quant-sim

Agent-based prediction market simulation with heterogeneous traders. Informed traders, noise traders, market makers, Kyle lambda price impact, order book dynamics, and emergent price discovery. Based on Gode & Sunder (1993), Farmer et al. (2005), and Kyle (1985).

Dependency modeling beyond correlation matrices. Gaussian, Student-t, Clayton, and Gumbel copulas for correlated prediction market outcomes. Tail dependence quantification and vine copulas for high-dimensional portfolios. The reason Gaussian copulas failed in 2008 — and what to use instead.

Foundation Monte Carlo simulation for binary contracts and prediction markets. GBM-based path simulation, binary payoff estimation, confidence intervals, and Brier score calibration. The base layer everything else builds on.

Sequential Monte Carlo (particle filters) for real-time probability updating. Bootstrap filter with systematic resampling, ESS monitoring, logit-space state evolution, and credible intervals. For live event tracking like election night or real-time market monitoring.

Importance sampling for rare events, antithetic variates, control variates, stratified sampling, and stacking all three. Achieves 100-10,000x variance reduction over crude Monte Carlo. Table stakes for production simulation.