$15K+ Profits: The 4 AI Trading Secrets WEEX Hackathon Prelim Winners Used to Dominate Volatile Crypto Markets

By: WEEX|2026/02/06 14:00:00

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The WEEX AI Trading Hackathon Preliminary Round has concluded in great success. Across volatile market conditions and live trading environments, participants pushed the boundaries of strategy design, execution discipline, and machine intelligence. Their performances not only reflected technical excellence, but also the growing maturity of AI as a force in modern markets. WEEX extends its deepest appreciation to every competing team and to the sponsors and partners whose unwavering support helped turn this vision into reality.

As the Finals commence on February 9, the spotlight now shifts to the ultimate test — where the strongest strategies will face the market head-on.

At its core, the WEEX AI Trading Hackathon is more than a competition — it is a statement about the future of trading. At a time when artificial intelligence is reshaping global industries, WEEX has chosen to place AI where it matters most: in real markets, under real pressure, with real consequences. For WEEX users and market participants worldwide, the hackathon offers a rare window into how elite AI systems think, adapt, and survive in live conditions. It is within this broader vision that the top three performers of the Preliminary Round emerge — not merely as winners, but as case studies in what the next generation of trading intelligence looks like. In the following sections, we examine the strategic foundations behind their success.

$6,452 in 7 Days: How WEEX Hackathon's Top AI Trader Dominated with 20x Leverage

NeuralEdge secured 1st place on the preliminary round leaderboard with a Net Realized PnL of $6,452. By maintaining a clear short bias and executing high-conviction trades with disciplined risk control, the strategy consistently capitalized on last week’s downside-dominated market, standing out as the most stable and profitable performer among all participants.

High-conviction directional trading, not frequency: Instead of chasing volatility, NeuralEdge limited activity to a handful of high-quality short setups. As ETH, SOL, XRP, and BNB showed repeated rejection at key resistance levels last week, the AI selectively engaged structural weakness rather than overtrading choppy intraday swings.
Consistent short bias aligned with market structure: With 91.72% of time spent short, the system maintained a clear bearish stance throughout the week, reflecting an accurate read of lower highs, weak follow-through on bounces, and sustained downside pressure — without unnecessary flip-flopping.
Capital deployed decisively during breakdown phases: Maintaining an average leverage of 20x, NeuralEdge scaled meaningfully into positions when downside momentum confirmed, such as ETHUSDT shorts sized at ~$19,600 notional — prioritizing conviction over gradual probing during fast-moving conditions.
Edge = “press the downside when structure confirms”: NeuralEdge’s core strength lies in recognizing when bearish market structure is reaffirmed, deploying leverage with intent, and allowing downside momentum to play out fully — resulting in a clean, decisive, and leaderboard-ready performance during a challenging market week.

Smart Money Tracker secured 2nd place on the preliminary round leaderboard with a Net Realized PnL of $6,532.51. By maintaining a consistent short bias and executing leveraged trades with a focus on high-probability setups, the strategy effectively captured downside moves while managing risk through controlled position sizing.

Asymmetric Short Bias Aligned with Market Conditions: The contestant maintained a clear directional tilt, with 55.66% of exposure in short positions and 43.40% in longs. This orientation aligned well with the bearish undercurrents observed across major cryptocurrencies last week, avoiding costly counter-trend trading during clear breakdown phases.
Focus on Major Pairs and Structural Setups: Trades were concentrated in high-liquidity majors (BTC, ETH, BNB, XRP, LTC, DOGE), avoiding volatile altcoins. Entries often coincided with rejections at key resistance or breakdowns from consolidation, such as the profitable LTC short from 59.30 to 56.97 and DOGE short from 0.1051 to 0.1019.
Risk Management Evident in Profit/Loss Profile: The contestant’s biggest win (+$943.88) significantly outweighed their biggest loss (-$507.39), indicating effective stop-loss discipline and profit-taking behavior. Several small-loss trades (e.g., -$65 on BTC, -$96 on XRP) suggest controlled risk exposure on less favorable moves.
Edge = "Leveraged Shorts on Breakdown Confirmation": The core strength lies in identifying structural weakness in major cryptocurrencies, entering with meaningful size and leverage, and holding through the core of the move. This resulted in a series of high-profit short trades (e.g., LTC, BNB, DOGE, XRP) that drove overall profitability, demonstrating a disciplined and trend-aware approach in a challenging market environment.

One More Round secured 3rd place on the preliminary round leaderboard with a Net Realized PnL of $3,235.85. By adopting an extreme short bias and concentrating almost exclusively on high-leverage BTC/USDT trades, the strategy aggressively capitalized on Bitcoin’s corrective phases, delivering outsized returns through bold, focused positioning.

Extreme directional conviction with near-total short exposure:

With 88.75% of time spent short and only 10.68% long, the contestant maintained one of the most consistently bearish stances on the leaderboard. This reflected a strong conviction that Bitcoin’s rally was facing exhaustion, allowing the strategy to profit repeatedly during pullbacks.

Ultra-focused, high-leverage trading on a single asset:

The portfolio shows remarkable concentration—virtually all trades were on BTC/USDT at 20x leverage. This focus eliminated noise from altcoins and allowed the trader to deeply align with Bitcoin’s intraweek structure, particularly its failure to sustain breaks above key levels like $84k–$92k.

Risk discipline visible despite aggressive posture:

While the biggest loss reached -$629.94, it remained well-contained relative to the biggest win, indicating the use of stops or timely exits when trades reversed. Several small losses (e.g., -$132.50) suggest the trader cut losing positions quickly rather than averaging into weakness.

Edge = “Maximum concentration on BTC’s failure to hold highs”:

The core strength lay in identifying precise moments when Bitcoin showed rejection at local tops—such as around $77.6k, $83k, and $87k—and entering concentrated, high-leverage shorts to capture the ensuing drop. This repetitive, structure-based approach turned Bitcoin’s choppy consolidation into a series of profitable swing trades, securing a top-three finish through clarity and conviction.

How the Top 3 AI Trading Strategies Won the WEEX Hackathon Preliminary Round

The top performers in the WEEX AI Wars Hackathon Preliminary Round shared a common secret: success comes from disciplined structure, high-conviction decisions, and patient execution, rather than chasing every market move or relying on guesswork. By focusing on clear signals, aligned biases, and controlled risk, they consistently captured opportunities even in a challenging, downside-dominated market.

Lesson 1: Market structure comes before prediction None of the top strategies tried to forecast bottoms or trade every bounce. Instead, they waited for clear signals of structural weakness—lower highs, failed breakouts, and confirmed breakdowns—before acting. This reinforces a key rule for traders: align with what the market is doing, not what you hope it will do.

Lesson 2: Directional conviction beats constant activity Rather than switching directions frequently, all three systems maintained a strong short bias once bearish conditions were established. By committing to a clear market view and avoiding unnecessary flip-flopping, they reduced noise, improved consistency, and avoided death by small losses—something many retail traders struggle with.

Lesson 3: Fewer trades, higher quality The most successful strategies did not chase every price movement. They traded selectively, focusing on high-liquidity pairs and waiting for high-confidence setups. This shows that overtrading is often the enemy of performance, especially in volatile markets.

Lesson 4: Let winners work, cut losers early A consistent pattern across the top performers was patience in winning trades and decisiveness in losing ones. Small losses were accepted quickly, while profitable trades were allowed to develop. This asymmetric mindset—small losses, larger gains—is foundational to long-term success.

The WEEX Hackathon not only provides a live, real-market proving ground for AI strategies but also fosters innovation in the crypto community, offering WEEX users a unique opportunity to learn from the cutting edge of AI trading, refine their own approaches, and participate in the evolution of intelligent market strategies.

How WEEX Traders Can Apply These AI Trading Principles

The convergence of these independent AI strategies around the same principles highlights an important truth: successful trading logic is universal, whether executed by humans or machines. The WEEX Hackathon Preliminary Round served as a real-market laboratory, proving that disciplined structure-based strategies can outperform in challenging conditions.

For WEEX users, this isn’t just a competition result—it’s a roadmap. By focusing on market structure, reducing overtrading, respecting risk, and deploying capital with conviction, traders can begin to think more like the AI systems that topped the leaderboard.

How WEEX Is Shaping the Future of AI Trading Through Real-Market Hackathons

As the WEEX AI Trading Hackathon advances beyond the preliminary stage, it has already demonstrated its value as more than a competitive arena — it is an open platform for technological exploration and talent discovery in AI-driven trading. By providing real-market environments, institutional-grade infrastructure, and open access to data and tools, WEEX is actively lowering the barrier for innovation while raising the standard for what AI trading can and should be. Looking forward, WEEX is committed to continuously expanding this platform: cultivating global AI trading talent, encouraging rigorous experimentation, and transforming cutting-edge ideas into scalable, production-ready strategies.

By bridging AI trading veterans, quant experts, AI tech entrepreneurs and the global AI and crypto community, WEEX aims to not only empower the next generation of quantitative traders, but also to help define the direction of AI trading itself — setting benchmarks, shaping best practices, and leading the industry toward a more intelligent, transparent, and resilient future.

About WEEX

Founded in 2018, WEEX has developed into a global crypto exchange with over 6.2 million users across more than 150 countries. The platform emphasizes security, liquidity, and usability, providing over 1,200 spot trading pairs and offering up to 400x leverage in crypto futures trading. In addition to traditional spot and derivatives markets, WEEX is expanding rapidly in the AI era — delivering real-time AI news, empowering users with AI trading tools, and exploring innovative trade-to-earn models that make intelligent trading more accessible to everyone. Its 1,000 BTC Protection Fund further strengthens asset safety and transparency, while features such as copy trading and advanced trading tools allow users to follow professional traders and experience a more efficient, intelligent trading journey.

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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.

Never Underestimate "Institutional Inertia"

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.

The Software Industry Has "Infinite Demand" for Labor

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.

Redemption of "Reindustrialization"

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.

Towards Abundance

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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