# na_list <- as.vector(which(is.na(Coffee_dt[,5]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Coffee_dt[na_list[i],5] <- round(mean((sum(Coffee_dt[(na_list[i]-5):(na_list[i]-1), 5])/5), (sum(Coffee_dt[(na_list[i]+1):(na_list[i]+5), 5]/5))), 2)
#
# } # end for
} # end outer if
# which(is.na(Coffee_dt[,1:ncol(Coffee_dt)]) == TRUE)
colnames(Coffee_dt) <- c("Coffee_Date", "Coffee_Open") # , "Coffee_High", "Coffee_Low", "Coffee_Close")
# View(head(Coffee_dt))
##### ##### End Coffee Setup ##### ##### #####
# *****
##### ##### Crude Setup ##### ##### #####
Crude_dt <- fread("../finance_data/Crude_CL=F.csv")
Crude_dt$`Date` <- lubridate::ymd(Crude_dt$`Date`)
Crude_dt <- Crude_dt[,1:2] # :5]
# class(Crude_dt$Open)
Crude_dt[,2] <- round(as.double(unlist(Crude_dt[,2])), 2)
# Crude_dt[,3] <- round(as.double(unlist(Crude_dt[,3])), 2)
# Crude_dt[,4] <- round(as.double(unlist(Crude_dt[,4])), 2)
# Crude_dt[,5] <- round(as.double(unlist(Crude_dt[,5])), 2)
na_list <- which(is.na(Crude_dt))
if(any(!is.na(na_list)) == TRUE)
{
na_list <- as.vector(which(is.na(Crude_dt[,2]) == TRUE))
for(i in 1:length(na_list))
{
Crude_dt[na_list[i],2] <- round(mean((sum(Crude_dt[(na_list[i]-5):(na_list[i]-1), 2])/5), (sum(Crude_dt[(na_list[i]+1):(na_list[i]+5), 2]/5))), 2)
} # end for
# na_list <- as.vector(which(is.na(Crude_dt[,3]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Crude_dt[na_list[i],3] <- round(mean((sum(Crude_dt[(na_list[i]-5):(na_list[i]-1), 3])/5), (sum(Crude_dt[(na_list[i]+1):(na_list[i]+5), 3]/5))), 2)
#
# } # end for
#
# na_list <- as.vector(which(is.na(Crude_dt[,4]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Crude_dt[na_list[i],4] <- round(mean((sum(Crude_dt[(na_list[i]-5):(na_list[i]-1), 4])/5), (sum(Crude_dt[(na_list[i]+1):(na_list[i]+5), 4]/5))), 2)
#
# } # end for
#
# na_list <- as.vector(which(is.na(Crude_dt[,5]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Crude_dt[na_list[i],5] <- round(mean((sum(Crude_dt[(na_list[i]-5):(na_list[i]-1), 5])/5), (sum(Crude_dt[(na_list[i]+1):(na_list[i]+5), 5]/5))), 2)
#
# } # end for
} # end outer if
# which(is.na(Crude_dt[,1:ncol(Crude_dt)]) == TRUE)
colnames(Crude_dt) <- c("Crude_Date", "Crude_Open") # , "Crude_High", "Crude_Low", "Crude_Close")
# View(head(Crude_dt))
##### ##### End Crude Setup ##### ##### #####
# *****
##### ##### Live Cattle Futures Setup ##### ##### #####
Cattle_dt <- fread("../finance_data/Live_Cattle_Futures_(LE=F)_.csv")
Cattle_dt <- Cattle_dt[,1:2] # :5]
colnames(Cattle_dt) <- c("Cattle_Date", "Cattle_Open") # , "Cattle_High", "Cattle_Low", "Cattle_Close")
Cattle_dt$Cattle_Date <- lubridate::mdy(Cattle_dt$Cattle_Date)
Cattle_dt <- purrr::map_df(Cattle_dt, rev)
# class(Cattle_dt$Cattle_Date)
Cattle_dt[,2] <- round(as.double(unlist(Cattle_dt[,2])), 2)
# Cattle_dt[,3] <- round(as.double(unlist(Cattle_dt[,3])), 2)
# Cattle_dt[,4] <- round(as.double(unlist(Cattle_dt[,4])), 2)
# Cattle_dt[,5] <- round(as.double(unlist(Cattle_dt[,5])), 2)
na_list <- which(is.na(Cattle_dt))
if(any(!is.na(na_list)) == TRUE)
{
na_list <- as.vector(which(is.na(Cattle_dt[,2]) == TRUE))
for(i in 1:length(na_list))
{
Cattle_dt[na_list[i],2] <- round(mean((sum(Cattle_dt[(na_list[i]-5):(na_list[i]-1), 2])/5), (sum(Cattle_dt[(na_list[i]+1):(na_list[i]+5), 2]/5))), 2)
} # end for
# na_list <- as.vector(which(is.na(Cattle_dt[,3]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Cattle_dt[na_list[i],3] <- round(mean((sum(Cattle_dt[(na_list[i]-5):(na_list[i]-1), 3])/5), (sum(Cattle_dt[(na_list[i]+1):(na_list[i]+5), 3]/5))), 2)
#
# } # end for
#
# na_list <- as.vector(which(is.na(Cattle_dt[,4]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Cattle_dt[na_list[i],4] <- round(mean((sum(Cattle_dt[(na_list[i]-5):(na_list[i]-1), 4])/5), (sum(Cattle_dt[(na_list[i]+1):(na_list[i]+5), 4]/5))), 2)
#
# } # end for
#
# na_list <- as.vector(which(is.na(Cattle_dt[,5]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Cattle_dt[na_list[i],5] <- round(mean((sum(Cattle_dt[(na_list[i]-5):(na_list[i]-1), 5])/5), (sum(Cattle_dt[(na_list[i]+1):(na_list[i]+5), 5]/5))), 2)
#
# } # end for
} # end outer if
# which(is.na(Cattle_dt[,1:ncol(Cattle_dt)]) == TRUE)
# View(head(Cattle_dt))
##### ##### End Live Cattle Futures Setup ##### ##### #####
# *****
##### ##### Gold Setup ##### ##### #####
Gold_dt <- fread("../finance_data/Gold_GC=F.csv")
Gold_dt$`Date` <- lubridate::ymd(Gold_dt$`Date`)
Gold_dt <- Gold_dt[,1:2] # :5]
# class(Gold_dt$Open)
Gold_dt[,2] <- round(as.double(unlist(Gold_dt[,2])), 2)
# Gold_dt[,3] <- round(as.double(unlist(Gold_dt[,3])), 2)
# Gold_dt[,4] <- round(as.double(unlist(Gold_dt[,4])), 2)
# Gold_dt[,5] <- round(as.double(unlist(Gold_dt[,5])), 2)
na_list <- which(is.na(Gold_dt))
if(any(!is.na(na_list)) == TRUE)
{
na_list <- as.vector(which(is.na(Gold_dt[,2]) == TRUE))
for(i in 1:length(na_list))
{
Gold_dt[na_list[i],2] <- round(mean((sum(Gold_dt[(na_list[i]-5):(na_list[i]-1), 2])/5), (sum(Gold_dt[(na_list[i]+1):(na_list[i]+5), 2]/5))), 2)
} # end for
# na_list <- as.vector(which(is.na(Gold_dt[,3]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Gold_dt[na_list[i],3] <- round(mean((sum(Gold_dt[(na_list[i]-5):(na_list[i]-1), 3])/5), (sum(Gold_dt[(na_list[i]+1):(na_list[i]+5), 3]/5))), 2)
#
# } # end for
#
# na_list <- as.vector(which(is.na(Gold_dt[,4]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Gold_dt[na_list[i],4] <- round(mean((sum(Gold_dt[(na_list[i]-5):(na_list[i]-1), 4])/5), (sum(Gold_dt[(na_list[i]+1):(na_list[i]+5), 4]/5))), 2)
#
# } # end for
#
# na_list <- as.vector(which(is.na(Gold_dt[,5]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Gold_dt[na_list[i],5] <- round(mean((sum(Gold_dt[(na_list[i]-5):(na_list[i]-1), 5])/5), (sum(Gold_dt[(na_list[i]+1):(na_list[i]+5), 5]/5))), 2)
#
# } # end for
} # end outer if
# which(is.na(Gold_dt[,1:ncol(Gold_dt)]) == TRUE)
colnames(Gold_dt) <- c("Gold_Date", "Gold_Open") # , "Gold_High", "Gold_Low", "Gold_Close")
# View(head(Gold_dt))
##### ##### End Gold Setup ##### ##### #####
# *****
##### ##### NYSE Setup ##### ##### #####
NYSE_dt <- fread("../finance_data/NYSE_^NYA.csv")
NYSE_dt$`Date` <- lubridate::ymd(NYSE_dt$`Date`)
NYSE_dt <- NYSE_dt[,1:2] # :5]
# class(NYSE_dt$Open)
NYSE_dt[,2] <- round(as.double(unlist(NYSE_dt[,2])), 2)
# NYSE_dt[,3] <- round(as.double(unlist(NYSE_dt[,3])), 2)
# NYSE_dt[,4] <- round(as.double(unlist(NYSE_dt[,4])), 2)
# NYSE_dt[,5] <- round(as.double(unlist(NYSE_dt[,5])), 2)
na_list <- which(is.na(NYSE_dt))
if(any(!is.na(na_list)) == TRUE)
{
na_list <- as.vector(which(is.na(NYSE_dt[,2]) == TRUE))
for(i in 1:length(na_list))
{
NYSE_dt[na_list[i],2] <- round(mean((sum(NYSE_dt[(na_list[i]-5):(na_list[i]-1), 2])/5), (sum(NYSE_dt[(na_list[i]+1):(na_list[i]+5), 2]/5))), 2)
} # end for
# na_list <- as.vector(which(is.na(NYSE_dt[,3]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   NYSE_dt[na_list[i],3] <- round(mean((sum(NYSE_dt[(na_list[i]-5):(na_list[i]-1), 3])/5), (sum(NYSE_dt[(na_list[i]+1):(na_list[i]+5), 3]/5))), 2)
#
# } # end for
#
# na_list <- as.vector(which(is.na(NYSE_dt[,4]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   NYSE_dt[na_list[i],4] <- round(mean((sum(NYSE_dt[(na_list[i]-5):(na_list[i]-1), 4])/5), (sum(NYSE_dt[(na_list[i]+1):(na_list[i]+5), 4]/5))), 2)
#
# } # end for
#
# na_list <- as.vector(which(is.na(NYSE_dt[,5]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   NYSE_dt[na_list[i],5] <- round(mean((sum(NYSE_dt[(na_list[i]-5):(na_list[i]-1), 5])/5), (sum(NYSE_dt[(na_list[i]+1):(na_list[i]+5), 5]/5))), 2)
#
# } # end for
} # end outer if
# which(is.na(NYSE_dt[,1:ncol(NYSE_dt)]) == TRUE)
colnames(NYSE_dt) <- c("NYSE_Date", "NYSE_Open") # , "NYSE_High", "NYSE_Low", "NYSE_Close")
# View(head(NYSE_dt))
##### ##### End NYSE Setup ##### ##### #####
# *****
##### ##### Silver Spot Setup ##### ##### #####
# rm(Silver_dt)
Silver_dt <- fread("../finance_data/Silver_spot_data__XAG_USA_.csv")
Silver_dt[1,1]
Silver_dt <- Silver_dt[,c(1, 2)] # :5]
colnames(Silver_dt) <- c("Silver_Date", "Silver_Open") # , "Silver_High", "Silver_Low", "Silver_Close")
Silver_dt$Silver_Date <- lubridate::mdy(Silver_dt$Silver_Date)
# class(Silver_dt$Silver_Data)
Silver_dt[,2] <- round(as.double(unlist(Silver_dt[,2])), 2)
# Silver_dt[,3] <- round(as.double(unlist(Silver_dt[,3])), 2)
# Silver_dt[,4] <- round(as.double(unlist(Silver_dt[,4])), 2)
# Silver_dt[,5] <- round(as.double(unlist(Silver_dt[,5])), 2)
na_list <- which(is.na(Silver_dt))
if(any(!is.na(na_list)) == TRUE)
{
na_list <- as.vector(which(is.na(Silver_dt[,2]) == TRUE))
for(i in 1:length(na_list))
{
Silver_dt[na_list[i],2] <- round(mean((sum(Silver_dt[(na_list[i]-5):(na_list[i]-1), 2])/5), (sum(Silver_dt[(na_list[i]+1):(na_list[i]+5), 2]/5))), 2)
} # end for
# na_list <- as.vector(which(is.na(Silver_dt[,3]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Silver_dt[na_list[i],3] <- round(mean((sum(Silver_dt[(na_list[i]-5):(na_list[i]-1), 3])/5), (sum(Silver_dt[(na_list[i]+1):(na_list[i]+5), 3]/5))), 2)
#
# } # end for
#
# na_list <- as.vector(which(is.na(Silver_dt[,4]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Silver_dt[na_list[i],4] <- round(mean((sum(Silver_dt[(na_list[i]-5):(na_list[i]-1), 4])/5), (sum(Silver_dt[(na_list[i]+1):(na_list[i]+5), 4]/5))), 2)
#
# } # end for
#
# na_list <- as.vector(which(is.na(Silver_dt[,5]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Silver_dt[na_list[i],5] <- round(mean((sum(Silver_dt[(na_list[i]-5):(na_list[i]-1), 5])/5), (sum(Silver_dt[(na_list[i]+1):(na_list[i]+5), 5]/5))), 2)
#
# } # end for
} # end outer if
# which(is.na(Silver_dt[,1:ncol(Silver_dt)]) == TRUE)
Silver_dt <- purrr::map_df(Silver_dt, rev)
# View(head(Silver_dt))
##### ##### End Silver Spot Setup ##### ##### #####
# *****
##### ##### S&P 500 Setup ##### ##### #####
sp500_dt <- fread("../finance_data/sp500_^GSPC.csv")
sp500_dt$`Date` <- lubridate::ymd(sp500_dt$`Date`)
sp500_dt <- sp500_dt[,1:2] # :5]
# class(sp500_dt$Open)
sp500_dt[,2] <- round(as.double(unlist(sp500_dt[,2])), 2)
# sp500_dt[,3] <- round(as.double(unlist(sp500_dt[,3])), 2)
# sp500_dt[,4] <- round(as.double(unlist(sp500_dt[,4])), 2)
# sp500_dt[,5] <- round(as.double(unlist(sp500_dt[,5])), 2)
na_list <- which(is.na(sp500_dt))
if(any(!is.na(na_list)) == TRUE)
{
na_list <- as.vector(which(is.na(sp500_dt[,2]) == TRUE))
for(i in 1:length(na_list))
{
sp500_dt[na_list[i],2] <- round(mean((sum(sp500_dt[(na_list[i]-5):(na_list[i]-1), 2])/5), (sum(sp500_dt[(na_list[i]+1):(na_list[i]+5), 2]/5))), 2)
} # end for
# na_list <- as.vector(which(is.na(sp500_dt[,3]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   sp500_dt[na_list[i],3] <- round(mean((sum(sp500_dt[(na_list[i]-5):(na_list[i]-1), 3])/5), (sum(sp500_dt[(na_list[i]+1):(na_list[i]+5), 3]/5))), 2)
#
# } # end for
#
# na_list <- as.vector(which(is.na(sp500_dt[,4]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   sp500_dt[na_list[i],4] <- round(mean((sum(sp500_dt[(na_list[i]-5):(na_list[i]-1), 4])/5), (sum(sp500_dt[(na_list[i]+1):(na_list[i]+5), 4]/5))), 2)
#
# } # end for
#
# na_list <- as.vector(which(is.na(sp500_dt[,5]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   sp500_dt[na_list[i],5] <- round(mean((sum(sp500_dt[(na_list[i]-5):(na_list[i]-1), 5])/5), (sum(sp500_dt[(na_list[i]+1):(na_list[i]+5), 5]/5))), 2)
#
# } # end for
} # end outer if
# which(is.na(sp500_dt[,1:ncol(sp500_dt)]) == TRUE)
colnames(sp500_dt) <- c("sp500_Date", "sp500_Open") # , "sp500_High", "sp500_Low", "sp500_Close")
# View(head(sp500_dt))
##### ##### End S&P 500 Setup ##### ##### #####
# *****
##### ##### Sugar Setup ##### ##### #####
Sugar_dt <- fread("../finance_data/Sugar_SB=F.csv")
Sugar_dt$`Date` <- lubridate::ymd(Sugar_dt$`Date`)
Sugar_dt <- Sugar_dt[,1:2] # :5]
# class(Sugar_dt$Open)
Sugar_dt[,2] <- round(as.double(unlist(Sugar_dt[,2])), 2)
# Sugar_dt[,3] <- round(as.double(unlist(Sugar_dt[,3])), 2)
# Sugar_dt[,4] <- round(as.double(unlist(Sugar_dt[,4])), 2)
# Sugar_dt[,5] <- round(as.double(unlist(Sugar_dt[,5])), 2)
na_list <- which(is.na(Sugar_dt))
if(any(!is.na(na_list)) == TRUE)
{
na_list <- as.vector(which(is.na(Sugar_dt[,2]) == TRUE))
for(i in 1:length(na_list))
{
Sugar_dt[na_list[i],2] <- round(mean((sum(Sugar_dt[(na_list[i]-5):(na_list[i]-1), 2])/5), (sum(Sugar_dt[(na_list[i]+1):(na_list[i]+5), 2]/5))), 2)
} # end for
# na_list <- as.vector(which(is.na(Sugar_dt[,3]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Sugar_dt[na_list[i],3] <- round(mean((sum(Sugar_dt[(na_list[i]-5):(na_list[i]-1), 3])/5), (sum(Sugar_dt[(na_list[i]+1):(na_list[i]+5), 3]/5))), 2)
#
# } # end for
#
# na_list <- as.vector(which(is.na(Sugar_dt[,4]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Sugar_dt[na_list[i],4] <- round(mean((sum(Sugar_dt[(na_list[i]-5):(na_list[i]-1), 4])/5), (sum(Sugar_dt[(na_list[i]+1):(na_list[i]+5), 4]/5))), 2)
#
# } # end for
#
# na_list <- as.vector(which(is.na(Sugar_dt[,5]) == TRUE))
#
# for(i in 1:length(na_list))
# {
#   Sugar_dt[na_list[i],5] <- round(mean((sum(Sugar_dt[(na_list[i]-5):(na_list[i]-1), 5])/5), (sum(Sugar_dt[(na_list[i]+1):(na_list[i]+5), 5]/5))), 2)
#
# } # end for
} # end outer if
# which(is.na(Sugar_dt[,1:ncol(Sugar_dt)]) == TRUE)
colnames(Sugar_dt) <- c("Sugar_Date", "Sugar_Open") #, "Sugar_High", "Sugar_Low", "Sugar_Close")
# View(head(Sugar_dt))
##### ##### End Sugar Setup ##### ##### #####
# *****
# nrow(Amazon_dt)
# nrow(Boeing_dt)
# nrow(Cattle_dt)
# nrow(Cocoa_dt)
# nrow(Coffee_dt)
# nrow(Crude_dt)
# nrow(Gold_dt)
# nrow(NYSE_dt)
# nrow(Silver_dt)
# nrow(sp500_dt)
# nrow(Sugar_dt)
# nrow(Wheat_dt)
#
# Amazon_dt$Amazon_Date[1]
# Boeing_dt$Boeing_Date[1]
# Cattle_dt$Cattle_Date[1]
# Cocoa_dt$Cocoa_Date[1]
# Coffee_dt$Coffee_Date[1]
# Crude_dt$Crude_Date[1]
# Gold_dt$Gold_Date[1]
# NYSE_dt$NYSE_Date[1]
# Silver_dt$Silver_Date[1]
# sp500_dt$sp500_Date[1]
# Sugar_dt$Sugar_Date[1]
# Wheat_dt$Wheat_Date[1]
#
#
# Amazon_dt[nrow(Amazon_dt),1]
#
# nrow(Amazon_dt)
##### Silver modifications #####
Silver_dt <- purrr::map_df(Silver_dt, rev)
nrow(Silver_dt)
Silver_dt <- Silver_dt %>% filter(Silver_Date %in% Amazon_dt$Amazon_Date)
nrow(Silver_dt)
# View(head(Silver_dt))
##### end Silver modifications #####
# Amazon_dt$Amazon_Date[length(Amazon_dt$Amazon_Date)]
# Boeing_dt$Boeing_Date[length(Boeing_dt$Boeing_Date)]
# Cattle_dt$Cattle_Date[length(Cattle_dt$Cattle_Date)]
# Cocoa_dt$Cocoa_Date[length(Cocoa_dt$Cocoa_Date)]
# Coffee_dt$Coffee_Date[length(Coffee_dt$Coffee_Date)]
# Crude_dt$Crude_Date[length(Crude_dt$Crude_Date)]
# Gold_dt$Gold_Date[length(Gold_dt$Gold_Date)]
# NYSE_dt$NYSE_Date[length(NYSE_dt$NYSE_Date)]
# Silver_dt$Silver_Date[length(Silver_dt$Silver_Date)]
# sp500_dt$sp500_Date[length(sp500_dt$sp500_Date)]
# Sugar_dt$Sugar_Date[length(Sugar_dt$Sugar_Date)]
# Wheat_dt$Wheat_Date[length(Wheat_dt$Wheat_Date)]
##### Cattle modifications #####
# nrow(Cattle_dt)
Cattle_dt <- Cattle_dt %>% filter(Cattle_Date %in% Amazon_dt$Amazon_Date)
# nrow(Cattle_dt)
# View(head(Cattle_dt))
##### end Cattle modifications #####
##### Amazon modifications #####
# nrow(Amazon_dt)
Amazon_dt <- Amazon_dt %>% filter(Amazon_Date %in% Cattle_dt$Cattle_Date)
# nrow(Amazon_dt)
# View(head(Amazon_dt))
##### end Amazon modifications #####
##### Boeing modifications #####
# nrow(Boeing_dt)
Boeing_dt <- Boeing_dt %>% filter(Boeing_Date %in% Cattle_dt$Cattle_Date)
# nrow(Boeing_dt)
# View(head(Boeing_dt))
##### end Boeing modifications #####
##### Cocoa modifications #####
# nrow(Cocoa_dt)
Cocoa_dt <- Cocoa_dt %>% filter(Cocoa_Date %in% Amazon_dt$Amazon_Date)
# nrow(Cocoa_dt)
# View(head(Cocoa_dt))
##### end Cocoa modifications #####
##### Coffee modifications #####
# nrow(Coffee_dt)
Coffee_dt <- Coffee_dt %>% filter(Coffee_Date %in% Amazon_dt$Amazon_Date)
# nrow(Coffee_dt)
# View(head(Coffee_dt))
##### end Coffee modifications #####
##### Crude modifications #####
# nrow(Crude_dt)
Crude_dt <- Crude_dt %>% filter(Crude_Date %in% Amazon_dt$Amazon_Date)
# nrow(Crude_dt)
# View(head(Crude_dt))
##### end Crude modifications #####
##### Gold modifications #####
# nrow(Gold_dt)
Gold_dt <- Gold_dt %>% filter(Gold_Date %in% Amazon_dt$Amazon_Date)
# nrow(Gold_dt)
# View(head(Gold_dt))
##### end Gold modifications #####
##### NYSE modifications #####
# nrow(NYSE_dt)
NYSE_dt <- NYSE_dt %>% filter(NYSE_Date %in% Amazon_dt$Amazon_Date)
# nrow(NYSE_dt)
# View(head(NYSE_dt))
##### end NYSE modifications #####
##### Silver modifications #####
# nrow(Silver_dt)
Silver_dt <- Silver_dt %>% filter(Silver_Date %in% Amazon_dt$Amazon_Date)
# nrow(Silver_dt)
# View(head(Silver_dt))
##### end Silver modifications #####
##### sp500 modifications #####
# nrow(sp500_dt)
sp500_dt <- sp500_dt %>% filter(sp500_Date %in% Amazon_dt$Amazon_Date)
# nrow(sp500_dt)
# View(head(sp500_dt))
##### end sp500 modifications #####
##### Sugar modifications #####
# nrow(Sugar_dt)
Sugar_dt <- Sugar_dt %>% filter(Sugar_Date %in% Amazon_dt$Amazon_Date)
# nrow(Sugar_dt)
# View(head(Sugar_dt))
##### end Sugar modifications #####
##### Wheat modifications #####
# nrow(Wheat_dt)
Wheat_dt <- Wheat_dt %>% filter(Wheat_Date %in% Amazon_dt$Amazon_Date)
# nrow(Wheat_dt)
# View(head(Wheat_dt))
##### end Wheat modifications #####
# nrow(Amazon_dt)
# nrow(Boeing_dt)
# nrow(Cattle_dt)
# nrow(Cocoa_dt)
# nrow(Coffee_dt)
# nrow(Crude_dt)
# nrow(Gold_dt)
# nrow(NYSE_dt)
# nrow(Silver_dt)
# nrow(sp500_dt)
# nrow(Sugar_dt)
# nrow(Wheat_dt)
##### EOF #####
paste("End of File")
##### EOF #####
amazon_full_scaled <- (Amazon_dt$Amazon_Open - mean(Amazon_dt$Amazon_Open)) / sd(Amazon_dt$Amazon_Open)
boeing_full_scaled <- (Boeing_dt$Boeing_Open - mean(Boeing_dt$Boeing_Open)) / sd(Boeing_dt$Boeing_Open)
cattle_full_scaled <- (Cattle_dt$Cattle_Open - mean(Cattle_dt$Cattle_Open)) / sd(Cattle_dt$Cattle_Open)
cocoa_full_scaled <- (Cocoa_dt$Cocoa_Open - mean(Cocoa_dt$Cocoa_Open)) / sd(Cocoa_dt$Cocoa_Open)
coffee_full_scaled <- (Coffee_dt$Coffee_Open - mean(Coffee_dt$Coffee_Open)) / sd(Coffee_dt$Coffee_Open)
crude_full_scaled  <- (Crude_dt$Crude_Open - mean(Crude_dt$Crude_Open)) / sd(Crude_dt$Crude_Open)
gold_full_scaled   <- (Gold_dt$Gold_Open - mean(Gold_dt$Gold_Open)) / sd(Gold_dt$Gold_Open)
nyse_full_scaled <- (NYSE_dt$NYSE_Open - mean(NYSE_dt$NYSE_Open)) / sd(NYSE_dt$NYSE_Open)
silver_full_scaled <- (Silver_dt$Silver_Open - mean(Silver_dt$Silver_Open)) / sd(Silver_dt$Silver_Open)
sp500_full_scaled <- (sp500_dt$sp500_Open - mean(sp500_dt$sp500_Open)) / sd(sp500_dt$sp500_Open)
sugar_full_scaled <- (Sugar_dt$Sugar_Open - mean(Sugar_dt$Sugar_Open)) / sd(Sugar_dt$Sugar_Open)
wheat_full_scaled <- (Wheat_dt$Wheat_Open - mean(Wheat_dt$Wheat_Open)) / sd(Wheat_dt$Wheat_Open)
ggplot(data_test, aes(x = Gold_Date)) +
# geom_point(aes(y = amazon_full_scaled, colour = "Amazon")) +
# geom_point(aes(y = boeing_full_scaled, colour = "Boeing")) +
scale_x_date(date_labels = "%Y-%m-%d") +
theme(axis.text.x = element_text(angle = 30, hjust = 1)) +
geom_point(aes(y = crude_full_scaled, colour  = "Crude")) +
geom_point(aes(y = sugar_full_scaled, colour  = "Sugar")) +
geom_point(aes(y = wheat_full_scaled, colour = "Wheat")) +
geom_point(aes(y = silver_full_scaled, colour = "Silver")) +
labs(x = "Date", y = "Crude, Sugar, Wheat, Coffee, and Silver (Scaled)", colour = "Variables")
data_test <- cbind(Gold_dt[,1], amazon_full_scaled, boeing_full_scaled, cattle_full_scaled, cocoa_full_scaled, coffee_full_scaled,
crude_full_scaled, nyse_full_scaled, silver_full_scaled, sp500_full_scaled, sugar_full_scaled, wheat_full_scaled)
rm(data_test)
data_test <- cbind(Gold_dt[,1], amazon_full_scaled, boeing_full_scaled, cattle_full_scaled, cocoa_full_scaled, coffee_full_scaled,
crude_full_scaled, nyse_full_scaled, silver_full_scaled, sp500_full_scaled, sugar_full_scaled, wheat_full_scaled)
ggplot(data_test, aes(x = Gold_Date)) +
# geom_point(aes(y = amazon_full_scaled, colour = "Amazon")) +
# geom_point(aes(y = boeing_full_scaled, colour = "Boeing")) +
scale_x_date(date_labels = "%Y-%m-%d") +
theme(axis.text.x = element_text(angle = 30, hjust = 1)) +
geom_point(aes(y = crude_full_scaled, colour  = "Crude")) +
geom_point(aes(y = sugar_full_scaled, colour  = "Sugar")) +
geom_point(aes(y = wheat_full_scaled, colour = "Wheat")) +
geom_point(aes(y = silver_full_scaled, colour = "Silver")) +
labs(x = "Date", y = "Crude, Sugar, Wheat, Coffee, and Silver (Scaled)", colour = "Variables")
