Neural network-based capital management for bitcoin trading: a risk-aware expert system for investment strategy optimization

Abstract

This study presents an expert system designed to generate Bitcoin investment strategies based on cryptocurrency market indicators. Historical BTC daily closing prices from 2015 to 2021 were processed to build the system’s predictive foundation. Multilayer perceptron (MLP) neural networks with various configurations were then employed to forecast both price levels and directional movements in Bitcoin’s value. These networks were trained using supervised learning techniques and assessed through multiple evaluation metrics. The configuration achieving the lowest RMSE and highest trend prediction accuracy was subsequently used to implement a capital management system capable of executing long, short, and combined trading positions in the Bitcoin market. An all-or-nothing investment scheme was applied and benchmarked against a traditional Buy & Hold (B&H) strategy. The proposed system achieved up to +68% profitability in the combined long/short configuration while reducing maximum drawdown by more than 40%. In addition, an expert supervisory layer was integrated, incorporating market indicators such as stop-loss, take-profit, and market withdrawal rules based on maximum adverse excursion (MAE) and maximum favorable excursion (MFE). Although this supervisory layer slightly reduced profitability in some scenarios, it enhanced risk control and capital protection during highly volatile periods. Overall, the proposed framework demonstrates that neural network–driven trading strategies, when combined with supervisory expert rules, can significantly outperform a passive Buy & Hold approach, offering a reproducible and fully automated solution for Bitcoin capital management.