Career: The ultimate Roadmap to become a Quant
A step-by-step guide to mastering quantitative research in finance
Table of contents:
Introduction.
Why become a quant?
Math bootcamp or, how to fall in love with calculus again.
Programming languages—if math is the engine, coding is the steering wheel.
Data analysis—no overfit here please.
Job opportunities—or which doors to knock on.
Before you begin, remember that you have an index with the newsletter content organized by clicking on “Read full story” in this image.
Introduction
Before you read any further, a quick disclaimer: The most effective path to becoming a quant is a structured one—pursue a degree in Data Science or Computational Mathematics, follow it with a postgraduate program in Mathematical or Financial Engineering, and secure an internship at a firm that truly delivers results.
However, if you already have a solid foundation and a profitable edge, working for a firm will simply provide leverage for your existing strategies.
Data science → Mathematical engineering → Intership → Leverage.
Here are the links to the study plans of some Spanish universities offering these degrees, in case you're around—you will find the masters also there:
UOC—online.
Viu—online.
Carlos III—on-site.
Okay! Let’s start with the basics: what even is a quant?
Imagine a hybrid creature—half mathematician, half programmer, with a dash of Gordon Gekko’s confidence (but better hair). Quants use math, stats, and code to predict financial markets, design trading algorithms, and occasionally argue with Excel spreadsheets until 3 a.m.
I translate it for you:
A professional who applies mathematical models, statistical techniques, and programming skills to analyze financial markets and securities.
They develop algorithms and models to identify trading opportunities, manage risks, and optimize investment strategies.
In essence, quants are the architects of modern financial analysis, blending theoretical knowledge with practical application.
Why become a quant?
You get to say I solve PDEs for fun at parties (and watch everyone leave).
You’ll earn enough to afford avocado toast and retirement (or at least a nicer gaming chair).
You can blame all your mistakes on market volatility (or Mercury retrograde).
No jokes! But a part of that:
Intellectual challenge: Engage in complex problem-solving that keeps your analytical skills sharp.
Financial reward: Competitive compensation packages that reflect the high demand for specialized skills.
Dynamic environment: Operate in fast-paced settings where innovation and agility are paramount.
Impactful work: Contribute to financial decision-making processes that influence global markets.
Some resources to start with:
Books:
The Concepts and Practice of Mathematical Finance by Mark S. Joshi.
Quantitative Finance by Paul Wilmott.
Inside the Black Box by Rishi K. Narang.
Algorithmic and High-Frequency Trading by Cartea, Jaimungal, and Penalva.
Advances in Financial Machine Learning by Marcos Lopez de Prado.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurelien Geron.
Designing Data-Intensive Applications by Martin Kleppmann.
Options, Futures, and Other Derivatives by John C. Hull.
Financial Markets and Institutions by Frederic S. Mishkin.
Online articles:
Quantitative Finance: Concepts, Tools, and Career Paths [link].
The vast majority of these resources can be found in Anna’s archive. So take a look and feel free to explore! Besides, in the appendix at the end, I’ll provide you with plenty of material for your bedside reading. You need an area of domain so, take a look at it!
Now that you’re sold, let’s talk about the skills you’ll need. Spoiler: It’s not just being good at Sudoku.
Math bootcamp or, how to fall in love with calculus again.
Quants eat math for breakfast because a solid foundation is crucial for any aspiring quant.
The following areas are particularly pertinent:
Linear algebra.
Understanding matrices, eigenvectors, and eigenvalues is essential for modeling and solving complex financial problems.
Key topics:
Matrix operations.
Determinants and inverses.
Eigenvalues and eigenvectors.
Diagonalization.
Recommended reading:
Linear Algebra Done Right by Sheldon Axler.
Probability and statistics.
Mastery of probability distributions, statistical inference, and hypothesis testing is vital for risk assessment and model validation.
Key topics:
Probability axioms.
Random variables and distributions.
Bayesian inference—better to avoid this one.
Central Limit Theorem.
Recommended reading:
Probability and Statistics by Morris H. DeGroot and Mark J. Schervish.
Stochastic calculus.
This field deals with modeling random processes, which is fundamental in pricing derivatives and managing financial risks.
Key topics:
Brownian motion.
Ito's Lemma.
Stochastic differential equations.
Martingales.
Recommended reading:
Stochastic Calculus for Finance II: Continuous-Time Models by Steven E. Shreve.
Numerical methods.
Numerical techniques are employed to approximate solutions to mathematical problems that cannot be solved analytically.
Key topics:
Finite difference methods.
Monte Carlo simulations.
Optimization algorithms.
Interpolation and extrapolation.
Recommended reading:
Numerical Methods in Finance and Economics by Paolo Brandimarte.
Optimization.
Optimization techniques are used to find the best possible solution under given constraints, crucial for portfolio management and risk assessment.
Key topics:
Linear and nonlinear programming.
Convex optimization.
Dynamic programming.
Constraint handling.
Recommended reading:
Convex Optimization by Stephen Boyd and Lieven Vandenberghe.
Other recommended resources:
The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman.
Stochastic Calculus for Finance by Steven Shreve.
Measure, Integral, and Probability by Capinski and Kopp.
Congrats! You’ve survived math bootcamp. Now let’s talk coding—because Excel macros won’t cut it.
Programming languages—if math is the engine, coding is the steering wheel.
Proficiency in programming is indispensable for implementing models, analyzing data, and automating processes. The following languages and tools are particularly relevant.
Python.
The Swiss Army knife of quants. Known for its readability and extensive libraries, Python is widely used in financial analysis and algorithmic trading. But, do you know why Python devs wear glasses? Because they can’t C#—sorry about that, check this [link] for a cheatsheet.
Key libraries:
NumPy.
pandas.
SciPy.
matplotlib.
scikit-learn.
Recommended reading:
Python for Data Analysis by Wes McKinney.
High-Performance Python by Micha Gorelick and Ian Ozsvald.
C++.
Valued for its performance, C++ is often utilized in high-frequency trading systems and performance-critical applications.
Key topics:
Object-oriented programming.
Memory management.
Template programming.
Concurrency.
There are alternatives like Rust or Java, but C++ is the standard in the industry—check this [link] for a cheatsheet.
Recommended reading:
Effective C++ by Scott Meyers.
C++ High Performance by Andrist, Downey, and Powell.
Effective Java by Joshua Bloch.
Java Concurrency in Practice by Brian Goetz.
The Rust Programming Language (aka The Rust Book).
Programming Rust by Jim Blandy & Jason Orendorff.
SQL/NoSQL.
It is essential for managing and querying relational databases, a common task in handling financial data—check this [link] for a cheatsheet.
Quants need data like vampires need blood. Here’s how to handle it:
Data warehousing: Store data in a way that makes future you cry less.
Big data architectures: Hadoop, Spark, and cloud computing for scalability.
Key topics:
Capture & data prep.
Data warehousing.
Analytical databases.
Non-relational databases.
Cloud database services.
Big data architectures.
Recommended reading:
SQL for Data Scientists by Renee M. P. Teate.
Designing Data-Intensive Applications by Martin Kleppmann.
Bash.
Bash is the duct tape of programming—ugly, practical, and shockingly powerful. Use it to automate everything: data pipelines, backtesting scripts, even your morning coffee order—check this [link] for a cheatsheet.
Recommended reading:
The Linux Command Line by William Shotts.
Shell scripting by Jason Cannon.
You’re now a math-coding ninja. Time to analyze data—like Sherlock Holmes, but with more dataframes.
Data analysis—no overfit here please.
This is the practice of working with financial data to glean useful information, which can then be used to make informed trading decisions.
Data mining—digging for gold in a mountain of rules.
Data mining involves extracting valuable information from vast datasets, akin to finding nuggets of gold amidst heaps of irrelevant data. It's the process of identifying patterns, correlations, and anomalies to make informed decisions.
Key topics:
Association rule learning: Discovering interesting relations between variables in large databases.
Clustering: Grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.
Classification: Assigning items to predefined categories or classes.
Anomaly detection: Identifying rare items, events, or observations which raise suspicions by differing significantly from the majority of the data.
Recommended reading:
Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, and Jian Pei.
An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.
Online courses:
Time series analysis—guessing the future, one autocorrelation plot at a time.
Time series analysis focuses on analyzing data points collected or recorded at specific time intervals to identify trends, seasonal patterns, and cyclical behaviors. It's essential for forecasting future values based on previously observed values.
Key topics:
Autocorrelation: Measuring the similarity between observations as a function of the time lag between them.
Stationarity: A property of a time series that its statistical properties do not change over time.
ARIMA models: AutoRegressive Integrated Moving Average models used for forecasting.
Seasonal decomposition: Breaking down a time series into trend, seasonal, and residual components.
Recommended reading:
Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos.
Time Series Analysis: Forecasting and Control by George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung.
Online courses:
Machine Learning—you need to understand classic ML, but don't stop there, too many pitfalls.
Supervised Learning—when you know the answer but pretend you don’t.
In supervised learning, algorithms are trained on labeled data, where the outcome is known, to predict outcomes for new, unseen data.
Key topics:
Linear regression: Modeling the relationship between a dependent variable and one or more independent variables.
Logistic regression: Used for binary classification problems.
Support vector machines: Finding the hyperplane that best divides a dataset into classes.
Decision trees: Using a tree-like model of decisions and their possible consequences.
Recommended reading:
Pattern Recognition and Machine Learning by Christopher M. Bishop.
Machine Learning: A Probabilistic Perspective by Kevin P. Murphy.
Online courses:
Unsupervised Learning—finding patterns in chaos, like astrology for data.
Unsupervised learning involves training algorithms on data without labeled responses, aiming to find hidden patterns or intrinsic structures.
Key topics:
K-means clustering: Partitioning data into K distinct clusters based on similarity.
Hierarchical clustering: Building a hierarchy of clusters.
Principal component analysis: Reducing the dimensionality of data while preserving as much variability as possible.
Recommended reading:
Unsupervised Learning: Foundations of Neural Computation by Geoffrey Hinton and Terrence Sejnowski.
Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
Online courses:
Reinforcement Learning—basically, parenting robots.
Reinforcement Learning is a branch of machine learning where an agent learns to make decisions by performing actions and receiving feedback from the environment. It's akin to training a pet: actions are encouraged or discouraged based on the outcomes they produce.
Key topics:
Agent: The learner or decision-maker.
Environment: Everything the agent interacts with.
State: A specific situation in the environment.
Action: A move the agent makes.
Reward: Feedback from the environment; can be positive or negative.
Policy: The strategy that the agent employs to determine actions based on the current state.
Value Function: Estimates the expected reward of states or state-action pairs.
Recommended reading:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto.
Algorithms for Reinforcement Learning by Csaba Szepesvári.
Online courses:
Deep Learning—because sometimes you need a neural net to tell you it’s going to rain.
Deep Learning is a subset of machine learning that uses neural networks with many layers (hence "deep") to model complex patterns in data. It's particularly effective in tasks like image and speech recognition.
Key topics:
Neural networks: Computational models inspired by the human brain, consisting of interconnected nodes (neurons) that process data.
Activation functions: Functions that determine the output of a neuron, introducing non-linearity into the network.
Backpropagation: A method for training neural networks by adjusting weights to minimize the error between predicted and actual outputs.
Convolutional neural networks: Specialized neural networks for processing structured grid data like images.
Recurrent neural networks: Neural networks designed for sequential data, such as time series or natural language.
Recommended readings:
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
Neural Networks and Deep Learning by Michael Nielsen.
Online courses:
NLP & Text Mining—teaching machines to read your ex’s texts.
Natural Language Processing is a field focused on enabling machines to understand, interpret, and generate human language. Text mining involves extracting meaningful information from text data.
Key topics:
Tokenization: Breaking text into individual words or phrases.
Part-of-speech tagging: Identifying the grammatical parts of speech in text.
Named entity recognition: Detecting and classifying entities like names, dates, and locations in text.
Sentiment analysis: Determining the emotional tone behind a body of text.
Topic modeling: Discovering abstract topics within a collection of documents.
Recommended readings:
Speech and Language Processing by Daniel Jurafsky and James H. Martin.
Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper.
Online Courses:
Job opportunities—or which doors to knock on.
Below is a list of prominent hedge funds known for their quantitative research roles, along with estimated compensation details. The compensation package doubles the base salary, and if you are a senior-level employee, the amount is doubled again. In other words, senior employees receive a compensation package that is three-four times their base salary:
If you're considering other mid-sized funds, they also offer excellent opportunities:
I don't want to go on much longer; next time, we'll talk about infrastructure—because that alone could fill another 20 articles.
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Appendix
Flash Boys by Michael Lewis.
Irrational Exuberance by Robert J. Shiller.
The Intelligent Investor by Benjamin Graham.
Pragmatic Capitalism by Cullen Roche.
The Black Swan by Nassim Taleb.
Fooled by Randomness by Nassim Taleb.
The Quants by Scott Patterson.
Dark Pools by Scott Patterson.
More Money Than God by Sebastian Mallaby.
Black Edge by Sheelah Kolhatkar.
The Man Who Solved the Markets by Gregory Zuckerman.
A Man for All Markets by Edward O. Thorp.
The (Mis)Behavior of Markets by Benoit Mandelbrot.
When Genius Failed by Roger Lowenstein.
A Random Walk Down Wall Street by Burton Malkiel.
Billion Dollar Whale by Tom Wright & Bradley Hope.
The Predators’ Ball by Connie Bruck.
Broken Markets by Sal Arnuk & Joseph Saluzzi.
The Problem of HFT by Edwin Lefèvre.
Trading at the Speed of Light by Donald MacKenzie.
Investing for Adults by William Bernstein.
The Rise of Carry by Timothy Lee.
Bombardiers by Po Bronson.
FTSE: The Inside Story by Mark Makepeace.
Flash Crash by Liam Vaughan.