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Here is the IAQF Trading Historical Backtesting Talk Summary for a quant model and strategy

(Last Updated On: January 14, 2014)

Here is the IAQF Trading Historical Backtesting Talk Summary for a quant model and strategy

 

– Hold-Out Data Testing is insufficient

– 100% Overfitting is where your model will model only the sample space instead of the thing you are trying to model.

– Overfitting is a % of bias in your test data and should be computed as a probability.

– Probability of Backtest Overfitting (PBO) is used to determine how much capital to allocate to each strategy

– Maximum PBO that should be  allowed for trading is .20

according to Marcos.

– Overfitting is unlikely with very simple strategies or with complex strategies that use a lot of of data (i.e tick data for HFT) but few variables.

-Neural Nets with 20 or more variables are highly susceptible to overfitting.

– Non-linear estimators like polynomials are more susceptible to noise than linear estimators like OLS

– Symmetric combinations of out of sample test data should

be used for testing (i.e multiple hold-outs)

– Transformations can be used to remove the bias in non-normal distributions and in linear estimators (OLS)

-Neural Nets/Machine Learning can be used to find the optimal transformation needed to remove the bias from the sample space (This is known as Deep Learning)

 

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2326253

 

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2308659

This came from my NYC contact so big thanks to him for sending this

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