fix(portfolio): provider 基本面前视偏差修复 + ETF paused + roic 接入

- local_parquet_provider: 新增 _latest_published_annual(NOTICE_DATE<=date +
  年报口径),替旧 _latest_row_before(REPORT_DATE)。修复两个 bug:
  1) 前视偏差:旧按报告期过滤会用未披露年报(如 3/31 取上年报,4 月才披露);
  2) 周期错配:income/balance 各自取最新致 NP(年)÷权益(季)垃圾值。
  income/balance 统一走该 helper → 同报告期 + 无前视。
  另:roic 接 calc_roic,_num(v==v)排 NaN 修 g() 对 NaN truthy 问题。
- local_unified_provider: get_price 缺失字段 paused 补 False / high·low_limit
  按 close±10% 估,修 bool(NaN)=True 被 bullet_trade get_current_data 误判
  停牌致 _process_orders cancel 所有 ETF 订单(0 交易)。

实证:roe/roa 跨时点一致(p50 从 0.016/0.064 不一致 → 0.074/0.070/0.065)。

sanguo_portfolio/providers/local_parquet_provider.py

@@ -327,6 +327,31 @@ class LocalParquetProvider(DataProvider):  # type: ignore[misc]
         sub = df[df[date_col] <= ts]
         return sub.iloc[-1] if not sub.empty else None
 
+    @staticmethod
+    def _latest_published_annual(
+        df: pd.DataFrame, date_str: str,
+    ) -> Optional[pd.Series]:
+        """最新**已披露年报**: ``NOTICE_DATE <= date_str`` 且 ``REPORT_TYPE`` 含"年"。
+
+        修复前视偏差: 旧逻辑按 ``REPORT_DATE``(报告期)取最新, 会用尚未披露的年报
+        (如 3/31 取 REPORT_DATE=上年 12/31 但 NOTICE_DATE=当年 4 月的年报 → 未来信息)。
+        年报口径保证 roe/roa 跨股可比(非季报累计); 最多滞后~1年, 月频策略可接受。
+        无 NOTICE_DATE 列 / 无已披露年报 → 回退 ``_latest_row_before(REPORT_DATE)`` 兜底。
+        """
+        if df is None or df.empty:
+            return None
+        if "NOTICE_DATE" not in df.columns:
+            return LocalParquetProvider._latest_row_before(df, "REPORT_DATE", date_str)
+        ts = pd.Timestamp(date_str)
+        d = df.assign(_notice=pd.to_datetime(df["NOTICE_DATE"], errors="coerce"))
+        sub = d[d["_notice"] <= ts]
+        if "REPORT_TYPE" in sub.columns:
+            sub = sub[sub["REPORT_TYPE"].astype(str).str.contains("年", na=False)]
+        sub = sub.sort_values("_notice")
+        if not sub.empty:
+            return sub.iloc[-1]
+        return LocalParquetProvider._latest_row_before(df, "REPORT_DATE", date_str)
+
     # ==================== get_fundamentals_df ====================
     def get_fundamentals_df(
         self,
@@ -358,8 +383,10 @@ class LocalParquetProvider(DataProvider):  # type: ignore[misc]
         row: Dict[str, Any] = {"code": jq_code}
 
         val = self._latest_row_before(self._read_valuation(fc), "date", date_str)
-        inc = self._latest_row_before(self._read_quarter("income", fc), "REPORT_DATE", date_str)
-        bal = self._latest_row_before(self._read_quarter("balance", fc), "REPORT_DATE", date_str)
+        # income/balance 取最新**已披露年报**(NOTICE_DATE<=date, REPORT_TYPE 含"年"):
+        # 修复旧按 REPORT_DATE 过滤的前视偏差(用了未披露年报) + 年报口径跨股可比
+        inc = self._latest_published_annual(self._read_quarter("income", fc), date_str)
+        bal = self._latest_published_annual(self._read_quarter("balance", fc), date_str)
 
         def g(d: Optional[pd.Series], k: str) -> Optional[float]:
             return _to_float(d.get(k)) if d is not None else None
@@ -415,9 +442,29 @@ class LocalParquetProvider(DataProvider):  # type: ignore[misc]
         # gross_profit_margin: 从 financial_abstract 读现成"毛利率"(百分数→小数)
         fa = self._read_financial_abstract(fc)
         row["gross_profit_margin"] = _pct_to_decimal(self._latest_indicator(fa, "毛利率"))
-        # roic: 需有息负债拆分 → V1 NaN
-        # TODO v2: roic = NOPAT / (权益 + 有息负债 - 现金)
-        row["roic"] = float("nan")
+        # roic = NOPAT / (归母权益 + 有息负债 - 货币资金)
+        # actual_tax_rate akshare 无现成指标, 传 None 让 calc_roic 用 inc_tax/total_profit 兜底
+        # _num: g() 的 _to_float 对 NaN 返 float('nan')(truthy), 需 v==v 排除 NaN 才能正确 or 0/条件
+        from ..factors.roic import calc_roic
+        def _num(d, k):
+            v = g(d, k)
+            return v if (v is not None and v == v) else None
+        oper_profit = _num(inc, "OPERATE_PROFIT")
+        inc_tax = _num(inc, "INCOME_TAX")
+        profit_before_tax = _num(inc, "TOTAL_PROFIT")
+        short_loan = _num(bal, "SHORT_LOAN") or 0
+        long_loan = _num(bal, "LONG_LOAN") or 0
+        bond_pay = (_num(bal, "BOND_PAYABLE") or 0) + (_num(bal, "SHORT_BOND_PAYABLE") or 0)
+        interest_bearing_debt = short_loan + long_loan + bond_pay
+        cash_equiv = _num(bal, "MONETARYFUNDS")
+        _parent = _num(bal, "TOTAL_PARENT_EQUITY")
+        if oper_profit is not None and _parent and cash_equiv is not None:
+            row["roic"] = calc_roic(
+                oper_profit, None, _parent, interest_bearing_debt, cash_equiv,
+                inc_tax=inc_tax, profit_before_tax=profit_before_tax,
+            )
+        else:
+            row["roic"] = float("nan")
         return row
 
     @staticmethod

sanguo_portfolio/providers/local_unified_provider.py

@@ -203,10 +203,20 @@ class LocalUnifiedProvider(DataProvider):  # type: ignore[misc]
                 "open_price": "open", "high_price": "high",
                 "low_price": "low", "close_price": "close",
             })
-            # 缺失字段(如 high_limit)补 NaN
+            # 缺失字段补默认: paused=False(避免 bool(NaN)=True 被 bullet_trade
+            # get_current_data 误判停牌→订单 cancel); high_limit/low_limit 按 close±10% 估
+            # (与 get_current_tick 同口径, 精确涨跌停/ST/创业科创规则 v2); 其他补 NaN
             if fields:
                 for f in fields:
-                    if f not in df.columns:
+                    if f in df.columns:
+                        continue
+                    if f == "paused":
+                        df[f] = False
+                    elif f == "high_limit" and "close" in df.columns:
+                        df[f] = df["close"] * 1.1
+                    elif f == "low_limit" and "close" in df.columns:
+                        df[f] = df["close"] * 0.9
+                    else:
                         df[f] = float("nan")
                 df = df[[f for f in fields if f in df.columns]]
             frames[jq_code] = df