牛津大学推出全球首个算法交易在线课程 - 互联网金融门户 未央网

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牛津大学推出全球首个算法交易在线课程

其他国际资讯

牛津大学推出全球首个算法交易在线课程

随着市场效益和投资业绩改善给基金带来的收益越来越多,算法/系统交易的发展也越来越快。

据统计,目前算法交易占整个对冲基金交易的20%左右,越来越多的交易者希望了解并参与基金和相关风险的管理。

面对这一趋势,近日牛津大学赛德商学院推出了全球首个算法交易在线课程,希望借此推动人们对算法交易规则的理解,帮助人们更好地进行基金投资。

该计划是在英国金融监管机构最近敦促算法交易公司加强控制之后发布的。此前英国金融行为监管局报告警告称,企业需要做更多工作来识别和减少其算法交易策略所带来的潜在风险。

计划召集人、牛津大学经济学副教授Nir Vulkan表示,算法交易是对冲基金管理的重要组成部分,并且其份额和规模都在增长。如果你有志从事这一行业,那么就应该从现在开始学习算法交易。

之前,人们认为技术、大数据和金融是相互冲突的,没有一门可能能够很好地将这些知识融合起来。然而,如今随着自动化的普及,系统交易的收益逐步走高,人工智能和机器学习的不断深化,这种融合就变得越发的重要和可能了。

据了解,本次课程将于2018年7月25日开始,为期6周。

在这6周中,学员将全面了解行为金融学理论,了解能够为算法所复制的人类行为模式。此外,该可能还会带领学员全面了解交易历史、如何参与和评估系统交易,并让学员有机会亲自建模进行测试。

Vulkan表示,一个好的交易员会遵循规则,同时通过算法去除初步规则中存在的人类偏见和情绪因素,从而提高算法交易的能力。

据悉,该课程还会邀请多位行业专业人士进行授课或提供参考意见。

 

 

 

Algorithmic, or systematic, trading is gaining momentum as funds benefit from market efficiencies and performance of investments improve. With algorithmic trading estimated to account for 20% of hedge funds, traders are looking to understand how to engage with funds and manage for risk.

Recognising these emerging trends Saïd Business School, University of Oxford has launched the Oxford Algorithmic Trading Programme, the first of its kind worldwide. It will provide an understanding of the rules that drive successful algorithmic trading strategies and give participants the ability to decide whether a fund is worth investing in.

Programme Convener Nir Vulkan, Associate Professor of Business Economics at Oxford Saïd commented: ‘Algorithmic trading is a significant part of hedge fund management and it is growing in share and size. If you’re starting in this industry or planning to stay in it, now is the time to educate yourself on algorithmic trading.

‘This is an area where technology, big data and finance collide. There hasn’t been a course of this nature before, that combines different disciplines. It’s relevant now more than ever, because of the increase in automation and because systematic funds have been gaining more momentum and becoming more popular, while AI and machine learning are getting better.’

The programme will be delivered online over 6 weeks giving finance professionals a thorough understanding of the behavioural finance theory that identifies patterns in human behaviour that can be replicated by algorithms. ‘Good traders follow good rules. The next logical step is to keep the rules but remove human bias and emotions completely from decisions, making algo funds potentially stronger,' said Vulkan.

The programme will explore the history of trading, how to engage and evaluate systems trading, and enable participants to get their hands dirty and see how a simple trading model is built. Alongside Professor Vulkan, programme participants will hear from a variety of industry professionals, including Martin Leuck, Director of Research, Aspect Capital, Matthew Sargaison, co-CEO, MAN AHL, Euan Kirk, CIO GAM Systematic, and Steve Mobbs, Founder, Oxford Asset Management, as well as academics leading research in the field.

Peter Tufano, Peter Moores Dean at Saïd Business School, commented: ‘Research suggests that some professional careers might be challenged as technology rapidly transforms markets, institutions, and business models. Supported by our partner GetSmarter, our suite of online programmes that includes fintech, blockchain strategy, and now algorithmic trading, will help entrepreneurs and executives future-proof their careers as they navigate the changing landscape.’

The programme comes after a British financial watchdog recently urged algorithmic trading firms to tighten controls. The British Financial Conduct Authority report warned 'firms need to do more work to identify and reduce potential conduct risks created by their algorithmic trading strategies.'

The report continued 'some firms lacked a suitable process to identify algorithmic trading across their business and did not have appropriate documentation in place to demonstrate suitable development and testing procedures are maintained.'

The online programme will begin on 25 July 2018, and will run for 6 weeks.


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