# A Risk-Aware Reinforcement Learning Reward for Financial Trading

arXiv: https://arxiv.org/abs/2506.04358 · HTML: https://mlsanity.com/paper/2506.04358/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Built on by (most cited first, 1 of 1)

- [Reward Hacking Mitigation using Verifiable Composite Rewards](https://mlsanity.com/paper/2509.15557/) (2025)

## Abstract (arXiv)

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