Get ready-to-use code for profiling with the package profvis from here.
This version of cash_backtest
takes a long data frame of prices and weights by date, which is a very convenient format for data analysis. We can make such a data frame of randomly generated prices and weights:
library(tidyverse)
# function for generating prices from GBM process
gbm_sim <- function(nsim = 100, t = 25, mu = 0, sigma = 0.1, S0 = 100, dt = 1./365) {
# matrix of random draws - one for each day for each simulation
epsilon <- matrix(rnorm(t*nsim), ncol = nsim, nrow = t)
# get GBM paths
gbm <- exp((mu - sigma * sigma / 2) * dt + sigma * epsilon * sqrt(dt)) - 1
# convert to price paths
gbm <- apply(rbind(rep(S0, nsim), gbm + 1), 2, cumprod)
gbm
}
# generate prices and weights
years <- 20
universe <- 100
x <- 1
tickers <- vector()
repeat{
tickers[[x]] <- paste0(sample(LETTERS, 5, replace = TRUE), collapse = "")
x <- x + 1
if(x == universe + 1)
break
}
stopifnot(n_distinct(tickers) == universe)
date <- seq(as.numeric(as.Date("1980-01-01")), as.numeric(as.Date("1980-01-01"))+(years*365))
prices <- cbind(date, gbm_sim(nsim = universe, t = years*365, mu = 0.1, sigma = 0.1))
colnames(prices) <- c("date", tickers)
weights <- cbind(date, rbind(rep(0, universe), matrix(rnorm(years*365*universe), nrow = years*365)))
colnames(weights) <- c("date", tickers)
backtest_df_long <- prices %>%
as.data.frame() %>%
mutate(date = as.Date(date, origin ="1970-01-01")) %>%
pivot_longer(-date, names_to = "ticker", values_to = "price") %>%
left_join(
weights %>%
as.data.frame() %>%
mutate(date = as.Date(date, origin ="1970-01-01")) %>%
pivot_longer(-date, names_to = "ticker", values_to = "theo_weight"),
by = c("date", "ticker")
)
head(backtest_df_long)
#> # A tibble: 6 x 4
#> date ticker price theo_weight
#> <date> <chr> <dbl> <dbl>
#> 1 1980-01-01 TVEZI 100 0
#> 2 1980-01-01 XIERO 100 0
#> 3 1980-01-01 XGYMU 100 0
#> 4 1980-01-01 PMVPF 100 0
#> 5 1980-01-01 KNCIP 100 0
#> 6 1980-01-01 JEBOY 100 0
backtest_df_long
has prices and weights for 100 tickers over 7301 days:
backtest_df_long %>%
summarise(
num_days = n_distinct(date),
num_tickers = n_distinct(ticker)
)
#> # A tibble: 1 x 2
#> num_days num_tickers
#> <int> <int>
#> 1 7301 100
To get some insight into how quickly the backtest runs and where the bottlenecks are, profvis
is your friend:
library(profvis)
profvis({cash_backtest_original(backtest_df_long)}, interval = 0.01)
(To get deeper insight, you can extract the function logic as a series of expressions and pass these directly to profvis
– but this shortcut is fine for our purposes)
profvis
tells us that cash_backtest_original
took about 7.5 seconds to run and that most of the time was spent messing around with data frames:
Stay tuned for the next installment in which Kris will discuss how we can benefit from switching our data frames to matrixes.
Visit Robot Wealth to download the complete R script: https://robotwealth.com/optimising-the-rsims-package-for-fast-backtesting-in-r/.
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