Knowledge library

This notebook screens candidate features for a CME futures case study against forward returns. For each feature, it computes cross-sectional rank correlations between feature values and subsequent returns, summarizes the correlation over dates, adjusts…

This notebook clusters monthly macroeconomic indicators to describe market conditions independently of equity returns. It aligns FRED series to month-end observations, standardizes unemployment, the federal funds rate, the yield-curve spread, and…

This notebook adapts Diffusion-TS to generate synthetic daily ETF return sequences. Its denoiser predicts the original series and decomposes that prediction into a polynomial trend, selected Fourier components, and a residual. A combined time-domain and…

This notebook builds a monitoring workflow for a deployed financial model using real holdout artifacts. It checks that incoming feature data is populated and structurally valid before measuring drift, then compares reference and current windows. Feature…

This notebook explains how to interpret Ridge regression forecasts for an ETF panel with SHAP. For a linear model, each feature’s attribution is its coefficient times the feature’s deviation from its training-background mean, so the attributions can be…

This notebook explains how a conditional autoencoder extends an instrumented principal component model: a neural network maps stock characteristics to factor exposures, while the model retains the same exposure-times-factor-return structure. It fits the…

This study screens financial and GJR-GARCH volatility features against forward equity returns. For each session, it calculates the rank correlation across stocks between a feature and subsequent returns, then assesses average association, consistency across…

This reference describes two financial news corpora used for sentiment analysis, text-feature development, and experiments linking news to returns. FNSPID connects headlines with stock tickers and covers a broad historical span; a smaller sample is available…

This notebook demonstrates deep hedging for a short European call. It simulates underlying price paths with geometric Brownian motion, calculates Black-Scholes delta as a benchmark, and trains a semi-recurrent neural network to choose hedge positions. The…

This notebook shows how to test whether four managed-portfolio strategies add explanatory power for SPY returns after controlling for broad movements captured by ten principal components. The candidate portfolios rank ETFs by recent momentum, low volatility,…

The document explains instrumented principal component analysis for modeling ETF returns. Unlike ordinary PCA, which assigns each fund fixed factor exposures, IPCA makes each fund’s exposures a shared linear function of its observable features. It estimates…

This notebook compares predictive models for ranking NASDAQ-100 stocks by their next 15-minute return using intraday microstructure information such as spreads, depth imbalance, signed volume, and price impact. It selects one representative prediction set…

This evaluation screens option-surface, price-derived, and conditional-volatility features against forward equity returns. For each session it computes a cross-sectional rank correlation between feature values and subsequent returns, then examines the time…

This analysis assesses whether signals selected in multiple trading case studies appear dependable enough for further use. It compares validation information coefficients (IC) with holdout evidence, examines stability and reproducibility, and contrasts…

This tutorial surveys features built from asset price and volume histories, including returns across horizons, trend and reversal measures, several volatility estimators, volatility regimes, liquidity, tail risk, and cross-sectional normalization. It…

This notebook explains instrumented principal component analysis (IPCA) for an equity panel with option-surface features. Unlike ordinary PCA, which estimates each stock's exposures from its return history, IPCA models exposures as a shared linear function…

This notebook outlines a pipeline for extracting supplier, customer, and competitor relationships from company 10-K filings and storing them as a Neo4j knowledge graph. A local language model proposes structured subject-predicate-object triples, after which…

This document describes registry checks for deciding whether to reuse or rerun model training and backtests. Runs are identified by hashes derived from their specifications; a run is reusable only when its expected artifacts are present. Missing pieces mark…

This notebook demonstrates fine-tuning a transformer for financial named entity recognition, turning text spans into structured organization, person, money, date, and percentage fields. It introduces BIO boundary labels and explains how word-level…

This notebook studies gradient boosting for cross-sectional prediction of CME futures returns. It varies tree capacity through leaf-count profiles and compares squared-error, absolute-error, and Huber objectives, which differ in how strongly extreme…

This notebook adds a temporal convolutional network (TCN) to a comparison of sequence models for forecasting cryptocurrency perpetual funding premiums. Causal padding ensures each position uses only current and earlier observations, while dilated…

This notebook compares four ways to retrieve passages from a corpus of company filing text: BM25, dense embeddings, reciprocal rank fusion (RRF), and a cross-encoder reranker. RRF combines ranked lists rather than raw scores, so it can join lexical and…

This document presents an end-to-end forecasting architecture that combines a research panel, an aggregate forecast, an adversarial debate, and a supervisor. The supervisor identifies disagreements, searches for clarifying evidence, and proposes a…

This case study evaluates daily cross-sectional signals across a broad US stock universe and lays out a long research pipeline, from point-in-time data and engineered features through model comparison, portfolio construction, costs, and holdout assessment.…