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import math
import polars as pl
import pytest
from microstructure.config import FeatureConfig
from microstructure.research.features import (
ResearchDataError,
TemporalLeakageError,
build_research_features,
build_research_frame,
model_feature_columns,
validate_temporal_contract,
)
SECOND = 1_000_000_000
def _feature_config() -> FeatureConfig:
return FeatureConfig(
trade_windows=(2,),
volatility_window=2,
intensity_window=2,
label_horizon_events=2,
large_trade_quantile=0.9,
)
def _books() -> pl.DataFrame:
rows = [
(0, "segment-a", 1, 100.0, 10.0, 102.0, 10.0),
(1, "segment-a", 2, 100.0, 12.0, 102.0, 8.0),
(2, "segment-a", 3, 101.0, 5.0, 102.0, 5.0),
(3, "segment-a", 4, 101.0, 4.0, 103.0, 6.0),
(4, "segment-a", 5, 102.0, 8.0, 103.0, 4.0),
(5, "segment-a", 6, 102.0, 6.0, 104.0, 6.0),
(6, "segment-b", 20, 110.0, 5.0, 112.0, 5.0),
(7, "segment-b", 21, 110.0, 7.0, 112.0, 3.0),
(8, "segment-b", 22, 111.0, 5.0, 112.0, 5.0),
]
return pl.DataFrame(
{
"symbol": ["BTCUSDT"] * len(rows),
"event_ts_ns": [row[0] * SECOND for row in rows],
"available_ts_ns": [row[0] * SECOND for row in rows],
"continuity_id": [row[1] for row in rows],
"sequence_end": [row[2] for row in rows],
"is_valid": [True] * len(rows),
"best_bid": [row[3] for row in rows],
"bid_quantity": [row[4] for row in rows],
"best_ask": [row[5] for row in rows],
"ask_quantity": [row[6] for row in rows],
}
)
def _trades() -> pl.DataFrame:
return pl.DataFrame(
{
"symbol": ["BTCUSDT", "BTCUSDT", "BTCUSDT", "BTCUSDT"],
"continuity_id": ["segment-a", "segment-a", "segment-a", "segment-b"],
"trade_id": [1, 2, 3, 4],
"available_ts_ns": [SECOND // 2, SECOND, 3 * SECOND // 2, 7 * SECOND],
"quantity": [2.0, 7.0, 3.0, 100.0],
"aggressor_side": ["buy", "sell", "sell", "buy"],
}
)
def test_hand_checked_causal_features_and_strict_trade_tie() -> None:
frame = build_research_frame(_books(), _trades(), _feature_config())
at_one = frame.filter(pl.col("decision_ts_ns") == SECOND).row(0, named=True)
assert at_one["mid_price"] == 101.0
assert at_one["spread"] == 2.0
assert at_one["queue_imbalance_l1"] == pytest.approx(0.2)
assert at_one["causal_microprice"] == pytest.approx(101.2)
assert at_one["ofi_l1"] == pytest.approx(4.0)
# The buy at 0.5 seconds is known. The sell timestamped exactly at this
# book decision is from a separately ordered archive stream and is excluded.
assert at_one["signed_trade_volume_w2"] == pytest.approx(2.0)
assert at_one["trade_feature_max_source_ts_ns"] == SECOND // 2
assert at_one["trade_feature_max_source_ts_ns"] < at_one["decision_ts_ns"]
assert at_one["realized_price_impact_bps_1"] == pytest.approx(0.0)
assert at_one["spread_recovery_bps_1"] == pytest.approx(0.0)
assert at_one["depth_recovery_l1_1"] == pytest.approx(0.0)
def test_feature_stage_can_be_reused_before_any_label_horizon_is_opened() -> None:
features = build_research_features(_books(), _trades(), _feature_config())
labeled = build_research_frame(_books(), _trades(), _feature_config())
assert "future_mid_return" not in features.columns
assert features.select("symbol", "continuity_id", "decision_sequence").equals(
labeled.select("symbol", "continuity_id", "decision_sequence")
)
assert (
features.get_column("max_feature_source_ts_ns").to_list()
== labeled.get_column("max_feature_source_ts_ns").to_list()
)
def test_optional_multilevel_depth_and_cancellation_enter_canonical_frame() -> None:
books = _books().with_columns(
pl.lit("binance_spot").alias("venue"),
(pl.col("bid_quantity") + 4.0).alias("depth_bid_5"),
(pl.col("ask_quantity") + 6.0).alias("depth_ask_5"),
(pl.col("bid_quantity") + 14.0).alias("depth_bid_10"),
(pl.col("ask_quantity") + 16.0).alias("depth_ask_10"),
)
depth_deltas = pl.DataFrame(
{
"venue": ["binance_spot"] * books.height,
"symbol": ["BTCUSDT"] * books.height,
"event_ts_ns": books.get_column("event_ts_ns"),
"available_ts_ns": books.get_column("available_ts_ns"),
"continuity_id": books.get_column("continuity_id"),
"first_update_id": books.get_column("sequence_end"),
"last_update_id": books.get_column("sequence_end"),
"bids": [
[{"price_ticks": 10_000, "quantity_lots": 0 if index == 1 else 10}]
for index in range(books.height)
],
"asks": [[{"price_ticks": 10_200, "quantity_lots": 10}] for _ in range(books.height)],
}
)
frame = build_research_frame(
books,
_trades(),
_feature_config(),
depth_deltas=depth_deltas,
)
at_one = frame.filter(pl.col("decision_ts_ns") == SECOND).row(0, named=True)
assert at_one["depth_total_l5"] == pytest.approx(30.0)
assert at_one["queue_imbalance_l5"] == pytest.approx(2.0 / 30.0)
assert at_one["cancellation_deletes_w2"] == 1
assert at_one["cancellation_intensity_w2"] == pytest.approx(0.25)
assert at_one["cancellation_feature_max_source_ts_ns"] == SECOND
assert "cancellation_intensity_w2" in model_feature_columns(frame)
validate_temporal_contract(frame)
def test_future_event_label_is_exact_right_censored_and_gap_local() -> None:
frame = build_research_frame(_books(), _trades(), _feature_config())
at_two = frame.filter(pl.col("decision_ts_ns") == 2 * SECOND).row(0, named=True)
assert at_two["future_mid_return"] == pytest.approx(math.log(102.5 / 101.5))
assert at_two["future_mid_direction"] == 1
assert at_two["future_mid_up"] == 1
assert at_two["label_information_end_ts_ns"] == 4 * SECOND
# The final two rows of segment A may not look into segment B even though
# later book observations exist globally.
segment_a_tail = frame.filter(
(pl.col("continuity_id") == "segment-a") & (pl.col("decision_ts_ns") >= 4 * SECOND)
)
assert segment_a_tail.get_column("right_censored").to_list() == [True, True]
assert segment_a_tail.get_column("future_mid_return").null_count() == 2
first_b = (
frame.filter(pl.col("continuity_id") == "segment-b")
.sort("decision_sequence")
.row(0, named=True)
)
assert first_b["ofi_l1"] == 0.0
assert first_b["signed_trade_volume_w2"] == 0.0
audit = validate_temporal_contract(frame)
assert audit.rows == 9
assert audit.right_censored_rows == 4
assert audit.continuity_segments == 2
def test_delayed_old_continuity_trade_cannot_enter_new_segment_features() -> None:
trades = pl.DataFrame(
{
"symbol": ["BTCUSDT", "BTCUSDT"],
"continuity_id": ["segment-b", "segment-a"],
"trade_id": [20, 21],
"available_ts_ns": [6 * SECOND + SECOND // 4, 6 * SECOND + SECOND // 2],
"quantity": [3.0, 100.0],
"aggressor_side": ["sell", "buy"],
}
)
frame = build_research_frame(_books(), trades, _feature_config())
second_b = frame.filter(
(pl.col("continuity_id") == "segment-b") & (pl.col("decision_ts_ns") == 7 * SECOND)
).row(0, named=True)
assert second_b["signed_trade_volume_w2"] == pytest.approx(-3.0)
assert second_b["trade_volume_w2"] == pytest.approx(3.0)
assert second_b["trade_feature_max_source_ts_ns"] == 6 * SECOND + SECOND // 4
def test_unsegmented_trades_fail_closed_for_multi_segment_books() -> None:
trades_without_continuity = _trades().drop("continuity_id")
with pytest.raises(ResearchDataError, match="require continuity_id"):
build_research_frame(_books(), trades_without_continuity, _feature_config())
def test_mutating_the_future_cannot_change_past_features() -> None:
original_books = _books()
original = build_research_frame(original_books, _trades(), _feature_config())
mutated_books = original_books.with_columns(
pl.when(pl.col("available_ts_ns") > 2 * SECOND)
.then(pl.col("best_bid") + 10_000.0)
.otherwise(pl.col("best_bid"))
.alias("best_bid"),
pl.when(pl.col("available_ts_ns") > 2 * SECOND)
.then(pl.col("best_ask") + 10_000.0)
.otherwise(pl.col("best_ask"))
.alias("best_ask"),
)
mutated_trades = pl.concat(
[
_trades(),
pl.DataFrame(
{
"symbol": ["BTCUSDT"],
"continuity_id": ["segment-a"],
"trade_id": [99],
"available_ts_ns": [3 * SECOND],
"quantity": [1_000_000.0],
"aggressor_side": ["buy"],
}
),
]
)
mutated = build_research_frame(mutated_books, mutated_trades, _feature_config())
feature_columns = [
"mid_price",
"spread_bps",
"queue_imbalance_l1",
"causal_microprice",
"ofi_l1",
"signed_trade_volume_w2",
"trade_volume_w2",
"realized_volatility_w2",
]
cutoff = pl.col("decision_ts_ns") <= 2 * SECOND
assert (
original.filter(cutoff)
.select(feature_columns)
.equals(mutated.filter(cutoff).select(feature_columns))
)
def test_lineage_guard_rejects_deliberate_future_source() -> None:
frame = build_research_frame(_books(), _trades(), _feature_config())
leaked = frame.with_columns(
(pl.col("feature_cutoff_ts_ns") + 1).alias("max_feature_source_ts_ns")
)
with pytest.raises(TemporalLeakageError, match="feature lineage"):
validate_temporal_contract(leaked)
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