Bayesian machine learning framework for characterizing structural dependency, dynamics, and volatility of cryptocurrency market using potential field theory

Identifying the structural dependence between the cryptocurrencies and predicting market trend are fundamental for effective portfolio management in cryptocurrency trading. In this paper, we present a unified Bayesian machine learning framework based on potential field theory and Gaussian Process to characterize the structural dependency of various cryptocurrencies, using historic price information. The following are our significant contributions: (i) Proposed a novel model for cryptocurrency price movements as a trajectory of a dynamical system governed by a time-varying non-linear potential field. (ii) Developed a Bayesian machine learning framework for inferring the non-linear potential function from observed cryptocurrency prices. (iii) Proposed that attractors and repellers inferred from the potential field are reliable cryptocurrency market indicators, surpassing existing attributes in the literature. (iv) Analysis of cryptocurrency market during various Bitcoin crash durations shows that attractors captured the market trend, volatility, and correlation. In addition, attractors aids explainability and visualization. (v) The proposed cryptocurrency market indicators (attractors and repellers) was used to improve the prediction performance of state-of-art deep learning price prediction models.