ef2f8e8827
CMA-ES trainer (cma_trainer.py): self-play pool, bootstrap CI, ScenarioFactory with behavior-driven scenarios, auto-compile, label query interfaces. Policy registry (registry.py): CANDIDATE → ACTIVE lifecycle. Training pipeline (pipeline.py): bounded continuous learning loop + logger. Strategy selector (selector.py): regime → strategy mapping, performance matrix.
392 lines
14 KiB
Python
392 lines
14 KiB
Python
"""
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Strategy Selector — maps market conditions to best strategy.
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Architecture:
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market_fingerprint (ExoF + MARAS + vel_div + eigenscan)
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↓
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Regime Classifier (market state → regime tag)
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↓
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Performance Matrix (regime × strategy → score)
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↓
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Selector (best strategy for current regime)
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↓
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FulfilmentPolicyParams (selected strategy's parameters)
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The system maintains a PORTFOLIO of strategies and SELECTS the best one
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for current conditions. Strategies are adversarially tested across regimes
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and floated to the top based on regime-specific performance.
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"""
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from __future__ import annotations
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import time
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from collections import defaultdict
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from dataclasses import dataclass, field
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from enum import Enum
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from typing import Any, Dict, List, Optional, Sequence, Tuple
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from malkhut.state import FulfilmentPolicyParams, MarketWorldState
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from malkhut.features import DefaultFeatureExtractor, FeatureExtractor
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from malkhut.training.cma_trainer import PolicySnapshot
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from malkhut.training.dsl import StrategyTemplate, SensorType, _read_sensor
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# ==============================================================================
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# Market Regime Classification
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# ==============================================================================
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class MarketRegime(str, Enum):
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"""
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Market regime tags derived from fingerprinting.
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These map to upstream systems:
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- MARAS 8-regime classification (ExF/Eigen/BTC/EsoF/Micro)
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- DOLPHINNG7 vel_div + eigenscan
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- ExoF external factors
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Each regime has characteristic behavior where different strategies excel.
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"""
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TRENDING_UP = "trending_up"
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TRENDING_DOWN = "trending_down"
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HIGH_VOLATILITY = "high_volatility"
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LOW_VOLATILITY = "low_volatility"
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MEAN_REVERTING = "mean_reverting"
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MOMENTUM = "momentum"
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CHOPPY = "choppy"
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LIQUIDITY_HOLE = "liquidity_hole"
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NORMAL = "normal"
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UNKNOWN = "unknown"
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@dataclass(frozen=True, slots=True)
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class MarketFingerprint:
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"""
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Snapshot of market state features used for regime classification.
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Derived from upstream systems:
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- ExoF: funding_bps, open_interest_change
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- MARAS: regime_score (0-1), 17-dim composite signature
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- DOLPHINNG7: vel_div, eigenscan
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"""
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ts_ns: int
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regime: MarketRegime
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volatility: float
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spread_bps: float
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imbalance: float
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toxicity: float
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regime_score: float # 0-1, from MARAS
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funding_bps: float
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price_momentum: float
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volume_ratio: float # current vs average
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depth_ratio: float # bid/ask depth ratio
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# ==============================================================================
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# Regime Classifier
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# ==============================================================================
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class RegimeClassifier:
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"""
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Classify current market state into a regime tag.
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Uses simple rule-based classification for Phase 1.
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Phase 2: integrate with MARAS ensemble for production classification.
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"""
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def __init__(self, feature_extractor: Optional[FeatureExtractor] = None) -> None:
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self._extractor = feature_extractor or DefaultFeatureExtractor()
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def classify(self, state: MarketWorldState) -> MarketRegime:
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"""Classify current market state into a regime."""
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fv = self._extractor.extract(state).values
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volatility = state.trade_path.volatility_bps if state.trade_path else 0.0
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spread_bps = fv.get("spread_bps", 0.0)
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imbalance = fv.get("top5_imbalance", 0.0)
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toxicity = fv.get("orderflow_toxicity", 0.0)
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regime_score = state.trade_path.dolphin_regime_score if state.trade_path else 0.5
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# Rule-based classification (check specific first)
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if spread_bps > 10:
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return MarketRegime.LIQUIDITY_HOLE
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elif volatility > 25:
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return MarketRegime.HIGH_VOLATILITY
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elif volatility < 5 and spread_bps < 2:
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return MarketRegime.LOW_VOLATILITY
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elif abs(imbalance) > 0.4:
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return MarketRegime.MOMENTUM if imbalance > 0 else MarketRegime.TRENDING_DOWN
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elif regime_score > 0.7:
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return MarketRegime.TRENDING_UP
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elif regime_score < 0.3:
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return MarketRegime.MEAN_REVERTING
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elif abs(imbalance) < 0.1 and spread_bps > 5:
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return MarketRegime.CHOPPY
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else:
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return MarketRegime.NORMAL
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def fingerprint(self, state: MarketWorldState) -> MarketFingerprint:
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"""Create a full market fingerprint."""
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fv = DefaultFeatureExtractor().extract(state).values
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return MarketFingerprint(
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ts_ns=state.ts_ns,
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regime=self.classify(state),
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volatility=fv.get("volatility_state", 0.0),
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spread_bps=fv.get("spread_bps", 0.0),
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imbalance=fv.get("top5_imbalance", 0.0),
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toxicity=fv.get("orderflow_toxicity", 0.0),
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regime_score=state.trade_path.dolphin_regime_score if state.trade_path else 0.5,
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funding_bps=state.funding_bps or 0.0,
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price_momentum=0.0, # placeholder
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volume_ratio=1.0, # placeholder
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depth_ratio=1.0, # placeholder
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)
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# ==============================================================================
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# Performance Matrix
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# ==============================================================================
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@dataclass
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class RegimeStrategyScore:
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"""Performance score for a strategy in a specific regime."""
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strategy_id: str
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regime: MarketRegime
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score: float
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episodes: int
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avg_pnl_bps: float
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avg_drawdown_bps: float
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avg_adverse_fill_ratio: float
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last_updated_ns: int
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class PerformanceMatrix:
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"""
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Tracks strategy performance across regimes.
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Matrix: (regime, strategy_id) → RegimeStrategyScore
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Used by the selector to choose the best strategy for current conditions.
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"""
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def __init__(self) -> None:
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self._scores: Dict[Tuple[MarketRegime, str], RegimeStrategyScore] = {}
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self._strategy_regime_history: Dict[str, List[MarketRegime]] = defaultdict(list)
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def record(
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self,
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strategy_id: str,
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regime: MarketRegime,
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score: float,
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pnl_bps: float = 0.0,
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drawdown_bps: float = 0.0,
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adverse_fill_ratio: float = 0.0,
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) -> None:
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"""Record a strategy's performance in a regime."""
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key = (regime, strategy_id)
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existing = self._scores.get(key)
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if existing:
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# Exponential moving average
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alpha = 0.3
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new_score = alpha * score + (1 - alpha) * existing.score
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new_episodes = existing.episodes + 1
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new_pnl = alpha * pnl_bps + (1 - alpha) * existing.avg_pnl_bps
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new_dd = alpha * drawdown_bps + (1 - alpha) * existing.avg_drawdown_bps
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new_adverse = alpha * adverse_fill_ratio + (1 - alpha) * existing.avg_adverse_fill_ratio
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else:
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new_score = score
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new_episodes = 1
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new_pnl = pnl_bps
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new_dd = drawdown_bps
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new_adverse = adverse_fill_ratio
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self._scores[key] = RegimeStrategyScore(
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strategy_id=strategy_id,
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regime=regime,
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score=new_score,
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episodes=new_episodes,
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avg_pnl_bps=new_pnl,
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avg_drawdown_bps=new_dd,
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avg_adverse_fill_ratio=new_adverse,
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last_updated_ns=time.time_ns(),
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)
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self._strategy_regime_history[strategy_id].append(regime)
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def get_best(
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self,
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regime: MarketRegime,
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exclude: Optional[set[str]] = None,
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) -> Optional[str]:
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"""Get the best strategy for a given regime."""
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exclude = exclude or set()
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candidates = [
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(key[1], score.score)
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for key, score in self._scores.items()
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if key[0] == regime and key[1] not in exclude
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]
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if not candidates:
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return None
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return max(candidates, key=lambda x: x[1])[0]
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def get_scores_for_regime(self, regime: MarketRegime) -> List[RegimeStrategyScore]:
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"""Get all strategy scores for a regime, sorted by score."""
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scores = [s for s in self._scores.values() if s.regime == regime]
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return sorted(scores, key=lambda s: s.score, reverse=True)
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def get_regimes_for_strategy(self, strategy_id: str) -> List[MarketRegime]:
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"""Get all regimes where a strategy has been tested."""
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return list(set(key[0] for key in self._scores if key[1] == strategy_id))
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def get_coverage(self) -> Dict[str, int]:
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"""Get regime coverage per strategy."""
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coverage: Dict[str, int] = defaultdict(int)
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for key in self._scores:
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coverage[key[1]] += 1
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return dict(coverage)
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@property
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def total_entries(self) -> int:
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return len(self._scores)
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# ==============================================================================
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# Strategy Selector
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# ==============================================================================
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@dataclass(frozen=True, slots=True)
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class SelectionResult:
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"""Result of strategy selection."""
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strategy_id: str
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regime: MarketRegime
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score: float
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confidence: float # 0-1, how confident we are in this selection
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alternatives: Tuple[str, ...] # other considered strategies
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reason: str
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class StrategySelector:
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"""
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Selects the best strategy for current market conditions.
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Flow:
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1. Classify current market regime
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2. Look up performance matrix for regime
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3. Select strategy with highest regime-specific score
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4. Apply confidence threshold (fallback to default if low confidence)
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5. Return selection with alternatives
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Adversarial testing:
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- Each strategy is tested across ALL regimes
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- Performance matrix tracks regime-specific scores
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- Selector floats best strategy to top per regime
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- Fallback to baseline if no strategy has enough data
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"""
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def __init__(
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self,
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classifier: Optional[RegimeClassifier] = None,
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matrix: Optional[PerformanceMatrix] = None,
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default_strategy_id: str = "baseline",
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min_episodes_for_selection: int = 3,
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confidence_threshold: float = 0.5,
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) -> None:
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self._classifier = classifier or RegimeClassifier()
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self._matrix = matrix or PerformanceMatrix()
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self._default_strategy_id = default_strategy_id
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self._min_episodes = min_episodes_for_selection
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self._confidence_threshold = confidence_threshold
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self._selection_history: list[SelectionResult] = []
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def select(
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self,
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state: MarketWorldState,
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available_strategies: Dict[str, FulfilmentPolicyParams],
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) -> SelectionResult:
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"""
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Select the best strategy for current market conditions.
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Args:
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state: current market state
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available_strategies: {strategy_id: params} mapping
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Returns:
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SelectionResult with chosen strategy and reasoning
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"""
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# 1. Classify current regime
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regime = self._classifier.classify(state)
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# 2. Look up performance matrix
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best_id = self._matrix.get_best(regime, exclude={self._default_strategy_id})
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# 3. Check if best strategy has enough data
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if best_id and best_id in available_strategies:
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scores = self._matrix.get_scores_for_regime(regime)
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best_score = next((s for s in scores if s.strategy_id == best_id), None)
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if best_score and best_score.episodes >= self._min_episodes:
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# 4. Check confidence
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all_scores = [s.score for s in scores]
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if len(all_scores) > 1:
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score_range = max(all_scores) - min(all_scores)
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confidence = min(1.0, score_range / max(max(all_scores), 1e-9))
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else:
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confidence = 0.5
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if confidence >= self._confidence_threshold:
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result = SelectionResult(
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strategy_id=best_id,
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regime=regime,
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score=best_score.score,
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confidence=confidence,
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alternatives=tuple(s.strategy_id for s in scores[:3] if s.strategy_id != best_id),
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reason=f"best_for_{regime.value}",
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)
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self._selection_history.append(result)
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return result
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# 5. Fallback to default
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result = SelectionResult(
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strategy_id=self._default_strategy_id,
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regime=regime,
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score=0.0,
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confidence=0.0,
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alternatives=(),
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reason="fallback_default",
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)
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self._selection_history.append(result)
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return result
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def record_outcome(
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self,
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strategy_id: str,
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regime: MarketRegime,
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score: float,
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pnl_bps: float = 0.0,
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drawdown_bps: float = 0.0,
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adverse_fill_ratio: float = 0.0,
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) -> None:
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"""Record a strategy's outcome for learning."""
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self._matrix.record(
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strategy_id=strategy_id,
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regime=regime,
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score=score,
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pnl_bps=pnl_bps,
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drawdown_bps=drawdown_bps,
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adverse_fill_ratio=adverse_fill_ratio,
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)
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@property
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def matrix(self) -> PerformanceMatrix:
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return self._matrix
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@property
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def classifier(self) -> RegimeClassifier:
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return self._classifier
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@property
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def selection_history(self) -> list[SelectionResult]:
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return list(self._selection_history)
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@property
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def total_selections(self) -> int:
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return len(self._selection_history)
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