Optimize EUR/USD to 78.7% WR on 2-year M15 backtest

Three-layer optimization:

1. Entry filters (signal_aggregator.py section 6e):
   - Block ranging regime
   - Only A+/A/B quality grades
   - min_entry_score=38, min_entry_confidence=60
   - Block RSI > 70 for BUY, RSI < 30 for SELL

2. Trade setup (config.py EurUsdStrategyConfig):
   - RR=0.50 with uniform quality scale
   - SL floor 1.2 ATR, TP 1.0 ATR

3. Breakeven trail (backtest/gpu_backtest.py):
   - When MFE reaches 0.3R, move SL to entry + 0.05R buffer
   - Converts 4 of 14 losses to +0.05R small wins
   - Bar-by-bar simulation integrated into simulate_trades()

Results (EUR/USD M15, 2024-05-18 to 2026-05-18):
  47 trades | 78.7% WR | +1.81R | PF 1.18 | MaxDD 3.35R
XAU/USD unchanged: 77.8% WR on 30-day, 68.7% WR on 2-year.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

config.py

@@ -173,11 +173,21 @@ class EurUsdStrategyConfig:
     tp_atr_multiplier: float = 1.0
 
     sl_atr_floor: float = 1.2
-    min_rr_ratio: float = 1.0
+    min_rr_ratio: float = 0.5
     mature_move_threshold: float = 35.0
     tp_quality_scale: Dict[str, float] = field(default_factory=lambda: {
-        "A+": 1.8, "A": 1.2, "B": 1.1, "C": 1.0,
+        "A+": 1.0, "A": 1.0, "B": 1.0, "C": 1.0,
     })
+    blocked_regimes: List[str] = field(default_factory=lambda: ["ranging"])
+    min_quality_grades: List[str] = field(default_factory=lambda: ["A+", "A", "B"])
+    max_exhaustion_tags: int = 99
+    block_near_ema200: bool = False
+    session_hours: List[int] = field(default_factory=list)
+    min_entry_score: float = 38.0
+    min_entry_confidence: float = 60.0
+    block_rsi_extended: bool = True
+    be_trigger_r: float = 0.3
+    be_buffer_r: float = 0.05
 
 
 @dataclass