Created
November 15, 2017 22:48
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| # ============= | |
| # === Setup === | |
| # ============= | |
| # install packages | |
| library(devtools) | |
| install_github("azure/razurebatch") | |
| install_github("azure/doazureparallel") | |
| # import the doAzureParallel library and its dependencies | |
| library(doAzureParallel) | |
| # set your credentials | |
| setCredentials("credentials.json") | |
| # Create your cluster if not exist | |
| cluster <- makeCluster("cluster.json") | |
| # register your parallel backend | |
| registerDoAzureParallel(cluster) | |
| # check that your workers are up | |
| getDoParWorkers() | |
| # ====================================== | |
| # === Monte Carlo Pricing Simulation === | |
| # ====================================== | |
| # set the parameters for the monte carlo simulation | |
| mean_change = 1.001 | |
| volatility = 0.01 | |
| opening_price = 100 | |
| # define a new function to simulate closing prices | |
| getClosingPrice <- function() { | |
| days <- 1825 # ~ 5 years | |
| movement <- rnorm(days, mean=mean_change, sd=volatility) | |
| path <- cumprod(c(opening_price, movement)) | |
| closingPrice <- path[days] | |
| return(closingPrice) | |
| } | |
| start_s <- Sys.time() | |
| # Run 10,000 simulations in series | |
| closingPrices_s <- foreach(i = 1:10, .combine='c') %do% { | |
| replicate(1000, getClosingPrice()) | |
| } | |
| end_s <- Sys.time() | |
| # plot the 50 closing prices in a histogram to show the distribution of outcomes | |
| hist(closingPrices_s) | |
| # Estimate runtime for 10 million | |
| difftime(end_s, start_s) | |
| 1000 * difftime(end_s, start_s, unit = "min") | |
| # Run 10 million simulations with doAzureParallel | |
| # We will run 100 iterations where each iteration executes 100000 simulations | |
| opt <- list(chunkSize = 2) # optimizie runtime | |
| start_p <- Sys.time() | |
| closingPrices_p <- foreach(i = 1:100, .combine='c', .options.azure = opt) %dopar% { | |
| replicate(100000, getClosingPrice()) | |
| } | |
| end_p <- Sys.time() | |
| difftime(end_p, start_p, unit = "min") | |
| # plot the 10 million closing prices in a histogram to show the distribution of outcomes | |
| hist(closingPrices_p) |
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