myquant/finance/cli/agent_cli.py

#!/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
    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。"""
    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

    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 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)

    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()

    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 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 args.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}")
        return 0

    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 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 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")
        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)))
        return 0

    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", "?")))
        md = result.get("report_markdown", "")
        # 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

    return 0  # 防御：未匹配（应不会到达）


if __name__ == "__main__":
    sys.exit(main())