QuantPy @TheQuantPy
👨💻Interested in quantitative finance with python 💡 https://t.co/HgIW2mragp 🔋 https://t.co/mqEC5lr0ns Build something useful! Joined February 2022-
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Feature importance with substitution effects: - mean decrease impurity (decision trees only) - mean decrease accuracy (any classifier, any performance score) Feature importance without substitution effects: - single feature importance (SFI)
How to handle multi-collinearity between features? Marcos Lopez de Prados suggestion -> Use an orthogonalization procedures! For example, perform PCA on raw features, then perform feature importance on transformed orthogonal features. So what are feature importance methods?
Feature importance. Marcos first law of backtesting - ignore at your own peril. “Backtesting is not a research tool. Feature importance is.” Marcos Lopez de Prado (2018). @lopezdeprado
Some of my takeaways/notes on feature importance from Marcos Lopez de Prados book advances in financial machine learning.
Have you ever used PCA in your quantitative finance work? What was your experience like?
Unravel the hidden dimensions of finance with Principal Component Analysis (PCA)!
Dive into the world of PCA and unlock the hidden potential of your financial data!
PCA in finance is like a compass that guides investors through the maze of data, helping them make informed decisions.
PCA used in portfolio optimization, risk management, and factor analysis - guiding informed decisions.
PCA works by transforming a set of correlated variables into a new set of uncorrelated variables called principal components. These components are ordered in terms of their importance, with the first component explaining the maximum amount of variance in the data.
Principal Component Analysis (PCA) is a powerful statistical technique used in finance to reduce the dimensionality of data while preserving its essential information.
What skills do you think are most valuable for a career in quantitative finance?
Looking to navigate the labyrinth of career paths in quantitative finance? Let's shed some light on the possibilities!
Share your favorite quantitative finance career path in the comments below!
The world of quantitative finance is your oyster, with endless opportunities to explore and grow. So, choose your path wisely and embark on a thrilling journey!
Choose your path: quantitative analyst, software engineer, or risk manager.
Quantitative finance offers a plethora of career paths, each requiring a unique set of skills. Whether you're passionate about coding, modeling, or risk analysis, there's a role for you.
In the dynamic world of quantitative finance, career paths are as diverse as the strategies themselves. From data scientists to risk managers, let's explore the exciting options available.
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