Stock-trading-programming/README.md

# 단타 자동매매 시스템 v3.0

기획서 v3.0 기준 / KIS Open API / Synology NAS Docker
AI: Claude Code headless (장 전 분석 + 장 후 피드백)

## 운영 모드

| KIS_MOCK | DRY_RUN | 동작 |
|----------|---------|------|
| true | true | 신호 확인만 (주문 없음) ← 처음 시작 |
| true | false | 모의투자 실제 주문 ← 3개월 검증 |
| false | false | 실거래 ← 조건 충족 후 |

## 빠른 시작

```bash
# 1. .env 설정
cp .env.example .env
# .env 열어서 KIS 키, Discord Webhook URL 입력

# 2. KIS 연결 테스트
pip install aiohttp python-dotenv
python test_connection.py

# 3. 신호 확인 (DRY_RUN=true)
python app/main.py

# 4. Docker 실행 (NAS)
docker-compose up -d
```

## 컨테이너 구성

| 컨테이너 | 역할 | 실행 시간 |
|---------|------|---------|
| stockbot-main | 매매 프로그램 | 상시 (09:00~15:00 활성) |
| stockbot-redis | 시세 캐시 | 상시 |
| stockbot-dashboard | Streamlit 모니터링 | 상시 (포트 8501) |
| claude-morning | 장 전 AI 분석 | 08:30 (실행 후 종료) |
| claude-evening | 장 후 AI 피드백 | 15:30 (실행 후 종료) |
| stockbot-killswitch | 긴급 청산 | 수동 트리거 |

## 긴급 청산

```bash
docker-compose --profile emergency up kill-switch
# 또는
python kill_switch/kill.py
```
# StockBot Current Status - 2026-05-28

This project is currently a paper-trading scalping bot with an AI training
pipeline in observation mode.

Active:
- Windows Task Scheduler operation for morning, midday, evening, watchdog, and training jobs.
- Approved `ENTRY_START = "09:20"` after the 2026-05-28 evening review.
- `FORCE_EXIT = "14:50"` remains unchanged.
- `avoid_sectors` filtering is active in runtime entry checks.
- Entry snapshots for model training.
- Post-entry snapshots at 1m, 3m, 5m, and 10m.
- Bot-data export to `data/training_dataset.csv`.
- External daily/minute data collection for pretraining.
- External daily collection falls back to KIS when pykrx same-day data fails.
- KIS minute collection excludes ETF/ETN by default and collects multiple windows from 09:30 to 14:00.
- RandomForest-based training engine.
- Optional AI entry scoring when `models/scalping_model.joblib` exists.
- Latest verified training run: 2026-05-28 20:24.
- Latest training rows: 3,156 total, including 3,146 external pretraining rows and 10 bot-trade rows.
- Latest metrics: `label_stop_loss` ROC-AUC 0.851, `label_win` ROC-AUC 0.719.

Not active yet:
- AI does not block buys.
- AI does not change position size.
- AI does not override exits.
- Real-cash trading is not ready until fill, unfilled-order, and partial-fill handling is hardened.

Daily schedule:

| Time | Task | Purpose |
|---|---|---|
| 08:15 | `StockBot_Morning` | Run `/morning`, build context, start bot |
| 09:00-15:10 | `StockBot_Watchdog` | Check/restart bot every 5 minutes |
| 11:20 | `StockBot_Midday` | Midday review and context update |
| 15:30 | `StockBot_Evening` | Daily review and proposal report |
| 16:00 | `StockBot_Training` | Collect data, export datasets, train model |

Useful commands:

```powershell
python -m pip install -r requirements.txt
powershell -ExecutionPolicy Bypass -File scripts\install_dependencies.ps1
powershell -ExecutionPolicy Bypass -File scripts\setup_scheduler.ps1
powershell -ExecutionPolicy Bypass -File scripts\run_training_pipeline.ps1
python scripts\export_training_dataset.py
python scripts\train_ai_model.py
```

Dependency portability:
- Root `requirements.txt` includes `app/requirements.txt`.
- `scripts/download_dependencies.ps1` downloads Windows/Python 3.11 wheels to `vendor/wheels`.
- `scripts/install_dependencies.ps1` installs from `vendor/wheels` first, then falls back to online pip.
- Raspberry Pi needs its own wheelhouse or online install because Windows wheels are not ARM/Linux compatible.

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