I've spent a lot of time researching for the best books to learn algo trading mostly focused on personal use (not to get an algo trading job) and I wanted to share it with you guys in case it would help anyone.
Its definitely a lot of books and I doubt anyone will read all of them, but maybe it can help you pick a few from each category to learn something new.
**If you have any suggestion of books that should definetly be added to the list or removes feel free to let me know! :D**
# Foundational Finance and Markets
1. Economics in One Lesson (Henry Hazlitt) - 218 pages
2. A Random Walk Down Wall Street (Burton Malkiel) - 480 pages
3. The Little Book of Common Sense Investing (John C. Bogle) - 320 pages
4. Reminiscences of a Stock Operator (Edwin Lefèvre) - 288 pages
5. Flash Boys (Michael Lewis) - 320 pages
6. Trading and Exchanges (Larry Harris) - 656 pages
# Financial Fundamentals Analysis
1. How to Read a Financial Report (John A. Tracy) - 240 pages
2. Financial Statements: A Step-by-Step Guide (Thomas R. Ittelson) - 304 pages
3. One Up on Wall Street (Peter Lynch) - 304 pages
4. The Intelligent Investor (Benjamin Graham) - 640 pages
5. Security Analysis (Benjamin Graham and David Dodd) - 816 pages
# Mathematics and Statistics for Quantitative Finance
1. The Mathematics of Money Management (Ralph Vince) - 400 pages
2. Cycle Analytics for Traders (John F. Ehlers) - 235 pages
3. A Primer for the Mathematics of Financial Engineering (Dan Stefanica) - 284 pages
4. Stochastic Calculus for Finance (Steven Shreve) - 187 pages
5. Time Series Analysis (James D. Hamilton) - 816 pages
6. Analysis of Financial Time Series (Ruey S. Tsay) - 720 pages
# Programming and Data Handling in Finance
1. Python for Finance (Yves Hilpisch) - 586 pages
2. Python for Algorithmic Trading (Yves Hilpisch) - 380 pages
3. Trading Evolved: Anyone Can Build Killer Trading Strategies in Python (Andreas Clenow) - 435 pages
4. The Algorithmic Trading Cookbook (Jason Strimpel) - 300 pages
5. Hands-On AI Trading with Python, QuantConnect, and AWS (Matthew Scarpino) - 416 pages
# Algorithmic Trading Frameworks and Backtesting
1. Quantitative Trading: How to Build Your Own Algorithmic Trading Business (Ernest Chan) - 182 pages
2. Building Winning Algorithmic Trading Systems (Kevin J. Davey) - 286 pages
3. Systematic Trading (Robert Carver) - 325 pages
4. Trading Systems and Methods (Perry J. Kaufman) - 1232 pages
5. The Science of Algorithmic Trading and Portfolio Management (Robert Kissell) - 492 pages
6. Algorithmic Trading Methods: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques (Robert Kissell) - 612 pages
7. Algorithmic Trading and DMA (Barry Johnson) - 574 pages
# Trading Strategies and Modeling
1. Inside the Black Box: A Simple Guide to Quantitative and High-Frequency Trading (Rishi K. Narang) - 336 pages
2. Algorithmic Trading: Winning Strategies and Their Rationale (Ernest Chan) - 224 pages
3. Stocks on the Move (Andreas F. Clenow) - 288 pages
4. Quantitative Momentum (Wes Gray) - 208 pages
5. Quantitative Value (Wes Gray) - 288 pages
6. The Art and Science of Technical Analysis (Adam Grimes) - 480 pages
7. Finding Alphas: A Quantitative Approach to Building Trading Strategies (Igor Tulchinsky) - 320 pages
8. Active Portfolio Management (Richard C. Grinold and Ronald N. Kahn) - 596 pages
# Risk Management and Portfolio Optimization
1. Machine Trading: Deploying Computer Algorithms to Conquer the Markets (Ernest P. Chan) - 264 pages
2. Leveraged Trading (Robert Carver) - 346 pages
3. Causal Factor Investing (Marcos López de Prado) - 100 pages
# Machine Learning and AI in Trading
1. Machine Learning for Asset Managers (Marcos López de Prado) - 141 pages
2. Advances in Financial Machine Learning (Marcos López de Prado) - 336 pages
3. Machine Learning for Algorithmic Trading (Stefan Jansen) - 820 pages
4. Machine Learning in Finance: From Theory to Practice (Matthew F. Dixon, Igor Halperin, and Paul Bilokon) - 548 pages
# Advanced Derivatives and Asset Classes
1. Option Volatility & Pricing: Advanced Trading Strategies and Techniques (Sheldon Natenberg) - 592 pages
2. Options, Futures, and Other Derivatives (John C. Hull) - 880 pages
3. Paul Wilmott Introduces Quantitative Finance (Paul Wilmott) - 736 pages