Building a High-Frequency Trading Architecture: A Deep Dive into C++, Redis Pub/Sub, and Rithmic API Integration

In the rapidly evolving world of algorithmic trading, the difference between profit and loss often comes down to microseconds. For quantitative developers and independent traders, the quest to build a robust, ultra-low latency High-Frequency trading architecture infrastructure is a continuous journey of optimization, architectural refinement, and technological integration.

This article provides an extensive walkthrough of a "Science Server Edition" trading system. Based on a recent live demonstration, we will deconstruct the architecture of a custom High-Frequency Trading (HFT) setup that leverages C++ for raw speed, Redis for efficient message passing, and the Rithmic API for institutional-grade market data. We will also explore the integration of modern AI coding assistants like Claude 3.5 Opus in the development workflow and discuss advanced market-making strategies such as the Avellaneda-Stoikov algorithm.

Whether you are a seasoned quant looking to refine your stack or a developer stepping into the world of financial engineering, this guide offers a practical look under the hood of a live trading environment.

Part 1: The Core Philosophy of Modern HFT Architecture

Before diving into the specific code and server configurations shown in the demo, it is crucial to understand the philosophy driving this specific architecture. The goal is not just speed; it is stability, modularity, and the ability to process vast amounts of data without bottlenecks.

1.1  The "Science Server" Concept

The term "Science Server" refers to a dedicated environment where experimental strategies meet live market data. It is a sandbox that mimics production reality as closely as possible without the immediate financial risk of a fully unleashed capital account. In this specific iteration, the system is designed to handle the Micro E-mini S&P 500 (MEES) futures.

The Micro E-mini is an ideal testing ground for HFT algorithms because:

1. Liquidity: It tracks the S&P 500, ensuring constant price movement and volume.

2. Accessibility: The capital requirements are significantly lower than standard E-mini contracts.

3. Data Fidelity: The price action mirrors the broader market, allowing for valid statistical analysis.

1.2 The Role of C++ in 2024 and Beyond

In an era where Python dominates data science and machine learning, C++ remains the undisputed king of trading infrastructure. The system demonstrated here relies on C++ for the heavy lifting. Why?

• Memory Management: C++ gives the developer absolute control over memory allocation and deallocation, preventing the "garbage collection pauses" that plague languages like Java or Python—pauses that can be fatal in HFT.

• Execution Speed: Compiled C++ code runs directly on the hardware, offering the lowest possible latency for execution logic.

• API Compatibility: Most institutional-grade APIs (like Rithmic or those provided by exchanges like CME) offer native C++ libraries.

However, a pure C++ system can be rigid. To balance this, the architecture uses a hybrid approach: C++ for the core engine and execution, and other tools (like Redis and Python) for communication and analysis.

Part 2: The Communication Backbone: Redis Pub/Sub

One of the most distinct features of the demonstrated system is its reliance on Redis, specifically utilizing the Publisher/Subscriber (Pub/Sub) pattern.

2.1 Why Redis?

Redis is an in-memory data structure store, used as a database, cache, and message broker. In this trading architecture, it serves as the central nervous system.

• Decoupling: By using Redis, the "Server" (which fetches data) and the "Client" (which makes trading decisions) are completely decoupled. They don't need to know the internal state of the other; they only need to listen to the message bus.

• Speed: Redis operates in memory. Reading and writing data to Redis takes nanoseconds to microseconds, making it fast enough for near-HFT applications.

2.2 Configuration and Legacy Compatibility

In the live demo, a specific configuration choice was highlighted: the use of an older version of Redis on Port 6380.

The Port Conflict: Standard Redis installations default to port 6379. In complex development environments, this port is often occupied by other services or background processes. By mapping the local host to 6380, the system ensures a dedicated channel for trading data without interference.

The "Old Version" Logic:The developer explicitly mentions using an older version of Redis. In the software world, "new" is often equated with "better," but in financial systems, "stable" is better.

"This coding is using Redis Pub/Sub. This will work with an older version of Redis and that's the only thing I need out of Redis. It's been proven to work over the years."

This highlights a critical lesson in system design: Avoid Feature Creep. If the Pub/Sub mechanism from five years ago works flawlessly and has zero overhead, upgrading to a newer version with unnecessary features introduces potential instability for no gain. The system utilizes the PUBLISH command to push market data and the SUBSCRIBE command to receive it, creating a lightweight, bidirectional data stream.

Part 3: The AI-Assisted Development Workflow

A fascinating aspect of this system's creation is the heavy utilization of Generative AI, specifically Claude 3.5 Opus.

3.1 Coding with Claude

The transcript reveals that much of the boilerplate and connection logic was generated by AI.

"This is all generated by Claude 3.5 Opus... getting a lot of praise for what it's doing especially Claude Code."

For quantitative developers, AI has shifted from a tool for "writing essays" to a legitimate pair programmer. In this context, Claude was likely used to:

1. Generate C++ Headers: Creating the structures for the Rithmic API callbacks.

2. Write Redis Connectors: Handling the socket programming required to talk to the Redis instance.

3. Debug Race Conditions: Identifying where the heartbeat logic might fail during high-load scenarios.

3.2 The "Human in the Loop" Necessity

Despite the AI's involvement, the developer emphasizes that this is his "own technique." AI can generate code, but it cannot architect a system. It doesn't understand the nuance of why the Rithmic API might return stale data on a specific futures contract, or why a heartbeat needs to be exactly 0.5 seconds. The AI accelerates the typing, but the engineering remains a human endeavor.

Part 4: Connecting to the Market: The Rithmic API

The engine of this car is the data feed. Without accurate, timely data, the fastest C++ code is useless. This system connects to Rithmic, a premier data service provider for futures trading.

4.1 Test vs. Production Environments

The demo runs in "Rithmic Test Mode."

• Rithmic Test: A simulated environment provided by the broker. It uses real historical data replayed or live data with simulated execution. It allows developers to test order routing without risking money.

• Rithmic Production (Rithmic 01): The live server located in Chicago (Aurora data centers), physically close to the CME matching engine.

The switch between these two is often a single line of code or a config change.

"It's connecting into Rithmic Test... I could easily do but this is under a test mode right now... pretty close to live data if I just change the server from Rithmic to the Rithmic 01."

4.2 Handling Market Data Flow

The data flow in the system is linear but high-speed:

1. Ingestion: The C++ Server subscribes to the MEES (Micro E-mini) instrument via the Rithmic API.

2. Normalization: The API returns a complex object containing Bid, Ask, Last Price, and Volume. The C++ code strips this down to the essentials.

3. Broadcasting: The normalized data is serialized (likely into JSON or a binary format) and PUBLISHED to the Redis channel.

4. Consumption: The Client script (running in VS Code) picks up the message, parses it, and feeds it into the strategy logic.

4.3 The Heartbeat Mechanism

Reliability is paramount. If the data feed dies, the algorithm must stop trading immediately. To ensure this, the system implements a "Heartbeat."

"There's a heartbeat in this every half second... maintaining the connection both into Redis and the server itself."

Every 500 milliseconds, a specific signal is sent. If the client does not receive this signal for, say, 1.5 seconds (3 missed heartbeats), it assumes the server is down and triggers a "Kill Switch," cancelling all open orders and flattening positions. This is a mandatory safety feature for any algorithmic system.

Part 5: The Client-Side Strategy and Visualization

While the server handles the raw data, the "Client" is where the intelligence lives. In the video, we see VS Code running a terminal that outputs the pulse of the market.

5.1 The Dashboard

The terminal output shown in the transcript provides a text-based dashboard:

• Market Data: Live Bid/Ask spreads.

• Position Status: Current holdings in MEES.

• Portfolio Value: Simulated at $100,000.

• Order Flow: Incoming buy/sell pressure.

• Heartbeat Status: Confirmation of server health.

This "text UI" is preferred by quants over graphical GUIs because it is lightweight. Rendering a chart takes CPU cycles; printing text to a console takes almost none.

5.2 Latency Considerations

The transcript mentions that the output could be removed to "speed it up even more."

"I could obviously take out all this output and speed it up even more make the code much more complicated but for now this is how I deal with it."

In true HFT (High-Frequency Trading), writing to the screen (I/O operation) is a massive bottleneck. In a production HFT environment, the system runs "headless"—no screens, no logs, just pure execution. Logs are written asynchronously to a fast NVMe drive to be analyzed after the trading day is over.

Part 6: Advanced Strategies: Market Making and Order Flow

The video transitions from the infrastructure to the actual math driving the trades. This is where the content from hftcode.com becomes relevant.

6.1 The Avellaneda-Stoikov Model

The transcript highlights a specific algorithm: Avellaneda-Stoikov.For those unfamiliar, this is a seminal paper in the world of quantitative finance ("High-frequency trading in a limit order book", 2008).

The Concept:Market making involves placing both a Buy limit order and a Sell limit order simultaneously. You profit from the "spread" (the difference between the two). The risk is "Inventory Risk"—the market moves against you, and you are stuck holding an asset that is losing value.

The Avellaneda-Stoikov model mathematically calculates:

1. The Reservation Price: The price at which the trader would be indifferent between buying and selling, adjusted for current inventory. If you have too much stock, your reservation price lowers to encourage selling.

2. The Optimal Spread: How far away from the mid-price your orders should be, based on market volatility.

The system demonstrated includes a full implementation of this model in Python and C++.

6.2 Order Flow Imbalance (OFI)

Another strategy mentioned is Order Flow Imbalance.

"Order flow imbalance which is also kind of used in high frequency trading as well. Full-on code, production ready."

OFI looks at the depth of the limit order book. If there are 500 contracts waiting to be bought at the best bid, but only 50 contracts waiting to be sold at the best ask, there is a "Buy Imbalance." This suggests upward pressure on the price. HFT algorithms use this micro-structure data to front-run price moves, buying just before the heavy buying pressure pushes the price up.

Part 7: Python Integration and Walk-Forward Analysis

While C++ handles the live fire, Python is used for the "War Room" analysis. The transcript describes a backtesting and forward-testing suite built with Streamlit.

7.1 Streamlit for Quants

Streamlit has become the standard for rapid prototyping in data science. It allows a Python script to be turned into an interactive web dashboard instantly.

In this ecosystem, Streamlit is used to:

• Visualize the Order Book.

• Run "Walk-Forward Analysis."

7.2 Walk-Forward Analysis

Standard backtesting (running a strategy on past data) is dangerous because of "overfitting"—tuning a strategy so perfectly to the past that it fails in the future.Walk-Forward Analysis fixes this. It optimizes the strategy on a window of data (e.g., January), then tests it on the next window (e.g., February). Then it rolls the window forward. If the strategy performs well in the "unseen" windows, it is robust.

The QuantLabs system includes a Python-based backtester that performs this rigorous validation before any C++ code is deployed to the live server.

Part 8: The QuantLabs Ecosystem: Learning and Trials

The technology described above is complex. It requires knowledge of C++, Network Engineering, Market Microstructure, and Statistics. This is where the broader QuantLabs ecosystem comes into play, as detailed in the provided URLs.

8.1 The "LEARN" Pathway (/registration)

For those intimidated by the C++ code or the Redis architecture, QuantLabs offers a structured learning path.

• The HFT C++ eBook: A foundational text that explains the infrastructure concepts (sockets, memory pointers, latency) required for trading.

• TradingView Integration: Not everyone needs a C++ black box. The "45 Minute Preview of TradingView" suggests a bridge for retail traders. TradingView uses "Pine Script," which is much easier to learn. It allows traders to prototype logic visually before committing to the heavy engineering of C++.

8.2 The "TRY" Pathway (/trials)

For those who want to see the results without writing the code from scratch, the Quant Analytics Trial offers a shortcut.According to the /trials page, this $47/month service provides:

1. AI Generated Source Code: Access to the very scripts discussed in the video.

2. Advanced Futures Options: Strategies that go beyond simple directional trading (buying/selling) into volatility trading.

3. Private Group Access: A community of like-minded developers debugging these architectures together.

This "Trial" model is crucial for the independent quant. Building an HFT system in isolation is difficult. Having access to a "Source Code Sample" acts as a Rosetta Stone—it is easier to modify working code than to stare at a blank screen.

Part 9: Technical Challenges and Future Roadmap

The transcript is honest about the current state of the system: it is a work in progress.

"There's still a lot of testing with this like for instance why is the data stale and so on so forth."

9.1 The "Stale Data" Problem

In HFT, "stale data" is the enemy. If the price of MEES is 4500.25, but your system thinks it is 4500.00 because of a 50-millisecond network lag, you will place orders that lose money instantly.

Debugging this involves:

• Network Sniffing: Using tools like Wireshark to see if the packets are delayed at the ISP level.

• Code Profiling: Checking if the Redis serialization is taking too long.

• Rithmic API Tuning: Ensuring the API callbacks are thread-safe and not blocking the main execution loop.

9.2 Future Integrations: Ethereum and Crypto

The developer hints at expanding beyond futures.

"Maybe an Ethereum. I don't know. It just depends."

The architecture described (C++ + Redis) is asset-agnostic. It doesn't care if the data is "S&P 500" or "Ethereum." However, crypto exchanges (like Binance or Coinbase) use different protocols (usually WebSocket and REST) compared to the FIX/API protocols of traditional finance. The modularity of using Redis means the "Client" strategy wouldn't need to change much; only the "Server" ingestion engine would need to be swapped out to talk to a Crypto API instead of Rithmic.

Part 10: Conclusion: The Democratization of HFT

Ten years ago, the system demonstrated in this video would have required a team of five engineers and a budget of $500,000. Today, thanks to open-source tools like Redis, accessible APIs like Rithmic, and AI assistants like Claude, a single developer can build it in a home office.

The "Science Server" represents the democratization of high-frequency trading. It is no longer the exclusive domain of Citadel or Renaissance Technologies. While the retail trader cannot compete on pure speed (physics dictates that the server closest to the exchange wins), they can compete on agility and complexity.

By combining the raw power of C++ with the analytical flexibility of Python and the safety of a rigorous testing environment, the modern independent quant can carve out a profitable niche in the global markets.

Key Takeaways for the Aspiring Quant:

1. Start with Architecture: Don't just write a script. Build a system. Separate your data feed from your strategy using a message bus like Redis.

2. Respect the Data: Use a "Heartbeat" to ensure your data is fresh. If the pulse stops, the trading stops.

3. Validate with Python: Use Python/Streamlit to prove the math (Walk-Forward Analysis) before you let C++ risk the money.

4. Leverage AI: Use tools like Claude to handle the boilerplate, but ensure you understand every line of code that goes into production.

5. Join a Community: Utilize resources like QuantLabs (/registration and /trials) to accelerate your learning curve and access proven source code.

The journey to a working HFT system is long, but as this demo shows, the roadmap is clearer than ever.

Appendix: Glossary of Terms

• HFT (High-Frequency Trading): A method of trading that uses powerful computer programs to transact a large number of orders in fractions of a second.

• Redis: An open-source, in-memory data structure store used as a database, cache, and message broker.

• Pub/Sub: A messaging pattern where senders (publishers) do not program the messages to be sent directly to specific receivers, but instead categorize published messages into classes without knowledge of which subscribers, if any, there may be.

• Rithmic: A low-latency trading platform and data API used primarily for futures trading.

• MEES (Micro E-mini S&P 500): A futures contract that is 1/10th the size of the standard E-mini S&P 500, making it accessible for smaller accounts.

• Market Making: A trading strategy where the trader provides liquidity by placing both buy and sell orders, profiting from the spread.

• Avellaneda-Stoikov: A mathematical model used to determine optimal bid and ask quotes for market making.

• Walk-Forward Analysis: A method of testing a trading strategy by optimizing it on a past segment of data and testing it on a subsequent segment, repeatedly moving the window forward.

Disclaimer: Algorithmic and high-frequency trading involve significant risk of loss and are not suitable for all investors. The code and strategies discussed in this article are for educational purposes only. Always test in a simulated environment before risking real capital.