diff --git a/vn.trader/ctaAlgo/strategyAtrRsi.py b/vn.trader/ctaAlgo/strategyAtrRsi.py new file mode 100644 index 00000000..74aabc6f --- /dev/null +++ b/vn.trader/ctaAlgo/strategyAtrRsi.py @@ -0,0 +1,270 @@ +# encoding: UTF-8 + +""" +一个ATR-RSI指标结合的交易策略，适合用在股指的1分钟和5分钟线上。 + +注意事项： +1. 作者不对交易盈利做任何保证，策略代码仅供参考 +2. 本策略需要用到talib，没有安装的用户请先参考www.vnpy.org上的教程安装 +3. 将IF0000_1min.csv用ctaHistoryData.py导入MongoDB后，直接运行本文件即可回测策略 + +""" + + +from ctaBase import * +from ctaTemplate import CtaTemplate + +import talib +import numpy as np + + +######################################################################## +class AtrRsiStrategy(CtaTemplate): + """结合ATR和RSI指标的一个分钟线交易策略""" + className = 'AtrRsiStrategy' + author = u'用Python的交易员' + + # 策略参数 + atrLength = 22 # 计算ATR指标的窗口数 + atrMaLength = 10 # 计算ATR均线的窗口数 + rsiLength = 5 # 计算RSI的窗口数 + rsiEntry = 16 # RSI的开仓信号 + trailingPercent = 0.8 # 百分比移动止损 + initDays = 10 # 初始化数据所用的天数 + + # 策略变量 + bar = None # K线对象 + barMinute = EMPTY_STRING # K线当前的分钟 + + bufferSize = 100 # 需要缓存的数据的大小 + bufferCount = 0 # 目前已经缓存了的数据的计数 + highArray = np.zeros(bufferSize) # K线最高价的数组 + lowArray = np.zeros(bufferSize) # K线最低价的数组 + closeArray = np.zeros(bufferSize) # K线收盘价的数组 + + atrCount = 0 # 目前已经缓存了的ATR的计数 + atrArray = np.zeros(bufferSize) # ATR指标的数组 + atrValue = 0 # 最新的ATR指标数值 + atrMa = 0 # ATR移动平均的数值 + + rsiValue = 0 # RSI指标的数值 + rsiBuy = 0 # RSI买开阈值 + rsiSell = 0 # RSI卖开阈值 + intraTradeHigh = 0 # 移动止损用的持仓期内最高价 + intraTradeLow = 0 # 移动止损用的持仓期内最低价 + + orderList = [] # 保存委托代码的列表 + + # 参数列表，保存了参数的名称 + paramList = ['name', + 'className', + 'author', + 'vtSymbol', + 'atrLength', + 'atrMaLength', + 'rsiLength', + 'rsiEntry', + 'trailingPercent'] + + # 变量列表，保存了变量的名称 + varList = ['inited', + 'trading', + 'pos', + 'atrValue', + 'atrMa', + 'rsiValue', + 'rsiBuy', + 'rsiSell'] + + #---------------------------------------------------------------------- + def __init__(self, ctaEngine, setting): + """Constructor""" + super(AtrRsiStrategy, self).__init__(ctaEngine, setting) + + #---------------------------------------------------------------------- + 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: + 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）""" + # 计算K线 + tickMinute = tick.datetime.minute + + if tickMinute != self.barMinute: + if self.bar: + self.onBar(self.bar) + + bar = CtaBarData() + bar.vtSymbol = tick.vtSymbol + bar.symbol = tick.symbol + bar.exchange = tick.exchange + + bar.open = tick.lastPrice + bar.high = tick.lastPrice + bar.low = tick.lastPrice + bar.close = tick.lastPrice + + bar.date = tick.date + bar.time = tick.time + bar.datetime = tick.datetime # K线的时间设为第一个Tick的时间 + + self.bar = bar # 这种写法为了减少一层访问，加快速度 + self.barMinute = tickMinute # 更新当前的分钟 + else: # 否则继续累加新的K线 + bar = self.bar # 写法同样为了加快速度 + + bar.high = max(bar.high, tick.lastPrice) + bar.low = min(bar.low, tick.lastPrice) + bar.close = tick.lastPrice + + #---------------------------------------------------------------------- + def onBar(self, bar): + """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 = [] + + # 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 + + self.bufferCount += 1 + if self.bufferCount < self.bufferSize: + return + + # 计算指标数值 + 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) + return + + if self.rsiValue < self.rsiSell: + self.short(bar.close-5, 1) + return + + # 持有多头仓位 + if self.pos == 1: + # 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) + return + + # 持有空头仓位 + if self.pos == -1: + 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) + return + + # 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 ctaBacktesting import * + from PyQt4 import QtCore, QtGui + + # 创建回测引擎 + engine = BacktestingEngine() + + # 设置引擎的回测模式为K线 + engine.setBacktestingMode(engine.BAR_MODE) + + # 设置回测用的数据起始日期 + engine.setStartDate('20120101') + + # 载入历史数据到引擎中 + engine.loadHistoryData(MINUTE_DB_NAME, 'IF0000') + + # 设置产品相关参数 + engine.setSlippage(0.2) # 股指1跳 + engine.setRate(0.3/10000) # 万0.3 + engine.setSize(300) # 股指合约大小 + + # 在引擎中创建策略对象 + engine.initStrategy(AtrRsiStrategy, {}) + + # 开始跑回测 + engine.runBacktesting() + + # 显示回测结果 + engine.showBacktestingResult() + + \ No newline at end of file