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
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 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 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 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 notebook evaluates stop-loss, trailing-stop, and fixed-duration exits as overlays on CME futures strategies. For each prediction horizon, it applies configured rules to the strongest validation-Sharpe parent selected from prior signal and allocation…
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 document describes a fixed out-of-sample backtest for a selected CME futures strategy. The configuration, predictions, allocator, rebalance cadence, concentration, and transaction-cost assumptions are inherited from earlier research steps and applied…
This notebook studies linear prediction models for a cross-section of CME futures products using feature columns grouped into related families, including carry, momentum, volatility, and rolling risk measures. Because columns within a family are often…
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 exploratory notebook profiles hourly CME futures data across products and asset classes, using the E-mini S&P 500 as an example. It explains the hierarchy from product to expiring contract to continuous series, summarizes coverage windows, and checks…
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 audit record defines how several backtesting frameworks are compared with ML4T across real strategy case studies and a synthetic stress workload. It specifies the comparison rules for ordered fills, timestamps, account-money values, quantities, and…
This notebook presents correctness and runtime comparisons for VectorBT Pro and VectorBT OSS against ML4T on supported case-study strategies. It covers ETF allocation, USD-quoted foreign exchange, and a US equity panel for both editions; VectorBT Pro also…
This notebook evaluates LSTM and NLinear sequence configurations on CME futures products. Sequence models consume ordered windows of observations, testing whether the recent path contains information beyond engineered summary features. The LSTM learns a…
This analysis explains how to read information coefficients for a catalog of CME futures prediction models. It defines each date’s IC as the rank correlation between predicted and realized returns across products, then averages those daily values 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…
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 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…
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 turns futures carry—the price gap between nearby expiries—into model-based features. It describes one-step ARIMA forecasts per product, rolling Fourier measures of seasonal cycle strength, and a two-state hidden Markov model that infers broad…
This notebook turns hourly continuous futures data into daily OHLCV bars aligned to CME Globex sessions. It assigns timestamps to the session ending at 4 PM Central Time, so Sunday evening trading belongs to Monday and Friday evening does not create a…
This audit compares multiple backtesting engines on shared historical inputs for ETF allocation, CME futures, crypto perpetuals with funding, foreign exchange, and US equities. Each supported pair receives the same content-addressed market data and frozen…