Datasets:
Tasks:
Tabular Classification
Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
economics
quantitative-finance
causal-inference
macroeconomics
housing-economics
market-microstructure
License:
| 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()) | |