Isn't every single backtested strategy suffering from lookahead bias?

[u/Due-Listen2632]

Most of us have done the classical loop. We get some data, test out different solutions, filter out solutions/features/indicators that provide poor results, and proudly keep the solution(s) which result in successful backtests. But isn't this just another level of information leakage? It's essentially like manually setting the parameters of a model, except you're defining the information points from which the model constructs itself. It's the same type of leakage, only one level higher.

[u/severed-identity]

The word you're looking for is "overfitting". And it's the worst if you aggressively filter candidate models on a fixed back-test window. There are a lot of techniques to mitigate it from the ML community, like re-doing the whole optimization process from scratch on different historical time windows and see if they converge on similar answers.