From 1f5eb90b0d091746866b115c00f28d6e51b09e4f Mon Sep 17 00:00:00 2001 From: kingchenc Date: Wed, 17 Jun 2026 03:40:18 +0200 Subject: [PATCH] docs: PyPI README reflects the zero-dependency Python binding (#321) The package README shown on **PyPI** still advertised a NumPy quick-start. Now that Python dropped its NumPy runtime dependency (#317), this updates it: - Tagline + install note: `pip install wickra` pulls **zero** third-party packages (not even NumPy); NumPy is an optional extra (`wickra[numpy]`). - Quick start imports no NumPy; `batch` returns `array.array('d')` (with a note that `np.asarray` wraps it zero-copy if you use NumPy). Ships with the data-layer release. --- bindings/python/README.md | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/bindings/python/README.md b/bindings/python/README.md index e01301d8..9c67249e 100644 --- a/bindings/python/README.md +++ b/bindings/python/README.md @@ -5,8 +5,8 @@ [![PyPI](https://img.shields.io/pypi/v/wickra.svg?logo=pypi&color=blue)](https://pypi.org/project/wickra/) [![License: MIT OR Apache-2.0](https://img.shields.io/badge/license-MIT_OR_Apache--2.0-blue)](https://github.com/wickra-lib/wickra#license) -**Streaming-first technical indicators for Python. `pip install wickra` — no -system dependencies, no C build tooling.** +**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 @@ -22,18 +22,20 @@ pip install wickra ``` Pre-built wheels ship for Linux, macOS, and Windows — there is nothing to -compile and no C library to track down. +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 ```python -import numpy as np -import wickra as ta +import wickra as ta # zero third-party deps — not even NumPy -# Batch: classic TA-Lib-style usage over a whole array. -prices = np.linspace(100, 200, 1000) +# 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) # numpy array, NaN during warmup +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)