IBKR Quant Blog Highlights – April 2026

Quants seeking to enhance their trading edge can find a curated collection of resources here, featuring advanced research and data-driven methodologies.

AI & Machine Learning in Finance

• A Conversation with Professor Lars Kotthoff – Symbolic AI, Financial Portfolios, and the Growth of LLMs – Part One – Burt Shulman of Interactive Brokers interviews Professor Lars Kotthoff, who shares his unique perspective on artificial intelligence. The discussion covers the evolution of AI from early neural networks to modern large language models, contrasts symbolic and statistical approaches, and examines the unexpected impact of financial portfolio theory on AI research.
• LLMs and the Shortening Shelf Life of Copyable Alpha – Visual Sectors examines how lowering the cost of translating ideas into code with LLMs benefits everyone equally. As a result, research becomes more diverse in syntax but converges in logic.
• Feature Selection in the Age of Generative AI – PredictNow.ai discusses that feature selection is the process of identifying the most relevant input variables for a machine learning model – reducing noise, limiting overfitting, and keeping computation tractable.

Backtesting & Trading Systems

• Brave New Backtest – Kris Longmore of Robot Wealth argues that although AI performs well in coding, its structural limitations, such as poor training data, proactive interference, and mode collapse, make it far from ideal for trading research.
• Backtest Trading: All You Need to Know – IBridgePy provides a backtesting framework that uses historical data to evaluate, optimize, and refine trading strategies.
• Algo Advantage 050 – Samir Varma – The Pre-Trading Show (on Quantum Physics) – Algo Advantage explains that data mining is not inherently flawed, as all empirical research involves analyzing data. The key difference lies in whether the search is rigorous or careless.

Programming, Data & Signals

• Statistical Arbitrage Using Cointegrated Stock Pairs in Indian Equity Market (2015-2025) | EPAT Project – Shant Tondon, guest contributor at QuantInsti, presents a project that develops and tests a market-neutral pairs trading strategy on 25 NSE large-cap stocks from multiple sectors.
• Momentum Investing Enhanced by Microsoft Foundry-Hosted Large Language Model – Selcuk Disci, DataGeeek, presents a coding tutorial on LLM-enhanced momentum investing, combining traditional momentum signals with real-time news interpretation by LLMs.
• Efficient Data Processing with Python Tools – PyQuant News highlights that list comprehensions and generator expressions in Python support efficient, readable, and scalable data processing in software development and data science.
• How to Draw a Candlestick Chart in R? – Both ggplot2 and plotly – Ozancan Ozdemir shows how to create both static and interactive candlestick charts in R using the ggplot2 and plotly libraries.
• Python Live Trading Strategies: The Power of Python in Financial Trading – IBridgePy illustrates how Python has transformed financial trading through automation, data analysis, and effective risk management, establishing coding as an essential skill for traders.
• Managing Dependencies with Virtual Environments – PyQuant News explains how virtual environments help developers efficiently manage dependencies and project-specific packages.

Market Strategies & Research

• The Many Facets of Stock Momentum: Distinguishing Factor and Stock Components – Elisabetta Basilico, Ph.D., CFA, Alpha Architect, reviews this paper by Xavier Gérard and Laura Jehl, which finds that stock momentum consists of multiple signals rather than a single factor.
• From Raw Chains to Research-Grade Signals – Visual Sectors highlights the value of a disciplined data engineering pipeline in supporting systematic strategies.
• Volatility Smile as a Distribution Map – Tribhuvan Bisen, Quant Insider, explains the concept of the “volatility smile” and discusses its practical implications for trading.
• The Skip-Month Mystery: What Last Month’s Returns Are Really Telling You – Larry Swedroe, Alpha Architect, reviews a recent paper by Dibyam Dikhit and discusses the Skip-Month Rule, recent volatility, and momentum.