quantra-go-algo’s gists

quantra-go-algo / plot_strategies_cum_returns.R

Created August 28, 2024 20:37

	# Plot the both buy-and-hold and the strategy cumulative returns
	ggplot(data=df_forecasts, aes(x = date)) +
	geom_line(aes(y = var_stra_cum_returns, color="VAR")) +
	geom_line(aes(y = tvp_var_sv_stra_cum_returns, color="TVP-VAR-SV")) +
	geom_line(aes(y = bnh_cum_returns,color="B&H")) +
	geom_line(aes(y = stra_improved_cum_returns, color="Improved TVP-VAR-SV")) +
	ggtitle("Buy and Hold and Strategies' Cumulative Returns") + xlab("Date") + ylab("Returns") +
	theme(plot.title = element_text(hjust = 0.5, size=25), legend.position="bottom", legend.text = element_text(size=20)) +
	scale_x_date(date_labels = "%b %y") +
	theme(

quantra-go-algo / create_strategies_returns.R

Created August 28, 2024 20:35

	# Create the VAR-based equally-weighted portfolio returns
	df_forecasts$var_stra_returns <- rowMeans((df_forecasts[,match(tickers,colnames(df_forecasts))] *
	df_forecasts[,match(ticker_var_forecasts,colnames(df_forecasts))]),
	na.rm=TRUE)

	# Set the NaN values of the strategy returns to zero
	df_forecasts$var_stra_returns[is.na(df_forecasts$var_stra_returns)] = 0.0

	# Create the strategy cumulative returns
	df_forecasts$var_stra_cum_returns <- exp(cumsum(df_forecasts$var_stra_returns))

quantra-go-algo / tvp_var_sv_signal_creation_loop.R

Created August 28, 2024 20:30

	print(paste0(strrep('=',50)))
	print(paste0(strrep('=',50)))
	print(paste0(strrep('=',50)))
	print(paste0('Estimation of TVP-VAR-SV forecasts'))

	if (length(initial_iloc_to_forecast<nrow(df_forecasts))!=0) {
	# The for loop to estimate the model each day
	for (i in initial_iloc_to_forecast:nrow(df_forecasts)) {

	# Set the current iteration date

quantra-go-algo / var_signal_creation_loop.R

Created August 28, 2024 20:26

	print(paste0(strrep('=',50)))
	print(paste0(strrep('=',50)))
	print(paste0(strrep('=',50)))
	print(paste0('Estimation of basic-VAR forecasts'))

	# Check if VAR forecasts have already been estimated
	if (length(as.numeric(rownames(tail(subset(df_forecasts, trade_done == 1) ,1))))==0) {

	print(paste0(strrep('=',50)))
	print(paste0(strrep('=',50)))

quantra-go-algo / create_forecasts_dataframe.R

Created August 28, 2024 20:20

	# Group the data dates by year and month
	options(dplyr.summarise.inform = FALSE)
	dates <- var_data %>%
	mutate(month = format(date, "%m"), year = format(date, "%Y")) %>%
	group_by(year, month) %>% summarise(first_dates = first(date))

	# Get the first date of Oct-2021
	initial_date = subset(dates, (dates$year=='2019') & (dates$month=='01'))$first_dates

	# Import the Excel file in case it exists

quantra-go-algo / open_xlsx_func.R

Created August 28, 2024 20:17

	# Function to open an XLSX file with error handling
	open_xlsx <- function(file_path) {
	# Output the Excel file in case it exists
	result <- tryCatch({
	# Read the Excel file
	data <- read.xlsx(file_path)
	# Set the date column as datetime type
	data$date <- as.Date(data$date, origin = "1899-12-30")

	# Return the data

quantra-go-algo / estimate_model_and_forecast_return.R

Created August 28, 2024 19:54

	# Set the seed to estimate the TVP-VAR-SV model
	set.seed(2024)

	# Estimate the TVP-VAR-SV model
	bv <- bvar.sv.tvp(tvp_var_data, tau= 250, nf=1, nrep = 300, nburn=20)

	# Obtain the forecasts of the model based on the mean of the posterior-distribution draws
	forecast_ys <- rowMeans(bv$fc.ydraws[1:7,1,])

quantra-go-algo / get_data_and_compute_log_returns.R

Created August 28, 2024 19:48

	# Set tickers
	tickers <- c('MSFT', 'AAPL', 'TSLA', 'NFLX', 'META', 'AMZN','GOOGL')

	# Set start and end dates
	start = "1990-01-01"
	end = "2024-08-01"

	df <- new.env()

	# Import the data

quantra-go-algo / import_packages.R

Created August 28, 2024 19:46

quantra-go-algo / install_packages.R

Created August 28, 2024 19:43

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