# -*- coding: utf-8 -*-
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.maps import Imports
from pandas_ta.momentum import rsi
from pandas_ta.utils import (
    non_zero_range,
    v_mamode,
    v_offset,
    v_pos_default,
    v_series,
    v_talib
)



def stochrsi(
    close: Series, length: Int = None, rsi_length: Int = None,
    k: Int = None, d: Int = None, mamode: str = None,
    talib: bool = None, offset: Int = None, **kwargs: DictLike
) -> DataFrame:
    """Stochastic RSI

    This indicator attempts to quantify RSI relative to its High-Low range.

    Sources:
        * "Stochastic RSI and Dynamic Momentum Index", Tushar Chande and
           Stanley Kroll, Stock & Commodities V.11:5 (189-199)
        * [tradingview](https://www.tradingview.com/wiki/Stochastic_(STOCH))

    Parameters:
        close (Series): ```close``` Series
        length (int): The period. Default: ```14```
        rsi_length (int): RSI period. Default: ```14```
        k (int): The Fast %K period. Default: ```3```
        d (int): The Slow %K period. Default: ```3```
        mamode (str): See ```help(ta.ma)```. Default: ```"sma"```
        talib (bool): If installed, use TA Lib. Default: ```True```
        offset (int): Post shift. Default: ```0```

    Other Parameters:
        fillna (value): ```pd.DataFrame.fillna(value)```

    Returns:
        (DataFrame): 2 columns

    Note:
        May be more sensitive to RSI and thus identify potential "overbought"
        or "oversold" signals.
    """
    # Validate
    length = v_pos_default(length, 14)
    rsi_length = v_pos_default(rsi_length, 14)
    k = v_pos_default(k, 3)
    d = v_pos_default(d, 3)
    _length = length + rsi_length + 2
    close = v_series(close, _length)

    if close is None:
        return

    mamode = v_mamode(mamode, "sma")
    mode_tal = v_talib(talib)
    offset = v_offset(offset)

    # Calculate
    # if Imports["talib"] and mode_tal:
    #     from talib import RSI
    #     rsi_ = RSI(close, length)
    # else:

    rsi_ = rsi(close, length=rsi_length)
    lowest_rsi = rsi_.rolling(length).min()
    highest_rsi = rsi_.rolling(length).max()

    stoch = 100 * (rsi_ - lowest_rsi) / non_zero_range(highest_rsi, lowest_rsi)

    stochrsi_k = ma(mamode, stoch, length=k)
    stochrsi_d = ma(mamode, stochrsi_k, length=d)

    # Offset
    if offset != 0:
        stochrsi_k = stochrsi_k.shift(offset)
        stochrsi_d = stochrsi_d.shift(offset)

    # Fill
    if "fillna" in kwargs:
        stochrsi_k.fillna(kwargs["fillna"], inplace=True)
        stochrsi_d.fillna(kwargs["fillna"], inplace=True)

    # Name and Category
    _name = "STOCHRSI"
    _props = f"_{length}_{rsi_length}_{k}_{d}"
    stochrsi_k.name = f"{_name}k{_props}"
    stochrsi_d.name = f"{_name}d{_props}"
    stochrsi_k.category = stochrsi_d.category = "momentum"

    data = {stochrsi_k.name: stochrsi_k, stochrsi_d.name: stochrsi_d}
    df = DataFrame(data, index=close.index)
    df.name = f"{_name}{_props}"
    df.category = stochrsi_k.category

    return df