Handling Overfitting in StrategyQuant-Generated Strategies

Hello
I have been using StrategyQuant to generate trading strategies but I’m concerned about overfitting. Some strategies perform exceptionally well in backtesting but fail in live trading. I suspect they may be too optimized for historical data & not robust enough for real market conditions.
Even after applying walk-forward analysis & out-of-sample testing; I still notice performance degradation.
What are the best practices to ensure a strategy is truly robust? Are there specific settings in StrategyQuant that help reduce overfitting, such as limiting certain indicators, using larger datasets, or applying stricter validation criteria? Checked StrategyQuant DocumentationMachine Learning Course Onlineguide related to this and found it quite informative.
I’d also love to hear about alternative robustness testing methods that experienced users rely on before deploying a strategy live.

Thank you!!

Hello,

this is kind of normal. Any tool you use you always fit performance to data that “happened” already and future will be very likely different at least slightly.

The key here is to focus on strategy logic meaning there should be at least “an idea” why the strategy should work and what is logic behind that. Relying strictly on randomly made strategies can work as well but the success rate is lower

We also recommend to always do various https://strategyquant.com/doc/strategyquant/cross-checks-automated-strategy-robustness-tests/

Apart from WF you should also do the system parameter permutation test as well https://strategyquant.com/doc/strategyquant/optimization-profile-system-parameter-permutation-strategyquant/#system-parameter-permutation-spp. You want to understand stable area of parameters for your strategy and use parameter set that is ideally inside one

Another new tool you could use is “regime filtering”. That is a new thing in SQX. Check https://strategyquant.com/blog/understanding-market-regimes-indicators-in-strategyquant-coding-base/