feat(ga): fitness v0 (DSR - dd_penalty * max_dd, kill on adversarial high)

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

src/multi_swarm/ga/fitness.py

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+"""Fitness function v0 della Phase 1.
+
+Combina :class:`FalsificationReport` (metriche di robustezza) e
+:class:`AdversarialReport` (findings euristici) in uno scalare ``>= 0`` che il
+GA usa per selezione e ranking.
+
+Logica deliberatamente coarse: DSR penalizzato dal max drawdown, con due
+kill-switch hard (no-trade, finding HIGH adversarial) che azzerano la fitness.
+La penalita' lineare sul drawdown e' un compromesso volutamente semplice;
+versioni successive potranno usare Calmar o utility convessa.
+"""
+
+from __future__ import annotations
+
+from ..agents.adversarial import AdversarialReport, Severity
+from ..agents.falsification import FalsificationReport
+
+
+def compute_fitness(
+    falsification: FalsificationReport,
+    adversarial: AdversarialReport,
+    drawdown_penalty: float = 0.5,
+) -> float:
+    """Calcola la fitness scalare di una strategia.
+
+    Args:
+        falsification: report con DSR, max_drawdown, n_trades.
+        adversarial: report con eventuali findings euristici.
+        drawdown_penalty: peso lineare sul max drawdown (default 0.5).
+
+    Returns:
+        Fitness ``>= 0``. Zero indica strategia da scartare.
+
+    Logica:
+        1. ``n_trades == 0`` → 0 (nessuna evidenza, sega subito).
+        2. Almeno un finding ``HIGH`` adversarial → 0 (kill).
+        3. Altrimenti: ``dsr - drawdown_penalty * max_drawdown``, clamped a 0.
+    """
+    if falsification.n_trades == 0:
+        return 0.0
+    if any(f.severity == Severity.HIGH for f in adversarial.findings):
+        return 0.0
+    raw = falsification.dsr - drawdown_penalty * falsification.max_drawdown
+    return max(0.0, float(raw))