Knowledge library

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 applies configured stop-loss, trailing-stop, and time-exit rules to the strongest validation-ranked signal or allocation strategy for each futures return horizon. Each rule is assessed on its own parent strategy, with parameters fixed in…

This notebook runs a previously selected US equities configuration on holdout predictions. The selected model, predictions, allocator, concentration, risk overlay, rebalance cadence, and transaction cost assumptions are inherited from earlier stages. Its…

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…

This document presents a cost sensitivity analysis for a fixed ETF momentum strategy. It holds the universe, weights, schedule, and simulator constant while rerunning the backtest over a range of per-leg fees. The resulting curve shows how Sharpe ratio and…

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 applies a previously selected ETF configuration to its reserved holdout window. It derives the matching holdout predictions from the selected training identity and checkpoint, then uses the strategy's existing concentration, allocation, risk…

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 document describes a daily US equities dataset sourced from NASDAQ Data Link’s Wiki Prices archive. It covers thousands of companies from 1962 through March 2018 and includes delisted firms, making it useful for historical research that needs to reduce…

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 notebook studies gradient-boosted models for forecasting returns and direction in crypto perpetuals. Its grid varies tree capacity and loss function, including squared-error, absolute-error, and Huber objectives, while scoring models at multiple points…

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 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 case study builds features for researching whether at-the-money implied variance exceeds subsequent realized variance for S&P 500 names, and whether the difference varies across securities. It combines straddle quotes with underlying prices to measure…

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…

This notebook reprices leading ETF allocation configurations across two cost grids while holding predictions, concentration, and allocation rules fixed. It compares the case study's per-share commission and half-spread model, which reflects how ETF trading…

This notebook measures how execution costs affect the already selected crypto perpetual futures configuration. It holds the model, checkpoint, entry rule, allocation, and risk control fixed, then reruns backtests across a grid of round-trip charges on traded…

This notebook compares linear models for predicting ETF returns from overlapping price-derived features. Since multiple return horizons and indicator families can encode similar information, ordinary least squares may assign unstable opposing coefficients to…

This configuration describes monthly long-short US equity portfolios ranked on firm characteristics. It specifies a complete-case universe with 46 characteristics, month-end decisions, next-open execution, equal weighting within the long and short legs, and…

This notebook introduces a stochastic discount factor model for pricing a cross-section of ETFs. Instead of separately estimating fund exposures and factor returns, it trains networks to find a common discount-factor series whose covariation with fund…