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Re: [amibroker] Data mining bias vs number of observations



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Hi Simon,

It all depends on the type of algorithms being used.  I use a similar  
system to the one described, and I have not found walk forward to add  
value in my case.  Though I would love to have 8 years of data to  
test, I currently have a much shorter history to work with.

The key is to understand your system well and understand what kinds of  
indicators, stops, etc., are subject to over optimization.  Looking at  
the equity curve and seeing how it performs in various market  
conditions that repeat over long cycles is the key to this type of  
trading.  It is also key to have algorithms that can accurately  
determine what kind of theoretical opportunities the market is making  
available to your chosen trading timeframe and instruments.  Adjusting  
parameters over large ranges and noting their effect on that equity  
curve will give insights about over optimization.  It is a matter of  
logical and intuitive understanding of the first principles.   
Automated optimization methods can not gain that understanding for you  
(yet).

However, OOS walk forward testing for most types of systems will give  
you a starting point to understanding if you really understand your  
system well.

Best regards,
Dennis


On Apr 17, 2008, at 5:29 AM, si00si00 wrote:
> Hi all,
>
> I have a friend who has developed a trading system. It is an intraday
> system that makes on average around 5 futures trades per day. We were
> discussing it the other day and a point of disagreement arose between
> us. He claims that there is no necessity for him to test the strategy
> on out of sample data because he has back tested it using over 8 years
> of historical intraday data, and the patterns the strategy predicts
> occur 70% of the time or more.
>
> My question is, does anyone know if the data-mining bias can be
> considered irrelvant when the sample size is so large? (in this case,
> the sample size is roughly 8400 trades). Put another way, with so many
> observations, how many different rules would have to be back tested in
> order for data-mining bias to creep in?
>
> Thanks in advance for any thoughts you might have!
>
> Simon
>
>
>
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