This month’s roundup covers a wide range of topics in quantitative trading, from factor research and Python tools to trading automation, as well as broader conversations about building an edge and scaling AUM.
Alpha Generation & Factor Research
Core theoretical content on what drives returns and mispricing
- Two Accounting Anomalies: One May Be Risk, the Other Is Mispricing – Larry Swedroe, Alpha Architect blog, reviews a study by Stephen Penman and Julie Lei Zhu on accrual anomaly and post-earnings-announcement drift (PEAD), and shares key insights.
- What Is the True Value of an Option? – ORATS discusses several option valuation methods, each offering different insights, with discrepancies among them signaling potential market shifts.
- Skewness as a Hidden Driver of Anomaly Returns – Larry Swedroe, Alpha Architect blog, examines research by Rui Gong, John Lynch, and Richard Ogden, who reveal that a large share of the performance driving well-known stock-market anomalies comes from just a handful of top-performing stocks.
Python Infrastructure and Automation
Tools and techniques for handling and processing financial data at scale
- Pandas in Financial Market Data Analysis – Jason from PyQuant News explains that Pandas is essential for financial market data analysis. It streamlines data cleaning, time series analysis, portfolio management, risk assessment, predictive modeling, and reporting, enabling analysts to make faster, data-driven decisions.
- Curated IBKR API Resources – This post compiles IBKR API learning resources, articles, and structured courses to help users build, automate, and improve trading systems with TWS, Client Portal API, Excel, Python, Pandas, R, and related tools.
- 5 Common IBridgePy Mistakes and How to Fix Them – Dr. Liu outlines common IBridgePy mistakes and provides sample code with explanations on how to resolve them.
- Unleashing Polars for Market Data Analysis – PyQuant News examines Polars, a fast, memory-efficient Python library built with Rust that helps financial analysts process large datasets and support applications like backtesting, risk analytics, and high-frequency trading analysis.
- Explores the Potential of Using IBKR API to Automate Your Trading Strategies – IBridgePy demonstrates how Interactive Brokers’ API enables traders to automate strategies by accessing market data, executing orders programmatically, and managing accounts.
- Process Large Financial Datasets With PySpark And Python – PyQuant News shows how PySpark processes large financial datasets, with practical examples of data loading, filtering, aggregation, result saving, and rolling statistics calculation.
Industry Perspectives
Practitioner interviews and broader market commentary
- Algo Advantage 055 – Toby Crabel – Short-Term Futures Trading with Size! – Algo Advantage’s Simon and guest Toby Crabel discuss how systematic trading success relies on thin, well-tested edges, strict risk controls, and continuous research.
- AI and Quant Insights: Podcasts and Webinars – This article curates IBKR podcasts and webinars showing how AI, machine learning, APIs, backtesting, and automation can help quants, academics, and investors build smarter, rules-based trading strategies while staying informed on emerging investing technologies.
- Algo Advantage 054 – Kieran Duff – Trading for a Living is Easier Now – Algo Advantage host Simon sits down with trader Kieran Duff to explore how he built a platform enabling traders to “trade their way” while attracting outside capital to grow assets under management (AUM).
Already an Interactive Brokers Client?
New to Interactive Brokers?
Disclosure: Interactive Brokers
The analysis in this material is provided for information only and is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad-based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation by IBKR to buy, sell or hold such investments. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.
The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Interactive Brokers, its affiliates, or its employees.
Disclosure: Options (with multiple legs)
Options involve risk and are not suitable for all investors. For information on the uses and risks of options read the "Characteristics and Risks of Standardized Options" also known as the options disclosure document (ODD). Multiple leg strategies, including spreads, will incur multiple transaction costs.
Disclosure: Futures Trading
Futures are not suitable for all investors. The amount you may lose may be greater than your initial investment. Before trading futures, please read the CFTC Risk Disclosure. A copy and additional information are available at the Warnings and Disclosures section of your local Interactive Brokers website.
Disclosure: API Examples Discussed
Throughout the lesson, please keep in mind that the examples discussed are purely for technical demonstration purposes, and do not constitute investment advice, an investment recommendation or investment research. Also, it is important to remember that placing trades in a paper account is recommended before any live trading.
Join The Conversation
If you have a general question, it may already be covered in our FAQs page. go to: IBKR Ireland FAQs or IBKR U.K. FAQs. If you have an account-specific question or concern, please reach out to Client Services: IBKR Ireland or IBKR U.K..
Visit IBKR U.K. Open an IBKR U.K. Account