Add minute bar feature pipeline

pipeline/features/compute.py

@@ -0,0 +1,75 @@
+"""Feature computation and validation."""
+
+import logging
+from pathlib import Path
+from typing import Iterable
+
+import pandas as pd
+from pandas.api.types import is_numeric_dtype
+
+from pipeline.features.registry import get_feature
+
+logger = logging.getLogger(__name__)
+
+FEATURE_KEY_COLUMNS = ["symbol_id", "date"]
+
+
+def validate_feature_frame(features: pd.DataFrame) -> pd.DataFrame:
+    """Validate and normalize a daily feature frame.
+
+    A valid feature frame is keyed by unique ``symbol_id,date`` rows and has at
+    least one numeric feature column beyond those keys.
+    """
+    duplicated = features.columns[features.columns.duplicated()].tolist()
+    if duplicated:
+        raise ValueError(f"Feature output has duplicate columns: {duplicated}")
+
+    missing = [col for col in FEATURE_KEY_COLUMNS if col not in features.columns]
+    if missing:
+        raise ValueError(f"Feature output missing required columns: {missing}")
+
+    out = features.copy()
+    out["date"] = pd.to_datetime(out["date"])
+
+    if out.duplicated(FEATURE_KEY_COLUMNS).any():
+        raise ValueError("Feature output has duplicate symbol_id,date rows")
+
+    feature_cols = [col for col in out.columns if col not in FEATURE_KEY_COLUMNS]
+    if not feature_cols:
+        raise ValueError("Feature output must include at least one feature column")
+
+    non_numeric = [col for col in feature_cols if not is_numeric_dtype(out[col])]
+    if non_numeric:
+        raise ValueError(f"Feature columns must be numeric: {non_numeric}")
+
+    out = out[FEATURE_KEY_COLUMNS + feature_cols].copy()
+    return out.sort_values(FEATURE_KEY_COLUMNS).reset_index(drop=True)
+
+
+def compute_feature(
+    minute: pd.DataFrame,
+    feature_type: str,
+    daily: pd.DataFrame | None = None,
+    **params,
+) -> pd.DataFrame:
+    """Compute one registered feature from raw minute bars."""
+    feature = get_feature(feature_type, **params)
+    result = validate_feature_frame(feature.compute(minute=minute, daily=daily))
+    feature_cols = [col for col in result.columns if col not in FEATURE_KEY_COLUMNS]
+    logger.info(
+        "Feature '%s' (%r): %d symbols × %d dates, columns=%s",
+        feature_type,
+        feature,
+        result["symbol_id"].nunique(),
+        result["date"].nunique(),
+        feature_cols,
+    )
+    return result
+
+
+def read_feature_frames(feature_paths: Iterable[str | Path]) -> list[pd.DataFrame]:
+    """Read and validate feature parquet files."""
+    return [
+        validate_feature_frame(pd.read_parquet(path))
+        for path in feature_paths
+    ]