# -*- coding: utf-8 -*-
# Williams %R (WILLR)
from typing import Any, Optional
from pandas import Series
from pandas_ta_classic import Imports
from pandas_ta_classic.utils import get_offset, verify_series


def willr(
    high: Series,
    low: Series,
    close: Series,
    length: Optional[int] = None,
    talib: Optional[bool] = None,
    offset: Optional[int] = None,
    **kwargs: Any,
) -> Optional[Series]:
    """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 None

    # 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:
        if "fill_method" in kwargs:

            if kwargs["fill_method"] == "ffill":

                willr.ffill(inplace=True)

            elif kwargs["fill_method"] == "bfill":

                willr.bfill(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.
"""
