Intellectual Property

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.

Trading strategies require thought

Your Algo is Your IP.

 

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Candlestick market data chart

Successful Starts in Algorithmic Trading

Getting Started in Algorithmic Trading Includes Overcoming Common Problems

Feb 24, 2018

by Tayloe Draughon, Product Manager, and FINTECH veteran

Many people have attempted to become the next great trader. They try assets like cryptocurrencies, or futures, or options and soon find out that it isn’t as easy as they originally thought. They run into many of the same problems.

Problem: Not enough cash.

I recently lost some personal cash on short-term volatility. I knew it was a gamble. I should have sold my short volatility ETF prior to the presidential state of the union speech. I lost. Oh well; it happens. It was a small position. Most people starting out can’t afford even a small loss. Any speculative trading has the possibility of a loss. People need capital to trade. If you don’t have it, then you need access to someone else’s capital, which usually comes with a professional trading position. This presents another problem. If you aren’t already a professional trader then leaving your job to become a full-time trader that is paid entirely out of profits is fear-worthy.

Problem: Not enough technology.

To build a trading system one needs computers that serve upmarket data, order processing to a broker or directly to the exchanges, risk systems, monitoring systems, data stores, and trade reconciliation systems. Once all of this is in place then you have to build an algorithmic trading platform that interfaces with all of these trading components. Also, you can use a third-party trading platform but this requires cash too.

Problem: Not enough data.

There are multiple types of data. Modern trading signals, including alpha signals, are derived from market data, fundamental data, news, social sentiment, and other “alternative data” (Alt-Data) sets.(1) The majority of trading algorithms require historical and streaming data. Historical data for the development phase and live data for trade execution. Most quantitative developers have an idea for their strategy. To figure out if it will actually work they will need access to the data to analyze, backtest, and forward test prior to beginning to trade.

Problem: Expensive data.

Market data is expensive. Access to the data is governed by the data owner. You can use it to trade with a retail account. However, access to the data for your research and analysis is a much bigger problem. The exchanges require legal agreements and charge for data. Fundamental data that provides earnings, calendars, estimates and corporate information is provided by other data vendors. They too require legal agreements and for you to contribute to their revenue stream. Alt-Data is also expensive and requires another paid licensing agreement. A recent conference pointed out that there are roughly 1,600 alternative data set providers. Lastly, you have to manage all this data. Storage, organization, and formatting all become an issue. One data source may use exchange symbols and another may use an internal identifier. You have to spend time and money storing and formatting the data.

Problem: Not enough protection of intellectual property

Your trading strategy is your intellectual property. Proprietary trading companies run custom algorithms every day. Quants entering into algorithmic trading need to be aware that their intellectual property may not actually be theirs. For example, quants at trading firms get a salary and in return, the employer has justifiable rights to the work their employees produce. Those that use free services to develop algorithms need to read and understand any user license agreement. (Don’t just click through!)

Problem: Limited understanding of trade expression

The markets don’t always work the way that an algo developer expects. Order types, time of day, order execution strategies, exchange rules, broker supplied algorithms, and a myriad of other order transaction items may cause problems for the algo creator. These items hinder the development of a quantitative algorithm because they distract the data scientist from studying trading strategies and may result in curve fitting.

Problem: Not enough mentoring or coaching

If good trading comes from experience then experience must come from bad trading. There is a reason we backtest and paper trade. Over time one gets better as one perfects one’s trading strategies. Traders develop operational “golden rules” that work and signals that improve their trading. They are constantly looking to improve. In algorithmic trading, the role of a mentor or coach helps develop these disciplines and helps you find new opportunities. If your algorithmic strategy development exists in a vacuum then it is likely to perform poorly. A coach or mentor helps point out new data sets, new ideas, new risk tools that you can leverage to create a fresh, proprietary strategy that is your unique property.

Overcoming the problems

For a retail trader or someone entering the market, these problems can be daunting. Rest assured, there are solutions. For the (highly) capitalized trader, these problems can be used as a roadmap to build a fully functioning algo development and trading environment. Solutions for those with capital depend upon the asset class being traded. When evaluating your choice of systems and approaches consider looking for technology that will allow you to be broker-neutral. Many (less capitalized) are coming to CloudQuant (where I work). The free tools allow you to test and build algo trading signals from market data, alt-data, and fundamental data. Signals can be joined together into a trading strategy. A strategy can be backtested to generate highly insightful results. The goal is to convert crowd-sourced algos into funded and licensed trading strategies that can be traded with our capital. Even with barriers lowered, algo development is still hard work A trading algorithm takes time to develop. Most people get stuck quickly and even give up. When you get stuck, or your algo doesn’t show progress, you need to stop and re-evaluate. An approach that works well within most algo shops is to break the strategy down into small components. Consider these steps:
  1. Identify a data feature.
  2. Repeat #1 as many times as possible
  3. Look for ways to combine your features into something that you can use to predict a price movement.
  4. Then test your algo
When you are stuck, ask your coach or your community for ideas. People are willing to help you out. Success comes with diligent work, support, and access to mentors, technology, and data.
Endnotes 1. Alternative Datasets are data sets that contain information not normally used in trading. These may be social or news sentiment analysis, satellite data, traffic data, or other information that is available. Natural Language Processing has created an explosion of new data sources for the imaginative quant.
data scientist researching trading strategies

Algos and Ethics – a response to a LinkedIn Post

Algorithmic Trading and Ethics

Alessio Farhadi posted “A.I. Trading – A Question of Ethics” on LinkedIn.  His main point is that machine learning and algos do not have ethics.  His thoughts may be overly influenced by the referenced Flash Boys book that painted the trading industry with a somewhat cynical brush. Just as AI technology has enabled growth in professional algorithms, it has also spurred growth and opportunity for the regulator, broker-dealer, exchanges, and even the average joe who wants to get involved. Fairness to the industry requires that one should review the steps that have been taken by innovators, regulators, broker-dealers, and exchanges to mitigate any potential dangers of using computers and algorithms to trade.

AI, Algo Trading and the Regulators and Watchdogs

The regulators, self-regulatory organizations, and exchanges have all been active in improving the integrity of the industry. These activities include:

AI, Algo Trading, and the Innovators

Innovative firms have seen the need and are helping the industry by responding to the urgent need. The term REGTECH applies here. A few examples include:
  • Vertex Analytics with their amazing ability to see patterns in the market data and highlight cheaters.
  • Trading Technologies’ Neurensic product that uses machine learning to catch spoofers, front-runners, layering, pump and dump, and more forms of illegal trading.
  • Edge Financial Technologies and their KillSwitchPlus tool that catches run away algos and limit breaches at the time of the order.
  • Catelas with its surveillance ability to catch collusion between traders or inappropriate use of insider data.
Even the algorithmic trading technologists and firms are getting into the game. At CloudQuant anyone can develop an algo. By democratizing the access to formerly restricted algorithm development tools anyone can participate in the algo world. The restrictions on historical data, technology, capital, and exchange membership are falling. 

AI, Algo Trading, and the Average Joe

Improved access to information is also making it easier for the world to see what is going on with their investments. StockTwits, Alexandria Technologies, Twitter, LinkedIn, Reddit, Quora, and more are all publishing insights into the world. Individuals, both professional and personal, are publishing more insights into the world. Google, Bing, Benzinga, Bloomberg, Reuters, and others are all making it easy to find data that previously were not easily available to the average investor. YouTube, LinkedIn, Quora, SMBTraining, QuantInsti and others are all teaching anyone interested in trading and algorithms secrets and insights that 5 years ago were not available to anyone other than a select privileged few at well-capitalized trading firms. Tools like Python programming language, Jupyter Notebooks, Technical Analysis Library TA-Lib, and our own CloudQuant are making it easier for anyone to enter the world of algorithmic trading.

AI and Machine Learning for Algo Trading isn’t to be Feared

Trading moved from being manual pit trading to computer screens. At that time people had legitimate concerns. Those were addressed. Similarly, we are moving to a more algorithmic world of trading. Concerns are again being addressed.   My point in all this is not to contradict Mr. Farhadi’s thoughts but to present additional, hopeful information. While the world of investing is changing, the safety systems and the participants are also changing. This doesn’t mean that the industry should stop adjusting to change. The technologists, regulators, broker-dealers, exchanges, and vendors all need to continue to innovate and adapt. This will lead to an ever-increasing stable and reasonable marketplace where all can fairly participate.

CloudQuant’s Production Algos & Ethics

CloudQuant licenses algos from algo creators who use our website and tools to backtest their strategies. During our due diligence process, we review the orders, positions, and trades that the algo did in backtesting. We are looking for any breach of trading rules that may cause issues. As a trading firm primarily we know that the issues of ethics and rule violations are never to be taken lightly. Our licensing process and ongoing oversight of algorithmic trading keep us on a solid ethical footing. Furthermore, all our trading is watched by compliance utilizing technology too.     Happy Algo Trading, Tayloe Draughon
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.