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| svmComputeOneForecast = function( | |
| id, | |
| data, | |
| response, | |
| startPoints, | |
| endPoints, | |
| len, | |
| history=500, | |
| trace=FALSE, | |
| kernel="radial", | |
| gamma=10^(-5:-1), | |
| cost=10^(0:2), | |
| sampling="cross", | |
| seed=1234, | |
| featureSelection=c("add", "prune", "all"), | |
| cross=10) | |
| { | |
| # Determine the forecast length | |
| startIndex = startPoints[id] | |
| endIndex = endPoints[id] | |
| forecastLength = endIndex - startIndex + 1 | |
| # A row in the data is responsible for the corresponding value in the | |
| # response. Thus, to forecast day X, we train the model on the previous | |
| # *history* days, and then use the features for day X to forecast. | |
| xtsData = data[index(data)[(startIndex-history):(startIndex-1)]] | |
| xtsResponse = response[index(response)[(startIndex-history):(startIndex-1)]] | |
| # Convert the input data and response to a matrix and a vector, respectively | |
| xx = as.matrix( coredata( xtsData ) ) | |
| yy = as.vector( coredata( xtsResponse ) ) | |
| # We need to set the seed to have reprodcible results | |
| set.seed( seed ) | |
| if(featureSelection[1] == "add") { | |
| # We add the features one by one, until we cannot improve the error | |
| best = NULL | |
| bestPerf = 1e9 | |
| # Maintained sorted, the content are the column indexes in the original matrix | |
| features = c() | |
| availableFeatures = seq(1,ncol(xx)) | |
| # Use greedy approach to add features | |
| repeat { | |
| bestColIdToAdd = 0L | |
| # print( features ) | |
| for(colId in 1:length(availableFeatures)) { | |
| # Get the matrix for the current tunning and tune | |
| zz = xx[,sort(c(features, availableFeatures[colId]))] | |
| # print(paste(sep="", "trying adding feature ", availableFeatures[colId])) | |
| newSvm = tune( svm, | |
| train.x=zz, | |
| train.y=yy, | |
| ranges=list( gamma=gamma, cost=cost ), | |
| tunecontrol=tune.control( sampling=sampling, cross=cross ), | |
| kernel=kernel ) | |
| # Check the performance improvement | |
| newPerf = round(newSvm$best.performance, 8) | |
| # print( paste( sep="", "new performance=", newPerf ) ) | |
| if(newPerf < bestPerf) { | |
| # print( paste( sep="", "old performance=", bestPerf, ", new performance=", newPerf ) ) | |
| best = newSvm | |
| bestPerf = newPerf | |
| bestColIdToAdd = colId | |
| } | |
| } | |
| if(bestColIdToAdd > 0) { | |
| # print( paste( sep="", "improvement, adding feature ", availableFeatures[bestColIdToAdd] ) ) | |
| # Found an improvement, update the features | |
| features = sort(c(features, availableFeatures[bestColIdToAdd])) | |
| availableFeatures = availableFeatures[-bestColIdToAdd] | |
| # Exit if no features left | |
| if(length(availableFeatures) == 0) break | |
| } else { | |
| # No improvements, done | |
| break | |
| } | |
| } | |
| } else { | |
| # Train the SVM | |
| # ss = svm( x=xx, y=yy, kernel=kernel, gamma=gamma[1], cost=cost[1] ) | |
| best = tune( svm, | |
| train.x=xx, | |
| train.y=yy, | |
| ranges=list( gamma=gamma, cost=cost ), | |
| tunecontrol=tune.control( sampling=sampling, cross=cross ), | |
| kernel=kernel ) | |
| # print( "gotBest" ) | |
| # print( paste( sep="", "performance=", round( best$best.performance, 6 ) ) ) | |
| # An array to keep track of the original participating features (by index) | |
| features = seq(1,ncol(xx)) | |
| # print( length( features ) ) | |
| # Use greedy approach to prune features | |
| if(featureSelection[1] == "prune") { | |
| repeat { | |
| bestColIdToRemove = 0L | |
| # print( features ) | |
| for(colId in 1:ncol(xx)) { | |
| # Remove column colId | |
| zz = xx[,-colId] | |
| # print( paste( sep="", "trying without feature ", colId ) ) | |
| # Tune with the reduced number of columns | |
| newBest = tune( svm, | |
| train.x=zz, | |
| train.y=yy, | |
| ranges=list( gamma=gamma, cost=cost ), | |
| tunecontrol=tune.control( sampling=sampling, cross=cross ), | |
| kernel=kernel ) | |
| # print( paste( sep="", "new performance=", round( newBest$best.performance, 6 ) ) ) | |
| if(round( newBest$best.performance, 6 ) < round( best$best.performance, 6)) { | |
| best = newBest | |
| bestColIdToRemove = colId | |
| # print( paste( sep="", "old performance=", round( best$best.performance, 6 ), | |
| # ", new performance=", round( newBest$best.performance, 6 ) ) ) | |
| } | |
| } | |
| if(bestColIdToRemove > 0) { | |
| # Found an improvement | |
| xx = xx[,-bestColIdToRemove] | |
| features = features[-bestColIdToRemove] | |
| # print( paste( sep="", "improvement, removed feature ", bestColIdToRemove ) ) | |
| # Break if there is only a single feature left | |
| if(length(features) == 1) break | |
| } else { | |
| # No improvements, done | |
| break | |
| } | |
| } | |
| } | |
| } | |
| # print( paste( sep="", "final features: (", paste( sep=",", collapse=",", features ), ")" ) ) | |
| # Predict using the SVM, use only the remaining features | |
| xtsNewData = data[index(data)[startIndex:endIndex]] | |
| newData = as.matrix( coredata( xtsNewData[,features] ) ) | |
| fore = predict( best$best.model, newData ) | |
| if( trace ) { | |
| str = paste( sep="", | |
| "\n", index(response)[startIndex], "\n", | |
| "=======================\n", | |
| " from: ", head(index(xtsResponse),1), | |
| ", to: ", tail(index(xtsResponse),1), | |
| ", length: ", length(index(xtsResponse)), | |
| "\n new data: from: ", head(index(xtsNewData), 1), | |
| ", to: ", tail(index(xtsNewData), 1), | |
| ", length: ", NROW(xtsNewData), | |
| "\n forecast length: ", forecastLength, | |
| "\n best model performance: ", round( best$best.performance, 6 ), | |
| "\n best model features: (", paste( collapse=",", features), ")", | |
| "\n best model gamma: ", best$best.model$gamma, | |
| "\n best model cost: ", best$best.model$cost, | |
| "\n forecasts: ", | |
| paste( collapse=", ", round( fore, 6 ) ), | |
| "\n" ) | |
| cat( sep="", str ) | |
| } | |
| return( list( index=startIndex, | |
| forecasts=fore, | |
| performance=best$best.performance, | |
| features=features, | |
| gamma=best$best.model$gamma, | |
| cost=best$best.model$cost ) ) | |
| } | |
| svmComputeForecasts = function( | |
| data, | |
| response, | |
| history=500, | |
| modelPeriod="days", | |
| modelPeriodMultiple=1, | |
| trace=TRUE, | |
| startDate, | |
| endDate, | |
| kernel="radial", | |
| gamma=10^(-5:-1), | |
| cost=10^(0:2), | |
| sampling="cross", | |
| cross=10, | |
| featureSelection=c("add", "prune", "all"), | |
| cores) | |
| { | |
| require( e1071 ) | |
| stopifnot( NROW( data ) == NROW( response ) ) | |
| len = NROW( response ) | |
| # Determine the starting index | |
| if( !missing( startDate ) ) | |
| { | |
| startIndex = max( len - NROW( index( data[paste( sep="", startDate, "/" )] ) ) + 1, | |
| history + 2 ) | |
| } | |
| else | |
| { | |
| startIndex = history + 2 | |
| } | |
| # Determine the ending index | |
| if( missing( endDate ) ) | |
| { | |
| lastIndex = len | |
| } | |
| else | |
| { | |
| lastIndex = NROW( index( data[paste( sep="", "/", endDate )] ) ) | |
| } | |
| if( startIndex > lastIndex ) | |
| { | |
| return( NULL ) | |
| } | |
| modelPeriod = tolower( modelPeriod[1] ) | |
| forecasts = rep( NA, len ) | |
| gammas = rep( NA, len ) | |
| costs = rep( NA, len ) | |
| performances = rep( NA, len ) | |
| features = rep( "", len ) | |
| # Get the interesting indexes | |
| periods = index(data)[startIndex:lastIndex] | |
| # Compute the end points for each period (day, week, month, etc) | |
| endPoints = endpoints( periods, modelPeriod, modelPeriodMultiple ) | |
| # Compute the starting points of each period, relative to the *data* index | |
| startPoints = endPoints + startIndex | |
| # Remove the last start point - it's outside | |
| length(startPoints) = length(startPoints) - 1 | |
| # Make the end points relative to the *data* index | |
| endPoints = endPoints + startIndex - 1 | |
| # Remove the first end point - it's always zero | |
| endPoints = tail( endPoints, -1 ) | |
| stopifnot( length( endPoints ) == length( startPoints ) ) | |
| if( missing( cores ) ) { | |
| cores = 1 | |
| } | |
| res = mclapply( seq(1,length(startPoints)), | |
| svmComputeOneForecast, | |
| data=data, | |
| response=response, | |
| startPoints=startPoints, | |
| endPoints=endPoints, | |
| len=len, | |
| history=history, | |
| trace=TRUE, | |
| kernel=kernel, | |
| gamma=gamma, | |
| cost=cost, | |
| featureSelection=featureSelection, | |
| mc.cores=cores ) | |
| for( ll in res ) | |
| { | |
| # Prepare the indexes | |
| ii = ll[["index"]] | |
| jj = ii + NROW( ll[["forecasts"]] ) - 1 | |
| # Copy the output | |
| forecasts[ii:jj] = ll[["forecasts"]] | |
| gammas[ii:jj] = ll[["gamma"]] | |
| costs[ii:jj] = ll[["cost"]] | |
| performances[ii:jj] = ll[["performance"]] | |
| # Encode the participating features as a bit mask stored in a single | |
| # integer. This representation limits us to max 32 features. | |
| features[ii:jj] = sum( 2^( ll[["features"]] - 1 ) ) | |
| } | |
| sigUp = ifelse( forecasts >= 0, 1, 0 ) | |
| sigUp[is.na( sigUp )] = 0 | |
| sigDown = ifelse( forecasts < 0, -1, 0 ) | |
| sigDown[is.na( sigDown)] = 0 | |
| # forecasts[is.na( forecasts )] = 0 | |
| sig = sigUp + sigDown | |
| res = merge( reclass( sig, response ), | |
| reclass( sigUp, response ), | |
| reclass( sigDown, response ), | |
| na.trim( reclass( forecasts, response ) ), | |
| reclass( performances, response ), | |
| reclass( gammas, response ), | |
| reclass( costs, response ), | |
| reclass( features, response ), | |
| all=F ) | |
| colnames( res ) = c( "Indicator", "Up", "Down", "Forecasts", "Performance", "Gamma", "Cost", "Features" ) | |
| return( res ) | |
| } | |
| svmFeatures = function(series) | |
| { | |
| require(PerformanceAnalytics) | |
| close = Cl(series) | |
| rets = na.trim(ROC(close, type="discrete")) | |
| # 1-day, 2-day, 3-day, 5-day, 10-day, 20-day and 50-day returns | |
| res = merge(na.trim(lag(rets, 1)), | |
| na.trim(lag(ROC(close, type="discrete", n=2), 1)), | |
| na.trim(lag(ROC(close, type="discrete", n=3), 1)), | |
| na.trim(lag(ROC(close, type="discrete", n=5), 1)), | |
| na.trim(lag(ROC(close, type="discrete", n=10), 1)), | |
| na.trim(lag(ROC(close, type="discrete", n=20), 1)), | |
| na.trim(lag(ROC(close, type="discrete", n=50), 1)), | |
| all=FALSE) | |
| # Add mean, median, sd, mad, skew and kurtosis | |
| res = merge(res, | |
| xts(na.trim(lag(rollmean(rets, k=21, align="right"),1))), | |
| xts(na.trim(lag(rollmedian(rets, k=21, align="right"),1))), | |
| xts(na.trim(lag(rollapply(rets, width=21, align="right", FUN=sd),1))), | |
| xts(na.trim(lag(rollapply(rets, width=21, align="right", FUN=mad),1))), | |
| xts(na.trim(lag(rollapply(rets, width=21, align="right", FUN=skewness),1))), | |
| xts(na.trim(lag(rollapply(rets, width=21, align="right", FUN=kurtosis),1))), | |
| all=FALSE) | |
| # Add volume with a lag of two | |
| res = merge(res, xts(na.trim(lag(Vo(series),2))), all=FALSE) | |
| colnames(res) = c("ROC.1", "ROC.2", "ROC.3", "ROC.5", "ROC.10", "ROC.20", "ROC.50", | |
| "MEAN", "MEDIAN", "SD", "MAD", "SKEW", "KURTOSIS", | |
| "VOLUME") | |
| return(res) | |
| } |
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