Architecting Highspeed Trading Systems: Why I Ditched C++ for C# and Pivoted to Strategy-First Design (Lessons Learned)
- Bryan Downing
- 2 minutes ago
- 9 min read
Introduction: The Relentless Pursuit of Alpha and Architecture
In the world of algorithmic trading, the ground beneath our feet is constantly shifting. What worked six months ago might be obsolete today—not just in terms of market strategies, but in the very software architecture that drives them.
I’m Brian, from quantlabsnet.com, and today we are doing a deep dive into the latest, hard-won lessons learned in building what I’m now classifying as highspeed trading systems.
If you are a quant developer, an aspiring algorithmic trader, or a software architect looking at the financial sector, this post is crucial. We are going to move beyond theory and look at the actual implementation challenges of connecting to real-world market data, the pitfalls of certain programming languages in specific environments, and a major architectural pivot that simplifies everything.
We will cover the journey of moving away from a complex, frustrating C++ implementation on Windows toward a sleek, manageable, and surprisingly fast C# (.NET) architecture using Redis as the backbone. We will also define what "high-speed" actually means for the majority of traders versus the institutional giants.
Section 1: Defining the Reality of "Highspeed Trading Systems"
Before we touch a single line of code, we must define our terms. The phrase "high-frequency trading" (HFT) is often thrown around loosely.
In the strictest institutional sense, HFT involves fighting for nanoseconds. It requires co-location at data centers (like the CME Aurora facility), utilizing expensive hardware like Field-Programmable GateArrays (FPGAs), and accessing premium, ultra-low latency APIs like Rhythmic’s Diamond API. To play in this arena requires significant capital—think "enough to buy a Ferrari" just to open the account—and an infrastructure cost that is prohibitive for most.
The "Highspeed-Like" Approach
What we are discussing today is practical high-speed trading. It is not about shaving the last nanosecond off a packet roundtrip time. It is about robust, low-latency processing that can handle high volumes of data during volatile market events without choking.
We are still using sophisticated tools—Rhythmic's API for market data and order routing, and Redis for inter-process communication. But we are making pragmatic choices based on the constraints of our environment (specifically, the need to run on Windows) and the size of our operation. This approach allows us to trade effectively in highly liquid markets without the eight-figure infrastructure investment of true HFT firms.
Section 2: The Architectural Pivot—Strategy Over Instrument
The most significant breakthrough in this recent development cycle wasn't technical; it was conceptual.
The Old Way: Instrument-Centric Clients
Initially, the architecture was designed around instruments. If I wanted to trade the S&P 500 E-mini futures, I would build a client dedicated to the ES. If I wanted to trade Crude Oil, I’d build another client for CL.
Each client contained its own logic, its own connections, and its own strategy parameters. This seemed logical at first. You trade an asset, therefore you build software for that asset.
The Realization: Liquidity Dictates Behavior
To be successful in futures and options trading, you must focus on high liquidity and high volume. You need markets where you can enter and exit positions instantly without slippage eating your profits. My focus narrowed down to the major players:
Market Indices: S&P 500, NASDAQ
Crypto Futures: Bitcoin, Ethereum
Commodities: Gold, Silver, Oil
When applying real-world market data and running backtests across these diverse instruments, a pattern emerged. The underlying behaviors of these liquid assets were often strikingly similar.
I found that out of four different highly liquid instruments, the exact same mathematical strategy could be applied effectively to all of them.
The New Way: Strategy-Centric Clients
This realization forced a massive pivot. Why duplicate code across four different instrument clients when they are all doing the same thing?
The new architecture flips the script. Instead of building clients based on instruments, I am now building clients based on strategies.
If I have a "Mean Reversion" strategy, I build one robust client for that logic. I can then spin up multiple instances of that single client, feeding different parameters into each—one instance trading the S&P, another trading Bitcoin, another trading Gold.
Benefits of this approach:
Drastically Reduced Codebase: Less code means fewer bugs and easier maintenance.
Scalability: Adding a new instrument to an existing strategy is merely a configuration change, not a coding project.
Focus: The development effort focuses on refining the mathematical edge of the strategy, not reinventing the wheel for every new ticker symbol.
Section 3: The Engine Room—The Core Strategies
The current architecture is built around four primary strategy clients based on this new philosophy. These were selected based on analysis using AI models tailored for quantitative techniques. They are simple yet effective starting points for high-liquidity markets.
1. MACD Momentum
Moving Average Convergence Divergence (MACD) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. In high-speed markets, catching strong momentum bursts early is crucial. This strategy client focuses on identifying those crossovers and divergences to ride short-term trends.
2. VWAP Mean Reversion
Volume-Weighted Average Price (VWAP) is vital in institutional trading as a benchmark. Price often gravitates back towards the VWAP line throughout the trading day. This strategy looks for extensions away from VWAP (overbought or oversold conditions relative to volume) and bets on a reversion back to the mean.
3. RSI Mean Reversion
The Relative Strength Index (RSI) is a classic momentum oscillator. This strategy uses RSI to identify extreme overbought (usually above 70) or oversold (below 30) conditions. In liquid futures markets, these extremes often lead to snap-back reactions, which this client is designed to capture.
4. Ornstein-Uhlenbeck (OU) Mean Reversion
This is the most mathematically rigorous of the four. The Ornstein-Uhlenbeck process is a stochastic process that describes the velocity of a massive Brownian particle under the influence of friction. In finance, it's used to model mean-reverting time series. Unlike simple RSI, OU provides a statistical framework for how strongly and quickly a price should pull back to its long-term mean, allowing for more sophisticated entry and exit signals.
Section 4: The Great C++ Depression (Why Windows Ruined It)
Now we arrive at the most painful, yet important, lesson of this entire journey. The attempt to build these highspeed trading systems using C++.
In the quant world, C++ is God. It is the language of high frequency. If you aren't using C++, aren't you just playing around? That was my mindset going in. I spent considerable time porting everything to C++, utilizing advanced AI coding assistants like Claude 3.5 Sonnet to speed up the process.
But reality hit hard. The theoretical speed advantages of C++ collapsed under the weight of practical implementation issues, specifically because of the operating system constraint.
The Windows Constraint
The core issue is the data provider. I am using Rhythmic Trader Pro. This critical piece of software, which acts as the gateway to the exchanges, only runs on Windows.
This means my entire trading infrastructure is effectively hostage to the Windows ecosystem. I cannot move the core C++ code to a high-performance Linux box using WSL (Windows Subsystem for Linux) or a standalone server without creating complex networking bridges (like TCP/IP sockets or WebSockets) back to the Windows host running Rhythmic. I wanted to avoid that added layer of complexity and latency. I wanted everything on one box.
The Nightmare of C++ Development on Windows
Developing complex C++ applications on Windows, even with modern tools like VS Code, proved to be a formidable barrier.
1. The Package Management Disaster (VCPKG)Coming from the world of Python, where pip install pandas just works, C++ package management on Windows is a shocking regression.
The AI tools repeatedly pointed me toward VCPKG, Microsoft's C++ library manager. In practice, it is horrible to work with. It requires complex integration with build systems, manual configuration of paths, and a level of friction that simply doesn't exist in modern languages like Python, C#, or Go. Instead of coding strategies, I was fighting with library linking.
2. The AI Context CollapseI rely heavily on AI coding assistants like Claude to accelerate development. Claude is fantastic for generating self-contained scripts or functions.
However, a real-world C++ trading system is vast. You have header files (.h), source files (.cpp), CMake configuration files, build artifacts, and JSON config files.
As the project grew, the sheer volume of files and cross-references overwhelmed the AI's context window. When asking for help debugging an issue spanning three different files and a header, Claude would lose the thread, hallucinate non-existent functions, or provide generic advice that didn't apply to my specific codebase.
Note: Anthropic recently announced updates to Claude's coding capabilities that might address context handling, but during this development cycle, it was a major roadblock. It forces a decision: do I spend a fortune using cutting-edge AI extensions like Windsurf or Roo Code just to manage C++ overhead?
3. Build System Overhead (CMake)Maintaining CMakeLists.txt files for a multi-project solution on Windows is a job unto itself. It adds another layer of non-trading-related cognitive load that slows down the actual goal: building a trading system.
The Speed Illusion
Given these immense challenges, I had to ask the hard question: even if I get this C++ beast working perfectly on Windows, what is the actual gain?
Without direct DMA access to the Aurora data center and FPGAs, the speed advantage of C++ over highly optimized C# on the same Windows box is likely negligible for my trading frequency. I was incurring 10x the development pain for perhaps a 5% performance gain that wouldn't even translate to P&L.
It was time to abandon ship.
Section 5: The Pragmatic Solution—C#, .NET, and Redis
The decision to scrap the C++ effort and move back to C# (.NET) was liberating.
Rhythmic provides four API options:
C++ (Windows only, high pain)
Python/Web (Uses Protocol Buffers, very cryptic and difficult to work with)
JavaScript (Not robust enough for this backend work)
C# / .NET
C# is the sweet spot. It is native to Windows, it has excellent performance (especially modern .NET), its tooling in VS Code and Visual Studio is superb, and its package management (NuGet) actually works.
The New Architecture Overview
The entire system is now structured as a series of .NET Console Applications within VS Code.
1. The Rhythmic Server GatewayThis is a standalone C# application. Its sole job is to connect to Rhythmic Trader Pro. It handles logins, requests market data subscriptions, and listens for order updates.
When market data comes in, the server doesn't process it. It immediately serializes it and blasts it out onto the message bus.
2. The Message Bus: RedisWe are using Redis (specifically, an open-source fork that supports the required features) as the central nervous system.
We utilize the Redis Pub/Sub (Publish/Subscribe) pattern.
The Server Gateway publishes market data ticks to specific channels (e.g., MarketData.ES, MarketData.BTC).
The Strategy Clients subscribe only to the channels they need.
Redis is incredibly fast, lightweight, and decouples the system components perfectly. The server doesn't care who is listening to the data; its only job is to publish it as fast as possible.
3. The Autonomous Strategy ClientsAs discussed earlier, we have separate C# console apps for each strategy type (MACD, OU, RSI, VWAP).
Each client is autonomous. It reads its own configuration file (which tells it which instruments to trade and what parameters to use), connects to Redis, subscribes to the relevant data channels, performs its calculations, and will eventually send buy/sell signals back through Redis to an order execution component.
Why This Works Better
Development Velocity: I can spin up a new C# console app, add the Redis library via NuGet, copy in my shared data structures, and start coding trading logic in minutes, not days.
Better Configuration: .NET's built-in support for JSON configuration files is seamless, replacing the clunky C++ alternatives.
AI Assistance: AI tools handle C# much better than C++. The structure is cleaner, there are no header files to manage, and the context is easier for the AI to maintain.
Stability: The system is modular. If the MACD client crashes, the RSI client keeps running. The Rhythmic server keeps running.
Section 6: Current Status and Future Outlook
As of this writing (a weekend), the system is in a fully runnable state. The VS Code project structure is clean, with sub-projects for the server, the four strategy clients, and a shared code library for common data models.
The two-way communication via Redis has been fully tested. The server can send data, and the clients receive it.
The next critical step is Tuesday morning when the markets reopen. That will be the first live test of the entire chain: Rhythmic live data -> C# Server -> Redis Pub/Sub -> C# Strategy Clients performing real-time calculations.
Conclusion: Lessons in Pragmatism
The journey to build highspeed trading systems is rarely a straight line. It is easy to get seduced by the theoretical purity of C++ and the allure of institutional-grade HFT architecture.
But if you are an independent quant or a small team, you must be ruthless about pragmatism. The constraints of your data provider (like Windows-only software) dictate your architecture more than your desire for speed.
Pivoting to a strategy-centric design pattern was a massive efficiency win. But the biggest lesson was accepting that for this specific setup, C# is simply the better tool for the job. It removes the friction of development, allowing focus to return where it belongs: the strategies themselves.
By combining the ease of C# with the raw speed and decoupling power of Redis, we have achieved a system that is high-speed, scalable, and most importantly, maintainable.
Comments