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 audits an annual-report corpus before it is indexed for financial research. It measures the delay between a fiscal period end and the public filing date, showing why point-in-time applications must use the publication date. It also identifies…
This notebook implements a daily educational adaptation of an adversarial stochastic discount factor model. A portfolio-weight network constructs a factor from next-day excess returns using characteristics and market state available at the prior close. An…
This notebook tests how a selected US firm characteristics strategy responds when its transaction cost assumption changes. It holds the strategy configuration fixed and reruns validation backtests across a declared grid of commission and slippage levels,…
The notebook previews S&P 100 10-K and 8-K filings intended as inputs to a financial knowledge graph. It describes schema checks for required fields, unique company and accession keys, consistent form labels, year alignment, and text-length metadata. Annual…
This notebook runs a fixed equity-characteristics strategy on a reserved holdout period using predictions and a portfolio allocator selected earlier. It keeps the configuration, position sizing, concentration, rebalance cadence, and cost assumption…
This case study assesses a weekly S&P 500 options short straddle using registered backtests. It selects a configuration from a nominated liquid universe, ranks candidates on validation, and evaluates the chosen configuration on holdout data without using…
This analysis reconstructs NASDAQ limit-order lifecycles from ITCH messages, following orders from submission to their first cancellation-related event or execution. It distinguishes adds, deletes, partial cancels, replacements, and executions, then measures…
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 turns stock-level predictions into validation portfolios. On each rebalance date, it sorts stocks by predicted return, buys the top group and shorts the bottom group, and assigns equal capital to every position. It applies this baseline across…
This notebook demonstrates how a fixed dollar order can impose very different costs across stocks because its size relative to available trading volume matters. It applies a square-root market-impact model to a long-when-positive momentum strategy using…
This notebook adapts TSMixer to predict forward ETF returns from historical momentum features. Its architecture alternates a shared linear transformation across the time axis with a feature MLP applied separately at each date. Pre-normalization and residual…
This notebook turns a supply chain knowledge graph and institutional ownership filings into features for downstream financial modeling. From the company network it derives topology measures, supplier concentration, competitive exposure, and bottleneck…
This notebook explains how to fine tune a transformer for financial named entity recognition, which identifies spans such as organizations, people, amounts, dates, and percentages in text. It focuses on BIO boundary labels and the alignment problem between…
This notebook adapts a supervised autoencoder to daily US equities. The model reconstructs ranked price and volume characteristics while auxiliary and main classification heads train a shared latent representation to predict whether returns are positive…
This notebook demonstrates a data-quality workflow for daily US equity OHLCV data. Structural checks cover nulls, duplicates, chronological order, price consistency, negative values, stale prices, and extreme returns. It then distinguishes structural…
This dataset guide describes a monthly US equity panel of anonymized firms and characteristics used for machine-learning asset-pricing research. The data includes accounting and technical characteristics, returns, and pre-defined training and test periods…
This notebook presents a workflow for reducing a large set of ETF features to candidates for later modeling. It ranks features using cross-sectional Spearman information coefficients against forward returns, computed by date and then averaged. Newey–West…
This notebook explains how a stochastic discount factor (SDF) prices a cross-section of equity option signals. Unlike factor models that estimate exposures and factor returns, the SDF approach estimates one random variable that makes asset returns price…
This document introduces latent factors as common return patterns across stocks, with each stock's loading describing its exposure to a pattern. It contrasts principal component analysis, which extracts factors and stock loadings from the return panel alone,…
This document explains how to construct forward-return and direction labels for NASDAQ-100 intraday research. It defines the return using an explicit execution convention: observe a bar close, enter at the next bar, and measure the outcome over a configured…
This notebook studies TabM, a neural network design for tabular data, on monthly firm characteristics. It compares three presets that increase hidden width and ensemble membership while keeping other training settings fixed. The shared runner fixes the…
This notebook applies a previously selected S&P 500 equity and options strategy to predictions for a separate 2021 holdout period. It carries the selected configuration forward unchanged, including its allocator, concentration rules, risk overlay, and…
This chapter outlines post-deployment governance for machine-learning trading systems. It separates technical failures, where identical inputs produce different outputs, from statistical decay, where outputs no longer predict returns. Its framework combines…
This index introduces latent factors as shared patterns extracted from the way stocks’ returns move together, with each stock’s loading indicating its exposure to a pattern. It contrasts principal component analysis, which estimates factors and stock…