Cryptocurrency Portfolio Management with Reinforcement Learning: Soft Actor--Critic and Deep Deterministic Policy Gradient Algorithms

TL;DR

This paper introduces a reinforcement learning framework using SAC and DDPG algorithms for cryptocurrency portfolio management, demonstrating improved performance and robustness over traditional methods in volatile markets.

Contribution

It applies advanced deep reinforcement learning algorithms to cryptocurrency portfolio management, showing their effectiveness and robustness in highly volatile market conditions.

Findings

SAC outperforms DDPG in stability and robustness.

Reinforcement learning agents outperform baseline strategies.

Deep RL methods adapt well to volatile cryptocurrency markets.

Abstract

This paper proposes a reinforcement learning--based framework for cryptocurrency portfolio management using the Soft Actor--Critic (SAC) and Deep Deterministic Policy Gradient (DDPG) algorithms. Traditional portfolio optimization methods often struggle to adapt to the highly volatile and nonlinear dynamics of cryptocurrency markets. To address this, we design an agent that learns continuous trading actions directly from historical market data through interaction with a simulated trading environment. The agent optimizes portfolio weights to maximize cumulative returns while minimizing downside risk and transaction costs. Experimental evaluations on multiple cryptocurrencies demonstrate that the SAC and DDPG agents outperform baseline strategies such as equal-weighted and mean--variance portfolios. The SAC algorithm, with its entropy-regularized objective, shows greater stability and robustness in noisy market conditions compared to DDPG. These results highlight the potential of deep reinforcement learning for adaptive and data-driven portfolio management in cryptocurrency markets.