u/priyo2902

• Which models do you use most frequently, and for what tasks?
• Which models have delivered the most practical value versus being primarily academic?
• How important are classical statistical models compared to modern ML methods?
• Are tree-based models still dominant, or is deep learning becoming more prevalent?
• If you were starting over today, which models would you prioritize learning?

Industry practitioners are invited to comment on any of the above. Thanks in advance.

I’d love to hear from people working in quant trading, HFT, or quantitative research:

1. Which models do you use most often? For what use case? Any details on features and data will be helpful.
2. Which models have proven useful in practice versus those that are mostly academic?
3. How important are classical statistical models compared to modern ML methods?
4. Are tree-based methods still dominant, or are deep learning approaches becoming more common?
5. If you were starting from scratch today, which models would you prioritize learning?

Thanks in advance for sharing your experience.

Over the past few weeks, I’ve decided to systematically learn quantitative modeling from first principles, covering both traditional statistical methods and modern machine learning techniques.

While following this path, I started wondering which models are genuinely useful in industry today, especially on the quantitative research side.
For example, I recently studied Hidden Markov Models (HMMs) for market regime detection, and it made me curious about what other models practitioners regularly use in production or research workflows.

I’d love to hear from people working in quant trading, portfolio management, systematic investing, HFT, or quantitative research:

1. Which models do you use most often? For what use case? Any details on features and data will be helpful.
2. Which models have proven useful in practice versus those that are mostly academic?
3. How important are classical statistical models compared to modern ML methods?
4. Are tree-based methods still dominant, or are deep learning approaches becoming more common?
5. If you were starting from scratch today, which models would you prioritize learning?

I’m looking for insights on both predictive modeling and broader quantitative research tasks such as forecasting, alpha generation, regime detection, risk modeling, execution, and portfolio construction.

Thanks in advance for sharing your experience.