import numpy as np
import pandas as pd
import backtrader as bt
from collections import deque
class BollingerMeanReversionStrategy(bt.Strategy):
    """
    Mechanical Bollinger Bands mean reversion strategy with rules-based entry/exit
    """
params = (
        ('bb_period', 20),
        ('bb_stdev', 2.0),
        ('rsi_period', 14),
        ('volume_lookback', 2),
        ('risk_percent', 2.0),
        ('target_percent', 5.0),
    )
def __init__(self):
        self.bb = bt.indicators.BollingerBands(
            self.data.close, period=self.params.bb_period, devfactor=self.params.bb_stdev
        )
        self.rsi = bt.indicators.RSI(self.data.close, period=self.params.rsi_period)
        self.volume_sma = bt.indicators.SimpleMovingAverage(
            self.data.volume, period=5
        )
# Track state
        self.in_long = False
        self.in_short = False
        self.entry_price = None
        self.entry_volume = None
        self.band_penetration_bar = None
        self.volume_signal_confirmed = False
def check_volume_exhaustion(self):
        """
        Check if volume is decreasing (exhaustion after spike)
        """
        if len(self.data) < 3:
            return False
vol_current = self.data.volume[0]
        vol_prev1 = self.data.volume[-1]
        vol_prev2 = self.data.volume[-2]
        vol_sma = self.volume_sma[0]
# Volume spike followed by decrease
        if vol_prev1 > vol_sma * 1.5 and vol_current < vol_prev1:
            return True
        return False
def next(self):
        # Long Entry: Lower Band Bounce
        if (self.data.close[0] < self.bb.lines.bot[0] and
            self.rsi[0] < 30 and
            self.check_volume_exhaustion() and
            not self.in_long and not self.in_short):
position_size = self.calculate_position_size(target_profit_percent=5.0)
            self.buy(size=position_size)
            self.entry_price = self.data.close[0]
            self.in_long = True
            self.stop_price = self.entry_price * 0.985  # 1.5% stop
# Short Entry: Upper Band Rejection
        elif (self.data.close[0] > self.bb.lines.top[0] and
              self.rsi[0] > 70 and
              self.check_volume_exhaustion() and
              not self.in_short and not self.in_long):
position_size = self.calculate_position_size(target_profit_percent=5.0)
            self.sell(size=position_size)
            self.entry_price = self.data.close[0]
            self.in_short = True
            self.stop_price = self.entry_price * 1.015  # 1.5% stop
# Exit Long Position
        if self.in_long:
            # Hit target: close above middle band
            if self.data.close[0] > self.bb.lines.mid[0]:
                self.close()
                self.in_long = False
# Hit stop loss
            elif self.data.close[0] < self.stop_price:
                self.close()
                self.in_long = False
# Time decay: hold > 10 days
            elif len(self) - self.bar_executed > 10:
                self.close()
                self.in_long = False
# Exit Short Position
        if self.in_short:
            # Hit target: close below middle band
            if self.data.close[0] < self.bb.lines.mid[0]:
                self.close()
                self.in_short = False
# Hit stop loss
            elif self.data.close[0] > self.stop_price:
                self.close()
                self.in_short = False
# Time decay
            elif len(self) - self.bar_executed > 10:
                self.close()
                self.in_short = False
def calculate_position_size(self, target_profit_percent):
        """
        Kelly criterion position sizing
        """
        win_rate = 0.68
        payoff_ratio = 2.0  # Avg win / avg loss
        kelly_fraction = (win_rate * payoff_ratio - (1 - win_rate)) / payoff_ratio
# Use 25% fractional Kelly
        position_fraction = kelly_fraction  0.25  0.02  # 2% risk
portfolio_value = self.broker.getvalue()
        position_value = portfolio_value * position_fraction
        position_size = int(position_value / self.data.close[0])
return max(1, position_size)