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【数据】【自动化交易】Python编写策略模拟股票交易
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,Volume
0,13.53,13.45,12.65,12.62,51597046.82,11.16,2010-01-04,572108102.0,11.37,10.62,10.76,1.0,51597044.0
1,13.64,13.72,12.91,12.92,48822968.82,11.27,2010-01-05,549279336.0,11.64,10.86,11.06,1.0,48822966.0
2,13.38,13.56,13.05,13.07,41360141.82,11.01,2010-01-06,461521697.0,11.48,11.02,11.21,1.0,41360139.0
... ...
简单交易程序
简单的策略程序：
# -*-coding:utf-8-*-
from __future__ import print_function
from pyalgotrade import strategy
from pyalgotrade.barfeed import quandlfeed
from pyalgotrade.technical import ma
class 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 strategy
from pyalgotrade.technical import ma
from pyalgotrade.technical import cross
from pyalgotrade import plotter
from pyalgotrade.barfeed import quandlfeed
from pyalgotrade.stratanalyzer import returns
class 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 file
feed = 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()