Pratik Bhadane
70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
"""
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Streaming / Incremental Indicators — bar-by-bar stateful classes.
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All streaming classes are implemented in Rust (PyO3) for maximum performance.
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The Python module re-exports the Rust classes from the ``_ferro_ta`` extension.
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The extension must be built; there is no Python fallback.
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Usage
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-----
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>>> from ferro_ta.data.streaming import StreamingSMA, StreamingEMA, StreamingRSI
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>>> import numpy as np
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>>> sma = StreamingSMA(period=3)
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>>> for close in [10.0, 11.0, 12.0, 13.0, 14.0]:
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... val = sma.update(close)
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... print(f"{close} → {val:.4f}" if not np.isnan(val) else f"{close} → NaN")
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10.0 → NaN
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11.0 → NaN
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12.0 → 11.0000
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13.0 → 12.0000
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14.0 → 13.0000
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Available classes
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-----------------
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StreamingSMA — Simple Moving Average
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StreamingEMA — Exponential Moving Average
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StreamingRSI — Relative Strength Index (Wilder seeding)
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StreamingATR — Average True Range (Wilder seeding)
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StreamingBBands — Bollinger Bands (upper, middle, lower)
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StreamingMACD — MACD line, signal, histogram
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StreamingStoch — Slow Stochastic (slowk, slowd)
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StreamingVWAP — Volume Weighted Average Price (cumulative)
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StreamingSupertrend — ATR-based Supertrend
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Rust backend
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------------
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All classes are PyO3 classes compiled into the ``_ferro_ta`` extension module.
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Import them directly from the extension for zero-overhead access::
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from ferro_ta._ferro_ta import StreamingSMA
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"""
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from __future__ import annotations
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# ---------------------------------------------------------------------------
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# Import Rust-backed streaming classes from the compiled extension.
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# ---------------------------------------------------------------------------
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from ferro_ta._ferro_ta import ( # noqa: F401
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StreamingATR,
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StreamingBBands,
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StreamingEMA,
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StreamingMACD,
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StreamingRSI,
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StreamingSMA,
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StreamingStoch,
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StreamingSupertrend,
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StreamingVWAP,
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)
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__all__ = [
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"StreamingSMA",
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"StreamingEMA",
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"StreamingRSI",
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"StreamingATR",
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"StreamingBBands",
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"StreamingMACD",
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"StreamingStoch",
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"StreamingVWAP",
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"StreamingSupertrend",
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]
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