759 lines
29 KiB
Python
759 lines
29 KiB
Python
# encoding: UTF-8
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'''
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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，
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可以使用和实盘相同的代码进行回测。
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'''
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from datetime import datetime, timedelta
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from collections import OrderedDict
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from itertools import product
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import pymongo
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from ctaBase import *
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from ctaSetting import *
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from vtConstant import *
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from vtGateway import VtOrderData, VtTradeData
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from vtFunction import loadMongoSetting
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########################################################################
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class BacktestingEngine(object):
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"""
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CTA回测引擎
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函数接口和策略引擎保持一样，
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从而实现同一套代码从回测到实盘。
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"""
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TICK_MODE = 'tick'
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BAR_MODE = 'bar'
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#----------------------------------------------------------------------
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def __init__(self):
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"""Constructor"""
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# 本地停止单编号计数
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self.stopOrderCount = 0
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# stopOrderID = STOPORDERPREFIX + str(stopOrderCount)
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# 本地停止单字典
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# key为stopOrderID，value为stopOrder对象
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self.stopOrderDict = {} # 停止单撤销后不会从本字典中删除
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self.workingStopOrderDict = {} # 停止单撤销后会从本字典中删除
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# 回测相关
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self.strategy = None # 回测策略
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self.mode = self.BAR_MODE # 回测模式，默认为K线
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self.slippage = 0 # 回测时假设的滑点
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self.rate = 0 # 回测时假设的佣金比例（适用于百分比佣金）
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self.size = 1 # 合约大小，默认为1
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self.dbClient = None # 数据库客户端
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self.dbCursor = None # 数据库指针
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#self.historyData = [] # 历史数据的列表，回测用
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self.initData = [] # 初始化用的数据
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#self.backtestingData = [] # 回测用的数据
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self.dbName = '' # 回测数据库名
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self.symbol = '' # 回测集合名
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self.dataStartDate = None # 回测数据开始日期，datetime对象
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self.dataEndDate = None # 回测数据结束日期，datetime对象
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self.strategyStartDate = None # 策略启动日期（即前面的数据用于初始化），datetime对象
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self.limitOrderDict = OrderedDict() # 限价单字典
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self.workingLimitOrderDict = OrderedDict() # 活动限价单字典，用于进行撮合用
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self.limitOrderCount = 0 # 限价单编号
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self.tradeCount = 0 # 成交编号
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self.tradeDict = OrderedDict() # 成交字典
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self.logList = [] # 日志记录
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# 当前最新数据，用于模拟成交用
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self.tick = None
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self.bar = None
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self.dt = None # 最新的时间
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#----------------------------------------------------------------------
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def setStartDate(self, startDate='20100416', initDays=10):
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"""设置回测的启动日期"""
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self.dataStartDate = datetime.strptime(startDate, '%Y%m%d')
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initTimeDelta = timedelta(initDays)
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self.strategyStartDate = self.dataStartDate + initTimeDelta
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#----------------------------------------------------------------------
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def setEndDate(self, endDate=''):
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"""设置回测的结束日期"""
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if endDate:
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self.dataEndDate= datetime.strptime(endDate, '%Y%m%d')
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#----------------------------------------------------------------------
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def setBacktestingMode(self, mode):
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"""设置回测模式"""
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self.mode = mode
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#----------------------------------------------------------------------
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def setDatabase(self, dbName, symbol):
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"""设置历史数据所用的数据库"""
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self.dbName = dbName
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self.symbol = symbol
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#----------------------------------------------------------------------
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def loadHistoryData(self):
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"""载入历史数据"""
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host, port = loadMongoSetting()
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self.dbClient = pymongo.MongoClient(host, port)
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collection = self.dbClient[self.dbName][self.symbol]
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self.output(u'开始载入数据')
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# 首先根据回测模式，确认要使用的数据类
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if self.mode == self.BAR_MODE:
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dataClass = CtaBarData
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func = self.newBar
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else:
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dataClass = CtaTickData
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func = self.newTick
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# 载入初始化需要用的数据
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flt = {'datetime':{'$gte':self.dataStartDate,
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'$lt':self.strategyStartDate}}
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initCursor = collection.find(flt)
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# 将数据从查询指针中读取出，并生成列表
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for d in initCursor:
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data = dataClass()
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data.__dict__ = d
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self.initData.append(data)
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# 载入回测数据
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if not self.dataEndDate:
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flt = {'datetime':{'$gte':self.strategyStartDate}} # 数据过滤条件
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else:
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flt = {'datetime':{'$gte':self.strategyStartDate,
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'$lte':self.dataEndDate}}
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self.dbCursor = collection.find(flt)
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self.output(u'载入完成，数据量：%s' %(initCursor.count() + self.dbCursor.count()))
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#----------------------------------------------------------------------
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def runBacktesting(self):
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"""运行回测"""
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# 载入历史数据
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self.loadHistoryData()
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# 首先根据回测模式，确认要使用的数据类
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if self.mode == self.BAR_MODE:
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dataClass = CtaBarData
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func = self.newBar
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else:
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dataClass = CtaTickData
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func = self.newTick
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self.output(u'开始回测')
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self.strategy.inited = True
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self.strategy.onInit()
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self.output(u'策略初始化完成')
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self.strategy.trading = True
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self.strategy.onStart()
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self.output(u'策略启动完成')
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self.output(u'开始回放数据')
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for d in self.dbCursor:
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data = dataClass()
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data.__dict__ = d
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func(data)
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self.output(u'数据回放结束')
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#----------------------------------------------------------------------
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def newBar(self, bar):
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"""新的K线"""
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self.bar = bar
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self.dt = bar.datetime
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self.crossLimitOrder() # 先撮合限价单
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self.crossStopOrder() # 再撮合停止单
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self.strategy.onBar(bar) # 推送K线到策略中
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#----------------------------------------------------------------------
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def newTick(self, tick):
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"""新的Tick"""
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self.tick = tick
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self.dt = tick.datetime
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self.crossLimitOrder()
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self.crossStopOrder()
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self.strategy.onTick(tick)
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#----------------------------------------------------------------------
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def initStrategy(self, strategyClass, setting=None):
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"""
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初始化策略
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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
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"""
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self.strategy = strategyClass(self, setting)
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self.strategy.name = self.strategy.className
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#----------------------------------------------------------------------
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def sendOrder(self, vtSymbol, orderType, price, volume, strategy):
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"""发单"""
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self.limitOrderCount += 1
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orderID = str(self.limitOrderCount)
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order = VtOrderData()
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order.vtSymbol = vtSymbol
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order.price = price
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order.totalVolume = volume
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order.status = STATUS_NOTTRADED # 刚提交尚未成交
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order.orderID = orderID
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order.vtOrderID = orderID
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order.orderTime = str(self.dt)
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# CTA委托类型映射
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if orderType == CTAORDER_BUY:
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order.direction = DIRECTION_LONG
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order.offset = OFFSET_OPEN
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elif orderType == CTAORDER_SELL:
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order.direction = DIRECTION_SHORT
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order.offset = OFFSET_CLOSE
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elif orderType == CTAORDER_SHORT:
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order.direction = DIRECTION_SHORT
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order.offset = OFFSET_OPEN
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elif orderType == CTAORDER_COVER:
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order.direction = DIRECTION_LONG
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order.offset = OFFSET_CLOSE
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# 保存到限价单字典中
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self.workingLimitOrderDict[orderID] = order
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self.limitOrderDict[orderID] = order
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return orderID
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#----------------------------------------------------------------------
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def cancelOrder(self, vtOrderID):
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"""撤单"""
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if vtOrderID in self.workingLimitOrderDict:
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order = self.workingLimitOrderDict[vtOrderID]
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order.status = STATUS_CANCELLED
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order.cancelTime = str(self.dt)
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del self.workingLimitOrderDict[vtOrderID]
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#----------------------------------------------------------------------
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def sendStopOrder(self, vtSymbol, orderType, price, volume, strategy):
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"""发停止单（本地实现）"""
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self.stopOrderCount += 1
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stopOrderID = STOPORDERPREFIX + str(self.stopOrderCount)
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so = StopOrder()
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so.vtSymbol = vtSymbol
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so.price = price
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so.volume = volume
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so.strategy = strategy
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so.stopOrderID = stopOrderID
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so.status = STOPORDER_WAITING
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if orderType == CTAORDER_BUY:
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so.direction = DIRECTION_LONG
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so.offset = OFFSET_OPEN
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elif orderType == CTAORDER_SELL:
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so.direction = DIRECTION_SHORT
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so.offset = OFFSET_CLOSE
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elif orderType == CTAORDER_SHORT:
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so.direction = DIRECTION_SHORT
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so.offset = OFFSET_OPEN
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elif orderType == CTAORDER_COVER:
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so.direction = DIRECTION_LONG
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so.offset = OFFSET_CLOSE
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# 保存stopOrder对象到字典中
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self.stopOrderDict[stopOrderID] = so
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self.workingStopOrderDict[stopOrderID] = so
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return stopOrderID
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#----------------------------------------------------------------------
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def cancelStopOrder(self, stopOrderID):
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"""撤销停止单"""
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# 检查停止单是否存在
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if stopOrderID in self.workingStopOrderDict:
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so = self.workingStopOrderDict[stopOrderID]
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so.status = STOPORDER_CANCELLED
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del self.workingStopOrderDict[stopOrderID]
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#----------------------------------------------------------------------
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def crossLimitOrder(self):
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"""基于最新数据撮合限价单"""
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# 先确定会撮合成交的价格
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if self.mode == self.BAR_MODE:
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buyCrossPrice = self.bar.low # 若买入方向限价单价格高于该价格，则会成交
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sellCrossPrice = self.bar.high # 若卖出方向限价单价格低于该价格，则会成交
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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
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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
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else:
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buyCrossPrice = self.tick.askPrice1
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sellCrossPrice = self.tick.bidPrice1
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buyBestCrossPrice = self.tick.askPrice1
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sellBestCrossPrice = self.tick.bidPrice1
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# 遍历限价单字典中的所有限价单
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for orderID, order in self.workingLimitOrderDict.items():
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# 判断是否会成交
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buyCross = order.direction==DIRECTION_LONG and order.price>=buyCrossPrice
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sellCross = order.direction==DIRECTION_SHORT and order.price<=sellCrossPrice
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# 如果发生了成交
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if buyCross or sellCross:
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# 推送成交数据
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self.tradeCount += 1 # 成交编号自增1
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tradeID = str(self.tradeCount)
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trade = VtTradeData()
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trade.vtSymbol = order.vtSymbol
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trade.tradeID = tradeID
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trade.vtTradeID = tradeID
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trade.orderID = order.orderID
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trade.vtOrderID = order.orderID
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trade.direction = order.direction
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trade.offset = order.offset
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# 以买入为例：
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# 1. 假设当根K线的OHLC分别为：100, 125, 90, 110
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# 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
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# 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
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if buyCross:
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trade.price = min(order.price, buyBestCrossPrice)
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self.strategy.pos += order.totalVolume
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else:
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trade.price = max(order.price, sellBestCrossPrice)
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self.strategy.pos -= order.totalVolume
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trade.volume = order.totalVolume
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trade.tradeTime = str(self.dt)
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trade.dt = self.dt
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self.strategy.onTrade(trade)
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self.tradeDict[tradeID] = trade
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# 推送委托数据
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order.tradedVolume = order.totalVolume
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order.status = STATUS_ALLTRADED
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self.strategy.onOrder(order)
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# 从字典中删除该限价单
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del self.workingLimitOrderDict[orderID]
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#----------------------------------------------------------------------
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def crossStopOrder(self):
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"""基于最新数据撮合停止单"""
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# 先确定会撮合成交的价格，这里和限价单规则相反
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if self.mode == self.BAR_MODE:
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buyCrossPrice = self.bar.high # 若买入方向停止单价格低于该价格，则会成交
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sellCrossPrice = self.bar.low # 若卖出方向限价单价格高于该价格，则会成交
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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
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else:
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buyCrossPrice = self.tick.lastPrice
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sellCrossPrice = self.tick.lastPrice
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bestCrossPrice = self.tick.lastPrice
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# 遍历停止单字典中的所有停止单
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for stopOrderID, so in self.workingStopOrderDict.items():
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# 判断是否会成交
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buyCross = so.direction==DIRECTION_LONG and so.price<=buyCrossPrice
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sellCross = so.direction==DIRECTION_SHORT and so.price>=sellCrossPrice
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# 如果发生了成交
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if buyCross or sellCross:
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# 推送成交数据
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self.tradeCount += 1 # 成交编号自增1
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tradeID = str(self.tradeCount)
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trade = VtTradeData()
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trade.vtSymbol = so.vtSymbol
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trade.tradeID = tradeID
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trade.vtTradeID = tradeID
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if buyCross:
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self.strategy.pos += so.volume
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trade.price = max(bestCrossPrice, so.price)
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else:
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self.strategy.pos -= so.volume
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trade.price = min(bestCrossPrice, so.price)
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self.limitOrderCount += 1
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orderID = str(self.limitOrderCount)
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trade.orderID = orderID
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trade.vtOrderID = orderID
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trade.direction = so.direction
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trade.offset = so.offset
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trade.volume = so.volume
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trade.tradeTime = str(self.dt)
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trade.dt = self.dt
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self.strategy.onTrade(trade)
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self.tradeDict[tradeID] = trade
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# 推送委托数据
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so.status = STOPORDER_TRIGGERED
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order = VtOrderData()
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order.vtSymbol = so.vtSymbol
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order.symbol = so.vtSymbol
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order.orderID = orderID
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order.vtOrderID = orderID
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order.direction = so.direction
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order.offset = so.offset
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order.price = so.price
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order.totalVolume = so.volume
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order.tradedVolume = so.volume
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order.status = STATUS_ALLTRADED
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order.orderTime = trade.tradeTime
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self.strategy.onOrder(order)
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self.limitOrderDict[orderID] = order
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# 从字典中删除该限价单
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del self.workingStopOrderDict[stopOrderID]
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#----------------------------------------------------------------------
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def insertData(self, dbName, collectionName, data):
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"""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"""
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pass
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#----------------------------------------------------------------------
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def loadBar(self, dbName, collectionName, startDate):
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"""直接返回初始化数据列表中的Bar"""
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return self.initData
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#----------------------------------------------------------------------
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def loadTick(self, dbName, collectionName, startDate):
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"""直接返回初始化数据列表中的Tick"""
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return self.initData
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#----------------------------------------------------------------------
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def writeCtaLog(self, content):
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"""记录日志"""
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log = str(self.dt) + ' ' + content
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self.logList.append(log)
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#----------------------------------------------------------------------
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def output(self, content):
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"""输出内容"""
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print str(datetime.now()) + "\t" + content
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#----------------------------------------------------------------------
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def calculateBacktestingResult(self):
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"""
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计算回测结果
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"""
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self.output(u'计算回测结果')
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# 首先基于回测后的成交记录，计算每笔交易的盈亏
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resultDict = OrderedDict() # 交易结果记录
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longTrade = [] # 未平仓的多头交易
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shortTrade = [] # 未平仓的空头交易
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for trade in self.tradeDict.values():
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# 多头交易
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if trade.direction == DIRECTION_LONG:
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# 如果尚无空头交易
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if not shortTrade:
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longTrade.append(trade)
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# 当前多头交易为平空
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else:
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entryTrade = shortTrade.pop(0)
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result = TradingResult(entryTrade.price, trade.price, -trade.volume,
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self.rate, self.slippage, self.size)
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resultDict[trade.dt] = result
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# 空头交易
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else:
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# 如果尚无多头交易
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if not longTrade:
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shortTrade.append(trade)
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# 当前空头交易为平多
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else:
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entryTrade = longTrade.pop(0)
|
||
|
||
result = TradingResult(entryTrade.price, trade.price, trade.volume,
|
||
self.rate, self.slippage, self.size)
|
||
resultDict[trade.dt] = result
|
||
|
||
# 检查是否有交易
|
||
if not resultDict:
|
||
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 time, result in resultDict.items():
|
||
capital += result.pnl
|
||
maxCapital = max(capital, maxCapital)
|
||
drawdown = capital - maxCapital
|
||
|
||
pnlList.append(result.pnl)
|
||
timeList.append(time)
|
||
capitalList.append(capital)
|
||
drawdownList.append(drawdown)
|
||
|
||
totalResult += 1
|
||
totalTurnover += result.turnover
|
||
totalCommission += result.commission
|
||
totalSlippage += result.slippage
|
||
|
||
# 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
|
||
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']))
|
||
|
||
# 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, entry, exit, volume, rate, slippage, size):
|
||
"""Constructor"""
|
||
self.entry = entry # 开仓价格
|
||
self.exit = exit # 平仓价格
|
||
self.volume = volume # 交易数量（+/-代表方向）
|
||
|
||
self.turnover = (self.entry+self.exit)*size # 成交金额
|
||
self.commission = self.turnover*rate # 手续费成本
|
||
self.slippage = slippage*2*size # 滑点成本
|
||
self.pnl = ((self.exit - self.entry) * 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()
|
||
|
||
|