# Parameter Optimisation & Backtesting – Part 2

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
Note that when performing the out of sample test, you will need to manual specify the parameter set that you wish to use.
?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)```

## 9 thoughts on “Parameter Optimisation & Backtesting – Part 2”

1. Hello from Poland!
Excellent job. Your code is not only very well commented (which is not common) it’s also WELL DESCRIBED.
Few months ago I dropped R but now I comming back to it.

Many thanks for sharing your work.

Tomasz

2. michal on said:

great test.
could You kindly help me to run this with Ducascopy’s historical data?

3. Husvar on said:

Hi,
Great example. Just one question, why do you sum returns (using colSums)? These are simple returns should you be multiplying them to account for compounding? Or am I missing something? Similarly should not you being using geometric chaining in the PerformanceAnalytics?

Thanks and keep up the good work

• GekkoQuant on said:

Hi Husvar,

Just quickly looking at the comments in my code it says that it goes long on the open and closes the position at the close.The choice of using arithmetic or geometric returns is down to your personal preference. Some people like to put their winnings into their next trade, others dont.

• woodoo on said:

hi!
I have the same question. If you use geometric=FALSE thats mean you need to use log(Cl(price)/Op(price)). Or Im wrong? Can you give your explain, how to use geometric/arithmetic and log/absolute returns.

4. Nic on said:

Sorry for my ignorance, but how do I interpret the “Out of Sample” graphic? What does “Buy&Hold” mean and what the direction (+0 / -0) mean right there? Thanks!

• Nic on said:

Also, I question why your SMA strategy applies to the “Open” (Op) price… is that a good reason to repeat in order strategies? (CCI, MACD,etc)

5. Daniel on said:

Very good one….!!! Code is very clear!!