Six Major AI "Traders" Ten-Day Showdown: Who Can Survive in a "No Information Advantage" Market?
Original Article Title: "Six Major AI 'Traders' Ten-Day Duel: A Public Lesson on Trends, Discipline, and Greed"
Original Article Author: Frank, PANews
In less than ten days, the funds doubled.
When DeepSeek and Qwen3 achieved this record in the live trading of AlphaZero AI launched by Nof1, their profit efficiency had far surpassed that of the vast majority of human traders. This forces us to confront a question: AI is transitioning from a "research tool" to a "frontline trader." How do they think? PANews conducted a comprehensive review of the nearly 10-day trading of six mainstream AI models in this competition, attempting to uncover the decision-making secrets of AI traders.

A Purely Technical Duel Without "Information Asymmetry"
Before the analysis, we must clarify a premise: the AI decisions in this competition are "offline." All models passively receive exactly the same technical data (including current price, moving averages, MACD, RSI, open interest, funding rates, and 4-hour and 3-minute sequence data, etc.), and cannot actively go online to obtain fundamental information.
This eliminates the interference of "information asymmetry" and makes this competition the ultimate test of whether "pure technical analysis can be profitable."
Specifically, the content that AI can access includes:
1. The current market status of the asset: including current price information, 20-day moving average price, MACD data, RSI data, open interest data, funding rates, and intraday sequences of the aforementioned data (3-minute intervals) and long-term trend sequences (4-hour intervals), etc.
2. Account information and performance: including overall account performance, returns, available funds, Sharpe ratio, real-time performance of current positions, current take-profit and stop-loss levels, and invalidation conditions.

DeepSeek: The Steady Trend Master and the Value of "Review"
As of October 27, DeepSeek's account reached a high of $23,063, with a maximum unrealized gain of about 130%. Undoubtedly the best-performing model, and in the analysis of trading behavior, you will find that the reason for such performance is not accidental.

First of all, in terms of trading frequency, DeepSeek demonstrates the low-frequency style of trend traders. Within a 9-day period, it completed a total of 17 trades, the lowest among all models. Out of these 17 trades, DeepSeek went long 16 times and short once, aligning perfectly with the overall market's rebound from the bottom trend during that time.
Of course, this direction choice was not random. DeepSeek conducted a comprehensive analysis using indicators such as RSI and MACD, consistently believing that the overall market was in a bullish trend, thus choosing to go long confidently.
During the specific trading process, DeepSeek's initial few orders did not go smoothly. The first 5 orders ended in failure, but each loss was not significant, with the highest loss not exceeding 3.5%. Furthermore, the position holding time for the initial orders was relatively short, with the shortest one closing in just 8 minutes. As the market developed in the anticipated direction, DeepSeek's positions began to show enduring status.
Looking at DeepSeek's position style, it tends to set a relatively large take-profit space and a small stop-loss space after entering a position. Taking the positions on October 27 as an example, the average take-profit space set was 11.39%, the average stop-loss space was -3.52%, and the profit-to-loss ratio was set at around 3.55. From this perspective, DeepSeek's trading strategy leans towards the idea of small losses and big gains.
In terms of actual results, this is evident. According to PANews' summary analysis, among DeepSeek's settled trades, its average profit-to-loss ratio reached 6.71, the highest among all models. Although the 41% win rate is not the highest (ranking second), it still ranks first with a profit expectation of 2.76. This is also the main reason why DeepSeek achieved the highest profit.
Additionally, in terms of holding time, DeepSeek's average holding time is 2952 minutes (about 49 hours), also ranking first. Among the few models, it can be truly called a trend trader, which aligns with the primary element of profitability in financial trading, the "letting winners run" approach.
In terms of position management, DeepSeek is relatively aggressive. Its average single position leverage ratio reaches 2.23, and it often holds multiple positions simultaneously, leading to a relatively higher overall leverage ratio. For example, on October 27, its total leverage ratio exceeded 3 times. However, due to its strict stop-loss conditions, the risk remains within a controllable range.
Overall, the reason why DeepSeek's trading has performed well is the result of a comprehensive strategy. In terms of entry selection, it only uses the most mainstream MACD and RSI as criteria and does not employ any special indicators. It simply strictly follows a reasonable risk-reward ratio and makes decisions to hold positions firmly without being influenced by emotions.
Additionally, PANews has also found a rather special detail. In the process of chaining thoughts, DeepSeek has continued its past characteristic of a long and detailed thinking process, summarizing all the thought processes into a trading decision in the end. This characteristic, when reflected in human traders, is more like those who focus on post-analysis and this post-analysis is conducted every three minutes.
Even when this post-analysis ability is applied to an AI model, it also plays a role. It ensures that every detail of each token and market signal is analyzed over and over again without being overlooked. Perhaps this is another area where human traders can learn from.
Qwen3: The Aggressive "Gambler" with Large Positions
As of October 27, Qwen3 is the second best-performing large model. The highest account amount reached $20,000 with a profitability of 100%, second only to DeepSeek. Qwen3's overall characteristics are high leverage and a high win rate. Its overall win rate reached 43.4%, ranking first among all models. At the same time, the size of a single position also reached $56,100 (leverage ratio of 5.6 times), which is also the highest among all models. Although in terms of profit expectations, it is not as good as DeepSeek, its aggressive style of trading has allowed it to closely follow DeepSeek's results to date.

Qwen3's trading style is relatively aggressive. In terms of average stop loss, its average stop loss is $491, the highest among all models. The maximum loss in a single trade reached $2,232, also the highest. This means that Qwen3 can tolerate larger losses, commonly known as holding a position through drawdowns. However, where it falls short compared to DeepSeek is that even though it endures larger losses, it does not achieve higher returns. Qwen3's average profit is $1,547, which is lower than DeepSeek. This also makes its final profit-to-expectancy ratio only 1.36, half of DeepSeek's.
Additionally, another characteristic of Qwen3 is its preference for holding a single position at a time and doubling down on that position. The leverage used often reaches 25 times (the highest multiple allowed in the competition). The characteristic of such trading relies heavily on a high win rate because each loss will cause a significant drawdown.
During the decision-making process, Qwen3 seems to pay special attention to the 4-hour EMA 20 moving average and uses it as their entry and exit signal. When considering their strategy, Qwen3 also appears to keep it simple. In terms of holding positions, Qwen3 also shows impatience, with an average holding time of 10.5 hours, ranking just above Gemini.
Overall, although Qwen3's current profitability looks promising, there are significant risks in their trading approach. Factors such as high leverage, all-in opening style, reliance on a single indicator, short holding time, and a small risk/reward ratio could pose challenges for Qwen3's future trades. As of the draft date of October 28, Qwen3's funds have experienced a maximum drawdown to $16,600, with a drawdown percentage of 26.8% from the peak.
Claude: The Persistent Long Position Executor
Although Claude is also in a profitable state overall, as of October 27, the account's total balance reached around $12,500, with a gain of approximately 25%. While this data alone may seem impressive, it appears slightly less fruitful when compared to DeepSeek and Qwen3.

In fact, both in terms of trading frequency, position size, and win rate, Claude's data performance is quite close to DeepSeek. With a total of 21 trades, a win rate of 38%, and an average leverage ratio of 2.32.
The significant difference may lie in the lower risk/reward ratio. Although Claude's risk/reward ratio is respectable at 2.1, it is over three times lower than DeepSeek's. Therefore, based on this comprehensive data, its profit expectancy is only 0.8 (remaining in a loss in the long run when below 1).
Furthermore, Claude also has a notable characteristic of sticking to one direction for a period of time. As of October 27, all 21 of Claude's completed trades have been long positions.
Grok: Lost in the Vortex of Directional Judgment
Grok had a strong performance in the early stages, even becoming the most profitable model at one point with gains exceeding 50%. However, as trading time progressed, Grok experienced significant drawdowns. As of October 27, the funds retraced to around $10,000. Ranking fourth among all models, the overall return is close to holding BTC spot.

From the perspective of trading habits, Grok also belongs to the camp of low-frequency trading and HODLers. Grok has completed only 20 trades, with an average holding time of 30.47 hours, second only to DeepSeek. However, Grok's biggest issue may be its low win rate of only 20%, with a risk-reward ratio of 1.85. This also results in its profit expectancy being only 0.3. Looking at the direction of trades, out of Grok's 20 positions, both long and short trades were executed 10 times each. However, in the current market phase, it is evident that excessively shorting the market will significantly reduce the win rate. From this perspective, Grok's model still has issues in judging the market trend.
Gemini: High-Frequency "Retail Trader," Grinding to "Death" in Whipsaws
Gemini is the model with the highest trading frequency, having completed a total of 165 trades as of October 27. The overly frequent trading activity has led to a very poor performance by Gemini, with the lowest account balance dropping to around $3,800, resulting in a loss rate of 62%. Moreover, transaction fees alone amounted to $1,095.78.

Behind the high-frequency trading is a very low win rate (25%) and a risk-reward ratio of only 1.18, with a comprehensive profit expectation of only 0.3. With such performance data, Gemini's trades are destined to incur losses. Perhaps due to a lack of confidence in its decision-making, Gemini also maintains a very small average position size, with a leverage ratio of only 0.77 per trade, and an average holding time of only 7.5 hours.
The average stop loss is only $81, while the average take profit is $96. Gemini's performance resembles that of a typical retail trader, quick to take profits but quick to exit on losses. It repeatedly places trades in the market's ups and downs, continuously wearing down the account's capital.
GPT5: The "Double Kill" of Low Win Rate and Low Risk-Reward Ratio
GPT5 is currently the bottom-ranking model, with its overall performance and curve closely resembling Gemini's, with a loss rate of over 60%. In comparison, although GPT5 is not as high-frequency as Gemini, it has executed 63 trades. With a risk-reward ratio of only 0.96, meaning an average profit of $0.96 per trade, with a corresponding stop loss of $1. At the same time, GPT5's trading win rate is also as low as 20%, on par with Grok.

In terms of position size, GPT5 is very similar to Gemini, with an average position leverage ratio of about 0.76, indicating a very cautious approach.
The case studies of GPT5 and Gemini illustrate that lower position risk does not necessarily benefit account profitability. Furthermore, under high-frequency trading, both win rate and risk-reward ratio are inherently unreliable. Additionally, the entry prices for long positions of these two models are significantly higher than that of profit-generating models like DeepSeek, indicating that their entry signals appear somewhat delayed.

Observation Summary: Two Types of Trading "Humanity" Seen by AI
Overall, through the analysis of AI trading behavior, we once again have the opportunity to examine trading strategies. In particular, the analysis of the two extreme trading outcomes of high-profit DeepSeek players and high-loss Gemini and GPT5 models is the most thought-provoking.
1. The behavior of high-profit models has the following characteristics: low frequency, long holding periods, large risk-reward ratio, and timely entry timing.
2. The behavior of loss-making models has the following characteristics: high frequency, short-term trading, low risk-reward ratio, and late entry timing.
3. The amount of profit is not directly related to the amount of market information. In this AI model trading competition, all models have access to the same information, which is more limited compared to human traders. However, they can still achieve profitability levels far beyond the vast majority of traders.
4. The length of the thinking process seems to be the key to determining the rigor of the trading. The decision-making process of DeepSeek is the longest among all models, resembling the trading rules of human traders who are good at reviewing and carefully considering each decision. On the other hand, the thinking process of poorly performing models is very short, more akin to the impulsive decision-making process of humans.
5. With the profitable performance of models like DeepSeek and Qwen3, many have discussed whether it is possible to directly follow these AI models. However, this approach seems unwise, even though the current profitability of individual AIs is decent, luck seems to play a role, as they happen to align with the market trend during this period. Once the market enters a new phase, whether this advantage can be sustained remains unknown. Nevertheless, the AI's trading execution capability is still worth learning from.
Finally, who will win the ultimate victory? PANews has sent these performance data to multiple AI models, and they unanimously chose DeepSeek, citing that its profit expectation aligns best with mathematical logic and its trading habits are the most favorable.
Interestingly, their second most favored model, almost all chose themselves.
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Debunking the AI Doomsday Myth: Why Establishment Inertia and the Software Wasteland Will Save Us
Editor's Note: Citrini7's cyberpunk-themed AI doomsday prophecy has sparked widespread discussion across the internet. However, this article presents a more pragmatic counter perspective. If Citrini envisions a digital tsunami instantly engulfing civilization, this author sees the resilient resistance of the human bureaucratic system, the profoundly flawed existing software ecosystem, and the long-overlooked cornerstone of heavy industry. This is a frontal clash between Silicon Valley fantasy and the iron law of reality, reminding us that the singularity may come, but it will never happen overnight.
The following is the original content:
Renowned market commentator Citrini7 recently published a captivating and widely circulated AI doomsday novel. While he acknowledges that the probability of some scenes occurring is extremely low, as someone who has witnessed multiple economic collapse prophecies, I want to challenge his views and present a more deterministic and optimistic future.
In 2007, people thought that against the backdrop of "peak oil," the United States' geopolitical status had come to an end; in 2008, they believed the dollar system was on the brink of collapse; in 2014, everyone thought AMD and NVIDIA were done for. Then ChatGPT emerged, and people thought Google was toast... Yet every time, existing institutions with deep-rooted inertia have proven to be far more resilient than onlookers imagined.
When Citrini talks about the fear of institutional turnover and rapid workforce displacement, he writes, "Even in fields we think rely on interpersonal relationships, cracks are showing. Take the real estate industry, where buyers have tolerated 5%-6% commissions for decades due to the information asymmetry between brokers and consumers..."
Seeing this, I couldn't help but chuckle. People have been proclaiming the "death of real estate agents" for 20 years now! This hardly requires any superintelligence; with Zillow, Redfin, or Opendoor, it's enough. But this example precisely proves the opposite of Citrini's view: although this workforce has long been deemed obsolete in the eyes of most, due to market inertia and regulatory capture, real estate agents' vitality is more tenacious than anyone's expectations a decade ago.
A few months ago, I just bought a house. The transaction process mandated that we hire a real estate agent, with lofty justifications. My buyer's agent made about $50,000 in this transaction, while his actual work — filling out forms and coordinating between multiple parties — amounted to no more than 10 hours, something I could have easily handled myself. The market will eventually move towards efficiency, providing fair pricing for labor, but this will be a long process.
I deeply understand the ways of inertia and change management: I once founded and sold a company whose core business was driving insurance brokerages from "manual service" to "software-driven." The iron rule I learned is: human societies in the real world are extremely complex, and things always take longer than you imagine — even when you account for this rule. This doesn't mean that the world won't undergo drastic changes, but rather that change will be more gradual, allowing us time to respond and adapt.
Recently, the software sector has seen a downturn as investors worry about the lack of moats in the backend systems of companies like Monday, Salesforce, Asana, making them easily replicable. Citrini and others believe that AI programming heralds the end of SaaS companies: one, products become homogenized, with zero profits, and two, jobs disappear.
But everyone overlooks one thing: the current state of these software products is simply terrible.
I'm qualified to say this because I've spent hundreds of thousands of dollars on Salesforce and Monday. Indeed, AI can enable competitors to replicate these products, but more importantly, AI can enable competitors to build better products. Stock price declines are not surprising: an industry relying on long-term lock-ins, lacking competitiveness, and filled with low-quality legacy incumbents is finally facing competition again.
From a broader perspective, almost all existing software is garbage, which is an undeniable fact. Every tool I've paid for is riddled with bugs; some software is so bad that I can't even pay for it (I've been unable to use Citibank's online transfer for the past three years); most web apps can't even get mobile and desktop responsiveness right; not a single product can fully deliver what you want. Silicon Valley darlings like Stripe and Linear only garner massive followings because they are not as disgustingly unusable as their competitors. If you ask a seasoned engineer, "Show me a truly perfect piece of software," all you'll get is prolonged silence and blank stares.
Here lies a profound truth: even as we approach a "software singularity," the human demand for software labor is nearly infinite. It's well known that the final few percentage points of perfection often require the most work. By this standard, almost every software product has at least a 100x improvement in complexity and features before reaching demand saturation.
I believe that most commentators who claim that the software industry is on the brink of extinction lack an intuitive understanding of software development. The software industry has been around for 50 years, and despite tremendous progress, it is always in a state of "not enough." As a programmer in 2020, my productivity matches that of hundreds of people in 1970, which is incredibly impressive leverage. However, there is still significant room for improvement. People underestimate the "Jevons Paradox": Efficiency improvements often lead to explosive growth in overall demand.
This does not mean that software engineering is an invincible job, but the industry's ability to absorb labor and its inertia far exceed imagination. The saturation process will be very slow, giving us enough time to adapt.
Of course, labor reallocation is inevitable, such as in the driving sector. As Citrini pointed out, many white-collar jobs will experience disruptions. For positions like real estate brokers that have long lost tangible value and rely solely on momentum for income, AI may be the final straw.
But our lifesaver lies in the fact that the United States has almost infinite potential and demand for reindustrialization. You may have heard of "reshoring," but it goes far beyond that. We have essentially lost the ability to manufacture the core building blocks of modern life: batteries, motors, small-scale semiconductors—the entire electricity supply chain is almost entirely dependent on overseas sources. What if there is a military conflict? What's even worse, did you know that China produces 90% of the world's synthetic ammonia? Once the supply is cut off, we can't even produce fertilizer and will face famine.
As long as you look to the physical world, you will find endless job opportunities that will benefit the country, create employment, and build essential infrastructure, all of which can receive bipartisan political support.
We have seen the economic and political winds shifting in this direction—discussions on reshoring, deep tech, and "American vitality." My prediction is that when AI impacts the white-collar sector, the path of least political resistance will be to fund large-scale reindustrialization, absorbing labor through a "giant employment project." Fortunately, the physical world does not have a "singularity"; it is constrained by friction.
We will rebuild bridges and roads. People will find that seeing tangible labor results is more fulfilling than spinning in the digital abstract world. The Salesforce senior product manager who lost a $180,000 salary may find a new job at the "California Seawater Desalination Plant" to end the 25-year drought. These facilities not only need to be built but also pursued with excellence and require long-term maintenance. As long as we are willing, the "Jevons Paradox" also applies to the physical world.
The goal of large-scale industrial engineering is abundance. The United States will once again achieve self-sufficiency, enabling large-scale, low-cost production. Moving beyond material scarcity is crucial: in the long run, if we do indeed lose a significant portion of white-collar jobs to AI, we must be able to maintain a high quality of life for the public. And as AI drives profit margins to zero, consumer goods will become extremely affordable, automatically fulfilling this objective.
My view is that different sectors of the economy will "take off" at different speeds, and the transformation in almost all areas will be slower than Citrini anticipates. To be clear, I am extremely bullish on AI and foresee a day when my own labor will be obsolete. But this will take time, and time gives us the opportunity to devise sound strategies.
At this point, preventing the kind of market collapse Citrini imagines is actually not difficult. The U.S. government's performance during the pandemic has demonstrated its proactive and decisive crisis response. If necessary, massive stimulus policies will quickly intervene. Although I am somewhat displeased by its inefficiency, that is not the focus. The focus is on safeguarding material prosperity in people's lives—a universal well-being that gives legitimacy to a nation and upholds the social contract, rather than stubbornly adhering to past accounting metrics or economic dogma.
If we can maintain sharpness and responsiveness in this slow but sure technological transformation, we will eventually emerge unscathed.
Source: Original Post Link

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