7ad123c4c1
Item 5 — PerformanceMatrix manifold: RegimeStrategyScore: added confidence, support_count, distance_to_nearest record() populates confidence from episode count (more evidence = more confidence) Item 6 — ActualsLoader: ActualsSnapshot: 12-field frozen dataclass for live market data ActualsLoader: reads CH tables (obf_universe, exf_data, maras_fingerprint, etc.) Synthetic fallback when CH unavailable Item 7 — OOD verdict in RiskGate: validate() now accepts daat_verdict parameter OUT_OF_DISTRIBUTION → veto action, fall back to doctrinal simple policy Backward compatible: default daat_verdict='KNOWN' Item 8 — Manifold query (three-phase recommendation): 1. DAAT classify live state (KNOWN/MARGINAL/OOD) 2. If KNOWN: find nearest regime in PerformanceMatrix → best strategy 3. If OOD: return doctrinal_simple fallback ManifoldRecommendation: strategy_id, confidence, regime, verdict, reason Item 10 — Book fidelity gap: BookFidelityConfig: n_levels, aggregation_window, min_depth synthesize_book_from_params: power-law D(d)=amplitude*d^(1-alpha) → OrderBookState Bridges OBF 15B rows → MALKHUT finite Tuple[PriceLevel] 5 files, 282 insertions.
99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
"""
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Manifold Query — cosine RETRIEVE → magnitude GATE → local MODEL.
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Uses the PerformanceMatrix manifold (item 5) + DAAT (item 9) + ActualsLoader (item 6)
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to recommend the best strategy for a live market state.
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Three phases:
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1. DAAT classifies: KNOWN / MARGINAL / OUT_OF_DISTRIBUTION
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2. If KNOWN: find nearest regime in manifold, get best strategy
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3. If OOD: return fall-back to doctrinal simple policy
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Optional
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from malkhut.daat.core import DaatQuery, DaatVerdict, daat_classify
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from malkhut.training.actuals_loader import ActualsSnapshot
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from malkhut.training.selector import PerformanceMatrix, MarketRegime
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@dataclass(frozen=True, slots=True)
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class ManifoldRecommendation:
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"""Output of manifold query — the best strategy recommendation."""
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strategy_id: str
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confidence: float # 0.0-1.0
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regime: str
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verdict: str # KNOWN / MARGINAL / OUT_OF_DISTRIBUTION
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reason: str
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# Simple mapping from actuals features to regime (for now)
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_REGIME_MAP = {
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"normal": MarketRegime.NORMAL,
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"crisis": MarketRegime.HIGH_VOLATILITY,
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"recovery": MarketRegime.MEAN_REVERTING,
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"transition": MarketRegime.TRENDING_UP,
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}
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def manifold_query(
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actuals: ActualsSnapshot,
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matrix: PerformanceMatrix,
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explored_states: list = None,
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explored_magnitudes: list = None,
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) -> ManifoldRecommendation:
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"""Query the performance manifold for the best strategy given live actuals.
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Three-phase:
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1. DAAT classify the live state
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2. If KNOWN/MARGINAL: find nearest regime in manifold
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3. If OUT_OF_DISTRIBUTION: return doctrinal fallback
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"""
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# Phase 1: DAAT classify
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query = DaatQuery(
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spread_bps=actuals.spread_bps,
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depth_usd=actuals.depth_usd,
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imbalance=actuals.imbalance,
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funding_bps=actuals.funding_bps,
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volatility=actuals.volatility,
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regime_score=0.0, # normalized from actuals.regime
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latency_ms=actuals.latency_ms,
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inventory_pct=0.0,
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)
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if explored_states is None:
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explored_states = [query] # self-referential: "we've seen this exact state"
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if explored_magnitudes is None:
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explored_magnitudes = [sum(abs(x) for x in [
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query.spread_bps, query.depth_usd, query.imbalance,
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query.funding_bps, query.volatility, query.regime_score,
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query.latency_ms, query.inventory_pct,
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])]
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daat_result = daat_classify(query, explored_states, explored_magnitudes)
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# Phase 2: If KNOWN or MARGINAL, find best strategy in manifold
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if daat_result.verdict in (DaatVerdict.KNOWN, DaatVerdict.MARGINAL):
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regime_str = actuals.regime if actuals.regime in _REGIME_MAP else "normal"
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regime = _REGIME_MAP.get(regime_str, MarketRegime.NORMAL)
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best = matrix.get_best(regime)
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if best:
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return ManifoldRecommendation(
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strategy_id=best,
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confidence=daat_result.confidence * 0.8,
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regime=regime_str,
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verdict=daat_result.verdict.value,
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reason=f"nearest regime: {regime_str}, cosine={daat_result.cosine_sim:.4f}",
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)
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# Phase 3: OOD → fall back to doctrinal
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return ManifoldRecommendation(
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strategy_id="doctrinal_simple",
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confidence=0.0,
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regime="unknown",
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verdict=daat_result.verdict.value,
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reason=f"OOD: cosine={daat_result.cosine_sim:.4f}, mag_ratio={daat_result.magnitude_ratio:.4f}",
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)
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