We describe three independent implementations of a new agent-based model (ABM) that simulates a contemporary sports-betting exchange, such as those offered commercially by companies including Betfair, Smarkets, and Betdaq. The motivation for constructing this ABM, which is known as the Bristol Betting Exchange (BBE), is so that it can serve as a synthetic data generator, producing large volumes of data that can be used to develop and test new betting strategies via advanced data analytics and machine learning techniques. Betting exchanges act as online platforms on which bettors can find willing counterparties to a bet, and they do this in a way that is directly comparable to the manner in which electronic financial exchanges, such as major stock markets, act as platforms that allow traders to find willing counterparties to buy from or sell to: the platform aggregates and anonymises orders from multiple participants, showing a summary of the market that is updated in real-time. In the first instance, BBE is aimed primarily at producing synthetic data for in-play betting (also known as in-race or in-game betting) where bettors can place bets on the outcome of a track-race event, such as a horse race, after the race has started and for as long as the race is underway, with betting only ceasing when the race ends. The rationale for, and design of, BBE has been described in detail in a previous paper that we summarise here, before discussing our comparative results which contrast a single-threaded implementation in Python, a multi-threaded implementation in Python, and an implementation where Python header-code calls simulations of the track-racing events written in OpenCL that execute on a 640-core GPU -- this runs approximately 1000 times faster than the single-threaded Python. Our source-code for BBE is freely available on GitHub.

中文翻译：

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实现基于 BBE 代理的体育博彩交易所模型

我们描述了基于代理的新模型 (ABM) 的三个独立实现，该模型模拟当代体育博彩交易，例如由 Betfair、Smarkets 和 Betdaq 等公司提供的商业交易。构建这个被称为布里斯托尔博彩交易所 (BBE) 的 ABM 的动机是，它可以充当合成数据生成器，生成大量数据，可用于通过高级数据开发和测试新的博彩策略分析和机器学习技术。博彩交易所充当在线平台，博彩玩家可以在该平台上找到愿意下注的交易对手，他们这样做的方式与电子金融交易所（例如主要股票市场）充当平台的方式直接可比，让交易者能够寻找愿意从以下位置购买或出售的交易对手：该平台汇总并匿名处理来自多个参与者的订单，显示实时更新的市场摘要。首先，BBE 的主要目的是为赛中投注（也称为赛中或比赛中投注）生成合成数据，其中投注者可以对田径赛事的结果下注，例如马比赛开始后和比赛进行期间，投注仅在比赛结束时停止。BBE 的基本原理和设计已在我们在此总结的前一篇论文中详细描述，然后讨论对比 Python 中的单线程实现和 Python 中的多线程实现的比较结果，以及 Python 标头代码调用在 640 核 GPU 上执行的用 OpenCL 编写的赛道赛事模拟的实现——这比单线程 Python 快大约 1000 倍。我们的 BBE 源代码可在 GitHub 上免费获得。