Learn K with Real Code Examples

K is optimized for processing large amounts of data efficiently.

It features a terse, symbolic syntax that allows complex operations in very few characters.

Commonly used in finance for real-time analytics, risk modeling, and market data processing.

Vectorized operations on arrays and tables

Functional programming constructs

Tacit (point-free) programming style

Efficient time-series and numeric calculations

Integration with kdb+ for persistent storage

Atoms, lists, and dictionaries

Tables and keyed tables

Tacit vs explicit functions

Vectorized operations

Integration with kdb+ queries

src/ - K scripts

lib/ - reusable functions and modules

data/ - input datasets or market data

tests/ - validation scripts

docs/ - function definitions and project notes

Write K scripts (.k files)

Load scripts into kdb+ or run via K interpreter

Test calculations and table manipulations

Optimize performance for large datasets

Integrate with real-time market data feeds

Beginner: simple array and list operations

Intermediate: table manipulations and time-series calculations

Advanced: functional/tacit programming with large datasets

Expert: high-frequency trading systems

Enterprise: real-time market analytics and integration with kdb+

Terser than Python or R for analytics

Faster than many general-purpose languages for large datasets

Specialized for time-series and financial data

Smaller community compared to mainstream languages

Integration with kdb+ provides unmatched performance for certain use cases

Early 1990s – K created by Arthur Whitney

1993–1995 – Used in financial applications

Late 1990s – Integration with kdb+

2000s – Continued adoption in high-frequency trading

2010s–2020s – Active in finance, particularly with real-time analytics

Atom: single value

List: ordered collection of items

Table: structured dataset

Keyed table: table with unique key column

Tacit function: function without explicit arguments