Add offline workflow and coverage tests

.gitignore

@@ -1,6 +1,7 @@
 __pycache__/
 *.py[cod]
 .pytest_cache/
+.coverage
 *.egg-info/
 .venv/
 venv/

pipeline/alpha/cli.py

@@ -160,7 +160,8 @@ def reversal_vol(data_path, output_dir, lookback, vol_window):
 @alpha.command("eval")
 @click.option("--alpha-path", required=True, help="Path to alpha parquet file")
 @click.option("--data-path", required=True, help="Path to data parquet (for price data)")
-def eval_(alpha_path, data_path):
+@click.option("--report-dir", default="reports", help="Directory to save JSON report")
+def eval_(alpha_path, data_path, report_dir):
     """Evaluate an alpha's performance (return, Sharpe, turnover).
 
     Alphas are interpreted as position WEIGHTS, not return predictors.
@@ -183,9 +184,9 @@ def eval_(alpha_path, data_path):
     click.echo("=" * 50)
 
     # Also dump JSON
-    os.makedirs("reports", exist_ok=True)
+    os.makedirs(report_dir, exist_ok=True)
     alpha_name = alpha_df["alpha_name"].iloc[0]
-    json_path = f"reports/{alpha_name}_eval.json"
+    json_path = os.path.join(report_dir, f"{alpha_name}_eval.json")
     with open(json_path, "w") as f:
         json.dump(metrics, f, indent=2)
     click.echo(f"\nReport saved: {json_path}")

pipeline/portfolio/simulator.py

@@ -166,13 +166,14 @@ class ReferenceSimulator(ExecutionSimulator):
         st = wide(data_df, "isST") if "isST" in data_df.columns else opn * 0.0
 
         symbols = sorted(set(tgt.columns) | set(opn.columns))
+        data_index = close.index
         tgt = tgt.reindex(columns=symbols)
-        opn = opn.reindex(columns=symbols)
+        opn = opn.reindex(index=data_index, columns=symbols)
         close = close.reindex(columns=symbols)
-        preclose = preclose.reindex(columns=symbols)
+        preclose = preclose.reindex(index=data_index, columns=symbols)
-        amount = amount.reindex(columns=symbols)
+        amount = amount.reindex(index=data_index, columns=symbols)
-        tstat = tstat.reindex(columns=symbols)
+        tstat = tstat.reindex(index=data_index, columns=symbols)
-        st = st.reindex(columns=symbols)
+        st = st.reindex(index=data_index, columns=symbols)
 
         sym_arr = np.asarray(symbols, dtype=object)
         n = len(symbols)

pyproject.toml

@@ -19,8 +19,32 @@ backtrader = [
 
 [dependency-groups]
 dev = [
+    "coverage>=7.14.1",
     "pytest>=7.0.0",
 ]
 
 [tool.uv]
 package = false
+
+[tool.pytest.ini_options]
+markers = [
+    "network: tests that call live external data providers and are skipped unless explicitly enabled",
+]
+
+[tool.coverage.run]
+branch = true
+relative_files = true
+source = [
+    ".",
+]
+
+[tool.coverage.report]
+fail_under = 80
+show_missing = true
+skip_covered = false
+omit = [
+    "tests/*",
+    "docs/*",
+    "scripts/*",
+    ".venv/*",
+]

scripts/run_coverage.py

@@ -0,0 +1,40 @@
+"""Run pytest under coverage in this environment.
+
+The plain ``coverage run -m pytest`` path reloads NumPy under the current VS Code
+Python startup environment, which breaks pandas/numpy reductions. Import NumPy
+before starting coverage so the measured test run uses one stable NumPy module.
+"""
+
+from __future__ import annotations
+
+import sys
+
+import numpy  # noqa: F401
+import coverage
+import pytest
+
+
+def main(argv: list[str]) -> int:
+    pytest_args = argv or ["tests/", "-v"]
+
+    cov = coverage.Coverage(config_file=True)
+    cov.erase()
+    cov.start()
+    test_status = pytest.main(pytest_args)
+    cov.stop()
+    cov.save()
+
+    if test_status != 0:
+        return int(test_status)
+
+    total = cov.report()
+    print(f"\nCoverage total: {total:.2f}%")
+    fail_under = float(cov.config.fail_under)
+    if total < fail_under:
+        print(f"Coverage failure: total {total:.2f}% is below {fail_under:.2f}%")
+        return 2
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main(sys.argv[1:]))

tests/helpers.py

@@ -0,0 +1,261 @@
+"""Shared deterministic test data for offline workflow tests."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from pipeline.common.schema import (
+    ALPHA_COLUMNS,
+    COMBO_COLUMNS,
+    DATA_COLUMNS,
+    MINUTE_BAR_COLUMNS,
+)
+
+
+GENERATED_SYMBOLS: tuple[str, ...] = (
+    "sh600000",
+    "sz000001",
+    "sh600519",
+    "sz300750",
+)
+
+GENERATED_SYMBOL_NAMES: dict[str, str] = {
+    "sh600000": "PF Bank",
+    "sz000001": "Ping An Bank",
+    "sh600519": "Kweichow Moutai",
+    "sz300750": "CATL",
+}
+
+
+def generated_sessions(n_sessions: int = 12) -> pd.DatetimeIndex:
+    """Return a fixed business-day calendar used by generated fixtures."""
+    return pd.bdate_range("2024-01-02", periods=n_sessions)
+
+
+def make_generated_daily_bars(
+    n_sessions: int = 12,
+    include_missing: bool = True,
+) -> pd.DataFrame:
+    """Build daily bars with explicit edge cases and no randomness.
+
+    The panel covers four A-share symbols and includes a suspended row, an ST
+    flag, a zero-volume row, a missing symbol-date, and limit-style open/close
+    moves. Values are deterministic so tests can assert exact identities.
+    """
+    dates = generated_sessions(n_sessions)
+    base_close = {
+        "sh600000": 10.00,
+        "sz000001": 15.00,
+        "sh600519": 1200.00,
+        "sz300750": 180.00,
+    }
+    returns = {
+        "sh600000": [0.000, 0.012, -0.006, 0.018, 0.100, -0.014, 0.006, 0.000, 0.008, -0.011, 0.004, 0.009],
+        "sz000001": [0.000, -0.008, 0.011, -0.004, 0.006, 0.000, -0.012, 0.009, 0.005, -0.007, 0.010, -0.003],
+        "sh600519": [0.000, 0.006, 0.004, -0.010, 0.012, -0.006, 0.005, 0.003, -0.009, 0.007, -0.004, 0.006],
+        "sz300750": [0.000, -0.010, 0.014, 0.006, -0.008, 0.011, -0.004, 0.009, -0.200, 0.012, -0.006, 0.008],
+    }
+    base_volume = {
+        "sh600000": 1_200_000.0,
+        "sz000001": 900_000.0,
+        "sh600519": 80_000.0,
+        "sz300750": 240_000.0,
+    }
+
+    rows: list[dict[str, object]] = []
+    for sym in GENERATED_SYMBOLS:
+        closes = [base_close[sym]]
+        pattern = returns[sym]
+        for step in range(1, n_sessions):
+            ret = pattern[step % len(pattern)]
+            closes.append(closes[-1] * (1.0 + ret))
+        closes_arr = np.asarray(closes, dtype=float)
+        precloses = np.concatenate([[closes_arr[0]], closes_arr[:-1]])
+
+        for i, date in enumerate(dates):
+            preclose = float(precloses[i])
+            close = float(closes_arr[i])
+            open_price = preclose * (1.0 + 0.25 * (close / preclose - 1.0))
+            high = max(open_price, close) * 1.01
+            low = min(open_price, close) * 0.99
+            volume = base_volume[sym] + 10_000.0 * i
+            tradestatus = 1
+            is_st = 0
+
+            if sym == "sh600000" and i == 4:
+                open_price = preclose * 1.10
+                close = open_price
+                high = open_price
+                low = open_price
+            if sym == "sh600000" and i == 7:
+                volume = 0.0
+            if sym == "sz000001" and i == 5:
+                open_price = preclose
+                close = preclose
+                high = preclose
+                low = preclose
+                volume = 0.0
+                tradestatus = 0
+            if sym == "sh600519" and i == 7:
+                is_st = 1
+            if sym == "sz300750" and i == 8:
+                open_price = preclose * 0.80
+                close = open_price
+                high = open_price
+                low = open_price
+
+            amount = volume * ((open_price + close) / 2.0)
+            vwap = amount / volume if volume > 0 else np.nan
+            pct_chg = (close / preclose - 1.0) * 100.0 if preclose else 0.0
+            rows.append({
+                "symbol_id": sym,
+                "symbol_name": GENERATED_SYMBOL_NAMES[sym],
+                "date": date,
+                "open": open_price,
+                "high": high,
+                "low": low,
+                "close": close,
+                "preclose": preclose,
+                "volume": volume,
+                "amount": amount,
+                "vwap": vwap,
+                "turn": volume / 1_000_000.0,
+                "pctChg": pct_chg,
+                "tradestatus": tradestatus,
+                "isST": is_st,
+                "peTTM": 8.0 + i,
+                "pbMRQ": 1.0 + 0.05 * i,
+                "psTTM": 2.0 + 0.03 * i,
+                "pcfNcfTTM": 5.0 + 0.1 * i,
+            })
+
+    result = pd.DataFrame(rows)
+    if include_missing and n_sessions > 6:
+        missing_mask = (
+            (result["symbol_id"] == "sz300750")
+            & (result["date"] == dates[6])
+        )
+        result = result.loc[~missing_mask].copy()
+
+    result = result[DATA_COLUMNS]
+    return result.sort_values(["date", "symbol_id"]).reset_index(drop=True)
+
+
+def make_generated_minute_bars(
+    daily: pd.DataFrame | None = None,
+) -> pd.DataFrame:
+    """Expand generated daily bars into a tiny deterministic intraday panel."""
+    daily = make_generated_daily_bars() if daily is None else daily.copy()
+    rows: list[dict[str, object]] = []
+    bar_times = ("09:35:00", "10:30:00", "14:55:00")
+
+    for daily_row in daily.sort_values(["date", "symbol_id"]).itertuples(index=False):
+        if int(getattr(daily_row, "tradestatus", 1)) == 0:
+            continue
+
+        volume = float(daily_row.volume)
+        volume_slices = [0.25 * volume, 0.35 * volume, 0.40 * volume]
+        prices = np.linspace(float(daily_row.open), float(daily_row.close), len(bar_times))
+        for j, time_text in enumerate(bar_times):
+            dt = pd.Timestamp(daily_row.date) + pd.Timedelta(time_text)
+            open_price = prices[j - 1] if j else float(daily_row.open)
+            close_price = float(prices[j])
+            high = max(open_price, close_price) * 1.002
+            low = min(open_price, close_price) * 0.998
+            minute_volume = float(volume_slices[j])
+            amount = minute_volume * ((open_price + close_price) / 2.0)
+            rows.append({
+                "symbol_id": daily_row.symbol_id,
+                "symbol_name": daily_row.symbol_name,
+                "datetime": dt,
+                "date": pd.Timestamp(daily_row.date).normalize(),
+                "time": time_text,
+                "frequency": "5m",
+                "open": open_price,
+                "high": high,
+                "low": low,
+                "close": close_price,
+                "volume": minute_volume,
+                "amount": amount,
+                "vwap": amount / minute_volume if minute_volume > 0 else np.nan,
+                "adjustflag": "3",
+            })
+
+    return pd.DataFrame(rows, columns=MINUTE_BAR_COLUMNS)
+
+
+def make_generated_derived_features(
+    daily: pd.DataFrame | None = None,
+) -> pd.DataFrame:
+    """Return numeric daily derived values, including NaN and infinity cells."""
+    daily = make_generated_daily_bars() if daily is None else daily.copy()
+    keys = (
+        daily[["symbol_id", "date"]]
+        .drop_duplicates()
+        .sort_values(["date", "symbol_id"])
+        .reset_index(drop=True)
+    )
+    date_rank = keys["date"].rank(method="dense").astype(float)
+    symbol_rank = keys["symbol_id"].map({
+        "sh600000": 1.0,
+        "sz000001": 2.0,
+        "sh600519": 3.0,
+        "sz300750": 4.0,
+    })
+    out = keys.copy()
+    out["toy_feature"] = symbol_rank + date_rank / 100.0
+    out["finite_feature"] = symbol_rank * date_rank
+    out["nan_feature"] = out["toy_feature"]
+    out["inf_feature"] = out["toy_feature"]
+    if len(out) >= 2:
+        out.loc[out.index[0], "nan_feature"] = np.nan
+        out.loc[out.index[1], "inf_feature"] = np.inf
+    return out
+
+
+def make_generated_alpha_weights(
+    alpha_name: str = "alpha_a",
+    *,
+    scale: float = 1.0,
+    offset: float = 0.0,
+    zero_date_index: int | None = None,
+    n_sessions: int = 10,
+) -> pd.DataFrame:
+    """Create a deterministic long alpha grid with optional zero-gross date."""
+    dates = generated_sessions(n_sessions)
+    even = np.array([1.20, -0.80, 0.40, -0.80], dtype=float)
+    odd = np.array([-0.60, 1.10, -0.90, 0.40], dtype=float)
+    rows: list[dict[str, object]] = []
+    for i, date in enumerate(dates):
+        vector = even.copy() if i % 2 == 0 else odd.copy()
+        vector = vector + offset
+        vector = vector - vector.mean()
+        if zero_date_index is not None and i == zero_date_index:
+            vector = np.zeros_like(vector)
+        for sym, weight in zip(GENERATED_SYMBOLS, scale * vector):
+            rows.append({
+                "symbol_id": sym,
+                "date": date,
+                "alpha_name": alpha_name,
+                "weight": float(weight),
+            })
+    result = pd.DataFrame(rows, columns=ALPHA_COLUMNS)
+    return result.sort_values(["symbol_id", "date"]).reset_index(drop=True)
+
+
+def make_generated_combo_weights(
+    combo_name: str = "combo",
+    *,
+    zero_date_index: int | None = 2,
+    n_sessions: int = 10,
+) -> pd.DataFrame:
+    """Create deterministic combo weights for portfolio construction tests."""
+    alpha = make_generated_alpha_weights(
+        "combo_source",
+        zero_date_index=zero_date_index,
+        n_sessions=n_sessions,
+    )
+    combo = alpha.rename(columns={"alpha_name": "combo_name"}).copy()
+    combo["combo_name"] = combo_name
+    return combo[COMBO_COLUMNS].sort_values(["symbol_id", "date"]).reset_index(drop=True)

tests/test_alpha.py

@@ -413,3 +413,92 @@ def test_feature_aware_alpha_reads_joined_feature_column(tmp_path):
     assert (result["alpha_name"] == "feature_run").all()
     last = result[result["date"] == result["date"].max()]
     assert last.set_index("symbol_id")["weight"].idxmax() == "sh600519"
+
+
+def test_feature_paths_join_multiple_files_and_normalize_dates(tmp_path):
+    module_path = tmp_path / "multi_feature_alpha.py"
+    module_path.write_text(textwrap.dedent('''
+        import pandas as pd
+        from pipeline.alpha.base import BaseAlpha
+        from pipeline.alpha.registry import register_alpha
+
+        @register_alpha
+        class MultiFeatureAlpha(BaseAlpha):
+            name = "multi_feature_test_alpha"
+
+            def signal_from_data(
+                self,
+                data: pd.DataFrame,
+                close: pd.DataFrame,
+            ) -> pd.DataFrame:
+                data = data.copy()
+                data["combined_feature"] = data["toy_a"] + data["toy_b"]
+                signal = data.pivot_table(
+                    index="date",
+                    columns="symbol_id",
+                    values="combined_feature",
+                    aggfunc="first",
+                )
+                return signal.reindex(index=close.index, columns=close.columns)
+    '''))
+
+    data = _make_data(n_days=8)
+    symbol_score = {"sh600000": 1.0, "sz000001": 2.0, "sh600519": 3.0}
+
+    feature_a = data[["symbol_id", "date"]].copy()
+    feature_a["date"] = feature_a["date"] + pd.Timedelta(hours=15)
+    feature_a["toy_a"] = feature_a["symbol_id"].map(symbol_score)
+
+    feature_b = data[["symbol_id", "date"]].copy()
+    feature_b["date"] = feature_b["date"].dt.strftime("%Y-%m-%d 09:30:00")
+    feature_b["toy_b"] = feature_b["symbol_id"].map(symbol_score) * 10.0
+
+    feature_a_path = tmp_path / "toy_a.pq"
+    feature_b_path = tmp_path / "toy_b.pq"
+    feature_a.to_parquet(feature_a_path, index=False)
+    feature_b.to_parquet(feature_b_path, index=False)
+
+    load_alpha_module(str(module_path))
+    result = compute_alpha(
+        data,
+        "multi_feature_run",
+        "multi_feature_test_alpha",
+        feature_paths=[str(feature_a_path), str(feature_b_path)],
+    )
+
+    assert list(result.columns) == ALPHA_COLUMNS
+    assert (result["alpha_name"] == "multi_feature_run").all()
+    last = result[result["date"] == result["date"].max()]
+    assert last.set_index("symbol_id")["weight"].idxmax() == "sh600519"
+
+
+def test_compute_alpha_rejects_duplicate_feature_frame_columns():
+    data = _make_data()
+    duplicate_columns = pd.DataFrame(
+        [["sh600000", pd.Timestamp("2024-01-01"), 1.0, 2.0]],
+        columns=["symbol_id", "date", "toy_feature", "toy_feature"],
+    )
+
+    with pytest.raises(ValueError, match="duplicate columns"):
+        compute_alpha(
+            data,
+            "bad_features",
+            "reversal",
+            feature_frames=[duplicate_columns],
+        )
+
+
+def test_compute_alpha_rejects_feature_path_collision_with_daily_data(tmp_path):
+    data = _make_data()
+    close_collision = data[["symbol_id", "date"]].copy()
+    close_collision["close"] = 1.0
+    close_collision_path = tmp_path / "close_collision.pq"
+    close_collision.to_parquet(close_collision_path, index=False)
+
+    with pytest.raises(ValueError, match="conflict"):
+        compute_alpha(
+            data,
+            "close_collision",
+            "reversal",
+            feature_paths=[str(close_collision_path)],
+        )

tests/test_cli_workflow.py

@@ -0,0 +1,427 @@
+"""CLI handoff tests for the offline daily workflow."""
+
+from __future__ import annotations
+
+import textwrap
+from pathlib import Path
+
+import pandas as pd
+from click.testing import CliRunner
+
+from cli import cli
+from tests.helpers import (
+    make_generated_daily_bars,
+    make_generated_derived_features,
+    make_generated_minute_bars,
+)
+
+
+FIXTURE_PATH = Path(__file__).parent / "fixtures" / "daily_bars_real_2024_01_sample.pq"
+
+
+def _invoke_ok(runner: CliRunner, args: list[str]):
+    result = runner.invoke(cli, args)
+    assert result.exit_code == 0, result.output
+    return result
+
+
+def _invoke_error(runner: CliRunner, args: list[str]):
+    result = runner.invoke(cli, args)
+    assert result.exit_code != 0, result.output
+    return result
+
+
+def test_cli_daily_workflow_handoffs_stay_in_tmp_path(tmp_path):
+    runner = CliRunner()
+    daily_bars = make_generated_daily_bars()
+    minute_bars = make_generated_minute_bars(daily_bars)
+    derived_features = make_generated_derived_features(daily_bars)
+
+    daily_path = tmp_path / "daily_bars.pq"
+    minute_path = tmp_path / "minute_bars.pq"
+    derived_input_path = tmp_path / "derived_input.pq"
+    daily_bars.to_parquet(daily_path, index=False)
+    minute_bars.to_parquet(minute_path, index=False)
+    derived_features.to_parquet(derived_input_path, index=False)
+
+    ingest_dir = tmp_path / "derived_ingested"
+    ingest_result = _invoke_ok(runner, [
+        "derived", "ingest",
+        "--input-path", str(derived_input_path),
+        "--derived-name", "toy_features",
+        "--output-dir", str(ingest_dir),
+    ])
+    ingested_feature_path = ingest_dir / "toy_features.pq"
+    assert "Saved derived data:" in ingest_result.output
+    assert ingested_feature_path.exists()
+
+    validate_result = _invoke_ok(runner, [
+        "derived", "validate",
+        "--input-path", str(ingested_feature_path),
+    ])
+    assert "Valid derived data:" in validate_result.output
+    assert "rows" in validate_result.output
+
+    computed_derived_dir = tmp_path / "derived_computed"
+    derived_compute_result = _invoke_ok(runner, [
+        "derived", "compute",
+        "--daily-path", str(daily_path),
+        "--minute-path", str(minute_path),
+        "--derived-type", "minute_daily_summary",
+        "--derived-name", "minute_summary",
+        "--output-dir", str(computed_derived_dir),
+    ])
+    minute_summary_path = computed_derived_dir / "minute_summary.pq"
+    assert "Loaded daily data:" in derived_compute_result.output
+    assert "Loaded minute bars:" in derived_compute_result.output
+    assert "Saved derived data:" in derived_compute_result.output
+    assert minute_summary_path.exists()
+    assert "minute_vwap" in pd.read_parquet(minute_summary_path).columns
+
+    alpha_module_path = tmp_path / "cli_feature_alpha.py"
+    alpha_module_path.write_text(textwrap.dedent("""
+        import pandas as pd
+        from pipeline.alpha.base import BaseAlpha
+        from pipeline.alpha.registry import register_alpha
+
+        @register_alpha
+        class CliFeatureAlpha(BaseAlpha):
+            name = "cli_feature_alpha_workflow"
+
+            def __init__(self, **kwargs):
+                self.kwargs = kwargs
+
+            def signal_from_data(
+                self,
+                data: pd.DataFrame,
+                close: pd.DataFrame,
+            ) -> pd.DataFrame:
+                signal = data.pivot_table(
+                    index="date",
+                    columns="symbol_id",
+                    values="minute_intraday_return",
+                    aggfunc="first",
+                )
+                fallback = close.pct_change(1, fill_method=None)
+                feature_signal = signal.reindex(index=close.index, columns=close.columns)
+                toy_signal = data.pivot_table(
+                    index="date",
+                    columns="symbol_id",
+                    values="toy_feature",
+                    aggfunc="first",
+                )
+                toy_signal = toy_signal.reindex(index=close.index, columns=close.columns)
+                return feature_signal.fillna(fallback) + toy_signal / 1000.0
+    """))
+
+    alpha_dir = tmp_path / "alphas"
+    alpha_result = _invoke_ok(runner, [
+        "alpha", "compute",
+        "--data-path", str(daily_path),
+        "--feature-path", str(minute_summary_path),
+        "--feature-path", str(ingested_feature_path),
+        "--alpha-module", str(alpha_module_path),
+        "--alpha-type", "cli_feature_alpha_workflow",
+        "--alpha-name", "cli_feature_alpha",
+        "--output-dir", str(alpha_dir),
+    ])
+    alpha_path = alpha_dir / "cli_feature_alpha.pq"
+    assert "Loaded data:" in alpha_result.output
+    assert "Saved alpha:" in alpha_result.output
+    assert "Weight stats" in alpha_result.output
+    assert alpha_path.exists()
+    assert not pd.read_parquet(alpha_path).empty
+
+    alpha_report_dir = tmp_path / "alpha_reports"
+    alpha_eval_result = _invoke_ok(runner, [
+        "alpha", "eval",
+        "--alpha-path", str(alpha_path),
+        "--data-path", str(daily_path),
+        "--report-dir", str(alpha_report_dir),
+    ])
+    alpha_report_path = alpha_report_dir / "cli_feature_alpha_eval.json"
+    assert "ALPHA EVALUATION" in alpha_eval_result.output
+    assert "Report saved:" in alpha_eval_result.output
+    assert alpha_report_path.exists()
+
+    combo_dir = tmp_path / "combos"
+    combo_result = _invoke_ok(runner, [
+        "combo", "combine",
+        "--alpha-paths", f"{alpha_path},{alpha_path}",
+        "--combo-name", "cli_combo",
+        "--method", "equal_weight",
+        "--output-dir", str(combo_dir),
+    ])
+    combo_path = combo_dir / "cli_combo.pq"
+    assert "Saved combo:" in combo_result.output
+    assert "Weight stats" in combo_result.output
+    assert combo_path.exists()
+
+    portfolio_dir = tmp_path / "portfolio"
+    build_result = _invoke_ok(runner, [
+        "portfolio", "build",
+        "--weights-path", str(combo_path),
+        "--data-path", str(daily_path),
+        "--booksize", "2000000",
+        "--portfolio-name", "cli_portfolio",
+        "--output-dir", str(portfolio_dir),
+    ])
+    positions_path = portfolio_dir / "cli_portfolio.pq"
+    assert "Saved positions:" in build_result.output
+    assert "Gross exposure" in build_result.output
+    assert positions_path.exists()
+
+    execution_dir = tmp_path / "execution"
+    simulate_result = _invoke_ok(runner, [
+        "portfolio", "simulate",
+        "--positions-path", str(positions_path),
+        "--data-path", str(daily_path),
+        "--constraint", "suspension",
+        "--constraint", "price_limit",
+        "--constraint", "volume_cap",
+        "--cost-bps", "5",
+        "--slippage-bps", "5",
+        "--volume-frac", "0.02",
+        "--output-dir", str(execution_dir),
+    ])
+    fills_path = execution_dir / "fills" / "cli_portfolio.pq"
+    pnl_path = execution_dir / "pnl" / "cli_portfolio.pq"
+    assert "Saved fills:" in simulate_result.output
+    assert "Saved pnl:" in simulate_result.output
+    assert "Total PnL:" in simulate_result.output
+    assert fills_path.exists()
+    assert pnl_path.exists()
+
+    eval_result = _invoke_ok(runner, [
+        "portfolio", "eval",
+        "--positions-path", str(positions_path),
+        "--data-path", str(daily_path),
+    ])
+    assert "Research-portfolio metrics:" in eval_result.output
+    assert "cumulative_return" in eval_result.output
+    assert "fitness" in eval_result.output
+
+    pqcat_result = _invoke_ok(runner, [
+        "pqcat",
+        str(positions_path),
+        "--info",
+    ])
+    assert "shape:" in pqcat_result.output
+    assert "dtypes:" in pqcat_result.output
+    assert "position_shares" in pqcat_result.output
+
+    alphaview_result = _invoke_ok(runner, [
+        "alphaview",
+        "--data-path", str(daily_path),
+        "--alpha-path", str(alpha_path),
+        "--symbol", "sh600000",
+        "--start-date", "2024-01-02",
+        "--end-date", "2024-01-12",
+        "--columns", "close,volume",
+    ])
+    assert "symbol: sh600000" in alphaview_result.output
+    assert "cli_feature_alpha" in alphaview_result.output
+
+
+def test_cli_pipeline_accepts_partitioned_daily_dataset(tmp_path):
+    runner = CliRunner()
+    daily_bars = make_generated_daily_bars(include_missing=False)
+    dataset_dir = tmp_path / "daily_dataset"
+    dataset_frame = daily_bars.copy()
+    dataset_frame["month"] = dataset_frame["date"].dt.strftime("%Y-%m")
+    dataset_frame.to_parquet(dataset_dir, partition_cols=["month"], index=False)
+
+    alpha_dir = tmp_path / "alphas"
+    alpha_result = _invoke_ok(runner, [
+        "alpha", "compute",
+        "--data-path", str(dataset_dir),
+        "--alpha-type", "reversal",
+        "--alpha-name", "dataset_reversal",
+        "--lookback", "3",
+        "--output-dir", str(alpha_dir),
+    ])
+    alpha_path = alpha_dir / "dataset_reversal.pq"
+    assert "Loaded data:" in alpha_result.output
+    assert alpha_path.exists()
+
+    combo_dir = tmp_path / "combos"
+    _invoke_ok(runner, [
+        "combo", "combine",
+        "--alpha-paths", str(alpha_path),
+        "--combo-name", "dataset_combo",
+        "--output-dir", str(combo_dir),
+    ])
+    combo_path = combo_dir / "dataset_combo.pq"
+    assert combo_path.exists()
+
+    portfolio_dir = tmp_path / "portfolio"
+    _invoke_ok(runner, [
+        "portfolio", "build",
+        "--weights-path", str(combo_path),
+        "--data-path", str(dataset_dir),
+        "--booksize", "1000000",
+        "--portfolio-name", "dataset_portfolio",
+        "--output-dir", str(portfolio_dir),
+    ])
+    positions_path = portfolio_dir / "dataset_portfolio.pq"
+    assert positions_path.exists()
+
+    execution_dir = tmp_path / "execution"
+    simulate_result = _invoke_ok(runner, [
+        "portfolio", "simulate",
+        "--positions-path", str(positions_path),
+        "--data-path", str(dataset_dir),
+        "--constraint", "suspension",
+        "--output-dir", str(execution_dir),
+    ])
+    assert "Saved fills:" in simulate_result.output
+    assert (execution_dir / "fills" / "dataset_portfolio.pq").exists()
+    assert (execution_dir / "pnl" / "dataset_portfolio.pq").exists()
+
+
+def test_cli_liquid_universe_masks_to_top_liquid_names(tmp_path):
+    runner = CliRunner()
+    daily_bars = make_generated_daily_bars(n_sessions=75, include_missing=False)
+    daily_path = tmp_path / "daily_bars_75d.pq"
+    daily_bars.to_parquet(daily_path, index=False)
+
+    alpha_dir = tmp_path / "alphas"
+    result = _invoke_ok(runner, [
+        "alpha", "compute",
+        "--data-path", str(daily_path),
+        "--alpha-type", "reversal_rank",
+        "--alpha-name", "liquid_rank",
+        "--lookback", "3",
+        "--liquid-universe",
+        "--universe-top-n", "2",
+        "--output-dir", str(alpha_dir),
+    ])
+
+    alpha_path = alpha_dir / "liquid_rank.pq"
+    alpha = pd.read_parquet(alpha_path)
+    nonzero = alpha[alpha["weight"] != 0.0]
+    assert "Saved alpha:" in result.output
+    assert alpha_path.exists()
+    assert not nonzero.empty
+    assert nonzero.groupby("date")["symbol_id"].nunique().max() <= 2
+
+
+def test_cli_real_fixture_round_trips_through_portfolio(tmp_path):
+    runner = CliRunner()
+
+    alpha_dir = tmp_path / "alphas"
+    _invoke_ok(runner, [
+        "alpha", "compute",
+        "--data-path", str(FIXTURE_PATH),
+        "--alpha-type", "reversal_vol",
+        "--alpha-name", "real_cli_reversal_vol",
+        "--lookback", "3",
+        "--vol-window", "3",
+        "--output-dir", str(alpha_dir),
+    ])
+    alpha_path = alpha_dir / "real_cli_reversal_vol.pq"
+    assert alpha_path.exists()
+    assert not pd.read_parquet(alpha_path).empty
+
+    combo_dir = tmp_path / "combos"
+    _invoke_ok(runner, [
+        "combo", "combine",
+        "--alpha-paths", str(alpha_path),
+        "--combo-name", "real_cli_combo",
+        "--output-dir", str(combo_dir),
+    ])
+    combo_path = combo_dir / "real_cli_combo.pq"
+    assert combo_path.exists()
+
+    portfolio_dir = tmp_path / "portfolio"
+    _invoke_ok(runner, [
+        "portfolio", "build",
+        "--weights-path", str(combo_path),
+        "--data-path", str(FIXTURE_PATH),
+        "--booksize", "1000000",
+        "--portfolio-name", "real_cli_portfolio",
+        "--output-dir", str(portfolio_dir),
+    ])
+    positions_path = portfolio_dir / "real_cli_portfolio.pq"
+    positions = pd.read_parquet(positions_path)
+    assert not positions.empty
+
+    eval_result = _invoke_ok(runner, [
+        "portfolio", "eval",
+        "--positions-path", str(positions_path),
+        "--data-path", str(FIXTURE_PATH),
+    ])
+    assert "Research-portfolio metrics:" in eval_result.output
+
+
+def test_cli_error_paths_are_clear_for_bad_user_inputs(tmp_path):
+    runner = CliRunner()
+    daily_bars = make_generated_daily_bars()
+    daily_path = tmp_path / "daily_bars.pq"
+    daily_bars.to_parquet(daily_path, index=False)
+
+    unknown_alpha = _invoke_error(runner, [
+        "alpha", "compute",
+        "--data-path", str(daily_path),
+        "--alpha-type", "does_not_exist",
+        "--alpha-name", "bad",
+        "--output-dir", str(tmp_path / "alphas"),
+    ])
+    assert "Unknown alpha-type" in unknown_alpha.output
+
+    malformed_param = _invoke_error(runner, [
+        "alpha", "compute",
+        "--data-path", str(daily_path),
+        "--alpha-type", "reversal",
+        "--alpha-name", "bad_param",
+        "--param", "not-an-assignment",
+        "--output-dir", str(tmp_path / "alphas"),
+    ])
+    assert "--param must be name=value" in malformed_param.output
+
+    unknown_derived = _invoke_error(runner, [
+        "derived", "compute",
+        "--daily-path", str(daily_path),
+        "--derived-type", "does_not_exist",
+        "--derived-name", "bad",
+        "--output-dir", str(tmp_path / "derived"),
+    ])
+    assert "Unknown derived-type" in unknown_derived.output
+
+    bad_constraint_positions = pd.DataFrame({
+        "symbol_id": ["sh600000"],
+        "date": [pd.Timestamp("2024-01-02")],
+        "portfolio_name": ["bad_constraint"],
+        "target_weight": [1.0],
+        "target_value": [1000.0],
+        "target_shares": [100.0],
+        "position_shares": [100],
+        "position_value": [1000.0],
+        "price": [10.0],
+    })
+    positions_path = tmp_path / "positions.pq"
+    bad_constraint_positions.to_parquet(positions_path, index=False)
+    unknown_constraint = _invoke_error(runner, [
+        "portfolio", "simulate",
+        "--positions-path", str(positions_path),
+        "--data-path", str(daily_path),
+        "--constraint", "not_a_constraint",
+        "--output-dir", str(tmp_path / "execution"),
+    ])
+    assert isinstance(unknown_constraint.exception, KeyError)
+    assert "not_a_constraint" in str(unknown_constraint.exception)
+
+    pqcat_missing_column = _invoke_error(runner, [
+        "pqcat",
+        str(daily_path),
+        "--columns", "close,not_a_column",
+    ])
+    assert "Columns not found: not_a_column" in pqcat_missing_column.output
+
+    alphaview_missing_symbol = _invoke_error(runner, [
+        "alphaview",
+        "--data-path", str(daily_path),
+        "--alpha-path", str(positions_path),
+        "--symbol", "sh999999",
+    ])
+    assert "Symbol 'sh999999' not found" in alphaview_missing_symbol.output

tests/test_downloader.py

@@ -1,6 +1,16 @@
+import os
+
 import pytest
 from data.downloader import download_daily
 
+pytestmark = [
+    pytest.mark.network,
+    pytest.mark.skipif(
+        os.environ.get("CEQ_RUN_LIVE_DOWNLOADER") != "1",
+        reason="set CEQ_RUN_LIVE_DOWNLOADER=1 to run live baostock/akshare smoke tests",
+    ),
+]
+
 
 def test_download_single_stock():
     """Smoke test: download data for 浦发银行 for a short window."""

tests/test_downloader_contracts.py

@@ -0,0 +1,306 @@
+"""Offline downloader contract tests with mocked data providers."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+import data.downloader as downloader
+import pipeline.data.downloader as pipeline_downloader
+from data.downloader import download_daily, download_daily_batch
+from pipeline.common.schema import DATA_COLUMNS
+from pipeline.data.downloader import download_universe
+
+
+class _FakeResult:
+    def __init__(self, rows, error_code="0", error_msg=""):
+        self.rows = rows
+        self.error_code = error_code
+        self.error_msg = error_msg
+        self._idx = -1
+
+    def next(self):
+        self._idx += 1
+        return self._idx < len(self.rows)
+
+    def get_row_data(self):
+        return self.rows[self._idx]
+
+
+def _daily_batch_row(
+    date: str = "2024-01-02",
+    open_: str = "10",
+    high: str = "11",
+    low: str = "9",
+    close: str = "10.5",
+    preclose: str = "10",
+    volume: str = "1000",
+    amount: str = "10500",
+) -> list[str]:
+    return [
+        date,
+        open_,
+        high,
+        low,
+        close,
+        preclose,
+        volume,
+        amount,
+        "1.23",
+        "5.0",
+        "1",
+        "0",
+        "8.0",
+        "1.1",
+        "2.2",
+        "3.3",
+    ]
+
+
+def test_download_daily_uses_baostock_before_akshare_in_auto(monkeypatch):
+    calls: list[str] = []
+    expected = pd.DataFrame({
+        "symbol": ["sh600000"],
+        "date": ["2024-01-02"],
+        "open": [10.0],
+        "high": [11.0],
+        "low": [9.0],
+        "close": [10.5],
+        "volume": [1000.0],
+        "amount": [10500.0],
+    })
+
+    def fake_baostock(symbol, start, end, adjust):
+        calls.append("baostock")
+        return expected
+
+    def fake_akshare(symbol, start, end, adjust):
+        calls.append("akshare")
+        raise AssertionError("akshare should not be called after baostock succeeds")
+
+    monkeypatch.setattr(downloader, "_download_baostock", fake_baostock)
+    monkeypatch.setattr(downloader, "_download_akshare", fake_akshare)
+
+    result = download_daily("sh600000", "2024-01-02", "2024-01-02", source="auto")
+
+    assert calls == ["baostock"]
+    assert result["date"].tolist() == [pd.Timestamp("2024-01-02")]
+    assert result["close"].tolist() == [10.5]
+
+
+def test_download_daily_falls_back_to_akshare_when_baostock_empty(monkeypatch):
+    calls: list[str] = []
+    fallback = pd.DataFrame({
+        "symbol": ["sz000001"],
+        "date": ["2024-01-02"],
+        "open": [20.0],
+        "high": [21.0],
+        "low": [19.0],
+        "close": [20.5],
+        "volume": [2000.0],
+        "amount": [41000.0],
+    })
+
+    monkeypatch.setattr(
+        downloader,
+        "_download_baostock",
+        lambda symbol, start, end, adjust: calls.append("baostock") or None,
+    )
+    monkeypatch.setattr(
+        downloader,
+        "_download_akshare",
+        lambda symbol, start, end, adjust: calls.append("akshare") or fallback,
+    )
+
+    result = download_daily("sz000001", "2024-01-02", "2024-01-02", source="auto")
+
+    assert calls == ["baostock", "akshare"]
+    assert result["symbol"].tolist() == ["sz000001"]
+    assert result["date"].tolist() == [pd.Timestamp("2024-01-02")]
+
+
+def test_download_daily_batch_maps_rich_schema_and_vwap(monkeypatch):
+    query_calls: list[dict] = []
+    login_count = 0
+    logout_count = 0
+
+    def fake_login():
+        nonlocal login_count
+        login_count += 1
+
+    def fake_logout():
+        nonlocal logout_count
+        logout_count += 1
+
+    def fake_query(**kwargs):
+        query_calls.append(kwargs)
+        rows = [
+            _daily_batch_row(volume="1000", amount="10500"),
+            _daily_batch_row(date="2024-01-03", volume="0", amount="0"),
+        ]
+        return _FakeResult(rows)
+
+    monkeypatch.setattr(downloader.bs, "login", fake_login)
+    monkeypatch.setattr(downloader.bs, "logout", fake_logout)
+    monkeypatch.setattr(downloader.bs, "query_history_k_data_plus", fake_query)
+
+    [(symbol, frame)] = list(
+        download_daily_batch(
+            ["sh600000"],
+            "2024-01-02",
+            "2024-01-03",
+            adjust="hfq",
+        )
+    )
+
+    assert symbol == "sh600000"
+    assert query_calls[0]["code"] == "sh.600000"
+    assert query_calls[0]["adjustflag"] == "1"
+    assert login_count == 1
+    assert logout_count == 1
+    assert frame is not None
+    assert frame.columns.tolist() == [
+        "symbol", "date", "open", "high", "low", "close", "preclose",
+        "volume", "amount", "vwap", "turn", "pctChg", "tradestatus", "isST",
+        "peTTM", "pbMRQ", "psTTM", "pcfNcfTTM",
+    ]
+    assert np.isclose(frame["vwap"].iloc[0], 10.5)
+    assert pd.isna(frame["vwap"].iloc[1])
+    assert pd.api.types.is_datetime64_any_dtype(frame["date"])
+    assert pd.api.types.is_numeric_dtype(frame["tradestatus"])
+
+
+def test_download_daily_batch_relogs_and_retries_session_loss(monkeypatch):
+    responses = [
+        _FakeResult([], error_code="10002007", error_msg="用户未登录"),
+        _FakeResult([_daily_batch_row()]),
+    ]
+    login_count = 0
+    logout_count = 0
+
+    def fake_login():
+        nonlocal login_count
+        login_count += 1
+
+    def fake_logout():
+        nonlocal logout_count
+        logout_count += 1
+
+    monkeypatch.setattr(downloader.bs, "login", fake_login)
+    monkeypatch.setattr(downloader.bs, "logout", fake_logout)
+    monkeypatch.setattr(
+        downloader.bs,
+        "query_history_k_data_plus",
+        lambda **kwargs: responses.pop(0),
+    )
+
+    [(symbol, frame)] = list(download_daily_batch(["sh600000"], "2024-01-02", "2024-01-02"))
+
+    assert symbol == "sh600000"
+    assert frame is not None
+    assert len(frame) == 1
+    assert login_count == 2
+    assert logout_count == 2
+
+
+def test_download_daily_batch_uses_akshare_fallback_when_enabled(monkeypatch):
+    fallback = pd.DataFrame({
+        "symbol": ["sh600000"],
+        "date": ["2024-01-02"],
+        "open": [10.0],
+        "high": [11.0],
+        "low": [9.0],
+        "close": [10.5],
+        "volume": [1000.0],
+        "amount": [10500.0],
+    })
+
+    monkeypatch.setattr(downloader.bs, "login", lambda: None)
+    monkeypatch.setattr(downloader.bs, "logout", lambda: None)
+    monkeypatch.setattr(
+        downloader.bs,
+        "query_history_k_data_plus",
+        lambda **kwargs: _FakeResult([], error_code="1", error_msg="no data"),
+    )
+    monkeypatch.setattr(
+        downloader,
+        "_download_akshare",
+        lambda symbol, start, end, adjust: fallback.copy(),
+    )
+
+    [(symbol, frame)] = list(
+        download_daily_batch(
+            ["sh600000"],
+            "2024-01-02",
+            "2024-01-02",
+            akshare_fallback=True,
+        )
+    )
+
+    assert symbol == "sh600000"
+    assert frame is not None
+    assert frame["date"].tolist() == [pd.Timestamp("2024-01-02")]
+    assert frame["close"].tolist() == [10.5]
+
+
+def test_download_universe_writes_daily_partitions_from_mock_batch(tmp_path, monkeypatch):
+    batch_frame = pd.DataFrame({
+        "symbol": ["sh600000", "sh600000"],
+        "date": pd.to_datetime(["2024-01-02", "2024-02-01"]),
+        "open": [10.0, 11.0],
+        "high": [11.0, 12.0],
+        "low": [9.0, 10.0],
+        "close": [10.5, 11.5],
+        "preclose": [10.0, 10.5],
+        "volume": [1000.0, 1200.0],
+        "amount": [10500.0, 13800.0],
+        "vwap": [10.5, 11.5],
+        "turn": [1.0, 1.1],
+        "pctChg": [5.0, 9.5],
+        "tradestatus": [1, 1],
+        "isST": [0, 0],
+        "peTTM": [8.0, 8.1],
+        "pbMRQ": [1.1, 1.2],
+        "psTTM": [2.1, 2.2],
+        "pcfNcfTTM": [3.1, 3.2],
+    })
+
+    monkeypatch.setattr(
+        pipeline_downloader,
+        "_resolve_universe",
+        lambda universe, max_symbols=0: pd.DataFrame({
+            "symbol_id": ["sh600000", "sz000001"],
+            "symbol_name": ["PF Bank", "Ping An Bank"],
+        }),
+    )
+
+    def fake_batch(symbols, start, end, adjust="qfq"):
+        assert symbols == ["sh600000", "sz000001"]
+        assert adjust == "qfq"
+        yield "sh600000", batch_frame
+        yield "sz000001", None
+
+    monkeypatch.setattr(pipeline_downloader, "download_daily_batch", fake_batch)
+
+    stats = download_universe(
+        universe="toy",
+        start_date="2024-01-02",
+        end_date="2024-02-01",
+        output_dir=str(tmp_path),
+        chunk_size=1,
+    )
+
+    dataset_path = tmp_path / "toy"
+    written = pd.read_parquet(dataset_path).sort_values(["date", "symbol_id"]).reset_index(drop=True)
+    assert stats == {
+        "dataset_path": str(dataset_path),
+        "n_symbols": 1,
+        "n_requested": 2,
+        "n_rows": 2,
+        "date_min": "2024-01-02",
+        "date_max": "2024-02-01",
+    }
+    assert (dataset_path / "month=2024-01").exists()
+    assert (dataset_path / "month=2024-02").exists()
+    assert written[DATA_COLUMNS].columns.tolist() == DATA_COLUMNS
+    assert written["symbol_name"].tolist() == ["PF Bank", "PF Bank"]

tests/test_features.py

@@ -13,6 +13,7 @@ from pipeline.features.registry import (
     get_feature,
     load_feature_module,
 )
+from pipeline.derived.compute import compute_derived
 
 
 def _minute_bars() -> pd.DataFrame:
@@ -77,6 +78,27 @@ def test_minute_daily_summary_feature_preserves_legacy_positional_compute():
     pd.testing.assert_frame_equal(direct, via_registry)
 
 
+def test_legacy_feature_compute_matches_canonical_derived_compute():
+    daily = pd.DataFrame({
+        "symbol_id": ["sh600000", "sz000001", "sh600000"],
+        "date": pd.to_datetime(["2024-01-02", "2024-01-02", "2024-01-03"]),
+        "close": [11.0, 20.5, 12.0],
+    })
+
+    legacy_feature = compute_feature(
+        minute=_minute_bars(),
+        daily=daily,
+        feature_type="minute_daily_summary",
+    )
+    canonical_derived = compute_derived(
+        "minute_daily_summary",
+        daily=daily,
+        minute=_minute_bars(),
+    )
+
+    pd.testing.assert_frame_equal(legacy_feature, canonical_derived)
+
+
 def test_load_external_feature_module_and_filter_params(tmp_path):
     module_path = tmp_path / "external_feature.py"
     module_path.write_text(textwrap.dedent('''

tests/test_portfolio.py

@@ -306,6 +306,47 @@ def test_construct_positions_schema():
     assert pos["position_shares"].dtype == np.int64
 
 
+def test_construct_positions_empty_weights_returns_schema():
+    data = _make_data(n_days=3)
+    empty_weights = pd.DataFrame(columns=["symbol_id", "date", "combo_name", "weight"])
+
+    pos = construct_positions(
+        empty_weights,
+        data,
+        booksize=1e6,
+        portfolio_name="empty",
+    )
+
+    assert list(pos.columns) == POSITION_COLUMNS
+    assert pos.empty
+
+
+def test_construct_positions_ignores_absent_or_bad_prices():
+    dates = pd.to_datetime(["2024-01-01", "2024-01-02"])
+    data = pd.DataFrame([
+        {"symbol_id": "sh600000", "date": dates[0], "close": np.nan, "isST": 0},
+        {"symbol_id": "sz000001", "date": dates[0], "close": 20.0, "isST": 0},
+        {"symbol_id": "sh600000", "date": dates[1], "close": 10.0, "isST": 0},
+        {"symbol_id": "sz000001", "date": dates[1], "close": 20.0, "isST": 0},
+    ])
+    weights = pd.DataFrame({
+        "symbol_id": ["sh600000", "sz000001", "sh600000", "sz000001"],
+        "date": [dates[0], dates[0], dates[1], dates[1]],
+        "combo_name": ["combo"] * 4,
+        "weight": [1.0, -1.0, 1.0, -1.0],
+    })
+
+    pos = construct_positions(weights, data, booksize=10000.0, portfolio_name="bad_price")
+
+    bad_price_rows = pos[
+        (pos["date"] == dates[0])
+        & (pos["symbol_id"] == "sh600000")
+    ]
+    assert bad_price_rows.empty or (bad_price_rows["target_weight"] == 0.0).all()
+    assert np.isfinite(pos["target_value"]).all()
+    assert np.isfinite(pos["position_value"]).all()
+
+
 def test_construct_positions_threads_state_and_closes_absent():
     data = _make_data()
     weights = _make_weights(data)
@@ -321,6 +362,34 @@ def test_construct_positions_threads_state_and_closes_absent():
     assert final.empty or (final["position_shares"] == 0).all()
 
 
+def test_construct_positions_closes_absent_short_position():
+    dates = pd.to_datetime(["2024-01-02", "2024-01-03"])
+    data = pd.DataFrame([
+        {"symbol_id": sym, "date": d, "close": price, "isST": 0}
+        for d in dates
+        for sym, price in (("sh600000", 10.0), ("sz000001", 20.0))
+    ])
+    weights = pd.DataFrame({
+        "symbol_id": ["sh600000", "sz000001", "sz000001"],
+        "date": [dates[0], dates[0], dates[1]],
+        "combo_name": ["combo", "combo", "combo"],
+        "weight": [-1.0, 1.0, 1.0],
+    })
+
+    pos = construct_positions(weights, data, booksize=20000.0, portfolio_name="absent_short")
+
+    first_day_short = pos[
+        (pos["date"] == dates[0])
+        & (pos["symbol_id"] == "sh600000")
+    ]
+    final_day_short = pos[
+        (pos["date"] == dates[1])
+        & (pos["symbol_id"] == "sh600000")
+    ]
+    assert (first_day_short["position_shares"] < 0).all()
+    assert final_day_short.empty or (final_day_short["position_shares"] == 0).all()
+
+
 def test_construct_positions_carries_book_on_zero_gross(caplog):
     dates = pd.to_datetime(["2024-01-01", "2024-01-02"])
     symbols = ["sh600000", "sz000001"]
@@ -392,6 +461,42 @@ def test_volume_cap_uses_traded_value():
     assert low[0] == -10000.0
 
 
+def test_constraints_compose_repeatably_regardless_of_order():
+    n = 1
+    sl = _slice(
+        n,
+        tradestatus=np.array([0.0]),
+        limit_status=np.array([LimitStatus.UP_LIMIT.value], dtype=np.int8),
+        amount=np.array([1_000.0]),
+        price=np.array([10.0]),
+    )
+    ctx = TradeContext(np.zeros(n, np.int64), np.array([500]), sl, 1e6)
+    first_order = ReferenceSimulator(
+        constraints=[
+            SuspensionConstraint(),
+            PriceLimitConstraint(),
+            VolumeCapConstraint(max_frac=0.1),
+        ],
+        cost_bps=10,
+    ).fill(ctx)
+    reversed_order = ReferenceSimulator(
+        constraints=[
+            VolumeCapConstraint(max_frac=0.1),
+            PriceLimitConstraint(),
+            SuspensionConstraint(),
+        ],
+        cost_bps=10,
+    ).fill(ctx)
+
+    assert first_order.traded_shares.tolist() == [0]
+    assert first_order.realized_shares.tolist() == [0]
+    assert first_order.blocked.tolist() == [1]
+    assert np.array_equal(first_order.traded_shares, reversed_order.traded_shares)
+    assert np.array_equal(first_order.realized_shares, reversed_order.realized_shares)
+    assert np.array_equal(first_order.blocked, reversed_order.blocked)
+    assert np.array_equal(first_order.cost, reversed_order.cost)
+
+
 # --- ReferenceSimulator ------------------------------------------------------
 
 def test_simulator_next_open_and_blocked_buy_holds_prev():
@@ -474,6 +579,91 @@ def test_simulator_cost_only_on_nonzero_realized_trades():
     assert np.isclose(res.cost[1], 50 * 20 * 10 / 1e4)
 
 
+def test_simulator_short_to_long_flip_trades_full_delta():
+    dates = pd.to_datetime(["2024-01-01", "2024-01-02", "2024-01-03"])
+    positions = pd.DataFrame({
+        "symbol_id": ["sh600000", "sh600000"],
+        "date": [dates[0], dates[1]],
+        "portfolio_name": ["flip", "flip"],
+        "target_weight": [-1.0, 1.0],
+        "target_value": [-1000.0, 1000.0],
+        "target_shares": [-100.0, 100.0],
+        "position_shares": [-100, 100],
+        "position_value": [-1000.0, 1000.0],
+        "price": [10.0, 10.0],
+    })
+    data = pd.DataFrame({
+        "symbol_id": ["sh600000"] * 3,
+        "date": dates,
+        "open": [10.0, 10.0, 10.0],
+        "close": [10.0, 10.0, 10.0],
+        "preclose": [10.0, 10.0, 10.0],
+        "amount": [1e9, 1e9, 1e9],
+        "tradestatus": [1, 1, 1],
+        "isST": [0, 0, 0],
+    })
+
+    fills, _ = ReferenceSimulator().run(positions, data)
+
+    by_date = fills.set_index("date")
+    assert by_date.loc[dates[1], "traded_shares"] == -100
+    assert by_date.loc[dates[1], "realized_shares"] == -100
+    assert by_date.loc[dates[2], "prev_shares"] == -100
+    assert by_date.loc[dates[2], "traded_shares"] == 200
+    assert by_date.loc[dates[2], "realized_shares"] == 100
+
+
+def test_simulator_volume_cap_partially_fills_sell():
+    sl = _slice(1, amount=np.array([10_000.0]), price=np.array([10.0]))
+    ctx = TradeContext(
+        np.array([1000], np.int64),
+        np.array([0], np.int64),
+        sl,
+        1_000_000.0,
+    )
+
+    result = ReferenceSimulator(
+        constraints=[VolumeCapConstraint(max_frac=0.10)]
+    ).fill(ctx)
+
+    assert result.traded_shares.tolist() == [-100]
+    assert result.realized_shares.tolist() == [900]
+    assert result.blocked.tolist() == [1]
+
+
+def test_simulator_missing_next_open_has_zero_cost_and_turnover():
+    dates = pd.to_datetime(["2024-01-01", "2024-01-02"])
+    positions = pd.DataFrame({
+        "symbol_id": ["sh600000"],
+        "date": [dates[0]],
+        "portfolio_name": ["missing_open"],
+        "target_weight": [1.0],
+        "target_value": [1000.0],
+        "target_shares": [100.0],
+        "position_shares": [100],
+        "position_value": [1000.0],
+        "price": [10.0],
+    })
+    data = pd.DataFrame({
+        "symbol_id": ["sh600000", "sh600000"],
+        "date": dates,
+        "open": [10.0, np.nan],
+        "close": [10.0, 10.0],
+        "preclose": [10.0, 10.0],
+        "amount": [1e9, 1e9],
+        "tradestatus": [1, 1],
+        "isST": [0, 0],
+    })
+
+    fills, pnl = ReferenceSimulator(cost_bps=10, slippage_bps=5).run(positions, data)
+
+    assert fills["traded_shares"].iloc[0] == 100
+    assert fills["trade_cost"].iloc[0] == 0.0
+    assert pnl["cost"].iloc[0] == 0.0
+    assert pnl["turnover"].iloc[0] == 0.0
+    assert pnl["gross_exposure"].iloc[0] == 1000.0
+
+
 def test_simple_cost_model_adds_cost_and_slippage_without_price_adjustment():
     model = SimpleProportionalCostModel(cost_bps=10, slippage_bps=5)
 

tests/test_schema_failures.py

@@ -0,0 +1,98 @@
+"""Malformed parquet/input tests for phase boundary contracts."""
+
+from __future__ import annotations
+
+import pandas as pd
+import pytest
+
+from pipeline.alpha.compute import compute_alpha
+from pipeline.combo.combine import combine_alphas
+from pipeline.derived.compute import validate_derived_frame
+from pipeline.portfolio.construct import construct_positions
+from pipeline.portfolio.simulator import ReferenceSimulator
+from tests.helpers import make_generated_daily_bars
+
+
+def test_alpha_compute_rejects_daily_data_without_close():
+    daily = make_generated_daily_bars().drop(columns=["close"])
+
+    with pytest.raises(KeyError, match="close"):
+        compute_alpha(daily, "bad", "reversal", lookback=3)
+
+
+def test_alpha_feature_path_rejects_duplicate_symbol_dates(tmp_path):
+    daily = make_generated_daily_bars()
+    feature = pd.DataFrame({
+        "symbol_id": ["sh600000", "sh600000"],
+        "date": ["2024-01-02 09:30:00", "2024-01-02 15:00:00"],
+        "toy_feature": [1.0, 2.0],
+    })
+    feature_path = tmp_path / "duplicate_feature_keys.pq"
+    feature.to_parquet(feature_path, index=False)
+
+    with pytest.raises(ValueError, match="duplicate symbol_id,date"):
+        compute_alpha(
+            daily,
+            "bad_features",
+            "reversal",
+            lookback=3,
+            feature_paths=[str(feature_path)],
+        )
+
+
+def test_derived_validation_rejects_bool_value_columns():
+    derived = pd.DataFrame({
+        "symbol_id": ["sh600000"],
+        "date": [pd.Timestamp("2024-01-02")],
+        "is_good": [True],
+    })
+
+    with pytest.raises(ValueError, match="numeric"):
+        validate_derived_frame(derived)
+
+
+def test_combo_combine_rejects_missing_weight_column(tmp_path):
+    bad_alpha = pd.DataFrame({
+        "symbol_id": ["sh600000"],
+        "date": [pd.Timestamp("2024-01-02")],
+        "alpha_name": ["bad"],
+    })
+    bad_alpha_path = tmp_path / "bad_alpha.pq"
+    bad_alpha.to_parquet(bad_alpha_path, index=False)
+
+    with pytest.raises(KeyError, match="weight"):
+        combine_alphas([str(bad_alpha_path)], "bad_combo")
+
+
+def test_portfolio_build_rejects_weights_without_symbol_id():
+    daily = make_generated_daily_bars()
+    bad_weights = pd.DataFrame({
+        "date": [pd.Timestamp("2024-01-02")],
+        "combo_name": ["bad"],
+        "weight": [1.0],
+    })
+
+    with pytest.raises(KeyError, match="symbol_id"):
+        construct_positions(
+            bad_weights,
+            daily,
+            booksize=1_000_000.0,
+            portfolio_name="bad_portfolio",
+        )
+
+
+def test_portfolio_simulate_rejects_positions_without_position_shares():
+    daily = make_generated_daily_bars()
+    bad_positions = pd.DataFrame({
+        "symbol_id": ["sh600000"],
+        "date": [pd.Timestamp("2024-01-02")],
+        "portfolio_name": ["bad"],
+        "target_weight": [1.0],
+        "target_value": [1000.0],
+        "target_shares": [100.0],
+        "position_value": [1000.0],
+        "price": [10.0],
+    })
+
+    with pytest.raises(KeyError, match="position_shares"):
+        ReferenceSimulator().run(bad_positions, daily)

tests/test_workflow_sanity.py

@@ -0,0 +1,508 @@
+"""Verbose offline checks for the daily research workflow."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from pipeline.alpha.compute import compute_alpha
+from pipeline.combo.combine import combine_alphas
+from pipeline.common.schema import (
+    ALPHA_COLUMNS,
+    COMBO_COLUMNS,
+    FILL_COLUMNS,
+    PNL_COLUMNS,
+    POSITION_COLUMNS,
+)
+from pipeline.portfolio.constraints import (
+    PriceLimitConstraint,
+    SuspensionConstraint,
+    VolumeCapConstraint,
+)
+from pipeline.portfolio.construct import construct_positions
+from pipeline.portfolio.research import evaluate_portfolio
+from pipeline.portfolio.simulator import ReferenceSimulator
+from tests.helpers import (
+    GENERATED_SYMBOLS,
+    generated_sessions,
+    make_generated_alpha_weights,
+    make_generated_combo_weights,
+    make_generated_daily_bars,
+)
+
+
+FIXTURE_PATH = Path(__file__).parent / "fixtures" / "daily_bars_real_2024_01_sample.pq"
+
+
+def _assert_sorted_by_symbol_date(frame: pd.DataFrame) -> None:
+    expected = frame.sort_values(["symbol_id", "date"]).reset_index(drop=True)
+    pd.testing.assert_frame_equal(frame.reset_index(drop=True), expected)
+
+
+def _assert_metric_dict_is_finite(metrics: dict[str, float]) -> None:
+    for key in (
+        "cumulative_return",
+        "sharpe_annual",
+        "turnover_annual",
+        "max_drawdown",
+        "hit_rate",
+        "n_dates",
+    ):
+        assert key in metrics
+        assert np.isfinite(metrics[key])
+    assert "ic" not in metrics
+    assert "rank_ic" not in metrics
+    assert "ir" not in metrics
+
+
+def test_tiny_workflow_golden_outputs_are_stable(tmp_path):
+    dates = pd.to_datetime(["2024-01-02", "2024-01-03", "2024-01-04"])
+    daily_bars = pd.DataFrame([
+        {
+            "symbol_id": "sh600000",
+            "symbol_name": "A",
+            "date": dates[0],
+            "open": 10.0,
+            "high": 10.0,
+            "low": 10.0,
+            "close": 10.0,
+            "preclose": 10.0,
+            "volume": 1_000_000.0,
+            "amount": 10_000_000.0,
+            "vwap": 10.0,
+            "turn": 1.0,
+            "pctChg": 0.0,
+            "tradestatus": 1,
+            "isST": 0,
+            "peTTM": 1.0,
+            "pbMRQ": 1.0,
+            "psTTM": 1.0,
+            "pcfNcfTTM": 1.0,
+        },
+        {
+            "symbol_id": "sz000001",
+            "symbol_name": "B",
+            "date": dates[0],
+            "open": 20.0,
+            "high": 20.0,
+            "low": 20.0,
+            "close": 20.0,
+            "preclose": 20.0,
+            "volume": 1_000_000.0,
+            "amount": 20_000_000.0,
+            "vwap": 20.0,
+            "turn": 1.0,
+            "pctChg": 0.0,
+            "tradestatus": 1,
+            "isST": 0,
+            "peTTM": 1.0,
+            "pbMRQ": 1.0,
+            "psTTM": 1.0,
+            "pcfNcfTTM": 1.0,
+        },
+        {
+            "symbol_id": "sh600000",
+            "symbol_name": "A",
+            "date": dates[1],
+            "open": 10.0,
+            "high": 12.0,
+            "low": 10.0,
+            "close": 12.0,
+            "preclose": 10.0,
+            "volume": 1_000_000.0,
+            "amount": 10_000_000.0,
+            "vwap": 10.0,
+            "turn": 1.0,
+            "pctChg": 20.0,
+            "tradestatus": 1,
+            "isST": 0,
+            "peTTM": 1.0,
+            "pbMRQ": 1.0,
+            "psTTM": 1.0,
+            "pcfNcfTTM": 1.0,
+        },
+        {
+            "symbol_id": "sz000001",
+            "symbol_name": "B",
+            "date": dates[1],
+            "open": 20.0,
+            "high": 20.0,
+            "low": 18.0,
+            "close": 18.0,
+            "preclose": 20.0,
+            "volume": 1_000_000.0,
+            "amount": 20_000_000.0,
+            "vwap": 20.0,
+            "turn": 1.0,
+            "pctChg": -10.0,
+            "tradestatus": 1,
+            "isST": 0,
+            "peTTM": 1.0,
+            "pbMRQ": 1.0,
+            "psTTM": 1.0,
+            "pcfNcfTTM": 1.0,
+        },
+        {
+            "symbol_id": "sh600000",
+            "symbol_name": "A",
+            "date": dates[2],
+            "open": 12.0,
+            "high": 13.0,
+            "low": 12.0,
+            "close": 13.0,
+            "preclose": 12.0,
+            "volume": 1_000_000.0,
+            "amount": 12_000_000.0,
+            "vwap": 12.0,
+            "turn": 1.0,
+            "pctChg": 8.33,
+            "tradestatus": 1,
+            "isST": 0,
+            "peTTM": 1.0,
+            "pbMRQ": 1.0,
+            "psTTM": 1.0,
+            "pcfNcfTTM": 1.0,
+        },
+        {
+            "symbol_id": "sz000001",
+            "symbol_name": "B",
+            "date": dates[2],
+            "open": 18.0,
+            "high": 21.0,
+            "low": 18.0,
+            "close": 21.0,
+            "preclose": 18.0,
+            "volume": 1_000_000.0,
+            "amount": 18_000_000.0,
+            "vwap": 18.0,
+            "turn": 1.0,
+            "pctChg": 16.67,
+            "tradestatus": 1,
+            "isST": 0,
+            "peTTM": 1.0,
+            "pbMRQ": 1.0,
+            "psTTM": 1.0,
+            "pcfNcfTTM": 1.0,
+        },
+    ])
+    alpha = pd.DataFrame({
+        "symbol_id": ["sh600000", "sz000001", "sh600000", "sz000001"],
+        "date": [dates[0], dates[0], dates[1], dates[1]],
+        "alpha_name": ["gold_alpha"] * 4,
+        "weight": [1.0, -1.0, -1.0, 1.0],
+    })
+    alpha_path = tmp_path / "gold_alpha.pq"
+    alpha.to_parquet(alpha_path, index=False)
+
+    combo = combine_alphas([str(alpha_path)], "gold_combo")
+    positions = construct_positions(combo, daily_bars, booksize=20_000.0, portfolio_name="gold_port")
+    fills, pnl = ReferenceSimulator().run(positions, daily_bars)
+
+    expected_combo = pd.DataFrame({
+        "symbol_id": ["sh600000", "sh600000", "sz000001", "sz000001"],
+        "date": [dates[0], dates[1], dates[0], dates[1]],
+        "combo_name": ["gold_combo"] * 4,
+        "weight": [1.0, -1.0, -1.0, 1.0],
+    })
+    expected_positions = pd.DataFrame({
+        "symbol_id": ["sh600000", "sh600000", "sz000001", "sz000001"],
+        "date": [dates[0], dates[1], dates[0], dates[1]],
+        "portfolio_name": ["gold_port"] * 4,
+        "target_weight": [0.5, -0.5, -0.5, 0.5],
+        "target_value": [10000.0, -10000.0, -10000.0, 10000.0],
+        "target_shares": [1000.0, -10000.0 / 12.0, -500.0, 10000.0 / 18.0],
+        "position_shares": [1000, -833, -500, 556],
+        "position_value": [10000.0, -9996.0, -10000.0, 10008.0],
+        "price": [10.0, 12.0, 20.0, 18.0],
+    })
+    expected_fills = pd.DataFrame({
+        "symbol_id": ["sh600000", "sz000001", "sh600000", "sz000001"],
+        "date": [dates[1], dates[1], dates[2], dates[2]],
+        "portfolio_name": ["gold_port"] * 4,
+        "prev_shares": [0, 0, 1000, -500],
+        "target_shares": [1000, -500, -833, 556],
+        "traded_shares": [1000, -500, -1833, 1056],
+        "realized_shares": [1000, -500, -833, 556],
+        "blocked": [0, 0, 0, 0],
+        "trade_cost": [0.0, 0.0, 0.0, 0.0],
+    })
+    expected_pnl = pd.DataFrame({
+        "date": [dates[1], dates[2]],
+        "portfolio_name": ["gold_port", "gold_port"],
+        "gross_exposure": [21000.0, 22505.0],
+        "net_exposure": [3000.0, 847.0],
+        "pnl": [3000.0, 835.0],
+        "cost": [0.0, 0.0],
+        "turnover": [1.0, 2.0502],
+        "n_positions": [2, 2],
+    })
+
+    pd.testing.assert_frame_equal(combo, expected_combo)
+    pd.testing.assert_frame_equal(positions, expected_positions)
+    pd.testing.assert_frame_equal(fills, expected_fills)
+    pd.testing.assert_frame_equal(pnl, expected_pnl)
+
+
+def test_generated_alpha_combo_portfolio_execution_workflow(tmp_path):
+    daily_bars = make_generated_daily_bars()
+
+    computed_alpha = compute_alpha(
+        data=daily_bars,
+        alpha_name="generated_reversal_3d",
+        alpha_type="reversal",
+        lookback=3,
+    )
+
+    assert list(computed_alpha.columns) == ALPHA_COLUMNS
+    assert not computed_alpha.empty
+    assert set(computed_alpha["symbol_id"]).issubset(set(GENERATED_SYMBOLS))
+    assert computed_alpha["date"].min() > daily_bars["date"].min()
+    assert computed_alpha["weight"].notna().all()
+    assert computed_alpha["weight"].abs().sum() > 0.0
+    assert {"ic", "rank_ic", "ir"}.isdisjoint(computed_alpha.columns)
+    _assert_sorted_by_symbol_date(computed_alpha)
+
+    alpha_a = make_generated_alpha_weights("alpha_a", zero_date_index=2)
+    alpha_b = make_generated_alpha_weights(
+        "alpha_b",
+        scale=0.5,
+        offset=0.25,
+        zero_date_index=2,
+    )
+    alpha_a_path = tmp_path / "alpha_a.pq"
+    alpha_b_path = tmp_path / "alpha_b.pq"
+    alpha_a.to_parquet(alpha_a_path, index=False)
+    alpha_b.to_parquet(alpha_b_path, index=False)
+
+    identity_combo = combine_alphas([str(alpha_a_path)], "identity_combo")
+    assert list(identity_combo.columns) == COMBO_COLUMNS
+    assert (identity_combo["combo_name"] == "identity_combo").all()
+    pd.testing.assert_frame_equal(
+        identity_combo[["symbol_id", "date", "weight"]],
+        alpha_a[["symbol_id", "date", "weight"]],
+    )
+
+    equal_combo = combine_alphas([str(alpha_a_path), str(alpha_b_path)], "equal_combo")
+    expected_equal_weights = (
+        pd.concat([alpha_a, alpha_b], ignore_index=True)
+        .groupby(["symbol_id", "date"], as_index=False)["weight"]
+        .mean()
+        .sort_values(["symbol_id", "date"])
+        .reset_index(drop=True)
+    )
+    pd.testing.assert_frame_equal(
+        equal_combo[["symbol_id", "date", "weight"]],
+        expected_equal_weights,
+    )
+
+    portfolio_weights = make_generated_combo_weights("workflow_combo", zero_date_index=2)
+    positions = construct_positions(
+        weights_df=portfolio_weights,
+        data_df=daily_bars,
+        booksize=2_000_000.0,
+        portfolio_name="workflow_portfolio",
+    )
+
+    assert list(positions.columns) == POSITION_COLUMNS
+    assert not positions.empty
+    assert (positions["portfolio_name"] == "workflow_portfolio").all()
+    assert pd.api.types.is_integer_dtype(positions["position_shares"])
+    assert np.allclose(
+        positions["position_value"],
+        positions["position_shares"].astype(float) * positions["price"].fillna(0.0),
+    )
+
+    target_gross_by_date = positions.groupby("date")["target_weight"].apply(lambda s: s.abs().sum())
+    nonzero_target_dates = target_gross_by_date[target_gross_by_date > 0.0]
+    assert np.allclose(nonzero_target_dates, 1.0)
+
+    nonzero_share_counts = positions.loc[positions["position_shares"] != 0, "position_shares"].abs()
+    assert (nonzero_share_counts >= 100).all()
+
+    zero_gross_date = generated_sessions(10)[2]
+    previous_date = generated_sessions(10)[1]
+    zero_gross_positions = positions[positions["date"] == zero_gross_date].set_index("symbol_id")
+    previous_positions = positions[positions["date"] == previous_date].set_index("symbol_id")
+    common_symbols = zero_gross_positions.index.intersection(previous_positions.index)
+    assert not common_symbols.empty
+    assert (zero_gross_positions.loc[common_symbols, "target_weight"] == 0.0).all()
+    pd.testing.assert_series_equal(
+        zero_gross_positions.loc[common_symbols, "position_shares"],
+        previous_positions.loc[common_symbols, "position_shares"],
+        check_names=False,
+    )
+
+    simulator = ReferenceSimulator(
+        constraints=[
+            SuspensionConstraint(),
+            PriceLimitConstraint(),
+            VolumeCapConstraint(max_frac=0.02),
+        ],
+        cost_bps=5,
+        slippage_bps=5,
+    )
+    fills, pnl = simulator.run(positions, daily_bars)
+
+    assert list(fills.columns) == FILL_COLUMNS
+    assert list(pnl.columns) == PNL_COLUMNS
+    assert not fills.empty
+    assert not pnl.empty
+    assert (fills["realized_shares"] == fills["prev_shares"] + fills["traded_shares"]).all()
+    assert fills["blocked"].sum() > 0
+
+    fill_prices = fills.merge(
+        daily_bars[["symbol_id", "date", "open"]],
+        on=["symbol_id", "date"],
+        how="left",
+        validate="many_to_one",
+    )
+    expected_trade_cost = (
+        fill_prices["traded_shares"].abs()
+        * fill_prices["open"].fillna(0.0)
+        * 10
+        / 10_000
+    )
+    assert np.allclose(fill_prices["trade_cost"], expected_trade_cost)
+
+    cost_by_date = fills.groupby("date")["trade_cost"].sum()
+    assert np.allclose(
+        pnl.set_index("date")["cost"],
+        cost_by_date.reindex(pnl["date"], fill_value=0.0),
+    )
+
+    booksize_used_by_simulator = positions.groupby("date")["target_value"].apply(lambda s: s.abs().sum()).max()
+    traded_value_by_date = (
+        fill_prices.assign(traded_value=fill_prices["traded_shares"].abs() * fill_prices["open"])
+        .groupby("date")["traded_value"]
+        .sum()
+    )
+    assert np.allclose(
+        pnl.set_index("date")["turnover"],
+        traded_value_by_date.reindex(pnl["date"], fill_value=0.0) / booksize_used_by_simulator,
+    )
+
+    metrics = evaluate_portfolio(positions, daily_bars)
+    _assert_metric_dict_is_finite(metrics)
+
+
+def test_generated_workflow_outputs_keep_parquet_schema_contracts(tmp_path):
+    daily_bars = make_generated_daily_bars(n_sessions=10, include_missing=False)
+    alpha = compute_alpha(
+        data=daily_bars,
+        alpha_name="schema_reversal",
+        alpha_type="reversal",
+        lookback=3,
+    )
+    alpha_path = tmp_path / "schema_reversal.pq"
+    alpha.to_parquet(alpha_path, index=False)
+
+    combo = combine_alphas([str(alpha_path)], "schema_combo")
+    positions = construct_positions(
+        weights_df=combo,
+        data_df=daily_bars,
+        booksize=1_000_000.0,
+        portfolio_name="schema_portfolio",
+    )
+    fills, pnl = ReferenceSimulator(cost_bps=5, slippage_bps=5).run(positions, daily_bars)
+
+    assert list(alpha.columns) == ALPHA_COLUMNS
+    assert pd.api.types.is_object_dtype(alpha["symbol_id"])
+    assert pd.api.types.is_datetime64_any_dtype(alpha["date"])
+    assert pd.api.types.is_object_dtype(alpha["alpha_name"])
+    assert pd.api.types.is_float_dtype(alpha["weight"])
+    assert not alpha.isna().any().any()
+    assert np.isfinite(alpha["weight"]).all()
+
+    assert list(combo.columns) == COMBO_COLUMNS
+    assert pd.api.types.is_object_dtype(combo["symbol_id"])
+    assert pd.api.types.is_datetime64_any_dtype(combo["date"])
+    assert pd.api.types.is_object_dtype(combo["combo_name"])
+    assert pd.api.types.is_float_dtype(combo["weight"])
+    assert not combo.isna().any().any()
+    assert np.isfinite(combo["weight"]).all()
+
+    assert list(positions.columns) == POSITION_COLUMNS
+    assert pd.api.types.is_integer_dtype(positions["position_shares"])
+    assert pd.api.types.is_datetime64_any_dtype(positions["date"])
+    assert not positions.isna().any().any()
+    position_numeric_columns = [
+        "target_weight",
+        "target_value",
+        "target_shares",
+        "position_value",
+        "price",
+    ]
+    assert np.isfinite(positions[position_numeric_columns]).all().all()
+
+    assert list(fills.columns) == FILL_COLUMNS
+    assert pd.api.types.is_integer_dtype(fills["prev_shares"])
+    assert pd.api.types.is_integer_dtype(fills["target_shares"])
+    assert pd.api.types.is_integer_dtype(fills["traded_shares"])
+    assert pd.api.types.is_integer_dtype(fills["realized_shares"])
+    assert pd.api.types.is_integer_dtype(fills["blocked"])
+    assert not fills.isna().any().any()
+    assert np.isfinite(fills["trade_cost"]).all()
+
+    assert list(pnl.columns) == PNL_COLUMNS
+    assert pd.api.types.is_integer_dtype(pnl["n_positions"])
+    assert not pnl.isna().any().any()
+    pnl_numeric_columns = [
+        "gross_exposure",
+        "net_exposure",
+        "pnl",
+        "cost",
+        "turnover",
+    ]
+    assert np.isfinite(pnl[pnl_numeric_columns]).all().all()
+
+
+def test_frozen_real_fixture_runs_high_level_workflow(tmp_path):
+    real_daily_bars = pd.read_parquet(FIXTURE_PATH)
+
+    assert real_daily_bars.shape == (36, 19)
+    assert set(real_daily_bars["symbol_id"]) == set(GENERATED_SYMBOLS)
+    assert real_daily_bars["date"].min() == pd.Timestamp("2024-01-02")
+    assert real_daily_bars["date"].max() == pd.Timestamp("2024-01-12")
+    assert real_daily_bars.groupby("date")["symbol_id"].nunique().eq(4).all()
+
+    reversal_alpha = compute_alpha(
+        data=real_daily_bars,
+        alpha_name="real_reversal_3d",
+        alpha_type="reversal",
+        lookback=3,
+    )
+    reversal_vol_alpha = compute_alpha(
+        data=real_daily_bars,
+        alpha_name="real_reversal_vol_3d",
+        alpha_type="reversal_vol",
+        lookback=3,
+        vol_window=3,
+    )
+
+    reversal_path = tmp_path / "real_reversal.pq"
+    reversal_vol_path = tmp_path / "real_reversal_vol.pq"
+    reversal_alpha.to_parquet(reversal_path, index=False)
+    reversal_vol_alpha.to_parquet(reversal_vol_path, index=False)
+
+    combo = combine_alphas([str(reversal_path), str(reversal_vol_path)], "real_equal_combo")
+    positions = construct_positions(
+        weights_df=combo,
+        data_df=real_daily_bars,
+        booksize=1_000_000.0,
+        portfolio_name="real_fixture_portfolio",
+    )
+    fills, pnl = ReferenceSimulator(cost_bps=5, slippage_bps=5).run(positions, real_daily_bars)
+    metrics = evaluate_portfolio(positions, real_daily_bars)
+
+    assert not reversal_alpha.empty
+    assert not reversal_vol_alpha.empty
+    assert not combo.empty
+    assert not positions.empty
+    assert not fills.empty
+    assert not pnl.empty
+    assert np.isfinite(combo["weight"]).all()
+    assert np.isfinite(positions["target_weight"]).all()
+    assert np.isfinite(pnl[["gross_exposure", "net_exposure", "pnl", "cost", "turnover"]]).all().all()
+    _assert_metric_dict_is_finite(metrics)

uv.lock

@@ -299,6 +299,7 @@ backtrader = [
 
 [package.dev-dependencies]
 dev = [
+    { name = "coverage" },
     { name = "pytest" },
 ]
 
@@ -315,7 +316,10 @@ requires-dist = [
 provides-extras = ["backtrader"]
 
 [package.metadata.requires-dev]
-dev = [{ name = "pytest", specifier = ">=7.0.0" }]
+dev = [
+    { name = "coverage", specifier = ">=7.14.1" },
+    { name = "pytest", specifier = ">=7.0.0" },
+]
 
 [[package]]
 name = "click"
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 ]
 
+[[package]]
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+version = "7.14.1"
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