Udemy – Statistical Inferencing for Quantitative Trading Strategies 2025-5

Statistical Inferencing for Quantitative Trading Strategies. This course examines the application of probability theory and statistical methods to assessing the validity and significance of quantitative trading strategies. Are the results of a trading strategy statistically significant? Does the machine learning model used truly identify market inefficiencies or is it overfitting? If, after backtesting ten strategies, only those with a Sharpe above 1 are selected, will the strategy lead to profitability or failure? This course helps to scientifically validate strategies by providing robust hypothesis tests and nonparametric methods. Topics such as hypothesis testing, type I and II errors, controlling family-wise error rates, and advanced techniques such as Monte Carlo permutation methods are covered. It also provides a comparison between rigorous scientific standards in industries such as pharmaceuticals and less rigorous approaches in trading. This course is designed for those who want to improve their trading decisions with powerful statistical tools.

What you will learn

• You will learn the fundamentals of finance and probability theory for algorithmic trading.
• You will learn statistical inference techniques such as parametric and nonparametric hypothesis tests.
• You apply statistical learning techniques to quantitative trading strategies in Python.
• You will learn practical validation methods that professionals use shortly before operationalizing strategies.

This course is suitable for people who:

• Traders who are interested in applying statistical theory to trading.
• Statisticians who are interested in the application of probability theory to trading.