Add minute bar feature pipeline

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.