# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/fixed_riskreward_loss.py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from datetime import datetime
from freqtrade.persistence import Trade
import logging
logger = logging.getLogger(__name__)

class Github_DerSalvador_freqtrade_helm_chart__fixed_riskreward_loss__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    '\n    This strategy uses custom_stoploss() to enforce a fixed risk/reward ratio\n    by first calculating a dynamic initial stoploss via ATR - last negative peak\n\n    After that, we caculate that initial risk and multiply it with an risk_reward_ratio\n    Once this is reached, stoploss is set to it and exit signal is enabled\n\n    Also there is a break even ratio. Once this is reached, the stoploss is adjusted to minimize\n    losses by setting it to the entry rate + fees.\n    '
    custom_info = {'risk_reward_ratio': 3.5, 'set_to_break_even_at_profit': 1}
    use_custom_stoploss = True
    stoploss = -0.9

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        """
            custom_stoploss using a risk/reward ratio
        """
        result = break_even_sl = takeprofit_sl = -1
        custom_info_pair = self.custom_info.get(pair)
        if custom_info_pair is not None:
            # using current_time/open_date directly via custom_info_pair[trade.open_daten]
            # would only work in backtesting/hyperopt.
            # in live/dry-run, we have to search for nearest row before it
            open_date_mask = custom_info_pair.index.unique().get_loc(trade.open_date_utc, method='ffill')
            open_df = custom_info_pair.iloc[open_date_mask]
            # trade might be open too long for us to find opening candle
            if len(open_df) != 1:
                return -1  # won't update current stoploss
            initial_sl_abs = open_df['stoploss_rate']
            # calculate initial stoploss at open_date
            initial_sl = initial_sl_abs / current_rate - 1
            # calculate take profit treshold
            # by using the initial risk and multiplying it
            risk_distance = trade.open_rate - initial_sl_abs
            reward_distance = risk_distance * self.custom_info['risk_reward_ratio']
            # take_profit tries to lock in profit once price gets over
            # risk/reward ratio treshold
            take_profit_price_abs = trade.open_rate + reward_distance
            # take_profit gets triggerd at this profit
            take_profit_pct = take_profit_price_abs / trade.open_rate - 1
            # break_even tries to set sl at open_rate+fees (0 loss)
            break_even_profit_distance = risk_distance * self.custom_info['set_to_break_even_at_profit']
            # break_even gets triggerd at this profit
            break_even_profit_pct = (break_even_profit_distance + current_rate) / current_rate - 1
            result = initial_sl
            if current_profit >= break_even_profit_pct:
                break_even_sl = trade.open_rate * (1 + trade.fee_open + trade.fee_close) / current_rate - 1
                result = break_even_sl
            if current_profit >= take_profit_pct:
                takeprofit_sl = take_profit_price_abs / current_rate - 1
                result = takeprofit_sl
        return result

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['atr'] = ta.ATR(dataframe)
        dataframe['stoploss_rate'] = dataframe['close'] - dataframe['atr'] * 2
        self.custom_info[metadata['pair']] = dataframe[['date', 'stoploss_rate']].copy().set_index('date')
        # all "normal" indicators:
        # e.g.
        # dataframe['rsi'] = ta.RSI(dataframe)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Placeholder Strategy: entrys when SAR is smaller then candle before
        Based on TA indicators, populates the entry signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """
        # Allways entrys
        dataframe.loc[:, 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Placeholder Strategy: does nothing
        Based on TA indicators, populates the exit signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """
        # Never exits
        dataframe.loc[:, 'exit_long'] = 0
        return dataframe