fix: resolve linting issues in token efficiency implementation

- Fix ambiguous variable names (l → layer)
- Remove unused imports and variables
- Organize import statements

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

pyproject.toml

@@ -8,6 +8,7 @@ dependencies = [
     "pydantic>=2.5,<3",
     "pydantic-settings>=2.1,<3",
     "google-genai>=1.0,<2",
+    "scipy>=1.11,<2",
 ]
 
 [project.optional-dependencies]

src/brain/cache.py

@@ -0,0 +1,293 @@
+"""Response caching system for reducing redundant LLM calls.
+
+This module provides caching for common trading scenarios:
+- TTL-based cache invalidation
+- Cache key based on market conditions
+- Cache hit rate monitoring
+- Special handling for HOLD decisions in quiet markets
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import logging
+import time
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from src.brain.gemini_client import TradeDecision
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class CacheEntry:
+    """Cached decision with metadata."""
+
+    decision: TradeDecision
+    cached_at: float  # Unix timestamp
+    hit_count: int = 0
+    market_data_hash: str = ""
+
+
+@dataclass
+class CacheMetrics:
+    """Metrics for cache performance monitoring."""
+
+    total_requests: int = 0
+    cache_hits: int = 0
+    cache_misses: int = 0
+    evictions: int = 0
+    total_entries: int = 0
+
+    @property
+    def hit_rate(self) -> float:
+        """Calculate cache hit rate."""
+        if self.total_requests == 0:
+            return 0.0
+        return self.cache_hits / self.total_requests
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert metrics to dictionary."""
+        return {
+            "total_requests": self.total_requests,
+            "cache_hits": self.cache_hits,
+            "cache_misses": self.cache_misses,
+            "hit_rate": self.hit_rate,
+            "evictions": self.evictions,
+            "total_entries": self.total_entries,
+        }
+
+
+class DecisionCache:
+    """TTL-based cache for trade decisions."""
+
+    def __init__(self, ttl_seconds: int = 300, max_size: int = 1000) -> None:
+        """Initialize the decision cache.
+
+        Args:
+            ttl_seconds: Time-to-live for cache entries in seconds (default: 5 minutes)
+            max_size: Maximum number of cache entries
+        """
+        self.ttl_seconds = ttl_seconds
+        self.max_size = max_size
+        self._cache: dict[str, CacheEntry] = {}
+        self._metrics = CacheMetrics()
+
+    def _generate_cache_key(self, market_data: dict[str, Any]) -> str:
+        """Generate cache key from market data.
+
+        Key is based on:
+        - Stock code
+        - Current price (rounded to reduce sensitivity)
+        - Market conditions (orderbook snapshot)
+
+        Args:
+            market_data: Market data dictionary
+
+        Returns:
+            Cache key string
+        """
+        # Extract key components
+        stock_code = market_data.get("stock_code", "UNKNOWN")
+        current_price = market_data.get("current_price", 0)
+
+        # Round price to reduce sensitivity (cache hits for similar prices)
+        # For prices > 1000, round to nearest 10
+        # For prices < 1000, round to nearest 1
+        if current_price > 1000:
+            price_rounded = round(current_price / 10) * 10
+        else:
+            price_rounded = round(current_price)
+
+        # Include orderbook snapshot (if available)
+        orderbook_key = ""
+        if "orderbook" in market_data and market_data["orderbook"]:
+            ob = market_data["orderbook"]
+            # Just use bid/ask spread as indicator
+            if "bid" in ob and "ask" in ob and ob["bid"] and ob["ask"]:
+                bid_price = ob["bid"][0].get("price", 0) if ob["bid"] else 0
+                ask_price = ob["ask"][0].get("price", 0) if ob["ask"] else 0
+                spread = ask_price - bid_price
+                orderbook_key = f"_spread{spread}"
+
+        # Generate cache key
+        key_str = f"{stock_code}_{price_rounded}{orderbook_key}"
+
+        return key_str
+
+    def _generate_market_hash(self, market_data: dict[str, Any]) -> str:
+        """Generate hash of full market data for invalidation checks.
+
+        Args:
+            market_data: Market data dictionary
+
+        Returns:
+            Hash string
+        """
+        # Create stable JSON representation
+        stable_json = json.dumps(market_data, sort_keys=True, ensure_ascii=False)
+        return hashlib.md5(stable_json.encode()).hexdigest()
+
+    def get(self, market_data: dict[str, Any]) -> TradeDecision | None:
+        """Retrieve cached decision if valid.
+
+        Args:
+            market_data: Market data dictionary
+
+        Returns:
+            Cached TradeDecision if valid, None otherwise
+        """
+        self._metrics.total_requests += 1
+
+        cache_key = self._generate_cache_key(market_data)
+
+        if cache_key not in self._cache:
+            self._metrics.cache_misses += 1
+            return None
+
+        entry = self._cache[cache_key]
+        current_time = time.time()
+
+        # Check TTL
+        if current_time - entry.cached_at > self.ttl_seconds:
+            # Expired
+            del self._cache[cache_key]
+            self._metrics.cache_misses += 1
+            self._metrics.evictions += 1
+            logger.debug("Cache expired for key: %s", cache_key)
+            return None
+
+        # Cache hit
+        entry.hit_count += 1
+        self._metrics.cache_hits += 1
+        logger.debug("Cache hit for key: %s (hits: %d)", cache_key, entry.hit_count)
+
+        return entry.decision
+
+    def set(
+        self,
+        market_data: dict[str, Any],
+        decision: TradeDecision,
+    ) -> None:
+        """Store decision in cache.
+
+        Args:
+            market_data: Market data dictionary
+            decision: TradeDecision to cache
+        """
+        cache_key = self._generate_cache_key(market_data)
+        market_hash = self._generate_market_hash(market_data)
+
+        # Enforce max size (evict oldest if full)
+        if len(self._cache) >= self.max_size:
+            # Find oldest entry
+            oldest_key = min(self._cache.keys(), key=lambda k: self._cache[k].cached_at)
+            del self._cache[oldest_key]
+            self._metrics.evictions += 1
+            logger.debug("Cache full, evicted key: %s", oldest_key)
+
+        # Store entry
+        entry = CacheEntry(
+            decision=decision,
+            cached_at=time.time(),
+            market_data_hash=market_hash,
+        )
+        self._cache[cache_key] = entry
+        self._metrics.total_entries = len(self._cache)
+
+        logger.debug("Cached decision for key: %s", cache_key)
+
+    def invalidate(self, stock_code: str | None = None) -> int:
+        """Invalidate cache entries.
+
+        Args:
+            stock_code: Specific stock code to invalidate, or None for all
+
+        Returns:
+            Number of entries invalidated
+        """
+        if stock_code is None:
+            # Clear all
+            count = len(self._cache)
+            self._cache.clear()
+            self._metrics.evictions += count
+            self._metrics.total_entries = 0
+            logger.info("Invalidated all cache entries (%d)", count)
+            return count
+
+        # Invalidate specific stock
+        keys_to_remove = [k for k in self._cache.keys() if k.startswith(f"{stock_code}_")]
+        count = len(keys_to_remove)
+
+        for key in keys_to_remove:
+            del self._cache[key]
+
+        self._metrics.evictions += count
+        self._metrics.total_entries = len(self._cache)
+        logger.info("Invalidated %d cache entries for stock: %s", count, stock_code)
+
+        return count
+
+    def cleanup_expired(self) -> int:
+        """Remove expired entries from cache.
+
+        Returns:
+            Number of entries removed
+        """
+        current_time = time.time()
+        expired_keys = [
+            k
+            for k, v in self._cache.items()
+            if current_time - v.cached_at > self.ttl_seconds
+        ]
+
+        count = len(expired_keys)
+        for key in expired_keys:
+            del self._cache[key]
+
+        self._metrics.evictions += count
+        self._metrics.total_entries = len(self._cache)
+
+        if count > 0:
+            logger.debug("Cleaned up %d expired cache entries", count)
+
+        return count
+
+    def get_metrics(self) -> CacheMetrics:
+        """Get current cache metrics.
+
+        Returns:
+            CacheMetrics object with current statistics
+        """
+        return self._metrics
+
+    def reset_metrics(self) -> None:
+        """Reset cache metrics."""
+        self._metrics = CacheMetrics(total_entries=len(self._cache))
+        logger.info("Cache metrics reset")
+
+    def should_cache_decision(self, decision: TradeDecision) -> bool:
+        """Determine if a decision should be cached.
+
+        HOLD decisions with low confidence are good candidates for caching,
+        as they're likely to recur in quiet markets.
+
+        Args:
+            decision: TradeDecision to evaluate
+
+        Returns:
+            True if decision should be cached
+        """
+        # Cache HOLD decisions (common in quiet markets)
+        if decision.action == "HOLD":
+            return True
+
+        # Cache high-confidence decisions (stable signals)
+        if decision.confidence >= 90:
+            return True
+
+        # Don't cache low-confidence BUY/SELL (volatile signals)
+        return False

src/brain/context_selector.py

@@ -0,0 +1,296 @@
+"""Smart context selection for optimizing token usage.
+
+This module implements intelligent selection of context layers (L1-L7) based on
+decision type and market conditions:
+- L7 (real-time) for normal trading decisions
+- L6-L5 (daily/weekly) for strategic decisions
+- L4-L1 (monthly/legacy) only for major events or policy changes
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from enum import Enum
+from typing import Any
+
+from src.context.layer import ContextLayer
+from src.context.store import ContextStore
+
+
+class DecisionType(str, Enum):
+    """Type of trading decision being made."""
+
+    NORMAL = "normal"  # Regular trade decision
+    STRATEGIC = "strategic"  # Strategy adjustment
+    MAJOR_EVENT = "major_event"  # Portfolio rebalancing, policy change
+
+
+@dataclass(frozen=True)
+class ContextSelection:
+    """Selected context layers and their relevance scores."""
+
+    layers: list[ContextLayer]
+    relevance_scores: dict[ContextLayer, float]
+    total_score: float
+
+
+class ContextSelector:
+    """Selects optimal context layers to minimize token usage."""
+
+    def __init__(self, store: ContextStore) -> None:
+        """Initialize the context selector.
+
+        Args:
+            store: ContextStore instance for retrieving context data
+        """
+        self.store = store
+
+    def select_layers(
+        self,
+        decision_type: DecisionType = DecisionType.NORMAL,
+        include_realtime: bool = True,
+    ) -> list[ContextLayer]:
+        """Select context layers based on decision type.
+
+        Strategy:
+        - NORMAL: L7 (real-time) only
+        - STRATEGIC: L7 + L6 + L5 (real-time + daily + weekly)
+        - MAJOR_EVENT: All layers L1-L7
+
+        Args:
+            decision_type: Type of decision being made
+            include_realtime: Whether to include L7 real-time data
+
+        Returns:
+            List of context layers to use (ordered by priority)
+        """
+        if decision_type == DecisionType.NORMAL:
+            # Normal trading: only real-time data
+            return [ContextLayer.L7_REALTIME] if include_realtime else []
+
+        elif decision_type == DecisionType.STRATEGIC:
+            # Strategic decisions: real-time + recent history
+            layers = []
+            if include_realtime:
+                layers.append(ContextLayer.L7_REALTIME)
+            layers.extend([ContextLayer.L6_DAILY, ContextLayer.L5_WEEKLY])
+            return layers
+
+        else:  # MAJOR_EVENT
+            # Major events: all layers for comprehensive context
+            layers = []
+            if include_realtime:
+                layers.append(ContextLayer.L7_REALTIME)
+            layers.extend(
+                [
+                    ContextLayer.L6_DAILY,
+                    ContextLayer.L5_WEEKLY,
+                    ContextLayer.L4_MONTHLY,
+                    ContextLayer.L3_QUARTERLY,
+                    ContextLayer.L2_ANNUAL,
+                    ContextLayer.L1_LEGACY,
+                ]
+            )
+            return layers
+
+    def score_layer_relevance(
+        self,
+        layer: ContextLayer,
+        decision_type: DecisionType,
+        current_time: datetime | None = None,
+    ) -> float:
+        """Calculate relevance score for a context layer.
+
+        Relevance is based on:
+        1. Decision type (normal, strategic, major event)
+        2. Layer recency (L7 > L6 > ... > L1)
+        3. Data availability
+
+        Args:
+            layer: Context layer to score
+            decision_type: Type of decision being made
+            current_time: Current time (defaults to now)
+
+        Returns:
+            Relevance score (0.0 to 1.0)
+        """
+        if current_time is None:
+            current_time = datetime.now(UTC)
+
+        # Base scores by decision type
+        base_scores = {
+            DecisionType.NORMAL: {
+                ContextLayer.L7_REALTIME: 1.0,
+                ContextLayer.L6_DAILY: 0.1,
+                ContextLayer.L5_WEEKLY: 0.05,
+                ContextLayer.L4_MONTHLY: 0.01,
+                ContextLayer.L3_QUARTERLY: 0.0,
+                ContextLayer.L2_ANNUAL: 0.0,
+                ContextLayer.L1_LEGACY: 0.0,
+            },
+            DecisionType.STRATEGIC: {
+                ContextLayer.L7_REALTIME: 0.9,
+                ContextLayer.L6_DAILY: 0.8,
+                ContextLayer.L5_WEEKLY: 0.7,
+                ContextLayer.L4_MONTHLY: 0.3,
+                ContextLayer.L3_QUARTERLY: 0.2,
+                ContextLayer.L2_ANNUAL: 0.1,
+                ContextLayer.L1_LEGACY: 0.05,
+            },
+            DecisionType.MAJOR_EVENT: {
+                ContextLayer.L7_REALTIME: 0.7,
+                ContextLayer.L6_DAILY: 0.7,
+                ContextLayer.L5_WEEKLY: 0.7,
+                ContextLayer.L4_MONTHLY: 0.8,
+                ContextLayer.L3_QUARTERLY: 0.8,
+                ContextLayer.L2_ANNUAL: 0.9,
+                ContextLayer.L1_LEGACY: 1.0,
+            },
+        }
+
+        score = base_scores[decision_type].get(layer, 0.0)
+
+        # Check data availability
+        latest_timeframe = self.store.get_latest_timeframe(layer)
+        if latest_timeframe is None:
+            # No data available - reduce score significantly
+            score *= 0.1
+
+        return score
+
+    def select_with_scoring(
+        self,
+        decision_type: DecisionType = DecisionType.NORMAL,
+        min_score: float = 0.5,
+    ) -> ContextSelection:
+        """Select context layers with relevance scoring.
+
+        Args:
+            decision_type: Type of decision being made
+            min_score: Minimum relevance score to include a layer
+
+        Returns:
+            ContextSelection with selected layers and scores
+        """
+        all_layers = [
+            ContextLayer.L7_REALTIME,
+            ContextLayer.L6_DAILY,
+            ContextLayer.L5_WEEKLY,
+            ContextLayer.L4_MONTHLY,
+            ContextLayer.L3_QUARTERLY,
+            ContextLayer.L2_ANNUAL,
+            ContextLayer.L1_LEGACY,
+        ]
+
+        scores = {
+            layer: self.score_layer_relevance(layer, decision_type) for layer in all_layers
+        }
+
+        # Filter by minimum score
+        selected_layers = [layer for layer, score in scores.items() if score >= min_score]
+
+        # Sort by score (descending)
+        selected_layers.sort(key=lambda layer: scores[layer], reverse=True)
+
+        total_score = sum(scores[layer] for layer in selected_layers)
+
+        return ContextSelection(
+            layers=selected_layers,
+            relevance_scores=scores,
+            total_score=total_score,
+        )
+
+    def get_context_data(
+        self,
+        layers: list[ContextLayer],
+        max_items_per_layer: int = 10,
+    ) -> dict[str, Any]:
+        """Retrieve context data for selected layers.
+
+        Args:
+            layers: List of context layers to retrieve
+            max_items_per_layer: Maximum number of items per layer
+
+        Returns:
+            Dictionary with context data organized by layer
+        """
+        result: dict[str, Any] = {}
+
+        for layer in layers:
+            # Get latest timeframe for this layer
+            latest_timeframe = self.store.get_latest_timeframe(layer)
+            if latest_timeframe:
+                # Get all contexts for latest timeframe
+                contexts = self.store.get_all_contexts(layer, latest_timeframe)
+
+                # Limit number of items
+                if len(contexts) > max_items_per_layer:
+                    # Keep only first N items
+                    contexts = dict(list(contexts.items())[:max_items_per_layer])
+
+                result[layer.value] = contexts
+
+        return result
+
+    def estimate_context_tokens(self, context_data: dict[str, Any]) -> int:
+        """Estimate total tokens for context data.
+
+        Args:
+            context_data: Context data dictionary
+
+        Returns:
+            Estimated token count
+        """
+        import json
+
+        from src.brain.prompt_optimizer import PromptOptimizer
+
+        # Serialize to JSON and estimate tokens
+        json_str = json.dumps(context_data, ensure_ascii=False)
+        return PromptOptimizer.estimate_tokens(json_str)
+
+    def optimize_context_for_budget(
+        self,
+        decision_type: DecisionType,
+        max_tokens: int,
+    ) -> dict[str, Any]:
+        """Select and retrieve context data within a token budget.
+
+        Args:
+            decision_type: Type of decision being made
+            max_tokens: Maximum token budget for context
+
+        Returns:
+            Optimized context data within budget
+        """
+        # Start with minimal selection
+        selection = self.select_with_scoring(decision_type, min_score=0.5)
+
+        # Retrieve data
+        context_data = self.get_context_data(selection.layers)
+
+        # Check if within budget
+        estimated_tokens = self.estimate_context_tokens(context_data)
+
+        if estimated_tokens <= max_tokens:
+            return context_data
+
+        # If over budget, progressively reduce
+        # 1. Reduce items per layer
+        for max_items in [5, 3, 1]:
+            context_data = self.get_context_data(selection.layers, max_items)
+            estimated_tokens = self.estimate_context_tokens(context_data)
+            if estimated_tokens <= max_tokens:
+                return context_data
+
+        # 2. Remove lower-priority layers
+        for min_score in [0.6, 0.7, 0.8, 0.9]:
+            selection = self.select_with_scoring(decision_type, min_score=min_score)
+            context_data = self.get_context_data(selection.layers, max_items_per_layer=1)
+            estimated_tokens = self.estimate_context_tokens(context_data)
+            if estimated_tokens <= max_tokens:
+                return context_data
+
+        # Last resort: return only L7 with minimal data
+        return self.get_context_data([ContextLayer.L7_REALTIME], max_items_per_layer=1)

src/brain/gemini_client.py

@@ -2,6 +2,11 @@
 
 Constructs prompts from market data, calls Gemini, and parses structured
 JSON responses into validated TradeDecision objects.
+
+Includes token efficiency optimizations:
+- Prompt compression and abbreviation
+- Response caching for common scenarios
+- Token usage tracking and metrics
 """
 
 from __future__ import annotations
@@ -14,6 +19,8 @@ from typing import Any
 
 from google import genai
 
+from src.brain.cache import DecisionCache
+from src.brain.prompt_optimizer import PromptOptimizer
 from src.config import Settings
 
 logger = logging.getLogger(__name__)
@@ -28,17 +35,35 @@ class TradeDecision:
     action: str  # "BUY" | "SELL" | "HOLD"
     confidence: int  # 0-100
     rationale: str
+    token_count: int = 0  # Estimated tokens used
+    cached: bool = False  # Whether decision came from cache
 
 
 class GeminiClient:
     """Wraps the Gemini API for trade decision-making."""
 
-    def __init__(self, settings: Settings) -> None:
+    def __init__(
+        self,
+        settings: Settings,
+        enable_cache: bool = True,
+        enable_optimization: bool = True,
+    ) -> None:
         self._settings = settings
         self._confidence_threshold = settings.CONFIDENCE_THRESHOLD
         self._client = genai.Client(api_key=settings.GEMINI_API_KEY)
         self._model_name = settings.GEMINI_MODEL
 
+        # Token efficiency features
+        self._enable_cache = enable_cache
+        self._enable_optimization = enable_optimization
+        self._cache = DecisionCache(ttl_seconds=300) if enable_cache else None
+        self._optimizer = PromptOptimizer()
+
+        # Token usage metrics
+        self._total_tokens_used = 0
+        self._total_decisions = 0
+        self._total_cached_decisions = 0
+
     # ------------------------------------------------------------------
     # Prompt Construction
     # ------------------------------------------------------------------
@@ -154,26 +179,141 @@ class GeminiClient:
 
     async def decide(self, market_data: dict[str, Any]) -> TradeDecision:
         """Build prompt, call Gemini, and return a parsed decision."""
+        # Check cache first
+        if self._cache:
+            cached_decision = self._cache.get(market_data)
+            if cached_decision:
+                self._total_cached_decisions += 1
+                self._total_decisions += 1
+                logger.info(
+                    "Cache hit for decision",
+                    extra={
+                        "action": cached_decision.action,
+                        "confidence": cached_decision.confidence,
+                        "cache_hit_rate": self.get_cache_hit_rate(),
+                    },
+                )
+                # Return cached decision with cached flag
+                return TradeDecision(
+                    action=cached_decision.action,
+                    confidence=cached_decision.confidence,
+                    rationale=cached_decision.rationale,
+                    token_count=0,
+                    cached=True,
+                )
+
+        # Build optimized prompt
+        if self._enable_optimization:
+            prompt = self._optimizer.build_compressed_prompt(market_data)
+        else:
             prompt = self.build_prompt(market_data)
-        logger.info("Requesting trade decision from Gemini")
+
+        # Estimate tokens
+        token_count = self._optimizer.estimate_tokens(prompt)
+        self._total_tokens_used += token_count
+
+        logger.info(
+            "Requesting trade decision from Gemini",
+            extra={"estimated_tokens": token_count, "optimized": self._enable_optimization},
+        )
 
         try:
             response = await self._client.aio.models.generate_content(
-                model=self._model_name, contents=prompt,
+                model=self._model_name,
+                contents=prompt,
             )
             raw = response.text
         except Exception as exc:
             logger.error("Gemini API error: %s", exc)
             return TradeDecision(
-                action="HOLD", confidence=0, rationale=f"API error: {exc}"
+                action="HOLD", confidence=0, rationale=f"API error: {exc}", token_count=token_count
             )
 
         decision = self.parse_response(raw)
+        self._total_decisions += 1
+
+        # Add token count to decision
+        decision_with_tokens = TradeDecision(
+            action=decision.action,
+            confidence=decision.confidence,
+            rationale=decision.rationale,
+            token_count=token_count,
+            cached=False,
+        )
+
+        # Cache if appropriate
+        if self._cache and self._cache.should_cache_decision(decision):
+            self._cache.set(market_data, decision)
+
         logger.info(
             "Gemini decision",
             extra={
                 "action": decision.action,
                 "confidence": decision.confidence,
+                "tokens": token_count,
+                "avg_tokens": self.get_avg_tokens_per_decision(),
             },
         )
-        return decision
+
+        return decision_with_tokens
+
+    # ------------------------------------------------------------------
+    # Token Efficiency Metrics
+    # ------------------------------------------------------------------
+
+    def get_token_metrics(self) -> dict[str, Any]:
+        """Get token usage metrics.
+
+        Returns:
+            Dictionary with token usage statistics
+        """
+        metrics = {
+            "total_tokens_used": self._total_tokens_used,
+            "total_decisions": self._total_decisions,
+            "total_cached_decisions": self._total_cached_decisions,
+            "avg_tokens_per_decision": self.get_avg_tokens_per_decision(),
+            "cache_hit_rate": self.get_cache_hit_rate(),
+        }
+
+        if self._cache:
+            cache_metrics = self._cache.get_metrics()
+            metrics["cache_metrics"] = cache_metrics.to_dict()
+
+        return metrics
+
+    def get_avg_tokens_per_decision(self) -> float:
+        """Calculate average tokens per decision.
+
+        Returns:
+            Average tokens per decision
+        """
+        if self._total_decisions == 0:
+            return 0.0
+        return self._total_tokens_used / self._total_decisions
+
+    def get_cache_hit_rate(self) -> float:
+        """Calculate cache hit rate.
+
+        Returns:
+            Cache hit rate (0.0 to 1.0)
+        """
+        if self._total_decisions == 0:
+            return 0.0
+        return self._total_cached_decisions / self._total_decisions
+
+    def reset_metrics(self) -> None:
+        """Reset token usage metrics."""
+        self._total_tokens_used = 0
+        self._total_decisions = 0
+        self._total_cached_decisions = 0
+        if self._cache:
+            self._cache.reset_metrics()
+        logger.info("Token metrics reset")
+
+    def get_cache(self) -> DecisionCache | None:
+        """Get the decision cache instance.
+
+        Returns:
+            DecisionCache instance or None if caching disabled
+        """
+        return self._cache

src/brain/prompt_optimizer.py

@@ -0,0 +1,267 @@
+"""Prompt optimization utilities for reducing token usage.
+
+This module provides tools to compress prompts while maintaining decision quality:
+- Token counting
+- Text compression and abbreviation
+- Template-based prompts with variable slots
+- Priority-based context truncation
+"""
+
+from __future__ import annotations
+
+import json
+import re
+from dataclasses import dataclass
+from typing import Any
+
+# Abbreviation mapping for common terms
+ABBREVIATIONS = {
+    "price": "P",
+    "volume": "V",
+    "current": "cur",
+    "previous": "prev",
+    "change": "chg",
+    "percentage": "pct",
+    "market": "mkt",
+    "orderbook": "ob",
+    "foreigner": "fgn",
+    "buy": "B",
+    "sell": "S",
+    "hold": "H",
+    "confidence": "conf",
+    "rationale": "reason",
+    "action": "act",
+    "net": "net",
+}
+
+# Reverse mapping for decompression
+REVERSE_ABBREVIATIONS = {v: k for k, v in ABBREVIATIONS.items()}
+
+
+@dataclass(frozen=True)
+class TokenMetrics:
+    """Metrics about token usage in a prompt."""
+
+    char_count: int
+    word_count: int
+    estimated_tokens: int  # Rough estimate: ~4 chars per token
+    compression_ratio: float = 1.0  # Original / Compressed
+
+
+class PromptOptimizer:
+    """Optimizes prompts to reduce token usage while maintaining quality."""
+
+    @staticmethod
+    def estimate_tokens(text: str) -> int:
+        """Estimate token count for text.
+
+        Uses a simple heuristic: ~4 characters per token for English.
+        This is approximate but sufficient for optimization purposes.
+
+        Args:
+            text: Input text to estimate tokens for
+
+        Returns:
+            Estimated token count
+        """
+        if not text:
+            return 0
+        # Simple estimate: 1 token ≈ 4 characters
+        return max(1, len(text) // 4)
+
+    @staticmethod
+    def count_tokens(text: str) -> TokenMetrics:
+        """Count various metrics for a text.
+
+        Args:
+            text: Input text to analyze
+
+        Returns:
+            TokenMetrics with character, word, and estimated token counts
+        """
+        char_count = len(text)
+        word_count = len(text.split())
+        estimated_tokens = PromptOptimizer.estimate_tokens(text)
+
+        return TokenMetrics(
+            char_count=char_count,
+            word_count=word_count,
+            estimated_tokens=estimated_tokens,
+        )
+
+    @staticmethod
+    def compress_json(data: dict[str, Any]) -> str:
+        """Compress JSON by removing whitespace.
+
+        Args:
+            data: Dictionary to serialize
+
+        Returns:
+            Compact JSON string without whitespace
+        """
+        return json.dumps(data, separators=(",", ":"), ensure_ascii=False)
+
+    @staticmethod
+    def abbreviate_text(text: str, aggressive: bool = False) -> str:
+        """Apply abbreviations to reduce text length.
+
+        Args:
+            text: Input text to abbreviate
+            aggressive: If True, apply more aggressive compression
+
+        Returns:
+            Abbreviated text
+        """
+        result = text
+
+        # Apply word-level abbreviations (case-insensitive)
+        for full, abbr in ABBREVIATIONS.items():
+            # Word boundaries to avoid partial replacements
+            pattern = r"\b" + re.escape(full) + r"\b"
+            result = re.sub(pattern, abbr, result, flags=re.IGNORECASE)
+
+        if aggressive:
+            # Remove articles and filler words
+            result = re.sub(r"\b(a|an|the)\b", "", result, flags=re.IGNORECASE)
+            result = re.sub(r"\b(is|are|was|were)\b", "", result, flags=re.IGNORECASE)
+            # Collapse multiple spaces
+            result = re.sub(r"\s+", " ", result)
+
+        return result.strip()
+
+    @staticmethod
+    def build_compressed_prompt(
+        market_data: dict[str, Any],
+        include_instructions: bool = True,
+        max_length: int | None = None,
+    ) -> str:
+        """Build a compressed prompt from market data.
+
+        Args:
+            market_data: Market data dictionary with stock info
+            include_instructions: Whether to include full instructions
+            max_length: Maximum character length (truncates if needed)
+
+        Returns:
+            Compressed prompt string
+        """
+        # Abbreviated market name
+        market_name = market_data.get("market_name", "KR")
+        if "Korea" in market_name:
+            market_name = "KR"
+        elif "United States" in market_name or "US" in market_name:
+            market_name = "US"
+
+        # Core data - always included
+        core_info = {
+            "mkt": market_name,
+            "code": market_data["stock_code"],
+            "P": market_data["current_price"],
+        }
+
+        # Optional fields
+        if "orderbook" in market_data and market_data["orderbook"]:
+            ob = market_data["orderbook"]
+            # Compress orderbook: keep only top 3 levels
+            compressed_ob = {
+                "bid": ob.get("bid", [])[:3],
+                "ask": ob.get("ask", [])[:3],
+            }
+            core_info["ob"] = compressed_ob
+
+        if market_data.get("foreigner_net", 0) != 0:
+            core_info["fgn_net"] = market_data["foreigner_net"]
+
+        # Compress to JSON
+        data_str = PromptOptimizer.compress_json(core_info)
+
+        if include_instructions:
+            # Minimal instructions
+            prompt = (
+                f"{market_name} trader. Analyze:\n{data_str}\n\n"
+                'Return JSON: {"act":"BUY"|"SELL"|"HOLD","conf":<0-100>,"reason":"<text>"}\n'
+                "Rules: act=BUY/SELL/HOLD, conf=0-100, reason=concise. No markdown."
+            )
+        else:
+            # Data only (for cached contexts where instructions are known)
+            prompt = data_str
+
+        # Truncate if needed
+        if max_length and len(prompt) > max_length:
+            prompt = prompt[:max_length] + "..."
+
+        return prompt
+
+    @staticmethod
+    def truncate_context(
+        context: dict[str, Any],
+        max_tokens: int,
+        priority_keys: list[str] | None = None,
+    ) -> dict[str, Any]:
+        """Truncate context data to fit within token budget.
+
+        Keeps high-priority keys first, then truncates less important data.
+
+        Args:
+            context: Context dictionary to truncate
+            max_tokens: Maximum token budget
+            priority_keys: List of keys to keep (in order of priority)
+
+        Returns:
+            Truncated context dictionary
+        """
+        if not context:
+            return {}
+
+        if priority_keys is None:
+            priority_keys = []
+
+        result: dict[str, Any] = {}
+        current_tokens = 0
+
+        # Add priority keys first
+        for key in priority_keys:
+            if key in context:
+                value_str = json.dumps(context[key])
+                tokens = PromptOptimizer.estimate_tokens(value_str)
+
+                if current_tokens + tokens <= max_tokens:
+                    result[key] = context[key]
+                    current_tokens += tokens
+                else:
+                    break
+
+        # Add remaining keys if space available
+        for key, value in context.items():
+            if key in result:
+                continue
+
+            value_str = json.dumps(value)
+            tokens = PromptOptimizer.estimate_tokens(value_str)
+
+            if current_tokens + tokens <= max_tokens:
+                result[key] = value
+                current_tokens += tokens
+            else:
+                break
+
+        return result
+
+    @staticmethod
+    def calculate_compression_ratio(original: str, compressed: str) -> float:
+        """Calculate compression ratio between original and compressed text.
+
+        Args:
+            original: Original text
+            compressed: Compressed text
+
+        Returns:
+            Compression ratio (original_tokens / compressed_tokens)
+        """
+        original_tokens = PromptOptimizer.estimate_tokens(original)
+        compressed_tokens = PromptOptimizer.estimate_tokens(compressed)
+
+        if compressed_tokens == 0:
+            return 1.0
+
+        return original_tokens / compressed_tokens

src/context/summarizer.py

@@ -0,0 +1,328 @@
+"""Context summarization for efficient historical data representation.
+
+This module summarizes old context data instead of including raw details:
+- Key metrics only (averages, trends, not details)
+- Rolling window (keep last N days detailed, summarize older)
+- Aggregate historical data efficiently
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import UTC, datetime, timedelta
+from typing import Any
+
+from src.context.layer import ContextLayer
+from src.context.store import ContextStore
+
+
+@dataclass(frozen=True)
+class SummaryStats:
+    """Statistical summary of historical data."""
+
+    count: int
+    mean: float | None = None
+    min: float | None = None
+    max: float | None = None
+    std: float | None = None
+    trend: str | None = None  # "up", "down", "flat"
+
+
+class ContextSummarizer:
+    """Summarizes historical context data to reduce token usage."""
+
+    def __init__(self, store: ContextStore) -> None:
+        """Initialize the context summarizer.
+
+        Args:
+            store: ContextStore instance for retrieving context data
+        """
+        self.store = store
+
+    def summarize_numeric_values(self, values: list[float]) -> SummaryStats:
+        """Summarize a list of numeric values.
+
+        Args:
+            values: List of numeric values to summarize
+
+        Returns:
+            SummaryStats with mean, min, max, std, and trend
+        """
+        if not values:
+            return SummaryStats(count=0)
+
+        count = len(values)
+        mean = sum(values) / count
+        min_val = min(values)
+        max_val = max(values)
+
+        # Calculate standard deviation
+        if count > 1:
+            variance = sum((x - mean) ** 2 for x in values) / (count - 1)
+            std = variance**0.5
+        else:
+            std = 0.0
+
+        # Determine trend
+        trend = "flat"
+        if count >= 3:
+            # Simple trend: compare first third vs last third
+            first_third = values[: count // 3]
+            last_third = values[-(count // 3) :]
+            first_avg = sum(first_third) / len(first_third)
+            last_avg = sum(last_third) / len(last_third)
+
+            # Trend threshold: 5% change
+            threshold = 0.05 * abs(first_avg) if first_avg != 0 else 0.01
+
+            if last_avg > first_avg + threshold:
+                trend = "up"
+            elif last_avg < first_avg - threshold:
+                trend = "down"
+
+        return SummaryStats(
+            count=count,
+            mean=round(mean, 4),
+            min=round(min_val, 4),
+            max=round(max_val, 4),
+            std=round(std, 4),
+            trend=trend,
+        )
+
+    def summarize_layer(
+        self,
+        layer: ContextLayer,
+        start_date: datetime | None = None,
+        end_date: datetime | None = None,
+    ) -> dict[str, Any]:
+        """Summarize all context data for a layer within a date range.
+
+        Args:
+            layer: Context layer to summarize
+            start_date: Start date (inclusive), None for all
+            end_date: End date (inclusive), None for now
+
+        Returns:
+            Dictionary with summarized metrics
+        """
+        if end_date is None:
+            end_date = datetime.now(UTC)
+
+        # Get all contexts for this layer
+        all_contexts = self.store.get_all_contexts(layer)
+
+        if not all_contexts:
+            return {"summary": "No data available", "count": 0}
+
+        # Group numeric values by key
+        numeric_data: dict[str, list[float]] = {}
+        text_data: dict[str, list[str]] = {}
+
+        for key, value in all_contexts.items():
+            # Try to extract numeric values
+            if isinstance(value, (int, float)):
+                if key not in numeric_data:
+                    numeric_data[key] = []
+                numeric_data[key].append(float(value))
+            elif isinstance(value, dict):
+                # Extract numeric fields from dict
+                for subkey, subvalue in value.items():
+                    if isinstance(subvalue, (int, float)):
+                        full_key = f"{key}.{subkey}"
+                        if full_key not in numeric_data:
+                            numeric_data[full_key] = []
+                        numeric_data[full_key].append(float(subvalue))
+            elif isinstance(value, str):
+                if key not in text_data:
+                    text_data[key] = []
+                text_data[key].append(value)
+
+        # Summarize numeric data
+        summary: dict[str, Any] = {}
+
+        for key, values in numeric_data.items():
+            stats = self.summarize_numeric_values(values)
+            summary[key] = {
+                "count": stats.count,
+                "avg": stats.mean,
+                "range": [stats.min, stats.max],
+                "trend": stats.trend,
+            }
+
+        # Summarize text data (just counts)
+        for key, values in text_data.items():
+            summary[f"{key}_count"] = len(values)
+
+        summary["total_entries"] = len(all_contexts)
+
+        return summary
+
+    def rolling_window_summary(
+        self,
+        layer: ContextLayer,
+        window_days: int = 30,
+        summarize_older: bool = True,
+    ) -> dict[str, Any]:
+        """Create a rolling window summary.
+
+        Recent data (within window) is kept detailed.
+        Older data is summarized to key metrics.
+
+        Args:
+            layer: Context layer to summarize
+            window_days: Number of days to keep detailed
+            summarize_older: Whether to summarize data older than window
+
+        Returns:
+            Dictionary with recent (detailed) and historical (summary) data
+        """
+        result: dict[str, Any] = {
+            "window_days": window_days,
+            "recent_data": {},
+            "historical_summary": {},
+        }
+
+        # Get all contexts
+        all_contexts = self.store.get_all_contexts(layer)
+
+        recent_values: dict[str, list[float]] = {}
+        historical_values: dict[str, list[float]] = {}
+
+        for key, value in all_contexts.items():
+            # For simplicity, treat all numeric values
+            if isinstance(value, (int, float)):
+                # Note: We don't have timestamps in context keys
+                # This is a simplified implementation
+                # In practice, would need to check timeframe field
+
+                # For now, put recent data in window
+                if key not in recent_values:
+                    recent_values[key] = []
+                recent_values[key].append(float(value))
+
+        # Detailed recent data
+        result["recent_data"] = {key: values[-10:] for key, values in recent_values.items()}
+
+        # Summarized historical data
+        if summarize_older:
+            for key, values in historical_values.items():
+                stats = self.summarize_numeric_values(values)
+                result["historical_summary"][key] = {
+                    "count": stats.count,
+                    "avg": stats.mean,
+                    "trend": stats.trend,
+                }
+
+        return result
+
+    def aggregate_to_higher_layer(
+        self,
+        source_layer: ContextLayer,
+        target_layer: ContextLayer,
+        metric_key: str,
+        aggregation_func: str = "mean",
+    ) -> float | None:
+        """Aggregate data from source layer to target layer.
+
+        Args:
+            source_layer: Source context layer (more granular)
+            target_layer: Target context layer (less granular)
+            metric_key: Key of metric to aggregate
+            aggregation_func: Aggregation function ("mean", "sum", "max", "min")
+
+        Returns:
+            Aggregated value, or None if no data available
+        """
+        # Get all contexts from source layer
+        source_contexts = self.store.get_all_contexts(source_layer)
+
+        # Extract values for metric_key
+        values = []
+        for key, value in source_contexts.items():
+            if key == metric_key and isinstance(value, (int, float)):
+                values.append(float(value))
+            elif isinstance(value, dict) and metric_key in value:
+                subvalue = value[metric_key]
+                if isinstance(subvalue, (int, float)):
+                    values.append(float(subvalue))
+
+        if not values:
+            return None
+
+        # Apply aggregation function
+        if aggregation_func == "mean":
+            return sum(values) / len(values)
+        elif aggregation_func == "sum":
+            return sum(values)
+        elif aggregation_func == "max":
+            return max(values)
+        elif aggregation_func == "min":
+            return min(values)
+        else:
+            return sum(values) / len(values)  # Default to mean
+
+    def create_compact_summary(
+        self,
+        layers: list[ContextLayer],
+        top_n_metrics: int = 5,
+    ) -> dict[str, Any]:
+        """Create a compact summary across multiple layers.
+
+        Args:
+            layers: List of context layers to summarize
+            top_n_metrics: Number of top metrics to include per layer
+
+        Returns:
+            Compact summary dictionary
+        """
+        summary: dict[str, Any] = {}
+
+        for layer in layers:
+            layer_summary = self.summarize_layer(layer)
+
+            # Keep only top N metrics (by count/relevance)
+            metrics = []
+            for key, value in layer_summary.items():
+                if isinstance(value, dict) and "count" in value:
+                    metrics.append((key, value, value["count"]))
+
+            # Sort by count (descending)
+            metrics.sort(key=lambda x: x[2], reverse=True)
+
+            # Keep top N
+            top_metrics = {m[0]: m[1] for m in metrics[:top_n_metrics]}
+
+            summary[layer.value] = top_metrics
+
+        return summary
+
+    def format_summary_for_prompt(self, summary: dict[str, Any]) -> str:
+        """Format summary for inclusion in a prompt.
+
+        Args:
+            summary: Summary dictionary
+
+        Returns:
+            Formatted string for prompt
+        """
+        lines = []
+
+        for layer, metrics in summary.items():
+            if not metrics:
+                continue
+
+            lines.append(f"{layer}:")
+            for key, value in metrics.items():
+                if isinstance(value, dict):
+                    # Format as: key: avg=X, trend=Y
+                    parts = []
+                    if "avg" in value and value["avg"] is not None:
+                        parts.append(f"avg={value['avg']:.2f}")
+                    if "trend" in value and value["trend"]:
+                        parts.append(f"trend={value['trend']}")
+                    if parts:
+                        lines.append(f"  {key}: {', '.join(parts)}")
+                else:
+                    lines.append(f"  {key}: {value}")
+
+        return "\n".join(lines)

src/core/criticality.py

@@ -0,0 +1,110 @@
+"""Criticality assessment for urgency-based response system.
+
+Evaluates market conditions to determine response urgency and enable
+faster reactions in critical situations.
+"""
+
+from __future__ import annotations
+
+from enum import StrEnum
+
+
+class CriticalityLevel(StrEnum):
+    """Urgency levels for market conditions and trading decisions."""
+
+    CRITICAL = "CRITICAL"  # <5s timeout - Emergency response required
+    HIGH = "HIGH"  # <30s timeout - Elevated priority
+    NORMAL = "NORMAL"  # <60s timeout - Standard processing
+    LOW = "LOW"  # No timeout - Batch processing
+
+
+class CriticalityAssessor:
+    """Assesses market conditions to determine response criticality level."""
+
+    def __init__(
+        self,
+        critical_pnl_threshold: float = -2.5,
+        critical_price_change_threshold: float = 5.0,
+        critical_volume_surge_threshold: float = 10.0,
+        high_volatility_threshold: float = 70.0,
+        low_volatility_threshold: float = 30.0,
+    ) -> None:
+        """Initialize the criticality assessor.
+
+        Args:
+            critical_pnl_threshold: P&L % that triggers CRITICAL (default -2.5%)
+            critical_price_change_threshold: Price change % that triggers CRITICAL
+                (default 5.0% in 1 minute)
+            critical_volume_surge_threshold: Volume surge ratio that triggers CRITICAL
+                (default 10x average)
+            high_volatility_threshold: Volatility score that triggers HIGH
+                (default 70.0)
+            low_volatility_threshold: Volatility score below which is LOW
+                (default 30.0)
+        """
+        self.critical_pnl_threshold = critical_pnl_threshold
+        self.critical_price_change_threshold = critical_price_change_threshold
+        self.critical_volume_surge_threshold = critical_volume_surge_threshold
+        self.high_volatility_threshold = high_volatility_threshold
+        self.low_volatility_threshold = low_volatility_threshold
+
+    def assess_market_conditions(
+        self,
+        pnl_pct: float,
+        volatility_score: float,
+        volume_surge: float,
+        price_change_1m: float = 0.0,
+        is_market_open: bool = True,
+    ) -> CriticalityLevel:
+        """Assess criticality level based on market conditions.
+
+        Args:
+            pnl_pct: Current P&L percentage
+            volatility_score: Momentum score from VolatilityAnalyzer (0-100)
+            volume_surge: Volume surge ratio (current / average)
+            price_change_1m: 1-minute price change percentage
+            is_market_open: Whether the market is currently open
+
+        Returns:
+            CriticalityLevel indicating required response urgency
+        """
+        # Market closed or very quiet → LOW priority (batch processing)
+        if not is_market_open or volatility_score < self.low_volatility_threshold:
+            return CriticalityLevel.LOW
+
+        # CRITICAL conditions: immediate action required
+        # 1. P&L near circuit breaker (-2.5% is close to -3.0% breaker)
+        if pnl_pct <= self.critical_pnl_threshold:
+            return CriticalityLevel.CRITICAL
+
+        # 2. Large sudden price movement (>5% in 1 minute)
+        if abs(price_change_1m) >= self.critical_price_change_threshold:
+            return CriticalityLevel.CRITICAL
+
+        # 3. Extreme volume surge (>10x average) indicates major event
+        if volume_surge >= self.critical_volume_surge_threshold:
+            return CriticalityLevel.CRITICAL
+
+        # HIGH priority: elevated volatility requires faster response
+        if volatility_score >= self.high_volatility_threshold:
+            return CriticalityLevel.HIGH
+
+        # NORMAL: standard trading conditions
+        return CriticalityLevel.NORMAL
+
+    def get_timeout(self, level: CriticalityLevel) -> float | None:
+        """Get timeout in seconds for a given criticality level.
+
+        Args:
+            level: Criticality level
+
+        Returns:
+            Timeout in seconds, or None for no timeout (LOW priority)
+        """
+        timeout_map = {
+            CriticalityLevel.CRITICAL: 5.0,
+            CriticalityLevel.HIGH: 30.0,
+            CriticalityLevel.NORMAL: 60.0,
+            CriticalityLevel.LOW: None,
+        }
+        return timeout_map[level]

src/core/priority_queue.py

@@ -0,0 +1,291 @@
+"""Priority-based task queue for latency control.
+
+Implements a thread-safe priority queue with timeout enforcement and metrics tracking.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import heapq
+import logging
+import time
+from collections.abc import Callable, Coroutine
+from dataclasses import dataclass, field
+from typing import Any
+
+from src.core.criticality import CriticalityLevel
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass(order=True)
+class PriorityTask:
+    """Task with priority and timestamp for queue ordering."""
+
+    # Lower priority value = higher urgency (CRITICAL=0, HIGH=1, NORMAL=2, LOW=3)
+    priority: int
+    timestamp: float
+    # Task data not used in comparison
+    task_id: str = field(compare=False)
+    task_data: dict[str, Any] = field(compare=False, default_factory=dict)
+    callback: Callable[[], Coroutine[Any, Any, Any]] | None = field(
+        compare=False, default=None
+    )
+
+
+@dataclass
+class QueueMetrics:
+    """Metrics for priority queue performance monitoring."""
+
+    total_enqueued: int = 0
+    total_dequeued: int = 0
+    total_timeouts: int = 0
+    total_errors: int = 0
+    current_size: int = 0
+    # Average wait time per criticality level (in seconds)
+    avg_wait_time: dict[CriticalityLevel, float] = field(default_factory=dict)
+    # P95 wait time per criticality level
+    p95_wait_time: dict[CriticalityLevel, float] = field(default_factory=dict)
+
+
+class PriorityTaskQueue:
+    """Thread-safe priority queue with timeout enforcement."""
+
+    # Priority mapping for criticality levels
+    PRIORITY_MAP = {
+        CriticalityLevel.CRITICAL: 0,
+        CriticalityLevel.HIGH: 1,
+        CriticalityLevel.NORMAL: 2,
+        CriticalityLevel.LOW: 3,
+    }
+
+    def __init__(self, max_size: int = 1000) -> None:
+        """Initialize the priority task queue.
+
+        Args:
+            max_size: Maximum queue size (default 1000)
+        """
+        self._queue: list[PriorityTask] = []
+        self._lock = asyncio.Lock()
+        self._max_size = max_size
+        self._metrics = QueueMetrics()
+        # Track wait times for metrics
+        self._wait_times: dict[CriticalityLevel, list[float]] = {
+            level: [] for level in CriticalityLevel
+        }
+
+    async def enqueue(
+        self,
+        task_id: str,
+        criticality: CriticalityLevel,
+        task_data: dict[str, Any],
+        callback: Callable[[], Coroutine[Any, Any, Any]] | None = None,
+    ) -> bool:
+        """Add a task to the priority queue.
+
+        Args:
+            task_id: Unique identifier for the task
+            criticality: Criticality level determining priority
+            task_data: Data associated with the task
+            callback: Optional async callback to execute
+
+        Returns:
+            True if enqueued successfully, False if queue is full
+        """
+        async with self._lock:
+            if len(self._queue) >= self._max_size:
+                logger.warning(
+                    "Priority queue full (size=%d), rejecting task %s",
+                    len(self._queue),
+                    task_id,
+                )
+                return False
+
+            priority = self.PRIORITY_MAP[criticality]
+            timestamp = time.time()
+
+            task = PriorityTask(
+                priority=priority,
+                timestamp=timestamp,
+                task_id=task_id,
+                task_data=task_data,
+                callback=callback,
+            )
+
+            heapq.heappush(self._queue, task)
+            self._metrics.total_enqueued += 1
+            self._metrics.current_size = len(self._queue)
+
+            logger.debug(
+                "Enqueued task %s with criticality %s (priority=%d, queue_size=%d)",
+                task_id,
+                criticality.value,
+                priority,
+                len(self._queue),
+            )
+
+            return True
+
+    async def dequeue(self, timeout: float | None = None) -> PriorityTask | None:
+        """Remove and return the highest priority task from the queue.
+
+        Args:
+            timeout: Maximum time to wait for a task (seconds)
+
+        Returns:
+            PriorityTask if available, None if queue is empty or timeout
+        """
+        start_time = time.time()
+        deadline = start_time + timeout if timeout else None
+
+        while True:
+            async with self._lock:
+                if self._queue:
+                    task = heapq.heappop(self._queue)
+                    self._metrics.total_dequeued += 1
+                    self._metrics.current_size = len(self._queue)
+
+                    # Calculate wait time
+                    wait_time = time.time() - task.timestamp
+                    criticality = self._get_criticality_from_priority(task.priority)
+                    self._wait_times[criticality].append(wait_time)
+                    self._update_wait_time_metrics()
+
+                    logger.debug(
+                        "Dequeued task %s (priority=%d, wait_time=%.2fs, queue_size=%d)",
+                        task.task_id,
+                        task.priority,
+                        wait_time,
+                        len(self._queue),
+                    )
+
+                    return task
+
+            # Queue is empty
+            if deadline and time.time() >= deadline:
+                return None
+
+            # Wait a bit before checking again
+            await asyncio.sleep(0.1)
+
+    async def execute_with_timeout(
+        self,
+        task: PriorityTask,
+        timeout: float | None,
+    ) -> Any:
+        """Execute a task with timeout enforcement.
+
+        Args:
+            task: Task to execute
+            timeout: Timeout in seconds (None = no timeout)
+
+        Returns:
+            Result from task callback
+
+        Raises:
+            asyncio.TimeoutError: If task exceeds timeout
+            Exception: Any exception raised by the task callback
+        """
+        if not task.callback:
+            logger.warning("Task %s has no callback, skipping execution", task.task_id)
+            return None
+
+        criticality = self._get_criticality_from_priority(task.priority)
+
+        try:
+            if timeout:
+                result = await asyncio.wait_for(task.callback(), timeout=timeout)
+            else:
+                result = await task.callback()
+
+            logger.debug(
+                "Task %s completed successfully (criticality=%s)",
+                task.task_id,
+                criticality.value,
+            )
+            return result
+
+        except TimeoutError:
+            self._metrics.total_timeouts += 1
+            logger.error(
+                "Task %s timed out after %.2fs (criticality=%s)",
+                task.task_id,
+                timeout or 0.0,
+                criticality.value,
+            )
+            raise
+
+        except Exception as exc:
+            self._metrics.total_errors += 1
+            logger.exception(
+                "Task %s failed with error (criticality=%s): %s",
+                task.task_id,
+                criticality.value,
+                exc,
+            )
+            raise
+
+    def _get_criticality_from_priority(self, priority: int) -> CriticalityLevel:
+        """Convert priority back to criticality level."""
+        for level, prio in self.PRIORITY_MAP.items():
+            if prio == priority:
+                return level
+        return CriticalityLevel.NORMAL
+
+    def _update_wait_time_metrics(self) -> None:
+        """Update average and p95 wait time metrics."""
+        for level, times in self._wait_times.items():
+            if not times:
+                continue
+
+            # Keep only last 1000 measurements to avoid memory bloat
+            if len(times) > 1000:
+                self._wait_times[level] = times[-1000:]
+                times = self._wait_times[level]
+
+            # Calculate average
+            self._metrics.avg_wait_time[level] = sum(times) / len(times)
+
+            # Calculate P95
+            sorted_times = sorted(times)
+            p95_idx = int(len(sorted_times) * 0.95)
+            self._metrics.p95_wait_time[level] = sorted_times[p95_idx]
+
+    async def get_metrics(self) -> QueueMetrics:
+        """Get current queue metrics.
+
+        Returns:
+            QueueMetrics with current statistics
+        """
+        async with self._lock:
+            return QueueMetrics(
+                total_enqueued=self._metrics.total_enqueued,
+                total_dequeued=self._metrics.total_dequeued,
+                total_timeouts=self._metrics.total_timeouts,
+                total_errors=self._metrics.total_errors,
+                current_size=self._metrics.current_size,
+                avg_wait_time=dict(self._metrics.avg_wait_time),
+                p95_wait_time=dict(self._metrics.p95_wait_time),
+            )
+
+    async def size(self) -> int:
+        """Get current queue size.
+
+        Returns:
+            Number of tasks in queue
+        """
+        async with self._lock:
+            return len(self._queue)
+
+    async def clear(self) -> int:
+        """Clear all tasks from the queue.
+
+        Returns:
+            Number of tasks cleared
+        """
+        async with self._lock:
+            count = len(self._queue)
+            self._queue.clear()
+            self._metrics.current_size = 0
+            logger.info("Cleared %d tasks from priority queue", count)
+            return count

src/evolution/__init__.py

@@ -0,0 +1,19 @@
+"""Evolution engine for self-improving trading strategies."""
+
+from src.evolution.ab_test import ABTester, ABTestResult, StrategyPerformance
+from src.evolution.optimizer import EvolutionOptimizer
+from src.evolution.performance_tracker import (
+    PerformanceDashboard,
+    PerformanceTracker,
+    StrategyMetrics,
+)
+
+__all__ = [
+    "EvolutionOptimizer",
+    "ABTester",
+    "ABTestResult",
+    "StrategyPerformance",
+    "PerformanceTracker",
+    "PerformanceDashboard",
+    "StrategyMetrics",
+]

src/evolution/ab_test.py

@@ -0,0 +1,220 @@
+"""A/B Testing framework for strategy comparison.
+
+Runs multiple strategies in parallel, tracks their performance,
+and uses statistical significance testing to determine winners.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from typing import Any
+
+import scipy.stats as stats
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class StrategyPerformance:
+    """Performance metrics for a single strategy."""
+
+    strategy_name: str
+    total_trades: int
+    wins: int
+    losses: int
+    total_pnl: float
+    avg_pnl: float
+    win_rate: float
+    sharpe_ratio: float | None = None
+
+
+@dataclass
+class ABTestResult:
+    """Result of an A/B test between two strategies."""
+
+    strategy_a: str
+    strategy_b: str
+    winner: str | None
+    p_value: float
+    confidence_level: float
+    is_significant: bool
+    performance_a: StrategyPerformance
+    performance_b: StrategyPerformance
+
+
+class ABTester:
+    """A/B testing framework for comparing trading strategies."""
+
+    def __init__(self, significance_level: float = 0.05) -> None:
+        """Initialize A/B tester.
+
+        Args:
+            significance_level: P-value threshold for statistical significance (default 0.05)
+        """
+        self._significance_level = significance_level
+
+    def calculate_performance(
+        self, trades: list[dict[str, Any]], strategy_name: str
+    ) -> StrategyPerformance:
+        """Calculate performance metrics for a strategy.
+
+        Args:
+            trades: List of trade records with pnl values
+            strategy_name: Name of the strategy
+
+        Returns:
+            StrategyPerformance object with calculated metrics
+        """
+        if not trades:
+            return StrategyPerformance(
+                strategy_name=strategy_name,
+                total_trades=0,
+                wins=0,
+                losses=0,
+                total_pnl=0.0,
+                avg_pnl=0.0,
+                win_rate=0.0,
+                sharpe_ratio=None,
+            )
+
+        total_trades = len(trades)
+        wins = sum(1 for t in trades if t.get("pnl", 0) > 0)
+        losses = sum(1 for t in trades if t.get("pnl", 0) < 0)
+        pnls = [t.get("pnl", 0.0) for t in trades]
+        total_pnl = sum(pnls)
+        avg_pnl = total_pnl / total_trades if total_trades > 0 else 0.0
+        win_rate = (wins / total_trades * 100) if total_trades > 0 else 0.0
+
+        # Calculate Sharpe ratio (risk-adjusted return)
+        sharpe_ratio = None
+        if len(pnls) > 1:
+            mean_return = avg_pnl
+            std_return = (
+                sum((p - mean_return) ** 2 for p in pnls) / (len(pnls) - 1)
+            ) ** 0.5
+            if std_return > 0:
+                sharpe_ratio = mean_return / std_return
+
+        return StrategyPerformance(
+            strategy_name=strategy_name,
+            total_trades=total_trades,
+            wins=wins,
+            losses=losses,
+            total_pnl=round(total_pnl, 2),
+            avg_pnl=round(avg_pnl, 2),
+            win_rate=round(win_rate, 2),
+            sharpe_ratio=round(sharpe_ratio, 4) if sharpe_ratio else None,
+        )
+
+    def compare_strategies(
+        self,
+        trades_a: list[dict[str, Any]],
+        trades_b: list[dict[str, Any]],
+        strategy_a_name: str = "Strategy A",
+        strategy_b_name: str = "Strategy B",
+    ) -> ABTestResult:
+        """Compare two strategies using statistical testing.
+
+        Uses a two-sample t-test to determine if performance difference is significant.
+
+        Args:
+            trades_a: List of trades from strategy A
+            trades_b: List of trades from strategy B
+            strategy_a_name: Name of strategy A
+            strategy_b_name: Name of strategy B
+
+        Returns:
+            ABTestResult with comparison details
+        """
+        perf_a = self.calculate_performance(trades_a, strategy_a_name)
+        perf_b = self.calculate_performance(trades_b, strategy_b_name)
+
+        # Extract PnL arrays for statistical testing
+        pnls_a = [t.get("pnl", 0.0) for t in trades_a]
+        pnls_b = [t.get("pnl", 0.0) for t in trades_b]
+
+        # Perform two-sample t-test
+        if len(pnls_a) > 1 and len(pnls_b) > 1:
+            t_stat, p_value = stats.ttest_ind(pnls_a, pnls_b, equal_var=False)
+            is_significant = p_value < self._significance_level
+            confidence_level = (1 - p_value) * 100
+        else:
+            # Not enough data for statistical test
+            p_value = 1.0
+            is_significant = False
+            confidence_level = 0.0
+
+        # Determine winner based on average PnL
+        winner = None
+        if is_significant:
+            if perf_a.avg_pnl > perf_b.avg_pnl:
+                winner = strategy_a_name
+            elif perf_b.avg_pnl > perf_a.avg_pnl:
+                winner = strategy_b_name
+
+        return ABTestResult(
+            strategy_a=strategy_a_name,
+            strategy_b=strategy_b_name,
+            winner=winner,
+            p_value=round(p_value, 4),
+            confidence_level=round(confidence_level, 2),
+            is_significant=is_significant,
+            performance_a=perf_a,
+            performance_b=perf_b,
+        )
+
+    def should_deploy(
+        self,
+        result: ABTestResult,
+        min_win_rate: float = 60.0,
+        min_trades: int = 20,
+    ) -> bool:
+        """Determine if a winning strategy should be deployed.
+
+        Args:
+            result: A/B test result
+            min_win_rate: Minimum win rate percentage for deployment (default 60%)
+            min_trades: Minimum number of trades required (default 20)
+
+        Returns:
+            True if the winning strategy meets deployment criteria
+        """
+        if not result.is_significant or result.winner is None:
+            return False
+
+        # Get performance of winning strategy
+        if result.winner == result.strategy_a:
+            winning_perf = result.performance_a
+        else:
+            winning_perf = result.performance_b
+
+        # Check deployment criteria
+        has_enough_trades = winning_perf.total_trades >= min_trades
+        has_good_win_rate = winning_perf.win_rate >= min_win_rate
+        is_profitable = winning_perf.avg_pnl > 0
+
+        meets_criteria = has_enough_trades and has_good_win_rate and is_profitable
+
+        if meets_criteria:
+            logger.info(
+                "Strategy '%s' meets deployment criteria: "
+                "win_rate=%.2f%%, trades=%d, avg_pnl=%.2f",
+                result.winner,
+                winning_perf.win_rate,
+                winning_perf.total_trades,
+                winning_perf.avg_pnl,
+            )
+        else:
+            logger.info(
+                "Strategy '%s' does NOT meet deployment criteria: "
+                "win_rate=%.2f%% (min %.2f%%), trades=%d (min %d), avg_pnl=%.2f",
+                result.winner if result.winner else "unknown",
+                winning_perf.win_rate if result.winner else 0.0,
+                min_win_rate,
+                winning_perf.total_trades if result.winner else 0,
+                min_trades,
+                winning_perf.avg_pnl if result.winner else 0.0,
+            )
+
+        return meets_criteria

src/evolution/optimizer.py

@@ -1,10 +1,10 @@
 """Evolution Engine — analyzes trade logs and generates new strategies.
 
 This module:
-1. Reads trade_logs.db to identify failing patterns
+1. Uses DecisionLogger.get_losing_decisions() to identify failing patterns
-2. Asks Gemini to generate a new strategy class
+2. Analyzes failure patterns by time, market conditions, stock characteristics
-3. Runs pytest on the generated file
+3. Asks Gemini to generate improved strategy recommendations
-4. Creates a simulated PR if tests pass
+4. Generates new strategy classes with enhanced decision-making logic
 """
 
 from __future__ import annotations
@@ -14,6 +14,7 @@ import logging
 import sqlite3
 import subprocess
 import textwrap
+from collections import Counter
 from datetime import UTC, datetime
 from pathlib import Path
 from typing import Any
@@ -21,6 +22,8 @@ from typing import Any
 from google import genai
 
 from src.config import Settings
+from src.db import init_db
+from src.logging.decision_logger import DecisionLogger
 
 logger = logging.getLogger(__name__)
 
@@ -53,29 +56,105 @@ class EvolutionOptimizer:
         self._db_path = settings.DB_PATH
         self._client = genai.Client(api_key=settings.GEMINI_API_KEY)
         self._model_name = settings.GEMINI_MODEL
+        self._conn = init_db(self._db_path)
+        self._decision_logger = DecisionLogger(self._conn)
 
     # ------------------------------------------------------------------
     # Analysis
     # ------------------------------------------------------------------
 
     def analyze_failures(self, limit: int = 50) -> list[dict[str, Any]]:
-        """Find trades where high confidence led to losses."""
+        """Find high-confidence decisions that resulted in losses.
-        conn = sqlite3.connect(self._db_path)
+
-        conn.row_factory = sqlite3.Row
+        Uses DecisionLogger.get_losing_decisions() to retrieve failures.
-        try:
-            rows = conn.execute(
         """
-                SELECT stock_code, action, confidence, pnl, rationale, timestamp
+        losing_decisions = self._decision_logger.get_losing_decisions(
-                FROM trades
+            min_confidence=80, min_loss=-100.0
-                WHERE confidence >= 80 AND pnl < 0
+        )
-                ORDER BY pnl ASC
+
-                LIMIT ?
+        # Limit results
-                """,
+        if len(losing_decisions) > limit:
-                (limit,),
+            losing_decisions = losing_decisions[:limit]
-            ).fetchall()
+
-            return [dict(r) for r in rows]
+        # Convert to dict format for analysis
-        finally:
+        failures = []
-            conn.close()
+        for decision in losing_decisions:
+            failures.append({
+                "decision_id": decision.decision_id,
+                "timestamp": decision.timestamp,
+                "stock_code": decision.stock_code,
+                "market": decision.market,
+                "exchange_code": decision.exchange_code,
+                "action": decision.action,
+                "confidence": decision.confidence,
+                "rationale": decision.rationale,
+                "outcome_pnl": decision.outcome_pnl,
+                "outcome_accuracy": decision.outcome_accuracy,
+                "context_snapshot": decision.context_snapshot,
+                "input_data": decision.input_data,
+            })
+
+        return failures
+
+    def identify_failure_patterns(
+        self, failures: list[dict[str, Any]]
+    ) -> dict[str, Any]:
+        """Identify patterns in losing decisions.
+
+        Analyzes:
+        - Time patterns (hour of day, day of week)
+        - Market conditions (volatility, volume)
+        - Stock characteristics (price range, market)
+        - Common failure modes in rationale
+        """
+        if not failures:
+            return {"pattern_count": 0, "patterns": {}}
+
+        patterns = {
+            "markets": Counter(),
+            "actions": Counter(),
+            "hours": Counter(),
+            "avg_confidence": 0.0,
+            "avg_loss": 0.0,
+            "total_failures": len(failures),
+        }
+
+        total_confidence = 0
+        total_loss = 0.0
+
+        for failure in failures:
+            # Market distribution
+            patterns["markets"][failure.get("market", "UNKNOWN")] += 1
+
+            # Action distribution
+            patterns["actions"][failure.get("action", "UNKNOWN")] += 1
+
+            # Time pattern (extract hour from ISO timestamp)
+            timestamp = failure.get("timestamp", "")
+            if timestamp:
+                try:
+                    dt = datetime.fromisoformat(timestamp)
+                    patterns["hours"][dt.hour] += 1
+                except (ValueError, AttributeError):
+                    pass
+
+            # Aggregate metrics
+            total_confidence += failure.get("confidence", 0)
+            total_loss += failure.get("outcome_pnl", 0.0)
+
+        patterns["avg_confidence"] = (
+            round(total_confidence / len(failures), 2) if failures else 0.0
+        )
+        patterns["avg_loss"] = (
+            round(total_loss / len(failures), 2) if failures else 0.0
+        )
+
+        # Convert Counters to regular dicts for JSON serialization
+        patterns["markets"] = dict(patterns["markets"])
+        patterns["actions"] = dict(patterns["actions"])
+        patterns["hours"] = dict(patterns["hours"])
+
+        return patterns
 
     def get_performance_summary(self) -> dict[str, Any]:
         """Return aggregate performance metrics from trade logs."""
@@ -109,14 +188,25 @@ class EvolutionOptimizer:
     async def generate_strategy(self, failures: list[dict[str, Any]]) -> Path | None:
         """Ask Gemini to generate a new strategy based on failure analysis.
 
+        Integrates failure patterns and market conditions to create improved strategies.
         Returns the path to the generated strategy file, or None on failure.
         """
+        # Identify failure patterns first
+        patterns = self.identify_failure_patterns(failures)
+
         prompt = (
             "You are a quantitative trading strategy developer.\n"
-            "Analyze these failed trades and generate an improved strategy.\n\n"
+            "Analyze these failed trades and their patterns, then generate an improved strategy.\n\n"
-            f"Failed trades:\n{json.dumps(failures, indent=2, default=str)}\n\n"
+            f"Failure Patterns:\n{json.dumps(patterns, indent=2)}\n\n"
-            "Generate a Python class that inherits from BaseStrategy.\n"
+            f"Sample Failed Trades (first 5):\n"
-            "The class must have an `evaluate(self, market_data: dict) -> dict` method.\n"
+            f"{json.dumps(failures[:5], indent=2, default=str)}\n\n"
+            "Based on these patterns, generate an improved trading strategy.\n"
+            "The strategy should:\n"
+            "1. Avoid the identified failure patterns\n"
+            "2. Consider market-specific conditions\n"
+            "3. Adjust confidence based on historical performance\n\n"
+            "Generate a Python method body that inherits from BaseStrategy.\n"
+            "The method signature is: evaluate(self, market_data: dict) -> dict\n"
             "The method must return a dict with keys: action, confidence, rationale.\n"
             "Respond with ONLY the method body (Python code), no class definition.\n"
         )
@@ -147,10 +237,15 @@ class EvolutionOptimizer:
         # Indent the body for the class method
         indented_body = textwrap.indent(body, "            ")
 
+        # Generate rationale from patterns
+        rationale = f"Auto-evolved from {len(failures)} failures. "
+        rationale += f"Primary failure markets: {list(patterns.get('markets', {}).keys())}. "
+        rationale += f"Average loss: {patterns.get('avg_loss', 0.0)}"
+
         content = STRATEGY_TEMPLATE.format(
             name=version,
             timestamp=datetime.now(UTC).isoformat(),
-            rationale="Auto-evolved from failure analysis",
+            rationale=rationale,
             class_name=class_name,
             body=indented_body.strip(),
         )

src/evolution/performance_tracker.py

@@ -0,0 +1,303 @@
+"""Performance tracking system for strategy monitoring.
+
+Tracks win rates, monitors improvement over time,
+and provides performance metrics dashboard.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import sqlite3
+from dataclasses import asdict, dataclass
+from datetime import UTC, datetime, timedelta
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class StrategyMetrics:
+    """Performance metrics for a strategy over a time period."""
+
+    strategy_name: str
+    period_start: str
+    period_end: str
+    total_trades: int
+    wins: int
+    losses: int
+    holds: int
+    win_rate: float
+    avg_pnl: float
+    total_pnl: float
+    best_trade: float
+    worst_trade: float
+    avg_confidence: float
+
+
+@dataclass
+class PerformanceDashboard:
+    """Comprehensive performance dashboard."""
+
+    generated_at: str
+    overall_metrics: StrategyMetrics
+    daily_metrics: list[StrategyMetrics]
+    weekly_metrics: list[StrategyMetrics]
+    improvement_trend: dict[str, Any]
+
+
+class PerformanceTracker:
+    """Tracks and monitors strategy performance over time."""
+
+    def __init__(self, db_path: str) -> None:
+        """Initialize performance tracker.
+
+        Args:
+            db_path: Path to the trade logs database
+        """
+        self._db_path = db_path
+
+    def get_strategy_metrics(
+        self,
+        strategy_name: str | None = None,
+        start_date: str | None = None,
+        end_date: str | None = None,
+    ) -> StrategyMetrics:
+        """Get performance metrics for a strategy over a time period.
+
+        Args:
+            strategy_name: Name of the strategy (None = all strategies)
+            start_date: Start date in ISO format (None = beginning of time)
+            end_date: End date in ISO format (None = now)
+
+        Returns:
+            StrategyMetrics object with performance data
+        """
+        conn = sqlite3.connect(self._db_path)
+        conn.row_factory = sqlite3.Row
+
+        try:
+            # Build query with optional filters
+            query = """
+                SELECT
+                    COUNT(*) as total_trades,
+                    SUM(CASE WHEN pnl > 0 THEN 1 ELSE 0 END) as wins,
+                    SUM(CASE WHEN pnl < 0 THEN 1 ELSE 0 END) as losses,
+                    SUM(CASE WHEN action = 'HOLD' THEN 1 ELSE 0 END) as holds,
+                    COALESCE(AVG(CASE WHEN pnl IS NOT NULL THEN pnl END), 0) as avg_pnl,
+                    COALESCE(SUM(CASE WHEN pnl IS NOT NULL THEN pnl ELSE 0 END), 0) as total_pnl,
+                    COALESCE(MAX(pnl), 0) as best_trade,
+                    COALESCE(MIN(pnl), 0) as worst_trade,
+                    COALESCE(AVG(confidence), 0) as avg_confidence,
+                    MIN(timestamp) as period_start,
+                    MAX(timestamp) as period_end
+                FROM trades
+                WHERE 1=1
+            """
+            params: list[Any] = []
+
+            if start_date:
+                query += " AND timestamp >= ?"
+                params.append(start_date)
+
+            if end_date:
+                query += " AND timestamp <= ?"
+                params.append(end_date)
+
+            # Note: Currently trades table doesn't have strategy_name column
+            # This is a placeholder for future extension
+
+            row = conn.execute(query, params).fetchone()
+
+            total_trades = row["total_trades"] or 0
+            wins = row["wins"] or 0
+            win_rate = (wins / total_trades * 100) if total_trades > 0 else 0.0
+
+            return StrategyMetrics(
+                strategy_name=strategy_name or "default",
+                period_start=row["period_start"] or "",
+                period_end=row["period_end"] or "",
+                total_trades=total_trades,
+                wins=wins,
+                losses=row["losses"] or 0,
+                holds=row["holds"] or 0,
+                win_rate=round(win_rate, 2),
+                avg_pnl=round(row["avg_pnl"], 2),
+                total_pnl=round(row["total_pnl"], 2),
+                best_trade=round(row["best_trade"], 2),
+                worst_trade=round(row["worst_trade"], 2),
+                avg_confidence=round(row["avg_confidence"], 2),
+            )
+        finally:
+            conn.close()
+
+    def get_daily_metrics(
+        self, days: int = 7, strategy_name: str | None = None
+    ) -> list[StrategyMetrics]:
+        """Get daily performance metrics for the last N days.
+
+        Args:
+            days: Number of days to retrieve (default 7)
+            strategy_name: Name of the strategy (None = all strategies)
+
+        Returns:
+            List of StrategyMetrics, one per day
+        """
+        metrics = []
+        end_date = datetime.now(UTC)
+
+        for i in range(days):
+            day_end = end_date - timedelta(days=i)
+            day_start = day_end - timedelta(days=1)
+
+            day_metrics = self.get_strategy_metrics(
+                strategy_name=strategy_name,
+                start_date=day_start.isoformat(),
+                end_date=day_end.isoformat(),
+            )
+            metrics.append(day_metrics)
+
+        return metrics
+
+    def get_weekly_metrics(
+        self, weeks: int = 4, strategy_name: str | None = None
+    ) -> list[StrategyMetrics]:
+        """Get weekly performance metrics for the last N weeks.
+
+        Args:
+            weeks: Number of weeks to retrieve (default 4)
+            strategy_name: Name of the strategy (None = all strategies)
+
+        Returns:
+            List of StrategyMetrics, one per week
+        """
+        metrics = []
+        end_date = datetime.now(UTC)
+
+        for i in range(weeks):
+            week_end = end_date - timedelta(weeks=i)
+            week_start = week_end - timedelta(weeks=1)
+
+            week_metrics = self.get_strategy_metrics(
+                strategy_name=strategy_name,
+                start_date=week_start.isoformat(),
+                end_date=week_end.isoformat(),
+            )
+            metrics.append(week_metrics)
+
+        return metrics
+
+    def calculate_improvement_trend(
+        self, metrics_history: list[StrategyMetrics]
+    ) -> dict[str, Any]:
+        """Calculate improvement trend from historical metrics.
+
+        Args:
+            metrics_history: List of StrategyMetrics ordered from oldest to newest
+
+        Returns:
+            Dictionary with trend analysis
+        """
+        if len(metrics_history) < 2:
+            return {
+                "trend": "insufficient_data",
+                "win_rate_change": 0.0,
+                "pnl_change": 0.0,
+                "confidence_change": 0.0,
+            }
+
+        oldest = metrics_history[0]
+        newest = metrics_history[-1]
+
+        win_rate_change = newest.win_rate - oldest.win_rate
+        pnl_change = newest.avg_pnl - oldest.avg_pnl
+        confidence_change = newest.avg_confidence - oldest.avg_confidence
+
+        # Determine overall trend
+        if win_rate_change > 5.0 and pnl_change > 0:
+            trend = "improving"
+        elif win_rate_change < -5.0 or pnl_change < 0:
+            trend = "declining"
+        else:
+            trend = "stable"
+
+        return {
+            "trend": trend,
+            "win_rate_change": round(win_rate_change, 2),
+            "pnl_change": round(pnl_change, 2),
+            "confidence_change": round(confidence_change, 2),
+            "period_count": len(metrics_history),
+        }
+
+    def generate_dashboard(
+        self, strategy_name: str | None = None
+    ) -> PerformanceDashboard:
+        """Generate a comprehensive performance dashboard.
+
+        Args:
+            strategy_name: Name of the strategy (None = all strategies)
+
+        Returns:
+            PerformanceDashboard with all metrics
+        """
+        # Get overall metrics
+        overall_metrics = self.get_strategy_metrics(strategy_name=strategy_name)
+
+        # Get daily metrics (last 7 days)
+        daily_metrics = self.get_daily_metrics(days=7, strategy_name=strategy_name)
+
+        # Get weekly metrics (last 4 weeks)
+        weekly_metrics = self.get_weekly_metrics(weeks=4, strategy_name=strategy_name)
+
+        # Calculate improvement trend
+        improvement_trend = self.calculate_improvement_trend(weekly_metrics[::-1])
+
+        return PerformanceDashboard(
+            generated_at=datetime.now(UTC).isoformat(),
+            overall_metrics=overall_metrics,
+            daily_metrics=daily_metrics,
+            weekly_metrics=weekly_metrics,
+            improvement_trend=improvement_trend,
+        )
+
+    def export_dashboard_json(
+        self, dashboard: PerformanceDashboard
+    ) -> str:
+        """Export dashboard as JSON string.
+
+        Args:
+            dashboard: PerformanceDashboard object
+
+        Returns:
+            JSON string representation
+        """
+        data = {
+            "generated_at": dashboard.generated_at,
+            "overall_metrics": asdict(dashboard.overall_metrics),
+            "daily_metrics": [asdict(m) for m in dashboard.daily_metrics],
+            "weekly_metrics": [asdict(m) for m in dashboard.weekly_metrics],
+            "improvement_trend": dashboard.improvement_trend,
+        }
+        return json.dumps(data, indent=2)
+
+    def log_dashboard(self, dashboard: PerformanceDashboard) -> None:
+        """Log dashboard summary to logger.
+
+        Args:
+            dashboard: PerformanceDashboard object
+        """
+        logger.info("=" * 60)
+        logger.info("PERFORMANCE DASHBOARD")
+        logger.info("=" * 60)
+        logger.info("Generated: %s", dashboard.generated_at)
+        logger.info("")
+        logger.info("Overall Performance:")
+        logger.info("  Total Trades: %d", dashboard.overall_metrics.total_trades)
+        logger.info("  Win Rate: %.2f%%", dashboard.overall_metrics.win_rate)
+        logger.info("  Average P&L: %.2f", dashboard.overall_metrics.avg_pnl)
+        logger.info("  Total P&L: %.2f", dashboard.overall_metrics.total_pnl)
+        logger.info("")
+        logger.info("Improvement Trend (%s):", dashboard.improvement_trend["trend"])
+        logger.info("  Win Rate Change: %+.2f%%", dashboard.improvement_trend["win_rate_change"])
+        logger.info("  P&L Change: %+.2f", dashboard.improvement_trend["pnl_change"])
+        logger.info("=" * 60)

src/main.py

@@ -19,7 +19,10 @@ from src.brain.gemini_client import GeminiClient
 from src.broker.kis_api import KISBroker
 from src.broker.overseas import OverseasBroker
 from src.config import Settings
+from src.context.layer import ContextLayer
 from src.context.store import ContextStore
+from src.core.criticality import CriticalityAssessor
+from src.core.priority_queue import PriorityTaskQueue
 from src.core.risk_manager import CircuitBreakerTripped, RiskManager
 from src.db import init_db, log_trade
 from src.logging.decision_logger import DecisionLogger
@@ -57,10 +60,14 @@ async def trading_cycle(
     risk: RiskManager,
     db_conn: Any,
     decision_logger: DecisionLogger,
+    context_store: ContextStore,
+    criticality_assessor: CriticalityAssessor,
     market: MarketInfo,
     stock_code: str,
 ) -> None:
     """Execute one trading cycle for a single stock."""
+    cycle_start_time = asyncio.get_event_loop().time()
+
     # 1. Fetch market data
     if market.is_domestic:
         orderbook = await broker.get_orderbook(stock_code)
@@ -106,6 +113,42 @@ async def trading_cycle(
         "foreigner_net": foreigner_net,
     }
 
+    # 1.5. Get volatility metrics from context store (L7_REALTIME)
+    latest_timeframe = context_store.get_latest_timeframe(ContextLayer.L7_REALTIME)
+    volatility_score = 50.0  # Default normal volatility
+    volume_surge = 1.0
+    price_change_1m = 0.0
+
+    if latest_timeframe:
+        volatility_data = context_store.get_context(
+            ContextLayer.L7_REALTIME,
+            latest_timeframe,
+            f"volatility_{stock_code}",
+        )
+        if volatility_data:
+            volatility_score = volatility_data.get("momentum_score", 50.0)
+            volume_surge = volatility_data.get("volume_surge", 1.0)
+            price_change_1m = volatility_data.get("price_change_1m", 0.0)
+
+    # 1.6. Assess criticality based on market conditions
+    criticality = criticality_assessor.assess_market_conditions(
+        pnl_pct=pnl_pct,
+        volatility_score=volatility_score,
+        volume_surge=volume_surge,
+        price_change_1m=price_change_1m,
+        is_market_open=True,
+    )
+
+    logger.info(
+        "Criticality for %s (%s): %s (pnl=%.2f%%, volatility=%.1f, volume_surge=%.1fx)",
+        stock_code,
+        market.name,
+        criticality.value,
+        pnl_pct,
+        volatility_score,
+        volume_surge,
+    )
+
     # 2. Ask the brain for a decision
     decision = await brain.decide(market_data)
     logger.info(
@@ -191,6 +234,27 @@ async def trading_cycle(
         exchange_code=market.exchange_code,
     )
 
+    # 7. Latency monitoring
+    cycle_end_time = asyncio.get_event_loop().time()
+    cycle_latency = cycle_end_time - cycle_start_time
+    timeout = criticality_assessor.get_timeout(criticality)
+
+    if timeout and cycle_latency > timeout:
+        logger.warning(
+            "Trading cycle exceeded timeout for %s (criticality=%s, latency=%.2fs, timeout=%.2fs)",
+            stock_code,
+            criticality.value,
+            cycle_latency,
+            timeout,
+        )
+    else:
+        logger.debug(
+            "Trading cycle completed within timeout for %s (criticality=%s, latency=%.2fs)",
+            stock_code,
+            criticality.value,
+            cycle_latency,
+        )
+
 
 async def run(settings: Settings) -> None:
     """Main async loop — iterate over open markets on a timer."""
@@ -212,6 +276,16 @@ async def run(settings: Settings) -> None:
         top_n=5,
     )
 
+    # Initialize latency control system
+    criticality_assessor = CriticalityAssessor(
+        critical_pnl_threshold=-2.5,  # Near circuit breaker at -3.0%
+        critical_price_change_threshold=5.0,  # 5% in 1 minute
+        critical_volume_surge_threshold=10.0,  # 10x average
+        high_volatility_threshold=70.0,
+        low_volatility_threshold=30.0,
+    )
+    priority_queue = PriorityTaskQueue(max_size=1000)
+
     # Track last scan time for each market
     last_scan_time: dict[str, float] = {}
 
@@ -315,6 +389,8 @@ async def run(settings: Settings) -> None:
                                 risk,
                                 db_conn,
                                 decision_logger,
+                                context_store,
+                                criticality_assessor,
                                 market,
                                 stock_code,
                             )
@@ -343,6 +419,18 @@ async def run(settings: Settings) -> None:
                             logger.exception("Unexpected error for %s: %s", stock_code, exc)
                             break  # Don't retry on unexpected errors
 
+            # Log priority queue metrics periodically
+            metrics = await priority_queue.get_metrics()
+            if metrics.total_enqueued > 0:
+                logger.info(
+                    "Priority queue metrics: enqueued=%d, dequeued=%d, size=%d, timeouts=%d, errors=%d",
+                    metrics.total_enqueued,
+                    metrics.total_dequeued,
+                    metrics.current_size,
+                    metrics.total_timeouts,
+                    metrics.total_errors,
+                )
+
             # Wait for next cycle or shutdown
             try:
                 await asyncio.wait_for(shutdown.wait(), timeout=TRADE_INTERVAL_SECONDS)

tests/test_evolution.py

@@ -0,0 +1,685 @@
+"""Tests for the Evolution Engine components.
+
+Tests cover:
+- EvolutionOptimizer: failure analysis and strategy generation
+- ABTester: A/B testing and statistical comparison
+- PerformanceTracker: metrics tracking and dashboard
+"""
+
+from __future__ import annotations
+
+import json
+import sqlite3
+import tempfile
+from datetime import UTC, datetime
+from pathlib import Path
+from unittest.mock import AsyncMock, Mock, patch
+
+import pytest
+
+from src.config import Settings
+from src.db import init_db, log_trade
+from src.evolution.ab_test import ABTester
+from src.evolution.optimizer import EvolutionOptimizer
+from src.evolution.performance_tracker import (
+    PerformanceDashboard,
+    PerformanceTracker,
+    StrategyMetrics,
+)
+from src.logging.decision_logger import DecisionLogger
+
+# ------------------------------------------------------------------
+# Fixtures
+# ------------------------------------------------------------------
+
+
+@pytest.fixture
+def db_conn() -> sqlite3.Connection:
+    """Provide an in-memory database with initialized schema."""
+    return init_db(":memory:")
+
+
+@pytest.fixture
+def settings() -> Settings:
+    """Provide test settings."""
+    return Settings(
+        KIS_APP_KEY="test_key",
+        KIS_APP_SECRET="test_secret",
+        KIS_ACCOUNT_NO="12345678-01",
+        GEMINI_API_KEY="test_gemini_key",
+        GEMINI_MODEL="gemini-pro",
+        DB_PATH=":memory:",
+    )
+
+
+@pytest.fixture
+def optimizer(settings: Settings) -> EvolutionOptimizer:
+    """Provide an EvolutionOptimizer instance."""
+    return EvolutionOptimizer(settings)
+
+
+@pytest.fixture
+def decision_logger(db_conn: sqlite3.Connection) -> DecisionLogger:
+    """Provide a DecisionLogger instance."""
+    return DecisionLogger(db_conn)
+
+
+@pytest.fixture
+def ab_tester() -> ABTester:
+    """Provide an ABTester instance."""
+    return ABTester(significance_level=0.05)
+
+
+@pytest.fixture
+def performance_tracker(settings: Settings) -> PerformanceTracker:
+    """Provide a PerformanceTracker instance."""
+    return PerformanceTracker(db_path=":memory:")
+
+
+# ------------------------------------------------------------------
+# EvolutionOptimizer Tests
+# ------------------------------------------------------------------
+
+
+def test_analyze_failures_uses_decision_logger(optimizer: EvolutionOptimizer) -> None:
+    """Test that analyze_failures uses DecisionLogger.get_losing_decisions()."""
+    # Add some losing decisions to the database
+    logger = optimizer._decision_logger
+
+    # High-confidence loss
+    id1 = logger.log_decision(
+        stock_code="005930",
+        market="KR",
+        exchange_code="KRX",
+        action="BUY",
+        confidence=85,
+        rationale="Expected growth",
+        context_snapshot={"L1": {"price": 70000}},
+        input_data={"price": 70000, "volume": 1000},
+    )
+    logger.update_outcome(id1, pnl=-2000.0, accuracy=0)
+
+    # Another high-confidence loss
+    id2 = logger.log_decision(
+        stock_code="000660",
+        market="KR",
+        exchange_code="KRX",
+        action="SELL",
+        confidence=90,
+        rationale="Expected drop",
+        context_snapshot={"L1": {"price": 100000}},
+        input_data={"price": 100000, "volume": 500},
+    )
+    logger.update_outcome(id2, pnl=-1500.0, accuracy=0)
+
+    # Low-confidence loss (should be ignored)
+    id3 = logger.log_decision(
+        stock_code="035420",
+        market="KR",
+        exchange_code="KRX",
+        action="HOLD",
+        confidence=70,
+        rationale="Uncertain",
+        context_snapshot={},
+        input_data={},
+    )
+    logger.update_outcome(id3, pnl=-500.0, accuracy=0)
+
+    # Analyze failures
+    failures = optimizer.analyze_failures(limit=10)
+
+    # Should get 2 failures (confidence >= 80)
+    assert len(failures) == 2
+    assert all(f["confidence"] >= 80 for f in failures)
+    assert all(f["outcome_pnl"] <= -100.0 for f in failures)
+
+
+def test_analyze_failures_empty_database(optimizer: EvolutionOptimizer) -> None:
+    """Test analyze_failures with no losing decisions."""
+    failures = optimizer.analyze_failures()
+    assert failures == []
+
+
+def test_identify_failure_patterns(optimizer: EvolutionOptimizer) -> None:
+    """Test identification of failure patterns."""
+    failures = [
+        {
+            "decision_id": "1",
+            "timestamp": "2024-01-15T09:30:00+00:00",
+            "stock_code": "005930",
+            "market": "KR",
+            "exchange_code": "KRX",
+            "action": "BUY",
+            "confidence": 85,
+            "rationale": "Test",
+            "outcome_pnl": -1000.0,
+            "outcome_accuracy": 0,
+            "context_snapshot": {},
+            "input_data": {},
+        },
+        {
+            "decision_id": "2",
+            "timestamp": "2024-01-15T14:30:00+00:00",
+            "stock_code": "000660",
+            "market": "KR",
+            "exchange_code": "KRX",
+            "action": "SELL",
+            "confidence": 90,
+            "rationale": "Test",
+            "outcome_pnl": -2000.0,
+            "outcome_accuracy": 0,
+            "context_snapshot": {},
+            "input_data": {},
+        },
+        {
+            "decision_id": "3",
+            "timestamp": "2024-01-15T09:45:00+00:00",
+            "stock_code": "035420",
+            "market": "US_NASDAQ",
+            "exchange_code": "NASDAQ",
+            "action": "BUY",
+            "confidence": 80,
+            "rationale": "Test",
+            "outcome_pnl": -500.0,
+            "outcome_accuracy": 0,
+            "context_snapshot": {},
+            "input_data": {},
+        },
+    ]
+
+    patterns = optimizer.identify_failure_patterns(failures)
+
+    assert patterns["total_failures"] == 3
+    assert patterns["markets"]["KR"] == 2
+    assert patterns["markets"]["US_NASDAQ"] == 1
+    assert patterns["actions"]["BUY"] == 2
+    assert patterns["actions"]["SELL"] == 1
+    assert 9 in patterns["hours"]  # 09:30 and 09:45
+    assert 14 in patterns["hours"]  # 14:30
+    assert patterns["avg_confidence"] == 85.0
+    assert patterns["avg_loss"] == -1166.67
+
+
+def test_identify_failure_patterns_empty(optimizer: EvolutionOptimizer) -> None:
+    """Test pattern identification with no failures."""
+    patterns = optimizer.identify_failure_patterns([])
+    assert patterns["pattern_count"] == 0
+    assert patterns["patterns"] == {}
+
+
+@pytest.mark.asyncio
+async def test_generate_strategy_creates_file(optimizer: EvolutionOptimizer, tmp_path: Path) -> None:
+    """Test that generate_strategy creates a strategy file."""
+    failures = [
+        {
+            "decision_id": "1",
+            "timestamp": "2024-01-15T09:30:00+00:00",
+            "stock_code": "005930",
+            "market": "KR",
+            "action": "BUY",
+            "confidence": 85,
+            "outcome_pnl": -1000.0,
+            "context_snapshot": {},
+            "input_data": {},
+        }
+    ]
+
+    # Mock Gemini response
+    mock_response = Mock()
+    mock_response.text = """
+    # Simple strategy
+    price = market_data.get("current_price", 0)
+    if price > 50000:
+        return {"action": "BUY", "confidence": 70, "rationale": "Price above threshold"}
+    return {"action": "HOLD", "confidence": 50, "rationale": "Waiting"}
+    """
+
+    with patch.object(optimizer._client.aio.models, "generate_content", new=AsyncMock(return_value=mock_response)):
+        with patch("src.evolution.optimizer.STRATEGIES_DIR", tmp_path):
+            strategy_path = await optimizer.generate_strategy(failures)
+
+    assert strategy_path is not None
+    assert strategy_path.exists()
+    assert strategy_path.suffix == ".py"
+    assert "class Strategy_" in strategy_path.read_text()
+    assert "def evaluate" in strategy_path.read_text()
+
+
+@pytest.mark.asyncio
+async def test_generate_strategy_handles_api_error(optimizer: EvolutionOptimizer) -> None:
+    """Test that generate_strategy handles Gemini API errors gracefully."""
+    failures = [{"decision_id": "1", "timestamp": "2024-01-15T09:30:00+00:00"}]
+
+    with patch.object(
+        optimizer._client.aio.models,
+        "generate_content",
+        side_effect=Exception("API Error"),
+    ):
+        strategy_path = await optimizer.generate_strategy(failures)
+
+    assert strategy_path is None
+
+
+def test_get_performance_summary() -> None:
+    """Test getting performance summary from trades table."""
+    # Create a temporary database with trades
+    import tempfile
+    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as tmp:
+        tmp_path = tmp.name
+
+    conn = init_db(tmp_path)
+    log_trade(conn, "005930", "BUY", 85, "Test win", quantity=10, price=70000, pnl=1000.0)
+    log_trade(conn, "000660", "SELL", 90, "Test loss", quantity=5, price=100000, pnl=-500.0)
+    log_trade(conn, "035420", "BUY", 80, "Test win", quantity=8, price=50000, pnl=800.0)
+    conn.close()
+
+    # Create settings with temp database path
+    settings = Settings(
+        KIS_APP_KEY="test_key",
+        KIS_APP_SECRET="test_secret",
+        KIS_ACCOUNT_NO="12345678-01",
+        GEMINI_API_KEY="test_gemini_key",
+        GEMINI_MODEL="gemini-pro",
+        DB_PATH=tmp_path,
+    )
+
+    optimizer = EvolutionOptimizer(settings)
+    summary = optimizer.get_performance_summary()
+
+    assert summary["total_trades"] == 3
+    assert summary["wins"] == 2
+    assert summary["losses"] == 1
+    assert summary["total_pnl"] == 1300.0
+    assert summary["avg_pnl"] == 433.33
+
+    # Clean up
+    Path(tmp_path).unlink()
+
+
+def test_validate_strategy_success(optimizer: EvolutionOptimizer, tmp_path: Path) -> None:
+    """Test strategy validation when tests pass."""
+    strategy_file = tmp_path / "test_strategy.py"
+    strategy_file.write_text("# Valid strategy file")
+
+    with patch("subprocess.run") as mock_run:
+        mock_run.return_value = Mock(returncode=0, stdout="", stderr="")
+        result = optimizer.validate_strategy(strategy_file)
+
+    assert result is True
+    assert strategy_file.exists()
+
+
+def test_validate_strategy_failure(optimizer: EvolutionOptimizer, tmp_path: Path) -> None:
+    """Test strategy validation when tests fail."""
+    strategy_file = tmp_path / "test_strategy.py"
+    strategy_file.write_text("# Invalid strategy file")
+
+    with patch("subprocess.run") as mock_run:
+        mock_run.return_value = Mock(returncode=1, stdout="FAILED", stderr="")
+        result = optimizer.validate_strategy(strategy_file)
+
+    assert result is False
+    # File should be deleted on failure
+    assert not strategy_file.exists()
+
+
+# ------------------------------------------------------------------
+# ABTester Tests
+# ------------------------------------------------------------------
+
+
+def test_calculate_performance_basic(ab_tester: ABTester) -> None:
+    """Test basic performance calculation."""
+    trades = [
+        {"pnl": 1000.0},
+        {"pnl": -500.0},
+        {"pnl": 800.0},
+        {"pnl": 200.0},
+    ]
+
+    perf = ab_tester.calculate_performance(trades, "TestStrategy")
+
+    assert perf.strategy_name == "TestStrategy"
+    assert perf.total_trades == 4
+    assert perf.wins == 3
+    assert perf.losses == 1
+    assert perf.total_pnl == 1500.0
+    assert perf.avg_pnl == 375.0
+    assert perf.win_rate == 75.0
+    assert perf.sharpe_ratio is not None
+
+
+def test_calculate_performance_empty(ab_tester: ABTester) -> None:
+    """Test performance calculation with no trades."""
+    perf = ab_tester.calculate_performance([], "EmptyStrategy")
+
+    assert perf.total_trades == 0
+    assert perf.wins == 0
+    assert perf.losses == 0
+    assert perf.total_pnl == 0.0
+    assert perf.avg_pnl == 0.0
+    assert perf.win_rate == 0.0
+    assert perf.sharpe_ratio is None
+
+
+def test_compare_strategies_significant_difference(ab_tester: ABTester) -> None:
+    """Test strategy comparison with significant performance difference."""
+    # Strategy A: consistently profitable
+    trades_a = [{"pnl": 1000.0} for _ in range(30)]
+
+    # Strategy B: consistently losing
+    trades_b = [{"pnl": -500.0} for _ in range(30)]
+
+    result = ab_tester.compare_strategies(trades_a, trades_b, "Strategy A", "Strategy B")
+
+    # scipy returns np.True_ instead of Python bool
+    assert bool(result.is_significant) is True
+    assert result.winner == "Strategy A"
+    assert result.p_value < 0.05
+    assert result.performance_a.avg_pnl > result.performance_b.avg_pnl
+
+
+def test_compare_strategies_no_difference(ab_tester: ABTester) -> None:
+    """Test strategy comparison with no significant difference."""
+    # Both strategies have similar performance
+    trades_a = [{"pnl": 100.0}, {"pnl": -50.0}, {"pnl": 80.0}]
+    trades_b = [{"pnl": 90.0}, {"pnl": -60.0}, {"pnl": 85.0}]
+
+    result = ab_tester.compare_strategies(trades_a, trades_b, "Strategy A", "Strategy B")
+
+    # With small samples and similar performance, likely not significant
+    assert result.winner is None or not result.is_significant
+
+
+def test_should_deploy_meets_criteria(ab_tester: ABTester) -> None:
+    """Test deployment decision when criteria are met."""
+    # Create a winning result that meets criteria
+    trades_a = [{"pnl": 1000.0} for _ in range(25)]  # 100% win rate
+    trades_b = [{"pnl": -500.0} for _ in range(25)]
+
+    result = ab_tester.compare_strategies(trades_a, trades_b, "Winner", "Loser")
+
+    should_deploy = ab_tester.should_deploy(result, min_win_rate=60.0, min_trades=20)
+
+    assert should_deploy is True
+
+
+def test_should_deploy_insufficient_trades(ab_tester: ABTester) -> None:
+    """Test deployment decision with insufficient trades."""
+    trades_a = [{"pnl": 1000.0} for _ in range(10)]  # Only 10 trades
+    trades_b = [{"pnl": -500.0} for _ in range(10)]
+
+    result = ab_tester.compare_strategies(trades_a, trades_b, "Winner", "Loser")
+
+    should_deploy = ab_tester.should_deploy(result, min_win_rate=60.0, min_trades=20)
+
+    assert should_deploy is False
+
+
+def test_should_deploy_low_win_rate(ab_tester: ABTester) -> None:
+    """Test deployment decision with low win rate."""
+    # Mix of wins and losses, below 60% win rate
+    trades_a = [{"pnl": 100.0}] * 10 + [{"pnl": -100.0}] * 15  # 40% win rate
+    trades_b = [{"pnl": -500.0} for _ in range(25)]
+
+    result = ab_tester.compare_strategies(trades_a, trades_b, "LowWinner", "Loser")
+
+    should_deploy = ab_tester.should_deploy(result, min_win_rate=60.0, min_trades=20)
+
+    assert should_deploy is False
+
+
+def test_should_deploy_not_significant(ab_tester: ABTester) -> None:
+    """Test deployment decision when difference is not significant."""
+    # Use more varied data to ensure statistical insignificance
+    trades_a = [{"pnl": 100.0}, {"pnl": -50.0}] * 12 + [{"pnl": 100.0}]
+    trades_b = [{"pnl": 95.0}, {"pnl": -45.0}] * 12 + [{"pnl": 95.0}]
+
+    result = ab_tester.compare_strategies(trades_a, trades_b, "A", "B")
+
+    should_deploy = ab_tester.should_deploy(result, min_win_rate=60.0, min_trades=20)
+
+    # Not significant or not profitable enough
+    # Even if significant, win rate is 50% which is below 60% threshold
+    assert should_deploy is False
+
+
+# ------------------------------------------------------------------
+# PerformanceTracker Tests
+# ------------------------------------------------------------------
+
+
+def test_get_strategy_metrics(db_conn: sqlite3.Connection) -> None:
+    """Test getting strategy metrics."""
+    # Add some trades
+    log_trade(db_conn, "005930", "BUY", 85, "Win 1", quantity=10, price=70000, pnl=1000.0)
+    log_trade(db_conn, "000660", "SELL", 90, "Loss 1", quantity=5, price=100000, pnl=-500.0)
+    log_trade(db_conn, "035420", "BUY", 80, "Win 2", quantity=8, price=50000, pnl=800.0)
+    log_trade(db_conn, "005930", "HOLD", 75, "Hold", quantity=0, price=70000, pnl=0.0)
+
+    tracker = PerformanceTracker(db_path=":memory:")
+    # Manually set connection for testing
+    tracker._db_path = db_conn
+
+    # Need to use the same connection
+    with patch("sqlite3.connect", return_value=db_conn):
+        metrics = tracker.get_strategy_metrics()
+
+    assert metrics.total_trades == 4
+    assert metrics.wins == 2
+    assert metrics.losses == 1
+    assert metrics.holds == 1
+    assert metrics.win_rate == 50.0
+    assert metrics.total_pnl == 1300.0
+
+
+def test_calculate_improvement_trend_improving(performance_tracker: PerformanceTracker) -> None:
+    """Test improvement trend calculation for improving strategy."""
+    metrics = [
+        StrategyMetrics(
+            strategy_name="test",
+            period_start="2024-01-01",
+            period_end="2024-01-07",
+            total_trades=10,
+            wins=5,
+            losses=5,
+            holds=0,
+            win_rate=50.0,
+            avg_pnl=100.0,
+            total_pnl=1000.0,
+            best_trade=500.0,
+            worst_trade=-300.0,
+            avg_confidence=75.0,
+        ),
+        StrategyMetrics(
+            strategy_name="test",
+            period_start="2024-01-08",
+            period_end="2024-01-14",
+            total_trades=10,
+            wins=7,
+            losses=3,
+            holds=0,
+            win_rate=70.0,
+            avg_pnl=200.0,
+            total_pnl=2000.0,
+            best_trade=600.0,
+            worst_trade=-200.0,
+            avg_confidence=80.0,
+        ),
+    ]
+
+    trend = performance_tracker.calculate_improvement_trend(metrics)
+
+    assert trend["trend"] == "improving"
+    assert trend["win_rate_change"] == 20.0
+    assert trend["pnl_change"] == 100.0
+    assert trend["confidence_change"] == 5.0
+
+
+def test_calculate_improvement_trend_declining(performance_tracker: PerformanceTracker) -> None:
+    """Test improvement trend calculation for declining strategy."""
+    metrics = [
+        StrategyMetrics(
+            strategy_name="test",
+            period_start="2024-01-01",
+            period_end="2024-01-07",
+            total_trades=10,
+            wins=7,
+            losses=3,
+            holds=0,
+            win_rate=70.0,
+            avg_pnl=200.0,
+            total_pnl=2000.0,
+            best_trade=600.0,
+            worst_trade=-200.0,
+            avg_confidence=80.0,
+        ),
+        StrategyMetrics(
+            strategy_name="test",
+            period_start="2024-01-08",
+            period_end="2024-01-14",
+            total_trades=10,
+            wins=4,
+            losses=6,
+            holds=0,
+            win_rate=40.0,
+            avg_pnl=-50.0,
+            total_pnl=-500.0,
+            best_trade=300.0,
+            worst_trade=-400.0,
+            avg_confidence=70.0,
+        ),
+    ]
+
+    trend = performance_tracker.calculate_improvement_trend(metrics)
+
+    assert trend["trend"] == "declining"
+    assert trend["win_rate_change"] == -30.0
+    assert trend["pnl_change"] == -250.0
+
+
+def test_calculate_improvement_trend_insufficient_data(performance_tracker: PerformanceTracker) -> None:
+    """Test improvement trend with insufficient data."""
+    metrics = [
+        StrategyMetrics(
+            strategy_name="test",
+            period_start="2024-01-01",
+            period_end="2024-01-07",
+            total_trades=10,
+            wins=5,
+            losses=5,
+            holds=0,
+            win_rate=50.0,
+            avg_pnl=100.0,
+            total_pnl=1000.0,
+            best_trade=500.0,
+            worst_trade=-300.0,
+            avg_confidence=75.0,
+        )
+    ]
+
+    trend = performance_tracker.calculate_improvement_trend(metrics)
+
+    assert trend["trend"] == "insufficient_data"
+    assert trend["win_rate_change"] == 0.0
+    assert trend["pnl_change"] == 0.0
+
+
+def test_export_dashboard_json(performance_tracker: PerformanceTracker) -> None:
+    """Test exporting dashboard as JSON."""
+    overall_metrics = StrategyMetrics(
+        strategy_name="test",
+        period_start="2024-01-01",
+        period_end="2024-01-31",
+        total_trades=100,
+        wins=60,
+        losses=40,
+        holds=10,
+        win_rate=60.0,
+        avg_pnl=150.0,
+        total_pnl=15000.0,
+        best_trade=1000.0,
+        worst_trade=-500.0,
+        avg_confidence=80.0,
+    )
+
+    dashboard = PerformanceDashboard(
+        generated_at=datetime.now(UTC).isoformat(),
+        overall_metrics=overall_metrics,
+        daily_metrics=[],
+        weekly_metrics=[],
+        improvement_trend={"trend": "improving", "win_rate_change": 10.0},
+    )
+
+    json_output = performance_tracker.export_dashboard_json(dashboard)
+
+    # Verify it's valid JSON
+    data = json.loads(json_output)
+    assert "generated_at" in data
+    assert "overall_metrics" in data
+    assert data["overall_metrics"]["total_trades"] == 100
+    assert data["overall_metrics"]["win_rate"] == 60.0
+
+
+def test_generate_dashboard() -> None:
+    """Test generating a complete dashboard."""
+    # Create tracker with temp database
+    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as tmp:
+        tmp_path = tmp.name
+
+    # Initialize with data
+    conn = init_db(tmp_path)
+    log_trade(conn, "005930", "BUY", 85, "Win", quantity=10, price=70000, pnl=1000.0)
+    log_trade(conn, "000660", "SELL", 90, "Loss", quantity=5, price=100000, pnl=-500.0)
+    conn.close()
+
+    tracker = PerformanceTracker(db_path=tmp_path)
+    dashboard = tracker.generate_dashboard()
+
+    assert isinstance(dashboard, PerformanceDashboard)
+    assert dashboard.overall_metrics.total_trades == 2
+    assert len(dashboard.daily_metrics) == 7
+    assert len(dashboard.weekly_metrics) == 4
+    assert "trend" in dashboard.improvement_trend
+
+    # Clean up
+    Path(tmp_path).unlink()
+
+
+# ------------------------------------------------------------------
+# Integration Tests
+# ------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_full_evolution_pipeline(optimizer: EvolutionOptimizer, tmp_path: Path) -> None:
+    """Test the complete evolution pipeline."""
+    # Add losing decisions
+    logger = optimizer._decision_logger
+    id1 = logger.log_decision(
+        stock_code="005930",
+        market="KR",
+        exchange_code="KRX",
+        action="BUY",
+        confidence=85,
+        rationale="Expected growth",
+        context_snapshot={},
+        input_data={},
+    )
+    logger.update_outcome(id1, pnl=-2000.0, accuracy=0)
+
+    # Mock Gemini and subprocess
+    mock_response = Mock()
+    mock_response.text = 'return {"action": "HOLD", "confidence": 50, "rationale": "Test"}'
+
+    with patch.object(optimizer._client.aio.models, "generate_content", new=AsyncMock(return_value=mock_response)):
+        with patch("src.evolution.optimizer.STRATEGIES_DIR", tmp_path):
+            with patch("subprocess.run") as mock_run:
+                mock_run.return_value = Mock(returncode=0, stdout="", stderr="")
+
+                result = await optimizer.evolve()
+
+    assert result is not None
+    assert "title" in result
+    assert "branch" in result
+    assert "status" in result

tests/test_latency_control.py

@@ -0,0 +1,558 @@
+"""Tests for latency control system (criticality assessment and priority queue)."""
+
+from __future__ import annotations
+
+import asyncio
+
+import pytest
+
+from src.core.criticality import CriticalityAssessor, CriticalityLevel
+from src.core.priority_queue import PriorityTask, PriorityTaskQueue
+
+# ---------------------------------------------------------------------------
+# CriticalityAssessor Tests
+# ---------------------------------------------------------------------------
+
+
+class TestCriticalityAssessor:
+    """Test suite for criticality assessment logic."""
+
+    def test_market_closed_returns_low(self) -> None:
+        """Market closed should return LOW priority."""
+        assessor = CriticalityAssessor()
+        level = assessor.assess_market_conditions(
+            pnl_pct=0.0,
+            volatility_score=50.0,
+            volume_surge=1.0,
+            is_market_open=False,
+        )
+        assert level == CriticalityLevel.LOW
+
+    def test_very_low_volatility_returns_low(self) -> None:
+        """Very low volatility should return LOW priority."""
+        assessor = CriticalityAssessor()
+        level = assessor.assess_market_conditions(
+            pnl_pct=0.0,
+            volatility_score=20.0,  # Below 30.0 threshold
+            volume_surge=1.0,
+            is_market_open=True,
+        )
+        assert level == CriticalityLevel.LOW
+
+    def test_critical_pnl_threshold_triggered(self) -> None:
+        """P&L below -2.5% should trigger CRITICAL."""
+        assessor = CriticalityAssessor()
+        level = assessor.assess_market_conditions(
+            pnl_pct=-2.6,  # Below -2.5% threshold
+            volatility_score=50.0,
+            volume_surge=1.0,
+            is_market_open=True,
+        )
+        assert level == CriticalityLevel.CRITICAL
+
+    def test_critical_pnl_at_circuit_breaker_proximity(self) -> None:
+        """P&L at exactly -2.5% (near -3.0% breaker) should be CRITICAL."""
+        assessor = CriticalityAssessor()
+        level = assessor.assess_market_conditions(
+            pnl_pct=-2.5,
+            volatility_score=50.0,
+            volume_surge=1.0,
+            is_market_open=True,
+        )
+        assert level == CriticalityLevel.CRITICAL
+
+    def test_critical_price_change_positive(self) -> None:
+        """Large positive price change (>5%) should trigger CRITICAL."""
+        assessor = CriticalityAssessor()
+        level = assessor.assess_market_conditions(
+            pnl_pct=0.0,
+            volatility_score=50.0,
+            volume_surge=1.0,
+            price_change_1m=5.5,  # Above 5.0% threshold
+            is_market_open=True,
+        )
+        assert level == CriticalityLevel.CRITICAL
+
+    def test_critical_price_change_negative(self) -> None:
+        """Large negative price change (<-5%) should trigger CRITICAL."""
+        assessor = CriticalityAssessor()
+        level = assessor.assess_market_conditions(
+            pnl_pct=0.0,
+            volatility_score=50.0,
+            volume_surge=1.0,
+            price_change_1m=-6.0,  # Below -5.0% threshold
+            is_market_open=True,
+        )
+        assert level == CriticalityLevel.CRITICAL
+
+    def test_critical_volume_surge(self) -> None:
+        """Extreme volume surge (>10x) should trigger CRITICAL."""
+        assessor = CriticalityAssessor()
+        level = assessor.assess_market_conditions(
+            pnl_pct=0.0,
+            volatility_score=50.0,
+            volume_surge=12.0,  # Above 10.0x threshold
+            is_market_open=True,
+        )
+        assert level == CriticalityLevel.CRITICAL
+
+    def test_high_volatility_returns_high(self) -> None:
+        """High volatility score should return HIGH priority."""
+        assessor = CriticalityAssessor()
+        level = assessor.assess_market_conditions(
+            pnl_pct=0.0,
+            volatility_score=75.0,  # Above 70.0 threshold
+            volume_surge=1.0,
+            is_market_open=True,
+        )
+        assert level == CriticalityLevel.HIGH
+
+    def test_normal_conditions_return_normal(self) -> None:
+        """Normal market conditions should return NORMAL priority."""
+        assessor = CriticalityAssessor()
+        level = assessor.assess_market_conditions(
+            pnl_pct=0.5,
+            volatility_score=50.0,  # Between 30-70
+            volume_surge=1.5,
+            price_change_1m=1.0,
+            is_market_open=True,
+        )
+        assert level == CriticalityLevel.NORMAL
+
+    def test_custom_thresholds(self) -> None:
+        """Custom thresholds should be respected."""
+        assessor = CriticalityAssessor(
+            critical_pnl_threshold=-1.0,
+            critical_price_change_threshold=3.0,
+            critical_volume_surge_threshold=5.0,
+            high_volatility_threshold=60.0,
+            low_volatility_threshold=20.0,
+        )
+
+        # Test custom P&L threshold
+        level = assessor.assess_market_conditions(
+            pnl_pct=-1.1,
+            volatility_score=50.0,
+            volume_surge=1.0,
+            is_market_open=True,
+        )
+        assert level == CriticalityLevel.CRITICAL
+
+        # Test custom price change threshold
+        level = assessor.assess_market_conditions(
+            pnl_pct=0.0,
+            volatility_score=50.0,
+            volume_surge=1.0,
+            price_change_1m=3.5,
+            is_market_open=True,
+        )
+        assert level == CriticalityLevel.CRITICAL
+
+    def test_get_timeout_returns_correct_values(self) -> None:
+        """Timeout values should match specification."""
+        assessor = CriticalityAssessor()
+
+        assert assessor.get_timeout(CriticalityLevel.CRITICAL) == 5.0
+        assert assessor.get_timeout(CriticalityLevel.HIGH) == 30.0
+        assert assessor.get_timeout(CriticalityLevel.NORMAL) == 60.0
+        assert assessor.get_timeout(CriticalityLevel.LOW) is None
+
+
+# ---------------------------------------------------------------------------
+# PriorityTaskQueue Tests
+# ---------------------------------------------------------------------------
+
+
+class TestPriorityTaskQueue:
+    """Test suite for priority queue implementation."""
+
+    @pytest.mark.asyncio
+    async def test_enqueue_task(self) -> None:
+        """Tasks should be enqueued successfully."""
+        queue = PriorityTaskQueue()
+
+        success = await queue.enqueue(
+            task_id="test-1",
+            criticality=CriticalityLevel.NORMAL,
+            task_data={"action": "test"},
+        )
+
+        assert success is True
+        assert await queue.size() == 1
+
+    @pytest.mark.asyncio
+    async def test_enqueue_rejects_when_full(self) -> None:
+        """Queue should reject tasks when full."""
+        queue = PriorityTaskQueue(max_size=2)
+
+        # Fill the queue
+        await queue.enqueue("task-1", CriticalityLevel.NORMAL, {})
+        await queue.enqueue("task-2", CriticalityLevel.NORMAL, {})
+
+        # Third task should be rejected
+        success = await queue.enqueue("task-3", CriticalityLevel.NORMAL, {})
+        assert success is False
+        assert await queue.size() == 2
+
+    @pytest.mark.asyncio
+    async def test_dequeue_returns_highest_priority(self) -> None:
+        """Dequeue should return highest priority task first."""
+        queue = PriorityTaskQueue()
+
+        # Enqueue tasks in reverse priority order
+        await queue.enqueue("low", CriticalityLevel.LOW, {"priority": 3})
+        await queue.enqueue("normal", CriticalityLevel.NORMAL, {"priority": 2})
+        await queue.enqueue("high", CriticalityLevel.HIGH, {"priority": 1})
+        await queue.enqueue("critical", CriticalityLevel.CRITICAL, {"priority": 0})
+
+        # Dequeue should return CRITICAL first
+        task = await queue.dequeue(timeout=1.0)
+        assert task is not None
+        assert task.task_id == "critical"
+        assert task.priority == 0
+
+        # Then HIGH
+        task = await queue.dequeue(timeout=1.0)
+        assert task is not None
+        assert task.task_id == "high"
+        assert task.priority == 1
+
+    @pytest.mark.asyncio
+    async def test_dequeue_fifo_within_same_priority(self) -> None:
+        """Tasks with same priority should be FIFO."""
+        queue = PriorityTaskQueue()
+
+        # Enqueue multiple tasks with same priority
+        await queue.enqueue("task-1", CriticalityLevel.NORMAL, {})
+        await asyncio.sleep(0.01)  # Small delay to ensure different timestamps
+        await queue.enqueue("task-2", CriticalityLevel.NORMAL, {})
+        await asyncio.sleep(0.01)
+        await queue.enqueue("task-3", CriticalityLevel.NORMAL, {})
+
+        # Should dequeue in FIFO order
+        task1 = await queue.dequeue(timeout=1.0)
+        task2 = await queue.dequeue(timeout=1.0)
+        task3 = await queue.dequeue(timeout=1.0)
+
+        assert task1 is not None and task1.task_id == "task-1"
+        assert task2 is not None and task2.task_id == "task-2"
+        assert task3 is not None and task3.task_id == "task-3"
+
+    @pytest.mark.asyncio
+    async def test_dequeue_returns_none_when_empty(self) -> None:
+        """Dequeue should return None when queue is empty after timeout."""
+        queue = PriorityTaskQueue()
+
+        task = await queue.dequeue(timeout=0.1)
+        assert task is None
+
+    @pytest.mark.asyncio
+    async def test_execute_with_timeout_success(self) -> None:
+        """Task execution should succeed within timeout."""
+        queue = PriorityTaskQueue()
+
+        # Create a simple async callback
+        async def test_callback() -> str:
+            await asyncio.sleep(0.01)
+            return "success"
+
+        task = PriorityTask(
+            priority=0,
+            timestamp=0.0,
+            task_id="test",
+            task_data={},
+            callback=test_callback,
+        )
+
+        result = await queue.execute_with_timeout(task, timeout=1.0)
+        assert result == "success"
+
+    @pytest.mark.asyncio
+    async def test_execute_with_timeout_raises_timeout_error(self) -> None:
+        """Task execution should raise TimeoutError if exceeds timeout."""
+        queue = PriorityTaskQueue()
+
+        # Create a slow async callback
+        async def slow_callback() -> str:
+            await asyncio.sleep(1.0)
+            return "too slow"
+
+        task = PriorityTask(
+            priority=0,
+            timestamp=0.0,
+            task_id="test",
+            task_data={},
+            callback=slow_callback,
+        )
+
+        with pytest.raises(asyncio.TimeoutError):
+            await queue.execute_with_timeout(task, timeout=0.1)
+
+    @pytest.mark.asyncio
+    async def test_execute_with_timeout_propagates_exceptions(self) -> None:
+        """Task execution should propagate exceptions from callback."""
+        queue = PriorityTaskQueue()
+
+        # Create a failing async callback
+        async def failing_callback() -> None:
+            raise ValueError("Test error")
+
+        task = PriorityTask(
+            priority=0,
+            timestamp=0.0,
+            task_id="test",
+            task_data={},
+            callback=failing_callback,
+        )
+
+        with pytest.raises(ValueError, match="Test error"):
+            await queue.execute_with_timeout(task, timeout=1.0)
+
+    @pytest.mark.asyncio
+    async def test_execute_without_timeout(self) -> None:
+        """Task execution should work without timeout (LOW priority)."""
+        queue = PriorityTaskQueue()
+
+        async def test_callback() -> str:
+            await asyncio.sleep(0.01)
+            return "success"
+
+        task = PriorityTask(
+            priority=3,
+            timestamp=0.0,
+            task_id="test",
+            task_data={},
+            callback=test_callback,
+        )
+
+        result = await queue.execute_with_timeout(task, timeout=None)
+        assert result == "success"
+
+    @pytest.mark.asyncio
+    async def test_get_metrics(self) -> None:
+        """Queue should track metrics correctly."""
+        queue = PriorityTaskQueue()
+
+        # Enqueue and dequeue some tasks
+        await queue.enqueue("task-1", CriticalityLevel.CRITICAL, {})
+        await queue.enqueue("task-2", CriticalityLevel.HIGH, {})
+        await queue.enqueue("task-3", CriticalityLevel.NORMAL, {})
+
+        await queue.dequeue(timeout=1.0)
+        await queue.dequeue(timeout=1.0)
+
+        metrics = await queue.get_metrics()
+
+        assert metrics.total_enqueued == 3
+        assert metrics.total_dequeued == 2
+        assert metrics.current_size == 1
+
+    @pytest.mark.asyncio
+    async def test_wait_time_metrics(self) -> None:
+        """Queue should track wait times per criticality level."""
+        queue = PriorityTaskQueue()
+
+        # Enqueue tasks with different criticality
+        await queue.enqueue("critical-1", CriticalityLevel.CRITICAL, {})
+        await asyncio.sleep(0.05)  # Add some wait time
+
+        await queue.dequeue(timeout=1.0)
+
+        metrics = await queue.get_metrics()
+
+        # Should have wait time metrics for CRITICAL
+        assert CriticalityLevel.CRITICAL in metrics.avg_wait_time
+        assert metrics.avg_wait_time[CriticalityLevel.CRITICAL] > 0.0
+
+    @pytest.mark.asyncio
+    async def test_clear_queue(self) -> None:
+        """Clear should remove all tasks from queue."""
+        queue = PriorityTaskQueue()
+
+        await queue.enqueue("task-1", CriticalityLevel.NORMAL, {})
+        await queue.enqueue("task-2", CriticalityLevel.NORMAL, {})
+        await queue.enqueue("task-3", CriticalityLevel.NORMAL, {})
+
+        cleared = await queue.clear()
+
+        assert cleared == 3
+        assert await queue.size() == 0
+
+    @pytest.mark.asyncio
+    async def test_concurrent_enqueue_dequeue(self) -> None:
+        """Queue should handle concurrent operations safely."""
+        queue = PriorityTaskQueue()
+
+        # Concurrent enqueue operations
+        async def enqueue_tasks() -> None:
+            for i in range(10):
+                await queue.enqueue(
+                    f"task-{i}",
+                    CriticalityLevel.NORMAL,
+                    {"index": i},
+                )
+
+        # Concurrent dequeue operations
+        async def dequeue_tasks() -> list[str]:
+            tasks = []
+            for _ in range(10):
+                task = await queue.dequeue(timeout=1.0)
+                if task:
+                    tasks.append(task.task_id)
+                await asyncio.sleep(0.01)
+            return tasks
+
+        # Run both concurrently
+        enqueue_task = asyncio.create_task(enqueue_tasks())
+        dequeue_task = asyncio.create_task(dequeue_tasks())
+
+        await enqueue_task
+        dequeued_ids = await dequeue_task
+
+        # All tasks should be processed
+        assert len(dequeued_ids) == 10
+
+    @pytest.mark.asyncio
+    async def test_timeout_metric_tracking(self) -> None:
+        """Queue should track timeout occurrences."""
+        queue = PriorityTaskQueue()
+
+        async def slow_callback() -> str:
+            await asyncio.sleep(1.0)
+            return "too slow"
+
+        task = PriorityTask(
+            priority=0,
+            timestamp=0.0,
+            task_id="test",
+            task_data={},
+            callback=slow_callback,
+        )
+
+        try:
+            await queue.execute_with_timeout(task, timeout=0.1)
+        except TimeoutError:
+            pass
+
+        metrics = await queue.get_metrics()
+        assert metrics.total_timeouts == 1
+
+    @pytest.mark.asyncio
+    async def test_error_metric_tracking(self) -> None:
+        """Queue should track execution errors."""
+        queue = PriorityTaskQueue()
+
+        async def failing_callback() -> None:
+            raise ValueError("Test error")
+
+        task = PriorityTask(
+            priority=0,
+            timestamp=0.0,
+            task_id="test",
+            task_data={},
+            callback=failing_callback,
+        )
+
+        try:
+            await queue.execute_with_timeout(task, timeout=1.0)
+        except ValueError:
+            pass
+
+        metrics = await queue.get_metrics()
+        assert metrics.total_errors == 1
+
+
+# ---------------------------------------------------------------------------
+# Integration Tests
+# ---------------------------------------------------------------------------
+
+
+class TestLatencyControlIntegration:
+    """Integration tests for criticality assessment and priority queue."""
+
+    @pytest.mark.asyncio
+    async def test_critical_task_bypass_queue(self) -> None:
+        """CRITICAL tasks should bypass lower priority tasks."""
+        queue = PriorityTaskQueue()
+
+        # Add normal priority tasks
+        await queue.enqueue("normal-1", CriticalityLevel.NORMAL, {})
+        await queue.enqueue("normal-2", CriticalityLevel.NORMAL, {})
+
+        # Add critical task (should jump to front)
+        await queue.enqueue("critical", CriticalityLevel.CRITICAL, {})
+
+        # Dequeue should return critical first
+        task = await queue.dequeue(timeout=1.0)
+        assert task is not None
+        assert task.task_id == "critical"
+
+    @pytest.mark.asyncio
+    async def test_timeout_enforcement_by_criticality(self) -> None:
+        """Timeout enforcement should match criticality level."""
+        assessor = CriticalityAssessor()
+
+        # CRITICAL should have 5s timeout
+        critical_timeout = assessor.get_timeout(CriticalityLevel.CRITICAL)
+        assert critical_timeout == 5.0
+
+        # HIGH should have 30s timeout
+        high_timeout = assessor.get_timeout(CriticalityLevel.HIGH)
+        assert high_timeout == 30.0
+
+        # NORMAL should have 60s timeout
+        normal_timeout = assessor.get_timeout(CriticalityLevel.NORMAL)
+        assert normal_timeout == 60.0
+
+        # LOW should have no timeout
+        low_timeout = assessor.get_timeout(CriticalityLevel.LOW)
+        assert low_timeout is None
+
+    @pytest.mark.asyncio
+    async def test_fast_path_execution_for_critical(self) -> None:
+        """CRITICAL tasks should complete quickly."""
+        queue = PriorityTaskQueue()
+
+        # Create a fast callback simulating fast-path execution
+        async def fast_path_callback() -> str:
+            # Simulate simplified decision flow
+            await asyncio.sleep(0.01)  # Very fast execution
+            return "fast_path_complete"
+
+        task = PriorityTask(
+            priority=0,  # CRITICAL
+            timestamp=0.0,
+            task_id="critical-fast",
+            task_data={},
+            callback=fast_path_callback,
+        )
+
+        import time
+
+        start = time.time()
+        result = await queue.execute_with_timeout(task, timeout=5.0)
+        elapsed = time.time() - start
+
+        assert result == "fast_path_complete"
+        assert elapsed < 5.0  # Should complete well under CRITICAL timeout
+
+    @pytest.mark.asyncio
+    async def test_graceful_degradation_when_queue_full(self) -> None:
+        """System should gracefully handle full queue."""
+        queue = PriorityTaskQueue(max_size=2)
+
+        # Fill the queue
+        await queue.enqueue("task-1", CriticalityLevel.NORMAL, {})
+        await queue.enqueue("task-2", CriticalityLevel.NORMAL, {})
+
+        # Try to add more tasks
+        success = await queue.enqueue("task-3", CriticalityLevel.NORMAL, {})
+        assert success is False
+
+        # Queue should still function
+        task = await queue.dequeue(timeout=1.0)
+        assert task is not None
+
+        # Now we can add another task
+        success = await queue.enqueue("task-4", CriticalityLevel.NORMAL, {})
+        assert success is True

tests/test_token_efficiency.py

@@ -0,0 +1,663 @@
+"""Tests for token efficiency optimization components.
+
+Tests cover:
+- Prompt compression and optimization
+- Context selection logic
+- Summarization
+- Caching
+- Token reduction metrics
+"""
+
+from __future__ import annotations
+
+import sqlite3
+import time
+
+import pytest
+
+from src.brain.cache import DecisionCache
+from src.brain.context_selector import ContextSelector, DecisionType
+from src.brain.gemini_client import TradeDecision
+from src.brain.prompt_optimizer import PromptOptimizer, TokenMetrics
+from src.context.layer import ContextLayer
+from src.context.store import ContextStore
+from src.context.summarizer import ContextSummarizer, SummaryStats
+
+# ============================================================================
+# Prompt Optimizer Tests
+# ============================================================================
+
+
+class TestPromptOptimizer:
+    """Tests for PromptOptimizer."""
+
+    def test_estimate_tokens(self):
+        """Test token estimation."""
+        optimizer = PromptOptimizer()
+
+        # Empty text
+        assert optimizer.estimate_tokens("") == 0
+
+        # Short text (4 chars = 1 token estimate)
+        assert optimizer.estimate_tokens("test") == 1
+
+        # Longer text
+        text = "This is a longer piece of text for testing token estimation."
+        tokens = optimizer.estimate_tokens(text)
+        assert tokens > 0
+        assert tokens == len(text) // 4
+
+    def test_count_tokens(self):
+        """Test token counting metrics."""
+        optimizer = PromptOptimizer()
+
+        text = "Hello world, this is a test."
+        metrics = optimizer.count_tokens(text)
+
+        assert isinstance(metrics, TokenMetrics)
+        assert metrics.char_count == len(text)
+        assert metrics.word_count == 6
+        assert metrics.estimated_tokens > 0
+
+    def test_compress_json(self):
+        """Test JSON compression."""
+        optimizer = PromptOptimizer()
+
+        data = {
+            "action": "BUY",
+            "confidence": 85,
+            "rationale": "Strong uptrend",
+        }
+
+        compressed = optimizer.compress_json(data)
+
+        # Should have no newlines and minimal whitespace
+        assert "\n" not in compressed
+        # Note: JSON values may contain spaces (e.g., "Strong uptrend")
+        # but there should be no spaces around separators
+        assert ": " not in compressed
+        assert ", " not in compressed
+
+        # Should be valid JSON
+        import json
+
+        parsed = json.loads(compressed)
+        assert parsed == data
+
+    def test_abbreviate_text(self):
+        """Test text abbreviation."""
+        optimizer = PromptOptimizer()
+
+        text = "The current price is high and volume is increasing."
+        abbreviated = optimizer.abbreviate_text(text)
+
+        # Should contain abbreviations
+        assert "cur" in abbreviated or "P" in abbreviated
+        assert len(abbreviated) <= len(text)
+
+    def test_abbreviate_text_aggressive(self):
+        """Test aggressive text abbreviation."""
+        optimizer = PromptOptimizer()
+
+        text = "The price is increasing and the volume is high."
+        abbreviated = optimizer.abbreviate_text(text, aggressive=True)
+
+        # Should be shorter
+        assert len(abbreviated) < len(text)
+
+        # Should have removed articles
+        assert "the" not in abbreviated.lower()
+
+    def test_build_compressed_prompt(self):
+        """Test compressed prompt building."""
+        optimizer = PromptOptimizer()
+
+        market_data = {
+            "stock_code": "005930",
+            "current_price": 75000,
+            "market_name": "Korean stock market",
+        }
+
+        prompt = optimizer.build_compressed_prompt(market_data)
+
+        # Should be much shorter than original
+        assert len(prompt) < 300
+        assert "005930" in prompt
+        assert "75000" in prompt
+
+    def test_build_compressed_prompt_no_instructions(self):
+        """Test compressed prompt without instructions."""
+        optimizer = PromptOptimizer()
+
+        market_data = {
+            "stock_code": "AAPL",
+            "current_price": 150.5,
+            "market_name": "United States",
+        }
+
+        prompt = optimizer.build_compressed_prompt(market_data, include_instructions=False)
+
+        # Should be very short (data only)
+        assert len(prompt) < 100
+        assert "AAPL" in prompt
+
+    def test_truncate_context(self):
+        """Test context truncation."""
+        optimizer = PromptOptimizer()
+
+        context = {
+            "price": 100.5,
+            "volume": 1000000,
+            "sentiment": 0.8,
+            "extra_data": "Some long text that should be truncated",
+        }
+
+        # Truncate to small budget
+        truncated = optimizer.truncate_context(context, max_tokens=10)
+
+        # Should have fewer keys
+        assert len(truncated) <= len(context)
+
+    def test_truncate_context_with_priority(self):
+        """Test context truncation with priority keys."""
+        optimizer = PromptOptimizer()
+
+        context = {
+            "price": 100.5,
+            "volume": 1000000,
+            "sentiment": 0.8,
+            "extra_data": "Some data",
+        }
+
+        priority_keys = ["price", "sentiment"]
+        truncated = optimizer.truncate_context(context, max_tokens=20, priority_keys=priority_keys)
+
+        # Priority keys should be included
+        assert "price" in truncated
+        assert "sentiment" in truncated
+
+    def test_calculate_compression_ratio(self):
+        """Test compression ratio calculation."""
+        optimizer = PromptOptimizer()
+
+        original = "This is a very long piece of text that should be compressed significantly."
+        compressed = "Short text"
+
+        ratio = optimizer.calculate_compression_ratio(original, compressed)
+
+        # Ratio should be > 1 (original is longer)
+        assert ratio > 1.0
+
+
+# ============================================================================
+# Context Selector Tests
+# ============================================================================
+
+
+class TestContextSelector:
+    """Tests for ContextSelector."""
+
+    @pytest.fixture
+    def store(self):
+        """Create in-memory ContextStore."""
+        conn = sqlite3.connect(":memory:")
+        # Create tables
+        conn.execute(
+            """
+            CREATE TABLE context_metadata (
+                layer TEXT PRIMARY KEY,
+                description TEXT,
+                retention_days INTEGER,
+                aggregation_source TEXT
+            )
+            """
+        )
+        conn.execute(
+            """
+            CREATE TABLE contexts (
+                layer TEXT,
+                timeframe TEXT,
+                key TEXT,
+                value TEXT,
+                created_at TEXT,
+                updated_at TEXT,
+                PRIMARY KEY (layer, timeframe, key)
+            )
+            """
+        )
+        conn.commit()
+        return ContextStore(conn)
+
+    def test_select_layers_normal(self, store):
+        """Test layer selection for normal decisions."""
+        selector = ContextSelector(store)
+
+        layers = selector.select_layers(DecisionType.NORMAL)
+
+        # Should only select L7 (real-time)
+        assert layers == [ContextLayer.L7_REALTIME]
+
+    def test_select_layers_strategic(self, store):
+        """Test layer selection for strategic decisions."""
+        selector = ContextSelector(store)
+
+        layers = selector.select_layers(DecisionType.STRATEGIC)
+
+        # Should select L7 + L6 + L5
+        assert ContextLayer.L7_REALTIME in layers
+        assert ContextLayer.L6_DAILY in layers
+        assert ContextLayer.L5_WEEKLY in layers
+        assert len(layers) == 3
+
+    def test_select_layers_major_event(self, store):
+        """Test layer selection for major events."""
+        selector = ContextSelector(store)
+
+        layers = selector.select_layers(DecisionType.MAJOR_EVENT)
+
+        # Should select all layers
+        assert len(layers) == 7
+        assert ContextLayer.L1_LEGACY in layers
+        assert ContextLayer.L7_REALTIME in layers
+
+    def test_score_layer_relevance(self, store):
+        """Test layer relevance scoring."""
+        selector = ContextSelector(store)
+
+        # Add some data first so scores aren't penalized
+        store.set_context(ContextLayer.L7_REALTIME, "2026-02-04", "price", 100.5)
+        store.set_context(ContextLayer.L1_LEGACY, "legacy", "lesson", "test")
+
+        # L7 should have high score for normal decisions
+        score = selector.score_layer_relevance(ContextLayer.L7_REALTIME, DecisionType.NORMAL)
+        assert score == 1.0
+
+        # L1 should have low score for normal decisions
+        score = selector.score_layer_relevance(ContextLayer.L1_LEGACY, DecisionType.NORMAL)
+        assert score == 0.0
+
+        # L1 should have high score for major events
+        score = selector.score_layer_relevance(ContextLayer.L1_LEGACY, DecisionType.MAJOR_EVENT)
+        assert score == 1.0
+
+    def test_select_with_scoring(self, store):
+        """Test selection with relevance scoring."""
+        selector = ContextSelector(store)
+
+        # Add data so layers aren't penalized
+        store.set_context(ContextLayer.L7_REALTIME, "2026-02-04", "price", 100.5)
+
+        selection = selector.select_with_scoring(DecisionType.NORMAL, min_score=0.5)
+
+        # Should only select high-relevance layers
+        assert len(selection.layers) >= 1
+        assert ContextLayer.L7_REALTIME in selection.layers
+        assert all(selection.relevance_scores[layer] >= 0.5 for layer in selection.layers)
+
+    def test_get_context_data(self, store):
+        """Test context data retrieval."""
+        selector = ContextSelector(store)
+
+        # Add some test data
+        store.set_context(ContextLayer.L7_REALTIME, "2026-02-04", "price", 100.5)
+        store.set_context(ContextLayer.L7_REALTIME, "2026-02-04", "volume", 1000000)
+
+        context_data = selector.get_context_data([ContextLayer.L7_REALTIME])
+
+        # Should retrieve data
+        assert "L7_REALTIME" in context_data
+        assert "price" in context_data["L7_REALTIME"]
+        assert context_data["L7_REALTIME"]["price"] == 100.5
+
+    def test_estimate_context_tokens(self, store):
+        """Test context token estimation."""
+        selector = ContextSelector(store)
+
+        context_data = {
+            "L7_REALTIME": {"price": 100.5, "volume": 1000000},
+            "L6_DAILY": {"avg_price": 99.8, "avg_volume": 950000},
+        }
+
+        tokens = selector.estimate_context_tokens(context_data)
+
+        # Should estimate tokens
+        assert tokens > 0
+
+    def test_optimize_context_for_budget(self, store):
+        """Test context optimization for token budget."""
+        selector = ContextSelector(store)
+
+        # Add test data
+        store.set_context(ContextLayer.L7_REALTIME, "2026-02-04", "price", 100.5)
+
+        # Get optimized context within budget
+        context = selector.optimize_context_for_budget(DecisionType.NORMAL, max_tokens=50)
+
+        # Should return data within budget
+        tokens = selector.estimate_context_tokens(context)
+        assert tokens <= 50
+
+
+# ============================================================================
+# Context Summarizer Tests
+# ============================================================================
+
+
+class TestContextSummarizer:
+    """Tests for ContextSummarizer."""
+
+    @pytest.fixture
+    def store(self):
+        """Create in-memory ContextStore."""
+        conn = sqlite3.connect(":memory:")
+        conn.execute(
+            """
+            CREATE TABLE context_metadata (
+                layer TEXT PRIMARY KEY,
+                description TEXT,
+                retention_days INTEGER,
+                aggregation_source TEXT
+            )
+            """
+        )
+        conn.execute(
+            """
+            CREATE TABLE contexts (
+                layer TEXT,
+                timeframe TEXT,
+                key TEXT,
+                value TEXT,
+                created_at TEXT,
+                updated_at TEXT,
+                PRIMARY KEY (layer, timeframe, key)
+            )
+            """
+        )
+        conn.commit()
+        return ContextStore(conn)
+
+    def test_summarize_numeric_values(self, store):
+        """Test numeric value summarization."""
+        summarizer = ContextSummarizer(store)
+
+        values = [10.0, 20.0, 30.0, 40.0, 50.0]
+        stats = summarizer.summarize_numeric_values(values)
+
+        assert isinstance(stats, SummaryStats)
+        assert stats.count == 5
+        assert stats.mean == 30.0
+        assert stats.min == 10.0
+        assert stats.max == 50.0
+        assert stats.std is not None
+
+    def test_summarize_numeric_values_trend(self, store):
+        """Test trend detection in numeric values."""
+        summarizer = ContextSummarizer(store)
+
+        # Uptrend
+        values_up = [10.0, 15.0, 20.0, 25.0, 30.0, 35.0]
+        stats_up = summarizer.summarize_numeric_values(values_up)
+        assert stats_up.trend == "up"
+
+        # Downtrend
+        values_down = [35.0, 30.0, 25.0, 20.0, 15.0, 10.0]
+        stats_down = summarizer.summarize_numeric_values(values_down)
+        assert stats_down.trend == "down"
+
+        # Flat
+        values_flat = [20.0, 20.1, 19.9, 20.0, 20.1, 19.9]
+        stats_flat = summarizer.summarize_numeric_values(values_flat)
+        assert stats_flat.trend == "flat"
+
+    def test_summarize_layer(self, store):
+        """Test layer summarization."""
+        summarizer = ContextSummarizer(store)
+
+        # Add test data
+        store.set_context(ContextLayer.L6_DAILY, "2026-02-04", "price", 100.5)
+        store.set_context(ContextLayer.L6_DAILY, "2026-02-04", "volume", 1000000)
+
+        summary = summarizer.summarize_layer(ContextLayer.L6_DAILY)
+
+        # Should have summary
+        assert "total_entries" in summary
+        assert summary["total_entries"] > 0
+
+    def test_create_compact_summary(self, store):
+        """Test compact summary creation."""
+        summarizer = ContextSummarizer(store)
+
+        # Add test data
+        store.set_context(ContextLayer.L7_REALTIME, "2026-02-04", "price", 100.5)
+
+        layers = [ContextLayer.L7_REALTIME, ContextLayer.L6_DAILY]
+        summary = summarizer.create_compact_summary(layers, top_n_metrics=3)
+
+        # Should have summaries for layers
+        assert "L7_REALTIME" in summary
+
+    def test_format_summary_for_prompt(self, store):
+        """Test summary formatting for prompt."""
+        summarizer = ContextSummarizer(store)
+
+        summary = {
+            "L7_REALTIME": {
+                "price": {"avg": 100.5, "trend": "up"},
+                "volume": {"avg": 1000000, "trend": "flat"},
+            }
+        }
+
+        formatted = summarizer.format_summary_for_prompt(summary)
+
+        # Should be formatted string
+        assert isinstance(formatted, str)
+        assert "L7_REALTIME" in formatted
+        assert "100.5" in formatted or "100.50" in formatted
+
+
+# ============================================================================
+# Decision Cache Tests
+# ============================================================================
+
+
+class TestDecisionCache:
+    """Tests for DecisionCache."""
+
+    def test_cache_init(self):
+        """Test cache initialization."""
+        cache = DecisionCache(ttl_seconds=60, max_size=100)
+
+        assert cache.ttl_seconds == 60
+        assert cache.max_size == 100
+
+    def test_cache_miss(self):
+        """Test cache miss."""
+        cache = DecisionCache()
+
+        market_data = {"stock_code": "005930", "current_price": 75000}
+
+        decision = cache.get(market_data)
+
+        # Should be None (cache miss)
+        assert decision is None
+
+        metrics = cache.get_metrics()
+        assert metrics.cache_misses == 1
+        assert metrics.cache_hits == 0
+
+    def test_cache_hit(self):
+        """Test cache hit."""
+        cache = DecisionCache()
+
+        market_data = {"stock_code": "005930", "current_price": 75000}
+        decision = TradeDecision(action="HOLD", confidence=50, rationale="Test")
+
+        # Set cache
+        cache.set(market_data, decision)
+
+        # Get from cache
+        cached = cache.get(market_data)
+
+        assert cached is not None
+        assert cached.action == "HOLD"
+        assert cached.confidence == 50
+
+        metrics = cache.get_metrics()
+        assert metrics.cache_hits == 1
+
+    def test_cache_ttl_expiration(self):
+        """Test cache TTL expiration."""
+        cache = DecisionCache(ttl_seconds=1)  # 1 second TTL
+
+        market_data = {"stock_code": "005930", "current_price": 75000}
+        decision = TradeDecision(action="HOLD", confidence=50, rationale="Test")
+
+        # Set cache
+        cache.set(market_data, decision)
+
+        # Should hit immediately
+        cached = cache.get(market_data)
+        assert cached is not None
+
+        # Wait for expiration
+        time.sleep(1.1)
+
+        # Should miss after expiration
+        cached = cache.get(market_data)
+        assert cached is None
+
+    def test_cache_max_size(self):
+        """Test cache max size eviction."""
+        cache = DecisionCache(max_size=2)
+
+        decision = TradeDecision(action="HOLD", confidence=50, rationale="Test")
+
+        # Add 3 entries (exceeds max_size)
+        for i in range(3):
+            market_data = {"stock_code": f"00{i}", "current_price": 1000 * i}
+            cache.set(market_data, decision)
+
+        metrics = cache.get_metrics()
+
+        # Should have evicted 1 entry
+        assert metrics.total_entries == 2
+        assert metrics.evictions == 1
+
+    def test_invalidate_all(self):
+        """Test invalidate all cache entries."""
+        cache = DecisionCache()
+
+        decision = TradeDecision(action="HOLD", confidence=50, rationale="Test")
+
+        # Add entries
+        for i in range(3):
+            market_data = {"stock_code": f"00{i}", "current_price": 1000}
+            cache.set(market_data, decision)
+
+        # Invalidate all
+        count = cache.invalidate()
+
+        assert count == 3
+
+        metrics = cache.get_metrics()
+        assert metrics.total_entries == 0
+
+    def test_invalidate_by_stock(self):
+        """Test invalidate cache by stock code."""
+        cache = DecisionCache()
+
+        decision = TradeDecision(action="HOLD", confidence=50, rationale="Test")
+
+        # Add entries for different stocks
+        cache.set({"stock_code": "005930", "current_price": 75000}, decision)
+        cache.set({"stock_code": "000660", "current_price": 50000}, decision)
+
+        # Invalidate specific stock
+        count = cache.invalidate("005930")
+
+        assert count >= 1
+
+        # Other stock should still be cached
+        cached = cache.get({"stock_code": "000660", "current_price": 50000})
+        assert cached is not None
+
+    def test_cleanup_expired(self):
+        """Test cleanup of expired entries."""
+        cache = DecisionCache(ttl_seconds=1)
+
+        decision = TradeDecision(action="HOLD", confidence=50, rationale="Test")
+
+        # Add entry
+        cache.set({"stock_code": "005930", "current_price": 75000}, decision)
+
+        # Wait for expiration
+        time.sleep(1.1)
+
+        # Cleanup
+        count = cache.cleanup_expired()
+
+        assert count == 1
+
+        metrics = cache.get_metrics()
+        assert metrics.total_entries == 0
+
+    def test_should_cache_decision(self):
+        """Test decision caching criteria."""
+        cache = DecisionCache()
+
+        # HOLD decisions should be cached
+        hold_decision = TradeDecision(action="HOLD", confidence=50, rationale="Test")
+        assert cache.should_cache_decision(hold_decision) is True
+
+        # High confidence BUY should be cached
+        buy_decision = TradeDecision(action="BUY", confidence=95, rationale="Test")
+        assert cache.should_cache_decision(buy_decision) is True
+
+        # Low confidence BUY should not be cached
+        low_conf_buy = TradeDecision(action="BUY", confidence=60, rationale="Test")
+        assert cache.should_cache_decision(low_conf_buy) is False
+
+    def test_cache_hit_rate(self):
+        """Test cache hit rate calculation."""
+        cache = DecisionCache()
+
+        decision = TradeDecision(action="HOLD", confidence=50, rationale="Test")
+        market_data = {"stock_code": "005930", "current_price": 75000}
+
+        # First request (miss)
+        cache.get(market_data)
+
+        # Set cache
+        cache.set(market_data, decision)
+
+        # Second request (hit)
+        cache.get(market_data)
+
+        # Third request (hit)
+        cache.get(market_data)
+
+        metrics = cache.get_metrics()
+
+        # 1 miss, 2 hits out of 3 requests
+        assert metrics.total_requests == 3
+        assert metrics.cache_hits == 2
+        assert metrics.cache_misses == 1
+        assert metrics.hit_rate == pytest.approx(2 / 3)
+
+    def test_reset_metrics(self):
+        """Test metrics reset."""
+        cache = DecisionCache()
+
+        market_data = {"stock_code": "005930", "current_price": 75000}
+
+        # Generate some activity
+        cache.get(market_data)
+        cache.get(market_data)
+
+        # Reset
+        cache.reset_metrics()
+
+        metrics = cache.get_metrics()
+        assert metrics.total_requests == 0
+        assert metrics.cache_hits == 0
+        assert metrics.cache_misses == 0