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Steve Cohen’s Point72 Ventures backs Quantopian

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Hedge fund titan Steve Cohen’s venture capital arm, Point72 Ventures LLC, has entered into an agreement with Quantopian to manage up to USD250 million of investment capital. Point72 Ventures, an affiliate of Point72 Asset Management LP, provides early stage capital to startups in fields such as data mining, artificial intelligence, and machine learning.

Quantopian is a platform that over the last five years has built a global network of more than 85,000 members in the pursuit of creating institutional-quality investment algorithms. The plan is for the best algorithms on Quantopian to manage a portion of Point72’s assets, with each author receiving a royalty based on their strategy’s performance.

“Quantopian has become a magnet for talented people who are really passionate about quantitative finance. As we’ve gotten to critical mass, people in the funds industry have started to take notice and those ripples made their way to Steve Cohen. He is very articulate about the challenges that the fund industry faces in relation to finding talent.

“The sizeable allocation by Point72 is an incredible validation for the platform and creates a significant financial opportunity for the authors whose algorithms get selected, as we will be able to make larger allocations,” explains Quantopian’s CEO, John ‘Fawce’ Fawcett. 

That Cohen has decided to back investment algorithms designed by people spanning the globe is, according to Fawcett, a “watershed moment for the entire industry”. Matthew Granade, the leader of Point72 Ventures, has been quoted as saying that the scarce resource in quantitative investing is talent. “Quantopian has demonstrated an innovative approach to finding that talent,” says Granade, adding that Point72 would benefit from an “ocean of untapped talent”.

Quantopian’s portfolio of investment algorithms is selected from the more than 400,000 algorithms written by its community of over 85,000 members, including professors, finance professionals, research scientists, developers, and students. By offering an institutional quality platform online and for free, with access to extensive data, Quantopian gives anyone the ability to create and test investment algorithms – and be rewarded for their work.

Quantopian evaluates the performance of each algorithm on its platform and makes allocations to selected algorithms based on factors including return, risk, style, capacity, and interaction effects. The authors of the selected algorithms receive a share of the profits generated from their investment strategies.

“Currently, in Quantopian’s proprietary account, which is separate from the forthcoming Point72 fund, we have ten algorithms running, with several on the horizon,” confirms Fawcett. “We are entering a new age of the internet where it’s not about consumption, but about creativity and producing new ideas and new intellectual property. 

“In my opinion, quantitative methods are still under-utilised for professional investing. They are heavily used in trading, but they have yet to be fully embraced and used in asset management; partly because there are not enough people with the requisite skills in financial hubs like New York and London.”

Platforms like Quantopian are changing the marketplace as individuals who, ten or 15 years ago, might have had to move to New York to make it in finance, are coming together from all regions of the world to connect with fellow Quantopian members, share ideas, learn how to build algorithms, refine them, test them, and pit their wits. 

“Quantopian decentralises things. People assume in the industry that talent is limited to places like London or New York, which I find startling. It’s an illusion. Based on the people we meet and the work they do on the platform, I feel like we know now that talent is not limited to those places; it’s pretty randomly distributed across the globe,” suggests Fawcett. 

Fawcett is keen to emphasise the culture at Quantopian. It is, he says, a pure relationship with the authors; one which doesn’t seek to harness people’s ideas and operate as a ‘net user’ of people. Community is a key part of the Quantopian strategy with benefits directly to everyone that participates as they learn about coding, about finance, about machine learning and so on.

“We’ve made it a real learning experience for all our members. I’d like to think we are inspiring people to do this work,” comments Fawcett. 

He goes on to explain that once a member has written a strategy, it then goes into a vast database where Quantopian records the exact version of the code they wrote, along with the date that they wrote it.

“That date marks a clear line between the data the author could have known about when building the algorithm; which we call ‘in sample’ data. After that date, we use true ‘out of sample’ data, which the author could not have known about.”

This gets to the heart of the most pressing problem within asset management and manager selection. It’s hard to tell if the historical track record of an investment strategy will perform in the future. 

“As everything is fully automated, we have a unique opportunity where we can test algorithms on data that the authors would not have seen before. That ‘out of sample’ testing is really our hallmark, our edge,” adds Fawcett. 

Quantopian uses a vast array of machines in the cloud to select algorithms for capital deployment. This involves going into the database to find all the strategies that were created six months ago, and running an extensive range of tests. The majority do not survive testing. 

Approximately 1 per cent get through, says Fawcett, adding that stage two involves a review by Quantopian’s research team: returns analysis, risk factor analysis, the behaviour of the algorithm in stress periods, transaction cost analysis, etc. 

“The final stage involves speaking to the author about the economic rationale behind the strategy, which provides us with a qualitative overlay. Providing that all comes together, we set up a licensing agreement and deploy capital to the algorithm,” concludes Fawcett.

A portion of Point72’s committed capital will be contingent on Quantopian meeting certain performance metrics based on the algorithmic strategies that eventually get selected.

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