[R-SIG-Finance] Are there genetic algorithm for trading strategy evolution in R?
Roupell, Darko
Darko.Roupell at cba.com.au
Thu Mar 8 23:23:10 CET 2012
To contribute to this discussion with a more concrete example of genetic algorithm usage for trading purpose I have disclosed simple code snippet how to implement it using DEoptim. GALGO is alternative to DEoptim in R.
Micheal, generally I do not provide complete examples so for this exception thank to Patrick Burns as he helped me in the past with PortfolioProbe Optimizer. Hope it helps.
******Disclaimer this is just example for learning purposes but no warranty is made as to accuracy and no liability is accepted if used for commercial purposes****
require(quantmod)
require(PerformanceAnalytics)
require(blotter)
require(DEoptim)
################ MACD TEST
z=read.csv(file="G:\\QERD\\GALGO\\first.csv",header=TRUE,sep=",",stringsAsFactors=FALSE, colClasses=c('character','numeric','numeric'))
z =as.xts(z, as.POSIXct(z$Date))
#force columns into numeric values
z$sum=as.numeric(z$INTC)+as.numeric(z$IEF)
z = z[,c("INTC", "IEF")]
# Let's think about returns instead of prices...
# Ra is the log return for a buy-and-hold strategy, Rb the 'benchmark.'
# We will extensively use these log-return series in the sequel.
z$Ra = Return.calculate(z$INTC)
z$Rb = Return.calculate(z$IEF)
# we will optimise over in sample data
insample=z['2008:']
#the fitness function must obtain the data
MACDFitness <- function(params) {
fast <- params[1]
slow <- params[2]
sig <- params[3]
if ((fast <= slow - 1) & (slow >= 4) & (fast >=4) & ((sig >= fast) & (sig <= slow))) { #certain conditions does not make sense
params = paste("F: ",fast," slow: ",slow,sep="")
x <- MACD(parent$INTC, nFast=fast, nSlow=slow, nSig=sig,maType="EMA")
position <- sign(x[,1]-x[,2])
s <- xts(position,order.by=index(parent))
s$Ra <- parent$Ra
s$Rb <- parent$Rb
s$rts <- (s$Ra*(s$macd>0)) + (s$Rb*(s$macd<=0))
Dt <- na.omit(s$rts-s$Rb)
sharpe = (mean(Dt)*252)/(sd(Dt)*sqrt(252))
} else {
sharpe = -100
}
#have to return negative sharpe because DEoptim minimises the fitness function
return(-sharpe)
}
lower = c(4,4,4)
upper = c(63,63,63)
set.seed(1234)
parent=z
#perform optimizaztion, set value to reach at -6 (i.e. sharpe ratio of 6 in this example) and maximum interations = 100
outDEoptim = DEoptim(MACDFitness,lower,upper, DEoptim.control(VTR=-6,itermax=100,))
summary(outDEoptim)
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Darko Roupell
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-----Original Message-----
From: r-sig-finance-bounces at r-project.org [mailto:r-sig-finance-bounces at r-project.org] On Behalf Of Patrick Burns
Sent: Friday, 9 March 2012 6:24 AM
To: Daniel Cegiełka
Cc: r-sig-finance at r-project.org
Subject: Re: [R-SIG-Finance] Are there genetic algorithm for trading strategy evolution in R?
Yes, serious chance of doing it poorly
without years of intense work.
On 08/03/2012 19:14, Daniel Cegiełka wrote:
> Let me add my two cents. Old Max Dama blog (mirror):
>
> http://smartdatacollective.com/maxdama/22571/voodoo-spectrum-machine-learning-and-data-sets
>
>
> Optimization is good to examine the sensitivity of the model and the
> selection of appropriate parameters - this is useful. But playing with
> the evolutionary strategy, what you Michael ask, is very risky.
>
> regards,
> Daniel
>
>
>
> 2012/3/8 Patrick Burns <patrick at burns-stat.com
> <mailto:patrick at burns-stat.com>>
>
> Comments inline.
>
>
> On 08/03/2012 18:16, Michael wrote:
>
> Thanks folks!
>
> After digging further on the Internet, I have the following
> questions:
>
> Q1: I read the following article:
>
> http://cran.r-project.org/web/__packages/DEoptim/vignettes/__DEoptimPortfolioOptimization.__pdf
> <http://cran.r-project.org/web/packages/DEoptim/vignettes/DEoptimPortfolioOptimization.pdf>
>
> It seems that there are a bunch of parameters in this optimizer
> and the
> results are sensitive to these parameters.
>
> So there is another layer of optimization with respect to these
> optimizer
> parameters.
>
> Is the "tweaking" of these optimizer parameters data-mining,
> which will
> lead to data-snooping bias?
>
>
> I wouldn't think so, but there might be
> a way to manage it.
>
>
>
> Q2: Due to the random nature of the optimizer, each time you run the
> backtest, you will have different performance.
>
> What do you do in that case?
>
>
> That may be a good thing, if you are
> willing to use it.
>
> In what I've done on backtesting:
>
> http://www.portfolioprobe.com/__2010/11/05/backtesting-almost-__wordless/
> <http://www.portfolioprobe.com/2010/11/05/backtesting-almost-wordless/>
>
> I show how to assess whether the strategy
> is better than luck by using random trades.
>
> The standard thing to assume (as I do in
> that piece) is that the optimization is
> noiseless. But really the optimization
> depends on a multitude of subtle influences.
> Even if you always got the exact global
> optimum, if a variance or expected return
> were slightly different, you could get a
> very different path. The "optimal" path
> is fuzzy in actuality.
>
>
>
> So for out-of-sample real-trading, we are trading a random strategy?
>
>
> Yes. But the inputs are random so even
> non-stochastic optimizers give you a
> random strategy in a sense.
>
>
>
> Q3: It's pretty easy to understand using Genetic Algorithms to
> serve as a
> replacement for regular optimizers;
>
> but using Genetic Algorithms to evolve trading strategies seem to be
> different. Anywhere we could find such an example in R?
>
>
> Yes, that is different.
>
> In
> https://stat.ethz.ch/__pipermail/r-sig-finance/__2010q4/007033.html
> <https://stat.ethz.ch/pipermail/r-sig-finance/2010q4/007033.html>
> you can find Josh quoting me quoting Lao-Tzu
> on why you are unlikely to find much useful
> on that subject.
>
> Pat
>
>
>
>
>
>
> On Thu, Mar 8, 2012 at 8:25 AM, Zachary
> Mayer<zach.mayer at gmail.com <mailto:zach.mayer at gmail.com>> wrote:
>
> There is the
> DEoptim<http://cran.r-project.__org/web/packages/DEoptim/__index.html
> <http://cran.r-project.org/web/packages/DEoptim/index.html>>library
> in r, which is an excellent library for differential
> evolution. If
> you can define your trading strategy in terms of a bunch of
> parameters to
> adjust and an objective function (i.e. turn it into an
> optimization
> problem), DEoptim will help you find the minimum (or maximum).
>
> DEoptim works well on non-differentiable problems with many
> local minima.
> Here is an example of using it to solve a portfolio
> optimization problem:
>
> http://cran.r-project.org/web/__packages/DEoptim/vignettes/__DEoptimPortfolioOptimization.__pdf
> <http://cran.r-project.org/web/packages/DEoptim/vignettes/DEoptimPortfolioOptimization.pdf>
>
>
>
> On Thu, Mar 8, 2012 at 12:43 AM, Sofian
> Hadiwijaya<reztinpeace at gmail.__com
> <mailto:reztinpeace at gmail.com>>wrote:
>
> how about quantmod library..
>
> On Wed, Mar 7, 2012 at 10:30 PM,
> Michael<comtech.usa at gmail.com
> <mailto:comtech.usa at gmail.com>> wrote:
>
> Hi all, Good morning, good afternoon and good evening!
>
> Could anybody please kindly point me to resources in
> R which shows about
> how to use Genetic algorithm to evolve trading
> strategies?
>
> I did a lot search on Google these days and
> certainly it's a
>
> well-covered
>
> and popular topic, but I don't see anywhere in R...
>
> Thanks a lot!
>
> [[alternative HTML version deleted]]
>
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> _________________________________________________
> R-SIG-Finance at r-project.org <mailto:R-SIG-Finance at r-project.org>
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> --
> Patrick Burns
> patrick at burns-stat.com <mailto:patrick at burns-stat.com>
> http://www.burns-stat.com
> http://www.portfolioprobe.com/__blog
> <http://www.portfolioprobe.com/blog>
> twitter: @portfolioprobe
>
>
> _________________________________________________
> R-SIG-Finance at r-project.org <mailto:R-SIG-Finance at r-project.org>
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>
--
Patrick Burns
patrick at burns-stat.com
http://www.burns-stat.com
http://www.portfolioprobe.com/blog
twitter: @portfolioprobe
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