Machine Learning and Big Data?

Book

Brief

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