Stop Choosing: Get C++ Performance in Python Algos with C++26

by Richard Hickling

The “religious war” between Python and C++ in algorithmic trading usually boils down to a single trade-off: Python is faster for getting ideas to market, while C++ is faster for getting orders into the book.

But why choose? With the advent of C++26 Reflection, you can now have the flexibility of a Python-based strategy without the performance penalty of slow loops.

Bring in C++ Rapidly: How Reflection Works

The biggest hurdle in hybrid trading systems has always been the “bridge.” Traditionally, if you wrote a complex pricer in C++, you had to manually write “boilerplate” code to tell Python how to talk to it. If you added a new function, you had to update the bridge. It was tedious and error-prone.

Reflection changes the game by allowing the code to “look in the mirror”. Instead of you manually describing your C++ functions to Python, the compiler does it for you. It programmatically inspects your classes and generates the bindings automatically.

Practical Reflection With C++26

by Barry Revzin

With the adoption of Reflection for C++26, the landscape of what is possible has shifted. This talk will focus on implementing Struct-of-Arrays for an arbitrary aggregate, but will also take some detours to cover some techniques that will prove useful for solving a wide range of problems.

A Brief History of Bjarne Stroustrup, the Creator of C++

by CultRepo

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Announcing Meeting C++ 2026

by Jens Weller

We'll be meeting from the 26th - 28th November in Berlin! You have the unique chance to spend the 1st advent in Berlin with C++ and Christmas Markets open!

With Mateusz Pusz and Kate Gregory I've chosen two well known speakers for the keynotes this year. Mateusz is well known for his units library, which currently also is proposed for the standard. It is also an important contribution to making C++ more safe and secure, the big topic of last year. Then Kate Gregory will be visiting us in Berlin again, she is known for her ability to create great talks around technical and social aspects in our daily lives as devs. You might remember her from giving a keynote in 2017, or speaking about the aging programmer two years ago.

For the 15th time Meeting C++ will organize a great event for 3 days filled with lots of content about C++, like last year the plan is to host 3 tracks in parallel in Berlin, with an optional 4th track. The 4th track will be unlocked either by sponsorships or ticket sales. You can be a part of this great C++ event by attending onsite and online. There is already great news for onsite attendees: Hudson River Trading is again this years t-shirt sponsor, a great and unique Meeting C++ 2026 t-shirt is an exclusive perk for onsite attendees!

Threads vs Coroutines: Why C++ Has Two Concurrency Models

by Conor Spilsbury

The C++11 standard introduced a powerful set of tools for concurrency such as threads, mutexes, condition variables, and futures. More recently, C++20 introduced another powerful but fundamentally different concurrency abstraction in the form of coroutines. But coroutines are not just an evolution or a replacement for threads. Instead, they each solve different problems in different ways. Choosing the right tool for the job requires understanding how each works under the hood and where they shine. This talk will help build that intuition by looking at how each interacts with the operating system and hardware which will help make better decisions when choosing which to use.

We'll explore how threads and synchronization primitives work at the operating-system and hardware level, from thread creation and scheduling to where context switching and synchronization introduce performance costs. We’ll then contrast this to the coroutine model introduced in C++20 which takes a fundamentally different approach by using a cooperative model based on the suspension and resumption of work.

Given this understanding, we’ll finish by applying this intuition to a set of real-world scenarios to identify whether threads or coroutines are a better fit for the problem at hand.

C++26: std::is_within_lifetime

by Sandor Dargo

When I was looking for the next topic for my posts, my eyes stopped on std::is_within_lifetime. Dealing with lifetime issues is a quite common source of bugs, after all. Then I clicked on the link and I read Checking if a union alternative is active. I scratched my head. Is the link correct?

It is — and it totally makes sense.

Let’s get into the details and first check what P2641R4 is about.

What does std::is_within_lifetime do?

C++26 adds bool std::is_within_lifetime(const T* p) to the <type_traits> header. This function checks whether p points to an object that is currently within its lifetime during constant evaluation.

CppCon 2025 More Speed & Simplicity: Practical Data-Oriented Design in C++

by Vittorio Romeo

Data-Oriented Design (DOD) presents a different way of thinking: prioritizing data layout not only unlocks significant performance gains via cache efficiency but can also lead to surprising simplicity in the code that actually processes the data.

This talk is a practical introduction for C++ developers familiar with OOP. Through a step-by-step refactoring of a conventional OOP design, we’ll both cover how data access patterns influence speed and how a data-first approach can clarify intent.

We’ll measure the performance impact with benchmarks and analyze how the refactored code, particularly the data processing loops, can become more direct and conceptually simpler.

Key techniques like Structure-of-Arrays (SoA) vs. Array-of-Structures (AoS) will be explained and benchmarked, considering their effects on both execution time and code clarity. We’ll pragmatically weigh the strengths (performance, simpler data logic) and weaknesses of DOD, highlighting how it can complement, not just replace, OOP.

We’ll also demonstrate that DOD doesn’t necessitate abandoning robust abstractions, showcasing C++ techniques for creating safe, expressive APIs that manage both complexity and performance.

Let’s learn how thinking “data-first” can make your C++ code faster and easier to reason about!

The cost of a function call

by Daniel Lemire

When programming, we chain functions together. Function A calls function B. And so forth.

You do not have to program this way, you could write an entire program using a single function. It would be a fun exercise to write a non-trivial program using a single function… as long as you delegate the code writing to AI because human beings quickly struggle with long functions.

A key compiler optimization is ‘inlining’: the compiler takes your function definition and it tries to substitute it at the call location. It is conceptually quite simple. Consider the following example where the function add3 calls the function add.

int add(int x, int y) { 
     return x + y; 
} 
int add3(int x, int y, int z) { 
     return add(add(x, y), z); 
} 

You can manually inline the call as follows.

int add3(int x, int y, int z) { 
     return x + y + z; 
}

Implementing vector<T>

by Quasar Chunawala

The most fundamental STL data-structure is the vector. In this article, I am going to explore writing a custom implementation of the vector data-structure. The standard library implementation std::vector is a work of art, it is extremely efficient at managing memory and has been tested ad nauseam. It is much better, in fact, than a homegrown alternative would be.

Why then write our own custom vector?

• Writing a naive implementation is challenging and rewarding. It is a lot of fun!
• Coding up these training implementations, thinking about corner cases, getting your code reviewed, revisiting your design is very effective at understanding the inner workings of STL data-structures and writing good C++ code.
• Its a good opportunity to learn about low-level memory management algorithms.

We are not aiming for an exhaustive representation or implementation, but we will write test cases for all basic functionalities expected out of a vector-like data-structure.

Informally, a std::vector<T> represents a dynamically allocated array that can grow as needed. As with any array, a std::vector<T> is a sequence of elements of type T arranged contiguously in memory. We will put our homegrown version of vector<T> under the dev namespace.

Flavours of Reflection: Comparing C++26 reflection with similar capabilities in other programming languages

by Bernard Teo

Reflection is imminent. It is set to be part of C++26, and it is perhaps the most anticipated feature accepted into this version of the standard. Lots of implementation work has already been done for Clang and GCC — they pretty much already work (both are on Compiler Explorer), and it hopefully won’t be too long before they get merged into their respective compilers.

Many programming languages already provide some reflection capabilities, so some people may think that C++ is simply playing catch-up. However, C++’s reflection feature does differ in important ways. This blog post explores the existing reflection capabilities of Python, Java, C#, and Rust, comparing them with one another as well as with the flavour of reflection slated for C++26.

In the rest of this blog post, the knowledge of C++ fundamentals is assumed (but not for the other programming languages, where non-commonsensical code will be explained).