rawdata <- readr::read_csv("BABA.CSV")
## Rows: 253 Columns: 7
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## dbl (6): Open, High, Low, Close, Adj Close, Volume
## date (1): Date
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
BABA <- read_csv("BABA.csv")
## Rows: 253 Columns: 7
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## dbl (6): Open, High, Low, Close, Adj Close, Volume
## date (1): Date
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
View(BABA)
evaluate <- function(PARAM, minVal = NA, maxVal = NA, y = 2021,
transform = FALSE, verbose = FALSE,
negative = FALSE, transformOnly = FALSE,
returnData = FALSE, accountParams = NULL){
# Step 1
# Convert and declare parameters if they exist on unbounded (-inf,inf) domain
if( transform | transformOnly ){
PARAM <- minVal +
(maxVal - minVal) * unlist(lapply( PARAM, function(v) (1 + exp(-v))^(-1) ))
if( transformOnly ){
}
}
return(PARAM)
} ## 修改
# Declare bounds and step size for optimization
lowerBound <- c(n1 = 5, nFact = 3, nSharpe = 22, shThresh = 0.05)
upperBound <- c(n1 = 80, nFact = 3, nSharpe = 22, shThresh = 0.95)
stepSize <- c(n1 = 5, nFact = 1, nSharpe = 1, shThresh = 0.05)
pnames <- names(stepSize)
np <- length(pnames)
# Declare list of all test points
POINTS <- list()
for( p in pnames ){
POINTS[[p]] <- seq(lowerBound[[p]], upperBound[[p]], stepSize[[p]])
}
OPTIM <- data.frame(matrix(NA, nrow = prod(unlist(lapply(POINTS, length))),
ncol = np + 1))
names(OPTIM)[1:np] <- names(POINTS)
names(OPTIM)[np+1] <- "obj"
# Store all possible combinations of parameters
for( i in 1:np ){
each <- prod(unlist(lapply(POINTS, length))[-(1:i)])
times <- prod(unlist(lapply(POINTS, length))[-(i:length(pnames))])
OPTIM[,i] <- rep(POINTS[[pnames[i]]], each = each, times = times)
}
# Test each row of OPTIM
timeLapse <- proc.time()[3]
for( i in 1:nrow(OPTIM) ){
OPTIM[i,np+1] <- evaluate(OPTIM[i,1:np], transform = FALSE, y = 2021)
cat(paste0("## ", floor( 100 * i / nrow(OPTIM)), "% complete\n"))
cat(paste0("## ",
round( ((proc.time()[3] - timeLapse) *
((nrow(OPTIM) - i)/ i))/60, 2),
" minutes remaining\n\n"))
}
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library(lattice)
wireframe(obj ~ n1*shThresh, data = OPTIM,
xlab = "n1", ylab = "shThresh",
main = "Long-Only MACD Exhaustive Optimization",
drape = TRUE,
colorkey = TRUE,
screen = list(z = 15, x = -60)
)
levelplot(obj ~ n1*shThresh, data = OPTIM,
xlab = "n1", ylab = "shThresh",
main = "Long-Only MACD Exhaustive Optimization"
)
