# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/custom_stoploss_with_psar_215.py



import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame

from freqtrade.strategy.interface import IStrategy


import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from datetime import datetime
from freqtrade.persistence import Trade


class Github_remiotore_freqtrade__custom_stoploss_with_psar_215__20260111_210550(IStrategy):
    minimal_roi = { "0": -1 }

    """
    this is an example class, implementing a PSAR based trailing stop loss
    you are supposed to take the `custom_stoploss()` and `populate_indicators()`
    parts and adapt it to your own strategy

    the populate_buy_trend() function is pretty nonsencial
    """
    timeframe = '1h'
    stoploss = -0.2
    custom_info = {}
    use_custom_stoploss = True

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:

        result = 1
        if self.custom_info and pair in self.custom_info and trade:


            relative_sl = None
            if self.dp:

                dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)


                last_candle = dataframe.iloc[-1].squeeze()
                relative_sl = last_candle['sar']

            if (relative_sl is not None):


                new_stoploss = (current_rate - relative_sl) / current_rate

                result = new_stoploss - 1

        return result

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['sar'] = ta.SAR(dataframe)
        if self.dp.runmode.value in ('backtest', 'hyperopt'):
            self.custom_info[metadata['pair']] = dataframe[['date', 'sar']].copy().set_index('date')



        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Placeholder Strategy: buys when SAR is smaller then candle before
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['sar'] < dataframe['sar'].shift())
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Placeholder Strategy: does nothing
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """

        dataframe.loc[:, 'sell'] = 0
        return dataframe