.Rhistory

dm <- dim(GAX)
dm
colnames(GAX)[dm[2]]
colnames(GAX)[dm[2]] <- "Direction"
colnames(GAX)
issd <- "2013-01-01"
ised <- "2017-12-31"
ossd <- "2018-01-01"
osed <- "2018-11-20"
isrow <- which(index(GAX) >= issd & index(GAX) <= ised)
osrow <- which(index(GAX) >= ossd & index(GAX) <= osed)
isGAX <- GAX[isrow,]
osGAX <- GAX[osrow,]
#표준화#
isme <- apply(isGAX, 2, mean)
isstd <- apply(isGAX, 2, sd)
isidn <- matrix(1, dim(isGAX)[1], dim(isGAX)[2])
norm_isGAX <- (isGAX - t(isme * t(isGAX))) / t(isstd * t(isidn))
dm<-dim(isGAX)
norm_isGAX[,dm[2]] <- direction[isrow]
formula <- paste("Direction ~ .", sep ="")
model <-glm(formula, family = "binomial", norm_isGAX)
summary(model)
pred <- predict(model, norm_isGAX)
prob <- 1/(1+exp(-(pred)))
par(mflow = c(2,1))
plot(pred,type = "l")
plot(prob, type = "l")
head(prob)
tail(prob)
pred_direction <- NULL
pred_direction[prob > 0.5] <- 1
pred_direction[prob <= 0.5] <- 0
pred_direction
#오차행렬에 집어넣을 때, pred_direction이랑 norm_isdji 간 객체 차 존재. Table로 집어넣어야 함, e1071패키지 설치 후 실행
matrix <- confusionMatrix(table(pred_direction,norm_isGAX$Direction))
matrix
#예측 정확도 94%
#정규화
osidn <- matrix(1,dim(osGAX)[1], dim(osGAX)[2])
norm_osGAX <- (osGAX - t(isme*t(osidn))) / t(isstd*t(osidn))
dm <- dim(osGAX)
norm_osGAX[,dm[2]] <- direction[osrow]
#표본 외 데이터 값, 확률
ospred <- predict(model, norm_osGAX)
osprob <- 1/(1+exp(-(ospred)))
ospred_direction <- NULL
ospred_direction[osprob >0.5]<-1
ospred_direction[osprob<=0.5]<-0
osmatrix <- confusionMatrix(table(ospred_direction, norm_osGAX$Direction))
osmatrix
#84% accuracy, 트레이딩 비용, 시장 슬리피지 고려X, 다른 전략 X 오로지 예측
#### 트레이딩 후 Cummulative return 구하는 코딩
#로지스틱 CUM RET 구하는 법
signal <- ifelse(ospred_direction == 1, 1, ifelse(ospred_direction == 0,-1,0))
testrow <- which(index(GAX) >= ossd & index(GAX) <= osed)
GAX <- GOLD.AX$GOLD.AX.Close
ret <- GAX/lag(GAX) -1
ret <- ret[testrow]
ret
cost <- 0
length(signal)
length(ret)
dim(ret)
length(testrow)
trade_ret <- ret * Lag(signal) - cost
dim(ret)
cumm_ret <- Return.cumulative(trade_ret)
anual_ret <- Return.annualized(trade_ret)
charts.PerformanceSummary(trade_ret)
### Data ###
getSymbols("GOLD.AX", src = "yahoo")
GAX <- GOLD.AX
GAX <- GAX[,"GOLD.AX.Close"]
GAX <- na.omit(GAX)
#Varaible #
avg10 <- rollapply(GAX, 10, mean)
avg20 <- rollapply(GAX, 20, mean)
std10 <- rollapply(GAX,10,sd)
std20 <- rollapply(GAX,20,sd)
rsi5 <- RSI(GAX, 5, "SMA")
rsi14 <- RSI(GAX, 14, "SMA")
macd12269 <- MACD(GAX, 12, 26, 9, "SMA")
macd7205 <- MACD(GAX, 7, 20, 5, "SMA")
bbands <- BBands(GAX, 20, "SMA", 2)
direction <- NULL
direction[GAX > Lag(GAX, 20)] <- 1
direction[GAX < Lag(GAX, 20)] <- 0
GAX <- cbind(GAX, avg10, avg20, std10, std20, rsi5, rsi14, macd12269, macd7205, bbands, direction)
dm <- dim(GAX)
dm
colnames(GAX)[dm[2]]
colnames(GAX)[dm[2]] <- "Direction"
colnames(GAX)
issd <- "2013-01-01"
ised <- "2017-12-31"
ossd <- "2018-01-01"
osed <- "2018-11-20"
isrow <- which(index(GAX) >= issd & index(GAX) <= ised)
osrow <- which(index(GAX) >= ossd & index(GAX) <= osed)
isGAX <- GAX[isrow,]
osGAX <- GAX[osrow,]
#표준화#
isme <- apply(isGAX, 2, mean)
isstd <- apply(isGAX, 2, sd)
isidn <- matrix(1, dim(isGAX)[1], dim(isGAX)[2])
norm_isGAX <- (isGAX - t(isme * t(isGAX))) / t(isstd * t(isidn))
dm<-dim(isGAX)
norm_isGAX[,dm[2]] <- direction[isrow]
formula <- paste("Direction ~ .", sep ="")
model <-glm(formula, family = "binomial", norm_isGAX)
summary(model)
pred <- predict(model, norm_isGAX)
prob <- 1/(1+exp(-(pred)))
par(mflow = c(2,1))
plot(pred,type = "l")
plot(prob, type = "l")
head(prob)
tail(prob)
pred_direction <- NULL
pred_direction[prob > 0.5] <- 1
pred_direction[prob <= 0.5] <- 0
pred_direction
#오차행렬에 집어넣을 때, pred_direction이랑 norm_isdji 간 객체 차 존재. Table로 집어넣어야 함, e1071패키지 설치 후 실행
matrix <- confusionMatrix(table(pred_direction,norm_isGAX$Direction))
matrix
#예측 정확도 94%
#정규화
osidn <- matrix(1,dim(osGAX)[1], dim(osGAX)[2])
norm_osGAX <- (osGAX - t(isme*t(osidn))) / t(isstd*t(osidn))
dm <- dim(osGAX)
norm_osGAX[,dm[2]] <- direction[osrow]
#표본 외 데이터 값, 확률
ospred <- predict(model, norm_osGAX)
osprob <- 1/(1+exp(-(ospred)))
ospred_direction <- NULL
ospred_direction[osprob >0.5]<-1
ospred_direction[osprob<=0.5]<-0
osmatrix <- confusionMatrix(table(ospred_direction, norm_osGAX$Direction))
osmatrix
#84% accuracy, 트레이딩 비용, 시장 슬리피지 고려X, 다른 전략 X 오로지 예측
#### 트레이딩 후 Cummulative return 구하는 코딩
#로지스틱 CUM RET 구하는 법
signal <- ifelse(ospred_direction == 1, 1, ifelse(ospred_direction == 0,-1,0))
testrow <- which(index(GAX) >= ossd & index(GAX) <= osed)
GAX <- GOLD.AX$GOLD.AX.Close
ret <- GAX/lag(GAX) -1
ret <- ret[testrow]
ret
cost <- 0
length(signal)
length(ret)
dim(ret)
length(testrow)
trade_ret <- ret * Lag(signal) - cost
dim(ret)
cumm_ret <- Return.cumulative(trade_ret)
anual_ret <- Return.annualized(trade_ret)
charts.PerformanceSummary(trade_ret)
cumm_ret
anual_ret
### Neural Network Machine Learning ###
rm(list=ls())
getSymbols("GOLD.AX", src = "yahoo")
GAX <- GOLD.AX[,"GOLD.AX.Close"]
GAX <- na.omit(GAX)
chartSeries(ClCl(GAX))
plot(GAX)
#수익률 계산, 델타 함수가 안되어요 ㅜㅜ
ret <- GAX/lag(GAX) -1 # 중요함
avg10 <- rollapply(GAX,10,mean)
avg20 <- rollapply(GAX,20,mean)
std10<-rollapply(GAX,10,sd)
std20<-rollapply(GAX,20,sd)
rsi5 <- RSI(GAX,5,"SMA")
rsi14<-RSI(GAX,14,"SMA")
macd12269 <- MACD(GAX,12,26,9,"SMA")
macd7205 <- MACD(GAX,7,20,5,"SMA")
bbands<-BBands(GAX,20,"SMA",2)
#지난 20일간 수익률 2% 이상이면 up, -2% down, 그사이 nowhere
direction <- data.frame(matrix(NA,dim(GAX)[1],1))
#20일 수익률
lagret <- (GAX - Lag(GAX,20)) / Lag(GAX,20)
direction[lagret > 0.02] <- "Up"
direction[lagret < -0.02] <- "Down"
direction[lagret < 0.02 & lagret > -0.02] <- "Nowhere"
GAX <- cbind(GAX,avg10,avg20, std10, std20,rsi5,rsi14,macd12269,macd7205,bbands)
#훈련용, 검증용, 평가용 데이터집합
train_sdate <- "2013-01-01"
train_edate <- "2016-12-31"
vali_sdate <- "2017-01-01"
vali_edate <- "2017-12-31"
test_sdate <- "2018-01-01"
test_edate <- "2018-12-31"
trainrow <- which(index(GAX) >= train_sdate & index(GAX) <= train_edate)
valirow <- which(index(GAX) >= vali_sdate & index(GAX) <= vali_edate)
testrow <- which(index(GAX) >= test_sdate & index(GAX) <= test_edate)
trainGAX <- GAX[trainrow,]
valiGAX <- GAX[valirow,]
testGAX <- GAX[testrow,]
trainme <- apply(trainGAX,2,mean)
trainstd <- apply(trainGAX,2,sd)
#정규화
trainidn <- (matrix(1,dim(trainGAX)[1],dim(trainGAX)[2]))
valiidn <- (matrix(1,dim(valiGAX)[1],dim(valiGAX)[2]))
testidn <- (matrix(1,dim(testGAX)[1],dim(testGAX)[2]))
norm_trainGAX <- (trainGAX - t(trainme*t(trainidn)))/t(trainstd*t(trainidn))
norm_valiGAX <- (valiGAX - t(trainme*t(valiidn)))/t(trainstd*t(valiidn))
norm_testGAX <- (testGAX - t(trainme*t(testidn)))/t(trainstd*t(testidn))
traindir <- direction[trainrow,1]
validir <- direction[valirow,1]
testdir<-direction[testrow,1]
#신경망 적합(정규화열,날짜별 방향, 신경수, 트레이스 출력 여부)
set.seed(1)
model <- nnet(norm_trainGAX, class.ind(traindir),size = 4, trace = F)
model
dim(norm_trainGAX)
vali_pred <- predict(model, norm_valiGAX)
head(vali_pred)
vali_pred_class <- data.frame(matrix(NA,dim(vali_pred)[1],1))
vali_pred_class[vali_pred[,"Down"]>0.5, 1]<- "Down"
vali_pred_class[vali_pred[,"Nowhere"]>0.5,1] <- "Nowhere"
vali_pred_class[vali_pred[,"Up"]>0.5, 1] <- "Up"
vali_pred_class
matrix<- confusionMatrix(table(vali_pred_class[,1],validir))
matrix
#87.65%
test_pred <- predict(model,norm_testGAX)
test_pred_class <- data.frame(matrix(NA,dim(test_pred)[1],1))
test_pred_class[test_pred[,"Down"]>0.5, 1]<- "Down"
test_pred_class[test_pred[,"Nowhere"]>0.5,1] <- "Nowhere"
test_pred_class[test_pred[,"Up"]>0.5, 1] <- "Up"
test_matrix <- confusionMatrix(table(test_pred_class[,1],testdir))
test_matrix
#82pro
#Signal generator
signal <- ifelse(test_pred_class == "Up", 1, ifelse(test_pred_class == "Down",-1,0))
ret <- ret[testrow]
ret
cost <- 0
trade_ret <- ret * Lag(signal) - cost
cumm_ret <- Return.cumulative(trade_ret)
anual_ret <- Return.annualized(trade_ret)
charts.PerformanceSummary(trade_ret)
#charts.PerformanceSummary(cumm_ret)
#plot(cumm_ret)
#Deep Neural Network
set.seed(1)
model <- dbn.dnn.train(norm_testGAX,class.ind(traindir),hidden=c(3,4,6))
nn.predict(model,norm_valiGAX)
nn.test(model,norm_valiGAX,class.ind(validir),t=0.4)
data <- cbind(as.data.frame(norm_trainGAX),traindir)
class(norm_trainGAX)
class(traindir)
h2o.init()
datah2o <- as.h2o(data,"h2o")
class(datah2o)
dim(datah2o)
#은닉층이 4개, 각 뉴런수 4,5,2,7
model <- h2o.deeplearning(1:15, 16,training_frame = datah2o,hidden = c(4,5,2,7))
vali_pred <- predict(model, as.h2o(norm_valiGAX,"h2o"))
vali_pred <- as.data.frame(vali_pred)
vali_pred_class <- data.frame(matrix(NA,dim(vali_pred)[1],1))
vali_pred_class[vali_pred[,"Down"]>0.5, 1]<- "Down"
vali_pred_class[vali_pred[,"Nowhere"]>0.5,1] <- "Nowhere"
vali_pred_class[vali_pred[,"Up"]>0.5, 1] <- "Up"
vali_matrix <- confusionMatrix(table(vali_pred_class[,1],validir))
vali_matrix
## Deep Graph ##
signal <- ifelse(vali_pred_class == "Up", 1, ifelse(vali_pred_class == "Down",-1,0))
ret <- GAX/lag(GAX) -1 # 중요함
ret <- ret[valirow]
ret <- ret[,1]
cost <- 0
trade_ret <- ret * Lag(signal) - cost
cumm_ret <- Return.cumulative(trade_ret)
anual_ret <- Return.annualized(trade_ret)
charts.PerformanceSummary(trade_ret)
rm(list=ls())
getSymbols("GOLD.AX", src = "yahoo")
GAX <- GOLD.AX
GAX <- GAX[,"GOLD.AX.Close"]
GAX <- na.omit(GAX)
avg10 <- rollapply(GAX, 10, mean)
avg20 <- rollapply(GAX, 20, mean)
std10 <- rollapply(GAX,10,sd)
std20 <- rollapply(GAX,20,sd)
rsi5 <- RSI(GAX, 5, "SMA")
rsi14 <- RSI(GAX, 14, "SMA")
macd12269 <- MACD(GAX, 12, 26, 9, "SMA")
macd7205 <- MACD(GAX, 7, 20, 5, "SMA")
bbands <- BBands(GAX, 20, "SMA", 2)
direction <- NULL
direction[GAX > Lag(GAX, 20)] <- 1
direction[GAX < Lag(GAX, 20)] <- 0
GAX <- cbind(GAX, avg10, avg20, std10, std20, rsi5, rsi14, macd12269, macd7205, bbands, direction)
dm <- dim(GAX)
dm
colnames(GAX)[dm[2]]
colnames(GAX)[dm[2]] <- "Direction"
colnames(GAX)
issd <- "2013-01-01"
ised <- "2017-12-31"
ossd <- "2018-01-01"
osed <- "2018-12-31"
isrow <- which(index(GAX) >= issd & index(GAX) <= ised)
osrow <- which(index(GAX) >= ossd & index(GAX) <= osed)
isGAX <- GAX[isrow,]
osGAX <- GAX[osrow,]
#표준화
isme <- apply(isGAX, 2, mean)
isstd <- apply(isGAX, 2, sd)
isidn <- matrix(1, dim(isGAX)[1], dim(isGAX)[2])
norm_isGAX <- (isGAX - t(isme * t(isidn))) / t(isstd * t(isidn))
norm_isGAX
dm<-dim(isGAX)
norm_isGAX[,dm[2]] <- direction[isrow]
#정규화
osidn <- matrix(1,dim(osGAX)[1], dim(osGAX)[2])
norm_osGAX <- (osGAX - t(isme*t(osidn))) / t(isstd*t(osidn))
dm <- dim(osGAX)
norm_osGAX[,dm[2]] <- direction[osrow]
#방향설정
GAX <- GOLD.AX
GAX <- GAX[,"GOLD.AX.Close"] #다시 설정
GAX <- na.omit(GAX)
lagret <- (GAX - Lag(GAX,20))/Lag(GAX,20)
direction[lagret >0.02]<-"Up"
direction[lagret < -0.02]<-"Down"
direction[lagret < 0.02 & lagret > -0.02]<-"Nowhere"
isdir <- direction[isrow]
osdir <- direction[osrow]
model <- svm(norm_isGAX, as.factor(isdir))
model
pred <- predict(model, norm_osGAX)
head(pred)
table(pred,osdir)
model
#적중률
sum(diag(table(pred,osdir)))/sum(table(pred,osdir))
####서포트벡터 트레이딩 후 Cummulative return 구하는 코딩
signal <- ifelse(pred == "Up", 1, ifelse(pred == "Down",-1,0))
#테스트할 날짜 데이터 집합
testrow <- which(index(GAX) >= ossd & index(GAX) <= osed)
testrow
#리턴 구하는 공식과 그 공식 활용 테스트 날짜별 리턴
ret <- GAX/lag(GAX) -1
ret <- ret[testrow]
ret
cost <- 0
trade_ret <- ret * Lag(signal) - cost
length(signal)
dim(ret)
cumm_ret <- Return.cumulative(trade_ret)
anual_ret <- Return.annualized(trade_ret)
charts.PerformanceSummary(trade_ret)
### K-clustering ###
rm(list=ls())
#Data
getSymbols("GOLD.AX", src = "yahoo")
GAX <- GOLD.AX
GAX <- GAX[,"GOLD.AX.Close"]
GAX <- na.omit(GAX)
#variable
avg10 <- rollapply(GAX, 10, mean)
avg20 <- rollapply(GAX, 20, mean)
std10 <- rollapply(GAX,10,sd)
std20 <- rollapply(GAX,20,sd)
rsi5 <- RSI(GAX, 5, "SMA")
rsi14 <- RSI(GAX, 14, "SMA")
macd12269 <- MACD(GAX, 12, 26, 9, "SMA")
macd7205 <- MACD(GAX, 7, 20, 5, "SMA")
bbands <- BBands(GAX, 20, "SMA", 2)
direction <- NULL
direction[GAX > Lag(GAX, 20)] <- 1
direction[GAX < Lag(GAX, 20)] <- 0
GAX <- cbind(GAX, avg10, avg20, std10, std20, rsi5, rsi14, macd12269, macd7205, bbands, direction)
dm <- dim(GAX)
dm
colnames(GAX)[dm[2]]
colnames(GAX)[dm[2]] <- "Direction"
colnames(GAX)
issd <- "2013-01-01"
ised <- "2017-12-31"
ossd <- "2018-01-01"
osed <- "2018-12-31"
isrow <- which(index(GAX) >= issd & index(GAX) <= ised)
osrow <- which(index(GAX) >= ossd & index(GAX) <= osed)
isGAX <- GAX[isrow,]
osGAX <- GAX[osrow,]
#표준화
isme <- apply(isGAX, 2, mean)
isstd <- apply(isGAX, 2, sd)
isidn <- matrix(1, dim(isGAX)[1], dim(isGAX)[2])
norm_isGAX <- (isGAX - t(isme * t(isidn))) / t(isstd * t(isidn))
dm<-dim(isGAX)
norm_isGAX[,dm[2]] <- direction[isrow]
osidn <- matrix(1,dim(osGAX)[1], dim(osGAX)[2])
norm_osGAX <- (osGAX - t(isme*t(osidn))) / t(isstd*t(osidn))
dm <- dim(osGAX)
norm_osGAX[,dm[2]] <- direction[osrow]
clusters <- 3
set.seed(1)
#디렉션 제거
dm<- dim(isGAX)
isGAX[,-dm[2]]
isGAX <- isGAX[,-dm[2]]
norm_isGAX <- norm_isGAX[,-dm[2]]
dm<-dim(osGAX)
osGAX<-osGAX[,-dm[2]]
norm_osGAX<-norm_osGAX[,-dm[2]]
#isdji 바탕으로 클러스터화 모델
model <- kmeans(norm_isGAX, clusters)
head(model$cluster)
model$cluster
model$center
model$size
#클러스터 내제곱합과 총제곱합 비율 최소화
model$tot.withinss
model$totss
model$tot.withinss/model$totss
ospredict <- cl_predict(model, norm_osGAX)
norm_osGAX
head(ospredict)
ospredict
GAX <- GOLD.AX
GAX <- GAX[,"GOLD.AX.Close"]
GAX <- na.omit(GAX)
lagret <- (GAX - Lag(GAX,20))/Lag(GAX,20)
direction[lagret >0.02]<-"Up"
direction[lagret < -0.02]<-"Down"
direction[lagret < 0.02 & lagret > -0.02]<-"Nowhere"
isdir <- direction[isrow]
osdir <- direction[osrow]
neighborhood <- 3
set.seed(1)
dim(norm_isGAX)
dim(norm_osGAX)
model <- knn(norm_isGAX, norm_osGAX, isdir, neighborhood)
model
head(model)
summary(model)
matrix <- confusionMatrix(table(model, osdir))
matrix
diag(matrix$table)
#for 사용해 confusionmatrix의 행렬 계산, 총 대각선 요소-총대각선 요소 수/총데이터요소수
accuracy<- NULL
for(i in c(1:100)){
model <- knn(isGAX, osGAX, isdir, i)
matrix <- confusionMatrix(table(model,osdir))
diag <- sum(diag(matrix$table))
total <- sum(matrix$table)
accuracy[i] <- (total - diag)/total
}
accuracy
plot(accuracy,type = "l")
plot(accuracy,label = "Accuracy of K % " ,type = "l")
#for 사용해 confusionmatrix의 행렬 계산, 총 대각선 요소-총대각선 요소 수/총데이터요소수
accuracy<- NULL
for(i in c(1:100)){
model <- knn(isGAX, osGAX, isdir, i)
matrix <- confusionMatrix(table(model,osdir))
diag <- sum(diag(matrix$table))
total <- sum(matrix$table)
accuracy[i] <- diag/total
}
accuracy
plot(accuracy ,type = "l")
install.packages('tidyverse')
### ##K Cluster 모형 CumRet 구하는법
GAX <- GOLD.AX
GAx <- GOLD.AX[,"GOLD.AX.Close"]
GAX <- na.omit(GAX)
signal <- ifelse( model== "Up", 1, ifelse( model== "Down",-1,0))
testrow <- which(index(GAX) >= ossd & index(GAX) <= osed)
ret <- GAX/lag(GAX,1) -1
ret <- ret[testrow]
ret
cost <- 0
length(signal)
length(ret)
length(testrow)
trade_ret <- ret * Lag(signal) - cost
signal
length(signal)
dim(ret)
cumm_ret <- Return.cumulative(trade_ret)
anual_ret <- Return.annualized(trade_ret)
charts.PerformanceSummary(trade_ret)
GAX <- GOLD.AX
GAx <- GOLD.AX[,"GOLD.AX.Close"]
GAX <- na.omit(GAX)
GAX <- GOLD.AX
GAX <- GOLD.AX[,"GOLD.AX.Close"]
GAX <- na.omit(GAX)
signal <- ifelse( model== "Up", 1, ifelse( model== "Down",-1,0))
testrow <- which(index(GAX) >= ossd & index(GAX) <= osed)
ret <- GAX/lag(GAX,1) -1
ret <- ret[testrow]
ret
cost <- 0
length(signal)
length(ret)
length(testrow)
trade_ret <- ret * Lag(signal) - cost
signal
length(signal)
dim(ret)
cumm_ret <- Return.cumulative(trade_ret)
anual_ret <- Return.annualized(trade_ret)
charts.PerformanceSummary(trade_ret)
plot(GAX)