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I just tried OpenAI’s updated o1 model. This technology will BREAK Wall Street

When I first tried the o1-preview model, released in mid-September, I was not impressed. Unlike traditional large language models, the o1 family of models do not respond instantly. They “think” about the question and possible solutions, and this process takes forever. Combined with the extraordinarily high cost of using the model and the lack of basic features (like function-calling), I seldom used the model, even though I’ve shown how to use it to create a market-beating trading strategy.

I used OpenAI’s o1 model to develop a trading strategy. It is DESTROYING the market. It literally took one try. I was shocked.

However, OpenAI just released the newest o1 model. Unlike its predecessor (o1-preview), this new reasoning model has the following upgrades:

• Better accuracy with less reasoning tokens: this new model is smarter and faster, operating at a PhD level of intelligence.
• Vision: Unlike the blind o1-preview model, the new o1 model can actually see with the vision API.
• Function-calling: Most importantly, the new model supports function-calling, allowing us to generate syntactically-valid JSON objects in the API.

With these new upgrades (particularly function-calling), I decided to see how powerful this new model was. And wow. I am beyond impressed. I didn’t just create a trading strategy that doubled the returns of the broader market. I also performed accurate financial research that even Wall Street would be jealous of.

Enhanced Financial Research Capabilities

Unlike the strongest traditional language models, the Large Reasoning Models are capable of thinking for as long as necessary to answer a question. This thinking isn’t wasted effort. It allows the model to generate extremely accurate queries to answer nearly any financial question, as long as the data is available in the database.

For example, I asked the model the following question:

Since Jan 1st 2000, how many times has SPY fallen 5% in a 7-day period? In other words, at time t, how many times has the percent return at time (t + 7 days) been -5% or more. Note, I’m asking 7 calendar days, not 7 trading days.

In the results, include the data ranges of these drops and show the percent return. Also, format these results in a markdown table.

O1 generates an accurate query on its very first try, with no manual tweaking required.

Transforming Insights into Trading Strategies

Staying with o1, I had a long conversation with the model. From this conversation, I extracted the following insights:

Essentially I learned that even in the face of large drawdowns, the market tends to recover over the next few months. This includes unprecedented market downturns, like the 2008 financial crisis and the COVID-19 pandemic.

We can transform these insights into algorithmic trading strategies, taking advantage of the fact that the market tends to rebound after a pullback. For example, I used the LLM to create the following rules:

• Buy 50% of our buying power if we have less than $500 of SPXL positions.
• Sell 20% of our portfolio value in SPXL if we haven’t sold in 10,000 (an arbitrarily large number) days and our positions are up 10%.
• Sell 20% of our portfolio value in SPXL if the SPXL stock price is up 10% from when we last sold it.
• Buy 40% of our buying power in SPXL if our SPXL positions are down 12% or more.

These rules take advantage of the fact that SPXL outperforms SPY in a bull market 3 to 1. If the market does happen to turn against us, we have enough buying power to lower our cost-basis. It’s a clever trick if we’re assuming the market tends to go up, but fair warning that this strategy is particularly dangerous during extended, multi-year market pullbacks.

I then tested this strategy from 01/01/2020 to 01/01/2022. Note that the start date is right before the infamous COVID-19 market crash. Even though the drawdown gets to as low as -69%, the portfolio outperforms the broader market by 85%.

Deploying Our Strategy to the Market

This is just one simple example. In reality, we can iteratively change the parameters to fit certain market conditions, or even create different strategies depending on the current market. All without writing a single line of code. Once we’re ready, we can deploy the strategy to the market with the click of a button.

Concluding Thoughts

The OpenAI O1 model is an enormous step forward for finance. It allows anybody to perform highly complex financial research without having to be a SQL expert. The impact of this can’t be understated.

The reality is that these models are getting better and cheaper. The fact that I was able to extract real insights from the market and transform them into automated investing strategies is something that was never heard of even 3 years ago.

The possibilities with OpenAI’s O1 model are just the beginning. For the first time ever, algorithmic trading and financial research is available to all who want it. This will transform finance and Wall Street as a whole