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Keras_1dCNN.R
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Keras_1dCNN.R
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library(dplyr)
library(data.table)
library(keras)
library(abind)
basePath = "~/Desktop/Delete/"
dataPrep = function(){
library(dplyr)
library(data.table)
library(lubridate)
kpath = "~/Desktop/Delete/rstudio-export/KR_DAUM_DAY.RDS"
dt =readRDS(kpath)
setorderv(dt, c("TICKER","DATE"),c(1,1))
# market
basePath ="~/Desktop/Delete/"
mkDT = fread(paste0(basePath, "^KS11.csv"))
colnames(mkDT) =c("DATE","OPEN","HIGH","LOW","CLOSE","ADJCLOSE","VOLUME")
mkDT[,DATE:=as.Date(DATE),]
mkDT = mkDT %>% mutate_if(is.character, as.numeric) %>% as.data.table()
dt = merge(dt, mkDT %>% select(DATE, ADJCLOSE) %>% as.data.table(), by="DATE")
# disparity and facevalue
pcmean = function(DT, date){
mDT = DT[VOLUME >0][,.(PCM=mean(CLOSE),PCSD=sd(CLOSE)),by=TICKER][,DATE:=date,]
mDT # return
}
dateSeq = dt$DATE %>% unique() %>% sort()
dateSeqLen = dateSeq %>% length()
allDT = data.table(NULL)
for(i in seq(21, dateSeqLen)){
minDate = dateSeq[i-20]
maxDate = dateSeq[i-1]
temp = dt[DATE >= minDate][DATE <= maxDate]
tempR = pcmean(temp, dateSeq[i])
allDT = rbindlist(list(allDT, tempR))
if(i %% 100 ==1){
print(paste(i, dateSeq[i]))
}
}
setorderv(dt, c("TICKER","DATE"),c(1,1))
for(i in c(1,21)){
shiftStr ="
cols=c('TICKER','CLOSE','ADJCLOSE')
days = _N_
anscols =paste0('_PREFIX_',days,cols)
_dataset_[,(anscols) := shift(.SD, days,NA,'_MOVE_'), .SDcols=cols]"
shiftStrExe = gsub("_N_",i, shiftStr)
shiftStrExe = gsub("_PREFIX_","P", shiftStrExe)
shiftStrExe = gsub("_dataset_","dt", shiftStrExe)
shiftStrExe = gsub("_MOVE_","lag", shiftStrExe)
eval(parse(text=shiftStrExe))
}
for(i in c(10)){
shiftStr ="
cols=c('TICKER','CLOSE','OPEN')
days = _N_
anscols =paste0('_PREFIX_',days,cols)
_dataset_[,(anscols) := shift(.SD, days,NA,'_MOVE_'), .SDcols=cols]"
shiftStrExe = gsub("_N_",i, shiftStr)
shiftStrExe = gsub("_PREFIX_","N", shiftStrExe)
shiftStrExe = gsub("_dataset_","dt", shiftStrExe)
shiftStrExe = gsub("_MOVE_","lead", shiftStrExe)
eval(parse(text=shiftStrExe))
}
dt=dt[TICKER==N10TICKER]
dt = dt[TICKER == P21TICKER]
comDT = merge(allDT, dt, by=c("TICKER","DATE"))[,AMT:=as.double(CLOSE)*as.double(VOLUME)/10^8,][VOLUME >0]
comDT[,DISPARITY:=CLOSE/PCM-1,][,ENTRY:=CLOSE,][,EXIT:=N10OPEN][,GAIN:=EXIT/ENTRY-1,][,PCVOL:=PCSD/CLOSE,]
comDTs = comDT[,DISPARITYRANK:=frank((DISPARITY),ties.method = 'first'),by=DATE]
comDTsf = comDTs[,FACERANKPERC:=frank(CLOSE, ties.method = 'first')/.N,by=DATE
][,OC:=CLOSE/OPEN-1,][,PCO:=OPEN/P1CLOSE-1,][,MKTPCC:=ADJCLOSE/P1ADJCLOSE-1,]
comDTsf # return
}
dt = dataPrep()
dateSeq = dt$DATE %>% unique() %>% sort()
pastDays = 20
selectRank = 150
dtSub =copy(dt) %>% select(TICKER,DATE, VOLUME, CLOSE, OC, PCO, DISPARITY,MKTPCC) %>% as.data.table()
seriesDTpre = data.table(NULL)
for( i in seq(pastDays+1,length(dateSeq))){
lastDT = dt[DATE == dateSeq[i]][DISPARITYRANK <= selectRank][,GAINRANKPERC:=frank(GAIN,ties.method = 'first')/.N,by=DATE] %>%
select(TICKER, ENTRY,EXIT,GAIN,LASTDATE=DATE, LASTVOLUME=VOLUME,LASTCLOSE=CLOSE, LASTDISPARITYRANK=DISPARITYRANK,GAINRANKPERC) %>%
as.data.table()
pastDT = dtSub[DATE <= dateSeq[i] & DATE >= dateSeq[i-pastDays]]
tempDT = merge(lastDT, pastDT, by="TICKER")[,DISPARITYRANKPERC:=frank(DISPARITY, ties.method = 'first')/.N,by=DATE
][,KEY:=paste0(TICKER,LASTDATE),]
seriesDTpre = rbindlist(list(seriesDTpre, tempDT))
if(i %% 100 == pastDays+1){
print(paste(i, dateSeq[i]))
}
}
# check full date series
seriesDTpreAgg = seriesDTpre[,.(CNT=.N),by=KEY]
goodKeyList = seriesDTpreAgg[CNT == pastDays+1]$KEY %>% unique()
seriesDTpre = seriesDTpre[KEY %in% goodKeyList]
seriesDTpre %>% dim()
saveRDS(seriesDTpre,paste0(basePath,"seriesDTre.rds"))
# data ops
# features: OC, PCO, VLM, disparityRank, disparityVal, mktPCC, facevalueRankPerd
# target: 0 or 1 (gain above median or not)
# flow:
# 1. disparity rank <= 100
# 2. n10gain rankPerc -> 0.5 threshold
# 3. past 20 days feature series
# data prep for keras
# data.table -> as.matrix()
trRatio= 0.7
trDateMax = dateSeq[floor(length(dateSeq)*trRatio)]
seriesDTpre[,CLOSERATIO:=CLOSE/LASTCLOSE-1,
][,VOLUMERATIO:=VOLUME/LASTVOLUME,
][,TARGET:=ifelse(GAINRANKPERC > 0.5,1,0),
]
seriesDT = copy(seriesDTpre)
trDT = seriesDT[LASTDATE <= trDateMax]
tsDT = seriesDT[LASTDATE > trDateMax]
featureCols = c("CLOSERATIO","VOLUMERATIO","OC","PCO","DISPARITY","DISPARITYRANKPERC","MKTPCC")
targetCols =c("TARGET")
reduceDF = function(DT, selectColList, filterCol){
smallDT = DT[KEY==filterCol][,selectColList,with=FALSE] %>% data.matrix()
return(smallDT)
}
arrayOpsFeature = function(seriesDT, featureCols){
seriesKey = seriesDT$KEY %>% unique()
print(paste("seriesKey length:", length(seriesKey)))
mxList = seriesKey %>% lapply(function(x) reduceDF(seriesDT, featureCols, x) )
mxList21 = mxList[!is.na(mxList)]
mxArray = abind(mxList21, along=3)
mxArray = aperm(mxArray,c(3,1,2))
print(paste("array dim:", dim(mxArray)[1]))
mxArray # return
}
arrayOpsTarget = function(seriesDT){
seriesKey = seriesDT$KEY %>% unique()
print(paste("seriesKey length:", length(seriesKey)))
mxArray = seriesDT %>% select(KEY,TARGET) %>% unique() %>% select(TARGET) %>% unlist() %>% array()
print(paste("array dim:", dim(mxArray)[1]))
mxArray # return
}
tr_x = arrayOpsFeature(trDT, featureCols)
tr_y = arrayOpsTarget(trDT)
ts_x = arrayOpsFeature(tsDT, featureCols)
ts_y = arrayOpsTarget(tsDT)
saveRDS(tr_x,paste0(basePath, "tr_x.rds"))
saveRDS(tr_y,paste0(basePath, "tr_y.rds"))
saveRDS(ts_x,paste0(basePath, "ts_x.rds"))
saveRDS(ts_y,paste0(basePath, "ts_y.rds"))
# 1D CNN
xtrain = array_reshape(tr_x, c(nrow(tr_x), 21,7))
ytrain = array_reshape(tr_y, c(nrow(tr_y)))
xtest = array_reshape(ts_x, c(nrow(ts_x), 21,7))
ytest = array_reshape(ts_y, c(nrow(ts_y)))
# parameters
filterSize = 40
filterSize2 =20
kernelSize = 2
maxpoolSize = 7L
model <- keras_model_sequential()
model %>%
layer_conv_1d( filterSize, kernelSize, padding="valid", activation = "relu", strides = 1L) %>%
layer_max_pooling_1d(pool_size = maxpoolSize, strides = 1L) %>%
layer_conv_1d( filterSize2, kernelSize, padding ="valid", activation = "relu", strides = 1L) %>%
layer_global_average_pooling_1d() %>%
layer_dropout(0.5) %>%
layer_dense(units = 1L, activation = "sigmoid")
# compile model
model %>% compile(
loss = "binary_crossentropy",
optimizer = "adam",
metrics = "accuracy"
)
model %>%
fit(
xtrain, ytrain,
batch_size = 5000,
epochs = 50,
validation_data = list(xtest, ytest),
view_metrics="auto"
)
pred = predict(model, xtest, verbose = 1)
predMap = data.table(KEY = tsDT$KEY %>% unique(), SCORE=pred, stringsAsFactors = F)
predDT = merge(tsDT, predMap, by="KEY")[!is.nan(SCORE.V1)]
highscoreDT = predDT[DATE==LASTDATE
][,AMT:=as.double(CLOSE)*as.double(VOLUME)/10^8,
][,SCORERANKPERC:=frank(desc(SCORE.V1),ties.method = 'first'),by=LASTDATE
][SCORERANKPERC <=5][AMT >1][,.(GM=mean(GAIN),AMTM=median(AMT),CNT=.N),by=DATE] %>% arrange(DATE) %>% as.data.table()
highscoreDT %>% summary
barplot(highscoreDT$GM )
barplot(highscoreDT$GM %>% sort())
highscoreDT$CR=1
fee = 0.003+0.015/100*2+0.01
for(i in seq(2, nrow(highscoreDT))){
highscoreDT[i,"CR"] =( 1+ (highscoreDT[i,"GM"]-fee)/10)*highscoreDT[i-1,"CR"]
}
barplot(highscoreDT$CR, log='y')
highscoreDT %>% tail()
highscoreDT %>% summary()
nrow(highscoreDT)
### save mode to file
model %>% save_model_hdf5(paste0(basePath,"disparity_1D_CNN_model.h5"))
model %>% summary
# CNN result :
# DATE GM AMTM CNT
# Min. :2016-07-11 Min. :-0.17334 Min. : 0.0068 Min. :1.000
# 1st Qu.:2016-12-08 1st Qu.:-0.01649 1st Qu.: 5.8486 1st Qu.:5.000
# Median :2017-05-15 Median : 0.01530 Median : 10.8046 Median :5.000
# Mean :2017-05-14 Mean : 0.02983 Mean : 18.2347 Mean :4.952
# 3rd Qu.:2017-10-19 3rd Qu.: 0.06905 3rd Qu.: 20.8000 3rd Qu.:5.000
# Max. :2018-04-11 Max. : 0.35438 Max. :334.2838 Max. :5.000
# manual result : disparityrank *4 -> facevalrank*1 -> pcvolrankperc < 0.3
# DATE GM CNT AMTM
# Min. :2016-07-11 Min. :-0.23427 Min. :6 Min. : 2.272
# 1st Qu.:2016-12-15 1st Qu.:-0.03357 1st Qu.:6 1st Qu.: 9.209
# Median :2017-05-26 Median : 0.01029 Median :6 Median : 13.280
# Mean :2017-05-26 Mean : 0.02622 Mean :6 Mean : 17.594
# 3rd Qu.:2017-11-06 3rd Qu.: 0.07364 3rd Qu.:6 3rd Qu.: 20.446
# Max. :2018-04-13 Max. : 0.58297 Max. :6 Max. :157.419
# CNN tune result : more filters, kernelsize =2, epoch=50
# DATE GM AMTM CNT
# Min. :2016-07-11 Min. :-0.16313 Min. : 0.00685 Min. :1.000
# 1st Qu.:2016-11-27 1st Qu.:-0.01070 1st Qu.: 6.72580 1st Qu.:5.000
# Median :2017-05-15 Median : 0.02685 Median : 11.66888 Median :5.000
# Mean :2017-05-14 Mean : 0.03908 Mean : 19.97580 Mean :4.947
# 3rd Qu.:2017-11-01 3rd Qu.: 0.07642 3rd Qu.: 22.38550 3rd Qu.:5.000
# Max. :2018-04-11 Max. : 0.76431 Max. :285.66997 Max. :5.000