1. 多进程并行优化
2. 解决计算胜率赔率时0整除的处理 3. 优化formatNumber函数
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chenxy123
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261b60759b
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5ad2c56845
@ -9,6 +9,7 @@ from __future__ import division
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from datetime import datetime, timedelta
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from collections import OrderedDict
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from itertools import product
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import multiprocessing
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import pymongo
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from ctaBase import *
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@ -49,6 +50,10 @@ class BacktestingEngine(object):
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self.strategy = None # 回测策略
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self.mode = self.BAR_MODE # 回测模式，默认为K线
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self.startDate = ''
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self.initDays = 0
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self.endDate = ''
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self.slippage = 0 # 回测时假设的滑点
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self.rate = 0 # 回测时假设的佣金比例（适用于百分比佣金）
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self.size = 1 # 合约大小，默认为1
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@ -84,6 +89,9 @@ class BacktestingEngine(object):
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#----------------------------------------------------------------------
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def setStartDate(self, startDate='20100416', initDays=10):
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"""设置回测的启动日期"""
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self.startDate = startDate
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self.initDays = initDays
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self.dataStartDate = datetime.strptime(startDate, '%Y%m%d')
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initTimeDelta = timedelta(initDays)
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@ -92,8 +100,11 @@ class BacktestingEngine(object):
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#----------------------------------------------------------------------
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def setEndDate(self, endDate=''):
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"""设置回测的结束日期"""
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self.endDate = endDate
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if endDate:
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self.dataEndDate= datetime.strptime(endDate, '%Y%m%d')
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# 若不修改时间则会导致不包含dataEndDate当天数据
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self.dataEndDate.replace(hour=23, minute=59)
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#----------------------------------------------------------------------
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def setBacktestingMode(self, mode):
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@ -595,10 +606,18 @@ class BacktestingEngine(object):
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totalLosing += result.pnl
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# 计算盈亏相关数据
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winningRate = winningResult/totalResult*100 # 胜率
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averageWinning = totalWinning/winningResult # 平均每笔盈利
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averageLosing = totalLosing/losingResult # 平均每笔亏损
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profitLossRatio = -averageWinning/averageLosing # 盈亏比
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winningRate = winningResult/totalResult*100 # 胜率
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averageWinning = 0 # 这里把数据都初始化为0
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averageLosing = 0
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profitLossRatio = 0
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if winningResult:
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averageWinning = totalWinning/winningResult # 平均每笔盈利
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if losingResult:
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averageLosing = totalLosing/losingResult # 平均每笔亏损
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if averageLosing:
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profitLossRatio = -averageWinning/averageLosing # 盈亏比
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# 返回回测结果
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d = {}
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@ -712,6 +731,7 @@ class BacktestingEngine(object):
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self.output(u'优化结果：')
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for result in resultList:
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self.output(u'%s: %s' %(result[0], result[1]))
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return result
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#----------------------------------------------------------------------
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def clearBacktestingResult(self):
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@ -730,6 +750,37 @@ class BacktestingEngine(object):
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self.tradeCount = 0
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self.tradeDict.clear()
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#----------------------------------------------------------------------
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def runParallelOptimization(self, strategyClass, optimizationSetting):
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"""并行优化参数"""
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# 获取优化设置
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settingList = optimizationSetting.generateSetting()
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targetName = optimizationSetting.optimizeTarget
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# 检查参数设置问题
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if not settingList or not targetName:
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self.output(u'优化设置有问题，请检查')
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# 多进程优化，启动一个对应CPU核心数量的进程池
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pool = multiprocessing.Pool(multiprocessing.cpu_count())
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l = []
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for setting in settingList:
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l.append(pool.apply_async(optimize, (strategyClass, setting,
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targetName, self.mode,
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self.startDate, self.initDays, self.endDate,
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self.slippage, self.rate, self.size,
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self.dbName, self.symbol)))
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pool.close()
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pool.join()
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# 显示结果
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resultList = [res.get() for res in l]
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resultList.sort(reverse=True, key=lambda result:result[1])
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self.output('-' * 30)
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self.output(u'优化结果：')
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for result in resultList:
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self.output(u'%s: %s' %(result[0], result[1]))
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########################################################################
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class TradingResult(object):
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@ -812,10 +863,32 @@ class OptimizationSetting(object):
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#----------------------------------------------------------------------
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def formatNumber(n):
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"""格式化数字到字符串"""
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n = round(n, 2) # 保留两位小数
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return format(n, ',') # 加上千分符
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rn = round(n, 2) # 保留两位小数
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return format(rn, ',') # 加上千分符
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#----------------------------------------------------------------------
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def optimize(strategyClass, setting, targetName,
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mode, startDate, initDays, endDate,
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slippage, rate, size,
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dbName, symbol):
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"""多进程优化时跑在每个进程中运行的函数"""
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engine = BacktestingEngine()
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engine.setBacktestingMode(mode)
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engine.setStartDate(startDate, initDays)
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engine.setSlippage(slippage)
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engine.setRate(rate)
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engine.setSize(size)
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engine.setDatabase(dbName, symbol)
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engine.initStrategy(strategyClass, setting)
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engine.runBacktesting()
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d = engine.calculateBacktestingResult()
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try:
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targetValue = d[targetName]
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except KeyError:
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targetValue = 0
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return (str(setting), targetValue)
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if __name__ == '__main__':
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@ -259,21 +259,31 @@ if __name__ == '__main__':
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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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## 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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## 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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## 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', 11, 12, 1) # 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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#engine.runOptimization(AtrRsiStrategy, setting) # 运行优化函数，自动输出结果
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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', 11, 20, 1) # 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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# 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)
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