296 lines
12 KiB
Python
296 lines
12 KiB
Python
# encoding: UTF-8
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"""
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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。
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注意事项：
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1. 作者不对交易盈利做任何保证，策略代码仅供参考
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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
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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
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"""
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import talib
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import numpy as np
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import os
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import sys
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cta_strategy_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
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trader_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
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sys.path.append(cta_strategy_path)
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from ctaBase import *
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from ctaTemplate import CtaTemplate
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########################################################################
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class AtrRsiStrategy(CtaTemplate):
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"""结合ATR和RSI指标的一个分钟线交易策略"""
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className = 'AtrRsiStrategy'
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author = u'用Python的交易员'
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# 策略参数
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atrLength = 22 # 计算ATR指标的窗口数
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atrMaLength = 10 # 计算ATR均线的窗口数
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rsiLength = 5 # 计算RSI的窗口数
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rsiEntry = 16 # RSI的开仓信号
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trailingPercent = 0.8 # 百分比移动止损
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initDays = 10 # 初始化数据所用的天数
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fixedSize = 1 # 每次交易的数量
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# 策略变量
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bar = None # K线对象
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barMinute = EMPTY_STRING # K线当前的分钟
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bufferSize = 100 # 需要缓存的数据的大小
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bufferCount = 0 # 目前已经缓存了的数据的计数
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highArray = np.zeros(bufferSize) # K线最高价的数组
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lowArray = np.zeros(bufferSize) # K线最低价的数组
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closeArray = np.zeros(bufferSize) # K线收盘价的数组
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atrCount = 0 # 目前已经缓存了的ATR的计数
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atrArray = np.zeros(bufferSize) # ATR指标的数组
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atrValue = 0 # 最新的ATR指标数值
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atrMa = 0 # ATR移动平均的数值
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rsiValue = 0 # RSI指标的数值
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rsiBuy = 0 # RSI买开阈值
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rsiSell = 0 # RSI卖开阈值
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intraTradeHigh = 0 # 移动止损用的持仓期内最高价
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intraTradeLow = 0 # 移动止损用的持仓期内最低价
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orderList = [] # 保存委托代码的列表
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# 参数列表，保存了参数的名称
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paramList = ['name',
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'className',
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'author',
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'vtSymbol',
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'atrLength',
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'atrMaLength',
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'rsiLength',
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'rsiEntry',
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'trailingPercent']
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# 变量列表，保存了变量的名称
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varList = ['inited',
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'trading',
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'pos',
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'atrValue',
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'atrMa',
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'rsiValue',
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'rsiBuy',
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'rsiSell']
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#----------------------------------------------------------------------
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def __init__(self, ctaEngine, setting):
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"""Constructor"""
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super(AtrRsiStrategy, self).__init__(ctaEngine, setting)
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# 注意策略类中的可变对象属性（通常是list和dict等），在策略初始化时需要重新创建，
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# 否则会出现多个策略实例之间数据共享的情况，有可能导致潜在的策略逻辑错误风险，
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# 策略类中的这些可变对象属性可以选择不写，全都放在__init__下面，写主要是为了阅读
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# 策略时方便（更多是个编程习惯的选择）
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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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self.onBar(bar)
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self.putEvent()
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#----------------------------------------------------------------------
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def onStart(self):
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"""启动策略（必须由用户继承实现）"""
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self.writeCtaLog(u'%s策略启动' %self.name)
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self.putEvent()
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#----------------------------------------------------------------------
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def onStop(self):
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"""停止策略（必须由用户继承实现）"""
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self.writeCtaLog(u'%s策略停止' %self.name)
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self.putEvent()
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#----------------------------------------------------------------------
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def onTick(self, tick):
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"""收到行情TICK推送（必须由用户继承实现）"""
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# 计算K线
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tickMinute = tick.datetime.minute
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if tickMinute != self.barMinute:
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if self.bar:
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self.onBar(self.bar)
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bar = CtaBarData()
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bar.vtSymbol = tick.vtSymbol
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bar.symbol = tick.symbol
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bar.exchange = tick.exchange
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bar.open = tick.lastPrice
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bar.high = tick.lastPrice
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bar.low = tick.lastPrice
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bar.close = tick.lastPrice
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bar.date = tick.date
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bar.time = tick.time
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bar.datetime = tick.datetime # K线的时间设为第一个Tick的时间
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self.bar = bar # 这种写法为了减少一层访问，加快速度
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self.barMinute = tickMinute # 更新当前的分钟
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else: # 否则继续累加新的K线
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bar = self.bar # 写法同样为了加快速度
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bar.high = max(bar.high, tick.lastPrice)
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bar.low = min(bar.low, tick.lastPrice)
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bar.close = tick.lastPrice
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#----------------------------------------------------------------------
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def onBar(self, bar):
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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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# 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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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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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, self.fixedSize)
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elif self.rsiValue < self.rsiSell:
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self.short(bar.close-5, self.fixedSize)
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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, abs(self.pos), 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, abs(self.pos), 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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def onOrder(self, order):
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"""收到委托变化推送（必须由用户继承实现）"""
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pass
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#----------------------------------------------------------------------
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def onTrade(self, trade):
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# 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 ctaBacktesting import *
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from PyQt4 import QtCore, QtGui
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# 创建回测引擎
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engine = BacktestingEngine()
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# 设置引擎的回测模式为K线
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engine.setBacktestingMode(engine.BAR_MODE)
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# 设置回测用的数据起始日期
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engine.setStartDate('20120101')
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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.setRate(0.3/10000) # 万0.3
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engine.setSize(300) # 股指合约大小
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engine.setPriceTick(0.2) # 股指最小价格变动
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# 设置使用的历史数据库
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engine.setDatabase(MINUTE_DB_NAME, 'IF0000')
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# 在引擎中创建策略对象
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d = {'atrLength': 11}
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engine.initStrategy(AtrRsiStrategy, d)
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# 开始跑回测
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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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## 跑优化
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#setting = OptimizationSetting() # 新建一个优化任务设置对象
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#setting.setOptimizeTarget('capital') # 设置优化排序的目标是策略净盈利
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#setting.addParameter('atrLength', 12, 20, 2) # 增加第一个优化参数atrLength，起始11，结束12，步进1
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#setting.addParameter('atrMa', 20, 30, 5) # 增加第二个优化参数atrMa，起始20，结束30，步进1
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#setting.addParameter('rsiLength', 5) # 增加一个固定数值的参数
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## 性能测试环境：I7-3770，主频3.4G, 8核心，内存16G，Windows 7 专业版
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## 测试时还跑着一堆其他的程序，性能仅供参考
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#import time
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#start = time.time()
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## 运行单进程优化函数，自动输出结果，耗时：359秒
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#engine.runOptimization(AtrRsiStrategy, setting)
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## 多进程优化，耗时：89秒
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##engine.runParallelOptimization(AtrRsiStrategy, setting)
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#print u'耗时：%s' %(time.time()-start) |