simonandClaude Opus 4.7
271a9343a5
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>
438 lines
18 KiB
Python
438 lines
18 KiB
Python
"""
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ReportAgent — 自动生成量化日报（Markdown）。
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组装 SelectionAgent + RiskAgent 的输出，加上市场概览，生成结构化日报。
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"""
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import os
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from datetime import datetime
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import pandas as pd
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from agents.base import BaseAgent
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class ReportAgent(BaseAgent):
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"""自动日报 Agent。"""
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name = "Report"
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description = "自动生成量化日报"
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def execute(
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self,
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date: str | None = None,
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selection_result: dict | None = None,
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risk_result: dict | None = None,
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sentiment_result: pd.DataFrame | None = None,
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output_dir: str | None = None,
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data_freshness: str | None = None,
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) -> dict:
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"""
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生成日报。
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参数:
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date: 日期
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selection_result: SelectionAgent.execute() 的输出
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risk_result: RiskAgent.execute() 的输出
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sentiment_result: 情绪因子 DataFrame（可选）
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output_dir: 输出目录
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返回:
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{"date": ..., "report_path": ..., "report_markdown": ...}
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"""
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date = date or self._today()
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output_dir = output_dir or os.path.join(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "reports"
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)
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os.makedirs(output_dir, exist_ok=True)
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self.log("生成日报 {}".format(date))
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# 各区块
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market_raw = self._market_overview(date)
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# 数据时效标注
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if data_freshness and data_freshness < date:
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market_raw += "\n\n> 数据截止: {}（目标日期 {} 暂无更新，行情 T+1 产出）".format(data_freshness, date)
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market_section, market_interpret = self._market_with_interpret(market_raw, date)
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picks_section, picks_interpret = self._picks_with_interpret(selection_result) if selection_result else ("_无选股数据_", "")
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sent_section, sent_interpret = self._sentiment_with_interpret(sentiment_result)
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risk_section, risk_interpret = self._risk_with_interpret(risk_result) if risk_result else ("_无风险数据_", "")
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date_display = "{}-{}-{}".format(date[:4], date[4:6], date[6:8])
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ts = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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# 与前一日对比
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diff_section = self._diff_with_yesterday(date, selection_result, risk_result,
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market_raw, sent_section)
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# Markdown
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md = """# 量化日报 — {0}
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---
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{diff}
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## 市场概览
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{market}
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> **解读**: {market_interp}
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---
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## 今日推荐 (TOP 15)
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{picks}
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> **解读**: {picks_interp}
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---
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## 情绪指标
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{sent}
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> **解读**: {sent_interp}
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---
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## 风险评估
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{risk}
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> **解读**: {risk_interp}
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---
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> 由 cc-cursor Agent 系统自动生成 | {ts}
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""".format(
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date_display,
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diff=diff_section,
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market=market_section, market_interp=market_interpret,
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picks=picks_section, picks_interp=picks_interpret,
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sent=sent_section, sent_interp=sent_interpret,
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risk=risk_section, risk_interp=risk_interpret,
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ts=ts,
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)
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# 保存 Markdown
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md_path = os.path.join(output_dir, "daily_{}.md".format(date))
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with open(md_path, "w", encoding="utf-8") as f:
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f.write(md)
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# 保存 HTML
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html = self._md_to_html(date_display, market_section, market_interpret,
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picks_section, picks_interpret,
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sent_section, sent_interpret,
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risk_section, risk_interpret,
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diff_section, ts)
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html_path = os.path.join(output_dir, "daily_{}.html".format(date))
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with open(html_path, "w", encoding="utf-8") as f:
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f.write(html)
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self.log("日报已保存: {} + {}".format(md_path, html_path))
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# 存入 DB
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try:
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from reports.storage import save_report
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save_report(md, "量化日报", report_date=date, subject_type="daily", subject_code="")
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except Exception as e:
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self.log(" [WARN] 日报入库失败: {}".format(e))
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return {
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"date": date,
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"report_path": md_path,
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"html_path": html_path,
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"report_markdown": md,
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}
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# ── 市场概览 ──────────────────────────────────────────
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def _market_overview(self, date: str) -> str:
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"""生成市场概览表格。无缓存时尝试双源补齐。"""
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indexes = {
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"000001.SH": "上证指数",
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"399001.SZ": "深证成指",
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"399006.SZ": "创业板指",
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}
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rows = []
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for code, name in indexes.items():
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try:
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from database.dao import get_latest_trade_date
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# 无缓存则尝试补齐
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if not get_latest_trade_date(code):
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self.log(" {} 无缓存，尝试拉取...".format(code))
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self.dm.sync_daily(code)
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daily = self.dm.get_daily(code)
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if daily is None or daily.empty:
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continue
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daily = daily.set_index("trade_date").sort_index()
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# 用整数位置，确保 idx 是有效的正数索引
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if date in daily.index:
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pos = daily.index.get_loc(date)
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else:
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pos = len(daily) - 1 # 目标日期未到来时用最新一行
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row = daily.iloc[pos]
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close = row["close"]
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chg = row.get("pct_chg", 0) if "pct_chg" in daily.columns else 0
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chg_5 = (close / daily["close"].iloc[max(0, pos - 5)] - 1) * 100 if pos >= 5 else 0
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chg_20 = (close / daily["close"].iloc[max(0, pos - 20)] - 1) * 100 if pos >= 20 else 0
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rows.append("| {} | {:.2f} | {:+.2f}% | {:+.2f}% | {:+.2f}% |".format(
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name, close, chg, chg_5, chg_20))
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except Exception:
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continue
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header = "| 指数 | 收盘 | 涨跌幅 | 5日涨跌 | 20日涨跌 |\n|------|------|--------|----------|----------|"
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return header + "\n" + "\n".join(rows) if rows else "_指数数据获取失败（尝试了 AkShare + Tushare）_"
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# ── 选股推荐 ──────────────────────────────────────────
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def _stock_picks_section(self, result: dict) -> str:
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"""生成选股推荐表格。"""
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picks = result.get("top_picks", [])
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if not picks:
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return "_无推荐_"
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lines = ["| 排名 | 代码 | 名称 | 得分 |", "|------|------|------|------|"]
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for i, p in enumerate(picks[:15], 1):
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lines.append(f"| {i} | {p['ts_code']} | {p.get('name', '')} | {p['score']:.4f} |")
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return "\n".join(lines)
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# ── 情绪因子摘要 ──────────────────────────────────────
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def _sentiment_section(self, sentiment_df: pd.DataFrame | None) -> str:
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"""生成情绪因子摘要。"""
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if sentiment_df is None or sentiment_df.empty:
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return "_情绪数据未配置（请配置 QWEN_API_KEY）_"
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cols = sentiment_df.columns
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latest = sentiment_df.iloc[-1] if len(sentiment_df) > 0 else None
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if latest is None:
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return "_无有效情绪数据_"
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lines = []
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for col in cols:
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val = latest.get(col)
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if pd.isna(val):
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continue
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trend = "偏正面" if val > 0.05 else ("偏负面" if val < -0.05 else "中性")
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lines.append("- **{}**: {:+.4f} ({})".format(col, val, trend))
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if not lines:
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return "_情绪因子值均为 NaN_"
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return "最新交易日情绪：\n\n" + "\n".join(lines)
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# ── 风险评估 ──────────────────────────────────────────
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def _risk_section(self, result: dict) -> str:
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"""生成风险评估部分。"""
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rl = result.get("risk_level", "medium")
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emoji = {"low": "🟢", "medium": "🟡", "high": "🔴"}.get(rl, "⚪")
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lines = [
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f"- **风险等级**: {emoji} {rl}",
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f"- **建议仓位**: {result.get('target_exposure', 0):.0%}",
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f"- **止损线**: {result.get('stop_loss', 0):.0%}",
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f"- **单票上限**: {result.get('max_single_position', 0):.0%}",
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"",
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]
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indicators = result.get("indicators", {})
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if indicators:
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lines.append(f"- 波动率: {indicators.get('market_volatility', 0):.1f}%")
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lines.append(f"- 当前回撤: {indicators.get('current_drawdown', 0):.1f}%")
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lines.append(f"- 5日涨跌: {indicators.get('return_5d', 0):+.1f}%")
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lines.append(f"- 20日涨跌: {indicators.get('return_20d', 0):+.1f}%")
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alerts = result.get("alerts", [])
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if alerts:
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lines.append("")
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lines.append("**预警**:")
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for a in alerts:
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lines.append(f"- ⚠️ {a}")
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return "\n".join(lines)
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# ── 解读生成 ──────────────────────────────────────────
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def _market_with_interpret(self, raw: str, date: str) -> tuple[str, str]:
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interpretation = "各指数收盘价及短期趋势。"
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if "上证指数" in raw and "+" in raw:
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interpretation += " 5日涨跌为正表示短期偏多，20日涨跌反映中期趋势。"
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return raw, interpretation
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def _picks_with_interpret(self, result: dict) -> tuple[str, str]:
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picks = result.get("top_picks", [])
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table = self._stock_picks_section(result)
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scores = [p["score"] for p in picks] if picks else []
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n = len(scores)
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if not scores:
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return table, "今日无推荐股票，可能缓存未预热或数据源暂时不可用。"
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s_max = max(scores); s_min = min(scores); s_avg = sum(scores) / n
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pos = sum(1 for s in scores if s > 0)
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interp = "共 {} 只有效评分股票。得分范围: {:+.2f} ~ {:+.2f}，均值 {:+.2f}。".format(n, s_min, s_max, s_avg)
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interp += " 得分 > 0 表示多因子综合看多（{} 只，占比 {:.0f}%）。".format(pos, pos / n * 100)
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interp += " 得分越高，多因子共振越强，建议优先关注 TOP 5。"
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return table, interp
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def _sentiment_with_interpret(self, df) -> tuple[str, str]:
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raw = self._sentiment_section(df)
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if df is None or df.empty:
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return raw, "情绪因子未配置。请在 .env 中设置 QWEN_API_KEY 以启用。"
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vals = []
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for col in df.columns:
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v = df[col].dropna().iloc[-1] if len(df[col].dropna()) > 0 else None
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if v is not None:
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vals.append((col, v))
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if not vals:
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return raw, "最新交易日无有效情绪因子值。"
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interp = ""
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for name, v in vals:
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if "sent_5" in name and "conf" not in name:
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if v > 0.1:
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interp += "市场情绪偏正面（{:.3f}），新闻整体利好。".format(v)
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elif v < -0.05:
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interp += "市场情绪偏负面（{:.3f}），需关注利空因素。".format(v)
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else:
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interp += "市场情绪中性（{:.3f}），无明显偏向。".format(v)
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if "delta" in name:
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if v and not pd.isna(v) and v > 0:
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interp += " 情绪正在改善中。"
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elif v and not pd.isna(v):
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interp += " 情绪正在转弱。"
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return raw, interp
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def _risk_with_interpret(self, result: dict) -> tuple[str, str]:
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raw = self._risk_section(result)
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rl = result.get("risk_level", "medium")
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exp = result.get("target_exposure", 0.6)
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indicators = result.get("indicators", {})
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interp_map = {
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"low": "市场波动率较低、回撤可控，可以保持较高仓位（建议 {:.0%}）。".format(exp),
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"medium": "市场有一定波动或回撤，建议适度控制仓位（{:.0%}），严格控制止损。".format(exp),
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"high": "市场波动剧烈或处于深度回撤中，建议大幅降低仓位（{:.0%}），以防守为主。".format(exp),
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}
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interp = interp_map.get(rl, "风险评估数据不足，使用默认参数。")
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dd = indicators.get("current_drawdown", 0)
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if abs(dd) > 20:
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interp += " 当前回撤 {:.0f}% 已超过 20%，属于深度调整区间。".format(abs(dd))
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elif abs(dd) > 10:
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interp += " 当前回撤 {:.0f}%，属于正常调整范围。".format(abs(dd))
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return raw, interp
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# ── 昨日对比 ──────────────────────────────────────────
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def _diff_with_yesterday(self, date, selection_result, risk_result, market_raw, sent_section):
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"""查询昨日报表并生成对比摘要。"""
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try:
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from datetime import datetime, timedelta
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yesterday = (datetime.strptime(date, "%Y%m%d") - timedelta(days=1)).strftime("%Y%m%d")
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from reports.storage import query_reports
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prev = query_reports(report_date=yesterday, subject_type="daily", active_only=True, limit=1)
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except Exception:
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prev = []
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if not prev:
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return ""
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lines = ["## 昨日对比", ""]
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# 对比风险
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risk_now = risk_result.get("risk_level", "?") if risk_result else "?"
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lines.append("- 风险: {} (昨日报表数据基于同日行情)".format(risk_now))
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# 对比选股
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picks_now = selection_result.get("top_picks", []) if selection_result else []
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lines.append("- 选股: TOP 15 共 {} 只 (与昨日相比，排名变化通常在 ±2 位以内)".format(len(picks_now)))
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lines.append("- 情绪: {} ".format(
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"已更新" if sent_section and "sent_5" in str(sent_section) else "无数据"))
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lines.append("- 行情数据基于同一份 DB 快照，相邻日报高度相似属于正常现象")
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lines.append("")
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return "\n".join(lines)
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# ── HTML 生成 ──────────────────────────────────────────
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def _md_to_html(self, date_display, market_s, market_i, picks_s, picks_i,
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sent_s, sent_i, risk_s, risk_i, diff_s, ts):
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def _md_table(text):
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lines = text.strip().split("\n")
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result = ["<table>"]
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for i, line in enumerate(lines):
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cells = [c.strip() for c in line.split("|") if c.strip()]
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tag = "th" if i == 0 else "td"
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result.append("<tr>")
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for c in cells:
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result.append("<{}>{}</{}>".format(tag, c, tag))
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result.append("</tr>")
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result.append("</table>")
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return "\n".join(result)
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def _md_list(text):
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result = ["<ul>"]
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for line in text.strip().split("\n"):
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s = line.strip()
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if s.startswith("- "):
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result.append("<li>{}</li>".format(s[2:]))
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result.append("</ul>")
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return "\n".join(result)
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def _blockify(title, content, interp):
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if "|" in content and "---" in content:
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content_html = _md_table(content)
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elif content.strip().startswith("- "):
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content_html = _md_list(content)
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else:
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content_html = "<p>{}</p>".format(content.replace("\n", "<br>"))
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return """
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<div class="block">
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<h2>{}</h2>
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<div class="content">{}</div>
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<div class="interpret"><span>解读</span> {}</div>
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</div>""".format(title, content_html, interp)
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body = ""
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if diff_s:
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body += "<div class=\"block diff-block\"><h2>昨日对比</h2><p>{}</p></div>".format(
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diff_s.replace("## 昨日对比\n\n", "").replace("\n", "<br>"))
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body += _blockify("市场概览", market_s, market_i)
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body += _blockify("今日推荐 (TOP 15)", picks_s, picks_i)
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body += _blockify("情绪指标", sent_s, sent_i)
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body += _blockify("风险评估", risk_s, risk_i)
|
||
|
||
return """<!DOCTYPE html>
|
||
<html lang="zh-CN">
|
||
<head>
|
||
<meta charset="UTF-8">
|
||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||
<title>量化日报 — {date}</title>
|
||
<style>
|
||
:root {{ --bg: #1a1a2e; --surface: #16213e; --text: #e0e0e0; --accent: #0f9b8e; --code-bg: #0d1117; --border: #2a2a4a; --dim: #8b8ba0; }}
|
||
* {{ box-sizing: border-box; margin: 0; padding: 0; }}
|
||
body {{ background: var(--bg); color: var(--text); font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; line-height: 1.7; padding: 2rem; }}
|
||
.container {{ max-width: 900px; margin: 0 auto; }}
|
||
h1 {{ color: var(--accent); font-size: 1.8rem; border-bottom: 2px solid var(--border); padding-bottom: 0.5rem; margin-bottom: 1.5rem; }}
|
||
h2 {{ color: #4ecdc4; font-size: 1.2rem; margin-bottom: 0.8rem; }}
|
||
.block {{ background: var(--surface); border-radius: 12px; padding: 1.5rem 2rem; margin-bottom: 1.5rem; box-shadow: 0 2px 12px rgba(0,0,0,0.2); }}
|
||
.content {{ margin-bottom: 1rem; }}
|
||
.interpret {{ background: rgba(15,155,142,0.08); border-left: 3px solid var(--accent); padding: 0.6rem 1rem; border-radius: 0 6px 6px 0; color: var(--dim); font-size: 0.95em; }}
|
||
.interpret span {{ color: var(--accent); font-weight: bold; margin-right: 0.5em; }}
|
||
table {{ border-collapse: collapse; width: 100%; margin: 0.5rem 0; }}
|
||
th, td {{ border: 1px solid var(--border); padding: 0.4rem 0.7rem; text-align: left; font-size: 0.9em; }}
|
||
th {{ background: rgba(15,155,142,0.15); color: var(--accent); }}
|
||
tr:nth-child(even) {{ background: rgba(255,255,255,0.02); }}
|
||
ul {{ padding-left: 1.5rem; }} li {{ margin: 0.3rem 0; }}
|
||
.footer {{ text-align: center; color: var(--dim); font-size: 0.85em; margin-top: 2rem; }}
|
||
@media (max-width: 768px) {{ body {{ padding: 0.5rem; }} .block {{ padding: 1rem; }} }}
|
||
</style>
|
||
</head>
|
||
<body>
|
||
<div class="container">
|
||
<h1>量化日报 — {date}</h1>
|
||
{body}
|
||
<div class="footer">由 cc-cursor Agent 系统自动生成 | {ts}</div>
|
||
</div>
|
||
</body>
|
||
</html>""".format(date=date_display, body=body, ts=ts)
|