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Tabular Classification
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economics
quantitative-finance
causal-inference
macroeconomics
housing-economics
market-microstructure
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ebcde1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | from __future__ import annotations
import polars as pl
import pytest
from microstructure.config import EvaluationConfig
from microstructure.research.splits import SplitError, expanding_walk_forward_splits
def _config(*, embargo: int = 2) -> EvaluationConfig:
return EvaluationConfig(
min_train_events=6,
validation_events=3,
test_events=3,
step_events=3,
embargo_events=embargo,
bootstrap_samples=50,
calibration_bins=5,
)
def _interval_frame(rows: int = 16) -> pl.DataFrame:
decision = list(range(rows))
censored = [index + 2 >= rows for index in decision]
return pl.DataFrame(
{
"symbol": ["BTCUSDT"] * rows,
"decision_ts_ns": decision,
"label_information_end_ts_ns": [
None if is_censored else index + 2
for index, is_censored in zip(decision, censored, strict=True)
],
"right_censored": censored,
}
)
def test_expanding_folds_purge_overlap_and_apply_embargo() -> None:
frame = _interval_frame()
plan = expanding_walk_forward_splits(frame, _config())
assert len(plan.folds) == 2
first = plan.folds[0]
assert first.validation_start_ts_ns == 6
assert first.validation_end_ts_ns == 8
assert first.train_indices.tolist() == [0, 1, 2, 3]
assert first.validation_indices.tolist() == [6, 7, 8]
assert first.purged_rows == 2
assert first.embargoed_time_buckets == 2
second = plan.folds[1]
assert second.train_indices.tolist() == list(range(7))
assert second.validation_indices.tolist() == [9, 10]
assert set(first.train_indices).issubset(set(second.train_indices))
for fold in plan.folds:
train = frame[fold.train_indices]
validation = frame[fold.validation_indices]
assert train.get_column("label_information_end_ts_ns").max() < fold.validation_start_ts_ns
assert validation.get_column("label_information_end_ts_ns").max() < plan.test_start_ts_ns
assert fold.train_end_ts_ns < fold.validation_start_ts_ns
def test_final_period_is_frozen_and_never_enters_development() -> None:
plan = expanding_walk_forward_splits(_interval_frame(), _config())
assert plan.test_start_ts_ns == 13
assert plan.test_end_ts_ns == 15
# The final two decisions are censored; only t=13 has a complete two-event target.
assert plan.test_indices.tolist() == [13]
development_validation = {
int(index) for fold in plan.folds for index in fold.validation_indices
}
assert development_validation.isdisjoint(set(plan.test_indices))
assert max(plan.final_train_indices) < min(plan.test_indices)
def test_validation_labels_cannot_end_inside_final_test() -> None:
frame = _interval_frame(rows=50)
config = EvaluationConfig(
min_train_events=20,
validation_events=10,
test_events=10,
step_events=10,
embargo_events=2,
bootstrap_samples=50,
calibration_bins=5,
)
plan = expanding_walk_forward_splits(frame, config)
assert plan.test_start_ts_ns == 40
last_validation = frame.with_row_index("row_id").filter(
pl.col("row_id").is_in(plan.folds[-1].validation_indices)
)
assert last_validation.get_column("decision_ts_ns").to_list() == list(range(30, 38))
assert last_validation.get_column("label_information_end_ts_ns").max() < plan.test_start_ts_ns
def test_same_timestamp_instruments_stay_in_the_same_fold() -> None:
base = _interval_frame()
eth = base.with_columns(pl.lit("ETHUSDT").alias("symbol"))
pooled = pl.concat([base, eth]).sort(["decision_ts_ns", "symbol"])
plan = expanding_walk_forward_splits(pooled, _config())
first_validation = pooled.with_row_index("row_id").filter(
pl.col("row_id").is_in(plan.folds[0].validation_indices)
)
counts = first_validation.group_by("decision_ts_ns").len().get_column("len")
assert counts.to_list() == [2, 2, 2]
def test_too_short_sample_fails_closed() -> None:
with pytest.raises(SplitError, match="need at least"):
expanding_walk_forward_splits(_interval_frame(rows=10), _config())
|