Allocations of Capital to Trading Algorithms

Capital allocation describes how CloudQuant, or another fund, divides their financial resources and other sources of capital to different trading strategies. Fund managers optimize capital allocation so that it generates as much return on capital as possible.

The heart of the CloudQuant business plan is our client algorithm license agreement. The algorithms created by our clients are their proprietary information. We clearly state this in our user agreement.

Once a researcher requests that we fund their strategy, we will review the simulation performance report. If the algo is selected for licensing, we provide the client with a proposal to enter into a mutually beneficial profit-sharing licensing agreement. When both parties agree to these terms we will fund (allocate) the strategy with our own capital.

To learn more about the requirements for a CloudQuant risk capital allocation see “The Trading Strategy Incubator.

See also: CloudQuant’s record of allocations.


$20M Allocation to Crowd Developed Trading Algorithm

CloudQuant Allocates Risk Capital to Crowd-Resourced Trading Algorithm



Chicago, Illinois, USA, May 18, 2018 – CloudQuant, one of 50 Most Promising FinTech Solution Providers of the year, has allocated risk capital to a crowd-resourced trading strategy. The strategy’s creator, an Australian based crowd researcher, leveraged CloudQuant’s market simulation and python based back-testing tools, to prove the algorithm’s performance and profitably within approved risk parameters. As a funded partner, the researcher will receive a share of the trading net profits. “Striking a balance between complexity and simplicity can be a major key towards success,” said the crowd researcher when giving advice to other researchers. The US equity strategy began trading immediately upon approval of the licensing agreement from both the Quantitative Trader and CloudQuant management. “Market enthusiasts are finding new alpha signals in alternative data sets, fundamental data, and market data. Our users are building exciting new trading strategies,” said CEO Morgan Slade. “We are excited about the talent we see emerging within our network of crowd researchers and confident new users will find trading opportunities.” Using research tools originally conceived and developed by proprietary traders in the parent company, Kershner Trading Group, CloudQuant provides data-driven resources to test an algorithm’s profitability. CloudQuant is proving that innovative trading strategies emerge when market enthusiasts are provided institutional grade research tools. The algorithm creator and CloudQuant can then enter into a profit-sharing agreement to trade the algorithm using CloudQuant provided risk capital. About Us CloudQuant is the cloud-based trading strategy incubator. Quantitative analysts, algorithmic developers, data scientists and traders around the world create and test trading strategies leveraging CloudQuant’s superior infrastructure. Approved strategies are licensed from the strategy creator and funded for production by the CloudQuant team, paying the creator a licensing fee from the net profits. By providing the capital, technology, and trading acumen to develop and utilize trading strategies, CloudQuant offers a mutually beneficial profit sharing agreement enabling both parties to profit. CloudQuant LLC, established in 2016, is a wholly owned subsidiary of Kershner Trading Group LLC   For Media Inquiries Please Contact: Jessica Titlebaum Darmoni + 1 312 358 3963
Machine Learning, Quantitative Investing News

Industry News: Machine Learning and Artificial Intelligence News 10/30/2017

AI and ML for CloudQuant, ArcaEx, Corporate earnings reports, Hedge Funds, Microsoft, Alexa, Saturday Night Live, the apocalypse, Elon Musk, and more …
Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

Industry News: Machine Learning and Artificial Intelligence News

ML for Banking, New York, Google/Deepmind,, God & Dan Brown, Natural Language Processing (NLP), IoT, and more…
Quantitative Strategy, Trading, and Algo Development Industry News

Industry News: Machine Learning and Artificial Intelligence News for the week ending October 2, 2017

Machine Learning and Artificial Intelligence News for the week ending Oct 2, 2017 that we found interesting from our FINTECH and CrowdSourcing perspective
Crowdsourcing Algorithmic Research

CloudQuant Continues To Allocate Risk Capital to Crowd Researchers

CloudQuant allocates risk capital to another crowd researcher by funding and leasing a crowdsourced trading algorithm. The licensor will receive a direct share of the monthly net trading profits.
John "Morgan" Slade

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

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.

CloudQuant Launches with Unprecedented Risk Capital Allocation to Crowd Researcher

CloudQuant, the trading strategy incubator, has launched its crowd research platform by licensing and allocating risk capital to a trading algorithm. The algorithm licensor will receive a direct share of the strategy’s monthly net trading profits.
Your algo is always yours, not ours.

Your Algos are Your Private Property on CloudQuant

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.

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


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


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.
Python Scripts in CloudQuant's Algorithmic Trading and Quantitative Strategy Backtesting Application

Algo Developers – Entry Level

CloudQuant is THE trading strategy incubator. We’re building a free python data research tool for ordinary people with extraordinary trading ideas. We license and fund the best trading strategies and pay our users a share of the profits. Our group is a FINTECH startup housed under the umbrella of a trading firm with existing infrastructure and financial resources.
World Market Access

CloudQuant Announces Users from 72 Countries

May 13, 2017, Chicago, Illinois, For Immediate Release

CloudQuant Announces Users from 72 Countries

CloudQuant’s innovative Trading Strategy Incubator has managed to attract new users from 72 countries. The users represent developers, financial analysts, data scientists, traders, and other trading enthusiast who are interested in developing a trading strategy that may be funded if it proves to be profitable.

These users hope to become the next funded trading strategy. They utilize the cloud based to develop trading strategies. All trading strategies are developed in python, a popular and easy to learn programming language.

About CloudQuant LLC

CloudQuant LLC, established in 2016, is a wholly owned subsidiary of Kersner Trading Group LLC. CloudQuant provides a cloud based trading strategy incubation service for algorithmic developers and traders around the world which includes a partnership agreement and profit sharing arrangements.