Source code for pandas_ta.momentum.cmo

# -*- coding: utf-8 -*-
from pandas_ta import Imports
from pandas_ta.overlap import rma
from pandas_ta.utils import get_drift, get_offset, verify_series


[docs]def cmo(close, length=None, scalar=None, talib=None, drift=None, offset=None, **kwargs): """Indicator: Chande Momentum Oscillator (CMO)""" # Validate Arguments length = int(length) if length and length > 0 else 14 scalar = float(scalar) if scalar else 100 close = verify_series(close, length) drift = get_drift(drift) offset = get_offset(offset) mode_tal = bool(talib) if isinstance(talib, bool) else True if close is None: return # Calculate Result if Imports["talib"] and mode_tal: from talib import CMO cmo = CMO(close, length) else: mom = close.diff(drift) positive = mom.copy().clip(lower=0) negative = mom.copy().clip(upper=0).abs() if mode_tal: pos_ = rma(positive, length) neg_ = rma(negative, length) else: pos_ = positive.rolling(length).sum() neg_ = negative.rolling(length).sum() cmo = scalar * (pos_ - neg_) / (pos_ + neg_) # Offset if offset != 0: cmo = cmo.shift(offset) # Handle fills if "fillna" in kwargs: cmo.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: cmo.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it cmo.name = f"CMO_{length}" cmo.category = "momentum" return cmo
cmo.__doc__ = \ """Chande Momentum Oscillator (CMO) Attempts to capture the momentum of an asset with overbought at 50 and oversold at -50. Sources: https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/chande-momentum-oscillator-cmo/ https://www.tradingview.com/script/hdrf0fXV-Variable-Index-Dynamic-Average-VIDYA/ Calculation: Default Inputs: drift=1, scalar=100 # Same Calculation as RSI except for this step CMO = scalar * (PSUM - NSUM) / (PSUM + NSUM) Args: close (pd.Series): Series of 'close's scalar (float): How much to magnify. Default: 100 talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib version. If TA Lib is not installed but talib is True, it runs the Python version TA Lib. Default: True drift (int): The short period. Default: 1 offset (int): How many periods to offset the result. Default: 0 Kwargs: talib (bool): If True, uses TA-Libs implementation. Otherwise uses EMA version. Default: True fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method Returns: pd.Series: New feature generated. """