b1ca6ab5d3
- backend/app/models/chronos.py: amazon/chronos-t5-small (env CHRONOS_MODEL
override 가능). lazy singleton, cuda + bf16 자동, q10/median/q90 + raw
samples 반환 (앙상블 가중평균용).
- backend/app/models/features.py: 종목별 학습/추론 피처 DataFrame.
OHLCV + TA(rsi/macd/atr/bb/sma/ema/vol_z) + 외인기관거래대금 + macro
(kospi/kosdaq/usdkrw/us10y + r1) + sentiment(v_sentiment_daily, 3d rolling).
학습용은 with_targets=True 로 y_close_h{1,3,5}, y_ret_h*, y_dir_h*
(±0.3% flat band) 추가.
- pyproject.toml: chronos-forecasting 1.4.1, accelerate 0.30.1, joblib 1.4.2.
이 단계까지는 코드만. 실제 모델 다운로드는 첫 ping/predict 호출 시점에.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
69 lines
1.3 KiB
TOML
69 lines
1.3 KiB
TOML
[project]
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name = "stock_chart_site_backend"
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version = "0.0.1"
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description = "Stock chart + prediction backend (FastAPI + Chronos + LightGBM + KR-FinBERT)"
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requires-python = ">=3.11,<3.13"
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dependencies = [
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# web
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"fastapi==0.111.0",
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"uvicorn[standard]==0.30.0",
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"pydantic==2.7.1",
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"pydantic-settings==2.3.0",
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# db
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"sqlalchemy==2.0.30",
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"psycopg[binary]==3.1.19",
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"alembic==1.13.1",
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# data
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"pandas==2.2.2",
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"numpy==1.26.4",
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"pykrx==1.0.45",
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"yfinance==0.2.40",
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"feedparser==6.0.11",
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"requests==2.32.3",
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"httpx==0.27.0",
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"beautifulsoup4==4.12.3",
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"lxml==5.2.2",
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# ml
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"transformers==4.41.2",
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"tokenizers==0.19.1",
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"sentencepiece==0.2.0",
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"accelerate==0.30.1",
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"chronos-forecasting==1.4.1",
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"scikit-learn==1.5.0",
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"lightgbm==4.3.0",
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"ta==0.11.0",
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"joblib==1.4.2",
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# scheduler
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"apscheduler==3.10.4",
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"pytz==2024.1",
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# utils
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"python-dotenv==1.0.1",
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"loguru==0.7.2",
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"tenacity==8.3.0",
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]
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[project.optional-dependencies]
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dev = [
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"pytest==8.2.1",
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"ruff==0.4.7",
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"mypy==1.10.0",
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]
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[build-system]
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requires = ["setuptools>=68"]
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build-backend = "setuptools.build_meta"
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[tool.setuptools.packages.find]
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where = ["."]
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include = ["app*"]
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[tool.ruff]
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line-length = 100
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target-version = "py311"
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