# -*- coding: utf-8 -*-
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.overlap import hl2
from pandas_ta.utils import v_offset, v_pos_default, v_series



def ttm_trend(
    high: Series, low: Series, close: Series,
    length: Int = None, offset: Int = None, **kwargs: DictLike
) -> DataFrame:
    """TTM Trend

    This indicator, by John Carter, labels bars green, ```1```, or
    red ```-1```, when above or below the average value.

    Sources:
        * John Carter, book “Mastering the Trade”
        * [prorealcode](https://www.prorealcode.com/prorealtime-indicators/ttm-trend-price/)

    Parameters:
        high (Series): ```high``` Series
        low (Series): ```low``` Series
        close (Series): ```close``` Series
        length (int): The period. Default: ```6```
        offset (int): Post shift. Default: ```0```

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

    Returns:
        (DataFrame): 1 column

    Tip:
        * Two bars of the opposite color is the signal to get in or out.
        * Recommended to stay in trade if colors do not change.
    """
    # Validate
    length = v_pos_default(length, 6)
    high = v_series(high, length)
    low = v_series(low, length)
    close = v_series(close, length)

    if high is None or low is None or close is None:
        return

    offset = v_offset(offset)

    # Calculate
    trend_avg = hl2(high, low)
    for i in range(1, length):
        trend_avg = trend_avg + hl2(high.shift(i), low.shift(i))

    trend_avg = trend_avg / length

    tm_trend = (close > trend_avg).astype(int)
    tm_trend.replace(0, -1, inplace=True)

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

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

    # Name and Category
    tm_trend.name = f"TTM_TRND_{length}"
    tm_trend.category = "momentum"

    df = DataFrame({tm_trend.name: tm_trend}, index=close.index)
    df.name = f"TTMTREND_{length}"
    df.category = tm_trend.category

    return df