kingchenc
ed7324115c
Completes the F1 family (Simple & Weighted MAs). The Rust core for both SMMA (Wilder's RMA) and TRIMA (triangular MA) already landed; this adds the remaining Definition-of-Done steps: - Python: PySmma / PyTrima PyO3 classes + module registration + .pyi stubs. - Node: SmmaNode / TrimaNode via the scalar-indicator macro; index.d.ts and index.js updated for the two new classes. - WASM: WasmSmma / WasmTrima via the scalar-indicator macro. - Wiki: Indicator-Smma.md and Indicator-Trima.md (full pages) plus rows in Indicators-Overview.md and entries in Home.md. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 208 core tests, 25 data tests and 31 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.