Quant AI Driven Journey into Algorithmic Trading with C++
- Bryan Downing
- Jul 31
- 10 min read
From Python Prototypes to C++ Powerhouses: An AI-Driven Journey into Algorithmic Trading
Good day, this is Bryan from quantabsnet.com. For a while now, my focus has been on demonstrating the capabilities of Python for creating trading simulations, dashboards, and various quantitative tools. It’s a fantastic language for rapid prototyping and data analysis. However, the time has come to venture into more demanding territory. Today, I’m going to share my experience transitioning these concepts into the world of C++, a language synonymous with high-performance computing and the backbone of many institutional quant AI trading systems.
This exploration is not just about a language switch; it's about proving that complex, robust trading applications can be built in C++ almost entirely with the help of modern Large Language Models (LLMs). This journey reveals the challenges, the surprising successes, and the profound implications of using AI as a co-pilot in the sophisticated domain of quantitative finance.
The C++ Challenge: Why Bother and What to Expect
Let's be clear from the outset: working with C++ is inherently more challenging than Python. The syntax is stricter, memory management is a manual affair, and the compilation process adds an extra layer of complexity. Embarking on this path with AI as your primary developer introduces its own set of considerations.
The LLM Factor: The quality of the generated C++ code is heavily dependent on the capability of your chosen LLM. You need a recent, powerful model to produce coherent, modern, and efficient code. In my experience, Anthropic's Claude 3.5 Sonnet (and its more advanced Opus version) remains the top contender for this kind of complex code generation, offering superior reasoning and output.
The Iteration Tax: The process of debugging and refining the AI-generated code is significantly more labor-intensive than in Python. I would estimate it takes two to three times the effort. The AI will generate a solid foundation, but you, the human developer, must guide it through iterations, correcting compilation errors, logical flaws, and runtime bugs. This is a partnership, not a push-button solution.
The GUI Dilemma: Simplicity Over Spectacle
One of the first major decisions was how to handle the user interface. My experiments with integrating third-party C++ GUI libraries proved to be a frustrating endeavor. It leads to bloated codebases, a myriad of compatibility issues with different operating systems (Windows, Linux), and an ever-expanding list of dependencies.
For these reasons, I made a strategic choice to focus exclusively on console applications. These terminal-based apps have no graphical front end. This approach offers several key advantages:
Reduced Complexity: The code remains lean and focused on the core trading logic.
Increased Portability: A console app is far easier to compile and run across different
environments.
Lower Latency: By eliminating the overhead of a GUI, the application can be more responsive, a critical factor in trading.
The primary requirement for such an application is that it must run in the same environment as the broker's software. For instance, if you are connecting to Interactive Brokers' Trader Workstation (TWS) on a Windows machine, your C++ application must also be hosted on that same Windows machine to establish a connection.
Choosing the Right Tools and Environment
The development environment plays a crucial role in managing the complexities of C++. I have experimented with both Microsoft's Visual Studio and the more lightweight VS Code. Honestly, for pure C++ development, I find them to be bloated and cumbersome. They come with a dizzying array of configuration files (JSON, etc.), making it difficult to manage and rebuild projects, especially when extraneous configurations for other languages like JavaScript are present.
As a result, my IDE of choice is JetBrains' CLion. It provides a much cleaner, more focused C++ development experience with less configuration "crap." It seamlessly integrates with the standard CMake build system, which is an industry-standard tool for managing C++ projects. The AI can even be prompted to generate the CMakeLists.txt file for you, a task that can be a significant headache when done manually.
The Windows Quagmire vs. Linux Liberation
My development journey began on Windows. Within the CLion IDE, the application compiled and ran without a hitch. However, the moment I tried to run the compiled executable outside of the IDE, Windows began complaining about missing system libraries (DLLs) and other dependencies. It's a classic Windows "DLL hell" scenario, an over-engineered mess that makes deployment a real pain.
Frustrated, I took the exact same codebase—with no file changes, no code modifications—and moved it into my Windows Subsystem for Linux (WSL) environment, which runs Ubuntu 22. Using the standard CMake build process (cmake . followed by make), the project compiled and produced a final executable file.
That executable ran perfectly.
This stark contrast highlights a significant advantage of developing in a Linux environment: it's cleaner, the code is smaller, and portability is a first-class citizen. This experience has solidified my preference for Linux for serious C++ development. While I still have a fondness for Windows, the unnecessary complexity it introduces is a major drawback. (And as for Apple's overpriced ecosystem, my days as a fanboy are long gone).
One caveat to consider is that the Linux versions of some broker software, like TWS, can be several major versions behind their Windows counterparts. This is a practical trade-off that traders must weigh when choosing their operating environment.
A Tour of the AI-Generated C++ Trading Console
Now, let's dive into the application itself. This is a 100% AI-generated console app that is capable of connecting to Interactive Brokers. It provides a menu-driven interface to manage and simulate a trading portfolio.
The Portfolio:The simulation is focused on a portfolio of commodities: Oil (CL), Silver (SI), and Natural Gas (NG). It starts with a simulated capital of $100,000, allocated as follows:
Crude Oil (CL): 30%
Silver (SI): 30%
Natural Gas (NG): 40%
The Strategies:Based on analysis from an AI-generated executive summary (a process I've detailed in previous discussions), the application implements specific options strategies for these instruments:
Bare Put on Silver
Iron Condor on Natural Gas
Iron Condor on Silver
The main screen displays the portfolio summary, including the expected monthly returns for each active strategy.
Key Features and Functionality:
Dynamic Strategy Toggling: One of the most powerful features is the ability to turn strategies on and off in real-time. For example, if I choose to disable the "Iron Condor on Silver" strategy, the system immediately reflects this change in the portfolio summary. Ideally, it would then reallocate the freed-up capital among the remaining active strategies.
Capital Simulation: The application allows you to change the starting capital on the fly. I can take the portfolio simulated at $100,000 and instantly re-run the simulation with a starting capital of just $5,000. The application automatically adjusts all position sizing and portfolio weightings, providing an immediate look at how the strategies would perform with a smaller account.
Instrument Details: You can drill down into each instrument to view its simulated market data, including contract size, tick size, current price, and implied volatility.
Backtesting and Forecasting: The app includes functionality for backtesting the strategies. Since it's a console application, it uses ASCII characters to generate rudimentary charts of the equity curve and drawdown. It displays key performance metrics like the Sharpe ratio, take-profit levels, and more. It also has a forecasting engine to project future price paths.
This entire, fully functional application was generated by an AI from a massive, detailed prompt. The process of iterating and correcting the code took time, but the end result is a testament to the power of this new development paradigm.
The Code: A Glimpse Under the Hood
The project structure is clean and logical, with header (.h) and source (.cpp) files for each major component:
IBConnector: Handles the connection to Interactive Brokers.
Instrument: Defines the properties of a tradable asset.
ForecastEngine: Contains logic for price forecasting.
Backtester: Manages the backtesting process.
Strategy: The heart of the system, defining the trading logic.
TradingEngine: Executes and manages trades.
Dashboard: Manages the console UI and user input.
Let's examine some of the most insightful parts of the code.
The Strategy.cpp File: Deconstructing an Iron Condor
The true power of this approach is revealed when you look at the implementation of complex strategies. An Iron Condor is a non-trivial, four-legged options strategy. Seeing it broken down into C++ code is incredibly educational.
The IronCondor class is initialized with its key parameters: the instrument, and the four strike prices (upper call, lower call, upper put, lower put). The AI has generated methods to calculate all the critical components of the trade.
Calculating Take Profit and Stop Loss: The code provides clear, commented logic. For instance, the take-profit might be set to 50% of the maximum potential profit, while the stop-loss is set at 150% of the maximum potential loss. These are simplified rules, but they form a solid baseline. A more advanced system could use a separate AI agent to dynamically adjust these percentages based on real-time market volatility.
Calculating Expected Profit: This is where the quant magic happens. The code calculates the probability of profit, noting that a real-world implementation would use a sophisticated option pricing model like Black-Scholes. The AI-generated code uses a simplified approximation based on the normal distribution and implied volatility:
calculation. In reality, this uses an option pricing model.
if (currentPrice > lowerPutStrike && currentPrice < upperCallStrike) {
// Price is within the profit zone. Approximation based on normal distribution.
probabilityOfProfit = 0.68; // ~1 standard deviation
} else {
// ... calculation for out-of-the-money scenarios
}
This is a goldmine for learning. You can see the mathematical formulas translated directly into code. You can reverse-engineer the logic, trace it in a debugger, and truly understand how the strategy works from the inside out. This was once the exclusive domain of those with access to expensive tools like MATLAB and its professional toolboxes. Now, AI can generate this knowledge for a fraction of the cost.
The ForecastEngine.cpp File: Predicting the Future
The forecasting engine is another impressive piece of AI generation. It includes methods for:
Calculating Implied Volatility (IV): The code demonstrates how to estimate IV from historical price volatility if a current IV value isn't available.
Forecasting Price Paths: It can generate future price scenarios, incorporating concepts like mean reversion to a long-term average.
Standard Mathematical Functions: The engine includes AI-generated code for calculating log returns, exponential moving averages (EMA), simple moving averages (SMA), and even linear regression.
The math is all there, laid bare in C++. For an aspiring quant, this is an invaluable resource. You can see how theoretical concepts are applied in a practical, performance-oriented language.
The TradingEngine.cpp File: The Execution Hub
The trading engine orchestrates the entire process. Its main loop consists of three steps:
Update Market Data: Fetch the latest prices and implied volatility.
Process Signals: Analyze the data to see if any strategy entry or exit conditions are met.
Execute Trades: Place orders to enter or exit positions.
A critical section of this code updates the option chain data. This brings us to a crucial point about data in trading.
The Unfair Advantage of Option Chain Data
You will often hear people claim that "AI doesn't work for trading because it can't predict the market." This statement is usually made by people who are only considering historical price data. They are correct in that context; predicting future prices from past prices alone is a fool's errand.
However, professional institutions don't rely solely on historical data. They use forward-looking data, and the richest source of this is the option chain. The option chain reflects the collective sentiment and positioning of all market participants—from small retail traders to billion-dollar hedge funds. It shows where the market believes the price will go. This data, especially from a tightly regulated exchange like the CME, is not easily manipulated and provides a much more accurate picture of future market sentiment.
When you build an AI forecasting model using option chain data, you are not just looking at the past. You are analyzing the market's own predictions about the future. This gives you a much higher probability of making accurate forecasts. The AI-generated trading engine understands this, with placeholders to pull data directly from the option chain to inform its decisions.
The Future is Agent-Based
What I've demonstrated here is a powerful, "old school" object-oriented approach to building a trading platform. The code is structured into classes and objects that interact with each other. However, the next evolution of AI-driven trading architecture will be radically different.
The future lies in trading agents.
Imagine a system where each piece of functionality is an independent, intelligent agent.
You would have a Data Ingestion Agent responsible for sourcing and cleaning market data.
A Forecasting Agent would consume this data and produce price predictions.
Multiple Strategy Agents (an Iron Condor agent, a Mean Reversion agent, etc.) would compete to find the best opportunities based on the forecasts.
A Risk Management Agent would oversee all activity, ensuring that the overall portfolio risk stays within acceptable limits.
An Execution Agent would be responsible for placing orders with the broker in the most efficient way possible.
In this paradigm, you build agents upon agents. You could use advanced AI techniques like Retrieval-Augmented Generation (RAG) to allow an agent to consult a library of research papers before making a decision. This is the direction where the industry is headed. While the legacy systems at major banks and HFT firms won't be replaced overnight, any new, enterprise-level trading platform built from the ground up will almost certainly be architected with AI agents in mind.
Conclusion: A New Era of Quantitative Development
This journey from Python to C++ has been a profound learning experience. It has proven that with the right AI tools and a willingness to iterate, it is entirely possible to build sophisticated, high-performance trading applications in a language that was once the exclusive domain of elite programmers.
The key takeaways are:
C++ is Viable: Don't be intimidated. AI can handle the heavy lifting of code generation, allowing you to focus on the logic and strategy.
Simplicity is Key: Forgo complex GUIs in favor of lean, portable console applications. Focus on the core trading engine.
AI is an Unparalleled Learning Tool: The ability to generate and reverse-engineer complex strategy code in C++ is a revolutionary way to learn both programming and quantitative finance.
Data is Everything: Your success hinges on using the right data. Prioritize forward-looking data from option chains over purely historical data.
The Future is Agents: Start thinking about how to architect systems not as monolithic applications, but as a collection of intelligent, interacting agents.
The world of algorithmic trading is undergoing a seismic shift. The barriers to entry are crumbling, and the tools available to individual developers and small firms are more powerful than ever. The ability to prompt an AI to build a C++ trading engine, and then to understand, refine, and deploy that engine, is a skill that will be in high demand in the years to come.
If you're interested in diving deeper into these topics, I invite you to visit my website at quantabsnet.com. You can sign up for my newsletter to get updates on all my latest projects, including the full source code for this C++ application, which will be available exclusively on the site. You'll also find a free ebook on building C++ HFT infrastructure.
Thank you for joining me on this exploration. The future of trading is here, and it's being written in C++—with a little help from our new AI partners.
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