feat: wire evolution loop into market close flow (issue #95)

Run EvolutionOptimizer.evolve() at US market close, skip for other
markets, and notify via Telegram when a strategy PR is generated.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

CLAUDE.md

@@ -45,6 +45,39 @@ Get real-time alerts for trades, circuit breakers, and system events via Telegra
 
 **Fail-safe**: Notifications never crash the trading system. Missing credentials or API errors are logged but trading continues normally.
 
+## Smart Volatility Scanner (Optional)
+
+Python-first filtering pipeline that reduces Gemini API calls by pre-filtering stocks using technical indicators.
+
+### How It Works
+
+1. **Fetch Rankings** — KIS API volume surge rankings (top 30 stocks)
+2. **Python Filter** — RSI + volume ratio calculations (no AI)
+   - Volume > 200% of previous day
+   - RSI(14) < 30 (oversold) OR RSI(14) > 70 (momentum)
+3. **AI Judgment** — Only qualified candidates (1-3 stocks) sent to Gemini
+
+### Configuration
+
+Add to `.env` (optional, has sensible defaults):
+```bash
+RSI_OVERSOLD_THRESHOLD=30    # 0-50, default 30
+RSI_MOMENTUM_THRESHOLD=70    # 50-100, default 70
+VOL_MULTIPLIER=2.0           # Volume threshold (2.0 = 200%)
+SCANNER_TOP_N=3              # Max candidates per scan
+```
+
+### Benefits
+
+- **Reduces API costs** — Process 1-3 stocks instead of 20-30
+- **Python-based filtering** — Fast technical analysis before AI
+- **Evolution-ready** — Selection context logged for strategy optimization
+- **Fault-tolerant** — Falls back to static watchlist on API failure
+
+### Realtime Mode Only
+
+Smart Scanner runs in `TRADE_MODE=realtime` only. Daily mode uses static watchlists for batch efficiency.
+
 ## Documentation
 
 - **[Workflow Guide](docs/workflow.md)** — Git workflow policy and agent-based development
@@ -75,6 +108,7 @@ User requirements and feedback are tracked in [docs/requirements-log.md](docs/re
 
 ```
 src/
+├── analysis/        # Technical analysis (RSI, volatility, smart scanner)
 ├── broker/          # KIS API client (domestic + overseas)
 ├── brain/           # Gemini AI decision engine
 ├── core/            # Risk manager (READ-ONLY)
@@ -85,7 +119,7 @@ src/
 ├── main.py          # Trading loop orchestrator
 └── config.py        # Settings (from .env)
 
-tests/               # 273 tests across 13 files
+tests/               # 343 tests across 14 files
 docs/                # Extended documentation
 ```
 

docs/agent-constraints.md

@@ -0,0 +1,45 @@
+# Agent Constraints
+
+This document records **persistent behavioral constraints** for agents working on this repository.
+It is distinct from `docs/requirements-log.md`, which records **project/product requirements**.
+
+## Scope
+
+- Applies to all AI agents and automation that modify this repo.
+- Supplements (does not replace) `docs/agents.md` and `docs/workflow.md`.
+
+## Persistent Rules
+
+1. **Workflow enforcement**
+   - Follow `docs/workflow.md` for all changes.
+   - Create a Gitea issue before any code or documentation change.
+   - Work on a feature branch `feature/issue-{N}-{short-description}` and open a PR.
+   - Never commit directly to `main`.
+
+2. **Document-first routing**
+   - When performing work, consult relevant `docs/` files *before* making changes.
+   - Route decisions to the documented policy whenever applicable.
+   - If guidance conflicts, prefer the stricter/safety-first rule and note it in the PR.
+
+3. **Docs with code**
+   - Any code change must be accompanied by relevant documentation updates.
+   - If no doc update is needed, state the reason explicitly in the PR.
+
+4. **Session-persistent user constraints**
+   - If the user requests that a behavior should persist across sessions, record it here
+     (or in a dedicated policy doc) and reference it when working.
+   - Keep entries short and concrete, with dates.
+
+## Change Control
+
+- Changes to this file follow the same workflow as code changes.
+- Keep the history chronological and minimize rewording of existing entries.
+
+## History
+
+### 2026-02-08
+
+- Always enforce Gitea workflow: issue -> feature branch -> PR before changes.
+- When work requires guidance, consult the relevant `docs/` policies first.
+- Any code change must be accompanied by relevant documentation updates.
+- Persist user constraints across sessions by recording them in this document.

docs/architecture.md

@@ -64,7 +64,39 @@ High-frequency trading with individual stock analysis:
 - `get_open_markets()` returns currently active markets
 - `get_next_market_open()` finds next market to open and when
 
-### 2. Brain (`src/brain/gemini_client.py`)
+**New API Methods** (added in v0.9.0):
+- `fetch_market_rankings()` — Fetch volume surge rankings from KIS API
+- `get_daily_prices()` — Fetch OHLCV history for technical analysis
+
+### 2. Analysis (`src/analysis/`)
+
+**VolatilityAnalyzer** (`volatility.py`) — Technical indicator calculations
+
+- ATR (Average True Range) for volatility measurement
+- RSI (Relative Strength Index) using Wilder's smoothing method
+- Price change percentages across multiple timeframes
+- Volume surge ratios and price-volume divergence
+- Momentum scoring (0-100 scale)
+- Breakout/breakdown pattern detection
+
+**SmartVolatilityScanner** (`smart_scanner.py`) — Python-first filtering pipeline
+
+- **Step 1**: Fetch volume rankings from KIS API (top 30 stocks)
+- **Step 2**: Calculate RSI and volume ratio for each stock
+- **Step 3**: Apply filters:
+  - Volume ratio >= `VOL_MULTIPLIER` (default 2.0x previous day)
+  - RSI < `RSI_OVERSOLD_THRESHOLD` (30) OR RSI > `RSI_MOMENTUM_THRESHOLD` (70)
+- **Step 4**: Score candidates by RSI extremity (60%) + volume surge (40%)
+- **Step 5**: Return top N candidates (default 3) for AI analysis
+- **Fallback**: Uses static watchlist if ranking API unavailable
+- **Realtime mode only**: Daily mode uses batch processing for API efficiency
+
+**Benefits:**
+- Reduces Gemini API calls from 20-30 stocks to 1-3 qualified candidates
+- Fast Python-based filtering before expensive AI judgment
+- Logs selection context (RSI, volume_ratio, signal, score) for Evolution system
+
+### 3. Brain (`src/brain/gemini_client.py`)
 
 **GeminiClient** — AI decision engine powered by Google Gemini
 
@@ -74,7 +106,7 @@ High-frequency trading with individual stock analysis:
 - Falls back to safe HOLD on any parse/API error
 - Handles markdown-wrapped JSON, malformed responses, invalid actions
 
-### 3. Risk Manager (`src/core/risk_manager.py`)
+### 4. Risk Manager (`src/core/risk_manager.py`)
 
 **RiskManager** — Safety circuit breaker and order validation
 
@@ -86,7 +118,7 @@ High-frequency trading with individual stock analysis:
 - **Fat-Finger Protection**: Rejects orders exceeding 30% of available cash
   - Must always be enforced, cannot be disabled
 
-### 4. Notifications (`src/notifications/telegram_client.py`)
+### 5. Notifications (`src/notifications/telegram_client.py`)
 
 **TelegramClient** — Real-time event notifications via Telegram Bot API
 
@@ -105,7 +137,7 @@ High-frequency trading with individual stock analysis:
 
 **Setup:** See [src/notifications/README.md](../src/notifications/README.md) for bot creation and configuration.
 
-### 5. Evolution (`src/evolution/optimizer.py`)
+### 6. Evolution (`src/evolution/optimizer.py`)
 
 **StrategyOptimizer** — Self-improvement loop
 
@@ -117,9 +149,11 @@ High-frequency trading with individual stock analysis:
 
 ## Data Flow
 
+### Realtime Mode (with Smart Scanner)
+
 ```
 ┌─────────────────────────────────────────────────────────────┐
-│ Main Loop (60s cycle per stock, per market)                │
+│ Main Loop (60s cycle per market)                           │
 └─────────────────────────────────────────────────────────────┘
                            │
                            ▼
@@ -132,6 +166,21 @@ High-frequency trading with individual stock analysis:
                            │
                            ▼
         ┌──────────────────────────────────┐
+        │ Smart Scanner (Python-first)      │
+        │ - Fetch volume rankings (KIS)    │
+        │ - Get 20d price history per stock│
+        │ - Calculate RSI(14) + vol ratio  │
+        │ - Filter: vol>2x AND RSI extreme │
+        │ - Return top 3 qualified stocks  │
+        └──────────────────┬────────────────┘
+                           │
+                           ▼
+        ┌──────────────────────────────────┐
+        │ For Each Qualified Candidate      │
+        └──────────────────┬────────────────┘
+                           │
+                           ▼
+        ┌──────────────────────────────────┐
         │ Broker: Fetch Market Data        │
         │ - Domestic: orderbook + balance  │
         │ - Overseas: price + balance      │
@@ -145,7 +194,7 @@ High-frequency trading with individual stock analysis:
                            │
                            ▼
         ┌──────────────────────────────────┐
-        │ Brain: Get Decision               │
+        │ Brain: Get Decision (AI)          │
         │ - Build prompt with market data   │
         │ - Call Gemini API                 │
         │ - Parse JSON response             │
@@ -181,6 +230,9 @@ High-frequency trading with individual stock analysis:
         │ - SQLite (data/trades.db)         │
         │ - Track: action, confidence,      │
         │   rationale, market, exchange     │
+        │ - NEW: selection_context (JSON)   │
+        │   - RSI, volume_ratio, signal     │
+        │   - For Evolution optimization    │
         └───────────────────────────────────┘
 ```
 
@@ -200,11 +252,24 @@ CREATE TABLE trades (
     price REAL,
     pnl REAL DEFAULT 0.0,
     market TEXT DEFAULT 'KR',       -- KR | US_NASDAQ | JP | etc.
-    exchange_code TEXT DEFAULT 'KRX' -- KRX | NASD | NYSE | etc.
+    exchange_code TEXT DEFAULT 'KRX', -- KRX | NASD | NYSE | etc.
+    selection_context TEXT          -- JSON: {rsi, volume_ratio, signal, score}
 );
 ```
 
-Auto-migration: Adds `market` and `exchange_code` columns if missing for backward compatibility.
+**Selection Context** (new in v0.9.0): Stores scanner selection criteria as JSON:
+```json
+{
+  "rsi": 28.5,
+  "volume_ratio": 2.7,
+  "signal": "oversold",
+  "score": 85.2
+}
+```
+
+Enables Evolution system to analyze correlation between selection criteria and trade outcomes.
+
+Auto-migration: Adds `market`, `exchange_code`, and `selection_context` columns if missing for backward compatibility.
 
 ## Configuration
 
@@ -236,6 +301,12 @@ SESSION_INTERVAL_HOURS=6      # Hours between sessions (daily mode only)
 TELEGRAM_BOT_TOKEN=1234567890:ABCdefGHIjklMNOpqrsTUVwxyz
 TELEGRAM_CHAT_ID=123456789
 TELEGRAM_ENABLED=true
+
+# Smart Scanner (optional, realtime mode only)
+RSI_OVERSOLD_THRESHOLD=30    # 0-50, oversold threshold
+RSI_MOMENTUM_THRESHOLD=70    # 50-100, momentum threshold
+VOL_MULTIPLIER=2.0           # Minimum volume ratio (2.0 = 200%)
+SCANNER_TOP_N=3              # Max qualified candidates per scan
 ```
 
 Tests use in-memory SQLite (`DB_PATH=":memory:"`) and dummy credentials via `tests/conftest.py`.

docs/requirements-log.md

@@ -26,3 +26,63 @@
 ### 문서화
 - 시스템 구조, 기능별 설명 등 코드 문서화 항상 신경쓸 것
 - 새로운 기능 추가 시 관련 문서 업데이트 필수
+
+---
+
+## 2026-02-06
+
+### Smart Volatility Scanner (Python-First, AI-Last 파이프라인)
+
+**배경:**
+- 정적 종목 리스트를 순회하는 방식은 비효율적
+- KIS API 거래량 순위를 통해 시장 주도주를 자동 탐지해야 함
+- Gemini API 호출 전에 Python 기반 기술적 분석으로 필터링 필요
+
+**요구사항:**
+1. KIS API 거래량 순위 API 통합 (`fetch_market_rankings`)
+2. 일별 가격 히스토리 API 추가 (`get_daily_prices`)
+3. RSI(14) 계산 기능 구현 (Wilder's smoothing method)
+4. 필터 조건:
+   - 거래량 > 전일 대비 200% (VOL_MULTIPLIER)
+   - RSI < 30 (과매도) OR RSI > 70 (모멘텀)
+5. 상위 1-3개 적격 종목만 Gemini에 전달
+6. 종목 선정 배경(RSI, volume_ratio, signal, score) 데이터베이스 기록
+
+**구현 결과:**
+- `src/analysis/smart_scanner.py`: SmartVolatilityScanner 클래스
+- `src/analysis/volatility.py`: calculate_rsi() 메서드 추가
+- `src/broker/kis_api.py`: 2개 신규 API 메서드
+- `src/db.py`: selection_context 컬럼 추가
+- 설정 가능한 임계값: RSI_OVERSOLD_THRESHOLD, RSI_MOMENTUM_THRESHOLD, VOL_MULTIPLIER, SCANNER_TOP_N
+
+**효과:**
+- Gemini API 호출 20-30개 → 1-3개로 감소
+- Python 기반 빠른 필터링 → 비용 절감
+- 선정 기준 추적 → Evolution 시스템 최적화 가능
+- API 장애 시 정적 watchlist로 자동 전환
+
+**참고:** Realtime 모드 전용. Daily 모드는 배치 효율성을 위해 정적 watchlist 사용.
+
+**이슈/PR:** #76, #77
+
+---
+
+## 2026-02-10
+
+### 코드 리뷰 시 플랜-구현 일치 검증 규칙
+
+**배경:**
+- 코드 리뷰 시 플랜(EnterPlanMode에서 승인된 계획)과 실제 구현이 일치하는지 확인하는 절차가 없었음
+- 플랜과 다른 구현이 리뷰 없이 통과될 위험
+
+**요구사항:**
+1. 모든 PR 리뷰에서 플랜-구현 일치 여부를 필수 체크
+2. 플랜에 없는 변경은 정당한 사유 필요
+3. 플랜 항목이 누락되면 PR 설명에 사유 기록
+4. 스코프가 플랜과 일치하는지 확인
+
+**구현 결과:**
+- `docs/workflow.md`에 Code Review Checklist 섹션 추가
+  - Plan Consistency (필수), Safety & Constraints, Quality, Workflow 4개 카테고리
+
+**이슈/PR:** #114

docs/workflow.md

@@ -6,6 +6,7 @@
 
 1. **Create Gitea Issue First** — All features, bug fixes, and policy changes require a Gitea issue before any code is written
 2. **Create Feature Branch** — Branch from `main` using format `feature/issue-{N}-{short-description}`
+   - After creating the branch, run `git pull origin main` and rebase to ensure the branch is up to date
 3. **Implement Changes** — Write code, tests, and documentation on the feature branch
 4. **Create Pull Request** — Submit PR to `main` branch referencing the issue number
 5. **Review & Merge** — After approval, merge via PR (squash or merge commit)
@@ -73,3 +74,37 @@ task_tool(
 ```
 
 Use `run_in_background=True` for independent tasks that don't block subsequent work.
+
+## Code Review Checklist
+
+**CRITICAL: Every PR review MUST verify plan-implementation consistency.**
+
+Before approving any PR, the reviewer (human or agent) must check ALL of the following:
+
+### 1. Plan Consistency (MANDATORY)
+
+- [ ] **Implementation matches the approved plan** — Compare the actual code changes against the plan created during `EnterPlanMode`. Every item in the plan must be addressed.
+- [ ] **No unplanned changes** — If the implementation includes changes not in the plan, they must be explicitly justified.
+- [ ] **No plan items omitted** — If any planned item was skipped, the reason must be documented in the PR description.
+- [ ] **Scope matches** — The PR does not exceed or fall short of the planned scope.
+
+### 2. Safety & Constraints
+
+- [ ] `src/core/risk_manager.py` is unchanged (READ-ONLY)
+- [ ] Circuit breaker threshold not weakened (only stricter allowed)
+- [ ] Fat-finger protection (30% max order) still enforced
+- [ ] Confidence < 80 still forces HOLD
+- [ ] No hardcoded API keys or secrets
+
+### 3. Quality
+
+- [ ] All new/modified code has corresponding tests
+- [ ] Test coverage >= 80%
+- [ ] `ruff check src/ tests/` passes (no lint errors)
+- [ ] No `assert` statements removed from tests
+
+### 4. Workflow
+
+- [ ] PR references the Gitea issue number
+- [ ] Feature branch follows naming convention (`feature/issue-N-description`)
+- [ ] Commit messages are clear and descriptive

src/analysis/__init__.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 from src.analysis.scanner import MarketScanner
+from src.analysis.smart_scanner import ScanCandidate, SmartVolatilityScanner
 from src.analysis.volatility import VolatilityAnalyzer
 
-__all__ = ["VolatilityAnalyzer", "MarketScanner"]
+__all__ = ["VolatilityAnalyzer", "MarketScanner", "SmartVolatilityScanner", "ScanCandidate"]

src/analysis/scanner.py

@@ -108,7 +108,7 @@ class MarketScanner:
             self.context_store.set_context(
                 ContextLayer.L7_REALTIME,
                 timeframe,
-                f"{market.code}_{stock_code}_volatility",
+                f"volatility_{market.code}_{stock_code}",
                 {
                     "price": metrics.current_price,
                     "atr": metrics.atr,
@@ -179,7 +179,7 @@ class MarketScanner:
         self.context_store.set_context(
             ContextLayer.L7_REALTIME,
             timeframe,
-            f"{market.code}_scan_result",
+            f"scan_result_{market.code}",
             {
                 "total_scanned": len(valid_metrics),
                 "top_movers": [m.stock_code for m in top_movers],

src/analysis/smart_scanner.py

@@ -0,0 +1,192 @@
+"""Smart Volatility Scanner with RSI and volume filters.
+
+Fetches market rankings from KIS API and applies technical filters
+to identify high-probability trading candidates.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from typing import Any
+
+from src.analysis.volatility import VolatilityAnalyzer
+from src.broker.kis_api import KISBroker
+from src.config import Settings
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ScanCandidate:
+    """A qualified candidate from the smart scanner."""
+
+    stock_code: str
+    name: str
+    price: float
+    volume: float
+    volume_ratio: float  # Current volume / previous day volume
+    rsi: float
+    signal: str  # "oversold" or "momentum"
+    score: float  # Composite score for ranking
+
+
+class SmartVolatilityScanner:
+    """Scans market rankings and applies RSI/volume filters.
+
+    Flow:
+    1. Fetch volume rankings from KIS API
+    2. For each ranked stock, fetch daily prices
+    3. Calculate RSI and volume ratio
+    4. Apply filters: volume > VOL_MULTIPLIER AND (RSI < 30 OR RSI > 70)
+    5. Return top N qualified candidates
+    """
+
+    def __init__(
+        self,
+        broker: KISBroker,
+        volatility_analyzer: VolatilityAnalyzer,
+        settings: Settings,
+    ) -> None:
+        """Initialize the smart scanner.
+
+        Args:
+            broker: KIS broker for API calls
+            volatility_analyzer: Analyzer for RSI calculation
+            settings: Application settings
+        """
+        self.broker = broker
+        self.analyzer = volatility_analyzer
+        self.settings = settings
+
+        # Extract scanner settings
+        self.rsi_oversold = settings.RSI_OVERSOLD_THRESHOLD
+        self.rsi_momentum = settings.RSI_MOMENTUM_THRESHOLD
+        self.vol_multiplier = settings.VOL_MULTIPLIER
+        self.top_n = settings.SCANNER_TOP_N
+
+    async def scan(
+        self,
+        fallback_stocks: list[str] | None = None,
+    ) -> list[ScanCandidate]:
+        """Execute smart scan and return qualified candidates.
+
+        Args:
+            fallback_stocks: Stock codes to use if ranking API fails
+
+        Returns:
+            List of ScanCandidate, sorted by score, up to top_n items
+        """
+        # Step 1: Fetch rankings
+        try:
+            rankings = await self.broker.fetch_market_rankings(
+                ranking_type="volume",
+                limit=30,  # Fetch more than needed for filtering
+            )
+            logger.info("Fetched %d stocks from volume rankings", len(rankings))
+        except ConnectionError as exc:
+            logger.warning("Ranking API failed, using fallback: %s", exc)
+            if fallback_stocks:
+                # Create minimal ranking data for fallback
+                rankings = [
+                    {
+                        "stock_code": code,
+                        "name": code,
+                        "price": 0,
+                        "volume": 0,
+                        "change_rate": 0,
+                        "volume_increase_rate": 0,
+                    }
+                    for code in fallback_stocks
+                ]
+            else:
+                return []
+
+        # Step 2: Analyze each stock
+        candidates: list[ScanCandidate] = []
+
+        for stock in rankings:
+            stock_code = stock["stock_code"]
+            if not stock_code:
+                continue
+
+            try:
+                # Fetch daily prices for RSI calculation
+                daily_prices = await self.broker.get_daily_prices(stock_code, days=20)
+
+                if len(daily_prices) < 15:  # Need at least 14+1 for RSI
+                    logger.debug("Insufficient price history for %s", stock_code)
+                    continue
+
+                # Calculate RSI
+                close_prices = [p["close"] for p in daily_prices]
+                rsi = self.analyzer.calculate_rsi(close_prices, period=14)
+
+                # Calculate volume ratio (today vs previous day avg)
+                if len(daily_prices) >= 2:
+                    prev_day_volume = daily_prices[-2]["volume"]
+                    current_volume = stock.get("volume", 0) or daily_prices[-1]["volume"]
+                    volume_ratio = (
+                        current_volume / prev_day_volume if prev_day_volume > 0 else 1.0
+                    )
+                else:
+                    volume_ratio = stock.get("volume_increase_rate", 0) / 100 + 1  # Fallback
+
+                # Apply filters
+                volume_qualified = volume_ratio >= self.vol_multiplier
+                rsi_oversold = rsi < self.rsi_oversold
+                rsi_momentum = rsi > self.rsi_momentum
+
+                if volume_qualified and (rsi_oversold or rsi_momentum):
+                    signal = "oversold" if rsi_oversold else "momentum"
+
+                    # Calculate composite score
+                    # Higher score for: extreme RSI + high volume
+                    rsi_extremity = abs(rsi - 50) / 50  # 0-1 scale
+                    volume_score = min(volume_ratio / 5, 1.0)  # Cap at 5x
+                    score = (rsi_extremity * 0.6 + volume_score * 0.4) * 100
+
+                    candidates.append(
+                        ScanCandidate(
+                            stock_code=stock_code,
+                            name=stock.get("name", stock_code),
+                            price=stock.get("price", daily_prices[-1]["close"]),
+                            volume=current_volume,
+                            volume_ratio=volume_ratio,
+                            rsi=rsi,
+                            signal=signal,
+                            score=score,
+                        )
+                    )
+
+                    logger.info(
+                        "Qualified: %s (%s) RSI=%.1f vol=%.1fx signal=%s score=%.1f",
+                        stock_code,
+                        stock.get("name", ""),
+                        rsi,
+                        volume_ratio,
+                        signal,
+                        score,
+                    )
+
+            except ConnectionError as exc:
+                logger.warning("Failed to analyze %s: %s", stock_code, exc)
+                continue
+            except Exception as exc:
+                logger.error("Unexpected error analyzing %s: %s", stock_code, exc)
+                continue
+
+        # Sort by score and return top N
+        candidates.sort(key=lambda c: c.score, reverse=True)
+        return candidates[: self.top_n]
+
+    def get_stock_codes(self, candidates: list[ScanCandidate]) -> list[str]:
+        """Extract stock codes from candidates for watchlist update.
+
+        Args:
+            candidates: List of scan candidates
+
+        Returns:
+            List of stock codes
+        """
+        return [c.stock_code for c in candidates]

src/analysis/volatility.py

@@ -124,6 +124,54 @@ class VolatilityAnalyzer:
             return 1.0
         return current_volume / avg_volume
 
+    def calculate_rsi(
+        self,
+        close_prices: list[float],
+        period: int = 14,
+    ) -> float:
+        """Calculate Relative Strength Index (RSI) using Wilder's smoothing.
+
+        Args:
+            close_prices: List of closing prices (oldest to newest, minimum period+1 values)
+            period: RSI period (default 14)
+
+        Returns:
+            RSI value between 0 and 100, or 50.0 (neutral) if insufficient data
+
+        Examples:
+            >>> analyzer = VolatilityAnalyzer()
+            >>> prices = [100 - i * 0.5 for i in range(20)]  # Downtrend
+            >>> rsi = analyzer.calculate_rsi(prices)
+            >>> assert rsi < 50  # Oversold territory
+        """
+        if len(close_prices) < period + 1:
+            return 50.0  # Neutral RSI if insufficient data
+
+        # Calculate price changes
+        changes = [close_prices[i] - close_prices[i - 1] for i in range(1, len(close_prices))]
+
+        # Separate gains and losses
+        gains = [max(0.0, change) for change in changes]
+        losses = [max(0.0, -change) for change in changes]
+
+        # Calculate initial average gain/loss (simple average for first period)
+        avg_gain = sum(gains[:period]) / period
+        avg_loss = sum(losses[:period]) / period
+
+        # Apply Wilder's smoothing for remaining periods
+        for i in range(period, len(changes)):
+            avg_gain = (avg_gain * (period - 1) + gains[i]) / period
+            avg_loss = (avg_loss * (period - 1) + losses[i]) / period
+
+        # Calculate RS and RSI
+        if avg_loss == 0:
+            return 100.0  # All gains, maximum RSI
+
+        rs = avg_gain / avg_loss
+        rsi = 100 - (100 / (1 + rs))
+
+        return rsi
+
     def calculate_pv_divergence(
         self,
         price_change: float,

src/broker/kis_api.py

@@ -280,3 +280,153 @@ class KISBroker:
                 return data
         except (TimeoutError, aiohttp.ClientError) as exc:
             raise ConnectionError(f"Network error sending order: {exc}") from exc
+
+    async def fetch_market_rankings(
+        self,
+        ranking_type: str = "volume",
+        limit: int = 30,
+    ) -> list[dict[str, Any]]:
+        """Fetch market rankings from KIS API.
+
+        Args:
+            ranking_type: Type of ranking ("volume" or "fluctuation")
+            limit: Maximum number of results to return
+
+        Returns:
+            List of stock data dicts with keys: stock_code, name, price, volume,
+            change_rate, volume_increase_rate
+
+        Raises:
+            ConnectionError: If API request fails
+        """
+        await self._rate_limiter.acquire()
+        session = self._get_session()
+
+        # TR_ID for volume ranking
+        tr_id = "FHPST01710000" if ranking_type == "volume" else "FHPST01710100"
+        headers = await self._auth_headers(tr_id)
+
+        params = {
+            "FID_COND_MRKT_DIV_CODE": "J",  # Stock/ETF/ETN
+            "FID_COND_SCR_DIV_CODE": "20001",  # Volume surge
+            "FID_INPUT_ISCD": "0000",  # All stocks
+            "FID_DIV_CLS_CODE": "0",  # All types
+            "FID_BLNG_CLS_CODE": "0",
+            "FID_TRGT_CLS_CODE": "111111111",
+            "FID_TRGT_EXLS_CLS_CODE": "000000",
+            "FID_INPUT_PRICE_1": "0",
+            "FID_INPUT_PRICE_2": "0",
+            "FID_VOL_CNT": "0",
+            "FID_INPUT_DATE_1": "",
+        }
+
+        url = f"{self._base_url}/uapi/domestic-stock/v1/quotations/volume-rank"
+
+        try:
+            async with session.get(url, headers=headers, params=params) as resp:
+                if resp.status != 200:
+                    text = await resp.text()
+                    raise ConnectionError(
+                        f"fetch_market_rankings failed ({resp.status}): {text}"
+                    )
+                data = await resp.json()
+
+            # Parse response - output is a list of ranked stocks
+            def _safe_float(value: str | float | None, default: float = 0.0) -> float:
+                if value is None or value == "":
+                    return default
+                try:
+                    return float(value)
+                except (ValueError, TypeError):
+                    return default
+
+            rankings = []
+            for item in data.get("output", [])[:limit]:
+                rankings.append({
+                    "stock_code": item.get("mksc_shrn_iscd", ""),
+                    "name": item.get("hts_kor_isnm", ""),
+                    "price": _safe_float(item.get("stck_prpr", "0")),
+                    "volume": _safe_float(item.get("acml_vol", "0")),
+                    "change_rate": _safe_float(item.get("prdy_ctrt", "0")),
+                    "volume_increase_rate": _safe_float(item.get("vol_inrt", "0")),
+                })
+            return rankings
+
+        except (TimeoutError, aiohttp.ClientError) as exc:
+            raise ConnectionError(f"Network error fetching rankings: {exc}") from exc
+
+    async def get_daily_prices(
+        self,
+        stock_code: str,
+        days: int = 20,
+    ) -> list[dict[str, Any]]:
+        """Fetch daily OHLCV price history for a stock.
+
+        Args:
+            stock_code: 6-digit stock code
+            days: Number of trading days to fetch (default 20 for RSI calculation)
+
+        Returns:
+            List of daily price dicts with keys: date, open, high, low, close, volume
+            Sorted oldest to newest
+
+        Raises:
+            ConnectionError: If API request fails
+        """
+        await self._rate_limiter.acquire()
+        session = self._get_session()
+
+        headers = await self._auth_headers("FHKST03010100")
+
+        # Calculate date range (today and N days ago)
+        from datetime import datetime, timedelta
+        end_date = datetime.now().strftime("%Y%m%d")
+        start_date = (datetime.now() - timedelta(days=days + 10)).strftime("%Y%m%d")
+
+        params = {
+            "FID_COND_MRKT_DIV_CODE": "J",
+            "FID_INPUT_ISCD": stock_code,
+            "FID_INPUT_DATE_1": start_date,
+            "FID_INPUT_DATE_2": end_date,
+            "FID_PERIOD_DIV_CODE": "D",  # Daily
+            "FID_ORG_ADJ_PRC": "0",  # Adjusted price
+        }
+
+        url = f"{self._base_url}/uapi/domestic-stock/v1/quotations/inquire-daily-itemchartprice"
+
+        try:
+            async with session.get(url, headers=headers, params=params) as resp:
+                if resp.status != 200:
+                    text = await resp.text()
+                    raise ConnectionError(
+                        f"get_daily_prices failed ({resp.status}): {text}"
+                    )
+                data = await resp.json()
+
+            # Parse response
+            def _safe_float(value: str | float | None, default: float = 0.0) -> float:
+                if value is None or value == "":
+                    return default
+                try:
+                    return float(value)
+                except (ValueError, TypeError):
+                    return default
+
+            prices = []
+            for item in data.get("output2", []):
+                prices.append({
+                    "date": item.get("stck_bsop_date", ""),
+                    "open": _safe_float(item.get("stck_oprc", "0")),
+                    "high": _safe_float(item.get("stck_hgpr", "0")),
+                    "low": _safe_float(item.get("stck_lwpr", "0")),
+                    "close": _safe_float(item.get("stck_clpr", "0")),
+                    "volume": _safe_float(item.get("acml_vol", "0")),
+                })
+
+            # Sort oldest to newest (KIS returns newest first)
+            prices.reverse()
+
+            return prices[:days]  # Return only requested number of days
+
+        except (TimeoutError, aiohttp.ClientError) as exc:
+            raise ConnectionError(f"Network error fetching daily prices: {exc}") from exc

src/config.py

@@ -33,6 +33,12 @@ class Settings(BaseSettings):
     FAT_FINGER_PCT: float = Field(default=30.0, gt=0.0, le=100.0)
     CONFIDENCE_THRESHOLD: int = Field(default=80, ge=0, le=100)
 
+    # Smart Scanner Configuration
+    RSI_OVERSOLD_THRESHOLD: int = Field(default=30, ge=0, le=50)
+    RSI_MOMENTUM_THRESHOLD: int = Field(default=70, ge=50, le=100)
+    VOL_MULTIPLIER: float = Field(default=2.0, gt=1.0, le=10.0)
+    SCANNER_TOP_N: int = Field(default=3, ge=1, le=10)
+
     # Database
     DB_PATH: str = "data/trade_logs.db"
 
@@ -49,8 +55,15 @@ class Settings(BaseSettings):
     DAILY_SESSIONS: int = Field(default=4, ge=1, le=10)
     SESSION_INTERVAL_HOURS: int = Field(default=6, ge=1, le=24)
 
+    # Pre-Market Planner
+    PRE_MARKET_MINUTES: int = Field(default=30, ge=10, le=120)
+    MAX_SCENARIOS_PER_STOCK: int = Field(default=5, ge=1, le=10)
+    PLANNER_TIMEOUT_SECONDS: int = Field(default=60, ge=10, le=300)
+    DEFENSIVE_PLAYBOOK_ON_FAILURE: bool = True
+    RESCAN_INTERVAL_SECONDS: int = Field(default=300, ge=60, le=900)
+
     # Market selection (comma-separated market codes)
-    ENABLED_MARKETS: str = "KR"
+    ENABLED_MARKETS: str = "KR,US"
 
     # Backup and Disaster Recovery (optional)
     BACKUP_ENABLED: bool = True

src/context/__init__.py

@@ -5,6 +5,7 @@ The context tree implements Pillar 2: hierarchical memory management across
 """
 
 from src.context.layer import ContextLayer
+from src.context.scheduler import ContextScheduler
 from src.context.store import ContextStore
 
-__all__ = ["ContextLayer", "ContextStore"]
+__all__ = ["ContextLayer", "ContextScheduler", "ContextStore"]

src/context/aggregator.py

@@ -18,16 +18,33 @@ class ContextAggregator:
         self.conn = conn
         self.store = ContextStore(conn)
 
-    def aggregate_daily_from_trades(self, date: str | None = None) -> None:
+    def aggregate_daily_from_trades(
+        self, date: str | None = None, market: str | None = None
+    ) -> None:
         """Aggregate L6 (daily) context from trades table.
 
         Args:
             date: Date in YYYY-MM-DD format. If None, uses today.
+            market: Market code filter (e.g., "KR", "US"). If None, aggregates all markets.
         """
         if date is None:
             date = datetime.now(UTC).date().isoformat()
 
-        # Calculate daily metrics from trades
+        if market is None:
+            cursor = self.conn.execute(
+                """
+                SELECT DISTINCT market
+                FROM trades
+                WHERE DATE(timestamp) = ?
+                """,
+                (date,),
+            )
+            markets = [row[0] for row in cursor.fetchall() if row[0]]
+        else:
+            markets = [market]
+
+        for market_code in markets:
+            # Calculate daily metrics from trades for the market
             cursor = self.conn.execute(
                 """
                 SELECT
@@ -41,29 +58,43 @@ class ContextAggregator:
                     SUM(CASE WHEN pnl > 0 THEN 1 ELSE 0 END) as wins,
                     SUM(CASE WHEN pnl < 0 THEN 1 ELSE 0 END) as losses
                 FROM trades
-            WHERE DATE(timestamp) = ?
+                WHERE DATE(timestamp) = ? AND market = ?
                 """,
-            (date,),
+                (date, market_code),
             )
             row = cursor.fetchone()
 
             if row and row[0] > 0:  # At least one trade
                 trade_count, buys, sells, holds, avg_conf, total_pnl, stocks, wins, losses = row
 
-            # Store daily metrics in L6
+                key_suffix = f"_{market_code}"
-            self.store.set_context(ContextLayer.L6_DAILY, date, "trade_count", trade_count)
+
-            self.store.set_context(ContextLayer.L6_DAILY, date, "buys", buys)
+                # Store daily metrics in L6 with market suffix
-            self.store.set_context(ContextLayer.L6_DAILY, date, "sells", sells)
-            self.store.set_context(ContextLayer.L6_DAILY, date, "holds", holds)
                 self.store.set_context(
-                ContextLayer.L6_DAILY, date, "avg_confidence", round(avg_conf, 2)
+                    ContextLayer.L6_DAILY, date, f"trade_count{key_suffix}", trade_count
+                )
+                self.store.set_context(ContextLayer.L6_DAILY, date, f"buys{key_suffix}", buys)
+                self.store.set_context(ContextLayer.L6_DAILY, date, f"sells{key_suffix}", sells)
+                self.store.set_context(ContextLayer.L6_DAILY, date, f"holds{key_suffix}", holds)
+                self.store.set_context(
+                    ContextLayer.L6_DAILY,
+                    date,
+                    f"avg_confidence{key_suffix}",
+                    round(avg_conf, 2),
                 )
                 self.store.set_context(
-                ContextLayer.L6_DAILY, date, "total_pnl", round(total_pnl, 2)
+                    ContextLayer.L6_DAILY,
+                    date,
+                    f"total_pnl{key_suffix}",
+                    round(total_pnl, 2),
+                )
+                self.store.set_context(
+                    ContextLayer.L6_DAILY, date, f"unique_stocks{key_suffix}", stocks
                 )
-            self.store.set_context(ContextLayer.L6_DAILY, date, "unique_stocks", stocks)
                 win_rate = round(wins / max(wins + losses, 1) * 100, 2)
-            self.store.set_context(ContextLayer.L6_DAILY, date, "win_rate", win_rate)
+                self.store.set_context(
+                    ContextLayer.L6_DAILY, date, f"win_rate{key_suffix}", win_rate
+                )
 
     def aggregate_weekly_from_daily(self, week: str | None = None) -> None:
         """Aggregate L5 (weekly) context from L6 (daily).
@@ -92,14 +123,25 @@ class ContextAggregator:
             daily_data[row[0]].append(json.loads(row[1]))
 
         if daily_data:
-            # Sum all PnL values
+            # Sum all PnL values (market-specific if suffixed)
             if "total_pnl" in daily_data:
                 total_pnl = sum(daily_data["total_pnl"])
                 self.store.set_context(
                     ContextLayer.L5_WEEKLY, week, "weekly_pnl", round(total_pnl, 2)
                 )
 
-            # Average all confidence values
+            for key, values in daily_data.items():
+                if key.startswith("total_pnl_"):
+                    market_code = key.split("total_pnl_", 1)[1]
+                    total_pnl = sum(values)
+                    self.store.set_context(
+                        ContextLayer.L5_WEEKLY,
+                        week,
+                        f"weekly_pnl_{market_code}",
+                        round(total_pnl, 2),
+                    )
+
+            # Average all confidence values (market-specific if suffixed)
             if "avg_confidence" in daily_data:
                 conf_values = daily_data["avg_confidence"]
                 avg_conf = sum(conf_values) / len(conf_values)
@@ -107,6 +149,17 @@ class ContextAggregator:
                     ContextLayer.L5_WEEKLY, week, "avg_confidence", round(avg_conf, 2)
                 )
 
+            for key, values in daily_data.items():
+                if key.startswith("avg_confidence_"):
+                    market_code = key.split("avg_confidence_", 1)[1]
+                    avg_conf = sum(values) / len(values)
+                    self.store.set_context(
+                        ContextLayer.L5_WEEKLY,
+                        week,
+                        f"avg_confidence_{market_code}",
+                        round(avg_conf, 2),
+                    )
+
     def aggregate_monthly_from_weekly(self, month: str | None = None) -> None:
         """Aggregate L4 (monthly) context from L5 (weekly).
 
@@ -135,8 +188,16 @@ class ContextAggregator:
 
         if weekly_data:
             # Sum all weekly PnL values
+            total_pnl_values: list[float] = []
             if "weekly_pnl" in weekly_data:
-                total_pnl = sum(weekly_data["weekly_pnl"])
+                total_pnl_values.extend(weekly_data["weekly_pnl"])
+
+            for key, values in weekly_data.items():
+                if key.startswith("weekly_pnl_"):
+                    total_pnl_values.extend(values)
+
+            if total_pnl_values:
+                total_pnl = sum(total_pnl_values)
                 self.store.set_context(
                     ContextLayer.L4_MONTHLY, month, "monthly_pnl", round(total_pnl, 2)
                 )
@@ -230,21 +291,44 @@ class ContextAggregator:
             )
 
     def run_all_aggregations(self) -> None:
-        """Run all aggregations from L7 to L1 (bottom-up)."""
+        """Run all aggregations from L7 to L1 (bottom-up).
+
+        All timeframes are derived from the latest trade timestamp so that
+        past data re-aggregation produces consistent results across layers.
+        """
+        cursor = self.conn.execute("SELECT MAX(timestamp) FROM trades")
+        row = cursor.fetchone()
+        if not row or row[0] is None:
+            return
+
+        ts_raw = row[0]
+        if ts_raw.endswith("Z"):
+            ts_raw = ts_raw.replace("Z", "+00:00")
+        latest_ts = datetime.fromisoformat(ts_raw)
+        trade_date = latest_ts.date()
+        date_str = trade_date.isoformat()
+
+        iso_year, iso_week, _ = trade_date.isocalendar()
+        week_str = f"{iso_year}-W{iso_week:02d}"
+        month_str = f"{trade_date.year}-{trade_date.month:02d}"
+        quarter = (trade_date.month - 1) // 3 + 1
+        quarter_str = f"{trade_date.year}-Q{quarter}"
+        year_str = str(trade_date.year)
+
         # L7 (trades) → L6 (daily)
-        self.aggregate_daily_from_trades()
+        self.aggregate_daily_from_trades(date_str)
 
         # L6 (daily) → L5 (weekly)
-        self.aggregate_weekly_from_daily()
+        self.aggregate_weekly_from_daily(week_str)
 
         # L5 (weekly) → L4 (monthly)
-        self.aggregate_monthly_from_weekly()
+        self.aggregate_monthly_from_weekly(month_str)
 
         # L4 (monthly) → L3 (quarterly)
-        self.aggregate_quarterly_from_monthly()
+        self.aggregate_quarterly_from_monthly(quarter_str)
 
         # L3 (quarterly) → L2 (annual)
-        self.aggregate_annual_from_quarterly()
+        self.aggregate_annual_from_quarterly(year_str)
 
         # L2 (annual) → L1 (legacy)
         self.aggregate_legacy_from_annual()

src/context/scheduler.py

@@ -0,0 +1,135 @@
+"""Context aggregation scheduler for periodic rollups and cleanup."""
+
+from __future__ import annotations
+
+import sqlite3
+from calendar import monthrange
+from dataclasses import dataclass
+from datetime import UTC, datetime
+
+from src.context.aggregator import ContextAggregator
+from src.context.store import ContextStore
+
+
+@dataclass(frozen=True)
+class ScheduleResult:
+    """Represents which scheduled tasks ran."""
+
+    weekly: bool = False
+    monthly: bool = False
+    quarterly: bool = False
+    annual: bool = False
+    legacy: bool = False
+    cleanup: bool = False
+
+
+class ContextScheduler:
+    """Run periodic context aggregations and cleanup when due."""
+
+    def __init__(
+        self,
+        conn: sqlite3.Connection | None = None,
+        aggregator: ContextAggregator | None = None,
+        store: ContextStore | None = None,
+    ) -> None:
+        if aggregator is None:
+            if conn is None:
+                raise ValueError("conn is required when aggregator is not provided")
+            aggregator = ContextAggregator(conn)
+        self.aggregator = aggregator
+
+        if store is None:
+            store = getattr(aggregator, "store", None)
+        if store is None:
+            if conn is None:
+                raise ValueError("conn is required when store is not provided")
+            store = ContextStore(conn)
+        self.store = store
+
+        self._last_run: dict[str, str] = {}
+
+    def run_if_due(self, now: datetime | None = None) -> ScheduleResult:
+        """Run scheduled aggregations if their schedule is due.
+
+        Args:
+            now: Current datetime (UTC). If None, uses current time.
+
+        Returns:
+            ScheduleResult indicating which tasks ran.
+        """
+        if now is None:
+            now = datetime.now(UTC)
+
+        today = now.date().isoformat()
+        result = ScheduleResult()
+
+        if self._should_run("cleanup", today):
+            self.store.cleanup_expired_contexts()
+            result = self._with(result, cleanup=True)
+
+        if self._is_sunday(now) and self._should_run("weekly", today):
+            week = now.strftime("%Y-W%V")
+            self.aggregator.aggregate_weekly_from_daily(week)
+            result = self._with(result, weekly=True)
+
+        if self._is_last_day_of_month(now) and self._should_run("monthly", today):
+            month = now.strftime("%Y-%m")
+            self.aggregator.aggregate_monthly_from_weekly(month)
+            result = self._with(result, monthly=True)
+
+        if self._is_last_day_of_quarter(now) and self._should_run("quarterly", today):
+            quarter = self._current_quarter(now)
+            self.aggregator.aggregate_quarterly_from_monthly(quarter)
+            result = self._with(result, quarterly=True)
+
+        if self._is_last_day_of_year(now) and self._should_run("annual", today):
+            year = str(now.year)
+            self.aggregator.aggregate_annual_from_quarterly(year)
+            result = self._with(result, annual=True)
+
+            # Legacy rollup runs after annual aggregation.
+            self.aggregator.aggregate_legacy_from_annual()
+            result = self._with(result, legacy=True)
+
+        return result
+
+    def _should_run(self, key: str, date_str: str) -> bool:
+        if self._last_run.get(key) == date_str:
+            return False
+        self._last_run[key] = date_str
+        return True
+
+    @staticmethod
+    def _is_sunday(now: datetime) -> bool:
+        return now.weekday() == 6
+
+    @staticmethod
+    def _is_last_day_of_month(now: datetime) -> bool:
+        last_day = monthrange(now.year, now.month)[1]
+        return now.day == last_day
+
+    @classmethod
+    def _is_last_day_of_quarter(cls, now: datetime) -> bool:
+        if now.month not in (3, 6, 9, 12):
+            return False
+        return cls._is_last_day_of_month(now)
+
+    @staticmethod
+    def _is_last_day_of_year(now: datetime) -> bool:
+        return now.month == 12 and now.day == 31
+
+    @staticmethod
+    def _current_quarter(now: datetime) -> str:
+        quarter = (now.month - 1) // 3 + 1
+        return f"{now.year}-Q{quarter}"
+
+    @staticmethod
+    def _with(result: ScheduleResult, **kwargs: bool) -> ScheduleResult:
+        return ScheduleResult(
+            weekly=kwargs.get("weekly", result.weekly),
+            monthly=kwargs.get("monthly", result.monthly),
+            quarterly=kwargs.get("quarterly", result.quarterly),
+            annual=kwargs.get("annual", result.annual),
+            legacy=kwargs.get("legacy", result.legacy),
+            cleanup=kwargs.get("cleanup", result.cleanup),
+        )

src/db.py

@@ -2,9 +2,11 @@
 
 from __future__ import annotations
 
+import json
 import sqlite3
 from datetime import UTC, datetime
 from pathlib import Path
+from typing import Any
 
 
 def init_db(db_path: str) -> sqlite3.Connection:
@@ -25,7 +27,8 @@ def init_db(db_path: str) -> sqlite3.Connection:
             price REAL,
             pnl REAL DEFAULT 0.0,
             market TEXT DEFAULT 'KR',
-            exchange_code TEXT DEFAULT 'KRX'
+            exchange_code TEXT DEFAULT 'KRX',
+            decision_id TEXT
         )
         """
     )
@@ -38,6 +41,10 @@ def init_db(db_path: str) -> sqlite3.Connection:
         conn.execute("ALTER TABLE trades ADD COLUMN market TEXT DEFAULT 'KR'")
     if "exchange_code" not in columns:
         conn.execute("ALTER TABLE trades ADD COLUMN exchange_code TEXT DEFAULT 'KRX'")
+    if "selection_context" not in columns:
+        conn.execute("ALTER TABLE trades ADD COLUMN selection_context TEXT")
+    if "decision_id" not in columns:
+        conn.execute("ALTER TABLE trades ADD COLUMN decision_id TEXT")
 
     # Context tree tables for multi-layered memory management
     conn.execute(
@@ -88,6 +95,27 @@ def init_db(db_path: str) -> sqlite3.Connection:
         """
     )
 
+    # Playbook storage for pre-market strategy persistence
+    conn.execute(
+        """
+        CREATE TABLE IF NOT EXISTS playbooks (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            date TEXT NOT NULL,
+            market TEXT NOT NULL,
+            status TEXT NOT NULL DEFAULT 'pending',
+            playbook_json TEXT NOT NULL,
+            generated_at TEXT NOT NULL,
+            token_count INTEGER DEFAULT 0,
+            scenario_count INTEGER DEFAULT 0,
+            match_count INTEGER DEFAULT 0,
+            UNIQUE(date, market)
+        )
+        """
+    )
+
+    conn.execute("CREATE INDEX IF NOT EXISTS idx_playbooks_date ON playbooks(date)")
+    conn.execute("CREATE INDEX IF NOT EXISTS idx_playbooks_market ON playbooks(market)")
+
     # Create indices for efficient context queries
     conn.execute("CREATE INDEX IF NOT EXISTS idx_contexts_layer ON contexts(layer)")
     conn.execute("CREATE INDEX IF NOT EXISTS idx_contexts_timeframe ON contexts(timeframe)")
@@ -118,15 +146,34 @@ def log_trade(
     pnl: float = 0.0,
     market: str = "KR",
     exchange_code: str = "KRX",
+    selection_context: dict[str, any] | None = None,
+    decision_id: str | None = None,
 ) -> None:
-    """Insert a trade record into the database."""
+    """Insert a trade record into the database.
+
+    Args:
+        conn: Database connection
+        stock_code: Stock code
+        action: Trade action (BUY/SELL/HOLD)
+        confidence: Confidence level (0-100)
+        rationale: AI decision rationale
+        quantity: Number of shares
+        price: Trade price
+        pnl: Profit/loss
+        market: Market code
+        exchange_code: Exchange code
+        selection_context: Scanner selection data (RSI, volume_ratio, signal, score)
+    """
+    # Serialize selection context to JSON
+    context_json = json.dumps(selection_context) if selection_context else None
+
     conn.execute(
         """
         INSERT INTO trades (
             timestamp, stock_code, action, confidence, rationale,
-            quantity, price, pnl, market, exchange_code
+            quantity, price, pnl, market, exchange_code, selection_context, decision_id
         )
-        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
         """,
         (
             datetime.now(UTC).isoformat(),
@@ -139,6 +186,31 @@ def log_trade(
             pnl,
             market,
             exchange_code,
+            context_json,
+            decision_id,
         ),
     )
     conn.commit()
+
+
+def get_latest_buy_trade(
+    conn: sqlite3.Connection, stock_code: str, market: str
+) -> dict[str, Any] | None:
+    """Fetch the most recent BUY trade for a stock and market."""
+    cursor = conn.execute(
+        """
+        SELECT decision_id, price, quantity
+        FROM trades
+        WHERE stock_code = ?
+          AND market = ?
+          AND action = 'BUY'
+          AND decision_id IS NOT NULL
+        ORDER BY timestamp DESC
+        LIMIT 1
+        """,
+        (stock_code, market),
+    )
+    row = cursor.fetchone()
+    if not row:
+        return None
+    return {"decision_id": row[0], "price": row[1], "quantity": row[2]}

src/evolution/__init__.py

@@ -1,12 +1,14 @@
 """Evolution engine for self-improving trading strategies."""
 
 from src.evolution.ab_test import ABTester, ABTestResult, StrategyPerformance
+from src.evolution.daily_review import DailyReviewer
 from src.evolution.optimizer import EvolutionOptimizer
 from src.evolution.performance_tracker import (
     PerformanceDashboard,
     PerformanceTracker,
     StrategyMetrics,
 )
+from src.evolution.scorecard import DailyScorecard
 
 __all__ = [
     "EvolutionOptimizer",
@@ -16,4 +18,6 @@ __all__ = [
     "PerformanceTracker",
     "PerformanceDashboard",
     "StrategyMetrics",
+    "DailyScorecard",
+    "DailyReviewer",
 ]

src/evolution/daily_review.py

@@ -0,0 +1,196 @@
+"""Daily review generator for market-scoped end-of-day scorecards."""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+import sqlite3
+from dataclasses import asdict
+
+from src.brain.gemini_client import GeminiClient
+from src.context.layer import ContextLayer
+from src.context.store import ContextStore
+from src.evolution.scorecard import DailyScorecard
+
+logger = logging.getLogger(__name__)
+
+
+class DailyReviewer:
+    """Builds daily scorecards and optional AI-generated lessons."""
+
+    def __init__(
+        self,
+        conn: sqlite3.Connection,
+        context_store: ContextStore,
+        gemini_client: GeminiClient | None = None,
+    ) -> None:
+        self._conn = conn
+        self._context_store = context_store
+        self._gemini = gemini_client
+
+    def generate_scorecard(self, date: str, market: str) -> DailyScorecard:
+        """Generate a market-scoped scorecard from decision logs and trades."""
+        decision_rows = self._conn.execute(
+            """
+            SELECT action, confidence, context_snapshot
+            FROM decision_logs
+            WHERE DATE(timestamp) = ? AND market = ?
+            """,
+            (date, market),
+        ).fetchall()
+
+        total_decisions = len(decision_rows)
+        buys = sum(1 for row in decision_rows if row[0] == "BUY")
+        sells = sum(1 for row in decision_rows if row[0] == "SELL")
+        holds = sum(1 for row in decision_rows if row[0] == "HOLD")
+        avg_confidence = (
+            round(sum(int(row[1]) for row in decision_rows) / total_decisions, 2)
+            if total_decisions > 0
+            else 0.0
+        )
+
+        matched = 0
+        for row in decision_rows:
+            try:
+                snapshot = json.loads(row[2]) if row[2] else {}
+            except json.JSONDecodeError:
+                snapshot = {}
+            scenario_match = snapshot.get("scenario_match", {})
+            if isinstance(scenario_match, dict) and scenario_match:
+                matched += 1
+        scenario_match_rate = (
+            round((matched / total_decisions) * 100, 2)
+            if total_decisions
+            else 0.0
+        )
+
+        trade_stats = self._conn.execute(
+            """
+            SELECT
+                COALESCE(SUM(pnl), 0.0),
+                SUM(CASE WHEN pnl > 0 THEN 1 ELSE 0 END),
+                SUM(CASE WHEN pnl < 0 THEN 1 ELSE 0 END)
+            FROM trades
+            WHERE DATE(timestamp) = ? AND market = ?
+            """,
+            (date, market),
+        ).fetchone()
+        total_pnl = round(float(trade_stats[0] or 0.0), 2) if trade_stats else 0.0
+        wins = int(trade_stats[1] or 0) if trade_stats else 0
+        losses = int(trade_stats[2] or 0) if trade_stats else 0
+        win_rate = round((wins / (wins + losses)) * 100, 2) if (wins + losses) > 0 else 0.0
+
+        top_winners = [
+            row[0]
+            for row in self._conn.execute(
+                """
+                SELECT stock_code, SUM(pnl) AS stock_pnl
+                FROM trades
+                WHERE DATE(timestamp) = ? AND market = ?
+                GROUP BY stock_code
+                HAVING stock_pnl > 0
+                ORDER BY stock_pnl DESC
+                LIMIT 3
+                """,
+                (date, market),
+            ).fetchall()
+        ]
+
+        top_losers = [
+            row[0]
+            for row in self._conn.execute(
+                """
+                SELECT stock_code, SUM(pnl) AS stock_pnl
+                FROM trades
+                WHERE DATE(timestamp) = ? AND market = ?
+                GROUP BY stock_code
+                HAVING stock_pnl < 0
+                ORDER BY stock_pnl ASC
+                LIMIT 3
+                """,
+                (date, market),
+            ).fetchall()
+        ]
+
+        return DailyScorecard(
+            date=date,
+            market=market,
+            total_decisions=total_decisions,
+            buys=buys,
+            sells=sells,
+            holds=holds,
+            total_pnl=total_pnl,
+            win_rate=win_rate,
+            avg_confidence=avg_confidence,
+            scenario_match_rate=scenario_match_rate,
+            top_winners=top_winners,
+            top_losers=top_losers,
+            lessons=[],
+            cross_market_note="",
+        )
+
+    async def generate_lessons(self, scorecard: DailyScorecard) -> list[str]:
+        """Generate concise lessons from scorecard metrics using Gemini."""
+        if self._gemini is None:
+            return []
+
+        prompt = (
+            "You are a trading performance reviewer.\n"
+            "Return ONLY a JSON array of 1-3 short lessons in English.\n"
+            f"Market: {scorecard.market}\n"
+            f"Date: {scorecard.date}\n"
+            f"Total decisions: {scorecard.total_decisions}\n"
+            f"Buys/Sells/Holds: {scorecard.buys}/{scorecard.sells}/{scorecard.holds}\n"
+            f"Total PnL: {scorecard.total_pnl}\n"
+            f"Win rate: {scorecard.win_rate}%\n"
+            f"Average confidence: {scorecard.avg_confidence}\n"
+            f"Scenario match rate: {scorecard.scenario_match_rate}%\n"
+            f"Top winners: {', '.join(scorecard.top_winners) or 'N/A'}\n"
+            f"Top losers: {', '.join(scorecard.top_losers) or 'N/A'}\n"
+        )
+
+        try:
+            decision = await self._gemini.decide(
+                {
+                    "stock_code": "REVIEW",
+                    "market_name": scorecard.market,
+                    "current_price": 0,
+                    "prompt_override": prompt,
+                }
+            )
+            return self._parse_lessons(decision.rationale)
+        except Exception as exc:
+            logger.warning("Failed to generate daily lessons: %s", exc)
+            return []
+
+    def store_scorecard_in_context(self, scorecard: DailyScorecard) -> None:
+        """Store scorecard in L6 using market-scoped key."""
+        self._context_store.set_context(
+            ContextLayer.L6_DAILY,
+            scorecard.date,
+            f"scorecard_{scorecard.market}",
+            asdict(scorecard),
+        )
+
+    def _parse_lessons(self, raw_text: str) -> list[str]:
+        """Parse lessons from JSON array response or fallback text."""
+        raw_text = raw_text.strip()
+        try:
+            parsed = json.loads(raw_text)
+            if isinstance(parsed, list):
+                return [str(item).strip() for item in parsed if str(item).strip()][:3]
+        except json.JSONDecodeError:
+            pass
+
+        match = re.search(r"\[.*\]", raw_text, re.DOTALL)
+        if match:
+            try:
+                parsed = json.loads(match.group(0))
+                if isinstance(parsed, list):
+                    return [str(item).strip() for item in parsed if str(item).strip()][:3]
+            except json.JSONDecodeError:
+                pass
+
+        lines = [line.strip("-* \t") for line in raw_text.splitlines() if line.strip()]
+        return lines[:3]

src/evolution/scorecard.py

@@ -0,0 +1,25 @@
+"""Daily scorecard model for end-of-day performance review."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+
+@dataclass
+class DailyScorecard:
+    """Structured daily performance snapshot for a single market."""
+
+    date: str
+    market: str
+    total_decisions: int
+    buys: int
+    sells: int
+    holds: int
+    total_pnl: float
+    win_rate: float
+    avg_confidence: float
+    scenario_match_rate: float
+    top_winners: list[str] = field(default_factory=list)
+    top_losers: list[str] = field(default_factory=list)
+    lessons: list[str] = field(default_factory=list)
+    cross_market_note: str = ""

src/notifications/telegram_client.py

@@ -304,6 +304,77 @@ class TelegramClient:
             NotificationMessage(priority=NotificationPriority.MEDIUM, message=message)
         )
 
+    async def notify_playbook_generated(
+        self,
+        market: str,
+        stock_count: int,
+        scenario_count: int,
+        token_count: int,
+    ) -> None:
+        """
+        Notify that a daily playbook was generated.
+
+        Args:
+            market: Market code (e.g., "KR", "US")
+            stock_count: Number of stocks in the playbook
+            scenario_count: Total number of scenarios
+            token_count: Gemini token usage for the playbook
+        """
+        message = (
+            f"<b>Playbook Generated</b>\n"
+            f"Market: {market}\n"
+            f"Stocks: {stock_count}\n"
+            f"Scenarios: {scenario_count}\n"
+            f"Tokens: {token_count}"
+        )
+        await self._send_notification(
+            NotificationMessage(priority=NotificationPriority.MEDIUM, message=message)
+        )
+
+    async def notify_scenario_matched(
+        self,
+        stock_code: str,
+        action: str,
+        condition_summary: str,
+        confidence: float,
+    ) -> None:
+        """
+        Notify that a scenario matched for a stock.
+
+        Args:
+            stock_code: Stock ticker symbol
+            action: Scenario action (BUY/SELL/HOLD/REDUCE_ALL)
+            condition_summary: Short summary of the matched condition
+            confidence: Scenario confidence (0-100)
+        """
+        message = (
+            f"<b>Scenario Matched</b>\n"
+            f"Symbol: <code>{stock_code}</code>\n"
+            f"Action: {action}\n"
+            f"Condition: {condition_summary}\n"
+            f"Confidence: {confidence:.0f}%"
+        )
+        await self._send_notification(
+            NotificationMessage(priority=NotificationPriority.HIGH, message=message)
+        )
+
+    async def notify_playbook_failed(self, market: str, reason: str) -> None:
+        """
+        Notify that playbook generation failed.
+
+        Args:
+            market: Market code (e.g., "KR", "US")
+            reason: Failure reason summary
+        """
+        message = (
+            f"<b>Playbook Failed</b>\n"
+            f"Market: {market}\n"
+            f"Reason: {reason[:200]}"
+        )
+        await self._send_notification(
+            NotificationMessage(priority=NotificationPriority.HIGH, message=message)
+        )
+
     async def notify_system_shutdown(self, reason: str) -> None:
         """
         Notify system shutdown.
@@ -492,7 +563,8 @@ class TelegramCommandHandler:
             if not command_parts:
                 return
 
-            command_name = command_parts[0]
+            # Remove @botname suffix if present (for group chats)
+            command_name = command_parts[0].split("@")[0]
 
             # Execute handler
             handler = self._commands.get(command_name)

src/strategy/models.py

@@ -0,0 +1,164 @@
+"""Pydantic models for pre-market scenario planning.
+
+Defines the data contracts for the proactive strategy system:
+- AI generates DayPlaybook before market open (structured JSON scenarios)
+- Local ScenarioEngine matches conditions during market hours (no API calls)
+"""
+
+from __future__ import annotations
+
+from datetime import UTC, date, datetime
+from enum import Enum
+
+from pydantic import BaseModel, Field, field_validator
+
+
+class ScenarioAction(str, Enum):
+    """Actions that can be taken by scenarios."""
+
+    BUY = "BUY"
+    SELL = "SELL"
+    HOLD = "HOLD"
+    REDUCE_ALL = "REDUCE_ALL"
+
+
+class MarketOutlook(str, Enum):
+    """AI's assessment of market direction."""
+
+    BULLISH = "bullish"
+    NEUTRAL_TO_BULLISH = "neutral_to_bullish"
+    NEUTRAL = "neutral"
+    NEUTRAL_TO_BEARISH = "neutral_to_bearish"
+    BEARISH = "bearish"
+
+
+class PlaybookStatus(str, Enum):
+    """Lifecycle status of a playbook."""
+
+    PENDING = "pending"
+    READY = "ready"
+    FAILED = "failed"
+    EXPIRED = "expired"
+
+
+class StockCondition(BaseModel):
+    """Condition fields for scenario matching (all optional, AND-combined).
+
+    The ScenarioEngine evaluates all non-None fields as AND conditions.
+    A condition matches only if ALL specified fields are satisfied.
+    """
+
+    rsi_below: float | None = None
+    rsi_above: float | None = None
+    volume_ratio_above: float | None = None
+    volume_ratio_below: float | None = None
+    price_above: float | None = None
+    price_below: float | None = None
+    price_change_pct_above: float | None = None
+    price_change_pct_below: float | None = None
+
+    def has_any_condition(self) -> bool:
+        """Check if at least one condition field is set."""
+        return any(
+            v is not None
+            for v in (
+                self.rsi_below,
+                self.rsi_above,
+                self.volume_ratio_above,
+                self.volume_ratio_below,
+                self.price_above,
+                self.price_below,
+                self.price_change_pct_above,
+                self.price_change_pct_below,
+            )
+        )
+
+
+class StockScenario(BaseModel):
+    """A single condition-action rule for one stock."""
+
+    condition: StockCondition
+    action: ScenarioAction
+    confidence: int = Field(ge=0, le=100)
+    allocation_pct: float = Field(ge=0, le=100, default=10.0)
+    stop_loss_pct: float = Field(le=0, default=-2.0)
+    take_profit_pct: float = Field(ge=0, default=3.0)
+    rationale: str = ""
+
+
+class StockPlaybook(BaseModel):
+    """All scenarios for a single stock (ordered by priority)."""
+
+    stock_code: str
+    stock_name: str = ""
+    scenarios: list[StockScenario] = Field(min_length=1)
+
+
+class GlobalRule(BaseModel):
+    """Portfolio-level rule (checked before stock-level scenarios)."""
+
+    condition: str  # e.g. "portfolio_pnl_pct < -2.0"
+    action: ScenarioAction
+    rationale: str = ""
+
+
+class CrossMarketContext(BaseModel):
+    """Summary of another market's state for cross-market awareness."""
+
+    market: str  # e.g. "US" or "KR"
+    date: str
+    total_pnl: float = 0.0
+    win_rate: float = 0.0
+    index_change_pct: float = 0.0  # e.g. KOSPI or S&P500 change
+    key_events: list[str] = Field(default_factory=list)
+    lessons: list[str] = Field(default_factory=list)
+
+
+class DayPlaybook(BaseModel):
+    """Complete playbook for a single trading day in a single market.
+
+    Generated by PreMarketPlanner (1 Gemini call per market per day).
+    Consumed by ScenarioEngine during market hours (0 API calls).
+    """
+
+    date: date
+    market: str  # "KR" or "US"
+    market_outlook: MarketOutlook = MarketOutlook.NEUTRAL
+    generated_at: str = ""  # ISO timestamp
+    gemini_model: str = ""
+    token_count: int = 0
+    global_rules: list[GlobalRule] = Field(default_factory=list)
+    stock_playbooks: list[StockPlaybook] = Field(default_factory=list)
+    default_action: ScenarioAction = ScenarioAction.HOLD
+    context_summary: dict = Field(default_factory=dict)
+    cross_market: CrossMarketContext | None = None
+
+    @field_validator("stock_playbooks")
+    @classmethod
+    def validate_unique_stocks(cls, v: list[StockPlaybook]) -> list[StockPlaybook]:
+        codes = [pb.stock_code for pb in v]
+        if len(codes) != len(set(codes)):
+            raise ValueError("Duplicate stock codes in playbook")
+        return v
+
+    def get_stock_playbook(self, stock_code: str) -> StockPlaybook | None:
+        """Find the playbook for a specific stock."""
+        for pb in self.stock_playbooks:
+            if pb.stock_code == stock_code:
+                return pb
+        return None
+
+    @property
+    def scenario_count(self) -> int:
+        """Total number of scenarios across all stocks."""
+        return sum(len(pb.scenarios) for pb in self.stock_playbooks)
+
+    @property
+    def stock_count(self) -> int:
+        """Number of stocks with scenarios."""
+        return len(self.stock_playbooks)
+
+    def model_post_init(self, __context: object) -> None:
+        """Set generated_at if not provided."""
+        if not self.generated_at:
+            self.generated_at = datetime.now(UTC).isoformat()

src/strategy/playbook_store.py

@@ -0,0 +1,184 @@
+"""Playbook persistence layer — CRUD for DayPlaybook in SQLite.
+
+Stores and retrieves market-specific daily playbooks with JSON serialization.
+Designed for the pre-market strategy system (one playbook per market per day).
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import sqlite3
+from datetime import date
+
+from src.strategy.models import DayPlaybook, PlaybookStatus
+
+logger = logging.getLogger(__name__)
+
+
+class PlaybookStore:
+    """CRUD operations for DayPlaybook persistence."""
+
+    def __init__(self, conn: sqlite3.Connection) -> None:
+        self._conn = conn
+
+    def save(self, playbook: DayPlaybook) -> int:
+        """Save or replace a playbook for a given date+market.
+
+        Uses INSERT OR REPLACE to enforce UNIQUE(date, market).
+
+        Returns:
+            The row id of the inserted/replaced record.
+        """
+        playbook_json = playbook.model_dump_json()
+        cursor = self._conn.execute(
+            """
+            INSERT OR REPLACE INTO playbooks
+                (date, market, status, playbook_json, generated_at,
+                 token_count, scenario_count, match_count)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+            """,
+            (
+                playbook.date.isoformat(),
+                playbook.market,
+                PlaybookStatus.READY.value,
+                playbook_json,
+                playbook.generated_at,
+                playbook.token_count,
+                playbook.scenario_count,
+                0,
+            ),
+        )
+        self._conn.commit()
+        row_id = cursor.lastrowid or 0
+        logger.info(
+            "Saved playbook for %s/%s (%d stocks, %d scenarios)",
+            playbook.date, playbook.market,
+            playbook.stock_count, playbook.scenario_count,
+        )
+        return row_id
+
+    def load(self, target_date: date, market: str) -> DayPlaybook | None:
+        """Load a playbook for a specific date and market.
+
+        Returns:
+            DayPlaybook if found, None otherwise.
+        """
+        row = self._conn.execute(
+            "SELECT playbook_json FROM playbooks WHERE date = ? AND market = ?",
+            (target_date.isoformat(), market),
+        ).fetchone()
+        if row is None:
+            return None
+        return DayPlaybook.model_validate_json(row[0])
+
+    def get_status(self, target_date: date, market: str) -> PlaybookStatus | None:
+        """Get the status of a playbook without deserializing the full JSON."""
+        row = self._conn.execute(
+            "SELECT status FROM playbooks WHERE date = ? AND market = ?",
+            (target_date.isoformat(), market),
+        ).fetchone()
+        if row is None:
+            return None
+        return PlaybookStatus(row[0])
+
+    def update_status(self, target_date: date, market: str, status: PlaybookStatus) -> bool:
+        """Update the status of a playbook.
+
+        Returns:
+            True if a row was updated, False if not found.
+        """
+        cursor = self._conn.execute(
+            "UPDATE playbooks SET status = ? WHERE date = ? AND market = ?",
+            (status.value, target_date.isoformat(), market),
+        )
+        self._conn.commit()
+        return cursor.rowcount > 0
+
+    def increment_match_count(self, target_date: date, market: str) -> bool:
+        """Increment the match_count for tracking scenario hits during the day.
+
+        Returns:
+            True if a row was updated, False if not found.
+        """
+        cursor = self._conn.execute(
+            "UPDATE playbooks SET match_count = match_count + 1 WHERE date = ? AND market = ?",
+            (target_date.isoformat(), market),
+        )
+        self._conn.commit()
+        return cursor.rowcount > 0
+
+    def get_stats(self, target_date: date, market: str) -> dict | None:
+        """Get playbook stats without full deserialization.
+
+        Returns:
+            Dict with status, token_count, scenario_count, match_count, or None.
+        """
+        row = self._conn.execute(
+            """
+            SELECT status, token_count, scenario_count, match_count, generated_at
+            FROM playbooks WHERE date = ? AND market = ?
+            """,
+            (target_date.isoformat(), market),
+        ).fetchone()
+        if row is None:
+            return None
+        return {
+            "status": row[0],
+            "token_count": row[1],
+            "scenario_count": row[2],
+            "match_count": row[3],
+            "generated_at": row[4],
+        }
+
+    def list_recent(self, market: str | None = None, limit: int = 7) -> list[dict]:
+        """List recent playbooks with summary info.
+
+        Args:
+            market: Filter by market code. None for all markets.
+            limit: Max number of results.
+
+        Returns:
+            List of dicts with date, market, status, scenario_count, match_count.
+        """
+        if market is not None:
+            rows = self._conn.execute(
+                """
+                SELECT date, market, status, scenario_count, match_count
+                FROM playbooks WHERE market = ?
+                ORDER BY date DESC LIMIT ?
+                """,
+                (market, limit),
+            ).fetchall()
+        else:
+            rows = self._conn.execute(
+                """
+                SELECT date, market, status, scenario_count, match_count
+                FROM playbooks
+                ORDER BY date DESC LIMIT ?
+                """,
+                (limit,),
+            ).fetchall()
+        return [
+            {
+                "date": row[0],
+                "market": row[1],
+                "status": row[2],
+                "scenario_count": row[3],
+                "match_count": row[4],
+            }
+            for row in rows
+        ]
+
+    def delete(self, target_date: date, market: str) -> bool:
+        """Delete a playbook.
+
+        Returns:
+            True if a row was deleted, False if not found.
+        """
+        cursor = self._conn.execute(
+            "DELETE FROM playbooks WHERE date = ? AND market = ?",
+            (target_date.isoformat(), market),
+        )
+        self._conn.commit()
+        return cursor.rowcount > 0

src/strategy/pre_market_planner.py

@@ -0,0 +1,472 @@
+"""Pre-market planner — generates DayPlaybook via Gemini before market open.
+
+One Gemini API call per market per day. Candidates come from SmartVolatilityScanner.
+On failure, returns a defensive playbook (all HOLD, no trades).
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from datetime import date, timedelta
+from typing import Any
+
+from src.analysis.smart_scanner import ScanCandidate
+from src.brain.context_selector import ContextSelector, DecisionType
+from src.brain.gemini_client import GeminiClient
+from src.config import Settings
+from src.context.store import ContextLayer, ContextStore
+from src.strategy.models import (
+    CrossMarketContext,
+    DayPlaybook,
+    GlobalRule,
+    MarketOutlook,
+    ScenarioAction,
+    StockCondition,
+    StockPlaybook,
+    StockScenario,
+)
+
+logger = logging.getLogger(__name__)
+
+# Mapping from string to MarketOutlook enum
+_OUTLOOK_MAP: dict[str, MarketOutlook] = {
+    "bullish": MarketOutlook.BULLISH,
+    "neutral_to_bullish": MarketOutlook.NEUTRAL_TO_BULLISH,
+    "neutral": MarketOutlook.NEUTRAL,
+    "neutral_to_bearish": MarketOutlook.NEUTRAL_TO_BEARISH,
+    "bearish": MarketOutlook.BEARISH,
+}
+
+_ACTION_MAP: dict[str, ScenarioAction] = {
+    "BUY": ScenarioAction.BUY,
+    "SELL": ScenarioAction.SELL,
+    "HOLD": ScenarioAction.HOLD,
+    "REDUCE_ALL": ScenarioAction.REDUCE_ALL,
+}
+
+
+class PreMarketPlanner:
+    """Generates a DayPlaybook by calling Gemini once before market open.
+
+    Flow:
+    1. Collect strategic context (L5-L7) + cross-market context
+    2. Build a structured prompt with scan candidates
+    3. Call Gemini for JSON scenario generation
+    4. Parse and validate response into DayPlaybook
+    5. On failure → defensive playbook (HOLD everything)
+    """
+
+    def __init__(
+        self,
+        gemini_client: GeminiClient,
+        context_store: ContextStore,
+        context_selector: ContextSelector,
+        settings: Settings,
+    ) -> None:
+        self._gemini = gemini_client
+        self._context_store = context_store
+        self._context_selector = context_selector
+        self._settings = settings
+
+    async def generate_playbook(
+        self,
+        market: str,
+        candidates: list[ScanCandidate],
+        today: date | None = None,
+    ) -> DayPlaybook:
+        """Generate a DayPlaybook for a market using Gemini.
+
+        Args:
+            market: Market code ("KR" or "US")
+            candidates: Stock candidates from SmartVolatilityScanner
+            today: Override date (defaults to date.today()). Use market-local date.
+
+        Returns:
+            DayPlaybook with scenarios. Empty/defensive if no candidates or failure.
+        """
+        if today is None:
+            today = date.today()
+
+        if not candidates:
+            logger.info("No candidates for %s — returning empty playbook", market)
+            return self._empty_playbook(today, market)
+
+        try:
+            # 1. Gather context
+            context_data = self._gather_context()
+            self_market_scorecard = self.build_self_market_scorecard(market, today)
+            cross_market = self.build_cross_market_context(market, today)
+
+            # 2. Build prompt
+            prompt = self._build_prompt(
+                market,
+                candidates,
+                context_data,
+                self_market_scorecard,
+                cross_market,
+            )
+
+            # 3. Call Gemini
+            market_data = {
+                "stock_code": "PLANNER",
+                "current_price": 0,
+                "prompt_override": prompt,
+            }
+            decision = await self._gemini.decide(market_data)
+
+            # 4. Parse response
+            playbook = self._parse_response(
+                decision.rationale, today, market, candidates, cross_market
+            )
+            playbook_with_tokens = playbook.model_copy(
+                update={"token_count": decision.token_count}
+            )
+            logger.info(
+                "Generated playbook for %s: %d stocks, %d scenarios, %d tokens",
+                market,
+                playbook_with_tokens.stock_count,
+                playbook_with_tokens.scenario_count,
+                playbook_with_tokens.token_count,
+            )
+            return playbook_with_tokens
+
+        except Exception:
+            logger.exception("Playbook generation failed for %s", market)
+            if self._settings.DEFENSIVE_PLAYBOOK_ON_FAILURE:
+                return self._defensive_playbook(today, market, candidates)
+            return self._empty_playbook(today, market)
+
+    def build_cross_market_context(
+        self, target_market: str, today: date | None = None,
+    ) -> CrossMarketContext | None:
+        """Build cross-market context from the other market's L6 data.
+
+        KR planner → reads US scorecard from previous night.
+        US planner → reads KR scorecard from today.
+
+        Args:
+            target_market: The market being planned ("KR" or "US")
+            today: Override date (defaults to date.today()). Use market-local date.
+        """
+        other_market = "US" if target_market == "KR" else "KR"
+        if today is None:
+            today = date.today()
+        timeframe_date = today - timedelta(days=1) if target_market == "KR" else today
+        timeframe = timeframe_date.isoformat()
+
+        scorecard_key = f"scorecard_{other_market}"
+        scorecard_data = self._context_store.get_context(
+            ContextLayer.L6_DAILY, timeframe, scorecard_key
+        )
+
+        if scorecard_data is None:
+            logger.debug("No cross-market scorecard found for %s", other_market)
+            return None
+
+        if isinstance(scorecard_data, str):
+            try:
+                scorecard_data = json.loads(scorecard_data)
+            except (json.JSONDecodeError, TypeError):
+                return None
+
+        if not isinstance(scorecard_data, dict):
+            return None
+
+        return CrossMarketContext(
+            market=other_market,
+            date=timeframe,
+            total_pnl=float(scorecard_data.get("total_pnl", 0.0)),
+            win_rate=float(scorecard_data.get("win_rate", 0.0)),
+            index_change_pct=float(scorecard_data.get("index_change_pct", 0.0)),
+            key_events=scorecard_data.get("key_events", []),
+            lessons=scorecard_data.get("lessons", []),
+        )
+
+    def build_self_market_scorecard(
+        self, market: str, today: date | None = None,
+    ) -> dict[str, Any] | None:
+        """Build previous-day scorecard for the same market."""
+        if today is None:
+            today = date.today()
+        timeframe = (today - timedelta(days=1)).isoformat()
+        scorecard_key = f"scorecard_{market}"
+        scorecard_data = self._context_store.get_context(
+            ContextLayer.L6_DAILY, timeframe, scorecard_key
+        )
+
+        if scorecard_data is None:
+            return None
+
+        if isinstance(scorecard_data, str):
+            try:
+                scorecard_data = json.loads(scorecard_data)
+            except (json.JSONDecodeError, TypeError):
+                return None
+
+        if not isinstance(scorecard_data, dict):
+            return None
+
+        return {
+            "date": timeframe,
+            "total_pnl": float(scorecard_data.get("total_pnl", 0.0)),
+            "win_rate": float(scorecard_data.get("win_rate", 0.0)),
+            "lessons": scorecard_data.get("lessons", []),
+        }
+
+    def _gather_context(self) -> dict[str, Any]:
+        """Gather strategic context using ContextSelector."""
+        layers = self._context_selector.select_layers(
+            decision_type=DecisionType.STRATEGIC,
+            include_realtime=True,
+        )
+        return self._context_selector.get_context_data(layers, max_items_per_layer=10)
+
+    def _build_prompt(
+        self,
+        market: str,
+        candidates: list[ScanCandidate],
+        context_data: dict[str, Any],
+        self_market_scorecard: dict[str, Any] | None,
+        cross_market: CrossMarketContext | None,
+    ) -> str:
+        """Build a structured prompt for Gemini to generate scenario JSON."""
+        max_scenarios = self._settings.MAX_SCENARIOS_PER_STOCK
+
+        candidates_text = "\n".join(
+            f"  - {c.stock_code} ({c.name}): price={c.price}, "
+            f"RSI={c.rsi:.1f}, volume_ratio={c.volume_ratio:.1f}, "
+            f"signal={c.signal}, score={c.score:.1f}"
+            for c in candidates
+        )
+
+        cross_market_text = ""
+        if cross_market:
+            cross_market_text = (
+                f"\n## Other Market ({cross_market.market}) Summary\n"
+                f"- P&L: {cross_market.total_pnl:+.2f}%\n"
+                f"- Win Rate: {cross_market.win_rate:.0f}%\n"
+                f"- Index Change: {cross_market.index_change_pct:+.2f}%\n"
+            )
+            if cross_market.lessons:
+                cross_market_text += f"- Lessons: {'; '.join(cross_market.lessons[:3])}\n"
+
+        self_market_text = ""
+        if self_market_scorecard:
+            self_market_text = (
+                f"\n## My Market Previous Day ({market})\n"
+                f"- Date: {self_market_scorecard['date']}\n"
+                f"- P&L: {self_market_scorecard['total_pnl']:+.2f}%\n"
+                f"- Win Rate: {self_market_scorecard['win_rate']:.0f}%\n"
+            )
+            lessons = self_market_scorecard.get("lessons", [])
+            if lessons:
+                self_market_text += f"- Lessons: {'; '.join(lessons[:3])}\n"
+
+        context_text = ""
+        if context_data:
+            context_text = "\n## Strategic Context\n"
+            for layer_name, layer_data in context_data.items():
+                if layer_data:
+                    context_text += f"### {layer_name}\n"
+                    for key, value in list(layer_data.items())[:5]:
+                        context_text += f"  - {key}: {value}\n"
+
+        return (
+            f"You are a pre-market trading strategist for the {market} market.\n"
+            f"Generate structured trading scenarios for today.\n\n"
+            f"## Candidates (from volatility scanner)\n{candidates_text}\n"
+            f"{self_market_text}"
+            f"{cross_market_text}"
+            f"{context_text}\n"
+            f"## Instructions\n"
+            f"Return a JSON object with this exact structure:\n"
+            f'{{\n'
+            f'  "market_outlook": "bullish|neutral_to_bullish|neutral'
+            f'|neutral_to_bearish|bearish",\n'
+            f'  "global_rules": [\n'
+            f'    {{"condition": "portfolio_pnl_pct < -2.0",'
+            f' "action": "REDUCE_ALL", "rationale": "..."}}\n'
+            f'  ],\n'
+            f'  "stocks": [\n'
+            f'    {{\n'
+            f'      "stock_code": "...",\n'
+            f'      "scenarios": [\n'
+            f'        {{\n'
+            f'          "condition": {{"rsi_below": 30, "volume_ratio_above": 2.0}},\n'
+            f'          "action": "BUY|SELL|HOLD",\n'
+            f'          "confidence": 85,\n'
+            f'          "allocation_pct": 10.0,\n'
+            f'          "stop_loss_pct": -2.0,\n'
+            f'          "take_profit_pct": 3.0,\n'
+            f'          "rationale": "..."\n'
+            f'        }}\n'
+            f'      ]\n'
+            f'    }}\n'
+            f'  ]\n'
+            f'}}\n\n'
+            f"Rules:\n"
+            f"- Max {max_scenarios} scenarios per stock\n"
+            f"- Only use stocks from the candidates list\n"
+            f"- Confidence 0-100 (80+ for actionable trades)\n"
+            f"- stop_loss_pct must be <= 0, take_profit_pct must be >= 0\n"
+            f"- Return ONLY the JSON, no markdown fences or explanation\n"
+        )
+
+    def _parse_response(
+        self,
+        response_text: str,
+        today: date,
+        market: str,
+        candidates: list[ScanCandidate],
+        cross_market: CrossMarketContext | None,
+    ) -> DayPlaybook:
+        """Parse Gemini's JSON response into a validated DayPlaybook."""
+        cleaned = self._extract_json(response_text)
+        data = json.loads(cleaned)
+
+        valid_codes = {c.stock_code for c in candidates}
+
+        # Parse market outlook
+        outlook_str = data.get("market_outlook", "neutral")
+        market_outlook = _OUTLOOK_MAP.get(outlook_str, MarketOutlook.NEUTRAL)
+
+        # Parse global rules
+        global_rules = []
+        for rule_data in data.get("global_rules", []):
+            action_str = rule_data.get("action", "HOLD")
+            action = _ACTION_MAP.get(action_str, ScenarioAction.HOLD)
+            global_rules.append(
+                GlobalRule(
+                    condition=rule_data.get("condition", ""),
+                    action=action,
+                    rationale=rule_data.get("rationale", ""),
+                )
+            )
+
+        # Parse stock playbooks
+        stock_playbooks = []
+        max_scenarios = self._settings.MAX_SCENARIOS_PER_STOCK
+        for stock_data in data.get("stocks", []):
+            code = stock_data.get("stock_code", "")
+            if code not in valid_codes:
+                logger.warning("Gemini returned unknown stock %s — skipping", code)
+                continue
+
+            scenarios = []
+            for sc_data in stock_data.get("scenarios", [])[:max_scenarios]:
+                scenario = self._parse_scenario(sc_data)
+                if scenario:
+                    scenarios.append(scenario)
+
+            if scenarios:
+                stock_playbooks.append(
+                    StockPlaybook(
+                        stock_code=code,
+                        scenarios=scenarios,
+                    )
+                )
+
+        return DayPlaybook(
+            date=today,
+            market=market,
+            market_outlook=market_outlook,
+            global_rules=global_rules,
+            stock_playbooks=stock_playbooks,
+            cross_market=cross_market,
+        )
+
+    def _parse_scenario(self, sc_data: dict) -> StockScenario | None:
+        """Parse a single scenario from JSON data. Returns None if invalid."""
+        try:
+            cond_data = sc_data.get("condition", {})
+            condition = StockCondition(
+                rsi_below=cond_data.get("rsi_below"),
+                rsi_above=cond_data.get("rsi_above"),
+                volume_ratio_above=cond_data.get("volume_ratio_above"),
+                volume_ratio_below=cond_data.get("volume_ratio_below"),
+                price_above=cond_data.get("price_above"),
+                price_below=cond_data.get("price_below"),
+                price_change_pct_above=cond_data.get("price_change_pct_above"),
+                price_change_pct_below=cond_data.get("price_change_pct_below"),
+            )
+
+            if not condition.has_any_condition():
+                logger.warning("Scenario has no conditions — skipping")
+                return None
+
+            action_str = sc_data.get("action", "HOLD")
+            action = _ACTION_MAP.get(action_str, ScenarioAction.HOLD)
+
+            return StockScenario(
+                condition=condition,
+                action=action,
+                confidence=int(sc_data.get("confidence", 50)),
+                allocation_pct=float(sc_data.get("allocation_pct", 10.0)),
+                stop_loss_pct=float(sc_data.get("stop_loss_pct", -2.0)),
+                take_profit_pct=float(sc_data.get("take_profit_pct", 3.0)),
+                rationale=sc_data.get("rationale", ""),
+            )
+        except (ValueError, TypeError) as e:
+            logger.warning("Failed to parse scenario: %s", e)
+            return None
+
+    @staticmethod
+    def _extract_json(text: str) -> str:
+        """Extract JSON from response, stripping markdown fences if present."""
+        stripped = text.strip()
+        if stripped.startswith("```"):
+            # Remove first line (```json or ```) and last line (```)
+            lines = stripped.split("\n")
+            lines = lines[1:]  # Remove opening fence
+            if lines and lines[-1].strip() == "```":
+                lines = lines[:-1]
+            stripped = "\n".join(lines)
+        return stripped.strip()
+
+    @staticmethod
+    def _empty_playbook(today: date, market: str) -> DayPlaybook:
+        """Return an empty playbook (no stocks, no scenarios)."""
+        return DayPlaybook(
+            date=today,
+            market=market,
+            market_outlook=MarketOutlook.NEUTRAL,
+            stock_playbooks=[],
+        )
+
+    @staticmethod
+    def _defensive_playbook(
+        today: date,
+        market: str,
+        candidates: list[ScanCandidate],
+    ) -> DayPlaybook:
+        """Return a defensive playbook — HOLD everything with stop-loss ready."""
+        stock_playbooks = [
+            StockPlaybook(
+                stock_code=c.stock_code,
+                scenarios=[
+                    StockScenario(
+                        condition=StockCondition(price_change_pct_below=-3.0),
+                        action=ScenarioAction.SELL,
+                        confidence=90,
+                        stop_loss_pct=-3.0,
+                        rationale="Defensive stop-loss (planner failure)",
+                    ),
+                ],
+            )
+            for c in candidates
+        ]
+        return DayPlaybook(
+            date=today,
+            market=market,
+            market_outlook=MarketOutlook.NEUTRAL_TO_BEARISH,
+            default_action=ScenarioAction.HOLD,
+            stock_playbooks=stock_playbooks,
+            global_rules=[
+                GlobalRule(
+                    condition="portfolio_pnl_pct < -2.0",
+                    action=ScenarioAction.REDUCE_ALL,
+                    rationale="Defensive: reduce on loss threshold",
+                ),
+            ],
+        )

src/strategy/scenario_engine.py

@@ -0,0 +1,270 @@
+"""Local scenario engine for playbook execution.
+
+Matches real-time market conditions against pre-defined scenarios
+without any API calls. Designed for sub-100ms execution.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from typing import Any
+
+from src.strategy.models import (
+    DayPlaybook,
+    GlobalRule,
+    ScenarioAction,
+    StockCondition,
+    StockScenario,
+)
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ScenarioMatch:
+    """Result of matching market conditions against scenarios."""
+
+    stock_code: str
+    matched_scenario: StockScenario | None
+    action: ScenarioAction
+    confidence: int
+    rationale: str
+    global_rule_triggered: GlobalRule | None = None
+    match_details: dict[str, Any] = field(default_factory=dict)
+
+
+class ScenarioEngine:
+    """Evaluates playbook scenarios against real-time market data.
+
+    No API calls — pure Python condition matching.
+
+    Expected market_data keys: "rsi", "volume_ratio", "current_price", "price_change_pct".
+    Callers must normalize data source keys to match this contract.
+    """
+
+    def __init__(self) -> None:
+        self._warned_keys: set[str] = set()
+
+    @staticmethod
+    def _safe_float(value: Any) -> float | None:
+        """Safely cast a value to float. Returns None on failure."""
+        if value is None:
+            return None
+        try:
+            return float(value)
+        except (ValueError, TypeError):
+            return None
+
+    def _warn_missing_key(self, key: str) -> None:
+        """Log a missing-key warning once per key per engine instance."""
+        if key not in self._warned_keys:
+            self._warned_keys.add(key)
+            logger.warning("Condition requires '%s' but key missing from market_data", key)
+
+    def evaluate(
+        self,
+        playbook: DayPlaybook,
+        stock_code: str,
+        market_data: dict[str, Any],
+        portfolio_data: dict[str, Any],
+    ) -> ScenarioMatch:
+        """Match market conditions to scenarios and return a decision.
+
+        Algorithm:
+        1. Check global rules first (portfolio-level circuit breakers)
+        2. Find the StockPlaybook for the given stock_code
+        3. Iterate scenarios in order (first match wins)
+        4. If no match, return playbook.default_action (HOLD)
+
+        Args:
+            playbook: Today's DayPlaybook for this market
+            stock_code: Stock ticker to evaluate
+            market_data: Real-time market data (price, rsi, volume_ratio, etc.)
+            portfolio_data: Portfolio state (pnl_pct, total_cash, etc.)
+
+        Returns:
+            ScenarioMatch with the decision
+        """
+        # 1. Check global rules
+        triggered_rule = self.check_global_rules(playbook, portfolio_data)
+        if triggered_rule is not None:
+            logger.info(
+                "Global rule triggered for %s: %s -> %s",
+                stock_code,
+                triggered_rule.condition,
+                triggered_rule.action.value,
+            )
+            return ScenarioMatch(
+                stock_code=stock_code,
+                matched_scenario=None,
+                action=triggered_rule.action,
+                confidence=100,
+                rationale=f"Global rule: {triggered_rule.rationale or triggered_rule.condition}",
+                global_rule_triggered=triggered_rule,
+            )
+
+        # 2. Find stock playbook
+        stock_pb = playbook.get_stock_playbook(stock_code)
+        if stock_pb is None:
+            logger.debug("No playbook for %s — defaulting to %s", stock_code, playbook.default_action)
+            return ScenarioMatch(
+                stock_code=stock_code,
+                matched_scenario=None,
+                action=playbook.default_action,
+                confidence=0,
+                rationale=f"No scenarios defined for {stock_code}",
+            )
+
+        # 3. Iterate scenarios (first match wins)
+        for scenario in stock_pb.scenarios:
+            if self.evaluate_condition(scenario.condition, market_data):
+                logger.info(
+                    "Scenario matched for %s: %s (confidence=%d)",
+                    stock_code,
+                    scenario.action.value,
+                    scenario.confidence,
+                )
+                return ScenarioMatch(
+                    stock_code=stock_code,
+                    matched_scenario=scenario,
+                    action=scenario.action,
+                    confidence=scenario.confidence,
+                    rationale=scenario.rationale,
+                    match_details=self._build_match_details(scenario.condition, market_data),
+                )
+
+        # 4. No match — default action
+        logger.debug("No scenario matched for %s — defaulting to %s", stock_code, playbook.default_action)
+        return ScenarioMatch(
+            stock_code=stock_code,
+            matched_scenario=None,
+            action=playbook.default_action,
+            confidence=0,
+            rationale="No scenario conditions met — holding position",
+        )
+
+    def check_global_rules(
+        self,
+        playbook: DayPlaybook,
+        portfolio_data: dict[str, Any],
+    ) -> GlobalRule | None:
+        """Check portfolio-level rules. Returns first triggered rule or None."""
+        for rule in playbook.global_rules:
+            if self._evaluate_global_condition(rule.condition, portfolio_data):
+                return rule
+        return None
+
+    def evaluate_condition(
+        self,
+        condition: StockCondition,
+        market_data: dict[str, Any],
+    ) -> bool:
+        """Evaluate all non-None fields in condition as AND.
+
+        Returns True only if ALL specified conditions are met.
+        Empty condition (no fields set) returns False for safety.
+        """
+        if not condition.has_any_condition():
+            return False
+
+        checks: list[bool] = []
+
+        rsi = self._safe_float(market_data.get("rsi"))
+        if condition.rsi_below is not None or condition.rsi_above is not None:
+            if "rsi" not in market_data:
+                self._warn_missing_key("rsi")
+        if condition.rsi_below is not None:
+            checks.append(rsi is not None and rsi < condition.rsi_below)
+        if condition.rsi_above is not None:
+            checks.append(rsi is not None and rsi > condition.rsi_above)
+
+        volume_ratio = self._safe_float(market_data.get("volume_ratio"))
+        if condition.volume_ratio_above is not None or condition.volume_ratio_below is not None:
+            if "volume_ratio" not in market_data:
+                self._warn_missing_key("volume_ratio")
+        if condition.volume_ratio_above is not None:
+            checks.append(volume_ratio is not None and volume_ratio > condition.volume_ratio_above)
+        if condition.volume_ratio_below is not None:
+            checks.append(volume_ratio is not None and volume_ratio < condition.volume_ratio_below)
+
+        price = self._safe_float(market_data.get("current_price"))
+        if condition.price_above is not None or condition.price_below is not None:
+            if "current_price" not in market_data:
+                self._warn_missing_key("current_price")
+        if condition.price_above is not None:
+            checks.append(price is not None and price > condition.price_above)
+        if condition.price_below is not None:
+            checks.append(price is not None and price < condition.price_below)
+
+        price_change_pct = self._safe_float(market_data.get("price_change_pct"))
+        if condition.price_change_pct_above is not None or condition.price_change_pct_below is not None:
+            if "price_change_pct" not in market_data:
+                self._warn_missing_key("price_change_pct")
+        if condition.price_change_pct_above is not None:
+            checks.append(price_change_pct is not None and price_change_pct > condition.price_change_pct_above)
+        if condition.price_change_pct_below is not None:
+            checks.append(price_change_pct is not None and price_change_pct < condition.price_change_pct_below)
+
+        return len(checks) > 0 and all(checks)
+
+    def _evaluate_global_condition(
+        self,
+        condition_str: str,
+        portfolio_data: dict[str, Any],
+    ) -> bool:
+        """Evaluate a simple global condition string against portfolio data.
+
+        Supports: "field < value", "field > value", "field <= value", "field >= value"
+        """
+        parts = condition_str.strip().split()
+        if len(parts) != 3:
+            logger.warning("Invalid global condition format: %s", condition_str)
+            return False
+
+        field_name, operator, value_str = parts
+        try:
+            threshold = float(value_str)
+        except ValueError:
+            logger.warning("Invalid threshold in condition: %s", condition_str)
+            return False
+
+        actual = portfolio_data.get(field_name)
+        if actual is None:
+            return False
+
+        try:
+            actual_val = float(actual)
+        except (ValueError, TypeError):
+            return False
+
+        if operator == "<":
+            return actual_val < threshold
+        elif operator == ">":
+            return actual_val > threshold
+        elif operator == "<=":
+            return actual_val <= threshold
+        elif operator == ">=":
+            return actual_val >= threshold
+        else:
+            logger.warning("Unknown operator in condition: %s", operator)
+            return False
+
+    def _build_match_details(
+        self,
+        condition: StockCondition,
+        market_data: dict[str, Any],
+    ) -> dict[str, Any]:
+        """Build a summary of which conditions matched and their normalized values."""
+        details: dict[str, Any] = {}
+
+        if condition.rsi_below is not None or condition.rsi_above is not None:
+            details["rsi"] = self._safe_float(market_data.get("rsi"))
+        if condition.volume_ratio_above is not None or condition.volume_ratio_below is not None:
+            details["volume_ratio"] = self._safe_float(market_data.get("volume_ratio"))
+        if condition.price_above is not None or condition.price_below is not None:
+            details["current_price"] = self._safe_float(market_data.get("current_price"))
+        if condition.price_change_pct_above is not None or condition.price_change_pct_below is not None:
+            details["price_change_pct"] = self._safe_float(market_data.get("price_change_pct"))
+
+        return details

tests/test_context.py

@@ -161,7 +161,7 @@ class TestContextAggregator:
         self, aggregator: ContextAggregator, db_conn: sqlite3.Connection
     ) -> None:
         """Test aggregating daily metrics from trades."""
-        date = "2026-02-04"
+        date = datetime.now(UTC).date().isoformat()
 
         # Create sample trades
         log_trade(db_conn, "005930", "BUY", 85, "Good signal", quantity=10, price=70000, pnl=500)
@@ -175,36 +175,44 @@ class TestContextAggregator:
         db_conn.commit()
 
         # Aggregate
-        aggregator.aggregate_daily_from_trades(date)
+        aggregator.aggregate_daily_from_trades(date, market="KR")
 
         # Verify L6 contexts
         store = aggregator.store
-        assert store.get_context(ContextLayer.L6_DAILY, date, "trade_count") == 3
+        assert store.get_context(ContextLayer.L6_DAILY, date, "trade_count_KR") == 3
-        assert store.get_context(ContextLayer.L6_DAILY, date, "buys") == 1
+        assert store.get_context(ContextLayer.L6_DAILY, date, "buys_KR") == 1
-        assert store.get_context(ContextLayer.L6_DAILY, date, "sells") == 1
+        assert store.get_context(ContextLayer.L6_DAILY, date, "sells_KR") == 1
-        assert store.get_context(ContextLayer.L6_DAILY, date, "holds") == 1
+        assert store.get_context(ContextLayer.L6_DAILY, date, "holds_KR") == 1
-        assert store.get_context(ContextLayer.L6_DAILY, date, "total_pnl") == 2000.0
+        assert store.get_context(ContextLayer.L6_DAILY, date, "total_pnl_KR") == 2000.0
-        assert store.get_context(ContextLayer.L6_DAILY, date, "unique_stocks") == 3
+        assert store.get_context(ContextLayer.L6_DAILY, date, "unique_stocks_KR") == 3
         # 2 wins, 0 losses
-        assert store.get_context(ContextLayer.L6_DAILY, date, "win_rate") == 100.0
+        assert store.get_context(ContextLayer.L6_DAILY, date, "win_rate_KR") == 100.0
 
     def test_aggregate_weekly_from_daily(self, aggregator: ContextAggregator) -> None:
         """Test aggregating weekly metrics from daily."""
         week = "2026-W06"
 
         # Set daily contexts
-        aggregator.store.set_context(ContextLayer.L6_DAILY, "2026-02-02", "total_pnl", 100.0)
+        aggregator.store.set_context(
-        aggregator.store.set_context(ContextLayer.L6_DAILY, "2026-02-03", "total_pnl", 200.0)
+            ContextLayer.L6_DAILY, "2026-02-02", "total_pnl_KR", 100.0
-        aggregator.store.set_context(ContextLayer.L6_DAILY, "2026-02-02", "avg_confidence", 80.0)
+        )
-        aggregator.store.set_context(ContextLayer.L6_DAILY, "2026-02-03", "avg_confidence", 85.0)
+        aggregator.store.set_context(
+            ContextLayer.L6_DAILY, "2026-02-03", "total_pnl_KR", 200.0
+        )
+        aggregator.store.set_context(
+            ContextLayer.L6_DAILY, "2026-02-02", "avg_confidence_KR", 80.0
+        )
+        aggregator.store.set_context(
+            ContextLayer.L6_DAILY, "2026-02-03", "avg_confidence_KR", 85.0
+        )
 
         # Aggregate
         aggregator.aggregate_weekly_from_daily(week)
 
         # Verify L5 contexts
         store = aggregator.store
-        weekly_pnl = store.get_context(ContextLayer.L5_WEEKLY, week, "weekly_pnl")
+        weekly_pnl = store.get_context(ContextLayer.L5_WEEKLY, week, "weekly_pnl_KR")
-        avg_conf = store.get_context(ContextLayer.L5_WEEKLY, week, "avg_confidence")
+        avg_conf = store.get_context(ContextLayer.L5_WEEKLY, week, "avg_confidence_KR")
 
         assert weekly_pnl == 300.0
         assert avg_conf == 82.5
@@ -214,9 +222,15 @@ class TestContextAggregator:
         month = "2026-02"
 
         # Set weekly contexts
-        aggregator.store.set_context(ContextLayer.L5_WEEKLY, "2026-W05", "weekly_pnl", 100.0)
+        aggregator.store.set_context(
-        aggregator.store.set_context(ContextLayer.L5_WEEKLY, "2026-W06", "weekly_pnl", 200.0)
+            ContextLayer.L5_WEEKLY, "2026-W05", "weekly_pnl_KR", 100.0
-        aggregator.store.set_context(ContextLayer.L5_WEEKLY, "2026-W07", "weekly_pnl", 150.0)
+        )
+        aggregator.store.set_context(
+            ContextLayer.L5_WEEKLY, "2026-W06", "weekly_pnl_KR", 200.0
+        )
+        aggregator.store.set_context(
+            ContextLayer.L5_WEEKLY, "2026-W07", "weekly_pnl_KR", 150.0
+        )
 
         # Aggregate
         aggregator.aggregate_monthly_from_weekly(month)
@@ -285,7 +299,7 @@ class TestContextAggregator:
         self, aggregator: ContextAggregator, db_conn: sqlite3.Connection
     ) -> None:
         """Test running all aggregations from L7 to L1."""
-        date = "2026-02-04"
+        date = datetime.now(UTC).date().isoformat()
 
         # Create sample trades
         log_trade(db_conn, "005930", "BUY", 85, "Good signal", quantity=10, price=70000, pnl=1000)
@@ -299,10 +313,18 @@ class TestContextAggregator:
 
         # Verify data exists in each layer
         store = aggregator.store
-        assert store.get_context(ContextLayer.L6_DAILY, date, "total_pnl") == 1000.0
+        assert store.get_context(ContextLayer.L6_DAILY, date, "total_pnl_KR") == 1000.0
-        current_week = datetime.now(UTC).strftime("%Y-W%V")
+        from datetime import date as date_cls
-        assert store.get_context(ContextLayer.L5_WEEKLY, current_week, "weekly_pnl") is not None
+        trade_date = date_cls.fromisoformat(date)
-        # Further layers depend on time alignment, just verify no crashes
+        iso_year, iso_week, _ = trade_date.isocalendar()
+        trade_week = f"{iso_year}-W{iso_week:02d}"
+        assert store.get_context(ContextLayer.L5_WEEKLY, trade_week, "weekly_pnl_KR") is not None
+        trade_month = f"{trade_date.year}-{trade_date.month:02d}"
+        trade_quarter = f"{trade_date.year}-Q{(trade_date.month - 1) // 3 + 1}"
+        trade_year = str(trade_date.year)
+        assert store.get_context(ContextLayer.L4_MONTHLY, trade_month, "monthly_pnl") == 1000.0
+        assert store.get_context(ContextLayer.L3_QUARTERLY, trade_quarter, "quarterly_pnl") == 1000.0
+        assert store.get_context(ContextLayer.L2_ANNUAL, trade_year, "annual_pnl") == 1000.0
 
 
 class TestLayerMetadata:

tests/test_context_scheduler.py

@@ -0,0 +1,104 @@
+"""Tests for ContextScheduler."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import UTC, datetime
+
+from src.context.scheduler import ContextScheduler
+
+
+@dataclass
+class StubAggregator:
+    """Stub aggregator that records calls."""
+
+    weekly_calls: list[str]
+    monthly_calls: list[str]
+    quarterly_calls: list[str]
+    annual_calls: list[str]
+    legacy_calls: int
+
+    def aggregate_weekly_from_daily(self, week: str) -> None:
+        self.weekly_calls.append(week)
+
+    def aggregate_monthly_from_weekly(self, month: str) -> None:
+        self.monthly_calls.append(month)
+
+    def aggregate_quarterly_from_monthly(self, quarter: str) -> None:
+        self.quarterly_calls.append(quarter)
+
+    def aggregate_annual_from_quarterly(self, year: str) -> None:
+        self.annual_calls.append(year)
+
+    def aggregate_legacy_from_annual(self) -> None:
+        self.legacy_calls += 1
+
+
+@dataclass
+class StubStore:
+    """Stub store that records cleanup calls."""
+
+    cleanup_calls: int = 0
+
+    def cleanup_expired_contexts(self) -> None:
+        self.cleanup_calls += 1
+
+
+def make_scheduler() -> tuple[ContextScheduler, StubAggregator, StubStore]:
+    aggregator = StubAggregator([], [], [], [], 0)
+    store = StubStore()
+    scheduler = ContextScheduler(aggregator=aggregator, store=store)
+    return scheduler, aggregator, store
+
+
+def test_run_if_due_weekly() -> None:
+    scheduler, aggregator, store = make_scheduler()
+    now = datetime(2026, 2, 8, 10, 0, tzinfo=UTC)  # Sunday
+
+    result = scheduler.run_if_due(now)
+
+    assert result.weekly is True
+    assert aggregator.weekly_calls == ["2026-W06"]
+    assert store.cleanup_calls == 1
+
+
+def test_run_if_due_monthly() -> None:
+    scheduler, aggregator, _store = make_scheduler()
+    now = datetime(2026, 2, 28, 12, 0, tzinfo=UTC)  # Last day of month
+
+    result = scheduler.run_if_due(now)
+
+    assert result.monthly is True
+    assert aggregator.monthly_calls == ["2026-02"]
+
+
+def test_run_if_due_quarterly() -> None:
+    scheduler, aggregator, _store = make_scheduler()
+    now = datetime(2026, 3, 31, 12, 0, tzinfo=UTC)  # Last day of Q1
+
+    result = scheduler.run_if_due(now)
+
+    assert result.quarterly is True
+    assert aggregator.quarterly_calls == ["2026-Q1"]
+
+
+def test_run_if_due_annual_and_legacy() -> None:
+    scheduler, aggregator, _store = make_scheduler()
+    now = datetime(2026, 12, 31, 12, 0, tzinfo=UTC)
+
+    result = scheduler.run_if_due(now)
+
+    assert result.annual is True
+    assert result.legacy is True
+    assert aggregator.annual_calls == ["2026"]
+    assert aggregator.legacy_calls == 1
+
+
+def test_cleanup_runs_once_per_day() -> None:
+    scheduler, _aggregator, store = make_scheduler()
+    now = datetime(2026, 2, 9, 9, 0, tzinfo=UTC)
+
+    scheduler.run_if_due(now)
+    scheduler.run_if_due(now)
+
+    assert store.cleanup_calls == 1

tests/test_daily_review.py

@@ -0,0 +1,383 @@
+"""Tests for DailyReviewer."""
+
+from __future__ import annotations
+
+import json
+import sqlite3
+from types import SimpleNamespace
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from src.context.layer import ContextLayer
+from src.context.store import ContextStore
+from src.db import init_db, log_trade
+from src.evolution.daily_review import DailyReviewer
+from src.evolution.scorecard import DailyScorecard
+from src.logging.decision_logger import DecisionLogger
+
+
+@pytest.fixture
+def db_conn() -> sqlite3.Connection:
+    return init_db(":memory:")
+
+
+@pytest.fixture
+def context_store(db_conn: sqlite3.Connection) -> ContextStore:
+    return ContextStore(db_conn)
+
+
+def _log_decision(
+    logger: DecisionLogger,
+    *,
+    stock_code: str,
+    market: str,
+    action: str,
+    confidence: int,
+    scenario_match: dict[str, float] | None = None,
+) -> str:
+    return logger.log_decision(
+        stock_code=stock_code,
+        market=market,
+        exchange_code="KRX" if market == "KR" else "NASDAQ",
+        action=action,
+        confidence=confidence,
+        rationale="test",
+        context_snapshot={"scenario_match": scenario_match or {}},
+        input_data={"stock_code": stock_code},
+    )
+
+
+def test_generate_scorecard_market_scoped(
+    db_conn: sqlite3.Connection, context_store: ContextStore,
+) -> None:
+    reviewer = DailyReviewer(db_conn, context_store)
+    logger = DecisionLogger(db_conn)
+
+    buy_id = _log_decision(
+        logger,
+        stock_code="005930",
+        market="KR",
+        action="BUY",
+        confidence=90,
+        scenario_match={"rsi": 29.0},
+    )
+    _log_decision(
+        logger,
+        stock_code="000660",
+        market="KR",
+        action="HOLD",
+        confidence=60,
+    )
+    _log_decision(
+        logger,
+        stock_code="AAPL",
+        market="US",
+        action="SELL",
+        confidence=80,
+        scenario_match={"volume_ratio": 2.1},
+    )
+
+    log_trade(
+        db_conn,
+        "005930",
+        "BUY",
+        90,
+        "buy",
+        quantity=1,
+        price=100.0,
+        pnl=10.0,
+        market="KR",
+        exchange_code="KRX",
+        decision_id=buy_id,
+    )
+    log_trade(
+        db_conn,
+        "000660",
+        "HOLD",
+        60,
+        "hold",
+        quantity=0,
+        price=0.0,
+        pnl=0.0,
+        market="KR",
+        exchange_code="KRX",
+    )
+    log_trade(
+        db_conn,
+        "AAPL",
+        "SELL",
+        80,
+        "sell",
+        quantity=1,
+        price=200.0,
+        pnl=-5.0,
+        market="US",
+        exchange_code="NASDAQ",
+    )
+
+    scorecard = reviewer.generate_scorecard("2026-02-14", "KR")
+
+    assert scorecard.market == "KR"
+    assert scorecard.total_decisions == 2
+    assert scorecard.buys == 1
+    assert scorecard.sells == 0
+    assert scorecard.holds == 1
+    assert scorecard.total_pnl == 10.0
+    assert scorecard.win_rate == 100.0
+    assert scorecard.avg_confidence == 75.0
+    assert scorecard.scenario_match_rate == 50.0
+
+
+def test_generate_scorecard_top_winners_and_losers(
+    db_conn: sqlite3.Connection, context_store: ContextStore,
+) -> None:
+    reviewer = DailyReviewer(db_conn, context_store)
+    logger = DecisionLogger(db_conn)
+
+    for code, pnl in [("005930", 30.0), ("000660", 10.0), ("035420", -15.0), ("051910", -5.0)]:
+        decision_id = _log_decision(
+            logger,
+            stock_code=code,
+            market="KR",
+            action="BUY" if pnl >= 0 else "SELL",
+            confidence=80,
+            scenario_match={"rsi": 30.0},
+        )
+        log_trade(
+            db_conn,
+            code,
+            "BUY" if pnl >= 0 else "SELL",
+            80,
+            "test",
+            quantity=1,
+            price=100.0,
+            pnl=pnl,
+            market="KR",
+            exchange_code="KRX",
+            decision_id=decision_id,
+        )
+
+    scorecard = reviewer.generate_scorecard("2026-02-14", "KR")
+    assert scorecard.top_winners == ["005930", "000660"]
+    assert scorecard.top_losers == ["035420", "051910"]
+
+
+def test_generate_scorecard_empty_day(
+    db_conn: sqlite3.Connection, context_store: ContextStore,
+) -> None:
+    reviewer = DailyReviewer(db_conn, context_store)
+    scorecard = reviewer.generate_scorecard("2026-02-14", "KR")
+
+    assert scorecard.total_decisions == 0
+    assert scorecard.total_pnl == 0.0
+    assert scorecard.win_rate == 0.0
+    assert scorecard.avg_confidence == 0.0
+    assert scorecard.scenario_match_rate == 0.0
+    assert scorecard.top_winners == []
+    assert scorecard.top_losers == []
+
+
+@pytest.mark.asyncio
+async def test_generate_lessons_without_gemini_returns_empty(
+    db_conn: sqlite3.Connection, context_store: ContextStore,
+) -> None:
+    reviewer = DailyReviewer(db_conn, context_store, gemini_client=None)
+    lessons = await reviewer.generate_lessons(
+        DailyScorecard(
+            date="2026-02-14",
+            market="KR",
+            total_decisions=1,
+            buys=1,
+            sells=0,
+            holds=0,
+            total_pnl=5.0,
+            win_rate=100.0,
+            avg_confidence=90.0,
+            scenario_match_rate=100.0,
+        )
+    )
+    assert lessons == []
+
+
+@pytest.mark.asyncio
+async def test_generate_lessons_parses_json_array(
+    db_conn: sqlite3.Connection, context_store: ContextStore,
+) -> None:
+    mock_gemini = MagicMock()
+    mock_gemini.decide = AsyncMock(
+        return_value=SimpleNamespace(rationale='["Cut losers earlier", "Reduce midday churn"]')
+    )
+    reviewer = DailyReviewer(db_conn, context_store, gemini_client=mock_gemini)
+
+    lessons = await reviewer.generate_lessons(
+        DailyScorecard(
+            date="2026-02-14",
+            market="KR",
+            total_decisions=3,
+            buys=1,
+            sells=1,
+            holds=1,
+            total_pnl=-2.5,
+            win_rate=50.0,
+            avg_confidence=70.0,
+            scenario_match_rate=66.7,
+        )
+    )
+    assert lessons == ["Cut losers earlier", "Reduce midday churn"]
+
+
+@pytest.mark.asyncio
+async def test_generate_lessons_fallback_to_lines(
+    db_conn: sqlite3.Connection, context_store: ContextStore,
+) -> None:
+    mock_gemini = MagicMock()
+    mock_gemini.decide = AsyncMock(
+        return_value=SimpleNamespace(rationale="- Keep risk tighter\n- Increase selectivity")
+    )
+    reviewer = DailyReviewer(db_conn, context_store, gemini_client=mock_gemini)
+
+    lessons = await reviewer.generate_lessons(
+        DailyScorecard(
+            date="2026-02-14",
+            market="US",
+            total_decisions=2,
+            buys=1,
+            sells=1,
+            holds=0,
+            total_pnl=1.0,
+            win_rate=50.0,
+            avg_confidence=75.0,
+            scenario_match_rate=100.0,
+        )
+    )
+    assert lessons == ["Keep risk tighter", "Increase selectivity"]
+
+
+@pytest.mark.asyncio
+async def test_generate_lessons_handles_gemini_error(
+    db_conn: sqlite3.Connection, context_store: ContextStore,
+) -> None:
+    mock_gemini = MagicMock()
+    mock_gemini.decide = AsyncMock(side_effect=RuntimeError("boom"))
+    reviewer = DailyReviewer(db_conn, context_store, gemini_client=mock_gemini)
+
+    lessons = await reviewer.generate_lessons(
+        DailyScorecard(
+            date="2026-02-14",
+            market="US",
+            total_decisions=0,
+            buys=0,
+            sells=0,
+            holds=0,
+            total_pnl=0.0,
+            win_rate=0.0,
+            avg_confidence=0.0,
+            scenario_match_rate=0.0,
+        )
+    )
+    assert lessons == []
+
+
+def test_store_scorecard_in_context(
+    db_conn: sqlite3.Connection, context_store: ContextStore,
+) -> None:
+    reviewer = DailyReviewer(db_conn, context_store)
+    scorecard = DailyScorecard(
+        date="2026-02-14",
+        market="KR",
+        total_decisions=5,
+        buys=2,
+        sells=1,
+        holds=2,
+        total_pnl=15.0,
+        win_rate=66.67,
+        avg_confidence=82.0,
+        scenario_match_rate=80.0,
+        lessons=["Keep position sizing stable"],
+        cross_market_note="US risk-off",
+    )
+
+    reviewer.store_scorecard_in_context(scorecard)
+
+    stored = context_store.get_context(
+        ContextLayer.L6_DAILY,
+        "2026-02-14",
+        "scorecard_KR",
+    )
+    assert stored is not None
+    assert stored["market"] == "KR"
+    assert stored["total_pnl"] == 15.0
+    assert stored["lessons"] == ["Keep position sizing stable"]
+
+
+def test_store_scorecard_key_is_market_scoped(
+    db_conn: sqlite3.Connection, context_store: ContextStore,
+) -> None:
+    reviewer = DailyReviewer(db_conn, context_store)
+    kr = DailyScorecard(
+        date="2026-02-14",
+        market="KR",
+        total_decisions=1,
+        buys=1,
+        sells=0,
+        holds=0,
+        total_pnl=1.0,
+        win_rate=100.0,
+        avg_confidence=90.0,
+        scenario_match_rate=100.0,
+    )
+    us = DailyScorecard(
+        date="2026-02-14",
+        market="US",
+        total_decisions=1,
+        buys=0,
+        sells=1,
+        holds=0,
+        total_pnl=-1.0,
+        win_rate=0.0,
+        avg_confidence=70.0,
+        scenario_match_rate=100.0,
+    )
+
+    reviewer.store_scorecard_in_context(kr)
+    reviewer.store_scorecard_in_context(us)
+
+    kr_ctx = context_store.get_context(ContextLayer.L6_DAILY, "2026-02-14", "scorecard_KR")
+    us_ctx = context_store.get_context(ContextLayer.L6_DAILY, "2026-02-14", "scorecard_US")
+
+    assert kr_ctx["market"] == "KR"
+    assert us_ctx["market"] == "US"
+    assert kr_ctx["total_pnl"] == 1.0
+    assert us_ctx["total_pnl"] == -1.0
+
+
+def test_generate_scorecard_handles_invalid_context_snapshot(
+    db_conn: sqlite3.Connection, context_store: ContextStore,
+) -> None:
+    reviewer = DailyReviewer(db_conn, context_store)
+    db_conn.execute(
+        """
+        INSERT INTO decision_logs (
+            decision_id, timestamp, stock_code, market, exchange_code,
+            action, confidence, rationale, context_snapshot, input_data
+        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+        """,
+        (
+            "d1",
+            "2026-02-14T09:00:00+00:00",
+            "005930",
+            "KR",
+            "KRX",
+            "HOLD",
+            50,
+            "test",
+            "{invalid_json",
+            json.dumps({}),
+        ),
+    )
+    db_conn.commit()
+
+    scorecard = reviewer.generate_scorecard("2026-02-14", "KR")
+    assert scorecard.total_decisions == 1
+    assert scorecard.scenario_match_rate == 0.0

tests/test_main.py

@@ -1,12 +1,68 @@
-"""Tests for main trading loop telegram integration."""
+"""Tests for main trading loop integration."""
 
-import asyncio
+from datetime import UTC, date, datetime
-from unittest.mock import AsyncMock, MagicMock, patch
+from unittest.mock import ANY, AsyncMock, MagicMock, patch
 
 import pytest
 
+from src.context.layer import ContextLayer
+from src.context.scheduler import ScheduleResult
 from src.core.risk_manager import CircuitBreakerTripped, FatFingerRejected
-from src.main import safe_float, trading_cycle
+from src.db import init_db, log_trade
+from src.evolution.scorecard import DailyScorecard
+from src.logging.decision_logger import DecisionLogger
+from src.main import (
+    _handle_market_close,
+    _run_context_scheduler,
+    _run_evolution_loop,
+    safe_float,
+    trading_cycle,
+)
+from src.strategy.models import (
+    DayPlaybook,
+    ScenarioAction,
+    StockCondition,
+    StockScenario,
+)
+from src.strategy.scenario_engine import ScenarioEngine, ScenarioMatch
+
+
+def _make_playbook(market: str = "KR") -> DayPlaybook:
+    """Create a minimal empty playbook for testing."""
+    return DayPlaybook(date=date(2026, 2, 8), market=market)
+
+
+def _make_buy_match(stock_code: str = "005930") -> ScenarioMatch:
+    """Create a ScenarioMatch that returns BUY."""
+    return ScenarioMatch(
+        stock_code=stock_code,
+        matched_scenario=None,
+        action=ScenarioAction.BUY,
+        confidence=85,
+        rationale="Test buy",
+    )
+
+
+def _make_hold_match(stock_code: str = "005930") -> ScenarioMatch:
+    """Create a ScenarioMatch that returns HOLD."""
+    return ScenarioMatch(
+        stock_code=stock_code,
+        matched_scenario=None,
+        action=ScenarioAction.HOLD,
+        confidence=0,
+        rationale="No scenario conditions met",
+    )
+
+
+def _make_sell_match(stock_code: str = "005930") -> ScenarioMatch:
+    """Create a ScenarioMatch that returns SELL."""
+    return ScenarioMatch(
+        stock_code=stock_code,
+        matched_scenario=None,
+        action=ScenarioAction.SELL,
+        confidence=90,
+        rationale="Test sell",
+    )
 
 
 class TestSafeFloat:
@@ -81,15 +137,16 @@ class TestTradingCycleTelegramIntegration:
         return broker
 
     @pytest.fixture
-    def mock_brain(self) -> MagicMock:
+    def mock_scenario_engine(self) -> MagicMock:
-        """Create mock brain that decides to buy."""
+        """Create mock scenario engine that returns BUY."""
-        brain = MagicMock()
+        engine = MagicMock(spec=ScenarioEngine)
-        decision = MagicMock()
+        engine.evaluate = MagicMock(return_value=_make_buy_match())
-        decision.action = "BUY"
+        return engine
-        decision.confidence = 85
+
-        decision.rationale = "Test buy"
+    @pytest.fixture
-        brain.decide = AsyncMock(return_value=decision)
+    def mock_playbook(self) -> DayPlaybook:
-        return brain
+        """Create a minimal day playbook."""
+        return _make_playbook()
 
     @pytest.fixture
     def mock_risk(self) -> MagicMock:
@@ -134,6 +191,7 @@ class TestTradingCycleTelegramIntegration:
         telegram.notify_trade_execution = AsyncMock()
         telegram.notify_fat_finger = AsyncMock()
         telegram.notify_circuit_breaker = AsyncMock()
+        telegram.notify_scenario_matched = AsyncMock()
         return telegram
 
     @pytest.fixture
@@ -151,7 +209,8 @@ class TestTradingCycleTelegramIntegration:
         self,
         mock_broker: MagicMock,
         mock_overseas_broker: MagicMock,
-        mock_brain: MagicMock,
+        mock_scenario_engine: MagicMock,
+        mock_playbook: DayPlaybook,
         mock_risk: MagicMock,
         mock_db: MagicMock,
         mock_decision_logger: MagicMock,
@@ -165,7 +224,8 @@ class TestTradingCycleTelegramIntegration:
             await trading_cycle(
                 broker=mock_broker,
                 overseas_broker=mock_overseas_broker,
-                brain=mock_brain,
+                scenario_engine=mock_scenario_engine,
+                playbook=mock_playbook,
                 risk=mock_risk,
                 db_conn=mock_db,
                 decision_logger=mock_decision_logger,
@@ -174,6 +234,7 @@ class TestTradingCycleTelegramIntegration:
                 telegram=mock_telegram,
                 market=mock_market,
                 stock_code="005930",
+                scan_candidates={},
             )
 
         # Verify notification was sent
@@ -189,7 +250,8 @@ class TestTradingCycleTelegramIntegration:
         self,
         mock_broker: MagicMock,
         mock_overseas_broker: MagicMock,
-        mock_brain: MagicMock,
+        mock_scenario_engine: MagicMock,
+        mock_playbook: DayPlaybook,
         mock_risk: MagicMock,
         mock_db: MagicMock,
         mock_decision_logger: MagicMock,
@@ -207,7 +269,8 @@ class TestTradingCycleTelegramIntegration:
             await trading_cycle(
                 broker=mock_broker,
                 overseas_broker=mock_overseas_broker,
-                brain=mock_brain,
+                scenario_engine=mock_scenario_engine,
+                playbook=mock_playbook,
                 risk=mock_risk,
                 db_conn=mock_db,
                 decision_logger=mock_decision_logger,
@@ -216,6 +279,7 @@ class TestTradingCycleTelegramIntegration:
                 telegram=mock_telegram,
                 market=mock_market,
                 stock_code="005930",
+                scan_candidates={},
             )
 
         # Verify notification was attempted
@@ -226,7 +290,8 @@ class TestTradingCycleTelegramIntegration:
         self,
         mock_broker: MagicMock,
         mock_overseas_broker: MagicMock,
-        mock_brain: MagicMock,
+        mock_scenario_engine: MagicMock,
+        mock_playbook: DayPlaybook,
         mock_risk: MagicMock,
         mock_db: MagicMock,
         mock_decision_logger: MagicMock,
@@ -248,7 +313,8 @@ class TestTradingCycleTelegramIntegration:
                 await trading_cycle(
                     broker=mock_broker,
                     overseas_broker=mock_overseas_broker,
-                    brain=mock_brain,
+                    scenario_engine=mock_scenario_engine,
+                    playbook=mock_playbook,
                     risk=mock_risk,
                     db_conn=mock_db,
                     decision_logger=mock_decision_logger,
@@ -257,6 +323,7 @@ class TestTradingCycleTelegramIntegration:
                     telegram=mock_telegram,
                     market=mock_market,
                     stock_code="005930",
+                    scan_candidates={},
                 )
 
         # Verify notification was sent
@@ -272,7 +339,8 @@ class TestTradingCycleTelegramIntegration:
         self,
         mock_broker: MagicMock,
         mock_overseas_broker: MagicMock,
-        mock_brain: MagicMock,
+        mock_scenario_engine: MagicMock,
+        mock_playbook: DayPlaybook,
         mock_risk: MagicMock,
         mock_db: MagicMock,
         mock_decision_logger: MagicMock,
@@ -296,7 +364,8 @@ class TestTradingCycleTelegramIntegration:
                 await trading_cycle(
                     broker=mock_broker,
                     overseas_broker=mock_overseas_broker,
-                    brain=mock_brain,
+                    scenario_engine=mock_scenario_engine,
+                    playbook=mock_playbook,
                     risk=mock_risk,
                     db_conn=mock_db,
                     decision_logger=mock_decision_logger,
@@ -305,6 +374,7 @@ class TestTradingCycleTelegramIntegration:
                     telegram=mock_telegram,
                     market=mock_market,
                     stock_code="005930",
+                    scan_candidates={},
                 )
 
         # Verify notification was attempted
@@ -315,7 +385,8 @@ class TestTradingCycleTelegramIntegration:
         self,
         mock_broker: MagicMock,
         mock_overseas_broker: MagicMock,
-        mock_brain: MagicMock,
+        mock_scenario_engine: MagicMock,
+        mock_playbook: DayPlaybook,
         mock_risk: MagicMock,
         mock_db: MagicMock,
         mock_decision_logger: MagicMock,
@@ -325,18 +396,15 @@ class TestTradingCycleTelegramIntegration:
         mock_market: MagicMock,
     ) -> None:
         """Test no trade notification sent when decision is HOLD."""
-        # Change brain decision to HOLD
+        # Scenario engine returns HOLD
-        decision = MagicMock()
+        mock_scenario_engine.evaluate = MagicMock(return_value=_make_hold_match())
-        decision.action = "HOLD"
-        decision.confidence = 50
-        decision.rationale = "Insufficient signal"
-        mock_brain.decide = AsyncMock(return_value=decision)
 
         with patch("src.main.log_trade"):
             await trading_cycle(
                 broker=mock_broker,
                 overseas_broker=mock_overseas_broker,
-                brain=mock_brain,
+                scenario_engine=mock_scenario_engine,
+                playbook=mock_playbook,
                 risk=mock_risk,
                 db_conn=mock_db,
                 decision_logger=mock_decision_logger,
@@ -345,6 +413,7 @@ class TestTradingCycleTelegramIntegration:
                 telegram=mock_telegram,
                 market=mock_market,
                 stock_code="005930",
+                scan_candidates={},
             )
 
         # Verify no trade notification sent
@@ -467,15 +536,16 @@ class TestOverseasBalanceParsing:
         return market
 
     @pytest.fixture
-    def mock_brain_hold(self) -> MagicMock:
+    def mock_scenario_engine_hold(self) -> MagicMock:
-        """Create mock brain that always holds."""
+        """Create mock scenario engine that always returns HOLD."""
-        brain = MagicMock()
+        engine = MagicMock(spec=ScenarioEngine)
-        decision = MagicMock()
+        engine.evaluate = MagicMock(return_value=_make_hold_match("AAPL"))
-        decision.action = "HOLD"
+        return engine
-        decision.confidence = 50
+
-        decision.rationale = "Testing balance parsing"
+    @pytest.fixture
-        brain.decide = AsyncMock(return_value=decision)
+    def mock_playbook(self) -> DayPlaybook:
-        return brain
+        """Create a minimal playbook."""
+        return _make_playbook("US")
 
     @pytest.fixture
     def mock_risk(self) -> MagicMock:
@@ -512,14 +582,17 @@ class TestOverseasBalanceParsing:
     @pytest.fixture
     def mock_telegram(self) -> MagicMock:
         """Create mock telegram client."""
-        return MagicMock()
+        telegram = MagicMock()
+        telegram.notify_scenario_matched = AsyncMock()
+        return telegram
 
     @pytest.mark.asyncio
     async def test_overseas_balance_list_format(
         self,
         mock_domestic_broker: MagicMock,
         mock_overseas_broker_with_list: MagicMock,
-        mock_brain_hold: MagicMock,
+        mock_scenario_engine_hold: MagicMock,
+        mock_playbook: DayPlaybook,
         mock_risk: MagicMock,
         mock_db: MagicMock,
         mock_decision_logger: MagicMock,
@@ -534,7 +607,8 @@ class TestOverseasBalanceParsing:
             await trading_cycle(
                 broker=mock_domestic_broker,
                 overseas_broker=mock_overseas_broker_with_list,
-                brain=mock_brain_hold,
+                scenario_engine=mock_scenario_engine_hold,
+                playbook=mock_playbook,
                 risk=mock_risk,
                 db_conn=mock_db,
                 decision_logger=mock_decision_logger,
@@ -543,6 +617,7 @@ class TestOverseasBalanceParsing:
                 telegram=mock_telegram,
                 market=mock_overseas_market,
                 stock_code="AAPL",
+                scan_candidates={},
             )
 
         # Verify balance API was called
@@ -553,7 +628,8 @@ class TestOverseasBalanceParsing:
         self,
         mock_domestic_broker: MagicMock,
         mock_overseas_broker_with_dict: MagicMock,
-        mock_brain_hold: MagicMock,
+        mock_scenario_engine_hold: MagicMock,
+        mock_playbook: DayPlaybook,
         mock_risk: MagicMock,
         mock_db: MagicMock,
         mock_decision_logger: MagicMock,
@@ -568,7 +644,8 @@ class TestOverseasBalanceParsing:
             await trading_cycle(
                 broker=mock_domestic_broker,
                 overseas_broker=mock_overseas_broker_with_dict,
-                brain=mock_brain_hold,
+                scenario_engine=mock_scenario_engine_hold,
+                playbook=mock_playbook,
                 risk=mock_risk,
                 db_conn=mock_db,
                 decision_logger=mock_decision_logger,
@@ -577,6 +654,7 @@ class TestOverseasBalanceParsing:
                 telegram=mock_telegram,
                 market=mock_overseas_market,
                 stock_code="AAPL",
+                scan_candidates={},
             )
 
         # Verify balance API was called
@@ -587,7 +665,8 @@ class TestOverseasBalanceParsing:
         self,
         mock_domestic_broker: MagicMock,
         mock_overseas_broker_with_empty: MagicMock,
-        mock_brain_hold: MagicMock,
+        mock_scenario_engine_hold: MagicMock,
+        mock_playbook: DayPlaybook,
         mock_risk: MagicMock,
         mock_db: MagicMock,
         mock_decision_logger: MagicMock,
@@ -602,7 +681,8 @@ class TestOverseasBalanceParsing:
             await trading_cycle(
                 broker=mock_domestic_broker,
                 overseas_broker=mock_overseas_broker_with_empty,
-                brain=mock_brain_hold,
+                scenario_engine=mock_scenario_engine_hold,
+                playbook=mock_playbook,
                 risk=mock_risk,
                 db_conn=mock_db,
                 decision_logger=mock_decision_logger,
@@ -611,6 +691,7 @@ class TestOverseasBalanceParsing:
                 telegram=mock_telegram,
                 market=mock_overseas_market,
                 stock_code="AAPL",
+                scan_candidates={},
             )
 
         # Verify balance API was called
@@ -621,7 +702,8 @@ class TestOverseasBalanceParsing:
         self,
         mock_domestic_broker: MagicMock,
         mock_overseas_broker_with_empty_price: MagicMock,
-        mock_brain_hold: MagicMock,
+        mock_scenario_engine_hold: MagicMock,
+        mock_playbook: DayPlaybook,
         mock_risk: MagicMock,
         mock_db: MagicMock,
         mock_decision_logger: MagicMock,
@@ -636,7 +718,8 @@ class TestOverseasBalanceParsing:
             await trading_cycle(
                 broker=mock_domestic_broker,
                 overseas_broker=mock_overseas_broker_with_empty_price,
-                brain=mock_brain_hold,
+                scenario_engine=mock_scenario_engine_hold,
+                playbook=mock_playbook,
                 risk=mock_risk,
                 db_conn=mock_db,
                 decision_logger=mock_decision_logger,
@@ -645,7 +728,629 @@ class TestOverseasBalanceParsing:
                 telegram=mock_telegram,
                 market=mock_overseas_market,
                 stock_code="AAPL",
+                scan_candidates={},
             )
 
         # Verify price API was called
         mock_overseas_broker_with_empty_price.get_overseas_price.assert_called_once()
+
+
+class TestScenarioEngineIntegration:
+    """Test scenario engine integration in trading_cycle."""
+
+    @pytest.fixture
+    def mock_broker(self) -> MagicMock:
+        """Create mock broker with standard domestic data."""
+        broker = MagicMock()
+        broker.get_orderbook = AsyncMock(
+            return_value={
+                "output1": {"stck_prpr": "50000", "frgn_ntby_qty": "100"}
+            }
+        )
+        broker.get_balance = AsyncMock(
+            return_value={
+                "output2": [
+                    {
+                        "tot_evlu_amt": "10000000",
+                        "dnca_tot_amt": "5000000",
+                        "pchs_amt_smtl_amt": "9500000",
+                    }
+                ]
+            }
+        )
+        broker.send_order = AsyncMock(return_value={"msg1": "OK"})
+        return broker
+
+    @pytest.fixture
+    def mock_market(self) -> MagicMock:
+        """Create mock KR market."""
+        market = MagicMock()
+        market.name = "Korea"
+        market.code = "KR"
+        market.exchange_code = "KRX"
+        market.is_domestic = True
+        return market
+
+    @pytest.fixture
+    def mock_telegram(self) -> MagicMock:
+        """Create mock telegram with all notification methods."""
+        telegram = MagicMock()
+        telegram.notify_trade_execution = AsyncMock()
+        telegram.notify_scenario_matched = AsyncMock()
+        telegram.notify_fat_finger = AsyncMock()
+        return telegram
+
+    @pytest.mark.asyncio
+    async def test_scenario_engine_called_with_enriched_market_data(
+        self, mock_broker: MagicMock, mock_market: MagicMock, mock_telegram: MagicMock,
+    ) -> None:
+        """Test scenario engine receives market_data enriched with scanner metrics."""
+        from src.analysis.smart_scanner import ScanCandidate
+
+        engine = MagicMock(spec=ScenarioEngine)
+        engine.evaluate = MagicMock(return_value=_make_hold_match())
+        playbook = _make_playbook()
+
+        candidate = ScanCandidate(
+            stock_code="005930", name="Samsung", price=50000,
+            volume=1000000, volume_ratio=3.5, rsi=25.0,
+            signal="oversold", score=85.0,
+        )
+
+        with patch("src.main.log_trade"):
+            await trading_cycle(
+                broker=mock_broker,
+                overseas_broker=MagicMock(),
+                scenario_engine=engine,
+                playbook=playbook,
+                risk=MagicMock(),
+                db_conn=MagicMock(),
+                decision_logger=MagicMock(),
+                context_store=MagicMock(get_latest_timeframe=MagicMock(return_value=None)),
+                criticality_assessor=MagicMock(
+                    assess_market_conditions=MagicMock(return_value=MagicMock(value="NORMAL")),
+                    get_timeout=MagicMock(return_value=5.0),
+                ),
+                telegram=mock_telegram,
+                market=mock_market,
+                stock_code="005930",
+                scan_candidates={"KR": {"005930": candidate}},
+            )
+
+        # Verify evaluate was called
+        engine.evaluate.assert_called_once()
+        call_args = engine.evaluate.call_args
+        market_data = call_args[0][2]  # 3rd positional arg
+        portfolio_data = call_args[0][3]  # 4th positional arg
+
+        # Scanner data should be enriched into market_data
+        assert market_data["rsi"] == 25.0
+        assert market_data["volume_ratio"] == 3.5
+        assert market_data["current_price"] == 50000.0
+
+        # Portfolio data should include pnl
+        assert "portfolio_pnl_pct" in portfolio_data
+        assert "total_cash" in portfolio_data
+
+    @pytest.mark.asyncio
+    async def test_trading_cycle_sets_l7_context_keys(
+        self, mock_broker: MagicMock, mock_market: MagicMock, mock_telegram: MagicMock,
+    ) -> None:
+        """Test L7 context is written with market-scoped keys."""
+        from src.analysis.smart_scanner import ScanCandidate
+
+        engine = MagicMock(spec=ScenarioEngine)
+        engine.evaluate = MagicMock(return_value=_make_hold_match())
+        playbook = _make_playbook()
+        context_store = MagicMock(get_latest_timeframe=MagicMock(return_value=None))
+
+        candidate = ScanCandidate(
+            stock_code="005930", name="Samsung", price=50000,
+            volume=1000000, volume_ratio=3.5, rsi=25.0,
+            signal="oversold", score=85.0,
+        )
+
+        with patch("src.main.log_trade"):
+            await trading_cycle(
+                broker=mock_broker,
+                overseas_broker=MagicMock(),
+                scenario_engine=engine,
+                playbook=playbook,
+                risk=MagicMock(),
+                db_conn=MagicMock(),
+                decision_logger=MagicMock(),
+                context_store=context_store,
+                criticality_assessor=MagicMock(
+                    assess_market_conditions=MagicMock(return_value=MagicMock(value="NORMAL")),
+                    get_timeout=MagicMock(return_value=5.0),
+                ),
+                telegram=mock_telegram,
+                market=mock_market,
+                stock_code="005930",
+                scan_candidates={"KR": {"005930": candidate}},
+            )
+
+        context_store.set_context.assert_any_call(
+            ContextLayer.L7_REALTIME,
+            ANY,
+            "volatility_KR_005930",
+            {"momentum_score": 50.0, "volume_surge": 1.0, "price_change_1m": 0.0},
+        )
+        context_store.set_context.assert_any_call(
+            ContextLayer.L7_REALTIME,
+            ANY,
+            "price_KR_005930",
+            {"current_price": 50000.0},
+        )
+        context_store.set_context.assert_any_call(
+            ContextLayer.L7_REALTIME,
+            ANY,
+            "rsi_KR_005930",
+            {"rsi": 25.0},
+        )
+        context_store.set_context.assert_any_call(
+            ContextLayer.L7_REALTIME,
+            ANY,
+            "volume_ratio_KR_005930",
+            {"volume_ratio": 3.5},
+        )
+
+    @pytest.mark.asyncio
+    async def test_scan_candidates_market_scoped(
+        self, mock_broker: MagicMock, mock_market: MagicMock, mock_telegram: MagicMock,
+    ) -> None:
+        """Test scan_candidates uses market-scoped lookup, ignoring other markets."""
+        from src.analysis.smart_scanner import ScanCandidate
+
+        engine = MagicMock(spec=ScenarioEngine)
+        engine.evaluate = MagicMock(return_value=_make_hold_match())
+
+        # Candidate stored under US market — should NOT be found for KR market
+        us_candidate = ScanCandidate(
+            stock_code="005930", name="Overlap", price=100,
+            volume=500000, volume_ratio=5.0, rsi=15.0,
+            signal="oversold", score=90.0,
+        )
+
+        with patch("src.main.log_trade"):
+            await trading_cycle(
+                broker=mock_broker,
+                overseas_broker=MagicMock(),
+                scenario_engine=engine,
+                playbook=_make_playbook(),
+                risk=MagicMock(),
+                db_conn=MagicMock(),
+                decision_logger=MagicMock(),
+                context_store=MagicMock(get_latest_timeframe=MagicMock(return_value=None)),
+                criticality_assessor=MagicMock(
+                    assess_market_conditions=MagicMock(return_value=MagicMock(value="NORMAL")),
+                    get_timeout=MagicMock(return_value=5.0),
+                ),
+                telegram=mock_telegram,
+                market=mock_market,  # KR market
+                stock_code="005930",
+                scan_candidates={"US": {"005930": us_candidate}},  # Wrong market
+            )
+
+        # Should NOT have rsi/volume_ratio because candidate is under US, not KR
+        market_data = engine.evaluate.call_args[0][2]
+        assert "rsi" not in market_data
+        assert "volume_ratio" not in market_data
+
+    @pytest.mark.asyncio
+    async def test_scenario_engine_called_without_scanner_data(
+        self, mock_broker: MagicMock, mock_market: MagicMock, mock_telegram: MagicMock,
+    ) -> None:
+        """Test scenario engine works when stock has no scan candidate."""
+        engine = MagicMock(spec=ScenarioEngine)
+        engine.evaluate = MagicMock(return_value=_make_hold_match())
+        playbook = _make_playbook()
+
+        with patch("src.main.log_trade"):
+            await trading_cycle(
+                broker=mock_broker,
+                overseas_broker=MagicMock(),
+                scenario_engine=engine,
+                playbook=playbook,
+                risk=MagicMock(),
+                db_conn=MagicMock(),
+                decision_logger=MagicMock(),
+                context_store=MagicMock(get_latest_timeframe=MagicMock(return_value=None)),
+                criticality_assessor=MagicMock(
+                    assess_market_conditions=MagicMock(return_value=MagicMock(value="NORMAL")),
+                    get_timeout=MagicMock(return_value=5.0),
+                ),
+                telegram=mock_telegram,
+                market=mock_market,
+                stock_code="005930",
+                scan_candidates={},  # No scanner data
+            )
+
+        # Should still work, just without rsi/volume_ratio
+        engine.evaluate.assert_called_once()
+        market_data = engine.evaluate.call_args[0][2]
+        assert "rsi" not in market_data
+        assert "volume_ratio" not in market_data
+        assert market_data["current_price"] == 50000.0
+
+    @pytest.mark.asyncio
+    async def test_scenario_matched_notification_sent(
+        self, mock_broker: MagicMock, mock_market: MagicMock, mock_telegram: MagicMock,
+    ) -> None:
+        """Test telegram notification sent when a scenario matches."""
+        # Create a match with matched_scenario (not None)
+        scenario = StockScenario(
+            condition=StockCondition(rsi_below=30),
+            action=ScenarioAction.BUY,
+            confidence=88,
+            rationale="RSI oversold bounce",
+        )
+        match = ScenarioMatch(
+            stock_code="005930",
+            matched_scenario=scenario,
+            action=ScenarioAction.BUY,
+            confidence=88,
+            rationale="RSI oversold bounce",
+            match_details={"rsi": 25.0},
+        )
+        engine = MagicMock(spec=ScenarioEngine)
+        engine.evaluate = MagicMock(return_value=match)
+
+        with patch("src.main.log_trade"):
+            await trading_cycle(
+                broker=mock_broker,
+                overseas_broker=MagicMock(),
+                scenario_engine=engine,
+                playbook=_make_playbook(),
+                risk=MagicMock(),
+                db_conn=MagicMock(),
+                decision_logger=MagicMock(),
+                context_store=MagicMock(get_latest_timeframe=MagicMock(return_value=None)),
+                criticality_assessor=MagicMock(
+                    assess_market_conditions=MagicMock(return_value=MagicMock(value="NORMAL")),
+                    get_timeout=MagicMock(return_value=5.0),
+                ),
+                telegram=mock_telegram,
+                market=mock_market,
+                stock_code="005930",
+                scan_candidates={},
+            )
+
+        # Scenario matched notification should be sent
+        mock_telegram.notify_scenario_matched.assert_called_once()
+        call_kwargs = mock_telegram.notify_scenario_matched.call_args.kwargs
+        assert call_kwargs["stock_code"] == "005930"
+        assert call_kwargs["action"] == "BUY"
+        assert "rsi=25.0" in call_kwargs["condition_summary"]
+
+    @pytest.mark.asyncio
+    async def test_no_scenario_matched_notification_on_default_hold(
+        self, mock_broker: MagicMock, mock_market: MagicMock, mock_telegram: MagicMock,
+    ) -> None:
+        """Test no scenario notification when default HOLD is returned."""
+        engine = MagicMock(spec=ScenarioEngine)
+        engine.evaluate = MagicMock(return_value=_make_hold_match())
+
+        with patch("src.main.log_trade"):
+            await trading_cycle(
+                broker=mock_broker,
+                overseas_broker=MagicMock(),
+                scenario_engine=engine,
+                playbook=_make_playbook(),
+                risk=MagicMock(),
+                db_conn=MagicMock(),
+                decision_logger=MagicMock(),
+                context_store=MagicMock(get_latest_timeframe=MagicMock(return_value=None)),
+                criticality_assessor=MagicMock(
+                    assess_market_conditions=MagicMock(return_value=MagicMock(value="NORMAL")),
+                    get_timeout=MagicMock(return_value=5.0),
+                ),
+                telegram=mock_telegram,
+                market=mock_market,
+                stock_code="005930",
+                scan_candidates={},
+            )
+
+        # No scenario matched notification for default HOLD
+        mock_telegram.notify_scenario_matched.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_decision_logger_receives_scenario_match_details(
+        self, mock_broker: MagicMock, mock_market: MagicMock, mock_telegram: MagicMock,
+    ) -> None:
+        """Test decision logger context includes scenario match details."""
+        match = ScenarioMatch(
+            stock_code="005930",
+            matched_scenario=None,
+            action=ScenarioAction.HOLD,
+            confidence=0,
+            rationale="No match",
+            match_details={"rsi": 45.0, "volume_ratio": 1.2},
+        )
+        engine = MagicMock(spec=ScenarioEngine)
+        engine.evaluate = MagicMock(return_value=match)
+        decision_logger = MagicMock()
+
+        with patch("src.main.log_trade"):
+            await trading_cycle(
+                broker=mock_broker,
+                overseas_broker=MagicMock(),
+                scenario_engine=engine,
+                playbook=_make_playbook(),
+                risk=MagicMock(),
+                db_conn=MagicMock(),
+                decision_logger=decision_logger,
+                context_store=MagicMock(get_latest_timeframe=MagicMock(return_value=None)),
+                criticality_assessor=MagicMock(
+                    assess_market_conditions=MagicMock(return_value=MagicMock(value="NORMAL")),
+                    get_timeout=MagicMock(return_value=5.0),
+                ),
+                telegram=mock_telegram,
+                market=mock_market,
+                stock_code="005930",
+                scan_candidates={},
+            )
+
+        decision_logger.log_decision.assert_called_once()
+        call_kwargs = decision_logger.log_decision.call_args.kwargs
+        assert "scenario_match" in call_kwargs["context_snapshot"]
+        assert call_kwargs["context_snapshot"]["scenario_match"]["rsi"] == 45.0
+
+    @pytest.mark.asyncio
+    async def test_reduce_all_does_not_execute_order(
+        self, mock_broker: MagicMock, mock_market: MagicMock, mock_telegram: MagicMock,
+    ) -> None:
+        """Test REDUCE_ALL action does not trigger order execution."""
+        match = ScenarioMatch(
+            stock_code="005930",
+            matched_scenario=None,
+            action=ScenarioAction.REDUCE_ALL,
+            confidence=100,
+            rationale="Global rule: portfolio loss > 2%",
+        )
+        engine = MagicMock(spec=ScenarioEngine)
+        engine.evaluate = MagicMock(return_value=match)
+
+        with patch("src.main.log_trade"):
+            await trading_cycle(
+                broker=mock_broker,
+                overseas_broker=MagicMock(),
+                scenario_engine=engine,
+                playbook=_make_playbook(),
+                risk=MagicMock(),
+                db_conn=MagicMock(),
+                decision_logger=MagicMock(),
+                context_store=MagicMock(get_latest_timeframe=MagicMock(return_value=None)),
+                criticality_assessor=MagicMock(
+                    assess_market_conditions=MagicMock(return_value=MagicMock(value="NORMAL")),
+                    get_timeout=MagicMock(return_value=5.0),
+                ),
+                telegram=mock_telegram,
+                market=mock_market,
+                stock_code="005930",
+                scan_candidates={},
+            )
+
+        # REDUCE_ALL is not BUY or SELL — no order sent
+        mock_broker.send_order.assert_not_called()
+        mock_telegram.notify_trade_execution.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_sell_updates_original_buy_decision_outcome() -> None:
+    """SELL should update the original BUY decision outcome in decision_logs."""
+    db_conn = init_db(":memory:")
+    decision_logger = DecisionLogger(db_conn)
+
+    buy_decision_id = decision_logger.log_decision(
+        stock_code="005930",
+        market="KR",
+        exchange_code="KRX",
+        action="BUY",
+        confidence=85,
+        rationale="Initial buy",
+        context_snapshot={},
+        input_data={},
+    )
+    log_trade(
+        conn=db_conn,
+        stock_code="005930",
+        action="BUY",
+        confidence=85,
+        rationale="Initial buy",
+        quantity=1,
+        price=100.0,
+        pnl=0.0,
+        market="KR",
+        exchange_code="KRX",
+        decision_id=buy_decision_id,
+    )
+
+    broker = MagicMock()
+    broker.get_orderbook = AsyncMock(
+        return_value={"output1": {"stck_prpr": "120", "frgn_ntby_qty": "0"}}
+    )
+    broker.get_balance = AsyncMock(
+        return_value={
+            "output2": [
+                {
+                    "tot_evlu_amt": "100000",
+                    "dnca_tot_amt": "10000",
+                    "pchs_amt_smtl_amt": "90000",
+                }
+            ]
+        }
+    )
+    broker.send_order = AsyncMock(return_value={"msg1": "OK"})
+
+    overseas_broker = MagicMock()
+    engine = MagicMock(spec=ScenarioEngine)
+    engine.evaluate = MagicMock(return_value=_make_sell_match())
+    risk = MagicMock()
+    context_store = MagicMock(
+        get_latest_timeframe=MagicMock(return_value=None),
+        set_context=MagicMock(),
+    )
+    criticality_assessor = MagicMock(
+        assess_market_conditions=MagicMock(return_value=MagicMock(value="NORMAL")),
+        get_timeout=MagicMock(return_value=5.0),
+    )
+    telegram = MagicMock()
+    telegram.notify_trade_execution = AsyncMock()
+    telegram.notify_fat_finger = AsyncMock()
+    telegram.notify_circuit_breaker = AsyncMock()
+    telegram.notify_scenario_matched = AsyncMock()
+
+    market = MagicMock()
+    market.name = "Korea"
+    market.code = "KR"
+    market.exchange_code = "KRX"
+    market.is_domestic = True
+
+    await trading_cycle(
+        broker=broker,
+        overseas_broker=overseas_broker,
+        scenario_engine=engine,
+        playbook=_make_playbook(),
+        risk=risk,
+        db_conn=db_conn,
+        decision_logger=decision_logger,
+        context_store=context_store,
+        criticality_assessor=criticality_assessor,
+        telegram=telegram,
+        market=market,
+        stock_code="005930",
+        scan_candidates={},
+    )
+
+    updated_buy = decision_logger.get_decision_by_id(buy_decision_id)
+    assert updated_buy is not None
+    assert updated_buy.outcome_pnl == 20.0
+    assert updated_buy.outcome_accuracy == 1
+
+
+@pytest.mark.asyncio
+async def test_handle_market_close_runs_daily_review_flow() -> None:
+    """Market close should aggregate, create scorecard, lessons, and notify."""
+    telegram = MagicMock()
+    telegram.notify_market_close = AsyncMock()
+    telegram.send_message = AsyncMock()
+
+    context_aggregator = MagicMock()
+    reviewer = MagicMock()
+    reviewer.generate_scorecard.return_value = DailyScorecard(
+        date="2026-02-14",
+        market="KR",
+        total_decisions=3,
+        buys=1,
+        sells=1,
+        holds=1,
+        total_pnl=12.5,
+        win_rate=50.0,
+        avg_confidence=75.0,
+        scenario_match_rate=66.7,
+    )
+    reviewer.generate_lessons = AsyncMock(return_value=["Cut losers faster"])
+
+    await _handle_market_close(
+        market_code="KR",
+        market_name="Korea",
+        market_timezone=UTC,
+        telegram=telegram,
+        context_aggregator=context_aggregator,
+        daily_reviewer=reviewer,
+    )
+
+    telegram.notify_market_close.assert_called_once_with("Korea", 0.0)
+    context_aggregator.aggregate_daily_from_trades.assert_called_once()
+    reviewer.generate_scorecard.assert_called_once()
+    assert reviewer.store_scorecard_in_context.call_count == 2
+    reviewer.generate_lessons.assert_called_once()
+    telegram.send_message.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_handle_market_close_without_lessons_stores_once() -> None:
+    """If no lessons are generated, scorecard should be stored once."""
+    telegram = MagicMock()
+    telegram.notify_market_close = AsyncMock()
+    telegram.send_message = AsyncMock()
+
+    context_aggregator = MagicMock()
+    reviewer = MagicMock()
+    reviewer.generate_scorecard.return_value = DailyScorecard(
+        date="2026-02-14",
+        market="US",
+        total_decisions=1,
+        buys=0,
+        sells=1,
+        holds=0,
+        total_pnl=-3.0,
+        win_rate=0.0,
+        avg_confidence=65.0,
+        scenario_match_rate=100.0,
+    )
+    reviewer.generate_lessons = AsyncMock(return_value=[])
+
+    await _handle_market_close(
+        market_code="US",
+        market_name="United States",
+        market_timezone=UTC,
+        telegram=telegram,
+        context_aggregator=context_aggregator,
+        daily_reviewer=reviewer,
+    )
+
+    assert reviewer.store_scorecard_in_context.call_count == 1
+
+
+def test_run_context_scheduler_invokes_scheduler() -> None:
+    """Scheduler helper should call run_if_due with provided datetime."""
+    scheduler = MagicMock()
+    scheduler.run_if_due = MagicMock(return_value=ScheduleResult(cleanup=True))
+
+    _run_context_scheduler(scheduler, now=datetime(2026, 2, 14, tzinfo=UTC))
+
+    scheduler.run_if_due.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_run_evolution_loop_skips_non_us_market() -> None:
+    optimizer = MagicMock()
+    optimizer.evolve = AsyncMock()
+    telegram = MagicMock()
+    telegram.send_message = AsyncMock()
+
+    await _run_evolution_loop(
+        evolution_optimizer=optimizer,
+        telegram=telegram,
+        market_code="KR",
+        market_date="2026-02-14",
+    )
+
+    optimizer.evolve.assert_not_called()
+    telegram.send_message.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_run_evolution_loop_notifies_when_pr_generated() -> None:
+    optimizer = MagicMock()
+    optimizer.evolve = AsyncMock(
+        return_value={
+            "title": "[Evolution] New strategy: v20260214_050000",
+            "branch": "evolution/v20260214_050000",
+            "status": "ready_for_review",
+        }
+    )
+    telegram = MagicMock()
+    telegram.send_message = AsyncMock()
+
+    await _run_evolution_loop(
+        evolution_optimizer=optimizer,
+        telegram=telegram,
+        market_code="US",
+        market_date="2026-02-14",
+    )
+
+    optimizer.evolve.assert_called_once()
+    telegram.send_message.assert_called_once()

tests/test_playbook_store.py

@@ -0,0 +1,289 @@
+"""Tests for playbook persistence (PlaybookStore + DB schema)."""
+
+from __future__ import annotations
+
+from datetime import date
+
+import pytest
+
+from src.db import init_db
+from src.strategy.models import (
+    DayPlaybook,
+    GlobalRule,
+    MarketOutlook,
+    PlaybookStatus,
+    ScenarioAction,
+    StockCondition,
+    StockPlaybook,
+    StockScenario,
+)
+from src.strategy.playbook_store import PlaybookStore
+
+
+@pytest.fixture
+def conn():
+    """Create an in-memory DB with schema."""
+    connection = init_db(":memory:")
+    yield connection
+    connection.close()
+
+
+@pytest.fixture
+def store(conn) -> PlaybookStore:
+    return PlaybookStore(conn)
+
+
+def _make_playbook(
+    target_date: date = date(2026, 2, 8),
+    market: str = "KR",
+    outlook: MarketOutlook = MarketOutlook.NEUTRAL,
+    stock_codes: list[str] | None = None,
+) -> DayPlaybook:
+    """Create a test playbook with sensible defaults."""
+    if stock_codes is None:
+        stock_codes = ["005930"]
+    return DayPlaybook(
+        date=target_date,
+        market=market,
+        market_outlook=outlook,
+        token_count=150,
+        stock_playbooks=[
+            StockPlaybook(
+                stock_code=code,
+                scenarios=[
+                    StockScenario(
+                        condition=StockCondition(rsi_below=30.0),
+                        action=ScenarioAction.BUY,
+                        confidence=85,
+                        rationale=f"Oversold bounce for {code}",
+                    ),
+                ],
+            )
+            for code in stock_codes
+        ],
+        global_rules=[
+            GlobalRule(
+                condition="portfolio_pnl_pct < -2.0",
+                action=ScenarioAction.REDUCE_ALL,
+                rationale="Near circuit breaker",
+            ),
+        ],
+    )
+
+
+# ---------------------------------------------------------------------------
+# Schema
+# ---------------------------------------------------------------------------
+
+
+class TestSchema:
+    def test_playbooks_table_exists(self, conn) -> None:
+        row = conn.execute(
+            "SELECT name FROM sqlite_master WHERE type='table' AND name='playbooks'"
+        ).fetchone()
+        assert row is not None
+
+    def test_unique_constraint(self, store: PlaybookStore) -> None:
+        pb = _make_playbook()
+        store.save(pb)
+        # Saving again for same date+market should replace, not error
+        pb2 = _make_playbook(stock_codes=["005930", "000660"])
+        store.save(pb2)
+        loaded = store.load(date(2026, 2, 8), "KR")
+        assert loaded is not None
+        assert loaded.stock_count == 2
+
+
+# ---------------------------------------------------------------------------
+# Save / Load
+# ---------------------------------------------------------------------------
+
+
+class TestSaveLoad:
+    def test_save_and_load(self, store: PlaybookStore) -> None:
+        pb = _make_playbook()
+        row_id = store.save(pb)
+        assert row_id > 0
+
+        loaded = store.load(date(2026, 2, 8), "KR")
+        assert loaded is not None
+        assert loaded.date == date(2026, 2, 8)
+        assert loaded.market == "KR"
+        assert loaded.stock_count == 1
+        assert loaded.scenario_count == 1
+
+    def test_load_not_found(self, store: PlaybookStore) -> None:
+        result = store.load(date(2026, 1, 1), "KR")
+        assert result is None
+
+    def test_save_preserves_all_fields(self, store: PlaybookStore) -> None:
+        pb = _make_playbook(
+            outlook=MarketOutlook.BULLISH,
+            stock_codes=["005930", "AAPL"],
+        )
+        store.save(pb)
+        loaded = store.load(date(2026, 2, 8), "KR")
+        assert loaded is not None
+        assert loaded.market_outlook == MarketOutlook.BULLISH
+        assert loaded.stock_count == 2
+        assert loaded.global_rules[0].action == ScenarioAction.REDUCE_ALL
+        assert loaded.token_count == 150
+
+    def test_save_different_markets(self, store: PlaybookStore) -> None:
+        kr = _make_playbook(market="KR")
+        us = _make_playbook(market="US", stock_codes=["AAPL"])
+        store.save(kr)
+        store.save(us)
+
+        kr_loaded = store.load(date(2026, 2, 8), "KR")
+        us_loaded = store.load(date(2026, 2, 8), "US")
+        assert kr_loaded is not None
+        assert us_loaded is not None
+        assert kr_loaded.market == "KR"
+        assert us_loaded.market == "US"
+        assert kr_loaded.stock_playbooks[0].stock_code == "005930"
+        assert us_loaded.stock_playbooks[0].stock_code == "AAPL"
+
+    def test_save_different_dates(self, store: PlaybookStore) -> None:
+        d1 = _make_playbook(target_date=date(2026, 2, 7))
+        d2 = _make_playbook(target_date=date(2026, 2, 8))
+        store.save(d1)
+        store.save(d2)
+
+        assert store.load(date(2026, 2, 7), "KR") is not None
+        assert store.load(date(2026, 2, 8), "KR") is not None
+
+    def test_replace_updates_data(self, store: PlaybookStore) -> None:
+        pb1 = _make_playbook(outlook=MarketOutlook.BEARISH)
+        store.save(pb1)
+
+        pb2 = _make_playbook(outlook=MarketOutlook.BULLISH)
+        store.save(pb2)
+
+        loaded = store.load(date(2026, 2, 8), "KR")
+        assert loaded is not None
+        assert loaded.market_outlook == MarketOutlook.BULLISH
+
+
+# ---------------------------------------------------------------------------
+# Status
+# ---------------------------------------------------------------------------
+
+
+class TestStatus:
+    def test_get_status(self, store: PlaybookStore) -> None:
+        store.save(_make_playbook())
+        status = store.get_status(date(2026, 2, 8), "KR")
+        assert status == PlaybookStatus.READY
+
+    def test_get_status_not_found(self, store: PlaybookStore) -> None:
+        assert store.get_status(date(2026, 1, 1), "KR") is None
+
+    def test_update_status(self, store: PlaybookStore) -> None:
+        store.save(_make_playbook())
+        updated = store.update_status(date(2026, 2, 8), "KR", PlaybookStatus.EXPIRED)
+        assert updated is True
+
+        status = store.get_status(date(2026, 2, 8), "KR")
+        assert status == PlaybookStatus.EXPIRED
+
+    def test_update_status_not_found(self, store: PlaybookStore) -> None:
+        updated = store.update_status(date(2026, 1, 1), "KR", PlaybookStatus.FAILED)
+        assert updated is False
+
+
+# ---------------------------------------------------------------------------
+# Match count
+# ---------------------------------------------------------------------------
+
+
+class TestMatchCount:
+    def test_increment_match_count(self, store: PlaybookStore) -> None:
+        store.save(_make_playbook())
+        store.increment_match_count(date(2026, 2, 8), "KR")
+        store.increment_match_count(date(2026, 2, 8), "KR")
+
+        stats = store.get_stats(date(2026, 2, 8), "KR")
+        assert stats is not None
+        assert stats["match_count"] == 2
+
+    def test_increment_not_found(self, store: PlaybookStore) -> None:
+        result = store.increment_match_count(date(2026, 1, 1), "KR")
+        assert result is False
+
+
+# ---------------------------------------------------------------------------
+# Stats
+# ---------------------------------------------------------------------------
+
+
+class TestStats:
+    def test_get_stats(self, store: PlaybookStore) -> None:
+        store.save(_make_playbook())
+        stats = store.get_stats(date(2026, 2, 8), "KR")
+        assert stats is not None
+        assert stats["status"] == "ready"
+        assert stats["token_count"] == 150
+        assert stats["scenario_count"] == 1
+        assert stats["match_count"] == 0
+        assert stats["generated_at"] != ""
+
+    def test_get_stats_not_found(self, store: PlaybookStore) -> None:
+        assert store.get_stats(date(2026, 1, 1), "KR") is None
+
+
+# ---------------------------------------------------------------------------
+# List recent
+# ---------------------------------------------------------------------------
+
+
+class TestListRecent:
+    def test_list_recent(self, store: PlaybookStore) -> None:
+        for day in range(5, 10):
+            store.save(_make_playbook(target_date=date(2026, 2, day)))
+        results = store.list_recent(market="KR", limit=3)
+        assert len(results) == 3
+        # Most recent first
+        assert results[0]["date"] == "2026-02-09"
+        assert results[2]["date"] == "2026-02-07"
+
+    def test_list_recent_all_markets(self, store: PlaybookStore) -> None:
+        store.save(_make_playbook(market="KR"))
+        store.save(_make_playbook(market="US", stock_codes=["AAPL"]))
+        results = store.list_recent(market=None, limit=10)
+        assert len(results) == 2
+
+    def test_list_recent_empty(self, store: PlaybookStore) -> None:
+        results = store.list_recent(market="KR")
+        assert results == []
+
+    def test_list_recent_filter_by_market(self, store: PlaybookStore) -> None:
+        store.save(_make_playbook(market="KR"))
+        store.save(_make_playbook(market="US", stock_codes=["AAPL"]))
+        kr_only = store.list_recent(market="KR")
+        assert len(kr_only) == 1
+        assert kr_only[0]["market"] == "KR"
+
+
+# ---------------------------------------------------------------------------
+# Delete
+# ---------------------------------------------------------------------------
+
+
+class TestDelete:
+    def test_delete(self, store: PlaybookStore) -> None:
+        store.save(_make_playbook())
+        deleted = store.delete(date(2026, 2, 8), "KR")
+        assert deleted is True
+        assert store.load(date(2026, 2, 8), "KR") is None
+
+    def test_delete_not_found(self, store: PlaybookStore) -> None:
+        deleted = store.delete(date(2026, 1, 1), "KR")
+        assert deleted is False
+
+    def test_delete_one_market_keeps_other(self, store: PlaybookStore) -> None:
+        store.save(_make_playbook(market="KR"))
+        store.save(_make_playbook(market="US", stock_codes=["AAPL"]))
+        store.delete(date(2026, 2, 8), "KR")
+        assert store.load(date(2026, 2, 8), "KR") is None
+        assert store.load(date(2026, 2, 8), "US") is not None

tests/test_pre_market_planner.py

@@ -0,0 +1,659 @@
+"""Tests for PreMarketPlanner — Gemini prompt builder + response parser."""
+
+from __future__ import annotations
+
+import json
+from datetime import date
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from src.analysis.smart_scanner import ScanCandidate
+from src.brain.context_selector import DecisionType
+from src.brain.gemini_client import TradeDecision
+from src.config import Settings
+from src.context.store import ContextLayer
+from src.strategy.models import (
+    CrossMarketContext,
+    DayPlaybook,
+    MarketOutlook,
+    ScenarioAction,
+)
+from src.strategy.pre_market_planner import PreMarketPlanner
+
+# ---------------------------------------------------------------------------
+# Fixtures
+# ---------------------------------------------------------------------------
+
+
+def _candidate(
+    code: str = "005930",
+    name: str = "Samsung",
+    price: float = 71000,
+    rsi: float = 28.5,
+    volume_ratio: float = 3.2,
+    signal: str = "oversold",
+    score: float = 82.0,
+) -> ScanCandidate:
+    return ScanCandidate(
+        stock_code=code,
+        name=name,
+        price=price,
+        volume=1_500_000,
+        volume_ratio=volume_ratio,
+        rsi=rsi,
+        signal=signal,
+        score=score,
+    )
+
+
+def _gemini_response_json(
+    outlook: str = "neutral_to_bullish",
+    stocks: list[dict] | None = None,
+    global_rules: list[dict] | None = None,
+) -> str:
+    """Build a valid Gemini JSON response."""
+    if stocks is None:
+        stocks = [
+            {
+                "stock_code": "005930",
+                "scenarios": [
+                    {
+                        "condition": {"rsi_below": 30, "volume_ratio_above": 2.5},
+                        "action": "BUY",
+                        "confidence": 85,
+                        "allocation_pct": 15.0,
+                        "stop_loss_pct": -2.0,
+                        "take_profit_pct": 4.0,
+                        "rationale": "Oversold bounce with high volume",
+                    }
+                ],
+            }
+        ]
+    if global_rules is None:
+        global_rules = [
+            {
+                "condition": "portfolio_pnl_pct < -2.0",
+                "action": "REDUCE_ALL",
+                "rationale": "Near circuit breaker",
+            }
+        ]
+    return json.dumps(
+        {"market_outlook": outlook, "global_rules": global_rules, "stocks": stocks}
+    )
+
+
+def _make_planner(
+    gemini_response: str = "",
+    token_count: int = 200,
+    context_data: dict | None = None,
+    scorecard_data: dict | None = None,
+    scorecard_map: dict[tuple[str, str, str], dict | None] | None = None,
+) -> PreMarketPlanner:
+    """Create a PreMarketPlanner with mocked dependencies."""
+    if not gemini_response:
+        gemini_response = _gemini_response_json()
+
+    # Mock GeminiClient
+    gemini = AsyncMock()
+    gemini.decide = AsyncMock(
+        return_value=TradeDecision(
+            action="HOLD",
+            confidence=0,
+            rationale=gemini_response,
+            token_count=token_count,
+        )
+    )
+
+    # Mock ContextStore
+    store = MagicMock()
+    if scorecard_map is not None:
+        store.get_context = MagicMock(
+            side_effect=lambda layer, timeframe, key: scorecard_map.get(
+                (layer.value if hasattr(layer, "value") else layer, timeframe, key)
+            )
+        )
+    else:
+        store.get_context = MagicMock(return_value=scorecard_data)
+
+    # Mock ContextSelector
+    selector = MagicMock()
+    selector.select_layers = MagicMock(
+        return_value=[ContextLayer.L7_REALTIME, ContextLayer.L6_DAILY]
+    )
+    selector.get_context_data = MagicMock(return_value=context_data or {})
+
+    settings = Settings(
+        KIS_APP_KEY="test",
+        KIS_APP_SECRET="test",
+        KIS_ACCOUNT_NO="12345678-01",
+        GEMINI_API_KEY="test",
+    )
+
+    return PreMarketPlanner(gemini, store, selector, settings)
+
+
+# ---------------------------------------------------------------------------
+# generate_playbook
+# ---------------------------------------------------------------------------
+
+
+class TestGeneratePlaybook:
+    @pytest.mark.asyncio
+    async def test_basic_generation(self) -> None:
+        planner = _make_planner()
+        candidates = [_candidate()]
+
+        pb = await planner.generate_playbook("KR", candidates, today=date(2026, 2, 8))
+
+        assert isinstance(pb, DayPlaybook)
+        assert pb.market == "KR"
+        assert pb.stock_count == 1
+        assert pb.scenario_count == 1
+        assert pb.market_outlook == MarketOutlook.NEUTRAL_TO_BULLISH
+        assert pb.token_count == 200
+
+    @pytest.mark.asyncio
+    async def test_empty_candidates_returns_empty_playbook(self) -> None:
+        planner = _make_planner()
+
+        pb = await planner.generate_playbook("KR", [], today=date(2026, 2, 8))
+
+        assert pb.stock_count == 0
+        assert pb.scenario_count == 0
+        assert pb.market_outlook == MarketOutlook.NEUTRAL
+
+    @pytest.mark.asyncio
+    async def test_gemini_failure_returns_defensive(self) -> None:
+        planner = _make_planner()
+        planner._gemini.decide = AsyncMock(side_effect=RuntimeError("API timeout"))
+        candidates = [_candidate()]
+
+        pb = await planner.generate_playbook("KR", candidates, today=date(2026, 2, 8))
+
+        assert pb.default_action == ScenarioAction.HOLD
+        assert pb.market_outlook == MarketOutlook.NEUTRAL_TO_BEARISH
+        assert pb.stock_count == 1
+        # Defensive playbook has stop-loss scenarios
+        assert pb.stock_playbooks[0].scenarios[0].action == ScenarioAction.SELL
+
+    @pytest.mark.asyncio
+    async def test_gemini_failure_empty_when_defensive_disabled(self) -> None:
+        planner = _make_planner()
+        planner._settings.DEFENSIVE_PLAYBOOK_ON_FAILURE = False
+        planner._gemini.decide = AsyncMock(side_effect=RuntimeError("fail"))
+        candidates = [_candidate()]
+
+        pb = await planner.generate_playbook("KR", candidates, today=date(2026, 2, 8))
+
+        assert pb.stock_count == 0
+
+    @pytest.mark.asyncio
+    async def test_multiple_candidates(self) -> None:
+        stocks = [
+            {
+                "stock_code": "005930",
+                "scenarios": [
+                    {
+                        "condition": {"rsi_below": 30},
+                        "action": "BUY",
+                        "confidence": 85,
+                        "rationale": "Oversold",
+                    }
+                ],
+            },
+            {
+                "stock_code": "AAPL",
+                "scenarios": [
+                    {
+                        "condition": {"rsi_above": 75},
+                        "action": "SELL",
+                        "confidence": 80,
+                        "rationale": "Overbought",
+                    }
+                ],
+            },
+        ]
+        planner = _make_planner(gemini_response=_gemini_response_json(stocks=stocks))
+        candidates = [_candidate(), _candidate(code="AAPL", name="Apple")]
+
+        pb = await planner.generate_playbook("US", candidates, today=date(2026, 2, 8))
+
+        assert pb.stock_count == 2
+        codes = [sp.stock_code for sp in pb.stock_playbooks]
+        assert "005930" in codes
+        assert "AAPL" in codes
+
+    @pytest.mark.asyncio
+    async def test_unknown_stock_in_response_skipped(self) -> None:
+        stocks = [
+            {
+                "stock_code": "005930",
+                "scenarios": [
+                    {
+                        "condition": {"rsi_below": 30},
+                        "action": "BUY",
+                        "confidence": 85,
+                        "rationale": "ok",
+                    }
+                ],
+            },
+            {
+                "stock_code": "UNKNOWN",
+                "scenarios": [
+                    {
+                        "condition": {"rsi_below": 20},
+                        "action": "BUY",
+                        "confidence": 90,
+                        "rationale": "bad",
+                    }
+                ],
+            },
+        ]
+        planner = _make_planner(gemini_response=_gemini_response_json(stocks=stocks))
+        candidates = [_candidate()]  # Only 005930
+
+        pb = await planner.generate_playbook("KR", candidates, today=date(2026, 2, 8))
+
+        assert pb.stock_count == 1
+        assert pb.stock_playbooks[0].stock_code == "005930"
+
+    @pytest.mark.asyncio
+    async def test_global_rules_parsed(self) -> None:
+        planner = _make_planner()
+        candidates = [_candidate()]
+
+        pb = await planner.generate_playbook("KR", candidates, today=date(2026, 2, 8))
+
+        assert len(pb.global_rules) == 1
+        assert pb.global_rules[0].action == ScenarioAction.REDUCE_ALL
+
+    @pytest.mark.asyncio
+    async def test_token_count_from_decision(self) -> None:
+        planner = _make_planner(token_count=450)
+        candidates = [_candidate()]
+
+        pb = await planner.generate_playbook("KR", candidates, today=date(2026, 2, 8))
+
+        assert pb.token_count == 450
+
+    @pytest.mark.asyncio
+    async def test_generate_playbook_uses_strategic_context_selector(self) -> None:
+        planner = _make_planner()
+        candidates = [_candidate()]
+
+        await planner.generate_playbook("KR", candidates, today=date(2026, 2, 8))
+
+        planner._context_selector.select_layers.assert_called_once_with(
+            decision_type=DecisionType.STRATEGIC,
+            include_realtime=True,
+        )
+        planner._context_selector.get_context_data.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_generate_playbook_injects_self_and_cross_scorecards(self) -> None:
+        scorecard_map = {
+            (ContextLayer.L6_DAILY.value, "2026-02-07", "scorecard_KR"): {
+                "total_pnl": -1.0,
+                "win_rate": 40,
+                "lessons": ["Tighten entries"],
+            },
+            (ContextLayer.L6_DAILY.value, "2026-02-07", "scorecard_US"): {
+                "total_pnl": 1.5,
+                "win_rate": 62,
+                "index_change_pct": 0.9,
+                "lessons": ["Follow momentum"],
+            },
+        }
+        planner = _make_planner(scorecard_map=scorecard_map)
+
+        await planner.generate_playbook("KR", [_candidate()], today=date(2026, 2, 8))
+
+        call_market_data = planner._gemini.decide.call_args.args[0]
+        prompt = call_market_data["prompt_override"]
+        assert "My Market Previous Day (KR)" in prompt
+        assert "Other Market (US)" in prompt
+
+
+# ---------------------------------------------------------------------------
+# _parse_response
+# ---------------------------------------------------------------------------
+
+
+class TestParseResponse:
+    def test_parse_full_response(self) -> None:
+        planner = _make_planner()
+        response = _gemini_response_json(outlook="bearish")
+        candidates = [_candidate()]
+
+        pb = planner._parse_response(response, date(2026, 2, 8), "KR", candidates, None)
+
+        assert pb.market_outlook == MarketOutlook.BEARISH
+        assert pb.stock_count == 1
+        assert pb.stock_playbooks[0].scenarios[0].confidence == 85
+
+    def test_parse_with_markdown_fences(self) -> None:
+        planner = _make_planner()
+        response = f"```json\n{_gemini_response_json()}\n```"
+        candidates = [_candidate()]
+
+        pb = planner._parse_response(response, date(2026, 2, 8), "KR", candidates, None)
+
+        assert pb.stock_count == 1
+
+    def test_parse_unknown_outlook_defaults_neutral(self) -> None:
+        planner = _make_planner()
+        response = _gemini_response_json(outlook="super_bullish")
+        candidates = [_candidate()]
+
+        pb = planner._parse_response(response, date(2026, 2, 8), "KR", candidates, None)
+
+        assert pb.market_outlook == MarketOutlook.NEUTRAL
+
+    def test_parse_scenario_with_all_condition_fields(self) -> None:
+        planner = _make_planner()
+        stocks = [
+            {
+                "stock_code": "005930",
+                "scenarios": [
+                    {
+                        "condition": {
+                            "rsi_below": 25,
+                            "volume_ratio_above": 3.0,
+                            "price_change_pct_below": -2.0,
+                        },
+                        "action": "BUY",
+                        "confidence": 92,
+                        "allocation_pct": 20.0,
+                        "stop_loss_pct": -3.0,
+                        "take_profit_pct": 5.0,
+                        "rationale": "Multi-condition entry",
+                    }
+                ],
+            }
+        ]
+        response = _gemini_response_json(stocks=stocks)
+        candidates = [_candidate()]
+
+        pb = planner._parse_response(response, date(2026, 2, 8), "KR", candidates, None)
+
+        sc = pb.stock_playbooks[0].scenarios[0]
+        assert sc.condition.rsi_below == 25
+        assert sc.condition.volume_ratio_above == 3.0
+        assert sc.condition.price_change_pct_below == -2.0
+        assert sc.allocation_pct == 20.0
+        assert sc.stop_loss_pct == -3.0
+        assert sc.take_profit_pct == 5.0
+
+    def test_parse_empty_condition_scenario_skipped(self) -> None:
+        planner = _make_planner()
+        stocks = [
+            {
+                "stock_code": "005930",
+                "scenarios": [
+                    {
+                        "condition": {},
+                        "action": "BUY",
+                        "confidence": 85,
+                        "rationale": "No conditions",
+                    },
+                    {
+                        "condition": {"rsi_below": 30},
+                        "action": "BUY",
+                        "confidence": 80,
+                        "rationale": "Valid",
+                    },
+                ],
+            }
+        ]
+        response = _gemini_response_json(stocks=stocks)
+        candidates = [_candidate()]
+
+        pb = planner._parse_response(response, date(2026, 2, 8), "KR", candidates, None)
+
+        # Empty condition scenario skipped, valid one kept
+        assert pb.stock_count == 1
+        assert pb.stock_playbooks[0].scenarios[0].confidence == 80
+
+    def test_parse_max_scenarios_enforced(self) -> None:
+        planner = _make_planner()
+        # Settings default MAX_SCENARIOS_PER_STOCK = 5
+        scenarios = [
+            {
+                "condition": {"rsi_below": 20 + i},
+                "action": "BUY",
+                "confidence": 80 + i,
+                "rationale": f"Scenario {i}",
+            }
+            for i in range(8)  # 8 scenarios, should be capped to 5
+        ]
+        stocks = [{"stock_code": "005930", "scenarios": scenarios}]
+        response = _gemini_response_json(stocks=stocks)
+        candidates = [_candidate()]
+
+        pb = planner._parse_response(response, date(2026, 2, 8), "KR", candidates, None)
+
+        assert len(pb.stock_playbooks[0].scenarios) == 5
+
+    def test_parse_invalid_json_raises(self) -> None:
+        planner = _make_planner()
+        candidates = [_candidate()]
+
+        with pytest.raises(json.JSONDecodeError):
+            planner._parse_response("not json at all", date(2026, 2, 8), "KR", candidates, None)
+
+    def test_parse_cross_market_preserved(self) -> None:
+        planner = _make_planner()
+        response = _gemini_response_json()
+        candidates = [_candidate()]
+        cross = CrossMarketContext(market="US", date="2026-02-07", total_pnl=1.5, win_rate=60)
+
+        pb = planner._parse_response(response, date(2026, 2, 8), "KR", candidates, cross)
+
+        assert pb.cross_market is not None
+        assert pb.cross_market.market == "US"
+        assert pb.cross_market.total_pnl == 1.5
+
+
+# ---------------------------------------------------------------------------
+# build_cross_market_context
+# ---------------------------------------------------------------------------
+
+
+class TestBuildCrossMarketContext:
+    def test_kr_reads_us_scorecard(self) -> None:
+        scorecard = {
+            "total_pnl": 2.5,
+            "win_rate": 65,
+            "index_change_pct": 0.8,
+            "lessons": ["Stay patient"],
+        }
+        planner = _make_planner(scorecard_data=scorecard)
+
+        ctx = planner.build_cross_market_context("KR", today=date(2026, 2, 8))
+
+        assert ctx is not None
+        assert ctx.market == "US"
+        assert ctx.total_pnl == 2.5
+        assert ctx.win_rate == 65
+        assert "Stay patient" in ctx.lessons
+
+        # Verify it queried scorecard_US
+        planner._context_store.get_context.assert_called_once_with(
+            ContextLayer.L6_DAILY, "2026-02-07", "scorecard_US"
+        )
+        assert ctx.date == "2026-02-07"
+
+    def test_us_reads_kr_scorecard(self) -> None:
+        scorecard = {"total_pnl": -1.0, "win_rate": 40, "index_change_pct": -0.5}
+        planner = _make_planner(scorecard_data=scorecard)
+
+        ctx = planner.build_cross_market_context("US", today=date(2026, 2, 8))
+
+        assert ctx is not None
+        assert ctx.market == "KR"
+        assert ctx.total_pnl == -1.0
+
+        planner._context_store.get_context.assert_called_once_with(
+            ContextLayer.L6_DAILY, "2026-02-08", "scorecard_KR"
+        )
+
+    def test_no_scorecard_returns_none(self) -> None:
+        planner = _make_planner(scorecard_data=None)
+
+        ctx = planner.build_cross_market_context("KR", today=date(2026, 2, 8))
+
+        assert ctx is None
+
+    def test_invalid_scorecard_returns_none(self) -> None:
+        planner = _make_planner(scorecard_data="not a dict and not json")
+
+        ctx = planner.build_cross_market_context("KR", today=date(2026, 2, 8))
+
+        assert ctx is None
+
+
+# ---------------------------------------------------------------------------
+# build_self_market_scorecard
+# ---------------------------------------------------------------------------
+
+
+class TestBuildSelfMarketScorecard:
+    def test_reads_previous_day_scorecard(self) -> None:
+        scorecard = {"total_pnl": -1.2, "win_rate": 45, "lessons": ["Reduce overtrading"]}
+        planner = _make_planner(scorecard_data=scorecard)
+
+        data = planner.build_self_market_scorecard("KR", today=date(2026, 2, 8))
+
+        assert data is not None
+        assert data["date"] == "2026-02-07"
+        assert data["total_pnl"] == -1.2
+        assert data["win_rate"] == 45
+        assert "Reduce overtrading" in data["lessons"]
+        planner._context_store.get_context.assert_called_once_with(
+            ContextLayer.L6_DAILY, "2026-02-07", "scorecard_KR"
+        )
+
+    def test_missing_scorecard_returns_none(self) -> None:
+        planner = _make_planner(scorecard_data=None)
+        assert planner.build_self_market_scorecard("US", today=date(2026, 2, 8)) is None
+
+
+# ---------------------------------------------------------------------------
+# _build_prompt
+# ---------------------------------------------------------------------------
+
+
+class TestBuildPrompt:
+    def test_prompt_contains_candidates(self) -> None:
+        planner = _make_planner()
+        candidates = [_candidate(code="005930", name="Samsung")]
+
+        prompt = planner._build_prompt("KR", candidates, {}, None, None)
+
+        assert "005930" in prompt
+        assert "Samsung" in prompt
+        assert "RSI=" in prompt
+        assert "volume_ratio=" in prompt
+
+    def test_prompt_contains_cross_market(self) -> None:
+        planner = _make_planner()
+        cross = CrossMarketContext(
+            market="US", date="2026-02-07", total_pnl=1.5,
+            win_rate=60, index_change_pct=0.8, lessons=["Cut losses early"],
+        )
+
+        prompt = planner._build_prompt("KR", [_candidate()], {}, None, cross)
+
+        assert "Other Market (US)" in prompt
+        assert "+1.50%" in prompt
+        assert "Cut losses early" in prompt
+
+    def test_prompt_contains_context_data(self) -> None:
+        planner = _make_planner()
+        context = {"L6_DAILY": {"win_rate": 0.65, "total_pnl": 2.5}}
+
+        prompt = planner._build_prompt("KR", [_candidate()], context, None, None)
+
+        assert "Strategic Context" in prompt
+        assert "L6_DAILY" in prompt
+        assert "win_rate" in prompt
+
+    def test_prompt_contains_max_scenarios(self) -> None:
+        planner = _make_planner()
+        prompt = planner._build_prompt("KR", [_candidate()], {}, None, None)
+
+        assert f"Max {planner._settings.MAX_SCENARIOS_PER_STOCK} scenarios" in prompt
+
+    def test_prompt_market_name(self) -> None:
+        planner = _make_planner()
+        prompt = planner._build_prompt("US", [_candidate()], {}, None, None)
+        assert "US market" in prompt
+
+    def test_prompt_contains_self_market_scorecard(self) -> None:
+        planner = _make_planner()
+        self_scorecard = {
+            "date": "2026-02-07",
+            "total_pnl": -0.8,
+            "win_rate": 45.0,
+            "lessons": ["Avoid midday entries"],
+        }
+        prompt = planner._build_prompt("KR", [_candidate()], {}, self_scorecard, None)
+
+        assert "My Market Previous Day (KR)" in prompt
+        assert "2026-02-07" in prompt
+        assert "-0.80%" in prompt
+        assert "Avoid midday entries" in prompt
+
+
+# ---------------------------------------------------------------------------
+# _extract_json
+# ---------------------------------------------------------------------------
+
+
+class TestExtractJson:
+    def test_plain_json(self) -> None:
+        assert PreMarketPlanner._extract_json('{"a": 1}') == '{"a": 1}'
+
+    def test_with_json_fence(self) -> None:
+        text = '```json\n{"a": 1}\n```'
+        assert PreMarketPlanner._extract_json(text) == '{"a": 1}'
+
+    def test_with_plain_fence(self) -> None:
+        text = '```\n{"a": 1}\n```'
+        assert PreMarketPlanner._extract_json(text) == '{"a": 1}'
+
+    def test_with_whitespace(self) -> None:
+        text = '  \n  {"a": 1}  \n  '
+        assert PreMarketPlanner._extract_json(text) == '{"a": 1}'
+
+
+# ---------------------------------------------------------------------------
+# Defensive playbook
+# ---------------------------------------------------------------------------
+
+
+class TestDefensivePlaybook:
+    def test_defensive_has_stop_loss(self) -> None:
+        candidates = [_candidate(code="005930"), _candidate(code="AAPL")]
+        pb = PreMarketPlanner._defensive_playbook(date(2026, 2, 8), "KR", candidates)
+
+        assert pb.default_action == ScenarioAction.HOLD
+        assert pb.market_outlook == MarketOutlook.NEUTRAL_TO_BEARISH
+        assert pb.stock_count == 2
+        for sp in pb.stock_playbooks:
+            assert sp.scenarios[0].action == ScenarioAction.SELL
+            assert sp.scenarios[0].stop_loss_pct == -3.0
+
+    def test_defensive_has_global_rule(self) -> None:
+        pb = PreMarketPlanner._defensive_playbook(date(2026, 2, 8), "KR", [_candidate()])
+
+        assert len(pb.global_rules) == 1
+        assert pb.global_rules[0].action == ScenarioAction.REDUCE_ALL
+
+    def test_empty_playbook(self) -> None:
+        pb = PreMarketPlanner._empty_playbook(date(2026, 2, 8), "US")
+
+        assert pb.stock_count == 0
+        assert pb.market == "US"
+        assert pb.market_outlook == MarketOutlook.NEUTRAL

tests/test_scenario_engine.py

@@ -0,0 +1,442 @@
+"""Tests for the local scenario engine."""
+
+from __future__ import annotations
+
+from datetime import date
+
+import pytest
+
+from src.strategy.models import (
+    DayPlaybook,
+    GlobalRule,
+    ScenarioAction,
+    StockCondition,
+    StockPlaybook,
+    StockScenario,
+)
+from src.strategy.scenario_engine import ScenarioEngine, ScenarioMatch
+
+
+@pytest.fixture
+def engine() -> ScenarioEngine:
+    return ScenarioEngine()
+
+
+def _scenario(
+    rsi_below: float | None = None,
+    rsi_above: float | None = None,
+    volume_ratio_above: float | None = None,
+    action: ScenarioAction = ScenarioAction.BUY,
+    confidence: int = 85,
+    **kwargs,
+) -> StockScenario:
+    return StockScenario(
+        condition=StockCondition(
+            rsi_below=rsi_below,
+            rsi_above=rsi_above,
+            volume_ratio_above=volume_ratio_above,
+            **kwargs,
+        ),
+        action=action,
+        confidence=confidence,
+        rationale=f"Test scenario: {action.value}",
+    )
+
+
+def _playbook(
+    stock_code: str = "005930",
+    scenarios: list[StockScenario] | None = None,
+    global_rules: list[GlobalRule] | None = None,
+    default_action: ScenarioAction = ScenarioAction.HOLD,
+) -> DayPlaybook:
+    if scenarios is None:
+        scenarios = [_scenario(rsi_below=30.0)]
+    return DayPlaybook(
+        date=date(2026, 2, 7),
+        market="KR",
+        stock_playbooks=[StockPlaybook(stock_code=stock_code, scenarios=scenarios)],
+        global_rules=global_rules or [],
+        default_action=default_action,
+    )
+
+
+# ---------------------------------------------------------------------------
+# evaluate_condition
+# ---------------------------------------------------------------------------
+
+
+class TestEvaluateCondition:
+    def test_rsi_below_match(self, engine: ScenarioEngine) -> None:
+        cond = StockCondition(rsi_below=30.0)
+        assert engine.evaluate_condition(cond, {"rsi": 25.0})
+
+    def test_rsi_below_no_match(self, engine: ScenarioEngine) -> None:
+        cond = StockCondition(rsi_below=30.0)
+        assert not engine.evaluate_condition(cond, {"rsi": 35.0})
+
+    def test_rsi_above_match(self, engine: ScenarioEngine) -> None:
+        cond = StockCondition(rsi_above=70.0)
+        assert engine.evaluate_condition(cond, {"rsi": 75.0})
+
+    def test_rsi_above_no_match(self, engine: ScenarioEngine) -> None:
+        cond = StockCondition(rsi_above=70.0)
+        assert not engine.evaluate_condition(cond, {"rsi": 65.0})
+
+    def test_volume_ratio_above_match(self, engine: ScenarioEngine) -> None:
+        cond = StockCondition(volume_ratio_above=3.0)
+        assert engine.evaluate_condition(cond, {"volume_ratio": 4.5})
+
+    def test_volume_ratio_below_match(self, engine: ScenarioEngine) -> None:
+        cond = StockCondition(volume_ratio_below=1.0)
+        assert engine.evaluate_condition(cond, {"volume_ratio": 0.5})
+
+    def test_price_above_match(self, engine: ScenarioEngine) -> None:
+        cond = StockCondition(price_above=50000)
+        assert engine.evaluate_condition(cond, {"current_price": 55000})
+
+    def test_price_below_match(self, engine: ScenarioEngine) -> None:
+        cond = StockCondition(price_below=50000)
+        assert engine.evaluate_condition(cond, {"current_price": 45000})
+
+    def test_price_change_pct_above_match(self, engine: ScenarioEngine) -> None:
+        cond = StockCondition(price_change_pct_above=2.0)
+        assert engine.evaluate_condition(cond, {"price_change_pct": 3.5})
+
+    def test_price_change_pct_below_match(self, engine: ScenarioEngine) -> None:
+        cond = StockCondition(price_change_pct_below=-3.0)
+        assert engine.evaluate_condition(cond, {"price_change_pct": -4.0})
+
+    def test_multiple_conditions_and_logic(self, engine: ScenarioEngine) -> None:
+        cond = StockCondition(rsi_below=30.0, volume_ratio_above=3.0)
+        # Both met
+        assert engine.evaluate_condition(cond, {"rsi": 25.0, "volume_ratio": 4.0})
+        # Only RSI met
+        assert not engine.evaluate_condition(cond, {"rsi": 25.0, "volume_ratio": 2.0})
+        # Only volume met
+        assert not engine.evaluate_condition(cond, {"rsi": 35.0, "volume_ratio": 4.0})
+        # Neither met
+        assert not engine.evaluate_condition(cond, {"rsi": 35.0, "volume_ratio": 2.0})
+
+    def test_empty_condition_returns_false(self, engine: ScenarioEngine) -> None:
+        cond = StockCondition()
+        assert not engine.evaluate_condition(cond, {"rsi": 25.0})
+
+    def test_missing_data_returns_false(self, engine: ScenarioEngine) -> None:
+        cond = StockCondition(rsi_below=30.0)
+        assert not engine.evaluate_condition(cond, {})
+
+    def test_none_data_returns_false(self, engine: ScenarioEngine) -> None:
+        cond = StockCondition(rsi_below=30.0)
+        assert not engine.evaluate_condition(cond, {"rsi": None})
+
+    def test_boundary_value_not_matched(self, engine: ScenarioEngine) -> None:
+        """rsi_below=30 should NOT match rsi=30 (strict less than)."""
+        cond = StockCondition(rsi_below=30.0)
+        assert not engine.evaluate_condition(cond, {"rsi": 30.0})
+
+    def test_boundary_value_above_not_matched(self, engine: ScenarioEngine) -> None:
+        """rsi_above=70 should NOT match rsi=70 (strict greater than)."""
+        cond = StockCondition(rsi_above=70.0)
+        assert not engine.evaluate_condition(cond, {"rsi": 70.0})
+
+    def test_string_value_no_exception(self, engine: ScenarioEngine) -> None:
+        """String numeric value should not raise TypeError."""
+        cond = StockCondition(rsi_below=30.0)
+        # "25" can be cast to float → should match
+        assert engine.evaluate_condition(cond, {"rsi": "25"})
+        # "35" → should not match
+        assert not engine.evaluate_condition(cond, {"rsi": "35"})
+
+    def test_percent_string_returns_false(self, engine: ScenarioEngine) -> None:
+        """Percent string like '30%' cannot be cast to float → False, no exception."""
+        cond = StockCondition(rsi_below=30.0)
+        assert not engine.evaluate_condition(cond, {"rsi": "30%"})
+
+    def test_decimal_value_no_exception(self, engine: ScenarioEngine) -> None:
+        """Decimal values should be safely handled."""
+        from decimal import Decimal
+
+        cond = StockCondition(rsi_below=30.0)
+        assert engine.evaluate_condition(cond, {"rsi": Decimal("25.0")})
+
+    def test_mixed_invalid_types_no_exception(self, engine: ScenarioEngine) -> None:
+        """Various invalid types should not raise exceptions."""
+        cond = StockCondition(
+            rsi_below=30.0, volume_ratio_above=2.0,
+            price_above=100, price_change_pct_below=-1.0,
+        )
+        data = {
+            "rsi": [25],           # list
+            "volume_ratio": "bad",  # non-numeric string
+            "current_price": {},    # dict
+            "price_change_pct": object(),  # arbitrary object
+        }
+        # Should return False (invalid types → None → False), never raise
+        assert not engine.evaluate_condition(cond, data)
+
+    def test_missing_key_logs_warning_once(self, caplog) -> None:
+        """Missing key warning should fire only once per key per engine instance."""
+        import logging
+
+        eng = ScenarioEngine()
+        cond = StockCondition(rsi_below=30.0)
+        with caplog.at_level(logging.WARNING):
+            eng.evaluate_condition(cond, {})
+            eng.evaluate_condition(cond, {})
+            eng.evaluate_condition(cond, {})
+        # Warning should appear exactly once despite 3 calls
+        assert caplog.text.count("'rsi' but key missing") == 1
+
+
+# ---------------------------------------------------------------------------
+# check_global_rules
+# ---------------------------------------------------------------------------
+
+
+class TestCheckGlobalRules:
+    def test_no_rules(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(global_rules=[])
+        result = engine.check_global_rules(pb, {"portfolio_pnl_pct": -1.0})
+        assert result is None
+
+    def test_rule_triggered(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(
+            global_rules=[
+                GlobalRule(
+                    condition="portfolio_pnl_pct < -2.0",
+                    action=ScenarioAction.REDUCE_ALL,
+                    rationale="Near circuit breaker",
+                ),
+            ]
+        )
+        result = engine.check_global_rules(pb, {"portfolio_pnl_pct": -2.5})
+        assert result is not None
+        assert result.action == ScenarioAction.REDUCE_ALL
+
+    def test_rule_not_triggered(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(
+            global_rules=[
+                GlobalRule(
+                    condition="portfolio_pnl_pct < -2.0",
+                    action=ScenarioAction.REDUCE_ALL,
+                ),
+            ]
+        )
+        result = engine.check_global_rules(pb, {"portfolio_pnl_pct": -1.0})
+        assert result is None
+
+    def test_first_rule_wins(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(
+            global_rules=[
+                GlobalRule(condition="portfolio_pnl_pct < -2.0", action=ScenarioAction.REDUCE_ALL),
+                GlobalRule(condition="portfolio_pnl_pct < -1.0", action=ScenarioAction.HOLD),
+            ]
+        )
+        result = engine.check_global_rules(pb, {"portfolio_pnl_pct": -2.5})
+        assert result is not None
+        assert result.action == ScenarioAction.REDUCE_ALL
+
+    def test_greater_than_operator(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(
+            global_rules=[
+                GlobalRule(condition="volatility_index > 30", action=ScenarioAction.HOLD),
+            ]
+        )
+        result = engine.check_global_rules(pb, {"volatility_index": 35})
+        assert result is not None
+
+    def test_missing_field_not_triggered(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(
+            global_rules=[
+                GlobalRule(condition="unknown_field < -2.0", action=ScenarioAction.REDUCE_ALL),
+            ]
+        )
+        result = engine.check_global_rules(pb, {"portfolio_pnl_pct": -5.0})
+        assert result is None
+
+    def test_invalid_condition_format(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(
+            global_rules=[
+                GlobalRule(condition="bad format", action=ScenarioAction.HOLD),
+            ]
+        )
+        result = engine.check_global_rules(pb, {})
+        assert result is None
+
+    def test_le_operator(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(
+            global_rules=[
+                GlobalRule(condition="portfolio_pnl_pct <= -2.0", action=ScenarioAction.REDUCE_ALL),
+            ]
+        )
+        assert engine.check_global_rules(pb, {"portfolio_pnl_pct": -2.0}) is not None
+        assert engine.check_global_rules(pb, {"portfolio_pnl_pct": -1.9}) is None
+
+    def test_ge_operator(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(
+            global_rules=[
+                GlobalRule(condition="volatility >= 80.0", action=ScenarioAction.HOLD),
+            ]
+        )
+        assert engine.check_global_rules(pb, {"volatility": 80.0}) is not None
+        assert engine.check_global_rules(pb, {"volatility": 79.9}) is None
+
+
+# ---------------------------------------------------------------------------
+# evaluate (full pipeline)
+# ---------------------------------------------------------------------------
+
+
+class TestEvaluate:
+    def test_scenario_match(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(scenarios=[_scenario(rsi_below=30.0)])
+        result = engine.evaluate(pb, "005930", {"rsi": 25.0}, {})
+        assert result.action == ScenarioAction.BUY
+        assert result.confidence == 85
+        assert result.matched_scenario is not None
+
+    def test_no_scenario_match_returns_default(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(scenarios=[_scenario(rsi_below=30.0)])
+        result = engine.evaluate(pb, "005930", {"rsi": 50.0}, {})
+        assert result.action == ScenarioAction.HOLD
+        assert result.confidence == 0
+        assert result.matched_scenario is None
+
+    def test_stock_not_in_playbook(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(stock_code="005930")
+        result = engine.evaluate(pb, "AAPL", {"rsi": 25.0}, {})
+        assert result.action == ScenarioAction.HOLD
+        assert result.confidence == 0
+
+    def test_global_rule_takes_priority(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(
+            scenarios=[_scenario(rsi_below=30.0)],
+            global_rules=[
+                GlobalRule(
+                    condition="portfolio_pnl_pct < -2.0",
+                    action=ScenarioAction.REDUCE_ALL,
+                    rationale="Loss limit",
+                ),
+            ],
+        )
+        result = engine.evaluate(
+            pb,
+            "005930",
+            {"rsi": 25.0},  # Would match scenario
+            {"portfolio_pnl_pct": -2.5},  # But global rule triggers first
+        )
+        assert result.action == ScenarioAction.REDUCE_ALL
+        assert result.global_rule_triggered is not None
+        assert result.matched_scenario is None
+
+    def test_first_scenario_wins(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(
+            scenarios=[
+                _scenario(rsi_below=30.0, action=ScenarioAction.BUY, confidence=90),
+                _scenario(rsi_below=25.0, action=ScenarioAction.BUY, confidence=95),
+            ]
+        )
+        result = engine.evaluate(pb, "005930", {"rsi": 20.0}, {})
+        # Both match, but first wins
+        assert result.confidence == 90
+
+    def test_sell_scenario(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(
+            scenarios=[
+                _scenario(rsi_above=75.0, action=ScenarioAction.SELL, confidence=80),
+            ]
+        )
+        result = engine.evaluate(pb, "005930", {"rsi": 80.0}, {})
+        assert result.action == ScenarioAction.SELL
+
+    def test_empty_playbook(self, engine: ScenarioEngine) -> None:
+        pb = DayPlaybook(date=date(2026, 2, 7), market="KR", stock_playbooks=[])
+        result = engine.evaluate(pb, "005930", {"rsi": 25.0}, {})
+        assert result.action == ScenarioAction.HOLD
+
+    def test_match_details_populated(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(scenarios=[_scenario(rsi_below=30.0, volume_ratio_above=2.0)])
+        result = engine.evaluate(
+            pb, "005930", {"rsi": 25.0, "volume_ratio": 3.0}, {}
+        )
+        assert result.match_details.get("rsi") == 25.0
+        assert result.match_details.get("volume_ratio") == 3.0
+
+    def test_custom_default_action(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(
+            scenarios=[_scenario(rsi_below=10.0)],  # Very unlikely to match
+            default_action=ScenarioAction.SELL,
+        )
+        result = engine.evaluate(pb, "005930", {"rsi": 50.0}, {})
+        assert result.action == ScenarioAction.SELL
+
+    def test_multiple_stocks_in_playbook(self, engine: ScenarioEngine) -> None:
+        pb = DayPlaybook(
+            date=date(2026, 2, 7),
+            market="US",
+            stock_playbooks=[
+                StockPlaybook(
+                    stock_code="AAPL",
+                    scenarios=[_scenario(rsi_below=25.0, confidence=90)],
+                ),
+                StockPlaybook(
+                    stock_code="MSFT",
+                    scenarios=[_scenario(rsi_above=75.0, action=ScenarioAction.SELL, confidence=80)],
+                ),
+            ],
+        )
+        aapl = engine.evaluate(pb, "AAPL", {"rsi": 20.0}, {})
+        assert aapl.action == ScenarioAction.BUY
+        assert aapl.confidence == 90
+
+        msft = engine.evaluate(pb, "MSFT", {"rsi": 80.0}, {})
+        assert msft.action == ScenarioAction.SELL
+
+    def test_complex_multi_condition(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(
+            scenarios=[
+                _scenario(
+                    rsi_below=30.0,
+                    volume_ratio_above=3.0,
+                    price_change_pct_below=-2.0,
+                    confidence=95,
+                ),
+            ]
+        )
+        # All conditions met
+        result = engine.evaluate(
+            pb,
+            "005930",
+            {"rsi": 22.0, "volume_ratio": 4.0, "price_change_pct": -3.0},
+            {},
+        )
+        assert result.action == ScenarioAction.BUY
+        assert result.confidence == 95
+
+        # One condition not met
+        result2 = engine.evaluate(
+            pb,
+            "005930",
+            {"rsi": 22.0, "volume_ratio": 4.0, "price_change_pct": -1.0},
+            {},
+        )
+        assert result2.action == ScenarioAction.HOLD
+
+    def test_scenario_match_returns_rationale(self, engine: ScenarioEngine) -> None:
+        pb = _playbook(scenarios=[_scenario(rsi_below=30.0)])
+        result = engine.evaluate(pb, "005930", {"rsi": 25.0}, {})
+        assert result.rationale != ""
+
+    def test_result_stock_code(self, engine: ScenarioEngine) -> None:
+        pb = _playbook()
+        result = engine.evaluate(pb, "005930", {"rsi": 25.0}, {})
+        assert result.stock_code == "005930"
+
+    def test_match_details_normalized(self, engine: ScenarioEngine) -> None:
+        """match_details should contain _safe_float normalized values, not raw."""
+        pb = _playbook(scenarios=[_scenario(rsi_below=30.0)])
+        # Pass string value — should be normalized to float in match_details
+        result = engine.evaluate(pb, "005930", {"rsi": "25.0"}, {})
+        assert result.action == ScenarioAction.BUY
+        assert result.match_details["rsi"] == 25.0
+        assert isinstance(result.match_details["rsi"], float)

tests/test_scorecard.py

@@ -0,0 +1,81 @@
+"""Tests for DailyScorecard model."""
+
+from __future__ import annotations
+
+from src.evolution.scorecard import DailyScorecard
+
+
+def test_scorecard_initialization() -> None:
+    scorecard = DailyScorecard(
+        date="2026-02-08",
+        market="KR",
+        total_decisions=10,
+        buys=3,
+        sells=2,
+        holds=5,
+        total_pnl=1234.5,
+        win_rate=60.0,
+        avg_confidence=78.5,
+        scenario_match_rate=70.0,
+        top_winners=["005930", "000660"],
+        top_losers=["035420"],
+        lessons=["Avoid chasing breakouts"],
+        cross_market_note="US volatility spillover",
+    )
+
+    assert scorecard.market == "KR"
+    assert scorecard.total_decisions == 10
+    assert scorecard.total_pnl == 1234.5
+    assert scorecard.top_winners == ["005930", "000660"]
+    assert scorecard.lessons == ["Avoid chasing breakouts"]
+    assert scorecard.cross_market_note == "US volatility spillover"
+
+
+def test_scorecard_defaults() -> None:
+    scorecard = DailyScorecard(
+        date="2026-02-08",
+        market="US",
+        total_decisions=0,
+        buys=0,
+        sells=0,
+        holds=0,
+        total_pnl=0.0,
+        win_rate=0.0,
+        avg_confidence=0.0,
+        scenario_match_rate=0.0,
+    )
+
+    assert scorecard.top_winners == []
+    assert scorecard.top_losers == []
+    assert scorecard.lessons == []
+    assert scorecard.cross_market_note == ""
+
+
+def test_scorecard_list_isolation() -> None:
+    a = DailyScorecard(
+        date="2026-02-08",
+        market="KR",
+        total_decisions=1,
+        buys=1,
+        sells=0,
+        holds=0,
+        total_pnl=10.0,
+        win_rate=100.0,
+        avg_confidence=90.0,
+        scenario_match_rate=100.0,
+    )
+    b = DailyScorecard(
+        date="2026-02-08",
+        market="US",
+        total_decisions=1,
+        buys=0,
+        sells=1,
+        holds=0,
+        total_pnl=-5.0,
+        win_rate=0.0,
+        avg_confidence=60.0,
+        scenario_match_rate=50.0,
+    )
+
+    a.top_winners.append("005930")
+    assert b.top_winners == []

tests/test_smart_scanner.py

@@ -0,0 +1,377 @@
+"""Tests for SmartVolatilityScanner."""
+
+from __future__ import annotations
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock
+
+from src.analysis.smart_scanner import ScanCandidate, SmartVolatilityScanner
+from src.analysis.volatility import VolatilityAnalyzer
+from src.broker.kis_api import KISBroker
+from src.config import Settings
+
+
+@pytest.fixture
+def mock_settings() -> Settings:
+    """Create test settings."""
+    return Settings(
+        KIS_APP_KEY="test",
+        KIS_APP_SECRET="test",
+        KIS_ACCOUNT_NO="12345678-01",
+        GEMINI_API_KEY="test",
+        RSI_OVERSOLD_THRESHOLD=30,
+        RSI_MOMENTUM_THRESHOLD=70,
+        VOL_MULTIPLIER=2.0,
+        SCANNER_TOP_N=3,
+        DB_PATH=":memory:",
+    )
+
+
+@pytest.fixture
+def mock_broker(mock_settings: Settings) -> MagicMock:
+    """Create mock broker."""
+    broker = MagicMock(spec=KISBroker)
+    broker._settings = mock_settings
+    broker.fetch_market_rankings = AsyncMock()
+    broker.get_daily_prices = AsyncMock()
+    return broker
+
+
+@pytest.fixture
+def scanner(mock_broker: MagicMock, mock_settings: Settings) -> SmartVolatilityScanner:
+    """Create smart scanner instance."""
+    analyzer = VolatilityAnalyzer()
+    return SmartVolatilityScanner(
+        broker=mock_broker,
+        volatility_analyzer=analyzer,
+        settings=mock_settings,
+    )
+
+
+class TestSmartVolatilityScanner:
+    """Test suite for SmartVolatilityScanner."""
+
+    @pytest.mark.asyncio
+    async def test_scan_finds_oversold_candidates(
+        self, scanner: SmartVolatilityScanner, mock_broker: MagicMock
+    ) -> None:
+        """Test that scanner identifies oversold stocks with high volume."""
+        # Mock rankings
+        mock_broker.fetch_market_rankings.return_value = [
+            {
+                "stock_code": "005930",
+                "name": "Samsung",
+                "price": 70000,
+                "volume": 5000000,
+                "change_rate": -3.5,
+                "volume_increase_rate": 250,
+            },
+        ]
+
+        # Mock daily prices - trending down (oversold)
+        prices = []
+        for i in range(20):
+            prices.append({
+                "date": f"2026020{i:02d}",
+                "open": 75000 - i * 200,
+                "high": 75500 - i * 200,
+                "low": 74500 - i * 200,
+                "close": 75000 - i * 250,  # Steady decline
+                "volume": 2000000,
+            })
+        mock_broker.get_daily_prices.return_value = prices
+
+        candidates = await scanner.scan()
+
+        # Should find at least one candidate (depending on exact RSI calculation)
+        mock_broker.fetch_market_rankings.assert_called_once()
+        mock_broker.get_daily_prices.assert_called_once_with("005930", days=20)
+
+        # If qualified, should have oversold signal
+        if candidates:
+            assert candidates[0].signal in ["oversold", "momentum"]
+            assert candidates[0].volume_ratio >= scanner.vol_multiplier
+
+    @pytest.mark.asyncio
+    async def test_scan_finds_momentum_candidates(
+        self, scanner: SmartVolatilityScanner, mock_broker: MagicMock
+    ) -> None:
+        """Test that scanner identifies momentum stocks with high volume."""
+        mock_broker.fetch_market_rankings.return_value = [
+            {
+                "stock_code": "035420",
+                "name": "NAVER",
+                "price": 250000,
+                "volume": 3000000,
+                "change_rate": 5.0,
+                "volume_increase_rate": 300,
+            },
+        ]
+
+        # Mock daily prices - trending up (momentum)
+        prices = []
+        for i in range(20):
+            prices.append({
+                "date": f"2026020{i:02d}",
+                "open": 230000 + i * 500,
+                "high": 231000 + i * 500,
+                "low": 229000 + i * 500,
+                "close": 230500 + i * 500,  # Steady rise
+                "volume": 1000000,
+            })
+        mock_broker.get_daily_prices.return_value = prices
+
+        candidates = await scanner.scan()
+
+        mock_broker.fetch_market_rankings.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_scan_filters_low_volume(
+        self, scanner: SmartVolatilityScanner, mock_broker: MagicMock
+    ) -> None:
+        """Test that stocks with low volume ratio are filtered out."""
+        mock_broker.fetch_market_rankings.return_value = [
+            {
+                "stock_code": "000660",
+                "name": "SK Hynix",
+                "price": 150000,
+                "volume": 500000,
+                "change_rate": -5.0,
+                "volume_increase_rate": 50,  # Only 50% increase (< 200%)
+            },
+        ]
+
+        # Low volume
+        prices = []
+        for i in range(20):
+            prices.append({
+                "date": f"2026020{i:02d}",
+                "open": 150000 - i * 100,
+                "high": 151000 - i * 100,
+                "low": 149000 - i * 100,
+                "close": 150000 - i * 150,  # Declining (would be oversold)
+                "volume": 1000000,  # Current 500k < 2x prev day 1M
+            })
+        mock_broker.get_daily_prices.return_value = prices
+
+        candidates = await scanner.scan()
+
+        # Should be filtered out due to low volume ratio
+        assert len(candidates) == 0
+
+    @pytest.mark.asyncio
+    async def test_scan_filters_neutral_rsi(
+        self, scanner: SmartVolatilityScanner, mock_broker: MagicMock
+    ) -> None:
+        """Test that stocks with neutral RSI are filtered out."""
+        mock_broker.fetch_market_rankings.return_value = [
+            {
+                "stock_code": "051910",
+                "name": "LG Chem",
+                "price": 500000,
+                "volume": 3000000,
+                "change_rate": 0.5,
+                "volume_increase_rate": 300,  # High volume
+            },
+        ]
+
+        # Flat prices (neutral RSI ~50)
+        prices = []
+        for i in range(20):
+            prices.append({
+                "date": f"2026020{i:02d}",
+                "open": 500000 + (i % 2) * 100,  # Small oscillation
+                "high": 500500,
+                "low": 499500,
+                "close": 500000 + (i % 2) * 50,
+                "volume": 1000000,
+            })
+        mock_broker.get_daily_prices.return_value = prices
+
+        candidates = await scanner.scan()
+
+        # Should be filtered out (RSI ~50, not < 30 or > 70)
+        assert len(candidates) == 0
+
+    @pytest.mark.asyncio
+    async def test_scan_uses_fallback_on_api_error(
+        self, scanner: SmartVolatilityScanner, mock_broker: MagicMock
+    ) -> None:
+        """Test fallback to static list when ranking API fails."""
+        mock_broker.fetch_market_rankings.side_effect = ConnectionError("API unavailable")
+
+        # Fallback stocks should still be analyzed
+        prices = []
+        for i in range(20):
+            prices.append({
+                "date": f"2026020{i:02d}",
+                "open": 50000 - i * 50,
+                "high": 51000 - i * 50,
+                "low": 49000 - i * 50,
+                "close": 50000 - i * 75,  # Declining
+                "volume": 1000000,
+            })
+        mock_broker.get_daily_prices.return_value = prices
+
+        candidates = await scanner.scan(fallback_stocks=["005930", "000660"])
+
+        # Should not crash
+        assert isinstance(candidates, list)
+
+    @pytest.mark.asyncio
+    async def test_scan_returns_top_n_only(
+        self, scanner: SmartVolatilityScanner, mock_broker: MagicMock
+    ) -> None:
+        """Test that scan returns at most top_n candidates."""
+        # Return many stocks
+        mock_broker.fetch_market_rankings.return_value = [
+            {
+                "stock_code": f"00{i}000",
+                "name": f"Stock{i}",
+                "price": 10000 * i,
+                "volume": 5000000,
+                "change_rate": -10,
+                "volume_increase_rate": 500,
+            }
+            for i in range(1, 10)
+        ]
+
+        # All oversold with high volume
+        def make_prices(code: str) -> list[dict]:
+            prices = []
+            for i in range(20):
+                prices.append({
+                    "date": f"2026020{i:02d}",
+                    "open": 10000 - i * 100,
+                    "high": 10500 - i * 100,
+                    "low": 9500 - i * 100,
+                    "close": 10000 - i * 150,
+                    "volume": 1000000,
+                })
+            return prices
+
+        mock_broker.get_daily_prices.side_effect = make_prices
+
+        candidates = await scanner.scan()
+
+        # Should respect top_n limit (3)
+        assert len(candidates) <= scanner.top_n
+
+    @pytest.mark.asyncio
+    async def test_scan_skips_insufficient_price_history(
+        self, scanner: SmartVolatilityScanner, mock_broker: MagicMock
+    ) -> None:
+        """Test that stocks with insufficient history are skipped."""
+        mock_broker.fetch_market_rankings.return_value = [
+            {
+                "stock_code": "005930",
+                "name": "Samsung",
+                "price": 70000,
+                "volume": 5000000,
+                "change_rate": -5.0,
+                "volume_increase_rate": 300,
+            },
+        ]
+
+        # Only 5 days of data (need 15+ for RSI)
+        mock_broker.get_daily_prices.return_value = [
+            {
+                "date": f"2026020{i:02d}",
+                "open": 70000,
+                "high": 71000,
+                "low": 69000,
+                "close": 70000,
+                "volume": 2000000,
+            }
+            for i in range(5)
+        ]
+
+        candidates = await scanner.scan()
+
+        # Should skip due to insufficient data
+        assert len(candidates) == 0
+
+    @pytest.mark.asyncio
+    async def test_get_stock_codes(
+        self, scanner: SmartVolatilityScanner
+    ) -> None:
+        """Test extraction of stock codes from candidates."""
+        candidates = [
+            ScanCandidate(
+                stock_code="005930",
+                name="Samsung",
+                price=70000,
+                volume=5000000,
+                volume_ratio=2.5,
+                rsi=28,
+                signal="oversold",
+                score=85.0,
+            ),
+            ScanCandidate(
+                stock_code="035420",
+                name="NAVER",
+                price=250000,
+                volume=3000000,
+                volume_ratio=3.0,
+                rsi=75,
+                signal="momentum",
+                score=88.0,
+            ),
+        ]
+
+        codes = scanner.get_stock_codes(candidates)
+
+        assert codes == ["005930", "035420"]
+
+
+class TestRSICalculation:
+    """Test RSI calculation in VolatilityAnalyzer."""
+
+    def test_rsi_oversold(self) -> None:
+        """Test RSI calculation for downtrending prices."""
+        analyzer = VolatilityAnalyzer()
+
+        # Steadily declining prices
+        prices = [100 - i * 0.5 for i in range(20)]
+        rsi = analyzer.calculate_rsi(prices, period=14)
+
+        assert rsi < 50  # Should be oversold territory
+
+    def test_rsi_overbought(self) -> None:
+        """Test RSI calculation for uptrending prices."""
+        analyzer = VolatilityAnalyzer()
+
+        # Steadily rising prices
+        prices = [100 + i * 0.5 for i in range(20)]
+        rsi = analyzer.calculate_rsi(prices, period=14)
+
+        assert rsi > 50  # Should be overbought territory
+
+    def test_rsi_neutral(self) -> None:
+        """Test RSI calculation for flat prices."""
+        analyzer = VolatilityAnalyzer()
+
+        # Flat prices with small oscillation
+        prices = [100 + (i % 2) * 0.1 for i in range(20)]
+        rsi = analyzer.calculate_rsi(prices, period=14)
+
+        assert 40 < rsi < 60  # Should be near neutral
+
+    def test_rsi_insufficient_data(self) -> None:
+        """Test RSI returns neutral when insufficient data."""
+        analyzer = VolatilityAnalyzer()
+
+        prices = [100, 101, 102]  # Only 3 prices, need 15+
+        rsi = analyzer.calculate_rsi(prices, period=14)
+
+        assert rsi == 50.0  # Default neutral
+
+    def test_rsi_all_gains(self) -> None:
+        """Test RSI returns 100 when all gains (no losses)."""
+        analyzer = VolatilityAnalyzer()
+
+        # Monotonic increase
+        prices = [100 + i for i in range(20)]
+        rsi = analyzer.calculate_rsi(prices, period=14)
+
+        assert rsi == 100.0  # Maximum RSI

tests/test_strategy_models.py

@@ -0,0 +1,366 @@
+"""Tests for strategy/playbook Pydantic models."""
+
+from __future__ import annotations
+
+from datetime import date
+
+import pytest
+from pydantic import ValidationError
+
+from src.strategy.models import (
+    CrossMarketContext,
+    DayPlaybook,
+    GlobalRule,
+    MarketOutlook,
+    PlaybookStatus,
+    ScenarioAction,
+    StockCondition,
+    StockPlaybook,
+    StockScenario,
+)
+
+
+# ---------------------------------------------------------------------------
+# StockCondition
+# ---------------------------------------------------------------------------
+
+
+class TestStockCondition:
+    def test_empty_condition(self) -> None:
+        cond = StockCondition()
+        assert not cond.has_any_condition()
+
+    def test_single_field(self) -> None:
+        cond = StockCondition(rsi_below=30.0)
+        assert cond.has_any_condition()
+
+    def test_multiple_fields(self) -> None:
+        cond = StockCondition(rsi_below=25.0, volume_ratio_above=3.0)
+        assert cond.has_any_condition()
+
+    def test_all_fields(self) -> None:
+        cond = StockCondition(
+            rsi_below=30,
+            rsi_above=10,
+            volume_ratio_above=2.0,
+            volume_ratio_below=10.0,
+            price_above=1000,
+            price_below=50000,
+            price_change_pct_above=-5.0,
+            price_change_pct_below=5.0,
+        )
+        assert cond.has_any_condition()
+
+
+# ---------------------------------------------------------------------------
+# StockScenario
+# ---------------------------------------------------------------------------
+
+
+class TestStockScenario:
+    def test_valid_scenario(self) -> None:
+        s = StockScenario(
+            condition=StockCondition(rsi_below=25.0),
+            action=ScenarioAction.BUY,
+            confidence=85,
+            allocation_pct=15.0,
+            stop_loss_pct=-2.0,
+            take_profit_pct=3.0,
+            rationale="Oversold bounce expected",
+        )
+        assert s.action == ScenarioAction.BUY
+        assert s.confidence == 85
+
+    def test_confidence_too_high(self) -> None:
+        with pytest.raises(ValidationError):
+            StockScenario(
+                condition=StockCondition(),
+                action=ScenarioAction.BUY,
+                confidence=101,
+            )
+
+    def test_confidence_too_low(self) -> None:
+        with pytest.raises(ValidationError):
+            StockScenario(
+                condition=StockCondition(),
+                action=ScenarioAction.BUY,
+                confidence=-1,
+            )
+
+    def test_allocation_too_high(self) -> None:
+        with pytest.raises(ValidationError):
+            StockScenario(
+                condition=StockCondition(),
+                action=ScenarioAction.BUY,
+                confidence=80,
+                allocation_pct=101.0,
+            )
+
+    def test_stop_loss_must_be_negative(self) -> None:
+        with pytest.raises(ValidationError):
+            StockScenario(
+                condition=StockCondition(),
+                action=ScenarioAction.BUY,
+                confidence=80,
+                stop_loss_pct=1.0,
+            )
+
+    def test_take_profit_must_be_positive(self) -> None:
+        with pytest.raises(ValidationError):
+            StockScenario(
+                condition=StockCondition(),
+                action=ScenarioAction.BUY,
+                confidence=80,
+                take_profit_pct=-1.0,
+            )
+
+    def test_defaults(self) -> None:
+        s = StockScenario(
+            condition=StockCondition(),
+            action=ScenarioAction.HOLD,
+            confidence=50,
+        )
+        assert s.allocation_pct == 10.0
+        assert s.stop_loss_pct == -2.0
+        assert s.take_profit_pct == 3.0
+        assert s.rationale == ""
+
+
+# ---------------------------------------------------------------------------
+# StockPlaybook
+# ---------------------------------------------------------------------------
+
+
+class TestStockPlaybook:
+    def test_valid_playbook(self) -> None:
+        pb = StockPlaybook(
+            stock_code="005930",
+            stock_name="Samsung Electronics",
+            scenarios=[
+                StockScenario(
+                    condition=StockCondition(rsi_below=25.0),
+                    action=ScenarioAction.BUY,
+                    confidence=85,
+                ),
+            ],
+        )
+        assert pb.stock_code == "005930"
+        assert len(pb.scenarios) == 1
+
+    def test_empty_scenarios_rejected(self) -> None:
+        with pytest.raises(ValidationError):
+            StockPlaybook(
+                stock_code="005930",
+                scenarios=[],
+            )
+
+    def test_multiple_scenarios(self) -> None:
+        pb = StockPlaybook(
+            stock_code="AAPL",
+            scenarios=[
+                StockScenario(
+                    condition=StockCondition(rsi_below=25.0),
+                    action=ScenarioAction.BUY,
+                    confidence=85,
+                ),
+                StockScenario(
+                    condition=StockCondition(rsi_above=75.0),
+                    action=ScenarioAction.SELL,
+                    confidence=80,
+                ),
+            ],
+        )
+        assert len(pb.scenarios) == 2
+
+
+# ---------------------------------------------------------------------------
+# GlobalRule
+# ---------------------------------------------------------------------------
+
+
+class TestGlobalRule:
+    def test_valid_rule(self) -> None:
+        rule = GlobalRule(
+            condition="portfolio_pnl_pct < -2.0",
+            action=ScenarioAction.REDUCE_ALL,
+            rationale="Risk limit approaching",
+        )
+        assert rule.action == ScenarioAction.REDUCE_ALL
+
+    def test_hold_rule(self) -> None:
+        rule = GlobalRule(
+            condition="volatility_index > 30",
+            action=ScenarioAction.HOLD,
+        )
+        assert rule.rationale == ""
+
+
+# ---------------------------------------------------------------------------
+# CrossMarketContext
+# ---------------------------------------------------------------------------
+
+
+class TestCrossMarketContext:
+    def test_valid_context(self) -> None:
+        ctx = CrossMarketContext(
+            market="US",
+            date="2026-02-07",
+            total_pnl=-1.5,
+            win_rate=40.0,
+            index_change_pct=-2.3,
+            key_events=["Fed rate decision"],
+            lessons=["Avoid tech sector on rate hike days"],
+        )
+        assert ctx.market == "US"
+        assert len(ctx.key_events) == 1
+
+    def test_defaults(self) -> None:
+        ctx = CrossMarketContext(market="KR", date="2026-02-07")
+        assert ctx.total_pnl == 0.0
+        assert ctx.key_events == []
+        assert ctx.lessons == []
+
+
+# ---------------------------------------------------------------------------
+# DayPlaybook
+# ---------------------------------------------------------------------------
+
+
+def _make_scenario(rsi_below: float = 25.0) -> StockScenario:
+    return StockScenario(
+        condition=StockCondition(rsi_below=rsi_below),
+        action=ScenarioAction.BUY,
+        confidence=85,
+    )
+
+
+def _make_playbook(**kwargs) -> DayPlaybook:
+    defaults = {
+        "date": date(2026, 2, 7),
+        "market": "KR",
+        "stock_playbooks": [
+            StockPlaybook(stock_code="005930", scenarios=[_make_scenario()]),
+        ],
+    }
+    defaults.update(kwargs)
+    return DayPlaybook(**defaults)
+
+
+class TestDayPlaybook:
+    def test_valid_playbook(self) -> None:
+        pb = _make_playbook()
+        assert pb.market == "KR"
+        assert pb.date == date(2026, 2, 7)
+        assert pb.default_action == ScenarioAction.HOLD
+        assert pb.scenario_count == 1
+        assert pb.stock_count == 1
+
+    def test_generated_at_auto_set(self) -> None:
+        pb = _make_playbook()
+        assert pb.generated_at != ""
+
+    def test_explicit_generated_at(self) -> None:
+        pb = _make_playbook(generated_at="2026-02-07T08:30:00")
+        assert pb.generated_at == "2026-02-07T08:30:00"
+
+    def test_duplicate_stocks_rejected(self) -> None:
+        with pytest.raises(ValidationError):
+            DayPlaybook(
+                date=date(2026, 2, 7),
+                market="KR",
+                stock_playbooks=[
+                    StockPlaybook(stock_code="005930", scenarios=[_make_scenario()]),
+                    StockPlaybook(stock_code="005930", scenarios=[_make_scenario(30)]),
+                ],
+            )
+
+    def test_empty_stock_playbooks_allowed(self) -> None:
+        pb = DayPlaybook(
+            date=date(2026, 2, 7),
+            market="KR",
+            stock_playbooks=[],
+        )
+        assert pb.stock_count == 0
+        assert pb.scenario_count == 0
+
+    def test_get_stock_playbook_found(self) -> None:
+        pb = _make_playbook()
+        result = pb.get_stock_playbook("005930")
+        assert result is not None
+        assert result.stock_code == "005930"
+
+    def test_get_stock_playbook_not_found(self) -> None:
+        pb = _make_playbook()
+        result = pb.get_stock_playbook("AAPL")
+        assert result is None
+
+    def test_with_global_rules(self) -> None:
+        pb = _make_playbook(
+            global_rules=[
+                GlobalRule(
+                    condition="portfolio_pnl_pct < -2.0",
+                    action=ScenarioAction.REDUCE_ALL,
+                ),
+            ],
+        )
+        assert len(pb.global_rules) == 1
+
+    def test_with_cross_market_context(self) -> None:
+        ctx = CrossMarketContext(market="US", date="2026-02-07", total_pnl=-1.5)
+        pb = _make_playbook(cross_market=ctx)
+        assert pb.cross_market is not None
+        assert pb.cross_market.market == "US"
+
+    def test_market_outlook(self) -> None:
+        pb = _make_playbook(market_outlook=MarketOutlook.BEARISH)
+        assert pb.market_outlook == MarketOutlook.BEARISH
+
+    def test_multiple_stocks_multiple_scenarios(self) -> None:
+        pb = DayPlaybook(
+            date=date(2026, 2, 7),
+            market="US",
+            stock_playbooks=[
+                StockPlaybook(
+                    stock_code="AAPL",
+                    scenarios=[_make_scenario(), _make_scenario(30)],
+                ),
+                StockPlaybook(
+                    stock_code="MSFT",
+                    scenarios=[_make_scenario()],
+                ),
+            ],
+        )
+        assert pb.stock_count == 2
+        assert pb.scenario_count == 3
+
+    def test_serialization_roundtrip(self) -> None:
+        pb = _make_playbook(
+            market_outlook=MarketOutlook.BULLISH,
+            cross_market=CrossMarketContext(market="US", date="2026-02-07"),
+        )
+        json_str = pb.model_dump_json()
+        restored = DayPlaybook.model_validate_json(json_str)
+        assert restored.market == pb.market
+        assert restored.date == pb.date
+        assert restored.scenario_count == pb.scenario_count
+        assert restored.cross_market is not None
+
+
+# ---------------------------------------------------------------------------
+# Enums
+# ---------------------------------------------------------------------------
+
+
+class TestEnums:
+    def test_scenario_action_values(self) -> None:
+        assert ScenarioAction.BUY.value == "BUY"
+        assert ScenarioAction.SELL.value == "SELL"
+        assert ScenarioAction.HOLD.value == "HOLD"
+        assert ScenarioAction.REDUCE_ALL.value == "REDUCE_ALL"
+
+    def test_market_outlook_values(self) -> None:
+        assert len(MarketOutlook) == 5
+
+    def test_playbook_status_values(self) -> None:
+        assert PlaybookStatus.READY.value == "ready"
+        assert PlaybookStatus.EXPIRED.value == "expired"

tests/test_telegram.py

@@ -160,6 +160,83 @@ class TestNotificationSending:
             assert "250.50" in payload["text"]
             assert "92%" in payload["text"]
 
+    @pytest.mark.asyncio
+    async def test_playbook_generated_format(self) -> None:
+        """Playbook generated notification has expected fields."""
+        client = TelegramClient(
+            bot_token="123:abc", chat_id="456", enabled=True
+        )
+
+        mock_resp = AsyncMock()
+        mock_resp.status = 200
+        mock_resp.__aenter__ = AsyncMock(return_value=mock_resp)
+        mock_resp.__aexit__ = AsyncMock(return_value=False)
+
+        with patch("aiohttp.ClientSession.post", return_value=mock_resp) as mock_post:
+            await client.notify_playbook_generated(
+                market="KR",
+                stock_count=4,
+                scenario_count=12,
+                token_count=980,
+            )
+
+            payload = mock_post.call_args.kwargs["json"]
+            assert "Playbook Generated" in payload["text"]
+            assert "Market: KR" in payload["text"]
+            assert "Stocks: 4" in payload["text"]
+            assert "Scenarios: 12" in payload["text"]
+            assert "Tokens: 980" in payload["text"]
+
+    @pytest.mark.asyncio
+    async def test_scenario_matched_format(self) -> None:
+        """Scenario matched notification has expected fields."""
+        client = TelegramClient(
+            bot_token="123:abc", chat_id="456", enabled=True
+        )
+
+        mock_resp = AsyncMock()
+        mock_resp.status = 200
+        mock_resp.__aenter__ = AsyncMock(return_value=mock_resp)
+        mock_resp.__aexit__ = AsyncMock(return_value=False)
+
+        with patch("aiohttp.ClientSession.post", return_value=mock_resp) as mock_post:
+            await client.notify_scenario_matched(
+                stock_code="AAPL",
+                action="BUY",
+                condition_summary="RSI < 30, volume_ratio > 2.0",
+                confidence=88.2,
+            )
+
+            payload = mock_post.call_args.kwargs["json"]
+            assert "Scenario Matched" in payload["text"]
+            assert "AAPL" in payload["text"]
+            assert "Action: BUY" in payload["text"]
+            assert "RSI < 30" in payload["text"]
+            assert "88%" in payload["text"]
+
+    @pytest.mark.asyncio
+    async def test_playbook_failed_format(self) -> None:
+        """Playbook failed notification has expected fields."""
+        client = TelegramClient(
+            bot_token="123:abc", chat_id="456", enabled=True
+        )
+
+        mock_resp = AsyncMock()
+        mock_resp.status = 200
+        mock_resp.__aenter__ = AsyncMock(return_value=mock_resp)
+        mock_resp.__aexit__ = AsyncMock(return_value=False)
+
+        with patch("aiohttp.ClientSession.post", return_value=mock_resp) as mock_post:
+            await client.notify_playbook_failed(
+                market="US",
+                reason="Gemini timeout",
+            )
+
+            payload = mock_post.call_args.kwargs["json"]
+            assert "Playbook Failed" in payload["text"]
+            assert "Market: US" in payload["text"]
+            assert "Gemini timeout" in payload["text"]
+
     @pytest.mark.asyncio
     async def test_circuit_breaker_priority(self) -> None:
         """Circuit breaker uses CRITICAL priority."""
@@ -309,6 +386,73 @@ class TestMessagePriorities:
             payload = mock_post.call_args.kwargs["json"]
             assert NotificationPriority.CRITICAL.emoji in payload["text"]
 
+    @pytest.mark.asyncio
+    async def test_playbook_generated_priority(self) -> None:
+        """Playbook generated uses MEDIUM priority emoji."""
+        client = TelegramClient(
+            bot_token="123:abc", chat_id="456", enabled=True
+        )
+
+        mock_resp = AsyncMock()
+        mock_resp.status = 200
+        mock_resp.__aenter__ = AsyncMock(return_value=mock_resp)
+        mock_resp.__aexit__ = AsyncMock(return_value=False)
+
+        with patch("aiohttp.ClientSession.post", return_value=mock_resp) as mock_post:
+            await client.notify_playbook_generated(
+                market="KR",
+                stock_count=2,
+                scenario_count=4,
+                token_count=123,
+            )
+
+            payload = mock_post.call_args.kwargs["json"]
+            assert NotificationPriority.MEDIUM.emoji in payload["text"]
+
+    @pytest.mark.asyncio
+    async def test_playbook_failed_priority(self) -> None:
+        """Playbook failed uses HIGH priority emoji."""
+        client = TelegramClient(
+            bot_token="123:abc", chat_id="456", enabled=True
+        )
+
+        mock_resp = AsyncMock()
+        mock_resp.status = 200
+        mock_resp.__aenter__ = AsyncMock(return_value=mock_resp)
+        mock_resp.__aexit__ = AsyncMock(return_value=False)
+
+        with patch("aiohttp.ClientSession.post", return_value=mock_resp) as mock_post:
+            await client.notify_playbook_failed(
+                market="KR",
+                reason="Invalid JSON",
+            )
+
+            payload = mock_post.call_args.kwargs["json"]
+            assert NotificationPriority.HIGH.emoji in payload["text"]
+
+    @pytest.mark.asyncio
+    async def test_scenario_matched_priority(self) -> None:
+        """Scenario matched uses HIGH priority emoji."""
+        client = TelegramClient(
+            bot_token="123:abc", chat_id="456", enabled=True
+        )
+
+        mock_resp = AsyncMock()
+        mock_resp.status = 200
+        mock_resp.__aenter__ = AsyncMock(return_value=mock_resp)
+        mock_resp.__aexit__ = AsyncMock(return_value=False)
+
+        with patch("aiohttp.ClientSession.post", return_value=mock_resp) as mock_post:
+            await client.notify_scenario_matched(
+                stock_code="AAPL",
+                action="BUY",
+                condition_summary="RSI < 30",
+                confidence=80.0,
+            )
+
+            payload = mock_post.call_args.kwargs["json"]
+            assert NotificationPriority.HIGH.emoji in payload["text"]
+
 
 class TestClientCleanup:
     """Test client cleanup behavior."""

tests/test_telegram_commands.py

@@ -230,6 +230,31 @@ class TestUpdateHandling:
         await handler._handle_update(update)
         assert executed is False
 
+    @pytest.mark.asyncio
+    async def test_handle_command_with_botname(self) -> None:
+        """Commands with @botname suffix are handled correctly."""
+        client = TelegramClient(bot_token="123:abc", chat_id="456", enabled=True)
+        handler = TelegramCommandHandler(client)
+
+        executed = False
+
+        async def test_command() -> None:
+            nonlocal executed
+            executed = True
+
+        handler.register_command("start", test_command)
+
+        update = {
+            "update_id": 1,
+            "message": {
+                "chat": {"id": 456},
+                "text": "/start@mybot",
+            },
+        }
+
+        await handler._handle_update(update)
+        assert executed is True
+
     @pytest.mark.asyncio
     async def test_handle_update_error_isolation(self) -> None:
         """Errors in handlers don't crash the system."""
@@ -524,7 +549,7 @@ class TestStatusCommands:
 
     @pytest.mark.asyncio
     async def test_positions_command_shows_holdings(self) -> None:
-        """Positions command displays current holdings."""
+        """Positions command displays account summary."""
         client = TelegramClient(bot_token="123:abc", chat_id="456", enabled=True)
         handler = TelegramCommandHandler(client)
 
@@ -536,12 +561,12 @@ class TestStatusCommands:
         async def mock_positions() -> None:
             """Mock /positions handler."""
             message = (
-                "<b>💼 Current Holdings</b>\n"
+                "<b>💼 Account Summary</b>\n\n"
-                "\n🇰🇷 <b>Korea</b>\n"
+                "<b>Total Evaluation:</b> ₩10,500,000\n"
-                "• 005930: 10 shares @ 70,000\n"
+                "<b>Available Cash:</b> ₩5,000,000\n"
-                "\n🇺🇸 <b>Overseas</b>\n"
+                "<b>Purchase Total:</b> ₩10,000,000\n"
-                "• AAPL: 15 shares @ 175\n"
+                "<b>P&L:</b> +5.00%\n\n"
-                "\n<b>Cash:</b> ₩5,000,000"
+                "<i>Note: Individual position details require API enhancement</i>"
             )
             await client.send_message(message)
 
@@ -561,8 +586,9 @@ class TestStatusCommands:
             # Verify message was sent
             assert mock_post.call_count == 1
             payload = mock_post.call_args.kwargs["json"]
-            assert "Current Holdings" in payload["text"]
+            assert "Account Summary" in payload["text"]
-            assert "shares" in payload["text"]
+            assert "Total Evaluation" in payload["text"]
+            assert "P&L" in payload["text"]
 
     @pytest.mark.asyncio
     async def test_positions_command_empty_holdings(self) -> None:
@@ -578,8 +604,8 @@ class TestStatusCommands:
         async def mock_positions_empty() -> None:
             """Mock /positions handler with no positions."""
             message = (
-                "<b>💼 Current Holdings</b>\n\n"
+                "<b>💼 Account Summary</b>\n\n"
-                "No positions currently held."
+                "No balance information available."
             )
             await client.send_message(message)
 
@@ -598,7 +624,7 @@ class TestStatusCommands:
 
             # Verify message was sent
             payload = mock_post.call_args.kwargs["json"]
-            assert "No positions" in payload["text"]
+            assert "No balance information available" in payload["text"]
 
     @pytest.mark.asyncio
     async def test_positions_command_error_handling(self) -> None:
@@ -638,51 +664,6 @@ class TestStatusCommands:
 class TestBasicCommands:
     """Test basic command implementations."""
 
-    @pytest.mark.asyncio
-    async def test_start_command_content(self) -> None:
-        """Start command contains welcome message and command list."""
-        client = TelegramClient(bot_token="123:abc", chat_id="456", enabled=True)
-        handler = TelegramCommandHandler(client)
-
-        mock_resp = AsyncMock()
-        mock_resp.status = 200
-        mock_resp.__aenter__ = AsyncMock(return_value=mock_resp)
-        mock_resp.__aexit__ = AsyncMock(return_value=False)
-
-        async def mock_start() -> None:
-            """Mock /start handler."""
-            message = (
-                "<b>🤖 The Ouroboros Trading Bot</b>\n\n"
-                "AI-powered global stock trading agent with real-time notifications.\n\n"
-                "<b>Available commands:</b>\n"
-                "/help - Show this help message\n"
-                "/status - Current trading status\n"
-                "/positions - View holdings\n"
-                "/stop - Pause trading\n"
-                "/resume - Resume trading"
-            )
-            await client.send_message(message)
-
-        handler.register_command("start", mock_start)
-
-        with patch("aiohttp.ClientSession.post", return_value=mock_resp) as mock_post:
-            update = {
-                "update_id": 1,
-                "message": {
-                    "chat": {"id": 456},
-                    "text": "/start",
-                },
-            }
-
-            await handler._handle_update(update)
-
-            # Verify message was sent
-            assert mock_post.call_count == 1
-            payload = mock_post.call_args.kwargs["json"]
-            assert "Ouroboros Trading Bot" in payload["text"]
-            assert "/help" in payload["text"]
-            assert "/status" in payload["text"]
-
     @pytest.mark.asyncio
     async def test_help_command_content(self) -> None:
         """Help command lists all available commands."""
@@ -698,7 +679,6 @@ class TestBasicCommands:
             """Mock /help handler."""
             message = (
                 "<b>📖 Available Commands</b>\n\n"
-                "/start - Welcome message\n"
                 "/help - Show available commands\n"
                 "/status - Trading status (mode, markets, P&L)\n"
                 "/positions - Current holdings\n"
@@ -724,7 +704,6 @@ class TestBasicCommands:
             assert mock_post.call_count == 1
             payload = mock_post.call_args.kwargs["json"]
             assert "Available Commands" in payload["text"]
-            assert "/start" in payload["text"]
             assert "/help" in payload["text"]
             assert "/status" in payload["text"]
             assert "/positions" in payload["text"]

tests/test_volatility.py

@@ -412,7 +412,7 @@ class TestMarketScanner:
         scan_result = context_store.get_context(
             ContextLayer.L7_REALTIME,
             latest_timeframe,
-            "KR_scan_result",
+            "scan_result_KR",
         )
         assert scan_result is not None
         assert scan_result["total_scanned"] == 3