Skip to main content
Ryan Orban

Ryan Orban

Subject
7 entries

Quantitative Finance

Bookmarks

  1. Didact AI: The Anatomy of an ML-Powered Stock Picking Engine

    A technical teardown of Didact AI's ML-powered stock picking engine — covering feature engineering, model architecture, training pipeline, and how uncertainty quantification informs position sizing. Rare public documentation of a production ML trading system.

  2. 46-Page Guide to Pricing Options and Implied Volatility with Python

    PyQuant News's 46-page guide to pricing options and calculating implied volatility in Python — covers Black-Scholes, Greeks, and the IV surface with working code. A self-contained practical reference for quant practitioners using Python.

  3. Quant-MELO-Portfolio: Bayesian Portfolio Optimization

    Quant-MELO-Portfolio is a Python project applying Bayesian architecture to stock portfolio optimization — finding optimal weights via Global Minimum Variance and Tangency portfolios. A concrete implementation of mean-variance optimization with Bayesian uncertainty quantification.

  4. Machine Learning in Finance: From Theory to Practice

    Springer 2020 textbook by Dixon, Halperin, and Bilokon bridging ML theory and quantitative finance practice — covering supervised learning, NLP for financial texts, RL for trading, and deep learning for derivatives pricing. The most rigorous academic treatment of ML applied to finance.

  5. All That Glitters Is Not Gold: Comparing Backtest and Out-of-Sample Performance on a Large Cohort of Trading Algorithms

  6. They Still Haven't Told You: Overnight vs. Intraday Market Anomaly

    Bruce Knuteson documents a decades-long anomaly in global stock markets where overnight returns consistently exceed intraday returns in ways consistent only with a large quant firm systematically expanding its book at open and contracting at close. A forensic finance argument that major market manipulation has gone unreported by the institutions supposed to catch it.

  7. Parameter Optimization with Zipline, PiCloud, StarCluster, and IPython Parallel

    Quantopian's blog post on running parameter optimization for trading strategies using Zipline backtester on PiCloud, StarCluster, and IPython Parallel — a 2013 example of cloud-distributed backtesting before it was a product. Shows the DIY infrastructure that Quantopian later packaged into their platform.

All bookmarks