Files
wickra/bindings/python
kingchenc 81962485af F9: add Accumulation/Distribution Line and Volume-Price Trend
Completes the F9 family (Cumulative volume) end to end:

- Rust core: adl.rs (Accumulation/Distribution Line — cumulative
  range-weighted volume) and vpt.rs (Volume-Price Trend — cumulative
  volume scaled by percentage price change). Each with a full Indicator
  impl, runnable doctest and reference / cumulative-property / warmup /
  reset / batch==streaming tests.
- Python: PyAdl / PyVolumePriceTrend PyO3 classes + module registration
  + .pyi stubs (no parameters, like OBV/VWAP).
- Node: explicit AdlNode and VolumePriceTrendNode; index.d.ts and
  index.js updated.
- WASM: WasmAdl and WasmVolumePriceTrend.
- Wiki: Indicator-Adl.md and Indicator-VolumePriceTrend.md plus rows in
  Indicators-Overview.md and entries in Home.md.

cargo fmt + clippy (core/wickra/data/wasm/node) clean; 373 core tests,
25 data tests and 53 doctests green.
2026-05-22 18:38:21 +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

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

  • Trend — SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA
  • Momentum — RSI (Wilder), MACD, Stochastic, CCI, ROC, WilliamsR, ADX, MFI, TRIX, AwesomeOscillator, Aroon
  • Volatility — BollingerBands, ATR, Keltner, Donchian, PSAR
  • Volume — OBV, VWAP

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.