    ### only compute this cross if it won't later be the primary model!!\n",
    "    fast_window = 20\n",
    "    slow_window = 60\n",
    "    col = 'Adj Close'\n",
    "    data['Fast EWMA {}'.format(col)] = data[col] # already averaged\n",
    "    data['Slow EWMA {}'.format(col)] = data[col].ewm(slow_window).mean()\n",
    "\n",
    "    # Compute sides\n",
    "    data['sp_cross_{}'.format(col)] = np.nan\n",
    "\n",
    "    long_signals = data['Fast EWMA {}'.format(col)] >= data['Slow EWMA {}'.format(col)]\n",
    "    short_signals = data['Fast EWMA {}'.format(col)] < data['Slow EWMA {}'.format(col)]\n",
    "    data.loc[long_signals, 'sp_cross_{}'.format(col)]  = 1\n",
    "    data.loc[short_signals, 'sp_cross_{}'.format(col)]  = -1\n",
    "\n",
    "    # Lagging our trading signals by one day\n",
    "    #data[['Fast EWMA', 'Slow EWMA']] = data[['Fast EWMA', 'Slow EWMA']].shift(1)\n",
    "\n",
    "    data.drop(['Fast EWMA {}'.format(col), 'Slow EWMA {}'.format(col)], axis=1, inplace=True)\n",
    "\n",
    "    return data\n",
    "\n",
    "sp500_train = add_technical_indicators(sp500_train)\n",
    "sp500_test = add_technical_indicators(sp500_test)\n",
    "\n",
    "sp500_train.Close[-100:].plot(legend=True);\n",
    "plt.figure()\n",
    "sp500_train.MACD[-100:].plot(legend=True);\n",
    "sp500_train.ADX[-100:].plot(legend=True);\n",
    "sp500_train.RSI[-100:].plot(legend=True);\n",
    "sp500_train.Stoch[-100:].plot(legend=True);\n",
    "sp500_train.Williams[-100:].plot(legend=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping close variable as it is the same as adjusted close here\n",
    "#sp500_train.drop(['High', 'Low', 'Open', 'Close'], axis=1, inplace=True)\n",
    "#sp500_test.drop(['High', 'Low', 'Open', 'Close'], axis=1, inplace=True)\n",
    "sp500_train.drop(['Close'], axis=1, inplace=True)\n",
    "sp500_test.drop(['Close'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We're shifting forward the financial variables (which are related to price) since news from the\n",
    "# previous day have to be used for predicting next day's prices\n",
    "sp500_train = sp500_train.shift(1) \n",
    "sp500_test = sp500_test.shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>ROC</th>\n",
       "      <th>RSI</th>\n",
       "      <th>Stoch</th>\n",
       "      <th>Williams</th>\n",
       "      <th>MACD</th>\n",
       "      <th>ADX</th>\n",
       "      <th>Close/Open</th>\n",
       "      <th>Daily Return</th>\n",
       "      <th>Daily Volatility</th>\n",
       "      <th>sp_cross_Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <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>2001-08-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-09-03</th>\n",
       "      <td>1141.829956</td>\n",
       "      <td>1126.380005</td>\n",
       "      <td>1129.030029</td>\n",
       "      <td>1133.579956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-09-04</th>\n",
       "      <td>1185.417436</td>\n",
       "      <td>1146.563254</td>\n",
       "      <td>1142.752676</td>\n",
       "      <td>1123.104688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.924087</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-09-05</th>\n",
       "      <td>1155.400024</td>\n",
       "      <td>1129.060059</td>\n",
       "      <td>1133.579956</td>\n",
       "      <td>1132.939941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.875720</td>\n",
       "      <td>1.272656</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-09-06</th>\n",
       "      <td>1135.520020</td>\n",
       "      <td>1114.859985</td>\n",
       "      <td>1132.939941</td>\n",
       "      <td>1131.739990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.105915</td>\n",
       "      <td>0.882187</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   High          Low         Open    Adj Close  ROC  RSI  \\\n",
       "Date                                                                       \n",
       "2001-08-31          NaN          NaN          NaN          NaN  NaN  NaN   \n",
       "2001-09-03  1141.829956  1126.380005  1129.030029  1133.579956  NaN  NaN   \n",
       "2001-09-04  1185.417436  1146.563254  1142.752676  1123.104688  NaN  NaN   \n",
       "2001-09-05  1155.400024  1129.060059  1133.579956  1132.939941  NaN  NaN   \n",
       "2001-09-06  1135.520020  1114.859985  1132.939941  1131.739990  NaN  NaN   \n",
       "\n",
       "            Stoch  Williams  MACD  ADX  Close/Open  Daily Return  \\\n",
       "Date                                                               \n",
       "2001-08-31    NaN       NaN   NaN  NaN         NaN           NaN   \n",
       "2001-09-03    NaN       NaN   NaN  0.0         1.0           NaN   \n",
       "2001-09-04    NaN       NaN   NaN  0.0         0.0     -0.924087   \n",
       "2001-09-05    NaN       NaN   NaN  0.0         0.0      0.875720   \n",
       "2001-09-06    NaN       NaN   NaN  0.0         0.0     -0.105915   \n",
       "\n",
       "            Daily Volatility  sp_cross_Adj Close  \n",
       "Date                                              \n",
       "2001-08-31               NaN                 NaN  \n",
       "2001-09-03               NaN                 1.0  \n",
       "2001-09-04               NaN                -1.0  \n",
       "2001-09-05          1.272656                 1.0  \n",
       "2001-09-06          0.882187                 1.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Before continuing, we'll concat the financial and the sentiment datasets\n",
    "X_train = pd.concat([sp500_train, sentiments_train], axis=1)\n",
    "X_test = pd.concat([sp500_test, sentiments_test], axis=1)\n",
    "X_train.dropna(inplace=True) # there are na values at the beginning, for the newly created variables (technical indicators)\n",
    "X_test.dropna(inplace=True)\n",
    "\n",
    "# Now we'll create y_train and y_test, our labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### New variables (crossovers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we'll add these ewmas as predictor or explanatory variables\n",
    "def add_crossing_ewmas(data, fast_window, slow_window):\n",
    "    \"\"\"\n",
    "    Adds two crossing exponentially weighted moving averages. \n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data: pandas dataframe\n",
    "    fast_window: int\n",
    "        Fast decay in terms of span.\n",
    "    slow_window: int\n",
    "        Slow decay in terms of span.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    Pandas dataframe.\n",
    "    \"\"\"\n",
    "\n",
    "    #fast_window = 20 # 1 working month\n",
    "    #slow_window = 90 # 5 working months\n",
    "\n",
    "    for col in ['sentiment_US500', 'stockIndexSentiment_USA']:\n",
    "\n",
    "        data['Fast EWMA {}'.format(col)] = data[col] # already averaged\n",
    "        data['Slow EWMA {}'.format(col)] = data[col].ewm(slow_window).mean()\n",
    "\n",
    "        # Compute sides\n",
    "        data['cross_{}'.format(col)] = np.nan\n",
    "\n",
    "        long_signals = data['Fast EWMA {}'.format(col)] >= data['Slow EWMA {}'.format(col)]\n",
    "        short_signals = data['Fast EWMA {}'.format(col)] < data['Slow EWMA {}'.format(col)]\n",
    "        data.loc[long_signals, 'cross_{}'.format(col)]  = 1\n",
    "        data.loc[short_signals, 'cross_{}'.format(col)]  = -1\n",
    "\n",
    "        # Lagging our trading signals by one day\n",
    "        #data[['Fast EWMA', 'Slow EWMA']] = data[['Fast EWMA', 'Slow EWMA']].shift(1)\n",
    "\n",
    "        data.drop(['Fast EWMA {}'.format(col), 'Slow EWMA {}'.format(col)], axis=1, inplace=True)\n",
    "    \n",
    "    return data\n",
    "\n",
    "X_train = add_crossing_ewmas(X_train, 10, 60)\n",
    "X_test = add_crossing_ewmas(X_test, 10, 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Labeling target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# overriding function get_bins\n",
    "# we want this to return the sign of the return when the vertical barrier is touched first\n",
    "# instead of what it's currently implemented (0 if vertical barrier is touched first)\n",
    "def get_bins(triple_barrier_events, close):\n",
    "    \"\"\"\n",
    "    Advances in Financial Machine Learning, Snippet 3.7, page 51.\n",
    "\n",
    "    Labeling for Side & Size with Meta Labels\n",
    "\n",
    "    Compute event's outcome (including side information, if provided).\n",
    "    events is a DataFrame where:\n",
    "\n",
    "    Now the possible values for labels in out['bin'] are {0,1}, whether to take the bet or pass,\n",
    "    a purely binary prediction. The previous feasible values were {−1,0,1}.\n",
    "    The ML algorithm will be trained to decide if it's 1, and we can use the probability of this secondary prediction\n",
    "    to derive the size of the bet, where the side (sign) of the position has been set by the primary model.\n",
    "\n",
    "    :param triple_barrier_events: (pd.DataFrame)\n",
    "                -events.index is event's starttime\n",
    "                -events['t1'] is event's endtime\n",
    "                -events['trgt'] is event's target\n",
    "                -events['side'] (optional) implies the algo's position side\n",
    "                Case 1: ('side' not in events): bin in (-1,1) <-label by price action\n",
    "                Case 2: ('side' in events): bin in (0,1) <-label by pnl (meta-labeling)\n",
    "    :param close: (pd.Series) Close prices\n",
    "    :return: (pd.DataFrame) Meta-labeled events\n",
    "    \"\"\"\n",
    "\n",
    "    # 1) Align prices with their respective events\n",
    "    events_ = triple_barrier_events.dropna(subset=['t1'])\n",
    "    all_dates = events_.index.union(other=events_['t1'].array).drop_duplicates()\n",
    "    prices = close.reindex(all_dates, method='bfill')\n",
    "\n",
    "    # 2) Create out DataFrame\n",
    "    out_df = pd.DataFrame(index=events_.index)\n",
    "    # Need to take the log returns, else your results will be skewed for short positions\n",
    "    #out_df['ret'] = np.log(prices.loc[events_['t1'].array].array) - np.log(prices.loc[events_.index])\n",
    "    out_df['ret'] = prices.loc[events_['t1'].values].values / prices.loc[events_.index] - 1\n",
    "    out_df['trgt'] = events_['trgt']\n",
    "\n",
    "    # Meta labeling: Events that were correct will have pos returns\n",
    "    if 'side' in events_:\n",
    "        out_df['ret'] = out_df['ret'] * events_['side']  # meta-labeling\n",
    "\n",
    "    # Added code: label 0 when vertical barrier reached\n",
    "    #-------------------we change this step, as we want the outcome to be the sign of the return\n",
    "    #out_df = barrier_touched(out_df, triple_barrier_events) \n",
    "    out_df['bin'] = np.sign(out_df['ret'])\n",
    "\n",
    "    # Meta labeling: label incorrect events with a 0\n",
    "    if 'side' in events_:\n",
    "        out_df.loc[out_df['ret'] <= 0, 'bin'] = 0\n",
    "\n",
    "    # Transform the log returns back to normal returns.\n",
    "    #out_df['ret'] = np.exp(out_df['ret']) - 1\n",
    "\n",
    "    # Add the side to the output. This is useful for when a meta label model must be fit\n",
    "    tb_cols = triple_barrier_events.columns\n",
    "    if 'side' in tb_cols:\n",
    "        out_df['side'] = triple_barrier_events['side']\n",
    "\n",
    "    return out_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we'll apply the labeling\n",
    "def apply_trading_labeling(data, \n",
    "                           compute_side=False, \n",
    "                           horizon=14,\n",
    "                           pt_sl=[1, 2], # multipliers for daily vol (contribution to horizontal barriers)\n",
    "                           min_ret=0.005,\n",
    "                           primary_model=False):\n",
    "    \"\"\"\n",
    "    Applies selected trading strategy with the given parameters. Labeling uses Triple Barrier Method.\n",
    "\n",
    "    Parameters\n",
    "    __________\n",
    "    dataframe: pandas dataframe \n",
    "        Data to use.\n",
    "    compute_side: boolean\n",
    "        Whether to use a primary model that tells the side (buy or sell).\n",
    "        When True, a trend-following strategy will be applied as primary model.\n",
    "        Default is False.\n",
    "    horizon: int\n",
    "        Prediction horizon in natural days.\n",
    "    pt_sl: list\n",
    "        Profit taking and stop loss multipliers to the volatility. Width of the TBM box.\n",
    "    min_ret: float\n",
    "        Minimum target return to run the search for triple barriers.\n",
    "    primary_model: boolean\n",
    "        Whether a primary model computed by the user has already decided the side.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    Labels dataframe and triple-barrier events dataframe.\n",
    "    \"\"\"\n",
    "\n",
    "    ####--------------------- Primary models ------------------------####\n",
    "    if compute_side is True:\n",
    "        # compute exponentially moving averages\n",
    "        fast_window = 20\n",
    "        slow_window = 90 # optimize the span for fast and slow averages\n",
    "\n",
    "        data['Fast EWMA'] = data['Adj Close'].ewm(fast_window).mean()\n",
    "        data['Slow EWMA'] = data['Adj Close'].ewm(slow_window).mean()\n",
    "    \n",
    "        # Compute sides\n",
    "        data['Side'] = np.nan\n",
    "\n",
    "        long_signals = data['Fast EWMA'] >= data['Slow EWMA']\n",
    "        short_signals = data['Fast EWMA'] < data['Slow EWMA']\n",
    "        data.loc[long_signals, 'Side'] = 1\n",
    "        data.loc[short_signals, 'Side'] = -1\n",
    "\n",
    "        # Lagging our trading signals by one day\n",
    "        data[['Fast EWMA', 'Slow EWMA']] = data[['Fast EWMA', 'Slow EWMA']].shift(1)\n",
    "\n",
    "        data[['Fast EWMA', 'Slow EWMA']].plot();\n",
    "    \n",
    "    data.dropna(inplace=True)\n",
    "\n",
    "    ####--------------------- CUSUM filters ------------------------####\n",
    "    # Apply Symmetric CUSUM Filter and get timestamps for events\n",
    "    cusum_events = mlfin.filters.cusum_filter(data['Adj Close'],\n",
    "                                              threshold=data['Daily Volatility']/100)\n",
    "\n",
    "    ####--------------------- Vertical barriers ------------------------####\n",
    "    # Compute vertical barrier\n",
    "    vertical_barriers = mlfin.labeling.add_vertical_barrier(t_events=cusum_events,\n",
    "                                                            close=data['Adj Close'],\n",
    "                                                            num_days=horizon) # this is the length of the tbm box\n",
    "    \n",
    "    ####--------------------- Triple barriers ------------------------####\n",
    "    # Computing triple barriers\n",
    "    if (compute_side is True) | (primary_model is True):\n",
    "        triple_barrier_events = mlfin.labeling.get_events(close=data['Adj Close'],\n",
    "                                                       t_events=cusum_events,\n",
    "                                                       pt_sl=pt_sl, # profit taking and stop loss multiples\n",
    "                                                       target=data['Daily Volatility']/100, # values in conjunction with pt_sl for width of barrier\n",
    "                                                       min_ret=min_ret,\n",
    "                                                       num_threads=3, # num of parallel tasks\n",
    "                                                       vertical_barrier_times=vertical_barriers,\n",
    "                                                       side_prediction=data.Side)\n",
    "        ####--------------------- Meta-labels ------------------------####\n",
    "        # now we compute the meta-labelling\n",
    "        meta_labeled_events = get_bins(triple_barrier_events, data['Adj Close'])\n",
    "    \n",
    "    else:\n",
    "        triple_barrier_events = mlfin.labeling.get_events(close=data['Adj Close'],\n",
    "                                                       t_events=cusum_events,\n",
    "                                                       pt_sl=pt_sl, # profit taking and stop loss multiples\n",
    "                                                       target=data['Daily Volatility']/100, # values in conjunction with pt_sl for width of barrier\n",
    "                                                       min_ret=min_ret,\n",
    "                                                       num_threads=3, # num of parallel tasks\n",
    "                                                       vertical_barrier_times=vertical_barriers,\n",
    "                                                       side_prediction=None)\n",
    "\n",
    "        ####--------------------- Side ------------------------####\n",
    "        # now we compute the side \n",
    "        # function that does meta-labeling returns side if no side prediction comes first\n",
    "        meta_labeled_events = get_bins(triple_barrier_events, data['Adj Close'])\n",
    "        meta_labeled_events['side'] = meta_labeled_events['bin']\n",
    "        meta_labeled_events['bin'] = 1\n",
    "\n",
    "    return meta_labeled_events, triple_barrier_events\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-05 19:11:32.864278 100.0% apply_pt_sl_on_t1 done after 0.09 minutes. Remaining 0.0 minutes..\n",
      "2021-11-05 19:11:38.634043 100.0% apply_pt_sl_on_t1 done after 0.08 minutes. Remaining 0.0 minutes..\n"
     ]
    }
   ],
   "source": [
    "# computing labels for side (+1, -1) \n",
    "# side will tell the sign of the bet\n",
    "train_labels, tbm_train = apply_trading_labeling(X_train, \n",
    "                                     primary_model=False, \n",
    "                                     compute_side=False,\n",
    "                                     horizon=14,\n",
    "                                     pt_sl=[0, 0],\n",
    "                                     min_ret=0.005)\n",
    "test_labels, tbm_test = apply_trading_labeling(X_test, \n",
    "                                     primary_model=False, \n",
    "                                     compute_side=False,\n",
    "                                     horizon=14,\n",
    "                                     pt_sl=[0, 0],\n",
    "                                     min_ret=0.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ret</th>\n",
       "      <th>trgt</th>\n",
       "      <th>bin</th>\n",
       "      <th>side</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-10-04</th>\n",
       "      <td>0.004486</td>\n",
       "      <td>0.017967</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-10-11</th>\n",
       "      <td>0.003895</td>\n",
       "      <td>0.015892</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-10-17</th>\n",
       "      <td>-0.034395</td>\n",
       "      <td>0.013810</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-10-18</th>\n",
       "      <td>-0.016071</td>\n",
       "      <td>0.014603</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-10-23</th>\n",
       "      <td>0.011873</td>\n",
       "      <td>0.013648</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ret      trgt  bin  side\n",
       "2001-10-04  0.004486  0.017967    1   1.0\n",
       "2001-10-11  0.003895  0.015892    1   1.0\n",
       "2001-10-17 -0.034395  0.013810    1  -1.0\n",
       "2001-10-18 -0.016071  0.014603    1  -1.0\n",
       "2001-10-23  0.011873  0.013648    1   1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t1</th>\n",
       "      <th>trgt</th>\n",
       "      <th>pt</th>\n",
       "      <th>sl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-10-04</th>\n",
       "      <td>2001-10-18</td>\n",
       "      <td>0.017967</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-10-11</th>\n",
       "      <td>2001-10-25</td>\n",
       "      <td>0.015892</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-10-17</th>\n",
       "      <td>2001-10-31</td>\n",
       "      <td>0.013810</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-10-18</th>\n",
       "      <td>2001-11-01</td>\n",
       "      <td>0.014603</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-10-23</th>\n",
       "      <td>2001-11-06</td>\n",
       "      <td>0.013648</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   t1      trgt  pt  sl\n",
       "2001-10-04 2001-10-18  0.017967   0   0\n",
       "2001-10-11 2001-10-25  0.015892   0   0\n",
       "2001-10-17 2001-10-31  0.013810   0   0\n",
       "2001-10-18 2001-11-01  0.014603   0   0\n",
       "2001-10-23 2001-11-06  0.013648   0   0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(train_labels.head())\n",
    "display(tbm_train.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27d0a025758c42199e0ade2059c7d564",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(RadioButtons(description='Data', index=1, options=('Train', 'Test'), value='Test'), Outp…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting labels to see outcome\n",
    "#configure_plotly_browser_state()\n",
    "@interact\n",
    "def plot_sentiment_index(data=widgets.RadioButtons(\n",
    "                            options=['Train', 'Test'],\n",
    "                            value='Test',\n",
    "                            # rows=10,\n",
    "                            description='Data',\n",
    "                            disabled=False)):\n",
    "\n",
    "    figura = make_subplots(specs=[[{\"secondary_y\": False}]])\n",
    "    if data == 'Test':\n",
    "        dataset = X_test.merge(test_labels,\n",
    "                            left_index=True,\n",
    "                            right_index=True,\n",
    "                            how='left')\n",
    "        figura.add_trace(go.Scatter(y=X_test['Adj Close'],\n",
    "                                    x=X_test.index,\n",
    "                                    mode='lines',\n",
    "                                    name='S&P 500 Close Price'),\n",
    "                         secondary_y=False,)\n",
    "    else:\n",
    "        dataset = X_train.merge(train_labels,\n",
    "                            left_index=True,\n",
    "                            right_index=True,\n",
    "                            how='left')\n",
    "        figura.add_trace(go.Scatter(y=X_train['Adj Close'],\n",
    "                                    x=X_train.index,\n",
    "                                    mode='lines',\n",
    "                                    name='SP500 Close Price'),\n",
    "                         secondary_y=False,)\n",
    "    if ('Fast EWMA' in dataset.columns) and ('Slow EWMA' in dataset.columns):\n",
    "        figura.add_trace(go.Scatter(y=dataset['Fast EWMA'],\n",
    "                                    x=dataset.index,\n",
    "                                    mode='lines',\n",
    "                                    name='SP500 Fast EWMA'),\n",
    "                         secondary_y=False,)\n",
    "        figura.add_trace(go.Scatter(y=dataset['Slow EWMA'],\n",
    "                                    x=dataset.index,\n",
    "                                    mode='lines',\n",
    "                                    name='SP500 Slow EWMA'),\n",
    "                         secondary_y=False,)\n",
    "\n",
    "    figura.add_trace(\n",
    "        go.Scatter(y=dataset[(dataset.bin == 1) & (dataset.side == 1)]['Adj Close'],\n",
    "                   x=dataset[(dataset.bin == 1) & (dataset.side == 1)].index,\n",
    "                   mode='markers',\n",
    "                   name='Buy',\n",
    "                   marker=dict(size=8, color='#008000'),\n",
    "                   marker_symbol=5),\n",
    "                   secondary_y=False,)\n",
    "    figura.add_trace(\n",
    "        go.Scatter(y=dataset[(dataset.bin == 1) & (dataset.side == -1)]['Adj Close'],\n",
    "                   x=dataset[(dataset.bin == 1) & (dataset.side == -1)].index,\n",
    "                   mode='markers',\n",
    "                   name='Sell',\n",
    "                   marker=dict(size=8, color='#FF0000'),\n",
    "                   marker_symbol=6),\n",
    "                   secondary_y=False,)\n",
    "\n",
    "    figura.update_layout(\n",
    "        title_text='S&P 500 Index and labeled positions | {}'.format(data),\n",
    "        colorway = bbva)\n",
    "\n",
    "    figura.update_xaxes(rangeslider_visible=True)\n",
    "    figura.update_yaxes(title_text=\"<b>S&P 500 Close Price</b>\", secondary_y=False)\n",
    "\n",
    "    figura.update_xaxes(\n",
    "        rangeslider_visible=True,\n",
    "        rangeselector=dict(\n",
    "            dict(font = dict(color = \"black\")),\n",
    "            buttons=list([\n",
    "                dict(count=1, label=\"1m\", step=\"month\", stepmode=\"backward\"),\n",
    "                dict(count=6, label=\"6m\", step=\"month\", stepmode=\"backward\"),\n",
    "                dict(count=1, label=\"YTD\", step=\"year\", stepmode=\"todate\"),\n",
    "                dict(count=1, label=\"1y\", step=\"year\", stepmode=\"backward\"),\n",
    "                dict(count=3, label=\"3y\", step=\"year\", stepmode=\"backward\"),\n",
    "                dict(count=5, label=\"5y\", step=\"year\", stepmode=\"backward\"),\n",
    "                dict(step=\"all\"),\n",
    "            ])\n",
    "        )\n",
    "    )\n",
    "\n",
    "    figura.update_layout(template='simple_white', hovermode='x')\n",
    "    iplot(figura)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:\n",
      "        ret\n",
      "label     \n",
      "-1.0   621\n",
      " 1.0   867\n",
      "\n",
      "Test:\n",
      "        ret\n",
      "label     \n",
      "-1.0    77\n",
      " 1.0   178\n"
     ]
    }
   ],
   "source": [
    "# number of observations per label\n",
    "train_labels['label'] = train_labels['bin'] * train_labels['side']\n",
    "print('Train:\\n', train_labels.groupby('label').count()[['ret']])\n",
    "\n",
    "test_labels['label'] = test_labels['bin'] * test_labels['side']\n",
    "print('\\nTest:\\n', test_labels.groupby('label').count()[['ret']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling price variables since we've already computed our sign labels\n",
    "X_train[sp500_train.columns], X_test[sp500_test.columns] = apply_transformation(X_train[sp500_train.columns],\n",
    "                                                                                X_test[sp500_test.columns],\n",
    "                                                                                'dispersion_and_scale')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>ROC</th>\n",
       "      <th>RSI</th>\n",
       "      <th>Stoch</th>\n",
       "      <th>Williams</th>\n",
       "      <th>MACD</th>\n",
       "      <th>ADX</th>\n",
       "      <th>...</th>\n",
       "      <th>bondPriceDirection_USA</th>\n",
       "      <th>bondPriceForecast_USA</th>\n",
       "      <th>bondVolatility_USA</th>\n",
       "      <th>centralBank_USA</th>\n",
       "      <th>debtDefault_USA</th>\n",
       "      <th>interestRates_USA</th>\n",
       "      <th>interestRatesForecast_USA</th>\n",
       "      <th>monetaryPolicyLooseVsTight_USA</th>\n",
       "      <th>cross_sentiment_US500</th>\n",
       "      <th>cross_stockIndexSentiment_USA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-10-04</th>\n",
       "      <td>-0.807969</td>\n",
       "      <td>-0.873574</td>\n",
       "      <td>-0.871239</td>\n",
       "      <td>-0.786130</td>\n",
       "      <td>1.808582</td>\n",
       "      <td>0.422597</td>\n",
       "      <td>1.165098</td>\n",
       "      <td>1.293337</td>\n",
       "      <td>-1.006822</td>\n",
       "      <td>-1.353784</td>\n",
       "      <td>...</td>\n",
       "      <td>1.166441</td>\n",
       "      <td>0.317978</td>\n",
       "      <td>0.891799</td>\n",
       "      <td>0.537591</td>\n",
       "      <td>-0.174197</td>\n",
       "      <td>-0.519892</td>\n",
       "      <td>-0.340690</td>\n",
       "      <td>0.161353</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-10-11</th>\n",
       "      <td>-0.782429</td>\n",
       "      <td>-0.827435</td>\n",
       "      <td>-0.848776</td>\n",
       "      <td>-0.751163</td>\n",
       "      <td>2.513995</td>\n",
       "      <td>0.551618</td>\n",
       "      <td>1.128432</td>\n",
       "      <td>1.220651</td>\n",
       "      <td>-0.226302</td>\n",
       "      <td>-1.530948</td>\n",
       "      <td>...</td>\n",
       "      <td>0.071350</td>\n",
       "      <td>0.210773</td>\n",
       "      <td>0.600524</td>\n",
       "      <td>-0.129103</td>\n",
       "      <td>0.380515</td>\n",
       "      <td>-0.996558</td>\n",
       "      <td>-0.762076</td>\n",
       "      <td>-0.416752</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-10-17</th>\n",
       "      <td>-0.701903</td>\n",
       "      <td>-0.691118</td>\n",
       "      <td>-0.714693</td>\n",
       "      <td>-0.685855</td>\n",
       "      <td>1.378815</td>\n",
       "      <td>0.851356</td>\n",
       "      <td>1.035913</td>\n",
       "      <td>1.059161</td>\n",
       "      <td>0.705309</td>\n",
       "      <td>-0.859799</td>\n",
       "      <td>...</td>\n",
       "      <td>0.331034</td>\n",
       "      <td>0.641730</td>\n",
       "      <td>0.572074</td>\n",
       "      <td>-0.370464</td>\n",
       "      <td>0.629447</td>\n",
       "      <td>-0.831857</td>\n",
       "      <td>-0.514771</td>\n",
       "      <td>-0.377919</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-10-18</th>\n",
       "      <td>-0.680350</td>\n",
       "      <td>-0.732335</td>\n",
       "      <td>-0.685032</td>\n",
       "      <td>-0.766768</td>\n",
       "      <td>-0.016683</td>\n",
       "      <td>-0.079297</td>\n",
       "      <td>-0.407315</td>\n",
       "      <td>-0.529535</td>\n",
       "      <td>0.534852</td>\n",
       "      <td>-0.897141</td>\n",
       "      <td>...</td>\n",
       "      <td>0.445600</td>\n",
       "      <td>0.690018</td>\n",
       "      <td>0.573608</td>\n",
       "      <td>-0.357195</td>\n",
       "      <td>0.643653</td>\n",
       "      <td>-0.797990</td>\n",
       "      <td>-0.467564</td>\n",
       "      <td>-0.378449</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-10-23</th>\n",
       "      <td>-0.746187</td>\n",
       "      <td>-0.755141</td>\n",
       "      <td>-0.780502</td>\n",
       "      <td>-0.715821</td>\n",
       "      <td>0.714642</td>\n",
       "      <td>0.351889</td>\n",
       "      <td>0.307352</td>\n",
       "      <td>0.146069</td>\n",
       "      <td>0.269745</td>\n",
       "      <td>-1.549311</td>\n",
       "      <td>...</td>\n",
       "      <td>0.306266</td>\n",
       "      <td>0.527152</td>\n",
       "      <td>0.434079</td>\n",
       "      <td>-0.604699</td>\n",
       "      <td>0.790327</td>\n",
       "      <td>-0.724182</td>\n",
       "      <td>-0.378894</td>\n",
       "      <td>-0.386394</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 104 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                High       Low      Open  Adj Close       ROC       RSI  \\\n",
       "2001-10-04 -0.807969 -0.873574 -0.871239  -0.786130  1.808582  0.422597   \n",
       "2001-10-11 -0.782429 -0.827435 -0.848776  -0.751163  2.513995  0.551618   \n",
       "2001-10-17 -0.701903 -0.691118 -0.714693  -0.685855  1.378815  0.851356   \n",
       "2001-10-18 -0.680350 -0.732335 -0.685032  -0.766768 -0.016683 -0.079297   \n",
       "2001-10-23 -0.746187 -0.755141 -0.780502  -0.715821  0.714642  0.351889   \n",
       "\n",
       "               Stoch  Williams      MACD       ADX  ...  \\\n",
       "2001-10-04  1.165098  1.293337 -1.006822 -1.353784  ...   \n",
       "2001-10-11  1.128432  1.220651 -0.226302 -1.530948  ...   \n",
       "2001-10-17  1.035913  1.059161  0.705309 -0.859799  ...   \n",
       "2001-10-18 -0.407315 -0.529535  0.534852 -0.897141  ...   \n",
       "2001-10-23  0.307352  0.146069  0.269745 -1.549311  ...   \n",
       "\n",
       "            bondPriceDirection_USA  bondPriceForecast_USA  bondVolatility_USA  \\\n",
       "2001-10-04                1.166441               0.317978            0.891799   \n",
       "2001-10-11                0.071350               0.210773            0.600524   \n",
       "2001-10-17                0.331034               0.641730            0.572074   \n",
       "2001-10-18                0.445600               0.690018            0.573608   \n",
       "2001-10-23                0.306266               0.527152            0.434079   \n",
       "\n",
       "            centralBank_USA  debtDefault_USA  interestRates_USA  \\\n",
       "2001-10-04         0.537591        -0.174197          -0.519892   \n",
       "2001-10-11        -0.129103         0.380515          -0.996558   \n",
       "2001-10-17        -0.370464         0.629447          -0.831857   \n",
       "2001-10-18        -0.357195         0.643653          -0.797990   \n",
       "2001-10-23        -0.604699         0.790327          -0.724182   \n",
       "\n",
       "            interestRatesForecast_USA  monetaryPolicyLooseVsTight_USA  \\\n",
       "2001-10-04                  -0.340690                        0.161353   \n",
       "2001-10-11                  -0.762076                       -0.416752   \n",
       "2001-10-17                  -0.514771                       -0.377919   \n",
       "2001-10-18                  -0.467564                       -0.378449   \n",
       "2001-10-23                  -0.378894                       -0.386394   \n",
       "\n",
       "            cross_sentiment_US500  cross_stockIndexSentiment_USA  \n",
       "2001-10-04                    1.0                            1.0  \n",
       "2001-10-11                    1.0                            1.0  \n",
       "2001-10-17                    1.0                            1.0  \n",
       "2001-10-18                    1.0                            1.0  \n",
       "2001-10-23                    1.0                            1.0  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# eliminating label 0 (0 would mean that pct change between days is null)\n",
    "train_labels = train_labels[train_labels.label != 0]\n",
    "test_labels = test_labels[test_labels.label != 0]\n",
    "\n",
    "# downsampling with events\n",
    "X_train = X_train.reindex(train_labels.index)\n",
    "X_test = X_test.reindex(test_labels.index)\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train.to_csv('x_train_pt_sl.csv', index=False)\n",
    "#X_test.to_csv('x_test_pt_sl.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# applying our model\n",
    "def apply_model(train_labels, test_labels, X_train_, X_test_, model, scoring, sfs=True):\n",
    "    \"\"\"\n",
    "    Applies model RF or XGB to data with Cross-Validation. It may perform variable selection on demand.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    train_labels: pandas dataframe\n",
    "        Dataframe containing train labels as returned by apply_trading_labeling.\n",
    "    test_labels: pandas dataframe\n",
    "        Dataframe containing test labels as returned by apply_trading_labeling.\n",
    "    X_train_: pandas dataframe\n",
    "        Training data.\n",
    "    X_test_: pandas dataframe\n",
    "        Test data.\n",
    "    model: str\n",
    "        One of 'RF' for Random Forest or 'XGB' for Extreme Gradient Boosting.\n",
    "    scoring: str\n",
    "        One of the admitted possibilities in sklearn's GridSearchCV .\n",
    "    sfs: boolean\n",
    "        Whether to perfrom Sequential Forward Selection with a simple RF for variable selection.\n",
    "        It will select from 10 to 30 total variables.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    Best estimator from CV and list of selected variables.\n",
    "    \"\"\"\n",
    "    \n",
    "    y_test = test_labels.label\n",
    "    y_train = train_labels.label\n",
    "    \n",
    "    pos_ratio = round(train_labels[train_labels.label <= 0][['label']].count() / \n",
    "                      train_labels[train_labels.label > 0][['label']].count(), 1)[0]\n",
    "    \n",
    "    if sfs is True:\n",
    "    # Sequential Forward Floating Selection\n",
    "        sffs = SFS(RandomForestClassifier(n_jobs=-1, random_state=1, n_estimators=40, max_depth=2,\n",
    "                                          class_weight='balanced_subsample'), \n",
    "                   k_features=(10, 30), \n",
    "                   forward=True, \n",
    "                   floating=True, \n",
    "                   scoring=scoring,\n",
    "                   cv=5,\n",
    "                   n_jobs=-1,\n",
    "                   verbose=0)\n",
    "\n",
    "        sffs = sffs.fit(X_train_, y_train)\n",
    "\n",
    "        print('\\nSequential Forward Floating Selection (k=30):')\n",
    "        sffs_score = sffs.k_score_\n",
    "        print('CV Score: %.2f' % sffs_score)\n",
    "\n",
    "        fig1 = plot_sfs(sffs.get_metric_dict(), kind='std_dev')\n",
    "\n",
    "        #plt.ylim([0.8, 1])\n",
    "        plt.title('Sequential Forward Selection (w. StdDev)')\n",
    "        #plt.grid()\n",
    "        plt.show()\n",
    "\n",
    "        selected_cols = []\n",
    "        for i, col in enumerate(X_train_.columns):\n",
    "            if i in sffs.k_feature_idx_:\n",
    "                selected_cols.append(col)\n",
    "\n",
    "        X_train_ = X_train_[selected_cols]\n",
    "        X_test_ = X_test_[selected_cols]\n",
    "    else:\n",
    "        selected_cols = X_train_.columns\n",
    "        \n",
    "    # Fitting model:\n",
    "    if model == 'RF':        \n",
    "        parameters = {'n_estimators': [40, 60, 100, 150, 180],\n",
    "                      'max_depth':[2, 3, 4, 5],\n",
    "                      'min_samples_split': [4, 6],\n",
    "                      'min_samples_leaf': [1, 2],\n",
    "                      'ccp_alpha': [0, 0.01, 0.02]}\n",
    "        \n",
    "        clf = RandomForestClassifier(n_jobs=-1, oob_score=True, criterion='gini', \n",
    "                                     random_state=1, class_weight='balanced_subsample')\n",
    "        \n",
    "        gridcv = GridSearchCV(clf, parameters, cv=TimeSeriesSplit(max_train_size=None, n_splits=5), \n",
    "                              scoring=scoring, verbose=1, n_jobs=-1, refit=True)\n",
    "    elif model == 'XGB':\n",
    "        parameters = {'n_estimators': [40, 60, 100, 180, 1000],\n",
    "                      'max_depth':[2, 3, 4, 5],\n",
    "                      'eta': [0.00005, 0.0005],\n",
    "                      'base_score': [0, 0.5, 1],\n",
    "                      'early_stopping_rounds':[5, 10]}#,\n",
    "                      #'scale_pos_weight': [0.5, 0.8, 1]}\n",
    "        \n",
    "        clf = XGBClassifier(objective='binary:logistic', predictor='gpu_predictor',\n",
    "                            random_state=1, min_child_weight=2, scale_pos_weight=pos_ratio)\n",
    "    \n",
    "        # As this is a time-series problem, we do not shuffle samples for Cross-Validation,\n",
    "        # and test always with newer registers:\n",
    "        gridcv = GridSearchCV(clf, parameters, cv=TimeSeriesSplit(max_train_size=None, n_splits=3), \n",
    "                              verbose=1, n_jobs=-1, refit=True, scoring=scoring, return_train_score=True)\n",
    "\n",
    "    gridcv.fit(X_train_, y_train)\n",
    "\n",
    "    # Results:\n",
    "    best_estimator = gridcv.best_estimator_\n",
    "    #cv_results, cv_results_index = gridcv.cv_results_, gridcv.best_index_\n",
    "    \n",
    "    scores = cross_val_score(best_estimator, X_train_, y_train,\n",
    "                             cv=TimeSeriesSplit(max_train_size=None, n_splits=3), scoring='f1')\n",
    "    print(\"Train F1-score: \", scores.mean())\n",
    "    scores = cross_val_score(best_estimator, X_train_, y_train,\n",
    "                             cv=TimeSeriesSplit(max_train_size=None, n_splits=3), scoring='accuracy')\n",
    "    print(\"Train accuracy: \", scores.mean())\n",
    "    scores = cross_val_score(best_estimator, X_train_, y_train,\n",
    "                             cv=TimeSeriesSplit(max_train_size=None, n_splits=3), scoring='roc_auc')\n",
    "    print(\"Train AUC: \", scores.mean())\n",
    "\n",
    "\n",
    "    print(\"Train best score %.2f\" % gridcv.best_score_)\n",
    "    print('Best parameters: {}'.format(gridcv.best_params_))\n",
    "\n",
    "    predictions = best_estimator.predict(X_test_)\n",
    "    accuracy = accuracy_score(y_test, predictions)*100\n",
    "    print(\"Accuracy: %.2f%%\" % accuracy)\n",
    "    y_pred = gridcv.predict_proba(X_test_)[:,1]\n",
    "    print(\"AUC: %.2f\" % roc_auc_score(y_test, y_pred))\n",
    "    if model == 'RF':\n",
    "        print(\"OOB Score: %.2f\" % best_estimator.oob_score_)\n",
    "    print(\"Classification report:\")\n",
    "    print(classification_report(y_test, predictions))\n",
    "\n",
    "    conf_mat = confusion_matrix(y_test, predictions)\n",
    "    print(\"Confusion matrix:\")\n",
    "    print(conf_mat)\n",
    "    #print(gridcv.cv_results_)\n",
    "    \n",
    "    with plt.style.context('seaborn-poster'):\n",
    "        features = X_train_.columns\n",
    "        plt.title('Feature Importances')\n",
    "        pd.Series(best_estimator.feature_importances_, index=features).nlargest(10).plot(kind='barh')\n",
    "        plt.xlabel('Relative Importance')\n",
    "        plt.show()\n",
    "    \n",
    "    return best_estimator, selected_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# choosing variables\n",
    "def choose_variables(X_train, X_test, var_type='technical'):\n",
    "    \"\"\"\n",
    "    Selects variables distinguishing between technical indicators and sentiment indices.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    X_train: pandas dataframe\n",
    "        Train dataframe.\n",
    "    X_test: pandas dataframe\n",
    "        Test dataframe.\n",
    "    var_type: str\n",
    "        One of 'technical', 'sentiment' or 'all'.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    Train and test dataframes with selected variables.\n",
    "    \"\"\"\n",
    "    sentiment_cols = [col for col in X_test.columns \n",
    "                      if col.endswith(('USA', 'US500', 'USD')) \n",
    "                      or col.startswith(('pc', 'cross'))]\n",
    "    if var_type == 'technical':\n",
    "        # only technical indicators or financial variables\n",
    "        X_train_ = X_train[[col for col in X_train.columns if col not in sentiment_cols]]\n",
    "        X_test_ = X_test[[col for col in X_test.columns if col not in sentiment_cols]]\n",
    "    elif var_type == 'sentiment':\n",
    "        # only sentiment variables\n",
    "        X_train_ = X_train[sentiment_cols]\n",
    "        X_test_ = X_test[sentiment_cols]\n",
    "    elif var_type == 'all':\n",
    "        X_train_ = X_train\n",
    "        X_test_ = X_test\n",
    "\n",
    "    return X_train_, X_test_\n",
    "\n",
    "X_train_, X_test_ = choose_variables(X_train, X_test, 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving here the sffs selections for different configurations\n",
    "\n",
    "# without stop loss and profit taking limits\n",
    "features_all = [ 'ROC',\n",
    "                 'ADX',\n",
    "                 'Daily Return',\n",
    "                 'fear_US500',\n",
    "                 'fundamentalStrength_US500',\n",
    "                 'optimism_USD',\n",
    "                 'surprise_USD',\n",
    "                 'timeUrgency_USD',\n",
    "                 'longShort_USD',\n",
    "                 'bondDefault_USA',\n",
    "                 'bondPriceForecast_USA',\n",
    "                 'interestRates_USA',\n",
    "                 'cross_stockIndexSentiment_USA' ] #all selection\n",
    "features_sent = ['fear_US500',\n",
    "                 'fundamentalStrength_US500',\n",
    "                 'optimism_USD',\n",
    "                 'surprise_USD',\n",
    "                 'longShort_USD',\n",
    "                 'priceForecast_USD',\n",
    "                 'stockIndexStress_USA',\n",
    "                 'bondUncertainty_USA',\n",
    "                 'bondPriceForecast_USA',\n",
    "                 'interestRates_USA',\n",
    "                 'cross_stockIndexSentiment_USA'] #sentiment selection\n",
    "\n",
    "# with stop loss and profit taking limits\n",
    "features_ptsl_all = ['Stoch',\n",
    "                     'Williams',\n",
    "                     'MACD',\n",
    "                     'ADX',\n",
    "                     'Close/Open',\n",
    "                     'sp_cross_Adj Close',\n",
    "                     'longShortForecast_US500',\n",
    "                     'analystRating_US500',\n",
    "                     'dividends_US500',\n",
    "                     'stress_USD',\n",
    "                     'stockIndexPriceDirection_USA',\n",
    "                     'stockIndexPriceForecast_USA',\n",
    "                     'cross_stockIndexSentiment_USA'] # pt sl all selection\n",
    "features_ptsl_sent = ['longShortForecast_US500',\n",
    "                     'priceForecast_US500',\n",
    "                     'analystRating_US500',\n",
    "                     'trust_USD',\n",
    "                     'stress_USD',\n",
    "                     'surprise_USD',\n",
    "                     'timeUrgency_USD',\n",
    "                     'longShort_USD',\n",
    "                     'volatility_USD',\n",
    "                     'stockIndexPriceDirection_USA',\n",
    "                     'bondUncertainty_USA',\n",
    "                     'interestRates_USA',\n",
    "                     'cross_stockIndexSentiment_USA'] # pt sl sentiment selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 240 candidates, totalling 720 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    3.7s\n",
      "[Parallel(n_jobs=-1)]: Done 720 out of 720 | elapsed:   38.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train F1-score:  0.471397248125701\n",
      "Train accuracy:  0.5008960573476703\n",
      "Train AUC:  0.5531392883621549\n",
      "Train best score -0.72\n",
      "Best parameters: {'base_score': 0.5, 'early_stopping_rounds': 5, 'eta': 5e-05, 'max_depth': 2, 'n_estimators': 40}\n",
      "Accuracy: 49.80%\n",
      "AUC: 0.53\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.32      0.61      0.42        77\n",
      "         1.0       0.73      0.45      0.56       178\n",
      "\n",
      "    accuracy                           0.50       255\n",
      "   macro avg       0.53      0.53      0.49       255\n",
      "weighted avg       0.61      0.50      0.52       255\n",
      "\n",
      "Confusion matrix:\n",
      "[[47 30]\n",
      " [98 80]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 921.6x633.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# applying first model for setting the side (sign) of the bet (buy or sell)\n",
    "first_model, features = apply_model(train_labels, test_labels, \n",
    "                                    X_train_[features_all], X_test_[features_all], \n",
    "                                    'XGB', 'neg_log_loss', sfs=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# only for xgb\n",
    "# plotting decision trees from the model\n",
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "plot_tree(first_model, num_trees=0, ax=ax); #first\n",
    "#plot_tree(first_model, num_trees=1, ax=ax, rankdir='LR'); #second\n",
    "#plot_tree(first_model, num_trees=2, ax=ax, rankdir='LR'); #third"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generating meta-labels with previous forecast\n",
    "# these will be the labels for the next model\n",
    "tbm_train = tbm_train.reindex(X_train_.index)\n",
    "tbm_test = tbm_test.reindex(X_test_.index)\n",
    "\n",
    "tbm_train['side'] = first_model.predict(X_train_[features_all]) # outcome of first model\n",
    "tbm_test['side'] = first_model.predict(X_test_[features_all])\n",
    "\n",
    "metalabels_train = get_bins(tbm_train, sp500_train['Adj Close'])\n",
    "metalabels_test = get_bins(tbm_test, sp500_test['Adj Close'])\n",
    "metalabels_train['label'] = metalabels_train.bin\n",
    "metalabels_test['label'] = metalabels_test.bin\n",
    "metalabels_train.dropna(inplace=True)\n",
    "metalabels_test.dropna(inplace=True)\n",
    "\n",
    "X_train_ = X_train_.reindex(metalabels_train.index)\n",
    "X_test_ = X_test_.reindex(metalabels_test.index)\n",
    "X_train_['side'] = metalabels_train.side\n",
    "X_test_['side'] = metalabels_test.side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.,  1.])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tbm_test.side.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ret</th>\n",
       "      <th>trgt</th>\n",
       "      <th>bin</th>\n",
       "      <th>side</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>451</td>\n",
       "      <td>451</td>\n",
       "      <td>451</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>1037</td>\n",
       "      <td>1037</td>\n",
       "      <td>1037</td>\n",
       "      <td>1037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ret  trgt   bin  side\n",
       "label                        \n",
       "0.0     451   451   451   451\n",
       "1.0    1037  1037  1037  1037"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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</th>\n",
       "      <th>trgt</th>\n",
       "      <th>bin</th>\n",
       "      <th>side</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ret  trgt  bin  side\n",
       "label                      \n",
       "0.0    128   128  128   128\n",
       "1.0    127   127  127   127"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(metalabels_train.groupby('label').count())\n",
    "display(metalabels_test.groupby('label').count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# appending side or previous outcome to the explanatory variables of the second model\n",
    "features_all.append('side')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 240 candidates, totalling 720 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 340 tasks      | elapsed:   21.3s\n",
      "[Parallel(n_jobs=-1)]: Done 720 out of 720 | elapsed:   35.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train F1-score:  0.6949332686001455\n",
      "Train accuracy:  0.5779569892473119\n",
      "Train AUC:  0.5135903756371402\n",
      "Train best score -0.68\n",
      "Best parameters: {'base_score': 0.5, 'early_stopping_rounds': 5, 'eta': 5e-05, 'max_depth': 3, 'n_estimators': 40}\n",
      "Accuracy: 67.84%\n",
      "AUC: 0.73\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.64      0.80      0.72       128\n",
      "         1.0       0.74      0.55      0.63       127\n",
      "\n",
      "    accuracy                           0.68       255\n",
      "   macro avg       0.69      0.68      0.67       255\n",
      "weighted avg       0.69      0.68      0.67       255\n",
      "\n",
      "Confusion matrix:\n",
      "[[103  25]\n",
      " [ 57  70]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 921.6x633.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training and testing second model for setting the size of the bet \n",
    "# (here, it will only tell whether to take the bet or not)\n",
    "second_model, _ = apply_model(metalabels_train, metalabels_test, \n",
    "                              X_train_[features_all], X_test_[features_all], \n",
    "                              'XGB', 'neg_log_loss', sfs=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generating final predictions\n",
    "final_predictions = second_model.predict(X_test_[features_all]) * X_test_.side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-02-12   -0.0\n",
       "2020-02-20   -0.0\n",
       "2020-02-24   -0.0\n",
       "2020-02-25   -0.0\n",
       "2020-02-26   -0.0\n",
       "2020-02-28   -0.0\n",
       "2020-03-03   -1.0\n",
       "2020-03-04   -0.0\n",
       "2020-03-05   -1.0\n",
       "2020-03-06   -0.0\n",
       "2020-03-10   -0.0\n",
       "2020-03-11   -1.0\n",
       "2020-03-12   -0.0\n",
       "2020-03-13   -1.0\n",
       "2020-03-16   -1.0\n",
       "2020-03-17   -1.0\n",
       "2020-03-23   -0.0\n",
       "2020-03-25   -1.0\n",
       "2020-03-27   -1.0\n",
       "2020-04-02   -0.0\n",
       "2020-04-07   -1.0\n",
       "2020-04-10   -0.0\n",
       "2020-04-20   -0.0\n",
       "2020-04-22   -1.0\n",
       "2020-04-27   -0.0\n",
       "2020-04-30   -0.0\n",
       "Name: side, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check if we have a reasonable/profitable outcome during the first stage of the covid-19 bear market\n",
    "final_predictions['2020-02-10': '2020-04-30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "mode": "lines",
         "name": "SP500 Close Price",
         "type": "scatter",
         "x": [
          "2016-10-05T00:00:00",
          "2016-10-12T00:00:00",
          "2016-10-20T00:00:00",
          "2016-10-27T00:00:00",
          "2016-11-08T00:00:00",
          "2016-11-10T00:00:00",
          "2016-11-16T00:00:00",
          "2016-11-22T00:00:00",
          "2016-11-28T00:00:00",
          "2016-12-01T00:00:00",
          "2016-12-08T00:00:00",
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       "            Plotly.purge(gd);\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
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     "metadata": {},
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   ],
   "source": [
    "# plotting labels to see outcome\n",
    "#configure_plotly_browser_state()\n",
    "figura = make_subplots(specs=[[{\"secondary_y\": False}]])\n",
    "\n",
    "dataset = X_test_\n",
    "dataset['Predictions'] = final_predictions\n",
    "dataset['Labels'] = test_labels['label']\n",
    "\n",
    "figura.add_trace(go.Scatter(y=X_test['Adj Close'],\n",
    "                            x=X_test.index,\n",
    "                            mode='lines',\n",
    "                            name='SP500 Close Price'),\n",
    "                 secondary_y=False,)\n",
    "figura.add_trace(\n",
    "    go.Scatter(y=dataset[(dataset.Labels == 1)]['Adj Close'],\n",
    "               x=dataset[(dataset.Labels == 1)].index,\n",
    "               mode='markers',\n",
    "               name='Real Buy',\n",
    "               marker=dict(size=8, color='#2DCCCD'),\n",
    "               marker_symbol=19),\n",
    "               secondary_y=False,)\n",
    "figura.add_trace(\n",
    "    go.Scatter(y=dataset[(dataset.Labels == -1)]['Adj Close'],\n",
    "               x=dataset[(dataset.Labels == -1)].index,\n",
    "               mode='markers',\n",
    "               name='Real Sell',\n",
    "               marker=dict(size=8, color='#D8BE75'),\n",
    "               marker_symbol=20),\n",
    "               secondary_y=False,)\n",
    "figura.add_trace(\n",
    "    go.Scatter(y=dataset[(dataset.Predictions == 0)]['Adj Close'],\n",
    "               x=dataset[(dataset.Predictions == 0)].index,\n",
    "               mode='markers',\n",
    "               name='Predicted Do Not Take Bet',\n",
    "               marker=dict(size=5, color='#bfbfbf'),\n",
    "               marker_symbol=0),\n",
    "               secondary_y=False,)\n",
    "figura.add_trace(\n",
    "    go.Scatter(y=dataset[(dataset.Predictions == 1)]['Adj Close'],\n",
    "               x=dataset[(dataset.Predictions == 1)].index,\n",
    "               mode='markers',\n",
    "               name='Predicted Buy',\n",
    "               marker=dict(size=8, color='#008000'),\n",
    "               marker_symbol=5),\n",
    "               secondary_y=False,)\n",
    "figura.add_trace(\n",
    "    go.Scatter(y=dataset[(dataset.Predictions == -1)]['Adj Close'],\n",
    "               x=dataset[(dataset.Predictions == -1)].index,\n",
    "               mode='markers',\n",
    "               name='Predicted Sell',\n",
    "               marker=dict(size=8, color='#FF0000'),\n",
    "               marker_symbol=6),\n",
    "               secondary_y=False,)\n",
    "\n",
    "figura.update_layout(\n",
    "    title_text='SP500 Index and predicted positions | Test',\n",
    "    colorway = bbva)\n",
    "\n",
    "figura.update_xaxes(rangeslider_visible=True)\n",
    "figura.update_yaxes(title_text=\"<b>Price</b>\", secondary_y=False)\n",
    "\n",
    "figura.update_xaxes(\n",
    "    rangeslider_visible=True,\n",
    "    rangeselector=dict(\n",
    "        dict(font = dict(color = \"black\")),\n",
    "        buttons=list([\n",
    "            dict(count=1, label=\"1m\", step=\"month\", stepmode=\"backward\"),\n",
    "            dict(count=6, label=\"6m\", step=\"month\", stepmode=\"backward\"),\n",
    "            dict(count=1, label=\"YTD\", step=\"year\", stepmode=\"todate\"),\n",
    "            dict(count=1, label=\"1y\", step=\"year\", stepmode=\"backward\"),\n",
    "            dict(count=3, label=\"3y\", step=\"year\", stepmode=\"backward\"),\n",
    "            dict(count=5, label=\"5y\", step=\"year\", stepmode=\"backward\"),\n",
    "            dict(step=\"all\"),\n",
    "        ])\n",
    "    )\n",
    ")\n",
    "\n",
    "figura.update_layout(template='plotly_white', hovermode='x')\n",
    "iplot(figura)"
   ]
  },
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   "metadata": {},
   "source": [
    "---"
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