Jason Strimpel, Matt Dancho – The Quant Scientist Algorithmic Trading System

The Quant Scientist Algorithmic Trading System by Jason Strimpel and Matt Dancho explores Python-based trading, backtesting and risk management. It combines foundational instruction with practical techniques for developing and evaluating algorithmic strategies.

Designed for beginners, experienced traders, investors and quantitative professionals, the course includes videos and PDFs. Its curriculum moves from setting up a trading environment to examining strategies, measuring risk and working with multiple assets.

Skills you’ll build in The Quant Scientist

• Develop algorithmic trading strategies using Python.
• Implement event-based backtesting techniques for accurate analysis.
• Utilize risk management strategies to protect investments.
• Engage in paper trading practice to build confidence.
• Access exclusive live sessions with industry experts.
• Join a supportive trading community for ongoing motivation.

Jason Strimpel and Matt Dancho’s approach

Jason Strimpel and Matt Dancho teach algorithmic trading and quantitative finance through data-driven methods. Their collaborative approach connects Python trading methodologies with event-based backtesting and risk analysis, explaining how strategy concepts can be put into practice.

The course combines trading theory with hands-on work, including paper trading, backtesting and real-time market strategies. Its lessons range from introductory setup to more advanced methods for analyzing and executing trades.

Setting up a trading environment

The opening phase covers creating an Interactive Brokers account and installing the Python tools needed for the trading environment. It then introduces order execution through Trader Workstation.

The setup instruction is organized step by step for learners who are new to the process. Paper trading provides a way to practice strategies before moving to live orders.

Three algorithmic trading strategies

The course examines three strategies used by the instructors: volatility targeting, momentum factor investing and advanced spread strategies. Each is broken down with attention to how it can be executed in practice.

Automated rebalancing is also part of the strategy instruction. The lessons consider how these methods can be incorporated into a trading routine rather than treating them only as theoretical concepts.

Event-based backtesting

Backtesting lessons use Zipline Reloaded to walk through event-based analysis of trading strategies. The instruction examines how historical testing can be used to assess a strategy before applying it in a live market.

The course also addresses common backtesting pitfalls and the implementation of factor-based trading methods. These topics connect the strategy lessons with the process of evaluating how an approach behaves under test conditions.

Risk analysis and paper trading

Risk management instruction uses Pyfolio Reloaded to examine performance metrics and risk indicators. The lessons focus on measures that traders can monitor when assessing a strategy and its exposure.

The course discusses when to revise a strategy and how to move from theoretical backtesting into real-time paper trading. This stage gives learners a way to examine a strategy in current market conditions before placing live trades.

Advanced backtesting and multi-asset trading

Advanced material introduces techniques for speeding up backtesting and explores strategies involving multiple assets. It extends the earlier testing instruction to more complex trading approaches.

The course also covers execution of multi-asset trades, considering their role in portfolio diversification and the management of more sophisticated investment portfolios.

Machine learning and Omega bonuses

The machine learning training explores predictive trading challenges and the use of machine learning strategies in a trading approach. It considers how those techniques can be used to construct predictive frameworks involving multiple assets.

Omega, described as a hedge fund in a box, is a Python application developed by Jason Strimpel. Its functions include trade execution, portfolio rebalancing, SQL database logging and connections to Interactive Brokers. The application brings these automation functions together for use in a trading workflow.

Who the course is for

The curriculum is intended for beginners seeking a structured introduction to algorithmic trading and experienced traders interested in advanced strategies and risk management. Investors exploring algorithmic portfolio methods can also follow the lessons on strategy implementation and analysis.

The material also addresses professionals interested in applying quantitative and machine learning techniques to trading.

Trading involves risk, and backtested results do not guarantee future performance.

Frequently asked questions

What does The Quant Scientist Algorithmic Trading System cover?

The course covers Python trading setup, volatility targeting, momentum factor investing, spread strategies, event-based backtesting and risk management. It also introduces multi-asset trading and machine learning applications.

Is the course suitable for beginners?

The course is designed for both beginners and experienced traders. Its initial instruction covers setting up an Interactive Brokers account, installing Python tools and practicing with paper trading.

Which tools are used in the course?

The curriculum uses Interactive Brokers, Trader Workstation, Zipline Reloaded and Pyfolio Reloaded. The Omega Python application is also included as a bonus.

What materials are included?

The course materials include videos and PDFs, along with machine learning training and the Omega trading application.