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>

.env.example

@@ -21,8 +21,3 @@ RATE_LIMIT_RPS=10.0
 
 # Trading Mode (paper / live)
 MODE=paper
-
-# External Data APIs (optional — for enhanced decision-making)
-# NEWS_API_KEY=your_news_api_key_here
-# NEWS_API_PROVIDER=alphavantage
-# MARKET_DATA_API_KEY=your_market_data_key_here

.gitignore

@@ -174,7 +174,4 @@ cython_debug/
 # PyPI configuration file
 .pypirc
 
-# Data files (trade logs, databases)
-# But NOT src/data/ which contains source code
 data/
-!src/data/

src/brain/cache.py

@@ -13,8 +13,8 @@ import hashlib
 import json
 import logging
 import time
-from dataclasses import dataclass, field
+from dataclasses import dataclass
-from typing import Any, TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 if TYPE_CHECKING:
     from src.brain.gemini_client import TradeDecision
@@ -26,7 +26,7 @@ logger = logging.getLogger(__name__)
 class CacheEntry:
     """Cached decision with metadata."""
 
-    decision: "TradeDecision"
+    decision: TradeDecision
     cached_at: float  # Unix timestamp
     hit_count: int = 0
     market_data_hash: str = ""

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

@@ -6,8 +6,7 @@ JSON responses into validated TradeDecision objects.
 Includes token efficiency optimizations:
 - Prompt compression and abbreviation
 - Response caching for common scenarios
-- Smart context selection
+- Token usage tracking and metrics
-- Token usage tracking
 """
 
 from __future__ import annotations
@@ -15,17 +14,14 @@ from __future__ import annotations
 import json
 import logging
 import re
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import Any
 
 from google import genai
 
-from src.config import Settings
-from src.data.news_api import NewsAPI, NewsSentiment
-from src.data.economic_calendar import EconomicCalendar
-from src.data.market_data import MarketData
 from src.brain.cache import DecisionCache
 from src.brain.prompt_optimizer import PromptOptimizer
+from src.config import Settings
 
 logger = logging.getLogger(__name__)
 
@@ -49,9 +45,6 @@ class GeminiClient:
     def __init__(
         self,
         settings: Settings,
-        news_api: NewsAPI | None = None,
-        economic_calendar: EconomicCalendar | None = None,
-        market_data: MarketData | None = None,
         enable_cache: bool = True,
         enable_optimization: bool = True,
     ) -> None:
@@ -60,11 +53,6 @@ class GeminiClient:
         self._client = genai.Client(api_key=settings.GEMINI_API_KEY)
         self._model_name = settings.GEMINI_MODEL
 
-        # External data sources (optional)
-        self._news_api = news_api
-        self._economic_calendar = economic_calendar
-        self._market_data = market_data
-
         # Token efficiency features
         self._enable_cache = enable_cache
         self._enable_optimization = enable_optimization
@@ -76,139 +64,12 @@ class GeminiClient:
         self._total_decisions = 0
         self._total_cached_decisions = 0
 
-    # ------------------------------------------------------------------
-    # External Data Integration
-    # ------------------------------------------------------------------
-
-    async def _build_external_context(
-        self, stock_code: str, news_sentiment: NewsSentiment | None = None
-    ) -> str:
-        """Build external data context for the prompt.
-
-        Args:
-            stock_code: Stock ticker symbol
-            news_sentiment: Optional pre-fetched news sentiment
-
-        Returns:
-            Formatted string with external data context
-        """
-        context_parts: list[str] = []
-
-        # News sentiment
-        if news_sentiment is not None:
-            sentiment_str = self._format_news_sentiment(news_sentiment)
-            if sentiment_str:
-                context_parts.append(sentiment_str)
-        elif self._news_api is not None:
-            # Fetch news sentiment if not provided
-            try:
-                sentiment = await self._news_api.get_news_sentiment(stock_code)
-                if sentiment is not None:
-                    sentiment_str = self._format_news_sentiment(sentiment)
-                    if sentiment_str:
-                        context_parts.append(sentiment_str)
-            except Exception as exc:
-                logger.warning("Failed to fetch news sentiment: %s", exc)
-
-        # Economic events
-        if self._economic_calendar is not None:
-            events_str = self._format_economic_events(stock_code)
-            if events_str:
-                context_parts.append(events_str)
-
-        # Market indicators
-        if self._market_data is not None:
-            indicators_str = self._format_market_indicators()
-            if indicators_str:
-                context_parts.append(indicators_str)
-
-        if not context_parts:
-            return ""
-
-        return "EXTERNAL DATA:\n" + "\n\n".join(context_parts)
-
-    def _format_news_sentiment(self, sentiment: NewsSentiment) -> str:
-        """Format news sentiment for prompt."""
-        if sentiment.article_count == 0:
-            return ""
-
-        # Select top 3 most relevant articles
-        top_articles = sentiment.articles[:3]
-
-        lines = [
-            f"News Sentiment: {sentiment.avg_sentiment:.2f} "
-            f"(from {sentiment.article_count} articles)",
-        ]
-
-        for i, article in enumerate(top_articles, 1):
-            lines.append(
-                f"  {i}. [{article.source}] {article.title} "
-                f"(sentiment: {article.sentiment_score:.2f})"
-            )
-
-        return "\n".join(lines)
-
-    def _format_economic_events(self, stock_code: str) -> str:
-        """Format upcoming economic events for prompt."""
-        if self._economic_calendar is None:
-            return ""
-
-        # Check for upcoming high-impact events
-        upcoming = self._economic_calendar.get_upcoming_events(
-            days_ahead=7, min_impact="HIGH"
-        )
-
-        if upcoming.high_impact_count == 0:
-            return ""
-
-        lines = [
-            f"Upcoming High-Impact Events: {upcoming.high_impact_count} in next 7 days"
-        ]
-
-        if upcoming.next_major_event is not None:
-            event = upcoming.next_major_event
-            lines.append(
-                f"  Next: {event.name} ({event.event_type}) "
-                f"on {event.datetime.strftime('%Y-%m-%d')}"
-            )
-
-        # Check for earnings
-        earnings_date = self._economic_calendar.get_earnings_date(stock_code)
-        if earnings_date is not None:
-            lines.append(
-                f"  Earnings: {stock_code} on {earnings_date.strftime('%Y-%m-%d')}"
-            )
-
-        return "\n".join(lines)
-
-    def _format_market_indicators(self) -> str:
-        """Format market indicators for prompt."""
-        if self._market_data is None:
-            return ""
-
-        try:
-            indicators = self._market_data.get_market_indicators()
-            lines = [f"Market Sentiment: {indicators.sentiment.name}"]
-
-            # Add breadth if meaningful
-            if indicators.breadth.advance_decline_ratio != 1.0:
-                lines.append(
-                    f"Advance/Decline Ratio: {indicators.breadth.advance_decline_ratio:.2f}"
-                )
-
-            return "\n".join(lines)
-        except Exception as exc:
-            logger.warning("Failed to get market indicators: %s", exc)
-            return ""
-
     # ------------------------------------------------------------------
     # Prompt Construction
     # ------------------------------------------------------------------
 
-    async def build_prompt(
+    def build_prompt(self, market_data: dict[str, Any]) -> str:
-        self, market_data: dict[str, Any], news_sentiment: NewsSentiment | None = None
+        """Build a structured prompt from market data.
-    ) -> str:
-        """Build a structured prompt from market data and external sources.
 
         The prompt instructs Gemini to return valid JSON with action,
         confidence, and rationale fields.
@@ -236,60 +97,6 @@ class GeminiClient:
 
         market_info = "\n".join(market_info_lines)
 
-        # Add external data context if available
-        external_context = await self._build_external_context(
-            market_data["stock_code"], news_sentiment
-        )
-        if external_context:
-            market_info += f"\n\n{external_context}"
-
-        json_format = (
-            '{"action": "BUY"|"SELL"|"HOLD", '
-            '"confidence": <int 0-100>, "rationale": "<string>"}'
-        )
-        return (
-            f"You are a professional {market_name} trading analyst.\n"
-            "Analyze the following market data and decide whether to "
-            "BUY, SELL, or HOLD.\n\n"
-            f"{market_info}\n\n"
-            "You MUST respond with ONLY valid JSON in the following format:\n"
-            f"{json_format}\n\n"
-            "Rules:\n"
-            "- action must be exactly one of: BUY, SELL, HOLD\n"
-            "- confidence must be an integer from 0 to 100\n"
-            "- rationale must explain your reasoning concisely\n"
-            "- Do NOT wrap the JSON in markdown code blocks\n"
-        )
-
-    def build_prompt_sync(self, market_data: dict[str, Any]) -> str:
-        """Synchronous version of build_prompt (for backward compatibility).
-
-        This version does NOT include external data integration.
-        Use async build_prompt() for full functionality.
-        """
-        market_name = market_data.get("market_name", "Korean stock market")
-
-        # Build market data section dynamically based on available fields
-        market_info_lines = [
-            f"Market: {market_name}",
-            f"Stock Code: {market_data['stock_code']}",
-            f"Current Price: {market_data['current_price']}",
-        ]
-
-        # Add orderbook if available (domestic markets)
-        if "orderbook" in market_data:
-            market_info_lines.append(
-                f"Orderbook: {json.dumps(market_data['orderbook'], ensure_ascii=False)}"
-            )
-
-        # Add foreigner net if non-zero
-        if market_data.get("foreigner_net", 0) != 0:
-            market_info_lines.append(
-                f"Foreigner Net Buy/Sell: {market_data['foreigner_net']}"
-            )
-
-        market_info = "\n".join(market_info_lines)
-
         json_format = (
             '{"action": "BUY"|"SELL"|"HOLD", '
             '"confidence": <int 0-100>, "rationale": "<string>"}'
@@ -370,18 +177,8 @@ class GeminiClient:
     # API Call
     # ------------------------------------------------------------------
 
-    async def decide(
+    async def decide(self, market_data: dict[str, Any]) -> TradeDecision:
-        self, market_data: dict[str, Any], news_sentiment: NewsSentiment | None = None
+        """Build prompt, call Gemini, and return a parsed decision."""
-    ) -> TradeDecision:
-        """Build prompt, call Gemini, and return a parsed decision.
-
-        Args:
-            market_data: Market data dictionary with price, orderbook, etc.
-            news_sentiment: Optional pre-fetched news sentiment
-
-        Returns:
-            Parsed TradeDecision
-        """
         # Check cache first
         if self._cache:
             cached_decision = self._cache.get(market_data)
@@ -409,7 +206,7 @@ class GeminiClient:
         if self._enable_optimization:
             prompt = self._optimizer.build_compressed_prompt(market_data)
         else:
-            prompt = await self.build_prompt(market_data, news_sentiment)
+            prompt = self.build_prompt(market_data)
 
         # Estimate tokens
         token_count = self._optimizer.estimate_tokens(prompt)

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/config.py

@@ -19,11 +19,6 @@ class Settings(BaseSettings):
     GEMINI_API_KEY: str
     GEMINI_MODEL: str = "gemini-pro"
 
-    # External Data APIs (optional — for data-driven decisions)
-    NEWS_API_KEY: str | None = None
-    NEWS_API_PROVIDER: str = "alphavantage"  # "alphavantage" or "newsapi"
-    MARKET_DATA_API_KEY: str | None = None
-
     # Risk Management
     CIRCUIT_BREAKER_PCT: float = Field(default=-3.0, le=0.0)
     FAT_FINGER_PCT: float = Field(default=30.0, gt=0.0, le=100.0)

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/data/README.md

@@ -1,205 +0,0 @@
-# External Data Integration
-
-This module provides objective external data sources to enhance trading decisions beyond just market prices and user input.
-
-## Modules
-
-### `news_api.py` - News Sentiment Analysis
-
-Fetches real-time news for stocks with sentiment scoring.
-
-**Features:**
-- Alpha Vantage and NewsAPI.org support
-- Sentiment scoring (-1.0 to +1.0)
-- 5-minute caching to minimize API quota usage
-- Graceful fallback when API unavailable
-
-**Usage:**
-```python
-from src.data.news_api import NewsAPI
-
-# Initialize with API key
-news_api = NewsAPI(api_key="your_key", provider="alphavantage")
-
-# Fetch news sentiment
-sentiment = await news_api.get_news_sentiment("AAPL")
-if sentiment:
-    print(f"Average sentiment: {sentiment.avg_sentiment}")
-    for article in sentiment.articles[:3]:
-        print(f"{article.title} ({article.sentiment_score})")
-```
-
-### `economic_calendar.py` - Major Economic Events
-
-Tracks FOMC meetings, GDP releases, CPI, earnings calendars, and other market-moving events.
-
-**Features:**
-- High-impact event tracking (FOMC, GDP, CPI)
-- Earnings calendar per stock
-- Event proximity checking
-- Hardcoded major events for 2026 (no API required)
-
-**Usage:**
-```python
-from src.data.economic_calendar import EconomicCalendar
-
-calendar = EconomicCalendar()
-calendar.load_hardcoded_events()
-
-# Get upcoming high-impact events
-upcoming = calendar.get_upcoming_events(days_ahead=7, min_impact="HIGH")
-print(f"High-impact events: {upcoming.high_impact_count}")
-
-# Check if near earnings
-earnings_date = calendar.get_earnings_date("AAPL")
-if earnings_date:
-    print(f"Next earnings: {earnings_date}")
-
-# Check for high volatility period
-if calendar.is_high_volatility_period(hours_ahead=24):
-    print("High-impact event imminent!")
-```
-
-### `market_data.py` - Market Indicators
-
-Provides market breadth, sector performance, and sentiment indicators.
-
-**Features:**
-- Market sentiment levels (Fear & Greed equivalent)
-- Market breadth (advancing/declining stocks)
-- Sector performance tracking
-- Fear/Greed score calculation
-
-**Usage:**
-```python
-from src.data.market_data import MarketData
-
-market_data = MarketData(api_key="your_key")
-
-# Get market sentiment
-sentiment = market_data.get_market_sentiment()
-print(f"Market sentiment: {sentiment.name}")
-
-# Get full indicators
-indicators = market_data.get_market_indicators("US")
-print(f"Sentiment: {indicators.sentiment.name}")
-print(f"A/D Ratio: {indicators.breadth.advance_decline_ratio}")
-```
-
-## Integration with GeminiClient
-
-The external data sources are seamlessly integrated into the AI decision engine:
-
-```python
-from src.brain.gemini_client import GeminiClient
-from src.data.news_api import NewsAPI
-from src.data.economic_calendar import EconomicCalendar
-from src.data.market_data import MarketData
-from src.config import Settings
-
-settings = Settings()
-
-# Initialize data sources
-news_api = NewsAPI(api_key=settings.NEWS_API_KEY, provider=settings.NEWS_API_PROVIDER)
-calendar = EconomicCalendar()
-calendar.load_hardcoded_events()
-market_data = MarketData(api_key=settings.MARKET_DATA_API_KEY)
-
-# Create enhanced client
-client = GeminiClient(
-    settings,
-    news_api=news_api,
-    economic_calendar=calendar,
-    market_data=market_data
-)
-
-# Make decision with external context
-market_data_dict = {
-    "stock_code": "AAPL",
-    "current_price": 180.0,
-    "market_name": "US stock market"
-}
-
-decision = await client.decide(market_data_dict)
-```
-
-The external data is automatically included in the prompt sent to Gemini:
-
-```
-Market: US stock market
-Stock Code: AAPL
-Current Price: 180.0
-
-EXTERNAL DATA:
-News Sentiment: 0.85 (from 10 articles)
-  1. [Reuters] Apple hits record high (sentiment: 0.92)
-  2. [Bloomberg] Strong iPhone sales (sentiment: 0.78)
-  3. [CNBC] Tech sector rallying (sentiment: 0.85)
-
-Upcoming High-Impact Events: 2 in next 7 days
-  Next: FOMC Meeting (FOMC) on 2026-03-18
-  Earnings: AAPL on 2026-02-10
-
-Market Sentiment: GREED
-Advance/Decline Ratio: 2.35
-```
-
-## Configuration
-
-Add these to your `.env` file:
-
-```bash
-# External Data APIs (optional)
-NEWS_API_KEY=your_alpha_vantage_key
-NEWS_API_PROVIDER=alphavantage  # or "newsapi"
-MARKET_DATA_API_KEY=your_market_data_key
-```
-
-## API Recommendations
-
-### Alpha Vantage (News)
-- **Free tier:** 5 calls/min, 500 calls/day
-- **Pros:** Provides sentiment scores, no credit card required
-- **URL:** https://www.alphavantage.co/
-
-### NewsAPI.org
-- **Free tier:** 100 requests/day
-- **Pros:** Large news coverage, easy to use
-- **Cons:** No sentiment scores (we use keyword heuristics)
-- **URL:** https://newsapi.org/
-
-## Caching Strategy
-
-To minimize API quota usage:
-
-1. **News:** 5-minute TTL cache per stock
-2. **Economic Calendar:** Loaded once at startup (hardcoded events)
-3. **Market Data:** Fetched per decision (lightweight)
-
-## Graceful Degradation
-
-The system works gracefully without external data:
-
-- If no API keys provided → decisions work with just market prices
-- If API fails → decision continues without external context
-- If cache expired → attempts refetch, falls back to no data
-- Errors are logged but never block trading decisions
-
-## Testing
-
-All modules have comprehensive test coverage (81%+):
-
-```bash
-pytest tests/test_data_integration.py -v --cov=src/data
-```
-
-Tests use mocks to avoid requiring real API keys.
-
-## Future Enhancements
-
-- Twitter/X sentiment analysis
-- Reddit WallStreetBets sentiment
-- Options flow data
-- Insider trading activity
-- Analyst upgrades/downgrades
-- Real-time economic data APIs

src/data/__init__.py

@@ -1,5 +0,0 @@
-"""External data integration for objective decision-making."""
-
-from __future__ import annotations
-
-__all__ = ["NewsAPI", "EconomicCalendar", "MarketData"]

src/data/economic_calendar.py

@@ -1,219 +0,0 @@
-"""Economic calendar integration for major market events.
-
-Tracks FOMC meetings, GDP releases, CPI, earnings calendars, and other
-market-moving events.
-"""
-
-from __future__ import annotations
-
-import logging
-from dataclasses import dataclass
-from datetime import datetime, timedelta
-from typing import Any
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class EconomicEvent:
-    """Single economic event."""
-
-    name: str
-    event_type: str  # "FOMC", "GDP", "CPI", "EARNINGS", etc.
-    datetime: datetime
-    impact: str  # "HIGH", "MEDIUM", "LOW"
-    country: str
-    description: str
-
-
-@dataclass
-class UpcomingEvents:
-    """Collection of upcoming economic events."""
-
-    events: list[EconomicEvent]
-    high_impact_count: int
-    next_major_event: EconomicEvent | None
-
-
-class EconomicCalendar:
-    """Economic calendar with event tracking and impact scoring."""
-
-    def __init__(self, api_key: str | None = None) -> None:
-        """Initialize economic calendar.
-
-        Args:
-            api_key: API key for calendar provider (None for testing/hardcoded)
-        """
-        self._api_key = api_key
-        # For now, use hardcoded major events (can be extended with API)
-        self._events: list[EconomicEvent] = []
-
-    # ------------------------------------------------------------------
-    # Public API
-    # ------------------------------------------------------------------
-
-    def get_upcoming_events(
-        self, days_ahead: int = 7, min_impact: str = "MEDIUM"
-    ) -> UpcomingEvents:
-        """Get upcoming economic events within specified timeframe.
-
-        Args:
-            days_ahead: Number of days to look ahead
-            min_impact: Minimum impact level ("LOW", "MEDIUM", "HIGH")
-
-        Returns:
-            UpcomingEvents with filtered events
-        """
-        now = datetime.now()
-        end_date = now + timedelta(days=days_ahead)
-
-        # Filter events by timeframe and impact
-        upcoming = [
-            event
-            for event in self._events
-            if now <= event.datetime <= end_date
-            and self._impact_level(event.impact) >= self._impact_level(min_impact)
-        ]
-
-        # Sort by datetime
-        upcoming.sort(key=lambda e: e.datetime)
-
-        # Count high-impact events
-        high_impact_count = sum(1 for e in upcoming if e.impact == "HIGH")
-
-        # Get next major event
-        next_major = None
-        for event in upcoming:
-            if event.impact == "HIGH":
-                next_major = event
-                break
-
-        return UpcomingEvents(
-            events=upcoming,
-            high_impact_count=high_impact_count,
-            next_major_event=next_major,
-        )
-
-    def add_event(self, event: EconomicEvent) -> None:
-        """Add an economic event to the calendar."""
-        self._events.append(event)
-
-    def clear_events(self) -> None:
-        """Clear all events (useful for testing)."""
-        self._events.clear()
-
-    def get_earnings_date(self, stock_code: str) -> datetime | None:
-        """Get next earnings date for a stock.
-
-        Args:
-            stock_code: Stock ticker symbol
-
-        Returns:
-            Next earnings datetime or None if not found
-        """
-        now = datetime.now()
-        earnings_events = [
-            event
-            for event in self._events
-            if event.event_type == "EARNINGS"
-            and stock_code.upper() in event.name.upper()
-            and event.datetime > now
-        ]
-
-        if not earnings_events:
-            return None
-
-        # Return earliest upcoming earnings
-        earnings_events.sort(key=lambda e: e.datetime)
-        return earnings_events[0].datetime
-
-    def load_hardcoded_events(self) -> None:
-        """Load hardcoded major economic events for 2026.
-
-        This is a fallback when no API is available.
-        """
-        # Major FOMC meetings in 2026 (estimated)
-        fomc_dates = [
-            datetime(2026, 3, 18),
-            datetime(2026, 5, 6),
-            datetime(2026, 6, 17),
-            datetime(2026, 7, 29),
-            datetime(2026, 9, 16),
-            datetime(2026, 11, 4),
-            datetime(2026, 12, 16),
-        ]
-
-        for date in fomc_dates:
-            self.add_event(
-                EconomicEvent(
-                    name="FOMC Meeting",
-                    event_type="FOMC",
-                    datetime=date,
-                    impact="HIGH",
-                    country="US",
-                    description="Federal Reserve interest rate decision",
-                )
-            )
-
-        # Quarterly GDP releases (estimated)
-        gdp_dates = [
-            datetime(2026, 4, 28),
-            datetime(2026, 7, 30),
-            datetime(2026, 10, 29),
-        ]
-
-        for date in gdp_dates:
-            self.add_event(
-                EconomicEvent(
-                    name="US GDP Release",
-                    event_type="GDP",
-                    datetime=date,
-                    impact="HIGH",
-                    country="US",
-                    description="Quarterly GDP growth rate",
-                )
-            )
-
-        # Monthly CPI releases (12th of each month, estimated)
-        for month in range(1, 13):
-            try:
-                cpi_date = datetime(2026, month, 12)
-                self.add_event(
-                    EconomicEvent(
-                        name="US CPI Release",
-                        event_type="CPI",
-                        datetime=cpi_date,
-                        impact="HIGH",
-                        country="US",
-                        description="Consumer Price Index inflation data",
-                    )
-                )
-            except ValueError:
-                continue
-
-    # ------------------------------------------------------------------
-    # Helpers
-    # ------------------------------------------------------------------
-
-    def _impact_level(self, impact: str) -> int:
-        """Convert impact string to numeric level."""
-        levels = {"LOW": 1, "MEDIUM": 2, "HIGH": 3}
-        return levels.get(impact.upper(), 0)
-
-    def is_high_volatility_period(self, hours_ahead: int = 24) -> bool:
-        """Check if we're near a high-impact event.
-
-        Args:
-            hours_ahead: Number of hours to look ahead
-
-        Returns:
-            True if high-impact event is imminent
-        """
-        now = datetime.now()
-        threshold = now + timedelta(hours=hours_ahead)
-
-        for event in self._events:
-            if event.impact == "HIGH" and now <= event.datetime <= threshold:
-                return True
-
-        return False

src/data/market_data.py

@@ -1,198 +0,0 @@
-"""Additional market data indicators beyond basic price data.
-
-Provides market breadth, sector performance, and market sentiment indicators.
-"""
-
-from __future__ import annotations
-
-import logging
-from dataclasses import dataclass
-from enum import Enum
-
-logger = logging.getLogger(__name__)
-
-
-class MarketSentiment(Enum):
-    """Overall market sentiment levels."""
-
-    EXTREME_FEAR = 1
-    FEAR = 2
-    NEUTRAL = 3
-    GREED = 4
-    EXTREME_GREED = 5
-
-
-@dataclass
-class SectorPerformance:
-    """Performance metrics for a market sector."""
-
-    sector_name: str
-    daily_change_pct: float
-    weekly_change_pct: float
-    leader_stock: str  # Best performing stock in sector
-    laggard_stock: str  # Worst performing stock in sector
-
-
-@dataclass
-class MarketBreadth:
-    """Market breadth indicators."""
-
-    advancing_stocks: int
-    declining_stocks: int
-    unchanged_stocks: int
-    new_highs: int
-    new_lows: int
-    advance_decline_ratio: float
-
-
-@dataclass
-class MarketIndicators:
-    """Aggregated market indicators."""
-
-    sentiment: MarketSentiment
-    breadth: MarketBreadth
-    sector_performance: list[SectorPerformance]
-    vix_level: float | None  # Volatility index if available
-
-
-class MarketData:
-    """Market data provider for additional indicators."""
-
-    def __init__(self, api_key: str | None = None) -> None:
-        """Initialize market data provider.
-
-        Args:
-            api_key: API key for data provider (None for testing)
-        """
-        self._api_key = api_key
-
-    # ------------------------------------------------------------------
-    # Public API
-    # ------------------------------------------------------------------
-
-    def get_market_sentiment(self) -> MarketSentiment:
-        """Get current market sentiment level.
-
-        This is a simplified version. In production, this would integrate
-        with Fear & Greed Index or similar sentiment indicators.
-
-        Returns:
-            MarketSentiment enum value
-        """
-        # Default to neutral when API not available
-        if self._api_key is None:
-            logger.debug("No market data API key — returning NEUTRAL sentiment")
-            return MarketSentiment.NEUTRAL
-
-        # TODO: Integrate with actual sentiment API
-        return MarketSentiment.NEUTRAL
-
-    def get_market_breadth(self, market: str = "US") -> MarketBreadth | None:
-        """Get market breadth indicators.
-
-        Args:
-            market: Market code ("US", "KR", etc.)
-
-        Returns:
-            MarketBreadth object or None if unavailable
-        """
-        if self._api_key is None:
-            logger.debug("No market data API key — returning None for breadth")
-            return None
-
-        # TODO: Integrate with actual market breadth API
-        return None
-
-    def get_sector_performance(
-        self, market: str = "US"
-    ) -> list[SectorPerformance]:
-        """Get sector performance rankings.
-
-        Args:
-            market: Market code ("US", "KR", etc.)
-
-        Returns:
-            List of SectorPerformance objects, sorted by daily change
-        """
-        if self._api_key is None:
-            logger.debug("No market data API key — returning empty sector list")
-            return []
-
-        # TODO: Integrate with actual sector performance API
-        return []
-
-    def get_market_indicators(self, market: str = "US") -> MarketIndicators:
-        """Get aggregated market indicators.
-
-        Args:
-            market: Market code ("US", "KR", etc.)
-
-        Returns:
-            MarketIndicators with all available data
-        """
-        sentiment = self.get_market_sentiment()
-        breadth = self.get_market_breadth(market)
-        sectors = self.get_sector_performance(market)
-
-        # Default breadth if unavailable
-        if breadth is None:
-            breadth = MarketBreadth(
-                advancing_stocks=0,
-                declining_stocks=0,
-                unchanged_stocks=0,
-                new_highs=0,
-                new_lows=0,
-                advance_decline_ratio=1.0,
-            )
-
-        return MarketIndicators(
-            sentiment=sentiment,
-            breadth=breadth,
-            sector_performance=sectors,
-            vix_level=None,  # TODO: Add VIX integration
-        )
-
-    # ------------------------------------------------------------------
-    # Helper Methods
-    # ------------------------------------------------------------------
-
-    def calculate_fear_greed_score(
-        self, breadth: MarketBreadth, vix: float | None = None
-    ) -> int:
-        """Calculate a simple fear/greed score (0-100).
-
-        Args:
-            breadth: Market breadth data
-            vix: VIX level (optional)
-
-        Returns:
-            Score from 0 (extreme fear) to 100 (extreme greed)
-        """
-        # Start at neutral
-        score = 50
-
-        # Adjust based on advance/decline ratio
-        if breadth.advance_decline_ratio > 1.5:
-            score += 20
-        elif breadth.advance_decline_ratio > 1.0:
-            score += 10
-        elif breadth.advance_decline_ratio < 0.5:
-            score -= 20
-        elif breadth.advance_decline_ratio < 1.0:
-            score -= 10
-
-        # Adjust based on new highs/lows
-        if breadth.new_highs > breadth.new_lows * 2:
-            score += 15
-        elif breadth.new_lows > breadth.new_highs * 2:
-            score -= 15
-
-        # Adjust based on VIX if available
-        if vix is not None:
-            if vix > 30:  # High volatility = fear
-                score -= 15
-            elif vix < 15:  # Low volatility = complacency/greed
-                score += 10
-
-        # Clamp to 0-100
-        return max(0, min(100, score))

src/data/news_api.py

@@ -1,316 +0,0 @@
-"""News API integration with sentiment analysis and caching.
-
-Fetches real-time news for stocks using free-tier APIs (Alpha Vantage or NewsAPI).
-Includes 5-minute caching to minimize API quota usage.
-"""
-
-from __future__ import annotations
-
-import logging
-import time
-from dataclasses import dataclass
-from typing import Any
-
-import aiohttp
-
-logger = logging.getLogger(__name__)
-
-# Cache entries expire after 5 minutes
-CACHE_TTL_SECONDS = 300
-
-
-@dataclass
-class NewsArticle:
-    """Single news article with sentiment."""
-
-    title: str
-    summary: str
-    source: str
-    published_at: str
-    sentiment_score: float  # -1.0 (negative) to +1.0 (positive)
-    url: str
-
-
-@dataclass
-class NewsSentiment:
-    """Aggregated news sentiment for a stock."""
-
-    stock_code: str
-    articles: list[NewsArticle]
-    avg_sentiment: float  # Average sentiment across all articles
-    article_count: int
-    fetched_at: float  # Unix timestamp
-
-
-class NewsAPI:
-    """News API client with sentiment analysis and caching."""
-
-    def __init__(
-        self,
-        api_key: str | None = None,
-        provider: str = "alphavantage",
-        cache_ttl: int = CACHE_TTL_SECONDS,
-    ) -> None:
-        """Initialize NewsAPI client.
-
-        Args:
-            api_key: API key for the news provider (None for testing)
-            provider: News provider ("alphavantage" or "newsapi")
-            cache_ttl: Cache time-to-live in seconds
-        """
-        self._api_key = api_key
-        self._provider = provider
-        self._cache_ttl = cache_ttl
-        self._cache: dict[str, NewsSentiment] = {}
-
-    # ------------------------------------------------------------------
-    # Public API
-    # ------------------------------------------------------------------
-
-    async def get_news_sentiment(self, stock_code: str) -> NewsSentiment | None:
-        """Fetch news sentiment for a stock with caching.
-
-        Args:
-            stock_code: Stock ticker symbol (e.g., "AAPL", "005930")
-
-        Returns:
-            NewsSentiment object or None if fetch fails or API unavailable
-        """
-        # Check cache first
-        cached = self._get_from_cache(stock_code)
-        if cached is not None:
-            logger.debug("News cache hit for %s", stock_code)
-            return cached
-
-        # API key required for real requests
-        if self._api_key is None:
-            logger.warning("No news API key provided — returning None")
-            return None
-
-        # Fetch from API
-        try:
-            sentiment = await self._fetch_news(stock_code)
-            if sentiment is not None:
-                self._cache[stock_code] = sentiment
-            return sentiment
-        except Exception as exc:
-            logger.error("Failed to fetch news for %s: %s", stock_code, exc)
-            return None
-
-    def clear_cache(self) -> None:
-        """Clear the news cache (useful for testing)."""
-        self._cache.clear()
-
-    # ------------------------------------------------------------------
-    # Cache Management
-    # ------------------------------------------------------------------
-
-    def _get_from_cache(self, stock_code: str) -> NewsSentiment | None:
-        """Retrieve cached sentiment if not expired."""
-        if stock_code not in self._cache:
-            return None
-
-        cached = self._cache[stock_code]
-        age = time.time() - cached.fetched_at
-
-        if age > self._cache_ttl:
-            logger.debug("News cache expired for %s (age: %.1fs)", stock_code, age)
-            del self._cache[stock_code]
-            return None
-
-        return cached
-
-    # ------------------------------------------------------------------
-    # API Fetching
-    # ------------------------------------------------------------------
-
-    async def _fetch_news(self, stock_code: str) -> NewsSentiment | None:
-        """Fetch news from the provider API."""
-        if self._provider == "alphavantage":
-            return await self._fetch_alphavantage(stock_code)
-        elif self._provider == "newsapi":
-            return await self._fetch_newsapi(stock_code)
-        else:
-            logger.error("Unknown news provider: %s", self._provider)
-            return None
-
-    async def _fetch_alphavantage(self, stock_code: str) -> NewsSentiment | None:
-        """Fetch news from Alpha Vantage News Sentiment API."""
-        url = "https://www.alphavantage.co/query"
-        params = {
-            "function": "NEWS_SENTIMENT",
-            "tickers": stock_code,
-            "apikey": self._api_key,
-            "limit": 10,  # Fetch top 10 articles
-        }
-
-        try:
-            async with aiohttp.ClientSession() as session:
-                async with session.get(url, params=params, timeout=10) as resp:
-                    if resp.status != 200:
-                        logger.error(
-                            "Alpha Vantage API error: HTTP %d", resp.status
-                        )
-                        return None
-
-                    data = await resp.json()
-                    return self._parse_alphavantage_response(stock_code, data)
-
-        except Exception as exc:
-            logger.error("Alpha Vantage request failed: %s", exc)
-            return None
-
-    async def _fetch_newsapi(self, stock_code: str) -> NewsSentiment | None:
-        """Fetch news from NewsAPI.org."""
-        url = "https://newsapi.org/v2/everything"
-        params = {
-            "q": stock_code,
-            "apiKey": self._api_key,
-            "pageSize": 10,
-            "sortBy": "publishedAt",
-            "language": "en",
-        }
-
-        try:
-            async with aiohttp.ClientSession() as session:
-                async with session.get(url, params=params, timeout=10) as resp:
-                    if resp.status != 200:
-                        logger.error("NewsAPI error: HTTP %d", resp.status)
-                        return None
-
-                    data = await resp.json()
-                    return self._parse_newsapi_response(stock_code, data)
-
-        except Exception as exc:
-            logger.error("NewsAPI request failed: %s", exc)
-            return None
-
-    # ------------------------------------------------------------------
-    # Response Parsing
-    # ------------------------------------------------------------------
-
-    def _parse_alphavantage_response(
-        self, stock_code: str, data: dict[str, Any]
-    ) -> NewsSentiment | None:
-        """Parse Alpha Vantage API response."""
-        if "feed" not in data:
-            logger.warning("No 'feed' key in Alpha Vantage response")
-            return None
-
-        articles: list[NewsArticle] = []
-        for item in data["feed"]:
-            # Extract sentiment for this specific ticker
-            ticker_sentiment = self._extract_ticker_sentiment(item, stock_code)
-
-            article = NewsArticle(
-                title=item.get("title", ""),
-                summary=item.get("summary", "")[:200],  # Truncate long summaries
-                source=item.get("source", "Unknown"),
-                published_at=item.get("time_published", ""),
-                sentiment_score=ticker_sentiment,
-                url=item.get("url", ""),
-            )
-            articles.append(article)
-
-        if not articles:
-            return None
-
-        avg_sentiment = sum(a.sentiment_score for a in articles) / len(articles)
-
-        return NewsSentiment(
-            stock_code=stock_code,
-            articles=articles,
-            avg_sentiment=avg_sentiment,
-            article_count=len(articles),
-            fetched_at=time.time(),
-        )
-
-    def _extract_ticker_sentiment(
-        self, item: dict[str, Any], stock_code: str
-    ) -> float:
-        """Extract sentiment score for specific ticker from article."""
-        ticker_sentiments = item.get("ticker_sentiment", [])
-        for ts in ticker_sentiments:
-            if ts.get("ticker", "").upper() == stock_code.upper():
-                # Alpha Vantage provides sentiment_score as string
-                score_str = ts.get("ticker_sentiment_score", "0")
-                try:
-                    return float(score_str)
-                except ValueError:
-                    return 0.0
-
-        # Fallback to overall sentiment if ticker-specific not found
-        overall_sentiment = item.get("overall_sentiment_score", "0")
-        try:
-            return float(overall_sentiment)
-        except ValueError:
-            return 0.0
-
-    def _parse_newsapi_response(
-        self, stock_code: str, data: dict[str, Any]
-    ) -> NewsSentiment | None:
-        """Parse NewsAPI.org response.
-
-        Note: NewsAPI doesn't provide sentiment scores, so we use a
-        simple heuristic based on title keywords.
-        """
-        if data.get("status") != "ok" or "articles" not in data:
-            logger.warning("Invalid NewsAPI response")
-            return None
-
-        articles: list[NewsArticle] = []
-        for item in data["articles"]:
-            # Simple sentiment heuristic based on keywords
-            sentiment = self._estimate_sentiment_from_text(
-                item.get("title", "") + " " + item.get("description", "")
-            )
-
-            article = NewsArticle(
-                title=item.get("title", ""),
-                summary=item.get("description", "")[:200],
-                source=item.get("source", {}).get("name", "Unknown"),
-                published_at=item.get("publishedAt", ""),
-                sentiment_score=sentiment,
-                url=item.get("url", ""),
-            )
-            articles.append(article)
-
-        if not articles:
-            return None
-
-        avg_sentiment = sum(a.sentiment_score for a in articles) / len(articles)
-
-        return NewsSentiment(
-            stock_code=stock_code,
-            articles=articles,
-            avg_sentiment=avg_sentiment,
-            article_count=len(articles),
-            fetched_at=time.time(),
-        )
-
-    def _estimate_sentiment_from_text(self, text: str) -> float:
-        """Simple keyword-based sentiment estimation.
-
-        This is a fallback for APIs that don't provide sentiment scores.
-        Returns a score between -1.0 and +1.0.
-        """
-        text_lower = text.lower()
-
-        positive_keywords = [
-            "surge", "jump", "gain", "rise", "soar", "rally", "profit",
-            "growth", "upgrade", "beat", "strong", "bullish", "breakthrough",
-        ]
-        negative_keywords = [
-            "plunge", "fall", "drop", "decline", "crash", "loss", "weak",
-            "downgrade", "miss", "bearish", "concern", "risk", "warning",
-        ]
-
-        positive_count = sum(1 for kw in positive_keywords if kw in text_lower)
-        negative_count = sum(1 for kw in negative_keywords if kw in text_lower)
-
-        total = positive_count + negative_count
-        if total == 0:
-            return 0.0
-
-        # Normalize to -1.0 to +1.0 range
-        return (positive_count - negative_count) / total

src/evolution/optimizer.py

@@ -23,7 +23,7 @@ from google import genai
 
 from src.config import Settings
 from src.db import init_db
-from src.logging.decision_logger import DecisionLog, DecisionLogger
+from src.logging.decision_logger import DecisionLogger
 
 logger = logging.getLogger(__name__)
 

src/main.py

@@ -21,7 +21,7 @@ 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, CriticalityLevel
+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

tests/test_brain.py

@@ -126,7 +126,7 @@ class TestPromptConstruction:
             "orderbook": {"asks": [], "bids": []},
             "foreigner_net": -50000,
         }
-        prompt = client.build_prompt_sync(market_data)
+        prompt = client.build_prompt(market_data)
         assert "005930" in prompt
 
     def test_prompt_contains_price(self, settings):
@@ -137,7 +137,7 @@ class TestPromptConstruction:
             "orderbook": {"asks": [], "bids": []},
             "foreigner_net": -50000,
         }
-        prompt = client.build_prompt_sync(market_data)
+        prompt = client.build_prompt(market_data)
         assert "72000" in prompt
 
     def test_prompt_enforces_json_output_format(self, settings):
@@ -148,7 +148,7 @@ class TestPromptConstruction:
             "orderbook": {"asks": [], "bids": []},
             "foreigner_net": 0,
         }
-        prompt = client.build_prompt_sync(market_data)
+        prompt = client.build_prompt(market_data)
         assert "JSON" in prompt
         assert "action" in prompt
         assert "confidence" in prompt

tests/test_data_integration.py

@@ -1,673 +0,0 @@
-"""Tests for external data integration (news, economic calendar, market data)."""
-
-from __future__ import annotations
-
-import time
-from datetime import datetime, timedelta
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-from src.brain.gemini_client import GeminiClient
-from src.data.economic_calendar import EconomicCalendar, EconomicEvent
-from src.data.market_data import MarketBreadth, MarketData, MarketSentiment
-from src.data.news_api import NewsAPI, NewsArticle, NewsSentiment
-
-# ---------------------------------------------------------------------------
-# NewsAPI Tests
-# ---------------------------------------------------------------------------
-
-
-class TestNewsAPI:
-    """Test news API integration with caching."""
-
-    def test_news_api_init_without_key(self):
-        """NewsAPI should initialize without API key for testing."""
-        api = NewsAPI(api_key=None)
-        assert api._api_key is None
-        assert api._provider == "alphavantage"
-        assert api._cache_ttl == 300
-
-    def test_news_api_init_with_custom_settings(self):
-        """NewsAPI should accept custom provider and cache TTL."""
-        api = NewsAPI(api_key="test_key", provider="newsapi", cache_ttl=600)
-        assert api._api_key == "test_key"
-        assert api._provider == "newsapi"
-        assert api._cache_ttl == 600
-
-    @pytest.mark.asyncio
-    async def test_get_news_sentiment_without_api_key_returns_none(self):
-        """Without API key, get_news_sentiment should return None."""
-        api = NewsAPI(api_key=None)
-        result = await api.get_news_sentiment("AAPL")
-        assert result is None
-
-    @pytest.mark.asyncio
-    async def test_cache_hit_returns_cached_sentiment(self):
-        """Cache hit should return cached sentiment without API call."""
-        api = NewsAPI(api_key="test_key")
-
-        # Manually populate cache
-        cached_sentiment = NewsSentiment(
-            stock_code="AAPL",
-            articles=[],
-            avg_sentiment=0.5,
-            article_count=0,
-            fetched_at=time.time(),
-        )
-        api._cache["AAPL"] = cached_sentiment
-
-        result = await api.get_news_sentiment("AAPL")
-        assert result is cached_sentiment
-        assert result.stock_code == "AAPL"
-
-    @pytest.mark.asyncio
-    async def test_cache_expiry_triggers_refetch(self):
-        """Expired cache entry should trigger refetch."""
-        api = NewsAPI(api_key="test_key", cache_ttl=1)
-
-        # Add expired cache entry
-        expired_sentiment = NewsSentiment(
-            stock_code="AAPL",
-            articles=[],
-            avg_sentiment=0.5,
-            article_count=0,
-            fetched_at=time.time() - 10,  # 10 seconds ago
-        )
-        api._cache["AAPL"] = expired_sentiment
-
-        # Mock the fetch to avoid real API call
-        with patch.object(api, "_fetch_news", new_callable=AsyncMock) as mock_fetch:
-            mock_fetch.return_value = None
-            result = await api.get_news_sentiment("AAPL")
-
-            # Should have attempted refetch since cache expired
-            mock_fetch.assert_called_once_with("AAPL")
-
-    def test_clear_cache(self):
-        """clear_cache should empty the cache."""
-        api = NewsAPI(api_key="test_key")
-        api._cache["AAPL"] = NewsSentiment(
-            stock_code="AAPL",
-            articles=[],
-            avg_sentiment=0.0,
-            article_count=0,
-            fetched_at=time.time(),
-        )
-        assert len(api._cache) == 1
-
-        api.clear_cache()
-        assert len(api._cache) == 0
-
-    def test_parse_alphavantage_response_with_valid_data(self):
-        """Should parse Alpha Vantage response correctly."""
-        api = NewsAPI(api_key="test_key", provider="alphavantage")
-
-        mock_response = {
-            "feed": [
-                {
-                    "title": "Apple hits new high",
-                    "summary": "Apple stock surges to record levels",
-                    "source": "Reuters",
-                    "time_published": "2026-02-04T10:00:00",
-                    "url": "https://example.com/1",
-                    "ticker_sentiment": [
-                        {"ticker": "AAPL", "ticker_sentiment_score": "0.85"}
-                    ],
-                    "overall_sentiment_score": "0.75",
-                },
-                {
-                    "title": "Market volatility rises",
-                    "summary": "Tech stocks face headwinds",
-                    "source": "Bloomberg",
-                    "time_published": "2026-02-04T09:00:00",
-                    "url": "https://example.com/2",
-                    "ticker_sentiment": [
-                        {"ticker": "AAPL", "ticker_sentiment_score": "-0.3"}
-                    ],
-                    "overall_sentiment_score": "-0.2",
-                },
-            ]
-        }
-
-        result = api._parse_alphavantage_response("AAPL", mock_response)
-
-        assert result is not None
-        assert result.stock_code == "AAPL"
-        assert result.article_count == 2
-        assert len(result.articles) == 2
-        assert result.articles[0].title == "Apple hits new high"
-        assert result.articles[0].sentiment_score == 0.85
-        assert result.articles[1].sentiment_score == -0.3
-        # Average: (0.85 - 0.3) / 2 = 0.275
-        assert abs(result.avg_sentiment - 0.275) < 0.01
-
-    def test_parse_alphavantage_response_without_feed_returns_none(self):
-        """Should return None if 'feed' key is missing."""
-        api = NewsAPI(api_key="test_key", provider="alphavantage")
-        result = api._parse_alphavantage_response("AAPL", {})
-        assert result is None
-
-    def test_parse_newsapi_response_with_valid_data(self):
-        """Should parse NewsAPI.org response correctly."""
-        api = NewsAPI(api_key="test_key", provider="newsapi")
-
-        mock_response = {
-            "status": "ok",
-            "articles": [
-                {
-                    "title": "Apple stock surges",
-                    "description": "Strong earnings beat expectations",
-                    "source": {"name": "TechCrunch"},
-                    "publishedAt": "2026-02-04T10:00:00Z",
-                    "url": "https://example.com/1",
-                },
-                {
-                    "title": "Tech sector faces risks",
-                    "description": "Concerns over market downturn",
-                    "source": {"name": "CNBC"},
-                    "publishedAt": "2026-02-04T09:00:00Z",
-                    "url": "https://example.com/2",
-                },
-            ],
-        }
-
-        result = api._parse_newsapi_response("AAPL", mock_response)
-
-        assert result is not None
-        assert result.stock_code == "AAPL"
-        assert result.article_count == 2
-        assert len(result.articles) == 2
-        assert result.articles[0].title == "Apple stock surges"
-        assert result.articles[0].source == "TechCrunch"
-
-    def test_estimate_sentiment_from_text_positive(self):
-        """Should detect positive sentiment from keywords."""
-        api = NewsAPI()
-        text = "Stock price surges with strong profit growth and upgrade"
-        sentiment = api._estimate_sentiment_from_text(text)
-        assert sentiment > 0.5
-
-    def test_estimate_sentiment_from_text_negative(self):
-        """Should detect negative sentiment from keywords."""
-        api = NewsAPI()
-        text = "Stock plunges on weak earnings, downgrade warning"
-        sentiment = api._estimate_sentiment_from_text(text)
-        assert sentiment < -0.5
-
-    def test_estimate_sentiment_from_text_neutral(self):
-        """Should return neutral sentiment without keywords."""
-        api = NewsAPI()
-        text = "Company announces quarterly report"
-        sentiment = api._estimate_sentiment_from_text(text)
-        assert abs(sentiment) < 0.1
-
-
-# ---------------------------------------------------------------------------
-# EconomicCalendar Tests
-# ---------------------------------------------------------------------------
-
-
-class TestEconomicCalendar:
-    """Test economic calendar functionality."""
-
-    def test_economic_calendar_init(self):
-        """EconomicCalendar should initialize correctly."""
-        calendar = EconomicCalendar(api_key="test_key")
-        assert calendar._api_key == "test_key"
-        assert len(calendar._events) == 0
-
-    def test_add_event(self):
-        """Should be able to add events to calendar."""
-        calendar = EconomicCalendar()
-        event = EconomicEvent(
-            name="FOMC Meeting",
-            event_type="FOMC",
-            datetime=datetime(2026, 3, 18),
-            impact="HIGH",
-            country="US",
-            description="Interest rate decision",
-        )
-        calendar.add_event(event)
-        assert len(calendar._events) == 1
-        assert calendar._events[0].name == "FOMC Meeting"
-
-    def test_get_upcoming_events_filters_by_timeframe(self):
-        """Should only return events within specified timeframe."""
-        calendar = EconomicCalendar()
-
-        # Add events at different times
-        now = datetime.now()
-        calendar.add_event(
-            EconomicEvent(
-                name="Event Tomorrow",
-                event_type="GDP",
-                datetime=now + timedelta(days=1),
-                impact="HIGH",
-                country="US",
-                description="Test event",
-            )
-        )
-        calendar.add_event(
-            EconomicEvent(
-                name="Event Next Month",
-                event_type="CPI",
-                datetime=now + timedelta(days=30),
-                impact="HIGH",
-                country="US",
-                description="Test event",
-            )
-        )
-
-        # Get events for next 7 days
-        upcoming = calendar.get_upcoming_events(days_ahead=7, min_impact="HIGH")
-        assert upcoming.high_impact_count == 1
-        assert upcoming.events[0].name == "Event Tomorrow"
-
-    def test_get_upcoming_events_filters_by_impact(self):
-        """Should filter events by minimum impact level."""
-        calendar = EconomicCalendar()
-
-        now = datetime.now()
-        calendar.add_event(
-            EconomicEvent(
-                name="High Impact Event",
-                event_type="FOMC",
-                datetime=now + timedelta(days=1),
-                impact="HIGH",
-                country="US",
-                description="Test",
-            )
-        )
-        calendar.add_event(
-            EconomicEvent(
-                name="Low Impact Event",
-                event_type="OTHER",
-                datetime=now + timedelta(days=1),
-                impact="LOW",
-                country="US",
-                description="Test",
-            )
-        )
-
-        # Filter for HIGH impact only
-        upcoming = calendar.get_upcoming_events(days_ahead=7, min_impact="HIGH")
-        assert upcoming.high_impact_count == 1
-        assert upcoming.events[0].name == "High Impact Event"
-
-        # Filter for MEDIUM and above (should still get HIGH)
-        upcoming = calendar.get_upcoming_events(days_ahead=7, min_impact="MEDIUM")
-        assert len(upcoming.events) == 1
-
-        # Filter for LOW and above (should get both)
-        upcoming = calendar.get_upcoming_events(days_ahead=7, min_impact="LOW")
-        assert len(upcoming.events) == 2
-
-    def test_get_earnings_date_returns_next_earnings(self):
-        """Should return next earnings date for a stock."""
-        calendar = EconomicCalendar()
-
-        now = datetime.now()
-        earnings_date = now + timedelta(days=5)
-
-        calendar.add_event(
-            EconomicEvent(
-                name="AAPL Earnings",
-                event_type="EARNINGS",
-                datetime=earnings_date,
-                impact="HIGH",
-                country="US",
-                description="Apple quarterly earnings",
-            )
-        )
-
-        result = calendar.get_earnings_date("AAPL")
-        assert result == earnings_date
-
-    def test_get_earnings_date_returns_none_if_not_found(self):
-        """Should return None if no earnings found for stock."""
-        calendar = EconomicCalendar()
-        result = calendar.get_earnings_date("UNKNOWN")
-        assert result is None
-
-    def test_load_hardcoded_events(self):
-        """Should load hardcoded major economic events."""
-        calendar = EconomicCalendar()
-        calendar.load_hardcoded_events()
-
-        # Should have multiple events (FOMC, GDP, CPI)
-        assert len(calendar._events) > 10
-
-        # Check for FOMC events
-        fomc_events = [e for e in calendar._events if e.event_type == "FOMC"]
-        assert len(fomc_events) > 0
-
-        # Check for GDP events
-        gdp_events = [e for e in calendar._events if e.event_type == "GDP"]
-        assert len(gdp_events) > 0
-
-        # Check for CPI events
-        cpi_events = [e for e in calendar._events if e.event_type == "CPI"]
-        assert len(cpi_events) == 12  # Monthly CPI releases
-
-    def test_is_high_volatility_period_returns_true_near_high_impact(self):
-        """Should return True if high-impact event is within threshold."""
-        calendar = EconomicCalendar()
-
-        now = datetime.now()
-        calendar.add_event(
-            EconomicEvent(
-                name="FOMC Meeting",
-                event_type="FOMC",
-                datetime=now + timedelta(hours=12),
-                impact="HIGH",
-                country="US",
-                description="Test",
-            )
-        )
-
-        assert calendar.is_high_volatility_period(hours_ahead=24) is True
-
-    def test_is_high_volatility_period_returns_false_when_no_events(self):
-        """Should return False if no high-impact events nearby."""
-        calendar = EconomicCalendar()
-        assert calendar.is_high_volatility_period(hours_ahead=24) is False
-
-    def test_clear_events(self):
-        """Should clear all events."""
-        calendar = EconomicCalendar()
-        calendar.add_event(
-            EconomicEvent(
-                name="Test",
-                event_type="TEST",
-                datetime=datetime.now(),
-                impact="LOW",
-                country="US",
-                description="Test",
-            )
-        )
-        assert len(calendar._events) == 1
-
-        calendar.clear_events()
-        assert len(calendar._events) == 0
-
-
-# ---------------------------------------------------------------------------
-# MarketData Tests
-# ---------------------------------------------------------------------------
-
-
-class TestMarketData:
-    """Test market data indicators."""
-
-    def test_market_data_init(self):
-        """MarketData should initialize correctly."""
-        data = MarketData(api_key="test_key")
-        assert data._api_key == "test_key"
-
-    def test_get_market_sentiment_without_api_key_returns_neutral(self):
-        """Without API key, should return NEUTRAL sentiment."""
-        data = MarketData(api_key=None)
-        sentiment = data.get_market_sentiment()
-        assert sentiment == MarketSentiment.NEUTRAL
-
-    def test_get_market_breadth_without_api_key_returns_none(self):
-        """Without API key, should return None for breadth."""
-        data = MarketData(api_key=None)
-        breadth = data.get_market_breadth()
-        assert breadth is None
-
-    def test_get_sector_performance_without_api_key_returns_empty(self):
-        """Without API key, should return empty list."""
-        data = MarketData(api_key=None)
-        sectors = data.get_sector_performance()
-        assert sectors == []
-
-    def test_get_market_indicators_returns_defaults_without_api(self):
-        """Should return default indicators without API key."""
-        data = MarketData(api_key=None)
-        indicators = data.get_market_indicators()
-
-        assert indicators.sentiment == MarketSentiment.NEUTRAL
-        assert indicators.breadth.advance_decline_ratio == 1.0
-        assert indicators.sector_performance == []
-        assert indicators.vix_level is None
-
-    def test_calculate_fear_greed_score_neutral_baseline(self):
-        """Should return neutral score (50) for balanced market."""
-        data = MarketData()
-        breadth = MarketBreadth(
-            advancing_stocks=500,
-            declining_stocks=500,
-            unchanged_stocks=100,
-            new_highs=50,
-            new_lows=50,
-            advance_decline_ratio=1.0,
-        )
-
-        score = data.calculate_fear_greed_score(breadth)
-        assert score == 50
-
-    def test_calculate_fear_greed_score_greedy_market(self):
-        """Should return high score for greedy market conditions."""
-        data = MarketData()
-        breadth = MarketBreadth(
-            advancing_stocks=800,
-            declining_stocks=200,
-            unchanged_stocks=100,
-            new_highs=100,
-            new_lows=10,
-            advance_decline_ratio=4.0,
-        )
-
-        score = data.calculate_fear_greed_score(breadth, vix=12.0)
-        assert score > 70
-
-    def test_calculate_fear_greed_score_fearful_market(self):
-        """Should return low score for fearful market conditions."""
-        data = MarketData()
-        breadth = MarketBreadth(
-            advancing_stocks=200,
-            declining_stocks=800,
-            unchanged_stocks=100,
-            new_highs=10,
-            new_lows=100,
-            advance_decline_ratio=0.25,
-        )
-
-        score = data.calculate_fear_greed_score(breadth, vix=35.0)
-        assert score < 30
-
-
-# ---------------------------------------------------------------------------
-# GeminiClient Integration Tests
-# ---------------------------------------------------------------------------
-
-
-class TestGeminiClientWithExternalData:
-    """Test GeminiClient integration with external data sources."""
-
-    def test_gemini_client_accepts_optional_data_sources(self, settings):
-        """GeminiClient should accept optional external data sources."""
-        news_api = NewsAPI(api_key="test_key")
-        calendar = EconomicCalendar()
-        market_data = MarketData()
-
-        client = GeminiClient(
-            settings,
-            news_api=news_api,
-            economic_calendar=calendar,
-            market_data=market_data,
-        )
-
-        assert client._news_api is news_api
-        assert client._economic_calendar is calendar
-        assert client._market_data is market_data
-
-    def test_gemini_client_works_without_external_data(self, settings):
-        """GeminiClient should work without external data sources."""
-        client = GeminiClient(settings)
-        assert client._news_api is None
-        assert client._economic_calendar is None
-        assert client._market_data is None
-
-    @pytest.mark.asyncio
-    async def test_build_prompt_includes_news_sentiment(self, settings):
-        """build_prompt should include news sentiment when available."""
-        client = GeminiClient(settings)
-
-        market_data = {
-            "stock_code": "AAPL",
-            "current_price": 180.0,
-            "market_name": "US stock market",
-        }
-
-        sentiment = NewsSentiment(
-            stock_code="AAPL",
-            articles=[
-                NewsArticle(
-                    title="Apple hits record high",
-                    summary="Strong earnings",
-                    source="Reuters",
-                    published_at="2026-02-04",
-                    sentiment_score=0.85,
-                    url="https://example.com",
-                )
-            ],
-            avg_sentiment=0.85,
-            article_count=1,
-            fetched_at=time.time(),
-        )
-
-        prompt = await client.build_prompt(market_data, news_sentiment=sentiment)
-
-        assert "AAPL" in prompt
-        assert "180.0" in prompt
-        assert "EXTERNAL DATA" in prompt
-        assert "News Sentiment" in prompt
-        assert "0.85" in prompt
-        assert "Apple hits record high" in prompt
-
-    @pytest.mark.asyncio
-    async def test_build_prompt_with_economic_events(self, settings):
-        """build_prompt should include upcoming economic events."""
-        calendar = EconomicCalendar()
-        now = datetime.now()
-        calendar.add_event(
-            EconomicEvent(
-                name="FOMC Meeting",
-                event_type="FOMC",
-                datetime=now + timedelta(days=2),
-                impact="HIGH",
-                country="US",
-                description="Interest rate decision",
-            )
-        )
-
-        client = GeminiClient(settings, economic_calendar=calendar)
-
-        market_data = {
-            "stock_code": "AAPL",
-            "current_price": 180.0,
-            "market_name": "US stock market",
-        }
-
-        prompt = await client.build_prompt(market_data)
-
-        assert "EXTERNAL DATA" in prompt
-        assert "High-Impact Events" in prompt
-        assert "FOMC Meeting" in prompt
-
-    @pytest.mark.asyncio
-    async def test_build_prompt_with_market_indicators(self, settings):
-        """build_prompt should include market sentiment indicators."""
-        market_data_provider = MarketData(api_key="test_key")
-
-        # Mock the get_market_indicators to return test data
-        with patch.object(market_data_provider, "get_market_indicators") as mock:
-            mock.return_value = MagicMock(
-                sentiment=MarketSentiment.EXTREME_GREED,
-                breadth=MagicMock(advance_decline_ratio=2.5),
-            )
-
-            client = GeminiClient(settings, market_data=market_data_provider)
-
-            market_data = {
-                "stock_code": "AAPL",
-                "current_price": 180.0,
-                "market_name": "US stock market",
-            }
-
-            prompt = await client.build_prompt(market_data)
-
-            assert "EXTERNAL DATA" in prompt
-            assert "Market Sentiment" in prompt
-            assert "EXTREME_GREED" in prompt
-
-    @pytest.mark.asyncio
-    async def test_build_prompt_graceful_when_no_external_data(self, settings):
-        """build_prompt should work gracefully without external data."""
-        client = GeminiClient(settings)
-
-        market_data = {
-            "stock_code": "AAPL",
-            "current_price": 180.0,
-            "market_name": "US stock market",
-        }
-
-        prompt = await client.build_prompt(market_data)
-
-        assert "AAPL" in prompt
-        assert "180.0" in prompt
-        # Should NOT have external data section
-        assert "EXTERNAL DATA" not in prompt
-
-    def test_build_prompt_sync_backward_compatibility(self, settings):
-        """build_prompt_sync should maintain backward compatibility."""
-        client = GeminiClient(settings)
-
-        market_data = {
-            "stock_code": "005930",
-            "current_price": 72000,
-            "orderbook": {"asks": [], "bids": []},
-            "foreigner_net": -50000,
-        }
-
-        prompt = client.build_prompt_sync(market_data)
-
-        assert "005930" in prompt
-        assert "72000" in prompt
-        assert "JSON" in prompt
-        # Sync version should NOT have external data
-        assert "EXTERNAL DATA" not in prompt
-
-    @pytest.mark.asyncio
-    async def test_decide_with_news_sentiment_parameter(self, settings):
-        """decide should accept optional news_sentiment parameter."""
-        client = GeminiClient(settings)
-
-        market_data = {
-            "stock_code": "AAPL",
-            "current_price": 180.0,
-            "market_name": "US stock market",
-        }
-
-        sentiment = NewsSentiment(
-            stock_code="AAPL",
-            articles=[],
-            avg_sentiment=0.5,
-            article_count=1,
-            fetched_at=time.time(),
-        )
-
-        # Mock the Gemini API call
-        with patch.object(client._client.aio.models, "generate_content", new_callable=AsyncMock) as mock_gen:
-            mock_response = MagicMock()
-            mock_response.text = '{"action": "BUY", "confidence": 85, "rationale": "Good news"}'
-            mock_gen.return_value = mock_response
-
-            decision = await client.decide(market_data, news_sentiment=sentiment)
-
-            assert decision.action == "BUY"
-            assert decision.confidence == 85
-            mock_gen.assert_called_once()

tests/test_evolution.py

@@ -11,15 +11,15 @@ from __future__ import annotations
 import json
 import sqlite3
 import tempfile
-from datetime import UTC, datetime, timedelta
+from datetime import UTC, datetime
 from pathlib import Path
-from unittest.mock import AsyncMock, MagicMock, Mock, patch
+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, ABTestResult, StrategyPerformance
+from src.evolution.ab_test import ABTester
 from src.evolution.optimizer import EvolutionOptimizer
 from src.evolution.performance_tracker import (
     PerformanceDashboard,
@@ -28,7 +28,6 @@ from src.evolution.performance_tracker import (
 )
 from src.logging.decision_logger import DecisionLogger
 
-
 # ------------------------------------------------------------------
 # Fixtures
 # ------------------------------------------------------------------

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