外汇市场中基于多智能体异步分布的深度强化学习交易优化方法
A Deep Reinforcement Learning Approach for Trading Optimization in the Forex Market with Multi-Agent Asynchronous Distribution
AI总结：
本文首次将多智能体A3C深度强化学习框架应用于外汇交易优化，通过多异步工作器并行探索多货币对，实验表明带锁与不带锁A3C分别在不同场景下优于PPO，能更快探索并显著提升交易收益。
摘要：
在当今外汇市场中，交易者越来越多地转向算法交易，利用计算机寻求更多利润。深度学习技术作为机器学习的前沿进展，能够识别金融数据中的模式。交易者利用这些模式执行更有效的交易，并遵循算法交易规则。深度强化学习方法（DRL）通过基于已识别模式直接执行交易并评估其盈利能力，相比传统深度学习方法具有优势。本研究首次将多智能体（MA）强化学习框架与最先进的异步优势演员-评论家（A3C）算法结合应用。所提出的方法采用跨多个异步工作器的并行学习，每个工作器专门负责多个货币对的交易，以探索针对不同市场条件和货币对量身定制的精细化策略的潜力。提出了两种不同的A3C模型，即带锁和不带锁的多智能体模型，并在单一货币和多货币场景下进行训练。结果表明，两种模型均优于近端策略优化（PPO）模型。带锁A3C在单一货币训练场景中优于其他模型，而不带锁A3C在多货币场景中优于其他模型。研究结果表明，该方法有助于对不同货币对进行更广泛、更快速的探索，显著提升交易收益。此外，智能体能够在更短时间内学习到更有利可图的交易策略。
英文摘要：
In today's forex market traders increasingly turn to algorithmic trading, leveraging computers to seek more profits. Deep learning techniques as cutting-edge advancements in machine learning, capable of identifying patterns in financial data. Traders utilize these patterns to execute more effective trades, adhering to algorithmic trading rules. Deep reinforcement learning methods (DRL), by directly executing trades based on identified patterns and assessing their profitability, offer advantages over traditional DL approaches. This research pioneers the application of a multi-agent (MA) RL framework with the state-of-the-art Asynchronous Advantage Actor-Critic (A3C) algorithm. The proposed method employs parallel learning across multiple asynchronous workers, each specialized in trading across multiple currency pairs to explore the potential for nuanced strategies tailored to different market conditions and currency pairs. Two different A3C with lock and without lock MA model was proposed and trained on single currency and multi-currency. The results indicate that both model outperform on Proximal Policy Optimization model. A3C with lock outperforms other in single currency training scenario and A3C without Lock outperforms other in multi-currency scenario. The findings demonstrate that this approach facilitates broader and faster exploration of different currency pairs, significantly enhancing trading returns. Additionally, the agent can learn a more profitable trading strategy in a shorter time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
