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.
87 documents
This case study tests weekly long-short carry-ranked strategies across a diversified set of CME futures, using daily data, walk-forward model evaluation, and costs for commissions, spreads, and roll slippage. It compares signal quality across model families,…
This notebook presents a neural stochastic discount factor model for a panel of CME futures. The model uses both product returns and observable characteristics, such as carry, momentum, and volatility, to construct latent factors and generate predictions for…
This notebook explains feature construction from data beyond a single asset’s price history. It derives annualized futures roll yield from contemporaneous front and deferred contract prices, and uses three tenors to calculate normalized curve slope and…
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 document explains a CME futures experiment using TabM, a parameter-efficient neural ensemble, on the same engineered feature rows and walk-forward folds used by linear and gradient-boosting models. TabM shares most weights across ensemble members to…
This notebook examines retained results from a real-strategy audit comparing the LEAN engine with matching ML4T Backtest profiles. It identifies the asset-class workloads supported by the frozen inputs and reports parity evidence across fills, valuations,…
The notebook assembles end-to-end pipelines for daily equities and hourly crypto perpetual futures, covering acquisition, validation, source labeling, and storage. Its equity example joins historical WikiPrices data with a more recent Yahoo feed. Since the…
This dataset guide describes a collection of continuous CME futures contracts spanning equity indexes, rates, energy, metals, currencies, agriculture, and livestock. It explains the hourly source data and derived daily frequency, multiple contract tenors,…
This notebook builds principal-component factors from a panel of CME futures returns, not from engineered characteristics such as carry, momentum, or volatility. PCA finds directions that explain variation across products; the first may resemble a common…
This document introduces two latent-factor approaches for futures returns. Principal component analysis finds uncorrelated directions that explain the most return variation, while a neural stochastic discount factor seeks combinations related to the…
This notebook describes how to produce holdout predictions for a selected CME futures model configuration. The configuration is chosen using validation results, then refitted on data ending before the holdout window opens. A label buffer separates training…
This guide introduces the public CFTC Commitment of Traders reports as a source of weekly futures positioning data. It distinguishes the Traders in Financial Futures report, which categorizes participants such as dealers, asset managers, and leveraged money,…
This notebook selects a CME futures case study from registered validation backtests spanning signal rules, allocation, and risk overlays. It compares candidates across both return horizons and chooses the configuration with the highest validation Sharpe.…
The document describes how a CME futures study generates predictions for a holdout period after selecting a model configuration on validation data. The selected configuration and checkpoint are recovered from the validation results, then the model is…
The document explains how to retrieve weekly CFTC Commitment of Traders data for selected futures products and save each product’s history as a Parquet file. COT reports capture Tuesday positioning and are released on Friday; trader categories vary between…