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重置项目

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This commit is contained in:
fish
2026-07-04 15:59:20 +08:00
parent 374e587f2d
commit 648a8b7f1c
224 changed files with 19700 additions and 9547 deletions
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refer/backend/app/indicators/pipeline.py
+25 -7
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@@ -1381,8 +1381,9 @@ def compute_enriched_today(
def _compute_limit_signals_today(df: pl.DataFrame, instruments: pl.DataFrame) -> pl.DataFrame:
"""盘中增量版的涨跌停/换手率/炸板/连板计算。"""
inst_cols = ["symbol"]
if "float_shares" in instruments.columns:
inst_cols.append("float_shares")
for c in ["float_shares", "limit_up", "limit_down"]:
if c in instruments.columns:
inst_cols.append(c)
inst_subset = instruments.select(inst_cols).unique(subset=["symbol"])
if "name" in instruments.columns:
st_flag = (
@@ -1431,14 +1432,31 @@ def _compute_limit_signals_today(df: pl.DataFrame, instruments: pl.DataFrame) ->
limit_up_price = _limit_price(prev_raw, limit_pct, up=True)
limit_down_price = _limit_price(prev_raw, limit_pct, up=False)
# 生效涨跌停价: 优先用维表权威值 (instruments.limit_up/down, 交易所级别精确价),
# 维表缺失 (新股上市前 5 日: limit_up 为 null 或哨兵 100000) 回退自算理论价。
# 哨兵阈值 10000 用于识别 "新股无涨跌停限制" 的占位值 (实际涨停价不可能上万)。
_SENTINEL = 10000.0
if "limit_up" in df.columns:
effective_limit_up = pl.when(
pl.col("limit_up").is_not_null() & (pl.col("limit_up") < _SENTINEL)
).then(pl.col("limit_up")).otherwise(limit_up_price)
else:
effective_limit_up = limit_up_price
if "limit_down" in df.columns:
effective_limit_down = pl.when(
pl.col("limit_down").is_not_null() & (pl.col("limit_down") < _SENTINEL)
).then(pl.col("limit_down")).otherwise(limit_down_price)
else:
effective_limit_down = limit_down_price
is_limit_up = (
pl.when((prev_raw > 0) & (pl.col("raw_close") > 0))
.then((pl.col("raw_close") - limit_up_price).abs() < 0.005)
.then(pl.col("raw_close") >= (effective_limit_up - 0.005))
.otherwise(None).cast(pl.Boolean)
)
is_limit_down = (
pl.when((prev_raw > 0) & (pl.col("raw_close") > 0))
.then((pl.col("raw_close") - limit_down_price).abs() < 0.005)
.then(pl.col("raw_close") <= (effective_limit_down + 0.005))
.otherwise(None).cast(pl.Boolean)
)
@@ -1449,7 +1467,7 @@ def _compute_limit_signals_today(df: pl.DataFrame, instruments: pl.DataFrame) ->
pl.when(prev_raw > 0)
.then(
(~is_limit_down.fill_null(True))
& (pl.col("low") <= limit_down_price + 0.005)
& (pl.col("low") <= effective_limit_down + 0.005)
& (pl.col("close") > pl.col("open"))
).otherwise(None).cast(pl.Boolean)
.alias("signal_limit_down_recovery"),
@@ -1457,7 +1475,7 @@ def _compute_limit_signals_today(df: pl.DataFrame, instruments: pl.DataFrame) ->
pl.when((prev_raw > 0) & (pl.col("raw_high") > 0))
.then(
(~is_limit_up.fill_null(True))
& (pl.col("raw_high") >= limit_up_price - 0.005)
& (pl.col("raw_high") >= effective_limit_up - 0.005)
).otherwise(None).cast(pl.Boolean)
.alias("signal_broken_limit_up"),
])
@@ -1482,7 +1500,7 @@ def _compute_limit_signals_today(df: pl.DataFrame, instruments: pl.DataFrame) ->
])
# 清理
cleanup = ["_limit_pct", "_is_st"]
cleanup = ["_limit_pct", "_is_st", "limit_up", "limit_down"]
for c in df.columns:
if c.endswith("_inst"):
cleanup.append(c)