Turning Financial Research into C++ HFT Code with AI Assistance

Introduction: The QuantLabs Experiment - From PDF to Potential C++ HFT Code

"Good day everybody, Bryan here from QuantLabs.net." With these familiar words, Bryan, a well-known figure in the quantitative trading community, recently unveiled an intriguing experiment: leveraging the power of modern AI, specifically Large Language Models (LLMs), to transform dense academic research into the skeleton of a C++ HFT code (High-Frequency Trading) application. As he showcased in a video, the goal was ambitious yet demonstrably achievable – take a publicly available PDF research paper, in this case focusing on Gamma Exposure (GEX) for SPX options, and generate a structured C++ codebase designed for speed and potentially capable of implementing the paper's core ideas.

This endeavor touches upon a critical challenge in quantitative finance: the often-significant gap between theoretical strategies published in research papers and their practical, low-latency implementation required for competitive trading. While academic papers might outline sophisticated models and potential market edges, translating them into robust, efficient, and near-instantaneous C++ code is a formidable task demanding expertise in both finance and high-performance computing.

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Bryan's demonstration, using what he informally termed "vibe coding," highlighted the potential of LLMs to act as powerful assistants in this process. The generated output wasn't presented as a finished, profitable trading system ready for deployment, but rather as a "pretty cool" proof-of-concept – a structured C++ project, complete with typical HFT components, built entirely using the Standard Template Library (STL) and based on the principles outlined in the GEX research paper.

This article delves deep into the concept presented by Bryan. We'll explore the significance of converting research into actionable code, dissect the specific C++ HFT application structure generated in his example, examine the underlying Gamma Exposure strategy, discuss the role and limitations of AI in this context, and reflect on the implications for learning, development, and the future of quantitative trading. It's a journey from academic theory to tangible code structure, highlighting a novel approach to tackling complexity in the fast-paced world of HFT.

The Genesis: Translating Theory into High-Speed Reality

The world of quantitative finance is rich with academic research. Universities, financial institutions, and independent researchers constantly publish papers exploring market microstructure, novel pricing models, statistical arbitrage opportunities, and behavioral finance insights. Many of these papers contain the seeds of potentially profitable trading strategies. However, a significant hurdle lies in the implementation.

A research paper might describe a strategy using mathematical notation, high-level algorithmic steps, or simulations in environments like MATLAB or Python/R. While excellent for validation and communication of the core idea, this is far removed from the requirements of an HFT system where performance is paramount. HFT demands:

1. Extreme Speed: Execution measured in microseconds or nanoseconds.

2. Low-Level Control: Direct manipulation of memory and hardware resources.

3. Robustness: Handling high-throughput data streams and edge cases reliably.

4. Minimal Dependencies: Avoiding external libraries that might introduce hidden latency or instability.

This is where C++ typically enters the picture, offering the necessary performance and control. But the translation process is non-trivial. It requires developers to:

• Understand the financial concepts deeply.

• Design efficient data structures and algorithms.

• Optimize code for specific hardware (CPU cache, instruction sets).

• Manage memory meticulously to avoid garbage collection pauses or fragmentation.

• Interface with high-speed market data feeds and order execution gateways.

Bryan's experiment focused on a specific, highly relevant area: Gamma Exposure (GEX), using a paper titled "Gamma Exposure: Qualifying Hedge Rebalancing for SPX Options." GEX relates to the collective gamma position of options market makers. Understanding GEX can potentially provide insights into market volatility and the points at which large hedging flows might occur, influencing price movements. This is precisely the kind of complex, data-driven concept that HFT firms seek to exploit.

The core idea presented was to feed the concepts and requirements derived from this paper into an LLM and task it with generating a C++ application structure. The AI acts as a code generator, scaffolding the project based on the provided prompts and constraints (like using only STL). As Bryan noted, the ability to "take a PDF white paper research paper and turn it into a potential C++ HFT high-frequency trading uh application" is a powerful capability, particularly for learning and rapid prototyping. It allows quants and developers to quickly visualize how a theoretical concept might be structured in a high-performance coding environment, even if significant refinement and validation are still required. The "pretty cool" factor isn't just about the novelty; it's about the potential acceleration of the research-to-implementation cycle.

Dissecting the Generated C++ HFT Application Structure

A key aspect of Bryan's demonstration was the resulting C++ project structure, which mirrors common practices in professional HFT development. The emphasis on using only the C++ Standard Template Library (STL) and avoiding third-party dependencies is particularly noteworthy and aligns with the philosophy of many HFT shops.

• Why No Third-Party Libraries? While libraries like Boost offer powerful tools, relying solely on the STL (and potentially low-level system/OS APIs) provides several advantages in an HFT context:

  • Maximum Control: Developers have full visibility into the code being executed.

  • Performance Predictability: Avoids hidden performance bottlenecks, memory allocation patterns, or unexpected behavior within external code.

  • Reduced Attack Surface: Fewer dependencies mean fewer potential security vulnerabilities.

  • Minimized Bloat: Only necessary components are included, potentially leading to smaller, faster binaries.

  • Build Simplicity: Avoids managing complex external dependencies and potential version conflicts.

The generated project structure, as described by Bryan, included the following key components:

1. Makefile: The classic build automation tool for C/C++ projects. It defines how the source code is compiled and linked into an executable, managing compiler flags (crucial for optimization, e.g., -O3, -march=native), dependencies between files, and build targets. Essential for reproducible builds.

2. include/ Directory: Contains the header files (.h or .hpp). These files declare the interfaces of classes and functions (function prototypes, class definitions, constants, type definitions). Separating interfaces from implementation promotes modularity and faster compilation times (when using precompiled headers or managing dependencies correctly).

3. src/ Directory: Contains the source implementation files (.cpp). These files provide the actual code definitions for the functions and methods declared in the header files. This separation is standard C++ practice.

4. Core Component Files (within src/ and include/): 

   • Config.(h/cpp): Handles loading and managing configuration parameters. This could include file paths, connection details (IP addresses, ports), risk limits, strategy parameters (thresholds, lookback periods), etc. Centralizing configuration makes the system easier to manage and adapt.

   • GexCalculator.(h/cpp): This is the heart of the strategy implementation, directly derived from the research paper. It would contain the logic to calculate Gamma Exposure based on input options data (prices, open interest, strikes, expirations) and potentially different weighting schemes or assumptions as mentioned in the paper and Bryan's README.

   • Main.(cpp): The entry point of the application (main function). It's responsible for initializing all other components (loading config, setting up market data connection, initializing the strategy engine, risk manager, etc.), starting the main processing loop, and handling graceful shutdown.

   • MarketData.(h/cpp): A critical component responsible for receiving, parsing, and processing real-time market data feeds (e.g., quotes and trades for the underlying asset like SPX or NQ futures, and the relevant options). Bryan correctly points out the challenge here: getting an LLM to generate code for specific, proprietary exchange APIs (like the CME's MDP 3.0) is highly unlikely due to the lack of public documentation and training data. However, the LLM could potentially generate code for standardized protocols like FIX (Financial Information eXchange), or at least create the necessary interface structure that a developer could then fill in with the specific API logic or connect to a data provider's library. This component needs to be incredibly fast and efficient.

   • OptionPricing.(h/cpp): Implements options pricing models (e.g., Black-Scholes-Merton or more advanced models suitable for the specific options traded) and, crucially, calculates the "Greeks" – Delta, Gamma, Vega, Theta, Rho. For a GEX strategy, accurate and fast calculation of Gamma (and Delta for hedging) is essential.

   • OrderManagement.(h/cpp): Handles the lifecycle of trading orders: creation, validation (pre-trade risk checks), submission to an exchange or broker, tracking order status (filled, partially filled, cancelled, rejected), and managing modifications or cancellations. Again, interfacing with a specific execution venue API is a challenge for LLMs, but FIX is a possibility, or a simulated execution handler could be generated.

   • PositionManagement.(h/cpp): Keeps track of the current portfolio holdings (positions in the underlying asset and options), calculates real-time Profit and Loss (PnL), and provides this information to the strategy and risk management components.

   • RiskManagement.(h/cpp): Implements risk controls. This includes pre-trade checks (e.g., fat finger checks, max order size, max position limits) and post-trade monitoring (e.g., overall portfolio risk limits, drawdown limits). Essential for preventing catastrophic losses.

   • Strategy.(h/cpp): Encapsulates the core trading logic based on the GEX calculations. It receives market data and GEX signals (from GexCalculator), makes trading decisions (e.g., initiate a hedge trade when delta drifts, enter/exit positions based on GEX thresholds), and interacts with OrderManagement to execute those decisions. It might implement logic for both trend-following and mean-reverting regimes based on GEX signals, as mentioned in the README.

   • Utilities.(h/cpp): Contains common helper functions and classes used across the system. This could include logging frameworks, high-precision timers, simple data structures, string manipulation functions, mathematical helpers, etc.

   • Types.(h): Often used to define common data structures and type aliases used throughout the application (e.g., struct Order, struct Trade, struct MarketUpdate, using Price = double;, using Timestamp = uint64_t;). Bryan notes this can sometimes reflect a more C-style approach but is common for clarity and consistency.

5. ReadMe.txt A Markdown file providing documentation for the project. As Bryan showed, the generated README outlined the project's purpose (GEX HFT system for futures/options using STL), listed the main components and their functions, potentially included build instructions, and summarized the strategy's core concepts (GEX calculation, different weightings, volatility prediction, signal generation, backtesting framework).

This structure represents a modular, well-organized approach suitable for a complex HFT system. Each component has a defined responsibility, making the system easier to understand, develop, test, and maintain. The AI's ability to generate this entire structure based on high-level requirements is the key takeaway from Bryan's demonstration.

The Gamma Exposure (GEX) Strategy: A Deeper Look

The specific strategy targeted in Bryan's example revolves around Gamma Exposure (GEX). Understanding GEX is crucial to appreciating the purpose of the generated codebase.

• What is Gamma? As one of the options "Greeks," Gamma measures the rate of change of an option's Delta with respect to changes in the underlying asset's price. High Gamma means the option's sensitivity to the underlying price (Delta) changes rapidly. Options that are at-the-money (strike price near the current underlying price) and close to expiration typically have the highest Gamma.

• What is Gamma Exposure (GEX)? GEX attempts to quantify the total Gamma position held by market makers or dealers in a particular options market (like SPX options). It's often calculated by summing the Gamma of all outstanding options contracts, weighted by their open interest, often separating calls and puts.

  • Calculation: A common simplified approach might look like:

    • GEX = Sum [(Gamma_call OpenInterest_call) - (Gamma_put OpenInterest_put)] for all strikes.

    • The subtraction reflects the typical positioning: market makers are often short calls (negative gamma contribution when calculating dealer exposure) and short puts (also negative gamma contribution). Variations exist, and the exact calculation method (including weighting assumptions) was part of what the generated code aimed to explore, as per the README.

• Why Does GEX Matter? Market makers aim to remain delta-neutral to avoid directional risk. When they are net short gamma (which is common), as the underlying price moves, their delta changes rapidly due to gamma.

  • Price Rises: Their negative delta (from short calls) becomes more negative, forcing them to buy the underlying asset to re-hedge towards delta-neutral.

  • Price Falls: Their positive delta (from short puts) becomes more positive, forcing them to sell the underlying asset to re-hedge.

  • Implication: Large aggregate short gamma exposure (negative GEX, depending on calculation convention) can amplify market moves, as dealer hedging reinforces the prevailing trend. Conversely, large long gamma exposure (positive GEX) can dampen volatility, as hedging activity acts counter to the market direction.

• GEX-Based Strategies: The generated C++ code, particularly the GexCalculator and Strategy components, would aim to:

  • Calculate GEX: Implement the formulas based on real-time options market data (prices, open interest). The README mentioned testing different assumptions and weightings, reflecting the nuances in GEX calculation.

  • Predict Hedging Flows: Use GEX levels to anticipate potential market impact from dealer hedging ("predict hedging flows"). High GEX might indicate levels where significant buying or selling pressure could emerge.

  • Generate Signals: Create trading signals based on GEX values or changes. For example:

    • Volatility Prediction: Extreme GEX levels might predict periods of high or low volatility.

    • Trend/Mean Reversion: Use GEX levels to identify potential support/resistance zones (where hedging might kick in) or to determine whether a trend-following or mean-reverting approach is more appropriate. The README explicitly mentioned generating signals for both "trend and reverse regimes."

  • Execute Trades: Implement logic (e.g., in NQ futures, as mentioned) based on these signals, potentially involving delta-hedging the resulting options positions or taking directional bets based on anticipated GEX-induced flows.

  • Backtesting: The structure included provisions for a backtesting framework, essential for validating the strategy's historical performance before considering live deployment.

The README also highlighted interesting aspects like addressing the observation that GEX is "typically positive due to higher call option open interest in indices" (this depends heavily on the calculation convention – if calculating dealer exposure where they are short calls, this might manifest differently) and testing "different assumptions on dealer positioning." This underscores the complexity and the need for nuanced implementation, going beyond simple textbook formulas.

The Role of AI ("Vibe Coding"): Potential and Pitfalls

Bryan's use of an LLM for "vibe coding" is a fascinating application of modern AI in a highly specialized domain. It highlights both the immense potential and the necessary caveats.

• Potential:

  • Rapid Scaffolding: LLMs excel at generating boilerplate code and standard structures. Creating the entire project layout (Makefile, include/, src/, basic class definitions) can save significant initial development time.

  • Exploring Alternatives: Quickly generate different structural approaches or implementations of specific algorithms (e.g., different ways to structure the market data handler or risk manager).

  • Learning Aid: As Bryan emphasized, it's "really good for self-learning." By prompting the AI based on a research paper and examining the generated code, developers can learn how theoretical concepts might translate into C++ structure, even if the code requires correction. It helps visualize the components and their interactions.

  • Bridging the Knowledge Gap: For someone strong in finance but less experienced in low-latency C++, or vice-versa, the AI can provide a starting point, suggesting common patterns and practices.

• Pitfalls and Caveats:

  • Code Quality Variability: LLM-generated code can range from excellent to subtly flawed or outright incorrect. It requires careful review by an experienced developer. Performance-critical code needs meticulous optimization beyond what an LLM might initially produce.

  • Lack of Deep Context: LLMs don't truly "understand" the nuances of HFT or specific market microstructure details. They generate code based on patterns in their training data. They might miss critical edge cases, subtle race conditions, or optimal memory management techniques for ultra-low latency.

  • API Hallucinations: As Bryan noted, generating accurate code for specific, non-public, or rapidly changing APIs (like exchange data/order entry APIs) is a major challenge. The LLM might "hallucinate" an API structure or generate code for an outdated version. Relying on standardized protocols like FIX is more feasible.

  • Over-Reliance Risk: Developers might be tempted to trust the generated code without sufficient scrutiny, leading to hard-to-debug errors or performance issues in production.

  • Not a Replacement for Expertise: Generating the structure is one thing; implementing the core logic correctly, optimizing for nanosecond latency, ensuring robustness, and validating the strategy requires deep domain knowledge and engineering skill. The AI is an assistant, not a replacement.

Bryan's approach seems pragmatic: use the AI for the initial structure and boilerplate, acknowledge its limitations (especially regarding specific APIs), and emphasize the need for human review, refinement, and understanding. The real value lies in accelerating the initial phase and providing a tangible learning tool.

Learning, Initiative, and the Quant Finance Landscape

Beyond the technical aspects, Bryan framed this experiment within the broader context of career development and learning in quantitative finance. He repeatedly stressed the value of self-learning and reverse engineering.

• Reverse Engineering for Understanding: Getting access to a structured C++ HFT codebase, even one generated by AI as a starting point, provides invaluable learning material. Developers can study how components interact, how data flows, how strategies are implemented, and how low-level C++ features are used. This practical exposure complements theoretical knowledge.

• Demonstrating Initiative: Bryan referenced hedge fund mogul Steven Cohen (Point72) and his emphasis on looking for people with initiative. Undertaking projects like translating research papers into code, even if challenging and imperfect, demonstrates passion, curiosity, and a proactive approach to skill development. This is often valued as highly as formal qualifications (like "Ivy league education PhD at whatever MIT"). Showing you can do something practical, bridge theory and implementation, makes a strong impression.

• Bridging Retail and Institutional: While acknowledging that running such code through a retail broker connection cannot be truly called "HFT" due to inherent latency limitations, Bryan highlighted that the principles and coding techniques are directly relevant. Understanding how institutional-grade systems are structured and how strategies like GEX are implemented provides valuable insights, regardless of the execution venue.

• The Value Proposition: Bryan explicitly linked access to this generated source code (and others) to his QuantLabs.net membership, positioning it as a resource for those serious about self-learning and gaining practical exposure to quantitative trading implementations.

The message is clear: in the competitive field of quantitative finance, continuous learning, practical application, and demonstrating initiative are key differentiators. Leveraging tools like AI to accelerate this process, while understanding their limitations, is a smart strategy.

Conclusion: A Glimpse into AI-Assisted Quant Development

Bryan's demonstration of using an LLM to generate a C++ HFT application structure from a research paper on Gamma Exposure is more than just a "pretty cool" tech demo. It represents a tangible example of how AI can potentially reshape aspects of quantitative development. By rapidly scaffolding complex projects based on theoretical concepts, LLMs can serve as powerful assistants, accelerating the initial phases of implementation and providing invaluable learning tools.

The generated structure, adhering to HFT best practices like STL-only dependencies and modular design, provides a realistic blueprint covering essential components from market data handling and option pricing to strategy execution and risk management. The focus on a sophisticated concept like GEX highlights the ambition to tackle real-world quantitative strategies.

However, the caveats remain crucial. AI-generated code requires rigorous human oversight, validation, and optimization. Deep domain expertise and low-level C++ proficiency are irreplaceable for building truly robust, high-performance systems, especially when interfacing with specific exchange protocols.

Ultimately, the QuantLabs experiment underscores the evolving synergy between human expertise and artificial intelligence in finance. It showcases a path for bridging the gap between academic research and practical implementation, fosters self-learning through reverse engineering, and emphasizes the kind of initiative valued in the demanding world of quantitative trading. While not a magic bullet, AI-assisted code generation, when used judiciously, offers a promising avenue for accelerating innovation and skill development in the quest for market edge. As Bryan puts it, taking the initiative to explore these frontiers, even without a PhD from MIT, "gets you places."