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