Mastering Backtesting for Algorithmic Trading
Unlock the Power of Historical Simulations – First Principles to Advanced
Requirements
- Familiarity with Python programming
- Basic understanding of financial markets and trading.
- Linear algebra and statistics is helpful
- Ability to read mathematical equations
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.
Code:
https://www.udemy.com/course/mastering-backtesting-for-algorithmic-trading/