kingchenc
21bbd521b3
- Rust core: super_trend.rs (SuperTrend — ATR-banded trailing stop with
flip logic; SuperTrendOutput { value, direction }), chandelier_exit.rs
(Chandelier Exit — ATR stop hung off the window's highest high / lowest
low; ChandelierExitOutput { long_stop, short_stop }),
chande_kroll_stop.rs (Chande Kroll Stop — a two-stage ATR stop;
ChandeKrollStopOutput { stop_long, stop_short }), atr_trailing_stop.rs
(ATR Trailing Stop — a single ratcheting close-based stop). Each with a
full Indicator impl, runnable doctest and reference / property / warmup
/ reset / batch==streaming tests.
- Python: PySuperTrend / PyChandelierExit / PyChandeKrollStop /
PyAtrTrailingStop PyO3 classes (struct outputs as tuples and (n, 2)
arrays) + module registration + .pyi stubs.
- Node: explicit SuperTrendNode / ChandelierExitNode / ChandeKrollStopNode
/ AtrTrailingStopNode with SuperTrendValue / ChandelierExitValue /
ChandeKrollStopValue objects; index.d.ts and index.js updated.
- WASM: WasmSuperTrend / WasmChandelierExit / WasmChandeKrollStop /
WasmAtrTrailingStop.
- Wiki: Indicator-SuperTrend/ChandelierExit/ChandeKrollStop/
AtrTrailingStop.md plus rows in the "Trailing stop" table of
Indicators-Overview.md and entries in Home.md.
- Add clippy.toml with doc-valid-idents for the proper noun "LeBeau".
cargo fmt + clippy (core/wickra/data/wasm/node) clean; 427 core tests,
25 data tests and 61 doctests green.
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.