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Curriculum / Phase 3 · Career Pathways

Modern C++ for Quantitative Finance

Explore the planned modern C++ programme for quantitative finance, its intended outcomes and free preparation resources.

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

Why study Modern C++ for Quantitative Finance?

Numerical software needs predictable ownership, clear interfaces and measurable performance. This planned programme will connect modern C++ to the engineering decisions behind reusable pricing and risk libraries.

Who this is for

For programmers already comfortable with functions, data structures, testing and numerical algorithms. The programme is planned; the resources below are available now.

Intended learning outcomes

  • Reason about ownership, lifetimes and numerical interfaces.
  • Design and test reusable numerical components.
  • Measure performance and understand concurrency trade-offs.

An example of the work this prepares you for

An example goal for independent preparation is a small numerical library with tests, an explicit ownership design and a measured performance comparison.

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.

Reason about ownership

Understand object lifetimes and the cost of copying or sharing data.

Design numerical interfaces

Make model assumptions and failure cases visible to a caller.

Measure performance

Separate a plausible speed improvement from a demonstrated one.

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. Tutorials · Free

    Learn C++

    LearnCpp

    Start with the free language tutorials on types, lifetimes and the standard library.

  2. Documentation · Free

    QuantLib

    QuantLib contributors

    Inspect an established C++ finance library after learning the language basics.

  3. Textbook · Free

    Operating Systems: Three Easy Pieces

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

    Read the memory and concurrency chapters for systems background.

  4. University course · Free

    The Missing Semester of Your CS Education

    MIT · notes and video lectures

    Use the tooling and profiling videos to support a measurable engineering workflow.

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

  • Derivatives Pricing and Model Quant · elective programme
  • Quant Developer and Trading Systems · required 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.PlannedQuantitative Data EngineeringExplore the planned quantitative data engineering programme, from reliable financial datasets to reproducible research pipelines.PlannedTrading Systems and Low-Latency InfrastructureExplore the planned trading systems programme, including systems preparation, latency measurement and reliable event processing.