feat: 量化引擎加固 — 新增测试 + 数据/因子/回测层优化

- 新增 finance/tests/ 6 个测试套件（agents/backtest/dao_upsert/factors/features/fundamental_lookahead）
- 数据层: data_manager / dao 优化，新增 upsert 逻辑
- 因子层: 基本面因子抽象定位 _mapping、ROE/PE/PB 重构
- 回测层: vectorbt/engine 大改动（251 行），report 增强
- ML 层: features/backtest_integration 特征工程与回测优化
- CLI: agent_cli 重构
- config/settings 扩充配置项

finance/factors/sentiment/news_source.py

@@ -10,6 +10,7 @@
 """
 
 import json
+import logging
 import os
 import time
 from datetime import datetime, timedelta
@@ -21,6 +22,8 @@ import requests
 # 确保 .env 已加载
 import config.settings  # noqa: F401
 
+logger = logging.getLogger(__name__)
+
 
 class NewsSource:
     """
@@ -87,7 +90,7 @@ class NewsSource:
                     frames.append(df)
                     time.sleep(self.request_delay)
             except Exception as e:
-                print("  [WARN] AkShare 新闻获取失败 ({}): {}".format(ts_code, e))
+                logger.warning("[news_source] AkShare 新闻获取失败 (%s): %s", ts_code, e)
 
         if self.use_xwlb:
             try:
@@ -99,7 +102,7 @@ class NewsSource:
                 if not df.empty:
                     frames.append(df)
             except Exception as e:
-                print("  [WARN] xwlb 新闻获取失败: {}".format(e))
+                logger.warning("[news_source] xwlb 新闻获取失败: %s", e)
 
         if self.use_mcp:
             try:
@@ -107,7 +110,7 @@ class NewsSource:
                 if not df.empty:
                     frames.append(df)
             except Exception as e:
-                print("  [WARN] MCP 新闻获取失败: {}".format(e))
+                logger.warning("[news_source] MCP 新闻获取失败: %s", e)
 
         if not frames:
             return pd.DataFrame(columns=["date", "title", "content", "source", "url"])
@@ -133,7 +136,8 @@ class NewsSource:
         symbol = ts_code.replace(".SZ", "").replace(".SH", "").replace(".BJ", "")
         try:
             df = ak.stock_news_em(symbol=symbol.zfill(6))
-        except Exception:
+        except Exception as e:
+            logger.warning("[news_source] AkShare 新闻接口失败 (%s): %s", ts_code, e)
             return pd.DataFrame()
 
         if df is None or df.empty:
@@ -204,7 +208,8 @@ class NewsSource:
             df["url"] = ""
             return df[["date", "title", "content", "source", "url"]]
 
-        except Exception:
+        except Exception as e:
+            logger.warning("[news_source] xwlb 新闻获取失败: %s", e)
             return pd.DataFrame()
 
     # ── MCP 数据源 ────────────────────────────────────────
@@ -232,7 +237,8 @@ class NewsSource:
             df = pd.DataFrame(records)
             df["source"] = "mcp_trendradar"
             return df[["date", "title", "content", "source", "url"]]
-        except Exception:
+        except Exception as e:
+            logger.warning("[news_source] MCP 新闻获取失败: %s", e)
             return pd.DataFrame()
 
     def _mcp_initialize(self) -> str | None:
@@ -267,10 +273,10 @@ class NewsSource:
             if session_id:
                 self._mcp_session_id = session_id
             else:
-                print("  [WARN] MCP initialize 未返回 session-id")
+                logger.warning("[news_source] MCP initialize 未返回 session-id")
             return session_id
         except Exception as e:
-            print("  [WARN] MCP 连接失败: {}".format(e))
+            logger.warning("[news_source] MCP 连接失败: %s", e)
             return None
 
     def _mcp_call_tool(
@@ -299,7 +305,8 @@ class NewsSource:
                     data = json.loads(line[5:].strip())
                     return data.get("result", {})
             return None
-        except Exception:
+        except Exception as e:
+            logger.warning("[news_source] MCP call_tool 失败: %s", e)
             return None
 
     @staticmethod

finance/factors/sentiment/sentiment_engine.py

@@ -128,11 +128,12 @@ class SentimentEngine:
         # 5. Qwen 情绪分析（有 API key 时才执行）
         sentiment_df = self._analyze_news(news_df)
 
-        # 6. 计算因子
+        # 6. 计算因子（对齐注册表，产出全部 4 个注册情绪因子，含 news_sent_20）
         factor_dfs = {}
         if not sentiment_df.empty:
             for factor_cls, kwargs in [
                 (NewsSentimentFactor, {"window": 5, "sentiment_df": sentiment_df}),
+                (NewsSentimentFactor, {"window": 20, "sentiment_df": sentiment_df}),
                 (SentimentConfidenceFactor, {"window": 5, "sentiment_df": sentiment_df}),
                 (SentimentMomentumFactor, {"period": 5, "sentiment_df": sentiment_df}),
             ]: