This is the strategy for mean-reversion - combines trend confirmation and RSI level for overbought and oversold conditions.

MeanReversionRSI.py

from jesse.strategies import Strategy
import jesse.indicators as ta
from jesse import utils

class MeanReversionRSI(Strategy):
    @property
    def higher_timeframe_candles(self):
        return self.get_candles(self.exchange, self.symbol, '4h')
    
    @property
    def rsi_higher(self):
        return ta.rsi(self.higher_timeframe_candles)
    
    @property
    def trend(self):
        s = ta.supertrend(self.higher_timeframe_candles)
        if self.price > s.trend:
            return 1
        else:
            return -1
    
    @property
    def adx(self):
        return ta.adx(self.candles)
    
    @property
    def adx_higher(self):
        return ta.adx(self.higher_timeframe_candles)
    
    @property
    def bb(self):
        return ta.bollinger_bands(self.candles)
    
    @property
    def atr(self):
        return ta.atr(self.candles)
    
    def should_long(self) -> bool:
        # Long when RSI on higher timeframe is above 70
        # Supertrend on higher timeframe is in uptrend (1)
        # ADX on current timeframe is above 20
        # ADX on higher timeframe is above 40
        return (
            self.rsi_higher > 70 and 
            self.trend == 1 and 
            self.adx > 20 and 
            self.adx_higher > 40
        )
    
    def should_short(self) -> bool:
        # Short when RSI on higher timeframe is below 30
        # Supertrend on higher timeframe is in downtrend (-1)
        # ADX on current timeframe is above 20
        # ADX on higher timeframe is above 40
        return (
            self.rsi_higher < 30 and 
            self.trend == -1 and 
            self.adx > 20 and 
            self.adx_higher > 40
        )
    
    def go_long(self):
        # Use Bollinger Bands lower band as entry point (limit order)
        entry_price = self.bb.lowerband
        stop_loss_price = entry_price - (self.atr * 6)
        
        # Risk 3% of account per trade
        qty = utils.risk_to_qty(self.available_margin, 3, entry_price, stop_loss_price, fee_rate=self.fee_rate)
        
        self.buy = qty*5, entry_price
    
    def go_short(self):
        # Use Bollinger Bands upper band as entry point (limit order)
        entry_price = self.bb.upperband
        stop_loss_price = entry_price + (self.atr * 6)
        
        # Risk 3% of account per trade
        qty = utils.risk_to_qty(self.available_margin, 3, entry_price, stop_loss_price, fee_rate=self.fee_rate)
        
        self.sell = qty*5, entry_price
    
    def on_open_position(self, order):
        # Set stop loss and take profit when position is opened
        if self.is_long:
            # Stop loss at entry price minus 6 ATR
            self.stop_loss = self.position.qty, self.position.entry_price - (self.atr * 6)
            # Take profit at upper band of Bollinger Bands
            self.take_profit = self.position.qty, self.bb.upperband
        
        elif self.is_short:
            # Stop loss at entry price plus 6 ATR
            self.stop_loss = self.position.qty, self.position.entry_price + (self.atr * 6)
            # Take profit at lower band of Bollinger Bands
            self.take_profit = self.position.qty, self.bb.lowerband
    
    def should_cancel_entry(self) -> bool:
        return True
        
    def watch_list(self) -> list:
        return [
            ('RSI 4H', self.rsi_higher),
            ('Trend', self.trend),
            ('ADX', self.adx),
            ('ADX 4H', self.adx_higher),
            ('BB Upper', self.bb.upperband),
            ('BB Middle', self.bb.middleband),
            ('BB Lower', self.bb.lowerband),
            ('ATR', self.atr),
            ('Long Signal', self.should_long()),
            ('Short Signal', self.should_short()),
            ('BBW', self.bbw)
        ]