Knowledge library

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 describes reconstructing a NASDAQ limit order book from DataBento market-by-order messages for a single symbol and trading day. It lays out a modular engine that tracks individual order state, aggregates orders into price levels, and maintains…

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 compares structured graph-style retrieval with embedding-based vector retrieval on institutional holdings from 13F filings. It constructs a point-in-time holdings snapshot at a fixed cutoff, limits the stock universe, and creates questions…

This notebook demonstrates a workflow for connecting losing trades to the model explanations associated with their decisions. It builds a single-asset SPY feature panel from price and macroeconomic data, trains a LightGBM model on earlier observations, and…

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 constructs market microstructure measures from NASDAQ ITCH trade data, aggregates trades into intraday bars, and distinguishes liquidity proxies from order-flow signals and order-book state. It classifies individual trades with a tick rule…

This demo outlines an always-on crypto trading loop connected to Alpaca’s USD spot market. It maps a perpetual-futures case-study universe to the venue’s supported spot pairs, making clear that only a subset can be traded there. The example signal is a…

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 demonstrates covariate-drift monitoring for ETF momentum features and crypto perpetual futures features across contrasting market windows. It compares Population Stability Index, which measures changes in individual feature distributions, with…

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…

This document presents a reusable diagnostic survey for financial datasets, covering index integrity, duplicates, missingness, outliers, calendar gaps, and domain-specific anomalies. It checks time types, ordering and uniqueness, including per-symbol…

This document demonstrates connecting a five-day ETF momentum strategy to Alpaca through a shared strategy interface. The strategy tracks momentum across SPY, QQQ, and IWM and generates signals when it crosses a threshold. Broker-specific adapters handle…

This document introduces a family of firm-return models that assumes a small set of unobserved common factors explains much of the cross-section. Rather than predicting returns directly from characteristics, these models infer each firm's factor exposures…