K-Nearest Neighbour Algo : Fail

In this post I’m going to look at an implementation of the k-nearest neighbour algorithm.

The algorithm is very simple and can be split into 3 components:

1. A Data Measure – What features / observation describe the current trading day? (vol, rsi, moving avg etc…, don’t forget to normalise your measurements) (variable dataMeasure)

2. Error Measure – How to measure the similarity between data measures (just use MSE) this identifies the K-most similar trading days to today (function calculateMSE)

3. Convert k-nearest neighbors to trading signal (function calculateTradeSignalFromKNeighbours)

In the data measure we look to come up with some quantitative measures that capture information about the current trading today. In the example presented below I’ve used a normalised volatility measure (vol(fast)/(vol(fast)+vol(slow)) where fast and slow indicate the window size, slower = longer window. The same procedure but for linear regression curves is used, additionally i’ve included a fast / slow rsi. We take this measure and compare it to the measures on all of the previous trading days, trying to identify the most similar K days in history.

You should look to normalise your signals in some fashion. The reason you need to do this is so that during the MSE calculation you haven’t unexpectedly put a large weight on one of your measurement variables.

Now that you’ve found a set of trading days that are most similar to your current trading day you still have to determine how to convert those days into a trading signal. In the code I take the Knearest neighbours and look at what occurred on the day after after them. I take the open close return and calculate the sharpe ratio of the K neighbors and use this as the number of contracts to buy the following day. If the K neighbors are unrelated their trading will be erratic and the sharpe ratio close to 0, hence we will only trade a small number of contracts.

This algo is potentially interesting when using vol as one of the data measures, it naturally captures the different regimes in the market. If today is a high vol day, it’ll be compared to the historical days that also have high vol. It is hoped that todays market still behaves in the same fashion as in a historically similar day.

gspc trading knearest neighbors

ftse trading knearest neighbors


arm trading knearest neighbors

Sadly the performance of this strategy is terrible (could just be poor input parameter selection / poor data measures). I suspect that there are better forms of K-nearest neighbour to use. I take today, and compare it to single days in history. There could be significant gains to be had if I take 1 month of data and find the historical most similar month. This will identify patterns of similar behavior which may be more tradeable. I will investigate this in my next post.

On to the code:

?View Code RSPLUS
marketSymbol <- "ARM.L"
nFastLookback <- 30 #The fast signal lookback used in linear regression curve
nSlowLookback <- 50 #The slow signal lookback used in linear regression curve
nFastVolLookback <- 30 #The fast signal lookback used to calculate the stdev
nSlowVolLookback <- 50 #The slow signal lookback used calculate the stdev
nFastRSILookback <- 30 #The fast signal lookback used to calculate the stdev
nSlowRSILookback <- 50 #The slow signal lookback used calculate the stdev
kNearestGroupSize <- 50 #How many neighbours to use
normalisedStrengthVolWeight <- 2 #Make some signals more important than others in the MSE
normalisedStrengthRegressionWeight <- 1
fastRSICurveWeight <- 2
slowRSICurveWeight <- 0.8
#Specify dates for downloading data, training models and running simulation
startDate = as.Date("2006-08-01") #Specify what date to get the prices from
symbolData <- new.env() #Make a new environment for quantmod to store data in
stockCleanNameFunc <- function(name){
getSymbols(marketSymbol, env = symbolData, src = "yahoo", from = startDate)
cleanName <- stockCleanNameFunc(marketSymbol)
mktData <- get(cleanName,symbolData)
linearRegressionCurve <- function(data,n){
    regression <- function(dataBlock){
           fit <-lm(dataBlock~seq(1,length(dataBlock),1))
    return (rollapply(data,width=n,regression,align="right",by.column=FALSE,na.pad=TRUE))
volCurve <- function(data,n){
    stdev <- function(dataBlock){
    return (rollapply(data,width=n,stdev,align="right",by.column=FALSE,na.pad=TRUE))^2
fastRegression <- linearRegressionCurve(Cl(mktData),nFastLookback)
slowRegression <- linearRegressionCurve(Cl(mktData),nSlowLookback)
normalisedStrengthRegression <- slowRegression / (slowRegression+fastRegression)
fastVolCurve <- volCurve(Cl(mktData),nFastVolLookback)
slowVolCurve <- volCurve(Cl(mktData),nSlowVolLookback)
normalisedStrengthVol <- slowVolCurve / (slowVolCurve+fastVolCurve)
fastRSICurve <-RSI(Cl(mktData),nFastRSILookback)/100 #rescale it to be in the same range as the other indicators
slowRSICurve <-RSI(Cl(mktData),nSlowRSILookback)/100
      #Lets plot the signals just to see what they look like
#DataMeasure will be used to determine how similar other days are to today
#It is used later on for calculate the days which are most similar to today according to MSE measure
dataMeasure <- cbind(normalisedStrengthVol*normalisedStrengthVolWeight,normalisedStrengthRegression*normalisedStrengthRegression,fastRSICurve*fastRSICurveWeight,slowRSICurve*slowRSICurveWeight)
colnames(dataMeasure) <- c("normalisedStrengthVol","normalisedStrengthRegression","fastRSICurve","slowRSICurve")
#Finds the nearest neighbour and calculates the trade signal
calculateNearestNeighbourTradeSignal <- function(dataMeasure,K,mktReturns){
        findKNearestNeighbours <- function(dataMeasure,K){
              calculateMSE <- function(dataMeasure){
                      calculateMSEInner <- function(dataA,dataB){
                        se <- ((as.matrix(dataA) - as.matrix(dataB))^2)
                      #Repeat the last row of dataMeasure multiple times
                      #This is so we can compare dataMeasure[today] with all the previous dates
                      lastMat <- last(dataMeasure)
                      setA <-  lastMat[rep(1, length(dataMeasure[,1])),]
                      setB <- dataMeasure
                      mse <- calculateMSEInner(setB,setA)
                      mse[is.na(mse)] <- Inf #Give it a terrible MSE if it's NA
                      colName <-  c(colnames(dataMeasure),"MSE")
                      dataMeasure <- cbind(dataMeasure,mse)
                      colnames(dataMeasure) <- colName
                      return (dataMeasure)
             rowNum <- seq(1,length(dataMeasure[,1]),1)
             dataMeasureWithMse <- as.data.frame(calculateMSE(dataMeasure))
             tmp <- c("rowNum", colnames(dataMeasureWithMse))
             dataMeasureWithMse <- cbind(rowNum,dataMeasureWithMse)
             colnames(dataMeasureWithMse) <- tmp
             dataMeasureWithMse <- dataMeasureWithMse[order(dataMeasureWithMse[,"MSE"]),]
             #Starting from the 2nd item as the 1st is the current day (MSE will be 0) want to drop it
             return (dataMeasureWithMse[seq(2,min(K,length(dataMeasureWithMse[,1]))),])
    calculateTradeSignalFromKNeighbours <- function(mktReturns,kNearestNeighbours){
         rowNums <- kNearestNeighbours[,"rowNum"]
         rowNums <- na.omit(rowNums)
         if(length(rowNums) <= 1) { return (0) }
         print("The kNearestNeighbours are:")
         #So lets see what happened on the day AFTER our nearest match
         mktRet <- mktReturns[rowNums+1]
         #return (sign(sum(mktRet)))
         return (SharpeRatio.annualized(mktRet))
    kNearestNeighbours <- findKNearestNeighbours(dataMeasure,K)
    tradeSignal <- calculateTradeSignalFromKNeighbours(mktReturns,kNearestNeighbours)
ret <- (Cl(mktData)/Op(mktData))-1
signalLog <- as.data.frame(ret)
signalLog[,1] <- 0
colnames(signalLog) <- c("TradeSignal")
#Loop through all the days we have data for, and calculate a signal for them using nearest neighbour
for(i in seq(1,length(ret))){
    print (paste("Simulating trading for day",i,"out of",length(ret),"@",100*i/length(ret),"%"))
    index <- seq(1,i)
    signal <- calculateNearestNeighbourTradeSignal(dataMeasure[index,],kNearestGroupSize,ret)
    signalLog[i,1] <- signal
tradeRet <- Lag(signalLog[,1])*ret[,1] #Combine todays signal with tomorrows return (no lookforward issues)
totalRet <- cbind(tradeRet,ret)
colnames(totalRet) <- c("Algo",paste(marketSymbol," Long OpCl Returns"))
charts.PerformanceSummary(totalRet,main=paste("K nearest trading algo for",marketSymbol),geometric=FALSE)
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Linear Regression Curves vs Bollinger Bands

In my last post I showed what a linear regression curve was, this post will use it as part of a mean reverting trading strategy.

The strategy is simple:

  • Calculate a rolling ‘average’ and a rolling ‘deviation’
  • If the Close price is greater than the average+n*deviation go short (and close when you cross the mean)
  • If the Close price is less than the average-n*deviation go long (and close when you cross the mean)

Two cases will be analysed, one strategy will use a simple moving average(SMA), the other will use the linear regression curve(LRC) for the average. The deviation function will be Standard Devation, Average True Range, and LRCDeviation (same as standard deviation but replace the mean with the LRC).

Results (Lookback = 20 and Deviation Multiplier = 2:

mean reversion linear regression curves

Annualized Sharpe Ratio (Rf=0%)

  • GSPC = 0.05257118
  • Simple Moving Avg – Standard Deviation = 0.2535342
  • Simple Moving Avg – Average True Range = 0.1165512
  • Simple Moving Avg – LRC Deviation 0.296234
  • Linear Regression Curve – Standard Deviation = 0.2818447
  • Linear Regression Curve – Average True Range = 0.5824727
  • Linear Regression Curve – LRC Deviation = 0.04672071

Optimisation analysis:

Annoyingly the colour scale is different between the two charts, however the sharpe ratio is written in each cell. Lighter colours indicate better performance.

Over a 13year period and trading the GSPC the LRC achieved a sharpe of ~0.6 where as the SMA achieved a sharpe of ~0.3. The LRC appears superior to the SMA.

Mean Reversion LRC STDEV Mean Reversion SMA STDEVI will update this post at a later point in time when my optimisation has finished running for the other strategies.

?View Code RSPLUS
marketSymbol <- "^GSPC"
nLookback <- 20 #The lookback to calcute the moving average / linear regression curve / average true range / standard deviation
nDeviation <- 2
#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
stockCleanNameFunc <- function(name){
getSymbols(marketSymbol, env = symbolData, src = "yahoo", from = startDate)
cleanName <- stockCleanNameFunc(marketSymbol)
mktData <- get(cleanName,symbolData)
linearRegressionCurve <- function(data,n){
    regression <- function(dataBlock){
           fit <-lm(dataBlock~seq(1,length(dataBlock),1))
    return (rollapply(data,width=n,regression,align="right",by.column=FALSE,na.pad=TRUE))
linearRegressionCurveStandardDeviation <- function(data,n){
    deviation <- function(dataBlock){
        fit <-lm(dataBlock~seq(1,length(dataBlock),1))
        quasiMean <- (last(fit$fitted.values))
        quasiMean <- rep(quasiMean,length(dataBlock))
        stDev <- sqrt((1/length(dataBlock))* sum((dataBlock - quasiMean)^2))
        return (stDev)
    return (rollapply(data,width=n,deviation,align="right",by.column=FALSE,na.pad=TRUE))
reduceLongTradeEntriesToTradOpenOrClosedSignal <- function(trades){
    #Takes something like
    #000011110000-1-1000011 (1 = go long, -1 = go short)
    #and turns it into
    #trades[is.na(trades)] <- 0
    out <- trades #copy the datastructure over
    currentPos <-0
    for(i in 1:length(out[,1])){
      if((currentPos == 0) & (trades[i,1]==1)){
        currentPos <- 1
        out[i,1] <- currentPos
      if((currentPos == 1) & (trades[i,1]==-1)){
        currentPos <- 0
        out[i,1] <- currentPos
      out[i,1] <- currentPos
reduceShortTradeEntriesToTradOpenOrClosedSignal <- function(trades){
generateTradingReturns <- function(mktPrices, nLookback, nDeviation, avgFunction, deviationFunction,title,showGraph=TRUE){
    quasiMean <- avgFunction(mktPrices,n=nLookback)
    quasiDeviation <- deviationFunction(mktPrices,n=nLookback)
    colnames(quasiMean) <- "QuasiMean"
    colnames(quasiDeviation) <- "QuasiDeviation"
    price <- Cl(mktPrices)
    upperThreshold = quasiMean + nDeviation*quasiDeviation
    lowerThreshold = quasiMean - nDeviation*quasiDeviation
    aboveUpperBand <- price>upperThreshold
    belowLowerBand <- price<lowerThreshold
    aboveMAvg <- price>quasiMean
    belowMAvg <- price<quasiMean
    rawShort <- (-1)*aboveUpperBand+belowMAvg
    shortPositions <- reduceShortTradeEntriesToTradOpenOrClosedSignal(rawShort)
    rawLong <- (-1)*aboveMAvg+belowLowerBand
    longPositions <- reduceLongTradeEntriesToTradOpenOrClosedSignal(rawLong)
    positions = longPositions + shortPositions
    signal <- positions
      plot(Cl(mktPrices),type="l",main=paste(marketSymbol, "close prices"))
      legend('bottomright',c("Close",paste("Band - ",title),paste("Average - ",title)),lty=1, col=c('black', 'red', 'blue'), bty='n', cex=.75)
    mktReturns <- Cl(mktPrices)/Lag(Cl(mktPrices)) - 1
    tradingReturns <- Lag(signal)*mktReturns
    tradingReturns[is.na(tradingReturns)] <- 0
    colnames(tradingReturns) <- title
    return (tradingReturns)
strategySMAandSTDEV <- function(mktData,nLookback,nDeviation){
       generateTradingReturns(mktData,nLookback,nDeviation,function(x,n) { SMA(Cl(x),n) },function(x,n) { rollapply(Cl(x),width=n, align="right",sd) },"Simple Moving Avg - Standard Deviation",FALSE)
strategySMAandATR <- function(mktData,nLookback,nDeviation){
       generateTradingReturns(mktData,nLookback,nDeviation,function(x,n) { SMA(Cl(x),n) },function(x,n) { atr <- ATR(x,n); return(atr$atr) },"Simple Moving Avg - Average True Range",FALSE)
strategySMAandLRCDev <- function(mktData,nLookback,nDeviation){
        generateTradingReturns(mktData,nLookback,nDeviation,function(x,n) { SMA(Cl(x),n) },function(x,n) { linearRegressionCurveStandardDeviation(Cl(x),n) },"Simple Moving Avg - LRC Deviation",FALSE)
strategyLRCandSTDEV <- function(mktData,nLookback,nDeviation){
       generateTradingReturns(mktData,nLookback,nDeviation,function(x,n) { linearRegressionCurve(Cl(x),n) },function(x,n) { rollapply(Cl(x),width=n, align="right",sd) },"Linear Regression Curve - Standard Deviation",FALSE)
strategyLRCandATR <- function(mktData,nLookback,nDeviation){
       generateTradingReturns(mktData,nLookback,nDeviation,function(x,n) { linearRegressionCurve(Cl(x),n) },function(x,n) { atr <- ATR(x,n); return(atr$atr) },"Linear Regression Curve - Average True Range",FALSE)
strategyLRCandLRCDev <- function(mktData,nLookback,nDeviation){
       generateTradingReturns(mktData,nLookback,nDeviation,function(x,n) { linearRegressionCurve(Cl(x),n) },function(x,n) { linearRegressionCurveStandardDeviation(Cl(x),n) },"Linear Regression Curve - LRC Deviation",FALSE)
bollingerBandsSMAandSTDEVTradingReturns <- strategySMAandSTDEV(mktData,nLookback,nDeviation)
bollingerBandsSMAandATRTradingReturns <- strategySMAandATR(mktData,nLookback,nDeviation)
bollingerBandsSMAandLRCDevTradingReturns <- strategySMAandLRCDev(mktData,nLookback,nDeviation)
bollingerBandsLRCandSTDEVTradingReturns <- strategyLRCandSTDEV(mktData,nLookback,nDeviation)
bollingerBandsLRCandATRTradingReturns <- strategyLRCandATR(mktData,nLookback,nDeviation)
bollingerBandsLRCandLRCDevTradingReturns <- strategyLRCandLRCDev(mktData,nLookback,nDeviation)
mktClClRet <- Cl(mktData)/Lag(Cl(mktData))-1
tradingReturns <- merge(as.zoo(mktClClRet),
charts.PerformanceSummary(tradingReturns,main=paste("Mean Reversion using nLookback",nLookback,"and nDeviation",nDeviation,"bands"),geometric=FALSE)
cat("Sharpe Ratio")
colorFunc <- function(x){
  x <- max(-4,min(4,x))
  if(x > 0){
  colorFunc <- rgb(0,(255*x/4)/255 , 0/255, 1)
  } else {
  colorFunc <- rgb((255*(-1*x)/4)/255,0 , 0/255, 1)
optimiseTradingStrat <- function(mktData,lookbackStart,lookbackEnd,lookbackStep,deviationStart,deviationEnd,deviationStep,strategy,title){
      lookbackRange <- seq(lookbackStart,lookbackEnd,lookbackStep)
      deviationRange <- seq(deviationStart,deviationEnd,deviationStep)
      combinations <- length(lookbackRange)*length(deviationRange)
      combLookback <- rep(lookbackRange,each=combinations/length(lookbackRange))
      combDeviation <- rep(deviationRange,combinations/length(deviationRange))
      optimisationMatrix <- t(rbind(t(combLookback),t(combDeviation),rep(NA,combinations),rep(NA,combinations),rep(NA,combinations)))
      colnames(optimisationMatrix) <- c("Lookback","Deviation","SharpeRatio","CumulativeReturns","MaxDrawDown")
        for(i in 1:length(optimisationMatrix[,1])){
            print(paste("On run",i,"out of",length(optimisationMatrix[,1]),"nLookback=",optimisationMatrix[i,"Lookback"],"nDeviation=",optimisationMatrix[i,"Deviation"]))
            runReturns <- strategy(mktData,optimisationMatrix[i,"Lookback"],optimisationMatrix[i,"Deviation"])
            optimisationMatrix[i,"SharpeRatio"] <- SharpeRatio.annualized(runReturns)
            optimisationMatrix[i,"CumulativeReturns"] <- sum(runReturns)
            optimisationMatrix[i,"MaxDrawDown"] <-  maxDrawdown(runReturns,geometric=FALSE)
          z <- matrix(optimisationMatrix[,"SharpeRatio"],nrow=length(lookbackRange),ncol=length(deviationRange),byrow=TRUE)
          colors <- colorFunc(optimisationMatrix[,"SharpeRatio"])
          rownames(z) <- lookbackRange
          colnames(z) <-deviationRange
          heatmap.2(z, key=TRUE,trace="none",cellnote=round(z,digits=2),Rowv=NA, Colv=NA, scale="column", margins=c(5,10),xlab="Deviation",ylab="Lookback",main=paste("Sharpe Ratio for Strategy",title))
  plot(Cl(mktData),type="l",main=paste(marketSymbol, "close prices"))
  legend('bottomright',c("Close",paste("Simple Moving Average Lookback=50"),paste("Linear Regression Curve Lookback=50")),lty=1, col=c('black', 'red', 'blue'), bty='n', cex=.75)
nLookbackStart <- 20
nLookbackEnd <- 200
nLookbackStep <- 20
nDeviationStart <- 1
nDeviationEnd <- 2.5
nDeviationStep <- 0.1
#optimiseTradingStrat(mktData,nLookbackStart,nLookbackEnd,nLookbackStep,nDeviationStart,nDeviationEnd,nDeviationStep,strategySMAandSTDEV,"AvgFunc=SMA and DeviationFunc=STDEV")
#optimiseTradingStrat(mktData,nLookbackStart,nLookbackEnd,nLookbackStep,nDeviationStart,nDeviationEnd,nDeviationStep,strategySMAandATR,"AvgFunc=SMA and DeviationFunc=ATR")
#optimiseTradingStrat(mktData,nLookbackStart,nLookbackEnd,nLookbackStep,nDeviationStart,nDeviationEnd,nDeviationStep,strategySMAandLRCDev,"AvgFunc=SMA and DeviationFunc=LRCDev")
#optimiseTradingStrat(mktData,nLookbackStart,nLookbackEnd,nLookbackStep,nDeviationStart,nDeviationEnd,nDeviationStep,strategyLRCandSTDEV,"AvgFunc=LRC and DeviationFunc=STDEV")
#optimiseTradingStrat(mktData,nLookbackStart,nLookbackEnd,nLookbackStep,nDeviationStart,nDeviationEnd,nDeviationStep,strategyLRCandATR,"AvgFunc=LRC and DeviationFunc=ATR")
#doptimiseTradingStrat(mktData,nLookbackStart,nLookbackEnd,nLookbackStep,nDeviationStart,nDeviationEnd,nDeviationStep,strategyLRCandLRCDev,"AvgFunc=LRC and DeviationFunc=LRCDev")
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High Probability Credit Spreads – Using Linear Regression Curves

I came across this video series over the weekend, an option trader discusses how he trades credit spreads (mainly looks for mean reversion). Most of you will be familiar with bollinger bands as a common mean reversion strategy, essentially you take the moving average and moving standard deviation of the stock. You then plot on to your chart the moving average and an upper and lower band(moving average +/- n*standard deviations).

It is assumed that the price will revert to the moving average hence any price move to the bands is a good entry point. A common problem with this strategy is that the moving average is a LAGGING indicator and is often very slow to track the price moves if a long lookback period is used.

Video 1 presents a technique called “linear regression curves” about 10mins in. Linear regression curves aim to solve the problem of the moving average being slow to track the price.

Linear Regression Curve vs Simple Moving Average

demo of linear regression curve good tracking


See how tightly the blue linear regression curve follows the close price, it’s significantly quicker to identify turns in the market where as the simple moving average has considerable tracking error. The MSE could be taken to quantify the tightness.

How to calculate the linear regression curve:

linear regression diagram

In this example you have 100 closing prices for your given stock. Bar 1 is the oldest price, bar 100 is the most recent price. We will use a 20day regression.

1. Take prices 1-20 and draw the line of best fit through them
2. At the end of your best fit line (so bar 20), draw a little circle
3. Take prices 2-21 and draw the line of best fit through them
4. At the end of your best fit line (so bar 21) draw a little circle
5. Repeat upto bar 100
6. Join all of your little circles, this is your ‘linear regression curve’
So in a nutshell you just join the ends of a rolling linear regression.

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Genetic Algorithm in R – Trend Following

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:

  1. A gene
  2. A fitness function
  3. 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 Google
01 Facebook
10 IBM
11 LinkedIn

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.

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

genetic algo sharpe 1.14

Annualized Sharpe Ratio (Rf=0%) 1.15

On to the code:

?View Code RSPLUS
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")
#Stock gene
stockGeneLength <- 3 #8stocks
#stockGeneLength<-6 #Allows 2^6 stocks (64)
#Strategy gene
#Paramter lookback gene
#Calculate the length of our chromozone, chromozone=[gene1,gene2,gene3...]
chromozoneLength <- stockGeneLength+strateyGeneLength+parameterLookbackGeneLength
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
  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){
getStrategyGeneFromChromozone <- function(chrome){
getParameterLookbackGeneFromChromozone <- function(chrome){
#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))
       return (signalMACross)
       return (function(mktdata,paramA,paramB) {signalMACross(mktdata,paramA,paramB,avgFunc=EMA)})
       return (function(mktdata,paramA,paramB) {signalMACross(mktdata,paramA,paramB,avgFunc=ZLEMA)})
      # return (signalBollingerReversion)
       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)
        return (10)
        return (20)
        return (30)
        return (40)
        return (50)
        return (60)
        return (70)
        return (80)
getLookbackBFromChromozone <- function(chrome){
    gene <- getParameterLookbackGeneFromChromozone(chrome)
    gene <- gene[,4:6]
    gene <- matrix(gene)
        return (15)
        return (25)
        return (35)
        return (45)
        return (55)
        return (65)
        return (75)
        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)
#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))
        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")
#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){
       } else {
  year <- 2002
  month <- 1
  totalRet <- 0
  fitnessEvoltion <- 0
  #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)
        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...
            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)
        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)
        currentMonthFitness <- calculateGeneFitnessFromTradingReturns(monthReturn)
        #Update the running total of P&L
        totalRet <- c(totalRet,monthReturn)
        fitnessEvoltion <- c(fitnessEvoltion,currentMonthFitness)
        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("After mating")
startingChromozones <- genetricMating(startingChromozones,topNToSelect,mutationProb)
tradingReturns <- doTrading(startingChromozones)
tradingReturns <- as.data.frame((as.matrix(tradingReturns[-1])))
charts.PerformanceSummary(tradingReturns,main=paste("Arithmetic Genetic Trading Returns"),geometric=FALSE)
cat("Sharpe Ratio")
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Is ‘risk’ rewarded in the equity markets?

This post looks to examine if the well known phrase “the higher the risk the higher the reward” applies to the FTSE 100 constituents. Numerous models have tried to capture risk reward metrics, the best known is the Capital Allocation Pricing Model (CAPM). CAPM tries to quantify the return on an investment an investor must receive in order to be adequately compensated for the risk they’ve taken.

The code below calculates the rolling standard deviation of returns, ‘the risk’, for the FTSE 100 constituents. It then groups stocks into quartiles by this risk metric, the groups are updated daily. Quartile 1 is the lowest volatility stocks, quartile 2 the highest. An equally weighted ($ amt) index is created for each quartile. According to the above theory Q4 (high vol) should produce the highest cumulative returns.

When using a 1 month lookback for the stdev calculation there is a clear winning index, the lowest vol index (black). Interestingly the 2nd best index is the highest vol index (blue). The graph above is calculated using arithmetic returns.

When using a longer lookback of 250 days, a trading year, the highest vol index is the best performer and the lowest vol index the worst performer.

For short lookback (30days) low vol index was the best performer

For long lookback (250days) high vol index was the best performer

One possible explanation (untested) is that for a short lookback the volatility risk metric is more sensitive to moves in the stock and hence on a news announcement / earnings the stock has a higher likelihood of moving from it’s current index into a higher vol index. Perhaps it isn’t unreasonable to assume that the high vol index contains only the stocks that have had a recent announcement / temporary volatility and are in a period of consolidation or mean reversion. Or to put it another way for short lookbacks the high vol index doesn’t contain the stocks that are permanently highly vol, whereas for long lookbacks any temporary vol deviations are smoothed out.

Below are the same charts as above but for geometric returns.

On to the code:

?View Code RSPLUS
#Script parameters
#Specify dates for downloading data
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
clClRet <- new.env()
downloadedSymbols <- list()
for(i in 1:length(symbolLst)){
  #Download one stock at a time
    getSymbols(symbolLst[i], env = symbolData, src = "yahoo", from = startDate)
     cleanName <- sub("^","",symbolLst[i],fixed=TRUE)
     mktData <- get(cleanName,symbolData)
     print(paste("-Calculating close close returns for:",cleanName))
      ret <-(Cl(mktData)/Lag(Cl(mktData)))-1
      print("-There is a abs(return) > 50% the data is odd lets not use this stock")
      downloadedSymbols <- c(downloadedSymbols,symbolLst[i])
      assign(cleanName,ret,envir = clClRet)
    }, error = function(e) {
    print(paste("Couldn't download: ", symbolLst[i]))
#Combine all the returns into a zoo object (joins the returns by date)
#Not a big fan of this loop, think it's suboptimal
zooClClRet <- zoo()
for(i in 1:length(downloadedSymbols)){
  cleanName <- sub("^","",downloadedSymbols[i],fixed=TRUE)
  print(paste("Combining the close close returns to the zoo:",cleanName))
    zooClClRet <- as.zoo(get(cleanName,clClRet))
  } else {
    zooClClRet <- merge(zooClClRet,as.zoo(get(cleanName,clClRet)))
#This will take inzoo or data frame
#And convert each row into quantiles
#Quantile 1 = 0-0.25
#Quantile 2 = 0.25-0.5 etc...
quasiQuantileFunction <- function(dataIn){
    quantileFun <- function(rowIn){
        quant <- quantile(rowIn,na.rm=TRUE)
        a <- (rowIn<=quant[5])
        b <- (rowIn<=quant[4])
        c <- (rowIn<=quant[3])
        d <- (rowIn<=quant[2])
        rowIn[a] <- 4
        rowIn[b] <- 3
        rowIn[c] <- 2
        rowIn[d] <- 1
  return (apply(dataIn,2,quantileFun))
avgReturnPerQuantile <- function(returnsData,quantileData){
      q1index <- (clClQuantiles==1)
      q2index <- (clClQuantiles==2)
      q3index <- (clClQuantiles==3)
      q4index <- (clClQuantiles==4)
      q1dat <- returnsData
      q1dat[!q1index] <- NaN
      q2dat <- returnsData
      q2dat[!q2index] <- NaN
      q3dat <- returnsData
      q3dat[!q3index] <- NaN
      q4dat <- returnsData
      q4dat[!q4index] <- NaN
      avgFunc <- function(x) {
           #apply(x,1,median,na.rm=TRUE) #median is more resistant to outliers
      res <- returnsData[,1:4] #just to maintain the time series (there must be a better way)
      res[,1] <- avgFunc(q1dat)
      res[,2] <- avgFunc(q2dat)
      res[,3] <- avgFunc(q3dat)
      res[,4] <- avgFunc(q4dat)
      colnames(res) <- c("Q1","Q2","Q3","Q4")
nLookback <- 250 #~1year trading calendar
clClVol <- rollapply(zooClClRet,nLookback,sd,na.rm=TRUE)
clClQuantiles <- quasiQuantileFunction(clClVol)
returnPerVolQuantile <- avgReturnPerQuantile(zooClClRet,clClQuantiles)
colnames(returnPerVolQuantile) <- c("Q1 min vol","Q2","Q3","Q4 max vol")
returnPerVolQuantile[is.nan(returnPerVolQuantile)]<-0 #Assume if there is no return data that it's return is 0
#returnPerVolQuantile[returnPerVolQuantile>0.2] <- 0 #I was having data issues leading to days with 150% returns! This filters them out
cumulativeReturnsByQuantile <- apply(returnPerVolQuantile,2,cumsum)
charts.PerformanceSummary(returnPerVolQuantile,main=paste("Arithmetic Cumulative Returns per Vol Quantile - Lookback=",nLookback),geometric=FALSE)
cat("Sharpe Ratio")
for(i in seq(2012,2004,-1)){
  windowedData <- window(as.zoo(returnPerVolQuantile),start=as.Date(paste(i,"-01-01",sep="")),end=as.Date(paste(i+1,"-01-01",sep="")))
title(main=paste("Arithmetic Cumulative Returns per Vol Quantile - Lookback=",nLookback),outer=T)
charts.PerformanceSummary(returnPerVolQuantile,main=paste("Geometric Cumulative Returns per Vol Quantile - Lookback=",nLookback),geometric=TRUE)
cat("Sharpe Ratio")
for(i in seq(2012,2004,-1)){
  windowedData <- window(as.zoo(returnPerVolQuantile),start=as.Date(paste(i,"-01-01",sep="")),end=as.Date(paste(i+1,"-01-01",sep="")))
title(main=paste("Geometric Cumulative Returns per Vol Quantile - Lookback=",nLookback),outer=T)
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