As an example, consider a strategy like “on Wednesdays, the market is more likely to have a large move, and signal XYZ predicts big moves accurately.” You can encode that as an algorithm: trade signal XYZ on Wednesdays. But the algorithm might make money on backtests even if the assumptions are wrong! By examining the individual components rather than just whether the algorithm made money, we get a better idea of whether the strategy works.
Is this an instance of the “theory” bullet point then? Because the probability of the statement “trading signal XYZ works on Wednesdays, because [specific reason]” cannot be higher than the probability of the statement “trading signal XYZ works” (the first statement involves a conjunction).
It’s a combination. The point is to throw out algorithms/parameters that do well on backtests when the assumptions are violated, because those are much more likely to be overfit.
I’d be interested to learn more about the “components” part.
As an example, consider a strategy like “on Wednesdays, the market is more likely to have a large move, and signal XYZ predicts big moves accurately.” You can encode that as an algorithm: trade signal XYZ on Wednesdays. But the algorithm might make money on backtests even if the assumptions are wrong! By examining the individual components rather than just whether the algorithm made money, we get a better idea of whether the strategy works.
Is this an instance of the “theory” bullet point then? Because the probability of the statement “trading signal XYZ works on Wednesdays, because [specific reason]” cannot be higher than the probability of the statement “trading signal XYZ works” (the first statement involves a conjunction).
It’s a combination. The point is to throw out algorithms/parameters that do well on backtests when the assumptions are violated, because those are much more likely to be overfit.