vnpy/examples/CtaBacktesting/.ipynb_checkpoints/backtesting_IF-checkpoint.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\n",
    "#from vnpy.trader.app.ctaStrategy.strategy.strategyMultiTimeframe import MultiTimeframeStrategy\n",
    "from vnpy.trader.app.ctaStrategy.strategy.strategyMultiSignal import MultiSignalStrategy"
   ]
  },
  {
   "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('20130101')               # 设置回测用的数据起始日期"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "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": false
   },
   "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\n",
    "#ngine.initStrategy(MultiTimeframeStrategy, d)    \n",
    "#engine.initStrategy(MultiSignalStrategy, {})    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-01-09 09:54:42.212000\t开始载入数据\n",
      "2018-01-09 09:54:42.331000\t载入完成，数据量：285480\n",
      "2018-01-09 09:54:42.331000\t开始回测\n",
      "2018-01-09 09:54:42.402000\t策略初始化完成\n",
      "2018-01-09 09:54:42.402000\t策略启动完成\n",
      "2018-01-09 09:54:42.402000\t开始回放数据\n",
      "2018-01-09 09:55:10.816000\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": [
      "2018-01-09 09:55:10.822000\t计算按日统计结果\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Github\\vnpy\\vnpy\\vnpy\\trader\\app\\ctaStrategy\\ctaBacktesting.py:971: RuntimeWarning: invalid value encountered in log\n",
      "  df['return'] = (np.log(df['balance']) - np.log(df['balance'].shift(1))).fillna(0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-01-09 09:55:11.113000\t------------------------------\n",
      "2018-01-09 09:55:11.113000\t首个交易日：\t2013-01-11\n",
      "2018-01-09 09:55:11.114000\t最后交易日：\t2017-07-14\n",
      "2018-01-09 09:55:11.114000\t总交易日：\t1095\n",
      "2018-01-09 09:55:11.114000\t盈利交易日\t399\n",
      "2018-01-09 09:55:11.114000\t亏损交易日：\t696\n",
      "2018-01-09 09:55:11.114000\t起始资金：\t1000000\n",
      "2018-01-09 09:55:11.114000\t结束资金：\t-1,583,699.42\n",
      "2018-01-09 09:55:11.114000\t总收益率：\t-258.37\n",
      "2018-01-09 09:55:11.114000\t年华收益率：\t-56.63\n",
      "2018-01-09 09:55:11.114000\t总盈亏：\t-2,583,699.42\n",
      "2018-01-09 09:55:11.114000\t最大回撤: \t-2,666,357.75\n",
      "2018-01-09 09:55:11.114000\t百分比最大回撤: \t-246.28\n",
      "2018-01-09 09:55:11.114000\t总手续费：\t1,772,319.42\n",
      "2018-01-09 09:55:11.114000\t总滑点：\t3,900,600.0\n",
      "2018-01-09 09:55:11.114000\t总成交金额：\t59,077,313,940.0\n",
      "2018-01-09 09:55:11.114000\t总成交笔数：\t50,395.0\n",
      "2018-01-09 09:55:11.114000\t日均盈亏：\t-2,359.54\n",
      "2018-01-09 09:55:11.114000\t日均手续费：\t1,618.56\n",
      "2018-01-09 09:55:11.114000\t日均滑点：\t3,562.19\n",
      "2018-01-09 09:55:11.114000\t日均成交金额：\t53,951,884.88\n",
      "2018-01-09 09:55:11.114000\t日均成交笔数：\t46.02\n",
      "2018-01-09 09:55:11.114000\t日均收益率：\t-0.97%\n",
      "2018-01-09 09:55:11.114000\t收益标准差：\t24.5%\n",
      "2018-01-09 09:55:11.114000\tSharpe Ratio：\t-0.61\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0xf4353b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显示逐日回测结果\n",
    "engine.showDailyResult()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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",
    "    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": 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('totalNetPnl')        # 设置优化排序的目标是策略净盈利\n",
    "setting.addParameter('atrLength', 12, 16, 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",
    "resultList = engine.runParallelOptimization(AtrRsiStrategy, setting)\n",
    "print u'耗时：%s' %(time.time()-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 显示优化的所有统计数据\n",
    "for result in resultList:\n",
    "    print '-' * 30\n",
    "    print u'参数：%s，目标：%s' %(result[0], result[1])\n",
    "    print u'统计数据：'\n",
    "    for k, v in result[2].items():\n",
    "        print u'%s：%s' %(k, v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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