"""
Backtest #27 — Regime-Aware Entry
==================================
Base: #24B (19B+20B+22D) — 739 trades, 80.4% WR, $2,235, Sharpe 2.87

HMM regime (low/medium/high vol) is only used for exits currently.
Goal: Use regime to FILTER entries and improve quality.

Configs:
  A: Regime-conditional confidence thresholds
     low_vol=0.50, medium=0.55, high_vol=0.70
  B: Skip SELL in low volatility (gold grinds up in calm markets, SELL fails)
  C: Skip ALL trades in high volatility (only trade low+medium vol)
  D: A+B combined (regime thresholds + skip SELL in low vol)
  E: Regime-direction matrix (skip worst combos: SELL in low vol, BUY in high vol crisis)

Usage:
    python backtests/backtest_27_regime_aware_entry.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

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
    regime_filtered: int = 0


# ─── Regime-Aware Entry Backtest ──────────────────────────────

class RegimeAwareBacktest:
    """#24B base + regime-aware entry filtering."""

    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,
        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,
        # #24B base settings
        skip_tokyo_london: bool = True,
        early_cut_momentum: float = -50.0,
        early_cut_loss_pct: float = 30.0,
        be_mult: float = 2.0,
        trail_start_mult: float = 4.0,
        trail_step_mult: float = 3.0,
        # ═══ #27 REGIME-AWARE ENTRY PARAMS ═══
        regime_conf_thresholds: Dict[str, float] = None,  # {regime: min_confidence}
        skip_sell_in_low_vol: bool = False,
        skip_all_in_high_vol: bool = False,
        regime_direction_filter: Dict[str, List[str]] = None,  # {regime: [blocked_directions]}
    ):
        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.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

        self.skip_tokyo_london = skip_tokyo_london
        self.early_cut_momentum = early_cut_momentum
        self.early_cut_loss_pct = early_cut_loss_pct
        self.be_mult = be_mult
        self.trail_start_mult = trail_start_mult
        self.trail_step_mult = trail_step_mult

        # #27 Regime params
        self.regime_conf_thresholds = regime_conf_thresholds or {}
        self.skip_sell_in_low_vol = skip_sell_in_low_vol
        self.skip_all_in_high_vol = skip_all_in_high_vol
        self.regime_direction_filter = regime_direction_filter or {}

        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 = 2270000

    # ── Session filter (#19B) ──

    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, 0.5
        elif 15 <= hour < 16:
            if self.skip_tokyo_london:
                return "Tokyo-London Overlap", False, 0.0
            return "Tokyo-London Overlap", True, 0.75
        elif 16 <= hour < 19:
            return "London Early", True, 0.8
        elif 19 <= hour < 24:
            return "London-NY Overlap (Golden)", True, 1.0
        elif 0 <= hour < 4:
            return "NY Session", True, 0.9
        else:
            return "Off Hours", False, 0.0

    def _hours_to_golden(self, dt: datetime) -> float:
        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)

    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 ──

    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)

    # ── Exit simulation (identical to #24B) ──

    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]:
        pip_value = 10
        highs = df["high"].to_list()
        lows = df["low"].to_list()
        closes = df["close"].to_list()
        times = df["time"].to_list()

        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]

        adaptive_breakeven_pips = atr * self.be_mult
        adaptive_trail_start_pips = atr * self.trail_start_mult
        adaptive_trail_step_pips = atr * self.trail_step_mult
        reversal_momentum_threshold = atr * self.trend_reversal_mult
        min_loss_for_reversal_exit = atr * 0.8

        profit_history = []
        peak_profit = 0.0
        stall_count = 0
        reversal_warnings = 0
        current_sl = stop_loss
        breakeven_moved = False

        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

        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]

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

            profit_history.append(current_profit)
            if current_profit > peak_profit:
                peak_profit = current_profit

            bars_since_entry = i - entry_idx

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

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

            # A.1 Breakeven
            if pip_profit_from_entry >= adaptive_breakeven_pips and not breakeven_moved:
                current_sl = entry_price + (2 if direction == "BUY" else -2)
                breakeven_moved = True

            # A.2 Trailing SL
            if pip_profit_from_entry >= adaptive_trail_start_pips:
                trail_distance = adaptive_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 protect
            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
            if bars_since_entry % 5 == 0 and bars_since_entry >= 5 and i >= 20:
                ma_fast = np.mean(closes[i-4:i+1])
                ma_slow = np.mean(closes[i-19:i+1])
                trend = "BULLISH" if ma_fast > ma_slow * 1.001 else ("BEARISH" if ma_fast < ma_slow * 0.999 else "NEUTRAL")
                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") or (direction == "SELL" and cached_ml_signal == "BUY"):
                        should_exit = True; urgency += 2
                if rsi_val:
                    if (rsi_val > 75 and direction == "BUY") or (rsi_val < 25 and direction == "SELL"):
                        should_exit = True; urgency += 2
                if (direction == "BUY" and trend == "BEARISH" and mom_dir == "BEARISH") or \
                   (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
            if self._is_near_weekend_close(current_time):
                if current_profit > 0 or current_profit > -10:
                    return current_profit, current_pips, ExitReason.WEEKEND_CLOSE, i, close

            # B.1 Smart TP
            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
            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
            if current_profit < 0:
                loss_percent_of_max = abs(current_profit) / self.max_loss_per_trade * 100
                if momentum < self.early_cut_momentum and loss_percent_of_max >= self.early_cut_loss_pct:
                    return current_profit, current_pips, ExitReason.EARLY_CUT, i, close

            # B.4 Trend Reversal
            is_ml_reversal = False
            if (direction == "BUY" and cached_ml_signal == "SELL" and cached_ml_confidence >= self.trend_reversal_threshold) or \
               (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
            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
            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
            if bars_since_entry >= 16 and current_profit < 5 and not profit_growing:
                if current_profit >= 0 or current_profit > -15:
                    return current_profit, current_pips, ExitReason.TIMEOUT, i, close
            if bars_since_entry >= 24 and (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) or \
                   (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

        final_idx = min(entry_idx + max_bars - 1, len(df) - 1)
        final_price = closes[final_idx]
        pips = ((final_price - entry_price) if direction == "BUY" else (entry_price - final_price)) / 0.1
        return pips * pip_value * lot_size, 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

        print(f"  #27 Regime filters: thresholds={self.regime_conf_thresholds}, skip_sell_low={self.skip_sell_in_low_vol}, skip_high={self.skip_all_in_high_vol}, dir_filter={self.regime_direction_filter}")
        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]
            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:
                if session_name == "Tokyo-London Overlap":
                    stats.session_blocked += 1
                continue

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

            df_slice = df.head(i + 1)

            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

            # ═══ #27 REGIME FILTER: Skip all in high vol ═══
            if self.skip_all_in_high_vol and regime == "high_volatility":
                stats.regime_filtered += 1
                continue

            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

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

            if smc_signal is None:
                continue

            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 = 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

            # ═══ #27 REGIME FILTERS ═══

            # Filter A: Regime-conditional confidence thresholds
            if self.regime_conf_thresholds:
                min_conf = self.regime_conf_thresholds.get(regime, 0.0)
                if min_conf > 0 and confidence < min_conf:
                    stats.regime_filtered += 1
                    continue

            # Filter B: Skip SELL in low volatility
            if self.skip_sell_in_low_vol and regime == "low_volatility" and smc_signal.signal_type == "SELL":
                stats.regime_filtered += 1
                continue

            # Filter E: Regime-direction matrix
            if self.regime_direction_filter:
                blocked_dirs = self.regime_direction_filter.get(regime, [])
                if smc_signal.signal_type in blocked_dirs:
                    stats.regime_filtered += 1
                    continue

            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
            drawdown_pct = (peak_capital - capital) / peak_capital * 100
            drawdown_usd = peak_capital - capital
            if drawdown_pct > stats.max_drawdown:
                stats.max_drawdown = drawdown_pct
                stats.max_drawdown_usd = drawdown_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_return = np.mean(returns)
                std_return = np.std(returns)
                stats.sharpe_ratio = (avg_return / std_return) * np.sqrt(252) if std_return > 0 else 0

        return stats


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

def main():
    print("=" * 70)
    print("XAUBOT AI — #27 Regime-Aware Entry")
    print("Base: #24B | Modified: Regime-based entry filtering")
    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")

    # ── First: analyze regime distribution in #24B baseline ──
    print("\n  Regime distribution analysis (pre-backtest)...")
    regime_col = df["regime_name"].to_list() if "regime_name" in df.columns else []
    if regime_col:
        from collections import Counter
        regime_counts = Counter(r for r in regime_col if r is not None)
        total_bars = sum(regime_counts.values())
        for r, c in sorted(regime_counts.items(), key=lambda x: -x[1]):
            print(f"    {r}: {c} bars ({c/total_bars*100:.1f}%)")

    baseline_24b_pnl = 2235.0

    # ═══ CONFIGS ═══
    configs = [
        {
            "name": "A: Regime thresholds",
            "regime_conf_thresholds": {"low_volatility": 0.50, "medium_volatility": 0.55, "high_volatility": 0.70},
            "skip_sell_in_low_vol": False,
            "skip_all_in_high_vol": False,
            "regime_direction_filter": {},
        },
        {
            "name": "B: Skip SELL in low vol",
            "regime_conf_thresholds": {},
            "skip_sell_in_low_vol": True,
            "skip_all_in_high_vol": False,
            "regime_direction_filter": {},
        },
        {
            "name": "C: Skip all high vol",
            "regime_conf_thresholds": {},
            "skip_sell_in_low_vol": False,
            "skip_all_in_high_vol": True,
            "regime_direction_filter": {},
        },
        {
            "name": "D: A+B combined",
            "regime_conf_thresholds": {"low_volatility": 0.50, "medium_volatility": 0.55, "high_volatility": 0.70},
            "skip_sell_in_low_vol": True,
            "skip_all_in_high_vol": False,
            "regime_direction_filter": {},
        },
        {
            "name": "E: Direction matrix",
            "regime_conf_thresholds": {},
            "skip_sell_in_low_vol": False,
            "skip_all_in_high_vol": False,
            "regime_direction_filter": {
                "low_volatility": ["SELL"],       # No SELL in calm markets
                "high_volatility": ["SELL"],       # No SELL in volatile markets (whipsaw)
            },
        },
    ]

    all_results = []

    for cfg in configs:
        cfg_name = cfg["name"]
        print(f"\n{'=' * 60}")
        print(f"  Config: {cfg_name}")

        bt = RegimeAwareBacktest(
            regime_conf_thresholds=cfg["regime_conf_thresholds"],
            skip_sell_in_low_vol=cfg["skip_sell_in_low_vol"],
            skip_all_in_high_vol=cfg["skip_all_in_high_vol"],
            regime_direction_filter=cfg["regime_direction_filter"],
        )
        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
        diff = net_pnl - baseline_24b_pnl

        # Direction breakdown
        buy_trades = [t for t in stats.trades if t.direction == "BUY"]
        sell_trades = [t for t in stats.trades if t.direction == "SELL"]
        buy_wins = sum(1 for t in buy_trades if t.result == TradeResult.WIN)
        sell_wins = sum(1 for t in sell_trades if t.result == TradeResult.WIN)
        buy_wr = buy_wins / len(buy_trades) * 100 if buy_trades else 0
        sell_wr = sell_wins / len(sell_trades) * 100 if sell_trades else 0
        buy_pnl = sum(t.profit_usd for t in buy_trades)
        sell_pnl = sum(t.profit_usd for t in sell_trades)

        # Regime breakdown
        regime_stats = {}
        for t in stats.trades:
            r = t.regime
            if r not in regime_stats:
                regime_stats[r] = {"w": 0, "l": 0, "p": 0.0}
            if t.result == TradeResult.WIN:
                regime_stats[r]["w"] += 1
            else:
                regime_stats[r]["l"] += 1
            regime_stats[r]["p"] += t.profit_usd

        print(f"\n  [{cfg_name}] 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"    Regime filtered: {stats.regime_filtered}")
        print(f"    BUY: {len(buy_trades)} trades, {buy_wr:.1f}% WR, ${buy_pnl:,.2f}")
        print(f"    SELL: {len(sell_trades)} trades, {sell_wr:.1f}% WR, ${sell_pnl:,.2f}")
        print(f"    Per regime:")
        for r, d in sorted(regime_stats.items(), key=lambda x: -x[1]["p"]):
            total = d["w"] + d["l"]
            wr = d["w"] / total * 100 if total > 0 else 0
            print(f"      {r:<20}: {total:>3} trades, {wr:>5.1f}% WR, ${d['p']:>8,.2f}")
        print(f"    vs #24B: ${diff:+,.2f}")

        all_results.append((cfg_name, stats, net_pnl, diff, len(buy_trades), buy_wr, buy_pnl, len(sell_trades), sell_wr, sell_pnl, regime_stats))

    # ═══ FINAL SUMMARY ═══
    print(f"\n{'=' * 70}")
    print("#27 REGIME-AWARE ENTRY — ALL CONFIGURATIONS")
    print("=" * 70)

    print(f"\n  {'Config':<25} {'Trades':>6} {'WR':>6} {'Net PnL':>10} {'DD':>6} {'Sharpe':>7} {'PF':>5} {'Filt':>5} {'vs #24B':>10}")
    print(f"  {'-' * 85}")
    print(f"  {'#1 BASELINE':<25} {'686':>6} {'72.2%':>6} {'$1,449.86':>10} {'5.4%':>6} {'1.98':>7} {'1.52':>5} {'—':>5} {'—':>10}")
    print(f"  {'#24B (base)':<25} {'739':>6} {'80.4%':>6} {'$2,235.00':>10} {'3.4%':>6} {'2.87':>7} {'1.77':>5} {'—':>5} {'—':>10}")
    for cfg_name, stats, net_pnl, diff, _, _, _, _, _, _, _ in all_results:
        print(f"  {cfg_name:<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} {stats.regime_filtered:>5} ${diff:>+9,.2f}")

    best_pnl = -999999
    best_name = ""
    best_stats = None
    best_data = None
    for entry in all_results:
        if entry[2] > best_pnl:
            best_pnl = entry[2]
            best_name = entry[0]
            best_stats = entry[1]
            best_data = entry

    print(f"\n  Best config: {best_name}")

    # Exit reasons for best
    print(f"\n  Exit Reasons (best config):")
    exit_counts = {}
    for t in best_stats.trades:
        r = t.exit_reason.value
        exit_counts[r] = exit_counts.get(r, 0) + 1
    for reason, count in sorted(exit_counts.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}%)")

    # Save
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "27_regime_aware_results")
    os.makedirs(output_dir, exist_ok=True)

    log_path = os.path.join(output_dir, f"regime_aware_{timestamp}.log")
    with open(log_path, "w") as f:
        f.write(f"#27 Regime-Aware Entry Results\n")
        f.write(f"Generated: {datetime.now()}\n")
        f.write(f"Base: #24B (739 trades, 80.4% WR, $2,235)\n\n")
        for cfg_name, stats, net_pnl, diff, buy_n, buy_wr, buy_pnl, sell_n, sell_wr, sell_pnl, regime_stats in all_results:
            f.write(f"  {cfg_name}: {stats.total_trades} trades, {stats.win_rate:.1f}% WR, "
                    f"${net_pnl:,.2f}, DD: {stats.max_drawdown:.1f}%, "
                    f"Sharpe: {stats.sharpe_ratio:.2f}, PF: {stats.profit_factor:.2f}, "
                    f"Filtered: {stats.regime_filtered}, "
                    f"BUY: {buy_n}@{buy_wr:.1f}%, SELL: {sell_n}@{sell_wr:.1f}%, "
                    f"vs #24B: ${diff:+,.2f}\n")
            for r, d in sorted(regime_stats.items(), key=lambda x: -x[1]["p"]):
                total = d["w"] + d["l"]
                wr = d["w"] / total * 100 if total > 0 else 0
                f.write(f"    {r}: {total} trades, {wr:.1f}% WR, ${d['p']:,.2f}\n")
        f.write(f"\nBest: {best_name}\n")
    print(f"  Log saved: {log_path}")

    try:
        from backtests.backtest_01_smc_only import generate_xlsx_report as gen_xlsx
        xlsx_path = os.path.join(output_dir, f"regime_aware_{timestamp}.xlsx")
        gen_xlsx(best_stats, xlsx_path, start_date, end_date)
        print(f"\n  Report saved: {xlsx_path}")
    except Exception as e:
        print(f"  [WARN] XLSX: {e}")

    mt5.disconnect()

    print(f"\n{'=' * 70}")
    print(f"Output: {output_dir}")
    print(f"  Log: {os.path.basename(log_path)}")
    print("=" * 70)
    print("Backtest complete!")


if __name__ == "__main__":
    main()
