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 pythonJupyter 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

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Alternative Data News. 27, September 2018

News covering: Social Market Analytics Sentiment Data for Forex; The Mosaic Effect with Big Data and AltData; Success as a Data Scientist; Data Ops Workbench; Turkish Volatility Showed Value of Alt Data
Algo Trading powered by Alternative Data Sets

Conversations: Effects of Alternative Data Sets on Trading Algorithms

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What effect can alternative data sets have on trading algorithms? We asked a few of our teammates and systematic traders what the effect of alternative data sets is on trading algos. We thought we could spread some insight as to why our alternative…
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Roles Played by Bayesian Networks in Machine Learning

Bayesian Network is a probabilistic graphical model which comprises variables and its relationships. It uses Bayesian inference and learning to develop the algorithm.
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Conversations: Learning Python within CloudQuant

What was your experience like learning Python within CloudQuant? We asked our portfolio managers and product management teammates who code in Python to explain their starting experiences in programming with Python with CloudQuant. We wanted to share with everyone what encouraged them to keep learning throughout the years. Everyone here codes as part of their job. This includes the CEO all the way down to the interns. We rely on our Backtesting Engine to ensure that trading algorithms work well before committing money to the automated trading strategies. But we also use JupyterLab in our daily work. We generate our reports, monitor our systems, and do all sorts of tasks in Python. Python has overtaken the spreadsheet in CloudQuant.
Sonal Gupta, Female Data Science Leader

What is it like to be a female in Data Science?

Sonal Gupta is an MBA graduate student at Case Western Reserve University in Ohio. She has five years experience in leading software development teams, product development and consulting engagements.  She has the ability to analyze large volumes of data and generating actionable insights.  We asked her what her experience has been like as a female in data science and invite you to read her response below.
John "Morgan" Slade

RavenPack - The State of Machine Intelligence in Capital Markets

The financial sector is making a massive shift towards machine intelligence in capital markets. This panel shares their experience in using data science and domain expertise in understanding data context.
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.