The Quant Roadmap: Everything I wish someone handed me on Day One

Getting into quantitative finance can feel overwhelming. There are hundreds of books, courses, projects and resources, but the bigger problem is knowing what to learn, and in what order.

I’m Priyanshu Priyank, and over the last few years I’ve worked across quantitative research, trading, alpha strategies, options, crypto, portfolio management and I am currently working as a Quant Portfolio Manager at 16 Alpha CAT III hedge fund.

Along the way, I realized how fragmented the learning journey can be. There are plenty of great resources, but figuring out what to learn next can take a lot of time. That’s also what led me to work on Quant Memo, a platform designed to make the quantitative finance learning journey more structured and accessible.

This is the exact roadmap I’d give anyone starting out in quant.

Tier 1: Foundations

C++ / Python

Learn both: C++ for speed, Python for flexibility. Mastering both lets you handle high-performance computing and leverage rich open-source libraries with ease.

C++ resources

• Programming: Principles and Practice Using C++, Bjarne Stroustrup: bit old but good fundamental book

• Effective Modern C++, Scott Meyers: my fav

• Udemy, C++, Abdul Bari: I learnt initially from here

• C++ Concurrency in Action, Anthony Williams: great to learn about multithreading

• Sams Teach Yourself C++ in 21 Days: great book for someone who needs a bit of layman content

Note: DON’T COVER ALL BOOKS. Read one or two according to your understanding and initial experience.

Python resources

• Fluent Python, Luciano Ramalho: a bit intermediate, can do it if you have basic Python knowledge

• Automate the Boring Stuff with Python, Al Sweigart: perfect for complete beginners to learn syntax

• freeCodeCamp Python tutorials: one-shot tutorials which can help before you dive into books

• Python for Finance, Yves Hilpisch: specialized to cover applications of Python

Note: You basically need to focus on certain Python libraries which are used, like NumPy, pandas, matplotlib, TA (great to build indicators) and yfinance (great for free data).

Maths

Quant roles demand strong problem-solving skills in maths, especially Probability & Statistics, along with Linear Algebra and Calculus. Prioritize Prob & Stats, as it forms the core of most quant models, ML algorithms and risk systems.

Linear Algebra

• Linear Algebra Done Right, Sheldon Axler: perfect book and I feel probably the only book that’s enough to get into quant

• Introduction to Linear Algebra, Gilbert Strang: classic MIT theory, can do if you start getting a thing for algebra

• Matrix Analysis, Horn & Johnson: absolutely not necessary for quant but can be used to go in-depth

Probability & Statistics

• Probability & Statistics: The Science of Uncertainty, University of Toronto: this book is very good, should solve the practice assignments

• Probability and Statistics, Morris H. DeGroot & Mark Schervish: a book that was suggested by a few of my seniors, can have a look at it

• NPTEL Prob & Stats course by Somesh Kumar Sir (IIT KGP): indeed a good explanation if you wanna skim through theory via video tutorials. One of my good friends from KGP suggested it

Calculus

• Calculus, James Stewart: it is a good beginner book to start with

• Calculus, Thomas & Finney: I did this one for my engineering mathematics, pretty much sufficient for quant

Note: You must have covered linear algebra and calculus during engineering mathematics 1/2, so that would be sufficient. Else you can refer to one of the suggested books. Majority focus should be on Prob & Stats, as it is asked in interviews quite a lot.

Finance basics

Learning finance and trading early signals strong interest in the field. It personally gave me an edge and helped me stand out in interviews and projects, thanks to my prior understanding of asset classes and market microstructure.

• Zerodha Varsity modules: ultimate resource, you can’t really ask for anything else for beginners for finance and trading

• A Beginner’s Guide to the Stock Market, Matthew Kratter: honestly a good book but won’t be needed if you cover Varsity

• Options Volatility and Pricing: personal favourite book but a bit advanced

• Options, Futures and Other Derivatives, John C. Hull: gold standard book to deep dive into finance and quant

Note: This section is not mandatory but would definitely give you an edge if you know about these basics already, so at least try to cover Zerodha Varsity modules.

DSA (Data Structures and Algorithms)

The most important topic generally asked in major quant firm interviews, as it is a very important problem-solving skill.

• Striver’s A2Z DSA sheet: honestly, in entirety it is very good. After this, only problem solving is enough

• Introduction to Algorithms (CLRS), Cormen, Leiserson, Rivest, Stein: probably the best and in-depth book for DSA. I would suggest people in first year can explore this one, although not mandatory

Note: Focus more on problem solving for DSA. That’s the ultimate rule to gain confidence for interview rounds.

Tier 2: Intermediate

ML and DL (and its use in trading)

This topic can come in handy a lot while building risk models and financial models. It can help you personally create really, really great projects.

Resources

• Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, Aurélien Géron: probably the best book to start ML

• 100 Days of ML, CampusX: a great friend of mine on X suggested this in combination with the first book, and it's actually a good combination to understand theory in detail

• Introduction to Statistical Learning (ISLR): covers a lot of the mathematical intuition part, not mandatory but very good

Specialised ML books for Quant/Finance

• Machine Learning for Asset Managers, Marcos López de Prado: really great book for application of ML for asset management, explore it once you're good with Python, finance and basic ML

• Machine Learning for Trading, Stefan Jansen: practically covers almost all the topics of ML we use in finance. Also make use of the official GitHub repo, it has a lot of useful content

• Quantitative Portfolio Management, Michael Isichenko: book for application of ML for portfolio management models

• Advances in Financial Machine Learning, Marcos López de Prado: considered the John C. Hull of applicative ML books for finance, heavily adapted and used a lot by quant funds

Note: Don't get too overwhelmed with the amount of content I have shared. Start slow, go with one book, and if it gives you a kick, try one of the applicative ML books. They will surely help you in building better quant projects for your resume.

Quant + Coding projects

For this topic I honestly don't have many resources, however I will share whatever project ideas I have which I believe will be really good for your resume for quant profiles.

1) Backtesting engine (multi-strategy + multi-asset classes) Build your own backtesting engine (Python preferred) and add good backtesting metrics to it (drawdown, Sharpe/Calmar ratio). Try to cover multiple strategies working on multiple asset classes.

2) Trading bot (live or paper trading) Try to at least implement a few trading bots for basic strategies: EMA divergence, RSI crossovers, Supertrend channel reversions. You can use the Binance API to create this for crypto for free, as most of the Indian APIs charge for these API keys. Honestly if you're working or planning to work as a Quant you will soon realise that these technical indicators have limited usecase for building models and trading engines as far better and advanced statistical and patter recognition models are used which help ensure slow decay of alpha over years.

3) Option chain analytics dashboard Pull NSE/Binance option chain data and analyze option Greeks, IV (implied volatility), PCR (put-call ratio) and expiry graph, and try to create a Streamlit dashboard for users to visualise them. Adding different option pricing models to this would be a great plus, making the project more detailed and complex.

4) Alpha signal research engine Create mean reversion/momentum signals using price, volatility and volume, and rank them via Sharpe/hit ratios. Extending them as multi-factor models (like WorldQuant style) would be a great plus.

5) Live market data handler (multi-timeframe data handler) Create your own market data handler for aggregating ticks into candles. The main issue to overcome in this project would be to handle the tick latency and correct database handling. Also try to add complexity by calculating real-time 10-12+ indicators for these candles.

Additional libraries which can help you in these projects

• Data: yfinance, ccxt, nsetools, Quandl

• ML & Stats: scikit-learn, statsmodels, xgboost, optuna, TA-Lib

• Backtest: backtrader, bt, zipline

• Infra: PostgreSQL, Redis, Streamlit, FastAPI

Note: Don't get overwhelmed due to a few jargons I used. Slowly, as you explore more finance/trading, ML and other books, these will be familiar for you.

CP Contests

Honestly, this isn't a topic, rather a goal you should set for yourself, as it helps you showcase your good problem-solving skills via your CP rating. Some tier 1 firms, if you're in a tier 1 college, apply rating shortlisting criteria too, so contests would be a great addition to your quant resume/profile.

1. Codeforces contests

2. CodeChef contests

3. LeetCode/AtCoder beginner contests

Companies usually prioritise CF ratings, so try to give those contests at least. Don't get disheartened if ratings don't increase much. It's not a necessity, but I'd suggest keep on grinding, it would help in interview rounds.

Tier 3: Advanced

Research & Alpha Papers

Research papers in finance and quant domains have long been a source of innovation. Reading and implementing them not only deepens your understanding but also gives you high-impact projects for your resume, especially if you add your own enhancements or experiments.

• SSRN (Social Science Research Network): honestly probably the best resource out there for quant/finance research papers

• arXiv Quantitative Finance section: it has a lot of good research papers related to applications of ML for quant finance

• WorldQuant BRAIN: the Brain platform has a lot of good research papers which can be read mostly for alpha research and generation

Note: This would be a great add-on, helping you with diving deep into research and stuff related to quant finance.

Interview Prep

This part doesn't have much advice, but a few extra resources I have which I wish to share with you.

• Heard on the Street, Timothy Crack: covers mental math, brainteaser puzzles and quant questions which are asked in interviews

• The Puzzle Palace (by a Jane Street alum): a great book for puzzle practice for interviews

• Quant Job Interview Questions and Answers, Xinfeng Zhou: has a lot of quant questions which are real-life asked in interviews, definitely should be done

• Fifty Challenging Problems in Probability, Frederick Mosteller: classic set of problem books

That's it. I tried my best to compile everything required to prepare for all the major topics in quant. If it feels too extensive and overwhelming, check out Quant Memo, where I've organized everything in one place. It is meant to provide what I did not have in my time ensuring that I give back to the community that made me thrive for absolutely free.

Keep grinding!!