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https://github.com/wassname/pandas-ta.git
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James Sawyer
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
# -*- coding: utf-8 -*-
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from ..utils import get_drift, get_offset, verify_series
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def cmo(close, length=None, scalar=None, drift=None, offset=None, **kwargs):
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"""Indicator: Chande Momentum Oscillator (CMO)"""
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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 14
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scalar = float(scalar) if scalar else 100
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talib = kwargs.pop("talib", True)
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drift = get_drift(drift)
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offset = get_offset(offset)
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# Calculate Result
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negative = close.diff(drift)
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positive = negative.copy()
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positive[positive < 0] = 0 # Make negatives 0 for the postive series
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negative[negative > 0] = 0 # Make postives 0 for the negative series
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if talib:
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pos_ = positive.ewm(com=length, adjust=False).mean()
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neg_ = negative.ewm(com=length, adjust=False).mean().abs()
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else:
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pos_ = positive.rolling(length).sum()
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neg_ = negative.abs().rolling(length).sum()
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cmo = scalar * (pos_ - neg_)
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cmo /= pos_ + neg_
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# Offset
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if offset != 0:
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cmo = cmo.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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cmo.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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cmo.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and Categorize it
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cmo.name = f"CMO_{length}"
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cmo.category = "momentum"
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return cmo
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cmo.__doc__ = """Chande Momentum Oscillator (CMO)
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Attempts to capture the momentum of an asset with overbought at 50 and
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oversold at -50.
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Sources:
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https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/chande-momentum-oscillator-cmo/
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Calculation:
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Default Inputs:
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drift=1, scalar=100
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# Same Calculation as RSI except for this step
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CMO = scalar * (PSUM - NSUM) / (PSUM + NSUM)
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Args:
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close (pd.Series): Series of 'close's
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scalar (float): How much to magnify. Default: 100
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drift (int): The short period. Default: 1
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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talib (bool): If True, uses TA-Libs implementation. Otherwise uses EMA version. Default: True
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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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