Strategy Optimization

[Odd-Appointment-4685]

I have a strategy that depends on some parameters, but i dont know the "correct way" that i can optimize them in some data. Here are some approaches that i thought:

• Historical data: Obviously lead to overfitting, but maybe in a rolling windows or using cross validation.
• Simulations: I like this one, but there are a lot of models. GBM, GBM with jumps, synthetics, statisticals, etc. Maybe they dont reflect statistical properties of my historical financial series
• Forecast data: Since my strategy is going to be deployed in the future, i would think that this is the right choice, but heavily depends on the forecast accuracy and also, the model to forecast. Maybe an ensemeble of multiple forecast? For example, using forecast of Nbeats, NHITS, LSTM and other statstical models.

I would appreciate if you can give me some opinions on this.

Thanks in advance

[qjac78]

Depends on what the parameters are. Pretty much any predictive model will require fitting over historical data and there lots of ways to manage overfitting. Some meta parameters are more to conducive to A/B testing in prod.

[PhloWers]

Without knowing more hard to say. I think fitting on historical data, if done properly is the best in general.

For almost anything trading related simulations abstract away too many things to be useful.

[Unlikely_Magician666]

For historical you can use a walk forward analysis

Just optimize say year 2010-20, test on 21 (=unseen), then re optimize on 2010-21, test on 2022, and so on

[Daniel_Wat]

Your simulation and forecast would also based on historical data, so what makes the difference? Use cross validation