Knowledge library

This notebook presents the Rademacher Anti-Serum (RAS) protocol as a way to adjust performance estimates for selecting from a finite class of strategies or signals. Empirical Rademacher complexity estimates how well the candidate performance matrix can align…

This notebook introduces NLinear as a baseline for predicting stock returns from ordered feature windows. Each example contains 60 consecutive sessions for one stock. For every feature, the model subtracts its latest value, maps the resulting history to a…

This notebook reconstructs the prevailing national best bid and offer (NBBO) for each AAPL trade during the regular session on March 16, 2020. It chronologically combines quote and trade events, carries the latest bid and ask forward, and removes trades…

This notebook fits TabM neural models to an S&P 500 options short-straddle return target and compares three model capacities on the same feature panel used by linear and boosting approaches. TabM shares a neural backbone across ensemble members, while…

This notebook evaluates position-level controls for an S&P 500 equity-and-options allocation, comparing fixed stop losses, trailing stops, and time exits with each strategy's own no-overlay baseline. It selects an eligible strategy lineage using validation…

This document describes a foreign exchange OHLCV dataset covering G10 majors and crosses, with daily and four-hour bars from OANDA. It outlines how the data can be downloaded, loaded, filtered by pair or date range, and explored through coverage summaries…

This document demonstrates Instrumented PCA, in which asset factor loadings are modeled as linear functions of characteristics observed before the return. It uses alternating least squares to estimate the characteristic-to-loading map and realized factors,…

This notebook compares zero-shot Chronos and TinyTimeMixer forecasts with an LSTM and a penalized linear model on an ETF panel. Every model receives the same univariate historical context, but the pretrained models forecast that context series rather than…

This notebook estimates whether a crypto perpetuals premium z-score is associated with subsequent eight-hour returns under an intervention, adjusting for price volatility, funding rate, and the premium’s deviation from its recent mean. Double machine…

This notebook explains how GARCH turns volatility clustering into a per-session feature. It first uses return plots and an ARCH-LM test to check whether squared returns depend on their own lags. In a GARCH(1,1) model, the response to a new shock and the…

This study asks whether option-market quantities can rank future stock returns. Its features include implied volatility across maturities, put-call skew, term-structure slope, and the variance risk premium. Because many measures are represented in several…

This notebook adapts an adversarial stochastic discount factor design to daily U.S. equities. A portfolio-weight network forms a factor from next-day excess returns, while a competing moment network helps train the pricing kernel; a separate beta network…

This tutorial constructs a continuous futures series from individual ES contracts. It identifies the front month using trading volume, with a monotonic roll constraint, and compares volume-based switching with calendar-based timing. It explains why outright…

This notebook turns a real supply-chain knowledge graph and institutional 13F holdings into machine-learning features. It derives network measures such as centrality and dependency indicators, estimates supplier concentration and competitive exposure, and…

This notebook compares LightGBM with a minimal multilayer perceptron, TabM, and optionally TabPFN for cross-sectional ETF return prediction. Models are evaluated on shared walk-forward folds using rank information coefficient, with chronological validation…

This notebook develops a framework for translating market activity into modeled trading costs and strategy capacity. It introduces the square-root impact model, identifies volatility and average daily volume as key inputs, and distinguishes quantities…

This notebook introduces a workflow that connects standardized ETF data loading, a feature registry, and signal diagnostics. It shows how to discover indicator metadata, compute features using defaults or explicit parameters, and store configurations for…

This notebook compares a Random Forest with XGBoost, LightGBM, and CatBoost on a panel of anonymized firm characteristics and returns. The dataset supplies temporal training, validation, and test splits. Models are evaluated using Spearman rank information…

The notebook explains value at risk as a loss quantile and conditional value at risk, or expected shortfall, as the average loss beyond that threshold. It estimates one-day tail risk for a broad equity ETF using four approaches: empirical historical…

This notebook describes how to produce holdout predictions for a selected crypto perpetuals funding strategy. It resolves the configuration from validation results, carries its checkpoint choice forward, and builds a new training specification using data…

This notebook assesses a selected S&P 500 equity and options strategy by reconstructing its validation configuration from registered, full-coverage candidates. It traces the funnel from equal-weight baseline through allocation, risk controls, and cost…

This notebook presents a cross-case-study inventory of engineered financial features and the strongest registry information coefficient for each market study. It counts features and feature-name families, compares feature-space sizes, and summarizes…

The document explains how to identify market regimes by dividing returns into overlapping windows and treating each window as an empirical distribution. It measures the distance between windows through matched, sorted quantiles, so differences across the…

This study builds a minute level measure of order flow pressure from market by order data for NVDA. It signs order additions, cancellations, and fills by side, weights events by size and distance from the midpoint, and smooths the resulting pressure series.…