2026-05-14 15:14:50 +09:00
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# 단타 자동매매 시스템 v3.0
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기획서 v3.0 기준 / KIS Open API / Synology NAS Docker
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AI: Claude Code headless (장 전 분석 + 장 후 피드백)
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## 운영 모드
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| KIS_MOCK | DRY_RUN | 동작 |
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|----------|---------|------|
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| true | true | 신호 확인만 (주문 없음) ← 처음 시작 |
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| true | false | 모의투자 실제 주문 ← 3개월 검증 |
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| false | false | 실거래 ← 조건 충족 후 |
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## 빠른 시작
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```bash
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# 1. .env 설정
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cp .env.example .env
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# .env 열어서 KIS 키, Discord Webhook URL 입력
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# 2. KIS 연결 테스트
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pip install aiohttp python-dotenv
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python test_connection.py
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# 3. 신호 확인 (DRY_RUN=true)
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python app/main.py
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# 4. Docker 실행 (NAS)
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docker-compose up -d
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```
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## 컨테이너 구성
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| 컨테이너 | 역할 | 실행 시간 |
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|---------|------|---------|
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| stockbot-main | 매매 프로그램 | 상시 (09:00~15:00 활성) |
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| stockbot-redis | 시세 캐시 | 상시 |
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| stockbot-dashboard | Streamlit 모니터링 | 상시 (포트 8501) |
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| claude-morning | 장 전 AI 분석 | 08:30 (실행 후 종료) |
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| claude-evening | 장 후 AI 피드백 | 15:30 (실행 후 종료) |
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| stockbot-killswitch | 긴급 청산 | 수동 트리거 |
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## 긴급 청산
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```bash
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docker-compose --profile emergency up kill-switch
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# 또는
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python kill_switch/kill.py
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```
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2026-05-27 16:53:52 +09:00
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# StockBot Current Status - 2026-05-27
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This project is currently a paper-trading scalping bot with an AI training
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pipeline in observation mode.
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Active:
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- Windows Task Scheduler operation for morning, midday, evening, watchdog, and training jobs.
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- Entry snapshots for model training.
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- Post-entry snapshots at 1m, 3m, 5m, and 10m.
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- Bot-data export to `data/training_dataset.csv`.
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- External daily/minute data collection for pretraining.
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- RandomForest-based training engine.
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- Optional AI entry scoring when `models/scalping_model.joblib` exists.
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Not active yet:
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- AI does not block buys.
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- AI does not change position size.
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- AI does not override exits.
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- Real-cash trading is not ready until fill, unfilled-order, and partial-fill handling is hardened.
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Daily schedule:
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| Time | Task | Purpose |
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|---|---|---|
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| 08:15 | `StockBot_Morning` | Run `/morning`, build context, start bot |
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| 09:00-15:10 | `StockBot_Watchdog` | Check/restart bot every 5 minutes |
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| 11:20 | `StockBot_Midday` | Midday review and context update |
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| 15:30 | `StockBot_Evening` | Daily review and proposal report |
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| 16:00 | `StockBot_Training` | Collect data, export datasets, train model |
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Useful commands:
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```powershell
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python -m pip install -r requirements.txt
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powershell -ExecutionPolicy Bypass -File scripts\install_dependencies.ps1
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powershell -ExecutionPolicy Bypass -File scripts\setup_scheduler.ps1
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powershell -ExecutionPolicy Bypass -File scripts\run_training_pipeline.ps1
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python scripts\export_training_dataset.py
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python scripts\train_ai_model.py
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```
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Dependency portability:
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- Root `requirements.txt` includes `app/requirements.txt`.
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- `scripts/download_dependencies.ps1` downloads Windows/Python 3.11 wheels to `vendor/wheels`.
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- `scripts/install_dependencies.ps1` installs from `vendor/wheels` first, then falls back to online pip.
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- Raspberry Pi needs its own wheelhouse or online install because Windows wheels are not ARM/Linux compatible.
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---
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