feat: unify domestic scanner and sizing; update docs

docs: reflect overseas ranking integration and volatility-first selection
feat: prioritize overseas volatility scoring over raw rankings
2026-02-17 06:29:36 +09:00 · 2026-02-17 06:29:16 +09:00 · 2026-02-17 06:25:45 +09:00 · 2026-02-17 06:25:45 +09:00 · 2026-02-16 21:50:49 +09:00 · 2026-02-16 21:48:49 +09:00
44 changed files with 8022 additions and 667 deletions
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -15,6 +15,9 @@ pytest -v --cov=src

 # Run (paper trading)
 python -m src.main --mode=paper
+
+# Run with dashboard
+python -m src.main --mode=paper --dashboard
 ```

 ## Telegram Notifications (Optional)
@@ -43,6 +46,10 @@ Get real-time alerts for trades, circuit breakers, and system events via Telegra
 - ℹ️ Market open/close notifications
 - 📝 System startup/shutdown status

+### Interactive Commands
+
+With `TELEGRAM_COMMANDS_ENABLED=true` (default), the bot supports 9 bidirectional commands: `/help`, `/status`, `/positions`, `/report`, `/scenarios`, `/review`, `/dashboard`, `/stop`, `/resume`.
+
 **Fail-safe**: Notifications never crash the trading system. Missing credentials or API errors are logged but trading continues normally.

 ## Smart Volatility Scanner (Optional)
@@ -109,17 +116,23 @@ User requirements and feedback are tracked in [docs/requirements-log.md](docs/re
 ```
 src/
 ├── analysis/        # Technical analysis (RSI, volatility, smart scanner)
+├── backup/          # Disaster recovery (scheduler, cloud storage, health)
+├── brain/           # Gemini AI decision engine (prompt optimizer, context selector)
 ├── broker/          # KIS API client (domestic + overseas)
-├── brain/           # Gemini AI decision engine
+├── context/         # L1-L7 hierarchical memory system
 ├── core/            # Risk manager (READ-ONLY)
-├── evolution/       # Self-improvement optimizer
+├── dashboard/       # FastAPI read-only monitoring (8 API endpoints)
+├── data/            # External data integration (news, market data, calendar)
+├── evolution/       # Self-improvement (optimizer, daily review, scorecard)
+├── logging/         # Decision logger (audit trail)
 ├── markets/         # Market schedules and timezone handling
-├── notifications/   # Telegram real-time alerts
+├── notifications/   # Telegram alerts + bidirectional commands (9 commands)
+├── strategy/        # Pre-market planner, scenario engine, playbook store
 ├── db.py            # SQLite trade logging
 ├── main.py          # Trading loop orchestrator
 └── config.py        # Settings (from .env)

-tests/               # 343 tests across 14 files
+tests/               # 551 tests across 25 files
 docs/                # Extended documentation
 ```

@@ -131,6 +144,7 @@ ruff check src/ tests/           # Lint
 mypy src/ --strict               # Type check

 python -m src.main --mode=paper  # Paper trading
+python -m src.main --mode=paper --dashboard  # With dashboard
 python -m src.main --mode=live   # Live trading (⚠️ real money)

 # Gitea workflow (requires tea CLI)
--- a/README.md
+++ b/README.md
@@ -10,28 +10,41 @@ KIS(한국투자증권) API로 매매하고, Google Gemini로 판단하며, 자
 │  (매매 실행) │     │ (거래 루프)  │     │  (의사결정)  │
 └─────────────┘     └──────┬──────┘     └─────────────┘
                           │
-                    ┌──────┴──────┐
-                    │Risk Manager │
-                    │  (안전장치)  │
-                    └──────┬──────┘
-                           │
-                    ┌──────┴──────┐
-                    │  Evolution  │
-                    │ (전략 진화)  │
-                    └─────────────┘
+              ┌────────────┼────────────┐
+              │            │            │
+       ┌──────┴──────┐ ┌──┴───┐ ┌──────┴──────┐
+       │Risk Manager │ │  DB  │ │  Telegram   │
+       │  (안전장치)  │ │      │ │ (알림+명령)  │
+       └──────┬──────┘ └──────┘ └─────────────┘
+              │
+     ┌────────┼────────┐
+     │        │        │
+┌────┴────┐┌──┴──┐┌────┴─────┐
+│Strategy ││Ctx  ││Evolution │
+│(플레이북)││(메모리)││ (진화)   │
+└─────────┘└─────┘└──────────┘
 ```

+**v2 핵심**: "Plan Once, Execute Locally" — 장 시작 전 AI가 시나리오 플레이북을 1회 생성하고, 거래 시간에는 로컬 시나리오 매칭만 수행하여 API 비용과 지연 시간을 대폭 절감.
+
 ## 핵심 모듈

-| 모듈 | 파일 | 설명 |
+| 모듈 | 위치 | 설명 |
 |------|------|------|
-| 설정 | `src/config.py` | Pydantic 기반 환경변수 로딩 및 타입 검증 |
-| 브로커 | `src/broker/kis_api.py` | KIS API 비동기 래퍼 (토큰 갱신, 레이트 리미터, 해시키) |
-| 두뇌 | `src/brain/gemini_client.py` | Gemini 프롬프트 구성 및 JSON 응답 파싱 |
-| 방패 | `src/core/risk_manager.py` | 서킷 브레이커 + 팻 핑거 체크 |
-| 알림 | `src/notifications/telegram_client.py` | 텔레그램 실시간 거래 알림 (선택사항) |
-| 진화 | `src/evolution/optimizer.py` | 실패 패턴 분석 → 새 전략 생성 → 테스트 → PR |
-| DB | `src/db.py` | SQLite 거래 로그 기록 |
+| 설정 | `src/config.py` | Pydantic 기반 환경변수 로딩 및 타입 검증 (35+ 변수) |
+| 브로커 | `src/broker/` | KIS API 비동기 래퍼 (국내 + 해외 9개 시장) |
+| 두뇌 | `src/brain/` | Gemini 프롬프트 구성, JSON 파싱, 토큰 최적화 |
+| 방패 | `src/core/risk_manager.py` | 서킷 브레이커 + 팻 핑거 체크 (READ-ONLY) |
+| 전략 | `src/strategy/` | Pre-Market Planner, Scenario Engine, Playbook Store |
+| 컨텍스트 | `src/context/` | L1-L7 계층형 메모리 시스템 |
+| 분석 | `src/analysis/` | RSI, ATR, Smart Volatility Scanner |
+| 알림 | `src/notifications/` | 텔레그램 양방향 (알림 + 9개 명령어) |
+| 대시보드 | `src/dashboard/` | FastAPI 읽기 전용 모니터링 (8개 API) |
+| 진화 | `src/evolution/` | 전략 진화 + Daily Review + Scorecard |
+| 의사결정 로그 | `src/logging/` | 전체 거래 결정 감사 추적 |
+| 데이터 | `src/data/` | 뉴스, 시장 데이터, 경제 캘린더 연동 |
+| 백업 | `src/backup/` | 자동 백업, S3 클라우드, 무결성 검증 |
+| DB | `src/db.py` | SQLite 거래 로그 (5개 테이블) |

 ## 안전장치

@@ -42,6 +55,7 @@ KIS(한국투자증권) API로 매매하고, Google Gemini로 판단하며, 자
 | 신뢰도 임계값 | Gemini 신뢰도 80 미만이면 강제 HOLD |
 | 레이트 리미터 | Leaky Bucket 알고리즘으로 API 호출 제한 |
 | 토큰 자동 갱신 | 만료 1분 전 자동으로 Access Token 재발급 |
+| 손절 모니터링 | 플레이북 시나리오 기반 실시간 포지션 보호 |

 ## 빠른 시작

@@ -67,7 +81,11 @@ pytest -v --cov=src --cov-report=term-missing
 ### 4. 실행 (모의투자)

 ```bash
+# 기본 실행
 python -m src.main --mode=paper
+
+# 대시보드 활성화
+python -m src.main --mode=paper --dashboard
 ```

 ### 5. Docker 실행
@@ -76,7 +94,20 @@ python -m src.main --mode=paper
 docker compose up -d ouroboros
 ```

-## 텔레그램 알림 (선택사항)
+## 지원 시장
+
+| 국가 | 거래소 | 코드 |
+|------|--------|------|
+| 🇰🇷 한국 | KRX | KR |
+| 🇺🇸 미국 | NASDAQ, NYSE, AMEX | US_NASDAQ, US_NYSE, US_AMEX |
+| 🇯🇵 일본 | TSE | JP |
+| 🇭🇰 홍콩 | SEHK | HK |
+| 🇨🇳 중국 | 상하이, 선전 | CN_SHA, CN_SZA |
+| 🇻🇳 베트남 | 하노이, 호치민 | VN_HNX, VN_HSX |
+
+`ENABLED_MARKETS` 환경변수로 활성 시장 선택 (기본: `KR,US`).
+
+## 텔레그램 (선택사항)

 거래 실행, 서킷 브레이커 발동, 시스템 상태 등을 텔레그램으로 실시간 알림 받을 수 있습니다.

@@ -102,25 +133,51 @@ docker compose up -d ouroboros
 - ℹ️ 장 시작/종료 알림
 - 📝 시스템 시작/종료 상태

-**안전장치**: 알림 실패해도 거래는 계속 진행됩니다. 텔레그램 API 오류나 설정 누락이 있어도 거래 시스템은 정상 작동합니다.
+### 양방향 명령어
+
+`TELEGRAM_COMMANDS_ENABLED=true` (기본값) 설정 시 9개 대화형 명령어 지원:
+
+| 명령어 | 설명 |
+|--------|------|
+| `/help` | 사용 가능한 명령어 목록 |
+| `/status` | 거래 상태 (모드, 시장, P&L) |
+| `/positions` | 계좌 요약 (잔고, 현금, P&L) |
+| `/report` | 일일 요약 (거래 수, P&L, 승률) |
+| `/scenarios` | 오늘의 플레이북 시나리오 |
+| `/review` | 최근 스코어카드 (L6_DAILY) |
+| `/dashboard` | 대시보드 URL 표시 |
+| `/stop` | 거래 일시 정지 |
+| `/resume` | 거래 재개 |
+
+**안전장치**: 알림 실패해도 거래는 계속 진행됩니다.

 ## 테스트

-35개 테스트가 TDD 방식으로 구현 전에 먼저 작성되었습니다.
+551개 테스트가 25개 파일에 걸쳐 구현되어 있습니다. 최소 커버리지 80%.

 ```
-tests/test_risk.py   — 서킷 브레이커, 팻 핑거, 통합 검증 (11개)
-tests/test_broker.py — 토큰 관리, 타임아웃, HTTP 에러, 해시키 (6개)
-tests/test_brain.py  — JSON 파싱, 신뢰도 임계값, 비정상 응답 처리 (15개)
+tests/test_scenario_engine.py    — 시나리오 매칭 (44개)
+tests/test_data_integration.py   — 외부 데이터 연동 (38개)
+tests/test_pre_market_planner.py — 플레이북 생성 (37개)
+tests/test_main.py               — 거래 루프 통합 (37개)
+tests/test_token_efficiency.py   — 토큰 최적화 (34개)
+tests/test_strategy_models.py    — 전략 모델 검증 (33개)
+tests/test_telegram_commands.py  — 텔레그램 명령어 (31개)
+tests/test_latency_control.py    — 지연시간 제어 (30개)
+tests/test_telegram.py           — 텔레그램 알림 (25개)
+... 외 16개 파일
 ```

+**상세**: [docs/testing.md](docs/testing.md)
+
 ## 기술 스택

 - **언어**: Python 3.11+ (asyncio 기반)
- **브로커**: KIS Open API (REST)
+- **브로커**: KIS Open API (REST, 국내+해외)
 - **AI**: Google Gemini Pro
- **DB**: SQLite
- **검증**: pytest + coverage
+- **DB**: SQLite (5개 테이블: trades, contexts, decision_logs, playbooks, context_metadata)
+- **대시보드**: FastAPI + uvicorn
+- **검증**: pytest + coverage (551 tests)
 - **CI/CD**: GitHub Actions
 - **배포**: Docker + Docker Compose

@@ -128,27 +185,50 @@ tests/test_brain.py  — JSON 파싱, 신뢰도 임계값, 비정상 응답 처

 ```
 The-Ouroboros/
-├── .github/workflows/ci.yml     # CI 파이프라인
 ├── docs/
-│   ├── agents.md                # AI 에이전트 페르소나 정의
-│   └── skills.md                # 사용 가능한 도구 목록
+│   ├── architecture.md          # 시스템 아키텍처
+│   ├── testing.md               # 테스트 가이드
+│   ├── commands.md              # 명령어 레퍼런스
+│   ├── context-tree.md          # L1-L7 메모리 시스템
+│   ├── workflow.md              # Git 워크플로우
+│   ├── agents.md                # 에이전트 정책
+│   ├── skills.md                # 도구 목록
+│   ├── disaster_recovery.md     # 백업/복구
+│   └── requirements-log.md      # 요구사항 기록
 ├── src/
-│   ├── config.py                      # Pydantic 설정
-│   ├── logging_config.py              # JSON 구조화 로깅
-│   ├── db.py                          # SQLite 거래 기록
-│   ├── main.py                        # 비동기 거래 루프
-│   ├── broker/kis_api.py              # KIS API 클라이언트
-│   ├── brain/gemini_client.py         # Gemini 의사결정 엔진
-│   ├── core/risk_manager.py           # 리스크 관리
-│   ├── notifications/telegram_client.py # 텔레그램 알림
-│   ├── evolution/optimizer.py         # 전략 진화 엔진
-│   └── strategies/base.py             # 전략 베이스 클래스
-├── tests/                       # TDD 테스트 스위트
+│   ├── analysis/                # 기술적 분석 (RSI, ATR, Smart Scanner)
+│   ├── backup/                  # 백업 (스케줄러, S3, 무결성 검증)
+│   ├── brain/                   # Gemini 의사결정 (프롬프트 최적화, 컨텍스트 선택)
+│   ├── broker/                  # KIS API (국내 + 해외)
+│   ├── context/                 # L1-L7 계층 메모리
+│   ├── core/                    # 리스크 관리 (READ-ONLY)
+│   ├── dashboard/               # FastAPI 모니터링 대시보드
+│   ├── data/                    # 외부 데이터 연동
+│   ├── evolution/               # 전략 진화 + Daily Review
+│   ├── logging/                 # 의사결정 감사 추적
+│   ├── markets/                 # 시장 스케줄 + 타임존
+│   ├── notifications/           # 텔레그램 알림 + 명령어
+│   ├── strategy/                # 플레이북 (Planner, Scenario Engine)
+│   ├── config.py                # Pydantic 설정
+│   ├── db.py                    # SQLite 데이터베이스
+│   └── main.py                  # 비동기 거래 루프
+├── tests/                       # 551개 테스트 (25개 파일)
 ├── Dockerfile                   # 멀티스테이지 빌드
 ├── docker-compose.yml           # 서비스 오케스트레이션
 └── pyproject.toml               # 의존성 및 도구 설정
 ```

+## 문서
+
+- **[아키텍처](docs/architecture.md)** — 시스템 설계, 컴포넌트, 데이터 흐름
+- **[테스트](docs/testing.md)** — 테스트 구조, 커버리지, 작성 가이드
+- **[명령어](docs/commands.md)** — CLI, Dashboard, Telegram 명령어
+- **[컨텍스트 트리](docs/context-tree.md)** — L1-L7 계층 메모리
+- **[워크플로우](docs/workflow.md)** — Git 워크플로우 정책
+- **[에이전트 정책](docs/agents.md)** — 안전 제약, 금지 행위
+- **[백업/복구](docs/disaster_recovery.md)** — 재해 복구 절차
+- **[요구사항](docs/requirements-log.md)** — 사용자 요구사항 추적
+
 ## 라이선스

 이 프로젝트의 라이선스는 [LICENSE](LICENSE) 파일을 참조하세요.
--- a/docs/architecture.md
+++ b/docs/architecture.md
@@ -2,7 +2,9 @@

 ## Overview

-Self-evolving AI trading agent for global stock markets via KIS (Korea Investment & Securities) API. The main loop in `src/main.py` orchestrates four components across multiple markets with two trading modes: daily (batch API calls) or realtime (per-stock decisions).
+Self-evolving AI trading agent for global stock markets via KIS (Korea Investment & Securities) API. The main loop in `src/main.py` orchestrates components across multiple markets with two trading modes: daily (batch API calls) or realtime (per-stock decisions).
+
+**v2 Proactive Playbook Architecture**: The system uses a "plan once, execute locally" approach. Pre-market, the AI generates a playbook of scenarios (one Gemini API call per market per day). During trading hours, a local scenario engine matches live market data against these pre-computed scenarios — no additional AI calls needed. This dramatically reduces API costs and latency.

 ## Trading Modes

@@ -46,9 +48,11 @@ High-frequency trading with individual stock analysis:
 **KISBroker** (`kis_api.py`) — Async KIS API client for domestic Korean market

 - Automatic OAuth token refresh (valid for 24 hours)
- Leaky-bucket rate limiter (10 requests per second)
+- Leaky-bucket rate limiter (configurable RPS, default 2.0)
 - POST body hash-key signing for order authentication
 - Custom SSL context with disabled hostname verification for VTS (virtual trading) endpoint due to known certificate mismatch
+- `fetch_market_rankings()` — Fetch volume surge rankings from KIS API
+- `get_daily_prices()` — Fetch OHLCV history for technical analysis

 **OverseasBroker** (`overseas.py`) — KIS overseas stock API wrapper

@@ -63,10 +67,11 @@ High-frequency trading with individual stock analysis:
 - `is_market_open()` checks weekends, trading hours, lunch breaks
 - `get_open_markets()` returns currently active markets
 - `get_next_market_open()` finds next market to open and when
+- 10 global markets defined (KR, US_NASDAQ, US_NYSE, US_AMEX, JP, HK, CN_SHA, CN_SZA, VN_HNX, VN_HSX)

-**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
+**Overseas Ranking API Methods** (added in v0.10.x):
+- `fetch_overseas_rankings()` — Fetch overseas ranking universe (fluctuation / volume)
+- Ranking endpoint paths and TR_IDs are configurable via environment variables

 ### 2. Analysis (`src/analysis/`)

@@ -81,24 +86,28 @@ High-frequency trading with individual stock analysis:

 **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
+- **Domestic (KR)**:
+  - **Step 1**: Fetch domestic fluctuation ranking as primary universe
+  - **Step 2**: Fetch domestic volume ranking for liquidity bonus
+  - **Step 3**: Compute volatility-first score (max of daily change% and intraday range%)
+  - **Step 4**: Apply liquidity bonus and return top N candidates
+- **Overseas (US/JP/HK/CN/VN)**:
+  - **Step 1**: Fetch overseas ranking universe (fluctuation rank + volume rank bonus)
+  - **Step 2**: Compute volatility-first score (max of daily change% and intraday range%)
+  - **Step 3**: Apply liquidity bonus from volume ranking
+  - **Step 4**: Return top N candidates (default 3)
+- **Fallback (overseas only)**: If ranking API is unavailable, uses dynamic universe
+  from runtime active symbols + recent traded symbols + current holdings (no static watchlist)
 - **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
+- Logs selection context (RSI-compatible proxy, volume_ratio, signal, score) for Evolution system

-### 3. Brain (`src/brain/gemini_client.py`)
+### 3. Brain (`src/brain/`)

-**GeminiClient** — AI decision engine powered by Google Gemini
+**GeminiClient** (`gemini_client.py`) — AI decision engine powered by Google Gemini

 - Constructs structured prompts from market data
 - Parses JSON responses into `TradeDecision` objects (`action`, `confidence`, `rationale`)
@@ -106,11 +115,20 @@ 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

+**PromptOptimizer** (`prompt_optimizer.py`) — Token efficiency optimization
+
+- Reduces prompt size while preserving decision quality
+- Caches optimized prompts
+
+**ContextSelector** (`context_selector.py`) — Relevant context selection for prompts
+
+- Selects appropriate context layers for current market conditions
+
 ### 4. Risk Manager (`src/core/risk_manager.py`)

 **RiskManager** — Safety circuit breaker and order validation

-⚠️ **READ-ONLY by policy** (see [`docs/agents.md`](./agents.md))
+> **READ-ONLY by policy** (see [`docs/agents.md`](./agents.md))

 - **Circuit Breaker**: Halts all trading via `SystemExit` when daily P&L drops below -3.0%
  - Threshold may only be made stricter, never relaxed
@@ -118,7 +136,79 @@ High-frequency trading with individual stock analysis:
 - **Fat-Finger Protection**: Rejects orders exceeding 30% of available cash
  - Must always be enforced, cannot be disabled

-### 5. Notifications (`src/notifications/telegram_client.py`)
+### 5. Strategy (`src/strategy/`)
+
+**Pre-Market Planner** (`pre_market_planner.py`) — AI playbook generation
+
+- Runs before market open (configurable `PRE_MARKET_MINUTES`, default 30)
+- Generates scenario-based playbooks via single Gemini API call per market
+- Handles timeout (`PLANNER_TIMEOUT_SECONDS`, default 60) with defensive playbook fallback
+- Persists playbooks to database for audit trail
+
+**Scenario Engine** (`scenario_engine.py`) — Local scenario matching
+
+- Matches live market data against pre-computed playbook scenarios
+- No AI calls during trading hours — pure Python matching logic
+- Returns matched scenarios with confidence scores
+- Configurable `MAX_SCENARIOS_PER_STOCK` (default 5)
+- Periodic rescan at `RESCAN_INTERVAL_SECONDS` (default 300)
+
+**Playbook Store** (`playbook_store.py`) — Playbook persistence
+
+- SQLite-backed storage for daily playbooks
+- Date and market-based retrieval
+- Status tracking (generated, active, expired)
+
+**Models** (`models.py`) — Pydantic data models
+
+- Scenario, Playbook, MatchResult, and related type definitions
+
+### 6. Context System (`src/context/`)
+
+**Context Store** (`store.py`) — L1-L7 hierarchical memory
+
+- 7-layer context system (see [docs/context-tree.md](./context-tree.md)):
+  - L1: Tick-level (real-time price)
+  - L2: Intraday (session summary)
+  - L3: Daily (end-of-day)
+  - L4: Weekly (trend analysis)
+  - L5: Monthly (strategy review)
+  - L6: Daily Review (scorecard)
+  - L7: Evolution (long-term learning)
+- Key-value storage with timeframe tagging
+- SQLite persistence in `contexts` table
+
+**Context Scheduler** (`scheduler.py`) — Periodic aggregation
+
+- Scheduled summarization from lower to higher layers
+- Configurable aggregation intervals
+
+**Context Summarizer** (`summarizer.py`) — Layer summarization
+
+- Aggregates lower-layer data into higher-layer summaries
+
+### 7. Dashboard (`src/dashboard/`)
+
+**FastAPI App** (`app.py`) — Read-only monitoring dashboard
+
+- Runs as daemon thread when enabled (`--dashboard` CLI flag or `DASHBOARD_ENABLED=true`)
+- Configurable host/port (`DASHBOARD_HOST`, `DASHBOARD_PORT`, default `127.0.0.1:8080`)
+- Serves static HTML frontend
+
+**8 API Endpoints:**
+
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/` | GET | Static HTML dashboard |
+| `/api/status` | GET | Daily trading status by market |
+| `/api/playbook/{date}` | GET | Playbook for specific date and market |
+| `/api/scorecard/{date}` | GET | Daily scorecard from L6_DAILY context |
+| `/api/performance` | GET | Trading performance metrics (by market + combined) |
+| `/api/context/{layer}` | GET | Query context by layer (L1-L7) |
+| `/api/decisions` | GET | Decision log entries with outcomes |
+| `/api/scenarios/active` | GET | Today's matched scenarios |
+
+### 8. Notifications (`src/notifications/telegram_client.py`)

 **TelegramClient** — Real-time event notifications via Telegram Bot API

@@ -126,7 +216,13 @@ High-frequency trading with individual stock analysis:
 - Non-blocking: failures are logged but never crash trading
 - Rate-limited: 1 message/second default to respect Telegram API limits
 - Auto-disabled when credentials missing
- Gracefully handles API errors, network timeouts, invalid tokens
+
+**TelegramCommandHandler** — Bidirectional command interface
+
+- Long polling from Telegram API (configurable `TELEGRAM_POLLING_INTERVAL`)
+- 9 interactive commands: `/help`, `/status`, `/positions`, `/report`, `/scenarios`, `/review`, `/dashboard`, `/stop`, `/resume`
+- Authorization filtering by `TELEGRAM_CHAT_ID`
+- Enable/disable via `TELEGRAM_COMMANDS_ENABLED` (default: true)

 **Notification Types:**
 - Trade execution (BUY/SELL with confidence)
@@ -134,12 +230,12 @@ High-frequency trading with individual stock analysis:
 - Fat-finger protection triggers (order rejection)
 - Market open/close events
 - System startup/shutdown status
+- Playbook generation results
+- Stop-loss monitoring alerts

-**Setup:** See [src/notifications/README.md](../src/notifications/README.md) for bot creation and configuration.
+### 9. Evolution (`src/evolution/`)

-### 6. Evolution (`src/evolution/optimizer.py`)
-
-**StrategyOptimizer** — Self-improvement loop
+**StrategyOptimizer** (`optimizer.py`) — Self-improvement loop

 - Analyzes high-confidence losing trades from SQLite
 - Asks Gemini to generate new `BaseStrategy` subclasses
@@ -147,99 +243,198 @@ High-frequency trading with individual stock analysis:
 - Simulates PR creation for human review
 - Only activates strategies that pass all tests

+**DailyReview** (`daily_review.py`) — End-of-day review
+
+- Generates comprehensive trade performance summary
+- Stores results in L6_DAILY context layer
+- Tracks win rate, P&L, confidence accuracy
+
+**DailyScorecard** (`scorecard.py`) — Performance scoring
+
+- Calculates daily metrics (trades, P&L, win rate, avg confidence)
+- Enables trend tracking across days
+
+**Stop-Loss Monitoring** — Real-time position protection
+
+- Monitors positions against stop-loss levels from playbook scenarios
+- Sends Telegram alerts when thresholds approached or breached
+
+### 10. Decision Logger (`src/logging/decision_logger.py`)
+
+**DecisionLogger** — Comprehensive audit trail
+
+- Logs every trading decision with full context snapshot
+- Captures input data, rationale, confidence, and outcomes
+- Supports outcome tracking (P&L, accuracy) for post-analysis
+- Stored in `decision_logs` table with indexed queries
+- Review workflow support (reviewed flag, review notes)
+
+### 11. Data Integration (`src/data/`)
+
+**External Data Sources** (optional):
+
+- `news_api.py` — News sentiment data
+- `market_data.py` — Extended market data
+- `economic_calendar.py` — Economic event calendar
+
+### 12. Backup (`src/backup/`)
+
+**Disaster Recovery** (see [docs/disaster_recovery.md](./disaster_recovery.md)):
+
+- `scheduler.py` — Automated backup scheduling
+- `exporter.py` — Data export to various formats
+- `cloud_storage.py` — S3-compatible cloud backup
+- `health_monitor.py` — Backup integrity verification
+
 ## Data Flow

+### Playbook Mode (Daily — Primary v2 Flow)
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│ Pre-Market Phase (before market open)                       │
+└─────────────────────────────────────────────────────────────┘
+                           │
+                           ▼
+        ┌──────────────────────────────────┐
+        │ Pre-Market Planner               │
+        │ - 1 Gemini API call per market   │
+        │ - Generate scenario playbook     │
+        │ - Store in playbooks table       │
+        └──────────────────┬───────────────┘
+                           │
+                           ▼
+┌─────────────────────────────────────────────────────────────┐
+│ Trading Hours (market open → close)                         │
+└─────────────────────────────────────────────────────────────┘
+                           │
+                           ▼
+        ┌──────────────────────────────────┐
+        │ Market Schedule Check            │
+        │ - Get open markets               │
+        │ - Filter by enabled markets      │
+        └──────────────────┬───────────────┘
+                           │
+                           ▼
+        ┌──────────────────────────────────┐
+        │ Scenario Engine (local)          │
+        │ - Match live data vs playbook    │
+        │ - No AI calls needed             │
+        │ - Return matched scenarios       │
+        └──────────────────┬───────────────┘
+                           │
+                           ▼
+        ┌──────────────────────────────────┐
+        │ Risk Manager: Validate Order     │
+        │ - Check circuit breaker          │
+        │ - Check fat-finger limit         │
+        └──────────────────┬───────────────┘
+                           │
+                           ▼
+        ┌──────────────────────────────────┐
+        │ Broker: Execute Order            │
+        │ - Domestic: send_order()         │
+        │ - Overseas: send_overseas_order()│
+        └──────────────────┬───────────────┘
+                           │
+                           ▼
+        ┌──────────────────────────────────┐
+        │ Decision Logger + DB             │
+        │ - Full audit trail               │
+        │ - Context snapshot               │
+        │ - Telegram notification          │
+        └──────────────────┬───────────────┘
+                           │
+                           ▼
+┌─────────────────────────────────────────────────────────────┐
+│ Post-Market Phase                                           │
+└─────────────────────────────────────────────────────────────┘
+                           │
+                           ▼
+        ┌──────────────────────────────────┐
+        │ Daily Review + Scorecard         │
+        │ - Performance summary            │
+        │ - Store in L6_DAILY context      │
+        │ - Evolution learning             │
+        └──────────────────────────────────┘
+```
+
 ### Realtime Mode (with Smart Scanner)

 ```
 ┌─────────────────────────────────────────────────────────────┐
-│ Main Loop (60s cycle per market)                           │
+│ Main Loop (60s cycle per market)                            │
 └─────────────────────────────────────────────────────────────┘
                           │
                           ▼
        ┌──────────────────────────────────┐
-        │ Market Schedule Check             │
-        │ - Get open markets                │
-        │ - Filter by enabled markets       │
-        │ - Wait if all closed              │
-        └──────────────────┬────────────────┘
+        │ Market Schedule Check            │
+        │ - Get open markets               │
+        │ - Filter by enabled markets      │
+        │ - Wait if all closed             │
+        └──────────────────┬───────────────┘
                           │
                           ▼
        ┌──────────────────────────────────┐
        │ 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 │
+        │ - Domestic: fluctuation rank     │
+        │   + volume rank bonus            │
+        │   + volatility-first scoring     │
+        │ - Overseas: ranking universe     │
+        │   + volatility-first scoring     │
+        │ - Fallback: dynamic universe     │
        │ - Return top 3 qualified stocks  │
-        └──────────────────┬────────────────┘
+        └──────────────────┬───────────────┘
                           │
                           ▼
        ┌──────────────────────────────────┐
-        │ For Each Qualified Candidate      │
-        └──────────────────┬────────────────┘
+        │ For Each Qualified Candidate     │
+        └──────────────────┬───────────────┘
                           │
                           ▼
        ┌──────────────────────────────────┐
        │ Broker: Fetch Market Data        │
        │ - Domestic: orderbook + balance  │
        │ - Overseas: price + balance      │
-        └──────────────────┬────────────────┘
+        └──────────────────┬───────────────┘
                           │
                           ▼
        ┌──────────────────────────────────┐
-        │ Calculate P&L                     │
-        │ pnl_pct = (eval - cost) / cost   │
-        └──────────────────┬────────────────┘
+        │ Brain: Get Decision (AI)         │
+        │ - Build prompt with market data  │
+        │ - Call Gemini API                │
+        │ - Parse JSON response            │
+        │ - Return TradeDecision           │
+        └──────────────────┬───────────────┘
                           │
                           ▼
        ┌──────────────────────────────────┐
-        │ Brain: Get Decision (AI)          │
-        │ - Build prompt with market data   │
-        │ - Call Gemini API                 │
-        │ - Parse JSON response             │
-        │ - Return TradeDecision            │
-        └──────────────────┬────────────────┘
+        │ Risk Manager: Validate Order     │
+        │ - Check circuit breaker          │
+        │ - Check fat-finger limit         │
+        └──────────────────┬───────────────┘
                           │
                           ▼
        ┌──────────────────────────────────┐
-        │ Risk Manager: Validate Order      │
-        │ - Check circuit breaker           │
-        │ - Check fat-finger limit          │
-        │ - Raise if validation fails       │
-        └──────────────────┬────────────────┘
+        │ Broker: Execute Order            │
+        │ - Domestic: send_order()         │
+        │ - Overseas: send_overseas_order()│
+        └──────────────────┬───────────────┘
                           │
                           ▼
        ┌──────────────────────────────────┐
-        │ Broker: Execute Order             │
-        │ - Domestic: send_order()          │
-        │ - Overseas: send_overseas_order() │
-        └──────────────────┬────────────────┘
-                           │
-                           ▼
-        ┌──────────────────────────────────┐
-        │ Notifications: Send Alert         │
-        │ - Trade execution notification    │
-        │ - Non-blocking (errors logged)    │
-        │ - Rate-limited to 1/sec           │
-        └──────────────────┬────────────────┘
-                           │
-                           ▼
-        ┌──────────────────────────────────┐
-        │ Database: Log Trade               │
-        │ - SQLite (data/trades.db)         │
-        │ - Track: action, confidence,      │
-        │   rationale, market, exchange     │
-        │ - NEW: selection_context (JSON)   │
-        │   - RSI, volume_ratio, signal     │
-        │   - For Evolution optimization    │
-        └───────────────────────────────────┘
+        │ Decision Logger + Notifications  │
+        │ - Log trade to SQLite            │
+        │ - selection_context (JSON)       │
+        │ - Telegram notification          │
+        └──────────────────────────────────┘
 ```

 ## Database Schema

-**SQLite** (`src/db.py`)
+**SQLite** (`src/db.py`) — Database: `data/trades.db`

+### trades
 ```sql
 CREATE TABLE trades (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
@@ -251,25 +446,73 @@ CREATE TABLE trades (
    quantity INTEGER,
    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.
-    selection_context TEXT          -- JSON: {rsi, volume_ratio, signal, score}
+    market TEXT DEFAULT 'KR',
+    exchange_code TEXT DEFAULT 'KRX',
+    selection_context TEXT,        -- JSON: {rsi, volume_ratio, signal, score}
+    decision_id TEXT              -- Links to decision_logs
 );
 ```

-**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
-}
+### contexts
+```sql
+CREATE TABLE contexts (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    layer TEXT NOT NULL,           -- L1 through L7
+    timeframe TEXT,
+    key TEXT NOT NULL,
+    value TEXT NOT NULL,           -- JSON data
+    created_at TEXT NOT NULL,
+    updated_at TEXT NOT NULL
+);
+-- Indices: idx_contexts_layer, idx_contexts_timeframe, idx_contexts_updated
 ```

-Enables Evolution system to analyze correlation between selection criteria and trade outcomes.
+### decision_logs
+```sql
+CREATE TABLE decision_logs (
+    decision_id TEXT PRIMARY KEY,
+    timestamp TEXT NOT NULL,
+    stock_code TEXT,
+    market TEXT,
+    exchange_code TEXT,
+    action TEXT,
+    confidence INTEGER,
+    rationale TEXT,
+    context_snapshot TEXT,         -- JSON: full context at decision time
+    input_data TEXT,              -- JSON: market data used
+    outcome_pnl REAL,
+    outcome_accuracy REAL,
+    reviewed INTEGER DEFAULT 0,
+    review_notes TEXT
+);
+-- Indices: idx_decision_logs_timestamp, idx_decision_logs_reviewed, idx_decision_logs_confidence
+```

-Auto-migration: Adds `market`, `exchange_code`, and `selection_context` columns if missing for backward compatibility.
+### playbooks
+```sql
+CREATE TABLE playbooks (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    date TEXT NOT NULL,
+    market TEXT NOT NULL,
+    status TEXT DEFAULT 'generated',
+    playbook_json TEXT NOT NULL,   -- Full playbook with scenarios
+    generated_at TEXT NOT NULL,
+    token_count INTEGER,
+    scenario_count INTEGER,
+    match_count INTEGER DEFAULT 0
+);
+-- Indices: idx_playbooks_date, idx_playbooks_market
+```
+
+### context_metadata
+```sql
+CREATE TABLE context_metadata (
+    layer TEXT PRIMARY KEY,
+    description TEXT,
+    retention_days INTEGER,
+    aggregation_source TEXT
+);
+```

 ## Configuration

@@ -284,29 +527,81 @@ KIS_APP_SECRET=your_app_secret
 KIS_ACCOUNT_NO=XXXXXXXX-XX
 GEMINI_API_KEY=your_gemini_key

-# Optional
+# Optional — Trading Mode
 MODE=paper                    # paper | live
-DB_PATH=data/trades.db
-CONFIDENCE_THRESHOLD=80
-MAX_LOSS_PCT=3.0
-MAX_ORDER_PCT=30.0
-ENABLED_MARKETS=KR,US_NASDAQ  # Comma-separated market codes
-
-# Trading Mode (API efficiency)
 TRADE_MODE=daily              # daily | realtime
 DAILY_SESSIONS=4              # Sessions per day (daily mode only)
 SESSION_INTERVAL_HOURS=6      # Hours between sessions (daily mode only)

-# Telegram Notifications (optional)
+# Optional — Database
+DB_PATH=data/trades.db
+
+# Optional — Risk
+CONFIDENCE_THRESHOLD=80
+MAX_LOSS_PCT=3.0
+MAX_ORDER_PCT=30.0
+
+# Optional — Markets
+ENABLED_MARKETS=KR,US         # Comma-separated market codes
+RATE_LIMIT_RPS=2.0            # KIS API requests per second
+
+# Optional — Pre-Market Planner (v2)
+PRE_MARKET_MINUTES=30         # Minutes before market open to generate playbook
+MAX_SCENARIOS_PER_STOCK=5     # Max scenarios per stock in playbook
+PLANNER_TIMEOUT_SECONDS=60    # Timeout for playbook generation
+DEFENSIVE_PLAYBOOK_ON_FAILURE=true  # Fallback on AI failure
+RESCAN_INTERVAL_SECONDS=300   # Scenario rescan interval during trading
+
+# Optional — Smart Scanner (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
+
+# Optional — Dashboard
+DASHBOARD_ENABLED=false       # Enable FastAPI dashboard
+DASHBOARD_HOST=127.0.0.1      # Dashboard bind address
+DASHBOARD_PORT=8080           # Dashboard port (1-65535)
+
+# Optional — Telegram
 TELEGRAM_BOT_TOKEN=1234567890:ABCdefGHIjklMNOpqrsTUVwxyz
 TELEGRAM_CHAT_ID=123456789
 TELEGRAM_ENABLED=true
+TELEGRAM_COMMANDS_ENABLED=true   # Enable bidirectional commands
+TELEGRAM_POLLING_INTERVAL=1.0    # Command polling interval (seconds)

-# 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
+# Optional — Backup
+BACKUP_ENABLED=false
+BACKUP_DIR=data/backups
+S3_ENDPOINT_URL=...
+S3_ACCESS_KEY=...
+S3_SECRET_KEY=...
+S3_BUCKET_NAME=...
+S3_REGION=...
+
+# Optional — External Data
+NEWS_API_KEY=...
+NEWS_API_PROVIDER=...
+MARKET_DATA_API_KEY=...
+
+# Position Sizing (optional)
+POSITION_SIZING_ENABLED=true
+POSITION_BASE_ALLOCATION_PCT=5.0
+POSITION_MIN_ALLOCATION_PCT=1.0
+POSITION_MAX_ALLOCATION_PCT=10.0
+POSITION_VOLATILITY_TARGET_SCORE=50.0
+
+# Legacy/compat scanner thresholds (kept for backward compatibility)
+RSI_OVERSOLD_THRESHOLD=30
+RSI_MOMENTUM_THRESHOLD=70
+VOL_MULTIPLIER=2.0
+
+# Overseas Ranking API (optional override; account-dependent)
+OVERSEAS_RANKING_ENABLED=true
+OVERSEAS_RANKING_FLUCT_TR_ID=HHDFS76200100
+OVERSEAS_RANKING_VOLUME_TR_ID=HHDFS76200200
+OVERSEAS_RANKING_FLUCT_PATH=/uapi/overseas-price/v1/quotations/inquire-updown-rank
+OVERSEAS_RANKING_VOLUME_PATH=/uapi/overseas-price/v1/quotations/inquire-volume-rank
 ```

 Tests use in-memory SQLite (`DB_PATH=":memory:"`) and dummy credentials via `tests/conftest.py`.
@@ -340,4 +635,9 @@ Tests use in-memory SQLite (`DB_PATH=":memory:"`) and dummy credentials via `tes
 - Invalid token → log error, trading unaffected
 - Rate limit exceeded → queued via rate limiter

-**Guarantee**: Notification failures never interrupt trading operations.
+### Playbook Generation Failure
+- Timeout → fall back to defensive playbook (`DEFENSIVE_PLAYBOOK_ON_FAILURE`)
+- API error → use previous day's playbook if available
+- No playbook → skip pre-market phase, fall back to direct AI calls
+
+**Guarantee**: Notification and dashboard failures never interrupt trading operations.
--- a/docs/commands.md
+++ b/docs/commands.md
@@ -119,7 +119,7 @@ No decorator needed for async tests.
 # Install all dependencies (production + dev)
 pip install -e ".[dev]"

-# Run full test suite with coverage
+# Run full test suite with coverage (551 tests across 25 files)
 pytest -v --cov=src --cov-report=term-missing

 # Run a single test file
@@ -137,11 +137,61 @@ mypy src/ --strict
 # Run the trading agent
 python -m src.main --mode=paper

+# Run with dashboard enabled
+python -m src.main --mode=paper --dashboard
+
 # Docker
 docker compose up -d ouroboros          # Run agent
 docker compose --profile test up test   # Run tests in container
 ```

+## Dashboard
+
+The FastAPI dashboard provides read-only monitoring of the trading system.
+
+### Starting the Dashboard
+
+```bash
+# Via CLI flag
+python -m src.main --mode=paper --dashboard
+
+# Via environment variable
+DASHBOARD_ENABLED=true python -m src.main --mode=paper
+```
+
+Dashboard runs as a daemon thread on `DASHBOARD_HOST:DASHBOARD_PORT` (default: `127.0.0.1:8080`).
+
+### API Endpoints
+
+| Endpoint | Description |
+|----------|-------------|
+| `GET /` | HTML dashboard UI |
+| `GET /api/status` | Daily trading status by market |
+| `GET /api/playbook/{date}` | Playbook for specific date (query: `market`) |
+| `GET /api/scorecard/{date}` | Daily scorecard from L6_DAILY context |
+| `GET /api/performance` | Performance metrics by market and combined |
+| `GET /api/context/{layer}` | Context data by layer L1-L7 (query: `timeframe`) |
+| `GET /api/decisions` | Decision log entries (query: `limit`, `market`) |
+| `GET /api/scenarios/active` | Today's matched scenarios |
+
+## Telegram Commands
+
+When `TELEGRAM_COMMANDS_ENABLED=true` (default), the bot accepts these interactive commands:
+
+| Command | Description |
+|---------|-------------|
+| `/help` | List available commands |
+| `/status` | Show trading status (mode, markets, P&L) |
+| `/positions` | Display account summary (balance, cash, P&L) |
+| `/report` | Daily summary metrics (trades, P&L, win rate) |
+| `/scenarios` | Show today's playbook scenarios |
+| `/review` | Display recent scorecards (L6_DAILY layer) |
+| `/dashboard` | Show dashboard URL if enabled |
+| `/stop` | Pause trading |
+| `/resume` | Resume trading |
+
+Commands are only processed from the authorized `TELEGRAM_CHAT_ID`.
+
 ## Environment Setup

 ```bash
--- a/docs/requirements-log.md
+++ b/docs/requirements-log.md
@@ -64,3 +64,104 @@
 **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
+
+---
+
+## 2026-02-16
+
+### 문서 v2 동기화 (전체 문서 현행화)
+
+**배경:**
+- v2 기능 구현 완료 후 문서가 실제 코드 상태와 크게 괴리
+- 문서에는 54 tests / 4 files로 기록되었으나 실제로는 551 tests / 25 files
+- v2 핵심 기능(Playbook, Scenario Engine, Dashboard, Telegram Commands, Daily Review, Context System, Backup) 문서화 누락
+
+**요구사항:**
+1. `docs/testing.md` — 551 tests / 25 files 반영, 전체 테스트 파일 설명
+2. `docs/architecture.md` — v2 컴포넌트(Strategy, Context, Dashboard, Decision Logger 등) 추가, Playbook Mode 데이터 플로우, DB 스키마 5개 테이블, v2 환경변수
+3. `docs/commands.md` — Dashboard 실행 명령어, Telegram 명령어 9종 레퍼런스
+4. `CLAUDE.md` — Project Structure 트리 확장, 테스트 수 업데이트, `--dashboard` 플래그
+5. `docs/skills.md` — DB 파일명 `trades.db`로 통일, Dashboard 명령어 추가
+6. 기존에 유효한 트러블슈팅, 코드 예제 등은 유지
+
+**구현 결과:**
+- 6개 문서 파일 업데이트
+- 이전 시도(2개 커밋)는 기존 내용을 과도하게 삭제하여 폐기, main 기준으로 재작업
+
+**이슈/PR:** #131, PR #134
+
+### 해외 스캐너 개선: 랭킹 연동 + 변동성 우선 선별
+
+**배경:**
+- `run_overnight` 실운영에서 미국장 동안 거래가 0건 지속
+- 원인: 해외 시장에서도 국내 랭킹/일봉 API 경로를 사용하던 구조적 불일치
+
+**요구사항:**
+1. 해외 시장도 랭킹 API 기반 유니버스 탐색 지원
+2. 단순 상승률/거래대금 상위가 아니라, **변동성이 큰 종목**을 우선 선별
+3. 고정 티커 fallback 금지
+
+**구현 결과:**
+- `src/broker/overseas.py`
+  - `fetch_overseas_rankings()` 추가 (fluctuation / volume)
+  - 해외 랭킹 API 경로/TR_ID를 설정값으로 오버라이드 가능하게 구현
+- `src/analysis/smart_scanner.py`
+  - market-aware 스캔(국내/해외 분리)
+  - 해외: 랭킹 API 유니버스 + 변동성 우선 점수(일변동률 vs 장중 고저폭)
+  - 거래대금/거래량 랭킹은 유동성 보정 점수로 활용
+  - 랭킹 실패 시에는 동적 유니버스(active/recent/holdings)만 사용
+- `src/config.py`
+  - `OVERSEAS_RANKING_*` 설정 추가
+
+**효과:**
+- 해외 시장에서 스캐너 후보 0개로 정지되는 상황 완화
+- 종목 선정 기준이 단순 상승률 중심에서 변동성 중심으로 개선
+- 고정 티커 없이도 시장 주도 변동 종목 탐지 가능
+
+### 국내 스캐너/주문수량 정렬: 변동성 우선 + 리스크 타기팅
+
+**배경:**
+- 해외만 변동성 우선으로 동작하고, 국내는 RSI/거래량 필터 중심으로 동작해 시장 간 전략 일관성이 낮았음
+- 매수 수량이 고정 1주라서 변동성 구간별 익스포저 관리가 어려웠음
+
+**요구사항:**
+1. 국내 스캐너도 변동성 우선 선별로 해외와 통일
+2. 고변동 종목일수록 포지션 크기를 줄이는 수량 산식 적용
+
+**구현 결과:**
+- `src/analysis/smart_scanner.py`
+  - 국내: `fluctuation ranking + volume ranking bonus` 기반 점수화로 전환
+  - 점수는 `max(abs(change_rate), intraday_range_pct)` 중심으로 계산
+  - 국내 랭킹 응답 스키마 키(`price`, `change_rate`, `volume`) 파싱 보강
+- `src/main.py`
+  - `_determine_order_quantity()` 추가
+  - BUY 시 변동성 점수 기반 동적 수량 산정 적용
+  - `trading_cycle`, `run_daily_session` 경로 모두 동일 수량 로직 사용
+- `src/config.py`
+  - `POSITION_SIZING_*` 설정 추가
+
+**효과:**
+- 국내/해외 스캐너 기준이 변동성 중심으로 일관화
+- 고변동 구간에서 자동 익스포저 축소, 저변동 구간에서 과소진입 완화
--- a/docs/skills.md
+++ b/docs/skills.md
@@ -34,6 +34,12 @@ python -m src.main --mode=paper
 ```
 Runs the agent in paper-trading mode (no real orders).

+### Start Trading Agent with Dashboard
+```bash
+python -m src.main --mode=paper --dashboard
+```
+Runs the agent with FastAPI dashboard on `127.0.0.1:8080` (configurable via `DASHBOARD_HOST`/`DASHBOARD_PORT`).
+
 ### Start Trading Agent (Production)
 ```bash
 docker compose up -d ouroboros
@@ -59,7 +65,7 @@ Analyze the last 30 days of trade logs and generate performance metrics.
 python -m src.evolution.optimizer --evolve
 ```
 Triggers the evolution engine to:
-1. Analyze `trade_logs.db` for failing patterns
+1. Analyze `trades.db` for failing patterns
 2. Ask Gemini to generate a new strategy
 3. Run tests on the new strategy
 4. Create a PR if tests pass
@@ -91,12 +97,12 @@ curl http://localhost:8080/health

 ### View Trade Logs
 ```bash
-sqlite3 data/trade_logs.db "SELECT * FROM trades ORDER BY timestamp DESC LIMIT 20;"
+sqlite3 data/trades.db "SELECT * FROM trades ORDER BY timestamp DESC LIMIT 20;"
 ```

 ### Export Trade History
 ```bash
-sqlite3 -header -csv data/trade_logs.db "SELECT * FROM trades;" > trades_export.csv
+sqlite3 -header -csv data/trades.db "SELECT * FROM trades;" > trades_export.csv
 ```

 ## Safety Checklist (Pre-Deploy)
--- a/docs/testing.md
+++ b/docs/testing.md
@@ -2,51 +2,29 @@

 ## Test Structure

-**54 tests** across four files. `asyncio_mode = "auto"` in pyproject.toml — async tests need no special decorator.
+**551 tests** across **25 files**. `asyncio_mode = "auto"` in pyproject.toml — async tests need no special decorator.

 The `settings` fixture in `conftest.py` provides safe defaults with test credentials and in-memory DB.

 ### Test Files

-#### `tests/test_risk.py` (11 tests)
- Circuit breaker boundaries
- Fat-finger edge cases
+#### Core Components
+
+##### `tests/test_risk.py` (14 tests)
+- Circuit breaker boundaries and exact threshold triggers
+- Fat-finger edge cases and percentage validation
 - P&L calculation edge cases
 - Order validation logic

-**Example:**
-```python
-def test_circuit_breaker_exact_threshold(risk_manager):
-    """Circuit breaker should trip at exactly -3.0%."""
-    with pytest.raises(CircuitBreakerTripped):
-        risk_manager.validate_order(
-            current_pnl_pct=-3.0,
-            order_amount=1000,
-            total_cash=10000
-        )
-```
-
-#### `tests/test_broker.py` (6 tests)
+##### `tests/test_broker.py` (11 tests)
 - OAuth token lifecycle
 - Rate limiting enforcement
 - Hash key generation
 - Network error handling
 - SSL context configuration

-**Example:**
-```python
-async def test_rate_limiter(broker):
-    """Rate limiter should delay requests to stay under 10 RPS."""
-    start = time.monotonic()
-    for _ in range(15):  # 15 requests
-        await broker._rate_limiter.acquire()
-    elapsed = time.monotonic() - start
-    assert elapsed >= 1.0  # Should take at least 1 second
-```
-
-#### `tests/test_brain.py` (18 tests)
- Valid JSON parsing
- Markdown-wrapped JSON handling
+##### `tests/test_brain.py` (24 tests)
+- Valid JSON parsing and markdown-wrapped JSON handling
 - Malformed JSON fallback
 - Missing fields handling
 - Invalid action validation
@@ -54,33 +32,143 @@ async def test_rate_limiter(broker):
 - Empty response handling
 - Prompt construction for different markets

-**Example:**
-```python
-async def test_confidence_below_threshold_forces_hold(brain):
-    """Decisions below confidence threshold should force HOLD."""
-    decision = brain.parse_response('{"action":"BUY","confidence":70,"rationale":"test"}')
-    assert decision.action == "HOLD"
-    assert decision.confidence == 70
-```
-
-#### `tests/test_market_schedule.py` (19 tests)
+##### `tests/test_market_schedule.py` (24 tests)
 - Market open/close logic
 - Timezone handling (UTC, Asia/Seoul, America/New_York, etc.)
 - DST (Daylight Saving Time) transitions
- Weekend handling
- Lunch break logic
+- Weekend handling and lunch break logic
 - Multiple market filtering
 - Next market open calculation

-**Example:**
-```python
-def test_is_market_open_during_trading_hours():
-    """Market should be open during regular trading hours."""
-    # KRX: 9:00-15:30 KST, no lunch break
-    market = MARKETS["KR"]
-    trading_time = datetime(2026, 2, 3, 10, 0, tzinfo=ZoneInfo("Asia/Seoul"))  # Monday 10:00
-    assert is_market_open(market, trading_time) is True
-```
+##### `tests/test_db.py` (3 tests)
+- Database initialization and table creation
+- Trade logging with all fields (market, exchange_code, decision_id)
+- Query and retrieval operations
+
+##### `tests/test_main.py` (37 tests)
+- Trading loop orchestration
+- Market iteration and stock processing
+- Dashboard integration (`--dashboard` flag)
+- Telegram command handler wiring
+- Error handling and graceful shutdown
+
+#### Strategy & Playbook (v2)
+
+##### `tests/test_pre_market_planner.py` (37 tests)
+- Pre-market playbook generation
+- Gemini API integration for scenario creation
+- Timeout handling and defensive playbook fallback
+- Multi-market playbook generation
+
+##### `tests/test_scenario_engine.py` (44 tests)
+- Scenario matching against live market data
+- Confidence scoring and threshold filtering
+- Multiple scenario type handling
+- Edge cases (no match, partial match, expired scenarios)
+
+##### `tests/test_playbook_store.py` (23 tests)
+- Playbook persistence to SQLite
+- Date-based retrieval and market filtering
+- Playbook status management (generated, active, expired)
+- JSON serialization/deserialization
+
+##### `tests/test_strategy_models.py` (33 tests)
+- Pydantic model validation for scenarios, playbooks, decisions
+- Field constraints and default values
+- Serialization round-trips
+
+#### Analysis & Scanning
+
+##### `tests/test_volatility.py` (24 tests)
+- ATR and RSI calculation accuracy
+- Volume surge ratio computation
+- Momentum scoring
+- Breakout/breakdown pattern detection
+- Market scanner watchlist management
+
+##### `tests/test_smart_scanner.py` (13 tests)
+- Python-first filtering pipeline
+- RSI and volume ratio filter logic
+- Candidate scoring and ranking
+- Fallback to static watchlist
+
+#### Context & Memory
+
+##### `tests/test_context.py` (18 tests)
+- L1-L7 layer storage and retrieval
+- Context key-value CRUD operations
+- Timeframe-based queries
+- Layer metadata management
+
+##### `tests/test_context_scheduler.py` (5 tests)
+- Periodic context aggregation scheduling
+- Layer summarization triggers
+
+#### Evolution & Review
+
+##### `tests/test_evolution.py` (24 tests)
+- Strategy optimization loop
+- High-confidence losing trade analysis
+- Generated strategy validation
+
+##### `tests/test_daily_review.py` (10 tests)
+- End-of-day review generation
+- Trade performance summarization
+- Context layer (L6_DAILY) integration
+
+##### `tests/test_scorecard.py` (3 tests)
+- Daily scorecard metrics calculation
+- Win rate, P&L, confidence tracking
+
+#### Notifications & Commands
+
+##### `tests/test_telegram.py` (25 tests)
+- Message sending and formatting
+- Rate limiting (leaky bucket)
+- Error handling (network timeout, invalid token)
+- Auto-disable on missing credentials
+- Notification types (trade, circuit breaker, fat-finger, market events)
+
+##### `tests/test_telegram_commands.py` (31 tests)
+- 9 command handlers (/help, /status, /positions, /report, /scenarios, /review, /dashboard, /stop, /resume)
+- Long polling and command dispatch
+- Authorization filtering by chat_id
+- Command response formatting
+
+#### Dashboard
+
+##### `tests/test_dashboard.py` (14 tests)
+- FastAPI endpoint responses (8 API routes)
+- Status, playbook, scorecard, performance, context, decisions, scenarios
+- Query parameter handling (market, date, limit)
+
+#### Performance & Quality
+
+##### `tests/test_token_efficiency.py` (34 tests)
+- Gemini token usage optimization
+- Prompt size reduction verification
+- Cache effectiveness
+
+##### `tests/test_latency_control.py` (30 tests)
+- API call latency measurement
+- Rate limiter timing accuracy
+- Async operation overhead
+
+##### `tests/test_decision_logger.py` (9 tests)
+- Decision audit trail completeness
+- Context snapshot capture
+- Outcome tracking (P&L, accuracy)
+
+##### `tests/test_data_integration.py` (38 tests)
+- External data source integration
+- News API, market data, economic calendar
+- Error handling for API failures
+
+##### `tests/test_backup.py` (23 tests)
+- Backup scheduler and execution
+- Cloud storage (S3) upload
+- Health monitoring
+- Data export functionality

 ## Coverage Requirements

@@ -91,20 +179,6 @@ Check coverage:
 pytest -v --cov=src --cov-report=term-missing
 ```

-Expected output:
-```
-Name                          Stmts   Miss  Cover   Missing
-----------------------------------------------------------
-src/brain/gemini_client.py       85      5    94%   165-169
-src/broker/kis_api.py           120     12    90%   ...
-src/core/risk_manager.py         35      2    94%   ...
-src/db.py                        25      1    96%   ...
-src/main.py                     150     80    47%   (excluded from CI)
-src/markets/schedule.py          95      3    97%   ...
-----------------------------------------------------------
-TOTAL                           510     103   80%
-```
-
 **Note:** `main.py` has lower coverage as it contains the main loop which is tested via integration/manual testing.

 ## Test Configuration
--- a/docs/workflow.md
+++ b/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
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,6 +9,8 @@ dependencies = [
    "pydantic-settings>=2.1,<3",
    "google-genai>=1.0,<2",
    "scipy>=1.11,<2",
+    "fastapi>=0.110,<1",
+    "uvicorn>=0.29,<1",
 ]

 [project.optional-dependencies]
--- a/src/analysis/scanner.py
+++ b/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],
--- a/src/analysis/smart_scanner.py
+++ b/src/analysis/smart_scanner.py
@@ -1,8 +1,4 @@
-"""Smart Volatility Scanner with RSI and volume filters.
-
-Fetches market rankings from KIS API and applies technical filters
-to identify high-probability trading candidates.
-"""
+"""Smart Volatility Scanner with volatility-first market ranking logic."""

 from __future__ import annotations

@@ -12,7 +8,9 @@ from typing import Any

 from src.analysis.volatility import VolatilityAnalyzer
 from src.broker.kis_api import KISBroker
+from src.broker.overseas import OverseasBroker
 from src.config import Settings
+from src.markets.schedule import MarketInfo

 logger = logging.getLogger(__name__)

@@ -32,19 +30,19 @@ class ScanCandidate:


 class SmartVolatilityScanner:
-    """Scans market rankings and applies RSI/volume filters.
+    """Scans market rankings and applies volatility-first 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
+    1. Fetch fluctuation rankings as primary universe
+    2. Fetch volume rankings for liquidity bonus
+    3. Score by volatility first, liquidity second
+    4. Return top N qualified candidates
    """

    def __init__(
        self,
        broker: KISBroker,
+        overseas_broker: OverseasBroker | None,
        volatility_analyzer: VolatilityAnalyzer,
        settings: Settings,
    ) -> None:
@@ -56,6 +54,7 @@ class SmartVolatilityScanner:
            settings: Application settings
        """
        self.broker = broker
+        self.overseas_broker = overseas_broker
        self.analyzer = volatility_analyzer
        self.settings = settings

@@ -67,107 +66,129 @@ class SmartVolatilityScanner:

    async def scan(
        self,
+        market: MarketInfo | None = None,
        fallback_stocks: list[str] | None = None,
    ) -> list[ScanCandidate]:
        """Execute smart scan and return qualified candidates.

        Args:
+            market: Target market info (domestic vs overseas behavior)
            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
+        if market and not market.is_domestic:
+            return await self._scan_overseas(market, fallback_stocks)
+
+        return await self._scan_domestic(fallback_stocks)
+
+    async def _scan_domestic(
+        self,
+        fallback_stocks: list[str] | None = None,
+    ) -> list[ScanCandidate]:
+        """Scan domestic market using volatility-first ranking + liquidity bonus."""
+        # 1) Primary universe from fluctuation ranking.
        try:
-            rankings = await self.broker.fetch_market_rankings(
-                ranking_type="volume",
-                limit=30,  # Fetch more than needed for filtering
+            fluct_rows = await self.broker.fetch_market_rankings(
+                ranking_type="fluctuation",
+                limit=50,
            )
-            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 []
+            logger.warning("Domestic fluctuation ranking failed: %s", exc)
+            fluct_rows = []
+
+        # 2) Liquidity bonus from volume ranking.
+        try:
+            volume_rows = await self.broker.fetch_market_rankings(
+                ranking_type="volume",
+                limit=50,
+            )
+        except ConnectionError as exc:
+            logger.warning("Domestic volume ranking failed: %s", exc)
+            volume_rows = []
+
+        if not fluct_rows and fallback_stocks:
+            logger.info(
+                "Domestic ranking unavailable; using fallback symbols (%d)",
+                len(fallback_stocks),
+            )
+            fluct_rows = [
+                {
+                    "stock_code": code,
+                    "name": code,
+                    "price": 0.0,
+                    "volume": 0.0,
+                    "change_rate": 0.0,
+                    "volume_increase_rate": 0.0,
+                }
+                for code in fallback_stocks
+            ]
+
+        if not fluct_rows:
+            return []
+
+        volume_rank_bonus: dict[str, float] = {}
+        for idx, row in enumerate(volume_rows):
+            code = _extract_stock_code(row)
+            if not code:
+                continue
+            volume_rank_bonus[code] = max(0.0, 15.0 - idx * 0.3)

-        # Step 2: Analyze each stock
        candidates: list[ScanCandidate] = []
-
-        for stock in rankings:
-            stock_code = stock["stock_code"]
+        for stock in fluct_rows:
+            stock_code = _extract_stock_code(stock)
            if not stock_code:
                continue

            try:
-                # Fetch daily prices for RSI calculation
-                daily_prices = await self.broker.get_daily_prices(stock_code, days=20)
+                price = _extract_last_price(stock)
+                change_rate = _extract_change_rate_pct(stock)
+                volume = _extract_volume(stock)

-                if len(daily_prices) < 15:  # Need at least 14+1 for RSI
-                    logger.debug("Insufficient price history for %s", stock_code)
+                intraday_range_pct = 0.0
+                volume_ratio = _safe_float(stock.get("volume_increase_rate"), 0.0) / 100.0 + 1.0
+
+                # Use daily chart to refine range/volume when available.
+                daily_prices = await self.broker.get_daily_prices(stock_code, days=2)
+                if daily_prices:
+                    latest = daily_prices[-1]
+                    latest_close = _safe_float(latest.get("close"), default=price)
+                    if price <= 0:
+                        price = latest_close
+                    latest_high = _safe_float(latest.get("high"))
+                    latest_low = _safe_float(latest.get("low"))
+                    if latest_close > 0 and latest_high > 0 and latest_low > 0 and latest_high >= latest_low:
+                        intraday_range_pct = (latest_high - latest_low) / latest_close * 100.0
+                    if volume <= 0:
+                        volume = _safe_float(latest.get("volume"))
+                    if len(daily_prices) >= 2:
+                        prev_day_volume = _safe_float(daily_prices[-2].get("volume"))
+                        if prev_day_volume > 0:
+                            volume_ratio = max(volume_ratio, volume / prev_day_volume)
+
+                volatility_pct = max(abs(change_rate), intraday_range_pct)
+                if price <= 0 or volatility_pct < 0.8:
                    continue

-                # Calculate RSI
-                close_prices = [p["close"] for p in daily_prices]
-                rsi = self.analyzer.calculate_rsi(close_prices, period=14)
+                volatility_score = min(volatility_pct / 10.0, 1.0) * 85.0
+                liquidity_score = volume_rank_bonus.get(stock_code, 0.0)
+                score = min(100.0, volatility_score + liquidity_score)
+                signal = "momentum" if change_rate >= 0 else "oversold"
+                implied_rsi = max(0.0, min(100.0, 50.0 + (change_rate * 4.0)))

-                # 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,
+                candidates.append(
+                    ScanCandidate(
+                        stock_code=stock_code,
+                        name=stock.get("name", stock_code),
+                        price=price,
+                        volume=volume,
+                        volume_ratio=max(1.0, volume_ratio, volatility_pct / 2.0),
+                        rsi=implied_rsi,
+                        signal=signal,
+                        score=score,
                    )
+                )

            except ConnectionError as exc:
                logger.warning("Failed to analyze %s: %s", stock_code, exc)
@@ -176,10 +197,161 @@ class SmartVolatilityScanner:
                logger.error("Unexpected error analyzing %s: %s", stock_code, exc)
                continue

-        # Sort by score and return top N
+        logger.info("Domestic ranking scan found %d candidates", len(candidates))
        candidates.sort(key=lambda c: c.score, reverse=True)
        return candidates[: self.top_n]

+    async def _scan_overseas(
+        self,
+        market: MarketInfo,
+        fallback_stocks: list[str] | None = None,
+    ) -> list[ScanCandidate]:
+        """Scan overseas symbols using ranking API first, then fallback universe."""
+        if self.overseas_broker is None:
+            logger.warning(
+                "Overseas scanner unavailable for %s: overseas broker not configured",
+                market.name,
+            )
+            return []
+
+        candidates = await self._scan_overseas_from_rankings(market)
+        if not candidates:
+            candidates = await self._scan_overseas_from_symbols(market, fallback_stocks)
+
+        candidates.sort(key=lambda c: c.score, reverse=True)
+        return candidates[: self.top_n]
+
+    async def _scan_overseas_from_rankings(
+        self,
+        market: MarketInfo,
+    ) -> list[ScanCandidate]:
+        """Build overseas candidates from ranking APIs using volatility-first scoring."""
+        assert self.overseas_broker is not None
+        try:
+            fluct_rows = await self.overseas_broker.fetch_overseas_rankings(
+                exchange_code=market.exchange_code,
+                ranking_type="fluctuation",
+                limit=50,
+            )
+        except Exception as exc:
+            logger.warning(
+                "Overseas fluctuation ranking failed for %s: %s", market.code, exc
+            )
+            fluct_rows = []
+
+        if not fluct_rows:
+            return []
+
+        volume_rank_bonus: dict[str, float] = {}
+        try:
+            volume_rows = await self.overseas_broker.fetch_overseas_rankings(
+                exchange_code=market.exchange_code,
+                ranking_type="volume",
+                limit=50,
+            )
+        except Exception as exc:
+            logger.warning(
+                "Overseas volume ranking failed for %s: %s", market.code, exc
+            )
+            volume_rows = []
+
+        for idx, row in enumerate(volume_rows):
+            code = _extract_stock_code(row)
+            if not code:
+                continue
+            # Top-ranked by traded value/volume gets higher liquidity bonus.
+            volume_rank_bonus[code] = max(0.0, 15.0 - idx * 0.3)
+
+        candidates: list[ScanCandidate] = []
+        for row in fluct_rows:
+            stock_code = _extract_stock_code(row)
+            if not stock_code:
+                continue
+
+            price = _extract_last_price(row)
+            change_rate = _extract_change_rate_pct(row)
+            volume = _extract_volume(row)
+            intraday_range_pct = _extract_intraday_range_pct(row, price)
+            volatility_pct = max(abs(change_rate), intraday_range_pct)
+
+            # Volatility-first filter (not simple gainers/value ranking).
+            if price <= 0 or volatility_pct < 0.8:
+                continue
+
+            volatility_score = min(volatility_pct / 10.0, 1.0) * 85.0
+            liquidity_score = volume_rank_bonus.get(stock_code, 0.0)
+            score = min(100.0, volatility_score + liquidity_score)
+            signal = "momentum" if change_rate >= 0 else "oversold"
+            implied_rsi = max(0.0, min(100.0, 50.0 + (change_rate * 4.0)))
+            candidates.append(
+                ScanCandidate(
+                    stock_code=stock_code,
+                    name=str(row.get("name") or row.get("ovrs_item_name") or stock_code),
+                    price=price,
+                    volume=volume,
+                    volume_ratio=max(1.0, volatility_pct / 2.0),
+                    rsi=implied_rsi,
+                    signal=signal,
+                    score=score,
+                )
+            )
+
+        if candidates:
+            logger.info(
+                "Overseas ranking scan found %d candidates for %s",
+                len(candidates),
+                market.name,
+            )
+        return candidates
+
+    async def _scan_overseas_from_symbols(
+        self,
+        market: MarketInfo,
+        symbols: list[str] | None,
+    ) -> list[ScanCandidate]:
+        """Fallback overseas scan from dynamic symbol universe."""
+        assert self.overseas_broker is not None
+        if not symbols:
+            logger.info("Overseas scanner: no symbol universe for %s", market.name)
+            return []
+
+        candidates: list[ScanCandidate] = []
+        for stock_code in symbols:
+            try:
+                price_data = await self.overseas_broker.get_overseas_price(
+                    market.exchange_code, stock_code
+                )
+                output = price_data.get("output", {})
+                price = _extract_last_price(output)
+                change_rate = _extract_change_rate_pct(output)
+                volume = _extract_volume(output)
+                intraday_range_pct = _extract_intraday_range_pct(output, price)
+                volatility_pct = max(abs(change_rate), intraday_range_pct)
+
+                if price <= 0 or volatility_pct < 0.8:
+                    continue
+
+                score = min(volatility_pct / 10.0, 1.0) * 100.0
+                signal = "momentum" if change_rate >= 0 else "oversold"
+                implied_rsi = max(0.0, min(100.0, 50.0 + (change_rate * 4.0)))
+                candidates.append(
+                    ScanCandidate(
+                        stock_code=stock_code,
+                        name=stock_code,
+                        price=price,
+                        volume=volume,
+                        volume_ratio=max(1.0, volatility_pct / 2.0),
+                        rsi=implied_rsi,
+                        signal=signal,
+                        score=score,
+                    )
+                )
+            except ConnectionError as exc:
+                logger.warning("Failed to analyze overseas %s: %s", stock_code, exc)
+            except Exception as exc:
+                logger.error("Unexpected error analyzing overseas %s: %s", stock_code, exc)
+        return candidates
+
    def get_stock_codes(self, candidates: list[ScanCandidate]) -> list[str]:
        """Extract stock codes from candidates for watchlist update.

@@ -190,3 +362,78 @@ class SmartVolatilityScanner:
            List of stock codes
        """
        return [c.stock_code for c in candidates]
+
+
+def _safe_float(value: Any, default: float = 0.0) -> float:
+    """Convert arbitrary values to float safely."""
+    if value in (None, ""):
+        return default
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def _extract_stock_code(row: dict[str, Any]) -> str:
+    """Extract normalized stock code from various API schemas."""
+    return (
+        str(
+            row.get("symb")
+            or row.get("ovrs_pdno")
+            or row.get("stock_code")
+            or row.get("pdno")
+            or ""
+        )
+        .strip()
+        .upper()
+    )
+
+
+def _extract_last_price(row: dict[str, Any]) -> float:
+    """Extract last/close-like price from API schema variants."""
+    return _safe_float(
+        row.get("last")
+        or row.get("ovrs_nmix_prpr")
+        or row.get("stck_prpr")
+        or row.get("price")
+        or row.get("close")
+    )
+
+
+def _extract_change_rate_pct(row: dict[str, Any]) -> float:
+    """Extract daily change rate (%) from API schema variants."""
+    return _safe_float(
+        row.get("rate")
+        or row.get("change_rate")
+        or row.get("prdy_ctrt")
+        or row.get("evlu_pfls_rt")
+        or row.get("chg_rt")
+    )
+
+
+def _extract_volume(row: dict[str, Any]) -> float:
+    """Extract volume/traded-amount proxy from schema variants."""
+    return _safe_float(
+        row.get("tvol") or row.get("acml_vol") or row.get("vol") or row.get("volume")
+    )
+
+
+def _extract_intraday_range_pct(row: dict[str, Any], price: float) -> float:
+    """Estimate intraday range percentage from high/low fields."""
+    if price <= 0:
+        return 0.0
+    high = _safe_float(
+        row.get("high")
+        or row.get("ovrs_hgpr")
+        or row.get("stck_hgpr")
+        or row.get("day_hgpr")
+    )
+    low = _safe_float(
+        row.get("low")
+        or row.get("ovrs_lwpr")
+        or row.get("stck_lwpr")
+        or row.get("day_lwpr")
+    )
+    if high <= 0 or low <= 0 or high < low:
+        return 0.0
+    return (high - low) / price * 100.0
--- a/src/broker/overseas.py
+++ b/src/broker/overseas.py
@@ -64,6 +64,65 @@ class OverseasBroker:
                f"Network error fetching overseas price: {exc}"
            ) from exc

+    async def fetch_overseas_rankings(
+        self,
+        exchange_code: str,
+        ranking_type: str = "fluctuation",
+        limit: int = 30,
+    ) -> list[dict[str, Any]]:
+        """Fetch overseas rankings (price change or volume amount).
+
+        Ranking API specs may differ by account/product. Endpoint paths and
+        TR_IDs are configurable via settings and can be overridden in .env.
+        """
+        if not self._broker._settings.OVERSEAS_RANKING_ENABLED:
+            return []
+
+        await self._broker._rate_limiter.acquire()
+        session = self._broker._get_session()
+
+        if ranking_type == "volume":
+            tr_id = self._broker._settings.OVERSEAS_RANKING_VOLUME_TR_ID
+            path = self._broker._settings.OVERSEAS_RANKING_VOLUME_PATH
+        else:
+            tr_id = self._broker._settings.OVERSEAS_RANKING_FLUCT_TR_ID
+            path = self._broker._settings.OVERSEAS_RANKING_FLUCT_PATH
+
+        headers = await self._broker._auth_headers(tr_id)
+        url = f"{self._broker._base_url}{path}"
+
+        # Try common param variants used by KIS overseas quotation APIs.
+        param_variants = [
+            {"AUTH": "", "EXCD": exchange_code, "NREC": str(max(limit, 30))},
+            {"AUTH": "", "OVRS_EXCG_CD": exchange_code, "NREC": str(max(limit, 30))},
+            {"AUTH": "", "EXCD": exchange_code},
+            {"AUTH": "", "OVRS_EXCG_CD": exchange_code},
+        ]
+
+        last_error: str | None = None
+        for params in param_variants:
+            try:
+                async with session.get(url, headers=headers, params=params) as resp:
+                    text = await resp.text()
+                    if resp.status != 200:
+                        last_error = f"HTTP {resp.status}: {text}"
+                        continue
+
+                    data = await resp.json()
+                    rows = self._extract_ranking_rows(data)
+                    if rows:
+                        return rows[:limit]
+
+                    # keep trying another param variant if response has no usable rows
+                    last_error = f"empty output (keys={list(data.keys())})"
+            except (TimeoutError, aiohttp.ClientError) as exc:
+                last_error = str(exc)
+                continue
+
+        raise ConnectionError(
+            f"fetch_overseas_rankings failed for {exchange_code}/{ranking_type}: {last_error}"
+        )
+
    async def get_overseas_balance(self, exchange_code: str) -> dict[str, Any]:
        """
        Fetch overseas account balance.
@@ -198,3 +257,11 @@ class OverseasBroker:
            "HSX": "VND",
        }
        return currency_map.get(exchange_code, "USD")
+
+    def _extract_ranking_rows(self, data: dict[str, Any]) -> list[dict[str, Any]]:
+        """Extract list rows from ranking response across schema variants."""
+        candidates = [data.get("output"), data.get("output1"), data.get("output2")]
+        for value in candidates:
+            if isinstance(value, list):
+                return [row for row in value if isinstance(row, dict)]
+        return []
--- a/src/config.py
+++ b/src/config.py
@@ -38,6 +38,11 @@ class Settings(BaseSettings):
    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)
+    POSITION_SIZING_ENABLED: bool = True
+    POSITION_BASE_ALLOCATION_PCT: float = Field(default=5.0, gt=0.0, le=30.0)
+    POSITION_MIN_ALLOCATION_PCT: float = Field(default=1.0, gt=0.0, le=20.0)
+    POSITION_MAX_ALLOCATION_PCT: float = Field(default=10.0, gt=0.0, le=50.0)
+    POSITION_VOLATILITY_TARGET_SCORE: float = Field(default=50.0, gt=0.0, le=100.0)

    # Database
    DB_PATH: str = "data/trade_logs.db"
@@ -83,6 +88,23 @@ class Settings(BaseSettings):
    TELEGRAM_COMMANDS_ENABLED: bool = True
    TELEGRAM_POLLING_INTERVAL: float = 1.0  # seconds

+    # Overseas ranking API (KIS endpoint/TR_ID may vary by account/product)
+    # Override these from .env if your account uses different specs.
+    OVERSEAS_RANKING_ENABLED: bool = True
+    OVERSEAS_RANKING_FLUCT_TR_ID: str = "HHDFS76200100"
+    OVERSEAS_RANKING_VOLUME_TR_ID: str = "HHDFS76200200"
+    OVERSEAS_RANKING_FLUCT_PATH: str = (
+        "/uapi/overseas-price/v1/quotations/inquire-updown-rank"
+    )
+    OVERSEAS_RANKING_VOLUME_PATH: str = (
+        "/uapi/overseas-price/v1/quotations/inquire-volume-rank"
+    )
+
+    # Dashboard (optional)
+    DASHBOARD_ENABLED: bool = False
+    DASHBOARD_HOST: str = "127.0.0.1"
+    DASHBOARD_PORT: int = Field(default=8080, ge=1, le=65535)
+
    model_config = {"env_file": ".env", "env_file_encoding": "utf-8"}

    @property
@@ -96,4 +118,7 @@ class Settings(BaseSettings):
    @property
    def enabled_market_list(self) -> list[str]:
        """Parse ENABLED_MARKETS into list of market codes."""
-        return [m.strip() for m in self.ENABLED_MARKETS.split(",") if m.strip()]
+        from src.markets.schedule import expand_market_codes
+
+        raw = [m.strip() for m in self.ENABLED_MARKETS.split(",") if m.strip()]
+        return expand_market_codes(raw)
--- a/src/context/init.py
+++ b/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"]
--- a/src/context/aggregator.py
+++ b/src/context/aggregator.py
@@ -18,52 +18,83 @@ 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
-        cursor = self.conn.execute(
-            """
-            SELECT
-                COUNT(*) as trade_count,
-                SUM(CASE WHEN action = 'BUY' THEN 1 ELSE 0 END) as buys,
-                SUM(CASE WHEN action = 'SELL' THEN 1 ELSE 0 END) as sells,
-                SUM(CASE WHEN action = 'HOLD' THEN 1 ELSE 0 END) as holds,
-                AVG(confidence) as avg_confidence,
-                SUM(pnl) as total_pnl,
-                COUNT(DISTINCT stock_code) as unique_stocks,
-                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) = ?
-            """,
-            (date,),
-        )
-        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
-            self.store.set_context(ContextLayer.L6_DAILY, date, "trade_count", trade_count)
-            self.store.set_context(ContextLayer.L6_DAILY, date, "buys", buys)
-            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)
+        if market is None:
+            cursor = self.conn.execute(
+                """
+                SELECT DISTINCT market
+                FROM trades
+                WHERE DATE(timestamp) = ?
+                """,
+                (date,),
            )
-            self.store.set_context(
-                ContextLayer.L6_DAILY, date, "total_pnl", round(total_pnl, 2)
+            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
+                    COUNT(*) as trade_count,
+                    SUM(CASE WHEN action = 'BUY' THEN 1 ELSE 0 END) as buys,
+                    SUM(CASE WHEN action = 'SELL' THEN 1 ELSE 0 END) as sells,
+                    SUM(CASE WHEN action = 'HOLD' THEN 1 ELSE 0 END) as holds,
+                    AVG(confidence) as avg_confidence,
+                    SUM(pnl) as total_pnl,
+                    COUNT(DISTINCT stock_code) as unique_stocks,
+                    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) = ? AND market = ?
+                """,
+                (date, market_code),
            )
-            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)
+            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
+
+                key_suffix = f"_{market_code}"
+
+                # Store daily metrics in L6 with market suffix
+                self.store.set_context(
+                    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,
+                    f"total_pnl{key_suffix}",
+                    round(total_pnl, 2),
+                )
+                self.store.set_context(
+                    ContextLayer.L6_DAILY, date, f"unique_stocks{key_suffix}", stocks
+                )
+                win_rate = round(wins / max(wins + losses, 1) * 100, 2)
+                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()
--- a/src/context/scheduler.py
+++ b/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),
+        )
--- a/src/dashboard/init.py
+++ b/src/dashboard/init.py
@@ -0,0 +1,5 @@
+"""FastAPI dashboard package for observability APIs."""
+
+from src.dashboard.app import create_dashboard_app
+
+__all__ = ["create_dashboard_app"]
--- a/src/dashboard/app.py
+++ b/src/dashboard/app.py
@@ -0,0 +1,361 @@
+"""FastAPI application for observability dashboard endpoints."""
+
+from __future__ import annotations
+
+import json
+import sqlite3
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+from fastapi import FastAPI, HTTPException, Query
+from fastapi.responses import FileResponse
+
+
+def create_dashboard_app(db_path: str) -> FastAPI:
+    """Create dashboard FastAPI app bound to a SQLite database path."""
+    app = FastAPI(title="The Ouroboros Dashboard", version="1.0.0")
+    app.state.db_path = db_path
+
+    @app.get("/")
+    def index() -> FileResponse:
+        index_path = Path(__file__).parent / "static" / "index.html"
+        return FileResponse(index_path)
+
+    @app.get("/api/status")
+    def get_status() -> dict[str, Any]:
+        today = datetime.now(UTC).date().isoformat()
+        with _connect(db_path) as conn:
+            market_rows = conn.execute(
+                """
+                SELECT DISTINCT market FROM (
+                    SELECT market FROM trades WHERE DATE(timestamp) = ?
+                    UNION
+                    SELECT market FROM decision_logs WHERE DATE(timestamp) = ?
+                    UNION
+                    SELECT market FROM playbooks WHERE date = ?
+                ) ORDER BY market
+                """,
+                (today, today, today),
+            ).fetchall()
+            markets = [row[0] for row in market_rows] if market_rows else []
+            market_status: dict[str, Any] = {}
+            total_trades = 0
+            total_pnl = 0.0
+            total_decisions = 0
+            for market in markets:
+                trade_row = conn.execute(
+                    """
+                    SELECT COUNT(*) AS c, COALESCE(SUM(pnl), 0.0) AS p
+                    FROM trades
+                    WHERE DATE(timestamp) = ? AND market = ?
+                    """,
+                    (today, market),
+                ).fetchone()
+                decision_row = conn.execute(
+                    """
+                    SELECT COUNT(*) AS c
+                    FROM decision_logs
+                    WHERE DATE(timestamp) = ? AND market = ?
+                    """,
+                    (today, market),
+                ).fetchone()
+                playbook_row = conn.execute(
+                    """
+                    SELECT status
+                    FROM playbooks
+                    WHERE date = ? AND market = ?
+                    LIMIT 1
+                    """,
+                    (today, market),
+                ).fetchone()
+                market_status[market] = {
+                    "trade_count": int(trade_row["c"] if trade_row else 0),
+                    "total_pnl": float(trade_row["p"] if trade_row else 0.0),
+                    "decision_count": int(decision_row["c"] if decision_row else 0),
+                    "playbook_status": playbook_row["status"] if playbook_row else None,
+                }
+                total_trades += market_status[market]["trade_count"]
+                total_pnl += market_status[market]["total_pnl"]
+                total_decisions += market_status[market]["decision_count"]
+
+            return {
+                "date": today,
+                "markets": market_status,
+                "totals": {
+                    "trade_count": total_trades,
+                    "total_pnl": round(total_pnl, 2),
+                    "decision_count": total_decisions,
+                },
+            }
+
+    @app.get("/api/playbook/{date_str}")
+    def get_playbook(date_str: str, market: str = Query("KR")) -> dict[str, Any]:
+        with _connect(db_path) as conn:
+            row = conn.execute(
+                """
+                SELECT date, market, status, playbook_json, generated_at,
+                       token_count, scenario_count, match_count
+                FROM playbooks
+                WHERE date = ? AND market = ?
+                """,
+                (date_str, market),
+            ).fetchone()
+            if row is None:
+                raise HTTPException(status_code=404, detail="playbook not found")
+            return {
+                "date": row["date"],
+                "market": row["market"],
+                "status": row["status"],
+                "playbook": json.loads(row["playbook_json"]),
+                "generated_at": row["generated_at"],
+                "token_count": row["token_count"],
+                "scenario_count": row["scenario_count"],
+                "match_count": row["match_count"],
+            }
+
+    @app.get("/api/scorecard/{date_str}")
+    def get_scorecard(date_str: str, market: str = Query("KR")) -> dict[str, Any]:
+        key = f"scorecard_{market}"
+        with _connect(db_path) as conn:
+            row = conn.execute(
+                """
+                SELECT value
+                FROM contexts
+                WHERE layer = 'L6_DAILY' AND timeframe = ? AND key = ?
+                """,
+                (date_str, key),
+            ).fetchone()
+            if row is None:
+                raise HTTPException(status_code=404, detail="scorecard not found")
+            return {"date": date_str, "market": market, "scorecard": json.loads(row["value"])}
+
+    @app.get("/api/performance")
+    def get_performance(market: str = Query("all")) -> dict[str, Any]:
+        with _connect(db_path) as conn:
+            if market == "all":
+                by_market_rows = conn.execute(
+                    """
+                    SELECT market,
+                           COUNT(*) AS total_trades,
+                           SUM(CASE WHEN pnl > 0 THEN 1 ELSE 0 END) AS wins,
+                           SUM(CASE WHEN pnl < 0 THEN 1 ELSE 0 END) AS losses,
+                           COALESCE(SUM(pnl), 0.0) AS total_pnl,
+                           COALESCE(AVG(confidence), 0.0) AS avg_confidence
+                    FROM trades
+                    GROUP BY market
+                    ORDER BY market
+                    """
+                ).fetchall()
+                combined = _performance_from_rows(by_market_rows)
+                return {
+                    "market": "all",
+                    "combined": combined,
+                    "by_market": [
+                        _row_to_performance(row)
+                        for row in by_market_rows
+                    ],
+                }
+
+            row = conn.execute(
+                """
+                SELECT market,
+                       COUNT(*) AS total_trades,
+                       SUM(CASE WHEN pnl > 0 THEN 1 ELSE 0 END) AS wins,
+                       SUM(CASE WHEN pnl < 0 THEN 1 ELSE 0 END) AS losses,
+                       COALESCE(SUM(pnl), 0.0) AS total_pnl,
+                       COALESCE(AVG(confidence), 0.0) AS avg_confidence
+                FROM trades
+                WHERE market = ?
+                GROUP BY market
+                """,
+                (market,),
+            ).fetchone()
+            if row is None:
+                return {"market": market, "metrics": _empty_performance(market)}
+            return {"market": market, "metrics": _row_to_performance(row)}
+
+    @app.get("/api/context/{layer}")
+    def get_context_layer(
+        layer: str,
+        timeframe: str | None = Query(default=None),
+        limit: int = Query(default=100, ge=1, le=1000),
+    ) -> dict[str, Any]:
+        with _connect(db_path) as conn:
+            if timeframe is None:
+                rows = conn.execute(
+                    """
+                    SELECT timeframe, key, value, updated_at
+                    FROM contexts
+                    WHERE layer = ?
+                    ORDER BY updated_at DESC
+                    LIMIT ?
+                    """,
+                    (layer, limit),
+                ).fetchall()
+            else:
+                rows = conn.execute(
+                    """
+                    SELECT timeframe, key, value, updated_at
+                    FROM contexts
+                    WHERE layer = ? AND timeframe = ?
+                    ORDER BY key
+                    LIMIT ?
+                    """,
+                    (layer, timeframe, limit),
+                ).fetchall()
+
+            entries = [
+                {
+                    "timeframe": row["timeframe"],
+                    "key": row["key"],
+                    "value": json.loads(row["value"]),
+                    "updated_at": row["updated_at"],
+                }
+                for row in rows
+            ]
+            return {
+                "layer": layer,
+                "timeframe": timeframe,
+                "count": len(entries),
+                "entries": entries,
+            }
+
+    @app.get("/api/decisions")
+    def get_decisions(
+        market: str = Query("KR"),
+        limit: int = Query(default=50, ge=1, le=500),
+    ) -> dict[str, Any]:
+        with _connect(db_path) as conn:
+            rows = conn.execute(
+                """
+                SELECT decision_id, timestamp, stock_code, market, exchange_code,
+                       action, confidence, rationale, context_snapshot, input_data,
+                       outcome_pnl, outcome_accuracy
+                FROM decision_logs
+                WHERE market = ?
+                ORDER BY timestamp DESC
+                LIMIT ?
+                """,
+                (market, limit),
+            ).fetchall()
+            decisions = []
+            for row in rows:
+                decisions.append(
+                    {
+                        "decision_id": row["decision_id"],
+                        "timestamp": row["timestamp"],
+                        "stock_code": row["stock_code"],
+                        "market": row["market"],
+                        "exchange_code": row["exchange_code"],
+                        "action": row["action"],
+                        "confidence": row["confidence"],
+                        "rationale": row["rationale"],
+                        "context_snapshot": json.loads(row["context_snapshot"]),
+                        "input_data": json.loads(row["input_data"]),
+                        "outcome_pnl": row["outcome_pnl"],
+                        "outcome_accuracy": row["outcome_accuracy"],
+                    }
+                )
+            return {"market": market, "count": len(decisions), "decisions": decisions}
+
+    @app.get("/api/scenarios/active")
+    def get_active_scenarios(
+        market: str = Query("US"),
+        date_str: str | None = Query(default=None),
+        limit: int = Query(default=50, ge=1, le=500),
+    ) -> dict[str, Any]:
+        if date_str is None:
+            date_str = datetime.now(UTC).date().isoformat()
+
+        with _connect(db_path) as conn:
+            rows = conn.execute(
+                """
+                SELECT timestamp, stock_code, action, confidence, rationale, context_snapshot
+                FROM decision_logs
+                WHERE market = ? AND DATE(timestamp) = ?
+                ORDER BY timestamp DESC
+                LIMIT ?
+                """,
+                (market, date_str, limit),
+            ).fetchall()
+            matches: list[dict[str, Any]] = []
+            for row in rows:
+                snapshot = json.loads(row["context_snapshot"])
+                scenario_match = snapshot.get("scenario_match", {})
+                if not isinstance(scenario_match, dict) or not scenario_match:
+                    continue
+                matches.append(
+                    {
+                        "timestamp": row["timestamp"],
+                        "stock_code": row["stock_code"],
+                        "action": row["action"],
+                        "confidence": row["confidence"],
+                        "rationale": row["rationale"],
+                        "scenario_match": scenario_match,
+                    }
+                )
+            return {"market": market, "date": date_str, "count": len(matches), "matches": matches}
+
+    return app
+
+
+def _connect(db_path: str) -> sqlite3.Connection:
+    conn = sqlite3.connect(db_path)
+    conn.row_factory = sqlite3.Row
+    return conn
+
+
+def _row_to_performance(row: sqlite3.Row) -> dict[str, Any]:
+    wins = int(row["wins"] or 0)
+    losses = int(row["losses"] or 0)
+    total = int(row["total_trades"] or 0)
+    win_rate = round((wins / (wins + losses) * 100), 2) if (wins + losses) > 0 else 0.0
+    return {
+        "market": row["market"],
+        "total_trades": total,
+        "wins": wins,
+        "losses": losses,
+        "win_rate": win_rate,
+        "total_pnl": round(float(row["total_pnl"] or 0.0), 2),
+        "avg_confidence": round(float(row["avg_confidence"] or 0.0), 2),
+    }
+
+
+def _performance_from_rows(rows: list[sqlite3.Row]) -> dict[str, Any]:
+    total_trades = 0
+    wins = 0
+    losses = 0
+    total_pnl = 0.0
+    confidence_weighted = 0.0
+    for row in rows:
+        market_total = int(row["total_trades"] or 0)
+        market_conf = float(row["avg_confidence"] or 0.0)
+        total_trades += market_total
+        wins += int(row["wins"] or 0)
+        losses += int(row["losses"] or 0)
+        total_pnl += float(row["total_pnl"] or 0.0)
+        confidence_weighted += market_total * market_conf
+    win_rate = round((wins / (wins + losses) * 100), 2) if (wins + losses) > 0 else 0.0
+    avg_confidence = round(confidence_weighted / total_trades, 2) if total_trades > 0 else 0.0
+    return {
+        "market": "all",
+        "total_trades": total_trades,
+        "wins": wins,
+        "losses": losses,
+        "win_rate": win_rate,
+        "total_pnl": round(total_pnl, 2),
+        "avg_confidence": avg_confidence,
+    }
+
+
+def _empty_performance(market: str) -> dict[str, Any]:
+    return {
+        "market": market,
+        "total_trades": 0,
+        "wins": 0,
+        "losses": 0,
+        "win_rate": 0.0,
+        "total_pnl": 0.0,
+        "avg_confidence": 0.0,
+    }
--- a/src/dashboard/static/index.html
+++ b/src/dashboard/static/index.html
@@ -0,0 +1,61 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>The Ouroboros Dashboard</title>
+    <style>
+      :root {
+        --bg: #0b1724;
+        --panel: #12263a;
+        --fg: #e6eef7;
+        --muted: #9fb3c8;
+        --accent: #3cb371;
+      }
+      body {
+        margin: 0;
+        font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
+        background: radial-gradient(circle at top left, #173b58, var(--bg));
+        color: var(--fg);
+      }
+      .wrap {
+        max-width: 900px;
+        margin: 48px auto;
+        padding: 0 16px;
+      }
+      .card {
+        background: color-mix(in oklab, var(--panel), black 12%);
+        border: 1px solid #28455f;
+        border-radius: 12px;
+        padding: 20px;
+      }
+      h1 {
+        margin-top: 0;
+      }
+      code {
+        color: var(--accent);
+      }
+      li {
+        margin: 6px 0;
+        color: var(--muted);
+      }
+    </style>
+  </head>
+  <body>
+    <div class="wrap">
+      <div class="card">
+        <h1>The Ouroboros Dashboard API</h1>
+        <p>Use the following endpoints:</p>
+        <ul>
+          <li><code>/api/status</code></li>
+          <li><code>/api/playbook/{date}?market=KR</code></li>
+          <li><code>/api/scorecard/{date}?market=KR</code></li>
+          <li><code>/api/performance?market=all</code></li>
+          <li><code>/api/context/{layer}</code></li>
+          <li><code>/api/decisions?market=KR</code></li>
+          <li><code>/api/scenarios/active?market=US</code></li>
+        </ul>
+      </div>
+    </div>
+  </body>
+</html>
--- a/src/db.py
+++ b/src/db.py
@@ -6,6 +6,7 @@ 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:
@@ -26,7 +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
        )
        """
    )
@@ -41,6 +43,8 @@ def init_db(db_path: str) -> sqlite3.Connection:
        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(
@@ -91,6 +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)")
@@ -122,6 +147,7 @@ def log_trade(
    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.

@@ -145,9 +171,9 @@ def log_trade(
        """
        INSERT INTO trades (
            timestamp, stock_code, action, confidence, rationale,
-            quantity, price, pnl, market, exchange_code, selection_context
+            quantity, price, pnl, market, exchange_code, selection_context, decision_id
        )
-        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
        """,
        (
            datetime.now(UTC).isoformat(),
@@ -161,6 +187,69 @@ def log_trade(
            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]}
+
+
+def get_open_position(
+    conn: sqlite3.Connection, stock_code: str, market: str
+) -> dict[str, Any] | None:
+    """Return open position if latest trade is BUY, else None."""
+    cursor = conn.execute(
+        """
+        SELECT action, decision_id, price, quantity
+        FROM trades
+        WHERE stock_code = ?
+          AND market = ?
+        ORDER BY timestamp DESC
+        LIMIT 1
+        """,
+        (stock_code, market),
+    )
+    row = cursor.fetchone()
+    if not row or row[0] != "BUY":
+        return None
+    return {"decision_id": row[1], "price": row[2], "quantity": row[3]}
+
+
+def get_recent_symbols(
+    conn: sqlite3.Connection, market: str, limit: int = 30
+) -> list[str]:
+    """Return recent unique symbols for a market, newest first."""
+    cursor = conn.execute(
+        """
+        SELECT stock_code, MAX(timestamp) AS last_ts
+        FROM trades
+        WHERE market = ?
+        GROUP BY stock_code
+        ORDER BY last_ts DESC
+        LIMIT ?
+        """,
+        (market, limit),
+    )
+    return [row[0] for row in cursor.fetchall() if row and row[0]]
--- a/src/evolution/init.py
+++ b/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",
 ]
--- a/src/evolution/daily_review.py
+++ b/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]
--- a/src/evolution/scorecard.py
+++ b/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 = ""
--- a/src/main.py
+++ b/src/main.py
--- a/src/markets/schedule.py
+++ b/src/markets/schedule.py
@@ -123,6 +123,23 @@ MARKETS: dict[str, MarketInfo] = {
    ),
 }

+MARKET_SHORTHAND: dict[str, list[str]] = {
+    "US": ["US_NASDAQ", "US_NYSE", "US_AMEX"],
+    "CN": ["CN_SHA", "CN_SZA"],
+    "VN": ["VN_HAN", "VN_HCM"],
+}
+
+
+def expand_market_codes(codes: list[str]) -> list[str]:
+    """Expand shorthand market codes into concrete exchange market codes."""
+    expanded: list[str] = []
+    for code in codes:
+        if code in MARKET_SHORTHAND:
+            expanded.extend(MARKET_SHORTHAND[code])
+        else:
+            expanded.append(code)
+    return expanded
+

 def is_market_open(market: MarketInfo, now: datetime | None = None) -> bool:
    """
--- a/src/notifications/telegram_client.py
+++ b/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.
--- a/src/strategy/playbook_store.py
+++ b/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
--- a/src/strategy/pre_market_planner.py
+++ b/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",
+                ),
+            ],
+        )
--- a/src/strategy/scenario_engine.py
+++ b/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
--- a/tests/test_context.py
+++ b/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, "buys") == 1
-        assert store.get_context(ContextLayer.L6_DAILY, date, "sells") == 1
-        assert store.get_context(ContextLayer.L6_DAILY, date, "holds") == 1
-        assert store.get_context(ContextLayer.L6_DAILY, date, "total_pnl") == 2000.0
-        assert store.get_context(ContextLayer.L6_DAILY, date, "unique_stocks") == 3
+        assert store.get_context(ContextLayer.L6_DAILY, date, "trade_count_KR") == 3
+        assert store.get_context(ContextLayer.L6_DAILY, date, "buys_KR") == 1
+        assert store.get_context(ContextLayer.L6_DAILY, date, "sells_KR") == 1
+        assert store.get_context(ContextLayer.L6_DAILY, date, "holds_KR") == 1
+        assert store.get_context(ContextLayer.L6_DAILY, date, "total_pnl_KR") == 2000.0
+        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(ContextLayer.L6_DAILY, "2026-02-03", "total_pnl", 200.0)
-        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, "2026-02-02", "total_pnl_KR", 100.0
+        )
+        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")
-        avg_conf = store.get_context(ContextLayer.L5_WEEKLY, week, "avg_confidence")
+        weekly_pnl = store.get_context(ContextLayer.L5_WEEKLY, week, "weekly_pnl_KR")
+        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(ContextLayer.L5_WEEKLY, "2026-W06", "weekly_pnl", 200.0)
-        aggregator.store.set_context(ContextLayer.L5_WEEKLY, "2026-W07", "weekly_pnl", 150.0)
+        aggregator.store.set_context(
+            ContextLayer.L5_WEEKLY, "2026-W05", "weekly_pnl_KR", 100.0
+        )
+        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
-        current_week = datetime.now(UTC).strftime("%Y-W%V")
-        assert store.get_context(ContextLayer.L5_WEEKLY, current_week, "weekly_pnl") is not None
-        # Further layers depend on time alignment, just verify no crashes
+        assert store.get_context(ContextLayer.L6_DAILY, date, "total_pnl_KR") == 1000.0
+        from datetime import date as date_cls
+        trade_date = date_cls.fromisoformat(date)
+        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:
--- a/tests/test_context_scheduler.py
+++ b/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
--- a/tests/test_daily_review.py
+++ b/tests/test_daily_review.py
@@ -0,0 +1,387 @@
+"""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
+
+from datetime import UTC, datetime
+
+TODAY = datetime.now(UTC).strftime("%Y-%m-%d")
+
+
+@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(TODAY, "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(TODAY, "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(TODAY, "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
--- a/tests/test_dashboard.py
+++ b/tests/test_dashboard.py
@@ -0,0 +1,298 @@
+"""Tests for dashboard endpoint handlers."""
+
+from __future__ import annotations
+
+import json
+import sqlite3
+from collections.abc import Callable
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+import pytest
+from fastapi import HTTPException
+from fastapi.responses import FileResponse
+
+from src.dashboard.app import create_dashboard_app
+from src.db import init_db
+
+
+def _seed_db(conn: sqlite3.Connection) -> None:
+    today = datetime.now(UTC).date().isoformat()
+
+    conn.execute(
+        """
+        INSERT INTO playbooks (
+            date, market, status, playbook_json, generated_at,
+            token_count, scenario_count, match_count
+        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+        """,
+        (
+            "2026-02-14",
+            "KR",
+            "ready",
+            json.dumps({"market": "KR", "stock_playbooks": []}),
+            "2026-02-14T08:30:00+00:00",
+            123,
+            2,
+            1,
+        ),
+    )
+    conn.execute(
+        """
+        INSERT INTO playbooks (
+            date, market, status, playbook_json, generated_at,
+            token_count, scenario_count, match_count
+        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+        """,
+        (
+            today,
+            "US_NASDAQ",
+            "ready",
+            json.dumps({"market": "US_NASDAQ", "stock_playbooks": []}),
+            f"{today}T08:30:00+00:00",
+            100,
+            1,
+            0,
+        ),
+    )
+    conn.execute(
+        """
+        INSERT INTO contexts (layer, timeframe, key, value, created_at, updated_at)
+        VALUES (?, ?, ?, ?, ?, ?)
+        """,
+        (
+            "L6_DAILY",
+            "2026-02-14",
+            "scorecard_KR",
+            json.dumps({"market": "KR", "total_pnl": 1.5, "win_rate": 60.0}),
+            "2026-02-14T15:30:00+00:00",
+            "2026-02-14T15:30:00+00:00",
+        ),
+    )
+    conn.execute(
+        """
+        INSERT INTO contexts (layer, timeframe, key, value, created_at, updated_at)
+        VALUES (?, ?, ?, ?, ?, ?)
+        """,
+        (
+            "L7_REALTIME",
+            "2026-02-14T10:00:00+00:00",
+            "volatility_KR_005930",
+            json.dumps({"momentum_score": 70.0}),
+            "2026-02-14T10:00:00+00:00",
+            "2026-02-14T10:00:00+00:00",
+        ),
+    )
+    conn.execute(
+        """
+        INSERT INTO decision_logs (
+            decision_id, timestamp, stock_code, market, exchange_code,
+            action, confidence, rationale, context_snapshot, input_data
+        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+        """,
+        (
+            "d-kr-1",
+            f"{today}T09:10:00+00:00",
+            "005930",
+            "KR",
+            "KRX",
+            "BUY",
+            85,
+            "signal matched",
+            json.dumps({"scenario_match": {"rsi": 28.0}}),
+            json.dumps({"current_price": 70000}),
+        ),
+    )
+    conn.execute(
+        """
+        INSERT INTO decision_logs (
+            decision_id, timestamp, stock_code, market, exchange_code,
+            action, confidence, rationale, context_snapshot, input_data
+        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+        """,
+        (
+            "d-us-1",
+            f"{today}T21:10:00+00:00",
+            "AAPL",
+            "US_NASDAQ",
+            "NASDAQ",
+            "SELL",
+            80,
+            "no match",
+            json.dumps({"scenario_match": {}}),
+            json.dumps({"current_price": 200}),
+        ),
+    )
+    conn.execute(
+        """
+        INSERT INTO trades (
+            timestamp, stock_code, action, confidence, rationale,
+            quantity, price, pnl, market, exchange_code, selection_context, decision_id
+        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+        """,
+        (
+            f"{today}T09:11:00+00:00",
+            "005930",
+            "BUY",
+            85,
+            "buy",
+            1,
+            70000,
+            2.0,
+            "KR",
+            "KRX",
+            None,
+            "d-kr-1",
+        ),
+    )
+    conn.execute(
+        """
+        INSERT INTO trades (
+            timestamp, stock_code, action, confidence, rationale,
+            quantity, price, pnl, market, exchange_code, selection_context, decision_id
+        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+        """,
+        (
+            f"{today}T21:11:00+00:00",
+            "AAPL",
+            "SELL",
+            80,
+            "sell",
+            1,
+            200,
+            -1.0,
+            "US_NASDAQ",
+            "NASDAQ",
+            None,
+            "d-us-1",
+        ),
+    )
+    conn.commit()
+
+
+def _app(tmp_path: Path) -> Any:
+    db_path = tmp_path / "dashboard_test.db"
+    conn = init_db(str(db_path))
+    _seed_db(conn)
+    conn.close()
+    return create_dashboard_app(str(db_path))
+
+
+def _endpoint(app: Any, path: str) -> Callable[..., Any]:
+    for route in app.routes:
+        if getattr(route, "path", None) == path:
+            return route.endpoint
+    raise AssertionError(f"route not found: {path}")
+
+
+def test_index_serves_html(tmp_path: Path) -> None:
+    app = _app(tmp_path)
+    index = _endpoint(app, "/")
+    resp = index()
+    assert isinstance(resp, FileResponse)
+    assert "index.html" in str(resp.path)
+
+
+def test_status_endpoint(tmp_path: Path) -> None:
+    app = _app(tmp_path)
+    get_status = _endpoint(app, "/api/status")
+    body = get_status()
+    assert "KR" in body["markets"]
+    assert "US_NASDAQ" in body["markets"]
+    assert "totals" in body
+
+
+def test_playbook_found(tmp_path: Path) -> None:
+    app = _app(tmp_path)
+    get_playbook = _endpoint(app, "/api/playbook/{date_str}")
+    body = get_playbook("2026-02-14", market="KR")
+    assert body["market"] == "KR"
+
+
+def test_playbook_not_found(tmp_path: Path) -> None:
+    app = _app(tmp_path)
+    get_playbook = _endpoint(app, "/api/playbook/{date_str}")
+    with pytest.raises(HTTPException, match="playbook not found"):
+        get_playbook("2026-02-15", market="KR")
+
+
+def test_scorecard_found(tmp_path: Path) -> None:
+    app = _app(tmp_path)
+    get_scorecard = _endpoint(app, "/api/scorecard/{date_str}")
+    body = get_scorecard("2026-02-14", market="KR")
+    assert body["scorecard"]["total_pnl"] == 1.5
+
+
+def test_scorecard_not_found(tmp_path: Path) -> None:
+    app = _app(tmp_path)
+    get_scorecard = _endpoint(app, "/api/scorecard/{date_str}")
+    with pytest.raises(HTTPException, match="scorecard not found"):
+        get_scorecard("2026-02-15", market="KR")
+
+
+def test_performance_all(tmp_path: Path) -> None:
+    app = _app(tmp_path)
+    get_performance = _endpoint(app, "/api/performance")
+    body = get_performance(market="all")
+    assert body["market"] == "all"
+    assert body["combined"]["total_trades"] == 2
+    assert len(body["by_market"]) == 2
+
+
+def test_performance_market_filter(tmp_path: Path) -> None:
+    app = _app(tmp_path)
+    get_performance = _endpoint(app, "/api/performance")
+    body = get_performance(market="KR")
+    assert body["market"] == "KR"
+    assert body["metrics"]["total_trades"] == 1
+
+
+def test_performance_empty_market(tmp_path: Path) -> None:
+    app = _app(tmp_path)
+    get_performance = _endpoint(app, "/api/performance")
+    body = get_performance(market="JP")
+    assert body["metrics"]["total_trades"] == 0
+
+
+def test_context_layer_all(tmp_path: Path) -> None:
+    app = _app(tmp_path)
+    get_context_layer = _endpoint(app, "/api/context/{layer}")
+    body = get_context_layer("L7_REALTIME", timeframe=None, limit=100)
+    assert body["layer"] == "L7_REALTIME"
+    assert body["count"] == 1
+
+
+def test_context_layer_timeframe_filter(tmp_path: Path) -> None:
+    app = _app(tmp_path)
+    get_context_layer = _endpoint(app, "/api/context/{layer}")
+    body = get_context_layer("L6_DAILY", timeframe="2026-02-14", limit=100)
+    assert body["count"] == 1
+    assert body["entries"][0]["key"] == "scorecard_KR"
+
+
+def test_decisions_endpoint(tmp_path: Path) -> None:
+    app = _app(tmp_path)
+    get_decisions = _endpoint(app, "/api/decisions")
+    body = get_decisions(market="KR", limit=50)
+    assert body["count"] == 1
+    assert body["decisions"][0]["decision_id"] == "d-kr-1"
+
+
+def test_scenarios_active_filters_non_matched(tmp_path: Path) -> None:
+    app = _app(tmp_path)
+    get_active_scenarios = _endpoint(app, "/api/scenarios/active")
+    body = get_active_scenarios(
+        market="KR",
+        date_str=datetime.now(UTC).date().isoformat(),
+        limit=50,
+    )
+    assert body["count"] == 1
+    assert body["matches"][0]["stock_code"] == "005930"
+
+
+def test_scenarios_active_empty_when_no_matches(tmp_path: Path) -> None:
+    app = _app(tmp_path)
+    get_active_scenarios = _endpoint(app, "/api/scenarios/active")
+    body = get_active_scenarios(market="US", date_str="2026-02-14", limit=50)
+    assert body["count"] == 0
--- a/tests/test_db.py
+++ b/tests/test_db.py
@@ -0,0 +1,60 @@
+"""Tests for database helper functions."""
+
+from src.db import get_open_position, init_db, log_trade
+
+
+def test_get_open_position_returns_latest_buy() -> None:
+    conn = init_db(":memory:")
+    log_trade(
+        conn=conn,
+        stock_code="005930",
+        action="BUY",
+        confidence=90,
+        rationale="entry",
+        quantity=2,
+        price=70000.0,
+        market="KR",
+        exchange_code="KRX",
+        decision_id="d-buy-1",
+    )
+
+    position = get_open_position(conn, "005930", "KR")
+    assert position is not None
+    assert position["decision_id"] == "d-buy-1"
+    assert position["price"] == 70000.0
+    assert position["quantity"] == 2
+
+
+def test_get_open_position_returns_none_when_latest_is_sell() -> None:
+    conn = init_db(":memory:")
+    log_trade(
+        conn=conn,
+        stock_code="005930",
+        action="BUY",
+        confidence=90,
+        rationale="entry",
+        quantity=1,
+        price=70000.0,
+        market="KR",
+        exchange_code="KRX",
+        decision_id="d-buy-1",
+    )
+    log_trade(
+        conn=conn,
+        stock_code="005930",
+        action="SELL",
+        confidence=95,
+        rationale="exit",
+        quantity=1,
+        price=71000.0,
+        market="KR",
+        exchange_code="KRX",
+        decision_id="d-sell-1",
+    )
+
+    assert get_open_position(conn, "005930", "KR") is None
+
+
+def test_get_open_position_returns_none_when_no_trades() -> None:
+    conn = init_db(":memory:")
+    assert get_open_position(conn, "AAPL", "US_NASDAQ") is None
--- a/tests/test_main.py
+++ b/tests/test_main.py
--- a/tests/test_market_schedule.py
+++ b/tests/test_market_schedule.py
@@ -7,6 +7,7 @@ import pytest

 from src.markets.schedule import (
    MARKETS,
+    expand_market_codes,
    get_next_market_open,
    get_open_markets,
    is_market_open,
@@ -199,3 +200,28 @@ class TestGetNextMarketOpen:
            enabled_markets=["INVALID", "KR"], now=test_time
        )
        assert market.code == "KR"
+
+
+class TestExpandMarketCodes:
+    """Test shorthand market expansion."""
+
+    def test_expand_us_shorthand(self) -> None:
+        assert expand_market_codes(["US"]) == ["US_NASDAQ", "US_NYSE", "US_AMEX"]
+
+    def test_expand_cn_shorthand(self) -> None:
+        assert expand_market_codes(["CN"]) == ["CN_SHA", "CN_SZA"]
+
+    def test_expand_vn_shorthand(self) -> None:
+        assert expand_market_codes(["VN"]) == ["VN_HAN", "VN_HCM"]
+
+    def test_expand_mixed_codes(self) -> None:
+        assert expand_market_codes(["KR", "US", "JP"]) == [
+            "KR",
+            "US_NASDAQ",
+            "US_NYSE",
+            "US_AMEX",
+            "JP",
+        ]
+
+    def test_expand_preserves_unknown_code(self) -> None:
+        assert expand_market_codes(["KR", "UNKNOWN"]) == ["KR", "UNKNOWN"]
--- a/tests/test_playbook_store.py
+++ b/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
--- a/tests/test_pre_market_planner.py
+++ b/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
--- a/tests/test_scenario_engine.py
+++ b/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)
--- a/tests/test_scorecard.py
+++ b/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 == []
--- a/tests/test_smart_scanner.py
+++ b/tests/test_smart_scanner.py
@@ -8,6 +8,7 @@ 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.broker.overseas import OverseasBroker
 from src.config import Settings


@@ -43,61 +44,70 @@ def scanner(mock_broker: MagicMock, mock_settings: Settings) -> SmartVolatilityS
    analyzer = VolatilityAnalyzer()
    return SmartVolatilityScanner(
        broker=mock_broker,
+        overseas_broker=None,
        volatility_analyzer=analyzer,
        settings=mock_settings,
    )


+@pytest.fixture
+def mock_overseas_broker() -> MagicMock:
+    """Create mock overseas broker."""
+    broker = MagicMock(spec=OverseasBroker)
+    broker.get_overseas_price = AsyncMock()
+    broker.fetch_overseas_rankings = AsyncMock(return_value=[])
+    return broker
+
+
 class TestSmartVolatilityScanner:
    """Test suite for SmartVolatilityScanner."""

    @pytest.mark.asyncio
-    async def test_scan_finds_oversold_candidates(
+    async def test_scan_domestic_prefers_volatility_with_liquidity_bonus(
        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 = [
+        """Domestic scan should score by volatility first and volume rank second."""
+        fluctuation_rows = [
            {
                "stock_code": "005930",
                "name": "Samsung",
                "price": 70000,
                "volume": 5000000,
-                "change_rate": -3.5,
+                "change_rate": -5.0,
                "volume_increase_rate": 250,
            },
+            {
+                "stock_code": "035420",
+                "name": "NAVER",
+                "price": 250000,
+                "volume": 3000000,
+                "change_rate": 3.0,
+                "volume_increase_rate": 200,
+            },
+        ]
+        volume_rows = [
+            {"stock_code": "035420", "name": "NAVER", "price": 250000, "volume": 3000000},
+            {"stock_code": "005930", "name": "Samsung", "price": 70000, "volume": 5000000},
+        ]
+        mock_broker.fetch_market_rankings.side_effect = [fluctuation_rows, volume_rows]
+        mock_broker.get_daily_prices.return_value = [
+            {"open": 1, "high": 1, "low": 1, "close": 1, "volume": 1000000},
+            {"open": 1, "high": 1, "low": 1, "close": 1, "volume": 1000000},
        ]
-
-        # 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
+        assert len(candidates) >= 1
+        # Samsung has higher absolute move, so it should lead despite lower volume rank bonus.
+        assert candidates[0].stock_code == "005930"
+        assert candidates[0].signal == "oversold"

    @pytest.mark.asyncio
-    async def test_scan_finds_momentum_candidates(
+    async def test_scan_domestic_finds_momentum_candidate(
        self, scanner: SmartVolatilityScanner, mock_broker: MagicMock
    ) -> None:
-        """Test that scanner identifies momentum stocks with high volume."""
-        mock_broker.fetch_market_rankings.return_value = [
+        """Positive change should be represented as momentum signal."""
+        fluctuation_rows = [
            {
                "stock_code": "035420",
                "name": "NAVER",
@@ -107,124 +117,67 @@ class TestSmartVolatilityScanner:
                "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
+        mock_broker.fetch_market_rankings.side_effect = [fluctuation_rows, fluctuation_rows]
+        mock_broker.get_daily_prices.return_value = [
+            {"open": 1, "high": 1, "low": 1, "close": 1, "volume": 1000000},
+            {"open": 1, "high": 1, "low": 1, "close": 1, "volume": 1000000},
+        ]

        candidates = await scanner.scan()

-        mock_broker.fetch_market_rankings.assert_called_once()
+        assert [c.stock_code for c in candidates] == ["035420"]
+        assert candidates[0].signal == "momentum"

    @pytest.mark.asyncio
-    async def test_scan_filters_low_volume(
+    async def test_scan_domestic_filters_low_volatility(
        self, scanner: SmartVolatilityScanner, mock_broker: MagicMock
    ) -> None:
-        """Test that stocks with low volume ratio are filtered out."""
-        mock_broker.fetch_market_rankings.return_value = [
+        """Domestic scan should drop symbols below volatility threshold."""
+        fluctuation_rows = [
            {
                "stock_code": "000660",
                "name": "SK Hynix",
                "price": 150000,
                "volume": 500000,
-                "change_rate": -5.0,
-                "volume_increase_rate": 50,  # Only 50% increase (< 200%)
+                "change_rate": 0.2,
+                "volume_increase_rate": 50,
            },
        ]
-
-        # 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
+        mock_broker.fetch_market_rankings.side_effect = [fluctuation_rows, fluctuation_rows]
+        mock_broker.get_daily_prices.return_value = [
+            {"open": 1, "high": 150100, "low": 149900, "close": 150000, "volume": 1000000},
+            {"open": 1, "high": 150100, "low": 149900, "close": 150000, "volume": 1000000},
+        ]

        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
+        """Domestic scan should remain operational using fallback symbols."""
+        mock_broker.fetch_market_rankings.side_effect = [
+            ConnectionError("API unavailable"),
+            ConnectionError("API unavailable"),
+        ]
+        mock_broker.get_daily_prices.return_value = [
+            {"open": 1, "high": 103, "low": 97, "close": 100, "volume": 1000000},
+            {"open": 1, "high": 103, "low": 97, "close": 100, "volume": 800000},
+        ]

        candidates = await scanner.scan(fallback_stocks=["005930", "000660"])

-        # Should not crash
        assert isinstance(candidates, list)
+        assert len(candidates) >= 1

    @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 = [
+        fluctuation_rows = [
            {
                "stock_code": f"00{i}000",
                "name": f"Stock{i}",
@@ -235,62 +188,17 @@ class TestSmartVolatilityScanner:
            }
            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
+        mock_broker.fetch_market_rankings.side_effect = [fluctuation_rows, fluctuation_rows]
+        mock_broker.get_daily_prices.return_value = [
+            {"open": 1, "high": 105, "low": 95, "close": 100, "volume": 1000000},
+            {"open": 1, "high": 105, "low": 95, "close": 100, "volume": 900000},
+        ]

        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
@@ -323,6 +231,124 @@ class TestSmartVolatilityScanner:

        assert codes == ["005930", "035420"]

+    @pytest.mark.asyncio
+    async def test_scan_overseas_uses_dynamic_symbols(
+        self, mock_broker: MagicMock, mock_overseas_broker: MagicMock, mock_settings: Settings
+    ) -> None:
+        """Overseas scan should use provided dynamic universe symbols."""
+        analyzer = VolatilityAnalyzer()
+        scanner = SmartVolatilityScanner(
+            broker=mock_broker,
+            overseas_broker=mock_overseas_broker,
+            volatility_analyzer=analyzer,
+            settings=mock_settings,
+        )
+
+        market = MagicMock()
+        market.name = "NASDAQ"
+        market.code = "US_NASDAQ"
+        market.exchange_code = "NASD"
+        market.is_domestic = False
+
+        mock_overseas_broker.get_overseas_price.side_effect = [
+            {"output": {"last": "210.5", "rate": "1.6", "tvol": "1500000"}},
+            {"output": {"last": "330.1", "rate": "0.2", "tvol": "900000"}},
+        ]
+
+        candidates = await scanner.scan(
+            market=market,
+            fallback_stocks=["AAPL", "MSFT"],
+        )
+
+        assert [c.stock_code for c in candidates] == ["AAPL"]
+        assert candidates[0].signal == "momentum"
+        assert candidates[0].price == 210.5
+
+    @pytest.mark.asyncio
+    async def test_scan_overseas_uses_ranking_api_first(
+        self, mock_broker: MagicMock, mock_overseas_broker: MagicMock, mock_settings: Settings
+    ) -> None:
+        """Overseas scan should prioritize ranking API when available."""
+        analyzer = VolatilityAnalyzer()
+        scanner = SmartVolatilityScanner(
+            broker=mock_broker,
+            overseas_broker=mock_overseas_broker,
+            volatility_analyzer=analyzer,
+            settings=mock_settings,
+        )
+        market = MagicMock()
+        market.name = "NASDAQ"
+        market.code = "US_NASDAQ"
+        market.exchange_code = "NASD"
+        market.is_domestic = False
+
+        mock_overseas_broker.fetch_overseas_rankings.return_value = [
+            {"symb": "NVDA", "last": "780.2", "rate": "2.4", "tvol": "1200000"},
+            {"symb": "MSFT", "last": "420.0", "rate": "0.3", "tvol": "900000"},
+        ]
+
+        candidates = await scanner.scan(market=market, fallback_stocks=["AAPL", "TSLA"])
+
+        assert mock_overseas_broker.fetch_overseas_rankings.call_count >= 1
+        mock_overseas_broker.get_overseas_price.assert_not_called()
+        assert [c.stock_code for c in candidates] == ["NVDA"]
+
+    @pytest.mark.asyncio
+    async def test_scan_overseas_without_symbols_returns_empty(
+        self, mock_broker: MagicMock, mock_overseas_broker: MagicMock, mock_settings: Settings
+    ) -> None:
+        """Overseas scan should return empty list when no symbol universe exists."""
+        analyzer = VolatilityAnalyzer()
+        scanner = SmartVolatilityScanner(
+            broker=mock_broker,
+            overseas_broker=mock_overseas_broker,
+            volatility_analyzer=analyzer,
+            settings=mock_settings,
+        )
+        market = MagicMock()
+        market.name = "NASDAQ"
+        market.code = "US_NASDAQ"
+        market.exchange_code = "NASD"
+        market.is_domestic = False
+
+        candidates = await scanner.scan(market=market, fallback_stocks=[])
+
+        assert candidates == []
+
+    @pytest.mark.asyncio
+    async def test_scan_overseas_picks_high_intraday_range_even_with_low_change(
+        self, mock_broker: MagicMock, mock_overseas_broker: MagicMock, mock_settings: Settings
+    ) -> None:
+        """Volatility selection should consider intraday range, not only change rate."""
+        analyzer = VolatilityAnalyzer()
+        scanner = SmartVolatilityScanner(
+            broker=mock_broker,
+            overseas_broker=mock_overseas_broker,
+            volatility_analyzer=analyzer,
+            settings=mock_settings,
+        )
+        market = MagicMock()
+        market.name = "NASDAQ"
+        market.code = "US_NASDAQ"
+        market.exchange_code = "NASD"
+        market.is_domestic = False
+
+        # change rate is tiny, but high-low range is large (15%).
+        mock_overseas_broker.fetch_overseas_rankings.return_value = [
+            {
+                "symb": "ABCD",
+                "last": "100",
+                "rate": "0.2",
+                "high": "110",
+                "low": "95",
+                "tvol": "800000",
+            }
+        ]
+
+        candidates = await scanner.scan(market=market, fallback_stocks=[])
+
+        assert [c.stock_code for c in candidates] == ["ABCD"]
+

 class TestRSICalculation:
    """Test RSI calculation in VolatilityAnalyzer."""
--- a/tests/test_telegram.py
+++ b/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."""
--- a/tests/test_telegram_commands.py
+++ b/tests/test_telegram_commands.py
@@ -682,6 +682,10 @@ class TestBasicCommands:
                "/help - Show available commands\n"
                "/status - Trading status (mode, markets, P&L)\n"
                "/positions - Current holdings\n"
+                "/report - Daily summary report\n"
+                "/scenarios - Today's playbook scenarios\n"
+                "/review - Recent scorecards\n"
+                "/dashboard - Dashboard URL/status\n"
                "/stop - Pause trading\n"
                "/resume - Resume trading"
            )
@@ -707,10 +711,106 @@ class TestBasicCommands:
            assert "/help" in payload["text"]
            assert "/status" in payload["text"]
            assert "/positions" in payload["text"]
+            assert "/report" in payload["text"]
+            assert "/scenarios" in payload["text"]
+            assert "/review" in payload["text"]
+            assert "/dashboard" in payload["text"]
            assert "/stop" in payload["text"]
            assert "/resume" in payload["text"]


+class TestExtendedCommands:
+    """Test additional bot commands."""
+
+    @pytest.mark.asyncio
+    async def test_report_command(self) -> None:
+        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_report() -> None:
+            await client.send_message("<b>📈 Daily Report</b>\n\nTrades: 1")
+
+        handler.register_command("report", mock_report)
+
+        with patch("aiohttp.ClientSession.post", return_value=mock_resp) as mock_post:
+            await handler._handle_update(
+                {"update_id": 1, "message": {"chat": {"id": 456}, "text": "/report"}}
+            )
+            payload = mock_post.call_args.kwargs["json"]
+            assert "Daily Report" in payload["text"]
+
+    @pytest.mark.asyncio
+    async def test_scenarios_command(self) -> None:
+        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_scenarios() -> None:
+            await client.send_message("<b>🧠 Today's Scenarios</b>\n\n- AAPL: BUY (85)")
+
+        handler.register_command("scenarios", mock_scenarios)
+
+        with patch("aiohttp.ClientSession.post", return_value=mock_resp) as mock_post:
+            await handler._handle_update(
+                {"update_id": 1, "message": {"chat": {"id": 456}, "text": "/scenarios"}}
+            )
+            payload = mock_post.call_args.kwargs["json"]
+            assert "Today's Scenarios" in payload["text"]
+
+    @pytest.mark.asyncio
+    async def test_review_command(self) -> None:
+        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_review() -> None:
+            await client.send_message("<b>📝 Recent Reviews</b>\n\n- 2026-02-14 KR")
+
+        handler.register_command("review", mock_review)
+
+        with patch("aiohttp.ClientSession.post", return_value=mock_resp) as mock_post:
+            await handler._handle_update(
+                {"update_id": 1, "message": {"chat": {"id": 456}, "text": "/review"}}
+            )
+            payload = mock_post.call_args.kwargs["json"]
+            assert "Recent Reviews" in payload["text"]
+
+    @pytest.mark.asyncio
+    async def test_dashboard_command(self) -> None:
+        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_dashboard() -> None:
+            await client.send_message("<b>🖥️ Dashboard</b>\n\nURL: http://127.0.0.1:8080")
+
+        handler.register_command("dashboard", mock_dashboard)
+
+        with patch("aiohttp.ClientSession.post", return_value=mock_resp) as mock_post:
+            await handler._handle_update(
+                {"update_id": 1, "message": {"chat": {"id": 456}, "text": "/dashboard"}}
+            )
+            payload = mock_post.call_args.kwargs["json"]
+            assert "Dashboard" in payload["text"]
+
+
 class TestGetUpdates:
    """Test getUpdates API interaction."""

--- a/tests/test_volatility.py
+++ b/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