Explore these valuable resources on algorithmic trading!
General Trends in Quantitative Finance
- What Can We Learn about Investors by Simulating Stock Markets? – Dr. Sandra Andraszewicz from ETH Zurich presents key findings from the ETH Zurich Trading Simulator (ZTS), a powerful tool designed to replicate the fast-paced environment of trading financial assets. The research covers several important topics, such as the effects of Social Trading Platforms, the application of physiological signals as indicators of financial success, and the complicated influence of overconfidence and incentives on trading behavior.
- A Navy SEAL Says Making Better Decisions Is SIMPLE. – Andrew Sridhar, a guest contributor on the Alpha Architect blog and a former Navy SEAL, outlines a process based on the principle that good decisions result from a good process. The goal is to enhance the quality of decisions made by individuals and teams, improving overall effectiveness.
- Expected Shortfall (ES) – Tostne Kutalia, QuantInsti blog, describes Expected Shortfall (ES), which is a concept that helps us understand the potential losses that could exceed the Value at Risk (VaR). The author demonstrates the steps for computing ES.
- How to Build LLM Agents with Magentic – Igor Radovanovic, AlgoTrading101, discusses Magentic, a framework that can integrate Large Language Models (LLMs) into Python code. One benefit is that it is a free, actively maintained open-source package that supports parallel function calling.
- Nonfarm Payrolls January 2025 – Will Klinke, Total Wealth Partners, examines the January 2025 Nonfarm Payrolls.
- Is It Time to Ditch International Stocks? – Jose Ordonez, Alpha Architect, discusses the merits of investing solely in the US compared to global markets and whether geographic diversification is advantageous.
- Exiting Your Trades – What Works Best? – Dave Mabe explores various trade exit strategies.
- Relax and Trust the Market Gods – Kris Longmore from Robot Wealth highlights that systematic trading involves sticking to a plan.
Python and R Use Cases: Specific Examples
- Concurrency in the TWS API – Discover the fundamental concept of concurrency with this IBKR API tutorial! Learn how to use Python to get account values, market data, and submit trades simultaneously.
- Automating Financial Strategies with Python Bots – PyQuant News discusses why Python is the preferred language for developing trading bots. This article illustrates how to set up your environment, develop the strategy, and backtest it.
- File Handling in Python: A Comprehensive Guide – PyQuant News provides practical examples highlighting the significance of file handling in Python, such as reading an entire file, reading line by line, and writing to files.
- R Code Snippet: Transform from Long Format to Wide Format – Sang-Heon Lee from SHLee AI Financial Model provides R code to read sample data and transform it from long to wide format and vice versa.
In addition, be sure to visit the community events page for details on the Open Source Quantitative Finance (formerly R/Finance) Conference, useR! – International R User Conference and posit::conf(2025).
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: API Examples Discussed
Throughout the lesson, please keep in mind that the examples discussed are purely for technical demonstration purposes, and do not constitute trading advice. Also, it is important to remember that placing trades in a paper account is recommended before any live trading.
Disclosure: Order Types / TWS
The order types available through Interactive Brokers LLC's Trader Workstation are designed to help you limit your loss and/or lock in a profit. Market conditions and other factors may affect execution. In general, orders guarantee a fill or guarantee a price, but not both. In extreme market conditions, an order may either be executed at a different price than anticipated or may not be filled in the marketplace.
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