Autonomous trading robots have been studied in ar-tificial intelligence area for quite some time. Many AI techniqueshave been tested in finance field including recent approaches likeconvolutional neural networks and deep reinforcement learning.There are many reported cases, where the developers are suc-cessful in creating robots with great performance when executingwith historical price series, so called backtesting. However, whenthese robots are used in real markets or data not used intheir training or evaluation frequently they present very poorperformance in terms of risks and return. In this paper, wediscussed some fundamental aspects of modelling autonomoustraders and the complex environment that is the financialworld. Furthermore, we presented a framework that helps thedevelopment and testing of autonomous traders. It may also beused in real or simulated operation in financial markets. Finally,we discussed some open problems in the area and pointed outsome interesting technologies that may contribute to advancein such task. We believe that mt5b3 may also contribute todevelopment of new autonomous traders.
翻译:自主交易机器人在人工智能领域已经研究了相当长一段时间了。许多AI技术在金融领域已经进行了测试,包括最近的一些方法,如革命神经网络和深层强化学习。有许多报告的案例,开发商在用历史价格序列执行时能够成功创造出具有良好性能的机器人,称为回测试。然而,当这些机器人在实际市场使用时,或者在培训或评估中没有使用过的数据,往往在风险和回报方面表现极差。在本文中,我们讨论了建立自主交易商模型的一些基本方面,以及金融界的复杂环境。此外,我们提出了一个框架,帮助自主交易商的发展和测试,也可能用于金融市场的实际或模拟操作。最后,我们讨论了该地区的一些公开问题,并指出了一些可能有助于推进这项工作的有趣技术。我们认为,MT5b3 也可能有助于新的自主交易商的发展。