feat(adversarial): flat_too_long_threshold parametrico (CLI ablation)

Estende AdversarialAgent con flat_too_long_threshold (default 0.95)
configurabile, simmetrico a fees_eat_alpha_threshold. Propagato a
RunConfig.flat_too_long_threshold e flag CLI --flat-too-long-threshold.

Motivazione: pop30-combo ha registrato 75 finding flat_too_long HIGH
(secondo killer dopo fees_eat_alpha 87). Rilassare la soglia 0.95→0.98
ammette strategie più passive ma marginalmente attive — analogo
all'ablation fees già verificata (+23% stabile).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

scripts/run_phase1.py

@@ -43,6 +43,12 @@ def parse_args() -> argparse.Namespace:
         default=0.5,
         help="Adversarial gate: kill se fees/gross_pnl > soglia (default 0.5, ablation 0.7)",
     )
+    p.add_argument(
+        "--flat-too-long-threshold",
+        type=float,
+        default=0.95,
+        help="Adversarial gate: kill se signal flat > soglia delle bar (default 0.95, ablation 0.98)",
+    )
     return p.parse_args()
 
 
@@ -98,6 +104,7 @@ def main() -> None:
         db_path=settings.db_path,
         prompt_mutation_weight=args.prompt_mutation_weight,
         fees_eat_alpha_threshold=args.fees_eat_alpha_threshold,
+        flat_too_long_threshold=args.flat_too_long_threshold,
     )
 
     run_id = run_phase1(cfg, ohlcv=ohlcv, llm=llm)

src/multi_swarm/agents/adversarial.py

@@ -63,9 +63,11 @@ class AdversarialAgent:
         self,
         fees_bp: float = 5.0,
         fees_eat_alpha_threshold: float = 0.5,
+        flat_too_long_threshold: float = 0.95,
     ) -> None:
         self._engine = BacktestEngine(fees_bp=fees_bp)
         self._fees_eat_alpha_threshold = fees_eat_alpha_threshold
+        self._flat_too_long_threshold = flat_too_long_threshold
 
     def review(self, strategy: Strategy, ohlcv: pd.DataFrame) -> AdversarialReport:
         signal_fn = compile_strategy(strategy)
@@ -133,12 +135,15 @@ class AdversarialAgent:
         n_active = int(((signals == Side.LONG) | (signals == Side.SHORT)).sum())
         n_flat_or_nan = n_bars - n_active
         flat_ratio = n_flat_or_nan / n_bars if n_bars > 0 else 1.0
-        if flat_ratio > 0.95:
+        if flat_ratio > self._flat_too_long_threshold:
             report.findings.append(
                 Finding(
                     name="flat_too_long",
                     severity=Severity.HIGH,
-                    detail=f"Signal flat for {flat_ratio * 100:.1f}% of bars (>95% threshold)",
+                    detail=(
+                        f"Signal flat for {flat_ratio * 100:.1f}% of bars "
+                        f"(>{self._flat_too_long_threshold * 100:.0f}% threshold)"
+                    ),
                 )
             )
 

src/multi_swarm/orchestrator/run.py

@@ -51,6 +51,7 @@ class RunConfig:
     db_path: Path = field(default_factory=lambda: Path("./runs.db"))
     prompt_mutation_weight: float = 0.0  # Phase 2.5: opt-in LLM mutator
     fees_eat_alpha_threshold: float = 0.5  # adversarial gate, allenta verso 0.7-0.8
+    flat_too_long_threshold: float = 0.95  # adversarial gate, allenta verso 0.98-0.99
 
 
 def run_phase1(
@@ -82,6 +83,7 @@ def run_phase1(
     adversarial_agent = AdversarialAgent(
         fees_bp=cfg.fees_bp,
         fees_eat_alpha_threshold=cfg.fees_eat_alpha_threshold,
+        flat_too_long_threshold=cfg.flat_too_long_threshold,
     )
     cost_tracker = CostTracker()