# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file

from datetime import datetime

import numpy as np
import pandas as pd
from pandas import DataFrame

from freqtrade.strategy import IStrategy, informative
import talib.abstract as ta


class TemaReversalBoth(IStrategy):
    """
    ML Labeling Strategy: TEMA Reversal (Long + Short)

    Triple Barrier Method for binary classification:
    - Long: Upper barrier TP = Label 1, Lower barrier SL = Label 0
    - Short: Lower barrier TP = Label 1, Upper barrier SL = Label 0
    - Time barrier: Timeout = Label 0 (or discard)

    Parameters tuned for ~50:50 label balance.
    """

    INTERFACE_VERSION = 3
    timeframe = "5s"
    can_short: bool = True

    minimal_roi = {"0": 100}
    stoploss = -0.05
    trailing_stop = False
    startup_candle_count: int = 200

    tema_length = 50
    atr_length = 20
    atr_multiplier = 1.5
    tp_risk_ratio = 1.0

    def informative_pairs(self):
        return []

    @informative("1m")
    def populate_indicators_1m(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["atr"] = ta.ATR(
            dataframe["high"], dataframe["low"], dataframe["close"],
            timeperiod=self.atr_length
        )
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # TEMA
        ema1 = ta.EMA(dataframe["close"], timeperiod=self.tema_length)
        ema2 = ta.EMA(ema1, timeperiod=self.tema_length)
        ema3 = ta.EMA(ema2, timeperiod=self.tema_length)
        dataframe["tema"] = 3 * ema1 - 3 * ema2 + ema3

        # Trend detection
        dataframe["tema_prev"] = dataframe["tema"].shift(1)
        dataframe["trend_up"] = dataframe["tema"] > dataframe["tema_prev"]
        dataframe["trend_down"] = dataframe["tema"] < dataframe["tema_prev"]
        dataframe["trend"] = np.where(
            dataframe["trend_up"],
            "UP",
            np.where(dataframe["trend_down"], "DOWN", "FLAT"),
        )

        # Trend flip detection
        dataframe["trend_prev"] = dataframe["trend"].shift(1)
        dataframe["trend_flip"] = (dataframe["trend"] != dataframe["trend_prev"]) & (
            dataframe["trend"] != "FLAT"
        )

        # ATR from 1m timeframe
        dataframe["atr"] = dataframe["atr_1m"]

        # Risk and TP/SL levels
        dataframe["risk"] = dataframe["atr"] * self.atr_multiplier
        dataframe["tp_long"] = dataframe["close"] + (self.tp_risk_ratio * dataframe["risk"])
        dataframe["sl_long"] = dataframe["close"] - dataframe["risk"]
        dataframe["tp_short"] = dataframe["close"] - (self.tp_risk_ratio * dataframe["risk"])
        dataframe["sl_short"] = dataframe["close"] + dataframe["risk"]

        # Reversal signals
        dataframe["reversal_to_up"] = dataframe["trend_flip"] & (dataframe["trend"] == "UP")
        dataframe["reversal_to_down"] = dataframe["trend_flip"] & (dataframe["trend"] == "DOWN")

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            dataframe["reversal_to_up"]
            & dataframe["atr"].notna()
            & (dataframe["atr"] > 0)
            & dataframe["tema"].notna(),
            "enter_long",
        ] = 1

        dataframe.loc[
            dataframe["reversal_to_down"]
            & dataframe["atr"].notna()
            & (dataframe["atr"] > 0)
            & dataframe["tema"].notna(),
            "enter_short",
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        return dataframe