finefinance/Logistic Regression.R at master · FinancialEngineerLab/finefinance · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
###Logistic Regression ###

rm(list=ls())

library(TTR)
library(xts)
library(quantmod)
library(caret)
library(nnet)
library(caret)
library(PerformanceAnalytics)
library(deepnet)
library(h2o)
library(caret)


### 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)