What you'll learn:
Master the Art of Backtesting: gain the skills to design and run your own custom backtests using historical data, starting from the very basics to advanced.
Spot and Avoid False Strategies: Uncover the secrets to identifying deceptive investment strategies.
Learn from Leading Experts: Benefit from the wisdom of pioneers in the field. Our curriculum is built around groundbreaking research and publications.
Infuse Causality in Your Strategies: Elevate your trading approach by integrating causal reasoning.
Adopt Best Practices in Quantitative Research: Forge your path in quantitative equity strategies using industry best practices.
Practical Insights and Real-World Application: This course doesn't just stop at theory. You'll get hands-on experience building your own backtester in Python
Innovate with Confidence: Equip yourself with the knowledge to not just follow but innovate in the field of quantitative finance.

Requirements:
Familiarity with Python programming
Basic understanding of financial markets and trading.
Linear algebra and statistics is helpful
Ability to read mathematical equations

Description:
This course is designed to equip you with the tools and knowledge needed to effectively backtest trading strategies using Python. It is tailored for those who want to test and validate their trading ideas with historical market data, ensuring a robust and data-driven approach to trading.Building Your Own Backtester in Python: Dive into the technicalities of building a backtester from scratch. Learn to code in Python and use popular libraries to create a versatile and reusable backtesting framework.Before You Backtest - Use This Protocol!: Understand the essential steps to prepare for backtesting. This module focuses on data collection, hypothesis formation, and setting up testing parameters.Best Practices in Research for Quantitative Equity Strategies: Learn the industry-standard research methodologies that quantitative analysts use for developing equity strategies. We cover data analysis techniques, statistical tests, and more.The Importance of Causality in Your Experiment Design: Understand the role of causality in trading strategy design. Learn how to differentiate between correlation and causation to build more effective trading strategies.What Not to Do!: A critical look at common pitfalls in strategy backtesting. Learn to identify and avoid mistakes that can lead to inaccurate conclusions and poor strategy performance.Detecting False Investment Strategies: Equip yourself with the knowledge to spot and avoid strategies that appear profitable but are actually flawed due to overfitting, data-snooping biases, or other errors.Bonus Lectures: Engage with additional content that delves into advanced topics, real-world case studies, and emerging trends in quantitative finance.

Who this course is for:
Aspiring quant traders and analysts looking to understand backtesting.
Finance professionals who want to incorporate data-driven methods into their trading strategies.
Students and academicians interested in quantitative finance.
Hobbyists looking to learn more about algorithmic trading.