Knowledge library

This notebook explains how model uncertainty can add information beyond a point estimate. It fits a stochastic volatility model in which latent log volatility evolves over time and returns follow a Student-t distribution. Posterior spread, interval width,…

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 chapter presents risk management as part of strategy design and live operations. It covers tail risk measurement with value at risk and conditional value at risk, drawdown depth and recovery, exposure decomposition, stress testing, adaptive controls,…

This notebook constructs features from minute-level NASDAQ-100 data using three procedures: a rolling heterogeneous autoregressive regression for near-term variance forecasts, a Fourier transform to describe periodic structure in recent activity, and…

This notebook compares volatility measurements, fits a heterogeneous autoregressive (HAR) model, and estimates roughness using Hurst exponents. It first contrasts intraday realized variance with daily estimators, separating overnight returns from session…

The chapter explains classical approaches to generating synthetic financial data, covering continuous-time price processes, GARCH volatility, bootstrap resampling and model comparison. It describes how models such as geometric Brownian motion,…

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 examines tick-level AlgoSeek trade and quote data for AAPL during the March 16, 2020 market crash. It filters to regular trading hours, separates trade prints from NBBO updates, and studies intraday activity, spreads, trading sizes, venue…

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 module defines a monthly ETF allocation baseline using a risk-adjusted momentum score: trailing cumulative return divided by annualized realized volatility. It selects the highest-ranked assets when the 10-year minus 2-year yield-curve slope exceeds a…

This notebook clusters monthly returns from nine equity, bond, currency, and commodity factor and market series using Gaussian mixture models. After dropping months with incomplete data, it standardizes the series so differences in scale do not dominate the…

This educational notebook surveys price- and volume-derived features used in quantitative research. It covers simple and logarithmic returns across horizons, skip-one momentum, overnight and intraday returns, moving-average distance, trend and reversal…

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 describes adapting Diffusion-TS to generate synthetic ETF return sequences. Its denoiser predicts the clean sequence as a sum of polynomial trend, Fourier seasonal, and residual components, while a combined time-domain and Fourier-domain loss…

This notebook presents an unconditional Sig-Wasserstein GAN for generating financial time series. It replaces a learned real-versus-generated discriminator with a distance based on expected path signatures, mathematical summaries of a path’s sequential…

This notebook uses daily ETF returns to explain how GARCH models capture volatility clustering and turn it into a session-by-session feature. It first uses visual diagnostics and an ARCH-LM test to assess whether squared returns depend on their own lags. A…

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 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 connects position sizing with analysis of maximum adverse and favorable excursions. It demonstrates fixed-fractional sizing, where shares are determined by a portfolio risk budget and entry-to-stop distance, with a cap on position…

This notebook explains feature construction from data beyond a single asset’s price history. It derives annualized futures roll yield from contemporaneous front and deferred contract prices, and uses three tenors to calculate normalized curve slope and…

This notebook explains how hidden Markov models infer unobserved market states from returns and recent volatility. It first establishes simple comparison rules: a volatility index threshold for stress and price relative to a long moving average for trend. A…

This notebook uses double machine learning to estimate whether a perpetual-futures premium z-score is associated with the following eight-hour return after adjustment for six pre-treatment controls. It compares effects across high- and low-volatility regimes…

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…