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fit_gold.R
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library(cmaes)
library(zoo)
library(lubridate)
library(dplyr)
library(parallel)
library(doParallel)
library(progress)
library(ggplot2)
library(nloptr)
library(optimx)
library(latticeExtra)
#get data
filename <- "GOLD.csv"
folder <- "data/GOLD_WINDOWS/"
filepath <- paste("./data/", filename, sep="")
filesname <- substr(filepath, nchar("./data/")+1, nchar(filepath)-4)
ticker <- read.csv(filepath)
#Generico yahoo finance
ticker <- ticker[,c(1,6)]
ticker$Date <- as.Date(ticker$Date, format = "%Y-%m-%d")
ticker$t <- decimal_date(ticker$Date)
names(ticker) <- c("Date", "Close", "t")
ticker$Close <- na_if(ticker$Close,"null")
ticker <- na.omit(ticker)
ticker$Close <- as.numeric(ticker$Close)
ticker <- na.omit(ticker)
lppl_est <- function(data, tc, m, w, a, b, c1, c2){
dif_time = abs(tc-data$t)
#dif_time = tc-data$t
est = a + dif_time^m*(b + ((c1 * cos(w * log(dif_time)))
+ (c2 * sin(w * log(dif_time)))))
return(est)
}
matrix_eq <- function(data, tc, m, w){
ti <- abs(tc - data$t)
#ti <- tc - data$t
fi <- ti ** m #B
gi <- ti ** m * cos(w * log(ti)) #C1
hi <- ti ** m * sin(w * log(ti)) #C2
yi <- log(data$Close)
MAT <- matrix(c(length(ti),sum(fi),sum(gi),sum(hi),
sum(fi),sum(fi**2),sum(fi*gi),sum(fi*hi),
sum(gi),sum(fi*gi),sum(gi**2),sum(gi*hi),
sum(hi),sum(fi*hi),sum(gi*hi),sum(hi**2)),ncol=4,nrow=4)
YY <- matrix(c(sum(yi),sum(yi*fi),sum(yi*gi),sum(yi*hi)),ncol=1)
coef <- solve(MAT,YY)
#reg <- coef(lm(logP ~ Xm + Xm.cos + Xm.sin, data=data))
return(coef)
}
funz_obj <- function(x,data){
tc = x[1]
m = x[2]
w = x[3]
lin_par <- matrix_eq(data,tc,m,w)
#c = (lin_par[3] ** 2 + lin_par[4] ** 2) ** 0.5
# Residual sum of squares
delta <- log(data$Close)-lppl_est(data,tc, m, w, lin_par[1], lin_par[2],
lin_par[3], lin_par[4])
RSS <- sum(delta^2)
return(RSS)
}
fitter <- function(data,plot=FALSE){
ticker <- data
last_row <- tail(ticker, 1)
first_row <- head(ticker, 1)
dt <- last_row$t -first_row$t
start_search <- c(runif(1,max(ticker$t)-0.2*dt,
max(ticker$t)+0.2*dt),
runif(1,0.01,1.99),
runif(1,1,50))
#
# start_search <- c(runif(1,max(ticker$t)+0.001*dt,
# max(ticker$t)+0.2*dt),
# runif(1,0.01,1.99),
# runif(1,1,50))
#
upper <- c(max(ticker$t)+0.2*dt,2,50)
lower <- c(max(ticker$t)-0.2*dt,0.01,1)
#lower <- c(max(ticker$t)+0.001*dt,0.01,1)
#
# if(type=="L-BFGS-B"){
#
# test <- optim(start_search,funz_obj,lower=lower,upper=upper,method="L-BFGS-B",data=ticker)
#
# }
#
# if(type=="CMAES"){
#
# nbre_generation <- 100
#
# vec_control <- data.frame(maxit = c(nbre_generation))
#
# test <- cmaes::cma_es(start_search, funz_obj, ticker,
# lower=c(max(ticker$t)-0.2*dt, 0.01, 1), upper=c(max(ticker$t)+0.2*dt, 1, 50), control=vec_control)
#
# }
#
#
# if(type=="contr"){
#
# test <- crs2lm(start_search, funz_obj, lower=lower, upper=upper, data=ticker)
#
#
# }
#
# if(type=="isres"){#MIGLIORE secondo
#
# test <- isres(start_search,funz_obj,lower=lower,upper=upper,data=ticker)
#
# }
#
#if(type=="mlsl"){#MIGLIORE IN ASSOLUTO
test <- mlsl(start_search,funz_obj,lower=lower,upper=upper,local.method = "LBFGS",data=ticker)
#}
#
# if(type=="nelder"){
#
# test <- neldermead(start_search,funz_obj,lower=lower,upper=upper, data=ticker)
#
# }
#
linear_param <- matrix_eq(ticker,test$par[1], test$par[2], test$par[3])
fitted <- lppl_est(ticker,test$par[1], test$par[2], test$par[3],
linear_param[1],linear_param[2],linear_param[3],linear_param[4])
if(plot==TRUE){
plot(log(ticker$Close),type="l",col="red")
lines(fitted, col="blue")
}
#Test radice unitaria su residui
residual <- log(ticker$Close)-fitted
test.resid <- suppressWarnings(tseries::kpss.test(residual)[1] )#Test stazionarieta' residui
rownames(test.resid) <- c()
results <- data.frame(first_row$Date,
last_row$Date,
last_row$Close,
as.integer(dt/(1/365)),
exp(max(fitted)),
test$par[1]-last_row$t,
as.integer((test$par[1]-last_row$t)/(1/365)),
test$par[2], #m
test$par[3], #w
test$par[1], #tc
linear_param[1], #A
linear_param[2], #B
linear_param[3], #C1
linear_param[4], #C2
(test$par[3]/(2*pi))*log(abs((test$par[1])/(test$par[1]-last_row$t))),
#(test$par[3]/(2))*log(abs((test$par[1]-first_row$t)/(last_row$t-first_row$t))),#number oscillation
(test$par[2]*abs(linear_param[2]))/(test$par[3]
*abs((linear_param[3]^2+linear_param[4]^2)^0.5)),
#*abs(linear_param[3]/(cos(atan(linear_param[4]/linear_param[3]))))
#abs((log(last_row$Close)-fitted[length(fitted)])/fitted[length(fitted)]),#relative error sbagliato
mean(abs(residual)/fitted, na.rm =TRUE), #relative error corretto
last_row$t-0.05*dt,
last_row$t+0.1*dt,
- linear_param[2] * test$par[2] - abs((linear_param[3]^2+linear_param[4]^2)^0.5)* sqrt(test$par[2]^2+test$par[3]^2),#fantazzini
test.resid,
sum(residual^2)
)
names(results) <- c("start_date","end_date","last_price","dt","LPPL_max","tc-end.t",
"day_to_tc","m","w","tc","A","B","C1","C2","oscill","damp","rel_err","dt_filter_low","dt_filter_high","hazard","test.resid","resid")
rownames(results) <- c()
return(results)
}
# Script che lo lancia tu tutte le finestre temporali
compute_conf <- function(data,clusters=9,size=10,diff=1){
ticker <- data
conf_ind <- data.frame(P.SS_EW=rep(0,nrow(ticker)),
P.SS_EF=rep(0,nrow(ticker)),
P.S_EW=rep(0,nrow(ticker)),
P.S_EF=rep(0,nrow(ticker)),
P.M_EW=rep(0,nrow(ticker)),
P.M_EF=rep(0,nrow(ticker)),
P.L_EW=rep(0,nrow(ticker)),
P.L_EF=rep(0,nrow(ticker)),
N.SS_EW=rep(0,nrow(ticker)),
N.SS_EF=rep(0,nrow(ticker)),
N.S_EW=rep(0,nrow(ticker)),
N.S_EF=rep(0,nrow(ticker)),
N.M_EW=rep(0,nrow(ticker)),
N.M_EF=rep(0,nrow(ticker)),
N.L_EW=rep(0,nrow(ticker)),
N.L_EF=rep(0,nrow(ticker)),#19
P.SS_tc=rep(0,nrow(ticker)),#20
P.S_tc=rep(0,nrow(ticker)),
P.M_tc=rep(0,nrow(ticker)),
P.L_tc=rep(0,nrow(ticker)),
N.SS_tc=rep(0,nrow(ticker)),
N.S_tc=rep(0,nrow(ticker)),
N.M_tc=rep(0,nrow(ticker)),
N.L_tc=rep(0,nrow(ticker))#27
)
ticker <- cbind(ticker,conf_ind)
cl <- parallel::makeForkCluster(clusters)
doParallel::registerDoParallel(cl)
for(j in diff:(size+diff)){
sub_ticker <- ticker[seq(nrow(ticker)-1463-j,nrow(ticker)-j),1:3]
df_result <- foreach (i = seq(1,1438,1), .combine = rbind) %dopar% {
r.ticker <- sub_ticker[i:nrow(sub_ticker),]
result <- NULL
attempt <- 3
while(is.null(result) && attempt <= 4){
attempt <- attempt +1
try(
result <- fitter(r.ticker)#,
#silent=TRUE
)
}
return(result)
}
#Prendo solo lunghezza intervallo che mi interessa dt tra 1460 e 40
df_result <- as_tibble(df_result) %>%
filter(dt >= 40 & dt<=1460)
#Salvare in csv risultato per singolo t2
nome <- paste("df_result","_",j,".csv",sep="")
write.csv(df_result,paste(folder,nome,sep=""))
}
parallel::stopCluster(cl)
#
# if(save==TRUE){
#
# write.csv(ticker,paste(folder,filesname,"_","ANALYSIS",".csv",sep=""))
#
# }
#
#return(ticker)
}
compute_conf(ticker,size=1000,diff=2593,clusters = 18)