# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/WyckoffStrategygpt.py
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import numpy as np

class Github_remiotore_freqtrade__WyckoffStrategygpt__20260111_210550(IStrategy):
    """
    This is a Freqtrade strategy template to implement a strategy based on Wyckoff concepts.
    """

    minimal_roi = {
        "0": 100
    }

    stoploss = -0.05

    trailing_stop = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.02
    trailing_only_offset_is_reached = True

    timeframe = '5m'

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Add various indicators needed for our strategy
        """

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)

        dataframe['volume'] = dataframe['volume']
        
        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on Wyckoff concepts, we need to identify specific phases.
        Here, we use simplified conditions for demonstration purposes.
        """

        dataframe.loc[
            (

                (dataframe['close'] < dataframe['ema200']) &
                (dataframe['close'] > dataframe['ema50']) &

                (dataframe['volume'] > dataframe['volume'].rolling(window=14).mean())
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Define sell signals based on Wyckoff concepts.
        """

        dataframe.loc[
            (

                (dataframe['close'] > dataframe['ema200']) &
                (dataframe['close'] < dataframe['ema50']) &

                (dataframe['volume'] > dataframe['volume'].rolling(window=14).mean())
            ),
            'sell'] = 1

        return dataframe