testing quantiles

test.R

set.seed((1234))
library(data.table)
annual_data <- data.table(year=1991:2000,ret=rnorm(200),group=1:20)

annual_data[ , decile2 := funchir::create_quantiles(ret, 10), by = year]

yearly_deciles <- annual_data[,data.table(t(quantile(ret,probs=seq(from=.1, to = 1, length.out=10)))),by=year]

find_decile <- function(thisReturn,year,decile_table){
  for(i in 2:ncol(decile_table)){
    if(thisReturn<decile_table[year==year,i,with=F])
      return(i-1)
  }
}

annual_data[,decile:=find_decile(ret,year,yearly_deciles),by=c("group","year")]

annual_data[decile2 != decile]

#      year        ret group decile2 decile
#   1: 1992  0.2774292     2       9      8
#   2: 1995  0.4291247     5       6      8
#   3: 1996  0.5060559     6       7      8
#   4: 1997 -0.5747400     7       5      3
#   5: 1998 -0.5466319     8       6      3
#  ---                                     
# 165: 1994  0.4878146    14       8      7
# 166: 1995  2.1680325    15      10      9
# 167: 1997  0.6202102    17       8      3
# 168: 1998 -0.9659032    18       4      2
# 169: 1999  0.1626547    19       5      2

yearly_deciles

#     year        10%        20%        30%         40%        50%
#  1: 1991 -1.2669623 -0.5999266 -0.4072242 -0.25117543 -0.1845911
#  2: 1992 -1.0054121 -0.6756371 -0.5181029 -0.47506832 -0.3580541
#  3: 1993 -1.1159853 -0.9060696 -0.7802718 -0.54810474 -0.3904718
#  4: 1994 -1.0956894 -0.7108254 -0.5016240 -0.22207968 -0.1411094
#  5: 1995 -1.0578154 -0.7201759 -0.2622917  0.01037561  0.2644080
#  6: 1996 -0.9884248 -0.4601983 -0.1232194  0.13221742  0.3774111
#  7: 1997 -1.2589292 -1.1354080 -1.1122516 -0.73735143 -0.5428747
#  8: 1998 -1.3582227 -1.0931034 -0.9832290 -0.82849022 -0.6351068
#  9: 1999 -0.6562070 -0.5319529 -0.3022577 -0.08572049  0.1988380
# 10: 2000 -1.1614805 -0.8992200 -0.5346564 -0.42043523 -0.2577377
#             60%         70%       80%       90%     100%
#  1: -0.02913816 0.218218814 0.7280088 1.1370174 1.704329
#  2: -0.14191268 0.059556665 0.2532393 0.3892011 2.548991
#  3: -0.21349855 0.237197769 0.9977136 1.6454666 2.058162
#  4:  0.20422286 0.468056999 0.6523721 0.7603787 1.001513
#  5:  0.46297992 0.868859228 1.0498477 1.8488146 2.168033
#  6:  0.50283913 0.581529012 0.7243151 1.4036260 1.983732
#  7: -0.28351943 0.557424862 0.7299899 1.6493214 1.678206
#  8: -0.46633500 0.002370059 0.1903998 0.9271660 3.043766
#  9:  0.29694436 0.556340728 0.7473556 1.0085816 1.605910
# 10: -0.04456651 0.049496996 0.3386886 1.5360169 2.415835