Welcome back to the newsletter.
If you’ve been drawing trendlines on a chart, trying to guess how the market will react to the latest geopolitical headline, I have some bad news: you are bringing a knife to a laser fight.
The landscape of quantitative trading has fundamentally shifted. For years, the barrier to entry for high-frequency trading (HFT) and serious algorithmic strategies was a PhD and a mastery of C++. Today? It’s a well-crafted prompt.
In our latest deep dive, we explored a massive breakthrough in our trading infrastructure. Here is what you need to know about the new era of AI-driven trading, “vibe coding,” and surviving extreme market volatility.
🐍 The Python & Rithmic Breakthrough
For a long time, if you wanted to use Rithmic—one of the premier data feeds and order routing platforms for futures—you had to navigate complex C++ APIs.
Not anymore. We have successfully integrated Python with Rithmic.
Why is this a game-changer? Because Python is the undisputed language of AI. By bridging Rithmic with Python, you can now pipe live, full depth-of-market (DOM) data directly into machine learning models and Large Language Models (LLMs). You can go from a backtested concept to live, automated execution in hours.
🤖 What is “Vibe Coding”?
If you hang around modern developer circles, you’ve heard of “vibe coding”—using AI to generate complex software via natural language instead of writing every line manually.
Here is how we are applying it to the markets right now:
The Brain: We use AI to generate massive, 41-page analytical reports synthesizing global news, stagflation data, and geopolitical events.
The Prompt: We feed this report into an LLM with a 1,500-line prompt, instructing it to act as a Systematic Portfolio Manager.
The Execution: The AI analyzes the news and automatically writes the Python code for the exact trading bots needed for that specific day’s market conditions.
⚔️ The Great LLM Showdown: Codex vs. Claude vs. GLM
We ran massive prompts through the top AI models to see which one builds the best trading bots. The results were surprising:
Claude 4.6 (Anthropic): The Overpriced Premium. Claude struggled with our massive 1,500-line prompts, frequently breaking connection. It’s also incredibly expensive. Unless you have capital to burn, it’s not the most efficient choice for high-frequency bot generation.
GLM 5 (Zhipu AI): The Budget Powerhouse. This Chinese LLM handled the massive prompts flawlessly and generated high-quality Python code at a fraction of the cost of Western models. It’s a bit slower, but if you run your generation overnight, it’s a massive winner.
Codex 5.3 (OpenAI): The Sweet Spot. Fast, reliable, and reasonably priced. When you need to generate and deploy a new bot in minutes based on breaking news, Codex is currently the king.
📊 Real Market Results: Crypto vs. Commodities
We let the AI loose during a recent period of extreme market stress (driven by war news and oil fluctuations).
The Winner: A Bitcoin futures momentum bot. It identified the crypto momentum and generated the highest profits with surprisingly low drawdowns.
The Wildcard: Natural Gas (NG). The AI deployed a mean-reverting NG strategy based on EU gas cap news. It achieved a massive Sharpe Ratio of 4.3, but experienced a violent $36,000 drawdown along the way. High profitability in commodities right now requires an iron stomach.
The Loser: Gold. Traditionally a safe haven, the AI’s gold tail-hedging strategy suffered worse drawdowns than Bitcoin. Human intuition (”buy gold during a war”) is losing to algorithmic reality.
⏳ Time is Running Out
The traditional discretionary trader is an endangered species. The future belongs to the Systematic Portfolio Manager—the trader who manages a fleet of AI agents adapting to the news in real-time.
Stay systematic,
Bryan at Quantlabs
