Data Science

Data Science is the application of scientific method, processes, systems, and tools to discover insights and knowledge or insights from all forms of data.  Modern Data Scientists use tools and skills including python,  Jupyter Notebooks, linear algebra, statistics, machine learning, and more in their quest to develop those insights.  Some in the business and trading consider Data Science to be synonymous with business analysis or quantitive research.

Posts

Sarah Leonard, MScA, University of Chicago

Interview with Sarah Leonard, STEM Woman and Data Scientist

“It’s exciting to see the growing number of women in Science, Technology, Engineering and Math (STEM); my advice is to not be afraid to jump in headfirst,” said Sarah Leonard, graduate student at the University of Chicago. “It is a difficult field but also lucrative and rapidly growing.” Leonard sat down with CloudQuant to talk about her experiences in data science, her insight as a female in a male dominated world, and the intensive process it took to find her dream job.
CloudQuant's Morgan Slade at Security Traders Association of Chicago

Artificial Intelligence (AI) and Machine Learning (ML) STAC Conference Summary

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Moderated by Jessica Titlebaum Darmoni from the Title Connection, Slade was joined by Brian Peterson, Algorithmic Trading Lead at DV Trading, Inderdeep Singh from CME Group’s Innovation Lab and Matthew Dixon, Assistant Professor of Finance and Statistics at Illinois Institute of Technology.
Backtest Research Life Cycle for Trading Strategies

Backtesting Trading Strategies

If you knew your trading strategy would work 50% of the time, would you commit your scarce savings to trade it? What if it worked 75% of the time? Backtesting gives one the confidence to know that your trading strategy will work.
Quantitative Strategy, Trading, and Algo Development Industry News

Industry News: Machine Learning and Artificial Intelligence for January 8, 2018

CloudQuant Thoughts: 80% of trading is being handled by robots? We know that 100% of trades are touched by automation these days. If it isn't in the actual order processing then it is in the clearing process, the risk process, or the account management process. When someone using a retail broker's website sees their portfolio there is added information that is presented. Almost all of that is touched by automation with some form of a "robot" or AI process.
85 Percent of Data is Unstructured

Is Crowdsourced Data Reliable?

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"Bring us your ideas and we will share the money with you,” agreed Morgan Slade, CEO of the crowdsourced algorithmic trading startup CloudQuant. “For us, engagement means breaking it down into a contractible problem."
www.futuresradioshow.com

Futures Radio Show interviews Morgan Slade December 12, 2017

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CloudQuant's CEO was interviewed by Anthony Crudele of Futures Radios show to discuss topic including Artificial Intelligence, Machine Learning, and Deep Learning applied to algorithmic trading. Alternative datasets are a major topic of discussion. People are saying that data is being created faster than ever before. That really isn't true. What is really happening is that data is being captured and stored at a faster rate than ever before. Vendors are now making AltData available for traders to change the way that they interact with the markets. This applies to futures and stocks with the popularity of Deep Learning in algorithmic trading strategy development.
Machine Learning, Quantitative Investing News

Industry News: Machine Learning and Artificial Intelligence for December 18, 2017

CloudQuant Thoughts: We really like the following quote in this article: “A huge part of financial services built technology in-house or used a small number of vendors and that old model is dying,” Whitcroft added. ‘Financial services is being componentisized.” This gets to the heart of new business models that include innovation and crowdsourcing. We have found that innovators, with atypical backgrounds and education, when given access to institutional grade tools and datasets can create something unique. There is significant value that can be derived. Our business model is proving this out daily.
Quantitative Strategy, Trading, and Algo Development Industry News

Industry News: Machine Learning and Artificial Intelligence for December 11, 2017

AI & ML news covering Hedge Funds, Career, Fraud Detection, Vineyards/Winemaking, Quanthouse, Apple, Quantitative Brokers, and more
Researcher reading iPad

Industry News: Machine Learning and Artificial Intelligence for December 4, 2017

Grace: Going back to what you are doing now with CloudQuant, I understand that you have a trading strategy incubator where your team has the experience, the technology, the capital and allows algo traders to essentially get a strategy funded. Can you tell us more about that and the vision behind it?
Morgan Slade, Python Data Scientist and Trader

QuantNews Interview with CEO Morgan Slade

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With over 20 years of experience as a trader, portfolio manager, executive, and entrepreneur, Morgan Slade is now the CEO of CloudQuant, a cloud based quantitative strategy incubator and systematic investment fund. He has built quantitative trading businesses at some of the world’s largest hedge funds and Investment Banks ...