R/make_gann_sigs.R

In causality-loop/mnmt: Mot Nam, Mot Thang

#' @importFrom magrittr '%$%' '%T>%'
.datatable.aware = TRUE

if (getRversion() >= '2.15.1')
  utils::globalVariables(c('.', 'Ret', 'Sig', 'Lag.3'), utils::packageName())

make_gann_sigs <- function()
{
  symbols <- c('SPY', 'DIA', 'QQQ')

  # just show EOW dates to avoid the scenario where you need to remake
  # model data mid-week and gann sigs = 0 for FRIDAY but it's still WEDNESDAY
  # or something
  dates <- readRDS('data/dates.rds')

  # make x in Sys.Date()-x a big number in case the market is down
  # more than 3 weeks
  prices <- lapply(symbols, function(x) {
    quantmod::getSymbols(x, env = NULL, from = Sys.Date()-70, to = Sys.Date()+1)[,4]
  }) %>% 
    do.call(what = xts::merge.xts) %>%
    `names<-`(symbols)

  rets <- prices %>% 
    .[xts::endpoints(., 'week')] %>%
    TTR::ROC(type = 'discrete', na.pad = F)

  gann_sigs <- parallel::mclapply(names(rets), function(x) {
    rets[,x] %>%
      `names<-`('Ret') %$%
      cbind(., quantmod::Lag(Ret, 1:2)) %>%
      cbind(., rowSums(sign(.)) == -3) %>%
      .[,c(1,4)] %T>%
      {.[is.na(.)] <- 0} %T>%
      {names(.)[2] <- 'Sig'} %$%
      cbind(Ret, diff(Sig) > 0) %T>% {.[!.] <- NA} %$%
      cbind(Ret, Sig, quantmod::Lag(Sig, 3) %T>%  {.[.==1] <- 0}) %$%
      cbind(Ret, ifelse(is.na(Sig), Lag.3, Sig)) %>%
      zoo::na.locf() %T>%
      {names(.)[2] <- 'Sig'} %>%
      .[,2] %>%
      `names<-`(x)
  }) %>%
    do.call(what = cbind) %>%
    PerformanceAnalytics::zerofill()

  # in case you make gann_sigs the first week of the year, in that case
  # dates$EOW would yield an xts with length 0
  if (length(gann_sigs[dates$EOW]) == 0) {
    gann_sigs <- xts::last(gann_sigs)
  } else {
    gann_sigs <- xts::last(gann_sigs[dates$EOW])
  }

  saveRDS(gann_sigs, file = 'data/gann_sigs.rds')
}