Monte Carlos and CAR25 Simulation by Backtrader (Credit to Howard Bandy)

[barton05]

Credit to CAR25 by Howard Bandy. I think Howard provides a good way to measure the required fraction (sizing) for any stock simulation.
I have created the monte carlos as Howard Bandy mentioned in his book. Hope it helps BT-ers as a quick start to have the Monte Carlos simulation. :)

Figures as follow:
I have randomly distributed the trade and set the data started as 1-1-1990. It will need some entry and exit price in your previous trade record as reference point. It will adjust the monte['fraction'] according to the simulated DD90 value.

Code as follow:

def runOneMonte(_,row,consoFeeds,monte,params):
	stockName,exchange,indName=row[:3]
	cerebro = bt.Cerebro()
	cerebro.broker.setcash(100000)
	simParams={'stockName':stockName,'exchange':exchange,'indicator':indName}
	#analyzer
	cerebro.addanalyzer(continueITDDAnalyzer,_name='contITDD')
	#addCommission
	IB = params['commission'](commission=0.0)
	cerebro.broker.addcommissioninfo(IB)
	#SetBroker
	cerebro.broker.set_checksubmit(False)

	#Shuffle and reindex Data
	#random.seed(1)
	datas=list(itertools.chain.from_iterable(itertools.repeat(consoFeeds,max(int(monte['tradeTime']/len(consoFeeds)),1))))
	shuffledDatas=random.sample(datas, len(datas))
	barlen = sum(len(f)-1 for f in shuffledDatas)
	consoFeed=pd.concat(shuffledDatas)
	mcIndex=pd.date_range(start=datetime.datetime.strptime('1990-01-01','%Y-%m-%d'),periods=len(consoFeed),freq='D')
	consoFeed.set_index(mcIndex,drop=True,inplace=True)
	_feed=bt.feeds.PandasData(dataname=consoFeed[['open','high','low','close','volume']])
	cerebro.adddata(_feed,name=stockName)
	tradeFeed = consoFeed[['BuyPrice','SellPrice']]
	cerebro.addstrategy(sizingStra, monte['fraction'],tradeFeed)
	cerebro.strats[0][0][0].contITDDSet={'ITDD':0,'ITDDLength':0,'initV':100000,'peak':float('-inf'),'ddlen':-1,'dd':0}
	strategies=cerebro.run()
	contITDDSet=strategies[0].analyzers.contITDD.get_analysis()
	contITDDSet['CARReturn']=(cerebro.broker.getvalue()/contITDDSet['initV'] - 1)*100*252/barlen
	return contITDDSet

def calculateDD90(row,params,monte,consoFeeds,q,loopRound=6):
	stockName=row.Index
	masterLogger=logging.getLogger('Master')
	loopTime=0
	while loopTime<loopRound:
		_prevFraction=0
		_loopCount=0
		if params['numMulti'] >1:
			print('{}-{}: Start Pooling with Monte fraction:{}'.format(stockName,row.indicator, monte['fraction']))
			pool = multiprocessing.Pool(params['numMulti'], worker_init, [q])
			#Run 1000 monte carlos
			startTime=datetime.datetime.now()
			with pool as p:
				simResSet=pd.DataFrame.from_dict(pool.starmap(runOneMonte,zip(range(0,monte['NumberSim']),itertools.repeat(list(row)),itertools.repeat(consoFeeds),itertools.repeat(monte),itertools.repeat(params))))
			pool.close()
			pool.join()
			timeRequired=datetime.datetime.now()-startTime
			print('{}-{}: Loop Time required for runOneMonte:{}'.format(stockName,row.indicator,timeRequired))
		else:
			print('{}:Start Single Thread Simulation'.format(stockName))
			simResSet = pd.DataFrame([runOneMonte(i,list(row), consoFeeds,monte,params) for i in range(0,monte['NumberSim'])])
		DD90 = np.quantile(simResSet['ITDD'] , 0.9, interpolation='nearest')
		DD90 = simResSet[simResSet['ITDD']==DD90].to_dict(orient='records')[0]
		print('{}-{}:Test Result: Fraction ={}, DD90={}, CAR25={}'.format(stockName,row.indicator,monte['fraction']*100,DD90['ITDD'],DD90['CARReturn']))
		if abs(monte['drawdownLimit'] - DD90['ITDD']) < monte['accuracyTolerance'] or abs(monte['fraction'] - _prevFraction) < 0.01 or _loopCount >5:
			return DD90
		else:
			_prevFraction = monte['fraction']
			_loopCount+=1
			monte['fraction']=monte['fraction']*monte['drawdownLimit']/(DD90['ITDD'])
			if monte['fraction']>1 :
				print('Overwhelming sizing Occurred')
				return DD90
		loopTime+=1
	return DD90

def setMonte(row,params,monte):
	#Read Order time and Trade Price and prepare the feeds, dont follow below script as it is minimized
	consoFeeds=[]
	for record in records:
		consoFeeds.append(consoFeed)

	#Multi-processing
	_stockStartTime=datetime.datetime.now()
	DD90=calculateDD90(row,params,monte,consoFeeds,q)
	timeRequired=datetime.datetime.now()-_stockStartTime
	print('{}: Total Loop Time required:{}\n================================='.format(row.Index,timeRequired))
	DD90.update({'fraction':monte['fraction'],'stockName':row.Index,'exchange':row.exchange,'indicator':row.indicator})
	return DD90