Difference between revisions of "Programming/Kdb/Resources"
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=APL and other related languages=
=APL and other related languages=
* The [https://britishaplassociation.org/ British APL Association (BAA)] was founded in 1984 to promote a family of interactive array-programming languages noted for elegance, conciseness, and fast development speed. Many of them were derived from Kenneth Iverson's mathematical notation.
* The [https://britishaplassociation.org/ British APL Association (BAA)]was founded in 1984 to promote a family of interactive array-programming languages noted for elegance, conciseness, and fast development speed. Many of them were derived from Kenneth Iverson's mathematical notation.
* [https://vector.org.uk/ Vector]—the journal of the BAA.
* [https://vector.org.uk/ Vector]—the journal of the BAA
Revision as of 22:55, 1 July 2021
Q for Mortals
- Author: Jeffry A. Borror
- Publisher: q4m LLC
- Paperback: 586 pages
- ISBN-10: 0692573674
- ISBN-13: 978-0692573679
Q for Mortals Version 3 is a thorough presentation of the q programming language and an introduction to the kdb+ database. It is a complete rewrite of the original Q for Mortals that is current with q 3.3. The presentation is derived from classes taught by the author at international financial institutions over the last decade. It is a series of tutorials based on q snippets intended to be entered interactively into the q console by the reader. The text takes its subject seriously but not itself. Technical explanations are augmented by mathematical observations, references to general programming concepts and other programming languages, and jokes. Coding style recommendations and advice to avoid gotchas appear liberally throughout. Examples are as simple as they can be but no simpler.
Chapter 1, Q Shock and Awe, provides a piquant panorama of the power of q and its dazzling zen-like nature. Chapter 2 describes the base data types of q. Chapter 3 discusses lists, the fundamental data structure of q. Chapter 4 presents the basic operators. Chapter 5 introduced dictionaries, which associate keys and values. Chapter 6 presents an in-depth description of functions and q's constructs for functional programming. Chapter 7 demonstrates transforming data from one type to another. Chapter 8 introduces tables and keyed tables, the fundamental data structures for kdb. Chapter 9 describes q-sql and all the methods to manipulate tables. Chapter 10 presents ways to control execution of q programs. Chapter 11 covers file and interprocess communication I/O. Chapter 12 describes workspace organization and management. Chapter 13 discusses system commands and command line parameters. Chapter 14 serves as an introduction to the kdb+ database. Chapter A has a complete rundown of the built-in functions. Chapter B lists common error messages. A cross-referenced index closes the book.
Version 3.1 of Q for Mortals is available online:
Q Tips: Fast, Scalable and Maintainable Kdb+
- Publisher: Vector Sigma
- Paperback: 463 pages
- ASIN: B00UZ8OMME
Learn q by building a real life application. Q Tips teaches you everything you need to know to build a fully functional Complex Event Processing (CEP) engine. Advanced topics include profiling an active q server, derivatives pricing, and histogram charting. As each new topic is introduced, tips are highlighted to help you write better q.
Machine Learning and Big Data with kdb+/q
- Authors: Jan Novotny, Paul A. Bilokon, Aris Galiotos, Frederic Deleze
- Publisher: Wiley
- Hardcover: 640 pages
- ISBN-10: 1119404754
- ISBN-13: 978-1119404750
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.
- Publisher: Vector Sigma
- Paperback: 415 pages
- ISBN-10: 1734467509
- ISBN-13: 978-1734467505
Bring the power of machine learning to the fastest time-series database. Fun Q uses the powerful q programming language to implement many of the most famous machine-learning algorithms. Using a meticulously factored machine-learning library, each algorithm is broken into its basic building blocks and then rebuilt from scratch. Famous machine-learning data sets are used to motivate each chapter as advanced q idioms are introduced. Whether you are a data scientist who is new to q or a kdb+ administrator who is new to machine learning, you'll have fun learning how machine-learning algorithms can be implemented in the concise vector-functional language q. With nothing but the q binary, you'll be able to download data sets, generate plots in the q terminal and get progress-bar-style feedback as model parameters iteratively improve. In addition to being a functional introduction to machine learning algorithms in q, it is designed to be a fun introduction as well!
- Tutorial introductions to the q language and the kdb+ database:
- kdb+ tutorials:
- thesweeheng's blog:
- qkdb's blog:
- me, q, and kdb+:
- The very useful reference card covers the keywords, operators, iterators, execution control, attributes, command-line options, system commands, datatypes, namespaces, and more:
- A Knowledge Base:
- A phrasebook:
Frequently asked questions
- The kdbfaq:
Hello! Perhaps you're just learning q and kdb, or maybe you've been using them for yours but something's got you stuck. You want answers. Fast. That's why you're using kdb in the first place, right?
Our names areand Nate, and we've spent — and continue to spend — much of our time reading and writing q code as well as building kdb databases and analytics on top of them. We also spend a lot of time with our users, teaching them how to use q and kdb more effectively as well as helping them implement their ideas. They come to us, because they know that's the fastest way to get the answers they need.
So, we created this site to host a growing collection of articles that will eventually become our book. Although we will be very fortunate indeed if any one of our writings inspires you, we are confident we can help you navigate the powerful yet cryptic waters of q and kdb. Fast.
- Kx Systems on GitHub:
- A list of code repositories maintained by Kx Systems:
- Interfaces to other languages, processes, and editor integrations:
- In particular, for Excel integration, see
- Nick Psaris's page on GitHub:
- The repository for the Machine Learning and Big Data with kdb+/q book by Novotny et al.:
- The was founded in 1984 to promote a family of interactive array-programming languages noted for elegance, conciseness, and fast development speed. Many of them were derived from Kenneth Iverson's mathematical notation.
- —the journal of the BAA.
- : high-performance data analysis, powerful operators for exploratory programming, built-in database.