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This document contains code to backtest a moving average trading strategy on S&P 500 index data from 2000-2010. It defines a TradingStrategy function that calculates signals based on short and long moving averages, and returns the daily profits/losses of following those signals. It then runs the TradingStrategy over a range of moving average periods, calculates performance metrics for each, and selects the top 5 strategies based on Sharpe ratio. The best strategy from the training period is then tested out of sample on new data to see how it performs versus a buy and hold approach.
![R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
[R.app GUI 1.52 (6188) x86_64-apple-darwin9.8.0]
[History restored from /Users/ananthraj/.Rapp.history]
> 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](https://image.slidesharecdn.com/rconsole-121219092605-phpapp02/85/R-console-1-320.jpg)
![{ 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),])](https://image.slidesharecdn.com/rconsole-121219092605-phpapp02/85/R-console-2-320.jpg)
![}
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)](https://image.slidesharecdn.com/rconsole-121219092605-phpapp02/85/R-console-3-320.jpg)
R console
- 1. R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. [R.app GUI 1.52 (6188) x86_64-apple-darwin9.8.0] [History restored from /Users/ananthraj/.Rapp.history] > 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
- 2. { 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),])
- 3. } 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)