python搭积木_从零实现”搭积木式实现策略“的回测系统 part VI

本篇我们将对比经典量化回测框架pyalgotrade与ailabx，二者同时实现均线策略。

先看pyalgotrade的代码实现：

from pyalgotrade import strategy

from pyalgotrade.technical import ma

from pyalgotrade.technical import cross

from pyalgotrade.tools import quandl

class SMACrossOver(strategy.BacktestingStrategy):

def __init__(self, feed, instrument, smaPeriod):

super(SMACrossOver, self).__init__(feed)

self.__instrument = instrument

self.__position = None

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

self.setUseAdjustedValues(True)

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

self.__sma = ma.SMA(self.__prices, smaPeriod)

def getSMA(self):

return self.__sma

def onEnterOk(self, position):

execInfo = position.getEntryOrder().getExecutionInfo()

self.info("BUY at %.2f" % (execInfo.getPrice()))

def onEnterCanceled(self, position):

self.__position = None

def onExitOk(self, position):

execInfo = position.getExitOrder().getExecutionInfo()

self.info("SELL at $%.2f" % (execInfo.getPrice()))

self.__position = None

def onBars(self, bars):

# If a position was not opened, check if we should enter a long position.

if self.__position is None:

if cross.cross_above(self.__prices, self.__sma) > 0:

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

# Enter a buy market order. The order is good till canceled.

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

# Check if we have to exit the position.

elif not self.__position.exitActive() and cross.cross_below(self.__prices, self.__sma) > 0:

self.__position.exitMarket()

from pyalgotrade import plotter

from pyalgotrade.barfeed import quandlfeed

from pyalgotrade.stratanalyzer import returns

#import sma_crossover

data = quandl.build_feed("WIKI", ['ORCL'], 2000, 2000, ".")

# Load the bar feed from the CSV file

feed = quandlfeed.Feed()

feed.addBarsFromCSV("orcl", "WIKI-ORCL-2000-quandl.csv")

# Evaluate the strategy with the feed's bars.

myStrategy = SMACrossOver(feed, "orcl", 20)

# Attach a returns analyzers to the strategy.

returnsAnalyzer = returns.Returns()

myStrategy.attachAnalyzer(returnsAnalyzer)

# Attach the plotter to the strategy.

plt = plotter.StrategyPlotter(myStrategy)

# Include the SMA in the instrument's subplot to get it displayed along with the closing prices.

plt.getInstrumentSubplot("orcl").addDataSeries("SMA", myStrategy.getSMA())

# Plot the simple returns on each bar.

plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())

# Run the strategy.

myStrategy.run()

myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())

from pyalgotrade.stratanalyzer import returns, sharpe, drawdown, trades

sharpe_ratio = sharpe.SharpeRatio()

myStrategy.attachAnalyzer(sharpe_ratio)

#print('sharpe:',sharpe_ratio.getSharpeRatio(0))

# Plot the strategy.

plt.plot()

再来看ailax积木式框架的实现：

'''

@author: 魏佳斌

@license: (C) Copyright 2018-2025, ailabx.com.

@contact: 86820609@qq.com

@file: test_trading_env.py

@time: 2018-10-17 10:29

@desc:

'''

import unittest,os

from quant.engine.trading_env import TradingEnv

from quant.engine.datafeed import DataFeed

from quant.engine.algos import *

class TestTradingEnv(unittest.TestCase):

def test_run_step(self):

path = os.path.abspath(os.path.join(os.getcwd(), "../../data"))

feed = DataFeed(data_path=path)

feed.download_or_get_data(['ORCL',], 2000, 2000)

long_expr = 'cross_up(close,ma(close,20))'

flat_expr = 'cross_down(close,ma(close,20))'

ma_cross = Strategy([

SelectByExpr(long_expr=long_expr,flat_expr=flat_expr),

WeighEqually(),

Constraint({'max_weight':0.9})

],name='均线交叉策略')

env = TradingEnv(strategy=ma_cross,feed=feed)

env.run_strategy()

stra_stats = env.get_statistics()

stats = [stra_stats]

from quant.engine.trading_env import EnvUtils

utils =EnvUtils(stats=stats)

utils.show_stats()

客观讲，ailabx只是做了一些配置。规则是通过两句表达式来给出，相当简洁：

long_expr = 'cross_up(close,ma(close,20))'

flat_expr = 'cross_down(close,ma(close,20))'

m

项目在github上开源，欢迎star。

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