Knowledge library

This notebook explains why a generic target-weight risk overlay cannot govern the described S&P 500 short-straddle strategy. The options engine allocates fixed capital fractions to weekly cohorts and normalizes weights within each cohort; scaling those…

This notebook builds forward share-return labels for a strategy that reads listed options data to rank equities and then holds the shares. It creates an adjusted price series that accounts for corporate actions and identifies companies by persistent security…

This case study examines whether information from listed options can help select S&P 500 stocks. It combines daily equity prices with option-implied volatility levels, skew, term structure, and variance risk premium measures across 633 stocks from 2017 to…

The document describes a specialized backtest for a weekly S&P 500 options strategy. Each cohort selects highly ranked constituents, sells near-the-money call and put options with roughly a month to expiration, and delta-hedges with shares when net delta…

This notebook explains instrumented principal component analysis (IPCA), which estimates common factors while making each stock’s factor exposures a shared linear function of its same-day option-surface features. Those features include implied volatility,…

This exploratory analysis introduces option contracts and chain structure, then profiles a 2020 sample of S&P 500 options across eight underlyings. It explains moneyness, intrinsic and time value, implied volatility, Greeks, and how strike, expiration, and…

This notebook evaluates position-level stop losses, trailing stops, and time exits on the allocation lineage that ranks highest by validation Sharpe among eligible full-coverage candidates. Each overlay is compared with its own no-overlay parent, isolating…

This notebook compares portfolio weighting rules for an S&P 500 options strategy that sells straddles. It holds the selected symbols and other strategy settings fixed while replacing the equal-weight baseline with allocators that use either model predictions…

This notebook applies TabM, a tabular neural architecture that shares a two-layer feature network across ensemble members. Each member scales the shared representation with its own learned vector and uses its own output layer; averaging member predictions…

This notebook describes producing an out-of-sample prediction set for a previously selected S&P 500 options model. It fixes the configuration using validation results, refits that configuration on pre-holdout data, and registers predictions for the holdout…

This notebook fits PatchTST, a transformer-style sequence model, as one member of a predeclared population of models for S&P 500 options research. It divides a lookback series into fixed-length patches, embeds them as tokens, and uses attention to relate…

The document describes how to compare validation predictions from linear, gradient-boosting, tabular deep-learning, and sequence models for S&P 500 options. Before reporting diagnostics, it checks that each registered prediction population is complete…

This notebook runs the NLinear member of a declared three-model sequence-learning population for S&P 500 options. It resolves the requests and checkpoints for the full population before fitting this member, so later notebooks can execute the LSTM and…

This notebook applies double machine learning to examine whether the implied-minus-realized volatility spread is associated with subsequent equity returns after adjustment for observed confounders. It identifies realized volatility, equity momentum, and…

This notebook builds conditional-volatility features for an S&P 500 equity and options study using a GJR-GARCH model. Unlike fixed rules based on past prices, fitted features depend on their estimation window. The method therefore declares a refit schedule,…

This document describes how to reduce large S&P 500 option chains into daily per-symbol datasets for research. Its surface summary selects options nearest target absolute deltas within maturity buckets, then derives at-the-money implied volatility at…

This case study evaluates short at-the-money straddles on S&P 500 constituents, entered weekly and held until expiry with a daily delta hedge. Its main methodological choice is to model hold-to-maturity returns: the option position incurs an entry spread but…

This notebook evaluates how transaction-cost assumptions affect the validation Sharpe of one selected S&P 500 equity and options allocation. It carries forward the configuration chosen through a frozen candidate set when available, then reruns the same…

This case study builds features for researching whether at-the-money implied variance exceeds subsequent realized variance for S&P 500 names, and whether the difference varies across securities. It combines straddle quotes with underlying prices to measure…

This notebook examines whether financial features help explain a 10-session delta-hedged options return label. It compares Ridge models using one implied-volatility feature, groups of implied-volatility-dependent and independent features, and the full…

This notebook measures how validation Sharpe for at-the-money S&P 500 option straddles changes across assumed execution costs and two option universes. It expresses spread cost as a fraction of the quoted option half-spread, since option premium and…

This notebook fits gradient-boosted tree models to options-market features that encode forecasts such as implied volatility, skew, term structure, and variance risk premium. It compares tree capacity and regression loss choices across forward-return and…

This notebook compares predictive, structural, and causal model evidence for a cross-sectional S&P 500 study combining equity features with options-derived measures such as implied volatility, skew, and term structure. It evaluates weekly forward-return…

This notebook uses double machine learning to test whether the association between the implied-minus-realized volatility spread and future equity returns persists after adjustment for observed confounders. It compares the adjusted estimate with naive…