Walk Forward demo for MACD
require(foreach,quietly=TRUE) require(iterators) require(quantstrat)
run the macd demo in this session to set things up
use source rather than demo so that everything will be local in this session
source(system.file(‘demo/macd.R’,package=‘quantstrat’),echo = TRUE)
example parallel initialization for doParallel. this or doMC, or doRedis are
most probably preferable to doSMP
#require(doParallel) #registerDoParallel() # by default number of physical cores -1
#please run macd demo before all these…
#retrieve the strategy from the environment, since the ‘macd’ strategy uses store=TRUE strategy.st <- ‘macd’
Set up Parameter Values
.FastMA = (1:10) .SlowMA = (5:25) .nsamples = 15 #for random parameter sampling, less important if you’re using doParallel or doMC
MA paramset
add.distribution(strategy.st, paramset.label = ‘MA’, component.type = ‘indicator’, component.label = ’_‘, #this is the label given to the indicator in the strat variable = list(n = .FastMA), label = ’nFAST’ )
add.distribution(strategy.st, paramset.label = ‘MA’, component.type = ‘indicator’, component.label = ’_‘, #this is the label given to the indicator in the strat variable = list(n = .SlowMA), label = ’nSLOW’ )
add.distribution.constraint(strategy.st, paramset.label = ‘MA’, distribution.label.1 = ‘nFAST’, distribution.label.2 = ‘nSLOW’, operator = ‘<’, label = ‘MA’ )
wfportfolio <- “wf.macd” initPortf(wfportfolio,symbols=stock.str) initOrders(portfolio=wfportfolio) wf_start <- Sys.time() wfresults <- walk.forward(strategy.st, paramset.label = ‘MA’, portfolio.st = wfportfolio, account.st = account.st, nsamples = .nsamples, period = ‘months’, k.training = 36, k.testing = 12, verbose =TRUE, anchored = TRUE, include.insamples = TRUE, savewf = FALSE ) wf_end <-Sys.time()
cat(“Running the walk forward search: ”) print(wf_end-wf_start) cat(” Total trials:“,.strategy\(macd\)trials,”“)
wfa.stats <- wfresults$tradeStats
print(wfa.stats)
chart.forward(wfresults)