Knowledge library

This notebook studies how Ridge regularization choices affect information coefficient on an ETF panel and how hyperparameter search can inflate reported performance. It first evaluates a wide alpha grid across walk-forward folds, scaling features within each…

This notebook presents a current audit comparing VectorBT Pro and VectorBT OSS with ML4T on supported real-data strategy workloads. Both VectorBT editions participate in ETF allocation, USD-quoted foreign-exchange allocation, and a US equity panel. Pro also…

This notebook explains instrumented principal component analysis (IPCA), which models each ETF’s factor exposures as a shared linear function of that fund’s observed features. Alternating least squares estimates the feature-to-exposure map and the factor…

This notebook fits a PCA latent-factor model to a panel of stock returns, without using option-surface features, characteristics, or target information. PCA extracts leading directions of historical cross-sectional co-movement, estimates each stock's…

This notebook compares equal weighting with two allocation rules for US firm characteristic strategies: score weighting, which assigns more capital to higher model predictions, and conformal weighting, which reduces capital for predictions with wider…

This analysis compares learned signals for monthly cross-sectional US stock returns using accounting and market characteristics, with next-month return as the target. It reviews linear models, boosted trees, deep tabular methods, latent-factor estimators,…

This audit compares Backtrader and Zipline with ML4T on real strategy cases, using only asset and contract combinations that each engine and the frozen data bundle can represent natively. It covers ETF allocation and a US equity panel for both engines, plus…

This notebook defines and validates monthly return labels for a cross-sectional US firm-characteristics study. It checks how the data provider pairs each row’s characteristics with the return outcome, emphasizing that the panel is already aligned and must…

This notebook builds an end-to-end portfolio allocator that places a Temporal Fusion Transformer style variable-selection network ahead of an LSTM. Separate gated residual networks embed individual features, and learned soft weights combine those embeddings…

This notebook compares daily, weekly, biweekly, and monthly rebalancing for a top-ranked momentum portfolio built from a fixed ETF universe. It measures turnover and gross risk-adjusted performance from historical prices, then estimates break-even alpha by…

This overview explains a family of models for predicting returns from firm characteristics. Rather than treating each characteristic as a direct return predictor, the models assume returns are driven by a small set of unobserved common factors and estimate…

This setup document defines a weekly S&P 500 equity research pipeline that uses options market features alongside price-based signals. It specifies the eligible universe, decision and execution timing, and distinct rebalance cadences for labels with…

This document presents a pedagogical Mamba-style selective state space model for predicting ETF returns from sequences of momentum features. Unlike attention and dense temporal mixing, a state space model carries a fixed-size state through the sequence with…

This document describes a conditional autoencoder that models firm exposures to latent return factors as a nonlinear function of firm characteristics. Like IPCA, it estimates a factor structure in which predicted returns combine firm-specific exposures with…

This document explains a TabM neural network for ranking ETFs from tabular features. Its shared two-layer backbone feeds multiple members, each with a learned scaling vector and output layer; averaging their predictions aims to reduce initialization noise…

This notebook explains how volume participation limits constrain the portion of market volume an order can consume in each interval. When a parent order exceeds the permitted amount, the broker fills part of it and carries the remainder forward. A…

This chapter overview explains how gradient-boosted trees build on decision trees and random forests for financial tabular prediction. Boosting adds learners sequentially to correct earlier errors, helping capture nonlinear relationships and threshold…

This document describes a monthly US equity dataset designed for machine learning research in asset pricing. It contains 94 rank-transformed firm characteristics, including accounting measures and technical features, alongside returns. Firms are anonymized,…

This notebook examines how market impact changes a momentum strategy’s backtest results across stocks with different liquidity. It holds the order size and strategy constant, estimates liquidity and volatility from a formation period, and applies several…

This notebook introduces a published panel for machine-learning research on US equities. It contains monthly observations with 46 firm characteristics and next-month excess returns, spanning several decades. The characteristics are grouped by economic…

This chapter explains how to build and use financial knowledge graphs when questions depend on relationships such as ownership, supply chains, or contagion. It covers entity identity, typed relationship schemas, provenance, and LLM-assisted extraction from…

This notebook compares two Transformer designs for forecasting 21-day ETF returns from eight trailing-momentum features. PatchTST groups consecutive days into patches, reducing the number of attention tokens and embedding local time patterns. iTransformer…

This notebook analyzes a portfolio of equity ETFs with CAPM and Fama-French factor regressions, then expands the analysis to rolling exposures, return attribution, and residual dependence. It uses aligned daily returns and factor data, estimates regression…

This demonstration runs one dual moving-average crossover strategy through both a historical backtest engine and a live engine replaying the same daily ETF bars. It holds the strategy, inputs, parameters, and fill convention fixed, then compares the…