RL4RF as a Reinforcement Learning Framework for a Realistic Forex Environment

Abstract

The paper introduces RL4RF, a novel framework designed to address the shortcomings of existing environments in Reinforcement Learning (RL) for Forex trading. RL4RF offers a comprehensive and authentic simulation of the Forex market, mitigating limitations associated with data diversity and market dynamics representation. Comprising distinct layers, including data handling, RL algorithms, and an environment model, RL4RF facilitates the utilization of various RL algorithms and data sources. Notably, RL4RF incorporates essential aspects such as transaction costs, market volatility, and fundamental factors that influence exchange rates. Its data layer efficiently manages CSV files, internet data retrieval, and synthetic dataset generation, enhancing the study of diverse market patterns. The RL algorithms layer accommodates multiple RL techniques, fostering adaptability to various trading scenarios. The environment layer of RL4RF faithfully replicates realistic trading conditions, encompassing factors like decimal precision, spread, lot size, initial balance, tick value, take profit, stop loss, and graphical representations of trading activities. This framework significantly enhances the feasibility of RL-based trading strategies, enabling
effective generalization to real-world market conditions. In this paper, we present the methodology, implementation specifics, and RL algorithms employed within RL4RF. Furthermore, we provide illustrative examples of RL4RF`s application in studying diverse market scenarios, showcasing its potential to revolutionize RL-based Forex trading strategies.