# Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response

arXiv: https://arxiv.org/abs/1906.02312 · HTML: https://mlsanity.com/paper/1906.02312/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 6 of 6)

- [Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators](https://mlsanity.com/paper/2108.00664/) (2021)
- [Limited or Biased: Modeling Sub-Rational Human Investors in Financial Markets](https://mlsanity.com/paper/2210.08569/) (2022)
- [Combining Deep Learning on Order Books with Reinforcement Learning for Profitable Trading](https://mlsanity.com/paper/2311.02088/) (2023)
- [Symbolic State Partitioning for Reinforcement Learning](https://mlsanity.com/paper/2409.16791/) (2024)
- [Reinforcement Learning Framework for Quantitative Trading](https://mlsanity.com/paper/2411.07585/) (2024)
- [Robust Market Making with Hawkes Order Flow and Price Impact via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2609.22785/) (2026)

## Abstract (arXiv)

We demonstrate an application of risk-sensitive reinforcement learning to optimizing execution in limit order book markets. We represent taking order execution decisions based on limit order book knowledge by a Markov Decision Process; and train a trading agent in a market simulator, which emulates multi-agent interaction by synthesizing market response to our agent's execution decisions from historical data. Due to market impact, executing high volume orders can incur significant cost. We learn trading signals from market microstructure in presence of simulated market response and derive explainable decision-tree-based execution policies using risk-sensitive Q-learning to minimize execution cost subject to constraints on cost variance.