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Machine Learning and Big Data with Kdb+/Q
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Machine Learning and Big Data with Kdb+/Q Tapa dura -

de Jan Novotny; Paul A. Bilokon; Aris Galiotos


Descripción de contraportada

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.

Detalles

  • Título Machine Learning and Big Data with Kdb+/Q
  • Autor Jan Novotny; Paul A. Bilokon; Aris Galiotos
  • Encuadernación Tapa dura
  • Páginas 640
  • Volúmenes 1
  • Idioma ENG
  • Editorial Wiley
  • Features Bibliography, Index
  • ISBN 9781119404750 / 1119404754
  • Peso 2.64 libras (1.20 kg)
  • Dimensiones 9.7 x 6.7 x 1.6 pulgadas (24.64 x 17.02 x 4.06 cm)
  • Library of Congress subjects Machine learning, Databases
  • Número de catálogo de la Librería del Congreso de EEUU 2019024955
  • Dewey Decimal Code 005.74

Acerca del autor

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.

FRDRIC DLZE 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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