start experiment 60 (exp/60-scheduled-algo-retrain-on-2026-08-21-tac)

code/tac-qlib/tac_qlib/data/config.py

@@ -61,6 +61,11 @@ UNKNOWN_FIELD_NAMES = ("factor", "change", "trade_unit", "suspend_flag")
 #: columns in the parquet files that are not features
 NON_FEATURE_COLUMNS = ("t", "date", "market", "timeframe", "symbol")
 
+#: Feature-family partitions merged by ``LakeConfig.load_features`` and scanned
+#: by the handler's field discovery. ``macro`` holds broadcast market-state
+#: columns (see skills/tac-qlib-custom/examples/persist_macro_broadcast.py).
+FEATURE_FAMILIES = ("ta", "sp", "macro")
+
 
 def timeframe_for_freq(freq: str) -> str:
     """Map a qlib frequency (e.g. ``day``, ``1min``) to a lake timeframe (e.g. ``1d``)."""
@@ -113,12 +118,12 @@ class LakeConfig:
         return self.features_dir(timeframe) / f"symbol={str(symbol).upper()}.parquet"
 
     def load_features(self, timeframe: str, symbol: str) -> pd.DataFrame:
-        """All feature columns for a symbol, merging the `family=ta` and
-        `family=sp` partitions by timestamp. Returns an empty frame when no
+        """All feature columns for a symbol, merging the `family=ta|sp|macro`
+        partitions by timestamp. Returns an empty frame when no
         feature files exist (legacy flat layout falls back transparently)."""
         sym = str(symbol).upper()
         frames = []
-        for family in ("ta", "sp"):
+        for family in FEATURE_FAMILIES:
             p = self.features_dir(timeframe) / f"family={family}" / f"symbol={sym}.parquet"
             if p.exists():
                 frames.append(pd.read_parquet(p))