From cbb7b1b510f60e010a47966b257c1fdd9a0fcecd Mon Sep 17 00:00:00 2001 From: "vn.py" Date: Fri, 15 Dec 2017 23:05:22 +0800 Subject: [PATCH] =?UTF-8?q?[Add]=E6=96=B0=E5=A2=9E=E5=A4=9A=E4=BF=A1?= =?UTF-8?q?=E5=8F=B7CTA=E7=AD=96=E7=95=A5=E5=BC=80=E5=8F=91=E5=8A=9F?= =?UTF-8?q?=E8=83=BD=EF=BC=8Cclose=20#567?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../backtesting_IF-checkpoint.ipynb | 38 ++- examples/CtaBacktesting/backtesting_IF.ipynb | 73 +---- vnpy/trader/app/ctaStrategy/ctaTemplate.py | 40 ++- .../strategy/strategyMultiSignal.py | 251 ++++++++++++++++++ 4 files changed, 327 insertions(+), 75 deletions(-) create mode 100644 vnpy/trader/app/ctaStrategy/strategy/strategyMultiSignal.py diff --git a/examples/CtaBacktesting/.ipynb_checkpoints/backtesting_IF-checkpoint.ipynb b/examples/CtaBacktesting/.ipynb_checkpoints/backtesting_IF-checkpoint.ipynb index 6d5f5b73..b6b586ee 100644 --- a/examples/CtaBacktesting/.ipynb_checkpoints/backtesting_IF-checkpoint.ipynb +++ b/examples/CtaBacktesting/.ipynb_checkpoints/backtesting_IF-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -11,13 +11,14 @@ "%matplotlib inline\n", "\n", "from vnpy.trader.app.ctaStrategy.ctaBacktesting import BacktestingEngine, OptimizationSetting, MINUTE_DB_NAME\n", - "from vnpy.trader.app.ctaStrategy.strategy.strategyAtrRsi import AtrRsiStrategy\n", - "from vnpy.trader.app.ctaStrategy.strategy.strategyMultiTimeframe import MultiTimeframeStrategy" + "#from vnpy.trader.app.ctaStrategy.strategy.strategyAtrRsi import AtrRsiStrategy\n", + "#from vnpy.trader.app.ctaStrategy.strategy.strategyMultiTimeframe import MultiTimeframeStrategy\n", + "from vnpy.trader.app.ctaStrategy.strategy.strategyMultiSignal import MultiSignalStrategy" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -29,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -38,12 +39,12 @@ "# 设置回测使用的数据\n", "engine.setBacktestingMode(engine.BAR_MODE) # 设置引擎的回测模式为K线\n", "engine.setDatabase(MINUTE_DB_NAME, 'IF0000') # 设置使用的历史数据库\n", - "engine.setStartDate('20120101') # 设置回测用的数据起始日期" + "engine.setStartDate('20100101') # 设置回测用的数据起始日期" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -59,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -68,16 +69,31 @@ "# 在引擎中创建策略对象\n", "d = {'atrLength': 11} # 策略参数配置\n", "#engine.initStrategy(AtrRsiStrategy, d) # 创建策略对象\n", - "engine.initStrategy(MultiTimeframeStrategy, d) # 创建策略对象" + "#ngine.initStrategy(MultiTimeframeStrategy, d) \n", + "engine.initStrategy(MultiSignalStrategy, {}) " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2017-12-15 23:01:54.728000\t开始载入数据\n", + "2017-12-15 23:01:54.765000\t载入完成，数据量：0\n", + "2017-12-15 23:01:54.765000\t开始回测\n", + "2017-12-15 23:01:54.765000\t策略初始化完成\n", + "2017-12-15 23:01:54.765000\t策略启动完成\n", + "2017-12-15 23:01:54.765000\t开始回放数据\n", + "2017-12-15 23:01:54.766000\t数据回放结束\n" + ] + } + ], "source": [ "# 运行回测\n", "engine.runBacktesting() # 运行回测" diff --git a/examples/CtaBacktesting/backtesting_IF.ipynb b/examples/CtaBacktesting/backtesting_IF.ipynb index 029af63a..00162a1c 100644 --- a/examples/CtaBacktesting/backtesting_IF.ipynb +++ b/examples/CtaBacktesting/backtesting_IF.ipynb @@ -11,8 +11,9 @@ "%matplotlib inline\n", "\n", "from vnpy.trader.app.ctaStrategy.ctaBacktesting import BacktestingEngine, OptimizationSetting, MINUTE_DB_NAME\n", - "from vnpy.trader.app.ctaStrategy.strategy.strategyAtrRsi import AtrRsiStrategy\n", - "from vnpy.trader.app.ctaStrategy.strategy.strategyMultiTimeframe import MultiTimeframeStrategy" + "#from vnpy.trader.app.ctaStrategy.strategy.strategyAtrRsi import AtrRsiStrategy\n", + "#from vnpy.trader.app.ctaStrategy.strategy.strategyMultiTimeframe import MultiTimeframeStrategy\n", + "from vnpy.trader.app.ctaStrategy.strategy.strategyMultiSignal import MultiSignalStrategy" ] }, { @@ -38,7 +39,7 @@ "# 设置回测使用的数据\n", "engine.setBacktestingMode(engine.BAR_MODE) # 设置引擎的回测模式为K线\n", "engine.setDatabase(MINUTE_DB_NAME, 'IF0000') # 设置使用的历史数据库\n", - "engine.setStartDate('20120101') # 设置回测用的数据起始日期" + "engine.setStartDate('20100101') # 设置回测用的数据起始日期" ] }, { @@ -68,30 +69,17 @@ "# 在引擎中创建策略对象\n", "d = {'atrLength': 11} # 策略参数配置\n", "#engine.initStrategy(AtrRsiStrategy, d) # 创建策略对象\n", - "engine.initStrategy(MultiTimeframeStrategy, d) # 创建策略对象" + "#ngine.initStrategy(MultiTimeframeStrategy, d) \n", + "engine.initStrategy(MultiSignalStrategy, {}) " ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2017-12-04 15:35:51.714000\t开始载入数据\n", - "2017-12-04 15:35:52.312000\t载入完成，数据量：351090\n", - "2017-12-04 15:35:52.312000\t开始回测\n", - "2017-12-04 15:35:52.333000\t策略初始化完成\n", - "2017-12-04 15:35:52.333000\t策略启动完成\n", - "2017-12-04 15:35:52.334000\t开始回放数据\n", - "2017-12-04 15:36:11.673000\t数据回放结束\n" - ] - } - ], + "outputs": [], "source": [ "# 运行回测\n", "engine.runBacktesting() # 运行回测" @@ -99,52 +87,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2017-12-04 15:36:11.682000\t计算按日统计结果\n", - "2017-12-04 15:36:11.743000\t------------------------------\n", - "2017-12-04 15:36:11.743000\t首个交易日：\t2012-01-11\n", - "2017-12-04 15:36:11.743000\t最后交易日：\t2017-07-14\n", - "2017-12-04 15:36:11.743000\t总交易日：\t1338\n", - "2017-12-04 15:36:11.743000\t盈利交易日\t442\n", - "2017-12-04 15:36:11.743000\t亏损交易日：\t632\n", - "2017-12-04 15:36:11.743000\t起始资金：\t1000000\n", - "2017-12-04 15:36:11.743000\t结束资金：\t1,415,037.22\n", - "2017-12-04 15:36:11.743000\t总收益率：\t41.5\n", - "2017-12-04 15:36:11.743000\t总盈亏：\t415,037.22\n", - "2017-12-04 15:36:11.743000\t最大回撤: \t-292,237.65\n", - "2017-12-04 15:36:11.743000\t总手续费：\t49,722.78\n", - "2017-12-04 15:36:11.743000\t总滑点：\t113,580.0\n", - "2017-12-04 15:36:11.743000\t总成交金额：\t1,657,426,020.0\n", - "2017-12-04 15:36:11.743000\t总成交笔数：\t1,893.0\n", - "2017-12-04 15:36:11.743000\t日均盈亏：\t310.19\n", - "2017-12-04 15:36:11.743000\t日均手续费：\t37.16\n", - "2017-12-04 15:36:11.743000\t日均滑点：\t84.89\n", - "2017-12-04 15:36:11.743000\t日均成交金额：\t1,238,733.95\n", - "2017-12-04 15:36:11.743000\t日均成交笔数：\t1.41\n", - "2017-12-04 15:36:11.743000\t日均收益率：\t0.03%\n", - "2017-12-04 15:36:11.743000\t收益标准差：\t0.87%\n", - "2017-12-04 15:36:11.743000\tSharpe Ratio：\t0.46\n" - ] - }, - { - "data": { - "image/png": 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Ac+fOZfr06VgsFkJCQujevTsAQUFBjBgxAsMwmD17NgCTJk0iIiKCmJgYvL29i45RmvpY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\nifcvgA8//JDo6Gj69u1bd41vICQIFBVmtVr9dRpvuukmpk2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weP7wXlOCx1NK166ZwEas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GAAAg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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# 显示逐日回测结果\n", "engine.showDailyResult()" diff --git a/vnpy/trader/app/ctaStrategy/ctaTemplate.py b/vnpy/trader/app/ctaStrategy/ctaTemplate.py index 1cd7376d..ef1acea2 100644 --- a/vnpy/trader/app/ctaStrategy/ctaTemplate.py +++ b/vnpy/trader/app/ctaStrategy/ctaTemplate.py @@ -600,4 +600,42 @@ class ArrayManager(object): if array: return up, down - return up[-1], down[-1] \ No newline at end of file + return up[-1], down[-1] + + +######################################################################## +class CtaSignal(object): + """ + CTA策略信号，负责纯粹的信号生成（目标仓位），不参与具体交易管理 + """ + + #---------------------------------------------------------------------- + def __init__(self): + """Constructor""" + self.signalPos = 0 # 信号仓位 + + #---------------------------------------------------------------------- + def onBar(self, bar): + """K线推送""" + pass + + #---------------------------------------------------------------------- + def onTick(self, tick): + """Tick推送""" + pass + + #---------------------------------------------------------------------- + def setSignalPos(self, pos): + """设置信号仓位""" + self.signalPos = pos + + #---------------------------------------------------------------------- + def getSignalPos(self): + """获取信号仓位""" + return self.signalPos + + + + + + \ No newline at end of file diff --git a/vnpy/trader/app/ctaStrategy/strategy/strategyMultiSignal.py b/vnpy/trader/app/ctaStrategy/strategy/strategyMultiSignal.py new file mode 100644 index 00000000..1e062ff7 --- /dev/null +++ b/vnpy/trader/app/ctaStrategy/strategy/strategyMultiSignal.py @@ -0,0 +1,251 @@ +# encoding: UTF-8 + +""" +一个多信号组合策略，基于的信号包括： +RSI（1分钟）：大于70为多头、低于30为空头 +CCI（1分钟）：大于10为多头、低于-10为空头 +MA（5分钟）：快速大于慢速为多头、低于慢速为空头 +""" + +from vnpy.trader.vtObject import VtBarData +from vnpy.trader.vtConstant import EMPTY_STRING +from vnpy.trader.app.ctaStrategy.ctaTemplate import (TargetPosTemplate, + CtaSignal, + BarGenerator, + ArrayManager) + + +######################################################################## +class RsiSignal(CtaSignal): + """RSI信号""" + + #---------------------------------------------------------------------- + def __init__(self): + """Constructor""" + super(RsiSignal, self).__init__() + + self.rsiWindow = 14 + self.rsiLevel = 20 + self.rsiLong = 50 + self.rsiLevel + self.rsiShort = 50 - self.rsiLevel + + self.bg = BarGenerator(self.onBar) + self.am = ArrayManager() + + #---------------------------------------------------------------------- + def onTick(self, tick): + """Tick更新""" + self.bg.updateTick(tick) + + #---------------------------------------------------------------------- + def onBar(self, bar): + """K线更新""" + self.am.updateBar(bar) + + if not self.am.inited: + self.setSignalPos(0) + + rsiValue = self.am.rsi(self.rsiWindow) + + if rsiValue >= self.rsiLong: + self.setSignalPos(1) + elif rsiValue <= self.rsiShort: + self.setSignalPos(-1) + else: + self.setSignalPos(0) + + +######################################################################## +class CciSignal(CtaSignal): + """CCI信号""" + + #---------------------------------------------------------------------- + def __init__(self): + """Constructor""" + super(CciSignal, self).__init__() + + self.cciWindow = 30 + self.cciLevel = 10 + self.cciLong = self.cciLevel + self.cciShort = -self.cciLevel + + self.bg = BarGenerator(self.onBar) + self.am = ArrayManager() + + #---------------------------------------------------------------------- + def onTick(self, tick): + """Tick更新""" + self.bg.updateTick(tick) + + #---------------------------------------------------------------------- + def onBar(self, bar): + """K线更新""" + self.am.updateBar(bar) + + if not self.am.inited: + self.setSignalPos(0) + + cciValue = self.am.cci(self.cciWindow) + + if cciValue >= self.cciLong: + self.setSignalPos(1) + elif cciValue<= self.cciShort: + self.setSignalPos(-1) + else: + self.setSignalPos(0) + + +######################################################################## +class MaSignal(CtaSignal): + """双均线信号""" + + #---------------------------------------------------------------------- + def __init__(self): + """Constructor""" + super(MaSignal, self).__init__() + + self.fastWindow = 5 + self.slowWindow = 20 + + self.bg = BarGenerator(self.onBar, 5, self.onFiveBar) + self.am = ArrayManager() + + #---------------------------------------------------------------------- + def onTick(self, tick): + """Tick更新""" + self.bg.updateTick(tick) + + #---------------------------------------------------------------------- + def onBar(self, bar): + """K线更新""" + self.bg.updateBar(bar) + + #---------------------------------------------------------------------- + def onFiveBar(self, bar): + """5分钟K线更新""" + self.am.updateBar(bar) + + if not self.am.inited: + self.setSignalPos(0) + + fastMa = self.am.sma(self.fastWindow) + slowMa = self.am.sma(self.slowWindow) + + if fastMa > slowMa: + self.setSignalPos(1) + elif fastMa < slowMa: + self.setSignalPos(-1) + else: + self.setSignalPos(0) + + +######################################################################## +class MultiSignalStrategy(TargetPosTemplate): + """跨时间周期交易策略""" + className = 'MultiSignalStrategy' + author = u'用Python的交易员' + + # 策略参数 + initDays = 10 # 初始化数据所用的天数 + fixedSize = 1 # 每次交易的数量 + + # 策略变量 + signalPos = {} # 信号仓位 + + # 参数列表，保存了参数的名称 + paramList = ['name', + 'className', + 'author', + 'vtSymbol'] + + # 变量列表，保存了变量的名称 + varList = ['inited', + 'trading', + 'pos', + 'signalPos', + 'targetPos'] + + # 同步列表，保存了需要保存到数据库的变量名称 + syncList = ['pos'] + + #---------------------------------------------------------------------- + def __init__(self, ctaEngine, setting): + """Constructor""" + super(MultiSignalStrategy, self).__init__(ctaEngine, setting) + + self.rsiSignal = RsiSignal() + self.cciSignal = CciSignal() + self.maSignal = MaSignal() + + self.signalPos = { + "rsi": 0, + "cci": 0, + "ma": 0 + } + + #---------------------------------------------------------------------- + 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: + self.onBar(bar) + + 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 onTick(self, tick): + """收到行情TICK推送（必须由用户继承实现）""" + super(MultiSignalStrategy, self).onTick(tick) + + self.rsiSignal.onTick(tick) + self.cciSignal.onTick(tick) + self.maSignal.onTick(tick) + + #---------------------------------------------------------------------- + def onBar(self, bar): + """收到Bar推送（必须由用户继承实现）""" + super(MultiSignalStrategy, self).onBar(bar) + + self.rsiSignal.onBar(bar) + self.cciSignal.onBar(bar) + self.maSignal.onBar(bar) + + self.signalPos['rsi'] = self.rsiSignal.getSignalPos() + self.signalPos['cci'] = self.cciSignal.getSignalPos() + self.signalPos['ma'] = self.maSignal.getSignalPos() + + targetPos = 0 + for v in self.signalPos.values(): + targetPos += v + + self.setTargetPos(targetPos) + + #---------------------------------------------------------------------- + def onOrder(self, order): + """收到委托变化推送（必须由用户继承实现）""" + super(MultiSignalStrategy, self).onOrder(order) + + #---------------------------------------------------------------------- + def onTrade(self, trade): + # 发出状态更新事件 + self.putEvent() + + #---------------------------------------------------------------------- + def onStopOrder(self, so): + """停止单推送""" + pass \ No newline at end of file