SBBTS: A Unified Schrödinger-Bass Framework for Synthetic Financial Time Series

SBBTS targets a sophisticated niche in quantitative finance: the creation of 'no-arbitrage' synthetic data. Current diffusion models often fail to satisfy martingale properties required for realistic financial simulations, while Martingale Optimal Transport (MOT) models often ignore drift. By unifying Schrödinger and Bass bridges, this project provides a mathematically rigorous way to handle both. Its defensibility is currently low (4) because, while the math is deep, the code is a fresh academic reference implementation with 0 stars and 5 forks—likely peer researchers. It lacks the 'moat' of a production-grade library or a proprietary dataset. Frontier labs (OpenAI/Anthropic) are unlikely to compete here as it is too domain-specific. The primary risk is from established financial software vendors or larger 'Quant-ML' libraries (like those from Bloomberg or various hedge fund open-source arms) absorbing the technique. The 5 forks within 9 days suggest immediate academic interest, but it needs an accessible API or integration into frameworks like 'stochastic-volatility-models' or 'synthetic-data-vault' to gain a real foothold.