BBRSIv2

from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

from freqtrade.persistence import Trade
from datetime import datetime, timedelta
from functools import reduce



class BBRSIv2(IStrategy):
    """
    author@: Gert Wohlgemuth
    converted from:
    https://github.com/sthewissen/Mynt/blob/master/src/Mynt.Core/Strategies/BbandRsi.cs
    Customized by StrongManBR
    """



    minimal_roi = {
        "0": 0.3
    }

    stoploss = -0.99
    
    process_only_new_candles = True  
    use_sell_signal = True
    sell_profit_only = True
    sell_profit_offset= 0.01
    ignore_roi_if_buy_signal = False    
    use_custom_stoploss = True
  

    startup_candle_count: int = 144

    timeframe = '15m'

    plot_config = {
        'main_plot': {
            'bb_lowerband': {},
            'bb_middleband': {},
            'bb_upperband': {},
            'tema': {}
        },
        'subplots': {
            "RSI": {
                'rsi': {'color': 'blue'}
            },
             "MARKET": {
                'close_max': {'color': 'green', 'type': 'bar'},
                'close_min': {'color': 'red','type': 'bar'},
                'dropped_by_percent': {'color': 'blue','type': 'bar'},
                'pumped_by_percent': {'color': 'orange','type': 'bar'}
            }            
       
            
        }
    }
    
    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        sl_new = 1

        if self.config['runmode'].value in ('live', 'dry_run'):
            sl_new = 0.001

        if (current_profit > 0.2):
            sl_new = 0.05
        elif (current_profit > 0.1):
            sl_new = 0.03
        elif (current_profit > 0.06):
            sl_new = 0.02
        elif (current_profit > 0.03):
            sl_new = 0.01

        return sl_new

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

        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["tema"] = ta.TEMA(dataframe, timeperiod=9, price="close")
        
        
        dataframe['close_max'] = dataframe['close'].rolling(window=60).max() #5h        
        dataframe['dropped_by_percent'] = (1 - (dataframe['close'] / dataframe['close_max']))        
        dataframe['close_min'] = dataframe['close'].rolling(window=60).min() #5h                        
        dataframe['pumped_by_percent'] =   (dataframe['high'] - dataframe['close_min'])/ dataframe['high']

        return dataframe 

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[:, 'buy_tag'] = ''
        conditions = []

        
        RB1 = ( 
               
                (qtpylib.crossed_above(dataframe['rsi'], 35)) &  # Signal: RSI crosses above 35
                (dataframe['close'] < dataframe['bb_lowerband']) 
                )
        dataframe.loc[RB1, 'buy_tag'] += 'RB1:BB_LOWER '        
        conditions.append(RB1)
        
        RB2 = ( 
                (dataframe['rsi'] < 23) &
                (dataframe["tema"] < dataframe["bb_lowerband"]) &  
                (dataframe["tema"] > dataframe["tema"].shift(1)) &  
                (dataframe["volume"] > 0)  # Make sure Volume is not 0 
                
                )
        dataframe.loc[RB2, 'buy_tag'] += 'RB2:RSI<23_ '        
        conditions.append(RB2)
        
        
        
        if conditions:

            dataframe.loc[ 




                           reduce(lambda x, y: x | y, conditions),'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'exit_tag'] = ''

        
        RS1 = ( dataframe['rsi'] >70 )
        dataframe.loc[RS1, 'exit_tag'] += 'RS1:RSI>70 '                
        conditions.append(RS1)
        
        RS2 = ( dataframe["high"] >  dataframe["close_max"])                                  
        dataframe.loc[RS2, 'exit_tag'] += 'RS2:>CLOSE_MAX '        
        conditions.append(RS2)
        
        if conditions:

            dataframe.loc[ 


                           reduce(lambda x, y: x | y, conditions),'sell'] = 1

        

        return dataframe