# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/f80d4d8b77c53435e9c0a9045636f1bfb2b8c539/chart/deployed_strategies/binance-michael-k8s-namespace/ReinforcedSmoothScalp.py
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import timeframe_to_minutes
from pandas import DataFrame
from technical.util import resample_to_interval, resampled_merge
import numpy  # noqa
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

class Github_DerSalvador_freqtrade_helm_chart__ReinforcedSmoothScalp__20260416_224245(IStrategy):
    INTERFACE_VERSION = 3
    '\n        this strategy is based around the idea of generating a lot of potentatils entrys and make tiny profits on each trade\n\n        we recommend to have at least 60 parallel trades at any time to cover non avoidable losses\n    '
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {'0': 0.02}
    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    # should not be below 3% loss
    stoploss = -0.1
    # Optimal timeframe for the strategy
    # the shorter the better
    timeframe = '1m'
    # resample factor to establish our general trend. Basically don't entry if a trend is not given
    resample_factor = 5

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        tf_res = timeframe_to_minutes(self.timeframe) * 5
        df_res = resample_to_interval(dataframe, tf_res)
        df_res['sma'] = ta.SMA(df_res, 50, price='close')
        dataframe = resampled_merge(dataframe, df_res, fill_na=True)
        dataframe['resample_sma'] = dataframe[f'resample_{tf_res}_sma']
        dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
        dataframe['ema_close'] = ta.EMA(dataframe, timeperiod=5, price='close')
        dataframe['ema_low'] = ta.EMA(dataframe, timeperiod=5, price='low')
        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        dataframe['adx'] = ta.ADX(dataframe)
        dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['mfi'] = ta.MFI(dataframe)
        # 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']
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # |
        # # try to get some sure things independent of resample
        # ((dataframe['rsi'] - dataframe['mfi']) < 10) &
        # (dataframe['mfi'] < 30) &
        # (dataframe['cci'] < -200)
        dataframe.loc[(dataframe['open'] < dataframe['ema_low']) & (dataframe['adx'] > 30) & (dataframe['mfi'] < 30) & ((dataframe['fastk'] < 30) & (dataframe['fastd'] < 30) & qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) & (dataframe['resample_sma'] < dataframe['close']), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[((dataframe['open'] >= dataframe['ema_high']) | (qtpylib.crossed_above(dataframe['fastk'], 70) | qtpylib.crossed_above(dataframe['fastd'], 70))) & (dataframe['cci'] > 100), 'exit_long'] = 1
        return dataframe