This post is going to explain what genetic algorithms are, it will also present R code for performing genetic optimisation.
A genetic algo consists of three things:
- A gene
- A fitness function
- Methods to breed/mate genes
The Gene
The gene is typically a binary number, each bit in the binary number controls various parts of your trading strategy. The gene below contains 4 sub gene, a stock gene to select what stock to trade, a strategy gene to select what strategy to use, paramA sets a parameter used in your strategy and paramB sets another parameter to use in your strategy.
Gene = [StockGene,StrategyGene,ParamA,ParamB]
Stock Gene | ||
---|---|---|
00 | ||
01 | ||
10 | IBM | |
11 |
Strategy Gene | ||
---|---|---|
0 | Simple Moving Average | |
1 | Exponential Moving Average |
ParamA Gene – Moving Average 1 Lookback | ||
---|---|---|
00 | 10 | |
01 | 20 | |
10 | 30 | |
11 | 40 |
ParamB Gene – Moving Average 2 Lookback | ||
---|---|---|
00 | 15 | |
01 | 25 | |
10 | 35 | |
11 | 45 |
So Gene = [01,1,00,11]
Would be stock=Facebook, strategy=Exponential Moving Average,paramA=10,paramB=45].
The strategy rules are simple, if the moving average(length=paramA) > moving average(length=paramB) then go long, and vice versa.
The fitness function
A gene is quantified as a good or bad gene using a fitness function. The success of a genetic trading strategy depends heavily upon your choice of fitness function and whether it makes sense with the strategies you intend to use. You will trade each of the strategies outlined by your active genes and then rank them by their fitness. A good starting point would be to use the sharp ratio as the fitness function.
You need to be careful that you apply the fitness function to statistically significant data. For example if you used a mean reverting strategy that might trade once a month (or what ever your retraining window is), then your fitness is determined by 1 or 2 datapoints!!! This will result in poor genetic optimisation (in my code i’ve commented out a mean reversion strategy test for yourself). Typically what happens is your sharpe ratio from 2 datapoints is very very high merely down to luck. You then mark this as a good gene and trade it the next month with terrible results.
Breeding Genes
With a genetic algo you need to breed genes, for the rest of this post i’ll assume you are breeding once a month. During breeding you take all of the genes in your gene pool and rank them according to the fitness function. You then select the top N genes and breed them (discard all the other genes they’re of no use).
Breeding consists of two parts:
Hybridisation – Take a gene and cut a chunk out of it, you can use whatever random number generator you want to determine the cut locations, swap this chunk with a corresponding chunk from another gene.
Eg.
Old gene: 00110010 and 11100110 (red is the randomly select bits to cut)
New gene: 00100110 and 11110010
You do this for every possible pair of genes in your top N list.
Mutation – After hybridisation go through all your genes and randomly flip the bits with an fixed probability. The mutation prevents your strategy from getting locked into an every shrinking gene pool.
For a more detailed explanation with diagrams please see:
http://blog.equametrics.com/ scroll down to Genetic Algorithms and its Application in Trading
Annualized Sharpe Ratio (Rf=0%) 1.15
On to the code:
library("quantmod") library("PerformanceAnalytics") library("zoo") #INPUTS topNToSelect <- 5 #Top n genes are selected during the mating, these will be mated with each other mutationProb <- 0.05 #A mutation can occur during the mating, this is the probability of a mutation for individual chromes symbolLst <- c("^GDAXI","^FTSE","^GSPC","^NDX","AAPL","ARMH","JPM","GS") #symbolLst <- c("ADN.L","ADM.L","AGK.L","AMEC.L","AAL.L","ANTO.L","ARM.L","ASHM.L","ABF.L","AZN.L","AV.L","BA.L","BARC.L","BG.L","BLT.L","BP.L","BATS.L","BLND.L","BSY.L","BNZL.L","BRBY.L","CSCG.L","CPI.L","CCL.L","CNA.L","CPG.L","CRH.L","CRDA.L","DGE.L","ENRC.L","EXPN.L","FRES.L","GFS.L","GKN.L","GSK.L","HMSO.L","HL.L","HSBA.L","IAP.L","IMI.L","IMT.L","IHG.L","IAG.L","IPR.L","ITRK.L","ITV.L","JMAT.L","KAZ.L","KGF.L","LAND.L","LGEN.L","LLOY.L","EMG.L","MKS.L","MGGT.L","MRW.L","NG.L","NXT.L","OML.L","PSON.L","PFC.L","PRU.L","RRS.L","RB.L","REL.L","RSL.L","REX.L","RIO.L","RR.L","RBS.L","RDSA.L","RSA.L","SAB.L","SGE.L","SBRY.L","SDR.L","SRP.L","SVT.L","SHP.L","SN.L","SMIN.L","SSE.L","STAN.L","SL.L","TATE.L","TSCO.L","TLW.L","ULVR.L","UU.L","VED.L","VOD.L","WEIR.L","WTB.L","WOS.L","WPP.L","XTA.L") #END INPUTS #Stock gene stockGeneLength <- 3 #8stocks #stockGeneLength<-6 #Allows 2^6 stocks (64) #Strategy gene strateyGeneLength<-2 #Paramter lookback gene parameterLookbackGeneLength<-6 #Calculate the length of our chromozone, chromozone=[gene1,gene2,gene3...] chromozoneLength <- stockGeneLength+strateyGeneLength+parameterLookbackGeneLength #TradingStrategies signalMACross <- function(mktdata, paramA, paramB, avgFunc=SMA){ signal = avgFunc(mktdata,n=paramA)/avgFunc(mktdata,n=paramB) signal[is.na(signal)] <- 0 signal <- (signal>1)*1 #converts bools into ints signal[signal==0] <- (-1) return (signal) } signalBollingerReversion <- function(mktdata, paramA, paramB){ avg <- SMA(mktdata,paramB) std <- 1*rollapply(mktdata, paramB,sd,align="right") shortSignal <- (mktdata > avg+std)*-1 longSignal <- (mktdata < avg-std)*1 signal <- shortSignal+longSignal signal[is.na(signal)]<-0 return (signal) } signalRSIOverBoughtOrSold <- function(mktdata, paramA, paramB){ upperLim <- min(60*(1+paramB/100),90) lowerLim <- max(40*(1-paramB/100),10) rsisignal <- RSI(mktdata,paramB) signal <- ((rsisignal>upperLim)*-1)+((rsisignal<lowerLim)*1) return (signal) } #Gene = [StockGene,StrategyGene,ParamAGene,ParamBGene] #The following functions extract specific parts of the gene getStockGeneFromChromozone <- function(chrome){ return(chrome[,seq(1,stockGeneLength)]) } getStrategyGeneFromChromozone <- function(chrome){ return(chrome[,seq(stockGeneLength+1,stockGeneLength+strateyGeneLength)]) } getParameterLookbackGeneFromChromozone <- function(chrome){ return(chrome[,seq(stockGeneLength+strateyGeneLength+1,stockGeneLength+strateyGeneLength+parameterLookbackGeneLength)]) } #Once parts of the gene have been extracted they are then converted into #lookback values, what stocks to trade, or what strategy to use getStockDataFromChromozone<- function(chrome){ #Basically a binary number to decimal converter gene <- getStockGeneFromChromozone(chrome) index <-sum(chrome*(2^(seq(1,length(gene),1)-1)))+1 #The +1 is to stop 0 since not a valid index cleanName <- sub("^","",symbolLst[index],fixed=TRUE) return (get(cleanName,symbolData)) } getStrategyFromChromozone <- function(chrome){ gene <- matrix(getStrategyGeneFromChromozone(chrome)) if(all(gene==matrix(c(0,0)))){ return (signalMACross) } if(all(gene==matrix(c(0,1)))){ return (function(mktdata,paramA,paramB) {signalMACross(mktdata,paramA,paramB,avgFunc=EMA)}) } if(all(gene==matrix(c(1,0)))){ return (function(mktdata,paramA,paramB) {signalMACross(mktdata,paramA,paramB,avgFunc=ZLEMA)}) # return (signalBollingerReversion) } if(all(gene==matrix(c(1,1)))){ return (function(mktdata,paramA,paramB) {signalMACross(mktdata,paramA,paramB,avgFunc=WMA)}) # return (signalRSIOverBoughtOrSold) } print("nothing found") } getLookbackAFromChromozone <- function(chrome){ gene <- getParameterLookbackGeneFromChromozone(chrome) gene <- gene[,1:3] gene <- matrix(gene) if(all(gene==matrix(c(0,0,0)))){ return (10) } if(all(gene==matrix(c(0,0,1)))){ return (20) } if(all(gene==matrix(c(0,1,0)))){ return (30) } if(all(gene==matrix(c(0,1,1)))){ return (40) } if(all(gene==matrix(c(1,0,0)))){ return (50) } if(all(gene==matrix(c(1,0,1)))){ return (60) } if(all(gene==matrix(c(1,1,0)))){ return (70) } if(all(gene==matrix(c(1,1,1)))){ return (80) } } getLookbackBFromChromozone <- function(chrome){ gene <- getParameterLookbackGeneFromChromozone(chrome) gene <- gene[,4:6] gene <- matrix(gene) if(all(gene==matrix(c(0,0,0)))){ return (15) } if(all(gene==matrix(c(0,0,1)))){ return (25) } if(all(gene==matrix(c(0,1,0)))){ return (35) } if(all(gene==matrix(c(0,1,1)))){ return (45) } if(all(gene==matrix(c(1,0,0)))){ return (55) } if(all(gene==matrix(c(1,0,1)))){ return (65) } if(all(gene==matrix(c(1,1,0)))){ return (75) } if(all(gene==matrix(c(1,1,1)))){ return (85) } } #The more positive the fitness, the better the gene calculateGeneFitnessFromTradingReturns <- function(tradingRet){ tradingFitness <- SharpeRatio.annualized(tradingRet) #tradingFitness <- SharpeRatio.annualized(tradingRet) * (1/maxDrawdown(tradingRet)) #tradingFitness <- max(cumsum(tradingRet))/maxDrawdown(tradingRet) #tradingFitness <- sum((tradingRet>0)*1)/length(tradingRet) #% of trades profitable #tradingFitness <- -1*maxDrawdown(tradingRet) return(tradingFitness) } #This function performs the mating between two chromozones genetricMating <- function(chromozoneFitness,useTopNPerformers,mutationProb){ selectTopNPerformers <- function(chromozoneFitness,useTopNPerformers){ #Ranks the chromozones by their fitness and select the topNPerformers orderedChromozones <- order(chromozoneFitness[,"Fitness"],decreasing=TRUE) orderedChromozones <- chromozoneFitness[orderedChromozones,] ##Often there are lots of overlapping strategies with the same fitness ##We should filter by unique fitness to stop the overweighting of lucky high fitness orderedChromozones <- subset(orderedChromozones, !duplicated(Fitness)) print(orderedChromozones) return(orderedChromozones[seq(1,min(nrow(orderedChromozones),useTopNPerformers)),]) } hybridize <- function(topChromozones,mutationProb){ crossoverFunc <- function(chromeA,chromeB){ chromeA <- chromeA[,!colnames(chromeA) %in% c("Fitness")] chromeB <- chromeB[,!colnames(chromeB) %in% c("Fitness")] #Takes a number of chromes from B and swaps them in to A nCross <- runif(min=0,max=ncol(chromeA)-1,1) #the number of individual chromes to swap swapStartLocation = round(runif(min=1,max=ncol(chromeA),1)) swapLocations <- seq(swapStartLocation,swapStartLocation+nCross) #Can run over the end of our vector, need to wrap around back to start swapLocations <- swapLocations %% ncol(chromeA)+1 #Performs the wrapping chromeA[1,swapLocations] <- chromeB[1,swapLocations] #Performs the swap return (chromeA) } mutateFunc <- function(chrome,mutationProb){ return((round(runif(min=0,max=1,ncol(chrome))<mutationProb)+chrome) %% 2) } #Take each chromozone and mate it with all the others (and it's self) a <- topChromozones[rep(seq(1,nrow(topChromozones)),each=nrow(topChromozones)),] #Repeat each row nrow times b <- topChromozones[rep(seq(1,nrow(topChromozones)),nrow(topChromozones)),] #Repeat whole matrix nrow times #Can this be vectorised (not huge amounts of data anyway so probs not an issue)? res <- matrix(nrow=0,ncol=ncol(a)-1) #The minus 1 is to drop the "Fitness" column for(i in 1:nrow(a)){ res <- rbind(res,mutateFunc(crossoverFunc(a[i,],b[i,]),mutationProb)) } return (res) } topChromozones <- selectTopNPerformers(chromozoneFitness,useTopNPerformers) #return ((hybridize(topChromozones,mutationProb))) #You may want duplicates to give more weight to 'good' genes return (unique(hybridize(topChromozones,mutationProb))) #Remove duplicate genes } #This function takes a chrome/gene and does the according trades #It takes market data and a start and an end date #It does not take responsibility for the mating and ranking of genes doGeneticTrading <- function(mktdata,chrome, startDate, endDate){ signalFunc <-getStrategyFromChromozone(chrome) paramA <- getLookbackAFromChromozone(chrome) paramB <- getLookbackBFromChromozone(chrome) signal <- signalFunc(Op(mktdata),paramA,paramB) opClRet <- (Cl(mktdata)/Op(mktdata)) - 1 tradingReturns = opClRet * signal dataWin <- (paste(startDate,"::",endDate,sep="")) tradingReturns <- tradingReturns[dataWin] colnames(tradingReturns) <- c("TradingRet") return(tradingReturns) } #This function mates genes every month #It also passes those genes into the doGeneticTrading function doTrading <- function(chromelist){ #Function for taking a year and a month and spitting out a clean date cleanDate <- function(y,m){ if(m == 13){ m <- 1 y <- y+1 } if(m < 10){ return(paste(y,paste("0",m,sep=""),sep="-")) } else { return(paste(y,m,sep="-")) } } year <- 2002 month <- 1 totalRet <- 0 fitnessEvoltion <- 0 dev.new() par(mfrow=c(2,1)) #Loop through many years and months for(y in 2002:2010){ for(m in 1:12){ chromeFitness <- as.data.frame(matrix(nrow=0,ncol=ncol(chromelist))) startD <- cleanDate(y-2,m) #Subtracting off 2 years to ensure we pass enough data in(should really be calculated from MA lookback) liveStart <- cleanDate(y,m) liveEnd <- cleanDate(y,m+1) print(paste("Start",startD,"LiveStart",liveStart,"LiveEnd",liveEnd)) dataWin <- (paste(startD,"::",liveEnd,sep="")) monthReturn <- data.frame() #Look through all the active chromes and use them for trading for(cn in 1:nrow(chromelist)){ #USE a try catch just incase there are data issue etc... try({ mktdata <- getStockDataFromChromozone(chromelist[cn,]) tradingRet <- doGeneticTrading(mktdata[dataWin],chromelist[cn,],liveStart,liveEnd) tradingRet <- tradingRet*(1/nrow(chromelist)) #even money given to each strategy tradingFitness <- calculateGeneFitnessFromTradingReturns(tradingRet) if(!is.nan(tradingFitness) && !is.nan(max(tradingRet)) && !is.nan(min(tradingRet))){ if(length(monthReturn) == 0 ){ monthReturn <- tradingRet } else { monthReturn <- cbind(monthReturn,tradingRet) } res <- cbind(chromelist[cn,],tradingFitness) colnames(res) <- c(colnames(chromelist[cn,]),"Fitness") chromeFitness <- rbind(chromeFitness,res) } },silent=FALSE) } print("Month return") #Collapse all the trades from each chromozone into a single P&L for each day in the month monthReturn <- apply(monthReturn,1,sum,na.rm=TRUE) print(monthReturn) currentMonthFitness <- calculateGeneFitnessFromTradingReturns(monthReturn) #Update the running total of P&L totalRet <- c(totalRet,monthReturn) fitnessEvoltion <- c(fitnessEvoltion,currentMonthFitness) plot(cumsum(totalRet)) plot(fitnessEvoltion) #print(chromeFitness) #print(chromeFitness[,"Fitness"]) chromelist <- genetricMating(chromeFitness,topNToSelect,mutationProb) print(paste("There are",nrow(chromelist), "chromes active")) print(paste("Min Fitness:",min(chromeFitness[,"Fitness"]))) print(paste("Max Fitness:",max(chromeFitness[,"Fitness"]))) print(paste("Average Fitness:",mean(chromeFitness[,"Fitness"]))) print(paste("Current Month Fitness:",currentMonthFitness)) } } return (totalRet) } #Specify dates for downloading data, training models and running simulation startDate = as.Date("2000-01-01") #Specify what date to get the prices from symbolData <- new.env() #Make a new environment for quantmod to store data in getSymbols(symbolLst, env = symbolData, src = "yahoo", from = startDate) #Create some genes at random #Make a diag matrix so that each chrome gets activated atleast once startingChromozones <- diag(chromozoneLength) rownames(startingChromozones) <- apply(t(seq(1,chromozoneLength)),2,function(x) { paste("Chrome",x,sep="") } ) fitness <- matrix(runif(min=-1,max=1,nrow(startingChromozones)),nrow=nrow(startingChromozones),ncol=1) colnames(fitness) <- c("Fitness") startingChromozones <- as.data.frame(cbind(startingChromozones,fitness)) print("Before mating") print(startingChromozones) print("After mating") startingChromozones <- genetricMating(startingChromozones,topNToSelect,mutationProb) print(startingChromozones) tradingReturns <- doTrading(startingChromozones) tradingReturns <- as.data.frame((as.matrix(tradingReturns[-1]))) tradingReturns<-as.zoo(tradingReturns) dev.new() charts.PerformanceSummary(tradingReturns,main=paste("Arithmetic Genetic Trading Returns"),geometric=FALSE) print(table.Stats(tradingReturns)) cat("Sharpe Ratio") print(SharpeRatio.annualized(tradingReturns)) |