Datasets:
Tasks:
Tabular Classification
Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
economics
quantitative-finance
causal-inference
macroeconomics
housing-economics
market-microstructure
License:
File size: 25,789 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 | """Auxiliary, explicitly future-dependent research labels.
These builders never produce model features. Each output records the end of
the information interval, right-censoring, and the assumptions needed to
interpret the label. Clock joins and fill evidence are strict with respect to
the decision/activation time and never cross ``continuity_id`` boundaries.
"""
from __future__ import annotations
import math
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Literal, cast
import polars as pl
class AuxiliaryLabelError(ValueError):
"""Raised when an auxiliary label cannot satisfy its temporal contract."""
Side = Literal["buy", "sell"]
@dataclass(frozen=True, slots=True)
class ClockTimeLabelSpec:
"""Clock horizons and admissible delay to the first observed target state."""
horizons_ns: tuple[int, ...]
max_target_staleness_ns: int | None = None
def __post_init__(self) -> None:
if not self.horizons_ns or any(value <= 0 for value in self.horizons_ns):
raise AuxiliaryLabelError("clock horizons must be nonempty and strictly positive")
if self.max_target_staleness_ns is not None and self.max_target_staleness_ns < 0:
raise AuxiliaryLabelError("max target staleness must be nonnegative")
@dataclass(frozen=True, slots=True)
class LimitFillAssumptions:
"""Observable-input queue proxy for a hypothetical best-quote limit order."""
side: Side
horizon_ns: int
order_quantity: float
queue_ahead_fraction: float = 1.0
activation_latency_ns: int = 0
def __post_init__(self) -> None:
if self.side not in {"buy", "sell"}:
raise AuxiliaryLabelError("limit side must be buy or sell")
if self.horizon_ns <= 0:
raise AuxiliaryLabelError("fill horizon must be positive")
if not math.isfinite(self.order_quantity) or self.order_quantity <= 0:
raise AuxiliaryLabelError("order quantity must be finite and positive")
if not 0.0 <= self.queue_ahead_fraction <= 1.0:
raise AuxiliaryLabelError("queue_ahead_fraction must be in [0, 1]")
if self.activation_latency_ns < 0:
raise AuxiliaryLabelError("activation latency must be nonnegative")
@dataclass(frozen=True, slots=True)
class AdverseSelectionSpec:
"""Post-fill markout horizons and target-state staleness bound."""
horizons_ns: tuple[int, ...]
max_target_staleness_ns: int | None = None
def __post_init__(self) -> None:
ClockTimeLabelSpec(self.horizons_ns, self.max_target_staleness_ns)
_GROUP_COLUMNS = ("symbol", "continuity_id")
def _require(
frame: pl.DataFrame,
columns: Sequence[str],
table: str,
*,
allow_empty: bool = False,
) -> None:
missing = sorted(set(columns).difference(frame.columns))
if missing:
raise AuxiliaryLabelError(f"{table} is missing required columns: {missing}")
if frame.is_empty() and not allow_empty:
raise AuxiliaryLabelError(f"{table} must not be empty")
def _valid_books(
books: pl.DataFrame,
*,
time_column: str,
mid_column: str,
) -> pl.DataFrame:
_require(books, (*_GROUP_COLUMNS, time_column, mid_column), "book states")
result = books.filter(pl.col("is_valid")) if "is_valid" in books.columns else books
if result.is_empty():
raise AuxiliaryLabelError("no valid book states are available")
invalid = result.filter(
pl.col("continuity_id").is_null()
| pl.col(time_column).is_null()
| pl.col(mid_column).is_null()
| (pl.col(mid_column) <= 0)
)
if not invalid.is_empty():
raise AuxiliaryLabelError("valid book states require segment, time, and positive mid")
return result.sort([time_column, *_GROUP_COLUMNS])
def _forward_mid_join(
decisions: pl.DataFrame,
books: pl.DataFrame,
*,
decision_time_column: str,
book_time_column: str,
mid_column: str,
target_column: str,
book_identity_column: str | None = None,
) -> pl.DataFrame:
right_columns: list[pl.Expr | str] = [
*_GROUP_COLUMNS,
pl.col(book_time_column).alias("_matched_target_ts_ns"),
pl.col(mid_column).alias("_matched_target_mid"),
]
if book_identity_column is not None:
right_columns.append(pl.col(book_identity_column).alias("_matched_target_identity"))
right = books.select(right_columns).sort(["_matched_target_ts_ns", *_GROUP_COLUMNS])
return (
decisions.sort([target_column, *_GROUP_COLUMNS])
.join_asof(
right,
left_on=target_column,
right_on="_matched_target_ts_ns",
by=list(_GROUP_COLUMNS),
strategy="forward",
allow_exact_matches=True,
check_sortedness=False,
)
.sort([decision_time_column, *_GROUP_COLUMNS])
)
def build_clock_time_mid_labels(
decisions: pl.DataFrame,
spec: ClockTimeLabelSpec,
*,
book_states: pl.DataFrame | None = None,
decision_time_column: str = "decision_ts_ns",
mid_column: str = "mid_price",
book_time_column: str | None = None,
book_mid_column: str | None = None,
book_identity_column: str | None = None,
) -> pl.DataFrame:
"""Build long-form clock-time return/direction labels within book segments."""
_require(decisions, (*_GROUP_COLUMNS, decision_time_column, mid_column), "decisions")
invalid_decisions = decisions.filter(
pl.col("continuity_id").is_null()
| pl.col(decision_time_column).is_null()
| pl.col(mid_column).is_null()
| (pl.col(mid_column) <= 0)
)
if invalid_decisions.height:
raise AuxiliaryLabelError("decisions require segment, time, and positive current mid")
target_states = decisions if book_states is None else book_states
target_time = book_time_column or decision_time_column
target_mid = book_mid_column or mid_column
books = _valid_books(target_states, time_column=target_time, mid_column=target_mid)
if book_identity_column is not None:
_require(books, (book_identity_column,), "clock-time target states")
if books.get_column(book_identity_column).null_count():
raise AuxiliaryLabelError("clock-time target identity must not contain nulls")
eligible_decisions = (
decisions.filter(pl.col("is_valid"))
if book_states is None and "is_valid" in decisions.columns
else decisions
)
outputs: list[pl.DataFrame] = []
for horizon_ns in sorted(set(spec.horizons_ns)):
with_target = eligible_decisions.with_columns(
(pl.col(decision_time_column) + horizon_ns).alias("clock_target_ts_ns")
)
joined = _forward_mid_join(
with_target,
books,
decision_time_column=decision_time_column,
book_time_column=target_time,
mid_column=target_mid,
target_column="clock_target_ts_ns",
book_identity_column=book_identity_column,
).with_columns(
(pl.col("_matched_target_ts_ns") - pl.col("clock_target_ts_ns")).alias(
"clock_target_staleness_ns"
)
)
censored = pl.col("_matched_target_ts_ns").is_null()
if spec.max_target_staleness_ns is not None:
censored = censored | (
pl.col("clock_target_staleness_ns") > spec.max_target_staleness_ns
)
labeled = (
joined.with_columns(
censored.alias("clock_right_censored"),
pl.lit(horizon_ns, dtype=pl.Int64).alias("clock_horizon_ns"),
pl.lit("clock_time_mid_return", dtype=pl.String).alias("clock_label_kind"),
pl.lit(
"first valid state at or after t+h in the same continuity segment",
dtype=pl.String,
).alias("clock_label_assumption"),
pl.lit(True).alias("clock_label_is_descriptive"),
pl.col(decision_time_column).alias("clock_label_start_ts_ns"),
pl.when(pl.col("_matched_target_ts_ns").is_null())
.then(pl.lit("no_same_segment_future_state"))
.when(censored)
.then(pl.lit("target_state_too_stale"))
.otherwise(None)
.alias("clock_censor_reason"),
)
.with_columns(
pl.when(~pl.col("clock_right_censored"))
.then((pl.col("_matched_target_mid") / pl.col(mid_column)).log())
.otherwise(None)
.alias("clock_future_mid_return"),
pl.when(~pl.col("clock_right_censored"))
.then(pl.col("_matched_target_mid"))
.otherwise(None)
.alias("clock_target_mid_price"),
pl.when(~pl.col("clock_right_censored"))
.then(pl.col("_matched_target_ts_ns"))
.otherwise(None)
.alias("clock_label_information_end_ts_ns"),
pl.when(~pl.col("clock_right_censored"))
.then(pl.col("clock_target_staleness_ns"))
.otherwise(None)
.alias("clock_observed_target_staleness_ns"),
)
.with_columns(
pl.when(pl.col("clock_future_mid_return").is_null())
.then(None)
.when(pl.col("clock_future_mid_return") > 0)
.then(1)
.when(pl.col("clock_future_mid_return") < 0)
.then(-1)
.otherwise(0)
.cast(pl.Int8)
.alias("clock_future_mid_direction")
)
)
if book_identity_column is not None:
labeled = labeled.with_columns(
pl.when(~pl.col("clock_right_censored"))
.then(pl.col("_matched_target_identity"))
.otherwise(None)
.alias("clock_label_information_end_identity")
)
outputs.append(labeled)
drop_columns = ["_matched_target_ts_ns", "_matched_target_mid"]
if book_identity_column is not None:
drop_columns.append("_matched_target_identity")
return (
pl.concat(outputs, how="diagonal_relaxed")
.drop(*drop_columns)
.sort([decision_time_column, "clock_horizon_ns", *_GROUP_COLUMNS])
)
def _side_expression(column: str) -> pl.Expr:
return (
pl.when(pl.col(column).cast(pl.String).str.to_lowercase().is_in(["buy", "1", "1.0"]))
.then(1.0)
.when(pl.col(column).cast(pl.String).str.to_lowercase().is_in(["sell", "-1", "-1.0"]))
.then(-1.0)
.otherwise(None)
)
def add_event_time_price_impact_labels(
frame: pl.DataFrame,
*,
side_column: str,
return_column: str = "future_mid_return",
information_end_column: str = "label_information_end_ts_ns",
right_censored_column: str = "right_censored",
) -> pl.DataFrame:
"""Side-sign a strictly future event-time return without changing its horizon."""
_require(
frame,
(
side_column,
return_column,
information_end_column,
right_censored_column,
"label_horizon_events",
),
"event-time impact frame",
)
result = frame.with_columns(_side_expression(side_column).alias("event_impact_side_sign"))
if result.filter(pl.col("event_impact_side_sign").is_null()).height:
raise AuxiliaryLabelError("event-time impact side must be buy/sell or +1/-1")
invalid_timing = result.filter(
(~pl.col(right_censored_column) & pl.col(information_end_column).is_null())
| (pl.col(right_censored_column) & pl.col(return_column).is_not_null())
)
if invalid_timing.height:
raise AuxiliaryLabelError("event-time return censoring and information end disagree")
return result.with_columns(
pl.when(~pl.col(right_censored_column))
.then(10_000.0 * pl.col("event_impact_side_sign") * pl.col(return_column))
.otherwise(None)
.alias("event_time_signed_price_impact_bps"),
pl.col(information_end_column).alias("event_impact_label_information_end_ts_ns"),
pl.col(right_censored_column).alias("event_impact_right_censored"),
pl.lit("event_time_signed_price_impact").alias("event_impact_label_kind"),
pl.lit(True).alias("event_impact_label_is_descriptive"),
pl.lit("aggressor-side sign times strictly future same-segment log-mid return").alias(
"event_impact_label_assumption"
),
)
def build_clock_time_price_impact_labels(
decisions: pl.DataFrame,
spec: ClockTimeLabelSpec,
*,
book_states: pl.DataFrame | None = None,
side_column: str = "trade_sign",
decision_time_column: str = "decision_ts_ns",
mid_column: str = "mid_price",
book_time_column: str | None = None,
book_mid_column: str | None = None,
) -> pl.DataFrame:
"""Add aggressor-signed clock-time price impact to mid-return labels."""
_require(decisions, (side_column,), "impact decisions")
result = build_clock_time_mid_labels(
decisions,
spec,
book_states=book_states,
decision_time_column=decision_time_column,
mid_column=mid_column,
book_time_column=book_time_column,
book_mid_column=book_mid_column,
).with_columns(_side_expression(side_column).alias("clock_impact_side_sign"))
if result.filter(pl.col("clock_impact_side_sign").is_null()).height:
raise AuxiliaryLabelError("impact side must be buy/sell or +1/-1")
return result.with_columns(
pl.when(~pl.col("clock_right_censored"))
.then(10_000.0 * pl.col("clock_impact_side_sign") * pl.col("clock_future_mid_return"))
.otherwise(None)
.alias("clock_signed_price_impact_bps"),
pl.lit("clock_time_signed_price_impact").alias("clock_label_kind"),
)
def build_hypothetical_limit_fill_labels(
book_states: pl.DataFrame,
trades: pl.DataFrame,
assumptions: LimitFillAssumptions,
*,
decision_time_column: str = "decision_ts_ns",
) -> pl.DataFrame:
"""Label a conservative best-quote fill proxy from subsequent trade prints.
The proxy ignores cancellations and hidden liquidity. Opposing prints
strictly after activation deplete a fixed fraction of displayed queue before
reaching the hypothetical order. Historical printed quantity is never
reused within one candidate order, but separate decision labels remain
counterfactual scenarios rather than simultaneously live orders.
"""
_require(
book_states,
(
*_GROUP_COLUMNS,
decision_time_column,
"best_bid",
"best_ask",
"bid_quantity",
"ask_quantity",
),
"limit-fill book states",
)
_require(
trades,
(*_GROUP_COLUMNS, "available_ts_ns", "price", "quantity", "aggressor_side"),
"limit-fill trades",
allow_empty=True,
)
invalid_trades = trades.filter(
(pl.col("quantity") < 0)
| (~pl.col("aggressor_side").str.to_lowercase().is_in(["buy", "sell"]))
)
if invalid_trades.height:
raise AuxiliaryLabelError("trades require nonnegative quantity and buy/sell side")
trade_groups: dict[tuple[str, str], list[dict[str, object]]] = {}
for row in trades.sort("available_ts_ns").iter_rows(named=True):
key = (str(row["symbol"]), str(row["continuity_id"]))
trade_groups.setdefault(key, []).append(row)
valid_for_coverage = (
book_states.filter(pl.col("is_valid")) if "is_valid" in book_states.columns else book_states
)
coverage: dict[tuple[str, str], int] = {}
for row in (
valid_for_coverage.group_by(list(_GROUP_COLUMNS))
.agg(pl.col(decision_time_column).max().alias("_coverage_end"))
.iter_rows(named=True)
):
coverage[(str(row["symbol"]), str(row["continuity_id"]))] = int(row["_coverage_end"])
output: list[dict[str, object]] = []
for row in book_states.sort([decision_time_column, *_GROUP_COLUMNS]).iter_rows(named=True):
symbol = str(row["symbol"])
continuity_id = str(row["continuity_id"])
decision_ts = int(row[decision_time_column])
activation_ts = decision_ts + assumptions.activation_latency_ns
deadline_ts = activation_ts + assumptions.horizon_ns
valid_decision = bool(row.get("is_valid", True))
limit_price = float(row["best_bid"] if assumptions.side == "buy" else row["best_ask"])
displayed_quantity = float(
row["bid_quantity"] if assumptions.side == "buy" else row["ask_quantity"]
)
queue_ahead = displayed_quantity * assumptions.queue_ahead_fraction
cumulative_executable = 0.0
full_fill_ts: int | None = None
if valid_decision:
for trade in trade_groups.get((symbol, continuity_id), []):
trade_ts = cast(int, trade["available_ts_ns"])
if trade_ts <= activation_ts:
continue
if trade_ts > deadline_ts:
break
trade_side = str(trade["aggressor_side"]).lower()
trade_price = cast(float, trade["price"])
marketable = (
assumptions.side == "buy"
and trade_side == "sell"
and trade_price <= limit_price
) or (
assumptions.side == "sell"
and trade_side == "buy"
and trade_price >= limit_price
)
if not marketable:
continue
cumulative_executable += cast(float, trade["quantity"])
if cumulative_executable >= queue_ahead + assumptions.order_quantity:
full_fill_ts = trade_ts
break
observed_fill = min(
assumptions.order_quantity, max(0.0, cumulative_executable - queue_ahead)
)
segment_covers_deadline = coverage.get((symbol, continuity_id), -1) >= deadline_ts
full_fill = full_fill_ts is not None
right_censored = (not valid_decision) or (not full_fill and not segment_covers_deadline)
information_end = (
full_fill_ts
if full_fill
else deadline_ts
if segment_covers_deadline and valid_decision
else None
)
base = {
name: row[name]
for name in (
"sample_id",
"symbol",
"continuity_id",
decision_time_column,
"decision_sequence",
)
if name in row
}
output.append(
{
**base,
"limit_label_kind": "hypothetical_best_quote_fill_proxy",
"limit_label_is_descriptive": True,
"limit_label_assumption": (
"trade-print depletion of fixed displayed queue; cancellations, hidden liquidity, "
"and endogenous impact ignored"
),
"limit_side": assumptions.side,
"limit_price": limit_price,
"limit_order_quantity": assumptions.order_quantity,
"limit_initial_displayed_quantity": displayed_quantity,
"limit_initial_queue_ahead": queue_ahead,
"limit_queue_ahead_fraction": assumptions.queue_ahead_fraction,
"limit_activation_latency_ns": assumptions.activation_latency_ns,
"limit_horizon_ns": assumptions.horizon_ns,
"limit_activation_ts_ns": activation_ts,
"limit_deadline_ts_ns": deadline_ts,
"limit_trade_evidence_required": True,
"limit_equal_time_ordering": "trade_at_activation_excluded",
"limit_cancellation_handling": "ignored_no_order_level_attribution",
"limit_observed_executable_quantity": cumulative_executable,
"limit_observed_fill_before_censoring": observed_fill,
"limit_right_censored": right_censored,
"limit_censor_reason": (
"invalid_decision_book"
if not valid_decision
else "segment_ends_before_horizon"
if right_censored
else None
),
"limit_fill_quantity": None if right_censored else observed_fill,
"limit_fill_fraction": (
None if right_censored else observed_fill / assumptions.order_quantity
),
"limit_full_fill": None if right_censored else full_fill,
"limit_full_fill_ts_ns": full_fill_ts,
"limit_label_start_ts_ns": decision_ts,
"limit_label_information_end_ts_ns": information_end,
}
)
return pl.DataFrame(output).sort([decision_time_column, *_GROUP_COLUMNS])
def build_post_fill_adverse_selection_labels(
fills: pl.DataFrame,
book_states: pl.DataFrame,
spec: AdverseSelectionSpec,
*,
fill_time_column: str = "fill_ts_ns",
fill_price_column: str = "fill_price",
side_column: str = "side",
book_time_column: str = "decision_ts_ns",
mid_column: str = "mid_price",
) -> pl.DataFrame:
"""Build side-aware post-fill markout and adverse-selection labels."""
_require(
fills,
(*_GROUP_COLUMNS, fill_time_column, fill_price_column, side_column),
"fills",
)
books = _valid_books(book_states, time_column=book_time_column, mid_column=mid_column)
prepared_fills = fills.with_columns(_side_expression(side_column).alias("_fill_side_sign"))
invalid = prepared_fills.filter(
pl.col("_fill_side_sign").is_null()
| (pl.col(fill_price_column) <= 0)
| pl.col("continuity_id").is_null()
)
if invalid.height:
raise AuxiliaryLabelError("fills require buy/sell side, positive price, and continuity_id")
outputs: list[pl.DataFrame] = []
for horizon_ns in sorted(set(spec.horizons_ns)):
with_target = prepared_fills.with_columns(
(pl.col(fill_time_column) + horizon_ns).alias("adverse_target_ts_ns")
)
joined = _forward_mid_join(
with_target,
books,
decision_time_column=fill_time_column,
book_time_column=book_time_column,
mid_column=mid_column,
target_column="adverse_target_ts_ns",
).with_columns(
(pl.col("_matched_target_ts_ns") - pl.col("adverse_target_ts_ns")).alias(
"adverse_target_staleness_ns"
)
)
censored = pl.col("_matched_target_ts_ns").is_null()
if spec.max_target_staleness_ns is not None:
censored = censored | (
pl.col("adverse_target_staleness_ns") > spec.max_target_staleness_ns
)
outputs.append(
joined.with_columns(
censored.alias("adverse_right_censored"),
pl.lit(horizon_ns, dtype=pl.Int64).alias("adverse_horizon_ns"),
pl.lit("post_fill_adverse_selection").alias("adverse_label_kind"),
pl.lit(True).alias("adverse_label_is_descriptive"),
pl.lit(
"first valid same-segment mid at or after fill+h; exogenous book and no own impact"
).alias("adverse_label_assumption"),
pl.col(fill_time_column).alias("adverse_label_start_ts_ns"),
pl.when(pl.col("_matched_target_ts_ns").is_null())
.then(pl.lit("no_same_segment_future_state"))
.when(censored)
.then(pl.lit("target_state_too_stale"))
.otherwise(None)
.alias("adverse_censor_reason"),
)
.with_columns(
pl.when(~pl.col("adverse_right_censored"))
.then(pl.col("_matched_target_mid"))
.otherwise(None)
.alias("adverse_target_mid_price"),
pl.when(~pl.col("adverse_right_censored"))
.then(pl.col("_matched_target_ts_ns"))
.otherwise(None)
.alias("adverse_label_information_end_ts_ns"),
pl.when(~pl.col("adverse_right_censored"))
.then(
10_000.0
* pl.col("_fill_side_sign")
* (pl.col("_matched_target_mid") - pl.col(fill_price_column))
/ pl.col(fill_price_column)
)
.otherwise(None)
.alias("post_fill_markout_bps"),
)
.with_columns(
(-pl.col("post_fill_markout_bps")).alias("adverse_selection_bps"),
pl.when(pl.col("post_fill_markout_bps").is_null())
.then(None)
.otherwise(pl.col("post_fill_markout_bps") < 0)
.alias("adverse_selection_indicator"),
)
)
return (
pl.concat(outputs, how="diagonal_relaxed")
.drop("_matched_target_ts_ns", "_matched_target_mid", "_fill_side_sign")
.sort([fill_time_column, "adverse_horizon_ns", *_GROUP_COLUMNS])
)
__all__ = [
"AdverseSelectionSpec",
"AuxiliaryLabelError",
"ClockTimeLabelSpec",
"LimitFillAssumptions",
"add_event_time_price_impact_labels",
"build_clock_time_mid_labels",
"build_clock_time_price_impact_labels",
"build_hypothetical_limit_fill_labels",
"build_post_fill_adverse_selection_labels",
]
|