# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/ReinforcedAverageStrategy.py
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, merge, DatetimeIndex
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.util import resample_to_interval, resampled_merge
from freqtrade.exchange import timeframe_to_minutes

class Github_DerSalvador_freqtrade_helm_chart__ReinforcedAverageStrategy__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    "\n\n    author@: Gert Wohlgemuth\n\n    idea:\n        entrys and exits on crossovers - doesn't really perfom that well and its just a proof of concept\n    "
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {'0': 0.5}
    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.2
    # Optimal timeframe for the strategy
    timeframe = '4h'
    # trailing stoploss
    trailing_stop = False
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.02
    trailing_only_offset_is_reached = False
    # run "populate_indicators" only for new candle
    process_only_new_candles = False
    # Experimental settings (configuration will overide these if set)
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['maShort'] = ta.EMA(dataframe, timeperiod=8)
        dataframe['maMedium'] = ta.EMA(dataframe, timeperiod=21)
        ##################################################################################
        # required for graphing
        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_middleband'] = bollinger['mid']
        self.resample_interval = timeframe_to_minutes(self.timeframe) * 12
        dataframe_long = resample_to_interval(dataframe, self.resample_interval)
        dataframe_long['sma'] = ta.SMA(dataframe_long, timeperiod=50, price='close')
        dataframe = resampled_merge(dataframe, dataframe_long, fill_na=True)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the entry signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """
        dataframe.loc[qtpylib.crossed_above(dataframe['maShort'], dataframe['maMedium']) & (dataframe['close'] > dataframe[f'resample_{self.resample_interval}_sma']) & (dataframe['volume'] > 0), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the exit signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """
        dataframe.loc[qtpylib.crossed_above(dataframe['maMedium'], dataframe['maShort']) & (dataframe['volume'] > 0), 'exit_long'] = 1
        return dataframe