refactor factor calculation and improve progress tracking

README.md

@@ -151,5 +151,5 @@ Streamlit `自检测试` 页签提供：
 TODO
 1. 在选股时，因子都已经提前算好，不需要再计算了，直接用就行。
 2. 因子计算的公式再确认下
-3. 审查整个项目的代码逻辑，从main.py开始，逐字逐句检查。如一些重复的检查可以去掉；未实现的功能请标记TODO，并给出实现思路；错误的、低效率的调用请修正；代码结构性的问题请指出。
+3. 审查整个项目的代码逻辑，从app/ui/streamlit_app.py开始，逐字逐句检查。如一些重复的安全检查可以去掉；明显果实的临时性代码请删除掉；未实现的功能请标记TODO，并给出实现思路；错误的、低效率的调用请修正；代码结构性的问题请指出并尝试修正；复杂不清晰的代码结构请尝试重构；
 4. 梳理整个项目的所有业务逻辑。针对每个业务，从业务实现角度评估代码功能是否存在问题，是否需要优化，是否需要重构。

app/features/evaluation.py

@@ -9,7 +9,8 @@ from app.features.factors import (
     DEFAULT_FACTORS,
     FactorResult,
     FactorSpec,
-    compute_factor_range
+    compute_factor_range,
+    lookup_factor_spec,
 )
 from app.utils.data_access import DataBroker
 from app.utils.logging import get_logger
@@ -90,14 +91,14 @@ def evaluate_factor(
     # )
     
     try:
-        # 计算因子值
+        spec = lookup_factor_spec(factor_name) or FactorSpec(factor_name, 0)
-        # 设置 skip_existing=False，确保即使因子已存在也会重新计算
+
         factor_results = compute_factor_range(
             start_date,
             end_date,
-            factors=[FactorSpec(factor_name, 0)],
+            factors=[spec],
             ts_codes=universe,
-            skip_existing=False
+            skip_existing=True,
         )
         
         # 因子计算完成（在异步线程中不直接访问factor_progress）

app/features/factors.py

@@ -90,6 +90,17 @@ DEFAULT_FACTORS: List[FactorSpec] = [
     FactorSpec("risk_penalty", 0),  # 风险惩罚因子
 ]
 
+_FACTOR_SPEC_MAP: Dict[str, FactorSpec] = {spec.name: spec for spec in DEFAULT_FACTORS}
+
+
+def lookup_factor_spec(name: str) -> Optional[FactorSpec]:
+    """Return a copy of the registered ``FactorSpec`` for ``name`` if available."""
+
+    base = _FACTOR_SPEC_MAP.get(name)
+    if base is None:
+        return None
+    return FactorSpec(name=base.name, window=base.window)
+
 
 def compute_factors(
     trade_date: date,
@@ -304,30 +315,33 @@ def _existing_factor_codes_with_factors(trade_date: str, factor_names: List[str]
     if not factor_names:
         return {}
         
-    # 构建检查条件
+    valid_names = [
-    conditions = []
+        name
-    for name in factor_names:
+        for name in factor_names
-        conditions.append(f"json_extract(factors, '$.{name}') IS NOT NULL")
+        if isinstance(name, str) and _IDENTIFIER_RE.match(name)
-    condition_str = " AND ".join(conditions)
+    ]
-    
+    if not valid_names:
-    # 构建SQL查询
+        return {}
-    query = """
-        SELECT ts_code
-        FROM factors
-        WHERE trade_date = ?
-        AND """ + condition_str + """
-        GROUP BY ts_code
-    """
 
     with db_session(read_only=True) as conn:
+        columns = {
+            row["name"]
+            for row in conn.execute("PRAGMA table_info(factors)").fetchall()
+        }
+        selected = [name for name in valid_names if name in columns]
+        if not selected:
+            return {}
+
+        predicates = " AND ".join(f"{col} IS NOT NULL" for col in selected)
+        query = (
+            "SELECT ts_code FROM factors "
+            "WHERE trade_date = ? AND "
+            f"{predicates} "
+            "GROUP BY ts_code"
+        )
         rows = conn.execute(query, (trade_date,)).fetchall()
 
-    # 返回结果
+    return {row["ts_code"]: True for row in rows if row and row["ts_code"]}
-    result = {}
-    for row in rows:
-        result[row["ts_code"]] = True
-    
-    return result
 
 
 def _list_trade_dates(

app/ui/progress_state.py

@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 from typing import Optional, Dict, Any
+import time
 import streamlit as st
 
 
@@ -25,7 +26,7 @@ class FactorProgressState:
                 'status': 'idle',  # idle, running, completed, error
                 'message': '',
                 'start_time': None,
-                'elapsed_time': 0
+                'elapsed_time': 0.0,
             }
     
     def start_calculation(self, total_securities: int, total_batches: int) -> None:
@@ -35,16 +36,17 @@ class FactorProgressState:
             total_securities: 总证券数量
             total_batches: 总批次数
         """
+        now = time.time()
         st.session_state.factor_progress.update({
             'current': 0,
-            'total': total_securities,
+            'total': max(total_securities, 0),
             'percentage': 0.0,
             'current_batch': 0,
-            'total_batches': total_batches,
+            'total_batches': max(total_batches, 0),
             'status': 'running',
             'message': '开始因子计算...',
-            'start_time': st.session_state.get('factor_progress', {}).get('start_time'),
+            'start_time': now,
-            'elapsed_time': 0
+            'elapsed_time': 0.0,
         })
     
     def update_progress(self, current_securities: int, current_batch: int, 
@@ -59,18 +61,26 @@ class FactorProgressState:
         progress = st.session_state.factor_progress
         
         # 计算百分比
-        if progress['total'] > 0:
+        total = progress.get('total', 0) or 0
-            percentage = (current_securities / progress['total']) * 100
+        if total > 0:
+            percentage = (current_securities / total) * 100
         else:
             percentage = 0.0
 
+        start_time = progress.get('start_time')
+        if isinstance(start_time, (int, float)):
+            elapsed = max(0.0, time.time() - start_time)
+        else:
+            elapsed = 0.0
+        
         # 更新状态
         progress.update({
             'current': current_securities,
             'current_batch': current_batch,
             'percentage': percentage,
-            'message': message or f'处理批次 {current_batch}/{progress["total_batches"]}',
+            'message': message or f'处理批次 {current_batch}/{progress["total_batches"] or 1}',
-            'status': 'running'
+            'status': 'running',
+            'elapsed_time': elapsed,
         })
     
     def complete_calculation(self, message: str = '因子计算完成') -> None:
@@ -80,11 +90,17 @@ class FactorProgressState:
             message: 完成消息
         """
         progress = st.session_state.factor_progress
+        start_time = progress.get('start_time')
+        if isinstance(start_time, (int, float)):
+            elapsed = max(0.0, time.time() - start_time)
+        else:
+            elapsed = progress.get('elapsed_time', 0.0) or 0.0
         progress.update({
-            'current': progress['total'],
+            'current': progress.get('total', 0),
-            'percentage': 100.0,
+            'percentage': 100.0 if progress.get('total', 0) else progress.get('percentage', 0.0),
             'status': 'completed',
-            'message': message
+            'message': message,
+            'elapsed_time': elapsed,
         })
     
     def error_occurred(self, error_message: str) -> None:
@@ -93,9 +109,16 @@ class FactorProgressState:
         Args:
             error_message: 错误消息
         """
-        st.session_state.factor_progress.update({
+        progress = st.session_state.factor_progress
+        start_time = progress.get('start_time')
+        if isinstance(start_time, (int, float)):
+            elapsed = max(0.0, time.time() - start_time)
+        else:
+            elapsed = progress.get('elapsed_time', 0.0) or 0.0
+        progress.update({
             'status': 'error',
-            'message': f'错误: {error_message}'
+            'message': f'错误: {error_message}',
+            'elapsed_time': elapsed,
         })
     
     def get_progress_info(self) -> Dict[str, Any]:
@@ -118,7 +141,7 @@ class FactorProgressState:
             'status': 'idle',
             'message': '',
             'start_time': None,
-            'elapsed_time': 0
+            'elapsed_time': 0.0,
         }
 
 

app/ui/views/backtest.py

@@ -2,16 +2,11 @@
 from __future__ import annotations
 
 import json
-import uuid
-from dataclasses import asdict
 from datetime import date, datetime
 from typing import Dict, List, Optional
 
-import numpy as np
 import pandas as pd
 import plotly.express as px
-import requests
-from requests.exceptions import RequestException
 import streamlit as st
 
 from app.agents.base import AgentContext

app/ui/views/factor_calculation.py

@@ -1,19 +1,20 @@
 """因子计算页面。"""
-from datetime import datetime, timedelta
+from datetime import date, datetime, timedelta
-from typing import List, Optional
+from typing import List, Optional, Sequence
 
 import streamlit as st
 
-from app.features.factors import compute_factors, DEFAULT_FACTORS, FactorSpec
+from app.features.factors import DEFAULT_FACTORS, FactorSpec, compute_factor_range
 from app.ui.progress_state import factor_progress
+from app.ui.shared import LOGGER, LOG_EXTRA
 from app.utils.data_access import DataBroker
 from app.utils.db import db_session
 
 
 def _get_latest_trading_date() -> datetime.date:
     """获取数据库中的最新交易日期"""
-    with db_session() as session:
+    with db_session(read_only=True) as conn:
-        result = session.execute(
+        result = conn.execute(
             """
             SELECT trade_date 
             FROM daily_basic 
@@ -34,9 +35,9 @@ def _get_all_stocks() -> List[str]:
     """获取所有股票代码"""
     try:
         # 从daily表获取所有股票代码
-        with db_session() as session:
+        with db_session(read_only=True) as conn:
             latest_date = _get_latest_trading_date()
-            result = session.execute(
+            result = conn.execute(
                 """
                 SELECT DISTINCT ts_code 
                 FROM daily 
@@ -45,12 +46,88 @@ def _get_all_stocks() -> List[str]:
                 {"trade_date": latest_date.strftime("%Y%m%d")}
             ).fetchall()
             
-            return [row[0] for row in result] if result else []
+            return [row["ts_code"] for row in result if row and row["ts_code"]] if result else []
-    except Exception as e:
+    except Exception as exc:
-        st.error(f"获取股票列表失败: {str(e)}")
+        LOGGER.exception("获取股票列表失败", extra={**LOG_EXTRA, "error": str(exc)})
+        st.error(f"获取股票列表失败: {exc}")
         return []
 
 
+def _normalize_universe(universe: Optional[Sequence[str]]) -> List[str]:
+    """去重并规范股票代码格式。"""
+    if not universe:
+        return []
+    seen: dict[str, None] = {}
+    for code in universe:
+        normalized = code.strip().upper()
+        if normalized and normalized not in seen:
+            seen[normalized] = None
+    return list(seen.keys())
+
+
+def _get_trade_dates_between(
+    start: date,
+    end: date,
+    universe: Optional[Sequence[str]] = None,
+) -> List[date]:
+    """获取区间内存在行情数据的交易日期列表。"""
+
+    if end < start:
+        return []
+
+    start_str = start.strftime("%Y%m%d")
+    end_str = end.strftime("%Y%m%d")
+    params: List[str] = [start_str, end_str]
+    query = (
+        "SELECT DISTINCT trade_date FROM daily "
+        "WHERE trade_date BETWEEN ? AND ?"
+    )
+    scoped_universe = _normalize_universe(universe)
+    if scoped_universe:
+        placeholders = ", ".join("?" for _ in scoped_universe)
+        query += f" AND ts_code IN ({placeholders})"
+        params.extend(scoped_universe)
+    query += " ORDER BY trade_date"
+
+    with db_session(read_only=True) as conn:
+        rows = conn.execute(query, params).fetchall()
+
+    return [
+        datetime.strptime(str(row["trade_date"]), "%Y%m%d").date()
+        for row in rows
+        if row and row["trade_date"]
+    ]
+
+
+def _estimate_total_workload(
+    trade_dates: Sequence[date],
+    universe: Optional[Sequence[str]],
+) -> int:
+    """估算本次计算需要处理的证券数量，用于驱动进度条。"""
+
+    trade_days = list(trade_dates)
+    if not trade_days:
+        return 0
+
+    scoped_universe = _normalize_universe(universe)
+    if scoped_universe:
+        return len(scoped_universe) * len(trade_days)
+
+    start_str = min(trade_days).strftime("%Y%m%d")
+    end_str = max(trade_days).strftime("%Y%m%d")
+    with db_session(read_only=True) as conn:
+        row = conn.execute(
+            """
+            SELECT COUNT(DISTINCT ts_code) AS cnt
+            FROM daily
+            WHERE trade_date BETWEEN ? AND ?
+            """,
+            (start_str, end_str),
+        ).fetchone()
+    universe_size = int(row["cnt"]) if row and row["cnt"] is not None else 0
+    return universe_size * len(trade_days)
+
+
 def render_factor_calculation() -> None:
     """渲染因子计算页面。"""
     st.subheader("📊 因子计算")
@@ -153,97 +230,55 @@ def render_factor_calculation() -> None:
         help="如果勾选，将跳过数据库中已存在的因子计算结果"
     )
     
-    # 5. 同步计算函数
+    # 5. 开始计算按钮
-    def run_factor_calculation_sync():
-        """同步执行因子计算"""
-        # 计算参数
-        total_stocks = len(universe) if universe else len(_get_all_stocks())
-        total_batches = len(selected_factors)
-        
-        try:
-            # 执行因子计算
-            results = []
-            for i, factor in enumerate(selected_factors):
-                # 更新批次进度
-                factor_progress.update_progress(
-                    current_securities=0,
-                    current_batch=i+1,
-                    message=f"正在计算因子: {factor.name}"
-                )
-                
-                # 计算单个交易日的因子
-                current_date = start_date
-                while current_date <= end_date:
-                    try:
-                        # 计算指定日期的因子
-                        daily_results = compute_factors(
-                            current_date,
-                            [factor],
-                            ts_codes=universe,
-                            skip_existing=skip_existing
-                        )
-                        results.extend(daily_results)
-                        
-                    except Exception as e:
-                        # 记录错误但不中断计算
-                        error_msg = f"计算因子 {factor.name} 在日期 {current_date} 时出错: {str(e)}"
-                        print(f"ERROR: {error_msg}")
-                    
-                    current_date += timedelta(days=1)
-            
-            # 计算完成
-            factor_progress.complete_calculation(f"因子计算完成！共计算 {len(results)} 条因子记录")
-            
-            return {
-                'success': True,
-                'results': results,
-                'factors': [f.name for f in selected_factors],
-                'date_range': f"{start_date} 至 {end_date}",
-                'stock_count': len(set(r.ts_code for r in results)) if results else 0,
-                'message': f"因子计算完成！共计算 {len(results)} 条因子记录"
-            }
-                
-        except Exception as e:
-            # 计算失败
-            factor_progress.error_occurred(f"因子计算失败: {str(e)}")
-            return {
-                'success': False,
-                'error': str(e),
-                'message': f"因子计算失败: {str(e)}"
-            }
-    
-    # 6. 开始计算按钮
     if st.button("开始计算因子", disabled=not selected_factors):
         # 重置状态
-        if 'factor_calculation_results' in st.session_state:
+        st.session_state.pop('factor_calculation_results', None)
-            st.session_state.factor_calculation_results = None
+        st.session_state.pop('factor_calculation_error', None)
-        if 'factor_calculation_error' in st.session_state:
+        factor_progress.reset()
-            st.session_state.factor_calculation_error = None
 
-        # 初始化进度状态
+        scoped_universe = _normalize_universe(universe) or None
-        total_stocks = len(universe) if universe else len(_get_all_stocks())
+        trade_dates = _get_trade_dates_between(start_date, end_date, scoped_universe)
+        if not trade_dates:
+            st.warning("所选时间窗口内无可用交易日数据，请先执行数据同步。")
+            return
+
+        total_workload = _estimate_total_workload(trade_dates, scoped_universe)
         factor_progress.start_calculation(
-            total_securities=total_stocks,
+            total_securities=max(total_workload, 1),
-            total_batches=len(selected_factors)
+            total_batches=len(trade_dates),
         )
 
-        # 直接调用同步计算函数
+        with st.spinner("正在计算因子..."):
-        result = run_factor_calculation_sync()
+            try:
-        
+                results = compute_factor_range(
-        # 处理计算结果
+                    start=min(trade_dates),
-        if result['success']:
+                    end=max(trade_dates),
+                    factors=selected_factors,
+                    ts_codes=scoped_universe,
+                    skip_existing=skip_existing,
+                )
+            except Exception as exc:
+                LOGGER.exception("因子计算失败", extra={**LOG_EXTRA, "error": str(exc)})
+                factor_progress.error_occurred(str(exc))
+                st.session_state.factor_calculation_error = str(exc)
+                st.error(f"❌ 因子计算失败: {exc}")
+            else:
+                factor_progress.complete_calculation(
+                    f"因子计算完成，共生成 {len(results)} 条因子记录"
+                )
+                factor_names = [spec.name for spec in selected_factors]
+                stock_count = len({item.ts_code for item in results}) if results else 0
                 st.session_state.factor_calculation_results = {
-                'results': result['results'],
+                    'results': results,
-                'factors': result['factors'],
+                    'factors': factor_names,
-                'date_range': result['date_range'],
+                    'date_range': f"{trade_dates[0]} 至 {trade_dates[-1]}",
-                'stock_count': result['stock_count']
+                    'stock_count': stock_count,
+                    'trade_days': len(trade_dates),
                 }
                 st.success("✅ 因子计算完成！")
-        else:
-            st.session_state.factor_calculation_error = result['error']
-            st.error(f"❌ 因子计算失败: {result['error']}")
     
-    # 7. 显示计算结果
+    # 6. 显示计算结果
     if 'factor_calculation_results' in st.session_state and st.session_state.factor_calculation_results:
         results = st.session_state.factor_calculation_results
         
@@ -255,7 +290,8 @@ def render_factor_calculation() -> None:
         with col2:
             st.metric("涉及股票数量", results['stock_count'])
         with col3:
-            st.metric("计算时间范围", results['date_range'])
+            st.metric("交易日数量", results.get('trade_days', 0))
+        st.caption(f"时间范围：{results['date_range']}")
         
         # 显示计算详情
         with st.expander("查看计算详情"):
@@ -279,8 +315,6 @@ def render_factor_calculation() -> None:
             else:
                 st.info("没有找到因子计算结果")
     
-    # 8. 移除异步线程检查逻辑（已改为同步模式）
+    # 7. 显示错误信息
-    
-    # 9. 显示错误信息
     if 'factor_calculation_error' in st.session_state and st.session_state.factor_calculation_error:
         st.error(f"❌ 因子计算失败: {st.session_state.factor_calculation_error}")

app/ui/views/stock_eval.py

@@ -1,6 +1,7 @@
 """股票筛选与评估视图。"""
-from datetime import datetime, timedelta
+from datetime import date, datetime, timedelta
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional
+import json
 
 import numpy as np
 import pandas as pd
@@ -15,11 +16,10 @@ from app.utils.db import db_session
 from app.utils.logging import get_logger
 
 
-def _get_latest_trading_date() -> datetime.date:
+def _get_latest_trading_date() -> date:
     """获取数据库中的最新交易日期"""
-    with db_session() as session:
+    with db_session(read_only=True) as conn:
-        # 获取当前日期的上一个有效交易日
+        result = conn.execute(
-        result = session.execute(
             """
             SELECT trade_date 
             FROM daily_basic 
@@ -35,6 +35,19 @@ def _get_latest_trading_date() -> datetime.date:
             return datetime.strptime(str(result[0]), "%Y%m%d").date()
         return datetime.now().date() - timedelta(days=1)  # 如果查询失败才返回昨天
 
+
+def _normalize_universe(universe: Optional[List[str]]) -> List[str]:
+    """标准化股票代码列表，去重并转为大写。"""
+
+    if not universe:
+        return []
+    normalized: Dict[str, None] = {}
+    for code in universe:
+        candidate = (code or "").strip().upper()
+        if candidate and candidate not in normalized:
+            normalized[candidate] = None
+    return list(normalized.keys())
+
 def render_stock_evaluation() -> None:
     """渲染股票筛选与评估页面。"""
     LOGGER = get_logger(__name__)
@@ -141,6 +154,9 @@ def render_stock_evaluation() -> None:
                 index_code,
                 end_date.strftime("%Y%m%d")
             )
+    universe = _normalize_universe(universe)
+    if universe == []:
+        universe = None
             
     # 4. 评估结果
     
@@ -167,11 +183,12 @@ def render_stock_evaluation() -> None:
             )
             
             st.session_state.evaluation_status = 'running'
+            st.session_state.pop('evaluation_error', None)
             results = []
             
             for i, factor_name in enumerate(selected_factors):
                 st.session_state.current_factor = factor_name
-                st.session_state.progress = (i / len(selected_factors)) * 100
+                st.session_state.progress = ((i + 1) / len(selected_factors)) * 100
                 
                 performance = evaluate_factor(
                     factor_name,
@@ -181,11 +198,11 @@ def render_stock_evaluation() -> None:
                 )
                 results.append({
                     "因子": factor_name,
-                    "IC均值": f"{performance.ic_mean:.4f}",
+                    "IC均值": performance.ic_mean,
-                    "RankIC均值": f"{performance.rank_ic_mean:.4f}",
+                    "RankIC均值": performance.rank_ic_mean,
-                    "IC信息比率": f"{performance.ic_ir:.4f}",
+                    "IC信息比率": performance.ic_ir,
-                    "夏普比率": f"{performance.sharpe_ratio:.4f}" if performance.sharpe_ratio else "N/A",
+                    "夏普比率": performance.sharpe_ratio,
-                    "换手率": f"{performance.turnover_rate*100:.1f}%" if performance.turnover_rate else "N/A"
+                    "换手率": performance.turnover_rate,
                 })
             
             st.session_state.evaluation_results = results
@@ -221,29 +238,45 @@ def render_stock_evaluation() -> None:
         
         st.markdown("##### 因子评估结果")
         result_df = pd.DataFrame(results)
+        if not result_df.empty:
+            display_df = result_df.copy()
+            for col in ["IC均值", "RankIC均值", "IC信息比率"]:
+                if col in display_df:
+                    display_df[col] = display_df[col].map(lambda v: f"{v:.4f}")
+            if "夏普比率" in display_df:
+                display_df["夏普比率"] = display_df["夏普比率"].map(
+                    lambda v: "N/A" if v is None else f"{v:.4f}"
+                )
+            if "换手率" in display_df:
+                display_df["换手率"] = display_df["换手率"].map(
+                    lambda v: "N/A" if v is None else f"{v * 100:.1f}%"
+                )
             st.dataframe(
-            result_df,
+                display_df,
                 hide_index=True,
                 width="stretch"
             )
+        else:
+            st.info("未产生任何因子评估结果。")
         
         # 绘制IC均值分布
-        ic_means = [float(r["IC均值"]) for r in results]
+        ic_means = result_df["IC均值"].astype(float).tolist() if not result_df.empty else []
         chart_df = pd.DataFrame({
             "因子": [r["因子"] for r in results],
             "IC均值": ic_means
         })
         st.bar_chart(chart_df.set_index("因子"))
 
-        # 生成股票评分
+        if not ic_means:
+            st.info("暂无足够的 IC 数据，无法生成股票评分。")
+            return
+
         with st.spinner("正在生成股票评分..."):
-            # 使用IC均值作为权重，但如果IC均值全为零，则使用均匀分布
             if all(mean == 0 for mean in ic_means):
                 factor_weights = [1.0 / len(ic_means)] * len(ic_means)
                 LOGGER.info("所有因子IC均值均为零，使用均匀权重", extra=LOG_EXTRA)
             else:
-                # 将IC均值归一化为权重
+                abs_sum = sum(abs(m) for m in ic_means) or 1.0
-                abs_sum = sum(abs(m) for m in ic_means)
                 factor_weights = [m / abs_sum for m in ic_means]
                 LOGGER.info("使用IC均值作为权重: %s", factor_weights, extra=LOG_EXTRA)
 
@@ -259,33 +292,35 @@ def render_stock_evaluation() -> None:
             score_df = pd.DataFrame(scores).sort_values(
                 "综合评分",
                 ascending=False
-                ).head(20)
+            )
+            top_df = score_df.head(20).reset_index(drop=True)
+            display_scores = top_df.copy()
+            display_scores["综合评分"] = display_scores["综合评分"].map(lambda v: f"{v:.4f}")
             st.dataframe(
-                    score_df,
+                display_scores,
                 hide_index=True,
                 width="stretch"
             )
 
-                # 添加入池功能
             if st.button("将Top 20股票加入股票池"):
-                    _add_to_stock_pool(
+                _add_to_stock_pool(top_df, end_date)
-                        score_df["股票代码"].tolist(),
-                        end_date
-                    )
                 st.success("已成功将选中股票加入股票池！")
+        else:
+            st.info("无法根据当前因子权重生成有效的股票评分结果。")
 
 
 def _calculate_stock_scores(
     universe: Optional[List[str]],
     factors: List[str],
-    eval_date: datetime.date,
+    eval_date: date,
     factor_weights: List[float]
-) -> List[Dict[str, str]]:
+) -> List[Dict[str, object]]:
     """计算股票的综合评分。"""
     LOGGER = get_logger(__name__)
     LOG_EXTRA = {"stage": "stock_evaluation"}
     
     broker = DataBroker()
+    trade_date_str = eval_date.strftime("%Y%m%d")
     
     # 记录评估开始
     LOGGER.info(
@@ -297,7 +332,7 @@ def _calculate_stock_scores(
     )
     
     # 标准化权重
-    weights = np.array(factor_weights)
+    weights = np.array(factor_weights, dtype=float)
     abs_sum = np.sum(np.abs(weights))
     if abs_sum > 0:  # 避免除以零
         weights = weights / abs_sum
@@ -306,7 +341,10 @@ def _calculate_stock_scores(
         weights = np.ones_like(weights) / len(weights)
     
     # 获取所有股票的因子值
-    stocks = universe or broker.get_all_stocks(eval_date.strftime("%Y%m%d"))
+    stocks = universe or broker.get_all_stocks(trade_date_str)
+    if not stocks:
+        LOGGER.warning("股票列表为空，无法生成评分", extra=LOG_EXTRA)
+        return []
     
     # 记录股票列表信息
     LOGGER.info(
@@ -320,42 +358,54 @@ def _calculate_stock_scores(
     
     evaluated_count = 0
     skipped_count = 0
+    factor_fields = [f"factors.{name}" for name in factors]
     
     for ts_code in stocks:
-        # 检查数据是否充分
+        if not check_data_sufficiency(ts_code, trade_date_str):
-        if not check_data_sufficiency(ts_code, eval_date.strftime("%Y%m%d")):
             skipped_count += 1
             continue
 
-        # 获取股票信息
+        latest_payload = broker.fetch_latest(
-        info = broker.get_stock_info(ts_code)
+            ts_code,
+            trade_date_str,
+            factor_fields,
+            auto_refresh=False,
+        )
+
+        if not latest_payload:
+            skipped_count += 1
+            continue
+
+        factor_values: List[float] = []
+        missing = False
+        for field in factor_fields:
+            value = latest_payload.get(field)
+            if value is None:
+                missing = True
+                break
+            try:
+                factor_values.append(float(value))
+            except (TypeError, ValueError):
+                missing = True
+                break
+
+        if missing or len(factor_values) != len(factors):
+            skipped_count += 1
+            continue
+
+        info = broker.get_stock_info(ts_code, trade_date_str)
         if not info:
             skipped_count += 1
             continue
 
-        # 获取因子值
+        score = float(np.dot(factor_values, weights))
-        factor_values = []
-        for factor in factors:
-            value = broker.fetch_latest_factor(ts_code, factor, eval_date)
-            if value is None:
-                skipped_count += 1
-                break
-            factor_values.append(value)
-            
-        # 检查是否所有因子值都已获取
-        if len(factor_values) != len(factors):
-            skipped_count += 1
-            continue
-            
-        # 计算综合评分
-        score = np.dot(factor_values, weights)
         evaluated_count += 1
         
         results.append({
             "股票代码": ts_code,
             "股票名称": info.get("name", ""),
             "行业": info.get("industry", ""),
-            "综合评分": f"{score:.4f}"
+            "综合评分": score,
         })
         
     # 记录评估完成信息
@@ -372,36 +422,51 @@ def _calculate_stock_scores(
 
 
 def _add_to_stock_pool(
-    ts_codes: List[str],
+    score_df: pd.DataFrame,
-    eval_date: datetime.date
+    eval_date: date
 ) -> None:
-    """将股票添加到股票池。"""
+    """将股票评分结果写入投资池。"""
-    with db_session() as session:
-        # 删除已有记录
-        session.execute(
-            """
-            DELETE FROM stock_pool 
-            WHERE entry_date = :entry_date
-            """,
-            {"entry_date": eval_date}
-        )
 
-        # 插入新记录
+    trade_date = eval_date.strftime("%Y%m%d")
-        values = [
+    payload: List[tuple] = []
+    ranked_df = score_df.reset_index(drop=True)
+
+    for rank, row in ranked_df.iterrows():
+        tags = json.dumps(["stock_evaluation", "top20"], ensure_ascii=False)
+        metadata = json.dumps(
             {
-                "ts_code": code,
+                "source": "stock_evaluation",
-                "entry_date": eval_date,
+                "rank": rank + 1,
-                "entry_reason": "factor_evaluation"
+                "score": float(row["综合评分"]),
-            }
+            },
-            for code in ts_codes
+            ensure_ascii=False,
-        ]
+        )
-        
+        payload.append(
-        session.execute(
+            (
-            """
+                trade_date,
-            INSERT INTO stock_pool (ts_code, entry_date, entry_reason)
+                row["股票代码"],
-            VALUES (:ts_code, :entry_date, :entry_reason)
+                float(row["综合评分"]),
-            """,
+                "candidate",
-            values
+                "factor_evaluation_top20",
+                tags,
+                metadata,
+            )
         )
 
-        session.commit()
+    with db_session() as conn:
+        conn.execute("DELETE FROM investment_pool WHERE trade_date = ?", (trade_date,))
+        if payload:
+            conn.executemany(
+                """
+                INSERT INTO investment_pool (
+                    trade_date,
+                    ts_code,
+                    score,
+                    status,
+                    rationale,
+                    tags,
+                    metadata
+                ) VALUES (?, ?, ?, ?, ?, ?, ?)
+                """,
+                payload,
+            )