论文标题
在模拟金融市场中，非平稳连续武装武装盗销策略
Nonstationary Continuum-Armed Bandit Strategies for Automated Trading in a Simulated Financial Market
论文作者
论文摘要
我们解决了设计一种自动交易策略的问题，该策略可以通过适应不断变化的市场状况来始终如一地获利。这项挑战可以作为非平稳连续武装强盗（NCAB）问题进行构架。为了解决NCAB问题，我们提出了PRBO，这是一种新颖的交易算法，它使用贝叶斯优化和``bandit-over-bandit''框架来动态调整策略参数，以响应市场条件。我们使用Bristol证券交易所（BSE）来模拟包含自动交易代理商异构种群的金融市场，并将PRBO与PRSH进行比较，PRSH是一种参考交易策略，通过随机山攀爬来适应策略参数。结果表明，尽管调节超参数较少，但PRBO比PRSH产生的利润明显高。 PRBO和执行实验的代码可在线开放源（https://github.com/harmonialeo/przi-bayesian-optimisation）。
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).
