VIOLET OA: add venue OB provider seam
Co-authored-by: Codex <codex@openai.com>
This commit is contained in:
Codex
178
prod/clean_arch/violet/venue_ob_provider.py
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178
prod/clean_arch/violet/venue_ob_provider.py
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"""VIOLET venue-agnostic OB provider seam.
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This is a read-only, in-memory seam for future non-BLUE order book sources.
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It produces the exact ``OBSnapshot`` shape expected by BLUE's
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``OBFeatureEngine`` without opening any live exchange connection.
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"""
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from __future__ import annotations
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from bisect import bisect_left
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try:
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from beartype.typing import Annotated, Callable, Iterable, Optional
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except ImportError: # pragma: no cover - beartype always present in prod
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from typing import Annotated, Callable, Iterable, Optional
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import numpy as np
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from pydantic import Field
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from .domain import StrictModel, Symbol, typed
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try:
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from nautilus_dolphin.nautilus.ob_provider import OBProvider, OBSnapshot
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except ImportError: # pragma: no cover - import path fallback for direct runs
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from nautilus_dolphin.nautilus.ob_provider import OBProvider, OBSnapshot # type: ignore
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OBLevel = Annotated[float, Field(ge=0.0, allow_inf_nan=False)]
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Level5 = tuple[OBLevel, OBLevel, OBLevel, OBLevel, OBLevel]
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class VenueOBTick(StrictModel):
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"""Normalized OB tick for one asset at one point in time."""
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timestamp: Annotated[float, Field(ge=0.0, allow_inf_nan=False)]
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asset: Symbol
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bid_notional_levels: Level5
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ask_notional_levels: Level5
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bid_depth_levels: Level5
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ask_depth_levels: Level5
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class VioletVenueOBProvider(OBProvider):
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"""Venue-agnostic, in-memory OB provider.
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Input comes from either an injected callable or a flat iterable of
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``VenueOBTick`` records. No exchange transport lives here.
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"""
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def __init__(
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self,
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*,
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ticks: Optional[Iterable[VenueOBTick]] = None,
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tick_source: Optional[Callable[[str], Iterable[VenueOBTick]]] = None,
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assets: Optional[Iterable[str]] = None,
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tolerance_s: float = 30.0,
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) -> None:
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self.tolerance_s = float(tolerance_s)
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self._tick_source = tick_source
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self._assets = sorted({str(a) for a in assets or [] if str(a).strip()})
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self._ticks: dict[str, list[VenueOBTick]] = {}
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self._timestamps: dict[str, np.ndarray] = {}
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if ticks is not None:
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self.load_ticks(ticks)
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if tick_source is not None and self._assets:
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self.refresh()
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@typed
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def load_ticks(self, ticks: Iterable[VenueOBTick]) -> None:
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grouped: dict[str, list[VenueOBTick]] = {}
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for tick in ticks:
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grouped.setdefault(tick.asset, []).append(tick)
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for asset, rows in grouped.items():
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rows.sort(key=lambda t: t.timestamp)
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self._ticks[asset] = rows
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self._timestamps[asset] = np.array([t.timestamp for t in rows], dtype=np.float64)
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if asset not in self._assets:
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self._assets.append(asset)
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self._assets.sort()
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@typed
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def refresh(self) -> None:
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if self._tick_source is None:
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return
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for asset in self._assets:
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self.load_ticks(self._tick_source(asset))
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def _to_snapshot(self, tick: VenueOBTick) -> OBSnapshot:
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return OBSnapshot(
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timestamp=float(tick.timestamp),
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asset=tick.asset,
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bid_notional=np.array(tick.bid_notional_levels, dtype=np.float64),
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ask_notional=np.array(tick.ask_notional_levels, dtype=np.float64),
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bid_depth=np.array(tick.bid_depth_levels, dtype=np.float64),
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ask_depth=np.array(tick.ask_depth_levels, dtype=np.float64),
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)
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def get_snapshot(self, asset: str, timestamp: float) -> Optional[OBSnapshot]:
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rows = self._ticks.get(asset)
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if not rows:
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return None
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ts_arr = self._timestamps.get(asset)
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if ts_arr is None or len(ts_arr) == 0:
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return None
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idx = bisect_left(ts_arr, float(timestamp))
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candidates = [max(0, idx - 1), min(idx, len(ts_arr) - 1)]
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best_idx = candidates[0]
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best_dist = abs(ts_arr[best_idx] - timestamp)
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for cand in candidates[1:]:
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dist = abs(ts_arr[cand] - timestamp)
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if dist < best_dist:
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best_dist = dist
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best_idx = cand
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if best_dist > self.tolerance_s:
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return None
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return self._to_snapshot(rows[best_idx])
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def get_assets(self) -> list[str]:
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return list(self._assets)
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def get_all_timestamps(self, asset: str) -> np.ndarray:
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ts = self._timestamps.get(asset)
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return np.array([], dtype=np.float64) if ts is None else ts.copy()
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def get_snapshot_count(self, asset: str) -> int:
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rows = self._ticks.get(asset)
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return len(rows) if rows is not None else 0
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def get_snapshot_by_index(self, asset: str, idx: int) -> Optional[OBSnapshot]:
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rows = self._ticks.get(asset)
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if rows is None or idx < 0 or idx >= len(rows):
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return None
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return self._to_snapshot(rows[idx])
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class MockTickVenueOBProvider(VioletVenueOBProvider):
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"""Deterministic synthetic tick source for tests."""
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def __init__(
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self,
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*,
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assets: Optional[Iterable[str]] = None,
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num_snapshots: int = 8,
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base_timestamp: float = 1_700_000_000.0,
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interval_s: float = 30.0,
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base_notional: float = 100_000.0,
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depth_scale: float = 1.0,
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imbalance_biases: Optional[dict[str, float]] = None,
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tolerance_s: float = 60.0,
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) -> None:
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assets = list(assets or ("BTCUSDT", "ETHUSDT"))
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ticks: list[VenueOBTick] = []
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level_weights = (1.0, 2.0, 3.0, 4.0, 5.0)
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for asset_idx, asset in enumerate(assets):
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bias = (imbalance_biases or {}).get(asset, 0.08 if asset_idx % 2 == 0 else -0.08)
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for snap_idx in range(num_snapshots):
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ts = base_timestamp + snap_idx * interval_s
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drift = 1.0 + 0.01 * snap_idx
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bid_mult = 1.0 + bias
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ask_mult = 1.0 - bias
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bid_not = tuple(
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float(base_notional * depth_scale * drift * w * bid_mult) for w in level_weights
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)
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ask_not = tuple(
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float(base_notional * depth_scale * drift * w * ask_mult) for w in level_weights
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)
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approx_price = 100.0 + 5.0 * asset_idx
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bid_dep = tuple(float(v / approx_price) for v in bid_not)
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ask_dep = tuple(float(v / approx_price) for v in ask_not)
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ticks.append(
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VenueOBTick(
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timestamp=float(ts),
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asset=asset,
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bid_notional_levels=bid_not,
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ask_notional_levels=ask_not,
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bid_depth_levels=bid_dep,
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ask_depth_levels=ask_dep,
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)
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)
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super().__init__(ticks=ticks, assets=assets, tolerance_s=tolerance_s)
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