Turn trading ideas into live strategies with one of the most trusted voices in quantitative finance. In this exclusive hands-on workshop, Jason will show you how to design, test, and deploy algorithmic trading strategies with Python. From discovering trading edges to building live trading apps, you’ll explore the complete workflow using powerful tools like pandas, VectorBT, and Interactive Brokers. With a blend of theory and practical coding exercises, you’ll leave ready to apply algorithmic trading techniques to real-world scenarios.
By the end of this workshop, you’ll be able to:
- How Python can give you an edge in algorithmic trading.
- Refine profitable trading ideas with practical techniques.
- Master essential Python libraries for quant trading.
- Build and backtest trading strategies the right way using VectorBT.
- Prototype and validate real-world trading models with pandas.
- Deploy a live trading application with the Interactive Brokers API.
- Access curated resources to keep improving your trading skills.
Who should attend?
- Aspiring quant/ retail traders who want to move from ideas to execution.
- Python developers interested in applying their skills to trading and financial markets.
- Data analysts and quants looking to explore backtesting, strategy design, and execution with real-world tools.
- Finance professionals who want to understand how algorithmic trading systems are built and deployed.
- Intermediate learners with basic knowledge of Python and trading concepts, eager to gain hands-on experience
This isn’t just a lecture—it’s a fully hands-on, end-to-end learning experience. Here’s what sets it apart:
- Practical, project-based approach: You won’t just learn theory—you’ll build a complete trading strategy from idea to live execution.
- Step-by-step workflow: The workshop mirrors the real-world algorithmic trading process—finding an edge, prototyping a strategy, backtesting, and deploying it with Interactive Brokers.
- Live coding with real tools: Work directly with Python, pandas, VectorBT, and the Interactive Brokers API in guided coding sessions.
- Capstone strategy build: Apply everything you learn to a real-world case study (the crack–refiner spread trade), so you walk away with a fully functional trading system.
- Execution focus: Beyond backtesting, you’ll explore how to reduce slippage, improve execution, and build trading apps that actually run in production.
- Expert instructor: Learn directly from Jason Strimpel, a recognized quant and educator with extensive experience bridging financial markets and machine learning.
Prerequisites:
Technical: Python 3.11 installedLibraries:pandas, VectorBT, Interactive Brokers API
Optional: Interactive Brokers account
Experience: Intermediate level Python experience. Market experience and understanding of basic technical terms and jargon.
