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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。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)

在此处输入图像描述 有没有人有更多例子的来源？

我试图弄清楚如何eventprofiler接收和使用 data，虽然有很多类方法被调用，但我发现剖析它有点棘手。

我想从简单开始，只使用priceand 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。

Ps：奖金问题：是否有类似的事件分析器zipline？

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”，但不知道它是什么。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)
4

2 回答 2

1

量化序言：

非常感谢教授。Tucker BALCH，GA-TECH [GA] 和他的团队，用于量化金融建模的QSTK倡议和创新方法。 在此处输入图像描述

如何eventprofiler获取数据？

简单地说，它以 -wrapped和-wrapped的形式接收对完整的访问feed，其中它通过 Class-parameter 获取所有 feed-details 对实例的访问权限，以便能够计算实现策略的所有细节微积分需要。聪明，不是吗？eventprofiler.run( feed , ... )eventprofiler.Predicatefeed.Predicate

其余的通过重用他们的类方法来完成。

如何建立自己的？

声明一个适当的装备就足够了eventprofiler.Predicate，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

其余的会像往常一样简单：

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，建议的代码不调用.getAdjClose()方法。原因是，深度回测可能会被非常接近的调整所扭曲，在调整尚不知道的时候做出决定。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。

于 2016-05-19T18:14:02.717 回答
1

奖励答案

虽然这个问题的动机很明确，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：

eventprofilerinQSTK和pyalgotrade建立在 Market 的表示DataSeries之上，其中外部数据通过 - 机制输入（而不是存储）feed。

该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QUOTE报价的原子角色级别上运行（异步外部事件到达和本地响应处理）。

事件流处理的顺序特性对于模拟的体外回测具有一定的吸引力。

QSTK相反， -originated的优势eventprofiler是基于一次处理全长DataSeries-表示（或一些智能迭代器重述或有效的-tricks &magics）。numpy stride

这种巨大的概念差异使得在事件处理环境中很难获得类似的智能 + 快速 + 高效 + 易于重用的工具，这QSTK eventprofiler是毫无疑问的。

于 2016-05-19T19:15:34.837 回答