I just tried OpenAIs updated o1 model. This technology will BREAK Wall Street

The Evolution of OpenAI’s O1 Model: A Game-Changer for Financial Research and Trading

When I first experimented with the O1-preview model, released in mid-September, my initial impression was underwhelming. Unlike traditional large language models that provide instant responses, the O1 family takes time to “think” through questions and solutions, which often felt excessively slow.

Adding to this, the high costs associated with using the model and the absence of critical features, such as function-calling, made it less appealing. Despite demonstrating its capability to create market-beating trading strategies, I seldom used it.

However, the release of OpenAI’s new O1 model marks a transformative upgrade. Unlike its predecessor, the new reasoning model introduces significant improvements:

Key Upgrades in the Latest O1 Model

1. Enhanced Accuracy with Fewer Reasoning Tokens
   Operating at a PhD-level intelligence, the model is both smarter and faster.

2. Vision Capabilities
   With the Vision API, the model can now “see,” unlike the vision-limited O1-preview version.

3. Function-Calling
   This feature enables the model to generate syntactically-valid JSON objects through the API, a crucial improvement for seamless integrations.

Armed with these advancements, I revisited the model's capabilities. The results were astonishing, leading not only to a trading strategy that outperformed the market but also to high-quality financial research that rivaled Wall Street standards.

Revolutionizing Financial Research

Unlike traditional language models, OpenAI’s O1 model uses extended reasoning time to deliver highly accurate responses. This capability allows it to answer intricate financial questions by formulating precise queries. For instance, I asked:

“Since Jan 1st, 2000, how many times has SPY dropped 5% in a 7-day period? Include the date ranges and percent returns, formatted in a markdown table.”

The O1 model provided a detailed, accurate response on the first attempt. In contrast, even advanced models like Claude 3.5 Sonnet failed to generate the correct query, requiring manual corrections that would be challenging for non-technical users.

Turning Insights into Trading Strategies

By engaging in an in-depth conversation with the O1 model, I uncovered key market insights:

• The market tends to recover after significant downturns, including events like the 2008 financial crisis and the COVID-19 pandemic.

Using these insights, I designed an algorithmic trading strategy with rules such as:

• Buy: 50% of buying power if SPXL positions are less than $500.
• Sell: 20% of SPXL portfolio value if no sales occurred in a long period (e.g., 10,000 days) and positions are up by 10%.
• Buy more: 40% of buying power if SPXL positions are down by 12% or more.

These rules capitalize on SPXL’s outperformance in bull markets, while retaining flexibility to manage downturns. Testing the strategy from 01/01/2020 to 01/01/2022—including the COVID-19 market crash—showed an impressive 85% outperformance of the broader market, even during a 69% drawdown.

Effortless Deployment and Adaptability

With the O1 model, creating and testing trading strategies no longer requires coding expertise. Parameters can be adjusted iteratively for specific market conditions, and strategies can be deployed instantly with just a click.

Conclusion: Empowering Retail Investors

The O1 model represents a groundbreaking advancement for finance, democratizing access to complex financial research. At NexusTrade, we’ve integrated this powerful AI tool to enable retail investors to design, test, and deploy market-beating strategies effortlessly.

Ready to transform your investing journey? Sign up today and take control of your financial future with the cutting-edge power of AI.