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>

README.md

@@ -111,7 +111,7 @@ Stack: Python 3.13, uv workspace, hatchling, pytest+pytest-mock+responses, opena
 ```bash
 uv sync                           # installa entrambi i workspace member come editable
 cp .env.example .env              # compila CERBERO_*_TOKEN e OPENROUTER_API_KEY
-uv run pytest                     # 250 test attesi (246 core + 4 smoke strategy_crypto)
+uv run pytest                     # 252 test attesi (248 core + 4 smoke strategy_crypto)
 ```
 
 ### Variabili .env richieste
@@ -151,15 +151,16 @@ uv run mypy src/ scripts/
 uv run python scripts/smoke_run.py
 
 # Run reale Phase 1/2 (Cerbero + OpenRouter, ~$0.15-0.25 per run K=20 10gen,
-# ~$0.40-0.55 per run esteso K=40 20gen con WFA OOS)
+# ~$0.40-0.55 per run esteso K=40 20gen con WFA OOS).
+# Default --start ora 2018-09-01 (7.3y, copre bear+halving+covid+ATH+winter+ETF).
 uv run python scripts/run_phase1.py \
   --name run-XXX \
   --exchange deribit --symbol BTC-PERPETUAL --timeframe 1h \
-  --start 2024-01-01T00:00:00+00:00 \
-  --end 2026-01-01T00:00:00+00:00 \
   --population-size 20 --n-generations 10 \
   --prompt-mutation-weight 0.30 --fitness-v2 \
   --llm-concurrency 8                          # 5-8x speedup wall time (default 1)
+# fitness-v2 hardened: hard-kill su {no_trades, degenerate, undertrading,
+# fees_eat_alpha, negative_net_pnl}. Override via --fitness-hard-kill CSV.
 # Default --prompt-library: importlib.resources del package strategy_crypto/prompts.json
 
 # Multi-fold validation di un run esistente (anti-overfit, 7y expanding-window)

scripts/run_phase1.py

@@ -19,6 +19,30 @@ def _default_prompt_library_path() -> Path:
     return Path(str(importlib.resources.files("strategy_crypto") / "prompts.json"))
 
 
+# Default v2 hard-kill list: oltre ai degenerate originali, fees_eat_alpha e
+# negative_net_pnl sono deal-breaker non recuperabili via soft penalty (vedi
+# 7y-overfit incident 2026-05-16: elite IS Sharpe 1.93 -> net -5% su 7y per fees).
+_DEFAULT_V2_HARD_KILL: tuple[str, ...] = (
+    "no_trades",
+    "degenerate",
+    "undertrading",
+    "fees_eat_alpha",
+    "negative_net_pnl",
+)
+
+
+def _resolve_hard_kill(args) -> tuple[str, ...] | None:
+    """Resolve la lista hard-kill da CLI args.
+
+    Priority: ``--fitness-hard-kill`` esplicito > default v2 > ``None`` (v1).
+    """
+    if args.fitness_hard_kill:
+        return tuple(s.strip() for s in args.fitness_hard_kill.split(",") if s.strip())
+    if args.fitness_v2:
+        return _DEFAULT_V2_HARD_KILL
+    return None
+
+
 def parse_args() -> argparse.Namespace:
     p = argparse.ArgumentParser(description="Multi-Swarm Phase 1 runner")
     p.add_argument("--name", default="phase1-spike-001")
@@ -35,7 +59,10 @@ def parse_args() -> argparse.Namespace:
     )
     p.add_argument("--symbol", default="BTC-PERPETUAL")
     p.add_argument("--timeframe", default="1h")
-    p.add_argument("--start", default="2024-01-01T00:00:00+00:00")
+    # Default esteso a 7.3 anni: copre bear 2018-19, halving 2020, COVID,
+    # ATH 2021, winter 2022, ETF rally 2024, regime corrente. Una finestra
+    # corta lascia il GA libero di overfit a un singolo regime.
+    p.add_argument("--start", default="2018-09-01T00:00:00+00:00")
     p.add_argument("--end", default="2026-01-01T00:00:00+00:00")
     p.add_argument("--fees-bp", type=float, default=5.0)
     p.add_argument("--n-trials-dsr", type=int, default=50)
@@ -67,8 +94,10 @@ def parse_args() -> argparse.Namespace:
         "--fitness-v2",
         action="store_true",
         help=(
-            "Attiva fitness v2: solo {no_trades, degenerate, undertrading} azzerano; "
+            "Attiva fitness v2: hard-kill su {no_trades, degenerate, undertrading, "
-            "gli altri HIGH applicano soft penalty multiplicativa"
+            "fees_eat_alpha, negative_net_pnl}; gli altri HIGH applicano soft penalty "
+            "multiplicativa. Versione hardened post 7y-overfit incident: fees + "
+            "net negativo non sono recuperabili."
         ),
     )
     p.add_argument(
@@ -77,6 +106,16 @@ 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(
+        "--fitness-hard-kill",
+        type=str,
+        default=None,
+        help=(
+            "Override comma-separated della lista di finding name che azzerano la "
+            "fitness in modalita' v2. Es: 'no_trades,degenerate'. Default v2: "
+            "no_trades,degenerate,undertrading,fees_eat_alpha,negative_net_pnl."
+        ),
+    )
     p.add_argument(
         "--wfa-train-split",
         type=float,
@@ -188,9 +227,7 @@ def main() -> None:
         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=(
+        fitness_hard_kill_findings=_resolve_hard_kill(args),
-            ("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,

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."""

state/validation-extended-001.json

@@ -0,0 +1,634 @@
+{
+  "run_id": "e263651598894da688d95fda90a34a96",
+  "run_name": "phase1-extended-001",
+  "n_folds": 4,
+  "top_k_requested": 10,
+  "top_k_evaluated": 10,
+  "symbol": "BTC-PERPETUAL",
+  "timeframe": "1h",
+  "start": "2018-09-01T00:00:00+00:00",
+  "end": "2026-01-01T00:00:00+00:00",
+  "ohlcv_bars": 64297,
+  "results": [
+    {
+      "genome_id": "fe6e01eb690d3960",
+      "fitness_is": 0.3513762485888574,
+      "sharpe_is": 0.9011072752402621,
+      "folds": [
+        {
+          "fold": 0,
+          "fitness": 0.18429629882863333,
+          "sharpe": 0.18578971215949266,
+          "dsr": 0.022696217108110216,
+          "dsr_pvalue": 0.9773037828918898,
+          "return": 0.03138679502492736,
+          "max_dd": 0.19089436189057732,
+          "n_trades": 90,
+          "test_start": "2022-05-02 12:00:00+00:00",
+          "test_end": "2023-04-02 08:00:00+00:00"
+        },
+        {
+          "fold": 1,
+          "fitness": 0.5261711065682141,
+          "sharpe": 1.875154815895858,
+          "dsr": 0.34945842578669783,
+          "dsr_pvalue": 0.6505415742133022,
+          "return": 0.684094224950746,
+          "max_dd": 0.26051011671170043,
+          "n_trades": 108,
+          "test_start": "2023-04-02 09:00:00+00:00",
+          "test_end": "2024-03-02 05:00:00+00:00"
+        },
+        {
+          "fold": 2,
+          "fitness": 0.29904777517998976,
+          "sharpe": 0.45027968136531676,
+          "dsr": 0.040236883094469954,
+          "dsr_pvalue": 0.9597631169055301,
+          "return": 0.16192920610625539,
+          "max_dd": 0.25615601205401484,
+          "n_trades": 87,
+          "test_start": "2024-03-02 06:00:00+00:00",
+          "test_end": "2025-01-31 02:00:00+00:00"
+        },
+        {
+          "fold": 3,
+          "fitness": 0.06652893772008044,
+          "sharpe": -0.7859068293026578,
+          "dsr": 0.0016949251764253048,
+          "dsr_pvalue": 0.9983050748235747,
+          "return": -0.1801701961968295,
+          "max_dd": 0.3050931306970407,
+          "n_trades": 89,
+          "test_start": "2025-01-31 03:00:00+00:00",
+          "test_end": "2025-12-31 23:00:00+00:00"
+        }
+      ],
+      "fitness_oos_mean": 0.2690110295742294,
+      "fitness_oos_min": 0.06652893772008044,
+      "fitness_oos_max": 0.5261711065682141,
+      "fitness_oos_std": 0.16971232602043682,
+      "sharpe_oos_mean": 0.43132934502950243,
+      "sharpe_oos_min": -0.7859068293026578,
+      "robust_score": 0.06652893772008044
+    },
+    {
+      "genome_id": "d98739b2ba8d65e8",
+      "fitness_is": 0.3581811122056351,
+      "sharpe_is": 1.5316294902683918,
+      "folds": [
+        {
+          "fold": 0,
+          "fitness": 0.03897301517910094,
+          "sharpe": -1.1213338499931884,
+          "dsr": 0.0007106609032094727,
+          "dsr_pvalue": 0.9992893390967905,
+          "return": -0.18358503193809717,
+          "max_dd": 0.24053109269341416,
+          "n_trades": 65,
+          "test_start": "2022-05-02 12:00:00+00:00",
+          "test_end": "2023-04-02 08:00:00+00:00"
+        },
+        {
+          "fold": 1,
+          "fitness": 0.13868820192362147,
+          "sharpe": 0.1139203144076397,
+          "dsr": 0.018885390702584475,
+          "dsr_pvalue": 0.9811146092974156,
+          "return": 0.019694298831973045,
+          "max_dd": 0.1528666578131679,
+          "n_trades": 21,
+          "test_start": "2023-04-02 09:00:00+00:00",
+          "test_end": "2024-03-02 05:00:00+00:00"
+        },
+        {
+          "fold": 2,
+          "fitness": 0.43784574830368517,
+          "sharpe": 1.922993791724599,
+          "dsr": 0.36463909799020594,
+          "dsr_pvalue": 0.6353609020097941,
+          "return": 0.31046814355338936,
+          "max_dd": 0.09604869735695161,
+          "n_trades": 48,
+          "test_start": "2024-03-02 06:00:00+00:00",
+          "test_end": "2025-01-31 02:00:00+00:00"
+        },
+        {
+          "fold": 3,
+          "fitness": 0.377910610011883,
+          "sharpe": 1.3313322991701542,
+          "dsr": 0.17322722607861918,
+          "dsr_pvalue": 0.8267727739213808,
+          "return": 0.12021252899342505,
+          "max_dd": 0.04712452925993322,
+          "n_trades": 32,
+          "test_start": "2025-01-31 03:00:00+00:00",
+          "test_end": "2025-12-31 23:00:00+00:00"
+        }
+      ],
+      "fitness_oos_mean": 0.24835439385457264,
+      "fitness_oos_min": 0.03897301517910094,
+      "fitness_oos_max": 0.43784574830368517,
+      "fitness_oos_std": 0.1647414807695958,
+      "sharpe_oos_mean": 0.5617281388273011,
+      "sharpe_oos_min": -1.1213338499931884,
+      "robust_score": 0.03897301517910094
+    },
+    {
+      "genome_id": "0dd6619fdcbe37f4",
+      "fitness_is": 0.3765498201912705,
+      "sharpe_is": 0.9388498977535525,
+      "folds": [
+        {
+          "fold": 0,
+          "fitness": 0.03550929012857722,
+          "sharpe": -1.1093436310362703,
+          "dsr": 0.0005959199839818188,
+          "dsr_pvalue": 0.9994040800160182,
+          "return": -0.24600377155172415,
+          "max_dd": 0.38950670401585935,
+          "n_trades": 122,
+          "test_start": "2022-05-02 12:00:00+00:00",
+          "test_end": "2023-04-02 08:00:00+00:00"
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