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.
106 documents
This notebook explains feature construction from data beyond a single asset’s price history. It derives annualized futures roll yield from contemporaneous front and deferred contract prices, and uses three tenors to calculate normalized curve slope and…
The notebook explains how to compare prediction models for S&P 500 options without allowing incomplete or mismatched evaluation samples to distort the results. It identifies each model by family, configuration, and checkpoint, checks that predictions meet…
This notebook develops forward return labels for short at-the-money straddles on S&P 500 stocks. Since the daily panel’s nominal 30-day straddle represents a different contract each session, a shifted price series would mix instruments. The label instead…
This notebook screens candidate features for a strategy that sells at-the-money straddles and holds them to expiration. For each trading day, it compares the cross-sectional ranking of available names by a feature with the ranking of their eventual straddle…
This notebook evaluates validation predictions as long-only equity portfolios, using equal-weight top-K selection and a decision schedule determined by each label's cadence. It checks the close-to-next-open event ordering with a seeded random-signal run,…
This case study compares sequence models on an S&P 500 equity and options feature panel. It pairs an LSTM, which carries gated state through a lookback window, with an N-Linear baseline that removes the latest level and fits a linear mapping. The baseline…
This notebook explains why a generic target-weight risk overlay is unsuitable for the described S&P 500 short-straddle engine. The engine assigns fixed capital shares to weekly cohorts and normalizes weights within each cohort to sum to one. Scaling those…
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 notebook fits a patched transformer to predict a primary label in an S&P 500 equity and options study. It divides each stock’s historical input window into contiguous patches and treats each patch as a token for attention. Attention can compare distant…
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…