My work focuses on crypto quantitative research, machine-learning signals, and research engineering for systematic trading.
📈 Crypto ML Signals
Active Research
Developing daily signal frameworks for crypto markets with cross-asset features and model diagnostics.
- Boosting-model pipelines for systematic signal generation
- Cross-asset feature construction and signal-accuracy evaluation
- Backtest reliability improvements for asset histories and missing-data handling
⚙️ Research Engineering
Active Research
Building maintainable quantitative research infrastructure from data pipelines to test coverage.
- Modular Python components for research pipelines
- Strategy configuration, diagnostics, and regression tests
- AI-assisted development workflows with iterative code review and failure-mode analysis
📊 Prediction Markets & Trading Systems
Researching prediction-market alpha and building high-frequency infrastructure for systematic trading.
- Market microstructure analysis and performance diagnostics
- Rust/Python infrastructure for data recording, replay, backtesting, and paper/live trading
- On-chain and alternative data integration for signal generation and risk monitoring
🔗 Blockchain Economics & Game Theory
Game-theoretic analysis of incentive structures in blockchain and data-factor markets.
- Verifier’s Dilemma — Validator incentive analysis for blockchain layer-2 scaling
- Bayesian game framework for data-property-right allocation
- Welfare analysis under heterogeneous growth assumptions