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CloudQuant Is a Trade Strategy Incubator That’s Looking to Develop and Fund Algorithm Traders

The company is looking to harness the quant trading revolution and is offering a free platform for aspiring algo traders to experiment.

Algorithmic trading has quickly become one of the hottest growth areas in the financial industry today, and is perhaps the most talked about trading approach on Wall Street right now. These advanced trading systems process massive amounts of data through complex financial models to identify optimal alpha opportunities. What’s more, these trades are typically automated and done in high volume, maximizing cost and time efficiency.

But algo trading has also drawn interest from professionals with more diverse backgrounds outside of finance, particularly those from the science and technology communities. A rising population of programmers, data scientists and mathematicians are now looking to write complex codes for automated investment strategies of their own. The challenge is gaining access to tools and data sets typically available only to institutional financial firms and hedge funds.

But now, one ambitious company looking to harness this quant trading revolution and partnering with those aspiring algo traders is CloudQuant, a free opensource, cloud-based trading strategy incubator that allows data scientists and programmers from all walks of life a chance to test their trading idea with historical data and an advanced backtesting engine. For strategies that show potential, CloudQuant will actually license the algorithm and allocate real capital to test it on the market, sharing any profits generated from the strategies with their creators.

The company just announced another $10 million allocation for a second trading algorithm. The funding went to an ETF-focused algorithmic strategy with significant capacity. Since September this year, the crowd researcher worked with CloudQuant’s Licensed Product quant execution team members to productionize the ETF strategy.

As stated above, the strategy was put in production with a $10 million allocation and the algorithm licensor will receive a direct share of the monthly net trading profits. CloudQuant licensed its first trading strategy earlier in August, allocating $15 million to fund it. spoke with Morgan Slade, CEO of CloudQuant, to learn more about the platform and about the company’s innovative business model.

EQ: What is CloudQuant and who is this platform for?

Morgan: CloudQuant is a trading strategy incubator. We target anybody globally who has an interest in developing quantitative trading strategies using institutional tools and would like to get funding to share in the profit. We lease strategies from the strategy creators, whether they would be an experienced trader, an engineer who has gotten into quantitative investing or a data scientist who lives in another country. Moreover, we give them the same tools that you’d have at a large hedge fund. If they’re successful at coming up with a good strategy, we lease it from them and they are able to earn 10% of the profit.

EQ: If someone is interested in using CloudQuant how does it work? Is there a vetting process? Can you walk through the basic steps to get started?

Morgan: it’s the world’s first free microsecond-level backtesting framework and data set that is available to everyone. It’s on the cloud and open to anyone with an internet connection. We work with people from 132 different countries. We do vet strategies before we fund them using some proprietary criteria and the machine learning tools that we have, but, in general, if you have something with a Sharpe Ratio [a measure for calculating risk-adjusted return] of over two, your profit-per-share traded is more than a penny or two and you win more than 60% of all months and 75% of all quarters, then we would probably have an interest in the strategy particularly if it has very low beta [a measure of a stock’s volatility compared to the market. Thus, “low beta” would mean low risk] to the S&P Index.

EQ: at that point, when you find a strategy that you like, do you reach out with the creators or is it done through the platform?

Morgan: the platform has a ‘fund my strategy’ button that allows you to bring your backtest results to our attention. Anything that you added in CloudQuant is your private property and your personal IP. We allow people the freedom to do whatever research they want, but we do – through our user agreement – have the right to look at certain aspects like Sharpe Ratios and profitability measures to see if it’s maybe worth having a conversation with a crowd researcher to see if they want to join one of our global research programs.

EQ: the concept of crowdsourcing algorithmic trading ideas is a very relevant and popular idea in modern terms, but amazingly CloudQuant launched in 2016 well before these ideas became mainstream. How did this idea originate?

Morgan: The idea was something that Kershner Trading Group, the parent company, had been thinking about and building in various substantiations since the 1990s. As time went on, they became more interested in doing quantitative trading. So, they ultimately started from scratch and built something using cloud technology, alternative data sets, high frequency data, machine learning and crowd researchers to do the research at scale.

For us, crowd research is a way to allow people to test us out and try this career without having to take any risks, as well as levelling the playing field for people who wouldn’t have gotten an interview on Wall Street because maybe they didn’t go to the right school to get recruiters attention but are still extremely bright. CloudQuant is a way for this demographic to contribute their own ideas and receive recognition.

In addition, we think there is a lot of group think in the large hedge funds because of all the players that work there who have gone from fund to fund taking ideas with them and repeatedly sharing them. As a result, the hedge fund space is fairly crowded with the same ideas. So, we hope to bring in new perspectives from people who are not affiliated with Wall Street.

EQ: to that point, algorithmic trading – and to a broader sense passive investing – has become extremely popular in recent years. In terms of timing, what would you say is the interest level from your addressable market base?

Morgan: we have had incredible interest in the product. We get emails every week talking about how wonderful the platform is and that people can’t believe it’s available for free. Think about it: people can do high frequency backtesting without paying any licensing fee. They have access to alternative data sets that normally you’d have to work at one of the top hedge funds in the world to access. We have the ability to let thousands and thousands of crowd researchers kick the tires on their data sets, which allows us to have a much bigger team in essence. It all amount to a very dynamic and flexible research team that we don’t pay upfront.

EQ: and if their strategy ends up losing money, they don’t have anything at risk?

Morgan: Exactly. It’s our capital from the family that funds Kershner Trading Group.

EQ: What are some of the tools people can use on CloudQuant that they can’t easily find elsewhere? What is the value proposition that you’re offering to the community?

Morgan: the value proposition is probably twofold. First, we have been trading for over 20 years and running a quantitative fund on CloudQuant for over five years. So, we have been producing trading strategy examples that people can learn from in our community and we will soon be posting those on the platform. Second, the primary value proposition for most people is going to be a way to take their trading ideas and get an allocation very easily – have a very transparent formula where you see your account. You can see how much was made each day and you can do the math in your head on how much of that is yours. This is, I think, is an incredible economic opportunity for most of our users.

EQ: Are users able to collaborate with each other on strategies to put their heads together to come up with a strategy that is more robust?

Morgan: absolutely. We have a forum where people can post questions and answers or share a code with each other. They can talk about strengths and weaknesses of various backtests. Furthermore, we are in the process of rolling out a new feature allowing people to collaborate through GitHub and other places, so that they can share code and work on projects together.

EQ: Is there anything else you think people should know about CloudQuant?

Morgan: CloudQuant is completely free with a 10% share of profit, which is half the usual incentive fee for typical hedge funds. We think this is a really exciting proposition for somebody who is not an equity holder in a management company. Lastly, we offer alternative data sets and the ability to employ machine learning on an advanced platform, which for many folks could be very interesting.

As the markets put the debt ceiling debacle in the rearview mirror, more than a few issues remain open.