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Quant-Finance-Resources

Courses, Articles and many more which can help beginners or professionals.

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创建于 2019-08-12 · 更新于 2026-10-15 · 今日第 5872 名
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PyPatel/Quant-Finance-Resources 排名第 5,872，有 1,070 个星标。

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关键参数与规格
关键参数与规格
参数值
类别Lists
子类别Domain-specific
关键词Finance
星标数1,070
分叉数158
贡献者1
创建日期2019-08-12
更新日期2026-10-15
1 天星标数1
7 天星标数6
排名5,872
上次排名11,692
排名变化5,820
7 天星标排名5,187
上次 7 天星标排名5,647
7 天星标排名变化460
总星标排名10,581
上次总星标排名10,582
总星标排名变化1
国家India
README 摘录

Quant-Finance-Resources **Courses, Articles and many more which can help beginners or professionals. ** *Finance is mostly details, and just having the ability to systematize and categorize and focus on details can be a huge advantage. * by Micheal Burry This resources are specifically meant for **STEM grads**. Most of the courses are Math or Coding heavy. About me I work as a Quant Trader in High Frequency Trading firm focused on Indian Markets in Equities and F&O. Tech) from

IIT Madras in Automotive Engineering. Courses and Lectures **Note:** The courses listed below will NOT show any Coursera or Youtube Fams, because those courses are open for all (Anyone can take it, Medical doctor or lawyer can if needed. ) I don't want to (or want you to) spend time in these courses because these courses give you a flavor of the subject and not deep understanding. Being an engineer, I want to (wish the same for you) use my already

learned mathematics skills to advance further in the topic and dive deep, to do some meaningful work and push the boundary. If you had Udemy courses and say "I know ML", I will lash you with a wet noodle (No offense to udemy). Mathematics **Numerical Linear Algebra for Coders** by fast. ai **Introduction to Probability** by MIT OCW **Topics in Mathematics with Applications in Finance** by *MIT Mathematics* Lecture Page AI **Machine Learning** by *Tom Mitchell* CMU Course Page **Introduction

to Reinforcement Learning** by *David Silver* from DeepMind (AlphaGo, AlphaZero etc. creator) Youtube Playlist of the course Quant Finance **Quantopian Lectures** for Python and Statistics Lectures **Global Financial Crisis** (Finance only) by *Timothy F. Secretary of the Treasury during crisis) Very good course for beginners in Finance or interested in Crisis analysis. Coursera course **Introduction to Market Microstructures** by *Paul Besson* (Paul heads Euronext's quantitative research department) Lecture Page Coding Python (Book) **Python for Finance**: Analyze Big Financial Data by

*Yves Hilpisch* (Citadel Recommended) C++ (Book) **Accelerated C++** by *Andrew Koenig, Barbara E. Moo* (If you have good background in coding and in OOP like python, Java programmers) (Book) **Effective Modern C++** by *Scott Meyers* Books Mathematics **Statistics** (4th edition) by *David Freedman, Robert Pisani, Roger Purves* (Citadel Recommended) **How to Lie with Statistics** by *Darrell Huff* (Interesting takes on how you can manipulate human perception with Graph) **Thinking Strategically** by *Avinash Dixit and Barry Nalebuff* (Game theory book) (Citadel

Recommended) **Analysis of Financial Time Series** by *Ruey S. Tsay* (Good for Time Series Analysis) AI **Deep Learning** by Ian Goodfellow et al. **Reinforcement Learning An Introduction** by *Sutton and Barto* Quant Finance **Options, Futures, and other Derivatives** by *John C Hull* (Citadel Recommended) **Advances in Financial Machine Learning** by *Marcos Lopez de Prado* **Financial Calculus: An Introduction to Derivative Pricing** by Martin Baxter (He ran Quant for Lehman and Nomura) Articles **The Gambler Who Cracked the Horse-Racing Code

** in Bloomberg Businessweek here **Learning and Understanding in the Mirror of Mathematics, Ch. 1,2** by Misha Gromov available here

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记录了多少星标和分叉？

PyPatel/Quant-Finance-Resources 有 1,070 个星标和 158 个分叉。

仓库排名是多少？

PyPatel/Quant-Finance-Resources 排名第 5,872。

哪个仓库排名最接近？

PyPatel/Quant-Finance-Resources 与 panxunying/ai-coding-welfare 比较，后者排名第 5,881，有 1,060 个星标。

README

Quant-Finance-Resources

Courses, Articles and many more which can help beginners or professionals.

Finance is mostly details, and just having the ability to systematize and categorize and focus on details can be a huge advantage. by Micheal Burry

This resources are specifically meant for STEM grads. Most of the courses are Math or Coding heavy. Take it at your own risk.

About me

I work as a Quant Trader in High Frequency Trading firm focused on Indian Markets in Equities and F&O. I did my Masters (M.Tech) and Bachelors (B.Tech) from IIT Madras in Automotive Engineering.

Courses and Lectures

Note: The courses listed below will NOT show any Coursera or Youtube Fams, because those courses are open for all (Anyone can take it, Medical doctor or lawyer can if needed.) I don't want to (or want you to) spend time in these courses because these courses give you a flavor of the subject and not deep understanding. Being an engineer, I want to (wish the same for you) use my already learned mathematics skills to advance further in the topic and dive deep, to do some meaningful work and push the boundary.

If you had Udemy courses and say "I know ML", I will lash you with a wet noodle (No offense to udemy).

Mathematics

  • Numerical Linear Algebra for Coders by fast.ai
  • Introduction to Probability by MIT OCW
  • Topics in Mathematics with Applications in Finance by MIT Mathematics Lecture Page

AI

Quant Finance

  • Quantopian Lectures for Python and Statistics Lectures
  • Global Financial Crisis (Finance only) by Timothy F. Geithner (U.S. Secretary of the Treasury during crisis) Very good course for beginners in Finance or interested in Crisis analysis. Coursera course
  • Introduction to Market Microstructures by Paul Besson (Paul heads Euronext's quantitative research department) Lecture Page

Coding

Python

  • (Book) Python for Finance: Analyze Big Financial Data by Yves Hilpisch (Citadel Recommended)

C++

  • (Book) Accelerated C++ by Andrew Koenig, Barbara E. Moo (If you have good background in coding and in OOP like python, Java programmers)
  • (Book) Effective Modern C++ by Scott Meyers

Books

Mathematics

  • Statistics (4th edition) by David Freedman, Robert Pisani, Roger Purves (Citadel Recommended)
  • How to Lie with Statistics by Darrell Huff (Interesting takes on how you can manipulate human perception with Graph)
  • Thinking Strategically by Avinash Dixit and Barry Nalebuff (Game theory book) (Citadel Recommended)
  • Analysis of Financial Time Series by Ruey S. Tsay (Good for Time Series Analysis)

AI

  • Deep Learning by Ian Goodfellow et al.
  • Reinforcement Learning An Introduction by Sutton and Barto

Quant Finance

  • Options, Futures, and other Derivatives by John C Hull (Citadel Recommended)
    • You will first need to have sound understanding of Financial Markets (Not expertise, but understanding is necessary.)
  • Advances in Financial Machine Learning by Marcos Lopez de Prado
  • Financial Calculus: An Introduction to Derivative Pricing by Martin Baxter (He ran Quant for Lehman and Nomura)

Articles

  • The Gambler Who Cracked the Horse-Racing Code in Bloomberg Businessweek here
  • Learning and Understanding in the Mirror of Mathematics, Ch.1,2 by Misha Gromov available here
  • High-Frequency Cross-Market Trading: Model Free measurements and Applications (Good view on US HFT Market and Networks) pdf of presentation

Papers

  • Beating the bookies with their own numbers - and how the online sports betting market is rigged Paper
  • Machine Learning for Trading by Gordon Ritter Paper (Talks about implementation of RL into Finance)
  • Deep Hedging by Hans Buehler et al. (JP Morgan Quants from London Office) Paper
    • This paper has been already implemented by JPM Article
    • Hedging Vanilla OTC Products using RL
  • A Stochastic Model for Order Book Dynamics by Rama Cont et al. Paper
  • Price dynamics in a Markovian limit order market by Rama Cont et al. Paper

Psychology

  • 48 Laws of Power by Robert Greene

Blogs

Interview Prep

  • Probability and Market by Jane Street pdf

PS: I am weak at grammar and someone told there are too many mistakes here, instead of fixing it..! It's open source for a reason...

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