Support Vector Machine RSI System

Better late than never, as promised, the R code for the SVM system discussed in a previous post.

For the record this code is based on the random forest system created by Max Dama. I thought that it would make it easier for common reader to compare and evaluate. I also want to state that this isn’t anywhere close to optimal programming, I did that I long time ago and I was only starting with R at the time.

Here is the system :

SVMClassifModel = function(data, targets, returns, lookback = 252, ktype = "C-svc", crossvalid = 10, C = 10) {
# Construct a predictive model using support vector machine
# Input data must be lagged one period to avoid look-ahead bias
# Print predictions and confidence, accuracy, equity curves plot, and performance statistics v.s. benchmark

# Libraries
require(kernlab)
require(quantmod)

# Make sure targets is a factor (for classification)
targets = as.factor(targets)
data$targets = as.factor(data$targets)

# Generate indexes for backtest
idx = data.frame(targets = lookback:(nrow(data)-1))

# Isolate index to be used later
inx = index(returns[idx$targets])

# Prediction function to be used for backtesting
pred1pd = function(t) {
# Train model
model = trainSVM(data[(t-lookback):t, ], ktype, C, crossvalid)
# Prediction
pred = predict(model, data[t+1, -1], type="prob")
# Print for user inspection
print(pred)
}

# backtest by looping over the calendar previously generated
preds = sapply(idx$targets, pred1pd)
# print output
print(preds)
print(max.col(preds))
preds = data.frame(t(rbind(mle = max.col(t(preds)), preds)))
print(preds)
print(summaryStats((returns[idx$targets] * (preds$mle*2-3)), returns[idx$targets], comp = TRUE))

#Equity curves
equity = xts(cumprod((returns[idx$targets] * (preds$mle*2-3))+1), inx)
Benchmark = xts(cumprod(returns[idx$targets] + 1), inx)

# y axis values range
yrngMin = abs(min(equity, Benchmark))
yrngMax = abs(max(equity, Benchmark))

# Plot curves
chartSeries(equity, log.scale = TRUE, name='Equity Curves', yrange=c(yrngMin, yrngMax))
addTA(Benchmark, on=1, col='gold')
}

trainSVM = function(data, ktype, C, crossvalid) {
# Return a trained svm model
trainedmodel = ksvm(targets ~ ., data = data, type = ktype, kernel="rbfdot", kpar=list(sigma=0.05), C = C, prob.model = TRUE, cross = crossvalid)
}

featureGen = function(sym, returns) {
# Return a data frame to be used as input by the SVM system

# Targets vector
targets = coredata(returns)
targets[targets>=0] = 1
targets[targets<0] = -1
targets = as.factor(targets)

#RSIs
rsi2 = RSI(Cl(sym), 2 )
rsi3 = RSI(Cl(sym), 3 )
rsi4 = RSI(Cl(sym), 4 )
rsi5 = RSI(Cl(sym), 5 )
rsi6 = RSI(Cl(sym), 6 )
rsi7 = RSI(Cl(sym), 7 )
rsi8 = RSI(Cl(sym), 8 )
rsi9 = RSI(Cl(sym), 9 )
rsi10 = RSI(Cl(sym), 10 )
rsi11 = RSI(Cl(sym), 11 )
rsi12 = RSI(Cl(sym), 12 )
rsi13 = RSI(Cl(sym), 13 )
rsi14 = RSI(Cl(sym), 14 )
rsi15 = RSI(Cl(sym), 15 )
rsi16 = RSI(Cl(sym), 16 )
rsi17 = RSI(Cl(sym), 17 )
rsi18 = RSI(Cl(sym), 18 )
rsi19 = RSI(Cl(sym), 19 )
rsi20 = RSI(Cl(sym), 20 )
rsi21 = RSI(Cl(sym), 21 )
rsi22 = RSI(Cl(sym), 22 )
rsi23 = RSI(Cl(sym), 23 )
rsi24 = RSI(Cl(sym), 24 )
rsi25 = RSI(Cl(sym), 25 )
rsi26 = RSI(Cl(sym), 26 )
rsi27 = RSI(Cl(sym), 27 )
rsi28 = RSI(Cl(sym), 28 )
rsi29 = RSI(Cl(sym), 29 )
rsi30 = RSI(Cl(sym), 30 )

# lagged RSIs to correspond RSI with target period
rsi2 = Lag(rsi2, 1)
rsi3 = Lag(rsi3, 1)
rsi4 = Lag(rsi4, 1)
rsi5 = Lag(rsi5, 1)
rsi6 = Lag(rsi6, 1)
rsi7 = Lag(rsi7, 1)
rsi8 = Lag(rsi8, 1)
rsi9 = Lag(rsi9, 1)
rsi10 = Lag(rsi10, 1)
rsi11 = Lag(rsi11, 1)
rsi12 = Lag(rsi12, 1)
rsi13 = Lag(rsi13, 1)
rsi14 = Lag(rsi14, 1)
rsi15 = Lag(rsi15, 1)
rsi16 = Lag(rsi16, 1)
rsi17 = Lag(rsi17, 1)
rsi18 = Lag(rsi18, 1)
rsi19 = Lag(rsi19, 1)
rsi20 = Lag(rsi20, 1)
rsi21 = Lag(rsi21, 1)
rsi22 = Lag(rsi22, 1)
rsi23 = Lag(rsi23, 1)
rsi24 = Lag(rsi24, 1)
rsi25 = Lag(rsi25, 1)
rsi26 = Lag(rsi26, 1)
rsi27 = Lag(rsi27, 1)
rsi28 = Lag(rsi28, 1)
rsi29 = Lag(rsi29, 1)
rsi30 = Lag(rsi30, 1)

# Data frame
data = data.frame(targets, rsi2, rsi3, rsi4, rsi5, rsi6, rsi7, rsi8, rsi9, rsi10, rsi11, rsi12, rsi13, rsi14, rsi15, rsi16, rsi17, rsi18, rsi19, rsi20, rsi21, rsi22, rsi23, rsi24, rsi25, rsi26, rsi27, rsi28, rsi29, rsi30)
# names(data) = c("targets", "data")

# Results
return(data)
}

summaryStats = function(x, bmk, comp = FALSE) {
#Required library
require(PerformanceAnalytics)

#Compute stats of interest for strategy
cumRetx = Return.cumulative(x)
annRetx = Return.annualized(x, scale=252)
sharpex = SharpeRatio.annualized(x, scale=252)
winpctx = length(x[x > 0])/length(x[x != 0])
annSDx = sd.annualized(x, scale=252)
maxDDx = maxDrawdown(x)
avDDx = mean(Drawdowns(x))

if(comp == TRUE) {
#Compute stats of interest for benchmark
cumRetbmk = Return.cumulative(bmk)
annRetbmk = Return.annualized(bmk, scale=252)
sharpebmk = SharpeRatio.annualized(bmk, scale=252)
winpctbmk = length(bmk[bmk > 0])/length(bmk)
annSDbmk = sd.annualized(bmk, scale=252)
maxDDbmk = maxDrawdown(bmk)
avDDbmk = mean(Drawdowns(bmk))
#Return result vectors
Benchmark = c(cumRetbmk, annRetbmk, sharpebmk, winpctbmk, annSDbmk, maxDDbmk, avDDbmk)
Strategy = c(cumRetx, annRetx, sharpex, winpctx, annSDx, maxDDx, avDDx)
nms = c("Cumulative Return", "Annualized Return", "Annualized Sharpe Ratio", "Winning Percentage", "Annualized Volatility", "Maximum Drawdown", "Average Drawdown")
result = data.frame(Strategy, Benchmark, row.names = nms)
} else {
#Return result vectors
nms = c("Cumulative Return", "Annualized Return", "Annualized Sharpe Ratio", "Winning Percentage", "Annualized Volatility", "Maximum Drawdown", "Average Drawdown")
Strategy = c(cumRetx, annRetx, sharpex, winpctx, annSDx, maxDDx, avDDx)
result = data.frame(Strategy, row.names = nms)
}
return(result)
}

Here is the harness used to use the system. Don’t forget to change the first two line of the code and replace with your directory.

For example:
setwd(“C:\Users\John Doe\Documents”)
source(“SVM System”)

setwd("INPUT DIRECTORY")
source("NAME OF THE RSI SYSTEM FILE IN THE FOLDER")
require(quantmod)
require(PerformanceAnalytics)

# Load data with quantmod
getSymbols('SPY', from='2000-06-01')
returns = dailyReturn(Cl(SPY), type='log')

# Generate data frame of data and targets
data = featureGen(SPY, returns)
targets = coredata(returns)
targets[targets>=0] = 1
targets[targets<0] = -1
targets = as.factor(targets)

# Run the system
SVMClassifModel(data[30:nrow(data),], targets[30:length(targets)], returns, lookback = 252, ktype = "C-svc", crossvalid = 10, C = 60)

Lastly I would like to know if anyone has a better idea to share code. This is not very good way and I would like to improve it. I also welcome suggestions to make the code more efficient. I also want to make clear that I do not think that this is a good system and I know that it could be improved by adding predictors and all, it is only to give an example to follow-up on the post mentioned above.

QF

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18 responses to “Support Vector Machine RSI System

  1. While you have interesting articles, it’s really really hard to read them. The combination of black background and gray letters is very unreadable and my eyes are in pain. Would you consider setting up your site to have white background like 95% of other sites and make it much more pleasing to the eyes and readable?

    Thanks.

    • Hi brownbear,

      I looking for a more appropriate background, but I don’t want to take a theme already used by someone else is the quant finance blogosphere. I always keep an eye open for new themes on wordpress and will update the theme when I find an interesting one.

      Cheers,
      QF

  2. Here’s a more compact version of your featureGen() function. It is not more computationally efficient, but it produces the same output (except for column names) with 80% fewer lines.

    featureGen2 = function(sym, n=30, steps=1) {
    # Return a data frame to be used as input by the SVM system
    targets = as.factor(ifelse(dailyReturn(Cl(sym), type=’log’)>=0,1,-1))
    data = data.frame(targets)

    # lagged RSIs to correspond RSI with target period
    for(i in seq.int(2,n,steps)) {
    data[,paste("rsi",i,sep="")] = as.numeric(Lag(RSI(Cl(sym),i),1))
    }
    rownames(data) = index(sym)

    # Results
    return(data)
    }

    Regarding a better way to share code, I know of a few people who put their scripts in their dropbox account and link to them.

  3. Pingback: Neural Networks and Genetic Optimizers - Page 2

  4. I was just looking over your code here and was curious about this line:

    predict(model, data[t+1, -1], type=”prob”)

    Does t+1 imply that you’re using data from the future (i.e. look-ahead bias)?

    • Hey Wilson,

      Thank you for the comment. The short answer is no, essentially, you can think of the period t+1 as being today’s data fed into the model in order to get tomorrow’s prediction. In [t+1, -1] the -1 implies that the first column, which is the dependent variable is excluded from the data frame. Let me know if there is still confusion.

      Cheers,
      QF

  5. I vote for github. R is biased towards subversion, but git is the future. It’s basically a distributed svn model. It’s very convenient to share code this way because others can clone your code from their command line (internet connection assumed).

    Interesting code, by the way. One may reasonably object that multiple RSI values have predictive value, but this modeling framework can easily be modified with inputs to the network of one’s choosing.

    I like dropbox, but I’m starting the get a feeling that it’s a hobbyists hangout, and that feeling just won’t go away.

  6. Thanks for publishing this code. I have been using it as a tool to learn R and it has been challenging. I am puzzled by one thing I have run into; the solution does not seem to be unique or stable. I run this code and copy the solution, shut down R, and then open R and rerun the same identical code a few minutes later, I get different results. Each run is different, and the differences are substantial. I thought that SVM typically prvided a unique solution, am I mistaken? ANy thoughts on how to approach debugging this?

    Thanks
    BruceT

    • Hey Bruce,

      It is a very good question and I forgot to mention it in the post, I shall rectify that today. The SVM does not always have a unique solution and when it doesn’t we need to find another way to end our threshold; minimizing the Bayes error is often employed.

      Best,

      QF

  7. Hey QF – great post, great use of R. Noticed a tiny problem with the indexing on the graphs. It doesn’t alter the strategy returns, but shifts the x-axis graphs out by the 30 days you chop off the front of the data. Email me for a full explain if you’d like. Other than that – awesome!

    tFB

  8. I am interested in R and Machine Learning for finance. What would be your thoughts into using Price and Time as the data.

    Price would be 30 points with the first point being 0. Each point after that would be assign +- the difference from the first point. Sort of a pattern….

    Time would be Minute/Hour/Weekday/Month.

    I am a total newb to machine learning and I am not even sure this data would be applicable. Any input or thoughts would be appreciated :D

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  10. It is an interesting post. I am just curious whether you have encountered the same situation. I used the same parameters and ran the model against SPY time series for multiple times. It turned out that sometimes it generated different results. Do you know why and how to control it? Thomas

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