Quant Links 

Here you find some interesting pages for Quants where you can learn more: 

• QuantLib : A free/open-source library for quantitative finance ( Books, Slides, Vidéos, blogs)

• Useful QuantLib-related posts appear in a number of blogs

  • • 1. Changwei Xiong (熊昌炜), VP Model validation Nomura Singapore: IR, HJM, Pricing, Heston, Local/Stochas Vol, FX, Market Risk 

      2. Yue Kuen KWOK, Citi Bank China, Hong Kong University of Science and Technology ***

      3. Gabriele Pompa PHD Italy /pyBlackScholesAnalytics***

      4. Nicola Cantarutti , PhD Portugal , Financial-Models-Numerical-Methods***

      5. M. Chataigner, S. Crepey and M. Dixon, Deep Local Volatility **

      6. Martin Haugh, Imperial College, UK *****

      7. Antoine JAcquier - Imperial College ******

      8. The Derivatives Academy Maxime de Bellefroid**** ( Structured products in Practice, Pricing, Greeks)

      9. Dauphine MAster Finance , Option pricing 

      10. Hatim Kagalwala,  Stochastic-Calculus-and-Option-Pricing , assignement 

      11. Maximum Likelihood Estimation in Vasicek Black Scholes and Jump Diffusion Models

      12. Stratégie automatique de trading en finance*****

      13. theodormunteanu/volatility-smile

      14. Python Programming in Finance,Zheng-Liang Lu 

      15. The Barcelona Probability Seminar 

      16. The Excell Derivatives Scope -Ioannis Rigopoulos 

      17. An Intuitive Introduction to Fractional and Rough Volatilities by Elisa Alòs, Jorge A. León 

      18. rBergomi simulation and turbocharged pricing

      19. Short-time near-the-money skew in rough fractional volatility models Christian Bayer, Peter K. Friz

      20. you finance blog

      21. A Brief History of Volatility Models Harel Jacobson Harel Jacobson

      22. Stratégie automatique de trading en finance,  Xavier Dupre

      23. A Simple Breakout Trading Strategy in Python. Sofien Kaabar

      24. Saeed Amen, Cuemacro, FX 

      25. Martin Haugh, Imperial College 

      26. Nicola Cantarutti  , An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code **

      27. Risk Engineering : Risk analysis, Risk Metrics , VaR, Sensivity, Copula, Monte Carlo, Python simulation ... 

      28. QuantPie: videos, slides, Stocks, FX, Basket, Short Rates, Yield Curves, Bonds, Rates

      29. Babak Mahdavi Damghani: Quant , Fx, Volatility 

      30. Mesures de risque de marché: Thomas GUIBERT, Société Générale 

      31. Option Pricing: Pricing exercice solution 

      32. Optimization: Ioannis Kourouklides ,Python

      33. Justin Lars Kirkby ICE, Atlanta, USA Matlab Projects 

      34. FRM I exam preparation , Prateek Yadav 

      35. Tabea , UPMC, Stat 

      36. Marco Avellaneda 

      37. Structured Products - Finance 

      38. Volatility tracking, Nicola Cantarutti  

      39. Python Pandas - Series & Dataframes, Numerical Computing in Python, A gallery of Jupyter Notebooks***

      40. Finance Training Course 

      41. Klaus Spanderen's blog;

      42. Peter Caspers's blog;

      43. Bojan Nikolic's blog;

      44. Édouard Tallent's blog;

      45. Cogito Learning's blog;

      46. Mick Hittesdorf's blog;

      47. John Orford's blog;

      48. Luigi Ballabio's blog;

      49. Matthias Groncki's blog.

      50. *Goutham Balaraman's blog.

      51. Mikael Katajamäki's blog.

      52. Suhas Ghorpadkar's blog. 

• Coding Links for interviews 

  • • 1. Solutions to Hackerrank practice problems:  185 solutions to Hackerrank practice problems with Python 3 and Oracle SQL by marinskiy 

      2. Sphere Online Judge 

      3. Best solutions for Codility Lessons.

      4. 100+ Python challenging programming exercises

      5. Hacker Rank-Python-Algorithm-Solution, by juhilsomaiya 

      6. Aryan Singh, 30 days coding**

      7. Best GitHub links for Interviews coding **

      8. Tech Interview Handbook : Algo, Questions to ask, ...

      9. Algorithmes et programmation, (ENSAE) 

      10. Yagnshun - https://yangshun.github.io/tech-interview-handbook/

      11. Jwasham - https://github.com/jwasham/coding-interview-university

      12. Kevin - https://github.com/kdn251/interviews

      13. Maxim - https://github.com/MaximAbramchuck/awesome-interview-questions

      14. Tushar Roy - https://github.com/mission-peace/interview

      15. Andreis - https://github.com/andreis/interview

      16. CTCI - https://github.com/careercup/CtCI-6th-Edition

      17. Ravi, C++ - https://github.com/mandliya/algorithms_and_data_structures

      18. Kenny, JS - https://github.com/kennymkchan/interview-questions-in-javascript

• Quant Questions Interview

  • • 1. Quant Questions 

      2. Quant Net Questions 

      3. Brillant 

      4. PROBABILITY AND STOCHASTIC CALCULUS QUANT INTERVIEW QUESTIONS , book

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• Books 

• Stochastic Analysis for Finance with Simulations, Choe, Geon Ho 

• Stochastic Simulation and Applications in Finance with MATLAB Programs,  Huu Tue Huynh ,Van Son Lai , Issouf Soumare - Biblio UPMC

• Interest Rate Modeling: Theory and Practice, Lixin Wu

• 2019 Mathematical Modeling And Computation In Finance: With Exercises And Python , By Oosterlee Cornelis W, Grzelak Lech A

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Awesome Optimal Transport in Deep Learning

https://github.com/Lapsilago/Awesome-Optimal-Transport-in-Deep-Learning?tab=readme-ov-file#generative-modeling

[Gabriel Peyré & Marco Cuturi] Computational Optimal Transport [pdf] [page] [course notes]

• kilianFatras/awesome-optimal-transport [GitHub]

• changwxx/Awesome-Optimal-Transport-in-Deep-Learning [GitHub]

• abdelwahed/OT_for_big_data [GitHub]

Mathematical Modeling and Computation in Finance: With Exercises and Python and MATLAB Computer Codes

https://github.com/Lapsilago/QuantFinanceBook