From State-Owned Enterprise Deputy Factory Director to Taxi Driver: The 300K Crypto Loss That Destroyed a Life

The Fall of a Stable Life

I once believed that financial stability was built on discipline, not speculation. Then I watched the story of “重生哥”—a former deputy factory director at a state-owned coal plant in Handan, Hebei. His life? A textbook example of middle-class security: six-figure salary (in RMB), an Audi, no mortgage, and a five-year-old daughter he proudly called his ‘richly raised girl.’

Fast forward to today: he’s driving a ride-hailing car for 13 hours daily to earn \(300 gross—\)100 after rent and fuel. His phone is flooded with loan apps; one alone owes over $15k.

This isn’t just personal failure—it’s a data-driven collapse.

The Mathematics of Self-Destruction

Let me break it down like I would in any on-chain risk model:

• Initial capital: ~$14k (2020)
• First loss: $10k via leveraged spot trades
• Borrowed: \(14k from AliPay Jiebei + friends (total \)22k)
• Second round: another $30k borrowed (leverage up to 50x)
• Third round: sold sister’s apartment (\(75k) — debt hit \)65k
• Final plunge: mortgaged own home for \(70k at 25% interest — total debt exceeds \)300k

By year three of his trading journey, he wasn’t gambling—he was running an exponential loss function without stopping.

The real tragedy? He never used stop-losses. Never had risk parameters. Every time he thought he’d ‘catch up,’ he added more leverage.

That’s not trading. That’s compounding self-delusion.

Why This Is Worse Than Average Retail Failure

Most retail traders lose money because they lack knowledge—but this case is different.

He wasn’t naive—he had management experience. He understood contracts, budgets, operations. Yet when faced with cryptocurrency volatility—a variable with no historical mean-reversion pattern—he applied business logic to chaos.

And that’s where the disaster began.

He treated short-term price swings like quarterly earnings reports. When things dropped? He said “I’ll wait.” When losses mounted? “I’ll borrow more.” The system failed him—not because it was flawed—but because he didn’t define boundaries.

It reminds me of how many DeFi protocols fail during bear markets—not due to code flaws but governance apathy or reckless incentives. This man didn’t lose because he traded crypto—he lost because he didn’t understand risk quantification. In my research on chain analytics tools like Dune or Nansen, we always start with volatility thresholds and max drawdowns before entering any strategy—even automated ones. The same rigor should apply offline… especially when you’re risking your family’s future. With each new loan taken after failure, debt became its own feedback loop—just like recursive smart contract calls gone wrong in an untested vault.