170 lines
6.1 KiB
Python
170 lines
6.1 KiB
Python
"""每日复盘报告生成"""
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import logging
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from app.config import settings, today_trade_date
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logger = logging.getLogger(__name__)
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async def generate_review() -> dict:
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"""生成每日复盘报告"""
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from app.data.tushare_client import tushare_client
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from app.data import tencent_client
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from app.engine.recommender import get_latest_recommendations, get_latest_sectors
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latest_trade_date = tushare_client.get_latest_trade_date()
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trade_date = today_trade_date()
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# 收集市场数据
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result = await get_latest_recommendations()
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mt = result.get("market_temp")
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sectors = await get_latest_sectors()
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recs = result.get("recommendations", [])
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# 实时指数
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try:
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index_data = await tencent_client.get_index_realtime()
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except Exception:
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index_data = {}
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# 构建数据摘要
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market_summary = ""
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if mt:
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market_summary = (
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f"市场温度: {mt.temperature}/100, "
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f"上涨{mt.up_count}家/下跌{mt.down_count}家, "
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f"涨停{mt.limit_up_count}家/跌停{mt.limit_down_count}家, "
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f"连板最高{mt.max_streak}板, 炸板率{mt.broken_rate}%"
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)
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index_summary = ""
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name_map = {"000001.SH": "上证", "399001.SZ": "深证", "399006.SZ": "创业板"}
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for code in ["000001.SH", "399001.SZ", "399006.SZ"]:
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d = index_data.get(code, {})
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if d:
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index_summary += f"{name_map[code]}: {d.get('price', 0):.2f} ({d.get('pct_chg', 0):+.2f}%), "
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sector_summary = "热门板块: " + "、".join(
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f"{s.sector_name}({(getattr(s, 'realtime_pct_change', None) or s.pct_change):+.1f}%)" for s in sectors[:5]
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)
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rec_summary = ""
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for r in recs[:5]:
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signal_map = {"breakout": "突破", "breakout_confirm": "确认", "pullback": "回踩",
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"launch": "启动", "reversal": "反转"}
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st = signal_map.get(r.entry_signal_type, r.entry_signal_type)
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rec_summary += f"\n- {r.name}({r.ts_code}): {st}型, 评分{r.score}, {r.signal}"
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user_msg = f"""请根据以下数据生成今日A股市场复盘报告（中文）：
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日期: {trade_date}
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Tushare 最新交易日: {latest_trade_date}
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{market_summary}
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{index_summary}
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{sector_summary}
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今日推荐股票:
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{rec_summary}
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请按以下格式输出（Markdown格式，总字数300-500字）：
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## 市场概况
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（指数走势、量能变化）
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## 板块热点
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（哪些板块领涨、资金流向）
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## 交易机会
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（今日推荐个股简要点评）
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## 明日关注
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（关注方向和操作建议）"""
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if settings.deepseek_api_key:
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try:
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from app.llm.client import get_client
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client = get_client()
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response = await client.chat.completions.create(
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model=settings.deepseek_model,
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messages=[
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{"role": "system", "content": "你是一位专业的A股市场分析师，擅长市场复盘和策略分析。回复使用Markdown格式，简洁专业。"},
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{"role": "user", "content": user_msg},
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],
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max_tokens=1500,
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temperature=0.5,
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)
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content = response.choices[0].message.content.strip()
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generated_by = "llm"
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except Exception as e:
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logger.error(f"生成复盘报告失败，使用规则兜底: {e}")
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content = _build_fallback_review(
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trade_date=trade_date,
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market_summary=market_summary,
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index_summary=index_summary,
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sector_summary=sector_summary,
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recommendations=recs,
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)
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generated_by = "rules"
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else:
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content = _build_fallback_review(
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trade_date=trade_date,
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market_summary=market_summary,
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index_summary=index_summary,
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sector_summary=sector_summary,
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recommendations=recs,
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)
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generated_by = "rules"
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try:
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# 保存到数据库
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from sqlalchemy import text
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from app.db.database import get_db
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async with get_db() as db:
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await db.execute(
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text(
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"INSERT OR REPLACE INTO daily_reviews (trade_date, content) "
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"VALUES (:td, :content)"
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),
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{"td": trade_date, "content": content},
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)
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await db.commit()
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logger.info(f"已生成 {trade_date} 复盘报告 ({generated_by})")
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return {"status": "ok", "trade_date": trade_date, "content": content, "generated_by": generated_by}
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except Exception as e:
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logger.error(f"保存复盘报告失败: {e}")
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return {"status": "error", "message": str(e)}
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def _build_fallback_review(
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trade_date: str,
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market_summary: str,
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index_summary: str,
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sector_summary: str,
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recommendations: list,
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) -> str:
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"""LLM 不可用时生成结构化规则复盘，避免页面空白。"""
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actionable = [r for r in recommendations if getattr(r, "action_plan", "") == "可操作"]
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watch = [r for r in recommendations if getattr(r, "action_plan", "") == "重点关注"]
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top_recs = recommendations[:5]
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rec_lines = "\n".join(
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f"- {r.name}({r.ts_code})：{getattr(r, 'action_plan', '观察')}，"
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f"{getattr(r, 'entry_signal_type', 'none')} 信号，评分 {getattr(r, 'score', 0)}。"
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for r in top_recs
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) or "- 暂无推荐标的。"
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return f"""## 市场概况
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{trade_date} 市场温度处于中性偏谨慎区间。{market_summary or "暂无市场温度数据。"} {index_summary or ""}
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## 板块热点
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{sector_summary or "暂无板块热度数据。"} 当前板块证据主要用于确认推荐方向是否有资金和赚钱效应支撑。
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## 交易机会
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今日推荐池共 {len(recommendations)} 只，其中可操作 {len(actionable)} 只、重点关注 {len(watch)} 只。当前更适合按触发条件等待确认，不宜把观察标的直接当作买入标的。
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{rec_lines}
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## 明日关注
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优先跟踪重点关注标的能否满足触发条件，同时观察主线板块是否延续。若市场温度回落或板块资金退潮，应降低仓位并把未确认标的转回观察池。"""
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