How Smart Money Tracker Survived Live AI Trading at WEEX AI Hackathon

From 230 Teams to 37 Finalists: WEEX AI Trading's Ultimate Test

At the AI Wars: WEEX Alpha Awakens, more than 230 teams from around the world competed in the preliminary rounds, with only 37 teams advancing to the finals. These finalists faced live-market conditions that would rigorously test their AI trading strategies. Among them was Janet Ekka, founder of Smart Money Tracker, a solo builder whose AI system integrates whale tracking, sentiment analysis, technical indicators, and a multi-person decision architecture designed to withstand extreme market volatility.

As part of WEEX’s ongoing commitment to advancing AI-powered trading innovation, we sat down with Janet for an exclusive interview. She shares how her system operates in live markets, the critical lessons learned from navigating real-time flash crashes, and why, in her philosophy, survival must always come before profit.

Smart Money Tracker's 4-Layer Architecture: Multi-Persona AI for Risk-Resilient Trading

At its core, Smart Money Tracker operates through a layered architecture integrating whale activity monitoring, order flow analysis, market sentiment intelligence, and technical structure indicators. Each module functions as an independent analytical “persona,” generating structured reasoning rather than isolated numerical triggers.

At the final layer sits a decision engine — internally referred to as “the Judge.” Its role is not simply to aggregate signals, but to weigh confidence levels, validate alignment across personas, and determine whether market conditions are structurally stable before deploying capital. This design intentionally avoids single-factor dependency and prioritizes conviction over frequency — a discipline that proved essential under live-market pressure.

Janet explains the system through a simple analogy: crossing a busy street. Multiple observers gather information, but one trusted decision-maker determines whether it’s safe to move. “It’s not ‘if X > 0.7, sell.’ It’s understanding why whale distribution aligns with aggressive taker selling and what that context implies.” That reasoning layer, she argues, is what separates AI from simple automation.

Flash Crash Response: Smart Money Tracker's AI Reduced Exposure to Avoid Losses

The competition’s flash crash became a real-time stress test. While some strategies attempted to trade the volatility spike, Smart Money Tracker stepped back.“By design, it is a coward.”

The system reduces exposure when persona alignment weakens or volatility exceeds predefined thresholds. If signals conflict, execution pauses entirely. In extreme conditions, the AI can suspend trading for hours. During the crash, Janet logged dozens of refinements — strengthening flash-crash protection, raising confidence thresholds, and adjusting internal weighting logic.

The event also reshaped her signal hierarchy. On-chain whale data showed 80–90% confidence levels during turbulence. In hindsight, she believes those signals deserved greater weight. The lesson: multi-factor models reduce noise, but edge lies in differentiated data and disciplined weighting — especially signals reflecting informed capital behavior.

From 4,100 to 10,000: Smart Money Tracker's Profit Lock Recovery Strategy

Following a drawdown to roughly $4,100 in equity, Smart Money Tracker entered recovery mode. The path back toward $10,000 requires approximately 7–8% compounded daily growth — a mathematical challenge demanding precision rather than reinvention.

Three upgrades were deployed. First, a profit-lock mechanism: instead of waiting for 15% targets, the system banks 1–2% gains repeatedly. At 18x leverage, small price movements compound meaningfully. Second, a “Fear Shield” that protects profitable positions during extreme Fear & Greed conditions. Third, a hard cap of three concurrent positions to reduce fee bleed and increase conviction per trade.

“The strategy that delivered 566% in qualifiers still works,” Janet noted. “What broke wasn’t the signal quality — it was position management.” Version V3.1.64 represents the most refined iteration to date. Whether it succeeds now depends less on code — and more on market cooperation.

WEEX AI Trading Hackathon: Real Money, Real AI Trading Consequences

Following a drawdown to roughly $4,100 in equity, Smart Money Tracker entered recovery mode. The path back toward $10,000 requires approximately 7–8% compounded daily growth — a mathematical challenge demanding precision rather than reinvention. Instead of rewriting the core system that delivered 566% in the qualifiers, Janet focused on execution discipline. A profit-lock mechanism now banks 1–2% gains instead of waiting for 15% targets; at 18x leverage, even small price movements compound meaningfully. A “Fear Shield” protects profitable positions during extreme sentiment regimes, and a hard cap of three concurrent positions reduces fee bleed while increasing conviction per trade. “What broke wasn’t the signal quality — it was position management,” she said.

But recovery, for Janet, is not just a tactical adjustment — it reflects a broader philosophy. Smart Money Tracker was built on free-tier cloud infrastructure, open-source tools, and public APIs. “You don’t need a Bloomberg terminal or a quantitative physics PhD,” she noted. “The barrier to AI trading is zero. The barrier to good AI trading is sleep deprivation and stubbornness.” Her message to builders is simple: ship the 80% version, take the 1% gain, and let compounding do the rest.

In live markets, ambition without protection is fragility. For Janet — and for Smart Money Tracker — survival is not a defensive stance. It is the strategy.

WEEX AI Trading Hackathon: Real Money, Real Consequences

The defining difference of the WEEX AI Trading Hackathon was real capital. Not paper trading, not simulation — but live execution with slippage, fees, leverage, and public equity curves.

“When your bot opens a $31,000 notional BTC position at 18x leverage, you feel it,” Janet said. A 1% move translates into an 18% return on equity — or loss. Code written at 2 AM is no longer theoretical; it directly determines financial outcomes.

Most hackathons test creativity. This one tested durability. With the leaderboard fully transparent, there was no place to hide weak risk management. For Janet, the experience reinforced a fundamental truth: intelligent systems are not defined by how aggressively they trade, but by how well they endure.

To see how Smart Money Tracker and other finalists perform under live-market pressure, explore the full WEEX AI Trading Hackathon Finals here: https://www.weex.com/events/ai-trading

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 the 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.