Statistical Inferencing for Quantitative Trading Strategies
Statistical Inferencing for Quantitative Trading Strategies
Last updated 7/2025
Duration: 4h 8m | .MP4 1920x1080 30 fps(r) | AAC, 44100 Hz, 2ch | 2.41 GB
Genre: eLearning | Language: English
Last updated 7/2025
Duration: 4h 8m | .MP4 1920x1080 30 fps(r) | AAC, 44100 Hz, 2ch | 2.41 GB
Genre: eLearning | Language: English
Learn how to apply probability theory and statistical inferencing techniques to validate algorithmic trading strategies.
What you'll learn
- Learn basics for finance and probability theory for algorithmic trading.
- Learn statistical inferencing techniques such as parametric and nonparametric hypothesis tests.
- Employ statistical learning techniques on quantitative trading strategies in Python.
- Learn practical validation methods quants use before taking strategies into production.
Requirements
- Basic-intermediate Python programming.
Description
Have you asked:
Is myquant trading strategy performance statistically significant?
Are my in-sample performancesstatistically significant while controlling for model complexity and bias?Is my ML model an inefficiency detector or a piece of overfitting poppycock software?
If I backtest10 strategies, pick those with Sharpe > 1, am I headed for wealth or ruin?
Statistical Inferencing for Quantitative Trading Strategiesisone-of-a-kind quantitative lecture series on applying probability theory and statistical methods to constructrobust hypothesis tests for validation of trading strategiesusing distribution-free methods.
The course takes the student on a whirlwind tour of finance basics, statistics basics as well as more advanced and modern techniques in statistical decision/inferencing theory.
Hypothesis testing concepts, Type I/II errors, powers, FWER control, multiple testing frameworks are introduced under both parametric and non-parametric assumptions for quantitative research.
Classical location tests(t,sign,rank-sum) tests are discussed in addition to cutting edge techniques usingmonte-carlo permutation methods. The lectures take you through the motivation for the need to employ rigorous scientific procedures in validating trading strategies.
In pharmaceuticals, medicine and other high-stakes industries, experimental design and implementation are key to decision-making, such as the acceptance of new chemicals in treatments. Unfortunately - hardly the same amount of scientific rigour is paid in deciding whether to take a trading strategy live. Apparently, moon cycles and lunar phases are enough! For these people, the writing is in the wall.
Who this course is for:
- Traders interested in applying statistical theory to trading.
- Statisticians interested in applying probability theory to trading.
More Info