Dimensional Gateway Traversal Initiated
Dimensional Coordinates: Alpha/Omega/Prime Coordinates Locked
Initiate Quantum Calibration Singularity Detected
Beginning Tesseract Unfolding
Hyperdimensional Matrices Aligned
Traversing
Dimensional Shift
Quantum Entanglement Stabilized
Cosmic Strings Vibrating in Harmony
Wormhole Aperture Expanding
Dimensional Gateway Stabilizing
Reality Parameters Reconfigured
Quantum Fluctuation Nominal
Initiating Spacetime Fold
Scanning Parallel Realities
Analyzing Dark Matter Density
Processing Gravitational Waves
Calibrating Temporal Displacement
Evaluating Dimensional Resonance
Stabilizing Quantum Foam
Traversal Sequence Complete Dimensional Gateway Open

Model. Test. Trade.

End-to-end quant research, from alpha discovery to execution and risk.

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Education

Columbia University M.A. in Mathematics of Finance

September 2024 – December 2025

GPA: 3.94/4.0

Coursework: Machine Learning, Time Series Modeling, Stochastic Process, Programming for Computational Finance, Numerical Methods, Derivatives Trading, Option Pricing.

University of Liverpool B.Sc. in Mathematics with Finance

September 2020 – May 2024

GPA: 3.83/4.0 · First Class Honors (WES report)

Coursework: Stochastic Theory, Linear Statistical Models, Mathematical Risk Theory, Applied Probability, Derivative Securities, Numerical Methods, Operational Research, Statistics and Probability, Financial Reporting.

Completed first two years at Xi’an Jiaotong–Liverpool University, GPA: 3.85/4.0 (Top 5%), then two years at the University of Liverpool as part of the international 2+2 program.

Experience

Numeraxial LLC Quantitative Research Intern

July 2025 – Present
  • Built a research-grade multi-asset data warehouse with a schema-first design, versioning for cross-asset research.
  • Standardized Bloomberg series across IDs, calendars, and FX; built modular ETL for cleaning, corporate actions.
  • Engineered documented features and regimes using statistical and macro signals using HMM and clustering.
  • Enabled portfolio construction using mean–variance, Black–Litterman, and HRP with constraints.

Western Securities Co., Ltd. Quantitative Analyst Intern

January 2024 – July 2024
  • Built a research-to-execution stack with a backtester and a pseudo-live setup using the Alpaca Crypto Python API; kept identical signal and state machines; produced deterministic, auditable runs; containerized with Docker, PostgreSQL, and TimescaleDB; added a data-quality guard and a risk worker.
  • Implemented two long-only modules: EMA–ADX trend and Z-score reversion gated by ADX and RSI; used conservative GTC limit placement with two-threshold hysteresis; applied affordability and inventory checks.
  • Results for a one-year in-sample period: trend-following Sharpe 2.13; reversion Sharpe 2.65; buy-and-hold Sharpe 1.65.
  • Built a stress program to define the acceptance envelope before scaling capital: tuned limit offset, reprice thresholds, and freshness gates, and set risk budgets for one-day parametric VaR and drawdown.
  • Shipped a Streamlit monitoring dashboard covering P&L, drawdown, turnover, VaR, and an order blotter.

Projects

Predicting Positive SPY Moves with Gradient Boosting CQF

June 2025
  • Framed next‑day SPY direction as a leakage‑safe classification task; built a reproducible, trading‑calendar–aligned OHLCV pipeline with rigorous timestamp alignment, missing‑data handling, and feature versioning.
  • Engineered 40+ economically motivated features including volatility, skew, kurtosis, RSI, MACD, ROC, Bollinger %B, lagged returns, and seasonality; a three‑stage feature funnel—MI → RF‑RFE → GBM.
  • Trained XGBoost with time‑series cross‑validation, nested tuning, probability calibration, and stability checks using SHAP and perturbation; achieved a held‑out ROC AUC of 0.714.
  • Converted calibrated probabilities into long or flat decisions; evaluated with a walk‑forward procedure.

GitHub

Portfolio Optimization of Factor‑Based Risk Model using Machine Learning CU

December 2024
  • Developed a multi‑factor risk model and optimized portfolio weights with factor exposures and covariance matrices.
  • Cleaned data with winsorization to mitigate outliers; computed daily residual returns using pseudoinverses.
  • Trained regularized least squares and neural networks with cross‑validation to estimate daily factor realizations.
  • Backtested on 3,061 stocks with 59 industry factors and 6 style factors (2003–2011); analyzed long/short market values, cumulative P&L, and risk decomposition, achieved consistent risk‑adjusted returns.

GitHub

Skills & Certificates

Programming

Python (3.x) C/C++ MATLAB R Git LaTeX Docker Jupyter Excel Bloomberg HTML

Languages

  • English & Chinese — Bilingual
  • Spanish — A2

Interests

  • Chess — Blitz rating 2300
  • Piano, Running, Badminton