"""Technical Analysis Trendline Module"""
__docformat__ = "numpy"

from pandas.core.frame import DataFrame
import yfinance as yf
from scipy.stats import linregress


def load_ticker(ticker: str, start_date: str) -> DataFrame:
    """
    Loads a ticker data from Yahoo Finance, adds a data index column data_id and Open-Close High/Low columns.

    Parameters
    ----------
    ticker : str
        The stock ticker.
    start_date : str
        Start date to load stock ticker data formatted YYYY-MM-DD.

    Returns
    -------
    DataFrame
        A Panda's data frame with columns Open, High, Low, Close, Adj Close, Volume, date_id, OC-High, OC-Low.
    """
    # print(f"Start date: {start_date}")
    df_data = yf.download(ticker, start=start_date, progress=False)

    df_data["date_id"] = (df_data.index.date - df_data.index.date.min()).astype(
        "timedelta64[D]"
    )
    df_data["date_id"] = df_data["date_id"].dt.days + 1

    df_data["OC_High"] = df_data[["Open", "Close"]].max(axis=1)
    df_data["OC_Low"] = df_data[["Open", "Close"]].min(axis=1)

    return df_data


def find_trendline(df_data: DataFrame, y_key: str, high_low: str = "high") -> DataFrame:
    """Attempts to find a trend line based on y_key column from a given stock ticker data frame.

    Parameters
    ----------
    df_data : DataFrame
        The stock ticker data frame with at least date_id, y_key columns.

    y_key : str
        Column name to base the trend line on.

    high_low: str, optional
        Either "high" or "low". High is the default.

    Returns
    -------
    DataFrame
        If a trend is successfully found,
            An updated Panda's data frame with a trend data {y_key}_trend column.
        If no trend was found,
            An original Panda's data frame
    """

    for iteration in [3, 4, 5, 6, 7]:
        df_temp = df_data.copy()
        while len(df_temp) > iteration:
            reg = linregress(
                x=df_temp["date_id"],
                y=df_temp[y_key],
            )

            if high_low == "high":
                df_temp = df_temp.loc[
                    df_temp[y_key] > reg[0] * df_temp["date_id"] + reg[1]
                ]
            else:
                df_temp = df_temp.loc[
                    df_temp[y_key] < reg[0] * df_temp["date_id"] + reg[1]
                ]

        if len(df_temp) > 1:
            break

    if len(df_temp) == 1:
        return df_data

    reg = linregress(
        x=df_temp["date_id"],
        y=df_temp[y_key],
    )

    df_data[f"{y_key}_trend"] = reg[0] * df_data["date_id"] + reg[1]

    return df_data