Quant Roadmap 2025 🚀 If you're just starting your quant journey (or...
Priyanshu Priyank@PriyanshuP1405
138 views
Dec 02, 2025
~9 min read
Advertisement
1
Quant Roadmap 2025 🚀
If you're just starting your quant journey (or feeling stuck), here's a roadmap broken into 3 tiers.
Work on the parts where you're weak and stack your edge from there.
Tier 1 ( Foundations )
->C++/Python
->Maths: Linear Algebra, Probability, Stats , Calculus
->Finance basics
->DSA ( Data structure and algorithms )
Tier 2 ( Intermediate )
-> ML ( and its use in trading )
-> Quant + coding projects
-> CP contests
Tier 3 ( Advanced )
-> Research & alpha papers
-> Interview Prep
If you're just starting your quant journey (or feeling stuck), here's a roadmap broken into 3 tiers.
Work on the parts where you're weak and stack your edge from there.
Tier 1 ( Foundations )
->C++/Python
->Maths: Linear Algebra, Probability, Stats , Calculus
->Finance basics
->DSA ( Data structure and algorithms )
Tier 2 ( Intermediate )
-> ML ( and its use in trading )
-> Quant + coding projects
-> CP contests
Tier 3 ( Advanced )
-> Research & alpha papers
-> Interview Prep
2
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 Practices 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 abit layman content )
Note :- DONT 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 ) , yfinance ( great for free data )
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 Practices 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 abit layman content )
Note :- DONT 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 ) , yfinance ( great for free data )
3
Maths
Quant roles demand strong problem-solving skills in math — 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
->Prob & stats : The science of uncertainity - 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 vid tutorials - one of my good friend 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/calculus during engineering mathematics 1/2 so that would be sufficient else you can refer to one of the suggested books - would be sufficient. Majority focus should be on prob & stats as it is asked in interviews quite a lot.
Quant roles demand strong problem-solving skills in math — 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
->Prob & stats : The science of uncertainity - 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 vid tutorials - one of my good friend 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/calculus during engineering mathematics 1/2 so that would be sufficient else you can refer to one of the suggested books - would be sufficient. Majority focus should be on prob & stats as it is asked in interviews quite a lot.
4
Finance & Trading
Learning finance and about trading early signals strong interest in the field — it personally gave me an edge and helped me stand out in interviews & projects with my prior understanding about asset classes and market microstructure.
Resources:-
->Zerodha varsity modules ( Ultimate resource you can't really ask for anything else for beginners for finance & trading )
->A Beginner’s Guide to the Stock Market – Matthew Kratter ( honestly a good book but wont be needed if you cover varsity )
->Options volatility and pricing ( personal favourite book but abit advanced )
-> Options futures and other derivatives - John C Hull ( gold standard book to deep dive into finance & quant )
Note:- This is section is not mandatory but would definitely give you an edge if you know about these basics already so atleast try to cover zerodha varsity modules
Learning finance and about trading early signals strong interest in the field — it personally gave me an edge and helped me stand out in interviews & projects with my prior understanding about asset classes and market microstructure.
Resources:-
->Zerodha varsity modules ( Ultimate resource you can't really ask for anything else for beginners for finance & trading )
->A Beginner’s Guide to the Stock Market – Matthew Kratter ( honestly a good book but wont be needed if you cover varsity )
->Options volatility and pricing ( personal favourite book but abit advanced )
-> Options futures and other derivatives - John C Hull ( gold standard book to deep dive into finance & quant )
Note:- This is section is not mandatory but would definitely give you an edge if you know about these basics already so atleast try to cover zerodha varsity modules
5
DSA ( Data Structure and Algorithms )
The most important topic which is generally asked in major quant firm interviews as it is a very important problem solving skill.
Resources:-
-> Honestly Striver's A2Z dsa sheet in entirety is literally very good after this only problem solving is enough
-> I had myself done codehelp's paid course for theory part but definitely not at all needed
-> Introduction to Algorithms – Cormen, Leiserson, Rivest, Stein (CLRS) ( Probably the best and in depth book for DSA I would suggest peeps 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
The most important topic which is generally asked in major quant firm interviews as it is a very important problem solving skill.
Resources:-
-> Honestly Striver's A2Z dsa sheet in entirety is literally very good after this only problem solving is enough
-> I had myself done codehelp's paid course for theory part but definitely not at all needed
-> Introduction to Algorithms – Cormen, Leiserson, Rivest, Stein (CLRS) ( Probably the best and in depth book for DSA I would suggest peeps 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
6
ML ( and its use in trading )
This topic can come handy alot while building risk models and financial models. This 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 - Campus X ( 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 alot of 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 porfolio 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 a lot of content that I have shared, start slow go with one book and if it gives you a kick try to do one of the applicative ML books will surely help you in building better quant projects for resume.
This topic can come handy alot while building risk models and financial models. This 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 - Campus X ( 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 alot of 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 porfolio 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 a lot of content that I have shared, start slow go with one book and if it gives you a kick try to do one of the applicative ML books will surely help you in building better quant projects for resume.
7
Quant + Coding projects
For this topic I honestly don't have much resources however I will share whatever project ideas I have which I believe will be really good for your resume for quant profiles
Project Ideas:-
1) Backtesting engine ( multi strategy + multi asset classes)
-> Build your own backtesting engines ( python preferred ) and add good backtesting metrics to it ( drawdown , sharpe/calmar ratio ) and try to cover for multiple strategies working on multiple asset classes
2) Trading bot ( Live or paper trading )
-> Try to atleast implement a few trading bots for basic strategies ema divergence , rsi crossovers , supertrend channel reversions - can use binance api to create this for crypto for free as most of the Indian APIs charge for these api keys
3) Option Chain analytics dashboard
-> Pull NSE/Binance option chain data and analyze option greeks , IV ( implied volatility ) , PCR ( put-call ratio ) , 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 , volume and rank them up 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 - 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
For this topic I honestly don't have much resources however I will share whatever project ideas I have which I believe will be really good for your resume for quant profiles
Project Ideas:-
1) Backtesting engine ( multi strategy + multi asset classes)
-> Build your own backtesting engines ( python preferred ) and add good backtesting metrics to it ( drawdown , sharpe/calmar ratio ) and try to cover for multiple strategies working on multiple asset classes
2) Trading bot ( Live or paper trading )
-> Try to atleast implement a few trading bots for basic strategies ema divergence , rsi crossovers , supertrend channel reversions - can use binance api to create this for crypto for free as most of the Indian APIs charge for these api keys
3) Option Chain analytics dashboard
-> Pull NSE/Binance option chain data and analyze option greeks , IV ( implied volatility ) , PCR ( put-call ratio ) , 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 , volume and rank them up 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 - 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
8
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 then 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/Atcode beginner contests
Companies usually prioritise CF ratings so try to give those contests atleast and 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
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 then 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/Atcode beginner contests
Companies usually prioritise CF ratings so try to give those contests atleast and 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
9
Research & Alpha Papers
Research papers in finance & 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.
Resources for Research Papers
-> 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
Research papers in finance & 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.
Resources for Research Papers
-> 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
10
Interview Prep
This part doesn't have much advice but a few extra resources I have which I wish to share with you
Resources:-
->Heard on the Street – Timothy Crack ( covers mental math , brainteaser puzzles and quant questions which are asked in interviews )
->The Puzzle Palace (by Jane Street alum) ( a great book for puzzle practice for interviews )
->Quant Job Interview Questions and Answers – Xinfeng Zhou ( has alot 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 guys I tried my best to compile all things required to prepare for all the major topics required for quant I guess this might be a one stop solution.
Keep grinding 🚀🙌
This part doesn't have much advice but a few extra resources I have which I wish to share with you
Resources:-
->Heard on the Street – Timothy Crack ( covers mental math , brainteaser puzzles and quant questions which are asked in interviews )
->The Puzzle Palace (by Jane Street alum) ( a great book for puzzle practice for interviews )
->Quant Job Interview Questions and Answers – Xinfeng Zhou ( has alot 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 guys I tried my best to compile all things required to prepare for all the major topics required for quant I guess this might be a one stop solution.
Keep grinding 🚀🙌
