157 lines
5.5 KiB
Python
157 lines
5.5 KiB
Python
#!/usr/bin/env python3
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"""
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每日投研简报 — 数据收集器（供 LLM 分析 cron 使用）
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====================================================
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牧尘批评（2026-08-02）：每天定时任务只推送原始数据，没有主动分析和建议。
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本脚本收集当天所有股票数据 → 输出结构化摘要，供 cron 的 LLM 分析后生成
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"今日解读 + 持仓建议 + 明日关注" 简报推给牧尘。
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数据源（全部读 stock_backtest/ 下的 JSON，不调外部 API）：
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1. 行业动量 industry_scan.json（周五扫描后更新）
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2. 宏观评分 macro_score.json
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3. 基本面 fundamental_scan.json
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4. 消息面 sentiment_scan.json
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5. 多账户持仓 multi_account/account_*.json
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6. paper 持仓 paper_trades_*.json
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7. 最近矛盾周报 contradiction_history.json
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8. 盘中信号状态 intraday_signal_state.json
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用法:
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python3 stock_daily_brief.py # 收集当天数据 → 输出摘要（供 cron prompt 注入）
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python3 stock_daily_brief.py --json # JSON 格式
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"""
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import json, sys
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from datetime import date, datetime
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from pathlib import Path
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HOME = Path.home()
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BT = HOME / ".hermes" / "stock_backtest"
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def load(name, default=None):
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f = BT / name
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if f.exists():
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try:
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return json.load(open(f))
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except Exception:
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return default
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return default
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def fmt_pct(v):
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if v is None:
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return "N/A"
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return f"{v*100:+.1f}%" if abs(v) < 3 else f"{v*100:+.1f}%"
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def main():
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today = date.today().isoformat()
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out = []
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out.append(f"【数据日期】{today}（周{'一二三四五六日'[date.today().weekday()]}）")
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out.append("")
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# 1. 行业动量
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scan = load("industry_scan.json")
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if scan:
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inds = scan.get("industries", {})
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mom = inds.get("avg_mom", {})
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sharpe = inds.get("avg_sharpe", {})
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ranked = sorted(mom.items(), key=lambda x: x[1], reverse=True)
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out.append("【行业动量排名】")
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for i, (ind, m) in enumerate(ranked[:8], 1):
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s = sharpe.get(ind, 0)
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out.append(f" {i}. {ind}: 动量{m*100:+.1f}% Sharpe{s:+.2f}")
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weak = [ind for ind, m in mom.items() if m < -0.2 and sharpe.get(ind, 0) < 0]
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if weak:
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out.append(f" 弱势(动量<20%且Sharpe负): {', '.join(weak)}")
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out.append("")
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# 2. 宏观
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macro = load("macro_score.json")
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if macro:
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score = macro.get("macro", "N/A")
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details = macro.get("details", {})
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out.append(f"【宏观评分】{score}")
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for k, v in details.items():
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out.append(f" {k}: {v}")
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out.append("")
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# 3. 基本面
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fund = load("fundamental_scan.json")
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if fund:
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out.append("【基本面扫描】")
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stocks = fund.get("stocks", fund.get("results", []))
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if isinstance(stocks, list):
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for s in stocks[:5]:
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fd = s.get("fundamental", s)
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name = fd.get("name", s.get("name", ""))
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score = s.get("score", "")
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out.append(f" {name}: {score}")
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out.append("")
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# 4. 消息面
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senti = load("sentiment_scan.json")
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if senti:
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ms = senti.get("message_score", senti.get("overall", senti.get("score", "N/A")))
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if isinstance(ms, (int, float)):
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ms = f"{ms:+.2f}"
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out.append(f"【消息面综合】{ms}")
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out.append("")
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# 5. 多账户持仓
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adir = BT / "multi_account"
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total_cap = 0
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positions = []
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if adir.exists():
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for f in sorted(adir.glob("account_*.json")):
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try:
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a = json.load(open(f))
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except Exception:
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continue
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total_cap += a.get("current_capital", 0)
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for p in a.get("positions", []):
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positions.append({
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"industry": a.get("industry", ""),
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"stock": a.get("stock", ""),
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"code": a.get("code", ""),
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"shares": p.get("shares", 0),
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"cost": p.get("avg_cost", 0),
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})
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out.append(f"【多账户】{len(list(adir.glob('account_*.json'))) if adir.exists() else 0} 账户 | 总资产 {total_cap:,.0f}")
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if positions:
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out.append(" 持仓:")
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for p in positions:
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out.append(f" {p['stock']}({p['industry']}) {p['shares']}股 成本{p['cost']:.2f}")
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else:
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out.append(" 持仓: 空仓")
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out.append("")
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# 6. paper 持仓
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for f in sorted(BT.glob("paper_trades_*.json")):
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try:
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d = json.load(open(f))
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except Exception:
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continue
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for p in d.get("positions", []):
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code = f.stem.replace("paper_trades_", "")
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out.append(f"【paper持仓】{d.get('stock','')}({code}) {p['shares']}股 成本{p['avg_cost']:.2f}")
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# 7. 最近矛盾周报结论
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hist = load("contradiction_history.json")
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if isinstance(hist, list) and hist:
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last = hist[-1]
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if isinstance(last, dict):
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out.append(f"【最近周报({last.get('date','')})】综合: {last.get('verdict', last.get('composite','N/A'))}")
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out.append("")
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# 8. 盘中信号状态
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state = load("intraday_signal_state.json")
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if state:
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out.append(f"【盘中信号状态】{json.dumps(state, ensure_ascii=False)[:200]}")
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text = "\n".join(out)
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if "--json" in sys.argv:
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print(json.dumps({"date": today, "brief": out}, ensure_ascii=False, indent=2))
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else:
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print(text)
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if __name__ == "__main__":
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main()
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