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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 | """Deterministic, explicitly synthetic L1 and trade event generation."""
from __future__ import annotations
import hashlib
import random
from collections.abc import Iterator, Sequence
from dataclasses import dataclass
import pyarrow as pa # type: ignore[import-untyped]
from microstructure.data.schemas import SCHEMA_VERSION, table_from_records
_NS_PER_MILLISECOND = 1_000_000
@dataclass(frozen=True, slots=True)
class SyntheticMarketData:
"""Small synthetic market tables; never evidence of observed market behavior."""
trades: pa.Table
book_observations: pa.Table
evidence_tier: str = "SYNTHETIC_SMOKE"
def _symbol_seed(seed: int, symbol: str) -> int:
material = f"{seed}:{symbol}".encode()
return int.from_bytes(hashlib.sha256(material).digest()[:8], "big")
def _initial_mid_ticks(symbol: str) -> int:
if symbol.upper().startswith("BTC"):
return 3_000_000
if symbol.upper().startswith("ETH"):
return 200_000
return 100_000
def _imbalance(bid: float, ask: float) -> float:
total = bid + ask
return (bid - ask) / total if total > 0.0 else 0.0
def _symbol_records(
*,
symbol: str,
events: int,
start_ts_ns: int,
seed: int,
event_spacing_ns: int,
tick_size: float,
lot_size: float,
) -> tuple[list[dict[str, object]], list[dict[str, object]]]:
rng = random.Random(_symbol_seed(seed, symbol))
mid_ticks = _initial_mid_ticks(symbol)
trades: list[dict[str, object]] = []
books: list[dict[str, object]] = []
continuity_id = f"synthetic:{symbol}:0"
source_artifact_id = f"synthetic-v1-seed-{seed}"
for index in range(events):
event_ts_ns = start_ts_ns + index * event_spacing_ns
bid_level_lots = rng.randint(50, 250)
ask_level_lots = rng.randint(50, 250)
imbalance_1 = _imbalance(float(bid_level_lots), float(ask_level_lots))
movement_draw = rng.random()
upward_probability = 0.50 + 0.20 * imbalance_1
if movement_draw < upward_probability - 0.10:
mid_ticks += 1
elif movement_draw > upward_probability + 0.10:
mid_ticks -= 1
mid_ticks = max(mid_ticks, 10)
spread_ticks = 1 if rng.random() < 0.85 else 2
best_bid_ticks = mid_ticks - spread_ticks // 2
best_ask_ticks = best_bid_ticks + spread_ticks
extra_bid_5 = sum(rng.randint(30, 180) for _ in range(4))
extra_ask_5 = sum(rng.randint(30, 180) for _ in range(4))
extra_bid_10 = sum(rng.randint(20, 140) for _ in range(5))
extra_ask_10 = sum(rng.randint(20, 140) for _ in range(5))
depth_bid_1_lots = bid_level_lots
depth_ask_1_lots = ask_level_lots
depth_bid_5_lots = depth_bid_1_lots + extra_bid_5
depth_ask_5_lots = depth_ask_1_lots + extra_ask_5
depth_bid_10_lots = depth_bid_5_lots + extra_bid_10
depth_ask_10_lots = depth_ask_5_lots + extra_ask_10
best_bid = best_bid_ticks * tick_size
best_ask = best_ask_ticks * tick_size
bid_quantity = bid_level_lots * lot_size
ask_quantity = ask_level_lots * lot_size
mid_price = (best_bid + best_ask) / 2.0
microprice = (best_ask * bid_quantity + best_bid * ask_quantity) / (
bid_quantity + ask_quantity
)
received_ts_ns = event_ts_ns + 100_000
books.append(
{
"schema_version": SCHEMA_VERSION,
"venue": "synthetic",
"symbol": symbol,
"event_ts_ns": event_ts_ns,
"received_ts_ns": received_ts_ns,
"available_ts_ns": received_ts_ns,
"availability_basis": "synthetic_receipt",
"capture_seq": index * 2,
"continuity_id": continuity_id,
"sequence_start": index + 1,
"sequence_end": index + 1,
"is_valid": True,
"best_bid_ticks": best_bid_ticks,
"best_ask_ticks": best_ask_ticks,
"bid_quantity_lots": bid_level_lots,
"ask_quantity_lots": ask_level_lots,
"tick_size": tick_size,
"lot_size": lot_size,
"best_bid": best_bid,
"best_ask": best_ask,
"bid_quantity": bid_quantity,
"ask_quantity": ask_quantity,
"spread": best_ask - best_bid,
"mid_price": mid_price,
"microprice": microprice,
"depth_bid_1": depth_bid_1_lots * lot_size,
"depth_ask_1": depth_ask_1_lots * lot_size,
"depth_bid_5": depth_bid_5_lots * lot_size,
"depth_ask_5": depth_ask_5_lots * lot_size,
"depth_bid_10": depth_bid_10_lots * lot_size,
"depth_ask_10": depth_ask_10_lots * lot_size,
"queue_imbalance_1": _imbalance(float(depth_bid_1_lots), float(depth_ask_1_lots)),
"queue_imbalance_5": _imbalance(float(depth_bid_5_lots), float(depth_ask_5_lots)),
"queue_imbalance_10": _imbalance(
float(depth_bid_10_lots), float(depth_ask_10_lots)
),
"source_artifact_id": source_artifact_id,
}
)
buy_probability = 0.50 + 0.25 * imbalance_1
aggressor_side = "buy" if rng.random() < buy_probability else "sell"
price_ticks = best_ask_ticks if aggressor_side == "buy" else best_bid_ticks
quantity_lots = rng.randint(1, 40)
trade_price = price_ticks * tick_size
trade_quantity = quantity_lots * lot_size
trade_event_ts_ns = event_ts_ns + 20_000
trade_received_ts_ns = event_ts_ns + 150_000
trades.append(
{
"schema_version": SCHEMA_VERSION,
"venue": "synthetic",
"symbol": symbol,
"event_ts_ns": trade_event_ts_ns,
"received_ts_ns": trade_received_ts_ns,
"available_ts_ns": trade_received_ts_ns,
"availability_basis": "synthetic_receipt",
"capture_seq": index * 2 + 1,
"continuity_id": continuity_id,
"trade_id": index + 1,
"first_trade_id": index + 1,
"last_trade_id": index + 1,
"price_ticks": price_ticks,
"quantity_lots": quantity_lots,
"tick_size": tick_size,
"lot_size": lot_size,
"price": trade_price,
"quantity": trade_quantity,
"quote_quantity": trade_price * trade_quantity,
"aggressor_side": aggressor_side,
"buyer_is_maker": aggressor_side == "sell",
"source_artifact_id": source_artifact_id,
}
)
return trades, books
def generate_synthetic_market(
*,
symbols: Sequence[str],
events_per_symbol: int,
start_ts_ns: int,
seed: int,
event_spacing_ns: int = 100 * _NS_PER_MILLISECOND,
tick_size: float = 0.01,
lot_size: float = 0.001,
) -> SyntheticMarketData:
"""Generate deterministic bounded tables for smoke tests and demos.
The generator is intentionally labelled synthetic in every row and result.
It is not calibrated to Binance and must never be reported as market data.
"""
if events_per_symbol < 1:
raise ValueError("events_per_symbol must be positive")
if event_spacing_ns < 1:
raise ValueError("event_spacing_ns must be positive")
if tick_size <= 0.0 or lot_size <= 0.0:
raise ValueError("tick_size and lot_size must be positive")
if not symbols:
raise ValueError("symbols must not be empty")
trade_records: list[dict[str, object]] = []
book_records: list[dict[str, object]] = []
for raw_symbol in symbols:
symbol = raw_symbol.upper()
trades, books = _symbol_records(
symbol=symbol,
events=events_per_symbol,
start_ts_ns=start_ts_ns,
seed=seed,
event_spacing_ns=event_spacing_ns,
tick_size=tick_size,
lot_size=lot_size,
)
trade_records.extend(trades)
book_records.extend(books)
return SyntheticMarketData(
trades=table_from_records("trades", trade_records),
book_observations=table_from_records("book_observations", book_records),
)
def iter_table_batches(table: pa.Table, batch_size: int = 100_000) -> Iterator[pa.RecordBatch]:
"""Expose bounded RecordBatches for the streaming storage interface."""
if batch_size < 1:
raise ValueError("batch_size must be positive")
yield from table.to_batches(max_chunksize=batch_size)
|