Knowledge library

This notebook examines the ADIA Lab causal discovery challenge as a benchmark for learning patterns within a known simulator family. Its task supplies many synthetic datasets paired with known directed acyclic graphs, then evaluates models on classifying…

This notebook explains Tail-GAN, a generative adversarial approach for creating financial return scenarios while targeting value at risk (VaR) and expected shortfall (ES). It uses differentiable sorting so gradients can pass through quantile estimates, and…

This feature-engineering notebook explains how slower information can condition faster trading signals through fundamentals, macroeconomic data, and calendar context. It outlines value ratios such as book-to-market and earnings yield, quality measures such…

This Python module describes methods for quantifying uncertainty in strategy backtests using daily out-of-sample returns. It combines stationary block bootstrap intervals for return-series statistics, a Sharpe standard error adjusted for autocorrelation and…

This notebook synthesizes registered validation results for five latent-factor estimators across a set of case studies. It first maps which estimators have results for each panel, then identifies the leading estimator by mean daily Spearman rank information…

This notebook demonstrates single-factor evaluation using real ETF data and a price-derived momentum example. It measures cross-sectional Spearman information coefficients against forward returns, compares horizons, and explains the distinction between…

This case study screens candidate financial and model-based features against forward returns for a NASDAQ-100 microstructure strategy. It calculates cross-sectional rank information coefficients through time, adjusts inference for dependence and multiple…

This chapter presents retrieval-augmented generation as a way to make language models more useful for open-ended financial research, where unsupported claims and hallucinations can be costly. It walks through the system from document ingestion and…

This notebook selects a CME futures case study from registered validation backtests spanning signal rules, allocation, and risk overlays. It compares candidates across both return horizons and chooses the configuration with the highest validation Sharpe.…

This notebook develops features that require multiple asset series. It compares Engle-Granger and Johansen tests for cointegration, explains why co-movement alone does not imply a stationary spread, and estimates hedge ratios both with a full-sample…

This notebook explains how to construct forward intraday return labels for NASDAQ-100 stocks using minute bars and NBBO quotes. It defines returns from the next bar after a signal to a specified future endpoint, uses quote midpoints to reduce bid-ask bounce,…

The document presents an operational workflow for keeping daily market data current: load an initial history, fetch a small overlapping delta, check for missing dates, and monitor freshness and validation issues. The overlap helps capture vendor revisions to…

This notebook fits a patched transformer to predict a primary label in an S&P 500 equity and options study. It divides each stock’s historical input window into contiguous patches and treats each patch as a token for attention. Attention can compare distant…

This exploratory analysis profiles hourly OHLCV data for Binance perpetual contracts alongside an eight-hour premium index. It checks dataset coverage, changing contract membership, units, missing values, and raw-bar OHLC consistency. The premium is stored…

This code presents a supervised autoencoder architecture for prediction from financial features. An encoder compresses normalized inputs into a bottleneck, while a decoder reconstructs the inputs as a regularizing task. An auxiliary prediction head operates…

This notebook explains an ETF stochastic discount factor model that fits a single series of weights to price returns across funds. Unlike models that separately estimate fund exposures and factor returns, it trains two networks against pricing errors: one…

This notebook presents a workflow for monitoring a deployed trading model using holdout predictions as an offline stand-in for live data. It validates the incoming feature panel before calculating drift, then compares reference and current windows with…

This notebook presents GReaT, a method for generating synthetic financial tables with a language model. It serializes each row as text, fine-tunes a GPT-based model on ETF features, and parses generated text back into mixed numerical and categorical columns.…

This notebook explains how to construct model-based features for a US equities panel without letting future observations influence historical values. Its central method is an estimation schedule: models use a burn-in period, fit only on data preceding the…