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/cli/agent_cli.py

@@ -3,19 +3,24 @@
 Agent 命令行入口。
 
 用法:
-    python cli/agent_cli.py daily          # 执行每日流程
-    python cli/agent_cli.py picks [N]      # 今日选股 Top N
-    python cli/agent_cli.py risk           # 风险评估
-    python cli/agent_cli.py research       # 因子研究
-    python cli/agent_cli.py report [DATE]  # 生成日报
+    python cli/agent_cli.py daily              # 执行每日流程
+    python cli/agent_cli.py picks [N]          # 今日选股 Top N
+    python cli/agent_cli.py risk               # 风险评估
+    python cli/agent_cli.py research           # 因子研究
+    python cli/agent_cli.py report [DATE]      # 生成日报
+    python cli/agent_cli.py warmup [N]         # 首次批量预热
 """
 
 import sys
 import os
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
+import argparse
+import logging
 from datetime import datetime
 
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s")
+
 
 def init_engines():
     """初始化所有引擎。"""
@@ -26,7 +31,6 @@ def init_engines():
     from factors.sentiment.sentiment_engine import SentimentEngine
     from factors.sentiment.news_source import NewsSource
     from factors.sentiment.qwen_client import QwenClient
-    from factors.registry import get_factor
 
     dm = DataManager()
     dm.init_db()
@@ -45,42 +49,62 @@ def init_engines():
     }
 
 
-def main():
-    if len(sys.argv) < 2:
-        print("用法: agent_cli.py <daily|picks|risk|research|report|warmup>")
-        print()
-        print("  daily [DATE]    — 执行每日完整流程")
-        print("  picks [N] [DATE] — 今日选股 Top N")
-        print("  risk            — 风险评估")
-        print("  research        — 因子发现与评估")
-        print("  report [DATE]   — 生成日报")
-        print("  warmup [N]      — 首次批量预热范围股票到 DB 缓存")
-        return
+def build_parser() -> argparse.ArgumentParser:
+    p = argparse.ArgumentParser(prog="agent_cli", description="cc-cursor 量化 Agent CLI")
+    sub = p.add_subparsers(dest="cmd", required=True)
 
-    cmd = sys.argv[1]
+    d = sub.add_parser("daily", help="执行每日完整流程")
+    d.add_argument("date", nargs="?", default=None, help="日期 YYYYMMDD（默认今天）")
+
+    pk = sub.add_parser("picks", help="今日选股 Top N")
+    pk.add_argument("top_n", nargs="?", type=int, default=15, help="返回 Top N")
+    pk.add_argument("date", nargs="?", default=None, help="日期 YYYYMMDD")
+
+    sub.add_parser("risk", help="风险评估")
+
+    sub.add_parser("research", help="因子发现与评估")
+
+    rp = sub.add_parser("report", help="生成日报")
+    rp.add_argument("date", nargs="?", default=None, help="日期 YYYYMMDD")
+
+    wm = sub.add_parser("warmup", help="首次批量预热范围股票到 DB 缓存")
+    wm.add_argument("batch_n", nargs="?", type=int, default=50, help="每批股票数")
+
+    return p
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = build_parser()
+    args = parser.parse_args(argv)
     engines = init_engines()
 
     from agents.orchestrator import AgentOrchestrator
     orch = AgentOrchestrator(**engines)
     orch.setup()
 
-    if cmd == "daily":
-        date = sys.argv[2] if len(sys.argv) > 2 else None
-        results = orch.run_daily(date=date)
-        # 打印日报内容
+    today = datetime.now().strftime("%Y%m%d")
+
+    if args.cmd == "daily":
+        results = orch.run_daily(date=args.date)
         report_md = results.get("report", {}).get("report_markdown", "")
         if report_md:
             print(report_md)
+        # 任一步骤失败 → 非零退出码，便于调度/CI 感知
+        failed = [k for k, v in results.items()
+                  if isinstance(v, dict) and "error" in v]
+        if failed:
+            print("每日流程有步骤未完成: {}".format(", ".join(failed)), file=sys.stderr)
+            return 1
+        return 0
 
-    elif cmd == "picks":
-        n = int(sys.argv[2]) if len(sys.argv) > 2 else 15
-        date = sys.argv[3] if len(sys.argv) > 3 else None
-        result = orch.picks(date=date, top_n=n)
+    elif args.cmd == "picks":
+        result = orch.picks(date=args.date, top_n=args.top_n)
         print(f"\n选股结果 ({result.get('date', '?')}):")
         for p in result.get("top_picks", []):
             print(f"  {p['ts_code']:12s} {p.get('name', ''):10s} {p['score']:.4f}")
+        return 0
 
-    elif cmd == "risk":
+    elif args.cmd == "risk":
         result = orch.risk_check()
         print(f"\n风险评估:")
         print(f"  等级: {result['risk_level']}")
@@ -93,22 +117,24 @@ def main():
             print(f"  回撤: {indicators.get('current_drawdown', 0):.1f}%")
         for a in result.get("alerts", []):
             print(f"  ⚠️ {a}")
+        return 0
 
-    elif cmd == "research":
+    elif args.cmd == "research":
         result = orch.run_research_cycle()
         top = result.get("research", {}).get("top_factors", [])
         print(f"\n因子评估结果:")
         if not top:
             print("  (无结果)")
-            return
+            return 0
         print(f"  {'因子':20s} {'IC':>8s} {'IC_IR':>8s} {'多头':>8s} {'空头':>8s} {'得分':>8s}")
         print(f"  {'─'*60}")
         for f in top:
             print(f"  {f['name']:20s} {f['ic_mean']:>+8.4f} {f['icir']:>8.3f} "
                   f"{f['long_ret']:>+7.1f}% {f['short_ret']:>+7.1f}% {f['score']:>8.4f}")
+        return 0
 
-    elif cmd == "warmup":
-        batch_n = int(sys.argv[2]) if len(sys.argv) > 2 else 50
+    elif args.cmd == "warmup":
+        batch_n = args.batch_n
         print("首次批量预热: 每次 {} 只股票，分批执行...".format(batch_n))
         sent = engines.get("sent")
         dm = engines.get("dm")
@@ -129,24 +155,20 @@ def main():
                     print("  {} 失败: {}".format(ts_code, e))
             print("  累计同步: {} 条".format(total_synced))
         print("预热完成: {} 条数据, {} 只新股票已缓存".format(total_synced, len(uncached)))
+        return 0
 
-    elif cmd == "report":
-        date = sys.argv[2] if len(sys.argv) > 2 else None
+    elif args.cmd == "report":
+        date = args.date or today
         result = orch.generate_report(date=date)
         print("\n日报已生成: {}".format(result.get("report_path", "?")))
-        # 存入 DB
         md = result.get("report_markdown", "")
-        if md:
-            from reports.storage import save_report
-            save_report(md, "量化日报", report_date=date or datetime.now().strftime("%Y%m%d"),
-                        subject_type="daily", subject_code="")
-            print("  已存入 DB")
+        # 注意: ReportAgent.execute 内部已保存到 DB，这里不再重复入库
         if md:
             print(md)
+        return 0
 
-    else:
-        print(f"未知命令: {cmd}")
+    return 0  # 防御：未匹配（应不会到达）
 
 
 if __name__ == "__main__":
-    main()
+    sys.exit(main())