feat(phase-0): scaffold backend + web + docker + DB schema

- docker-compose.yml: timescaledb-ha (timescaledb 2.27 + vectorscale + pgvector + pgai)
  + backend (FastAPI, CUDA 12.1) + web (Next.js 14)
- docker-compose.gpu.yml: GPU profile overlay for RTX 3070 Ti
- build.bat: Windows bootstrap, auto-detects nvidia-smi and selects GPU/CPU compose
- backend: Dockerfile, pyproject.toml, FastAPI skeleton with /health and /health/db
- DB migration 001_init.sql: symbols (with trigram search), ohlcv_daily/1m (hypertables),
  macro_daily, trading_value_daily, news (vector embedding), predictions
  (with user_triggered flag for on-demand UX), prediction_outcomes, model_performance
- web: Next.js 14 + Tailwind + lightweight-charts placeholder
- README: KIS/DART/HuggingFace token issuance guides + 10 seed tickers + run instructions
This commit is contained in:
tkrmagid
2026-05-20 14:37:35 +09:00
parent 619dc7811b
commit cacddf5adf
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# Copy to .env and fill in. .env is gitignored.
# --- Database ---
POSTGRES_USER=stock
POSTGRES_PASSWORD=stockpw
POSTGRES_DB=stock
POSTGRES_HOST=db
POSTGRES_PORT=5432
DATABASE_URL=postgresql+psycopg://stock:stockpw@db:5432/stock
# --- Timezone ---
TZ=Asia/Seoul
# --- API keys (모두 무료 발급. 없어도 pykrx만으로 일단 동작) ---
# 한국투자증권 OpenAPI (실시간 시세, EOD 등)
# 발급: https://apiportal.koreainvestment.com (계좌 + Open API 서비스 신청)
KIS_APP_KEY=
KIS_APP_SECRET=
KIS_ACCOUNT_NO=
# DART OpenAPI (전자공시 본문)
# 발급: https://opendart.fss.or.kr (회원가입 후 인증키 신청)
DART_API_KEY=
# HuggingFace (선택. 없어도 공개 모델 다운로드 가능. 토큰 있으면 rate limit 완화)
# 발급: https://huggingface.co/settings/tokens
HUGGINGFACE_TOKEN=
# --- 모델 디바이스 ---
# auto | cuda | cpu
MODEL_DEVICE=auto
# --- Backend ---
BACKEND_PORT=8000
LOG_LEVEL=INFO
# --- Web ---
WEB_PORT=3000
NEXT_PUBLIC_API_BASE=http://localhost:8000

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# Env / secrets
.env
.env.local
.env.*.local
# Python
__pycache__/
*.py[cod]
*.pyo
.Python
.venv/
venv/
.pytest_cache/
.mypy_cache/
.ruff_cache/
*.egg-info/
dist/
build/
# Models / artifacts (downloaded HF caches, trained LGBM)
backend/artifacts/
backend/.cache/
.huggingface/
# Node
node_modules/
.next/
out/
*.tsbuildinfo
# OS
.DS_Store
Thumbs.db
# Docker volumes mounted locally
postgres_data/
# Logs
*.log
logs/

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# stock_chart_site # stock_chart_site
개인용 주식 차트 + 단기 예측 사이트. 한국 종목 검색 → 현재 차트 표시 → "예상차트 보기" 클릭 시 Chronos+LightGBM 앙상블로 향후 1~5거래일 예측을 차트에 이어 붙임. 사용자가 한 번이라도 예측을 확인한 종목은 자동 저장해서 다음날 실제 가격과 비교 → 오차/방향성 적중률을 누적 → 앙상블 가중치를 자동 보정.
스펙 원문: `/home/claude/EJClaw/groups/stock_predictor/SPEC.md` (별도 채팅 그룹).
## 빠른 시작 (Windows + Docker Desktop + RTX 3070 Ti)
전제: Docker Desktop이 이미 설치되어 있고, GPU 사용하려면 `Settings → Resources → WSL Integration → GPU support`가 켜져 있어야 합니다.
```cmd
git clone https://git.tkrmagid.kr/tkrmagid/stock_chart_site.git
cd stock_chart_site
build.bat
```
`build.bat`이 자동으로
1. `.env.example``.env` 복사 (없는 경우)
2. `nvidia-smi` 로 GPU 감지 → GPU 있으면 GPU 모드, 없으면 CPU 폴백
3. `docker compose build` + `up -d`
확인:
- Web: http://localhost:3000
- Backend health: http://localhost:8000/health
- DB extensions: http://localhost:8000/health/db (`timescaledb`, `vector`, `pg_trgm` 셋 다 켜져 있어야 정상)
정지:
```cmd
docker compose down
```
## 빌드 모드 (수동)
```bash
# GPU 모드 (RTX 3070 Ti 등 NVIDIA GPU 사용)
docker compose -f docker-compose.yml -f docker-compose.gpu.yml up -d --build
# CPU 모드
docker compose up -d --build
```
## API 키 발급 (모두 무료)
키 없어도 pykrx 기반 일봉/뉴스 RSS 만으로 일단 동작합니다. 다음 키를 받으면 데이터 품질이 좋아집니다.
### 1) 한국투자증권 KIS OpenAPI (실시간 시세 + EOD)
1. https://apiportal.koreainvestment.com 회원가입 (한국투자증권 계좌 필요)
2. 좌측 "Open API 신청" → 모의/실전 중 하나 신청
3. 발급 완료 후 마이페이지에서 App Key, App Secret, 계좌번호 확인
4. `.env` 에 입력:
```
KIS_APP_KEY=...
KIS_APP_SECRET=...
KIS_ACCOUNT_NO=...
```
### 2) DART OpenAPI (전자공시 본문)
1. https://opendart.fss.or.kr 회원가입
2. 마이페이지 → 인증키 신청 → 즉시 발급
3. `.env` 에 입력:
```
DART_API_KEY=...
```
### 3) HuggingFace (선택, 모델 다운로드 가속)
토큰 없어도 공개 모델 (`amazon/chronos-bolt-base`, `snunlp/KR-FinBert`) 다운로드가 됩니다. 토큰이 있으면 rate limit이 완화되고 첫 다운로드가 빨라집니다.
1. https://huggingface.co 회원가입
2. https://huggingface.co/settings/tokens 에서 Read 토큰 생성
3. `.env` 에 입력:
```
HUGGINGFACE_TOKEN=hf_...
```
## 학습/배치 대상 시드 종목 (10개)
검색은 KRX 전 종목을 대상으로 동작하지만, 일별 배치/재학습/메트릭 누적은 아래 10개를 우선 대상으로 합니다. 운영하면서 더 의미있는 종목이 보이면 교체합니다.
| 분류 | 종목 | 코드 |
|---|---|---|
| 대형 인기주 | 삼성전자 | 005930 |
| 대형 인기주 | SK하이닉스 | 000660 |
| 변동성 큰 종목 | 에코프로비엠 | 247540 |
| 변동성 큰 종목 | 한미반도체 | 042700 |
| 최근 인기 테마 | 두산에너빌리티 | 034020 |
| 최근 인기 테마 | 한화에어로스페이스 | 012450 |
| 최근 인기 테마 | HD현대중공업 | 329180 |
| 전통 IT/플랫폼 | NAVER | 035420 |
| 방어주/저변동 | KT&G | 033780 |
| 방어주/저변동 | 한국가스공사 | 036460 |
## 디렉토리 구조
```
stock_chart_site/
├── build.bat # Windows: 빌드+기동
├── docker-compose.yml # db + backend + web
├── docker-compose.gpu.yml # GPU 오버레이 (NVIDIA reservation)
├── .env.example # 환경 변수 템플릿
├── backend/
│ ├── Dockerfile # CUDA 12.1 + Python 3.11
│ ├── pyproject.toml
│ └── app/
│ ├── main.py # FastAPI entry
│ ├── config.py # env settings
│ ├── db/
│ │ ├── connection.py
│ │ └── migrations/
│ │ └── 001_init.sql # DB 스키마
│ ├── fetch/ # KIS / pykrx / DART / 뉴스 (Phase 1)
│ ├── models/ # Chronos / LightGBM / KR-FinBERT (Phase 2~4)
│ ├── pipelines/ # daily_batch / inference / retrain (Phase 1, 4)
│ └── api/ # FastAPI 라우터 (Phase 5)
└── web/
├── Dockerfile
├── package.json
└── app/
├── layout.tsx
└── page.tsx # 검색 + 차트 UI (Phase 6)
```
## 진행 계획
- Phase 0 — 스캐폴드 (현재): Docker 환경 + DB 스키마 + 빈 FastAPI/Next.js + build.bat
- Phase 1a — pykrx 데이터 파이프: 일봉/외인기관/지수 + DART + 뉴스 RSS + 거시
- Phase 1b — KIS EOD (키 받으면)
- Phase 2 — KR-FinBERT 감성 점수 + 일별 집계
- Phase 3 — Chronos zero-shot 예측 적재
- Phase 4 — LightGBM walk-forward + `prediction_outcomes` 누적 시작
- Phase 5 — FastAPI 엔드포인트 (검색, 차트, on-demand 예측, 메트릭)
- Phase 6 — Next.js UI (검색 + 현재 차트 + 예상차트 토글)
- Phase 7 (옵션) — 백테스트 페이지 + 주간 자동 재학습
## 동작 모델 메모
- 예측 트리거: 사용자가 "예상차트 보기" 누른 종목에 대해 즉시 inference. 결과는 `predictions(user_triggered=TRUE)` 로 저장.
- 다음날 00:30 (또는 16:30) 배치: `user_triggered=TRUE` 인 예측 중 `target_date`가 도래한 것들에 대해 실제 가격과 매칭 → `prediction_outcomes` 적재.
- 주간 02:00: 종목/모델별 최근 30일 hit rate 기반으로 앙상블 가중치를 자동 보정. hit rate가 임계 미만이면 LGBM 재학습.
## 안전/한계
- 본인 1인 개인용. 외부 공개/상업 사용 안 함.
- 자동매매 연결 없음. 예측은 참고용.
- 백테스트 정확도와 라이브 정확도는 다르며 단기 방향성 모델의 라이브 상한은 보통 55~60%.

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__pycache__
*.pyc
.venv
.pytest_cache
.mypy_cache
.ruff_cache
tests
artifacts
.cache

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# CUDA 12.1 + Python 3.11. CPU 환경에서는 torch.cuda.is_available()==False 가 되어 자동 폴백.
# Windows + Docker Desktop + WSL2 GPU 패스스루로 RTX 3070 Ti 에서 동작.
FROM nvidia/cuda:12.1.1-runtime-ubuntu22.04 AS base
ENV DEBIAN_FRONTEND=noninteractive \
PYTHONUNBUFFERED=1 \
PYTHONDONTWRITEBYTECODE=1 \
PIP_NO_CACHE_DIR=1 \
PIP_DISABLE_PIP_VERSION_CHECK=1 \
TZ=Asia/Seoul
RUN apt-get update && apt-get install -y --no-install-recommends \
python3.11 python3.11-venv python3-pip \
build-essential git curl ca-certificates tzdata \
libgomp1 \
&& ln -sf /usr/bin/python3.11 /usr/local/bin/python \
&& ln -sf /usr/bin/python3.11 /usr/local/bin/python3 \
&& rm -rf /var/lib/apt/lists/*
WORKDIR /app
COPY pyproject.toml ./
# Install PyTorch (CUDA 12.1 wheels) first so the rest of deps don't downgrade it.
RUN pip install --extra-index-url https://download.pytorch.org/whl/cu121 \
torch==2.3.1 torchvision==0.18.1
RUN pip install --no-deps -e . || true
RUN pip install -e .
COPY app ./app
EXPOSE 8000
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]

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from __future__ import annotations
from pydantic_settings import BaseSettings, SettingsConfigDict
class Settings(BaseSettings):
model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")
database_url: str = "postgresql+psycopg://stock:stockpw@db:5432/stock"
tz: str = "Asia/Seoul"
log_level: str = "INFO"
# 모델 디바이스 선택. 'auto'는 torch.cuda.is_available() 기반
model_device: str = "auto"
# External keys (옵션)
kis_app_key: str | None = None
kis_app_secret: str | None = None
kis_account_no: str | None = None
dart_api_key: str | None = None
huggingface_token: str | None = None
settings = Settings()

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from __future__ import annotations
from sqlalchemy import create_engine, text
from sqlalchemy.engine import Engine
from app.config import settings
_engine: Engine | None = None
def get_engine() -> Engine:
global _engine
if _engine is None:
_engine = create_engine(settings.database_url, pool_pre_ping=True, future=True)
return _engine
def ping() -> dict[str, object]:
"""Smoke-test: DB 연결 + 확장 확인."""
eng = get_engine()
with eng.connect() as conn:
version = conn.execute(text("SELECT version()")).scalar()
exts = conn.execute(
text(
"SELECT extname FROM pg_extension "
"WHERE extname IN ('timescaledb','vector','pg_trgm') ORDER BY extname"
)
).scalars().all()
return {"server": version, "extensions": list(exts)}

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-- Init schema for stock_chart_site
-- Loaded automatically on first DB container start via /docker-entrypoint-initdb.d
\set ON_ERROR_STOP on
CREATE EXTENSION IF NOT EXISTS timescaledb;
CREATE EXTENSION IF NOT EXISTS vector;
CREATE EXTENSION IF NOT EXISTS pg_trgm;
-- 종목 마스터 (Phase 1 에서 KRX 전체 종목 시드. 검색은 name 또는 code 둘 다 지원)
CREATE TABLE IF NOT EXISTS symbols (
code TEXT PRIMARY KEY,
name TEXT NOT NULL,
market TEXT NOT NULL, -- 'KOSPI' / 'KOSDAQ' / 'NASDAQ'
sector TEXT,
active BOOLEAN DEFAULT TRUE,
is_seed BOOLEAN DEFAULT FALSE, -- 학습/배치 대상 10종목 여부
created_at TIMESTAMPTZ DEFAULT NOW()
);
CREATE INDEX IF NOT EXISTS symbols_name_trgm ON symbols USING gin (name gin_trgm_ops);
CREATE INDEX IF NOT EXISTS symbols_active ON symbols(active);
-- 일별 시세
CREATE TABLE IF NOT EXISTS ohlcv_daily (
code TEXT NOT NULL REFERENCES symbols(code),
date DATE NOT NULL,
open NUMERIC,
high NUMERIC,
low NUMERIC,
close NUMERIC,
volume BIGINT,
PRIMARY KEY (code, date)
);
SELECT create_hypertable('ohlcv_daily', 'date', if_not_exists => TRUE);
CREATE INDEX IF NOT EXISTS ohlcv_daily_code_date ON ohlcv_daily(code, date DESC);
-- 분봉 (M8 인트라데이용, 스키마만 미리 둔다)
CREATE TABLE IF NOT EXISTS ohlcv_1m (
code TEXT NOT NULL,
ts TIMESTAMPTZ NOT NULL,
open NUMERIC,
high NUMERIC,
low NUMERIC,
close NUMERIC,
volume BIGINT,
PRIMARY KEY (code, ts)
);
SELECT create_hypertable('ohlcv_1m', 'ts', if_not_exists => TRUE);
-- 거시 / 환율 / 지수
CREATE TABLE IF NOT EXISTS macro_daily (
date DATE NOT NULL,
key TEXT NOT NULL, -- 'kospi','kosdaq','usdkrw','us10y',...
value NUMERIC,
PRIMARY KEY (date, key)
);
-- 외인 / 기관 순매수 (KRW 기준 거래대금)
CREATE TABLE IF NOT EXISTS trading_value_daily (
code TEXT NOT NULL REFERENCES symbols(code),
date DATE NOT NULL,
foreign_net NUMERIC,
institution_net NUMERIC,
individual_net NUMERIC,
PRIMARY KEY (code, date)
);
-- 뉴스 / 공시
CREATE TABLE IF NOT EXISTS news (
id BIGSERIAL PRIMARY KEY,
code TEXT REFERENCES symbols(code),
source TEXT NOT NULL, -- 'naver_finance' / 'dart' / 'google_rss'
published_at TIMESTAMPTZ NOT NULL,
title TEXT NOT NULL,
url TEXT NOT NULL UNIQUE,
body TEXT,
sentiment_score REAL, -- KR-FinBERT 출력 -1..+1
sentiment_label TEXT, -- 'positive' / 'neutral' / 'negative'
embedding VECTOR(768),
created_at TIMESTAMPTZ DEFAULT NOW()
);
CREATE INDEX IF NOT EXISTS news_code_pub ON news(code, published_at DESC);
CREATE INDEX IF NOT EXISTS news_pub ON news(published_at DESC);
-- 모델 예측 이력
-- user_triggered=TRUE 인 행만 다음날 outcomes 매칭/오차수정 학습에 사용
CREATE TABLE IF NOT EXISTS predictions (
id BIGSERIAL PRIMARY KEY,
code TEXT NOT NULL REFERENCES symbols(code),
predicted_at TIMESTAMPTZ NOT NULL,
base_date DATE NOT NULL, -- 예측 기준일(=마지막 관측일)
target_date DATE NOT NULL,
horizon INT NOT NULL, -- 1, 3, 5
model TEXT NOT NULL, -- 'chronos2' / 'lgbm' / 'ensemble'
direction TEXT, -- 'up' / 'flat' / 'down'
prob_up REAL,
prob_flat REAL,
prob_down REAL,
expected_return REAL,
point_forecast NUMERIC, -- median 가격
ci_low NUMERIC, -- quantile 10
ci_high NUMERIC, -- quantile 90
features_snapshot JSONB,
user_triggered BOOLEAN NOT NULL DEFAULT FALSE,
created_at TIMESTAMPTZ DEFAULT NOW(),
UNIQUE (code, base_date, target_date, horizon, model)
);
CREATE INDEX IF NOT EXISTS predictions_code_target ON predictions(code, target_date DESC);
CREATE INDEX IF NOT EXISTS predictions_user_triggered ON predictions(user_triggered) WHERE user_triggered = TRUE;
-- 예측 vs 실제 결과 (오차 수정 / 메트릭 / 가중치 튜닝의 입력)
CREATE TABLE IF NOT EXISTS prediction_outcomes (
prediction_id BIGINT PRIMARY KEY REFERENCES predictions(id) ON DELETE CASCADE,
code TEXT NOT NULL REFERENCES symbols(code),
target_date DATE NOT NULL,
horizon INT NOT NULL,
model TEXT NOT NULL,
predicted_close NUMERIC,
actual_close NUMERIC,
actual_return REAL,
direction_hit BOOLEAN, -- 방향성 적중 여부
abs_error REAL,
resolved_at TIMESTAMPTZ DEFAULT NOW()
);
CREATE INDEX IF NOT EXISTS po_code_target ON prediction_outcomes(code, target_date DESC);
CREATE INDEX IF NOT EXISTS po_model ON prediction_outcomes(model);
-- 모델별 롤링 성능 (앙상블 가중치 튜닝에 사용)
CREATE TABLE IF NOT EXISTS model_performance (
code TEXT NOT NULL REFERENCES symbols(code),
model TEXT NOT NULL,
window_days INT NOT NULL, -- 7, 30 등
as_of DATE NOT NULL,
hit_rate REAL,
mae REAL,
brier REAL,
sample_count INT,
PRIMARY KEY (code, model, window_days, as_of)
);

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from __future__ import annotations
import logging
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from app.config import settings
from app.db.connection import ping as db_ping
logging.basicConfig(level=settings.log_level)
logger = logging.getLogger(__name__)
app = FastAPI(title="stock_chart_site", version="0.0.1")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
def _resolved_device() -> str:
if settings.model_device != "auto":
return settings.model_device
try:
import torch # noqa: WPS433
return "cuda" if torch.cuda.is_available() else "cpu"
except Exception: # noqa: BLE001
return "cpu"
@app.get("/health")
def health() -> dict[str, object]:
return {
"ok": True,
"device": _resolved_device(),
"version": "0.0.1",
}
@app.get("/health/db")
def health_db() -> dict[str, object]:
return {"ok": True, **db_ping()}

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[project]
name = "stock_chart_site_backend"
version = "0.0.1"
description = "Stock chart + prediction backend (FastAPI + Chronos + LightGBM + KR-FinBERT)"
requires-python = ">=3.11,<3.13"
dependencies = [
# web
"fastapi==0.111.0",
"uvicorn[standard]==0.30.0",
"pydantic==2.7.1",
"pydantic-settings==2.3.0",
# db
"sqlalchemy==2.0.30",
"psycopg[binary]==3.1.19",
"alembic==1.13.1",
# data
"pandas==2.2.2",
"numpy==1.26.4",
"pykrx==1.0.45",
"yfinance==0.2.40",
"feedparser==6.0.11",
"requests==2.32.3",
"httpx==0.27.0",
"beautifulsoup4==4.12.3",
"lxml==5.2.2",
# ml
"transformers==4.41.2",
"tokenizers==0.19.1",
"sentencepiece==0.2.0",
"scikit-learn==1.5.0",
"lightgbm==4.3.0",
"ta==0.11.0",
# scheduler
"apscheduler==3.10.4",
"pytz==2024.1",
# utils
"python-dotenv==1.0.1",
"loguru==0.7.2",
"tenacity==8.3.0",
]
[project.optional-dependencies]
dev = [
"pytest==8.2.1",
"ruff==0.4.7",
"mypy==1.10.0",
]
[build-system]
requires = ["setuptools>=68"]
build-backend = "setuptools.build_meta"
[tool.setuptools.packages.find]
where = ["."]
include = ["app*"]
[tool.ruff]
line-length = 100
target-version = "py311"

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@echo off
REM stock_chart_site - Windows 빌드/기동 스크립트
REM 더블클릭하면 .env 준비 -> GPU 감지 -> docker compose up
setlocal enabledelayedexpansion
cd /d "%~dp0"
echo === stock_chart_site bootstrap ===
REM 1) .env 준비
if not exist .env (
echo .env not found. Copying .env.example -> .env
copy /Y .env.example .env >nul
echo - 키 발급 안내는 README.md 참조. 비워둬도 pykrx 만으로 일단 동작합니다.
)
REM 2) Docker 설치 / 실행 확인
where docker >nul 2>&1
if errorlevel 1 (
echo [ERROR] docker 명령을 찾을 수 없습니다. Docker Desktop 설치/실행을 확인하세요.
pause
exit /b 1
)
docker info >nul 2>&1
if errorlevel 1 (
echo [ERROR] Docker Desktop이 실행 중이 아닙니다.
pause
exit /b 1
)
REM 3) GPU 감지
set USE_GPU=0
where nvidia-smi >nul 2>&1
if not errorlevel 1 (
nvidia-smi >nul 2>&1
if not errorlevel 1 set USE_GPU=1
)
if "%USE_GPU%"=="1" (
echo [GPU] NVIDIA GPU detected. Using GPU profile.
set COMPOSE_FILES=-f docker-compose.yml -f docker-compose.gpu.yml
) else (
echo [CPU] NVIDIA GPU not detected. Falling back to CPU mode.
echo Docker Desktop ^> Settings ^> Resources ^> WSL Integration ^> GPU support 를 켜면 GPU 사용 가능합니다.
set COMPOSE_FILES=-f docker-compose.yml
)
REM 4) Build + up
echo.
echo === docker compose build ===
docker compose %COMPOSE_FILES% build
if errorlevel 1 (
echo [ERROR] build 실패.
pause
exit /b 1
)
echo.
echo === docker compose up -d ===
docker compose %COMPOSE_FILES% up -d
if errorlevel 1 (
echo [ERROR] up 실패.
pause
exit /b 1
)
echo.
echo === 상태 ===
docker compose %COMPOSE_FILES% ps
echo.
echo 접속:
echo Web http://localhost:3000
echo Backend http://localhost:8000/health
echo DB ext http://localhost:8000/health/db
echo.
echo 로그 보기: docker compose logs -f backend
echo 정지: docker compose down
echo.
pause
endlocal

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services:
backend:
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: all
capabilities: [gpu]
environment:
MODEL_DEVICE: cuda
NVIDIA_VISIBLE_DEVICES: all

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name: stock_chart_site
services:
db:
image: timescale/timescaledb-ha:pg16
container_name: scs_db
restart: unless-stopped
environment:
POSTGRES_USER: ${POSTGRES_USER:-stock}
POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-stockpw}
POSTGRES_DB: ${POSTGRES_DB:-stock}
TZ: ${TZ:-Asia/Seoul}
volumes:
- postgres_data:/home/postgres/pgdata/data
- ./backend/app/db/migrations:/docker-entrypoint-initdb.d:ro
ports:
- "5432:5432"
healthcheck:
test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER:-stock} -d ${POSTGRES_DB:-stock}"]
interval: 10s
timeout: 5s
retries: 10
backend:
build:
context: ./backend
dockerfile: Dockerfile
container_name: scs_backend
restart: unless-stopped
env_file: .env
environment:
DATABASE_URL: postgresql+psycopg://${POSTGRES_USER:-stock}:${POSTGRES_PASSWORD:-stockpw}@db:5432/${POSTGRES_DB:-stock}
depends_on:
db:
condition: service_healthy
ports:
- "${BACKEND_PORT:-8000}:8000"
volumes:
- ./backend:/app
- hf_cache:/root/.cache/huggingface
web:
build:
context: ./web
dockerfile: Dockerfile
container_name: scs_web
restart: unless-stopped
env_file: .env
environment:
NEXT_PUBLIC_API_BASE: ${NEXT_PUBLIC_API_BASE:-http://localhost:8000}
depends_on:
- backend
ports:
- "${WEB_PORT:-3000}:3000"
volumes:
- ./web:/app
- /app/node_modules
- /app/.next
volumes:
postgres_data:
hf_cache:

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node_modules
.next
out
.git
.DS_Store

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FROM node:20-alpine
WORKDIR /app
ENV NEXT_TELEMETRY_DISABLED=1
COPY package.json package-lock.json* ./
RUN npm install --no-audit --no-fund
COPY . .
EXPOSE 3000
CMD ["npm", "run", "dev"]

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@tailwind base;
@tailwind components;
@tailwind utilities;
html, body {
background: #0b0d12;
color: #e6e8eb;
}

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import "./globals.css";
import type { Metadata } from "next";
export const metadata: Metadata = {
title: "Stock Chart Site",
description: "개인용 주식 예측 차트",
};
export default function RootLayout({ children }: { children: React.ReactNode }) {
return (
<html lang="ko">
<body className="min-h-screen">{children}</body>
</html>
);
}

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export default function HomePage() {
return (
<main className="mx-auto max-w-3xl px-6 py-16">
<h1 className="text-3xl font-bold tracking-tight">Stock Chart Site</h1>
<p className="mt-3 text-sm text-zinc-400">
Phase 0 scaffold. UI는 Phase 6 .
</p>
<div className="mt-8 rounded-md border border-zinc-800 bg-zinc-900/50 p-4 text-sm">
<div className="font-medium">Backend health</div>
<code className="mt-2 block text-zinc-400">GET {process.env.NEXT_PUBLIC_API_BASE}/health</code>
</div>
</main>
);
}

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/// <reference types="next" />
/// <reference types="next/image-types/global" />
// NOTE: This file should not be edited
// see https://nextjs.org/docs/basic-features/typescript for more information.

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/** @type {import('next').NextConfig} */
const nextConfig = {
reactStrictMode: true,
output: "standalone",
};
module.exports = nextConfig;

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{
"name": "stock-chart-site-web",
"version": "0.0.1",
"private": true,
"scripts": {
"dev": "next dev -p 3000 -H 0.0.0.0",
"build": "next build",
"start": "next start -p 3000 -H 0.0.0.0",
"lint": "next lint"
},
"dependencies": {
"next": "14.2.3",
"react": "18.3.1",
"react-dom": "18.3.1",
"lightweight-charts": "4.1.7"
},
"devDependencies": {
"@types/node": "20.12.12",
"@types/react": "18.3.3",
"@types/react-dom": "18.3.0",
"typescript": "5.4.5",
"tailwindcss": "3.4.4",
"postcss": "8.4.38",
"autoprefixer": "10.4.19"
}
}

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module.exports = {
plugins: { tailwindcss: {}, autoprefixer: {} },
};

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/** @type {import('tailwindcss').Config} */
module.exports = {
content: [
"./app/**/*.{ts,tsx}",
"./components/**/*.{ts,tsx}",
],
theme: { extend: {} },
plugins: [],
};

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{
"compilerOptions": {
"target": "ES2022",
"lib": ["dom", "dom.iterable", "esnext"],
"allowJs": false,
"skipLibCheck": true,
"strict": true,
"noEmit": true,
"esModuleInterop": true,
"module": "esnext",
"moduleResolution": "bundler",
"resolveJsonModule": true,
"isolatedModules": true,
"jsx": "preserve",
"incremental": true,
"plugins": [{ "name": "next" }],
"paths": { "@/*": ["./*"] }
},
"include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
"exclude": ["node_modules"]
}