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/migrations/versions/0011_alpha_list_checks_and_metrics.py

@@ -0,0 +1,125 @@
+"""Index Alpha checks and metrics; backfill existing snapshots without upstream calls."""
+
+import math
+
+import sqlalchemy as sa
+from alembic import op
+
+revision = "0011"
+down_revision = "0010"
+branch_labels = None
+depends_on = None
+
+
+# Frozen normalization for historical snapshots; do not import mutable app code.
+def number(value):
+    if value is None or isinstance(value, bool):
+        return None
+    try:
+        result = float(value)
+        return result if math.isfinite(result) else None
+    except (ValueError, TypeError):
+        return None
+
+
+def failed_checks(checks):
+    """Return failed platform check names; local correlation never changes this list."""
+    return (
+        [
+            check.get("name") if isinstance(check.get("name"), str) else "未命名检查"
+            for check in checks
+            if isinstance(check, dict) and check.get("result") == "FAIL"
+        ]
+        if isinstance(checks, list)
+        else []
+    )
+
+
+def snapshot_columns(settings, metrics, checks):
+    """Derive list fields from a platform snapshot, preserving missing metrics as null.
+
+    Only explicit FAIL results count. Empty, malformed and unfinished checks are
+    pending; all known checks passing without PROD_CORRELATION is only a pre-check.
+    No submission eligibility or activity eligibility is inferred here.
+    """
+    settings = settings if isinstance(settings, dict) else {}
+    metrics = metrics if isinstance(metrics, dict) else {}
+    checks = checks if isinstance(checks, list) else []
+    valid = [check for check in checks if isinstance(check, dict)]
+    failures = len(failed_checks(checks))
+    by_name = {check["name"]: check for check in valid if isinstance(check.get("name"), str)}
+    if failures:
+        check_type = "FAIL_1" if failures == 1 else "FAIL_2"
+    elif not checks or len(valid) != len(checks) or any(check.get("result") != "PASS" for check in valid):
+        check_type = "PENDING"
+    else:
+        check_type = "PASS" if "PROD_CORRELATION" in by_name else "PRE_CHECK"
+    neutralization = settings.get("neutralization")
+    return {
+        "check_type": check_type,
+        "neutralization": neutralization if isinstance(neutralization, str) else None,
+        "pnl": number(metrics.get("pnl")),
+        **{
+            field: number(by_name.get(name, {}).get("value"))
+            for field, name in (
+                ("sub_universe_sharpe", "LOW_SUB_UNIVERSE_SHARPE"),
+                ("robust_universe_sharpe", "LOW_ROBUST_UNIVERSE_SHARPE"),
+                ("two_year_sharpe", "LOW_2Y_SHARPE"),
+                ("prod_correlation", "PROD_CORRELATION"),
+            )
+        },
+    }
+
+
+METRICS = ("sub_universe_sharpe", "robust_universe_sharpe", "two_year_sharpe", "prod_correlation", "pnl")
+
+
+def upgrade():
+    columns = [sa.Column(name, sa.Float(), nullable=True) for name in METRICS]
+    columns += [
+        sa.Column("neutralization", sa.Text(), nullable=True),
+        sa.Column("check_type", sa.String(20), nullable=False, server_default="PENDING"),
+    ]
+    for column in columns:
+        op.add_column("alphas", column)
+    op.create_index("ix_alphas_check_type", "alphas", ["check_type"])
+    table = sa.table(
+        "alphas",
+        sa.column("id", sa.String()),
+        sa.column("settings", sa.JSON()),
+        sa.column("is_metrics", sa.JSON()),
+        sa.column("checks", sa.JSON()),
+        *(sa.column(column.name, column.type) for column in columns),
+    )
+    connection = op.get_bind()
+    last_id = None
+    while True:
+        query = (
+            sa.select(table.c.id, table.c.settings, table.c.is_metrics, table.c.checks)
+            .order_by(table.c.id)
+            .limit(500)
+        )
+        if last_id is not None:
+            query = query.where(table.c.id > last_id)
+        rows = connection.execute(query).mappings().all()
+        if not rows:
+            break
+        connection.execute(
+            table.update()
+            .where(table.c.id == sa.bindparam("snapshot_id"))
+            .values({column.name: sa.bindparam(column.name) for column in columns}),
+            [
+                {
+                    "snapshot_id": row["id"],
+                    **snapshot_columns(row["settings"], row["is_metrics"], row["checks"]),
+                }
+                for row in rows
+            ],
+        )
+        last_id = rows[-1]["id"]
+
+
+def downgrade():
+    op.drop_index("ix_alphas_check_type", table_name="alphas")
+    for name in ("check_type", "neutralization", *reversed(METRICS)):
+        op.drop_column("alphas", name)