backtesting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/sh: pip: command not found\n",
      "/bin/sh: pip: command not found\n",
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "!pip install poloniex\n",
    "!pip install git+https://github.com/preslavrachev/pandas-ta.git\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import pandas as pd\n",
    "import poloniex\n",
    "import time\n",
    "from pandasta import indicators as pdt\n",
    "from pandasta.indicators import Order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fetch the Data\n",
    "For training and testing data, we will be using the [Poloniex public API](https://poloniex.com/support/api/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "end = int(time.time())\n",
    "period = 300\n",
    "start = end - 60000 * period\n",
    "data = poloniex.Poloniex().returnChartData('USDT_BTC',\n",
    "                                           period=period,\n",
    "                                           start=start,\n",
    "                                           end=end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)\n",
    "df['time'] = df['date']\n",
    "df['close'] = df['close'].astype('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add Indicators / Features\n",
    "Using the Pandas TA conventions, we will request a few indicators, based on different tiem windows. In our current case, this will be the fast-moving part (K) of the [Stochastic Oscillator](https://en.wikipedia.org/wiki/Stochastic_oscillator) index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cluster_features = ['stochk_14',\n",
    "                    'stochk_30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>close</th>\n",
       "      <th>date</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>quoteVolume</th>\n",
       "      <th>volume</th>\n",
       "      <th>weightedAverage</th>\n",
       "      <th>stochk_14</th>\n",
       "      <th>stochk_30</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</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>2018-08-28 05:35:00</th>\n",
       "      <td>6904.0</td>\n",
       "      <td>1535434500</td>\n",
       "      <td>6906.69999999</td>\n",
       "      <td>6904.00000011</td>\n",
       "      <td>6906.69999999</td>\n",
       "      <td>0.35863438</td>\n",
       "      <td>2476.19524381</td>\n",
       "      <td>6904.51161936</td>\n",
       "      <td>72.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-28 05:40:00</th>\n",
       "      <td>6905.0</td>\n",
       "      <td>1535434800</td>\n",
       "      <td>6907.99999709</td>\n",
       "      <td>6901</td>\n",
       "      <td>6904</td>\n",
       "      <td>0.12103803</td>\n",
       "      <td>835.87090383</td>\n",
       "      <td>6905.85350601</td>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-28 05:45:00</th>\n",
       "      <td>6908.0</td>\n",
       "      <td>1535435100</td>\n",
       "      <td>6907.99999882</td>\n",
       "      <td>6905</td>\n",
       "      <td>6905.00000001</td>\n",
       "      <td>0.51925218</td>\n",
       "      <td>3585.51854367</td>\n",
       "      <td>6905.1583831</td>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-28 05:50:00</th>\n",
       "      <td>6910.0</td>\n",
       "      <td>1535435400</td>\n",
       "      <td>6910</td>\n",
       "      <td>6904.09984155</td>\n",
       "      <td>6908.54307989</td>\n",
       "      <td>4.56309809</td>\n",
       "      <td>31525.22731567</td>\n",
       "      <td>6908.73321017</td>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-28 05:55:00</th>\n",
       "      <td>6904.0</td>\n",
       "      <td>1535435700</td>\n",
       "      <td>6905</td>\n",
       "      <td>6903.99984675</td>\n",
       "      <td>6905</td>\n",
       "      <td>0.3164425</td>\n",
       "      <td>2184.77654387</td>\n",
       "      <td>6904.18178303</td>\n",
       "      <td>67.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      close        date           high            low  \\\n",
       "time                                                                    \n",
       "2018-08-28 05:35:00  6904.0  1535434500  6906.69999999  6904.00000011   \n",
       "2018-08-28 05:40:00  6905.0  1535434800  6907.99999709           6901   \n",
       "2018-08-28 05:45:00  6908.0  1535435100  6907.99999882           6905   \n",
       "2018-08-28 05:50:00  6910.0  1535435400           6910  6904.09984155   \n",
       "2018-08-28 05:55:00  6904.0  1535435700           6905  6903.99984675   \n",
       "\n",
       "                              open quoteVolume          volume  \\\n",
       "time                                                             \n",
       "2018-08-28 05:35:00  6906.69999999  0.35863438   2476.19524381   \n",
       "2018-08-28 05:40:00           6904  0.12103803    835.87090383   \n",
       "2018-08-28 05:45:00  6905.00000001  0.51925218   3585.51854367   \n",
       "2018-08-28 05:50:00  6908.54307989  4.56309809  31525.22731567   \n",
       "2018-08-28 05:55:00           6905   0.3164425   2184.77654387   \n",
       "\n",
       "                    weightedAverage  stochk_14  stochk_30  \n",
       "time                                                       \n",
       "2018-08-28 05:35:00   6904.51161936       72.0       58.0  \n",
       "2018-08-28 05:40:00   6905.85350601       70.0       60.0  \n",
       "2018-08-28 05:45:00    6905.1583831       70.0       60.0  \n",
       "2018-08-28 05:50:00   6908.73321017       70.0       60.0  \n",
       "2018-08-28 05:55:00   6904.18178303       67.0       64.0  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pdt.BacktestingTaDataFrame(df, indicators=cluster_features, funds=1000, min_amount=0.001)\n",
    "df[cluster_features] = (df[cluster_features] * 100).round()\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add a basic strategy\n",
    "The strategy will use the following assumption:\n",
    "- Every time the `stochk_14` indicator goes below 20, buy a certain amount\n",
    "- Every time the `stochk_14` indicator goes above 80, sell a certain amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14d680f28>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7VtWGqirgir5tSZJGYFrOqSSZD7wSuAE4uKq+1xY9ABzcpg8F7u9bbXOr7ai+eYK6JGlE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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x137dcbba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['stochk_14'] <= 100]['stochk_14'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class StochKStrategy(pdt.TradingStrategy):\n",
    "    def generate_order(self, record, funds, balance) -> pdt.Order:\n",
    "        amount = 0.001\n",
    "        if record['stochk_14'] <= 15:\n",
    "            return Order(decision=pdt.Order.Decision.BUY, amount=funds/record['close']*0.05)\n",
    "        elif record['stochk_14'] >= 90:\n",
    "            return Order(decision=pdt.Order.Decision.SELL, amount=balance*0.04)\n",
    "        #print(\"reached this point: \", record['stochk_14'])\n",
    "        return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res = df.apply_strategy(strategy = StochKStrategy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>amounts</th>\n",
       "      <th>balance</th>\n",
       "      <th>buy_hold</th>\n",
       "      <th>decisions</th>\n",
       "      <th>funds</th>\n",
       "      <th>statuses</th>\n",
       "      <th>worth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</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>2018-08-26 10:10:00</th>\n",
       "      <td>0.003670</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>659.408875</td>\n",
       "      <td>SELL</td>\n",
       "      <td>187.553549</td>\n",
       "      <td>FILLED</td>\n",
       "      <td>774.904254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-26 11:35:00</th>\n",
       "      <td>0.003523</td>\n",
       "      <td>0.084549</td>\n",
       "      <td>663.450852</td>\n",
       "      <td>SELL</td>\n",
       "      <td>211.191588</td>\n",
       "      <td>FILLED</td>\n",
       "      <td>778.504536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-26 13:50:00</th>\n",
       "      <td>0.003382</td>\n",
       "      <td>0.081167</td>\n",
       "      <td>663.092665</td>\n",
       "      <td>SELL</td>\n",
       "      <td>233.871855</td>\n",
       "      <td>FILLED</td>\n",
       "      <td>778.198253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-26 13:55:00</th>\n",
       "      <td>0.003247</td>\n",
       "      <td>0.077920</td>\n",
       "      <td>665.237974</td>\n",
       "      <td>SELL</td>\n",
       "      <td>255.715353</td>\n",
       "      <td>FILLED</td>\n",
       "      <td>779.959316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-26 14:00:00</th>\n",
       "      <td>0.003117</td>\n",
       "      <td>0.074804</td>\n",
       "      <td>665.638116</td>\n",
       "      <td>SELL</td>\n",
       "      <td>276.697725</td>\n",
       "      <td>FILLED</td>\n",
       "      <td>780.274650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      amounts   balance    buy_hold decisions       funds  \\\n",
       "time                                                                        \n",
       "2018-08-26 10:10:00  0.003670  0.088072  659.408875      SELL  187.553549   \n",
       "2018-08-26 11:35:00  0.003523  0.084549  663.450852      SELL  211.191588   \n",
       "2018-08-26 13:50:00  0.003382  0.081167  663.092665      SELL  233.871855   \n",
       "2018-08-26 13:55:00  0.003247  0.077920  665.237974      SELL  255.715353   \n",
       "2018-08-26 14:00:00  0.003117  0.074804  665.638116      SELL  276.697725   \n",
       "\n",
       "                    statuses       worth  \n",
       "time                                      \n",
       "2018-08-26 10:10:00   FILLED  774.904254  \n",
       "2018-08-26 11:35:00   FILLED  778.504536  \n",
       "2018-08-26 13:50:00   FILLED  778.198253  \n",
       "2018-08-26 13:55:00   FILLED  779.959316  \n",
       "2018-08-26 14:00:00   FILLED  780.274650  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#res.describe()\n",
    "res[res['amounts'] > 0].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x150dfc160>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XtBs4RCqnyz+462rSE6fX1Eur3cVLiz1r7AwBpky52oiU8ktr/Y7FmNp+n0Df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gMxfWEvePo6pxfl4KRQk+uvlDGqq/O/P4X5358EAJucwuy9qbLRyemRQk/g/9YDTzHG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\nAAAgAgjuAAAAACAC/j/By/WV3PDEDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150e79668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res['buy_hold'].plot.line(figsize=(15, 10))\n",
    "res['worth'].plot.line()\n",
    "res['funds'].plot.line()\n",
    "res[res['decisions'] == 'BUY']['worth'].plot.line()\n",
    "res[res['decisions'] == 'SELL']['worth'].plot.line()"
   ]
  },
  {
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}