diff --git a/vnpy/trader/app/ctaStrategy/tools/README.md b/vnpy/trader/app/ctaStrategy/tools/README.md deleted file mode 100644 index 67c59f15..00000000 --- a/vnpy/trader/app/ctaStrategy/tools/README.md +++ /dev/null @@ -1,10 +0,0 @@ -# CTA策略开发相关的工具代码 - -### ctaLineBar.py -* 简介：CTA策略开发中常用的K线类，可以基于tick自动生成K线，并提供EMA、DMI、ATR、RSI等常用技术指标的计算 -* 贡献者：李来佳 -* WeChat/QQ: 28888502 - -### multiTimeFrame -* 简介：基于CTA模块扩展了回测和交易功能，允许策略中引用辅助品种信息（其他时间框架、其他合约），同时提供了一个突破策略的例子 -* 贡献者：周正舟 diff --git a/vnpy/trader/app/ctaStrategy/tools/ctaLineBar.py b/vnpy/trader/app/ctaStrategy/tools/ctaLineBar.py deleted file mode 100644 index 8d72331a..00000000 --- a/vnpy/trader/app/ctaStrategy/tools/ctaLineBar.py +++ /dev/null @@ -1,842 +0,0 @@ -# encoding: UTF-8 - -# AUTHOR:李来佳 -# WeChat/QQ: 28888502 - -from vtConstant import * -from vnpy.trader.vtObject import VtBarData - -from datetime import datetime - -import talib as ta -import numpy -import copy,csv - - -DEBUGCTALOG = True - -class CtaLineBar(object): - """CTA K线""" - """ 使用方法: - 1、在策略构造函数__init()中初始化 - self.lineM = None # 1分钟K线 - lineMSetting = {} - lineMSetting['name'] = u'M1' - lineMSetting['barTimeInterval'] = 60 # 1分钟对应60秒 - lineMSetting['inputEma1Len'] = 7 # EMA线1的周期 - lineMSetting['inputEma2Len'] = 21 # EMA线2的周期 - lineMSetting['inputBollLen'] = 20 # 布林特线周期 - lineMSetting['inputBollStdRate'] = 2 # 布林特线标准差 - lineMSetting['minDiff'] = self.minDiff # 最小条 - lineMSetting['shortSymbol'] = self.shortSymbol #商品短号 - self.lineM = CtaLineBar(self, self.onBar, lineMSetting) - 2、在onTick()中，需要导入tick数据 - self.lineM.onTick(tick) - self.lineM5.onTick(tick) # 如果你使用2个周期 - 3、在onBar事件中，按照k线结束使用；其他任何情况下bar内使用，通过对象使用即可，self.lineM.lineBar[-1].close - """ - - # 参数列表，保存了参数的名称 - paramList = ['vtSymbol'] - - def __init__(self, strategy, onBarFunc, setting=None,): - - # OnBar事件回调函数 - self.onBarFunc = onBarFunc - - # 参数列表 - self.paramList.append('barTimeInterval') - self.paramList.append('inputPreLen') - self.paramList.append('inputEma1Len') - self.paramList.append('inputEma2Len') - self.paramList.append('inputDmiLen') - self.paramList.append('inputDmiMax') - self.paramList.append('inputAtr1Len') - self.paramList.append('inputAtr2Len') - self.paramList.append('inputAtr3Len') - self.paramList.append('inputVolLen') - self.paramList.append('inputRsiLen') - self.paramList.append('inputCmiLen') - self.paramList.append('inputBollLen') - self.paramList.append('inputBollStdRate') - - - self.paramList.append('minDiff') - self.paramList.append('shortSymbol') - self.paramList.append('activeDayJump') - self.paramList.append('name') - - - # 输入参数 - self.name = u'LineBar' - self.barTimeInterval = 300 - - self.inputPreLen = EMPTY_INT #1 - - self.inputEma1Len = EMPTY_INT # 13 - self.inputEma2Len = EMPTY_INT # 21 - - self.inputDmiLen = EMPTY_INT # 14 # DMI的计算周期 - self.inputDmiMax = EMPTY_FLOAT # 30 # Dpi和Mdi的突破阈值 - - self.inputAtr1Len = EMPTY_INT # 10 # ATR波动率的计算周期(近端） - self.inputAtr2Len = EMPTY_INT # 26 # ATR波动率的计算周期（常用） - self.inputAtr3Len = EMPTY_INT # 50 # ATR波动率的计算周期（远端） - - self.inputVolLen = EMPTY_INT # 14 # 平均交易量的计算周期 - - self.inputRsiLen = EMPTY_INT # 7 # RSI 相对强弱指数 - - self.shortSymbol = EMPTY_STRING # 商品的短代码 - self.minDiff = 1 # 商品的最小价格单位 - - self.activeDayJump = False # 隔夜跳空 - - # 当前的Tick - self.curTick = None - - # K 线服务的策略 - self.strategy = strategy - - # K线保存数据 - self.bar = None # K线数据对象 - self.lineBar = [] # K线缓存数据队列 - self.barFirstTick =False # K线的第一条Tick数据 - - # K 线的相关计算结果数据 - - self.preHigh = [] # K线的前inputPreLen的的最高 - self.preLow = [] # K线的前inputPreLen的的最低 - - self.lineEma1 = [] # K线的EMA1均线，周期是InputEmaLen1，包含当前bar - self.lineEma1MtmRate = [] # K线的EMA1均线 的momentum(3) 动能 - - self.lineEma2 = [] # K线的EMA2均线，周期是InputEmaLen2，包含当前bar - self.lineEma2MtmRate = [] # K线的EMA2均线 的momentum(3) 动能 - - # K线的DMI( Pdi，Mdi，ADX，Adxr) 计算数据 - self.barPdi = EMPTY_FLOAT # bar内的升动向指标，即做多的比率 - self.barMdi = EMPTY_FLOAT # bar内的下降动向指标，即做空的比率 - - self.linePdi = [] # 升动向指标，即做多的比率 - self.lineMdi = [] # 下降动向指标，即做空的比率 - - self.lineDx = [] # 趋向指标列表，最大长度为inputM*2 - self.barAdx = EMPTY_FLOAT # Bar内计算的平均趋向指标 - self.lineAdx = [] # 平均趋向指标 - self.barAdxr = EMPTY_FLOAT # 趋向平均值，为当日ADX值与M日前的ADX值的均值 - self.lineAdxr = [] # 平均趋向变化指标 - - # K线的基于DMI、ADX计算的结果 - self.barAdxTrend = EMPTY_FLOAT # ADX值持续高于前一周期时，市场行情将维持原趋势 - self.barAdxrTrend = EMPTY_FLOAT # ADXR值持续高于前一周期时,波动率比上一周期高 - - self.buyFilterCond = False # 多过滤器条件,做多趋势的判断，ADX高于前一天，上升动向> inputMM - self.sellFilterCond = False # 空过滤器条件,做空趋势的判断，ADXR高于前一天，下降动向> inputMM - - # K线的ATR技术数据 - self.lineAtr1 = [] # K线的ATR1,周期为inputAtr1Len - self.lineAtr2 = [] # K线的ATR2,周期为inputAtr2Len - self.lineAtr3 = [] # K线的ATR3,周期为inputAtr3Len - - self.barAtr1 = EMPTY_FLOAT - self.barAtr2 = EMPTY_FLOAT - self.barAtr3 = EMPTY_FLOAT - - # K线的交易量平均 - self.lineAvgVol = [] # K 线的交易量平均 - - # K线的RSI计算数据 - self.lineRsi = [] # 记录K线对应的RSI数值，只保留inputRsiLen*8 - - self.lowRsi = 30 # RSI的最低线 - self.highRsi = 70 # RSI的最高线 - - self.lineRsiTop = [] # 记录RSI的最高峰，只保留 inputRsiLen个 - self.lineRsiButtom = [] # 记录RSI的最低谷，只保留 inputRsiLen个 - self.lastRsiTopButtom = None # 最近的一个波峰/波谷 - - # K线的CMI计算数据 - self.inputCmiLen = EMPTY_INT - self.lineCmi = [] # 记录K线对应的Cmi数值，只保留inputCmiLen*8 - - # K线的布林特计算数据 - self.inputBollLen = EMPTY_INT # K线周期 - self.inputBollStdRate = 1.5 # 两倍标准差 - - self.lineUpperBand = [] # 上轨 - self.lineMiddleBand = [] # 中线 - self.lineLowerBand = [] # 下轨 - - if setting: - self.setParam(setting) - - def setParam(self, setting): - """设置参数""" - d = self.__dict__ - for key in self.paramList: - if key in setting: - - d[key] = setting[key] - - def onTick(self, tick): - """行情更新 - :type tick: object - """ - # Tick 有效性检查 - #if (tick.datetime- datetime.now()).seconds > 10: - # self.writeCtaLog(u'无效的tick时间:{0}'.format(tick.datetime)) - # return - - if tick.datetime.hour == 8 or tick.datetime.hour == 20: - self.writeCtaLog(u'竞价排名tick时间:{0}'.format(tick.datetime)) - return - - self.curTick = tick - - # 3.生成x K线，若形成新Bar，则触发OnBar事件 - self.__drawLineBar(tick) - - def addBar(self,bar): - """予以外部初始化程序增加bar""" - l1 = len(self.lineBar) - - if l1 == 0: - self.lineBar.append(bar) - self.onBar(bar) - return - - # 与最后一个BAR的时间比对，判断是否超过K线的周期 - lastBar = self.lineBar[-1] - - if (bar.datetime - lastBar.datetime).seconds >= self.barTimeInterval: - self.lineBar.append(bar) - self.onBar(bar) - return - - # 更新最后一个bar - lastBar.close = bar.close - lastBar.high = max(lastBar.high, bar.high) - lastBar.low = min(lastBar.low, bar.low) - lastBar.volume = lastBar.volume + bar.volume - - - def onBar(self, bar): - """OnBar事件""" - # 计算相关数据 - self.__recountPreHighLow() - self.__recountEma() - self.__recountDmi() - self.__recountAtr() - self.__recountAvgVol() - self.__recountRsi() - self.__recountCmi() - self.__recountBoll() - - - # 回调上层调用者 - self.onBarFunc(bar) - - - def __firstTick(self,tick): - """ K线的第一个Tick数据""" - self.bar = VtBarData() # 创建新的K线 - - self.bar.vtSymbol = tick.vtSymbol - self.bar.symbol = tick.symbol - self.bar.exchange = tick.exchange - - self.bar.open = tick.lastPrice # O L H C - self.bar.high = tick.lastPrice - self.bar.low = tick.lastPrice - self.bar.close = tick.lastPrice - - # K线的日期时间 - self.bar.date = tick.date # K线的日期时间（去除秒）设为第一个Tick的时间 - self.bar.time = tick.time # K线的日期时间（去除秒）设为第一个Tick的时间 - self.bar.datetime = tick.datetime - - self.bar.volume = tick.volume - self.bar.openInterest = tick.openInterest - - self.barFirstTick = True # 标识该Tick属于该Bar的第一个tick数据 - - self.lineBar.append(self.bar) # 推入到lineBar队列 - - # ---------------------------------------------------------------------- - def __drawLineBar(self, tick): - """生成 line Bar """ - - l1 = len(self.lineBar) - - # 保存第一个K线数据 - if l1 == 0: - self.__firstTick(tick) - self.onBar(self.bar) - return - - # 清除8交易小时前的数据， - if l1 > 60 * 8: - del self.lineBar[0] - - # 与最后一个BAR的时间比对，判断是否超过5分钟 - lastBar = self.lineBar[-1] - - # 专门处理隔夜跳空。隔夜跳空会造成开盘后EMA和ADX的计算错误。 - if len(self.lineAtr2) < 1: - priceInBar = 5 * self.minDiff - else: - priceInBar = self.lineAtr2[-1] - - jumpBars = int(abs(tick.lastPrice - lastBar.close)/priceInBar) - - # 开盘时间 - if (tick.datetime.hour == 9 or tick.datetime.hour == 21) \ - and tick.datetime.minute == 0 and tick.datetime.second == 0 \ - and lastBar.datetime.hour != tick.datetime.hour \ - and jumpBars > 0 and self.activeDayJump: - - priceInYesterday = lastBar.close - - self.writeCtaLog(u'line Bar jumpbars:{0}'.format(jumpBars)) - - if tick.lastPrice > priceInYesterday: # 价格往上跳 - - # 生成砖块递增K线,减小ATR变动 - for i in range(0, jumpBars, 1): - upbar = copy.deepcopy(lastBar) - upbar.open = priceInYesterday + float(i * priceInBar) - upbar.low = upbar.open - upbar.close = priceInYesterday + float((i+1) * priceInBar) - upbar.high = upbar.close - upbar.volume = 0 - - self.lineBar.append(upbar) - self.onBar(upbar) - - else: # 价格往下跳 - # 生成递减K线,减小ATR变动 - for i in range(0, jumpBars, 1): - - downbar = copy.deepcopy(lastBar) - downbar.open = priceInYesterday - float(i * priceInBar) - downbar.high = downbar.open - downbar.close = priceInYesterday - float((i+1) * priceInBar) - downbar.low = downbar.close - downbar.volume = 0 - - self.lineBar.append(downbar) - self.onBar(downbar) - - # 生成平移K线，减小Pdi，Mdi、ADX变动 - for i in range(0, jumpBars*2, 1): - equalbar=copy.deepcopy(self.lineBar[-1]) - equalbar.volume = 0 - self.lineBar.append(equalbar) - self.onBar(equalbar) - - # 重新指定为最后一个Bar - lastBar = self.lineBar[-1] - - # 处理日内的间隔时段最后一个tick，如10:15分，11:30分，15:00 和 2:30分 - endtick = False - if (tick.datetime.hour == 10 and tick.datetime.minute == 15 ) \ - or (tick.datetime.hour == 11 and tick.datetime.minute == 30 ) \ - or (tick.datetime.hour == 15 and tick.datetime.minute == 00 ) \ - or (tick.datetime.hour == 2 and tick.datetime.minute == 30 ): - endtick = True - - if self.shortSymbol in NIGHT_MARKET_SQ2 and tick.datetime.hour == 1 and tick.datetime.minute == 00: - endtick = True - - if self.shortSymbol in NIGHT_MARKET_SQ3 and tick.datetime.hour == 23 and tick.datetime.minute == 00: - endtick = True - - if self.shortSymbol in NIGHT_MARKET_ZZ or self.shortSymbol in NIGHT_MARKET_DL: - if tick.datetime.hour == 23 and tick.datetime.minute == 30: - endtick = True - - # 满足时间要求 - if (tick.datetime-lastBar.datetime).seconds >= self.barTimeInterval and not endtick: - - # 创建并推入新的Bar - self.__firstTick(tick) - # 触发OnBar事件 - self.onBar(lastBar) - - else: - # 更新当前最后一个bar - self.barFirstTick = False - - # 更新最高价、最低价、收盘价、成交量 - lastBar.high = max(lastBar.high, tick.lastPrice) - lastBar.low = min(lastBar.low, tick.lastPrice) - lastBar.close = tick.lastPrice - lastBar.volume = lastBar.volume + tick.volume - - # 更新Bar的颜色 - if lastBar.close > lastBar.open: - lastBar.color = COLOR_RED - elif lastBar.close < lastBar.open: - lastBar.color = COLOR_BLUE - else: - lastBar.color = COLOR_EQUAL - - # ---------------------------------------------------------------------- - def __recountPreHighLow(self): - """计算 K线的前周期最高和最低""" - - if self.inputPreLen <= 0: # 不计算 - return - - # 1、lineBar满足长度才执行计算 - if len(self.lineBar) < self.inputPreLen: - self.writeCtaLog(u'数据未充分,当前Bar数据数量：{0}，计算High、Low需要：{1}'. - format(len(self.lineBar), self.inputPreLen)) - return - - # 2.计算前inputPreLen周期内(不包含当前周期）的Bar高点和低点 - preHigh = EMPTY_FLOAT - preLow = EMPTY_FLOAT - - for i in range(len(self.lineBar)-2, len(self.lineBar)-2-self.inputPreLen, -1): - - if self.lineBar[i].high > preHigh or preHigh == EMPTY_FLOAT: - preHigh = self.lineBar[i].high # 前InputPreLen周期高点 - - if self.lineBar[i].low < preLow or preLow == EMPTY_FLOAT: - preLow = self.lineBar[i].low # 前InputPreLen周期低点 - - # 保存 - if len(self.preHigh) > self.inputPreLen * 8: - del self.preHigh[0] - self.preHigh.append(preHigh) - - # 保存 - if len(self.preLow)> self.inputPreLen * 8: - del self.preLow[0] - self.preLow.append(preLow) - - #---------------------------------------------------------------------- - - - def __recountEma(self): - """计算K线的EMA1 和EMA2""" - l = len(self.lineBar) - - # 1、lineBar满足长度才执行计算 - if len(self.lineBar) < max(7, self.inputEma1Len, self.inputEma2Len)+2: - self.debugCtaLog(u'数据未充分,当前Bar数据数量：{0}，计算EMA需要：{1}'. - format(len(self.lineBar), max(7, self.inputEma1Len, self.inputEma2Len)+2)) - return - - # 计算第一条EMA均线 - if self.inputEma1Len > 0: - - if self.inputEma1Len > l: - ema1Len = l - else: - ema1Len = self.inputEma1Len - - # 3、获取前InputN周期(不包含当前周期）的自适应均线 - listClose=[x.close for x in self.lineBar[-ema1Len - 1:-1]] - - barEma1 = ta.EMA(numpy.array(listClose, dtype=float), ema1Len)[-1] - - barEma1 = round(float(barEma1), 3) - - if len(self.lineEma1) > self.inputEma1Len*8: - del self.lineEma1[0] - self.lineEma1.append(barEma1) - - # 计算第二条EMA均线 - if self.inputEma2Len > 0: - - if self.inputEma2Len > l: - ema2Len = l - else: - ema2Len = self.inputEma2Len - - # 3、获取前InputN周期(不包含当前周期）的自适应均线 - listClose=[x.close for x in self.lineBar[-ema2Len - 1:-1]] - barEma2 = ta.EMA(numpy.array(listClose, dtype=float), ema2Len)[-1] - - barEma2 = round(float(barEma2), 3) - - if len(self.lineEma2) > self.inputEma1Len*8: - del self.lineEma2[0] - self.lineEma2.append(barEma2) - - - def __recountDmi(self): - """计算K线的DMI数据和条件""" - - if self.inputDmiLen <= 0: # 不计算 - return - - # 1、lineMx满足长度才执行计算 - if len(self.lineBar) < self.inputDmiLen+1: - self.debugCtaLog(u'数据未充分,当前Bar数据数量：{0}，计算DMI需要：{1}'.format(len(self.lineBar), self.inputDmiLen+1)) - return - - - # 2、根据当前High，Low，(不包含当前周期）重新计算TR1，PDM，MDM和ATR - barTr1 = EMPTY_FLOAT # 获取InputP周期内的价差最大值之和 - barPdm = EMPTY_FLOAT # InputP周期内的做多价差之和 - barMdm = EMPTY_FLOAT # InputP周期内的做空价差之和 - - for i in range(len(self.lineBar)-2, len(self.lineBar)-2-self.inputDmiLen, -1): # 周期 inputDmiLen - # 3.1、计算TR1 - - # 当前周期最高与最低的价差 - high_low_spread = self.lineBar[i].high - self.lineBar[i].low - # 当前周期最高与昨收价的价差 - high_preclose_spread = abs(self.lineBar[i].high - self.lineBar[i - 1].close) - # 当前周期最低与昨收价的价差 - low_preclose_spread = abs(self.lineBar[i].low - self.lineBar[i - 1].close) - - # 最大价差 - max_spread = max(high_low_spread, high_preclose_spread, low_preclose_spread) - barTr1 = barTr1 + float(max_spread) - - # 今高与昨高的价差 - high_prehigh_spread = self.lineBar[i].high - self.lineBar[i - 1].high - # 昨低与今低的价差 - low_prelow_spread = self.lineBar[i - 1].low - self.lineBar[i].low - - # 3.2、计算周期内的做多价差之和 - if high_prehigh_spread > 0 and high_prehigh_spread > low_prelow_spread: - barPdm = barPdm + high_prehigh_spread - - # 3.3、计算周期内的做空价差之和 - if low_prelow_spread > 0 and low_prelow_spread > high_prehigh_spread: - barMdm = barMdm + low_prelow_spread - - # 6、计算上升动向指标，即做多的比率 - if barTr1 == 0: - self.barPdi = 0 - else: - self.barPdi = barPdm * 100 / barTr1 - - if len(self.linePdi) > self.inputDmiLen+1: - del self.linePdi[0] - - self.linePdi.append(self.barPdi) - - # 7、计算下降动向指标，即做空的比率 - if barTr1 == 0: - self.barMdi = 0 - else: - self.barMdi = barMdm * 100 / barTr1 - - # 8、计算平均趋向指标 Adx，Adxr - if self.barMdi + self.barPdi == 0: - dx = 0 - else: - dx = 100 * abs(self.barMdi - self.barPdi) / (self.barMdi + self.barPdi) - - if len(self.lineMdi) > self.inputDmiLen+1: - del self.lineMdi[0] - - self.lineMdi.append(self.barMdi) - - if len(self.lineDx) > self.inputDmiLen+1: - del self.lineDx[0] - - self.lineDx.append(dx) - - # 平均趋向指标，MA计算 - if len(self.lineDx) < self.inputDmiLen+1: - self.barAdx = dx - else: - self.barAdx = ta.EMA(numpy.array(self.lineDx, dtype=float), self.inputDmiLen)[-1] - - # 保存Adx值 - if len(self.lineAdx) > self.inputDmiLen+1: - del self.lineAdx[0] - - self.lineAdx.append(self.barAdx) - - # 趋向平均值，为当日ADX值与1周期前的ADX值的均值 - if len(self.lineAdx) == 1: - self.barAdxr = self.lineAdx[-1] - else: - self.barAdxr = (self.lineAdx[-1] + self.lineAdx[-2]) / 2 - - # 保存Adxr值 - if len(self.lineAdxr) > self.inputDmiLen+1: - del self.lineAdxr[0] - self.lineAdxr.append(self.barAdxr) - - # 7、计算A，ADX值持续高于前一周期时，市场行情将维持原趋势 - if len(self.lineAdx) < 2: - self.barAdxTrend = False - elif self.lineAdx[-1] > self.lineAdx[-2]: - self.barAdxTrend = True - else: - self.barAdxTrend = False - - # ADXR值持续高于前一周期时,波动率比上一周期高 - if len(self.lineAdxr) < 2: - self.barAdxrTrend = False - elif self.lineAdxr[-1] > self.lineAdxr[-2]: - self.barAdxrTrend = True - else: - self.barAdxrTrend = False - - # 多过滤器条件,做多趋势，ADX高于前一天，上升动向> inputDmiMax - if self.barPdi > self.barMdi and self.barAdxTrend and self.barAdxrTrend and self.barPdi >= self.inputDmiMax: - self.buyFilterCond = True - - self.writeCtaLog(u'{0}[DEBUG]Buy Signal On Bar,Pdi:{1}>Mdi:{2},adx[-1]:{3}>Adx[-2]:{4}' - .format(self.curTick.datetime, self.barPdi, self.barMdi, self.lineAdx[-1], self.lineAdx[-2])) - else: - self.buyFilterCond = False - - # 空过滤器条件 做空趋势，ADXR高于前一天，下降动向> inputMM - if self.barPdi < self.barMdi and self.barAdxTrend and self.barAdxrTrend and self.barMdi >= self.inputDmiMax: - self.sellFilterCond = True - - self.writeCtaLog(u'{0}[DEBUG]Short Signal On Bar,Pdi:{1}Adx[-2]:{4}' - .format(self.curTick.datetime, self.barPdi, self.barMdi, self.lineAdx[-1], self.lineAdx[-2])) - else: - self.sellFilterCond = False - - def __recountAtr(self): - """计算Mx K线的各类数据和条件""" - - # 1、lineMx满足长度才执行计算 - maxAtrLen = max(self.inputAtr1Len, self.inputAtr2Len, self.inputAtr3Len) - - if maxAtrLen <= 0: # 不计算 - return - - if len(self.lineBar) < maxAtrLen+1: - self.debugCtaLog(u'数据未充分,当前Bar数据数量：{0}，计算ATR需要：{1}'. - format(len(self.lineBar), maxAtrLen+1)) - return - - # 首次计算 - if (self.inputAtr1Len > 0 and len(self.lineAtr1) < 1) \ - or (self.inputAtr2Len > 0 and len(self.lineAtr2) < 1) \ - or (self.inputAtr3Len > 0 and len(self.lineAtr3) < 1): - - # 根据当前High，Low，(不包含当前周期）重新计算TR1和ATR - barTr1 = EMPTY_FLOAT # 获取inputAtr1Len周期内的价差最大值之和 - barTr2 = EMPTY_FLOAT # 获取inputAtr2Len周期内的价差最大值之和 - barTr3 = EMPTY_FLOAT # 获取inputAtr3Len周期内的价差最大值之和 - - j = 0 - for i in range(len(self.lineBar)-2, len(self.lineBar)-2-maxAtrLen, -1): # 周期 inputP - # 3.1、计算TR - - # 当前周期最高与最低的价差 - high_low_spread = self.lineBar[i].high - self.lineBar[i].low - # 当前周期最高与昨收价的价差 - high_preclose_spread = abs(self.lineBar[i].high - self.lineBar[i - 1].close) - # 当前周期最低与昨收价的价差 - low_preclose_spread = abs(self.lineBar[i].low - self.lineBar[i - 1].close) - - # 最大价差 - max_spread = max(high_low_spread, high_preclose_spread, low_preclose_spread) - if j < self.inputAtr1Len: - barTr1 = barTr1 + float(max_spread) - if j < self.inputAtr2Len: - barTr2 = barTr2 + float(max_spread) - if j < self.inputAtr3Len: - barTr3 = barTr3 + float(max_spread) - - j = j + 1 - - else: # 只计算一个 - - # 当前周期最高与最低的价差 - high_low_spread = self.lineBar[-2].high - self.lineBar[-2].low - # 当前周期最高与昨收价的价差 - high_preclose_spread = abs(self.lineBar[-2].high - self.lineBar[-3].close) - # 当前周期最低与昨收价的价差 - low_preclose_spread = abs(self.lineBar[-2].low - self.lineBar[-3].close) - - # 最大价差 - barTr1 = max(high_low_spread, high_preclose_spread, low_preclose_spread) - barTr2 = barTr1 - barTr3 = barTr1 - - # 计算 ATR - if self.inputAtr1Len > 0: - if len(self.lineAtr1) < 1: - self.barAtr1 = round(barTr1 / self.inputAtr1Len, 3) - else: - self.barAtr1 = round((self.lineAtr1[-1]*(self.inputAtr1Len -1) + barTr1) / self.inputAtr1Len, 3) - - if len(self.lineAtr1) > self. inputAtr1Len+1 : - del self.lineAtr1[0] - self.lineAtr1.append(self.barAtr1) - - if self.inputAtr2Len > 0: - if len(self.lineAtr2) < 1: - self.barAtr2 = round(barTr2 / self.inputAtr2Len, 3) - else: - self.barAtr2 = round((self.lineAtr2[-1]*(self.inputAtr2Len -1) + barTr2) / self.inputAtr2Len, 3) - - if len(self.lineAtr2) > self. inputAtr2Len+1: - del self.lineAtr2[0] - self.lineAtr2.append(self.barAtr2) - - if self.inputAtr3Len > 0: - if len(self.lineAtr3) < 1: - self.barAtr3 = round(barTr3 / self.inputAtr3Len, 3) - else: - self.barAtr3 = round((self.lineAtr3[-1]*(self.inputAtr3Len -1) + barTr3) / self.inputAtr3Len, 3) - - if len(self.lineAtr3) > self. inputAtr3Len+1: - del self.lineAtr3[0] - - self.lineAtr3.append(self.barAtr3) - - #---------------------------------------------------------------------- - def __recountAvgVol(self): - """计算平均成交量""" - - # 1、lineBar满足长度才执行计算 - if self.inputVolLen <= 0: # 不计算 - return - - if len(self.lineBar) < self.inputVolLen+1: - self.debugCtaLog(u'数据未充分,当前Bar数据数量：{0}，计算Avg Vol需要：{1}'. - format(len(self.lineBar), self.inputVolLen+1)) - return - - listVol = [x.volume for x in self.lineBar[-self.inputVolLen-1: -1]] - - sumVol = ta.SUM(numpy.array(listVol, dtype=float), timeperiod=self.inputVolLen)[-1] - - avgVol = round(sumVol/self.inputVolLen, 0) - - self.lineAvgVol.append(avgVol) - - # ---------------------------------------------------------------------- - def __recountRsi(self): - """计算K线的RSI""" - - if self.inputRsiLen <= 0: return - - # 1、lineBar满足长度才执行计算 - if len(self.lineBar) < self.inputRsiLen+2: - self.debugCtaLog(u'数据未充分,当前Bar数据数量：{0}，计算RSI需要：{1}'. - format(len(self.lineBar), self.inputRsiLen+2)) - return - - # 3、inputRsiLen(包含当前周期）的相对强弱 - listClose=[x.close for x in self.lineBar[-self.inputRsiLen - 2:]] - barRsi = ta.RSI(numpy.array(listClose, dtype=float), self.inputRsiLen)[-1] - barRsi = round(float(barRsi), 3) - - l = len(self.lineRsi) - if l > self.inputRsiLen*8: - del self.lineRsi[0] - - self.lineRsi.append(barRsi) - - if l > 3: - # 峰 - if self.lineRsi[-1] < self.lineRsi[-2] and self.lineRsi[-3] < self.lineRsi[-2]: - - t={} - t["Type"] = u'T' - t["RSI"] = self.lineRsi[-2] - t["Close"] = self.lineBar[-2].close - - - if len(self.lineRsiTop) > self.inputRsiLen: - del self.lineRsiTop[0] - - self.lineRsiTop.append( t ) - self.lastRsiTopButtom = self.lineRsiTop[-1] - - # 谷 - elif self.lineRsi[-1] > self.lineRsi[-2] and self.lineRsi[-3] > self.lineRsi[-2]: - - b={} - b["Type"] = u'B' - b["RSI"] = self.lineRsi[-2] - b["Close"] = self.lineBar[-2].close - - if len(self.lineRsiButtom) > self.inputRsiLen: - del self.lineRsiButtom[0] - self.lineRsiButtom.append(b) - self.lastRsiTopButtom = self.lineRsiButtom[-1] - - def __recountCmi(self): - """市场波动指数（Choppy Market Index，CMI）是一个用来判断市场走势类型的技术分析指标。 - 它通过计算当前收盘价与一定周期前的收盘价的差值与这段时间内价格波动的范围的比值，来判断目前的股价走势是趋势还是盘整。 - 市场波动指数CMI的计算公式： - CMI=(Abs(Close-ref(close,(n-1)))*100/(HHV(high,n)-LLV(low,n)) - 其中，Abs是绝对值。 - n是周期数，例如３０。 - 市场波动指数CMI的使用方法： - 这个指标的重要用途是来区分目前的股价走势类型：盘整，趋势。当CMI指标小于２０时，市场走势是盘整；当CMI指标大于２０时，市场在趋势期。 - CMI指标还可以用于预测股价走势类型的转变。因为物极必反，当CMI长期处于０附近，此时，股价走势很可能从盘整转为趋势；当CMI长期处于１００附近，此时，股价趋势很可能变弱，形成盘整。 - """ - - if self.inputCmiLen <= EMPTY_INT: return - - # 1、lineBar满足长度才执行计算 - if len(self.lineBar) < self.inputCmiLen: - self.debugCtaLog(u'数据未充分,当前Bar数据数量：{0}，计算CMI需要：{1}'. - format(len(self.lineBar), self.inputCmiLen)) - return - - listClose =[x.close for x in self.lineBar[-self.inputCmiLen:]] - hhv = max(listClose) - llv = min(listClose) - - if hhv==llv: - cmi = 100 - else: - cmi = abs(self.lineBar[-1].close-self.lineBar[-2].close)*100/(hhv-llv) - - cmi = round(cmi, 2) - - if len(self.lineCmi) > self.inputCmiLen: - del self.lineCmi[0] - - self.lineCmi.append(cmi) - - def __recountBoll(self): - """布林特线""" - if self.inputBollLen < EMPTY_INT: return - - l = len(self.lineBar) - - if l < min(7, self.inputBollLen)+1: - self.debugCtaLog(u'数据未充分,当前Bar数据数量：{0}，计算Boll需要：{1}'. - format(len(self.lineBar), min(7, self.inputBollLen)+1)) - return - - if l < self.inputBollLen+2: - bollLen = l-1 - else: - bollLen = self.inputBollLen - - # 不包含当前最新的Bar - listClose=[x.close for x in self.lineBar[-bollLen - 1:-1]] - # - upper, middle, lower = ta.BBANDS(numpy.array(listClose, dtype=float), - timeperiod=bollLen, nbdevup=self.inputBollStdRate, - nbdevdn=self.inputBollStdRate, matype=0) - - self.lineUpperBand.append(upper[-1]) - self.lineMiddleBand.append(middle[-1]) - self.lineLowerBand.append(lower[-1]) - - - # ---------------------------------------------------------------------- - def writeCtaLog(self, content): - """记录CTA日志""" - self.strategy.writeCtaLog(u'['+self.name+u']'+content) - - def debugCtaLog(self,content): - """记录CTA日志""" - if DEBUGCTALOG: - self.strategy.writeCtaLog(u'['+self.name+u'-DEBUG]'+content) \ No newline at end of file diff --git a/vnpy/trader/app/ctaStrategy/tools/multiTimeFrame/Screen Shot 2016-11-08 at 9.08.47 PM.png b/vnpy/trader/app/ctaStrategy/tools/multiTimeFrame/Screen Shot 2016-11-08 at 9.08.47 PM.png deleted file mode 100644 index ca857f31..00000000 Binary files a/vnpy/trader/app/ctaStrategy/tools/multiTimeFrame/Screen Shot 2016-11-08 at 9.08.47 PM.png and /dev/null differ diff --git a/vnpy/trader/app/ctaStrategy/tools/multiTimeFrame/ctaBacktestMultiTF.py b/vnpy/trader/app/ctaStrategy/tools/multiTimeFrame/ctaBacktestMultiTF.py deleted file mode 100644 index ab492b79..00000000 --- a/vnpy/trader/app/ctaStrategy/tools/multiTimeFrame/ctaBacktestMultiTF.py +++ /dev/null @@ -1,204 +0,0 @@ -# encoding: UTF-8 - -''' -这个文件加入在CTA回测引擎的基础上加入了辅助品种信息, 保持接口的一致, 可以在原CTA引擎上执行的代码, -也可以在这个引擎上执行 -This file add multi Time Frame functionalities to CTA backtesting engine, the APIs are the -same as CTA engine. Real trading code can be directly used for backtesting. -''' - -from __future__ import division - -from vnpy.trader.vtObject import VtTickData, VtBarData -from ctaBacktesting import * - -class BacktestEngineMultiTF(BacktestingEngine): - - def __init__(self): - """Constructor""" - super(BacktestEngineMultiTF, self).__init__() - - self.info_symbols = [] # List, 输入辅助品种的2值tuple, 左边为数据库名, 右边为collection名 - self.InfoCursor = {} # Dict, 放置回测用辅助品种数据库 - self.initInfoCursor = {} # Dict, 放置初始化用辅助品种数据库 - self.infobar = {} # Dict, 放置辅助品种最新一个K线数据 - self.MultiOn = False # Boolean, 判断是否传入了辅助品种 - - # ---------------------------------------------------------------------- - def setDatabase(self, dbName, symbol, **kwargs): - """set database that provide historical data""" - - self.dbName = dbName - - # Set executed symbol and information symbols - self.symbol = symbol - if "info_symbol" in kwargs: - self.info_symbols = kwargs["info_symbol"] - - # Turn on MultiTF switch - if len(self.info_symbols) > 0: - self.MultiOn = True - - # ---------------------------------------------------------------------- - def loadInitData(self, collection, **kwargs): - """Load initializing data""" - # 载入初始化需要用的数据 - # Load initialised data - - # $gte means "greater and equal to" - # $lt means "less than" - flt = {'datetime': {'$gte': self.dataStartDate, - '$lt': self.strategyStartDate}} - self.initCursor = collection.find(flt) - - # 初始化辅助品种数据 - # Initializing information data - if "inf" in kwargs: - for name in kwargs["inf"]: - DB = kwargs["inf"][name] - self.initInfoCursor[name] = DB.find(flt) - - # 将数据从查询指针中读取出，并生成列表 - # Read data from cursor, generate a list - self.initData = [] - - for d in self.initCursor: - data = self.dataClass() - data.__dict__ = d - self.initData.append(data) - - # ---------------------------------------------------------------------- - def loadHistoryData(self): - """载入历史数据""" - self.dbClient = pymongo.MongoClient(globalSetting['mongoHost'], globalSetting['mongoPort']) - collection = self.dbClient[self.dbName][self.symbol] - - # Load historical data of information symbols, construct a dictionary of Database - # Values of dictionary are mongo.Client. - info_collection = {} - if self.MultiOn is True: - for DBname, symbol in self.info_symbols: - info_collection[DBname + " " + symbol] = self.dbClient[DBname][symbol] - - self.output("Start loading historical data") - - # 首先根据回测模式，确认要使用的数据类 - # Choose data type based on backtest mode - if self.mode == self.BAR_MODE: - self.dataClass = VtBarData - self.func = self.newBar - else: - self.dataClass = VtTickData - self.func = self.newTick - - # Load initializing data - self.loadInitData(collection, inf=info_collection) - - # 载入回测数据 - # Load backtest data (exclude initializing data) - if not self.dataEndDate: - # If "End Date" is not set, retreat data up to today - flt = {'datetime': {'$gte': self.strategyStartDate}} - else: - flt = {'datetime': {'$gte': self.strategyStartDate, - '$lte': self.dataEndDate}} - self.dbCursor = collection.find(flt) - - if self.MultiOn is True: - for db in info_collection: - self.InfoCursor[db] = info_collection[db].find(flt) - self.output( - "Data loading completed, data volumn: %s" % (self.initCursor.count() + self.dbCursor.count() + \ - sum([i.count() for i in self.InfoCursor.values()]))) - else: - self.output("Data loading completed, data volumn: %s" % (self.initCursor.count() + self.dbCursor.count())) - - # ---------------------------------------------------------------------- - def runBacktesting(self): - """运行回测""" - """Run backtesting""" - - # 载入历史数据 - # Load historical data - self.loadHistoryData() - - self.output("Start backtesing!") - - self.strategy.inited = True - self.strategy.onInit() - self.output("Strategy initialsing complete") - - self.strategy.trading = True - self.strategy.onStart() - self.output("Strategy started") - - self.output("Processing historical data...") - - dataClass = self.dataClass - func = self.func - for d in self.dbCursor: - data = dataClass() - data.__dict__ = d - func(data) - - self.output("No more historical data") - - # ---------------------------------------------------------------------- - def checkInformationData(self): - """Update information symbols' data""" - - # If infobar is empty, which means it is the first time calling this method - if self.infobar == {}: - for info_symbol in self.InfoCursor: - try: - self.infobar[info_symbol] = next(self.InfoCursor[info_symbol]) - except StopIteration: - print "Data of information symbols is empty! Input must be a list, not str." - raise - - temp = {} - for info_symbol in self.infobar: - - data = self.infobar[info_symbol] - - # Update data only when Time Stamp is matched - if (data is not None) and (data['datetime'] <= self.dt): - - try: - temp[info_symbol] = VtBarData() - temp[info_symbol].__dict__ = data - self.infobar[info_symbol] = next(self.InfoCursor[info_symbol]) - except StopIteration: - self.infobar[info_symbol] = None - self.output("No more data in information database.") - else: - temp[info_symbol] = None - - return temp - - # ---------------------------------------------------------------------- - def newBar(self, bar): - """新的K线""" - """new ohlc Bar""" - self.bar = bar - self.dt = bar.datetime - self.updatePosition() # Update total position value based on new Bar - self.crossLimitOrder() # 先撮合限价单 - self.crossStopOrder() # 再撮合停止单 - if self.MultiOn is True: - self.strategy.onBar(bar, infobar=self.checkInformationData()) # 推送K线到策略中 - else: - self.strategy.onBar(bar) # 推送K线到策略中 - - # ---------------------------------------------------------------------- - def newTick(self, tick): - """新的Tick""" - """new Tick""" - self.tick = tick - self.dt = tick.datetime - self.crossLimitOrder() - self.crossStopOrder() - self.strategy.onTick(tick) - -######################################################################## - diff --git a/vnpy/trader/app/ctaStrategy/tools/multiTimeFrame/ctaStrategyMultiTF.py b/vnpy/trader/app/ctaStrategy/tools/multiTimeFrame/ctaStrategyMultiTF.py deleted file mode 100644 index d12ca378..00000000 --- a/vnpy/trader/app/ctaStrategy/tools/multiTimeFrame/ctaStrategyMultiTF.py +++ /dev/null @@ -1,411 +0,0 @@ -# encoding: UTF-8 -""" -This file tweaks ctaTemplate Module to suit multi-TimeFrame strategies. -""" - - -from strategyAtrRsi import * -from ctaBase import * -from ctaTemplate import CtaTemplate - -######################################################################## -class TC11(CtaTemplate): - - # Strategy name and author - className = "TC11" - author = "Zenacon" - - # Set MongoDB DataBase - barDbName = "TestData" - - # Strategy parameters - pGeneric_prd = 21 - pGeneric_on = True - - pATRprd_F = 13 - pATRprd_M = 21 - pATRprd_S = 63 - - pBOSSplus_prd = 98 - pBOSSminus_prd = 22 - - if pGeneric_on == 0: - pRSIprd = 20 - pBBprd = 10 - pBB_ATRprd = 15 - pATRprd = 21 - pDMIprd = 21 - else: - pRSIprd = \ - pBBprd = \ - pBB_ATRprd = \ - pATRprd = \ - pDMIprd = pGeneric_prd - - pBOSS_Mult = 1.75 - - # Strategy variables - vOBO_initialpoint = EMPTY_FLOAT - vOBO_Stretch = EMPTY_FLOAT - vOBO_level_L = EMPTY_FLOAT - vOBO_level_S = EMPTY_FLOAT - - # parameters' list, record names of parameters - paramList = ['name', - 'className', - 'author', - 'vtSymbol'] - - # variables' list, record names of variables - varList = ['inited', - 'trading', - 'pos'] - - def __init__(self, ctaEngine, setting): - """Constructor""" - super(TC11, self).__init__(ctaEngine, setting) - - # ---------------------------------------------------------------------- - def onBar(self, bar, **kwargs): - """收到Bar推送（必须由用户继承实现）""" - # 撤销之前发出的尚未成交的委托（包括限价单和停止单） - for orderID in self.orderList: - self.cancelOrder(orderID) - self.orderList = [] - - # Record new information bar - if "infobar" in kwargs: - for i in kwargs["infobar"]: - if kwargs["infobar"][i] is None: - pass - else: - # print kwargs["infobar"][i]["close"] - self.closeArray[0:self.bufferSize - 1] = self.closeArray[1:self.bufferSize] - self.highArray[0:self.bufferSize - 1] = self.highArray[1:self.bufferSize] - self.lowArray[0:self.bufferSize - 1] = self.lowArray[1:self.bufferSize] - - self.closeArray[-1] = bar.close - self.highArray[-1] = bar.high - self.lowArray[-1] = bar.low - - """ - Record new bar - """ - self.closeArray[0:self.bufferSize - 1] = self.closeArray[1:self.bufferSize] - self.highArray[0:self.bufferSize - 1] = self.highArray[1:self.bufferSize] - self.lowArray[0:self.bufferSize - 1] = self.lowArray[1:self.bufferSize] - - self.closeArray[-1] = bar.close - self.highArray[-1] = bar.high - self.lowArray[-1] = bar.low - - self.bufferCount += 1 - if self.bufferCount < self.bufferSize: - return - - """ - Calculate Indicators - """ - - vOBO_initialpoint = self.dataHTF_filled['Open'] - vOBO_Stretch = self.vATR['htf'].m * self.pBOSS_Mult - - self.atrValue = talib.ATR(self.highArray, - self.lowArray, - self.closeArray, - self.atrLength)[-1] - self.atrArray[0:self.bufferSize - 1] = self.atrArray[1:self.bufferSize] - self.atrArray[-1] = self.atrValue - - self.atrCount += 1 - if self.atrCount < self.bufferSize: - return - - self.atrMa = talib.MA(self.atrArray, - self.atrMaLength)[-1] - self.rsiValue = talib.RSI(self.closeArray, - self.rsiLength)[-1] - - # 判断是否要进行交易 - - # 当前无仓位 - if self.pos == 0: - self.intraTradeHigh = bar.high - self.intraTradeLow = bar.low - - # ATR数值上穿其移动平均线，说明行情短期内波动加大 - # 即处于趋势的概率较大，适合CTA开仓 - if self.atrValue > self.atrMa: - # 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 - if self.rsiValue > self.rsiBuy: - # 这里为了保证成交，选择超价5个整指数点下单 - self.buy(bar.close + 5, 1) - - elif self.rsiValue < self.rsiSell: - self.short(bar.close - 5, 1) - - # 持有多头仓位 - elif self.pos > 0: - # 计算多头持有期内的最高价，以及重置最低价 - self.intraTradeHigh = max(self.intraTradeHigh, bar.high) - self.intraTradeLow = bar.low - # 计算多头移动止损 - longStop = self.intraTradeHigh * (1 - self.trailingPercent / 100) - # 发出本地止损委托，并且把委托号记录下来，用于后续撤单 - orderID = self.sell(longStop, 1, stop=True) - self.orderList.append(orderID) - - # 持有空头仓位 - elif self.pos < 0: - self.intraTradeLow = min(self.intraTradeLow, bar.low) - self.intraTradeHigh = bar.high - - shortStop = self.intraTradeLow * (1 + self.trailingPercent / 100) - orderID = self.cover(shortStop, 1, stop=True) - self.orderList.append(orderID) - - # 发出状态更新事件 - self.putEvent() - -######################################################################## -class Prototype(AtrRsiStrategy): - - """ - "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。 - - 调用的方法: - self.infoArray["数据库名 + 空格 + collection名"]["close"] - self.infoArray["数据库名 + 空格 + collection名"]["high"] - self.infoArray["数据库名 + 空格 + collection名"]["low"] - """ - infoArray = {} - initInfobar = {} - - def __int__(self): - super(Prototype, self).__int__() - - # ---------------------------------------------------------------------- - def onInit(self): - """初始化策略（必须由用户继承实现）""" - self.writeCtaLog(u'%s策略初始化' % self.name) - - # 初始化RSI入场阈值 - self.rsiBuy = 50 + self.rsiEntry - self.rsiSell = 50 - self.rsiEntry - - # 载入历史数据，并采用回放计算的方式初始化策略数值 - initData = self.loadBar(self.initDays) - for bar in initData: - - # 推送新数据, 同时检查是否有information bar需要推送 - # Update new bar, check whether the Time Stamp matching any information bar - ibar = self.checkInfoBar(bar) - self.onBar(bar, infobar=ibar) - - self.putEvent() - - # ---------------------------------------------------------------------- - def checkInfoBar(self, bar): - """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)""" - - initInfoCursorDict = self.ctaEngine.initInfoCursor - - # 如果"initInfobar"字典为空, 初始化字典, 插入第一个数据 - # If dictionary "initInfobar" is empty, insert first data record - if self.initInfobar == {}: - for info_symbol in initInfoCursorDict: - try: - self.initInfobar[info_symbol] = next(initInfoCursorDict[info_symbol]) - except StopIteration: - print "Data of information symbols is empty! Input is a list, not str." - raise - - # 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, - # 然后更新该品种的数据 - # If any symbol's TimeStamp is matched with execution symbol's TimeStamp, return data - # in "initInfobar", and update new data. - temp = {} - for info_symbol in self.initInfobar: - - data = self.initInfobar[info_symbol] - - # Update data only when Time Stamp is matched - if data['datetime'] <= bar.datetime: - try: - temp[info_symbol] = CtaBarData() - temp[info_symbol].__dict__ = data - self.initInfobar[info_symbol] = next(initInfoCursorDict[info_symbol]) - except StopIteration: - self.ctaEngine.output("No more data for initializing %s." % (info_symbol,)) - else: - temp[info_symbol] = None - - return temp - - # ---------------------------------------------------------------------- - def updateInfoArray(self, infobar): - """收到Infomation Data, 更新辅助品种缓存字典""" - - for name in infobar: - - data = infobar[name] - - # Construct empty array - if len(self.infoArray) < len(infobar) : - self.infoArray[name] = { - "close": np.zeros(self.bufferSize), - "high": np.zeros(self.bufferSize), - "low": np.zeros(self.bufferSize) - } - - if data is None: - pass - - else: - self.infoArray[name]["close"][0:self.bufferSize - 1] = \ - self.infoArray[name]["close"][1:self.bufferSize] - self.infoArray[name]["high"][0:self.bufferSize - 1] = \ - self.infoArray[name]["high"][1:self.bufferSize] - self.infoArray[name]["low"][0:self.bufferSize - 1] = \ - self.infoArray[name]["low"][1:self.bufferSize] - - self.infoArray[name]["close"][-1] = data.close - self.infoArray[name]["high"][-1] = data.high - self.infoArray[name]["low"][-1] = data.low - - # ---------------------------------------------------------------------- - def onBar(self, bar, **kwargs): - """收到Bar推送（必须由用户继承实现）""" - # 撤销之前发出的尚未成交的委托（包括限价单和停止单） - for orderID in self.orderList: - self.cancelOrder(orderID) - self.orderList = [] - - # Update infomation data - # "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, - # 则传入的是"None", 当time stamp和执行品种匹配时, 传入的是"Bar" - self.updateInfoArray(kwargs["infobar"]) - - # 保存K线数据 - self.closeArray[0:self.bufferSize - 1] = self.closeArray[1:self.bufferSize] - self.highArray[0:self.bufferSize - 1] = self.highArray[1:self.bufferSize] - self.lowArray[0:self.bufferSize - 1] = self.lowArray[1:self.bufferSize] - - self.closeArray[-1] = bar.close - self.highArray[-1] = bar.high - self.lowArray[-1] = bar.low - - # 若读取的缓存数据不足, 不考虑交易 - self.bufferCount += 1 - if self.bufferCount < self.bufferSize: - return - - # 计算指标数值 - - # 计算不同时间下的ATR数值 - - # Only trading when information bar changes - # 只有在30min或者1d K线更新后才可以交易 - TradeOn = False - if any([i is not None for i in kwargs["infobar"].values()]): - - TradeOn = True - self.scaledAtrValue1M = talib.ATR(self.highArray, - self.lowArray, - self.closeArray, - self.atrLength)[-1] * (25) ** (0.5) - self.atrValue30M = talib.abstract.ATR(self.infoArray["TestData @GC_30M"])[-1] - self.rsiValue = talib.abstract.RSI(self.infoArray["TestData @GC_30M"], self.rsiLength)[-1] - - self.atrCount += 1 - if self.atrCount < self.bufferSize: - return - - # 判断是否要进行交易 - - # 当前无仓位 - if (self.pos == 0 and TradeOn == True): - self.intraTradeHigh = bar.high - self.intraTradeLow = bar.low - - # 1Min调整后ATR大于30MinATR - # 即处于趋势的概率较大，适合CTA开仓 - if self.atrValue30M < self.scaledAtrValue1M: - # 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 - if self.rsiValue > self.rsiBuy: - # 这里为了保证成交，选择超价5个整指数点下单 - self.buy(bar.close+5, 1) - - elif self.rsiValue < self.rsiSell: - self.short(bar.close-5, 1) - - # 下单后, 在下一个30Min K线之前不交易 - TradeOn = False - - # 持有多头仓位 - elif self.pos > 0: - # 计算多头持有期内的最高价，以及重置最低价 - self.intraTradeHigh = max(self.intraTradeHigh, bar.high) - self.intraTradeLow = bar.low - # 计算多头移动止损 - longStop = self.intraTradeHigh * (1 - self.trailingPercent / 100) - # 发出本地止损委托，并且把委托号记录下来，用于后续撤单 - orderID = self.sell(longStop, 1, stop=True) - self.orderList.append(orderID) - - # 持有空头仓位 - elif self.pos < 0: - self.intraTradeLow = min(self.intraTradeLow, bar.low) - self.intraTradeHigh = bar.high - - shortStop = self.intraTradeLow * (1 + self.trailingPercent / 100) - orderID = self.cover(shortStop, 1, stop=True) - self.orderList.append(orderID) - - # 发出状态更新事件 - self.putEvent() - - -if __name__ == '__main__': - # 提供直接双击回测的功能 - # 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 - from ctaBacktestMultiTF import * - from PyQt4 import QtCore, QtGui - import time - - ''' - 创建回测引擎 - 设置引擎的回测模式为K线 - 设置回测用的数据起始日期 - 载入历史数据到引擎中 - 在引擎中创建策略对象 - - Create backtesting engine - Set backtest mode as "Bar" - Set "Start Date" of data range - Load historical data to engine - Create strategy instance in engine - ''' - engine = BacktestEngineMultiTF() - engine.setBacktestingMode(engine.BAR_MODE) - engine.setStartDate('20100101') - engine.setDatabase("TestData", "@GC_1M", info_symbol=[("TestData","@GC_30M")]) - - # Set parameters for strategy - d = {'atrLength': 11} - engine.initStrategy(Prototype, d) - - # 设置产品相关参数 - engine.setSlippage(0.2) # 股指1跳 - engine.setCommission(0.3 / 10000) # 万0.3 - engine.setSize(300) # 股指合约大小 - - # 开始跑回测 - start = time.time() - - engine.runBacktesting() - - # 显示回测结果 - engine.showBacktestingResult() - - print 'Time consumed：%s' % (time.time() - start) \ No newline at end of file diff --git a/vnpy/trader/app/ctaStrategy/tools/multiTimeFrame/strategyBreakOut.py b/vnpy/trader/app/ctaStrategy/tools/multiTimeFrame/strategyBreakOut.py deleted file mode 100644 index 3236ec0b..00000000 --- a/vnpy/trader/app/ctaStrategy/tools/multiTimeFrame/strategyBreakOut.py +++ /dev/null @@ -1,317 +0,0 @@ -# encoding: UTF-8 -""" -This file tweaks ctaTemplate Module to suit multi-TimeFrame strategies. -""" - -from ctaBase import * -from ctaTemplate import CtaTemplate -import numpy as np - -######################################################################## -class BreakOut(CtaTemplate): - - """ - "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。 - - 调用的方法: - 价格序列: - self.infoArray["数据库名 + 空格 + collection名"]["close"] - self.infoArray["数据库名 + 空格 + collection名"]["high"] - self.infoArray["数据库名 + 空格 + collection名"]["low"] - - 单个价格: - self.infoBar["数据库名 + 空格 + collection名"] - 返回的值为一个ctaBarData 或 None - """ - - #---------------------------------------------------------------------- - def __init__(self, ctaEngine, setting): - """日内突破交易策略, 出场方式非常多, 本文件使用指标出场""" - - className = 'BreakOut' - author = 'Joe' - super(BreakOut, self).__init__(ctaEngine, setting) - - # 设置辅助品种数据字典 - self.infoArray = {} - self.initInfobar = {} - self.infoBar = {} - - # 缓存数据量 - self.bufferSize = 100 - self.bufferCount = 0 - self.initDays = 10 - - # 设置参数 - self.pOBO_Mult = 0.5 # 计算突破点位 - # self.pProtMult = 2 # 止损的ATR倍数 - # self.pProfitMult = 2 # 止盈相对于止损的倍数 - # self.SlTp_On = False # 止损止盈功能 - # self.EODTime = 15 # 设置日内平仓时间 - - self.vOBO_stretch = EMPTY_FLOAT - self.vOBO_initialpoint = EMPTY_FLOAT - self.vOBO_level_L = EMPTY_FLOAT - self.vOBO_level_S = EMPTY_FLOAT - - self.orderList = [] - - # 参数列表，保存了参数的名称 - paramList = ['name', - 'className', - 'author', - 'pOBO_Mult', - 'pProtMult', - 'pProfitMult', - 'SlTp_On', - 'EODTime'] - - # 变量列表，保存了变量的名称 - varList = ['vOBO_stretch', - 'vOBO_initialpoint', - 'vOBO_level_L', - 'vOBO_level_S'] - - # ---------------------------------------------------------------------- - def onInit(self): - """初始化策略（必须由用户继承实现）""" - self.writeCtaLog(u'%s策略初始化' % self.name) - - # 载入历史数据，并采用回放计算的方式初始化策略数值 - initData = self.loadBar(self.initDays) - for bar in initData: - - # 推送新数据, 同时检查是否有information bar需要推送 - # Update new bar, check whether the Time Stamp matching any information bar - ibar = self.checkInfoBar(bar) - self.onBar(bar, infobar=ibar) - - self.putEvent() - - #---------------------------------------------------------------------- - def onStart(self): - """启动策略（必须由用户继承实现）""" - self.writeCtaLog(u'%s策略启动' %self.name) - self.putEvent() - - #---------------------------------------------------------------------- - def onStop(self): - """停止策略（必须由用户继承实现）""" - self.writeCtaLog(u'%s策略停止' %self.name) - self.putEvent() - - # ---------------------------------------------------------------------- - def checkInfoBar(self, bar): - """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)""" - - initInfoCursorDict = self.ctaEngine.initInfoCursor - - # 如果"initInfobar"字典为空, 初始化字典, 插入第一个数据 - # If dictionary "initInfobar" is empty, insert first data record - if self.initInfobar == {}: - for info_symbol in initInfoCursorDict: - try: - self.initInfobar[info_symbol] = next(initInfoCursorDict[info_symbol]) - except StopIteration: - print "Data of information symbols is empty! Input is a list, not str." - raise - - # 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, - # 然后更新该品种的数据 - # If any symbol's TimeStamp is matched with execution symbol's TimeStamp, return data - # in "initInfobar", and update new data. - temp = {} - for info_symbol in self.initInfobar: - - data = self.initInfobar[info_symbol] - - # Update data only when Time Stamp is matched - - if (data is not None) and (data['datetime'] <= bar.datetime): - - try: - temp[info_symbol] = CtaBarData() - temp[info_symbol].__dict__ = data - self.initInfobar[info_symbol] = next(initInfoCursorDict[info_symbol]) - except StopIteration: - self.initInfobar[info_symbol] = None - self.ctaEngine.output("No more data for initializing %s." % (info_symbol,)) - else: - temp[info_symbol] = None - - return temp - - # ---------------------------------------------------------------------- - def updateInfoArray(self, infobar): - """收到Infomation Data, 更新辅助品种缓存字典""" - - for name in infobar: - - data = infobar[name] - - # Construct empty array - if len(self.infoArray) < len(infobar) : - self.infoArray[name] = { - "close": np.zeros(self.bufferSize), - "high": np.zeros(self.bufferSize), - "low": np.zeros(self.bufferSize), - "open": np.zeros(self.bufferSize) - } - - if data is None: - pass - - else: - self.infoArray[name]["close"][0:self.bufferSize - 1] = \ - self.infoArray[name]["close"][1:self.bufferSize] - self.infoArray[name]["high"][0:self.bufferSize - 1] = \ - self.infoArray[name]["high"][1:self.bufferSize] - self.infoArray[name]["low"][0:self.bufferSize - 1] = \ - self.infoArray[name]["low"][1:self.bufferSize] - self.infoArray[name]["open"][0:self.bufferSize - 1] = \ - self.infoArray[name]["open"][1:self.bufferSize] - - self.infoArray[name]["close"][-1] = data.close - self.infoArray[name]["high"][-1] = data.high - self.infoArray[name]["low"][-1] = data.low - self.infoArray[name]["open"][-1] = data.open - - # ---------------------------------------------------------------------- - def onBar(self, bar, **kwargs): - """收到Bar推送（必须由用户继承实现）""" - - # Update infomation data - # "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, - # 则传入的是"None", 当time stamp和执行品种匹配时, 传入的是"Bar" - if "infobar" in kwargs: - self.infoBar = kwargs["infobar"] - self.updateInfoArray(kwargs["infobar"]) - - # 若读取的缓存数据不足, 不考虑交易 - self.bufferCount += 1 - if self.bufferCount < self.bufferSize: - return - - # 计算指标数值 - a = np.sum(self.infoArray["TestData @GC_1D"]["close"]) - if a == 0.0: - return - - # Only updating indicators when information bar changes - # 只有在30min或者1d K线更新后才更新指标 - TradeOn = False - if any([i is not None for i in self.infoBar]): - TradeOn = True - self.vRange = self.infoArray["TestData @GC_1D"]["high"][-1] -\ - self.infoArray["TestData @GC_1D"]["low"][-1] - self.vOBO_stretch = self.vRange * self.pOBO_Mult - self.vOBO_initialpoint = self.infoArray["TestData @GC_1D"]["close"][-1] - self.vOBO_level_L = self.vOBO_initialpoint + self.vOBO_stretch - self.vOBO_level_S = self.vOBO_initialpoint - self.vOBO_stretch - - self.atrValue30M = talib.abstract.ATR(self.infoArray["TestData @GC_30M"])[-1] - - # 判断是否要进行交易 - - # 当前无仓位 - if (self.pos == 0 and TradeOn == True): - - # 撤销之前发出的尚未成交的委托（包括限价单和停止单） - for orderID in self.orderList: - self.cancelOrder(orderID) - self.orderList = [] - - # 若上一个30分钟K线的最高价大于OBO_level_L - # 且当前的价格大于OBO_level_L, 则买入 - if self.infoArray["TestData @GC_30M"]["high"][-1] > self.vOBO_level_L: - - if bar.close > self.vOBO_level_L: - - self.buy(bar.close + 0.5, 1) - - # 下单后, 在下一个30Min K线之前不交易 - TradeOn = False - - # 若上一个30分钟K线的最高价低于OBO_level_S - # 且当前的价格小于OBO_level_S, 则卖出 - elif self.infoArray["TestData @GC_30M"]["low"][-1] < self.vOBO_level_S: - - if bar.close < self.vOBO_level_S: - - self.short(bar.close - 0.5, 1) - - # 下单后, 在下一个30Min K线之前不交易 - TradeOn = False - - # 持有多头仓位 - elif self.pos > 0: - - # 当价格低于initialpoint水平, 出场 - if bar.close < self.vOBO_initialpoint: - self.sell(bar.close - 0.5 , 1) - - # 持有空头仓位 - elif self.pos < 0: - - # 当价格高于initialpoint水平, 出场 - if bar.close > self.vOBO_initialpoint: - self.cover(bar.close + 0.5, 1) - - - # 发出状态更新事件 - self.putEvent() - - # ---------------------------------------------------------------------- - def onOrder(self, order): - """收到委托变化推送（必须由用户继承实现）""" - pass - - # ---------------------------------------------------------------------- - def onTrade(self, trade): - pass - - -if __name__ == '__main__': - # 提供直接双击回测的功能 - # 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 - from ctaBacktestMultiTF import * - from PyQt4 import QtCore, QtGui - import time - - ''' - 创建回测引擎 - 设置引擎的回测模式为K线 - 设置回测用的数据起始日期 - 载入历史数据到引擎中 - 在引擎中创建策略对象 - - Create backtesting engine - Set backtest mode as "Bar" - Set "Start Date" of data range - Load historical data to engine - Create strategy instance in engine - ''' - engine = BacktestEngineMultiTF() - engine.setBacktestingMode(engine.BAR_MODE) - engine.setStartDate('20120101') - engine.setEndDate('20150101') - engine.setDatabase("TestData", "@GC_1M", info_symbol=[("TestData","@GC_30M"), - ("TestData","@GC_1D")]) - - # Set parameters for strategy - engine.initStrategy(BreakOut, {}) - - # 设置产品相关参数 - engine.setSlippage(0.2) # 股指1跳 - engine.setCommission(0.3 / 10000) # 万0.3 - engine.setSize(1) # 股指合约大小 - - # 开始跑回测 - start = time.time() - - engine.runBacktesting() - - # 显示回测结果 - engine.showBacktestingResult() - - print 'Time consumed：%s' % (time.time() - start) \ No newline at end of file