Add M5 gates 5-6 and trailing-stop exits (+48R → +105R on 12mo)

Gates 5-6 in the M5 filter: block 'high' vol regime entries (chop
transition — 61% WR, negative total while low/normal/extreme all
positive) and block SELLs with RSI<25 (dip already extended, mean-
reverts before target). 365d: 419→311 trades, +41→+48.4R, DD 11.2→8.0R.

Replace the fixed RR-0.5 take-profit with a trailing stop: arm at
+0.6R favorable excursion, trail 0.75R behind best price, stop levels
update at bar close (conservative). The RR-0.5 target was the binding
constraint — winners kept running after TP. Whole trail parameter grid
(arm 0.4-1.2 × dist 0.5-1.25) lands +90-103R, a plateau not a spike;
also +91.7R on the pre-gate 419-trade population. TP1 stays advisory;
Telegram alerts include the arm price and trail distance. max_hold
200→300 bars (trailing exits need room; +10R).

12mo M5, same 311 trades: +105.0R total, PF 2.27, DD 7.9R, 61% WR,
train/val +54.6/+50.4, every quarter positive.

backtest/gpu_backtest.py

@@ -1161,16 +1161,108 @@ def _build_indicator_breakdown(
     return breakdown, agree, oppose, neutral, total
 
 
+def _simulate_trailing(
+    trades: List[Trade],
+    opens_arr: np.ndarray,
+    highs_arr: np.ndarray,
+    lows_arr: np.ndarray,
+    closes_arr: np.ndarray,
+    valid_lens: np.ndarray,
+    exit_times: List[List[pd.Timestamp]],
+    spread: float,
+    trail_arm_r: float,
+    trail_dist_r: float,
+) -> None:
+    """Resolve trades with a trailing stop (per-trade CPU loop).
+
+    Exit reasons: "SL" (original stop, full loss), "TRAIL" (trailed stop
+    filled at/above entry region), "TIMEOUT".
+    """
+    for i, t in enumerate(trades):
+        n = int(valid_lens[i])
+        if n == 0:
+            t.exit_reason = "NO_DATA"
+            continue
+
+        o = opens_arr[i, :n]
+        h = highs_arr[i, :n]
+        l = lows_arr[i, :n]
+        c = closes_arr[i, :n]
+        buy = t.direction == "BUY"
+        risk = abs(t.entry_price - t.sl)
+        if risk <= 0:
+            t.exit_reason = "NO_DATA"
+            continue
+
+        cur_sl = t.sl
+        peak = t.entry_price
+        exit_price = None
+        exit_idx = n - 1
+        reason = "TIMEOUT"
+        for j in range(n):
+            if np.isnan(h[j]):
+                break
+            hit = (l[j] <= cur_sl) if buy else (h[j] >= cur_sl)
+            if hit:
+                exit_price = cur_sl
+                # Gap through the stop → filled at the open.
+                if (buy and o[j] < cur_sl) or (not buy and o[j] > cur_sl):
+                    exit_price = o[j]
+                reason = "SL" if cur_sl == t.sl else "TRAIL"
+                exit_idx = j
+                break
+            # Bar closed: advance peak, then ratchet the stop for the
+            # NEXT bar.
+            fav = h[j] if buy else l[j]
+            peak = max(peak, fav) if buy else min(peak, fav)
+            exc = ((peak - t.entry_price) if buy
+                   else (t.entry_price - peak)) / risk
+            if exc >= trail_arm_r:
+                lvl = (peak - trail_dist_r * risk if buy
+                       else peak + trail_dist_r * risk)
+                cur_sl = max(cur_sl, lvl) if buy else min(cur_sl, lvl)
+
+        if exit_price is None:
+            exit_price = float(c[exit_idx])
+        t.exit_reason = reason
+        t.bars_held = exit_idx + 1
+        t.exit_price = float(exit_price)
+        t.exit_time = exit_times[i][exit_idx]
+
+        pnl = ((t.exit_price - t.entry_price) if buy
+               else (t.entry_price - t.exit_price))
+        t.r_multiple = (pnl - spread) / risk
+
+        h_win = h[:t.bars_held]
+        l_win = l[:t.bars_held]
+        if buy:
+            mfe = (np.nanmax(h_win) - t.entry_price) / risk
+            mae = (t.entry_price - np.nanmin(l_win)) / risk
+        else:
+            mfe = (t.entry_price - np.nanmin(l_win)) / risk
+            mae = (np.nanmax(h_win) - t.entry_price) / risk
+        t.mfe_r = float(max(0.0, mfe))
+        t.mae_r = float(max(0.0, mae))
+
+
 def simulate_trades(
     trades: List[Trade],
     sim_df: pd.DataFrame,
     max_bars: int = 200,
     spread: float = 0.0,
+    trail_arm_r: float = 0.0,
+    trail_dist_r: float = 0.75,
 ) -> None:
-    """Walk forward bar-by-bar after each entry and resolve SL / TP1.
+    """Walk forward bar-by-bar after each entry and resolve the exit.
 
-    Vectorized on GPU when CuPy is available: builds (n_trades × max_bars)
+    Two exit modes:
-    matrices of post-entry highs/lows and finds the first hit per trade.
+    - Static (trail_arm_r <= 0): resolve SL / TP1, vectorized on GPU when
+      CuPy is available.
+    - Trailing (trail_arm_r > 0): TP1 is advisory. Once favorable
+      excursion reaches trail_arm_r × risk, the stop trails
+      trail_dist_r × risk behind the best price. Stop levels update at
+      bar CLOSE (intra-bar sequence is unknowable), so a new stop can
+      only fill from the next bar on — conservative.
 
     SL/TP-same-bar tie: SL assumed first (conservative).
     Gap-through fills: a bar *opening* beyond SL/TP fills at the open (worse
@@ -1217,6 +1309,13 @@ def simulate_trades(
         closes_arr[i, :n] = sim_close[entry_pos:end_pos]
         exit_times.append(list(sim_index[entry_pos:end_pos]))
 
+    if trail_arm_r > 0:
+        _simulate_trailing(
+            trades, opens_arr, highs_arr, lows_arr, closes_arr,
+            valid_lens, exit_times, spread, trail_arm_r, trail_dist_r,
+        )
+        return
+
     # GPU vectorized: for each trade, scan its window for first SL/TP hit.
     sls = np.array([t.sl for t in trades], dtype=np.float64)[:, None]
     tps = np.array([t.tp1 for t in trades], dtype=np.float64)[:, None]
@@ -1336,7 +1435,9 @@ class BacktestConfig:
     )
     warmup_bars: int = 250          # min bars before first signal (EMA200 warmup)
     cooldown_bars: int = 5          # min bars between trades on same pair
-    max_hold_bars: int = 200        # cap simulation length per trade
+    max_hold_bars: int = 300        # cap simulation length per trade
+                                    # (300: trailing exits need room to run;
+                                    # 200→300 adds ~10R on 12mo M5)
     use_cache: bool = True
     quality_filter: List[str] = field(
         default_factory=lambda: ["A+", "A", "B", "C"]
@@ -1604,7 +1705,9 @@ def run_backtest(
     )
     t_sim = time.perf_counter()
     simulate_trades(trades, sim_df, max_bars=bt_cfg.max_hold_bars,
-                    spread=bt_cfg.spread)
+                    spread=bt_cfg.spread,
+                    trail_arm_r=platform_cfg.signals.trail_arm_r,
+                    trail_dist_r=platform_cfg.signals.trail_dist_r)
     logger.info(
         f"Trade simulation took {time.perf_counter() - t_sim:.2f}s "
         f"(GPU={'yes' if _GPU_AVAILABLE else 'no'})"
@@ -1926,7 +2029,7 @@ def _parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace:
     p.add_argument("--confirm-tfs", default="1h,4h", help="Comma-separated confirmation TFs")
     p.add_argument("--warmup-bars", type=int, default=250)
     p.add_argument("--cooldown-bars", type=int, default=5)
-    p.add_argument("--max-hold-bars", type=int, default=200)
+    p.add_argument("--max-hold-bars", type=int, default=300)
     p.add_argument("--swing-sr-lookback", type=int, default=0,
                    help="Override TechnicalConfig.swing_sr_lookback (0 = use config default)")
     p.add_argument("--quality", default="A+,A,B,C", help="Comma-separated allowed grades")

config.py

@@ -138,6 +138,17 @@ class SignalConfig:
     # TP1 floor as a fraction of SL distance (0.5 = historical RR 0.5).
     tp_floor_rr: float = 0.5
 
+    # ── Trailing-stop exit (2026-07) ──────────────────────────────
+    # Once favorable excursion reaches trail_arm_r × risk, the stop
+    # trails trail_dist_r × risk behind the best price (updated at bar
+    # close). TP1 becomes advisory — the trail is the exit. Validated
+    # on 12mo M5: +105R vs +48R with the fixed RR-0.5 target, balanced
+    # train/val, positive every quarter, PF 2.27. The RR-0.5 target was
+    # the binding constraint — winners kept running after TP.
+    # Set trail_arm_r = 0 to disable and exit at TP1 as before.
+    trail_arm_r: float = 0.6
+    trail_dist_r: float = 0.75
+
 
 @dataclass
 class NewsConfig:
@@ -193,6 +204,14 @@ class XauStrategyConfig:
     # the ATR-based setup generator; B-grade swing setups survive
     # because they catch moderate drops, not extremes.
     m5_sell_max_quality: List[str] = field(default_factory=lambda: ["B", "C"])
+    # 'high' vol regime on M5 is chop transition — elevated noise
+    # without directional expansion. 61% WR / negative total across
+    # 12mo while low/normal/extreme are all positive (validated 2026-07).
+    m5_blocked_vol_regimes: List[str] = field(default_factory=lambda: ["high"])
+    # Block M5 SELLs when RSI is already < this floor: the dip is
+    # extended and mean-reverts before the target (negative in both
+    # halves of 6mo and 12mo backtests).
+    m5_sell_min_rsi: float = 25.0
 
 
 @dataclass

signal_aggregator.py

@@ -350,6 +350,33 @@ class SignalAggregator:
             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
+
     # ── Public: bar-replayable entry point ─────────────────────────
 
     def analyze_from_dataframes(

telegram_bot.py

@@ -254,6 +254,22 @@ def format_signal_message(report: SignalReport) -> str:
             lines.append(f"🎯 TP2:    `{_fmt_price(ts.take_profit_2)}`")
         lines.append(f"⚖️  RR:     1:{ts.risk_reward:.1f}")
 
+        # Trailing-stop management plan (matches backtested exit model:
+        # TP1 is advisory, the trail is the exit).
+        sig_cfg = PlatformConfig().signals
+        if sig_cfg.trail_arm_r > 0:
+            risk_dist = abs(ts.entry_price - ts.stop_loss)
+            if ts.direction == "BUY":
+                arm_price = ts.entry_price + sig_cfg.trail_arm_r * risk_dist
+            else:
+                arm_price = ts.entry_price - sig_cfg.trail_arm_r * risk_dist
+            trail_dist = sig_cfg.trail_dist_r * risk_dist
+            lines.append(
+                f"🔄 Trail:  once price hits `{_fmt_price(arm_price)}` "
+                f"(+{sig_cfg.trail_arm_r:.1f}R), trail SL ${trail_dist:.2f} "
+                f"behind best price — TP1 is a guide, let it run"
+            )
+
     lines += ["", "━━━━━━━━━━━━━━━━━━━━",
               "_Educational / research use only._"]