pyalgotrade 中事件分析器的更多示例

Question

我正在尝试学习如何在 pyalgotrade 的事件分析器中实施自定义策略。 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)

谁有更多例子的来源？

我试图弄清楚 eventprofiler 如何接收和使用数据，虽然调用了很多类方法，但我发现剖析它有点棘手.

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

任何帮助将不胜感激。

P.s.：额外问题：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]

我查看了源“eventprofiler.py”，但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

量化序言：

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

eventprofiler 是如何获取数据的？

简单地说，它可以在eventprofiler.run( feed , ... ) 和eventprofiler.Predicate-wrapped 中访问完整的feed >feed，它通过一个Class-parameter获取所有feed-details对.Predicate实例的访问权限，以便能够计算那里实现的所有细节战略演算要求。 很聪明，不是吗？

其余的都是通过重用他们的类方法来完成的。

---

如何自建？

声明一个适当装备的eventprofiler.Predicate就足够了，它将被注入evenprofiler实例化：

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

其余的将一如既往地简单：

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

---

量化结语：

一位细心的读者注意到，提议的代码没有调用.getAdjClose() 方法。原因是，深度回测可能会被非常接近的调整所扭曲，在调整未知的时候做出决定。每个专业的量化分析师都有公平的责任避免任何窥探未来的方式，并仔细决定在投资组合估值方面需要调整的地方，如果工具持有时间在其各自的生命周期内经历了调整，或者不是。

Answer 2

奖励答案

虽然问题的动机很明确，但有几个原因，为什么答案不像看起来那么简单。

首先，事情是如何运作的：

QSTK和pyalgotrade中的eventprofiler建立在DataSeries 表示市场，外部数据通过 feed-机制输入（未存储）。

zipline 方法有所不同，其核心功能集中在其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-QUOTE- 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对于模拟体外回测具有一定的吸引力。

相反，QSTK-originated eventprofiler 的优势在于同时处理完整-长度的DataSeries-representations（或一些智能迭代器重述或高效numpy stride-tricks&magics）。

这种巨大的概念差异使得很难获得类似的智能 + 快速 + 高效 + 易于重用的工具，因为 QSTK eventprofiler 毫无疑问是，在一个事件处理环境。