410 lines
15 KiB
Python
410 lines
15 KiB
Python
# 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 strategyAtrRsi import *
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from ctaBase import *
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from ctaTemplate import CtaTemplate
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########################################################################
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class TC11(CtaTemplate):
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# Strategy name and author
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className = "TC11"
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author = "Zenacon"
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# Set MongoDB DataBase
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barDbName = "TestData"
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# Strategy parameters
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pGeneric_prd = 21
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pGeneric_on = True
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pATRprd_F = 13
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pATRprd_M = 21
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pATRprd_S = 63
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pBOSSplus_prd = 98
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pBOSSminus_prd = 22
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if pGeneric_on == 0:
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pRSIprd = 20
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pBBprd = 10
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pBB_ATRprd = 15
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pATRprd = 21
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pDMIprd = 21
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else:
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pRSIprd = \
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pBBprd = \
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pBB_ATRprd = \
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pATRprd = \
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pDMIprd = pGeneric_prd
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pBOSS_Mult = 1.75
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# Strategy variables
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vOBO_initialpoint = EMPTY_FLOAT
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vOBO_Stretch = EMPTY_FLOAT
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vOBO_level_L = EMPTY_FLOAT
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vOBO_level_S = EMPTY_FLOAT
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# parameters' list, record names of parameters
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paramList = ['name',
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'className',
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'author',
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'vtSymbol']
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# variables' list, record names of variables
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varList = ['inited',
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'trading',
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'pos']
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def __init__(self, ctaEngine, setting):
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"""Constructor"""
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super(TC11, self).__init__(ctaEngine, setting)
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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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# 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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# Record new information bar
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if "infobar" in kwargs:
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for i in kwargs["infobar"]:
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if kwargs["infobar"][i] is None:
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pass
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else:
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# print kwargs["infobar"][i]["close"]
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self.closeArray[0:self.bufferSize - 1] = self.closeArray[1:self.bufferSize]
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self.highArray[0:self.bufferSize - 1] = self.highArray[1:self.bufferSize]
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self.lowArray[0:self.bufferSize - 1] = self.lowArray[1:self.bufferSize]
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self.closeArray[-1] = bar.close
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self.highArray[-1] = bar.high
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self.lowArray[-1] = bar.low
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"""
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Record new bar
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"""
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self.closeArray[0:self.bufferSize - 1] = self.closeArray[1:self.bufferSize]
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self.highArray[0:self.bufferSize - 1] = self.highArray[1:self.bufferSize]
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self.lowArray[0:self.bufferSize - 1] = self.lowArray[1:self.bufferSize]
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self.closeArray[-1] = bar.close
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self.highArray[-1] = bar.high
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self.lowArray[-1] = bar.low
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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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"""
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Calculate Indicators
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"""
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vOBO_initialpoint = self.dataHTF_filled['Open']
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vOBO_Stretch = self.vATR['htf'].m * self.pBOSS_Mult
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self.atrValue = talib.ATR(self.highArray,
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self.lowArray,
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self.closeArray,
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self.atrLength)[-1]
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self.atrArray[0:self.bufferSize - 1] = self.atrArray[1:self.bufferSize]
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self.atrArray[-1] = self.atrValue
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self.atrCount += 1
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if self.atrCount < self.bufferSize:
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return
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self.atrMa = talib.MA(self.atrArray,
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self.atrMaLength)[-1]
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self.rsiValue = talib.RSI(self.closeArray,
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self.rsiLength)[-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:
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self.intraTradeHigh = bar.high
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self.intraTradeLow = bar.low
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# ATR数值上穿其移动平均线，说明行情短期内波动加大
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# 即处于趋势的概率较大，适合CTA开仓
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if self.atrValue > self.atrMa:
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# 使用RSI指标的趋势行情时，会在超买超卖区钝化特征，作为开仓信号
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if self.rsiValue > self.rsiBuy:
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# 这里为了保证成交，选择超价5个整指数点下单
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self.buy(bar.close + 5, 1)
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elif self.rsiValue < self.rsiSell:
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self.short(bar.close - 5, 1)
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# 持有多头仓位
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elif self.pos > 0:
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# 计算多头持有期内的最高价，以及重置最低价
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self.intraTradeHigh = max(self.intraTradeHigh, bar.high)
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self.intraTradeLow = bar.low
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# 计算多头移动止损
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longStop = self.intraTradeHigh * (1 - self.trailingPercent / 100)
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# 发出本地止损委托，并且把委托号记录下来，用于后续撤单
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orderID = self.sell(longStop, 1, stop=True)
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self.orderList.append(orderID)
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# 持有空头仓位
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elif self.pos < 0:
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self.intraTradeLow = min(self.intraTradeLow, bar.low)
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self.intraTradeHigh = bar.high
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shortStop = self.intraTradeLow * (1 + self.trailingPercent / 100)
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orderID = self.cover(shortStop, 1, stop=True)
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self.orderList.append(orderID)
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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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class Prototype(AtrRsiStrategy):
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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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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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"""
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infoArray = {}
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initInfobar = {}
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def __int__(self):
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super(Prototype, self).__int__()
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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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# 初始化RSI入场阈值
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self.rsiBuy = 50 + self.rsiEntry
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self.rsiSell = 50 - self.rsiEntry
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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 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['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.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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}
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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]["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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# ----------------------------------------------------------------------
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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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# 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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# 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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self.updateInfoArray(kwargs["infobar"])
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# 保存K线数据
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self.closeArray[0:self.bufferSize - 1] = self.closeArray[1:self.bufferSize]
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self.highArray[0:self.bufferSize - 1] = self.highArray[1:self.bufferSize]
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self.lowArray[0:self.bufferSize - 1] = self.lowArray[1:self.bufferSize]
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self.closeArray[-1] = bar.close
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self.highArray[-1] = bar.high
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self.lowArray[-1] = bar.low
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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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# 计算不同时间下的ATR数值
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# Only trading when information bar changes
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# 只有在30min或者1d K线更新后才可以交易
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TradeOn = False
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if any([i is not None for i in kwargs["infobar"].values()]):
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TradeOn = True
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self.scaledAtrValue1M = talib.ATR(self.highArray,
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self.lowArray,
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self.closeArray,
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self.atrLength)[-1] * (25) ** (0.5)
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self.atrValue30M = talib.abstract.ATR(self.infoArray["TestData @GC_30M"])[-1]
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self.rsiValue = talib.abstract.RSI(self.infoArray["TestData @GC_30M"], self.rsiLength)[-1]
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self.atrCount += 1
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if self.atrCount < self.bufferSize:
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return
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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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self.intraTradeHigh = bar.high
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self.intraTradeLow = bar.low
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# 1Min调整后ATR大于30MinATR
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# 即处于趋势的概率较大，适合CTA开仓
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if self.atrValue30M < self.scaledAtrValue1M:
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# 使用RSI指标的趋势行情时，会在超买超卖区钝化特征，作为开仓信号
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if self.rsiValue > self.rsiBuy:
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# 这里为了保证成交，选择超价5个整指数点下单
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self.buy(bar.close+5, 1)
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elif self.rsiValue < self.rsiSell:
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self.short(bar.close-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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# 计算多头持有期内的最高价，以及重置最低价
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self.intraTradeHigh = max(self.intraTradeHigh, bar.high)
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self.intraTradeLow = bar.low
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# 计算多头移动止损
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longStop = self.intraTradeHigh * (1 - self.trailingPercent / 100)
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# 发出本地止损委托，并且把委托号记录下来，用于后续撤单
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orderID = self.sell(longStop, 1, stop=True)
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self.orderList.append(orderID)
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# 持有空头仓位
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elif self.pos < 0:
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self.intraTradeLow = min(self.intraTradeLow, bar.low)
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self.intraTradeHigh = bar.high
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shortStop = self.intraTradeLow * (1 + self.trailingPercent / 100)
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orderID = self.cover(shortStop, 1, stop=True)
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self.orderList.append(orderID)
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# 发出状态更新事件
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self.putEvent()
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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('20100101')
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engine.setDatabase("TestData", "@GC_1M", info_symbol=[("TestData","@GC_30M")])
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# Set parameters for strategy
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d = {'atrLength': 11}
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engine.initStrategy(Prototype, d)
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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(300) # 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) |