feat: Implement Alpha list and metrics enhancements

- Added new metrics fields: sub_universe_sharpe, robust_universe_sharpe, two_year_sharpe, prod_correlation, pnl, check_type, and neutralization to the Alpha model.
- Updated snapshot_columns function to derive new metrics and check types from platform snapshots.
- Enhanced API to include failed checks and check types in responses.
- Created migration script to backfill existing Alpha records with new metrics and check types.
- Updated frontend components to display new metrics and allow editing of custom tags.
- Improved filtering and sorting capabilities for new metrics in the Alpha list.
- Added tests for new functionality including checks classification and metrics filtering.

backend/app/main.py

@@ -16,7 +16,7 @@ from sqlalchemy import delete, select, text
 
 from .ai.routes import router as ai_router
 from .ai.runtime import AIRuntime
-from .alphas import list_statement, sorted_statement
+from .alphas import failed_checks, list_statement, sorted_statement
 from .backtests.routes import router as backtest_router
 from .business import Business, notify_job
 from .catalog.research_routes import router as research_catalog_router
@@ -375,11 +375,18 @@ def create_app(settings=None, wq_client=None, ai_model_factory=None):
                 "turnover",
                 "margin",
                 "drawdown",
+                "sub_universe_sharpe",
+                "robust_universe_sharpe",
+                "two_year_sharpe",
+                "prod_correlation",
+                "pnl",
+                "neutralization",
+                "check_type",
                 "date_created",
                 "date_submitted",
                 "synced_at",
             ]
-            writer.writerow(columns + ["research_state", "favorite", "tags", "note"])
+            writer.writerow(columns + ["failed_checks", "research_state", "favorite", "tags", "note"])
             yield buffer.getvalue()
             buffer.seek(0)
             buffer.truncate(0)
@@ -388,7 +395,7 @@ def create_app(settings=None, wq_client=None, ai_model_factory=None):
                 async for a, r in rows:
                     writer.writerow(
                         [csv_cell(getattr(a, key)) for key in columns]
-                        + [r.state, r.favorite, csv_cell(";".join(r.tags)), csv_cell(r.note)]
+                        + [csv_cell(";".join(failed_checks(a.checks))), r.state, r.favorite, csv_cell(";".join(r.tags)), csv_cell(r.note)]
                     )
                     yield buffer.getvalue()
                     buffer.seek(0)