RSI(2) Evaluation

# Attach packages. You can install packages via:
# install.packages(c("quantmod","TTR","PerformanceAnalytics"))
library(quantmod)
library(TTR)
library(PerformanceAnalytics)

# Pull S&P500 index data from Yahoo! Finance
getSymbols("^GSPC", from="2000-01-01")

# Calculate the RSI indicator
rsi <- RSI(Cl(GSPC), 2)

# Calculate Close-to-Close returns
ret <- ROC(Cl(GSPC))
ret[1] <- 0

# This function gives us some standard summary
# statistics for our trades.
tradeStats <- function(signals, returns) {
# Inputs:
# signals : trading signals
# returns : returns corresponding to signals

# Combine data and convert to data.frame
sysRet <- signals * returns * 100
posRet <- sysRet > 0 # Positive rule returns
negRet <- sysRet < 0 # Negative rule returns
dat <- cbind(signals,posRet*100,sysRet[posRet],sysRet[negRet],1)
dat <- as.data.frame(dat)

# Aggreate data for summary statistics
means <- aggregate(dat[,2:4], by=list(dat[,1]), mean, na.rm=TRUE)
medians <- aggregate(dat[,3:4], by=list(dat[,1]), median, na.rm=TRUE)
sums <- aggregate(dat[,5], by=list(dat[,1]), sum)

colnames(means) <- c("Signal","% Win","Mean Win","Mean Loss")
colnames(medians) <- c("Signal","Median Win","Median Loss")
colnames(sums) <- c("Signal","# Trades")

all <- merge(sums,means)
all <- merge(all,medians)

wl <- cbind( abs(all[,"Mean Win"]/all[,"Mean Loss"]),
abs(all[,"Median Win"]/all[,"Median Loss"]) )
colnames(wl) <- c("Mean W/L","Median W/L")

all <- cbind(all,wl)
return(all)
}

# This function determines position size and
# enables us to test several ideas with much
# greater speed and flexibility.
rsi2pos <- function(ind, indIncr=5, posIncr=0.25) {
# Inputs:
# ind : indicator vector
# indIncr : indicator value increments/breakpoints
# posIncr : position value increments/breakpoints

# Initialize result vector
size <- rep(0,NROW(ind))

# Long
size <- ifelse(ind < 4*indIncr, (1-posIncr*3), size)
size <- ifelse(ind < 3*indIncr, (1-posIncr*2), size)
size <- ifelse(ind < 2*indIncr, (1-posIncr*1), size)
size <- ifelse(ind < 1*indIncr, (1-posIncr*0), size)

# Short
size <- ifelse(ind > 100-4*indIncr, 3*posIncr-1, size)
size <- ifelse(ind > 100-3*indIncr, 2*posIncr-1, size)
size <- ifelse(ind > 100-2*indIncr, 1*posIncr-1, size)
size <- ifelse(ind > 100-1*indIncr, 0*posIncr-1, size)

# Today's position ('size') is based on today's
# indicator, but we need to apply today's position
# to the Close-to-Close return at tomorrow's close.
size <- lag(size)

# Replace missing signals with no position
# (generally just at beginning of series)
size[is.na(size)] <- 0

# Return results
return(size)
}

# Calculate signals with the 'rsi2pos()' function,
# using 5 as the RSI step: 5, 10, 15, 20, 80, 85, 90, 95
# and 0.25 as the size step: 0.25, 0.50, 0.75, 1.00
sig <- rsi2pos(rsi, 5, 0.25)

# Break out the long (up) and short (dn) signals
sigup <- ifelse(sig > 0, sig, 0)
sigdn <- ifelse(sig < 0, sig, 0)

# Calculate rule returns
ret_up <- ret * sigup
colnames(ret_up) <- 'Long System Return'
ret_dn <- ret * sigdn
colnames(ret_dn) <- 'Short System Return'
ret_all <- ret * sig
colnames(ret_all) <- 'Total System Return'

# Create performance graphs
png(filename="20090606_rsi2_performance.png", 720, 720)
charts.PerformanceSummary(cbind(ret_up,ret_dn),methods='none',
main='RSI(2) Performance - RSI steps = 5, Size steps = 0.25')
dev.off()

# Print trade statistics table
cat('\nRSI(2) Trade Statistics - RSI steps = 5, Size steps = 0.25\n')
print(tradeStats(sig,ret))

# Print drawdown table
cat('\nRSI(2) Drawdowns - RSI steps = 5, Size steps = 0.25\n')
print(table.Drawdowns(ret_all, top=10))

# Print downside risk table
cat('\nRSI(2) Downside Risk - RSI steps = 5, Size steps = 0.25\n')
print(table.DownsideRisk(ret_all))

# Calculate signals with the 'rsi2pos()' function
# using new RSI and size step values
sig <- rsi2pos(rsi, 10, 0.3)

# Break out the long (up) and short (dn) signals
sigup <- ifelse(sig > 0, sig, 0)
sigdn <- ifelse(sig < 0, sig, 0)

# Calculate rule returns
ret_up <- ret * sigup
colnames(ret_up) <- 'Long System Return'
ret_dn <- ret * sigdn
colnames(ret_dn) <- 'Short System Return'
ret_all <- ret * sig
colnames(ret_all) <- 'Total System Return'

# Calculate performance statistics
png(filename="20090606_rsi2_performance_updated.png", 720, 720)
charts.PerformanceSummary(cbind(ret_up,ret_dn),methods='none',
main='RSI(2) Performance - RSI steps = 10, Size steps = 0.30')
dev.off()

# Print trade statistics table
cat('\nRSI(2) Trade Statistics - RSI steps = 10, Size steps = 0.30\n')
print(tradeStats(sig,ret))

# Print drawdown table
cat('\nRSI(2) Drawdowns - RSI steps = 10, Size steps = 0.30\n')
print(table.Drawdowns(ret_all, top=10))

# Print downside risk table
cat('\nRSI(2) Downside Risk - RSI steps = 10, Size steps = 0.30\n')
print(table.DownsideRisk(ret_all))