{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from turtleEngine import BacktestingEngine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = BacktestingEngine()\n",
    "engine.setPeriod(datetime(2015, 1, 1), datetime(2018, 11, 9))\n",
    "engine.initPortfolio('setting.csv', 10000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15:23:05.294000:IF99数据加载完成，总数据量：940\n",
      "15:23:05.323000:I99数据加载完成，总数据量：940\n",
      "15:23:05.348000:CU99数据加载完成，总数据量：940\n",
      "15:23:05.375000:TA99数据加载完成，总数据量：940\n",
      "15:23:05.375000:全部数据加载完成\n"
     ]
    }
   ],
   "source": [
    "engine.loadData()\n",
    "engine.runBacktesting()\n",
    "engine.calculateResult()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for dt, l in engine.tradeDict.items():\n",
    "    print dt\n",
    "    for trade in l:\n",
    "        print trade.vtSymbol, trade.direction, trade.offset, trade.price, trade.volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for result in engine.resultList:\n",
    "    if result.totalPnl:\n",
    "        print result.date, result.totalPnl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x83b8a50>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x80a01d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l = [result.totalPnl for result in engine.resultList]\n",
    "equity = np.cumsum(l)\n",
    "plt.plot(equity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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