Machine Learning and Big Data with kdb+/q

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.   

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.

Develop solid high-frequency strategies with q's unprecedented speed and efficiency In the world of high-frequency trading, the q programming language and kdb+ database have risen to the top of the ranks as tools for implementing quantitative analyses of all types. Until now, there has been a lack of accessible, implementation-focused books to assist in Data Science and Machine Learning using this technology. Machine Learning and Big Data with kdb+/q bridges this conspicuous gap, providing you with a practical introduction to the q language and a guide to using data science to enable data-driven decision making. You'll also learn the basic principles and techniques underpinning powerful trading mechanisms based upon machine learning. This book opens the world of q and kdb+ to a wide audience, as it emphasises solutions to problems of practical importance. Implementations covered include: Data description and summary statistics Basic regression methods and cointegration Volatility estimation and time series modelling Advanced machine learning techniques, including neural networks, random forests, and principal component analysis Techniques useful beyond finance related to text analysis, game engines and agent based models Written by four top figures in global quantitative finance and technology, Machine Learning and Big Data with kdb+/q is a valuable resource in high-frequency trading.

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