XauBot/backtests/backtest_19_session_optimize.py

"""
Backtest #19 — Session Optimization
=====================================
Base: SMC-Only v4 (Backtest #1)
Added: Skip unprofitable sessions, optimize session multipliers

Data from Baseline #1:
  Sydney-Tokyo:           76.2% WR, +$794  (BEST)
  NY Session:             72.9% WR, +$555  (OK)
  Golden (London-NY):     71.6% WR, +$363  (OK)
  London Early:           63.3% WR, -$205  (BLEEDING)
  Tokyo-London Overlap:   54.5% WR, -$57   (WORST)

Configurations:
  A) Skip London Early only
  B) Skip Tokyo-London only
  C) Skip both (London Early + Tokyo-London)
  D) Skip both + boost Golden (1.0 -> 1.2x lot)
  E) Skip both + boost Sydney (0.5 -> 0.7x lot)

Usage:
    python backtests/backtest_19_session_optimize.py
"""

import polars as pl
import pandas as pd
import numpy as np
from datetime import datetime, timedelta, date
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass, field
from enum import Enum
import sys
import os
from zoneinfo import ZoneInfo
from openpyxl import Workbook
from openpyxl.styles import Font, Alignment, PatternFill, Border, Side
from openpyxl.chart import LineChart, Reference
from openpyxl.utils import get_column_letter

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from src.mt5_connector import MT5Connector
from src.smc_polars import SMCAnalyzer, SMCSignal
from src.feature_eng import FeatureEngineer
from src.regime_detector import MarketRegimeDetector, MarketRegime
from src.ml_model import TradingModel
from src.config import get_config
from src.dynamic_confidence import DynamicConfidenceManager, create_dynamic_confidence, MarketQuality
from loguru import logger

logger.remove()
logger.add(sys.stderr, level="WARNING")

WIB = ZoneInfo("Asia/Jakarta")


# ─── Enums & Dataclasses ──────────────────────────────────────

class TradeResult(Enum):
    WIN = "WIN"
    LOSS = "LOSS"
    BREAKEVEN = "BREAKEVEN"


class ExitReason(Enum):
    TAKE_PROFIT = "take_profit"
    SMART_TP = "smart_tp"
    PEAK_PROTECT = "peak_protect"
    EARLY_EXIT = "early_exit"
    EARLY_CUT = "early_cut"
    MAX_LOSS = "max_loss"
    STALL = "stall"
    TREND_REVERSAL = "trend_reversal"
    TIMEOUT = "timeout"
    WEEKEND_CLOSE = "weekend_close"
    TRAILING_SL = "trailing_sl"
    BREAKEVEN_EXIT = "breakeven_exit"
    DAILY_LIMIT = "daily_limit"
    REGIME_DANGER = "regime_danger"
    MARKET_SIGNAL = "market_signal"


class TradingMode(Enum):
    NORMAL = "normal"
    RECOVERY = "recovery"
    PROTECTED = "protected"
    STOPPED = "stopped"


@dataclass
class SimulatedTrade:
    ticket: int
    entry_time: datetime
    exit_time: datetime
    direction: str
    entry_price: float
    exit_price: float
    stop_loss: float
    take_profit: float
    lot_size: float
    profit_usd: float
    profit_pips: float
    result: TradeResult
    exit_reason: ExitReason
    smc_confidence: float
    regime: str
    session: str
    signal_reason: str
    has_bos: bool = False
    has_choch: bool = False
    has_fvg: bool = False
    has_ob: bool = False
    atr_at_entry: float = 0.0
    rr_ratio: float = 0.0
    trading_mode: str = "normal"


@dataclass
class BacktestStats:
    total_trades: int = 0
    wins: int = 0
    losses: int = 0
    total_profit: float = 0.0
    total_loss: float = 0.0
    max_drawdown: float = 0.0
    max_drawdown_usd: float = 0.0
    win_rate: float = 0.0
    profit_factor: float = 0.0
    avg_win: float = 0.0
    avg_loss: float = 0.0
    avg_trade: float = 0.0
    expectancy: float = 0.0
    sharpe_ratio: float = 0.0
    trades: List[SimulatedTrade] = field(default_factory=list)
    equity_curve: List[float] = field(default_factory=list)
    avoided_signals: int = 0
    daily_limit_stops: int = 0
    recovery_mode_trades: int = 0
    session_blocked: int = 0


# ─── Session Optimize Backtest ─────────────────────────────────

class SessionOptimizeBacktest:
    """SMC-Only + Optimized session filter."""

    def __init__(
        self,
        capital: float = 5000.0,
        max_daily_loss_percent: float = 5.0,
        max_loss_per_trade_percent: float = 1.0,
        base_lot_size: float = 0.01,
        max_lot_size: float = 0.02,
        recovery_lot_size: float = 0.01,
        trend_reversal_threshold: float = 0.75,
        max_concurrent_positions: int = 2,
        breakeven_pips: float = 30.0,
        trail_start_pips: float = 50.0,
        trail_step_pips: float = 30.0,
        min_profit_to_protect: float = 5.0,
        max_drawdown_from_peak: float = 50.0,
        trade_cooldown_bars: int = 10,
        trend_reversal_mult: float = 0.6,
        # Session optimization params
        skip_london_early: bool = False,
        skip_tokyo_london: bool = False,
        sydney_mult: float = 0.5,
        tokyo_london_mult: float = 0.75,
        london_early_mult: float = 0.8,
        golden_mult: float = 1.0,
        ny_mult: float = 0.9,
    ):
        self.capital = capital
        self.max_daily_loss_usd = capital * (max_daily_loss_percent / 100)
        self.max_loss_per_trade = capital * (max_loss_per_trade_percent / 100)
        self.base_lot_size = base_lot_size
        self.max_lot_size = max_lot_size
        self.recovery_lot_size = recovery_lot_size
        self.trend_reversal_threshold = trend_reversal_threshold
        self.max_concurrent_positions = max_concurrent_positions
        self.breakeven_pips = breakeven_pips
        self.trail_start_pips = trail_start_pips
        self.trail_step_pips = trail_step_pips
        self.min_profit_to_protect = min_profit_to_protect
        self.max_drawdown_from_peak = max_drawdown_from_peak
        self.trade_cooldown_bars = trade_cooldown_bars
        self.trend_reversal_mult = trend_reversal_mult

        # Session params
        self.skip_london_early = skip_london_early
        self.skip_tokyo_london = skip_tokyo_london
        self.sydney_mult = sydney_mult
        self.tokyo_london_mult = tokyo_london_mult
        self.london_early_mult = london_early_mult
        self.golden_mult = golden_mult
        self.ny_mult = ny_mult

        config = get_config()
        self.smc = SMCAnalyzer(
            swing_length=config.smc.swing_length,
            ob_lookback=config.smc.ob_lookback,
        )
        self.features = FeatureEngineer()
        self.dynamic_confidence = create_dynamic_confidence()

        self.ml_model = TradingModel(model_path="models/xgboost_model.pkl")
        try:
            self.ml_model.load()
            print("  ML model loaded (for exit evaluation)")
        except Exception:
            print("  [WARN] ML model not loaded")

        self.regime_detector = MarketRegimeDetector(model_path="models/hmm_regime.pkl")
        try:
            self.regime_detector.load()
        except Exception:
            print("  [WARN] HMM model not loaded")

        self._ticket_counter = 2190000

    # ── Optimized session filter ──

    def _get_session_from_time(self, dt: datetime) -> Tuple[str, bool, float]:
        if dt.tzinfo is None:
            dt = dt.replace(tzinfo=ZoneInfo("UTC"))
        wib_time = dt.astimezone(WIB)
        hour = wib_time.hour

        if 6 <= hour < 15:
            return "Sydney-Tokyo", True, self.sydney_mult
        elif 15 <= hour < 16:
            if self.skip_tokyo_london:
                return "Tokyo-London Overlap", False, 0.0
            return "Tokyo-London Overlap", True, self.tokyo_london_mult
        elif 16 <= hour < 19:
            if self.skip_london_early:
                return "London Early", False, 0.0
            return "London Early", True, self.london_early_mult
        elif 19 <= hour < 24:
            return "London-NY Overlap (Golden)", True, self.golden_mult
        elif 0 <= hour < 4:
            return "NY Session", True, self.ny_mult
        else:
            return "Off Hours", False, 0.0

    def _is_near_weekend_close(self, dt: datetime) -> bool:
        if dt.tzinfo is None:
            dt = dt.replace(tzinfo=ZoneInfo("UTC"))
        wib = dt.astimezone(WIB)
        if wib.weekday() == 5 and wib.hour >= 4 and wib.minute >= 30:
            return True
        return False

    # ── Lot sizing (synced) ──

    def _calculate_lot_size(self, confidence, regime, trading_mode, session_mult):
        if trading_mode == TradingMode.STOPPED:
            return 0
        lot = self.base_lot_size
        if trading_mode in (TradingMode.RECOVERY, TradingMode.PROTECTED):
            lot = self.recovery_lot_size
        else:
            if confidence >= 0.65:
                lot = self.max_lot_size
            elif confidence >= 0.55:
                lot = self.base_lot_size
            else:
                lot = self.recovery_lot_size
        if regime.lower() in ["high_volatility", "crisis"]:
            lot = self.recovery_lot_size
        lot = max(0.01, lot * session_mult)
        return round(lot, 2)

    def _hours_to_golden(self, dt: datetime) -> float:
        """Hours until golden time (19:00 WIB). Returns 0 if already in golden."""
        if dt.tzinfo is None:
            dt = dt.replace(tzinfo=ZoneInfo("UTC"))
        wib = dt.astimezone(WIB)
        if 19 <= wib.hour < 24:
            return 0
        target = wib.replace(hour=19, minute=0, second=0, microsecond=0)
        if wib.hour >= 19:
            target += timedelta(days=1)
        return max(0, (target - wib).total_seconds() / 3600)

    # ── Full exit simulation (synced with #1) ──

    def _simulate_trade_exit(
        self, df, entry_idx, direction, entry_price, take_profit, stop_loss,
        lot_size, daily_loss_so_far, feature_cols, max_bars=100,
    ) -> Tuple[float, float, ExitReason, int, float]:
        """
        Simulate trade exit with ALL 3 exit systems synced with main_live.py:
        A) SmartPositionManager (breakeven, trailing, peak protect, market signal)
        B) SmartRiskManager (smart TP, early cut, stall, daily limit, reversal)
        C) Time/Trend exit (4h/6h/8h timeout, ATR momentum)
        """
        pip_value = 10  # XAUUSD: 1 pip = $10 per lot

        highs = df["high"].to_list()
        lows = df["low"].to_list()
        closes = df["close"].to_list()
        times = df["time"].to_list()

        # ATR at entry
        atr = 12.0
        if "atr" in df.columns:
            atr_list = df["atr"].to_list()
            if entry_idx < len(atr_list) and atr_list[entry_idx] is not None:
                atr = atr_list[entry_idx]

        reversal_momentum_threshold = atr * self.trend_reversal_mult
        min_loss_for_reversal_exit = atr * 0.8

        # ── State tracking (simulating SmartRiskManager PositionGuard) ──
        profit_history = []
        price_history = []
        peak_profit = 0.0
        stall_count = 0
        reversal_warnings = 0

        # SmartPositionManager state
        current_sl = stop_loss  # broker SL (mutable via trailing)
        breakeven_moved = False

        # Target TP profit for probability estimation
        if direction == "BUY":
            target_tp_profit = (take_profit - entry_price) / 0.1 * pip_value * lot_size
        else:
            target_tp_profit = (entry_price - take_profit) / 0.1 * pip_value * lot_size

        # ML prediction cache (evaluate every 4 bars like live)
        cached_ml_signal = ""
        cached_ml_confidence = 0.5

        for i in range(entry_idx + 1, min(entry_idx + max_bars, len(df))):
            high = highs[i]
            low = lows[i]
            close = closes[i]
            current_time = times[i]

            # Current P/L
            if direction == "BUY":
                current_pips = (close - entry_price) / 0.1
                pip_profit_from_entry = (close - entry_price) / 0.1
            else:
                current_pips = (entry_price - close) / 0.1
                pip_profit_from_entry = (entry_price - close) / 0.1
            current_profit = current_pips * pip_value * lot_size

            # Track history
            profit_history.append(current_profit)
            price_history.append(close)
            if current_profit > peak_profit:
                peak_profit = current_profit

            bars_since_entry = i - entry_idx

            # ── ML prediction (every 4 bars, synced with live) ──
            if bars_since_entry % 4 == 0 and self.ml_model.fitted:
                try:
                    df_slice = df.head(i + 1)
                    ml_pred = self.ml_model.predict(df_slice, feature_cols)
                    cached_ml_signal = ml_pred.signal
                    cached_ml_confidence = ml_pred.confidence
                except Exception:
                    pass

            # ── Momentum calculation (synced with PositionGuard.calculate_momentum) ──
            momentum = 0.0
            if len(profit_history) >= 3:
                recent = profit_history[-5:] if len(profit_history) >= 5 else profit_history
                profit_change = recent[-1] - recent[0]
                momentum = max(-100, min(100, (profit_change / 10) * 50))

            profit_growing = momentum > 0

            # ════════════════════════════════════════════════
            # A) SmartPositionManager checks (every bar)
            # ════════════════════════════════════════════════

            # A.0 TP hit by price action (high/low)
            if direction == "BUY" and high >= take_profit:
                pips = (take_profit - entry_price) / 0.1
                profit = pips * pip_value * lot_size
                return profit, pips, ExitReason.TAKE_PROFIT, i, take_profit
            elif direction == "SELL" and low <= take_profit:
                pips = (entry_price - take_profit) / 0.1
                profit = pips * pip_value * lot_size
                return profit, pips, ExitReason.TAKE_PROFIT, i, take_profit

            # A.0b Trailing SL hit check
            if breakeven_moved and current_sl > 0:
                if direction == "BUY" and low <= current_sl:
                    pips = (current_sl - entry_price) / 0.1
                    profit = pips * pip_value * lot_size
                    reason = ExitReason.TRAILING_SL if pip_profit_from_entry >= self.trail_start_pips else ExitReason.BREAKEVEN_EXIT
                    return profit, pips, reason, i, current_sl
                elif direction == "SELL" and high >= current_sl:
                    pips = (entry_price - current_sl) / 0.1
                    profit = pips * pip_value * lot_size
                    reason = ExitReason.TRAILING_SL if pip_profit_from_entry >= self.trail_start_pips else ExitReason.BREAKEVEN_EXIT
                    return profit, pips, reason, i, current_sl

            # A.1 Breakeven move (after 30 pips / $3 profit)
            if pip_profit_from_entry >= self.breakeven_pips and not breakeven_moved:
                if direction == "BUY":
                    current_sl = entry_price + 2  # 2 points buffer
                else:
                    current_sl = entry_price - 2
                breakeven_moved = True

            # A.2 Trailing SL (after 50 pips / $5 profit)
            if pip_profit_from_entry >= self.trail_start_pips:
                trail_distance = self.trail_step_pips * 0.1
                if direction == "BUY":
                    new_trail_sl = close - trail_distance
                    if new_trail_sl > current_sl:
                        current_sl = new_trail_sl
                else:
                    new_trail_sl = close + trail_distance
                    if current_sl == 0 or new_trail_sl < current_sl:
                        current_sl = new_trail_sl

            # A.3 Peak profit drawdown protection (50% drawdown from peak for $5+ profit)
            if peak_profit > self.min_profit_to_protect:
                drawdown_pct = ((peak_profit - current_profit) / peak_profit) * 100 if peak_profit > 0 else 0
                if drawdown_pct > self.max_drawdown_from_peak:
                    return current_profit, current_pips, ExitReason.PEAK_PROTECT, i, close

            # A.4 Market analysis: trend + momentum + RSI (synced with position_manager)
            if bars_since_entry % 5 == 0 and bars_since_entry >= 5:
                if i >= 20:
                    ma_fast = np.mean(closes[i-4:i+1])
                    ma_slow = np.mean(closes[i-19:i+1])
                    trend = "NEUTRAL"
                    if ma_fast > ma_slow * 1.001:
                        trend = "BULLISH"
                    elif ma_fast < ma_slow * 0.999:
                        trend = "BEARISH"

                    roc = (closes[i] / closes[max(0,i-4)] - 1) * 100
                    mom_dir = "BULLISH" if roc > 0.3 else ("BEARISH" if roc < -0.3 else "NEUTRAL")

                    rsi_val = None
                    if "rsi" in df.columns:
                        rsi_list = df["rsi"].to_list()
                        if i < len(rsi_list):
                            rsi_val = rsi_list[i]

                    urgency = 0
                    should_exit = False

                    if cached_ml_confidence > 0.75:
                        if direction == "BUY" and cached_ml_signal == "SELL":
                            should_exit = True
                            urgency += 2
                        elif direction == "SELL" and cached_ml_signal == "BUY":
                            should_exit = True
                            urgency += 2

                    if rsi_val:
                        if rsi_val > 75 and direction == "BUY":
                            should_exit = True
                            urgency += 2
                        elif rsi_val < 25 and direction == "SELL":
                            should_exit = True
                            urgency += 2

                    if direction == "BUY" and trend == "BEARISH" and mom_dir == "BEARISH":
                        should_exit = True
                        urgency += 3
                    elif direction == "SELL" and trend == "BULLISH" and mom_dir == "BULLISH":
                        should_exit = True
                        urgency += 3

                    if should_exit and current_profit > self.min_profit_to_protect / 2:
                        return current_profit, current_pips, ExitReason.MARKET_SIGNAL, i, close

                    if urgency >= 7 and current_profit > 0:
                        return current_profit, current_pips, ExitReason.MARKET_SIGNAL, i, close

            # A.5 Weekend close check
            if self._is_near_weekend_close(current_time):
                if current_profit > 0:
                    return current_profit, current_pips, ExitReason.WEEKEND_CLOSE, i, close
                elif current_profit > -10:
                    return current_profit, current_pips, ExitReason.WEEKEND_CLOSE, i, close

            # ════════════════════════════════════════════════
            # B) SmartRiskManager checks
            # ════════════════════════════════════════════════

            # B.1 Smart TP ($15+ with momentum analysis)
            if current_profit >= 15:
                if current_profit >= 40:
                    return current_profit, current_pips, ExitReason.SMART_TP, i, close
                if current_profit >= 25 and momentum < -30:
                    return current_profit, current_pips, ExitReason.SMART_TP, i, close
                if peak_profit > 30 and current_profit < peak_profit * 0.6:
                    return current_profit, current_pips, ExitReason.PEAK_PROTECT, i, close
                if current_profit >= 20:
                    progress = (current_profit / target_tp_profit) * 100 if target_tp_profit > 0 else 0
                    progress_score = min(40, max(0, progress * 0.4))
                    momentum_score = ((momentum + 100) / 200) * 30
                    time_penalty = min(10, bars_since_entry / 4 * 2)
                    tp_probability = progress_score + momentum_score + 10 - time_penalty
                    if tp_probability < 25:
                        return current_profit, current_pips, ExitReason.SMART_TP, i, close

            # B.2 Smart Early Exit ($5-15 profit + reversal)
            if 5 <= current_profit < 15:
                if momentum < -50 and cached_ml_confidence >= 0.65:
                    is_reversal = (
                        (direction == "BUY" and cached_ml_signal == "SELL") or
                        (direction == "SELL" and cached_ml_signal == "BUY")
                    )
                    if is_reversal:
                        return current_profit, current_pips, ExitReason.EARLY_EXIT, i, close

            # B.3 Early cut: loss significant + momentum negative
            if current_profit < 0:
                loss_percent_of_max = abs(current_profit) / self.max_loss_per_trade * 100
                if momentum < -30 and loss_percent_of_max >= 30:
                    return current_profit, current_pips, ExitReason.EARLY_CUT, i, close

            # B.4 Trend Reversal: ML 75%+ opposite
            is_ml_reversal = False
            if direction == "BUY" and cached_ml_signal == "SELL" and cached_ml_confidence >= self.trend_reversal_threshold:
                is_ml_reversal = True
                reversal_warnings += 1
            elif direction == "SELL" and cached_ml_signal == "BUY" and cached_ml_confidence >= self.trend_reversal_threshold:
                is_ml_reversal = True
                reversal_warnings += 1

            loss_moderate = abs(current_profit) > (self.max_loss_per_trade * 0.4)
            if is_ml_reversal and current_profit < -8 and loss_moderate:
                return current_profit, current_pips, ExitReason.TREND_REVERSAL, i, close

            if reversal_warnings >= 3 and current_profit < -10:
                return current_profit, current_pips, ExitReason.TREND_REVERSAL, i, close

            # B.5 Max loss per trade — 50% of max
            if current_profit <= -(self.max_loss_per_trade * 0.50):
                htg = self._hours_to_golden(current_time)
                if htg <= 1 and htg > 0 and momentum > -40:
                    pass
                else:
                    return current_profit, current_pips, ExitReason.MAX_LOSS, i, close

            # B.6 Stall detection
            if len(profit_history) >= 10:
                recent_range = max(profit_history[-10:]) - min(profit_history[-10:])
                if recent_range < 3 and current_profit < -15:
                    stall_count += 1
                    if stall_count >= 5:
                        return current_profit, current_pips, ExitReason.STALL, i, close

            # B.7 Daily loss limit
            potential_daily_loss = daily_loss_so_far + abs(min(0, current_profit))
            if potential_daily_loss >= self.max_daily_loss_usd:
                return current_profit, current_pips, ExitReason.DAILY_LIMIT, i, close

            # ════════════════════════════════════════════════
            # C) Time-based exit
            # ════════════════════════════════════════════════

            ml_agrees = (
                (direction == "BUY" and cached_ml_signal == "BUY") or
                (direction == "SELL" and cached_ml_signal == "SELL")
            )

            if bars_since_entry >= 16:
                if current_profit < 5 and not profit_growing:
                    if current_profit >= 0:
                        return current_profit, current_pips, ExitReason.TIMEOUT, i, close
                    elif current_profit > -15:
                        return current_profit, current_pips, ExitReason.TIMEOUT, i, close

            if bars_since_entry >= 24:
                if current_profit < 10 or not profit_growing:
                    return current_profit, current_pips, ExitReason.TIMEOUT, i, close

            if bars_since_entry >= 32:
                return current_profit, current_pips, ExitReason.TIMEOUT, i, close

            # C.2 ATR trend reversal
            if bars_since_entry > 10:
                recent_closes = closes[i-5:i+1]
                mom = recent_closes[-1] - recent_closes[0]
                if direction == "BUY" and mom < -reversal_momentum_threshold:
                    if current_profit < -min_loss_for_reversal_exit:
                        return current_profit, current_pips, ExitReason.TREND_REVERSAL, i, close
                elif direction == "SELL" and mom > reversal_momentum_threshold:
                    if current_profit < -min_loss_for_reversal_exit:
                        return current_profit, current_pips, ExitReason.TREND_REVERSAL, i, close

        # End of data — close at last price
        final_idx = min(entry_idx + max_bars - 1, len(df) - 1)
        final_price = closes[final_idx]
        if direction == "BUY":
            pips = (final_price - entry_price) / 0.1
        else:
            pips = (entry_price - final_price) / 0.1
        profit = pips * pip_value * lot_size
        return profit, pips, ExitReason.TIMEOUT, final_idx, final_price

    # ── Main run ──

    def run(self, df, start_date=None, end_date=None, initial_capital=5000.0):
        stats = BacktestStats()
        capital = initial_capital
        peak_capital = initial_capital
        stats.equity_curve.append(capital)

        daily_loss = 0.0
        daily_profit = 0.0
        daily_trades = 0
        consecutive_losses = 0
        trading_mode = TradingMode.NORMAL
        current_date = None

        feature_cols = []
        if self.ml_model.fitted and self.ml_model.feature_names:
            feature_cols = [f for f in self.ml_model.feature_names if f in df.columns]

        times = df["time"].to_list()
        start_idx = next((i for i, t in enumerate(times) if t >= start_date), 100) if start_date else 100
        end_idx = next((i for i, t in enumerate(times) if t > end_date), len(df) - 100) if end_date else len(df) - 100

        last_trade_idx = -self.trade_cooldown_bars * 2

        skipped = []
        if self.skip_london_early:
            skipped.append("London Early")
        if self.skip_tokyo_london:
            skipped.append("Tokyo-London")

        print(f"  Skip sessions: {', '.join(skipped) if skipped else 'none'}")
        print(f"  Multipliers: Syd={self.sydney_mult}, TL={self.tokyo_london_mult}, LE={self.london_early_mult}, Gold={self.golden_mult}, NY={self.ny_mult}")
        print(f"  Date range: {times[start_idx]} to {times[end_idx - 1]}")
        print(f"  Total bars: {end_idx - start_idx}")

        for i in range(start_idx, end_idx):
            if i - last_trade_idx < self.trade_cooldown_bars:
                continue

            current_time = times[i]

            # Daily reset
            trade_date = current_time.date() if hasattr(current_time, 'date') else current_time
            if current_date is None or trade_date != current_date:
                daily_loss = 0.0
                daily_profit = 0.0
                daily_trades = 0
                current_date = trade_date
                if consecutive_losses < 2:
                    trading_mode = TradingMode.NORMAL

            if trading_mode == TradingMode.STOPPED:
                continue

            session_name, can_trade, lot_mult = self._get_session_from_time(current_time)
            if not can_trade:
                stats.session_blocked += 1
                continue

            if hasattr(current_time, 'weekday') and current_time.weekday() >= 5:
                continue

            df_slice = df.head(i + 1)

            # Regime check
            regime = "normal"
            try:
                if self.regime_detector.fitted:
                    regime_state = self.regime_detector.get_current_state(df_slice)
                    if regime_state:
                        regime = regime_state.regime.value
                        if regime_state.regime == MarketRegime.CRISIS:
                            continue
                        if regime_state.recommendation == "SLEEP":
                            continue
            except Exception:
                pass

            # Dynamic confidence AVOID filter
            try:
                ml_signal = ""
                ml_confidence = 0.5
                if self.ml_model.fitted and feature_cols:
                    ml_pred = self.ml_model.predict(df_slice, feature_cols)
                    ml_signal = ml_pred.signal
                    ml_confidence = ml_pred.confidence

                market_analysis = self.dynamic_confidence.analyze_market(
                    session=session_name,
                    regime=regime,
                    volatility="medium",
                    trend_direction=regime,
                    has_smc_signal=True,
                    ml_signal=ml_signal,
                    ml_confidence=ml_confidence,
                )
                if market_analysis.quality == MarketQuality.AVOID:
                    stats.avoided_signals += 1
                    continue
            except Exception:
                pass

            # SMC signal
            try:
                smc_signal = self.smc.generate_signal(df_slice)
            except Exception:
                continue

            if smc_signal is None:
                continue

            # SMC details
            recent_df = df_slice.tail(10)
            recent_bos = recent_df["bos"].to_list() if "bos" in df_slice.columns else []
            recent_choch = recent_df["choch"].to_list() if "choch" in df_slice.columns else []
            recent_fvg_bull = recent_df["is_fvg_bull"].to_list() if "is_fvg_bull" in df_slice.columns else []
            recent_fvg_bear = recent_df["is_fvg_bear"].to_list() if "is_fvg_bear" in df_slice.columns else []
            recent_obs = recent_df["ob"].to_list() if "ob" in df_slice.columns else []

            has_bos = 1 in recent_bos or -1 in recent_bos
            has_choch = 1 in recent_choch or -1 in recent_choch
            has_fvg = any(recent_fvg_bull) or any(recent_fvg_bear)
            has_ob = 1 in recent_obs or -1 in recent_obs

            atr_at_entry = 12.0
            if "atr" in df_slice.columns:
                atr_val = df_slice.tail(1)["atr"].item()
                if atr_val is not None and atr_val > 0:
                    atr_at_entry = atr_val

            # Confidence
            confidence = smc_signal.confidence
            ml_agrees = (
                (smc_signal.signal_type == "BUY" and ml_signal == "BUY") or
                (smc_signal.signal_type == "SELL" and ml_signal == "SELL")
            )
            if ml_agrees:
                confidence = (smc_signal.confidence + ml_confidence) / 2
            if regime == "high_volatility":
                confidence *= 0.9

            lot_size = self._calculate_lot_size(confidence, regime, trading_mode, lot_mult)
            if lot_size <= 0:
                continue

            if trading_mode == TradingMode.RECOVERY:
                stats.recovery_mode_trades += 1

            entry_price = smc_signal.entry_price
            take_profit_price = smc_signal.take_profit
            stop_loss_price = smc_signal.stop_loss
            risk = abs(entry_price - stop_loss_price)
            rr = abs(take_profit_price - entry_price) / risk if risk > 0 else 0

            profit, pips, exit_reason, exit_idx, exit_price = self._simulate_trade_exit(
                df=df, entry_idx=i, direction=smc_signal.signal_type,
                entry_price=entry_price, take_profit=take_profit_price,
                stop_loss=stop_loss_price, lot_size=lot_size,
                daily_loss_so_far=daily_loss, feature_cols=feature_cols,
            )

            self._ticket_counter += 1
            result = TradeResult.WIN if profit > 0 else (TradeResult.LOSS if profit < 0 else TradeResult.BREAKEVEN)

            trade = SimulatedTrade(
                ticket=self._ticket_counter,
                entry_time=current_time,
                exit_time=times[exit_idx] if exit_idx < len(times) else times[-1],
                direction=smc_signal.signal_type,
                entry_price=entry_price, exit_price=exit_price,
                stop_loss=stop_loss_price, take_profit=take_profit_price,
                lot_size=lot_size, profit_usd=profit, profit_pips=pips,
                result=result, exit_reason=exit_reason,
                smc_confidence=confidence, regime=regime,
                session=session_name, signal_reason=smc_signal.reason,
                has_bos=has_bos, has_choch=has_choch,
                has_fvg=has_fvg, has_ob=has_ob,
                atr_at_entry=atr_at_entry, rr_ratio=rr,
                trading_mode=trading_mode.value,
            )
            stats.trades.append(trade)

            stats.total_trades += 1
            daily_trades += 1
            capital += profit

            if profit > 0:
                stats.wins += 1
                stats.total_profit += profit
                daily_profit += profit
                consecutive_losses = 0
                if trading_mode == TradingMode.RECOVERY:
                    trading_mode = TradingMode.NORMAL
            else:
                stats.losses += 1
                stats.total_loss += abs(profit)
                daily_loss += abs(profit)
                consecutive_losses += 1

            if daily_loss >= self.max_daily_loss_usd:
                trading_mode = TradingMode.STOPPED
                stats.daily_limit_stops += 1
            elif consecutive_losses >= 3 or daily_loss >= self.max_daily_loss_usd * 0.6:
                trading_mode = TradingMode.PROTECTED
            elif consecutive_losses >= 2:
                trading_mode = TradingMode.RECOVERY

            if capital > peak_capital:
                peak_capital = capital
            dd_pct = (peak_capital - capital) / peak_capital * 100
            dd_usd = peak_capital - capital
            if dd_pct > stats.max_drawdown:
                stats.max_drawdown = dd_pct
                stats.max_drawdown_usd = dd_usd

            stats.equity_curve.append(capital)
            last_trade_idx = exit_idx

            if stats.total_trades % 100 == 0:
                print(f"  {stats.total_trades} trades processed...")

        if stats.total_trades > 0:
            stats.win_rate = stats.wins / stats.total_trades * 100
            stats.avg_win = stats.total_profit / stats.wins if stats.wins > 0 else 0
            stats.avg_loss = stats.total_loss / stats.losses if stats.losses > 0 else 0
            stats.avg_trade = (stats.total_profit - stats.total_loss) / stats.total_trades
            stats.profit_factor = stats.total_profit / stats.total_loss if stats.total_loss > 0 else float("inf")
            win_prob = stats.wins / stats.total_trades
            loss_prob = stats.losses / stats.total_trades
            stats.expectancy = (win_prob * stats.avg_win) - (loss_prob * stats.avg_loss)
            returns = [t.profit_usd for t in stats.trades]
            if len(returns) > 1:
                avg_r = np.mean(returns)
                std_r = np.std(returns)
                stats.sharpe_ratio = (avg_r / std_r) * np.sqrt(252) if std_r > 0 else 0

        return stats


# ─── Report generators ─────────────────────────────────────────

def generate_xlsx_report(stats, filepath, start_date, end_date, config_label):
    wb = Workbook()
    hf = Font(name="Calibri", bold=True, size=12, color="FFFFFF")
    hfill = PatternFill(start_color="1F4E79", end_color="1F4E79", fill_type="solid")
    sf = Font(name="Calibri", bold=True, size=10)
    sfill = PatternFill(start_color="D6E4F0", end_color="D6E4F0", fill_type="solid")
    wf = PatternFill(start_color="C6EFCE", end_color="C6EFCE", fill_type="solid")
    lf = PatternFill(start_color="FFC7CE", end_color="FFC7CE", fill_type="solid")
    bdr = Border(left=Side(style="thin"), right=Side(style="thin"), top=Side(style="thin"), bottom=Side(style="thin"))

    net = stats.total_profit - stats.total_loss
    ws = wb.active
    ws.title = "Summary"
    ws.merge_cells("A1:F1")
    ws["A1"] = f"XAUBot AI — #19 Session Optimize ({config_label})"
    ws["A1"].font = Font(name="Calibri", bold=True, size=16, color="1F4E79")
    ws["A2"] = f"Period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}"

    data = [
        ("Performance", "", True),
        ("Total Trades", stats.total_trades, False),
        ("Wins / Losses", f"{stats.wins} / {stats.losses}", False),
        ("Win Rate", f"{stats.win_rate:.1f}%", False),
        ("Session Blocked", stats.session_blocked, False),
        ("", "", False),
        ("Profit/Loss", "", True),
        ("Net PnL", f"${net:,.2f}", False),
        ("Profit Factor", f"{stats.profit_factor:.2f}", False),
        ("Avg Win / Avg Loss", f"${stats.avg_win:,.2f} / ${stats.avg_loss:,.2f}", False),
        ("Expectancy", f"${stats.expectancy:,.2f}", False),
        ("", "", False),
        ("Risk", "", True),
        ("Max Drawdown", f"{stats.max_drawdown:.1f}% (${stats.max_drawdown_usd:,.2f})", False),
        ("Sharpe Ratio", f"{stats.sharpe_ratio:.2f}", False),
    ]
    row = 5
    for label, value, is_h in data:
        ws.cell(row=row, column=1, value=label)
        ws.cell(row=row, column=2, value=value)
        if is_h:
            ws.cell(row=row, column=1).font = sf
            ws.cell(row=row, column=1).fill = sfill
            ws.cell(row=row, column=2).fill = sfill
        row += 1

    # Session breakdown
    ws.cell(row=5, column=4, value="Session Performance")
    ws.cell(row=5, column=4).font = sf
    ws.cell(row=5, column=4).fill = sfill
    for c in range(5, 8):
        ws.cell(row=5, column=c).fill = sfill
    row = 6
    for lbl, col in [("Session", 4), ("Trades", 5), ("WR", 6), ("Net PnL", 7)]:
        ws.cell(row=row, column=col, value=lbl).font = Font(bold=True)
    row += 1
    sess_stats = {}
    for t in stats.trades:
        s = t.session
        if s not in sess_stats:
            sess_stats[s] = {"w": 0, "l": 0, "p": 0.0}
        if t.result == TradeResult.WIN:
            sess_stats[s]["w"] += 1
        else:
            sess_stats[s]["l"] += 1
        sess_stats[s]["p"] += t.profit_usd
    for sess, d in sorted(sess_stats.items(), key=lambda x: -x[1]["p"]):
        total = d["w"] + d["l"]
        wr = d["w"] / total * 100 if total > 0 else 0
        ws.cell(row=row, column=4, value=sess)
        ws.cell(row=row, column=5, value=total)
        ws.cell(row=row, column=6, value=f"{wr:.1f}%")
        ws.cell(row=row, column=7, value=f"${d['p']:,.2f}")
        row += 1

    ws.column_dimensions["A"].width = 24
    ws.column_dimensions["B"].width = 28
    for c in range(4, 8):
        ws.column_dimensions[get_column_letter(c)].width = 20

    # Trade log
    ws2 = wb.create_sheet("Trade Log")
    headers = ["Ticket", "Entry Time", "Exit Time", "Dir", "Entry", "Exit", "SL", "TP",
               "Lot", "Profit ($)", "Pips", "Result", "Exit Reason", "Conf", "Regime", "Session"]
    for col, h in enumerate(headers, 1):
        cell = ws2.cell(row=1, column=col, value=h)
        cell.font = hf
        cell.fill = hfill
    for ri, t in enumerate(stats.trades, 2):
        vals = [t.ticket, t.entry_time.strftime("%Y-%m-%d %H:%M"), t.exit_time.strftime("%Y-%m-%d %H:%M"),
                t.direction, t.entry_price, t.exit_price, t.stop_loss, t.take_profit,
                t.lot_size, round(t.profit_usd, 2), round(t.profit_pips, 1), t.result.value,
                t.exit_reason.value, round(t.smc_confidence, 2), t.regime, t.session]
        for ci, v in enumerate(vals, 1):
            cell = ws2.cell(row=ri, column=ci, value=v)
            cell.border = bdr
            if ci == 10 and isinstance(v, (int, float)):
                cell.fill = wf if v > 0 else (lf if v < 0 else PatternFill())

    # Equity curve
    ws3 = wb.create_sheet("Equity Curve")
    for c, h in enumerate(["Trade #", "Equity"], 1):
        ws3.cell(row=1, column=c, value=h).font = hf
        ws3.cell(row=1, column=c).fill = hfill
    for idx, eq in enumerate(stats.equity_curve):
        ws3.cell(row=idx + 2, column=1, value=idx)
        ws3.cell(row=idx + 2, column=2, value=round(eq, 2))
    if len(stats.equity_curve) > 1:
        chart = LineChart()
        chart.title = "Equity Curve"
        chart.width = 30
        chart.height = 15
        d = Reference(ws3, min_col=2, min_row=1, max_row=len(stats.equity_curve) + 1)
        chart.add_data(d, titles_from_data=True)
        ws3.add_chart(chart, "D2")

    wb.save(filepath)
    print(f"\n  Report saved: {filepath}")


def generate_log(stats, filepath, start_date, end_date, config_label):
    net = stats.total_profit - stats.total_loss
    lines = ["=" * 80, f"XAUBOT AI — #19 Session Optimize ({config_label})", "=" * 80]
    lines.append(f"Period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}")
    lines.append(f"Session blocked: {stats.session_blocked}")
    lines.append("")
    lines.append(f"  Trades: {stats.total_trades} | WR: {stats.win_rate:.1f}%")
    lines.append(f"  Net PnL: ${net:,.2f} | PF: {stats.profit_factor:.2f}")
    lines.append(f"  Max DD: {stats.max_drawdown:.1f}% | Sharpe: {stats.sharpe_ratio:.2f}")
    lines.append(f"  Avg Win: ${stats.avg_win:,.2f} | Avg Loss: ${stats.avg_loss:,.2f}")
    lines.append("")

    lines.append("--- SESSION BREAKDOWN ---")
    ss = {}
    for t in stats.trades:
        if t.session not in ss:
            ss[t.session] = {"w": 0, "l": 0, "p": 0.0}
        if t.result == TradeResult.WIN:
            ss[t.session]["w"] += 1
        else:
            ss[t.session]["l"] += 1
        ss[t.session]["p"] += t.profit_usd
    for s, d in sorted(ss.items(), key=lambda x: -x[1]["p"]):
        total = d["w"] + d["l"]
        wr = d["w"] / total * 100 if total > 0 else 0
        lines.append(f"  {s:30s}: {total:3d} trades, {wr:5.1f}% WR, ${d['p']:>8,.2f}")
    lines.append("")

    lines.append("--- DIRECTION ---")
    for d in ["BUY", "SELL"]:
        dt = [t for t in stats.trades if t.direction == d]
        dw = sum(1 for t in dt if t.result == TradeResult.WIN)
        dp = sum(t.profit_usd for t in dt)
        dwr = dw / len(dt) * 100 if dt else 0
        lines.append(f"  {d}: {len(dt)} trades, {dwr:.1f}% WR, ${dp:,.2f}")
    lines.append("")

    lines.append("--- EXIT REASONS ---")
    ec = {}
    for t in stats.trades:
        r = t.exit_reason.value
        ec[r] = ec.get(r, 0) + 1
    for reason, count in sorted(ec.items(), key=lambda x: -x[1]):
        pct = count / stats.total_trades * 100 if stats.total_trades > 0 else 0
        lines.append(f"  {reason:20s}: {count:4d} ({pct:5.1f}%)")

    with open(filepath, "w", encoding="utf-8") as f:
        f.write("\n".join(lines))
    print(f"  Log saved: {filepath}")


# ─── Main ──────────────────────────────────────────────────────

def main():
    BASELINE_NET = 1449.86

    print("=" * 70)
    print("XAUBOT AI — #19 Session Optimization")
    print("Base: SMC-Only v4 | Added: Skip unprofitable sessions")
    print("=" * 70)

    config = get_config()
    mt5 = MT5Connector(
        login=config.mt5_login, password=config.mt5_password,
        server=config.mt5_server, path=config.mt5_path,
    )
    mt5.connect()
    print(f"\nConnected to MT5")

    print("Fetching XAUUSD M15 historical data...")
    df = mt5.get_market_data(symbol="XAUUSD", timeframe="M15", count=50000)
    if len(df) == 0:
        print("ERROR: No data")
        mt5.disconnect()
        return

    print(f"  Received {len(df)} bars")
    times = df["time"].to_list()
    print(f"  Data range: {times[0]} to {times[-1]}")

    end_date = datetime.now()
    start_date = datetime(2025, 8, 1)
    data_start = times[0]
    if hasattr(data_start, 'replace') and data_start.tzinfo:
        start_date = start_date.replace(tzinfo=data_start.tzinfo)
        end_date = end_date.replace(tzinfo=data_start.tzinfo)
    if data_start > start_date:
        start_date = data_start + timedelta(days=5)

    print(f"\n  Backtest period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}")

    print("\nCalculating indicators...")
    features = FeatureEngineer()
    smc = SMCAnalyzer(swing_length=config.smc.swing_length, ob_lookback=config.smc.ob_lookback)
    df = features.calculate_all(df, include_ml_features=True)
    df = smc.calculate_all(df)

    regime_detector = MarketRegimeDetector(model_path="models/hmm_regime.pkl")
    try:
        regime_detector.load()
        df = regime_detector.predict(df)
        print("  HMM regime loaded")
    except Exception:
        print("  [WARN] HMM not available")
    print("  Indicators calculated")

    # ═══ Test configurations ═══
    configs = [
        # label, skip_LE, skip_TL, syd, tl, le, gold, ny
        ("A: skip_LE",           True,  False, 0.5,  0.75, 0.8,  1.0,  0.9),
        ("B: skip_TL",           False, True,  0.5,  0.75, 0.8,  1.0,  0.9),
        ("C: skip_both",         True,  True,  0.5,  0.75, 0.8,  1.0,  0.9),
        ("D: skip+boost_gold",   True,  True,  0.5,  0.75, 0.8,  1.2,  0.9),
        ("E: skip+boost_syd",    True,  True,  0.7,  0.75, 0.8,  1.0,  0.9),
        ("F: skip+boost_both",   True,  True,  0.7,  0.75, 0.8,  1.2,  1.0),
    ]

    results = {}

    for label, skip_le, skip_tl, syd, tl, le, gold, ny in configs:
        print(f"\n{'='*60}")
        print(f"  Config: {label}")

        bt = SessionOptimizeBacktest(
            capital=5000.0,
            max_daily_loss_percent=5.0,
            max_loss_per_trade_percent=1.0,
            base_lot_size=0.01, max_lot_size=0.02, recovery_lot_size=0.01,
            breakeven_pips=30.0, trail_start_pips=50.0, trail_step_pips=30.0,
            min_profit_to_protect=5.0, max_drawdown_from_peak=50.0,
            trade_cooldown_bars=10, trend_reversal_mult=0.6,
            skip_london_early=skip_le,
            skip_tokyo_london=skip_tl,
            sydney_mult=syd, tokyo_london_mult=tl, london_early_mult=le,
            golden_mult=gold, ny_mult=ny,
        )

        stats = bt.run(df=df, start_date=start_date, end_date=end_date, initial_capital=5000.0)
        net_pnl = stats.total_profit - stats.total_loss
        results[label] = (stats, net_pnl)

        print(f"\n  [{label}] Results:")
        print(f"    Trades: {stats.total_trades} | WR: {stats.win_rate:.1f}%")
        print(f"    Net PnL: ${net_pnl:,.2f} | PF: {stats.profit_factor:.2f}")
        print(f"    Max DD: {stats.max_drawdown:.1f}% | Sharpe: {stats.sharpe_ratio:.2f}")
        print(f"    Blocked: {stats.session_blocked}")
        print(f"    vs BASELINE: ${net_pnl - BASELINE_NET:+,.2f}")

        # Session breakdown
        ss = {}
        for t in stats.trades:
            if t.session not in ss:
                ss[t.session] = {"w": 0, "l": 0, "p": 0.0}
            if t.result == TradeResult.WIN:
                ss[t.session]["w"] += 1
            else:
                ss[t.session]["l"] += 1
            ss[t.session]["p"] += t.profit_usd
        for s, d in sorted(ss.items(), key=lambda x: -x[1]["p"]):
            total = d["w"] + d["l"]
            wr = d["w"] / total * 100 if total > 0 else 0
            print(f"      {s:30s}: {total:3d} trades, {wr:5.1f}% WR, ${d['p']:>8,.2f}")

    # ═══ Comparison table ═══
    print("\n" + "=" * 70)
    print("#19 SESSION OPTIMIZATION — ALL CONFIGURATIONS")
    print("=" * 70)
    print(f"\n  {'Config':<25} {'Trades':>6} {'WR':>6} {'Net PnL':>10} {'DD':>6} {'Sharpe':>7} {'PF':>5} {'vs Base':>10}")
    print("  " + "-" * 85)
    print(f"  {'BASELINE (#1)':<25} {'686':>6} {'72.2%':>6} {'$1,449.86':>10} {'5.4%':>6} {'1.98':>7} {'1.52':>5} {'—':>10}")
    print(f"  {'#8 Stoch+Sell':<25} {'416':>6} {'76.7%':>6} {'$1,320.41':>10} {'2.8%':>6} {'3.17':>7} {'1.76':>5} {'—':>10}")

    best_label = None
    best_pnl = -float("inf")

    for label, (stats, net_pnl) in results.items():
        diff = net_pnl - BASELINE_NET
        print(f"  {label:<25} {stats.total_trades:>6} {stats.win_rate:>5.1f}% ${net_pnl:>9,.2f} {stats.max_drawdown:>5.1f}% {stats.sharpe_ratio:>7.2f} {stats.profit_factor:>5.2f} ${diff:>+9,.2f}")
        if net_pnl > best_pnl:
            best_pnl = net_pnl
            best_label = label

    # Save best
    if best_label and best_label in results:
        best_stats, best_net = results[best_label]
        print(f"\n  Best config: {best_label}")

        print(f"\n  Direction:")
        for d in ["BUY", "SELL"]:
            dt = [t for t in best_stats.trades if t.direction == d]
            dw = sum(1 for t in dt if t.result == TradeResult.WIN)
            dp = sum(t.profit_usd for t in dt)
            dwr = dw / len(dt) * 100 if dt else 0
            print(f"    {d}: {len(dt)} trades, {dwr:.1f}% WR, ${dp:,.2f}")

        print(f"\n  Exit Reasons:")
        ec = {}
        for t in best_stats.trades:
            r = t.exit_reason.value
            ec[r] = ec.get(r, 0) + 1
        for reason, count in sorted(ec.items(), key=lambda x: -x[1]):
            pct = count / best_stats.total_trades * 100 if best_stats.total_trades > 0 else 0
            print(f"    {reason:20s}: {count} ({pct:.1f}%)")

        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "19_session_optimize_results")
        os.makedirs(output_dir, exist_ok=True)
        log_path = os.path.join(output_dir, f"session_opt_{timestamp}.log")
        xlsx_path = os.path.join(output_dir, f"session_opt_{timestamp}.xlsx")
        generate_log(best_stats, log_path, start_date, end_date, best_label)
        generate_xlsx_report(best_stats, xlsx_path, start_date, end_date, best_label)

        print("\n" + "=" * 70)
        print(f"Output: {output_dir}")
        print(f"  Log:    {os.path.basename(log_path)}")
        print(f"  Report: {os.path.basename(xlsx_path)}")
        print("=" * 70)

    mt5.disconnect()
    print("Backtest complete!")


if __name__ == "__main__":
    main()