feat: add fundamentals weighting backtest research

tests/unit/test_fundamentals_research_runner.py

@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+import zipfile
+
+from scripts import run_fundamentals_research as runner
+
+
+def _window(name, sharpe, drawdown):
+    return {
+        "window": name,
+        "sharpe": sharpe,
+        "sharpe_se": 0.2,
+        "dsr": 0.8,
+        "cagr_pct": 10.0,
+        "max_drawdown_pct": drawdown,
+        "calmar": 1.0,
+        "trades": 20,
+    }
+
+
+def test_arm_matrix_is_bounded_and_pre_registered():
+    assert runner.N_TRIALS == 13
+    assert runner.ARMS[0]["id"] == "control_w00"
+    assert {arm["weight"] for arm in runner.ARMS[1:]} == {0.1, 0.2, 0.3, 0.4}
+    assert {arm["composite"] for arm in runner.ARMS[1:]} == {
+        "quality",
+        "growth",
+        "balanced",
+    }
+
+
+def test_development_grade_does_not_read_test_window():
+    control = {
+        "windows": [
+            _window("train", 1.0, 10.0),
+            _window("validation", 1.0, 10.0),
+            _window("test", 9.0, 1.0),
+        ]
+    }
+    arm = {
+        "windows": [
+            _window("train", 1.1, 10.0),
+            _window("validation", 1.2, 11.0),
+            _window("test", -9.0, 90.0),
+        ]
+    }
+    assert runner._development_grade(control, arm)["pass"] is True
+
+
+def test_output_bundle_is_self_contained(tmp_path):
+    report = {
+        "generated_at": "2026-07-23T00:00:00Z",
+        "splits": {"train_end": "2024-01-01", "test_start": "2025-01-01"},
+        "n_trials": 13,
+        "warnings": ["survivorship bias"],
+        "factor_ic": {"full": []},
+        "arms": [],
+        "development_selection": None,
+        "final_check": None,
+    }
+    output = tmp_path / "result.json"
+    runner._write_outputs(report, output, bundle=True)
+    with zipfile.ZipFile(output.with_suffix(".zip")) as archive:
+        names = set(archive.namelist())
+    assert {
+        "result.json",
+        "result.md",
+        "result-arms.csv",
+        "result-factor-ic.csv",
+        "result-trades.csv",
+    } <= names
+
+
+def test_winner_concentration_exposes_top_five_dependence():
+    details = [
+        {"pnl": value, "r": value / 10} for value in (100, 90, 80, 70, 60, -10, -20)
+    ]
+    result = runner._winner_concentration(details)
+    assert result["top5_pnl"] == 400
+    assert result["net_pnl_ex_top5"] == -30
+    assert result["avg_r_ex_top5"] == -1.5