Datasets:
Tasks:
Tabular Classification
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Tags:
economics
quantitative-finance
causal-inference
macroeconomics
housing-economics
market-microstructure
License:
| """Versioned normalized Arrow schemas and their temporal contract. | |
| All timestamps are signed UTC epoch nanoseconds. ``available_ts_ns`` is the | |
| earliest time at which a row may enter a research information set. Archive | |
| rows explicitly identify exchange event time as a proxy; live rows use local | |
| receipt time. Prices and quantities retain exact integer tick/lot columns and | |
| also expose documented floating convenience columns for research consumers. | |
| """ | |
| from __future__ import annotations | |
| from collections.abc import Iterable, Mapping | |
| from typing import Any | |
| import pyarrow as pa # type: ignore[import-untyped] | |
| SCHEMA_VERSION = "1.0.0" | |
| class SchemaError(ValueError): | |
| """Raised when a normalized table violates its declared schema.""" | |
| def _metadata(name: str) -> dict[bytes, bytes]: | |
| return { | |
| b"schema_name": name.encode(), | |
| b"schema_version": SCHEMA_VERSION.encode(), | |
| b"timestamp_unit": b"UTC epoch nanoseconds", | |
| b"temporal_contract": ( | |
| b"available_ts_ns is the information-set clock; event_ts_ns alone is not receipt proof" | |
| ), | |
| b"numeric_contract": ( | |
| b"price_ticks and quantity_lots are exact; float columns are convenience units" | |
| ), | |
| } | |
| _COMMON_EVENT_FIELDS = [ | |
| pa.field("schema_version", pa.string(), nullable=False), | |
| pa.field("venue", pa.string(), nullable=False), | |
| pa.field("symbol", pa.string(), nullable=False), | |
| pa.field("event_ts_ns", pa.int64(), nullable=False), | |
| pa.field("received_ts_ns", pa.int64()), | |
| pa.field("available_ts_ns", pa.int64(), nullable=False), | |
| pa.field("availability_basis", pa.string(), nullable=False), | |
| pa.field("capture_seq", pa.int64()), | |
| pa.field("continuity_id", pa.string()), | |
| ] | |
| TRADE_SCHEMA = pa.schema( | |
| [ | |
| *_COMMON_EVENT_FIELDS, | |
| pa.field("trade_id", pa.int64(), nullable=False), | |
| pa.field("first_trade_id", pa.int64()), | |
| pa.field("last_trade_id", pa.int64()), | |
| pa.field("price_ticks", pa.int64(), nullable=False), | |
| pa.field("quantity_lots", pa.int64(), nullable=False), | |
| pa.field("tick_size", pa.float64(), nullable=False), | |
| pa.field("lot_size", pa.float64(), nullable=False), | |
| pa.field("price", pa.float64(), nullable=False), | |
| pa.field("quantity", pa.float64(), nullable=False), | |
| pa.field("quote_quantity", pa.float64(), nullable=False), | |
| pa.field("aggressor_side", pa.string(), nullable=False), | |
| pa.field("buyer_is_maker", pa.bool_(), nullable=False), | |
| pa.field("source_artifact_id", pa.string(), nullable=False), | |
| ], | |
| metadata=_metadata("trades"), | |
| ) | |
| BOOK_OBSERVATION_SCHEMA = pa.schema( | |
| [ | |
| *_COMMON_EVENT_FIELDS, | |
| pa.field("sequence_start", pa.int64(), nullable=False), | |
| pa.field("sequence_end", pa.int64(), nullable=False), | |
| pa.field("is_valid", pa.bool_(), nullable=False), | |
| pa.field("best_bid_ticks", pa.int64(), nullable=False), | |
| pa.field("best_ask_ticks", pa.int64(), nullable=False), | |
| pa.field("bid_quantity_lots", pa.int64(), nullable=False), | |
| pa.field("ask_quantity_lots", pa.int64(), nullable=False), | |
| pa.field("tick_size", pa.float64(), nullable=False), | |
| pa.field("lot_size", pa.float64(), nullable=False), | |
| pa.field("best_bid", pa.float64(), nullable=False), | |
| pa.field("best_ask", pa.float64(), nullable=False), | |
| pa.field("bid_quantity", pa.float64(), nullable=False), | |
| pa.field("ask_quantity", pa.float64(), nullable=False), | |
| pa.field("spread", pa.float64(), nullable=False), | |
| pa.field("mid_price", pa.float64(), nullable=False), | |
| pa.field("microprice", pa.float64(), nullable=False), | |
| pa.field("depth_bid_1", pa.float64(), nullable=False), | |
| pa.field("depth_ask_1", pa.float64(), nullable=False), | |
| pa.field("depth_bid_5", pa.float64(), nullable=False), | |
| pa.field("depth_ask_5", pa.float64(), nullable=False), | |
| pa.field("depth_bid_10", pa.float64(), nullable=False), | |
| pa.field("depth_ask_10", pa.float64(), nullable=False), | |
| pa.field("queue_imbalance_1", pa.float64(), nullable=False), | |
| pa.field("queue_imbalance_5", pa.float64(), nullable=False), | |
| pa.field("queue_imbalance_10", pa.float64(), nullable=False), | |
| pa.field("source_artifact_id", pa.string(), nullable=False), | |
| ], | |
| metadata=_metadata("book_observations"), | |
| ) | |
| LEVEL_TYPE = pa.struct( | |
| [ | |
| pa.field("price_ticks", pa.int64(), nullable=False), | |
| pa.field("quantity_lots", pa.int64(), nullable=False), | |
| ] | |
| ) | |
| DEPTH_DELTA_SCHEMA = pa.schema( | |
| [ | |
| *_COMMON_EVENT_FIELDS, | |
| pa.field("first_update_id", pa.int64(), nullable=False), | |
| pa.field("last_update_id", pa.int64(), nullable=False), | |
| pa.field("previous_update_id", pa.int64()), | |
| pa.field("bids", pa.list_(LEVEL_TYPE), nullable=False), | |
| pa.field("asks", pa.list_(LEVEL_TYPE), nullable=False), | |
| pa.field("tick_size", pa.float64(), nullable=False), | |
| pa.field("lot_size", pa.float64(), nullable=False), | |
| pa.field("source_artifact_id", pa.string(), nullable=False), | |
| ], | |
| metadata=_metadata("depth_deltas"), | |
| ) | |
| BOOK_SNAPSHOT_SCHEMA = pa.schema( | |
| [ | |
| pa.field("schema_version", pa.string(), nullable=False), | |
| pa.field("venue", pa.string(), nullable=False), | |
| pa.field("symbol", pa.string(), nullable=False), | |
| pa.field("snapshot_id", pa.string(), nullable=False), | |
| pa.field("request_ts_ns", pa.int64(), nullable=False), | |
| pa.field("received_ts_ns", pa.int64(), nullable=False), | |
| pa.field("available_ts_ns", pa.int64(), nullable=False), | |
| pa.field("continuity_id", pa.string(), nullable=False), | |
| pa.field("last_update_id", pa.int64(), nullable=False), | |
| pa.field("depth_limit", pa.int32(), nullable=False), | |
| pa.field("bids", pa.list_(LEVEL_TYPE), nullable=False), | |
| pa.field("asks", pa.list_(LEVEL_TYPE), nullable=False), | |
| pa.field("tick_size", pa.float64(), nullable=False), | |
| pa.field("lot_size", pa.float64(), nullable=False), | |
| pa.field("source_artifact_id", pa.string(), nullable=False), | |
| ], | |
| metadata=_metadata("book_snapshots"), | |
| ) | |
| SEQUENCE_GAP_SCHEMA = pa.schema( | |
| [ | |
| pa.field("schema_version", pa.string(), nullable=False), | |
| pa.field("venue", pa.string(), nullable=False), | |
| pa.field("symbol", pa.string(), nullable=False), | |
| pa.field("continuity_id", pa.string(), nullable=False), | |
| pa.field("expected_sequence", pa.int64(), nullable=False), | |
| pa.field("observed_sequence_start", pa.int64(), nullable=False), | |
| pa.field("observed_sequence_end", pa.int64(), nullable=False), | |
| pa.field("missing_start", pa.int64(), nullable=False), | |
| pa.field("missing_end", pa.int64(), nullable=False), | |
| pa.field("detected_ts_ns", pa.int64(), nullable=False), | |
| pa.field("reason", pa.string(), nullable=False), | |
| pa.field("source_artifact_id", pa.string(), nullable=False), | |
| ], | |
| metadata=_metadata("sequence_gaps"), | |
| ) | |
| SCHEMAS: Mapping[str, pa.Schema] = { | |
| "trades": TRADE_SCHEMA, | |
| "book_observations": BOOK_OBSERVATION_SCHEMA, | |
| "depth_deltas": DEPTH_DELTA_SCHEMA, | |
| "book_snapshots": BOOK_SNAPSHOT_SCHEMA, | |
| "sequence_gaps": SEQUENCE_GAP_SCHEMA, | |
| } | |
| def get_schema(name: str, version: str = SCHEMA_VERSION) -> pa.Schema: | |
| """Return a schema by stable name and fail closed on unknown versions.""" | |
| if version != SCHEMA_VERSION: | |
| raise SchemaError(f"unsupported schema version {version!r}; expected {SCHEMA_VERSION!r}") | |
| try: | |
| return SCHEMAS[name] | |
| except KeyError as exc: | |
| raise SchemaError(f"unknown normalized schema: {name!r}") from exc | |
| def table_from_records(name: str, records: Iterable[Mapping[str, Any]]) -> pa.Table: | |
| """Construct a table using the registry rather than inferred Arrow types.""" | |
| schema = get_schema(name) | |
| try: | |
| return pa.Table.from_pylist(list(records), schema=schema) | |
| except (pa.ArrowException, TypeError, ValueError) as exc: | |
| raise SchemaError(f"records do not conform to {name} {SCHEMA_VERSION}: {exc}") from exc | |
| def ensure_schema(table: pa.Table | pa.RecordBatch, name: str) -> None: | |
| """Require exact field order/types/nullability; metadata may be absent on batches.""" | |
| expected = get_schema(name) | |
| actual = table.schema | |
| if not actual.equals(expected, check_metadata=False): | |
| raise SchemaError(f"schema mismatch for {name}: expected {expected}, got {actual}") | |
| metadata = actual.metadata or {} | |
| declared_name = metadata.get(b"schema_name") | |
| declared_version = metadata.get(b"schema_version") | |
| if declared_name is not None and declared_name != name.encode(): | |
| raise SchemaError( | |
| f"schema metadata name mismatch: expected {name!r}, got {declared_name.decode()}" | |
| ) | |
| if declared_version is not None and declared_version != SCHEMA_VERSION.encode(): | |
| raise SchemaError( | |
| "schema metadata version mismatch: " | |
| f"expected {SCHEMA_VERSION!r}, got {declared_version.decode()}" | |
| ) | |
| version_column = table.column(actual.get_field_index("schema_version")) | |
| observed_versions = set(version_column.to_pylist()) | |
| if observed_versions.difference({SCHEMA_VERSION}): | |
| raise SchemaError( | |
| f"row schema_version mismatch: expected only {SCHEMA_VERSION!r}, " | |
| f"got {sorted(observed_versions)!r}" | |
| ) | |