计划 API：桥容器常驻改 uvicorn(:8300)，GET /plan 取每日选股计划；
plan.py 拆装配/渲染两层供 API 与 cron 共用；requirements 加 fastapi/uvicorn
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zlt
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"""桥侧计划 API（07-30 用户需求）：对外提供每日选股计划。
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容器常驻命令改为 uvicorn 后随容器启动（docker-compose 已配端口，默认 8300）；
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cron 的 docker exec 构建/出计划照旧，互不影响。局域网内部服务，v1 无鉴权。
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GET /health 存活 + 最新计划日
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GET /plan 最新一天的计划（JSON）
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GET /plan?date=2026-07-30 指定日期
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GET /plan?format=md Markdown 原文（浏览器直接可读）
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GET /plan/dates 可用日期列表
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POST /plan/refresh?date=... 重新生成该日计划文件（data/plan/*.md）
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将来接 XXL-JOB / 事件回调，触发器打这层即可，不必进容器。
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"""
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import pandas as pd
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from fastapi import FastAPI, HTTPException
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from fastapi.responses import PlainTextResponse
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import db
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import plan
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app = FastAPI(title="akg-factor-bridge · 每日选股计划", version="0.1")
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@app.get("/health")
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def health():
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try:
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d = plan._latest_date("t_factor_akg_score") # noqa: SLF001 —— 桥内自用
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except Exception as e: # noqa: BLE001 —— 库连不上也要能回答"我还活着"
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return {"ok": False, "error": repr(e)}
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return {"ok": True, "latest_plan_date": d}
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@app.get("/plan/dates")
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def plan_dates(limit: int = 30):
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df = db.read_mysql(
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"factor", "SELECT DISTINCT trade_date FROM t_factor_akg_score "
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"ORDER BY trade_date DESC LIMIT %s", (int(limit),))
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if df.empty:
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return {"dates": []}
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return {"dates": [pd.Timestamp(x).date().isoformat()
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for x in df["trade_date"]]}
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@app.get("/plan")
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def get_plan(date: str | None = None, format: str = "json",
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top: int = 20, obs_top: int = 10, theme_cap: int = 5):
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try:
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data = plan.collect(date, top, obs_top, theme_cap)
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except RuntimeError as e:
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raise HTTPException(status_code=404, detail=str(e))
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if format == "md":
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return PlainTextResponse(plan.render_md(data),
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media_type="text/markdown; charset=utf-8")
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return data
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@app.post("/plan/refresh")
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def refresh(date: str | None = None):
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try:
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out = plan.generate(date)
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except SystemExit as e:
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raise HTTPException(status_code=404, detail=str(e))
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return {"ok": True, "file": out}
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@ -1,13 +1,16 @@
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# akg-factor-bridge：独立部署单元，可落在任意能同时连通「基座PG/153/平台MySQL」的服务器。
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# 容器常驻（sleep infinity），由宿主 cron 或平台 XXL-JOB 以 docker exec 触发 build；
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# 也可改 command 为一次性任务由外部调度拉起。
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# 容器常驻并提供计划 API（uvicorn :8300，见 api.py，07-30）；构建/出计划仍由宿主
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# cron 以 docker exec 触发。将来接 XXL-JOB / 事件回调，触发器打这层 API 即可。
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services:
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akg-factor-bridge:
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build: .
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container_name: akg_factor_bridge
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env_file: .env
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restart: unless-stopped
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command: sleep infinity
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# uvicorn 起不来时（如镜像未重建、缺 fastapi）退回 sleep——保住 cron 的 docker exec
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command: sh -c "uvicorn api:app --host 0.0.0.0 --port 8300 || sleep infinity"
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ports:
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- "${BRIDGE_API_PORT:-8300}:8300"
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volumes:
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- .:/app # 代码卷挂载：改码即生效，不用重构镜像（开发期）。冻结成 prod 镜像时删此行并 --build。
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# 跨机部署（默认）：.env 三处用 LAN IP，默认 bridge 网络出网到 LAN 即可。
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221
plan.py
221
plan.py
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@ -1,11 +1,15 @@
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"""每日选股计划（R4 首版）：把 akg_gate / akg_score 变成一份人能读的榜单。
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"""每日选股计划（R4）：数据装配 / Markdown 渲染 / 产出，三段分离。
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collect() -> dict 结构化计划——api.py 直接当 JSON 返回
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render_md() -> str 从 dict 渲染 Markdown
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generate() CLI 与 cron 的入口：collect + render + 落盘 + 打印
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数据全部来自已落库的表，不重算：
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平台因子表 t_factor_akg_score / _gate / _upside / _heat —— 当日截面
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基座只读视图 v_factor_transmission —— 传导证据（主题、源数、已动比例）
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基座只读视图 v_factor_transmission —— 传导证据
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基座 industry_pools —— 股票名称
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产出：终端打印 + data/plan/plan_<日期>.md。升降档一节对比前一交易日的档位表，
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体现"数据到达本身是信号"（首次覆盖 / 新进传导链即升档）。
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升降档一节对比前一交易日的档位表——数据到达本身是信号（首次覆盖 /
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新进传导链即升档）。
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"""
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import json
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import os
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@ -86,46 +90,24 @@ def _tier_label(score: float) -> str:
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int((score - 190.0) // 20), "?")
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def _pct(v) -> str:
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return "—" if v is None or pd.isna(v) else f"{v:+.0%}"
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def _val(series: pd.Series, k: str):
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v = series.get(k)
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return None if v is None or pd.isna(v) else float(v)
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def _num(v) -> str:
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return "—" if v is None or pd.isna(v) else f"{v:.2f}"
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def _pick(ranked: pd.Series, ev: dict, top: int, theme_cap: int):
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"""按分数从高到低取 top 条；每个传导主题最多 theme_cap 条（0=不设限）。
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为什么设限：传导目标是环节级，同环节全体成员共享同一条证据，不设限时
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榜单会被三四个环节刷屏——20 个名额实际只是 4 注。限额后变成
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"多条线索 × 每条线索取最冷最便宜的几只"，被挤掉的仍在完整档位表里。"""
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out, cnt = [], {}
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for k, s in ranked.items():
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e = ev.get(k)
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theme = e[0] if e else "（无传导）"
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if theme_cap and cnt.get(theme, 0) >= theme_cap:
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continue
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cnt[theme] = cnt.get(theme, 0) + 1
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out.append((k, s))
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if len(out) >= top:
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break
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return out
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def generate(date: str | None = None, top: int = 20, obs_top: int = 10,
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theme_cap: int = 5) -> str:
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def collect(date: str | None = None, top: int = 20, obs_top: int = 10,
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theme_cap: int = 5) -> dict:
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"""装配一天的计划为结构化字典。数据缺失抛 RuntimeError（api 侧转 404）。"""
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ds = date or _latest_date("t_factor_akg_score")
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if not ds:
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raise SystemExit("t_factor_akg_score 还没有数据——先 build akg_score。")
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raise RuntimeError("t_factor_akg_score 还没有数据——先 build akg_score。")
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score = _factor("t_factor_akg_score", ds)
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gate = _factor("t_factor_akg_gate", ds)
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if score.empty or gate.empty:
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raise SystemExit(f"{ds} 缺 akg_score / akg_gate——先 build 该日再出计划。")
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raise RuntimeError(f"{ds} 缺 akg_score / akg_gate——先 build 该日再出计划。")
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upside = _factor("t_factor_akg_upside", ds)
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if upside.empty:
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# 晚间 18:40 建当日 upside 表时行情源（~19:50 发布）还没到，当日表常为空。
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# 这里现算兜底：此刻库里已有晚到的当日价，as-of 口径不变（consensus<=当日）。
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# 当日 upside 表为空时现算兜底（as-of 口径不变：consensus<=当日、当日收盘价）
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import factors
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df_up = factors.build_upside(ds, ds)
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if df_up is not None and not df_up.empty:
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@ -140,81 +122,154 @@ def generate(date: str | None = None, top: int = 20, obs_top: int = 10,
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main = score[score >= _MAIN_MIN].sort_values(ascending=False)
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obs = score[score < _MAIN_MIN].sort_values(ascending=False)
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L = [f"# 每日选股计划 · {ds}", ""]
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L.append(f"主榜 {len(main)} 只 / 观察档 {len(obs)} 只 / 全池档位覆盖 {len(gate)} 只。")
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stale = sorted(d for d in mkt_days if d != ds)
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def _pick(ranked: pd.Series, n: int):
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"""分数从高到低取 n 条；每个传导主题最多 theme_cap 条（0=不设限）——
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传导目标是环节级、同环节成员共享同一条证据，不限额会被少数环节刷屏。"""
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out, cnt = [], {}
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for k, s in ranked.items():
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e = ev.get(k)
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theme = e[0] if e else "（无传导）"
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if theme_cap and cnt.get(theme, 0) >= theme_cap:
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continue
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cnt[theme] = cnt.get(theme, 0) + 1
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out.append((k, s))
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if len(out) >= n:
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break
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return out
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def _row(rank: int, k: str, s: float, with_tier: bool) -> dict:
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e = ev.get(k)
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r = {"rank": rank, "code": k, "name": names.get(k),
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"score": round(float(s), 2),
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"evidence": ({"theme": e[0], "n_sources": e[1],
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"moved_ratio": round(e[2], 4)} if e else None),
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"heat": _val(heat, k), "upside": _val(upside, k)}
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if with_tier:
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r["tier"] = _tier_label(s)
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return r
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changes = None
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prev_ds = _prev_date("t_factor_akg_gate", ds)
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if prev_ds:
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prev = _factor("t_factor_akg_gate", prev_ds)
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both = pd.concat([prev.rename("prev"), gate.rename("cur")],
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axis=1).fillna(-1.0) # -1 = 当日不在面板
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lab = {-1.0: "池外", 0.0: "不采纳", 1.0: "观察档", 2.0: "主榜"}
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up_df = both[both["cur"] > both["prev"]].sort_values("cur", ascending=False)
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down_df = both[both["cur"] < both["prev"]].sort_values("prev", ascending=False)
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def _mv(d: pd.DataFrame):
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return [{"code": k, "name": names.get(k),
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"from": lab.get(r["prev"], "?"), "to": lab.get(r["cur"], "?")}
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for k, r in d.iterrows()]
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changes = {"base_date": prev_ds,
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"upgrades_total": int(len(up_df)),
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"downgrades_total": int(len(down_df)),
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"upgrades": _mv(up_df.head(15)),
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"downgrades": _mv(down_df.head(15))}
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return {
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"date": ds,
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"counts": {"main": int(len(main)), "observe": int(len(obs)),
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"gate_covered": int(len(gate))},
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"market_snapshot_days": sorted(mkt_days),
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"heat_date": hd,
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"theme_cap": theme_cap,
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"main": [_row(i, k, s, True)
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for i, (k, s) in enumerate(_pick(main, top), 1)],
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"observe": [_row(i, k, s, False)
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for i, (k, s) in enumerate(_pick(obs, obs_top), 1)],
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"changes": changes,
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"encoding": "主榜分=200+传导档位×20+组内分（还没热、还便宜）；"
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"观察档分=100+0.6z(传导)+0.4z(−热度)",
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}
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def _fmt_pct(v) -> str:
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return "—" if v is None else f"{v:+.0%}"
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def _fmt_num(v) -> str:
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return "—" if v is None else f"{v:.2f}"
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def _fmt_ev(e) -> str:
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if not e:
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return "—"
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return f"{e['theme']}（{e['n_sources']} 源，已动 {e['moved_ratio']:.0%}）"
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def render_md(d: dict) -> str:
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L = [f"# 每日选股计划 · {d['date']}", ""]
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c = d["counts"]
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L.append(f"主榜 {c['main']} 只 / 观察档 {c['observe']} 只 / "
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f"全池档位覆盖 {c['gate_covered']} 只。")
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stale = [x for x in d["market_snapshot_days"] if x != d["date"]]
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if stale:
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L.append(f"⚠️ 本日传导用的行情快照 = {'、'.join(stale)}（T−1 口径："
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f"\"谁已经动了\"看的是上个交易日收盘；拍点方案定版前均如此）。")
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L.append(f"注：本日传导用的行情快照 = {'、'.join(stale)}"
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f"（与计划日不同——历史降级日口径）。")
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L.append("")
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cap_txt = f"，每主题限额 {theme_cap}" if theme_cap else ""
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main_rows = _pick(main, ev, top, theme_cap)
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L.append(f"## 主榜 Top {len(main_rows)}（有券商预期、目标价不低于现价{cap_txt}）")
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cap_txt = f"，每主题限额 {d['theme_cap']}" if d["theme_cap"] else ""
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L.append(f"## 主榜 Top {len(d['main'])}（有券商预期、目标价不低于现价{cap_txt}）")
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L.append("")
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L.append("| # | 代码 | 名称 | 总分 | 档位 | 传导证据 | 热度 | 预期空间 |")
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L.append("|---|------|------|------|------|----------|------|----------|")
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for i, (k, s) in enumerate(main_rows, 1):
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e = ev.get(k)
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etxt = f"{e[0]}（{e[1]} 源，已动 {e[2]:.0%}）" if e else "—"
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L.append(f"| {i} | {k} | {names.get(k, '—')} | {s:.1f} | {_tier_label(s)} "
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f"| {etxt} | {_num(heat.get(k))} | {_pct(upside.get(k))} |")
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for r in d["main"]:
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L.append(f"| {r['rank']} | {r['code']} | {r['name'] or '—'} | {r['score']:.1f} "
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f"| {r['tier']} | {_fmt_ev(r['evidence'])} "
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f"| {_fmt_num(r['heat'])} | {_fmt_pct(r['upside'])} |")
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L.append("")
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obs_rows = _pick(obs, ev, obs_top, theme_cap)
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L.append(f"## 观察档 Top {len(obs_rows)}"
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L.append(f"## 观察档 Top {len(d['observe'])}"
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f"（无券商预期、但在传导链上——没有估值锚，置信度低{cap_txt}）")
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L.append("")
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L.append("| # | 代码 | 名称 | 分 | 传导证据 | 热度 |")
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L.append("|---|------|------|----|----------|------|")
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for i, (k, s) in enumerate(obs_rows, 1):
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e = ev.get(k)
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etxt = f"{e[0]}（{e[1]} 源，已动 {e[2]:.0%}）" if e else "—"
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L.append(f"| {i} | {k} | {names.get(k, '—')} | {s:.1f} "
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f"| {etxt} | {_num(heat.get(k))} |")
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for r in d["observe"]:
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L.append(f"| {r['rank']} | {r['code']} | {r['name'] or '—'} | {r['score']:.1f} "
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f"| {_fmt_ev(r['evidence'])} | {_fmt_num(r['heat'])} |")
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L.append("")
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L.append("## 今日升降档")
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L.append("")
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prev_ds = _prev_date("t_factor_akg_gate", ds)
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if not prev_ds:
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ch = d["changes"]
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if not ch:
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L.append("（没有更早的档位表可比，升降档从下一个交易日开始。）")
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else:
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prev = _factor("t_factor_akg_gate", prev_ds)
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both = pd.concat([prev.rename("prev"), gate.rename("cur")], axis=1)
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both = both.fillna(-1.0) # -1 = 当日不在面板
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up = both[both["cur"] > both["prev"]]
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down = both[both["cur"] < both["prev"]]
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lab = {-1.0: "池外", 0.0: "不采纳", 1.0: "观察档", 2.0: "主榜"}
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L.append(f"对比 {prev_ds}：升档 {len(up)} 只，降档 {len(down)} 只。"
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L.append(f"对比 {ch['base_date']}：升档 {ch['upgrades_total']} 只，"
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f"降档 {ch['downgrades_total']} 只。"
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f"升档=拿到新锚（首次覆盖 / 新进传导链），本身就是值得看的信号。")
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|
||||
def _rows(d: pd.DataFrame, cap: int = 15):
|
||||
lines = []
|
||||
for k, r in d.iterrows():
|
||||
lines.append(f"- {k} {names.get(k, '')}："
|
||||
f"{lab.get(r['prev'], '?')} → {lab.get(r['cur'], '?')}")
|
||||
if len(lines) >= cap:
|
||||
lines.append(f"- ……共 {len(d)} 只，其余见档位表")
|
||||
break
|
||||
return lines
|
||||
|
||||
if not up.empty:
|
||||
if ch["upgrades"]:
|
||||
L.append("")
|
||||
L.append("**升档**：")
|
||||
L += _rows(up.sort_values("cur", ascending=False))
|
||||
if not down.empty:
|
||||
L += [f"- {m['code']} {m['name'] or ''}：{m['from']} → {m['to']}"
|
||||
for m in ch["upgrades"]]
|
||||
if ch["upgrades_total"] > len(ch["upgrades"]):
|
||||
L.append(f"- ……共 {ch['upgrades_total']} 只，其余见档位表")
|
||||
if ch["downgrades"]:
|
||||
L.append("")
|
||||
L.append("**降档**：")
|
||||
L += _rows(down.sort_values("prev", ascending=False))
|
||||
L += [f"- {m['code']} {m['name'] or ''}：{m['from']} → {m['to']}"
|
||||
for m in ch["downgrades"]]
|
||||
if ch["downgrades_total"] > len(ch["downgrades"]):
|
||||
L.append(f"- ……共 {ch['downgrades_total']} 只，其余见档位表")
|
||||
L.append("")
|
||||
L.append("---")
|
||||
L.append("口径：主榜分 = 200 + 传导档位×20 + 组内分（还没热、还便宜）；"
|
||||
"观察档分 = 100 + 0.6z(传导) + 0.4z(−热度)。")
|
||||
L.append(f"口径：{d['encoding']}。")
|
||||
return "\n".join(L)
|
||||
|
||||
text = "\n".join(L)
|
||||
|
||||
def generate(date: str | None = None, top: int = 20, obs_top: int = 10,
|
||||
theme_cap: int = 5) -> str:
|
||||
try:
|
||||
data = collect(date, top, obs_top, theme_cap)
|
||||
except RuntimeError as e:
|
||||
raise SystemExit(str(e))
|
||||
text = render_md(data)
|
||||
os.makedirs("data/plan", exist_ok=True)
|
||||
out = f"data/plan/plan_{ds}.md"
|
||||
out = f"data/plan/plan_{data['date']}.md"
|
||||
with open(out, "w", encoding="utf-8") as f:
|
||||
f.write(text + "\n")
|
||||
print(text)
|
||||
|
|
|
|||
|
|
@ -4,3 +4,5 @@ psycopg[binary]>=3.1
|
|||
PyMySQL>=1.1
|
||||
python-dotenv>=1.0
|
||||
PyYAML>=6.0
|
||||
fastapi>=0.110
|
||||
uvicorn>=0.29
|
||||
|
|
|
|||
Loading…
Reference in New Issue