Mastering Backtesting For Algorithmic Trading

Unlock the Power of Historical Simulations – First Principles to Advanced

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.

Overview

Section 1: Useful Resources

Lecture 1 Free 1 Month: MlFinLab License

Lecture 2 Join the Reading Group

Section 2: Build Your Own Backtester in Python

Lecture 3 Lecture: Introduction to Backtesting

Lecture 4 Lecture: Backtesting Tutorial

Lecture 5 Notebook Practical: Downloading Price Data with YFinance

Lecture 6 Backtest Statistics and Libraries in Python

Lecture 7 External Lecture: Using Pyfolio to Analyze your Trading Strategies

Lecture 8 External Lecture: QuantStats - Portfolio Analytics with Python Tutorial

Lecture 9 Notebook Practical: Build your Own Backtest

Lecture 10 Paper: The Impact of Volatility Targeting

Lecture 11 Lecture: The Impact of Volatility Targeting

Lecture 12 What About Transaction Costs?

Lecture 13 External Lecture: When Should You Build Your Own Backtester?

Lecture 14 Overview of Backtesting Platforms (Python)

Lecture 15 Recommended Readings for Building your Own Backtester

Section 3: Before you Backtest - Use this Protocol!

Lecture 16 Paper: A Backtesting Protocol in the Era of Machine Learning

Lecture 17 The Protocol

Lecture 18 Lecture: 7 Point Backtesting Protocol

Section 4: Best Practices in Research for Quantitative Equity Strategies

Lecture 19 Paper: Best Practices in Research for Quantitative Equity Strategies

Lecture 20 Heart of the Quantitative Model: Data

Lecture 21 A Taxonomy of Quant Models

Lecture 22 How to Develop a Quant Strategy

Lecture 23 9 Tips for Better Model Development

Lecture 24 External Lecture: Enhancing Statistical Significance of Backtests

Section 5: The Importance of Causality in your Experiment Design

Lecture 25 Introduction to Causality in Finance

Lecture 26 Paper: Causal Factor Investing: Can Factor Investing Become Scientific?

Lecture 27 Lecture: Scientific Discovery in Quantitative Finance

Section 6: What Not to Do!

Lecture 28 Lecture: Introduction

Lecture 29 Paper: Seven Sins of Quantitative Investing

Lecture 30 Lecture: The 7 Sins of Quantitative Investing

Lecture 31 Paper: The Four Horsemen of Machine Learning in Finance

Lecture 32 Lecture: The Four Horsemen of Machine Learning in Finance

Lecture 33 Principles for Effective Machine Learning in Finance

Lecture 34 External Lecture: 10 Ways Backtests Lie

Lecture 35 Paper: The 10 Reasons Most Machine Learning Funds Fail

Lecture 36 External Lecture: The 7 Reasons Most Machine Learning Funds Fail

Section 7: Detecting False Investment Strategies

Lecture 37 Paper: The False Strategy Theorem

Lecture 38 Lecture: The False Strategy Theorem

Lecture 39 Paper: Detection of False Investment Strategies

Lecture 40 Lecture: Detection of False Investment Strategies

Lecture 41 Paper: Backtesting (An Alternative Approach to the DSR and PSR)

Lecture 42 Lecture on the paper: Backtesting

Lecture 43 Paper: Evaluating Trading Strategies

Lecture 44 External Lecture: The Risks of Historical Backtests

Section 8: Extra Content: Position Sizing, Stop Losses, and Costs

Lecture 45 Paper: Risk-Constrained Kelly Gambling

Lecture 46 Paper: The Kelly Criterion in Blackjack Sports Betting, and the Stock Market

Lecture 47 Lecture: Four Approaches to Kelly Bet Sizing

Lecture 48 Paper: When do stop-loss rules stop losses?

Lecture 49 Lecture: When do Stop-Loss Rules Stop Losses?

Lecture 50 Paper: A Practitioner Perspective on Trading and Implementation of Strategies

Lecture 51 Lecture: A Practitioner Perspective on Trading and Implementation of Strategies

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.