Reinforcement Learning for Crypto Futures Portfolios with Lending and Shorting

Summary

The study proposes a reinforcement learning portfolio manager for a high-risk setting. It combines two-sided transactions with lending and borrowing, uses a profit-and-loss reward, and aims to improve downside risk management and capital use. Its implementation pairs a Soft Actor-Critic agent with a convolutional network and multi-head attention. The portfolio contains crypto assets traded in perpetual futures, with USDT used for lending and borrowing and periodic rebalancing based on recent market data.

The model is evaluated over two periods spanning different volatility conditions, and the authors report stronger benchmark performance, especially in high-volatility scenarios. The excerpt gives no benchmark identities, detailed risk measures, transaction-cost treatment, or evidence beyond those historical tests. Results therefore describe the reported evaluation and do not establish that the approach will remain profitable in other periods or live trading.

Key ideas

• The portfolio manager combines long and short transactions with lending and borrowing.
• A profit-and-loss reward is designed to support downside risk management and capital allocation.
• The implementation uses Soft Actor-Critic with a convolutional network and multi-head attention.
• The model is evaluated on a diversified crypto perpetual futures portfolio with periodic rebalancing.
• Reported benchmark outperformance is strongest in high-volatility test periods, with generalization limits left unclear.

Tags

# Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework


# Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework









This study presents a Reinforcement Learning (RL)-based portfolio management model tailored for high-risk environments, addressing the limitations of traditional RL models and exploiting market opportunities through two-sided transactions and lending. Our approach integrates a new environmental formulation with a Profit and Loss (PnL)-based reward function, enhancing the RL agent's ability in downside risk management and capital optimization. We implemented the model using the Soft Actor-Critic (SAC) agent with a Convolutional Neural Network with Multi-Head Attention (CNN-MHA). This setup effectively manages a diversified 12-crypto asset portfolio in the Binance perpetual futures market, leveraging USDT for both granting and receiving loans and rebalancing every 4 hours, utilizing market data from the preceding 48 hours. Tested over two 16-month periods of varying market volatility, the model significantly outperformed benchmarks, particularly in high-volatility scenarios, achieving higher return-to-risk ratios and demonstrating robust profitability. These results confirm the model's effectiveness in leveraging market dynamics and managing risks in volatile environments like the cryptocurrency market.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.