Exploiting Risk-Aversion and Size-dependent fees in FX Trading with Fitted Natural Actor-Critic
This work addresses automated trading in the Foreign Exchange market, focusing on realistic transaction costs and risk management, but appears incremental as it applies an existing RL method to this specific domain.
The authors tackled the problem of recognizing and leveraging intraday price patterns in the Foreign Exchange market by implementing a Reinforcement Learning algorithm called Fitted Natural Actor-Critic, which enabled continuous actions for variable trading sizes to model transaction costs and integrate risk-averse approaches, and empirically validated the approach on EUR-USD historical data.
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.