Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
124 documents
This notebook explains Tail-GAN, a generative adversarial approach for creating financial return scenarios while targeting value at risk (VaR) and expected shortfall (ES). It uses differentiable sorting so gradients can pass through quantile estimates, and…
This case study compares gradient boosting models that predict returns to expiry for short at-the-money straddles. It varies tree capacity and three fitting objectives: squared error, absolute error, and Huber loss. Walk-forward validation uses two periods,…
This configuration describes a weekly at-the-money straddle-selling study on S&P 500 constituents. Positions are opened on Friday and held toward expiry, with daily stock hedges triggered by changes in option delta. The portfolio spreads capital across…
This notebook explains how to construct model-based features for a US equities panel without letting future observations influence historical values. Its central method is an estimation schedule: models use a burn-in period, fit only on data preceding the…