Stock Trading Contest with AI Models Spr

Stock Trading Contest with AI Models Springs a Surprise: Gemini Becomes an "Inverse Indicator" While DeepSeek Leads—Controversies and Reflections Behind the Scenes

 

A highly-watched stock trading contest featuring AI models wrapped up a few days ago, and the results caught most people off guard. Gemini, the star model under Google, unexpectedly turned into an "inverse indicator," with the investment portfolio it recommended failing to perform as expected. In sharp contrast, DeepSeek, a model from China, took the lead and claimed top honors with impressive returns, sparking heated discussions inside and outside the industry almost overnight.

 

After the contest, debates about "why DeepSeek stood out" never died down, and the most popular explanation centered on its "quantitative trading roots." Many argued that DeepSeek's team had deep experience in quantitative trading early on, giving them a better grasp of how market fluctuations work and the logic behind trading strategies. This "inherent edge" seemed to make the model naturally good at tasks like stock trading—where you need to analyze data in real time and spot short-term trends. After all, quantitative trading relies on algorithms to dig out hidden signals in data, which aligns closely with how AI models process massive amounts of financial data and make investment decisions. In a way, this background seemed to predestine DeepSeek's fit for stock trading scenarios from the start.

 

But if you think about it, the contest results can't be summed up simply by "background advantages." There are more questions worth exploring beneath the surface. First, there's the gap in how well different AI models fit stock trading scenarios. As a general-purpose large model, Gemini excels more at integrating cross-field knowledge and complex logical reasoning. But stock trading demands a precise understanding of the market's "unique quirks"—things like how short-term policy changes affect industries, tiny shifts in capital flows, or even swings in market sentiment. For a model to handle these, it needs tons of targeted financial data and real trading experience built into its training. If Gemini's training data lacked enough labeled financial trading content or coverage of relevant scenarios, even its strong general capabilities wouldn't help it perform well in a niche area like stock trading.

 

Second, is the label of "inverse indicator" fair to Gemini? The outcome of a single contest carries a degree of randomness. Market volatility itself, the contest's rules (like holding periods or allowed investment types), and even short-term news could all skew an AI model's performance. Gemini's poor showing this time might not mean it's incapable—it could just be that it hasn't found the best way to turn its "general intelligence" into "specialized stock trading skills." After all, an AI model's performance depends heavily on scenario settings and data input. A single contest loss is more like a "scenario adaptation test" than a judgment on its overall ability.

 

What's more, DeepSeek's victory also makes us wonder: what really gives an AI an edge in stock trading? Is it the strategy experience from quantitative trading roots, or the speed at which it processes market data in real time? The truth is, you need both. A quantitative background helps the model understand trading logic faster and avoid making decisions that "go against market rules." Meanwhile, strong data processing skills let it spot hidden opportunities that humans might miss in a fast-changing market. But even more important—can the AI "understand risk"? Stock trading isn't just about chasing returns; it's even more about controlling risk. Balancing returns and risk in a volatile market is probably a more crucial measure than "short-term returns," yet this point seemed to be overlooked in most post-contest discussions.

 

The surprising results of this AI stock trading contest aren't so much a "which model is better" showdown as a wake-up call for the industry: AI still has a long way to go before it's truly useful in financial trading. For general-purpose large models to succeed in niche areas, they need more precise scenario adaptation and targeted data training. And models with niche backgrounds (like quantitative trading) also need to keep refining their risk control and long-term stability. For regular investors, there's another takeaway: don't blindly trust AI just because of one contest's results. The market is far too complex and uncertain for current AI models to fully handle. A more practical attitude is to see AI as a helpful tool—not a magic solution.

 

In the future, as AI tech deepens its reach in finance, more contests like this will likely pop up. But the real value won't lie in "who wins." It will be in using these contests to spot problems, refine models, and make AI better at boosting the "safety" and "stability" of financial trading. That's what AI in stock trading should really be about.