sentiment-engine/MALKHUT/malkhut/training/selector.py

"""
Strategy Selector — maps market conditions to best strategy.

Architecture:
    market_fingerprint (ExoF + MARAS + vel_div + eigenscan)
        ↓
    Regime Classifier (market state → regime tag)
        ↓
    Performance Matrix (regime × strategy → score)
        ↓
    Selector (best strategy for current regime)
        ↓
    FulfilmentPolicyParams (selected strategy's parameters)

The system maintains a PORTFOLIO of strategies and SELECTS the best one
for current conditions. Strategies are adversarially tested across regimes
and floated to the top based on regime-specific performance.
"""
from __future__ import annotations

import time
from collections import defaultdict
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Dict, List, Optional, Sequence, Tuple

from malkhut.state import FulfilmentPolicyParams, MarketWorldState
from malkhut.features import DefaultFeatureExtractor, FeatureExtractor
from malkhut.training.cma_trainer import PolicySnapshot
from malkhut.training.dsl import StrategyTemplate, SensorType, _read_sensor


# ==============================================================================
# Market Regime Classification
# ==============================================================================

class MarketRegime(str, Enum):
    """
    Market regime tags derived from fingerprinting.

    These map to upstream systems:
      - MARAS 8-regime classification (ExF/Eigen/BTC/EsoF/Micro)
      - DOLPHINNG7 vel_div + eigenscan
      - ExoF external factors

    Each regime has characteristic behavior where different strategies excel.
    """
    TRENDING_UP = "trending_up"
    TRENDING_DOWN = "trending_down"
    HIGH_VOLATILITY = "high_volatility"
    LOW_VOLATILITY = "low_volatility"
    MEAN_REVERTING = "mean_reverting"
    MOMENTUM = "momentum"
    CHOPPY = "choppy"
    LIQUIDITY_HOLE = "liquidity_hole"
    NORMAL = "normal"
    UNKNOWN = "unknown"


@dataclass(frozen=True, slots=True)
class MarketFingerprint:
    """
    Snapshot of market state features used for regime classification.

    Derived from upstream systems:
      - ExoF: funding_bps, open_interest_change
      - MARAS: regime_score (0-1), 17-dim composite signature
      - DOLPHINNG7: vel_div, eigenscan
    """
    ts_ns: int
    regime: MarketRegime
    volatility: float
    spread_bps: float
    imbalance: float
    toxicity: float
    regime_score: float  # 0-1, from MARAS
    funding_bps: float
    price_momentum: float
    volume_ratio: float  # current vs average
    depth_ratio: float  # bid/ask depth ratio


# ==============================================================================
# Regime Classifier
# ==============================================================================

class RegimeClassifier:
    """
    Classify current market state into a regime tag.

    Uses simple rule-based classification for Phase 1.
    Phase 2: integrate with MARAS ensemble for production classification.
    """

    def __init__(self, feature_extractor: Optional[FeatureExtractor] = None) -> None:
        self._extractor = feature_extractor or DefaultFeatureExtractor()

    def classify(self, state: MarketWorldState) -> MarketRegime:
        """Classify current market state into a regime."""
        fv = self._extractor.extract(state).values

        volatility = state.trade_path.volatility_bps if state.trade_path else 0.0
        spread_bps = fv.get("spread_bps", 0.0)
        imbalance = fv.get("top5_imbalance", 0.0)
        toxicity = fv.get("orderflow_toxicity", 0.0)
        regime_score = state.trade_path.dolphin_regime_score if state.trade_path else 0.5

        # Rule-based classification (check specific first)
        if spread_bps > 10:
            return MarketRegime.LIQUIDITY_HOLE
        elif volatility > 25:
            return MarketRegime.HIGH_VOLATILITY
        elif volatility < 5 and spread_bps < 2:
            return MarketRegime.LOW_VOLATILITY
        elif abs(imbalance) > 0.4:
            return MarketRegime.MOMENTUM if imbalance > 0 else MarketRegime.TRENDING_DOWN
        elif regime_score > 0.7:
            return MarketRegime.TRENDING_UP
        elif regime_score < 0.3:
            return MarketRegime.MEAN_REVERTING
        elif abs(imbalance) < 0.1 and spread_bps > 5:
            return MarketRegime.CHOPPY
        else:
            return MarketRegime.NORMAL

    def fingerprint(self, state: MarketWorldState) -> MarketFingerprint:
        """Create a full market fingerprint."""
        fv = DefaultFeatureExtractor().extract(state).values
        return MarketFingerprint(
            ts_ns=state.ts_ns,
            regime=self.classify(state),
            volatility=fv.get("volatility_state", 0.0),
            spread_bps=fv.get("spread_bps", 0.0),
            imbalance=fv.get("top5_imbalance", 0.0),
            toxicity=fv.get("orderflow_toxicity", 0.0),
            regime_score=state.trade_path.dolphin_regime_score if state.trade_path else 0.5,
            funding_bps=state.funding_bps or 0.0,
            price_momentum=0.0,  # placeholder
            volume_ratio=1.0,  # placeholder
            depth_ratio=1.0,  # placeholder
        )


# ==============================================================================
# Performance Matrix
# ==============================================================================

@dataclass
class RegimeStrategyScore:
    """Performance score for a strategy in a specific regime.

    Manifold fields (for Mode 2 recommendation):
      confidence: 0.0-1.0, how reliable is this score
      support_count: how many evaluations produced this score
      distance_to_nearest: distance to nearest other evaluated point
    """
    strategy_id: str
    regime: MarketRegime
    score: float
    episodes: int
    avg_pnl_bps: float
    avg_drawdown_bps: float
    avg_adverse_fill_ratio: float
    last_updated_ns: int
    confidence: float = 1.0
    support_count: int = 1
    distance_to_nearest: float = 0.0


class PerformanceMatrix:
    """
    Tracks strategy performance across regimes.

    Matrix: (regime, strategy_id) → RegimeStrategyScore

    Used by the selector to choose the best strategy for current conditions.
    """

    def __init__(self) -> None:
        self._scores: Dict[Tuple[MarketRegime, str], RegimeStrategyScore] = {}
        self._strategy_regime_history: Dict[str, List[MarketRegime]] = defaultdict(list)

    def record(
        self,
        strategy_id: str,
        regime: MarketRegime,
        score: float,
        pnl_bps: float = 0.0,
        drawdown_bps: float = 0.0,
        adverse_fill_ratio: float = 0.0,
    ) -> None:
        """Record a strategy's performance in a regime."""
        key = (regime, strategy_id)
        existing = self._scores.get(key)

        if existing:
            # Exponential moving average
            alpha = 0.3
            new_score = alpha * score + (1 - alpha) * existing.score
            new_episodes = existing.episodes + 1
            new_pnl = alpha * pnl_bps + (1 - alpha) * existing.avg_pnl_bps
            new_dd = alpha * drawdown_bps + (1 - alpha) * existing.avg_drawdown_bps
            new_adverse = alpha * adverse_fill_ratio + (1 - alpha) * existing.avg_adverse_fill_ratio
        else:
            new_score = score
            new_episodes = 1
            new_pnl = pnl_bps
            new_dd = drawdown_bps
            new_adverse = adverse_fill_ratio

        self._scores[key] = RegimeStrategyScore(
            strategy_id=strategy_id,
            regime=regime,
            score=new_score,
            episodes=new_episodes,
            avg_pnl_bps=new_pnl,
            avg_drawdown_bps=new_dd,
            avg_adverse_fill_ratio=new_adverse,
            last_updated_ns=time.time_ns(),
            confidence=min(1.0, new_episodes / 10.0),  # confidence grows with evidence
            support_count=new_episodes,
            distance_to_nearest=0.0,  # computed lazily on query
        )
        self._strategy_regime_history[strategy_id].append(regime)

    def get_best(
        self,
        regime: MarketRegime,
        exclude: Optional[set[str]] = None,
    ) -> Optional[str]:
        """Get the best strategy for a given regime."""
        exclude = exclude or set()
        candidates = [
            (key[1], score.score)
            for key, score in self._scores.items()
            if key[0] == regime and key[1] not in exclude
        ]
        if not candidates:
            return None
        return max(candidates, key=lambda x: x[1])[0]

    def get_scores_for_regime(self, regime: MarketRegime) -> List[RegimeStrategyScore]:
        """Get all strategy scores for a regime, sorted by score."""
        scores = [s for s in self._scores.values() if s.regime == regime]
        return sorted(scores, key=lambda s: s.score, reverse=True)

    def get_regimes_for_strategy(self, strategy_id: str) -> List[MarketRegime]:
        """Get all regimes where a strategy has been tested."""
        return list(set(key[0] for key in self._scores if key[1] == strategy_id))

    def get_coverage(self) -> Dict[str, int]:
        """Get regime coverage per strategy."""
        coverage: Dict[str, int] = defaultdict(int)
        for key in self._scores:
            coverage[key[1]] += 1
        return dict(coverage)

    @property
    def total_entries(self) -> int:
        return len(self._scores)


# ==============================================================================
# Strategy Selector
# ==============================================================================

@dataclass(frozen=True, slots=True)
class SelectionResult:
    """Result of strategy selection."""
    strategy_id: str
    regime: MarketRegime
    score: float
    confidence: float  # 0-1, how confident we are in this selection
    alternatives: Tuple[str, ...]  # other considered strategies
    reason: str


class StrategySelector:
    """
    Selects the best strategy for current market conditions.

    Flow:
        1. Classify current market regime
        2. Look up performance matrix for regime
        3. Select strategy with highest regime-specific score
        4. Apply confidence threshold (fallback to default if low confidence)
        5. Return selection with alternatives

    Adversarial testing:
        - Each strategy is tested across ALL regimes
        - Performance matrix tracks regime-specific scores
        - Selector floats best strategy to top per regime
        - Fallback to baseline if no strategy has enough data
    """

    def __init__(
        self,
        classifier: Optional[RegimeClassifier] = None,
        matrix: Optional[PerformanceMatrix] = None,
        default_strategy_id: str = "baseline",
        min_episodes_for_selection: int = 3,
        confidence_threshold: float = 0.5,
    ) -> None:
        self._classifier = classifier or RegimeClassifier()
        self._matrix = matrix or PerformanceMatrix()
        self._default_strategy_id = default_strategy_id
        self._min_episodes = min_episodes_for_selection
        self._confidence_threshold = confidence_threshold
        self._selection_history: list[SelectionResult] = []

    def select(
        self,
        state: MarketWorldState,
        available_strategies: Dict[str, FulfilmentPolicyParams],
    ) -> SelectionResult:
        """
        Select the best strategy for current market conditions.

        Args:
            state: current market state
            available_strategies: {strategy_id: params} mapping

        Returns:
            SelectionResult with chosen strategy and reasoning
        """
        # 1. Classify current regime
        regime = self._classifier.classify(state)

        # 2. Look up performance matrix
        best_id = self._matrix.get_best(regime, exclude={self._default_strategy_id})

        # 3. Check if best strategy has enough data
        if best_id and best_id in available_strategies:
            scores = self._matrix.get_scores_for_regime(regime)
            best_score = next((s for s in scores if s.strategy_id == best_id), None)

            if best_score and best_score.episodes >= self._min_episodes:
                # 4. Check confidence
                all_scores = [s.score for s in scores]
                if len(all_scores) > 1:
                    score_range = max(all_scores) - min(all_scores)
                    confidence = min(1.0, score_range / max(max(all_scores), 1e-9))
                else:
                    confidence = 0.5

                if confidence >= self._confidence_threshold:
                    result = SelectionResult(
                        strategy_id=best_id,
                        regime=regime,
                        score=best_score.score,
                        confidence=confidence,
                        alternatives=tuple(s.strategy_id for s in scores[:3] if s.strategy_id != best_id),
                        reason=f"best_for_{regime.value}",
                    )
                    self._selection_history.append(result)
                    return result

        # 5. Fallback to default
        result = SelectionResult(
            strategy_id=self._default_strategy_id,
            regime=regime,
            score=0.0,
            confidence=0.0,
            alternatives=(),
            reason="fallback_default",
        )
        self._selection_history.append(result)
        return result

    def record_outcome(
        self,
        strategy_id: str,
        regime: MarketRegime,
        score: float,
        pnl_bps: float = 0.0,
        drawdown_bps: float = 0.0,
        adverse_fill_ratio: float = 0.0,
    ) -> None:
        """Record a strategy's outcome for learning."""
        self._matrix.record(
            strategy_id=strategy_id,
            regime=regime,
            score=score,
            pnl_bps=pnl_bps,
            drawdown_bps=drawdown_bps,
            adverse_fill_ratio=adverse_fill_ratio,
        )

    @property
    def matrix(self) -> PerformanceMatrix:
        return self._matrix

    @property
    def classifier(self) -> RegimeClassifier:
        return self._classifier

    @property
    def selection_history(self) -> list[SelectionResult]:
        return list(self._selection_history)

    @property
    def total_selections(self) -> int:
        return len(self._selection_history)