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.
4 documents
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 diagnoses how a fixed, long-only ETF momentum baseline performed across market conditions from 2010 to 2024. It labels each daily return using volatility and trend measures known before that return began, with expanding historical medians…
The document turns a cross-asset momentum hypothesis into a monthly exploratory test using a 100-ETF universe. It constructs adjusted month-end prices, measures 12-to-1-month momentum, then ranks eligible ETFs into five equal-weight groups and compares their…
This feature-engineering module prepares daily price data for systematic macro portfolio models. It creates horizon returns scaled by estimated volatility, several multi-scale MACD signals, and rolling z-scores of log prices. The return horizons can use a…