# Market Making with Scaled Beta Policies

arXiv: https://arxiv.org/abs/2207.03352 · HTML: https://mlsanity.com/paper/2207.03352/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 42 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Market Making via Reinforcement Learning](https://mlsanity.com/paper/1804.04216/) (2018)
- [Reinforcement Learning for Market Making in a Multi-agent Dealer Market](https://mlsanity.com/paper/1911.05892/) (2019)
- [How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?](https://mlsanity.com/paper/1906.12010/) (2019)
- [Robust Market Making via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2003.01820/) (2020)
- [Optimizing Market Making using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1812.10252/) (2018)

## Built on by (most cited first, 5 of 5)

- [Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness](https://mlsanity.com/paper/2306.12806/) (2023)
- [JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading](https://mlsanity.com/paper/2308.13289/) (2023)
- [IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making](https://mlsanity.com/paper/2308.08918/) (2023)
- [Model-based gym environments for limit order book trading](https://mlsanity.com/paper/2209.07823/) (2022)
- [Resolving Latency and Inventory Risk in Market Making with Reinforcement Learning](https://mlsanity.com/paper/2505.12465/) (2025)

## Abstract (arXiv)

This paper introduces a new representation for the actions of a market maker in an order-driven market. This representation uses scaled beta distributions, and generalises three approaches taken in the artificial intelligence for market making literature: single price-level selection, ladder strategies and "market making at the touch". Ladder strategies place uniform volume across an interval of contiguous prices. Scaled beta distribution based policies generalise these, allowing volume to be skewed across the price interval. We demonstrate that this flexibility is useful for inventory management, one of the key challenges faced by a market maker. In this paper, we conduct three main experiments: first, we compare our more flexible beta-based actions with the special case of ladder strategies; then, we investigate the performance of simple fixed distributions; and finally, we devise and evaluate a simple and intuitive dynamic control policy that adjusts actions in a continuous manner depending on the signed inventory that the market maker has acquired. All empirical evaluations use a high-fidelity limit order book simulator based on historical data with 50 levels on each side.