fix(fitness): hardening anti-overfit post-7y incident — 3 correzioni

Incident: extended run elite (Sharpe IS 1.93) net-negativo su 7y
continuous (fees=101% del gross). Multi-fold validation NON sufficiente:
ogni fold restarta equity, mascherando accumulo fees compound.

Correzioni:

1) Default --start esteso a 2018-09-01 (7.3 anni)
   - Copre bear 2018-19, halving 2020, COVID, ATH 2021, winter 2022,
     ETF rally 2024, regime corrente.
   - Una finestra corta (2y) lasciava il GA libero di overfit single-regime.

2) fees_eat_alpha promosso a hard-kill in fitness v2
   - Da soft-penalty 0.4x a hard-kill 0 fitness.
   - Una strategia con fees > 50% del gross non e' recuperabile via
     selection: il prodotto del GA non puo' deployare con quel cost burden.

3) Nuovo finding negative_net_pnl (HIGH, hard-kill)
   - Fires quando sum(trade.net_pnl) < 0 sul training window.
   - Cattura: gross negativo (no edge direzionale) E gross positivo ma
     fees > gross (edge insufficiente).
   - Sintesi del net-after-fees su finestra continua come "deal-breaker"
     non negoziabile via soft penalty.

CLI:
- --fitness-hard-kill <csv> per override esplicito.
- Default v2: no_trades,degenerate,undertrading,fees_eat_alpha,negative_net_pnl.

Test:
- 252 pass (251 + 1 nuovo: test_negative_net_pnl_fires_on_negative_gross).
- Test e2e WFA aggiornato: passa fitness_hard_kill_findings=('no_trades',)
  perche' il fixture sintetico non produce strategie profittevoli.
- test_no_findings_on_reasonable_strategy rinominato:
  test_rsi_mean_reversion_loses_money_on_synthetic_data (riflette
  semantica reale: RSI mean-rev su synthetic ohlcv ha net negativo).

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

src/multi_swarm_core/multi_swarm_core/agents/adversarial.py

@@ -172,10 +172,11 @@ class AdversarialAgent:
                 )
             )
 
-        # Fees-eat-alpha: gross_pnl > 0 ma fees > 50% del lordo.
+        # Fees-eat-alpha: gross_pnl > 0 ma fees > soglia del lordo.
         # La strategia ha edge teorico ma il margine viene mangiato dai
         # costi di transazione: non sostenibile in produzione.
-        # Se gross_pnl <= 0 il check non si applica (gia' perdente).
+        # Se gross_pnl <= 0 il check non si applica (la condizione e' coperta
+        # da ``negative_net_pnl`` sotto).
         gross_pnl = sum(t.gross_pnl for t in result.trades)
         total_fees = sum(t.fees for t in result.trades)
         if gross_pnl > 0 and total_fees / gross_pnl > self._fees_eat_alpha_threshold:
@@ -190,4 +191,22 @@ class AdversarialAgent:
                 )
             )
 
+        # Negative-net-pnl: somma di ``trade.net_pnl`` < 0 sul training.
+        # Cattura sia il caso "gross negativo" (no edge direzionale) sia il
+        # caso "gross positivo ma fees superiori a gross" (edge insufficiente).
+        # Sintesi del net-after-fees su finestra continua: deal-breaker, non
+        # negoziabile via soft penalty.
+        net_pnl = gross_pnl - total_fees
+        if net_pnl < 0:
+            report.findings.append(
+                Finding(
+                    name="negative_net_pnl",
+                    severity=Severity.HIGH,
+                    detail=(
+                        f"Net PnL ${net_pnl:.2f} < 0 after fees over {n_bars} bars; "
+                        f"gross ${gross_pnl:.2f}, fees ${total_fees:.2f}"
+                    ),
+                )
+            )
+
         return report

src/multi_swarm_core/tests/integration/test_e2e_minimal_run.py

@@ -108,7 +108,13 @@ def test_e2e_wfa_populates_fitness_oos(
     fake_llm,
     mocker,
 ):
-    """WFA: train_split=0.7 → top genomi devono avere fitness_oos popolato."""
+    """WFA: train_split=0.7 → top genomi devono avere fitness_oos popolato.
+
+    Usa fitness v2 con hard-kill minimale (solo no_trades): il fixture sintetico
+    non produce strategie profittevoli, quindi i check aggressivi
+    fees_eat_alpha/negative_net_pnl azzererebbero tutti i genomi rendendo
+    inverificabile il wiring WFA.
+    """
     cfg = RunConfig(
         run_name="e2e-wfa-test",
         population_size=5,
@@ -125,6 +131,7 @@ def test_e2e_wfa_populates_fitness_oos(
         db_path=tmp_path / "runs.db",
         wfa_train_split=0.7,
         wfa_top_k=3,
+        fitness_hard_kill_findings=("no_trades",),
     )
     run_id = run_phase1(cfg, ohlcv=synthetic_ohlcv, llm=fake_llm)
     repo = Repository(db_path=tmp_path / "runs.db")

src/multi_swarm_core/tests/unit/test_adversarial.py

@@ -3,7 +3,6 @@ import json
 import numpy as np
 import pandas as pd
 import pytest
-
 from multi_swarm_core.agents.adversarial import (
     AdversarialAgent,
     AdversarialReport,
@@ -54,7 +53,10 @@ def test_degenerate_always_long_flagged(ohlcv: pd.DataFrame) -> None:
     assert any(f.name == "degenerate" and f.severity == Severity.HIGH for f in report.findings)
 
 
-def test_no_findings_on_reasonable_strategy(ohlcv: pd.DataFrame) -> None:
+def test_rsi_mean_reversion_loses_money_on_synthetic_data(ohlcv: pd.DataFrame) -> None:
+    """RSI mean-reversion sul fixture sintetico ha net negativo: deve firare
+    negative_net_pnl (deal-breaker). Conferma che il check cattura strategie
+    che perdono sul training, indipendentemente dal motivo (no edge / fees)."""
     src = json.dumps(
         {
             "rules": [
@@ -84,8 +86,59 @@ def test_no_findings_on_reasonable_strategy(ohlcv: pd.DataFrame) -> None:
     ast = parse_strategy(src)
     agent = AdversarialAgent()
     report = agent.review(ast, ohlcv)
+    assert any(
+        f.name == "negative_net_pnl" and f.severity == Severity.HIGH
+        for f in report.findings
+    )
+
+
+def test_profitable_strategy_no_high_findings(
+    monkeypatch: pytest.MonkeyPatch, ohlcv: pd.DataFrame
+) -> None:
+    """Sanity test: una strategia con gross > 0 e fees << gross + n_trades ragionevole
+    + signal misto non deve produrre nessun finding HIGH."""
+    n = 15
+    # entry=100 exit=110 gross=10 per trade, fees a 5bp -> 0.105 per trade
+    # totali: gross=150, fees=1.575 -> net=+148.4
+    fake_trades = [
+        _make_trade(
+            ohlcv.index[i * 30],
+            ohlcv.index[i * 30 + 1],
+            entry_price=100.0,
+            exit_price=110.0,
+        )
+        for i in range(n)
+    ]
+    # 50/50 LONG/FLAT per evitare degenerate/flat_too_long/time_in_market.
+    fake_signals = pd.Series(
+        [Side.LONG if i % 2 == 0 else Side.FLAT for i in range(len(ohlcv))],
+        index=ohlcv.index,
+        dtype=object,
+    )
+
+    def fake_run(self, ohlcv: pd.DataFrame, signals: pd.Series) -> BacktestResult:  # type: ignore[no-untyped-def]
+        return BacktestResult(
+            equity_curve=pd.Series([0.0] * len(ohlcv), index=ohlcv.index, name="equity"),
+            returns=pd.Series([0.0] * len(ohlcv), index=ohlcv.index, name="returns"),
+            trades=fake_trades,
+        )
+
+    def fake_compile(strategy):  # type: ignore[no-untyped-def]
+        return lambda df: fake_signals
+
+    monkeypatch.setattr(
+        "multi_swarm_core.agents.adversarial.BacktestEngine.run", fake_run
+    )
+    monkeypatch.setattr(
+        "multi_swarm_core.agents.adversarial.compile_strategy", fake_compile
+    )
+
+    ast = parse_strategy(_MINIMAL_STRATEGY_SRC)
+    report = AdversarialAgent().review(ast, ohlcv)
     high_findings = [f for f in report.findings if f.severity == Severity.HIGH]
-    assert len(high_findings) == 0
+    assert high_findings == [], (
+        f"expected no HIGH findings, got: {[f.name for f in high_findings]}"
+    )
 
 
 def test_zero_trade_strategy_flagged(ohlcv: pd.DataFrame) -> None:
@@ -383,6 +436,55 @@ def test_fees_eat_alpha_flagged(monkeypatch: pytest.MonkeyPatch,
     )
 
 
+def test_negative_net_pnl_fires_on_negative_gross(
+    monkeypatch: pytest.MonkeyPatch, ohlcv: pd.DataFrame
+) -> None:
+    """gross_pnl < 0 (perdente direzionale) -> HIGH negative_net_pnl.
+    fees_eat_alpha NON deve firare perche' la sua condizione richiede gross > 0.
+    """
+    n = 15
+    # entry=100 exit=95 gross=-5 per trade (LONG perdente)
+    fake_trades = [
+        _make_trade(
+            ohlcv.index[i * 30],
+            ohlcv.index[i * 30 + 1],
+            entry_price=100.0,
+            exit_price=95.0,
+        )
+        for i in range(n)
+    ]
+    fake_signals = pd.Series(
+        [Side.LONG if i % 2 == 0 else Side.FLAT for i in range(len(ohlcv))],
+        index=ohlcv.index,
+        dtype=object,
+    )
+
+    def fake_run(self, ohlcv, signals):  # type: ignore[no-untyped-def]
+        return BacktestResult(
+            equity_curve=pd.Series([0.0] * len(ohlcv), index=ohlcv.index, name="equity"),
+            returns=pd.Series([0.0] * len(ohlcv), index=ohlcv.index, name="returns"),
+            trades=fake_trades,
+        )
+
+    def fake_compile(strategy):  # type: ignore[no-untyped-def]
+        return lambda df: fake_signals
+
+    monkeypatch.setattr(
+        "multi_swarm_core.agents.adversarial.BacktestEngine.run", fake_run
+    )
+    monkeypatch.setattr(
+        "multi_swarm_core.agents.adversarial.compile_strategy", fake_compile
+    )
+
+    ast = parse_strategy(_MINIMAL_STRATEGY_SRC)
+    report = AdversarialAgent().review(ast, ohlcv)
+    assert any(
+        f.name == "negative_net_pnl" and f.severity == Severity.HIGH
+        for f in report.findings
+    )
+    assert not any(f.name == "fees_eat_alpha" for f in report.findings)
+
+
 def test_time_in_market_too_high_flagged(monkeypatch: pytest.MonkeyPatch,
                                           ohlcv: pd.DataFrame) -> None:
     """Signal LONG per >80% delle bar -> HIGH time_in_market_too_high."""