Knowledge library

This chapter argues that durable trading research depends on a disciplined process that can adapt to changing markets, noisy evidence, and implementation costs. It introduces distinctions among structural breaks, regimes, data drift, and concept drift, then…

This evaluation notebook screens financial and model-based features for their relationship to the next eight-hour return of crypto perpetual futures. It builds the evaluation sample from walk-forward validation windows, keeps the final holdout outside the…

This notebook checks whether a daily US equity ranking strategy has enough usable data for a fair evaluation before fitting any model. It explains how to form a point-in-time universe with historical constituents, using printed prices and prior-session…

This notebook studies how regularization affects a CME futures model built from correlated feature families, including carry, rolling Sharpe, momentum, and volatility measures. It compares Ridge, Lasso, and ElasticNet: Ridge shrinks coefficients while…

This notebook describes adapting Diffusion-TS to generate synthetic ETF return sequences. Its denoiser predicts the clean sequence as a sum of polynomial trend, Fourier seasonal, and residual components, while a combined time-domain and Fourier-domain loss…

The chapter argues that repeatable research discipline is a more durable source of trading advantage than any individual strategy. It presents a workflow built around falsifiable hypotheses, out-of-sample evaluation, and corrections for multiple testing to…

The document explains why TabM is used to model perpetual funding panel data: its shared neural backbone combines all input features, while nonlinear layers can represent interactions. Unlike a tree that chooses one feature at each split, this design can use…

This notebook models institutional equity holdings from SEC 13F filings as a graph linking managers to securities, with sector relationships for aggregation. Its schema retains both the quarter-end report date and the later filing availability date: the…

This evaluation workflow screens financial and model-based features against forward returns for a NASDAQ-100 intraday panel. It first reserves the final test period, excluding observations whose forward labels would settle within that window. For each…

This analysis estimates whether skip-recent momentum is associated with a causal change in ETFs’ forward returns, rather than asking how well momentum predicts returns. Double machine learning models the outcome and the treatment using declared confounders,…

This benchmark-generation method creates an equal-weighted daily return series from the declared assets in a case study. It first takes each symbol’s last close for each session, then calculates close-to-close returns and averages the available returns…

This analysis profiles a fixed universe of 100 ETFs across nine asset groups. It checks coverage by fund and year, confirms that the price panel matches the group configuration, reviews nulls and OHLC ordering, and compares trading volume across groups. It…

This document presents the PatchTST neural-network backbone for forecasting multivariate time series. It divides each input series into overlapping patches, projects each patch into a learned feature space, adds positional encodings, and processes the…

This document explains how to measure returns around events such as signal triggers, earnings announcements, or macro releases. Its worked example uses momentum breakouts in liquid ETFs. For each event, a market model fitted to a pre-event estimation window…

This notebook estimates whether the premium z-score of a crypto perpetual affects subsequent eight-hour returns when three declared drivers—price volatility, funding rate, and average premium deviation—are held fixed. Because treatment is continuous, the…

This notebook presents an unconditional Sig-Wasserstein GAN for generating financial time series. It replaces a learned real-versus-generated discriminator with a distance based on expected path signatures, mathematical summaries of a path’s sequential…

This notebook uses daily ETF returns to explain how GARCH models capture volatility clustering and turn it into a session-by-session feature. It first uses visual diagnostics and an ARCH-LM test to assess whether squared returns depend on their own lags. A…

This notebook explains how to rebuild a single-symbol NASDAQ limit order book from message-by-order data. It processes adds, executions, cancellations, deletes, and replacements while maintaining the remaining size of each live order. Replacements require…

This source module provides loaders for U.S. equity prices, index data, options, market microstructure, fundamentals, and institutional holdings. Its most substantive methodological detail concerns resampling intraday trade data: open, high, low, close, and…

This notebook explains how to reconstruct a model experiment catalog from a case-study registry and compare its contents with MLflow. It follows experiments through training runs, prediction sets, and backtest runs, linking each stage to the preceding one.…

This exploratory analysis profiles hourly OHLCV for crypto perpetual contracts alongside an eight-hour premium index. It examines symbol and date coverage, changing contract membership, premium units and distribution, OHLC integrity, missing values, and…

This notebook compares ordinary least squares with Ridge, LASSO, and Elastic Net for predicting forward returns across an ETF universe. All models use the same walk-forward folds, with a purge gap matched to the label horizon, so differences can be…

This chapter outlines how to build time-consistent fundamentals and alternative-data research across equities, macroeconomic series, futures, crypto, and prediction markets. Its central methods are bitemporal storage and as-of queries, source-aware…

This notebook builds the long-only, fully invested mean-variance framework for a diversified ETF universe. It constructs feasible portfolios and an efficient frontier, then solves for maximum-Sharpe and minimum-variance weights using estimated returns and…