关于pyalgotrade中事件分析器的更多示例

Question

我正在学习如何在pyalgotrade的事件分析器中实现自定义策略。这是他们给出的默认示例。。

from pyalgotrade           import eventprofiler
from pyalgotrade.technical import stats
from pyalgotrade.technical import roc
from pyalgotrade.technical import ma
from pyalgotrade.tools     import yahoofinance

#     Event inspired on an example from Ernie Chan's book:
#    'Algorithmic Trading: Winning Strategies and Their Rationale'

class BuyOnGap(eventprofiler.Predicate):
    def __init__(self, feed):
        stdDevPeriod = 90
        smaPeriod = 20
        self.__returns = {}
        self.__stdDev = {}
        self.__ma = {}
        for instrument in feed.getRegisteredInstruments():
            priceDS = feed[instrument].getAdjCloseDataSeries()
            #      Returns over the adjusted close values.
            self.__returns[instrument] = roc.RateOfChange(priceDS, 1)
            #      StdDev over those returns.
            self.__stdDev[instrument] = stats.StdDev(self.__returns[instrument], stdDevPeriod)
            #      MA over the adjusted close values.
            self.__ma[instrument] = ma.SMA(priceDS, smaPeriod)

    def __gappedDown(self, instrument, bards):
        ret = False
        if self.__stdDev[instrument][-1] != None:
            prevBar = bards[-2]
            currBar = bards[-1]
            low2OpenRet = (currBar.getAdjOpen() - prevBar.getAdjLow()) / float(prevBar.getAdjLow())
            if low2OpenRet < (self.__returns[instrument][-1] - self.__stdDev[instrument][-1]):
                ret = True
        return ret

    def __aboveSMA(self, instrument, bards):
        ret = False
        if self.__ma[instrument][-1] != None and bards[-1].getAdjOpen() > self.__ma[instrument][-1]:
            ret = True
        return ret

    def eventOccurred(self, instrument, bards):
        ret = False
        if self.__gappedDown(instrument, bards) and self.__aboveSMA(instrument, bards):
            ret = True
        return ret

def main(plot):
    instruments = ["AA", "AES", "AIG"]
    feed = yahoofinance.build_feed(instruments, 2008, 2009, ".")

    predicate = BuyOnGap(feed)
    eventProfiler = eventprofiler.Profiler(predicate, 5, 5)
    eventProfiler.run(feed, True)

    results = eventProfiler.getResults()
    print "%d events found" % (results.getEventCount())
    if plot:
        eventprofiler.plot(results)

if __name__ == "__main__":
    main(True)

​

还有其他例子的来源吗?

我试图弄清楚eventprofiler 是如何接收和使用data的，但是有相当多的类方法被调用，我发现对其进行剖析有点棘手。

我想从简单开始，只使用price和volume。是的，一种策略是

if volume > 1000 and close < 50: event == True

任何帮助都将不胜感激。

P.s.：的额外问题：是否有类似的zipline事件剖析器？

编辑:多亏了user3666197，我能够做我想做的更改，但是我得到了这个错误：

Traceback (most recent call last):
  File "C:\Users\David\Desktop\Python\Coursera\Computational Finance\Week2\PyAlgoTrade\Bitfinex\FCT\FCT_single_event_test.py", line 43, in <module>
    main(True)
  File "C:\Users\David\Desktop\Python\Coursera\Computational Finance\Week2\PyAlgoTrade\Bitfinex\FCT\FCT_single_event_test.py", line 35, in main
    eventProfiler.run(feed, True)
  File "C:\Python27\lib\site-packages\pyalgotrade\eventprofiler.py", line 215, in run
    disp.run()
  File "C:\Python27\lib\site-packages\pyalgotrade\dispatcher.py", line 102, in run
    eof, eventsDispatched = self.__dispatch()
  File "C:\Python27\lib\site-packages\pyalgotrade\dispatcher.py", line 90, in __dispatch
    if self.__dispatchSubject(subject, smallestDateTime):
  File "C:\Python27\lib\site-packages\pyalgotrade\dispatcher.py", line 68, in __dispatchSubject
    ret = subject.dispatch() is True
  File "C:\Python27\lib\site-packages\pyalgotrade\feed\__init__.py", line 105, in dispatch
    self.__event.emit(dateTime, values)
  File "C:\Python27\lib\site-packages\pyalgotrade\observer.py", line 59, in emit
    handler(*args, **kwargs)
  File "C:\Python27\lib\site-packages\pyalgotrade\eventprofiler.py", line 172, in __onBars
    eventOccurred = self.__predicate.eventOccurred(instrument, self.__feed[instrument])
  File "C:\Python27\lib\site-packages\pyalgotrade\eventprofiler.py", line 89, in eventOccurred
    raise NotImplementedError()
NotImplementedError
[Finished in 1.9s]

我看了一下源'eventprofiler.py‘，不知道它是什么。这是密码

from pyalgotrade import eventprofiler
from pyalgotrade.technical import stats
from pyalgotrade.technical import roc
from pyalgotrade.technical import ma
from pyalgotrade.barfeed import csvfeed

# Event inspired on an example from Ernie Chan's book:
# 'Algorithmic Trading: Winning Strategies and Their Rationale'

class single_event_strat( eventprofiler.Predicate ):
    def __init__(self,feed):
        self.__returns = {} # CLASS ATTR
        for inst in feed.getRegisteredInstruments():

            priceDS = feed[inst].getAdjCloseDataSeries() # STORE: priceDS ( a temporary representation )

            self.__returns[inst] = roc.RateOfChange( priceDS, 1 )
            # CALC:  ATTR <- Returns over the adjusted close values, consumed priceDS 
            #( could be expressed as self.__returns[inst] = roc.RateOfChange( ( feed[inst].getAdjCloseDataSeries() ), 1 ), 
            #but would be less readable

    def eventOccoured( self, instrument, aBarDS):
        if (aBarDS[-1].getVolume() > 1000 and aBarDS[-1].getClose() > 50 ):
            return True
        else: 
            return False

def main(plot):
    feed = csvfeed.GenericBarFeed(0)

    feed.addBarsFromCSV('FCT', "FCT_daily_converted.csv")

    predicate = single_event_strat(feed)
    eventProfiler = eventprofiler.Profiler(predicate, 5, 5)
    eventProfiler.run(feed, True)

    results = eventProfiler.getResults()
    print "%d events found" % (results.getEventCount())
    if plot:
        eventprofiler.plot(results)

if __name__ == "__main__":
    main(True)

Answer 1

定量序言：

-initiative和定量金融模型的创新方法。

eventprofiler是如何接收数据的？

简单地说，它接收对完整feed的访问--在eventprofiler.run( feed , ... )中，也作为eventprofiler.Predicate-wrapped feed，，它通过一个类参数获得.Predicate实例的所有提要-详细信息的访问权限，以便能够计算出策略演算所需的所有细节。Smart，不是吗？

其余部分是通过重用它们的类方法来完成的。

如何建立自己的自己？

声明一个装备适当的eventprofiler.Predicate，就足够了，它将被注入到evenprofiler实例化中：

class DavidsSTRATEGY( eventprofiler.Predicate ):
      def __init__( self, feed ):                         # mandatory .__init__
          """                                               no need for this code
                                                            left here for didactic
                                                            purposes only
                                                            to show the principle

          self.__returns = {}                                         # CLASS ATTR

          for                inst in feed.getRegisteredInstruments():
              priceDS = feed[inst].getAdjCloseDataSeries()            # STORE: priceDS ( a temporary representation )
              self.__returns[inst] = roc.RateOfChange( priceDS, 1 )   # CALC:  ATTR <- Returns over the adjusted close values, consumed priceDS ( could be expressed as self.__returns[inst] = roc.RateOfChange( ( feed[inst].getAdjCloseDataSeries() ), 1 ), but would be less readable
          """

      def eventOccurred( self, instrument, aBarDS ):       # mandatory .eventOccured
          if (   aBarDS[-1].getVolume() > 1000             # "last" Bar's Volume >
             and aBarDS[-1].getClose()  <   50             # "last" Bar's Close  <
             ):
                 return True
          else:
                 return False

其余的将像往常一样简单：

eventProfiler = eventprofiler.Profiler( predicate = DavidsSTRATEGY( feed ), 5, 5 )
eventProfiler.run( feed, True )

定量催眠术：

一位细心的读者注意到，建议的代码不调用.getAdjClose()方法。其理由是，深反测试可能会被非常密切的调整所扭曲，即在尚不清楚调整的时候做出决定。每一个专业的量化分析师都有责任避免窥视未来的任何手段，并仔细决定在投资组合估值方面需要调整的地方，如果一个工具持有时间在其各自生命周期内已经经历了调整，或者没有调整。