MACD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/igoracmorais/igor/Wise&Trust/CPM_trading2/Trading/Rotines/Python'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import talib as ta\n",
    "from matplotlib.finance import candlestick_ohlc\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import os\n",
    "from scipy.signal import argrelextrema\n",
    "import json\n",
    "from bs4 import BeautifulSoup\n",
    "import requests \n",
    "import urllib.request\n",
    "import datetime\n",
    "from datetime import timedelta, datetime\n",
    "import time\n",
    "diretorio= os.getcwd()\n",
    "diretorio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv(diretorio+\"/btc.csv\",sep=\";\",index_col=\"datetime\")\n",
    "def macd(data,n=200,nome_coluna_datas=\"datetime\",EMA_fast=[],EMA_slow=[]):\n",
    "    for a in EMA_fast:\n",
    "        for b in EMA_slow:\n",
    "            data['macd'+str(a)+str(b)],data['macdsignal'+str(a)+str(b)],data['macdhist'+str(a)+str(b)]=ta.MACD(data.close,\n",
    "                                                                                                               fastperiod=a,\n",
    "                                                                                                               slowperiod=b,\n",
    "                                                                                                               signalperiod=9)\n",
    "            data['EMA'+str(b)]=ta.EMA(data.close,timeperiod=b)\n",
    "        data['EMA'+str(a)]=ta.EMA(data.close,timeperiod=a)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Candlestick graph\n",
    "def candle_graph(data,n=200,nome_coluna_datas=\"datetime\",coluna_macdisthist=\"macdhist\",\n",
    "                 coluna_macdsignal=\"macdsignal\",EMA_fast=[],EMA_slow=[]):\n",
    "    ohlc=data.reset_index()[-n:]\n",
    "    ohlc[nome_coluna_datas]=pd.to_datetime(ohlc[nome_coluna_datas])\n",
    "    ohlc[nome_coluna_datas]=ohlc[nome_coluna_datas].apply(mdates.date2num)\n",
    "    #\n",
    "    plt.style.use('fivethirtyeight')\n",
    "    fig = plt.figure(figsize=(6,8)) \n",
    "    ax1 = plt.subplot2grid((6,1), (0, 0),rowspan=3)\n",
    "    ax2 = plt.subplot2grid((6,1), (3, 0),rowspan=2)\n",
    "    ax3 = plt.subplot2grid((6,1), (5, 0),rowspan=1)\n",
    "    ax1.axes.get_xaxis().set_visible(False)\n",
    "    ax2.axes.get_xaxis().set_visible(False)\n",
    "    candlestick_ohlc(ax1, ohlc.values, width=.6, colorup='green', colordown='red')\n",
    "    g1,=ax1.plot(ohlc[nome_coluna_datas],ohlc['EMA'+str(EMA_fast)],lw=1,label='EMA'+str(EMA_fast),color='black')\n",
    "    g2,=ax1.plot(ohlc[nome_coluna_datas],ohlc['EMA'+str(EMA_slow)],lw=1,label='EMA'+str(EMA_slow),color='blue')\n",
    "    # macdhist\n",
    "    pos_signal = ohlc[coluna_macdisthist].copy()\n",
    "    neg_signal = ohlc[coluna_macdisthist].copy()\n",
    "    pos_signal[pos_signal <= 0] = np.nan\n",
    "    neg_signal[neg_signal > 0] = np.nan\n",
    "    ax2.bar(ohlc[nome_coluna_datas], pos_signal, color = 'green', lw=1,label=\"macdhist\")\n",
    "    ax2.bar(ohlc[nome_coluna_datas], neg_signal, color = 'red', lw=1)\n",
    "    # macdsignal\n",
    "    ax3.plot(ohlc[nome_coluna_datas],ohlc[coluna_macdsignal],lw=1,label=\"macdsignal\",color='black')\n",
    "    ax3.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "    import matplotlib.ticker as ticker\n",
    "    ax3.xaxis.set_major_locator(ticker.MaxNLocator(10))\n",
    "    plt.xticks(fontsize=8)\n",
    "    #\n",
    "    ax1.legend((g1,g2),['EMA'+str(EMA_fast),'EMA'+str(EMA_slow)],loc=\"best\",fontsize=8)\n",
    "    ax2.legend(loc=1,fontsize=8)\n",
    "    ax3.legend(loc=1,fontsize=8)\n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def turning_point(data,coluna=[],ordem=3):\n",
    "    for coluna in coluna:\n",
    "        data[coluna+'_min'] = data.iloc[argrelextrema(data[coluna].values, \n",
    "                                                  np.less_equal, order=ordem)[0]][coluna]\n",
    "        data[coluna+'_max'] = data.iloc[argrelextrema(data[coluna].values, \n",
    "                                                  np.greater_equal, order=ordem)[0]][coluna]\n",
    "        data[coluna+'_min'].fillna(0,inplace=True)\n",
    "        data[coluna+'_max'].fillna(0,inplace=True)\n",
    "        data[\"tp_\"+coluna]=0\n",
    "        data.loc[data[coluna+'_min']>0,\"tp_\"+coluna]=1\n",
    "        data.loc[data[coluna+'_max']>0,\"tp_\"+coluna]=-1\n",
    "        data[[coluna+'_min',coluna+'_max']]=np.nan\n",
    "        data.drop([coluna+'_min',coluna+'_max'],axis=1,inplace=True)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## exemplo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/igoracmorais/anaconda3/lib/python3.6/site-packages/scipy/signal/_peak_finding.py:68: RuntimeWarning: invalid value encountered in less_equal\n",
      "  results &= comparator(main, plus)\n",
      "/Users/igoracmorais/anaconda3/lib/python3.6/site-packages/scipy/signal/_peak_finding.py:69: RuntimeWarning: invalid value encountered in less_equal\n",
      "  results &= comparator(main, minus)\n",
      "/Users/igoracmorais/anaconda3/lib/python3.6/site-packages/scipy/signal/_peak_finding.py:68: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  results &= comparator(main, plus)\n",
      "/Users/igoracmorais/anaconda3/lib/python3.6/site-packages/scipy/signal/_peak_finding.py:69: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  results &= comparator(main, minus)\n"
     ]
    },
    {
     "data": {
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       "<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>macd</th>\n",
       "      <th>macdsignal</th>\n",
       "      <th>macdhist</th>\n",
       "      <th>EMA12</th>\n",
       "      <th>EMA26</th>\n",
       "      <th>tp_close</th>\n",
       "      <th>tp_macdhist</th>\n",
       "      <th>tp_macd</th>\n",
       "      <th>tp_macdsignal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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>2018-10-13</th>\n",
       "      <td>6252.10</td>\n",
       "      <td>6280.73</td>\n",
       "      <td>6238.92</td>\n",
       "      <td>6267.27</td>\n",
       "      <td>1.377050e+08</td>\n",
       "      <td>-61.925808</td>\n",
       "      <td>-25.310928</td>\n",
       "      <td>-36.614880</td>\n",
       "      <td>6457.530621</td>\n",
       "      <td>6519.456429</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-14</th>\n",
       "      <td>6267.27</td>\n",
       "      <td>6346.56</td>\n",
       "      <td>6247.17</td>\n",
       "      <td>6277.73</td>\n",
       "      <td>2.154229e+08</td>\n",
       "      <td>-71.681781</td>\n",
       "      <td>-34.585099</td>\n",
       "      <td>-37.096682</td>\n",
       "      <td>6429.868987</td>\n",
       "      <td>6501.550768</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2018-10-15</th>\n",
       "      <td>6277.73</td>\n",
       "      <td>7234.83</td>\n",
       "      <td>6244.77</td>\n",
       "      <td>6612.36</td>\n",
       "      <td>1.190524e+09</td>\n",
       "      <td>-51.814332</td>\n",
       "      <td>-38.030945</td>\n",
       "      <td>-13.783386</td>\n",
       "      <td>6457.944528</td>\n",
       "      <td>6509.758859</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-16</th>\n",
       "      <td>6612.69</td>\n",
       "      <td>6612.99</td>\n",
       "      <td>6583.30</td>\n",
       "      <td>6606.12</td>\n",
       "      <td>8.020117e+06</td>\n",
       "      <td>-36.155967</td>\n",
       "      <td>-37.655950</td>\n",
       "      <td>1.499982</td>\n",
       "      <td>6480.740754</td>\n",
       "      <td>6516.896721</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "               open     high      low    close        volume       macd  \\\n",
       "datetime                                                                  \n",
       "2018-10-13  6252.10  6280.73  6238.92  6267.27  1.377050e+08 -61.925808   \n",
       "2018-10-14  6267.27  6346.56  6247.17  6277.73  2.154229e+08 -71.681781   \n",
       "2018-10-15  6277.73  7234.83  6244.77  6612.36  1.190524e+09 -51.814332   \n",
       "2018-10-16  6612.69  6612.99  6583.30  6606.12  8.020117e+06 -36.155967   \n",
       "\n",
       "            macdsignal   macdhist        EMA12        EMA26  tp_close  \\\n",
       "datetime                                                                \n",
       "2018-10-13  -25.310928 -36.614880  6457.530621  6519.456429         0   \n",
       "2018-10-14  -34.585099 -37.096682  6429.868987  6501.550768         0   \n",
       "2018-10-15  -38.030945 -13.783386  6457.944528  6509.758859        -1   \n",
       "2018-10-16  -37.655950   1.499982  6480.740754  6516.896721         0   \n",
       "\n",
       "            tp_macdhist  tp_macd  tp_macdsignal  \n",
       "datetime                                         \n",
       "2018-10-13            0        0              0  \n",
       "2018-10-14            0        0              0  \n",
       "2018-10-15            0        0              0  \n",
       "2018-10-16           -1        0              0  "
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=macd(data,EMA_fast=12,EMA_slow=26)\n",
    "df=turning_point(df,coluna=[\"macd\",\"macdsignal\",\"macdhist\"])\n",
    "df.tail(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
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uJbOtjKlZj/GqeG7H36Zuxbrs7rfboKyWjpeXFz/88EO245Mnp7Jhg5y0kt05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\nRU0IIcSE8R/DyTQbd94KvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0f1c9ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "candle_graph(df,EMA_fast=12,EMA_slow=26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}