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
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Simon
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@@ -3,19 +3,24 @@
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Agent 命令行入口。
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用法:
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python cli/agent_cli.py daily # 执行每日流程
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python cli/agent_cli.py picks [N] # 今日选股 Top N
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python cli/agent_cli.py risk # 风险评估
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python cli/agent_cli.py research # 因子研究
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python cli/agent_cli.py report [DATE] # 生成日报
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python cli/agent_cli.py daily # 执行每日流程
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python cli/agent_cli.py picks [N] # 今日选股 Top N
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python cli/agent_cli.py risk # 风险评估
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python cli/agent_cli.py research # 因子研究
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python cli/agent_cli.py report [DATE] # 生成日报
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python cli/agent_cli.py warmup [N] # 首次批量预热
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"""
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import argparse
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import logging
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from datetime import datetime
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logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s")
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def init_engines():
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"""初始化所有引擎。"""
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@@ -26,7 +31,6 @@ def init_engines():
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from factors.sentiment.sentiment_engine import SentimentEngine
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from factors.sentiment.news_source import NewsSource
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from factors.sentiment.qwen_client import QwenClient
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from factors.registry import get_factor
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dm = DataManager()
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dm.init_db()
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@@ -45,42 +49,62 @@ def init_engines():
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}
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def main():
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if len(sys.argv) < 2:
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print("用法: agent_cli.py <daily|picks|risk|research|report|warmup>")
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print()
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print(" daily [DATE] — 执行每日完整流程")
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print(" picks [N] [DATE] — 今日选股 Top N")
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print(" risk — 风险评估")
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print(" research — 因子发现与评估")
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print(" report [DATE] — 生成日报")
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print(" warmup [N] — 首次批量预热范围股票到 DB 缓存")
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return
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def build_parser() -> argparse.ArgumentParser:
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p = argparse.ArgumentParser(prog="agent_cli", description="cc-cursor 量化 Agent CLI")
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sub = p.add_subparsers(dest="cmd", required=True)
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cmd = sys.argv[1]
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d = sub.add_parser("daily", help="执行每日完整流程")
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d.add_argument("date", nargs="?", default=None, help="日期 YYYYMMDD（默认今天）")
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pk = sub.add_parser("picks", help="今日选股 Top N")
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pk.add_argument("top_n", nargs="?", type=int, default=15, help="返回 Top N")
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pk.add_argument("date", nargs="?", default=None, help="日期 YYYYMMDD")
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sub.add_parser("risk", help="风险评估")
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sub.add_parser("research", help="因子发现与评估")
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rp = sub.add_parser("report", help="生成日报")
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rp.add_argument("date", nargs="?", default=None, help="日期 YYYYMMDD")
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wm = sub.add_parser("warmup", help="首次批量预热范围股票到 DB 缓存")
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wm.add_argument("batch_n", nargs="?", type=int, default=50, help="每批股票数")
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return p
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def main(argv: list[str] | None = None) -> int:
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parser = build_parser()
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args = parser.parse_args(argv)
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engines = init_engines()
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from agents.orchestrator import AgentOrchestrator
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orch = AgentOrchestrator(**engines)
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orch.setup()
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if cmd == "daily":
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date = sys.argv[2] if len(sys.argv) > 2 else None
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results = orch.run_daily(date=date)
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# 打印日报内容
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today = datetime.now().strftime("%Y%m%d")
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if args.cmd == "daily":
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results = orch.run_daily(date=args.date)
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report_md = results.get("report", {}).get("report_markdown", "")
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if report_md:
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print(report_md)
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# 任一步骤失败 → 非零退出码，便于调度/CI 感知
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failed = [k for k, v in results.items()
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if isinstance(v, dict) and "error" in v]
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if failed:
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print("每日流程有步骤未完成: {}".format(", ".join(failed)), file=sys.stderr)
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return 1
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return 0
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elif cmd == "picks":
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n = int(sys.argv[2]) if len(sys.argv) > 2 else 15
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date = sys.argv[3] if len(sys.argv) > 3 else None
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result = orch.picks(date=date, top_n=n)
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elif args.cmd == "picks":
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result = orch.picks(date=args.date, top_n=args.top_n)
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print(f"\n选股结果 ({result.get('date', '?')}):")
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for p in result.get("top_picks", []):
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print(f" {p['ts_code']:12s} {p.get('name', ''):10s} {p['score']:.4f}")
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return 0
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elif cmd == "risk":
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elif args.cmd == "risk":
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result = orch.risk_check()
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print(f"\n风险评估:")
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print(f" 等级: {result['risk_level']}")
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@@ -93,22 +117,24 @@ def main():
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print(f" 回撤: {indicators.get('current_drawdown', 0):.1f}%")
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for a in result.get("alerts", []):
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print(f" ⚠️ {a}")
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return 0
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elif cmd == "research":
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elif args.cmd == "research":
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result = orch.run_research_cycle()
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top = result.get("research", {}).get("top_factors", [])
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print(f"\n因子评估结果:")
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if not top:
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print(" (无结果)")
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return
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return 0
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print(f" {'因子':20s} {'IC':>8s} {'IC_IR':>8s} {'多头':>8s} {'空头':>8s} {'得分':>8s}")
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print(f" {'─'*60}")
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for f in top:
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print(f" {f['name']:20s} {f['ic_mean']:>+8.4f} {f['icir']:>8.3f} "
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f"{f['long_ret']:>+7.1f}% {f['short_ret']:>+7.1f}% {f['score']:>8.4f}")
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return 0
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elif cmd == "warmup":
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batch_n = int(sys.argv[2]) if len(sys.argv) > 2 else 50
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elif args.cmd == "warmup":
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batch_n = args.batch_n
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print("首次批量预热: 每次 {} 只股票，分批执行...".format(batch_n))
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sent = engines.get("sent")
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dm = engines.get("dm")
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@@ -129,24 +155,20 @@ def main():
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print(" {} 失败: {}".format(ts_code, e))
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print(" 累计同步: {} 条".format(total_synced))
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print("预热完成: {} 条数据, {} 只新股票已缓存".format(total_synced, len(uncached)))
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return 0
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elif cmd == "report":
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date = sys.argv[2] if len(sys.argv) > 2 else None
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elif args.cmd == "report":
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date = args.date or today
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result = orch.generate_report(date=date)
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print("\n日报已生成: {}".format(result.get("report_path", "?")))
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# 存入 DB
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md = result.get("report_markdown", "")
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if md:
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from reports.storage import save_report
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save_report(md, "量化日报", report_date=date or datetime.now().strftime("%Y%m%d"),
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subject_type="daily", subject_code="")
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print(" 已存入 DB")
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# 注意: ReportAgent.execute 内部已保存到 DB，这里不再重复入库
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if md:
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print(md)
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return 0
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else:
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print(f"未知命令: {cmd}")
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return 0 # 防御：未匹配（应不会到达）
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if __name__ == "__main__":
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main()
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sys.exit(main())
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@@ -89,7 +89,7 @@ def main():
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test_price = price_df.loc[X_test.index]
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test_factor = factor_df.loc[X_test.index]
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bt_engine = VectorBTEngine()
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benchmark = MLBenchmark([lgb_model, cb_model], fe, test_price, test_factor, bt_engine)
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benchmark = MLBenchmark([lgb_model, cb_model], fe, test_factor, test_price, bt_engine)
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result = benchmark.run()
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print(result.round(2).to_string())
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print(" > 解读: 回测结果反映 ML 策略在测试集上的实盘表现。")
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