Recent articles include interviews with quantitative analysts, as well as discussions on general topics and tutorials related to R, Python and Excel programming.
Quant and Econometrics Topics
- Philip Sun on Generative AI and the Risk of Market Manipulation – Philip Sun, co-author of “Hands-On AI Trading with Python, QuantConnect, and AWS”, discusses his perspective that generative AI introduces both new opportunities and new risks in the market.
- Kris Boudt Part Three: Sentometrics, A Match Made in Belgium – Kris Boudt, professor and quant, explains his transition from academia and research to co-founding Sentometrics, where he puts his theories into practice.
- The Aggregated Equity Risk Premium – In this Alpha Architect blog story, Elisabetta Basilico, Ph.D., CFA, analyzes a paper on the use of Machine Learning techniques to predict market returns by compiling the expected returns of individual stocks.
- Bayesian Inference Methods and Formula Explained – Vivek Krishnamoorthy, QuantInsti, introduces us to common methods and formulas used in Bayesian inference.
- Unlocking the Power of Automated Trading with IBKR: A Comprehensive Guide – Dr. Hui Liu, IBridgePy, discusses the essential aspects of algorithmic trading using the IBKR API.
- Understanding Market Impact in Active Trading: A Comprehensive Guide – PyQuant News explores various factors that influence market impact.
- What Can We Expect from Long-Run Asset Returns? – Quantpedia examines the academic paper “Long-Run Asset Returns” by David Chambers, Elroy Dimson, Antti Ilmanen, and Paul Rintamäki.
- US Value Stocks Trading at Historically High Discounts – Larry Swedroe, guest contributor to Alpha Architect blog, highlights a trend where growth stocks are trading at historically high valuations, while value stocks are trading at their average valuation over the past 25 years.
- From Logistic to Random Forests: Mastering Non-linear Regression Models – In this tutorial by QuantInsti, readers can explore the fundamentals of non-linear regression models.
R, Python and Excel Tutorials
- yfscreen: Yahoo Finance Screener in R and Python – Jason Foster showcases yfscreen, an open-source package that simplifies interaction with Yahoo Finance APIs. It handles session management, query construction, pagination, and JSON payload generation.
- Functions Applied to Vectors in Quantitative Analysis in R: Performing Payments Traceability – Roberto Delgado Castro illustrates how utilizing functions in R is an effective method for conducting quantitative analysis and tracking payments across various financial databases.
- Quick Fix: Yahoo Finance Data Access with Excel – Kris Longmore, Robot Wealth, showcases an Excel spreadsheet designed to retrieve historical price and volume data from Yahoo Finance.
- Unlocking Real-Time Financial Data with Python – PyQuant News demonstrates how to access financial data using the Yahoo Finance API.
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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.
The new ForecastTrader lesson is a solid addition—Jose Torres’s macro insights offer a practical angle that’s easy to integrate into quant models. It’s especially helpful for those of us refining risk management strategies in volatile conditions.
We are happy to hear you had a positive experience!