# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/Leveraged.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
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy  # noqa

class Github_DerSalvador_freqtrade_helm_chart__Leveraged__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    '\n\n    author@: Gert Wohlgemuth\n\n    works on new objectify branch!\n\n    idea:\n        only entry on an upward tending market\n    '
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    #minimal_roi = {
    #    "0": 0.01
    #}
    minimal_roi = {'120': 0.0, '45': 0.01, '30': 0.015, '0': 0.025}
    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.05
    # Optimal timeframe for the strategy
    timeframe = '5m'
    # resample factor to establish our general trend. Basically don't entry if a trend is not given
    resample_factor = 12
    EMA_SHORT_TERM = 5
    EMA_MEDIUM_TERM = 12
    EMA_LONG_TERM = 21

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = self.resample(dataframe, self.timeframe, self.resample_factor)
        ##################################################################################
        # entry and exit indicators
        dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] = ta.EMA(dataframe, timeperiod=self.EMA_SHORT_TERM)
        dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)] = ta.EMA(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
        dataframe['ema_{}'.format(self.EMA_LONG_TERM)] = ta.EMA(dataframe, timeperiod=self.EMA_LONG_TERM)
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['min'] = ta.MIN(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
        dataframe['max'] = ta.MAX(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
        dataframe['cci'] = ta.CCI(dataframe)
        dataframe['mfi'] = ta.MFI(dataframe)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)
        dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4
        ##################################################################################
        # 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']
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']
        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
        """
        # simple v bottom shape (lopsided to the left to increase reactivity)
        # which has to be below a very slow average
        # this pattern only catches a few, but normally very good entry points
        # entry in very oversold conditions
        # safeguard against down trending markets and a pump and dump
        dataframe.loc[((dataframe['close'] < dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) & (dataframe['close'] < dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) & (dataframe['close'] == dataframe['min']) & (dataframe['close'] <= dataframe['bb_lowerband']) | (dataframe['average'].shift(5) > dataframe['average'].shift(4)) & (dataframe['average'].shift(4) > dataframe['average'].shift(3)) & (dataframe['average'].shift(3) > dataframe['average'].shift(2)) & (dataframe['average'].shift(2) > dataframe['average'].shift(1)) & (dataframe['average'].shift(1) < dataframe['average'].shift(0)) & (dataframe['low'].shift(1) < dataframe['bb_middleband']) & (dataframe['cci'].shift(1) < -100) & (dataframe['rsi'].shift(1) < 30) & (dataframe['mfi'].shift(1) < 30) | (dataframe['low'] < dataframe['bb_middleband']) & (dataframe['cci'] < -200) & (dataframe['rsi'] < 30) & (dataframe['mfi'] < 30)) & ((dataframe['volume'] < dataframe['volume'].rolling(window=30).mean().shift(1) * 20) & (dataframe['resample_sma'] < dataframe['close']) & (dataframe['resample_sma'].shift(1) < dataframe['resample_sma'])), 'entry'] = 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
        """
        # always exit on eight green candles
        # with a high rsi
        # in case of very overbought market, like some one pumping
        # exit
        dataframe.loc[(dataframe['close'] > dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) & (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) & (dataframe['close'] >= dataframe['max']) & (dataframe['close'] >= dataframe['bb_upperband']) & (dataframe['mfi'] > 80) | (dataframe['open'] < dataframe['close']) & (dataframe['open'].shift(1) < dataframe['close'].shift(1)) & (dataframe['open'].shift(2) < dataframe['close'].shift(2)) & (dataframe['open'].shift(3) < dataframe['close'].shift(3)) & (dataframe['open'].shift(4) < dataframe['close'].shift(4)) & (dataframe['open'].shift(5) < dataframe['close'].shift(5)) & (dataframe['open'].shift(6) < dataframe['close'].shift(6)) & (dataframe['open'].shift(7) < dataframe['close'].shift(7)) & (dataframe['rsi'] > 70) | (dataframe['cci'] > 200) & (dataframe['rsi'] > 70), 'exit'] = 1
        return dataframe

    def resample(self, dataframe, interval, factor):
        # defines the reinforcement logic
        # resampled dataframe to establish if we are in an uptrend, downtrend or sideways trend
        df = dataframe.copy()
        df = df.set_index(DatetimeIndex(df['date']))
        ohlc_dict = {'open': 'first', 'high': 'max', 'low': 'min', 'close': 'last'}
        df = df.resample(str(int(interval[:-1]) * factor) + 'min', label='right').agg(ohlc_dict).dropna(how='any')
        df['resample_sma'] = ta.SMA(df, timeperiod=25, price='close')
        df = df.drop(columns=['open', 'high', 'low', 'close'])
        df = df.resample(interval[:-1] + 'min')
        df = df.interpolate(method='time')
        df['date'] = df.index
        df.index = range(len(df))
        dataframe = merge(dataframe, df, on='date', how='left')
        return dataframe