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# Kaufman's Adaptive Moving Average (3) Strategy with Eniso's RAI as signal damper
# -----------------------------------------------------------------------------
# STRATEGY
strategy.kama3 <- function(prices, weights=NULL, indicators=NULL, parameters=list(), printSteps=F) {
# DEFAULT Parameters
.stratFUN.defaultParams <- list(lambda = 0.01, vola.periods = 10, rf = 0, threshold = 0, period="weeks")
# DECLARE Parameters
parameters <- .stratFUN.declareParams(defaultParams = .stratFUN.defaultParams, parameters = parameters)
# VALIDATION Input Parameters!
vola.periods <- parameters[["vola.periods"]]
strat.thre <- parameters[["threshold"]]
rf <- parameters[["rf"]]
lambda <- parameters[["lambda"]]
period <- parameters[["period"]]
# GET RAI
if( is.null(indicators) || !"rai" %in% tolower(names(indicators)) ) stop("Please provide RAI indicator!")
rai <- indicators[["rai"]][,1]
if (! is.xts(rai)) stop("Please provide RAI as xts!")
if(printSteps==T) print("Parameters set.")
# PERIODICAL prices
if (period != "none") {
prices <- .toPeriod(data=prices[paste0(min(index(rai)), "::", max(index(rai)))], period=period)
rai <- .toPeriod(data=rai, period=period)
}
prices <- prices[index(rai)]
logReturns <- .PricesToLogReturns(prices)
# SET weights xts if NULL
if(is.null(weights)) weights <- xts()
if(printSteps==T) print("Periods calculated.")
# cut Data
rai <- rai[index(prices)]
# PREVENT RAI from being too close to 0
# rai[rai<0.01 & rai>=0] <- 0.01
# rai[rai>(-0.01) & rai<=0] <- -0.01
# FUNCTION to linear rescale values between bounds below and above border
rescaleFUN <- function(values, lbound, ubound, border){
above <- below <- values
above[values<border] <- border
below[values>border] <- border
# abore border
currentRange <- max(above) - border
if (currentRange == 0) {
above.new <- 0
} else {
above.new <- ((above - border) / currentRange ) * ubound
}
# below border
currentRange <- border - min(below)
if (currentRange == 0) {
below.new <- 0
} else {
below.new <- ((border - below) / currentRange ) * lbound
}
return(above.new+below.new)
}
# FUNCTION to calculate a rolling window volatility
# with a certain lag (window size)
# data: xts
rollingVola <- function(data, lag) {
len <- length(data)
if (lag > len) stop("Lag is larger than length!")
vola <- data
if (!is.null(nrow(data))) {
vola[,] <- NA
len <- nrow(data)
if (lag > len) stop("Lag is larger than length!")
for (i in (lag+1):len) { #i<-2
vola[i,] <- sd(data[(i-lag):i,])
}
} else {
vola[] <- NA
for (i in (lag+1):len) { #i<-1
vola[i] <- sd(data[(i-lag):i])
}
}
return(vola)
}
# FUNCTION ewmaFUN to calculate EWMA for an x-vector
ewmaFUN <- function(x, lambda) {
m.t <- as.numeric(x[1])
m.ts <- vapply(as.vector(x), function(x_t) return((m.t <<- lambda * x_t + (1 - lambda) * m.t)), 0)
return(m.ts)
}
## RETURNS
returns <- logReturns
# Rolling Vola
returns.vola <- rollingVola(returns, lag=vola.periods)[(vola.periods+1):nrow(prices),]
# Rolling mean
returns.mean <- Reduce(cbind, lapply(returns, rollmean, k=vola.periods, align="right"))
# Rolling sharpe
returns.sharpe <- (returns.mean - log(1+rf)) / returns.vola
## RAI
# Rolling Vola
rai.vola <- rollingVola(rai, lag=vola.periods)[(vola.periods+1):nrow(prices),]
# Rolling mean
rai.mean <- Reduce(cbind, lapply(rai, rollmean, k=vola.periods, align="right"))
# Rolling sharpe
rai.sharpe <- rai.mean / rai.vola
# OBTAIN strategy values
strat.vals <- Reduce(cbind, lapply(returns.sharpe, function(return.sharpe) {
factor <- rep(1, nrow(return.sharpe))
# Exception: both values are negative --> strat vals also negative
factor[return.sharpe<0 & rai.sharpe<0] <- -1
return(factor * return.sharpe * rai.sharpe)
}))
# RESCALE strat vals to fit in (-1,1)
strat.vals <- rescaleFUN(strat.vals, lbound=-1, ubound=1, border=0)
if(printSteps==T) print("Strategy values set.")
# REDUCE prices to same period as strategy values are available
prices.reduced <- prices[index(strat.vals),]
# EXTRACT signals
signals <- -1 * (strat.vals <= -strat.thre)
signals <- signals + (strat.vals > strat.thre)
if(printSteps==T) print("Signal matrix calculated.")
# SHIFT signals for next trading day period -> shift dates + 1
signals <- lag(signals, k=1, na.pad=F)
if(printSteps==T) print("Signal matrix shifted by 1 time period.")
indicators <- list(kama3=strat.vals, RAI=rai)
names(indicators) <- c(paste0("KAMA3(", lambda, ",", vola.periods, ",", rf, ")"), "RAI")
# OUTPUT
return( list(filters=list(), signals=signals, prices=prices, logReturns=logReturns, weights=weights, indicators=list(rai=rai), parameters=parameters) )
}
# plot.kama3 <- function(object, from=NULL, until=NULL, which=NULL, main=NULL) {
# # GET VALUES
# prices <- getPrices(object, from=from, until=until, which=which)
# strat.vals <- getStratVals(object)[["KAMA3.vals"]][index(prices), colnames(prices)]
# performance <- performance(object, of="assets", from=start(prices), until=end(prices), which=which)
#
# # DECLARE Parameters
# parameters <- getParameters(object)
# vola.periods <- parameters[["vola.periods"]]
# strat.thre <- parameters[["threshold"]]
#
# # RAI
# rai <- getIndicators(object)[["rai"]][index(prices),1]
#
# # PLOT main
# if (is.null(main)) {
# plot.main <- colnames(prices)
# } else {
# if (!is.character(main)) stop("Please provide plot headings as character!")
# if (length(main) == 1) plot.main <- rep(main, ncol(prices))
# }
# if (length(plot.main) != ncol(prices))
# stop("Please provide as many headings as graphics!")
#
# par.mar <- par()$mar # keep standard margins
# margins <- c(7, 4.1, 4.1, 3)
#
# for (i in 1:ncol(prices)) { #i<-1
# layout(matrix(1:6, ncol=2, byrow=T), widths=c(0.8, 0.2), heights=c(0.5, 0.2, 0.3))
# #layout.show(1)
#
# # PLOT1: Plot prices
# par(mar=c(0, margins[2:4]))
# plot(prices[,i], main=plot.main[i], minor.ticks=F, axes=F)
# axis(2, las=2)
#
# # PLOT2: LEGEND Prices
# par(mar=rep(0,4))
# plot(1:2, 1:2, type="n", axes=F, ann=F) #only for layout
# legend("left", legend=colnames(prices)[i], lty=c(1), cex=0.8, bty="n")
#
# # PLOT3: Plot KAMA3 and RAI
# par(mar=c(0, margins[2], 0, margins[4]))
# ylim <- c(min(cbind(strat.vals[,i]-strat.thre,rai[index(strat.vals)]), rm.na=T), max(cbind(strat.vals[,i]+strat.thre,rai[index(strat.vals)]), rm.na=T))
# plot(prices[,i], ylim=ylim, type="n", main="", minor.ticks=F, axes=F) # no data
# abline(h=0, col="gray")
# # RAI
# lines(rai, col="black")
# axis(4, at=pretty(range(strat.vals[,i])), las=2) # right axis
# # KAMA3
# lines(strat.vals[,i], col="red")
# if (strat.thre > 0) {
# abline(h=strat.thre, lty=2, col="blue")
# abline(h=-strat.thre, lty=2, col="blue")
# }
#
# # PLOT4: LEGEND KAMA3 i + RAI
# par(mar=rep(0,4))
# plot(1:2, 1:2, type="n", axes=F, ann=F) #only for layout
# if (strat.thre > 0) {
# legend("left", legend=c(paste0("KAMA3(", vola.periods, ")"), paste0("threshold(", strat.thre, ")"), "RAI"), col=c("red", "blue", "black"), lty=c(1,2,1), cex=0.8, bty="n")
# } else {
# legend("left", legend=c(paste0("KAMA3(", vola.periods, ")"), "RAI"), col=c("red", "black"), lty=c(1,1), cex=0.8, bty="n")
# }
#
# # PLOT5: PERFORMANCE
# par(mar=c(7, margins[2], 0, margins[4]))
# # pseudo for same time domain
# plot(prices[,i], ylim=range(performance[,i]), type="n", main="", axes=F, minor.ticks=F)
# axis(1, at=.index(prices[,i])[axTicksByTime(prices)], labels=names(axTicksByTime(prices)), las=2)
# axis(2, las=2) # right axis
# # PERFORMANCE
# lines(performance[,i], col="darkgray")
#
# # PLOT6: LEGEND performance
# par(mar=c(margins[1],0,0,0))
# plot(1:2, 1:2, type="n", axes=F, ann=F) #only for layout
# # LEGEND
# legend("left", legend="Performance", col=c("darkgray"), lty=c(1), cex=0.8, bty="n")
# } # for prices
#
# layout(1) #reset layout
# par(mar=par.mar) #reset margins
# }
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