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>
2026-05-10 23:54:46 +02:00
parent 23c9e37f94
commit d3662f6098
2 changed files with 123 additions and 5 deletions
@@ -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