# Nonstationary Continuum-Armed Bandit Strategies for Automated Trading in a Simulated Financial Market

arXiv: https://arxiv.org/abs/2208.02901 · HTML: https://mlsanity.com/paper/2208.02901/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [BSE: A Minimal Simulation of a Limit-Order-Book Stock Exchange](https://mlsanity.com/paper/1809.06027/) (2018)
- [Finite Continuum-Armed Bandits](https://mlsanity.com/paper/2010.12236/) (2020)
- [Parameterised-Response Zero-Intelligence Traders](https://mlsanity.com/paper/2103.11341/) (2021)

## Abstract (arXiv)

We approach the problem of designing an automated trading strategy that can consistently profit by adapting to changing market conditions. This challenge can be framed as a Nonstationary Continuum-Armed Bandit (NCAB) problem. To solve the NCAB problem, we propose PRBO, a novel trading algorithm that uses Bayesian optimization and a ``bandit-over-bandit'' framework to dynamically adjust strategy parameters in response to market conditions. We use Bristol Stock Exchange (BSE) to simulate financial markets containing heterogeneous populations of automated trading agents and compare PRBO with PRSH, a reference trading strategy that adapts strategy parameters through stochastic hill-climbing. Results show that PRBO generates significantly more profit than PRSH, despite having fewer hyperparameters to tune. The code for PRBO and performing experiments is available online open-source (https://github.com/HarmoniaLeo/PRZI-Bayesian-Optimisation).