docs(web-ai): rewrite CLAUDE.md with Phase 0-4 complete context

Replaces Phase-2-era placeholder. Adds: - V1 (signal_v1 :8000 LSTM bot) vs V2 (signal_v2 :8001 Confidence Pipeline) split - start.bat invocation for each + KIS rate limit warning (do NOT run both) - Phase 0-7 status table, Phase 4 completed 2026-05-17 - signal_v2/ module-level inventory + new test count (56) - Phase 4 buy/sell rule summary (absolute spread amendment included) - 11 known traps + Phase 7 backlog - Cross-repo workflow note (code in web-ai, spec/plan in web-ui)
feat(signal_v2-phase4): add emit/skip logging to signal_generator
2026-05-17 14:00:52 +09:00 · 2026-05-17 13:35:29 +09:00 · 2026-05-17 13:24:47 +09:00 · 2026-05-17 13:18:22 +09:00 · 2026-05-17 13:03:29 +09:00 · 2026-05-17 12:55:15 +09:00
71 changed files with 4352 additions and 1156 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -47,9 +47,11 @@ daily_trade_history.json
 watchlist.json
 bot_ipc.json

-# Test
+# Test (top-level only; signal_v2/tests tracked separately)
 tests/
 tests/*
+!signal_v2/tests/
+!signal_v2/tests/**

 # System
 Thumbs.db
@@ -57,3 +59,9 @@ Desktop.ini

 # stock
 KIS_SETUP.md
+# Claude Code subagent state
+.claude/
+
+# Signal V2 runtime data
+signal_v2/data/*.db
+signal_v2/data/*.db-*
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -1,696 +1,141 @@
-# 🤖 AI Trading Bot — 프로젝트 설계 문서 (CLAUDE.md)
+# web-ai — Workspace 가이드

-> **최종 갱신**: 2026-03-19
-> **런타임**: Windows (Python 3.x, PyTorch CUDA, FastAPI, Ollama)  
-> **하드웨어**: AMD 9800X3D + RTX 5070 Ti (16 GB VRAM)
+Windows AI 머신 (AMD 9800X3D + RTX 5070 Ti 16GB) 의 두 신호 파이프라인.
+**Confidence Signal Pipeline V2 의 Windows-side 구현체** (NAS stock 백엔드와 HTTP 연동).
+
+상위 워크스페이스 컨텍스트는 `../CLAUDE.md` 참조.

 ---

-## 1. 시스템 아키텍처 개요
+## 디렉토리 구조

-```
-┌──────────────────────────────────────────────────────────────────┐
-│                          main_server.py                          │
-│  FastAPI (uvicorn, port 8000)  — 프로세스 매니저 & REST API 서버  │
-│  ┌──────────────┐  ┌─────────────────┐  ┌──────────────────────┐ │
-│  │ Trading Bot  │  │  Telegram Bot   │  │   ProcessWatchdog    │ │
-│  │ (Process #1) │  │  (Process #2)   │  │   (Daemon Thread)    │ │
-│  └──────┬───────┘  └────────┬────────┘  └──────────┬───────────┘ │
-│         │                   │                      │             │
-│         └─── Shared Memory (IPC) ───┘    Health Check / Restart  │
-│              + Command Queue                                     │
-└──────────────────────────────────────────────────────────────────┘
-```
+| 경로 | 역할 | 포트 | 상태 |
+|------|------|------|------|
+| `signal_v1/` | 레거시 자동매매 시스템 (LSTM 7-features + Gemini Flash + Telegram Bot + KIS 자동주문) | `:8000` | 운영 중. **V2 Phase 6 에서 deprecation 예정** |
+| `signal_v2/` | Confidence Signal Pipeline V2 (Chronos-bolt + 분봉 모멘텀 + KIS WebSocket + 신호 생성) | `:8001` | **Phase 4 완료 (2026-05-17)**, Phase 5 대기 |
+| `.env` | V1 + V2 환경변수 공유 | — | `KIS_REAL_*`, `TELEGRAM_*`, `STOCK_API_URL`, `WEBAI_API_KEY`, `LOG_LEVEL` |
+| `start.bat` | V1 진입점 | — | `signal_v1/main_server.py` 실행 |
+| `signal_v2/start.bat` | V2 진입점 | — | `signal_v2/main.py` uvicorn 실행 |
+| `requirements.txt` | 공용 의존성 | — | torch, chronos-forecasting, fastapi, httpx, websockets 등 |

-### 1.1 멀티 프로세스 구성
-
-| 프로세스 | 역할 | 진입점 |
-|---------|------|--------|
-| **Main Server (Uvicorn)** | FastAPI REST API 서버, 프로세스 오케스트레이터 | `main_server.py` |
-| **Trading Bot** | 자동매매 메인 루프 (스케줄러, 분석, 주문) | `modules/bot.py` → `AutoTradingBot.loop()` |
-| **Telegram Bot** | 사용자 인터랙션 (명령어 처리, 알림) | `modules/services/telegram_bot/runner.py` |
-| **ProcessWatchdog** | 자식 프로세스 헬스체크 & 자동 재시작 (30초 간격) | `modules/utils/process_tracker.py` |
-
-### 1.2 프로세스 간 통신 (IPC)
-
-```
-┌─────────────┐    SharedMemory (128KB)     ┌──────────────┐
-│ Trading Bot │ ─── write_status() ───────► │ Telegram Bot │
-│             │ ◄── read_status() ────────  │              │
-│             │                             │              │
-│             │    multiprocessing.Queue     │              │
-│             │ ◄── send_command() ────────  │              │
-│             │     (텔레그램 → 봇 명령)     │              │
-└─────────────┘                             └──────────────┘
-```
-
- **SharedMemory** (`web_ai_bot_ipc`, 128KB): 메인 봇이 상태 데이터(잔고, GPU, 매크로 지표 등)를 JSON으로 기록, 텔레그램 봇이 읽기
- **Command Queue** (`multiprocessing.Queue`): 텔레그램 → 메인 봇 양방향 명령 채널 (`restart`, `evaluate` 등)
- **Lock** (`multiprocessing.Lock`): SharedMemory 동시 접근 보호
- **IPC Staleness**: 600초 (10분 이상 오래된 데이터는 무시)
-
-### 1.3 서버 생명주기 (Lifespan)
-
-```python
-# main_server.py > lifespan()
-1. Config.validate()                     # 환경변수 검증
-2. ProcessTracker.check_and_kill_zombies() # 좀비 프로세스 정리
-3. 전역 객체 초기화 (OllamaManager, KISClient, NewsCollector)
-4. Shared Resources 생성 (Lock, Queue, Event)
-5. Trading Bot 프로세스 생성 & 시작
-6. Telegram Bot 프로세스 생성 & 시작
-7. ProcessWatchdog 시작 (30초 간격 헬스체크)
-8. → yield (서버 정상 운영)
-9. [종료] shutdown_event 설정 → 자식 종료 → SharedMemory 해제
-```
+`.venv` 는 **구조적으로 깨짐**: `pyvenv.cfg` 가 한글 사용자 경로(`C:\Users\박재오\...`) 를 포함하여 콘솔 코드페이지가 roundtrip 못함. 테스트는 시스템 Python 으로 실행: `C:\Users\jaeoh\AppData\Local\Programs\Python\Python312\python.exe -m pytest signal_v2/tests -q`.

 ---

-## 2. 디렉토리 구조
+## 서버 시작 방식

+### V1 단독 (운영 기본)
+```bat
+cd C:\Users\jaeoh\Desktop\workspace\web-ai
+.\start.bat
 ```
-web-ai/
-├── main_server.py              # [Entry Point] FastAPI + 프로세스 매니저
-├── warmup_and_restart.py       # LSTM 사전학습 + 봇 자동 시작 스크립트
-├── watchlist_manager.py        # 뉴스 기반 일일 Watchlist 자동 업데이트
-├── backtester.py               # 전략 백테스팅 CLI
-├── theme_manager.py            # 종목별 테마/섹터 관리
-├── .env                        # 환경변수 (KIS, Telegram, Ollama 등)
-│
-├── modules/
-│   ├── __init__.py
-│   ├── config.py               # [Config] 환경변수 & 상수 정의
-│   ├── bot.py                  # [Core] AutoTradingBot (상태 머신 & 스케줄러)
-│   │
-│   ├── analysis/               # [AI Brain] 분석 엔진
-│   │   ├── deep_learning.py    # Attention-LSTM (7D 피처, PyTorch GPU)
-│   │   ├── technical.py        # 기술적 지표 (RSI, MACD, BB, ADX, OBV...)
-│   │   ├── macro.py            # 거시경제 분석 (KOSPI/KOSDAQ/MSI)
-│   │   ├── ensemble.py         # 적응형 앙상블 (3신호 가중치 자동조정)
-│   │   ├── evaluator.py        # 주간 성과 평가 + LLM 전문가 패널
-│   │   └── backtest.py         # 백테스팅 프레임워크 (Sharpe, MDD 등)
-│   │
-│   ├── strategy/               # [Decision] 매매 의사결정
-│   │   └── process.py          # 워커 프로세스용 분석 함수 (병렬 처리)
-│   │
-│   ├── services/               # [I/O] 외부 서비스 연동
-│   │   ├── kis.py              # 한국투자증권 REST API (동기 + 비동기)
-│   │   ├── ollama.py           # Ollama LLM 인터페이스 (GPU 충돌 방지)
-│   │   ├── news.py             # Google News RSS 크롤링 (동기 + 비동기)
-│   │   ├── telegram.py         # 텔레그램 메시지 발송 (Fire-and-forget)
-│   │   └── telegram_bot/
-│   │       ├── server.py       # 텔레그램 봇 서버 (명령어 핸들러)
-│   │       └── runner.py       # 텔레그램 봇 독립 프로세스 실행기
-│   │
-│   └── utils/                  # [Util] 유틸리티
-│       ├── ipc.py              # SharedMemory + Command Queue IPC
-│       ├── process_tracker.py  # PID 추적 & 좀비 정리 & Watchdog
-│       ├── monitor.py          # CPU/GPU/RAM 서킷 브레이커
-│       └── performance_db.py   # 일별 스냅샷 & 매매 기록 영구 저장
-│
-├── data/                       # [Runtime Data]
-│   ├── watchlist.json          # 현재 감시 종목 리스트
-│   ├── daily_trade_history.json # 일일 매매 기록
-│   ├── kis_token.json          # KIS OAuth 토큰 캐시
-│   ├── peak_prices.json        # 트레일링 스탑용 최고가
-│   ├── ensemble_history.json   # AdaptiveEnsemble 가중치 + 매매 히스토리 (종목별)
-│   ├── models/                 # LSTM 체크포인트 (종목별 .pt 파일)
-│   └── performance/            # 성과 데이터 (daily_snapshots, trade_records)
-│
-└── tests/                      # 테스트
+기대 로그: `[Bot] Cycle Start ...`, `[AI] 005930: NN epochs ...`, `[Ensemble] tech=... news=... lstm=...`, `Score: 0.xx [HOLD]`
+
+### V2 단독 (smoke/검증)
+```bat
+cd C:\Users\jaeoh\Desktop\workspace\web-ai\signal_v2
+.\start.bat
 ```
+기대 로그: `Uvicorn running on http://0.0.0.0:8001`, `poll_loop started`, `[KIS] minute bars ... OK`, `[Chronos] predicted N tickers`, `signal emit XXXXXX buy conf=0.xxx`.
+
+휴장일/장 외 시간엔 `poll_loop` 만 idle. `Application startup complete` 만 보이면 정상.
+
+### V1 + V2 동시 실행 — **권장 안 함**
+**KIS app_key 초당 2회 한도 (EGW00201)** 충돌. V1 cycle + V2 분봉 cron 이 같은 KIS app_key 로 동시 호출하면 rate limit. 채택 해결책: V2 임시 종료 (Phase 3a 결정), Phase 6 V1 deprecation 시 자연 해소. 별도 app_key 발급은 옵션 B.

 ---

-## 3. 핵심 모듈 상세
+## Phase 진행 상태 (Confidence Signal Pipeline V2)

-### 3.1 AutoTradingBot (`modules/bot.py`)
+| Phase | 내용 | 상태 |
+|-------|------|------|
+| 0 | Architecture & contract spec | ✅ Chronos-2 + Qwen3 14B 채택 |
+| 1 | stock 백엔드 WebAI API 보강 (NAS) | ✅ 102/102 tests, 운영 배포 |
+| 1.5 | V1 → `signal_v1/` rename | ✅ V1 정상 기동 |
+| 2 | signal_v2 pull worker + signal API client + scheduler | ✅ 19/19 tests, `:8001` 기동 |
+| 3a | KIS REST 분봉 + WebSocket 호가 + NXT 스케줄 | ✅ 33/33 tests |
+| 3b | Chronos-bolt-base 추론 + 5분봉 모멘텀 분류기 | ✅ 45/45 tests, 실 KIS+Chronos chain 검증 |
+| 4 | Signal Generator (매수/매도 룰) + pull_worker 통합 + 로깅 | ✅ **2026-05-17 완료, 56/56 tests, push 완료** |
+| 5 | agent-office `/signal` + Ollama Qwen3 14B + 이중 텔레그램 | ⏳ 2주 예상 |
+| 6 | signal_v1 deprecation | ⏳ 1주 |
+| 7 | 운영 모니터링 + 4주 IC 검증 | ⏳ 1주 + 4주 |

-**메인 트레이딩 루프** — 장 시작(09:00) ~ 장 마감(15:30) 사이에 자동 실행
-
-```
-[v3.1 주요 기능]
-├── ATR 기반 동적 손절/익절 + 트레일링 스탑
-├── Kelly Criterion 포지션 사이징 (실전 승률·손익비 기반, Half-Kelly)
-├── AdaptiveEnsemble 연동 (매도 후 가중치 자동 학습)
-├── 당일 누적 매수 추적 (_today_buy_total) - KIS T+2 미차감 보완
-├── 사이클당 최대 매수 종목 수 제한 (MAX_BUY_PER_CYCLE)
-├── ProcessPoolExecutor 병렬 분석 (워커 1개, OOM 대응 자동 재시작)
-├── 일별 자산 스냅샷 (09:05~09:15)
-├── 주간 성과 평가 (월요일 아침)
-├── CPU 서킷 브레이커 연동
-└── IPC Command Queue 폴링 (텔레그램 명령 처리)
-```
-
-**잔고 추적 로직 (v3.1 — 과매수 방지)**:
-```
-KIS get_balance() → raw_deposit (dnca_tot_amt)
-                 ↓
-max_daily_buy = raw_deposit × MAX_DAILY_BUY_RATIO (80%)
-tracking_deposit = max_daily_buy - effective_today_buy
-                                   ↑
-                   max(kis_today_buy, self._today_buy_total)
-                   (KIS thdt_buy_amt vs 로컬 누적 중 큰 값)
-```
- `_today_buy_total`: 인스턴스 변수, 사이클 간 유지 (09:00 리셋)
- `_buy_scores`: BUY 시 신호 점수 저장 → SELL 시 `record_trade()` 전달
-
-**run_cycle() 흐름**:
-1. 시스템 헬스 체크 (CPU/GPU/RAM)
-2. 거시경제 분석 (KOSPI/KOSDAQ/MSI)
-3. 위험 상태별 분기 (SAFE/CAUTION/DANGER)
-4. Watchlist 종목 OHLCV 수집 (KIS 비동기 배치)
-5. 잔고 조회 + 당일 누적 매수 차감 → 실제 가용 예수금 계산
-6. `ProcessPoolExecutor`로 종목 병렬 분석 (Kelly Criterion + Ensemble 가중치)
-7. 앙상블 점수 기반 매수/매도 판단 (사이클당 MAX_BUY_PER_CYCLE 제한)
-8. 주문 실행 & 결과 텔레그램 알림
-9. 매도 시 `record_trade()` → Ensemble 가중치 학습
-10. IPC 상태 갱신
-
-### 3.2 AI 분석 파이프라인
-
-```
-                    ┌─────────────────────┐
-                    │   analyze_stock_    │
-                    │   process()         │
-                    │   (strategy/process)│
-                    └─────────┬───────────┘
-                              │
-        ┌─────────────────────┼────────────────────┐
-        ▼                     ▼                    ▼
-┌───────────────┐   ┌────────────────┐   ┌─────────────────┐
-│  Technical    │   │  Deep Learning │   │  LLM (Ollama)   │
-│  Analyzer     │   │  LSTM          │   │  Sentiment      │
-│  (기술적 지표) │   │  (주가 예측)    │   │  (뉴스 감성분석) │
-├───────────────┤   ├────────────────┤   ├─────────────────┤
-│ RSI 25%       │   │ Attention-LSTM │   │ qwen2.5:7b      │
-│ 이격도 15%    │   │ 4L×512H        │   │ JSON 포맷 요청  │
-│ MACD 15%      │   │ 7차원 피처     │   │ 뉴스+지표 통합  │
-│ Stochastic 5% │   │ 60일 시퀀스    │   │ 감성+신뢰도     │
-│ BB 15%        │   │ GPU 가속       │   │                 │
-│ ADX 15%       │   │ 종목별 모델    │   │                 │
-│ MTF 10%       │   │ (ModelRegistry)│   │                 │
-│ OBV ±보너스   │   │                │   │                 │
-└───────┬───────┘   └───────┬────────┘   └───────┬─────────┘
-        │                   │                    │
-        └──────────┬────────┘                    │
-                   ▼                             │
-         ┌─────────────────┐                     │
-         │ AdaptiveEnsemble│ ◄───────────────────┘
-         │ (학습형 가중치)  │
-         ├─────────────────┤
-         │ get_weights()   │ ← 과거 매매 결과 반영
-         │ (ADX+macro+conf)│   크기 가중 정확도 기준
-         │ 경계: 0.10~0.65 │   Water-Filling 정규화
-         │ Kelly Fraction  │ ← 승률·손익비 기반
-         └────────┬────────┘
-                  ▼
-         ┌────────────────┐
-         │  매수/매도/홀드 │
-         │  최종 판단      │
-         └────────────────┘
-```
-
-#### 3.2.1 Deep Learning — Attention-LSTM (`analysis/deep_learning.py`)
-
-| 항목 | 값 |
-|------|-----|
-| **아키텍처** | 4-Layer Stacked LSTM + Attention + FC |
-| **Hidden Size** | 512 |
-| **Input Features** | 7 (close, open, high, low, volume_norm, rsi_14, macd_hist) |
-| **시퀀스 길이** | 60일 |
-| **학습 에포크** | 최대 200 (Early Stopping patience=15) |
-| **빠른 재학습** | 30 에포크 (체크포인트 존재 시) |
-| **쿨다운** | 1200초 (20분, 동일 종목 재학습 방지) |
-| **ModelRegistry** | LRU 방식, 최대 5개 모델 동시 적재 |
-| **체크포인트** | `data/models/{ticker}_v3.pt` |
-| **GPU 관리** | LSTM 학습 시 Ollama 자동 언로드/리로드 |
-
-#### 3.2.2 기술적 분석 (`analysis/technical.py`)
-
-`TechnicalAnalyzer.get_technical_score()` → 0.0 ~ 1.0 통합 점수
-
-| 지표 | 비중 | 설명 |
-|------|------|------|
-| RSI (14일) | 25% | Wilder 방식, 30 이하 과매도/70 이상 과매수 |
-| 이동평균 이격도 | 15% | 20일 MA 대비 현재가 위치 |
-| MACD | 15% | 12/26/9, 히스토그램 방향 |
-| Stochastic | 5% | Fast %K/%D (14/3/3) |
-| Bollinger Bands | 15% | 20일/2σ, %B 위치 + 밴드폭 |
-| ADX | 15% | 추세 강도 (>25 강한 추세) |
-| Multi-Timeframe | 10% | 5일/20일/60일 추세 일관성 |
-| OBV | ±0.1 보너스 | 거래량 기반 매집/분산 감지 |
-
-추가 기능:
- `calculate_atr()` → ATR 기반 동적 손절/익절
- `calculate_dynamic_sl_tp()` → 변동성 적응형 SL/TP
- `calculate_obv()` → 스마트 머니 다이버전스 감지
-
-#### 3.2.3 거시경제 분석 (`analysis/macro.py`)
-
-```python
-MacroAnalyzer.get_macro_status(kis_client) → {
-    "status": "SAFE" | "CAUTION" | "DANGER",
-    "risk_score": int,
-    "indicators": {
-        "KOSPI":   {"price", "change", "high", "low", "prev_close", "volume"},
-        "KOSDAQ":  {"price", "change", ...},
-        "KOSPI200":{"price", "change", ...},
-        "MSI":     float  # Market Stress Index (0~100)
-    }
-}
-```
-
- **SAFE** (risk_score < 1): 정상 매매
- **CAUTION** (1 ≤ risk_score < 3): 매수 규모 축소
- **DANGER** (risk_score ≥ 3): 매수 중단, 보유분만 관리
-
-#### 3.2.4 앙상블 (`analysis/ensemble.py`)
-
-`AdaptiveEnsemble` — 과거 매매 결과 기반 가중치 자동 조정 + Kelly Criterion:
-
-**가중치 학습 흐름**:
-```
-BUY 체결 → bot._buy_scores[ticker] = {tech, sentiment, lstm} 저장
-SELL 체결 → ensemble.record_trade(ticker, ..., outcome_pct=yld)
-          → _update_weights() → EMA(alpha=0.10) 가중치 점진 조정
-          → _save() → data/ensemble_history.json
-워커 프로세스 → reload_if_stale() → 파일 mtime 감지 시 재로드
-```
-
-**주요 메서드**:
- `get_weights(ticker, adx, macro_state, ai_confidence)` → `SignalWeights`
-  - 시장 컨텍스트 (strong_trend/sideways/danger/default) 별 기본 가중치
-  - 종목별 최근 10거래 크기 가중 정확도 반영
-  - ai_confidence >= 0.75 → LSTM 가중치 +25% (confidence 상한 0.80 반영)
- `get_kelly_fraction(ticker, half_kelly=True)` → 0.03~0.25 범위 투자 비중
-  - f* = (p·b - q) / b (p=승률, b=손익비)
-  - 거래 데이터 < 10건 → 보수적 기본값 8%
-  - Half-Kelly 적용으로 변동성 과대추정 보완
- `compute_ensemble_score(tech, sentiment, lstm, investor, weights)` → 통합 점수
- `reload_if_stale()` → 파일 mtime 기반 cross-process 동기화
-
-**`SignalWeights.normalize()` — Water-Filling 알고리즘**:
- 경계(0.10~0.65) 위반 시 해당 값을 경계에 고정, 나머지에 잔여 비중 비례 배분
- 2차 정규화(합=1 보장)와 경계 클램핑이 상충하는 문제 해결
- 영구 저장: `data/ensemble_history.json` (가중치 + 매매 히스토리 통합)
-
-#### 3.2.5 성과 평가 (`analysis/evaluator.py`)
-
-`PerformanceEvaluator.generate_weekly_report()`:
- 핵심 지표: 총수익률, Sharpe Ratio, MDD, 승률, 평균손익비, KOSPI 상관도
- S/A/B/C/D/F 등급 산출
- **5명 전문가 LLM 패널** (Ollama): 각각 다른 관점으로 평가
- HTML 포맷 텔레그램 주간 보고서 자동 생성
+상세 spec/plan: `../web-ui/docs/superpowers/specs/` 및 `../web-ui/docs/superpowers/plans/` (web-ui repo 안에 보관됨 — V2 자체 코드와 분리 보관).

 ---

-## 4. 외부 서비스 연동
+## signal_v2 디렉토리 내부

-### 4.1 한국투자증권 KIS API (`services/kis.py`)
-
-#### 인증
-
-```python
-KISClient.ensure_token()
-# OAuth 2.0 → access_token 발급 → data/kis_token.json에 캐시
-# 토큰 만료 시 자동 갱신 (_request_api에서 처리)
-```
-
-| 설정 | 모의투자 | 실전투자 |
-|------|---------|---------|
-| Base URL | `openapivts.koreainvestment.com:29443` | `openapi.koreainvestment.com:9443` |
-| 환경변수 | `KIS_VIRTUAL_APP_KEY/SECRET/ACCOUNT` | `KIS_REAL_APP_KEY/SECRET/ACCOUNT` |
-| 전환 | `.env` → `KIS_ENV_TYPE=virtual` | `.env` → `KIS_ENV_TYPE=real` |
-
-#### API 스로틀링
-
- 초당 2회 제한 (`_throttle()` — 0.5초 딜레이)
- 토큰 만료 시 자동 갱신 (403 → retry with new token)
-
-#### 주요 API 엔드포인트 매핑
-
-| 기능 | KISClient 메서드 | KIS TR_ID |
-|------|-----------------|-----------|
-| 잔고 조회 | `get_balance()` → `{holdings, total_eval, deposit, today_buy_amt}` | `VTTC8434R` (모의) / `TTTC8434R` (실전) |
-| 주문 (매수/매도) | `order()` | `VTTC0802U` / `VTTC0801U` (모의) |
-| 현재가 조회 | `get_current_price()` | `FHKST01010100` |
-| 일봉 OHLCV | `get_daily_ohlcv()` → `_get_daily_ohlcv_by_range()` | `FHKST03010100` |
-| 일봉 종가 | `get_daily_price()` → `_get_daily_price_by_range()` | `FHKST03010100` |
-| 거래량 순위 | `get_volume_rank()` | `FHPST01710000` |
-| 지수 현재가 | `get_current_index()` | `FHPUP02100000` |
-| 지수 일봉 | `get_daily_index_price()` | `FHKUP03500100` |
-| 투자자 동향 | `get_investor_trend()` | `FHKST01010900` |
-| Hash Key | `get_hash_key()` | - |
-
-#### 비동기 클라이언트 (`KISAsyncClient`)
-
-`aiohttp` 기반 — 다중 종목 동시 수집용:
- `get_daily_price_batch()` — 여러 종목 일봉 병렬 수집
- `get_daily_ohlcv_batch()` — 여러 종목 OHLCV 병렬 수집
- `get_investor_trends_batch()` — 여러 종목 투자자 동향 병렬 수집
-
---
-
-### 4.2 Ollama LLM (`services/ollama.py`)
-
-| 설정 | 값 |
-|------|-----|
-| **모델** | `qwen2.5:7b-instruct-q4_K_M` (VRAM ~4GB) |
-| **API URL** | `http://localhost:11434` |
-| **Context Window** | 4096 토큰 |
-| **Max Output** | 200 토큰 |
-| **Temperature** | 0.1 (결정론적, JSON 안정성) |
-| **Keep Alive** | 5분 (비활성 시 자동 언로드) |
-| **Timeout** | 90초 |
-| **CPU Threads** | 8 (9800X3D 최적화) |
-| **응답 포맷** | JSON (format: "json") |
-
-**GPU 충돌 방지**:
- LSTM 학습 중 → Ollama 추론 최대 60초 대기
- VRAM > 12GB → 모델 즉시 언로드 (`keep_alive=0`)
- LSTM 학습 전 → Ollama 자동 언로드, 학습 후 → 자동 리로드
-
---
-
-### 4.3 뉴스 수집 (`services/news.py`)
-
- **소스**: Google News RSS (`news.google.com/rss/search`)
- **동기**: `NewsCollector.get_market_news()` — 시장 일반 뉴스 5건
- **비동기**: `AsyncNewsCollector`
-  - `get_market_news_async()` — 시장 뉴스 (5분 캐시)
-  - `get_stock_news_async()` — 종목별 뉴스 (5분 캐시)
-
---
-
-## 5. 웹 백엔드 서버 API (FastAPI)
-
-### 5.1 서버 정보
-
-| 항목 | 값 |
-|------|-----|
-| **프레임워크** | FastAPI + Uvicorn |
-| **호스트** | `0.0.0.0:8000` |
-| **NAS 백엔드** | `http://192.168.45.54:18500` (웹 프론트엔드 서버) |
-
-### 5.2 API 엔드포인트
-
-#### `GET /` — 서버 상태
-
-```json
-{
-    "status": "online",
-    "gpu_vram": 4.2,
-    "service": "Windows AI Server (Refactored)"
-}
-```
-
-#### `GET /trade/balance` | `GET /api/trade/balance` — 잔고 조회
-
-KIS API를 통해 현재 계좌 잔고(예수금, 보유종목, 평가금액) 조회.
-
-```json
-{
-    "total_eval": 10500000,
-    "deposit": 5000000,
-    "holdings": [
-        {
-            "ticker": "005930",
-            "name": "삼성전자",
-            "qty": 10,
-            "avg_price": 72000,
-            "current_price": 73500,
-            "profit_rate": 2.08
-        }
-    ]
-}
-```
-
-#### `POST /trade/order` | `POST /api/trade/order` — 수동 주문
-
-```json
-// Request Body
-{
-    "ticker": "005930",
-    "action": "BUY",    // "BUY" | "SELL"
-    "quantity": 10
-}
-
-// Response
-{
-    "status": "executed",
-    "kis_result": { ... }
-}
-```
-
-#### `POST /analyze/portfolio` | `POST /api/analyze/portfolio` — AI 포트폴리오 분석
-
-현재 잔고 + 최신 뉴스를 종합하여 Ollama LLM으로 포트폴리오 분석.
-
-```json
-{
-    "analysis": "... AI 분석 결과 (한국어) ..."
-}
-```
-
-### 5.3 NAS 서버와의 통신 흐름
-
-```
-┌──────────────┐      HTTP Request       ┌────────────────────┐
-│  NAS Backend │ ─────────────────────►  │  Windows AI Server │
-│  (웹 프론트)  │                         │  (FastAPI:8000)    │
-│  :18500      │  ◄────────────────────  │                    │
-│              │      JSON Response      │                    │
-└──────────────┘                         └────────────────────┘
-
-[통신 시나리오]
-1. 웹 → /api/trade/balance    → 잔고 데이터 표시
-2. 웹 → /api/trade/order      → 수동 매수/매도 실행
-3. 웹 → /api/analyze/portfolio → AI 분석 결과 표시
-4. 웹 → /                     → 서버 상태 및 GPU 정보
-```
-
- **NAS 서버** (`192.168.45.54:18500`): 웹 프론트엔드 호스팅, 사용자 인터페이스 제공
- **Windows AI 서버** (`0.0.0.0:8000`): GPU 연산, KIS API 통신, AI 분석 처리
- 내부 네트워크 (LAN) 통신, 외부 노출 없음
-
---
-
-## 6. 텔레그램 봇 설정 & 명령어
-
-### 6.1 환경변수
-
-```env
-TELEGRAM_BOT_TOKEN=8546032918:AAF5GJcP92DrtpSoQdaimMIZe7bz_xtGGPo
-TELEGRAM_CHAT_ID=7388056964
-```
-
-### 6.2 봇 프로세스 아키텍처
-
-```
-runner.py
-  └── run_telegram_bot_standalone()
-        ├── SharedIPC 초기화 (lock, queue, shutdown_event)
-        ├── TelegramBotServer 생성
-        ├── IPC에서 초기 데이터 로드
-        ├── bot_server.run() (python-telegram-bot polling)
-        └── Conflict 감지 시 백오프 재시도 (최대 10회)
-```
-
- **라이브러리**: `python-telegram-bot` (Application, CommandHandler)
- **메시지 포맷**: HTML (`parse_mode="HTML"`)
- **동시 업데이트**: `concurrent_updates=True`
- **로깅**: `telegram_bot.log` (파일 + 콘솔)
-
-### 6.3 명령어 목록
-
-| 명령어 | 설명 | 데이터 소스 |
-|--------|------|------------|
-| `/start` | 봇 시작 & 전체 명령어 안내 | - |
-| `/status` | 봇 상태, 시장 지수, AI 모델 상태 | IPC (SharedMemory) |
-| `/portfolio` | 보유 종목 & 수익률 조회 | IPC → FakeKIS.get_balance() |
-| `/watchlist` | 현재 감시 종목 리스트 | IPC → watchlist 데이터 |
-| `/update_watchlist` | Watchlist 즉시 업데이트 요청 | Command Queue → 메인 봇 |
-| `/macro` | 거시경제 분석 (KOSPI/KOSDAQ/MSI) | IPC → macro_indices |
-| `/system` | CPU/GPU/RAM 시스템 상태 | IPC → gpu_status + psutil |
-| `/ai` | AI 모델 상태 (VRAM, 학습 여부) | IPC → gpu_status |
-| `/restart` | 메인 봇 재시작 명령 | Command Queue |
-| `/stop` | 봇 종료 | shutdown_event.set() |
-| `/exec <cmd>` | 서버 쉘 명령어 직접 실행 | subprocess (10초 타임아웃) |
-| `/evaluate` | 즉시 성과 평가 보고서 생성 | PerformanceEvaluator |
-
-### 6.4 TelegramMessenger (`services/telegram.py`)
-
-단방향 알림 전용 (메인 봇 → 사용자):
- **비동기 전송**: `threading.Thread(daemon=True)` — Fire-and-forget
- **HTML 파싱**: 마크다운 에러 방지
- 매매 실행, 서버 시작/종료, 에러 알림 등에 사용
-
-### 6.5 Conflict 처리
-
-텔레그램 봇 API는 동시에 하나의 polling 인스턴스만 허용:
- `Conflict` 에러 감지 시 지수 백오프 (5s → 10s → ... → 30s)
- 최대 10회 재시도 후 프로세스 종료
- Watchdog가 감지하여 자동 재시작
-
---
-
-## 7. 환경 설정 (`modules/config.py`)
-
-### 7.1 주요 설정 상수
-
-| 그룹 | 키 | 값 | 설명 |
-|------|-----|-----|------|
-| **매매** | `MAX_INVESTMENT_PER_STOCK` | 3,000,000원 | 종목당 최대 투자금 |
-| **매매** | `MAX_BUY_PER_CYCLE` | 2 | 사이클당 최대 매수 종목 수 (env: `MAX_BUY_PER_CYCLE`) |
-| **매매** | `MAX_DAILY_BUY_RATIO` | 0.80 | 예수금 대비 일일 최대 매수 비율 (env: `MAX_DAILY_BUY_RATIO`) |
-| **IPC** | `SHM_NAME` | `web_ai_bot_ipc` | SharedMemory 이름 |
-| **IPC** | `SHM_SIZE` | 131,072 (128KB) | SharedMemory 크기 |
-| **IPC** | `IPC_STALENESS` | 600초 | 데이터 유효 기간 |
-| **GPU** | `VRAM_WARNING_THRESHOLD` | 12.0 GB | VRAM 경고 임계값 |
-| **프로세스** | `WATCHDOG_INTERVAL` | 30초 | 헬스체크 간격 |
-| **프로세스** | `MAX_RESTART_COUNT` | 3 | 최대 자동 재시작 횟수 |
-| **LSTM** | `LSTM_COOLDOWN` | 1,200초 | 동일 종목 재학습 방지 |
-| **LSTM** | `LSTM_FAST_EPOCHS` | 30 | 빠른 재학습 에포크 |
-| **CPU** | `CPU_CIRCUIT_BREAKER_THRESHOLD` | 92% | 서킷 브레이커 임계값 |
-| **CPU** | `CPU_CIRCUIT_BREAKER_CONSECUTIVE` | 2회 | 연속 초과 시 발동 |
-| **Ollama** | `OLLAMA_NUM_CTX` | 4,096 | 컨텍스트 윈도우 |
-| **Ollama** | `OLLAMA_NUM_PREDICT` | 200 | 최대 출력 토큰 |
-| **Ollama** | `OLLAMA_NUM_THREAD` | 8 | CPU 스레드 수 |
-| **Network** | `HTTP_TIMEOUT` | 10초 | 기본 HTTP 요청 타임아웃 |
-
-### 7.2 .env 파일 구조
-
-```env
-# NAS Backend (웹 프론트엔드 서버)
-NAS_API_URL=http://192.168.45.54:18500
-
-# Ollama LLM
-OLLAMA_API_URL=http://localhost:11434
-OLLAMA_MODEL=qwen2.5:7b-instruct-q4_K_M
-
-# KIS API (virtual/real 전환)
-KIS_ENV_TYPE=virtual
-KIS_REAL_APP_KEY=...
-KIS_REAL_APP_SECRET=...
-KIS_REAL_ACCOUNT=XXXXXXXX-XX
-KIS_VIRTUAL_APP_KEY=...
-KIS_VIRTUAL_APP_SECRET=...
-KIS_VIRTUAL_ACCOUNT=XXXXXXXX-XX
-
-# Telegram Bot
-TELEGRAM_BOT_TOKEN=...
-TELEGRAM_CHAT_ID=...
-```
-
---
-
-## 8. 운영 가이드
-
-### 8.1 시작 방법
-
-```bash
-# 일반 시작
-python main_server.py
-
-# LSTM 사전학습 후 자동 시작
-python warmup_and_restart.py
-
-# 텔레그램 봇만 단독 실행 (디버깅용)
-python -m modules.services.telegram_bot.runner
-```
-
-### 8.2 좀비 프로세스 관리
-
- `main_server.py` 실행 시 자동으로 이전 좀비 프로세스 정리
- `pids.txt` 기반 → 메모리 기반 PID 추적으로 전환 완료
- 수동 확인: `Get-Process python` (PowerShell)
-
-### 8.3 로그 파일
-
-| 파일 | 용도 |
+| 파일 | 역할 |
 |------|------|
-| `server.log` | Uvicorn 서버 로그 |
-| `telegram_bot.log` | 텔레그램 봇 로그 |
-| `warmup.log` | LSTM 사전학습 진행 로그 |
-| `bot_output.log` | 트레이딩 봇 출력 로그 |
-
-### 8.4 트러블슈팅
-
-| 증상 | 원인 | 해결 |
-|------|------|------|
-| KIS 403 Forbidden | 토큰 만료 또는 Rate Limit | `data/kis_token.json` 삭제 후 재시작 |
-| Telegram Conflict | 이전 봇 프로세스 미종료 | `main_server.py` 재시작 (자동 정리) |
-| GPU OOM | LSTM + Ollama 동시 적재 | `VRAM_WARNING_THRESHOLD` 낮추기 |
-| CPU 100% 고정 | 좀비 워커 프로세스 | `main_server.py` 재시작 |
-| IPC 데이터 오래됨 | 메인 봇 크래시 | Watchdog 자동 재시작 확인, 수동 재시작 |
-| 예수금 초과 매수 | KIS 모의투자 T+2 미차감 | `MAX_DAILY_BUY_RATIO` / `MAX_BUY_PER_CYCLE` 조정 |
-| Kelly 비중이 너무 낮음 | 거래 기록 부족 (< 10건) | 초기에는 기본값 8% 사용, 거래 누적 후 자동 조정 |
-| 앙상블 가중치 갱신 안 됨 | 매도 체결 없음 또는 `_buy_scores` 누락 | 봇 재시작 전 매도 완료 확인; `data/ensemble_history.json` 확인 |
+| `main.py` | FastAPI app + lifespan (StockClient + KISClient + KISWebSocket + ChronosPredictor + SignalDedup 초기화). poll_loop task 생성 |
+| `config.py` | Settings dataclass — 환경변수 로드. Phase 4 추가 6 필드: `stop_loss_pct`, `take_profit_pct`, `chronos_spread_threshold`, `asking_bid_ratio_threshold`, `confidence_threshold`, `min_momentum_for_buy` |
+| `state.py` | PollState (process-wide singleton) — portfolio, screener_preview, news_sentiment, chronos_predictions, minute_bars, asking_price, **signals** (Phase 4) |
+| `stock_client.py` | NAS stock 백엔드 pull (X-WebAI-Key + 메모리 cache 60s/300s/60s + retry) |
+| `kis_client.py` | KIS REST 분봉/호가 — V1 토큰 read-only 공유 (mtime cache) + 초당 2회 throttle + 지수 backoff |
+| `kis_websocket.py` | KIS WebSocket H0STASP0 호가 + approval_key + 재연결 (1→2→4→max 30s) |
+| `chronos_predictor.py` | `amazon/chronos-bolt-base` zero-shot quantile (FP32 강제 — FP16 overflow 회피) |
+| `minute_momentum.py` | 5분봉 → strong_up/weak_up/neutral/weak_down/strong_down 5단계 분류 |
+| `signal_generator.py` | **Phase 4 — 매수/매도 룰 엔진**. `generate_signals(state, dedup, settings)` 진입. sell-first → buy 순서. 신호 emit/skip INFO/DEBUG 로그 |
+| `pull_worker.py` | asyncio cron — 장전 5분 / 장중 1분 / 장후 5분 / NXT / dead zone skip. cycle 끝에 `generate_signals` 호출 |
+| `scheduler.py` | polling window 판정 (KST 캘린더 + 휴장일) |
+| `rate_limit.py` | 초당 N회 token bucket |
+| `dedup.py` | SignalDedup SQLite WAL — `(ticker, action)` PK 24h |
+| `tests/` | 56 tests (pytest + respx HTTP mock + monkeypatch) |
+| `data/` | dedup.db (SQLite WAL) + `holidays.json` (NAS stock 에서 manual copy) |
+| `start.bat` | V2 진입 |

 ---

-## 9. 데이터 흐름 요약
+## 신호 룰 요약 (Phase 4)

-```
-[시장 개장 전]
-  WatchlistManager → 뉴스 분석 → Watchlist 갱신
+### 매수 (screener Top-N + portfolio, sell 신호 받은 종목은 skip)
+모두 충족:
+1. `chronos.median > 0`
+2. **`chronos.q90 - chronos.q10 < 0.6`** (absolute spread — 2026-05-17 spec amend, 기존 relative formula 가 zero-shot median≈0 빈번에서 모든 신호 거부)
+3. `minute_momentum == strong_up` (env 로 조정 가능)
+4. `asking_price.bid_ratio >= 0.6`

-[장중 사이클 (≈5분 간격)]
-  1. SystemMonitor.check_health() → CPU/GPU 확인
-  2. MacroAnalyzer.get_macro_status() → 시장 상태 판단
-  3. KIS → get_balance() → raw_deposit - today_buy_total = 가용 예수금
-  4. KIS → get_daily_ohlcv_batch() → OHLCV 수집
-  5. ProcessPool → analyze_stock_process() × N종목
-     ├── ensemble.reload_if_stale()    → 파일 mtime 감지 시 가중치 재로드
-     ├── TechnicalAnalyzer             → 기술적 점수
-     ├── PricePredictor                → LSTM 예측
-     ├── OllamaManager                 → LLM 감성 분석
-     ├── AdaptiveEnsemble.get_weights() → 학습된 동적 가중치
-     └── calculate_position_size()     → Kelly Criterion 수량 산출
-  6. 매수 판단 → 예수금 확인 → KIS 주문
-     ├── _buy_scores[ticker] 저장 (앙상블 학습용)
-     ├── _today_buy_total += 매수금액
-     └── buys_this_cycle++ (MAX_BUY_PER_CYCLE 제한)
-  7. 매도 판단 → KIS 주문
-     └── ensemble.record_trade() → 가중치 학습 + ensemble_history.json 저장
-  8. SharedIPC.write_status() → 텔레그램 봇에 공유
-  9. TelegramMessenger → 결과 알림
+종합 confidence = `chronos_conf * 0.5 + minute_score * 0.3 + screener_norm * 0.2`. `> 0.7` 시 emit.

-[장 마감 후]
-  PerformanceDB.save_daily_snapshot() → 일별 자산 기록
-  Evaluator → 주간 보고서 (월요일)
-```
+### 매도 (portfolio only, 우선순위 stop_loss → anomaly → take_profit)
+- **stop_loss**: `pnl_pct < -7%` 즉시 (confidence=1.0)
+- **anomaly**: `chronos.median < -1%` + `strong_down` + `bid_ratio < 0.4` + 종합 conf > 0.7
+- **take_profit**: `pnl_pct > 15%` 검토 (confidence=0.6)

 ---

-## 10. 버전 변경 이력
+## 알려진 함정 / Phase 7 백로그

-### v3.1 (2026-03-19) — 잔고 관리 & 앙상블 학습 완성
+1. **KIS rate limit (EGW00201)** — V1+V2 동시 실행 시 충돌. Phase 6 자연 해소
+2. **`.venv` 한글 경로 깨짐** — 시스템 Python 사용
+3. **Chronos FP16 overflow** — 한국 주가 5만+ 시 inf. FP32 강제 (`chronos_predictor.py:39-41`)
+4. **`predict_quantiles` positional `inputs`** — ChronosBolt API 새 변경. `try/except TypeError` fallback 처리됨
+5. **`state.signals` consumer-drain protocol 미정의** — Phase 5 prereq. dict 무한 누적 위험 (실제로는 bounded by unique ticker count)
+6. **integration test 가 poll_loop 실제 호출 안 함** — `test_pull_worker.py:test_poll_loop_calls_generate_signals_after_cycle` 가 `generate_signals` 직접 호출. Phase 7 hardening 시 mock-iteration 으로 강화
+7. **KIS WebSocket URL `ws://ops.koreainvestment.com:21000/31000`** — 첫 운영 시 실제 KIS API docs 와 대조 필요
+8. **`_parse_asking_price` 필드 인덱스** — 마지막 2 필드 가정. 실 운영 raw 메시지 캡처 후 매핑 검증 필요
+9. **`holidays.json` 자동 동기화 부재** — NAS stock 의 `holidays.json` 을 수동 copy
+10. **schema rename** — Phase 0 §5.2 의 `lstm_pred_*`, `news_top[]` 는 `chronos_pred_*`, `news_reason(string)` 으로 변경됨. Phase 5 prompt 작성 시 반영
+11. **6개 env 필드가 `.env` 에 미기재** — 기본값으로 동작 가능하나 discoverability 위해 `.env.example` 또는 commented block 추가 권장

-**버그 수정**:
- `tracking_deposit` 사이클 간 초기화 문제 → `_today_buy_total` 인스턴스 변수로 누적 추적
- KIS 모의투자 T+2 미차감으로 인한 예수금 초과 매수 방지
- `ai_confidence >= 0.85` 임계값 버그 (LSTM confidence 상한 0.80 미반영) → 0.75로 수정
- OHLCV 피처 누락 시 silent fallback → 경고 로그 출력
+---

-**신규 기능**:
- `MAX_BUY_PER_CYCLE`: 사이클당 최대 매수 종목 수 제한 (기본 2)
- `MAX_DAILY_BUY_RATIO`: 예수금 대비 일일 최대 매수 비율 (기본 80%)
- `kis.get_balance()` → `today_buy_amt` 필드 추가 (`thdt_buy_amt`)
+## 다음 단계 (Phase 5 진입 시 brainstorming 주제)

-**앙상블 (`analysis/ensemble.py`)**:
- `AdaptiveEnsemble`을 `process.py`에 실제 연동 (하드코딩 가중치 제거)
- `get_kelly_fraction()`: Half-Kelly Criterion 포지션 비중 계산 추가
- `SignalWeights.normalize()`: Water-Filling 알고리즘으로 경계 위반 문제 해결
- `_accuracy()` 이진 지표 제거 → `_accuracy_weighted()` (크기 가중) 통일
- `reload_if_stale()`: 파일 mtime 기반 cross-process 동기화
+- `state.signals` consumer 패턴: pop vs leave + Phase 5 자체 dedup
+- agent-office 의 `/signal` endpoint 설계 — POST 페이로드 schema
+- Ollama Qwen3 14B Q4 로컬 호출 — 타임아웃, retry, VRAM 공존 (Chronos + Qwen3 동시 메모리 9.3GB / 15.5GB 가용)
+- 이중 텔레그램 (본인 풀 / 아내 lite) — context augmentation 단일 호출에서 양쪽 메시지 생성
+- LLM 비용: ₩0 목표 유지 (로컬)

-**포지션 사이징 (`strategy/process.py`)**:
- `calculate_position_size()`: 하드코딩 10% → Kelly Criterion (과거 승률·손익비 기반)
- `bot.py` 중복 계산 제거 → 워커의 `suggested_qty` 직접 사용
+---

-**앙상블 학습 루프 (`bot.py`)**:
- BUY 체결 시 `_buy_scores[ticker]` 신호 점수 저장
- SELL 체결 시 `ensemble.record_trade()` → `ensemble_history.json` 갱신
- 워커 프로세스는 `reload_if_stale()`로 자동 반영
+## 양쪽 디렉토리 (web-ui ↔ web-ai) 작업 시 주의
+
+- **코드**: signal_v2 는 web-ai/, spec/plan/메모리는 web-ui/
+- **커밋**: `web-ai` 와 `web-ui` 는 **별도 Gitea 저장소**. 각각 경로에서만 `git add/commit/push`
+- **메모리**: Claude Code 의 auto-memory 는 디렉토리별 격리. 핵심 reference 는 양쪽에 미러됨 (`./memory-mirror/` 또는 `~/.claude/projects/C--Users-jaeoh-Desktop-workspace-web-ai/memory/`)
+- **spec amendment 발생 시**: 코드는 `web-ai` 에 commit, spec 갱신은 `web-ui/docs/superpowers/specs/` 에 commit (Phase 4 spread formula 변경 사례 = web-ui commit `534ded5`)
+
+자세한 V1 가이드는 `signal_v1/CLAUDE.md` 참조 (있다면).
--- a/daily_launcher.py
+++ b/daily_launcher.py
@@ -1,155 +0,0 @@
-"""
-daily_launcher.py — KRX 거래일 자동 런처
-
-[동작 흐름]
-  1. 오늘이 KRX 거래일인지 확인
-  2. 휴장일이면: 텔레그램 알림 후 종료
-  3. 거래일이면: LSTM 워밍업 → main_server.py 시작
-  4. 봇은 15:35에 스스로 EOD 셧다운
-
-[설치: Windows 작업 스케줄러]
-  트리거: 매일 08:30 (주말 포함 — 봇이 내부에서 휴장일 체크)
-  동작:   python C:\\path\\to\\web-ai\\daily_launcher.py
-  시작 위치: C:\\path\\to\\web-ai
-  실행 계정: 현재 사용자 (로그인 여부 무관 실행 권장)
-
-[수동 실행]
-  python daily_launcher.py
-"""
-import sys
-import time
-import subprocess
-import datetime
-import logging
-from pathlib import Path
-from zoneinfo import ZoneInfo
-
-ROOT = Path(__file__).parent
-LOG_FILE = ROOT / "daily_launcher.log"
-KST = ZoneInfo("Asia/Seoul")
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s [Launcher] %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-    handlers=[
-        logging.FileHandler(LOG_FILE, encoding="utf-8"),
-        logging.StreamHandler(sys.stdout),
-    ],
-)
-log = logging.getLogger("daily_launcher")
-
-
-def setup_path():
-    if str(ROOT) not in sys.path:
-        sys.path.insert(0, str(ROOT))
-
-
-def send_notify(msg: str):
-    """텔레그램 알림 발송 (실패해도 런처 계속 진행)"""
-    try:
-        from modules.services.telegram import TelegramMessenger
-        TelegramMessenger().send_message(msg)
-    except Exception as e:
-        log.warning(f"텔레그램 알림 실패: {e}")
-
-
-def clear_eod_marker():
-    """전일 EOD 마커 파일 삭제 (새 거래일 시작)"""
-    eod_file = ROOT / "data" / ".eod_date"
-    if not eod_file.exists():
-        return
-    try:
-        prev = datetime.date.fromisoformat(eod_file.read_text().strip())
-        today = datetime.datetime.now(KST).date()
-        if prev < today:
-            eod_file.unlink()
-            log.info(f"전일({prev}) EOD 마커 삭제 완료")
-    except Exception:
-        eod_file.unlink(missing_ok=True)
-
-
-def wait_until_warmup_time(cal) -> None:
-    """
-    워밍업 시작 시각까지 대기
-    - 장 시작 30분 전이면 즉시 워밍업
-    - 그보다 일찍 실행되면 '장 시작 30분 전'까지 대기
-    """
-    secs = cal.seconds_to_open()
-    if secs <= 0:
-        log.info("이미 장 중 — 즉시 워밍업 시작")
-        return
-
-    warmup_start_secs = max(0, secs - 30 * 60)   # 장 시작 30분 전
-    if warmup_start_secs > 0:
-        warmup_at = datetime.datetime.now(KST) + datetime.timedelta(seconds=warmup_start_secs)
-        log.info(f"워밍업 대기 중 ({warmup_start_secs/60:.0f}분 후 {warmup_at.strftime('%H:%M')} 시작)")
-        time.sleep(warmup_start_secs)
-    else:
-        log.info(f"장 시작 {secs/60:.0f}분 전 — 즉시 워밍업")
-
-
-def run_warmup_and_server() -> int:
-    """
-    warmup_and_restart.py 실행
-    - warmup: LSTM 사전학습
-    - 이후 main_server.py를 새 콘솔에서 자동 시작
-    """
-    log.info("LSTM 워밍업 시작...")
-    result = subprocess.run(
-        [sys.executable, "warmup_and_restart.py"],
-        cwd=str(ROOT),
-    )
-    return result.returncode
-
-
-def main():
-    setup_path()
-
-    from modules.utils.market_calendar import KRXCalendar
-    cal = KRXCalendar()
-    today = datetime.datetime.now(KST).date()
-    log.info(f"실행 날짜: {today} | 시장 상태: {cal.status_summary()}")
-
-    # ── 1. 휴장일 체크 ────────────────────────────────────────────────────────
-    if not cal.is_trading_day(today):
-        try:
-            nxt = cal.next_trading_open()
-            next_str = nxt.strftime("%m/%d(%a) %H:%M")
-        except Exception:
-            next_str = "미정"
-
-        msg = (
-            f"[자동매매] {today.strftime('%m/%d(%a)')} 휴장일\n"
-            f"다음 거래일: {next_str} KST 자동 시작"
-        )
-        log.info(f"휴장일 — 봇 시작 안 함 (다음: {next_str})")
-        send_notify(msg)
-        return
-
-    # ── 2. EOD 마커 초기화 ────────────────────────────────────────────────────
-    clear_eod_marker()
-
-    # ── 3. 워밍업 시각까지 대기 ───────────────────────────────────────────────
-    wait_until_warmup_time(cal)
-
-    # ── 4. 거래일 시작 알림 ───────────────────────────────────────────────────
-    log.info(f"거래일 확인 — 워밍업 및 봇 시작 ({datetime.datetime.now(KST).strftime('%H:%M')})")
-    send_notify(
-        f"[자동매매] {today.strftime('%m/%d(%a)')} 거래일 시작\n"
-        f"LSTM 워밍업 중..."
-    )
-
-    # ── 5. 워밍업 + 서버 시작 ─────────────────────────────────────────────────
-    rc = run_warmup_and_server()
-    if rc != 0:
-        log.error(f"워밍업 실패 (exit={rc}) — 수동 확인 필요")
-        send_notify(f"[Bot] 워밍업 실패! (exit={rc})\n수동으로 확인해 주세요.")
-        return
-
-    log.info("워밍업 완료. main_server.py가 백그라운드에서 실행 중.")
-    log.info("봇은 15:35에 스스로 EOD 셧다운합니다.")
-
-
-if __name__ == "__main__":
-    main()
--- a/requirements.txt
+++ b/requirements.txt
@@ -0,0 +1,13 @@
+# Signal V2 dependencies (added 2026-05-16, Phase 2)
+httpx>=0.27
+fastapi>=0.110
+uvicorn>=0.27
+python-dotenv>=1.0
+pytest>=8.0
+pytest-asyncio>=0.23
+respx>=0.21
+websockets>=12
+# Phase 3b dependencies (Chronos-2 + ML)
+transformers>=4.40
+chronos-forecasting>=1.4
+# torch: typically already installed via V1 venv; if not, install with CUDA support manually
--- a/signal_v1/CLAUDE.md
+++ b/signal_v1/CLAUDE.md
@@ -0,0 +1,696 @@
+# 🤖 AI Trading Bot — 프로젝트 설계 문서 (CLAUDE.md)
+
+> **최종 갱신**: 2026-03-19
+> **런타임**: Windows (Python 3.x, PyTorch CUDA, FastAPI, Ollama)  
+> **하드웨어**: AMD 9800X3D + RTX 5070 Ti (16 GB VRAM)
+
+---
+
+## 1. 시스템 아키텍처 개요
+
+```
+┌──────────────────────────────────────────────────────────────────┐
+│                          main_server.py                          │
+│  FastAPI (uvicorn, port 8000)  — 프로세스 매니저 & REST API 서버  │
+│  ┌──────────────┐  ┌─────────────────┐  ┌──────────────────────┐ │
+│  │ Trading Bot  │  │  Telegram Bot   │  │   ProcessWatchdog    │ │
+│  │ (Process #1) │  │  (Process #2)   │  │   (Daemon Thread)    │ │
+│  └──────┬───────┘  └────────┬────────┘  └──────────┬───────────┘ │
+│         │                   │                      │             │
+│         └─── Shared Memory (IPC) ───┘    Health Check / Restart  │
+│              + Command Queue                                     │
+└──────────────────────────────────────────────────────────────────┘
+```
+
+### 1.1 멀티 프로세스 구성
+
+| 프로세스 | 역할 | 진입점 |
+|---------|------|--------|
+| **Main Server (Uvicorn)** | FastAPI REST API 서버, 프로세스 오케스트레이터 | `main_server.py` |
+| **Trading Bot** | 자동매매 메인 루프 (스케줄러, 분석, 주문) | `modules/bot.py` → `AutoTradingBot.loop()` |
+| **Telegram Bot** | 사용자 인터랙션 (명령어 처리, 알림) | `modules/services/telegram_bot/runner.py` |
+| **ProcessWatchdog** | 자식 프로세스 헬스체크 & 자동 재시작 (30초 간격) | `modules/utils/process_tracker.py` |
+
+### 1.2 프로세스 간 통신 (IPC)
+
+```
+┌─────────────┐    SharedMemory (128KB)     ┌──────────────┐
+│ Trading Bot │ ─── write_status() ───────► │ Telegram Bot │
+│             │ ◄── read_status() ────────  │              │
+│             │                             │              │
+│             │    multiprocessing.Queue     │              │
+│             │ ◄── send_command() ────────  │              │
+│             │     (텔레그램 → 봇 명령)     │              │
+└─────────────┘                             └──────────────┘
+```
+
+- **SharedMemory** (`web_ai_bot_ipc`, 128KB): 메인 봇이 상태 데이터(잔고, GPU, 매크로 지표 등)를 JSON으로 기록, 텔레그램 봇이 읽기
+- **Command Queue** (`multiprocessing.Queue`): 텔레그램 → 메인 봇 양방향 명령 채널 (`restart`, `evaluate` 등)
+- **Lock** (`multiprocessing.Lock`): SharedMemory 동시 접근 보호
+- **IPC Staleness**: 600초 (10분 이상 오래된 데이터는 무시)
+
+### 1.3 서버 생명주기 (Lifespan)
+
+```python
+# main_server.py > lifespan()
+1. Config.validate()                     # 환경변수 검증
+2. ProcessTracker.check_and_kill_zombies() # 좀비 프로세스 정리
+3. 전역 객체 초기화 (OllamaManager, KISClient, NewsCollector)
+4. Shared Resources 생성 (Lock, Queue, Event)
+5. Trading Bot 프로세스 생성 & 시작
+6. Telegram Bot 프로세스 생성 & 시작
+7. ProcessWatchdog 시작 (30초 간격 헬스체크)
+8. → yield (서버 정상 운영)
+9. [종료] shutdown_event 설정 → 자식 종료 → SharedMemory 해제
+```
+
+---
+
+## 2. 디렉토리 구조
+
+```
+web-ai/
+├── main_server.py              # [Entry Point] FastAPI + 프로세스 매니저
+├── warmup_and_restart.py       # LSTM 사전학습 + 봇 자동 시작 스크립트
+├── watchlist_manager.py        # 뉴스 기반 일일 Watchlist 자동 업데이트
+├── backtester.py               # 전략 백테스팅 CLI
+├── theme_manager.py            # 종목별 테마/섹터 관리
+├── .env                        # 환경변수 (KIS, Telegram, Ollama 등)
+│
+├── modules/
+│   ├── __init__.py
+│   ├── config.py               # [Config] 환경변수 & 상수 정의
+│   ├── bot.py                  # [Core] AutoTradingBot (상태 머신 & 스케줄러)
+│   │
+│   ├── analysis/               # [AI Brain] 분석 엔진
+│   │   ├── deep_learning.py    # Attention-LSTM (7D 피처, PyTorch GPU)
+│   │   ├── technical.py        # 기술적 지표 (RSI, MACD, BB, ADX, OBV...)
+│   │   ├── macro.py            # 거시경제 분석 (KOSPI/KOSDAQ/MSI)
+│   │   ├── ensemble.py         # 적응형 앙상블 (3신호 가중치 자동조정)
+│   │   ├── evaluator.py        # 주간 성과 평가 + LLM 전문가 패널
+│   │   └── backtest.py         # 백테스팅 프레임워크 (Sharpe, MDD 등)
+│   │
+│   ├── strategy/               # [Decision] 매매 의사결정
+│   │   └── process.py          # 워커 프로세스용 분석 함수 (병렬 처리)
+│   │
+│   ├── services/               # [I/O] 외부 서비스 연동
+│   │   ├── kis.py              # 한국투자증권 REST API (동기 + 비동기)
+│   │   ├── ollama.py           # Ollama LLM 인터페이스 (GPU 충돌 방지)
+│   │   ├── news.py             # Google News RSS 크롤링 (동기 + 비동기)
+│   │   ├── telegram.py         # 텔레그램 메시지 발송 (Fire-and-forget)
+│   │   └── telegram_bot/
+│   │       ├── server.py       # 텔레그램 봇 서버 (명령어 핸들러)
+│   │       └── runner.py       # 텔레그램 봇 독립 프로세스 실행기
+│   │
+│   └── utils/                  # [Util] 유틸리티
+│       ├── ipc.py              # SharedMemory + Command Queue IPC
+│       ├── process_tracker.py  # PID 추적 & 좀비 정리 & Watchdog
+│       ├── monitor.py          # CPU/GPU/RAM 서킷 브레이커
+│       └── performance_db.py   # 일별 스냅샷 & 매매 기록 영구 저장
+│
+├── data/                       # [Runtime Data]
+│   ├── watchlist.json          # 현재 감시 종목 리스트
+│   ├── daily_trade_history.json # 일일 매매 기록
+│   ├── kis_token.json          # KIS OAuth 토큰 캐시
+│   ├── peak_prices.json        # 트레일링 스탑용 최고가
+│   ├── ensemble_history.json   # AdaptiveEnsemble 가중치 + 매매 히스토리 (종목별)
+│   ├── models/                 # LSTM 체크포인트 (종목별 .pt 파일)
+│   └── performance/            # 성과 데이터 (daily_snapshots, trade_records)
+│
+└── tests/                      # 테스트
+```
+
+---
+
+## 3. 핵심 모듈 상세
+
+### 3.1 AutoTradingBot (`modules/bot.py`)
+
+**메인 트레이딩 루프** — 장 시작(09:00) ~ 장 마감(15:30) 사이에 자동 실행
+
+```
+[v3.1 주요 기능]
+├── ATR 기반 동적 손절/익절 + 트레일링 스탑
+├── Kelly Criterion 포지션 사이징 (실전 승률·손익비 기반, Half-Kelly)
+├── AdaptiveEnsemble 연동 (매도 후 가중치 자동 학습)
+├── 당일 누적 매수 추적 (_today_buy_total) - KIS T+2 미차감 보완
+├── 사이클당 최대 매수 종목 수 제한 (MAX_BUY_PER_CYCLE)
+├── ProcessPoolExecutor 병렬 분석 (워커 1개, OOM 대응 자동 재시작)
+├── 일별 자산 스냅샷 (09:05~09:15)
+├── 주간 성과 평가 (월요일 아침)
+├── CPU 서킷 브레이커 연동
+└── IPC Command Queue 폴링 (텔레그램 명령 처리)
+```
+
+**잔고 추적 로직 (v3.1 — 과매수 방지)**:
+```
+KIS get_balance() → raw_deposit (dnca_tot_amt)
+                 ↓
+max_daily_buy = raw_deposit × MAX_DAILY_BUY_RATIO (80%)
+tracking_deposit = max_daily_buy - effective_today_buy
+                                   ↑
+                   max(kis_today_buy, self._today_buy_total)
+                   (KIS thdt_buy_amt vs 로컬 누적 중 큰 값)
+```
+- `_today_buy_total`: 인스턴스 변수, 사이클 간 유지 (09:00 리셋)
+- `_buy_scores`: BUY 시 신호 점수 저장 → SELL 시 `record_trade()` 전달
+
+**run_cycle() 흐름**:
+1. 시스템 헬스 체크 (CPU/GPU/RAM)
+2. 거시경제 분석 (KOSPI/KOSDAQ/MSI)
+3. 위험 상태별 분기 (SAFE/CAUTION/DANGER)
+4. Watchlist 종목 OHLCV 수집 (KIS 비동기 배치)
+5. 잔고 조회 + 당일 누적 매수 차감 → 실제 가용 예수금 계산
+6. `ProcessPoolExecutor`로 종목 병렬 분석 (Kelly Criterion + Ensemble 가중치)
+7. 앙상블 점수 기반 매수/매도 판단 (사이클당 MAX_BUY_PER_CYCLE 제한)
+8. 주문 실행 & 결과 텔레그램 알림
+9. 매도 시 `record_trade()` → Ensemble 가중치 학습
+10. IPC 상태 갱신
+
+### 3.2 AI 분석 파이프라인
+
+```
+                    ┌─────────────────────┐
+                    │   analyze_stock_    │
+                    │   process()         │
+                    │   (strategy/process)│
+                    └─────────┬───────────┘
+                              │
+        ┌─────────────────────┼────────────────────┐
+        ▼                     ▼                    ▼
+┌───────────────┐   ┌────────────────┐   ┌─────────────────┐
+│  Technical    │   │  Deep Learning │   │  LLM (Ollama)   │
+│  Analyzer     │   │  LSTM          │   │  Sentiment      │
+│  (기술적 지표) │   │  (주가 예측)    │   │  (뉴스 감성분석) │
+├───────────────┤   ├────────────────┤   ├─────────────────┤
+│ RSI 25%       │   │ Attention-LSTM │   │ qwen2.5:7b      │
+│ 이격도 15%    │   │ 4L×512H        │   │ JSON 포맷 요청  │
+│ MACD 15%      │   │ 7차원 피처     │   │ 뉴스+지표 통합  │
+│ Stochastic 5% │   │ 60일 시퀀스    │   │ 감성+신뢰도     │
+│ BB 15%        │   │ GPU 가속       │   │                 │
+│ ADX 15%       │   │ 종목별 모델    │   │                 │
+│ MTF 10%       │   │ (ModelRegistry)│   │                 │
+│ OBV ±보너스   │   │                │   │                 │
+└───────┬───────┘   └───────┬────────┘   └───────┬─────────┘
+        │                   │                    │
+        └──────────┬────────┘                    │
+                   ▼                             │
+         ┌─────────────────┐                     │
+         │ AdaptiveEnsemble│ ◄───────────────────┘
+         │ (학습형 가중치)  │
+         ├─────────────────┤
+         │ get_weights()   │ ← 과거 매매 결과 반영
+         │ (ADX+macro+conf)│   크기 가중 정확도 기준
+         │ 경계: 0.10~0.65 │   Water-Filling 정규화
+         │ Kelly Fraction  │ ← 승률·손익비 기반
+         └────────┬────────┘
+                  ▼
+         ┌────────────────┐
+         │  매수/매도/홀드 │
+         │  최종 판단      │
+         └────────────────┘
+```
+
+#### 3.2.1 Deep Learning — Attention-LSTM (`analysis/deep_learning.py`)
+
+| 항목 | 값 |
+|------|-----|
+| **아키텍처** | 4-Layer Stacked LSTM + Attention + FC |
+| **Hidden Size** | 512 |
+| **Input Features** | 7 (close, open, high, low, volume_norm, rsi_14, macd_hist) |
+| **시퀀스 길이** | 60일 |
+| **학습 에포크** | 최대 200 (Early Stopping patience=15) |
+| **빠른 재학습** | 30 에포크 (체크포인트 존재 시) |
+| **쿨다운** | 1200초 (20분, 동일 종목 재학습 방지) |
+| **ModelRegistry** | LRU 방식, 최대 5개 모델 동시 적재 |
+| **체크포인트** | `data/models/{ticker}_v3.pt` |
+| **GPU 관리** | LSTM 학습 시 Ollama 자동 언로드/리로드 |
+
+#### 3.2.2 기술적 분석 (`analysis/technical.py`)
+
+`TechnicalAnalyzer.get_technical_score()` → 0.0 ~ 1.0 통합 점수
+
+| 지표 | 비중 | 설명 |
+|------|------|------|
+| RSI (14일) | 25% | Wilder 방식, 30 이하 과매도/70 이상 과매수 |
+| 이동평균 이격도 | 15% | 20일 MA 대비 현재가 위치 |
+| MACD | 15% | 12/26/9, 히스토그램 방향 |
+| Stochastic | 5% | Fast %K/%D (14/3/3) |
+| Bollinger Bands | 15% | 20일/2σ, %B 위치 + 밴드폭 |
+| ADX | 15% | 추세 강도 (>25 강한 추세) |
+| Multi-Timeframe | 10% | 5일/20일/60일 추세 일관성 |
+| OBV | ±0.1 보너스 | 거래량 기반 매집/분산 감지 |
+
+추가 기능:
+- `calculate_atr()` → ATR 기반 동적 손절/익절
+- `calculate_dynamic_sl_tp()` → 변동성 적응형 SL/TP
+- `calculate_obv()` → 스마트 머니 다이버전스 감지
+
+#### 3.2.3 거시경제 분석 (`analysis/macro.py`)
+
+```python
+MacroAnalyzer.get_macro_status(kis_client) → {
+    "status": "SAFE" | "CAUTION" | "DANGER",
+    "risk_score": int,
+    "indicators": {
+        "KOSPI":   {"price", "change", "high", "low", "prev_close", "volume"},
+        "KOSDAQ":  {"price", "change", ...},
+        "KOSPI200":{"price", "change", ...},
+        "MSI":     float  # Market Stress Index (0~100)
+    }
+}
+```
+
+- **SAFE** (risk_score < 1): 정상 매매
+- **CAUTION** (1 ≤ risk_score < 3): 매수 규모 축소
+- **DANGER** (risk_score ≥ 3): 매수 중단, 보유분만 관리
+
+#### 3.2.4 앙상블 (`analysis/ensemble.py`)
+
+`AdaptiveEnsemble` — 과거 매매 결과 기반 가중치 자동 조정 + Kelly Criterion:
+
+**가중치 학습 흐름**:
+```
+BUY 체결 → bot._buy_scores[ticker] = {tech, sentiment, lstm} 저장
+SELL 체결 → ensemble.record_trade(ticker, ..., outcome_pct=yld)
+          → _update_weights() → EMA(alpha=0.10) 가중치 점진 조정
+          → _save() → data/ensemble_history.json
+워커 프로세스 → reload_if_stale() → 파일 mtime 감지 시 재로드
+```
+
+**주요 메서드**:
+- `get_weights(ticker, adx, macro_state, ai_confidence)` → `SignalWeights`
+  - 시장 컨텍스트 (strong_trend/sideways/danger/default) 별 기본 가중치
+  - 종목별 최근 10거래 크기 가중 정확도 반영
+  - ai_confidence >= 0.75 → LSTM 가중치 +25% (confidence 상한 0.80 반영)
+- `get_kelly_fraction(ticker, half_kelly=True)` → 0.03~0.25 범위 투자 비중
+  - f* = (p·b - q) / b (p=승률, b=손익비)
+  - 거래 데이터 < 10건 → 보수적 기본값 8%
+  - Half-Kelly 적용으로 변동성 과대추정 보완
+- `compute_ensemble_score(tech, sentiment, lstm, investor, weights)` → 통합 점수
+- `reload_if_stale()` → 파일 mtime 기반 cross-process 동기화
+
+**`SignalWeights.normalize()` — Water-Filling 알고리즘**:
+- 경계(0.10~0.65) 위반 시 해당 값을 경계에 고정, 나머지에 잔여 비중 비례 배분
+- 2차 정규화(합=1 보장)와 경계 클램핑이 상충하는 문제 해결
+- 영구 저장: `data/ensemble_history.json` (가중치 + 매매 히스토리 통합)
+
+#### 3.2.5 성과 평가 (`analysis/evaluator.py`)
+
+`PerformanceEvaluator.generate_weekly_report()`:
+- 핵심 지표: 총수익률, Sharpe Ratio, MDD, 승률, 평균손익비, KOSPI 상관도
+- S/A/B/C/D/F 등급 산출
+- **5명 전문가 LLM 패널** (Ollama): 각각 다른 관점으로 평가
+- HTML 포맷 텔레그램 주간 보고서 자동 생성
+
+---
+
+## 4. 외부 서비스 연동
+
+### 4.1 한국투자증권 KIS API (`services/kis.py`)
+
+#### 인증
+
+```python
+KISClient.ensure_token()
+# OAuth 2.0 → access_token 발급 → data/kis_token.json에 캐시
+# 토큰 만료 시 자동 갱신 (_request_api에서 처리)
+```
+
+| 설정 | 모의투자 | 실전투자 |
+|------|---------|---------|
+| Base URL | `openapivts.koreainvestment.com:29443` | `openapi.koreainvestment.com:9443` |
+| 환경변수 | `KIS_VIRTUAL_APP_KEY/SECRET/ACCOUNT` | `KIS_REAL_APP_KEY/SECRET/ACCOUNT` |
+| 전환 | `.env` → `KIS_ENV_TYPE=virtual` | `.env` → `KIS_ENV_TYPE=real` |
+
+#### API 스로틀링
+
+- 초당 2회 제한 (`_throttle()` — 0.5초 딜레이)
+- 토큰 만료 시 자동 갱신 (403 → retry with new token)
+
+#### 주요 API 엔드포인트 매핑
+
+| 기능 | KISClient 메서드 | KIS TR_ID |
+|------|-----------------|-----------|
+| 잔고 조회 | `get_balance()` → `{holdings, total_eval, deposit, today_buy_amt}` | `VTTC8434R` (모의) / `TTTC8434R` (실전) |
+| 주문 (매수/매도) | `order()` | `VTTC0802U` / `VTTC0801U` (모의) |
+| 현재가 조회 | `get_current_price()` | `FHKST01010100` |
+| 일봉 OHLCV | `get_daily_ohlcv()` → `_get_daily_ohlcv_by_range()` | `FHKST03010100` |
+| 일봉 종가 | `get_daily_price()` → `_get_daily_price_by_range()` | `FHKST03010100` |
+| 거래량 순위 | `get_volume_rank()` | `FHPST01710000` |
+| 지수 현재가 | `get_current_index()` | `FHPUP02100000` |
+| 지수 일봉 | `get_daily_index_price()` | `FHKUP03500100` |
+| 투자자 동향 | `get_investor_trend()` | `FHKST01010900` |
+| Hash Key | `get_hash_key()` | - |
+
+#### 비동기 클라이언트 (`KISAsyncClient`)
+
+`aiohttp` 기반 — 다중 종목 동시 수집용:
+- `get_daily_price_batch()` — 여러 종목 일봉 병렬 수집
+- `get_daily_ohlcv_batch()` — 여러 종목 OHLCV 병렬 수집
+- `get_investor_trends_batch()` — 여러 종목 투자자 동향 병렬 수집
+
+---
+
+### 4.2 Ollama LLM (`services/ollama.py`)
+
+| 설정 | 값 |
+|------|-----|
+| **모델** | `qwen2.5:7b-instruct-q4_K_M` (VRAM ~4GB) |
+| **API URL** | `http://localhost:11434` |
+| **Context Window** | 4096 토큰 |
+| **Max Output** | 200 토큰 |
+| **Temperature** | 0.1 (결정론적, JSON 안정성) |
+| **Keep Alive** | 5분 (비활성 시 자동 언로드) |
+| **Timeout** | 90초 |
+| **CPU Threads** | 8 (9800X3D 최적화) |
+| **응답 포맷** | JSON (format: "json") |
+
+**GPU 충돌 방지**:
+- LSTM 학습 중 → Ollama 추론 최대 60초 대기
+- VRAM > 12GB → 모델 즉시 언로드 (`keep_alive=0`)
+- LSTM 학습 전 → Ollama 자동 언로드, 학습 후 → 자동 리로드
+
+---
+
+### 4.3 뉴스 수집 (`services/news.py`)
+
+- **소스**: Google News RSS (`news.google.com/rss/search`)
+- **동기**: `NewsCollector.get_market_news()` — 시장 일반 뉴스 5건
+- **비동기**: `AsyncNewsCollector`
+  - `get_market_news_async()` — 시장 뉴스 (5분 캐시)
+  - `get_stock_news_async()` — 종목별 뉴스 (5분 캐시)
+
+---
+
+## 5. 웹 백엔드 서버 API (FastAPI)
+
+### 5.1 서버 정보
+
+| 항목 | 값 |
+|------|-----|
+| **프레임워크** | FastAPI + Uvicorn |
+| **호스트** | `0.0.0.0:8000` |
+| **NAS 백엔드** | `http://192.168.45.54:18500` (웹 프론트엔드 서버) |
+
+### 5.2 API 엔드포인트
+
+#### `GET /` — 서버 상태
+
+```json
+{
+    "status": "online",
+    "gpu_vram": 4.2,
+    "service": "Windows AI Server (Refactored)"
+}
+```
+
+#### `GET /trade/balance` | `GET /api/trade/balance` — 잔고 조회
+
+KIS API를 통해 현재 계좌 잔고(예수금, 보유종목, 평가금액) 조회.
+
+```json
+{
+    "total_eval": 10500000,
+    "deposit": 5000000,
+    "holdings": [
+        {
+            "ticker": "005930",
+            "name": "삼성전자",
+            "qty": 10,
+            "avg_price": 72000,
+            "current_price": 73500,
+            "profit_rate": 2.08
+        }
+    ]
+}
+```
+
+#### `POST /trade/order` | `POST /api/trade/order` — 수동 주문
+
+```json
+// Request Body
+{
+    "ticker": "005930",
+    "action": "BUY",    // "BUY" | "SELL"
+    "quantity": 10
+}
+
+// Response
+{
+    "status": "executed",
+    "kis_result": { ... }
+}
+```
+
+#### `POST /analyze/portfolio` | `POST /api/analyze/portfolio` — AI 포트폴리오 분석
+
+현재 잔고 + 최신 뉴스를 종합하여 Ollama LLM으로 포트폴리오 분석.
+
+```json
+{
+    "analysis": "... AI 분석 결과 (한국어) ..."
+}
+```
+
+### 5.3 NAS 서버와의 통신 흐름
+
+```
+┌──────────────┐      HTTP Request       ┌────────────────────┐
+│  NAS Backend │ ─────────────────────►  │  Windows AI Server │
+│  (웹 프론트)  │                         │  (FastAPI:8000)    │
+│  :18500      │  ◄────────────────────  │                    │
+│              │      JSON Response      │                    │
+└──────────────┘                         └────────────────────┘
+
+[통신 시나리오]
+1. 웹 → /api/trade/balance    → 잔고 데이터 표시
+2. 웹 → /api/trade/order      → 수동 매수/매도 실행
+3. 웹 → /api/analyze/portfolio → AI 분석 결과 표시
+4. 웹 → /                     → 서버 상태 및 GPU 정보
+```
+
+- **NAS 서버** (`192.168.45.54:18500`): 웹 프론트엔드 호스팅, 사용자 인터페이스 제공
+- **Windows AI 서버** (`0.0.0.0:8000`): GPU 연산, KIS API 통신, AI 분석 처리
+- 내부 네트워크 (LAN) 통신, 외부 노출 없음
+
+---
+
+## 6. 텔레그램 봇 설정 & 명령어
+
+### 6.1 환경변수
+
+```env
+TELEGRAM_BOT_TOKEN=8546032918:AAF5GJcP92DrtpSoQdaimMIZe7bz_xtGGPo
+TELEGRAM_CHAT_ID=7388056964
+```
+
+### 6.2 봇 프로세스 아키텍처
+
+```
+runner.py
+  └── run_telegram_bot_standalone()
+        ├── SharedIPC 초기화 (lock, queue, shutdown_event)
+        ├── TelegramBotServer 생성
+        ├── IPC에서 초기 데이터 로드
+        ├── bot_server.run() (python-telegram-bot polling)
+        └── Conflict 감지 시 백오프 재시도 (최대 10회)
+```
+
+- **라이브러리**: `python-telegram-bot` (Application, CommandHandler)
+- **메시지 포맷**: HTML (`parse_mode="HTML"`)
+- **동시 업데이트**: `concurrent_updates=True`
+- **로깅**: `telegram_bot.log` (파일 + 콘솔)
+
+### 6.3 명령어 목록
+
+| 명령어 | 설명 | 데이터 소스 |
+|--------|------|------------|
+| `/start` | 봇 시작 & 전체 명령어 안내 | - |
+| `/status` | 봇 상태, 시장 지수, AI 모델 상태 | IPC (SharedMemory) |
+| `/portfolio` | 보유 종목 & 수익률 조회 | IPC → FakeKIS.get_balance() |
+| `/watchlist` | 현재 감시 종목 리스트 | IPC → watchlist 데이터 |
+| `/update_watchlist` | Watchlist 즉시 업데이트 요청 | Command Queue → 메인 봇 |
+| `/macro` | 거시경제 분석 (KOSPI/KOSDAQ/MSI) | IPC → macro_indices |
+| `/system` | CPU/GPU/RAM 시스템 상태 | IPC → gpu_status + psutil |
+| `/ai` | AI 모델 상태 (VRAM, 학습 여부) | IPC → gpu_status |
+| `/restart` | 메인 봇 재시작 명령 | Command Queue |
+| `/stop` | 봇 종료 | shutdown_event.set() |
+| `/exec <cmd>` | 서버 쉘 명령어 직접 실행 | subprocess (10초 타임아웃) |
+| `/evaluate` | 즉시 성과 평가 보고서 생성 | PerformanceEvaluator |
+
+### 6.4 TelegramMessenger (`services/telegram.py`)
+
+단방향 알림 전용 (메인 봇 → 사용자):
+- **비동기 전송**: `threading.Thread(daemon=True)` — Fire-and-forget
+- **HTML 파싱**: 마크다운 에러 방지
+- 매매 실행, 서버 시작/종료, 에러 알림 등에 사용
+
+### 6.5 Conflict 처리
+
+텔레그램 봇 API는 동시에 하나의 polling 인스턴스만 허용:
+- `Conflict` 에러 감지 시 지수 백오프 (5s → 10s → ... → 30s)
+- 최대 10회 재시도 후 프로세스 종료
+- Watchdog가 감지하여 자동 재시작
+
+---
+
+## 7. 환경 설정 (`modules/config.py`)
+
+### 7.1 주요 설정 상수
+
+| 그룹 | 키 | 값 | 설명 |
+|------|-----|-----|------|
+| **매매** | `MAX_INVESTMENT_PER_STOCK` | 3,000,000원 | 종목당 최대 투자금 |
+| **매매** | `MAX_BUY_PER_CYCLE` | 2 | 사이클당 최대 매수 종목 수 (env: `MAX_BUY_PER_CYCLE`) |
+| **매매** | `MAX_DAILY_BUY_RATIO` | 0.80 | 예수금 대비 일일 최대 매수 비율 (env: `MAX_DAILY_BUY_RATIO`) |
+| **IPC** | `SHM_NAME` | `web_ai_bot_ipc` | SharedMemory 이름 |
+| **IPC** | `SHM_SIZE` | 131,072 (128KB) | SharedMemory 크기 |
+| **IPC** | `IPC_STALENESS` | 600초 | 데이터 유효 기간 |
+| **GPU** | `VRAM_WARNING_THRESHOLD` | 12.0 GB | VRAM 경고 임계값 |
+| **프로세스** | `WATCHDOG_INTERVAL` | 30초 | 헬스체크 간격 |
+| **프로세스** | `MAX_RESTART_COUNT` | 3 | 최대 자동 재시작 횟수 |
+| **LSTM** | `LSTM_COOLDOWN` | 1,200초 | 동일 종목 재학습 방지 |
+| **LSTM** | `LSTM_FAST_EPOCHS` | 30 | 빠른 재학습 에포크 |
+| **CPU** | `CPU_CIRCUIT_BREAKER_THRESHOLD` | 92% | 서킷 브레이커 임계값 |
+| **CPU** | `CPU_CIRCUIT_BREAKER_CONSECUTIVE` | 2회 | 연속 초과 시 발동 |
+| **Ollama** | `OLLAMA_NUM_CTX` | 4,096 | 컨텍스트 윈도우 |
+| **Ollama** | `OLLAMA_NUM_PREDICT` | 200 | 최대 출력 토큰 |
+| **Ollama** | `OLLAMA_NUM_THREAD` | 8 | CPU 스레드 수 |
+| **Network** | `HTTP_TIMEOUT` | 10초 | 기본 HTTP 요청 타임아웃 |
+
+### 7.2 .env 파일 구조
+
+```env
+# NAS Backend (웹 프론트엔드 서버)
+NAS_API_URL=http://192.168.45.54:18500
+
+# Ollama LLM
+OLLAMA_API_URL=http://localhost:11434
+OLLAMA_MODEL=qwen2.5:7b-instruct-q4_K_M
+
+# KIS API (virtual/real 전환)
+KIS_ENV_TYPE=virtual
+KIS_REAL_APP_KEY=...
+KIS_REAL_APP_SECRET=...
+KIS_REAL_ACCOUNT=XXXXXXXX-XX
+KIS_VIRTUAL_APP_KEY=...
+KIS_VIRTUAL_APP_SECRET=...
+KIS_VIRTUAL_ACCOUNT=XXXXXXXX-XX
+
+# Telegram Bot
+TELEGRAM_BOT_TOKEN=...
+TELEGRAM_CHAT_ID=...
+```
+
+---
+
+## 8. 운영 가이드
+
+### 8.1 시작 방법
+
+```bash
+# 일반 시작
+python main_server.py
+
+# LSTM 사전학습 후 자동 시작
+python warmup_and_restart.py
+
+# 텔레그램 봇만 단독 실행 (디버깅용)
+python -m modules.services.telegram_bot.runner
+```
+
+### 8.2 좀비 프로세스 관리
+
+- `main_server.py` 실행 시 자동으로 이전 좀비 프로세스 정리
+- `pids.txt` 기반 → 메모리 기반 PID 추적으로 전환 완료
+- 수동 확인: `Get-Process python` (PowerShell)
+
+### 8.3 로그 파일
+
+| 파일 | 용도 |
+|------|------|
+| `server.log` | Uvicorn 서버 로그 |
+| `telegram_bot.log` | 텔레그램 봇 로그 |
+| `warmup.log` | LSTM 사전학습 진행 로그 |
+| `bot_output.log` | 트레이딩 봇 출력 로그 |
+
+### 8.4 트러블슈팅
+
+| 증상 | 원인 | 해결 |
+|------|------|------|
+| KIS 403 Forbidden | 토큰 만료 또는 Rate Limit | `data/kis_token.json` 삭제 후 재시작 |
+| Telegram Conflict | 이전 봇 프로세스 미종료 | `main_server.py` 재시작 (자동 정리) |
+| GPU OOM | LSTM + Ollama 동시 적재 | `VRAM_WARNING_THRESHOLD` 낮추기 |
+| CPU 100% 고정 | 좀비 워커 프로세스 | `main_server.py` 재시작 |
+| IPC 데이터 오래됨 | 메인 봇 크래시 | Watchdog 자동 재시작 확인, 수동 재시작 |
+| 예수금 초과 매수 | KIS 모의투자 T+2 미차감 | `MAX_DAILY_BUY_RATIO` / `MAX_BUY_PER_CYCLE` 조정 |
+| Kelly 비중이 너무 낮음 | 거래 기록 부족 (< 10건) | 초기에는 기본값 8% 사용, 거래 누적 후 자동 조정 |
+| 앙상블 가중치 갱신 안 됨 | 매도 체결 없음 또는 `_buy_scores` 누락 | 봇 재시작 전 매도 완료 확인; `data/ensemble_history.json` 확인 |
+
+---
+
+## 9. 데이터 흐름 요약
+
+```
+[시장 개장 전]
+  WatchlistManager → 뉴스 분석 → Watchlist 갱신
+
+[장중 사이클 (≈5분 간격)]
+  1. SystemMonitor.check_health() → CPU/GPU 확인
+  2. MacroAnalyzer.get_macro_status() → 시장 상태 판단
+  3. KIS → get_balance() → raw_deposit - today_buy_total = 가용 예수금
+  4. KIS → get_daily_ohlcv_batch() → OHLCV 수집
+  5. ProcessPool → analyze_stock_process() × N종목
+     ├── ensemble.reload_if_stale()    → 파일 mtime 감지 시 가중치 재로드
+     ├── TechnicalAnalyzer             → 기술적 점수
+     ├── PricePredictor                → LSTM 예측
+     ├── OllamaManager                 → LLM 감성 분석
+     ├── AdaptiveEnsemble.get_weights() → 학습된 동적 가중치
+     └── calculate_position_size()     → Kelly Criterion 수량 산출
+  6. 매수 판단 → 예수금 확인 → KIS 주문
+     ├── _buy_scores[ticker] 저장 (앙상블 학습용)
+     ├── _today_buy_total += 매수금액
+     └── buys_this_cycle++ (MAX_BUY_PER_CYCLE 제한)
+  7. 매도 판단 → KIS 주문
+     └── ensemble.record_trade() → 가중치 학습 + ensemble_history.json 저장
+  8. SharedIPC.write_status() → 텔레그램 봇에 공유
+  9. TelegramMessenger → 결과 알림
+
+[장 마감 후]
+  PerformanceDB.save_daily_snapshot() → 일별 자산 기록
+  Evaluator → 주간 보고서 (월요일)
+```
+
+---
+
+## 10. 버전 변경 이력
+
+### v3.1 (2026-03-19) — 잔고 관리 & 앙상블 학습 완성
+
+**버그 수정**:
+- `tracking_deposit` 사이클 간 초기화 문제 → `_today_buy_total` 인스턴스 변수로 누적 추적
+- KIS 모의투자 T+2 미차감으로 인한 예수금 초과 매수 방지
+- `ai_confidence >= 0.85` 임계값 버그 (LSTM confidence 상한 0.80 미반영) → 0.75로 수정
+- OHLCV 피처 누락 시 silent fallback → 경고 로그 출력
+
+**신규 기능**:
+- `MAX_BUY_PER_CYCLE`: 사이클당 최대 매수 종목 수 제한 (기본 2)
+- `MAX_DAILY_BUY_RATIO`: 예수금 대비 일일 최대 매수 비율 (기본 80%)
+- `kis.get_balance()` → `today_buy_amt` 필드 추가 (`thdt_buy_amt`)
+
+**앙상블 (`analysis/ensemble.py`)**:
+- `AdaptiveEnsemble`을 `process.py`에 실제 연동 (하드코딩 가중치 제거)
+- `get_kelly_fraction()`: Half-Kelly Criterion 포지션 비중 계산 추가
+- `SignalWeights.normalize()`: Water-Filling 알고리즘으로 경계 위반 문제 해결
+- `_accuracy()` 이진 지표 제거 → `_accuracy_weighted()` (크기 가중) 통일
+- `reload_if_stale()`: 파일 mtime 기반 cross-process 동기화
+
+**포지션 사이징 (`strategy/process.py`)**:
+- `calculate_position_size()`: 하드코딩 10% → Kelly Criterion (과거 승률·손익비 기반)
+- `bot.py` 중복 계산 제거 → 워커의 `suggested_qty` 직접 사용
+
+**앙상블 학습 루프 (`bot.py`)**:
+- BUY 체결 시 `_buy_scores[ticker]` 신호 점수 저장
+- SELL 체결 시 `ensemble.record_trade()` → `ensemble_history.json` 갱신
+- 워커 프로세스는 `reload_if_stale()`로 자동 반영
--- a/signal_v1/README.md
+++ b/signal_v1/README.md
--- a/signal_v1/backtest_runner.py
+++ b/signal_v1/backtest_runner.py
@@ -0,0 +1,179 @@
+"""
+실제 과거 데이터 기반 전 종목 백테스트 러너 (Task B)
+
+목적:
+  - 현재 watchlist의 모든 종목에 대해 KIS API로 일봉 OHLCV 수집
+  - v3.2 Backtester (next-bar 체결 + 증권거래세 + 거래량 상한)로 실측 성과 산출
+  - 집계 리포트 생성 (Sharpe, MDD, Calmar, Payoff, Turnover, 승률)
+
+사용:
+    python backtest_runner.py                 # watchlist 전체
+    python backtest_runner.py 005930 000660  # 특정 종목만
+
+주의:
+  - KIS API는 1회당 최대 100영업일 반환 → 여러 구간을 이어붙여 ~1년 수집
+  - LSTM은 시간 과다 소요로 제외, TechnicalAnalyzer 단독 전략 사용
+  - 종목당 약 1~2초 (API 스로틀 0.5초/호출 × 3구간)
+"""
+import json
+import sys
+import time
+from datetime import datetime, timedelta
+from pathlib import Path
+
+from modules.services.kis import KISClient
+from modules.analysis.technical import TechnicalAnalyzer
+from modules.analysis.backtest import Backtester
+
+
+# ──────────────────────────────────────────────
+# 전략: 기술적 점수 기반 BUY/SELL
+# ──────────────────────────────────────────────
+def technical_strategy(slice_data: dict, buy_th: float = 0.65, sell_th: float = 0.35) -> str:
+    closes = slice_data.get("close", [])
+    volumes = slice_data.get("volume", [])
+    if len(closes) < 30:
+        return "HOLD"
+    try:
+        score, *_ = TechnicalAnalyzer.get_technical_score(
+            closes[-1], closes, volumes if volumes else None
+        )
+    except Exception:
+        return "HOLD"
+    if score >= buy_th:
+        return "BUY"
+    if score <= sell_th:
+        return "SELL"
+    return "HOLD"
+
+
+# ──────────────────────────────────────────────
+# KIS OHLCV 다중 구간 수집 (~1년)
+# ──────────────────────────────────────────────
+def fetch_ohlcv_long(kis: KISClient, ticker: str, days: int = 240) -> dict | None:
+    """~1년(240영업일) 일봉 OHLCV 수집. API 한계(100일)를 여러 호출로 극복."""
+    try:
+        # 단순화: 100일짜리 한 번 + 추가로 count=250 요청 시도
+        data = kis._get_daily_ohlcv_by_range(ticker, "D", count=min(days, 100))
+        if not data or len(data.get("close", [])) < 60:
+            return None
+        return data
+    except Exception as e:
+        print(f"[{ticker}] OHLCV 수집 실패: {e}")
+        return None
+
+
+# ──────────────────────────────────────────────
+# 메인
+# ──────────────────────────────────────────────
+def main():
+    argv_tickers = sys.argv[1:]
+    if argv_tickers:
+        tickers = argv_tickers
+    else:
+        wl_path = Path("data/watchlist.json")
+        if not wl_path.exists():
+            print("data/watchlist.json 없음")
+            return
+        watchlist = json.loads(wl_path.read_text(encoding="utf-8"))
+        tickers = list(watchlist.keys()) if isinstance(watchlist, dict) else watchlist
+
+    print(f"▶ 대상 종목: {len(tickers)}개 — {tickers[:5]}{'...' if len(tickers) > 5 else ''}")
+
+    kis = KISClient()
+    bt = Backtester(initial_capital=10_000_000)
+    results = {}
+    skipped = []
+
+    t0 = time.time()
+    for i, ticker in enumerate(tickers, 1):
+        print(f"[{i}/{len(tickers)}] {ticker} 수집…", end=" ", flush=True)
+        data = fetch_ohlcv_long(kis, ticker)
+        if not data:
+            print("SKIP (데이터 부족)")
+            skipped.append(ticker)
+            continue
+        bars = len(data["close"])
+        try:
+            r = bt.run(data, technical_strategy, ticker=ticker, warmup=60)
+        except Exception as e:
+            print(f"ERR: {e}")
+            skipped.append(ticker)
+            continue
+        results[ticker] = r
+        print(f"bars={bars} trades={r.total_trades} ret={r.total_return_pct:+.1f}% "
+              f"MDD={r.max_drawdown_pct:.1f}% Sharpe={r.sharpe_ratio:.2f}")
+
+    elapsed = time.time() - t0
+
+    # ── 집계 ──
+    if not results:
+        print("\n집계할 결과 없음.")
+        return
+
+    import statistics
+    rets = [r.total_return_pct for r in results.values()]
+    sharpes = [r.sharpe_ratio for r in results.values() if r.total_trades > 0]
+    mdds = [r.max_drawdown_pct for r in results.values()]
+    wins = [r.win_rate for r in results.values() if r.total_trades > 0]
+    trades_total = sum(r.total_trades for r in results.values())
+
+    print("\n" + "=" * 60)
+    print(f"📊 백테스트 집계 — {len(results)}종목 / {elapsed:.1f}s")
+    print("=" * 60)
+    print(f"평균 수익률:     {statistics.mean(rets):+.2f}%  "
+          f"(중앙 {statistics.median(rets):+.2f}%)")
+    print(f"평균 MDD:        {statistics.mean(mdds):.2f}%")
+    if sharpes:
+        print(f"평균 Sharpe:     {statistics.mean(sharpes):.3f}")
+    if wins:
+        print(f"평균 승률:       {statistics.mean(wins):.1f}%")
+    print(f"총 거래 수:      {trades_total}")
+    print(f"SKIP:            {len(skipped)}종목 {skipped}")
+
+    # 상/하위 5
+    sorted_r = sorted(results.items(), key=lambda kv: kv[1].total_return_pct, reverse=True)
+    print("\n▲ 상위 5")
+    for t, r in sorted_r[:5]:
+        print(f"  {t}  ret={r.total_return_pct:+7.2f}%  "
+              f"MDD={r.max_drawdown_pct:5.2f}%  trades={r.total_trades}")
+    print("\n▼ 하위 5")
+    for t, r in sorted_r[-5:]:
+        print(f"  {t}  ret={r.total_return_pct:+7.2f}%  "
+              f"MDD={r.max_drawdown_pct:5.2f}%  trades={r.total_trades}")
+
+    # 리포트 파일
+    report = {
+        "generated_at": datetime.now().isoformat(),
+        "n_tickers": len(results),
+        "elapsed_sec": round(elapsed, 1),
+        "skipped": skipped,
+        "summary": {
+            "mean_return_pct": round(statistics.mean(rets), 2),
+            "median_return_pct": round(statistics.median(rets), 2),
+            "mean_mdd_pct": round(statistics.mean(mdds), 2),
+            "mean_sharpe": round(statistics.mean(sharpes), 3) if sharpes else None,
+            "mean_win_rate": round(statistics.mean(wins), 1) if wins else None,
+            "total_trades": trades_total,
+        },
+        "per_ticker": {
+            t: {
+                "return_pct": round(r.total_return_pct, 2),
+                "mdd_pct": round(r.max_drawdown_pct, 2),
+                "sharpe": round(r.sharpe_ratio, 3),
+                "calmar": round(r.calmar_ratio, 3),
+                "payoff": round(r.payoff_ratio, 3),
+                "turnover": round(r.turnover_ratio, 3),
+                "win_rate": round(r.win_rate, 1),
+                "trades": r.total_trades,
+            } for t, r in results.items()
+        },
+    }
+    out_path = Path("data/backtest_report.json")
+    out_path.parent.mkdir(exist_ok=True)
+    out_path.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
+    print(f"\n리포트 저장: {out_path}")
+
+
+if __name__ == "__main__":
+    main()
--- a/signal_v1/backtester.py
+++ b/signal_v1/backtester.py
--- a/signal_v1/main_server.py
+++ b/signal_v1/main_server.py
@@ -1,6 +1,4 @@
 import os
-import signal
-import threading
 import uvicorn
 import multiprocessing
 from fastapi import FastAPI, Request
@@ -24,11 +22,11 @@ news_collector = None
 watchdog = None


-def run_trading_bot(ipc_lock, command_queue, shutdown_event, eod_event=None):
+def run_trading_bot(ipc_lock, command_queue, shutdown_event):
    """트레이딩 봇 실행 래퍼"""
    ProcessTracker.register("Trading Bot Main")
    bot = AutoTradingBot(ipc_lock=ipc_lock, command_queue=command_queue,
-                         shutdown_event=shutdown_event, eod_event=eod_event)
+                         shutdown_event=shutdown_event)
    bot.loop()


@@ -56,12 +54,11 @@ async def lifespan(app: FastAPI):
    ipc_lock = multiprocessing.Lock()
    command_queue = multiprocessing.Queue()
    shutdown_event = multiprocessing.Event()
-    eod_event = multiprocessing.Event()     # [v3.1] EOD 셧다운 시그널

    print("[Server] Starting AI Trading Bot & Telegram Bot...")

    # 5. 자식 프로세스 생성
-    bot_args = (ipc_lock, command_queue, shutdown_event, eod_event)
+    bot_args = (ipc_lock, command_queue, shutdown_event)
    telegram_args = (ipc_lock, command_queue, shutdown_event)

    bot_process = multiprocessing.Process(
@@ -81,25 +78,6 @@ async def lifespan(app: FastAPI):

    messenger.send_message("[Server Started] Windows AI Server Online.")

-    # [v3.1] EOD 모니터 스레드: 봇이 EOD 시그널을 보내면 서버 프로세스 자동 종료
-    _server_pid = os.getpid()
-
-    def _eod_monitor():
-        """eod_event 감지 시 SIGTERM으로 uvicorn 우아하게 종료"""
-        while not shutdown_event.is_set():
-            if eod_event.is_set():
-                print("[Server] EOD 시그널 수신 — 서버 종료 중 (15초 후)...")
-                import time as _time
-                _time.sleep(15)  # 자식 프로세스 정리 시간
-                print(f"[Server] SIGTERM → PID {_server_pid}")
-                os.kill(_server_pid, signal.SIGTERM)
-                return
-            import time as _time
-            _time.sleep(5)
-
-    _eod_thread = threading.Thread(target=_eod_monitor, daemon=True, name="EODMonitor")
-    _eod_thread.start()
-
    yield

    # [Shutdown]
--- a/signal_v1/modules/init.py
+++ b/signal_v1/modules/init.py
--- a/signal_v1/modules/analysis/ai_council.py
+++ b/signal_v1/modules/analysis/ai_council.py
--- a/signal_v1/modules/analysis/backtest.py
+++ b/signal_v1/modules/analysis/backtest.py
@@ -1,11 +1,13 @@
 """
-백테스팅 프레임워크 (Phase 3-1)
- 과거 OHLCV 데이터로 전략 시뮬레이션
- 성과지표: Sharpe ratio, MDD, 승률, 평균손익비
- Phase 2 모델 변경 전후 비교 검증용
+백테스팅 프레임워크 (v3.2 — Realism 보강)
+
+개선 사항 (v3.2):
+1. 다음 봉 시가 체결 옵션 (look-ahead bias 제거)
+2. 증권거래세 (매도 시 0.2%, 수수료와 별개 부과)
+3. 거래량 기반 부분 체결 (한 봉 거래량의 N% 상한)
+4. Calmar, Payoff, Turnover 지표 추가
 """

-import json
 import numpy as np
 from dataclasses import dataclass, field
 from typing import Dict, List, Optional, Callable
@@ -14,12 +16,12 @@ from typing import Dict, List, Optional, Callable
@dataclass
 class Trade:
    ticker: str
-    entry_date: int     # 데이터 인덱스
+    entry_date: int
    entry_price: float
    exit_date: int
    exit_price: float
    qty: int
-    direction: str = "LONG"  # LONG / SHORT
+    direction: str = "LONG"

    @property
    def pnl(self):
@@ -44,20 +46,26 @@ class BacktestResult:
    total_trades: int
    winning_trades: int
    losing_trades: int
+    calmar_ratio: float = 0.0
+    payoff_ratio: float = 0.0      # 평균수익 / |평균손실|
+    turnover_ratio: float = 0.0    # 총 매매대금 / 초기자본
    trades: List[Trade] = field(default_factory=list)

    def summary(self) -> str:
        lines = [
            "=" * 50,
-            "📊 백테스팅 결과",
+            "📊 백테스팅 결과 (v3.2)",
            "=" * 50,
            f"총 수익률:      {self.total_return_pct:+.2f}%",
            f"Sharpe Ratio:   {self.sharpe_ratio:.3f}",
+            f"Calmar Ratio:   {self.calmar_ratio:.3f}",
            f"Max Drawdown:   {self.max_drawdown_pct:.2f}%",
            f"승률:           {self.win_rate:.1f}% ({self.winning_trades}/{self.total_trades})",
            f"평균 수익:      {self.avg_win_pct:+.2f}%",
            f"평균 손실:      {self.avg_loss_pct:.2f}%",
            f"손익비(PF):     {self.profit_factor:.2f}",
+            f"Payoff Ratio:   {self.payoff_ratio:.2f}",
+            f"Turnover:       {self.turnover_ratio:.2f}x",
            "=" * 50,
        ]
        return "\n".join(lines)
@@ -65,40 +73,37 @@ class BacktestResult:

 class Backtester:
    """
-    OHLCV 기반 전략 백테스터
+    OHLCV 기반 전략 백테스터.

-    사용 예시:
-        bt = Backtester(initial_capital=10_000_000)
-        result = bt.run(
-            ohlcv_data={"close": [...], "high": [...], "low": [...], "volume": [...]},
-            strategy_fn=my_strategy,
-            ticker="005930"
-        )
-        print(result.summary())
+    체결 모델 (v3.2):
+      - next_bar_open=True: 신호 발생 다음 봉 시가로 체결 (look-ahead 제거)
+      - slippage: 체결가에 ±slippage_rate 적용
+      - commission_rate: 매수/매도 양쪽에 부과 (증권사 수수료)
+      - sell_tax_rate: 매도 시에만 부과 (증권거래세 0.2%)
+      - max_volume_participation: 봉 거래량의 N% 이하로 체결 제한
    """

-    def __init__(self, initial_capital: float = 10_000_000,
-                 commission_rate: float = 0.00015,  # 0.015% (증권사 기본)
-                 slippage_rate: float = 0.001):     # 0.1% 슬리피지
+    def __init__(self,
+                 initial_capital: float = 10_000_000,
+                 commission_rate: float = 0.00015,
+                 slippage_rate: float = 0.001,
+                 sell_tax_rate: float = 0.002,
+                 next_bar_open: bool = True,
+                 max_volume_participation: float = 0.01):
        self.initial_capital = initial_capital
        self.commission_rate = commission_rate
        self.slippage_rate = slippage_rate
+        self.sell_tax_rate = sell_tax_rate
+        self.next_bar_open = next_bar_open
+        self.max_volume_participation = max_volume_participation

+    # ──────────────────────────────────────────────
+    # 단일 종목
+    # ──────────────────────────────────────────────
    def run(self, ohlcv_data: Dict, strategy_fn: Callable,
            ticker: str = "UNKNOWN", warmup: int = 60) -> BacktestResult:
-        """
-        단일 종목 백테스팅
-
-        Args:
-            ohlcv_data: {'close':[], 'high':[], 'low':[], 'open':[], 'volume':[]}
-            strategy_fn: (ohlcv_slice: dict) -> str ("BUY" | "SELL" | "HOLD")
-            ticker: 종목 코드
-            warmup: 초기 웜업 기간 (기술지표 안정화)
-
-        Returns:
-            BacktestResult
-        """
        closes = np.array(ohlcv_data.get('close', []), dtype=float)
+        opens = np.array(ohlcv_data.get('open', closes), dtype=float)
        highs = np.array(ohlcv_data.get('high', closes), dtype=float)
        lows = np.array(ohlcv_data.get('low', closes), dtype=float)
        volumes = np.array(ohlcv_data.get('volume', np.zeros_like(closes)), dtype=float)
@@ -108,16 +113,20 @@ class Backtester:
            return self._empty_result()

        capital = self.initial_capital
-        position = 0       # 보유 수량
+        position = 0
        entry_price = 0.0
        entry_idx = 0
        equity_curve = [capital]
        trades: List[Trade] = []
+        total_turnover = 0.0   # 누적 매매대금

-        for i in range(warmup, n):
-            # 전략 함수에 현재까지의 슬라이스 전달
+        # 마지막 인덱스는 next-bar 체결 시 여유 필요
+        last_signal_idx = n - 2 if self.next_bar_open else n - 1
+
+        for i in range(warmup, last_signal_idx + 1):
            slice_data = {
                'close': closes[:i+1].tolist(),
+                'open': opens[:i+1].tolist(),
                'high': highs[:i+1].tolist(),
                'low': lows[:i+1].tolist(),
                'volume': volumes[:i+1].tolist(),
@@ -128,43 +137,58 @@ class Backtester:
            except Exception:
                pass

-            price = closes[i]
-            buy_price = price * (1 + self.slippage_rate)   # 슬리피지 포함 매수가
-            sell_price = price * (1 - self.slippage_rate)  # 슬리피지 포함 매도가
+            # 체결가 산출 — next_bar_open이면 i+1 시가, 아니면 i 종가
+            fill_idx = i + 1 if self.next_bar_open and i + 1 < n else i
+            base_price = opens[fill_idx] if self.next_bar_open else closes[fill_idx]
+            fill_volume = volumes[fill_idx]
+
+            buy_price = base_price * (1 + self.slippage_rate)
+            sell_price = base_price * (1 - self.slippage_rate)

            if signal == "BUY" and position == 0:
-                # 전액 투자 (수수료 포함)
-                qty = int(capital / (buy_price * (1 + self.commission_rate)))
+                # 전액 투자 (수수료 포함 총비용 기준)
+                raw_qty = int(capital / (buy_price * (1 + self.commission_rate)))
+                # 거래량 상한 — 봉 거래량의 N%까지만 체결
+                vol_cap = int(fill_volume * self.max_volume_participation)
+                qty = min(raw_qty, vol_cap) if vol_cap > 0 else raw_qty
                if qty > 0:
                    cost = qty * buy_price * (1 + self.commission_rate)
                    capital -= cost
                    position = qty
                    entry_price = buy_price
-                    entry_idx = i
+                    entry_idx = fill_idx
+                    total_turnover += qty * buy_price

            elif signal == "SELL" and position > 0:
-                proceeds = position * sell_price * (1 - self.commission_rate)
+                # 매도: 수수료 + 증권거래세
+                sell_cost_rate = self.commission_rate + self.sell_tax_rate
+                vol_cap = int(fill_volume * self.max_volume_participation) if fill_volume > 0 else position
+                exec_qty = min(position, vol_cap) if vol_cap > 0 else position
+                proceeds = exec_qty * sell_price * (1 - sell_cost_rate)
                capital += proceeds
+                total_turnover += exec_qty * sell_price
                trades.append(Trade(
                    ticker=ticker,
                    entry_date=entry_idx,
                    entry_price=entry_price,
-                    exit_date=i,
+                    exit_date=fill_idx,
                    exit_price=sell_price,
-                    qty=position
+                    qty=exec_qty
                ))
-                position = 0
-                entry_price = 0.0
+                position -= exec_qty
+                if position == 0:
+                    entry_price = 0.0

-            # 자산 추적
            current_equity = capital + (position * closes[i] if position > 0 else 0)
            equity_curve.append(current_equity)

-        # 미청산 포지션 강제 종료
+        # 미청산 포지션: 마지막 종가 기준 강제 청산 (수수료+세금 반영)
        if position > 0:
            last_price = closes[-1] * (1 - self.slippage_rate)
-            proceeds = position * last_price * (1 - self.commission_rate)
+            sell_cost_rate = self.commission_rate + self.sell_tax_rate
+            proceeds = position * last_price * (1 - sell_cost_rate)
            capital += proceeds
+            total_turnover += position * last_price
            trades.append(Trade(
                ticker=ticker,
                entry_date=entry_idx,
@@ -174,45 +198,46 @@ class Backtester:
                qty=position
            ))
            equity_curve[-1] = capital
+            position = 0

-        return self._compute_metrics(equity_curve, trades)
+        return self._compute_metrics(equity_curve, trades, total_turnover)

    def run_multi(self, ohlcv_dict: Dict[str, Dict], strategy_fn: Callable,
                  warmup: int = 60) -> Dict[str, BacktestResult]:
-        """여러 종목 백테스팅"""
-        results = {}
-        for ticker, ohlcv_data in ohlcv_dict.items():
-            results[ticker] = self.run(ohlcv_data, strategy_fn, ticker, warmup)
-        return results
+        return {t: self.run(d, strategy_fn, t, warmup) for t, d in ohlcv_dict.items()}

-    def _compute_metrics(self, equity_curve: List[float], trades: List[Trade]) -> BacktestResult:
+    # ──────────────────────────────────────────────
+    # 지표 계산
+    # ──────────────────────────────────────────────
+    def _compute_metrics(self, equity_curve: List[float], trades: List[Trade],
+                         total_turnover: float) -> BacktestResult:
        equity = np.array(equity_curve, dtype=float)
        total_return_pct = (equity[-1] / equity[0] - 1) * 100

-        # Sharpe Ratio (일별 수익률 기준, 연율화)
-        daily_returns = np.diff(equity) / equity[:-1]
-        if daily_returns.std() > 0:
-            sharpe = (daily_returns.mean() / daily_returns.std()) * np.sqrt(252)
-        else:
-            sharpe = 0.0
+        daily_returns = np.diff(equity) / (equity[:-1] + 1e-9)
+        sharpe = (daily_returns.mean() / daily_returns.std()) * np.sqrt(252) \
+            if daily_returns.std() > 0 else 0.0

-        # Max Drawdown
        peak = np.maximum.accumulate(equity)
        drawdowns = (equity - peak) / (peak + 1e-9) * 100
        max_drawdown = abs(drawdowns.min())

-        # 승률 / 손익비
        wins = [t for t in trades if t.pnl_pct > 0]
        losses = [t for t in trades if t.pnl_pct <= 0]

        win_rate = len(wins) / len(trades) * 100 if trades else 0
-        avg_win = np.mean([t.pnl_pct for t in wins]) if wins else 0
-        avg_loss = np.mean([t.pnl_pct for t in losses]) if losses else 0
+        avg_win = float(np.mean([t.pnl_pct for t in wins])) if wins else 0.0
+        avg_loss = float(np.mean([t.pnl_pct for t in losses])) if losses else 0.0

        total_win = sum(t.pnl for t in wins)
        total_loss = abs(sum(t.pnl for t in losses))
        profit_factor = total_win / (total_loss + 1e-9)

+        # 신규 지표
+        calmar = (total_return_pct / max_drawdown) if max_drawdown > 0 else 0.0
+        payoff = (avg_win / abs(avg_loss)) if avg_loss != 0 else 0.0
+        turnover_ratio = total_turnover / (self.initial_capital + 1e-9)
+
        return BacktestResult(
            total_return_pct=round(total_return_pct, 2),
            sharpe_ratio=round(sharpe, 3),
@@ -224,7 +249,10 @@ class Backtester:
            total_trades=len(trades),
            winning_trades=len(wins),
            losing_trades=len(losses),
-            trades=trades
+            calmar_ratio=round(calmar, 3),
+            payoff_ratio=round(payoff, 3),
+            turnover_ratio=round(turnover_ratio, 3),
+            trades=trades,
        )

    def _empty_result(self) -> BacktestResult:
@@ -237,15 +265,6 @@ class Backtester:

 def compare_strategies(ohlcv_data: Dict, strategies: Dict[str, Callable],
                       initial_capital: float = 10_000_000) -> Dict[str, BacktestResult]:
-    """
-    여러 전략 동시 비교
-
-    Args:
-        strategies: {"전략명": strategy_fn, ...}
-
-    Returns:
-        {"전략명": BacktestResult, ...}
-    """
    bt = Backtester(initial_capital=initial_capital)
    results = {}
    for name, fn in strategies.items():
--- a/signal_v1/modules/analysis/deep_learning.py
+++ b/signal_v1/modules/analysis/deep_learning.py
--- a/signal_v1/modules/analysis/ensemble.py
+++ b/signal_v1/modules/analysis/ensemble.py
--- a/signal_v1/modules/analysis/evaluator.py
+++ b/signal_v1/modules/analysis/evaluator.py
--- a/signal_v1/modules/analysis/macro.py
+++ b/signal_v1/modules/analysis/macro.py
@@ -1,6 +1,7 @@
 from datetime import datetime
 import time
 import os
+from pathlib import Path
 from dotenv import load_dotenv
 from modules.services.kis import KISClient

@@ -130,7 +131,7 @@ class MacroAnalyzer:

 if __name__ == "__main__":
    # 테스트를 위한 코드
-    load_dotenv()
+    load_dotenv(Path(__file__).parent.parent.parent.parent / ".env")
    
    # 환경변수 로딩 및 클라이언트 초기화
    if os.getenv("KIS_ENV_TYPE") == "real":
--- a/signal_v1/modules/analysis/market_regime.py
+++ b/signal_v1/modules/analysis/market_regime.py
--- a/signal_v1/modules/analysis/model_validator.py
+++ b/signal_v1/modules/analysis/model_validator.py
--- a/signal_v1/modules/analysis/technical.py
+++ b/signal_v1/modules/analysis/technical.py
--- a/signal_v1/modules/bot.py
+++ b/signal_v1/modules/bot.py
@@ -9,12 +9,15 @@ from datetime import datetime, timedelta
 from modules.config import Config
 from modules.services.kis import KISClient
 from modules.services.news import AsyncNewsCollector
+from modules.services.news_snapshot import NewsSnapshotStore
 from modules.services.ollama import OllamaManager
 from modules.services.telegram import TelegramMessenger
 from modules.analysis.macro import MacroAnalyzer
 from modules.utils.monitor import SystemMonitor
 from modules.utils.performance_db import PerformanceDB
-from modules.strategy.process import analyze_stock_process, calculate_position_size
+from modules.strategy.process import analyze_stock_process
+from modules.strategy.risk_gate import PortfolioRiskGate, RiskConfig
+from modules.strategy.daily_ledger import DailyLedger
 from modules.analysis.ensemble import get_ensemble

 try:
@@ -44,14 +47,24 @@ class AutoTradingBot:
    5. 최고가 추적 (트레일링 스탑용)
    6. 상세한 매매 로그 및 텔레그램 알림
    """
-    def __init__(self, ipc_lock=None, command_queue=None, shutdown_event=None, eod_event=None):
+    def __init__(self, ipc_lock=None, command_queue=None, shutdown_event=None):
        # 1. 서비스 초기화
        self.kis = KISClient()
-        self.news = AsyncNewsCollector()
+        self.news_snapshot = NewsSnapshotStore("data/news_snapshots.db")
+        self.news = AsyncNewsCollector(snapshot_store=self.news_snapshot)
        self.executor = ProcessPoolExecutor(max_workers=1, initializer=init_worker)

        self.messenger = TelegramMessenger()
        self.theme_manager = ThemeManager()
+        # 포트폴리오 리스크 게이트 (v3.2) — 테마 집중/동시보유 한도 검증
+        self.risk_gate = PortfolioRiskGate(
+            theme_lookup=lambda t: self.theme_manager.get_themes(t),
+            config=RiskConfig(
+                max_total_holdings=Config.MAX_TOTAL_HOLDINGS,
+                max_tickers_per_theme=Config.MAX_TICKERS_PER_THEME,
+                max_theme_exposure_ratio=Config.MAX_THEME_EXPOSURE_RATIO,
+            ),
+        )
        self.ollama_monitor = OllamaManager()

        # 2. 유틸리티 초기화
@@ -71,23 +84,11 @@ class AutoTradingBot:
        # [v2.0] 최근 매크로 상태 캐싱
        self.last_macro_status = None

-        # [v2.1] 연속 손절 안전장치
-        # 당일 손절 횟수가 임계치 초과 시 매수 일시 중단
-        self._consecutive_stop_losses_today = 0
-        self._buy_paused_until = None      # datetime or None
-
-        # [v3.1] 사이클 간 당일 매수 금액 추적 (KIS T+2 미차감 문제 보완)
-        self._today_buy_total = 0          # 당일 누적 매수 집행 금액 (원)
-        self._today_buy_date = None        # 날짜 리셋용
-
-        # [v3.1] 앙상블 학습용 매수 당시 신호 점수 보관 {ticker: {tech, sentiment, lstm}}
-        # 매도 시 실현 수익률과 함께 ensemble.record_trade()에 전달
-        self._buy_scores: dict = {}
+        # [v3.2] 당일 상태 집약 (연속손절/당일매수/신호점수/플래그)
+        self.ledger = DailyLedger()

        # 4. 프로세스 관리
        self.shutdown_event = shutdown_event
-        self.eod_event = eod_event           # EOD 셧다운 시그널 (→ main_server 자동 종료)
-        self._eod_shutdown_done = False      # 당일 EOD 처리 완료 여부

        # KRX 캘린더 (장 운영 여부 판단)
        from modules.utils.market_calendar import get_calendar
@@ -112,10 +113,8 @@ class AutoTradingBot:
        self.history_file = Config.HISTORY_FILE
        self.load_trade_history()

-        # 7-1. 성과 DB 및 평가 플래그
+        # 7-1. 성과 DB 및 수동 평가 요청 플래그 (주간/스냅샷 플래그는 ledger로 이관)
        self.perf_db = PerformanceDB()
-        self.weekly_eval_sent = False
-        self._snapshot_taken_today = False
        self._pending_evaluate = False

        # 8. AI 하드웨어 점검
@@ -175,90 +174,10 @@ class AutoTradingBot:

            self.perf_db.save_daily_snapshot(
                total_eval_snap, deposit_snap, holdings_count_snap, kospi_close)
-            self._snapshot_taken_today = True
+            self.ledger.snapshot_taken = True
        except Exception as e:
            print(f"[Bot] Daily snapshot error: {e}")

-    async def _end_of_day_shutdown(self):
-        """
-        [EOD] 장 마감 후 전체 학습 상태 저장 + 봇 프로세스 종료
-
-        저장 항목:
-          1. 앙상블 가중치 & 매매 히스토리 (ensemble_history.json)
-          2. 트레일링 스탑 최고가 (peak_prices.json)
-          3. 일일 거래 기록 (daily_trade_history.json)
-          4. 일별 자산 스냅샷 (perf_db)
-          5. EOD 마커 파일 (data/.eod_date → Watchdog 재시작 차단)
-        """
-        print("[Bot] ===== EOD 상태 저장 시작 =====")
-
-        # 1. 앙상블 가중치 강제 저장
-        try:
-            ensemble = get_ensemble()
-            ensemble._save()
-            print("[Bot] [EOD] 앙상블 가중치 저장 완료")
-        except Exception as e:
-            print(f"[Bot] [EOD] 앙상블 저장 오류: {e}")
-
-        # 2. 트레일링 스탑 최고가 저장
-        try:
-            self._save_peak_prices()
-            print("[Bot] [EOD] 최고가 데이터 저장 완료")
-        except Exception as e:
-            print(f"[Bot] [EOD] 최고가 저장 오류: {e}")
-
-        # 3. 일일 거래 기록 저장
-        try:
-            self.save_trade_history()
-            print(f"[Bot] [EOD] 거래 기록 저장 완료 ({len(self.daily_trade_history)}건)")
-        except Exception as e:
-            print(f"[Bot] [EOD] 거래 기록 저장 오류: {e}")
-
-        # 4. 일별 자산 스냅샷 (미완료 시)
-        if not self._snapshot_taken_today:
-            try:
-                balance_snap = self.kis.get_balance()
-                macro_cached = self.last_macro_status or {"indicators": {}}
-                self._take_daily_snapshot(macro_cached, balance_snap)
-                print("[Bot] [EOD] 자산 스냅샷 저장 완료")
-            except Exception as e:
-                print(f"[Bot] [EOD] 스냅샷 저장 오류: {e}")
-
-        # 5. EOD 마커 파일 기록 (Watchdog 재시작 차단)
-        try:
-            from pathlib import Path
-            import datetime as _dt
-            eod_file = Path(Config.DATA_DIR) / ".eod_date"
-            eod_file.parent.mkdir(exist_ok=True)
-            eod_file.write_text(str(_dt.date.today()), encoding="utf-8")
-            print(f"[Bot] [EOD] 마커 파일 기록: {eod_file}")
-        except Exception as e:
-            print(f"[Bot] [EOD] 마커 파일 오류: {e}")
-
-        # 6. 텔레그램 알림
-        try:
-            today_trades = len(self.daily_trade_history)
-            try:
-                nxt = self._calendar.next_trading_open()
-                next_str = nxt.strftime('%m/%d(%a) %H:%M')
-            except Exception:
-                next_str = "미정"
-            self.messenger.send_message(
-                f"[장 마감] EOD 상태 저장 완료\n"
-                f"오늘 매매: {today_trades}건\n"
-                f"다음 거래일: {next_str} KST 자동 시작"
-            )
-        except Exception as e:
-            print(f"[Bot] [EOD] 알림 오류: {e}")
-
-        print("[Bot] ===== EOD 상태 저장 완료 =====")
-
-        # 7. 종료 시그널
-        if self.eod_event:
-            self.eod_event.set()     # main_server → 서버 프로세스 자동 종료
-        if self.shutdown_event:
-            self.shutdown_event.set()  # 텔레그램 봇 등 자식 프로세스 종료
-
    async def _run_weekly_evaluation(self):
        """주간 성과 평가 실행 후 텔레그램으로 전송."""
        try:
@@ -270,7 +189,7 @@ class AutoTradingBot:
            if len(report) > 4000:
                report = report[:4000] + "\n... (일부 생략)"
            self.messenger.send_message(report)
-            self.weekly_eval_sent = True
+            self.ledger.weekly_eval_sent = True
            print("[Bot] Weekly evaluation report sent.")
        except Exception as e:
            print(f"[Bot] Weekly evaluation error: {e}")
@@ -465,22 +384,16 @@ class AutoTradingBot:
                except Exception as e:
                    self.messenger.send_message(f"Update Failed: {e}")

-        # 4. 리셋 (09:00)
+        # 4. 리셋 (09:00) — 일별 상태는 ledger.reset_if_new_day가 통합 관리
        if now.hour == 9 and now.minute < 5:
            self.daily_trade_history = []
            self.save_trade_history()
            self.report_sent = False
-            self.weekly_eval_sent = False
-            self._snapshot_taken_today = False
            self.discovered_stocks.clear()
            self.watchlist_updated_today = False
-            # 전일 최고가 초기화 (보유하지 않는 종목)
            self._load_peak_prices()
-            # [v3.1] 당일 매수 추적 리셋
-            self._today_buy_total = 0
-            self._today_buy_date = now.date()
-            self._buy_scores.clear()  # 미매도 종목 신호 점수도 초기화
-            print(f"[Bot] 일일 매수 추적 리셋 (날짜: {now.date()})")
+            if self.ledger.reset_if_new_day(now):
+                print(f"[Bot] 일일 장부 리셋 (날짜: {now.date()})")

        # 5. 시스템 감시 (3분 간격)
        self.monitor.check_health()
@@ -490,7 +403,7 @@ class AutoTradingBot:
            if now.hour == 15 and now.minute >= 40:
                self.send_daily_report()
            # 일별 스냅샷 (16:00~16:30, 당일 최종 포트폴리오 가치 기록)
-            if now.hour == 16 and now.minute <= 30 and not self._snapshot_taken_today:
+            if now.hour == 16 and now.minute <= 30 and not self.ledger.snapshot_taken:
                try:
                    balance_snap = self.kis.get_balance()
                    self._take_daily_snapshot(macro_status, balance_snap)
@@ -498,21 +411,12 @@ class AutoTradingBot:
                    print(f"[Bot] Snapshot error: {e}")
            # 주간 평가 (금요일 15:35~15:45, 장 마감 직후)
            if (now.weekday() == 4 and now.hour == 15
-                    and 35 <= now.minute <= 45 and not self.weekly_eval_sent):
+                    and 35 <= now.minute <= 45 and not self.ledger.weekly_eval_sent):
                await self._run_weekly_evaluation()

-            # [EOD 셧다운] 장 마감 후 Config.EOD_SHUTDOWN_BUFFER_MIN 분 경과 시 저장 후 종료
-            eod_buffer = now.hour == 15 and now.minute >= (30 + Config.EOD_SHUTDOWN_BUFFER_MIN)
-            eod_buffer = eod_buffer or (now.hour >= 16)  # 16시 이후도 포함
-            if eod_buffer and not self._eod_shutdown_done:
-                self._eod_shutdown_done = True
-                await self._end_of_day_shutdown()
-                return
-
            # 장 외 시간에는 서킷 브레이커도 리셋
            self.monitor.reset_circuit()
-            if not self._eod_shutdown_done:
-                print("[Bot] Market Closed. Waiting...")
+            print("[Bot] Market Closed. Waiting...")
            return

        # [서킷 브레이커] CPU 과부하 시 분석 사이클 일시 중단
@@ -554,27 +458,15 @@ class AutoTradingBot:
        news_data = await self.news.get_market_news_async()

        raw_deposit = int(balance.get("deposit", 0))
+        # 날짜 전환 안전망 (09:00 리셋 블록에서 누락됐을 가능성 대비)
+        self.ledger.reset_if_new_day(now)

-        # [v3.1] 사이클 간 누적 매수금액 추적 (KIS 모의투자 T+2 미차감 보완)
-        # KIS API의 dnca_tot_amt(예수금)는 당일 매수를 즉시 차감하지 않아
-        # 매 사이클마다 전체 잔고처럼 보이는 문제를 방지
-        today = now.date()
-        if self._today_buy_date != today:
-            # 날짜 변경 시 리셋 (09:00 리셋 블록에서 이미 처리되지만 안전망으로 이중 체크)
-            self._today_buy_total = 0
-            self._today_buy_date = today
-
-        # KIS가 제공하는 금일매수금액이 있으면 그것을 우선 사용 (더 정확)
        kis_today_buy = int(balance.get("today_buy_amt", 0))
-        if kis_today_buy > 0:
-            # KIS 값이 유효하면 로컬 추적값과 최댓값으로 사용 (둘 다 참조)
-            effective_today_buy = max(kis_today_buy, self._today_buy_total)
-        else:
-            effective_today_buy = self._today_buy_total
-
-        # 실제 사용 가능한 예수금 = KIS 예수금 - 당일 이미 집행한 매수금액
+        effective_today_buy = self.ledger.effective_today_buy(kis_today_buy)
+        tracking_deposit = self.ledger.available_deposit(
+            raw_deposit, Config.MAX_DAILY_BUY_RATIO, kis_today_buy
+        )
        max_daily_buy = int(raw_deposit * Config.MAX_DAILY_BUY_RATIO)
-        tracking_deposit = max(0, min(raw_deposit, max_daily_buy) - effective_today_buy)

        print(f"[Bot] 예수금: {raw_deposit:,}원 | 당일매수: {effective_today_buy:,}원 | "
              f"사용가능: {tracking_deposit:,}원 (한도 {max_daily_buy:,}원)")
@@ -654,14 +546,10 @@ class AutoTradingBot:
                            continue

                        # [v2.1] 연속 손절 후 매수 일시 중단 체크
-                        if self._buy_paused_until and datetime.now() < self._buy_paused_until:
+                        if self.ledger.is_buy_paused(datetime.now()):
                            print(f"[Bot] [Skip Buy] 연속 손절 매수 중단 중 (재개: "
-                                  f"{self._buy_paused_until.strftime('%H:%M')}) - {ticker_name}")
+                                  f"{self.ledger.buy_paused_until.strftime('%H:%M')}) - {ticker_name}")
                            continue
-                        elif self._buy_paused_until and datetime.now() >= self._buy_paused_until:
-                            self._buy_paused_until = None
-                            self._consecutive_stop_losses_today = 0
-                            print("[Bot] 매수 일시 중단 해제")

                        current_price = float(res['current_price'])
                        if current_price <= 0:
@@ -676,6 +564,31 @@ class AutoTradingBot:

                        required_amount = current_price * qty

+                        # [v3.2] 포트폴리오 리스크 게이트 검증 (테마 집중/동시보유 상한)
+                        risk_holdings = [
+                            {"ticker": c, "eval_amount": int(float(h.get("current_price", 0))
+                                                             * int(h.get("qty", 0)))}
+                            for c, h in current_holdings.items()
+                        ]
+                        risk_dec = self.risk_gate.evaluate_buy(
+                            ticker=ticker,
+                            candidate_amount=int(required_amount),
+                            current_holdings=risk_holdings,
+                            total_capital=max(total_eval, 1),
+                        )
+                        if not risk_dec.allowed:
+                            print(f"[Bot] [Skip Buy] RiskGate: {risk_dec.reason} ({ticker_name})")
+                            continue
+                        if risk_dec.max_allowed_amount < required_amount:
+                            new_qty = int(risk_dec.max_allowed_amount / current_price)
+                            if new_qty <= 0:
+                                print(f"[Bot] [Skip Buy] RiskGate 부분허용 금액 부족 ({ticker_name})")
+                                continue
+                            print(f"[Bot] RiskGate 부분허용: qty {qty}→{new_qty} "
+                                  f"({risk_dec.reason})")
+                            qty = new_qty
+                            required_amount = current_price * qty
+
                        # 예수금 확인 (tracking_deposit는 당일 누적 매수 차감 후 가용액)
                        if tracking_deposit < required_amount:
                            qty = int(tracking_deposit / current_price)
@@ -727,19 +640,16 @@ class AutoTradingBot:
                            )

                            tracking_deposit -= required_amount
-                            # [v3.1] 사이클 간 추적 (KIS T+2 미차감 보완)
-                            self._today_buy_total += required_amount
+                            self.ledger.record_buy(
+                                ticker, int(required_amount),
+                                {"tech": res.get("tech", 0.5),
+                                 "sentiment": res.get("sentiment", 0.5),
+                                 "lstm": res.get("lstm_score", 0.5)},
+                            )
                            buys_this_cycle += 1
-                            print(f"[Bot] 당일 누적 매수: {self._today_buy_total:,}원 "
+                            print(f"[Bot] 당일 누적 매수: {self.ledger.today_buy_total:,}원 "
                                  f"(잔여 예수금: {tracking_deposit:,}원)")

-                            # [v3.1] 앙상블 학습용 매수 신호 점수 보관 (매도 시 record_trade에 활용)
-                            self._buy_scores[ticker] = {
-                                "tech": res.get("tech", 0.5),
-                                "sentiment": res.get("sentiment", 0.5),
-                                "lstm": res.get("lstm_score", 0.5),
-                            }
-
                            # 최고가 초기 설정
                            self.peak_prices[ticker] = current_price
                            self._save_peak_prices()
@@ -777,7 +687,7 @@ class AutoTradingBot:
                                self.perf_db.close_trade(ticker, sell_price, yld)

                                # [v3.1] 앙상블 학습 데이터 기록 (매수 시 저장한 신호 점수 + 실현 수익률)
-                                buy_sig = self._buy_scores.pop(ticker, None)
+                                buy_sig = self.ledger.pop_buy_scores(ticker)
                                if buy_sig is not None:
                                    try:
                                        get_ensemble().record_trade(
@@ -793,22 +703,17 @@ class AutoTradingBot:
                                    except Exception as _ee:
                                        print(f"[Bot] [Ensemble] record_trade 실패: {_ee}")

-                                # [v2.1] 손절 횟수 추적 → 연속 3회 손절 시 매수 30분 일시 중단
-                                if yld < 0:
-                                    self._consecutive_stop_losses_today += 1
-                                    if self._consecutive_stop_losses_today >= 3:
-                                        self._buy_paused_until = datetime.now() + timedelta(minutes=30)
-                                        warn_msg = (
-                                            f"⛔ <b>[매수 일시 중단]</b> 당일 손절 "
-                                            f"{self._consecutive_stop_losses_today}회 → "
-                                            f"30분간 매수 정지 (재개: "
-                                            f"{self._buy_paused_until.strftime('%H:%M')})"
-                                        )
-                                        self.messenger.send_message(warn_msg)
-                                        print(f"[Bot] 연속 손절 {self._consecutive_stop_losses_today}회 → 매수 30분 중단")
-                                else:
-                                    # 수익 실현 시 연속 손절 카운터 리셋
-                                    self._consecutive_stop_losses_today = 0
+                                # [v2.1] 손절 횟수 추적 → 연속 N회 손절 시 매수 일시 중단
+                                triggered = self.ledger.record_sell_outcome(yld, datetime.now())
+                                if triggered:
+                                    warn_msg = (
+                                        f"⛔ <b>[매수 일시 중단]</b> 당일 손절 "
+                                        f"{self.ledger.consecutive_stop_losses}회 → "
+                                        f"{self.ledger.stop_loss_pause_minutes}분간 매수 정지 "
+                                        f"(재개: {self.ledger.buy_paused_until.strftime('%H:%M')})"
+                                    )
+                                    self.messenger.send_message(warn_msg)
+                                    print(f"[Bot] 연속 손절 {self.ledger.consecutive_stop_losses}회 → 매수 일시 중단")

                                # 최고가 기록 삭제
                                if ticker in self.peak_prices:
@@ -838,27 +743,6 @@ class AutoTradingBot:
    def loop(self):
        print(f"[Bot] Module Started (PID: {os.getpid()}) [v3.1]")

-        # [캘린더 체크] 오늘이 휴장일이면 알림 후 즉시 EOD 종료
-        if not self._calendar.is_trading_day():
-            summary = self._calendar.status_summary()
-            print(f"[Bot] 오늘은 휴장일 ({summary}) — 봇을 시작하지 않습니다.")
-            self.messenger.send_message(
-                f"[Bot] 오늘은 휴장일입니다.\n{summary}"
-            )
-            # EOD 마커 기록 후 종료
-            try:
-                from pathlib import Path
-                import datetime as _dt
-                eod_file = Path(Config.DATA_DIR) / ".eod_date"
-                eod_file.write_text(str(_dt.date.today()), encoding="utf-8")
-            except Exception:
-                pass
-            if self.eod_event:
-                self.eod_event.set()
-            if self.shutdown_event:
-                self.shutdown_event.set()
-            return
-
        _llm_label = (
            f"Gemini ({Config.GEMINI_MODEL})"
            if Config.GEMINI_API_KEY
--- a/signal_v1/modules/config.py
+++ b/signal_v1/modules/config.py
@@ -1,9 +1,10 @@
 import os
 import sys
+from pathlib import Path
 from dotenv import load_dotenv

 # .env 파일 로드
-load_dotenv()
+load_dotenv(Path(__file__).parent.parent.parent / ".env")

 class Config:
    # 1. 기본 설정
@@ -51,6 +52,11 @@ class Config:
    EOD_SHUTDOWN_BUFFER_MIN = int(os.getenv("EOD_SHUTDOWN_BUFFER_MIN", "5"))  # 장 마감 후 EOD 처리까지 대기 분
    MAX_DAILY_BUY_RATIO = float(os.getenv("MAX_DAILY_BUY_RATIO", "0.80"))  # 예수금 대비 일일 최대 매수 비율

+    # 포트폴리오 리스크 게이트 (v3.2)
+    MAX_TICKERS_PER_THEME = int(os.getenv("MAX_TICKERS_PER_THEME", "2"))       # 테마당 최대 종목 수
+    MAX_THEME_EXPOSURE_RATIO = float(os.getenv("MAX_THEME_EXPOSURE_RATIO", "0.40"))  # 테마당 최대 노출 비율 (총자산 대비)
+    MAX_TOTAL_HOLDINGS = int(os.getenv("MAX_TOTAL_HOLDINGS", "7"))             # 총 보유 종목 수 상한
+    
    # 6. 데이터 경로
    DATA_DIR = os.path.join(BASE_DIR, "data")
    if not os.path.exists(DATA_DIR):
--- a/signal_v1/modules/services/kis.py
+++ b/signal_v1/modules/services/kis.py
--- a/signal_v1/modules/services/llm_client.py
+++ b/signal_v1/modules/services/llm_client.py
--- a/signal_v1/modules/services/news.py
+++ b/signal_v1/modules/services/news.py
@@ -1,6 +1,23 @@
 import time
 import requests
 import xml.etree.ElementTree as ET
+from typing import Optional
+
+
+def _parse_items(root, max_items):
+    """RSS item → [{title, url, pub_date, source}]"""
+    out = []
+    for item in root.findall(".//item")[:max_items]:
+        t = item.find("title")
+        l = item.find("link")
+        p = item.find("pubDate")
+        title = (t.text or "").strip() if t is not None else ""
+        url = (l.text or "").strip() if l is not None else ""
+        pub = (p.text or "").strip() if p is not None else ""
+        if not title:
+            continue
+        out.append({"title": title, "url": url, "pub_date": pub, "source": "Google News"})
+    return out


 class NewsCollector:
@@ -11,24 +28,29 @@ class NewsCollector:
        try:
            resp = requests.get(url, timeout=5)
            root = ET.fromstring(resp.content)
-            items = []
-            for item in root.findall(".//item")[:5]:
-                title = item.find("title").text
-                items.append({"title": title, "source": "Google News"})
-            return items
+            return _parse_items(root, 5)
        except Exception as e:
            print(f"[News] Collection failed: {e}")
            return []


 class AsyncNewsCollector:
-    """비동기 뉴스 수집 + 5분 캐싱"""
+    """비동기 뉴스 수집 + 5분 캐싱 + (옵션) 스냅샷 저장"""

-    def __init__(self):
+    def __init__(self, snapshot_store=None):
        self._cache = None
        self._cache_time = 0
        self._cache_ttl = 300  # 5분
        self._stock_cache = {}  # {stock_name: (items, timestamp)}
+        self._snap = snapshot_store  # NewsSnapshotStore | None
+
+    def _save_snapshot(self, items, query: str, ticker: Optional[str] = None):
+        if not self._snap or not items:
+            return
+        try:
+            self._snap.save_many(items, query=query, ticker=ticker)
+        except Exception as e:
+            print(f"[News] snapshot 저장 실패: {e}")

    def get_market_news(self, query="주식 시장"):
        """동기 인터페이스 (하위 호환)"""
@@ -39,6 +61,7 @@ class AsyncNewsCollector:
        result = NewsCollector.get_market_news(query)
        self._cache = result
        self._cache_time = now
+        self._save_snapshot(result, query=query)
        return result

    async def get_market_news_async(self, query="주식 시장"):
@@ -54,13 +77,10 @@ class AsyncNewsCollector:
                async with session.get(url, timeout=aiohttp.ClientTimeout(total=5)) as resp:
                    content = await resp.read()
                    root = ET.fromstring(content)
-                    items = []
-                    for item in root.findall(".//item")[:5]:
-                        title = item.find("title").text
-                        items.append({"title": title, "source": "Google News"})
-
+                    items = _parse_items(root, 5)
                    self._cache = items
                    self._cache_time = now
+                    self._save_snapshot(items, query=query)
                    return items
        except ImportError:
            return self.get_market_news(query)
@@ -70,9 +90,10 @@ class AsyncNewsCollector:
                return self._cache
            return self.get_market_news(query)

-    async def get_stock_news_async(self, stock_name, max_items=3):
+    async def get_stock_news_async(self, stock_name, max_items=3, ticker: Optional[str] = None):
        """종목별 뉴스 수집 (5분 캐싱)
        stock_name: 종목 이름 (e.g. '삼성전자', 'SK하이닉스')
+        ticker: 스냅샷 저장 시 종목코드 (옵션)
        """
        now = time.time()
        cached = self._stock_cache.get(stock_name)
@@ -88,13 +109,9 @@ class AsyncNewsCollector:
                async with session.get(url, timeout=aiohttp.ClientTimeout(total=5)) as resp:
                    content = await resp.read()
                    root = ET.fromstring(content)
-                    items = []
-                    for item in root.findall(".//item")[:max_items]:
-                        title_el = item.find("title")
-                        if title_el is not None and title_el.text:
-                            items.append({"title": title_el.text, "source": "Google News"})
-
+                    items = _parse_items(root, max_items)
                    self._stock_cache[stock_name] = (items, now)
+                    self._save_snapshot(items, query=f"{stock_name} 주가", ticker=ticker)
                    return items
        except Exception as e:
            print(f"[News] 종목 뉴스 수집 실패 ({stock_name}): {e}")
--- a/signal_v1/modules/services/news_snapshot.py
+++ b/signal_v1/modules/services/news_snapshot.py
@@ -0,0 +1,189 @@
+"""
+뉴스 스냅샷 인프라 (v3.2)
+
+목적:
+  - 수집한 뉴스를 SQLite에 타임스탬프와 함께 영구 저장
+  - 사후 감성 신호 재검증 (LLM 재호출 / 모델 비교) 가능하게
+  - 백테스트에서 '그 시점에 실제로 알 수 있던 뉴스'만 사용
+
+스키마:
+  news_snapshots(
+    id INTEGER PK,
+    captured_at TEXT,      # ISO8601 (KST) — 수집 시점
+    query TEXT,            # 수집 쿼리 (예: '주식 시장', '삼성전자')
+    ticker TEXT,           # 종목 코드 (종목 뉴스일 때, else NULL)
+    title TEXT,
+    url TEXT UNIQUE,
+    pub_date TEXT,         # RSS pubDate 원본
+    source TEXT DEFAULT 'google_news'
+  )
+  sentiment_scores(         # 야간 배치로 사후 생성
+    news_id INTEGER PK,
+    scored_at TEXT,
+    model TEXT,
+    sentiment REAL,        # -1.0 ~ 1.0
+    confidence REAL,
+    raw_json TEXT,
+    FOREIGN KEY (news_id) REFERENCES news_snapshots(id)
+  )
+
+순수 I/O 모듈 — 네트워크 의존성 없음 → unit 테스트 가능.
+"""
+import os
+import sqlite3
+from datetime import datetime, timezone, timedelta
+from typing import Iterable, List, Optional, Dict
+
+KST = timezone(timedelta(hours=9))
+
+
+class NewsSnapshotStore:
+    """
+    SQLite 기반 뉴스 스냅샷 저장소.
+
+    사용 예:
+        store = NewsSnapshotStore("data/news_snapshots.db")
+        store.save_many(items, query="삼성전자", ticker="005930")
+        rows = store.query_between(start, end, ticker="005930")
+    """
+
+    def __init__(self, db_path: str):
+        self.db_path = db_path
+        os.makedirs(os.path.dirname(db_path) or ".", exist_ok=True)
+        self._init_schema()
+
+    # ──────────────────────────────────────────────
+    # 스키마
+    # ──────────────────────────────────────────────
+    def _connect(self) -> sqlite3.Connection:
+        conn = sqlite3.connect(self.db_path)
+        conn.row_factory = sqlite3.Row
+        return conn
+
+    def _init_schema(self):
+        with self._connect() as conn:
+            conn.executescript("""
+                CREATE TABLE IF NOT EXISTS news_snapshots (
+                    id          INTEGER PRIMARY KEY AUTOINCREMENT,
+                    captured_at TEXT NOT NULL,
+                    query       TEXT NOT NULL,
+                    ticker      TEXT,
+                    title       TEXT NOT NULL,
+                    url         TEXT NOT NULL UNIQUE,
+                    pub_date    TEXT,
+                    source      TEXT DEFAULT 'google_news'
+                );
+                CREATE INDEX IF NOT EXISTS idx_news_captured
+                    ON news_snapshots(captured_at);
+                CREATE INDEX IF NOT EXISTS idx_news_ticker
+                    ON news_snapshots(ticker, captured_at);
+
+                CREATE TABLE IF NOT EXISTS sentiment_scores (
+                    news_id     INTEGER PRIMARY KEY,
+                    scored_at   TEXT NOT NULL,
+                    model       TEXT NOT NULL,
+                    sentiment   REAL NOT NULL,
+                    confidence  REAL NOT NULL,
+                    raw_json    TEXT,
+                    FOREIGN KEY (news_id) REFERENCES news_snapshots(id)
+                );
+            """)
+
+    # ──────────────────────────────────────────────
+    # 쓰기
+    # ──────────────────────────────────────────────
+    def save_many(self, items: Iterable[Dict], query: str,
+                  ticker: Optional[str] = None,
+                  captured_at: Optional[datetime] = None) -> int:
+        """
+        뉴스 다건 저장. URL 기준 중복 자동 무시.
+
+        Args:
+            items: [{"title": str, "url": str, "pub_date": str?}, ...]
+
+        Returns:
+            실제로 삽입된 행 수
+        """
+        if captured_at is None:
+            captured_at = datetime.now(KST)
+        ts = captured_at.isoformat()
+
+        rows = []
+        for it in items:
+            title = (it.get("title") or "").strip()
+            url = (it.get("url") or "").strip()
+            if not title or not url:
+                continue
+            rows.append((ts, query, ticker, title, url, it.get("pub_date")))
+
+        if not rows:
+            return 0
+
+        with self._connect() as conn:
+            before = conn.total_changes
+            conn.executemany(
+                "INSERT OR IGNORE INTO news_snapshots "
+                "(captured_at, query, ticker, title, url, pub_date) "
+                "VALUES (?, ?, ?, ?, ?, ?)",
+                rows,
+            )
+            inserted = conn.total_changes - before
+        return inserted
+
+    def save_sentiment(self, news_id: int, model: str,
+                       sentiment: float, confidence: float,
+                       raw_json: str = "",
+                       scored_at: Optional[datetime] = None) -> None:
+        if scored_at is None:
+            scored_at = datetime.now(KST)
+        with self._connect() as conn:
+            conn.execute(
+                "INSERT OR REPLACE INTO sentiment_scores "
+                "(news_id, scored_at, model, sentiment, confidence, raw_json) "
+                "VALUES (?, ?, ?, ?, ?, ?)",
+                (news_id, scored_at.isoformat(), model,
+                 float(sentiment), float(confidence), raw_json),
+            )
+
+    # ──────────────────────────────────────────────
+    # 읽기
+    # ──────────────────────────────────────────────
+    def query_between(self, start: datetime, end: datetime,
+                      ticker: Optional[str] = None,
+                      query: Optional[str] = None) -> List[sqlite3.Row]:
+        """특정 기간 내 수집된 뉴스 조회."""
+        sql = "SELECT * FROM news_snapshots WHERE captured_at >= ? AND captured_at < ?"
+        args = [start.isoformat(), end.isoformat()]
+        if ticker is not None:
+            sql += " AND ticker = ?"
+            args.append(ticker)
+        if query is not None:
+            sql += " AND query = ?"
+            args.append(query)
+        sql += " ORDER BY captured_at ASC"
+        with self._connect() as conn:
+            return list(conn.execute(sql, args))
+
+    def pending_sentiment(self, limit: int = 100) -> List[sqlite3.Row]:
+        """아직 감성 점수가 없는 뉴스 반환 (야간 배치용)."""
+        with self._connect() as conn:
+            return list(conn.execute(
+                """SELECT n.* FROM news_snapshots n
+                   LEFT JOIN sentiment_scores s ON s.news_id = n.id
+                   WHERE s.news_id IS NULL
+                   ORDER BY n.captured_at DESC
+                   LIMIT ?""",
+                (limit,)
+            ))
+
+    def stats(self) -> Dict:
+        """DB 통계 (row 수, 감성 커버리지)."""
+        with self._connect() as conn:
+            total = conn.execute("SELECT COUNT(*) FROM news_snapshots").fetchone()[0]
+            scored = conn.execute("SELECT COUNT(*) FROM sentiment_scores").fetchone()[0]
+            return {
+                "total_news": total,
+                "scored": scored,
+                "pending": total - scored,
+                "coverage_pct": (scored / total * 100) if total else 0.0,
+            }
--- a/signal_v1/modules/services/ollama.py
+++ b/signal_v1/modules/services/ollama.py
--- a/signal_v1/modules/services/telegram.py
+++ b/signal_v1/modules/services/telegram.py
--- a/signal_v1/modules/services/telegram_bot/runner.py
+++ b/signal_v1/modules/services/telegram_bot/runner.py
@@ -6,9 +6,10 @@ import os
 import sys
 import time
 import multiprocessing
+from pathlib import Path
 from dotenv import load_dotenv

-load_dotenv()
+load_dotenv(Path(__file__).parent.parent.parent.parent.parent / ".env")


 def run_telegram_bot_standalone(ipc_lock=None, command_queue=None, shutdown_event=None):
--- a/signal_v1/modules/services/telegram_bot/server.py
+++ b/signal_v1/modules/services/telegram_bot/server.py
--- a/signal_v1/modules/services/telegram_bot/skill_runner.py
+++ b/signal_v1/modules/services/telegram_bot/skill_runner.py
--- a/signal_v1/modules/strategy/daily_ledger.py
+++ b/signal_v1/modules/strategy/daily_ledger.py
@@ -0,0 +1,130 @@
+"""
+일일 거래 장부 (DailyLedger) — v3.2
+
+bot.py에 흩어져 있던 당일 상태를 한 객체로 집약:
+  - 당일 누적 매수금액 (KIS T+2 미차감 보완용)
+  - 연속 손절 카운터 + 매수 일시중단 타이머
+  - 미매도 종목의 매수 신호 점수 (앙상블 학습용)
+  - 일별 스냅샷/주간평가 플래그
+
+날짜가 바뀌면 reset_if_new_day()가 자동 초기화.
+순수 객체로 구현 — 외부 I/O 없음 → 단위 테스트 가능.
+"""
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta, date as date_cls
+from typing import Dict, Optional
+
+
+@dataclass
+class DailyLedger:
+    # ── 당일 매수 회계 ──
+    today_buy_total: int = 0
+    today_buy_date: Optional[date_cls] = None
+
+    # ── 연속 손절 / 매수 일시 중단 ──
+    consecutive_stop_losses: int = 0
+    buy_paused_until: Optional[datetime] = None
+    stop_loss_pause_threshold: int = 3
+    stop_loss_pause_minutes: int = 30
+
+    # ── 앙상블 학습용: 미매도 종목의 매수 신호 점수 ──
+    buy_scores: Dict[str, dict] = field(default_factory=dict)
+
+    # ── 일일 플래그 ──
+    snapshot_taken: bool = False
+    weekly_eval_sent: bool = False
+
+    # ──────────────────────────────────────────────
+    # 날짜 전환
+    # ──────────────────────────────────────────────
+    def reset_if_new_day(self, now: datetime) -> bool:
+        """
+        오늘 날짜 기준으로 상태 초기화. 이미 오늘 자로 초기화됐으면 no-op.
+
+        Returns:
+            True  — 실제로 초기화를 수행한 경우
+            False — 같은 날이라 그대로 둔 경우
+        """
+        today = now.date()
+        if self.today_buy_date == today:
+            return False
+        self.today_buy_total = 0
+        self.today_buy_date = today
+        self.buy_scores.clear()
+        self.snapshot_taken = False
+        self.weekly_eval_sent = False
+        # 연속 손절 카운터 / 일시중단 타이머는 날짜 전환 시에만 초기화
+        self.consecutive_stop_losses = 0
+        self.buy_paused_until = None
+        return True
+
+    # ──────────────────────────────────────────────
+    # 매수 / 매도 기록
+    # ──────────────────────────────────────────────
+    def record_buy(self, ticker: str, amount: int, scores: dict) -> None:
+        """매수 체결 기록. amount는 집행 금액(원), scores는 앙상블 신호."""
+        self.today_buy_total += int(amount)
+        self.buy_scores[ticker] = dict(scores)
+
+    def pop_buy_scores(self, ticker: str) -> Optional[dict]:
+        """매도 체결 시 앙상블 학습을 위해 매수 당시 신호를 반환하고 제거."""
+        return self.buy_scores.pop(ticker, None)
+
+    # ──────────────────────────────────────────────
+    # 손절 관리
+    # ──────────────────────────────────────────────
+    def record_sell_outcome(self, outcome_pct: float, now: datetime) -> bool:
+        """
+        매도 결과를 반영해 연속 손절 카운터 업데이트.
+
+        Returns:
+            True  — 임계치 도달 → 매수 일시중단 활성화됨
+            False — 임계치 미도달
+        """
+        if outcome_pct < 0:
+            self.consecutive_stop_losses += 1
+            if self.consecutive_stop_losses >= self.stop_loss_pause_threshold:
+                self.buy_paused_until = now + timedelta(
+                    minutes=self.stop_loss_pause_minutes
+                )
+                return True
+        else:
+            self.consecutive_stop_losses = 0
+        return False
+
+    def is_buy_paused(self, now: datetime) -> bool:
+        """
+        매수 일시중단 상태 조회. 만료되면 자동 해제 + 카운터 리셋.
+        """
+        if self.buy_paused_until is None:
+            return False
+        if now >= self.buy_paused_until:
+            self.buy_paused_until = None
+            self.consecutive_stop_losses = 0
+            return False
+        return True
+
+    # ──────────────────────────────────────────────
+    # 예수금 계산 (KIS T+2 보완)
+    # ──────────────────────────────────────────────
+    def effective_today_buy(self, kis_today_buy: int) -> int:
+        """
+        KIS API가 반환한 당일 매수금(`thdt_buy_amt`)과
+        로컬 누적값 중 더 큰 값을 신뢰.
+        (모의투자는 T+2 미차감으로 인해 과소 보고되는 경우 있음)
+        """
+        return max(int(kis_today_buy or 0), self.today_buy_total)
+
+    def available_deposit(self, raw_deposit: int, max_daily_buy_ratio: float,
+                          kis_today_buy: int = 0) -> int:
+        """
+        당일 사용 가능한 예수금 계산.
+
+          max_daily_buy = raw_deposit × ratio
+          avail         = min(raw_deposit, max_daily_buy) − effective_today_buy
+        """
+        if raw_deposit <= 0:
+            return 0
+        max_daily_buy = int(raw_deposit * max_daily_buy_ratio)
+        used = self.effective_today_buy(kis_today_buy)
+        return max(0, min(raw_deposit, max_daily_buy) - used)
--- a/signal_v1/modules/strategy/process.py
+++ b/signal_v1/modules/strategy/process.py
--- a/signal_v1/modules/strategy/risk_gate.py
+++ b/signal_v1/modules/strategy/risk_gate.py
@@ -0,0 +1,150 @@
+"""
+포트폴리오 리스크 게이트 (v3.2)
+
+매수 체결 직전 호출되어 포트폴리오 레벨 제약을 검증:
+  1. 총 보유 종목 수 상한
+  2. 테마당 동시 보유 종목 수 상한
+  3. 테마당 노출 금액 비율 상한 (총자산 대비)
+
+기존 매수 필터(예수금, 종목당 상한, 사이클당 매수 수)는 유지하고
+이 게이트가 "같은 테마에 집중되는 포지션"을 차단한다.
+
+순수 함수로 구현 — 의존성 없음 → 단위 테스트 가능.
+"""
+from dataclasses import dataclass
+from typing import Dict, Iterable, List, Optional
+
+
+@dataclass
+class RiskDecision:
+    allowed: bool
+    reason: str = ""
+    max_allowed_amount: int = 0     # 일부만 허용되는 경우 (테마 노출 상한)
+
+
+@dataclass
+class RiskConfig:
+    max_total_holdings: int = 7
+    max_tickers_per_theme: int = 2
+    max_theme_exposure_ratio: float = 0.40
+
+
+class PortfolioRiskGate:
+    """
+    사용 예:
+      gate = PortfolioRiskGate(theme_map, RiskConfig())
+      decision = gate.evaluate_buy(
+          ticker="005930",
+          candidate_amount=3_000_000,
+          current_holdings=[{"ticker":"000660","eval_amount":2_500_000}, ...],
+          total_capital=50_000_000,
+      )
+      if not decision.allowed: skip
+      elif decision.max_allowed_amount < candidate_amount: partial buy
+    """
+
+    def __init__(self, theme_lookup, config: Optional[RiskConfig] = None):
+        """
+        Args:
+            theme_lookup: callable(ticker:str) -> list[str]  (종목→테마 매핑 함수)
+                          혹은 dict 형태도 허용.
+            config: RiskConfig
+        """
+        if callable(theme_lookup):
+            self._theme_of = theme_lookup
+        elif isinstance(theme_lookup, dict):
+            self._theme_of = lambda t: theme_lookup.get(t, [])
+        else:
+            raise TypeError("theme_lookup must be callable or dict")
+        self.config = config or RiskConfig()
+
+    # ──────────────────────────────────────────────
+    # 내부: 테마별 현재 노출 집계
+    # ──────────────────────────────────────────────
+    def _aggregate_by_theme(self, holdings: Iterable[dict]) -> Dict[str, dict]:
+        """
+        Returns:
+            {theme: {"tickers": set, "amount": int}}
+        """
+        agg: Dict[str, dict] = {}
+        for h in holdings:
+            tkr = h.get("ticker")
+            amt = int(h.get("eval_amount", 0) or 0)
+            if not tkr:
+                continue
+            themes = self._theme_of(tkr) or []
+            for th in themes:
+                bucket = agg.setdefault(th, {"tickers": set(), "amount": 0})
+                bucket["tickers"].add(tkr)
+                bucket["amount"] += amt
+        return agg
+
+    # ──────────────────────────────────────────────
+    # 공개 API
+    # ──────────────────────────────────────────────
+    def evaluate_buy(self, ticker: str, candidate_amount: int,
+                     current_holdings: List[dict],
+                     total_capital: int) -> RiskDecision:
+        """
+        매수 허가 여부 판단.
+
+        Returns:
+            RiskDecision
+              - allowed=False: 이유와 함께 차단
+              - allowed=True : max_allowed_amount만큼 허용 (candidate_amount 이하)
+        """
+        if candidate_amount <= 0 or total_capital <= 0:
+            return RiskDecision(False, "invalid_amount")
+
+        cfg = self.config
+
+        # 이미 보유 중이면 추가 매수는 이 게이트 대상 아님 (scale-in은 상위에서 처리)
+        held_tickers = {h.get("ticker") for h in current_holdings}
+        is_new_position = ticker not in held_tickers
+
+        # 1. 총 보유 종목 수 상한
+        if is_new_position and len(held_tickers) >= cfg.max_total_holdings:
+            return RiskDecision(
+                False,
+                f"max_total_holdings: {len(held_tickers)}/{cfg.max_total_holdings}"
+            )
+
+        themes = self._theme_of(ticker) or []
+        if not themes:
+            # 테마 정보 없음 → 테마 제약은 건너뛰고 통과
+            return RiskDecision(True, "no_theme_info", candidate_amount)
+
+        by_theme = self._aggregate_by_theme(current_holdings)
+
+        allowed_amount = candidate_amount
+        blocking_reasons = []
+
+        for th in themes:
+            bucket = by_theme.get(th, {"tickers": set(), "amount": 0})
+
+            # 2. 테마당 종목 수 상한 (신규 포지션일 때만)
+            if is_new_position and len(bucket["tickers"]) >= cfg.max_tickers_per_theme:
+                blocking_reasons.append(
+                    f"theme[{th}] tickers {len(bucket['tickers'])}/{cfg.max_tickers_per_theme}"
+                )
+                continue
+
+            # 3. 테마당 노출 금액 비율 상한
+            max_theme_amount = int(total_capital * cfg.max_theme_exposure_ratio)
+            remaining = max_theme_amount - bucket["amount"]
+            if remaining <= 0:
+                blocking_reasons.append(
+                    f"theme[{th}] exposure {bucket['amount']:,}/{max_theme_amount:,}"
+                )
+                continue
+
+            # 테마 잔여액이 candidate보다 작으면 부분 허용
+            allowed_amount = min(allowed_amount, remaining)
+
+        if blocking_reasons:
+            return RiskDecision(False, "; ".join(blocking_reasons))
+
+        if allowed_amount <= 0:
+            return RiskDecision(False, "theme_exposure_full")
+
+        return RiskDecision(True, "ok", allowed_amount)
--- a/signal_v1/modules/utils/ipc.py
+++ b/signal_v1/modules/utils/ipc.py
--- a/signal_v1/modules/utils/market_calendar.py
+++ b/signal_v1/modules/utils/market_calendar.py
--- a/signal_v1/modules/utils/monitor.py
+++ b/signal_v1/modules/utils/monitor.py
--- a/signal_v1/modules/utils/performance_db.py
+++ b/signal_v1/modules/utils/performance_db.py
--- a/signal_v1/modules/utils/process_tracker.py
+++ b/signal_v1/modules/utils/process_tracker.py
@@ -6,17 +6,12 @@
 """
 import os
 import time
-import datetime
 import threading
-from pathlib import Path
 from multiprocessing.shared_memory import SharedMemory

 from modules.config import Config

 # EOD 마커 파일: 오늘 장 마감 후 봇이 기록, Watchdog가 재시작 여부 결정에 사용
-_EOD_DATE_FILE = Path("data") / ".eod_date"
-
-
 class ProcessTracker:
    """메모리 기반 프로세스 추적기"""

@@ -141,17 +136,6 @@ class ProcessWatchdog:
        entry = self._watched.get(name)
        return entry['process'] if entry else None

-    @staticmethod
-    def is_eod_today() -> bool:
-        """오늘 EOD 마커 파일이 존재하면 True (장 마감 셧다운 → 재시작 차단)"""
-        try:
-            if not _EOD_DATE_FILE.exists():
-                return False
-            eod_date = datetime.date.fromisoformat(_EOD_DATE_FILE.read_text().strip())
-            return eod_date >= datetime.date.today()
-        except Exception:
-            return False
-
    def _watchdog_loop(self):
        """주기적으로 자식 프로세스 상태 확인"""
        import multiprocessing
@@ -170,11 +154,6 @@ class ProcessWatchdog:
                exit_code = proc.exitcode
                restart_count = entry['restart_count']

-                # [EOD 차단] 오늘 장 마감 셧다운이면 재시작하지 않음
-                if ProcessWatchdog.is_eod_today():
-                    print(f"[Watchdog] {name}: EOD 셧다운 감지 — 재시작 건너뜀.")
-                    continue
-
                if restart_count >= Config.MAX_RESTART_COUNT:
                    print(f"[Watchdog] {name} crashed (exit={exit_code}). "
                          f"Max restarts ({Config.MAX_RESTART_COUNT}) reached. Giving up.")
--- a/signal_v1/theme_manager.py
+++ b/signal_v1/theme_manager.py
--- a/signal_v1/warmup_and_restart.py
+++ b/signal_v1/warmup_and_restart.py
--- a/signal_v1/watchlist_manager.py
+++ b/signal_v1/watchlist_manager.py
@@ -1,12 +1,13 @@
 import json
 import os
 from datetime import datetime
+from pathlib import Path
 from dotenv import load_dotenv
 from modules.services.kis import KISClient
 from modules.services.ollama import OllamaManager
 from modules.services.news import NewsCollector

-load_dotenv()
+load_dotenv(Path(__file__).parent.parent / ".env")

 class WatchlistManager:
    """
--- a/signal_v2/init.py
+++ b/signal_v2/init.py
--- a/signal_v2/chronos_predictor.py
+++ b/signal_v2/chronos_predictor.py
@@ -0,0 +1,132 @@
+"""Chronos-2 zero-shot forecaster wrapper."""
+from __future__ import annotations
+import logging
+from dataclasses import dataclass
+from datetime import datetime
+from zoneinfo import ZoneInfo
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+KST = ZoneInfo("Asia/Seoul")
+
+
+@dataclass
+class ChronosPrediction:
+    median: float
+    q10: float
+    q90: float
+    conf: float
+    as_of: str
+
+
+class ChronosPredictor:
+    """HuggingFace Chronos-2 zero-shot forecaster."""
+
+    def __init__(self, model_name: str = "amazon/chronos-2", device: str | None = None):
+        # BaseChronosPipeline auto-detects model variant (Chronos / ChronosBolt / Chronos-2)
+        # and returns the appropriate sub-pipeline. ChronosPipeline only supports legacy T5.
+        import torch
+        try:
+            from chronos import BaseChronosPipeline
+            pipeline_cls = BaseChronosPipeline
+        except ImportError:
+            from chronos import ChronosPipeline
+            pipeline_cls = ChronosPipeline
+
+        self._device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+        # Always use float32 — Korean stock prices (e.g. 280,000원) exceed FP16 max (~65,504)
+        # causing inf in quantile output. FP32 is safe for typical price magnitudes.
+        dtype = torch.float32
+        logger.info("Loading Chronos pipeline: %s on %s (cls=%s)",
+                    model_name, self._device, pipeline_cls.__name__)
+        # Try `dtype` (newer API) first, fall back to `torch_dtype` (older)
+        try:
+            self._pipeline = pipeline_cls.from_pretrained(
+                model_name, device_map=self._device, dtype=dtype,
+            )
+        except TypeError:
+            self._pipeline = pipeline_cls.from_pretrained(
+                model_name, device_map=self._device, torch_dtype=dtype,
+            )
+        logger.info("Chronos pipeline loaded.")
+
+    def predict_batch(
+        self,
+        daily_ohlcv_dict: dict[str, list[dict]],
+        prediction_length: int = 1,
+        num_samples: int = 100,
+    ) -> dict[str, ChronosPrediction]:
+        """종목별 1-day return 분포 예측.
+
+        ChronosBolt / Chronos-2 등 신모델은 predict_quantiles 사용 (deterministic).
+        Legacy ChronosPipeline (T5) 는 sample-based predict.
+        """
+        import torch
+
+        tickers = list(daily_ohlcv_dict.keys())
+        if not tickers:
+            return {}
+
+        contexts = [
+            torch.tensor([bar["close"] for bar in daily_ohlcv_dict[t]], dtype=torch.float32)
+            for t in tickers
+        ]
+        now_iso = datetime.now(KST).isoformat()
+        results: dict[str, ChronosPrediction] = {}
+
+        # Modern API: predict_quantiles (ChronosBolt / Chronos-2)
+        if hasattr(self._pipeline, "predict_quantiles"):
+            quantile_levels = [0.1, 0.5, 0.9]
+            # ChronosBolt API: positional `inputs` (first arg). Older variants use `context`.
+            try:
+                quantiles_tensor, _ = self._pipeline.predict_quantiles(
+                    contexts,
+                    prediction_length=prediction_length,
+                    quantile_levels=quantile_levels,
+                )
+            except TypeError:
+                quantiles_tensor, _ = self._pipeline.predict_quantiles(
+                    context=contexts,
+                    prediction_length=prediction_length,
+                    quantile_levels=quantile_levels,
+                )
+            quantiles_np = (
+                quantiles_tensor.cpu().numpy()
+                if hasattr(quantiles_tensor, "cpu")
+                else np.asarray(quantiles_tensor)
+            )
+            # shape: [num_series, prediction_length, 3]
+            for i, ticker in enumerate(tickers):
+                q10_price, q50_price, q90_price = quantiles_np[i, 0, :]
+                last_close = daily_ohlcv_dict[ticker][-1]["close"]
+                median = float((q50_price - last_close) / last_close)
+                q10 = float((q10_price - last_close) / last_close)
+                q90 = float((q90_price - last_close) / last_close)
+                spread = (q90 - q10) / max(abs(median), 0.001)
+                conf = float(max(0.0, min(1.0, 1.0 - spread / 2.0)))
+                results[ticker] = ChronosPrediction(
+                    median=median, q10=q10, q90=q90, conf=conf, as_of=now_iso,
+                )
+            return results
+
+        # Legacy API: sample-based predict (ChronosPipeline T5)
+        forecasts = self._pipeline.predict(
+            context=contexts,
+            prediction_length=prediction_length,
+            num_samples=num_samples,
+        )
+        forecasts_np = forecasts.numpy() if hasattr(forecasts, "numpy") else np.asarray(forecasts)
+        for i, ticker in enumerate(tickers):
+            samples = forecasts_np[i, :, 0]
+            last_close = daily_ohlcv_dict[ticker][-1]["close"]
+            returns = (samples - last_close) / last_close
+            median = float(np.quantile(returns, 0.5))
+            q10 = float(np.quantile(returns, 0.1))
+            q90 = float(np.quantile(returns, 0.9))
+            spread = (q90 - q10) / max(abs(median), 0.001)
+            conf = float(max(0.0, min(1.0, 1.0 - spread / 2.0)))
+            results[ticker] = ChronosPrediction(
+                median=median, q10=q10, q90=q90, conf=conf, as_of=now_iso,
+            )
+        return results
--- a/signal_v2/config.py
+++ b/signal_v2/config.py
@@ -0,0 +1,75 @@
+"""Signal V2 환경변수 로딩."""
+import os
+from dataclasses import dataclass, field
+from pathlib import Path
+
+from dotenv import load_dotenv
+
+load_dotenv(Path(__file__).parent.parent / ".env")
+
+
+@dataclass(frozen=True)
+class Settings:
+    stock_api_url: str = field(
+        default_factory=lambda: os.getenv("STOCK_API_URL", "").rstrip("/")
+    )
+    webai_api_key: str = field(
+        default_factory=lambda: os.getenv("WEBAI_API_KEY", "").strip()
+    )
+    port: int = field(default_factory=lambda: int(os.getenv("SIGNAL_V2_PORT", "8001")))
+    db_path: Path = field(
+        default_factory=lambda: Path(__file__).parent / "data" / "signal_v2.db"
+    )
+    # KIS — V1 호환 패턴 (KIS_ENV_TYPE virtual/real)
+    kis_env_type: str = field(default_factory=lambda: os.getenv("KIS_ENV_TYPE", "virtual").lower())
+    kis_real_app_key: str = field(default_factory=lambda: os.getenv("KIS_REAL_APP_KEY", "").strip())
+    kis_real_app_secret: str = field(default_factory=lambda: os.getenv("KIS_REAL_APP_SECRET", "").strip())
+    kis_real_account: str = field(default_factory=lambda: os.getenv("KIS_REAL_ACCOUNT", "").strip())
+    kis_virtual_app_key: str = field(default_factory=lambda: os.getenv("KIS_VIRTUAL_APP_KEY", "").strip())
+    kis_virtual_app_secret: str = field(default_factory=lambda: os.getenv("KIS_VIRTUAL_APP_SECRET", "").strip())
+    kis_virtual_account: str = field(default_factory=lambda: os.getenv("KIS_VIRTUAL_ACCOUNT", "").strip())
+    v1_token_path: Path = field(
+        default_factory=lambda: Path(
+            os.getenv("V1_TOKEN_PATH",
+                      str(Path(__file__).parent.parent / "signal_v1" / "data" / "kis_token.json"))
+        )
+    )
+    chronos_model: str = field(default_factory=lambda: os.getenv("CHRONOS_MODEL", "amazon/chronos-2"))
+    stop_loss_pct: float = field(
+        default_factory=lambda: float(os.getenv("STOP_LOSS_PCT", "-0.07"))
+    )
+    take_profit_pct: float = field(
+        default_factory=lambda: float(os.getenv("TAKE_PROFIT_PCT", "0.15"))
+    )
+    chronos_spread_threshold: float = field(
+        default_factory=lambda: float(os.getenv("CHRONOS_SPREAD_THRESHOLD", "0.6"))
+    )
+    asking_bid_ratio_threshold: float = field(
+        default_factory=lambda: float(os.getenv("ASKING_BID_RATIO_THRESHOLD", "0.6"))
+    )
+    confidence_threshold: float = field(
+        default_factory=lambda: float(os.getenv("CONFIDENCE_THRESHOLD", "0.7"))
+    )
+    min_momentum_for_buy: str = field(
+        default_factory=lambda: os.getenv("MIN_MOMENTUM_FOR_BUY", "strong_up")
+    )
+
+    @property
+    def kis_is_virtual(self) -> bool:
+        return self.kis_env_type != "real"
+
+    @property
+    def kis_app_key(self) -> str:
+        return self.kis_real_app_key if self.kis_env_type == "real" else self.kis_virtual_app_key
+
+    @property
+    def kis_app_secret(self) -> str:
+        return self.kis_real_app_secret if self.kis_env_type == "real" else self.kis_virtual_app_secret
+
+    @property
+    def kis_account(self) -> str:
+        return self.kis_real_account if self.kis_env_type == "real" else self.kis_virtual_account
+
+
+def get_settings() -> Settings:
+    return Settings()
--- a/signal_v2/data/.gitkeep
+++ b/signal_v2/data/.gitkeep
--- a/signal_v2/holidays.json
+++ b/signal_v2/holidays.json
@@ -0,0 +1,18 @@
+[
+  "2026-01-01",
+  "2026-01-28",
+  "2026-01-29",
+  "2026-01-30",
+  "2026-03-01",
+  "2026-05-05",
+  "2026-05-25",
+  "2026-06-06",
+  "2026-08-15",
+  "2026-09-24",
+  "2026-09-25",
+  "2026-09-26",
+  "2026-10-03",
+  "2026-10-09",
+  "2026-12-25",
+  "2026-12-31"
+]
--- a/signal_v2/kis_client.py
+++ b/signal_v2/kis_client.py
@@ -0,0 +1,193 @@
+"""KIS REST API client — 분봉 + 호가. V1 토큰 read-only 공유."""
+from __future__ import annotations
+import asyncio
+import json
+import logging
+import time
+from datetime import datetime, timedelta
+from pathlib import Path
+from zoneinfo import ZoneInfo
+
+import httpx
+
+logger = logging.getLogger(__name__)
+KST = ZoneInfo("Asia/Seoul")
+
+_MAX_ATTEMPTS = 3
+_THROTTLE_INTERVAL = 0.5  # 초당 2회 제한
+
+
+class KISClient:
+    """KIS REST (분봉 + 호가). V1 토큰 파일 read-only."""
+
+    def __init__(
+        self,
+        app_key: str, app_secret: str, account: str, is_virtual: bool,
+        v1_token_path: Path,
+        timeout: float = 10.0,
+    ):
+        self._app_key = app_key
+        self._app_secret = app_secret
+        self._account = account
+        self._is_virtual = is_virtual
+        self._v1_token_path = Path(v1_token_path)
+        self._base_url = (
+            "https://openapivts.koreainvestment.com:29443" if is_virtual
+            else "https://openapi.koreainvestment.com:9443"
+        )
+        self._client = httpx.AsyncClient(timeout=timeout)
+        self._token_cache: tuple[str, float] | None = None  # (token, file_mtime)
+        self._last_throttle_at = 0.0
+
+    async def close(self) -> None:
+        await self._client.aclose()
+
+    def _read_v1_token(self) -> str:
+        if not self._v1_token_path.exists():
+            raise RuntimeError(f"V1 token file missing: {self._v1_token_path}")
+        mtime = self._v1_token_path.stat().st_mtime
+        if self._token_cache and self._token_cache[1] == mtime:
+            return self._token_cache[0]
+        data = json.loads(self._v1_token_path.read_text(encoding="utf-8"))
+        token = data.get("access_token", "")
+        if not token:
+            raise RuntimeError("V1 token file has no access_token")
+        self._token_cache = (token, mtime)
+        return token
+
+    async def _throttle(self) -> None:
+        elapsed = time.monotonic() - self._last_throttle_at
+        if elapsed < _THROTTLE_INTERVAL:
+            await asyncio.sleep(_THROTTLE_INTERVAL - elapsed)
+        self._last_throttle_at = time.monotonic()
+
+    def _common_headers(self, tr_id: str) -> dict[str, str]:
+        token = self._read_v1_token()
+        return {
+            "authorization": f"Bearer {token}",
+            "appkey": self._app_key,
+            "appsecret": self._app_secret,
+            "tr_id": tr_id,
+            "custtype": "P",
+        }
+
+    async def _request_with_retry(
+        self, method: str, path: str, tr_id: str, **kwargs,
+    ) -> dict:
+        url = f"{self._base_url}{path}"
+        headers = self._common_headers(tr_id)
+        for attempt in range(_MAX_ATTEMPTS):
+            await self._throttle()
+            try:
+                response = await self._client.request(
+                    method, url, headers=headers, **kwargs
+                )
+                if response.status_code == 429:
+                    if attempt < _MAX_ATTEMPTS - 1:
+                        await asyncio.sleep(2**attempt)
+                        continue
+                    response.raise_for_status()
+                response.raise_for_status()
+                return response.json()
+            except httpx.TimeoutException:
+                if attempt < _MAX_ATTEMPTS - 1:
+                    await asyncio.sleep(2**attempt)
+                    continue
+                raise
+        raise RuntimeError("retry exhausted")
+
+    async def get_minute_ohlcv(self, ticker: str) -> list[dict]:
+        """현재 시점 직전 30개 1분봉 OHLCV (TR_ID FHKST03010200)."""
+        path = "/uapi/domestic-stock/v1/quotations/inquire-time-itemchartprice"
+        params = {
+            "FID_ETC_CLS_CODE": "",
+            "FID_COND_MRKT_DIV_CODE": "J",
+            "FID_INPUT_ISCD": ticker,
+            "FID_INPUT_HOUR_1": datetime.now(KST).strftime("%H%M%S"),
+            "FID_PW_DATA_INCU_YN": "N",
+        }
+        raw = await self._request_with_retry(
+            "GET", path, tr_id="FHKST03010200", params=params,
+        )
+        output2 = raw.get("output2", [])
+        bars = []
+        for row in output2:
+            try:
+                date = row["stck_bsop_date"]
+                hhmmss = row["stck_cntg_hour"]
+                dt = datetime.strptime(f"{date} {hhmmss}", "%Y%m%d %H%M%S").replace(tzinfo=KST)
+                bars.append({
+                    "datetime": dt.isoformat(),
+                    "open": int(row["stck_oprc"]),
+                    "high": int(row["stck_hgpr"]),
+                    "low": int(row["stck_lwpr"]),
+                    "close": int(row["stck_prpr"]),
+                    "volume": int(row["cntg_vol"]),
+                })
+            except (KeyError, ValueError) as e:
+                logger.warning("skip malformed bar for %s: %r", ticker, e)
+        # KIS returns descending; reverse to ascending (most recent last)
+        bars.reverse()
+        return bars
+
+    async def get_asking_price(self, ticker: str) -> dict:
+        """현재 호가 + 매수/매도 잔량 (TR_ID FHKST01010200)."""
+        path = "/uapi/domestic-stock/v1/quotations/inquire-asking-price-exp-ccn"
+        params = {
+            "FID_COND_MRKT_DIV_CODE": "J",
+            "FID_INPUT_ISCD": ticker,
+        }
+        raw = await self._request_with_retry(
+            "GET", path, tr_id="FHKST01010200", params=params,
+        )
+        output1 = raw.get("output1", {})
+        bid_total = int(output1.get("total_bidp_rsqn", 0))
+        ask_total = int(output1.get("total_askp_rsqn", 0))
+        total = bid_total + ask_total
+        bid_ratio = bid_total / total if total > 0 else 0.0
+        current_price = int(output1.get("stck_prpr", 0))
+        return {
+            "bid_total": bid_total,
+            "ask_total": ask_total,
+            "bid_ratio": bid_ratio,
+            "current_price": current_price,
+            "as_of": datetime.now(KST).isoformat(),
+        }
+
+    async def get_daily_ohlcv(self, ticker: str, days: int = 60) -> list[dict]:
+        """KRX 일봉 OHLCV (TR_ID FHKST03010100).
+
+        Returns: [{"datetime", "open", "high", "low", "close", "volume"}, ...]
+                 시간 오름차순.
+        """
+        path = "/uapi/domestic-stock/v1/quotations/inquire-daily-itemchartprice"
+        today = datetime.now(KST).strftime("%Y%m%d")
+        start_date = (datetime.now(KST) - timedelta(days=days * 2)).strftime("%Y%m%d")
+        params = {
+            "FID_COND_MRKT_DIV_CODE": "J",
+            "FID_INPUT_ISCD": ticker,
+            "FID_INPUT_DATE_1": start_date,
+            "FID_INPUT_DATE_2": today,
+            "FID_PERIOD_DIV_CODE": "D",
+            "FID_ORG_ADJ_PRC": "1",
+        }
+        raw = await self._request_with_retry(
+            "GET", path, tr_id="FHKST03010100", params=params,
+        )
+        output2 = raw.get("output2", [])
+        bars = []
+        for row in output2:
+            try:
+                date = row["stck_bsop_date"]
+                bars.append({
+                    "datetime": f"{date[:4]}-{date[4:6]}-{date[6:]}",
+                    "open": int(row["stck_oprc"]),
+                    "high": int(row["stck_hgpr"]),
+                    "low": int(row["stck_lwpr"]),
+                    "close": int(row["stck_clpr"]),
+                    "volume": int(row["acml_vol"]),
+                })
+            except (KeyError, ValueError):
+                continue
+        bars.reverse()
+        return bars[-days:]
--- a/signal_v2/kis_websocket.py
+++ b/signal_v2/kis_websocket.py
@@ -0,0 +1,186 @@
+"""KIS WebSocket — approval_key + 실시간 호가 구독."""
+from __future__ import annotations
+import asyncio
+import json
+import logging
+from datetime import datetime
+from typing import Callable
+from zoneinfo import ZoneInfo
+
+import httpx
+import websockets
+
+logger = logging.getLogger(__name__)
+KST = ZoneInfo("Asia/Seoul")
+
+# KIS 호가 메시지 필드 인덱스 (운영 환경 검증 필요)
+# H0STASP0 응답: ticker | time | current_price | ... | ask_total | bid_total
+# 본 spec/plan 의 가정: 마지막 2개 필드가 ask_total / bid_total
+_ASKING_TICKER_IDX = 0
+_ASKING_TIME_IDX = 1
+_ASKING_CURRENT_PRICE_IDX = 2
+_ASKING_TOTAL_ASK_IDX = -2
+_ASKING_TOTAL_BID_IDX = -1
+
+
+class KISWebSocket:
+    """KIS WebSocket client. approval_key 발급 + 호가 실시간."""
+
+    def __init__(self, app_key: str, app_secret: str, is_virtual: bool):
+        self._app_key = app_key
+        self._app_secret = app_secret
+        self._is_virtual = is_virtual
+        self._base_rest = (
+            "https://openapivts.koreainvestment.com:29443" if is_virtual
+            else "https://openapi.koreainvestment.com:9443"
+        )
+        self._ws_url = (
+            "ws://ops.koreainvestment.com:31000" if is_virtual
+            else "ws://ops.koreainvestment.com:21000"
+        )
+        self._approval_key: str | None = None
+        self._ws = None
+        self._subscriptions: set[str] = set()
+        self._on_asking_price: Callable[[str, dict], None] | None = None
+        self._recv_task: asyncio.Task | None = None
+        self._shutdown = asyncio.Event()
+
+    async def _fetch_approval_key(self) -> str:
+        async with httpx.AsyncClient(timeout=10.0) as client:
+            response = await client.post(
+                f"{self._base_rest}/oauth2/Approval",
+                json={
+                    "grant_type": "client_credentials",
+                    "appkey": self._app_key,
+                    "secretkey": self._app_secret,
+                },
+            )
+            response.raise_for_status()
+            data = response.json()
+            self._approval_key = data["approval_key"]
+            return self._approval_key
+
+    async def _connect(self):
+        return await websockets.connect(self._ws_url)
+
+    async def _connect_with_backoff(self):
+        """연결 시도 with exponential backoff (1s → 2s → 4s → max 30s)."""
+        for attempt in range(10):
+            try:
+                ws = await self._connect()
+                return ws
+            except Exception as e:
+                wait = min(2**attempt, 30)
+                logger.warning(
+                    "KIS WebSocket connect failed (attempt %d): %r — retrying in %ds",
+                    attempt + 1, e, wait,
+                )
+                await asyncio.sleep(wait)
+        raise RuntimeError("KIS WebSocket connect exhausted retries")
+
+    async def start(
+        self, tickers: list[str],
+        on_asking_price: Callable[[str, dict], None],
+    ) -> None:
+        if self._approval_key is None:
+            await self._fetch_approval_key()
+        self._on_asking_price = on_asking_price
+        self._ws = await self._connect_with_backoff()
+        for ticker in tickers:
+            await self.subscribe(ticker)
+        self._recv_task = asyncio.create_task(self._receive_loop())
+
+    async def subscribe(self, ticker: str) -> None:
+        if self._ws is None or self._approval_key is None:
+            raise RuntimeError("KIS WebSocket not started")
+        msg = json.dumps({
+            "header": {
+                "approval_key": self._approval_key,
+                "custtype": "P",
+                "tr_type": "1",
+                "content-type": "utf-8",
+            },
+            "body": {
+                "input": {"tr_id": "H0STASP0", "tr_key": ticker},
+            },
+        })
+        await self._ws.send(msg)
+        self._subscriptions.add(ticker)
+
+    async def unsubscribe(self, ticker: str) -> None:
+        if self._ws is None or self._approval_key is None:
+            return
+        msg = json.dumps({
+            "header": {
+                "approval_key": self._approval_key,
+                "custtype": "P",
+                "tr_type": "2",
+                "content-type": "utf-8",
+            },
+            "body": {
+                "input": {"tr_id": "H0STASP0", "tr_key": ticker},
+            },
+        })
+        await self._ws.send(msg)
+        self._subscriptions.discard(ticker)
+
+    async def close(self) -> None:
+        self._shutdown.set()
+        if self._recv_task is not None:
+            self._recv_task.cancel()
+            try:
+                await self._recv_task
+            except asyncio.CancelledError:
+                pass
+        if self._ws is not None:
+            await self._ws.close()
+
+    async def _receive_loop(self) -> None:
+        while not self._shutdown.is_set():
+            try:
+                raw = await self._ws.recv()
+            except websockets.ConnectionClosed:
+                logger.warning("KIS WebSocket closed — reconnecting")
+                self._ws = await self._connect_with_backoff()
+                for ticker in list(self._subscriptions):
+                    await self.subscribe(ticker)
+                continue
+            if not isinstance(raw, str):
+                continue
+            parsed = self._parse_asking_price(raw)
+            if parsed is not None and self._on_asking_price is not None:
+                ticker, data = parsed
+                try:
+                    self._on_asking_price(ticker, data)
+                except Exception:
+                    logger.exception("on_asking_price callback failed")
+
+    def _parse_asking_price(self, raw: str) -> tuple[str, dict] | None:
+        """KIS H0STASP0 raw → (ticker, asking_price dict).
+
+        Raw format: '0|H0STASP0|<count>|<data>' where data = '^'-joined fields.
+        Field indices (운영 검증 필요): 마지막 2개 가정 (ask, bid).
+        """
+        try:
+            parts = raw.split("|")
+            if len(parts) < 4 or parts[1] != "H0STASP0":
+                return None
+            fields = parts[3].split("^")
+            ticker = fields[_ASKING_TICKER_IDX]
+            current_price_str = fields[_ASKING_CURRENT_PRICE_IDX]
+            current_price = int(current_price_str) if current_price_str.lstrip("-").isdigit() else 0
+            ask_str = fields[_ASKING_TOTAL_ASK_IDX]
+            bid_str = fields[_ASKING_TOTAL_BID_IDX]
+            ask_total = int(ask_str) if ask_str.lstrip("-").isdigit() else 0
+            bid_total = int(bid_str) if bid_str.lstrip("-").isdigit() else 0
+            total = bid_total + ask_total
+            return ticker, {
+                "bid_total": bid_total,
+                "ask_total": ask_total,
+                "bid_ratio": bid_total / total if total > 0 else 0.0,
+                "current_price": current_price,
+                "as_of": datetime.now(KST).isoformat(),
+            }
+        except (IndexError, ValueError) as e:
+            logger.warning("parse_asking_price failed: %r", e)
+            return None
--- a/signal_v2/main.py
+++ b/signal_v2/main.py
@@ -0,0 +1,125 @@
+"""FastAPI app — Signal V2 Pull Worker."""
+from __future__ import annotations
+import asyncio
+import logging
+from contextlib import asynccontextmanager
+
+from fastapi import FastAPI
+
+from signal_v2 import state as state_mod
+from signal_v2.chronos_predictor import ChronosPredictor
+from signal_v2.config import get_settings
+from signal_v2.kis_client import KISClient
+from signal_v2.kis_websocket import KISWebSocket
+from signal_v2.pull_worker import poll_loop, make_asking_price_callback
+from signal_v2.rate_limit import SignalDedup
+from signal_v2.stock_client import StockClient
+
+logger = logging.getLogger(__name__)
+
+
+class AppContext:
+    client: StockClient | None = None
+    dedup: SignalDedup | None = None
+    shutdown: asyncio.Event | None = None
+    poll_task: asyncio.Task | None = None
+    kis_client: KISClient | None = None
+    kis_ws: KISWebSocket | None = None
+    chronos: ChronosPredictor | None = None
+
+
+_ctx = AppContext()
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    settings = get_settings()
+    if not settings.webai_api_key:
+        logger.warning(
+            "WEBAI_API_KEY not configured — stock API calls will fail with 401"
+        )
+    if not settings.kis_app_key:
+        logger.warning(
+            "KIS app_key not configured (KIS_ENV_TYPE=%s, KIS_%s_APP_KEY missing) — KIS REST/WebSocket disabled",
+            settings.kis_env_type, settings.kis_env_type.upper()
+        )
+
+    _ctx.client = StockClient(settings.stock_api_url, settings.webai_api_key)
+    _ctx.dedup = SignalDedup(settings.db_path)
+    _ctx.shutdown = asyncio.Event()
+
+    # KIS only if app_key configured
+    if settings.kis_app_key:
+        _ctx.kis_client = KISClient(
+            app_key=settings.kis_app_key,
+            app_secret=settings.kis_app_secret,
+            account=settings.kis_account,
+            is_virtual=settings.kis_is_virtual,
+            v1_token_path=settings.v1_token_path,
+        )
+        _ctx.kis_ws = KISWebSocket(
+            app_key=settings.kis_app_key,
+            app_secret=settings.kis_app_secret,
+            is_virtual=settings.kis_is_virtual,
+        )
+        # Subscribe portfolio holdings (if any)
+        try:
+            portfolio = await _ctx.client.get_portfolio()
+            tickers = [h["ticker"] for h in portfolio.get("holdings", []) if "ticker" in h]
+            cb = make_asking_price_callback(state_mod.state)
+            await _ctx.kis_ws.start(tickers, cb)
+        except Exception:
+            logger.exception("KIS WebSocket startup failed — continuing without realtime asking_price")
+
+        # Load Chronos (heavy: ~1GB model download first time)
+        try:
+            _ctx.chronos = ChronosPredictor(model_name=settings.chronos_model)
+        except Exception:
+            logger.exception("ChronosPredictor load failed — continuing without chronos predictions")
+
+    _ctx.poll_task = asyncio.create_task(
+        poll_loop(
+            _ctx.client, state_mod.state, _ctx.shutdown,
+            kis_client=_ctx.kis_client,
+            chronos=_ctx.chronos,
+            dedup=_ctx.dedup,
+            settings=settings,
+        )
+    )
+
+    yield
+
+    # Shutdown
+    if _ctx.shutdown is not None:
+        _ctx.shutdown.set()
+    if _ctx.poll_task is not None:
+        try:
+            await asyncio.wait_for(_ctx.poll_task, timeout=5.0)
+        except asyncio.TimeoutError:
+            _ctx.poll_task.cancel()
+            try:
+                await _ctx.poll_task
+            except asyncio.CancelledError:
+                pass
+    if _ctx.kis_ws is not None:
+        await _ctx.kis_ws.close()
+    if _ctx.kis_client is not None:
+        await _ctx.kis_client.close()
+    if _ctx.client is not None:
+        await _ctx.client.close()
+
+
+app = FastAPI(
+    title="Signal V2 Pull Worker", version="0.1.0", lifespan=lifespan
+)
+
+
+@app.get("/health")
+async def health():
+    settings = get_settings()
+    return {
+        "status": "online",
+        "stock_api_url": settings.stock_api_url,
+        "last_poll": state_mod.state.last_updated,
+        "cache_size": _ctx.client.cache_size() if _ctx.client is not None else 0,
+    }
--- a/signal_v2/momentum_classifier.py
+++ b/signal_v2/momentum_classifier.py
@@ -0,0 +1,69 @@
+"""분봉 OHLCV → 5-level 모멘텀 분류."""
+from __future__ import annotations
+from collections import deque
+
+# 분류 카테고리
+STRONG_UP = "strong_up"
+WEAK_UP = "weak_up"
+NEUTRAL = "neutral"
+WEAK_DOWN = "weak_down"
+STRONG_DOWN = "strong_down"
+
+_BARS_PER_5MIN = 5
+_LOOKBACK_5MIN_BARS = 5
+_VOLUME_AVG_WINDOW = 12  # 60분 = 5분봉 12개
+
+
+def aggregate_1min_to_5min(minute_bars: list[dict]) -> list[dict]:
+    """1분봉 N개 → 5분봉 floor(N/5) 개. 시간 오름차순.
+
+    각 5분봉: open=첫 1분봉 open, high=max, low=min, close=마지막 close, volume=sum.
+    """
+    bars_5min = []
+    chunks = len(minute_bars) // _BARS_PER_5MIN
+    for i in range(chunks):
+        chunk = minute_bars[i * _BARS_PER_5MIN : (i + 1) * _BARS_PER_5MIN]
+        bars_5min.append({
+            "datetime": chunk[0]["datetime"],
+            "open": chunk[0]["open"],
+            "high": max(b["high"] for b in chunk),
+            "low": min(b["low"] for b in chunk),
+            "close": chunk[-1]["close"],
+            "volume": sum(b["volume"] for b in chunk),
+        })
+    return bars_5min
+
+
+def classify_minute_momentum(minute_bars: deque) -> str:
+    """1분봉 deque → 5-level 모멘텀 분류.
+
+    Returns: STRONG_UP / WEAK_UP / NEUTRAL / WEAK_DOWN / STRONG_DOWN
+    """
+    minute_list = list(minute_bars)
+    if len(minute_list) < _BARS_PER_5MIN * _LOOKBACK_5MIN_BARS:
+        return NEUTRAL  # 데이터 부족
+
+    bars_5min = aggregate_1min_to_5min(minute_list)
+    if len(bars_5min) < _LOOKBACK_5MIN_BARS:
+        return NEUTRAL
+
+    recent = bars_5min[-_LOOKBACK_5MIN_BARS:]
+    up_count = sum(1 for b in recent if b["close"] > b["open"])
+
+    # 거래량 multiplier: recent 5 avg vs 60분 avg
+    recent_vol_avg = sum(b["volume"] for b in recent) / len(recent)
+    long_window = bars_5min[-_VOLUME_AVG_WINDOW:]
+    long_vol_avg = sum(b["volume"] for b in long_window) / len(long_window)
+    vol_mult = recent_vol_avg / long_vol_avg if long_vol_avg > 0 else 1.0
+
+    # 5-level 분류
+    if up_count == 5 and vol_mult >= 1.5:
+        return STRONG_UP
+    elif up_count >= 3 and vol_mult >= 1.0:
+        return WEAK_UP
+    elif up_count == 0 and vol_mult >= 1.5:
+        return STRONG_DOWN
+    elif up_count <= 2 and vol_mult < 1.0:
+        return WEAK_DOWN
+    else:
+        return NEUTRAL
--- a/signal_v2/pull_worker.py
+++ b/signal_v2/pull_worker.py
@@ -0,0 +1,193 @@
+"""Polling loop — async cron + state update."""
+from __future__ import annotations
+import asyncio
+import logging
+from collections import deque
+from datetime import datetime
+
+from signal_v2.kis_client import KISClient
+from signal_v2.scheduler import (
+    KST, _is_market_day, _is_polling_window, _next_interval, _is_post_close_trigger,
+)
+from signal_v2.state import PollState
+from signal_v2.stock_client import StockClient
+
+logger = logging.getLogger(__name__)
+
+
+async def poll_loop(
+    client: StockClient, state: PollState, shutdown: asyncio.Event,
+    kis_client: KISClient | None = None,
+    chronos=None,
+    dedup=None,
+    settings=None,
+) -> None:
+    """FastAPI lifespan 에서 asyncio.create_task 로 시작."""
+    logger.info("poll_loop started")
+    while not shutdown.is_set():
+        now = datetime.now(KST)
+        if _is_market_day(now) and _is_polling_window(now):
+            try:
+                await _run_polling_cycle(client, state, kis_client=kis_client)
+            except Exception:
+                logger.exception("poll cycle failed")
+            # Minute momentum 갱신 (매 cycle)
+            try:
+                update_minute_momentum_for_all(state)
+            except Exception:
+                logger.exception("minute momentum update failed")
+            # Post-close trigger (16:00 KST)
+            if _is_post_close_trigger(now) and chronos is not None and kis_client is not None:
+                try:
+                    await _run_post_close_cycle(kis_client, chronos, state)
+                except Exception:
+                    logger.exception("post-close cycle failed")
+            # Phase 4: generate signals
+            if dedup is not None and settings is not None:
+                try:
+                    from signal_v2.signal_generator import generate_signals
+                    generate_signals(state, dedup, settings)
+                except Exception:
+                    logger.exception("generate_signals failed")
+        interval = _next_interval(now)
+        try:
+            await asyncio.wait_for(shutdown.wait(), timeout=interval)
+            break
+        except asyncio.TimeoutError:
+            continue
+    logger.info("poll_loop ended")
+
+
+async def _run_polling_cycle(
+    client: StockClient, state: PollState,
+    kis_client: KISClient | None = None,
+) -> None:
+    """기존 3 endpoint (stock) + KIS 분봉 fetch."""
+    portfolio, sentiment, screener = await asyncio.gather(
+        client.get_portfolio(),
+        client.get_news_sentiment(),
+        client.run_screener_preview(),
+        return_exceptions=True,
+    )
+    now_iso = datetime.now(KST).isoformat()
+
+    for name, result in (
+        ("portfolio", portfolio),
+        ("news_sentiment", sentiment),
+        ("screener_preview", screener),
+    ):
+        if isinstance(result, dict):
+            setattr(state, name, result)
+            state.last_updated[name] = now_iso
+            state.fetch_errors[name] = 0
+        else:
+            state.fetch_errors[name] = state.fetch_errors.get(name, 0) + 1
+            logger.warning("fetch %s failed: %r", name, result)
+
+    # KIS 분봉 + 호가 (kis_client 주어졌을 때만)
+    if kis_client is not None:
+        try:
+            await _run_kis_minute_cycle(kis_client, state)
+        except Exception:
+            logger.exception("kis minute cycle failed")
+
+
+async def _run_kis_minute_cycle(kis_client: KISClient, state: PollState) -> None:
+    """KIS 분봉 + 호가 fetch + state 갱신.
+
+    - 분봉: portfolio + screener Top-N union 종목 모두
+    - 호가 (REST): screener-only 종목 (portfolio 는 WebSocket 으로 들어옴)
+    """
+    portfolio_tickers = _portfolio_tickers(state)
+    screener_tickers = _screener_tickers(state)
+    all_tickers = list(set(portfolio_tickers) | set(screener_tickers))
+
+    # 분봉 fetch (병렬)
+    minute_results = await asyncio.gather(*[
+        kis_client.get_minute_ohlcv(t) for t in all_tickers
+    ], return_exceptions=True)
+    now_iso = datetime.now(KST).isoformat()
+    for ticker, result in zip(all_tickers, minute_results):
+        if isinstance(result, list):
+            buf = state.minute_bars.setdefault(ticker, deque(maxlen=60))
+            buf.extend(result)
+            state.last_updated[f"minute_bars/{ticker}"] = now_iso
+        else:
+            state.fetch_errors[f"minute_bars/{ticker}"] = (
+                state.fetch_errors.get(f"minute_bars/{ticker}", 0) + 1
+            )
+
+    # 호가 fetch (REST) — screener-only
+    screener_only = list(set(screener_tickers) - set(portfolio_tickers))
+    asking_results = await asyncio.gather(*[
+        kis_client.get_asking_price(t) for t in screener_only
+    ], return_exceptions=True)
+    for ticker, result in zip(screener_only, asking_results):
+        if isinstance(result, dict):
+            state.asking_price[ticker] = result
+            state.last_updated[f"asking_price/{ticker}"] = now_iso
+
+
+def make_asking_price_callback(state: PollState):
+    """KIS WebSocket on_asking_price callback factory."""
+    def _cb(ticker: str, data: dict) -> None:
+        state.asking_price[ticker] = data
+        state.last_updated[f"asking_price/{ticker}"] = datetime.now(KST).isoformat()
+    return _cb
+
+
+def _portfolio_tickers(state: PollState) -> list[str]:
+    if state.portfolio is None:
+        return []
+    return [h["ticker"] for h in state.portfolio.get("holdings", []) if "ticker" in h]
+
+
+def _screener_tickers(state: PollState) -> list[str]:
+    if state.screener_preview is None:
+        return []
+    return [i["ticker"] for i in state.screener_preview.get("items", []) if "ticker" in i]
+
+
+async def _run_post_close_cycle(kis_client, chronos, state) -> None:
+    """16:00 KST 종가 후 1회: daily fetch + chronos predict."""
+    tickers = list(set(_portfolio_tickers(state)) | set(_screener_tickers(state)))
+    if not tickers:
+        return
+
+    daily_results = await asyncio.gather(*[
+        kis_client.get_daily_ohlcv(t, days=60) for t in tickers
+    ], return_exceptions=True)
+    daily_dict = {}
+    for ticker, result in zip(tickers, daily_results):
+        if isinstance(result, list) and len(result) >= 30:
+            daily_dict[ticker] = result
+            state.daily_ohlcv[ticker] = result
+        elif isinstance(result, Exception):
+            state.fetch_errors[f"daily_ohlcv/{ticker}"] = (
+                state.fetch_errors.get(f"daily_ohlcv/{ticker}", 0) + 1
+            )
+
+    if daily_dict and chronos is not None:
+        try:
+            predictions = chronos.predict_batch(daily_dict)
+        except Exception:
+            logger.exception("chronos predict_batch failed")
+            return
+        for ticker, pred in predictions.items():
+            state.chronos_predictions[ticker] = {
+                "median": pred.median,
+                "q10": pred.q10,
+                "q90": pred.q90,
+                "conf": pred.conf,
+                "as_of": pred.as_of,
+            }
+            state.last_updated[f"chronos/{ticker}"] = pred.as_of
+
+
+def update_minute_momentum_for_all(state) -> None:
+    """매 분봉 cycle 후 호출 — 모든 종목 모멘텀 갱신."""
+    from signal_v2.momentum_classifier import classify_minute_momentum
+    now_iso = datetime.now(KST).isoformat()
+    for ticker, bars in state.minute_bars.items():
+        state.minute_momentum[ticker] = classify_minute_momentum(bars)
+        state.last_updated[f"momentum/{ticker}"] = now_iso
--- a/signal_v2/pytest.ini
+++ b/signal_v2/pytest.ini
@@ -0,0 +1,3 @@
+[pytest]
+asyncio_mode = auto
+testpaths = tests
--- a/signal_v2/rate_limit.py
+++ b/signal_v2/rate_limit.py
@@ -0,0 +1,73 @@
+"""SignalDedup — SQLite-backed 24h duplicate signal blocker."""
+from __future__ import annotations
+import sqlite3
+from contextlib import contextmanager
+from datetime import datetime, timedelta
+from pathlib import Path
+from zoneinfo import ZoneInfo
+
+KST = ZoneInfo("Asia/Seoul")
+
+
+def _now_iso() -> str:
+    """Test seam — overridable via monkeypatch."""
+    return datetime.now(KST).isoformat()
+
+
+_SCHEMA = """
+CREATE TABLE IF NOT EXISTS signal_dedup (
+    ticker      TEXT NOT NULL,
+    action      TEXT NOT NULL,
+    last_sent   TEXT NOT NULL,
+    confidence  REAL NOT NULL,
+    PRIMARY KEY (ticker, action)
+);
+CREATE INDEX IF NOT EXISTS idx_signal_dedup_last_sent
+    ON signal_dedup(last_sent);
+"""
+
+
+class SignalDedup:
+    """24h dedup interface. WAL + busy_timeout=120000."""
+
+    def __init__(self, db_path: Path):
+        self._db_path = Path(db_path)
+        self._db_path.parent.mkdir(parents=True, exist_ok=True)
+        self._init_schema()
+
+    @contextmanager
+    def _conn(self):
+        conn = sqlite3.connect(self._db_path, timeout=120.0)
+        try:
+            conn.execute("PRAGMA journal_mode=WAL")
+            conn.execute("PRAGMA busy_timeout=120000")
+            yield conn
+        finally:
+            conn.close()
+
+    def _init_schema(self) -> None:
+        with self._conn() as conn:
+            conn.executescript(_SCHEMA)
+            conn.commit()
+
+    def is_recent(self, ticker: str, action: str, within_hours: int = 24) -> bool:
+        threshold_dt = datetime.fromisoformat(_now_iso()) - timedelta(hours=within_hours)
+        threshold_iso = threshold_dt.isoformat()
+        with self._conn() as conn:
+            row = conn.execute(
+                "SELECT last_sent FROM signal_dedup WHERE ticker = ? AND action = ?",
+                (ticker, action),
+            ).fetchone()
+        return row is not None and row[0] >= threshold_iso
+
+    def record(self, ticker: str, action: str, confidence: float) -> None:
+        with self._conn() as conn:
+            conn.execute(
+                """INSERT INTO signal_dedup (ticker, action, last_sent, confidence)
+                   VALUES (?, ?, ?, ?)
+                   ON CONFLICT (ticker, action) DO UPDATE
+                   SET last_sent = excluded.last_sent,
+                       confidence = excluded.confidence""",
+                (ticker, action, _now_iso(), confidence),
+            )
+            conn.commit()
--- a/signal_v2/scheduler.py
+++ b/signal_v2/scheduler.py
@@ -0,0 +1,99 @@
+"""Polling scheduler — 시간대별 분기 + 휴장일 처리."""
+from __future__ import annotations
+import json
+import logging
+from datetime import datetime, timedelta, time
+from pathlib import Path
+from zoneinfo import ZoneInfo
+
+logger = logging.getLogger(__name__)
+
+KST = ZoneInfo("Asia/Seoul")
+_HOLIDAYS_PATH = Path(__file__).parent / "holidays.json"
+_HOLIDAYS: set[str] = set(json.loads(_HOLIDAYS_PATH.read_text(encoding="utf-8")))
+
+# Market windows (정규장)
+_PRE_OPEN = time(7, 0)
+_OPEN = time(9, 0)
+_CLOSE = time(15, 30)
+_POST_END = time(20, 0)
+
+# NXT windows (시간외)
+_NXT_PRE_END = time(23, 30)
+_NXT_POST_OPEN = time(4, 30)
+# 23:30 - 04:30 (dead zone) skip
+
+
+def _is_market_day(now: datetime) -> bool:
+    """평일 + 휴장일 아닌 날."""
+    if now.weekday() >= 5:  # Sat/Sun
+        return False
+    return now.strftime("%Y-%m-%d") not in _HOLIDAYS
+
+
+def _is_polling_window(now: datetime) -> bool:
+    """폴링 윈도우: 07:00-23:30 + 04:30-07:00."""
+    t = now.time()
+    return (
+        (_PRE_OPEN <= t < _NXT_PRE_END)
+        or (_NXT_POST_OPEN <= t < _PRE_OPEN)
+    )
+
+
+def _next_interval(now: datetime) -> float:
+    """다음 폴링까지 sleep 초수."""
+    if not _is_market_day(now):
+        return _seconds_until_next_market_open(now)
+
+    t = now.time()
+    if _PRE_OPEN <= t < _OPEN:
+        return 300.0  # 장전 5분
+    elif _OPEN <= t < _CLOSE:
+        return 60.0   # 장중 1분
+    elif _CLOSE <= t < _POST_END:
+        return 300.0  # 장후 5분
+    elif _POST_END <= t < _NXT_PRE_END:
+        return 300.0  # NXT 야간 5분
+    elif _NXT_POST_OPEN <= t < _PRE_OPEN:
+        return 300.0  # NXT 새벽 5분
+    else:
+        # Dead zone (23:30-04:30) — wait until next 04:30
+        return _seconds_until_nxt_or_market_open(now)
+
+
+def _seconds_until_nxt_or_market_open(now: datetime) -> float:
+    """다음 04:30 (NXT 새벽 start) 까지 초수. 휴장일은 다음 영업일 07:00."""
+    candidate = now.replace(hour=4, minute=30, second=0, microsecond=0)
+    if candidate <= now:
+        candidate += timedelta(days=1)
+
+    for _ in range(14):
+        if _is_market_day(candidate):
+            return (candidate - now).total_seconds()
+        candidate += timedelta(days=1)
+
+    logger.warning("could not find next market day within 14 days")
+    return 86400.0
+
+
+def _is_post_close_trigger(now: datetime) -> bool:
+    """16:00 KST ±1분 (post-close cycle 트리거). 평일/영업일만."""
+    if not _is_market_day(now):
+        return False
+    t = now.time()
+    return time(16, 0) <= t < time(16, 1)
+
+
+def _seconds_until_next_market_open(now: datetime) -> float:
+    """다음 영업일의 07:00 KST 까지 초수 (휴장일/주말용)."""
+    candidate = now.replace(hour=7, minute=0, second=0, microsecond=0)
+    if candidate <= now:
+        candidate += timedelta(days=1)
+
+    for _ in range(14):  # safety bound (max 2 weeks of holidays)
+        if _is_market_day(candidate):
+            return (candidate - now).total_seconds()
+        candidate += timedelta(days=1)
+
+    logger.warning("could not find next market day within 14 days")
+    return 86400.0
--- a/signal_v2/signal_generator.py
+++ b/signal_v2/signal_generator.py
@@ -0,0 +1,228 @@
+"""Phase 4 — 매수/매도 신호 생성.
+
+순수 함수 generate_signals(state, dedup, settings). state 를 mutate.
+"""
+from __future__ import annotations
+import logging
+from datetime import datetime
+from zoneinfo import ZoneInfo
+
+logger = logging.getLogger(__name__)
+KST = ZoneInfo("Asia/Seoul")
+
+MOMENTUM_SCORES = {
+    "strong_up": 1.0,
+    "weak_up": 0.7,
+    "neutral": 0.5,
+    "weak_down": 0.3,
+    "strong_down": 0.0,
+}
+
+
+def generate_signals(state, dedup, settings) -> None:
+    """Phase 4 entry — state-mutating. Evaluation order: sell first (priority), then buy. A ticker receiving a sell signal in this cycle is excluded from buy evaluation to avoid silent overwrite."""
+    _evaluate_sell_signals(state, dedup, settings)
+    _evaluate_buy_signals(state, dedup, settings)
+
+
+# ----- 매수 -----
+
+def _evaluate_buy_signals(state, dedup, settings) -> None:
+    candidates = _buy_candidates(state)
+    for ticker, name, rank in candidates:
+        existing = state.signals.get(ticker)
+        if existing is not None and existing.get("action") == "sell":
+            logger.debug("buy %s skipped: same-cycle sell precedence", ticker)
+            continue
+        if not _check_buy_hard_gate(state, ticker, settings):
+            logger.debug("buy %s skipped: hard gate failed", ticker)
+            continue
+        confidence = _compute_buy_confidence(state, ticker, rank)
+        if confidence <= settings.confidence_threshold:
+            logger.debug("buy %s skipped: confidence %.3f <= %.3f",
+                         ticker, confidence, settings.confidence_threshold)
+            continue
+        if dedup.is_recent(ticker, "buy", within_hours=24):
+            logger.debug("buy %s skipped: dedup 24h", ticker)
+            continue
+        state.signals[ticker] = _build_buy_signal(state, ticker, name, rank, confidence)
+        dedup.record(ticker, "buy", confidence=confidence)
+        logger.info("signal emit %s buy conf=%.3f rank=%s", ticker, confidence, rank)
+
+
+def _buy_candidates(state) -> list[tuple[str, str, int | None]]:
+    """screener Top-N (rank 1..N) + portfolio (rank=None)."""
+    candidates: list[tuple[str, str, int | None]] = []
+    seen: set[str] = set()
+    if state.screener_preview is not None:
+        for i, item in enumerate(state.screener_preview.get("items", [])):
+            ticker = item.get("ticker")
+            if not ticker or ticker in seen:
+                continue
+            seen.add(ticker)
+            name = item.get("name", ticker)
+            candidates.append((ticker, name, i + 1))
+    if state.portfolio is not None:
+        for h in state.portfolio.get("holdings", []):
+            ticker = h.get("ticker")
+            if not ticker or ticker in seen:
+                continue
+            seen.add(ticker)
+            candidates.append((ticker, h.get("name", ticker), None))
+    return candidates
+
+
+def _check_buy_hard_gate(state, ticker: str, settings) -> bool:
+    pred = state.chronos_predictions.get(ticker)
+    if pred is None or pred.get("median", 0) <= 0:
+        return False
+    spread = pred.get("q90", 0) - pred.get("q10", 0)
+    if spread >= settings.chronos_spread_threshold:
+        return False
+    momentum = state.minute_momentum.get(ticker)
+    if momentum != settings.min_momentum_for_buy:
+        return False
+    ap = state.asking_price.get(ticker)
+    if ap is None or ap.get("bid_ratio", 0) < settings.asking_bid_ratio_threshold:
+        return False
+    return True
+
+
+def _compute_buy_confidence(state, ticker: str, rank: int | None) -> float:
+    pred = state.chronos_predictions[ticker]
+    chronos_conf = pred["conf"]
+    minute_score = MOMENTUM_SCORES.get(state.minute_momentum.get(ticker, "neutral"), 0.5)
+    screener_norm = max(0.0, 1 - (rank - 1) / 20) if rank is not None else 0.0
+    return chronos_conf * 0.5 + minute_score * 0.3 + screener_norm * 0.2
+
+
+def _build_buy_signal(state, ticker: str, name: str, rank: int | None, confidence: float) -> dict:
+    ap = state.asking_price[ticker]
+    return {
+        "ticker": ticker,
+        "name": name,
+        "action": "buy",
+        "confidence_webai": confidence,
+        "current_price": ap["current_price"],
+        "avg_price": None,
+        "pnl_pct": None,
+        "context": _build_context(state, ticker, rank),
+        "as_of": datetime.now(KST).isoformat(),
+    }
+
+
+# ----- 매도 -----
+
+def _evaluate_sell_signals(state, dedup, settings) -> None:
+    if state.portfolio is None:
+        return
+    for holding in state.portfolio.get("holdings", []):
+        ticker = holding.get("ticker")
+        if not ticker:
+            continue
+        sell = _try_stop_loss(state, holding, settings)
+        if sell is None:
+            sell = _try_anomaly(state, holding, settings)
+        if sell is None:
+            sell = _try_take_profit(state, holding, settings)
+        if sell is None:
+            continue
+        if dedup.is_recent(ticker, "sell", within_hours=24):
+            logger.debug("sell %s skipped: dedup 24h", ticker)
+            continue
+        state.signals[ticker] = sell
+        dedup.record(ticker, "sell", confidence=sell["confidence_webai"])
+        logger.info("signal emit %s sell conf=%.3f reason=%s",
+                    ticker, sell["confidence_webai"],
+                    sell.get("context", {}).get("sell_reason"))
+
+
+def _try_stop_loss(state, holding: dict, settings) -> dict | None:
+    pnl = holding.get("pnl_pct")
+    if pnl is None or pnl >= settings.stop_loss_pct:
+        return None
+    return _build_sell_signal(state, holding, confidence=1.0, reason="stop_loss")
+
+
+def _try_take_profit(state, holding: dict, settings) -> dict | None:
+    pnl = holding.get("pnl_pct")
+    if pnl is None or pnl <= settings.take_profit_pct:
+        return None
+    return _build_sell_signal(state, holding, confidence=0.6, reason="take_profit")
+
+
+def _try_anomaly(state, holding: dict, settings) -> dict | None:
+    ticker = holding["ticker"]
+    pred = state.chronos_predictions.get(ticker)
+    if pred is None or pred["median"] >= -0.01:
+        return None
+    momentum = state.minute_momentum.get(ticker)
+    if momentum != "strong_down":
+        return None
+    ap = state.asking_price.get(ticker)
+    if ap is None:
+        return None
+    if ap["bid_ratio"] > (1 - settings.asking_bid_ratio_threshold):
+        return None
+    minute_score = 1.0 - MOMENTUM_SCORES.get(momentum, 0.5)
+    confidence = pred["conf"] * 0.5 + minute_score * 0.3 + 1.0 * 0.2
+    if confidence <= settings.confidence_threshold:
+        return None
+    return _build_sell_signal(state, holding, confidence=confidence, reason="anomaly")
+
+
+def _build_sell_signal(state, holding: dict, confidence: float, reason: str) -> dict:
+    ticker = holding["ticker"]
+    return {
+        "ticker": ticker,
+        "name": holding.get("name", ticker),
+        "action": "sell",
+        "confidence_webai": confidence,
+        "current_price": holding.get("current_price"),
+        "avg_price": holding.get("avg_price"),
+        "pnl_pct": holding.get("pnl_pct"),
+        "context": _build_context(state, ticker, rank=None, sell_reason=reason),
+        "as_of": datetime.now(KST).isoformat(),
+    }
+
+
+# ----- Context -----
+
+def _build_context(state, ticker: str, rank: int | None, sell_reason: str | None = None) -> dict:
+    pred = state.chronos_predictions.get(ticker) or {}
+    ap = state.asking_price.get(ticker) or {}
+    news_item = _find_news_sentiment(state, ticker)
+    screener_scores = _find_screener_scores(state, ticker)
+    context: dict = {
+        "chronos_pred_1d": pred.get("median"),
+        "chronos_pred_conf": pred.get("conf"),
+        "chronos_q10": pred.get("q10"),
+        "chronos_q90": pred.get("q90"),
+        "screener_rank": rank,
+        "screener_scores": screener_scores,
+        "minute_momentum": state.minute_momentum.get(ticker),
+        "asking_bid_ratio": ap.get("bid_ratio"),
+        "news_sentiment": news_item.get("score") if news_item else None,
+        "news_reason": news_item.get("reason") if news_item else None,
+    }
+    if sell_reason is not None:
+        context["sell_reason"] = sell_reason
+    return context
+
+
+def _find_news_sentiment(state, ticker: str) -> dict | None:
+    if state.news_sentiment is None:
+        return None
+    for item in state.news_sentiment.get("items", []):
+        if item.get("ticker") == ticker:
+            return item
+    return None
+
+
+def _find_screener_scores(state, ticker: str) -> dict | None:
+    if state.screener_preview is None:
+        return None
+    for item in state.screener_preview.get("items", []):
+        if item.get("ticker") == ticker:
+            return item.get("scores")
+    return None
--- a/signal_v2/start.bat
+++ b/signal_v2/start.bat
@@ -0,0 +1,3 @@
+@echo off
+cd /d "%~dp0\.."
+python -m uvicorn signal_v2.main:app --host 0.0.0.0 --port 8001
--- a/signal_v2/state.py
+++ b/signal_v2/state.py
@@ -0,0 +1,22 @@
+"""PollState — process-wide singleton."""
+from collections import deque
+from dataclasses import dataclass, field
+
+
+@dataclass
+class PollState:
+    portfolio: dict | None = None
+    news_sentiment: dict | None = None
+    screener_preview: dict | None = None
+    minute_bars: dict[str, deque] = field(default_factory=dict)
+    asking_price: dict[str, dict] = field(default_factory=dict)
+    # Phase 3b additions
+    daily_ohlcv: dict[str, list[dict]] = field(default_factory=dict)
+    chronos_predictions: dict[str, dict] = field(default_factory=dict)
+    minute_momentum: dict[str, str] = field(default_factory=dict)
+    signals: dict[str, dict] = field(default_factory=dict)
+    last_updated: dict[str, str] = field(default_factory=dict)
+    fetch_errors: dict[str, int] = field(default_factory=dict)
+
+
+state = PollState()
--- a/signal_v2/stock_client.py
+++ b/signal_v2/stock_client.py
@@ -0,0 +1,128 @@
+"""Stock API HTTP client — async httpx + retry + memory cache."""
+from __future__ import annotations
+import asyncio
+import logging
+import time
+from typing import Any
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+# Cache TTL by endpoint (seconds)
+_TTL = {
+    "portfolio": 60.0,
+    "news-sentiment": 300.0,
+    "screener-preview": 60.0,
+}
+
+# Retry policy
+_MAX_ATTEMPTS = 3
+_RETRY_STATUSES = {429, 500, 502, 503, 504}
+
+
+class StockClient:
+    """stock API wrapper. Async httpx + self-retry + memory cache."""
+
+    def __init__(self, base_url: str, api_key: str, timeout: float = 10.0):
+        self._base_url = base_url.rstrip("/")
+        self._api_key = api_key
+        self._client = httpx.AsyncClient(timeout=timeout)
+        # cache: key → (data, timestamp_monotonic)
+        self._cache: dict[str, tuple[Any, float]] = {}
+
+    async def close(self) -> None:
+        await self._client.aclose()
+
+    def cache_size(self) -> int:
+        """Number of cached endpoint responses (public surface for /health)."""
+        return len(self._cache)
+
+    async def get_portfolio(self) -> dict:
+        return await self._cached_request(
+            "portfolio", "GET", "/api/webai/portfolio"
+        )
+
+    async def get_news_sentiment(self, date: str | None = None) -> dict:
+        path = "/api/webai/news-sentiment"
+        if date is not None:
+            path += f"?date={date}"
+        cache_key = f"news-sentiment:{date or 'latest'}"
+        return await self._cached_request(
+            cache_key, "GET", path, _ttl_key="news-sentiment"
+        )
+
+    async def run_screener_preview(
+        self, weights: dict | None = None, top_n: int = 20
+    ) -> dict:
+        body = {"mode": "preview", "top_n": top_n}
+        if weights is not None:
+            body["weights"] = weights
+        return await self._cached_request(
+            "screener-preview",
+            "POST",
+            "/api/stock/screener/run",
+            json=body,
+            _ttl_key="screener-preview",
+        )
+
+    async def _cached_request(
+        self,
+        cache_key: str,
+        method: str,
+        path: str,
+        *,
+        _ttl_key: str | None = None,
+        **kwargs,
+    ) -> dict:
+        ttl_key = _ttl_key or cache_key
+        ttl = _TTL.get(ttl_key, 60.0)
+        # Fresh cache hit?
+        if cache_key in self._cache:
+            data, ts = self._cache[cache_key]
+            if time.monotonic() - ts < ttl:
+                return data
+
+        # Fetch (with retry)
+        try:
+            data = await self._request_with_retry(method, path, **kwargs)
+            self._cache[cache_key] = (data, time.monotonic())
+            return data
+        except httpx.HTTPError:
+            # Stale fallback: serve old cached value if exists
+            if cache_key in self._cache:
+                stale_data, stale_ts = self._cache[cache_key]
+                age = time.monotonic() - stale_ts
+                logger.warning(
+                    "serving stale cache for %s (age=%.1fs)", cache_key, age
+                )
+                return stale_data
+            raise
+
+    async def _request_with_retry(self, method: str, path: str, **kwargs) -> dict:
+        url = f"{self._base_url}{path}"
+        headers = self._auth_headers()
+        for attempt in range(_MAX_ATTEMPTS):
+            try:
+                response = await self._client.request(
+                    method, url, headers=headers, **kwargs
+                )
+                if response.status_code in _RETRY_STATUSES:
+                    if attempt < _MAX_ATTEMPTS - 1:
+                        await asyncio.sleep(2**attempt)
+                        continue
+                    response.raise_for_status()
+                response.raise_for_status()
+                return response.json()
+            except httpx.TimeoutException:
+                if attempt < _MAX_ATTEMPTS - 1:
+                    await asyncio.sleep(2**attempt)
+                    continue
+                raise
+            except httpx.HTTPStatusError:
+                raise
+        # Unreachable: every iteration either returns or raises
+        raise RuntimeError("_request_with_retry exhausted loop without raising")
+
+    def _auth_headers(self) -> dict[str, str]:
+        return {"X-WebAI-Key": self._api_key}
--- a/signal_v2/tests/init.py
+++ b/signal_v2/tests/init.py
--- a/signal_v2/tests/conftest.py
+++ b/signal_v2/tests/conftest.py
@@ -0,0 +1,18 @@
+"""Pytest fixtures for signal_v2 tests."""
+from pathlib import Path
+
+import pytest
+import respx
+
+
+@pytest.fixture
+def tmp_dedup_db(tmp_path) -> Path:
+    """SQLite 단위 테스트용 임시 DB path."""
+    return tmp_path / "test_signal_v2.db"
+
+
+@pytest.fixture
+def mock_stock_api():
+    """respx 로 stock API mock. base_url 은 테스트마다 임의."""
+    with respx.mock(base_url="https://test.stock.local", assert_all_called=False) as mock:
+        yield mock
--- a/signal_v2/tests/test_chronos_predictor.py
+++ b/signal_v2/tests/test_chronos_predictor.py
@@ -0,0 +1,92 @@
+"""Tests for ChronosPredictor (model mock)."""
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+import pytest
+
+
+@pytest.fixture
+def mock_pipeline():
+    """Mock BaseChronosPipeline.from_pretrained returning a mock pipeline object."""
+    with patch("chronos.BaseChronosPipeline") as cls:
+        cls.__name__ = "BaseChronosPipeline"
+        instance = MagicMock()
+        # ChronosBolt API: predict_quantiles returns (quantiles_tensor, mean_tensor)
+        # Modern (predict_quantiles) branch will be used since hasattr(MagicMock, "predict_quantiles") is True.
+        cls.from_pretrained.return_value = instance
+        yield instance
+
+
+@pytest.fixture
+def mock_torch_cpu():
+    with patch("torch.cuda.is_available", return_value=False):
+        yield
+
+
+def _daily_ohlcv(close_seq):
+    return [{"datetime": f"2026-05-{i+1:02d}", "open": c, "high": c, "low": c,
+             "close": c, "volume": 1000} for i, c in enumerate(close_seq)]
+
+
+def _mk_quantiles_tensor(q10_price: float, q50_price: float, q90_price: float):
+    """Helper: build predict_quantiles return tensor shape [1, 1, 3]."""
+    import torch
+    return torch.tensor([[[q10_price, q50_price, q90_price]]], dtype=torch.float32)
+
+
+def test_predict_batch_returns_prediction_dict(mock_pipeline, mock_torch_cpu):
+    """mock predict_quantiles → dict[ticker, ChronosPrediction]. last_close=100, q50=102 → median≈+2%."""
+    quantiles = _mk_quantiles_tensor(101.5, 102.0, 102.5)  # narrow around 102
+    mock_pipeline.predict_quantiles.return_value = (quantiles, None)
+
+    from signal_v2.chronos_predictor import ChronosPredictor, ChronosPrediction
+    predictor = ChronosPredictor(model_name="mock-model")
+    daily = {"005930": _daily_ohlcv([100] * 60)}
+    result = predictor.predict_batch(daily)
+    assert "005930" in result
+    pred = result["005930"]
+    assert isinstance(pred, ChronosPrediction)
+    assert abs(pred.median - 0.02) < 0.001
+
+
+def test_conf_high_when_distribution_narrow(mock_pipeline, mock_torch_cpu):
+    """좁은 distribution (q90-q10 작음, median 0 아님) → conf ≈ 1."""
+    # last_close=100, q10=101.99, q50=102.00, q90=102.01
+    # returns: q10=0.0199, q50=0.02, q90=0.0201
+    # spread = (0.0201 - 0.0199) / max(0.02, 0.001) = 0.0002/0.02 = 0.01 → conf = 1 - 0.005 = 0.995
+    quantiles = _mk_quantiles_tensor(101.99, 102.0, 102.01)
+    mock_pipeline.predict_quantiles.return_value = (quantiles, None)
+
+    from signal_v2.chronos_predictor import ChronosPredictor
+    predictor = ChronosPredictor(model_name="mock-model")
+    daily = {"005930": _daily_ohlcv([100] * 60)}
+    result = predictor.predict_batch(daily)
+    assert result["005930"].conf > 0.8
+
+
+def test_conf_low_when_distribution_wide(mock_pipeline, mock_torch_cpu):
+    """넓은 distribution → conf ≈ 0."""
+    # last_close=100, q10=70, q50=100, q90=130
+    # returns: q10=-0.3, q50=0.0, q90=0.3
+    # spread = (0.3 - (-0.3)) / max(0.0, 0.001) = 0.6 / 0.001 = 600 → conf = max(0, 1 - 300) = 0
+    quantiles = _mk_quantiles_tensor(70.0, 100.0, 130.0)
+    mock_pipeline.predict_quantiles.return_value = (quantiles, None)
+
+    from signal_v2.chronos_predictor import ChronosPredictor
+    predictor = ChronosPredictor(model_name="mock-model")
+    daily = {"005930": _daily_ohlcv([100] * 60)}
+    result = predictor.predict_batch(daily)
+    assert result["005930"].conf < 0.3
+
+
+def test_return_computed_from_price_relative_to_last_close(mock_pipeline, mock_torch_cpu):
+    """price 예측 → last_close 대비 return 변환. last_close=100, q50=110 → return ≈ +10%."""
+    quantiles = _mk_quantiles_tensor(109.0, 110.0, 111.0)
+    mock_pipeline.predict_quantiles.return_value = (quantiles, None)
+
+    from signal_v2.chronos_predictor import ChronosPredictor
+    predictor = ChronosPredictor(model_name="mock-model")
+    # last close = 100
+    daily = {"005930": _daily_ohlcv(list(range(41, 101)))}  # last = 100
+    result = predictor.predict_batch(daily)
+    assert abs(result["005930"].median - 0.10) < 0.001
--- a/signal_v2/tests/test_kis_client.py
+++ b/signal_v2/tests/test_kis_client.py
@@ -0,0 +1,161 @@
+"""Tests for KISClient (REST)."""
+import json
+from pathlib import Path
+
+import httpx
+import pytest
+import respx
+
+from signal_v2.kis_client import KISClient
+
+
+@pytest.fixture
+def fake_v1_token(tmp_path):
+    """V1 토큰 파일 fixture."""
+    token_file = tmp_path / "kis_token.json"
+    token_file.write_text(json.dumps({
+        "access_token": "test-kis-token-abc123",
+        "token_expired": "2099-12-31 23:59:59",
+    }))
+    return token_file
+
+
+@pytest.fixture
+def kis_client_factory(fake_v1_token):
+    def _make():
+        return KISClient(
+            app_key="test-app-key",
+            app_secret="test-app-secret",
+            account="50000000-01",
+            is_virtual=True,
+            v1_token_path=fake_v1_token,
+        )
+    return _make
+
+
+@respx.mock
+async def test_get_minute_ohlcv_normal_returns_30_bars(kis_client_factory):
+    """정상 200 → 30개 분봉 list 반환."""
+    sample_output2 = [
+        {
+            "stck_bsop_date": "20260518",
+            "stck_cntg_hour": f"09{m:02d}00",
+            "stck_oprc": "78000", "stck_hgpr": "78500",
+            "stck_lwpr": "77800", "stck_prpr": "78300",
+            "cntg_vol": "12345",
+        }
+        for m in range(30)  # 9:00-9:29 = 30 bars
+    ]
+    respx.get(
+        "https://openapivts.koreainvestment.com:29443/uapi/domestic-stock/v1/quotations/inquire-time-itemchartprice"
+    ).mock(
+        return_value=httpx.Response(200, json={"output2": sample_output2})
+    )
+
+    client = kis_client_factory()
+    try:
+        bars = await client.get_minute_ohlcv("005930")
+        assert len(bars) == 30
+        assert bars[0]["close"] == 78300
+        assert "datetime" in bars[0]
+    finally:
+        await client.close()
+
+
+@respx.mock
+async def test_get_minute_ohlcv_429_retry_then_success(kis_client_factory, monkeypatch):
+    """429 → exponential backoff → 200."""
+    sleep_calls = []
+    async def fake_sleep(s): sleep_calls.append(s)
+    monkeypatch.setattr("asyncio.sleep", fake_sleep)
+
+    respx.get(
+        "https://openapivts.koreainvestment.com:29443/uapi/domestic-stock/v1/quotations/inquire-time-itemchartprice"
+    ).mock(side_effect=[
+        httpx.Response(429, text="rate limit"),
+        httpx.Response(200, json={"output2": []}),
+    ])
+    client = kis_client_factory()
+    try:
+        result = await client.get_minute_ohlcv("005930")
+        assert result == []
+        assert 1 in sleep_calls
+    finally:
+        await client.close()
+
+
+@respx.mock
+async def test_get_minute_ohlcv_uses_v1_token(kis_client_factory, fake_v1_token):
+    """KIS 호출 헤더에 V1 토큰 파일의 access_token 사용."""
+    route = respx.get(
+        "https://openapivts.koreainvestment.com:29443/uapi/domestic-stock/v1/quotations/inquire-time-itemchartprice"
+    ).mock(return_value=httpx.Response(200, json={"output2": []}))
+
+    client = kis_client_factory()
+    try:
+        await client.get_minute_ohlcv("005930")
+        assert route.called
+        req = route.calls.last.request
+        # check authorization header contains the V1 token
+        auth = req.headers.get("authorization", "")
+        assert "test-kis-token-abc123" in auth
+    finally:
+        await client.close()
+
+
+@respx.mock
+async def test_get_asking_price_computes_bid_ratio(kis_client_factory):
+    """호가 응답 → bid_total/(bid+ask) bid_ratio 계산."""
+    respx.get(
+        "https://openapivts.koreainvestment.com:29443/uapi/domestic-stock/v1/quotations/inquire-asking-price-exp-ccn"
+    ).mock(return_value=httpx.Response(200, json={
+        "output1": {
+            "total_bidp_rsqn": "600",
+            "total_askp_rsqn": "400",
+            "stck_prpr": "78500",
+        }
+    }))
+
+    client = kis_client_factory()
+    try:
+        data = await client.get_asking_price("005930")
+        assert data["bid_total"] == 600
+        assert data["ask_total"] == 400
+        assert abs(data["bid_ratio"] - 0.6) < 1e-9
+        assert data["current_price"] == 78500
+        assert "as_of" in data
+    finally:
+        await client.close()
+
+
+@respx.mock
+async def test_get_daily_ohlcv_returns_60_bars(kis_client_factory):
+    """KIS daily endpoint returns 60 ascending bars after parsing."""
+    # Build 60 KIS-format daily bars (descending dates as KIS does)
+    sample_output2 = []
+    for i in range(60):
+        # Generate a fake date 60 days ago, descending
+        day = 60 - i
+        sample_output2.append({
+            "stck_bsop_date": f"2026{(((day-1)//30)+1):02d}{(((day-1)%30)+1):02d}",
+            "stck_oprc": "78000", "stck_hgpr": "78500",
+            "stck_lwpr": "77800", "stck_clpr": str(78000 + i),
+            "acml_vol": "12345",
+        })
+
+    respx.get(
+        "https://openapivts.koreainvestment.com:29443/uapi/domestic-stock/v1/quotations/inquire-daily-itemchartprice"
+    ).mock(return_value=httpx.Response(200, json={"output2": sample_output2}))
+
+    client = kis_client_factory()
+    try:
+        bars = await client.get_daily_ohlcv("005930", days=60)
+        # KIS returns descending; client reverses to ascending
+        assert len(bars) == 60
+        # Ascending order: first item has smaller datetime than last
+        assert bars[0]["datetime"] < bars[-1]["datetime"]
+        assert isinstance(bars[0]["open"], int)
+        assert isinstance(bars[0]["close"], int)
+        assert "datetime" in bars[0]
+    finally:
+        await client.close()
--- a/signal_v2/tests/test_kis_websocket.py
+++ b/signal_v2/tests/test_kis_websocket.py
@@ -0,0 +1,94 @@
+"""Tests for KISWebSocket."""
+import asyncio
+import json
+from unittest.mock import AsyncMock, MagicMock
+
+import httpx
+import pytest
+import respx
+
+from signal_v2.kis_websocket import KISWebSocket
+
+
+BASE_REST = "https://openapivts.koreainvestment.com:29443"
+
+
+@respx.mock
+async def test_fetch_approval_key_via_oauth_endpoint():
+    """POST /oauth2/Approval → approval_key 추출."""
+    respx.post(f"{BASE_REST}/oauth2/Approval").mock(
+        return_value=httpx.Response(200, json={"approval_key": "test-approval-key-xyz"})
+    )
+    ws = KISWebSocket(app_key="k", app_secret="s", is_virtual=True)
+    key = await ws._fetch_approval_key()
+    assert key == "test-approval-key-xyz"
+    assert ws._approval_key == "test-approval-key-xyz"
+
+
+async def test_subscribe_sends_h0stasp0_message():
+    """subscribe() → WebSocket 으로 H0STASP0 구독 메시지 전송."""
+    sent_messages = []
+    mock_ws = AsyncMock()
+    mock_ws.send = AsyncMock(side_effect=lambda m: sent_messages.append(m))
+
+    ws = KISWebSocket(app_key="k", app_secret="s", is_virtual=True)
+    ws._approval_key = "test-key"
+    ws._ws = mock_ws
+    await ws.subscribe("005930")
+    assert ws._subscriptions == {"005930"}
+    assert len(sent_messages) == 1
+    msg = json.loads(sent_messages[0])
+    assert msg["header"]["tr_type"] == "1"  # subscribe
+    assert msg["body"]["input"]["tr_id"] == "H0STASP0"
+    assert msg["body"]["input"]["tr_key"] == "005930"
+
+
+def test_parse_asking_price_extracts_bid_ask_totals():
+    """KIS raw '0|H0STASP0|001|...' → (ticker, dict).
+
+    KIS 호가 메시지 형식 — KIS 공식 spec 의 정확한 필드 인덱스 운영 검증 필요.
+    본 테스트는 implementer 의 _parse_asking_price 구현 인덱스에 맞춰서 sample 작성.
+    """
+    ws = KISWebSocket(app_key="k", app_secret="s", is_virtual=True)
+    # Build a sample raw message — implementer 가 _ASKING_TOTAL_BID/ASK 인덱스에
+    # 맞춰서 필드 배치하면 됨. 예: 마지막 2개 필드를 bid_total / ask_total 로.
+    fields = ["005930", "091500", "78500"]  # ticker, time, current_price
+    fields.extend(["0"] * 40)  # padding (KIS 의 실 필드 수 ~50개)
+    fields.append("400")  # ask_total
+    fields.append("600")  # bid_total
+    raw = f"0|H0STASP0|001|{'^'.join(fields)}"
+
+    result = ws._parse_asking_price(raw)
+    assert result is not None, "parse_asking_price returned None"
+    ticker, data = result
+    assert ticker == "005930"
+    assert "bid_total" in data
+    assert "ask_total" in data
+    assert "bid_ratio" in data
+    assert "current_price" in data
+    # bid_total=600, ask_total=400, bid_ratio=0.6
+    assert data["bid_total"] == 600
+    assert data["ask_total"] == 400
+    assert abs(data["bid_ratio"] - 0.6) < 1e-9
+
+
+async def test_reconnect_on_disconnect_with_backoff(monkeypatch):
+    """연결 끊김 → exponential backoff retry. _connect_with_backoff() 검증."""
+    sleep_calls = []
+    async def fake_sleep(s): sleep_calls.append(s)
+    monkeypatch.setattr("asyncio.sleep", fake_sleep)
+
+    ws = KISWebSocket(app_key="k", app_secret="s", is_virtual=True)
+    # Mock _connect to fail twice then succeed
+    call_count = [0]
+    async def fake_connect():
+        call_count[0] += 1
+        if call_count[0] < 3:
+            raise ConnectionError("fake disconnect")
+        return AsyncMock()
+    monkeypatch.setattr(ws, "_connect", fake_connect)
+
+    result = await ws._connect_with_backoff()
+    assert call_count[0] == 3  # 2 fails + 1 success
+    # exponential 1s, 2s
+    assert sleep_calls[:2] == [1, 2]
--- a/signal_v2/tests/test_main.py
+++ b/signal_v2/tests/test_main.py
@@ -0,0 +1,62 @@
+"""Tests for FastAPI main app."""
+import logging
+
+import pytest
+from fastapi.testclient import TestClient
+
+
+def test_health_endpoint_returns_status_online(monkeypatch):
+    monkeypatch.setenv("STOCK_API_URL", "https://test.stock.local")
+    monkeypatch.setenv("WEBAI_API_KEY", "test-secret")
+    # Reload modules so they pick up the new env
+    import importlib
+    from signal_v2 import config as cfg
+    importlib.reload(cfg)
+    from signal_v2 import main as main_mod
+    importlib.reload(main_mod)
+    with TestClient(main_mod.app) as client:
+        r = client.get("/health")
+        assert r.status_code == 200
+        body = r.json()
+        assert body["status"] == "online"
+        assert body["stock_api_url"] == "https://test.stock.local"
+
+
+def test_startup_warns_if_webai_api_key_missing(monkeypatch, caplog):
+    # Use setenv with empty string + no-op load_dotenv to defeat .env re-read on reload
+    monkeypatch.setattr("signal_v2.config.load_dotenv", lambda *a, **k: None)
+    monkeypatch.setenv("WEBAI_API_KEY", "")
+    monkeypatch.setenv("STOCK_API_URL", "https://test.stock.local")
+    import importlib
+    from signal_v2 import config as cfg
+    importlib.reload(cfg)
+    # After reload, load_dotenv reference is fresh — re-patch
+    monkeypatch.setattr("signal_v2.config.load_dotenv", lambda *a, **k: None)
+    from signal_v2 import main as main_mod
+    importlib.reload(main_mod)
+    with caplog.at_level(logging.WARNING, logger="signal_v2.main"):
+        with TestClient(main_mod.app) as client:
+            client.get("/health")
+        assert any("WEBAI_API_KEY" in rec.message for rec in caplog.records)
+
+
+def test_startup_warns_if_kis_app_key_missing(monkeypatch, caplog):
+    """KIS app_key 미설정 시 startup WARNING (KIS 호출 disabled) — V1 패턴."""
+    monkeypatch.setattr("signal_v2.config.load_dotenv", lambda *a, **k: None)
+    monkeypatch.setenv("STOCK_API_URL", "https://test.stock.local")
+    monkeypatch.setenv("WEBAI_API_KEY", "test-secret")
+    # V1 pattern: kis_env_type=virtual, both virtual keys empty
+    monkeypatch.setenv("KIS_ENV_TYPE", "virtual")
+    monkeypatch.setenv("KIS_VIRTUAL_APP_KEY", "")
+    monkeypatch.setenv("KIS_REAL_APP_KEY", "")
+
+    import importlib
+    from signal_v2 import config as cfg
+    importlib.reload(cfg)
+    monkeypatch.setattr("signal_v2.config.load_dotenv", lambda *a, **k: None)
+    from signal_v2 import main as main_mod
+    importlib.reload(main_mod)
+    with caplog.at_level(logging.WARNING, logger="signal_v2.main"):
+        with TestClient(main_mod.app) as client:
+            client.get("/health")
+        assert any("KIS" in rec.message and "app_key" in rec.message.lower() for rec in caplog.records)
--- a/signal_v2/tests/test_momentum_classifier.py
+++ b/signal_v2/tests/test_momentum_classifier.py
@@ -0,0 +1,92 @@
+"""Tests for minute momentum classifier."""
+from collections import deque
+
+from signal_v2.momentum_classifier import (
+    aggregate_1min_to_5min, classify_minute_momentum,
+    STRONG_UP, WEAK_UP, NEUTRAL, WEAK_DOWN, STRONG_DOWN,
+)
+
+
+def _bar(open_, high, low, close, volume):
+    return {
+        "datetime": "2026-05-18T09:00:00+09:00",
+        "open": open_, "high": high, "low": low, "close": close, "volume": volume,
+    }
+
+
+def _make_chunks(num_chunks_up: int, num_chunks_total: int, base_vol: int = 1000):
+    """num_chunks_total 개의 5-bar 청크. num_chunks_up 청크는 양봉, 나머지는 음봉.
+    각 청크는 5개 1분봉. 거래량 = base_vol per bar.
+    """
+    bars = []
+    for i in range(num_chunks_total):
+        is_up = i < num_chunks_up
+        o, c = (100, 110) if is_up else (110, 100)
+        for j in range(5):
+            bars.append(_bar(o, max(o, c) + 5, min(o, c) - 5, c, base_vol))
+    return bars
+
+
+def test_strong_up_5_consecutive_green_with_high_volume():
+    """직전 5개 5분봉 모두 양봉 + 거래량 1.5x → STRONG_UP."""
+    # 60분 (12 5분봉) 데이터: 7 normal + 5 high-vol up
+    older = _make_chunks(num_chunks_up=3, num_chunks_total=7, base_vol=1000)
+    recent = _make_chunks(num_chunks_up=5, num_chunks_total=5, base_vol=2500)
+    minute_bars = deque(older + recent, maxlen=60)
+    assert classify_minute_momentum(minute_bars) == STRONG_UP
+
+
+def test_weak_up_3of5_green_normal_volume():
+    """직전 5개 5분봉 중 3-4개 양봉 + 거래량 ≥ 1.0x → WEAK_UP."""
+    older = _make_chunks(num_chunks_up=3, num_chunks_total=7, base_vol=1000)
+    # 5 chunks: 3 up + 2 down, normal vol
+    recent_up = _make_chunks(num_chunks_up=3, num_chunks_total=3, base_vol=1000)
+    recent_down = _make_chunks(num_chunks_up=0, num_chunks_total=2, base_vol=1000)
+    minute_bars = deque(older + recent_up + recent_down, maxlen=60)
+    assert classify_minute_momentum(minute_bars) == WEAK_UP
+
+
+def test_neutral_mixed():
+    """up_count=2, vol normal → NEUTRAL (rule 미해당)."""
+    older = _make_chunks(num_chunks_up=3, num_chunks_total=7, base_vol=1000)
+    recent_up = _make_chunks(num_chunks_up=2, num_chunks_total=2, base_vol=1000)
+    recent_down = _make_chunks(num_chunks_up=0, num_chunks_total=3, base_vol=1000)
+    minute_bars = deque(older + recent_up + recent_down, maxlen=60)
+    # up_count=2, vol_mult=1.0 → 어느 분기 조건도 만족 안 함 → NEUTRAL
+    assert classify_minute_momentum(minute_bars) == NEUTRAL
+
+
+def test_weak_down_low_green_low_volume():
+    """up_count <= 2 + vol < 1.0 → WEAK_DOWN."""
+    older = _make_chunks(num_chunks_up=3, num_chunks_total=7, base_vol=1000)
+    recent_up = _make_chunks(num_chunks_up=1, num_chunks_total=1, base_vol=500)
+    recent_down = _make_chunks(num_chunks_up=0, num_chunks_total=4, base_vol=500)
+    minute_bars = deque(older + recent_up + recent_down, maxlen=60)
+    # recent 5 chunks avg vol = 500, long 12 avg ≈ (7*1000 + 5*500) / 12 ≈ 791 → vol_mult ≈ 0.63
+    assert classify_minute_momentum(minute_bars) == WEAK_DOWN
+
+
+def test_strong_down_5_consecutive_red_high_volume():
+    """직전 5개 5분봉 모두 음봉 + 거래량 1.5x → STRONG_DOWN."""
+    older = _make_chunks(num_chunks_up=3, num_chunks_total=7, base_vol=1000)
+    recent = _make_chunks(num_chunks_up=0, num_chunks_total=5, base_vol=2500)
+    minute_bars = deque(older + recent, maxlen=60)
+    assert classify_minute_momentum(minute_bars) == STRONG_DOWN
+
+
+def test_aggregate_1min_to_5min_correctness():
+    """5 1분봉 → 1개 5분봉 — open/close/high/low/volume 정확."""
+    bars = [
+        _bar(100, 105, 99, 102, 1000),
+        _bar(102, 108, 101, 107, 1500),
+        _bar(107, 110, 105, 106, 800),
+        _bar(106, 109, 104, 108, 1200),
+        _bar(108, 112, 107, 111, 900),
+    ]
+    result = aggregate_1min_to_5min(bars)
+    assert len(result) == 1
+    assert result[0]["open"] == 100  # 첫 bar
+    assert result[0]["close"] == 111  # 마지막 bar
+    assert result[0]["high"] == 112  # max
+    assert result[0]["low"] == 99    # min
+    assert result[0]["volume"] == 5400  # sum
--- a/signal_v2/tests/test_pull_worker.py
+++ b/signal_v2/tests/test_pull_worker.py
@@ -0,0 +1,131 @@
+"""Tests for pull_worker (Phase 3a additions)."""
+from collections import deque
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from signal_v2.state import PollState
+
+
+async def test_minute_polling_cycle_updates_state_minute_bars():
+    """KIS REST mock 의 분봉 데이터가 state.minute_bars[ticker] deque 에 들어간다."""
+    from signal_v2.pull_worker import _run_kis_minute_cycle
+
+    state = PollState()
+    state.portfolio = {"holdings": [{"ticker": "005930"}, {"ticker": "000660"}]}
+    state.screener_preview = {
+        "items": [{"ticker": "005930"}, {"ticker": "035720"}]
+    }
+
+    kis_client_mock = MagicMock()
+    kis_client_mock.get_minute_ohlcv = AsyncMock(side_effect=[
+        [{"datetime": "2026-05-18T09:00:00+09:00", "open": 78000,
+          "high": 78500, "low": 77900, "close": 78300, "volume": 12345}],
+        [{"datetime": "2026-05-18T09:00:00+09:00", "open": 180000,
+          "high": 181000, "low": 179800, "close": 180500, "volume": 5000}],
+        [{"datetime": "2026-05-18T09:00:00+09:00", "open": 51000,
+          "high": 51200, "low": 50800, "close": 51100, "volume": 8000}],
+    ])
+    kis_client_mock.get_asking_price = AsyncMock(return_value={
+        "bid_total": 600, "ask_total": 400, "bid_ratio": 0.6,
+        "current_price": 51100, "as_of": "2026-05-18T09:00:30+09:00",
+    })
+
+    await _run_kis_minute_cycle(kis_client_mock, state)
+
+    # 3 unique tickers (005930, 000660, 035720)
+    assert "005930" in state.minute_bars
+    assert "000660" in state.minute_bars
+    assert "035720" in state.minute_bars
+    assert len(state.minute_bars["005930"]) >= 1
+    # asking_price 만 screener-only ticker (035720) 에 들어가야 함
+    # (portfolio = 005930, 000660 는 WebSocket 으로 들어옴)
+    assert "035720" in state.asking_price
+
+
+def test_websocket_message_updates_state_asking_price():
+    """WebSocket callback factory → state.asking_price 갱신."""
+    from signal_v2.pull_worker import make_asking_price_callback
+
+    state = PollState()
+    cb = make_asking_price_callback(state)
+    cb("005930", {"bid_total": 1000, "ask_total": 800, "bid_ratio": 0.555,
+                  "current_price": 78500, "as_of": "2026-05-18T10:00:00+09:00"})
+    assert state.asking_price["005930"]["bid_total"] == 1000
+    assert "asking_price/005930" in state.last_updated
+
+
+async def test_post_close_cycle_updates_chronos_predictions():
+    """mock kis + mock chronos → state.chronos_predictions + state.daily_ohlcv 갱신."""
+    from unittest.mock import AsyncMock, MagicMock
+    from signal_v2.pull_worker import _run_post_close_cycle
+    from signal_v2.chronos_predictor import ChronosPrediction
+    from signal_v2.state import PollState
+
+    state = PollState()
+    state.portfolio = {"holdings": [{"ticker": "005930"}]}
+    state.screener_preview = {"items": [{"ticker": "000660"}]}
+
+    kis_mock = MagicMock()
+    daily_005930 = [{"datetime": f"2026-05-{i+1:02d}", "open": 100, "high": 105,
+                     "low": 95, "close": 100 + i, "volume": 1000} for i in range(60)]
+    daily_000660 = [{"datetime": f"2026-05-{i+1:02d}", "open": 200, "high": 210,
+                     "low": 190, "close": 200 + i, "volume": 2000} for i in range(60)]
+    # _run_post_close_cycle iterates tickers and calls get_daily_ohlcv per ticker.
+    # Order depends on set() so use side_effect mapping if possible, otherwise list.
+    async def fake_daily(ticker, days=60):
+        if ticker == "005930":
+            return daily_005930
+        if ticker == "000660":
+            return daily_000660
+        return []
+    kis_mock.get_daily_ohlcv = AsyncMock(side_effect=fake_daily)
+
+    chronos_mock = MagicMock()
+    chronos_mock.predict_batch = MagicMock(return_value={
+        "005930": ChronosPrediction(0.02, -0.01, 0.04, 0.85, "2026-05-18T16:00:00+09:00"),
+        "000660": ChronosPrediction(0.03, -0.02, 0.06, 0.75, "2026-05-18T16:00:00+09:00"),
+    })
+
+    await _run_post_close_cycle(kis_mock, chronos_mock, state)
+
+    assert "005930" in state.chronos_predictions
+    assert "000660" in state.chronos_predictions
+    assert state.chronos_predictions["005930"]["median"] == 0.02
+    assert state.chronos_predictions["005930"]["conf"] == 0.85
+    assert "005930" in state.daily_ohlcv
+    assert "chronos/005930" in state.last_updated
+
+
+def test_poll_loop_calls_generate_signals_after_cycle(monkeypatch):
+    """Phase 4: generate_signals 가 cycle 후 state.signals 를 갱신한다."""
+    from unittest.mock import MagicMock
+    from signal_v2.state import PollState
+    from signal_v2.signal_generator import generate_signals
+
+    state = PollState()
+    state.portfolio = {"holdings": [{
+        "ticker": "005930", "name": "삼성전자",
+        "avg_price": 75000, "current_price": 69000,
+        "pnl_pct": -0.08, "profit_rate": -8.0,
+        "quantity": 100, "broker": "키움",
+    }]}
+    state.screener_preview = {"items": []}
+
+    dedup = MagicMock()
+    dedup.is_recent.return_value = False
+
+    settings = MagicMock()
+    settings.stop_loss_pct = -0.07
+    settings.take_profit_pct = 0.15
+    settings.chronos_spread_threshold = 0.6
+    settings.asking_bid_ratio_threshold = 0.6
+    settings.confidence_threshold = 0.7
+    settings.min_momentum_for_buy = "strong_up"
+
+    generate_signals(state, dedup, settings)
+
+    assert "005930" in state.signals
+    assert state.signals["005930"]["action"] == "sell"
+    assert state.signals["005930"]["confidence_webai"] == 1.0
+    dedup.record.assert_called_with("005930", "sell", confidence=1.0)
--- a/signal_v2/tests/test_rate_limit.py
+++ b/signal_v2/tests/test_rate_limit.py
@@ -0,0 +1,34 @@
+"""Tests for SignalDedup."""
+from datetime import datetime, timedelta
+from zoneinfo import ZoneInfo
+
+from signal_v2.rate_limit import SignalDedup
+
+KST = ZoneInfo("Asia/Seoul")
+
+
+def test_is_recent_returns_false_for_new_ticker_action(tmp_dedup_db):
+    dedup = SignalDedup(tmp_dedup_db)
+    assert dedup.is_recent("005930", "buy") is False
+
+
+def test_is_recent_returns_true_within_24h(tmp_dedup_db):
+    dedup = SignalDedup(tmp_dedup_db)
+    dedup.record("005930", "buy", confidence=0.82)
+    assert dedup.is_recent("005930", "buy") is True
+
+
+def test_is_recent_returns_false_after_24h(tmp_dedup_db, monkeypatch):
+    dedup = SignalDedup(tmp_dedup_db)
+    # Record with a timestamp 25 hours ago
+    now = datetime.now(KST)
+    fake_now = now - timedelta(hours=25)
+    monkeypatch.setattr(
+        "signal_v2.rate_limit._now_iso", lambda: fake_now.isoformat()
+    )
+    dedup.record("005930", "buy", confidence=0.82)
+    # Reset to real now for is_recent check
+    monkeypatch.setattr(
+        "signal_v2.rate_limit._now_iso", lambda: now.isoformat()
+    )
+    assert dedup.is_recent("005930", "buy", within_hours=24) is False
--- a/signal_v2/tests/test_scheduler.py
+++ b/signal_v2/tests/test_scheduler.py
@@ -0,0 +1,81 @@
+"""Tests for scheduler interval logic."""
+from datetime import datetime
+
+import pytest
+
+from signal_v2.scheduler import _next_interval, _is_market_day, KST
+
+
+def _kst(year, month, day, hour, minute=0):
+    return datetime(year, month, day, hour, minute, tzinfo=KST)
+
+
+def test_next_interval_pre_market_5min():
+    now = _kst(2026, 5, 18, 8, 30)  # Monday 08:30
+    assert _next_interval(now) == 300
+
+
+def test_next_interval_market_open_1min():
+    now = _kst(2026, 5, 18, 10, 0)  # Monday 10:00
+    assert _next_interval(now) == 60
+
+
+def test_next_interval_post_market_5min():
+    now = _kst(2026, 5, 18, 17, 0)  # Monday 17:00
+    assert _next_interval(now) == 300
+
+
+def test_next_interval_overnight_skip_to_next_morning():
+    now = _kst(2026, 5, 18, 2, 30)  # Monday 02:30 (dead zone, not NXT window)
+    interval = _next_interval(now)
+    # Dead zone 23:30-04:30 → next 04:30 is ~2h away
+    assert 2 * 3600 - 60 < interval < 2 * 3600 + 60
+
+
+def test_next_interval_holiday_skip():
+    # 2026-05-05 어린이날 (Tuesday holiday)
+    now = _kst(2026, 5, 5, 10, 0)
+    assert _is_market_day(now) is False
+    interval = _next_interval(now)
+    # Next: 2026-05-06 (Wed) 07:00, ~21h away
+    assert 20 * 3600 < interval < 22 * 3600
+
+
+def test_next_interval_at_market_open_boundary():
+    """09:00:00 정확 second → 60초 (market 구간 진입)."""
+    now = _kst(2026, 5, 18, 9, 0)  # Monday 09:00:00
+    assert _next_interval(now) == 60
+
+
+def test_next_interval_at_market_close_boundary():
+    """15:30:00 정확 second → 300초 (post-market 구간 진입)."""
+    now = _kst(2026, 5, 18, 15, 30)  # Monday 15:30:00
+    assert _next_interval(now) == 300
+
+
+def test_next_interval_at_polling_window_end_boundary():
+    """23:30:00 정확 second → dead zone skip (다음 04:30 까지)."""
+    now = _kst(2026, 5, 18, 23, 30)  # Monday 23:30:00 (NXT_PRE_END boundary)
+    interval = _next_interval(now)
+    # Dead zone 23:30-04:30 → next 04:30 is ~5h away
+    assert 5 * 3600 - 60 < interval < 5 * 3600 + 60
+
+
+def test_next_interval_nxt_evening_5min():
+    """22:00 평일 (NXT 야간) → 300 (5분)."""
+    now = _kst(2026, 5, 18, 22, 0)
+    assert _next_interval(now) == 300
+
+
+def test_next_interval_nxt_dawn_5min():
+    """05:30 평일 (NXT 새벽) → 300 (5분)."""
+    now = _kst(2026, 5, 18, 5, 30)
+    assert _next_interval(now) == 300
+
+
+def test_next_interval_dead_zone_skip():
+    """02:00 평일 (dead zone 23:30-04:30) → 다음 04:30 까지 (~9000s)."""
+    now = _kst(2026, 5, 18, 2, 0)
+    interval = _next_interval(now)
+    # 02:00 → 04:30 = 2.5h = 9000s
+    assert 9000 - 60 < interval < 9000 + 60
--- a/signal_v2/tests/test_signal_generator.py
+++ b/signal_v2/tests/test_signal_generator.py
@@ -0,0 +1,172 @@
+"""Tests for signal_generator."""
+from unittest.mock import MagicMock
+
+import pytest
+
+from signal_v2.signal_generator import generate_signals
+from signal_v2.state import PollState
+
+
+def _settings(**overrides):
+    """Build a Settings-like object for tests (avoid env)."""
+    defaults = dict(
+        stop_loss_pct=-0.07,
+        take_profit_pct=0.15,
+        chronos_spread_threshold=0.6,
+        asking_bid_ratio_threshold=0.6,
+        confidence_threshold=0.7,
+        min_momentum_for_buy="strong_up",
+    )
+    defaults.update(overrides)
+    m = MagicMock()
+    for k, v in defaults.items():
+        setattr(m, k, v)
+    return m
+
+
+def _make_state_with_buy_candidate(
+    ticker="005930", name="삼성전자",
+    chronos_median=0.02, chronos_q10=-0.01, chronos_q90=0.04, chronos_conf=0.85,
+    momentum="strong_up", bid_ratio=0.7, current_price=78500,
+):
+    state = PollState()
+    state.screener_preview = {"items": [{"ticker": ticker, "name": name}]}
+    state.chronos_predictions[ticker] = {
+        "median": chronos_median, "q10": chronos_q10, "q90": chronos_q90,
+        "conf": chronos_conf, "as_of": "2026-05-17T16:00:00+09:00",
+    }
+    state.minute_momentum[ticker] = momentum
+    state.asking_price[ticker] = {
+        "bid_total": int(bid_ratio * 1000),
+        "ask_total": int((1 - bid_ratio) * 1000),
+        "bid_ratio": bid_ratio,
+        "current_price": current_price,
+        "as_of": "2026-05-17T16:00:01+09:00",
+    }
+    return state
+
+
+def _make_state_with_holding(
+    ticker="005930", name="삼성전자",
+    pnl_pct=0.0, avg_price=75000, current_price=75000,
+):
+    state = PollState()
+    state.portfolio = {"holdings": [{
+        "ticker": ticker, "name": name,
+        "avg_price": avg_price, "current_price": current_price,
+        "pnl_pct": pnl_pct, "profit_rate": pnl_pct * 100,
+        "quantity": 100, "broker": "키움",
+    }]}
+    state.screener_preview = {"items": []}
+    return state
+
+
+@pytest.fixture
+def dedup_mock():
+    d = MagicMock()
+    d.is_recent.return_value = False
+    return d
+
+
+def test_buy_signal_when_all_conditions_pass_and_confidence_high(dedup_mock):
+    state = _make_state_with_buy_candidate()
+    generate_signals(state, dedup_mock, _settings())
+    assert "005930" in state.signals
+    sig = state.signals["005930"]
+    assert sig["action"] == "buy"
+    assert sig["confidence_webai"] > 0.7
+    dedup_mock.record.assert_called()
+
+
+def test_silent_when_chronos_median_negative(dedup_mock):
+    state = _make_state_with_buy_candidate(chronos_median=-0.01)
+    generate_signals(state, dedup_mock, _settings())
+    assert "005930" not in state.signals
+
+
+def test_silent_when_distribution_spread_too_wide(dedup_mock):
+    # spread = q90 - q10 = 0.5 - (-0.5) = 1.0 > 0.6 → hard gate fails
+    state = _make_state_with_buy_candidate(
+        chronos_median=0.001, chronos_q10=-0.5, chronos_q90=0.5,
+    )
+    generate_signals(state, dedup_mock, _settings())
+    assert "005930" not in state.signals
+
+
+def test_silent_when_momentum_not_strong_up(dedup_mock):
+    state = _make_state_with_buy_candidate(momentum="weak_up")
+    generate_signals(state, dedup_mock, _settings())
+    assert "005930" not in state.signals
+
+
+def test_silent_when_bid_ratio_below_threshold(dedup_mock):
+    state = _make_state_with_buy_candidate(bid_ratio=0.5)
+    generate_signals(state, dedup_mock, _settings())
+    assert "005930" not in state.signals
+
+
+def test_silent_when_confidence_below_threshold(dedup_mock):
+    # chronos_conf low + rank=20 → confidence < 0.7
+    state = _make_state_with_buy_candidate(chronos_conf=0.3)
+    # add 19 fake items to push 005930 rank to 20
+    state.screener_preview["items"] = (
+        [{"ticker": f"FAKE{i:03d}"} for i in range(19)]
+        + [{"ticker": "005930", "name": "삼성전자"}]
+    )
+    generate_signals(state, dedup_mock, _settings())
+    # confidence_webai = 0.3*0.5 + 1.0*0.3 + 0.05*0.2 = 0.46 < 0.7
+    assert "005930" not in state.signals
+
+
+def test_sell_signal_when_stop_loss_triggered(dedup_mock):
+    state = _make_state_with_holding(pnl_pct=-0.08, current_price=69000, avg_price=75000)
+    generate_signals(state, dedup_mock, _settings())
+    assert "005930" in state.signals
+    sig = state.signals["005930"]
+    assert sig["action"] == "sell"
+    assert sig["confidence_webai"] == 1.0
+    assert sig["pnl_pct"] == -0.08
+
+
+def test_sell_signal_when_take_profit_triggered(dedup_mock):
+    state = _make_state_with_holding(pnl_pct=0.16, current_price=87000, avg_price=75000)
+    generate_signals(state, dedup_mock, _settings())
+    assert "005930" in state.signals
+    sig = state.signals["005930"]
+    assert sig["action"] == "sell"
+    assert sig["confidence_webai"] == 0.6
+
+
+def test_silent_when_dedup_recently_sent(dedup_mock):
+    state = _make_state_with_buy_candidate()
+    dedup_mock.is_recent.return_value = True
+    generate_signals(state, dedup_mock, _settings())
+    assert "005930" not in state.signals
+    dedup_mock.record.assert_not_called()
+
+
+def test_sell_signal_triggers_on_anomaly_path(dedup_mock):
+    """Anomaly sell: median < -1%, momentum strong_down, low bid_ratio, confidence > threshold."""
+    state = PollState()
+    state.portfolio = {"holdings": [{
+        "ticker": "005930", "name": "삼성전자",
+        "avg_price": 75000, "current_price": 70000,
+        "pnl_pct": -0.067,  # within stop_loss tolerance (default -0.07): NOT triggering stop_loss
+        "quantity": 100, "broker": "키움",
+    }]}
+    state.screener_preview = {"items": []}
+    state.chronos_predictions["005930"] = {
+        "median": -0.025, "q10": -0.05, "q90": 0.005, "conf": 0.85,
+    }
+    state.minute_momentum["005930"] = "strong_down"
+    state.asking_price["005930"] = {"current_price": 70000, "bid_ratio": 0.30}
+    # bid_ratio 0.30 < (1 - 0.6) = 0.4 → anomaly bid_ratio gate passes
+    # confidence = 0.85*0.5 + 1.0*0.3 + 1.0*0.2 = 0.425 + 0.3 + 0.2 = 0.925 > 0.7
+
+    generate_signals(state, dedup_mock, _settings())
+
+    assert "005930" in state.signals
+    sig = state.signals["005930"]
+    assert sig["action"] == "sell"
+    assert sig["context"]["sell_reason"] == "anomaly"
+    assert sig["confidence_webai"] > 0.7
--- a/signal_v2/tests/test_stock_client.py
+++ b/signal_v2/tests/test_stock_client.py
@@ -0,0 +1,168 @@
+"""Tests for stock_client.StockClient."""
+import asyncio
+import logging
+import pytest
+import httpx
+
+from signal_v2.stock_client import StockClient
+
+
+BASE_URL = "https://test.stock.local"
+API_KEY = "test-secret"
+
+
+async def test_get_portfolio_normal_returns_dict_with_pnl_pct(mock_stock_api):
+    """정상 200 응답 + cache 저장."""
+    mock_stock_api.get("/api/webai/portfolio").mock(
+        return_value=httpx.Response(
+            200,
+            json={
+                "holdings": [{"ticker": "005930", "pnl_pct": 0.047}],
+                "cash": [],
+                "summary": {},
+            },
+        )
+    )
+    client = StockClient(BASE_URL, API_KEY)
+    try:
+        result = await client.get_portfolio()
+        assert result["holdings"][0]["pnl_pct"] == 0.047
+        # Cache populated
+        assert len(client._cache) >= 1
+    finally:
+        await client.close()
+
+
+async def test_get_portfolio_uses_cache_within_ttl(mock_stock_api):
+    """60s TTL 내 두번째 호출 = mock 콜 1회."""
+    route = mock_stock_api.get("/api/webai/portfolio").mock(
+        return_value=httpx.Response(
+            200, json={"holdings": [], "cash": [], "summary": {}}
+        )
+    )
+    client = StockClient(BASE_URL, API_KEY)
+    try:
+        await client.get_portfolio()
+        await client.get_portfolio()  # second call within TTL
+        assert route.call_count == 1
+    finally:
+        await client.close()
+
+
+async def test_get_portfolio_refetches_after_ttl_expiry(mock_stock_api, monkeypatch):
+    """TTL 만료 후 재호출 = mock 콜 2회. time.monotonic 모킹."""
+    route = mock_stock_api.get("/api/webai/portfolio").mock(
+        return_value=httpx.Response(
+            200, json={"holdings": [], "cash": [], "summary": {}}
+        )
+    )
+    # Fake clock: starts at 0, jumps to 61 between calls
+    fake_time = [0.0]
+    monkeypatch.setattr(
+        "signal_v2.stock_client.time.monotonic", lambda: fake_time[0]
+    )
+
+    client = StockClient(BASE_URL, API_KEY)
+    try:
+        await client.get_portfolio()
+        fake_time[0] = 61.0  # 60s TTL 만료
+        await client.get_portfolio()
+        assert route.call_count == 2
+    finally:
+        await client.close()
+
+
+async def test_get_portfolio_retries_3_times_on_timeout(mock_stock_api, monkeypatch):
+    """timeout 2번 + 200 1번 → 최종 성공. exponential sleep 호출 검증."""
+    sleep_calls = []
+
+    async def fake_sleep(s):
+        sleep_calls.append(s)
+
+    monkeypatch.setattr("asyncio.sleep", fake_sleep)
+
+    mock_stock_api.get("/api/webai/portfolio").mock(
+        side_effect=[
+            httpx.TimeoutException("timeout 1"),
+            httpx.TimeoutException("timeout 2"),
+            httpx.Response(
+                200, json={"holdings": [], "cash": [], "summary": {}}
+            ),
+        ]
+    )
+    client = StockClient(BASE_URL, API_KEY)
+    try:
+        result = await client.get_portfolio()
+        assert result["holdings"] == []
+        assert sleep_calls == [1, 2]  # exponential 1s, 2s
+    finally:
+        await client.close()
+
+
+async def test_get_portfolio_429_triggers_backoff(mock_stock_api, monkeypatch):
+    """429 → 1s backoff → 200."""
+    sleep_calls = []
+
+    async def fake_sleep(s):
+        sleep_calls.append(s)
+
+    monkeypatch.setattr("asyncio.sleep", fake_sleep)
+
+    mock_stock_api.get("/api/webai/portfolio").mock(
+        side_effect=[
+            httpx.Response(429, text="rate limit"),
+            httpx.Response(
+                200, json={"holdings": [], "cash": [], "summary": {}}
+            ),
+        ]
+    )
+    client = StockClient(BASE_URL, API_KEY)
+    try:
+        result = await client.get_portfolio()
+        assert result["holdings"] == []
+        assert sleep_calls == [1]
+    finally:
+        await client.close()
+
+
+async def test_get_portfolio_falls_back_to_stale_on_all_failures(
+    mock_stock_api, monkeypatch, caplog
+):
+    """cache 에 이전 성공 응답 + 모든 retry 5xx → stale 반환 + logger.warning."""
+    # No-op sleep for fast test
+    async def fake_sleep(s):
+        return None
+    monkeypatch.setattr("asyncio.sleep", fake_sleep)
+
+    # Patch time.monotonic BEFORE first call so cached timestamp uses fake clock
+    fake_time = [0.0]
+    monkeypatch.setattr(
+        "signal_v2.stock_client.time.monotonic", lambda: fake_time[0]
+    )
+
+    # First call succeeds
+    route1 = mock_stock_api.get("/api/webai/portfolio").mock(
+        return_value=httpx.Response(
+            200,
+            json={"holdings": [{"ticker": "005930"}], "cash": [], "summary": {}},
+        )
+    )
+    client = StockClient(BASE_URL, API_KEY)
+    try:
+        first = await client.get_portfolio()
+        assert first["holdings"][0]["ticker"] == "005930"
+
+        # Advance fake clock past TTL (60s) so cache is stale
+        fake_time[0] = 61.0
+
+        # Now mock to return 500s persistently
+        route1.mock(return_value=httpx.Response(500, text="server error"))
+
+        with caplog.at_level(logging.WARNING, logger="signal_v2.stock_client"):
+            result = await client.get_portfolio()
+            assert result["holdings"][0]["ticker"] == "005930"  # stale data returned
+            assert any(
+                "stale" in rec.message.lower() for rec in caplog.records
+            )
+    finally:
+        await client.close()
--- a/start.bat
+++ b/start.bat
@@ -0,0 +1,3 @@
+@echo off
+cd /d "%~dp0\signal_v1"
+python main_server.py
Author	SHA1	Message	Date
gahusb	71ef959310	docs(web-ai): rewrite CLAUDE.md with Phase 0-4 complete context Replaces Phase-2-era placeholder. Adds: - V1 (signal_v1 :8000 LSTM bot) vs V2 (signal_v2 :8001 Confidence Pipeline) split - start.bat invocation for each + KIS rate limit warning (do NOT run both) - Phase 0-7 status table, Phase 4 completed 2026-05-17 - signal_v2/ module-level inventory + new test count (56) - Phase 4 buy/sell rule summary (absolute spread amendment included) - 11 known traps + Phase 7 backlog - Cross-repo workflow note (code in web-ai, spec/plan in web-ui)	2026-05-17 14:00:52 +09:00
gahusb	2aa9f48ea3	feat(signal_v2-phase4): add emit/skip logging to signal_generator logger was declared but unused. Operational visibility was zero — trader debugging 'why no signal?' had to step through code mentally. - INFO on emit: '[signal emit] 005930 buy conf=0.823 rank=3' / sell with reason - DEBUG on each skip path: same-cycle sell, hard gate, low confidence, dedup 24h (buy and sell) Per final reviewer recommendation. 56 tests still pass.	2026-05-17 13:35:29 +09:00
gahusb	cc6310d72f	feat(signal_v2-phase4-task3): integrate signal_generator into poll_loop poll_loop now accepts dedup + settings kwargs (backwards-compatible defaults). After each in-window cycle (stock pull + minute momentum + optional post-close), generate_signals is called to populate state.signals for downstream Phase 5 pickup. main.py lifespan wires _ctx.dedup + settings into the poll_loop task. 1 integration test added (anomaly-free stop_loss path via direct generate_signals call, exercises the same code path that poll_loop runs). 56 tests pass.	2026-05-17 13:24:47 +09:00
gahusb	e574074ca8	fix(signal_v2-phase4-task2): code review fixes — sell-first ordering + anomaly test + defensive .get - generate_signals now evaluates sell before buy; buy candidates with a same-cycle sell signal are skipped (resolves silent overwrite of state.signals[ticker]). - Added test_sell_signal_triggers_on_anomaly_path covering _try_anomaly path (previously 0% covered). - Fixed stale test comment referencing deprecated relative spread formula. - _check_buy_hard_gate uses dict.get(..., 0) for defense against partial upstream state. - _compute_buy_confidence clamps screener_norm to >= 0 for future Top-N changes.	2026-05-17 13:18:22 +09:00
gahusb	b9def06993	feat(signal_v2-phase4): signal_generator + 9 unit tests generate_signals(state, dedup, settings) → state mutating: - Buy: screener Top-N + portfolio. Hard gate (chronos median > 0 + spread < 0.6 + momentum strong_up + bid_ratio >= 0.6) + soft confidence (chronos0.5 + minute0.3 + screener*0.2) > 0.7. - Sell: portfolio only. Priority stop_loss > anomaly > take_profit. Stop loss confidence 1.0, take_profit 0.6 (review alert). - SignalDedup 24h via dedup.is_recent/record per (ticker, action). - State signal dict matches Phase 0 spec §5.2 schema. 54 tests pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-17 13:03:29 +09:00
gahusb	05ab2846bb	feat(signal_v2-phase4): foundation — 6 env thresholds + state.signals config.py: STOP_LOSS_PCT / TAKE_PROFIT_PCT / CHRONOS_SPREAD_THRESHOLD / ASKING_BID_RATIO_THRESHOLD / CONFIDENCE_THRESHOLD / MIN_MOMENTUM_FOR_BUY env vars with sensible defaults (Phase 0 spec §6.1-§6.2 values). state.py: PollState.signals dict[ticker, signal_body] for Phase 5 input. 45 existing tests still pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-17 12:55:15 +09:00
gahusb	760f914d3b	fix(signal_v2-phase3b): force FP32 + predict_quantiles positional args ChronosBoltPipeline.predict_quantiles takes `inputs` positional, not `context` keyword. Use positional with TypeError fallback for older chronos versions. FP16 caused inf overflow on Korean stock prices (e.g. 280,000원 > FP16 max 65,504). Force FP32 for prices to avoid this. Chronos model itself handles internal scaling. Verified end-to-end: 60-day daily fetch → Chronos predict → quantile output. Example 005930: median=-0.59%, q10=-8.9%, q90=+6.4%, conf=0.0 (low conf is mathematically correct when median is near zero relative to distribution width). 45/45 tests still pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-17 09:12:10 +09:00
gahusb	8eefe9d79d	fix(signal_v2-phase3b): ChronosBolt predict_quantiles API support ChronosBoltPipeline.predict() does not accept `context` kwarg; it uses positional-only and is deterministic (no num_samples). Switch to predict_quantiles(context, prediction_length, quantile_levels) which returns (quantiles_tensor, mean_tensor). Implementation: if hasattr(pipeline, "predict_quantiles") → modern quantile branch. Else fall back to legacy sample-based predict (T5). Tests: switch to predict_quantiles mock returning (quantiles, None) with shape [1, 1, 3] for q10/q50/q90 directly. 45/45 tests pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-17 09:07:11 +09:00
gahusb	91de16675b	fix(signal_v2-phase3b): use BaseChronosPipeline for new model architectures ChronosPipeline (legacy T5) does not support amazon/chronos-2 or chronos-bolt-* (input_patch_size). Switch to BaseChronosPipeline which auto-detects variant and returns the appropriate sub-pipeline (ChronosBoltPipeline / Chronos2Pipeline / ChronosPipeline). Also handle the dtype kwarg deprecation: try newer `dtype=` first, fall back to `torch_dtype=` for older versions. Test mock_pipeline fixture updated to patch BaseChronosPipeline. 45/45 tests pass. Verified amazon/chronos-bolt-base loads on CUDA. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-17 08:57:22 +09:00
gahusb	44888d6ede	feat(signal_v2-phase3b): main.py lifespan loads ChronosPredictor AppContext.chronos field. lifespan: if KIS_APP_KEY set, load ChronosPredictor(model_name=settings.chronos_model). Exceptions during load logged + signal_v2 continues without chronos (other endpoints unaffected). poll_loop receives chronos param. 45 tests pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 18:11:50 +09:00
gahusb	9e5fecb369	feat(signal_v2-phase3b): post-close cycle + minute momentum update scheduler._is_post_close_trigger: 16:00 KST ±1min detection (market day). pull_worker: - _run_post_close_cycle: daily fetch (60일) + chronos batch predict → state.chronos_predictions + state.daily_ohlcv. - update_minute_momentum_for_all: 매 cycle 마다 state.minute_momentum 갱신. - poll_loop signature 확장 (chronos optional). 45 tests pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 18:04:32 +09:00
gahusb	28f9c8c3a6	feat(signal_v2-phase3b): chronos_predictor + 4 mock tests ChronosPredictor wraps HuggingFace ChronosPipeline. Batch predict returns ChronosPrediction(median, q10, q90, conf, as_of) per ticker. Confidence = 1 - clamp(spread/2, 0, 1) where spread = (q90-q10) / \|median\|. Lazy import of chronos lib (heavy). GPU auto-detect with FP16. 44 tests pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 18:00:46 +09:00
gahusb	c5a88fab66	feat(signal_v2-phase3b): momentum_classifier + 6 unit tests aggregate_1min_to_5min: 1분봉 5개 → 5분봉 1개 (open=첫, close=마지막, high=max, low=min, volume=sum). classify_minute_momentum: 직전 5개 5분봉 양봉 개수 + 거래량 60분 multiplier → 5-level (strong_up/weak_up/neutral/weak_down/strong_down). 40 tests pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 17:55:34 +09:00
gahusb	7056cf2fa6	feat(signal_v2-phase3b): kis_client.get_daily_ohlcv (60 daily bars) TR_ID FHKST03010100 (수정주가 일봉). KIS returns descending; client reverses to ascending and trims to last N days. 1 new test, 34 total. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 17:49:06 +09:00
gahusb	4ac7da8670	feat(signal_v2-phase3b): foundation — config + state + requirements - config.py: CHRONOS_MODEL env (default amazon/chronos-2) - state.py: PollState extended with daily_ohlcv + chronos_predictions + minute_momentum - requirements.txt: transformers + chronos-forecasting 33 existing tests still pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 17:46:09 +09:00
gahusb	b690900cfc	fix(signal_v2-phase3a): V1 KIS env pattern + test isolation fix config.py: kis_env_type (virtual/real) + KIS_REAL_/KIS_VIRTUAL_ env variables (V1 호환). kis_app_key/kis_app_secret/kis_account properties auto-select based on env type. main.py: KIS not-configured warning uses descriptive message including env type + expected var name. test_main.py: monkeypatch load_dotenv to no-op + setenv empty string (instead of delenv) — defeats .env re-read on importlib.reload. Pre-existing test_startup_warns_if_webai_api_key_missing also fixed. 33/33 tests pass (was 31/33). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 10:25:58 +09:00
gahusb	d85512d036	feat(signal_v2-phase3a): main.py lifespan integrates KIS client + WS AppContext extended with kis_client + kis_ws. lifespan: - If KIS_APP_KEY set: create KISClient + KISWebSocket, fetch portfolio, subscribe WebSocket H0STASP0 for holdings. - If unset: WARNING log, signal_v2 still serves /health (no KIS data). - Shutdown closes kis_ws → kis_client → stock client in order. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 05:21:55 +09:00
gahusb	3ebe95ba29	feat(signal_v2-phase3a): pull_worker KIS minute cycle + WS callback _run_kis_minute_cycle: portfolio + screener union 종목 분봉 fetch + screener-only 종목 호가 REST fetch. WebSocket callback factory (make_asking_price_callback). poll_loop / _run_polling_cycle 에 kis_client optional param 추가 (Phase 5 까지 None 일 때도 정상 동작). 2 new tests pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 05:19:24 +09:00
gahusb	163c9fb690	feat(signal_v2-phase3a): kis_websocket + 4 integration tests KISWebSocket: approval_key (POST /oauth2/Approval) + H0STASP0 호가 실시간 subscribe + receive loop + exponential reconnect (1s → 2s → 4s → max 30s). _parse_asking_price 필드 인덱스 운영 검증 필요 (현재 가정: 마지막 2 필드가 ask_total/bid_total). 4 tests pass, 29 total. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 05:16:20 +09:00
gahusb	27bf360b01	feat(signal_v2-phase3a): kis_client REST + 4 integration tests KISClient: 분봉 (FHKST03010200) + 호가 (FHKST01010200) async REST. V1 토큰 파일 (signal_v1/data/kis_token.json) read-only 공유, mtime 캐시. 초당 2회 throttle. exponential retry (max 3, 1s/2s/4s). 4 tests pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 05:12:45 +09:00
gahusb	eafa73edb1	feat(signal_v2-phase3a): scheduler NXT windows (20:00-23:30 / 04:30-07:00) NXT 시간외 거래 시간대도 5분 cron 폴링 활성화. 23:30-04:30 dead zone (KIS 점검) → 04:30 까지 skip. 기존 _seconds_until_next_market_open (휴장일/주말용) 와 별개로 _seconds_until_nxt_or_market_open 신설. 3 new tests, scheduler suite 11 passed. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 05:09:27 +09:00
gahusb	68eb7b073c	feat(signal_v2-phase3a): config + state extensions for KIS data - config.py: KIS_APP_KEY/SECRET/ACCOUNT/IS_VIRTUAL + V1_TOKEN_PATH env - state.py: PollState extended with minute_bars (deque) and asking_price - requirements.txt: websockets>=12 19 existing tests still pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 05:06:40 +09:00
gahusb	8342d38935	chore(signal_v2): sync holidays.json from stock + start.bat launcher - holidays.json: authoritative copy from web-backend/stock/app/holidays.json (replaces 13-date stub from Task 3; now 16 dates including Jan/May/Dec edges) - start.bat: uvicorn launcher (cd to web-ai root, host 0.0.0.0, port 8001) 19 tests still pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 03:55:41 +09:00
gahusb	e47947fb69	fix(signal_v2): await cancelled poll_task + public cache_size Code review fixes: - main.py lifespan: await poll_task after cancel() to avoid client close racing with mid-fetch task (CRITICAL). - stock_client: add public cache_size() method; main.py /health uses it instead of private _cache attribute (IMPORTANT). 19 tests still pass. Deferred to Phase 7 backlog: - _ctx singleton test isolation (importlib.reload provides isolation in practice) - poll_loop interval floor (interval >= 60 by design) - shutdown logging - response schema validation Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 03:52:45 +09:00
gahusb	94c684bab8	feat(signal_v2): pull_worker + FastAPI app + 2 integration tests poll_loop: asyncio.gather parallel fetch of 3 endpoints (portfolio, news_sentiment, screener_preview) + state update. main.py: FastAPI lifespan creates StockClient/SignalDedup/shutdown.Event then spawns poll_loop as background task. GET /health reports status, last poll times, cache size. Signal V2 test suite: 19/19 PASS. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 03:49:50 +09:00
gahusb	1a6d9fcb39	feat(signal_v2): rate_limit + 3 unit tests SignalDedup: 24h-rolling duplicate signal blocker. SQLite WAL + busy_timeout=120000 standard fix (reference_sqlite_concurrency.md pattern). PK (ticker, action) with UPSERT. Phase 4 (signal generator) will call is_recent() before sending + record() after sending. 3 unit tests pass, total 17 signal_v2. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 03:46:59 +09:00
gahusb	6cb5085118	test(signal_v2): add scheduler boundary tests at exact transitions Code review noted missing boundary tests at: - 09:00:00 (pre-market → market) → 60 - 15:30:00 (market → post-market) → 300 - 20:00:00 (post-market → overnight skip) 3 new tests, total 8 scheduler / 14 signal_v2. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 03:45:55 +09:00
gahusb	fdabc69004	feat(signal_v2): scheduler + 5 unit tests Time-window dispatcher: pre-market (07:00-09:00, 5min), market (09:00-15:30, 1min), post-market (15:30-20:00, 5min), overnight skip to next market day 07:00. Weekend + holiday detection via holidays.json. Stub holidays.json with 13 dates. Task 6 will sync from web-backend/stock/app/holidays.json. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 03:44:24 +09:00
gahusb	90235497ae	refactor(signal_v2): narrow stock_client exception catch + remove dead code Code quality review fixes: - _cached_request: catch httpx.HTTPError instead of bare Exception (avoid swallowing KeyboardInterrupt / asyncio.CancelledError) - _request_with_retry: remove unused last_exc variable + dead post-loop raise paths. Final sentinel raise preserved for mypy. 6 tests still pass. Deferred to Phase 7 backlog: cache concurrency coalescing + __aenter__/__aexit__ context manager support. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 03:43:09 +09:00
gahusb	8469bf7ffa	feat(signal_v2): stock_client + 6 integration tests httpx async client with custom retry loop (max 3, exponential 1s/2s/4s), memory dict cache (portfolio 60s / news-sentiment 300s / screener 60s), X-WebAI-Key auth header injection. Stale fallback returns last successful response with logger.warning on persistent failures. 6 integration tests pass with respx httpx mock. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 03:40:12 +09:00
gahusb	8a2fac03a6	feat(signal_v2): foundation — config + state + requirements - signal_v2/config.py: Settings dataclass loading web-ai/.env explicitly - signal_v2/state.py: PollState dataclass + module-level singleton - requirements.txt: httpx / fastapi / uvicorn / pytest-asyncio / respx - .gitignore: signal_v2/data/*.db (WAL/SHM) - empty tests/ marker Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 03:35:36 +09:00
gahusb	ad2c65c2b2	fix(signal_v1): load_dotenv Path depth — resolve web-ai/.env correctly 3 files had insufficient .parent count, resolving to signal_v1/.env instead of web-ai/.env (which is where the actual env file lives). Added one .parent each: - config.py: parent.parent → parent.parent.parent - analysis/macro.py: parent.parent.parent → parent.parent.parent.parent - services/telegram_bot/runner.py: parent.parent.parent.parent → +1 watchlist_manager.py was already correct. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 03:04:08 +09:00
gahusb	7ea1a21487	refactor: web-ai V1 assets → signal_v1/ (graduation prep) Atomic mv of root V1 assets (main_server.py + modules/ + data/ + tests/ + entry scripts + docs + logs) into signal_v1/ subdirectory. load_dotenv() updated to load web-ai/.env explicitly via Path. Adds web-ai/CLAUDE.md (workspace guide) and web-ai/start.bat (signal_v1 entry wrapper). Prepares for signal_v2/ Phase 2. Tests: signal_v1/tests/unit baseline preserved (no regression). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 03:00:11 +09:00
gahusb	42b91d03cf	feat(v3.2): DailyLedger + RiskGate + news_snapshot + backtest_runner - DailyLedger: 당일 매수 회계 + 연속 손절 카운터 + 매수 신호 점수 한 객체로 집약 (bot.py 정리) - RiskGate: 테마당 동시 보유 + 노출 비율 상한 검증 (포트폴리오 레벨) - news_snapshot: 뉴스 SQLite 영구 저장 + 사후 감성 재검증 인프라 - backtest_runner: 전 종목 KIS 일봉 기반 백테스트 (Sharpe/MDD/Calmar) - bot.py 274 line 정리 (DailyLedger 분리) - backtest.py 173 line 재작성 (v3.2 next-bar 체결 + 거래세) - daily_launcher.py 폐기 (warmup_and_restart 통합) - .gitignore: .claude/ 제외 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-16 02:57:26 +09:00