## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----eval=FALSE---------------------------------------------------------------
# my_corr <- function(todo,rv) {
#     # Get Data
#     tickers_to_get=strsplit(rv$assetline,";")[[1]]
#     roll_window <- fcoalesce(as.numeric(rv$todoargs),22) # default to 22 day rolling correlation
#     if(length(tickers_to_get)<3) {
#         avsh_quick_message("Need at least 3 tickers")
#         return() }
#     # Dont forget to add data in case it is needed
#     av_add_px(equitylist=tickers_to_get)
#     # Load the internal data store
#     allpx <- av_load_shinydata("pxd")[data.table(symbol=tickers_to_get),on=.(symbol)]
#     allpx <- allpx[,rtn:=c(NA_real_,diff(log(adjusted_close),1)), by=.(symbol)]
#     newdtstr <- FinanceGraphs::extenddtstr(rv$dtstr_hist,begchg=-ceiling(31/22*roll_window))
#     allpx <- allpx [,.(symbol,timestamp,rtn)] |> FinanceGraphs::narrowbydtstr(newdtstr)
# 
#     # Make pairwise data
#     pairs <- CJ(var1=tickers_to_get,var2=tickers_to_get)[var1<var2,]
#     corDT1<- allpx[,.(timestamp, var1=symbol, rtn1=rtn)][pairs,on=.(var1),allow.cartesian=TRUE]
#     corDT<- allpx[,.(timestamp, var2=symbol, rtn2=rtn)][corDT1,on=.(var2,timestamp),allow.cartesian=TRUE]
# 
#     # Rolling correlations
#     rollcor_DT <- corDT[,rcorr:=frollapply(.SD,roll_window,\(x) cor(x$rtn1,x$rtn2),by.column=FALSE), by=.(var1,var2)]
#     cornames <- c("corr_p25","corr_p50","corr_p75")
#     rollcor_toplot <- rollcor_DT[,
#                         (cornames):=as.list(quantile(.SD$rcorr,probs=c(0.25,0.5,0.75),na.rm=TRUE)), by=.(timestamp)][
#                         ,.SD, .SDcols=c("timestamp",cornames)]
#     rollcorr_dyg <- fgts_dygraph(rollcor_toplot,title=paste0("Rolling percentiles of ",roll_window," bd correlations"),
#                 roller=1,events=rv$ts_events)
#     # Overall correlations for period
#     corDT <- corDT |> FinanceGraphs::narrowbydtstr(rv$dtstr_hist)
#     allcorr <- corDT[,.(allcorr=cor(rtn1,rtn2,use="pairwise.complete.obs")),by=.(var1,var2)]
#     allcorr_gt <- dcast(allcorr,var1 ~ var2,value.var="allcorr") |> gt() |>
#                     tab_header(title=paste0("Correlation matrix for ",rv$dtstr_hist)) |>
#                     sub_missing(missing_text="--")
#     # Last Percentiles
#     corrpct <- rollcor_DT[,.(lastpctile=100*last(frank(rcorr,na.last=NA))/.N), by=.(var1,var2)]
#     pctlast_gt <- dcast(corrpct,var1 ~ var2,value.var="lastpctile") |> gt() |>
#             tab_header(title=paste0("Last correlation percentile ",rv$dtstr_hist)) |>
#             fmt_number(decimals=1) |> sub_missing(missing_text="--")
# 
#     # Return list
#     return(list("GT3L"=allcorr_gt,"TS1"=rollcorr_dyg,"GT3R"=pctlast_gt))
# }

## ----eval=FALSE---------------------------------------------------------------
# av_add_analytic("RCOR","my_corr",helpstr="Rolling Correlations")