Files
wickra/bindings/python
kingchenc e452d35a27 F13a: add Accelerator Oscillator, Balance of Power, Choppiness Index and Vertical Horizontal Filter
First half of the eight indicators that fill out the new family taxonomy.

- Rust core: accelerator_oscillator.rs (AcceleratorOscillator — AO minus a
  short SMA of itself), balance_of_power.rs (BalanceOfPower — per-bar
  (close-open)/(high-low)), choppiness_index.rs (ChoppinessIndex — summed
  true range over the high-low span, log-scaled) and
  vertical_horizontal_filter.rs (VerticalHorizontalFilter — net move over
  total move). Each with a full Indicator impl, runnable doctest and
  reference / property / warmup / reset / batch==streaming tests.
- Python / Node / WASM: classes wired through all three bindings
  (BalanceOfPower carries an explicit open column; VHF rides the scalar
  macros) plus .pyi stubs and __init__.py / __all__ entries.
- Wiki: four new Indicator-*.md pages.

The eight-family taxonomy restructure (Overview / Home / README / folder
layout) lands in F13c once F13b's four indicators are in.

cargo fmt + clippy (core/wickra/data/wasm/node) clean; 481 core tests,
25 data tests and 70 doctests green.
2026-05-22 20:57:52 +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

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

  • Trend — SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA, SMMA, TRIMA, ZLEMA, T3, VWMA
  • Momentum — RSI (Wilder), MACD, Stochastic, CCI, ROC, WilliamsR, ADX, MFI, TRIX, AwesomeOscillator, Aroon, MOM, CMO, TSI, PMO, StochRSI, UltimateOscillator, PPO, DPO, Coppock, AroonOscillator, Vortex, MassIndex
  • Volatility — BollingerBands, ATR, Keltner, Donchian, PSAR, NATR, StdDev, UlcerIndex, HistoricalVolatility, BollingerBandwidth, PercentB, SuperTrend, ChandelierExit, ChandeKrollStop, AtrTrailingStop
  • Volume — OBV, VWAP, ADL, VolumePriceTrend, ChaikinMoneyFlow, ChaikinOscillator, ForceIndex, EaseOfMovement
  • Statistics — TypicalPrice, MedianPrice, WeightedClose, LinearRegression, LinRegSlope

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.