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In VNPY, optimization parameters are often done in batches. One is a group of different policies to optimize a variety in batches, and another strategy corresponds to different credentials. The following is the source code, which is placed under the example\ CtaBacktesting folder, mainly referring to the original optimization code.

There is also the output time, due to optimization, the results may be many, the default output of 30 to excel.

# encoding: UTF-8

Import pandas as pd

From vnpy.trader.app.ctaStrategy.ctaBacktesting import BacktestingEngine, MINUTE_DB_NAME, OptimizationSetting

From vnpy.trader.app.ctaStrategy.strategy.strategyBollChannel import BollChannelStrategy

Class BatchOptimization (object):

Def _ init__ (self):

"

Def calculateBacktesting (self,symbollist,strategylist, sort = 'totalNetPnl'):

# enter the variety queue and policy queue, return the result resultlist, and add the variety name, policy name and policy parameters in order to output for easy retrieval

Resultlist = []

For symbol in symbollist:

For strategy in strategylist:

Result = self.runBacktesting (symbol,strategy,sort)

# add variety name, policy name and policy parameters

If isinstance (result,dict):

# if dict is returned, join directly

Result ["Symbolname"] = str (symbol ["vtSymbol"])

Result ["strategyname"] = str (strategy [0])

Result ["strategysetting"] = str (strategy [1])

Resultlist.append (result)

Else:

# it is found that the optimized queue contains a tuple, which is composed of three components: the first ranking policy parameter, the second testing target value, and the third policy parameter running result.

# what we want here is the third, insert the dict first, insert symbolname, and strategysetting in the dict

For resultraw in result:

Resultlist.append (resultraw [2])

Resultlist [- 1] ["Symbolname"] = str (symbol ["vtSymbol"])

Resultlist [- 1] ["strategysetting"] = str (resultraw [0])

Return resultlist

Def runBacktesting (self, symbol, strategy, sort):

# write the test variety and parameters, and return the test data set including the test results

# create a policy object in the engine

# create a backtest engine

Engine = BacktestingEngine ()

# set the backtest mode of the engine to K line

Engine.setBacktestingMode (engine.BAR_MODE)

# set the data start date for back testing

Engine.setStartDate (symbol ["StartDate"])

Engine.setSlippage (symbol ["Slippage"]) # 1 Hop

Engine.setRate (symbol ["Rate"]) # Commission size

Engine.setSize (symbol ["Size"]) # contract size

Engine.setPriceTick (symbol ["Slippage"]) # minimum price change

Engine.setCapital (symbol ["Capital"])

# set the history database used

Engine.setDatabase (MINUTE_DB_NAME, symbol ["vtSymbol"])

# call optimization method to integrate optimization test

Setting = OptimizationSetting () # create a new optimization task setting object

Setting.setOptimizeTarget (sort) # sets the goal of optimal ranking is policy net profit

Print strategy [1]

For settingKey in strategy [1]:

If isinstance (strategy [1] [settingKey], tuple):

Setting.addParameter (settingKey,strategy [1] [settingKey] [0], strategy [1] [settingKey] [1], strategy [1] [settingKey] [2])

Else:

Setting.addParameter (settingKey,strategy [1] [settingKey])

#

Optimizationresult = engine.runParallelOptimization (strategy [0], setting)

Engine.output (u' output statistics')

# if optimization mode is used, the list of the dict returned in the policy response test is returned. If the normal return test is a single dict,

# if it is greater than 30, return within 30, otherwise all

If len (optimizationresult) > 30:

Return optimizationresult [: 30]

Else:

Return optimizationresult

Def toExcel (self, resultlist, path = "C:\ data\ datframe.xlsx"):

# output excel according to input statistics queue and path. No new mode is provided here. If you want, you can change it.

# dft.to_csv (path,index=False,header=True, mode ='a')

SummayKey = resultlist [0] .keys ()

# summayValue = result.values ()

Dft = pd.DataFrame (columns=summayKey)

For result_ in resultlist:

New = pd.DataFrame (result_, index= ["0"])

Dft = dft.append (new,ignore_index=True)

Dft.to_excel (path,index=False,header=True)

Print "output statistical results of back test to" + path "

If _ name__ = = "_ _ main__":

# create a variety queue. Here you can import it with json, and write it directly for ease of use.

Symbollist = [{

"vtSymbol": 'm1809'

"StartDate": "20180101"

"Slippage": 1

"Size": 10

"Rate": 2 / 10000

"Capital": 10000

}

{

"vtSymbol": 'rb1810'

"StartDate": "20180101"

"Slippage": 1

"Size": 10

"Rate": 2 / 10000

"Capital": 10000

}

]

# here is the case of the same policy and different parameters, of course, there can be multiple policies and multiple parameter combinations

Strategylist2 = []

# Policy list, if it is a tuple, then there are three, according to the first beginning, the second ending, and the third step.

Settinglist = [

{'bollWindow': (10, 20, 2)}]

# merge a tuple

If settinglist! = []:

For para1 in settinglist:

Strategylist2.append ((BollChannelStrategy, para1))

NT = BatchOptimization ()

Resultlist = NT.calculateBacktesting (symbollist,Strategylist2,sort = 'totalNetPnl')

# define the path

Path = "C:\ Project\ OptimizationResult.xlsx"

NT.toExcel (resultlist,path)

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