Exploiting Risk-Aversion and Size-dependent fees in FX Trading with Fitted Natural Actor-Critic

TL;DR

This paper presents a reinforcement learning approach using Fitted Natural Actor-Critic to develop a risk-averse FX trading agent that optimizes order sizes and captures intraday price patterns, validated on EUR-USD data.

Contribution

It introduces a novel application of Fitted Natural Actor-Critic in FX trading, incorporating size-dependent transaction costs and risk aversion for improved trading strategies.

Findings

Effective recognition of intraday FX patterns

Enhanced trading performance with size-dependent costs

Risk-averse strategies improve trading robustness

Abstract

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.