Who should I follow to learn about Quant Trading from experts?

If you want to **actually learn quant trading** rather than get sucked into “5 strategies that make 100% a year” content, I’d follow a relatively small group of serious practitioners and researchers. ## My top follows - **Ernest Chan** - Probably my #1 recommendation for getting started with systematic trading. His material is practical: strategy research, backtesting, statistical arbitrage, portfolio construction, and the realities of implementation. His *Quantitative Trading* is a particularly good starting point. [Resources & Backtesting Tutorials | Om Arora](https://omarora.in/quantitative-finance/resources-backtesting-tutorials/?utm_source=chatgpt.com) - **Robert Carver** - Excellent for understanding how professional systematic traders think about signals, volatility scaling, position sizing, diversification, and portfolio construction. His framework is especially useful for futures/systematic macro. [Resources & Backtesting Tutorials | Om Arora](https://omarora.in/quantitative-finance/resources-backtesting-tutorials/?utm_source=chatgpt.com) - **Marcos López de Prado** - Follow him for the more rigorous side of quant research: financial ML, overfitting, cross-validation, feature engineering, and statistical validity. I'd study him *after* you understand basic systematic trading. [Resources & Backtesting Tutorials | Om Arora](https://omarora.in/quantitative-finance/resources-backtesting-tutorials/?utm_source=chatgpt.com) - **Corey Hoffstein / Flirting with Models** - One of my favorites for learning how professional systematic investors actually reason about factors, portfolio construction, trend following, and risk. [Best Quant Finance Podcasts 2026: 12 Shows Every Quant Should Follow | Quantt](https://www.quantt.co.uk/resources/best-quant-finance-podcasts-2026?utm_source=chatgpt.com) - **Dimitri Bianco (Fancy Quant)** - Particularly useful if you're interested in **becoming a quant**, rather than merely building retail algorithms. His content covers quant careers, research, risk, and interviews with industry professionals. [Learning Center - Best YouTube Channels for Quant Finance | AlgoTradingMap](https://algotradingmap.com/learning-center?utm_source=chatgpt.com) - **QuantStart** - Great practical bridge between theory and implementation. They cover quantitative finance, statistics, ML, systematic trading and provide code-oriented tutorials. [About Us | QuantStart](https://www.quantstart.com/about/?utm_source=chatgpt.com) - **Jane Street** - Follow their educational material for probability, statistics, market-making concepts and the kind of problem-solving expected at elite trading firms. Their public content is unusually good for interview preparation. [Learning Center - Best YouTube Channels for Quant Finance | AlgoTradingMap](https://algotradingmap.com/learning-center?utm_source=chatgpt.com) - **Two Sigma / G-Research / Hudson River Trading** - Good accounts to follow once you want to see how machine learning, statistics and engineering are applied in institutional trading rather than just in toy backtests. [Learning Center - Best YouTube Channels for Quant Finance | AlgoTradingMap](https://algotradingmap.com/learning-center?utm_source=chatgpt.com) ## Podcasts worth your time I'd put these on your regular rotation: 1. **Flirting with Models** - systematic investing/factor research. 2. **Top Traders Unplugged** - trend following, managed futures and systematic macro. 3. **The Algorithmic Advantage** - interviews with systematic traders and portfolio managers. [Best Quant Finance Podcasts 2026: 12 Shows Every Quant Should Follow | Quantt](https://www.quantt.co.uk/resources/best-quant-finance-podcasts-2026?utm_source=chatgpt.com) 4. **Chat With Traders** - broader trading stories and practitioner perspectives. 5. **Better System Trader** - systematic strategy development and research. [Resources – SMART Trading Strategies](https://smarttradingstrategies.com/resources/?utm_source=chatgpt.com) ## One important distinction If your goal is **a quant career at Jane Street/Citadel/Two Sigma/HRT/etc.**, don't primarily follow retail algo-trading influencers. I'd spend your time roughly like this: **40%** probability/statistics/math **25%** programming/data structures/Python/C++ **20%** market microstructure + trading **15%** strategy research/backtesting And learn to be suspicious of anyone who shows you a backtest without discussing **transaction costs, slippage, look-ahead bias, survivorship bias, multiple testing, out-of-sample validation, and capacity**. Those are much more important than finding the next “alpha strategy.” [Top 25 Algorithmic Trading YouTube Channels (Intermediate/Advanced)](https://insights.wisdomchain.com/top-25-algorithmic-trading-youtube-channels-intermediate-advanced/?utm_source=chatgpt.com) If you tell me your current level (**beginner / Python programmer / finance background / math-heavy**) and whether you're aiming for **a quant job or your own trading**, I can give you a **3–6 month quant-trading learning roadmap with exactly what to read, watch, and build each week**.

**To learn about Quant Trading, follow pioneers like Jim Simons and Ed Thorp, leading academics such as Jim Gatheral and Petter Kolm, and modern practitioners sharing insights on YouTube like Patrick Boyle and Dimitri Bianco.** This mix of legendary traders, professors, and active professionals will give you both historical foundations and practical, current perspectives. ## 📊 Legendary Quant Traders These figures shaped the field and remain essential to study: * **Jim Simons** – Founder of Renaissance Technologies, built the Medallion Fund with ~40% annual returns. His approach proved that hiring mathematicians and scientists could revolutionize finance. * **Ed Thorp** – Mathematician, author of _Beat the Market_, pioneer of statistical arbitrage and convertible bond trading. * **David Shaw** – Founder of D.E. Shaw, one of the earliest large-scale quant hedge funds. * **Cliff Asness** – Co-founder of AQR Capital, known for factor investing and bridging academia with practice.[Greatest Quant Traders of All Time: 12 Legends Who Shaped Quantitative Finance | Quantt](https://www.quantt.co.uk/resources/greatest-quant-traders-of-all-time) ## 🎓 Top Quant Professors (2025 Rankings) Academics who actively shape quant education and industry: * **Jim Gatheral (Baruch College)** – Leading researcher in volatility modeling. * **Petter Kolm (NYU Courant)** – Expert in portfolio optimization and risk management. * **Charles-Albert Lehalle (École Polytechnique)** – Authority on market microstructure and execution. * **Frank Fabozzi (Johns Hopkins)** – Author of seminal texts on fixed income and quantitative investing.[2025's Top 20 Quant Professors](https://www.rebellionresearch.com/2025s-top-20-quant-professors) ## 📺 YouTube Channels for Practical Learning Accessible, practitioner-led content: * **Patrick Boyle on Finance** – Hedge fund manager and professor, explains derivatives and markets with humor. * **Dimitri Bianco (Fancy Quant)** – Head of Quant Risk & Research, shares career advice and technical insights. * **Coding Jesus** – Quant developer, covers interview prep, compensation, and coding strategies. * **QuantPy** – Focuses on coding quant strategies in Python, bridging theory and practice.[Learning Center - Best YouTube Channels for Quant Finance | AlgoTradingMap](https://algotradingmap.com/learning-center.html) ## 🏫 Training Programs & Faculty Structured learning from institutions: * **QuantInsti Faculty** – Includes experts like Dr. Thomas Starke (AAAQuants), Dr. Yves Hilpisch (Python Quants), and Nitesh Khandelwal (QuantInsti CEO). They specialize in algorithmic trading, Python for finance, and systematic strategies.[QuantInsti Faculty: Top Algorithmic Traders and Quantitative Finance Experts](https://www.quantinsti.com/faculty) ## ⚠️ Risks & Considerations * **Beware of hype**: Many online “quant trading” influencers are not credible. Stick to verified academics, practitioners, and audited track records. * **Quant ≠ guaranteed profits**: Even top funds face drawdowns; strategies require constant adaptation. * **Technical depth required**: Strong foundations in math, statistics, and programming are essential before applying strategies. ✅ **Best approach:** Start with foundational books by Ed Thorp and Jim Gatheral, follow practitioner channels like Patrick Boyle, and explore structured programs (QuantInsti, NYU Courant). This combination ensures you learn both the theory and the real-world application of quant trading. Would you like me to create a **step-by-step learning roadmap** (books, courses, channels, and practice projects) so you can follow a structured path into quant trading?

Learning quantitative trading requires a mix of statistics, programming, and market mechanics. Rather than relying solely on social media influencers, the most effective way to learn is by following industry practitioners, academics, and platform educators who share rigorous, technical content. [How to Become a Quant - Master of Science in Computational Finance](https://www.cmu.edu/mscf/news/how-to-become-a-quant#:~:text=In%20addition%20to%20mathematics%20and%20computer%20programming%2C,analysis%20to%20set%20up%20these%20algorithmic%20models.) **Industry Practitioners & Authors** * **Ernie Chan:** A veteran quant hedge fund manager and author of foundational books like _Algorithmic Trading_ and _Quantitative Trading_. His blog and writings provide practical insight into strategy backtesting and implementation details. [Self-Study Plan for Becoming a Quantitative Trader - Part I - QuantStart](https://www.quantstart.com/articles/Self-Study-Plan-for-Becoming-a-Quantitative-Trader-Part-I/#:~:text=The%20following%20will%20provide%20you%20with%20a,significant%20implementation%20detail%20on%20quant%20trading%20strategies.) * **Marcos López de Prado:** A leading authority in machine learning for finance. Author of _Advances in Financial Machine Learning_ , he is essential reading for understanding how to properly apply modern data science to financial data without falling for common statistical traps. * **Dr. Ernest P. Chan and Yves Hilpisch:** Yves Hilpisch is great for Python-specific financial applications, algorithmic trading architecture, and using AI in finance. **Platforms & Communities** * **QuantStart (Michael Halls-Moore):** An exceptional self-study resource providing tutorials on mathematical foundations, Python programming, and time-series analysis for aspiring quantitative traders. * **QuantConnect & Numerai:** Platforms where you can see what active algorithmic traders are building, test your own models against live data, and compete in data science tournaments. * **QuantInsti / Quantra:** Offer structured learning tracks ranging from beginner introductions to advanced algorithmic trading modules. **Essential Textbooks to Anchor Your Learning** * **John Hull:** _Options, Futures, and Other Derivatives_ (the undisputed "bible" for derivatives and market structure). [How I would start learning Quantitative Trading again - Medium](https://medium.com/@ammar--ansari/how-i-would-start-learning-quantitative-trading-again-53d86492c785) * **Ruey S. Tsay:** _Analysis of Financial Time Series_ (critical for learning how to model market data). [How I would start learning Quantitative Trading again - Medium](https://medium.com/@ammar--ansari/how-i-would-start-learning-quantitative-trading-again-53d86492c785#:~:text=*%20Analysis%20of%20Financial%20Time%20Series%20\(Ruey,an%20important%20part%20of%20building%20a%20strategy.) * **Sheldon Ross:** _A First Course in Probability_ (essential for building the mental framework required for quant interviews). [How I would start learning Quantitative Trading again - Medium](https://medium.com/@ammar--ansari/how-i-would-start-learning-quantitative-trading-again-53d86492c785#:~:text=This%20summary%20is%20an%20attempt%20to%20shed,Heard%20on%20the%20Street%20\(Chapter%201%2C%204\)) What is your current background in programming or mathematics? Knowing where you are starting from can help narrow down the best first steps.

Learning quantitative trading requires a mix of market mechanics, statistical rigor, programming, and financial theory. Depending on whether you want academic depth, practical coding strategies, or industry insights, here are the top practitioners, writers, researchers, and resources to follow: Newsletters & Substacks (Deep Dives & Strategy) * **QuantStart (Michael Halls-Moore)** : Essential for learning how to implement quantitative trading systems, backtesting, and algorithmic setups using Python and C++. * **Young and Calculated** : Excellent for modern insights into what quantitative researchers actually read, breaking down academic machine learning papers and cross-sectional equity returns. [What the Best Quant Researchers Actually Read](https://youngandcalculated.substack.com/p/what-the-best-quant-researchers-actually) * **Money Stuff by Matt Levine (Bloomberg)** : While not strictly a pure quant newsletter, Matt Levine explains market structure, complex financial products, and high-finance mechanics with unmatched clarity and wit. Industry Legends & Researchers (X / Twitter & Blogs) * * * **_Ernie Chan_ (`@chanbot`)**: A veteran quant trader and author of classic books like _Algorithmic Trading_ and _Quantitative Trading_. His blog and social media feeds offer practical, realistic advice on strategy building and risk management. * * * **_Marcos López de Prado_** : A prominent quant researcher and author of _Advances in Financial Machine Learning_. Follow his published academic papers and institutional commentary if you want rigorous, institutional-grade machine learning applications for finance. * * * **_Dr. Howard Bandy_** : A great follow for systematic trading, asset allocation, and rigorous backtesting methodology, particularly for technical and quantitative validation. Open Communities & Platforms * **QuantConnect Community & Forums**: An algorithmic trading platform where institutional and retail quants share open-source code, discuss factor models, and debate live strategy performance. * **r/algotrading on Reddit** : A massive community for discussing data sources, brokers, statistics, and programming hurdles. (Filter for high-upvote educational posts rather than day-to-day beginner questions). To help me narrow this down, are you more interested in: * **Coding and building backtests** (Python/C++ implementation)? * **Academic theory and statistics** (Machine learning, stochastic calculus)? * **Macro/market structure** insights?