This is a follow on from: http://gekkoquant.com/2012/08/29/parameter-optimisation-backtesting-part1/

The code presented here will aim to optimise a strategy based upon the simple moving average indicator. The strategy will go Long when moving average A > moving average B. The optimisation is to determine what period to make each of the moving averages A & B.

Please note that this isn’t intended to be a good strategy, it is merely here to give an example of how to optimise a parameter.

Onto the code:

**Functions**

**TradingStrategy**this function implements the trading logic and calculates the returns**RunIterativeStrategy**this function iterates through possible parameter combinations and calls TradingStrategy for each new parameter set**CalculatePerformanceMetric**takes in a table of returns (from RunIterativeStrategy) and runs a function/metric over each set of returns.**PerformanceTable**calls CalculatePerformanceMetric for lots of different metric and compiles the results into a table**OrderPerformanceTable**lets us order the performance table by a given metric, ie order by highest sharpe ratio**SelectTopNStrategies**selects the best N strategies for a specified performance metric (charts.PerformanceSummary can only plot ~20 strategies, hence this function to select a sample)**FindOptimumStrategy**does what it says on the tin

^{?}View Code RSPLUS

library("quantmod") library("PerformanceAnalytics") nameOfStrategy <- "GSPC Moving Average Strategy" #Specify dates for downloading data, training models and running simulation trainingStartDate = as.Date("2000-01-01") trainingEndDate = as.Date("2010-01-01") outofSampleStartDate = as.Date("2010-01-02") #Download the data symbolData <- new.env() #Make a new environment for quantmod to store data in getSymbols("^GSPC", env = symbolData, src = "yahoo", from = trainingStartDate) trainingData <- window(symbolData$GSPC, start = trainingStartDate, end = trainingEndDate) testData <- window(symbolData$GSPC, start = outofSampleStartDate) indexReturns <- Delt(Cl(window(symbolData$GSPC, start = outofSampleStartDate))) colnames(indexReturns) <- "GSPC Buy&Hold" TradingStrategy <- function(mktdata,mavga_period,mavgb_period){ #This is where we define the trading strategy #Check moving averages at start of the day and use as the direciton signal #Enter trade at the start of the day and exit at the close #Lets print the name of whats running runName <- paste("MAVGa",mavga_period,".b",mavgb_period,sep="") print(paste("Running Strategy: ",runName)) #Calculate the Open Close return returns <- (Cl(mktdata)/Op(mktdata))-1 #Calculate the moving averages mavga <- SMA(Op(mktdata),n=mavga_period) mavgb <- SMA(Op(mktdata),n=mavgb_period) signal <- mavga / mavgb #If mavga > mavgb go long signal <- apply(signal,1,function (x) { if(is.na(x)){ return (0) } else { if(x>1){return (1)} else {return (-1)}}}) tradingreturns <- signal * returns colnames(tradingreturns) <- runName return (tradingreturns) } RunIterativeStrategy <- function(mktdata){ #This function will run the TradingStrategy #It will iterate over a given set of input variables #In this case we try lots of different periods for the moving average firstRun <- TRUE for(a in 1:10) { for(b in 1:10) { runResult <- TradingStrategy(mktdata,a,b) if(firstRun){ firstRun <- FALSE results <- runResult } else { results <- cbind(results,runResult) } } } return(results) } CalculatePerformanceMetric <- function(returns,metric){ #Get given some returns in columns #Apply the function metric to the data print (paste("Calculating Performance Metric:",metric)) metricFunction <- match.fun(metric) metricData <- as.matrix(metricFunction(returns)) #Some functions return the data the wrong way round #Hence cant label columns to need to check and transpose it if(nrow(metricData) == 1){ metricData <- t(metricData) } colnames(metricData) <- metric return (metricData) } PerformanceTable <- function(returns){ pMetric <- CalculatePerformanceMetric(returns,"colSums") pMetric <- cbind(pMetric,CalculatePerformanceMetric(returns,"SharpeRatio.annualized")) pMetric <- cbind(pMetric,CalculatePerformanceMetric(returns,"maxDrawdown")) colnames(pMetric) <- c("Profit","SharpeRatio","MaxDrawDown") print("Performance Table") print(pMetric) return (pMetric) } OrderPerformanceTable <- function(performanceTable,metric){ return (performanceTable[order(performanceTable[,metric],decreasing=TRUE),]) } SelectTopNStrategies <- function(returns,performanceTable,metric,n){ #Metric is the name of the function to apply to the column to select the Top N #n is the number of strategies to select pTab <- OrderPerformanceTable(performanceTable,metric) if(n > ncol(returns)){ n <- ncol(returns) } strategyNames <- rownames(pTab)[1:n] topNMetrics <- returns[,strategyNames] return (topNMetrics) } FindOptimumStrategy <- function(trainingData){ #Optimise the strategy trainingReturns <- RunIterativeStrategy(trainingData) pTab <- PerformanceTable(trainingReturns) toptrainingReturns <- SelectTopNStrategies(trainingReturns,pTab,"SharpeRatio",5) charts.PerformanceSummary(toptrainingReturns,main=paste(nameOfStrategy,"- Training"),geometric=FALSE) return (pTab) } pTab <- FindOptimumStrategy(trainingData) #pTab is the performance table of the various parameters tested #Test out of sample dev.new() #Manually specify the parameter that we want to trade here, just because a strategy is at the top of #pTab it might not be good (maybe due to overfit) outOfSampleReturns <- TradingStrategy(testData,mavga_period=9,mavgb_period=6) finalReturns <- cbind(outOfSampleReturns,indexReturns) charts.PerformanceSummary(finalReturns,main=paste(nameOfStrategy,"- Out of Sample"),geometric=FALSE) |