MatheLinuxQuant research · software · education
Research
Each research line is grounded in courses we already teach. Write-ups, tools, and results are published here as they are ready.
Rule-based strategies built from a stated hypothesis, measured against costs and execution delay, and validated out of sample before any capital is committed.
Open →Derivative pricing and risk models from first principles: stochastic calculus, jump and stochastic-volatility dynamics, and the numerical methods that make them usable.
Open →Machine learning for return prediction and risk, paired with backtesting infrastructure that controls for look-ahead, selection bias, and overfitting. This is the research line behind the Strategy Validation Engine.
Open →Neural-network methods for pricing, hedging, and PDE solving, together with the convex and stochastic optimization that portfolio construction depends on.
Open →The engineering side of quantitative finance: low-latency C++ services, parallel numerics, market-data architecture, and risk systems that hold up in production.
Open →Macro and fundamental research across FX, energies and commodities, interest rates, futures, and derivatives: what drives each market and how those drivers can be measured.
Open →Where quantum computing may matter for finance: quantum machine learning, optimization, and simulation, studied with an honest view of current hardware limits.
Open →