Source code for pandas_ta.momentum.willr

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

[docs]def willr(high, low, close, length=None, talib=None, offset=None, **kwargs): """Indicator: William's Percent R (WILLR)""" # Validate arguments length = int(length) if length and length > 0 else 14 min_periods = int(kwargs["min_periods"]) if "min_periods" in kwargs and kwargs["min_periods"] is not None else length _length = max(length, min_periods) high = verify_series(high, _length) low = verify_series(low, _length) close = verify_series(close, _length) 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 WILLR willr = WILLR(high, low, close, length) else: lowest_low = low.rolling(length, min_periods=min_periods).min() highest_high = high.rolling(length, min_periods=min_periods).max() willr = 100 * ((close - lowest_low) / (highest_high - lowest_low) - 1) # Offset if offset != 0: willr = willr.shift(offset) # Handle fills if "fillna" in kwargs: willr.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: willr.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it = f"WILLR_{length}" willr.category = "momentum" return willr
willr.__doc__ = \ """William's Percent R (WILLR) William's Percent R is a momentum oscillator similar to the RSI that attempts to identify overbought and oversold conditions. Sources: Calculation: Default Inputs: length=20 LL = low.rolling(length).min() HH = high.rolling(length).max() WILLR = 100 * ((close - LL) / (HH - LL) - 1) 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 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 generated. """