We present first results from the use of XGBoost, a highly effective machine learning (ML) method, within the Bristol Betting Exchange (BBE), an open-source agent-based model (ABM) designed to simulate a contemporary sports-betting exchange with in-play betting during track-racing events such as horse races. We use the BBE ABM and its array of minimally-simple bettor-agents as a synthetic data generator which feeds into our XGBoost ML system, with the intention that XGBoost discovers profitable dynamic betting strategies by learning from the more profitable bets made by the BBE bettor-agents. After this XGBoost training, which results in one or more decision trees, a bettor-agent with a betting strategy determined by the XGBoost-learned decision tree(s) is added to the BBE ABM and made to bet on a sequence of races under various conditions and betting-market scenarios, with profitability serving as the primary metric of comparison and evaluation. Our initial findings presented here show that XGBoost trained in this way can indeed learn profitable betting strategies, and can generalise to learn strategies that outperform each of the set of strategies used for creation of the training data. To foster further research and enhancements, the complete version of our extended BBE, including the XGBoost integration, has been made freely available as an open-source release on GitHub.

翻译：我们首次展示了在布里斯托尔博彩交易所（BBE）中应用XGBoost（一种高效机器学习方法）的研究成果。BBE是一个开源智能体模型（ABM），旨在模拟包含赛马等赛道赛事实时投注的当代体育博彩交易所。我们将BBE ABM及其一系列最小简约型投注智能体作为合成数据生成器，为我们的XGBoost机器学习系统提供数据，目的是让XGBoost通过学习BBE投注智能体中获利更高的投注行为来发现可盈利的动态投注策略。经过上述XGBoost训练（生成一个或多个决策树）后，我们将具有由XGBoost学习所得决策树决定的投注策略的智能体重新嵌入BBE ABM，使其在不同条件和投注市场场景下对一系列赛事进行投注，并以盈利性作为主要比较与评估指标。我们初步研究结果表明：以该方式训练的XGBoost确实能学习到可盈利的投注策略，并能泛化出优于训练数据生成策略集中所有单一策略的新策略。为促进后续研究与改进，包含XGBoost集成的完整扩展版BBE已作为开源版本在GitHub上免费发布。