QuantPy

👨‍💻Interested in quantitative finance with python 💡 🔋

[QuantPy @TheQuantPy]

Here is a broad picture of Mathematical & Computational #Finance .

Shock!!! Not all #options can be priced using black-scholes.

Although we live in a generalised black-scholes world, non-vanilla contracts require numerical methods to accurately measure and control risk.

[QuantPy @TheQuantPy]

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)

[QuantPy @TheQuantPy]

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

[QuantPy @TheQuantPy]

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?

[QuantPy @TheQuantPy]

Some of my takeaways/notes on feature importance from Marcos Lopez de Prados book advances in financial machine learning.

[QuantPy @TheQuantPy]

Have you ever used PCA in your quantitative finance work? What was your experience like?

[QuantPy @TheQuantPy]

Unravel the hidden dimensions of finance with Principal Component Analysis (PCA)!

[QuantPy @TheQuantPy]

Dive into the world of PCA and unlock the hidden potential of your financial data!

[QuantPy @TheQuantPy]

PCA in finance is like a compass that guides investors through the maze of data, helping them make informed decisions.

[QuantPy @TheQuantPy]

PCA used in portfolio optimization, risk management, and factor analysis - guiding informed decisions.

[QuantPy @TheQuantPy]

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.

[QuantPy @TheQuantPy]

Principal Component Analysis (PCA) is a powerful statistical technique used in finance to reduce the dimensionality of data while preserving its essential information.

[QuantPy @TheQuantPy]

What skills do you think are most valuable for a career in quantitative finance?

[QuantPy @TheQuantPy]

Looking to navigate the labyrinth of career paths in quantitative finance? Let's shed some light on the possibilities!

[QuantPy @TheQuantPy]

Share your favorite quantitative finance career path in the comments below!

[QuantPy @TheQuantPy]

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!

[QuantPy @TheQuantPy]

Choose your path: quantitative analyst, software engineer, or risk manager.

[QuantPy @TheQuantPy]

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.

[QuantPy @TheQuantPy]

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.