PyalgoTrade 优化（六）

满足优化器组件。这个想法很简单：

有一个服务器负责：

提供数据来运行策略。
提供运行策略的参数。
记录每个工作线程的策略结果。

有多名工作人员负责：

使用服务器提供的数据和参数运行策略。

为了说明这一点，我们将使用一种称为相对强弱指标RSI2的策略，
它需要以下参数：

SMA期间用于趋势识别。我们称这个entrySMA为150到250。
退出点的SMA周期较小。我们称这个exitSMA为5到15之间。
进入短期/长仓的RSI期间。我们称之为rsiPeriod，范围介于2到10之间。
长期进仓的RSI超卖阈值。我们称此overSoldThreshold为5到25之间。
RSI超买买入门槛。我们称之为OverBoughtThreshold，范围为75到95。

如果我的数学是好的，那些是4409559不同的组合。

测试这个策略为一组参数花了大约0.16秒。如果我连续执行所有组合，我需要大约8.5天的时间来评估所有组合，并找到最佳参数。那是很长一段时间，但是如果我能够拿到10台8核电脑来完成这项工作，总时间将会下降到大约2.5个小时。
长话短说，我们需要平行
我们先从“道琼斯工业平均水平”下载3年的每日k线数据：

python -c "from pyalgotrade.tools import yahoofinance; yahoofinance.download_daily_bars('dia', 2009, 'dia-2009.csv')"

python -c "from pyalgotrade.tools import yahoofinance; yahoofinance.download_daily_bars('dia', 2010, 'dia-2010.csv')"

python -c "from pyalgotrade.tools import yahoofinance; yahoofinance.download_daily_bars('dia', 2011, 'dia-2011.csv')"

from pyalgotrade import strategy

from pyalgotrade.technical import ma

from pyalgotrade.technical import rsi

from pyalgotrade.technical import cross

class RSI2(strategy.BacktestingStrategy):

    def __init__(self, feed, instrument, entrySMA, exitSMA, rsiPeriod, overBoughtThreshold, overSoldThreshold):

        super(RSI2, self).__init__(feed)

        self.__instrument = instrument

        # We'll use adjusted close values, if available, instead of regular close values.

        if feed.barsHaveAdjClose():

            self.setUseAdjustedValues(True)

        self.__priceDS = feed[instrument].getPriceDataSeries()

        self.__entrySMA = ma.SMA(self.__priceDS, entrySMA)

        self.__exitSMA = ma.SMA(self.__priceDS, exitSMA)

        self.__rsi = rsi.RSI(self.__priceDS, rsiPeriod)

        self.__overBoughtThreshold = overBoughtThreshold

        self.__overSoldThreshold = overSoldThreshold

        self.__longPos = None

        self.__shortPos = None

    def getEntrySMA(self):

        return self.__entrySMA

    def getExitSMA(self):

        return self.__exitSMA

    def getRSI(self):

        return self.__rsi

    def onEnterCanceled(self, position):

        if self.__longPos == position:

            self.__longPos = None

        elif self.__shortPos == position:

            self.__shortPos = None

        else:

            assert(False)

    def onExitOk(self, position):

        if self.__longPos == position:

            self.__longPos = None

        elif self.__shortPos == position:

            self.__shortPos = None

        else:

            assert(False)

    def onExitCanceled(self, position):

        # If the exit was canceled, re-submit it.

        position.exitMarket()

    def onBars(self, bars):

        # Wait for enough bars to be available to calculate SMA and RSI.

        if self.__exitSMA[-1] is None or self.__entrySMA[-1] is None or self.__rsi[-1] is None:

            return

        bar = bars[self.__instrument]

        if self.__longPos is not None:

            if self.exitLongSignal():

                self.__longPos.exitMarket()

        elif self.__shortPos is not None:

            if self.exitShortSignal():

                self.__shortPos.exitMarket()

        else:

            if self.enterLongSignal(bar):

                shares = int(self.getBroker().getCash() * 0.9 / bars[self.__instrument].getPrice())

                self.__longPos = self.enterLong(self.__instrument, shares, True)

            elif self.enterShortSignal(bar):

                shares = int(self.getBroker().getCash() * 0.9 / bars[self.__instrument].getPrice())

                self.__shortPos = self.enterShort(self.__instrument, shares, True)

    def enterLongSignal(self, bar):

        return bar.getPrice() > self.__entrySMA[-1] and self.__rsi[-1] <= self.__overSoldThreshold

    def exitLongSignal(self):

        return cross.cross_above(self.__priceDS, self.__exitSMA) and not self.__longPos.exitActive()

    def enterShortSignal(self, bar):

        return bar.getPrice() < self.__entrySMA[-1] and self.__rsi[-1] >= self.__overBoughtThreshold

    def exitShortSignal(self):

        return cross.cross_below(self.__priceDS, self.__exitSMA) and not self.__shortPos.exitActive()

服务器脚本

import itertools

from pyalgotrade.barfeed import yahoofeed

from pyalgotrade.optimizer import server

def parameters_generator():

    instrument = ["dia"]

    entrySMA = range(150, 251)

    exitSMA = range(5, 16)

    rsiPeriod = range(2, 11)

    overBoughtThreshold = range(75, 96)

    overSoldThreshold = range(5, 26)

    return itertools.product(instrument, entrySMA, exitSMA, rsiPeriod, overBoughtThreshold, overSoldThreshold)

# The if __name__ == '__main__' part is necessary if running on Windows.

if __name__ == '__main__':

    # Load the feed from the CSV files.

    feed = yahoofeed.Feed()

    feed.addBarsFromCSV("dia", "dia-2009.csv")

    feed.addBarsFromCSV("dia", "dia-2010.csv")

    feed.addBarsFromCSV("dia", "dia-2011.csv")

    # Run the server.

    server.serve(feed, parameters_generator(), "localhost", 5000)

服务器代码正在做3件事情：

声明生成函数，为该策略产生不同的参数组合。
使用我们下载的CSV文件加载Feed。
运行端口5000上等待传入连接的服务器。
这是使用pyalgotrade.optimizer.worker模块与服务器提供的数据并行运行策略的工作脚本：

from pyalgotrade.optimizer import worker

import rsi2

# The if __name__ == '__main__' part is necessary if running on Windows.

if __name__ == '__main__':

    worker.run(rsi2.RSI2, "localhost", 5000, workerName="localworker")

当您运行服务器和客户端时，您将在服务器控制台上看到类似的内容：

2014-05-03 15:04:01,083 server [INFO] Loading bars

2014-05-03 15:04:01,348 server [INFO] Waiting for workers

2014-05-03 15:04:58,277 server [INFO] Partial result 1242173.28754 with parameters: ('dia', 150, 5, 2, 91, 19) from localworker

2014-05-03 15:04:58,566 server [INFO] Partial result 1203266.33502 with parameters: ('dia', 150, 5, 2, 81, 19) from localworker

2014-05-03 15:05:50,965 server [INFO] Partial result 1220763.1579 with parameters: ('dia', 150, 5, 3, 83, 24) from localworker

2014-05-03 15:05:51,325 server [INFO] Partial result 1221627.50793 with parameters: ('dia', 150, 5, 3, 80, 24) from localworker

.

.

在工作台的控制台上有这样的东西：

2014-05-03 15:02:25,360 localworker [INFO] Running strategy with parameters ('dia', 150, 5, 2, 84, 15)

2014-05-03 15:02:25,377 localworker [INFO] Running strategy with parameters ('dia', 150, 5, 2, 94, 5)

2014-05-03 15:02:25,661 localworker [INFO] Result 1090481.06342

2014-05-03 15:02:25,661 localworker [INFO] Result 1031470.23717

2014-05-03 15:02:25,662 localworker [INFO] Running strategy with parameters ('dia', 150, 5, 2, 93, 25)

2014-05-03 15:02:25,665 localworker [INFO] Running strategy with parameters ('dia', 150, 5, 2, 84, 14)

2014-05-03 15:02:25,995 localworker [INFO] Result 1135558.55667

2014-05-03 15:02:25,996 localworker [INFO] Running strategy with parameters ('dia', 150, 5, 2, 93, 24)

2014-05-03 15:02:26,006 localworker [INFO] Result 1083987.18174

2014-05-03 15:02:26,007 localworker [INFO] Running strategy with parameters ('dia', 150, 5, 2, 84, 13)

2014-05-03 15:02:26,256 localworker [INFO] Result 1093736.17175

2014-05-03 15:02:26,257 localworker [INFO] Running strategy with parameters ('dia', 150, 5, 2, 84, 12)

2014-05-03 15:02:26,280 localworker [INFO] Result 1135558.55667

.

.

请注意，您应该只运行一个服务器和一个或多个工作。
如果您只想在自己的桌面上并行运行策略，您可以利用pyalgotrade.optimizer.local 模块，如下所示：

import itertools

from pyalgotrade.optimizer import local

from pyalgotrade.barfeed import yahoofeed

import rsi2

def parameters_generator():

    instrument = ["dia"]

    entrySMA = range(150, 251)

    exitSMA = range(5, 16)

    rsiPeriod = range(2, 11)

    overBoughtThreshold = range(75, 96)

    overSoldThreshold = range(5, 26)

    return itertools.product(instrument, entrySMA, exitSMA, rsiPeriod, overBoughtThreshold, overSoldThreshold)

# The if __name__ == '__main__' part is necessary if running on Windows.

if __name__ == '__main__':

    # Load the feed from the CSV files.

    feed = yahoofeed.Feed()

    feed.addBarsFromCSV("dia", "dia-2009.csv")

    feed.addBarsFromCSV("dia", "dia-2010.csv")

    feed.addBarsFromCSV("dia", "dia-2011.csv")

    local.run(rsi2.RSI2, feed, parameters_generator())

代码正在做3件事情：
1.声明生成不同参数组合的生成函数。
2.使用我们下载的CSV文件加载Feed。
3.使用pyalgotrade.optimizer.local模块并行运行策略，找到最佳结果。
当您运行此代码时，您应该看到如下：

2014-05-03 15:08:06,587 server [INFO] Loading bars

2014-05-03 15:08:06,910 server [INFO] Waiting for workers

2014-05-03 15:08:58,347 server [INFO] Partial result 1242173.28754 with parameters: ('dia', 150, 5, 2, 91, 19) from worker-95583

2014-05-03 15:08:58,967 server [INFO] Partial result 1203266.33502 with parameters: ('dia', 150, 5, 2, 81, 19) from worker-95584

2014-05-03 15:09:52,097 server [INFO] Partial result 1220763.1579 with parameters: ('dia', 150, 5, 3, 83, 24) from worker-95584

2014-05-03 15:09:52,921 server [INFO] Partial result 1221627.50793 with parameters: ('dia', 150, 5, 3, 80, 24) from worker-95583

2014-05-03 15:10:40,826 server [INFO] Partial result 1142162.23912 with parameters: ('dia', 150, 5, 4, 76, 17) from worker-95584

2014-05-03 15:10:41,318 server [INFO] Partial result 1107487.03214 with parameters: ('dia', 150, 5, 4, 83, 17) from worker-95583

.

.

为了记录，发现的最佳结果是$ 2314.40，具有以下参数：

entrySMA：154
exitSMA：5
rsiPeriod：2
overBoughtThreshold：91
overSoldThreshold：18

作者：readilen
链接：http://www.jianshu.com/p/8c43f54cf7a1
來源：简书
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