# 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 pymongo from ctaBase import * from ctaSetting 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 # 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.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.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""" if endDate: self.dataEndDate= datetime.strptime(endDate, '%Y%m%d') #---------------------------------------------------------------------- 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 = 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 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 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 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 = totalWinning/winningResult # 平均每笔盈利 averageLosing = totalLosing/losingResult # 平均每笔亏损 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'平均每笔盈利\t%s' %formatNumber(d['averageWinning'])) self.output(u'平均每笔亏损\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])) #---------------------------------------------------------------------- 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() ######################################################################## 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, step): """增加优化参数""" if end <= start: print u'参数起始点必须小于终止点' 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""" n = round(n, 2) # 保留两位小数 return format(n, ',') # 加上千分符 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 ctaDemo 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(DoubleEmaDemo, {}) # 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()