From liquidation to annual income of over 100 million: How Cai Jiamin uses quantitative trading to navigate bull and bear markets

At age 12, Cai Jiamin had only 20 HKD for lunch. He would eat a 12-dollar bento from a small shop near school, leaving him with 8 HKD each day, dreaming of one day being able to spend freely like other students. This desire drove him to start researching the Hong Kong Mark Six lottery, attempting to win the 8 million HKD jackpot with 5 HKD bets, but ultimately failed. But this poor student did not give up; he turned to stock trading. After two account wipeouts at ages 16 and 19, he finally found his own profitable path—quantitative trading. Today, Cai Jiamin manages a quant fund worth 160 million, consistently earning profits in both bull and bear markets. His story is a perfect illustration of how data-driven strategies can overcome human weaknesses.

The Enlightenment After Two Wipeouts: Why Quantitative Trading Wastes Time but Not Money

At 16, Cai Jiamin used a borrowed account to make his first high-leverage trade. He invested 40,000 HKD into warrants and turbo certificates, trying to get rich quickly with 10x or 20x leverage. The result was instant: his account dropped from 40,000 HKD to just a few hundred HKD. That night, the young Cai even thought his account was hacked, completely clueless about how to face this disaster.

A conversation with a friend in the park changed his perspective. The friend said, “You don’t have kids to support, no family to care for, so having three or four thousand or three or four hundred HKD in your account makes no difference to you.” This realization opened his eyes. Cai Jiamin understood that youth was the best time to experiment with leverage.

At 19, he started over. He accumulated 150,000 HKD through tutoring but was wiped out again overnight trading options and futures. This blow was even heavier but more enlightening. Cai Jiamin began to reflect calmly: why, after reading so many news articles, studying countless charts, and analyzing numerous technical indicators, could he still not achieve stable profits?

The answer came from a seemingly simple insight—he had never validated his methods with historical data. All his trading decisions were based on others’ advice, KOL recommendations, or intuition, not data. This cognitive shift led Cai Jiamin into the world of quantitative trading.

The core logic of quant trading is clear: backtest strategies using historical data, verify their effectiveness, then invest real money. This means the potential waste is time (the backtesting cycle), but not money. Unlike manual trading, quant trading transforms emotional decisions into rational data calculations, fundamentally eliminating the influence of emotions.

CTA Strategies and Risk Management: Achieving 240% Returns in a Bear Market

In May 2020, Bitcoin experienced its third halving. Cai Jiamin observed that trading volume and scale in the crypto market were gradually expanding, so he decided to transfer his accumulated quantitative strategies from traditional finance to Bitcoin trading. Starting in May 2021, he deployed strategies in the crypto market, and by January 2023, his account grew from a few million HKD to 100 million HKD—achieving about 20x returns in a year and a half.

The secret to this success lies in Cai Jiamin’s adoption of CTA (trend-following) strategies. Unlike high-frequency or arbitrage trading, CTA focuses on market direction—using hourly price signals to determine when to go long or short. When he believes the market will rise, he establishes long positions; when he expects a decline, he switches to short. This pure directional trading has the advantage of profiting in any market environment.

For example, in the 2022 bear market, most traders chose to wait or cut losses, but Cai Jiamin achieved a 240% annual return through short strategies. This highlights the core advantage of quant trading over simply holding assets: holding Bitcoin profits only in bull markets, but quant strategies can hedge with short positions and make profits even in bear markets.

However, CTA strategies are not risk-free. Cai Jiamin openly admits that such strategies can have drawdowns of 20-30% or more. In contrast, high-frequency trading typically has drawdowns under 1%, and arbitrage strategies usually see 3-5%. This requires him to face a significant psychological challenge—when investors see their accounts drop by 20-30%, panic can set in.

The way to handle this is through expectation management and data support. Cai Jiamin carefully explains the characteristics of CTA strategies to investors, showing them the full cycle of “losses first, then gains.” When investors see a complete case of “a 30% loss followed by a new high and a 100% gain,” their understanding of volatility shifts.

Two main metrics evaluate strategy effectiveness: the Sharpe ratio (risk-adjusted return) and the Calmar ratio (annual return divided by maximum drawdown). Cai emphasizes not to overweight any single factor. Once, he heavily weighted a factor that performed extremely well—over 50% of the portfolio. When that factor failed, the entire portfolio experienced huge swings. This lesson led him to adopt a balanced factor weighting strategy—keeping each factor’s weight similar, so that even if one fails, the overall portfolio isn’t catastrophically affected.

Transitioning from Traditional Finance to Crypto: Cai Jiamin’s Strategy Migration and Innovation

Cai Jiamin’s journey into quant trading did not start in crypto. During university, he self-taught programming, participated in multiple quant trading competitions, and won awards. These achievements helped him join a proprietary trading firm, then later a well-known hedge fund, working in traditional finance for about five years.

His experience in traditional finance gave him two key insights: first, a more detailed view of data. In hedge funds, teams manage stocks, commodities, futures, and forex simultaneously. Cai learned to analyze data from multiple angles and dimensions, rather than blindly trusting a single source. Second, client expectation management—something he couldn’t learn from trading alone at home. When a fund’s annual return reaches 50%, but clients are told to expect 20-30%, the actual 30-40% return surprises them and encourages further investment. This “discounted” approach creates long-term value.

In migrating to crypto markets, Cai found that most strategies are transferable across asset classes. Simple trend-following strategies (like a 20-day moving average breakout) work on Bitcoin, US stocks, and commodities. But he also observed two key differences: traditional markets have opening and closing times, causing overnight gaps; crypto markets trade 24/7, with no such gaps.

More importantly, crypto markets have unique data dimensions not found in traditional markets. On-chain data (wallet flows, exchange inflows/outflows, whale movements), community sentiment data, on-chain sentiment indices—these are unavailable in traditional finance. Cai leverages these differences to develop strategies exclusive to crypto.

The AI Era: Quantitative Challenges and Opportunities—The Core of Differentiation

When ChatGPT and other AI tools emerged, Cai Jiamin’s initial reaction was to embrace them. He found AI greatly aided two aspects: signal generation—using machine learning time-series models to train factors and data, enabling AI to automatically generate long or short signals; and coding efficiency—tasks that once took 10 hours now take 5-10 minutes with DeepSeek.

But he also points out the risks: competitors are using the same tools. If all quant teams adopt identical machine learning models and AI tools, competitive advantages diminish. He reviewed AI’s development trajectory—2021-2022, machine learning effects were modest; by 2023, with the AI wave rising, AI-driven strategies started producing effective signals; by 2024-2025, results continued to improve. The reason is the “self-fulfilling prophecy”—more people using AI tools, market reactions becoming more pronounced, and the tools’ effectiveness continually increasing.

So, how to maintain a competitive edge in the AI era? Cai’s answer: Focus on data others ignore, do what others don’t. Many teams still rely on charts and price data; he turns to on-chain data and sentiment analysis. Many adopt generic machine learning models; he seeks customized algorithm optimization. This is the core of quant trading’s competitive advantage over the next five to ten years—not better tools, but more unique data perspectives.

The Growth Path of Young Traders: Using Time to Exchange for Money and Experience

Reflecting on his own growth, Cai Jiamin offers a key piece of advice for young traders: Use time to exchange for money. The biggest advantage of youth is abundant time and tolerance for mistakes. During university, he self-taught programming, participated in competitions, and engaged in simulated trading, spending three to six months proving whether his strategies could be consistently profitable—much less costly than for middle-aged traders.

His experience with margin calls also changed his mindset. After multiple losses, he gradually developed a “ego-less” trading mentality—not rejoicing over profits, nor getting upset over losses. This emotional stability is the foundation of rational decision-making. He says, “Profitable traders love trading itself, not just the money.” That’s why he later shifted from pure prop trading to education and sharing—his passion for trading transcended mere profits.

When asked if he’s a natural-born trading genius, Cai Jiamin gives a clear answer: There’s no innate trading genius, only relentless learning and perseverance. Maintaining rationality, avoiding emotional bias; correcting cognitive errors promptly; staying humble and continuously learning—these are the fundamental secrets to surviving bull and bear cycles and building an independent trading system.

From a poor student at 12 to a quant trading champion managing a billion-dollar fund, Cai Jiamin proves that the key to winning in markets is neither luck nor genius, but respect for data, reverence for risk, and relentless learning. This is the true reason why quant trading surpasses traditional trading.