Timeline for Finding robust regions of multidimensional parameter combinations in trading strategies
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Mar 10, 2017 at 10:38 | vote | accept | vonjd | ||
| Mar 8, 2017 at 9:59 | comment | added | Quantuple | ... and of course you are right, CV for time series data is challenging, at least the standard $K$-fold CV. So I guess that what I'm saying is that what you suggest is OK but should be part of the "strategy identification" and the whole should be run through (cross-)validation to have a good idea of the generalisation error. | |
| Mar 8, 2017 at 9:52 | comment | added | Quantuple | What I'm saying is on the contrary that if a strategy does not generalise well (which is eventually the only thing you are interested in) you should not even care whether the numerical estimation procedure of its parameters is robust. In other words, by picking a "robust" solution (here focusing on local extrema of the objective function whose neighbourhood in the parameter space is flat instead of a global extrema), you effectively restrict your model class, which can potentially lead to restricting yourself to strategies that are bound to be poor at generalising. | |
| Mar 8, 2017 at 9:20 | comment | added | vonjd | @Quantuple: Another thing is of course that using cross-validation on ordered data like time series has challenges of its own - see also here: stats.stackexchange.com/questions/14099/… | |
| Mar 8, 2017 at 9:09 | comment | added | vonjd | @Quantuple: I of course know that, yet the above could be a first step to understand more about the behaviour of the strategy within its parameter space. If the strategy is not even robust you need not worry about generalizability. | |
| Mar 8, 2017 at 9:05 | comment | added | Quantuple | I agree with @madilyn. IMO, you should be more scared about the generalisation capability of your model than its robustness. I would therefore recommend cross-validation techniques since it is well know that the error measured on a backtest is an optimistically biased estimator of the expected generalisation error of your model (since your model is generally chosen so as to minimise the former error). | |
| Mar 8, 2017 at 8:21 | answer | added | madilyn | timeline score: 1 | |
| Mar 8, 2017 at 7:01 | history | asked | vonjd | CC BY-SA 3.0 |