Datasets:

ShawnChamberlain
/

open-economic-quant-research-data

	from __future__ import annotations

	from datetime import datetime

	import polars as pl

	from microstructure.research.l2_analysis import build_l2_descriptive_analysis

	SECOND = 1_000_000_000
	DATES = (
	("2026-08-10", "train"),
	("2026-08-11", "validation"),
	("2026-08-12", "primary_test"),
	("2026-08-13", "replication_test"),
	)
	ENDPOINTS = (
	("event_20", "event", 20, "events"),
	("event_100", "event", 100, "events"),
	("clock_1000ms", "clock", 1_000, "milliseconds"),
	("clock_5000ms", "clock", 5_000, "milliseconds"),
	)
	FEATURES = (
	"spread_bps",
	"depth_total_l1",
	"ofi_w20",
	"realized_volatility_w100",
	)


	def _frame(
	study_date: str,
	study_role: str,
	symbol: str,
	endpoint_name: str,
	domain: str,
	horizon: int,
	unit: str,
	) -> pl.DataFrame:
	start = int(datetime.fromisoformat(f"{study_date}T14:00:00+00:00").timestamp()) * SECOND
	continuity = f"{study_date}::{symbol}::observed-0000"
	rows: list[dict[str, object]] = []
	for sequence in range(140):
	decision = start + sequence * SECOND
	censored = sequence >= 120
	label_end = decision + (horizon * SECOND if domain == "event" else horizon * 1_000_000)
	ofi = float((sequence % 9) - 4)
	future_return = ofi * 1e-5 + (0.25e-5 if symbol == "BTCUSDT" else -0.1e-5)
	bid_quantity = 1.0 if sequence % 19 == 0 else 5.0 + (sequence % 7) * 0.2
	ask_quantity = 1.2 if sequence % 23 == 0 else 4.5 + (sequence % 5) * 0.2
	spread = 8.0 if sequence % 17 == 0 else 1.0 + (sequence % 3) * 0.1
	rows.append(
	{
	"study_date": study_date,
	"study_role": study_role,
	"endpoint_name": endpoint_name,
	"endpoint_domain": domain,
	"endpoint_horizon_value": horizon,
	"endpoint_horizon_unit": unit,
	"symbol": symbol,
	"continuity_id": continuity,
	"observed_interval_id": continuity,
	"observed_interval_start_ns": start,
	"observed_interval_end_ns_exclusive": start + 3_600 * SECOND,
	"decision_ts_ns": decision,
	"decision_sequence": sequence,
	"feature_cutoff_ts_ns": decision,
	"max_feature_source_ts_ns": decision,
	"max_feature_source_sequence": sequence,
	"feature_continuity_id": continuity,
	"label_start_ts_ns": decision,
	"label_start_sequence": sequence,
	"right_censored": censored,
	"future_mid_return": None if censored else future_return,
	"future_mid_up": None if censored else int(future_return > 0),
	"label_information_end_ts_ns": None if censored else label_end,
	"label_information_end_sequence": None if censored else sequence + horizon,
	"label_continuity_id": None if censored else continuity,
	"ofi_signed_future_mid_markout_bps": (
	None
	if censored
	else (1.0 if ofi > 0 else -1.0 if ofi < 0 else 0.0) * future_return * 10_000.0
	),
	"signed_markout_side_source": (
	"ofi_w20" if endpoint_name in {"event_20", "clock_1000ms"} else "ofi_w100"
	),
	"sample_id": f"{study_date}::{symbol}::{endpoint_name}::{sequence}",
	"mid_price": 100.0 + sequence * 0.01,
	"bid_quantity": bid_quantity,
	"ask_quantity": ask_quantity,
	"spread_bps": spread,
	"depth_total_l1": bid_quantity + ask_quantity,
	"depth_total_l5": bid_quantity + ask_quantity + 10.0,
	"depth_total_l10": bid_quantity + ask_quantity + 20.0,
	"queue_imbalance_l1": (bid_quantity - ask_quantity) / (bid_quantity + ask_quantity),
	"realized_volatility_w100": 0.001 + (sequence % 13) * 0.0001,
	"ofi_w20": ofi,
	"ofi_w100": ofi * 0.8,
	"volatility_regime": ("low" if sequence % 3 == 0 else "high"),
	"liquidity_regime": ("liquid" if sequence % 4 else "stressed"),
	}
	)
	return pl.DataFrame(rows, infer_schema_length=None)


	def _all_frames() -> list[pl.DataFrame]:
	return [
	_frame(study_date, role, symbol, name, domain, horizon, unit)
	for study_date, role in DATES
	for symbol in ("BTCUSDT", "ETHUSDT")
	for name, domain, horizon, unit in ENDPOINTS
	]


	def test_l2_descriptive_outputs_are_date_symbol_endpoint_explicit() -> None:
	result = build_l2_descriptive_analysis(
	_all_frames(), feature_columns=FEATURES, stability_bins=5
	)

	assert result.intraday_liquidity.height == 8 * 3
	assert result.ofi_return_association.height == 32
	assert result.signal_half_life.height == 32
	assert result.liquidity_recovery.height == 16
	assert result.regime_diagnostics.height > 32
	assert result.feature_stability.height == 2 * 2 * 4 * len(FEATURES)
	assert result.cross_instrument_stability.height == 16
	assert result.cross_instrument_stability.get_column("cross_instrument_pooling").not_().all()
	assert set(result.ofi_return_association.get_column("interpretation").unique()) == {
	"descriptive_book_flow_markout_not_trade_impact"
	}


	def test_shock_thresholds_and_stability_reference_are_development_only() -> None:
	frames = _all_frames()
	original = build_l2_descriptive_analysis(frames, feature_columns=FEATURES, stability_bins=5)
	mutated = [
	frame.with_columns(
	pl.when(pl.col("study_role").is_in(["primary_test", "replication_test"]))
	.then(pl.col("spread_bps") * 100.0)
	.otherwise(pl.col("spread_bps"))
	.alias("spread_bps")
	)
	for frame in frames
	]
	changed = build_l2_descriptive_analysis(mutated, feature_columns=FEATURES, stability_bins=5)

	assert (
	original.liquidity_recovery.select(
	"symbol", "train_spread_q95", "train_executable_depth_q05"
	)
	.unique()
	.sort("symbol")
	.equals(
	changed.liquidity_recovery.select(
	"symbol", "train_spread_q95", "train_executable_depth_q05"
	)
	.unique()
	.sort("symbol")
	)
	)
	assert set(changed.feature_stability.get_column("reference_scope").unique()) == {
	"train_plus_validation_only"
	}