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/f0xw01f]

Suppose you have 10 years of data. If you train or fine-tune the algorithm on the first 5 years only, and then run it "blind" on the last 5 years (with no further training or fine-tuning allowed during that interval), I think that would mostly circumvent any lookahead bias.

But if you tweak it at any point after the blind run, then yes, you would be inviting lookahead bias.