Knowledge library

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 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 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 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 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…

This document explains how wavelet decompositions and rolling spectral estimates describe financial returns at different time scales. Wavelets divide returns into components spanning progressively slower periods, making it possible to see which scales…

This notebook uses double machine learning to estimate whether futures carry has an effect on subsequent returns after adjusting for volatility, momentum, and cross-sectional carry rank. It distinguishes causal explanation from predictive performance: a…

This document explains how to compare an incumbent and a candidate model in shadow evaluation before allocating capital. It fixes their roles and promotion thresholds in advance, then assesses the candidate on five dimensions: improvement in annualized…

This notebook demonstrates how to configure Feast with Parquet sources, entities, feature views, timestamps, and a time-to-live policy, then retrieve features for training examples and a live-style as-of query. It compares Feast’s output feature by feature…

The notebook introduces a supervised autoencoder for equity factor modeling. Unlike unsupervised autoencoders that learn a bottleneck by reconstructing the cross-section, this model also includes forward returns in its training objective. The target…

The notebook explains how to separate continuous price variation from jumps in intraday equity returns. It estimates daily realized variance from squared returns and uses bipower variation as a jump-robust estimate of the continuous component; their…

This notebook applies a neural stochastic discount-factor model to CME futures. In asset-pricing terms, a stochastic discount factor is a random variable whose product with each asset's return has the same expected value under no arbitrage. The model…

This configuration note distinguishes settings that appear in a backtest identity from settings that actually affect simulated trading costs. In the described case study, all registered runs use a vectorized, return-to-expiry path. The configured…

This notebook presents a cross-case-study inventory of engineered financial features across nine asset classes. It counts features and name-based families from each available feature panel, compares feature-space sizes, and summarizes the strongest…

This chapter surveys financial text representations, from dictionaries and word counts through TF-IDF, static embeddings, recurrent networks, and Transformers. It explains the trade-offs: simpler methods are fast, interpretable, and adaptable to finance,…

This notebook builds a financial retrieval-augmented generation evaluation harness with separate measures for retrieval coverage, answer faithfulness, appropriate abstention, and security behavior. It uses six hand-written fixtures, with context drawn in…

This notebook uses Optuna to tune LightGBM return models while considering both cross-sectional information coefficient (IC) and prediction turnover. A single-objective search maximizes IC for comparison; an NSGA-II multi-objective search identifies…

This notebook compares embedded, server-based, general-purpose, and trading-focused databases on financial time-series tasks: bulk writes, full scans, time-window queries, OHLCV aggregation, and trade-quote alignment. Its main lesson is that storage layout…

This educational treatment compares parametric stochastic models and bootstrap methods for generating synthetic financial paths. It covers geometric Brownian motion, jump diffusion, mean reversion, Heston stochastic volatility, and GARCH, alongside…

This notebook turns 21-day forward ETF returns into an up-or-down target and evaluates L1- and L2-regularized logistic regression on chronological walk-forward folds. It scales features using each fold’s training data, then assesses predictions with…

This notebook configures Feast as a feature store for financial and model-based data, then compares its historical and point-in-time retrievals with a transparent, hand-built join. It declares entities, timestamped sources, feature views, and a time to live,…