Machine Learning and Big Data with KDB+/Q

Machine Learning and Big Data with KDB+/Q
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Book Description
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.

Content

PART ONE - Language Fundamentals
CHAPTER 1. Fundamentals of the q Programming Language
CHAPTER 2. Dictionaries and Tables: The q Fundamentals
CHAPTER 3. Functions
CHAPTER 4. Editors and Other Tools
CHAPTER 5. Debugging q Code

PART TWO - Data Operations
CHAPTER 6. Splayed and Partitioned Tables
CHAPTER 7. Joins
CHAPTER 8. Parallelisation
CHAPTER 9. Data Cleaning and Filtering
CHAPTER 10. Parse Trees
CHAPTER 11. A Few Use Cases

PART THREE - Data Science
CHAPTER 12. Basic Overview of Statistics
CHAPTER 13. Linear Regression
CHAPTER 14. Time Series Econometrics
CHAPTER 15. Fourier Transform
CHAPTER 16. Eigensystem and PCA
CHAPTER 17. Outlier Detection
CHAPTER 18. Simulating Asset Prices

PART FOUR - Machine Learning
CHAPTER 19. Basic Principles of Machine Learning
CHAPTER 20. Linear Regression with Regularisation
CHAPTER 21. Nearest Neighbours
CHAPTER 22. Neural Networks
CHAPTER 23. AdaBoost with Stumps
CHAPTER 24. Trees
CHAPTER 25. Forests
CHAPTER 26. Unsupervised Machine Learning: The Apriori Algorithm
CHAPTER 27. Processing Information
CHAPTER 28. Towards AI – Monte Carlo Tree Search
CHAPTER 29. Econophysics: The Agent-Based Computational Models
CHAPTER 30. Epilogue: Art

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