ForexBot2/signal_aggregator.py

"""
ForexBot2 — Signal Aggregator
==============================
Combines multi-timeframe technical analysis with optional news context
into a graded actionable signal.
"""

import logging
import os
from dataclasses import dataclass, field
from datetime import datetime, timezone
from typing import Dict, List, Optional

import pandas as pd

from config import PlatformConfig, SignalStrength, Timeframe
from data_provider import ForexDataProvider
from engine import TechnicalEngine, TechnicalAnalysis
from news_engine import NewsEngine, FundamentalContext

logger = logging.getLogger(__name__)


def _is_forex_weekend(now_utc: datetime) -> bool:
    """Forex market closed: Friday 22:00 UTC → Sunday 22:00 UTC (Sydney open)."""
    wd = now_utc.weekday()
    hour = now_utc.hour
    if wd == 4 and hour >= 22:  # Friday after 22:00
        return True
    if wd == 5:  # All Saturday
        return True
    if wd == 6 and hour < 22:  # Sunday before 22:00
        return True
    return False


@dataclass
class TradeSetup:
    pair: str
    display_name: str
    direction: str  # "BUY" / "SELL"
    entry_price: float
    stop_loss: float
    take_profit_1: float
    take_profit_2: float
    risk_reward: float
    risk_pips: float
    reward_pips: float
    pip_value: float
    # Entry archetype ("GENERIC", "XAU_PB") — selects trail params.
    strategy: str = "GENERIC"


@dataclass
class SignalReport:
    symbol: str
    display_name: str
    timestamp: str

    primary_signal: SignalStrength = SignalStrength.NEUTRAL
    primary_score: float = 0.0
    primary_confidence: float = 0.0

    mtf_agreement: bool = False
    mtf_signals: Dict[str, SignalStrength] = field(default_factory=dict)
    mtf_scores: Dict[str, float] = field(default_factory=dict)

    primary_analysis: Optional[TechnicalAnalysis] = None
    confirmation_analyses: List[TechnicalAnalysis] = field(default_factory=list)

    event_risk: str = "clear"
    event_description: str = ""
    news_sentiment: float = 0.0
    news_description: str = ""

    final_signal: SignalStrength = SignalStrength.NEUTRAL
    final_score: float = 0.0
    final_confidence: float = 0.0
    action_recommendation: str = ""
    signal_quality: str = ""  # A+, A, B, C, D, F

    trade_setup: Optional[TradeSetup] = None

    bullish_reasons: List[str] = field(default_factory=list)
    bearish_reasons: List[str] = field(default_factory=list)
    warnings: List[str] = field(default_factory=list)

    current_price: float = 0.0
    spread_estimate: float = 0.0
    market_regime: str = ""
    volatility_regime: str = ""
    exhaustion_count: int = 0


class SignalAggregator:
    """Master aggregator for live signal analysis."""

    # Diagnostic funnel for the M5 pullback route (shared across
    # instances; read by backtest tooling, harmless in live use).
    pb_funnel: Dict[str, int] = {}

    def __init__(self, config: PlatformConfig):
        self.config = config
        self.data_provider = ForexDataProvider(config)
        self.tech_engine = TechnicalEngine(config.technical)
        self._news_engine: Optional[NewsEngine] = None  # lazy

    def _get_news_engine(self) -> NewsEngine:
        if self._news_engine is None:
            self._news_engine = NewsEngine(self.config.news)
        return self._news_engine

    # ── Public: live mode entry point ──────────────────────────────

    def analyze_pair(
        self,
        symbol: Optional[str] = None,
        context: Optional[FundamentalContext] = None,
    ) -> SignalReport:
        """Live-mode entry: fetch all needed timeframes then delegate."""
        if symbol is None:
            symbol = self.config.symbol

        pair_info = self.data_provider.get_pair_info(symbol)
        now_utc = datetime.now(timezone.utc)

        if pair_info["display"] == "XAU/USD" and self.config.xau_strategy.enabled:
            xau_cfg = self.config.xau_strategy

            if xau_cfg.weekend_filter and _is_forex_weekend(now_utc):
                report = SignalReport(
                    symbol=symbol,
                    display_name=pair_info["display"],
                    timestamp=str(now_utc),
                )
                report.final_signal = SignalStrength.NEUTRAL
                report.signal_quality = "F"
                report.trade_setup = None
                report.warnings.append("WEEKEND FILTER: XAU/USD market closed — no liquidity")
                report.action_recommendation = "NO TRADE — Weekend (market closed)"
                return report

            if xau_cfg.allowed_weekdays and now_utc.weekday() not in xau_cfg.allowed_weekdays:
                day_names = {0: "Mon", 1: "Tue", 2: "Wed", 3: "Thu", 4: "Fri", 5: "Sat", 6: "Sun"}
                allowed = "/".join(day_names.get(d, str(d)) for d in xau_cfg.allowed_weekdays)
                report = SignalReport(
                    symbol=symbol,
                    display_name=pair_info["display"],
                    timestamp=str(now_utc),
                )
                report.final_signal = SignalStrength.NEUTRAL
                report.signal_quality = "F"
                report.trade_setup = None
                report.warnings.append(
                    f"DAY FILTER: {day_names.get(now_utc.weekday(), '?')} not in allowed days ({allowed})"
                )
                report.action_recommendation = f"NO TRADE — Only trading {allowed}"
                return report

        if pair_info["display"] == "EUR/USD" and self.config.eur_strategy.enabled:
            eur_cfg = self.config.eur_strategy

            if eur_cfg.weekend_filter and _is_forex_weekend(now_utc):
                report = SignalReport(
                    symbol=symbol,
                    display_name=pair_info["display"],
                    timestamp=str(now_utc),
                )
                report.final_signal = SignalStrength.NEUTRAL
                report.signal_quality = "F"
                report.trade_setup = None
                report.warnings.append("WEEKEND FILTER: EUR/USD market closed — no liquidity")
                report.action_recommendation = "NO TRADE — Weekend (market closed)"
                return report

            if eur_cfg.allowed_weekdays and now_utc.weekday() not in eur_cfg.allowed_weekdays:
                day_names = {0: "Mon", 1: "Tue", 2: "Wed", 3: "Thu", 4: "Fri", 5: "Sat", 6: "Sun"}
                allowed = "/".join(day_names.get(d, str(d)) for d in eur_cfg.allowed_weekdays)
                report = SignalReport(
                    symbol=symbol,
                    display_name=pair_info["display"],
                    timestamp=str(now_utc),
                )
                report.final_signal = SignalStrength.NEUTRAL
                report.signal_quality = "F"
                report.trade_setup = None
                report.warnings.append(
                    f"DAY FILTER: {day_names.get(now_utc.weekday(), '?')} not in allowed days ({allowed})"
                )
                report.action_recommendation = f"NO TRADE — Only trading {allowed}"
                return report

        timeframes = [self.config.primary_timeframe] + list(
            self.config.confirmation_timeframes
        )

        bt = self.config.backtest
        if bt.regime_filter and bt.regime_filter_timeframe not in timeframes:
            timeframes.append(bt.regime_filter_timeframe)

        dfs: Dict[Timeframe, pd.DataFrame] = {}
        stale_tfs = []
        for tf in timeframes:
            try:
                df = self.data_provider.fetch_ohlcv(symbol, tf)
                dfs[tf] = df
                if not self.data_provider._is_data_fresh(df, symbol, tf):
                    stale_tfs.append(tf.value)
            except Exception as e:
                logger.warning(f"Fetch failed for {symbol} {tf.value}: {e}")

        report = self.analyze_from_dataframes(
            symbol=symbol,
            dfs=dfs,
            context=context,
            use_news=True,
        )

        if stale_tfs:
            report.warnings.append(f"STALE DATA on {', '.join(stale_tfs)} — signal may be unreliable")
            if self.config.primary_timeframe.value in stale_tfs:
                report.final_signal = SignalStrength.NEUTRAL
                report.signal_quality = "F"
                report.trade_setup = None
                report.warnings.append("Primary timeframe stale — signal suppressed")

        if bt.regime_filter:
            self._apply_regime_filter_live(report, dfs)

        # Session window + blocked hours on the signal bar's CLOSE time —
        # the exact predicate the backtester's replay loop applies before
        # each bar, so live and backtest cannot diverge. Bar-close time
        # (not wall clock) makes manual mid-candle scans behave like
        # boundary auto-scans. This replaces an older wall-clock filter
        # that was guarded by a marker string no code wrote.
        if bt.session_filter and report.primary_analysis is not None:
            try:
                bar_close = (
                    pd.Timestamp(report.timestamp)
                    + pd.Timedelta(
                        seconds=ForexDataProvider.CANDLE_SECONDS[
                            self.config.primary_timeframe
                        ]
                    )
                )
                close_h = bar_close.hour
                _is_xau = pair_info["display"] == "XAU/USD"
                _blocked = (self.config.xau_strategy.blocked_hours_utc
                            if _is_xau else bt.blocked_hours_utc)
                outside = not (bt.session_start_hour <= close_h < bt.session_end_hour)
                if outside or close_h in _blocked:
                    report.warnings.append(
                        f"SESSION FILTER: bar closes {close_h:02}:00 UTC — "
                        + ("outside window" if outside else "blocked hour")
                    )
                    report.final_signal = SignalStrength.NEUTRAL
                    report.signal_quality = "F"
                    report.action_recommendation = "NO TRADE — Outside tradeable session"
                    report.trade_setup = None
            except Exception:
                pass

        return report

    def _apply_regime_filter_live(
        self, report: SignalReport, dfs: Dict[Timeframe, pd.DataFrame]
    ) -> None:
        """If higher-TF ADX is below threshold, downgrade signal to NEUTRAL."""
        bt = self.config.backtest
        regime_tf = bt.regime_filter_timeframe
        df = dfs.get(regime_tf)
        if df is None or df.empty:
            report.warnings.append(
                f"Regime filter requested but {regime_tf.value} data unavailable"
            )
            return

        adx_value = self._latest_adx(df, self.config.technical.adx_period)
        if adx_value is None:
            report.warnings.append(
                f"Regime filter: ADX could not be computed on {regime_tf.value}"
            )
            return

        if adx_value < bt.regime_filter_min_adx:
            report.warnings.append(
                f"REGIME FILTER: {regime_tf.value} ADX={adx_value:.1f} < "
                f"{bt.regime_filter_min_adx:.0f} — market choppy, skipping signal"
            )
            report.final_signal = SignalStrength.NEUTRAL
            report.signal_quality = "F"
            report.action_recommendation = (
                f"NO TRADE — Regime filter (ADX {adx_value:.1f} on {regime_tf.value})"
            )
            report.trade_setup = None
        else:
            report.bullish_reasons.append(
                f"Regime: {regime_tf.value} ADX={adx_value:.1f} confirms trending market"
            ) if report.final_score > 0 else report.bearish_reasons.append(
                f"Regime: {regime_tf.value} ADX={adx_value:.1f} confirms trending market"
            )

    @staticmethod
    def _latest_adx(df: pd.DataFrame, period: int = 14) -> Optional[float]:
        """Wilder ADX on a fresh df; returns the last finite value or None."""
        if df is None or len(df) < period * 3:
            return None
        high, low, close = df["High"], df["Low"], df["Close"]
        tr = pd.concat([
            high - low,
            (high - close.shift()).abs(),
            (low - close.shift()).abs(),
        ], axis=1).max(axis=1)
        plus_dm_raw = high.diff()
        minus_dm_raw = -low.diff()
        plus_dm = plus_dm_raw.where((plus_dm_raw > minus_dm_raw) & (plus_dm_raw > 0), 0.0)
        minus_dm = minus_dm_raw.where((minus_dm_raw > plus_dm_raw) & (minus_dm_raw > 0), 0.0)
        atr_s = tr.ewm(alpha=1/period, min_periods=period, adjust=False).mean()
        plus_di = 100 * (plus_dm.ewm(alpha=1/period, min_periods=period, adjust=False).mean() / atr_s)
        minus_di = 100 * (minus_dm.ewm(alpha=1/period, min_periods=period, adjust=False).mean() / atr_s)
        denom = (plus_di + minus_di).replace(0, float("nan"))
        dx = 100 * (plus_di - minus_di).abs() / denom
        adx = dx.ewm(alpha=1/period, min_periods=period, adjust=False).mean().dropna()
        if adx.empty:
            return None
        return float(adx.iloc[-1])

    def _apply_m5_filters(self, report: SignalReport) -> None:
        """Extra selectivity for M5 — noisy TF needs stronger confirmation."""
        if report.signal_quality not in ("A+", "A", "B", "C"):
            return

        xau_cfg = self.config.xau_strategy
        abs_score = abs(report.final_score)

        # Gate 1: minimum absolute score (M5 composite scores near the
        # threshold are noise — require a wider margin).
        if abs_score < xau_cfg.m5_min_abs_score:
            report.warnings.append(
                f"M5 FILTER: |score| {abs_score:.1f} < {xau_cfg.m5_min_abs_score:.0f} minimum"
            )
            report.signal_quality = "D"
            report.trade_setup = None
            return

        # Gate 2: require MTF agreement (M5 alone is too noisy).
        if xau_cfg.m5_require_mtf and not report.mtf_agreement:
            report.warnings.append(
                "M5 FILTER: no MTF agreement — M5 signals require confirmation"
            )
            report.signal_quality = "D"
            report.trade_setup = None
            return

        # Gate 3: at least one confirmation TF must have a meaningful
        # score in the same direction — NEUTRAL confirmations don't
        # count for M5 since they indicate the move isn't visible on
        # higher timeframes yet.
        min_conf_score = xau_cfg.m5_min_confirmation_score
        is_buy = report.final_score > 0
        confirmed = False
        primary_tf = self.config.primary_timeframe.value
        for tf_val, tf_score in report.mtf_scores.items():
            if tf_val == primary_tf:
                continue
            if is_buy and tf_score >= min_conf_score:
                confirmed = True
                break
            if not is_buy and tf_score <= -min_conf_score:
                confirmed = True
                break

        if not confirmed:
            report.warnings.append(
                "M5 FILTER: no confirmation TF shows directional conviction"
            )
            report.signal_quality = "D"
            report.trade_setup = None
            return

        # Gate 4: cap SELL quality on M5 — A-grade SELLs enter at the
        # extreme of temporary dips in gold's macro uptrend and lose
        # money. B-grade SELLs (weaker conviction, shallower entries)
        # are marginally profitable.
        is_sell = report.final_score < 0
        if is_sell and report.signal_quality not in xau_cfg.m5_sell_max_quality:
            report.warnings.append(
                f"M5 FILTER: SELL quality {report.signal_quality} capped — "
                f"high-conviction M5 shorts enter at extremes"
            )
            report.signal_quality = "D"
            report.trade_setup = None
            return

        # Gate 5: block chop-transition volatility. 'high' vol on M5 is
        # elevated noise without the directional expansion of 'extreme'
        # — stops get swept before targets (61% WR, negative total in
        # every backtest window; low/normal/extreme all positive).
        if report.volatility_regime in xau_cfg.m5_blocked_vol_regimes:
            report.warnings.append(
                f"M5 FILTER: '{report.volatility_regime}' volatility — "
                f"chop-transition regime, stops get swept"
            )
            report.signal_quality = "D"
            report.trade_setup = None
            return

        # Gate 6: SELL RSI floor — shorting with RSI already < 25 means
        # entering at the extreme of the dip (same pathology as Gate 4,
        # visible through RSI instead of quality grade).
        if is_sell:
            rsi_ind = report.primary_analysis.indicators.get("rsi")
            if rsi_ind is not None and rsi_ind.value < xau_cfg.m5_sell_min_rsi:
                report.warnings.append(
                    f"M5 FILTER: SELL blocked — RSI {rsi_ind.value:.0f} < "
                    f"{xau_cfg.m5_sell_min_rsi:.0f}, dip already extended"
                )
                report.signal_quality = "D"
                report.trade_setup = None
                return

    # ── M5 pullback entry (XAU_PB) ──────────────────────────────────

    def _detect_m5_pullback(
        self, report: SignalReport, primary_df: pd.DataFrame
    ) -> Optional[Dict]:
        """Detect a pullback-continuation candidate on the primary TF.

        Trend intact (EMA alignment >= 3/4, ADX floor) + price retraced
        to EMA21/KC-mid within the last few bars + resumption bar closed
        back in trend direction + RSI in the pullback zone (not the
        extended zone breakout entries fire in).
        """
        cfg = self.config.xau_strategy
        pa = report.primary_analysis
        if pa is None or len(primary_df) < 60:
            return None
        levels = pa.key_levels
        atr = levels.get("atr", 0.0)
        ema_f = levels.get("ema_fast")
        ema_m = levels.get("ema_medium")
        ema_s = levels.get("ema_slow")
        ema_t = levels.get("ema_trend")
        if not atr or None in (ema_f, ema_m, ema_s, ema_t):
            return None

        adx_ind = pa.indicators.get("adx")
        rsi_ind = pa.indicators.get("rsi")
        if adx_ind is None or rsi_ind is None:
            return None
        if adx_ind.value < cfg.m5_pullback_adx_min:
            return None

        close = float(primary_df["Close"].iloc[-1])
        open_ = float(primary_df["Open"].iloc[-1])
        n_touch = cfg.m5_pullback_touch_bars
        recent_low = float(primary_df["Low"].iloc[-n_touch:].min())
        recent_high = float(primary_df["High"].iloc[-n_touch:].max())
        # Pullback swing anchor for SL: extreme of the last 8 bars.
        pb_low = float(primary_df["Low"].iloc[-8:].min())
        pb_high = float(primary_df["High"].iloc[-8:].max())
        kc_mid = levels.get("kc_mid", ema_m)
        lo_atr, hi_atr = cfg.m5_pullback_retrace_atr
        rsi = rsi_ind.value

        # BUY: uptrend structure, dip to the band, bullish resumption.
        up_align = sum([ema_f > ema_m, ema_m > ema_s, ema_s > ema_t,
                        close > ema_t])
        if up_align >= 3:
            touched = recent_low <= max(ema_m, kc_mid)
            swing_high = float(primary_df["High"].iloc[-40:].max())
            depth = (swing_high - pb_low) / atr
            resumed = close > open_ and close > ema_f
            lo_z, hi_z = cfg.m5_pullback_rsi_buy
            if (touched and resumed and lo_atr <= depth <= hi_atr
                    and lo_z <= rsi <= hi_z):
                return {"direction": "BUY", "anchor": pb_low,
                        "retrace_atr": depth}

        # SELL: mirrored.
        dn_align = sum([ema_f < ema_m, ema_m < ema_s, ema_s < ema_t,
                        close < ema_t])
        if dn_align >= 3:
            touched = recent_high >= min(ema_m, kc_mid)
            swing_low = float(primary_df["Low"].iloc[-40:].min())
            depth = (pb_high - swing_low) / atr
            resumed = close < open_ and close < ema_f
            lo_z, hi_z = cfg.m5_pullback_rsi_sell
            if (touched and resumed and lo_atr <= depth <= hi_atr
                    and lo_z <= rsi <= hi_z):
                return {"direction": "SELL", "anchor": pb_high,
                        "retrace_atr": depth}
        return None

    def _try_m5_pullback(
        self, report: SignalReport, primary_df: pd.DataFrame, pip_value: float
    ) -> None:
        """Build an XAU_PB trade setup if a pullback candidate passes gates."""
        cfg = self.config.xau_strategy
        # Rejection funnel counters (class-level diagnostic, cheap).
        funnel = SignalAggregator.pb_funnel

        def _reject(reason: str) -> None:
            funnel[reason] = funnel.get(reason, 0) + 1

        pb = self._detect_m5_pullback(report, primary_df)
        if pb is None:
            return
        _reject("detected")
        direction = pb["direction"]
        is_buy = direction == "BUY"

        # Directional composite floor (lower than Gate 1 by design —
        # price mid-pullback scores weaker on the momentum indicators).
        signed = report.final_score if is_buy else -report.final_score
        if signed < cfg.m5_pullback_min_score:
            _reject("score")
            return

        # Confirmation TF must agree (reuse Gate-3 threshold).
        min_conf = cfg.m5_min_confirmation_score
        confirmed = False
        primary_tf = self.config.primary_timeframe.value
        for tf_val, tf_score in report.mtf_scores.items():
            if tf_val == primary_tf:
                continue
            if (is_buy and tf_score >= min_conf) or \
               (not is_buy and tf_score <= -min_conf):
                confirmed = True
                break
        if not confirmed:
            _reject("confirmation_tf")
            return

        # Never against multi-TF consensus (mirror of gate 6f).
        sigs = list(report.mtf_signals.values())
        bull_n = sum(1 for s in sigs if s in (SignalStrength.BUY, SignalStrength.STRONG_BUY))
        bear_n = sum(1 for s in sigs if s in (SignalStrength.SELL, SignalStrength.STRONG_SELL))
        thr = self.config.signals.mtf_agreement_threshold
        if (is_buy and bear_n >= thr) or (not is_buy and bull_n >= thr):
            _reject("mtf_opposition")
            return

        # Volatility gates match the breakout path.
        if report.volatility_regime in cfg.m5_blocked_vol_regimes:
            _reject("vol_regime")
            return
        if (not is_buy and report.volatility_regime
                and report.volatility_regime not in cfg.sell_vol_regimes):
            _reject("sell_vol")
            return

        atr = report.primary_analysis.key_levels.get("atr", 0.0)
        entry = report.current_price
        min_sl_price = self.config.signals.min_sl_pips * pip_value
        if is_buy:
            sl_dist = max(entry - (pb["anchor"] - 0.5 * atr), min_sl_price)
            sl = entry - sl_dist
            tp1 = entry + 1.5 * sl_dist
            tp2 = entry + 2.5 * sl_dist
        else:
            sl_dist = max((pb["anchor"] + 0.5 * atr) - entry, min_sl_price)
            sl = entry + sl_dist
            tp1 = entry - 1.5 * sl_dist
            tp2 = entry - 2.5 * sl_dist

        # Spread gate — same economics as the breakout generators.
        min_tp = (self.config.signals.round_trip_cost
                  * self.config.signals.min_tp_cost_multiple)
        if 1.5 * sl_dist < min_tp:
            return

        report.trade_setup = TradeSetup(
            pair=report.symbol,
            display_name=report.display_name,
            direction=direction,
            entry_price=entry,
            stop_loss=sl,
            take_profit_1=tp1,
            take_profit_2=tp2,
            risk_reward=1.5,
            risk_pips=sl_dist / pip_value,
            reward_pips=1.5 * sl_dist / pip_value,
            pip_value=pip_value,
            strategy="XAU_PB",
        )
        if report.signal_quality not in ("A+", "A", "B"):
            report.signal_quality = "B"
        report.final_signal = (SignalStrength.BUY if is_buy
                               else SignalStrength.SELL)
        report.action_recommendation = (
            f"{direction} — pullback continuation entry"
        )
        marker = (f"XAU PULLBACK STRATEGY: retrace {pb['retrace_atr']:.1f} "
                  f"ATR to EMA21/KC-mid, trend resumed")
        target = report.bullish_reasons if is_buy else report.bearish_reasons
        target.insert(0, marker)

    # ── Public: bar-replayable entry point ─────────────────────────

    def analyze_from_dataframes(
        self,
        symbol: str,
        dfs: Dict[Timeframe, pd.DataFrame],
        context: Optional[FundamentalContext] = None,
        use_news: bool = True,
    ) -> SignalReport:
        """
        Run the full analysis pipeline on already-fetched dataframes.

        `dfs` must include the primary timeframe and ideally the
        confirmation timeframes. Each df is treated as the visible history
        at decision time — the backtester is responsible for slicing so
        no future bars leak in.
        """
        pair_info = self.data_provider.get_pair_info(symbol)
        display = pair_info["display"]
        pip_value = pair_info["pip_value"]

        report = SignalReport(
            symbol=symbol,
            display_name=display,
            timestamp=str(datetime.now(timezone.utc)),
        )

        # ── 1. Primary timeframe ───────────────────────────────────
        primary_df = dfs.get(self.config.primary_timeframe)
        if primary_df is None or primary_df.empty:
            report.warnings.append("Primary timeframe data unavailable")
            report.signal_quality = "F"
            return report

        primary_analysis = self.tech_engine.analyze(
            primary_df, symbol, self.config.primary_timeframe.value
        )
        report.primary_analysis = primary_analysis
        report.primary_signal = primary_analysis.composite_signal
        report.primary_score = primary_analysis.composite_score
        report.primary_confidence = primary_analysis.confidence
        report.market_regime = primary_analysis.market_regime
        report.volatility_regime = primary_analysis.volatility_regime
        report.current_price = float(primary_df["Close"].iloc[-1])
        report.timestamp = str(primary_df.index[-1])
        report.mtf_signals[self.config.primary_timeframe.value] = primary_analysis.composite_signal
        report.mtf_scores[self.config.primary_timeframe.value] = primary_analysis.composite_score

        # ── 2. Confirmation timeframes ─────────────────────────────
        for tf in self.config.confirmation_timeframes:
            cf_df = dfs.get(tf)
            if cf_df is None or cf_df.empty:
                continue
            try:
                cf_analysis = self.tech_engine.analyze(cf_df, symbol, tf.value)
                report.confirmation_analyses.append(cf_analysis)
                report.mtf_signals[tf.value] = cf_analysis.composite_signal
                report.mtf_scores[tf.value] = cf_analysis.composite_score
            except Exception as e:
                logger.warning(f"Confirmation TF {tf.value} failed for {symbol}: {e}")

        # ── 3. MTF agreement ───────────────────────────────────────
        report.mtf_agreement = self._check_mtf_agreement(report)

        # ── 4. Fundamental context (optional) ──────────────────────
        if use_news:
            news = self._get_news_engine()
            if context is None:
                context = news.get_fundamental_context()
            event_risk, event_desc = news.get_event_risk_level(symbol, context)
            report.event_risk = event_risk
            report.event_description = event_desc
            sentiment, sent_desc = news.get_pair_sentiment(symbol, context)
            report.news_sentiment = sentiment
            report.news_description = sent_desc
        else:
            report.event_risk = "clear"
            report.event_description = ""
            report.news_sentiment = 0.0
            report.news_description = ""

        # ── 5. Spread estimate from primary data (avoids extra API call) ─
        try:
            report.spread_estimate = float(
                (primary_df["High"] - primary_df["Low"]).median()
            )
        except Exception:
            report.spread_estimate = 0.0

        # ── 6. Final signal ────────────────────────────────────────
        self._compute_final_signal(report)

        # ── 6b. Session filter (works for both live and backtest) ─
        if self.config.backtest.session_filter:
            try:
                bar_ts = pd.Timestamp(report.timestamp)
                bar_hour = bar_ts.hour
                bar_wd = bar_ts.weekday()
                _is_xau_pair = report.display_name == "XAU/USD"
                _blocked_hours = (self.config.xau_strategy.blocked_hours_utc
                                  if _is_xau_pair else self.config.backtest.blocked_hours_utc)
                if bar_hour in _blocked_hours:
                    report.final_signal = SignalStrength.NEUTRAL
                    report.signal_quality = "F"
                    report.warnings.append(f"SESSION FILTER: {bar_hour:02}:00 UTC blocked")
                _allowed_wd = (self.config.xau_strategy.allowed_weekdays
                               if _is_xau_pair else self.config.backtest.allowed_weekdays)
                if _allowed_wd and bar_wd not in _allowed_wd:
                    day_names = {0: "Mon", 1: "Tue", 2: "Wed", 3: "Thu",
                                 4: "Fri", 5: "Sat", 6: "Sun"}
                    report.final_signal = SignalStrength.NEUTRAL
                    report.signal_quality = "F"
                    report.warnings.append(
                        f"DAY FILTER: {day_names.get(bar_wd, '?')} not in allowed days"
                    )
            except Exception:
                pass

        # ── 6c. Transitioning regime quality boost ──────────────────
        if report.market_regime == "transitioning" and report.signal_quality in ("B", "C", "D"):
            abs_score = abs(report.final_score)
            if abs_score >= 30 and report.final_confidence >= 50 and report.mtf_agreement:
                report.signal_quality = "A"
                report.action_recommendation = f"{report.final_signal.name.replace('_', ' ')} — Trend emerging, early entry"
            elif abs_score >= 25 and report.final_confidence >= 45:
                report.signal_quality = "B"
                report.action_recommendation = f"{report.final_signal.name.replace('_', ' ')} — Trend forming, watch closely"

        # ── 6d. Exhausted trend quality cap ───────────────────────
        if (report.market_regime.startswith("trending")
                and report.exhaustion_count >= 2
                and report.signal_quality in ("C", "D")):
            report.warnings.append(
                "EXHAUSTED TREND FILTER: C-grade blocked in exhausted trend"
            )
            report.signal_quality = "D"
            report.trade_setup = None

        # ── 6e. SELL quality gate ─────────────────────────────────
        xau_is_sell_dir = (
            report.final_signal in (SignalStrength.SELL, SignalStrength.STRONG_SELL)
            or (report.final_signal == SignalStrength.NEUTRAL and report.final_score < 0)
        )
        # Actionable grades only: without this guard the gate would "upgrade"
        # a session-blocked F report to D, which re-opens the M5 pullback
        # path (its entry check is quality != "F").
        if (self.config.xau_strategy.enabled
                and report.display_name == "XAU/USD"
                and xau_is_sell_dir
                and report.signal_quality in ("A+", "A", "B", "C")
                and report.signal_quality not in self.config.xau_strategy.sell_min_quality):
            report.warnings.append(
                f"SELL FILTER: quality {report.signal_quality} below minimum for SELL"
            )
            report.signal_quality = "D"
            report.trade_setup = None

        # ── 6f. MTF opposition gate ───────────────────────────────
        # Never trade against a multi-timeframe consensus: if the
        # threshold number of TFs point opposite to the trade direction,
        # block the setup (counter-trend entries in gold bleed).
        if report.signal_quality in ("A+", "A", "B", "C") and report.mtf_signals:
            sigs = list(report.mtf_signals.values())
            bull_n = sum(1 for s in sigs if s in (SignalStrength.BUY, SignalStrength.STRONG_BUY))
            bear_n = sum(1 for s in sigs if s in (SignalStrength.SELL, SignalStrength.STRONG_SELL))
            thr = self.config.signals.mtf_agreement_threshold
            opposed = (bear_n >= thr and report.final_score > 0) or \
                      (bull_n >= thr and report.final_score < 0)
            if opposed:
                report.warnings.append(
                    "MTF OPPOSITION: majority of timeframes point against signal — no trade"
                )
                report.signal_quality = "D"
                report.trade_setup = None

        # ── 6g. SELL volatility gate ──────────────────────────────
        # Shorts in quiet markets fade against gold's macro uptrend;
        # only take sells when volatility regime confirms real selling.
        if (self.config.xau_strategy.enabled
                and report.display_name == "XAU/USD"
                and xau_is_sell_dir
                and report.signal_quality in ("A+", "A", "B", "C")
                and report.volatility_regime
                and report.volatility_regime not in self.config.xau_strategy.sell_vol_regimes):
            report.warnings.append(
                f"SELL FILTER: volatility '{report.volatility_regime}' too quiet for shorts"
            )
            report.signal_quality = "D"
            report.trade_setup = None

        # ── 6g2. XAU regime gates (opt-in via per-TF factory overrides) ──
        if (self.config.xau_strategy.enabled
                and report.display_name == "XAU/USD"
                and report.signal_quality in ("A+", "A", "B", "C")):
            if report.market_regime in self.config.xau_strategy.blocked_regimes:
                report.warnings.append(
                    f"REGIME FILTER: '{report.market_regime}' blocked for XAU"
                )
                report.signal_quality = "D"
                report.trade_setup = None
            elif (xau_is_sell_dir
                    and report.market_regime
                    in self.config.xau_strategy.sell_blocked_regimes):
                report.warnings.append(
                    f"SELL FILTER: regime '{report.market_regime}' blocked for shorts"
                )
                report.signal_quality = "D"
                report.trade_setup = None

        # ── 6h. EUR/USD regime + quality + risk gate ──────────────
        if report.display_name == "EUR/USD" and self.config.eur_strategy.enabled:
            eur = self.config.eur_strategy
            if eur.blocked_regimes and report.market_regime in eur.blocked_regimes:
                report.warnings.append(
                    f"EUR REGIME FILTER: {report.market_regime} blocked"
                )
                report.signal_quality = "F"
            if (eur.min_quality_grades
                    and report.signal_quality not in eur.min_quality_grades
                    and report.signal_quality != "F"):
                report.warnings.append(
                    f"EUR QUALITY FILTER: {report.signal_quality} below minimum"
                )
                report.signal_quality = "F"
            if (report.signal_quality != "F"
                    and getattr(report, 'exhaustion_count', 0) > eur.max_exhaustion_tags):
                report.warnings.append(
                    f"EUR EXHAUSTION FILTER: {report.exhaustion_count} tags > max {eur.max_exhaustion_tags}"
                )
                report.signal_quality = "F"
            if (report.signal_quality != "F"
                    and eur.block_near_ema200
                    and any(w.startswith("NEAR EMA200") for w in report.warnings)):
                report.warnings.append("EUR EMA200 FILTER: too close to EMA200")
                report.signal_quality = "F"
            if eur.session_hours and report.signal_quality != "F":
                try:
                    bar_hour = pd.Timestamp(report.timestamp).hour
                    if bar_hour not in eur.session_hours:
                        report.warnings.append(
                            f"EUR SESSION FILTER: {bar_hour:02}:00 UTC outside allowed hours"
                        )
                        report.signal_quality = "F"
                except Exception:
                    pass
            if report.signal_quality != "F":
                abs_s = abs(report.final_score)
                if abs_s < eur.min_entry_score:
                    report.warnings.append(
                        f"EUR SCORE FILTER: |{abs_s:.1f}| < {eur.min_entry_score}"
                    )
                    report.signal_quality = "F"
                elif report.final_confidence < eur.min_entry_confidence:
                    report.warnings.append(
                        f"EUR CONF FILTER: {report.final_confidence} < {eur.min_entry_confidence}"
                    )
                    report.signal_quality = "F"
            if (report.signal_quality != "F"
                    and eur.blocked_vol_regimes
                    and report.volatility_regime):
                if report.volatility_regime in eur.blocked_vol_regimes:
                    report.warnings.append(
                        f"EUR VOL FILTER: {report.volatility_regime} volatility blocked"
                    )
                    report.signal_quality = "F"
            if (report.signal_quality != "F"
                    and eur.min_indicator_agreement > 0
                    and report.primary_analysis):
                agree_count = sum(
                    1 for ind in report.primary_analysis.indicators.values()
                    if ind.signal.value != 0
                    and (ind.signal.value > 0) == (report.final_score > 0)
                )
                if agree_count < eur.min_indicator_agreement:
                    report.warnings.append(
                        f"EUR AGREE FILTER: {agree_count} < {eur.min_indicator_agreement}"
                    )
                    report.signal_quality = "F"
            if (report.signal_quality != "F"
                    and eur.block_rsi_extended
                    and report.primary_analysis):
                rsi_ind = report.primary_analysis.indicators.get("rsi")
                if rsi_ind:
                    rsi_v = rsi_ind.value
                    is_buy = report.final_score > 0
                    if is_buy and rsi_v > 70:
                        report.warnings.append(
                            f"EUR RSI FILTER: RSI {rsi_v:.0f} overbought for BUY"
                        )
                        report.signal_quality = "F"
                    elif not is_buy and rsi_v < 30:
                        report.warnings.append(
                            f"EUR RSI FILTER: RSI {rsi_v:.0f} oversold for SELL"
                        )
                        report.signal_quality = "F"

        # ── 6i. M5 noise gate (gold-tuned thresholds — XAU only) ──
        if (self.config.primary_timeframe == Timeframe.M5
                and report.display_name == "XAU/USD"):
            self._apply_m5_filters(report)

        # ── 7. Trade setup for actionable signals ──────────────────
        # ATR generator is XAU-only: it hardcodes gold pip value and
        # 2-decimal price rounding.
        if (report.trade_setup is None and report.signal_quality in ("A+", "A")
                and report.display_name == "XAU/USD"):
            atr = (report.primary_analysis.key_levels.get("atr", 0)
                   if report.primary_analysis else 0)
            if atr > 0:
                report.trade_setup = self._generate_xau_atr_trade_setup(report, atr)
        if report.trade_setup is None and report.signal_quality in ("A+", "A", "B", "C"):
            report.trade_setup = self._generate_trade_setup(report, pip_value)

        # ── 8. Reasons ─────────────────────────────────────────────
        self._collect_reasons(report)

        # ── 8b. EUR strategy marker ────────────────────────────────
        # The backtester tags trades from reasons[0]; mark EUR setups so
        # they report under the EUR strategy instead of GENERIC.
        if (report.display_name == "EUR/USD" and self.config.eur_strategy.enabled
                and report.trade_setup is not None):
            marker = (f"EUR STRATEGY: {report.market_regime} regime, "
                      f"score {report.final_score:+.1f}, quality {report.signal_quality}")
            target = (report.bullish_reasons if report.trade_setup.direction == "BUY"
                      else report.bearish_reasons)
            target.insert(0, marker)

        # ── 9. M5 pullback entry (XAU_PB) ──────────────────────────
        # Second archetype: when no breakout setup fired, look for a
        # trend-continuation pullback (retrace to EMA21/KC-mid). Runs
        # after reasons so its marker lands at reasons[0] for tagging.
        if (self.config.primary_timeframe == Timeframe.M5
                and report.display_name == "XAU/USD"
                and self.config.xau_strategy.m5_pullback_enabled
                and report.trade_setup is None
                and report.signal_quality != "F"):
            self._try_m5_pullback(report, primary_df, pip_value)

        return report

    # ── Internals ──────────────────────────────────────────────────

    _TP_QUALITY_SCALE = {"A+": 2.0, "A": 1.5, "B": 1.0, "C": 1.0}

    def _generate_xau_atr_trade_setup(
        self, report: SignalReport, atr: float
    ) -> Optional[TradeSetup]:
        """XAU ATR-based trade setup with quality-tiered TP scaling."""
        cfg = self.config.xau_strategy
        price = report.current_price
        pip_value = 0.10

        if report.final_signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY):
            is_buy = True
        elif report.final_signal in (SignalStrength.SELL, SignalStrength.STRONG_SELL):
            is_buy = False
        else:
            is_buy = report.final_score >= 0

        quality_scale = self._TP_QUALITY_SCALE.get(report.signal_quality, 0.75)
        if report.signal_quality == "A" and report.exhaustion_count >= 2:
            quality_scale = 1.0
        tp_mult = cfg.tp_atr_multiplier * quality_scale

        if is_buy:
            sl = price - cfg.sl_atr_multiplier * atr
            tp1 = price + tp_mult * atr
            tp2 = price + tp_mult * 1.5 * atr
            direction = "BUY"
        else:
            sl = price + cfg.sl_atr_multiplier * atr
            tp1 = price - tp_mult * atr
            tp2 = price - tp_mult * 1.5 * atr
            direction = "SELL"

        tf = self.config.signals.tp_tightening_factor
        tp1 = price + (tp1 - price) * tf
        tp2 = price + (tp2 - price) * tf

        sl_distance = abs(price - sl)
        tp_distance = abs(tp1 - price)
        rr = tp_distance / sl_distance if sl_distance > 0 else 0

        min_rr = self.config.signals.min_rr_ratio
        if rr < min_rr - 1e-9:
            return None

        # Spread gate (same rule as _generate_trade_setup).
        min_tp = self.config.signals.round_trip_cost * self.config.signals.min_tp_cost_multiple
        if tp_distance < min_tp:
            report.warnings.append(
                f"SPREAD GATE: target ${tp_distance:.2f} < ${min_tp:.2f} minimum — "
                f"costs would eat the edge"
            )
            return None

        return TradeSetup(
            pair=report.symbol,
            display_name=report.display_name,
            direction=direction,
            entry_price=price,
            stop_loss=round(sl, 2),
            take_profit_1=round(tp1, 2),
            take_profit_2=round(tp2, 2),
            risk_reward=round(rr, 2),
            risk_pips=round(sl_distance / pip_value, 1),
            reward_pips=round(tp_distance / pip_value, 1),
            pip_value=pip_value,
        )

    def _check_mtf_agreement(self, report: SignalReport) -> bool:
        signals = list(report.mtf_signals.values())
        if len(signals) < 2:
            return False

        bullish = sum(1 for s in signals if s in (SignalStrength.BUY, SignalStrength.STRONG_BUY))
        bearish = sum(1 for s in signals if s in (SignalStrength.SELL, SignalStrength.STRONG_SELL))

        threshold = self.config.signals.mtf_agreement_threshold
        # Agreement must point the same way as the primary timeframe —
        # two bullish confirmation TFs must not "confirm" a bearish primary.
        if report.primary_score > 0:
            return bullish >= threshold
        if report.primary_score < 0:
            return bearish >= threshold
        return bullish >= threshold or bearish >= threshold

    def _compute_final_signal(self, report: SignalReport):
        score = report.primary_score
        confidence = report.primary_confidence

        # ── Bonuses (additive, applied first) ─────────────────────
        if report.mtf_agreement:
            confidence += 10
            all_scores = list(report.mtf_scores.values())
            if all_scores:
                mtf_avg = sum(all_scores) / len(all_scores)
                score = score * 0.6 + mtf_avg * 0.4

        if abs(report.news_sentiment) > 0.2:
            if (report.news_sentiment > 0 and score > 0) or \
               (report.news_sentiment < 0 and score < 0):
                confidence += 5

        primary = report.primary_analysis
        if primary and report.market_regime.startswith("trending"):
            rsi_ind = primary.indicators.get("rsi")
            bb_ind = primary.indicators.get("bollinger")
            # Pullback bonuses require a KNOWN trend direction — the mixed
            # "trending" regime (EMAs unaligned) gets no directional bonus.
            if rsi_ind:
                rsi_val = rsi_ind.value
                if report.market_regime == "trending_up" and score > 0 and 38 <= rsi_val <= 55:
                    confidence += 5
                elif report.market_regime == "trending_down" and score < 0 and 45 <= rsi_val <= 62:
                    confidence += 5
            if bb_ind:
                bb_pos = bb_ind.value
                if report.market_regime == "trending_up" and score > 0 and bb_pos < 0.4:
                    confidence += 5
                elif report.market_regime == "trending_down" and score < 0 and bb_pos > 0.6:
                    confidence += 5

        # ── Event risk (hard cap, separate from penalty system) ───
        # Warning always shown; the confidence cap is config-gated
        # (news.event_risk_caps_signals) — see NewsConfig for the audit
        # showing the cap costs R on XAU.
        if report.event_risk == "danger":
            report.warnings.append(f"EVENT RISK: {report.event_description}")
            if self.config.news.event_risk_caps_signals:
                confidence = min(confidence, 30)
                report.action_recommendation = f"WAIT — {report.event_description}"

        # ── Penalties (diminishing cap system) ────────────────────
        # Collect all applicable penalties, then apply with exponential
        # decay so stacking never guts a signal beyond 30%.
        penalties = []

        if not report.mtf_agreement:
            penalties.append(("mtf_disagreement", 15))
            if len(report.mtf_scores) >= 2:
                scores_list = list(report.mtf_scores.values())
                if any(s > 10 for s in scores_list) and any(s < -10 for s in scores_list):
                    report.warnings.append("Timeframes CONFLICT — mixed signals across timeframes")

        if abs(report.news_sentiment) > 0.2:
            if (report.news_sentiment > 0 and score < -10) or \
               (report.news_sentiment < 0 and score > 10):
                report.warnings.append("News sentiment CONFLICTS with technical signal")
                penalties.append(("news_conflict", 10))

        if report.event_risk == "caution":
            report.warnings.append(f"Upcoming event: {report.event_description}")
            if self.config.news.event_risk_caps_signals:
                penalties.append(("event_caution", 15))

        is_counter_trend = (
            (report.market_regime == "trending_up" and score < 0) or
            (report.market_regime == "trending_down" and score > 0)
        )
        if is_counter_trend:
            report.warnings.append("Counter-trend signal — reduced confidence")
            penalties.append(("counter_trend", 15))

        if report.volatility_regime == "extreme":
            report.warnings.append("EXTREME volatility — wider stops needed")
            penalties.append(("extreme_vol", 10))
        elif report.volatility_regime == "low":
            report.warnings.append("Low volatility — potential for breakout")

        if primary is not None:
            ema200 = primary.key_levels.get("ema_trend")
            if ema200 and ema200 > 0 and report.current_price > 0:
                ema_dist_pct = abs(report.current_price - ema200) / ema200 * 100.0
                _lo = float(os.environ.get("FB2_EMA_LO", "0.5"))
                if ema_dist_pct < _lo:
                    report.warnings.append(f"NEAR EMA200 ({ema_dist_pct:.2f}%) — chop risk")
                    penalties.append(("ema200_near", 5))
                elif ema_dist_pct > 2.5:
                    report.warnings.append(f"FAR FROM EMA200 ({ema_dist_pct:.2f}%) — over-extended")
                    penalties.append(("ema200_far", 5))

            atr = primary.key_levels.get("atr", 0)
            if atr > 0 and score != 0:
                if score > 0:
                    swing_lows = sorted(
                        v for k, v in primary.key_levels.items() if k.startswith("swing_low_")
                    )
                    if swing_lows and report.current_price - swing_lows[0] > 3.5 * atr:
                        report.warnings.append("TREND EXHAUSTION — price extended >3.5 ATR past swing low")
                        penalties.append(("exhaustion", 8))
                else:
                    swing_highs = sorted(
                        (v for k, v in primary.key_levels.items() if k.startswith("swing_high_")),
                        reverse=True,
                    )
                    if swing_highs and swing_highs[0] - report.current_price > 3.5 * atr:
                        report.warnings.append("TREND EXHAUSTION — price extended >3.5 ATR past swing high")
                        penalties.append(("exhaustion", 8))

            ema_medium = primary.key_levels.get("ema_medium")
            if atr > 0 and ema_medium and report.market_regime.startswith("trending"):
                pullback_dist = abs(report.current_price - ema_medium) / atr
                if pullback_dist > 2.5:
                    report.warnings.append(f"NO PULLBACK — price {pullback_dist:.1f} ATR from EMA21")
                    penalties.append(("no_pullback", 8))
                elif pullback_dist > 2.0:
                    report.warnings.append(f"Weak pullback — price {pullback_dist:.1f} ATR from EMA21")
                    penalties.append(("weak_pullback", 5))

            if report.market_regime.startswith("trending"):
                rsi_ind = primary.indicators.get("rsi")
                bb_ind = primary.indicators.get("bollinger")
                # Extension penalties apply in ANY strong-ADX regime; the
                # mixed "trending" regime penalizes BOTH directions (it
                # previously penalized only BUYs — the SELL side of the
                # same bullish-bias bug fixed in the RSI reinterpretation).
                if rsi_ind:
                    rsi_val = rsi_ind.value
                    if report.market_regime in ("trending_up", "trending") and score > 0 and rsi_val > 65:
                        report.warnings.append(f"Extended — RSI {rsi_val:.0f} in uptrend")
                        penalties.append(("rsi_extended", 5))
                    elif report.market_regime in ("trending_down", "trending") and score < 0 and rsi_val < 35:
                        report.warnings.append(f"Extended — RSI {rsi_val:.0f} in downtrend")
                        penalties.append(("rsi_extended", 5))
                if bb_ind:
                    bb_pos = bb_ind.value
                    if report.market_regime in ("trending_up", "trending") and score > 0 and bb_pos > 0.85:
                        penalties.append(("bb_overextended", 8))
                    elif report.market_regime in ("trending_down", "trending") and score < 0 and bb_pos < 0.15:
                        penalties.append(("bb_overextended", 8))

        # Hard block: static exhaustion + fading momentum → kill the trade
        # Static signals alone are normal in gold trends. Only block when
        # price is extended AND momentum indicators confirm deceleration.
        _EXHAUSTION_TAGS = {"exhaustion", "no_pullback", "rsi_extended", "bb_overextended"}
        exhaustion_count = sum(1 for name, _ in penalties if name in _EXHAUSTION_TAGS)
        report.exhaustion_count = exhaustion_count
        momentum_fading = False
        if primary is not None and exhaustion_count >= 2:
            roc_ind = primary.indicators.get("roc")
            macd_ind = primary.indicators.get("macd")
            roc_fading = False
            macd_fading = False
            if roc_ind:
                if score > 0 and roc_ind.value < 0:
                    roc_fading = True
                elif score < 0 and roc_ind.value > 0:
                    roc_fading = True
            if macd_ind:
                if score > 0 and macd_ind.signal in (SignalStrength.SELL, SignalStrength.STRONG_SELL):
                    macd_fading = True
                elif score < 0 and macd_ind.signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY):
                    macd_fading = True
            momentum_fading = roc_fading or macd_fading
        exhaustion_blocked = exhaustion_count >= 2 and momentum_fading

        MAX_PENALTY = 30.0
        penalties.sort(key=lambda x: x[1], reverse=True)
        total_penalty = 0.0
        decay = 1.0
        for _name, amount in penalties:
            applied = amount * decay
            if total_penalty + applied > MAX_PENALTY:
                applied = MAX_PENALTY - total_penalty
            total_penalty += applied
            decay *= 0.5
            if total_penalty >= MAX_PENALTY:
                break

        confidence -= total_penalty
        confidence = max(0.0, min(95.0, confidence))

        if exhaustion_blocked:
            report.warnings.append(
                f"EXHAUSTION BLOCK — {exhaustion_count} exhaustion signals fired, trade rejected"
            )
            confidence = 0.0

        # Final signal
        if score >= 35 and confidence >= 55:
            report.final_signal = SignalStrength.STRONG_BUY
        elif score >= 25 and confidence >= 50:
            report.final_signal = SignalStrength.BUY
        elif score <= -35 and confidence >= 55:
            report.final_signal = SignalStrength.STRONG_SELL
        elif score <= -25 and confidence >= 50:
            report.final_signal = SignalStrength.SELL
        else:
            report.final_signal = SignalStrength.NEUTRAL

        report.final_score = score
        report.final_confidence = confidence

        # Quality grade
        abs_score = abs(score)
        if abs_score >= 40 and confidence >= 65 and report.mtf_agreement:
            report.signal_quality = "A+"
        elif abs_score >= 33 and confidence >= 55 and report.mtf_agreement:
            report.signal_quality = "A"
        elif abs_score >= 28 and confidence >= 50:
            report.signal_quality = "B"
        elif abs_score >= 25 and confidence >= 45:
            report.signal_quality = "C"
        elif abs_score >= 15:
            report.signal_quality = "D"
        else:
            report.signal_quality = "F"

        if not report.action_recommendation:
            sig_name = report.final_signal.name.replace("_", " ")
            if report.signal_quality in ("A+", "A"):
                report.action_recommendation = f"{sig_name} — High-quality setup, consider entry"
            elif report.signal_quality == "B":
                report.action_recommendation = f"{sig_name} — Decent setup, wait for confirmation"
            elif report.signal_quality in ("C", "D"):
                report.action_recommendation = "WATCH — Signal present but low confidence"
            else:
                report.action_recommendation = "NO TRADE — No clear signal"

    def _generate_trade_setup(self, report: SignalReport, pip_value: float) -> Optional[TradeSetup]:
        if not report.primary_analysis:
            return None

        price = report.current_price
        atr = report.primary_analysis.key_levels.get("atr", 0)
        if atr == 0:
            return None
        if pip_value <= 0:
            return None

        # Fix 2: Minimum ATR threshold for XAU/USD
        if report.display_name == "XAU/USD" and atr < self.config.xau_strategy.min_atr_threshold:
            report.warnings.append(
                f"LOW ATR FILTER: ATR ${atr:.2f} < ${self.config.xau_strategy.min_atr_threshold:.2f} minimum"
            )
            return None

        if report.final_signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY):
            is_buy = True
        elif report.final_signal in (SignalStrength.SELL, SignalStrength.STRONG_SELL):
            is_buy = False
        else:
            is_buy = report.final_score >= 0
        levels = report.primary_analysis.key_levels

        _is_eur = report.display_name == "EUR/USD" and self.config.eur_strategy.enabled

        if _is_eur:
            # EUR geometry reads only EurUsdStrategyConfig — nothing
            # gold-calibrated from SignalConfig applies here.
            eur = self.config.eur_strategy
            min_sl_price = eur.min_sl_pips * pip_value
            min_rr = eur.min_rr_ratio
            # No TP-tightening interplay for EUR: the RR floor used for
            # target selection IS min_rr (tp_tightening_factor is 1.0;
            # revisit if EUR ever tunes it).
            pre_tight_min_rr = min_rr
            mature_move = abs(report.final_score) > eur.mature_move_threshold
            quality_scale = eur.tp_quality_scale.get(report.signal_quality, 1.0)
            tp_mult = (eur.tp_atr_multiplier if mature_move
                       else eur.tp_atr_multiplier * 1.5) * quality_scale
            tp_swing_cap = (2.0 if mature_move else 2.5) * quality_scale
            sl_atr_floor = eur.sl_atr_floor
            sl_swing_buffer = eur.sl_atr_multiplier
        else:
            min_sl_price = self.config.signals.min_sl_pips * pip_value
            min_rr = self.config.signals.min_rr_ratio
            tp_floor = max(min_rr, self.config.signals.tp_floor_rr)
            tf = self.config.signals.tp_tightening_factor
            pre_tight_min_rr = tp_floor / tf if tf > 0 else tp_floor

            tp_mult = 1.5
            tp_swing_cap = 2.5

            sl_atr_floor = (self.config.xau_strategy.v2_sl_atr_multiplier
                            if report.display_name == "XAU/USD" else 1.5)
            sl_swing_buffer = 1.0

        if is_buy:
            swing_lows = sorted(
                [v for k, v in levels.items() if k.startswith("swing_low_") and v < price],
                reverse=True,
            )
            if swing_lows:
                sl_price = swing_lows[0] - atr * sl_swing_buffer
            else:
                sl_price = price - atr * (1.0 + sl_swing_buffer)
            min_sl = max(atr * sl_atr_floor, min_sl_price)
            if price - sl_price < min_sl:
                sl_price = price - min_sl

            sl_distance = price - sl_price

            swing_highs = sorted(
                v for k, v in levels.items() if k.startswith("swing_high_") and v > price + atr * 0.3
            )
            if (swing_highs
                    and swing_highs[0] - price <= atr * tp_swing_cap
                    and swing_highs[0] - price >= sl_distance * pre_tight_min_rr):
                tp1 = swing_highs[0]
            else:
                tp1 = price + max(atr * tp_mult, sl_distance * pre_tight_min_rr)
            if len(swing_highs) >= 2 and swing_highs[1] - price <= atr * 5.0:
                tp2 = swing_highs[1]
            else:
                tp2 = price + max(atr * (tp_mult + 1.5), sl_distance * pre_tight_min_rr * 1.5)

            direction = "BUY"
            entry, sl = price, sl_price
        else:
            swing_highs = sorted(
                v for k, v in levels.items() if k.startswith("swing_high_") and v > price
            )
            if swing_highs:
                sl_price = swing_highs[0] + atr * sl_swing_buffer
            else:
                sl_price = price + atr * (1.0 + sl_swing_buffer)
            min_sl = max(atr * sl_atr_floor, min_sl_price)
            if sl_price - price < min_sl:
                sl_price = price + min_sl

            sl_distance = sl_price - price

            swing_lows = sorted(
                [v for k, v in levels.items() if k.startswith("swing_low_") and v < price - atr * 0.3],
                reverse=True,
            )
            if (swing_lows
                    and price - swing_lows[0] <= atr * tp_swing_cap
                    and price - swing_lows[0] >= sl_distance * pre_tight_min_rr):
                tp1 = swing_lows[0]
            else:
                tp1 = price - max(atr * tp_mult, sl_distance * pre_tight_min_rr)
            if len(swing_lows) >= 2 and price - swing_lows[1] <= atr * 5.0:
                tp2 = swing_lows[1]
            else:
                tp2 = price - max(atr * (tp_mult + 1.5), sl_distance * pre_tight_min_rr * 1.5)

            direction = "SELL"
            entry, sl = price, sl_price

        tf = self.config.signals.tp_tightening_factor
        tp1 = entry + (tp1 - entry) * tf
        tp2 = entry + (tp2 - entry) * tf

        # TP2 must sit beyond TP1 — swing picks can invert when TP1 falls
        # back to the ATR target while TP2 takes a nearer second swing.
        if is_buy:
            tp2 = max(tp2, tp1 + atr)
        else:
            tp2 = min(tp2, tp1 - atr)

        tp1_distance = abs(tp1 - entry)
        rr = tp1_distance / sl_distance if sl_distance > 0 else 0

        if rr < min_rr - 1e-9:
            return None

        # Spread gate: a target too close to entry hands most of the win
        # to transaction costs (validated on M5: sub-$10 targets lose).
        if _is_eur:
            min_tp = eur.round_trip_cost * eur.min_tp_cost_multiple
            if tp1_distance < min_tp:
                report.warnings.append(
                    f"SPREAD GATE: target {tp1_distance / pip_value:.1f} pips "
                    f"< {min_tp / pip_value:.1f} pip minimum — costs would eat the edge"
                )
                return None
        else:
            min_tp = self.config.signals.round_trip_cost * self.config.signals.min_tp_cost_multiple
            if tp1_distance < min_tp:
                report.warnings.append(
                    f"SPREAD GATE: target ${tp1_distance:.2f} < ${min_tp:.2f} minimum — "
                    f"costs would eat the edge"
                )
                return None

        risk_pips = sl_distance / pip_value
        reward_pips = tp1_distance / pip_value

        return TradeSetup(
            pair=report.symbol,
            display_name=report.display_name,
            direction=direction,
            entry_price=entry,
            stop_loss=sl,
            take_profit_1=tp1,
            take_profit_2=tp2,
            risk_reward=round(rr, 2),
            risk_pips=risk_pips,
            reward_pips=reward_pips,
            pip_value=pip_value,
        )

    def _collect_reasons(self, report: SignalReport):
        if not report.primary_analysis:
            return

        for name, ind in report.primary_analysis.indicators.items():
            if name.startswith("_") or ind.weight == 0:
                continue
            if ind.signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY):
                prefix = "STRONG: " if ind.signal == SignalStrength.STRONG_BUY else ""
                report.bullish_reasons.append(f"{prefix}{ind.name}: {ind.description}")
            elif ind.signal in (SignalStrength.SELL, SignalStrength.STRONG_SELL):
                prefix = "STRONG: " if ind.signal == SignalStrength.STRONG_SELL else ""
                report.bearish_reasons.append(f"{prefix}{ind.name}: {ind.description}")

        if report.news_sentiment > 0.1:
            report.bullish_reasons.append(f"News: {report.news_description}")
        elif report.news_sentiment < -0.1:
            report.bearish_reasons.append(f"News: {report.news_description}")