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Using Genetic Algorithms in Quantitative Trading
############################################################
## 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
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