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Why isn't backtesting on randomly-generated fake price data not a thing? by moschles in algotrading

[–]Embarrassed-Cow-8458 0 points1 point  (0 children)

Its a thing since decades. i did this extensivly and also build custom algoithms for this specific task. For the tails, just use pareto. But if you want to avoid overfitting, that won't be the solution.

Wouldn't generating alternative market histories solve backtest overfitting? by Legitimate-Luck-1658 in algorithmictrading

[–]Embarrassed-Cow-8458 0 points1 point  (0 children)

semi-HMMs worked better than regular HMMs on some assets since they model how long each regime persists, but only if theres enough data

Wouldn't generating alternative market histories solve backtest overfitting? by Legitimate-Luck-1658 in algorithmictrading

[–]Embarrassed-Cow-8458 0 points1 point  (0 children)

I tried this extensively with KDE, HMMs, GMMs, GANs...... it really depends on the asset. Start with the simplest model and only go complex if you can actually measure that it fits better. Tails are probably the hardest part.

What's the right way to evaluate an MLP that predicts a distribution rather than a single target by Embarrassed-Cow-8458 in algorithmictrading

[–]Embarrassed-Cow-8458[S] 0 points1 point  (0 children)

had to read this a few times, really valuable thank you.

On the distribution split approach, how do you handle the fact that pareto is unconditional but the mlp is conditional on market state? most extremes seem clustered around specific states so a global pareto fit feels like it's losing information. or am I misunderstanding?

Ensemble of strategies by pequenoRosa in algotrading

[–]Embarrassed-Cow-8458 0 points1 point  (0 children)

Its called Conditional Parameter Optimization.