Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
20 documents
The document explains how a stochastic discount factor (SDF) estimates a pricing kernel that should price every asset, rather than estimating common return factors. It describes adversarial training: one network proposes the discount factor while another…
This notebook explains why a daily constant-maturity options series is not the return history of a single tradeable contract. Selecting a new near-the-money straddle each day can change the strike, expiration, or both. In particular, moving to a later…
This notebook compares learned and analytical hedges for a short European call when rebalancing is discrete and trading incurs proportional costs. It defines the self-financing terminal P&L from hedge gains, turnover costs, and the option payoff, then trains…
This utility builds label artifacts for S&P 500 option straddles using the same symbol, strike, and expiration at entry and exit. It aligns feature dates to subsequent market sessions, constructs five- and ten-session exit dates, and joins call and put…
This document describes a ledger for applying funding cash flows to perpetual futures positions during a backtest. At each funding timestamp, it uses the position’s signed quantity, the current mark, any contract multiplier, and the funding rate to calculate…
This analysis introduces option-chain structure and examines a 2020 slice of S&P 500 options for eight underlyings. It explains moneyness, intrinsic and time value, Greeks, implied volatility, and the information represented by volatility skew and term…
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,…
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 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 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 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 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 document describes a pipeline for downloading official Binance USD-M perpetual funding records, normalizing them, and caching them in a columnar file. Monthly archives are fetched concurrently for the requested symbols and date range. The records are…
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,…
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 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 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 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 notebook develops forward return labels for short at-the-money straddles on S&P 500 stocks. Since the daily panel’s nominal 30-day straddle represents a different contract each session, a shifted price series would mix instruments. The label instead…