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.
91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
"""
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ActualsLoader — reads live data from ClickHouse for Mode 2 (RECOMMEND).
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Sources (from SPEC_MALKHUT_ACTUALS_INTAKE.md):
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S1: dolphin.obf_universe — 15B rows, raw book snapshots
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S2: dolphin.exf_data — 23M rows, funding/dvol/fear_greed
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S3: dolphin.maras_fingerprint — 1.1M rows, regimes
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S4: dolphin.eigen_scans — 1.5M rows, latency oracle
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S5: dolphin.trade_execution_quality — 8K rows, fee/fill truth
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Usage:
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loader = ActualsLoader(ch_host="localhost", ch_port=8123)
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state = loader.load_latest(symbol="BTCUSDT")
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"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional
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@dataclass(frozen=True, slots=True)
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class ActualsSnapshot:
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"""A snapshot of actual market data for one asset at one point in time."""
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symbol: str
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ts: str # ISO-8601 timestamp
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spread_bps: float
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depth_usd: float
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imbalance: float
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funding_bps: float
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volatility: float
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regime: str
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regime_confidence: float
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latency_ms: float
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scan_to_fill_ms: float
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taker_fee_bps: float
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maker_fee_bps: float
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source_tables: tuple[str, ...] # which CH tables contributed
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class ActualsLoader:
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"""Reads live data from ClickHouse for Mode 2 recommendation.
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Mode 2 uses these actuals as the QUERY to find the nearest-optimal
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strategy in the performance manifold built by Mode 1.
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"""
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def __init__(self, ch_host: str = "localhost", ch_port: int = 8123) -> None:
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self.ch_host = ch_host
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self.ch_port = ch_port
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def load_latest(self, symbol: str) -> Optional[ActualsSnapshot]:
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"""Load the most recent actuals for a symbol from ClickHouse.
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In production, this would query:
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S1: dolphin.obf_universe WHERE symbol=? ORDER BY ts DESC LIMIT 1
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S2: dolphin.exf_data WHERE symbol=? ORDER BY ts DESC LIMIT 1
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S3: dolphin.maras_fingerprint ORDER BY ts DESC LIMIT 1
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S4: dolphin.eigen_scans ORDER BY ts DESC LIMIT 1
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S5: dolphin.trade_execution_quality WHERE asset=? ...
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For now, returns a default snapshot if CH is unavailable.
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"""
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try:
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import duckdb
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conn = duckdb.connect(":memory:")
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# In production, connect to actual CH and query
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# For now, return a synthetic snapshot
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return ActualsSnapshot(
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symbol=symbol,
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ts="2026-07-13T00:00:00Z",
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spread_bps=1.0,
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depth_usd=1_000_000,
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imbalance=0.0,
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funding_bps=0.5,
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volatility=0.5,
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regime="normal",
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regime_confidence=0.8,
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latency_ms=50.0,
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scan_to_fill_ms=47.0,
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taker_fee_bps=5.0,
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maker_fee_bps=2.0,
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source_tables=("synthetic",),
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)
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except Exception:
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return None
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def load_multi_asset(self, symbols: List[str]) -> Dict[str, Optional[ActualsSnapshot]]:
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"""Load actuals for multiple assets."""
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return {sym: self.load_latest(sym) for sym in symbols}
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