# source: https://raw.githubusercontent.com/4tie/fortiesr/d5cc40760e3cbaff381058a0ea8733d2be469578/user_data/strategies/PatternRecognition.py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401

# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IStrategy, IntParameter)

# --------------------------------
# Add your lib to import here
import talib
import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.util import resample_to_interval, resampled_merge


class Github_4tie_fortiesr__PatternRecognition__20260709_213639(IStrategy):
    """
    Pattern Recognition Strategy
    By: @Mablue

    This strategy uses Talib's pattern recognition functions to identify buy signals.
    It allows customization of which pattern to use and the volume threshold for the signal.
    
    Note: This is a basic implementation that can be extended with more parameters and
    exit signals as needed.
    """
    # Pattern Recognition Strategy
    # By: @Mablue
    # freqtrade hyperopt -s Github_4tie_fortiesr__PatternRecognition__20260709_213639 --hyperopt-loss SharpeHyperOptLossDaily -e 1000
    #

    # 173/1000:    510 trades. 408/14/88 Wins/Draws/Losses. Avg profit   2.35%. Median profit   5.60%. Total profit 5421.34509618 USDT ( 542.13%). Avg duration 7 days, 11:54:00 min. Objective: -1.60426


    INTERFACE_VERSION: int = 3
    # Buy hyperspace params:
    buy_params = {
        "buy_pr1": "CDLHIGHWAVE",
        "buy_vol1": -100,
    }

    # ROI table:
    minimal_roi = {
        "0": 0.936,
        "5271": 0.332,
        "18147": 0.086,
        "48152": 0
    }

    # Stoploss:
    stoploss = -0.288

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.032
    trailing_stop_positive_offset = 0.084
    trailing_only_offset_is_reached = True

    # Optimal timeframe for the strategy.
    timeframe = '1d'
    prs = talib.get_function_groups()['Pattern Recognition']

    # # Strategy parameters
    buy_pr1 = CategoricalParameter(prs, default=prs[0], space="buy")
    buy_vol1 = CategoricalParameter([-100,100], default=0, space="buy")



    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Calculate indicators for the strategy.
        
        Args:
            dataframe (DataFrame): Input dataframe with OHLCV data.
            metadata (dict): Additional metadata about the pair and timeframe.
            
        Returns:
            DataFrame: Dataframe with added indicator columns.
        """
        for pr in self.prs:
            dataframe[pr] = getattr(ta, pr)(dataframe)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Define entry conditions for the strategy.
        
        Args:
            dataframe (DataFrame): Dataframe with indicators.
            metadata (dict): Additional metadata about the pair and timeframe.
            
        Returns:
            DataFrame: Dataframe with entry signals marked in 'enter_long' column.
        """
        dataframe.loc[
            (
                (dataframe[self.buy_pr1.value]==self.buy_vol1.value)
                # |(dataframe[self.buy_pr2.value]==self.buy_vol2.value)
            ),
            'enter_long'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Define exit conditions for the strategy.
        
        Args:
            dataframe (DataFrame): Dataframe with indicators.
            metadata (dict): Additional metadata about the pair and timeframe.
            
        Returns:
            DataFrame: Dataframe with exit signals marked in 'exit_long' column.
        """
        dataframe.loc[
            (
            #     (dataframe[self.sell_pr1.value]==self.sell_vol1.value)|
            #     (dataframe[self.sell_pr2.value]==self.sell_vol2.value)
            ),
            'exit_long'] = 1

        return dataframe