vnpy/examples/CtaBacktesting/backtesting_IF.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('20120101')               # 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": [
      "2017-09-23 19:05:15.863000\t开始载入数据\n",
      "2017-09-23 19:05:16.009000\t载入完成，数据量：351090\n",
      "2017-09-23 19:05:16.009000\t开始回测\n",
      "2017-09-23 19:05:16.027000\t策略初始化完成\n",
      "2017-09-23 19:05:16.027000\t策略启动完成\n",
      "2017-09-23 19:05:16.027000\t开始回放数据\n",
      "2017-09-23 19:05:39.203000\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": [
      "2017-09-23 19:05:39.209000\t计算按日统计结果\n",
      "2017-09-23 19:05:39.282000\t------------------------------\n",
      "2017-09-23 19:05:39.282000\t首个交易日：\t2012-01-11\n",
      "2017-09-23 19:05:39.282000\t最后交易日：\t2017-07-14\n",
      "2017-09-23 19:05:39.282000\t总交易日：\t1338\n",
      "2017-09-23 19:05:39.282000\t盈利交易日\t651\n",
      "2017-09-23 19:05:39.282000\t亏损交易日：\t686\n",
      "2017-09-23 19:05:39.282000\t起始资金：\t1000000\n",
      "2017-09-23 19:05:39.282000\t结束资金：\t1,601,119.34\n",
      "2017-09-23 19:05:39.282000\t总收益率：\t60.11\n",
      "2017-09-23 19:05:39.282000\t总盈亏：\t601,119.34\n",
      "2017-09-23 19:05:39.282000\t最大回撤: \t-185,949.45\n",
      "2017-09-23 19:05:39.282000\t总手续费：\t220,760.66\n",
      "2017-09-23 19:05:39.282000\t总滑点：\t457,980.0\n",
      "2017-09-23 19:05:39.282000\t总成交金额：\t7,358,688,660.0\n",
      "2017-09-23 19:05:39.282000\t总成交笔数：\t7,632.0\n",
      "2017-09-23 19:05:39.282000\t日均盈亏：\t449.27\n",
      "2017-09-23 19:05:39.282000\t日均手续费：\t164.99\n",
      "2017-09-23 19:05:39.282000\t日均滑点：\t342.29\n",
      "2017-09-23 19:05:39.282000\t日均成交金额：\t5,499,767.31\n",
      "2017-09-23 19:05:39.282000\t日均成交笔数：\t5.7\n",
      "2017-09-23 19:05:39.282000\t日均收益率：\t0.04%\n",
      "2017-09-23 19:05:39.282000\t收益标准差：\t1.07%\n",
      "2017-09-23 19:05:39.282000\tSharpe Ratio：\t0.52\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoEAAAOlCAYAAAASGT0sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKiAmJoaAgABrN0MBCQk
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcf3a810>"
      ]
     },
     "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": [
      "2017-09-23 19:09:02.702000\t计算回测结果\n",
      "2017-09-23 19:09:02.824000\t------------------------------\n",
      "2017-09-23 19:09:02.824000\t第一笔交易：\t2012-01-11 10:18:00\n",
      "2017-09-23 19:09:02.824000\t最后一笔交易：\t2017-07-14 15:00:00\n",
      "2017-09-23 19:09:02.824000\t总交易次数：\t3,817.0\n",
      "2017-09-23 19:09:02.824000\t总盈亏：\t601,026.05\n",
      "2017-09-23 19:09:02.824000\t最大回撤: \t-215,542.46\n",
      "2017-09-23 19:09:02.824000\t平均每笔盈利：\t157.46\n",
      "2017-09-23 19:09:02.824000\t平均每笔滑点：\t120.0\n",
      "2017-09-23 19:09:02.824000\t平均每笔佣金：\t57.84\n",
      "2017-09-23 19:09:02.824000\t胜率\t\t37.1%\n",
      "2017-09-23 19:09:02.824000\t盈利交易平均值\t8,540.18\n",
      "2017-09-23 19:09:02.824000\t亏损交易平均值\t-4,786.29\n",
      "2017-09-23 19:09:02.824000\t盈亏比：\t1.78\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11504410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显示逐笔回测结果\n",
    "engine.showBacktestingResult()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TradeID: 1, Time: 2012-01-11 09:18:00, Direction: 多, Price: 2464.4, Volume: 1\n",
      "TradeID: 2, Time: 2012-01-11 10:18:00, Direction: 空, Price: 2448.8, Volume: 1\n",
      "TradeID: 3, Time: 2012-01-11 10:19:00, Direction: 空, Price: 2448.4, Volume: 1\n",
      "TradeID: 4, Time: 2012-01-11 14:21:00, Direction: 多, Price: 2456.2, Volume: 1\n",
      "TradeID: 5, Time: 2012-01-11 14:22:00, Direction: 多, Price: 2455.6, Volume: 1\n",
      "TradeID: 6, Time: 2012-01-11 14:57:00, Direction: 空, Price: 2444.0, Volume: 1\n",
      "TradeID: 7, Time: 2012-01-11 14:58:00, Direction: 空, Price: 2445.8, Volume: 1\n",
      "TradeID: 8, Time: 2012-01-12 09:51:00, Direction: 多, Price: 2452.8, Volume: 1\n",
      "TradeID: 9, Time: 2012-01-12 09:52:00, Direction: 多, Price: 2452.0, Volume: 1\n",
      "TradeID: 10, Time: 2012-01-12 10:31:00, Direction: 空, Price: 2455.2, Volume: 1\n"
     ]
    }
   ],
   "source": [
    "# 显示前10条成交记录\n",
    "for i in range(10):\n",
    "    d = engine.tradeDict[str(i+1)].__dict__\n",
    "    print 'TradeID: %s, Time: %s, Direction: %s, Price: %s, Volume: %s' %(d['tradeID'], d['dt'], d['direction'], d['price'], d['volume'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-09-23 19:22:56.071000\t------------------------------\n",
      "2017-09-23 19:22:56.071000\t优化结果：\n",
      "2017-09-23 19:22:56.071000\t{'rsiLength': 5, 'atrMa': 20, 'atrLength': 20}: 812328.0084\n",
      "2017-09-23 19:22:56.071000\t{'rsiLength': 5, 'atrMa': 25, 'atrLength': 20}: 812328.0084\n",
      "2017-09-23 19:22:56.071000\t{'rsiLength': 5, 'atrMa': 30, 'atrLength': 20}: 812328.0084\n",
      "2017-09-23 19:22:56.071000\t{'rsiLength': 5, 'atrMa': 20, 'atrLength': 18}: 750899.3112\n",
      "2017-09-23 19:22:56.071000\t{'rsiLength': 5, 'atrMa': 25, 'atrLength': 18}: 750899.3112\n",
      "2017-09-23 19:22:56.071000\t{'rsiLength': 5, 'atrMa': 30, 'atrLength': 18}: 750899.3112\n",
      "2017-09-23 19:22:56.071000\t{'rsiLength': 5, 'atrMa': 20, 'atrLength': 16}: 640937.2914\n",
      "2017-09-23 19:22:56.071000\t{'rsiLength': 5, 'atrMa': 25, 'atrLength': 16}: 640937.2914\n",
      "2017-09-23 19:22:56.071000\t{'rsiLength': 5, 'atrMa': 30, 'atrLength': 16}: 640937.2914\n",
      "2017-09-23 19:22:56.071000\t{'rsiLength': 5, 'atrMa': 20, 'atrLength': 14}: 640663.1826\n",
      "2017-09-23 19:22:56.071000\t{'rsiLength': 5, 'atrMa': 25, 'atrLength': 14}: 640663.1826\n",
      "2017-09-23 19:22:56.071000\t{'rsiLength': 5, 'atrMa': 30, 'atrLength': 14}: 640663.1826\n",
      "2017-09-23 19:22:56.071000\t{'rsiLength': 5, 'atrMa': 20, 'atrLength': 12}: 624225.045\n",
      "2017-09-23 19:22:56.071000\t{'rsiLength': 5, 'atrMa': 25, 'atrLength': 12}: 624225.045\n",
      "2017-09-23 19:22:56.071000\t{'rsiLength': 5, 'atrMa': 30, 'atrLength': 12}: 624225.045\n",
      "耗时：175.118999958\n"
     ]
    }
   ],
   "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",
    "start = time.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": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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