feat(analysis): 网页汇总表自定义列

- analyze 支持 custom_columns：白名单表达式树由服务端 Decimal 求值，
  自定义列进入汇总、分组行与明细（缺失保持不可用，不转零）
- 新增「选择网页自定义列」弹窗：从定义库（含共享）多选最多 10 列，
  应用后进入主分析表格与热力图；公式工作簿可一键导出网页已选列
- 请求去重纳入自定义列；Playwright 覆盖选择后显示与数值断言

finance/analysis.py

@@ -167,9 +167,9 @@ def metrics(agg, formula, products=None):
     }
 
 
-def comparison(current, baseline, comparable):
+def comparison(current, baseline, comparable, keys=None):
     result = {}
-    for key in METRICS:
+    for key in keys or METRICS:
         c, b = current.get(key), baseline.get(key)
         delta = c - b if comparable and c is not None and b is not None else None
         change = Decimal(delta) / abs(Decimal(b)) if delta is not None and b != 0 else None
@@ -177,8 +177,31 @@ def comparison(current, baseline, comparable):
     return result
 
 
+def custom_columns(ds, body):
+    from .formula_columns import ensure_available_metrics, validate_column
+
+    columns = body.get("custom_columns", [])
+    if columns is None:
+        return []
+    if not isinstance(columns, list) or len(columns) > 10:
+        raise ValueError("自定义列最多 10 个")
+    result = [validate_column(column) for column in columns]
+    for column in result:
+        ensure_available_metrics(ds, column)
+    return result
+
+
+def custom_values(values, columns):
+    from .formula_columns import evaluate
+
+    return {f"custom_{index}": evaluate(column["expression"], values) for index, column in enumerate(columns)}
+
+
 def analyze(ds, member, body, preferences):
-    ctx = context(ds, body)
+    columns = custom_columns(ds, body)
+    ctx = {**context(ds, body), "custom_columns": columns}
+    custom_keys = [f"custom_{index}" for index in range(len(columns))]
+    value_keys = [*METRICS, *custom_keys]
     qs = scoped_facts(ds, member, ctx["filters"])
     formula = ds.mapping["roi_formula"]
     products = ds.mapping.get("self_operated_products", [])
@@ -190,8 +213,14 @@ def analyze(ds, member, body, preferences):
     )
     comparable = not ctx["missing"]
     if set(ctx["current"]) - set(ds.periods):
-        total_c = {**total_c, **{key: None for key in METRICS}}
-    summary = comparison(total_c, total_b, comparable)
+        total_c = {**total_c, **{key: None for key in value_keys}}
+    current_missing = bool(set(ctx["current"]) - set(ds.periods))
+    total_c = {
+        **total_c,
+        **({key: None for key in custom_keys} if current_missing else custom_values(total_c, columns)),
+    }
+    total_b = {**total_b, **custom_values(total_b, columns)}
+    summary = comparison(total_c, total_b, comparable, value_keys)
     group = "dimensions__" + ctx["group"]
     buckets = {}
     for side in ("current", "baseline"):
@@ -205,7 +234,7 @@ def analyze(ds, member, body, preferences):
                 k: Decimal(0)
                 if k in ("revenue", "cost", "gross", "customers", "self_revenue") and total.get(k) is not None
                 else None
-                for k in METRICS
+                for k in value_keys
             },
         }
 
@@ -217,8 +246,10 @@ def analyze(ds, member, body, preferences):
     for label, sides in buckets.items():
         c, b = sides.get("current", empty_side(total_c)), sides.get("baseline", empty_side(total_b))
         if set(ctx["current"]) - set(ds.periods):
-            c = {**c, **{key: None for key in METRICS}}
-        values = comparison(c, b, comparable)
+            c = {**c, **{key: None for key in value_keys}}
+        c = {**c, **({key: None for key in custom_keys} if current_missing else custom_values(c, columns))}
+        b = {**b, **custom_values(b, columns)}
+        values = comparison(c, b, comparable, value_keys)
         v = values[metric]
         change, delta = v["change"], v["delta"]
         impact = (
@@ -249,7 +280,10 @@ def analyze(ds, member, body, preferences):
         )
     rows.sort(key=lambda r: (not r["anomaly"], -r["impact"], r["label"]))
     observed = {
-        r["period"]: metrics(r, formula, products)
+        r["period"]: {
+            **(values := metrics(r, formula, products)),
+            **custom_values(values, columns),
+        }
         for r in qs.values("period").annotate(**aggregations(products))
     }
     trend = []
@@ -257,7 +291,7 @@ def analyze(ds, member, body, preferences):
         period = month_text(index)
         values = observed.get(period)
         if values is None:
-            values = empty_side(total_c) if period in ds.periods else {key: None for key in METRICS}
+            values = empty_side(total_c) if period in ds.periods else {key: None for key in value_keys}
         trend.append({"period": period, **values})
     return {
         "dataset_id": str(ds.pk),