Big Data in Quantitative Trading Research and Algo Development

Big Data is data sets that are complex data sets, typically with large volume. Examples of Big Data in FINTECH include historical market data, news service information, tweets, and sentiment data.  Challenges in using big data include capture, storage, security, analysis, and visualization. Data scientists use Big Data (including alternative data) for predictive analytics. CloudQuant’s quantitative crowd researchers use historical market data, news data, and sentiment data to develop predictive trading strategies that are tested using a complex, proprietary backtesting trade simulator.


Morgan Slade, Python Data Scientist and Trader

CloudQuant CEO John Morgan Slade presenting at THE TRADING SHOW - CHICAGO 2019

CloudQuant CEO John Morgan Slade will be taking part in THE TRADING SHOW - CHICAGO 2019 on Wednesday 8th May 2019. At 13:20 (Central) he will be partaking in a Panel to discuss "Deep Neural Networks - how supervised do deep learning machines…
Happy Data-Driven Holidays

A Data-Driven Christmas

‘Twas the week before Christmas at the North Pole finishing all the project was the Head Quant’s Goal. Santa was wanting the newest naughty and nice list. Presents are needed for all good engineers and analysts. In California, they want a fire restoration tree and plant seeding robot. In Michigan, they want distribution channels for their new legal pot.
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CloudQuant Partners with RavenPack to Expand Use of Alternative Data

CloudQuant LLC today announced the addition of RavenPack analytics within their trading strategy incubator. Crowd researchers can now use RavenPack historical data to discover tradable alpha signals on CloudQuant’s online Python and JupyterLab-based tools.

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
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.
Trevor Trinkino Quantitative Trader

Machine Learning FXCM Webinar with Trevor Trinkino of CloudQuant - Part 2/3

On May 15th Trevor Trinkino presented part two of a three-part Machine Learning webinar with FXCM. Part one is here. Part 2  - Preprocess data for Random Forest. PnL and prediciton improvements... In part two Trevor goes over…
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.
Morgan Slade to Speak at RavenPack in London April 24, 2018

The RavenPack Big Data & Machine Learning Revolution Comes to London

April 24, 2018, Top finance professionals who will share their latest research and experience with Ravenpack big data and machine learning in London.
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Industry News: Machine Learning and Artificial Intelligence for January 15, 2018

... While budgets may be tight we believe that innovators always find a way. Look for innovation to happen outside of mainstream information technology. Almost everyone trading today has some technology skills. It may be as simple as someone with an Excel spreadsheet macro, or a data scientists with a Jupyter Notebook. ...
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.