Skip to content
Curriculum / Phase 3 · Career Pathways

Trading Systems and Low-Latency Infrastructure

Explore the planned trading systems programme, including systems preparation, latency measurement and reliable event processing.

This programme is planned. The outcomes below describe its intended direction; you can use the external resources now to prepare independently.

Why study Trading Systems and Low-Latency Infrastructure?

A trading decision depends on the system delivering data, processing events and sending orders reliably. This planned programme will connect operating-system and networking ideas to latency, resilience and observability.

Who this is for

For experienced programmers with concurrency and networking foundations. The programme is planned; its preparation resources can be studied independently now.

Intended learning outcomes

  • Explain operating-system and network contributions to latency.
  • Reason about concurrency, memory and event ordering.
  • Measure performance and design observable failure handling.

An example of the work this prepares you for

An example goal for independent preparation is an event-processing prototype with latency measurements, failure scenarios and a written reliability analysis.

Prerequisites and suggested preparation

You should be comfortable with the background covered in these programmes. Equivalent knowledge from university study or independent learning is also suitable.

The goal is to use the foundations in context: explain the assumptions, carry out a calculation and interpret the result. If one of those steps feels unfamiliar, review the linked preparation first.

A preview of the learning journey

The proposed learning journey is organised around these capabilities.

Follow an event through a system

Explain where processing, scheduling and communication add delay.

Reason about concurrency

Identify correctness risks in shared state and event ordering.

Measure and recover

Assess latency distributions and behaviour under failure.

Free resources to start learning

Use these alongside the programme or to prepare independently. Start with the introductory material, work through an example, then return to the research papers when the underlying ideas are familiar.

  1. Textbook · Free

    Operating Systems: Three Easy Pieces

    Remzi H. Arpaci-Dusseau and Andrea C. Arpaci-Dusseau · University of Wisconsin–Madison

    Study processes, memory, concurrency and persistence in the free online textbook.

  2. University course · Free

    Distributed Systems

    Yale · James Aspnes · CS 422 notes

    Read the university notes for distributed coordination and failure reasoning.

  3. Tutorials · Free

    Learn C++

    LearnCpp

    Strengthen language and lifetime knowledge before performance-sensitive systems work.

  4. University course · Free

    The Missing Semester of Your CS Education

    MIT · notes and video lectures

    Use debugging and profiling lectures as practical preparation.

Links reviewed 14 October 2026. Free refers to the linked reading, notes or videos; printed books and certificates may cost extra.

Find more papers and learning materials in the SelfMadeQuant Directory.

Where this programme fits

  • Quant Developer and Trading Systems · elective programme

Continue preparing with the free resources, or explore the programmes available now.

Explore the curriculum
Phase 3Advanced Derivatives and Exotic OptionsStudy volatility surfaces, local and stochastic volatility, and exotic derivatives beyond foundational option pricing.Phase 3Counterparty Credit Risk and CVAStudy counterparty credit exposure, CVA, DVA, collateral and Monte Carlo exposure simulation.Phase 3Convertible ArbitrageStudy convertible bond valuation, delta hedging, credit overlays and the financing risks of convertible arbitrage.Phase 3Statistical ArbitrageStudy pairs trading, cointegration, factor residuals and the research and execution of relative-value strategies.Phase 3Financial Machine LearningStudy financial machine learning with event labels, finance-aware validation, feature importance and model monitoring.Phase 3Text as Data and NLP for FinanceStudy financial text analysis with lexicons, embeddings, classification and validation of NLP measurements.Phase 3Event Studies and Causal Measurement in FinanceStudy event-study design, abnormal returns and robust causal measurement in financial data.Phase 3Empirical Asset Pricing and Factor InvestingStudy empirical asset pricing, factor construction and the costs, capacity and risks of factor investing.PlannedModern C++ for Quantitative FinanceExplore the planned modern C++ programme for quantitative finance, its intended outcomes and free preparation resources.PlannedQuantitative Data EngineeringExplore the planned quantitative data engineering programme, from reliable financial datasets to reproducible research pipelines.