Career: Have you ever seen a quantitative team get wiped out?
A practical guide to building a career in finance and fund management
Table of contents:
Introduction.
Learning, failing, and evolving.
The birth of a pod shop.
Crafting the blueprint.
The dream team.
Legal, compliance, and regulatory maze.
Lessons learned and what I’d do differently today.
Before you begin, remember that you have an index with the newsletter content organized by clicking on “Read full story” in this image.
Introduction
I have always believed that success in quantitative trading is not about looking at charts, but rather about combining rigorous mathematical modeling with the innate passion of human creativity. Now, at 38 years old, I reflect on a journey that began with an insatiable curiosity and has become the meal I put on the table every day.
I'm sure if you're reading this, you've fantasized about starting your own fund at some point. Is it worth it? What does it entail? I'm sure more than one of you imagines it as a sort of Wolf of Wall Street, with money, cocaine, and whores. But you should rather imagine it as:
Sleepless nights spent coding and iterating algorithms.
Anxiety, fear, and shame followed by moments when every sacrifice is worth it.
Missteps that taught invaluable lessons—including layoffs.
A transformation from a hopeful young trader to a seasoned entrepreneur.
I invite you to join me today as I share my personal story. Whether you’re an aspiring trader, a tech enthusiast, or simply curious about the world of quant trading, I hope you find this account both informative and inspiring.
Learning, failing, and evolving
My journey began in the summer of 2011. The world was on the brink of significant financial and technological transformations, and I was a bright-eyed 23-year-old, freshly stepping into the quant trading arena. The technology landscape was rapidly evolving:
Dial-up was swiftly giving way to faster, more efficient internet connections.
Early smartphones hinted at the mobile revolution that would soon reshape communication.
I attended a seminar on quantitative finance where a speaker described multi-strategy funds and pod shops as living, breathing cells in a trading organism. I jotted down every idea with fervor, thinking:
“What if every trader could operate like an independent cell—free to innovate yet working together to drive overall success?”
That single idea ignited an obsession. I spent countless nights in front of my setup, sketching models on napkins and dreaming of a firm where brilliant minds could thrive within a supportive yet fiercely competitive ecosystem.
Between 2011 and 2015, my career unfolded, and I embarked on a path to merging trading, technology, and data. I joined a trading firm that specialized in american stocks—a realm where every trade carried its own lesson and every day presented a new challenge.
During these formative years, I immersed myself in the core principles of quant trading:
Data: The raw material powering every buy or sell decision.
Algorithms: The engines transforming data into actionable insights.
Risk Management: The safety net ensuring we could weather market volatility.
I vividly recall the surge of adrenaline when a model executed a flawless trade—only to be jolted by a sudden market twist that nullified what looked like a sure win.
The early days were riddled with setbacks. I learned quickly that:
Every mistake is an expensive learning opportunity.
Backtested models guarantee nothing and often struggled in live markets.
One particularly volatile day, our models—performing well in controlled environments—began to falter. Instead of succumbing to panic, our team gathered, analyzed the situation, and managed to close the day with a positive balance—were we lucky? For sure, I won’t deny it. This experience underscored an invaluable lesson: The devil is in the details, and a trading system has details that even its own developer doesn't know.
The birth of a pod shop
By the early 2016s, I had absorbed enough from the quant trading world to recognize that traditional, centralized management had its limits. I was ready for a paradigm shift—one that would eventually form the foundation of my business.
In 2017, during a hectic morning at our firm due to a change of manager, I watched disparate teams compete for internal capital like athletes in a high-stakes game. Suddenly, I realized everything had changed. The new boss was from the USA and had something to tell us:
“What if we could harness this competitive energy more efficiently?”
He brought the vision of a structure that was virtually unknown to Spanish firms—and perhaps still is. In this structure, each team would operate autonomously, developing unique strategies and benefiting from a centralized framework that allocated capital based on performance—no profits? No money or compassion for you, it's that simple and heartbreaking.
Armed with a marker, I walked over to the whiteboard in our small office. There, I began outlining a plan for tackling this new approach. I debated with my colleagues the advantages of complete freedom versus the need for strict risk controls. In the end, I summed it up in:
Every team could pursue their own strategies.
The collective strength of the firm was maximized by dynamic capital allocation.
This marked the birth of a satellite structure that would forever change our approach to trading. A quick summary:
The Agile PODs model core principles we followed:
Revenue increased with improved productivity.
Minimize risk while maximizing returns per unit of time.
Talent scalability.
Crafting the blueprint
With a clear vision in place, the monumental task was to build the infrastructure to support it. There were days when technical challenges seemed insurmountable, yet the thrill of creating something innovative kept us moving forward.
As a unit, the main company expected us to do things on our own. They would provide the money, and we would have to justify every cent spent. So our initial investment was for countless cups of coffee and a high-performance trading engine.
High-performance computing hardware: Vital for processing large volumes of data rapidly.
Low-latency connectivity: Essential for capturing real-time market opportunities.
A data provider: Without data, our systems would have been the same as everyone else's.
Every breakthrough—whether reducing latency by a microsecond or designing a robust data pipeline—felt like a personal triumph.
We needed to build a system that:
Ingested real-time market data feeds: Ensuring immediate access to pricing, volume, and order book information.
Stored historical data: Enabling extensive testing and model refinement.
Integrated alternative data sources: Such as news, SEC, form 13F, statements, financial scores, etc.
Custom ETL processes are tedious, but they ensure that every data point is clean, accurate, and actionable.
A major objective was to free our quants to focus on what they did best: research. To achieve this, we automated nearly every operational facet:
Order management systems: For automatic trade execution.
Smart order routing: To reduce slippage and optimize execution.
Automated risk controls: Constantly monitoring positions and enforcing risk parameters.
Although automation was the cornerstone, we continued to monitor trades on a large screen where everyone could see the balance, the monthly target, and what was happening in the market.