Create recommendation_engine.py

analytics/recommendation_engine.py

@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+from typing import Any
+
+import pandas as pd
+
+from analytics.confidence import compute_confidence
+from analytics.recommendation_rules import apply_recommendation_rules
+from models.live_fair_simulator_v3 import build_upcoming_simulated_rows
+
+
+def build_upcoming_hitter_recommendations(
+    game_row: dict[str, Any],
+    statcast_df: pd.DataFrame,
+    odds_df: pd.DataFrame | None = None,
+    weather_row: dict[str, Any] | None = None,
+) -> list[dict[str, Any]]:
+    """
+    Batch 3 decision-layer wrapper.
+    Uses simulated fair rows, then adds confidence + recommendation tier.
+    """
+    rows = build_upcoming_simulated_rows(
+        game_row=game_row,
+        statcast_df=statcast_df,
+        weather_row=weather_row,
+    )
+
+    recommendations: list[dict[str, Any]] = []
+
+    for row in rows:
+        confidence_block = compute_confidence(row, game_row=game_row)
+        row.update(confidence_block)
+
+        rules_block = apply_recommendation_rules(row)
+        row.update(rules_block)
+
+        recommendations.append(row)
+
+    return recommendations