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About Kand

The Motivation

TALib has long been a cornerstone for financial indicator calculations, valued for its comprehensive feature set. However, as modern workflows demand higher performance and flexibility, its limitations have become apparent:

  • Performance Bottlenecks: TALib’s C-based core, while fast, is constrained by Python’s Global Interpreter Lock (GIL), limiting multi-threaded potential in today’s multi-core world. This issue has been a persistent challenge, as noted in discussions around the Python bindings.
  • Complex Setup: Installing TALib often involves wrangling C library dependencies, a hurdle for users seeking quick deployment. The fact that installation issues dominate their GitHub issues speaks volumes about this challenge.
  • Batch-Only Design: TALib focuses on full-batch computations, lacking efficient incremental updates needed for real-time systems. While its Python bindings offer a stream feature for incremental calculations, it still relies on batch processing underneath, resulting in slower performance. Even attempts to address parallelism in the native C library highlight its multi-threading constraints.

These pain points inspired us to rethink how financial tools should work in a modern, high-performance context.

Why We Built Kand

kand was created to address TALib’s shortcomings and deliver a next-generation solution for financial developers. Leveraging Rust’s speed and safety, we set out to build a library that’s not just an alternative, but a leap forward:

  • Elite Performance: Written in Rust, kand matches or exceeds TALib’s speed while adding GIL-free multi-threading for true parallelism.
  • Seamless Integration: Powered by rust-numpy, kand shares array memory addresses directly between Python and Rust, enabling true zero-copy data access without any overhead in cross-language operations.
  • Real-Time Ready: True O(1) complexity with near-zero overhead—each update is just a pure variable computation without loops or batching, making it ideal for real-time streaming data processing.
  • Frictionless Setup: A single pip install command replaces TALib’s cumbersome C setup, with precompiled wheels for all major platforms.
  • Cross-Platform Power: Runs effortlessly on Linux, macOS, and Windows—musl Linux included.

Our Vision

kand isn’t just about fixing what’s broken—it’s about enabling what’s possible. Whether you’re a quant trader, data scientist, or developer, we aim to provide a tool that’s fast, reliable, and effortless to use, so you can focus on building, not battling your tools.

To see kand in action, check out our Installation Guide or dive into the API Documentation.