From Coding to Conviction: How One Engineer Built a Decade-Long Faith in Ethereum

Wesley didn’t rush to change cars or increase positions the moment ETH climbed back to 4,350. Instead, his first action was deliberate: redeeming a license plate he’d surrendered during harder times. “Cars can be replaced, capital can be rebuilt, but integrity must be restored first,” he said quietly. That small piece of metal wasn’t about vanity—it was a signature on a bear market decision.

The story of how he got here spans a decade of calculated moves, from abandoning investment banking to running a peer-to-peer lending MVP built on Facebook’s SDK, to becoming an arbitrage trader managing tens of millions in AUM, to ultimately learning that true wealth accumulation required letting go of material symbols. Today, with ETH at $2.93K, his commitment remains unchanged: “I won’t sell. After it reaches 10,000, I’ll deposit into Aave or Compound, borrow some stablecoins, and still won’t sell.”

The Accidental Engineer: From Finance to Code

Wesley’s entry into technology wasn’t planned. Fresh from Hong Kong’s investment banking world where he worked as a bond salesman, he felt more like an actor than a professional. “I’m naturally introverted. The performative side of traditional finance didn’t suit me,” he explained. So he came home and built something nobody asked for: a chatbot-based lending system for students using Facebook’s SDK.

The MVP was crude by today’s standards—built by someone who had been coding for barely two months. But it worked. He decomposed the lending flow into conditional logic and keyword parsing, guiding users through transactions via text. With just a handful of team members, the platform processed roughly 10 million RMB in volume across 500-600 users within two to three months, reaching breakeven. Remarkably, there wasn’t a single defaulted loan.

His motivation was personal: a poor family background, dreams of studying abroad, and a landlord who wouldn’t accept credit cards. “I wondered if there could be a better way for students like me. Just verify your ID and scholarship, get approved instantly.”

When disagreements with his business partner arose, necessity forced him deeper into coding. “The company would’ve collapsed if I didn’t learn,” he joked. The venture sold shortly after, delivering his first meaningful wealth accumulation.

The Australian Detour: Financial Analysis by Day, Self-Teaching by Night

Seeking a working holiday, Wesley moved to Australia in 2016. His finance degree locked him into finance-related work—a community bank job that included counting ATM cash, analyzing equity valuations, and modeling mergers. His colleagues left by 3 PM; he stayed late, building the foundation of his engineering career through self-directed learning: data structures, algorithms, operating systems, all pieced together from online courses and PDFs.

The plan had been to pursue a U.S. master’s degree, but reality intervened. His resume, despite a year of coding experience, faced repeated rejection. After two years in Australia, having accumulated roughly 400,000 RMB, he made a decision: if he truly wanted to master engineering, he needed to return to China’s tech ecosystem. That choice eventually opened the door to Web3.

The Crypto Entry: Funding Rate Arbitrage and the Birth of a Philosophy

Wesley’s formal entry into cryptocurrency came via an unlikely path—joining a Hong Kong insurance startup in 2018 where dozens of laid-off crypto exchange workers suddenly filled the office with blockchain jargon. By 2019, he began allocating to ETH and Synthetix (SNX), a year before DeFi Summer exploded.

But what truly mobilized him wasn’t narrative hype—it was a specific opportunity: funding rate arbitrage. Working with a colleague, he developed an algorithm exploiting the basis spread between spot and perpetual futures markets. By late 2020, it was live and generating 80-90% annualized returns.

“The problem was capital,” he recalled. Armed with a single PDF explaining the strategy to traditional finance professionals—investment bankers, private banking managers, and high-net-worth individuals—he raised nearly 10 million dollars across Hong Kong and Singapore. The strategy delivered approximately 87% returns in its first year.

Yet success bred curiosity. “I was running on APIs but didn’t truly understand the blockchain underneath,” he admitted. So he stepped back from trading, systematically studying the EVM: from the Yellow Paper to Solidity, from bytecode analysis to writing personal tools. He absorbed lessons from the 2020-2021 bull market’s darker moments—watching major hacks drain millions from projects he worked on, learning the hard way that multi-signature controls, time locks, and bytecode verification weren’t optional.

The Choice: Why Ethereum Wins Through Verifiability

By the DeFi Summer of 2020-2021, Wesley had gravitated toward Ethereum, though not for the reasons most traders cite. His answer was purely technical: “It’s verifiable.”

His logic is elegant in its simplicity. EVM contracts, once deployed, execute according to on-chain code. You don’t need to trust the team—you can read the source code or bytecode beforehand and make an informed decision. “For an engineer, that’s trust,” he said.

When asked about Solana’s superior performance and throughput, he acknowledged the technical merits but noted a critical limitation: “You can’t verify the execution on-chain the way you can with EVM. It’s more of a black box.” Regarding Bitcoin’s store-of-value narrative, he respected it as digital gold and conceded that a long-term portfolio combining BTC and ETH made sense. “But for me personally, it’s almost entirely ETH. Call it professional bias—Ethereum feels like an operating system. iOS, Android, EVM. That’s the frame I see.”

This wasn’t dismissal of other chains; it was preference weighted toward what could be audited, tested, and reproduced.

The Bear Market Clarity: Selling Symbols, Building Discipline

The bull market’s excesses caught him too. In 2021, he purchased a Bored Ape Yacht Club NFT for 35 ETH (worth 140 ETH at peak), never selling. He deployed hundreds of ETH into Otherside NFTs containing rare Koda and Azuki pieces. The floor collapsed almost immediately.

“I realized that collecting these symbols to attract others didn’t serve me,” he reflected. When the bear market arrived in 2022, he sold the Sydney villa with ocean views, the sports cars (including the one bearing his ETH10K plate), and temporarily worked as a digital nomad across Asia. “I felt quite empty in that big house,” he admitted. “Sweat from farm work picking apples—that cleared my mind more than any luxury view.”

In 2022, with Ethereum dropping from 4,871 to 880, he faced a choice: sell or commit. “Around 800-900, I did consider cutting losses,” he confessed. Instead, he pivoted to discipline: systematic purchasing starting around 1,200, accumulating through every dip. “At 1,000 I kept buying. If it drops 50 dollars, I treat it as a crash and add funds.”

He cleared external client accounts—a decision crystallized by the FTX collapse. “If my position goes to zero, my clients shouldn’t suffer that counterparty risk.” His funding rate arbitrage, stripped of leverage and external capital, still delivers approximately 10% annualized returns today, supplemented by contract work writing smart contracts and NFT systems.

The License Plate as Metaphor: Verifiability Extended to Life

On the day ETH climbed past 4,350 this summer, Wesley redeemed the “ETH10K” plate. Not as a boast, but as a statement: the bear market beliefs he held are still valid today.

This wasn’t emotional. It was structural. Just as he trusts code over people, he trusts principles over feelings. Low leverage, non-directional strategies, heavy auditing—these aren’t cautious; they’re sustainable. “Going forward, the arb margins will likely compress,” he assessed, “but small volumes can still profit.”

He doesn’t recruit others into crypto with promises of 10X. Instead, he shares learning paths: start with Udemy’s Python Bootcamp to get code running, then move to O’Reilly’s “Introducing Python” for fundamentals, finally take Coursera’s data structures specialization to solidify theory. “Learn to do first, then understand why.”

The license plate remained symbolic: a reminder that when price surges threaten to intoxicate judgment, the brake lights illuminate first.