Застосування reinforcement learning для алгоритмічної торгівлі на фінансових ринках

Abstract

This work explores the application of reinforcement learning for algorithmic trading in financial markets. It identifies the challenge of automating decision-making amid market volatility and uncertainty. The study reviews existing solutions such as Q-learning, deep Q-networks, and Actor-Critic architectures. It highlights the potential of hybrid models that integrate supervised pre-training with reinforcement learning. The proposed approach also incorporates risk management mechanisms to dynamically adapt trading strategies.