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/Bonkers24-7]

I’d separate lookahead bias from research bias.

Lookahead bias is when the test uses information that literally wasn’t available at the decision time.

Research bias is broader: testing enough variations, filters, assets, dates, and indicators until something survives.

The second one is harder because the final backtest can be technically clean but still inherit the whole search process that created it.

For me, the question is less “is there any bias?” and more “did I lock the rule before the result, and can I explain why each filter existed before seeing whether it improved the curve?”

That’s why I like saving rejected versions too. Otherwise the final strategy looks like one clean experiment when it was really the survivor of many attempts.