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
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…
This notebook explains how to apply four market impact models in a backtest: no impact, linear impact, square-root impact, and a configurable power law. Each model estimates a signed per-share price move based on order direction, quantity, price, and volume.…
This document explains how hidden Markov models infer unobserved market regimes from returns and recent volatility. It first sets two transparent benchmarks: a volatility index threshold for stress and price relative to a long moving average for trend. It…
This notebook explores ARIMA as a feature generator for models that may use its output alongside other predictors. It explains how ACF and PACF plots can suggest model orders, how information criteria can compare candidate orders on training data, and why…
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 notebook presents lightweight falsification diagnostics for feature triage, explicitly distinguishing mechanism consistency from causal identification. It first scans ETF features across forward-return horizons with multiple-testing correction, then…
This notebook surveys supervised-learning labels using ETF price data. It covers fixed-horizon forward returns for regression or direction classification, time-series rolling percentiles, cross-sectional percentile labels, triple-barrier labels with fixed or…
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 notebook explains how to build a cross-sectional futures feature matrix from three contract tenors per product. It derives carry and curve curvature from exchange-settled prices, while using roll-adjusted prices for return, momentum, and volatility…
This US equities feature study explains how to generate features from estimated models without allowing future data into earlier observations. Its estimation schedule uses a history burn-in, fits parameters only on data before each output block, then…
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 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 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 rule-based features for a cross-section of CME futures, centered on carry from the spread between nearby delivery contracts. It also constructs momentum, volatility, curve-shape, and calendar features. The design distinguishes raw…
This data exploration examines eight-hour premium-index observations for USDT-margined crypto perpetual contracts and explains how the premium relates to funding payments. The index uses executable impact bid and ask prices relative to the price index,…
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 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,…
This environment models a market maker that adjusts quote center and spread in response to inventory, volatility, and a discrete action choice. Synthetic prices use conditional volatility generated with a GARCH process, while order imbalance evolves over…
This notebook trains a vanilla autoencoder on hourly returns for a basket of crypto perpetual markets. Returns are standardized using the training period, and a neural network compresses the multi-asset input into a two-dimensional latent representation…
This notebook compares reference ETF portfolios under historical crises, hand-defined simultaneous asset shocks, and Monte Carlo loss scenarios. Historical windows include major equity and rate-driven selloffs, with portfolio returns compounded over explicit…
The document explains how to create currency-pair features from three fitted time-series models: a state-space filter that estimates a slowly changing price level, a two-state hidden Markov model for dollar volatility regimes, and an ARIMA model whose…