XauBot/backtests/backtest_20_early_cut_tune.py

"""
Backtest #20 — Early Cut Tuning
================================
Base: SMC-Only v4 (Backtest #1)
Modified: Tune early_cut exit sensitivity

Current baseline early cut logic (B.3):
  if current_profit < 0:
    loss_percent_of_max = abs(current_profit) / max_loss_per_trade * 100
    if momentum < -30 and loss_percent_of_max >= 30:
        exit EARLY_CUT

From corrected #19B results:
  early_cut accounts for 12.4% of all exits
  Many early_cut trades might have recovered if given more time

Configurations:
  A: momentum < -40 (more patient on momentum, keep loss at 30%)
  B: momentum < -50 (very patient on momentum)
  C: momentum < -30, loss >= 40% (allow bigger losses before cutting)
  D: momentum < -30, loss >= 50% (allow even bigger losses)
  E: momentum < -40, loss >= 40% (combined patience)
  F: Disable early cut entirely

Usage:
    python backtests/backtest_20_early_cut_tune.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


# ─── Early Cut Tune Backtest ─────────────────────────────────

class EarlyCutTuneBacktest:
    """SMC-Only + Tunable early cut parameters."""

    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,
        # Early cut tuning params
        early_cut_momentum: float = -30.0,     # momentum threshold (default: -30)
        early_cut_loss_pct: float = 30.0,      # loss % of max threshold (default: 30%)
        early_cut_enabled: bool = True,         # can disable entirely
    ):
        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

        # Early cut params
        self.early_cut_momentum = early_cut_momentum
        self.early_cut_loss_pct = early_cut_loss_pct
        self.early_cut_enabled = early_cut_enabled

        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 = 2200000

    # ── Session filter (synced) ──

    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:
            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:
        """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)

    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)

    # ── Full exit simulation (synced with #1, early cut tunable) ──

    def _simulate_trade_exit(
        self,
        df: pl.DataFrame,
        entry_idx: int,
        direction: str,
        entry_price: float,
        take_profit: float,
        stop_loss: float,
        lot_size: float,
        daily_loss_so_far: float,
        feature_cols: list,
        max_bars: int = 100,
    ) -> Tuple[float, float, ExitReason, int, float]:
        """
        Simulate trade exit — identical to baseline #1 EXCEPT:
        B.3 Early cut uses self.early_cut_momentum and self.early_cut_loss_pct
        """
        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]

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

        profit_history = []
        price_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 = (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

            profit_history.append(current_profit)
            price_history.append(close)
            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) SmartPositionManager checks
            # ════════════════════════════════════════════════

            # A.0 TP hit
            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
            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
            if pip_profit_from_entry >= self.breakeven_pips and not breakeven_moved:
                if direction == "BUY":
                    current_sl = entry_price + 2
                else:
                    current_sl = entry_price - 2
                breakeven_moved = True

            # A.2 Trailing SL
            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 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:
                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
            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
            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 — TUNABLE THRESHOLDS          ║
            # ╚══════════════════════════════════════════════╝
            if self.early_cut_enabled and 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:
                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
            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
            # ════════════════════════════════════════════════

            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
        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 (synced with #1) ──

    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

        ec_info = f"mom<{self.early_cut_momentum}, loss>={self.early_cut_loss_pct}%"
        if not self.early_cut_enabled:
            ec_info = "DISABLED"
        print(f"  Early cut: {ec_info}")
        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:
                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

            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

            # 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 = 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
            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 — #20 Early Cut Tuning")
    print("Base: SMC-Only v4 | Modified: Early cut sensitivity")
    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")

    # ═══ Configurations ═══
    configs = [
        {
            "name": "A: mom<-40",
            "early_cut_momentum": -40.0,
            "early_cut_loss_pct": 30.0,
            "early_cut_enabled": True,
        },
        {
            "name": "B: mom<-50",
            "early_cut_momentum": -50.0,
            "early_cut_loss_pct": 30.0,
            "early_cut_enabled": True,
        },
        {
            "name": "C: loss>=40%",
            "early_cut_momentum": -30.0,
            "early_cut_loss_pct": 40.0,
            "early_cut_enabled": True,
        },
        {
            "name": "D: loss>=50%",
            "early_cut_momentum": -30.0,
            "early_cut_loss_pct": 50.0,
            "early_cut_enabled": True,
        },
        {
            "name": "E: mom<-40+loss>=40%",
            "early_cut_momentum": -40.0,
            "early_cut_loss_pct": 40.0,
            "early_cut_enabled": True,
        },
        {
            "name": "F: disabled",
            "early_cut_momentum": -30.0,
            "early_cut_loss_pct": 30.0,
            "early_cut_enabled": False,
        },
    ]

    baseline_pnl = 1449.86
    baseline_stoch_pnl = 1320.41
    all_results = []

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

        bt = EarlyCutTuneBacktest(
            early_cut_momentum=cfg["early_cut_momentum"],
            early_cut_loss_pct=cfg["early_cut_loss_pct"],
            early_cut_enabled=cfg["early_cut_enabled"],
        )

        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

        # Count early_cut exits
        ec_count = sum(1 for t in stats.trades if t.exit_reason == ExitReason.EARLY_CUT)
        ec_pct = ec_count / stats.total_trades * 100 if stats.total_trades > 0 else 0

        # Early cut P/L breakdown
        ec_trades = [t for t in stats.trades if t.exit_reason == ExitReason.EARLY_CUT]
        ec_total_loss = sum(t.profit_usd for t in ec_trades)

        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"    Early cuts: {ec_count} ({ec_pct:.1f}%) | EC total P/L: ${ec_total_loss:,.2f}")
        print(f"    vs BASELINE: ${net_pnl - baseline_pnl:+,.2f}")

        all_results.append({
            "name": cfg["name"],
            "stats": stats,
            "net_pnl": net_pnl,
            "ec_count": ec_count,
            "ec_pct": ec_pct,
            "ec_pnl": ec_total_loss,
        })

    # ═══ Summary ═══
    print(f"\n{'=' * 70}")
    print("#20 EARLY CUT TUNING — ALL CONFIGURATIONS")
    print("=" * 70)

    print(f"\n  {'Config':<25} {'Trades':>6} {'WR':>6} {'Net PnL':>10} {'DD':>6} {'Sharpe':>7} {'PF':>5} {'EC#':>5} {'EC%':>6} {'EC P/L':>10} {'vs Base':>10}")
    print(f"  {'-' * 110}")
    print(f"  {'BASELINE (#1)':<25} {'686':>6} {'72.2%':>6} {'$1,449.86':>10} {'5.4%':>6} {'1.98':>7} {'1.52':>5} {'—':>5} {'—':>6} {'—':>10} {'—':>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} {'—':>5} {'—':>6} {'—':>10} {'—':>10}")

    best_result = None
    best_pnl = -999999

    for r in all_results:
        s = r["stats"]
        diff = r["net_pnl"] - baseline_pnl
        print(f"  {r['name']:<25} {s.total_trades:>6} {s.win_rate:>5.1f}% ${r['net_pnl']:>9,.2f} {s.max_drawdown:>5.1f}% {s.sharpe_ratio:>7.2f} {s.profit_factor:>5.2f} {r['ec_count']:>5} {r['ec_pct']:>5.1f}% ${r['ec_pnl']:>9,.2f} ${diff:>+9,.2f}")
        if r["net_pnl"] > best_pnl:
            best_pnl = r["net_pnl"]
            best_result = r

    print(f"\n  Best config: {best_result['name']}")

    # Direction breakdown for best
    best_stats = best_result["stats"]
    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}")

    # Exit reasons for best
    print(f"\n  Exit Reasons:")
    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 reports
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "20_early_cut_results")
    os.makedirs(output_dir, exist_ok=True)

    log_path = os.path.join(output_dir, f"early_cut_{timestamp}.log")
    with open(log_path, "w") as f:
        f.write(f"#20 Early Cut Tuning Results\n")
        f.write(f"Generated: {datetime.now()}\n\n")
        for r in all_results:
            s = r["stats"]
            f.write(f"Config: {r['name']}\n")
            f.write(f"  Trades: {s.total_trades}, WR: {s.win_rate:.1f}%, Net: ${r['net_pnl']:,.2f}\n")
            f.write(f"  DD: {s.max_drawdown:.1f}%, Sharpe: {s.sharpe_ratio:.2f}, PF: {s.profit_factor:.2f}\n")
            f.write(f"  Early cuts: {r['ec_count']} ({r['ec_pct']:.1f}%), EC P/L: ${r['ec_pnl']:,.2f}\n\n")
    print(f"  Log saved: {log_path}")

    # XLSX for best config
    try:
        from backtests.backtest_01_smc_only import generate_xlsx_report as gen_xlsx
        xlsx_path = os.path.join(output_dir, f"early_cut_{timestamp}.xlsx")
        gen_xlsx(best_stats, xlsx_path, start_date, end_date)
    except Exception as e:
        print(f"  [WARN] XLSX generation skipped: {e}")

    mt5.disconnect()

    print(f"\n{'=' * 70}")
    print(f"Output: {output_dir}")
    print(f"  Log: early_cut_{timestamp}.log")
    print("=" * 70)
    print("Backtest complete!")


if __name__ == "__main__":
    main()