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.
715 documents
This notebook compares two causal questions about extreme premiums in Bitcoin perpetual futures: whether a premium state predicts forward returns, and whether it predicts subsequent premium reversion. The treatment is a binary indicator for a premium z-score…
This notebook presents a workflow for deciding how financial time series should be modelled. It begins with plots of SPY prices and returns, VIX levels, and the return distribution to identify drifting levels, clustered moves, and heavy tails. It then…
This notebook builds prediction intervals around gradient boosting forecasts and compares split conformal prediction with quantile regression, conformalized quantile regression, and adaptive conformal inference. Split conformal uses a chronological training…
This US equities panel study uses a penalized linear model as a baseline for forecasting forward returns from features observed at the close. Its feature set contains overlapping momentum and volatility measurements, creating multicollinearity that can make…
This ETF feature study builds three model-derived inputs: a hidden Markov model (HMM) that estimates market regime, fractionally differenced prices for reference funds, and per-ETF GARCH(1,1) conditional volatility. It emphasizes that avoiding look-ahead…
This notebook explains instrumented principal component analysis (IPCA), which estimates common factors while making each stock’s factor exposures a shared linear function of its same-day option-surface features. Those features include implied volatility,…
This study compares gradient-boosted trees with a penalized linear model for ranking currency pairs by future returns. Its motivation is that trees can represent conditional relationships, such as using momentum in one market regime and carry in another,…
This exploratory analysis profiles a four-hour OANDA dataset covering twenty currency pairs. It explains that the volume field counts quote updates at one venue, not consolidated traded size, and therefore cannot support a market-wide liquidity ranking. It…
This notebook assembles a forecasting agent from a language-model client, a search tool, and a bounded turn loop. The agent searches for evidence and returns a binary probability with a rationale. Its response parser handles common formatting failures and…
This notebook applies Bayesian structural time-series event-study methods to estimate the impact of Federal Reserve announcements on a bond ETF. It builds a counterfactual from pre-event relationships between the target’s daily log returns and returns on…
This notebook tests whether four managed-portfolio strategies explain SPY returns after controlling for broad ETF co-movement. The strategies rank ETFs using lagged momentum, volatility, or recent returns. Ten principal components from a balanced set of…
This notebook adapts Word2Vec skip-gram to institutional equity holdings. It treats each institution’s portfolio as a sentence, stock identifiers as tokens, and positions ordered by reported holding value as word order. Stocks that repeatedly appear in…
This notebook explains why raw equity prices can produce misleading returns when they cross stock splits or cash dividend dates. It describes backward adjustment, which scales historical prices for later corporate actions, and distinguishes raw prices for…
This notebook evaluates whether a signal’s information coefficient (IC) is distinguishable from noise when successive observations are dependent. It illustrates the issue using a momentum signal and ETF data, explaining how overlapping forward returns,…
This notebook explains how to inspect a shipped panel of economic series from FRED and interpret its calendar-day grid, metadata, and derived columns. Because the panel has been flattened onto a daily calendar, row counts conceal the source release…
This notebook treats ARIMA forecasts as input columns for later models rather than as standalone predictions. It fits models to ETF returns, uses stationarity checks and autocorrelation diagnostics to propose orders, and compares candidate orders with an…
This notebook describes how to prepare and register NLinear forecasts for FX labels. NLinear subtracts the last observed level from each fixed-length input history, focusing the model on changes within the lookback. A shared sequence eligibility process…
This shared analysis utility supports comparing trading-model predictions through registry metrics and prediction-level diagnostics. It loads precomputed information-coefficient results, optionally filters by label or model family, and by default excludes…
The document sets out how to construct forward-return labels for a cross-sectional futures strategy that ranks products by term structure. It distinguishes roll-adjusted prices, which are appropriate for returns across contract rolls, from raw settlement…
The document describes three model-based features built from futures carry: an ARIMA forecast of next-session carry, rolling Fourier measures of selected cycle lengths, and a two-state hidden Markov model that infers the market regime from average carry. Its…
The document explains two ways to estimate bid-ask spreads when only daily OHLCV data is available. Corwin-Schultz compares high-low ranges over adjacent one-day and two-day intervals, using the different behavior of volatility and spread to separate them;…
The document describes converting rolling ETF return windows into two-channel images for a conventional convolutional neural network. A Gramian angular field maps normalized observations to angles and records pairwise angular relationships; a Markov…
This document introduces the Chen, Pelger, and Zhu monthly US equity panel used to benchmark asset-pricing models. It describes 46 firm characteristics grouped by economic theme, next-month excess returns, and the panel’s time-ordered train, validation, and…
This notebook presents a workflow for characterizing market series before modeling. It begins with plots of SPY prices and returns, VIX levels, and the return distribution to identify trends, volatility clustering, and heavy tails. It then explains how to…