[R-SIG-Finance] Processing time of backtests on a single computer
Jersey Fanatic
jerseyfanatic1 at gmail.com
Wed Apr 6 22:20:17 CEST 2016
The email with the CSV data attached is waiting for moderator's approval.
The reproducible code is below.
2016-04-06 23:17 GMT+03:00 Jersey Fanatic <jerseyfanatic1 at gmail.com>:
> Thanks for the quick reply. I did not understand what you meant by path
> dependent looping or copying, but my aim is to decrease the processing time
> of the backtesting of several combinations of parameters.
>
> Here is the reproducible example, and the data I am using is attached as
> well:
>
> library(lattice)
> library(foreach)
> library(doSNOW)
> library(ggplot2)
> library(PerformanceAnalytics)
> library(doSNOW)
> require(latticeExtra)
> require(grid)
> library(gridExtra)
> library(reshape)
> library(quantstrat)
> no.cores <- 8
>
> .strategy<- new.env()
> .blotter<- new.env()
>
> currency(c('USD', 'EUR'))
> exchange_rate(primary_id="EURUSD", tick_size=0.0001)
>
> data.location.r <- "processingtime_q_rsigfinance.csv"
> symbol.data <- as.xts(read.zoo(data.location.r, sep=',',
> tz="",header=TRUE, format='%d/%m/%Y %H:%M', index.column = 1))
> symbol.data <- symbol.data[symbol.data$VOLUME!=0,]
> # Delete rows with no volume (when the
> market is closed)
> symbol.data[,c(1,2,3,4)] <-
> round(as.numeric(symbol.data[,c(1,2,3,4)]),abs(log10(0.0001))) #datayı tick
> sizela uyumlu yapma
> assign("EURUSD", symbol.data)
>
> strategy.st <- "rsigfinance"
> rm.strat(strategy.st)
>
>
> initDate = as.character(as.Date(index(symbol.data[1])-1))
>
> initPortf(strategy.st, "EURUSD", initDate=initDate, currency = "USD")
> initAcct(strategy.st, portfolios=strategy.st, initDate=initDate,
> initEq=100000, currency = "USD")
> initOrders(portfolio=strategy.st,initDate=initDate)
>
> strategy(strategy.st,store=TRUE)
> summary(getStrategy(strategy.st))
>
>
> macdFastMARange <- seq(2,17,by=5)
> macdSlowMARange <- seq(5,35,by=10)
> macdSignalRange <- seq(2,18,by=8)
>
> StopLossDistanceRange <- seq(0.01,0.02,by=0.01)
>
> TrailingDistanceRange <- seq(0.01,0.02,by=0.01)
>
>
> positionSizeLong = round(100000 / as.numeric(symbol.data$CLOSE[1]),-2)
> positionSizeShort = - round(100000 / as.numeric(symbol.data$CLOSE[1]),-2)
> txn.model <- 0
> sltsltp.txn.fee <- 0
>
>
> add.indicator(strategy.st,
> name = "MACD",
> arguments = list(x=Cl(eval(parse(text = "EURUSD")))),
> label='macd')
>
> add.signal(strategy.st,name="sigCrossover",
> arguments =
> list(columns=c("macd.macd","signal.macd"),relationship="gt"),
> label="macd.gt.signal")
>
> add.signal(strategy.st,name="sigCrossover",
> arguments =
> list(columns=c("macd.macd","signal.macd"),relationship="lt"),
> label="macd.lt.signal")
>
> add.rule(strategy.st,
> name='ruleSignal',
> arguments = list(sigcol="macd.gt.signal",
> sigval=TRUE,
> prefer="Open",
> orderqty= positionSizeLong,
> ordertype='market',
> orderside='long',
> orderset='ocolong',
> TxnFees = txn.model),
> type='enter',
> label='longenter',
> enabled=TRUE
> )
>
> add.rule(strategy.st,
> name='ruleSignal',
> arguments = list(sigcol="macd.lt.signal",
> sigval=TRUE,
> prefer="Open",
> orderqty='all',
> ordertype='market',
> orderside='long',
> orderset='ocolong',
> TxnFees = txn.model),
> type='exit',
> label='longexit',
> enabled=TRUE
> )
>
> add.rule(strategy.st,name='ruleSignal',
> arguments = list( sigcol="macd.lt.signal", sigval=TRUE,
> replace=FALSE,
> orderside='long',
> ordertype='stoplimit',
> tmult=TRUE,
> threshold=quote( longStopLossDistance ),
> orderqty='all',
> orderset='ocolong',
> TxnFees = txn.model),
> type='chain', parent="longenter",
> label='StopLossLong',
> enabled=TRUE)
>
> add.rule(strategy.st, name = 'ruleSignal',
> arguments=list(sigcol="macd.lt.signal" , sigval=TRUE,
> replace=FALSE,
> orderside='long',
> ordertype='stoptrailing',
> tmult=TRUE,
> threshold=quote(longTrailingStopDistance),
> orderqty='all',
> orderset='ocolong',
> TxnFees = txn.model),
> type='chain', parent="longenter",
> label='StopTrailingLong',
> enabled=TRUE
> )
>
> add.rule(strategy.st, name = "ruleSignal",
> arguments = list(sigcol="macd.lt.signal",
> sigval=TRUE,
> ordertype="limit",
> orderside="long",
> replace=FALSE,
> tmult=TRUE,
> threshold=quote(longTakeProfitDistance),
> orderqty="all",
> orderset="ocolong",
> TxnFees = txn.model),
> type = "chain", parent="longenter",
> label = "takeProfitLong",
> enabled = FALSE
> )
>
> add.rule(strategy.st,
> name='ruleSignal',
> arguments = list(sigcol="macd.lt.signal",
> sigval=TRUE,
> prefer="Open",
> orderqty=positionSizeShort,
> ordertype='market',
> orderside='short',
> orderset='ocoshort',
> TxnFees = txn.model),
> type='enter',
> label='shortenter',
> enabled=TRUE
> )
>
> add.rule(strategy.st,
> name='ruleSignal',
> arguments = list(sigcol="macd.gt.signal",
> sigval=TRUE,
> prefer="Open",
> orderqty='all',
> ordertype='market',
> orderside='short',
> orderset='ocoshort',
> TxnFees = txn.model),
> type='exit',
> label='shortexit',
> enabled=TRUE
> )
>
> add.rule(strategy.st,name='ruleSignal',
> arguments = list( sigcol="macd.gt.signal", sigval=TRUE,
> replace=FALSE,
> orderside='short',
> ordertype='stoplimit',
> tmult=TRUE,
> threshold=quote( shortStopLossDistance ),
> orderqty='all',
> orderset='ocoshort',
> TxnFees = txn.model),
> type='chain', parent="shortenter",
> label='StopLossShort',
> enabled=TRUE)
>
> add.rule(strategy.st, name = 'ruleSignal',
> arguments=list(sigcol="macd.gt.signal" , sigval=TRUE,
> replace=FALSE,
> orderside='short',
> ordertype='stoptrailing',
> tmult=TRUE,
> threshold=quote( shortTrailingStopDistance),
> orderqty='all',
> orderset='ocoshort',
> TxnFees = txn.model),
> type='chain', parent="shortenter",
> label='StopTrailingShort',
> enabled=TRUE
> )
>
> add.rule(strategy.st, name = "ruleSignal",
> arguments = list(sigcol="macd.gt.signal",
> sigval=TRUE,
> ordertype="limit",
> orderside="short",
> replace=FALSE,
> tmult=TRUE,
> threshold=quote( -shortTakeProfitDistance),
> orderqty="all",
> orderset="ocoshort",
> TxnFees = txn.model),
> type = "chain", parent="shortenter",
> label = "takeProfitShort",
> enabled = FALSE
> )
>
>
> add.distribution(strategy.st,
> paramset.label = "MACD_OPT",
> component.type = 'indicator',
> component.label = "macd",
> variable = list( nFast = macdFastMARange ),
> label = "macdFastMARANGE")
>
>
> add.distribution(strategy.st,
> paramset.label = "MACD_OPT",
> component.type = 'indicator',
> component.label = "macd",
> variable = list( nSlow = macdSlowMARange ),
> label = "macdSlowMARANGE")
>
> add.distribution(strategy.st,
> paramset.label = "MACD_OPT",
> component.type = 'indicator',
> component.label = "macd",
> variable = list( nSig = macdSignalRange ),
> label = "macdSignalRANGE")
>
> add.distribution.constraint(strategy.st,
> paramset.label = 'MACD_OPT',
> distribution.label.1 = 'macdFastMARANGE',
> distribution.label.2 = 'macdSlowMARANGE',
> operator = '<',
> label = 'FastMA<SlowMA')
>
> add.distribution(strategy.st,
> paramset.label = "MACD_OPT",
> component.type = "chain",
> component.label = "StopLossLong",
> variable = list( threshold = StopLossDistanceRange ),
> label = "StopLossLONG")
>
> add.distribution(strategy.st,
> paramset.label = "MACD_OPT",
> component.type = "chain",
> component.label = "StopTrailingLong",
> variable = list( threshold = TrailingDistanceRange ),
> label = "StopTrailingLONG")
>
> add.distribution(strategy.st,
> paramset.label = "MACD_OPT",
> component.type = "chain",
> component.label = "StopLossShort",
> variable = list( threshold = StopLossDistanceRange ),
> label = "StopLossSHORT")
>
> add.distribution(strategy.st,
> paramset.label = "MACD_OPT",
> component.type = "chain",
> component.label = "StopTrailingShort",
> variable = list( threshold = TrailingDistanceRange ),
> label = "StopTrailingSHORT")
>
> add.distribution.constraint(strategy.st,
> paramset.label = "MACD_OPT",
> distribution.label.1 = "StopLossLONG",
> distribution.label.2 = "StopLossSHORT",
> operator = "==",
> label = "StoplossEquality")
>
> add.distribution.constraint(strategy.st,
> paramset.label = "MACD_OPT",
> distribution.label.1 = "StopTrailingLONG",
> distribution.label.2 = "StopTrailingSHORT",
> operator = "==",
> label = "TrailingStopEquality")
>
> summary(getStrategy(strategy.st))
>
>
>
> start.t <- Sys.time()
> paramsetenv<-new.env()
> cl <- snow::makeCluster(no.cores, type = "SOCK")
> registerDoSNOW(cl)
> results <- apply.paramset(strategy.st,paramset.label="MACD_OPT",
> portfolio=strategy.st, account=strategy.st,nsamples=0,verbose
> = FALSE,
> audit=paramsetenv)
> snow::stopCluster(cl)
> finish.t <- Sys.time()
> print(finish.t-start.t)
>
>
>
>
>
> If there are any problems with the code when reproducing, sorry. I ran it
> on my computer and it did seem to work as it did normally.
>
>
>
> 2016-04-06 21:05 GMT+03:00 Brian G. Peterson <brian at braverock.com>:
>
>> On Wed, 2016-04-06 at 20:58 +0300, Jersey Fanatic wrote:
>> > Hi everyone,
>> >
>> > I am trying to backtest a simple strategy of mine, but it took approx 10
>> > hours of processing so I was wondering if it is normal.
>> >
>> > My computer has an i7 core 2.10 GHz, 8GB RAM with Windows 7 Ultimate
>> OS. My
>> > code uses doSNOW package for parallel processing and all the 8 cores, so
>> > the CPU is %100 all the time for the complete 10 hours.
>> >
>> > The data the backtest is done on is M5 OHLC FX (24 hour) data for
>> approx 1
>> > year which makes a total of 58000 data points (counting OHLC as 1). The
>> > strategy has 4 entry&exit and 4 stoploss&trailingstoploss&takeprofit
>> rules.
>> > 144 combinations of parameters are tested.
>> >
>> > I would just like to know if this time is normal for just 1 home-use
>> > computer. And I would be grateful for any recommendations to speed up
>> this
>> > process to decrease computing time.
>>
>> No, this sounds very high.
>>
>> You haven't told us enough about what you're trying to do for anyone
>> here to help you in a specific way. You'd need to provide a minimal
>> reproducible example.
>>
>> However, we typically use a benchmark time of one core minute per day
>> per instrument on tick data (millions of observations per day).
>>
>> Is seems likely that you are doing too much path dependent looping,
>> copying, or both.
>>
>> Regards,
>>
>> Brian
>>
>>
>
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