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 describes how to produce holdout predictions for a selected NASDAQ-100 microstructure configuration after model and strategy choices have been made on validation folds. It reconstructs the selected training specification, derives a holdout fold…
This notebook explains how a volume participation limit constrains the quantity of a parent order that can fill during each market interval. A broker calculates the allowed slice from observed traded volume and carries any unfilled remainder forward. Lower…
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…
This notebook demonstrates single-factor evaluation using real ETF data and a price-derived momentum example. It measures cross-sectional Spearman information coefficients against forward returns, compares horizons, and explains the distinction between…
This case study screens candidate financial and model-based features against forward returns for a NASDAQ-100 microstructure strategy. It calculates cross-sectional rank information coefficients through time, adjusts inference for dependence and multiple…
This chapter presents retrieval-augmented generation as a way to make language models more useful for open-ended financial research, where unsupported claims and hallucinations can be costly. It walks through the system from document ingestion and…
This notebook explains how to construct forward intraday return labels for NASDAQ-100 stocks using minute bars and NBBO quotes. It defines returns from the next bar after a signal to a specified future endpoint, uses quote midpoints to reduce bid-ask bounce,…
The document presents an operational workflow for keeping daily market data current: load an initial history, fetch a small overlapping delta, check for missing dates, and monitor freshness and validation issues. The overlap helps capture vendor revisions to…
This notebook fits a patched transformer to predict a primary label in an S&P 500 equity and options study. It divides each stock’s historical input window into contiguous patches and treats each patch as a token for attention. Attention can compare distant…
This code presents a supervised autoencoder architecture for prediction from financial features. An encoder compresses normalized inputs into a bottleneck, while a decoder reconstructs the inputs as a regularizing task. An auxiliary prediction head operates…
This notebook explains an ETF stochastic discount factor model that fits a single series of weights to price returns across funds. Unlike models that separately estimate fund exposures and factor returns, it trains two networks against pricing errors: one…
This notebook describes how to create holdout predictions for a previously selected US equities model configuration. Since model choices and strategy settings were made using validation results, those same results cannot independently establish the quality…
This notebook presents GReaT, a method for generating synthetic financial tables with a language model. It serializes each row as text, fine-tunes a GPT-based model on ETF features, and parses generated text back into mixed numerical and categorical columns.…
This notebook explains how to construct model-based features for a US equities panel without letting future observations influence historical values. Its central method is an estimation schedule: models use a burn-in period, fit only on data preceding the…