Quant AI Pro Options Trading Strategies in a C++ Code Breakdown

Hello everybody, Brian here from quantlabsnet.com. In this comprehensive code breakdown, we are going to embark on a deep dive into a C++ options trading application with quant AI in mind, specifically designed for Windows using Visual C++ within the Visual Studio 2022 environment. This project was born from a sophisticated portfolio prompt, derived from one of my detailed executive summary documents, with the goal of creating a trading interface that connects into Interactive Brokers.

I'll be making the complete source code for this project available to anyone interested through my website. For access, please check the description in the original video, which will direct you to the relevant blog post. See the download link below.

This endeavor proved to be a fascinating and revealing project, not just from a software engineering perspective, but also as a case study in the current capabilities and idiosyncrasies of modern AI-driven code generation. The journey to a functional application was not straightforward and involved navigating the complexities of different Integrated Development Environments (IDEs) and comparing the output of several leading Large Language Models (LLMs).

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Initially, I attempted to set up the project using both Visual Studio Code and the full Visual Studio 2022 IDE. However, the process quickly became bogged down by the sheer volume of extraneous configuration files and project-specific metadata that these environments generate. As a developer who values clean, simple, and portable codebases, this was a significant point of friction. My preference leans towards tools like the CLion IDE, which facilitates the creation of projects that can be seamlessly moved from a Windows development environment to a Linux production server. The complexity of the Visual Studio ecosystem, in my opinion, works against this principle of portability and simplicity.

Given these challenges and my personal preferences, the core development for this project was undertaken by a seasoned developer with multiple years of experience at Microsoft. Their deep expertise in the Windows and Visual Studio ecosystem was invaluable in navigating these hurdles. A very big thank you is owed to them for their efforts.

The development process itself became a gauntlet for different AI models, providing a stark comparison of their real-world performance. The developer, having access to all my executive summaries and prompts, first attempted to generate the solution using ChatGPT. The result, unfortunately, was what can only be described as a "dog's breakfast." The generated Visual Studio solution file was fundamentally broken, and the code itself lacked a crucial entry point, rendering it completely non-functional. This experience aligns with my observations over the last six months, where I've found code generated by OpenAI's models to be consistently disappointing and often unusable for complex tasks.

In stark contrast, my own experiences have shown that models from Anthropic, particularly Claude 3.7 with its advanced reasoning capabilities, are far superior. I consistently find myself to be more productive, and the quality of the generated code is significantly higher. However, the developer on this project took a different, and ultimately very successful, path. They turned to Qwen 3 Coder, a powerful open-source LLM. This model produced a much more viable and coherent code base.

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The process didn't stop there. The code generated by Qwen was then loaded into the Cursor IDE, an AI-first code editor. From there, as I was told, GitHub Copilot was leveraged to systematically correct and refactor the code, addressing the numerous errors and exceptions that are an inevitable part of AI-generated projects. This hybrid approach—using a powerful generative model like Qwen and a sophisticated corrective tool like Copilot—proved to be the winning combination.

An interesting side note on workflow: I learned that both Qwen and ChatGPT have the capability to generate a complete, self-contained zip file for an entire IDE project. This is a significant convenience, bundling all source files, headers, and project configurations into a single downloadable archive. This stands in contrast to my typical method of using APIs, which requires me to manually copy and paste each generated file into the IDE—a tedious and error-prone process. This discovery suggests that a more efficient development workflow may be possible.

Now, let's explore the running application and see how this AI-driven development process translated into a functional product.

The "Alchemist" Application: A Functional Overview

The resulting application, aptly named "The Alchemist" by one of the AI processes, is surprisingly sophisticated. While I won't be connecting it to my live Interactive Brokers account in this demonstration, we can walk through the rich feature set presented in its graphical user interface.

The main window is organized into several logical panels:

• Core Controls: At the top, we have the essential buttons to manage the trading session: "Connect to IB," "Start Trading," "Stop Trading," and "Refresh Data." These provide the primary control over the application's lifecycle and its connection to the broker.

• Strategy Management: A significant portion of the UI is dedicated to strategy selection. Users can individually toggle on or off a variety of strategies that were generated by Qwen based on my executive summary. These include:

  • ES ZN Cash Carry (with a 35% capital allocation)

  • Arbitrage Strategies (with a 40% allocation)

  • Income Strategies

  • ZT ARMA Iron Condor

  • High Volume Iron Condor

This level of versatility and modularity in strategy management is a notable improvement over some of my previous, more monolithic methodologies. And remember, this is all implemented in high-performance C++.

• Portfolio Dashboard: A top-level view of the portfolio's health is provided through key metrics, including:

  • Starting Capital

  • Current P&L (Profit & Loss)

  • Available Cash

  • Volatility

  • Max Drawdown

• Performance Analytics: The interface features two distinct tables to display performance results—one for "Active Results" from live or paper trading and another for "Backtest Results." These tables provide a granular look at performance with metrics such as:

  • Total P&L

  • Win Ratio

  • Max Drawdown

  • Sharpe Ratio

  • Total Trades

While the functionality is impressive, it's unclear if the back-end implementation is as feature-rich as what I can achieve with Streamlit applications in Python for data visualization. However, the real power here lies in its C++ foundation for execution. This brings us to a crucial philosophical debate.

The Great Debate: GUI vs. Console for Production Trading

When you build a trading application in Python, you eventually hit a wall when it comes to robust, low-latency order execution. At that point, you are often forced to move to a console application. As you may have seen in my other C++ breakdowns, there are compelling reasons to keep the entire execution stack in C++, and perhaps even more compelling reasons to keep it within a Linux environment.

Let's be frank: if you decide to take a trading system into full production for real, live trading, the Windows environment and its associated GUI overhead become significant liabilities. This extra graphical user interface layer, while useful for demonstrations, introduces a cascade of problems:

1. Increased Complexity and Fragility: The code required to create and maintain a GUI is extensive and complex. It dramatically increases the probability of bugs, errors, and unexpected behavior.

2. Dependency Hell: GUI applications create hard dependencies on the operating system's graphical libraries. This makes the code inherently non-portable. The application is tethered to Windows, making a move to a more stable and cost-effective Linux server environment a massive undertaking.

3. Performance Overhead: GUIs are resource-intensive. They consume CPU cycles and memory that, in a trading context, are far better allocated to the core logic of strategy execution, market data processing, and risk management.

The more sensible route, in my opinion, is to embrace a "no-frills" approach. A lean, console-based application, particularly one designed for Linux, is superior for production deployment. All the charting, performance analytics, and journaling can be effectively offloaded to specialized third-party services. Platforms like TraderSync or even the tools provided directly by your broker (like Interactive Brokers) are designed specifically for this purpose and do it exceptionally well.

By decoupling the execution engine from the presentation layer, you gain immense benefits. The core trading logic remains clean, fast, and robust. Furthermore, when it comes to hosting in a cloud environment, a stripped-down console application is vastly cheaper. It requires fewer resources, smaller virtual machine instances, and is generally easier to manage and automate.

With that critical distinction made, let's now dive into the code itself and see what the AI has constructed under the hood.

Deep Dive into the "Alchemist" C++ Codebase

Opening the project in Visual Studio, we find a well-organized structure. I won't spend time on the header files (.h); if you're familiar with C++, you understand their role in declaring the interfaces for the corresponding source files (.cpp). Our focus will be on the implementation logic.

Instrument.cpp

This file is responsible for defining the financial instruments the system will trade.

• Initialization: The initializeInstrument function uses a simple if-else if chain to set properties based on the instrument's symbol (e.g., "ES", "ZN"). This data is hardcoded, directly reflecting the inputs from my executive summary.

• Data Handling: We see functions to get_default_size, get_contract_size, and crucially, get_price_history and get_volatility. These functions operate on std::vector, a standard C++ library container. This adheres to a key directive in my prompts: to avoid external dependencies and rely on standard libraries wherever possible, generating custom code for other needs.

• Code Quality: One of the first things I noticed is that the code generated by Qwen here looks more generic and less rigidly hardcoded than other versions I've worked with. This is a positive attribute, suggesting a more flexible and maintainable design.

• Validation and Risk: The file also includes stubs for validateMarketData, validateOptionChain, and validateFutureContract, which are essential for data integrity. Furthermore, it contains a calculateRiskMetrics function to compute mean, variance, and correlation, showing an early integration of risk concepts at the instrument level.

TradingDashboard.cpp

This file is the beast that powers the GUI. As I scroll through it, my arguments against GUI-based trading systems are vividly illustrated. The code is a sprawling landscape of Windows-specific API calls: RegisterClass, CreateWindowEx, and endless message handling loops. It's filled with the meticulous setup for every button, checkbox, panel, and label we saw in the running application.

While it's impressive that an AI can generate this level of detailed, platform-specific GUI code, it's also a clear demonstration of the maintenance nightmare it represents. I can easily see how the developer encountered 180+ errors, and I would wager a significant portion of them originated within this complex GUI component. If we were to strip out this file and replace it with a simple console interface, the entire project would become leaner, faster, and more robust.

Strategy.cpp

Here, we get into the meat of the trading logic.

• Strategy Definitions: The file contains hardcoded parameters for various option strategies like the Iron Condor, Bear Put, Bull Call Spread, Straddle, and Strangle. These parameters—stop-loss, take-profit, quantity, max risk—are all directly sourced from the executive summary document fed to the LLM.

• Option Greeks: The code correctly incorporates the essential option Greeks: Delta, Gamma, Theta, and Vega, which are fundamental to any serious options trading system.

• Code Quality: I am genuinely impressed by the quality of the C++ here. It's clean, object-oriented, and logically structured. It reinforces my thought that I should experiment with Qwen 3 for building Linux console applications, as the core logic it generates is quite good.

Before we move on, a quick word on AI hallucination. You discover hallucinations when you run the code and it produces nonsensical results or crashes due to flawed logic. It's a form of bug unique to AI generation. Interestingly, after months of use, I recently started seeing hallucinations for the first time in Claude 3.7's output. I am not yet familiar with the hallucination patterns of Qwen 3, but I assume that the developer's iterative debugging process with Copilot was instrumental in identifying and correcting any such issues among the 180+ errors.

RiskManager.cpp - The Crown Jewel

This file is, without a doubt, the most impressive and revealing part of the entire project. What Qwen has generated here is nothing short of a professional-grade quantitative risk management module.

• Standard Metrics: It correctly implements a suite of standard institutional risk metrics: Value at Risk (VaR), Volatility, Beta, and Sharpe Ratio. It also includes concepts like Concentration and Correlation.

• Unprompted Quant Techniques: This is where it gets fascinating. The code includes a RiskAlert system and calculations for a risk-free rate. I never mentioned a risk-free rate in any of my prompts. The LLM inferred its necessity and implemented it. This strongly suggests that Qwen is not just translating prompts into code; it's reasoning from a deep and specialized knowledge base. You simply do not get this level of quantitative sophistication from a model like Claude.

• HFT-Style Code: The coding style is tight, efficient, and minimalist. It is highly reminiscent of the style seen in open-source high-frequency trading projects. This leads me to a hypothesis: Chinese LLMs like Qwen and DeepSeek, some of which originated from algorithmic quant funds, may be trained on a vast corpus of academic papers, research, and proprietary code from the world of HFT. They seem to have a built-in architectural bias towards quantitative finance.

• Advanced Metrics: The module goes even further, providing functions to calculate the Sortino Ratio, Calmar Ratio, Skewness, and Kurtosis. It even includes frameworks for decomposed risk (Marginal VaR, Incremental VaR) and stress testing. This is a quant's toolkit, generated on demand.

Portfolio.cpp - Intelligent Scaffolding

The portfolio management code continues this trend of professional-grade design.

• HFT Design: It manages initial capital, sector exposure, cash, and total value in a way that, once again, feels like it was lifted from an HFT shop's internal library. It's clean, uses standard C++ libraries, and is reasonably well-commented.

• Intelligent Placeholders: This is another brilliant feature. For calculations that require external data, the AI has generated placeholder comments. For example, to calculate Beta, the code includes the comment: // Would need market data for actual calculation. To calculate inter-portfolio correlation, it adds: // Would need other portfolios for actual calculation. This is incredibly useful. It's not a bug; it's a feature. The AI has built the scaffolding and left clear, annotated hooks for a developer to add the necessary data feeds or additional code.

• More "Free" Features: The code also includes logic for leverage and a risk limit check that functions similarly to the Kelly Criterion. I did not ask for any of this. These are valuable features that the LLM added proactively, demonstrating a deep understanding of the problem domain.

AlchemistStrategies.cpp - Unearthing "Trade Secrets"

If the Risk Manager was the crown jewel, this file is the hidden treasure map. This is where the specific, named strategies are implemented, and the code contains what can only be described as "gold nuggets" of expert knowledge.

• Insider Logic: While implementing an ES Reversal Arbitrage strategy, the code contains comments and logic that feel like proprietary trade secrets. For instance, a comment // PVK for short-term option appears. What is PVK? It seems to be a specific, non-obvious heuristic. The code also contains logic for implementing a more aggressive exit for high volatility instrument. These are not generic programming patterns; they are domain-specific rules that an HFT shop would consider its intellectual property.

• Embedded Knowledge: It appears that Qwen, in its training, has absorbed and is now able to reproduce the nuanced conditions and heuristics that trading firms use to gain an edge. It's suggesting how to trade, not just providing the tools to do so.

• AI-Driven Forecasting: The code contains hardcoded prices and volatility figures (e.g., volatility of 23% allowing for wider strikes). It seems the AI, based on the context from the executive summary, is estimating market conditions and embedding those estimates directly into the strategy logic.

• Realized vs. Unrealized Volatility: The code makes a distinction between realized and unrealized volatility, a sophisticated concept that I have never seen in AI-generated code before. This again points to a source of training data that is far beyond the public internet.

It's important to note that the generated numbers are for full-size contracts. To trade micros, one would need to be very specific in the prompt, for example, "only focus on micro contracts for MES and MNQ."

Conclusion and Future Outlook

This deep dive into the "Alchemist" project has been an eye-opening experience. The final code, a product of Qwen 3 Coder's generation and Copilot's refinement, is incredibly impressive. The overarching conclusion is that certain LLMs, particularly Qwen, appear to have been trained on a deep, specialized corpus of quantitative finance and high-frequency trading material.

The implications are profound. The AI is no longer just a tool for writing boilerplate code. It is evolving into a knowledge partner, capable of architecting complex, professional-grade systems and, most surprisingly, embedding deep, domain-specific, and potentially proprietary expertise directly into the generated code. The discovery of what appear to be trade secrets and advanced quantitative techniques, all unprompted, is a testament to this new paradigm.

The journey of this project offers several key takeaways for developers in the quant space:

1. Don't Settle for One LLM: The difference in quality between ChatGPT and Qwen was night and day. It is crucial to experiment with multiple models to find the one best suited for your specific, complex domain.

2. Embrace a Hybrid AI Workflow: The combination of a powerful generative model (Qwen) to create the initial structure and a sophisticated refinement tool (Copilot in Cursor) to debug and iterate proved highly effective.

3. Choose Your Architecture Wisely: The debate between GUI and console is not trivial. For production trading systems, a lean, portable, console-based architecture is strategically superior in terms of robustness, performance, and cost.

I want to extend my sincere gratitude once again to the developer who put in the long hours to bring this project to life. Their expertise was essential in navigating the complexities of the toolchain and iterating the AI-generated code into a functional state.

If you want to learn more about my approach to quantitative trading and automated systems, I invite you to visit my website at quantlabs.net. There, you can sign up for my newsletter and receive a free ebook on C++ HFT infrastructure. The full source code for this "Alchemist" project will be available via a link in the description of the original video, which will guide you to the correct blog post.

Thank you for joining me on this detailed breakdown. Have a good day.