用强化学习优化外汇交易，考虑订单大小和风险规避
Exploiting Risk-Aversion and Size-dependent fees in FX Trading with Fitted Natural Actor-Critic
- 用拟合自然演员-评论家算法实现连续交易动作
- 在欧元兑美元数据上验证，能有效处理订单规模相关的交易成本
- 适合关注风险控制的量化交易研究者
近年来，人工智能因在多个领域的广泛应用而日益流行。金融领域已将其用于开发自动化交易系统，使代理能自主与市场互动以达成不同目标。本文聚焦于识别并利用外汇市场中的日内价格模式，该市场以高流动性和灵活性著称。我们采用一种名为拟合自然演员-评论家（Fitted Natural Actor-Critic）的强化学习算法，训练一个能执行连续动作的交易代理，从而支持可变订单规模的交易。这一特性有助于真实建模交易成本，因为交易成本通常随订单大小变化。此外，该方法还支持融入风险厌恶机制，引导代理采取更保守的交易行为。所提方法在欧元兑美元历史数据上进行了实证验证。
原文摘要 · Abstract (English)
In recent years, the popularity of artificial intelligence has surged due to its widespread application in various fields. The financial sector has harnessed its advantages for multiple purposes, including the development of automated trading systems designed to interact autonomously with markets to pursue different aims. In this work, we focus on the possibility of recognizing and leveraging intraday price patterns in the Foreign Exchange market, known for its extensive liquidity and flexibility. Our approach involves the implementation of a Reinforcement Learning algorithm called Fitted Natural Actor-Critic. This algorithm allows the training of an agent capable of effectively trading by means of continuous actions, which enable the possibility of executing orders with variable trading sizes. This feature is instrumental to realistically model transaction costs, as they typically depend on the order size. Furthermore, it facilitates the integration of risk-averse approaches to induce the agent to adopt more conservative behavior. The proposed approaches have been empirically validated on EUR-USD historical data.
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