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
The document describes a feature pipeline that combines equity prices with summaries of listed options implied-volatility surfaces. Its central hypothesis is that disagreement between option-implied volatility and realized share volatility can help rank…
The document explains how a stochastic discount factor (SDF) estimates a pricing kernel that should price every asset, rather than estimating common return factors. It describes adversarial training: one network proposes the discount factor while another…
This analysis compares predictive, latent-factor, and causal models for a cross-sectional S&P 500 stock strategy using weekly forward returns. Its feature set combines equity momentum and volatility measures with option information such as implied-volatility…
This read-only assessment reconstructs the selected strategy from configured, full-coverage registry results. It follows the progression from an equal-weight baseline through allocation, risk controls, and transaction-cost sensitivity, then reads the holdout…
This notebook explains why a daily constant-maturity options series is not the return history of a single tradeable contract. Selecting a new near-the-money straddle each day can change the strike, expiration, or both. In particular, moving to a later…
This case study describes producing an out-of-sample prediction set for an already selected S&P 500 options model. The holdout configuration is fixed using validation results, then fitted again on data ending before the holdout window. A label buffer…
This notebook compares learned and analytical hedges for a short European call when rebalancing is discrete and trading incurs proportional costs. It defines the self-financing terminal P&L from hedge gains, turnover costs, and the option payoff, then trains…
This utility measures the absolute relative change in an entered options straddle's premium over specified session horizons. It builds a valid straddle premium from paired call and put quotes with positive bids and asks above bids, then follows the exact…
This utility builds label artifacts for S&P 500 option straddles using the same symbol, strike, and expiration at entry and exit. It aligns feature dates to subsequent market sessions, constructs five- and ten-session exit dates, and joins call and put…
This analysis checks whether daily options and share data can support a weekly S&P 500 strategy that ranks constituents by thirty day at the money implied volatility and buys the highest ranked shares. Options provide the signal, while the portfolio holds…
This document describes a one year out of sample backtest of S&P 500 option straddles. It applies predictions from a model refit on pre holdout history, together with the previously selected strategy, allocation, concentration, weekly entry schedule, hedge…
This notebook specifies and executes a double machine learning analysis of the effect of the variance risk premium on short-option returns through expiry. Before execution, it resolves the treatment, outcome, confounders, timing, nuisance model, temporal…
This case study fits regularized linear models to returns from short at-the-money straddles held to expiry. The trade collects call and put premiums, giving it a capped maximum gain but potentially very large losses when the underlying moves sharply.…
This study converts registered model predictions into comparable S&P 500 option strategies. On weekly decision dates it ranks predicted returns, filters to a liquid universe, and sells equally weighted at-the-money straddles on the highest-ranked symbols.…
This notebook evaluates one previously selected S&P 500 options configuration on a holdout period. It reuses the registered model predictions and strategy settings, including the signal schedule, allocation, hedge rule, and trading costs, without tuning them…
This document describes refitting the configuration selected by earlier validation stages on all eligible pre-2021 history, then generating predictions for the 2021 holdout. It derives the training interval from the declared evaluation window, label buffer,…
This notebook evaluates gradient-boosted trees on equity option analytics, where features such as implied volatility, skew, term structure, and variance risk premium encode market expectations. It asks whether a nonlinear model can combine those forecasts…
This notebook outlines validation-only diagnostics for a 10-session delta-hedged S&P 500 options return label. It organizes financial predictors into implied-volatility-dependent and independent groups, then compares Ridge models using a single volatility…
This notebook builds model-based features for S&P 500 options research from underlying returns. It fits GJR-GARCH, which gives extra weight to negative return shocks, and a stochastic-volatility model whose latent variance is estimated with MCMC and tracked…
This guide organizes US equity datasets into market data, company fundamentals, investor positioning, and firm characteristics. It inventories loaders for daily and intraday bars, options, and market microstructure records, as well as SEC filing text, XBRL…
This notebook compares ways to hedge a short European call when rebalancing is discrete and trades incur proportional costs. It defines self-financing terminal P&L, then trains a neural hedger to minimize expected shortfall under Heston simulated prices. The…
This notebook refits the configuration selected by earlier validation stages using pre-2021 history, then publishes predictions for a 2021 holdout. It retrieves the chosen configuration from a recorded candidate set or applies the same ranking rule when that…
This analysis introduces option-chain structure and examines a 2020 slice of S&P 500 options for eight underlyings. It explains moneyness, intrinsic and time value, Greeks, implied volatility, and the information represented by volatility skew and term…
This feasibility analysis asks whether the data and assumptions for a weekly, delta-hedged short-straddle strategy on S&P 500 constituents are plausible before fitting a model. It explains how calls and puts at the same strike and expiration form a straddle,…