mirror of
https://github.com/mihakralj/QuanTAlib.git
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73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
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"""pandas-ta compatibility aliases.
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Maps pandas-ta function names to quantalib equivalents where signatures
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overlap. Import ``from quantalib._compat import ALIASES`` then look up
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the target function in ``quantalib.indicators``.
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Usage::
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from quantalib._compat import get_compat
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fn = get_compat("midprice") # returns indicators.medprice
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"""
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from __future__ import annotations
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from collections.abc import Callable
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# pandas-ta name → quantalib indicators function name
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ALIASES: dict[str, str] = {
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# Core
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"midprice": "medprice",
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"typical_price": "typprice",
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"average_price": "avgprice",
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"mid_body": "midbody",
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# Momentum
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"momentum": "mom",
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# Trends
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"simple_moving_average": "sma",
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"weighted_moving_average": "wma",
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"hull_moving_average": "hma",
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"triangular_moving_average": "trima",
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"exponential_moving_average": "ema",
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"double_exponential_moving_average": "dema",
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"triple_exponential_moving_average": "tema",
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"least_squares_moving_average": "lsma",
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"time_series_forecast": "tsf",
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"linreg": "lsma",
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"sinwma": "sinema",
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# Core (pandas-ta price transforms)
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"hl2": "medprice",
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"hlc3": "typprice",
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"ohlc4": "avgprice",
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# Volatility
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"true_range": "tr",
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"standard_deviation": "stddev",
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"stdev": "stddev",
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# Volume
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"on_balance_volume": "obv",
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"price_volume_trend": "pvt",
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"volume_weighted_moving_average": "vwma",
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"money_flow_index": "mfi",
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"chaikin_money_flow": "cmf",
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"ease_of_movement": "eom",
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# Channels
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"bollinger_bands": "bbands",
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"aberration": "aberr",
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# Statistics
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"z_score": "zscore",
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# Filters
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"butterworth": "butter2",
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}
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def get_compat(name: str) -> Callable[..., object] | None:
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"""Resolve a pandas-ta alias to the quantalib function, or None."""
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from . import indicators
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target = ALIASES.get(name)
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if target is None:
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return None
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return getattr(indicators, target, None)
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