Integrate factor attribution into backtest CLI

main.py

@@ -5,6 +5,7 @@ import numpy as np
 import pandas as pd
 
 import data_manager
+import factor_attribution
 import metrics
 from strategies.adaptive_momentum import AdaptiveMomentumStrategy
 from strategies.buy_and_hold import BuyAndHoldStrategy
@@ -163,6 +164,18 @@ def main() -> None:
         help="Execution mode: 'close' (default, signal & execute on close) or "
              "'open-close' (signal on morning open, execute at close)",
     )
+    parser.add_argument(
+        "--attribution", action="store_true",
+        help="Run factor attribution after performance metrics",
+    )
+    parser.add_argument(
+        "--attribution-model", choices=["capm", "ff5", "ff5plus", "all"], default="all",
+        help="Factor model selection for attribution output",
+    )
+    parser.add_argument(
+        "--attribution-export", default=None,
+        help="Directory to export factor attribution CSVs",
+    )
     args = parser.parse_args()
     initial_capital = args.capital if args.capital is not None else 10_000
     use_open = args.execution == "open-close"
@@ -238,6 +251,20 @@ def main() -> None:
             continue
         metrics.summary(eq, name=name)
 
+    if args.attribution:
+        summary_df, loadings_df = factor_attribution.attribute_strategies(
+            results_df=results_df,
+            benchmark_label=benchmark_label,
+            benchmark=benchmark,
+            price_data=data,
+            market=args.market,
+            model_selection=args.attribution_model,
+        )
+        factor_attribution.print_attribution_summary(summary_df)
+        if args.attribution_export:
+            factor_attribution.export_attribution(summary_df, loadings_df, args.attribution_export)
+            print(f"Attribution CSVs written to {args.attribution_export}")
+
     # --- Visualization ---
     if not args.no_plot:
         plot_results(results_df.dropna())