# source: https://raw.githubusercontent.com/MaoBui2907/freqtrade-strategy-analysis/8727737e03425e269872c9d7e972bbf326292bb6/server/strategies/Minmax.py
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------

from scipy.signal import argrelextrema
import numpy as np


class Github_MaoBui2907_freqtrade_strategy_analysis__Minmax__20250617_150144(IStrategy):
    minimal_roi = {"0": 10}

    stoploss = -0.05

    timeframe = "1h"

    trailing_stop = False

    process_only_new_candles = False

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe_copy = dataframe.copy()
        frame_size = 500
        len_df = len(dataframe)
        dataframe["buy_signal"] = False
        dataframe["exit_signal"] = False
        lookback_size = 100
        # Let's calculate argrelextrema on separated data slices and get only last result to avoid lookahead bias!
        for i in range(len_df):
            if i + frame_size < len_df:
                slice = dataframe_copy[i : i + frame_size]
                min_peaks = argrelextrema(slice["close"].values, np.less, order=lookback_size)
                max_peaks = argrelextrema(slice["close"].values, np.greater, order=lookback_size)
                # Somehow we never getting last index of a frame as min or max. What a surprise :)
                # So lets take penultimate result and use it as a signal to buy/sell.
                if len(min_peaks[0]) and min_peaks[0][-1] == frame_size - 2:
                    # signal that penultimate candle is min
                    # lets buy here
                    dataframe.at[i + frame_size, "buy_signal"] = True
                if len(max_peaks[0]) and max_peaks[0][-1] == frame_size - 2:
                    # oh it seams that penultimate candle is max
                    # lets sell ASAP
                    dataframe.at[i + frame_size, "exit_signal"] = True

                if i + frame_size == len_df - 1:
                    print(min_peaks)

        #                                                                               A
        # Wow what a pathetic results!!!Where is my Trillions of BTC?!?!?!              |
        # Let's make it in a lookahead way to make more numbers in backtesting!!        |
        #                                                                               |
        #                                                                               |
        # Comment this section!!  ------------------------------------------------------|
        # Uncomment this section ASAP!
        #           |
        #           |
        #           |
        #           |
        #           V

        # min_peaks = argrelextrema(dataframe['close'].values, np.less, order=loockback_size)
        # max_peaks = argrelextrema(dataframe['close'].values, np.greater, order=loockback_size)
        #
        #
        # for mp in min_peaks[0]:
        #     dataframe.at[mp, 'buy_signal'] = True
        #
        # for mp in max_peaks[0]:
        #     dataframe.at[mp, 'exit_signal'] = True

        # Uhhh that's better! Ordering Lambo now!

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        print(dataframe.tail(30))

        dataframe.loc[(dataframe["buy_signal"]), "enter_long"] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe["exit_signal"]), "exit_long"] = 1
        return dataframe