My view on DeepSeek: How it became a game-changer for market analysis

Can the DeepSeek AI model outperform ChatGPT and revolutionize AI-powered trading? Trading expert Eric Chia explores how DeepSeek is reshaping financial markets and AI technology.

When I first heard about the DeepSeek AI model, my immediate reaction was, “Great, another AI model trying to make waves in an already crowded space.”

I mean, we’ve got ChatGPT, Perplexity, Claude, and a dozen other AI tools vying for attention—which is why comparisons like DeepSeek vs ChatGPT are starting to matter more in trading and market analysis contexts. But then, something interesting happened. DeepSeek actually turned out to be good. Like, really good. 

This got me thinking: How did a Chinese company, under scrutiny for advanced chip restrictions, still manage to build something that sent shockwaves through the market? More importantly, what does a model like DeepSeek actually offer traders and market participants when it comes to analysing information, interpreting context, and supporting decision-making—and how does it compare to other AI models for trading that focus on reasoning rather than execution?

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DeepSeek's impact on the AI industry

The magnitude of this achievement is undeniable—it’s not just another AI model, but a product that has directly influenced market sentiment, particularly in the US. The sheer impact has even been felt by Nvidia (NVDA), a key player in the AI race. For traders, these shifts matter because changes in AI leadership and capability can influence market sentiment, capital flows, and how information is processed across financial markets.

What’s even more intriguing is how the DeepSeek AI model, which seemingly started as a niche project, evolved into something capable of genuinely shifting the AI landscape. It raises bigger questions about China’s AI strategy, its ability to innovate despite supply chain constraints, and whether this signals a broader shift in the balance of power within the AI industry.

The cost factor and market disruption

And then there’s the cost factor. If the DeepSeek AI model truly delivers high performance at a lower cost—something the market has yet to fully verify—it could mark a significant disruption, forcing Western AI firms to rethink their pricing and accessibility strategies.

DeepSeek wasn’t supposed to be a revolution. It started as an experimental project, aiming to push the boundaries of open-source AI models. But what set it apart was its laser focus on efficiency, scalability, and user-friendly adaptability. Unlike some of its competitors that prioritize broad, generic responses, DeepSeek zeroed in on being sharp, to the point, and slightly less robotic, while using better reasoning to anticipate what the user really wants to ask or what user objectives/expectations are.

DeepSeek vs. ChatGPT: Performance comparison

To assess how each model performs in a trading-related analysis context, I applied the same market-focused prompt to both tools. I tested DeepSeek vs ChatGPT by applying the same prompt to both AI tools to generate an answer:

“You're a trader focusing on Gold instruments. You will need to analyze both technical and fundamental analysis and tell me what is the best strategy.”

The aim wasn’t to follow the output as a trading instruction, but to assess how each model reasoned through market context.

Contextual retention and accuracy

My results showed that DeepSeek demonstrates a significant advantage in contextual retention, meaning it excels in maintaining continuity throughout a conversation. It accurately remembers prior details, ensuring responses remain consistent and aligned with the user’s past inputs. This reduces the likelihood of contradictory statements or the need to repeatedly remind the AI tool of earlier discussions.

Moreover, the DeepSeek AI model exhibits a lower tendency to hallucinate information, making it a more reliable choice for users who require precise, fact-based responses. While some AI models may inject unrelated or fabricated details, DeepSeek remains focused, delivering answers that are not only relevant but also closer to what the user actually wants.

This strong contextual awareness enables DeepSeek to better grasp user intent, adapt to preferences, and refine its responses accordingly. As a result, users experience a more intuitive and personalized interaction, where the AI understands them better and provides insights that align with their expectations.

How DeepSeek helps traders and impacts financial markets

The impact of the DeepSeek AI model on financial markets extends beyond AI innovation, highlighting how AI models in trading are increasingly being used to support analysis rather than execution. Its ability to retain context, process large data sets, and provide deeper market insights makes it a valuable tool for traders.

For example, DeepSeek’s enhanced contextual retention enables it to track ongoing market trends and react to financial news more informatively. Unlike traditional AI models that generate static responses, DeepSeek can analyze multiple factors—including economic reports, interest rate changes, and geopolitical events—to produce more accurate analytical perspectives.

Moreover, its lower tendency to hallucinate means traders can rely on it for factual, data-driven insights rather than speculative or misleading information. Whether used for risk assessment, portfolio optimization, or technical analysis, DeepSeek’s ability to reason through complex financial scenarios can help traders make more informed investment decisions with greater confidence.

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Final thoughts

One of the biggest challenges in AI today is balancing speed, accuracy, and reasoning depth. DeepSeek performs strongly across these dimensions, particularly in contexts where structured thinking and contextual retention matter.

No AI model is perfect, and DeepSeek is no exception. But its emergence highlights a broader shift in how AI tools are evolving—not just as conversational assistants, but as analytical frameworks capable of supporting complex decision-making.

For traders, the significance isn’t about which model “wins.” It’s about understanding how different AI models in trading can support clearer reasoning, stronger context management, and more disciplined market analysis. Tools like DeepSeek are most valuable when used to enhance interpretation and structured thinking—not to replace judgement or automate execution.

Ultimately, the real advantage lies in how effectively traders integrate AI into their analytical process, using it to refine perspective rather than outsource responsibility.