317 lines
12 KiB
Python
317 lines
12 KiB
Python
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# encoding: UTF-8
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"""
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This file tweaks ctaTemplate Module to suit multi-TimeFrame strategies.
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"""
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from ctaBase import *
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from ctaTemplate import CtaTemplate
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import numpy as np
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########################################################################
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class BreakOut(CtaTemplate):
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"""
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"infoArray" 字典是用来储存辅助品种信息的, 可以是同品种的不同分钟k线, 也可以是不同品种的价格。
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调用的方法:
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价格序列:
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self.infoArray["数据库名 + 空格 + collection名"]["close"]
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self.infoArray["数据库名 + 空格 + collection名"]["high"]
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self.infoArray["数据库名 + 空格 + collection名"]["low"]
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单个价格:
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self.infoBar["数据库名 + 空格 + collection名"]
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返回的值为一个ctaBarData 或 None
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"""
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#----------------------------------------------------------------------
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def __init__(self, ctaEngine, setting):
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"""日内突破交易策略, 出场方式非常多, 本文件使用指标出场"""
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className = 'BreakOut'
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author = 'Joe'
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super(BreakOut, self).__init__(ctaEngine, setting)
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# 设置辅助品种数据字典
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self.infoArray = {}
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self.initInfobar = {}
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self.infoBar = {}
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# 缓存数据量
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self.bufferSize = 100
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self.bufferCount = 0
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self.initDays = 10
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# 设置参数
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self.pOBO_Mult = 0.5 # 计算突破点位
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# self.pProtMult = 2 # 止损的ATR倍数
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# self.pProfitMult = 2 # 止盈相对于止损的倍数
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# self.SlTp_On = False # 止损止盈功能
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# self.EODTime = 15 # 设置日内平仓时间
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self.vOBO_stretch = EMPTY_FLOAT
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self.vOBO_initialpoint = EMPTY_FLOAT
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self.vOBO_level_L = EMPTY_FLOAT
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self.vOBO_level_S = EMPTY_FLOAT
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self.orderList = []
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# 参数列表，保存了参数的名称
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paramList = ['name',
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'className',
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'author',
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'pOBO_Mult',
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'pProtMult',
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'pProfitMult',
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'SlTp_On',
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'EODTime']
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# 变量列表，保存了变量的名称
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varList = ['vOBO_stretch',
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'vOBO_initialpoint',
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'vOBO_level_L',
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'vOBO_level_S']
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# ----------------------------------------------------------------------
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def onInit(self):
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"""初始化策略（必须由用户继承实现）"""
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self.writeCtaLog(u'%s策略初始化' % self.name)
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# 载入历史数据，并采用回放计算的方式初始化策略数值
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initData = self.loadBar(self.initDays)
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for bar in initData:
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# 推送新数据, 同时检查是否有information bar需要推送
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# Update new bar, check whether the Time Stamp matching any information bar
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ibar = self.checkInfoBar(bar)
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self.onBar(bar, infobar=ibar)
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self.putEvent()
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#----------------------------------------------------------------------
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def onStart(self):
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"""启动策略（必须由用户继承实现）"""
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self.writeCtaLog(u'%s策略启动' %self.name)
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self.putEvent()
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#----------------------------------------------------------------------
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def onStop(self):
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"""停止策略（必须由用户继承实现）"""
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self.writeCtaLog(u'%s策略停止' %self.name)
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self.putEvent()
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# ----------------------------------------------------------------------
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def checkInfoBar(self, bar):
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"""在初始化时, 检查辅助品种数据的推送(初始化结束后, 回测时不会调用)"""
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initInfoCursorDict = self.ctaEngine.initInfoCursor
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# 如果"initInfobar"字典为空, 初始化字典, 插入第一个数据
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# If dictionary "initInfobar" is empty, insert first data record
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if self.initInfobar == {}:
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for info_symbol in initInfoCursorDict:
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try:
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self.initInfobar[info_symbol] = next(initInfoCursorDict[info_symbol])
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except StopIteration:
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print "Data of information symbols is empty! Input is a list, not str."
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raise
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# 若有某一品种的 TimeStamp 和执行报价的 TimeStamp 匹配, 则将"initInfobar"中的数据推送,
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# 然后更新该品种的数据
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# If any symbol's TimeStamp is matched with execution symbol's TimeStamp, return data
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# in "initInfobar", and update new data.
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temp = {}
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for info_symbol in self.initInfobar:
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data = self.initInfobar[info_symbol]
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# Update data only when Time Stamp is matched
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if (data is not None) and (data['datetime'] <= bar.datetime):
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try:
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temp[info_symbol] = CtaBarData()
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temp[info_symbol].__dict__ = data
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self.initInfobar[info_symbol] = next(initInfoCursorDict[info_symbol])
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except StopIteration:
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self.initInfobar[info_symbol] = None
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self.ctaEngine.output("No more data for initializing %s." % (info_symbol,))
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else:
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temp[info_symbol] = None
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return temp
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# ----------------------------------------------------------------------
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def updateInfoArray(self, infobar):
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"""收到Infomation Data, 更新辅助品种缓存字典"""
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for name in infobar:
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data = infobar[name]
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# Construct empty array
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if len(self.infoArray) < len(infobar) :
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self.infoArray[name] = {
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"close": np.zeros(self.bufferSize),
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"high": np.zeros(self.bufferSize),
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"low": np.zeros(self.bufferSize),
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"open": np.zeros(self.bufferSize)
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}
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if data is None:
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pass
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else:
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self.infoArray[name]["close"][0:self.bufferSize - 1] = \
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self.infoArray[name]["close"][1:self.bufferSize]
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self.infoArray[name]["high"][0:self.bufferSize - 1] = \
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self.infoArray[name]["high"][1:self.bufferSize]
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self.infoArray[name]["low"][0:self.bufferSize - 1] = \
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self.infoArray[name]["low"][1:self.bufferSize]
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self.infoArray[name]["open"][0:self.bufferSize - 1] = \
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self.infoArray[name]["open"][1:self.bufferSize]
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self.infoArray[name]["close"][-1] = data.close
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self.infoArray[name]["high"][-1] = data.high
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self.infoArray[name]["low"][-1] = data.low
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self.infoArray[name]["open"][-1] = data.open
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# ----------------------------------------------------------------------
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def onBar(self, bar, **kwargs):
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"""收到Bar推送（必须由用户继承实现）"""
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# Update infomation data
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# "infobar"是由不同时间或不同品种的品种数据组成的字典, 如果和执行品种的 TimeStamp 不匹配,
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# 则传入的是"None", 当time stamp和执行品种匹配时, 传入的是"Bar"
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if "infobar" in kwargs:
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self.infoBar = kwargs["infobar"]
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self.updateInfoArray(kwargs["infobar"])
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# 若读取的缓存数据不足, 不考虑交易
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self.bufferCount += 1
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if self.bufferCount < self.bufferSize:
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return
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# 计算指标数值
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a = np.sum(self.infoArray["TestData @GC_1D"]["close"])
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if a == 0.0:
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return
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# Only updating indicators when information bar changes
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# 只有在30min或者1d K线更新后才更新指标
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TradeOn = False
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if any([i is not None for i in self.infoBar]):
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TradeOn = True
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self.vRange = self.infoArray["TestData @GC_1D"]["high"][-1] -\
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self.infoArray["TestData @GC_1D"]["low"][-1]
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self.vOBO_stretch = self.vRange * self.pOBO_Mult
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self.vOBO_initialpoint = self.infoArray["TestData @GC_1D"]["close"][-1]
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self.vOBO_level_L = self.vOBO_initialpoint + self.vOBO_stretch
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self.vOBO_level_S = self.vOBO_initialpoint - self.vOBO_stretch
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self.atrValue30M = talib.abstract.ATR(self.infoArray["TestData @GC_30M"])[-1]
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# 判断是否要进行交易
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# 当前无仓位
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if (self.pos == 0 and TradeOn == True):
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# 撤销之前发出的尚未成交的委托（包括限价单和停止单）
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for orderID in self.orderList:
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self.cancelOrder(orderID)
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self.orderList = []
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# 若上一个30分钟K线的最高价大于OBO_level_L
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# 且当前的价格大于OBO_level_L, 则买入
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if self.infoArray["TestData @GC_30M"]["high"][-1] > self.vOBO_level_L:
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if bar.close > self.vOBO_level_L:
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self.buy(bar.close + 0.5, 1)
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# 下单后, 在下一个30Min K线之前不交易
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TradeOn = False
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# 若上一个30分钟K线的最高价低于OBO_level_S
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# 且当前的价格小于OBO_level_S, 则卖出
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elif self.infoArray["TestData @GC_30M"]["low"][-1] < self.vOBO_level_S:
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if bar.close < self.vOBO_level_S:
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self.short(bar.close - 0.5, 1)
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# 下单后, 在下一个30Min K线之前不交易
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TradeOn = False
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# 持有多头仓位
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elif self.pos > 0:
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# 当价格低于initialpoint水平, 出场
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if bar.close < self.vOBO_initialpoint:
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self.sell(bar.close - 0.5 , 1)
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# 持有空头仓位
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elif self.pos < 0:
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# 当价格高于initialpoint水平, 出场
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if bar.close > self.vOBO_initialpoint:
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self.cover(bar.close + 0.5, 1)
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# 发出状态更新事件
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self.putEvent()
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# ----------------------------------------------------------------------
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def onOrder(self, order):
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"""收到委托变化推送（必须由用户继承实现）"""
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pass
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# ----------------------------------------------------------------------
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def onTrade(self, trade):
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pass
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if __name__ == '__main__':
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# 提供直接双击回测的功能
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# 导入PyQt4的包是为了保证matplotlib使用PyQt4而不是PySide，防止初始化出错
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from ctaBacktestMultiTF import *
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from PyQt4 import QtCore, QtGui
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import time
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'''
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创建回测引擎
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设置引擎的回测模式为K线
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设置回测用的数据起始日期
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载入历史数据到引擎中
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在引擎中创建策略对象
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Create backtesting engine
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Set backtest mode as "Bar"
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Set "Start Date" of data range
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Load historical data to engine
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Create strategy instance in engine
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'''
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engine = BacktestEngineMultiTF()
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engine.setBacktestingMode(engine.BAR_MODE)
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engine.setStartDate('20120101')
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engine.setEndDate('20150101')
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engine.setDatabase("TestData", "@GC_1M", info_symbol=[("TestData","@GC_30M"),
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("TestData","@GC_1D")])
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# Set parameters for strategy
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engine.initStrategy(BreakOut, {})
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# 设置产品相关参数
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engine.setSlippage(0.2) # 股指1跳
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engine.setCommission(0.3 / 10000) # 万0.3
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engine.setSize(1) # 股指合约大小
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# 开始跑回测
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start = time.time()
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engine.runBacktesting()
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# 显示回测结果
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engine.showBacktestingResult()
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print 'Time consumed：%s' % (time.time() - start)
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