Knowledge library

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 notebook fits PatchTST as one member of a declared sequence-model population for S&P 500 options. PatchTST divides a lookback window into fixed-size patches, embeds them as tokens, and uses attention to learn relationships among portions of the history.…

This document describes an LSTM sequence model for an S&P 500 options study. Unlike cross-sectional models that rely on engineered features to summarize a symbol’s history, the LSTM receives chronological windows and learns which parts of the sequence…

This notebook develops Black-Scholes pricing for European calls and puts, checks put-call parity, and computes implied volatility with Brent root-finding. It also defines Delta, Gamma, Vega, Theta, and Rho and uses plots to illustrate how option…

This notebook describes a patched transformer for sequence prediction in an S&P 500 equity and options study. It divides a time window into contiguous blocks and represents each block as a token, allowing attention to compare distant parts of the window…

This chapter frames reinforcement learning as a tool for sequential control, distinguishing it from supervised forecasting. It focuses on tasks where actions affect later outcomes and rewards are comparatively concrete: trade execution, market making, and…

This case study constructs features for comparing variance risk premiums across S&P 500 stocks using 30-day at-the-money straddles and underlying price data. It measures implied volatility against realized volatility at several horizons, adds option-market…

This analysis checks whether daily option data can support a weekly strategy that sells at-the-money straddles on S&P 500 constituents, delta hedges shares, and holds contracts to expiration. It explains how calls and puts form a straddle, how implied and…

This notebook presents a family-level view of five latent-factor approaches applied to an equity option analytics case study. PCA estimates common movements directly from the return panel; IPCA maps characteristics to exposures linearly; a conditional…

This feasibility analysis checks whether the data and assumptions can support a weekly strategy that ranks S&P 500 stocks by at-the-money implied volatility and buys the leading shares. Options supply the signal, but positions are held in equities. The…

This notebook evaluates sequence models for ranking S&P 500 stocks using windows of their option-surface and price histories. It pairs a normalized linear model, which has no recurrent memory, with an LSTM whose gates carry information through time. The…

This notebook builds forward-return labels for a strategy that reads listed-option signals and trades the underlying shares. It adjusts daily share prices for splits and dividends, uses a persistent security identifier, and defines returns between executable…

This notebook turns model prediction sets into comparable S&P 500 options backtests. On each weekly decision date, it ranks predicted returns, selects the highest-ranked symbols in the liquid universe, and sells equally weighted at-the-money straddles.…

This configuration note distinguishes settings that appear in a backtest identity from settings that actually affect simulated trading costs. In the described case study, all registered runs use a vectorized, return-to-expiry path. The configured…

This notebook screens candidate features for a strategy that sells at-the-money call and put options on the same stock with the same expiry, holding the straddle to expiration. For each feature, it measures whether the feature ranks available names in the…

This notebook evaluates validation predictions derived from equity and options research by translating them into long-only equity portfolios. It uses an equal-weight top-K baseline, compares several portfolio concentrations, and follows the label-specific…

This notebook builds forward return labels for short at-the-money straddles on S&P 500 stocks. Because the daily 30-day straddle panel rolls to a different strike or expiration each session, a shifted price series would compare different contracts. Instead,…

This notebook fits a declared LSTM model for forecasting in an S&P 500 options study. The network receives chronological windows of each symbol’s history and uses gated recurrent state to represent information across sessions, rather than relying only on…

This study screens financial and GJR-GARCH volatility features against forward equity returns. For each session, it calculates the rank correlation across stocks between a feature and subsequent returns, then assesses average association, consistency across…

The document derives European call and put prices under Black-Scholes, checks their relationship through put-call parity, and computes implied volatility by numerically solving for the volatility that matches an observed option price. It also defines Delta,…

This notebook demonstrates deep hedging for a short European call. It simulates underlying price paths with geometric Brownian motion, calculates Black-Scholes delta as a benchmark, and trains a semi-recurrent neural network to choose hedge positions. The…

This evaluation screens option-surface, price-derived, and conditional-volatility features against forward equity returns. For each session it computes a cross-sectional rank correlation between feature values and subsequent returns, then examines the time…

This notebook measures how an S&P 500 options strategy's validation performance changes under different assumed fractions of the quoted spread paid. It selects one baseline strategy per model family, then reruns each across spread-cost assumptions and both a…

This notebook explains instrumented principal component analysis (IPCA) for an equity panel with option-surface features. Unlike ordinary PCA, which estimates each stock's exposures from its return history, IPCA models exposures as a shared linear function…