############################################################
## Using Genetic Algorithms in Quantitative Trading   
##
## thertrader@gmail.com - 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