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看股功能更新

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cirry
2026-08-16 20:20:59 +08:00
parent 63f8ed8b69
commit 65d2f54d1b
9 changed files with 296 additions and 107 deletions
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97
backend/app/screener/engine.py
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@@ -1,6 +1,6 @@
"""选股执行引擎：SQL 快照预筛缩小范围 -> 逐股指标计算过滤。
性能：预筛在 SQLite 索引上完成（毫秒级）；指标阶段候选集通常数百~数千只 × ~90 根 bar，
性能：预筛在数据库索引上完成（毫秒级）；指标阶段候选集通常数百~数千只 × ~90 根 bar，
pandas 逐股计算（复用 app/indicators，指标按 (族, 参数) 去重计算），秒级完成。
"""
from __future__ import annotations
@@ -14,7 +14,8 @@ from sqlalchemy import and_, func, not_, or_, select
from sqlalchemy.ext.asyncio import AsyncSession
from .. import indicators as ind
from ..models import DailySnapshot, MarketDaily, StockBasic
from ..data.symbols import plain_code
from ..models import Candle, DailySnapshot, StockBasic
from ..schemas import IndicatorCondition, ScreenConditions, SnapshotCondition
# 快照字段 -> (DB 列, 中文标签, LLM 值 -> DB 值的换算乘数)
@@ -25,7 +26,7 @@ SNAPSHOT_FIELDS: dict[str, tuple[str, str, float]] = {
"pe_ttm": ("pe_ttm", "市盈率TTM", 1.0),
"pb": ("pb", "市净率", 1.0),
"turnover_rate": ("turnover_rate", "换手率%", 1.0),
"close": ("close", "最新价", 1.0), # 实际取 market_daily.close，无需快照表
"close": ("close", "最新价", 1.0), # 实际取 candles 最新 bar，无需快照表
}
@@ -198,7 +199,7 @@ def _snapshot_clause(cond: SnapshotCondition):
if cond.field not in SNAPSHOT_FIELDS:
raise ValueError(f"未知快照字段: {cond.field}")
col_name, _, scale = SNAPSHOT_FIELDS[cond.field]
col = MarketDaily.close if cond.field == "close" else getattr(DailySnapshot, col_name)
col = Candle.close if cond.field == "close" else getattr(DailySnapshot, col_name)
lo = cond.value * scale
hi = (cond.value2 * scale) if cond.op == "between" and cond.value2 is not None else None
if cond.op == "gt":
@@ -219,17 +220,22 @@ _CAND_COLS = ["ts_code", "name", "close", "pct_chg",
async def _prefilter(session: AsyncSession, conds: ScreenConditions, target_date: datetime) -> pd.DataFrame:
"""最新交易日截面预筛：快照条件 + 排除项 + 名称/收盘价。返回候选 DataFrame。"""
"""最新交易日截面预筛：candles(全量不复权底座) + stock_basic + daily_snapshot。
只取 target_date 当日有交易的股票（停牌股无当日 bar，自然排除，与原先 market_daily
的 trade_date == target_date 行为一致）。pct_chg 用前一交易日收盘价计算。
"""
snap_date = await session.scalar(select(func.max(DailySnapshot.trade_date))) or target_date
stmt = (
select(
MarketDaily.ts_code, StockBasic.name, MarketDaily.close, MarketDaily.pct_chg,
Candle.symbol, StockBasic.ts_code, StockBasic.name, Candle.close,
DailySnapshot.total_mv, DailySnapshot.circ_mv,
DailySnapshot.pe_ttm, DailySnapshot.pb, DailySnapshot.turnover_rate,
)
.join(StockBasic, StockBasic.ts_code == MarketDaily.ts_code)
.outerjoin(DailySnapshot, and_(DailySnapshot.ts_code == MarketDaily.ts_code,
DailySnapshot.trade_date == target_date))
.where(MarketDaily.trade_date == target_date)
.join(StockBasic, StockBasic.symbol == Candle.symbol)
.outerjoin(DailySnapshot, and_(DailySnapshot.ts_code == StockBasic.ts_code,
DailySnapshot.trade_date == snap_date))
.where(Candle.timeframe == "1d", Candle.ts == target_date)
)
if conds.exclude_delisted:
@@ -237,13 +243,39 @@ async def _prefilter(session: AsyncSession, conds: ScreenConditions, target_date
if conds.exclude_st:
stmt = stmt.where(not_(or_(StockBasic.name.like("%ST%"), StockBasic.name.like("%退%"))))
if conds.exclude_bj:
stmt = stmt.where(not_(MarketDaily.ts_code.like("%.BJ")))
stmt = stmt.where(not_(StockBasic.ts_code.like("%.BJ")))
for c in conds.snapshot:
stmt = stmt.where(_snapshot_clause(c))
rows = (await session.execute(stmt)).all()
return pd.DataFrame(rows, columns=_CAND_COLS)
df = pd.DataFrame(rows, columns=["symbol", "ts_code", "name", "close",
"total_mv", "circ_mv", "pe_ttm", "pb", "turnover_rate"])
if df.empty:
return df[_CAND_COLS]
# pct_chg：candles 无现成涨跌幅列，用前一交易日的收盘价计算
prev_dt = await session.scalar(
select(func.max(Candle.ts)).where(Candle.timeframe == "1d", Candle.ts < target_date)
)
prev_map: dict[str, float] = {}
if prev_dt is not None:
pr = await session.execute(
select(Candle.symbol, Candle.close).where(
Candle.timeframe == "1d", Candle.ts == prev_dt,
Candle.symbol.in_(df["symbol"].tolist()),
)
)
prev_map = {r.symbol: r.close for r in pr}
prev = df["symbol"].map(prev_map)
def _pct(c, p) -> float | None:
if p is None or p != p or float(p) == 0:
return None
return (float(c) / float(p) - 1) * 100
df["pct_chg"] = [_pct(c, p) for c, p in zip(df["close"], prev)]
return df[_CAND_COLS]
# ---------- 主流程 ----------
@@ -260,18 +292,30 @@ def _max_needed_bars(conds: ScreenConditions) -> int:
async def _load_bars(session: AsyncSession, ts_codes: list[str],
target_date: datetime, min_date: datetime) -> pd.DataFrame:
"""载入候选股的 K 线窗口。候选 <= 2000 用 IN 精确圈定；否则拉全窗口再 pandas 过滤。"""
"""载入候选股的 K 线窗口（candles 全量不复权底座）。
候选 <= 2000 用 IN 精确圈定；否则拉全窗口再 pandas 过滤。
ts_code 按候选集映射回 symbol 查询，pct_chg 用每股收盘价环比计算。
"""
symbols = [plain_code(t) for t in ts_codes]
stmt = select(
MarketDaily.ts_code, MarketDaily.trade_date, MarketDaily.open, MarketDaily.high,
MarketDaily.low, MarketDaily.close, MarketDaily.pct_chg,
).where(MarketDaily.trade_date >= min_date, MarketDaily.trade_date <= target_date)
if len(ts_codes) <= 2000:
stmt = stmt.where(MarketDaily.ts_code.in_(set(ts_codes)))
Candle.symbol, Candle.ts, Candle.open, Candle.high, Candle.low, Candle.close,
).where(Candle.timeframe == "1d", Candle.ts >= min_date, Candle.ts <= target_date)
if len(symbols) <= 2000:
stmt = stmt.where(Candle.symbol.in_(set(symbols)))
rows = (await session.execute(stmt)).all()
df = pd.DataFrame(rows, columns=["ts_code", "trade_date", "open", "high", "low", "close", "pct_chg"])
if not df.empty and len(ts_codes) > 2000:
df = df[df["ts_code"].isin(set(ts_codes))]
return df.sort_values(["ts_code", "trade_date"]).reset_index(drop=True)
df = pd.DataFrame(rows, columns=["symbol", "trade_date", "open", "high", "low", "close"])
if not df.empty and len(symbols) > 2000:
df = df[df["symbol"].isin(set(symbols))]
df = df.sort_values(["symbol", "trade_date"]).reset_index(drop=True)
if df.empty:
df["ts_code"] = pd.Series(dtype=object)
df["pct_chg"] = pd.Series(dtype=object)
else:
df["pct_chg"] = df.groupby("symbol")["close"].pct_change() * 100
ts_map = {plain_code(t): t for t in ts_codes}
df["ts_code"] = df["symbol"].map(ts_map)
return df[["ts_code", "trade_date", "open", "high", "low", "close", "pct_chg"]]
def _f(v) -> float | None:
@@ -303,7 +347,9 @@ async def run_screen(session: AsyncSession, conds: ScreenConditions, limit: int)
if not conds.indicator and not conds.snapshot:
raise ValueError("筛选条件为空")
target_date = await session.scalar(select(func.max(MarketDaily.trade_date)))
target_date = await session.scalar(
select(func.max(Candle.ts)).where(Candle.timeframe == "1d")
)
if target_date is None:
raise DataNotReadyError("全市场数据未同步：请先在选股页点击「同步市场数据」")
@@ -330,10 +376,11 @@ async def run_screen(session: AsyncSession, conds: ScreenConditions, limit: int)
# 纯快照条件：预筛结果即命中
items = [_item_from_row(row) | {"indicators": {}} for _, row in cand.iterrows()]
else:
# 圈定 K 线窗口：按已同步交易日序列回溯 needed 根
# 圈定 K 线窗口：按 candles 全量交易日序列回溯 needed 根（不再受同步窗口限制）
need = _max_needed_bars(conds)
dates_res = await session.execute(
select(MarketDaily.trade_date).distinct().order_by(MarketDaily.trade_date.desc()).limit(need)
select(Candle.ts).where(Candle.timeframe == "1d").distinct()
.order_by(Candle.ts.desc()).limit(need)
)
min_date = min(r[0] for r in dates_res)
bars = await _load_bars(session, cand["ts_code"].tolist(), target_date, min_date)