Datasets:
Tasks:
Tabular Classification
Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
economics
quantitative-finance
causal-inference
macroeconomics
housing-economics
market-microstructure
License:
File size: 5,310 Bytes
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | from __future__ import annotations
import pytest
from microstructure.data.book import (
BookInvariantError,
BookSnapshot,
DepthDelta,
IncrementalBookReconstructor,
reconstruct_snapshot_and_deltas,
)
def _snapshot() -> BookSnapshot:
return BookSnapshot(
venue="binance_spot",
symbol="BTCUSDT",
snapshot_id="snapshot-fixture",
request_ts_ns=1_000,
received_ts_ns=1_100,
available_ts_ns=1_100,
continuity_id="session-1",
last_update_id=100,
depth_limit=5000,
bids=((10_000, 10), (9_999, 20), (9_998, 30)),
asks=((10_001, 15), (10_002, 25), (10_003, 35)),
tick_size=0.01,
lot_size=0.001,
source_artifact_id="snapshot-fixture",
)
def _delta(
start: int,
end: int,
*,
bids: tuple[tuple[int, int], ...] = (),
asks: tuple[tuple[int, int], ...] = (),
event_ts_ns: int = 2_000,
previous: int | None = None,
continuity_id: str = "session-1",
tick_size: float = 0.01,
lot_size: float = 0.001,
) -> DepthDelta:
return DepthDelta(
venue="binance_spot",
symbol="BTCUSDT",
event_ts_ns=event_ts_ns,
received_ts_ns=event_ts_ns + 100,
available_ts_ns=event_ts_ns + 100,
availability_basis="local_receive_time",
capture_seq=end,
continuity_id=continuity_id,
first_update_id=start,
last_update_id=end,
previous_update_id=previous,
bids=bids,
asks=asks,
tick_size=tick_size,
lot_size=lot_size,
source_artifact_id=f"delta-{start}-{end}",
)
def test_reconstruction_discards_stale_bridges_snapshot_and_accepts_overlap() -> None:
result = reconstruct_snapshot_and_deltas(
_snapshot(),
[
_delta(98, 100, bids=((10_000, 999),)),
_delta(100, 102, bids=((10_000, 0),)),
_delta(102, 104, bids=((10_000, 12),), asks=((10_001, 18),)),
],
)
assert result.status == "LIVE"
assert result.stale_events == 1
assert result.final_update_id == 104
assert result.observations.num_rows == 2
first, second = result.observations.to_pylist()
assert first["best_bid_ticks"] == 9_999
assert second["best_bid_ticks"] == 10_000
assert second["sequence_start"] == 102
assert result.gaps.num_rows == 0
def test_forward_gap_is_recorded_and_later_events_are_not_applied() -> None:
result = reconstruct_snapshot_and_deltas(
_snapshot(),
[
_delta(100, 102),
_delta(105, 106, bids=((10_000, 20),)),
_delta(107, 108, bids=((10_000, 30),)),
],
)
assert result.status == "GAPPED"
assert result.final_update_id == 102
assert result.observations.num_rows == 1
[gap] = result.gaps.to_pylist()
assert gap["expected_sequence"] == 103
assert gap["missing_start"] == 103
assert gap["missing_end"] == 104
assert gap["reason"] == "forward_sequence_gap"
def test_crossed_book_is_emitted_as_invalid_then_epoch_stops() -> None:
result = reconstruct_snapshot_and_deltas(
_snapshot(),
[_delta(100, 101, bids=((10_002, 5),)), _delta(102, 102)],
)
assert result.status == "INVALID"
assert result.observations.num_rows == 1
assert result.observations.column("is_valid").to_pylist() == [False]
assert result.gaps.column("reason").to_pylist() == ["crossed_or_locked_book"]
assert result.final_update_id == 101
def test_sequence_order_not_timestamp_order_and_availability_waits_for_snapshot() -> None:
result = reconstruct_snapshot_and_deltas(
_snapshot(),
[
_delta(100, 101, event_ts_ns=5_000),
_delta(102, 102, event_ts_ns=4_000),
],
)
assert result.status == "LIVE"
assert result.final_update_id == 102
assert result.observations.column("event_ts_ns").to_pylist() == [5_000, 4_000]
assert all(
value >= _snapshot().available_ts_ns
for value in result.observations.column("available_ts_ns").to_pylist()
)
def test_previous_update_id_mismatch_requires_resynchronization() -> None:
result = reconstruct_snapshot_and_deltas(_snapshot(), [_delta(100, 102, previous=99)])
assert result.status == "GAPPED"
assert result.observations.num_rows == 0
assert result.gaps.column("reason").to_pylist() == ["previous_update_id_mismatch"]
def test_delta_scale_mismatch_fails_closed_before_reinterpretation() -> None:
with pytest.raises(BookInvariantError, match="scales do not match"):
reconstruct_snapshot_and_deltas(
_snapshot(),
[_delta(100, 101, tick_size=0.1)],
)
def test_malformed_stale_looking_range_is_invalid_and_later_delta_is_audited() -> None:
reconstructor = IncrementalBookReconstructor(_snapshot())
malformed = reconstructor.update(_delta(101, 99))
excluded = reconstructor.update(_delta(101, 101))
assert malformed.outcome == "INVALID"
assert malformed.gap is not None
assert malformed.gap.reason == "invalid_sequence_range"
assert reconstructor.stale_events == 0
assert excluded.outcome == "EXCLUDED_AFTER_TERMINAL"
assert excluded.gap is not None
assert excluded.gap.reason == "epoch_already_invalid"
|