A Risk-Aware Reinforcement Learning Reward for Financial Trading

TL;DR

This paper introduces a flexible, risk-aware reward function for reinforcement learning in financial trading, balancing return and risk with customizable weights, enabling tailored investment strategies.

Contribution

It presents a modular, parameterized reward function incorporating multiple risk and return metrics, with derived gradients for efficient training and theoretical analysis.

Findings

Effective tuning of weights achieves desired risk-return profiles.

The reward function demonstrates robustness across trading scenarios.

The framework allows extension with additional risk measures.

Abstract

We propose a novel composite reward function for reinforcement learning in financial trading that balances return and risk using four differentiable terms: annualized return downside risk differential return and the Treynor ratio Unlike single metric objectives for example the Sharpe ratio our formulation is modular and parameterized by weights w1 w2 w3 and w4 enabling practitioners to encode diverse investor preferences We tune these weights via grid search to target specific risk return profiles We derive closed form gradients for each term to facilitate gradient based training and analyze key theoretical properties including monotonicity boundedness and modularity This framework offers a general blueprint for building robust multi objective reward functions in complex trading environments and can be extended with additional risk measures or adaptive weighting