# -*- 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
willr.name = 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:
https://www.tradingview.com/wiki/Williams_%25R_(%25R)
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.
"""