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LondonR 18 th September 2012

LondonR 18 th September 2012. SPONSORED BY. R TRAINING, CONSULTING & APPLICATION DEVELOPMENT. From Back-test to Trade using R Parallelization in OneTick. jjames@mango-solutions.com andrew.diamond@onetick.com. What the presentation shows. EURJPY FX tick data

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LondonR 18 th September 2012

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  1. LondonR18th September 2012 SPONSORED BY R TRAINING, CONSULTING & APPLICATION DEVELOPMENT

  2. From Back-test to Trade using R Parallelization in OneTick jjames@mango-solutions.com andrew.diamond@onetick.com

  3. What the presentation shows • EURJPY FX tick data • Take a small section of historic tick data • Look at rugarch analyses • Ability to use parallelisation effectively • How? • 5 minute architectural overview of OneTick • Questions around tick data management • Q & A?

  4. OneTick

  5. Deal with idiosyncrasies of tick data > setClass("TK", representation="POSIXct") > TK <- function(x)new("TK", as.POSIXct(x, format="%Y%m%d %H:%M:%OS")) > setOldClass("TK", S4Class="TK", where=.GlobalEnv) > > as.TK <- function(from)TK(as.POSIXct(from, format="%Y%m%d %H:%M:%OS", tz="GMT")) > setAs("character", "TK", as.TK) > setAs("TK", "POSIXct", function(from)as.POSIXct(as.numeric(from), origin="1970-01-01", tz="GMT")) [1] "coerce<-" > as.TK <- function(from)TK(as.POSIXct(from, format="%Y%m%d %H:%M:%OS", tz="GMT")) > setAs("character", "TK", as.TK) > setAs("TK", "POSIXct", function(from)as.POSIXct(as.numeric(from), origin="1970-01-01", tz="GMT")) [1] "coerce<-" > print.TK <- function(x, ...)print(format(as(x,"POSIXct"), format="%Y%m%d %H:%M:%OS3",...)) > setGeneric("print.TK", print.TK) [1] "print.TK" > setMethod("show", signature(object="TK"), function(object)print(format(object, format="%Y%m%d %H:%M:%OS3"))) [1] "show"

  6. perform (arbitrarily) garch ####### require(rugarch) spec = ugarchspec() ## Assume that the points are distributed fit = ugarchfit(data = diff(EURJPY[select,3]), spec = spec) plot(fit,which="all") # return just to the rate data fit = ugarchfit(data = EURJPY[select,3], spec = spec) plot(fit,which="all") forc = ugarchforecast(fit, n.ahead=20) plot(forc, which="all")

  7. Look for rolling values ctrl = list(rho = 1, delta = 1e-9, outer.iter = 100, tol = 1e-7) spec = ugarchspec(variance.model = list(model = "eGARCH"), distribution.model = "jsu") bktest <- ugarchroll(spec, data = EURJPY[select,3], n.ahead = 1, # parallel = TRUE, parallel.control = list(pkg = c("snowfall"), cores = 4), forecast.length = 100, refit.every = 25, refit.window = "recursive", solver = "solnp", fit.control = list(), solver.control = ctrl, calculate.VaR = TRUE, VaR.alpha = c(0.01, 0.025, 0.05)) plot(bktest, which="all")

  8. Look for rolling values ## BID=BID_PRICE,ASK=ASK_PRICE len<- length(BID) mu=NA alpha1=NA bktest <- NA if(len>110){ tryCatch({bktest <- ugarchroll(spec, data = BID, n.ahead = 1, forecast.length = 100, refit.every = 25, refit.window = "recursive", parallel = TRUE, parallel.control = list(pkg = "snowfall", cores = 4), solver = "solnp", fit.control = list(), solver.control = ctrl, calculate.VaR = TRUE, VaR.alpha = c(0.01, 0.025, 0.05)) mu=bktest@roll["coefs"][[1]][1,"mu"] alpha1=bktest@roll["coefs"][[1]][1,"alpha1"]}, error=function(e)cat("Error\n")) } ## OneTick outputs ## MU=mu,ALPHA1=alpha1, LEN=len

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