# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/RSIDirectionalWithTrend.py
from freqtrade.strategy import IStrategy, merge_informative_pair
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.exchange import timeframe_to_minutes
import numpy  # noqa

class Github_DerSalvador_freqtrade_helm_chart__RSIDirectionalWithTrend__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    '\n    Github_DerSalvador_freqtrade_helm_chart__RSIDirectionalWithTrend__20260115_122204\n    author@: Paul Csapak\n    github@: https://github.com/paulcpk/freqtrade-strategies-that-work\n\n    How to use it?\n\n    > freqtrade download-data --timeframes 1h --timerange=20180301-20200301\n    > freqtrade backtesting --export trades -s DoubleEMACrossoverWithTrend --timeframe 1h --timerange=20180301-20200301\n    > freqtrade plot-dataframe -s DoubleEMACrossoverWithTrend --indicators1 ema100 --timeframe 1h --timerange=20180301-20200301\n\n    '
    # Optimal timeframe for the strategy
    timeframe = '1h'
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    # timeframe_mins = timeframe_to_minutes(timeframe)
    # minimal_roi = {
    #     "0": 0.08,                       # 5% for the first 3 candles
    #     str(timeframe_mins * 12): 0.04,  # 2% after 3 candles
    #     str(timeframe_mins * 24): 0.02,  # 1% After 6 candles
    # }
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.1
    # trailing stoploss
    trailing_stop = True

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # RSI crosses above 30
        # Candle low is above EMA
        # Ensure this candle had volume (important for backtesting)
        dataframe.loc[qtpylib.crossed_above(dataframe['rsi'], 15) & (dataframe['low'] > dataframe['ema100']) & (dataframe['volume'] > 0), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # RSI crosses above 70
        # OR price is below trend ema
        dataframe.loc[qtpylib.crossed_above(dataframe['rsi'], 85) | (dataframe['low'] < dataframe['ema100']), 'exit_long'] = 1
        return dataframe