000			02806 a2200289 4500
003			OSt
005			20220124164020.0
008			220120b xxu||||| |||| 00| 0 eng d
020			_a9781119404750
040			_cIIT Kanpur
041			_aeng
082			_a005.74 _bM184
245			_aMachine learning and big data with kdb+/q _cJan Novotny ...[et al.]
260			_bWiley _c2020 _aChichester
300			_axxiv, 613p
440			_aWiley finance series
520			_aMachine 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 the “meat” of q programming to solve practical economic problems. -Perform everyday operations including basic regressions, cointegration, volatility estimation, modeling, 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 modeling 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.
650			_aMachine learning
650			_aBig data
650			_aQuery languages (Computer science)
650			_aDatabases
700			_aNovotny, Jan
700			_aBilokon, Paul A.
700			_aGaliotos, Aris
700			_aDeleze, Frederic
942			_cBK
999			_c565284 _d565284