Question

我正在尝试学习如何在pyalgotrade的事件探查器中实现自定义策略。 This is the default example they give。

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)

是否有人有更多示例来源？

我试图找出 eventprofiler如何接受和使用数据，但是有很多类方法可以调用，而且我是＆＃39我觉得解剖它有点棘手。

我想从简单开始，只使用price和volume。它是，一种策略是if volume > 1000 and close < 50: event == True

任何帮助将不胜感激。

P.s。：红利问题： zipline是否有类似的事件分析器？

编辑：感谢user3666197，我能够进行我想要的更改，但是我收到了这个错误：

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]

我已经查看过来源＆event;事件发展日期＆＃39;并且无法弄清楚它是什么。这是代码

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

Quant Prologue：

Many thanks to prof. Tucker BALCH, GA-TECH [GA], and his team, for QSTK-initiative以及量化金融建模的创新方法。

eventprofiler如何获取数据？

简单地说，它可以在feed eventprofiler.run( feed中同时访问完整的 , ... ) eventprofiler.Predicate - 包裹 feed ，其中包含所有Feed详情＆＃39;通过Class-parameter访问 .Predicate 实例，以便能够计算出策略演算所需的所有细节。 聪明，不是吗？

其余的是通过重复使用他们的类方法来完成的。

如何建立自己的？

宣布一个配备适当的 eventprofiler.Predicate 就足够了，它将被注入evenprofiler实例中：

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

剩下的就像往常一样简单：

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

Quant Epilogue：

一位细心的读者已经注意到，建议的代码不会调用.getAdjClose()方法。理由是，深度回测可能会因为非常接近的调整，在做出决策时有所偏差，而调整尚不清楚。每个专业定量分析师的公平义务是避免任何窥视未来的方法，并在需要调整的情况下仔细决定投资组合估值，如果持有时间的工具已进行调整，则在其中进行调整。相应的生命周期，或不是。

Answer 2

奖金回答

虽然问题的动机是明确的，但有几个原因，为什么答案不是那么简单，因为它可能看起来。

首先，事情是如何运作的：

eventprofiler 和 QSTK 中的 pyalgotrade 是建立在市场的 DataSeries 表示之上，外部数据通过 feed 机制输入（未存储）。

zipline 方法与众不同，其核心功能主要集中在基于事件的模拟引擎上，该引擎在每个价格的原子角色级别上运行 - {{ 1}} - tick（异步外部事件到达和本地响应处理）。

事件流处理的顺序性质对模拟的体外回溯测试具有一定的吸引力。

QUOTE - 来源QSTK的优势正相反，基于一次处理完整 - 长度eventprofiler - 表示（或某些智能迭代器重新验证或高效 DataSeries numpy - 技巧和魔法）。

这种巨大的概念差异使得很难获得类似智能+快速+高效+易于重复使用的工具，因为stride QSTK 问题是，在事件处理环境中。

pyalgotrade中事件分析器的更多示例

2 个答案:

Quant Prologue：

`eventprofiler`如何获取数据？

如何建立自己的？

Quant Epilogue：

奖金回答

pyalgotrade中事件分析器的更多示例

2 个答案:

Quant Prologue：

eventprofiler如何获取数据？

如何建立自己的？

Quant Epilogue：

奖金回答

`eventprofiler`如何获取数据？