feat(ga): fitness continua v1 con tanh(sharpe) + penalita' moltiplicativa di drawdown

Phase 1 v0 usava `max(0, dsr - 0.5*max_dd)` che azzerava brutalmente la fitness
quando max_dd > 2*dsr. Real run v4 aveva 55/55 strategie a fitness=0 (DSR ~0.001,
max_dd > 0.5), zero pressione selettiva sul GA.

v1: base = 0.5*dsr + 0.5*0.5*(tanh(sharpe)+1) in [0,1], modulata da penalty
moltiplicativa 1/(1+k*max_dd) in (0,1]. Hard kill (no-trade, HIGH adversarial)
preservati. Fitness sempre >0 per strategie con almeno 1 trade -> il GA
puo' preferire "meno cattivo" a "catastrofico" anche su sharpe negativo.

Tests: +3 nuovi (continuous mediocre, bounded, monotonic drawdown), 4 esistenti
restano verdi. Suite 138 -> 141 passed. ruff + mypy strict puliti.

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

tests/unit/test_fitness.py

@@ -1,13 +1,18 @@
+from itertools import pairwise
+
 from multi_swarm.agents.adversarial import AdversarialReport, Finding, Severity
 from multi_swarm.agents.falsification import FalsificationReport
 from multi_swarm.ga.fitness import compute_fitness
 
 
 def make_falsification(
-    dsr: float = 0.7, max_dd: float = 0.2, n_trades: int = 30
+    dsr: float = 0.7,
+    max_dd: float = 0.2,
+    n_trades: int = 30,
+    sharpe: float = 1.5,
 ) -> FalsificationReport:
     return FalsificationReport(
-        sharpe=1.5,
+        sharpe=sharpe,
         dsr=dsr,
         dsr_pvalue=0.05,
         max_drawdown=max_dd,
@@ -43,3 +48,44 @@ def test_fitness_zeroed_by_high_severity_finding() -> None:
         findings=[Finding(name="degenerate", severity=Severity.HIGH, detail="x")]
     )
     assert compute_fitness(f, a) == 0.0
+
+
+def test_fitness_continuous_signal_for_mediocre() -> None:
+    """Strategie mediocri (DSR ~0, Sharpe negativo) hanno comunque fitness>0
+    e la meno cattiva e' preferita."""
+    a = AdversarialReport()
+    less_bad = make_falsification(dsr=0.001, sharpe=-0.5, max_dd=0.3)
+    worse = make_falsification(dsr=0.001, sharpe=-2.0, max_dd=0.3)
+    f_less = compute_fitness(less_bad, a)
+    f_worse = compute_fitness(worse, a)
+    assert f_less > 0.0
+    assert f_worse > 0.0
+    assert f_less > f_worse
+
+
+def test_fitness_bounded() -> None:
+    """Fitness e' bounded in [0, 2.0] per input tipici."""
+    a = AdversarialReport()
+    cases = [
+        make_falsification(dsr=0.0, sharpe=-5.0, max_dd=0.0),
+        make_falsification(dsr=0.0, sharpe=0.0, max_dd=0.0),
+        make_falsification(dsr=0.5, sharpe=1.0, max_dd=0.2),
+        make_falsification(dsr=0.9, sharpe=2.0, max_dd=0.15),
+        make_falsification(dsr=1.0, sharpe=5.0, max_dd=0.0),
+        make_falsification(dsr=1.0, sharpe=10.0, max_dd=5.0),
+    ]
+    for f in cases:
+        v = compute_fitness(f, a)
+        assert 0.0 <= v <= 2.0, f"fitness {v} fuori range per {f}"
+
+
+def test_fitness_normalizes_drawdown() -> None:
+    """Con DSR e Sharpe fissi, fitness e' monotona decrescente in max_dd."""
+    a = AdversarialReport()
+    dds = [0.0, 0.1, 0.5, 1.0, 2.0, 5.0]
+    fitnesses = [
+        compute_fitness(make_falsification(dsr=0.5, sharpe=1.0, max_dd=dd), a)
+        for dd in dds
+    ]
+    for prev, curr in pairwise(fitnesses):
+        assert prev > curr, f"non monotona: {fitnesses}"