random strategy test

# visit http://reakkt.com for description

setwd("C:/!REPO2/Test")

library(parallel)
library(doSNOW)
registerDoSNOW(cl <- makeCluster(detectCores(),outfile="cluster.txt"))

library(TTR)

###

asset <- "spx"

data.source <- "stooq"

sd.treshold     <- 6

test.period     <- 250 # 1 year

minimum.return  <- 0.5

repetitions     <- 10

l               <- 1000 # 4 years

###

source(file="../../!REPO/Common/stooq all.R")
# source(file="../../!REPO/Common/quantmod data.R")
source(file="../../!REPO/Common/zigzag.R")

### ladowanie danych (z pliku stooq albo z Yahoo!)

if (data.source=="stooq") {

  stooq.file   <- "../../!REPO/Stooq/stooq data.zip"

  x <- as.matrix(GetStooqDataZip(asset)$data)

}

if (data.source=="yahoo") {

  x <- getMarketData(asset,data.source)

}

###

x <- tail(x,l+test.period)

n <- nrow(x)

p <- x[,"Close"]

r <- log(x[-1,"Close"]/x[-n,"Close"])

v.month <- runSD(r,25)
v.year  <- runSD(r,250)

change.treshold <- 100*stats::sd(r)*sd.treshold # dla SD=6, srednia istotnosc sygnalu jest wieksza niz dla SD=5

# change.treshold <- 35

zz  <- TTR::ZigZag(p,change=change.treshold)

zz.rev <- ZigZagReversals(zz)

###

# reps - no. of repetitions
GenerateDesiredReturn <- function(idx, reps, minimum.return=0.5, test.period=250) {

  cat(idx,"\n") # outfile

  market.result   <- 0
  strategy.result <- rep(-Inf,reps)
  j               <- numeric(reps)

  period.returns <- r[(idx-test.period+1):idx]

  market.result  <- sum(period.returns)

  i2 <- 1

  for (i2 in 1:reps) {

    while (strategy.result[i2]<market.result ||
           strategy.result[i2]<=0 ||
           strategy.result[i2]<minimum.return) {

      j[i2] <- j[i2]+1

      signal <- runif(test.period,0,1)

      strategy.result[i2] <- sum(ifelse(signal>0.5,+1,-1)*period.returns)

    } # while

  } # for

  return( c(market.result,
            sum(abs(period.returns)), # theoretical max. return in the period
            mean(strategy.result),    # mean (<-reps) strategy result in the period
            mean(j)) )                # average no. of draws to reach tresholds (>=market, >0, >=minimum.return)

}

test.window <- test.period:(n-1)

returns <- matrix(NA,length(test.window),4)

range(test.window)

returns <- foreach ( idx=test.window, .combine=rbind ) %dopar% GenerateDesiredReturn(idx, reps=repetitions, minimum.return, test.period)

colnames(returns) <- c("market","max. possible", "strategy", "intensity")

stopCluster(cl)

par(mfrow=c(5,1))

plot(tail(p[-1],l),type="l", main="Price",ylab="price")
abline(v=zz.rev$min[which(zz.rev$min>=(n-l))]-(n-l), col="Green")
abline(v=zz.rev$max[which(zz.rev$max>=(n-l))]-(n-l), col="Red")

plot(tail(r,l),type="l", main="Returns",ylab="%")
abline(v=zz.rev$min[which(zz.rev$min>=(n-l))]-(n-l), col="Green")
abline(v=zz.rev$max[which(zz.rev$max>=(n-l))]-(n-l), col="Red")

matplot(cbind(tail(v.year,l),tail(v.month,l)),type="l", lty="solid", log="y", main="Realized Volatility 1Y & 1M",ylab="volatility")
abline(v=zz.rev$min[which(zz.rev$min>=(n-l))]-(n-l), col="Green")
abline(v=zz.rev$max[which(zz.rev$max>=(n-l))]-(n-l), col="Red")

matplot(cbind( tail(returns[,"max. possible"],l),
               tail(returns[,"strategy"],l) ),
        type="l", lty="solid",log="y",
        main="Theoretical Max Return vs Strategy Return 1Y", ylab="return")
abline(v=zz.rev$min[which(zz.rev$min>=(n-l))]-(n-l), col="Green")
abline(v=zz.rev$max[which(zz.rev$max>=(n-l))]-(n-l), col="Red")

plot(tail(returns[,"intensity"],l),type="l",log="y", main="Difficulty 1Y",ylab="trials")
abline(v=zz.rev$min[which(zz.rev$min>=(n-l))]-(n-l), col="Green")
abline(v=zz.rev$max[which(zz.rev$max>=(n-l))]-(n-l), col="Red")

par(mfrow=c(1,1))