Files
wickra/bindings/python
kingchenc efcd6216c1 feat(bindings): expose RollingVWAP in Python, Node and WASM (R4)
The rolling-window VWAP indicator (`wickra_core::RollingVwap`) was only
available in the Rust crate, even though the README's Volume-family
table already advertised "VWAP (cumulative + rolling)" as a cross-
language feature. Users on Python, Node or in the browser had to fall
back to the cumulative `VWAP` or re-implement the rolling variant
themselves.

This commit closes the gap end-to-end:

- Python: `wickra.RollingVWAP(period)` — same constructor / `update` /
  `batch` / `reset` / `is_ready` / `warmup_period` surface as `VWAP`,
  plus a `period` property and a typed `__repr__`. The `__init__.py`
  re-exports it and `__all__` lists it; the `.pyi` stub matches.
- Node: `RollingVWAP(period)` — napi class with the same lifecycle,
  exported from `index.js` and declared in `index.d.ts`.
- WASM: `RollingVWAP(period)` — wasm-bindgen class with the same
  `Float64Array` I/O as `VWAP`.

Tests added:

- Python: `test_rolling_vwap_streaming_matches_batch` — exercises
  `update == batch` plus the full lifecycle on the shared OHLC fixture.
- Node: `RollingVWAP` row in the `candleScalar` parity table — covered
  by the generic streaming-vs-batch + lifecycle harness.
- WASM: dedicated `wasm-bindgen-test` mirrors the Python test.

The wiki page `Indicator-Vwap.md` drops the "Rust-only" caveat and
gains Python / Node / WASM examples.
2026-05-23 01:43:00 +02:00
..

Wickra — Python bindings

Streaming-first technical indicators powered by a Rust core.

pip install wickra

Quick start

import numpy as np
import wickra as ta

# Batch — TA-Lib-style usage
prices = np.linspace(100, 200, 1000)
rsi = ta.RSI(14).batch(prices)            # NumPy array; NaN during warmup

# Streaming — feed ticks one at a time
rsi = ta.RSI(14)
for price in live_prices:
    v = rsi.update(price)                 # O(1) per tick
    if v is not None and v > 70:
        ...

What's included

71 streaming-first indicators across eight families. Every one passes a batch == streaming equivalence test and reference-value tests:

  • Moving Averages — SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA, SMMA, TRIMA, ZLEMA, T3, VWMA
  • Momentum Oscillators — RSI (Wilder), Stochastic, CCI, ROC, Williams %R, MFI, Awesome Oscillator, MOM, CMO, TSI, PMO, StochRSI, Ultimate Oscillator
  • Trend & Directional — MACD, ADX (+DI/-DI), Aroon, TRIX, Aroon Oscillator, Vortex, Mass Index, Choppiness Index, Vertical Horizontal Filter
  • Price Oscillators — PPO, DPO, Coppock, Accelerator Oscillator, Balance of Power
  • Volatility & Bands — ATR, Bollinger Bands, Keltner Channels, Donchian Channels, NATR, StdDev, Ulcer Index, Historical Volatility, Bollinger Bandwidth, %B, True Range, Chaikin Volatility
  • Trailing Stops — Parabolic SAR, SuperTrend, Chandelier Exit, Chande Kroll Stop, ATR Trailing Stop
  • Volume — OBV, VWAP (cumulative + rolling), ADL, Volume-Price Trend, Chaikin Money Flow, Chaikin Oscillator, Force Index, Ease of Movement
  • Price Statistics — Typical Price, Median Price, Weighted Close, Linear Regression, Linear Regression Slope, Z-Score, Linear Regression Angle

Why streaming-first matters

Classic TA libraries are batch-only: every live tick triggers a full recomputation over the entire history. Wickra updates indicator state in O(1) per tick. On a 5K-bar history the streaming RSI gap is ~17× over the nearest peer with a streaming API and 100×+ over batch-only libraries.

Full project

See https://github.com/kingchenc/wickra for benchmarks, the Rust core, Node.js and WebAssembly bindings, examples, and CI.

License

Licensed under the PolyForm Noncommercial License 1.0.0. Personal, research, educational, and non-profit use are all permitted. Commercial sale requires a separate license — contact via the GitHub repo.