1"""Row-level and session-level validation of canonical bars. Flags, never mutates prices."""2from __future__ import annotations34from datetime import date5from typing import Any67import polars as pl8import pyarrow as pa9from findata_schema import INTERVAL_SECONDS, Interval, QualityFlag1011from findata_quality.calendar import SessionCalendar, xnys1213PRICE_JUMP_THRESHOLD = 0.25 # 25 % between consecutive 1-minute closes inside a session (flag only)141516def validate_bars(table: pa.Table, interval: Interval, calendar: SessionCalendar | None = None,17 check_sessions: bool = True) -> tuple[pa.Table, list[dict[str, Any]]]:18 """Return (table with `quality_flags` filled, anomalies list). Input must follow BARS_SCHEMA columns."""19 cal = calendar or xnys()20 step = INTERVAL_SECONDS[interval]21 df = pl.from_arrow(table)22 if df.height == 0:23 return table, []24 df = df.sort(["symbol_as_reported", "timestamp_utc"])25 ts_s = pl.col("timestamp_utc").dt.epoch("s")26 flags: list[pl.Expr] = [27 pl.when((pl.col("high") < pl.max_horizontal("open", "close")) | (pl.col("low") > pl.min_horizontal("open", "close"))28 | (pl.col("high") < pl.col("low"))).then(pl.lit(QualityFlag.ohlc_inconsistent.value)),29 pl.when(pl.min_horizontal("open", "high", "low", "close") <= 0).then(pl.lit(QualityFlag.nonpositive_price.value)),30 pl.when(pl.col("volume") < 0).then(pl.lit(QualityFlag.negative_volume.value)),31 pl.when(pl.col("volume") == 0).then(pl.lit(QualityFlag.zero_volume.value)),32 pl.when(pl.col("volume") != pl.col("volume").floor()).then(pl.lit(QualityFlag.fractional_volume.value)),33 pl.when((ts_s % step) != 0).then(pl.lit(QualityFlag.off_interval.value)),34 pl.when(pl.struct(["symbol_as_reported", "timestamp_utc"]).is_duplicated()).then(pl.lit(QualityFlag.duplicate.value)),35 ]36 if interval != Interval.d1:37 prev_close = pl.col("close").shift(1).over(["symbol_as_reported", "session_date"])38 flags.append(pl.when(((pl.col("close") / prev_close) - 1).abs() > PRICE_JUMP_THRESHOLD)39 .then(pl.lit(QualityFlag.price_jump.value)))40 if check_sessions and interval != Interval.d1:41 # outside extended hours / non trading day: computed per distinct session_date42 dates = df.get_column("session_date").unique().to_list()43 bounds = {d: cal.extended_bounds_utc(d) for d in dates}44 non_sessions = [d for d in dates if not cal.is_session(d)]45 lo = pl.col("session_date").replace_strict({d: b[0] for d, b in bounds.items()}, return_dtype=pl.Datetime("us", "UTC"))46 hi = pl.col("session_date").replace_strict({d: b[1] for d, b in bounds.items()}, return_dtype=pl.Datetime("us", "UTC"))47 flags.append(pl.when((pl.col("timestamp_utc") < lo) | (pl.col("timestamp_utc") >= hi))48 .then(pl.lit(QualityFlag.outside_session.value)))49 if non_sessions:50 flags.append(pl.when(pl.col("session_date").is_in(non_sessions)).then(pl.lit(QualityFlag.non_trading_day.value)))51 df = df.with_columns(52 pl.concat_list([f.otherwise(None) for f in flags]).list.drop_nulls().alias("quality_flags")53 )54 anomalies = _summarize(df)55 out = df.to_arrow()56 # cast back to the canonical schema (polars may widen list/int types)57 out = out.select(table.column_names).cast(table.schema)58 return out, anomalies596061def _summarize(df: pl.DataFrame) -> list[dict[str, Any]]:62 flagged = df.filter(pl.col("quality_flags").list.len() > 0)63 if flagged.height == 0:64 return []65 agg = (flagged.explode("quality_flags")66 .group_by(["symbol_as_reported", "session_date", "quality_flags"]).agg(pl.len().alias("n"))67 .sort(["symbol_as_reported", "session_date", "quality_flags"]))68 sev = {QualityFlag.zero_volume.value: "info", QualityFlag.fractional_volume.value: "info",69 QualityFlag.price_jump.value: "warning", QualityFlag.outside_session.value: "warning",70 QualityFlag.gap.value: "info"}71 return [{"symbol": r["symbol_as_reported"], "session_date": r["session_date"], "code": r["quality_flags"],72 "severity": sev.get(r["quality_flags"], "error"), "detail": {"rows": r["n"]}}73 for r in agg.iter_rows(named=True)]747576def session_coverage(table: pa.Table, interval: Interval, calendar: SessionCalendar | None = None) -> list[dict[str, Any]]:77 """Per (symbol, session_date): bar count, first/last ts, expected regular-hours bars, gap flags."""78 cal = calendar or xnys()79 step = INTERVAL_SECONDS[interval]80 df = pl.from_arrow(table)81 if df.height == 0:82 return []83 g = (df.group_by(["symbol_as_reported", "session_date"])84 .agg(pl.len().alias("bar_count"), pl.col("timestamp_utc").min().alias("first_ts"),85 pl.col("timestamp_utc").max().alias("last_ts"))86 .sort(["symbol_as_reported", "session_date"]))87 rows = []88 for r in g.iter_rows(named=True):89 d: date = r["session_date"]90 exp = cal.expected_regular_bars(d, step) if interval != Interval.d1 else 191 flags = []92 if not cal.is_session(d):93 flags.append(QualityFlag.non_trading_day.value)94 rows.append({"symbol": r["symbol_as_reported"], "session_date": d, "bar_count": r["bar_count"],95 "first_ts": r["first_ts"], "last_ts": r["last_ts"], "expected_bars": exp, "flags": flags})96 return rows97