AI Coding Revolution: How New LLMs Are Generating Institutional-Grade Quant Finance Tools

"Hello everybody, Bryan here . It's uh May 22nd..." With these familiar words, Bryan, a prominent voice in the quantitative trading and development space, recently unveiled a series of developments that signal a seismic shift in how sophisticated financial software can be created. The announcement, centered around the power of newly released Large Language Models (LLMs), showcases the rapidly evolving capabilities of AI coding in generating complex, high-performance C++ code for institutional-level trading strategies. This isn't just about automating simple scripts; it's about AI tackling the intricate, demanding world of quantitative finance, a domain traditionally requiring deep expertise and years of development experience.

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The core of Bryan's update revolves around the recent releases of Anthropic's Claude 3 series, specifically mentioning Sonnet and Opus ("version 4" as he referred to it, likely indicating the most powerful Opus variant), and Google's Gemini 2.5 Pro. These cutting-edge models, he reports, have demonstrated an unprecedented ability to understand complex requirements, generate robust code, and even debug intricate errors, pushing the boundaries of what was previously thought possible with AI-driven code generation.

A Flurry of AI-Generated Sophistication

Bryan's excitement was palpable as he listed a series of "institutional level projects" he had managed to code up, primarily using these new AI tools, in a remarkably short timeframe – "just today alone I've generated six projects... sorry five projects." These are not trivial undertakings:

• Mean Reverting LPI Swap: A strategy likely involving trading instruments based on their tendency to revert to a historical mean, potentially linked to a Liquidity Preference Index or similar.

• Implied Volatility Log Normal Simulation: This points to complex options pricing or risk management tools, simulating asset price movements based on implied volatility and log-normal distributions.

• Option Pricing: A broad category, but given the context, likely sophisticated models beyond basic Black-Scholes.

• Monotonicity (referred to as "monotisticity"): This likely relates to ensuring that financial models or pricing functions behave consistently and predictably (e.g., an option's price not decreasing as volatility increases, all else being equal).

• Cybernetic Oscillator: Suggests an advanced technical indicator, possibly incorporating feedback loops or adaptive mechanisms inspired by cybernetics.

These projects, now coded in C++, represent a significant leap. Bryan emphasized his growing proficiency in generating these "fairly quickly," highlighting a move towards "headless projects" – applications designed to run without a graphical user interface, crucial for backend trading systems.

The Crown Jewel: The Beta Trading Analyzer

The centerpiece of Bryan's demonstration, however, was the "Beta Trading Analyzer." He described it as "the most advanced one yet" and "very sophisticated," a testament to the capabilities of the latest AI models. This project, he explained, was intentionally designed to challenge the AI, and the results were illuminating.

The development process for the Beta Trading Analyzer was a fascinating interplay between different AI models:

1. Initial Generation with Anthropic Claude 4 Opus: Bryan tasked "version 4" with generating the initial C++ code. While it "kind of got the code there," the build process revealed it was "full of errors, like it was a lot."

2. Iterative Debugging with Gemini 2.5 Pro: Undeterred, Bryan embarked on a "two-hour process" of iteration with Claude, trying to get it to correct its own errors. When this proved challenging, he took a different approach: "I handed the same job, all the code that I generated plus all the exceptions and errors, and handed it off to Gemini 2.5 (Gemini 2.5 Pro)."

3. Success with Gemini: Over "probably an hour of iterating," Gemini 2.5 Pro was able to "fix it and be able to... finally got it linked and working."

This collaborative AI workflow—one model for initial generation, another for refinement and debugging—is a powerful paradigm. It underscores that the current state of AI development isn't always about a single perfect model, but potentially about leveraging the strengths of different architectures.

A Technical Glimpse into the Beta Trading Analyzer

Bryan provided a walkthrough of the Beta Trading Analyzer, emphasizing its "heavily quant stuff." The application utilizes IMGUI, a popular C++ library for creating graphical user interfaces, allowing for interactive control and visualization. Despite some minor bugs (like a "stupid thing that follows you around"), the core functionality was impressive.

The analyzer features a menuing system and various demonstration modules:

• Beta Distribution Demo: This allows users to adjust parameters like "bid alpha" and "ask alpha," which in turn modify bid and ask volumes, with changes reflected in a visual panel. This is crucial for modeling the probability distribution of trading parameters.

• Volume Profile Inventory Management Demo: This section likely visualizes and manages how inventory levels are affected by trading activity across different price levels, a key component for market makers.

• Market Simulator MDP (Markov Decision Process): This is where the application truly shines in terms of sophistication. Bryan described this as being for "market making and high frequency trading (HFT)." The MDP is a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision-maker. In HFT, an MDP agent can be trained to make optimal trading decisions (like placing bid/ask quotes) to maximize profit while managing risk (like inventory).

  • The simulator allows for running up to 10,000 steps.

  • It includes parameters for inventory, P&L, epoch, signaling, quotes, and total traded volume.

Bryan stressed that his primary goal was to "get it to a running state." The entire program, impressively, is contained within a single C++ file of "just under a thousand lines." This self-contained nature, with minimal external dependencies beyond IMGUI and the Standard Template Library (STL), is a deliberate choice. "I only wanted one library, one framework, and that was IMGUI. Everything else is using standard template library. And what you'll find most of the time is that's how HFT shops [operate]... they're going to try to strip away and minimize as many dependencies as possible." This mirrors real-world HFT practices where performance and control are paramount.

The AI-Generated Project Structure and Build System

The sophistication extends to the project's architecture, also laid out by the AI:

• build script: For compiling the project.

• main.cpp: The single source file containing all the logic.

• CMakeLists.txt: A CMake file to manage the build process. Bryan marveled at this: "To handcode this, I mean I've seen full-time jobs just to build out make files. No more with AI, right?"

• setup script: Likely for environment configuration.

• lib folder: Containing IMGUI-related files (.make, .ini, library files).

• third_party folder: Housing the IMGUI and IMPlot source code (IMGUI being an open-source project).

The AI's ability to generate not just the core application code but also the entire build system and project structure is a significant time-saver and skill-multiplier.

Deconstructing the AI-Generated README: A Blueprint of Complexity

Bryan shared insights from the AI-generated README file for the Beta Trading Analyzer, which further detailed the program's capabilities and underlying concepts. The prompt given to the AI was to "create a C++ based on the specs with an IMGUI interface and plots."

The README outlined key concepts:

• Beta Distribution & Scaled Beta Distribution for Volume Profile: Used to model the shape of order book depth or trading interest.

• Market Making with Inventory Management: The core strategy.

• HFT Decision Making with State Space: Defining the possible states the market or agent can be in.

• Inventory, Signal, Quotes, Epoch: Standard HFT parameters.

• Beta Distribution Parameterization (Alpha, Beta, Concentration, Mode): Fine-tuning the distribution.

• Inventory-Driven Beta Skewing: Adjusting trading parameters based on current inventory levels.

• Market Simulation and Replay: For testing and analysis.

Key components implemented:

• Beta Distribution Class

• Scaled Beta Volume Profile Class

• Market Maker Agent with Inventory Management

• State Space Representation

• MDP Solver

• Market Simulation

• Plotting Functionality (IMGUI Interface): Real-time plotting of distributions, inventory, simulation control. The AI even recognized and aimed to replicate plots mentioned in the reference article, such as beta distribution shapes, volume profiles, inventory evolution, and P&L.

The README also included comprehensive build instructions for various platforms (Ubuntu, Debian, CentOS, Red Hat, and even Visual Studio for Windows), a direct result of Bryan's prompt to "assume I'm a 5-year-old, I don't know what I'm doing. You want everything."

Mind-Blowing Performance Metrics: AI-Optimized Code

Perhaps the most startling revelations came from the "Performance and Advantages" section of the AI-generated README. These metrics suggest that the AI didn't just write functional code, but highly optimized code suitable for demanding HFT environments:

• Processes 1 million order book events per second in real-time. This is a staggering figure, indicative of extremely efficient data handling and processing logic.

• 5 milliseconds (ms) decision latency on commodity hardware. Low latency is the holy grail in HFT. Achieving 5ms on standard hardware is a significant feat, suggesting the AI implemented efficient algorithms and data structures.

• Implements hardware-accelerated PDF (Probability Density Function) calculations. This implies the code is designed to leverage specific hardware features for speed.

• Supports distributed backtesting across GPU clusters. This is an advanced feature typically found in large-scale quantitative research platforms, allowing for rapid testing of strategies over vast datasets.

Bryan was clearly impressed: "That's mind-blowing... it's coded optimally with that from the AI. This is how advanced it is." The README further stated the implementation "balances academic rigor with production-grade considerations, particularly around low-latency execution and numerical stability in financial calculations." It also noted the architecture "allows for both interactive research and headless batch processing."

Dissecting the Code: A Walkthrough of AI-Generated Logic

The AI also provided a breakdown of the code structure, revealing a modular design even within the single C++ file:

• Header Files: Standard C++ libraries (iostream, vector, cmath, random, map, algorithm, chrono, thread, numeric, iomanip, sstream) and IMGUI headers.

• Core Mathematical Models: 

  • BetaDistribution class: For calculations related to the beta distribution.

  • ScaledBetaProfile class: For managing the volume profile based on the scaled beta distribution.

• MDP Agent and Simulation: This is the heart of the HFT logic.

  • Inspiration: The README noted this section draws inspiration from academic papers like "Dynamic Trading with Predictable Returns and Transaction Costs" by Avellaneda & Stoikov, and potentially Ho & Stoll or Sahalia & Saglam models, which are foundational in market making theory.

  • MDPParams struct: Container for fixed model parameters (e.g., risk aversion, transaction costs).

  • MarketState struct: Represents the current state (e.g., price, inventory).

  • StateIndexer class: Maps market states to integer indices for efficient use in value iteration arrays (using only STL).

  • MDPSolver class: Implements value iteration to find the optimal trading policy (bid/ask decisions) that maximizes expected reward (spread capture) while penalizing inventory risk.

  • MarketSimulator class: Simulates market progression and events, allowing the HFT agent to interact with this environment.

The AI's ability to draw upon academic literature and implement complex algorithms like value iteration for an MDP is a profound demonstration of its advanced understanding.

The "Writing's on the Wall": Implications for Developers and the Future

Bryan didn't mince words about the implications of these advancements: "People say, 'Can I keep my job? I have my career.' I hate to tell you folks, the writing's on the wall right here, right now." This isn't necessarily a death knell for developers, but rather a signal of a profound transformation in their roles and required skills.

• The Rise of the AI Collaborator: Developers will increasingly work with AI, guiding it, prompting it, and refining its output. The iterative process Bryan described with Claude and Gemini is a prime example.

• The Importance of Reverse Engineering: Bryan advocates for learning by "reverse engineering these examples." Understanding AI-generated code, its structure, and its optimizations will be a crucial skill.

• Questioning Traditional Learning: "Do you really need books now to generate stuff like this? I don't know, I don't think so, because now you can generate this kind of code just from a blog." While foundational knowledge will always be important, AI can rapidly translate concepts from various sources into working code, accelerating the learning and application process.

• Democratization of Sophisticated Tools: The ability to generate institutional-grade tools with AI could lower the barrier to entry for smaller firms or individual traders, though expertise in prompting and validating the output will remain key.

QuantLabs.net: Embracing the AI-Driven Future

Bryan is positioning QuantLabs.net to be at the forefront of this revolution. He plans to make these sophisticated, AI-generated projects available to paying members of his community. This move reflects the significant time and iterative effort still required to guide the AI to produce polished, working applications, even with their advanced capabilities. "This is why I'm going to be charging for this, okay? Because it does take a lot of time to get everything I just showed you."

He's also ramping up his content creation, with more emails, TikTok videos, and YouTube content to share these developments. His call to action is clear: engage with the material, join his email list (which offers a free C++ HFT ebook), and subscribe to his channels to stay updated on this rapidly evolving landscape.

Conclusion: A New Era of Code Generation

The developments showcased by Bryan from QuantLabs.net are more than just an interesting tech demo; they are a profound illustration of the transformative power of modern AI. The ability of models like Anthropic's Claude 3 Opus and Google's Gemini 2.5 Pro to generate, debug, and optimize complex C++ code for demanding fields like high-frequency trading is a watershed moment.

While the AI doesn't eliminate the need for human expertise – indeed, Bryan's deep quant knowledge was crucial in guiding the AI and validating its output – it drastically alters the development lifecycle. It empowers individuals and smaller teams to create tools of a sophistication previously reserved for large institutions with extensive R&D budgets.

The performance metrics cited – millions of order book events per second, millisecond latencies, GPU acceleration support, all from AI-generated code – are particularly striking. This suggests AI is not just mimicking human coding patterns but is capable of producing highly optimized, performance-critical software.

The "writing is on the wall," as Bryan puts it, not for the end of programming, but for the dawn of a new era of human-AI collaboration in software development. For those in quantitative finance and beyond, the ability to effectively "vibe code" with these advanced AI partners will be the key to innovation and competitive advantage in the years to come. The times are indeed "getting good" for those ready to embrace this paradigm shift, even as it challenges traditional notions of skill, learning, and value creation in the world of software.