Knowledge library

This notebook compares portfolio weighting rules for selling straddles on selected S&P 500 symbols. It holds the chosen symbols constant and varies allocation, using equal weight as the baseline. The methods include weighting by predicted score, inverse…

This case study assesses a weekly S&P 500 options short straddle using registered backtests. It selects a configuration from a nominated liquid universe, ranks candidates on validation, and evaluates the chosen configuration on holdout data without using…

This notebook explains how a stochastic discount factor (SDF) prices a cross-section of equity option signals. Unlike factor models that estimate exposures and factor returns, the SDF approach estimates one random variable that makes asset returns price…

This notebook applies a previously selected S&P 500 equity and options strategy to predictions for a separate 2021 holdout period. It carries the selected configuration forward unchanged, including its allocator, concentration rules, risk overlay, and…

This document describes building a cross-sectional feature matrix for S&P 500 stocks by combining adjusted share-price histories with summarized option implied-volatility surfaces. It organizes features by role, input, lookback, and observability delay.…

This feature-engineering notebook constructs variables that require information beyond one asset’s price history. For futures, it computes annualized roll yield from contemporaneous near and deferred contract levels, plus term-structure slope and curvature…

This notebook builds model-based conditional volatility features for S&P 500 shares with a GJR-GARCH(1,1) model, then compares an option-implied volatility spread measured against realized volatility with one measured against a model forecast. Since…

This notebook fits a PCA latent-factor model to a panel of stock returns, without using option-surface features, characteristics, or target information. PCA extracts leading directions of historical cross-sectional co-movement, estimates each stock's…

This setup document defines a weekly S&P 500 equity research pipeline that uses options market features alongside price-based signals. It specifies the eligible universe, decision and execution timing, and distinct rebalance cadences for labels with…

This notebook compares squared error, absolute error, and Huber loss for gradient-boosted models predicting short at-the-money straddle returns. The target has a capped gain and potentially severe losses, so the fitting loss may affect how well predictions…

This document outlines a two-pass method for extracting source observations used in an S&P 500 options straddle study. First, it identifies call and put contracts meeting a near-the-money candidate screen based on days to expiration, absolute delta,…

The document evaluates how trading costs change the validation performance of one previously selected S&P 500 equity and options allocation. It sweeps one-way charges as a fraction of traded value and compares them with a flat per-share commission and…

This notebook applies TabM, a weight-sharing neural ensemble, to stock-return targets built from equity-option features. Its members use a common two-layer network backbone, with separate activation scaling vectors and output heads whose predictions are…

This notebook declares and runs a double machine learning analysis of the variance risk premium’s effect on S&P 500 option returns to expiry. It makes the estimand, observed timing, confounders, temporal cross-validation setup, nuisance model, HAC covariance…

This document explains why a daily constant-maturity option series is not a return series for a position actually held. It selects same-strike, same-expiration call and put legs that pass liquidity, maturity, volatility-estimation, and delta filters, then…

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 demonstrates deep hedging for a short European call. It simulates geometric Brownian motion paths, uses Black–Scholes delta hedging as a benchmark, and trains a semi-recurrent neural network to choose underlying positions that minimize a…

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 notebook evaluates whether option-market measures can rank future returns across stocks. It fits declared linear models on option-derived features, using walk-forward validation, and compares penalty strengths for ridge, lasso, and elastic net. 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 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 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 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 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…