Powered by Kx: Quantile Technologies

7 Nov 2018 | , ,

By Stephen O’Connor, Chairman of Quantile Quantile Technologies, a Kx Ventures company, was formed in 2015 by a team of experienced professionals with backgrounds in risk management, clearing, quantitative analysis and trading technology. Quantile is headquartered in London, and serves the global community of derivatives users. Quantile is a FinTech company that helps major financial institutions

Using q in Machine Learning with Neural Network and Clustering Examples

4 Apr 2017 | , , ,

Tokyo-based kdb+ programmer, and algorithmic quantitative analyst, Mark Lefevre recently gave a couple of talks about using high-performance machine learning with kdb+ at the Kx Community Tokyo Meetup. His talk “Using Q to Read Japanese” focused on utilizing neural networks and how supervised learning can be used in q to teach a machine to recognize Japanese characters from handwritten images. His second talk, “Kx for Wine Tasting” focused on utilizing the k-means clustering algorithm and unsupervised learning in q to teach a machine to appreciate wine!

On Implementing an XML Standard In Kx

6 Mar 2017 | ,

London-based kdb+ developer Eoghan Page recently developed a solution to generate FpML (Financial products Markup Language) for some interest rate derivatives, specifically interest rate swaps, tenor basis swaps and forward rate agreements. In his informative blog post with coding examples, Eoghan describes how he originally had done a similar task in Python, which became an "unmaintainable giant," so he decided to start from scratch and use kdb+. After you read the article, check out his code on GitHub.

Distributed Computing in kdb+

21 Dec 2016 | ,

Kx financial engineer, Connor Gervin, gave a talk at a Kx Community NYC Meetup in 2015 that is worth revisiting. Connor described kdb+’s built-in multithreading and multiprocessing capabilities, which are an essential part of every serious kdb+ programmer's’ toolkit. With these features, programmers can make the best use of multicore hardware when solving increasingly complex problems over ever-expanding datasets.