最有看点的互联网金融门户

最有看点的互联网金融门户
传统金融的互联网化国际资讯基于互联网平台的金融业务

《经济学人》:众包型量化投资平台Quantopian的发展之路

一直以来,量化对冲基金被誉为资产管理行业"皇冠上的明珠",行业中汇集了大量尖端人才。Renaissance Technologies、Two Sigma和Man AHL等对冲基金管理的资产数额均已达到数百亿美元,为了跑赢市场,它们聘请多名天才数学家和物理学家,通宵达旦地编写交易算法。然而,对于普通投资者来说,如此高大上的量化对冲基金距离较远,一直较为神秘,不过随着众包(Crowdsourcing)概念的出现,量化投资平台的发展模式也逐渐向"群众化"靠拢。

众包(Crowdsourcing),是由美国《连线》杂志的记者Jeff Howe在2006年6月提出的。它是指一个公司或机构把过去由员工执行的工作任务,以自由自愿的形式外包给非特定的(而且通常是大型的)大众网络的做法。在以信奉精英的金融投资领域,互联网更是给这一模式增添了新的活力。

Quantopian成立于2011年,是一家提供在线策略编写、回测、交易服务及社区交流的众包型量化投资平台。Quantopian不仅提供美国证券市场上市公司的海量历史行情数据,并且具备非常高效的回测服务后台,同时也接入了盈透证券(Interactive Broker)的交易通道。用户不仅能通过提交Python策略程序进行在线历史数据回测,同时也可以将交易策略投入实盘交易。

自成立以来,Quantopian一直坚持走"群众化路线"。它瞄准的并非投资领域的顶级专业人士,而是量化投资行业的新手,因此,相比于其他平台,庞大的用户群就成为了Quantopian发展中的一个显著优势。一般来说,量化分析领域人才较为稀缺,再加上各公司保密条款的限制,导致各平台职员人数只有100人上下,例如,管理资产总额达到424亿美元的Man AHL实则只有120名员工。相比之下,Quantopian的注册会员人数已经达到12万,相当于Man AHL的1000倍。

Quantopian首席执行官John Fawcett表示,平台的注册会员只是业余的"宽客",并非公司的正式员工。"宽客"通常已经在金融投资领域具有一定的算法编写经验,他们登陆Quantopian的主要目的是为了进一步了解如何将算法应用到实际的交易当中。

Quantopian会挑选部分算法,募集基金进行投资。同时,平台会收取一定的管理费用,而算法作者也从基金收益中获取大约10%作为回报。

上个月,Quantopian刚刚挑选了平台上的15种算法进行投资。John Fawcett表示,公司将在未来加大投资力度,为"宽客"提供更多的机会。

去年11月,Quantopian获得了由Andreessen Horowitz领投的2500万美元C轮融资,表明了众包型量化投资平台在未来巨大的发展潜力。类似于Quantopian,WorldQuant的WebSim平台也选择了走"群众化"之路。该平台也为用户提供包括略编写、回测等方面的服务,不同之处在于,WorldQuant专注于更为基础的量化投资分析。其中,杰出的算法作者还有机会被平台的兼职研究顾问。目前,该平台兼职顾问人数已经超过500,相当于WorldQuant的正式员工数量。

根据Quantopian发布的报告显示,公司对选中的每个算法的投资额大约为10万美元到300万美元。当然,Quantopian作为行业中的先驱者,难以判断其做法是否有效。此外,尽管Quantopian在2016年对算法的投资回报高达40%,但由于公司尚未开展外部资产管理业务,投资所用资金均来自自有资产。因此,当Quantopian在未来真正引入外部资金时,必将面临更为严峻的监管压力,到那时,"群众智慧"还能否成为Quantopian发展的取胜之匙?

A new sort of hedge fund bets on the expertise of amateurs

quant hedge funds have long been seen as the nerdyvanguard of finance. Firms such as Renaissance Technologies, Two Sigma and Man AHL, each ofwhich manages tens of billions of dollars, hire talented mathematicians and physicists to sit in their airy offices and develop trading algorithms. But what if such talent could be harnessed without the hassle of an expensive and time-consuming recruitment process? That is the proposition Quantopian, a hedge fund and online crowd-sourcing platform founded in 2011, is testing. Anyone can learn to build trading algorithms on its platform. The most successful are then picked to manage money. Last month the firm announced it had made its first allocations of funds to 15 algorithms it had selected.

Quantopian would appear to have one striking advantage over its competitors: sheerweight of numbers. The difficulty of hiring and a desire for secrecy limit even big quant funds to a full-time research staff in the low hundreds (Man AHL, for instance, has 120). Quantopian boasts 120,000 members on its platform.

These are amateurs, however, not fulltime employees. John Fawcett, Quantopian’s CEO, says many sign up to learn how to apply algorithms to trading; they usually already have experience in coding and modellingin domainsoutside finance. Few will have their algorithms selected, an honour that comes with a licensing fee of 10% of net profits . The rest can at least use their algorithms to trade their own money.

MrFawcett plans both to allocate funds to more algorithms, and to increase allocations to those already picked. There is no dearth of capital. Steve Cohen, a big-name investor who survived an insider-trading scandal at his previous hedge fund, provided some of Quantopian’s venture-capital funding and has pledged up to $250m to promising algorithms on the platform. The firm intends to launch a fund open to other investors this year.

Quantopian-like models have the potential to bring the gig economy to high finance. Most people on its platform hold full-time jobs or are students, earning some income on the side. At least one quant hedge fund has already bet on the trend. WorldQuant’s WebSim platform, like Quantopian’s, offers access to financial data and a wayto test ideas, though it is geared towards more basic research. The best performers on WebSim can become paid part-time research consultants, of whom there are now close to 500, nearly asmany asWorldQuant’s full-time staff.

It is still early to judge Quantopian’s allocations (ranging from $100,000 to $3m per algorithm) by their financial return. As a pioneer, it has no obvious comparators. Some algorithms at Quantiacs, a competitor with only around 6,000 members on its platform, have generated up to 40% returns in the past year, but that is with small allocations of capital (Quantiacs has yet to manage outside assets). So the real test for the crowd-sourcers lies ahead: will a deeper talent pool mean better performance, even when seriousmoney is involved?


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