Knowledge library

This notebook uses SPY daily returns to explain frequency-domain analysis and its limits as a source of trading features. A wavelet decomposition separates variation into bands at successively slower time scales, while spectral estimates describe how return…

This notebook compares three ways to use market regimes in a forecasting model: omit regime information, add the filtered probability of a volatile state as a feature, or fit separate experts for hard-assigned regimes. It builds regime probabilities from a…

This case study uses linear models as a baseline for predicting forward returns across a broad US equity panel. It explains why overlapping price and volume features create collinearity, making ordinary least squares coefficients unstable, and compares Ridge…

This analysis compares registered double machine learning treatment effects from nine trading case studies. It loads current results from each study’s registry, checks registry integrity and duplicate labels, and makes coverage explicit. Effects and…

This notebook presents a framework for assessing an ETF allocation backtest using daily portfolio returns and a matched SPY benchmark. It covers risk and return measures such as Sharpe, Sortino, Calmar, and maximum drawdown; rolling metrics; drawdown…

This notebook explains how model uncertainty can add information beyond a point estimate. It fits a stochastic volatility model in which latent log volatility evolves over time and returns follow a Student-t distribution. Posterior spread, interval width,…

This evaluation harness separates four kinds of failure in a financial retrieval-augmented generation pipeline: retrieving relevant evidence, grounding answers in that evidence, refusing unanswerable questions, and resisting unsafe instructions in retrieved…

This notebook presents a multi-round bull-versus-bear debate for a contested probability forecast. Each side argues from shared research-agent summaries, sees the other side's prior position, and revises its probability. The process stops when the forecast…

This exploratory analysis introduces option contracts and chain structure, then profiles a 2020 sample of S&P 500 options across eight underlyings. It explains moneyness, intrinsic and time value, implied volatility, Greeks, and how strike, expiration, and…

This analysis reads a registered backtest pipeline for a monthly US equity strategy based on firm characteristics. It reports Sharpe uncertainty, probability and deflated Sharpe measures, paired strategy comparisons, holdout decay, and a cost analysis…

This notebook builds fitted-model features from foreign-exchange price histories, complementing indicators calculated directly from past prices. It describes three approaches: a state-space filter that estimates a slowly changing price level and related…

This notebook describes a workflow for turning S&P 500 companies’ quarterly MD&A disclosures into trading features. It uses filing acceptance dates as the point-in-time anchor, collapses multiple filings for a company on the same date to the latest period,…

This chapter surveys deep learning for time series through a practical question: when does observation order contain information that cross-sectional prediction misses, and which model designs can use it? It covers recurrent networks and their sequential…

This read-only analysis brings validation predictions from several model families together to assess whether they are complete, how well they rank stocks, whether ranking skill persists across folds, and whether apparently similar models contribute distinct…

This notebook applies TabM, a tabular neural architecture that shares a two-layer feature network across ensemble members. Each member scales the shared representation with its own learned vector and uses its own output layer; averaging member predictions…

This notebook explains risk-premium PCA, which modifies the return covariance matrix by adding a weighted outer product of the training-period mean returns. Its leading eigenvectors define static factor portfolios; raising the weight tilts the factors toward…

This notebook uses SHAP to explain Ridge regression forecasts of ETF returns. For a linear model, feature attributions have a closed-form relationship to the fitted coefficients and deviations from background feature values, allowing the analysis to check…

This notebook explains Kalman filtering as a way to extract features from financial prices. A local linear trend state tracks both a latent price level and its slope, while an observation model accounts for noisy closes. Each update produces a filtered…

This notebook presents instrumented principal component analysis (IPCA), a latent-factor approach to forecasting returns from firm characteristics. Instead of assigning each characteristic a direct return weight, IPCA models each firm’s factor exposures as a…

This utility describes a method for explaining strategy returns with Fama–French three-factor or five-factor models, optionally including momentum. It aligns daily factor data with strategy returns, detects the return frequency, and compounds factor returns…

This analysis compares registered model families on a cross-asset ETF panel using walk-forward validation results. Models predict 21-trading-day returns and are assessed by cross-sectional information coefficients, which measure how well forecasts rank funds…

This notebook analyzes NASDAQ TotalView-ITCH messages to describe market-wide activity and create reusable trade data. It counts messages by type, then enriches order-execution messages by mapping stock-location identifiers to tickers and order references to…

This notebook builds price-derived features for ranking a broad US stock universe. It describes momentum, volatility, moving-average, and technical-indicator families, then evaluates them against a one-session forward-return label. Its central design rule is…

This study uses ten trading days of NVDA market-by-order data to examine how daily trading activity affects bar sampling. It filters trades to regular US market hours, classifies aggressor sides from venue labels, and reports day-to-day variation in trade…