Four repositories form a pipeline for financial AI, quant analysis, RAG-based knowledge search, paper trading, and brokerage Open API auto-trading.

flowchart LR
    DRL["domain-rag-lab<br/>(finance RAG & LEAN backtests)"] -->|"backtest results / strategy specs (HTTP API)"| LUM["lumina-invest<br/>(robo-advisor & quant signals)"]
    LUM -->|"order / balance Open API (API key)"| SCT["stock-coin-trade<br/>(paper trading & Open API broker)"]
    DRL -.->|"fork & frontend integration"| SKP["stock-kms-portal<br/>(learning, charts, KMS, Lex/RAG portal)"]

• Integration principle: domain-rag-lab, lumina-invest, and stock-coin-trade are deployed independently with separate databases and communicate only via REST APIs (API key auth).
• Portal: stock-kms-portal consolidates domain-rag-lab and the investment-analysis web app into a single entry point (port 80).

Integrated Korean financial learning and investment-analysis KMS portal. FastAPI Vanilla JS ApexCharts Qdrant pgvector AWS Lex LEAN

• Market charts: Query domestic and global stock/index prices (Yahoo Finance-based API) with candlestick charts
• Financial learning: 10 learning units, with Mermaid.js diagrams
• LEAN backtesting: Built-in local Docker runner for the QuantConnect LEAN engine
• Hybrid chat & RAG: AWS Lex v2 chatbot combined with Qdrant document RAG
• Origin: Forked from the domain-rag-lab structure, with frontends migrated to a single origin

Paper-trading platform for stocks (KRX), crypto, and alternative assets, plus brokerage Open API practice. FastAPI AWS SAM MariaDB PostgreSQL Vanilla JS Nginx

• Open API: Virtual account issuing and order execution (/api/v1/orders, etc.) for external systems such as lumina-invest
• Data: MariaDB for members/trades, PostgreSQL (pg-stock) for OHLCV minute/daily bars
• Broker integrations: Korea Investment & Securities (KIS) API/paper trading, KB Securities Open API, Alpaca Paper Trading, TradingView Pine Script
• Tooling: KIS MCP (Model Context Protocol) server with VS Code/Codex integration
• Frontend: Vanilla JavaScript (ES Modules) served by Nginx

Robo-advisor and quant-signal orchestrator for automated trading. FastAPI Celery PostgreSQL Redis LangChain LangGraph Qdrant Neo4j LightGBM

• Auto-trading engine: Celery Beat runs the quant.auto_trade_cycle task every 10 minutes
• Orchestration: Receives strategy specs backtested in domain-rag-lab, computes risk limits, and sends orders to stock-coin-trade via Open API
• AI & ML: LangChain/LangGraph agents, Scikit-learn and LightGBM models
• AWS: SageMaker training-result lookup and AWS Comprehend sentiment analysis
• Storage: PostgreSQL 16 (SQLAlchemy, asyncpg, Alembic), Redis (sessions, Celery broker/backend)

RAG for financial products and asset allocation, with QuantConnect LEAN backtest workflows. FastAPI Streamlit Qdrant pgvector Redis Docker

• Hybrid search: Qdrant vector search and PostgreSQL keyword search fused with Reciprocal Rank Fusion (RRF)
• Multi-turn orchestrator: Session short-term memory and pgvector-based long-term memory
• Ingestion: PDF/TXT document ingestion and chunking pipeline
• LEAN backtesting: Local Docker-based QuantConnect LEAN engine
• Infrastructure: Docker Compose, Caddy (TLS reverse proxy), AWS EC2 deployment