Source code for pandas_ta.volatility.natr

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

[docs]def natr(high, low, close, length=None, scalar=None, mamode=None, talib=None, drift=None, offset=None, **kwargs): """Indicator: Normalized Average True Range (NATR)""" # Validate arguments length = int(length) if length and length > 0 else 14 mamode = mamode if isinstance(mamode, str) else "ema" 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) mode_tal = bool(talib) if isinstance(talib, bool) else True if high is None or low is None or close is None: return # Calculate Result if Imports["talib"] and mode_tal: from talib import NATR natr = NATR(high, low, close, length) else: natr = scalar / close natr *= atr(high=high, low=low, close=close, length=length, mamode=mamode, drift=drift, offset=offset, **kwargs) # Offset if offset != 0: natr = natr.shift(offset) # Handle fills if "fillna" in kwargs: natr.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: natr.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it = f"NATR_{length}" natr.category = "volatility" return natr
natr.__doc__ = \ """Normalized Average True Range (NATR) Normalized Average True Range attempt to normalize the average true range. Sources: Calculation: Default Inputs: length=20 ATR = Average True Range NATR = (100 / close) * ATR(high, low, close) Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's close (pd.Series): Series of 'close's length (int): The short period. Default: 20 scalar (float): How much to magnify. Default: 100 mamode (str): See ```help(```. Default: 'ema' talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib version. Default: True 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 """