# source: https://raw.githubusercontent.com/tradingstrategy-ai/gmx-data-collector/60b9b49ef0433e302dc97d32778daedf5f23f59a/examples/strategies/ADXMomentum.py
# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta


class Github_tradingstrategy_ai_gmx_data_collector__ADXMomentum__20260127_192319(IStrategy):
    """
    Trend-following momentum strategy that enters long positions during strong upward trends and exits when momentum reverses.

    Entry: ADX > 25 (strong trend), MOM > 0 (positive momentum), PLUS_DI > 25 and PLUS_DI > MINUS_DI (upward directional strength).
    Exit: ADX > 25, MOM < 0 (negative momentum), MINUS_DI > 25 and PLUS_DI < MINUS_DI (downward directional strength).
    """

    INTERFACE_VERSION: int = 3

    # Minimal ROI designed for the strategy.
    # adjust based on market conditions. We would recommend to keep it low for quick turn arounds
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {"0": 0.05}

    # Optimal stoploss designed for the strategy
    stoploss = -0.25

    # Optimal timeframe for the strategy
    timeframe = "1h"

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 20
    exit_profit_only = False

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
        dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=25)
        dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=25)
        dataframe["sar"] = ta.SAR(dataframe)
        dataframe["mom"] = ta.MOM(dataframe, timeperiod=14)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["adx"] > 25)
                & (dataframe["mom"] > 0)
                & (dataframe["plus_di"] > 25)
                & (dataframe["plus_di"] > dataframe["minus_di"])
            ),
            "enter_long",
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["adx"] > 25)
                & (dataframe["mom"] < 0)
                & (dataframe["minus_di"] > 25)
                & (dataframe["plus_di"] < dataframe["minus_di"])
            ),
            "exit_long",
        ] = 1
        return dataframe