I used Optuna to optimize my ETH experiment. I only optimized the learning rate of the Adam optimizer, and yet was able to achieve a precision of 0.65. Basically this means that if it predicts that today’s return will be greater than 1% it will be right twice as often as it is wrong, approximately. Maybe with some more input features, or with some optimization of the model itself, in terms of number of layers and number of nodes, number of dropout layers, etc, I could improve on this. Very interesting.
ETA: Alas, that result was something of an outlier. I repeated the experiment and out of 100 trials that best learning rate gave a value greater than 0.5 only about once, where many other trials using similar learning rate were below 0.5 That’s the problem with finding the best result, there’s obviously quite a lot of variance in this process. Back to the drawing board.