50 Smartest Companies Of The Year 2018

Providing a Platform That Brings Ideas and Approaches to Trading To Life: CloudQuant LLC

thesiliconreview-morgan-slade-ceo-cloudquant-2018With the rise of electronic trading, the trading and investment industry has evolved from one of a floor-based business to a world of quants, data scientists and software engineers. It is one of the most powerful industries to work in, competing for talent with the likes of Apple, Google and Amazon. While opportunity could be plentiful in this space, manpower is limited and many firms lack diversity of ideas.  Designing new trading signals and scouting alternative datasets poses challenges in this ever-changing environment.

One firm in the midst of all this development has found a way to leverage new trading strategies, identify market signals and alternative datasets, as well as provide financial gain to anyone with a computer and coding skills.

The Genesis

CloudQuant LLC launched in 2016, however it was in production much earlier than that as internal research tool at Kershner Trading Group. With over 23 years experience as a proprietary trading firm, Kershner Trading built a cloud-based exchange trading strategy simulation engine as a resource for their traders. Many of their traders were looking for ways to explore extraordinary quantitative strategies. By building this open access platform where traders were able to go from idea to product in a matter of hours, Kershner stumbled upon a revolutionary concept.

They spun the product off and CloudQuant was established to serve the needs of independent market researchers, traders and data scientists around the world. Anyone with knowledge of the coding language Python could build and backtest their trading algorithms for free. Impressed by the level of talent and ideas emerging from the crowd researchers, CloudQuant quickly secured funding from Kershner and began working out licensing agreements with independent algorithm creators.

In August 2017, CloudQuant officially launched with a $15 million USD allocation to it’s first crowd-sourced algorithm, which was quickly followed by a second $10 million USD allocation. The company has allotted $92 million in funding to crowd-sourced trading strategies since it’s inception.

Today, by empowering market enthusiasts, AI researchers and data scientists with the access to industrial strength research tools, CloudQuant is able to leverage alpha signals and unique data sets making it one of the leading firms in the investment trading industry.

“We believe that one doesn’t have to be on Wall Street to create an innovative, profitable trading strategy and signal.”

The First Project Roll-On

After the soft-launch of  CloudQuant’s algorithmic trading simulator, they received an overwhelming and encouraging response. The company quickly began building out it’s suite of products starting with CloudQuant (CQ) Elite.

CQ Elite provides users with impressive abilities to backtest trading signals and strategies with micro-second level tick data at no charge. It is a web-enabled application for algorithm development, married with scheduled backtests and a sophisticated market simulation engine. With simple coding knowledge, anyone with an idea, thought or insight is able to create a profitable trading strategy and test it out. 

Users have access to historical market data, institutional-grade technology, capital, alternative datasets, strategic backtesting tools and professional, standardized reports. With detailed trade and quote data, CloudQuant prides itself on creating an environment where anyone can confidentially test trading ideas and strategies.

As the project continued to roll out, CloudQuant enhanced the user experience by adding online forums and a customer success team. They work with crowd researchers to improve the trading strategy and offer guidance as well as coaching.

Overcoming Hurdles

By offering high-quality research and simulation tools, CloudQuant makes money when it is funding and trading the crowd-resourced algorithm. They are committed to growing their network of financial partners but are challenged to find new talent.

They are also finding that there is a balance between turning a profit and building the functionality that attracts unique talent and increases revenue. The CloudQuant team wants the product to be exceptional and meet user demands. They regularly add new features and prioritize by building those that increase research loops among the successful crowd researchers.

The company also focuses on the onboarding process and ensures a successful user experience. If platform registrants have a good first week, CloudQuant sees long-term use of their services and products.

The Future Sight

With the ability to license and allocate institutional-sized funding to crowd-researched trading algorithms, CloudQuant has made data available and created opportunities for anyone around the world to explore and make money from their trading ideas.

CloudQuant is focused on alternative data sets and identifying new trading signals for trading algorithms. They will be offering another internal product with Artificial Intelligence (AI) capabilities later this year. Data scientists, technical traders and AI researchers will be able to explore alternative datasets to create trading signals and alpha signals. They are leveraging JupyterLab, the latest data science environment.

Greet the Chief

Morgan Slade, CEO of CloudQuant: Morgan has over 20 years of experience as a trader, portfolio manager, researcher, technologist, executive and entrepreneur in the financial services industry. Prior to CloudQuant, he has built quantitative trading businesses at some of the world’s largest Hedge Funds and Investment Banks. In the past, he has served as a Portfolio Manager for both Citadel Investments and Carlson Capital and has served as Global Head of Equity Trading for several large high-frequency trading firms. 

With a BS and MS in Materials Science and Engineering from MIT, Morgan is a recognized expert in Prediction, Machine Learning, High-Frequency Trading, Autonomous Market Making and is interested in the application of Deep Learning to quantitative trading.

“We are breaking down barriers in the path to a career on Wall Street.  Our product levels the playing field for anyone in the world with an internet connection to access institutional quality research tools.”