BBRSIv2.py

# --- Do not remove these libs ---
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 designed for the strategy.
    # adjust based on market conditions. We would recommend to keep it low for quick turn arounds
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "0": 0.3
    }

    # Optimal stoploss designed for the strategy
    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

    # Optimal timeframe for the strategy
    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 bands
        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']
        
        # Custom
        # TEMA - Triple Exponential Moving Average
        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 = []
#        dont_buy_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[ 
                           #is_bull &                    
                           #is_additional_check & 
                           #can_buy &
                           #is_live_data & 
                           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'] = ''
        #sell_now = []     
        
        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[ 
                          
                           #can_sell &
                           #is_live_data & 
                           reduce(lambda x, y: x | y, conditions),'sell'] = 1

        

        return dataframe