feat(adversarial): time_in_market_too_high HIGH (>80% always-in-market)

Simmetrico opposto di flat_too_long: penalizza strategie LONG/SHORT su
piu' dell'80% delle bar. Una sempre-in-market e' leveraged B&H camuffato,
esposto a funding cumulato (perp ogni 8h), tail risk eventi notturni e
nessuna opportunity-cost flexibility. Sweet spot fitness positiva: 5-80%
time in market.

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

src/multi_swarm/agents/adversarial.py

@@ -1,6 +1,7 @@
 """Adversarial agent: ispeziona una :class:`Strategy` con check euristici
 hand-crafted per scovare patologie note (degenerate, no-trade, over/under
-trading, flat-too-long, fees-eat-alpha) prima del training vero e proprio.
+trading, flat-too-long, time-in-market-too-high, fees-eat-alpha) prima
+del training vero e proprio.
 
 Pipeline:
 
@@ -11,10 +12,13 @@ falsificazione, ma sega presto i casi degeneri (es. ``gt close -1e9`` →
 sempre long) che inquinerebbero il leaderboard del swarm.
 
 Phase 1.5 hardening: soglie strette per overtrading (n_trades > n_bars/20)
-e undertrading (HIGH se n_trades < 10), piu' due nuovi check HIGH:
+e undertrading (HIGH se n_trades < 10), piu' tre nuovi check HIGH:
-``flat_too_long`` (signal flat >95% delle bar) e ``fees_eat_alpha``
+``flat_too_long`` (signal flat >95% delle bar),
-(fees > 50% del gross_pnl positivo). Killano le strategie "lucky shot"
+``time_in_market_too_high`` (signal long/short >80% delle bar, di fatto
-e quelle con margine sottile non sostenibile in produzione.
+leveraged buy-and-hold con funding/tail-risk cumulato) e
+``fees_eat_alpha`` (fees > 50% del gross_pnl positivo). Killano le
+strategie "lucky shot", le sempre-in-market e quelle con margine sottile
+non sostenibile in produzione.
 """
 
 from __future__ import annotations
@@ -133,6 +137,26 @@ class AdversarialAgent:
                 )
             )
 
+        # Time-in-market-too-high: signal LONG o SHORT >80% delle bar.
+        # Simmetrico opposto di flat_too_long: una strategia sempre-in-market
+        # e' di fatto leveraged buy-and-hold, esposta a funding cumulato su
+        # perp (paid ogni 8h), tail risk eventi notturni/weekend, nessuna
+        # opportunity-cost flexibility. Sweet spot fitness positiva: 5-80%
+        # time in market (combinato con flat_too_long).
+        active_ratio = n_active / n_bars if n_bars > 0 else 0.0
+        if active_ratio > 0.80:
+            report.findings.append(
+                Finding(
+                    name="time_in_market_too_high",
+                    severity=Severity.HIGH,
+                    detail=(
+                        f"Signal long/short for {active_ratio * 100:.1f}% of bars "
+                        "(>80% threshold); esposizione cumulativa funding + tail risk, "
+                        "di fatto leveraged B&H"
+                    ),
+                )
+            )
+
         # Fees-eat-alpha: gross_pnl > 0 ma fees > 50% del lordo.
         # La strategia ha edge teorico ma il margine viene mangiato dai
         # costi di transazione: non sostenibile in produzione.

tests/unit/test_adversarial.py

@@ -338,3 +338,97 @@ def test_fees_eat_alpha_flagged(monkeypatch: pytest.MonkeyPatch,
         f.name == "fees_eat_alpha" and f.severity == Severity.HIGH
         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."""
+    n_bars = len(ohlcv)
+    # 90% LONG, 10% FLAT iniziali (warmup-like) per evitare degenerate.
+    n_flat = int(n_bars * 0.10)
+    sig_values = [Side.FLAT] * n_flat + [Side.LONG] * (n_bars - n_flat)
+    fake_signals = pd.Series(sig_values, index=ohlcv.index, dtype=object)
+    # 15 trade per evitare undertrading HIGH.
+    fake_trades = [
+        _make_trade(
+            ohlcv.index[i * 30],
+            ohlcv.index[i * 30 + 1],
+            entry_price=100.0,
+            exit_price=101.0,
+        )
+        for i in range(15)
+    ]
+
+    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.agents.adversarial.BacktestEngine.run", fake_run
+    )
+    monkeypatch.setattr(
+        "multi_swarm.agents.adversarial.compile_strategy", fake_compile
+    )
+
+    src = _MINIMAL_STRATEGY_SRC
+    ast = parse_strategy(src)
+    agent = AdversarialAgent()
+    report = agent.review(ast, ohlcv)
+    assert any(
+        f.name == "time_in_market_too_high" and f.severity == Severity.HIGH
+        for f in report.findings
+    )
+
+
+def test_reasonable_balanced_strategy_not_flagged(monkeypatch: pytest.MonkeyPatch,
+                                                    ohlcv: pd.DataFrame) -> None:
+    """Mix ~50% flat, ~25% long, ~25% short: no HIGH sui gate temporali."""
+    n_bars = len(ohlcv)
+    # Pattern ciclico: 2 flat, 1 long, 1 short per ogni gruppo da 4 bar.
+    # Risultato: ~50% FLAT, ~25% LONG, ~25% SHORT. flat_ratio=0.5 < 0.95,
+    # active_ratio=0.5 < 0.80.
+    pattern = [Side.FLAT, Side.FLAT, Side.LONG, Side.SHORT]
+    sig_values = [pattern[i % 4] for i in range(n_bars)]
+    fake_signals = pd.Series(sig_values, index=ohlcv.index, dtype=object)
+    # 15 trade per evitare undertrading HIGH.
+    fake_trades = [
+        _make_trade(
+            ohlcv.index[i * 30],
+            ohlcv.index[i * 30 + 1],
+            entry_price=100.0,
+            exit_price=101.0,
+        )
+        for i in range(15)
+    ]
+
+    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.agents.adversarial.BacktestEngine.run", fake_run
+    )
+    monkeypatch.setattr(
+        "multi_swarm.agents.adversarial.compile_strategy", fake_compile
+    )
+
+    src = _MINIMAL_STRATEGY_SRC
+    ast = parse_strategy(src)
+    agent = AdversarialAgent()
+    report = agent.review(ast, ohlcv)
+    # I due gate temporali non devono triggerare.
+    names = [f.name for f in report.findings]
+    assert "flat_too_long" not in names
+    assert "time_in_market_too_high" not in names