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economics
quantitative-finance
causal-inference
macroeconomics
housing-economics
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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 | """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}"
)
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