feat(data): 添加每日指标接口并优化因子引擎

- 新增 api_daily_basic.py 封装 Tushare 每日指标接口
- 因子引擎移除 lookback_days，支持 daily_basic 表字段路由
- 将每日指标纳入自动同步流程
- 删除废弃的 training/main.py

src/factors/engine/data_router.py

@@ -69,16 +69,11 @@ class DataRouter:
 
         # 收集所有需要的表和字段
         required_tables: Dict[str, Set[str]] = {}
-        max_lookback = 0
 
         for spec in data_specs:
             if spec.table not in required_tables:
                 required_tables[spec.table] = set()
             required_tables[spec.table].update(spec.columns)
-            max_lookback = max(max_lookback, spec.lookback_days)
-
-        # 调整日期范围以包含回看期
-        adjusted_start = self._adjust_start_date(start_date, max_lookback)
 
         # 从数据源获取各表数据
         table_data = {}
@@ -86,7 +81,7 @@ class DataRouter:
             df = self._load_table(
                 table_name=table_name,
                 columns=list(columns),
-                start_date=adjusted_start,
+                start_date=start_date,
                 end_date=end_date,
                 stock_codes=stock_codes,
             )
@@ -95,11 +90,6 @@ class DataRouter:
         # 组装核心宽表
         core_table = self._assemble_wide_table(table_data, required_tables)
 
-        # 过滤到实际请求日期范围
-        core_table = core_table.filter(
-            (pl.col("trade_date") >= start_date) & (pl.col("trade_date") <= end_date)
-        )
-
         return core_table
 
     def _load_table(
@@ -265,34 +255,6 @@ class DataRouter:
 
         return result
 
-    def _adjust_start_date(self, start_date: str, lookback_days: int) -> str:
-        """根据回看天数调整开始日期。
-
-        Args:
-            start_date: 原始开始日期 (YYYYMMDD)
-            lookback_days: 需要回看的交易日数
-
-        Returns:
-            调整后的开始日期
-        """
-        # 简化的日期调整：假设每月30天，向前推移
-        # 实际应用中应该使用交易日历
-        year = int(start_date[:4])
-        month = int(start_date[4:6])
-        day = int(start_date[6:8])
-
-        total_days = lookback_days + 30  # 额外缓冲
-
-        day -= total_days
-        while day <= 0:
-            month -= 1
-            if month <= 0:
-                month = 12
-                year -= 1
-            day += 30
-
-        return f"{year:04d}{month:02d}{day:02d}"
-
     def clear_cache(self) -> None:
         """清除数据缓存。"""
         with self._lock:

src/factors/engine/data_spec.py

@@ -18,12 +18,10 @@ class DataSpec:
     Attributes:
         table: 数据表名称
         columns: 需要的字段列表
-        lookback_days: 回看天数（用于时序计算）
     """
 
     table: str
     columns: List[str]
-    lookback_days: int = 1
 
 
 @dataclass

src/factors/engine/planner.py

@@ -73,9 +73,9 @@ class ExecutionPlanner:
     ) -> List[DataSpec]:
         """从依赖推导数据规格。
 
-        根据表达式中的函数类型推断回看天数需求。
         基础行情字段（open, high, low, close, vol, amount, pre_close, change, pct_chg）
         默认从 pro_bar 表获取。
+        每日指标字段（total_mv, circ_mv, pe, pb 等）从 daily_basic 表获取。
 
         Args:
             dependencies: 依赖的字段集合
@@ -84,10 +84,6 @@ class ExecutionPlanner:
         Returns:
             数据规格列表
         """
-        # 计算最大回看窗口
-        max_window = self._extract_max_window(expression)
-        lookback_days = max(1, max_window)
-
         # 基础行情字段集合（这些字段从 pro_bar 表获取）
         pro_bar_fields = {
             "open",
@@ -103,9 +99,27 @@ class ExecutionPlanner:
             "volume_ratio",
         }
 
-        # 将依赖分为 pro_bar 字段和其他字段
+        # 每日指标字段集合（这些字段从 daily_basic 表获取）
+        daily_basic_fields = {
+            "turnover_rate_f",
+            "pe",
+            "pe_ttm",
+            "pb",
+            "ps",
+            "ps_ttm",
+            "dv_ratio",
+            "dv_ttm",
+            "total_share",
+            "float_share",
+            "free_share",
+            "total_mv",
+            "circ_mv",
+        }
+
+        # 将依赖分为不同表的字段
         pro_bar_deps = dependencies & pro_bar_fields
-        other_deps = dependencies - pro_bar_fields
+        daily_basic_deps = dependencies & daily_basic_fields
+        other_deps = dependencies - pro_bar_fields - daily_basic_fields
 
         data_specs = []
 
@@ -115,7 +129,15 @@ class ExecutionPlanner:
                 DataSpec(
                     table="pro_bar",
                     columns=sorted(pro_bar_deps),
-                    lookback_days=lookback_days,
+                )
+            )
+
+        # daily_basic 表的数据规格
+        if daily_basic_deps:
+            data_specs.append(
+                DataSpec(
+                    table="daily_basic",
+                    columns=sorted(daily_basic_deps),
                 )
             )
 
@@ -125,46 +147,7 @@ class ExecutionPlanner:
                 DataSpec(
                     table="daily",
                     columns=sorted(other_deps),
-                    lookback_days=lookback_days,
                 )
             )
 
         return data_specs
-
-    def _extract_max_window(self, node: Node) -> int:
-        """从表达式中提取最大窗口大小。
-
-        Args:
-            node: AST 节点
-
-        Returns:
-            最大窗口大小，无时序函数返回 1
-        """
-        if isinstance(node, FunctionNode):
-            window = 1
-            # 检查函数参数中的窗口大小
-            for arg in node.args:
-                if (
-                    isinstance(arg, Constant)
-                    and isinstance(arg.value, int)
-                    and arg.value > window
-                ):
-                    window = arg.value
-
-            # 递归检查子表达式
-            for arg in node.args:
-                if isinstance(arg, Node) and not isinstance(arg, Constant):
-                    window = max(window, self._extract_max_window(arg))
-
-            return window
-
-        elif isinstance(node, BinaryOpNode):
-            return max(
-                self._extract_max_window(node.left),
-                self._extract_max_window(node.right),
-            )
-
-        elif isinstance(node, UnaryOpNode):
-            return self._extract_max_window(node.operand)
-
-        return 1