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.
30 documents
This notebook describes an out-of-sample backtest for a selected crypto perpetual funding strategy. It reuses predictions generated from training history that ends before the holdout period, then applies the chosen strategy configuration, including its…
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 document describes data access and alignment conventions for crypto perpetual futures and related on-chain series. It explains that premium-index bars are timestamped at their opening time: an eight-hour bar records the premium leading into the funding…
This notebook checks whether four-hour spot FX data can support a daily cross-sectional strategy that ranks currency pairs using momentum and carry. It tests whether the declared instruments have prices at each decision point, whether the universe represents…
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 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 data exploration examines eight-hour premium-index observations for USDT-margined crypto perpetual contracts and explains how the premium relates to funding payments. The index uses executable impact bid and ask prices relative to the price index,…
This dataset guide presents Binance perpetual futures price and volume data alongside an eight-hour premium index. Hourly OHLCV records describe market activity, while the premium measures the difference between perpetual and spot prices relative to spot. A…
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 case study evaluates daily momentum and carry signals across a small, correlated universe of G10 spot currency pairs. Its workflow moves from feasibility checks and forward-return labels through engineered and model-based features, cross-validation,…
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 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 assesses whether four-hour spot FX data can support a daily cross-sectional strategy that ranks currency pairs using momentum and carry, buys the strongest, and sells the weakest. It does not fit a model or make forecasts. Instead, it checks…
This configuration describes a daily long-short strategy across 20 FX pairs. It sets a New York close decision time, next-bar execution, equal-weight sizing, and ranking signals based on momentum or carry. It also specifies spread and swap-point costs,…
This analysis explains how Binance’s perpetual-futures premium index relates to spot prices and how the exchange transforms that premium into periodic funding. The index uses executable impact bid and ask prices relative to an underlying price index,…
This notebook describes how to evaluate a selected crypto perpetual funding strategy on a later holdout period. It reuses predictions generated from training data ending before the holdout, carries the chosen signal, allocation method, and exit overlay…
This document studies position-level exits added to an existing crypto perpetual strategy: fixed stop losses, trailing stops, and time-based exits. It explains that these controls only close positions selected by the underlying strategy, and evaluates them…
This notebook uses double machine learning to estimate whether futures carry predicts subsequent returns independently of volatility, momentum, and cross-sectional carry rank. It residualizes both carry and returns against these confounders with flexible…
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 analysis checks whether settlement data can support a weekly, cross-sectional CME futures strategy before any model is fit. It tests whether enough products are quoted on rebalance dates to fill the intended long and short portfolios, estimates spread…
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,…
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…
The document describes three model-based features built from futures carry: an ARIMA forecast of next-session carry, rolling Fourier measures of selected cycle lengths, and a two-state hidden Markov model that infers the market regime from average carry. Its…
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…