mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
54 lines
1.3 KiB
Python
54 lines
1.3 KiB
Python
# -*- coding: utf-8 -*-
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from numpy import fabs as npfabs
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from ..utils import get_offset, verify_series
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def mad(close, length=None, offset=None, **kwargs):
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"""Indicator: Mean Absolute Deviation"""
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# Validate Arguments
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close = verify_series(close)
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length = int(length) if length and length > 0 else 30
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min_periods = (int(kwargs["min_periods"]) if "min_periods" in kwargs and
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kwargs["min_periods"] is not None else length)
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offset = get_offset(offset)
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# Calculate Result
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def mad_(series):
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"""Mean Absolute Deviation"""
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return npfabs(series - series.mean()).mean()
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mad = close.rolling(length, min_periods=min_periods).apply(mad_, raw=True)
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# Offset
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if offset != 0:
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mad = mad.shift(offset)
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# Name & Category
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mad.name = f"MAD_{length}"
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mad.category = "statistics"
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return mad
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mad.__doc__ = """Rolling Mean Absolute Deviation
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Sources:
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Calculation:
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Default Inputs:
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length=30
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mad = close.rolling(length).mad()
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Args:
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close (pd.Series): Series of 'close's
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length (int): It's period. Default: 30
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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fillna (value, optional): pd.DataFrame.fillna(value)
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fill_method (value, optional): Type of fill method
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Returns:
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pd.Series: New feature generated.
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"""
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