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修复停复牌股票实时涨跌停漏算

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This commit is contained in:
shy3130
2026-07-30 13:09:07 +08:00
parent 4b6e94e020
commit f8fca96f42
3 changed files with 425 additions and 32 deletions
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backend/app/indicators/pipeline.py
+47 -12
View File
@@ -1382,8 +1382,11 @@ def compute_enriched_today(
alpha = _ema_alpha
# ---- JOIN: 今天的 OHLCV + 昨天的递推状态 ----
df = today_ohlcv.join(live_agg, on="symbol", how="inner")
# ---- JOIN: 今天的 OHLCV + 各股票最后一个有效交易日的递推状态 ----
# 当日行情是主表, 复牌或新上市股票不能因为没有历史状态而被静默删除。
live_state = live_agg.with_columns(pl.lit(True).alias("_has_history_state"))
df = today_ohlcv.join(live_state, on="symbol", how="left")
has_history_state = pl.col("_has_history_state").fill_null(False)
# ---- 前复权: 保存原始价 → 调整 OHLCV ----
df = df.with_columns([
@@ -1527,8 +1530,14 @@ def compute_enriched_today(
# ---- 极值 60 日 ----
df = df.with_columns([
pl.max_horizontal(pl.col("_high_59d"), pl.col("high")).alias("high_60d"),
pl.min_horizontal(pl.col("_low_59d"), pl.col("low")).alias("low_60d"),
pl.when(has_history_state)
.then(pl.max_horizontal(pl.col("_high_59d"), pl.col("high")))
.otherwise(None)
.alias("high_60d"),
pl.when(has_history_state)
.then(pl.min_horizontal(pl.col("_low_59d"), pl.col("low")))
.otherwise(None)
.alias("low_60d"),
])
# ---- 动量 (5d/10d/20d/30d/60d) ----
@@ -1548,7 +1557,7 @@ def compute_enriched_today(
vol_mean = total_sum / 20
vol_var = total_sq_sum / 20 - vol_mean ** 2
df = df.with_columns(
pl.when(vol_var > 0)
pl.when(has_history_state & (vol_var > 0))
.then(vol_var.sqrt() * (252 ** 0.5))
.otherwise(None)
.alias("annual_vol_20d"),
@@ -1638,6 +1647,7 @@ def compute_enriched_today(
"_adj_factor",
"_vol_19d_pct_sum", "_vol_19d_pct_sq_sum",
"_prev_consec_up", "_prev_consec_down",
"_has_history_state",
]
df = df.drop([c for c in drop_cols if c in df.columns])
@@ -1722,7 +1732,7 @@ def _compute_limit_signals_today(df: pl.DataFrame, instruments: pl.DataFrame) ->
limit_down_price = polars_limit_price(prev_raw, limit_pct, up=False)
# 生效涨跌停价: 维表日期与行情日期一致时优先使用交易所权威值;
# 维表过期、价格缺失或新股哨兵值均回退自算理论价。旧版无 as_of 维表保持兼容。
# 维表过期或价格缺失时回退自算理论价。旧版无 as_of 维表保持兼容。
# 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
authoritative_date = (
@@ -1730,30 +1740,51 @@ def _compute_limit_signals_today(df: pl.DataFrame, instruments: pl.DataFrame) ->
if "_instrument_as_of" in df.columns
else pl.lit(True)
)
has_authoritative_up = pl.lit(False)
has_authoritative_down = pl.lit(False)
no_price_limit = pl.lit(False)
if "limit_up" in df.columns:
effective_limit_up = pl.when(
has_authoritative_up = (
authoritative_date
& pl.col("limit_up").is_not_null()
& (pl.col("limit_up") > 0)
& (pl.col("limit_up") < _SENTINEL)
)
no_price_limit = (
authoritative_date
& pl.col("limit_up").is_not_null()
& (pl.col("limit_up") >= _SENTINEL)
)
effective_limit_up = pl.when(
has_authoritative_up
).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(
has_authoritative_down = (
authoritative_date
& pl.col("limit_down").is_not_null()
& (pl.col("limit_down") > 0)
& (pl.col("limit_down") < _SENTINEL)
)
effective_limit_down = pl.when(
has_authoritative_down
).then(pl.col("limit_down")).otherwise(limit_down_price)
else:
effective_limit_down = limit_down_price
valid_prev_raw = prev_raw.is_not_null() & (prev_raw > 0)
is_limit_up = (
pl.when((prev_raw > 0) & (pl.col("raw_close") > 0))
pl.when(no_price_limit)
.then(False)
.when((valid_prev_raw | has_authoritative_up) & (pl.col("raw_close") > 0))
.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))
pl.when(no_price_limit)
.then(False)
.when((valid_prev_raw | has_authoritative_down) & (pl.col("raw_close") > 0))
.then(pl.col("raw_close") <= (effective_limit_down + 0.005))
.otherwise(None).cast(pl.Boolean)
)
@@ -1762,7 +1793,9 @@ def _compute_limit_signals_today(df: pl.DataFrame, instruments: pl.DataFrame) ->
is_limit_up.alias("signal_limit_up"),
is_limit_down.alias("signal_limit_down"),
# 跌停翘板
pl.when(prev_raw > 0)
pl.when(no_price_limit)
.then(False)
.when(valid_prev_raw | has_authoritative_down)
.then(
(~is_limit_down.fill_null(True))
& (pl.col("low") <= effective_limit_down + 0.005)
@@ -1770,7 +1803,9 @@ def _compute_limit_signals_today(df: pl.DataFrame, instruments: pl.DataFrame) ->
).otherwise(None).cast(pl.Boolean)
.alias("signal_limit_down_recovery"),
# 炸板: 最高价曾触及涨停价 + 最终未封住
pl.when((prev_raw > 0) & (pl.col("raw_high") > 0))
pl.when(no_price_limit)
.then(False)
.when((valid_prev_raw | has_authoritative_up) & (pl.col("raw_high") > 0))
.then(
(~is_limit_up.fill_null(True))
& (pl.col("raw_high") >= effective_limit_up - 0.005)
backend/app/tickflow/repository.py
+127 -20
View File
@@ -36,6 +36,17 @@ def enriched_dirname(asset_type: str) -> str:
return "kline_etf_enriched" if asset_type == "etf" else "kline_daily_enriched"
def _last_available_rows(df: pl.DataFrame, cutoff: date) -> pl.DataFrame:
"""从已按 symbol/date 排序的数据中取每只标的最后一条有效状态。"""
if df.is_empty():
return df
return (
df.filter(pl.col("date") <= cutoff)
.group_by("symbol", maintain_order=True)
.last()
)
class DataStore:
"""唯一的存储入口 — 进程启动时创建。"""
@@ -586,6 +597,17 @@ class KlineRepository:
logger.info("enriched refresh step start: build live agg")
self._build_live_agg(self._live_agg_baseline_date(latest))
logger.info("enriched refresh step done: build live agg (%.2fs)", time.perf_counter() - step)
repaired_today = self._restore_missing_latest_rows(
latest, df_today, df_full,
)
if len(repaired_today) > len(df_today):
df_full = pl.concat(
[df_full.filter(pl.col("date") != latest), repaired_today],
how="diagonal_relaxed",
).sort(["symbol", "date"])
self._enriched_history_cache = df_full
self._enriched_cache = repaired_today
df_today = repaired_today
logger.info("enriched 缓存已计算: %d 只, 日期 %s (即时计算)", len(df_today), latest)
logger.info("enriched refresh done (%.2fs)", time.perf_counter() - started)
return
@@ -605,6 +627,67 @@ class KlineRepository:
except Exception as e: # noqa: BLE001
logger.warning("enriched 缓存刷新失败: %s", e)
def _restore_missing_latest_rows(
self,
latest: date,
df_today: pl.DataFrame,
history: pl.DataFrame,
) -> pl.DataFrame:
"""用同日原始日K补齐旧实时快照漏写的正常成交股票。"""
if self._live_agg_cache is None or self._live_agg_cache.is_empty():
return df_today
daily_path = (
self.store.data_dir
/ "kline_daily"
/ f"date={latest.isoformat()}"
/ "part.parquet"
)
if not daily_path.exists():
return df_today
try:
from app.indicators.pipeline import compute_enriched_today, filter_halt_days
daily = filter_halt_days(pl.read_parquet(daily_path))
missing = daily.join(
df_today.select("symbol").unique(),
on="symbol",
how="anti",
)
if missing.is_empty():
return df_today
missing_symbols = missing.select("symbol").unique()
previous = history.filter(pl.col("date") < latest).join(
missing_symbols,
on="symbol",
how="semi",
)
if not previous.is_empty():
previous = _last_available_rows(previous, latest)
recovered = compute_enriched_today(
self._live_agg_cache,
previous,
missing,
self.get_instruments(),
)
if recovered.is_empty():
return df_today
recovered = self._with_instrument_metadata("stock", recovered)
result = pl.concat(
[df_today, recovered],
how="diagonal_relaxed",
).unique(subset=["symbol", "date"], keep="last").sort("symbol")
logger.info(
"enriched latest cache restored from daily: date=%s, rows=%d",
latest,
len(result) - len(df_today),
)
return result
except Exception as e: # noqa: BLE001
logger.warning("enriched latest cache restore skipped: %s", e)
return df_today
def _build_live_agg(self, latest: date) -> None:
"""从 OHLCV 即时计算递推状态 + 窗口聚合, 构建盘中实时聚合表。
@@ -622,8 +705,11 @@ class KlineRepository:
hist_all = self._enriched_history_cache
if "date" in hist_all.columns and hist_all["date"].min() <= start_60d:
# 从历史缓存中提取所需列 (历史缓存已有指标列)
base_cols = ["symbol", "date", "open", "high", "low", "close", "volume",
"raw_close", "raw_high", "raw_low"]
base_cols = [
"symbol", "date", "open", "high", "low", "close", "volume",
"raw_close", "raw_high", "raw_low",
"consecutive_limit_ups", "consecutive_limit_downs",
]
needed = [c for c in base_cols if c in hist_all.columns]
step = time.perf_counter()
logger.info("live agg step start: slice history cache")
@@ -632,9 +718,6 @@ class KlineRepository:
).select(needed).sort(["symbol", "date"])
logger.info("live agg step done: slice history cache rows=%d (%.2fs)", len(df_hist), time.perf_counter() - step)
# 用历史缓存的指标列提取最新日状态 (无需再次 compute_indicators)
state_source = hist_all.filter(pl.col("date") == latest)
state_cols = [
"symbol",
"ema5", "ema10", "ema20", "ema30", "ema60",
@@ -644,7 +727,10 @@ class KlineRepository:
"close", "high", "low",
"annual_vol_20d",
]
existing_state = [c for c in state_cols if c in state_source.columns]
existing_state = [c for c in state_cols if c in hist_all.columns]
state_source = _last_available_rows(
hist_all.select("date", *existing_state), latest,
)
agg_a = state_source.select(existing_state)
else:
df_hist = pl.DataFrame()
@@ -668,10 +754,13 @@ class KlineRepository:
# 单独计算 _ema12 / _ema26 (compute_indicators 内部会 drop 掉)
step = time.perf_counter()
logger.info("live agg step start: ema state")
df_ema = df_hist.sort(["symbol", "date"]).with_columns([
pl.col("close").ewm_mean(alpha=_ema_alpha(12), adjust=False).over("symbol").alias("_ema12"),
pl.col("close").ewm_mean(alpha=_ema_alpha(26), adjust=False).over("symbol").alias("_ema26"),
]).filter(pl.col("date") == latest).select("symbol", "_ema12", "_ema26")
df_ema = _last_available_rows(
df_hist.sort(["symbol", "date"]).with_columns([
pl.col("close").ewm_mean(alpha=_ema_alpha(12), adjust=False).over("symbol").alias("_ema12"),
pl.col("close").ewm_mean(alpha=_ema_alpha(26), adjust=False).over("symbol").alias("_ema26"),
]).select("symbol", "date", "_ema12", "_ema26"),
latest,
).select("symbol", "_ema12", "_ema26")
agg_a = agg_a.join(df_ema, on="symbol", how="inner")
logger.info("live agg step done: ema state (%.2fs)", time.perf_counter() - step)
@@ -690,9 +779,9 @@ class KlineRepository:
rsi_exprs.append(gain.ewm_mean(alpha=a, adjust=False).over("symbol").alias(f"_rsi_avg_gain_{n}"))
rsi_exprs.append(loss.ewm_mean(alpha=a, adjust=False).over("symbol").alias(f"_rsi_avg_loss_{n}"))
df_rsi = (
df_rsi_base
.with_columns(rsi_exprs)
.filter(pl.col("date") == latest)
_last_available_rows(
df_rsi_base.with_columns(rsi_exprs), latest,
)
.select("symbol", *[f"_rsi_avg_gain_{n}" for n in (6, 14, 24)],
*[f"_rsi_avg_loss_{n}" for n in (6, 14, 24)])
)
@@ -704,7 +793,9 @@ class KlineRepository:
step = time.perf_counter()
logger.info("live agg step start: adj factor state")
adj_factor_df = (
df_hist.filter(pl.col("date") == latest)
_last_available_rows(
df_hist.select("symbol", "date", "close", "raw_close"), latest,
)
.select("symbol", (pl.col("close") / pl.col("raw_close")).alias("_adj_factor"))
)
agg_a = agg_a.join(adj_factor_df, on="symbol", how="left")
@@ -728,14 +819,27 @@ class KlineRepository:
agg_a = agg_a.join(df_vol, on="symbol", how="left")
logger.info("live agg step done: annual vol state (%.2fs)", time.perf_counter() - step)
# 昨日连板数: 从 enriched parquet 取 (用于增量计算同向 +1)
# 昨日连板数: 使用每只股票最后一个有效交易日状态 (用于增量计算同向 +1)
step = time.perf_counter()
logger.info("live agg step start: consecutive state")
lf = scan_enriched_parquet(self._enriched_glob).filter(pl.col("date") == latest)
consec_cols = [c for c in ["symbol", "consecutive_limit_ups", "consecutive_limit_downs"]
if c in lf.collect_schema().names()]
if c in df_hist.columns]
consec_source = df_hist
if len(consec_cols) != 3:
lf = (
scan_enriched_parquet(self._enriched_glob)
.filter((pl.col("date") >= start_60d) & (pl.col("date") <= latest))
.sort(["symbol", "date"])
)
consec_cols = [
c for c in ["symbol", "consecutive_limit_ups", "consecutive_limit_downs"]
if c in lf.collect_schema().names()
]
consec_source = lf.select("date", *consec_cols).collect()
if len(consec_cols) == 3:
consec_df = lf.select(consec_cols).collect()
consec_df = _last_available_rows(
consec_source.select("date", *consec_cols), latest,
)
if not consec_df.is_empty():
consec = consec_df.select(
"symbol",
@@ -815,7 +919,8 @@ class KlineRepository:
)
read_cols = [c for c in ["symbol", "date", "open", "high", "low", "close", "volume",
"raw_close", "raw_high", "raw_low"]
"raw_close", "raw_high", "raw_low",
"consecutive_limit_ups", "consecutive_limit_downs"]
if c in lf.collect_schema().names()]
df_hist = lf.select(read_cols).collect()
@@ -834,7 +939,9 @@ class KlineRepository:
"annual_vol_20d",
]
existing_state = [c for c in state_cols if c in df_with_indicators.columns]
agg_a = df_with_indicators.filter(pl.col("date") == latest).select(existing_state)
agg_a = _last_available_rows(
df_with_indicators.select("date", *existing_state), latest,
).select(existing_state)
return df_hist, agg_a
backend/tests/test_realtime_enriched_resume.py
+251
View File
@@ -0,0 +1,251 @@
from __future__ import annotations
from datetime import date, timedelta
import polars as pl
from app.indicators.pipeline import (
ENRICHED_COLUMNS_BY_CATEGORY,
SIGNAL_DEPENDENCIES,
compute_enriched_today,
compute_indicators,
)
from app.tickflow.repository import DataStore, KlineRepository
def _historical_cache(latest: date) -> pl.DataFrame:
rows = []
for symbol, days in (("600001.SH", 100), ("000820.SZ", 98)):
first = latest - timedelta(days=99)
for offset in range(days):
trade_date = first + timedelta(days=offset)
close = 10.0 + offset * 0.01
rows.append({
"symbol": symbol,
"date": trade_date,
"open": close,
"high": close + 0.1,
"low": close - 0.1,
"close": close,
"raw_close": close,
"raw_high": close + 0.1,
"raw_low": close - 0.1,
"volume": 1000.0 + offset,
"amount": close * (1000.0 + offset),
})
return compute_indicators(pl.DataFrame(rows).sort(["symbol", "date"]))
def test_live_agg_uses_each_symbols_last_available_trading_state(tmp_path):
latest = date(2026, 7, 29)
repo = KlineRepository(DataStore(tmp_path))
repo._enriched_history_cache = _historical_cache(latest)
partition = tmp_path / "kline_daily_enriched" / f"date={latest.isoformat()}"
partition.mkdir(parents=True)
pl.DataFrame({
"symbol": ["600001.SH"],
"date": [latest],
"open": [10.99],
"high": [11.09],
"low": [10.89],
"close": [10.99],
"volume": [1099.0],
"amount": [12078.01],
"raw_close": [10.99],
"raw_high": [11.09],
"raw_low": [10.89],
"turnover_rate": [1.0],
"consecutive_limit_ups": pl.Series([0], dtype=pl.UInt32),
"consecutive_limit_downs": pl.Series([0], dtype=pl.UInt32),
}).write_parquet(partition / "part.parquet")
resumed_date = latest - timedelta(days=2)
resumed_partition = (
tmp_path / "kline_daily_enriched" / f"date={resumed_date.isoformat()}"
)
resumed_partition.mkdir(parents=True)
pl.DataFrame({
"symbol": ["000820.SZ"],
"date": [resumed_date],
"open": [10.97],
"high": [11.07],
"low": [10.87],
"close": [10.97],
"volume": [1097.0],
"amount": [12034.09],
"raw_close": [10.97],
"raw_high": [11.07],
"raw_low": [10.87],
"turnover_rate": [1.0],
"consecutive_limit_ups": pl.Series([2], dtype=pl.UInt32),
"consecutive_limit_downs": pl.Series([0], dtype=pl.UInt32),
}).write_parquet(resumed_partition / "part.parquet")
repo._build_live_agg(latest)
states = repo.get_live_agg().sort("symbol")
assert states["symbol"].to_list() == ["000820.SZ", "600001.SH"]
resumed = states.filter(pl.col("symbol") == "000820.SZ").row(0, named=True)
assert resumed["_prev_consec_up"] == 2
def _live_state() -> pl.DataFrame:
return pl.DataFrame({
"symbol": ["600001.SH"],
"ema5": [10.0],
"ema10": [10.0],
"ema20": [10.0],
"ema30": [10.0],
"ema60": [10.0],
"macd_dea": [0.0],
"kdj_k": [50.0],
"kdj_d": [50.0],
"atr_14": [0.2],
"close": [10.0],
"high": [10.1],
"low": [9.9],
"annual_vol_20d": [0.1],
"_ema12": [10.0],
"_ema26": [10.0],
"_adj_factor": [1.0],
"_vol_19d_pct_sum": [0.0],
"_vol_19d_pct_sq_sum": [0.0],
"_prev_consec_up": pl.Series([0], dtype=pl.UInt32),
"_prev_consec_down": pl.Series([0], dtype=pl.UInt32),
"_ma5_partial_sum": [40.0],
"_ma10_partial_sum": [90.0],
"_ma20_partial_sum": [190.0],
"_ma30_partial_sum": [290.0],
"_ma60_partial_sum": [590.0],
"_boll_partial_sum": [190.0],
"_boll_partial_sq_sum": [1900.0],
"_high_59d": [10.1],
"_low_59d": [9.9],
"_close_5d_ago": [10.0],
"_close_10d_ago": [10.0],
"_close_20d_ago": [10.0],
"_close_30d_ago": [10.0],
"_close_60d_ago": [10.0],
"_vol_ma5_partial_sum": [4000.0],
"_vol_ma10_partial_sum": [9000.0],
"_vol_ma5_prev_sum": [5000.0],
"_kdj_8d_low": [9.9],
"_kdj_8d_high": [10.1],
"_window_len": [59],
"_rsi_avg_gain_6": [0.01],
"_rsi_avg_loss_6": [0.01],
"_rsi_avg_gain_14": [0.01],
"_rsi_avg_loss_14": [0.01],
"_rsi_avg_gain_24": [0.01],
"_rsi_avg_loss_24": [0.01],
})
def _previous_enriched() -> pl.DataFrame:
return pl.DataFrame({
"symbol": ["600001.SH"],
"ma5": [10.0],
"ma10": [10.0],
"ma20": [10.0],
"ma60": [10.0],
"macd_dif": [0.0],
"macd_dea": [0.0],
"boll_upper": [10.2],
"boll_lower": [9.8],
"close": [10.0],
})
def test_realtime_enriched_keeps_rows_without_history_and_limits_technical_fields():
today = date(2026, 7, 30)
today_rows = pl.DataFrame({
"symbol": ["600001.SH", "000820.SZ", "001000.SZ", "600002.SH"],
"date": [today] * 4,
"open": [10.1, 11.0, 11.0, 0.0],
"high": [10.2, 11.0, 11.0, 0.0],
"low": [10.0, 11.0, 11.0, 0.0],
"close": [10.2, 11.0, 11.0, 0.0],
"volume": [1200.0, 3000.0, 2000.0, 0.0],
"amount": [12240.0, 33000.0, 22000.0, 0.0],
"prev_close": [10.0, None, 10.0, 10.0],
})
instruments = pl.DataFrame({
"symbol": ["600001.SH", "000820.SZ", "001000.SZ", "600002.SH"],
"name": ["已有历史", "复牌股票", "上市新股", "停牌股票"],
"float_shares": [1_000_000.0] * 4,
"limit_up": [11.0, 11.0, 100000.0, 11.0],
"limit_down": [9.0, 9.0, 0.0, 9.0],
"as_of": [today] * 4,
})
result = compute_enriched_today(
_live_state(),
_previous_enriched(),
today_rows,
instruments,
)
assert result["symbol"].to_list() == today_rows["symbol"].to_list()
existing = result.filter(pl.col("symbol") == "600001.SH").row(0, named=True)
resumed = result.filter(pl.col("symbol") == "000820.SZ").row(0, named=True)
ipo = result.filter(pl.col("symbol") == "001000.SZ").row(0, named=True)
halted = result.filter(pl.col("symbol") == "600002.SH").row(0, named=True)
assert existing["ma5"] is not None
assert resumed["raw_close"] == 11.0
assert resumed["signal_limit_up"] is True
assert resumed["consecutive_limit_ups"] == 1
technical_columns = {
column
for category in (
"ma", "ema", "macd", "boll", "kdj", "atr", "volume",
"extremes", "momentum", "volatility", "rsi",
)
for column in ENRICHED_COLUMNS_BY_CATEGORY[category]
} | set(SIGNAL_DEPENDENCIES)
assert all(resumed[column] is None for column in technical_columns)
assert ipo["signal_limit_up"] is False
assert halted["signal_limit_up"] is not True
assert "_has_history_state" not in result.columns
def test_repository_restores_active_rows_missing_from_latest_enriched(tmp_path):
today = date(2026, 7, 30)
repo = KlineRepository(DataStore(tmp_path))
repo._live_agg_cache = _live_state()
repo._instruments_cache = pl.DataFrame({
"symbol": ["600001.SH", "000820.SZ", "600002.SH"],
"name": ["已有历史", "复牌股票", "停牌股票"],
"float_shares": [1_000_000.0] * 3,
"limit_up": [11.0, 11.0, 11.0],
"limit_down": [9.0, 9.0, 9.0],
"as_of": [today] * 3,
})
daily_partition = tmp_path / "kline_daily" / f"date={today.isoformat()}"
daily_partition.mkdir(parents=True)
pl.DataFrame({
"symbol": ["600001.SH", "000820.SZ", "600002.SH"],
"date": [today] * 3,
"open": [10.2, 11.0, 0.0],
"high": [10.2, 11.0, 0.0],
"low": [10.2, 11.0, 0.0],
"close": [10.2, 11.0, 0.0],
"volume": [1200.0, 3000.0, 0.0],
"amount": [12240.0, 33000.0, 0.0],
}).write_parquet(daily_partition / "part.parquet")
existing_today = compute_enriched_today(
_live_state(),
_previous_enriched(),
pl.read_parquet(daily_partition / "part.parquet").head(1),
repo._instruments_cache,
)
history = _previous_enriched().with_columns(
pl.lit(today - timedelta(days=1)).alias("date"),
)
restored = repo._restore_missing_latest_rows(today, existing_today, history)
assert restored["symbol"].to_list() == ["000820.SZ", "600001.SH"]
resumed = restored.filter(pl.col("symbol") == "000820.SZ").row(0, named=True)
assert resumed["signal_limit_up"] is True
assert resumed["ma5"] is None