This is where I open-source quant research, ship trading tooling, and occasionally break things 🤣

• 🔭 Currently building deep asset pricing & portfolio optimization systems at Qraft Technologies
• 🎓 M.S. in Artificial Intelligence @ Kyung Hee University · KHU AIMS Lab member
• 🌱 Currently learning: JAX, market microstructure, prediction-market mechanics
• 💬 Ask me about: PyTorch · quantitative ML · deep portfolio optimization · time-series forecasting
• 👨‍💻 Read more about my work at choiinyeol.github.io
• ⚡ Fun fact: I take numpy.random.seed(42) very personally

• 금융공학을 배우는 방법, 근데 이제 발표를 곁들인
• 개발자에 대해 평소 해왔던 생각을 정리하는 글
• S&P Quantitative Investment Model Development Competition Report
• 옵시디언으로 개발 블로그 구축하기 - (1)
• AI 프로젝트를 위한 WSL2 개발 환경 세팅 가이드

I'm a Quant AI Researcher at Qraft Technologies, pursuing an M.S. in Artificial Intelligence at Kyung Hee University. My research sits at the intersection of deep learning and financial modeling — empirical asset pricing, portfolio construction, and alternative data signals.

Most of my exploratory work and side projects live as open-source on GitHub. A few highlights:

• Asset-pricing-models-using-deep-learning-networks — Empirical asset pricing with DNN / RNN factor models. Replicates and extends the modern empirical asset pricing literature with deep architectures.

• Portfolio-Optimization-Deep-Learning-With-Candlestick-Image — Mean-variance allocation conditioned on candlestick chart images. Explores whether visual chart features carry exploitable allocation signal beyond standard tabular features.

• prediction-market-analysis — A framework for collecting and analyzing Polymarket and Kalshi market & trade data. Treats event-market flow as a structured alpha signal.

• snusmic-quant-terminal — A terminal-based quant research workbench.

• RuleBase-VS-TimeSeries-Algorithm — Capstone benchmark of rule-based trading vs. deep time-series strategies.

⏩ and many more

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            hunt the alpha, ship the code.

During my graduate research at KHU AIMS Lab, I've worked across empirical asset pricing, mean-variance optimization, candlestick-conditioned allocation, and prediction-market microstructure. My research training began under an advisor now affiliated with Korea University Department of Financial Engineering. I believe the most interesting quant problems sit where machine learning meets economic structure — neither pure curve-fitting nor pure theory.

I keep my hands dirty through domestic algorithmic-trading and data-analysis competitions on the side.

• KHU AIMS Lab — graduate researcher
• KRX 주식 투자 알고리즘 경진대회 — algorithmic trading competition
• DB 투자대회 — investment-strategy competition
• 미래에셋증권 빅데이터 페스티벌 (ESG) — big-data festival
• 2022 통계데이터 분석·활용대회 — statistical data analysis competition