数据】【自动化交易】Python编写策略模拟股票交易

gysgold

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:
简单交易程序

简单的策略程序：

1. # -*-coding:utf-8-*-
   
2. from __future__ import print_function
   
3. from pyalgotrade import strategy
   
4. from pyalgotrade.barfeed import quandlfeed
   
5. from pyalgotrade.technical import ma
   
6. 
   
7. 
   
8. class MyStrategy(strategy.BacktestingStrategy):
   
9.     def __init__(self, feed, instrument, smaPeriod):
   
10.         super(MyStrategy, self).__init__(feed, 1000)
    
11.         self.__position = None
    
12.         self.__instrument = instrument
    
13.         # We'll use adjusted close values instead of regular close values.
    
14.         self.setUseAdjustedValues(True)
    
15.         self.__sma = ma.SMA(feed[instrument].getPriceDataSeries(), smaPeriod)
    
16. 
    
17.     #---- BUY ----
    
18.     def onEnterOk(self, position):
    
19.         execInfo = position.getEntryOrder().getExecutionInfo()
    
20.         #self.info("BUY at $%.2f" % (execInfo.getPrice()))
    
21.         self.info("在价格 ￥%.2f 时买入" % (execInfo.getPrice()));
    
22. 
    
23.     #---- NO BUY ----
    
24.     def onEnterCanceled(self, position):
    
25.         self.__position = None
    
26. 
    
27.     #---- SELL ----
    
28.     def onExitOk(self, position):
    
29.         execInfo = position.getExitOrder().getExecutionInfo()
    
30.         #self.info("SELL at $%.2f" % (execInfo.getPrice()))
    
31.         self.info("在价格 ￥%.2f 时抛出" % (execInfo.getPrice()));
    
32.         self.__position = None
    
33. 
    
34.     #---- NO SELL ----
    
35.     def onExitCanceled(self, position):
    
36.         # If the exit was canceled, re-submit it.
    
37.         self.__position.exitMarket()
    
38. 
    
39.     def onBars(self, bars):
    
40.         # Wait for enough bars to be available to calculate a SMA.
    
41.         if self.__sma[-1] is None:
    
42.             return
    
43. 
    
44.         bar = bars[self.__instrument]
    
45.         # If a position was not opened, check if we should enter a long position.
    
46.         if self.__position is None:
    
47.             if bar.getPrice() > self.__sma[-1]:
    
48.                 # Enter a buy market order for 10 shares. The order is good till canceled.
    
49.                 self.__position = self.enterLong(self.__instrument, 10, True)
    
50.         # Check if we have to exit the position.
    
51.         elif bar.getPrice() < self.__sma[-1] and not self.__position.exitActive():
    
52.             self.__position.exitMarket()
    
53. 
    
54. 
    
55. def run_strategy(smaPeriod):
    
56.     # Load the bar feed from the CSV file
    
57.     feed = quandlfeed.Feed()
    
58.     feed.addBarsFromCSV("orcl", "600288SH.csv")
    
59. 
    
60.     # Evaluate the strategy with the feed.
    
61.     myStrategy = MyStrategy(feed, "orcl", smaPeriod)
    
62.     myStrategy.run()
    
63.     #print("Final portfolio value: $%.2f" % myStrategy.getBroker().getEquity())
    
64.     print("最终盈亏情况: ￥ %.2f" % myStrategy.getBroker().getEquity())
    
65. 
    
66. run_strategy(15);
    
67. 
    

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策略和绘制曲线程序：

1. # -*-coding:utf-8-*-
   
2. from pyalgotrade import strategy
   
3. from pyalgotrade.technical import ma
   
4. from pyalgotrade.technical import cross
   
5. from pyalgotrade import plotter
   
6. from pyalgotrade.barfeed import quandlfeed
   
7. from pyalgotrade.stratanalyzer import returns
   
8. 
   
9. class SMACrossOver(strategy.BacktestingStrategy):
   
10.     def __init__(self, feed, instrument, smaPeriod):
    
11.         super(SMACrossOver, self).__init__(feed)
    
12.         self.__instrument = instrument
    
13.         self.__position   = None
    
14.         # We'll use adjusted close values instead of regular close values.
    
15.         self.setUseAdjustedValues(True)
    
16.         self.__prices     = feed[instrument].getPriceDataSeries()
    
17.         self.__sma        = ma.SMA(self.__prices, smaPeriod)
    
18. 
    
19.     def getSMA(self):
    
20.         return self.__sma
    
21. 
    
22.     def onEnterCanceled(self, position):
    
23.         self.__position = None
    
24. 
    
25.     def onExitOk(self, position):
    
26.         self.__position = None
    
27. 
    
28.     def onExitCanceled(self, position):
    
29.         # If the exit was canceled, re-submit it.
    
30.         self.__position.exitMarket()
    
31. 
    
32.     def onBars(self, bars):
    
33.         # If a position was not opened, check if we should enter a long position.
    
34.         if self.__position is None:
    
35.             if cross.cross_above(self.__prices, self.__sma) > 0:
    
36.                 shares = int(self.getBroker().getCash() * 0.9 / bars[self.__instrument].getPrice())
    
37.                 # Enter a buy market order. The order is good till canceled.
    
38.                 self.__position = self.enterLong(self.__instrument, shares, True)
    
39.         # Check if we have to exit the position.
    
40.         elif not self.__position.exitActive() and cross.cross_below(self.__prices, self.__sma) > 0:
    
41.             self.__position.exitMarket()
    
42. 
    
43. # Load the bar feed from the CSV file
    
44. feed = quandlfeed.Feed()
    
45. #feed.addBarsFromCSV("orcl", "WIKI-ORCL-2000-quandl.csv")
    
46. feed.addBarsFromCSV("600288SH", "600288SH.csv")
    
47. 
    
48. # Evaluate the strategy with the feed's bars.
    
49. myStrategy = sma_crossover.SMACrossOver(feed, "600288SH", 20)
    
50. 
    
51. # Attach a returns analyzers to the strategy.
    
52. returnsAnalyzer = returns.Returns()
    
53. myStrategy.attachAnalyzer(returnsAnalyzer)
    
54. 
    
55. # Attach the plotter to the strategy.
    
56. plt = plotter.StrategyPlotter(myStrategy)
    
57. # Include the SMA in the instrument's subplot to get it displayed along with the closing prices.
    
58. plt.getInstrumentSubplot("600288SH").addDataSeries("SMA", myStrategy.getSMA())
    
59. # Plot the simple returns on each bar.
    
60. plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
    
61. 
    
62. # Run the strategy.
    
63. myStrategy.run()
    
64. myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
    
65. 
    
66. # Plot the strategy.
    
67. plt.plot()
    
68. 
    
69. 
    

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