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

# 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

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:

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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