Knowledge library

This notebook uses principal component analysis on a liquid subset of US stocks to build gross-normalized eigenportfolios and study their role as latent risk factors. It examines explained variance, interprets components through sector ETF correlations,…

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 builds a monthly ETF rotation backtest from portfolio returns through to simulated trades. It ranks ten funds using trailing risk-adjusted returns, applies a Treasury yield-curve regime to choose between the leaders and a defensive bond…

This configuration describes a daily, long-short US equity strategy that ranks a broad stock universe by model predictions, buys the top decile, and shorts the bottom decile with equal weights within each group. It specifies next-session open execution,…

This notebook turns model rankings into simple crypto perpetual portfolios. At each funding timestamp, an entry rule selects contracts from the top or bottom of the ranking and assigns equal weights. Equal weighting provides a reference point: comparisons…

This notebook shows how to translate gross returns into net performance under explicit trading, financing, and fund-expense assumptions. Its cost stack includes spread and market impact, commissions and exchange fees, margin interest, stock borrow,…

This study re-prices leading ETF allocation and risk-control combinations across two cost grids while holding each registered strategy configuration fixed. One grid uses basis points per trade; the other uses per-share commission and half-spread, the…

This notebook compares six long-only portfolio allocations for a multi-asset ETF universe. It uses Ledoit–Wolf covariance shrinkage to reduce the sampling variation that can destabilize optimization, and compares maximum Sharpe, minimum variance, risk…

This material explains how to evaluate a strategy chosen on validation data using predictions from a separate 2021 holdout. It keeps the selected allocation, concentration, risk controls, trading schedule, and costs fixed, changing only the predictions…

This notebook explains a conditional autoencoder that models stock returns as characteristic-dependent factor loadings multiplied by latent factors. It first estimates characteristic-managed portfolio returns with a joint cross-sectional least-squares fit,…

This notebook studies how allocation rules affect ETF strategies after a model has already ranked assets. It holds the prediction sets and rebalance calendar fixed, selects the same top-k basket for each comparison, and varies the weights assigned within…

This notebook tests whether portfolio construction can rescue a cost-defeated, every-bar NASDAQ-100 strategy. It selects leading baseline predictions using the declared selection universe, then runs the allocation comparisons on the full universe. The sweep…

This notebook takes leading validation prediction configurations from an equal-weight signal-stage baseline and tests alternative allocation methods on the same predictions. It compares score-based and conformal weighting with inverse volatility, risk…

This notebook evaluates how registered ETF prediction sets perform when converted into portfolios. It crosses each validation prediction set with declared entry schemes, such as different top-k holdings, while keeping the trading calendar, rebalance cadence,…

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 evaluates validation predictions as long-only equity portfolios, using equal-weight top-K selection and a decision schedule determined by each label's cadence. It checks the close-to-next-open event ordering with a seeded random-signal run,…

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…

This shared utility module provides a common gradient-boosting pipeline for quantitative research. It defines capacity presets and maps canonical parameter names into the corresponding settings for LightGBM, XGBoost, CatBoost, and scikit-learn histogram…

This notebook explains Tail-GAN, a generative adversarial approach for creating financial return scenarios while targeting value at risk (VaR) and expected shortfall (ES). It uses differentiable sorting so gradients can pass through quantile estimates, and…

This Python module describes methods for quantifying uncertainty in strategy backtests using daily out-of-sample returns. It combines stationary block bootstrap intervals for return-series statistics, a Sharpe standard error adjusted for autocorrelation and…

This notebook synthesizes registered validation results for five latent-factor estimators across a set of case studies. It first maps which estimators have results for each panel, then identifies the leading estimator by mean daily Spearman rank information…

This notebook selects a CME futures case study from registered validation backtests spanning signal rules, allocation, and risk overlays. It compares candidates across both return horizons and chooses the configuration with the highest validation Sharpe.…

This notebook explains an ETF stochastic discount factor model that fits a single series of weights to price returns across funds. Unlike models that separately estimate fund exposures and factor returns, it trains two networks against pricing errors: one…

The document describes using the Graph-based Coalition Structure Generation algorithm, GCS-Q, to cluster assets represented as a signed, weighted graph of return correlations. The motivation is that common clustering approaches can lose information when they…