PPO.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 mpl_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, find_peaks_cwt\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",
    "from scipy import signal\n",
    "diretorio= os.getcwd()\n",
    "diretorio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv(diretorio+\"/btc.csv\",sep=\";\",index_col=\"datetime\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ppo(data,EMA_fast=[],EMA_slow=[]):\n",
    "    for pf in EMA_fast:\n",
    "        for ps in EMA_slow:\n",
    "            data[\"ppo_\"+str(pf)+'_'+str(ps)]=ta.PPO(data.close,fastperiod=pf,slowperiod=ps,matype=0)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Candlestick graph\n",
    "def candle_graph_ppo(data,nome_coluna_datas=\"datetime\",n=200,**colunas):\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",
    "    ax1.axes.get_xaxis().set_visible(False)\n",
    "    candlestick_ohlc(ax1, ohlc.values, width=.6, colorup='green', colordown='red')\n",
    "    #\n",
    "    from matplotlib.pyplot import cm\n",
    "    colors=[\"r\",\"b\",\"gray\",\"green\",\"w\"]\n",
    "    for cor in range(len(colunas)):\n",
    "        for key,value in colunas.items():\n",
    "            ax2.plot(ohlc[nome_coluna_datas], ohlc[value], color = colors[cor], lw=1,label=value)\n",
    "    ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "    import matplotlib.ticker as ticker\n",
    "    ax2.xaxis.set_major_locator(ticker.MaxNLocator(10))\n",
    "    plt.xticks(fontsize=8)\n",
    "    ax2.legend(loc=1,fontsize=8)\n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>ppo_7_26</th>\n",
       "      <th>ppo_7_36</th>\n",
       "      <th>ppo_7_40</th>\n",
       "      <th>ppo_14_26</th>\n",
       "      <th>ppo_14_36</th>\n",
       "      <th>ppo_14_40</th>\n",
       "      <th>ppo_21_26</th>\n",
       "      <th>ppo_21_36</th>\n",
       "      <th>ppo_21_40</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>2018-10-21</th>\n",
       "      <td>6490.60</td>\n",
       "      <td>6557.39</td>\n",
       "      <td>6474.47</td>\n",
       "      <td>6509.87</td>\n",
       "      <td>1.052839e+08</td>\n",
       "      <td>0.131794</td>\n",
       "      <td>0.141411</td>\n",
       "      <td>0.239905</td>\n",
       "      <td>-0.809902</td>\n",
       "      <td>-0.800375</td>\n",
       "      <td>-0.702807</td>\n",
       "      <td>-0.285718</td>\n",
       "      <td>-0.276141</td>\n",
       "      <td>-0.178057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-22</th>\n",
       "      <td>6509.87</td>\n",
       "      <td>6531.23</td>\n",
       "      <td>6460.38</td>\n",
       "      <td>6483.23</td>\n",
       "      <td>2.027669e+08</td>\n",
       "      <td>-0.163106</td>\n",
       "      <td>-0.133204</td>\n",
       "      <td>-0.099155</td>\n",
       "      <td>-1.004607</td>\n",
       "      <td>-0.974958</td>\n",
       "      <td>-0.941195</td>\n",
       "      <td>-0.379401</td>\n",
       "      <td>-0.349564</td>\n",
       "      <td>-0.315588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-23</th>\n",
       "      <td>6484.32</td>\n",
       "      <td>6490.71</td>\n",
       "      <td>6450.55</td>\n",
       "      <td>6450.55</td>\n",
       "      <td>1.040892e+08</td>\n",
       "      <td>-0.314778</td>\n",
       "      <td>-0.503503</td>\n",
       "      <td>-0.373609</td>\n",
       "      <td>-1.065628</td>\n",
       "      <td>-1.252931</td>\n",
       "      <td>-1.124016</td>\n",
       "      <td>-0.295623</td>\n",
       "      <td>-0.484384</td>\n",
       "      <td>-0.354466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               open     high      low    close        volume  ppo_7_26  \\\n",
       "datetime                                                                 \n",
       "2018-10-21  6490.60  6557.39  6474.47  6509.87  1.052839e+08  0.131794   \n",
       "2018-10-22  6509.87  6531.23  6460.38  6483.23  2.027669e+08 -0.163106   \n",
       "2018-10-23  6484.32  6490.71  6450.55  6450.55  1.040892e+08 -0.314778   \n",
       "\n",
       "            ppo_7_36  ppo_7_40  ppo_14_26  ppo_14_36  ppo_14_40  ppo_21_26  \\\n",
       "datetime                                                                     \n",
       "2018-10-21  0.141411  0.239905  -0.809902  -0.800375  -0.702807  -0.285718   \n",
       "2018-10-22 -0.133204 -0.099155  -1.004607  -0.974958  -0.941195  -0.379401   \n",
       "2018-10-23 -0.503503 -0.373609  -1.065628  -1.252931  -1.124016  -0.295623   \n",
       "\n",
       "            ppo_21_36  ppo_21_40  \n",
       "datetime                          \n",
       "2018-10-21  -0.276141  -0.178057  \n",
       "2018-10-22  -0.349564  -0.315588  \n",
       "2018-10-23  -0.484384  -0.354466  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=ppo(data,EMA_fast=[7,14,21],EMA_slow=[26,36,40])\n",
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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RyzqIyBcwMJKJjHMZKKspM9kXUhsSApSVIWD5cgCWAyR5J2HtKr9XIj0GRjJRqaxEWGAY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\niG1/MwL2p8kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cc575f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "candle_graph_ppo(df,linha=['ppo_7_26','ppo_14_40'])"
   ]
  },
  {
   "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
}