Knowledge library

This notebook demonstrates an operational cycle for an ETF prediction strategy: refresh market data, recompute financial features, retrain a Ridge model, save deployment artifacts, generate current cross-sectional predictions, replay the predictions through…

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 isolates results for the LEAN engine from an audit comparing retained real-strategy runs with matching ML4T Backtest profiles. It identifies four supported workloads: ETF allocation, crypto perpetual funding, USD-quoted FX allocation, and a US…

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 explains Hierarchical Risk Parity as an alternative to mean-variance allocation when covariance estimates are noisy. HRP clusters assets using correlation distances, orders them according to the hierarchy, then recursively bisects the ordered…

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 notebook assesses a weekly S&P 500 options straddle strategy using registered backtests and a fixed selection process. It resolves the selected configuration from the registry after applying a liquid-universe restriction, then evaluates validation and…

This document explains how to create predictions for a reserved NASDAQ-100 microstructure holdout period using a configuration selected on validation data. It refits the chosen model using only history that ends before the holdout, with a label buffer wide…

This case study fits regularized linear models to returns from short at-the-money straddles held to expiry. Because premium income caps the gain while losses can grow without bound, a few severe losses dominate a squared-error regression objective. The…

This notebook uses real ETF data to assess whether a momentum signal remains useful under reasonable changes to its lookback, market regime, and implementation. It computes cross-sectional information coefficients between momentum and forward returns, then…

This notebook builds a monthly model matrix for a US firm-characteristics study from a released panel whose characteristics have already been ranked across firms. It adds composites and interactions using values in the same firm-month row, without fitting…

This notebook constructs four model-based features for S&P 500 option research: next-day volatility forecasts from GJR-GARCH and stochastic volatility, plus the differences between each forecast and the option market’s implied volatility. It explains how the…

This document fits TabM, a weight-sharing neural ensemble, to option-derived features for predicting forward equity returns and risk-adjusted returns. Its members use one shared two-layer network, with separate activation scaling and output heads, then…

This document presents a structured state record for research agents, separate from their conversation transcript. The record stores the question and cutoff, gathered evidence, unresolved questions, tool-call history, gate outcomes, and synthesis status.…

This document studies position-level exits added to an existing crypto perpetual strategy: fixed stop losses, trailing stops, and time-based exits. It explains that these controls only close positions selected by the underlying strategy, and evaluates them…

This document explains how a forecasting agent’s search interface can constrain its evidence and make its activity auditable. A provider-neutral protocol returns typed results with source, publication date, relevance score, and content, while a cutoff filter…

This notebook examines linear-model regularization for predicting returns across currency pairs. It distinguishes collinearity among input features from dependence among the assets: currency pairs share underlying currencies, so their returns and rankings…

This module describes checks for whether prediction and backtest inputs cover the sessions and folds specified by a study. Relative checks compare candidate models with one another, so they can miss a shared failure: stale fold boundaries or an upstream join…

This notebook studies a supervised autoencoder for predicting forward returns from firm characteristics. It retains a narrow neural-network bottleneck but removes the conditional-factor interpretation and pricing restriction used by related models. The…

This notebook converts FX pair predictions into baseline trading results. At each decision time it ranks pairs, takes equal-sized long and short sleeves, and runs the selected prediction configurations and checkpoints through an existing backtest engine.…

This feasibility analysis checks whether a monthly long-short strategy based on US firm characteristics can be evaluated with the available panel. It verifies that the measures are encoded as ranks, describes how long firms remain observable, counts firms at…