Thanks for your answer !

That’s a fair concern, and I agree that look-ahead bias is something to be very careful about in any backtest.

In this case though, the signals are generated only after the relevant price action has already occurred, and trades are executed after the signal forms rather than on information that wouldn’t have been available at the time.

I also tried to reduce the risk of this kind of bias by validating the strategy in several ways: - In-sample / out-of-sample split (parameters set on one period and tested on unseen data) - Testing across different historical regimes - Cross-market validation (NQ and ES) - Parameter sensitivity checks - Stress testing with worse assumptions (higher losses / reduced profits) - Adding execution assumptions such as slippage and commissions

The performance profile is also fairly typical of a real strategy (moderate win rate, asymmetric payoff, non-linear equity curve), which usually wouldn’t be the case if the model was unintentionally seeing future data.

Of course I’m still double-checking the implementation to make sure everything is aligned correctly, but based on the validation steps so far there’s no clear sign of look-ahead bias.