| > They also don't compare to baselines This is certainly worth criticising > they can still overfit to test data by running a lot of experiments with different hyperparameters, architectures and parts of the data, and only report what has worked. but this is a different accusation from accidentally overfitting or leaking, i.e. it would mean that they're dishonest and cherrypicked their data in such a way that it hides overfitting and leakage. This criticism can be levelled at every ML paper, but in this case they detail their architecture, provide the code, and provide a Jupyter notebook to let people try it themselves. > just assume they can trade at whatever price the data tells them. It's completely unrealistic. I think that this is a fair assumption for highly liquid markets and relatively small trades, and if it's a fair assumption then all of your criticisms (slippage etc) don't apply to the extent that they'll break the approach. Also, if the approach works then trade size (fees aside) and being frontrun also wont apply because presumably large HFT firms can use it. Overall I think your criticisms are valid, but imo they don't invalidate a promising approach, they're just the next thing to test. |