Younghwan Chae, Ph.D. · 채영환

PhD in Mechanical Engineering  —  Mathematical Optimization  ·  ML & Perception Engineer @ Doosan Robotics
Mathematical optimization is the through-line — numerical optimization, surrogate modeling & state estimation, carried from theory into 3D perception, sensor fusion, and production systems.

한국어 · English

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Three stacks that share a shape — collect → store → research. One Airflow deployment feeds both equity markets; the standalone services sit outside the pipeline.

flowchart TB
    subgraph KR ["🇰🇷 Korean equities"]
        direction LR
        K["kiwoom-rest-api"] --> AF["quant-airflow<br/>DART · KRX · Naver"] --> DB[("TimescaleDB<br/>delisted included")] --> Q["kr-quant"]
    end

    subgraph US ["🇺🇸 US equities"]
        direction LR
        SH["Sharadar"] --> AFU["quant-airflow<br/>bulk snapshot rebuild"] --> DD[("DuckDB<br/>point-in-time")] --> O["portfolio-research"]
        YF["yfinance"] --> AT["automated-stock-trading-systems"]
    end

    subgraph CX ["🪙 Crypto"]
        direction LR
        EX["Exchange APIs"] --> CR["quantbox-engine"]
    end

    subgraph SVC ["Standalone services & tools"]
        direction TB
        F["krx-fundamentals-api"]
        NW["krx-news-rest-api"]
        FC["fin-checkup"]
    end

    %% 세로 정렬용 — `~~~` 는 그 자체로 보이지 않는 링크다(linkStyle 불필요).
    KR ~~~ US ~~~ CX ~~~ SVC

    classDef source fill:#2563EB,stroke:#1E40AF,color:#FFFFFF
    classDef move   fill:#B45309,stroke:#78350F,color:#FFFFFF
    classDef out    fill:#059669,stroke:#065F46,color:#FFFFFF

    class K,SH,YF,EX,F,NW,FC source
    class AF,AFU,DB,DD move
    class Q,O,AT,CR out

    style KR  fill:#0F172A08,stroke:#64748B
    style US  fill:#0F172A08,stroke:#64748B
    style CX  fill:#0F172A08,stroke:#64748B
    style SVC fill:#0F172A08,stroke:#64748B,stroke-dasharray:4 3

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_{Blue — data sources & standalone services · amber — collection & storage · green — research & engines. Dashed — outside the pipeline.}

Project	What it is
kiwoom-rest-api	Kiwoom Securities REST API wrapper — full domestic-equity endpoint coverage & real-time WebSocket feeds · sync + async, auto token refresh · pip install kiwoom-client
quant-airflow	The one pipeline behind both equity stacks — 12 DAGs. Korea: prices, supply/demand, earnings, consensus & shares outstanding into TimescaleDB over DART · Kiwoom · KRX · Naver, with delisted-stock backfill so downstream backtests aren't survivorship-biased. US: a daily Sharadar bulk snapshot rebuilt into a DuckDB store and published atomically
krx-fundamentals-api	Korean corporate fundamentals API — financial statements, valuation metrics, dividends & stock screening (DART + KRX + Naver), served cache-first
krx-news-rest-api	Korean market news & disclosure collection API (FastAPI + Redis) — one schema over sources that each word the same event differently
fin-checkup	Risk-disclosure alerts + a financial health checkup over DART & SEC EDGAR — rights offerings, CB issues, audit opinions and delistings pushed to Telegram; 17 statement metrics read as a traffic-light chart against last year, the sector median and the peer percentile. Reports measurements and facts only — never a recommendation
kr-quant	KOSPI/KOSDAQ alpha research at the trade-distribution level — walk-forward, random null controls, purged CV, Deflated Sharpe & survivorship-corrected universes, all enforced as CI guardrails. The rejections are the product — pure noise clears “5 of 6 folds positive” 46% of the time, so the test is whether a strategy beats its own randomized version. A daily sector money-flow observation axis lives alongside it
portfolio-research	US equity factor engine — point-in-time & survivorship-bias-free, walk-forward optimization gated by Deflated Sharpe & PBO · plus tactical ETF allocation. Ships the rejections too: all 9 pre-registered TAA configs failed the PBO gate, and one headline number was retracted · writeup
quantbox-engine	Crypto futures backtest & execution engine — zero lookahead, backtest↔live parity. Exits are placed as exchange-side algo orders
automated-stock-trading-systems	Backtester for Bensdorp's seven non-correlated trading systems (educational reimplementation)

Strategies and parameters stay closed. Only structure and discipline are written down. Happy to walk through any of these on request.

Project	What it is
macro-sector-agent	Top-down sector-cycle research pipeline — it asks which industry has been forgotten, not what to buy. The market is cut at a resolution standard sector labels can't show, and agents argue from evidence over whether a theme is a cycle trough or a structural death. The LLM sits only at the narrow waist; everything above and below is deterministic. The machine never picks — it only excludes
scalp-it	Korean intraday strategy validation framework + live tick/orderbook collection — ticks cannot be backfilled, so a missed day is gone for good. Pre-register, measure once. No re-tuning to revive a rejected hypothesis
quantbox	Crypto pair-trading engine — statistical arbitrage. quantbox-engine is the public extract with the strategies removed
momentum	US equity screener — Minervini Trend Template + VCP pattern, DuckDB-cached, CLI-driven
gpt-quant-v2	News-driven algorithmic trading experiment — sentiment analysis + ML signal generation over an MCP tool interface (archived)
trading_code	First iteration of the crypto pair-trading framework — predecessor of quantbox (archived)
resume-private	Private résumé source (LaTeX)