Machine Learning and Big Data with kdb+/q
Q, High Frequency Financial Data and Algorithmic Trading
$98.99
- Hardcover
640 pages
- Release Date
21 November 2019
Summary
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 kn…
Book Details
| ISBN-13: | 9781119404750 |
|---|---|
| ISBN-10: | 1119404754 |
| Author: | Paul A. Bilokon, Jan Novotny, Aris Galiotos, Frederic Deleze |
| Publisher: | John Wiley & Sons Inc |
| Imprint: | John Wiley & Sons Inc |
| Format: | Hardcover |
| Number of Pages: | 640 |
| Release Date: | 21 November 2019 |
| Weight: | 1.23kg |
| Dimensions: | 41mm x 168mm x 246mm |
| Series: | Wiley Finance |

Paul A. Bilokon
JAN NOVOTNY is an eFX quant trader at Deutsche Bank. Previously, he worked at the Centre for Econometric Analysis on high-frequency econometric models. He holds a PhD from CERGE-EI, Charles University, Prague.
PAUL A. BILOKON is CEO and founder of Thalesians Ltd and an expert in algorithmic trading. He previously worked at Nomura, Lehman Brothers, and Morgan Stanley. Paul was educated at Christ Church College, Oxford, and Imperial College.
ARIS GALIOTOS is the global technical lead for the eFX kdb+ team at HSBC, where he helps develop a big data installation processing billions of real-time records per day. Aris holds an MSc in Financial Mathematics with Distinction from the University of Edinburgh.
FRÉDÉRIC DÉLÈZE is an independent algorithm trader and consultant. He has designed automated trading strategies for hedge funds and developed quantitative risk models for investment banks. He holds a PhD in Finance from Hanken School of Economics, Helsinki.
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