Systematic signal discovery through applied mathematics. Autonomous execution across CME futures and prediction markets.
A 27-model mathematical engine spanning stochastic calculus, information theory, fractal geometry, topology, and beyond — discovering persistent structure in non-stationary markets.
Jump-diffusion processes, local/stochastic vol surfaces, and realized variance decomposition for CME futures.
Shannon entropy, KL-divergence, and mutual information applied to market microstructure and regime detection.
Persistent homology and Betti curves identifying structural invariants in high-dimensional price manifolds.
Marchenko-Pastur filtering and eigenvalue decomposition for covariance estimation and noise reduction.
Wasserstein distances for regime classification, strategy rotation, and non-parametric signal construction.
Iterated integrals capturing sequential path geometry for feature-invariant strategy encoding.
Original research applying advanced mathematics to quantitative trading and market microstructure. Full methodology breakdowns and reproducible results.
Applying TDA to identify topological features in CME futures price data that persist across temporal scales, revealing structural market regimes invisible to classical statistical methods.
A framework for daily strategy lifecycle management using information-theoretic criteria. Strategies are mined, validated, deployed for one session, then replaced — exploiting short-lived statistical structure.
Gaussian HMM with BIC-optimal state selection applied to volatility and momentum features, gating position sizing and strategy selection by detected market regime.
Additional papers in development. Research updates published on an ongoing basis.
Fully autonomous CME futures trading factory. Daily strategy rotation — overfit to current conditions each evening, deploy for one session, then replace. Five-phase lifecycle pipeline with HMM regime detection, persistent strategy pools, and a 27-model mathematical signal engine.
Integrated prediction and sports betting analysis platform. Walk-forward logistic regression, entropy-based line movement detection, sharpness-weighted consensus modeling, Poisson scoring, Elo power ratings, and SGP correlation modeling — unified under Kelly-optimal bankroll management.
Quantitative researcher and systems builder at the intersection of pure mathematics and autonomous market execution. Building fully autonomous trading infrastructure from first principles — where mathematical theory meets real capital, real risk, and real-time execution.
Research spans stochastic calculus, topological data analysis, information geometry, optimal transport, and rough path theory applied to non-stationary financial systems.
Targeting graduate programs in quantitative finance — Baruch MFE, CMU MSCF, Columbia, NYU Courant — with a research focus on geometric and topological methods in financial signal discovery.
Quantitative Finance & Applied Mathematics — Baruch MFE, CMU MSCF, Columbia, NYU Courant.
Interested in quantitative research collaboration, system architecture, or mathematical approaches to financial markets.
cormac@lydonfinancial.com