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    Statistical Inferencing for Quantitative Trading Strategies

    Posted By: lucky_aut
    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

    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

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