# source: https://raw.githubusercontent.com/rainfrog123/blue/8ac55020b956060fcab469dca2835000af7540a2/freq/project/user_data/strategies/TemaTrendLabel.py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file

# --- Standard Library Imports ---
from datetime import datetime
from typing import Optional, Union

# --- Third Party Imports ---
import numpy as np
import pandas as pd
from pandas import DataFrame

# --- Freqtrade Imports ---
from freqtrade.strategy import (
    IStrategy,
    IntParameter
)

# --- Technical Analysis Library Imports ---
import talib.abstract as ta

# --- Custom Strategy Class ---
class Github_rainfrog123_blue__TemaTrendLabel__20260124_181148(IStrategy):
    """
    Strategy for labeling trend reversals in trading data for machine learning tasks.
    """
    INTERFACE_VERSION = 3
    timeframe = '5s'
    can_short: bool = True
    minimal_roi = {"10": 10000}  # Adjusted for 5s timeframe (60s → 10s)
    stoploss = -0.001  # Adjusted stoploss for faster timeframe
    trailing_stop = True
    startup_candle_count: int = 150  # Increased for more reliable indicators on faster timeframe

    def informative_pairs(self):
        """
        Define pairs for additional, informative data.
        Currently returns an empty list.
        """
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Add all necessary technical indicators to the DataFrame.
        Currently includes Triple Exponential Moving Average (TEMA).
        """
        tema_period = 150  # Increased for 5s timeframe to maintain similar time window
        dataframe['tema'] = ta.TEMA(dataframe['close'], timeperiod=tema_period)
        dataframe['trend'] = np.where(dataframe['tema'] > dataframe['tema'].shift(1), 'UP', 'DOWN')
        dataframe['trend_duration'] = (dataframe['trend'] != dataframe['trend'].shift(1)).cumsum()
        dataframe['trend_count'] = dataframe.groupby('trend_duration').cumcount() + 1
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Populate the entry signals based on trend reversal logic.
        Signals for long and short entries based on trend direction changes.
        """
        dataframe.loc[
            (dataframe['trend'] == 'UP') & (dataframe['trend'].shift(1) == 'DOWN'),
            'enter_long'] = 1
        dataframe.loc[
            (dataframe['trend'] == 'DOWN') & (dataframe['trend'].shift(1) == 'UP'),
            'enter_short'] = 1
        return dataframe
    
    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Populate the exit signals for the strategy.
        This method is left empty for further customization.
        """
       
        return dataframe