This seems like massive historical overfit, which can lead to arbitrarily precise fit, but no predictive capability. Any model, if given enough parameters, can be made to match historical data to an arbitrary degree. I also run several Bitcoin bots. I can tell you that slippage is not insignificant. If you make transactions every ~10 seconds and incur 0.1% fees each time, this is an extremely significant effect in ag…
There was no cross validation involved in assessing the performance of this model. Using one subset for training, and one subset for testing and calling that conclusive is naive.
No interesting conclusions can be taken away from this paper due to flawed methodology and failure to take into account real world variables like bid-ask spread, commissions, how quickly trades can be executed, whether orders will even be filled, etc.