# 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("\n Running the walk forward search: \n ")
print(wf_end-wf_start)
cat(" Total trials:",.strategy$macd$trials,"\n")
wfa.stats <- wfresults$tradeStats
print(wfa.stats)
chart.forward(wfresults)
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