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.
566 documents
This notebook evaluates how registered ETF prediction sets perform when converted into portfolios. It crosses each validation prediction set with declared entry schemes, such as different top-k holdings, while keeping the trading calendar, rebalance cadence,…
This chapter describes a path from research to live execution built around strategy logic that can run in backtest, paper, and live settings. It covers broker and platform integrations, deployment loops, data and feature parity checks, order lifecycle…
This notebook describes an LSTM model that predicts returns from ordered windows of stock features in a US equities panel. Unlike a flat tabular model, the LSTM processes sessions sequentially and carries a hidden state through each window. Learned gates…
This notebook presents TimeGAN, a recurrent generative adversarial model for producing synthetic financial sequences. Its five modules learn an embedding and reconstruction, predict successive latent states, generate sequences from noise, and distinguish…
This notebook applies two sequential detectors to real validation prediction errors from two US equities linear-model configurations over a calendar year. A two-window mean-shift detector looks for changes in error magnitude, while a bad-day frequency…
This document explains how TWAP and VWAP divide a large parent order into child orders over a trading session. TWAP allocates equal shares to each time interval; VWAP allocates shares in proportion to a forecast of interval volume. The notebook estimates…
This notebook presents a supervised autoencoder for predicting ETF forward returns. The network compresses a fund’s feature row into a low-dimensional representation and predicts returns directly from that representation. Unlike conditional autoencoders in…
This notebook explains how to test whether events such as trading signals or announcements predict returns beyond a market benchmark. It demonstrates the workflow using momentum breakouts in liquid ETFs: estimate a market model from pre-event returns,…
This feasibility analysis evaluates whether quote data can support a market-neutral, cross-sectional strategy that ranks NASDAQ-100 constituents at a fifteen-minute cadence and holds both ends of the ranking. It checks whether enough eligible symbols are…
This notebook explains how to extract insider transactions from raw SEC Form 4 XML for equity research. It treats each transaction block as the unit of parsing, preserving the association among transaction code, date, share count, price, and direction. XML…
This notebook compares stop-loss, trailing-stop, and time-exit overlays on fixed NASDAQ-100 microstructure backtests. The strategy makes decisions every fifteen minutes, while risk rules monitor prices every minute, allowing an overlay to exit a position…
This notebook describes slow-moving contextual features for daily trading decisions: accounting ratios, macroeconomic conditions, and calendar encodings. It outlines value and quality measures such as earnings yield, profitability, accruals, leverage, and…
This document explains how to build daily forward-return labels for ETFs using adjusted closing prices at the observation date and a later trading session. It creates a primary monthly-horizon label and a shorter weekly variant, with labels computed on each…
This notebook compares split-conformal prediction, conformalized quantile regression, and adaptive conformal inference for forecasting ETF forward returns. In walk-forward folds, it trains on one subset, calibrates interval errors on another, and evaluates…
This notebook evaluates validation predictions as long-only equity portfolios, using equal-weight top-K selection and a decision schedule determined by each label's cadence. It checks the close-to-next-open event ordering with a seeded random-signal run,…
The notebook studies whether order-book activity predicts near-term returns for NVDA. It builds a distance-weighted measure of book pressure from market-by-order adds, cancels, and fills, then smooths the signed activity to represent liquidity changes near…
This case study compares sequence models on an S&P 500 equity and options feature panel. It pairs an LSTM, which carries gated state through a lookback window, with an N-Linear baseline that removes the latest level and fits a linear mapping. The baseline…
This notebook explains TimeGAN’s five-part architecture: an embedder and recovery network map between raw observations and latent space, a supervisor learns temporal transitions, and a generator and discriminator train adversarially. It describes staged…
This notebook demonstrates connecting a shared ETF momentum strategy interface to Alpaca, with a simulated offline path available by default. It explains how broker and market-data adapters can change while the strategy, order log, and shadow-mode wrapper…
This notebook converts rolling ETF return windows into two-channel images for a convolutional neural network. The Gramian Angular Summation Field rescales values within each window, maps them to angles, and records trigonometric relationships between pairs…
This notebook measures how a selected US firm-characteristics strategy’s validation performance changes as assumed commissions and slippage vary. It re-runs the same configuration across a declared cost grid, splitting each total charge evenly between…
This notebook describes how to produce predictions for an untouched holdout period after choosing a configuration on validation data. It resolves the selected configuration, rebuilds its training specification for the holdout, and derives the training window…
This configuration describes an intraday study of order-flow signals across a point-in-time NASDAQ-100 universe. It defines a cost-screened subset using liquidity information available before each evaluation period, specifies decision schedules for several…
This feature-engineering notebook explains how slower information can condition faster trading signals through fundamentals, macroeconomic data, and calendar context. It outlines value ratios such as book-to-market and earnings yield, quality measures such…