Knowledge library

This notebook presents instrumented principal component analysis as a model of cross-sectional returns. Instead of assigning each firm’s characteristics direct return weights, IPCA estimates a shared linear map from characteristics to factor exposures,…

This notebook compares four neural network designs on the same task: predicting the next daily return from a window of past returns, pooled across eight exchange-traded funds. It describes how a dense network, a one-dimensional convolution, an LSTM, and a…

This notebook evaluates sequence models for ranking S&P 500 stocks using windows of their option-surface and price histories. It pairs a normalized linear model, which has no recurrent memory, with an LSTM whose gates carry information through time. The…

This notebook explains why ordinary t-tests can overstate the strength of an information coefficient (IC) when daily IC observations are dependent. It constructs a momentum signal on ETF data, examines the IC autocorrelation, and introduces Newey–West…

This notebook demonstrates a unified OHLCV interface across Yahoo Finance, WikiPrices, and FRED, then uses the sources for current ETF data, long-history equities, and macroeconomic series. It outlines a priority-ordered fallback approach for acquisition and…

This notebook fits a long short-term memory network to NASDAQ-100 microstructure features to predict forward returns over several minute-scale horizons. Unlike a flattened linear model, an LSTM processes each observation in sequence and learns how much past…

This chapter explains how to evaluate financial machine-learning models while respecting the information available at each decision time. It distinguishes training, validation, and a final holdout, and explains why randomly shuffled k-fold validation lets…

This notebook queries registries for multiple trading case studies and produces cross-dataset tables for signal, allocation, cost, and risk comparisons. It reports diagnostics on the validation-ranked configuration cluster, including the top-to-tenth Sharpe…

This capstone notebook compares equal weight, inverse volatility, mean-variance optimization, and hierarchical risk parity using a common signal on a diversified ETF universe. A rolling Ridge model uses momentum, moving-average distance, and volatility…

This notebook builds N-BEATS, a fully connected neural forecasting architecture that processes a whole input window and predicts a future path. Its interpretable variant constrains blocks to express components through polynomial trend and Fourier seasonality…

This notebook isolates portfolio allocation by fixing each case study’s selected signal and comparing allocation-stage backtests. It considers methods such as equal weight, inverse volatility, score weighting, mean-variance optimization, risk parity, and…

This notebook compares portfolio allocation methods across case studies while holding each study’s selected signal prediction fixed. It loads allocation-stage backtests tied to the same spine prediction, then examines methods such as equal weight, inverse…

This notebook examines how rebalancing cadence affects turnover, gross performance, and cost-adjusted results for a top-ranked momentum portfolio of ETFs. It estimates turnover from historical target-weight changes at daily, weekly, biweekly, and monthly…

This notebook compares LightGBM models with a ridge baseline for predicting ETF returns over two horizons. It explains why trees can discover regime-dependent interactions, such as momentum behaving differently under market stress, while greedy splits can be…

This notebook evaluates a DeFi total value locked series as alternative data for trading ether. It organizes the review around four questions: whether the series relates to forward returns, whether the data is sound and reconstructible, whether its use is…

This notebook describes a 15-minute backtest workflow for predictions on NASDAQ-100 stocks. It first runs a random-signal plumbing check: because random trading should lose after costs, a persistently profitable result can indicate problems such as…

This notebook compares portfolio allocations from PyPortfolioOpt, Riskfolio-Lib, and skfolio using the same ETF return panel. It fits allocators on a training period, checks that equivalent objectives and aligned weights agree within solver tolerances, and…

This notebook builds forward-return labels for a strategy that reads listed-option signals and trades the underlying shares. It adjusts daily share prices for splits and dividends, uses a persistent security identifier, and defines returns between executable…

This notebook estimates how much an equal-weight portfolio of surviving US equities can overstate returns relative to a portfolio that includes firms leaving the dataset. It first examines when the panel records exits, finding that the usable exit history…

This notebook studies how transaction costs affect a frequently rebalanced NASDAQ-100 strategy. It first applies basis-point cost assumptions to existing pre-cost backtests to show how Sharpe changes as costs rise. It then compares full-universe and…

This notebook explains instrumented principal component analysis as a way to make latent-factor loadings depend on observable stock characteristics rather than assigning each stock a fixed loading. A shared mapping from characteristics to factor exposures…

This notebook constructs the long-only, fully invested Markowitz framework, including feasible portfolios, the efficient frontier, maximum-Sharpe and minimum-variance solutions. It explains how portfolio variance depends on the covariance matrix and how…

This notebook turns model rankings into simple crypto perpetual-futures portfolios so later experiments can measure the effect of changing sizing, costs, or risk controls. It applies entry rules such as selecting the highest-scored contracts for longs and…

This notebook demonstrates how to tune StopLoss, TakeProfit, and TrailingStop rules for a momentum portfolio while preserving a chronological separation between calibration and evaluation. It tests individual rule widths and joint combinations using…