kingchenc
b4613a74c8
Candle batch() methods that index parallel high/low/close/volume arrays without first checking their lengths panic on a length mismatch. Adds an equal-length guard returning a clean error to the 11 affected Node methods (Stochastic, ADX, CCI, WilliamsR, MFI, PSAR, Keltner, Donchian, VWAP, AO, Aroon), the 10 affected WASM methods, and the 8 affected Python methods (WilliamsR, ADX, MFI, PSAR, Keltner, VWAP, AO, Aroon) -- matching the guard ATR/OBV already had. Verified in Node: mismatched arrays now throw instead of crashing.
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.