Add daily derived data pipeline

pipeline/features/library/minute_daily_summary.py

@@ -1,84 +1,16 @@
-"""Daily summary features derived from raw minute bars."""
+"""Compatibility wrapper for the built-in minute daily summary plugin."""
 
-import numpy as np
 import pandas as pd
 
-from pipeline.features.base import BaseFeature
-from pipeline.features.registry import register_feature
+from pipeline.derived.library.minute_daily_summary import MinuteDailySummaryDerived
 
 
-@register_feature
-class MinuteDailySummaryFeature(BaseFeature):
-    """Aggregate intraday bars into daily summary columns."""
-
-    name = "minute_daily_summary"
+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:
-        minute = minute.copy()
-        minute["date"] = pd.to_datetime(minute["date"])
-        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"])
-            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"])
-                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",
-            ]
-        ]
+        return super().compute(daily=daily, minute=minute)