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.
Note: 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 gives you flavour of subject and not deep understanding. Being an engineer, I want to (wish the same for you) use my already learnt mathematics skills to advance further in topic and dive deep, to do some meaningful work or research.
If you had Udemy courses and say "I know ML", I will lash you with a wet noodle (No offense to udemy).
- 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
- 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
- (Book) Python for Finance: Analyze Big Financial Data by Yves Hilpisch (Citadel Recommended)
- (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
- 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)
- Deep Learning by Ian Goodfellow et al.
- Reinforcement Learning An Introduction by Sutton and Barto
- 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)
- 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
- 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
- Fast AI Awesome courses and blogs
- http://koaning.io/ by Vincent D. Warmerdam (Use of Simple Mathematics into ML)
- Colah's Blog by Christopher Olah (Ex OpenAI and Google Brain)
- Quantitative Trading by E P Chan (Authored multiple books including Machine Trading, Algorithmic Trading)
- Distill Research Journal (About ML algorithms research papers presented with interactive visualization for better understanding)
- Sebastian Ruder's Blog (Seb is Research Scientist at Deep Mind, NLP Research)
- Systematic Edge (HFT Market making and relative value strategy, Algo trader in Chicago)
- SNIPER IN MAHWAH & FRIENDS Blog on HFT Networks and latency
- Quantitative Research and Trading by Jonathan Kinlay (PhD in Economics, Previously NYU Stern and CMU Prof)
- Applied Probability Notes by Neil Walton (PhD, Ex Head of Probability and Statistics group at University of Manchester) Applied Probability is main theme for Optimization, Game theory, Dynamic Programming and RL
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...