What is NovoLabs?
I started NovoLabs as a personal side project, but from the beginning my intention was to build it with professional standards and long-term vision. It is an engineering-driven platform focused on understanding how modern financial markets actually work, and how the connections form between markets, infrastructure, and the broader economy. Alongside this, I want to create educational content that helps make these complex systems more approachable from a technical perspective.
This is not an investment advisory platform. You will not find predictions, buy or sell recommendations, or market timing strategies here. Instead, the focus is on how investors and quantitative practitioners think, how trading systems are designed, and how real market mechanisms operate under the surface. NovoLabs is a technical laboratory where software engineering, market microstructure, quantitative research, and real-world trading infrastructure meet.
Since I am also at the beginning of my journey in this domain, I see NovoLabs as a living documentation of my learning process. Financial markets are among the most complex and fast-evolving systems, and understanding them requires continuous exploration. Here I share what I learn, what I build, and how my thinking evolves. I am open to feedback and collaboration ideas from others who approach markets with an engineering mindset.
👨🔬 Who is behind it?
I originally started my career as an electrical engineer, which deeply shaped how I think about problems. At my core, I am still an engineer: I care about structure, efficiency, and understanding how systems behave under real conditions. Over time, I gradually gravitated toward software development, and today I work primarily as a backend engineer, although I am comfortable working across the stack when needed. Looking back, I realized that a major reason for this transition was the creative freedom of software: building complex systems is faster, more flexible, and far more iterative in code than in hardware.
I have always been drawn to complex systems and difficult problems because they push my thinking the furthest. I am especially fascinated by mathematical problems, as they demand precision, patience, and deep concentration. For me, the difficulty itself is what makes them exciting and rewarding to solve.
This is also what naturally led me toward finance and the quantitative world. Financial markets combine complexity, mathematics, and real-world impact in a unique way. They are living systems shaped by data, human behavior, and infrastructure, making them one of the most challenging and intellectually stimulating domains to explore as an engineer.
📚 What kind of content will appear here?
The content published on NovoLabs will revolve around three main areas. Each of them approaches financial markets from a slightly different angle, but all share the same engineering-driven mindset.
Publishing frequency will be irregular but intentional. Each piece of content is meant to be durable and technical rather than frequent and superficial.
Articles
Articles will not only include original written material like the one you are reading now, but also discussions, analyses, and summaries of books, papers, and articles published by others. The goal is not repetition, but interpretation: extracting the ideas that matter and connecting them to real systems and practical understanding.
The topics will cover how financial markets are structured and how different markets operate, such as foreign exchange, equities, cryptocurrencies, derivatives, and fixed income. This includes explaining what instruments like bonds and treasuries are, how they work, and what role institutions like central banks and the Federal Reserve play in the global financial system.
From an engineering perspective, I also want to explore how trading actually happens at a low level: how orders are placed and matched, what market makers and takers do, who the different participants are, and how retail traders, institutions, and liquidity providers differ in behavior and objectives.
Beyond market structure, there will be articles about quantitative thinking and decision-making. This includes trading strategies, risk management, and portfolio construction, but always with an emphasis on robustness, assumptions, and system behavior rather than short-term profits.
Some articles will be more theoretical, some more practical, and some deeply technical. Together, they are meant to build a coherent picture of financial markets as complex engineered systems shaped by data, infrastructure, and human behavior.
Podcast summaries
Podcast summaries are meant to be a shortcut to the most valuable ideas found in high-quality quantitative finance and market infrastructure podcasts. Instead of passively consuming long episodes, I use this format to extract the core concepts, technical insights, and mental models that are actually worth keeping.
These summaries are also a form of documentation: a structured record of what I learned, how it changed my understanding, and how different ideas connect to engineering, quantitative research, and real trading systems.
The main focus will be on podcasts that approach markets from a quantitative and systems-level perspective, such as:
- Flirting with Models – a deep dive into quantitative finance, market structure, asset pricing, and the realities of institutional trading.
- Becoming a Hedge Fund Analyst: Inside Point72 Academy – insights into how professional investment firms think and train analysts, useful even beyond a pure PnL-focused career path.
- Chat With Traders – conversations with practitioners that provide real-world perspectives on markets, execution, and decision-making.
The goal is not to summarize every episode, but to selectively document the ones that contain ideas relevant to engineering-driven thinking about markets, quantitative infrastructure, and system design.
Projects
This section is dedicated to longer-form, research-driven projects. These are the most demanding pieces of work on NovoLabs, but also the most meaningful ones.
Each project starts from a concrete question rather than a predefined solution. The focus is on understanding why certain approaches work or fail, how assumptions break down in real data, and what can realistically be inferred from noisy, non-stationary systems.
The topics will naturally evolve during the research process. Rather than committing upfront to rigid scopes, projects are allowed to shift, or deepen as the most interesting aspects reveal themselves through experimentation.
Broadly, these projects explore areas such as:
- Stability and robustness of quantitative models under changing market conditions
- Statistical and distributional properties of financial time series
- Applying engineering and physics-inspired tools (e.g. Kalman filtering, frequency-domain analysis) to market data
- Bridging theoretical models and empirical behavior in noisy, non-stationary systems
Together, these projects form the research backbone of NovoLabs: a growing body of structured experiments that reflect how I think about complex systems, data, and uncertainty.
🛠 Technology Stack Overview
⚙️ Backend & API
Python
The core language of the platform. Python provides the balance between rapid prototyping and production-grade engineering, making it ideal for quantitative research, backend systems, and data infrastructure.
FastAPI
Async-first web framework used to build high-performance APIs with automatic OpenAPI documentation, strong typing, and predictable request handling under load.
Pydantic
Enforces strict schema validation and data integrity at system boundaries, making incorrect states and silent type errors explicit and impossible to ignore.
SQLAlchemy
Provides explicit control over database interactions through modern ORM patterns and polymorphic domain models, keeping the data layer expressive and compact.
🗄 Database & Data Layer
PostgreSQL
The primary relational datastore of the platform, chosen for its transactional guarantees, extensibility, and ability to support both content storage and future market data research.
🌐 Web & Frontend
Tailwind CSS
Utility-first CSS framework that allows precise control over visual structure while keeping styles consistent, minimal, and easy to evolve.
📨 Email Infrastructure
AWS SES & SNS
SES handles outbound transactional emails, while SNS processes bounce and complaint feedback, enabling automated suppression and delivery health monitoring.
🛡 Security & Edge Protection
Cloudflare
Acts as the edge security layer providing DNS, CDN, DDoS protection, bot mitigation, and request filtering before traffic reaches the application.
📦 Infrastructure
Docker
Provides reproducible, isolated environments that make deployments predictable and simplify dependency management across development and production.
Caddy
Terminates HTTPS automatically, handles reverse proxying, and acts as the clean, minimal entry point for all incoming traffic.
🚀 What comes next?
NovoLabs will evolve together with my understanding of markets and systems. Some projects will be theoretical, some practical, some experimental. All of them are meant to increase depth, not noise.
If this platform ends up becoming useful to others who think like engineers about markets, that is a welcomed side effect.
📬 Communication
NovoLabs exposes a single communication endpoint:
This is not a social channel. It is a technical interface for feedback, collaboration ideas, or infrastructure-related discussion.