COMP226: Assignment 2 Slides 1 of 2: The backtester framework Rahul Savani [email protected] Assignment 2 Continuous Assessment Number 2 (of 2) Weighting 15% Assignment Circulated Thu 31 March 2022 (week 9) Deadline 17:00 Fri 13th May 2022 (week 12) Submission Mode CodeGrade (via Canvas) Two handouts / sets of slides For this assignment, there are two handouts and two corresponding sets of slides with videos: 1. backtester.pdf and a2_slides_01.pdf (these slides) provide an an introduction to the backtester_2022 framework 2. a2.pdf and a2_slides_02.pdf describes the assignment, which asks you to implement and optimize a trading strategy within the backtester_2022 framework Please read the handouts carefully and in full Getting started — main.R Download backtester_2022.zip and unzip it Go to resulting backtester_2022 directory, open R, and set this directory as the working directoty (e.g. with setwd) Now you can run example strategies via main.R: source(‘main.R’) 1. It sources the framework and example strategy helper 2. Loads data and subsets it; loads an example strategy 3. Running a backtest, and plots the results Sourcing source(‘framework/data.R’) source(‘framework/backtester.R’) source(‘framework/processResults.R’) source(‘example_strategies.R’) Loading and subsetting data dataList <- getData(directory="EXAMPLE") > for (x in dataList) print(paste(class(x)[1],start(x),end(x))) [1] “xts 1970-01-02 1970-07-20” [1] “xts 1970-01-02 1970-07-20” [1] “xts 1970-01-02 1970-07-20” [1] “xts 1970-01-02 1970-07-20” [1] “xts 1970-01-02 1970-07-20” > head(dataList[[1]],n=1) Open High Low Close Volume 1970-01-02 1903.75 1917.75 1902.75 1915 39101 # just use first 200 days dataList <- lapply(dataList, function(x) x[1:200]) Example strategies example_strategies.R contains helper functions for: example_strategies <- c("fixed", "copycat", "rsi_contrarian", "bbands_trend_following", "bbands_contrarian", "bbands_holding_period") The helper functions are used in main.R as follows: # choose strategy from example_strategies strategy <- "fixed" # check that the choice is valid stopifnot(is_valid_example_strategy(strategy)) # load in strategy and params load_strategy(strategy) # fu
nction from example_strategies.R fixed.R getOrders <- function(store, newRowList, currentPos, info, params) { allzero <- rep(0,length(newRowList)) marketOrders <- allzero if (is.null(store)) { marketOrders <- params$sizes store <- 1 # not null } return(list(store=store,marketOrders=marketOrders, limitOrders1=allzero, limitPrices1=allzero, limitOrders2=allzero, limitPrices2=allzero)) } getOrders All strategies are implemented via getOrders, which always uses these arguments: getOrders <- function(store,newRowList,currentPos,info,params) store: contains all data you save from one period to the next newRowList: new day's data (a list of rows from the series) currentPos: the vector of current positions in each series info: not needed for this assignment params: list of parameters (facilitates parameter optimization) Running a backtest # Do backtest results <- backtest(dataList,getOrders,params,sMult) plotResults(dataList,results) cat("Profit:", results$aggProfit, 'n') Market and limit orders In the framework, trading decisions are made after the close of day k, and trades are executed on day k+1. Each day, the framework allows one market order for each series, and two limit orders for each series; these are returned from getOrders as follows: return(list(store=store,marketOrders=marketOrders, limitOrders1=limitOrders1, limitPrices1=limitPrices1, limitOrders2=limitOrders2, limitPrices2=limitPrices2)) We will not use limit orders for assignment 2 (leave limitOrders1, limitPrices1, limitOrders2, limitPrices2 as zero vectors) Market orders - example Market orders will be executed at the open on day k+1 (incurring slippage as described in slides 4.5) Their size (number of units to trade) and direction (buy/sell) are specified in the vector marketOrders of signed numbers, which is returned by getOrders. E.g. c(0,-5,0,1,0) represents a market order for 5 units short in series 2, and 1 unit long in series 4 Exercise Run the example strategies and play with their parameters The second set of slides describes the assignment, where you will implement and optimise the Triple Moving Average (TMA) momentum strategy from slides 4.7