Crowdsourcing News & Topics in Algorithmic Trading, Quantitative Finance, …

Crowdsourcing is the internet model where anyone with a computer can be involved in an activity. At CloudQuant this is an opportunity for students, recent graduates, and career-changers to be involved in quantitative research and algorithmic trading. Anyone on the internet can use the free tools provided to create and prove that a trading algorithm works.

This is our CrowdSourced Trading Strategy Incubator. As a member of the crowd, we partner with you to bring your idea to market. This partnership is properly licensed so that you retain your rights to your strategy. When we both agree to the terms of the profit sharing deal, then we fund and run the algo you have already proven. Having a larger amount of capital results in a stronger possibility of succeeding in the markets. Your algo is able to run “At Scale” instead of inside the constraints of your own capital.

Posts

News for Machine Learning and Algo Trading

Deep Dive With JLN: How Can The Financial Industry Draw In The Next Generation Of Talent?

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The former trading floors provided a sense of community. Electronic trading communities like CloudQuant provide a bright future for new traders.
Morgan Slade and Tayloe Draughon with Jill Malandrino of NASDAQ

Discussing Crowd Sourcing Algos with Jill Malandrino of NASDAQ

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NASDAQ’s Jill Malandrino discusses crowd research in an Algo World with Morgan Slade and Tayloe Draughon at the FIA Expo 2017
Crowdsourcing Algorithmic Research

CloudQuant at FIA Expo in Chicago: perfect storm for open source revolution in quant trading

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The impact of machine learning and open source resources on quant trading could be described as explosive. At FIA Expo in Chicago, CloudQuant’s CEO Morgan Slade will be discussing how that’s translating into opportunity for a wider variety of participants.
chicago cityscape and sears tower

Built in Chicago: Wanna try your hand at high-frequency trading? There’s an app for that

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Built in Chicago discusses CloudQuant, a Chicago-based algorithmic trading startup, lets anyone try their hand at devising their own strategies.
John "Morgan" Slade

FintekNews: 3 Questions with John “Morgan” Slade of CloudQuant

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FintekNews recently asked 3 Questions of our CEO Morgan Slade. This is in response to our recently announced launch with a $15M allocation to a crowd based trading strategy algo creator.
Quantitative Strategy, Trading, and Algo Development Industry News

Quantitative Trading and Data Science in the News August 28, 2017

Quantitative Trading and Data Science in the News August 28 2017: CloudQuant opens door to crowd algo traders, RBC AI pilot, RBC pilots AI-based financial insight tools, AI focussed Chip, Momentum trading guide…
Morgan Slade, Python Data Scientist and Trader

Quant Trading and Superpowers: Morgan Slade speaks on Opportunity

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“You have a chance to try and change an industry” said Slade, CEO of CloudQuant at the MarketsWiki Education’s World of Opportunity event in New York.
Algo developer getting paid

Why the top jobs in finance will go to gig workers

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eFinancial Careers reported on the Battle of the Quants meeting in June 2017.  The article quotes CloudQuant CEO Morgan Slade in discussing opportunities to be paid by becoming a crowdsourcing algorithmic trading developer.   Quantitative research and data science applied to create trading strategies Morgan Slade, the CEO of CloudQuant, formerly with Merrill Lynch and Citadel, said they officially put their website up publicly six months ago and now researchers in 70 different countries are using it already. He sees this crowdsourced development model as an opportunity for students, recent graduates and career-changers alike. “We’re tapping into the new skills coming out of educational institutions and students’ and graduates’ new ways of looking at things, but there are also opportunities for experienced people to connect the dots related to the ontological relationships between the data and the stock markets and other assets,” Slade said. “There are huge untapped resources out there, and we try to engage with the researchers as if they were employees and support them as such.” Two of the freelance researchers wrote trading algorithms that were so impressive that Slade hired them as full-time portfolio managers. “We’re building [in-house] teams to support [freelance] researchers’ investment strategies, and we expect that to continue,” Slade said. “If we see people who are especially talented, then we might decide to have remote teams centered on individuals who stand out. “To be successful, they have to master a prediction step, a portfolio construction step and a trading expression step – we give researchers the chance to take a stab at portfolio construction and trade expression on their own,” he said. “We help them with things they’re not going to be good at, such as trade expression – we help them get better execution and give advice on portfolio construction if they need it. “People are spending their free time doing research, and if they weren’t getting something out of it, then they wouldn’t be engaged with our site, and there are also experienced traders who don’t have the ability to build what we have – people are eager to retain their IP, which we let them, so add it to our platform and we can both make money off of it.” Read the full article on eFinancial Careers
Your algo is always yours, not ours.

Your Algos are Your Private Property on CloudQuant

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Your Proprietary Trading Algorithm is always your property on CloudQuant. Any trading strategy that you develop is yours. Not ours.
  • You do not transfer ownership of the algo to CloudQuant.
  • You do not transfer any copyrights to CloudQuant.
  • We have no rights to reverse engineer your algo from you backtest outputs.
This is fundamental to the operations and success of CloudQuant.  We are most interested in our relationship with you, the crowd researcher, not an algorithm.  Algorithms come and go, but people are what makes our ecosystem extraordinary.  We know that over the long run you will come up with many great ideas once you develop your first profitable strategy and license it.  We want to develop a mutually profitable partnership with you.  It ordinary people with extraordinary ideas on a great platform that will revolutionize the industry. The User Agreement clearly states the following:
  • Everything you submit to the CloudQuant Services is “Input.”
  • We may also review the Output… (i.e. the results of the backtest)
Your code, your algo, your data used by your algo are all your “Input” which is specifically exempted from our ownership clause where we protect the services that we run in the cloud:
  • Excluding your Input, you acknowledge that we and our licensors have and shall continue to have exclusive ownership of the CloudQuant Services, Our Content, ..

Who See’s my Algos on CloudQuant?

Until you are in the licensing process no one sees your algos or your proprietary inputs. Our User Agreement (think Terms of Service) specifically states “We will not use performance data in a manner that would reveal the details and workings of the underlying Input.” The output of your algo, simulated trades, timing of trades, log files etc cannot be used by CloudQuant or any of our affiliates to reverse engineer your trading strategy. We are interested in seeing the scorecard when you submit your Fund My Strategy request. This covers the following information and is clearly visible on your backtest summary page.
ScoreCard

CloudQuant Backtest ScoreCard

 

The Licensing Process

The licensing process is where a crowd researcher asks for CloudQuant to review the performance of the algo and to consider licensing it from the algo owner for the purposes of live trading. The process covers:

Algo Creator

CloudQuant

Request Process
Request to “Fund My Strategy”  
Backtest Review Process
CloudQuant reviews algo output from algo creators specific backtests.
Mutual Non-Discloser Process
CloudQuant sends mutual Non-Disclosure Agreement (NDA) to client
Client signs mutual Non-Disclosure Agreement (NDA) and sends to CloudQuant   
Algo Review Process
Discuss the algo output and generalities of the algorithm. Discuss the algo output and generalities of the algorithm.
Compliance and Risk Review of Algo
Discuss sensitivity studies to determine risk allocation capacity and optimal trade execution
License Process
License Proposal with Profit Sharing Specifics
Agree to License the Algo to CloudQuant?
Signed License Agreement
Licensed Product Group Operations
Setup Operational Procedures Setup Operational Procedures
Complete final compliance and risk management review including reviewing algorithm code and installing required risk and compliance checks.
CloudQuant Crowd Supporting Operations runs algo.
Regular Reports on Performance and Feedback.
Monthly profit sharing
  You can clearly see the steps where an algo creator’s rights are clearly protected with the NDA and the license agreement. At any point in this process either party may stop this process at which point CloudQuant will not use, or attempt to duplicate your algo.

Fund My Strategy Request

When an algo creator requests the “Fund My Strategy” request they see the following statement. By clicking “Fund My Strategy” you are specifically requesting that Cloudquant LLC, in accordance with the User Agreement, review the Output and Simulation Performance Report (as defined in the User Agreement) for the script you have selected for licensing consideration. If your Input (as defined by the User Agreement) is selected for licensing, we will make a proposal to enter into a profit-sharing licensing agreement and we will Fund Your Strategy with our capital. We will independently fund and operate your strategy. You will receive royalty payments on any net trading profits in accordance with the licensing agreement.

The Algo Belongs to The Algo Creator

During the licensing process, which our users fully control, we will first evaluate the output but only when requested. If the output from your performance reports looks promising, then we both decide to proceed. We re-affirm that the algo belongs to the user at multiple steps throughout this licensing process. There is language on the website, it is covered in the mutual NDA, and in the licensing agreement. At no point is the copyright of the algo transferred to CloudQuant. The algo belongs to the algo creator not to CloudQuant. We will not operate the algo without a proper license agreement in place.

Other Protections

Technology

The algorithms and data that support the algorithms (“Input”) are stored within CloudQuant’s services. They are protected by a username based security mechanism. The services prevent all users from seeing someone else’s algos and input data.

The CloudQuant Employment Agreement

Every CloudQuant employee and contractor signs an employment agreement that clearly specifies that accessing or sharing protected inputs is forbidden. Employees are prevented from duplicating any user algo. This is a very restrictive and specific agreement written to protect the rights of the algo creator and of CloudQuant LLC.

Operating Algos by CloudQuant Crowd Researcher Support Team

Algos are operated by the CloudQuant crowd trader support team known as the Licensed Product Group. They are dedicated to the algo creators and running only those algos licensed from our user community. These traders, are experienced professionals, but are not proprietary traders and do not trade for their own accounts, or for any CloudQuant or Kershner Trading account. The compliance and risk review ensures that these operators are aware of the generalities of how the algo operates. This is required by our regulators. The operators are responsible for ensuring that the algo is performing in accordance to agreed upon parameters and within risk parameters. Information barriers prevent the traders at our parent company, Kershner Trading Group, from having knowledge of our operations in the Licensed Product Group.

The Algo Doesn’t Belong to CloudQuant

Your Proprietary Trading Algorithm is always your property. Any trading strategy that you develop is yours. Not ours. You do not transfer ownership of the algo to CloudQuant. We are most interested in our relationship with you, the crowd researcher, not an algorithm.  We know that over the long run you will come up with many great ideas once you develop your first profitable strategy.  We want to develop a mutually profitable partnership with you. We take steps to properly protect your property through legal agreements between you and CloudQuant and between CloudQuant and our team. We take steps to protect your property with technology. You can have the utmost confidence in CloudQuant and in everything you do with our cloud services.
World Market Access

2017 – The Year of Artificial Intelligence

2017 is the year of artificial intelligence. Here’s why

World Economic Forum published that Artificial Intelligence (AI) is a rapidly growing discussion point in corporations and governments. This is driven by: 1. Everything is now becoming a connected device

The internet of things is collecting data in ways never before possible.

2. Computing is becoming free

The cost of computing continues to drop, especially with crowdsourced research platforms like CloudQuant.

3. Data is becoming the new oil

“The amounts and types of data available digitally have proliferated exponentially over the last decade, as everything has moved online, been made mobile with smartphones, and tracked via sensors. New sources of data emerged through things like social media, digital images and video.” 

4. Machine learning is becoming the new combustion engine

“new machine learning models have emerged recently that seem to be able to take better advantage of all the new data. For example, deep learning enables computers to ‘see’ or distinguish objects and text in images and videos much better than before.”

At CloudQuant our crowd researchers are finding that access to markets, and to data is allowing them to research and develop profitable algos in ways never before conceived. Access to new data sets, like social sentiment, allow new dimensions of quantitative strategies that were not conceived even five years ago. We anticipate that the new data “oil” and machine learning “engines” will continue to grow our world of trading.   See the full article on World Economic Forum’s web site by Sandhya Venkatachalam (24 May 2017).