Professional Experiences
MYR - Private Investment Fund
Quantitative Researcher
Active market maker on Hyperliquid (DeFi LBO), developing proprietary quantitative strategies in digital asset markets.
Key Responsibilities & Achievements
- Designed and deployed short-horizon market-making strategies using L1/L2/L3 order book data, microprice signals, order book imbalance and FIFO queue dynamics to optimize execution probability and spread capture across 12 liquid crypto order books
- Developed microstructure-driven alpha signals from depth imbalance, order flow autocorrelation and spread dynamics, supported by event-time backtesting on 5+ years of tick-level data
- Implemented inventory risk control (Avellaneda–Stoikov inspired) and low-latency execution systems (<100ms), optimizing quote refresh, queue positioning and mitigating adverse selection
- Modeled limit order book event arrivals using stochastic intensity frameworks (Poisson-type limit/market/cancel flows) to estimate short-term price pressure and fill probabilities while analyzing competing algorithmic trader behavior
Technical Stack: Python, NumPy, Pandas, WebSocket APIs, REST APIs, Linux
La Valériane - Investment Branch
Quantitative Developer
Designed and built an end-to-end delta-neutral statistical arbitrage strategy between Binance (CEX) and dYdX (DeFi DEX), targeting cross-venue microstructure inefficiencies.
Key Responsibilities & Achievements
- Reconstructed full L2 order books and built 2-year tick-level datasets to analyze cross-exchange price formation, latency asymmetries and liquidity distribution
- Developed arbitrage signals based on microprice deviations and depth-adjusted fair value estimators, incorporating slippage modeling and execution constraints
- Implemented, backtested and deployed the trading bot under realistic latency and partial-fill assumptions, achieving ~8% annualized returns with $800k–$1M daily volume while maintaining strict market neutrality
Technical Stack: Python, Binance API, dYdX API, NumPy, Pandas