Knowledge library

This notebook synthesizes registered validation results for five latent-factor estimators—PCA, IPCA, CAE, SDF, and SAE—across nine case studies. It first reports which estimators have results in each panel, then identifies the highest mean daily Spearman…

This notebook applies TabM, a weight-sharing neural ensemble, to a US equities panel represented as one feature row per stock and session. A shared two-layer backbone feeds multiple members, each with its own scaling vector and output layer; averaging their…

This notebook defines forward-return targets for ranking US stocks and explains how to make their horizons correspond to actual trading sessions. It uses split- and dividend-adjusted prices for returns, but uses the contemporaneous printed close and dollar…

This pipeline constructs financial features for an ETF deployment workflow from OHLCV prices and a yield curve. It derives returns and risk-adjusted returns across multiple lookback periods, momentum acceleration and volatility ratios, then adds common…

This script gathers institutional holdings from SEC 13F filings in two ways: it can retrieve filings for a curated set of managers or download the SEC’s quarterly bulk files. Both paths normalize holdings into a shared schema, with the bulk path designed for…

This notebook applies principal component analysis to a panel of US stock returns to represent broad shared movements with a smaller set of factors. Each factor is a direction of common variation, and each stock’s loading describes its exposure to that…

This notebook explains how to extract insider transactions from raw SEC Form 4 XML for quantitative equity research. It uses an XML parser to keep each trade’s code, date, share count, price, and direction attached to its transaction block, while separately…

This case study applies causal estimation to ETF momentum and asks whether its effect on forward returns varies with market volatility. It uses a continuous momentum treatment, a 21-day forward-return outcome, volatility and yield-curve controls, and a…

This notebook introduces NLinear as a simple sequence-model baseline for predicting returns across US equities. Each example uses a window of 60 consecutive sessions for one stock, with features arranged in time order. For each feature, the model subtracts…

The document describes a walk-forward study of TabM neural networks predicting monthly returns from firm characteristics. Because the shared training runner fixes the objective to squared error for regression and cross-entropy for classification, the…

The document presents a workflow connecting standardized market-data loaders, a registry of prebuilt features, and signal diagnostics. Feature computation can be specified by names, parameter dictionaries, or YAML, supporting repeatable pipelines and…

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 feasibility analysis checks whether the data and assumptions can support a weekly strategy that ranks S&P 500 stocks by at-the-money implied volatility and buys the leading shares. Options supply the signal, but positions are held in equities. The…

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 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 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 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 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…