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.
22 documents
The document shows how to turn weekly Commitment of Traders reports into futures positioning features. It explains the trader categories in the financial futures and disaggregated commodity formats, and why participant groups matter when aggregate net…
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 explains how to turn weekly Commitment of Traders reports into futures positioning features. It outlines the report categories for financial futures and physical commodities, describes how net positions reflect different participant roles, and…
This notebook explains how to build a cross-sectional futures feature matrix from three contract tenors per product. It derives carry and curve curvature from exchange-settled prices, while using roll-adjusted prices for return, momentum, and volatility…
This guide explains how to turn hourly continuous futures data into daily bars aligned to CME trading sessions. Because a session ends at 4 PM Central Time, bars from Sunday evening belong to Monday's session, and bars after the close generally count toward…
This notebook builds rule-based features for a cross-section of CME futures, centered on carry from the spread between nearby delivery contracts. It also constructs momentum, volatility, curve-shape, and calendar features. The design distinguishes raw…
This notebook defines forward-return targets for a cross-sectional futures strategy that ranks products by term structure, going long those with stronger carry and short those with weaker carry. It distinguishes roll-adjusted prices, appropriate for…
This notebook measures how transaction costs affect a fixed CME futures strategy configuration. It selects the reported configuration through a shared validation-stage process, carries its risk overlay into the cost runs, and applies a declared grid of…
This notebook explains how futures contract specifications affect backtest accounting. It shows how the contract multiplier converts price moves into dollar P&L, how price times multiplier determines notional value for sizing, and why per-contract…
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 analysis explains how CME futures data is organized into products, expiring contracts, and volume-rolled continuous series. It uses E-mini S&P 500 data to show how individual contracts have finite trading windows that overlap around a roll, while a…
This reference summarizes listed contract months for 35 CME futures products across equity indexes, Treasuries, energy, metals, currencies, interest rates, agriculture, livestock, and crypto. It explains the exchange’s month-code system and distinguishes…
This document compares alternative position-sizing methods for CME futures strategies selected from a baseline ranked by equal-weight validation Sharpe. Equal weighting treats every selected product alike, although futures contracts can have very different…
This notebook uses double machine learning to estimate whether futures carry has an effect on subsequent returns after adjusting for volatility, momentum, and cross-sectional carry rank. It distinguishes causal explanation from predictive performance: a…
This notebook checks whether the data can support a weekly, cross-sectional futures strategy before any model is fitted. It describes a design that ranks CME products, takes long positions in the highest-ranked contracts and short positions in the lowest,…
This notebook studies gradient boosting for cross-sectional prediction of CME futures returns. It varies tree capacity through leaf-count profiles and compares squared-error, absolute-error, and Huber objectives, which differ in how strongly extreme…
The document sets out how to construct forward-return labels for a cross-sectional futures strategy that ranks products by term structure. It distinguishes roll-adjusted prices, which are appropriate for returns across contract rolls, from raw settlement…
This document describes a CME futures dataset with hourly and daily bars, continuous front-month contracts, and two deferred tenors across several product groups. It explains the dataset’s coverage, fields, loading options, and related weekly CFTC…
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 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 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…
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…