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docs: PyPI README reflects the zero-dependency Python binding (#321)

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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.
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kingchenc
2026-06-17 03:40:18 +02:00
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@@ -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)