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.
1,124 documents
This case study constructs features for comparing variance risk premiums across S&P 500 stocks using 30-day at-the-money straddles and underlying price data. It measures implied volatility against realized volatility at several horizons, adds option-market…
This case study explains how to generate predictions for a holdout period after model and strategy choices have been made on validation data. It resolves the previously selected configuration, reconstructs its training specification, and fits it again using…
This notebook builds an LSTM allocator that maps recent ETF return and volatility features directly to long-only portfolio weights. A softmax output keeps weights nonnegative and fully invested, while a differentiable Sharpe-ratio objective trains the…
This notebook introduces EdgarTools for exploring SEC filings by company and filing type. It demonstrates looking up issuers using stable CIK identifiers, retrieving annual and quarterly reports, extracting XBRL financial statements, and inspecting insider…
This notebook applies principal component analysis to changes in eight Treasury constant-maturity yields, spanning one to thirty years. It removes forward-filled calendar rows with no yield changes and standardizes the remaining observations before PCA, so…
This notebook describes an LSTM sequence model for predicting stock returns from ordered windows of recent features. Each example uses 60 consecutive sessions for one stock; windows that cross listing gaps, halts, or other interruptions are excluded so that…
This analysis checks whether daily option data can support a weekly strategy that sells at-the-money straddles on S&P 500 constituents, delta hedges shares, and holds contracts to expiration. It explains how calls and puts form a straddle, how implied and…
This reference summarizes listed contract months for 35 CME futures products across equity indexes, Treasuries, energy, metals, currencies, interest rates, agriculture, livestock, and crypto. It explains the exchange’s month-code system and distinguishes…
This notebook synthesizes registered validation results for five latent-factor estimators—PCA, IPCA, CAE, SDF, and SAE—across nine case studies. It first reports which estimators have results in each panel, then identifies the highest mean daily Spearman…
This document describes a pipeline for downloading official Binance USD-M perpetual funding records, normalizing them, and caching them in a columnar file. Monthly archives are fetched concurrently for the requested symbols and date range. The records are…
This notebook applies TabM, a weight-sharing neural ensemble, to a US equities panel represented as one feature row per stock and session. A shared two-layer backbone feeds multiple members, each with its own scaling vector and output layer; averaging their…
This analysis selects one configuration from a frozen pool of crypto perpetual-futures candidates spanning four prediction labels and multiple strategy stages. It chooses the highest validation Sharpe and treats label, split, and execution details as part of…
This notebook defines forward-return targets for ranking US stocks and explains how to make their horizons correspond to actual trading sessions. It uses split- and dividend-adjusted prices for returns, but uses the contemporaneous printed close and dollar…
This pipeline constructs financial features for an ETF deployment workflow from OHLCV prices and a yield curve. It derives returns and risk-adjusted returns across multiple lookback periods, momentum acceleration and volatility ratios, then adds common…
This script gathers institutional holdings from SEC 13F filings in two ways: it can retrieve filings for a curated set of managers or download the SEC’s quarterly bulk files. Both paths normalize holdings into a shared schema, with the bulk path designed for…
This notebook applies principal component analysis to a panel of US stock returns to represent broad shared movements with a smaller set of factors. Each factor is a direction of common variation, and each stock’s loading describes its exposure to that…
This notebook compares gradient boosting configurations for crypto perpetual futures using features built around the premium of perpetual contracts over spot. It varies tree capacity and loss function, including squared, absolute, and Huber losses, and…
This notebook explains how to extract insider transactions from raw SEC Form 4 XML for quantitative equity research. It uses an XML parser to keep each trade’s code, date, share count, price, and direction attached to its transaction block, while separately…
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 develops model-based features for crypto perpetual futures: per-asset conditional volatility forecasts from GJR-GARCH and a market-wide stressed-regime probability from a two-state Gaussian hidden Markov model fitted to funding settlements.…
This case study lays out a research pipeline for crypto perpetual futures, treating funding payments exchanged between long and short positions at regular settlements as a potential return source. It describes data and model stages from label construction…
This analysis compares feature triage results across nine trading case studies and asks whether features that pass screening predict stronger strategies. Its screening combines Benjamini–Hochberg false-discovery control with a second route based on a case…
This notebook describes DeePM, an end-to-end ETF portfolio policy designed to improve performance across varying market conditions. Its components include volatility-normalized return and trend features, an LSTM-based model, variable selection, conditioning…
This case study applies causal estimation to ETF momentum and asks whether its effect on forward returns varies with market volatility. It uses a continuous momentum treatment, a 21-day forward-return outcome, volatility and yield-curve controls, and a…