Source code for pandas_ta.momentum.cfo

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

[docs]def cfo(close, length=None, scalar=None, drift=None, offset=None, **kwargs): """Indicator: Chande Forcast Oscillator (CFO)""" # Validate Arguments length = int(length) if length and length > 0 else 9 scalar = float(scalar) if scalar else 100 close = verify_series(close, length) drift = get_drift(drift) offset = get_offset(offset) if close is None: return # Finding linear regression of Series cfo = scalar * (close - linreg(close, length=length, tsf=True)) cfo /= close # Offset if offset != 0: cfo = cfo.shift(offset) # Handle fills if "fillna" in kwargs: cfo.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: cfo.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it = f"CFO_{length}" cfo.category = "momentum" return cfo
cfo.__doc__ = \ """Chande Forcast Oscillator (CFO) The Forecast Oscillator calculates the percentage difference between the actual price and the Time Series Forecast (the endpoint of a linear regression line). Sources: Calculation: Default Inputs: length=9, drift=1, scalar=100 LINREG = Linear Regression CFO = scalar * (close - LINERREG(length, tdf=True)) / close Args: close (pd.Series): Series of 'close's length (int): The period. Default: 9 scalar (float): How much to magnify. Default: 100 drift (int): The short period. Default: 1 offset (int): How many periods to offset the result. Default: 0 Kwargs: fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method Returns: pd.Series: New feature generated. """