Knowledge library

This notebook develops forward return labels for short at-the-money straddles on S&P 500 stocks. Since the daily panel’s nominal 30-day straddle represents a different contract each session, a shifted price series would mix instruments. The label instead…

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 analysis compares feature-triage ledgers across nine trading case studies. It distinguishes Benjamini–Hochberg false-discovery-rate significance from a second route to a PROCEED decision, which combines a case-specific effect-size floor with fold sign…

This notebook fine-tunes three transformer checkpoints for three-class financial sentence sentiment and compares their accuracy, macro F1, confusion matrices, parameter counts, and training time. It uses a stratified train, validation, and test split of the…

This document records a structured bull and bear debate about whether the Federal Reserve will raise its target rate during 2026. Three agents assess the question, with arguments drawing on Fed projections, inflation, labor-market conditions, forecasts from…

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 document introduces two latent-factor approaches for futures returns. Principal component analysis finds uncorrelated directions that explain the most return variation, while a neural stochastic discount factor seeks combinations related to the…

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 notebook screens candidate features for a strategy that sells at-the-money straddles and holds them to expiration. For each trading day, it compares the cross-sectional ranking of available names by a feature with the ranking of their eventual straddle…

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 document describes ways to turn dated entity records into arrays for latent-factor case studies. One method keeps only entities that meet a coverage threshold learned from an eligibility dataset, producing a consistent entity axis with missing…

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 notebook inventories model-based feature columns across nine market case studies by reading Parquet schemas rather than loading full datasets. It excludes identifier fields, counts columns by market, and assigns feature families through tokens in their…

This notebook explains how searching across many factors or strategies inflates the apparent performance of the selected winner, even when individual estimates are computed correctly. A simulated set of noise factors illustrates that the largest information…

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 compares gross and net Sharpe across strategy cost sweeps drawn from case-study backtests. For each deployed strategy configuration, it reads the assumed per-leg cost and rebalance cadence from recorded artifacts, interpolates Sharpe at the…

This record documents a multi-agent research run on whether the United States would enter a recession by the end of 2026 under a specified resolution rule based on consecutive quarterly GDP contractions or an NBER recession date. Three agents searched for…

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…

Risk-Premium PCA modifies the covariance matrix by adding a weighted outer product of the training mean-return vector. Its leading eigenvectors define static factor portfolios: ordinary PCA is recovered when the weight is zero, while positive weights tilt…

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 notebook uses a Gaussian mixture model to group months by the joint behavior of nine factor and asset-class return series. It standardizes monthly observations, drops months with missing values, and compares candidate cluster counts using BIC, AIC, and…

This notebook builds a forecasting agent that alternates between reasoning and actions in a ReAct loop. Its action space is deliberately small: the agent can search for evidence or submit a probability forecast. A shared two-method interface lets the loop…