{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f3af64b1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T00:34:31.916808Z",
     "start_time": "2021-11-25T00:34:31.900888Z"
    }
   },
   "source": [
    "# Desafio Quantamental Itaú 2021"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fedca98",
   "metadata": {},
   "source": [
    "## Objetivo do desafio\n",
    "\n",
    "<cite>A forma mais fácil de entender o objetivo do desafio é se colocar na posição de um\n",
    "analista quantitativo de um fundo de investimento.\n",
    "Seu objetivo é criar e apresentar uma estratégia quantitativa de investimento e,\n",
    "assim, convencer o gestor do fundo a colocá-la no portfólio ou, pelo menos,\n",
    "considerar a possibilidade de saber mais sobre sua proposta, aprofundando seu\n",
    "entendimento.\n",
    "Portanto, sugerimos utilizar uma linguagem mais prática e direta, evitando uma\n",
    "comunicação acadêmica.\n",
    "**O objetivo é apresentar a sua ideia para o gestor do fundo e não escrever um \n",
    "artigo acadêmico.** </cite>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f96770fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T02:45:18.022247Z",
     "start_time": "2021-11-25T02:45:18.005259Z"
    }
   },
   "source": [
    "## Abordagem Utilizando Aprendizado de maquina"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc3a94b9",
   "metadata": {},
   "source": [
    "$\\quad$Técnicas de aprendizado de máquina ganharam notoriedade por conseguirem resultados promissores em diversas áreas. Isso se deve pelo volume de dados e pelo aumento do processamento dos computadores. \\\n",
    "$\\quad$O mercado financeiro não foi uma exceção, também adotou essas técnicas. Com o crescimento de fundos quantitativos, cada vez mais empresas empregam o uso de aprendizado de máquina para identificação de tendências do mercado e gerenciamento de portfólio. \\\n",
    "$\\quad$O objetivo da ideia de investimento apresentada neste trabalho é uma tentativa de empregar o uso do aprendizado supervisionado na identificação de tendências de mercado de uma ação. Para tal, os valores de fechamento de ações diários foram discretizados e transformados em um problema de classificação.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be033e6f",
   "metadata": {},
   "source": [
    "## De onde a ideia foi tirada?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01480f14",
   "metadata": {},
   "source": [
    "$\\quad$A ideia de classificação dos dados foi baseada no livro “Advances in Financial Machine Learning” (2018), de Marcos López de Prado. Utilizei um algoritmo parecido com o do livro, mas com uma implementação adaptada para dados com intervalos diários. A adaptação foi pensada como uma implementação da barreira tripla do código, a partir do artigo “Data Labelling: The Triple-barrier Method” (Gui, 2020). Após a classificação, utilizei técnicas conhecidas de aprendizado de máquina. <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "325f020e",
   "metadata": {},
   "source": [
    "## Dados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a1d43bd",
   "metadata": {},
   "source": [
    "$\\quad$Na literatura revisada sobre análise de dados financeiros para modelos de aprendizado de máquina, me deparei com alguns possíveis casos de modelagem de dados. Usualmente tem-se uma tabela com informações de ações nas colunas e agrupamentos de períodos de tempo nas linhas. Quando nos aproximamos dos dados mais crus, encontramos as entradas de compras e vendas nas linhas com seus respectivos horários - denominaremos essas linhas como “barras de tempo”. Destaco a representação dos dados mais comuns para poder ressaltar um erro muito usual em modelos financeiros de aprendizado de máquina.\n",
    "\n",
    "### Barras de tempo\n",
    "\n",
    "$\\quad$É a representação de dados que mais encontramos. Os dados são disponibilizados de acordo com um intervalo de tempo, podendo ser minutos, horas, dias etc. \\\n",
    "$\\quad$Apesar de sua utilização ser muito popular, esse tipo de representação deve ser evitada pois o mercado não processa a informação em um tempo constante. O horário em que o mercado abre, por exemplo, tem uma atividade mais intensa do que  por volta do meio-dia. Isso significa que por volta do meio-dia, ou seja, no período de baixa atividade, as barras de tempo terão pouca frequência de informação, enquanto que no momento de abertura do mercado, ou seja, no período de alta atividade, terão alta frequência de informação. Essa inconstância na frequência de informações ocasiona um desbalanceamento da frequência dos dados.\\\n",
    "$\\quad$Para podermos contornar esse problema precisamos de dados mais “crus”, com a informação do horário em que ocorreu o trade, o seu preço, a quantidade e se foi uma venda ou compra. No presente trabalho não temos um intervalo de tempo agrupando os trades, e sim um fluxo natural dos dados do mercado. Isso é devido à difícil obtenção destes dados de forma gratuita, ainda mais para um histórico longo. Quando podem ser encontrados de forma gratuita, esses dados contêm apenas alguns dias ou, no máximo, alguns meses. Dessa forma, apesar de não ser o ideal, optou-se por utilizar dados de barra de tempo, por serem encontrados de forma gratuita e em um volume maior, o que é essencial para podermos treinar um modelo de aprendizado de máquina.\\\n",
    "$\\quad$Os dados que utilizei foram extraídos do Yahoo Finance. A API construída para o python nos permite apenas extrair dados diários. Esbarramos aqui em outro problema: analogamente aos dados, imagine que os dados diários são uma imagem com péssima resolução, com uma distância muito grande entre os pixels, o que dificulta uma análise mais precisa da imagem. O ideal seria menor tempo de intervalo dos dados, o que significaria maior resolução e qualidade da imagem, e assim seria possível conclusões com uma precisão muito maior. Contudo, acabamos por utilizar os dados diários devido a falta de acesso à dados com uma frequência de minutos ou horas de forma gratuita e em grande volume.\\\n",
    "$\\quad$Os dados diários foram extraídos do período indicado no desafio, de 30-12-2009 até 31-12-2020. Cada dia é uma linha e as colunas são as variáveis de abertura de preço, seu valor mais alto, mais baixo, preço de fechamento. A ação escolhida foi a do Itaú (ITUB4.SA)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "11a35cd1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T00:42:14.943015Z",
     "start_time": "2021-11-25T00:41:38.769651Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import yfinance as yf\n",
    "import seaborn as sns\n",
    "import pandas_ta as ta\n",
    "\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4630059b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T00:51:10.403999Z",
     "start_time": "2021-11-25T00:49:45.267251Z"
    }
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-30</th>\n",
       "      <td>10.888960</td>\n",
       "      <td>11.030743</td>\n",
       "      <td>10.840754</td>\n",
       "      <td>10.971194</td>\n",
       "      <td>8940087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>11.027909</td>\n",
       "      <td>11.481616</td>\n",
       "      <td>11.027909</td>\n",
       "      <td>11.376697</td>\n",
       "      <td>11843397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>11.405057</td>\n",
       "      <td>11.450428</td>\n",
       "      <td>11.314316</td>\n",
       "      <td>11.450428</td>\n",
       "      <td>8593315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>11.413557</td>\n",
       "      <td>11.481614</td>\n",
       "      <td>11.257596</td>\n",
       "      <td>11.351173</td>\n",
       "      <td>10602572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>11.271773</td>\n",
       "      <td>11.356844</td>\n",
       "      <td>11.215060</td>\n",
       "      <td>11.234911</td>\n",
       "      <td>9966567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close    Volume\n",
       "Date                                                            \n",
       "2009-12-30  10.888960  11.030743  10.840754  10.971194   8940087\n",
       "2010-01-04  11.027909  11.481616  11.027909  11.376697  11843397\n",
       "2010-01-05  11.405057  11.450428  11.314316  11.450428   8593315\n",
       "2010-01-06  11.413557  11.481614  11.257596  11.351173  10602572\n",
       "2010-01-07  11.271773  11.356844  11.215060  11.234911   9966567"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticker = 'ITUB4.SA'\n",
    "yfObj = yf.Ticker(ticker)\n",
    "data = yfObj.history(start=\"2009-12-30\", end=\"2020-12-31\")\n",
    "data.drop(columns = ['Dividends', 'Stock Splits'], inplace = True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "147f1474",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T00:54:28.206532Z",
     "start_time": "2021-11-25T00:54:27.945533Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.title('Preço de Fechamento')\n",
    "plt.plot(data.Close)\n",
    "plt.xlabel('Data')\n",
    "plt.ylabel('Close')\n",
    "plt.show()\n",
    "\n",
    "num_time_bars = len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c18d482f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:12:22.592470Z",
     "start_time": "2021-11-25T01:12:22.345206Z"
    }
   },
   "source": [
    "## Indicadores do Mercado\n",
    "\n",
    "$\\quad$Alguns indicadores foram criados a partir dos dados obtidos, como RSI, MACD, bbands, médias móveis, preço médio ponderado por volume (VWAP) e ATR (Average True Range). \\\n",
    "$\\quad$O objetivo é escolher indicadores que possam indicar tendências do mercado, em razão do nosso modelo ter o intuito de aprender a classificar a partir desses dados.\\\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9c92fb23",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:14:55.744818Z",
     "start_time": "2021-11-25T01:14:55.481809Z"
    }
   },
   "outputs": [],
   "source": [
    "#MA indicator\n",
    "def SMA(values, n):\n",
    "    \"\"\"\n",
    "    Retorna media movel n de values\n",
    "    \"\"\"\n",
    "    return pd.Series(values).rolling(n).mean()\n",
    "\n",
    "\n",
    "def cross_over(data, sma1, sma2):\n",
    "    sinal = []\n",
    "    n = len(data)\n",
    "    \n",
    "    for i in range(n):\n",
    "        if (i < n2 -2):\n",
    "            sinal.append(0)\n",
    "        elif (sma1[i-2] < sma2[i-2] and sma1[i-1] > sma2[i-1]):\n",
    "            sinal.append(1)\n",
    "        elif (sma1[i-2] > sma2[i-2] and sma1[i-1] < sma2[i-1]):\n",
    "            sinal.append(-1)\n",
    "        else:\n",
    "            sinal.append(sinal[i-1])\n",
    "    return sinal\n",
    "\n",
    "#media movel curta\n",
    "n1 = 10\n",
    "#media movel longa\n",
    "n2 = 30\n",
    "sma1 = SMA(data.Close, n1)\n",
    "sma2 = SMA(data.Close, n2)\n",
    "\n",
    "sma1.fillna(0, inplace = True)\n",
    "sma2.fillna(0, inplace = True)\n",
    "\n",
    "data['cross_over'] = cross_over(data, sma1, sma2)\n",
    "\n",
    "def BBANDS(data, n_lookback, n_std):\n",
    "    \"\"\"Bollinger bands indicator\"\"\"\n",
    "    #bollinger bands com a media\n",
    "    TP = (data.High + data.Low + data.Close) / 3\n",
    "    std = TP.rolling(n_lookback).std() \n",
    "    MA = TP.rolling(n_lookback).mean()\n",
    "    upper = TP + n_std*std\n",
    "    lower = TP - n_std*std\n",
    "    \n",
    "    return upper, lower\n",
    "\n",
    "def tec_indicators(data):\n",
    "    data['vwap'] = data.ta.vwap()\n",
    "    #average true range\n",
    "    data['atr'] = data.ta.atr()\n",
    "    #Bbands\n",
    "    data['upper'], data['lower'] = BBANDS(data, 20, 2)\n",
    "    #Rsi\n",
    "    data['rsi'] = data.ta.rsi(close='Close', lenght = 2)\n",
    "    macd = data.ta.macd(fast = 8, slow = 21)\n",
    "    data = pd.concat([data, macd], axis = 1)\n",
    "    \n",
    "    data['SMA_15'] = pd.Series(data.Close).rolling(20).mean()\n",
    "    #data['SMA_50'] = pd.Series(data.Close).rolling(50).mean()\n",
    "    data['SMA_100'] = pd.Series(data.Close).rolling(100).mean()\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bdb0dab5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:15:03.800921Z",
     "start_time": "2021-11-25T01:15:02.065806Z"
    }
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>cross_over</th>\n",
       "      <th>vwap</th>\n",
       "      <th>atr</th>\n",
       "      <th>upper</th>\n",
       "      <th>lower</th>\n",
       "      <th>rsi</th>\n",
       "      <th>MACD_8_21_9</th>\n",
       "      <th>MACDh_8_21_9</th>\n",
       "      <th>MACDs_8_21_9</th>\n",
       "      <th>SMA_15</th>\n",
       "      <th>SMA_100</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-30</th>\n",
       "      <td>10.888960</td>\n",
       "      <td>11.030743</td>\n",
       "      <td>10.840754</td>\n",
       "      <td>10.971194</td>\n",
       "      <td>8940087</td>\n",
       "      <td>0</td>\n",
       "      <td>10.947564</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>2010-01-04</th>\n",
       "      <td>11.027909</td>\n",
       "      <td>11.481616</td>\n",
       "      <td>11.027909</td>\n",
       "      <td>11.376697</td>\n",
       "      <td>11843397</td>\n",
       "      <td>0</td>\n",
       "      <td>11.295407</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>2010-01-05</th>\n",
       "      <td>11.405057</td>\n",
       "      <td>11.450428</td>\n",
       "      <td>11.314316</td>\n",
       "      <td>11.450428</td>\n",
       "      <td>8593315</td>\n",
       "      <td>0</td>\n",
       "      <td>11.405057</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>2010-01-06</th>\n",
       "      <td>11.413557</td>\n",
       "      <td>11.481614</td>\n",
       "      <td>11.257596</td>\n",
       "      <td>11.351173</td>\n",
       "      <td>10602572</td>\n",
       "      <td>0</td>\n",
       "      <td>11.363461</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>2010-01-07</th>\n",
       "      <td>11.271773</td>\n",
       "      <td>11.356844</td>\n",
       "      <td>11.215060</td>\n",
       "      <td>11.234911</td>\n",
       "      <td>9966567</td>\n",
       "      <td>0</td>\n",
       "      <td>11.268939</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close    Volume  cross_over  \\\n",
       "Date                                                                           \n",
       "2009-12-30  10.888960  11.030743  10.840754  10.971194   8940087           0   \n",
       "2010-01-04  11.027909  11.481616  11.027909  11.376697  11843397           0   \n",
       "2010-01-05  11.405057  11.450428  11.314316  11.450428   8593315           0   \n",
       "2010-01-06  11.413557  11.481614  11.257596  11.351173  10602572           0   \n",
       "2010-01-07  11.271773  11.356844  11.215060  11.234911   9966567           0   \n",
       "\n",
       "                 vwap  atr  upper  lower  rsi  MACD_8_21_9  MACDh_8_21_9  \\\n",
       "Date                                                                       \n",
       "2009-12-30  10.947564  NaN    NaN    NaN  NaN          NaN           NaN   \n",
       "2010-01-04  11.295407  NaN    NaN    NaN  NaN          NaN           NaN   \n",
       "2010-01-05  11.405057  NaN    NaN    NaN  NaN          NaN           NaN   \n",
       "2010-01-06  11.363461  NaN    NaN    NaN  NaN          NaN           NaN   \n",
       "2010-01-07  11.268939  NaN    NaN    NaN  NaN          NaN           NaN   \n",
       "\n",
       "            MACDs_8_21_9  SMA_15  SMA_100  \n",
       "Date                                       \n",
       "2009-12-30           NaN     NaN      NaN  \n",
       "2010-01-04           NaN     NaN      NaN  \n",
       "2010-01-05           NaN     NaN      NaN  \n",
       "2010-01-06           NaN     NaN      NaN  \n",
       "2010-01-07           NaN     NaN      NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = tec_indicators(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9771bc84",
   "metadata": {},
   "source": [
    "Linhas duplicadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bfe3f098",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:16:22.174962Z",
     "start_time": "2021-11-25T01:16:21.834365Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Porcentagem de linhas duplicadas: 0.0\n"
     ]
    }
   ],
   "source": [
    "nLinhas , nColunas = data.shape\n",
    "\n",
    "dupl = data.duplicated().sum()/nLinhas\n",
    "\n",
    "print('Porcentagem de linhas duplicadas:', round(dupl*100,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f47a72f5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T19:05:42.085942Z",
     "start_time": "2021-11-09T19:05:42.078964Z"
    }
   },
   "source": [
    "Porcentagem de nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "706c8bb2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:16:27.777966Z",
     "start_time": "2021-11-25T01:16:27.625633Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequencia treino:\n"
     ]
    },
    {
     "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>Qtd Nan</th>\n",
       "      <th>Qtd Nan %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Open</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Close</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volume</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cross_over</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vwap</th>\n",
       "      <td>34</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atr</th>\n",
       "      <td>14</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upper</th>\n",
       "      <td>19</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lower</th>\n",
       "      <td>19</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsi</th>\n",
       "      <td>14</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MACD_8_21_9</th>\n",
       "      <td>20</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MACDh_8_21_9</th>\n",
       "      <td>28</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MACDs_8_21_9</th>\n",
       "      <td>28</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMA_15</th>\n",
       "      <td>19</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMA_100</th>\n",
       "      <td>99</td>\n",
       "      <td>3.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Qtd Nan  Qtd Nan %\n",
       "Open                0       0.00\n",
       "High                0       0.00\n",
       "Low                 0       0.00\n",
       "Close               0       0.00\n",
       "Volume              0       0.00\n",
       "cross_over          0       0.00\n",
       "vwap               34       1.25\n",
       "atr                14       0.51\n",
       "upper              19       0.70\n",
       "lower              19       0.70\n",
       "rsi                14       0.51\n",
       "MACD_8_21_9        20       0.73\n",
       "MACDh_8_21_9       28       1.03\n",
       "MACDs_8_21_9       28       1.03\n",
       "SMA_15             19       0.70\n",
       "SMA_100            99       3.63"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificado a quantidade de NaN por atributo\n",
    "nan_train= pd.DataFrame()\n",
    "nan_train['Qtd Nan'] = data.isna().sum()\n",
    "nan_train['Qtd Nan %'] = round(100*data.isna().sum()/len(data),2)\n",
    "print('Frequencia treino:')\n",
    "nan_train.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e38753",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T19:05:17.130213Z",
     "start_time": "2021-11-09T19:05:17.022466Z"
    }
   },
   "source": [
    "Porcentagem de dados igual a 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d87c4a4c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:16:34.919077Z",
     "start_time": "2021-11-25T01:16:34.889112Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open            0.000000\n",
       "High            0.000000\n",
       "Low             0.000000\n",
       "Close           0.000000\n",
       "Volume          1.247249\n",
       "cross_over      1.100514\n",
       "vwap            0.000000\n",
       "atr             0.000000\n",
       "upper           0.000000\n",
       "lower           0.000000\n",
       "rsi             0.000000\n",
       "MACD_8_21_9     0.000000\n",
       "MACDh_8_21_9    0.000000\n",
       "MACDs_8_21_9    0.000000\n",
       "SMA_15          0.000000\n",
       "SMA_100         0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data == 0).astype(int).sum(axis=0)/len(data)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d733b882",
   "metadata": {},
   "source": [
    "## Variável a ser predita\n",
    "\n",
    "$\\quad$Para podermos treinar um modelo de aprendizado de máquina supervisionado precisamos da variável que queremos prever. O preço das ações do mercado tem um comportamento estocástico, e, portanto, tem seu estado indeterminado. Assim, vamos utilizar a estratégia do de Prado (2018), chamada de método de barreira tripla. \\\n",
    "$\\quad$Antes disso, cito o modelo mais encontrado na literatura e o porquê dele não ter um resultado eficaz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23ff0134",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:19:29.749635Z",
     "start_time": "2021-11-25T01:19:29.730700Z"
    }
   },
   "source": [
    "### Método de horizonte\n",
    "Nesse método calculamos o retorno do preço em n dias e classificamos a sua predição utilizando uma constante, sendo o retorno definido por: \\\n",
    "\n",
    "$$\\frac{p_1 - p_2}{p_2}$$\n",
    "p1: preço atual \\\n",
    "p2: preço anterior a n intervalos de tempo \\\n",
    " \n",
    "| 1  | Retorno >= Constante              |\n",
    "|----|-----------------------------------|\n",
    "| -1 | Retorno <= Constante              |\n",
    "| 0  | - Constante < Retorno < Constante |\n",
    "\n",
    "O grande problema dessa abordagem é não considerar a volatilidade dos preços ao atribuir a constante\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "99002b34",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:30:22.553128Z",
     "start_time": "2021-11-25T01:30:22.437057Z"
    }
   },
   "source": [
    "### Método de Barreira Tripla\n",
    "\n",
    "$\\quad$Nesse método temos a construção de uma barreira tripla. A sua grande vantagem está em considerar a volatilidade para classificarmos cada linha da nossa tabela de acordo com a barreira que o preço de fechamento atingiu primeiro. \\\n",
    "$\\quad$Uma abordagem desse método seria considerar transações apenas de long. A barreira superior será o limite de lucro que iremos obter antes de sair, a barreira inferior será o stop loss, e a barreira vertical, o tempo limite que seguramos uma ação.\\ \n",
    "$\\quad$Entretanto, queremos um modelo que trate tanto de long quanto short. Dessa forma, optei por uma outra abordagem possível, na qual vamos classificar como uma operação de venda quando o preço futuro atingir a barreira inferior; compra quando atingir a barreira superior, e saímos da posição atual caso atingimos a barreira lateral. Fazemos isso pois nosso modelo está tentando prever tendências no futuro e, assim, quando ele estiver prevendo uma queda ou compra queremos apostar nisso. \\\n",
    "$\\quad$Utilizei 10 dias futuros para a construção da barreira vertical, e 2 como multiplicador tanto da barreira superior quanto inferior.\\\n",
    "\n",
    "**Barreira horizontal superior:** Preço futuro + volatilidade de n intervalos de tempo anterior\\\n",
    "**Barreira horizontal inferior:** Preço futuro - volatilidade de n intervalos de tempo anterior\\\n",
    "**Barreira vertical:** temos a opção de atribuir 0 ou o sinal do retorno, optamos por 0\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6bf7e6dc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:41:42.298493Z",
     "start_time": "2021-11-25T01:41:40.392392Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_Daily_Volatility(close,span0=20):\n",
    "    # simple percentage returns\n",
    "    df0=close.pct_change()\n",
    "    # 20 days, a month EWM's std as boundary\n",
    "    df0=df0.ewm(span=span0).std()\n",
    "    df0.dropna(inplace=True)\n",
    "    return df0\n",
    "\n",
    "def get_3_barriers():\n",
    "    #create a container\n",
    "    barriers = pd.DataFrame(columns=['days_passed', \n",
    "              'price', 'vert_barrier', \\\n",
    "              'top_barrier', 'bottom_barrier'], \\\n",
    "               index = daily_volatility.index)\n",
    "    for day, vol in daily_volatility.iteritems():\n",
    "        days_passed = len(daily_volatility.loc \\\n",
    "                      [daily_volatility.index[0] : day])\n",
    "        #set the vertical barrier \n",
    "        if (days_passed + t_final < len(daily_volatility.index) \\\n",
    "            and t_final != 0):\n",
    "            vert_barrier = daily_volatility.index[\n",
    "                                days_passed + t_final]\n",
    "        else:\n",
    "            vert_barrier = np.nan\n",
    "        #set the top barrier\n",
    "        if upper_lower_multipliers[0] > 0:\n",
    "            top_barrier = prices.loc[day] + prices.loc[day] * \\\n",
    "                          upper_lower_multipliers[0] * vol\n",
    "        else:\n",
    "            #set it to NaNs\n",
    "            top_barrier = pd.Series(index=prices.index)\n",
    "        #set the bottom barrier\n",
    "        if upper_lower_multipliers[1] > 0:\n",
    "            bottom_barrier = prices.loc[day] - prices.loc[day] * \\\n",
    "                          upper_lower_multipliers[1] * vol\n",
    "        else: \n",
    "            #set it to NaNs\n",
    "            bottom_barrier = pd.Series(index=prices.index)\n",
    "        barriers.loc[day, ['days_passed', 'price','vert_barrier','top_barrier', 'bottom_barrier']] = days_passed, prices.loc[day], vert_barrier, top_barrier, bottom_barrier\n",
    "    return barriers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "074b03a2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:41:56.678131Z",
     "start_time": "2021-11-25T01:41:52.679706Z"
    }
   },
   "outputs": [],
   "source": [
    "price = data.Close\n",
    "#set the boundary of barriers, based on 20 days EWM\n",
    "daily_volatility = get_Daily_Volatility(price)\n",
    "# how many days we hold the stock which set the vertical barrier\n",
    "t_final = 10 \n",
    "#the up and low boundary multipliers\n",
    "upper_lower_multipliers = [2, 2]\n",
    "#allign the index\n",
    "prices = price[daily_volatility.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bd30144b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:42:02.123943Z",
     "start_time": "2021-11-25T01:41:56.716835Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>days_passed</th>\n",
       "      <th>price</th>\n",
       "      <th>vert_barrier</th>\n",
       "      <th>top_barrier</th>\n",
       "      <th>bottom_barrier</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>1</td>\n",
       "      <td>11.4504</td>\n",
       "      <td>2010-01-20 00:00:00</td>\n",
       "      <td>11.944</td>\n",
       "      <td>10.9569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>2</td>\n",
       "      <td>11.3512</td>\n",
       "      <td>2010-01-21 00:00:00</td>\n",
       "      <td>11.8726</td>\n",
       "      <td>10.8297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>3</td>\n",
       "      <td>11.2349</td>\n",
       "      <td>2010-01-22 00:00:00</td>\n",
       "      <td>11.7102</td>\n",
       "      <td>10.7596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>4</td>\n",
       "      <td>11.0648</td>\n",
       "      <td>2010-01-26 00:00:00</td>\n",
       "      <td>11.5099</td>\n",
       "      <td>10.6197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>5</td>\n",
       "      <td>10.9684</td>\n",
       "      <td>2010-01-27 00:00:00</td>\n",
       "      <td>11.361</td>\n",
       "      <td>10.5757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-22</th>\n",
       "      <td>2720</td>\n",
       "      <td>30.1984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.2072</td>\n",
       "      <td>29.1897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-23</th>\n",
       "      <td>2721</td>\n",
       "      <td>30.8652</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.907</td>\n",
       "      <td>29.8234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>2722</td>\n",
       "      <td>31.0875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0864</td>\n",
       "      <td>30.0885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>2723</td>\n",
       "      <td>31.0198</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.9779</td>\n",
       "      <td>30.0618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>2724</td>\n",
       "      <td>30.5657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.5302</td>\n",
       "      <td>29.6011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2724 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           days_passed    price         vert_barrier top_barrier  \\\n",
       "Date                                                               \n",
       "2010-01-05           1  11.4504  2010-01-20 00:00:00      11.944   \n",
       "2010-01-06           2  11.3512  2010-01-21 00:00:00     11.8726   \n",
       "2010-01-07           3  11.2349  2010-01-22 00:00:00     11.7102   \n",
       "2010-01-08           4  11.0648  2010-01-26 00:00:00     11.5099   \n",
       "2010-01-11           5  10.9684  2010-01-27 00:00:00      11.361   \n",
       "...                ...      ...                  ...         ...   \n",
       "2020-12-22        2720  30.1984                  NaN     31.2072   \n",
       "2020-12-23        2721  30.8652                  NaN      31.907   \n",
       "2020-12-28        2722  31.0875                  NaN     32.0864   \n",
       "2020-12-29        2723  31.0198                  NaN     31.9779   \n",
       "2020-12-30        2724  30.5657                  NaN     31.5302   \n",
       "\n",
       "           bottom_barrier  \n",
       "Date                       \n",
       "2010-01-05        10.9569  \n",
       "2010-01-06        10.8297  \n",
       "2010-01-07        10.7596  \n",
       "2010-01-08        10.6197  \n",
       "2010-01-11        10.5757  \n",
       "...                   ...  \n",
       "2020-12-22        29.1897  \n",
       "2020-12-23        29.8234  \n",
       "2020-12-28        30.0885  \n",
       "2020-12-29        30.0618  \n",
       "2020-12-30        29.6011  \n",
       "\n",
       "[2724 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barriers = get_3_barriers()\n",
    "barriers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9baf964a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:42:31.019575Z",
     "start_time": "2021-11-25T01:42:26.437118Z"
    }
   },
   "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>days_passed</th>\n",
       "      <th>price</th>\n",
       "      <th>vert_barrier</th>\n",
       "      <th>top_barrier</th>\n",
       "      <th>bottom_barrier</th>\n",
       "      <th>out</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>1</td>\n",
       "      <td>11.4504</td>\n",
       "      <td>2010-01-20 00:00:00</td>\n",
       "      <td>11.944</td>\n",
       "      <td>10.9569</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>2</td>\n",
       "      <td>11.3512</td>\n",
       "      <td>2010-01-21 00:00:00</td>\n",
       "      <td>11.8726</td>\n",
       "      <td>10.8297</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>3</td>\n",
       "      <td>11.2349</td>\n",
       "      <td>2010-01-22 00:00:00</td>\n",
       "      <td>11.7102</td>\n",
       "      <td>10.7596</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>4</td>\n",
       "      <td>11.0648</td>\n",
       "      <td>2010-01-26 00:00:00</td>\n",
       "      <td>11.5099</td>\n",
       "      <td>10.6197</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>5</td>\n",
       "      <td>10.9684</td>\n",
       "      <td>2010-01-27 00:00:00</td>\n",
       "      <td>11.361</td>\n",
       "      <td>10.5757</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-22</th>\n",
       "      <td>2720</td>\n",
       "      <td>30.1984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.2072</td>\n",
       "      <td>29.1897</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-23</th>\n",
       "      <td>2721</td>\n",
       "      <td>30.8652</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.907</td>\n",
       "      <td>29.8234</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>2722</td>\n",
       "      <td>31.0875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0864</td>\n",
       "      <td>30.0885</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>2723</td>\n",
       "      <td>31.0198</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.9779</td>\n",
       "      <td>30.0618</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>2724</td>\n",
       "      <td>30.5657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.5302</td>\n",
       "      <td>29.6011</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2724 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           days_passed    price         vert_barrier top_barrier  \\\n",
       "Date                                                               \n",
       "2010-01-05           1  11.4504  2010-01-20 00:00:00      11.944   \n",
       "2010-01-06           2  11.3512  2010-01-21 00:00:00     11.8726   \n",
       "2010-01-07           3  11.2349  2010-01-22 00:00:00     11.7102   \n",
       "2010-01-08           4  11.0648  2010-01-26 00:00:00     11.5099   \n",
       "2010-01-11           5  10.9684  2010-01-27 00:00:00      11.361   \n",
       "...                ...      ...                  ...         ...   \n",
       "2020-12-22        2720  30.1984                  NaN     31.2072   \n",
       "2020-12-23        2721  30.8652                  NaN      31.907   \n",
       "2020-12-28        2722  31.0875                  NaN     32.0864   \n",
       "2020-12-29        2723  31.0198                  NaN     31.9779   \n",
       "2020-12-30        2724  30.5657                  NaN     31.5302   \n",
       "\n",
       "           bottom_barrier   out  \n",
       "Date                             \n",
       "2010-01-05        10.9569    -1  \n",
       "2010-01-06        10.8297    -1  \n",
       "2010-01-07        10.7596    -1  \n",
       "2010-01-08        10.6197    -1  \n",
       "2010-01-11        10.5757    -1  \n",
       "...                   ...   ...  \n",
       "2020-12-22        29.1897  None  \n",
       "2020-12-23        29.8234  None  \n",
       "2020-12-28        30.0885  None  \n",
       "2020-12-29        30.0618  None  \n",
       "2020-12-30        29.6011  None  \n",
       "\n",
       "[2724 rows x 6 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barriers['out'] = None\n",
    "barriers.head()\n",
    "\n",
    "def get_labels():\n",
    "    '''\n",
    "    start: first day of the window\n",
    "    end:last day of the window\n",
    "    price_initial: first day stock price\n",
    "    price_final:last day stock price\n",
    "    top_barrier: profit taking limit\n",
    "    bottom_barrier:stop loss limt\n",
    "    condition_pt:top_barrier touching conditon\n",
    "    condition_sl:bottom_barrier touching conditon\n",
    "    '''\n",
    "    for i in range(len(barriers.index)):\n",
    "        start = barriers.index[i]\n",
    "        end = barriers.vert_barrier[i]\n",
    "        if pd.notna(end):\n",
    "            # assign the initial and final price\n",
    "            price_initial = barriers.price[start]\n",
    "            price_final = barriers.price[end]\n",
    "            # assign the top and bottom barriers\n",
    "            top_barrier = barriers.top_barrier[i]\n",
    "            bottom_barrier = barriers.bottom_barrier[i]\n",
    "            #set the profit taking and stop loss conditons\n",
    "            condition_pt = (barriers.price[start: end] >= \\\n",
    "             top_barrier).any()\n",
    "            condition_sl = (barriers.price[start: end] <= \\\n",
    "             bottom_barrier).any()\n",
    "            #assign the labels\n",
    "            if condition_pt: #Atingiu barreira superior\n",
    "                barriers['out'][i] = 1\n",
    "            elif condition_sl: #Atingiu barreira inferior\n",
    "                barriers['out'][i] = -1  \n",
    "            else:  #Atingiu barreira vertical\n",
    "                barriers['out'][i] = 0\n",
    "    return\n",
    "get_labels()\n",
    "barriers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6af7585a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:42:55.182183Z",
     "start_time": "2021-11-25T01:42:54.912534Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (12,8)\n",
    "\n",
    "sns.barplot(x = barriers.out.value_counts().index, y = barriers.out.value_counts())\n",
    "plt.title('Distribuição Classificação')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "564a7199",
   "metadata": {},
   "source": [
    "$\\quad$O método de barreira tripla rotulou a maioria dos dados como compra (1), seguido de venda (-1) e saída da posição (0), como não ocorreu um grande desbalanceamento, não vamos optar por técnicas de validação estratificadas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "642876a5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:46:21.057384Z",
     "start_time": "2021-11-25T01:46:20.865756Z"
    }
   },
   "source": [
    "## Analise Exploratoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4fd7085c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:46:56.010730Z",
     "start_time": "2021-11-25T01:46:48.720390Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 6))\n",
    "# define the mask to set the values in the upper triangle to True\n",
    "mask = np.triu(np.ones_like(data.corr(), dtype=bool))\n",
    "heatmap = sns.heatmap(data.corr(), mask=mask, vmin=-1, vmax=1, annot=True, cmap='BrBG')\n",
    "heatmap.set_title('Triangle Correlation Heatmap', fontdict={'fontsize':18}, pad=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "32ebf68c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:46:56.225919Z",
     "start_time": "2021-11-25T01:46:56.016407Z"
    }
   },
   "outputs": [],
   "source": [
    "df = data.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1893312a",
   "metadata": {},
   "source": [
    "## Aprendizado Supervisionado"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "537d5478",
   "metadata": {},
   "source": [
    "$\\quad$Essa etapa é quando realmente entra nossa estratégia de investimento. A ideia final é ter uma função f que para um determinado dia no futuro (x) consiga prever se devemos estar com sinal de compra (1), venda (-1) ou fora da posição (0).\\\n",
    "$\\quad$Com os dados e suas respectivas classificações, iremos treinar um modelo de aprendizado supervisionado. Dado nossa matriz de dados (X) e nosso vetor de variáveis a serem preditas Y, nosso objetivo é aprender a função f que leva de X a Y. \\\n",
    " \n",
    " $$f: X \\rightarrow Y$$\n",
    "\n",
    "$\\quad$Apliquei quatro modelos de aprendizado nos nossos dados: random forest, SVM, naive bayes e uma variação do ramdom forest para series temporais. Utilizei os modelos das bibliotecas sklearn e pyts do python\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b075bc77",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:48:58.466733Z",
     "start_time": "2021-11-25T01:48:58.303399Z"
    }
   },
   "outputs": [],
   "source": [
    "out = barriers.out\n",
    "data['out'] = out\n",
    "data.out.fillna(0, inplace = True)\n",
    "\n",
    "df = data.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5492467b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:48:59.309633Z",
     "start_time": "2021-11-25T01:48:58.914888Z"
    }
   },
   "outputs": [],
   "source": [
    "Y = df.out.to_numpy()\n",
    "\n",
    "X = df.drop(columns = 'out').to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18f93010",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:50:33.918877Z",
     "start_time": "2021-11-25T01:50:33.901921Z"
    }
   },
   "source": [
    "## Validação\n",
    "\n",
    "$\\quad$Utilizei duas estratégias focadas em classificação de series temporais para validar nossos modelos de aprendizado de máquina, sempre tendo o cuidado de deixar um gap de 10 dias entre o treno e o teste, para não termos vazamento de dados futuros, a escolha de 10 dias está baseada na nossa barreira vertical:"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "de5f98de",
   "metadata": {},
   "source": [
    "### Multiple Train test split\n",
    "\n",
    "$\\quad$Nessa abordagem, dividimos os dados em 4 partes de tamanhos aproximados. Treinamos e testamos nosso modelo na primeira parte, guardando sua acurácia. Entramos então na segunda parte, adicionamos uma quantidade de dados no treino e repetimos o procedimento quatro vezes\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "Tiramos uma média das eficácias de cada modelo nas quatro partes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f36a7ac5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:54:06.361190Z",
     "start_time": "2021-11-25T01:52:20.458758Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimeSeriesSplit(gap=10, max_train_size=None, n_splits=4, test_size=None)\n",
      "TRAIN: 1 535 TEST: 547 1090\n",
      "TRAIN: 1 1080 TEST: 1092 1635\n",
      "TRAIN: 1 1625 TEST: 1637 2180\n",
      "TRAIN: 1 2170 TEST: 2182 2725\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from pyts.classification import TimeSeriesForest\n",
    "from sklearn import svm\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from pyts.classification import TimeSeriesForest\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "normalizer = StandardScaler()\n",
    "\n",
    "\n",
    "acuracia = pd.DataFrame()\n",
    "acur_RF = []\n",
    "acur_svm = []\n",
    "acur_TSF = []\n",
    "acur_NB = []\n",
    "\n",
    "#Separando treino e teste com um gap de 10 para nao termos vazamento de dados\n",
    "tscv = TimeSeriesSplit(gap = 10, n_splits = 4)\n",
    "print(tscv)\n",
    "\n",
    "for train_index, test_index in tscv.split(X):\n",
    "\n",
    "    print(\"TRAIN:\", train_index[1], train_index[-1], \"TEST:\", test_index[1], test_index[-1])\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = Y[train_index], Y[test_index]\n",
    "    \n",
    "    #Padronizando os dados\n",
    "    X_train = normalizer.fit_transform(X_train)\n",
    "    X_test = normalizer.fit_transform(X_test)\n",
    "    \n",
    "    #Random forest\n",
    "    clf=RandomForestClassifier(n_estimators=100)\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    a = metrics.accuracy_score(y_test, y_pred)\n",
    "    acur_RF.append(a)\n",
    "    \n",
    "    #SVM\n",
    "    clf=svm.SVC()\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    a = metrics.accuracy_score(y_test, y_pred)\n",
    "    acur_svm.append(a)\n",
    "    \n",
    "    #TimeSeriesForest()\n",
    "    clf =TimeSeriesForest(random_state=43)\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    #clf.score(X_test, y_test)\n",
    "    a = metrics.accuracy_score(y_test, y_pred)\n",
    "    acur_TSF.append(a)\n",
    "    \n",
    "    #Naive Bayes\n",
    "    gnb = GaussianNB()\n",
    "    gnb.fit(X_train, y_train)\n",
    "    y_pred=gnb.predict(X_test)\n",
    "    a = metrics.accuracy_score(y_test, y_pred)\n",
    "    acur_NB.append(a)\n",
    "    \n",
    "acuracia['acur_RF'] = acur_RF \n",
    "acuracia['acur_svm'] = acur_svm \n",
    "acuracia['TimeSeriesForest'] = acur_TSF\n",
    "acuracia['acur_NB'] = acur_NB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3ff84562",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:54:17.594491Z",
     "start_time": "2021-11-25T01:54:17.553599Z"
    }
   },
   "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>acur_RF</th>\n",
       "      <th>acur_svm</th>\n",
       "      <th>TimeSeriesForest</th>\n",
       "      <th>acur_NB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.499083</td>\n",
       "      <td>0.491743</td>\n",
       "      <td>0.475229</td>\n",
       "      <td>0.409174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.521101</td>\n",
       "      <td>0.466055</td>\n",
       "      <td>0.506422</td>\n",
       "      <td>0.458716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.412844</td>\n",
       "      <td>0.477064</td>\n",
       "      <td>0.403670</td>\n",
       "      <td>0.502752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.359633</td>\n",
       "      <td>0.390826</td>\n",
       "      <td>0.366972</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    acur_RF  acur_svm  TimeSeriesForest   acur_NB\n",
       "0  0.499083  0.491743          0.475229  0.409174\n",
       "1  0.521101  0.466055          0.506422  0.458716\n",
       "2  0.412844  0.477064          0.403670  0.502752\n",
       "3  0.359633  0.390826          0.366972  0.400000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acuracia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6153e16e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:54:25.719046Z",
     "start_time": "2021-11-25T01:54:25.696106Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "acur_RF             0.448165\n",
       "acur_svm            0.456422\n",
       "TimeSeriesForest    0.438073\n",
       "acur_NB             0.442661\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acuracia.mean(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "64d3d827",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:54:30.257005Z",
     "start_time": "2021-11-25T01:54:30.232070Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "acur_RF             0.075272\n",
       "acur_svm            0.044979\n",
       "TimeSeriesForest    0.064008\n",
       "acur_NB             0.047645\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acuracia.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "291f9463",
   "metadata": {},
   "source": [
    "$\\quad$Note que não obtemos uma boa eficácia, e isso é resultado da falta de robustez do modelo nos passos iniciais, temos uma escassez de informações de qualidade para a classificação. Por curiosidade rodamos o modelo com o vazamento de dados de 10 dias e obtive uma acurácia de 70% com o random forest.\\\n",
    "$\\quad$Utilizei dessa etapa para podermos decidir qual dos modelos utilizar na próxima validação, que pela estratégia escolhida tem um custo computacional muito alto.\n",
    "\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "101b48d1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:56:07.506290Z",
     "start_time": "2021-11-25T01:56:07.498315Z"
    }
   },
   "source": [
    "### Walk Forward Validation\n",
    "\n",
    "$\\quad$Nessa validação já iremos gerar as previsões finais do nosso modelo que irão alimentar o backtest, iremos utilizar da estratégia do naive bayes, pois apesar de não ter tido a melhor eficácia, foi o que apresentou o menor desvio padrão os 4 folds testados, além de ter o menor tempo computacional de treino (alguns segundos), contra os outros modelos que tiveram uma estimativa de tempo de horas nessa etapa. \\\n",
    "$\\quad$Essa validação é parecida com a anterior, iremos treinar o nosso modelo a partir do centésimo dia para podermos ter dados suficientes para treino e iremos prever o teste de apenas um dia, que será o último dia do dado de treino + 10 dias (gap necessário para não vazar dados futuros), depois calculamos a sua acurácia, no próximo passo adicionamos 1 dia no treino e tentamos prever o próximo dia, repetimos esta operação até chegar ao final do conjunto de dados, nossa tabela de dados tem cerca de 2726 dias, o que torna esse processo computacionalmente caro, entretanto caso fossemos colocar em produção só precisaríamos treinar os dados uma vez por dia após o treinamento histórico.\n",
    "\n",
    "![image.png](attachment:image.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "334241bd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:57:28.390953Z",
     "start_time": "2021-11-25T01:57:19.789249Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 2606/2606 [00:08<00:00, 321.41it/s]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics\n",
    "from tqdm import tqdm\n",
    "\n",
    "clf = GaussianNB()\n",
    "\n",
    "#X = datadata.drop(columns = ['Open', 'High', 'Low', 'Close', 'SMA_15', 'SMA_100'], inplace = True)\n",
    "n_train = 100\n",
    "n_records = len(X)\n",
    "pred = pd.DataFrame(index = df.index, columns = ['out', 'acuracia'])\n",
    "acura = []\n",
    "\n",
    "for i in tqdm(range(n_train+10, n_records-10)):\n",
    "    #temos que tentar descobrir o i+10 pra nao ter vazamento de dados\n",
    "    #sempre tera um gap entre o treino e o teste do tamanho da barreira vertical\n",
    "    X_train, X_test = X[n_train:i], X[i+10:i+11]\n",
    "    y_train, y_test = Y[n_train:i], Y[i+10:i+11]\n",
    "    \n",
    "    clf.fit(X_train,y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    \n",
    "    #adicionando previsao\n",
    "    pred.out[i+10:i+11] = y_pred\n",
    "    \n",
    "    acuracia = metrics.accuracy_score(y_test, y_pred)\n",
    "    #adicionando acuracia\n",
    "    pred.acuracia[i+10:i+11] = acuracia\n",
    "    acura.append(acuracia) \n",
    "    #print('tMrain=%d, test=%d' % (len(X_train), len(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "eeb3616e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:57:59.212936Z",
     "start_time": "2021-11-25T01:57:58.992074Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (8,6)\n",
    "\n",
    "sns.barplot(x = pred.out.value_counts().index, y = pred.out.value_counts())\n",
    "plt.title('Distribuição Predição')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "181df135",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T01:58:56.564926Z",
     "start_time": "2021-11-25T01:58:56.502153Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acertos: 43.48%\n",
      "Erros: 56.52%\n"
     ]
    }
   ],
   "source": [
    "erros = pred.acuracia.value_counts()[0]\n",
    "acertos = pred.acuracia.value_counts()[1]\n",
    "total = erros + acertos\n",
    "\n",
    "print('Acertos: {}%'.format(round(acertos/total*100, 2)))\n",
    "print('Erros: {}%'.format(round(erros/total*100, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "306a0807",
   "metadata": {},
   "source": [
    "$\\quad$O modelo classificou a maioria dos dados como sinal de compra, o que era esperado, os sinais de venda e saída que preocupam por não estarem próximos da classificação da barreira tripla. \\\n",
    "$\\quad$Nessa etapa tivemos 43.48% acertos, um desempenho baixo.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57dc12f9",
   "metadata": {},
   "source": [
    "## Backtest\n",
    "$\\quad$ A implementação do backtest é autoral. Embasei no formato e na lógica do curso Quant Expert, de Rafael Lavrado, e implementei no python utilizando orientação a objeto. \\\n",
    "$\\quad$Gerei as tabelas dos trades short e long e algumas estatísticas de performance da estratégia. \\\n",
    "$\\quad$O codigo da implementação do backtest pode ser encontrado aqui [Backtest](https://github.com/mathsinis/mathsinis.github.io/blob/main/codigos/Backtest.py)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f3adde24",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T02:00:37.601255Z",
     "start_time": "2021-11-25T02:00:37.595274Z"
    }
   },
   "outputs": [],
   "source": [
    "import Backtest as bt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4cd69fef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T02:00:53.750503Z",
     "start_time": "2021-11-25T02:00:53.720548Z"
    }
   },
   "outputs": [],
   "source": [
    "out = pred.fillna(0).out\n",
    "bt2 = bt.backtest(data.index, data.Close.values, out.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b242b9",
   "metadata": {},
   "source": [
    "### Trades Long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6cc23d46",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T02:01:50.714819Z",
     "start_time": "2021-11-25T02:01:50.232552Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         trade    entrada      saida   ndias  perfomance\n",
      "0      Trade 1 2010-07-02 2010-07-05  3 days   -0.024191\n",
      "1      Trade 2 2010-07-20 2010-07-21  1 days   -0.003225\n",
      "2      Trade 3 2010-07-22 2010-07-23  1 days    0.010499\n",
      "3      Trade 4 2010-07-28 2010-08-30 33 days   -0.061846\n",
      "4      Trade 5 2010-09-13 2010-09-15  2 days    0.020259\n",
      "..         ...        ...        ...     ...         ...\n",
      "189  Trade 190 2020-11-13 2020-11-16  3 days    0.035125\n",
      "190  Trade 191 2020-11-19 2020-11-24  5 days    0.031239\n",
      "191  Trade 192 2020-11-25 2020-11-27  2 days   -0.013624\n",
      "192  Trade 193 2020-12-02 2020-12-10  8 days    0.052368\n",
      "193  Trade 194 2020-12-11 2020-12-30 19 days    0.010543\n",
      "\n",
      "[194 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "b = bt2.trade_long()\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6a082a46",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T02:01:51.502757Z",
     "start_time": "2021-11-25T02:01:51.474831Z"
    }
   },
   "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>trade</th>\n",
       "      <th>entrada</th>\n",
       "      <th>saida</th>\n",
       "      <th>ndias</th>\n",
       "      <th>perfomance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Trade 1</td>\n",
       "      <td>2010-07-02</td>\n",
       "      <td>2010-07-05</td>\n",
       "      <td>3 days</td>\n",
       "      <td>-0.024191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Trade 2</td>\n",
       "      <td>2010-07-20</td>\n",
       "      <td>2010-07-21</td>\n",
       "      <td>1 days</td>\n",
       "      <td>-0.003225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Trade 3</td>\n",
       "      <td>2010-07-22</td>\n",
       "      <td>2010-07-23</td>\n",
       "      <td>1 days</td>\n",
       "      <td>0.010499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Trade 4</td>\n",
       "      <td>2010-07-28</td>\n",
       "      <td>2010-08-30</td>\n",
       "      <td>33 days</td>\n",
       "      <td>-0.061846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Trade 5</td>\n",
       "      <td>2010-09-13</td>\n",
       "      <td>2010-09-15</td>\n",
       "      <td>2 days</td>\n",
       "      <td>0.020259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade    entrada      saida   ndias  perfomance\n",
       "0  Trade 1 2010-07-02 2010-07-05  3 days   -0.024191\n",
       "1  Trade 2 2010-07-20 2010-07-21  1 days   -0.003225\n",
       "2  Trade 3 2010-07-22 2010-07-23  1 days    0.010499\n",
       "3  Trade 4 2010-07-28 2010-08-30 33 days   -0.061846\n",
       "4  Trade 5 2010-09-13 2010-09-15  2 days    0.020259"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e2187ad3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T02:01:52.947771Z",
     "start_time": "2021-11-25T02:01:52.847748Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Perf Long\n",
      "\n",
      "N Trades:194 \n",
      "Win:96 \n",
      "Acerto:49.48% \n",
      "Avg Win:5.27% \n",
      "Erro:-95% \n",
      "Avg Loss:-3.86%  \n",
      "Avg Total:0.68%  \n",
      "Avg Holding:17 days 00:14:50.721649484  \n",
      "Avg Holding Win:21 days 00:30:00  \n",
      "Avg Holding Loss:13 days 04:27:12.989690721\n"
     ]
    }
   ],
   "source": [
    "bt2.long_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f85526f",
   "metadata": {},
   "source": [
    "### Trades Short"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "486a695f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-17T01:44:36.577502Z",
     "start_time": "2021-11-17T01:44:36.514669Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         trade    entrada      saida  ndias  performance\n",
      "0      Trade 1 2010-07-06 2010-07-07 1 days     0.058635\n",
      "1      Trade 2 2010-07-14 2010-07-20 6 days     0.003506\n",
      "2      Trade 3 2010-07-21 2010-07-22 1 days     0.027231\n",
      "3      Trade 4 2010-07-23 2010-07-28 5 days     0.023376\n",
      "4      Trade 5 2010-08-30 2010-08-31 1 days     0.029116\n",
      "..         ...        ...        ...    ...          ...\n",
      "158  Trade 159 2020-10-23 2020-10-26 3 days     0.007076\n",
      "159  Trade 160 2020-10-27 2020-11-03 7 days    -0.033533\n",
      "160  Trade 161 2020-11-05 2020-11-06 1 days    -0.001173\n",
      "161  Trade 162 2020-11-12 2020-11-13 1 days     0.018969\n",
      "162  Trade 163 2020-11-17 2020-11-19 2 days    -0.023059\n",
      "\n",
      "[163 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "s = bt2.trade_short()\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "4789271b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-17T01:44:37.117056Z",
     "start_time": "2021-11-17T01:44:37.100103Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Perf Short\n",
      "\n",
      "N Trades:163 \n",
      "Win:79 \n",
      "Acerto:48.47% \n",
      "Avg Win:2.73% \n",
      "Erro:51.53% \n",
      "Avg Loss:-1.72%  \n",
      "Avg Total:0.45%  \n",
      "Avg Holding:2 days 05:09:12.147239263  \n",
      "Avg Holding Win:2 days 12:45:34.177215189  \n",
      "Avg Holding Loss:1 days 22:15:54.216867469\n"
     ]
    }
   ],
   "source": [
    "bt2.short_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "566ec2af",
   "metadata": {},
   "source": [
    "Analisando as estatísticas, a estratégia divergiu bastante quanto a operação de long e short , com uma média de holding muito maior que o esperado(10 dias) para long e muito menor para short, quanto a performance, o resultado foi insatisfatório com um número de acertos baixo e com uma média do percentual de rendimento próximo a zero."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c508b2ed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T02:05:18.794289Z",
     "start_time": "2021-11-25T02:05:18.783319Z"
    }
   },
   "source": [
    "## Benchmark\n",
    "\n",
    "Simulei o percentual de rendimento durante o período da estratégia e comparei com a IBOV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a185e691",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-25T02:08:40.454958Z",
     "start_time": "2021-11-25T02:07:09.213051Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\msini\\Anaconda3\\envs\\finances\\lib\\site-packages\\pandas\\core\\indexing.py:670: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  iloc._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ticker = '^BVSP'\n",
    "yfObj = yf.Ticker(ticker)\n",
    "ibov = yfObj.history(start=\"2009-12-30\", end=\"2020-12-31\")\n",
    "\n",
    "ibov['pct_change'] = ibov.Close.pct_change(1).fillna(0)\n",
    "\n",
    "#acumulando\n",
    "ibov_acum = []\n",
    "#adicionando primeiro dia\n",
    "ibov_acum.append(ibov['pct_change'][0]+1)\n",
    "for i in range(1,len(ibov.Close)):\n",
    "    ibov_acum.append((ibov['pct_change'][i]+1)*ibov_acum[i-1])\n",
    "\n",
    "ibov_acum = pd.Series(ibov_acum, index = ibov.index)\n",
    "\n",
    "\n",
    "out_s = pd.Series(out.values, index = data.index)\n",
    "\n",
    "df2 = pd.DataFrame()\n",
    "df2['out_s'] = out_s\n",
    "df2['Close'] = data.Close.pct_change(1)\n",
    "df2['Close'].iloc[0] = 0\n",
    "df2['estrategia'] = df2['out_s'] * df2['Close']\n",
    "\n",
    "est_acum = []\n",
    "est_acum.append(1)\n",
    "\n",
    "for i in range(1, len(df2['estrategia'])):\n",
    "    est_acum.append((df2['estrategia'].iloc[i] + 1)*est_acum[i-1])\n",
    "\n",
    "df2['pct_estrategia'] = est_acum\n",
    "\n",
    "X = df2.index\n",
    "y = df2.pct_estrategia\n",
    "\n",
    "X2 = ibov_acum.index\n",
    "y2 = ibov_acum\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (20,12)\n",
    "\n",
    "p2 = sns.lineplot(x = X,y = y, label = 'Estrategia')\n",
    "p3 = sns.lineplot(x = X2,y = y2, label = 'IBOV')\n",
    "\n",
    "#p2, = plt.plot(X, df2.pct_estrategia, label = 'close', color = 'blue')\n",
    "#p2, = plt.plot(X2, cdi_dia_acum.values, label = 'close', color = 'red')\n",
    "#plt.legend(handles=[p0, p1, p3, p4])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90a2f0fd",
   "metadata": {},
   "source": [
    "Pela analise visual do desempenho da estratégia, verifica-se que no geral, ela não conseguiu ultrapassar a IBOV e retornou em prejuízo ao final do período. O único período que parece dar bons retornos ocorreu por volta de 2014"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b809ee6",
   "metadata": {},
   "source": [
    "## Conclusão\n",
    "\n",
    "$\\quad$A estratégia de investimento apresentada não obteve um resultado satisfatório. Diversos motivos podem ter contribuído para esse desempenho: base de dados pobre e com quantidade de dados insatisfatória, indicadores insuficientes e até mesmo os modelos selecionados podem não ter sido os melhores para esse tipo de problema. Diferentes tipos de abordagem dentro desse trabalho podem acabar gerando resultados melhores, desde a busca de uma base de dados mais próxima da realidade até modelos de complexidade maior (aprendizado profundo)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "775cd0af",
   "metadata": {},
   "source": [
    "## Bibliografia\n",
    "\n",
    "De Prado, M. L. (2018). Advances in financial machine learning. John Wiley & Sons.\n",
    "\n",
    "Gui, K. (30 de Agosto de 2020). Data Labelling: The Triple-barrier Method. Fonte: Towards Data Science: https://towardsdatascience.com/the-triple-barrier-method-251268419dcd\n",
    "\n",
    "Quant Expert: Estratégias Quantitativas. Rafael Lavrado: https://www.udemy.com/course/quant-expert/\n"
   ]
  }
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