vnpy/examples/CtaBacktesting/.ipynb_checkpoints/backtesting_IF-checkpoint.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
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    "%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('20100101')               # 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": 8,
   "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": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-12-15 23:01:54.728000\t开始载入数据\n",
      "2017-12-15 23:01:54.765000\t载入完成，数据量：0\n",
      "2017-12-15 23:01:54.765000\t开始回测\n",
      "2017-12-15 23:01:54.765000\t策略初始化完成\n",
      "2017-12-15 23:01:54.765000\t策略启动完成\n",
      "2017-12-15 23:01:54.765000\t开始回放数据\n",
      "2017-12-15 23:01:54.766000\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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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('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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