最快的速度扫描了一遍pyalgotrade的文档,从可理解性角度来讲,确实比backtrader好很多,但是功能方面似乎就有缺失了。功能缺失也有好处,就是能够更加灵活,不用再受到文档描述不清楚,不了解功能怎么用的痛苦了吧。
1.pyalgotrade的交易
这里,还是老样子,用简单的SMA策略来学习一下pyalgotrade的基本交易方法。当现在价格上穿SMA时,开多单;当现在的价格,下穿SMA时,平掉先前的多头头寸。
def onBars(self, bars):# 每一个数据都会抵达这里,就像becktest中的next # SMA的计算存在窗口,所以前面的几个bar下是没有SMA的数据的. if self.__sma[-1] is None: return #bar.getTyoicalPrice = (bar.getHigh() + bar.getLow() + bar.getClose())/ 3.0 bar = bars[self.__instrument] # If a position was not opened, check if we should enter a long position. if self.__position is None:# 如果手上没有头寸,那么 if bar.getPrice() > self.__sma[-1]: # 开多,如果现价大于移动均线,且当前没有头寸. self.__position = self.enterLong(self.__instrument, 100, True) # 当前有多头头寸,平掉多头头寸. elif bar.getPrice() < self.__sma[-1] and not self.__position.exitActive(): self.__position.exitMarket()这一部分基本就是交易逻辑。
enterLong(self.__instrument, 100, True)
这就是开多单的命令,第一个就是开多单的标的,100是下单数目,最后一个是allOrNone参数,也就是说,是不是要么全部成交,要么一个不成,默认是false。
与backtrader一样,也存在各种各样的订单监听函数,
def onEnterOk(self, position): execInfo = position.getEntryOrder().getExecutionInfo() self.info("BUY at %.2f" % (execInfo.getPrice())) def onEnterCanceled(self, position): self.__position = None def onExitOk(self, position): execInfo = position.getExitOrder().getExecutionInfo() self.info("SELL at $%.2f" % (execInfo.getPrice())) self.__position = None def onExitCanceled(self, position): # If the exit was canceled, re-submit it. self.__position.exitMarket()
基本都是大同小异,功能上没有什么实质性区别。
2.SMA策略示例完整代码
# coding=utf-8 from pyalgotrade import strategy from pyalgotrade.bar import Frequency from pyalgotrade.barfeed.csvfeed import GenericBarFeed from pyalgotrade.technical import ma # 1.构建一个策略 class MyStrategy(strategy.BacktestingStrategy): def __init__(self, feed, instrument): super(MyStrategy, self).__init__(feed) self.__position = None self.__sma = ma.SMA(feed[instrument].getCloseDataSeries(), 150) self.__instrument = instrument self.getBroker() def onEnterOk(self, position): execInfo = position.getEntryOrder().getExecutionInfo() self.info("BUY at %.2f" % (execInfo.getPrice())) def onEnterCanceled(self, position): self.__position = None def onExitOk(self, position): execInfo = position.getExitOrder().getExecutionInfo() self.info("SELL at $%.2f" % (execInfo.getPrice())) self.__position = None def onExitCanceled(self, position): # If the exit was canceled, re-submit it. self.__position.exitMarket() def onBars(self, bars):# 每一个数据都会抵达这里,就像becktest中的next # SMA的计算存在窗口,所以前面的几个bar下是没有SMA的数据的. if self.__sma[-1] is None: return #bar.getTyoicalPrice = (bar.getHigh() + bar.getLow() + bar.getClose())/ 3.0 bar = bars[self.__instrument] # If a position was not opened, check if we should enter a long position. if self.__position is None:# 如果手上没有头寸,那么 if bar.getPrice() > self.__sma[-1]: # 开多,如果现价大于移动均线,且当前没有头寸. self.__position = self.enterLong(self.__instrument, 100, True) # 当前有多头头寸,平掉多头头寸. elif bar.getPrice() < self.__sma[-1] and not self.__position.exitActive(): self.__position.exitMarket() # 2.获得回测数据,官网来源于yahoo,由于墙的关系,我们用本地数据 feed = GenericBarFeed(Frequency.DAY, None, None) feed.addBarsFromCSV("fd", "fd.csv") # 3.把策略跑起来 myStrategy = MyStrategy(feed, "fd") myStrategy.run() myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())最后,我们看到把myStrategy.getResult()输出,就可以看到最后我们的资产净值。
2015-08-14 00:00:00 strategy [INFO] BUY at 3976.41
2015-08-19 00:00:00 strategy [INFO] SELL at $3646.80
2016-07-13 00:00:00 strategy [INFO] BUY at 3049.51
2016-08-02 00:00:00 strategy [INFO] SELL at $2950.08
2016-08-03 00:00:00 strategy [INFO] BUY at 2963.22
2016-12-30 00:00:00 strategy [INFO] Final portfolio value: $1394503.20