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.
20 documents
This document describes a daily ETF candidate universe covering equities, fixed income, commodities, and currencies. It outlines a workflow for downloading market data, loading it for analysis, inspecting coverage by symbol and category, and filtering by…
This research-agent record considers whether the Federal Reserve will raise the upper bound of its target rate during 2026. It contains a market price, search traces, and agent probability estimates. The first rationale favors a hike, citing inflation risks…
This notebook applies principal component analysis to changes in Treasury yields across maturities. Standardizing changes gives each maturity equal influence, and the resulting components are interpreted as level shifts, steepening or flattening, and…
This notebook explains how to interpret Kalshi’s federal funds rate contracts and prepare their prices for quantitative research. A binary contract price represents an implied event probability, but the feed contains the highest standing YES bid rather than…
This notebook compares six long-only ETF allocation methods designed to reduce reliance on unstable estimates. It applies Ledoit-Wolf covariance shrinkage to estimators that use covariance, and contrasts mean-variance maximum Sharpe with minimum variance,…
This event study uses Bayesian structural time-series models to estimate how Federal Reserve announcements affect a bond ETF. It learns the target’s relationship with selected international equity and commodity ETF returns during a pre-event window, then…
This chapter surveys methods for recovering common structure from return and characteristic panels, from PCA and eigenportfolios to IPCA, risk-premium PCA, conditional autoencoders, stochastic discount factor estimation, and supervised autoencoders. Its…
This dataset note describes a diversified collection of exchange traded funds used in a momentum strategy and a broader sequence of financial research examples. It provides daily open, high, low, close, and volume observations beginning in 2006, grouped…
This notebook explains Hierarchical Risk Parity as an alternative to mean-variance allocation when covariance estimates are noisy. HRP clusters assets using correlation distances, orders them according to the hierarchy, then recursively bisects the ordered…
This notebook applies principal component analysis to changes in eight Treasury constant-maturity yields, spanning one to thirty years. It removes forward-filled calendar rows with no yield changes and standardizes the remaining observations before PCA, so…
This notebook explores an economic data panel built from Federal Reserve Economic Data series. It establishes that the shipped panel uses calendar dates, including weekends and holidays, and shows how accompanying metadata identifies each series’ meaning,…
This configuration specifies a historical macroeconomic dataset from FRED for regime filtering and cross-asset analysis. It organizes series by daily, weekly, monthly, and quarterly frequency, including Treasury yields, the federal funds rate, the VIX, labor…
This document describes a workflow for collecting FRED Treasury yields and economic indicators, aligning series with different reporting frequencies to a daily calendar, and loading or filtering the resulting dataset. The indicators include Treasury rates…
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 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 dataset guide describes a workflow for obtaining FRED series, aligning observations to a daily calendar, and loading selected indicators for analysis. The examples include Treasury yields, the 10-year minus 2-year spread, VIX, employment measures,…
This document records a multi-stage forecast of whether the Federal Reserve will raise rates during 2026. Three agents search for evidence and produce probabilities, followed by debate and a supervisor update. Their views differ: two assign a low probability…
This record captures one LangGraph forecasting run on whether the Federal Reserve would raise its target rate during 2026. Three agents produced probabilities, followed by a debate and a supervisor update. The logged outputs include an aggregate probability,…
This document records a structured bull and bear debate about whether the Federal Reserve will raise its target rate during 2026. Three agents assess the question, with arguments drawing on Fed projections, inflation, labor-market conditions, forecasts from…
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…