55 lines
1.6 KiB
Markdown
55 lines
1.6 KiB
Markdown
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# Wickra — Python bindings
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Streaming-first technical indicators powered by a Rust core.
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```bash
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pip install wickra
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```
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## Quick start
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```python
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import numpy as np
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import wickra as ta
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# Batch — TA-Lib-style usage
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prices = np.linspace(100, 200, 1000)
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rsi = ta.RSI(14).batch(prices) # NumPy array; NaN during warmup
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# Streaming — feed ticks one at a time
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rsi = ta.RSI(14)
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for price in live_prices:
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v = rsi.update(price) # O(1) per tick
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if v is not None and v > 70:
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...
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```
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## What's included
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25 streaming-first indicators across four families. Every one passes a
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`batch == streaming` equivalence test and reference-value tests:
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- **Trend** — SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA
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- **Momentum** — RSI (Wilder), MACD, Stochastic, CCI, ROC, WilliamsR, ADX,
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MFI, TRIX, AwesomeOscillator, Aroon
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- **Volatility** — BollingerBands, ATR, Keltner, Donchian, PSAR
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- **Volume** — OBV, VWAP
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## Why streaming-first matters
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Classic TA libraries are batch-only: every live tick triggers a full
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recomputation over the entire history. Wickra updates indicator state in
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O(1) per tick. On a 5K-bar history the streaming RSI gap is ~17× over the
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nearest peer with a streaming API and 100×+ over batch-only libraries.
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## Full project
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See <https://github.com/kingchenc/wickra> for benchmarks, the Rust core,
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Node.js and WebAssembly bindings, examples, and CI.
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## License
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Licensed under the **PolyForm Noncommercial License 1.0.0**. Personal,
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research, educational, and non-profit use are all permitted. Commercial
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sale requires a separate license — contact via the GitHub repo.
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