第五节 交易

我们继续采取简单的策略，这次模拟实际交易。这个想法很简单：

• 如果调整后的收盘价高于SMA（15），我们将进入多头仓位（我们下单买入市价）。
• 如果调整后的收盘价低于SMA（15），我们退出多头头寸（我们出售)

from pyalgotrade import strategy
from pyalgotrade.barfeed import yahoofeed
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)

    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):
        # 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 yahoo feed from the CSV file
    feed = yahoofeed.Feed()
    feed.addBarsFromCSV("orcl", "orcl-2000.csv")

    # Evaluate the strategy with the feed.
    myStrategy = MyStrategy(feed, "orcl", smaPeriod)
    myStrategy.run()
    print "Final portfolio value: $%.2f" % myStrategy.getBroker().getEquity()

run_strategy(15)

运行策略后看到如下结果

2000-01-26 00:00:00 strategy [INFO] BUY at $27.26
2000-01-28 00:00:00 strategy [INFO] SELL at $24.74
2000-02-03 00:00:00 strategy [INFO] BUY at $26.60
2000-02-22 00:00:00 strategy [INFO] SELL at $28.40
2000-02-23 00:00:00 strategy [INFO] BUY at $28.91
2000-03-31 00:00:00 strategy [INFO] SELL at $38.51
2000-04-07 00:00:00 strategy [INFO] BUY at $40.19
2000-04-12 00:00:00 strategy [INFO] SELL at $37.44
2000-04-19 00:00:00 strategy [INFO] BUY at $37.76
2000-04-20 00:00:00 strategy [INFO] SELL at $35.45
2000-04-28 00:00:00 strategy [INFO] BUY at $37.70
2000-05-05 00:00:00 strategy [INFO] SELL at $35.54
2000-05-08 00:00:00 strategy [INFO] BUY at $36.17
2000-05-09 00:00:00 strategy [INFO] SELL at $35.39
2000-05-16 00:00:00 strategy [INFO] BUY at $37.28
2000-05-19 00:00:00 strategy [INFO] SELL at $34.58
2000-05-31 00:00:00 strategy [INFO] BUY at $35.18
2000-06-23 00:00:00 strategy [INFO] SELL at $38.81
2000-06-27 00:00:00 strategy [INFO] BUY at $39.56
2000-06-28 00:00:00 strategy [INFO] SELL at $39.42
2000-06-29 00:00:00 strategy [INFO] BUY at $39.41
2000-06-30 00:00:00 strategy [INFO] SELL at $38.60
2000-07-03 00:00:00 strategy [INFO] BUY at $38.96
2000-07-05 00:00:00 strategy [INFO] SELL at $36.89
2000-07-21 00:00:00 strategy [INFO] BUY at $37.19
2000-07-24 00:00:00 strategy [INFO] SELL at $37.04
2000-07-26 00:00:00 strategy [INFO] BUY at $35.93
2000-07-28 00:00:00 strategy [INFO] SELL at $36.08
2000-08-01 00:00:00 strategy [INFO] BUY at $36.11
2000-08-02 00:00:00 strategy [INFO] SELL at $35.06
2000-08-04 00:00:00 strategy [INFO] BUY at $37.61
2000-09-11 00:00:00 strategy [INFO] SELL at $41.34
2000-09-29 00:00:00 strategy [INFO] BUY at $39.07
2000-10-02 00:00:00 strategy [INFO] SELL at $38.30
2000-10-20 00:00:00 strategy [INFO] BUY at $34.71
2000-10-31 00:00:00 strategy [INFO] SELL at $31.34
2000-11-20 00:00:00 strategy [INFO] BUY at $23.35
2000-11-21 00:00:00 strategy [INFO] SELL at $23.83
2000-12-01 00:00:00 strategy [INFO] BUY at $25.33
2000-12-21 00:00:00 strategy [INFO] SELL at $26.72
2000-12-22 00:00:00 strategy [INFO] BUY at $29.17
Final portfolio value: $979.44

如果调整sma的测试周期，讲得到不一样的结果

for i in range(10, 30):
    run_strategy(i)

我们发现sma(20)的结果最好

Final portfolio value: $1075.38