12-year-old saves lunch money to buy Mark Six! Cai Jiamin, at 19, experiences a second liquidation, earning over 100 million annually through quantification

Cai Jiamin, at just 12 years old with only 20 HKD for lunch, researched the Mark Six lottery in hopes of winning 8 million. At 16, he experienced leverage liquidation, losing 40,000 to zero, and at 19, he lost another 150,000. He transitioned into quantitative trading, turning millions into billions from 2021 to 2023 (20x). During the bear market in 2022, he profited 240% from short selling. Using CTA strategies, his drawdowns reached 20%-30%.

From Mark Six to Stocks: The 12-Year-Old’s Money-Making Dream

At 12, Cai Jiamin’s family was poor; his daily lunch was only 20 HKD. He couldn’t afford the expensive school meals costing 25 HKD, so he chose the cheaper 15 HKD options. He would go to the school sports field kiosk to buy 12 HKD meals, leaving 8 HKD. He had no money for toys, couldn’t join extracurricular activities, and envied his classmates’ freedom. He began thinking of ways to make money. His first idea was to buy Hong Kong lottery tickets. A ticket cost 5 HKD, with a jackpot of 8 million HKD, offering the highest leverage ratio.

He spent two months studying over 100 past lottery draws, trying to predict the next result, but failed each time, eventually giving up, thinking it was purely random. His second idea was trading. Working at 12 was illegal, and without leverage, earning a few dozen HKD per hour wouldn’t make him rich quickly. His older brother, who was 8 years older and just started university, opened a stock account. Cai watched his brother analyze moving averages and became interested, thinking if he could predict stock movements tomorrow, he could make money.

At 14, he used his middle school textbooks to write predictions for stock movements the next day. The next day, he verified these predictions on his computer, achieving a high success rate over a year. He then asked his brother to open a securities account at the bank using his ID, setting a password so his brother wouldn’t access the money. In the first year, he earned over 30%. At 16, he wanted to try high leverage, transferring funds into bull and bear warrants and warrants, achieving 10x to 20x leverage.

Cai Jiamin’s Three-Stage Enlightenment in Making Money

12 Years Old Researching the Mark Six: Bet 5 HKD for an 8 million HKD jackpot, failed after analyzing 100 data periods

14 Years Old Stock Analogy: Used textbooks to predict rises and falls, high success rate over a year

16 Years Old Leverage Liquidation: 40,000 HKD principal hit zero after high leverage, a painful first lesson

One day, he saw his capital drop from over 40,000 HKD to just over 200 HKD on his computer. He thought he had logged into the wrong account or was robbed. Later, he realized the market had “stolen” it. He was afraid to tell his family, so he took his best middle school friend to the park for a walk and asked how he would feel about losing money. His friend said, “Even if you lose everything, it doesn’t matter; you can still eat, go to school, and sleep. Life goes on.” That statement had a huge impact on him, making him realize that “trading when young is a great opportunity; leverage more while you’re young.”

At 19, Second Zeroing, Quantitative Revelation, and Data Validation

After the 16-year-old liquidation, Cai Jiamin continued learning trading, reading investment books, fundamental analysis, technical analysis, and charts. By his first year in university, he had saved 150,000 HKD from tutoring middle school students and re-entered the market. He traded Hong Kong stocks, options, futures, and other high-leverage products, losing his 150,000 HKD again to zero. This time, he deeply reflected: “I’ve read news, analyzed charts (seconds, minutes, hours, daily), and understood fundamental analysis. Why am I still losing money?”

He realized his method was flawed. He searched online and found the term “quantitative trading.” He then understood he had been ignoring a key point: not verifying whether his methods could make money using historical data. He had only listened to others, believing that a 20-day moving average could be profitable without testing it. He discovered that nearly all top ten hedge funds in the world used quantitative trading; Renaissance Technologies, for example, had an annual return of 67% over forty years. He understood that quantitative trading had been proven to be a sustainable way to make money.

Cai Jiamin began self-learning quantitative trading, reading all books on systematic trading in the library, taking notes from the first to the last page. He searched online for shared knowledge. Using Hong Kong stock data, he tested strategies initially with Excel because he couldn’t code, pulling 100 days of price data, then expanding to a year and four years. He tested data he previously used for manual trading, gradually developing a feel and a set of techniques.

In university, he won trading competitions, and friends introduced him to proprietary trading firms that provided capital for profit sharing. He attended interviews, showing his competition awards and monthly statements, and made consistent profits for several months. After explaining his trading logic clearly, he successfully entered the industry. After a few months of simulated trading, he accumulated several million HKD in capital. After graduation, he worked at a hedge fund for five years, learning to analyze data in detail and manage client expectations.

The Path from Millions to Billions in Quantitative Trading

At 20, working at a hedge fund, he noticed Bitcoin’s halving in 2020 and began applying traditional strategies to the crypto market using historical data. He initially invested 1 to 2 million HKD. 2021 was his best year; from May 2021 to January 2023, his account grew from a few million to 100 million HKD, about 20x. In the following years, he averaged about 100 million HKD in profit annually, with an annual return of over 100% for his quantitative strategies.

Cai Jiamin emphasizes that trading is more profitable than just holding coins. Simply holding Bitcoin from 2021’s 60,000 HKD to 120,000 HKD only doubled; buying at 20,000 HKD and now at 80,000 HKD only quadrupled. The key to trading is earning from market volatility and drawdowns, and even during bear markets, short selling can be profitable. In the 2022 bear market, they earned 240%. They use medium- to high-frequency CTA (trend-following) strategies, judging market movements on hourly levels—buying when bullish, shorting when bearish, not doing both simultaneously.

CTA strategies can have significant drawdowns, sometimes exceeding 10 or even 20 points, and nearly every year, they encounter over 20-point corrections. Investors then ask if the strategy still works; they need to analyze data to determine if it can continue. The strategy might not make money for three or six months, and one must judge whether it has failed or will reach new highs again. Cai emphasizes avoiding over-reliance on a single factor; weights should be balanced so that if one factor fails, it doesn’t severely impact the portfolio. He values risk-adjusted returns like the Sharpe ratio and Calmar ratio.

The three steps in strategy development are: first, determine which factors to use; second, verify with historical data whether they can make money; third, simulate trading for one or two weeks to test system stability, then gradually increase position size from $100, $1,000 to the target size, and finally, conduct risk control checks to see if the strategy has failed. Cai stresses that doing things others don’t do is how to earn money others can’t—such as analyzing on-chain data and discussing market sentiment, rather than relying solely on charts and prices.

The Rational Philosophy and Educational Mission of Quantitative Trading

Two of Cai Jiamin’s margin calls made him emotionally detached from profits and losses, helping him stay rational. He chose to study pharmacy instead of finance because having a license was necessary for employment, providing a stable career backup to support trading. At the hedge fund, he learned to analyze data meticulously and manage client expectations—such as aiming for 50% annual profit in real trading but telling clients to expect 20%-30%, and ultimately earning 40%, which satisfied clients.

AI has helped quantitative trading; using machine learning to generate signals with two or three layers of strategies, programming efficiency improved from 10 hours to 5-10 minutes. But competitors can also use AI, making it both an opportunity and a risk. He started testing machine learning in 2021 but saw limited results. After the AI wave in 2023, he began making profits, and the more people use it, the better it works—an example of self-fulfilling prophecy.

Cai Jiamin believes that successful traders love trading itself, not just making money. Money is just a score to verify the method. True happiness comes from seeing personal progress and optimizing methods amid different risks. Since 2017, he has been teaching courses, helping over a dozen students achieve financial freedom—from tens of thousands to over a hundred million HKD. He believes that the fulfillment from education surpasses earning an extra or two billion; seeing students make money and achieve freedom allows them to pursue what they truly want.

He advises beginners to stay rational, reduce human biases (like not selling to avoid losses), and maintain an open learning mindset. He emphasizes that effort after birth is more important than innate talent—no one is born knowing how to trade; it’s learned gradually. There is no free lunch in the world; only by doing what others haven’t can you earn what others can’t.