feat(phase-2.6): Walk-Forward Validation + min-trades filter parametrico

Due fondamenta scientifiche per filtrare overfit e lucky-shot:

1) undertrading_threshold parametrico (era hardcoded 10):
   - AdversarialAgent.__init__(undertrading_threshold=10)
   - CLI flag --undertrading-threshold
   - Aggiunto a hard_kill_findings v2 default
     {"no_trades", "degenerate", "undertrading"}: ora un genome con 1 trade
     fortunato (es. genome 80be6bcc-1trade-fit-0.21 di fitness-v2-combo) viene
     killato anche sotto fitness v2 soft-kill.
   - Test parametric: undertrading_threshold=25 → 15 trade triggerano HIGH.

2) Walk-Forward Validation (WFA):
   - RunConfig.wfa_train_split (None=off, 0<x<1=on) + wfa_top_k=5
   - run_phase1: split ohlcv in train/test; GA usa solo train; a fine GA
     i top_k genomi (by fitness in-sample, fitness>0) vengono rivalutati
     sul test_ohlcv via falsification+adversarial+compute_fitness.
   - Schema migration: evaluations + fitness_oos, sharpe_oos, return_oos,
     max_dd_oos, n_trades_oos (ALTER TABLE con try/except per DB pre-2.6).
   - Repository.update_evaluation_oos helper per popolare colonne OOS.
   - CLI flags --wfa-train-split, --wfa-top-k.
   - Test integration: train_split=0.7 → fitness_oos popolato per top_k.

Motivazione: la fase 2.5 ha generato 17 run con fitness fino a 0.36 + DSR
positivo, ma OOS test su 7 anni mostra che flat-ablation top crolla -37%
mentre fitness-v2 top regge (+143%). WFA in-run permette ora di vedere
direttamente il degradation train→test senza eseguire backtest separati,
rendendo possibile filtrare overfit early durante l'ottimizzazione.

Tests (+2 → 193 totale):
- test_undertrading_threshold_parametric
- test_e2e_wfa_populates_fitness_oos

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

scripts/run_phase1.py

@@ -49,11 +49,17 @@ def parse_args() -> argparse.Namespace:
         default=0.95,
         help="Adversarial gate: kill se signal flat > soglia delle bar (default 0.95, ablation 0.98)",
     )
+    p.add_argument(
+        "--undertrading-threshold",
+        type=int,
+        default=10,
+        help="Adversarial: kill se n_trades < soglia (default 10, bump per filtrare lucky-shot)",
+    )
     p.add_argument(
         "--fitness-v2",
         action="store_true",
         help=(
-            "Attiva fitness v2: solo {no_trades, degenerate} azzerano; "
+            "Attiva fitness v2: solo {no_trades, degenerate, undertrading} azzerano; "
             "gli altri HIGH applicano soft penalty multiplicativa"
         ),
     )
@@ -63,6 +69,18 @@ def parse_args() -> argparse.Namespace:
         default=0.4,
         help="v2: fattore soft penalty per HIGH non-hard (default 0.4 → 1 HIGH → 0.71x)",
     )
+    p.add_argument(
+        "--wfa-train-split",
+        type=float,
+        default=None,
+        help="Walk-forward: frazione bar usate per training (es. 0.7 = primi 70%% in-sample, ultimi 30%% OOS)",
+    )
+    p.add_argument(
+        "--wfa-top-k",
+        type=int,
+        default=5,
+        help="Walk-forward: quanti top genomi rivalutare OOS (default 5)",
+    )
     return p.parse_args()
 
 
@@ -119,10 +137,13 @@ def main() -> None:
         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,
+        undertrading_threshold=args.undertrading_threshold,
         fitness_hard_kill_findings=(
-            ("no_trades", "degenerate") if args.fitness_v2 else None
+            ("no_trades", "degenerate", "undertrading") if args.fitness_v2 else None
         ),
         fitness_adversarial_soft_penalty=args.fitness_soft_penalty,
+        wfa_train_split=args.wfa_train_split,
+        wfa_top_k=args.wfa_top_k,
     )
 
     run_id = run_phase1(cfg, ohlcv=ohlcv, llm=llm)