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.
742 documents
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…
This notebook turns cross-sectional stock predictions into long-short portfolios by ranking stocks at each rebalance, buying the top k and shorting the bottom k with equal capital per position. Equal weighting provides a baseline that isolates differences in…
This notebook presents a feature-selection workflow for ETF research, intended to narrow many candidate transforms and lookbacks to a smaller set for later modeling. It computes cross-sectional Spearman information coefficients between features and forward…
This notebook evaluates model predictions as NASDAQ-100 trading strategies using a shared backtest engine. It first runs a plumbing check with random signals: persistent profits after costs would point to issues such as lookahead, misaligned data, or…
This notebook checks whether a fifteen-minute cross-sectional stock-ranking strategy is feasible before fitting a model or making forecasts. It uses NASDAQ-100 quote data, midpoint returns, and a pre-holdout development window to examine the strategy’s…
This code describes how backtest settings are resolved from case study configuration and how run identity accounts for changes to the traded universe and prediction age. Its central lesson is that any filter that changes the portfolio or prediction set must…
The document outlines ways to compare strategy returns with two external benchmarks: daily SPY total returns and Fama-French market returns formed by adding the market risk premium and risk-free rate. It provides date filtering and alignment helpers,…
The document describes fitting a simple linear sequence model to NASDAQ-100 one-minute features to forecast forward returns at several horizons. Each input contains the preceding hour for one stock, expressed as changes from the latest observation, and the…
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…
This CME futures study varies gradient boosting tree capacity and loss function while recording predictions at multiple training checkpoints. Tree leaf count controls how finely a model partitions the feature space, while squared, absolute, and Huber losses…
This notebook studies how Ridge regularization choices affect information coefficient on an ETF panel and how hyperparameter search can inflate reported performance. It first evaluates a wide alpha grid across walk-forward folds, scaling features within each…
This notebook explains how to construct one-, five-, and 21-session forward spot returns for a fixed FX-pair universe. It first maps four-hour bars into trading sessions using a New York rollover calendar, then builds returns without removing rows before…
This notebook runs the already selected FX strategy specification against registered holdout predictions. It preserves the strategy’s signal, allocation, risk controls, rebalance rule, costs, and account settings; the prediction set and price-frame identity…
This notebook explains instrumented principal component analysis (IPCA), which models each ETF’s factor exposures as a shared linear function of that fund’s observed features. Alternating least squares estimates the feature-to-exposure map and the factor…
This notebook studies six allocation methods applied to selected crypto perpetual strategy configurations. The rankings and entry rules remain fixed while the capital assigned to each position changes. The methods use model scores, individual contract…
The notebook presents an autonomous research workflow in which a language model uses general-purpose tools to inspect files, run commands, query a backtest registry, and examine data. Rather than hard-coding research actions, the operator discovers…
This notebook explains why a reported Sharpe ratio is an uncertain estimate, not a definitive measure of strategy quality. It develops confidence intervals and the probabilistic Sharpe ratio, which estimates the chance that a strategy's true Sharpe exceeds a…
This notebook compares equal weighting with two allocation rules for US firm characteristic strategies: score weighting, which assigns more capital to higher model predictions, and conformal weighting, which reduces capital for predictions with wider…
This analysis compares learned signals for monthly cross-sectional US stock returns using accounting and market characteristics, with next-month return as the target. It reviews linear models, boosted trees, deep tabular methods, latent-factor estimators,…
This chapter presents agentic workflows for financial research tasks where gathering, filtering, and synthesizing uneven evidence is the main challenge. It contrasts these workflows with conventional models that work well on stable structured inputs, and…
This audit compares Backtrader and Zipline with ML4T on real strategy cases, using only asset and contract combinations that each engine and the frozen data bundle can represent natively. It covers ETF allocation and a US equity panel for both engines, plus…
This notebook defines and validates monthly return labels for a cross-sectional US firm-characteristics study. It checks how the data provider pairs each row’s characteristics with the return outcome, emphasizing that the panel is already aligned and must…
This notebook explains how to evaluate financial models without allowing information unavailable at a decision date to leak into training or model selection. It distinguishes training, validation, and a final holdout set, and shows why randomly shuffled…
This notebook compares four ways to discover relationships in a panel of ETF returns: NOTEARS for contemporaneous linear DAGs, VAR-LiNGAM for lagged and instantaneous structure, PCMCI for conditional-independence tests, and Granger tests as pairwise…