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.
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CloudQuant Press, Industry NewsYour Algos are Your Private Property on CloudQuant
Blog, CloudQuant Press- 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.
- Everything you submit to the CloudQuant Services is “Input.”
- We may also review the Output… (i.e. the results of the backtest)
- 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 |
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 |
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.2017 – The Year of Artificial Intelligence
Industry News2017 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 deviceThe internet of things is collecting data in ways never before possible.
2. Computing is becoming freeThe 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).The next wave of broker innovation will be Crowdsourced algos
CloudQuant Press, Industry NewsAre Institutional Brokers Innovative?
Markets Media is reporting that “47% of buy-side firms, mostly those with modest levels of assets under management, did not think their brokers were doing anything innovative.” Andy Kershner, the CEO of CloudQuant’s parent company was interviewed. He believes:The next wave of broker innovation likely will be geared toward democratizing quantitative trading, according to Kershner Trading Group Founder and CEO Andy Kershner. That would vastly expand the universe of high-level quant traders globally, which Kershner roughly estimated stands at perhaps 5,000 today.

Andy Kershner, Kershner Trading
“It will be very similar to what you saw when you had access to the market open up in the mid-90s for day traders,” Kershner said. “You had lots of innovation, lots of people coming in with lots of ideas, and lots of software that really changed and knocked out all the market makers. Then high-frequency came along and killed all the specialists and also knocked out some of the day traders. I think now we’ll see a reversion.”
“You’ve got ‘big data’ out there everywhere that everybody talks about, but the moat is access to the data, access to capital, and access to some knowledge of what to do with it,” Kershner continued. “I think in the next three years if you do not have an auto- or quantitative-trading system that’s more than just APIs — you’ll need back-testing, forward-testing, live, the whole package — if you don’t have that for people to sign up for, your brokerage will be left behind.”
Read the full story on Markets Media
Battle of The Quants – Discusses Crowd Researching in NY
CloudQuant Press, Industry NewsTrading Strategy development—Powered by Machine Learning
CloudQuant Press, Industry News- How large funds and institutions put on $100-million positions; how they work orders into the market, structure the trade and handle market impact etc.
- Morgan explains why he feels as though the common approach to strategy development is counter intuitive, and shares an alternative 3-step formula.
- A simple description of how machine learning and data science is being used by traders, and an example of how ML has been used to improve existing strategies.