After considering what kind of ‘other model’ I could use to provide input to my RL agent, I think now that a supervised learning classification model might be most appropriate, and I’ve head that XGBoost is one of the best. I’m also thinking of using different data for that than the instrument I’m actually trading with the RL algorithm. For example if I’m trading ADA or ETH I could use BTC or a crypto index as the source of data for the other model. All crypto coins are fairly highly correlated, although BTC seems to take the lead most of the time.
I guess it would be fairly straight forward to turn BTC prices into a supervised learning problem, with maybe a positive daily return indicating good to buy, a negatiive return as good to sell, and close to zero being good to hold. Actually probably some threshold above and below zero might work better.
I could potentially use this approach with lots of different input data, as long as they have some correlation to crypto prices and therefore some predictive power. Anyway, time to get up to speed on using XGBoost effectively, so naturally I now have a small book on the subject, courtesy of machinelearningmastery, as usual with ML books. I’m sure I play a significant part in keeping the ML publishing industry afloat.