I recently came across a particularly interesting case— a quantitative trading expert turned a few million Hong Kong dollars into 100 million in just a year and a half. This trader's name is Calvin Cai, and his story made me rethink what a true "money printing machine" really is.

Speaking of which, Calvin's story is a bit legendary. At 12 years old, with only 20 Hong Kong dollars for lunch money, he started pondering how to make money—initially studying the Mark Six lottery, then shifting to stocks. At 14, he entered the market using his brother’s account; at 16, he used 10x leverage to lose his 40,000 HKD principal down to a few hundred. That liquidation should have scared him off, but it didn’t. At 19, he tried again, losing his 150,000 HKD back to zero. After two harsh lessons from the market, he finally understood a key principle: manual trading tests your emotions, while quantitative trading tests your data.

This shift was crucial. He began validating each trading strategy with historical data—only investing when it was effective, and iterating when it wasn’t. Starting from May 2021, he moved his quantitative strategies into the crypto market, and by January 2023, his account grew from a few million HKD to 100 million—roughly a 20-fold return. Even more impressive, during the 2022 bear market, he still made 240%. This wasn’t from holding coins, but from genuine trading ability.

Calvin’s method is called CTA strategy (trend trading), which uses hourly signals to determine market direction—long or short. Unlike other quantitative methods, CTA strategies tend to have larger drawdowns, usually around 20%-30%, but because of this characteristic, they can profit in any market condition. He emphasizes balancing factor weights—no single signal should dominate; otherwise, if that signal fails, the entire portfolio is at risk. Sharpe ratio and Calmar ratio are the two metrics he values most.

The process from inspiration to implementation involves three steps: first, decide which factors to use; second, backtest with historical data to see if it’s profitable over the past five years, setting standards like 50% annual return and less than 20% drawdown; third, simulate trading to verify system stability, then gradually increase position sizes in live trading. The entire process is rigorous because he’s seen too many "paper profits" strategies that lose money once they go live.

Interestingly, Calvin is now also using AI to optimize signal generation. He started testing machine learning in 2021, but the results weren’t obvious at first. By 2023, with the proliferation of AI tools, he found that machine learning strategies began to truly generate profits. The quantitative fund he manages has grown to 160 million HKD, with an average annual return exceeding 100%.

But he also admits that the biggest challenge of CTA strategies is managing investor expectations. Sometimes a strategy will go several months without making money, and investors start doubting. At this point, it’s crucial to explain from a data perspective why holding onto it is justified, rather than blindly cutting it. The most important lesson he learned working at a traditional hedge fund for five years is expectation management—setting conservative expectations for investors and ultimately exceeding them, which keeps them willing to continue investing.

Looking at Calvin’s growth path, what moved me most is his understanding of the "advantage of youth." After two margin calls, he didn’t give up—instead, he thought, “When you’re young, you have the capital to make mistakes; losing everything isn’t a big deal.” This mindset gave him the courage to use high leverage to learn, and also helped him stay rational in later quantitative trading. He says there are no born trading geniuses—only learning and relentless effort afterward. Staying rational, correcting cognitive biases in time, and always remaining humble in learning—these are the core secrets to surviving bull and bear cycles.