Can I generate code using generative ai models​ with HFT in 15 minutes?

Hello everybody, Brian here from QuantLabs.net, and today I want to pull back the curtain on what is arguably the most sophisticated strategy project, simulation – whatever you want to call it – that I’ve developed to date. This isn't just another coding exercise; this is a leap into what feels remarkably close to the realm of high-frequency trading (HFT), all orchestrated with an unprecedented level of AI assistance. We're talking about a robust backend built in C++, powering a dynamic and insightful frontend developed in Python using Streamlit, a framework I've grown to admire for its clean code generation and efficiency. The question becomes: Can I generate code using generative AI models​ with HFT in 15 minutes? Answer: Why not? So that is a definite yes!

The real star of this show, the invisible hand guiding the creation of this entire system, is Anthropic's Claude 4 Opus Reasoning model, specifically leveraged for its advanced reasoning capabilities. Make no mistake, harnessing this level of AI is a significant investment, but the results, as you'll soon see, are nothing short of revolutionary. This project underscores a paradigm shift in how complex financial applications can be conceptualized, designed, and deployed.

The Genesis: From Raw Data to AI-Synthesized Strategy

The foundation of this project lies in its ability to process and interpret a vast amount of information. We began by feeding the AI reports on 46 different financial instruments. This isn't a narrow selection; we're talking about a diverse portfolio covering major indices like the NASDAQ, commodities such as heating oil and gold, forex pairs like EUR/USD, and even cryptocurrencies including Ethereum and Bitcoin. The challenge was not just to analyze these instruments in isolation but to derive a cohesive, actionable strategy from the collective intelligence.

From these 46 individual reports, the AI, Claude 4 Opus, was tasked with generating a summary report. This wasn't a simple aggregation; it was an act of synthesis, identifying optimal combined futures and options strategies designed for the highest potential profit. The output was a detailed blueprint, 100% generated by AI, outlining sophisticated approaches:

• Options Hedging & Straddles: For instruments demonstrating particular volatility profiles or requiring risk mitigation.

• Covered Calls: For a bullish outlook on specific assets while generating income.

• Protective Puts: For a bullish outlook but with crucial downside protection.

• Calendar Spreads: Suited for environments with stable or predictable volatility.

Furthermore, the AI didn't just suggest strategies; it provided context. It allocated these strategies to specific instruments based on its analysis of the individual reports and even proposed weightings for a hypothetical portfolio – initially conceived as $100,000 in the coding phase, though the visual interface dynamically adapts and in some views might reflect a larger conceptual sum like a million-dollar account. This level of nuanced, AI-driven decision-making, from instrument analysis to strategy allocation and portfolio weighting, is what sets this project apart. This comprehensive strategic output, complete with the underlying logic, is the kind of cutting-edge material we make available to our Elite programming members at QuantLabs.net, who receive full source code and ongoing insights.

The Architecture: A Symphony of C++ and Python

To bring these AI-generated strategies to life, we needed a robust and performant architecture. This led to a two-pronged approach: a C++ backend for the heavy lifting and a Python Streamlit frontend for visualization and interaction.

1. The C++ Backend: The Engine Room

   
   

   The backend is the workhorse of this simulation. Written in C++, it’s responsible for serving up the actual set of strategies, managing the simulated market data feed, executing trades within the simulation, and calculating P&L in real-time. C++ is the language of choice for HFT and latency-sensitive applications due to its raw speed, memory management capabilities, and direct hardware interaction potential. In our simulation, this C++ application runs continuously, updating market conditions and strategy performance every couple of seconds, providing a constant stream of data that mimics a live trading environment. This backend is where the core logic of the AI-suggested strategies is implemented and tested.

2. The Python Streamlit Frontend: The Visual Command Center

   
   

   For the frontend, I opted for Python with the Streamlit ASGI framework. Streamlit is a lightweight, high-performance framework that's excellent for building asynchronous web services and applications. Its appeal lies in its simplicity, speed, and the clean, maintainable code it facilitates – especially when working with AI code generation tools like Claude.

The Python frontend consumes the summary report generated by Claude 4 Opus. This report, detailing the optimal strategies, is fed back into Opus with a prompt to generate the Streamlit application code for the user interface. This UI then connects to the live C++ backend, fetching and displaying data in real-time. The result is a sophisticated dashboard that visualizes the ongoing simulation, launched and managed from a separate terminal.

A Tour of the Live HFT Simulation Dashboard

Once the C++ backend is running and the Python Streamlit frontend is launched, we are greeted with a dynamic dashboard that truly showcases the system's capabilities. Starting with a simulated portfolio of $100,000, the dashboard offers a wealth of live information:

• Portfolio Overview:

  • Current Portfolio Value: Tracks the initial $100,000 as it fluctuates.

  • Total P&L (USD): A real-time dollar value of profit or loss.

  • Average Active Positions: The number of currently open trades.

  • Strategy Focus: Emphasizes a bid-ask, low-latency approach, characteristic of HFT.

  • Average Win Rate: Dynamically updated, for instance, showing 54% in the initial run.

• Strategy Allocation (AI-Driven):

  
  

  This is a direct reflection of the AI's strategic recommendations from the summary report. For example, the dashboard might display:

  • Arbitrage: 40%

  • Strangle/Straddle: 25%

  • Covered Call/Protective Put: 15%

  • Calendar Spread: (Remaining Percentage)

    
    

    It's crucial to remember these are for futures and options on futures, not direct equity plays outside of indices.

• P&L Breakdown by Strategy:

  
  

  A visual representation (e.g., bar chart or pie chart) showing the profit and loss contribution of each active strategy type (Arbitrage, Straddle, Covered Call, etc.). This gives an immediate sense of what's working in the current simulated market conditions.

• Strategy Metrics:

  
  

  Key performance indicators (KPIs) for the overall portfolio:

  • Win Ratio: The percentage of profitable trades.

  • Sharpe Ratio: A measure of risk-adjusted return (though with simulated data, its interpretation needs care).

  • Trade Execution Quality: (Potentially metrics like slippage if the simulation were more granular).

  • Max Drawdown: The largest peak-to-trough decline during a specific period.

• Live Market Data Feed (Simulated):

  
  

  The dashboard displays a stream of simulated market data, which is being fed by the C++ backend. This includes:

  • Bid-Ask Spreads: For various instruments, allowing identification of tight spreads and good volume. Examples include Swiss Franc (CHF), Australian Dollar (AUD), Canadian Dollar (CAD), Oil (CL), etc.

  • Real-Time Quotes: An interactive section where users can select an instrument (e.g., E-mini S&P 500 futures - ES, or Crude Oil - CL) and see its live simulated bid, ask, and spread. This feature, remarkably, was an addition suggested and implemented by the AI (Claude 4 Opus) itself, not something I explicitly requested. It demonstrates the LLM's capacity to anticipate user needs.

  • Filtering Options: The ability to filter by minimum volume or maximum spread, and a manual data refresh button – again, thoughtful additions by the AI.

• Positions Dashboard:

  
  

  A detailed look at the currently held positions:

  • Total Position Value: The current market value of all open positions (e.g., $52,000).

  • Total Position P&L: The unrealized profit or loss on these positions (e.g., up 4.5%).

  • Winning Positions: Ratio of profitable to total open positions (e.g., 4 out of 5).

  • Average Position Size: The typical capital allocated per trade (e.g., ~$10,000).

  • Active Positions List: Details on each open trade, including the instrument (e.g., Bitcoin, ZO, CC, ZT, ZC – these are ticker symbols for various futures contracts) and the strategy being employed (Arbitrage, Straddle, Covered Call, Protective Put).

• P&L by Position (Visualized):

  
  

  Often shown as a pie chart or bar graph, this breaks down the P&L contribution of each individual active position, offering granular insight into performance.

The "10-Minute Miracle": AI's Unprecedented Speed

One of the most astounding aspects of this entire project is the speed of its creation. The initial summary report, derived from 46 instrument analyses, was generated by Claude 4 Opus in about five minutes. The subsequent generation of the Python Streamlit frontend code, based on that summary report and the directive to create an interactive dashboard, took no more than an additional 10 minutes. This means that a sophisticated, dual-language (C++/Python) trading simulation system, complete with a dynamic UI and multiple analytical views, went from concept to functional prototype in roughly 15 minutes of AI processing time. This is a testament to the power of modern LLMs with strong reasoning capabilities. It’s the kind of rapid development that would have been unthinkable just a few years ago, potentially taking weeks or months for a human development team.

Beyond Explicit Instructions: AI's Creative Input

Throughout the development of the frontend, Claude 4 Opus didn't just translate instructions into code; it actively contributed to the design and functionality. Features like the instrument-specific quote lookup, the volume and spread filters, and the refresh buttons were not part of my original, minimal prompt. The AI inferred their utility and incorporated them. This "creative reasoning" or "anticipatory design" is a hallmark of advanced LLMs like Opus. It’s as if you're collaborating with an extremely fast and knowledgeable junior developer who occasionally suggests brilliant additions. The refresh rate for the dashboard, for instance, was set at 2 seconds by default – a sensible choice for a pseudo-live system.

The Performance Tab: Rethinking Backtesting

Perhaps the most strategically significant part of the dashboard is the "Performance" tab. This section directly addresses a common question in quantitative trading: backtesting. My evolving perspective, reinforced by projects like this, is: why get bogged down in traditional backtesting if you can observe strategy performance in a dynamic, forward-looking (albeit simulated, for now) environment?

If one had access to real-time, high-quality futures data, and critically, real market data for options chains (which, as I've often mentioned, can be very expensive, especially from providers like the CME), this kind of live performance monitoring becomes incredibly powerful. The Performance tab in this simulation offers:

• Strategy Performance Analysis (Radar Chart):

  
  

  This visually striking radar chart plots various metrics (like Sharpe Ratio, Total P&L, Win Ratio) for each strategy type. It provides an at-a-glance comparison of their risk-reward profiles. Again, this sophisticated visualization was not explicitly requested but generated by the AI, showcasing its ability to select appropriate and insightful data representations. It helps identify which strategies are performing best across multiple dimensions.

• Risk-Return Scatter Plot:

  
  

  Another AI-generated gem, this scatter plot typically charts Max Drawdown against Total P&L for each strategy. The ideal strategy would be in the quadrant representing high P&L and low Max Drawdown. In the demonstration, "Protective Puts" might appear in this favorable zone, suggesting it as a candidate for increased allocation.

The core idea here is iterative refinement. Instead of spending weeks setting up a historical backtest, one could, with real data, run a diverse set of AI-suggested strategies for a limited period (say, a day). The live performance dashboard would clearly indicate which 1-2 strategy types are most profitable or have the best risk-adjusted returns. You could then take this insight, feed it back to the AI as an updated prompt (e.g., "Focus only on Arbitrage and Protective Puts, using these specific risk parameters"), and regenerate a new, more focused C++ backend logic and potentially a refined Python frontend in another 10-15 minutes. This rapid iteration cycle, driven by live (or high-fidelity simulated) performance, is a powerful alternative or complement to traditional backtesting. It allows for dynamic adaptation to changing market conditions and quick pivoting in strategy development.

Deployment and Scalability: The Path to Production

While this is a simulation, the architecture and the AI-generated codebase are designed with an eye toward potential deployment. The use of Streamlit for the frontend and C++ for the backend aligns with industry best practices for performance and scalability. The AI can even assist in generating Dockerfiles and Kubernetes configurations, potentially allowing for deployment to cloud platforms like Streamlit Community Cloud (though this particular app is more complex) or custom cloud infrastructure with just a few more prompts. Adding custom authentication layers would also be a guided process with the AI.

The Implications: A Paradigm Shift in Quantitative Finance

This project is more than just a technical demonstration; it's a window into the future of quantitative finance and software development.

1. Speed and Agility: The ability to generate complex, multi-component applications in minutes rather than months is a game-changer. It allows for rapid prototyping, testing of new ideas, and quick adaptation to new market insights.

2. The Evolving Role of the Quant Developer: The human expert's role shifts. Instead of writing every line of code, the quant becomes a "conductor" of the AI, an architect of prompts, a validator of AI-generated logic, and a strategist who guides the AI's powerful capabilities. Deep domain knowledge remains crucial for crafting effective prompts and interpreting results.

3. Democratization (with a Caveat): While tools like Claude 4 Opus can empower smaller teams or even individuals to build sophisticated systems, access to these top-tier LLMs comes at a significant cost. This "reasoning" power is computationally expensive.

4. Innovation in Strategy Discovery: AI can analyze vast datasets and suggest novel strategy combinations or parameterizations that humans might not have considered.

5. Enhanced Visualization and Reporting: As demonstrated, AI can autonomously create insightful and user-friendly UIs, making complex data more accessible.

QuantLabs.net: Your Gateway to This Evolving World

What I've shown you today is a glimpse of what's becoming possible. If this piques your interest and you want to delve deeper into the world of quantitative trading, AI-driven development, and advanced analytics, I invite you to connect with us at QuantLabs.net.

There are several ways to engage:

• Join Our Email List: Sign up on our website. We're increasing our communication, sending out more emails with valuable insights, project updates, and announcements. Engaging with our content helps us tailor what we send to your interests.

• Explore the "Learn" Tab: This section of our website is a hub for educational content and resources.

• Consider Our Memberships: 

  • 7-Day Free Trial: If you're curious about what lies behind our paywall, we offer a 7-day risk-free trial. This gives you access to our Quant Analytics platform, our private news group, and a selection of our courses (some may be older, but still foundational). It’s a great way to sample our offerings.

  • Elite Programming Membership: If you're serious about programming and want access to the source code for projects like the HFT simulation I've just detailed, along with ongoing advanced content, the Elite membership is the way to go. This is where we share our most cutting-edge work.

Conclusion: Embracing the AI Revolution in Finance

The project I've shared today – a C++ backend, Python Streamlit frontend, HFT-like simulation, all largely conceived and coded by Anthropic's Claude 4 Opus – is a powerful illustration of where technology is heading. The speed, sophistication, and even the creative input of the AI are genuinely "beyond crazy" in the best possible way. We're moving from a world where building such systems required large teams and extensive timelines to one where a knowledgeable individual, armed with the right AI tools and domain expertise, can achieve incredible results in remarkably short periods.

This isn't about replacing human ingenuity but augmenting it. The ability to rapidly prototype, test, and iterate on complex trading strategies by leveraging advanced AI is no longer a far-off dream; it's a present-day reality. The key is to understand how to effectively prompt, guide, and validate these powerful new tools.

Thank you for watching and reading. I hope this has been as exciting for you as it has been for me to develop and share. The landscape of quantitative finance is changing rapidly, and at QuantLabs.net, we're committed to exploring the frontier. Have a great day, and I look forward to sharing even more advanced quant finance and AI content with you soon.