Machine Learning and Big Data with kdb+/q

Machine Learning and Big Data with kdb+/q
Author :
Publisher : John Wiley & Sons
Total Pages : 640
Release :
ISBN-10 : 9781119404750
ISBN-13 : 1119404754
Rating : 4/5 (754 Downloads)

Book Synopsis Machine Learning and Big Data with kdb+/q by : Jan Novotny

Download or read book Machine Learning and Big Data with kdb+/q written by Jan Novotny and published by John Wiley & Sons. This book was released on 2019-12-31 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: Upgrade your programming language to more effectively handle high-frequency data Machine Learning and Big Data with KDB+/Q offers quants, programmers and algorithmic traders a practical entry into the powerful but non-intuitive kdb+ database and q programming language. Ideally designed to handle the speed and volume of high-frequency financial data at sell- and buy-side institutions, these tools have become the de facto standard; this book provides the foundational knowledge practitioners need to work effectively with this rapidly-evolving approach to analytical trading. The discussion follows the natural progression of working strategy development to allow hands-on learning in a familiar sphere, illustrating the contrast of efficiency and capability between the q language and other programming approaches. Rather than an all-encompassing “bible”-type reference, this book is designed with a focus on real-world practicality to help you quickly get up to speed and become productive with the language. Understand why kdb+/q is the ideal solution for high-frequency data Delve into “meat” of q programming to solve practical economic problems Perform everyday operations including basic regressions, cointegration, volatility estimation, modelling and more Learn advanced techniques from market impact and microstructure analyses to machine learning techniques including neural networks The kdb+ database and its underlying programming language q offer unprecedented speed and capability. As trading algorithms and financial models grow ever more complex against the markets they seek to predict, they encompass an ever-larger swath of data – more variables, more metrics, more responsiveness and altogether more “moving parts.” Traditional programming languages are increasingly failing to accommodate the growing speed and volume of data, and lack the necessary flexibility that cutting-edge financial modelling demands. Machine Learning and Big Data with KDB+/Q opens up the technology and flattens the learning curve to help you quickly adopt a more effective set of tools.


Machine Learning and Big Data with kdb+/q Related Books

Machine Learning and Big Data with kdb+/q
Language: en
Pages: 640
Authors: Jan Novotny
Categories: Business & Economics
Type: BOOK - Published: 2019-12-31 - Publisher: John Wiley & Sons

DOWNLOAD EBOOK

Upgrade your programming language to more effectively handle high-frequency data Machine Learning and Big Data with KDB+/Q offers quants, programmers and algori
Machine Learning in Finance
Language: en
Pages: 565
Authors: Matthew F. Dixon
Categories: Business & Economics
Type: BOOK - Published: 2020-07-01 - Publisher: Springer Nature

DOWNLOAD EBOOK

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational discipli
Q Tips
Language: en
Pages: 314
Authors: Nick Psaris
Categories: Database design
Type: BOOK - Published: 2015-03-19 - Publisher:

DOWNLOAD EBOOK

Learn q by building a real life application. Q Tips teaches you everything you need to know to build a fully functional CEP engine. Advanced topics include prof
Fun Q
Language: en
Pages: 416
Authors: Nick Psaris
Categories:
Type: BOOK - Published: 2020-07-16 - Publisher:

DOWNLOAD EBOOK

Malliavin Calculus in Finance
Language: en
Pages: 350
Authors: Elisa Alos
Categories: Mathematics
Type: BOOK - Published: 2021-07-13 - Publisher: CRC Press

DOWNLOAD EBOOK

Malliavin Calculus in Finance: Theory and Practice aims to introduce the study of stochastic volatility (SV) models via Malliavin Calculus. Malliavin calculus h