Drop the `gap_dt_unit: str` column
We don't need it in `detect_time_gaps()` since doing straight up datetime diffs in `polars` already has a humanized `str` representation but with higher precision like '2d 1h 24m 1s' B)account_tests
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@ -18,7 +18,8 @@
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Financial time series processing utilities usually
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Financial time series processing utilities usually
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pertaining to OHLCV style sampled data.
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pertaining to OHLCV style sampled data.
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Routines are generally implemented in either ``numpy`` or ``polars`` B)
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Routines are generally implemented in either ``numpy`` or
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``polars`` B)
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'''
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'''
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from __future__ import annotations
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from __future__ import annotations
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@ -269,9 +270,14 @@ def detect_time_gaps(
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# gap_dt_unit: t_unit = 'minutes',
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# gap_dt_unit: t_unit = 'minutes',
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# gap_thresh: int = 1,
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# gap_thresh: int = 1,
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# legacy stock mkts
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# NOTE: legacy stock mkts have venue operating hours
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# and thus gaps normally no more then 1-2 days at
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# a time.
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# XXX -> must be valid ``polars.Expr.dt.<name>``
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# TODO: allow passing in a frame of operating hours
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# durations/ranges for faster legit gap checks.
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gap_dt_unit: t_unit = 'days',
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gap_dt_unit: t_unit = 'days',
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gap_thresh: int = 2,
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gap_thresh: int = 1,
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) -> pl.DataFrame:
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) -> pl.DataFrame:
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'''
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'''
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@ -281,18 +287,17 @@ def detect_time_gaps(
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actual missing data segments.
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actual missing data segments.
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'''
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'''
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dt_gap_col: str = f'{gap_dt_unit}_diff'
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return (
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return with_dts(
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with_dts(df)
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df
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.filter(
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).filter(
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pl.col('s_diff').abs() > expect_period
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pl.col('s_diff').abs() > expect_period
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)
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).with_columns(
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.filter(
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getattr(
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getattr(
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pl.col('dt_diff').dt,
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pl.col('dt_diff').dt,
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gap_dt_unit, # NOTE: must be valid ``Expr.dt.<name>``
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gap_dt_unit,
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)().alias(dt_gap_col)
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)().abs() > gap_thresh
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).filter(
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)
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pl.col(dt_gap_col).abs() > gap_thresh
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)
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)
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