Improve offline coverage for data boundaries

.gitignore

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

README.md

@@ -120,6 +120,49 @@ partitions already written. Pass the **dataset directory** (`{output_dir}/{unive
 as `--data-path` to later phases — `pd.read_parquet` reads the whole partitioned
 set. Symbols use the internal `sh600000` / `sz000001` form (exchange prefix + code).
 
+### `derived` — daily custom/derived data
+
+Derived data is daily-only v1 research data keyed by `symbol_id,date`, with one
+or more numeric value columns. It can come from user CSV/parquet files or Python
+plugins, and is written as a single parquet file at `derived/{name}.pq`.
+
+The validator normalizes `date` to the trading day, requires unique
+`symbol_id,date` keys, rejects duplicate columns, and rejects non-numeric value
+columns. Alpha computation consumes derived data through the existing
+`--feature-path` flag.
+
+```bash
+# Validate a user file without writing output.
+uv run python cli.py derived validate --input-path vendor_factor.csv
+
+# Ingest CSV/parquet into the canonical derived/ layout.
+uv run python cli.py derived ingest \
+    --input-path vendor_factor.csv \
+    --derived-name vendor_factor
+
+# List built-in and external derived-data plugin types.
+uv run python cli.py derived list
+uv run python cli.py derived list --derived-module path/to/my_derived.py
+
+# Compute a derived file from daily and/or minute inputs.
+uv run python cli.py derived compute \
+    --minute-path data/minute_bars/sh600000 \
+    --daily-path data/daily_bars/sh600000 \
+    --derived-type minute_daily_summary \
+    --derived-name minute_summary
+
+# Join derived columns into a feature-aware alpha.
+uv run python cli.py alpha compute \
+    --data-path data/daily_bars/sh600000 \
+    --feature-path derived/minute_summary.pq \
+    --alpha-type my_feature_aware_alpha \
+    --alpha-name my_run
+```
+
+For compatibility, `feature list` and `feature compute` remain available and
+delegate to the same derived-data registry. Existing `features/*.pq` files are
+still valid `--feature-path` inputs when they satisfy the daily numeric contract.
+
 ### `alpha list` — show registered alpha types
 
 ```bash
@@ -137,6 +180,7 @@ uv run python cli.py alpha list --alpha-module path/to/my_alpha.py   # include a
 | `--output-dir` | `alphas` | Output directory |
 | `--lookback` | `5` | Lookback days (passed to alphas that accept it) |
 | `--vol-window` | `20` | Volatility window (passed to alphas that accept it) |
+| `--feature-path` | — | Daily derived/feature parquet file or dataset to left-join on `symbol_id,date`; repeatable |
 | `--alpha-module` | — | External module(s) to import first; repeatable. Dotted path or `.py` file |
 | `--param` | — | Extra constructor param as `name=value`; repeatable |
 
@@ -370,6 +414,8 @@ between phases (data is stored long/tidy):
   OHLC scale under `qfq`/`hfq`; `turn` is turnover %, `pctChg` daily % change,
   `tradestatus`/`isST` are 0/1 flags, and `peTTM`/`pbMRQ`/`psTTM`/`pcfNcfTTM` are
   baostock valuation ratios.)
+- **derived** (`DERIVED_KEY_COLUMNS` + values): required keys `symbol_id, date`;
+  value columns are user/plugin-defined and must be numeric in v1.
 - **alpha** (`ALPHA_COLUMNS`): `symbol_id, date, alpha_name, weight`
 - **combo** (`COMBO_COLUMNS`): `symbol_id, date, combo_name, weight`
 - **portfolio positions** (`POSITION_COLUMNS`): `symbol_id, date, portfolio_name, target_weight, target_value, target_shares, position_shares, position_value, price`
@@ -387,8 +433,11 @@ directory yields an extra `month` (`YYYY-MM`) partition column on top of
 
 - `cli.py` — entry point wiring the file-based phases together
 - `pipeline/data/` — universe resolution + download → `data/daily_bars/{universe}/month=YYYY-MM/*.pq`
+- `pipeline/derived/` — daily derived-data ingestion, validation, plugin registry,
+  and built-in derived computations → `derived/*.pq`
 - `pipeline/alpha/` — `base.py` (`BaseAlpha`), `registry.py` (factory + plugin loader),
   `library/` (built-in alphas), `compute.py` (`compute_alpha` / `evaluate_alpha`)
+- `pipeline/features/` — compatibility wrappers for the derived-data registry
 - `pipeline/combo/` — alpha combination → `combos/*.pq`
 - `pipeline/portfolio/` — construction, A-share lot/limit rules, constraints,
   reference next-open simulator, and research metrics
@@ -410,6 +459,9 @@ constructed positions, fills/costs, P&L, and target-weight research metrics.
 - [x] **Reference execution simulation** — next-open fills over constructed
   `position_shares`, with suspension, price-limit, volume-cap, transaction-cost,
   and slippage controls.
+- [x] **Derived/custom daily data ("Level 2")** — ingest user CSV/parquet files
+  or compute plugin outputs as validated numeric daily datasets under
+  `derived/{name}.pq`; alpha joins continue through `--feature-path`.
 - [ ] **Optional Backtrader adapter** — Backtrader is available as the
   `backtrader` extra for possible future event-driven/broker-style experiments,
   but it is not part of the current canonical portfolio workflow.
@@ -419,3 +471,29 @@ constructed positions, fills/costs, P&L, and target-weight research metrics.
   and intraday VWAP. These need a tick / L1–L2 quote feed (typically a paid or
   brokerage data tier); the free daily sources here only expose daily bars, so
   this is a separate data phase rather than extra columns on the daily schema.
+
+### Additional TODOs
+
+The following items are intended extensions beyond the current daily
+alpha-to-portfolio pipeline:
+
+- **Long-only portfolio mode** — add a construction option that converts
+  alpha/combo weights into a long-only book while preserving existing lot,
+  price, suspension, and volume-cap handling.
+- **Index-short hedging mode** — support portfolios that hold long A-share
+  names while shorting an index or index proxy for market exposure control.
+- **Expanded universe presets** — add explicit universe aliases for CSI 300,
+  CSI 500, CSI 1000, and CSI 1800, while keeping file-based and comma-separated
+  custom universes available.
+- **Categorical derived data** — extend the numeric-only derived-data v1 contract
+  to support categorical inputs such as industry classifications. In this
+  project, "Level 2" means customized second-level research data produced by
+  users or plugins; it does not necessarily mean exchange order-book/L2 quote
+  feeds.
+- **Minute bar data** — continue extending the raw minute-bar and feature
+  workflow. The initial Baostock 5-minute download and daily feature plugin path
+  exist; intraday execution and replacing canonical daily bars remain out of
+  scope unless explicitly added later.
+- **Industry data** — add industry classification inputs for filtering,
+  grouping, exposure reporting, neutralization, or industry-aware portfolio
+  construction.

cli.py

@@ -3,7 +3,9 @@
 
 Phases:
     data      — Download daily bars to parquet
+    derived   — Ingest or compute daily derived data
     alpha     — Compute alpha weights from data
+    feature   — Compute daily features from minute bars
     combo     — Combine alphas into a single weight
     portfolio — Build tradable positions and simulate execution
 """
@@ -13,7 +15,9 @@ import logging
 import click
 
 from pipeline.data.cli import data
+from pipeline.derived.cli import derived
 from pipeline.alpha.cli import alpha
+from pipeline.features.cli import feature
 from pipeline.combo.cli import combo
 from pipeline.portfolio.cli import portfolio
 from tools.pqcat import pqcat
@@ -39,7 +43,9 @@ def cli(log_level):
 
 
 cli.add_command(data)
+cli.add_command(derived)
 cli.add_command(alpha)
+cli.add_command(feature)
 cli.add_command(combo)
 cli.add_command(portfolio)
 cli.add_command(pqcat)

data/downloader.py

@@ -31,11 +31,68 @@ _BATCH_COLUMNS = [
     "peTTM", "pbMRQ", "psTTM", "pcfNcfTTM",
 ]
 
+# Raw Baostock minute bars. The ``time`` field is usually compact
+# YYYYMMDDHHMMSSmmm; parsing below also tolerates HH:MM:SS strings in tests.
+_MINUTE_FIELDS = "date,time,code,open,high,low,close,volume,amount,adjustflag"
+_MINUTE_NUMERIC = ["open", "high", "low", "close", "volume", "amount"]
+_MINUTE_COLUMNS = [
+    "symbol", "datetime", "date", "time", "frequency",
+    "open", "high", "low", "close", "volume", "amount", "vwap", "adjustflag",
+]
+_MINUTE_FREQUENCIES = {"5", "15", "30", "60"}
+
 
 class _SessionLost(Exception):
     """baostock reported the session was dropped (``用户未登录``)."""
 
 
+def _normalize_minute_frequency(frequency: str | int) -> tuple[str, str]:
+    """Return Baostock frequency and partition label for a minute interval."""
+    raw = str(frequency).strip().lower()
+    if raw.endswith("m"):
+        raw = raw[:-1]
+    if raw not in _MINUTE_FREQUENCIES:
+        raise ValueError(
+            f"Unsupported minute frequency '{frequency}'. "
+            f"Expected one of {sorted(_MINUTE_FREQUENCIES)} minutes."
+        )
+    return raw, f"{raw}m"
+
+
+def _parse_minute_datetime(date: pd.Series, time: pd.Series) -> pd.Series:
+    """Parse Baostock minute timestamps into pandas datetimes."""
+    date_dt = pd.to_datetime(date, errors="coerce")
+    date_compact = date_dt.dt.strftime("%Y%m%d")
+    time_text = time.astype(str).str.strip()
+    time_digits = time_text.str.replace(r"\D", "", regex=True)
+
+    full_digits = time_digits.str.slice(0, 14)
+    from_full = pd.to_datetime(full_digits, format="%Y%m%d%H%M%S", errors="coerce")
+
+    from_short = pd.Series(pd.NaT, index=date.index, dtype="datetime64[ns]")
+    short_time = time_digits.str.len().between(1, 6)
+    if short_time.any():
+        short_digits = (
+            time_digits.loc[short_time]
+            .str.pad(6, side="right", fillchar="0")
+            .str.slice(0, 6)
+        )
+        from_short.loc[short_time] = pd.to_datetime(
+            date_compact.loc[short_time] + short_digits,
+            format="%Y%m%d%H%M%S",
+            errors="coerce",
+        )
+
+    from_text = pd.Series(pd.NaT, index=date.index, dtype="datetime64[ns]")
+    text_time = time_text.str.contains(":", regex=False)
+    if text_time.any():
+        from_text.loc[text_time] = pd.to_datetime(
+            date.astype(str).loc[text_time] + " " + time_text.loc[text_time],
+            errors="coerce",
+        )
+    return from_full.fillna(from_short).fillna(from_text)
+
+
 def _download_akshare(symbol: str, start: str, end: str, adjust: str = "qfq") -> Optional[pd.DataFrame]:
     """Download daily bars from akshare. Returns DataFrame with OHLCV columns."""
     try:
@@ -239,3 +296,104 @@ def download_daily_batch(
         except Exception:
             pass
 
+
+def download_minute_batch(
+    symbols: Iterable[str],
+    start: str,
+    end: str,
+    frequency: str | int = 5,
+    relogin_every: int = 200,
+) -> Iterator[Tuple[str, Optional[pd.DataFrame]]]:
+    """Download raw Baostock minute bars for many symbols.
+
+    Minute bars are intentionally unadjusted (`adjustflag='3'`) because the
+    output is raw intraday market data for downstream feature aggregation, not a
+    tradable daily price series.
+
+    Args:
+        symbols: Internal-form symbols (``sh600000`` / ``sz000001``).
+        start, end: ``YYYY-MM-DD`` bounds.
+        frequency: Baostock minute frequency. ``5``/``"5"``/``"5m"`` all mean
+            5-minute bars.
+        relogin_every: Proactively refresh the baostock session every N symbols.
+
+    Yields:
+        ``(symbol, df)`` where ``df`` has raw minute bars or ``None`` when no
+        data is available.
+    """
+    query_frequency, frequency_label = _normalize_minute_frequency(frequency)
+    adjustflag = _BAOSTOCK_ADJUST["none"]
+
+    def _relogin() -> None:
+        try:
+            bs.logout()
+        except Exception:
+            pass
+        bs.login()
+
+    def _fetch(symbol: str) -> Optional[pd.DataFrame]:
+        """One Baostock minute query; returns df, None, or raises _SessionLost."""
+        code = f"{symbol[:2]}.{symbol[2:]}"
+        rs = bs.query_history_k_data_plus(
+            code=code,
+            fields=_MINUTE_FIELDS,
+            start_date=start,
+            end_date=end,
+            frequency=query_frequency,
+            adjustflag=adjustflag,
+        )
+        if rs.error_code != "0":
+            if "未登录" in (rs.error_msg or ""):
+                raise _SessionLost(rs.error_msg)
+            logger.warning("baostock minute error for %s: %s", symbol, rs.error_msg)
+            return None
+        rows = []
+        while rs.next():
+            rows.append(rs.get_row_data())
+        if not rows:
+            return None
+
+        df = pd.DataFrame(rows, columns=_MINUTE_FIELDS.split(","))
+        df[_MINUTE_NUMERIC] = df[_MINUTE_NUMERIC].apply(pd.to_numeric, errors="coerce")
+        df["datetime"] = _parse_minute_datetime(df["date"], df["time"])
+        bad_timestamps = df["datetime"].isna()
+        if bad_timestamps.any():
+            raise ValueError(
+                f"Could not parse {int(bad_timestamps.sum())} minute timestamp(s)"
+            )
+        df["date"] = pd.to_datetime(df["date"], errors="coerce")
+        df["time"] = df["datetime"].dt.strftime("%H:%M:%S")
+        df["frequency"] = frequency_label
+        df["vwap"] = (df["amount"] / df["volume"]).where(df["volume"] > 0)
+        df["symbol"] = symbol
+        return df[_MINUTE_COLUMNS].sort_values("datetime").reset_index(drop=True)
+
+    bs.login()
+    try:
+        for i, symbol in enumerate(symbols):
+            if i and relogin_every and i % relogin_every == 0:
+                _relogin()
+
+            df: Optional[pd.DataFrame] = None
+            for attempt in (1, 2):
+                try:
+                    df = _fetch(symbol)
+                    break
+                except _SessionLost:
+                    if attempt == 1:
+                        _relogin()
+                        continue
+                    logger.warning("baostock minute session lost for %s after relogin", symbol)
+                except Exception as e:
+                    logger.warning("baostock minute download failed for %s: %s", symbol, e)
+                    break
+
+            if df is not None and not df.empty:
+                yield symbol, df
+            else:
+                yield symbol, None
+    finally:
+        try:
+            bs.logout()
+        except Exception:
+            pass

docs/minute_bar_data.md

@@ -0,0 +1,70 @@
+# Minute Bar Data Notes
+
+The minute-bar path downloads raw Baostock intraday bars and stores them as a
+Hive-partitioned dataset:
+
+```bash
+uv run python cli.py data download-minute \
+    --universe sh600000 \
+    --start-date 2024-01-02 --end-date 2024-01-05 \
+    --frequency 5
+```
+
+The default layout is:
+
+```text
+data/minute_bars/{universe}/frequency=5m/month=YYYY-MM/*.pq
+```
+
+Derived-data plugins can aggregate those bars to daily `symbol_id,date` numeric
+files, for example:
+
+```bash
+uv run python cli.py derived compute \
+    --minute-path data/minute_bars/sh600000 \
+    --daily-path data/daily_bars/sh600000 \
+    --derived-type minute_daily_summary \
+    --derived-name minute_summary
+```
+
+The legacy `feature compute` command delegates to the same derived-data
+registry and remains available for existing scripts.
+
+## Daily vs Minute Reconciliation
+
+Baostock's daily raw bars and 5-minute raw bars are close, but they should not
+be treated as perfectly reconstructible from each other.
+
+When checking consistency, compare daily raw bars (`data download --adjust none`)
+against minute bars on the same raw price scale. The minute aggregation should
+use:
+
+- `open`: first minute open
+- `high`: max minute high
+- `low`: min minute low
+- `close`: last minute close
+- `volume`: sum minute volume
+- `amount`: sum minute amount
+- `vwap`: `sum(amount) / sum(volume)`
+
+In a sanity check for `sh600000` from `2024-01-02` through `2024-01-05`, Baostock
+returned 4 daily rows and 192 5-minute bars, exactly 48 bars per day. Open, low,
+and close matched daily exactly on all 4 days. High matched on 3 of 4 days; on
+`2024-01-04`, the daily high was `6.67` while the max 5-minute high was `6.66`.
+Minute-summed volume and amount were higher than daily by roughly `0.16%` to
+`1.23%`. VWAP remained very close, with max relative difference around
+`0.0043%`.
+
+This appears to be a source-level Baostock reconciliation caveat, not a parser
+or ordering issue: the minute bars covered the regular `09:35:00` through
+`15:00:00` range and sorted correctly by timestamp.
+
+Practical guidance:
+
+- Use tolerance-based daily-vs-minute checks; do not require exact equality for
+  high, volume, or amount.
+- Expect open/close alignment to be a stronger sanity check than exact volume
+  reconstruction.
+- Use minute-derived values as separate daily features, not as replacements for
+  the canonical daily bar dataset unless a strategy explicitly wants that
+  source convention.

pipeline/alpha/base.py

@@ -5,7 +5,7 @@ position weights. Subclasses implement :meth:`signal` — the raw, unnormalized
 score. The base class turns a signal into cross-sectionally z-scored weights
 via :meth:`to_weights` (override it for a different normalization).
 """
-from abc import ABC, abstractmethod
+from abc import ABC
 
 import numpy as np
 import pandas as pd
@@ -15,15 +15,14 @@ class BaseAlpha(ABC):
     """A position-weight alpha over a cross-section of stocks.
 
     Concrete subclasses must set a unique class-level :attr:`name` (the registry
-    key) and implement :meth:`signal`. Construct subclasses with their own typed
-    parameters (e.g. ``lookback``); the factory passes only the parameters a
-    given ``__init__`` accepts.
+    key) and implement either :meth:`signal` or :meth:`signal_from_data`.
+    Construct subclasses with their own typed parameters (e.g. ``lookback``);
+    the factory passes only the parameters a given ``__init__`` accepts.
     """
 
     #: Unique registry key. Every concrete alpha must set this to a non-empty str.
     name: str = ""
 
-    @abstractmethod
     def signal(self, close: pd.DataFrame) -> pd.DataFrame:
         """Compute the raw signal.
 
@@ -34,6 +33,21 @@ class BaseAlpha(ABC):
             A wide DataFrame aligned to ``close`` where higher values indicate a
             stronger long. Use NaN where the signal is undefined.
         """
+        raise NotImplementedError(
+            f"{type(self).__name__} must implement signal() or signal_from_data()"
+        )
+
+    def signal_from_data(
+        self,
+        data: pd.DataFrame,
+        close: pd.DataFrame,
+    ) -> pd.DataFrame:
+        """Compute the raw signal from long daily data plus wide closes.
+
+        Feature-aware alphas can override this to pivot joined feature columns
+        from ``data``. The default preserves the existing close-only alpha API.
+        """
+        return self.signal(close)
 
     def to_weights(self, signal: pd.DataFrame) -> pd.DataFrame:
         """Cross-sectionally z-score a signal into signed position weights.

pipeline/alpha/cli.py

@@ -56,6 +56,10 @@ def list_(alpha_modules):
 @click.option("--output-dir", default="alphas", help="Directory to save alpha parquet")
 @click.option("--lookback", default=5, type=int, help="Lookback days")
 @click.option("--vol-window", default=20, type=int, help="Volatility window (reversal_vol only)")
+@click.option(
+    "--feature-path", "feature_paths", multiple=True,
+    help="Daily derived/feature parquet file or dataset to left-join on symbol_id,date (repeatable)",
+)
 @click.option(
     "--alpha-module", "alpha_modules", multiple=True,
     help="External module(s) to import so their alphas register (dotted path or .py file)",
@@ -74,7 +78,7 @@ def list_(alpha_modules):
     help="Most-liquid names kept per date when --liquid-universe is set",
 )
 def compute(data_path, alpha_name, alpha_type, output_dir, lookback, vol_window,
-            alpha_modules, extra_params, liquid_universe, universe_top_n):
+            feature_paths, alpha_modules, extra_params, liquid_universe, universe_top_n):
     """Compute one alpha from raw data and save as parquet."""
     for spec in alpha_modules:
         load_alpha_module(spec)
@@ -100,6 +104,7 @@ def compute(data_path, alpha_name, alpha_type, output_dir, lookback, vol_window,
         alpha_name=alpha_name,
         alpha_type=alpha_type,
         universe=universe,
+        feature_paths=feature_paths,
         **params,
     )
 
@@ -155,7 +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.
@@ -178,9 +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/alpha/compute.py

@@ -7,12 +7,19 @@ through :mod:`pipeline.alpha.registry`.
 """
 
 import logging
+from pathlib import Path
+from typing import Iterable
 
 import numpy as np
 import pandas as pd
 
 from pipeline.alpha.registry import get_alpha
 from pipeline.common.schema import ALPHA_COLUMNS
+from pipeline.derived.compute import (
+    DERIVED_KEY_COLUMNS,
+    read_derived_frames,
+    validate_derived_frame,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -33,6 +40,38 @@ def _pivot_open(df: pd.DataFrame) -> pd.DataFrame:
     return pivot.sort_index()
 
 
+def join_feature_frames(
+    data: pd.DataFrame,
+    feature_frames: Iterable[pd.DataFrame],
+) -> pd.DataFrame:
+    """Left-join validated daily derived/feature frames onto long daily data."""
+    out = data.copy()
+    out["date"] = pd.to_datetime(out["date"])
+    existing = set(out.columns)
+    joined_cols: list[str] = []
+
+    for frame in feature_frames:
+        features = validate_derived_frame(frame)
+        feature_cols = [col for col in features.columns if col not in DERIVED_KEY_COLUMNS]
+        overlap = sorted(existing.intersection(feature_cols))
+        if overlap:
+            raise ValueError(
+                f"Feature columns conflict with existing daily data columns: {overlap}"
+            )
+        out = out.merge(
+            features,
+            on=DERIVED_KEY_COLUMNS,
+            how="left",
+            validate="many_to_one",
+        )
+        existing.update(feature_cols)
+        joined_cols.extend(feature_cols)
+
+    if joined_cols:
+        logger.info("Joined feature columns into daily data: %s", joined_cols)
+    return out
+
+
 def _forward_open_to_open_returns(open_: pd.DataFrame) -> pd.DataFrame:
     """Return earned by a close-formed signal after next-open execution.
 
@@ -105,6 +144,8 @@ def compute_alpha(
     alpha_name: str,
     alpha_type: str,
     universe: dict | None = None,
+    feature_paths: Iterable[str | Path] | None = None,
+    feature_frames: Iterable[pd.DataFrame] | None = None,
     **params,
 ) -> pd.DataFrame:
     """Compute alpha weights from raw data.
@@ -118,6 +159,10 @@ def compute_alpha(
             :func:`investable_universe_mask`) *before* it is turned into
             weights, so unheld names get weight 0. Keys are forwarded as keyword
             arguments to :func:`investable_universe_mask`.
+        feature_paths: Optional parquet files/datasets keyed by ``symbol_id``
+            and ``date``. Their numeric feature columns are left-joined onto
+            ``data`` before alpha logic runs.
+        feature_frames: Optional in-memory feature frames with the same schema.
         **params: Constructor parameters for the alpha (e.g. ``lookback``,
             ``vol_window``). Only the params the alpha's ``__init__`` accepts are
             used; extras are ignored.
@@ -128,12 +173,20 @@ def compute_alpha(
     Raises:
         KeyError: If ``alpha_type`` is not registered.
     """
+    feature_inputs: list[pd.DataFrame] = []
+    if feature_paths:
+        feature_inputs.extend(read_derived_frames(feature_paths))
+    if feature_frames:
+        feature_inputs.extend(feature_frames)
+    if feature_inputs:
+        data = join_feature_frames(data, feature_inputs)
+
     alpha = get_alpha(alpha_type, **params)
     close = _pivot_close(data)
+    signal = alpha.signal_from_data(data, close)
     if universe is None:
-        weights = alpha.weights(close)
+        weights = alpha.to_weights(signal)
     else:
-        signal = alpha.signal(close)
         mask = investable_universe_mask(data, signal, **universe)
         weights = alpha.to_weights(signal.where(mask))
 

pipeline/common/schema.py

@@ -26,6 +26,31 @@ DATA_COLUMNS: Final[list[str]] = [
     "pcfNcfTTM",     # float64: P/CF (net cash flow, TTM)
 ]
 
+# Required columns for raw intraday minute bar parquet files.
+MINUTE_BAR_COLUMNS: Final[list[str]] = [
+    "symbol_id",     # str: internal code like 'sh600000'
+    "symbol_name",   # str: stock name like '浦发银行'
+    "datetime",      # datetime64: intraday bar timestamp
+    "date",          # date component, aligned with daily DATA_COLUMNS date
+    "time",          # str: HH:MM:SS bar time
+    "frequency",     # str: e.g. '5m'
+    "open",          # float64
+    "high",          # float64
+    "low",           # float64
+    "close",         # float64
+    "volume",        # float64 (shares)
+    "amount",        # float64 (turnover in yuan, raw/unadjusted)
+    "vwap",          # float64: amount / volume
+    "adjustflag",    # str: baostock adjustment flag; '3' for raw/unadjusted
+]
+
+# Required key columns for daily derived-data parquet files. Value columns are
+# user/plugin-defined and must be numeric.
+DERIVED_KEY_COLUMNS: Final[list[str]] = [
+    "symbol_id",     # str
+    "date",          # date: normalized daily timestamp
+]
+
 # Required columns for alpha parquet files.
 # Alphas are position WEIGHTS: positive=long, negative=short.
 ALPHA_COLUMNS: Final[list[str]] = [

pipeline/data/cli.py

@@ -3,7 +3,7 @@
 import click
 from datetime import date
 
-from pipeline.data.downloader import download_universe
+from pipeline.data.downloader import download_minute_universe, download_universe
 
 
 @click.group(name="data")
@@ -42,3 +42,37 @@ def download(universe, start_date, end_date, output_dir, symbols, chunk_size, ad
         f"{stats['n_rows']:,} bars, {stats['date_min']} → {stats['date_max']}"
     )
     click.echo(f"Dataset: {stats['dataset_path']}")
+
+
+@data.command("download-minute")
+@click.option(
+    "--universe", default="csi500",
+    help="Which universe: hs300, csi500, all (~5000 A-shares), file path, or comma-separated symbols",
+)
+@click.option("--start-date", default="2017-01-01", help="Start date YYYY-MM-DD")
+@click.option("--end-date", default=str(date.today()), help="End date YYYY-MM-DD")
+@click.option("--output-dir", default="data/minute_bars", help="Root for the partitioned dataset")
+@click.option("--symbols", default=0, type=int, help="Max symbols (0=all)")
+@click.option("--chunk-size", default=100, type=int, help="Symbols per durability flush")
+@click.option("--frequency", default="5", help="Minute frequency: 5, 15, 30, or 60")
+def download_minute(universe, start_date, end_date, output_dir, symbols, chunk_size, frequency):
+    """Download raw Baostock minute bars into a partitioned parquet dataset.
+
+    Writes ``{output_dir}/{universe}/frequency=5m/month=YYYY-MM/*.pq`` for the
+    default 5-minute frequency.
+    """
+    stats = download_minute_universe(
+        universe=universe,
+        start_date=start_date,
+        end_date=end_date,
+        output_dir=output_dir,
+        max_symbols=symbols,
+        chunk_size=chunk_size,
+        frequency=frequency,
+    )
+    click.echo(
+        f"\nSummary: {stats['n_symbols']}/{stats['n_requested']} symbols, "
+        f"{stats['n_rows']:,} bars, {stats['date_min']} → {stats['date_max']}, "
+        f"frequency={stats['frequency']}"
+    )
+    click.echo(f"Dataset: {stats['dataset_path']}")

pipeline/data/downloader.py

@@ -11,9 +11,13 @@ import pyarrow.dataset as pads
 
 # Reuse existing downloader and universe modules
 sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
-from data.downloader import download_daily_batch
+from data.downloader import (
+    _normalize_minute_frequency,
+    download_daily_batch,
+    download_minute_batch,
+)
 from data.universe import get_all_stocks, get_hs300_stocks, get_zz500_stocks
-from pipeline.common.schema import DATA_COLUMNS
+from pipeline.common.schema import DATA_COLUMNS, MINUTE_BAR_COLUMNS
 
 logger = logging.getLogger(__name__)
 
@@ -89,6 +93,25 @@ def _write_month_partitions(df: pd.DataFrame, base_dir: Path, basename_prefix: s
     )
 
 
+def _write_minute_partitions(df: pd.DataFrame, base_dir: Path, basename_prefix: str) -> None:
+    """Append rows to a Hive-partitioned minute dataset.
+
+    Layout: ``frequency=5m/month=YYYY-MM/*.pq``.
+    """
+    out = df.copy()
+    out["month"] = pd.to_datetime(out["date"]).dt.strftime("%Y-%m")
+    table = pa.Table.from_pandas(out, preserve_index=False)
+    pads.write_dataset(
+        table,
+        str(base_dir),
+        format="parquet",
+        partitioning=["frequency", "month"],
+        partitioning_flavor="hive",
+        basename_template=f"{basename_prefix}-{{i}}.pq",
+        existing_data_behavior="overwrite_or_ignore",
+    )
+
+
 def download_universe(
     universe: str = "csi500",
     start_date: str = "2017-01-01",
@@ -177,3 +200,104 @@ def download_universe(
         "date_min": None if date_min is None else str(pd.Timestamp(date_min).date()),
         "date_max": None if date_max is None else str(pd.Timestamp(date_max).date()),
     }
+
+
+def download_minute_universe(
+    universe: str = "csi500",
+    start_date: str = "2017-01-01",
+    end_date: str = "2026-12-31",
+    output_dir: str = "data/minute_bars",
+    max_symbols: int = 0,
+    chunk_size: int = 100,
+    frequency: str | int = 5,
+) -> dict:
+    """Download raw minute bars into a frequency/month-partitioned dataset.
+
+    Args:
+        universe: ``hs300``, ``csi500``, ``all``/``full``, a file path, or a
+            comma-separated symbol list.
+        start_date, end_date: ``YYYY-MM-DD`` bounds.
+        output_dir: Root under which ``{universe}/frequency=5m/month=YYYY-MM``
+            is written.
+        max_symbols: Cap on symbols (0 = all).
+        chunk_size: Symbols per durability flush.
+        frequency: Minute interval. ``5``/``"5"``/``"5m"`` are 5-minute bars.
+
+    Returns:
+        Stats dict with dataset path, row count, symbol count, date range, and
+        frequency label.
+    """
+    _, frequency_label = _normalize_minute_frequency(frequency)
+    constituents = _resolve_universe(universe, max_symbols)
+    symbols = constituents["symbol_id"].tolist()
+    names = dict(zip(constituents["symbol_id"], constituents["symbol_name"]))
+    n_requested = len(symbols)
+    logger.info(
+        "Minute universe %s: %d symbols, %s → %s, frequency=%s",
+        universe,
+        n_requested,
+        start_date,
+        end_date,
+        frequency,
+    )
+
+    base_dir = Path(output_dir) / universe
+    target_frequency_dir = base_dir / f"frequency={frequency_label}"
+    if target_frequency_dir.exists():
+        shutil.rmtree(target_frequency_dir)
+    base_dir.mkdir(parents=True, exist_ok=True)
+
+    buffer: list[pd.DataFrame] = []
+    chunk_idx = 0
+    succeeded = 0
+    n_rows = 0
+    date_min = None
+    date_max = None
+
+    def flush() -> None:
+        nonlocal buffer, chunk_idx, n_rows, date_min, date_max
+        if not buffer:
+            return
+        chunk = pd.concat(buffer, ignore_index=True)
+        _write_minute_partitions(chunk, base_dir, basename_prefix=f"chunk{chunk_idx:04d}")
+        n_rows += len(chunk)
+        cmin, cmax = chunk["date"].min(), chunk["date"].max()
+        date_min = cmin if date_min is None else min(date_min, cmin)
+        date_max = cmax if date_max is None else max(date_max, cmax)
+        logger.info(
+            "Flushed minute chunk %d: %d rows (%d symbols done)",
+            chunk_idx,
+            len(chunk),
+            succeeded,
+        )
+        buffer = []
+        chunk_idx += 1
+
+    for i, (symbol, df) in enumerate(
+        download_minute_batch(symbols, start_date, end_date, frequency=frequency), start=1
+    ):
+        if df is None:
+            logger.warning("  %s: no minute data", symbol)
+        else:
+            df["symbol_id"] = symbol
+            df["symbol_name"] = names.get(symbol, symbol)
+            buffer.append(df[MINUTE_BAR_COLUMNS])
+            succeeded += 1
+        if len(buffer) >= chunk_size:
+            flush()
+        if i % 100 == 0:
+            logger.info("Minute progress: %d/%d symbols", i, n_requested)
+    flush()
+
+    if succeeded == 0:
+        raise RuntimeError("No minute data downloaded for any symbol")
+
+    return {
+        "dataset_path": str(base_dir),
+        "frequency": frequency_label,
+        "n_symbols": succeeded,
+        "n_requested": n_requested,
+        "n_rows": n_rows,
+        "date_min": None if date_min is None else str(pd.Timestamp(date_min).date()),
+        "date_max": None if date_max is None else str(pd.Timestamp(date_max).date()),
+    }

pipeline/derived/__init__.py

@@ -0,0 +1,2 @@
+"""Daily derived-data plugin package."""
+

pipeline/derived/base.py

@@ -0,0 +1,38 @@
+"""Base class for daily derived-data plugins."""
+
+from abc import ABC, abstractmethod
+
+import pandas as pd
+
+
+class BaseDerivedData(ABC):
+    """Compute daily, symbol-keyed numeric derived data.
+
+    Derived-data plugins may use daily bars, minute bars, or both as inputs, but
+    they must always return daily rows keyed by ``symbol_id,date``.
+    """
+
+    #: Unique registry key. Every concrete derived-data plugin must set this.
+    name: str = ""
+
+    @abstractmethod
+    def compute(
+        self,
+        daily: pd.DataFrame | None = None,
+        minute: pd.DataFrame | None = None,
+    ) -> pd.DataFrame:
+        """Compute daily derived data.
+
+        Args:
+            daily: Optional daily market data.
+            minute: Optional raw minute bars.
+
+        Returns:
+            DataFrame with ``symbol_id``, ``date``, and one or more numeric
+            derived-data columns.
+        """
+
+    def __repr__(self) -> str:
+        params = ", ".join(f"{k}={v!r}" for k, v in vars(self).items())
+        return f"{type(self).__name__}({params})"
+

pipeline/derived/cli.py

@@ -0,0 +1,145 @@
+"""CLI for daily derived-data ingestion and computation."""
+
+import click
+import pandas as pd
+
+from pipeline.derived.compute import (
+    DERIVED_KEY_COLUMNS,
+    compute_derived,
+    read_derived_frame,
+    write_derived_frame,
+)
+from pipeline.derived.registry import (
+    available_derived,
+    load_derived_module,
+)
+
+
+@click.group(name="derived")
+def derived():
+    """Ingest, compute, and validate daily derived data."""
+
+
+def _coerce(value: str):
+    """Best-effort coercion of a CLI string to int, then float, else str."""
+    for cast in (int, float):
+        try:
+            return cast(value)
+        except ValueError:
+            continue
+    return value
+
+
+def _parse_params(pairs: tuple[str, ...]) -> dict:
+    """Parse repeated ``name=value`` options into a params dict."""
+    params: dict = {}
+    for pair in pairs:
+        if "=" not in pair:
+            raise click.BadParameter(f"--param must be name=value, got '{pair}'")
+        key, value = pair.split("=", 1)
+        params[key.strip()] = _coerce(value.strip())
+    return params
+
+
+def _read_optional_parquet(path: str | None) -> pd.DataFrame | None:
+    return None if path is None else pd.read_parquet(path)
+
+
+def _summarize(result: pd.DataFrame) -> str:
+    value_cols = [col for col in result.columns if col not in DERIVED_KEY_COLUMNS]
+    return f"{len(result):,} rows, {len(value_cols)} columns"
+
+
+@derived.command("list")
+@click.option(
+    "--derived-module", "derived_modules", multiple=True,
+    help="External module(s) to import first (dotted path or .py file)",
+)
+def list_(derived_modules):
+    """List the registered derived-data plugin types."""
+    for spec in derived_modules:
+        load_derived_module(spec)
+    for name in available_derived():
+        click.echo(name)
+
+
+@derived.command("validate")
+@click.option("--input-path", required=True, help="CSV/parquet file or parquet dataset to validate")
+def validate(input_path):
+    """Validate a daily derived-data file without writing output."""
+    try:
+        result = read_derived_frame(input_path)
+    except Exception as exc:
+        raise click.ClickException(str(exc)) from exc
+    click.echo(f"Valid derived data: {input_path}  ({_summarize(result)})")
+
+
+@derived.command("ingest")
+@click.option("--input-path", required=True, help="CSV/parquet file to ingest")
+@click.option("--derived-name", required=True, help="Name for this derived-data output file")
+@click.option("--output-dir", default="derived", help="Directory to save derived parquet")
+def ingest(input_path, derived_name, output_dir):
+    """Ingest a user-provided daily derived-data CSV/parquet file."""
+    try:
+        result = read_derived_frame(input_path)
+        out_path = write_derived_frame(result, derived_name, output_dir=output_dir)
+    except Exception as exc:
+        raise click.ClickException(str(exc)) from exc
+    click.echo(f"Saved derived data: {out_path}  ({_summarize(result)})")
+
+
+@derived.command("compute")
+@click.option("--daily-path", default=None, help="Optional daily data parquet/dataset")
+@click.option("--minute-path", default=None, help="Optional minute parquet/dataset")
+@click.option("--derived-type", required=True, help="Registry key of the derived-data plugin")
+@click.option("--derived-name", required=True, help="Name for this derived-data output file")
+@click.option("--output-dir", default="derived", help="Directory to save derived parquet")
+@click.option(
+    "--derived-module", "derived_modules", multiple=True,
+    help="External module(s) to import so their derived-data plugins register",
+)
+@click.option(
+    "--param", "extra_params", multiple=True,
+    help="Extra derived-data constructor param as name=value (repeatable)",
+)
+def compute(
+    daily_path,
+    minute_path,
+    derived_type,
+    derived_name,
+    output_dir,
+    derived_modules,
+    extra_params,
+):
+    """Compute one daily derived-data file from daily and/or minute inputs."""
+    for spec in derived_modules:
+        load_derived_module(spec)
+
+    options = available_derived()
+    if derived_type not in options:
+        raise click.BadParameter(
+            f"Unknown derived-type '{derived_type}'. Available: {options}. "
+            f"Use --derived-module to register an external derived-data plugin.",
+            param_hint="--derived-type",
+        )
+    if daily_path is None and minute_path is None:
+        raise click.UsageError("At least one of --daily-path or --minute-path is required")
+
+    daily = _read_optional_parquet(daily_path)
+    if daily_path:
+        click.echo(f"Loaded daily data: {len(daily):,} rows from {daily_path}")
+    minute = _read_optional_parquet(minute_path)
+    if minute_path:
+        click.echo(f"Loaded minute bars: {len(minute):,} rows from {minute_path}")
+
+    try:
+        result = compute_derived(
+            derived_type=derived_type,
+            daily=daily,
+            minute=minute,
+            **_parse_params(extra_params),
+        )
+        out_path = write_derived_frame(result, derived_name, output_dir=output_dir)
+    except Exception as exc:
+        raise click.ClickException(str(exc)) from exc
+    click.echo(f"Saved derived data: {out_path}  ({_summarize(result)})")

pipeline/derived/compute.py

@@ -0,0 +1,115 @@
+"""Derived-data computation and validation."""
+
+import csv
+import logging
+from pathlib import Path
+from typing import Iterable
+
+import pandas as pd
+from pandas.api.types import is_bool_dtype, is_numeric_dtype
+
+from pipeline.common.schema import DERIVED_KEY_COLUMNS
+from pipeline.derived.registry import get_derived
+
+logger = logging.getLogger(__name__)
+
+
+def validate_derived_frame(derived: pd.DataFrame) -> pd.DataFrame:
+    """Validate and normalize a daily derived-data frame.
+
+    A valid derived frame is keyed by unique ``symbol_id,date`` rows and has at
+    least one numeric value column beyond those keys. Dates are normalized to
+    daily timestamps before duplicate-key checks.
+    """
+    duplicated = derived.columns[derived.columns.duplicated()].tolist()
+    if duplicated:
+        raise ValueError(f"Derived data has duplicate columns: {duplicated}")
+
+    missing = [col for col in DERIVED_KEY_COLUMNS if col not in derived.columns]
+    if missing:
+        raise ValueError(f"Derived data missing required columns: {missing}")
+
+    out = derived.copy()
+    out["date"] = pd.to_datetime(out["date"]).dt.normalize()
+
+    if out.duplicated(DERIVED_KEY_COLUMNS).any():
+        raise ValueError("Derived data has duplicate symbol_id,date rows")
+
+    value_cols = [col for col in out.columns if col not in DERIVED_KEY_COLUMNS]
+    if not value_cols:
+        raise ValueError("Derived data must include at least one value column")
+
+    non_numeric = [
+        col
+        for col in value_cols
+        if is_bool_dtype(out[col]) or not is_numeric_dtype(out[col])
+    ]
+    if non_numeric:
+        raise ValueError(f"Derived data value columns must be numeric: {non_numeric}")
+
+    out = out[DERIVED_KEY_COLUMNS + value_cols].copy()
+    return out.sort_values(DERIVED_KEY_COLUMNS).reset_index(drop=True)
+
+
+def compute_derived(
+    derived_type: str,
+    daily: pd.DataFrame | None = None,
+    minute: pd.DataFrame | None = None,
+    **params,
+) -> pd.DataFrame:
+    """Compute one registered derived-data plugin."""
+    if daily is None and minute is None:
+        raise ValueError("Derived data computation requires --daily-path or --minute-path")
+
+    derived = get_derived(derived_type, **params)
+    result = validate_derived_frame(derived.compute(daily=daily, minute=minute))
+    value_cols = [col for col in result.columns if col not in DERIVED_KEY_COLUMNS]
+    logger.info(
+        "Derived data '%s' (%r): %d symbols × %d dates, columns=%s",
+        derived_type,
+        derived,
+        result["symbol_id"].nunique(),
+        result["date"].nunique(),
+        value_cols,
+    )
+    return result
+
+
+def read_derived_frame(path: str | Path) -> pd.DataFrame:
+    """Read and validate one derived CSV/parquet file or parquet dataset."""
+    path = Path(path)
+    if path.suffix.lower() == ".csv":
+        return validate_derived_frame(_read_csv_with_duplicate_header_check(path))
+    return validate_derived_frame(pd.read_parquet(path))
+
+
+def read_derived_frames(derived_paths: Iterable[str | Path]) -> list[pd.DataFrame]:
+    """Read and validate derived-data files."""
+    return [read_derived_frame(path) for path in derived_paths]
+
+
+def write_derived_frame(
+    derived: pd.DataFrame,
+    derived_name: str,
+    output_dir: str | Path = "derived",
+) -> Path:
+    """Validate and write derived data to ``{output_dir}/{derived_name}.pq``."""
+    result = validate_derived_frame(derived)
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    out_path = output_dir / f"{derived_name}.pq"
+    result.to_parquet(out_path, index=False)
+    return out_path
+
+
+def _read_csv_with_duplicate_header_check(path: Path) -> pd.DataFrame:
+    with path.open(newline="") as fh:
+        reader = csv.reader(fh)
+        try:
+            header = next(reader)
+        except StopIteration as exc:
+            raise ValueError("CSV input is empty") from exc
+    duplicated = sorted({col for col in header if header.count(col) > 1})
+    if duplicated:
+        raise ValueError(f"Derived data has duplicate columns: {duplicated}")
+    return pd.read_csv(path)

pipeline/derived/library/__init__.py

@@ -0,0 +1,4 @@
+"""Built-in derived-data library."""
+
+from pipeline.derived.library import minute_daily_summary  # noqa: F401
+

pipeline/derived/library/minute_daily_summary.py

@@ -0,0 +1,88 @@
+"""Daily summary data derived from raw minute bars."""
+
+import numpy as np
+import pandas as pd
+
+from pipeline.derived.base import BaseDerivedData
+from pipeline.derived.registry import register_derived
+
+
+@register_derived
+class MinuteDailySummaryDerived(BaseDerivedData):
+    """Aggregate intraday bars into daily summary columns."""
+
+    name = "minute_daily_summary"
+
+    def compute(
+        self,
+        daily: pd.DataFrame | None = None,
+        minute: pd.DataFrame | None = None,
+    ) -> pd.DataFrame:
+        if minute is None:
+            raise ValueError("minute_daily_summary requires minute input")
+
+        minute = minute.copy()
+        minute["date"] = pd.to_datetime(minute["date"]).dt.normalize()
+        sort_cols = ["symbol_id", "date"]
+        if "datetime" in minute.columns:
+            minute["datetime"] = pd.to_datetime(minute["datetime"])
+            sort_cols.append("datetime")
+        elif "time" in minute.columns:
+            sort_cols.append("time")
+        minute = minute.sort_values(sort_cols)
+
+        grouped = minute.groupby(["symbol_id", "date"], sort=True)
+        summary = grouped.agg(
+            minute_bar_count=("close", "count"),
+            first_open=("open", "first"),
+            last_close=("close", "last"),
+            high=("high", "max"),
+            low=("low", "min"),
+            volume_sum=("volume", "sum"),
+            amount_sum=("amount", "sum"),
+        )
+        summary["minute_intraday_return"] = (
+            summary["last_close"] / summary["first_open"] - 1.0
+        )
+        summary["minute_intraday_range"] = summary["high"] / summary["low"] - 1.0
+        summary["minute_vwap"] = (
+            summary["amount_sum"] / summary["volume_sum"].where(summary["volume_sum"] > 0)
+        )
+        summary = summary.reset_index()
+
+        if daily is not None:
+            daily_keys = daily[["symbol_id", "date"]].copy()
+            daily_keys["date"] = pd.to_datetime(daily_keys["date"]).dt.normalize()
+            daily_keys = daily_keys.drop_duplicates(["symbol_id", "date"])
+            result = daily_keys.merge(summary, on=["symbol_id", "date"], how="left")
+            if "close" in daily.columns:
+                daily_close = daily[["symbol_id", "date", "close"]].copy()
+                daily_close["date"] = pd.to_datetime(daily_close["date"]).dt.normalize()
+                daily_close = daily_close.drop_duplicates(["symbol_id", "date"])
+                result = result.merge(
+                    daily_close.rename(columns={"close": "daily_close"}),
+                    on=["symbol_id", "date"],
+                    how="left",
+                )
+                reference_close = result["daily_close"].fillna(result["last_close"])
+            else:
+                reference_close = result["last_close"]
+        else:
+            result = summary
+            reference_close = result["last_close"]
+
+        result["minute_vwap_deviation"] = (
+            result["minute_vwap"] / reference_close.replace(0.0, np.nan) - 1.0
+        )
+        return result[
+            [
+                "symbol_id",
+                "date",
+                "minute_bar_count",
+                "minute_intraday_return",
+                "minute_intraday_range",
+                "minute_vwap",
+                "minute_vwap_deviation",
+            ]
+        ]
+

pipeline/derived/registry.py

@@ -0,0 +1,80 @@
+"""Registry and factory for daily derived-data plugins."""
+
+import importlib
+import importlib.util
+import inspect
+from pathlib import Path
+from typing import Optional, Type
+
+from pipeline.derived.base import BaseDerivedData
+
+_REGISTRY: dict[str, Type[BaseDerivedData]] = {}
+_builtins_loaded = False
+
+
+def register_derived(cls: Type[BaseDerivedData]) -> Type[BaseDerivedData]:
+    """Class decorator that registers derived data under ``BaseDerivedData.name``."""
+    if not (isinstance(cls, type) and issubclass(cls, BaseDerivedData)):
+        raise TypeError(f"{cls!r} is not a BaseDerivedData subclass")
+    key = getattr(cls, "name", "")
+    if not key:
+        raise ValueError(f"{cls.__name__} must set a non-empty class attribute `name`")
+    existing = _REGISTRY.get(key)
+    if existing is not None and existing is not cls:
+        raise ValueError(
+            f"Derived data name '{key}' already registered by {existing.__name__}"
+        )
+    _REGISTRY[key] = cls
+    return cls
+
+
+def available_derived() -> list[str]:
+    """Sorted names of all registered derived-data plugins."""
+    _ensure_builtins()
+    return sorted(_REGISTRY)
+
+
+def get_derived(name: str, **params) -> BaseDerivedData:
+    """Instantiate a registered derived-data plugin by name.
+
+    Only parameters accepted by the plugin class's ``__init__`` are forwarded.
+    """
+    _ensure_builtins()
+    if name not in _REGISTRY:
+        raise KeyError(f"Unknown derived data '{name}'. Available: {sorted(_REGISTRY)}")
+    cls = _REGISTRY[name]
+    accepted = _accepted_params(cls)
+    kwargs = params if accepted is None else {k: v for k, v in params.items() if k in accepted}
+    return cls(**kwargs)
+
+
+def load_derived_module(spec: str) -> None:
+    """Import an external module so its ``@register_derived`` classes register."""
+    looks_like_file = spec.endswith(".py") or Path(spec).expanduser().exists()
+    if looks_like_file:
+        path = Path(spec).expanduser().resolve()
+        if not path.exists():
+            raise FileNotFoundError(f"Derived data module not found: {path}")
+        module_spec = importlib.util.spec_from_file_location(path.stem, path)
+        if module_spec is None or module_spec.loader is None:
+            raise ImportError(f"Cannot load derived data module from {path}")
+        module = importlib.util.module_from_spec(module_spec)
+        module_spec.loader.exec_module(module)
+    else:
+        importlib.import_module(spec)
+
+
+def _accepted_params(cls: Type[BaseDerivedData]) -> Optional[set[str]]:
+    """Param names ``cls.__init__`` accepts, or None if it takes ``**kwargs``."""
+    sig = inspect.signature(cls.__init__)
+    if any(p.kind is p.VAR_KEYWORD for p in sig.parameters.values()):
+        return None
+    return {name for name in sig.parameters if name != "self"}
+
+
+def _ensure_builtins() -> None:
+    global _builtins_loaded
+    if not _builtins_loaded:
+        import pipeline.derived.library  # noqa: F401
+        _builtins_loaded = True
+

pipeline/features/__init__.py

@@ -0,0 +1 @@
+"""Daily feature plugin package."""

pipeline/features/base.py

@@ -0,0 +1,8 @@
+"""Compatibility alias for daily feature plugins.
+
+The canonical plugin API is ``pipeline.derived``. ``BaseFeature`` remains as an
+alias so existing external feature modules continue to register unchanged.
+"""
+
+from pipeline.derived.base import BaseDerivedData as BaseFeature
+

pipeline/features/cli.py

@@ -0,0 +1,108 @@
+"""CLI for daily feature computation."""
+
+import os
+
+import click
+import pandas as pd
+
+from pipeline.features.compute import compute_feature
+from pipeline.features.registry import available_features, load_feature_module
+
+
+@click.group(name="feature")
+def feature():
+    """Compute daily feature parquet files from minute bars."""
+
+
+def _coerce(value: str):
+    """Best-effort coercion of a CLI string to int, then float, else str."""
+    for cast in (int, float):
+        try:
+            return cast(value)
+        except ValueError:
+            continue
+    return value
+
+
+def _parse_params(pairs: tuple[str, ...]) -> dict:
+    """Parse repeated ``name=value`` options into a params dict."""
+    params: dict = {}
+    for pair in pairs:
+        if "=" not in pair:
+            raise click.BadParameter(f"--param must be name=value, got '{pair}'")
+        key, value = pair.split("=", 1)
+        params[key.strip()] = _coerce(value.strip())
+    return params
+
+
+@feature.command("list")
+@click.option(
+    "--feature-module", "feature_modules", multiple=True,
+    help="External module(s) to import first (dotted path or .py file)",
+)
+def list_(feature_modules):
+    """List the registered feature types."""
+    for spec in feature_modules:
+        load_feature_module(spec)
+    for name in available_features():
+        click.echo(name)
+
+
+@feature.command("compute")
+@click.option("--minute-path", required=True, help="Path to minute parquet dataset/file")
+@click.option("--daily-path", default=None, help="Optional daily data parquet for alignment")
+@click.option("--feature-type", required=True, help="Registry key of the feature class")
+@click.option("--feature-name", required=True, help="Name for this feature run/output file")
+@click.option("--output-dir", default="features", help="Directory to save feature parquet")
+@click.option(
+    "--feature-module", "feature_modules", multiple=True,
+    help="External module(s) to import so their features register (dotted path or .py file)",
+)
+@click.option(
+    "--param", "extra_params", multiple=True,
+    help="Extra feature constructor param as name=value (repeatable)",
+)
+def compute(
+    minute_path,
+    daily_path,
+    feature_type,
+    feature_name,
+    output_dir,
+    feature_modules,
+    extra_params,
+):
+    """Compute one daily feature file from raw minute bars."""
+    for spec in feature_modules:
+        load_feature_module(spec)
+
+    options = available_features()
+    if feature_type not in options:
+        raise click.BadParameter(
+            f"Unknown feature-type '{feature_type}'. Available: {options}. "
+            f"Use --feature-module to register an external feature.",
+            param_hint="--feature-type",
+        )
+
+    minute = pd.read_parquet(minute_path)
+    click.echo(f"Loaded minute bars: {len(minute):,} rows from {minute_path}")
+
+    daily = None
+    if daily_path:
+        daily = pd.read_parquet(daily_path)
+        click.echo(f"Loaded daily data: {len(daily):,} rows from {daily_path}")
+
+    result = compute_feature(
+        minute=minute,
+        daily=daily,
+        feature_type=feature_type,
+        **_parse_params(extra_params),
+    )
+
+    os.makedirs(output_dir, exist_ok=True)
+    out_path = f"{output_dir}/{feature_name}.pq"
+    result.to_parquet(out_path, index=False)
+    feature_cols = [col for col in result.columns if col not in ("symbol_id", "date")]
+    click.echo(
+        f"Saved feature: {out_path}  ({len(result):,} rows, "
+        f"{len(feature_cols)} columns)"
+    )

pipeline/features/compute.py

@@ -0,0 +1,41 @@
+"""Compatibility wrappers for daily feature computation and validation."""
+
+from pathlib import Path
+from typing import Iterable
+
+import pandas as pd
+
+from pipeline.derived.compute import (
+    DERIVED_KEY_COLUMNS,
+    compute_derived,
+    read_derived_frames,
+    validate_derived_frame,
+)
+
+FEATURE_KEY_COLUMNS = DERIVED_KEY_COLUMNS
+
+
+def validate_feature_frame(features: pd.DataFrame) -> pd.DataFrame:
+    """Validate and normalize a legacy daily feature frame."""
+    return validate_derived_frame(features)
+
+
+def compute_feature(
+    minute: pd.DataFrame,
+    feature_type: str,
+    daily: pd.DataFrame | None = None,
+    **params,
+) -> pd.DataFrame:
+    """Compute one registered feature through the derived-data registry."""
+    return compute_derived(
+        derived_type=feature_type,
+        daily=daily,
+        minute=minute,
+        **params,
+    )
+
+
+def read_feature_frames(feature_paths: Iterable[str | Path]) -> list[pd.DataFrame]:
+    """Read and validate feature/derived-data parquet files."""
+    return read_derived_frames(feature_paths)
+

pipeline/features/library/__init__.py

@@ -0,0 +1,3 @@
+"""Built-in feature library."""
+
+from pipeline.features.library import minute_daily_summary  # noqa: F401

pipeline/features/library/minute_daily_summary.py

@@ -0,0 +1,16 @@
+"""Compatibility wrapper for the built-in minute daily summary plugin."""
+
+import pandas as pd
+
+from pipeline.derived.library.minute_daily_summary import MinuteDailySummaryDerived
+
+
+class MinuteDailySummaryFeature(MinuteDailySummaryDerived):
+    """Legacy minute-first wrapper around the derived-data implementation."""
+
+    def compute(
+        self,
+        minute: pd.DataFrame,
+        daily: pd.DataFrame | None = None,
+    ) -> pd.DataFrame:
+        return super().compute(daily=daily, minute=minute)

pipeline/features/registry.py

@@ -0,0 +1,36 @@
+"""Compatibility registry wrappers for daily feature plugins."""
+
+from typing import Type
+
+from pipeline.derived.base import BaseDerivedData
+from pipeline.derived.registry import (
+    available_derived,
+    get_derived,
+    load_derived_module,
+    register_derived,
+)
+from pipeline.features.base import BaseFeature
+
+
+def register_feature(cls: Type[BaseFeature]) -> Type[BaseFeature]:
+    """Register a legacy feature plugin in the derived-data registry."""
+    return register_derived(cls)
+
+
+def available_features() -> list[str]:
+    """Sorted names of all registered feature/derived-data plugins."""
+    return available_derived()
+
+
+def get_feature(name: str, **params) -> BaseDerivedData:
+    """Instantiate a registered feature/derived-data plugin by name."""
+    if name == "minute_daily_summary":
+        from pipeline.features.library.minute_daily_summary import MinuteDailySummaryFeature
+
+        return MinuteDailySummaryFeature(**params)
+    return get_derived(name, **params)
+
+
+def load_feature_module(spec: str) -> None:
+    """Import an external module so its ``@register_feature`` classes register."""
+    load_derived_module(spec)

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)
-        amount = amount.reindex(columns=symbols)
-        tstat = tstat.reindex(columns=symbols)
-        st = st.reindex(columns=symbols)
+        preclose = preclose.reindex(index=data_index, columns=symbols)
+        amount = amount.reindex(index=data_index, columns=symbols)
+        tstat = tstat.reindex(index=data_index, 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

@@ -347,3 +347,158 @@ def test_universe_filter_does_not_corrupt_signal_history():
     held = set(filtered.loc[filtered["weight"] != 0.0, "symbol_id"].unique())
     # The two most liquid names (highest amount) are sh600519, sz300750.
     assert held == {"sh600519", "sz300750"}
+
+
+# --- feature-aware alpha integration ----------------------------------------
+
+def test_compute_alpha_without_feature_path_matches_empty_feature_paths():
+    data = _make_data()
+
+    base = compute_alpha(data, "rev5", "reversal", lookback=5)
+    with_empty_features = compute_alpha(
+        data,
+        "rev5",
+        "reversal",
+        lookback=5,
+        feature_paths=[],
+    )
+
+    pd.testing.assert_frame_equal(base, with_empty_features)
+
+
+def test_feature_aware_alpha_reads_joined_feature_column(tmp_path):
+    module_path = tmp_path / "feature_aware_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 FeatureAwareAlpha(BaseAlpha):
+            name = "feature_aware_test_alpha"
+
+            def signal_from_data(
+                self,
+                data: pd.DataFrame,
+                close: pd.DataFrame,
+            ) -> pd.DataFrame:
+                signal = data.pivot_table(
+                    index="date",
+                    columns="symbol_id",
+                    values="toy_feature",
+                    aggfunc="first",
+                )
+                return signal.reindex(index=close.index, columns=close.columns)
+    '''))
+
+    data = _make_data()
+    feature = data[["symbol_id", "date"]].copy()
+    feature["toy_feature"] = feature["symbol_id"].map({
+        "sh600000": 1.0,
+        "sz000001": 2.0,
+        "sh600519": 3.0,
+    })
+    feature_path = tmp_path / "toy_feature.pq"
+    feature.to_parquet(feature_path, index=False)
+
+    load_alpha_module(str(module_path))
+    result = compute_alpha(
+        data,
+        "feature_run",
+        "feature_aware_test_alpha",
+        feature_paths=[str(feature_path)],
+    )
+
+    assert list(result.columns) == ALPHA_COLUMNS
+    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 +1,545 @@
+"""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
+
+
+def test_cli_list_and_legacy_feature_paths(tmp_path):
+    runner = CliRunner()
+    daily_bars = make_generated_daily_bars(n_sessions=3, include_missing=False)
+    minute_bars = make_generated_minute_bars(daily_bars)
+    daily_path = tmp_path / "daily_bars.pq"
+    minute_path = tmp_path / "minute_bars.pq"
+    daily_bars.to_parquet(daily_path, index=False)
+    minute_bars.to_parquet(minute_path, index=False)
+
+    derived_list = _invoke_ok(runner, ["derived", "list"])
+    feature_list = _invoke_ok(runner, ["feature", "list"])
+    assert "minute_daily_summary" in derived_list.output
+    assert "minute_daily_summary" in feature_list.output
+
+    feature_dir = tmp_path / "features"
+    feature_compute = _invoke_ok(runner, [
+        "feature",
+        "compute",
+        "--minute-path",
+        str(minute_path),
+        "--daily-path",
+        str(daily_path),
+        "--feature-type",
+        "minute_daily_summary",
+        "--feature-name",
+        "legacy_summary",
+        "--output-dir",
+        str(feature_dir),
+    ])
+    feature_path = feature_dir / "legacy_summary.pq"
+    assert "Loaded minute bars:" in feature_compute.output
+    assert "Loaded daily data:" in feature_compute.output
+    assert "Saved feature:" in feature_compute.output
+    assert feature_path.exists()
+
+    no_input = _invoke_error(runner, [
+        "derived",
+        "compute",
+        "--derived-type",
+        "minute_daily_summary",
+        "--derived-name",
+        "missing_inputs",
+        "--output-dir",
+        str(tmp_path / "derived"),
+    ])
+    assert "At least one of --daily-path or --minute-path is required" in no_input.output
+
+    missing_minute = _invoke_error(runner, [
+        "derived",
+        "compute",
+        "--daily-path",
+        str(daily_path),
+        "--derived-type",
+        "minute_daily_summary",
+        "--derived-name",
+        "daily_only",
+        "--output-dir",
+        str(tmp_path / "derived"),
+    ])
+    assert "minute_daily_summary requires minute input" in missing_minute.output
+
+    malformed_feature_param = _invoke_error(runner, [
+        "feature",
+        "compute",
+        "--minute-path",
+        str(minute_path),
+        "--feature-type",
+        "minute_daily_summary",
+        "--feature-name",
+        "bad_param",
+        "--param",
+        "not-an-assignment",
+        "--output-dir",
+        str(tmp_path / "features_bad"),
+    ])
+    assert "--param must be name=value" in malformed_feature_param.output
+
+    unknown_feature = _invoke_error(runner, [
+        "feature",
+        "compute",
+        "--minute-path",
+        str(minute_path),
+        "--feature-type",
+        "does_not_exist",
+        "--feature-name",
+        "bad_feature",
+        "--output-dir",
+        str(tmp_path / "features_unknown"),
+    ])
+    assert "Unknown feature-type" in unknown_feature.output
+
+
+def test_cli_pqcat_row_modes(tmp_path):
+    runner = CliRunner()
+    daily_bars = make_generated_daily_bars(n_sessions=3, include_missing=False)
+    daily_path = tmp_path / "daily_bars.pq"
+    daily_bars.to_parquet(daily_path, index=False)
+
+    head_result = _invoke_ok(runner, [
+        "pqcat",
+        str(daily_path),
+        "--head",
+        "2",
+        "--columns",
+        "symbol_id,close",
+    ])
+    tail_result = _invoke_ok(runner, [
+        "pqcat",
+        str(daily_path),
+        "--tail",
+        "1",
+    ])
+
+    assert "symbol_id" in head_result.output
+    assert "close" in head_result.output
+    assert "date" in tail_result.output

tests/test_data_cli_and_resolve.py

@@ -0,0 +1,148 @@
+"""Offline coverage for data CLI and universe resolution glue."""
+
+from __future__ import annotations
+
+import pandas as pd
+from click.testing import CliRunner
+
+from cli import cli
+import pipeline.data.cli as data_cli
+import pipeline.data.downloader as pipeline_downloader
+
+
+def test_resolve_universe_handles_named_file_all_and_symbol_list(tmp_path, monkeypatch):
+    hs300_raw = pd.DataFrame({
+        "updateDate": ["2024-01-12", "2024-01-12"],
+        "stockName": ["浦发银行", "平安银行"],
+        "stockCode": ["sh.600000", "sz.000001"],
+    })
+    zz500_raw = pd.DataFrame({
+        "name": ["东风汽车"],
+        "code": ["sh.600006"],
+        "date": ["2024-01-12"],
+    })
+
+    monkeypatch.setattr(pipeline_downloader, "get_hs300_stocks", lambda: hs300_raw)
+    monkeypatch.setattr(pipeline_downloader, "get_zz500_stocks", lambda: zz500_raw)
+    monkeypatch.setattr(
+        pipeline_downloader,
+        "get_all_stocks",
+        lambda: pd.DataFrame({
+            "code": ["sh600000", "sz000001", "sh600519"],
+            "name": ["浦发银行", "平安银行", "贵州茅台"],
+        }),
+    )
+
+    symbol_file = tmp_path / "symbols.txt"
+    symbol_file.write_text("sh600000\n\nsz000001\n")
+
+    hs300 = pipeline_downloader._resolve_universe("hs300")
+    zz500 = pipeline_downloader._resolve_universe("csi500")
+    all_capped = pipeline_downloader._resolve_universe("all", max_symbols=2)
+    from_file = pipeline_downloader._resolve_universe(str(symbol_file))
+    from_list = pipeline_downloader._resolve_universe("sh600000, sz000001")
+
+    assert hs300.to_dict("list") == {
+        "symbol_name": ["浦发银行", "平安银行"],
+        "symbol_id": ["sh600000", "sz000001"],
+    }
+    assert zz500.to_dict("list") == {
+        "symbol_name": ["东风汽车"],
+        "symbol_id": ["sh600006"],
+    }
+    assert all_capped["symbol_id"].tolist() == ["sh600000", "sz000001"]
+    assert from_file["symbol_id"].tolist() == ["sh600000", "sz000001"]
+    assert from_file["symbol_name"].tolist() == ["sh600000", "sz000001"]
+    assert from_list["symbol_id"].tolist() == ["sh600000", "sz000001"]
+
+
+def test_data_cli_download_commands_print_summaries_without_network(monkeypatch, tmp_path):
+    runner = CliRunner()
+    daily_calls: list[dict] = []
+    minute_calls: list[dict] = []
+
+    def fake_daily(**kwargs):
+        daily_calls.append(kwargs)
+        return {
+            "dataset_path": str(tmp_path / "daily" / kwargs["universe"]),
+            "n_symbols": 2,
+            "n_requested": 3,
+            "n_rows": 18,
+            "date_min": "2024-01-02",
+            "date_max": "2024-01-12",
+        }
+
+    def fake_minute(**kwargs):
+        minute_calls.append(kwargs)
+        return {
+            "dataset_path": str(tmp_path / "minute" / kwargs["universe"]),
+            "frequency": "15m",
+            "n_symbols": 1,
+            "n_requested": 1,
+            "n_rows": 32,
+            "date_min": "2024-01-02",
+            "date_max": "2024-01-03",
+        }
+
+    monkeypatch.setattr(data_cli, "download_universe", fake_daily)
+    monkeypatch.setattr(data_cli, "download_minute_universe", fake_minute)
+
+    daily_result = runner.invoke(cli, [
+        "data",
+        "download",
+        "--universe",
+        "sh600000,sz000001",
+        "--start-date",
+        "2024-01-02",
+        "--end-date",
+        "2024-01-12",
+        "--output-dir",
+        str(tmp_path / "daily"),
+        "--symbols",
+        "3",
+        "--chunk-size",
+        "2",
+        "--adjust",
+        "none",
+    ])
+    minute_result = runner.invoke(cli, [
+        "data",
+        "download-minute",
+        "--universe",
+        "toy",
+        "--start-date",
+        "2024-01-02",
+        "--end-date",
+        "2024-01-03",
+        "--output-dir",
+        str(tmp_path / "minute"),
+        "--symbols",
+        "1",
+        "--chunk-size",
+        "1",
+        "--frequency",
+        "15",
+    ])
+
+    assert daily_result.exit_code == 0, daily_result.output
+    assert "Summary: 2/3 symbols, 18 bars" in daily_result.output
+    assert daily_calls == [{
+        "universe": "sh600000,sz000001",
+        "start_date": "2024-01-02",
+        "end_date": "2024-01-12",
+        "output_dir": str(tmp_path / "daily"),
+        "max_symbols": 3,
+        "chunk_size": 2,
+        "adjust": "none",
+    }]
+    assert minute_result.exit_code == 0, minute_result.output
+    assert "frequency=15m" in minute_result.output
+    assert minute_calls == [{
+        "universe": "toy",
+        "start_date": "2024-01-02",
+        "end_date": "2024-01-03",
+        "output_dir": str(tmp_path / "minute"),
+        "max_symbols": 1,
+        "chunk_size": 1,
+        "frequency": "15",
+    }]

tests/test_derived.py

@@ -0,0 +1,261 @@
+"""Tests for daily derived-data ingestion and plugins."""
+
+import textwrap
+
+import numpy as np
+import pandas as pd
+import pytest
+from click.testing import CliRunner
+
+from cli import cli
+from pipeline.alpha.compute import join_feature_frames
+from pipeline.derived.compute import compute_derived, validate_derived_frame
+from pipeline.derived.registry import available_derived, get_derived, load_derived_module
+
+
+def _daily_bars() -> pd.DataFrame:
+    return pd.DataFrame({
+        "symbol_id": ["sh600000", "sz000001", "sh600000"],
+        "date": pd.to_datetime(["2024-01-02", "2024-01-02", "2024-01-03"]),
+        "open": [10.0, 20.0, 11.0],
+        "close": [10.5, 20.5, 11.5],
+        "volume": [1000.0, 2000.0, 1200.0],
+    })
+
+
+def _minute_bars() -> pd.DataFrame:
+    return pd.DataFrame({
+        "symbol_id": ["sh600000", "sh600000", "sz000001"],
+        "datetime": pd.to_datetime([
+            "2024-01-02 09:35:00",
+            "2024-01-02 09:40:00",
+            "2024-01-02 09:35:00",
+        ]),
+        "date": pd.to_datetime(["2024-01-02", "2024-01-02", "2024-01-02"]),
+        "time": ["09:35:00", "09:40:00", "09:35:00"],
+        "open": [10.0, 10.5, 20.0],
+        "high": [11.0, 12.0, 21.0],
+        "low": [9.0, 10.0, 19.0],
+        "close": [10.5, 11.0, 20.5],
+        "volume": [100.0, 300.0, 200.0],
+        "amount": [1000.0, 3300.0, 4100.0],
+    })
+
+
+def test_validate_derived_frame_normalizes_and_sorts():
+    result = validate_derived_frame(pd.DataFrame({
+        "symbol_id": ["sz000001", "sh600000"],
+        "date": ["2024-01-02 15:00:00", "2024-01-02 09:30:00"],
+        "custom_value": [2.0, 1.0],
+    }))
+
+    assert result["symbol_id"].tolist() == ["sh600000", "sz000001"]
+    assert result["date"].tolist() == [
+        pd.Timestamp("2024-01-02"),
+        pd.Timestamp("2024-01-02"),
+    ]
+
+
+def test_validate_derived_frame_rejects_missing_keys():
+    with pytest.raises(ValueError, match="missing required"):
+        validate_derived_frame(pd.DataFrame({"symbol_id": ["sh600000"], "x": [1.0]}))
+
+
+def test_validate_derived_frame_rejects_duplicate_normalized_keys():
+    with pytest.raises(ValueError, match="duplicate symbol_id,date"):
+        validate_derived_frame(pd.DataFrame({
+            "symbol_id": ["sh600000", "sh600000"],
+            "date": ["2024-01-02 09:30:00", "2024-01-02 15:00:00"],
+            "x": [1.0, 2.0],
+        }))
+
+
+def test_validate_derived_frame_rejects_duplicate_columns():
+    bad = pd.DataFrame(
+        [["sh600000", pd.Timestamp("2024-01-02"), 1.0, 2.0]],
+        columns=["symbol_id", "date", "dup", "dup"],
+    )
+    with pytest.raises(ValueError, match="duplicate columns"):
+        validate_derived_frame(bad)
+
+
+def test_validate_derived_frame_rejects_non_numeric_values():
+    with pytest.raises(ValueError, match="numeric"):
+        validate_derived_frame(pd.DataFrame({
+            "symbol_id": ["sh600000"],
+            "date": [pd.Timestamp("2024-01-02")],
+            "bad": ["not numeric"],
+        }))
+
+
+def test_derived_ingest_cli_accepts_csv_and_parquet(tmp_path):
+    runner = CliRunner()
+    source = pd.DataFrame({
+        "symbol_id": ["sz000001", "sh600000"],
+        "date": ["2024-01-02", "2024-01-02"],
+        "custom_value": [2.0, 1.0],
+    })
+    csv_path = tmp_path / "custom.csv"
+    parquet_path = tmp_path / "custom.pq"
+    out_dir = tmp_path / "derived"
+    source.to_csv(csv_path, index=False)
+    source.to_parquet(parquet_path, index=False)
+
+    csv_result = runner.invoke(cli, [
+        "derived", "ingest",
+        "--input-path", str(csv_path),
+        "--derived-name", "csv_custom",
+        "--output-dir", str(out_dir),
+    ])
+    assert csv_result.exit_code == 0, csv_result.output
+
+    parquet_result = runner.invoke(cli, [
+        "derived", "ingest",
+        "--input-path", str(parquet_path),
+        "--derived-name", "parquet_custom",
+        "--output-dir", str(out_dir),
+    ])
+    assert parquet_result.exit_code == 0, parquet_result.output
+
+    written = pd.read_parquet(out_dir / "csv_custom.pq")
+    assert written["symbol_id"].tolist() == ["sh600000", "sz000001"]
+    assert (out_dir / "parquet_custom.pq").exists()
+
+
+def test_derived_validate_cli_rejects_duplicate_csv_columns(tmp_path):
+    runner = CliRunner()
+    csv_path = tmp_path / "bad.csv"
+    csv_path.write_text("symbol_id,date,x,x\nsh600000,2024-01-02,1.0,2.0\n")
+
+    result = runner.invoke(cli, [
+        "derived", "validate",
+        "--input-path", str(csv_path),
+    ])
+
+    assert result.exit_code != 0
+    assert "duplicate columns" in result.output
+
+
+def test_external_derived_plugin_loads_filters_params_and_uses_inputs(tmp_path):
+    module_path = tmp_path / "external_derived.py"
+    module_path.write_text(textwrap.dedent('''
+        import pandas as pd
+        from pipeline.derived.base import BaseDerivedData
+        from pipeline.derived.registry import register_derived
+
+        @register_derived
+        class FlexibleDerived(BaseDerivedData):
+            name = "flexible_derived_test"
+
+            def __init__(self, scale: float = 1.0):
+                self.scale = scale
+
+            def compute(self, daily=None, minute=None) -> pd.DataFrame:
+                result = None
+                if daily is not None:
+                    result = daily[["symbol_id", "date", "close"]].copy()
+                    result["daily_scaled_close"] = result.pop("close") * self.scale
+                if minute is not None:
+                    minute_out = (
+                        minute.groupby(["symbol_id", "date"], as_index=False)["volume"]
+                        .sum()
+                        .rename(columns={"volume": "minute_volume_sum"})
+                    )
+                    minute_out["minute_volume_sum"] *= self.scale
+                    result = minute_out if result is None else result.merge(
+                        minute_out, on=["symbol_id", "date"], how="left"
+                    )
+                return result
+    '''))
+
+    load_derived_module(str(module_path))
+    assert "flexible_derived_test" in available_derived()
+
+    instance = get_derived("flexible_derived_test", scale=2.0, ignored=99)
+    assert instance.scale == 2.0
+    assert not hasattr(instance, "ignored")
+
+    daily_result = compute_derived(
+        "flexible_derived_test",
+        daily=_daily_bars(),
+        scale=2.0,
+        ignored=99,
+    )
+    assert "daily_scaled_close" in daily_result.columns
+    assert np.isclose(daily_result["daily_scaled_close"].iloc[0], 21.0)
+
+    minute_result = compute_derived(
+        "flexible_derived_test",
+        minute=_minute_bars(),
+        scale=2.0,
+    )
+    assert "minute_volume_sum" in minute_result.columns
+    assert np.isclose(
+        minute_result.loc[minute_result["symbol_id"] == "sh600000", "minute_volume_sum"].iloc[0],
+        800.0,
+    )
+
+    both_result = compute_derived(
+        "flexible_derived_test",
+        daily=_daily_bars(),
+        minute=_minute_bars(),
+        scale=1.0,
+    )
+    assert {"daily_scaled_close", "minute_volume_sum"}.issubset(both_result.columns)
+
+
+def test_derived_compute_cli_writes_builtin_minute_summary(tmp_path):
+    runner = CliRunner()
+    minute_path = tmp_path / "minute.pq"
+    out_dir = tmp_path / "derived"
+    _minute_bars().to_parquet(minute_path, index=False)
+
+    result = runner.invoke(cli, [
+        "derived", "compute",
+        "--minute-path", str(minute_path),
+        "--derived-type", "minute_daily_summary",
+        "--derived-name", "minute_summary",
+        "--output-dir", str(out_dir),
+    ])
+
+    assert result.exit_code == 0, result.output
+    written = pd.read_parquet(out_dir / "minute_summary.pq")
+    assert "minute_vwap" in written.columns
+
+
+def test_alpha_feature_join_rejects_derived_column_collisions():
+    data = _daily_bars()
+    derived_a = data[["symbol_id", "date"]].copy()
+    derived_a["custom_value"] = 1.0
+    derived_b = data[["symbol_id", "date"]].copy()
+    derived_b["custom_value"] = 2.0
+
+    with pytest.raises(ValueError, match="conflict"):
+        join_feature_frames(data, [derived_a, derived_b])
+
+    close_collision = data[["symbol_id", "date"]].copy()
+    close_collision["close"] = 1.0
+    with pytest.raises(ValueError, match="conflict"):
+        join_feature_frames(data, [close_collision])
+
+
+def test_legacy_feature_cli_delegates_to_derived_registry(tmp_path):
+    runner = CliRunner()
+    minute_path = tmp_path / "minute.pq"
+    out_dir = tmp_path / "features"
+    _minute_bars().to_parquet(minute_path, index=False)
+
+    list_result = runner.invoke(cli, ["feature", "list"])
+    assert list_result.exit_code == 0, list_result.output
+    assert "minute_daily_summary" in list_result.output
+
+    compute_result = runner.invoke(cli, [
+        "feature", "compute",
+        "--minute-path", str(minute_path),
+        "--feature-type", "minute_daily_summary",
+        "--feature-name", "minute_summary",
+        "--output-dir", str(out_dir),
+    ])
+    assert compute_result.exit_code == 0, compute_result.output
+    assert (out_dir / "minute_summary.pq").exists()
+

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 +1,462 @@
+"""Offline downloader contract tests with mocked data providers."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+
+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_raises_when_requested_source_has_no_data(monkeypatch):
+    monkeypatch.setattr(downloader, "_download_baostock", lambda *args: None)
+
+    with pytest.raises(RuntimeError, match="Failed to download data for sh600000"):
+        download_daily(
+            "sh600000",
+            "2024-01-02",
+            "2024-01-02",
+            source="baostock",
+        )
+
+
+def test_akshare_daily_downloader_maps_columns_and_failures(monkeypatch):
+    calls: list[dict] = []
+    raw = pd.DataFrame({
+        "日期": ["2024-01-02"],
+        "开盘": [10.0],
+        "最高": [11.0],
+        "最低": [9.0],
+        "收盘": [10.5],
+        "成交量": [1000.0],
+        "成交额": [10500.0],
+        "换手率": [1.2],
+    })
+
+    def fake_hist(**kwargs):
+        calls.append(kwargs)
+        return raw.copy()
+
+    monkeypatch.setattr(downloader.ak, "stock_zh_a_hist", fake_hist)
+
+    result = downloader._download_akshare(
+        "sh600000",
+        "20240102",
+        "20240102",
+        adjust="",
+    )
+
+    assert calls == [{
+        "symbol": "600000",
+        "period": "daily",
+        "start_date": "20240102",
+        "end_date": "20240102",
+        "adjust": "",
+    }]
+    assert result is not None
+    assert result.columns.tolist() == [
+        "symbol", "date", "open", "high", "low", "close", "volume", "amount",
+    ]
+    assert result["symbol"].tolist() == ["sh600000"]
+
+    monkeypatch.setattr(downloader.ak, "stock_zh_a_hist", lambda **kwargs: pd.DataFrame())
+    assert downloader._download_akshare("sh600000", "20240102", "20240102") is None
+
+    monkeypatch.setattr(
+        downloader.ak,
+        "stock_zh_a_hist",
+        lambda **kwargs: (_ for _ in ()).throw(RuntimeError("provider down")),
+    )
+    assert downloader._download_akshare("sh600000", "20240102", "20240102") is None
+
+
+def test_baostock_daily_downloader_maps_errors_and_logout_failures(monkeypatch):
+    query_calls: list[dict] = []
+    row = ["2024-01-02", "10", "11", "9", "10.5", "1000", "10500"]
+
+    monkeypatch.setattr(downloader.bs, "login", lambda: None)
+    monkeypatch.setattr(
+        downloader.bs,
+        "logout",
+        lambda: (_ for _ in ()).throw(RuntimeError("logout failed")),
+    )
+
+    def fake_query(**kwargs):
+        query_calls.append(kwargs)
+        return _FakeResult([row])
+
+    monkeypatch.setattr(downloader.bs, "query_history_k_data_plus", fake_query)
+
+    result = downloader._download_baostock(
+        "sz000001",
+        "2024-01-02",
+        "2024-01-02",
+        adjust="none",
+    )
+
+    assert query_calls[0]["code"] == "sz.000001"
+    assert query_calls[0]["adjustflag"] == "3"
+    assert result is not None
+    assert result["symbol"].tolist() == ["sz000001"]
+    assert pd.api.types.is_numeric_dtype(result["close"])
+
+    monkeypatch.setattr(downloader.bs, "logout", lambda: None)
+    monkeypatch.setattr(
+        downloader.bs,
+        "query_history_k_data_plus",
+        lambda **kwargs: _FakeResult([], error_code="1", error_msg="bad symbol"),
+    )
+    assert downloader._download_baostock("sz000001", "2024-01-02", "2024-01-02") is None
+
+    monkeypatch.setattr(
+        downloader.bs,
+        "query_history_k_data_plus",
+        lambda **kwargs: _FakeResult([]),
+    )
+    assert downloader._download_baostock("sz000001", "2024-01-02", "2024-01-02") is None
+
+    monkeypatch.setattr(
+        downloader.bs,
+        "query_history_k_data_plus",
+        lambda **kwargs: (_ for _ in ()).throw(RuntimeError("query failed")),
+    )
+    assert downloader._download_baostock("sz000001", "2024-01-02", "2024-01-02") is None
+
+
+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_periodic_relogin_and_none_result(monkeypatch):
+    responses = [
+        _FakeResult([], error_code="1", error_msg="bad symbol"),
+        _FakeResult([_daily_batch_row(date="2024-01-03")]),
+    ]
+    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),
+    )
+
+    results = list(
+        download_daily_batch(
+            ["sh600000", "sz000001"],
+            "2024-01-02",
+            "2024-01-03",
+            relogin_every=1,
+        )
+    )
+
+    assert results[0] == ("sh600000", None)
+    assert results[1][0] == "sz000001"
+    assert results[1][1] is not None
+    assert login_count == 2
+    assert logout_count == 2
+
+
+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

@@ -0,0 +1,172 @@
+"""Tests for minute-derived daily feature plugins."""
+
+import textwrap
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from pipeline.features.compute import compute_feature, validate_feature_frame
+from pipeline.features.library.minute_daily_summary import MinuteDailySummaryFeature
+from pipeline.features.registry import (
+    available_features,
+    get_feature,
+    load_feature_module,
+)
+from pipeline.derived.compute import compute_derived
+
+
+def _minute_bars() -> pd.DataFrame:
+    return pd.DataFrame({
+        "symbol_id": ["sh600000", "sh600000", "sz000001"],
+        "symbol_name": ["PF Bank", "PF Bank", "Ping An"],
+        "datetime": pd.to_datetime([
+            "2024-01-02 09:35:00",
+            "2024-01-02 09:40:00",
+            "2024-01-02 09:35:00",
+        ]),
+        "date": pd.to_datetime(["2024-01-02", "2024-01-02", "2024-01-02"]),
+        "time": ["09:35:00", "09:40:00", "09:35:00"],
+        "frequency": ["5m", "5m", "5m"],
+        "open": [10.0, 10.5, 20.0],
+        "high": [11.0, 12.0, 21.0],
+        "low": [9.0, 10.0, 19.0],
+        "close": [10.5, 11.0, 20.5],
+        "volume": [100.0, 300.0, 200.0],
+        "amount": [1000.0, 3300.0, 4100.0],
+        "vwap": [10.0, 11.0, 20.5],
+        "adjustflag": ["3", "3", "3"],
+    })
+
+
+def test_built_in_minute_daily_summary():
+    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],
+    })
+
+    result = compute_feature(
+        minute=_minute_bars(),
+        daily=daily,
+        feature_type="minute_daily_summary",
+    )
+
+    assert "minute_daily_summary" in available_features()
+    row = result[
+        (result["symbol_id"] == "sh600000")
+        & (result["date"] == pd.Timestamp("2024-01-02"))
+    ].iloc[0]
+    assert row["minute_bar_count"] == 2
+    assert np.isclose(row["minute_intraday_return"], 11.0 / 10.0 - 1.0)
+    assert np.isclose(row["minute_intraday_range"], 12.0 / 9.0 - 1.0)
+    assert np.isclose(row["minute_vwap"], 4300.0 / 400.0)
+    assert np.isclose(row["minute_vwap_deviation"], (4300.0 / 400.0) / 11.0 - 1.0)
+
+    missing = result[
+        (result["symbol_id"] == "sh600000")
+        & (result["date"] == pd.Timestamp("2024-01-03"))
+    ].iloc[0]
+    assert pd.isna(missing["minute_vwap"])
+
+
+def test_minute_daily_summary_feature_preserves_legacy_positional_compute():
+    direct = MinuteDailySummaryFeature().compute(_minute_bars())
+    via_registry = get_feature("minute_daily_summary").compute(_minute_bars())
+
+    assert "minute_vwap" in direct.columns
+    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('''
+        import pandas as pd
+        from pipeline.features.base import BaseFeature
+        from pipeline.features.registry import register_feature
+
+        @register_feature
+        class ExternalVolumeFeature(BaseFeature):
+            name = "external_volume_feature"
+
+            def __init__(self, scale: float = 1.0):
+                self.scale = scale
+
+            def compute(self, minute: pd.DataFrame, daily=None) -> pd.DataFrame:
+                out = (
+                    minute.groupby(["symbol_id", "date"], as_index=False)["volume"]
+                    .sum()
+                    .rename(columns={"volume": "scaled_volume"})
+                )
+                out["scaled_volume"] *= self.scale
+                return out
+    '''))
+
+    load_feature_module(str(module_path))
+    assert "external_volume_feature" in available_features()
+
+    instance = get_feature("external_volume_feature", scale=2.0, ignored=99)
+    assert instance.scale == 2.0
+    assert not hasattr(instance, "ignored")
+
+    result = compute_feature(
+        minute=_minute_bars(),
+        feature_type="external_volume_feature",
+        scale=2.0,
+        ignored=99,
+    )
+    row = result[result["symbol_id"] == "sh600000"].iloc[0]
+    assert np.isclose(row["scaled_volume"], 800.0)
+
+
+def test_validate_feature_frame_rejects_missing_keys():
+    with pytest.raises(ValueError, match="missing required"):
+        validate_feature_frame(pd.DataFrame({"symbol_id": ["sh600000"], "x": [1.0]}))
+
+
+def test_validate_feature_frame_rejects_duplicate_keys_after_date_normalization():
+    with pytest.raises(ValueError, match="duplicate symbol_id,date"):
+        validate_feature_frame(pd.DataFrame({
+            "symbol_id": ["sh600000", "sh600000"],
+            "date": ["2024-01-02", pd.Timestamp("2024-01-02")],
+            "x": [1.0, 2.0],
+        }))
+
+
+def test_validate_feature_frame_rejects_duplicate_columns():
+    bad = pd.DataFrame(
+        [["sh600000", pd.Timestamp("2024-01-02"), 1.0, 2.0]],
+        columns=["symbol_id", "date", "dup", "dup"],
+    )
+    with pytest.raises(ValueError, match="duplicate columns"):
+        validate_feature_frame(bad)
+
+
+def test_validate_feature_frame_rejects_non_numeric_feature_columns():
+    with pytest.raises(ValueError, match="numeric"):
+        validate_feature_frame(pd.DataFrame({
+            "symbol_id": ["sh600000"],
+            "date": [pd.Timestamp("2024-01-02")],
+            "bad": ["not numeric"],
+        }))

tests/test_minute_downloader.py

@@ -0,0 +1,288 @@
+"""Tests for raw Baostock minute bar download plumbing."""
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import data.downloader as low_level_downloader
+import pipeline.data.downloader as pipeline_downloader
+from data.downloader import download_minute_batch
+from pipeline.common.schema import MINUTE_BAR_COLUMNS
+from pipeline.data.downloader import download_minute_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 test_download_minute_batch_maps_and_parses_baostock_rows(monkeypatch):
+    rows = [
+        [
+            "2024-01-02",
+            "20240102093500000",
+            "sh.600000",
+            "10",
+            "11",
+            "9",
+            "10.5",
+            "1000",
+            "10500",
+            "3",
+        ],
+        [
+            "2024-01-02",
+            "20240102094000000",
+            "sh.600000",
+            "10.5",
+            "12",
+            "10",
+            "11",
+            "2000",
+            "22000",
+            "3",
+        ],
+    ]
+    calls = []
+
+    def fake_query(**kwargs):
+        calls.append(kwargs)
+        return _FakeResult(rows)
+
+    monkeypatch.setattr(low_level_downloader.bs, "login", lambda: None)
+    monkeypatch.setattr(low_level_downloader.bs, "logout", lambda: None)
+    monkeypatch.setattr(
+        low_level_downloader.bs,
+        "query_history_k_data_plus",
+        fake_query,
+    )
+
+    [(symbol, df)] = list(
+        download_minute_batch(
+            ["sh600000"],
+            "2024-01-02",
+            "2024-01-02",
+            frequency=5,
+        )
+    )
+
+    assert symbol == "sh600000"
+    assert calls[0]["code"] == "sh.600000"
+    assert calls[0]["frequency"] == "5"
+    assert calls[0]["adjustflag"] == "3"
+    assert df is not None
+    assert df["datetime"].iloc[0] == pd.Timestamp("2024-01-02 09:35:00")
+    assert df["time"].tolist() == ["09:35:00", "09:40:00"]
+    assert (df["frequency"] == "5m").all()
+    assert np.isclose(df["open"].iloc[0], 10.0)
+    assert np.isclose(df["vwap"].iloc[0], 10.5)
+    assert pd.api.types.is_numeric_dtype(df["volume"])
+
+
+def test_minute_frequency_and_timestamp_parsing_edge_cases():
+    frequency, label = low_level_downloader._normalize_minute_frequency("15m")
+    assert (frequency, label) == ("15", "15m")
+
+    with pytest.raises(ValueError, match="Unsupported minute frequency"):
+        low_level_downloader._normalize_minute_frequency("1m")
+
+    parsed = low_level_downloader._parse_minute_datetime(
+        pd.Series(["2024-01-02", "2024-01-02"]),
+        pd.Series(["0935", "09:40:00"]),
+    )
+
+    assert parsed.tolist() == [
+        pd.Timestamp("2024-01-02 09:35:00"),
+        pd.Timestamp("2024-01-02 09:40:00"),
+    ]
+
+
+def test_download_minute_batch_empty_result_yields_none(monkeypatch):
+    monkeypatch.setattr(low_level_downloader.bs, "login", lambda: None)
+    monkeypatch.setattr(low_level_downloader.bs, "logout", lambda: None)
+    monkeypatch.setattr(
+        low_level_downloader.bs,
+        "query_history_k_data_plus",
+        lambda **kwargs: _FakeResult([]),
+    )
+
+    assert list(download_minute_batch(["sh600000"], "2024-01-02", "2024-01-02")) == [
+        ("sh600000", None)
+    ]
+
+
+def test_download_minute_batch_non_login_error_and_periodic_relogin(monkeypatch):
+    responses = [
+        _FakeResult([], error_code="1", error_msg="bad symbol"),
+        _FakeResult([]),
+    ]
+    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(low_level_downloader.bs, "login", fake_login)
+    monkeypatch.setattr(low_level_downloader.bs, "logout", fake_logout)
+    monkeypatch.setattr(
+        low_level_downloader.bs,
+        "query_history_k_data_plus",
+        lambda **kwargs: responses.pop(0),
+    )
+
+    assert list(
+        download_minute_batch(
+            ["sh600000", "sz000001"],
+            "2024-01-02",
+            "2024-01-02",
+            relogin_every=1,
+        )
+    ) == [("sh600000", None), ("sz000001", None)]
+    assert login_count == 2
+    assert logout_count == 2
+
+
+def test_download_minute_batch_second_session_loss_yields_none(monkeypatch):
+    responses = [
+        _FakeResult([], error_code="10002007", error_msg="用户未登录"),
+        _FakeResult([], error_code="10002007", error_msg="用户未登录"),
+    ]
+
+    monkeypatch.setattr(low_level_downloader.bs, "login", lambda: None)
+    monkeypatch.setattr(low_level_downloader.bs, "logout", lambda: None)
+    monkeypatch.setattr(
+        low_level_downloader.bs,
+        "query_history_k_data_plus",
+        lambda **kwargs: responses.pop(0),
+    )
+
+    assert list(download_minute_batch(["sh600000"], "2024-01-02", "2024-01-02")) == [
+        ("sh600000", None)
+    ]
+
+
+def test_download_minute_batch_rejects_unparsed_timestamps(monkeypatch):
+    bad_rows = [[
+        "2024-01-02",
+        "not-a-time",
+        "sh.600000",
+        "10",
+        "11",
+        "9",
+        "10.5",
+        "1000",
+        "10500",
+        "3",
+    ]]
+    monkeypatch.setattr(low_level_downloader.bs, "login", lambda: None)
+    monkeypatch.setattr(low_level_downloader.bs, "logout", lambda: None)
+    monkeypatch.setattr(
+        low_level_downloader.bs,
+        "query_history_k_data_plus",
+        lambda **kwargs: _FakeResult(bad_rows),
+    )
+
+    assert list(download_minute_batch(["sh600000"], "2024-01-02", "2024-01-02")) == [
+        ("sh600000", None)
+    ]
+
+
+def test_download_minute_universe_writes_frequency_month_partitions(tmp_path, monkeypatch):
+    minute = pd.DataFrame({
+        "symbol": ["sh600000", "sh600000"],
+        "datetime": pd.to_datetime(["2024-01-02 09:35:00", "2024-01-02 09:40:00"]),
+        "date": pd.to_datetime(["2024-01-02", "2024-01-02"]),
+        "time": ["09:35:00", "09:40:00"],
+        "frequency": ["5m", "5m"],
+        "open": [10.0, 10.5],
+        "high": [11.0, 12.0],
+        "low": [9.0, 10.0],
+        "close": [10.5, 11.0],
+        "volume": [1000.0, 2000.0],
+        "amount": [10500.0, 22000.0],
+        "vwap": [10.5, 11.0],
+        "adjustflag": ["3", "3"],
+    })
+
+    monkeypatch.setattr(
+        pipeline_downloader,
+        "_resolve_universe",
+        lambda universe, max_symbols=0: pd.DataFrame({
+            "symbol_id": ["sh600000"],
+            "symbol_name": ["PF Bank"],
+        }),
+    )
+
+    def fake_batch(symbols, start, end, frequency=5):
+        assert symbols == ["sh600000"]
+        assert frequency == "5"
+        yield "sh600000", minute
+
+    monkeypatch.setattr(pipeline_downloader, "download_minute_batch", fake_batch)
+
+    preserved = tmp_path / "toy" / "frequency=15m" / "month=2024-01" / "old.pq"
+    preserved.parent.mkdir(parents=True)
+    preserved_minute = minute.copy()
+    preserved_minute["frequency"] = "15m"
+    preserved_minute["symbol_id"] = "sh600000"
+    preserved_minute["symbol_name"] = "PF Bank"
+    preserved_minute[MINUTE_BAR_COLUMNS].to_parquet(preserved, index=False)
+
+    stats = download_minute_universe(
+        universe="toy",
+        start_date="2024-01-02",
+        end_date="2024-01-02",
+        output_dir=str(tmp_path),
+        chunk_size=1,
+        frequency="5",
+    )
+
+    dataset_path = Path(stats["dataset_path"])
+    assert (dataset_path / "frequency=5m" / "month=2024-01").is_dir()
+    assert preserved.exists()
+    out = pd.read_parquet(dataset_path / "frequency=5m")
+    assert (set(MINUTE_BAR_COLUMNS) - {"frequency"}) <= set(out.columns)
+    assert set(out["symbol_id"]) == {"sh600000"}
+    assert set(out["symbol_name"]) == {"PF Bank"}
+    assert stats["n_rows"] == 2
+
+
+def test_download_minute_universe_raises_when_all_symbols_empty(tmp_path, monkeypatch):
+    monkeypatch.setattr(
+        pipeline_downloader,
+        "_resolve_universe",
+        lambda universe, max_symbols=0: pd.DataFrame({
+            "symbol_id": ["sh600000"],
+            "symbol_name": ["PF Bank"],
+        }),
+    )
+    monkeypatch.setattr(
+        pipeline_downloader,
+        "download_minute_batch",
+        lambda symbols, start, end, frequency=5: iter([("sh600000", None)]),
+    )
+
+    with pytest.raises(RuntimeError, match="No minute data"):
+        download_minute_universe(
+            universe="toy",
+            start_date="2024-01-02",
+            end_date="2024-01-02",
+            output_dir=str(tmp_path),
+        )

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_universe.py

@@ -0,0 +1,92 @@
+"""Offline tests for baostock-backed universe helpers."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+import data.universe as universe
+
+
+class _FakeResult:
+    def __init__(self, rows, fields=None):
+        self.rows = rows
+        self.fields = fields or ["code", "name", "date"]
+        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 test_index_constituent_helpers_normalize_dotted_codes(monkeypatch):
+    calls: list[str] = []
+
+    monkeypatch.setattr(universe.bs, "login", lambda: calls.append("login"))
+    monkeypatch.setattr(universe.bs, "logout", lambda: calls.append("logout"))
+    monkeypatch.setattr(
+        universe.bs,
+        "query_hs300_stocks",
+        lambda: _FakeResult([
+            ["sh.600000", "浦发银行", "2024-01-12"],
+            ["sz.000001", "平安银行", "2024-01-12"],
+        ]),
+    )
+    monkeypatch.setattr(
+        universe.bs,
+        "query_zz500_stocks",
+        lambda: _FakeResult([
+            ["sh.600006", "东风汽车", "2024-01-12"],
+        ]),
+    )
+
+    hs300 = universe.get_hs300_stocks()
+    zz500 = universe.get_zz500_stocks()
+
+    assert calls == ["login", "logout", "login", "logout"]
+    assert hs300["code"].tolist() == ["sh600000", "sz000001"]
+    assert zz500["code"].tolist() == ["sh600006"]
+    assert hs300["name"].tolist() == ["浦发银行", "平安银行"]
+
+
+def test_get_all_stocks_walks_back_and_filters_to_listed_a_shares(monkeypatch):
+    fields = ["code", "tradeStatus", "code_name"]
+    responses = [
+        _FakeResult([], fields=fields),
+        _FakeResult(
+            [
+                ["sh.600000", "1", "浦发银行"],
+                ["sh.688001", "1", "华兴源创"],
+                ["sz.000001", "1", "平安银行"],
+                ["sz.300750", "1", "宁德时代"],
+                ["sz.399001", "1", "深证成指"],
+                ["sz.200001", "1", "深物业B"],
+                ["bj.430047", "1", "北交所样本"],
+            ],
+            fields=fields,
+        ),
+    ]
+    query_days: list[str] = []
+
+    monkeypatch.setattr(universe.bs, "login", lambda: None)
+    monkeypatch.setattr(universe.bs, "logout", lambda: None)
+
+    def fake_query_all_stock(day):
+        query_days.append(day)
+        return responses.pop(0)
+
+    monkeypatch.setattr(universe.bs, "query_all_stock", fake_query_all_stock)
+
+    result = universe.get_all_stocks("2024-01-07")
+
+    assert query_days == ["2024-01-07", "2024-01-06"]
+    assert result.columns.tolist() == ["code", "name"]
+    assert result["code"].tolist() == [
+        "sh600000",
+        "sh688001",
+        "sz000001",
+        "sz300750",
+    ]
+    assert result["name"].tolist() == ["浦发银行", "华兴源创", "平安银行", "宁德时代"]

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"
@@ -498,6 +502,119 @@ wheels = [
     { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/0c/58/bd257695f39d05594ca4ad60df5bcb7e32247f9951fd09a9b8edb82d1daa/contourpy-1.3.3-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:3d1a3799d62d45c18bafd41c5fa05120b96a28079f2393af559b843d1a966a77", size = 225315, upload-time = "2025-07-26T12:02:58.801Z" },
 ]
 
+[[package]]
+name = "coverage"
+version = "7.14.1"
+source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/54/fd/0ab2772530e946e1be1abd0bc09e647ec9b02e88f0867857601fefca8953/coverage-7.14.1.tar.gz", hash = "sha256:30c08f7d90415aa98b3c990385dea2939b0da55f38515e5b369b83655f8523be", size = 920132, upload-time = "2026-05-26T20:41:36.783Z" }
+wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/92/69/0d2ef01ff4b8fcecd4cba920d11e92fa4f96ae412441d3b56a90a258e69b/coverage-7.14.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:3e3680291c4a1d0dadfa84a2c459576a4af5133abb617905714339a0c73138cf", size = 219722, upload-time = "2026-05-26T20:38:14.002Z" },
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