aa22529330
T1 scaffold: 42 frozen dataclasses (state.py), action model + PlannedPolicy + RiskDecision (actions.py), 17-feature extraction (features.py), FulfilmentEngine hot-path orchestrator (engine.py).
55 lines
2.1 KiB
Python
55 lines
2.1 KiB
Python
"""
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Feature extraction for planner and reward functions.
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Rule: every human-obvious feature is allowed, but the CMA-ES optimiser
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must be allowed to discover non-obvious interactions (queue churn, time
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since MFE, recovery velocity, cross-venue lead, etc.).
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"""
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from __future__ import annotations
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import math
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from dataclasses import dataclass
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from typing import Mapping, Protocol
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from malkhut.state import MarketWorldState
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@dataclass(frozen=True, slots=True)
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class FeatureVector:
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values: Mapping[str, float]
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class FeatureExtractor(Protocol):
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def extract(self, state: MarketWorldState) -> FeatureVector: ...
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class DefaultFeatureExtractor:
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def extract(self, state: MarketWorldState) -> FeatureVector:
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b = state.book
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bid_qty = sum(x.qty for x in b.bids[:5])
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ask_qty = sum(x.qty for x in b.asks[:5])
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imbalance = (bid_qty - ask_qty) / max(bid_qty + ask_qty, 1e-12)
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path = state.trade_path
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values = {
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"mid": b.mid if b.bids and b.asks else 0.0,
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"spread_bps": b.spread_bps if b.bids and b.asks else 0.0,
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"top5_imbalance": imbalance,
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"funding_bps": state.funding_bps or 0.0,
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"volatility_state": state.volatility_state or 0.0,
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"pnl_bps": path.pnl_bps if path else 0.0,
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"mae_bps": path.mae_bps if path else 0.0,
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"mfe_bps": path.mfe_bps if path else 0.0,
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"distance_from_mfe_bps": path.distance_from_mfe_bps if path else 0.0,
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"seconds_held": path.seconds_held if path else 0.0,
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"time_in_loss_s": path.time_in_loss_s if path else 0.0,
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"time_since_deep_mae_s": path.time_since_deep_mae_s if path else 0.0,
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"recovery_velocity_bps_per_s": path.recovery_velocity_bps_per_s if path else 0.0,
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"adverse_velocity_bps_per_s": path.adverse_velocity_bps_per_s if path else 0.0,
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"orderflow_toxicity": path.orderflow_toxicity if path else 0.0,
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"queue_churn_score": path.queue_churn_score if path else 0.0,
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"cross_venue_lead_score": path.cross_venue_lead_score if path else 0.0,
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}
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return FeatureVector(values=values)
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