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.
105 documents
This configuration defines a weekly futures research universe spanning equity indexes, government bonds, energy, metals, currencies, agriculture, and livestock. It sets Friday settlement as the decision snapshot and Monday open as the execution point. The…
This notebook presents a framework for checking whether a momentum signal’s apparent quality survives reasonable research choices. Using real ETF data before the designated holdout period, it varies one lookback parameter at a time and measures…
This notebook compares linear models on ETF features built from overlapping return windows and related momentum, volatility, and oscillator measures. Such predictors are collinear, making ordinary least squares coefficients unstable. The experiment fits…
This notebook presents a supervised autoencoder for predicting ETF forward returns. The network compresses a fund’s feature row into a low-dimensional representation and predicts returns directly from that representation. Unlike conditional autoencoders in…
This notebook explains how to test whether events such as trading signals or announcements predict returns beyond a market benchmark. It demonstrates the workflow using momentum breakouts in liquid ETFs: estimate a market model from pre-event returns,…
This document explains how to build daily forward-return labels for ETFs using adjusted closing prices at the observation date and a later trading session. It creates a primary monthly-horizon label and a shorter weekly variant, with labels computed on each…
This notebook demonstrates connecting a shared ETF momentum strategy interface to Alpaca, with a simulated offline path available by default. It explains how broker and market-data adapters can change while the strategy, order log, and shadow-mode wrapper…
This document describes a daily ETF dataset intended as a candidate pool for momentum and cross-asset research. It covers nine categories, including US and international equities, fixed income, commodities, specialty funds, and currencies. Data is sourced…
This notebook demonstrates single-factor evaluation using real ETF data and a price-derived momentum example. It measures cross-sectional Spearman information coefficients against forward returns, compares horizons, and explains the distinction between…