这节我就用上节提到的pyalgotrade来编写回测策略程序，模拟股票交易。本篇文章里用的是SMA均线策略。
数据数据我使用的是 大恒科技(600288.SH) 2010年到2016年的day级数据，我将其变换成了pyalgotrade教程的格式:
Adj. Close,Adj. High,Adj. Low,Adj. Open,Adj. Volume,Close,Date,Ex-Dividend,High,Low,Open,Split Ratio,Volume0,13.53,13.45,12.65,12.62,51597046.82,11.16,2010-01-04,572108102.0,11.37,10.62,10.76,1.0,51597044.01,13.64,13.72,12.91,12.92,48822968.82,11.27,2010-01-05,549279336.0,11.64,10.86,11.06,1.0,48822966.02,13.38,13.56,13.05,13.07,41360141.82,11.01,2010-01-06,461521697.0,11.48,11.02,11.21,1.0,41360139.0... ... 简单交易程序简单的策略程序：
# -*-coding:utf-8-*-from __future__ import print_functionfrom pyalgotrade import strategyfrom pyalgotrade.barfeed import quandlfeedfrom pyalgotrade.technical import maclass MyStrategy(strategy.BacktestingStrategy): def __init__(self, feed, instrument, smaPeriod): super(MyStrategy, self).__init__(feed, 1000) self.__position = None self.__instrument = instrument # We'll use adjusted close values instead of regular close values. self.setUseAdjustedValues(True) self.__sma = ma.SMA(feed[instrument].getPriceDataSeries(), smaPeriod) #---- BUY ---- def onEnterOk(self, position): execInfo = position.getEntryOrder().getExecutionInfo() #self.info("BUY at $%.2f" % (execInfo.getPrice())) self.info("在价格 ￥%.2f 时买入" % (execInfo.getPrice())); #---- NO BUY ---- def onEnterCanceled(self, position): self.__position = None #---- SELL ---- def onExitOk(self, position): execInfo = position.getExitOrder().getExecutionInfo() #self.info("SELL at $%.2f" % (execInfo.getPrice())) self.info("在价格 ￥%.2f 时抛出" % (execInfo.getPrice())); self.__position = None #---- NO SELL ---- def onExitCanceled(self, position): # If the exit was canceled, re-submit it. self.__position.exitMarket() def onBars(self, bars): # Wait for enough bars to be available to calculate a SMA. if self.__sma[-1] is None: return bar = bars[self.__instrument] # If a position was not opened, check if we should enter a long position. if self.__position is None: if bar.getPrice() > self.__sma[-1]: # Enter a buy market order for 10 shares. The order is good till canceled. self.__position = self.enterLong(self.__instrument, 10, True) # Check if we have to exit the position. elif bar.getPrice() < self.__sma[-1] and not self.__position.exitActive(): self.__position.exitMarket()def run_strategy(smaPeriod): # Load the bar feed from the CSV file feed = quandlfeed.Feed() feed.addBarsFromCSV("orcl", "600288SH.csv") # Evaluate the strategy with the feed. myStrategy = MyStrategy(feed, "orcl", smaPeriod) myStrategy.run() #print("Final portfolio value: $%.2f" % myStrategy.getBroker().getEquity()) print("最终盈亏情况: ￥ %.2f" % myStrategy.getBroker().getEquity())run_strategy(15);输出结果：
... ...2016-06-22 00:00:00 strategy [INFO] 在价格 ￥14.62 时抛出2016-06-23 00:00:00 strategy [INFO] 在价格 ￥14.94 时买入2016-06-27 00:00:00 strategy [INFO] 在价格 ￥14.68 时抛出2016-06-28 00:00:00 strategy [INFO] 在价格 ￥14.95 时买入2016-07-19 00:00:00 strategy [INFO] 在价格 ￥15.35 时抛出2016-07-20 00:00:00 strategy [INFO] 在价格 ￥15.34 时买入2016-07-28 00:00:00 strategy [INFO] 在价格 ￥15.12 时抛出2016-08-11 00:00:00 strategy [INFO] 在价格 ￥15.88 时买入2016-08-26 00:00:00 strategy [INFO] 在价格 ￥15.58 时抛出最终盈亏情况: ￥ 925.78策略和绘制曲线程序：
# -*-coding:utf-8-*-from pyalgotrade import strategyfrom pyalgotrade.technical import mafrom pyalgotrade.technical import crossfrom pyalgotrade import plotterfrom pyalgotrade.barfeed import quandlfeedfrom pyalgotrade.stratanalyzer import returnsclass SMACrossOver(strategy.BacktestingStrategy): def __init__(self, feed, instrument, smaPeriod): super(SMACrossOver, self).__init__(feed) self.__instrument = instrument self.__position = None # We'll use adjusted close values instead of regular close values. self.setUseAdjustedValues(True) self.__prices = feed[instrument].getPriceDataSeries() self.__sma = ma.SMA(self.__prices, smaPeriod) def getSMA(self): return self.__sma def onEnterCanceled(self, position): self.__position = None def onExitOk(self, position): self.__position = None def onExitCanceled(self, position): # If the exit was canceled, re-submit it. self.__position.exitMarket() def onBars(self, bars): # If a position was not opened, check if we should enter a long position. if self.__position is None: if cross.cross_above(self.__prices, self.__sma) > 0: shares = int(self.getBroker().getCash() * 0.9 / bars[self.__instrument].getPrice()) # Enter a buy market order. The order is good till canceled. self.__position = self.enterLong(self.__instrument, shares, True) # Check if we have to exit the position. elif not self.__position.exitActive() and cross.cross_below(self.__prices, self.__sma) > 0: self.__position.exitMarket()# Load the bar feed from the CSV filefeed = quandlfeed.Feed()#feed.addBarsFromCSV("orcl", "WIKI-ORCL-2000-quandl.csv")feed.addBarsFromCSV("600288SH", "600288SH.csv")# Evaluate the strategy with the feed's bars.myStrategy = sma_crossover.SMACrossOver(feed, "600288SH", 20)# Attach a returns analyzers to the strategy.returnsAnalyzer = returns.Returns()myStrategy.attachAnalyzer(returnsAnalyzer)# Attach the plotter to the strategy.plt = plotter.StrategyPlotter(myStrategy)# Include the SMA in the instrument's subplot to get it displayed along with the closing prices.plt.getInstrumentSubplot("600288SH").addDataSeries("SMA", myStrategy.getSMA())# Plot the simple returns on each bar.plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())# Run the strategy.myStrategy.run()myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())# Plot the strategy.plt.plot()