Using Genetic Algorithms in Quantitative Trading

geneticAlgorithms

############################################################
## Using Genetic Algorithms in Quantitative Trading   
##
## [email protected] - Mar 2014
############################################################
library(PerformanceAnalytics)
library(rgenoud)
library(quantmod)
library(TTR)


###############
outputPath <- "your_path"
theInstrument <- "SPY"

data <- getSymbols(Symbols = theInstrument, 
                   src = "yahoo", 
                   from = "2000-01-01", 
                   auto.assign = FALSE)

colnames(data) <- c("open","high","low","close","volume","adj.")


###############
fitnessFunction <- function(xx=c(1,1,1,1)){
  print(xx)
  rtn <- ROC(data[,"close"],n=1)
  rsi <- RSI(data[,"close"],n=xx[1],maType="SMA")
  smas <- SMA(data[,"close"],n=xx[3])
  smal <- SMA(data[,"close"],n=xx[4])
  aa <- cbind(data[,"close"],rtn,rsi,smas,smal)
  colnames(aa) <- c("close","rtn","rsi","smas","smal")
  
  isData <- aa[index(aa) < "2011-01-01"]
   
  posBuySignal <- which(isData[,"rsi"] <= (1 - xx[2]) & isData[,"smas"] > isData[,"smal"]) + 1
  if (length(posBuySignal) == 0)
    posBuySignal <- NULL
  posSellSignal <- which(isData[,"rsi"] > xx[2] & isData[,"smas"] < isData[,"smal"]) + 1
  if (length(posSellSignal) == 0)
    posSellSignal <- NULL
  allSignals <- c(posBuySignal,posSellSignal)
  allSignals <- allSignals[which(allSignals <= nrow(isData))]
  
  if (!is.null(allSignals) && length(allSignals) >= 50)
    theStat <- SharpeRatio.annualized(isData[sort(allSignals),"rtn"])
  if (is.null(allSignals) | length(allSignals) < 50)
    theStat <- 0  

  return(theStat)
}


###############
tradingStatistics <- function(isOrOos = TRUE, xx = c(1,1,1,1)){
  print(xx)
  rtn <- ROC(data[,"close"],n=1)
  rsi <- RSI(data[,"close"],n=xx[1],maType="SMA")
  smas <- SMA(data[,"close"],n=xx[3])
  smal <- SMA(data[,"close"],n=xx[4])
  aa <- cbind(data[,"close"],rtn,rsi,smas,smal)
  colnames(aa) <- c("close","rtn","rsi","smas","smal")
  
  if (isOrOos == TRUE)
    sampleData <- aa[index(aa) < "2011-01-01"]
  if (isOrOos == FALSE)
    sampleData <- aa[index(aa) >= "2011-01-01"]
  
  posBuySignal <- which(sampleData[,"rsi"] <= (1 - xx[2]) & sampleData[,"smas"] > sampleData[,"smal"]) + 1
  if (length(posBuySignal) == 0)
    posBuySignal <- NULL
  posSellSignal <- which(sampleData[,"rsi"] > xx[2] & sampleData[,"smas"] < sampleData[,"smal"]) + 1
  if (length(posSellSignal) == 0)
    posSellSignal <- NULL
  allSignals <- c(posBuySignal,posSellSignal)
  allSignals <- allSignals[which(allSignals <= nrow(sampleData))]
  
  totalRtn <- sum(sampleData[sort(allSignals),"rtn"])
  numberOfTrades <- length(sampleData[sort(allSignals),"rtn"])
  hitRatio <- length(which(sampleData[sort(allSignals),"rtn"] > 0))/numberOfTrades
  
  return(list(totalRtn=totalRtn,numberOfTrades=numberOfTrades,hitRatio=hitRatio))
} 


###########
optimum <- genoud(fitnessFunction, 
                  nvars = 4,  
                  max = TRUE,  
                  pop.size = 30, 
                  max.generations = 50, 
                  wait.generations = 15,
                  hard.generation.limit = TRUE, 
                  starting.values = c(5,70,30,100), 
                  MemoryMatrix = TRUE,
                  Domains = matrix(c(5,50,10,50,
                                     50,90,50,200),
                                   nrow=4,ncol=2), 
                  default.domains = 4, 
                  solution.tolerance = 0.00001,
                  gr = NULL, 
                  boundary.enforcement = 2, 
                  lexical = FALSE, 
                  gradient.check = FALSE, 
                  data.type.int = TRUE,  
                  hessian = FALSE, 
                  unif.seed = 812821, 
                  int.seed = 53058,
                  print.level = 2, 
                  share.type = 0, 
                  instance.number = 0,
                  output.path = paste(outputPath,theInstrument,"_Summary.txt",sep=""), 
                  output.append = FALSE,  
                  project.path = paste(outputPath,theInstrument,"_Details.txt",sep=""), 
                  P1=2, P2=8, P3=8, P4=6, P5=6, P6=6, P7=8, P8=6, P9=0,
                  P9mix = NULL, 
                  BFGSburnin = 0, 
                  BFGSfn = NULL, 
                  BFGShelp = NULL,
                  cluster = FALSE, 
                  balance = FALSE, 
                  debug = FALSE, 
                  control = list())

solution <- optimum$par

HI! As i understand, posBuySignal is index of signal to buy. Why it is always empty?

Hi! I'd like to know how you can use integer value inside the genoud Domain. For instance if you have to optimize crossing of fast and slow ExponentialMAs finding their optima length. Thanks and my compliments. Giorgio