This paper presents novel results generated from a new simulation model of a contemporary financial market, that cast serious doubt on the previously widely accepted view of the relative performance of various well-known public-domain automated-trading algorithms. Various public-domain trading algorithms have been proposed over the past 25 years in a kind of arms-race, where each new trading algorithm was compared to the previous best, thereby establishing a "pecking order", i.e. a partially-ordered dominance hierarchy from best to worst of the various trading algorithms. Many of these algorithms were developed and tested using simple minimal simulations of financial markets that only weakly approximated the fact that real markets involve many different trading systems operating asynchronously and in parallel. In this paper we use BSE, a public-domain market simulator, to run a set of experiments generating benchmark results from several well-known trading algorithms. BSE incorporates a very simple time-sliced approach to simulating parallelism, which has obvious known weaknesses. We then alter and extend BSE to make it threaded, so that different trader algorithms operate asynchronously and in parallel: we call this simulator Threaded-BSE (TBSE). We then re-run the trader experiments on TBSE and compare the TBSE results to our earlier benchmark results from BSE. Our comparison shows that the dominance hierarchy in our more realistic experiments is different from the one given by the original simple simulator. We conclude that simulated parallelism matters a lot, and that earlier results from simple simulations comparing different trader algorithms are no longer to be entirely trusted.

中文翻译：

---

哪个交易代理最好？使用金融市场的线程并行模拟改变啄食顺序

本文提出了由当代金融市场的新模拟模型产生的新结果，这对以前广泛接受的各种知名公共领域自动交易算法的相对性能的观点提出了严重质疑。在过去的 25 年里，在一场军备竞赛中提出了各种公共领域的交易算法，将每种新的交易算法与以前最好的交易算法进行比较，从而建立“啄食顺序”，即从各种交易算法中的最佳到最差。许多这些算法是使用金融市场的简单最小模拟来开发和测试的，这些模拟只是微弱地近似了真实市场涉及许多不同的交易系统异步和并行运行的事实。在本文中，我们使用 BSE，一个公共领域的市场模拟器，运行一组实验，从几个著名的交易算法生成基准结果。BSE 结合了一种非常简单的时间切片方法来模拟并行性，该方法具有明显的已知弱点。然后我们改变和扩展 BSE 以使其成为线程，以便不同的交易者算法异步和并行运行：我们将此模拟器称为线程 BSE (TBSE)。然后我们在 TBSE 上重新运行交易者实验，并将 TBSE 结果与我们早期的 BSE 基准结果进行比较。我们的比较表明，我们更现实的实验中的支配层次与原始简单模拟器给出的不同。我们得出结论，模拟并行性很重要，