Articles & Video
Staying afloat: How is quant finance changing under Covid-19?
Is the worst behind us? Or is it still to come?
Analysing Covid-19 Data with AWS Data Exchange, Amazon Redshift, and Tableau
How to create COVID-19 dashboards using Tableau and different AWS services, such as AWS Data Exchange, AWS COVID-19 Data Lake, Amazon Redshift, and Amazon Athena.
From emotions to decisions – a framework for using big data in portfolio management
A case study in using alternative data by SESAMm and La Française Investment Solutions.
Artificial intelligence, machine learning, and data in quant finance
In this compilation of articles, FutureQuantMinds explore AI, ML, and the impact of data on quant finance
Data challenges in applications of machine learning to quant finance problems
Is machine learning made for quant finance? Dr. Svetlana Borovkova (Vrije Universiteit Amsterdam and Probablity & Partners) shares some of the biggest breakthroughs in machine learning application and evaluates the uses and limitations of ML in various scenarios.
How to survive the new challenges in quant finance?
The latest QuantMinds eMagazine delves into the use of machine learning, alternative data, blockchain applications, diversity, and more!
What is alternative data and how can quants approach it?
Saeed Amen, Co-Founder of Cuemacro, discusses why alternative data is so exciting for quant finance.
Democratising data science in your organisation
As digital disruption finds its way to every industry, the traditional centralised way of storing data is not good enough to keep up with exponential developments.
Quantitative finance in the digital age
Download our latest eMagazine for exclusive articles focusing on data, alternative data, AAD, vectorisation, and quantum computing.
Data’s big year: The last frontiers of quantitative edge
Stocktwits’ Director of Business Development and Revenue Strategy Pierce Crosby on the 2 data trends that quants should look out for in 2019.
The best python tools to analyze alternative investment data for $0
Building an alternative data effort can be expensive but fortunately unlike buying datasets and hiring people, the technology infrastructure required to analyze alternative data can be acquired at minimal to zero cost.