feat(quant): consolidate research artifact contract (#7)

tests/test_data_adapter.py

@@ -7,10 +7,12 @@ import pandas as pd
 import pytest
 
 from quant_engine.data_adapter import (
+    AssetReturnSnapshot,
     add_vwap_proxy,
     apply_adj_factor,
     load_qtdb_daily,
     long_to_wide,
+    prepare_asset_return_snapshot,
     prepare_execution_inputs,
     prepare_stock_series,
     rename_tushare_columns,
@@ -266,6 +268,17 @@ def test_prepare_execution_inputs_basic(tushare_long: pd.DataFrame) -> None:
     assert volumes.iloc[0, 0] == pytest.approx(1000.0)
 
 
+def test_prepare_execution_inputs_can_select_next_session_open_price(
+    tushare_long: pd.DataFrame,
+) -> None:
+    """显式 price_col=open 时应生成开盘执行价矩阵。"""
+    renamed = rename_tushare_columns(tushare_long)
+
+    prices, _volumes = prepare_execution_inputs(renamed, price_col="open")
+
+    assert prices.iloc[0, 0] == pytest.approx(10.0)
+
+
 def test_prepare_execution_inputs_no_volume() -> None:
     """无 volume 列 → volumes 全 1.0。"""
     df = pd.DataFrame(
@@ -286,6 +299,177 @@ def test_prepare_execution_inputs_missing_close_raises() -> None:
         prepare_execution_inputs(df)
 
 
+def test_prepare_execution_inputs_missing_selected_price_raises() -> None:
+    df = pd.DataFrame({"stock_code": ["A"], "trade_date": ["2024-01-01"], "close": [10.0]})
+    with pytest.raises(ValueError, match="缺 open"):
+        prepare_execution_inputs(df, price_col="open")
+
+
+# ── prepare_asset_return_snapshot ────────────────────────────
+
+
+def _daily_prices() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "stock_code": ["B", "A", "B", "A", "B", "A"],
+            "trade_date": [
+                "2024-01-02",
+                "2024-01-01",
+                "2024-01-01",
+                "2024-01-03",
+                "2024-01-03",
+                "2024-01-02",
+            ],
+            "close": [18.0, 10.0, 20.0, 12.1, 19.8, 11.0],
+        }
+    )
+
+
+def test_prepare_asset_return_snapshot_is_stable_and_immutable_by_interface() -> None:
+    snapshot = prepare_asset_return_snapshot(
+        _daily_prices(),
+        source="qtdb_pro.hq_daily",
+        source_snapshot_id="hq-daily:2024-01-03:v1",
+        adjustment="qfq",
+    )
+
+    assert isinstance(snapshot, AssetReturnSnapshot)
+    assert snapshot.data_snapshot_id.startswith("asset-returns-v1:")
+    assert snapshot.source == "qtdb_pro.hq_daily"
+    assert snapshot.source_snapshot_id == "hq-daily:2024-01-03:v1"
+    assert snapshot.price_field == "close"
+    assert snapshot.adjustment == "qfq"
+    assert snapshot.return_method == "simple"
+    assert snapshot.start_date.isoformat() == "2024-01-01"
+    assert snapshot.end_date.isoformat() == "2024-01-03"
+    assert snapshot.sessions == 3
+    assert snapshot.assets == ("A", "B")
+
+    expected = pd.DataFrame(
+        {
+            "A": [np.nan, 0.1, 0.1],
+            "B": [np.nan, -0.1, 0.1],
+        },
+        index=pd.to_datetime(["2024-01-01", "2024-01-02", "2024-01-03"]),
+    )
+    expected.index.name = "trade_date"
+    expected.columns.name = "stock_code"
+    pd.testing.assert_frame_equal(snapshot.returns, expected)
+
+    exposed = snapshot.returns
+    exposed.iloc[1, 0] = 999.0
+    assert snapshot.returns.iloc[1, 0] == pytest.approx(0.1)
+
+
+def test_asset_return_snapshot_identity_is_order_independent_and_content_addressed() -> None:
+    kwargs = {
+        "source": "qtdb_pro.hq_daily",
+        "source_snapshot_id": "hq-daily:2024-01-03:v1",
+        "adjustment": "none",
+    }
+    baseline = prepare_asset_return_snapshot(_daily_prices(), **kwargs)
+    shuffled = prepare_asset_return_snapshot(
+        _daily_prices().sample(frac=1.0, random_state=7),
+        **kwargs,
+    )
+    changed_prices = _daily_prices().copy()
+    changed_prices.loc[changed_prices["close"] == 12.1, "close"] = 12.2
+    changed_content = prepare_asset_return_snapshot(changed_prices, **kwargs)
+    changed_source = prepare_asset_return_snapshot(
+        _daily_prices(),
+        source="qtdb_pro.hq_daily",
+        source_snapshot_id="hq-daily:2024-01-03:v2",
+        adjustment="none",
+    )
+
+    assert shuffled.data_snapshot_id == baseline.data_snapshot_id
+    assert changed_content.data_snapshot_id != baseline.data_snapshot_id
+    assert changed_source.data_snapshot_id != baseline.data_snapshot_id
+
+
+def test_prepare_asset_return_snapshot_does_not_fill_missing_prices() -> None:
+    prices = _daily_prices()
+    prices.loc[
+        (prices["stock_code"] == "A") & (prices["trade_date"] == "2024-01-02"),
+        "close",
+    ] = np.nan
+
+    snapshot = prepare_asset_return_snapshot(
+        prices,
+        source="qtdb_pro.hq_daily",
+        source_snapshot_id="hq-daily:missing-middle",
+    )
+
+    assert pd.isna(snapshot.returns.loc[pd.Timestamp("2024-01-02"), "A"])
+    assert pd.isna(snapshot.returns.loc[pd.Timestamp("2024-01-03"), "A"])
+
+
+def test_prepare_asset_return_snapshot_rejects_duplicate_sessions() -> None:
+    duplicate = pd.concat([_daily_prices(), _daily_prices().iloc[[0]]], ignore_index=True)
+
+    with pytest.raises(ValueError, match="duplicate"):
+        prepare_asset_return_snapshot(
+            duplicate,
+            source="qtdb_pro.hq_daily",
+            source_snapshot_id="hq-daily:duplicate",
+        )
+
+
+@pytest.mark.parametrize("invalid_price", [0.0, -1.0, np.inf])
+def test_prepare_asset_return_snapshot_rejects_invalid_prices(invalid_price: float) -> None:
+    prices = _daily_prices()
+    prices.loc[0, "close"] = invalid_price
+
+    with pytest.raises(ValueError, match="positive finite"):
+        prepare_asset_return_snapshot(
+            prices,
+            source="qtdb_pro.hq_daily",
+            source_snapshot_id="hq-daily:invalid-price",
+        )
+
+
+@pytest.mark.parametrize(
+    ("source", "source_snapshot_id", "adjustment"),
+    [
+        ("", "source-1", "none"),
+        ("qtdb_pro.hq_daily", "", "none"),
+        ("qtdb_pro.hq_daily", "source-1", ""),
+    ],
+)
+def test_prepare_asset_return_snapshot_requires_explicit_identity_semantics(
+    source: str,
+    source_snapshot_id: str,
+    adjustment: str,
+) -> None:
+    with pytest.raises(ValueError, match="must be non-empty"):
+        prepare_asset_return_snapshot(
+            _daily_prices(),
+            source=source,
+            source_snapshot_id=source_snapshot_id,
+            adjustment=adjustment,
+        )
+
+
+def test_asset_return_snapshot_feeds_reproducible_covariance_lineage() -> None:
+    from quant_engine.risk import estimate_covariance_snapshot
+
+    market_snapshot = prepare_asset_return_snapshot(
+        _daily_prices(),
+        source="qtdb_pro.hq_daily",
+        source_snapshot_id="hq-daily:2024-01-03:v1",
+    )
+    covariance_snapshot = estimate_covariance_snapshot(
+        market_snapshot.returns,
+        as_of_date=market_snapshot.end_date,
+        lookback_sessions=3,
+        min_observations=2,
+        data_snapshot_id=market_snapshot.data_snapshot_id,
+    )
+
+    assert covariance_snapshot.data_snapshot_id == market_snapshot.data_snapshot_id
+    assert covariance_snapshot.snapshot_id.startswith("sample-cov-v1:")
+
+
 # ── 端到端：长表 → 适配 → alpha158 + execution ──────────────