vnpy/vn.trader/ctaAlgo/ctaBacktesting.py

# encoding: UTF-8

'''
本文件中包含的是CTA模块的回测引擎，回测引擎的API和CTA引擎一致，
可以使用和实盘相同的代码进行回测。
'''
from __future__ import division

from datetime import datetime, timedelta
from collections import OrderedDict
from itertools import product
import multiprocessing
import pymongo

from ctaBase import *
from strategy import *

from vtConstant import *
from vtGateway import VtOrderData, VtTradeData
from vtFunction import loadMongoSetting


########################################################################
class BacktestingEngine(object):
    """
    CTA回测引擎
    函数接口和策略引擎保持一样，
    从而实现同一套代码从回测到实盘。
    """
    
    TICK_MODE = 'tick'
    BAR_MODE = 'bar'

    #----------------------------------------------------------------------
    def __init__(self):
        """Constructor"""
        # 本地停止单编号计数
        self.stopOrderCount = 0
        # stopOrderID = STOPORDERPREFIX + str(stopOrderCount)
        
        # 本地停止单字典
        # key为stopOrderID，value为stopOrder对象
        self.stopOrderDict = {}             # 停止单撤销后不会从本字典中删除
        self.workingStopOrderDict = {}      # 停止单撤销后会从本字典中删除
        
        # 引擎类型为回测
        self.engineType = ENGINETYPE_BACKTESTING
        
        # 回测相关
        self.strategy = None        # 回测策略
        self.mode = self.BAR_MODE   # 回测模式，默认为K线
        
        self.startDate = ''
        self.initDays = 0        
        self.endDate = ''

        self.slippage = 0           # 回测时假设的滑点
        self.rate = 0               # 回测时假设的佣金比例（适用于百分比佣金）
        self.size = 1               # 合约大小，默认为1        
        
        self.dbClient = None        # 数据库客户端
        self.dbCursor = None        # 数据库指针
        
        #self.historyData = []       # 历史数据的列表，回测用
        self.initData = []          # 初始化用的数据
        #self.backtestingData = []   # 回测用的数据
        
        self.dbName = ''            # 回测数据库名
        self.symbol = ''            # 回测集合名
        
        self.dataStartDate = None       # 回测数据开始日期，datetime对象
        self.dataEndDate = None         # 回测数据结束日期，datetime对象
        self.strategyStartDate = None   # 策略启动日期（即前面的数据用于初始化），datetime对象
        
        self.limitOrderDict = OrderedDict()         # 限价单字典
        self.workingLimitOrderDict = OrderedDict()  # 活动限价单字典，用于进行撮合用
        self.limitOrderCount = 0                    # 限价单编号
        
        self.tradeCount = 0             # 成交编号
        self.tradeDict = OrderedDict()  # 成交字典
        
        self.logList = []               # 日志记录
        
        # 当前最新数据，用于模拟成交用
        self.tick = None
        self.bar = None
        self.dt = None      # 最新的时间
        
    #----------------------------------------------------------------------
    def setStartDate(self, startDate='20100416', initDays=10):
        """设置回测的启动日期"""
        self.startDate = startDate
        self.initDays = initDays
        
        self.dataStartDate = datetime.strptime(startDate, '%Y%m%d')
        
        initTimeDelta = timedelta(initDays)
        self.strategyStartDate = self.dataStartDate + initTimeDelta
        
    #----------------------------------------------------------------------
    def setEndDate(self, endDate=''):
        """设置回测的结束日期"""
        self.endDate = endDate
        if endDate:
            self.dataEndDate= datetime.strptime(endDate, '%Y%m%d')
            # 若不修改时间则会导致不包含dataEndDate当天数据
            self.dataEndDate.replace(hour=23, minute=59)    
        
    #----------------------------------------------------------------------
    def setBacktestingMode(self, mode):
        """设置回测模式"""
        self.mode = mode
    
    #----------------------------------------------------------------------
    def setDatabase(self, dbName, symbol):
        """设置历史数据所用的数据库"""
        self.dbName = dbName
        self.symbol = symbol
    
    #----------------------------------------------------------------------
    def loadHistoryData(self):
        """载入历史数据"""
        host, port, logging = loadMongoSetting()
        
        self.dbClient = pymongo.MongoClient(host, port)
        collection = self.dbClient[self.dbName][self.symbol]          

        self.output(u'开始载入数据')
      
        # 首先根据回测模式，确认要使用的数据类
        if self.mode == self.BAR_MODE:
            dataClass = CtaBarData
            func = self.newBar
        else:
            dataClass = CtaTickData
            func = self.newTick

        # 载入初始化需要用的数据
        flt = {'datetime':{'$gte':self.dataStartDate,
                           '$lt':self.strategyStartDate}}        
        initCursor = collection.find(flt)
        
        # 将数据从查询指针中读取出，并生成列表
        self.initData = []              # 清空initData列表
        for d in initCursor:
            data = dataClass()
            data.__dict__ = d
            self.initData.append(data)      
        
        # 载入回测数据
        if not self.dataEndDate:
            flt = {'datetime':{'$gte':self.strategyStartDate}}   # 数据过滤条件
        else:
            flt = {'datetime':{'$gte':self.strategyStartDate,
                               '$lte':self.dataEndDate}}  
        self.dbCursor = collection.find(flt)
        
        self.output(u'载入完成，数据量：%s' %(initCursor.count() + self.dbCursor.count()))
        
    #----------------------------------------------------------------------
    def runBacktesting(self):
        """运行回测"""
        # 载入历史数据
        self.loadHistoryData()
        
        # 首先根据回测模式，确认要使用的数据类
        if self.mode == self.BAR_MODE:
            dataClass = CtaBarData
            func = self.newBar
        else:
            dataClass = CtaTickData
            func = self.newTick

        self.output(u'开始回测')
        
        self.strategy.inited = True
        self.strategy.onInit()
        self.output(u'策略初始化完成')
        
        self.strategy.trading = True
        self.strategy.onStart()
        self.output(u'策略启动完成')
        
        self.output(u'开始回放数据')

        for d in self.dbCursor:
            data = dataClass()
            data.__dict__ = d
            func(data)     
            
        self.output(u'数据回放结束')
        
    #----------------------------------------------------------------------
    def newBar(self, bar):
        """新的K线"""
        self.bar = bar
        self.dt = bar.datetime
        self.crossLimitOrder()      # 先撮合限价单
        self.crossStopOrder()       # 再撮合停止单
        self.strategy.onBar(bar)    # 推送K线到策略中
    
    #----------------------------------------------------------------------
    def newTick(self, tick):
        """新的Tick"""
        self.tick = tick
        self.dt = tick.datetime
        self.crossLimitOrder()
        self.crossStopOrder()
        self.strategy.onTick(tick)
        
    #----------------------------------------------------------------------
    def initStrategy(self, strategyClass, setting=None):
        """
        初始化策略
        setting是策略的参数设置，如果使用类中写好的默认设置则可以不传该参数
        """
        self.strategy = strategyClass(self, setting)
        self.strategy.name = self.strategy.className
        
    #----------------------------------------------------------------------
    def sendOrder(self, vtSymbol, orderType, price, volume, strategy):
        """发单"""
        self.limitOrderCount += 1
        orderID = str(self.limitOrderCount)
        
        order = VtOrderData()
        order.vtSymbol = vtSymbol
        order.price = price
        order.totalVolume = volume
        order.status = STATUS_NOTTRADED     # 刚提交尚未成交
        order.orderID = orderID
        order.vtOrderID = orderID
        order.orderTime = str(self.dt)
        
        # CTA委托类型映射
        if orderType == CTAORDER_BUY:
            order.direction = DIRECTION_LONG
            order.offset = OFFSET_OPEN
        elif orderType == CTAORDER_SELL:
            order.direction = DIRECTION_SHORT
            order.offset = OFFSET_CLOSE
        elif orderType == CTAORDER_SHORT:
            order.direction = DIRECTION_SHORT
            order.offset = OFFSET_OPEN
        elif orderType == CTAORDER_COVER:
            order.direction = DIRECTION_LONG
            order.offset = OFFSET_CLOSE     
        
        # 保存到限价单字典中
        self.workingLimitOrderDict[orderID] = order
        self.limitOrderDict[orderID] = order
        
        return orderID
    
    #----------------------------------------------------------------------
    def cancelOrder(self, vtOrderID):
        """撤单"""
        if vtOrderID in self.workingLimitOrderDict:
            order = self.workingLimitOrderDict[vtOrderID]
            order.status = STATUS_CANCELLED
            order.cancelTime = str(self.dt)
            del self.workingLimitOrderDict[vtOrderID]
        
    #----------------------------------------------------------------------
    def sendStopOrder(self, vtSymbol, orderType, price, volume, strategy):
        """发停止单（本地实现）"""
        self.stopOrderCount += 1
        stopOrderID = STOPORDERPREFIX + str(self.stopOrderCount)
        
        so = StopOrder()
        so.vtSymbol = vtSymbol
        so.price = price
        so.volume = volume
        so.strategy = strategy
        so.stopOrderID = stopOrderID
        so.status = STOPORDER_WAITING
        
        if orderType == CTAORDER_BUY:
            so.direction = DIRECTION_LONG
            so.offset = OFFSET_OPEN
        elif orderType == CTAORDER_SELL:
            so.direction = DIRECTION_SHORT
            so.offset = OFFSET_CLOSE
        elif orderType == CTAORDER_SHORT:
            so.direction = DIRECTION_SHORT
            so.offset = OFFSET_OPEN
        elif orderType == CTAORDER_COVER:
            so.direction = DIRECTION_LONG
            so.offset = OFFSET_CLOSE           
        
        # 保存stopOrder对象到字典中
        self.stopOrderDict[stopOrderID] = so
        self.workingStopOrderDict[stopOrderID] = so
        
        return stopOrderID
    
    #----------------------------------------------------------------------
    def cancelStopOrder(self, stopOrderID):
        """撤销停止单"""
        # 检查停止单是否存在
        if stopOrderID in self.workingStopOrderDict:
            so = self.workingStopOrderDict[stopOrderID]
            so.status = STOPORDER_CANCELLED
            del self.workingStopOrderDict[stopOrderID]
            
    #----------------------------------------------------------------------
    def crossLimitOrder(self):
        """基于最新数据撮合限价单"""
        # 先确定会撮合成交的价格
        if self.mode == self.BAR_MODE:
            buyCrossPrice = self.bar.low        # 若买入方向限价单价格高于该价格，则会成交
            sellCrossPrice = self.bar.high      # 若卖出方向限价单价格低于该价格，则会成交
            buyBestCrossPrice = self.bar.open   # 在当前时间点前发出的买入委托可能的最优成交价
            sellBestCrossPrice = self.bar.open  # 在当前时间点前发出的卖出委托可能的最优成交价
        else:
            buyCrossPrice = self.tick.askPrice1
            sellCrossPrice = self.tick.bidPrice1
            buyBestCrossPrice = self.tick.askPrice1
            sellBestCrossPrice = self.tick.bidPrice1
        
        # 遍历限价单字典中的所有限价单
        for orderID, order in self.workingLimitOrderDict.items():
            # 判断是否会成交
            buyCross = order.direction==DIRECTION_LONG and order.price>=buyCrossPrice
            sellCross = order.direction==DIRECTION_SHORT and order.price<=sellCrossPrice
            
            # 如果发生了成交
            if buyCross or sellCross:
                # 推送成交数据
                self.tradeCount += 1            # 成交编号自增1
                tradeID = str(self.tradeCount)
                trade = VtTradeData()
                trade.vtSymbol = order.vtSymbol
                trade.tradeID = tradeID
                trade.vtTradeID = tradeID
                trade.orderID = order.orderID
                trade.vtOrderID = order.orderID
                trade.direction = order.direction
                trade.offset = order.offset
                
                # 以买入为例：
                # 1. 假设当根K线的OHLC分别为：100, 125, 90, 110
                # 2. 假设在上一根K线结束(也是当前K线开始)的时刻，策略发出的委托为限价105
                # 3. 则在实际中的成交价会是100而不是105，因为委托发出时市场的最优价格是100
                if buyCross:
                    trade.price = min(order.price, buyBestCrossPrice)
                    self.strategy.pos += order.totalVolume
                else:
                    trade.price = max(order.price, sellBestCrossPrice)
                    self.strategy.pos -= order.totalVolume
                
                trade.volume = order.totalVolume
                trade.tradeTime = str(self.dt)
                trade.dt = self.dt
                self.strategy.onTrade(trade)
                
                self.tradeDict[tradeID] = trade
                
                # 推送委托数据
                order.tradedVolume = order.totalVolume
                order.status = STATUS_ALLTRADED
                self.strategy.onOrder(order)
                
                # 从字典中删除该限价单
                del self.workingLimitOrderDict[orderID]
                
    #----------------------------------------------------------------------
    def crossStopOrder(self):
        """基于最新数据撮合停止单"""
        # 先确定会撮合成交的价格，这里和限价单规则相反
        if self.mode == self.BAR_MODE:
            buyCrossPrice = self.bar.high    # 若买入方向停止单价格低于该价格，则会成交
            sellCrossPrice = self.bar.low    # 若卖出方向限价单价格高于该价格，则会成交
            bestCrossPrice = self.bar.open   # 最优成交价，买入停止单不能低于，卖出停止单不能高于
        else:
            buyCrossPrice = self.tick.lastPrice
            sellCrossPrice = self.tick.lastPrice
            bestCrossPrice = self.tick.lastPrice
        
        # 遍历停止单字典中的所有停止单
        for stopOrderID, so in self.workingStopOrderDict.items():
            # 判断是否会成交
            buyCross = so.direction==DIRECTION_LONG and so.price<=buyCrossPrice
            sellCross = so.direction==DIRECTION_SHORT and so.price>=sellCrossPrice
            
            # 如果发生了成交
            if buyCross or sellCross:
                # 推送成交数据
                self.tradeCount += 1            # 成交编号自增1
                tradeID = str(self.tradeCount)
                trade = VtTradeData()
                trade.vtSymbol = so.vtSymbol
                trade.tradeID = tradeID
                trade.vtTradeID = tradeID
                
                if buyCross:
                    self.strategy.pos += so.volume
                    trade.price = max(bestCrossPrice, so.price)
                else:
                    self.strategy.pos -= so.volume
                    trade.price = min(bestCrossPrice, so.price)                
                
                self.limitOrderCount += 1
                orderID = str(self.limitOrderCount)
                trade.orderID = orderID
                trade.vtOrderID = orderID
                
                trade.direction = so.direction
                trade.offset = so.offset
                trade.volume = so.volume
                trade.tradeTime = str(self.dt)
                trade.dt = self.dt
                self.strategy.onTrade(trade)
                
                self.tradeDict[tradeID] = trade
                
                # 推送委托数据
                so.status = STOPORDER_TRIGGERED
                
                order = VtOrderData()
                order.vtSymbol = so.vtSymbol
                order.symbol = so.vtSymbol
                order.orderID = orderID
                order.vtOrderID = orderID
                order.direction = so.direction
                order.offset = so.offset
                order.price = so.price
                order.totalVolume = so.volume
                order.tradedVolume = so.volume
                order.status = STATUS_ALLTRADED
                order.orderTime = trade.tradeTime
                self.strategy.onOrder(order)
                
                self.limitOrderDict[orderID] = order
                
                # 从字典中删除该限价单
                if stopOrderID in self.workingStopOrderDict:
                    del self.workingStopOrderDict[stopOrderID]        

    #----------------------------------------------------------------------
    def insertData(self, dbName, collectionName, data):
        """考虑到回测中不允许向数据库插入数据，防止实盘交易中的一些代码出错"""
        pass
    
    #----------------------------------------------------------------------
    def loadBar(self, dbName, collectionName, startDate):
        """直接返回初始化数据列表中的Bar"""
        return self.initData
    
    #----------------------------------------------------------------------
    def loadTick(self, dbName, collectionName, startDate):
        """直接返回初始化数据列表中的Tick"""
        return self.initData
    
    #----------------------------------------------------------------------
    def writeCtaLog(self, content):
        """记录日志"""
        log = str(self.dt) + ' ' + content 
        self.logList.append(log)
        
    #----------------------------------------------------------------------
    def output(self, content):
        """输出内容"""
        print str(datetime.now()) + "\t" + content 
    
    #----------------------------------------------------------------------
    def calculateBacktestingResult(self):
        """
        计算回测结果
        """
        self.output(u'计算回测结果')
        
        # 首先基于回测后的成交记录，计算每笔交易的盈亏
        resultList = []             # 交易结果列表
        
        longTrade = []              # 未平仓的多头交易
        shortTrade = []             # 未平仓的空头交易
        
        for trade in self.tradeDict.values():
            # 多头交易
            if trade.direction == DIRECTION_LONG:
                # 如果尚无空头交易
                if not shortTrade:
                    longTrade.append(trade)
                # 当前多头交易为平空
                else:
                    while True:
                        entryTrade = shortTrade[0]
                        exitTrade = trade
                        
                        # 清算开平仓交易
                        closedVolume = min(exitTrade.volume, entryTrade.volume)
                        result = TradingResult(entryTrade.price, entryTrade.dt, 
                                               exitTrade.price, exitTrade.dt,
                                               -closedVolume, self.rate, self.slippage, self.size)
                        resultList.append(result)
                        
                        # 计算未清算部分
                        entryTrade.volume -= closedVolume
                        exitTrade.volume -= closedVolume
                        
                        # 如果开仓交易已经全部清算，则从列表中移除
                        if not entryTrade.volume:
                            shortTrade.pop(0)
                        
                        # 如果平仓交易已经全部清算，则退出循环
                        if not exitTrade.volume:
                            break
                        
                        # 如果平仓交易未全部清算，
                        if exitTrade.volume:
                            # 且开仓交易已经全部清算完，则平仓交易剩余的部分
                            # 等于新的反向开仓交易，添加到队列中
                            if not shortTrade:
                                longTrade.append(exitTrade)
                                break
                            # 如果开仓交易还有剩余，则进入下一轮循环
                            else:
                                pass
                        
            # 空头交易        
            else:
                # 如果尚无多头交易
                if not longTrade:
                    shortTrade.append(trade)
                # 当前空头交易为平多
                else:                    
                    while True:
                        entryTrade = longTrade[0]
                        exitTrade = trade
                        
                        # 清算开平仓交易
                        closedVolume = min(exitTrade.volume, entryTrade.volume)
                        result = TradingResult(entryTrade.price, entryTrade.dt, 
                                               exitTrade.price, exitTrade.dt,
                                               closedVolume, self.rate, self.slippage, self.size)
                        resultList.append(result)
                        
                        # 计算未清算部分
                        entryTrade.volume -= closedVolume
                        exitTrade.volume -= closedVolume
                        
                        # 如果开仓交易已经全部清算，则从列表中移除
                        if not entryTrade.volume:
                            longTrade.pop(0)
                        
                        # 如果平仓交易已经全部清算，则退出循环
                        if not exitTrade.volume:
                            break
                        
                        # 如果平仓交易未全部清算，
                        if exitTrade.volume:
                            # 且开仓交易已经全部清算完，则平仓交易剩余的部分
                            # 等于新的反向开仓交易，添加到队列中
                            if not longTrade:
                                shortTrade.append(exitTrade)
                                break
                            # 如果开仓交易还有剩余，则进入下一轮循环
                            else:
                                pass                    
                    
        # 检查是否有交易
        if not resultList:
            self.output(u'无交易结果')
            return {}
        
        # 然后基于每笔交易的结果，我们可以计算具体的盈亏曲线和最大回撤等        
        capital = 0             # 资金
        maxCapital = 0          # 资金最高净值
        drawdown = 0            # 回撤
        
        totalResult = 0         # 总成交数量
        totalTurnover = 0       # 总成交金额（合约面值）
        totalCommission = 0     # 总手续费
        totalSlippage = 0       # 总滑点
        
        timeList = []           # 时间序列
        pnlList = []            # 每笔盈亏序列
        capitalList = []        # 盈亏汇总的时间序列
        drawdownList = []       # 回撤的时间序列
        
        winningResult = 0       # 盈利次数
        losingResult = 0        # 亏损次数		
        totalWinning = 0        # 总盈利金额		
        totalLosing = 0         # 总亏损金额        
        
        for result in resultList:
            capital += result.pnl
            maxCapital = max(capital, maxCapital)
            drawdown = capital - maxCapital
            
            pnlList.append(result.pnl)
            timeList.append(result.exitDt)      # 交易的时间戳使用平仓时间
            capitalList.append(capital)
            drawdownList.append(drawdown)
            
            totalResult += 1
            totalTurnover += result.turnover
            totalCommission += result.commission
            totalSlippage += result.slippage
            
            if result.pnl >= 0:
                winningResult += 1
                totalWinning += result.pnl
            else:
                losingResult += 1
                totalLosing += result.pnl
                
        # 计算盈亏相关数据
        winningRate = winningResult/totalResult*100         # 胜率
        
        averageWinning = 0                                  # 这里把数据都初始化为0
        averageLosing = 0
        profitLossRatio = 0
        
        if winningResult:
            averageWinning = totalWinning/winningResult     # 平均每笔盈利
        if losingResult:
            averageLosing = totalLosing/losingResult        # 平均每笔亏损
        if averageLosing:
            profitLossRatio = -averageWinning/averageLosing # 盈亏比

        # 返回回测结果
        d = {}
        d['capital'] = capital
        d['maxCapital'] = maxCapital
        d['drawdown'] = drawdown
        d['totalResult'] = totalResult
        d['totalTurnover'] = totalTurnover
        d['totalCommission'] = totalCommission
        d['totalSlippage'] = totalSlippage
        d['timeList'] = timeList
        d['pnlList'] = pnlList
        d['capitalList'] = capitalList
        d['drawdownList'] = drawdownList
        d['winningRate'] = winningRate
        d['averageWinning'] = averageWinning
        d['averageLosing'] = averageLosing
        d['profitLossRatio'] = profitLossRatio
        
        return d
        
    #----------------------------------------------------------------------
    def showBacktestingResult(self):
        """显示回测结果"""
        d = self.calculateBacktestingResult()
        
        # 输出
        self.output('-' * 30)
        self.output(u'第一笔交易：\t%s' % d['timeList'][0])
        self.output(u'最后一笔交易：\t%s' % d['timeList'][-1])
        
        self.output(u'总交易次数：\t%s' % formatNumber(d['totalResult']))        
        self.output(u'总盈亏：\t%s' % formatNumber(d['capital']))
        self.output(u'最大回撤: \t%s' % formatNumber(min(d['drawdownList'])))                
        
        self.output(u'平均每笔盈利：\t%s' %formatNumber(d['capital']/d['totalResult']))
        self.output(u'平均每笔滑点：\t%s' %formatNumber(d['totalSlippage']/d['totalResult']))
        self.output(u'平均每笔佣金：\t%s' %formatNumber(d['totalCommission']/d['totalResult']))
        
        self.output(u'胜率\t\t%s%%' %formatNumber(d['winningRate']))
        self.output(u'盈利交易平均值\t%s' %formatNumber(d['averageWinning']))
        self.output(u'亏损交易平均值\t%s' %formatNumber(d['averageLosing']))
        self.output(u'盈亏比：\t%s' %formatNumber(d['profitLossRatio']))
    
        # 绘图
        import matplotlib.pyplot as plt
        
        pCapital = plt.subplot(3, 1, 1)
        pCapital.set_ylabel("capital")
        pCapital.plot(d['capitalList'])
        
        pDD = plt.subplot(3, 1, 2)
        pDD.set_ylabel("DD")
        pDD.bar(range(len(d['drawdownList'])), d['drawdownList'])         
        
        pPnl = plt.subplot(3, 1, 3)
        pPnl.set_ylabel("pnl")
        pPnl.hist(d['pnlList'], bins=50)
        
        plt.show()
    
    #----------------------------------------------------------------------
    def putStrategyEvent(self, name):
        """发送策略更新事件，回测中忽略"""
        pass

    #----------------------------------------------------------------------
    def setSlippage(self, slippage):
        """设置滑点点数"""
        self.slippage = slippage
        
    #----------------------------------------------------------------------
    def setSize(self, size):
        """设置合约大小"""
        self.size = size
        
    #----------------------------------------------------------------------
    def setRate(self, rate):
        """设置佣金比例"""
        self.rate = rate

    #----------------------------------------------------------------------
    def runOptimization(self, strategyClass, optimizationSetting):
        """优化参数"""
        # 获取优化设置        
        settingList = optimizationSetting.generateSetting()
        targetName = optimizationSetting.optimizeTarget
        
        # 检查参数设置问题
        if not settingList or not targetName:
            self.output(u'优化设置有问题，请检查')
        
        # 遍历优化
        resultList = []
        for setting in settingList:
            self.clearBacktestingResult()
            self.output('-' * 30)
            self.output('setting: %s' %str(setting))
            self.initStrategy(strategyClass, setting)
            self.runBacktesting()
            d = self.calculateBacktestingResult()
            try:
                targetValue = d[targetName]
            except KeyError:
                targetValue = 0
            resultList.append(([str(setting)], targetValue))
        
        # 显示结果
        resultList.sort(reverse=True, key=lambda result:result[1])
        self.output('-' * 30)
        self.output(u'优化结果：')
        for result in resultList:
            self.output(u'%s: %s' %(result[0], result[1]))
        return result
            
    #----------------------------------------------------------------------
    def clearBacktestingResult(self):
        """清空之前回测的结果"""
        # 清空限价单相关
        self.limitOrderCount = 0
        self.limitOrderDict.clear()
        self.workingLimitOrderDict.clear()        
        
        # 清空停止单相关
        self.stopOrderCount = 0
        self.stopOrderDict.clear()
        self.workingStopOrderDict.clear()
        
        # 清空成交相关
        self.tradeCount = 0
        self.tradeDict.clear()
        
    #----------------------------------------------------------------------
    def runParallelOptimization(self, strategyClass, optimizationSetting):
        """并行优化参数"""
        # 获取优化设置        
        settingList = optimizationSetting.generateSetting()
        targetName = optimizationSetting.optimizeTarget
        
        # 检查参数设置问题
        if not settingList or not targetName:
            self.output(u'优化设置有问题，请检查')
        
        # 多进程优化，启动一个对应CPU核心数量的进程池
        pool = multiprocessing.Pool(multiprocessing.cpu_count())
        l = []

        for setting in settingList:
            l.append(pool.apply_async(optimize, (strategyClass, setting,
                                                 targetName, self.mode, 
                                                 self.startDate, self.initDays, self.endDate,
                                                 self.slippage, self.rate, self.size,
                                                 self.dbName, self.symbol)))
        pool.close()
        pool.join()
        
        # 显示结果
        resultList = [res.get() for res in l]
        resultList.sort(reverse=True, key=lambda result:result[1])
        self.output('-' * 30)
        self.output(u'优化结果：')
        for result in resultList:
            self.output(u'%s: %s' %(result[0], result[1]))    
        

########################################################################
class TradingResult(object):
    """每笔交易的结果"""

    #----------------------------------------------------------------------
    def __init__(self, entryPrice, entryDt, exitPrice, 
                 exitDt, volume, rate, slippage, size):
        """Constructor"""
        self.entryPrice = entryPrice    # 开仓价格
        self.exitPrice = exitPrice      # 平仓价格
        
        self.entryDt = entryDt          # 开仓时间datetime    
        self.exitDt = exitDt            # 平仓时间
        
        self.volume = volume    # 交易数量（+/-代表方向）
        
        self.turnover = (self.entryPrice+self.exitPrice)*size*abs(volume)   # 成交金额
        self.commission = self.turnover*rate                                # 手续费成本
        self.slippage = slippage*2*size*abs(volume)                         # 滑点成本
        self.pnl = ((self.exitPrice - self.entryPrice) * volume * size 
                    - self.commission - self.slippage)                      # 净盈亏


########################################################################
class OptimizationSetting(object):
    """优化设置"""

    #----------------------------------------------------------------------
    def __init__(self):
        """Constructor"""
        self.paramDict = OrderedDict()
        
        self.optimizeTarget = ''        # 优化目标字段
        
    #----------------------------------------------------------------------
    def addParameter(self, name, start, end=None, step=None):
        """增加优化参数"""
        if end is None and step is None:
            self.paramDict[name] = [start]
            return 
        
        if end < start:
            print u'参数起始点必须不大于终止点'
            return
        
        if step <= 0:
            print u'参数布进必须大于0'
            return
        
        l = []
        param = start
        
        while param <= end:
            l.append(param)
            param += step
        
        self.paramDict[name] = l
        
    #----------------------------------------------------------------------
    def generateSetting(self):
        """生成优化参数组合"""
        # 参数名的列表
        nameList = self.paramDict.keys()
        paramList = self.paramDict.values()
        
        # 使用迭代工具生产参数对组合
        productList = list(product(*paramList))
        
        # 把参数对组合打包到一个个字典组成的列表中
        settingList = []
        for p in productList:
            d = dict(zip(nameList, p))
            settingList.append(d)
    
        return settingList
    
    #----------------------------------------------------------------------
    def setOptimizeTarget(self, target):
        """设置优化目标字段"""
        self.optimizeTarget = target


#----------------------------------------------------------------------
def formatNumber(n):
    """格式化数字到字符串"""
    rn = round(n, 2)        # 保留两位小数
    return format(rn, ',')  # 加上千分符
    

#----------------------------------------------------------------------
def optimize(strategyClass, setting, targetName,
             mode, startDate, initDays, endDate,
             slippage, rate, size,
             dbName, symbol):
    """多进程优化时跑在每个进程中运行的函数"""
    engine = BacktestingEngine()
    engine.setBacktestingMode(mode)
    engine.setStartDate(startDate, initDays)
    engine.setEndDate(endDate)
    engine.setSlippage(slippage)
    engine.setRate(rate)
    engine.setSize(size)
    engine.setDatabase(dbName, symbol)
    
    engine.initStrategy(strategyClass, setting)
    engine.runBacktesting()
    d = engine.calculateBacktestingResult()
    try:
        targetValue = d[targetName]
    except KeyError:
        targetValue = 0            
    return (str(setting), targetValue)    


if __name__ == '__main__':
    # 以下内容是一段回测脚本的演示，用户可以根据自己的需求修改
    # 建议使用ipython notebook或者spyder来做回测
    # 同样可以在命令模式下进行回测（一行一行输入运行）
    from strategy.strategyEmaDemo import *
    
    # 创建回测引擎
    engine = BacktestingEngine()
    
    # 设置引擎的回测模式为K线
    engine.setBacktestingMode(engine.BAR_MODE)

    # 设置回测用的数据起始日期
    engine.setStartDate('20110101')
    
    # 载入历史数据到引擎中
    engine.setDatabase(MINUTE_DB_NAME, 'IF0000')
    
    # 设置产品相关参数
    engine.setSlippage(0.2)     # 股指1跳
    engine.setRate(0.3/10000)   # 万0.3
    engine.setSize(300)         # 股指合约大小    
    
    # 在引擎中创建策略对象
    engine.initStrategy(EmaDemoStrategy, {})
    
    # 开始跑回测
    engine.runBacktesting()
    
    # 显示回测结果
    # spyder或者ipython notebook中运行时，会弹出盈亏曲线图
    # 直接在cmd中回测则只会打印一些回测数值
    engine.showBacktestingResult()