Initial commit: cc-cursor 全链路量化研究平台

7 Sprints 全部完成:
  Sprint 0: 基础设施 (DataManager + MariaDB)
  Sprint 1: 因子引擎 (34因子/12分类)
  Sprint 2: VectorBT 回测 (5策略+截面)
  Sprint 3: Optuna 优化 (+Walk-Forward)
  Sprint 4: ML 模型 (LightGBM+CatBoost)
  Sprint 5: Qwen 情绪因子 (三源新闻+日期对齐)
  Sprint 6: Agent 系统 (4Agent+日报.md/.html)

生产加固 (15项): Tushare双源fallback, SSH自动恢复, pool_pre_ping,
  save_daily先删后插, load_dotenv绝对路径, 日报5d/20d修复,
  RiskAgent改上证指数, 昨日对比+数据截止, mac_report utf8mb4,
  CLAUDE-*.md 9条已知Bug, demo全参数化, djapi数据源归一化,
  indexDatas API修正

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

finance/cli/agent_cli.py

@@ -0,0 +1,152 @@
+#!/usr/bin/env python
+"""
+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
+"""
+
+import sys
+import os
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from datetime import datetime
+
+
+def init_engines():
+    """初始化所有引擎。"""
+    from data.data_manager import DataManager
+    from factors.engine import FactorEngine
+    from backtest.vectorbt.engine import VectorBTEngine
+    from optimizer.engine import OptunaEngine
+    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()
+    fe = FactorEngine(dm)
+    bt = VectorBTEngine()
+    opt = OptunaEngine(bt)
+    sent = SentimentEngine(dm, qwen_client=QwenClient(), news_source=NewsSource())
+    fe._sentiment_engine = sent
+
+    return {
+        "dm": dm,
+        "fe": fe,
+        "bt": bt,
+        "opt": opt,
+        "sent": sent,
+    }
+
+
+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
+
+    cmd = sys.argv[1]
+    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
+        report_md = results.get("report", {}).get("report_markdown", "")
+        if report_md:
+            print(report_md)
+
+    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)
+        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}")
+
+    elif cmd == "risk":
+        result = orch.risk_check()
+        print(f"\n风险评估:")
+        print(f"  等级: {result['risk_level']}")
+        print(f"  建议仓位: {result['target_exposure']:.0%}")
+        print(f"  止损线: {result['stop_loss']:.0%}")
+        print(f"  单票上限: {result['max_single_position']:.0%}")
+        indicators = result.get("indicators", {})
+        if indicators:
+            print(f"  波动率: {indicators.get('market_volatility', 0):.1f}%")
+            print(f"  回撤: {indicators.get('current_drawdown', 0):.1f}%")
+        for a in result.get("alerts", []):
+            print(f"  ⚠️ {a}")
+
+    elif 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
+        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}")
+
+    elif cmd == "warmup":
+        batch_n = int(sys.argv[2]) if len(sys.argv) > 2 else 50
+        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")
+        scope = sent.get_scope_stocks() if sent else list(dm.get_stock_list().index[:100])
+        from database.dao import get_latest_trade_date
+        uncached = [c for c in scope if not get_latest_trade_date(c)]
+        print("范围: {} 只, 未缓存: {} 只".format(len(scope), len(uncached)))
+
+        total_synced = 0
+        for i in range(0, len(uncached), batch_n):
+            batch = uncached[i:i + batch_n]
+            print("[warmup] 批次 {}/{} ({}~{})".format(i // batch_n + 1, (len(uncached) - 1) // batch_n + 1, i, i + len(batch)))
+            for ts_code in batch:
+                try:
+                    n = dm.sync_daily(ts_code)
+                    total_synced += n
+                except Exception as e:
+                    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)))
+
+    elif cmd == "report":
+        date = sys.argv[2] if len(sys.argv) > 2 else None
+        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")
+        if md:
+            print(md)
+
+    else:
+        print(f"未知命令: {cmd}")
+
+
+if __name__ == "__main__":
+    main()