BbandRsiRolling

# --- 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 typing import Dict, List
from functools import reduce
# --------------------------------

class BbandRsiRolling(IStrategy):
    INTERFACE_VERSION = 3
    '\n\n    author@: Michael Fourie\n\n    This strategy uses Bollinger Bands and the rolling rsi to determine when it should make a entry.\n    Selling is completley determined by the minimal roi.\n\n    '
    # Minimal ROI designed for the strategy.
    # This has been determined through hyperopt in a timerange of 270 days.
    minimal_roi = {'0': 0.03279, '259': 0.02964, '536': 0.02467, '818': 0.02326, '965': 0.01951, '1230': 0.01492, '1279': 0.01502, '1448': 0.00945, '1525': 0.00698, '1616': 0.00319, '1897': 0}
    # Optimal stoploss designed for the strategy
    stoploss = -0.08
    # Optimal timeframe for the strategy
    timeframe = '5m'

    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']
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['rsi'].rolling(8).min() < 37) & (dataframe['close'] < dataframe['bb_lowerband']), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(), 'exit_long'] = 1
        return dataframe