vnpy/examples/CtaBacktesting/backtesting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from vnpy.trader.app.ctaStrategy.ctaBacktesting import BacktestingEngine, OptimizationSetting, MINUTE_DB_NAME\n",
    "from vnpy.trader.app.ctaStrategy.strategy.strategyAtrRsi import AtrRsiStrategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 创建回测引擎对象\n",
    "engine = BacktestingEngine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 设置回测使用的数据\n",
    "engine.setBacktestingMode(engine.BAR_MODE)    # 设置引擎的回测模式为K线\n",
    "engine.setDatabase(MINUTE_DB_NAME, 'IF0000')  # 设置使用的历史数据库\n",
    "engine.setStartDate('20160101')               # 设置回测用的数据起始日期"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 配置回测引擎参数\n",
    "engine.setSlippage(0.2)     # 设置滑点为股指1跳\n",
    "engine.setRate(0.3/10000)   # 设置手续费万0.3\n",
    "engine.setSize(300)         # 设置股指合约大小 \n",
    "engine.setPriceTick(0.2)    # 设置股指最小价格变动   \n",
    "engine.setCapital(1000000)  # 设置回测本金"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 在引擎中创建策略对象\n",
    "d = {'atrLength': 11}                     # 策略参数配置\n",
    "engine.initStrategy(AtrRsiStrategy, d)    # 创建策略对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-07-14 20:10:59.807000\t开始载入数据\n",
      "2017-07-14 20:10:59.859000\t载入完成，数据量：69994\n",
      "2017-07-14 20:10:59.859000\t开始回测\n",
      "2017-07-14 20:10:59.872000\t策略初始化完成\n",
      "2017-07-14 20:10:59.872000\t策略启动完成\n",
      "2017-07-14 20:10:59.872000\t开始回放数据\n",
      "2017-07-14 20:11:04.727000\t数据回放结束\n"
     ]
    }
   ],
   "source": [
    "# 运行回测\n",
    "engine.runBacktesting()          # 运行回测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-07-14 20:11:05.963000\t计算按日统计结果\n",
      "2017-07-14 20:11:05.981000\t------------------------------\n",
      "2017-07-14 20:11:05.981000\t首个交易日：\t2016-01-11\n",
      "2017-07-14 20:11:05.982000\t最后交易日：\t2017-03-20\n",
      "2017-07-14 20:11:05.982000\t总交易日：\t289\n",
      "2017-07-14 20:11:05.982000\t盈利交易日\t146\n",
      "2017-07-14 20:11:05.982000\t亏损交易日：\t143\n",
      "2017-07-14 20:11:05.982000\t起始资金：\t1000000\n",
      "2017-07-14 20:11:05.982000\t结束资金：\t973,109.83\n",
      "2017-07-14 20:11:05.982000\t总收益率：\t-2.69\n",
      "2017-07-14 20:11:05.982000\t总盈亏：\t-26,890.17\n",
      "2017-07-14 20:11:05.982000\t最大回撤: \t-162,300.57\n",
      "2017-07-14 20:11:05.982000\t总手续费：\t28,270.17\n",
      "2017-07-14 20:11:05.982000\t总滑点：\t60,300.0\n",
      "2017-07-14 20:11:05.982000\t总成交金额：\t942,339,000.0\n",
      "2017-07-14 20:11:05.982000\t总成交笔数：\t1,004.0\n",
      "2017-07-14 20:11:05.982000\t日均盈亏：\t-93.05\n",
      "2017-07-14 20:11:05.982000\t日均手续费：\t97.82\n",
      "2017-07-14 20:11:05.982000\t日均滑点：\t208.65\n",
      "2017-07-14 20:11:05.982000\t日均成交金额：\t3,260,688.58\n",
      "2017-07-14 20:11:05.982000\t日均成交笔数：\t3.47\n",
      "2017-07-14 20:11:05.982000\t日均收益率：\t-0.02%\n",
      "2017-07-14 20:11:05.982000\t收益标准差：\t1.11%\n",
      "2017-07-14 20:11:05.982000\tSharpe Ratio：\t-0.27\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAOlCAYAAAD921YSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJ0yYwKxZs/jXv/6Fp6c
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119e6350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显示逐日回测结果\n",
    "df = engine.showDailyResult()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-07-14 20:11:13.701000\t计算回测结果\n",
      "2017-07-14 20:11:13.722000\t------------------------------\n",
      "2017-07-14 20:11:13.722000\t第一笔交易：\t2016-01-11 09:33:00\n",
      "2017-07-14 20:11:13.722000\t最后一笔交易：\t2017-03-20 15:00:00\n",
      "2017-07-14 20:11:13.722000\t总交易次数：\t503.0\n",
      "2017-07-14 20:11:13.722000\t总盈亏：\t-26,980.93\n",
      "2017-07-14 20:11:13.722000\t最大回撤: \t-160,499.28\n",
      "2017-07-14 20:11:13.722000\t平均每笔盈利：\t-53.64\n",
      "2017-07-14 20:11:13.722000\t平均每笔滑点：\t120.0\n",
      "2017-07-14 20:11:13.722000\t平均每笔佣金：\t56.26\n",
      "2017-07-14 20:11:13.722000\t胜率\t\t38.17%\n",
      "2017-07-14 20:11:13.722000\t盈利交易平均值\t7,882.85\n",
      "2017-07-14 20:11:13.722000\t亏损交易平均值\t-4,953.34\n",
      "2017-07-14 20:11:13.722000\t盈亏比：\t1.59\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x123c1670>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显示逐笔回测结果\n",
    "engine.showBacktestingResult()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 显示前10条成交记录\n",
    "for i in range(10):\n",
    "    print engine.tradeDict[str(i+1)].__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 优化配置\n",
    "setting = OptimizationSetting()                 # 新建一个优化任务设置对象\n",
    "setting.setOptimizeTarget('capital')            # 设置优化排序的目标是策略净盈利\n",
    "setting.addParameter('atrLength', 12, 20, 2)    # 增加第一个优化参数atrLength，起始12，结束20，步进2\n",
    "setting.addParameter('atrMa', 20, 30, 5)        # 增加第二个优化参数atrMa，起始20，结束30，步进5\n",
    "setting.addParameter('rsiLength', 5)            # 增加一个固定数值的参数\n",
    "\n",
    "# 执行多进程优化\n",
    "import time\n",
    "engine.runParallelOptimization(AtrRsiStrategy, setting)\n",
    "print u'耗时：%s' %(time.time()-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}