Source code for pandas_ta.trend.chop

# -*- coding: utf-8 -*-
from numpy import log10 as npLog10
from numpy import log as npLn
from pandas_ta.volatility import atr
from pandas_ta.utils import get_drift, get_offset, verify_series

[docs]def chop(high, low, close, length=None, atr_length=None, ln=None, scalar=None, drift=None, offset=None, **kwargs): """Indicator: Choppiness Index (CHOP)""" # Validate Arguments length = int(length) if length and length > 0 else 14 atr_length = int(atr_length) if atr_length is not None and atr_length > 0 else 1 ln = bool(ln) if isinstance(ln, bool) else False scalar = float(scalar) if scalar else 100 high = verify_series(high, length) low = verify_series(low, length) close = verify_series(close, length) drift = get_drift(drift) offset = get_offset(offset) if high is None or low is None or close is None: return # Calculate Result diff = high.rolling(length).max() - low.rolling(length).min() atr_ = atr(high=high, low=low, close=close, length=atr_length) atr_sum = atr_.rolling(length).sum() chop = scalar if ln: chop *= (npLn(atr_sum) - npLn(diff)) / npLn(length) else: chop *= (npLog10(atr_sum) - npLog10(diff)) / npLog10(length) # Offset if offset != 0: chop = chop.shift(offset) # Handle fills if "fillna" in kwargs: chop.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: chop.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it = f"CHOP{'ln' if ln else ''}_{length}_{atr_length}_{scalar}" chop.category = "trend" return chop
chop.__doc__ = \ """Choppiness Index (CHOP) The Choppiness Index was created by Australian commodity trader E.W. Dreiss and is designed to determine if the market is choppy (trading sideways) or not choppy (trading within a trend in either direction). Values closer to 100 implies the underlying is choppier whereas values closer to 0 implies the underlying is trending. Sources: Calculation: Default Inputs: length=14, scalar=100, drift=1 HH = high.rolling(length).max() LL = low.rolling(length).min() ATR_SUM = SUM(ATR(drift), length) CHOP = scalar * (LOG10(ATR_SUM) - LOG10(HH - LL)) CHOP /= LOG10(length) Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's close (pd.Series): Series of 'close's length (int): It's period. Default: 14 atr_length (int): Length for ATR. Default: 1 ln (bool): If True, uses ln otherwise log10. Default: False scalar (float): How much to magnify. Default: 100 drift (int): The difference 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. """