wickra/bindings/python

Wickra — Python

Streaming-first technical indicators for Python. pip install wickra — zero third-party dependencies (not even NumPy), no system dependencies, no C build tooling.

Wickra is a multi-language technical-analysis library with a Rust core and bindings for Python, Node.js and WASM, plus a C ABI for C, C++, C#, Go, Java, R and any other C-capable language. Every indicator is an O(1) streaming state machine, so live trading bots and historical backtests share the exact same implementation. This package is the Python binding (PyO3); it exposes all 514 streaming-first indicators across twenty-four families.

Install

pip install wickra

Pre-built wheels ship for Linux, macOS, and Windows — there is nothing to compile and no C library to track down. pip install wickra pulls zero third-party packages; NumPy is an optional extra (pip install wickra[numpy]) for zero-copy interop.

Quick start

import wickra as ta                     # zero third-party deps — not even NumPy

# Batch: classic TA-Lib-style usage over a whole series.
prices = [100.0 + i * 0.1 for i in range(1000)]   # list, array.array or NumPy all work
rsi = ta.RSI(14)
values = rsi.batch(prices)              # array.array('d'), NaN during warmup
                                        # np.asarray(values) wraps it zero-copy if you use NumPy

# Streaming: the same indicator, fed tick by tick in O(1).
rsi = ta.RSI(14)
for price in live_feed:
    value = rsi.update(price)           # no recomputation over history
    if value is not None and value > 70:
        print("overbought")

batch(prices) and feeding the same prices through update() produce identical values — the equivalence is enforced by the test suite.

Benchmark

Two benchmarks ship with the binding:

• benchmarks/throughput.py — streaming and batch updates-per-second for SMA, ATR and MACD. This is per-binding FFI overhead (the same Rust core runs under every binding), not a cross-library ratio.
• benchmarks/compare_libraries.py — the cross-library comparison against TA-Lib, pandas-ta, tulipy and finta that backs the headline speedups.

maturin develop --release
python -m benchmarks.throughput
python -m benchmarks.compare_libraries   # cross-library; auto-detects installed peers

See the repository BENCHMARKS.md.

Documentation

The full indicator catalogue, guides, quickstarts, and API reference live in the main repository and documentation site:

• Repository & full indicator list: https://github.com/wickra-lib/wickra
• Docs (quickstarts, cookbook, TA-Lib migration): https://docs.wickra.org
• Runnable examples: examples/python/

Wickra ships native bindings for Python, Node.js, WASM and Rust, plus a C ABI hub that any C-capable language (C, C++, C#, Go, Java, R) links against — all exposing the same indicators from the shared, unsafe-forbidden Rust core.

Security

Found a security issue? Please don't open a public issue. Report it privately via the affected repository's Security tab ("Report a vulnerability") or email support@wickra.org with a subject line starting [wickra security]. Full policy: https://github.com/wickra-lib/wickra/blob/main/SECURITY.md.

Disclaimer

Wickra is an indicator toolkit, not a trading system. The values it computes are deterministic transforms of the input data — they are not financial advice and do not predict the market. Any use in a live trading context is at your own risk. The library is provided as is, without warranty of any kind.

License

Licensed under either of Apache-2.0 or MIT at your option.