189 lines
7.6 KiB
Python
189 lines
7.6 KiB
Python
"""核心股历史接口：活跃核心股 + 指定日核心股/题材前20"""
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from datetime import date as date_cls
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from fastapi import APIRouter, Query
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from fastapi.responses import JSONResponse
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from database import get_connection, dict_from_row
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router = APIRouter()
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_NO_CACHE_HEADERS = {"Cache-Control": "no-store, no-cache, must-revalidate, max-age=0"}
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def _recent_trade_dates(conn, n: int = 10) -> list[str]:
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"""最近 n 个有数据的交易日（升序）"""
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rows = conn.execute(
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"SELECT DISTINCT trade_date FROM daily_core_stocks ORDER BY trade_date DESC LIMIT ?",
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(n,),
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).fetchall()
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return [r["trade_date"] for r in reversed(rows)]
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@router.get("/active", summary="活跃核心股 + 最近N日涨幅矩阵")
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async def active_core_stocks(days: int = Query(10, ge=1, le=30, description="交易日天数")):
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conn = get_connection()
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try:
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dates = _recent_trade_dates(conn, days)
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if not dates:
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return JSONResponse({"dates": [], "stocks": []}, headers=_NO_CACHE_HEADERS)
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# 窗口内出现过且最近一次出现距今天数 <= 10 个交易日
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placeholders = ",".join("?" * len(dates))
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rows = conn.execute(
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f"""SELECT trade_date, stock_code, stock_name, f3, cover_count FROM daily_core_stocks
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WHERE trade_date IN ({placeholders})
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ORDER BY trade_date DESC, rank ASC""",
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dates,
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).fetchall()
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# 组装 per-stock:每日涨幅 + 出现次数 + 最近上榜
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stock_days: dict[str, dict] = {}
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for r in rows:
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code = r["stock_code"]
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s = stock_days.setdefault(code, {
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"stockCode": code,
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"stockName": r["stock_name"],
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"coverCount": r["cover_count"],
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"dailyGains": {},
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"appearCount": 0,
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"lastAppear": None,
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})
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s["dailyGains"][r["trade_date"]] = r["f3"]
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s["appearCount"] += 1
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if s["lastAppear"] is None or r["trade_date"] > s["lastAppear"]:
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s["lastAppear"] = r["trade_date"]
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stocks = list(stock_days.values())
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# 稳定排序：先按出现次数降序，再按最近上榜日降序
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stocks.sort(key=lambda x: x.get("lastAppear") or "", reverse=True)
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stocks.sort(key=lambda x: -x["appearCount"])
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# 一次性取窗口内全部题材关联，按 stock_code 分组（跨天题材去重）
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themes_rows = conn.execute(
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f"""SELECT stock_code, theme_code, theme_name FROM daily_core_stock_themes
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WHERE trade_date IN ({placeholders})""",
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dates,
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).fetchall()
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themes_by_stock: dict[str, dict[str, dict]] = {}
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for t in themes_rows:
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per = themes_by_stock.setdefault(t["stock_code"], {})
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per.setdefault(t["theme_code"], {"theme_code": t["theme_code"], "theme_name": t["theme_name"]})
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for s in stocks:
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s["themes"] = list(themes_by_stock.get(s["stockCode"], {}).values())
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# daysSinceLastAppear:最近上榜距窗口最新交易日的自然日差(简单口径)
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latest = dates[-1] if dates else None
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for s in stocks:
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if s.get("lastAppear") and latest:
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try:
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d1 = date_cls.fromisoformat(latest)
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d2 = date_cls.fromisoformat(s["lastAppear"])
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s["daysSinceLastAppear"] = (d1 - d2).days
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except ValueError:
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s["daysSinceLastAppear"] = 0
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else:
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s["daysSinceLastAppear"] = 0
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return JSONResponse({"dates": dates, "stocks": stocks}, headers=_NO_CACHE_HEADERS)
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finally:
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conn.close()
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@router.get("/consecutive", summary="连续上榜的核心股")
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async def consecutive_core_stocks(min_days: int = Query(3, ge=2, le=10, description="最小连续天数")):
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from datetime import date as date_cls
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conn = get_connection()
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try:
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# 最近30个有数据的交易日
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rows = conn.execute(
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"SELECT DISTINCT trade_date FROM daily_core_stocks ORDER BY trade_date DESC LIMIT 30"
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).fetchall()
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all_dates = [r["trade_date"] for r in reversed(rows)]
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if not all_dates:
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return JSONResponse({"stocks": []}, headers=_NO_CACHE_HEADERS)
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# 查询所有上榜记录
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placeholders = ",".join("?" * len(all_dates))
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rows = conn.execute(
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f"""SELECT trade_date, stock_code, stock_name, f3, cover_count
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FROM daily_core_stocks
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WHERE trade_date IN ({placeholders})
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ORDER BY stock_code, trade_date""",
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all_dates,
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).fetchall()
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# 按股票分组，记录上榜日期
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stock_dates: dict[str, dict] = {}
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for r in rows:
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code = r["stock_code"]
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s = stock_dates.setdefault(code, {
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"stockCode": code,
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"stockName": r["stock_name"],
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"dates": [],
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"dailyGains": {},
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})
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s["dates"].append(r["trade_date"])
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s["dailyGains"][r["trade_date"]] = r["f3"]
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# 计算连续上榜天数（从最新交易日往回数）
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results = []
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for code, s in stock_dates.items():
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dates_list = sorted(s["dates"])
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consecutive = 0
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last_n = []
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for i in range(len(dates_list) - 1, -1, -1):
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if i == len(dates_list) - 1:
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consecutive = 1
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last_n.append(dates_list[i])
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else:
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prev_idx = all_dates.index(dates_list[i + 1]) if dates_list[i + 1] in all_dates else -1
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curr_idx = all_dates.index(dates_list[i]) if dates_list[i] in all_dates else -1
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if prev_idx - curr_idx <= 2:
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consecutive += 1
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last_n.append(dates_list[i])
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else:
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break
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if consecutive >= min_days:
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results.append({
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"stockCode": code,
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"stockName": s["stockName"],
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"consecutiveDays": consecutive,
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"recentDates": last_n,
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"dailyGains": {d: s["dailyGains"].get(d) for d in last_n},
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})
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results.sort(key=lambda x: -x["consecutiveDays"])
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return JSONResponse({"stocks": results}, headers=_NO_CACHE_HEADERS)
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finally:
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conn.close()
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@router.get("/history", summary="指定交易日核心股(含所属题材)")
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async def core_stock_history(date: str = Query(..., description="交易日 YYYY-MM-DD")):
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conn = get_connection()
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try:
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rows = conn.execute(
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"SELECT * FROM daily_core_stocks WHERE trade_date = ? ORDER BY rank ASC", (date,)
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).fetchall()
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# 一次性取该日全部题材关联，按 stock_code 分组，避免逐股 N+1 查询
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themes_rows = conn.execute(
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"SELECT stock_code, theme_code, theme_name FROM daily_core_stock_themes WHERE trade_date = ?",
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(date,),
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).fetchall()
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themes_by_stock: dict[str, list] = {}
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for t in themes_rows:
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themes_by_stock.setdefault(t["stock_code"], []).append(
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{"theme_code": t["theme_code"], "theme_name": t["theme_name"]}
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)
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items = []
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for r in rows:
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d = dict_from_row(r)
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d["themes"] = themes_by_stock.get(d["stock_code"], [])
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items.append(d)
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return JSONResponse({"date": date, "items": items}, headers=_NO_CACHE_HEADERS)
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finally:
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conn.close()
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