Mastering Machine Learning for Cutting-Edge Quantitative Trading Strategies
About This Course
In today's fast-paced financial markets, the ability to develop, implement, and evaluate quantitative trading strategies using machine learning models is essential for gaining a competitive advantage.
The overall objective of this course is to equip participants with the necessary knowledge and skills to achieve this. By understanding market dynamics and various trading techniques, participants will gain practical experience in developing strategies that can be applied in real-world financial markets. The course emphasises both theoretical understanding and practical application, utilising Python for strategy implementation.
What You'll Learn
• Distinguish between different types of trading strategies such as low-latency, algorithmic, high-frequency, and quantitative trading
• Understand how to process and work with financial data to develop trading strategies
• Evaluate and implement statistical arbitrage strategies, including pairs trading and momentum-based trading strategies
• Apply risk management techniques in trading, including spread-trading strategies and market-making
• Use Python to implement and backtest trading strategies, assessing their performance in a scientific and systematic manner
• Apply machine learning models to improve the performance of trading strategies
Entry Requirements
• Participants are encouraged to have a fundamental understanding of Python or R, and those without programming experience should consider taking introductory online courses.
• Basic knowledge of financial concepts, such as interest, compounding, and investment strategies, is recommended to understand the financial applications of machine learning.
• Familiarity with data handling, particularly using tools like Excel or Python for basic data processing.