stoch.ipynb

{
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  {
   "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": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv(diretorio+\"/btc.csv\",sep=\";\",index_col=\"datetime\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def stoch(data,EMA_fast=[],EMA_slow=[]):\n",
    "    for ft in EMA_fast:\n",
    "        for st in EMA_slow:\n",
    "            data[\"slowk_\"+str(ft)+\"_\"+str(st)],data[\"slowd_\"+str(ft)+\"_\"+str(st)]= ta.STOCH(data.high,\n",
    "                                                                                            data.low,\n",
    "                                                                                            data.close,\n",
    "                                                                                           fastk_period=ft,\n",
    "                                                                                           slowk_period=st,\n",
    "                                                                                           slowk_matype=0,\n",
    "                                                                                           slowd_period=3,\n",
    "                                                                                           slowd_matype=0)\n",
    "            # signal turning point\n",
    "            data=turning_point(data,coluna=[\"slowk_\"+str(ft)+\"_\"+str(st)])\n",
    "            #\n",
    "            data[\"fastk_\"+str(ft)+\"_\"+str(st)],data[\"fastd_\"+str(ft)+\"_\"+str(st)]= ta.STOCHF(data.high,\n",
    "                                                                                            data.low,\n",
    "                                                                                            data.close,\n",
    "                                                                                            fastk_period=ft,\n",
    "                                                                                            fastd_period=3,\n",
    "                                                                                            fastd_matype=0)\n",
    "            # signal fastk\n",
    "            data[\"fastk_\"+str(ft)+\"_\"+str(st)+\"_\"+\"s\"]=0\n",
    "            data.loc[(data[\"fastk_\"+str(ft)+\"_\"+str(st)] > 50),\"fastk_\"+str(ft)+\"_\"+str(st)+\"_\"+\"s\"]=1\n",
    "            data.loc[(data[\"fastk_\"+str(ft)+\"_\"+str(st)] <= 50),\"fastk_\"+str(ft)+\"_\"+str(st)+\"_\"+\"s\"]=-1\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Candlestick graph\n",
    "def candle_graph_stoch(data,nome_coluna_datas=\"datetime\",n=200,fast=\"fastk_14_26\",slow=\"slowk_14_26\"):\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=(7,8)) \n",
    "    ax1 = plt.subplot2grid((7,1), (0, 0),rowspan=3)\n",
    "    ax2 = plt.subplot2grid((7,1), (3, 0),rowspan=2)\n",
    "    ax3 = plt.subplot2grid((7,1), (5, 0),rowspan=2)\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",
    "    ax2.plot(ohlc[nome_coluna_datas], ohlc[fast], color = \"r\", lw=1,label=fast)\n",
    "    ax3.plot(ohlc[nome_coluna_datas], ohlc[slow], color = \"b\", lw=1,label=slow)\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",
    "    ax2.legend(loc=1,fontsize=8)\n",
    "    ax3.legend(loc=1,fontsize=8)\n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/igoracmorais/anaconda3/lib/python3.6/site-packages/scipy/signal/_peak_finding.py:73: 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:74: 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:73: 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:74: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  results &= comparator(main, minus)\n"
     ]
    },
    {
     "data": {
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       "  <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>slowk_7_26</th>\n",
       "      <th>slowd_7_26</th>\n",
       "      <th>tp_slowk_7_26</th>\n",
       "      <th>fastk_7_26</th>\n",
       "      <th>fastd_7_26</th>\n",
       "      <th>...</th>\n",
       "      <th>tp_slowk_21_35</th>\n",
       "      <th>fastk_21_35</th>\n",
       "      <th>fastd_21_35</th>\n",
       "      <th>fastk_21_35_s</th>\n",
       "      <th>slowk_21_50</th>\n",
       "      <th>slowd_21_50</th>\n",
       "      <th>tp_slowk_21_50</th>\n",
       "      <th>fastk_21_50</th>\n",
       "      <th>fastd_21_50</th>\n",
       "      <th>fastk_21_50_s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <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>2018-10-20</th>\n",
       "      <td>6469.28</td>\n",
       "      <td>6645.20</td>\n",
       "      <td>6395.45</td>\n",
       "      <td>6490.56</td>\n",
       "      <td>1.271858e+08</td>\n",
       "      <td>42.561732</td>\n",
       "      <td>44.319865</td>\n",
       "      <td>0</td>\n",
       "      <td>24.825768</td>\n",
       "      <td>25.877663</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>29.976103</td>\n",
       "      <td>29.251029</td>\n",
       "      <td>-1</td>\n",
       "      <td>42.522852</td>\n",
       "      <td>43.744144</td>\n",
       "      <td>0</td>\n",
       "      <td>29.976103</td>\n",
       "      <td>29.251029</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\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>41.914622</td>\n",
       "      <td>42.852434</td>\n",
       "      <td>0</td>\n",
       "      <td>26.776155</td>\n",
       "      <td>24.910842</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>31.792865</td>\n",
       "      <td>29.914321</td>\n",
       "      <td>-1</td>\n",
       "      <td>41.258075</td>\n",
       "      <td>42.508939</td>\n",
       "      <td>0</td>\n",
       "      <td>31.792865</td>\n",
       "      <td>29.914321</td>\n",
       "      <td>-1</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>41.946609</td>\n",
       "      <td>42.140988</td>\n",
       "      <td>0</td>\n",
       "      <td>26.643599</td>\n",
       "      <td>26.081841</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>29.286467</td>\n",
       "      <td>30.351811</td>\n",
       "      <td>-1</td>\n",
       "      <td>39.955187</td>\n",
       "      <td>41.245371</td>\n",
       "      <td>0</td>\n",
       "      <td>29.286467</td>\n",
       "      <td>30.351811</td>\n",
       "      <td>-1</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>40.087313</td>\n",
       "      <td>41.316182</td>\n",
       "      <td>1</td>\n",
       "      <td>22.062062</td>\n",
       "      <td>25.160605</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>26.211802</td>\n",
       "      <td>29.097045</td>\n",
       "      <td>-1</td>\n",
       "      <td>38.632628</td>\n",
       "      <td>39.948630</td>\n",
       "      <td>1</td>\n",
       "      <td>26.211802</td>\n",
       "      <td>29.097045</td>\n",
       "      <td>-1</td>\n",
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       "<p>4 rows × 59 columns</p>\n",
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      "text/plain": [
       "               open     high      low    close        volume  slowk_7_26  \\\n",
       "datetime                                                                   \n",
       "2018-10-20  6469.28  6645.20  6395.45  6490.56  1.271858e+08   42.561732   \n",
       "2018-10-21  6490.60  6557.39  6474.47  6509.87  1.052839e+08   41.914622   \n",
       "2018-10-22  6509.87  6531.23  6460.38  6483.23  2.027669e+08   41.946609   \n",
       "2018-10-23  6484.32  6490.71  6450.55  6450.55  1.040892e+08   40.087313   \n",
       "\n",
       "            slowd_7_26  tp_slowk_7_26  fastk_7_26  fastd_7_26      ...        \\\n",
       "datetime                                                           ...         \n",
       "2018-10-20   44.319865              0   24.825768   25.877663      ...         \n",
       "2018-10-21   42.852434              0   26.776155   24.910842      ...         \n",
       "2018-10-22   42.140988              0   26.643599   26.081841      ...         \n",
       "2018-10-23   41.316182              1   22.062062   25.160605      ...         \n",
       "\n",
       "            tp_slowk_21_35  fastk_21_35  fastd_21_35  fastk_21_35_s  \\\n",
       "datetime                                                              \n",
       "2018-10-20               0    29.976103    29.251029             -1   \n",
       "2018-10-21               0    31.792865    29.914321             -1   \n",
       "2018-10-22               0    29.286467    30.351811             -1   \n",
       "2018-10-23              -1    26.211802    29.097045             -1   \n",
       "\n",
       "            slowk_21_50  slowd_21_50  tp_slowk_21_50  fastk_21_50  \\\n",
       "datetime                                                            \n",
       "2018-10-20    42.522852    43.744144               0    29.976103   \n",
       "2018-10-21    41.258075    42.508939               0    31.792865   \n",
       "2018-10-22    39.955187    41.245371               0    29.286467   \n",
       "2018-10-23    38.632628    39.948630               1    26.211802   \n",
       "\n",
       "            fastd_21_50  fastk_21_50_s  \n",
       "datetime                                \n",
       "2018-10-20    29.251029             -1  \n",
       "2018-10-21    29.914321             -1  \n",
       "2018-10-22    30.351811             -1  \n",
       "2018-10-23    29.097045             -1  \n",
       "\n",
       "[4 rows x 59 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=stoch(data,EMA_fast=[7,14,21],EMA_slow=[26,35,50])\n",
    "df.tail(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
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Hjx8HAJw6dQoqlcpgm6FDhyI1NZXfpqKiAqmpqRg6dCi/zX333Yfu7m6cOnVK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\nQgikIBBCCCEE8H+Rp+LhEDq2hgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a94d828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "candle_graph_stoch(df)"
   ]
  },
  {
   "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",
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