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RE: [amibroker] Optimization -- again



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So 
according to your last paragraph it is impossbile to develop a system that is 
consistently successful since just looking for it voids it.  Right?  

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  face=Tahoma size=2>-----Original Message-----From: Howard Bandy 
  [mailto:howardbandy@xxxxxxxxx] Sent: Thursday, October 16, 2003 
  2:20 PMTo: amibroker@xxxxxxxxxxxxxxxSubject: [amibroker] 
  Optimization -- againGreetings --In my 
  opinion, anything we do in development of trading systems involves asearch 
  for a pattern than precedes a profitable trading opportunity.  
  Anytime we examine the results of alternative systems, we are involved 
  insearching; and when we select the most promising of those alternatives, 
  weare optimizing.  Only a system based on truly random entries and 
  exits wouldnot be the result some optimization.  So the question of 
  "should weoptimize?" is moot -- we have no choice but to optimize.  
  Consequently, weshould be aware of our optimization 
  techniques.Chuck referred to an optimization technique recommendation 
  I made to thecompany we both worked for in Denver a few years ago.  
  This is a shortdescription of it.The company is a Commodity 
  Trading Advisor which traded futures, notindividual stocks, but the 
  procedures are equally valid for both.When I joined the company, they 
  were using very long data series whendeveloping their models.  They 
  used a technique sometimes called folding orjackknifing, where the data 
  was divided into several periods -- say ten.The modeling process made ten 
  passes.  During each pass, one period was heldback to be used as 
  out-of-sample data, the other nine were used to selectthe best parameter 
  values.  After all ten passes, the results were gatheredtogether and 
  the parameter values that scored best overall were chosen.There are 
  several problems with this method.  One is the difficulty with 
  the"ramp up" period at the start of each segment, another is that it is 
  notvalid to use older data for out-of-sample testing than was used 
  forin-sample development, and another is that the data series were too 
  long.Chuck and I and others had many interesting discussions about how 
  long thein-sample data should be.  My background is strong in 
  both the theory and the practice of modeling andsimulation, and includes a 
  great deal of experience with analysis offinancial time series.  I 
  proposed the following method, which I continue tobelieve is 
  valid.First, before any modeling begins.  Using judgment of 
  management andcomparison of trading profiles of many trading runs (real, 
  simulated, orimagined), pick an objective function by which the "goodness" 
  of a tradingsystem will be measured.  This is important, it is a 
  personal or corporatejudgment, and it should not be subject to 
  optimization.  Divide each data series into a sequence of 
  in-sample and out-of-sampleperiods.  The length of the out-of-sample 
  period is the "reoptimization"period.  Say there are about ten years 
  of historical data available(1/1/1993 through 1/1/2003.  Set the 
  in-sample period to two years and theout-of-sample period to one 
  year.  Run the following sequence:  Search /optimize using 1993 
  and 1994; pick the "best" model for 1993-1994; forwardtest this model for 
  1995 and save the results; step forward onereoptimization period and 
  repeat until all the full in-sample periods havebeen used.  The final 
  optimization will have been 2001 and 2002, with noout-of-sample data to 
  test.  Ignore all in-sample results!!  Examine theconcatenated 
  out-of-sample equity curve.  If it is acceptable, you have 
  someconfidence that the parameters select by the final optimization (2001 
  and2002) will be profitable for 2003.  No guarantees -- only some 
  confidence.How did I pick two years for in-sample and one year for 
  out-of-sample?  Thatwas just an example.  The method is to set 
  up an automated search where thelength of the in-sample period and the 
  length of the out-of-sample period --the reoptimization period -- are 
  variables, and then search through thatspace.  Trading 
  systems work because they identify inefficiencies in markets.  
  Everyprofitable trade reduces the inefficiency until, finally, the trading 
  systemcannot overcome the frictional forces of commission and 
  slippage.  This isthe same phenomenon that physicists talk about as 
  entropy.My feeling -- and it may be different than Chuck's -- is that 
  the market isnot only non-stationary, but that the probability that it 
  will return to aprevious state is near zero.  Being 
  non-stationary means that market conditions change with respect to 
  ourtrading systems.  If I am modeling a physical process, such as a 
  chemicalreaction, I can count on a predictable modelable output for a 
  given set ofinputs.  If I am modeling a financial time series, the 
  output following agiven set of inputs changes over time.  If a market 
  were stationary withrespect to an RSI oscillator system, I could always 
  buy a rise of the RSIthrough the 20 percent line, to use a very simplistic 
  example. I feel that the introduction of microcomputers, trading 
  system developmentsoftware, inexpensive individual brokerage accounts, and 
  discussion groupssuch as this one have permanently changed the realm of 
  trading.  One,everyone who is interested can afford to buy a 
  computer, run AmiBroker, anddesign and test trading systems.  Two, if 
  someone develops a profitablesystem and trades it, the profits it takes 
  reduce the potential profitsavailable to anyone else who trades it.  
  Consequently, the characteristicsof the market change in a way that moves 
  the market away from that modeluntil that trading system is no longer 
  profitable enough to overcomecommission and slippage.  Three, a new 
  person beginning to study tradingsystem development typically tests a lot 
  of old systems.  If one is found tobe profitable and they start 
  trading it, the market moves back to beingefficient.  Consequently, 
  trading systems that used to work, but no longerwork, are very unlikely to 
  ever work again.So, I feel that the in-sample period should be short 
  so that the marketconditions do not change much over that period.  
  That is, I am looking for adata series that is stationary relative to my 
  model.  The stationaryrelationship must extend beyond the in-sample 
  period far enough that themodel will be profitable when used for trading 
  in the out-of-sample data.The length of the extension determines the 
  reoptimization period.  It couldbe years, months, or even one 
  day.  Note that the holding period of atypical trade is very much 
  related to the length of both the in-sample andout-of-sample 
  periods.  The typical trade should be much shorter than thein-sample 
  period and somewhat shorter than the out-of-sample period.The 
  important point in all this is that the only results being analyzed arethe 
  concatenated out-of-sample trades.As with all model development, every 
  time I look at the out-of-sampleresults in any way, I reduce the 
  probability that future trading resultswill be profitable.  That 
  means that I should not perform thousands of testsof model parameters, 
  in-sample periods, and out-of-sample periods, on thesame data series and 
  then pick the best model base on my examination ofthousands of 
  out-of-sample results.  In effect, I will have just convertedall 
  those out-of-sample results into in-sample data for another step in 
  thedevelopment.  That is legitimate, just be aware of what is 
  happening.Thanks for listening,HowardSend 
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