JupyterLab and Jupyter Notebooks

CloudQuant uses JuypterLab and Jupyter Notebooks in all of our data science projects. We find that this popular python environment with the standard and extended packages allow our internal and external quantitative analysts to create and share research that includes python code, equations, visualizations and research text. Our new CloudQuant.AI environment which is only available for internal users is built on JupyterLab. Watch for announcements from CloudQuant as we plan on opening this platform with our market data, fundamental data, and news/sentiment Alternative Datasets for registered crowd researchers.

The Jupyter Project is an open source project and can be found at http://jupyter.org/

Posts

Quantitative Strategies and Capital for Trading

Industry News: Machine Learning and Artificial Intelligence News for the week ending September 25, 2017

Big Data, Data Analytics News: Wall Street Robots, SEC, Microsoft, Nvidia, REGTECH, financial services firms, KPIs all using Data Science...
Algo developer getting paid

Intro to Machine Learning with CloudQuant and Jupyter Notebooks

Trevor Trinkino, a quantitative analysts and trader at Kershner Trading Group recently put together an introduction to Machine Learning utilizing CloudQuant and Jupyter Notebooks. In this video he walks you through a high-level process for implementing machine learning into a trading algorithm, ...
SPY Benchmark Trader

Full Stack UI Developer - Chicago

We are looking for a Web Developer responsible for managing the interchange of data between the server and the users, as well as translating the UI/UX design wireframes to actual code that will produce the visual elements of the application.
Quantitative Strategies and Capital for Trading

Quantitative Trading and Data Science in the News August 7 2017

Citadel fund up 7%, Hedge funds betting against Tesla (and losing), Data Analytic trends, ...