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.
86 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 document outlines a shared data system for quantitative trading research, cataloging datasets across equities, options, futures, crypto, foreign exchange, factors, macroeconomics, filings, positioning, news, and prediction markets. It describes the…
This case study compares predictive models for monthly cross-asset rotation across ETFs spanning equities, fixed income, commodities, currencies, and real estate. Its central lesson is that information coefficient (IC) and trading performance can rank models…
This notebook shows how to align macroeconomic observations with the dates traders could actually have known them. It distinguishes the period a value measures from its publication date, estimates release dates from period length and agency lag schedules,…
This notebook queries backtest registries across nine case studies and assembles comparable tables for downstream analysis. It organizes results by asset class and data frequency, then records performance across signal, allocation, cost, and risk stages,…
This notebook develops a financial feature matrix for a cross-asset ETF momentum hypothesis: assets with stronger relative performance may continue to outperform over the following month. It combines trailing returns at several horizons, risk-adjusted…
This document presents a cross-market inventory of model-based feature artifacts from nine case studies. It reads parquet schemas rather than loading their rows, excludes identifier columns, counts feature columns, and groups names by tokens associated with…
This document explains how to build and inspect a feature matrix for a cross-sectional ETF momentum and rotation hypothesis. It defines each feature’s lookback and information lag, then constructs trailing returns, risk-adjusted returns, volatility, trend…
This utility module supports deep learning workflows for financial time series across multiple assets. It resolves dataset aliases and loads canonical case study data, then creates sliding-window sequences independently for each symbol. The sequence…
This chapter presents strategy research as the design and evaluation of an executable decision process, from the initial economic idea through position sizing, constraints, costs, and live-like testing. It recommends classifying strategy families and…
This notebook checks whether historical ETF data can support a monthly ranking strategy before fitting a model or making forecasts. It tests the tradable universe using prior-year liquidity, counts eligible funds on rebalance dates, converts per-share…
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 notebook compares single-objective hyperparameter tuning with a multiobjective search for LightGBM prediction models. The baseline maximizes validation information coefficient (IC). The NSGA-II search instead maximizes IC while minimizing normalized…
This shared analysis module describes ways to estimate trading frictions across asset classes. It includes high-low and return-autocovariance estimators for bid-ask spreads, rolling average volume measures, and regression approaches for calibrating…
This notebook develops a unit-aware framework for estimating trading costs across equities, crypto perpetuals, futures, ETFs, and foreign exchange. It distinguishes share volume, contract volume, base-asset volume, and price-update counts, converting…
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…
This notebook brings an ETF case study’s registered backtests together into an uncertainty-aware strategy assessment. It reads performance measures with block-bootstrap confidence intervals and uses paired bootstraps to compare successive pipeline stages,…
This notebook explains how a macro panel stamped with the period it measures can leak future information into a trading backtest. It estimates publication dates by adding the period length to the stamped date and then applying a conservative release lag…
This notebook implements DeePM, an end-to-end portfolio policy for a diversified ETF universe. Its model combines temporal features, asset metadata, cross-sectional attention, and a macro graph prior that permits attention within asset classes and across…
This feature-engineering notebook constructs variables that require information beyond one asset’s price history. For futures, it computes annualized roll yield from contemporaneous near and deferred contract levels, plus term-structure slope and curvature…
The notebook diagnoses how a fixed monthly ETF momentum strategy performed across volatility, trend, and yield-curve conditions. Regime labels are designed to be known before the return they describe: volatility and index trend use prior closes and are…
The document presents a machine-learning trading research workflow that carries ideas from data and feature construction through model training, backtesting, transaction costs, portfolio and risk decisions, deployment, and monitoring. It emphasizes an…
The document describes loading ETF market data with optional symbol and date filters, plus a deterministic limit on the symbols returned. Its main analytical point is to match the price series to the quantity being measured: adjusted prices are appropriate…
This notebook presents a current audit comparing VectorBT Pro and VectorBT OSS with ML4T on supported real-data strategy workloads. Both VectorBT editions participate in ETF allocation, USD-quoted foreign-exchange allocation, and a US equity panel. Pro also…